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Bootstrapping with alchemlyb
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Bootstrapping.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Bootstrapping with `alchemlyb`"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Calculating the uncertainty for free energies obtained from some estimators, such as BAR, is not straightforward. Despite this, bootstrap sampling is a computational approach that can be employed to obtain robust uncertainty values, regardless of estimator used or the distribution of samples involved. For background on how this method works, see the [alchemistry wiki section](http://www.alchemistry.org/wiki/Analyzing_Simulation_Results#Bootstrap_Sampling).\n",
"\n",
"Here we will demonstrate how to employ bootstrap sampling with `alchemlyb`. At this time, the tooling presented is only available on a [feature branch](https://github.com/alchemistry/alchemlyb/tree/feature-bootstrapping) of the library, currently a work-in-progress."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We'll start by loading a test dataset created with Gromacs.\n",
"We'll also import the `BAR` and `TI` estimators, as well as parsers for both `u_nk` and `dHdl` data types."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from alchemlyb.estimators import BAR, TI\n",
"from alchemlyb.parsing.gmx import extract_u_nk, extract_dHdl\n",
"\n",
"from alchemtest.gmx import load_expanded_ensemble_case_1"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gromacs: Host CB7 and Guest C3 in water\n",
"=======================================\n",
"\n",
"Host CB7 and Guest C3 in water, Guest C3 alchemically turned into Guest C3 in vacuum separated from water and Host CB7. This unpublished data uses Host CB7 and Guest C3 from [Muddana2014a]_. Similar published data can be found in [Monroe2014a]_.\n",
"\n",
"Notes\n",
"-----\n",
"Data Set Characteristics:\n",
" :Number of Legs: 2 (Coulomb, VDW)\n",
" :Number of Windows: 32 total, 20 for Coulomb, 12 for VDW\n",
" :Number of Simulations: 1\n",
" :Length of Simulation: 100ns\n",
" :System Size: 8286 atoms\n",
" :Temperature: 300 K\n",
" :Alchemical Pathway: vdw + coul --> vdw --> vacuum\n",
" :Missing Values: None\n",
" :Energy unit: kJ/mol\n",
" :Time unit: ps\t\t \t\t \n",
" :Creator: \\T. Jensen\n",
" :Donor: Travis Jensen (travis.jensen@colorado.edu)\n",
" :Date: November 2017\n",
" :License: `CC0 <https://creativecommons.org/publicdomain/zero/1.0/>`_ Public Domain Dedication \n",
"\n",
"\n",
"This dataset was generated using the expanded ensemble algorithm in the `Gromacs <http://www.gromacs.org/>`_ molecular dynamics engine.\n",
"\n",
".. [Muddana2014a] H. Muddana, A. Fenley, D. Mobley, and M. Gilson. The SAMPL4 host–guest blind prediction challenge: an overview. Journal of Computer-Aided Molecular Design, 28(4):305–317, 2014. PMID: 24599514. DOI: `10.1007/s10822-014-9735-1 <https://doi.org/10.1007/s10822-014-9735-1>`_.\n",
"\n",
".. [Monroe2014a] J. Monroe and M. Shirts. Converging free energies of binding in cucurbit[7]uril and octa-acid host-guest systems from SAMPL4 using expanded ensemble simulations. Journal of Computer-Aided Molecular Design, 28(4):401–415, 2014. PMID: 24610238 DOI: `10.1007/s10822-014-9716-4 <https://doi.org/10.1007/s10822-014-9716-4>`_.\n",
"\n"
]
}
],
"source": [
"data = load_expanded_ensemble_case_1()\n",
"\n",
"print(data.DESCR)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'AllStates': ['/home/david/Library/alchemistry/alchemtest/src/alchemtest/gmx/expanded_ensemble/case_1/CB7_Guest3_dhdl.xvg.gz']}"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.data"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
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],
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" (0.0, 0.0, 0.0, 0.0) \\\n",
"time fep-lambda coul-lambda vdw-lambda restraint-lambda \n",
"0.0 0.0 1.00 0.1 0.0002 -36416.048617 \n",
"2.0 0.0 1.00 0.4 0.0020 -36472.655852 \n",
"4.0 0.0 1.00 0.3 0.0010 -36420.169969 \n",
"6.0 0.0 0.22 0.0 0.0000 -36480.483060 \n",
"8.0 0.0 0.16 0.0 0.0000 -36556.217962 \n",
"... ... \n",
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"100000.0 0.0 0.68 0.0 0.0000 -36566.406054 \n",
"\n",
" (0.0, 0.05, 0.0, 0.0) \\\n",
"time fep-lambda coul-lambda vdw-lambda restraint-lambda \n",
"0.0 0.0 1.00 0.1 0.0002 -36417.317190 \n",
"2.0 0.0 1.00 0.4 0.0020 -36474.116119 \n",
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"8.0 0.0 0.16 0.0 0.0000 -36550.639739 \n",
"... ... \n",
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"99994.0 0.0 0.60 0.0 0.0000 -36547.664008 \n",
"99996.0 0.0 0.00 0.0 0.0000 -36793.801417 \n",
"99998.0 0.0 1.00 0.0 0.0001 -36405.480799 \n",
"100000.0 0.0 0.68 0.0 0.0000 -36566.014993 \n",
"\n",
" (0.0, 0.1, 0.0, 0.0) \\\n",
"time fep-lambda coul-lambda vdw-lambda restraint-lambda \n",
"0.0 0.0 1.00 0.1 0.0002 -36418.585730 \n",
"2.0 0.0 1.00 0.4 0.0020 -36475.576386 \n",
"4.0 0.0 1.00 0.3 0.0010 -36422.445070 \n",
"6.0 0.0 0.22 0.0 0.0000 -36470.672758 \n",
"8.0 0.0 0.16 0.0 0.0000 -36545.061520 \n",
"... ... \n",
"99992.0 0.0 0.16 0.0 0.0000 -36535.438229 \n",
"99994.0 0.0 0.60 0.0 0.0000 -36547.084795 \n",
"99996.0 0.0 0.00 0.0 0.0000 -36787.401306 \n",
"99998.0 0.0 1.00 0.0 0.0001 -36406.442786 \n",
"100000.0 0.0 0.68 0.0 0.0000 -36565.623970 \n",
"\n",
" (0.0, 0.16, 0.0, 0.0) \\\n",
"time fep-lambda coul-lambda vdw-lambda restraint-lambda \n",
"0.0 0.0 1.00 0.1 0.0002 -36420.107981 \n",
"2.0 0.0 1.00 0.4 0.0020 -36477.328670 \n",
"4.0 0.0 1.00 0.3 0.0010 -36423.810059 \n",
"6.0 0.0 0.22 0.0 0.0000 -36464.786695 \n",
"8.0 0.0 0.16 0.0 0.0000 -36538.367687 \n",
"... ... \n",
"99992.0 0.0 0.16 0.0 0.0000 -36528.949560 \n",
"99994.0 0.0 0.60 0.0 0.0000 -36546.389831 \n",
"99996.0 0.0 0.00 0.0 0.0000 -36779.721219 \n",
"99998.0 0.0 1.00 0.0 0.0001 -36407.597235 \n",
"100000.0 0.0 0.68 0.0 0.0000 -36565.154682 \n",
"\n",
" (0.0, 0.22, 0.0, 0.0) \\\n",
"time fep-lambda coul-lambda vdw-lambda restraint-lambda \n",
"0.0 0.0 1.00 0.1 0.0002 -36421.630235 \n",
"2.0 0.0 1.00 0.4 0.0020 -36479.080913 \n",
"4.0 0.0 1.00 0.3 0.0010 -36425.175113 \n",
"6.0 0.0 0.22 0.0 0.0000 -36458.900530 \n",
"8.0 0.0 0.16 0.0 0.0000 -36531.673818 \n",
"... ... \n",
"99992.0 0.0 0.16 0.0 0.0000 -36522.460915 \n",
"99994.0 0.0 0.60 0.0 0.0000 -36545.694809 \n",
"99996.0 0.0 0.00 0.0 0.0000 -36772.041130 \n",
"99998.0 0.0 1.00 0.0 0.0001 -36408.751553 \n",
"100000.0 0.0 0.68 0.0 0.0000 -36564.685451 \n",
"\n",
" (0.0, 0.28, 0.0, 0.0) \\\n",
"time fep-lambda coul-lambda vdw-lambda restraint-lambda \n",
"0.0 0.0 1.00 0.1 0.0002 -36423.152521 \n",
"2.0 0.0 1.00 0.4 0.0020 -36480.833176 \n",
"4.0 0.0 1.00 0.3 0.0010 -36426.540117 \n",
"6.0 0.0 0.22 0.0 0.0000 -36453.014437 \n",
"8.0 0.0 0.16 0.0 0.0000 -36524.979976 \n",
"... ... \n",
"99992.0 0.0 0.16 0.0 0.0000 -36515.972369 \n",
"99994.0 0.0 0.60 0.0 0.0000 -36544.999807 \n",
"99996.0 0.0 0.00 0.0 0.0000 -36764.361004 \n",
"99998.0 0.0 1.00 0.0 0.0001 -36409.905883 \n",
"100000.0 0.0 0.68 0.0 0.0000 -36564.216229 \n",
"\n",
" (0.0, 0.34, 0.0, 0.0) \\\n",
"time fep-lambda coul-lambda vdw-lambda restraint-lambda \n",
"0.0 0.0 1.00 0.1 0.0002 -36424.674698 \n",
"2.0 0.0 1.00 0.4 0.0020 -36482.585473 \n",
"4.0 0.0 1.00 0.3 0.0010 -36427.905183 \n",
"6.0 0.0 0.22 0.0 0.0000 -36447.128270 \n",
"8.0 0.0 0.16 0.0 0.0000 -36518.286112 \n",
"... ... \n",
"99992.0 0.0 0.16 0.0 0.0000 -36509.483672 \n",
"99994.0 0.0 0.60 0.0 0.0000 -36544.304813 \n",
"99996.0 0.0 0.00 0.0 0.0000 -36756.680928 \n",
"99998.0 0.0 1.00 0.0 0.0001 -36411.060332 \n",
"100000.0 0.0 0.68 0.0 0.0000 -36563.746979 \n",
"\n",
" (0.0, 0.4, 0.0, 0.0) \\\n",
"time fep-lambda coul-lambda vdw-lambda restraint-lambda \n",
"0.0 0.0 1.00 0.1 0.0002 -36426.196979 \n",
"2.0 0.0 1.00 0.4 0.0020 -36484.337738 \n",
"4.0 0.0 1.00 0.3 0.0010 -36429.270165 \n",
"6.0 0.0 0.22 0.0 0.0000 -36441.242239 \n",
"8.0 0.0 0.16 0.0 0.0000 -36511.592274 \n",
"... ... \n",
"99992.0 0.0 0.16 0.0 0.0000 -36502.995070 \n",
"99994.0 0.0 0.60 0.0 0.0000 -36543.609792 \n",
"99996.0 0.0 0.00 0.0 0.0000 -36749.000852 \n",
"99998.0 0.0 1.00 0.0 0.0001 -36412.214688 \n",
"100000.0 0.0 0.68 0.0 0.0000 -36563.277746 \n",
"\n",
" (0.0, 0.46, 0.0, 0.0) \\\n",
"time fep-lambda coul-lambda vdw-lambda restraint-lambda \n",
"0.0 0.0 1.00 0.1 0.0002 -36427.719234 \n",
"2.0 0.0 1.00 0.4 0.0020 -36486.090016 \n",
"4.0 0.0 1.00 0.3 0.0010 -36430.635267 \n",
"6.0 0.0 0.22 0.0 0.0000 -36435.356066 \n",
"8.0 0.0 0.16 0.0 0.0000 -36504.898418 \n",
"... ... \n",
"99992.0 0.0 0.16 0.0 0.0000 -36496.506480 \n",
"99994.0 0.0 0.60 0.0 0.0000 -36542.914817 \n",
"99996.0 0.0 0.00 0.0 0.0000 -36741.320756 \n",
"99998.0 0.0 1.00 0.0 0.0001 -36413.369122 \n",
"100000.0 0.0 0.68 0.0 0.0000 -36562.808486 \n",
"\n",
" (0.0, 0.52, 0.0, 0.0) \\\n",
"time fep-lambda coul-lambda vdw-lambda restraint-lambda \n",
"0.0 0.0 1.00 0.1 0.0002 -36429.241427 \n",
"2.0 0.0 1.00 0.4 0.0020 -36487.842279 \n",
"4.0 0.0 1.00 0.3 0.0010 -36432.000316 \n",
"6.0 0.0 0.22 0.0 0.0000 -36429.469962 \n",
"8.0 0.0 0.16 0.0 0.0000 -36498.204568 \n",
"... ... \n",
"99992.0 0.0 0.16 0.0 0.0000 -36490.017839 \n",
"99994.0 0.0 0.60 0.0 0.0000 -36542.219793 \n",
"99996.0 0.0 0.00 0.0 0.0000 -36733.640693 \n",
"99998.0 0.0 1.00 0.0 0.0001 -36414.523467 \n",
"100000.0 0.0 0.68 0.0 0.0000 -36562.339245 \n",
"\n",
" ... \\\n",
"time fep-lambda coul-lambda vdw-lambda restraint-lambda ... \n",
"0.0 0.0 1.00 0.1 0.0002 ... \n",
"2.0 0.0 1.00 0.4 0.0020 ... \n",
"4.0 0.0 1.00 0.3 0.0010 ... \n",
"6.0 0.0 0.22 0.0 0.0000 ... \n",
"8.0 0.0 0.16 0.0 0.0000 ... \n",
"... ... \n",
"99992.0 0.0 0.16 0.0 0.0000 ... \n",
"99994.0 0.0 0.60 0.0 0.0000 ... \n",
"99996.0 0.0 0.00 0.0 0.0000 ... \n",
"99998.0 0.0 1.00 0.0 0.0001 ... \n",
"100000.0 0.0 0.68 0.0 0.0000 ... \n",
"\n",
" (0.0, 1.0, 0.3, 0.001) \\\n",
"time fep-lambda coul-lambda vdw-lambda restraint-lambda \n",
"0.0 0.0 1.00 0.1 0.0002 -36433.674464 \n",
"2.0 0.0 1.00 0.4 0.0020 -36521.062826 \n",
"4.0 0.0 1.00 0.3 0.0010 -36441.414131 \n",
"6.0 0.0 0.22 0.0 0.0000 -36377.732738 \n",
"8.0 0.0 0.16 0.0 0.0000 -36433.927662 \n",
"... ... \n",
"99992.0 0.0 0.16 0.0 0.0000 -36423.390664 \n",
"99994.0 0.0 0.60 0.0 0.0000 -36520.191829 \n",
"99996.0 0.0 0.00 0.0 0.0000 -36662.057176 \n",
"99998.0 0.0 1.00 0.0 0.0001 -36410.186669 \n",
"100000.0 0.0 0.68 0.0 0.0000 -36544.017270 \n",
"\n",
" (0.0, 1.0, 0.4, 0.002) \\\n",
"time fep-lambda coul-lambda vdw-lambda restraint-lambda \n",
"0.0 0.0 1.00 0.1 0.0002 -36428.199010 \n",
"2.0 0.0 1.00 0.4 0.0020 -36519.140146 \n",
"4.0 0.0 1.00 0.3 0.0010 -36436.793983 \n",
"6.0 0.0 0.22 0.0 0.0000 -36372.767346 \n",
"8.0 0.0 0.16 0.0 0.0000 -36428.332808 \n",
"... ... \n",
"99992.0 0.0 0.16 0.0 0.0000 -36417.209752 \n",
"99994.0 0.0 0.60 0.0 0.0000 -36513.682061 \n",
"99996.0 0.0 0.00 0.0 0.0000 -36656.450596 \n",
"99998.0 0.0 1.00 0.0 0.0001 -36404.319812 \n",
"100000.0 0.0 0.68 0.0 0.0000 -36537.780082 \n",
"\n",
" (0.0, 1.0, 0.48, 0.004) \\\n",
"time fep-lambda coul-lambda vdw-lambda restraint-lambda \n",
"0.0 0.0 1.00 0.1 0.0002 -36423.369178 \n",
"2.0 0.0 1.00 0.4 0.0020 -36516.315498 \n",
"4.0 0.0 1.00 0.3 0.0010 -36432.357601 \n",
"6.0 0.0 0.22 0.0 0.0000 -36368.208474 \n",
"8.0 0.0 0.16 0.0 0.0000 -36423.464440 \n",
"... ... \n",
"99992.0 0.0 0.16 0.0 0.0000 -36412.020063 \n",
"99994.0 0.0 0.60 0.0 0.0000 -36508.302519 \n",
"99996.0 0.0 0.00 0.0 0.0000 -36651.555755 \n",
"99998.0 0.0 1.00 0.0 0.0001 -36399.373632 \n",
"100000.0 0.0 0.68 0.0 0.0000 -36532.534533 \n",
"\n",
" (0.0, 1.0, 0.52, 0.01) \\\n",
"time fep-lambda coul-lambda vdw-lambda restraint-lambda \n",
"0.0 0.0 1.00 0.1 0.0002 -36420.842397 \n",
"2.0 0.0 1.00 0.4 0.0020 -36514.585065 \n",
"4.0 0.0 1.00 0.3 0.0010 -36429.948794 \n",
"6.0 0.0 0.22 0.0 0.0000 -36365.779974 \n",
"8.0 0.0 0.16 0.0 0.0000 -36420.927202 \n",
"... ... \n",
"99992.0 0.0 0.16 0.0 0.0000 -36409.360316 \n",
"99994.0 0.0 0.60 0.0 0.0000 -36505.566767 \n",
"99996.0 0.0 0.00 0.0 0.0000 -36649.002457 \n",
"99998.0 0.0 1.00 0.0 0.0001 -36396.834263 \n",
"100000.0 0.0 0.68 0.0 0.0000 -36529.844659 \n",
"\n",
" (0.0, 1.0, 0.6, 0.02) \\\n",
"time fep-lambda coul-lambda vdw-lambda restraint-lambda \n",
"0.0 0.0 1.00 0.1 0.0002 -36415.626166 \n",
"2.0 0.0 1.00 0.4 0.0020 -36510.646409 \n",
"4.0 0.0 1.00 0.3 0.0010 -36424.838460 \n",
"6.0 0.0 0.22 0.0 0.0000 -36360.699541 \n",
"8.0 0.0 0.16 0.0 0.0000 -36415.695615 \n",
"... ... \n",
"99992.0 0.0 0.16 0.0 0.0000 -36403.946548 \n",
"99994.0 0.0 0.60 0.0 0.0000 -36500.035230 \n",
"99996.0 0.0 0.00 0.0 0.0000 -36643.735197 \n",
"99998.0 0.0 1.00 0.0 0.0001 -36391.658260 \n",
"100000.0 0.0 0.68 0.0 0.0000 -36524.367682 \n",
"\n",
" (0.0, 1.0, 0.68, 0.04) \\\n",
"time fep-lambda coul-lambda vdw-lambda restraint-lambda \n",
"0.0 0.0 1.00 0.1 0.0002 -36410.237611 \n",
"2.0 0.0 1.00 0.4 0.0020 -36506.202578 \n",
"4.0 0.0 1.00 0.3 0.0010 -36419.411703 \n",
"6.0 0.0 0.22 0.0 0.0000 -36355.379530 \n",
"8.0 0.0 0.16 0.0 0.0000 -36410.292820 \n",
"... ... \n",
"99992.0 0.0 0.16 0.0 0.0000 -36398.428717 \n",
"99994.0 0.0 0.60 0.0 0.0000 -36494.434418 \n",
"99996.0 0.0 0.00 0.0 0.0000 -36638.294797 \n",
"99998.0 0.0 1.00 0.0 0.0001 -36386.375961 \n",
"100000.0 0.0 0.68 0.0 0.0000 -36518.784104 \n",
"\n",
" (0.0, 1.0, 0.76, 0.1) \\\n",
"time fep-lambda coul-lambda vdw-lambda restraint-lambda \n",
"0.0 0.0 1.00 0.1 0.0002 -36404.705317 \n",
"2.0 0.0 1.00 0.4 0.0020 -36501.354671 \n",
"4.0 0.0 1.00 0.3 0.0010 -36413.725517 \n",
"6.0 0.0 0.22 0.0 0.0000 -36349.861210 \n",
"8.0 0.0 0.16 0.0 0.0000 -36404.746083 \n",
"... ... \n",
"99992.0 0.0 0.16 0.0 0.0000 -36392.816424 \n",
"99994.0 0.0 0.60 0.0 0.0000 -36488.758621 \n",
"99996.0 0.0 0.00 0.0 0.0000 -36632.711686 \n",
"99998.0 0.0 1.00 0.0 0.0001 -36381.000093 \n",
"100000.0 0.0 0.68 0.0 0.0000 -36513.104863 \n",
"\n",
" (0.0, 1.0, 0.84, 0.2) \\\n",
"time fep-lambda coul-lambda vdw-lambda restraint-lambda \n",
"0.0 0.0 1.00 0.1 0.0002 -36399.065603 \n",
"2.0 0.0 1.00 0.4 0.0020 -36496.198430 \n",
"4.0 0.0 1.00 0.3 0.0010 -36407.839587 \n",
"6.0 0.0 0.22 0.0 0.0000 -36344.191979 \n",
"8.0 0.0 0.16 0.0 0.0000 -36399.089537 \n",
"... ... \n",
"99992.0 0.0 0.16 0.0 0.0000 -36387.132412 \n",
"99994.0 0.0 0.60 0.0 0.0000 -36483.024528 \n",
"99996.0 0.0 0.00 0.0 0.0000 -36627.020775 \n",
"99998.0 0.0 1.00 0.0 0.0001 -36375.553599 \n",
"100000.0 0.0 0.68 0.0 0.0000 -36507.353322 \n",
"\n",
" (0.0, 1.0, 0.92, 0.4) \\\n",
"time fep-lambda coul-lambda vdw-lambda restraint-lambda \n",
"0.0 0.0 1.00 0.1 0.0002 -36393.305828 \n",
"2.0 0.0 1.00 0.4 0.0020 -36490.746462 \n",
"4.0 0.0 1.00 0.3 0.0010 -36401.758340 \n",
"6.0 0.0 0.22 0.0 0.0000 -36338.365268 \n",
"8.0 0.0 0.16 0.0 0.0000 -36393.317579 \n",
"... ... \n",
"99992.0 0.0 0.16 0.0 0.0000 -36381.356231 \n",
"99994.0 0.0 0.60 0.0 0.0000 -36477.191283 \n",
"99996.0 0.0 0.00 0.0 0.0000 -36621.220139 \n",
"99998.0 0.0 1.00 0.0 0.0001 -36370.021459 \n",
"100000.0 0.0 0.68 0.0 0.0000 -36501.510110 \n",
"\n",
" (0.0, 1.0, 1.0, 1.0) \n",
"time fep-lambda coul-lambda vdw-lambda restraint-lambda \n",
"0.0 0.0 1.00 0.1 0.0002 -36387.246310 \n",
"2.0 0.0 1.00 0.4 0.0020 -36484.758644 \n",
"4.0 0.0 1.00 0.3 0.0010 -36395.308852 \n",
"6.0 0.0 0.22 0.0 0.0000 -36332.194787 \n",
"8.0 0.0 0.16 0.0 0.0000 -36387.288730 \n",
"... ... \n",
"99992.0 0.0 0.16 0.0 0.0000 -36375.309819 \n",
"99994.0 0.0 0.60 0.0 0.0000 -36470.999236 \n",
"99996.0 0.0 0.00 0.0 0.0000 -36615.185064 \n",
"99998.0 0.0 1.00 0.0 0.0001 -36364.252395 \n",
"100000.0 0.0 0.68 0.0 0.0000 -36495.400679 \n",
"\n",
"[50001 rows x 28 columns]"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"u_nk = extract_u_nk(data.data['AllStates'][0], T=300)\n",
"u_nk"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
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" <td>128.001486</td>\n",
" <td>5.099843</td>\n",
" <td>0.487645</td>\n",
" </tr>\n",
" <tr>\n",
" <th>99998.0</th>\n",
" <th>0.0</th>\n",
" <th>1.00</th>\n",
" <th>0.0</th>\n",
" <th>0.0001</th>\n",
" <td>0.0</td>\n",
" <td>-19.239824</td>\n",
" <td>28.486784</td>\n",
" <td>0.552438</td>\n",
" </tr>\n",
" <tr>\n",
" <th>100000.0</th>\n",
" <th>0.0</th>\n",
" <th>0.68</th>\n",
" <th>0.0</th>\n",
" <th>0.0000</th>\n",
" <td>0.0</td>\n",
" <td>7.820713</td>\n",
" <td>31.069375</td>\n",
" <td>0.630800</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>50001 rows × 4 columns</p>\n",
"</div>"
],
"text/plain": [
" fep coul \\\n",
"time fep-lambda coul-lambda vdw-lambda restraint-lambda \n",
"0.0 0.0 1.00 0.1 0.0002 0.0 -25.370747 \n",
"2.0 0.0 1.00 0.4 0.0020 0.0 -29.204609 \n",
"4.0 0.0 1.00 0.3 0.0010 0.0 -22.750548 \n",
"6.0 0.0 0.22 0.0 0.0000 0.0 98.102009 \n",
"8.0 0.0 0.16 0.0 0.0000 0.0 111.564190 \n",
"... ... ... \n",
"99992.0 0.0 0.16 0.0 0.0000 0.0 108.143665 \n",
"99994.0 0.0 0.60 0.0 0.0000 0.0 11.583392 \n",
"99996.0 0.0 0.00 0.0 0.0000 0.0 128.001486 \n",
"99998.0 0.0 1.00 0.0 0.0001 0.0 -19.239824 \n",
"100000.0 0.0 0.68 0.0 0.0000 0.0 7.820713 \n",
"\n",
" vdw \\\n",
"time fep-lambda coul-lambda vdw-lambda restraint-lambda \n",
"0.0 0.0 1.00 0.1 0.0002 18.818422 \n",
"2.0 0.0 1.00 0.4 0.0020 29.146286 \n",
"4.0 0.0 1.00 0.3 0.0010 39.792327 \n",
"6.0 0.0 0.22 0.0 0.0000 -31.308051 \n",
"8.0 0.0 0.16 0.0 0.0000 11.168792 \n",
"... ... \n",
"99992.0 0.0 0.16 0.0 0.0000 33.530695 \n",
"99994.0 0.0 0.60 0.0 0.0000 40.429575 \n",
"99996.0 0.0 0.00 0.0 0.0000 5.099843 \n",
"99998.0 0.0 1.00 0.0 0.0001 28.486784 \n",
"100000.0 0.0 0.68 0.0 0.0000 31.069375 \n",
"\n",
" restraint \n",
"time fep-lambda coul-lambda vdw-lambda restraint-lambda \n",
"0.0 0.0 1.00 0.1 0.0002 0.669507 \n",
"2.0 0.0 1.00 0.4 0.0020 0.980692 \n",
"4.0 0.0 1.00 0.3 0.0010 0.697221 \n",
"6.0 0.0 0.22 0.0 0.0000 0.714655 \n",
"8.0 0.0 0.16 0.0 0.0000 0.543152 \n",
"... ... \n",
"99992.0 0.0 0.16 0.0 0.0000 0.641591 \n",
"99994.0 0.0 0.60 0.0 0.0000 0.900641 \n",
"99996.0 0.0 0.00 0.0 0.0000 0.487645 \n",
"99998.0 0.0 1.00 0.0 0.0001 0.552438 \n",
"100000.0 0.0 0.68 0.0 0.0000 0.630800 \n",
"\n",
"[50001 rows x 4 columns]"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dHdl = extract_dHdl(data.data['AllStates'][0], T=300)\n",
"dHdl"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We'll use a function called `bootstrap` that produces a bootstrapped version of the `DataFrame` it is fed."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"\n",
"import seaborn as sns\n",
"\n",
"from alchemlyb.preprocessing.bootstrapping import bootstrap"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
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" <tr style=\"text-align: right;\">\n",
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" </tr>\n",
" <tr>\n",
" <th>time</th>\n",
" <th>fep-lambda</th>\n",
" <th>coul-lambda</th>\n",
" <th>vdw-lambda</th>\n",
" <th>restraint-lambda</th>\n",
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" <td>-36356.746089</td>\n",
" <td>-36351.958455</td>\n",
" <td>-36346.894632</td>\n",
" <td>-36341.614102</td>\n",
" <td>-36336.170149</td>\n",
" <td>-36330.583426</td>\n",
" <td>-36324.783604</td>\n",
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" <tr>\n",
" <th>66836.0</th>\n",
" <th>0.0</th>\n",
" <th>0.0</th>\n",
" <th>0.0</th>\n",
" <th>0.0</th>\n",
" <td>-3.675944e+04</td>\n",
" <td>-3.675148e+04</td>\n",
" <td>-3.674352e+04</td>\n",
" <td>-3.673397e+04</td>\n",
" <td>-3.672442e+04</td>\n",
" <td>-3.671487e+04</td>\n",
" <td>-3.670532e+04</td>\n",
" <td>-3.669577e+04</td>\n",
" <td>-3.668621e+04</td>\n",
" <td>-3.667666e+04</td>\n",
" <td>...</td>\n",
" <td>-36594.955469</td>\n",
" <td>-36590.397135</td>\n",
" <td>-36586.240831</td>\n",
" <td>-36584.031876</td>\n",
" <td>-36579.413810</td>\n",
" <td>-36574.580946</td>\n",
" <td>-36569.574184</td>\n",
" <td>-36564.435303</td>\n",
" <td>-36559.169234</td>\n",
" <td>-36553.664219</td>\n",
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" <th>66436.0</th>\n",
" <th>0.0</th>\n",
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" <td>-3.643946e+04</td>\n",
" <td>-3.643125e+04</td>\n",
" <td>-3.642305e+04</td>\n",
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" <td>-36345.180430</td>\n",
" <td>-36340.876186</td>\n",
" <td>-36338.605087</td>\n",
" <td>-36333.879967</td>\n",
" <td>-36328.955010</td>\n",
" <td>-36323.862626</td>\n",
" <td>-36318.641014</td>\n",
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" <td>-3.640428e+04</td>\n",
" <td>-3.639257e+04</td>\n",
" <td>-3.638085e+04</td>\n",
" <td>...</td>\n",
" <td>-36282.429287</td>\n",
" <td>-36278.152947</td>\n",
" <td>-36274.223184</td>\n",
" <td>-36272.127265</td>\n",
" <td>-36267.733383</td>\n",
" <td>-36263.122654</td>\n",
" <td>-36258.337674</td>\n",
" <td>-36253.420310</td>\n",
" <td>-36248.379219</td>\n",
" <td>-36243.120743</td>\n",
" </tr>\n",
" <tr>\n",
" <th>53478.0</th>\n",
" <th>0.0</th>\n",
" <th>0.0</th>\n",
" <th>0.0</th>\n",
" <th>0.0</th>\n",
" <td>-3.632835e+04</td>\n",
" <td>-3.632104e+04</td>\n",
" <td>-3.631373e+04</td>\n",
" <td>-3.630496e+04</td>\n",
" <td>-3.629619e+04</td>\n",
" <td>-3.628742e+04</td>\n",
" <td>-3.627865e+04</td>\n",
" <td>-3.626988e+04</td>\n",
" <td>-3.626110e+04</td>\n",
" <td>-3.625233e+04</td>\n",
" <td>...</td>\n",
" <td>-36182.943593</td>\n",
" <td>-36178.916750</td>\n",
" <td>-36174.991344</td>\n",
" <td>-36172.850909</td>\n",
" <td>-36168.297113</td>\n",
" <td>-36163.452995</td>\n",
" <td>-36158.379558</td>\n",
" <td>-36153.132340</td>\n",
" <td>-36147.729236</td>\n",
" <td>-36142.079750</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <th>...</th>\n",
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" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>42380.0</th>\n",
" <th>0.0</th>\n",
" <th>1.0</th>\n",
" <th>1.0</th>\n",
" <th>1.0</th>\n",
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" <td>2.153912e+07</td>\n",
" <td>2.153911e+07</td>\n",
" <td>2.153911e+07</td>\n",
" <td>2.153911e+07</td>\n",
" <td>2.153911e+07</td>\n",
" <td>2.153911e+07</td>\n",
" <td>2.153911e+07</td>\n",
" <td>2.153910e+07</td>\n",
" <td>2.153910e+07</td>\n",
" <td>...</td>\n",
" <td>-35873.259518</td>\n",
" <td>-36177.516563</td>\n",
" <td>-36298.845217</td>\n",
" <td>-36338.738654</td>\n",
" <td>-36393.419838</td>\n",
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" <td>-36462.026210</td>\n",
" <td>-36461.744571</td>\n",
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" <tr>\n",
" <th>26644.0</th>\n",
" <th>0.0</th>\n",
" <th>1.0</th>\n",
" <th>1.0</th>\n",
" <th>1.0</th>\n",
" <td>2.154413e+09</td>\n",
" <td>2.154413e+09</td>\n",
" <td>2.154413e+09</td>\n",
" <td>2.154413e+09</td>\n",
" <td>2.154413e+09</td>\n",
" <td>2.154413e+09</td>\n",
" <td>2.154413e+09</td>\n",
" <td>2.154413e+09</td>\n",
" <td>2.154413e+09</td>\n",
" <td>2.154413e+09</td>\n",
" <td>...</td>\n",
" <td>-35614.808002</td>\n",
" <td>-35924.812434</td>\n",
" <td>-36046.615214</td>\n",
" <td>-36086.415588</td>\n",
" <td>-36140.717530</td>\n",
" <td>-36173.359462</td>\n",
" <td>-36192.756552</td>\n",
" <td>-36203.681727</td>\n",
" <td>-36208.930674</td>\n",
" <td>-36209.986853</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23406.0</th>\n",
" <th>0.0</th>\n",
" <th>1.0</th>\n",
" <th>1.0</th>\n",
" <th>1.0</th>\n",
" <td>1.807774e+11</td>\n",
" <td>1.807774e+11</td>\n",
" <td>1.807774e+11</td>\n",
" <td>1.807774e+11</td>\n",
" <td>1.807774e+11</td>\n",
" <td>1.807774e+11</td>\n",
" <td>1.807774e+11</td>\n",
" <td>1.807774e+11</td>\n",
" <td>1.807774e+11</td>\n",
" <td>1.807774e+11</td>\n",
" <td>...</td>\n",
" <td>-35737.970710</td>\n",
" <td>-36068.530575</td>\n",
" <td>-36198.987569</td>\n",
" <td>-36241.730342</td>\n",
" <td>-36300.170731</td>\n",
" <td>-36335.384642</td>\n",
" <td>-36356.341241</td>\n",
" <td>-36368.145383</td>\n",
" <td>-36373.795653</td>\n",
" <td>-36374.881866</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22924.0</th>\n",
" <th>0.0</th>\n",
" <th>1.0</th>\n",
" <th>1.0</th>\n",
" <th>1.0</th>\n",
" <td>1.664938e+09</td>\n",
" <td>1.664938e+09</td>\n",
" <td>1.664938e+09</td>\n",
" <td>1.664938e+09</td>\n",
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" <td>1.664938e+09</td>\n",
" <td>1.664938e+09</td>\n",
" <td>1.664938e+09</td>\n",
" <td>1.664938e+09</td>\n",
" <td>...</td>\n",
" <td>-36043.950977</td>\n",
" <td>-36347.574307</td>\n",
" <td>-36466.344467</td>\n",
" <td>-36505.013896</td>\n",
" <td>-36557.544037</td>\n",
" <td>-36588.836058</td>\n",
" <td>-36607.121386</td>\n",
" <td>-36617.077710</td>\n",
" <td>-36621.403332</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>82110.0</th>\n",
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],
"text/plain": [
" (0.0, 0.0, 0.0, 0.0) \\\n",
"time fep-lambda coul-lambda vdw-lambda restraint-lambda \n",
"12406.0 0.0 0.0 0.0 0.0 -3.652022e+04 \n",
"66836.0 0.0 0.0 0.0 0.0 -3.675944e+04 \n",
"66436.0 0.0 0.0 0.0 0.0 -3.649414e+04 \n",
"66334.0 0.0 0.0 0.0 0.0 -3.648241e+04 \n",
"53478.0 0.0 0.0 0.0 0.0 -3.632835e+04 \n",
"... ... \n",
"42380.0 0.0 1.0 1.0 1.0 2.153912e+07 \n",
"26644.0 0.0 1.0 1.0 1.0 2.154413e+09 \n",
"23406.0 0.0 1.0 1.0 1.0 1.807774e+11 \n",
"22924.0 0.0 1.0 1.0 1.0 1.664938e+09 \n",
"82110.0 0.0 1.0 1.0 1.0 8.194179e+13 \n",
"\n",
" (0.0, 0.05, 0.0, 0.0) \\\n",
"time fep-lambda coul-lambda vdw-lambda restraint-lambda \n",
"12406.0 0.0 0.0 0.0 0.0 -3.651271e+04 \n",
"66836.0 0.0 0.0 0.0 0.0 -3.675148e+04 \n",
"66436.0 0.0 0.0 0.0 0.0 -3.648731e+04 \n",
"66334.0 0.0 0.0 0.0 0.0 -3.647264e+04 \n",
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"... ... \n",
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"82110.0 0.0 1.0 1.0 1.0 8.194179e+13 \n",
"\n",
" (0.0, 0.1, 0.0, 0.0) \\\n",
"time fep-lambda coul-lambda vdw-lambda restraint-lambda \n",
"12406.0 0.0 0.0 0.0 0.0 -3.650520e+04 \n",
"66836.0 0.0 0.0 0.0 0.0 -3.674352e+04 \n",
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"... ... \n",
"42380.0 0.0 1.0 1.0 1.0 2.153911e+07 \n",
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"22924.0 0.0 1.0 1.0 1.0 1.664938e+09 \n",
"82110.0 0.0 1.0 1.0 1.0 8.194179e+13 \n",
"\n",
" (0.0, 0.16, 0.0, 0.0) \\\n",
"time fep-lambda coul-lambda vdw-lambda restraint-lambda \n",
"12406.0 0.0 0.0 0.0 0.0 -3.649619e+04 \n",
"66836.0 0.0 0.0 0.0 0.0 -3.673397e+04 \n",
"66436.0 0.0 0.0 0.0 0.0 -3.647227e+04 \n",
"66334.0 0.0 0.0 0.0 0.0 -3.645116e+04 \n",
"53478.0 0.0 0.0 0.0 0.0 -3.630496e+04 \n",
"... ... \n",
"42380.0 0.0 1.0 1.0 1.0 2.153911e+07 \n",
"26644.0 0.0 1.0 1.0 1.0 2.154413e+09 \n",
"23406.0 0.0 1.0 1.0 1.0 1.807774e+11 \n",
"22924.0 0.0 1.0 1.0 1.0 1.664938e+09 \n",
"82110.0 0.0 1.0 1.0 1.0 8.194179e+13 \n",
"\n",
" (0.0, 0.22, 0.0, 0.0) \\\n",
"time fep-lambda coul-lambda vdw-lambda restraint-lambda \n",
"12406.0 0.0 0.0 0.0 0.0 -3.648718e+04 \n",
"66836.0 0.0 0.0 0.0 0.0 -3.672442e+04 \n",
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"66334.0 0.0 0.0 0.0 0.0 -3.643944e+04 \n",
"53478.0 0.0 0.0 0.0 0.0 -3.629619e+04 \n",
"... ... \n",
"42380.0 0.0 1.0 1.0 1.0 2.153911e+07 \n",
"26644.0 0.0 1.0 1.0 1.0 2.154413e+09 \n",
"23406.0 0.0 1.0 1.0 1.0 1.807774e+11 \n",
"22924.0 0.0 1.0 1.0 1.0 1.664938e+09 \n",
"82110.0 0.0 1.0 1.0 1.0 8.194179e+13 \n",
"\n",
" (0.0, 0.28, 0.0, 0.0) \\\n",
"time fep-lambda coul-lambda vdw-lambda restraint-lambda \n",
"12406.0 0.0 0.0 0.0 0.0 -3.647817e+04 \n",
"66836.0 0.0 0.0 0.0 0.0 -3.671487e+04 \n",
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"... ... \n",
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"82110.0 0.0 1.0 1.0 1.0 8.194179e+13 \n",
"\n",
" (0.0, 0.34, 0.0, 0.0) \\\n",
"time fep-lambda coul-lambda vdw-lambda restraint-lambda \n",
"12406.0 0.0 0.0 0.0 0.0 -3.646916e+04 \n",
"66836.0 0.0 0.0 0.0 0.0 -3.670532e+04 \n",
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"66334.0 0.0 0.0 0.0 0.0 -3.641600e+04 \n",
"53478.0 0.0 0.0 0.0 0.0 -3.627865e+04 \n",
"... ... \n",
"42380.0 0.0 1.0 1.0 1.0 2.153911e+07 \n",
"26644.0 0.0 1.0 1.0 1.0 2.154413e+09 \n",
"23406.0 0.0 1.0 1.0 1.0 1.807774e+11 \n",
"22924.0 0.0 1.0 1.0 1.0 1.664938e+09 \n",
"82110.0 0.0 1.0 1.0 1.0 8.194179e+13 \n",
"\n",
" (0.0, 0.4, 0.0, 0.0) \\\n",
"time fep-lambda coul-lambda vdw-lambda restraint-lambda \n",
"12406.0 0.0 0.0 0.0 0.0 -3.646015e+04 \n",
"66836.0 0.0 0.0 0.0 0.0 -3.669577e+04 \n",
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"66334.0 0.0 0.0 0.0 0.0 -3.640428e+04 \n",
"53478.0 0.0 0.0 0.0 0.0 -3.626988e+04 \n",
"... ... \n",
"42380.0 0.0 1.0 1.0 1.0 2.153911e+07 \n",
"26644.0 0.0 1.0 1.0 1.0 2.154413e+09 \n",
"23406.0 0.0 1.0 1.0 1.0 1.807774e+11 \n",
"22924.0 0.0 1.0 1.0 1.0 1.664938e+09 \n",
"82110.0 0.0 1.0 1.0 1.0 8.194179e+13 \n",
"\n",
" (0.0, 0.46, 0.0, 0.0) \\\n",
"time fep-lambda coul-lambda vdw-lambda restraint-lambda \n",
"12406.0 0.0 0.0 0.0 0.0 -3.645114e+04 \n",
"66836.0 0.0 0.0 0.0 0.0 -3.668621e+04 \n",
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"... ... \n",
"42380.0 0.0 1.0 1.0 1.0 2.153910e+07 \n",
"26644.0 0.0 1.0 1.0 1.0 2.154413e+09 \n",
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"22924.0 0.0 1.0 1.0 1.0 1.664938e+09 \n",
"82110.0 0.0 1.0 1.0 1.0 8.194179e+13 \n",
"\n",
" (0.0, 0.52, 0.0, 0.0) \\\n",
"time fep-lambda coul-lambda vdw-lambda restraint-lambda \n",
"12406.0 0.0 0.0 0.0 0.0 -3.644213e+04 \n",
"66836.0 0.0 0.0 0.0 0.0 -3.667666e+04 \n",
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"53478.0 0.0 0.0 0.0 0.0 -3.625233e+04 \n",
"... ... \n",
"42380.0 0.0 1.0 1.0 1.0 2.153910e+07 \n",
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"82110.0 0.0 1.0 1.0 1.0 8.194179e+13 \n",
"\n",
" ... \\\n",
"time fep-lambda coul-lambda vdw-lambda restraint-lambda ... \n",
"12406.0 0.0 0.0 0.0 0.0 ... \n",
"66836.0 0.0 0.0 0.0 0.0 ... \n",
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"... ... \n",
"42380.0 0.0 1.0 1.0 1.0 ... \n",
"26644.0 0.0 1.0 1.0 1.0 ... \n",
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"82110.0 0.0 1.0 1.0 1.0 ... \n",
"\n",
" (0.0, 1.0, 0.3, 0.001) \\\n",
"time fep-lambda coul-lambda vdw-lambda restraint-lambda \n",
"12406.0 0.0 0.0 0.0 0.0 -36367.622499 \n",
"66836.0 0.0 0.0 0.0 0.0 -36594.955469 \n",
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"53478.0 0.0 0.0 0.0 0.0 -36182.943593 \n",
"... ... \n",
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"23406.0 0.0 1.0 1.0 1.0 -35737.970710 \n",
"22924.0 0.0 1.0 1.0 1.0 -36043.950977 \n",
"82110.0 0.0 1.0 1.0 1.0 -35837.437277 \n",
"\n",
" (0.0, 1.0, 0.4, 0.002) \\\n",
"time fep-lambda coul-lambda vdw-lambda restraint-lambda \n",
"12406.0 0.0 0.0 0.0 0.0 -36363.202995 \n",
"66836.0 0.0 0.0 0.0 0.0 -36590.397135 \n",
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"53478.0 0.0 0.0 0.0 0.0 -36178.916750 \n",
"... ... \n",
"42380.0 0.0 1.0 1.0 1.0 -36177.516563 \n",
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"23406.0 0.0 1.0 1.0 1.0 -36068.530575 \n",
"22924.0 0.0 1.0 1.0 1.0 -36347.574307 \n",
"82110.0 0.0 1.0 1.0 1.0 -36192.985048 \n",
"\n",
" (0.0, 1.0, 0.48, 0.004) \\\n",
"time fep-lambda coul-lambda vdw-lambda restraint-lambda \n",
"12406.0 0.0 0.0 0.0 0.0 -36359.010176 \n",
"66836.0 0.0 0.0 0.0 0.0 -36586.240831 \n",
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"66334.0 0.0 0.0 0.0 0.0 -36274.223184 \n",
"53478.0 0.0 0.0 0.0 0.0 -36174.991344 \n",
"... ... \n",
"42380.0 0.0 1.0 1.0 1.0 -36298.845217 \n",
"26644.0 0.0 1.0 1.0 1.0 -36046.615214 \n",
"23406.0 0.0 1.0 1.0 1.0 -36198.987569 \n",
"22924.0 0.0 1.0 1.0 1.0 -36466.344467 \n",
"82110.0 0.0 1.0 1.0 1.0 -36331.279314 \n",
"\n",
" (0.0, 1.0, 0.52, 0.01) \\\n",
"time fep-lambda coul-lambda vdw-lambda restraint-lambda \n",
"12406.0 0.0 0.0 0.0 0.0 -36356.746089 \n",
"66836.0 0.0 0.0 0.0 0.0 -36584.031876 \n",
"66436.0 0.0 0.0 0.0 0.0 -36338.605087 \n",
"66334.0 0.0 0.0 0.0 0.0 -36272.127265 \n",
"53478.0 0.0 0.0 0.0 0.0 -36172.850909 \n",
"... ... \n",
"42380.0 0.0 1.0 1.0 1.0 -36338.738654 \n",
"26644.0 0.0 1.0 1.0 1.0 -36086.415588 \n",
"23406.0 0.0 1.0 1.0 1.0 -36241.730342 \n",
"22924.0 0.0 1.0 1.0 1.0 -36505.013896 \n",
"82110.0 0.0 1.0 1.0 1.0 -36376.168131 \n",
"\n",
" (0.0, 1.0, 0.6, 0.02) \\\n",
"time fep-lambda coul-lambda vdw-lambda restraint-lambda \n",
"12406.0 0.0 0.0 0.0 0.0 -36351.958455 \n",
"66836.0 0.0 0.0 0.0 0.0 -36579.413810 \n",
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"... ... \n",
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"22924.0 0.0 1.0 1.0 1.0 -36557.544037 \n",
"82110.0 0.0 1.0 1.0 1.0 -36437.068875 \n",
"\n",
" (0.0, 1.0, 0.68, 0.04) \\\n",
"time fep-lambda coul-lambda vdw-lambda restraint-lambda \n",
"12406.0 0.0 0.0 0.0 0.0 -36346.894632 \n",
"66836.0 0.0 0.0 0.0 0.0 -36574.580946 \n",
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"53478.0 0.0 0.0 0.0 0.0 -36163.452995 \n",
"... ... \n",
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"82110.0 0.0 1.0 1.0 1.0 -36473.429331 \n",
"\n",
" (0.0, 1.0, 0.76, 0.1) \\\n",
"time fep-lambda coul-lambda vdw-lambda restraint-lambda \n",
"12406.0 0.0 0.0 0.0 0.0 -36341.614102 \n",
"66836.0 0.0 0.0 0.0 0.0 -36569.574184 \n",
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"53478.0 0.0 0.0 0.0 0.0 -36158.379558 \n",
"... ... \n",
"42380.0 0.0 1.0 1.0 1.0 -36446.069398 \n",
"26644.0 0.0 1.0 1.0 1.0 -36192.756552 \n",
"23406.0 0.0 1.0 1.0 1.0 -36356.341241 \n",
"22924.0 0.0 1.0 1.0 1.0 -36607.121386 \n",
"82110.0 0.0 1.0 1.0 1.0 -36494.934422 \n",
"\n",
" (0.0, 1.0, 0.84, 0.2) \\\n",
"time fep-lambda coul-lambda vdw-lambda restraint-lambda \n",
"12406.0 0.0 0.0 0.0 0.0 -36336.170149 \n",
"66836.0 0.0 0.0 0.0 0.0 -36564.435303 \n",
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"53478.0 0.0 0.0 0.0 0.0 -36153.132340 \n",
"... ... \n",
"42380.0 0.0 1.0 1.0 1.0 -36457.046159 \n",
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"23406.0 0.0 1.0 1.0 1.0 -36368.145383 \n",
"22924.0 0.0 1.0 1.0 1.0 -36617.077710 \n",
"82110.0 0.0 1.0 1.0 1.0 -36507.024430 \n",
"\n",
" (0.0, 1.0, 0.92, 0.4) \\\n",
"time fep-lambda coul-lambda vdw-lambda restraint-lambda \n",
"12406.0 0.0 0.0 0.0 0.0 -36330.583426 \n",
"66836.0 0.0 0.0 0.0 0.0 -36559.169234 \n",
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"53478.0 0.0 0.0 0.0 0.0 -36147.729236 \n",
"... ... \n",
"42380.0 0.0 1.0 1.0 1.0 -36462.026210 \n",
"26644.0 0.0 1.0 1.0 1.0 -36208.930674 \n",
"23406.0 0.0 1.0 1.0 1.0 -36373.795653 \n",
"22924.0 0.0 1.0 1.0 1.0 -36621.403332 \n",
"82110.0 0.0 1.0 1.0 1.0 -36512.894676 \n",
"\n",
" (0.0, 1.0, 1.0, 1.0) \n",
"time fep-lambda coul-lambda vdw-lambda restraint-lambda \n",
"12406.0 0.0 0.0 0.0 0.0 -36324.783604 \n",
"66836.0 0.0 0.0 0.0 0.0 -36553.664219 \n",
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"... ... \n",
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"23406.0 0.0 1.0 1.0 1.0 -36374.881866 \n",
"22924.0 0.0 1.0 1.0 1.0 -36621.346397 \n",
"82110.0 0.0 1.0 1.0 1.0 -36514.376157 \n",
"\n",
"[50001 rows x 28 columns]"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bootstrap(u_nk, random_state=42)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
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" <td>...</td>\n",
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" <td>...</td>\n",
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" <tr>\n",
" <th>42380.0</th>\n",
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" <th>1.0</th>\n",
" <th>1.0</th>\n",
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" <td>-26.654287</td>\n",
" <td>-4.481644</td>\n",
" <td>3.552037</td>\n",
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" <tr>\n",
" <th>26644.0</th>\n",
" <th>0.0</th>\n",
" <th>1.0</th>\n",
" <th>1.0</th>\n",
" <th>1.0</th>\n",
" <td>0.0</td>\n",
" <td>-435.223144</td>\n",
" <td>-2.518275</td>\n",
" <td>1.020132</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23406.0</th>\n",
" <th>0.0</th>\n",
" <th>1.0</th>\n",
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" <td>0.0</td>\n",
" <td>-411.861360</td>\n",
" <td>-2.037621</td>\n",
" <td>1.124172</td>\n",
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" <tr>\n",
" <th>22924.0</th>\n",
" <th>0.0</th>\n",
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" <th>1.0</th>\n",
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],
"text/plain": [
" fep coul \\\n",
"time fep-lambda coul-lambda vdw-lambda restraint-lambda \n",
"12406.0 0.0 0.0 0.0 0.0 0.0 150.173387 \n",
"66836.0 0.0 0.0 0.0 0.0 0.0 159.195895 \n",
"66436.0 0.0 0.0 0.0 0.0 0.0 136.716144 \n",
"66334.0 0.0 0.0 0.0 0.0 0.0 195.312595 \n",
"53478.0 0.0 0.0 0.0 0.0 0.0 146.183223 \n",
"... ... ... \n",
"42380.0 0.0 1.0 1.0 1.0 0.0 -26.654287 \n",
"26644.0 0.0 1.0 1.0 1.0 0.0 -435.223144 \n",
"23406.0 0.0 1.0 1.0 1.0 0.0 -411.861360 \n",
"22924.0 0.0 1.0 1.0 1.0 NaN -47.555592 \n",
"82110.0 0.0 1.0 1.0 1.0 0.0 277.096591 \n",
"\n",
" vdw \\\n",
"time fep-lambda coul-lambda vdw-lambda restraint-lambda \n",
"12406.0 0.0 0.0 0.0 0.0 -38.781438 \n",
"66836.0 0.0 0.0 0.0 0.0 -18.474767 \n",
"66436.0 0.0 0.0 0.0 0.0 -3.769272 \n",
"66334.0 0.0 0.0 0.0 0.0 -18.703157 \n",
"53478.0 0.0 0.0 0.0 0.0 -67.798597 \n",
"... ... \n",
"42380.0 0.0 1.0 1.0 1.0 -4.481644 \n",
"26644.0 0.0 1.0 1.0 1.0 -2.518275 \n",
"23406.0 0.0 1.0 1.0 1.0 -2.037621 \n",
"22924.0 0.0 1.0 1.0 1.0 6.036154 \n",
"82110.0 0.0 1.0 1.0 1.0 -2.932633 \n",
"\n",
" restraint \n",
"time fep-lambda coul-lambda vdw-lambda restraint-lambda \n",
"12406.0 0.0 0.0 0.0 0.0 0.341422 \n",
"66836.0 0.0 0.0 0.0 0.0 0.456740 \n",
"66436.0 0.0 0.0 0.0 0.0 0.553704 \n",
"66334.0 0.0 0.0 0.0 0.0 0.396809 \n",
"53478.0 0.0 0.0 0.0 0.0 0.411901 \n",
"... ... \n",
"42380.0 0.0 1.0 1.0 1.0 3.552037 \n",
"26644.0 0.0 1.0 1.0 1.0 1.020132 \n",
"23406.0 0.0 1.0 1.0 1.0 1.124172 \n",
"22924.0 0.0 1.0 1.0 1.0 1.685209 \n",
"82110.0 0.0 1.0 1.0 1.0 0.590915 \n",
"\n",
"[50001 rows x 4 columns]"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bootstrap(dHdl, random_state=42)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Notice that when we use `bootstrap` on one of our datasets, the `time` index becomes visibly scrambled. For the set of samples for each `lambda`-index combination, this function effectively replaces each sample in the set with a sample uniformly chosen, with replacement, from that set. The result is a `DataFrame` with the same *number of samples* as the original, and the same number of samples for each `lambda`-index combination.\n",
"\n",
"Note the `random_seed` keyword argument makes the call to `bootstrap` deterministic. Not specifying a seed will give a different result every time the function is called on the same data:"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
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"text/plain": [
" (0.0, 0.0, 0.0, 0.0) \\\n",
"time fep-lambda coul-lambda vdw-lambda restraint-lambda \n",
"37326.0 0.0 0.0 0.0 0.0 -3.658737e+04 \n",
"37322.0 0.0 0.0 0.0 0.0 -3.633149e+04 \n",
"80412.0 0.0 0.0 0.0 0.0 -3.665674e+04 \n",
"67374.0 0.0 0.0 0.0 0.0 -3.658091e+04 \n",
"62036.0 0.0 0.0 0.0 0.0 -3.638524e+04 \n",
"... ... \n",
"70898.0 0.0 1.0 1.0 1.0 2.348421e+08 \n",
"87138.0 0.0 1.0 1.0 1.0 2.911213e+16 \n",
"51192.0 0.0 1.0 1.0 1.0 1.051405e+10 \n",
"23302.0 0.0 1.0 1.0 1.0 2.532350e+08 \n",
"46170.0 0.0 1.0 1.0 1.0 2.026784e+06 \n",
"\n",
" (0.0, 0.05, 0.0, 0.0) \\\n",
"time fep-lambda coul-lambda vdw-lambda restraint-lambda \n",
"37326.0 0.0 0.0 0.0 0.0 -3.657885e+04 \n",
"37322.0 0.0 0.0 0.0 0.0 -3.632223e+04 \n",
"80412.0 0.0 0.0 0.0 0.0 -3.664870e+04 \n",
"67374.0 0.0 0.0 0.0 0.0 -3.657336e+04 \n",
"62036.0 0.0 0.0 0.0 0.0 -3.637933e+04 \n",
"... ... \n",
"70898.0 0.0 1.0 1.0 1.0 2.348421e+08 \n",
"87138.0 0.0 1.0 1.0 1.0 2.911213e+16 \n",
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"23302.0 0.0 1.0 1.0 1.0 2.532350e+08 \n",
"46170.0 0.0 1.0 1.0 1.0 2.026780e+06 \n",
"\n",
" (0.0, 0.1, 0.0, 0.0) \\\n",
"time fep-lambda coul-lambda vdw-lambda restraint-lambda \n",
"37326.0 0.0 0.0 0.0 0.0 -3.657034e+04 \n",
"37322.0 0.0 0.0 0.0 0.0 -3.631297e+04 \n",
"80412.0 0.0 0.0 0.0 0.0 -3.664065e+04 \n",
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"62036.0 0.0 0.0 0.0 0.0 -3.637343e+04 \n",
"... ... \n",
"70898.0 0.0 1.0 1.0 1.0 2.348421e+08 \n",
"87138.0 0.0 1.0 1.0 1.0 2.911213e+16 \n",
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"23302.0 0.0 1.0 1.0 1.0 2.532350e+08 \n",
"46170.0 0.0 1.0 1.0 1.0 2.026775e+06 \n",
"\n",
" (0.0, 0.16, 0.0, 0.0) \\\n",
"time fep-lambda coul-lambda vdw-lambda restraint-lambda \n",
"37326.0 0.0 0.0 0.0 0.0 -3.656013e+04 \n",
"37322.0 0.0 0.0 0.0 0.0 -3.630186e+04 \n",
"80412.0 0.0 0.0 0.0 0.0 -3.663100e+04 \n",
"67374.0 0.0 0.0 0.0 0.0 -3.655674e+04 \n",
"62036.0 0.0 0.0 0.0 0.0 -3.636634e+04 \n",
"... ... \n",
"70898.0 0.0 1.0 1.0 1.0 2.348421e+08 \n",
"87138.0 0.0 1.0 1.0 1.0 2.911213e+16 \n",
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"23302.0 0.0 1.0 1.0 1.0 2.532350e+08 \n",
"46170.0 0.0 1.0 1.0 1.0 2.026770e+06 \n",
"\n",
" (0.0, 0.22, 0.0, 0.0) \\\n",
"time fep-lambda coul-lambda vdw-lambda restraint-lambda \n",
"37326.0 0.0 0.0 0.0 0.0 -3.654991e+04 \n",
"37322.0 0.0 0.0 0.0 0.0 -3.629075e+04 \n",
"80412.0 0.0 0.0 0.0 0.0 -3.662135e+04 \n",
"67374.0 0.0 0.0 0.0 0.0 -3.654768e+04 \n",
"62036.0 0.0 0.0 0.0 0.0 -3.635925e+04 \n",
"... ... \n",
"70898.0 0.0 1.0 1.0 1.0 2.348421e+08 \n",
"87138.0 0.0 1.0 1.0 1.0 2.911213e+16 \n",
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"23302.0 0.0 1.0 1.0 1.0 2.532350e+08 \n",
"46170.0 0.0 1.0 1.0 1.0 2.026765e+06 \n",
"\n",
" (0.0, 0.28, 0.0, 0.0) \\\n",
"time fep-lambda coul-lambda vdw-lambda restraint-lambda \n",
"37326.0 0.0 0.0 0.0 0.0 -3.653970e+04 \n",
"37322.0 0.0 0.0 0.0 0.0 -3.627963e+04 \n",
"80412.0 0.0 0.0 0.0 0.0 -3.661170e+04 \n",
"67374.0 0.0 0.0 0.0 0.0 -3.653861e+04 \n",
"62036.0 0.0 0.0 0.0 0.0 -3.635216e+04 \n",
"... ... \n",
"70898.0 0.0 1.0 1.0 1.0 2.348421e+08 \n",
"87138.0 0.0 1.0 1.0 1.0 2.911213e+16 \n",
"51192.0 0.0 1.0 1.0 1.0 1.051405e+10 \n",
"23302.0 0.0 1.0 1.0 1.0 2.532350e+08 \n",
"46170.0 0.0 1.0 1.0 1.0 2.026760e+06 \n",
"\n",
" (0.0, 0.34, 0.0, 0.0) \\\n",
"time fep-lambda coul-lambda vdw-lambda restraint-lambda \n",
"37326.0 0.0 0.0 0.0 0.0 -3.652949e+04 \n",
"37322.0 0.0 0.0 0.0 0.0 -3.626852e+04 \n",
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"67374.0 0.0 0.0 0.0 0.0 -3.652955e+04 \n",
"62036.0 0.0 0.0 0.0 0.0 -3.634507e+04 \n",
"... ... \n",
"70898.0 0.0 1.0 1.0 1.0 2.348421e+08 \n",
"87138.0 0.0 1.0 1.0 1.0 2.911213e+16 \n",
"51192.0 0.0 1.0 1.0 1.0 1.051405e+10 \n",
"23302.0 0.0 1.0 1.0 1.0 2.532351e+08 \n",
"46170.0 0.0 1.0 1.0 1.0 2.026755e+06 \n",
"\n",
" (0.0, 0.4, 0.0, 0.0) \\\n",
"time fep-lambda coul-lambda vdw-lambda restraint-lambda \n",
"37326.0 0.0 0.0 0.0 0.0 -3.651927e+04 \n",
"37322.0 0.0 0.0 0.0 0.0 -3.625741e+04 \n",
"80412.0 0.0 0.0 0.0 0.0 -3.659239e+04 \n",
"67374.0 0.0 0.0 0.0 0.0 -3.652049e+04 \n",
"62036.0 0.0 0.0 0.0 0.0 -3.633798e+04 \n",
"... ... \n",
"70898.0 0.0 1.0 1.0 1.0 2.348421e+08 \n",
"87138.0 0.0 1.0 1.0 1.0 2.911213e+16 \n",
"51192.0 0.0 1.0 1.0 1.0 1.051405e+10 \n",
"23302.0 0.0 1.0 1.0 1.0 2.532351e+08 \n",
"46170.0 0.0 1.0 1.0 1.0 2.026750e+06 \n",
"\n",
" (0.0, 0.46, 0.0, 0.0) \\\n",
"time fep-lambda coul-lambda vdw-lambda restraint-lambda \n",
"37326.0 0.0 0.0 0.0 0.0 -3.650906e+04 \n",
"37322.0 0.0 0.0 0.0 0.0 -3.624629e+04 \n",
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"67374.0 0.0 0.0 0.0 0.0 -3.651142e+04 \n",
"62036.0 0.0 0.0 0.0 0.0 -3.633089e+04 \n",
"... ... \n",
"70898.0 0.0 1.0 1.0 1.0 2.348421e+08 \n",
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"46170.0 0.0 1.0 1.0 1.0 2.026745e+06 \n",
"\n",
" (0.0, 0.52, 0.0, 0.0) \\\n",
"time fep-lambda coul-lambda vdw-lambda restraint-lambda \n",
"37326.0 0.0 0.0 0.0 0.0 -3.649884e+04 \n",
"37322.0 0.0 0.0 0.0 0.0 -3.623518e+04 \n",
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"62036.0 0.0 0.0 0.0 0.0 -3.632381e+04 \n",
"... ... \n",
"70898.0 0.0 1.0 1.0 1.0 2.348421e+08 \n",
"87138.0 0.0 1.0 1.0 1.0 2.911213e+16 \n",
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"46170.0 0.0 1.0 1.0 1.0 2.026740e+06 \n",
"\n",
" ... \\\n",
"time fep-lambda coul-lambda vdw-lambda restraint-lambda ... \n",
"37326.0 0.0 0.0 0.0 0.0 ... \n",
"37322.0 0.0 0.0 0.0 0.0 ... \n",
"80412.0 0.0 0.0 0.0 0.0 ... \n",
"67374.0 0.0 0.0 0.0 0.0 ... \n",
"62036.0 0.0 0.0 0.0 0.0 ... \n",
"... ... \n",
"70898.0 0.0 1.0 1.0 1.0 ... \n",
"87138.0 0.0 1.0 1.0 1.0 ... \n",
"51192.0 0.0 1.0 1.0 1.0 ... \n",
"23302.0 0.0 1.0 1.0 1.0 ... \n",
"46170.0 0.0 1.0 1.0 1.0 ... \n",
"\n",
" (0.0, 1.0, 0.3, 0.001) \\\n",
"time fep-lambda coul-lambda vdw-lambda restraint-lambda \n",
"37326.0 0.0 0.0 0.0 0.0 -36417.119231 \n",
"37322.0 0.0 0.0 0.0 0.0 -36143.463821 \n",
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"67374.0 0.0 0.0 0.0 0.0 -36420.077798 \n",
"62036.0 0.0 0.0 0.0 0.0 -36256.564022 \n",
"... ... \n",
"70898.0 0.0 1.0 1.0 1.0 -36053.225580 \n",
"87138.0 0.0 1.0 1.0 1.0 -35479.849422 \n",
"51192.0 0.0 1.0 1.0 1.0 -35716.197283 \n",
"23302.0 0.0 1.0 1.0 1.0 -36061.153533 \n",
"46170.0 0.0 1.0 1.0 1.0 -36293.649812 \n",
"\n",
" (0.0, 1.0, 0.4, 0.002) \\\n",
"time fep-lambda coul-lambda vdw-lambda restraint-lambda \n",
"37326.0 0.0 0.0 0.0 0.0 -36413.710972 \n",
"37322.0 0.0 0.0 0.0 0.0 -36139.644756 \n",
"80412.0 0.0 0.0 0.0 0.0 -36486.723777 \n",
"67374.0 0.0 0.0 0.0 0.0 -36415.106697 \n",
"62036.0 0.0 0.0 0.0 0.0 -36251.458656 \n",
"... ... \n",
"70898.0 0.0 1.0 1.0 1.0 -36362.742315 \n",
"87138.0 0.0 1.0 1.0 1.0 -35957.118581 \n",
"51192.0 0.0 1.0 1.0 1.0 -36046.796088 \n",
"23302.0 0.0 1.0 1.0 1.0 -36384.207003 \n",
"46170.0 0.0 1.0 1.0 1.0 -36487.395298 \n",
"\n",
" (0.0, 1.0, 0.48, 0.004) \\\n",
"time fep-lambda coul-lambda vdw-lambda restraint-lambda \n",
"37326.0 0.0 0.0 0.0 0.0 -36410.413858 \n",
"37322.0 0.0 0.0 0.0 0.0 -36136.053556 \n",
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"67374.0 0.0 0.0 0.0 0.0 -36410.803007 \n",
"62036.0 0.0 0.0 0.0 0.0 -36247.065536 \n",
"... ... \n",
"70898.0 0.0 1.0 1.0 1.0 -36484.729485 \n",
"87138.0 0.0 1.0 1.0 1.0 -36144.931777 \n",
"51192.0 0.0 1.0 1.0 1.0 -36175.623476 \n",
"23302.0 0.0 1.0 1.0 1.0 -36512.532623 \n",
"46170.0 0.0 1.0 1.0 1.0 -36563.379480 \n",
"\n",
" (0.0, 1.0, 0.52, 0.01) \\\n",
"time fep-lambda coul-lambda vdw-lambda restraint-lambda \n",
"37326.0 0.0 0.0 0.0 0.0 -36408.622136 \n",
"37322.0 0.0 0.0 0.0 0.0 -36134.120795 \n",
"80412.0 0.0 0.0 0.0 0.0 -36480.151061 \n",
"67374.0 0.0 0.0 0.0 0.0 -36408.566174 \n",
"62036.0 0.0 0.0 0.0 0.0 -36244.788054 \n",
"... ... \n",
"70898.0 0.0 1.0 1.0 1.0 -36524.580361 \n",
"87138.0 0.0 1.0 1.0 1.0 -36206.532707 \n",
"51192.0 0.0 1.0 1.0 1.0 -36217.569304 \n",
"23302.0 0.0 1.0 1.0 1.0 -36554.638690 \n",
"46170.0 0.0 1.0 1.0 1.0 -36588.009075 \n",
"\n",
" (0.0, 1.0, 0.6, 0.02) \\\n",
"time fep-lambda coul-lambda vdw-lambda restraint-lambda \n",
"37326.0 0.0 0.0 0.0 0.0 -36404.822356 \n",
"37322.0 0.0 0.0 0.0 0.0 -36130.040912 \n",
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"67374.0 0.0 0.0 0.0 0.0 -36403.961550 \n",
"62036.0 0.0 0.0 0.0 0.0 -36240.110197 \n",
"... ... \n",
"70898.0 0.0 1.0 1.0 1.0 -36578.918401 \n",
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"51192.0 0.0 1.0 1.0 1.0 -36274.677749 \n",
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"46170.0 0.0 1.0 1.0 1.0 -36621.158686 \n",
"\n",
" (0.0, 1.0, 0.68, 0.04) \\\n",
"time fep-lambda coul-lambda vdw-lambda restraint-lambda \n",
"37326.0 0.0 0.0 0.0 0.0 -36400.793063 \n",
"37322.0 0.0 0.0 0.0 0.0 -36125.732880 \n",
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"62036.0 0.0 0.0 0.0 0.0 -36235.298733 \n",
"... ... \n",
"70898.0 0.0 1.0 1.0 1.0 -36611.537864 \n",
"87138.0 0.0 1.0 1.0 1.0 -36342.868406 \n",
"51192.0 0.0 1.0 1.0 1.0 -36308.960305 \n",
"23302.0 0.0 1.0 1.0 1.0 -36646.868170 \n",
"46170.0 0.0 1.0 1.0 1.0 -36640.449250 \n",
"\n",
" (0.0, 1.0, 0.76, 0.1) \\\n",
"time fep-lambda coul-lambda vdw-lambda restraint-lambda \n",
"37326.0 0.0 0.0 0.0 0.0 -36396.579974 \n",
"37322.0 0.0 0.0 0.0 0.0 -36121.247864 \n",
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"62036.0 0.0 0.0 0.0 0.0 -36230.375031 \n",
"... ... \n",
"70898.0 0.0 1.0 1.0 1.0 -36630.848991 \n",
"87138.0 0.0 1.0 1.0 1.0 -36374.530534 \n",
"51192.0 0.0 1.0 1.0 1.0 -36329.360913 \n",
"23302.0 0.0 1.0 1.0 1.0 -36667.478723 \n",
"46170.0 0.0 1.0 1.0 1.0 -36651.202311 \n",
"\n",
" (0.0, 1.0, 0.84, 0.2) \\\n",
"time fep-lambda coul-lambda vdw-lambda restraint-lambda \n",
"37326.0 0.0 0.0 0.0 0.0 -36392.227546 \n",
"37322.0 0.0 0.0 0.0 0.0 -36116.629309 \n",
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"62036.0 0.0 0.0 0.0 0.0 -36225.366072 \n",
"... ... \n",
"70898.0 0.0 1.0 1.0 1.0 -36641.649211 \n",
"87138.0 0.0 1.0 1.0 1.0 -36393.413351 \n",
"51192.0 0.0 1.0 1.0 1.0 -36340.915217 \n",
"23302.0 0.0 1.0 1.0 1.0 -36679.088860 \n",
"46170.0 0.0 1.0 1.0 1.0 -36656.466962 \n",
"\n",
" (0.0, 1.0, 0.92, 0.4) \\\n",
"time fep-lambda coul-lambda vdw-lambda restraint-lambda \n",
"37326.0 0.0 0.0 0.0 0.0 -36387.741587 \n",
"37322.0 0.0 0.0 0.0 0.0 -36111.899907 \n",
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"... ... \n",
"70898.0 0.0 1.0 1.0 1.0 -36646.643015 \n",
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"51192.0 0.0 1.0 1.0 1.0 -36346.594124 \n",
"23302.0 0.0 1.0 1.0 1.0 -36684.695799 \n",
"46170.0 0.0 1.0 1.0 1.0 -36657.981625 \n",
"\n",
" (0.0, 1.0, 1.0, 1.0) \n",
"time fep-lambda coul-lambda vdw-lambda restraint-lambda \n",
"37326.0 0.0 0.0 0.0 0.0 -36383.008882 \n",
"37322.0 0.0 0.0 0.0 0.0 -36107.031201 \n",
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"62036.0 0.0 0.0 0.0 0.0 -36214.963587 \n",
"... ... \n",
"70898.0 0.0 1.0 1.0 1.0 -36646.810461 \n",
"87138.0 0.0 1.0 1.0 1.0 -36407.609179 \n",
"51192.0 0.0 1.0 1.0 1.0 -36348.118459 \n",
"23302.0 0.0 1.0 1.0 1.0 -36686.020855 \n",
"46170.0 0.0 1.0 1.0 1.0 -36656.429043 \n",
"\n",
"[50001 rows x 28 columns]"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bootstrap(u_nk)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## subsampling + bootstrapping + estimation"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We'll now fit 100 BAR estimators on bootstrapped sets of the original samples. We can do this in a single loop, gathering up our resulting `delta_f_`s for each fitted estimator in a list:"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We'll need to subsample the data first in an effort to create a set of uncorrelated samples, which is a key assumption we must satisfy for bootstrapping."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"from alchemlyb.preprocessing import equilibrium_detection"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
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" <th>(0.0, 0.52, 0.0, 0.0)</th>\n",
" <th>...</th>\n",
" <th>(0.0, 1.0, 0.3, 0.001)</th>\n",
" <th>(0.0, 1.0, 0.4, 0.002)</th>\n",
" <th>(0.0, 1.0, 0.48, 0.004)</th>\n",
" <th>(0.0, 1.0, 0.52, 0.01)</th>\n",
" <th>(0.0, 1.0, 0.6, 0.02)</th>\n",
" <th>(0.0, 1.0, 0.68, 0.04)</th>\n",
" <th>(0.0, 1.0, 0.76, 0.1)</th>\n",
" <th>(0.0, 1.0, 0.84, 0.2)</th>\n",
" <th>(0.0, 1.0, 0.92, 0.4)</th>\n",
" <th>(0.0, 1.0, 1.0, 1.0)</th>\n",
" </tr>\n",
" <tr>\n",
" <th>time</th>\n",
" <th>fep-lambda</th>\n",
" <th>coul-lambda</th>\n",
" <th>vdw-lambda</th>\n",
" <th>restraint-lambda</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0.0</th>\n",
" <th>0.0</th>\n",
" <th>1.00</th>\n",
" <th>0.1</th>\n",
" <th>0.0002</th>\n",
" <td>-36416.048617</td>\n",
" <td>-36417.317190</td>\n",
" <td>-36418.585730</td>\n",
" <td>-36420.107981</td>\n",
" <td>-36421.630235</td>\n",
" <td>-36423.152521</td>\n",
" <td>-36424.674698</td>\n",
" <td>-36426.196979</td>\n",
" <td>-36427.719234</td>\n",
" <td>-36429.241427</td>\n",
" <td>...</td>\n",
" <td>-36433.674464</td>\n",
" <td>-36428.199010</td>\n",
" <td>-36423.369178</td>\n",
" <td>-36420.842397</td>\n",
" <td>-36415.626166</td>\n",
" <td>-36410.237611</td>\n",
" <td>-36404.705317</td>\n",
" <td>-36399.065603</td>\n",
" <td>-36393.305828</td>\n",
" <td>-36387.246310</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2.0</th>\n",
" <th>0.0</th>\n",
" <th>1.00</th>\n",
" <th>0.4</th>\n",
" <th>0.0020</th>\n",
" <td>-36472.655852</td>\n",
" <td>-36474.116119</td>\n",
" <td>-36475.576386</td>\n",
" <td>-36477.328670</td>\n",
" <td>-36479.080913</td>\n",
" <td>-36480.833176</td>\n",
" <td>-36482.585473</td>\n",
" <td>-36484.337738</td>\n",
" <td>-36486.090016</td>\n",
" <td>-36487.842279</td>\n",
" <td>...</td>\n",
" <td>-36521.062826</td>\n",
" <td>-36519.140146</td>\n",
" <td>-36516.315498</td>\n",
" <td>-36514.585065</td>\n",
" <td>-36510.646409</td>\n",
" <td>-36506.202578</td>\n",
" <td>-36501.354671</td>\n",
" <td>-36496.198430</td>\n",
" <td>-36490.746462</td>\n",
" <td>-36484.758644</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4.0</th>\n",
" <th>0.0</th>\n",
" <th>1.00</th>\n",
" <th>0.3</th>\n",
" <th>0.0010</th>\n",
" <td>-36420.169969</td>\n",
" <td>-36421.307675</td>\n",
" <td>-36422.445070</td>\n",
" <td>-36423.810059</td>\n",
" <td>-36425.175113</td>\n",
" <td>-36426.540117</td>\n",
" <td>-36427.905183</td>\n",
" <td>-36429.270165</td>\n",
" <td>-36430.635267</td>\n",
" <td>-36432.000316</td>\n",
" <td>...</td>\n",
" <td>-36441.414131</td>\n",
" <td>-36436.793983</td>\n",
" <td>-36432.357601</td>\n",
" <td>-36429.948794</td>\n",
" <td>-36424.838460</td>\n",
" <td>-36419.411703</td>\n",
" <td>-36413.725517</td>\n",
" <td>-36407.839587</td>\n",
" <td>-36401.758340</td>\n",
" <td>-36395.308852</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6.0</th>\n",
" <th>0.0</th>\n",
" <th>0.22</th>\n",
" <th>0.0</th>\n",
" <th>0.0000</th>\n",
" <td>-36480.483060</td>\n",
" <td>-36475.577879</td>\n",
" <td>-36470.672758</td>\n",
" <td>-36464.786695</td>\n",
" <td>-36458.900530</td>\n",
" <td>-36453.014437</td>\n",
" <td>-36447.128270</td>\n",
" <td>-36441.242239</td>\n",
" <td>-36435.356066</td>\n",
" <td>-36429.469962</td>\n",
" <td>...</td>\n",
" <td>-36377.732738</td>\n",
" <td>-36372.767346</td>\n",
" <td>-36368.208474</td>\n",
" <td>-36365.779974</td>\n",
" <td>-36360.699541</td>\n",
" <td>-36355.379530</td>\n",
" <td>-36349.861210</td>\n",
" <td>-36344.191979</td>\n",
" <td>-36338.365268</td>\n",
" <td>-36332.194787</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8.0</th>\n",
" <th>0.0</th>\n",
" <th>0.16</th>\n",
" <th>0.0</th>\n",
" <th>0.0000</th>\n",
" <td>-36556.217962</td>\n",
" <td>-36550.639739</td>\n",
" <td>-36545.061520</td>\n",
" <td>-36538.367687</td>\n",
" <td>-36531.673818</td>\n",
" <td>-36524.979976</td>\n",
" <td>-36518.286112</td>\n",
" <td>-36511.592274</td>\n",
" <td>-36504.898418</td>\n",
" <td>-36498.204568</td>\n",
" <td>...</td>\n",
" <td>-36433.927662</td>\n",
" <td>-36428.332808</td>\n",
" <td>-36423.464440</td>\n",
" <td>-36420.927202</td>\n",
" <td>-36415.695615</td>\n",
" <td>-36410.292820</td>\n",
" <td>-36404.746083</td>\n",
" <td>-36399.089537</td>\n",
" <td>-36393.317579</td>\n",
" <td>-36387.288730</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <th>...</th>\n",
" <th>...</th>\n",
" <th>...</th>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>99992.0</th>\n",
" <th>0.0</th>\n",
" <th>0.16</th>\n",
" <th>0.0</th>\n",
" <th>0.0000</th>\n",
" <td>-36546.252507</td>\n",
" <td>-36540.845355</td>\n",
" <td>-36535.438229</td>\n",
" <td>-36528.949560</td>\n",
" <td>-36522.460915</td>\n",
" <td>-36515.972369</td>\n",
" <td>-36509.483672</td>\n",
" <td>-36502.995070</td>\n",
" <td>-36496.506480</td>\n",
" <td>-36490.017839</td>\n",
" <td>...</td>\n",
" <td>-36423.390664</td>\n",
" <td>-36417.209752</td>\n",
" <td>-36412.020063</td>\n",
" <td>-36409.360316</td>\n",
" <td>-36403.946548</td>\n",
" <td>-36398.428717</td>\n",
" <td>-36392.816424</td>\n",
" <td>-36387.132412</td>\n",
" <td>-36381.356231</td>\n",
" <td>-36375.309819</td>\n",
" </tr>\n",
" <tr>\n",
" <th>99994.0</th>\n",
" <th>0.0</th>\n",
" <th>0.60</th>\n",
" <th>0.0</th>\n",
" <th>0.0000</th>\n",
" <td>-36548.243186</td>\n",
" <td>-36547.664008</td>\n",
" <td>-36547.084795</td>\n",
" <td>-36546.389831</td>\n",
" <td>-36545.694809</td>\n",
" <td>-36544.999807</td>\n",
" <td>-36544.304813</td>\n",
" <td>-36543.609792</td>\n",
" <td>-36542.914817</td>\n",
" <td>-36542.219793</td>\n",
" <td>...</td>\n",
" <td>-36520.191829</td>\n",
" <td>-36513.682061</td>\n",
" <td>-36508.302519</td>\n",
" <td>-36505.566767</td>\n",
" <td>-36500.035230</td>\n",
" <td>-36494.434418</td>\n",
" <td>-36488.758621</td>\n",
" <td>-36483.024528</td>\n",
" <td>-36477.191283</td>\n",
" <td>-36470.999236</td>\n",
" </tr>\n",
" <tr>\n",
" <th>99996.0</th>\n",
" <th>0.0</th>\n",
" <th>0.00</th>\n",
" <th>0.0</th>\n",
" <th>0.0000</th>\n",
" <td>-36800.201423</td>\n",
" <td>-36793.801417</td>\n",
" <td>-36787.401306</td>\n",
" <td>-36779.721219</td>\n",
" <td>-36772.041130</td>\n",
" <td>-36764.361004</td>\n",
" <td>-36756.680928</td>\n",
" <td>-36749.000852</td>\n",
" <td>-36741.320756</td>\n",
" <td>-36733.640693</td>\n",
" <td>...</td>\n",
" <td>-36662.057176</td>\n",
" <td>-36656.450596</td>\n",
" <td>-36651.555755</td>\n",
" <td>-36649.002457</td>\n",
" <td>-36643.735197</td>\n",
" <td>-36638.294797</td>\n",
" <td>-36632.711686</td>\n",
" <td>-36627.020775</td>\n",
" <td>-36621.220139</td>\n",
" <td>-36615.185064</td>\n",
" </tr>\n",
" <tr>\n",
" <th>99998.0</th>\n",
" <th>0.0</th>\n",
" <th>1.00</th>\n",
" <th>0.0</th>\n",
" <th>0.0001</th>\n",
" <td>-36404.518763</td>\n",
" <td>-36405.480799</td>\n",
" <td>-36406.442786</td>\n",
" <td>-36407.597235</td>\n",
" <td>-36408.751553</td>\n",
" <td>-36409.905883</td>\n",
" <td>-36411.060332</td>\n",
" <td>-36412.214688</td>\n",
" <td>-36413.369122</td>\n",
" <td>-36414.523467</td>\n",
" <td>...</td>\n",
" <td>-36410.186669</td>\n",
" <td>-36404.319812</td>\n",
" <td>-36399.373632</td>\n",
" <td>-36396.834263</td>\n",
" <td>-36391.658260</td>\n",
" <td>-36386.375961</td>\n",
" <td>-36381.000093</td>\n",
" <td>-36375.553599</td>\n",
" <td>-36370.021459</td>\n",
" <td>-36364.252395</td>\n",
" </tr>\n",
" <tr>\n",
" <th>100000.0</th>\n",
" <th>0.0</th>\n",
" <th>0.68</th>\n",
" <th>0.0</th>\n",
" <th>0.0000</th>\n",
" <td>-36566.406054</td>\n",
" <td>-36566.014993</td>\n",
" <td>-36565.623970</td>\n",
" <td>-36565.154682</td>\n",
" <td>-36564.685451</td>\n",
" <td>-36564.216229</td>\n",
" <td>-36563.746979</td>\n",
" <td>-36563.277746</td>\n",
" <td>-36562.808486</td>\n",
" <td>-36562.339245</td>\n",
" <td>...</td>\n",
" <td>-36544.017270</td>\n",
" <td>-36537.780082</td>\n",
" <td>-36532.534533</td>\n",
" <td>-36529.844659</td>\n",
" <td>-36524.367682</td>\n",
" <td>-36518.784104</td>\n",
" <td>-36513.104863</td>\n",
" <td>-36507.353322</td>\n",
" <td>-36501.510110</td>\n",
" <td>-36495.400679</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>50001 rows × 28 columns</p>\n",
"</div>"
],
"text/plain": [
" (0.0, 0.0, 0.0, 0.0) \\\n",
"time fep-lambda coul-lambda vdw-lambda restraint-lambda \n",
"0.0 0.0 1.00 0.1 0.0002 -36416.048617 \n",
"2.0 0.0 1.00 0.4 0.0020 -36472.655852 \n",
"4.0 0.0 1.00 0.3 0.0010 -36420.169969 \n",
"6.0 0.0 0.22 0.0 0.0000 -36480.483060 \n",
"8.0 0.0 0.16 0.0 0.0000 -36556.217962 \n",
"... ... \n",
"99992.0 0.0 0.16 0.0 0.0000 -36546.252507 \n",
"99994.0 0.0 0.60 0.0 0.0000 -36548.243186 \n",
"99996.0 0.0 0.00 0.0 0.0000 -36800.201423 \n",
"99998.0 0.0 1.00 0.0 0.0001 -36404.518763 \n",
"100000.0 0.0 0.68 0.0 0.0000 -36566.406054 \n",
"\n",
" (0.0, 0.05, 0.0, 0.0) \\\n",
"time fep-lambda coul-lambda vdw-lambda restraint-lambda \n",
"0.0 0.0 1.00 0.1 0.0002 -36417.317190 \n",
"2.0 0.0 1.00 0.4 0.0020 -36474.116119 \n",
"4.0 0.0 1.00 0.3 0.0010 -36421.307675 \n",
"6.0 0.0 0.22 0.0 0.0000 -36475.577879 \n",
"8.0 0.0 0.16 0.0 0.0000 -36550.639739 \n",
"... ... \n",
"99992.0 0.0 0.16 0.0 0.0000 -36540.845355 \n",
"99994.0 0.0 0.60 0.0 0.0000 -36547.664008 \n",
"99996.0 0.0 0.00 0.0 0.0000 -36793.801417 \n",
"99998.0 0.0 1.00 0.0 0.0001 -36405.480799 \n",
"100000.0 0.0 0.68 0.0 0.0000 -36566.014993 \n",
"\n",
" (0.0, 0.1, 0.0, 0.0) \\\n",
"time fep-lambda coul-lambda vdw-lambda restraint-lambda \n",
"0.0 0.0 1.00 0.1 0.0002 -36418.585730 \n",
"2.0 0.0 1.00 0.4 0.0020 -36475.576386 \n",
"4.0 0.0 1.00 0.3 0.0010 -36422.445070 \n",
"6.0 0.0 0.22 0.0 0.0000 -36470.672758 \n",
"8.0 0.0 0.16 0.0 0.0000 -36545.061520 \n",
"... ... \n",
"99992.0 0.0 0.16 0.0 0.0000 -36535.438229 \n",
"99994.0 0.0 0.60 0.0 0.0000 -36547.084795 \n",
"99996.0 0.0 0.00 0.0 0.0000 -36787.401306 \n",
"99998.0 0.0 1.00 0.0 0.0001 -36406.442786 \n",
"100000.0 0.0 0.68 0.0 0.0000 -36565.623970 \n",
"\n",
" (0.0, 0.16, 0.0, 0.0) \\\n",
"time fep-lambda coul-lambda vdw-lambda restraint-lambda \n",
"0.0 0.0 1.00 0.1 0.0002 -36420.107981 \n",
"2.0 0.0 1.00 0.4 0.0020 -36477.328670 \n",
"4.0 0.0 1.00 0.3 0.0010 -36423.810059 \n",
"6.0 0.0 0.22 0.0 0.0000 -36464.786695 \n",
"8.0 0.0 0.16 0.0 0.0000 -36538.367687 \n",
"... ... \n",
"99992.0 0.0 0.16 0.0 0.0000 -36528.949560 \n",
"99994.0 0.0 0.60 0.0 0.0000 -36546.389831 \n",
"99996.0 0.0 0.00 0.0 0.0000 -36779.721219 \n",
"99998.0 0.0 1.00 0.0 0.0001 -36407.597235 \n",
"100000.0 0.0 0.68 0.0 0.0000 -36565.154682 \n",
"\n",
" (0.0, 0.22, 0.0, 0.0) \\\n",
"time fep-lambda coul-lambda vdw-lambda restraint-lambda \n",
"0.0 0.0 1.00 0.1 0.0002 -36421.630235 \n",
"2.0 0.0 1.00 0.4 0.0020 -36479.080913 \n",
"4.0 0.0 1.00 0.3 0.0010 -36425.175113 \n",
"6.0 0.0 0.22 0.0 0.0000 -36458.900530 \n",
"8.0 0.0 0.16 0.0 0.0000 -36531.673818 \n",
"... ... \n",
"99992.0 0.0 0.16 0.0 0.0000 -36522.460915 \n",
"99994.0 0.0 0.60 0.0 0.0000 -36545.694809 \n",
"99996.0 0.0 0.00 0.0 0.0000 -36772.041130 \n",
"99998.0 0.0 1.00 0.0 0.0001 -36408.751553 \n",
"100000.0 0.0 0.68 0.0 0.0000 -36564.685451 \n",
"\n",
" (0.0, 0.28, 0.0, 0.0) \\\n",
"time fep-lambda coul-lambda vdw-lambda restraint-lambda \n",
"0.0 0.0 1.00 0.1 0.0002 -36423.152521 \n",
"2.0 0.0 1.00 0.4 0.0020 -36480.833176 \n",
"4.0 0.0 1.00 0.3 0.0010 -36426.540117 \n",
"6.0 0.0 0.22 0.0 0.0000 -36453.014437 \n",
"8.0 0.0 0.16 0.0 0.0000 -36524.979976 \n",
"... ... \n",
"99992.0 0.0 0.16 0.0 0.0000 -36515.972369 \n",
"99994.0 0.0 0.60 0.0 0.0000 -36544.999807 \n",
"99996.0 0.0 0.00 0.0 0.0000 -36764.361004 \n",
"99998.0 0.0 1.00 0.0 0.0001 -36409.905883 \n",
"100000.0 0.0 0.68 0.0 0.0000 -36564.216229 \n",
"\n",
" (0.0, 0.34, 0.0, 0.0) \\\n",
"time fep-lambda coul-lambda vdw-lambda restraint-lambda \n",
"0.0 0.0 1.00 0.1 0.0002 -36424.674698 \n",
"2.0 0.0 1.00 0.4 0.0020 -36482.585473 \n",
"4.0 0.0 1.00 0.3 0.0010 -36427.905183 \n",
"6.0 0.0 0.22 0.0 0.0000 -36447.128270 \n",
"8.0 0.0 0.16 0.0 0.0000 -36518.286112 \n",
"... ... \n",
"99992.0 0.0 0.16 0.0 0.0000 -36509.483672 \n",
"99994.0 0.0 0.60 0.0 0.0000 -36544.304813 \n",
"99996.0 0.0 0.00 0.0 0.0000 -36756.680928 \n",
"99998.0 0.0 1.00 0.0 0.0001 -36411.060332 \n",
"100000.0 0.0 0.68 0.0 0.0000 -36563.746979 \n",
"\n",
" (0.0, 0.4, 0.0, 0.0) \\\n",
"time fep-lambda coul-lambda vdw-lambda restraint-lambda \n",
"0.0 0.0 1.00 0.1 0.0002 -36426.196979 \n",
"2.0 0.0 1.00 0.4 0.0020 -36484.337738 \n",
"4.0 0.0 1.00 0.3 0.0010 -36429.270165 \n",
"6.0 0.0 0.22 0.0 0.0000 -36441.242239 \n",
"8.0 0.0 0.16 0.0 0.0000 -36511.592274 \n",
"... ... \n",
"99992.0 0.0 0.16 0.0 0.0000 -36502.995070 \n",
"99994.0 0.0 0.60 0.0 0.0000 -36543.609792 \n",
"99996.0 0.0 0.00 0.0 0.0000 -36749.000852 \n",
"99998.0 0.0 1.00 0.0 0.0001 -36412.214688 \n",
"100000.0 0.0 0.68 0.0 0.0000 -36563.277746 \n",
"\n",
" (0.0, 0.46, 0.0, 0.0) \\\n",
"time fep-lambda coul-lambda vdw-lambda restraint-lambda \n",
"0.0 0.0 1.00 0.1 0.0002 -36427.719234 \n",
"2.0 0.0 1.00 0.4 0.0020 -36486.090016 \n",
"4.0 0.0 1.00 0.3 0.0010 -36430.635267 \n",
"6.0 0.0 0.22 0.0 0.0000 -36435.356066 \n",
"8.0 0.0 0.16 0.0 0.0000 -36504.898418 \n",
"... ... \n",
"99992.0 0.0 0.16 0.0 0.0000 -36496.506480 \n",
"99994.0 0.0 0.60 0.0 0.0000 -36542.914817 \n",
"99996.0 0.0 0.00 0.0 0.0000 -36741.320756 \n",
"99998.0 0.0 1.00 0.0 0.0001 -36413.369122 \n",
"100000.0 0.0 0.68 0.0 0.0000 -36562.808486 \n",
"\n",
" (0.0, 0.52, 0.0, 0.0) \\\n",
"time fep-lambda coul-lambda vdw-lambda restraint-lambda \n",
"0.0 0.0 1.00 0.1 0.0002 -36429.241427 \n",
"2.0 0.0 1.00 0.4 0.0020 -36487.842279 \n",
"4.0 0.0 1.00 0.3 0.0010 -36432.000316 \n",
"6.0 0.0 0.22 0.0 0.0000 -36429.469962 \n",
"8.0 0.0 0.16 0.0 0.0000 -36498.204568 \n",
"... ... \n",
"99992.0 0.0 0.16 0.0 0.0000 -36490.017839 \n",
"99994.0 0.0 0.60 0.0 0.0000 -36542.219793 \n",
"99996.0 0.0 0.00 0.0 0.0000 -36733.640693 \n",
"99998.0 0.0 1.00 0.0 0.0001 -36414.523467 \n",
"100000.0 0.0 0.68 0.0 0.0000 -36562.339245 \n",
"\n",
" ... \\\n",
"time fep-lambda coul-lambda vdw-lambda restraint-lambda ... \n",
"0.0 0.0 1.00 0.1 0.0002 ... \n",
"2.0 0.0 1.00 0.4 0.0020 ... \n",
"4.0 0.0 1.00 0.3 0.0010 ... \n",
"6.0 0.0 0.22 0.0 0.0000 ... \n",
"8.0 0.0 0.16 0.0 0.0000 ... \n",
"... ... \n",
"99992.0 0.0 0.16 0.0 0.0000 ... \n",
"99994.0 0.0 0.60 0.0 0.0000 ... \n",
"99996.0 0.0 0.00 0.0 0.0000 ... \n",
"99998.0 0.0 1.00 0.0 0.0001 ... \n",
"100000.0 0.0 0.68 0.0 0.0000 ... \n",
"\n",
" (0.0, 1.0, 0.3, 0.001) \\\n",
"time fep-lambda coul-lambda vdw-lambda restraint-lambda \n",
"0.0 0.0 1.00 0.1 0.0002 -36433.674464 \n",
"2.0 0.0 1.00 0.4 0.0020 -36521.062826 \n",
"4.0 0.0 1.00 0.3 0.0010 -36441.414131 \n",
"6.0 0.0 0.22 0.0 0.0000 -36377.732738 \n",
"8.0 0.0 0.16 0.0 0.0000 -36433.927662 \n",
"... ... \n",
"99992.0 0.0 0.16 0.0 0.0000 -36423.390664 \n",
"99994.0 0.0 0.60 0.0 0.0000 -36520.191829 \n",
"99996.0 0.0 0.00 0.0 0.0000 -36662.057176 \n",
"99998.0 0.0 1.00 0.0 0.0001 -36410.186669 \n",
"100000.0 0.0 0.68 0.0 0.0000 -36544.017270 \n",
"\n",
" (0.0, 1.0, 0.4, 0.002) \\\n",
"time fep-lambda coul-lambda vdw-lambda restraint-lambda \n",
"0.0 0.0 1.00 0.1 0.0002 -36428.199010 \n",
"2.0 0.0 1.00 0.4 0.0020 -36519.140146 \n",
"4.0 0.0 1.00 0.3 0.0010 -36436.793983 \n",
"6.0 0.0 0.22 0.0 0.0000 -36372.767346 \n",
"8.0 0.0 0.16 0.0 0.0000 -36428.332808 \n",
"... ... \n",
"99992.0 0.0 0.16 0.0 0.0000 -36417.209752 \n",
"99994.0 0.0 0.60 0.0 0.0000 -36513.682061 \n",
"99996.0 0.0 0.00 0.0 0.0000 -36656.450596 \n",
"99998.0 0.0 1.00 0.0 0.0001 -36404.319812 \n",
"100000.0 0.0 0.68 0.0 0.0000 -36537.780082 \n",
"\n",
" (0.0, 1.0, 0.48, 0.004) \\\n",
"time fep-lambda coul-lambda vdw-lambda restraint-lambda \n",
"0.0 0.0 1.00 0.1 0.0002 -36423.369178 \n",
"2.0 0.0 1.00 0.4 0.0020 -36516.315498 \n",
"4.0 0.0 1.00 0.3 0.0010 -36432.357601 \n",
"6.0 0.0 0.22 0.0 0.0000 -36368.208474 \n",
"8.0 0.0 0.16 0.0 0.0000 -36423.464440 \n",
"... ... \n",
"99992.0 0.0 0.16 0.0 0.0000 -36412.020063 \n",
"99994.0 0.0 0.60 0.0 0.0000 -36508.302519 \n",
"99996.0 0.0 0.00 0.0 0.0000 -36651.555755 \n",
"99998.0 0.0 1.00 0.0 0.0001 -36399.373632 \n",
"100000.0 0.0 0.68 0.0 0.0000 -36532.534533 \n",
"\n",
" (0.0, 1.0, 0.52, 0.01) \\\n",
"time fep-lambda coul-lambda vdw-lambda restraint-lambda \n",
"0.0 0.0 1.00 0.1 0.0002 -36420.842397 \n",
"2.0 0.0 1.00 0.4 0.0020 -36514.585065 \n",
"4.0 0.0 1.00 0.3 0.0010 -36429.948794 \n",
"6.0 0.0 0.22 0.0 0.0000 -36365.779974 \n",
"8.0 0.0 0.16 0.0 0.0000 -36420.927202 \n",
"... ... \n",
"99992.0 0.0 0.16 0.0 0.0000 -36409.360316 \n",
"99994.0 0.0 0.60 0.0 0.0000 -36505.566767 \n",
"99996.0 0.0 0.00 0.0 0.0000 -36649.002457 \n",
"99998.0 0.0 1.00 0.0 0.0001 -36396.834263 \n",
"100000.0 0.0 0.68 0.0 0.0000 -36529.844659 \n",
"\n",
" (0.0, 1.0, 0.6, 0.02) \\\n",
"time fep-lambda coul-lambda vdw-lambda restraint-lambda \n",
"0.0 0.0 1.00 0.1 0.0002 -36415.626166 \n",
"2.0 0.0 1.00 0.4 0.0020 -36510.646409 \n",
"4.0 0.0 1.00 0.3 0.0010 -36424.838460 \n",
"6.0 0.0 0.22 0.0 0.0000 -36360.699541 \n",
"8.0 0.0 0.16 0.0 0.0000 -36415.695615 \n",
"... ... \n",
"99992.0 0.0 0.16 0.0 0.0000 -36403.946548 \n",
"99994.0 0.0 0.60 0.0 0.0000 -36500.035230 \n",
"99996.0 0.0 0.00 0.0 0.0000 -36643.735197 \n",
"99998.0 0.0 1.00 0.0 0.0001 -36391.658260 \n",
"100000.0 0.0 0.68 0.0 0.0000 -36524.367682 \n",
"\n",
" (0.0, 1.0, 0.68, 0.04) \\\n",
"time fep-lambda coul-lambda vdw-lambda restraint-lambda \n",
"0.0 0.0 1.00 0.1 0.0002 -36410.237611 \n",
"2.0 0.0 1.00 0.4 0.0020 -36506.202578 \n",
"4.0 0.0 1.00 0.3 0.0010 -36419.411703 \n",
"6.0 0.0 0.22 0.0 0.0000 -36355.379530 \n",
"8.0 0.0 0.16 0.0 0.0000 -36410.292820 \n",
"... ... \n",
"99992.0 0.0 0.16 0.0 0.0000 -36398.428717 \n",
"99994.0 0.0 0.60 0.0 0.0000 -36494.434418 \n",
"99996.0 0.0 0.00 0.0 0.0000 -36638.294797 \n",
"99998.0 0.0 1.00 0.0 0.0001 -36386.375961 \n",
"100000.0 0.0 0.68 0.0 0.0000 -36518.784104 \n",
"\n",
" (0.0, 1.0, 0.76, 0.1) \\\n",
"time fep-lambda coul-lambda vdw-lambda restraint-lambda \n",
"0.0 0.0 1.00 0.1 0.0002 -36404.705317 \n",
"2.0 0.0 1.00 0.4 0.0020 -36501.354671 \n",
"4.0 0.0 1.00 0.3 0.0010 -36413.725517 \n",
"6.0 0.0 0.22 0.0 0.0000 -36349.861210 \n",
"8.0 0.0 0.16 0.0 0.0000 -36404.746083 \n",
"... ... \n",
"99992.0 0.0 0.16 0.0 0.0000 -36392.816424 \n",
"99994.0 0.0 0.60 0.0 0.0000 -36488.758621 \n",
"99996.0 0.0 0.00 0.0 0.0000 -36632.711686 \n",
"99998.0 0.0 1.00 0.0 0.0001 -36381.000093 \n",
"100000.0 0.0 0.68 0.0 0.0000 -36513.104863 \n",
"\n",
" (0.0, 1.0, 0.84, 0.2) \\\n",
"time fep-lambda coul-lambda vdw-lambda restraint-lambda \n",
"0.0 0.0 1.00 0.1 0.0002 -36399.065603 \n",
"2.0 0.0 1.00 0.4 0.0020 -36496.198430 \n",
"4.0 0.0 1.00 0.3 0.0010 -36407.839587 \n",
"6.0 0.0 0.22 0.0 0.0000 -36344.191979 \n",
"8.0 0.0 0.16 0.0 0.0000 -36399.089537 \n",
"... ... \n",
"99992.0 0.0 0.16 0.0 0.0000 -36387.132412 \n",
"99994.0 0.0 0.60 0.0 0.0000 -36483.024528 \n",
"99996.0 0.0 0.00 0.0 0.0000 -36627.020775 \n",
"99998.0 0.0 1.00 0.0 0.0001 -36375.553599 \n",
"100000.0 0.0 0.68 0.0 0.0000 -36507.353322 \n",
"\n",
" (0.0, 1.0, 0.92, 0.4) \\\n",
"time fep-lambda coul-lambda vdw-lambda restraint-lambda \n",
"0.0 0.0 1.00 0.1 0.0002 -36393.305828 \n",
"2.0 0.0 1.00 0.4 0.0020 -36490.746462 \n",
"4.0 0.0 1.00 0.3 0.0010 -36401.758340 \n",
"6.0 0.0 0.22 0.0 0.0000 -36338.365268 \n",
"8.0 0.0 0.16 0.0 0.0000 -36393.317579 \n",
"... ... \n",
"99992.0 0.0 0.16 0.0 0.0000 -36381.356231 \n",
"99994.0 0.0 0.60 0.0 0.0000 -36477.191283 \n",
"99996.0 0.0 0.00 0.0 0.0000 -36621.220139 \n",
"99998.0 0.0 1.00 0.0 0.0001 -36370.021459 \n",
"100000.0 0.0 0.68 0.0 0.0000 -36501.510110 \n",
"\n",
" (0.0, 1.0, 1.0, 1.0) \n",
"time fep-lambda coul-lambda vdw-lambda restraint-lambda \n",
"0.0 0.0 1.00 0.1 0.0002 -36387.246310 \n",
"2.0 0.0 1.00 0.4 0.0020 -36484.758644 \n",
"4.0 0.0 1.00 0.3 0.0010 -36395.308852 \n",
"6.0 0.0 0.22 0.0 0.0000 -36332.194787 \n",
"8.0 0.0 0.16 0.0 0.0000 -36387.288730 \n",
"... ... \n",
"99992.0 0.0 0.16 0.0 0.0000 -36375.309819 \n",
"99994.0 0.0 0.60 0.0 0.0000 -36470.999236 \n",
"99996.0 0.0 0.00 0.0 0.0000 -36615.185064 \n",
"99998.0 0.0 1.00 0.0 0.0001 -36364.252395 \n",
"100000.0 0.0 0.68 0.0 0.0000 -36495.400679 \n",
"\n",
"[50001 rows x 28 columns]"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"u_nk"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
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"</table>\n",
"<p>36907 rows × 28 columns</p>\n",
"</div>"
],
"text/plain": [
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" ... \\\n",
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"756.0 0.0 0.0 0.0 0.0 -36266.284534 \n",
"884.0 0.0 0.0 0.0 0.0 -36490.913621 \n",
"888.0 0.0 0.0 0.0 0.0 -36454.603283 \n",
"894.0 0.0 0.0 0.0 0.0 -36400.117349 \n",
"898.0 0.0 0.0 0.0 0.0 -36555.440261 \n",
"... ... \n",
"96880.0 0.0 1.0 1.0 1.0 -36609.561640 \n",
"96882.0 0.0 1.0 1.0 1.0 -36731.030500 \n",
"96886.0 0.0 1.0 1.0 1.0 -36483.288921 \n",
"96888.0 0.0 1.0 1.0 1.0 -36694.230175 \n",
"96896.0 0.0 1.0 1.0 1.0 -36539.761463 \n",
"\n",
" (0.0, 1.0, 0.84, 0.2) \\\n",
"time fep-lambda coul-lambda vdw-lambda restraint-lambda \n",
"756.0 0.0 0.0 0.0 0.0 -36260.667217 \n",
"884.0 0.0 0.0 0.0 0.0 -36485.772194 \n",
"888.0 0.0 0.0 0.0 0.0 -36449.340774 \n",
"894.0 0.0 0.0 0.0 0.0 -36395.201729 \n",
"898.0 0.0 0.0 0.0 0.0 -36550.861869 \n",
"... ... \n",
"96880.0 0.0 1.0 1.0 1.0 -36618.049801 \n",
"96882.0 0.0 1.0 1.0 1.0 -36736.937278 \n",
"96886.0 0.0 1.0 1.0 1.0 -36494.933908 \n",
"96888.0 0.0 1.0 1.0 1.0 -36702.391771 \n",
"96896.0 0.0 1.0 1.0 1.0 -36549.818624 \n",
"\n",
" (0.0, 1.0, 0.92, 0.4) \\\n",
"time fep-lambda coul-lambda vdw-lambda restraint-lambda \n",
"756.0 0.0 0.0 0.0 0.0 -36254.968789 \n",
"884.0 0.0 0.0 0.0 0.0 -36480.538579 \n",
"888.0 0.0 0.0 0.0 0.0 -36443.989732 \n",
"894.0 0.0 0.0 0.0 0.0 -36390.180835 \n",
"898.0 0.0 0.0 0.0 0.0 -36546.174334 \n",
"... ... \n",
"96880.0 0.0 1.0 1.0 1.0 -36621.634447 \n",
"96882.0 0.0 1.0 1.0 1.0 -36739.031984 \n",
"96886.0 0.0 1.0 1.0 1.0 -36500.449121 \n",
"96888.0 0.0 1.0 1.0 1.0 -36705.716350 \n",
"96896.0 0.0 1.0 1.0 1.0 -36554.442952 \n",
"\n",
" (0.0, 1.0, 1.0, 1.0) \n",
"time fep-lambda coul-lambda vdw-lambda restraint-lambda \n",
"756.0 0.0 0.0 0.0 0.0 -36249.078206 \n",
"884.0 0.0 0.0 0.0 0.0 -36475.134120 \n",
"888.0 0.0 0.0 0.0 0.0 -36438.467186 \n",
"894.0 0.0 0.0 0.0 0.0 -36384.979140 \n",
"898.0 0.0 0.0 0.0 0.0 -36541.323990 \n",
"... ... \n",
"96880.0 0.0 1.0 1.0 1.0 -36621.825482 \n",
"96882.0 0.0 1.0 1.0 1.0 -36738.326905 \n",
"96886.0 0.0 1.0 1.0 1.0 -36500.770146 \n",
"96888.0 0.0 1.0 1.0 1.0 -36705.643692 \n",
"96896.0 0.0 1.0 1.0 1.0 -36555.318874 \n",
"\n",
"[36907 rows x 28 columns]"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"u_nk_s = equilibrium_detection(u_nk, u_nk.columns[0])\n",
"u_nk_s"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 49.7 s, sys: 75.5 ms, total: 49.7 s\n",
"Wall time: 49.9 s\n"
]
}
],
"source": [
"%%time\n",
"delta_fs = list()\n",
"for i in range(100):\n",
" print(i, end='\\r')\n",
" bar = BAR().fit(bootstrap(u_nk_s))\n",
" delta_fs.append(bar.delta_f_)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Once we have all our `delta_f_`s, which tell us the free energy differences in units of `kT` between all states, we can, for example, stack them and calculate the mean (and make a new `DataFrame` out of the resulting array):"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f120c301190>"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"mean_d = pd.DataFrame(np.stack(delta_fs).mean(axis=0), index=delta_fs[0].index, columns=delta_fs[0].columns)\n",
"sns.heatmap(mean_d, center=0)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can calculate the standard deviation for each difference across bootstrap results in the same way:"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f120bde9d30>"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"stds = pd.DataFrame(np.stack(delta_fs).std(axis=0), index=delta_fs[0].index, columns=delta_fs[0].columns)\n",
"sns.heatmap(stds)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If we want to know the standard deviation between any two states, such as (0.0, 0.0, 0.0, 0.0) and (0.0, 1.0, 1.0, 1.0), we can do:"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>(0.0, 1.0, 1.0, 1.0)</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>(0.0, 0.0, 0.0, 0.0)</th>\n",
" <td>0.175837</td>\n",
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"</div>"
],
"text/plain": [
" (0.0, 1.0, 1.0, 1.0)\n",
"(0.0, 0.0, 0.0, 0.0) 0.175837"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"stds.loc[[(0.0, 0.0, 0.0, 0.0)], [(0.0, 1.0, 1.0, 1.0)]]"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.17583727787143485"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
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"source": [
"stds.loc[[(0.0, 0.0, 0.0, 0.0)], [(0.0, 1.0, 1.0, 1.0)]].values.flatten()[0]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can use the exact same approach for our `dHdl` dataset, using `TI` instead of `BAR` as our estimator:"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
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"text/plain": [
" fep coul \\\n",
"time fep-lambda coul-lambda vdw-lambda restraint-lambda \n",
"0.0 0.0 1.00 0.1 0.0002 0.0 -25.370747 \n",
"2.0 0.0 1.00 0.4 0.0020 0.0 -29.204609 \n",
"4.0 0.0 1.00 0.3 0.0010 0.0 -22.750548 \n",
"6.0 0.0 0.22 0.0 0.0000 0.0 98.102009 \n",
"8.0 0.0 0.16 0.0 0.0000 0.0 111.564190 \n",
"... ... ... \n",
"99992.0 0.0 0.16 0.0 0.0000 0.0 108.143665 \n",
"99994.0 0.0 0.60 0.0 0.0000 0.0 11.583392 \n",
"99996.0 0.0 0.00 0.0 0.0000 0.0 128.001486 \n",
"99998.0 0.0 1.00 0.0 0.0001 0.0 -19.239824 \n",
"100000.0 0.0 0.68 0.0 0.0000 0.0 7.820713 \n",
"\n",
" vdw \\\n",
"time fep-lambda coul-lambda vdw-lambda restraint-lambda \n",
"0.0 0.0 1.00 0.1 0.0002 18.818422 \n",
"2.0 0.0 1.00 0.4 0.0020 29.146286 \n",
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"8.0 0.0 0.16 0.0 0.0000 11.168792 \n",
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"99992.0 0.0 0.16 0.0 0.0000 33.530695 \n",
"99994.0 0.0 0.60 0.0 0.0000 40.429575 \n",
"99996.0 0.0 0.00 0.0 0.0000 5.099843 \n",
"99998.0 0.0 1.00 0.0 0.0001 28.486784 \n",
"100000.0 0.0 0.68 0.0 0.0000 31.069375 \n",
"\n",
" restraint \n",
"time fep-lambda coul-lambda vdw-lambda restraint-lambda \n",
"0.0 0.0 1.00 0.1 0.0002 0.669507 \n",
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"99996.0 0.0 0.00 0.0 0.0000 0.487645 \n",
"99998.0 0.0 1.00 0.0 0.0001 0.552438 \n",
"100000.0 0.0 0.68 0.0 0.0000 0.630800 \n",
"\n",
"[50001 rows x 4 columns]"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dHdl"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
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],
"text/plain": [
" fep coul \\\n",
"time fep-lambda coul-lambda vdw-lambda restraint-lambda \n",
"330.0 0.0 0.0 0.0 0.0 0.0 161.047660 \n",
"1764.0 0.0 0.0 0.0 0.0 0.0 160.991429 \n",
"2574.0 0.0 0.0 0.0 0.0 0.0 165.497080 \n",
"3076.0 0.0 0.0 0.0 0.0 NaN 127.295298 \n",
"4860.0 0.0 0.0 0.0 0.0 0.0 131.460735 \n",
"... ... ... \n",
"96880.0 0.0 1.0 1.0 1.0 0.0 -57.343249 \n",
"96882.0 0.0 1.0 1.0 1.0 0.0 466.215728 \n",
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"96888.0 0.0 1.0 1.0 1.0 0.0 -44.661570 \n",
"96896.0 0.0 1.0 1.0 1.0 0.0 44.294170 \n",
"\n",
" vdw \\\n",
"time fep-lambda coul-lambda vdw-lambda restraint-lambda \n",
"330.0 0.0 0.0 0.0 0.0 -24.641202 \n",
"1764.0 0.0 0.0 0.0 0.0 -14.005734 \n",
"2574.0 0.0 0.0 0.0 0.0 -42.095259 \n",
"3076.0 0.0 0.0 0.0 0.0 24.899755 \n",
"4860.0 0.0 0.0 0.0 0.0 6.792276 \n",
"... ... \n",
"96880.0 0.0 1.0 1.0 1.0 12.085948 \n",
"96882.0 0.0 1.0 1.0 1.0 16.068576 \n",
"96886.0 0.0 1.0 1.0 1.0 -5.530314 \n",
"96888.0 0.0 1.0 1.0 1.0 14.952239 \n",
"96896.0 0.0 1.0 1.0 1.0 5.813643 \n",
"\n",
" restraint \n",
"time fep-lambda coul-lambda vdw-lambda restraint-lambda \n",
"330.0 0.0 0.0 0.0 0.0 0.506337 \n",
"1764.0 0.0 0.0 0.0 0.0 0.556419 \n",
"2574.0 0.0 0.0 0.0 0.0 0.374377 \n",
"3076.0 0.0 0.0 0.0 0.0 0.488477 \n",
"4860.0 0.0 0.0 0.0 0.0 0.392852 \n",
"... ... \n",
"96880.0 0.0 1.0 1.0 1.0 0.206656 \n",
"96882.0 0.0 1.0 1.0 1.0 0.665225 \n",
"96886.0 0.0 1.0 1.0 1.0 2.854807 \n",
"96888.0 0.0 1.0 1.0 1.0 0.264065 \n",
"96896.0 0.0 1.0 1.0 1.0 0.150873 \n",
"\n",
"[26660 rows x 4 columns]"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dHdl_s = equilibrium_detection(dHdl, 'coul')\n",
"dHdl_s"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 19.4 s, sys: 19.9 ms, total: 19.5 s\n",
"Wall time: 19.5 s\n"
]
}
],
"source": [
"%%time\n",
"delta_fs = list()\n",
"for i in range(100):\n",
" print(i, end='\\r')\n",
" ti = TI().fit(bootstrap(dHdl_s))\n",
" delta_fs.append(ti.delta_f_)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f120c5d1100>"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"mean = pd.DataFrame(np.stack(delta_fs).mean(axis=0), index=delta_fs[0].index, columns=delta_fs[0].columns)\n",
"sns.heatmap(mean, center=0)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f120bc88040>"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"stds = pd.DataFrame(np.stack(delta_fs).std(axis=0), index=delta_fs[0].index, columns=delta_fs[0].columns)\n",
"sns.heatmap(stds)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>(0.0, 1.0, 1.0, 1.0)</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>(0.0, 0.0, 0.0, 0.0)</th>\n",
" <td>0.199248</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" (0.0, 1.0, 1.0, 1.0)\n",
"(0.0, 0.0, 0.0, 0.0) 0.199248"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"stds.loc[[(0.0, 0.0, 0.0, 0.0)], [(0.0, 1.0, 1.0, 1.0)]]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Scaling out when bootstrapping is expensive"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Bootstrapping in a loop is rather slow. Fortunately, each bootstrap sampling + estimation is done independently, which translates to a largely parallelizable problem. We can use, e.g. [dask](https://dask.org/) to build out a dependency graph from our loop, which can then be fed to a [dask cluster](https://distributed.dask.org/en/latest/) (local or remote) for computation. This might be absolutely vital for computing at a reasonable speed with a larger dataset."
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
"from dask.distributed import Client\n",
"from dask.delayed import delayed"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [],
"source": [
"cl = Client('vulkan:8786')"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<table style=\"border: 2px solid white;\">\n",
"<tr>\n",
"<td style=\"vertical-align: top; border: 0px solid white\">\n",
"<h3 style=\"text-align: left;\">Client</h3>\n",
"<ul style=\"text-align: left; list-style: none; margin: 0; padding: 0;\">\n",
" <li><b>Scheduler: </b>tcp://vulkan:8786</li>\n",
" <li><b>Dashboard: </b><a href='http://vulkan:8787/status' target='_blank'>http://vulkan:8787/status</a>\n",
"</ul>\n",
"</td>\n",
"<td style=\"vertical-align: top; border: 0px solid white\">\n",
"<h3 style=\"text-align: left;\">Cluster</h3>\n",
"<ul style=\"text-align: left; list-style:none; margin: 0; padding: 0;\">\n",
" <li><b>Workers: </b>8</li>\n",
" <li><b>Cores: </b>8</li>\n",
" <li><b>Memory: </b>33.59 GB</li>\n",
"</ul>\n",
"</td>\n",
"</tr>\n",
"</table>"
],
"text/plain": [
"<Client: 'tcp://192.168.1.244:8786' processes=8 threads=8, memory=33.59 GB>"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cl"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We'll first send our raw `u_nk` and `dHdl` data to the worker processes; we'll hold on to a [Future](https://distributed.dask.org/en/latest/api.html#future) object that we can use for constructing our graph. Sending the data out is valuable to ensure that all workers have these rather large structures in memory from the start."
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [],
"source": [
"L_u_nk = cl.scatter(u_nk_s)\n",
"L_dHdl = cl.scatter(dHdl_s)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We'll start with our `BAR` approach; we can modify our loop with [delayed](https://docs.dask.org/en/latest/delayed.html) wrappers around our function and `fit` method calls. For each loop iteration, the result will be a graph like this one:"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"delayed(BAR().fit)(delayed(bootstrap)(L_u_nk)).visualize()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This graph defines the set of operations required, in what dependency order, to calculate the resulting `delta_f_` values. The operations can be performed by worker nodes outside this notebook's Python process, with parallelism defined by the graph. For our 100 bootstrap + estimations, we'll get a very parallelizable result:"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [],
"source": [
"delta_fs = list()\n",
"for i in range(100):\n",
" bar = delayed(BAR().fit)(delayed(bootstrap)(L_u_nk))\n",
" delta_fs.append(bar.delta_f_)"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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33z/8h/9wt9u98IUvvPvuu7eec3R0dHTXXXd953d+5263e9nLXnb//fdvPWd7GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KfRZ9o9li/j0d15553PeMYzvuRLvuStb33r1ls+28033/zFX/zF11577Yc//OGtt2xJoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jT6LLvH7E08uhtvvPHUqVPf8A3f8NGPfnTrLZ/bXXfd9exnP/vqq6/+xV/8xa23bEOjPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NNq3e2xew6P7iZ/4iWPHjv2zf/bPHnrooa23PJoHH3zwla985bFjx37yJ39y6y2PNY36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrT6HPaPQbv4FGcP3/+Va961WWXXfbzP//zW2851M/+7M8eP378H//jf/zII49sveWxoFGfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0aPYrdJX06j+7hhx9+yUtecuLEiZtvvnnrLZ+fX/qlXzpx4sT3fM/3PPzww1tvubQ06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6NHt7t0j+bRnT9//vu///tPnTp1yy23bL1liV/7tV87efLk3//7f//8+fNbb7lUNOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jWbtL9Fxm/ciP/MgVV1zxzne+c+shy73nPe+58sorf/RHf3TrIZeKRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9Wk0a3eJnsuj+/f//t8fO3bspptu2nrIWjfeeOOxY8d++qd/eushF59GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1aXSI3aV4KI/uve997/Hjx3/qp35q6yEXxw033HDZZZfdcsstWw+5mDTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPowPtLvoTeXR/9Ed/9Ff/6l998YtfvPWQi+mlL33pE5/4xHvuuWfrIReHRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9Wl0uGNHR0c7Hivnz5//O3/n7/ze7/3e7bff/pf/8l/ees5Fc/bs2Wc961lPetKT3vOe91x22WVbz1lFoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jT6/BzxGLrhhhtOnDhx++23bz3k4nv/+99/4sSJf/tv/+3WQ9bSqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo0+L7uL+Cwe3d13333q1KnXvOY1Ww+5VP71v/7Xp06d+v3f//2thyynUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRp9vo4dHR3teEy86EUvuvPOOz/wgQ+cOHFi6y2XxEMPPXTttdc+4xnPuPnmm7fespBGfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1afR5O+Ix8a53vWu32/3Kr/zK1kMurV/91V/d7XbveMc7th6yhEZ9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVptMCxo6OjHZfY+fPnv/qrv/oZz3jGm9/85q23XHIvfvGLP/zhD//f//t/jx8/vvWWz4NGfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1abTQEZfezTfffPz48Q9+8INbD3ksfOhDHzp+/Pgv//Ivbz3k86NRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9Gi1z7OjoaMcl9nVf93Vf/uVf/pa3vGXrIY+R7/7u777rrrtuv/32Y8eObb3lUBr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSn0UJHXGLvfOc7d7vd+9///q2HPHbuuOOOY8eOvetd79p6yKE06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo094vFoQAAIABJREFU6tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6PFjh0dHe24lL7jO77j05/+9Lve9a6thzymvu3bvu3KK698xzvesfWQg2jUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRssdcSn90R/90eWXX/6mN71p6yGPtTe+8Y2XX375H/7hH249ZJ5GWw+Zp9HWQ+ZptPWQeRptPWSeRlsPmafR1kPmabT1kHkabT1knkZbD5mn0dZD5mm09ZB5Gm09ZJ5GWw+Zp9HWQ+ZptPWQeRptPWSeRlsPmafR1kPmabT1kHkabT1knkZbD5mn0dZD5mm09ZB5Gm09ZJ5GWw+Zp9HWQ+ZptPWQeRptPWSeRlsPmafR1kPmabT1kHkabT1knkZbD5mn0dZD5mm09ZB5Gm09ZJ5GWw+Zp9HWQ+ZptPWQeRptPWSeRlsPmafR1kPmabT1kHkabT1knkZbD5mn0dZD5mm09ZB5Gm09ZJ5GWw+Zp9HWQ+ZptPWQeRptPWSeRlsPmafR1kPmabT1kHkabT1knkZbD5mn0dZD5mm09ZB5Gm09ZJ5GWw+Zp9HWQ+ZptPWQeRptPWSeRlsPmafR1kPmabT1kHkabT1knkZbD5mn0dZD5mm09ZB5Gm09ZJ5GWw+Zp9HWQ+ZptPWQeRptPWSeRlsPmafR1kPmabT1kHkabT1knkZbD5mn0dZD5mm09ZB5Gm09ZJ5GWw+Zp9HWQ+ZptPWQeRptPWSeRlsPmafR1kPmabT1kHkabT1knkZbD5mn0dZD5mm09ZB5Gm09ZJ5GWw+Zp9HWQ+ZptOYhx3dcSr/4i7948uTJ7/qu79p6yGPthS984alTp970pjdtPWSeRlsPmafR1kPmabT1kHkabT1knkZbD5mn0dZD5mm09ZB5Gm09ZJ5GWw+Zp9HWQ+ZptPWQeRptPWSeRlsPmafR1kPmabT1kHkabT1knkZbD5mn0dZD5mm09ZB5Gm09ZJ5GWw+Zp9HWQ+ZptPWQeRptPWSeRlsPmafR1kPmabT1kHkabT1knkZbD5mn0dZD5mm09ZB5Gm09ZJ5GWw+Zp9HWQ+ZptPWQeRptPWSeRlsPmafR1kPmabT1kHkabT1knkZbD5mn0dZD5mm09ZB5Gm09ZJ5GWw+Zp9HWQ+ZptPWQeRptPWSeRlsPmafR1kPmabT1kHkabT1knkZbD5mn0dZD5mm09ZB5Gm09ZJ5GWw+Zp9HWQ+ZptPWQeRptPWSeRlsPmafR1kPmabT1kHkabT1knkZbD5mn0dZD5mm09ZB5Gm09ZJ5GWw+Zp9HWQ+ZptPWQeRptPWSeRlsPmafR1kPmabT1kHkabT1knkZbD5mn0dZD5mm09ZB5Gm09ZJ5GWw+Zp9HWQ+ZptPWQeRptPWSeRlsPmafR1kPmabT1kHkabT1knkZbD5mn0dZD5mm09ZB5Gm09ZJ5GWw+Zp9HWQ+ZptPWQeRptPWSeRlsPmafR1kPmabTmIccv1ho+p5tuuuklL3nJVVddtfWQx9pVV131d//u373pppu2HjJPo62HzNNo6yHzNNp6yDyNth4yT6Oth8zTaOsh8zTaesg8jbYeMk+jrYfM02jrIfM02nrIPI22HjJPo62HzNNo6yHzNNp6yDyNth4yT6Oth8zTaOsh8zTaesg8jbYeMk+jrYfM02jrIfM02nrIPI22HjJPo62HzNNo6yHzNNp6yDyNth4yT6Oth8zTaOsh8zTaesg8jbYeMk+jrYfM02jrIfM02nrIPI22HjJPo62HzNNo6yHzNNp6yDyNth4yT6Oth8zTaOsh8zTaesg8jbYeMk+jrYfM02jrIfM02nrIPI22HjJPo62HzNNo6yHzNNp6yDyNth4yT6Oth8zTaOsh8zTaesg8jbYeMk+jrYfM02jrIfM02nrIPI22HjJPo62HzNNo6yHzNNp6yDyNth4yT6Oth8zTaOsh8zTaesg8jbYeMk+jrYfM02jrIfM02nrIPI22HjJPo62HzNNo6yHzNNp6yDyNth4yT6Oth8zTaOsh8zTaesg8jbYeMk+jrYfM02jrIfM02nrIPI22HjJPo62HzNNo6yHzNNp6yDyNth4yT6Oth8zTaOsh8zTaesg8jbYeMk+jrYfM02jrIfM02nrIPI22HjJPo62HzNNo6yHzNFr1lCMumd/5nd/Z7Xa33HLL1kO28Z73vGe32911111bD3k0GmkUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1Hf+kbHd1wyt9xyy8mTJ5/znOdsPWQb119//VVXXXXrrbduPeTRaKRRnEZ9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z96xsdv4hr+Cy33nrr9ddff+LEia2HbOPEiRPPec5z4v8H0UijOI36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo361jc6fhHX8Fn++3//78997nO3XrGl5z3veb/2a7+29YpHo5FGfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1rWx0/CJO4TP99m//9j333PPN3/zNWw/Z0nOf+9x77rnnd3/3d7ce8rlptNPoC4FGfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfSsbHb+4a5jceeedx44d++qv/uqth2zp2muvPXbs2Ic+9KGth3xuGu00+kKgUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUd/KRscv7homZ86cefKTn3zy5MnH/tV33HHHgo8d+K3Py6lTp770S7/0zJkzF/3JF4VGO40uTKMDabTT6MI0OpBGO40uTKMDabTT6MI0OpBGO40uTKMDabTT6MI0OpBGO40uTKMDabTT6MI0OpBGO40uTKMDabTT6MI0OpBGO40uTKMDabTT6MI0OpBGO40uTKMDabTT6MI0OpBGO40uTKMDabTT6MI0OpBGO40uTKMDabTT6MI0OpBGO40uTKMDabTT6MI0OpBGO40uTKMDabTT6MI0OpBGO40uTKMDabTT6MI0OpBGO40uTKMDabTT6MI0OpBGO40uTKMDabTT6MI0OpBGO40uTKMDabTT6MI0OpBGO40uTKMDabTT6MI0OpBGO40uTKMDabTT6MI0OpBGO40uTKMDabTT6MI0OpBGO40uTKMDabTT6MI0OpBGO40uTKMDabTT6MI0OpBGO40uTKMDabTT6MI0OpBGO40uTKMDabTT6MI0OpBGO40uTKMDabTT6MI0OpBGO40uTKMDabTT6MI0OpBGO40uTKMDabRb3ej4xV3D5MyZM6dPn37s3/vII4/8xE/8xOf7sQO/tcDp06fL/wfRaKfR56LR4TQaNNqn0eE0GjTap9HhNBo02qfR4TQaNNqn0eE0GjTap9HhNBo02qfR4TQaNNqn0eE0GjTap9HhNBo02qfR4TQaNNqn0eE0GjTap9HhNBo02qfR4TQaNNqn0eE0GjTap9HhNBo02qfR4TQaNNqn0eE0GjTap9HhNBo02qfR4TQaNNqn0eE0GjTap9HhNBo02qfR4TQaNNqn0eE0GjTap9HhNBo02qfR4TQaNNqn0eE0GjTap9HhNBo02qfR4TQaNNqn0eE0GjTap9HhNBo02qfR4TQaNNqn0eE0GjTap9HhNBo02qfR4TQaNNqn0eE0GjTap9HhNBo02qfR4TQaNNqn0eE0GjTap9HhNBo02qfR4TQaNNqn0eE0GjTap9HhNBo02qfR4TQaNNqn0eE0GjTap9HhNBo02qfR4TQaNNqn0eE0GjTap9HhNBo02qfR4TQa1jQ6fnGnMNnqb+e/+lf/6q677vp8P3bgtxZ42tOe9pGPfORSPHk9jQaN9ml0OI0GjfZpdDiNBo32aXQ4jQaN9ml0OI0GjfZpdDiNBo32aXQ4jQaN9ml0OI0GjfZpdDiNBo32aXQ4jQaN9ml0OI0GjfZpdDiNBo32aXQ4jQaN9ml0OI0GjfZpdDiNBo32aXQ4jQaN9ml0OI0GjfZpdDiNBo32aXQ4jQaN9ml0OI0GjfZpdDiNBo32aXQ4jQaN9ml0OI0GjfZpdDiNBo32aXQ4jQaN9ml0OI0GjfZpdDiNBo32aXQ4jQaN9ml0OI0GjfZpdDiNBo32aXQ4jQaN9ml0OI0GjfZpdDiNBo32aXQ4jQaN9ml0OI0GjfZpdDiNBo32aXQ4jQaN9ml0OI0GjfZpdDiNBo32aXQ4jQaN9ml0OI0GjfZpdDiNBo32aXQ4jQaN9ml0OI0GjfZpdDiNBo32aXQ4jQaN9ml0OI0GjfZpdDiNBo32aXQ4jQaN9ml0OI2GNY0u+/Ef//GLOob/32tf+9pv/uZv/sZv/MaVz/nkJz958803v/e9773yyiu/9Eu/9DNPf/Inf/KmN73p3e9+97lz5/76X//ru93uxhtv/JEf+ZHz589/6lOf+q3f+q3rrrtut9u9973vfetb3/qbv/mbV1999ROe8IT9j33wgx/8rG/97u/+7tve9rav+Zqvectb3vKBD3zgGc94xuL9/+f//J/bbrvtFa94xbp/DZeERoNGGq2h0aCRRmtoNGik0RoaDRpptIZGg0YaraHRoJFGa2g0aKTRGhoNGmm0hkaDRhqtodGgkUZraDRopNEaGg0aabSGRoNGGq2h0aCRRmtoNGik0RoaDRpptIZGg0YaraHRoJFGa2g0aKTRGhoNGmm0hkaDRhqtodGgkUZraDRopNEaGg0aabSGRoNGGq2h0aCRRmtoNGik0RoaDRpptIZGg0YaraHRoJFGa2g0aKTRGhoNGmm0hkaDRhqtodGgkUZraDRopNEaGg0aabSGRoNGGq2h0aCRRmtoNGik0RoaDRpptIZGg0YaraHRoJFGa2g0aKTRGhoNGmm0hkaDRhqtodGgkUZraDRopNEaGg0aabSGRoNGGq2h0bCm0fHFb+XR3Xvvvddcc83Kh9x+++1PfepT3/jGN37sYx/79m//9h/8wR/88R//8f/0n/7Tbre77bbb/tbf+lt33HHHHXfc8a3f+q0//MM//OCDD951111f/uVfPn39/Pnz3/Vd3/UDP/ADH//4x3/5l3/5mc985tvf/vbP+thn/fHee+993vOe95SnPOWnfuqnbrrpphtuuOGlL33p3XffvfhXuOaaa86ePbv465eURoNGGq2h0aCRRmtoNGik0RoaDRpptIZGg0YaraHRoJFGa2g0aKTRGhoNGmm0hkaDRhqtodGgkUZraDRopNEaGg0aabSGRoNGGq2h0aCRRmtoNGik0RoaDRpptIZGg0YaraHRoJFGa2g0aKTRGhoNGmm0hkaDRhqtodGgkUZraDRopNEaGg0aabSGRoNGGq2h0aCRRmtoNGik0RoaDRpptIZGg0YaraHRoJFGa2g0aKTRGhoNGmm0hkaDRhqtodGgkUZraDRopNEaGg0aabSGRoNGGq2h0aCRRmtoNGik0RoaDRpptIZGg0YaraHRoJFGa2g0aKTRGhoNGmm0hkaDRhqtodGgkUZraDRopNEaGg0aabSGRoNGGq2h0bCq0RGXxlVXXfULv/ALKx9y3XXXff3Xf/345//xP/7Hbrd78Ytf/DM/8zP33Xffk570pA996EPj9MpXvnK3273vfe87Ojp6wQte8MxnPnP8/AMf+MBut/uv//W/Hh0dnTt37i/9pb/0fd/3feP0mR/7zD+eO3fuZ37mZx7/+Mc/7nGP+83f/M1PfvKTr3/969f8Cm94wxtOnjy55gmXjkaDRuOk0TIaDRqNk0bLaDRoNE4aLaPRoNE4abSMRoNG46TRMhoNGo2TRstoNGg0Thoto9Gg0ThptIxGg0bjpNEyGg0ajZNGy2g0aDROGi2j0aDROGm0jEaDRuOk0TIaDRqNk0bLaDRoNE4aLaPRoNE4abSMRoNG46TRMhoNGo2TRstoNGg0Thoto9Gg0ThptIxGg0bjpNEyGg0ajZNGy2g0aDROGi2j0aDROGm0jEaDRuOk0TIaDRqNk0bLaDRoNE4aLaPRoNE4abSMRoNG46TRMhoNGo2TRstoNGg0Thoto9Gg0ThptIxGg0bjpNEyGg0ajZNGy2g0aDROGi2j0aDROGm0jEaDRuOk0TIaDRqNk0bLaDRoNE4aLaPRoNE4abSMRoNG46TRMhoNGo2TRstoNGg0Thoto9Gg0ThptIxGg0bjpNEyGg0ajZNGy2g0aDROGi2j0aD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DP5j9D3Lf/fff//GPfzw7WKOdRl8INOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOpb2ejyi7uGyVd91VedP3/+zjvvfNaznrXsCd/0Td/0B3/wBxd31WPsgx/84Pnz55/+9KdvPeRz02in0RcCjfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfpWNjp+cdcwOX369BOf+MT/9t/+29ZDtnTrrbc+8YlPfMpTnrL1kM9No51GXwg06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06lvZ6PjFXcPk2LFj3/RN33TrrbduPWRLt9566/Of//xjx45tPeRz02in0RcCjfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jf4/duol1PO68P/493zHMzqVzSBmVyiinFyYhHRflLZoUS2zLA2iiNzYKmgRSAUtgsBuQosWlU5hSTdLsVKwm4tKGAlMJkgCK+3iZZRxPDPf30L44z/N9zh+Z97Pxsdjdc7ne3i/X1+eH06fRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1Hf02y0XO8aHuu88867+eabH3nkkdlD5jh48OAvf/nL8847b/aQJ6ORRnEa9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9T39Rss1ruE/nH/++fv377/llltmD5nj17/+9YMPPnj++efPHvJkNNIoTqM+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6O+p99oucY1/Iczzzzz7LPP/ta3vjV7yBx79uw555xzXv7yl88e8mQ00ihOoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPo76n32i5xjU83kUXXfTtb3/74Ycfnj3keDtw4MB3vvOdiy++ePaQMY1mDxnTaPaQMY1mDxnTaPaQMY1mDxnTaPaQMY1mDxnTaPaQMY1mDxnTaPaQMY1mDxnTaPaQMY1mDxnTaPaQMY1mDxnTaPaQMY1mDxnTaPaQMY1mDxnTaPaQMY1mDxnTaPaQMY1mDxnTaPaQMY1mDxnTaPaQMY1mDxnTaPaQMY1mDxnTaPaQMY1mDxnTaPaQMY1mDxnTaPaQMY1mDxnTaPaQMY1mDxnTaPaQMY1mDxnTaPaQMY1mDxnTaPaQMY1mDxnTaPaQMY1mDxnTaPaQMY1mDxnTaPaQMY1mDxnTaPaQMY1mDxnTaPaQMY1mDxnTaPaQMY1mDxnTaPaQMY1mDxnTaPaQMY1mDxnTaPaQMY1mDxnTaPaQMY1mDxnTaPaQMY1mDxnTaPaQMY1mDxnTaPaQMY1mDxnTaPaQMY1mDxnTaPaQMY1mDxnTaPaQMY1mDxnTaPaQMY1mDxnTaPaQMY1mDxnTaPaQMY1mDxnTaPaQMY1mDxnTaPaQMY1mDxnTaPaQMY1mDxnTaPaQMY1mDxnTaPaQMY1mDxnTaPaQMY1mDxnTaPaQMY1mDxnTaPaQMY1mDxnTaPaQMY2eziHLda3hCV100UX333//tddeO3vI8fajH/3o/vvvf+973zt7yJhGs4eMaTR7yJhGs4eMaTR7yJhGs4eMaTR7yJhGs4eMaTR7yJhGs4eMaTR7yJhGs4eMaTR7yJhGs4eMaTR7yJhGs4eMaTR7yJhGs4eMaTR7yJhGs4eMaTR7yJhGs4eMaTR7yJhGs4eMaTR7yJhGs4eMaTR7yJhGs4eMaTR7yJhGs4eMaTR7yJhGs4eMaTR7yJhGs4eMaTR7yJhGs4eMaTR7yJhGs4eMaTR7yJhGs4eMaTR7yJhGs4eMaTR7yJhGs4eMaTR7yJhGs4eMaTR7yJhGs4eMaTR7yJhGs4eMaTR7yJhGs4eMaTR7yJhGs4eMaTR7yJhGs4eMaTR7yJhGs4eMaTR7yJhGs4eMaTR7yJhGs4eMaTR7yJhGs4eMaTR7yJhGs4eMaTR7yJhGs4eMaTR7yJhGs4eMaTR7yJhGs4eMaTR7yJhGs4eMaTR7yJhGs4eMaTR7yJhGs4eMaTR7yJhGs4eMaTR7yJhGs4eMaTR7yJhGs4eMaTR7yJhGs4eMaTR7yJhGs4eMaTR7yJhGs4eMaTR7yJhGs4eMaTR7yJhGs4eMaTR7yJhGs4eMaTR7yJhGs4eMaTR7yJhGT+eQjdVqta5BPKG3v/3t27dv/9GPfjR7yHH1zne+c2tr6/rrr5895Iho1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0ZHb8Ux9v3vf39jY+O2226bPeT42bt378bGxg9+8IPZQ46URn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9Wl01DZWq9WCY2m1Wp1zzjlnn332VVddNXvLcXLhhRfedttte/fuXS6Xs7ccEY36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrT6OitOPb27Nmzbdu2O+64Y/aQ42Hfvn3btm379re/PXvIU6NRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9Gh2djdVqteAYO3To0FlnnfWGN7zhG9/4xuwtx9xFF13029/+9g9/+MO2bdtmb3kKNOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jo7TiuLjmmms2NjZuuumm2UOOrV/84hcbGxvf+973Zg85Ghr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSn0VHYWK1WC46Ld77znX/+859vvfXWzc3N2VuOia2trXPPPfeFL3zh9ddfP3vLUdKoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jZ6yFcfLvn37TjnllM9//vOzhxwrn/vc53bs2PGnP/1p9pCjp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0aPVWLdR3EkfjUpz71rGc967bbbps9ZP327t27Y8eOz3zmM7OHPF0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp9FTsrFarRYcL1tbW+edd97dd9/929/+9tRTT509Z20efPDB1772taeffvqNN9540kknzZ7ztGjUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRk/NiuPrL3/5y+mnn37hhRfOHrJOH/jAB0477bQ777xz9pD10KhPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6NjtxivcdxJK699tqNjY0rrrhi9pD1+PKXv7xcLq+77rrZQ9ZJoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jQ6Qou1n8iR+PSnP71t27Zrrrlm9pCn67vf/e62bds++9nPzh6yfhr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSn0ZFYHItDORIf+9jHtm/ffsMNN8wecvRuuummU0455ZJLLpk95FjRqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo2GFsfoXIYOHTr07ne/e+fOnbfccsvsLUfjlltu2blz53ve855Dhw7N3nKsaNSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfZvaH98AACAASURBVBr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GQ4tjdC5H4sCBA+94xzue/exnX3fddbO3PDU//vGPn/3sZ7/rXe96+OGHZ285tjTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoye3OHZHcyS2trY+/OEPn3TSSV/72tdmbzlS3/zmNzc3Nz/wgQ8cPHhw9pbjQaM+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo0ehKLY3o6R+Lw4cMf//jHNzY2Lrvssq2trdlznszW1tYnP/nJjY2NT3ziE4cPH5495/jRqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo3+m8WxvoAjdMUVV5x88slve9vb/vrXv87e8sTuuuuut771raeccspXv/rV2Vvm0KhPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6N+jTq06hPoz6NHm9xfK7hSPzud797xSte8YIXvOCnP/3p7C3/6frrrz/jjDNe+cpX3nrrrbO3zKRRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9GvVp1KdRn0Z9Gv2HxXG7iSNx3333XXDBBRsbGxdffPHf/va32XNWq9Xqrrvuev/7379YLC688ML7779/9pz5NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jx1ocz8s4Qj/84Q9f9rKX7dy58/LLL9/a2po149ChQ1/96ld37tz5kpe85Otf//qsGU0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp9GjFsf/So7EQw89dNlll5188skvf/nLL7/88gMHDhzP2w8ePPj1r3999+7dm5ubl1566QMPPHA8b/9foVGfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0arVarxZRbOUJ33HHHBz/4wc3NzZe97GVf+cpXHnzwwWN94/79+7/85S+/9KUv3b59+4c+9KF9+/Yd6xv/12nUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1HfM7zRYuLdHKE777zz0ksv3bFjx44dO9797nf/8Ic/fOSRR9Z7xaFDh37xi1985CMfOfXUU7dv337xxRf73/GUaNSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUd8zttFi9gCO1D333PPFL37xda973WKxeMlLXnLJJZd85zvfueeee57mmVdfffVHP/rRF7/4xYvF4vWvf/2XvvSlf/zjH+va/EyjUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9Gfc/ARhur1WrB/5Tbb7/9qquuuuGGG373u9+tVqtXv/rVb3zjG3fv3v2qV73qzDPPfOlLX7pcLh/9y717925sbJx99tmP/nr48OE777zzj3/84+233/7HP/7xN7/5zd69e7dt23buuee+/e1vf9/73rd79+55X+uEolGfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn0a9WnUp1GfRn3PnEYbq9Vq9gaO0n333XfzzTffeOONv//972+//fa77757sVhs3779Oc95zq5du57znOf885//3NjYOO200/bv33/vvffu37//4MGDi8XijDPOOOuss17zmtecf/75b3nLW5773OfO/ionLI36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOo74RttrFar2RtYj3//+9933HHHvn37Hn0XH3jggS984QuLxeJjH/vYqaee+uj7+opXvGL37t27du2aPfYZSqM+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfo06tOoT6M+jfpOvEYbq9Vq9gaOiV//+tdvfvObF4vFr371qze96U2z5/AENOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqE+jPo36NOrTqO8EaLScPYBjZc+ePZubm5ubm9/61rdmb+GJadSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr1adSnUZ9GfRr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\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"delayed(delta_fs).visualize()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can pass this to the cluster `Client` to compute, then gather back the result here:"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 326 ms, sys: 29.7 ms, total: 356 ms\n",
"Wall time: 5.52 s\n"
]
}
],
"source": [
"%%time\n",
"L_r = cl.compute(delta_fs)\n",
"delta_fs = cl.gather(L_r)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This is much faster when we have 8 processes on a faster machine crunching away at the problem."
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f120c857ac0>"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"mean_d = pd.DataFrame(np.stack(delta_fs).mean(axis=0), index=delta_fs[0].index, columns=delta_fs[0].columns)\n",
"sns.heatmap(mean_d, center=0)"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f1200fdef40>"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"stds_d = pd.DataFrame(np.stack(delta_fs).std(axis=0), index=delta_fs[0].index, columns=delta_fs[0].columns)\n",
"sns.heatmap(stds_d)"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>(0.0, 1.0, 1.0, 1.0)</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>(0.0, 0.0, 0.0, 0.0)</th>\n",
" <td>0.159725</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" (0.0, 1.0, 1.0, 1.0)\n",
"(0.0, 0.0, 0.0, 0.0) 0.159725"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"stds_d.loc[[(0.0, 0.0, 0.0, 0.0)], [(0.0, 1.0, 1.0, 1.0)]]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note that our resulting uncertainty between these states differs than that from our serial calculation earlier. Perhaps we need to bootstrap sample more to see greater convergence in the uncertainty we observe. Parallelism helps us here, since we can now scale up our approach by a factor of 10 and wait about as long as we did earlier for the serial approach. We'll do a 1000 bootstrap + estimation runs:"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [],
"source": [
"delta_fs = list()\n",
"for i in range(1000):\n",
" bar = delayed(BAR().fit)(delayed(bootstrap)(L_u_nk))\n",
" delta_fs.append(bar.delta_f_)"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 1.91 s, sys: 125 ms, total: 2.03 s\n",
"Wall time: 53.4 s\n"
]
}
],
"source": [
"%%time\n",
"L_r = cl.compute(delta_fs)\n",
"delta_fs = cl.gather(L_r)"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f120c4ab190>"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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SPTBeBhwo6XkpEebAtK+ZunwSNElHJcmlZlx2JW3f8OthwO3Jz23MbGsz2xo4H/igmV2Y3nN7i/N6GFghaa+UHbqI1hJPFwGLUrboXsAz9WXhTtkFdgBuafGeIAiCTcJknSl2qk6xLh11pZktl1SXPIImiSfgm2a2mEwu6TxJxwD3A4en8W2lo7x2Jb0SqCW7x43luKQtoG2B1weB7wIzgUvShqTjkk/fBC4G3gosA1YB7+2k3cS+ZNqQY7Jq85l5Q5i2um1O0DoGh5/LHQMwa3hF/iBgdDR/kuvVZhwadtR9OesszVE/OeJUmak5x3k0HAcd9XtQbp3iasf1X+usUxwa8D2xGPLUicpX87jaocwx3XG9AFY7vsRXO2oZvTjkRgHwdlWb7qxznSi1igU7L50KiqcBHwGuhHXNwTdqEG5m72/4+Ulg/xa2Xp/sbYTT7juaX28x/uiGX/ca43iLgde22P/Nhp8N+FCLt3fKLsChZLPgIAiCSjBUsWVRLyEdtfHxfjaZ7Ep6PvCltGwdBEFQCWKm2ERIR3WHVLwftRZBEFSKqj0r9BK9T4MgCILSGe1Oi9XSiaAYBEEQlM6oM3GpakRQDIIgCEonZopB5emb6yhXmDc3d8zsVS9wHW/G2vzyDoBhR+nGyGi+JBRAzRxlFI6yDYC1li8x5c0jq9V8xxyx/D9Jj4wTQN/GYisb4S0VGXKcp9eWtwxkuN9RktHnlO5S/nX1SnJ5yjtWO0vAPWUzq51Nt7wZjf1dClY+gbbqEUExCIIgKJ3Junw65fUUG8b+nSRLRfT1ffskncWlkq5xHG8bSb9OmoY/TM3PW407KY25Q9JbOmlX0nRJ10qOW+EgCIIuMWrm3vKQNEPS9ZJ+m76vP532u747i9ALeopIeilZG7gHGvYtAE4HDjWz1wDvcpzX54Evm9l2wFPAMS2OtSNwBPAaMimr0+s3B52wa2ZDZNfr3Q7/gyAIusJogc3BWmA/M9sF2BU4KLW7zP3uLMqU11NMfBn4GBsqb/w5WeB+AMDMxmxAnvqS7kfWJ3UsHw8DzjWztWZ2L1lbtj07bDf0FIMgqBRlBsWkI7sy/TotbYbvu7MQU15PUdJhwB/M7LdNL+0APE/SLyXdKGlRzvE2B542W5fJUZaeYhl2bwH2aGW8UU/x0esvGMONIAiC8hjC3Fvj91Tajm22J6lf0hIyBaUrgLvxfXcWohPPoUrRU5Q04RwpSbOAfyRbOm1mANidrN/qTOBXkq4zszsnetxuk5aphyTNNbNnm147AzgD4P+cvGSSJkkHQTDZKNLfs/F7aowxo8Cu6dHXj4FXTcC9tkx1PcVtgW2A3yrTTdwKuEnSi8juKi4zs+fM7AngWmCXMY73JLCgIaGlLD3FsuwOAr66hSAIgg5TZqJNI2b2NHA18AZ8352FKH2maGZPpWnuDDNbQ6Zx+LmGJJgDgZNavLWup3gyTXqKwPFm1ry8mWvXzH4HrCuqS4FxoZk9IeknwKnpgk4nS9j5chp3FbDIzP7QYMskXU0mSHxuo48tzuP7kr4EvBjYHri+w3Y3B54wszH1fWY56hRXzsuX4hlcmZsYDMDAqi3yBwGDQ8tzxwwPr8wdAzA6mi8LZc46vxFH9fHQ2Jd8HXLWmpmjztJbT+eh5q0ZdPjvteW7ElBzHHPUWXQ37Khn9F7X0b78v5EhfNJRnlH+OkXftfDURpbBhJQgmkjCB8Nm9rSkmcCbyZJsPN+dhehUok1dT5GkcVjXPbyBJt1DSQvTe04G3izpLuCA9DuMoafotNsSM7sNuBS4mSy4fMvMbpHUB2wHtPqm/jjwUUnLyJ4Ffjsd71BJn0l2lwLnAbcm+x9Ky5sdsZve79JTDIIg6BajmHtzsCVwtaSbyb7vr0jKQy2/OyeCrODU1WVU2g34iJm9pwRbpwDnmNnNE/fMdbzXAu8zs49OBrvJ9o+AE/Oehx5w2m9y/7FXPpR/nzTvoWdcfg08dodr3Kpn8x/jrlx5r8/W6kdyxwwN+cSPR0ZX5Q9yzhQHnXfn0x33qVWdKc7s882OZjlmWgCzHWLKc/p9ZWkeW97rOs/h/0xH1xuo9kzxC7+7ZkJTyk/uvK87uHz65qsrU+kfeoobH+8WoPTA1Sm7Kdv3wsmYIBQEwdRlqAMTrm4QeoqTnFS8f/am9iMIgqAR78y1akRrsCAIgqB0IigGQRAEQcKrmlI1IigGQRAEpRMzxaDyzJ2T/yFd5dJcnO063pyVPt3FweH8BkgjDs1FgNHRjap3NsK8NYOOirraqC9pzqPNCDDqqO7yZIJ68X5tjThGet1ySku6ZhplZs96s089fq1xZuL69BSdmp3OmZlHZ7MMJmtQ7FSdYktUMUkpSSdIuj1JkXzBYfegJNu0TFJLFQ9Jg0nCZFmSNNm6k3Yl7STpu3nHCIIg6CZDVnNvVaKrQZEKSUpJ2pdMeWKXJB31xRy7/cBpwMHAjsCRSc6pmWOAp5KUyZfJui50zG7q2rOVpJeNdZwgCIJuUjNzb1Wi20GxSpJSfw2cbGZrIV86iizALjOze1IZxLnJt7H8PR/YP8lDddLuT8m0FoMgCCpByR1tukbXgmLVJKXIpKP2TkuR10hqKb00DrvrxiVJk2fI2g910u5iYO9WxhslWe775Y/GcCMIgqA8JmtQ7GaiTWUkpRIDwGbAXmRahOdJeoV1ou9d53mMrEn4RjRKsrztrJsm47kFQTAJqdqyqJduLp9WSVIKshnZj5Ki8/VkzfvHknXw2l03LilwzCeTh+qk3Rm0aJoeBEGwqYhEmxzS871+SfXAeBlwoKTnpUSYA9O+ZuqSUtAkKSWpVXszr90LydQlkLQDmXzUE5JekiSemrkB2F7SNmkp+Ijk21j+vhP4RZrhdsRu+n0H4JYW7wmCINgk1DD3ViW6XadYl5S60syWS6pLP0GT9BPwTTNbTCYhdZ6kY4D7gcPT+LaSUk67ZwJnSroFGAKOSsFrS2AjUTszG5F0PFmA7QfOTHJOJHmnxWZ2EZl0yTlJymQ56xNgOmUXnNJRswbzRsD0mfkf0LWzfDVYo7Pmucb1r1qQO2Zgms/W9Onzc8eMjPq0mGu1fAWMEa+CgVfD0VGT5v0S8VSjeb+OPGc55FRK9D4A6XOM66uVV3M37FS28FCmYsUaZ52i+5jWpTrFSbp82hHpqLYHmwSSUilAPZAC0WSwOwhcA7zJchRq/+Lc/GeKf3g0/w9m7SO+P6p5Dz3lGtf/xLLcMR55KYDVq/OFt1etftRlyyMxNTLynMvWaM0XiHF8AXqXd7odFL132IPOM/BIUXllqDy2BpxBcZZDhsojVQWbKCg6paO+d9sNE4qeh+6wqzu4XHTnkqktHdWOySApZWanTia7ZDPmE/MCYhAEQTepWlapl663eQtJqXIxs7uAuza1H0EQBI3UKpZA4yV6nwZBEASlU2aP3m4SQTEIgiAonapllXqJoBgEQRCUzmQt3o+g2EPMnJ4/ZrpnzMuMFY/mZxCOTvNlBg5My5eiGhjwyVX19eWfwED/jNwxACN9+f0Q+pzZjzVnHpQnG7zmLH0ol/zMTI/sFfhkqABXUfeAs9TFk+866qwV8UhMeTNGvZmgHnveEoj+LklHTc7F0wiKwTjwBMQgCMrBG2CrxmSdKXbk261KuolJg3BJ2u6TtKThtZ0l/SrpKf6uodtOu+O5fPTqOZZlV9KVnusVBEHQLSZrR5tO3fJXRjfRzN5tZrua2a7ABcCP0vsHgP8Ejkt6ivsAeS1Mcn0scL5l2j0H+GDOMYIgCLrGiNXcW5XoVFCskm4iAEl78HDgB2nXgcDNZvZbADN70tFQwONjIb9KsnsRcGTOMYIgCLpGzBQTFdRNrLM38GgqdoesibZJukzSTZI+lnM8r49F/Zqw3RQkByVtpNvYqKd4++WhpxgEQXeomX+rEp1ItKmabmKdI1k/S4Ts3N9EpqW4CrhK0o1m1krJols+TsRuXVNxA5mqRj3F9/8o9BSDIOgOVZsBeunE8mnVdBPrzw/fDvywYfeDwLVm9oSZrQIuBnbLOabHR7dfJdsNTcUgCCpDLJ8mKqibCHAAcLuZPdj0/p0kzUpB84+BW9Mxz5a0p9dHr18dtCvgRcB9rU6+Tl9fORvOzfr6XJsHqd+5DTg2r62+3K185Ni6fTy5hhjlblWlZpa7jXo3HJvTlsevIr5NFPc1qFjpRqcSbeq6iSQtw7q+4Q006RtKWpjeczLwZkl3kQWxk9P+trqJTruQaQ/+oOn9TwFfSu9dAtxkZnVNwp2Bh1qcV0sfJS1MWo1j+tVBu7sD14VSRhAEVWGy3gB1RE9xMugmjnG8ecC3zexdk8Fusv1V4KK856HHXpj/TPEBh57i087i/dkP+rQGZzx2X+6YoWfvdtl67rn7c8esWZO3Mp/GrV2eO2Z4+FmXrZFR38q21Tz3NWUWcztnnp5ZsbPIfLrzmDMcx5wlX1rEDJeeou9z7dFwHHQcD3zdZdY4u/Z4i+X7nB1tLrzjNxNalnjVy7d2B5fb779vauspTgbdxDGOtwIoPXB1ym7iFm+CUBAEQTeo2gzQS8favIVuYvcws//Y1D4EQRA0MlmDYjSxDIIgCEqn7OxTSQdJukPSMkljdjybCBEUgyAIgtKpFdjySH20TwMOBnYEjpS0YwfcjqAYBEEQlE/J2ad7AsvM7B4zGwLOJWuPWTohHdVD1By3ZJ4xXqE0uYz5Mi69+VqeqhS/rXz/3dnb7izv8p7EeNL5zHs8xzBv+qD3TrzPYdGbSekZ59UZ9NiaXmINa9n6h93SUyzySZZ0LHBsw64zUjeuOq3aXL5+Au61pReko3ZJ8lC/k/TTVBqBpDdLujHtv1HSfo7jhXRUEARByZjZGWa2sGE7I/9dnWHKS0cB3wJONLOdgB8D9RKPJ4A/TfuPIpNfyiOko4IgCFw4uyX51hjG0z5zXPSCdNQOwLXp5yuAdwCY2W/MrN5dZikwU9JgzjFDOioIgsBFqUHxBmB7SdskJaYjyL73SqcXpKOWsv6B7LvY8G6jzjvI2rytzTlmSEcFQRB4kPxbDqmF5fFk/Z5vA84zs6WdcLsTM8VSpKMoL+PgfcAHJd0IzAWGGl+U9Brg88AHNqGPZditS0c121u3Vv+qA98+Yf+CIAh8FFAPcGBmF5vZDma2rZl9tiMuu70pRqWko8zsdjM70Mx2J2sKvq6JpqStyJ4zLjIzT3PNkI4KgiBwoAL/VYkpLx0l6QXp/33APwHfTL8vAH5OloTzv03vmZLSUUEQBF2jxOXTbtKpOsW6dNSVZrZcUl3yCJoknoBvmtliMrmk8yQdA9wPHJ7Gt5WOcto9UtKH0pgfAd9JPx8PbAf8s6R/TvsONLPHGFviaSMfk0zVcWb2/rH86qBdl3TUc2vGejVj7RpHbdgq3+ruwNq8R7QZNpKvpjE8/IzLlkeNYnhklcvWaC3f/1pt2GXL3xc/vzbSp7/grVP0MepQ5pjmvOOf5rwX99T6eesBB5V/1aY5xoBPcWOG05an5rHPfNd12FFXCzCtIzqgG1O1GaCXkI7a+HhTVjrqyO/nS0c95JCFGn44dwgA8x5+0jVOT96ZO2bVs/ljAFatbnXPsSGrV/uko4aGV+SOGRn2BdiaI8Bm5AefTRMU8/FKQg06g+JMR/CZ1ee7r5/pGOcNirP6823NdkpaeYLiGucNVdlB8Zxbr59QVNv2FTu6g8vd99xamQga0lEbHy+ko4IgCCZKl2akZRPSUVOAkI4KgqBqaJK21o7ep0EQBEHpqGIJNF4iKAZBEATlE8unQRAEQZChCIpB1Zk7K3/M7DmOhLHtYbkjS3VoxWyHVzBz5Ubd6TZicOgFLlujjnKLwWkLWLU6P4XWkyM2bWA2Q8PP5js24ssMrDku/6gzy9CfW+pY5nJkUw4BmKdExem/9zRLMzXCgGPJb9RxXZ9l2JUZ65VxWlPL/yxWrSSjz5mBWzUmp9fBJsUTEKuMJyB6cQXESlPicx9XQKwunoDoxVsq4sETEKuInCUuVSP3262L2oiXSnpa0s/GGDMo6YeSlkn6taStHXYPknRHek9LOSpJfyTpJkkjkt6ZZzO9Z/ekxSmqweQAACAASURBVLhM0tfU4qmyMr6Wxtyc6jfrr7XTRmxpV9Ipkm5Pdn6cOvIgaSdJ3/X4HARB0C2kPvdWJTzedFwbMXEKkFfsfwzwlJltB3yZrJF3W1IgPw04GNiRrLvNji2GPgAcDXzf4WedbwB/BWyftlbyUAc3vH5sek+eNmI7u1cArzWznYE7gZMAzOx3wFaSXlbA9yAIgo4ylYNiN7QRScXneWtRjXbPB/ZvNUNrYE9gmZndY2ZDwLmsl5FqPPZ9qWOOa1E+Ne2eZ2bXJVWLs2l9jocBZ1vGdcCC9N6W13Esu2Z2eUMbt+vIGoLX+SmZvlgQBEElkPrdW5UYMyh2URvRy7rjpwDxDDBWlsZ4tA29fjzosNvu+GPt99h9H3BJw++Lgb1bOdqop3hb6CkGQdAl+vqmubcqkfc0uBRtREnlN1jtUSR9AhgBvtewu6WWImR6isAZAMdemN/7NAiCoAyqNgP0krd82i1tRC/rji9pAJgPjNV1ejzahl4/Gpcv29ltd/yx9re1K+lo4BDgL2zDTu6hpRgEQaXoU597qxJjzhTN7ClJ/ZJmmNkaMv2+zzUkhRxISvhooq4PeDJN2ojA8Wa2aJz+1u3+Cngn8Is0E30J2bO7/ZvG3wBsL2kbsuByBPDnRQ4o6XYze1XjPjN7WNIKSXsBvwYWAV9v4+/xks4lS6p5Jr235XVM8lAt7Uo6CPgY8Mdm1lyMtwNwS965bD4rP+X86Zn5k8nnZvsmnGvnDLrGTX92Qe6YgaEtfLYcElMeSSjw1Smat2bQO240/wvCGFMhrGFc/r+TN8nBU3M26jzFIXfphsOgu+Qx/1oMOHt11hzKQjV3LWk+a52aCh6/wKfMUQZTdaYI67URSfp9dU2/G2jSMEz6f5AFwzdLugs4IP0ObbQR0/v/G/gvsuSZByW9Je3/jKRD07BvA5tLWgZ8lPVZrVvCxt8U6bnj8WTB/DbgPDNb2mxX0h6SHiRTsfh3SfUxW9C+kOuDwLeAZcDdpGd8ko6TdFwaczFwTxrzH+k9Y17HdnaBU4G5wBWSlkj6ZoMv+5IJJgdBEFSCyZpok6unOBm0ESUdDzxgZheVbPcQskSjr5Vpt0wkDQLXAG/KExk+6fLf5N5K3v1Y/t3mo4/77qhH/uC7c5378OO5Y/qevt9la83Ku3PHrF7ziM/Wmny/1g75xI+HnUX+o6NDuWNy/pnXj+v6TDHfd8Bd5O/RZ/RqMw725Y/zzhQ9IsMe/UYvm2qm+ItlE9M4XLjbO905DItvOr8y3cNz/+UmgzaimZ3aIbttGwlUiJcBJ+YFxCAIgm7S3z99U7swLly3M6GNWF3M7C7grk3tRxAEQSNVWxb1Er1PgyAIgtKJoBgEQRAECYVKRhAEQRBk9MVMMag682fmJ3jNm5WfMLbCMQbg6Tm+bL7h2XNyxwyuyddcBJg2nN+AyVunWHNkU3rr0dz1jI5s0FqtPIkmOTMu+/odrbicqV7l1jM6dSodQpX9Ks/WSImZoENO6ShPLSZAX5lyYWMgR5ZuFZlQK4EekZU6Lkk5LZH0P21UNsZjt62/kk5K+++o12um/WdKekzSLU22vihpvzy/giAIukVf36B7qxIT7a/TC7JS3zezncxsV+ALwJdKstvS3zT2COA1ZAokp2v9E+vv0lqV5Ov4rmMQBEFXkAbcW5WYaFDsBVmpFQ2/zobcNQqX3TH8PQw418zWmtm9ZJ1t9ky+XAssbzZkZveTdfp5UY5vQRAEXaFP/e6tSow7KPaSrJSkD0m6m2ym+GGvHzl22/k7Xrmrm4A3tvB9nXTU9Rdd4DATBEEwcdQ34N6qxERmiqXISpE/89rkmNlpZrYt8HHgnza1P21oKR9lZmeY2UIzW7jnoe/YBG4FQdCL9OLyaS/KSp1L/nKv1247f8crdxXyUUEQVIaeS7RJzw37JdUD42XAgZKelxJsDkz7mqnLP0GTrJSks8frT5PdDWSlJF3VYvw6Wam0FHxEsrEBkrZv+PVPSC3VJmq3nb9p/xEpO3UbYHvg+jHPPMMlHxUEQdANJuvy6US9qctKXZm0AOtySNAkKwV808wWk8lInSfpGOB+4PA0Pk9W6lXAnCTxdIyZXSbpM8DipI7xbeCcJCu1nCwYwRiyUkld4zKgHzizUVaqwe7xkg4AhoGnWB/IJmq3pb9mtlTSecCtyf6H6o3YJf0A2AfYIl2HT5rZtyVNA7YDFre6fnXmzcx/oD3foaf4tLNO8VnnuDXzZuaOmbZ6vstWv0N3cfroGpetmqOesebske/tpe9RrRgd9dVZlnU8gP7+Eu/mS6xn9Gozemr4+s1Xv+cpQRyRt04xf8yQuxbWNYwuySl2bVk0xZ3DyIpWHwOONrOHUuLiV4G3AqvS/pty7eVJR+U408uyUh2xO05f3gbsZmb/d6xxpy/+Xb501OP531j3P+H7zDz8sHMhwiFXNffRjZJuW9L39AO5Y4ZXPeiytWbNw/lj1o61Qr+etWufco0bGWnWj96YqgbF4ZHnXLZGR3yr/C4pKmdQ9Hw99zuL2qc7FtimuSW58sdsqqB48333TCh8HnTQ193B5dJLTxj3sSTNq1cJSPowsKOZHSfprcAJZEHx9cBXzez1efYmFMp7XFaqI3bHyQDwb5vaiSAIgjrdWhYdo2zuMODs9FjqOkkLJG3ZUP3Qkgl7HbJSmx4z+69N7UMQBEEjRZZPJR0LHNuw6wwzO6PA+z8LLCIrbds37W5X3tbZoBgEQRAEG1HgOXQKgG2DoKQrgVbNST5hZj8xs08An5B0EnA8WXe1cRFBMQiCICidMpdPzewA59DvAReTBcVxlbdNtM1bEARBEGyMBvzbRA6zYdncYcDt6eeLgEXK2At4Ju95IsRMMQiCIOgA1r36w5MlvZKsJON+4Li0/2KyzNNlZCUZ7/UYi6DYQ8yZkb8wMMfxGGCW81HBoKPmEWDVzHy/hmfm1zICDK7Kr2fsH17psjXNUWIwOuooGwBqznEevGUUPlu+Zsz9/TNyx3g1I/3j8j8/Naee4ogjOd6rR+jRcKx59RQdtZHDznP0eu8soZw4XdJTNLOW/StT1umHitrL/etSD2gmNox9hySTtDD9Pk3SWcr0FG9LD3HzjrdN8m1Z8nV6m3HtNBPH9FfS1yStbPj9eEnvy/MrCIKgq/T1+7cK4bnl7AXNRCTNBf4G+HXD7ncBg2a2E7A78AFHIP488OXk41PJ5+ZjtdRMzPM3Bevm630mWYFqEARBZagNTHdvVcITFKe8ZmLiX8gCWmMPMANmp4bdM4EhYEWL9wKQfNkv+Qbtz72dZmJbf1PAPAX4WKMhM1sF3Cdpz3Z+BUEQdJ2pOFNUj2gmpnZ1LzWznze9dD7wHFmx5wPAF+v9XNuwOfB08q3t8cbwayx/jwcuapM9tRjYu5VDatBTvPq/osY/CILuYH197q1K5CXalKKZKHXt0W5hlGUtfAk4usXLewKjZDqFzwP+W9KVZnZP9zwESS8mW8rdp82Qx8gapm9EY1Hs2bcsrey/QxAEUwur2AzQS16I7gXNxLnAa4FfSroP2Au4KD2/+3PgUjMbNrPHgP8FFo5xvCeBBVrf3yhXS7FpXLv9ryNTwViWfJyV1DXqhJZiEASVotbf596qxJgzRTN7KiWAzDCzNWRySJ9rSK45EGiVkVnXCjyZJs1E4HgzWzROf+t2f0WTZiJZ49f9m8av0zYkCy5HkAW6xnN8hmxGTPLxl8Dfm9liSfuTPSM8R9JssoD5lTTuKmCRmf2hwZZJujr5dm7jubc4j+9L+hLZLLSumahW/ibpqXUtjiStTIk8dXYgC9hjMn0gP/3bN8Y34RzwFvxMyx8y6jXmeGjf5ygv8I7r7/clCfQ5x/U7lCEm2Ht/A9wlGX35/0ijjjEAfc5i7ZpjpqFRn//mKFjwlneMOmx5JR88f0k+r/wlJX1u7yZGzf0FUC08IbqumUh6nlbXTLyBJs3EeikDWTB8s6S7gAPS75CvmfhfZMkzD9bLFCR9RtKhadi3gc3TLOmjrM9qbattSPYs7jLgNuC8Rm3DBrvtOI1Mw3FpOt/vmNnNacl1OzIdxGY+Dnw0+bh58hlJhyrTUyT5UNdMvJSkmTiWvzm8kSzpKQiCoBLU+vrcW5XI1VNUD2smjnG81wLvM7OPduN4Ob68Dvio59/n3NtvzddTfCx/pnLvE7571wce9d2Rrng8/49ixiM+DcFZTzpW6lfm6yQCDK3ObZPI2rW+JwNe3cURR2OBEadIsgfvTHHAMWseGs5LHs8YdjZPGBnNfyJQc2pLrs99G9Oay5Zn/jPgnI15Zm0jzhlg2TPFO++/b0JTyj8+YbE7h+Gary/skvRxPrn/vr2smTjG8W4hm6lWgS2AMcWFgyAIuo15FJQriGvRNzQTq4uZxbJpEASVo9Y/hYNiEARBEBRhSs8UgyAIgqAItWkRFIMgCIIAiJliMAmYPSM/03DOYH4m6OxBX5bezBm+5LPnHOOGZ/lq4Eafm507ZmAoX14KYGAkP0vSl9UIViuvtrCvz1fz6MmLc2efDuRLd3nz8NzjPNmgThmqmscUPr88MlSeukgAOcZ5Pzlu6Sj3yAlSrUoLNxEUgyAIgvKZnF3eOhPLe0WDUdLhkm6VtFTS9x12d0/ajMuSLuJG6wvK+Foac3OqE0XSrpJ+lY51s6R3N7znXEnb5x0/CIKga/QV2CpEp9yZ8hqMKQidBLzRzF4D/K3D328Af0XW1m17WstuHdzw+rHpPQCryNrK1TUYvyJpQYPdjzUbCoIg2GREUNyAXtBg/CvgtHROpIbhbUmN0eeZ2XWWtRE6m/Zai2dbxnVkDca3NLM7zeyudKyHyJqsPz+957+BAxoakTced5101KXf/+FYLgZBEJRG34C5typRelDsFQ1GsibcO0j6X0nXSWoV6JvtPuiwm3v81Fh9OnA3gJnVyISKd2k2ZmZnmNlCM1t40J+/u/nlIAiCjqA+/1YlOpFoM+U1GBMDZEuc+5BJPF0raSczm9C555FmnOcAR6VgWOcxMsWNGzt5/CAIAg+TVE6xI8unvaDBCNkM7qKktXgvcCdZkBzL7lYOu22PL2ke8HPgE2lptZHQVAyCoDL09fm3KlH6TLEXNBgTFwJHAt+RtAXZcuo9yefbzexVjYPN7GFJKyTtBfwaWAR8vY2/x0s6lyw56Zn03unAj5PP57d43w7ALWNdCE+d4mxHneLcQV9R7qxB32R/mmPcmpm+v5zhmfn1dH1Dc122+kYW5I7pdypW9A885xo3zVEDN6pVLls1h61+Z83jQP+sUo6XjfPWFubXgFqfs040Rw0ow/e59pgadVcXevBGDaf/XapTVF/VF/ta06kY3QsajJcBT0q6Fbga+AczezIFyHafzg8C3yJ7/nc3cEmye5yk49KYi8mC6zLgP9J7AA4H/gg4WtKStO2a3v9CYLWZPdLmuEEQBF1lYMC/VYlcPcVxGe1hDUZJh5AlGn2tTLs5x/wIsMLMvj3WuF88siz3H/ueR/JnPvc9Mezy6+7HfJ+thx/Pv8Nd84Tv/m3W4/kryIPPPOWypZX59xgjq333IV7dxWGHJuHoaDVnimuHfY/Th4ZWOMc9kztmZMQ3Ax8dzdcJ9Xa0MccM1t+HxkO5M0Vv75v77r93Qn3aDvzGb9zB5fK/fl1lesJ1JEb3sgajmbVtJNBBniZLvgmCIKgEVXtW6KVjE9fQYOweZvadTe1DEARBIxEUgyAIgiDRP0kTbSIoBkEQBKUTM8UgCIIgSEybpNFlkroddIp+x92dZwz47xRd45zdMWpl3p46tAbV5/sTatGWtiV9jmPWnBqIZd6oy9GexON7oWN6+n85e4SN3e44Yc46P8+wTbFyWG7y6YSZpBrDIR3V5j250lGSvtxQL3inpKcbXnuZpMsl3ZakpcY8ptdHSWdKekzSLU37vyhpv7zzCoIg6Bb9ff6tSoR0VBNe6Sgz+4iZ7Wpmu5J1pvlRw8tnA6eY2avJzjmvSM3r43dprTDydXzXKwiCoCtM1jZvIR21MV7pqEaOBH4AkALogJldkXxcaWZ51dYuH83sWmB5i/33k3XteVHOcYIgCLpCN2eKkk6QdHsSYf9Cw/6T0grcHfWOZ3mEdNQY4xPt/AVA0suBbYBfpF07AE9L+pGk30g6pb6MXKKPrbgJeGML/9bpKf7snHMLmgyCIBgf0wf820SQtC/ZxGKXJML+xbR/R7Le1XVh9tMd38UhHVUCRwDnN3TuGQD2Bl4HPAD8EDiarAdrJ6lLR22AmZ0BnAG+Nm9BEARl0MVl0b8GTjaztbCB4PthwLlp/72p//WeZOIQbQnpqDHGJ9r5W+cI0tJp4kFgSVp+HSFT09itZB9bEdJRQRBUhn75t8YVrbQdW+BQOwB7pyTFayTtkfYXWvWrE9JR45eOQtKrgOex4Z3HDcACSc83s8eB/YDFafy/Ateb2Y89PhY8zx3IFEPaMjSSL9mzeij/sKt9/cBZ6xw3PJSfu621vssxMJTf+FlDvnsHczSbHnU3pPaNG3E0+x4ZcfrvaDvsWE1yj/P6NeqU2xodXZs7plbzfchq5pChckpa4RhX7myjhsszZ0lJt2oyijwrbFzRaoWkK4FWOROfIItjmwF7AXsA50l6RRFfGwnpqCYKSEdBFjDPbQxgaRn174GrJP2OrHroP9LLO5E9L22mpY+SXizp4obz/QFZ4HxlOt9j0v5pwHak4BsEwdTBGaorR5mJNmZ2gJm9tsX2E7IZ4I8s43qyS7YFxVf9gM4V758GfAS4EtY1B9+oQbiZvb/h5yeB5lkbZKUcp7U6iJnt3Wb/Pzf8vAZ4V4the41h92IyXcO2dtPvn2rz/iuAnVu8NM3MNlrPbuejmT0EvLXh9yNbHQ84hOy5pk9xNQiCoMMMdK96/0JgX+BqSTsA04EnyFbgvi/pS2T5FtsD1+cZC+moLmJmrpTgcTAA/FuHbAdBEBRmerkNjsbiTODM1NRkCDgqrd4tlXQecCvZquCHPPEopKOmAGY25rPEIAiCbtOtTjWpnvwv27z2WeCzRexF79MgCIKgdKrWvs1LBMUgCIKgdPonaUfwCIpBEARB6cRMMag8wyP59UnDo/lj1jrsAIw6c8lrjlSsvmHfMfscB1XNl6Rbc4zzJvya5yTx1RZ6c9cmmOO2AbVafv2nZwwU8N9TjOCuLfR8frzFD/m2vHOkMlWozDmyW/O3/sk5UYygGARBEJTP9IHJGRVDT7H1ezx6ii+XdJWkmyX9UtJWaf+ukn6VurXfLOndjuO5fUzdgn7TeM6SzpW0fd5xgiAIukV/n9xblQg9xSa8eopkndjPNrOdgc8A/5r2rwIWpW7tBwFfkbSgRB//hqzTTiPfAD6Wc4wgCIKuESLDG9ILeoo7sl4u6ur6GDO708zuSj8/RNbY/Pll+Jhmo38CfKvppf8GDkjNxIMgCDY5fZJ7qxKhpzjG+EQ7f38LvD39/DZgrqQN7KZm5tOBu0vy8StkM8INMgIs62S8DNil+Q2N3ecv/f4Pc9wIgiAoh8k6Uww9xfHz98Cpko4GriVrNLsurS7JX51D1nJowj19JR0CPGZmN0rap8WQup7ijY07G7vP//S+O6p+TYMgmCJEos16ekJP0cweMrO3m9nryORLMLOn03HmAT8HPmFm15Xk4xuBQyXdR7aku5+k/2x4PfQUgyCoDJM10Sb0FMeppyhpC2B5mgWeRFIBScvHP062z296z7j1FM3spHQc0kzx782ssd/fDsAtY12IaY47t2mO4qJB5x1gf5+zttDROHhkmu+YNcdajA0MumypL//Pw/sYV56TxKdb6NVALOt4AH1900sZU+SY8tyzy3lf73luZd45Qv7Cj7dm0DPKv7zj+xvx+jZRqhbsvISeYhMF9BT3Ae6QdCfZ889609nDgT8Cjpa0JG27ptcmpKfYDkkvBFabWSvbQRAEXWeyPlNUcYF3h1FpN+AjZpZXLuGxdQpwjpndPHHPNrB7PPCAmV1Upt2cY17WCfkoSR8BVpjZt8cad+mDd+b+Y9/zSL7a+X1P+rq43POY77P16GP5fxUjT7hMMfvxlbljpj8z1ur5euy5h3LHDK951GVr7Vrf04Dh4bxkar/CvadzjHfWNm3anNwxQ0PPuGytHfKlHAwP5/9bjoz6roWn24770b+ji1FfibMxf0KCd2bm8+2++++f0FTvvDtvc1+Ew3d4dWWmlaGn2EU6qKf4NFlSTxAEQSWYrMunoac4BTCz72xqH4IgCBqZrNmnUewdBEEQlE5fzBSDIAiCIKNqCTReIigGQRAEpTNZnyliZrH18AYcG7bC1mTxLWxt2s9FL2yTdIIblMixYStsddhe2JoatnqCCIpBEARBkIigGARBEASJCIrBGWErbHXYXtiaGrZ6go60eQuCIAiCyUjMFIMgCIIgEUExCIIgCBIRFIMgCIIgER1teghJM4BDgL2BF5PpVN4C/NySZmQBW1uRCTBvZAu4xNw6PBvYfF6DrfvGY6OTSJoNrLFxKr9U/ZqVYavsc5T0AuCNTbYWj9O3hS38usLMnipqK9nrA3ZptGdmPo2wjW2VeZ6l+dWLRKJNjyDp02QB8ZfAjcBjwAxgB2Df9PPfmUO3UtJ3gJcAPwMWt7C1O3CimV3rsDUf+BBwJDAdeDzZeiFwHXC6mV1d4FSR9AbgL8m+ALdkwy/m/zQzl/Bf+nI5AvgLYA9gLTAIPJFs/buZLXPaquQ1K9lWmee4L5nY9mbAb5psbQucD/ybma1w2HovcAJwLxt/9t9I9tn4v2b2gPM8twU+TiaGfhfrr9kOwCrg34GzPAGt5PMsza+eZlO31ImtOxvwJzmvvwBY6LT12pzXpwPbOW1dAbwHWNDitd2BrwDHFDjPS4BvA4eS3SkPAHOA3YC/I7spONRp6xrg/wI7A30N+zcD3gFcAPzlZL5mJdsq8xxPAV7W5rUB4M+AdzhtfQiYOcbruwL7F/iM/QD4I9Kkoum1FwB/Cxy1Cc6zNL96eYuZYjAhJG0GYGbLN7UvAJK2MLMnJjomjZtmZsMTHdPiPZW6ZkEQrCeCYo+QlslOIrvzfAFgZEs1PwFONrOnC9h6GfAFYH/gaUDAPOAXZMtj943Dt4PIlt4A/gBcVsSnFjZf2GjPzB4dpx0Bezb5dr0V/MOp8jUry5akecA/JjuXmNn3G1473cw+WNS3Nsd5r01QWFvSnWa2wwTePw94vpnd3bR/Z3M8gnAeo4zzPNvMFpXhT68Q2ae9w3nAU8A+ZraZmW1O9pznqfRaEX4I/Bh4kZltb2bbkT27uxA4t4ghSYuAm4B9gFlp2xe4Mb1WCEm7SrqObJn0C2m7RtJ1knYraOtAsmcznwLemrZPA3el14pQyWtW8vWvf4FfABwh6QJJg2nfXgVtjcWniwyW9KykFWl7VtKzwLb1/UUPLulw4HbgAklLJe3R8PJ3i9obg6LneVHT9lPg7fXfS/RrShMzxR5B0h1m9sqir7UZf5eZbV/0tXbHBl7fPCtJmZC/Lno3L2kJ8AEz+3XT/r3IEmN2KWDrNuDg5lmcpG2Ai83s1QVsVfKalWxriZnt2vD7J8huJA4ly/J035RIajfbErCDmQ22eb2Vra8BC4B/qK8YSLrXzLbx2miyt4Tsc/GwpD2Bs4GTzOzHkn5jZq8rYKvM87wJuBX4FtlKkMieMx4BYGbXeG31MlGS0TvcL+ljZNln9S+GFwJHA78vaOtGSacDZzW896XAUWQZdEUQ2R9wM7X0WlFmNwdEADO7LpVUFGEAeLDF/j8A0wraquo1K9PWoKQ+S9mNZvZZSX8AriVLdirCC4G3kK1kNPv7/4oYMrMPS9od+IGkC4FTaX3OXvrN7OFk+/qUQfozSS8dh93SzhNYCPwN8AmyG4AlklZHMCxGBMXe4d1kqd/XpJoogEeBi4DDC9paBBxDtrxTfw71IPBTsszPInwWuEnS5awPFi8D3gz8S0FbAJdI+jnZ3Xtj8FkEXFrQ1pnADZLObbJ1BMXPs6rXrExbPwX2A66s7zCz70p6BPh6QVs/A+aY2ZLmFyT9sqAtzOxGSQcAx5NlFc8oaqOBZyVtW3+emGaM+5Athb+moK3SzjPdjHxZ0n+l/z9KfMcXJpZPg01OWqp7Cxsneoy3qPpg4LAmexeZ2cXjsPXqNrZuHY9vZVHmNSv7+lcdSVsCrxvP5yG9fxdglZnd1bR/GnC4mX2vBDcnjKQ/Ad5oZv+4qX2ZTERQDJC0m5ndVJKtQ8zsZ2XY6hWm0jWT9BayDOfGAPsTMys6Sy8t8zfZehWtb25uK2qrwWYpGc5BtYjs0wDgr0u0tUf+EB+SStWCk3RsibY+VZYtKnrNitqS9BWyZ1rX0JD5C3xY0lcL2iot81fSx8kyfAVcnzaRPWM8sYitZG+sDGd3ko3jOKXdKJVpa6oTM8Wgskja3cxuLNHeB8zs30uy9adm9tMybJVJmdesqC21qf1LM747C2bYlpn5eyfwmuYmC5KmA0uL+JXeV1qGc85xtqwn9FTJ1lQnHsL2ECq34Lv05ahmygyIyV4pATHZKhwQJ9s1G4etNZL2MLMbmvbvAawpaKvMzN8aWcu/+5v2b5leK0qZGc7rUFOno4kEsTJt9RoRFHsEZYXYnwQuJ/tigaxI+3OSPm1mZxew9XGyBtLnki1FAWxFthx1rpmdXMBWaZ12GmyW8lxL0gBZxujbyL5U19kCvl2kvVtVr1nJ1/9o4BuS5rI+oL0UeCa9VoQyM3//FrhK0l1smGG7HVk2alFKy3BWi05HyrrlFO50VKatXiaWT3uEkou0S1uOknQZ2R/tWWb2SNr3IrL6vf3NrOjzo6+QqQKczfov5q3IvrDuMrO/KWDrB2RfLmc12ToK2MzM3l3AViWvWdnXrFT1+AAAF69JREFUv+H9jQkojxS1kezsSFb4P+HMX2WKJ81JOzfY+GXASslwlvQrsqbr59d9kdQPvAv4WzNzdwIq01YvE0GxR0hfyntYk2xSmiksLvilfDvwFjO7v2n/y4HLrVh3nNI67aT3lPlcq21/zLFeazO+ktesA9e/E31sJ9xAvcxM1jJRuZ2OSrPVy8Tyae9QZpF2mctR96u8TjtQ7nOt5ZLeBVxQ79KSZhzvYuMOJHlU9ZqVZqvkJfr6UuB+ZMuvE1lWPBA4nSybte7XVsB2kj5oZpd7bTmOdayZFcnaLbPTUZm2epaYKfYQKrfgu5TlqOTTiWRLUc2ddj5fdHagrOn3N4BWz7U+VDCbcmvg82RfzPVrtAC4muyL+d6CvlXumpVsq8wl+jKXFUvLZHUcq1CGc1o+P4YNl2LXdToys7WbwlYvE0ExmJKU9Vyrwd7mAGb25ER9m6qUvERf6rIi8GozG2naPx241TLFkiAAYvk0mKKkIDihQNhkL4JhPmUu0Ze5FFhmJitQXoZzUD1iphgEQWmUtURf9lKgSuxhW2aGc1A9IigGQVAqmuI9QcvMcA6qR/Q+7XEknSXpG5JeW4KtKyVdIumQEmwtlPTi/JHdR9KWWq8oP1Fblbxm47GlDXuCfp4Ne4K6BYYdx5nwtWqw9alxvG2NpFb9aseT4dwSSYdJen3VbPUC8UwxOJXsuc97gI9P0NYistZZZRQJnwDsnO7K3UXy7UgZiACnmdmpEzR3DrCtpAvM7O8naKuq12w8tr5L+56g3wFK6QlKFnzKanA9nrZ4R1Ne5552vB7YSdKAmR1cIVtTnlg+DSZEGYXVOfbnmtmzJdnaHNjLzH5egi0BO5rZ0nG8dzJdM7etnIzRZVMty7PsDOegGkRQ7BFUbr/MjXosAuPusdihLiilPNcqqxNKla9ZWbYkfQ3YltY9Qe81s0JNClRSA3WV2MO2bNLn4jEzW5M+a0cDuwG3Av/RXEbisDcPeL6Z3d20f2czu7kkt6c08UyxdziPrAB9HzPbzMw2J+s28lR6rQg/BH4MvMjMtk8zgC2BC8kaXrtJXVBuAvYBZqVtX7KU/EUF/Wp+rtWsdVfouZZK1PSjotesTFtm9mGy5fh9yW7ATko/nzaOgFimBuI5wK5s/O+4C/CfBW2VzcWs/x4+GfgT4NdkS8RF9SwPB24HLpC0tOm553cn7mqPYGax9cAG3DGe19qMv2s8r7U7NrCgxf7nkWXyFT3PJWRdVZr37wX8tqCt24CtW+zfBrhtKlyzsq9/WRtwJzCtxf7p47hebc9jU55jOv6tDT/fCPQ1/F7087oE2DL9vCdZgHxb+v03m/I8J9MWM8Xe4X5JH0vLikC2xJjuyIv2y7xR0umSXi/pxWl7fSq2LlpYLbKl3GZq6bWitNW6A4pq3ZWp6VfVa1b29W99EOnYgm+payA2Mx4NxOWS3qWszV7dnz5J76Z4D9uy+b2k/dLP95EtN6/roFSQfku6iWZ2Pdks/Z8kfZjW/8ZBCyL7tHd4N1mPy2skNfe4PLygrUVkz2g+TYvC6oK2yuyCAiVq3VFuJ5SqXrOyr387igbYMhuoH0FWInK6pOYetkcUtNWWcWY4vx84O5WGPAMskbQk+ffRgi48K2lbS88TzexhSfuQLdG/pqCtniUSbYJNjkpsVJ7slaJ1l2yV1gmlTMq8ZmVf/7JQyRqIyWZHe9iON8M5fc52YP3qxA2WlFkK2NgFWGVmdzXtnwYcbmbfK2KvV4mgGCBpNzO7qSRbh5hZWTVkPcFUumbqwZ6gKeNze+CeTX0jUSfd6Iya2YpN7ctkI54pBgB/XaKtVp0+xoWkQtl3DntFn2uNZetTZdmiotesqC1lPUH/BriGhsxf4MOSvlqWX5saSf8paYv081uAW8iWZ5co098sYmu5pG9J2j+VZEzErxdLOlvSM8ATwC2SHpD0qTRbDBzETDGoLJJ2twL6hw57hbTucmz9qZn9tAxbZVLmNStqSz3SE1TS78xsp/Tz/wP+3MzuS4HyKjNzd+5RpkH5deBIYGvgfOAHKTGsqF+/AD5jZr+U9HZgb+CfyEpjXmBmpd0UTmUiKPYQJRd8l1JY3UtM9Wsm6WbgGDO7oWn/nmRF8jttGs9aI2lLYLkVV9xYCrzBzFZI+h/gj+rP/yQtNTN3Uoukm8xst/Tzy8gSf44gS7Q518z+sYCt3zYGZEk3mtnu6efbzexVXlu9TCyf9gglF3yXVlgtab6kkyXdnpaSnpR0W9q3oIitBptvUdbk/KK0fUPSQeOwMyDpA5IulXRz2i6RdFzR5aiqXrOSr//RwKmSbpV0edpuA75GST1BVWIDdbKi/tslfbHg+z4NXC3pfcD/Av8l6ShJ36V4hvO6JVMze8DMvpCC5FuBQsEaeFzSX0p6iaQTyEo86jP1+K53EjPFHiEt07y+eVaYHsj/utWy1xi27gReY03tsZRp4C0tskwm6TKyVmdnWeodqayn5FHA/mZWqHOMStS6k/QDspZsZzXZOgrYzAo03a7qNSv7+je8vyM9QZUpd2xJluF5Wgn2xtXDVtJ2wF+xYcbohWZ2WUE7XzKzoqUX7Wy9DPgisCNZIf8/pLKMzck6WV1QxnGmOhEUe4T0pbyHmT3TtH8+sLjgl/LtwFvM7P6m/S8HLjezVxawdUe78WO9Noa90p5rtbOV91qb8ZW8ZmVf/6qSAsYKM3ta0tbAQuB2M7tlkzoWVI6YUvcO9SLtb0j6x7R9k2xJ9bMFbdULqy+RdEbaLgWuIss+LML9Kq/TDpSrdVdmJ5SqXrOyr38pSHpVulY/l7StpO9KelrS9cpq+orYOpEsC/Y6Se8nW+I8GPihpFJmaQ3HKlPrsZK2pjoxU+whVG7BdymF1cmnE8kSUJo77XzeCsorKWv6/Q2gldbdhwpmU25Nlmq/H+uDYL0Tyolmdm9B3yp3zcq+/mUh6VrgFGAOWaPsj5M1VT8E+Fsz27+AraVkM8NZZM/ZXmFmj0uaTfboYMIC2w3H+rSZfXIq25rqRFAMpiRlP9dShzuhBBsi6Tdm9rr08wZajGrI2HTautnMdpbUDzxMplRSzxa9pcygGEx+ovdpMCVJQbC0BI8IhuNH4+sJ2t/w85eaXpte0IWbJH2frCH8VcBZael6PzLdwsKoxPKaqtrqVeKZYhAEHcXMXk1WSF5kufk0SXPS+0+v70xZn1cWdOH9ZI3XfwC8h2x5/Q1kslnvLWir7PKaStrqZWL5NAiCnkMT6PdbcnlNJW31MjFT7HEknZUyUif8XEUlFlZLWphq0iqHpC0lDZZkq5LXrOzrL+l3JdoqdK0k7da8ARdJel36uShlaj1W1VbPEs8Ug1PJdOreQ5bhNxEWkQqrJ+oUcAKwc6oHdBfJt2Ocz7XacQ6wraQLzOzvJ2irqtessC1l/TZbvgS8aIL+NLIHUERVZDFwHRt2iNmc7FmlkT1bLEKZWo9VtdWzxPJpUGkkzTWzZ0uyNS6tuza2xtUJpdmfTiTwlHzN3LYkDQPfo7XK+zvNbG4ZPhVF0juADwMnm9klad+9ZrbNBGyWpvVYVVu9SgTFHkFZ55qTyLTuXkD2xfUY8BOyL4vCTcHbHOcSMzt4HO+b1uJZyBZm9kQJPh1qZheVYGc7YBfgNisoMizpZOCLZvaEpIXAeWRLWtOARWZ2TQFb04FhS3+8kvYFdgNurX/pF6WM6y/pRuCoVl1iJP3ezF5a0KcyszLnAP9C1qbv74BfmtkritoJpj7xTLF3OI+sAH0fM9vMzDYnawj+VHrNTatnNGnbHdi1oK19JT0IPKysgfTWDS9fXsRWsvf2pu0dwBn13wvaulrrdfPeA1zM+k4oJxR07U8aAswpwLtT7d2bgX8raOsGsiYCSPoHso5EM4GPpuDrpuTr/7dAO1HbtxX0q9RMSjNbaWYfAT5H1st2TlEbQW8QM8UeQeX2yxwla5vVShR1LzObWcDWDcDRZrZU0juBfwXeY2bXqaGAu4C9YeAysllw3b93kunUmZm9r4CtdYXdyc+DzOxJSbOA68xs5wK2bgN2MrMRSdeZ2V4Nr63T5xuHX4uBvc1staQB4KaCfpV6/cuik5mUael7roUqfdCCmCn2DvervB6XtwEfMLN9mzcyxe8iTK8/lzOz88mWd8+S9Ge0fjaVx/8hmzXdYGbvNbP3Ak+kn90BMTEsqb50txJ4Lv28lg2Lyz2cDlwsaT/gUklflfTHkj5NpmhQhBVany38BDAj/TxA8b/psq9/S8aRXduxTErLWDFOv4IpTmSf9g7vJutxeY2k5h6Xhxe09Snaf/kWXVYclvSiehu2NGPZnyy7cNuCtjCzGyS9GThB0tVkGbXj/XL/CHC5pAuApcAvlEktvQn4TkG/vp7KEv6a9XJD2wMXAv9fQb+OA74n6bdkM+LFynqF7kS2PFiEUq//GBTNGO1WJmVRv9oi6UpgmCzDeUI2q2qrF4jl0/+/vXOLtaOs4vjv/+AFLxQKtoVowXiJD0aNae0LJAR6izE1MYq2WEpqfNBofDAiIiFKYpQmGqJQE5CkRxNpG4lpjYGWhrb6ICgtIgULfaglBkobW5posSF2+fDNgdPds885M2ftPd/sb/2S/XBmOv+9Zs3u/vasWZegVSQtBY6b2VM92+cAXzOzuhM8JmpcDtwFLGqaVFHZsYZz5+ZtM7ODTe3yQKmP5/Ieu3bUTZiawv8XkRqoN/b/bOlaJqUcZz3mqlUCsSgGs+ruMUitHJE0z8yOOWuOjM88M0Y9GZRdkuYCmMM0kVy1SiOeKQaQQnqtaEm6UNIPJf1K0pqefRv7HTeF3gKlDj33SLpE0vck/U3SVkmX1dSa2/sC/izp4vEvHSfq+my/pNskzTq8Kekdku6Q9IykU5KOS3pM0k0NtLLsveltl6SFkjZLOg48TvpMHKu2XTkKWiUTd4pBq1TP6w6ROo6sJz37WGNmZ1RzRFCl9zDwe9JEhDWkYvJfkxJIlprZp2tonQWO9Gx+NylUaW3VuUk6DDxIehZ8lNToeouZvdhAaxvwW1KT7etJftsM3EYauXVrDa0se2962yXpT6Sw/G/GQ7lVOPtzpFmPM+5OlKtWycSiWBDV87GVnD9kuHbhvpeWpL+a2ccm/P1d4JPAKuCRBovixDl8L5jZwn7vNQOtb5LqCL9lZk9X2w5bw04ojj57/ceCpKuB1cBnSFnBD5jZvTW0njKzj074+y9mtrh6nvesmX2ohtZBYIWZHenZfgWws07Zjyfedkk61G8hnWpfl7RKJsKnhSDpRmA/cA1pAvnbSMX7+6p9rWgBb6m+gAGoEjvuA/5A6k9Zl4mf6V9OsW9azOzHpLFDt0v6iaR30jCT1dlnE238o5l9lbTQ3kkaiVSH/0i6qrJxFXCi0j3L5HWoUzGeMfqQpHur18OkGYbfqKk1KWrWQN3brn2SNkpaIuny6rWkCvc/OSJaxRJ3ioUg6TlgSe9diaSLgcfN7IMtaW0g/Vrf1bN9JfCzBqGtO4ANZvbvnu3vJ7Wz+2wdvQnHrwJuBa40s9rNrZ19ttnMvlDXhj5aHwF+QSoPeQZYb2bPS3oXsNrMflpTb6AZo2qYSelpVxV2/RLnJu78kzSz8X4zO9Pv2K5olUwsioVQPVdZbGanerbPAZ6oGaZx0+oSki4A3meT9PacwbFF+swDRSZlMESieL8cfgDsl7STc4uhl5EaJbel1Rc5lyrMVs/MXgUONNTqnM+8/V/zvRcCG4DrgFfSJl0IPArcYmb/aMOu6ZD0Ka8C+Vy1Rp14plgIZjYGLCL1LD1TvfaQCts3taU1DZ6lIt56tbQ66jNv/9dhCykrdoGZfcBS8/TLSB2ANrdo13QsLkBrpInwaRAE2RGZlEFbRPg0aB3PUhFvPW/bvOjSOapZ7819VdbkGG+Em98DrMMpk7KhXdNpLjOzR0ZZa9SJ8GnQKt6lChmXnrjRwXO8kdQM4IqaxzwNfJ80CmwHqRH9AWBti3ZNx/0FaI00ET4NWsWzVMFbz9s2L7pwjrlmjHrYJWl7v13AtWb29q5rlUyETwtH0hhwmhRCql1q4KAlJi+Ib1I87q3nbdv5b9C+z9y0hpUxWjeTcgB2XQ18kTRj85y3ItVCjoJWscSiGNxNKg1YS5o9OGwt71KFrpWetO0zT60tpN6bN0zSe3Mz4NV7s+4MRG+7HgNOm9ne3h3VnfcoaBVLhE+D1qlCdSs4P9HjZNt63rZ5keM55poxmqtdQZ7EolgIVYbhd0jTIuaRQmbHgG2k9mczzjT01CqFEnwmaTOpd+pkGaOXmtn1Tu9TK5NyWHYFo0EsioUgaQfpGcqYmR2tti0gfTFcZ2bL29AqhRJ8Nqzem+qZfpKLXcFoEItiIUh6rt+InKn2DVqrFMJn9YhMyqAtItGmHI5Iupl0p/IygKT5wE28EVJqQ6sUivZZg96bQ8mkjJ6gQS9RvF8OnyfNJ9wr6YSkE6Tem3NJE9fb0poUSWOSfi7pw7npNdTqlM+8/U/93puvZ1L2vPYAnpmUbj1Bc/X/AK7lSBPh0yBLJC0mlQZ8wsxmWyriqudtmxclnGPO5Or/uJb1iEUxGJlxQ12lBJ/VzRgdFrnaFbRHhE8DaHHckKQ5kn4k6WAVVvyXpL9X2y6q++aeet62TUFrPhviOebae7O2Xbn6f4jXcqSJRTHAzL7cotZW4CRwjZnNNbNLSA2pT1b76uKp523bpLTsMzctSdv7vH5Hep7aCgOwK0v/O2sVS4RPC0IZjhuSc6mCp94AbMvOZ85aJ+mfMbrFzObPVMsTb7sy9n+U/TgQd4qFoHzHDR2RdLNSecK4/nxJ36ZZqYKnnptWxj7z1Bp4xqiaZVJ625Wr/73/LxVJ3CkWgjIdN1Qdcwup28i8avPLwHbgTqs54sdTz1krS595+3/QKINMylz937VrmSuxKBaCpOeBxWZ2qmf7HOCJOk2RPbVKIXwWBN0gOtqUQ67jhvoi51IFT70GWp3zmbf/a753Jxuo5+r/Nq9l14hnioVgZmPAImAvcKZ67QEWmdmmtrSmwbNUxFuvllZHfebt/zp0NZMyV/+3eS07RYRPC0GSbJqLPZN/461VCuGzekQmZdAWET4th92SHgS2mdkL4xuVxupcRRphtBvYNGQt11IRbz1HrWx95u3/SfTHgNPAPWZ2YIaHHdGAG6g3tGv82Cz9P+hrWQIRPi2HlcD/gAckvSjpWUmHgUPAauCuGmE8Ny3nUoWcS0+y9Jm3//twN7ALWFvjmIE3UG9oV7b+H9K1HHkifFogkt4EXAq8OttfkLPV8ixV8Nbztm3C8dn4bFDnOMrk6v+4lj7EnWKBmNlrZvaSR0jFQUukzMJezlb72tTztg3IzmduWhpS701JH2/Zriz976xVLPFMMWgb71KFzpWeNCDXc9wKPErKGD0KIGkB6XnpVmB5Tb1+fAWo0y/W265c/Z/r57VTRPg0aJ0qvLOC85MDTrat522bFzmeY64Zo4OwK0f/e2uVSiyKQat4lyqUUHqS6zlWdyi7mDxjdJmZLZ1Oo0fPq4G6t125+j/Lz2vXiGeKQdvslvR1SQsnbpT0ZknXKqXNr2tJz9s2L3I9R7eMUedMSu9M1lz9n+vntVPEnWLQKpLeCqwHbgDeC7wCXED6wbYT2GhmT7ah522bF4WcY7aZlLn6P9dr2TViUQyyQY6lIt563rZ50ZVzVM3emxpSA/W6dk1yfJb+z/Xz2gViUQyCYOBIus/MZpwxKmkdcDvpDue8TMoajSZc7QpGn1gUgyDIksikDNogFsUgCNxwzBj1zkqOnqDBjIjs0yAIXHDOGHXLpHS2Kxhx4k4xCAIXPDNGnbMys81kDfIj2rwFQeCFW+9NM/svsBHY6JBJGT1BgxkTi2IQBF4MpPemmb0GvJSbXcFoEuHTIAjcyDVjNFe7gvyIRTEIAhdy7b2Zq11BnkT2aRAEXuTaezNXu4IMiTvFIAhcyLX3Zq52BXkSi2IQBO7k2nszV7uCfIhFMQiCIAgq4pliEARBEFTEohgEQRAEFbEoBkEQBEFFLIpBEARBUBGLYhAEQRBU/B/qss4G6w9ggQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"mean_d = pd.DataFrame(np.stack(delta_fs).mean(axis=0), index=delta_fs[0].index, columns=delta_fs[0].columns)\n",
"sns.heatmap(mean_d, center=0)"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f120289bbe0>"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"stds_d = pd.DataFrame(np.stack(delta_fs).std(axis=0), index=delta_fs[0].index, columns=delta_fs[0].columns)\n",
"sns.heatmap(stds_d)"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>(0.0, 1.0, 1.0, 1.0)</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>(0.0, 0.0, 0.0, 0.0)</th>\n",
" <td>0.179414</td>\n",
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"text/plain": [
" (0.0, 1.0, 1.0, 1.0)\n",
"(0.0, 0.0, 0.0, 0.0) 0.179414"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"stds_d.loc[[(0.0, 0.0, 0.0, 0.0)], [(0.0, 1.0, 1.0, 1.0)]]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We'll do it again as another roll of the dice:"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [],
"source": [
"delta_fs = list()\n",
"for i in range(1000):\n",
" bar = delayed(BAR().fit)(delayed(bootstrap)(L_u_nk))\n",
" delta_fs.append(bar.delta_f_)"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 1.86 s, sys: 113 ms, total: 1.97 s\n",
"Wall time: 51.9 s\n"
]
}
],
"source": [
"%%time\n",
"L_r = cl.compute(delta_fs)\n",
"delta_fs = cl.gather(L_r)"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f1200d9bf70>"
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"mean_d = pd.DataFrame(np.stack(delta_fs).mean(axis=0), index=delta_fs[0].index, columns=delta_fs[0].columns)\n",
"sns.heatmap(mean_d, center=0)"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f1200d65f70>"
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"stds_d = pd.DataFrame(np.stack(delta_fs).std(axis=0), index=delta_fs[0].index, columns=delta_fs[0].columns)\n",
"sns.heatmap(stds_d)"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>(0.0, 1.0, 1.0, 1.0)</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>(0.0, 0.0, 0.0, 0.0)</th>\n",
" <td>0.176048</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" (0.0, 1.0, 1.0, 1.0)\n",
"(0.0, 0.0, 0.0, 0.0) 0.176048"
]
},
"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"stds_d.loc[[(0.0, 0.0, 0.0, 0.0)], [(0.0, 1.0, 1.0, 1.0)]]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Pretty close to the value we got just before. To quantify convergence in production work, it's worth running many of these many-bootstrap+estimation runs. This is where parallelism really pays off."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.1"
}
},
"nbformat": 4,
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