diff --git a/jupyter-example.ipynb b/jupyter-example.ipynb new file mode 100644 index 0000000..fc8b0a7 --- /dev/null +++ b/jupyter-example.ipynb @@ -0,0 +1,5155 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Setup" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Populating the interactive namespace from numpy and matplotlib\n" + ] + } + ], + "source": [ + "%pylab --no-import-all inline\n", + "import matplotlib as mpl" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Don't necessarily actually want the figures to be inline (not really necessary here as I really just want to save them)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import matplotlib as mpl\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import ParaTemp.CoordinateAnalysis as ca\n", + "\n", + "from ParaTemp import cd\n", + "import pandas as pd\n", + "\n", + "import os\n", + "\n", + "import MDAnalysis\n", + "import MDAnalysis as mda\n", + "import MDAnalysis.analysis.rdf as mdardf\n", + "from MDAnalysis.analysis.base import AnalysisBase" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "configs = {'MaEn': 'major-endo/13-3htmf-etc/05',\n", + " 'MaEx': 'major-exo/13-3htmf-etc/05',\n", + " 'MiEn': 'minor-endo/13-3htmf-etc/05',\n", + " 'MiEx': 'minor-exo/13-3htmf-etc/05'\n", + " }" + ] + }, + { + "cell_type": "code", + "execution_count": 177, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 177, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "reload(ca)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "heading_collapsed": true + }, + "source": [ + "# Test stuff" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": true, + "hidden": true + }, + "outputs": [], + "source": [ + "i = 0\n", + "key = 'MaEn'\n", + "with cd(configs[key]):\n", + " univ = ca.Taddol('../../../solutes.gro', \n", + " 'npt-PT-{}-out{}.xtc'.format(key, i))" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "hidden": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "775ns\n" + ] + } + ], + "source": [ + "final_time = str(int(univ.data['Time'].iat[-1]/1000))+'ns'\n", + "print final_time" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "collapsed": true, + "hidden": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['205.0', '215.51057475708495', '226.5600382055078', '238.17601975929801', '250.38756542283483', '263.22521042098697', '276.72105555308065', '290.90884746161777', '305.8240630164604', '321.50399802548463', '337.9878604935263', '355.31686866281603', '373.53435408005436', '392.68586994784874', '412.8193050314477', '433.9850034055983']\n", + "\n" + ] + } + ], + "source": [ + "with cd(configs[key]):\n", + " with open('TOPO/temperatures.dat', 'r') as t_file:\n", + " temps = list(t_file.read()[1:-2].split(', '))\n", + "print temps\n", + "print type(temps)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "hidden": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MaEx\n", + "MiEn\n", + "MiEx\n", + "MaEn\n" + ] + } + ], + "source": [ + "for key in configs:\n", + " print key" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "heading_collapsed": true + }, + "source": [ + "# Run it" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": true, + "hidden": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Now starting on MaEn 0...\n", + "\"all\" given or implied, calculating distances for oxygens and CVs\n", + "Done importing and calculating, making figures...\n", + "\n", + "\n", + "!!!! Not enough data to plot for closed MaEn 0 !!!!\n", + "\n", + "\n", + "Done making and saving figures for MaEn 0, closing and moving on...\n", + "Now starting on MaEn 1...\n", + "\"all\" given or implied, calculating distances for oxygens and CVs\n", + "Done importing and calculating, making figures...\n", + "\n", + "\n", + "!!!! Not enough data to plot for closed MaEn 1 !!!!\n", + "\n", + "\n", + "Done making and saving figures for MaEn 1, closing and moving on...\n", + "Now starting on MaEn 2...\n", + "\"all\" given or implied, calculating distances for oxygens and CVs\n", + "Done importing and calculating, making figures...\n", + "Done making and saving figures for MaEn 2, closing and moving on...\n", + "Now starting on MaEn 3...\n", + "\"all\" given or implied, calculating distances for oxygens and CVs\n", + "Done importing and calculating, making figures...\n", + "Done making and saving figures for MaEn 3, closing and moving on...\n", + "Now starting on MaEn 4...\n", + "\"all\" given or implied, calculating distances for oxygens and CVs\n", + "Done importing and calculating, making figures...\n", + "Done making and saving figures for MaEn 4, closing and moving on...\n", + "Now starting on MaEn 5...\n", + "\"all\" given or implied, calculating distances for oxygens and CVs\n", + "Done importing and calculating, making figures...\n", + "Done making and saving figures for MaEn 5, closing and moving on...\n", + "Now starting on MaEn 6...\n", + "\"all\" given or implied, calculating distances for oxygens and CVs\n", + "Done importing and calculating, making figures...\n", + "Done making and saving figures for MaEn 6, closing and moving on...\n", + "Now starting on MaEn 7...\n", + "\"all\" given or implied, calculating distances for oxygens and CVs\n", + "Done importing and calculating, making figures...\n", + "Done making and saving figures for MaEn 7, closing and moving on...\n", + "Now starting on MaEn 8...\n", + "\"all\" given or implied, calculating distances for oxygens and CVs\n", + "Done importing and calculating, making figures...\n", + "Done making and saving figures for MaEn 8, closing and moving on...\n", + "Now starting on MaEn 9...\n", + "\"all\" given or implied, calculating distances for oxygens and CVs\n", + "Done importing and calculating, making figures...\n", + "Done making and saving figures for MaEn 9, closing and moving on...\n", + "Now starting on MaEn 10...\n", + "\"all\" given or implied, calculating distances for oxygens and CVs\n", + "Done importing and calculating, making figures...\n", + "Done making and saving figures for MaEn 10, closing and moving on...\n", + "Now starting on MaEn 11...\n", + "\"all\" given or implied, calculating distances for oxygens and CVs\n", + "Done importing and calculating, making figures...\n", + "Done making and saving figures for MaEn 11, closing and moving on...\n", + "Now starting on MaEn 12...\n", + "\"all\" given or implied, calculating distances for oxygens and CVs\n", + "Done importing and calculating, making figures...\n", + "Done making and saving figures for MaEn 12, closing and moving on...\n", + "Now starting on MaEn 13...\n", + "\"all\" given or implied, calculating distances for oxygens and CVs\n", + "Done importing and calculating, making figures...\n", + "Done making and saving figures for MaEn 13, closing and moving on...\n", + "Now starting on MaEn 14...\n", + "\"all\" given or implied, calculating distances for oxygens and CVs\n", + "Done importing and calculating, making figures...\n", + "Done making and saving figures for MaEn 14, closing and moving on...\n", + "Now starting on MaEn 15...\n", + "\"all\" given or implied, calculating distances for oxygens and CVs\n", + "Done importing and calculating, making figures...\n", + "Done making and saving figures for MaEn 15, closing and moving on...\n", + "\n", + "\n", + "---Done with all MaEn, moving to next config\n" + ] + } + ], + "source": [ + "for key in configs:\n", + " with cd(configs[key]):\n", + " with open('TOPO/temperatures.dat', 'r') as t_file:\n", + " temps = list(t_file.read()[1:-2].split(', '))\n", + " fig_fes_cvs, axes_fes_cvs = plt.subplots(4, 4, sharex=True, sharey=True)\n", + " fig_fes_cvs_open, axes_fes_cvs_open = plt.subplots(4, 4, sharex=True, sharey=True)\n", + " fig_fes_cvs_closed, axes_fes_cvs_closed = plt.subplots(4, 4, sharex=True, sharey=True)\n", + " fig_fes_ox_dists, axes_fes_ox_dists = plt.subplots(4, 4, sharex=True, sharey=True)\n", + " for i in xrange(16):\n", + " print 'Now starting on {} {}...'.format(key, i)\n", + " temp = float(temps[i])\n", + " univ = ca.Taddol('../../../solutes.gro', \n", + " 'npt-PT-{}-out{}.xtc'.format(key, i))\n", + " final_time = str(int(univ.data['Time'].iat[-1]/1000))+'ns'\n", + " file_name_end = '-rjm-PT-{}-{}-{}.pdf'.format(key, i, final_time)\n", + " if os.path.isfile('fes-ox-dists'+file_name_end):\n", + " print '{} {} seems to already '.format(key, i) \\\n", + " 'be complete; moving on...'\n", + " continue\n", + " try:\n", + " univ.read_data()\n", + " except IOError:\n", + " univ.calculate_distances()\n", + " univ.save_data()\n", + " univ.calc_open_closed() # This is fast, so doesn't really matter if repeated\n", + " print 'Done importing and calculating, making figures...'\n", + " \n", + " ax = axes_fes_cvs.flat[i]\n", + " univ.fes_2d_cvs(temp=temp, display=False, ax=ax)\n", + " filename = 'fes-cvs'+file_name_end\n", + " if not os.path.isfile(filename):\n", + " fig = univ.fes_2d_cvs(temp=temp)\n", + " fig.savefig(filename)\n", + " \n", + " if len(univ.data[univ.data['open_TAD']]['CV1']) > 1:\n", + " ax = axes_fes_cvs_open.flat[i]\n", + " univ.fes_2d_cvs(univ.data[univ.data['open_TAD']]['CV1'], \n", + " univ.data[univ.data['open_TAD']]['CV2'],\n", + " temp=temp, display=False, ax=ax)\n", + " filename = 'fes-cvs-open'+file_name_end\n", + " if not os.path.isfile(filename):\n", + " fig = univ.fes_2d_cvs(univ.data[univ.data['open_TAD']]['CV1'], \n", + " univ.data[univ.data['open_TAD']]['CV2'],\n", + " temp=temp)\n", + " fig.savefig(filename)\n", + " else:\n", + " print '\\n\\n!!!! Not enough data to plot for open ' \\\n", + " '{} {} !!!!\\n\\n'.format(key, i)\n", + " \n", + " if len(univ.data[univ.data['closed_TAD']]['CV1']) > 1:\n", + " ax = axes_fes_cvs_closed.flat[i]\n", + " univ.fes_2d_cvs(univ.data[univ.data['closed_TAD']]['CV1'], \n", + " univ.data[univ.data['closed_TAD']]['CV2'],\n", + " temp=temp, display=False, ax=ax)\n", + " filename = 'fes-cvs-closed'+file_name_end\n", + " if not os.path.isfile(filename):\n", + " fig = univ.fes_2d_cvs(univ.data[univ.data['closed_TAD']]['CV1'], \n", + " univ.data[univ.data['closed_TAD']]['CV2'],\n", + " temp=temp)\n", + " fig.savefig(filename)\n", + " else:\n", + " print '\\n\\n!!!! Not enough data to plot for closed ' \\\n", + " '{} {} !!!!\\n\\n'.format(key, i)\n", + " \n", + " ax = axes_fes_ox_dists.flat[i]\n", + " univ.fes_ox_dists(temp=temp, save=False, display=False, linewidth=3,\n", + " ax=ax)\n", + " filename = 'fes-ox-dists'+file_name_end\n", + " if not os.path.isfile(filename):\n", + " fig = univ.fes_ox_dists(temp=temp, save=False, display=True, \n", + " linewidth=3)\n", + " fig.savefig(filename)\n", + " print('Done making and saving figures for '\n", + " '{} {}, closing and moving on...'.format(key, i))\n", + " #plt.close('all') # Not necessary because they pass out of scope?\n", + " fig_fes_cvs.savefig('fes-cvs-rjm-PT-{}-comb-{}.pdf'.format(key, final_time))\n", + " fig_fes_cvs_open.savefig('fes-cvs-open-rjm-PT-{}-comb-{}.pdf'.format(key, final_time))\n", + " fig_fes_cvs_closed.savefig('fes-cvs-closed-rjm-PT-{}-comb-{}.pdf'.format(key, final_time))\n", + " fig_fes_ox_dists.savefig('fes-ox-dists-rjm-PT-{}-comb-{}.pdf'.format(key, final_time))\n", + " print '\\n\\n---Done with all {}, moving to next config'.format(key)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "heading_collapsed": true, + "hidden": true + }, + "source": [ + "##### Output from a nearly complete run:" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "hidden": true + }, + "source": [ + "Output from a nearly complete run that died at the end:\n", + "\n", + "Now starting on MiEx 0...\n", + "\"all\" given or implied, calculating distances for oxygens and CVs\n", + "Done importing and calculating, making figures...\n", + "\n", + "\n", + "!!!! Not enough data to plot for closed MiEx 0 !!!!\n", + "\n", + "\n", + "Done making and saving figures for MiEx 0, closing and moving on...\n", + "Now starting on MiEx 1...\n", + "\"all\" given or implied, calculating distances for oxygens and CVs\n", + "Done importing and calculating, making figures...\n", + "\n", + "\n", + "!!!! Not enough data to plot for closed MiEx 1 !!!!\n", + "\n", + "\n", + "Done making and saving figures for MiEx 1, closing and moving on...\n", + "Now starting on MiEx 2...\n", + "\"all\" given or implied, calculating distances for oxygens and CVs\n", + "Done importing and calculating, making figures...\n", + "Done making and saving figures for MiEx 2, closing and moving on...\n", + "Now starting on MiEx 3...\n", + "\"all\" given or implied, calculating distances for oxygens and CVs\n", + "Done importing and calculating, making figures...\n", + "\n", + "\n", + "!!!! Not enough data to plot for closed MiEx 3 !!!!\n", + "\n", + "\n", + "Done making and saving figures for MiEx 3, closing and moving on...\n", + "Now starting on MiEx 4...\n", + "\"all\" given or implied, calculating distances for oxygens and CVs\n", + "Done importing and calculating, making figures...\n", + "Done making and saving figures for MiEx 4, closing and moving on...\n", + "Now starting on MiEx 5...\n", + "\"all\" given or implied, calculating distances for oxygens and CVs\n", + "Done importing and calculating, making figures...\n", + "Done making and saving figures for MiEx 5, closing and moving on...\n", + "Now starting on MiEx 6...\n", + "\"all\" given or implied, calculating distances for oxygens and CVs\n", + "Done importing and calculating, making figures...\n", + "Done making and saving figures for MiEx 6, closing and moving on...\n", + "Now starting on MiEx 7...\n", + "\"all\" given or implied, calculating distances for oxygens and CVs\n", + "Done importing and calculating, making figures...\n", + "Done making and saving figures for MiEx 7, closing and moving on...\n", + "Now starting on MiEx 8...\n", + "\"all\" given or implied, calculating distances for oxygens and CVs\n", + "Done importing and calculating, making figures...\n", + "Done making and saving figures for MiEx 8, closing and moving on...\n", + "Now starting on MiEx 9...\n", + "\"all\" given or implied, calculating distances for oxygens and CVs\n", + "Done importing and calculating, making figures...\n", + "Done making and saving figures for MiEx 9, closing and moving on...\n", + "Now starting on MiEx 10...\n", + "\"all\" given or implied, calculating distances for oxygens and CVs\n", + "Done importing and calculating, making figures...\n", + "Done making and saving figures for MiEx 10, closing and moving on...\n", + "Now starting on MiEx 11...\n", + "\"all\" given or implied, calculating distances for oxygens and CVs\n", + "Done importing and calculating, making figures...\n", + "Done making and saving figures for MiEx 11, closing and moving on...\n", + "Now starting on MiEx 12...\n", + "\"all\" given or implied, calculating distances for oxygens and CVs\n", + "Done importing and calculating, making figures...\n", + "Done making and saving figures for MiEx 12, closing and moving on...\n", + "Now starting on MiEx 13...\n", + "\"all\" given or implied, calculating distances for oxygens and CVs\n", + "Done importing and calculating, making figures...\n", + "Done making and saving figures for MiEx 13, closing and moving on...\n", + "Now starting on MiEx 14...\n", + "\"all\" given or implied, calculating distances for oxygens and CVs\n", + "Done importing and calculating, making figures...\n", + "Done making and saving figures for MiEx 14, closing and moving on...\n", + "Now starting on MiEx 15...\n", + "\"all\" given or implied, calculating distances for oxygens and CVs\n", + "Done importing and calculating, making figures...\n", + "Done making and saving figures for MiEx 15, closing and moving on...\n", + "\n", + "\n", + "---Done with all MiEx, moving to next config\n", + "Now starting on MiEn 0...\n", + "\"all\" given or implied, calculating distances for oxygens and CVs\n", + "Done importing and calculating, making figures...\n", + "Done making and saving figures for MiEn 0, closing and moving on...\n", + "Now starting on MiEn 1...\n", + "\"all\" given or implied, calculating distances for oxygens and CVs\n", + "Done importing and calculating, making figures...\n", + "Done making and saving figures for MiEn 1, closing and moving on...\n", + "Now starting on MiEn 2...\n", + "\"all\" given or implied, calculating distances for oxygens and CVs\n", + "Done importing and calculating, making figures...\n", + "\n", + "\n", + "!!!! Not enough data to plot for closed MiEn 2 !!!!\n", + "\n", + "\n", + "Done making and saving figures for MiEn 2, closing and moving on...\n", + "Now starting on MiEn 3...\n", + "\"all\" given or implied, calculating distances for oxygens and CVs\n", + "Done importing and calculating, making figures...\n", + "Done making and saving figures for MiEn 3, closing and moving on...\n", + "Now starting on MiEn 4...\n", + "\"all\" given or implied, calculating distances for oxygens and CVs\n", + "Done importing and calculating, making figures...\n", + "Done making and saving figures for MiEn 4, closing and moving on...\n", + "Now starting on MiEn 5...\n", + "\"all\" given or implied, calculating distances for oxygens and CVs\n", + "Done importing and calculating, making figures...\n", + "Done making and saving figures for MiEn 5, closing and moving on...\n", + "Now starting on MiEn 6...\n", + "\"all\" given or implied, calculating distances for oxygens and CVs\n", + "Done importing and calculating, making figures...\n", + "Done making and saving figures for MiEn 6, closing and moving on...\n", + "Now starting on MiEn 7...\n", + "\"all\" given or implied, calculating distances for oxygens and CVs\n", + "Done importing and calculating, making figures...\n", + "Done making and saving figures for MiEn 7, closing and moving on...\n", + "Now starting on MiEn 8...\n", + "\"all\" given or implied, calculating distances for oxygens and CVs\n", + "Done importing and calculating, making figures...\n", + "Done making and saving figures for MiEn 8, closing and moving on...\n", + "Now starting on MiEn 9...\n", + "\"all\" given or implied, calculating distances for oxygens and CVs\n", + "Done importing and calculating, making figures...\n", + "Done making and saving figures for MiEn 9, closing and moving on...\n", + "Now starting on MiEn 10...\n", + "\"all\" given or implied, calculating distances for oxygens and CVs\n", + "Done importing and calculating, making figures...\n", + "Done making and saving figures for MiEn 10, closing and moving on...\n", + "Now starting on MiEn 11...\n", + "\"all\" given or implied, calculating distances for oxygens and CVs\n", + "Done importing and calculating, making figures...\n", + "Done making and saving figures for MiEn 11, closing and moving on...\n", + "Now starting on MiEn 12...\n", + "\"all\" given or implied, calculating distances for oxygens and CVs\n", + "Done importing and calculating, making figures...\n", + "Done making and saving figures for MiEn 12, closing and moving on...\n", + "Now starting on MiEn 13...\n", + "\"all\" given or implied, calculating distances for oxygens and CVs\n", + "Done importing and calculating, making figures...\n", + "Done making and saving figures for MiEn 13, closing and moving on...\n", + "Now starting on MiEn 14...\n", + "\"all\" given or implied, calculating distances for oxygens and CVs\n", + "Done importing and calculating, making figures...\n", + "Done making and saving figures for MiEn 14, closing and moving on...\n", + "Now starting on MiEn 15...\n", + "\"all\" given or implied, calculating distances for oxygens and CVs\n", + "Done importing and calculating, making figures...\n", + "Done making and saving figures for MiEn 15, closing and moving on...\n", + "\n", + "\n", + "---Done with all MiEn, moving to next config\n", + "Now starting on MaEn 0...\n", + "\"all\" given or implied, calculating distances for oxygens and CVs\n", + "Done importing and calculating, making figures...\n", + "\n", + "\n", + "!!!! Not enough data to plot for closed MaEn 0 !!!!\n", + "\n", + "\n", + "Done making and saving figures for MaEn 0, closing and moving on...\n", + "Now starting on MaEn 1...\n", + "\"all\" given or implied, calculating distances for oxygens and CVs\n", + "Done importing and calculating, making figures...\n", + "\n", + "\n", + "!!!! Not enough data to plot for closed MaEn 1 !!!!\n", + "\n", + "\n", + "Done making and saving figures for MaEn 1, closing and moving on...\n", + "Now starting on MaEn 2...\n", + "\"all\" given or implied, calculating distances for oxygens and CVs\n", + "Done importing and calculating, making figures...\n", + "Done making and saving figures for MaEn 2, closing and moving on...\n", + "Now starting on MaEn 3...\n", + "\"all\" given or implied, calculating distances for oxygens and CVs\n", + "Done importing and calculating, making figures...\n", + "Done making and saving figures for MaEn 3, closing and moving on...\n", + "Now starting on MaEn 4...\n", + "\"all\" given or implied, calculating distances for oxygens and CVs\n", + "Done importing and calculating, making figures...\n", + "Done making and saving figures for MaEn 4, closing and moving on...\n", + "Now starting on MaEn 5...\n", + "\"all\" given or implied, calculating distances for oxygens and CVs\n", + "Done importing and calculating, making figures...\n", + "Done making and saving figures for MaEn 5, closing and moving on...\n", + "Now starting on MaEn 6...\n", + "\"all\" given or implied, calculating distances for oxygens and CVs\n", + "Done importing and calculating, making figures...\n", + "Done making and saving figures for MaEn 6, closing and moving on...\n", + "Now starting on MaEn 7...\n", + "\"all\" given or implied, calculating distances for oxygens and CVs\n", + "Done importing and calculating, making figures...\n", + "Done making and saving figures for MaEn 7, closing and moving on...\n", + "Now starting on MaEn 8...\n", + "\"all\" given or implied, calculating distances for oxygens and CVs\n", + "Done importing and calculating, making figures...\n", + "Done making and saving figures for MaEn 8, closing and moving on...\n", + "Now starting on MaEn 9...\n", + "\"all\" given or implied, calculating distances for oxygens and CVs\n", + "Done importing and calculating, making figures...\n", + "Done making and saving figures for MaEn 9, closing and moving on...\n", + "Now starting on MaEn 10...\n", + "\"all\" given or implied, calculating distances for oxygens and CVs" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true, + "heading_collapsed": true + }, + "source": [ + "# Combined open/closed FESs" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "heading_collapsed": true, + "hidden": true + }, + "source": [ + "## Test stuff" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true, + "hidden": true + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "df = pd.DataFrame()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": true, + "hidden": true + }, + "outputs": [], + "source": [ + "df['a'] = [1, 2]" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "hidden": true + }, + "outputs": [ + { + "ename": "TypeError", + "evalue": "'NoneType' object has no attribute '__getitem__'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mndf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhistogram\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mndf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'b'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m: 'NoneType' object has no attribute '__getitem__'" + ] + } + ], + "source": [ + "ndf = None\n", + "np.histogram(ndf['b'])" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": true, + "hidden": true + }, + "outputs": [], + "source": [ + "tdf = pd.DataFrame(columns=['O-O', 'O(l)-Cy', 'O(r)-Cy'])" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "hidden": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
O-OO(l)-CyO(r)-Cy
0123
1234
\n", + "
" + ], + "text/plain": [ + " O-O O(l)-Cy O(r)-Cy\n", + "0 1 2 3\n", + "1 2 3 4" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tdf.append(pd.DataFrame([[1,2,3], [2,3,4]], columns=['O-O', 'O(l)-Cy', 'O(r)-Cy']), in_p)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "hidden": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
O-OO(l)-CyO(r)-Cy
\n", + "
" + ], + "text/plain": [ + "Empty DataFrame\n", + "Columns: [O-O, O(l)-Cy, O(r)-Cy]\n", + "Index: []" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tdf" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "hidden": true + }, + "source": [ + "## Production" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "collapsed": true, + "hidden": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Now starting on MaEx 0...\n", + "Calculating oxygen distances...\n", + "This may take a few minutes.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr3/graduate/theavey/anaconda_envs/py2.7/lib/python2.7/site-packages/tables/path.py:112: NaturalNameWarning: object name is not a valid Python identifier: '754462'; it does not match the pattern ``^[a-zA-Z_][a-zA-Z0-9_]*$``; you will not be able to use natural naming to access this object; using ``getattr()`` will still work, though\n", + " NaturalNameWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved data to npt-PT-MaEx-out0.h5[754462]\n", + "Now starting on MiEn 0...\n", + "Calculating oxygen distances...\n", + "This may take a few minutes.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr3/graduate/theavey/anaconda_envs/py2.7/lib/python2.7/site-packages/tables/path.py:112: NaturalNameWarning: object name is not a valid Python identifier: '776242'; it does not match the pattern ``^[a-zA-Z_][a-zA-Z0-9_]*$``; you will not be able to use natural naming to access this object; using ``getattr()`` will still work, though\n", + " NaturalNameWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved data to npt-PT-MiEn-out0.h5[776242]\n", + "Now starting on MiEx 0...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr3/graduate/theavey/.local/lib/python2.7/site-packages/MDAnalysis/coordinates/XDR.py:126: UserWarning: Reload offsets from trajectory\n", + " ctime or size or n_atoms did not match\n", + " warnings.warn(\"Reload offsets from trajectory\\n \"\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Calculating oxygen distances...\n", + "This may take a few minutes.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr3/graduate/theavey/anaconda_envs/py2.7/lib/python2.7/site-packages/tables/path.py:112: NaturalNameWarning: object name is not a valid Python identifier: '773192'; it does not match the pattern ``^[a-zA-Z_][a-zA-Z0-9_]*$``; you will not be able to use natural naming to access this object; using ``getattr()`` will still work, though\n", + " NaturalNameWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved data to npt-PT-MiEx-out0.h5[773192]\n", + "Now starting on MaEn 0...\n", + "Calculating oxygen distances...\n", + "This may take a few minutes.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr3/graduate/theavey/anaconda_envs/py2.7/lib/python2.7/site-packages/tables/path.py:112: NaturalNameWarning: object name is not a valid Python identifier: '775548'; it does not match the pattern ``^[a-zA-Z_][a-zA-Z0-9_]*$``; you will not be able to use natural naming to access this object; using ``getattr()`` will still work, though\n", + " NaturalNameWarning)\n", + "/usr3/graduate/theavey/anaconda_envs/py2.7/lib/python2.7/site-packages/tables/path.py:112: NaturalNameWarning: object name is not a valid Python identifier: '750ns'; it does not match the pattern ``^[a-zA-Z_][a-zA-Z0-9_]*$``; you will not be able to use natural naming to access this object; using ``getattr()`` will still work, though\n", + " NaturalNameWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved data to npt-PT-MaEn-out0.h5[775548]\n", + "Now starting on MaEx 1...\n", + "Calculating oxygen distances...\n", + "This may take a few minutes.\n", + "Saved data to npt-PT-MaEx-out1.h5[754462]\n", + "Now starting on MiEn 1...\n", + "Calculating oxygen distances...\n", + "This may take a few minutes.\n", + "Saved data to npt-PT-MiEn-out1.h5[776242]\n", + "Now starting on MiEx 1...\n", + "Calculating oxygen distances...\n", + "This may take a few minutes.\n", + "Saved data to npt-PT-MiEx-out1.h5[773192]\n", + "Now starting on MaEn 1...\n", + "Calculating oxygen distances...\n", + "This may take a few minutes.\n", + "Saved data to npt-PT-MaEn-out1.h5[775548]\n", + "Now starting on MaEx 2...\n", + "Calculating oxygen distances...\n", + "This may take a few minutes.\n", + "Saved data to npt-PT-MaEx-out2.h5[754462]\n", + "Now starting on MiEn 2...\n", + "Calculating oxygen distances...\n", + "This may take a few minutes.\n", + "Saved data to npt-PT-MiEn-out2.h5[776242]\n", + "Now starting on MiEx 2...\n", + "Calculating oxygen distances...\n", + "This may take a few minutes.\n", + "Saved data to npt-PT-MiEx-out2.h5[773192]\n", + "Now starting on MaEn 2...\n", + "Calculating oxygen distances...\n", + "This may take a few minutes.\n", + "Saved data to npt-PT-MaEn-out2.h5[775548]\n", + "Now starting on MaEx 3...\n", + "Calculating oxygen distances...\n", + "This may take a few minutes.\n", + "Saved data to npt-PT-MaEx-out3.h5[754462]\n", + "Now starting on MiEn 3...\n", + "Calculating oxygen distances...\n", + "This may take a few minutes.\n", + "Saved data to npt-PT-MiEn-out3.h5[776242]\n", + "Now starting on MiEx 3...\n", + "Calculating oxygen distances...\n", + "This may take a few minutes.\n", + "Saved data to npt-PT-MiEx-out3.h5[773192]\n", + "Now starting on MaEn 3...\n", + "Calculating oxygen distances...\n", + "This may take a few minutes.\n", + "Saved data to npt-PT-MaEn-out3.h5[775548]\n", + "Now starting on MaEx 4...\n", + "Calculating oxygen distances...\n", + "This may take a few minutes.\n", + "Saved data to npt-PT-MaEx-out4.h5[754462]\n", + "Now starting on MiEn 4...\n", + "Calculating oxygen distances...\n", + "This may take a few minutes.\n", + "Saved data to npt-PT-MiEn-out4.h5[776242]\n", + "Now starting on MiEx 4...\n", + "Calculating oxygen distances...\n", + "This may take a few minutes.\n", + "Saved data to npt-PT-MiEx-out4.h5[773192]\n", + "Now starting on MaEn 4...\n", + "Calculating oxygen distances...\n", + "This may take a few minutes.\n", + "Saved data to npt-PT-MaEn-out4.h5[775548]\n", + "Now starting on MaEx 5...\n", + "Calculating oxygen distances...\n", + "This may take a few minutes.\n", + "Saved data to npt-PT-MaEx-out5.h5[754462]\n", + "Now starting on MiEn 5...\n", + "Calculating oxygen distances...\n", + "This may take a few minutes.\n", + "Saved data to npt-PT-MiEn-out5.h5[776242]\n", + "Now starting on MiEx 5...\n", + "Calculating oxygen distances...\n", + "This may take a few minutes.\n", + "Saved data to npt-PT-MiEx-out5.h5[773192]\n", + "Now starting on MaEn 5...\n", + "Calculating oxygen distances...\n", + "This may take a few minutes.\n", + "Saved data to npt-PT-MaEn-out5.h5[775548]\n", + "Now starting on MaEx 6...\n", + "Calculating oxygen distances...\n", + "This may take a few minutes.\n", + "Saved data to npt-PT-MaEx-out6.h5[754462]\n", + "Now starting on MiEn 6...\n", + "Calculating oxygen distances...\n", + "This may take a few minutes.\n", + "Saved data to npt-PT-MiEn-out6.h5[776242]\n", + "Now starting on MiEx 6...\n", + "Calculating oxygen distances...\n", + "This may take a few minutes.\n", + "Saved data to npt-PT-MiEx-out6.h5[773192]\n", + "Now starting on MaEn 6...\n", + "Calculating oxygen distances...\n", + "This may take a few minutes.\n", + "Saved data to npt-PT-MaEn-out6.h5[775548]\n", + "Now starting on MaEx 7...\n", + "Calculating oxygen distances...\n", + "This may take a few minutes.\n", + "Saved data to npt-PT-MaEx-out7.h5[754462]\n", + "Now starting on MiEn 7...\n", + "Calculating oxygen distances...\n", + "This may take a few minutes.\n", + "Saved data to npt-PT-MiEn-out7.h5[776242]\n", + "Now starting on MiEx 7...\n", + "Calculating oxygen distances...\n", + "This may take a few minutes.\n", + "Saved data to npt-PT-MiEx-out7.h5[773192]\n", + "Now starting on MaEn 7...\n", + "Calculating oxygen distances...\n", + "This may take a few minutes.\n", + "Saved data to npt-PT-MaEn-out7.h5[775548]\n", + "Now starting on MaEx 8...\n", + "Calculating oxygen distances...\n", + "This may take a few minutes.\n", + "Saved data to npt-PT-MaEx-out8.h5[754462]\n", + "Now starting on MiEn 8...\n", + "Calculating oxygen distances...\n", + "This may take a few minutes.\n", + "Saved data to npt-PT-MiEn-out8.h5[776242]\n", + "Now starting on MiEx 8...\n", + "Calculating oxygen distances...\n", + "This may take a few minutes.\n", + "Saved data to npt-PT-MiEx-out8.h5[773192]\n", + "Now starting on MaEn 8...\n", + "Calculating oxygen distances...\n", + "This may take a few minutes.\n", + "Saved data to npt-PT-MaEn-out8.h5[775548]\n", + "Now starting on MaEx 9...\n", + "Calculating oxygen distances...\n", + "This may take a few minutes.\n", + "Saved data to npt-PT-MaEx-out9.h5[754462]\n", + "Now starting on MiEn 9...\n", + "Calculating oxygen distances...\n", + "This may take a few minutes.\n", + "Saved data to npt-PT-MiEn-out9.h5[776242]\n", + "Now starting on MiEx 9...\n", + "Calculating oxygen distances...\n", + "This may take a few minutes.\n", + "Saved data to npt-PT-MiEx-out9.h5[773192]\n", + "Now starting on MaEn 9...\n", + "Calculating oxygen distances...\n", + "This may take a few minutes.\n", + "Saved data to npt-PT-MaEn-out9.h5[775548]\n", + "Now starting on MaEx 10...\n", + "Calculating oxygen distances...\n", + "This may take a few minutes.\n", + "Saved data to npt-PT-MaEx-out10.h5[754462]\n", + "Now starting on MiEn 10...\n", + "Calculating oxygen distances...\n", + "This may take a few minutes.\n", + "Saved data to npt-PT-MiEn-out10.h5[776242]\n", + "Now starting on MiEx 10...\n", + "Calculating oxygen distances...\n", + "This may take a few minutes.\n", + "Saved data to npt-PT-MiEx-out10.h5[773192]\n", + "Now starting on MaEn 10...\n", + "Calculating oxygen distances...\n", + "This may take a few minutes.\n", + "Saved data to npt-PT-MaEn-out10.h5[775548]\n", + "Now starting on MaEx 11...\n", + "Calculating oxygen distances...\n", + "This may take a few minutes.\n", + "Saved data to npt-PT-MaEx-out11.h5[754462]\n", + "Now starting on MiEn 11...\n", + "Calculating oxygen distances...\n", + "This may take a few minutes.\n", + "Saved data to npt-PT-MiEn-out11.h5[776242]\n", + "Now starting on MiEx 11...\n", + "Calculating oxygen distances...\n", + "This may take a few minutes.\n", + "Saved data to npt-PT-MiEx-out11.h5[773192]\n", + "Now starting on MaEn 11...\n", + "Calculating oxygen distances...\n", + "This may take a few minutes.\n", + "Saved data to npt-PT-MaEn-out11.h5[775548]\n", + "Now starting on MaEx 12...\n", + "Calculating oxygen distances...\n", + "This may take a few minutes.\n", + "Saved data to npt-PT-MaEx-out12.h5[754462]\n", + "Now starting on MiEn 12...\n", + "Calculating oxygen distances...\n", + "This may take a few minutes.\n", + "Saved data to npt-PT-MiEn-out12.h5[776242]\n", + "Now starting on MiEx 12...\n", + "Calculating oxygen distances...\n", + "This may take a few minutes.\n", + "Saved data to npt-PT-MiEx-out12.h5[773192]\n", + "Now starting on MaEn 12...\n", + "Calculating oxygen distances...\n", + "This may take a few minutes.\n", + "Saved data to npt-PT-MaEn-out12.h5[775548]\n", + "Now starting on MaEx 13...\n", + "Calculating oxygen distances...\n", + "This may take a few minutes.\n", + "Saved data to npt-PT-MaEx-out13.h5[754462]\n", + "Now starting on MiEn 13...\n", + "Calculating oxygen distances...\n", + "This may take a few minutes.\n", + "Saved data to npt-PT-MiEn-out13.h5[776242]\n", + "Now starting on MiEx 13...\n", + "Calculating oxygen distances...\n", + "This may take a few minutes.\n", + "Saved data to npt-PT-MiEx-out13.h5[773192]\n", + "Now starting on MaEn 13...\n", + "Calculating oxygen distances...\n", + "This may take a few minutes.\n", + "Saved data to npt-PT-MaEn-out13.h5[775548]\n", + "Now starting on MaEx 14...\n", + "Calculating oxygen distances...\n", + "This may take a few minutes.\n", + "Saved data to npt-PT-MaEx-out14.h5[754462]\n", + "Now starting on MiEn 14...\n", + "Calculating oxygen distances...\n", + "This may take a few minutes.\n", + "Saved data to npt-PT-MiEn-out14.h5[776242]\n", + "Now starting on MiEx 14...\n", + "Calculating oxygen distances...\n", + "This may take a few minutes.\n", + "Saved data to npt-PT-MiEx-out14.h5[773192]\n", + "Now starting on MaEn 14...\n", + "Calculating oxygen distances...\n", + "This may take a few minutes.\n", + "Saved data to npt-PT-MaEn-out14.h5[775548]\n", + "Now starting on MaEx 15...\n", + "Calculating oxygen distances...\n", + "This may take a few minutes.\n", + "Saved data to npt-PT-MaEx-out15.h5[754462]\n", + "Now starting on MiEn 15...\n", + "Calculating oxygen distances...\n", + "This may take a few minutes.\n", + "Saved data to npt-PT-MiEn-out15.h5[776242]\n", + "Now starting on MiEx 15...\n", + "Calculating oxygen distances...\n", + "This may take a few minutes.\n", + "Saved data to npt-PT-MiEx-out15.h5[773192]\n", + "Now starting on MaEn 15...\n", + "Calculating oxygen distances...\n", + "This may take a few minutes.\n", + "Saved data to npt-PT-MaEn-out15.h5[775548]\n" + ] + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX8AAAEPCAYAAACqZsSmAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX8AAAEPCAYAAACqZsSmAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nhCFL/sYYE4Ys+RtjTBj6f6CKZ3jy+3EsAAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX8AAAEPCAYAAACqZsSmAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nZMnfGGMikCV/Y4yJQP8PYL+it3Ol2uQAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX8AAAEPCAYAAACqZsSmAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX8AAAEPCAYAAACqZsSmAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n/sYYE4Us+RtjTBT6/yVZz5SyfdawAAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX8AAAEPCAYAAACqZsSmAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX8AAAEPCAYAAACqZsSmAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nzgAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX8AAAEPCAYAAACqZsSmAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngU2CfCkAAAAGSURBVP4/Jy57A2brQxQAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX8AAAEPCAYAAACqZsSmAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX8AAAEPCAYAAACqZsSmAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nGBOBLPkbY0wE+n842vY0g/H0bgAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX8AAAEPCAYAAACqZsSmAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3Xl4VNX5wPHvO9k3EiBhDwn7LgJhRxTUirKIu1bqDrW4\n1qrV1rbaVutSilutKGqt+nOpKG64s4ggu+yEPexIwhpCEkjm/f2RwSIEMpPMzJ3JvJ/nmSeZmTv3\nvBfueefmnHPPEVXFGGNMZHE5HYAxxpjgs+RvjDERyJK/McZEIEv+xhgTgSz5G2NMBLLkb4wxEciS\nvzHGRCBL/sYYE4Es+RtjTASKdjqAk0lPT9fs7GynwzC11MKFCwtUNSPY5dp5bQLJl/M6ZJN/dnY2\nCxYscDoMU0uJyCYnyrXz2gSSL+e1NfsYY0wEsuRvjDERyJK/McZEIEv+xhgTgSz5G2NMBLLkb4wx\nEciSvzHGRKCwSv4HSo7w6NdrOVzmdjoUY4wJa2GV/Gfn7eX+Kbn8c9ZGp0Mxxpiw5nPyF5EkEYkK\nRDBVGdK+Aee1y+DPX65ld9FhJ0Iw5iecrA/G1ESVyV9EXCLycxH5RER2AbnADhFZISJPiEgbbwoS\nkTQReVdEckVklYj0rU7A40Z0orC0jAc/X12djxtTI/6qD8Y4zZsr/2lAK+B+oJGqZqpqA+AMYA7w\nqIiM8mI/TwGfqWp7oCuwqjoBd2qUwpg+zfnXd5tYubOwOrswpib8VR+McZQ3E7udo6pHjn9RVfcA\nk4BJIhJzqh2ISB1gIHCd57OHgWq32zx0Xjv+b9E27v5oJVNG967uboypjhrXB2NCQZVX/pWd6NXY\npiWQD7wiIt+LyEQRSfIyxhNkJMfxh3Pb8mnuLj7P3VXd3RjjMz/VB2Mc502bf6GIHDjmUXjsTy/L\niQa6A/9S1W5AEXBfJWWNEZEFIrIgPz//lDu8dUA2reoncteHKygrt6GfJjiqUx98Oa+NCRZvrvxT\nVLXOMY+UY396Wc5WYKuqzvU8f5eKL4Pjy3pBVXNUNScj49TrEcRFR/HE8I6s/OEgL87d7GUYxtRM\ndeqDL+e1McHi01BPEekqIrd6Hqd5+zlV3QlsEZF2npfOBlb6UnZlRnZuxJmt6vPHz1azr9j+0jbB\nVd36YEwo8Dr5i8gdwBtAA8/jDRG5zYeybvN8ZilwOvCIL4GeJCbGj+jE7kOHefirtTXdnTFe80N9\nMMZRvizjeCPQW1WLAETkMeA74BlvPqyqi4EcnyOsQrdmqVyXk8lTMzfwy75ZtE6vdj+yMb6oUX0w\nxmm+NPsIUH7M83LPa457+IL2xEa5uPfjGrckGeOtkK0PxnjDlyv/V4C5IvI+FSf5SODlgETlo8Z1\n4rlvcGv+8Nlqpq8r4KzW6U6HZGq/kK0PxnjD6yt/Vf0HcD2w2/O4VlXHByowX/3mrFZkpsVz14cr\nKHer0+GYWi7U64MxVfGlwzcH+ANwAzAaeM3TeRsSEmKieGxoR77fdoD/LNjidDimlgv1+mBMVXxp\n9nkDuAdYBoTkXVVXdmvC099u5HdTcrmsaxOS43w5PGN8EvL1wZhT8aXDN19VP1TVjaq66egjYJFV\ng4gw/sJO7Cws5bGp65wOx9RuIV8fjDkVX5L/nzxz8lwlIhcffQQssmrqk1WXq7o15e/T17N57yGn\nwzG1V1jUh0iQX3KQnA+fZObODU6HElZ8aRe5HmgPxPC/P3MVeM/fQdXUo0Pb8/6yHdz/SS5vjDph\nFglj/CFs6kNt9++181m4eyv14xOdDiWs+JL8u6pql4BF4kfN6ybym7Na8fBXa7ntjBb0yarrdEim\n9gmb+lCbudXNC6vnMqBhCzqmNXI6nLDiS7PPHBHpGLBI/Oy+wa1plBLHrz9YgaoN/TR+F1b1obaa\nvmM96woLGNO2j9OhhB1fkv8AYLGIrBaRpSKyLJSHtiXHRfPw+e2Zs2kvb32/3elwTO0TVvWhtpqw\neg51YxO4NNvm1fOVL80+QwIWRYBc2zOTZ77dyG8/WcnILo1IiLF1to3fhF19qG12FRfy/ubl3NK+\nHwnRtniar3y5w3dTZY9ABldTUa6KoZ9b9pXwjxnrnQ7H1CLhWB9qm3+vW8ARdzlj2lmTT3X4NJ9/\nODqrdToXdWnE375ex44DJU6HY4zxg4qO3jmc0bAFHdIaOh1OWKr1yR/g8WEdOVzu5oFPc50OxRjj\nB9N2rGd94W5+2a6v06GELW/W8O0rImE9VW3r9CRuH9CCV+Zv4fut+50Ox4Sx2lAfaoMJq7+jXlwi\nl2TZaNvq8ubK/1pgoYi8JSLXiUhYDqZ94Ny2pCfFcul/FtidvxHAHbiZXWtFfQhnPxQX8v6m5Vzb\nOod46+itNm8WcL9ZVbsDDwJ1gX+LyHci8oiIDBSRsBhCk5YQw0c39GJ30WHOfG42eXvsC6C2UlUG\n/nMWf/lyTSD2XSvqQzj799r5lKnbxvbXkC+jfXJVdbyqDgEGA98ClwFzvd2HiESJyPci8rHvodZc\n76y6fHVzX/YXl3Hmc7NZX1DkRBgmwGbn7WVW3l4ykmIDVoY/6oPxnVvdvLBmLgMbtqR9WgOnwwlr\n1erwVdViVZ2iqrepqi/r8t4BrKpOmf6Sk5nG1zf3pai04gtgbf5BJ8MxAfDUzA2kJcTwix7NglJe\nDepDjRSXHQlWUSFj6o51bCjczS9teGeNBW20j4g0A4YCE4NV5sl0a5bK1F/1o7TMzZnPzSb3h0Kn\nQzJ+smVvMe8t28no3s1JqsXrOQyc8k+unvGG02EE3YTVc6gfl8jF1tFbY8Ec6vkkcC+nWPhCRMaI\nyAIRWZCfnx/QYE5rUofpY/vhVjjrX9+xYqd9AdQGz83OQ1W5pX+206H8KBDndXZyPWbtyouoeat+\nKC5ksnX0+k1Qkr+IDAN2qerCU22nqi+oao6q5mRkZAQ8rk6NUpj+q764BAb9azZLtx8IeJkmcA4d\nLuOFOZsY2bkRWfVCZ3rfQJzX/Rtks6vkIOsLd/tlf+HglaMdvdbk4xfejPMvFJEDlTwKRcTbbNkf\nGCEiecBbwGAReb0GcftN+4YpzBjbj9goF4P/NZvF2+w+gHD1xqJt7Dl0hDvOaBmwMvxUH2qsf8Ns\nAGb9sDFYRTrKrW5eXDOXsxq1ol2qdfT6gzdDPVNUtU4ljxRVreNNIap6v6o2U9Vs4EpgqqqOqmHs\nftMmI5kZY/uRFBfN4H99x8It+5wOyfhIVXlq5kZOb1KHM1rWC2Q5Na4P/tAxrSFpsQnM2pUXrCId\n9fX2io5eu+r3H5+afUSkroj08oxnHigiAwMVWLC1Sk9ixth+pCZEc/bz3zF3016nQzI+mLZuNyt2\nFnL7GS0I1g24TtYHl7jo1yCLbyPkyv/pVTOto9fPvE7+InIT8A3wOfCQ5+eDvhaoqtNVdZivnwuG\n7HqJzBjbj/SkWM6dMIfZG/c4HZLx0lMzN5CRHMtV3ZoGpTx/1Yea6N+gBav272JPae2+YXHq9rV8\nvGUVd3U6k7io2juCK9h8ufK/A+gJbFLVQUA3ILBDchzQvG4i08f2o1FKHOe9OIeZGyKnQy1crS8o\n4qOVP/DLPlnEB2/NBsfrw9F2/9m1uOmn3O3m1/M+JCu5Lnd1qjUNDSHBl+RfoqolACISp6q5QLvA\nhOWsZmkJTB/bj2apCQx5cS7T1hU4HZI5hX/OyiNKhF/1yw5msY7Xh57pmUSLi1k/5AWz2KB6ae08\nlu7dwRM5w2x4p5/5kvy3ikgaMBn4UkQ+AGrt+ohNUuOZPrYf2XUTGDpxLl+tqXV/5NQKhSVlvDRv\nM5d1bUKT1PhgFu14fUiMjqV7/abM2lU72/33Hy7mgUWfckbDFrZMYwD4MrfPRaq6T1UfBP4AvARc\nGKjAQkHDlDimj+1H6/Qkhr00j89ydzkdkjnOqwu2cKCkjDvOaBHUckOlPvRv2IL5BVsoLS8LdtEB\n9/CSrykoOcT4XhcGrRM/kvjS4fuq50oHVZ0BzAQmBCqwUJGRHMfUm/vSoUEyQyfO5db3lrH30GGn\nwzJUTNv89MyN9G6eRu+sukEtO1Tqw4AGLSgpL2PR7q3BLjqg1h0o4MmVM7muTQ490oMzR1Ok8aXZ\n5zRV/XEAvKrupaKTq9ZLT674C2Bsv2z+NTuPdo9N49/ztgRyznjjhc9X72JtQRG3B/mq3yMk6sP/\nbvbKC3bRAXXP/I+JdUXxcPfznQ6l1vIl+btE5MfLKxGpB0TMuKvUhBieubgLC+4cSOv6SVz/9mIG\nPDvLVgZz0FMzN9K4ThyXntbEieJDoj40TEihVUr9WnWz19Tta5m8eTm/O+1sGicG7b65iONL8h8H\nzBaRv4jIn4HZwOOBCSt0dWuWyre39ueVK05n3e4icp78xpqCHJD7QyGfr85nbL9sYqMdWYo6ZOpD\n/wbZzNq1sVZM8mZDO4PHlw7f/wCXAD9QMZ75YlV9LVCBhTKXS7iuVyarfzvImoIc8vS3G4mNcjGm\nT5Yj5YdSfejfsAX5JUWsOxD+Q5JftqGdQeNLh28PVV2pqs+q6jOqulJEhgcyuFBXNzGWZy7uwsJf\nD6RNujUFBcu+4iO8umArP+/elAYpcY7EEEr1oX+DbICwb/rZf7iY3y/6lAE2tDMofPl7+UUR+XFi\nDRG5CnjA/yGFn9ObpjLzFmsKCpaX5m7m0OHyoA/vPE7I1IcOaQ2oG5sQ9vP8HB3a+aQN7QwKX5L/\npcCrItJBREYDY4GfBSas8GNNQcFR7laenbWRgS3rcXrTVCdDCZn6UDHJW3ZYX/kv3bOdJ1fO5NrW\nPWxoZ5D40ua/gYrpmCdRceL/TFWtfeM41hQUWB+t2EnenmKnhnf+KNTqQ/+G2eTu38XukiKnQqi2\n7Yf2M/TLl8iIT+JvPS5wOpyI4c1iLstEZKmILAXeBeoB2cBcz2umEpU1Bd05eTmFJbXvTsxgemrm\nRprXTeDCTo0cKT9U68PRdv9wm+Tt4JFShn35MvsOl/DJOTfSyIZ2Bo0345JDcvrlcHC0KWhkl0b8\nfkouT3+7kUlLd/DsxV24sLMzySucLd1+gOnrd/P4sA5ERzkyvBNCtD70TG9OjCuKWbvyGN68k9Ph\neKXMXc6V019n6d4dfHTO9ZxePzjTcZsK3iT/zVrFAGIRkaq2iWRpCTH885Iu/CKnGWP+u4SRr8xn\nZOdGPD2yM5l1E5wOL2w8NXMDCTEubuzd3MkwQrI+JETHeCZ5ywtmsdWmqtwx9wM+2bqKf/W9mPOb\ndXA6pIjjzeXTNBG5TUR+UuNEJFZEBovIq8C1gQmvdumTVZeFvx7IY0M78PnqXXR8YhpPfbOBcusQ\nrlLBwVLeWLSNa3IyqZcY62QoIVsfBjQIn0nexq/4hudyZ3NP57O4uX0/p8OJSN4k/yFAOfCmiGwX\nkZUisgFYC1wFjFfVf59qByKSKSLTRGSViKwQkTtqHHmYiolyce/g1qy4ZxADWtTjzg9W0OfpmSza\nausGn8oLczZTWubm9gHOdvTih/oQKP0bZlNaXsbCgtCe5G1S3lLunv8xl2afxqM51sHrlCqbfTwL\nVjwHPCciMUA6UHzspFZeKAN+o6qLRCQFWCgiX6rqympFXQu0qJ/IlJt6887i7dzxwQp6PjmTOwe2\n5KHz2pEcFzFTJnnlSLmb52bncW7bdDo2SnE0Fj/Vh4Do9+PNXhvp55nwLdTMzd/EqG/+j94ZzfnP\nGVfhEsf6biKeT//yqnpEVXf4eqJ7PrPI83shsAqI+N4dEeGKbk3J/e0gRvfJ4h8zNtDx8Wl8tGKn\n06GFlPeW7mDb/hJuP6Ol06H8RHXrQ6A0TEihdUp6yLb7byjczfCvXqZJYiofnnM9CTZ9g6OC/rUr\nItlUTH07t5L3xojIAhFZkJ8fOStnpSXE8PylpzHr1v7UiY9hxMvzufTVBWzbX+x0aCHhqZkbaZ2e\nxAXtGzgdSrUE87zu3zCb2bvyQm6St8IjJYz46mXK3G6mnHsjGfHJTocU8YKa/EUkmYqbYu5U1QPH\nv6+qL6hqjqrmZGRkBDO0kNCvRT0W/Xogf7ugPZ+s/IEOj03n2W83RnSH8PzN+/hu015uG5CNyxWe\nt/wH87zu3yCb/JIi1obQJG+qyvUz32bV/l38d9A1tEsNzy/x2sabm7z6ih8m2vC0j04C3lDV92q6\nv9oqNtrFfWe3Yfk9Z9EnK43b3l9Ov2e+ZfmOE74rI8JTMzeQEhfNdT0znQ4F8F99CJQBDSs6xENp\nnp/Hlk1j0qZlPJ4zlLObtHE6HOPhzZX/tVR00L4lIteJiM93J3kqy0vAKlX9h6+fj0St0pP4fEwf\n3ri6G3l7DtH/2VnM3bTX6bCCaseBEt5Zsp3re2VSJz5k2odrXB8CqV1qBvXiEkOm3f/zbav53cJP\nubLF6dzV6UynwzHHqDL5q+rNqtodeBCoC/xbRL4TkUdEZKCIRHlRTn/gF8BgEVnsedgYryqICD/v\n3owFdw4kIymWn70whzkR9AXw/OxNlLmV25wf3vkjP9WHgKmY5C2LWbucv/LfULibq6a/Tue6jZjY\n/zKbqTPE+DKxW66qjlfVIcBg4FvgMirpuK3ks9+qqqjqaap6uucxpfphR5bMuglMH9uv4gtgwhy+\ny9vjdEgBV1pWzvPf5XFB+wa0Tk9yOpwT1KQ+BFr/Bi1YvT+fAgcneSs6UspFX/8bgPcHX0tSjDPr\nLpiTq1aHr6oWq+oUVb1NVXP8HZQ5UbO0BGbc0o9GKXGc98JcZm+svV8Aqspt7y9n18HD3DkwtIZ3\nVibU6sPgxq0BeGfjYkfKV1VumvVflu3dyZtnjaJVnXRH4jCnZndYhJGmqQlMG9uXxnXiOO/FOcyq\nhV8AqsqvP1jBi3M28/tz2nBO28gb9VVTPdMz6ZmeyfgVMyl3u4Ne/vgV3/DWxsU83GMI5zVtF/Ty\njXcs+YeZpqkJTPtVP5rUiee8F+Ywc8Nup0Pyqz98tpqnZm7kzoEt+MsQSxzVISLc3flM1hUW8NGW\n4N5EP3X7Wu5Z8DGXZHXhvi6Dg1q28Y03Qz3riUiTYARjvNMkNZ7pY/vRLDWe81+cyzfra8cXwN++\nXsvDX63lpt7N+ceITiHZQRgu9eHirC5kJddl3IoZQStza9E+Lp/+Gu1TG/DKGVeE5P+f+R9vrvz/\nzjGzFIrIbBF5R0TuE5GIn6LBKY3rVHwBZKYlcP7EucxYHzo39VTH0zM38Lspufy8W1Oev/S0UE4c\nYVEfol1R/LrjQL79YSNz8zcFvDy3urlu5tuUlJfx/uDrSImJD3iZpma8Sf49gEePeZ5CxZj9dOD+\nQARlvNPI8wWQXTeBCybOY/q68PwCeGnuZu6YvIKLujTi1atOJyq07+QNm/pwQ9uepMbGM2554K/+\nn1k5i693rGV8rxG0TbV+mnDgTfIvPW5hiqmq+jlwD+D4yIZI1zAljqm/6keLeolcMHEuU9eG1xfA\nm4u2Mfq/SxjSPoM3R3V3coUub4VNfUiJieeXbfswadMyNhYGrmlw5b6d3LfwE4ZlduCmtr0DVo7x\nL29qWomIZB19oqp3eH4qEDK3XUayhilxTL25L63qJzHspbl8vSY8JsX7YPlOfvHm9wxsWZ9J1+YQ\nF+3o/VHeCqv6cHvHM3AhPLliZkD2f7i8jF988ybJ0XG82M9u5Aon3iT/h4HJItL+2BdFpDHeLQNp\ngqBBShxTf9WX1ulJDHtpHl+F+BfAF6t3cfl/FpLTLJWPbuhFYmzYnEphVR+aJqVyVctuvLR2HntL\nD/l9/39Z8hWLdm/jhf6X2uLrYcab6R0+Bx6hYvm6T0XkCRH5OxV3ND566k+bYMpIjuPrm/vSNiOZ\n4S/N44vVu5wOqVIzN+xm5Cvz6dAwmU9H9yYlPuRy5kmFY334TeczKSo7zITVc/y63+925fHI0q+5\nrnUOF2V18eu+TeB51cCqqv8FWlHRsXUQ+IGKEQ8DAheaqY6KL4A+tGuQzIiX5/N5bmh9AczbvJeh\nE+eRVTeRL8b0oa6z6/FWS7jVh671mnBOkzY8s+pbDvtpfd+DR0q55pu3yExK46neI/2yTxNcvszt\ncwhYByQBtwLjgFEBisvUQLrnL4AODZK58JX5fBYiXwBLtx9gyAtzyUiO5aub+9AgJXznewm3+vCb\nTmey/dAB3vLTlA93z/+I9YW7efWMK6kTa8M6w5E3N3m1FZE/ikguMBHYDZypqr2B2je/QC1RPymW\nr27uS8eGFU1Av3p3qaMrg63edZBzJ3xHUmwUX9/cl6apCY7FUhPhWh/Oa9qOzmmN+PvyGTVe5WvK\nllVMWD2H33QeyJmNWvkpQhNs3lz55wJDgUs9qxE9pqp5nvcid4mpMFA/KZavb+7L6D7NmTh3M60f\nmco9H62k4GBpUOPYuPsQZz//HQBf3dyX7HqJQS3fz8KyPogId3UeyLK9O/hq+9pq76egpIgbZr1D\nl7qN+Wv38/0YoQk2b5L/JUAe8KWIvCYiwz2rcpkwUDcxlucuOY3V9w3i8tObMG7Gelo+MpWHPl/N\ngZIjAS9/2/5izn7+Ow4dLufLX/alXYOwX7s1bOvDz1t2p1FCSrWnfNhTeojLp/2HPaWHeG3gVcRF\nhU9HvTmRN6N93lfVK4DWwGfAL4GtIvIKYGO7wkTL+km8elU3lt19Fue2TefBL9bQ8uGvGTd9PcVH\nygNS5o4DJZzz/BwKig7z+Zg+nNYk/E+XcK4PcVHR3NZhAJ9vW82yPTt8+uyigq30+HA8s3bl8VL/\ny+laL+SnNzJV8KXDt0hV31DVYUAHYA6wLGCRmYDo1CiFSdf1ZN4dZ9CjWRp3f7SS1o9MZcJ3eRwp\nr9n0v4fL3MxYX8ADn+bS56mZNPvzl2zae4hPbupFz+Zp/jmAEBGu9eHm9n1JjI7hljnvsXLfTq8+\n88raefSf8izlqsy84BZ+0bpHgKM0wSA17fzxuiCRIcBTQBQwUVVPOSY6JydHFyxYEJTYItn0dQX8\n/tNcZuftpWX9RP58Xjuu7NbUq/l1VJWVPxzkyzX5fLkmnxnrd1N0uJwol9ArM41z22Zw+elN6NQo\nJQhH4hsRWejEwiuhcF6/uHoOd837iKKyw1zRoit/PP1cOqQ1PGG70vIybp8zmRfWzOHsxm1486yr\nyYgP+2a7Ws2X8zooyd+zruka4FxgKzAfuEpVTzrZeChUkkihqkxZtYvff5rLku0H6Nwohb+e354R\nnRqecLv+jgMlfLUmny/XFPDV2nx2HKjoPG6TnsS5bTM4t206g1qnk5oQ2s3gkZz8oaLjdtzyGTyz\n6lsOlR3hypan88eu59I+rQEAmw/u5dJp/2F+wRbuP20wf+k2hChXyM+7FPFCMfn3BR5U1fM8z+8H\nUNW/newzoVJJIonbrfx3yXb+8Nlq1hYU0at5Gn8+rx1uVb5cU8CXa/JZvrMQgPqJMZzTNoNz22Zw\nTpt0ssJsBE+kJ/+j8ksOMm75DJ5dNYvi8iNc1aIbQ5q14865H3DE7ebVM65kZFZnp8M0XvLlvA5W\nd31TYMsxz7cCNv1fiHG5hCu6NeWS0xrznwVbefCL1Qx5sWI98rhoFwNa1GNUj2ac2zad05uk4grt\nqZeNFzLik3k0Zyi/6XwmTyybzj9zZ/HGhkV0SmvIe4Ovs+mZa7FgJf/KssQJf3KIyBhgDEDz5s0D\nHZM5iegoFzf0bs7PuzflvWU7SE+K5YyW9UmICYtZN0NOOJzXGfHJPN5zGHd3PpNPtq7isuyuJMeE\n7x3YpmrBasTbCmQe87wZsP34jVT1Bc+NMzkZGXbF4bT4mCh+3r0ZP2vXwBJ/DYTTed0gIYXr2/Sy\nxB8BgpX85wNtRKSFiMQCVwIfBqlsY4wxxwlKs4+qlonIrcDnVAz1fFlVVwSjbGOMMScK2jh/X4lI\nPuDPlafTgfBa49B3kXCM4J/jzFLVoLfB2HldbZFwnEE9r0M2+fubiCxwYmhfMEXCMULkHKc3IuXf\nIhKOM9jHaHdtGGNMBLLkb4wxESiSkv8LTgcQBJFwjBA5x+mNSPm3iITjDOoxRkybvzHGmP+JpCt/\nY4wxHpb8jVdE5CYRWSYi1zsdizH+FKnntiV/461LgMHAZU4HYoyfReS5bck/wETkQRG52/P77FNs\nlyYiY4MXWaUxTBCR/id5ey6wy/PTGDu3w5wl/yBS1X6neDsNcLSCUDHN9pyTvJcMzARSgxeOCRd2\nbocfS/4BICK/F5HVIvIV0O6Y1w96fiaJyCciskRElovIFcCjQCsRWSwiT3i2mywiC0VkhWdaYEQk\nW0RWiciLnte/EJEEz3vXiMhSz35fO6bcUSIyz7PvCZ6V1Y6PuQOwRlVPWM1dRFzARcA1wEWVfd5E\nBju3axFVtYcfH0APKhbyTgTqAOuAuz3vHfT8vAR48ZjPpALZwPLj9lXP8zMBWA7U92xXBpzuee8d\nYBTQCVgNpB/32Q7AR0CM5/lzwDWVxH0XcMNJjukc4H3P75OBc53+d7ZH8B92bteuh135+98ZVJxM\nh1T1AJVPXb0MOEdEHhORM1R1/0n2dbuILKHiz9VMoI3n9Y2qutjz+0IqKs1g4F1VLQBQ1T2e98+m\notLOF5HFnuctKynrPOCzk8RxNfCm5/c3Pc9N5LFzuxYJ1kpekeaUd86p6hoR6QFcAPxNRL4A/nPs\nNiJyFhVXJX1V9ZCITAfiPW+XHrNpORVXT3KScgV4VVXvP1k8IpIIpKnqCQvseP7svhA4W0Qep6Kp\nMEVEElS1+FTHaWolO7drCbvy979vqGg7TBCRFGD48RuISBPgkKq+Dvwd6A4UAinHbJYK7PVUjvZA\nnyrK/Rq4XETqe8qod8zrl4pIg6Ovi0jWcZ8dBEw7yX5HAJ+qanNVzVbV5lT8qX3CcZlaz87tWsSu\n/P1MVReJyNvAYirmbZ9ZyWZdgCdExA0cAX6lqrtFZJaILAc+BR4AbhaRpVS0d55spMLRcleIyMPA\nDBEpB74HrlPVlSLyAPCFp3PrCHALP51T/nzg3ZPs+mqOu3ID3geup6JN1kQIO7drF5vbxyAii4De\nqnrE6VhdHbBjAAAeL0lEQVSM8Sc7t0/Okr8xxkQga/M3xpgIFLJt/unp6Zqdne10GKaWWrhwYYE6\nsIavMaEiZJN/dnY2CxYscDoMU0uJiD8XUTcm7FizjzHGRCBL/mHs8K4NFC7+2OkwjDFhyJJ/GNvz\n9XNsffYytMxGsRljfGPJP4wltOyJHimhZNtyp0MxxoQZS/5hLKFlLwBKNsxzOBJjTLix5B/GYtKz\niUpJp3jDfKdDMcaEGUv+YUxESGjRk2K78jfG+MiSf5iLb9mL0m0rcJcWOR2KMSaMWPIPcwkteoK6\nKc5b5HQoxpgw4nPy96zRGTnrXIa4hJY9Aev0Ncb4psrkLyIuEfm5Z1HmXUAusMOzwPITItKmqn14\n9pMmIu+KSK5nkea+NQ3eQHSdBsSkZ1u7vzHGJ95c+U8DWgH3A41UNVNVG1Cxnucc4FERGeXFfp4C\nPlPV9kBXYFU1YzbHSWjRk+KNNuLHGOM9byZ2O6eyhRA8iyhPAiaJSMypdiAidYCBwHWezx4GDvsc\nralUfMteHJj/X8oO5BNdxyaqNMZUrcorf29WwPFim5ZAPvCKiHwvIhNFJMnLGE0Vjrb7F2+0WVCN\nMd7xps2/UEQOHPMoPPanl+VEU7GQ879UtRtQBNxXSVljRGSBiCzIz8/36UAiWUJ2DxCXdfoaY7zm\nzZV/iqrWOeaRcuxPL8vZCmxV1bme5+9S8WVwfFkvqGqOquZkZFjzhbdc8cnENe1I8UZL/sYY7/i0\nmIuIdKWioxfgG1Vd6s3nVHWniGwRkXaquho4G1jpW6jmVBJa9KRw8ceoKiLidDjGmBDn9Th/EbkD\neANo4Hm8ISK3+VDWbZ7PLAVOBx7xJVBzavEte1FemM+RAlugyhhTNV+u/G8EeqtqEYCIPAZ8Bzzj\nzYdVdTGQ43OExisJLTydvhvmEZuR7WwwxpiQ58sdvgKUH/O83POaCQHxmV2QmDhKbLy/McYLvlz5\nvwLMFZH3qUj6I4GXAxKV8ZlExxLfvJvd6WuM8YrXV/6q+g/gemC353Gtqo4PVGDGdwkte1KctxB1\nl1e9sTEmovnS4ZsD/AG4ARgNvObpvDUhIr5FL7S0iNLtNnOGMebUfGn2eQO4B1gGuAMTjqmJH+/0\n3TCP+GadHY7GhKOFCxc2iI6Ongh0xqZ8P5YbWF5WVnZTjx49djkdjD/4kvzzVfXDgEViaiy2YRtc\niamUbJgPA29wOhwThqKjoyc2atSoQ0ZGxl6Xy6VOxxMq3G635Ofnd9y5c+dEYITT8fiDL8n/TyIy\nEfgaKD36oqq+5/eoTLWIy2XLOpqa6myJ/0Qul0szMjL279y5s9b8Se1L8r8eaA/E8L9mHwUs+YeQ\n+BY92f3pE7gPl+CKjXc6HBN+XJb4K+f5d6k1TWG+JP+uqtolYJEYv0ho2QvKyyjZvJjE1n2cDscY\nE6J8+RabIyIdAxaJ8Ytj7/Q1JlytX78+5uyzz26VlZXVOTMzs/P111+fWVJScsJNpW63m3vvvbdx\nVlZW5+zs7M69e/duu2DBAvuT1wu+JP8BwGIRWS0iS0VkmQ31DD0x9ZoSndbE7vQ1YcvtdjNy5MjW\nI0aM2Ldp06blGzduXF5UVOS64447mh6/7aOPPpoxd+7cpOXLl6/My8tb/tvf/nbnRRdd1PrQoUM2\n+0AVfEn+Q4A2wM+A4cAwz08TYhJa9rIrfxO2Pvroo5S4uDj3HXfcsRsgOjqa559/fsvbb7+dXlhY\n+JOc9fTTTzd+7rnntqSkpLgBLr744gM9evQomjBhQn0nYg8nXrf5q6pNFxkm4lv2pHDRZMqL9hGV\nlOZ0OCZM3fDW4szlOwsT/bnPzo1SDr185elbTrXNsmXLErp27Xro2Nfq1avnbty48eGVK1fG9e7d\nuxhgz549ruLiYlenTp1Kj922R48eRStWrLCmnyrUmp5r8z8JLXsBUJxnyzqa8ONZk+KEEUferlVh\na1p4x6fFXEx4SMjOARGK18wiudM5TodjwlRVV+iB0qVLl+IPPvig7rGv7dmzx7Vz587Yxx9/vOHy\n5csTGzZseHjGjBnrEhIS3CtXrozt2LHj4aPbfv/994kDBw48GPzIw4s3a/j2FfsaDStRSWnEN+9G\nUe40p0MxxmcjRowoLCkpcT377LP1AcrKyhg7dmzmZZddVvDuu+/m5ebmrpwxY8Y6gFtvvXXnLbfc\n0vzgwYMCMHny5JT58+enjB49ereTxxAOvLnyvxb4p4isAT4DPlPVnYENy9RUUsfB7PnyadyHi3HF\nJjgdjjFec7lcTJ48ed2YMWOynnjiicZut5vBgwfvf/rpp7cdv+3vfve7XXv37o3q2LFjJ5fLRUZG\nxpH33ntvXXJyst2oVoUqk7+q3gwgIu2B84F/i0gqMI2KL4NZqurVHMIiEgUsALap6rBqR22qlNhh\nELs//TuH1s4mudPZTodTI/vnvkPBB3+m+d2fEVOvmdPhmCBo3br1kalTp66rajuXy8W4ceN2jBs3\nbkcw4qpNfJnPP1dVx6vqEGAw8C1wGTDXh/LuAGy+4SBIbHsGuKI4tGqq06HUWEneQkp3riG6TkOn\nQzGm1qjWaB9VLVbVKap6m6p6tS6viDQDhgITq1Om8U1UQgoJLXpStCr82/1LtiwlrmlHJDrG6VCM\nqTWCOdTzSeBebC2AoEnqOJjiDfMoLy50OpQaKd2ylPjMrk6HYUytEpTkLyLDgF2qurCK7caIyAIR\nWZCfnx+M0Gq1xA6DwF3OoTXfOh1KtZUVFlC2bzvxmac5HYoxtUqwrvz7AyNEJA94CxgsIq8fv5Gq\nvqCqOaqak5GREaTQaq/E1v2Q6Niwbvcv3VIxfVScJX9j/KrK0T4iUkjFvP0nvAWoqtapah+qej9w\nv2d/ZwF3q+oo30I1vnLFJZLQqk9Yt/uXeJJ/fHNr9jHGn6q88lfVFFWtU8kjxZvEb5yV1GEwJZsW\nUV601+lQqqVk8xKiUhsSXaeB06GYIDrVlM6zZs1KuOKKK7IAnn766frXXHNNc4BHHnkk46mnnjrp\nhG6bN2+OHjZsWMvMzMzOrVq16nTmmWe2Xrp0aVxwjij0+NTsIyJ1RaSXiAw8+vC1QFWdbmP8gyex\nwyBQ5dDqb5wOpVpKtywlvpk1+USSqqZ0/utf/9r4zjvvPGER9dtuu233888/X+l4YLfbzYgRI1oP\nHDiwcMuWLcvXr1+/4m9/+9u27du3R+wQMq+Tv4jcBHwDfA485Pn5YGDCMv6S0Ko3EptA0crwa/fX\n8jJKt6+wJp8Ic6opnXfv3h21atWqxL59+xYf/7mUlBR3s2bNSqdNm3bCTKQff/xxSnR0tN57770/\njiTp169f8ZAhQw6OHDmyxeuvv/7j9LcjRoxo8cYbb6QG6vhChS8Tu90B9ATmqOogzx2/DwUmLOMv\nrpg4Etv0D8t2/8M716BHSq2z1yE3fPt25vK9O/07pXPdRodeHnBFtad0fu655+q3a9fuhMR/VPfu\n3YumT5+eMmjQoJ98funSpSfs86jRo0fnjx8/vuGoUaP27d69O2rhwoXJkyZN2ujLcYUjX5p9SlS1\nBEBE4lQ1F2gXmLCMPyV1GEzp1mWUHQiv4bM/dvbaGP+Icqopnfft2xdVv379Iyf7bIMGDcp8bcoZ\nOnTowU2bNsVv27Yt+qWXXqo3dOjQvTExtb81yJcr/60ikgZMBr4Ukb3A9sCEZfwpscMgAA7lTqdO\nr8scjsZ7JZuXQFQ0cU3aOx1KRKrqCj1QTjWlc+vWrUvz8vJO2klbUlLiSkhIcK9bty5m2LBhbQBu\nuOGG/C5duhRPnjy57sk+d/nll++eOHFivUmTJtV7+eWX8/x2MCHMl7l9LlLVfar6IPAH4CXgwkAF\nZvwnoUUOrviUsGv3L926lLjGHZDoWKdDMUF0qimd+/Tpc+hUyX/NmjVxnTt3Lm7duvWR3Nzclbm5\nuSvvvffe/OHDhxcePnxYxo0bl3502xkzZiR+8sknyQA333xzwYQJExoC5OTklAT6GEOBLx2+r3qu\n/FHVGcBMYEKgAjP+I1HRJLY7I+zm9y/ZstQ6eyPQ0Smd33vvvbpZWVmdW7Ro0TkuLs799NNPb+vW\nrVtJYWFh1N69eyvNXfPnz08ePnz4CfOZuFwuPvzww/Vff/11nczMzM6tW7fu9Kc//alJ8+bNjwBk\nZmaWtWrVqmTUqFERsw6AL80+p6nqvqNPVHWviHQLQEwmAJI6DObgkikc2budmLpNnA6nSuUH91C2\nZ6t19kaoU03pfPXVVxe88sor9e66666C22+/fTewGyrG/7dt27akcePGZZV9Ljs7+8iUKVM2VPZe\nYWGhKy8vL+7GG2/c47eDCHG+dPi6ROTHNjMRqYctAxk2fmz3D5NRP//r7LXkb37qnnvuyY+Lizth\ngshdu3bFPPbYYycs+FKVyZMnp7Rt27bT6NGjd9WvX9+rtUlqA1+S9zhgtoi8S8V0D5cDDwckKuN3\n8c274kqqS9GqqaT2u9rpcKpkI33MySQmJuott9xywhX6RRdddKA6+xs5cmThyJEjl9U8svDidfJX\n1f+IyAIqFnIR4GJVXRmwyIxfiSuKpHZnhs14/9ItS4hKySAq1RZwMSYQfOnw7aGqK1X1WVV9RlVX\nisjwQAZn/Cup42CO5G/kcH6e06FU6Whnr4g4HYoxtZIvbf4vikiXo09E5CrgAf+HZAIlXNr9tbyM\n0q3LrbPXmADyJflfCrwqIh1EZDQwFvhZYMIygRDXtBNRKRkUhfj8/od/WIceKbHOXmMCyJebvDYA\nVwKTqPgi+Jmq7g9UYMb/RISkjmdzcNlnuA+fdHoUx1lnr/F2SufjzZs3L+GSSy7JPtl+S0tLZezY\nsU2zsrI6t2nTplOXLl06vPPOOxE5NX2VyV9ElonIUhFZCrwL1AOygbme10wYqTv4ZsoLC9g34yWn\nQzmp0i1LwBVFbJMOTodiHFDdKZ2PHDlCr169infs2BG7du3aSm8L//Wvf91k586dMbm5uSvWrl27\nYsqUKWsPHDgQFehjCkXeXPkPA4Yf8+hNRXPP0ecmjCS2G0hCm/4UTHkcLTvsdDiVKtmylLjG7XHF\nROw6GxHNlymd77rrriZXXXVVVv/+/dtcfPHFLQDOP//8fa+++uoJ8/gUFha6/u///i9j4sSJmxMS\nEhQq7uy96aab9o4fPz79xhtvzDy67bhx49JvuummZsE5Ymd4M9Rzs6pWtozjj0REqtrGhAYRIX34\n79nyjwvYN/t16g68wemQTlC6ZSkJbQc4HUbE2z7xhsySrcv9OqVzfLPOh5rc9LJfp3ReunRp4ty5\nc3OTk5MVoHfv3kWPPvpoY+CHY7dbuXJlXOPGjQ/Xq1fvhBvEbrzxxj2dOnXqWFpaujUuLk5ff/31\n9AkTJmyq9oGGAW+u/KeJyG0i0vzYF0UkVkQGi8irwLWn2oGIZIrINBFZJSIrROSOmgRtaib5tCHE\nZ3Vn98ePou7QuqGxvGgvR3Zvts7eCObrlM5DhgzZdzTxAzRu3Ljshx9+8GlO5jp16rj79+9f+Pbb\nb6d+//338UeOHJFevXqFbseYH3hz5T8EuAF4U0RaAPuAeCAK+AIYr6qLq9hHGfAbVV0kIinAQhH5\n0m4Sc0bF1f/v2PrspRyY919S+1zpdEg/KtlScaNlnC3d6LiqrtADxdcpnZOSkn5yJV9cXOyKj493\nAwwYMKBNQUFBTNeuXYsmTpy4ZceOHbF79+511a1b94Sr/zFjxhQ8/PDDjdq2bVsyatSogkAcWyjx\nZgH3ElV9TlX7A1nA2UB3Vc1S1dFeJH5UdYeqLvL8XgisAprWMHZTAyk9LiK2SQcKPnoEdZ9QDxxT\nenSkj83mGbFqMqUzVDTvHG0a+vbbb9fm5uaufPvttzelpKS4r7zyyoLRo0c3PzpyaNOmTTHPPfdc\nPYDBgwcX7dixI/b999+vHwkTvPm0gLuqHvEk8n1Vb105EckGugFzq7sPU3PicpE+7H5Kty7j4OKP\nnQ7nRyVblhCVXJ/otMZOh2IcUpMpnQGmTp1aZ9iwYZUOQ3/yySe3paenl7Vt27ZTmzZtOg0fPrxV\nw4YNf5wFdOTIkXtzcnIOZmRkhFZ7aABIMPtpRSQZmAE8rKrvVfL+GGAMQPPmzXts2lSr+1scp+Vl\nrPttW6JSMmjxxzkhMZXChod644pPJvu3Xwe0HBFZqKo5AS0kDC1ZsiSva9euId3k8dBDDzVISUlx\n33XXXSfEWVxcLH369Gm3YMGC3OosxTho0KDWd9555w8XXnjhCWsCACxZsiS9a9eu2b5HHXp8uvKv\nCRGJoeIGsTcqS/wAqvqCquaoak5GRkawQotYEhVN+tDfUrJhHkUrA5tsvaHuckq3LiPe2vvNKZxs\nSmeAdevWxT788MPbfE38BQUFUdnZ2Z3j4+PdJ0v8tY03N3n1lRpeEno+/xKwSlX/UZN9Gf9KHXAd\n0WlNKPjQ+dm5D+9ajx4utpE+5pRONqUzQJcuXUqHDRvmc/JOT08vz8vLW/7pp59WuthLbeTNlf+1\nVIzOeUtErhORRtUopz/wC2CwiCz2PC6oxn6Mn7li4qh//t0cyp3OobWzHY3laGdvnHX2Osntdrud\nb/8LQZ5/l9AZHVFD3oz2uVlVuwMPAnWBf4vIdyLyiIgMFJEqb41W1W9VVVT1NFU93fOYUvPwjT/U\nHTSGqJR0Cj5y9uq/ZPMSEBdxTTo6GkeEW56fn59qXwA/5Xa7JT8/PxVY7nQs/uLLYi65QC4wXkQS\ngEHAZcA/AOs4C2OuuCTq/exO8ic9wM7Xbyep4zkkthtIVFJaUOMo2bKU2MbtcMXGB7Vc8z9lZWU3\n7dy5c+LOnTs7E8Q+wTDgBpaXlZXd5HQg/lKtNXhVtRiY4nmYWqDeubdRvGEee6e/yJ4vnwFxEZ/d\nnaQOg0nqMIjEdgNxxfn1Tv+fUHc5JRvm/bjmgHFGjx49dgEjnI7DBJ4twG4AiEqoQ/M7P8B9uITi\nDXMpWjmVQ6umsfvz8eye8jgxGS3J+u1XxGa0CEj5RaumU7Z/J3V6XBSQ/RtjfsqSv/kJV2w8Se3P\nJKn9mcBDuEsPUbTiS7ZPvIG8vw4g67dfEReAqZb3z34NV0Idkk8f5vd9G2NO5M1Qz3oi0iQYwZjQ\n44pLJKX7hWTdPx3VcvIeGUjxpu/9Woa79BCFCyZRp+dluGIT/LpvY0zlvOnQ+TvHzNopIrNF5B0R\nuU9EbH6eCBGf2YXs381EYhLY9OggDq37zm/7Llz0Ae6Sg6T2/4Xf9mmMOTVvkn8P4NFjnqdQccNW\nOnB/IIIyoSmuURtaPPAt0SkZbHr8XA6u8M9dwftnv0ZM/eYktj3DL/szxlTNm+RfetxCLVNV9XPg\nHmyIZ8SJqd+c7N/NJDajBVvGD6Xw+49qtL+y/T9wcPkX1Ol7NeKykYXGBIs3ta1ERH5cLFlV7/D8\nVMD3mZNM2ItOa0T2/TOIa3YaW565mOIN86u9r/1z3wJ3OWn9RvkxQmNMVbxJ/g8Dk0Wk/bEvikhj\nbLRQxIpKrkfWvV8SlViXXe/+vtr72T/rNeKzuhPX1O7qNSaYqkzeqvq5iNShYjnHxVTc3izARcAD\nAY7PhLCoxFTSh93HD2/+hqLcGZ7hod4r3b6KkryFNPz5+ABFaIw5Ga8aWVX1v0ArKjp6D1KxMPK1\ngK2yHeHqDv4V0WmNyZ/0B3xdG2LfrNfAFUVqn6sCFJ0x5mS87mFT1UPAOiAJuBUYB1hDbYRzxSaQ\nPvz3HFozk6IVX3n9OXW7OfDdGyR3/hnRqQ0DGKExpjLe3OTVVkT+KCK5wERgN3CmqvYGav06l6Zq\naWfeREz95uya9IDXV/+H1szkyO7NpFpHrzGO8ObKPxcYClzqWWXrMVXN87wXvDUgTchyxcSRfuEf\nKdkwz+v1gPfPeg1XfDIp3UcGODpjTGW8Sf6XAHnAlyLymogM9yzJaMyP0vpfQ2zD1ux67w+o+9Tr\nXbgPF3Ng/n9JybkkoDOFGmNOzpvFXN5X1SuA1sBnwC+BrSLyClAnwPGZMCHRMaRf+CdKNy+hcGGl\nSzT/6ODij3EXH7AmH2Mc5EuHb5GqvqGqw4AOwBxgWcAiM2Ente9VxDbpwK73/oi6y0+63b5ZrxGd\n1oQkm7vfGMdU6356Vd2jqhNU1evaKyJDRGS1iKwTkfuqU64JbeKKosHFf+bw9lXs/+7NSrcp3b6K\ng8s+JbXf1YiryhVAjTEBEpQ7dD3r/P4TOBfYCswXkQ9VdWUwyjfBk9LjYuKbn07+5AdJ7X0FiFC8\nfg6F33/EwcUfU7p9JRITR9qA65wO1ZiIFqzpGXoB61R1A4CIvAVcCFjyr2XE5SLjkr+wZfxwNj1+\nDqVbl1NetAeioklqdyZpZ40mpfuFAVsRzBjjnWAl/6bAlmOebwV6H7+RiIwBxgA0b948OJEZv0vu\nOpTE9mdRunUZyV2HktJtOEmdf0ZUYqrToRljPIKV/KWS1064R0BVXwBeAMjJybF7CMKUiJB131RQ\ntWmajQlRwUr+W4HMY543A7YHqWzjABEBqew73xgTCoJ1WTYfaCMiLUQkFrgS+DBIZRtjjDlOUK78\nVbVMRG4FPgeigJdVdUUwyjbGGHMi8XUa3mARkXxgkx93mQ4U+HF/oSgSjhH8c5xZqprhj2CMCUch\nm/z9TUQWqGqtXnM4Eo4RIuc4jQkkG4phjDERyJK/McZEoEhK/i84HUAQRMIxQuQcpzEBEzFt/sYY\nY/4nkq78jTHGeATrDl9HiEg88A0QR8Wxvquqf3I2qsDwzJy6ANjmWXOh1hGRPKAQKAfKbMSPMdVX\nq5M/UAoMVtWDnqUnvxWRT1V1jtOBBcAdwCpq/+pqg1Q1Eu5lMCaganWzj1Y46Hka43nUuk4OEWkG\nDAUmOh2LMSY81OrkDxXNISKyGNgFfKmqc52OKQCeBO4FTr1yevhT4AsRWeiZ/tsYU021Pvmrarmq\nnk7FTKK9RKSz0zH5k4gMA3ap6kKnYwmC/qraHTgfuEVEBjodkDHhqtYn/6NUdR8wHRjicCj+1h8Y\n4ekMfQsYLCKvOxtSYKjqds/PXcD7VKwQZ4yphlqd/EUkQ0TSPL8nAOcAuc5G5V+qer+qNlPVbCqm\nyp6qqqMcDsvvRCRJRFKO/g78DFjubFTGhK/aPtqnMfCqZxikC3hHVT92OCZTPQ2B96VigZho4P9U\n9TNnQzImfNkdvsYYE4FqdbOPMcaYylnyN8aYCGTJ3xhjIpAlf2OMiUCW/I0xJgJZ8jfGmAhkyd8Y\nYyKQJX/jFRG5SUSWicj1TsdijKk5S/7GW5cAg4HLnA7EGFNzlvwDTEQeFJG7Pb/PPsV2aSIyNniR\nVRrDBBHpf5K351IxLXZtnBLbmIhjyT+IVLXfKd5OAxxN/kBv4GSrnCUDM4HU4IVjjAkUS/4BICK/\nF5HVIvIV0O6Y1w96fiaJyCciskRElovIFcCjQCsRWSwiT3i2m+xZuGTF0cVLRCRbRFaJyIue17/w\nzFiKiFwjIks9+33tmHJHicg8z74neCa6Oz7mDsAaVS2v5D0XcBFwDXBRZZ83xoSX2j6rZ9CJSA8q\nplbuRsW/7yLg+IVWhgDbVXWo5zOpVDSndPYsPHPUDaq6x5Pc54vIJM/rbYCrVHW0iLwDXCIi3wO/\np2LBkwIRqefZdwfgCs/rR0TkOeBq4D/HxXQ+cLJZMgcDS1U1T0SWeJ5/6cu/izEmtNiVv/+dAbyv\nqodU9QDwYSXbLAPOEZHHROQMVd1/kn3d7km2c4BMKpI+wEZVXez5fSGQTUVCfvfo4uaqusfz/tlA\nDyq+PBZ7nrespKzzOHnyvxp40/P7m57nxpgwZlf+gXHKebJVdY3nL4QLgL+JyBccdyUuImdRsfhM\nX1U9JCLTgXjP26XHbFoOJAByknIFeFVV7z9ZPCKSCKQdXSnruPcSgAuBs0XkcSouGFJEJEFVi091\nnMaY0GVX/v73DRXt4gmelaeGH7+BiDQBDqnq68Dfge5AIZByzGapwF5P4m8P9Kmi3K+By0WkvqeM\nese8fqmINDj6uohkHffZQcC0k+x3BPCpqjZX1WxVbQ58VNlxGWPCh135+5mqLhKRt4HFwCYqRsgc\nrwvwhIi4gSPAr1R1t4jMEpHlwKfAA8DNIrIUWM3JR+EcLXeFiDwMzBCRcuB74DpVXSkiDwBfeDpu\njwC3eGI76nzg3ZPsurL+gfeB64F3ThWTMSZ02UpeBhFZBPRW1SNOx2KMCQ5L/sYYE4Gszd8YYyKQ\nJX9jjIlAlvyNMSYCWfI3xpgIZMnfGGMikCV/Y4yJQJb8jTEmAlnyN8aYCPT/na9i3AhPgCIAAAAA\nSUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYgAAAEPCAYAAABY9lNGAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nGOOVJQhjjDFe/T+DdZn1AdoamgAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX8AAAEPCAYAAACqZsSmAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYgAAAEPCAYAAABY9lNGAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX8AAAEPCAYAAACqZsSmAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nJgL9PzPIOXsquCoDAAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYgAAAEPCAYAAABY9lNGAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "for i in xrange(16):\n", + " comb_df = pd.DataFrame(columns=['O-O', 'O(l)-Cy', 'O(r)-Cy'])\n", + " if os.path.exists('comb-o-data-{}.h5'.format(i)):\n", + " with pd.HDFStore('comb-o-data-{}.h5'.format(i)) as store:\n", + " try:\n", + " comb_df = store['time_750ns']\n", + " except KeyError:\n", + " comb_df = store['750ns']\n", + " else:\n", + " for key in configs:\n", + " with cd(configs[key]):\n", + " with open('TOPO/temperatures.dat', 'r') as t_file:\n", + " temps = list(t_file.read()[1:-2].split(', '))\n", + " temp = float(temps[i])\n", + " print 'Now starting on {} {}...'.format(key, i)\n", + " univ = ca.Taddol('../../../solutes.gro', \n", + " 'npt-PT-{}-out{}.xtc'.format(key, i))\n", + " try:\n", + " univ.read_data()\n", + " except IOError:\n", + " save_data = True\n", + " pass\n", + " else:\n", + " save_data = False\n", + " comb_df = comb_df.append(univ.ox_dists, ignore_index=True)\n", + " if save_data:\n", + " univ.save_data()\n", + " with pd.HDFStore('comb-o-data-{}.h5'.format(i)) as store:\n", + " store['time_750ns'] = comb_df\n", + " print('Saved combined data as {}'.format('comb-o-data-{}.h5'.format(i)))\n", + " fig = univ.fes_ox_dists(data=comb_df, temp=temp, linewidth=3)\n", + " for axes in fig.axes[:3]:\n", + " axes.set_ylim[0:8]\n", + " fig.savefig('fes-ox-dists-rjm-PT-comb-{}.pdf'.format(i))\n", + " plt.close('all')\n", + " print('Saved figure as {}'.format('fes-ox-dists-rjm-PT-comb-{}.pdf'.format(i)))\n", + " print('Done with temp {:.0f} K ({} of 16).'.format(temp, i+1) \\\n", + " ' Moving to next temperature...\\n\\n')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Do Again at 1 us" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "heading_collapsed": true + }, + "source": [ + "## Playground" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": true, + "hidden": true + }, + "outputs": [], + "source": [ + "td = {'a': 1, 'b': 2}" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": true, + "hidden": true + }, + "outputs": [], + "source": [ + "td.update({'c': 3})" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": true, + "hidden": true + }, + "outputs": [], + "source": [ + "td.update(c=3)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": true, + "hidden": true + }, + "outputs": [], + "source": [ + "td.update(c=3)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "hidden": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "a\n", + "c\n", + "b\n" + ] + } + ], + "source": [ + "for key in td:\n", + " print(key)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "hidden": true + }, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAawAAAEYCAYAAAAAk8LPAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "for axes in axes_fes_cvs[0:3]:\n", + " for ax in axes:\n", + " ax.set_xlabel('')\n", + "for axes in axes_fes_cvs[:,1:]:\n", + " for ax in axes:\n", + " ax.set_ylabel('')\n", + "fig_fes_cvs.tight_layout()\n", + "fig_fes_cvs" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "hidden": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fig_fes_cvs.patches" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true, + "heading_collapsed": true + }, + "source": [ + "## Production" + ] + }, + { + "cell_type": "code", + "execution_count": 183, + "metadata": { + "hidden": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Now starting on MaEx 0...\n", + "Done importing and calculating, making figures...\n", + "Using default data: self.ox_dists.\n", + "Using default data: self.ox_dists.\n", + "Done making and saving figures for MaEx 0, closing and moving on...\n", + "Now starting on MaEx 1...\n", + "Done importing and calculating, making figures...\n", + "Using default data: self.ox_dists.\n", + "Using default data: self.ox_dists.\n", + "Done making and saving figures for MaEx 1, closing and moving on...\n", + "Now starting on MaEx 2...\n", + "Done importing and calculating, making figures...\n", + "Using default data: self.ox_dists.\n", + "Using default data: self.ox_dists.\n", + "Done making and saving figures for MaEx 2, closing and moving on...\n", + "Now starting on MaEx 3...\n", + "Done importing and calculating, making figures...\n", + "Using default data: self.ox_dists.\n", + "Using default data: self.ox_dists.\n", + "Done making and saving figures for MaEx 3, closing and moving on...\n", + "Now starting on MaEx 4...\n", + "Done importing and calculating, making figures...\n", + "Using default data: self.ox_dists.\n", + "Using default data: self.ox_dists.\n", + "Done making and saving figures for MaEx 4, closing and moving on...\n", + "Now starting on MaEx 5...\n", + "Done importing and calculating, making figures...\n", + "Using default data: self.ox_dists.\n", + "Using default data: self.ox_dists.\n", + "Done making and saving figures for MaEx 5, closing and moving on...\n", + "Now starting on MaEx 6...\n", + "Done importing and calculating, making figures...\n", + "Using default data: self.ox_dists.\n", + "Using default data: self.ox_dists.\n", + "Done making and saving figures for MaEx 6, closing and moving on...\n", + "Now starting on MaEx 7...\n", + "Done importing and calculating, making figures...\n", + "Using default data: self.ox_dists.\n", + "Using default data: self.ox_dists.\n", + "Done making and saving figures for MaEx 7, closing and moving on...\n", + "Now starting on MaEx 8...\n", + "Done importing and calculating, making figures...\n", + "Using default data: self.ox_dists.\n", + "Using default data: self.ox_dists.\n", + "Done making and saving figures for MaEx 8, closing and moving on...\n", + "Now starting on MaEx 9...\n", + "Done importing and calculating, making figures...\n", + "Using default data: self.ox_dists.\n", + "Using default data: self.ox_dists.\n", + "Done making and saving figures for MaEx 9, closing and moving on...\n", + "Now starting on MaEx 10...\n", + "Done importing and calculating, making figures...\n", + "Using default data: self.ox_dists.\n", + "Using default data: self.ox_dists.\n", + "Done making and saving figures for MaEx 10, closing and moving on...\n", + "Now starting on MaEx 11...\n", + "Done importing and calculating, making figures...\n", + "Using default data: self.ox_dists.\n", + "Using default data: self.ox_dists.\n", + "Done making and saving figures for MaEx 11, closing and moving on...\n", + "Now starting on MaEx 12...\n", + "Done importing and calculating, making figures...\n", + "Using default data: self.ox_dists.\n", + "Using default data: self.ox_dists.\n", + "Done making and saving figures for MaEx 12, closing and moving on...\n", + "Now starting on MaEx 13...\n", + "Done importing and calculating, making figures...\n", + "Using default data: self.ox_dists.\n", + "Using default data: self.ox_dists.\n", + "Done making and saving figures for MaEx 13, closing and moving on...\n", + "Now starting on MaEx 14...\n", + "Done importing and calculating, making figures...\n", + "Using default data: self.ox_dists.\n", + "Using default data: self.ox_dists.\n", + "Done making and saving figures for MaEx 14, closing and moving on...\n", + "Now starting on MaEx 15...\n", + "Done importing and calculating, making figures...\n", + "Using default data: self.ox_dists.\n", + "Using default data: self.ox_dists.\n", + "Done making and saving figures for MaEx 15, closing and moving on...\n", + "\n", + "\n", + "---Done with all MaEx, moving to next config\n", + "Now starting on MiEn 0...\n", + "Done importing and calculating, making figures...\n", + "Using default data: self.ox_dists.\n", + "Using default data: self.ox_dists.\n", + "Done making and saving figures for MiEn 0, closing and moving on...\n", + "Now starting on MiEn 1...\n", + "Done importing and calculating, making figures...\n", + "Using default data: self.ox_dists.\n", + "Using default data: self.ox_dists.\n", + "Done making and saving figures for MiEn 1, closing and moving on...\n", + "Now starting on MiEn 2...\n", + "Done importing and calculating, making figures...\n", + "\n", + "\n", + "!!!! Not enough data to plot for closed MiEn 2 !!!!\n", + "\n", + "\n", + "Using default data: self.ox_dists.\n", + "Using default data: self.ox_dists.\n", + "Done making and saving figures for MiEn 2, closing and moving on...\n", + "Now starting on MiEn 3...\n", + "Done importing and calculating, making figures...\n", + "Using default data: self.ox_dists.\n", + "Using default data: self.ox_dists.\n", + "Done making and saving figures for MiEn 3, closing and moving on...\n", + "Now starting on MiEn 4...\n", + "Done importing and calculating, making figures...\n", + "Using default data: self.ox_dists.\n", + "Using default data: self.ox_dists.\n", + "Done making and saving figures for MiEn 4, closing and moving on...\n", + "Now starting on MiEn 5...\n", + "Done importing and calculating, making figures...\n", + "Using default data: self.ox_dists.\n", + "Using default data: self.ox_dists.\n", + "Done making and saving figures for MiEn 5, closing and moving on...\n", + "Now starting on MiEn 6...\n", + "Done importing and calculating, making figures...\n", + "Using default data: self.ox_dists.\n", + "Using default data: self.ox_dists.\n", + "Done making and saving figures for MiEn 6, closing and moving on...\n", + "Now starting on MiEn 7...\n", + "Done importing and calculating, making figures...\n", + "Using default data: self.ox_dists.\n", + "Using default data: self.ox_dists.\n", + "Done making and saving figures for MiEn 7, closing and moving on...\n", + "Now starting on MiEn 8...\n", + "Done importing and calculating, making figures...\n", + "Using default data: self.ox_dists.\n", + "Using default data: self.ox_dists.\n", + "Done making and saving figures for MiEn 8, closing and moving on...\n", + "Now starting on MiEn 9...\n", + "Done importing and calculating, making figures...\n", + "Using default data: self.ox_dists.\n", + "Using default data: self.ox_dists.\n", + "Done making and saving figures for MiEn 9, closing and moving on...\n", + "Now starting on MiEn 10...\n", + "Done importing and calculating, making figures...\n", + "Using default data: self.ox_dists.\n", + "Using default data: self.ox_dists.\n", + "Done making and saving figures for MiEn 10, closing and moving on...\n", + "Now starting on MiEn 11...\n", + "Done importing and calculating, making figures...\n", + "Using default data: self.ox_dists.\n", + "Using default data: self.ox_dists.\n", + "Done making and saving figures for MiEn 11, closing and moving on...\n", + "Now starting on MiEn 12...\n", + "Done importing and calculating, making figures...\n", + "Using default data: self.ox_dists.\n", + "Using default data: self.ox_dists.\n", + "Done making and saving figures for MiEn 12, closing and moving on...\n", + "Now starting on MiEn 13...\n", + "Done importing and calculating, making figures...\n", + "Using default data: self.ox_dists.\n", + "Using default data: self.ox_dists.\n", + "Done making and saving figures for MiEn 13, closing and moving on...\n", + "Now starting on MiEn 14...\n", + "Done importing and calculating, making figures...\n", + "Using default data: self.ox_dists.\n", + "Using default data: self.ox_dists.\n", + "Done making and saving figures for MiEn 14, closing and moving on...\n", + "Now starting on MiEn 15...\n", + "Done importing and calculating, making figures...\n", + "Using default data: self.ox_dists.\n", + "Using default data: self.ox_dists.\n", + "Done making and saving figures for MiEn 15, closing and moving on...\n", + "\n", + "\n", + "---Done with all MiEn, moving to next config\n", + "Now starting on MiEx 0...\n", + "Done importing and calculating, making figures...\n", + "\n", + "\n", + "!!!! Not enough data to plot for closed MiEx 0 !!!!\n", + "\n", + "\n", + "Using default data: self.ox_dists.\n", + "Using default data: self.ox_dists.\n", + "Done making and saving figures for MiEx 0, closing and moving on...\n", + "Now starting on MiEx 1...\n", + "Done importing and calculating, making figures...\n", + "\n", + "\n", + "!!!! Not enough data to plot for closed MiEx 1 !!!!\n", + "\n", + "\n", + "Using default data: self.ox_dists.\n", + "Using default data: self.ox_dists.\n", + "Done making and saving figures for MiEx 1, closing and moving on...\n", + "Now starting on MiEx 2...\n", + "Done importing and calculating, making figures...\n", + "Using default data: self.ox_dists.\n", + "Using default data: self.ox_dists.\n", + "Done making and saving figures for MiEx 2, closing and moving on...\n", + "Now starting on MiEx 3...\n", + "Done importing and calculating, making figures...\n", + "\n", + "\n", + "!!!! Not enough data to plot for closed MiEx 3 !!!!\n", + "\n", + "\n", + "Using default data: self.ox_dists.\n", + "Using default data: self.ox_dists.\n", + "Done making and saving figures for MiEx 3, closing and moving on...\n", + "Now starting on MiEx 4...\n", + "Done importing and calculating, making figures...\n", + "Using default data: self.ox_dists.\n", + "Using default data: self.ox_dists.\n", + "Done making and saving figures for MiEx 4, closing and moving on...\n", + "Now starting on MiEx 5...\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Done importing and calculating, making figures...\n", + "Using default data: self.ox_dists.\n", + "Using default data: self.ox_dists.\n", + "Done making and saving figures for MiEx 5, closing and moving on...\n", + "Now starting on MiEx 6...\n", + "Done importing and calculating, making figures...\n", + "Using default data: self.ox_dists.\n", + "Using default data: self.ox_dists.\n", + "Done making and saving figures for MiEx 6, closing and moving on...\n", + "Now starting on MiEx 7...\n", + "Done importing and calculating, making figures...\n", + "Using default data: self.ox_dists.\n", + "Using default data: self.ox_dists.\n", + "Done making and saving figures for MiEx 7, closing and moving on...\n", + "Now starting on MiEx 8...\n", + "Done importing and calculating, making figures...\n", + "Using default data: self.ox_dists.\n", + "Using default data: self.ox_dists.\n", + "Done making and saving figures for MiEx 8, closing and moving on...\n", + "Now starting on MiEx 9...\n", + "Done importing and calculating, making figures...\n", + "Using default data: self.ox_dists.\n", + "Using default data: self.ox_dists.\n", + "Done making and saving figures for MiEx 9, closing and moving on...\n", + "Now starting on MiEx 10...\n", + "Done importing and calculating, making figures...\n", + "Using default data: self.ox_dists.\n", + "Using default data: self.ox_dists.\n", + "Done making and saving figures for MiEx 10, closing and moving on...\n", + "Now starting on MiEx 11...\n", + "Done importing and calculating, making figures...\n", + "Using default data: self.ox_dists.\n", + "Using default data: self.ox_dists.\n", + "Done making and saving figures for MiEx 11, closing and moving on...\n", + "Now starting on MiEx 12...\n", + "Done importing and calculating, making figures...\n", + "Using default data: self.ox_dists.\n", + "Using default data: self.ox_dists.\n", + "Done making and saving figures for MiEx 12, closing and moving on...\n", + "Now starting on MiEx 13...\n", + "Done importing and calculating, making figures...\n", + "Using default data: self.ox_dists.\n", + "Using default data: self.ox_dists.\n", + "Done making and saving figures for MiEx 13, closing and moving on...\n", + "Now starting on MiEx 14...\n", + "Done importing and calculating, making figures...\n", + "Using default data: self.ox_dists.\n", + "Using default data: self.ox_dists.\n", + "Done making and saving figures for MiEx 14, closing and moving on...\n", + "Now starting on MiEx 15...\n", + "Done importing and calculating, making figures...\n", + "Using default data: self.ox_dists.\n", + "Using default data: self.ox_dists.\n", + "Done making and saving figures for MiEx 15, closing and moving on...\n", + "\n", + "\n", + "---Done with all MiEx, moving to next config\n", + "Now starting on MaEn 0...\n", + "Done importing and calculating, making figures...\n", + "\n", + "\n", + "!!!! Not enough data to plot for closed MaEn 0 !!!!\n", + "\n", + "\n", + "Using default data: self.ox_dists.\n", + "Using default data: self.ox_dists.\n", + "Done making and saving figures for MaEn 0, closing and moving on...\n", + "Now starting on MaEn 1...\n", + "Done importing and calculating, making figures...\n", + "Using default data: self.ox_dists.\n", + "Using default data: self.ox_dists.\n", + "Done making and saving figures for MaEn 1, closing and moving on...\n", + "Now starting on MaEn 2...\n", + "Done importing and calculating, making figures...\n", + "Using default data: self.ox_dists.\n", + "Using default data: self.ox_dists.\n", + "Done making and saving figures for MaEn 2, closing and moving on...\n", + "Now starting on MaEn 3...\n", + "Done importing and calculating, making figures...\n", + "Using default data: self.ox_dists.\n", + "Using default data: self.ox_dists.\n", + "Done making and saving figures for MaEn 3, closing and moving on...\n", + "Now starting on MaEn 4...\n", + "Done importing and calculating, making figures...\n", + "Using default data: self.ox_dists.\n", + "Using default data: self.ox_dists.\n", + "Done making and saving figures for MaEn 4, closing and moving on...\n", + "Now starting on MaEn 5...\n", + "Done importing and calculating, making figures...\n", + "Using default data: self.ox_dists.\n", + "Using default data: self.ox_dists.\n", + "Done making and saving figures for MaEn 5, closing and moving on...\n", + "Now starting on MaEn 6...\n", + "Done importing and calculating, making figures...\n", + "Using default data: self.ox_dists.\n", + "Using default data: self.ox_dists.\n", + "Done making and saving figures for MaEn 6, closing and moving on...\n", + "Now starting on MaEn 7...\n", + "Done importing and calculating, making figures...\n", + "Using default data: self.ox_dists.\n", + "Using default data: self.ox_dists.\n", + "Done making and saving figures for MaEn 7, closing and moving on...\n", + "Now starting on MaEn 8...\n", + "Done importing and calculating, making figures...\n", + "Using default data: self.ox_dists.\n", + "Using default data: self.ox_dists.\n", + "Done making and saving figures for MaEn 8, closing and moving on...\n", + "Now starting on MaEn 9...\n", + "Done importing and calculating, making figures...\n", + "Using default data: self.ox_dists.\n", + "Using default data: self.ox_dists.\n", + "Done making and saving figures for MaEn 9, closing and moving on...\n", + "Now starting on MaEn 10...\n", + "Done importing and calculating, making figures...\n", + "Using default data: self.ox_dists.\n", + "Using default data: self.ox_dists.\n", + "Done making and saving figures for MaEn 10, closing and moving on...\n", + "Now starting on MaEn 11...\n", + "Done importing and calculating, making figures...\n", + "Using default data: self.ox_dists.\n", + "Using default data: self.ox_dists.\n", + "Done making and saving figures for MaEn 11, closing and moving on...\n", + "Now starting on MaEn 12...\n", + "Done importing and calculating, making figures...\n", + "Using default data: self.ox_dists.\n", + "Using default data: self.ox_dists.\n", + "Done making and saving figures for MaEn 12, closing and moving on...\n", + "Now starting on MaEn 13...\n", + "Done importing and calculating, making figures...\n", + "Using default data: self.ox_dists.\n", + "Using default data: self.ox_dists.\n", + "Done making and saving figures for MaEn 13, closing and moving on...\n", + "Now starting on MaEn 14...\n", + "Done importing and calculating, making figures...\n", + "Using default data: self.ox_dists.\n", + "Using default data: self.ox_dists.\n", + "Done making and saving figures for MaEn 14, closing and moving on...\n", + "Now starting on MaEn 15...\n", + "Done importing and calculating, making figures...\n", + "Using default data: self.ox_dists.\n", + "Using default data: self.ox_dists.\n", + "Done making and saving figures for MaEn 15, closing and moving on...\n", + "\n", + "\n", + "---Done with all MaEn, moving to next config\n" + ] + } + ], + "source": [ + "overwrite = True\n", + "\n", + "for key in configs:\n", + " with cd(configs[key]):\n", + " with open('TOPO/temperatures.dat', 'r') as t_file:\n", + " temps = list(t_file.read()[1:-2].split(', '))\n", + " univ = ca.Taddol('../../../solutes.gro', \n", + " 'npt-PT-{}-out{}.xtc'.format(key, '0'))\n", + " final_time = int(univ.data['Time'].iat[-1]/1000)\n", + " if final_time >= 1000:\n", + " final_time = str(final_time/1000)+'us'\n", + " else:\n", + " final_time = str(final_time) + 'ns'\n", + " if os.path.isfile('fes-ox-dists-rjm-PT-{}-comb-{}.pdf'.format(key, final_time)) or not overwrite:\n", + " print('\\nAll of {} seems to be complete, moving on to next config...\\n\\n'.format(key))\n", + " continue\n", + " fig_fes_cvs, axes_fes_cvs = plt.subplots(4, 4, sharex=True, sharey=True)\n", + " fig_fes_cvs_open, axes_fes_cvs_open = plt.subplots(4, 4, sharex=True, sharey=True)\n", + " fig_fes_cvs_closed, axes_fes_cvs_closed = plt.subplots(4, 4, sharex=True, sharey=True)\n", + " fig_fes_ox_dists, axes_fes_ox_dists = plt.subplots(8, 8, sharex=True, sharey=True)\n", + " for i in xrange(16):\n", + " print 'Now starting on {} {}...'.format(key, i)\n", + " temp = float(temps[i])\n", + " univ = ca.Taddol('../../../solutes.gro', \n", + " 'npt-PT-{}-out{}.xtc'.format(key, i))\n", + " final_time = str(int(univ.data['Time'].iat[-1]/1000))+'ns'\n", + " file_name_end = '-rjm-PT-{}-{}-{}.pdf'.format(key, i, final_time)\n", + "# if os.path.isfile('fes-ox-dists'+file_name_end):\n", + "# print('{} {} seems to already '.format(key, i) +\n", + "# 'be complete; moving on...')\n", + "# continue\n", + " try:\n", + " univ.read_data()\n", + " except IOError:\n", + " univ.calculate_distances()\n", + " univ.save_data()\n", + " univ.calc_open_closed() # This is fast, so doesn't really matter if repeated\n", + " print 'Done importing and calculating, making figures...'\n", + " \n", + " ax = axes_fes_cvs.flat[i]\n", + " univ.fes_2d_cvs(temp=temp, display=False, ax=ax)\n", + " filename = 'fes-cvs'+file_name_end\n", + " if (not os.path.isfile(filename)) or overwrite:\n", + " fig = univ.fes_2d_cvs(temp=temp)\n", + " fig.savefig(filename)\n", + " \n", + " if len(univ.data[univ.data['open_TAD']]['CV1']) > 1:\n", + " ax = axes_fes_cvs_open.flat[i]\n", + " univ.fes_2d_cvs(univ.data[univ.data['open_TAD']]['CV1'], \n", + " univ.data[univ.data['open_TAD']]['CV2'],\n", + " temp=temp, display=False, ax=ax)\n", + " filename = 'fes-cvs-open'+file_name_end\n", + " if (not os.path.isfile(filename)) or overwrite:\n", + " fig = univ.fes_2d_cvs(univ.data[univ.data['open_TAD']]['CV1'], \n", + " univ.data[univ.data['open_TAD']]['CV2'],\n", + " temp=temp)\n", + " fig.savefig(filename)\n", + " else:\n", + " print '\\n\\n!!!! Not enough data to plot for open ' \\\n", + " '{} {} !!!!\\n\\n'.format(key, i)\n", + " \n", + " if len(univ.data[univ.data['closed_TAD']]['CV1']) > 1:\n", + " ax = axes_fes_cvs_closed.flat[i]\n", + " univ.fes_2d_cvs(univ.data[univ.data['closed_TAD']]['CV1'], \n", + " univ.data[univ.data['closed_TAD']]['CV2'],\n", + " temp=temp, display=False, ax=ax)\n", + " filename = 'fes-cvs-closed'+file_name_end\n", + " if (not os.path.isfile(filename)) or overwrite:\n", + " fig = univ.fes_2d_cvs(univ.data[univ.data['closed_TAD']]['CV1'], \n", + " univ.data[univ.data['closed_TAD']]['CV2'],\n", + " temp=temp)\n", + " fig.savefig(filename)\n", + " else:\n", + " print '\\n\\n!!!! Not enough data to plot for closed ' \\\n", + " '{} {} !!!!\\n\\n'.format(key, i)\n", + " \n", + " axes = axes_fes_ox_dists.flat[4*i:4*i+4]\n", + " univ.fes_ox_dists(temp=temp, save=False, display=False, linewidth=3,\n", + " axes=axes)\n", + " filename = 'fes-ox-dists'+file_name_end\n", + " if (not os.path.isfile(filename)) or overwrite:\n", + " fig = univ.fes_ox_dists(temp=temp, save=False, display=True, \n", + " linewidth=3)\n", + " fig.savefig(filename)\n", + " print('Done making and saving figures for '\n", + " '{} {}, closing and moving on...'.format(key, i))\n", + " #plt.close('all') # Not necessary because they pass out of scope?\n", + " for axes in axes_fes_cvs[0:3]:\n", + " for ax in axes:\n", + " ax.set_xlabel('')\n", + " for axes in axes_fes_cvs[:,1:]:\n", + " for ax in axes:\n", + " ax.set_ylabel('')\n", + " fig_fes_cvs.tight_layout()\n", + "# fig_fes_cvs.savefig('fes-cvs-rjm-PT-{}-comb-{}.pdf'.format(key, final_time))\n", + " for axes in axes_fes_cvs_open[0:3]:\n", + " for ax in axes:\n", + " ax.set_xlabel('')\n", + " for axes in axes_fes_cvs_open[:,1:]:\n", + " for ax in axes:\n", + " ax.set_ylabel('')\n", + " fig_fes_cvs_open.tight_layout()\n", + "# fig_fes_cvs_open.savefig('fes-cvs-open-rjm-PT-{}-comb-{}.pdf'.format(key, final_time))\n", + " for axes in axes_fes_cvs_closed[0:3]:\n", + " for ax in axes:\n", + " ax.set_xlabel('')\n", + " for axes in axes_fes_cvs_closed[:,1:]:\n", + " for ax in axes:\n", + " ax.set_ylabel('')\n", + " fig_fes_cvs_closed.tight_layout()\n", + "# fig_fes_cvs_closed.savefig('fes-cvs-closed-rjm-PT-{}-comb-{}.pdf'.format(key, final_time))\n", + "# fig_fes_ox_dists.savefig('fes-ox-dists-rjm-PT-{}-comb-{}.pdf'.format(key, final_time))\n", + " print '\\n\\n---Done with all {}, moving to next config'.format(key)\n", + " plt.close('all')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "heading_collapsed": true + }, + "source": [ + "## Combined Ox FESs Playground" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "hidden": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[,\n", + " ,\n", + " ]" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fig.axes[:3]" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "hidden": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "axes.set_ylim" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "heading_collapsed": true + }, + "source": [ + "## Combined Ox FESs Production" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "hidden": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved figure as fes-ox-dists-rjm-PT-comb-0.pdf\n", + "Done with temp 205 K (1 of 16). Moving to next temperature...\n", + "\n", + "\n", + "Now starting on MaEx 1...\n", + "Now starting on MiEn 1...\n", + "Now starting on MiEx 1...\n", + "Now starting on MaEn 1...\n", + "Saved combined data as comb-o-data-1.h5\n", + "Saved figure as fes-ox-dists-rjm-PT-comb-1.pdf\n", + "Done with temp 216 K (2 of 16). Moving to next temperature...\n", + "\n", + "\n", + "Now starting on MaEx 2...\n", + "Now starting on MiEn 2...\n", + "Now starting on MiEx 2...\n", + "Now starting on MaEn 2...\n", + "Saved combined data as comb-o-data-2.h5\n", + "Saved figure as fes-ox-dists-rjm-PT-comb-2.pdf\n", + "Done with temp 227 K (3 of 16). Moving to next temperature...\n", + "\n", + "\n", + "Now starting on MaEx 3...\n", + "Now starting on MiEn 3...\n", + "Now starting on MiEx 3...\n", + "Now starting on MaEn 3...\n", + "Saved combined data as comb-o-data-3.h5\n", + "Saved figure as fes-ox-dists-rjm-PT-comb-3.pdf\n", + "Done with temp 238 K (4 of 16). Moving to next temperature...\n", + "\n", + "\n", + "Now starting on MaEx 4...\n", + "Now starting on MiEn 4...\n", + "Now starting on MiEx 4...\n", + "Now starting on MaEn 4...\n", + "Saved combined data as comb-o-data-4.h5\n", + "Saved figure as fes-ox-dists-rjm-PT-comb-4.pdf\n", + "Done with temp 250 K (5 of 16). Moving to next temperature...\n", + "\n", + "\n", + "Now starting on MaEx 5...\n", + "Now starting on MiEn 5...\n", + "Now starting on MiEx 5...\n", + "Now starting on MaEn 5...\n", + "Saved combined data as comb-o-data-5.h5\n", + "Saved figure as fes-ox-dists-rjm-PT-comb-5.pdf\n", + "Done with temp 263 K (6 of 16). Moving to next temperature...\n", + "\n", + "\n", + "Now starting on MaEx 6...\n", + "Now starting on MiEn 6...\n", + "Now starting on MiEx 6...\n", + "Now starting on MaEn 6...\n", + "Saved combined data as comb-o-data-6.h5\n", + "Saved figure as fes-ox-dists-rjm-PT-comb-6.pdf\n", + "Done with temp 277 K (7 of 16). Moving to next temperature...\n", + "\n", + "\n", + "Now starting on MaEx 7...\n", + "Now starting on MiEn 7...\n", + "Now starting on MiEx 7...\n", + "Now starting on MaEn 7...\n", + "Saved combined data as comb-o-data-7.h5\n", + "Saved figure as fes-ox-dists-rjm-PT-comb-7.pdf\n", + "Done with temp 291 K (8 of 16). Moving to next temperature...\n", + "\n", + "\n", + "Now starting on MaEx 8...\n", + "Now starting on MiEn 8...\n", + "Now starting on MiEx 8...\n", + "Now starting on MaEn 8...\n", + "Saved combined data as comb-o-data-8.h5\n", + "Saved figure as fes-ox-dists-rjm-PT-comb-8.pdf\n", + "Done with temp 306 K (9 of 16). Moving to next temperature...\n", + "\n", + "\n", + "Now starting on MaEx 9...\n", + "Now starting on MiEn 9...\n", + "Now starting on MiEx 9...\n", + "Now starting on MaEn 9...\n", + "Saved combined data as comb-o-data-9.h5\n", + "Saved figure as fes-ox-dists-rjm-PT-comb-9.pdf\n", + "Done with temp 322 K (10 of 16). Moving to next temperature...\n", + "\n", + "\n", + "Now starting on MaEx 10...\n", + "Now starting on MiEn 10...\n", + "Now starting on MiEx 10...\n", + "Now starting on MaEn 10...\n", + "Saved combined data as comb-o-data-10.h5\n", + "Saved figure as fes-ox-dists-rjm-PT-comb-10.pdf\n", + "Done with temp 338 K (11 of 16). Moving to next temperature...\n", + "\n", + "\n", + "Now starting on MaEx 11...\n", + "Now starting on MiEn 11...\n", + "Now starting on MiEx 11...\n", + "Now starting on MaEn 11...\n", + "Saved combined data as comb-o-data-11.h5\n", + "Saved figure as fes-ox-dists-rjm-PT-comb-11.pdf\n", + "Done with temp 355 K (12 of 16). Moving to next temperature...\n", + "\n", + "\n", + "Now starting on MaEx 12...\n", + "Now starting on MiEn 12...\n", + "Now starting on MiEx 12...\n", + "Now starting on MaEn 12...\n", + "Saved combined data as comb-o-data-12.h5\n", + "Saved figure as fes-ox-dists-rjm-PT-comb-12.pdf\n", + "Done with temp 374 K (13 of 16). Moving to next temperature...\n", + "\n", + "\n", + "Now starting on MaEx 13...\n", + "Now starting on MiEn 13...\n", + "Now starting on MiEx 13...\n", + "Now starting on MaEn 13...\n", + "Saved combined data as comb-o-data-13.h5\n", + "Saved figure as fes-ox-dists-rjm-PT-comb-13.pdf\n", + "Done with temp 393 K (14 of 16). Moving to next temperature...\n", + "\n", + "\n", + "Now starting on MaEx 14...\n", + "Now starting on MiEn 14...\n", + "Now starting on MiEx 14...\n", + "Now starting on MaEn 14...\n", + "Saved combined data as comb-o-data-14.h5\n", + "Saved figure as fes-ox-dists-rjm-PT-comb-14.pdf\n", + "Done with temp 413 K (15 of 16). Moving to next temperature...\n", + "\n", + "\n", + "Now starting on MaEx 15...\n", + "Now starting on MiEn 15...\n", + "Now starting on MiEx 15...\n", + "Now starting on MaEn 15...\n", + "Saved combined data as comb-o-data-15.h5\n", + "Saved figure as fes-ox-dists-rjm-PT-comb-15.pdf\n", + "Done with temp 434 K (16 of 16). Moving to next temperature...\n", + "\n", + "\n" + ] + } + ], + "source": [ + "for i in xrange(16):\n", + " comb_df = pd.DataFrame(columns=['O-O', 'O(l)-Cy', 'O(r)-Cy'])\n", + " if os.path.exists('comb-o-data-{}.h5'.format(i)):\n", + " with pd.HDFStore('comb-o-data-{}.h5'.format(i)) as store:\n", + " comb_df = store['time_1us']\n", + " else:\n", + " for key in configs:\n", + " with cd(configs[key]):\n", + " with open('TOPO/temperatures.dat', 'r') as t_file:\n", + " temps = list(t_file.read()[1:-2].split(', '))\n", + " temp = float(temps[i])\n", + " print 'Now starting on {} {}...'.format(key, i)\n", + " univ = ca.Taddol('../../../solutes.gro', \n", + " 'npt-PT-{}-out{}.xtc'.format(key, i))\n", + " try:\n", + " univ.read_data()\n", + " except IOError:\n", + " save_data = True\n", + " pass\n", + " else:\n", + " save_data = False\n", + " comb_df = comb_df.append(univ.ox_dists, ignore_index=True)\n", + " if save_data:\n", + " univ.save_data()\n", + " with pd.HDFStore('comb-o-data-{}.h5'.format(i)) as store:\n", + " store['time_1us'] = comb_df\n", + " print('Saved combined data as {}'.format('comb-o-data-{}.h5'.format(i)))\n", + " fig = univ.fes_ox_dists(data=comb_df, temp=temp, linewidth=3)\n", + " for axes in fig.axes[:3]:\n", + " axes.set_ylim([0,8])\n", + " fig.savefig('fes-ox-dists-rjm-PT-comb-{}.pdf'.format(i))\n", + " plt.close('all')\n", + " print('Saved figure as {}'.format('fes-ox-dists-rjm-PT-comb-{}.pdf'.format(i)))\n", + " print('Done with temp {:.0f} K ({} of 16).'.format(temp, i+1) +\n", + " ' Moving to next temperature...\\n\\n')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "# CV Ox Dist Cross Peak" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Playground" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "univ = ca.Taddol('solutes.gro', 'major-endo/13-3htmf-etc/05/npt-PT-MaEn-out0.xtc')\n", + "univ.read_data()" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQ8AAAEYCAYAAABC/HMdAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQmCC\n", + "text/plain": [ + "" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fig, ax = plt.subplots()\n", + "ax.hist2d(univ.data['O(r)-Cy'], univ.data['CV1'])\n", + "ax.set_aspect(0.1,'box-forced')\n", + "ax.set_xlabel('O(r)-Cy')\n", + "ax.set_ylabel('CV 1')\n", + "fig.tight_layout()\n", + "fig.savefig('CV1-v-Or-Cy.pdf')\n", + "fig" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr3/graduate/theavey/anaconda_envs/py2.7/lib/python2.7/site-packages/matplotlib/pyplot.py:524: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n", + " max_open_warning, RuntimeWarning)\n" + ] + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQwAAAEYCAYAAACpy8geAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nmYUhqdn/AmEsOVh32P2VAAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fig, ax = plt.subplots()\n", + "ax.hist2d(univ.data['O(r)-Cy'], univ.data['CV2'])\n", + "ax.set_aspect(0.1,'box-forced')\n", + "ax.set_xlabel('O(r)-Cy')\n", + "ax.set_ylabel('CV 2')\n", + "fig.tight_layout()\n", + "fig.savefig('CV2-v-Or-Cy.pdf')\n", + "fig" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note, `CV2` is the CV involving the hydrogen attached to the right oxygen binding to the ketone on the 3-HTMF.\n", + "\n", + "This makes sense with the relative energetics of the two wells as the temperature rises: \n", + "The slightly farther well goes down in free energy at higher temperature. \n", + "An unbound state would likely become more entropically favored at higher temperatures, so these two wells are associated with reactant-bound and reactant-unbound states." + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "plt.close('all')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true, + "heading_collapsed": true + }, + "source": [ + "# Limit z-range" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "heading_collapsed": true, + "hidden": true + }, + "source": [ + "## Playground" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "heading_collapsed": true, + "hidden": true + }, + "source": [ + "### Test out parsing stuff" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "hidden": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0., 2., 4., 6., 8., 10., 12., 14., 16., 18., 20.])" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "zrange = list((0,20)) + [11]\n", + "np.linspace(*zrange)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "hidden": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[0, 20, 11]" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "zrange" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "hidden": true + }, + "outputs": [ + { + "ename": "TypeError", + "evalue": "object of type 'int' has no len()", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m11\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m: object of type 'int' has no len()" + ] + } + ], + "source": [ + "len(11)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "hidden": true + }, + "outputs": [ + { + "ename": "TypeError", + "evalue": "float() argument must be a string or a number", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mzrange\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m: float() argument must be a string or a number" + ] + } + ], + "source": [ + "float(zrange)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "hidden": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[0, 11, 20]" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tx = [11]\n", + "[0, tx[0], 20]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "hidden": true + }, + "source": [ + "### Try new argument to fes_2d_cvs" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": { + "collapsed": true, + "hidden": true + }, + "outputs": [], + "source": [ + "univ = ca.Taddol('solutes.gro', 'major-endo/13-3htmf-etc/05/npt-PT-MaEn-out0.xtc')\n", + "univ.read_data()" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": { + "collapsed": true, + "hidden": true + }, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAVIAAAEYCAYAAAAOFn7lAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nihKkEREVJUgjIipKkEZEVPT/AZG1vOZUBuQnAAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fig = univ.fes_2d_cvs(zrange=[0,4,3], zfinal=6)\n", + "fig" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": { + "collapsed": true, + "hidden": true + }, + "outputs": [], + "source": [ + "fig.savefig('fes-cvs-PT-MaEn-0-1us-zrange-fb.pdf')" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": { + "hidden": true + }, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAVIAAAEYCAYAAAAOFn7lAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "execution_count": 65, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fig = univ.fes_2d_cvs(bins=[0,4,8])\n", + "fig" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": { + "collapsed": true, + "hidden": true + }, + "outputs": [], + "source": [ + "fig.savefig('fes-cvs-PT-MaEn-0-1us-zrange-2b.pdf')" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": { + "hidden": true + }, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAVEAAAFPCAYAAAD0qv2pAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nF/j3qGm5AAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "execution_count": 64, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fig = univ.hist_2d_cvs()\n", + "fig" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "hidden": true + }, + "source": [ + "### Compare edges vs. midpoints" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": { + "hidden": true + }, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAVkAAAEYCAYAAAD29oUSAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nU5seKQAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "execution_count": 67, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fig_edges = univ.fes_2d_cvs()\n", + "fig_edges" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": { + "hidden": true + }, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAVkAAAEYCAYAAAD29oUSAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n7qQAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "execution_count": 79, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fig_mids = univ.fes_2d_cvs()\n", + "fig_mids" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "hidden": true + }, + "source": [ + "It seems that it shifts the FES by about 0.25 angstroms, which seems insignificant enough to not worry much about this. \n", + "I'll use the midpoints going forward, but I don't think it's necessary to discard all the old plots." + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "metadata": { + "hidden": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[1, 2, 3, 4, 5]" + ] + }, + "execution_count": 81, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tl" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "metadata": { + "hidden": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 1.5, 2.5, 3.5, 4.5])" + ] + }, + "execution_count": 82, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ca.Taddol.running_mean(tl,2)" + ] + }, + { + "cell_type": "code", + "execution_count": 84, + "metadata": { + "hidden": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "numpy.ndarray" + ] + }, + "execution_count": 84, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "type(np.array([1]))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true, + "heading_collapsed": true + }, + "source": [ + "# CV Cut Plots" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "heading_collapsed": true, + "hidden": true + }, + "source": [ + "## Sandbox" + ] + }, + { + "cell_type": "code", + "execution_count": 150, + "metadata": { + "hidden": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 150, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "univ.trajectory" + ] + }, + { + "cell_type": "code", + "execution_count": 127, + "metadata": { + "collapsed": true, + "hidden": true + }, + "outputs": [], + "source": [ + "univ.calc_open_closed()" + ] + }, + { + "cell_type": "code", + "execution_count": 128, + "metadata": { + "collapsed": true, + "hidden": true + }, + "outputs": [], + "source": [ + "x = univ.data.loc[lambda x: (2.5 < x.CV2) & (x.CV2 < 3.5) & x.closed_TAD]['CV1']\n", + "y = univ.data.loc[lambda x: (2.5 < x.CV2) & (x.CV2 < 3.5)]['CV2']" + ] + }, + { + "cell_type": "code", + "execution_count": 130, + "metadata": { + "hidden": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 130, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(x)" + ] + }, + { + "cell_type": "code", + "execution_count": 118, + "metadata": { + "hidden": true + }, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAVkAAAEYCAYAAAD29oUSAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQ9bMLEUOWTOzFP1/ApbJFmPqfAQAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "execution_count": 118, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "univ.fes_2d_cvs(x=x, y=y)" + ] + }, + { + "cell_type": "code", + "execution_count": 129, + "metadata": { + "hidden": true + }, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAD8CAYAAABzTgP2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nosSSTQAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "execution_count": 129, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fig, ax = plt.subplots()\n", + "ax.hist(x)\n", + "fig" + ] + }, + { + "cell_type": "code", + "execution_count": 124, + "metadata": { + "hidden": true + }, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYgAAAEPCAYAAABY9lNGAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "execution_count": 124, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "temp = 205\n", + "\n", + "r = 0.0019872 # kcal_th/(K mol)\n", + "fig, ax = plt.subplots()\n", + "\n", + "x = univ.data.loc[lambda x: (2.5 < x.CV2) & (x.CV2 < 3.5)]['CV1']\n", + "y = univ.data.loc[lambda x: (2.5 < x.CV2) & (x.CV2 < 3.5)]['CV2']\n", + "\n", + "n, bins = np.histogram(x)\n", + "n = [float(j) for j in n]\n", + "prob = np.array([j / max(n) for j in n]) + 1e-40\n", + "delta_g = np.array([-r * temp * np.log(p) for p in prob])\n", + "delta_g\n", + "line, = ax.plot(bins[:-1], delta_g)\n", + "ax.set_ylabel(r'$\\Delta G$ / (kcal / mol)')\n", + "ax.set_xlabel(r'distance / $\\mathrm{\\AA}$')\n", + "\n", + "fig" + ] + }, + { + "cell_type": "code", + "execution_count": 148, + "metadata": { + "hidden": true + }, + "outputs": [ + { + "ename": "IOError", + "evalue": "Data of this name already exists in this store! filename: npt-PT-MaEn-out15.h5, key: time_1000ns", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mIOError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[0mps\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0muniv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Time'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0muniv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'OrCOC2'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'O(l)-O(c2)'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlegend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhandles\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mps\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'upper left'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m \u001b[0muniv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 15\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr3/graduate/theavey/.local/lib/python2.7/site-packages/ParaTemp/CoordinateAnalysis.py\u001b[0m in \u001b[0;36msave_data\u001b[0;34m(self, filename, overwrite)\u001b[0m\n\u001b[1;32m 109\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 110\u001b[0m raise IOError('Data of this name already exists in this '\n\u001b[0;32m--> 111\u001b[0;31m \u001b[0;34m'store! filename: '\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 112\u001b[0m '{}, key: {}'.format(filename, time))\n\u001b[1;32m 113\u001b[0m \u001b[0mstore\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mIOError\u001b[0m: Data of this name already exists in this store! filename: npt-PT-MaEn-out15.h5, key: time_1000ns" + ] + } + ], + "source": [ + "key = 'MaEn'\n", + "i = 15\n", + "\n", + "with cd(configs[key]):\n", + " univ = ca.Taddol('../../../solutes.gro', \n", + " 'npt-PT-{}-out{}.xtc'.format(key, i))\n", + " univ.calculate_distances(OlCOC1='9 11', OlCOC2='9 12', OrCOC1='7 11', OrCOC2='7 12')\n", + " ps = []\n", + " ps.append(plt.plot(univ.data['Time'], univ.data['OlCOC1'], label='O(l)-O(c1)')[0])\n", + " ps.append(plt.plot(univ.data['Time'], univ.data['OlCOC2'], label='O(l)-O(c2)')[0])\n", + " ps.append(plt.plot(univ.data['Time'], univ.data['OrCOC1'], label='O(r)-O(c1)')[0])\n", + " ps.append(plt.plot(univ.data['Time'], univ.data['OrCOC2'], label='O(l)-O(c2)')[0])\n", + " plt.legend(handles=ps, loc='upper left')\n", + " univ.save_data()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 149, + "metadata": { + "hidden": true + }, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAD8CAYAAAB5Pm/hAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nItQAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 146, + "metadata": { + "hidden": true + }, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAD8CAYAAAB5Pm/hAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 147, + "metadata": { + "hidden": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved data to npt-PT-MaEn-out0.h5[time_1000ns]\n" + ] + } + ], + "source": [ + "univ.save_data()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "heading_collapsed": true, + "hidden": true + }, + "source": [ + "## Production" + ] + }, + { + "cell_type": "code", + "execution_count": 139, + "metadata": { + "hidden": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Now starting on MaEx 0...\n", + "Not enough closed frames for MaEx 0\n", + "Now starting on MiEn 0...\n", + "Not enough closed frames for MiEn 0\n", + "Now starting on MiEx 0...\n", + "Not enough closed frames for MiEx 0\n", + "Now starting on MaEn 0...\n", + "Not enough closed frames for MaEn 0\n" + ] + } + ], + "source": [ + "r = 0.0019872 # kcal_th/(K mol)\n", + "\n", + "for key in configs:\n", + " with cd(configs[key]):\n", + " i = 0\n", + " print 'Now starting on {} {}...'.format(key, i)\n", + " univ = ca.Taddol('../../../solutes.gro', \n", + " 'npt-PT-{}-out{}.xtc'.format(key, i))\n", + " temp = 205\n", + " final_time = int(univ.data['Time'].iat[-1]/1000)\n", + " if final_time > 1000:\n", + " final_time = str(final_time/1000)+'us'\n", + " else:\n", + " final_time = str(final_time) + 'ns'\n", + " file_name_end = '-rjm-PT-{}-{}-{}.pdf'.format(key, i, final_time)\n", + " try:\n", + " univ.read_data()\n", + " except IOError:\n", + " univ.calculate_distances()\n", + " univ.calc_open_closed()\n", + " univ.save_data()\n", + " univ.calc_open_closed()\n", + "\n", + " x = univ.data.loc[lambda x: (2.5 < x.CV2) & (x.CV2 < 3.5) & x.closed_TAD]['CV1']\n", + " if len(x) > 2:\n", + " n, bins = np.histogram(x, bins=20)\n", + " n = [float(j) for j in n]\n", + " prob = np.array([j / max(n) for j in n]) + 1e-40\n", + " delta_g = np.array([-r * temp * np.log(p) for p in prob])\n", + " delta_g\n", + " fig, ax = plt.subplots()\n", + " line, = ax.plot(bins[:-1], delta_g)\n", + " ax.set_ylabel(r'$\\Delta G$ / (kcal / mol)')\n", + " ax.set_xlabel(r'CV 1 / $\\mathrm{\\AA}$')\n", + " fig.tight_layout()\n", + " fig.savefig('fes-CV1-closed-shortCV2'+file_name_end)\n", + " else:\n", + " print('Not enough closed frames for {} {}'.format(key, i))\n", + " \n", + " x = univ.data.loc[lambda x: (7.5 < x.CV2) & (x.CV2 < 8.5) & x.open_TAD]['CV1']\n", + " if len(x) > 2:\n", + " n, bins = np.histogram(x, bins=20)\n", + " n = [float(j) for j in n]\n", + " prob = np.array([j / max(n) for j in n]) + 1e-40\n", + " delta_g = np.array([-r * temp * np.log(p) for p in prob])\n", + " delta_g\n", + " fig, ax = plt.subplots()\n", + " line, = ax.plot(bins[:-1], delta_g)\n", + " ax.set_ylabel(r'$\\Delta G$ / (kcal / mol)')\n", + " ax.set_xlabel(r'CV 1 / $\\mathrm{\\AA}$')\n", + " fig.tight_layout()\n", + " fig.savefig('fes-CV1-open-longCV2'+file_name_end)\n", + " else:\n", + " print('Not enough open frames for {} {}'.format(key, i))" + ] + }, + { + "cell_type": "code", + "execution_count": 140, + "metadata": { + "collapsed": true, + "hidden": true + }, + "outputs": [], + "source": [ + "plt.close('all')" + ] + }, + { + "cell_type": "code", + "execution_count": 178, + "metadata": { + "collapsed": true, + "hidden": true + }, + "outputs": [], + "source": [ + "univ = ca.Taddol('solutes.gro', 'major-endo/13-3htmf-etc/05/npt-PT-MaEn-out0.xtc')\n", + "univ.read_data()" + ] + }, + { + "cell_type": "code", + "execution_count": 179, + "metadata": { + "collapsed": true, + "hidden": true + }, + "outputs": [], + "source": [ + "fig = univ.fes_2d_cvs(bins=np.linspace(0,10,11))" + ] + }, + { + "cell_type": "code", + "execution_count": 180, + "metadata": { + "hidden": true + }, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAVkAAAEYCAYAAAD29oUSAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "execution_count": 180, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fig" + ] + }, + { + "cell_type": "code", + "execution_count": 181, + "metadata": { + "collapsed": true, + "hidden": true + }, + "outputs": [], + "source": [ + "ax = fig.gca()" + ] + }, + { + "cell_type": "code", + "execution_count": 182, + "metadata": { + "hidden": true + }, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAVkAAAEYCAYAAAD29oUSAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngg==\n", + "text/plain": [ + "" + ] + }, + "execution_count": 182, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ax.set_ylim([1.5,12])\n", + "ax.set_xlim([1.5, 12])\n", + "fig" + ] + }, + { + "cell_type": "code", + "execution_count": 156, + "metadata": { + "hidden": true + }, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAD8CAYAAACMwORRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSWrIuEtSQ8Zdkhoy7pLU0P8BV5ki56dpYKYAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist2d(univ.data['CV1'], univ.data['O(r)-Cy'])\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 157, + "metadata": { + "hidden": true + }, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAD8CAYAAACMwORRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nd0lqyLhLUkPGXZIa+n9Img2MMaA/cwAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist2d(univ.data['CV2'], univ.data['O(r)-Cy'])\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 158, + "metadata": { + "hidden": true + }, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAD8CAYAAACMwORRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n4y5JHTLuktQh4y5JHfp/+yCJ2ifl64IAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist2d(univ.data['CV1'], univ.data['O(l)-Cy'])\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 159, + "metadata": { + "hidden": true + }, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAD8CAYAAACMwORRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nl6QGGXdJapBxl6QGGXdJatD/ArEVieP2rgVDAAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist2d(univ.data['CV2'], univ.data['O(l)-Cy'])\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true, + "heading_collapsed": true + }, + "source": [ + "# Select geoms from min" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "hidden": true + }, + "source": [ + "## Sandbox" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": true, + "hidden": true + }, + "outputs": [], + "source": [ + "univ = ca.Taddol('solutes.gro', 'major-endo/13-3htmf-etc/05/pbc-MaEn-0.xtc')" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": true, + "hidden": true + }, + "outputs": [], + "source": [ + "univ.read_data(filename='major-endo/13-3htmf-etc/05/npt-PT-MaEn-out0.h5')" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": true, + "hidden": true + }, + "outputs": [], + "source": [ + "bool_array = (univ.data['CV1'] > 6.5) & (univ.data['CV1'] < 9.) & (univ.data['CV2'] < 3.) & (univ.data['CV2'] > 1.5)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "hidden": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "univ.trajectory.frame" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "hidden": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "False" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bool_array[10000]" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": true, + "hidden": true + }, + "outputs": [], + "source": [ + "solutes = univ.select_atoms('resname is 3HT or resname is CIN or resname is TAD')\n", + "\n", + "with mda.Writer('minim-structs-lCV1-sCV2-rjm-PT-MaEn-0.xtc', solutes.n_atoms) as W:\n", + " for ts in univ.trajectory:\n", + " if bool_array[univ.trajectory.frame]:\n", + " W.write(solutes)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "hidden": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "solutes" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "heading_collapsed": true, + "hidden": true + }, + "source": [ + "## Production" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "hidden": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "These cutoffs include 85723 frames.\n" + ] + } + ], + "source": [ + "cv1_cuts = [6.5, 9.]\n", + "cv2_cuts = [1.5, 3.]\n", + "name_set = 'lCV1-sCV2'\n", + "\n", + "univ = ca.Taddol('solutes.gro', 'major-endo/13-3htmf-etc/05/pbc-MaEn-0.xtc')\n", + "try:\n", + " univ.data['CV1']\n", + "except KeyError:\n", + " univ.read_data(filename='major-endo/13-3htmf-etc/05/npt-PT-MaEn-out0.h5')\n", + "\n", + "bool_array = ((univ.data['CV1'] > cv1_cuts[0]) & (univ.data['CV1'] < cv1_cuts[1]) \n", + " & (univ.data['CV2'] > cv2_cuts[0]) & (univ.data['CV2'] < cv2_cuts[1]))\n", + "num = len(univ.data[bool_array])\n", + "print('These cutoffs include {} frames.'.format(num))\n", + "\n", + "solutes = univ.select_atoms('resname is 3HT or resname is CIN or resname is TAD')\n", + "\n", + "with mda.Writer('minim-structs-'+name_set+'-rjm-PT-MaEn-0.xtc', \n", + " solutes.n_atoms) as W:\n", + " for ts in univ.trajectory:\n", + " if bool_array[univ.trajectory.frame]:\n", + " W.write(solutes)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "hidden": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "These cutoffs include 65794 frames.\n" + ] + } + ], + "source": [ + "cv1_cuts = [1.5, 4.]\n", + "cv2_cuts = [6.75, 8.5]\n", + "name_set = 'sCV1-lCV2'\n", + "\n", + "univ = ca.Taddol('solutes.gro', 'major-endo/13-3htmf-etc/05/pbc-MaEn-0.xtc')\n", + "try:\n", + " univ.data['CV1']\n", + "except KeyError:\n", + " univ.read_data(filename='major-endo/13-3htmf-etc/05/npt-PT-MaEn-out0.h5')\n", + "\n", + "bool_array = ((univ.data['CV1'] > cv1_cuts[0]) & (univ.data['CV1'] < cv1_cuts[1]) \n", + " & (univ.data['CV2'] > cv2_cuts[0]) & (univ.data['CV2'] < cv2_cuts[1]))\n", + "num = len(univ.data[bool_array])\n", + "print('These cutoffs include {} frames.'.format(num))\n", + "\n", + "solutes = univ.select_atoms('resname is 3HT or resname is CIN or resname is TAD')\n", + "\n", + "with mda.Writer('minim-structs-'+name_set+'-rjm-PT-MaEn-0.xtc', \n", + " solutes.n_atoms) as W:\n", + " for ts in univ.trajectory:\n", + " if bool_array[univ.trajectory.frame]:\n", + " W.write(solutes)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "hidden": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "These cutoffs include 54987 frames.\n" + ] + } + ], + "source": [ + "cv1_cuts = [1.5, 4.]\n", + "cv2_cuts = [6.75, 8.5]\n", + "name_set = 'sCV1-lCV2'\n", + "\n", + "univ = ca.Taddol('solutes.gro', 'major-exo/13-3htmf-etc/05/pbc-MaEx-0.xtc')\n", + "try:\n", + " univ.data['CV1']\n", + "except KeyError:\n", + " univ.read_data(filename='major-exo/13-3htmf-etc/05/npt-PT-MaEx-out0.h5')\n", + "\n", + "bool_array = ((univ.data['CV1'] > cv1_cuts[0]) & (univ.data['CV1'] < cv1_cuts[1]) \n", + " & (univ.data['CV2'] > cv2_cuts[0]) & (univ.data['CV2'] < cv2_cuts[1]))\n", + "num = len(univ.data[bool_array])\n", + "print('These cutoffs include {} frames.'.format(num))\n", + "\n", + "solutes = univ.select_atoms('resname is 3HT or resname is CIN or resname is TAD')\n", + "\n", + "with mda.Writer('minim-structs-'+name_set+'-rjm-PT-MaEx-0.xtc', \n", + " solutes.n_atoms) as W:\n", + " for ts in univ.trajectory:\n", + " if bool_array[univ.trajectory.frame]:\n", + " W.write(solutes)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "hidden": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "These cutoffs include 133980 frames.\n" + ] + } + ], + "source": [ + "cv1_cuts = [6.5, 9.]\n", + "cv2_cuts = [1.5, 3.]\n", + "name_set = 'lCV1-sCV2'\n", + "\n", + "univ = ca.Taddol('solutes.gro', 'major-exo/13-3htmf-etc/05/pbc-MaEx-0.xtc')\n", + "try:\n", + " univ.data['CV1']\n", + "except KeyError:\n", + " univ.read_data(filename='major-exo/13-3htmf-etc/05/npt-PT-MaEx-out0.h5')\n", + "\n", + "bool_array = ((univ.data['CV1'] > cv1_cuts[0]) & (univ.data['CV1'] < cv1_cuts[1]) \n", + " & (univ.data['CV2'] > cv2_cuts[0]) & (univ.data['CV2'] < cv2_cuts[1]))\n", + "num = len(univ.data[bool_array])\n", + "print('These cutoffs include {} frames.'.format(num))\n", + "\n", + "solutes = univ.select_atoms('resname is 3HT or resname is CIN or resname is TAD')\n", + "\n", + "with mda.Writer('minim-structs-'+name_set+'-rjm-PT-MaEx-0.xtc', \n", + " solutes.n_atoms) as W:\n", + " for ts in univ.trajectory:\n", + " if bool_array[univ.trajectory.frame]:\n", + " W.write(solutes)\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "hidden": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "500001" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(univ.data)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true, + "hidden": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": { + "heading_collapsed": true + }, + "source": [ + "# g(R)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true, + "hidden": true + }, + "outputs": [], + "source": [ + "univ = ca.Taddol('solutes.gro', 'major-endo/13-3htmf-etc/05/pbc-MaEn-0.xtc')\n", + "\n", + "reactants = univ.select_atoms('resname is 3HT or resname in CIN')\n", + "catalyst = univ.select_atoms('resname in TAD')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "heading_collapsed": true, + "hidden": true + }, + "source": [ + "## g(R) class" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "heading_collapsed": true, + "hidden": true + }, + "source": [ + "### General Test Classes" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "hidden": true + }, + "outputs": [ + { + "ename": "TypeError", + "evalue": "object.__init__() takes no parameters", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[0;32mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 13\u001b[0;31m \u001b[0mtc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0muniv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrajectory\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'hey'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, traj, b)\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0;32mclass\u001b[0m \u001b[0mtc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mAnalysisBase\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtraj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtraj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 11\u001b[0m \u001b[0;32mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mTypeError\u001b[0m: object.__init__() takes no parameters" + ] + } + ], + "source": [ + "from __future__ import division, absolute_import\n", + "\n", + "\n", + "from MDAnalysis.lib.util import blocks_of\n", + "from MDAnalysis.lib import distances\n", + "from MDAnalysis.analysis.base import AnalysisBase\n", + "\n", + "class tc(AnalysisBase):\n", + " def __init__(self, traj, b):\n", + " super(tc, self).__init__(traj)\n", + " print(b)\n", + " \n", + "tc(univ.trajectory, 'hey')" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "collapsed": true, + "hidden": true + }, + "outputs": [], + "source": [ + "class base(object):\n", + " def __init__(self, a):\n", + " print('base var is {}'.format(a))\n", + " \n", + "class child(base):\n", + " def __init__(self, a, b):\n", + " super(child, self).__init__(a)\n", + " print('child var is {}'.format(b))" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "hidden": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "base var is 1st\n", + "child var is 2nd\n" + ] + }, + { + "data": { + "text/plain": [ + "<__main__.child at 0x7ff07b8e0390>" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "child('1st', '2nd')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "heading_collapsed": true, + "hidden": true + }, + "source": [ + "### AnalysisBase" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "collapsed": true, + "hidden": true + }, + "outputs": [], + "source": [ + "from __future__ import absolute_import\n", + "import six\n", + "from six.moves import range, zip\n", + "import inspect\n", + "import logging\n", + "\n", + "import numpy as np\n", + "from MDAnalysis import coordinates\n", + "from MDAnalysis.core import AtomGroup\n", + "from MDAnalysis.lib.log import ProgressMeter#, _set_verbose\n", + "\n", + "logger = logging.getLogger(__name__)\n", + "\n", + "\n", + "class AnalysisBase(object):\n", + " \"\"\"Base class for defining multi frame analysis\n", + " The class it is designed as a template for creating multiframe analyses.\n", + " This class will automatically take care of setting up the trajectory\n", + " reader for iterating, and it offers to show a progress meter.\n", + " To define a new Analysis, `AnalysisBase` needs to be subclassed\n", + " `_single_frame` must be defined. It is also possible to define\n", + " `_prepare` and `_conclude` for pre and post processing. See the example\n", + " below.\n", + " .. code-block:: python\n", + " class NewAnalysis(AnalysisBase):\n", + " def __init__(self, atomgroup, parameter, **kwargs):\n", + " super(NewAnalysis, self).__init__(atomgroup.universe.trajectory,\n", + " **kwargs)\n", + " self._parameter = parameter\n", + " self._ag = atomgroup\n", + " def _prepare(self):\n", + " # OPTIONAL\n", + " # Called before iteration on the trajectory has begun.\n", + " # Data structures can be set up at this time\n", + " self.result = []\n", + " def _single_frame(self):\n", + " # REQUIRED\n", + " # Called after the trajectory is moved onto each new frame.\n", + " # store result of `some_function` for a single frame\n", + " self.result.append(some_function(self._ag, self._parameter))\n", + " def _conclude(self):\n", + " # OPTIONAL\n", + " # Called once iteration on the trajectory is finished.\n", + " # Apply normalisation and averaging to results here.\n", + " self.result = np.asarray(self.result) / np.sum(self.result)\n", + " Afterwards the new analysis can be run like this.\n", + " .. code-block:: python\n", + " na = NewAnalysis(u.select_atoms('name CA'), 35).run()\n", + " print(na.result)\n", + " \"\"\"\n", + "\n", + " def __init__(self, trajectory, start=None,\n", + " stop=None, step=None, verbose=None, quiet=None):\n", + " \"\"\"\n", + " Parameters\n", + " ----------\n", + " trajectory : mda.Reader\n", + " A trajectory Reader\n", + " start : int, optional\n", + " start frame of analysis\n", + " stop : int, optional\n", + " stop frame of analysis\n", + " step : int, optional\n", + " number of frames to skip between each analysed frame\n", + " verbose : bool, optional\n", + " Turn on verbosity\n", + " \"\"\"\n", + "# self._verbose = _set_verbose(verbose, quiet, default=False)\n", + "# self._quiet = not self._verbose\n", + " self._setup_frames(trajectory, start, stop, step)\n", + "\n", + " def _setup_frames(self, trajectory, start=None, stop=None, step=None):\n", + " \"\"\"\n", + " Pass a Reader object and define the desired iteration pattern\n", + " through the trajectory\n", + " Parameters\n", + " ----------\n", + " trajectory : mda.Reader\n", + " A trajectory Reader\n", + " start : int, optional\n", + " start frame of analysis\n", + " stop : int, optional\n", + " stop frame of analysis\n", + " step : int, optional\n", + " number of frames to skip between each analysed frame\n", + " \"\"\"\n", + " self._trajectory = trajectory\n", + " start, stop, step = trajectory.check_slice_indices(start, stop, step)\n", + " self.start = start\n", + " self.stop = stop\n", + " self.step = step\n", + " self.n_frames = len(range(start, stop, step))\n", + " interval = int(self.n_frames // 100)\n", + " if interval == 0:\n", + " interval = 1\n", + "\n", + " # ensure _verbose is set when __init__ wasn't called, this is to not\n", + " # break pre 0.16.0 API usage of AnalysisBase\n", + " if not hasattr(self, '_verbose'):\n", + " if hasattr(self, '_quiet'):\n", + " # Here, we are in the odd case where a children class defined\n", + " # self._quiet without going through AnalysisBase.__init__.\n", + " warnings.warn(\"The *_quiet* attribute of analyses is \"\n", + " \"deprecated (from 0.16)use *_verbose* instead.\",\n", + " DeprecationWarning)\n", + " self._verbose = not self._quiet\n", + " else:\n", + " self._verbose = True\n", + " self._quiet = not self._verbose\n", + " self._pm = ProgressMeter(self.n_frames if self.n_frames else 1,\n", + " interval=interval#, verbose=self._verbose\n", + " )\n", + "\n", + " def _single_frame(self):\n", + " \"\"\"Calculate data from a single frame of trajectory\n", + " Don't worry about normalising, just deal with a single frame.\n", + " \"\"\"\n", + " raise NotImplementedError(\"Only implemented in child classes\")\n", + "\n", + " def _prepare(self):\n", + " \"\"\"Set things up before the analysis loop begins\"\"\"\n", + " pass\n", + "\n", + " def _conclude(self):\n", + " \"\"\"Finalise the results you've gathered.\n", + " Called at the end of the run() method to finish everything up.\n", + " \"\"\"\n", + " pass\n", + "\n", + " def run(self):\n", + " \"\"\"Perform the calculation\"\"\"\n", + " logger.info(\"Starting preparation\")\n", + " self._prepare()\n", + " for i, ts in enumerate(\n", + " self._trajectory[self.start:self.stop:self.step]):\n", + " self._frame_index = i\n", + " self._ts = ts\n", + " # logger.info(\"--> Doing frame {} of {}\".format(i+1, self.n_frames))\n", + " self._single_frame()\n", + " self._pm.echo(self._frame_index)\n", + " logger.info(\"Finishing up\")\n", + " self._conclude()\n", + " return self" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "heading_collapsed": true, + "hidden": true + }, + "source": [ + "### singleRDF" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "collapsed": true, + "hidden": true + }, + "outputs": [], + "source": [ + "from __future__ import division, absolute_import\n", + "\n", + "\n", + "from MDAnalysis.lib.util import blocks_of\n", + "from MDAnalysis.lib import distances\n", + "# from MDAnalysis.analysis.base import AnalysisBase\n", + "\n", + "class singleRDF(AnalysisBase):\n", + " \n", + " def __init__(self, g1, g2, nbins=75, range=(0.0, 15.0), exclusion_block=None, **kwargs):\n", + " super(singleRDF, self).__init__(g1.universe.trajectory, **kwargs)\n", + "# super(InterRDF, self).__init__()\n", + "# AnalysisBase.__init__(g1.universe.trajectory, **kwargs)\n", + " self.g1 = g1\n", + " self.g2 = g2\n", + " self.u = g1.universe\n", + "# self._trajectory = self.u.trajectory\n", + "\n", + " self.rdf_settings = {'bins': nbins,\n", + " 'range': range}\n", + " self._exclusion_block = exclusion_block\n", + "\n", + " def _prepare(self):\n", + " # Empty histogram to store the RDF\n", + " count, edges = np.histogram([-1], **self.rdf_settings)\n", + " count = count.astype(np.float64)\n", + " count *= 0.0\n", + " self.count = count\n", + " self.edges = edges\n", + " self.bins = 0.5 * (edges[:-1] + edges[1:])\n", + "\n", + " # Need to know average volume\n", + " self.volume = 0.0\n", + "\n", + " # Allocate a results array which we will reuse\n", + " self._result = np.zeros((1,), dtype=np.float64)\n", + " # If provided exclusions, create a mask of _result which\n", + " # lets us take these out\n", + " if self._exclusion_block is not None:\n", + " self._exclusion_mask = blocks_of(self._result,\n", + " *self._exclusion_block)\n", + " self._maxrange = self.rdf_settings['range'][1] + 1.0\n", + " else:\n", + " self._exclusion_mask = None\n", + "\n", + " def _single_frame(self):\n", + " mda.lib.distances.calc_bonds(np.array([self.g1.center_of_mass()], dtype='float32'),\n", + " np.array([self.g2.center_of_mass()], dtype='float32'),\n", + " box=self.u.dimensions, result=self._result)\n", + " # Maybe exclude same molecule distances\n", + " if self._exclusion_mask is not None:\n", + " self._exclusion_mask[:] = self._maxrange\n", + "\n", + " count = np.histogram(self._result, **self.rdf_settings)[0]\n", + " self.count += count\n", + "\n", + " self.volume += self._ts.volume\n", + "\n", + " def _conclude(self):\n", + " # Number of each selection\n", + " nA = 1\n", + " nB = 1\n", + " N = nA * nB\n", + "\n", + " # If we had exclusions, take these into account\n", + " if self._exclusion_block:\n", + " xA, xB = self._exclusion_block\n", + " nblocks = nA / xA\n", + " N -= xA * xB * nblocks\n", + "\n", + " # Volume in each radial shell\n", + " vol = np.power(self.edges[1:], 3) - np.power(self.edges[:-1], 3)\n", + " vol *= 4/3.0 * np.pi\n", + "\n", + " # Average number density\n", + " box_vol = self.volume / self.n_frames\n", + " density = N / box_vol\n", + "\n", + " rdf = self.count / (density * vol * self.n_frames)\n", + "\n", + " self.rdf = rdf" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "heading_collapsed": true, + "hidden": true + }, + "source": [ + "### Original InterRDF" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": true, + "hidden": true + }, + "outputs": [], + "source": [ + "class InterRDF(AnalysisBase):\n", + " \"\"\"Intermolecular pair distribution function\n", + " InterRDF(g1, g2, nbins=75, range=(0.0, 15.0))\n", + " Arguments\n", + " ---------\n", + " g1 : AtomGroup\n", + " First AtomGroup\n", + " g2 : AtomGroup\n", + " Second AtomGroup\n", + " nbins : int (optional)\n", + " Number of bins in the histogram [75]\n", + " range : tuple or list (optional)\n", + " The size of the RDF [0.0, 15.0]\n", + " exclusion_block : tuple (optional)\n", + " A tuple representing the tile to exclude from the distance\n", + " array. [None]\n", + " start : int (optional)\n", + " The frame to start at (default is first)\n", + " stop : int (optional)\n", + " The frame to end at (default is last)\n", + " step : int (optional)\n", + " The step size through the trajectory in frames (default is\n", + " every frame)\n", + " Example\n", + " -------\n", + " First create the :class:`InterRDF` object, by supplying two\n", + " AtomGroups then use the :meth:`run` method ::\n", + " rdf = InterRDF(ag1, ag2)\n", + " rdf.run()\n", + " Results are available through the :attr:`bins` and :attr:`rdf`\n", + " attributes::\n", + " plt.plot(rdf.bins, rdf.rdf)\n", + " The `exclusion_block` keyword allows the masking of pairs from\n", + " within the same molecule. For example, if there are 7 of each\n", + " atom in each molecule, the exclusion mask `(7, 7)` can be used.\n", + " .. versionadded:: 0.13.0\n", + " \"\"\"\n", + " def __init__(self, g1, g2,\n", + " nbins=75, range=(0.0, 15.0), exclusion_block=None,\n", + " **kwargs):\n", + " super(InterRDF, self).__init__(g1.universe.trajectory, **kwargs)\n", + " self.g1 = g1\n", + " self.g2 = g2\n", + " self.u = g1.universe\n", + "\n", + " self.rdf_settings = {'bins': nbins,\n", + " 'range': range}\n", + " self._exclusion_block = exclusion_block\n", + "\n", + " def _prepare(self):\n", + " # Empty histogram to store the RDF\n", + " count, edges = np.histogram([-1], **self.rdf_settings)\n", + " count = count.astype(np.float64)\n", + " count *= 0.0\n", + " self.count = count\n", + " self.edges = edges\n", + " self.bins = 0.5 * (edges[:-1] + edges[1:])\n", + "\n", + " # Need to know average volume\n", + " self.volume = 0.0\n", + "\n", + " # Allocate a results array which we will reuse\n", + " self._result = np.zeros((len(self.g1), len(self.g2)), dtype=np.float64)\n", + " # If provided exclusions, create a mask of _result which\n", + " # lets us take these out\n", + " if self._exclusion_block is not None:\n", + " self._exclusion_mask = blocks_of(self._result,\n", + " *self._exclusion_block)\n", + " self._maxrange = self.rdf_settings['range'][1] + 1.0\n", + " else:\n", + " self._exclusion_mask = None\n", + "\n", + " def _single_frame(self):\n", + " distances.distance_array(self.g1.positions, self.g2.positions,\n", + " box=self.u.dimensions, result=self._result)\n", + " # Maybe exclude same molecule distances\n", + " if self._exclusion_mask is not None:\n", + " self._exclusion_mask[:] = self._maxrange\n", + "\n", + " count = np.histogram(self._result, **self.rdf_settings)[0]\n", + " self.count += count\n", + "\n", + " self.volume += self._ts.volume\n", + "\n", + " def _conclude(self):\n", + " # Number of each selection\n", + " nA = len(self.g1)\n", + " nB = len(self.g2)\n", + " N = nA * nB\n", + "\n", + " # If we had exclusions, take these into account\n", + " if self._exclusion_block:\n", + " xA, xB = self._exclusion_block\n", + " nblocks = nA / xA\n", + " N -= xA * xB * nblocks\n", + "\n", + " # Volume in each radial shell\n", + " vol = np.power(self.edges[1:], 3) - np.power(self.edges[:-1], 3)\n", + " vol *= 4/3.0 * np.pi\n", + "\n", + " # Average number density\n", + " box_vol = self.volume / self.n_frames\n", + " density = N / box_vol\n", + "\n", + " rdf = self.count / (density * vol * self.n_frames)\n", + "\n", + " self.rdf = rdf" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "heading_collapsed": true, + "hidden": true + }, + "source": [ + "### Test it out" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "collapsed": true, + "hidden": true + }, + "outputs": [], + "source": [ + "rcrdf = singleRDF(reactants, catalyst)" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "hidden": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Step 500001/500001 [100.0%]\n" + ] + }, + { + "data": { + "text/plain": [ + "<__main__.singleRDF at 0x7ff07b8e0150>" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rcrdf.run()" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": { + "hidden": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nKNBFRFKEAl1EJEUo0EVEUsT/B4tT4/e2p7EDAAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(rcrdf.bins, rcrdf.rdf)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "heading_collapsed": true, + "hidden": true + }, + "source": [ + "## Sandbox" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "hidden": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 16.76177446, 16.50244847, 16.50584995])" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "solutes.center_of_mass()" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": { + "hidden": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 12.56000042, 18.52000046, 16.40999985],\n", + " [ 11.56000042, 17.28000069, 16. ]], dtype=float32)" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "univ.atoms[0:2].positions" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": true, + "hidden": true + }, + "outputs": [], + "source": [ + "reactants = univ.select_atoms('resname is 3HT or resname in CIN')\n", + "catalyst = univ.select_atoms('resname in TAD')" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": { + "hidden": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 24.56289245 15.61536654 16.61624764]\n", + "[ 24.14600269 15.43795875 16.02795708]\n", + "[ 23.6555312 15.40071902 15.20888833]\n", + "[ 24.03353669 14.90177947 15.56263439]\n", + "[ 24.07699284 14.99378214 14.78060282]\n", + "[ 23.96306046 14.42012566 14.93890745]\n", + "[ 23.49715232 15.04642216 14.85584878]\n", + "[ 23.55577468 14.61994775 14.94998444]\n", + "[ 24.09964669 15.09156878 14.67031495]\n", + "[ 23.61167564 15.88590142 14.54943811]\n", + "[ 24.03531234 15.84271269 14.21178254]\n" + ] + } + ], + "source": [ + "for ts in univ.trajectory:\n", + " if univ.trajectory.frame > 10:\n", + " break\n", + " else:\n", + " print(reactants.center_of_mass())" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": { + "collapsed": true, + "hidden": true + }, + "outputs": [], + "source": [ + "dists = np.zeros([univ.trajectory.n_frames, 1])\n", + "\n", + "for ts in univ.trajectory:\n", + " mda.lib.distances.calc_bonds(np.array([reactants.center_of_mass()], dtype='float32'),\n", + " np.array([catalyst.center_of_mass()], dtype='float32'),\n", + " box=univ.dimensions,\n", + " result=dists[univ.trajectory.frame])" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": { + "collapsed": true, + "hidden": true + }, + "outputs": [], + "source": [ + "rcrdf = mdardf.InterRDF(reactants, catalyst)" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": { + "collapsed": true, + "hidden": true + }, + "outputs": [], + "source": [ + "rcrdf.run()" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": { + "hidden": true + }, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAD8CAYAAACMwORRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(rcrdf.bins, rcrdf.rdf)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "hidden": true + }, + "source": [ + "## Production" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "heading_collapsed": true, + "hidden": true + }, + "source": [ + "### Center of mass $g(R)$" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "hidden": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Now starting on MaEx 0...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Step 500001/500001 [100.0%]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Now starting on MiEn 0...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Step 500001/500001 [100.0%]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Now starting on MiEx 0...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Step 500001/500001 [100.0%]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Now starting on MaEn 0...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Step 500001/500001 [100.0%]\n" + ] + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nnVXo9QAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nnVXo9QAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots()\n", + "\n", + "for key in configs:\n", + " with cd(configs[key]):\n", + " i = 0\n", + " print 'Now starting on {} {}...'.format(key, i)\n", + " univ = ca.Taddol('../../../solutes.gro', \n", + " 'npt-PT-{}-out{}.xtc'.format(key, i))\n", + " temp = 205\n", + " final_time = int(univ.data['Time'].iat[-1]/1000)\n", + " if final_time > 1000:\n", + " final_time = str(final_time/1000)+'us'\n", + " else:\n", + " final_time = str(final_time) + 'ns'\n", + " file_name_end = '-rjm-PT-{}-{}-{}.pdf'.format(key, i, final_time)\n", + " \n", + " reactants = univ.select_atoms('resname is 3HT or resname in CIN')\n", + " catalyst = univ.select_atoms('resname in TAD')\n", + " \n", + " rcrdf = singleRDF(reactants, catalyst)\n", + " rcrdf.run()\n", + " \n", + " ax.plot(rcrdf.bins, rcrdf.rdf, label=key)\n", + "\n", + "ax.legend()\n", + "fig" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": { + "collapsed": true, + "hidden": true + }, + "outputs": [], + "source": [ + "fig.savefig('g-of-r-rjm-PT-comb-0.pdf')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "heading_collapsed": true, + "hidden": true + }, + "source": [ + "### Intermolecular O-O $g(R)$" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": { + "hidden": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "univ.select_atoms('resname is 3HT or resname in CIN and name is O*')" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": { + "hidden": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "univ.select_atoms('resname in TAD and name is O*')" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": { + "hidden": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Now starting on MaEx 0...\n", + "Now starting on MiEn 0...\n", + "Now starting on MiEx 0...\n", + "Now starting on MaEn 0...\n" + ] + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", + "text/plain": [ + "" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots()\n", + "\n", + "bins_all_Os = {}\n", + "rdfs_all_Os = {}\n", + "\n", + "for key in sorted(configs):\n", + " with cd(configs[key]):\n", + " i = 0\n", + " print 'Now starting on {} {}...'.format(key, i)\n", + " univ = ca.Taddol('../../../solutes.gro', \n", + " 'npt-PT-{}-out{}.xtc'.format(key, i))\n", + " temp = 205\n", + " final_time = int(univ.data['Time'].iat[-1]/1000)\n", + " if final_time > 1000:\n", + " final_time = str(final_time/1000)+'us'\n", + " else:\n", + " final_time = str(final_time) + 'ns'\n", + " file_name_end = '-rjm-PT-{}-{}-{}.pdf'.format(key, i, final_time)\n", + " \n", + " reactant_Os = univ.select_atoms('resname is 3HT or resname in CIN and name is O*')\n", + " catalyst_Os = univ.select_atoms('resname in TAD and name is O*')\n", + " \n", + " rcrdf = mdardf.InterRDF(reactant_Os, catalyst_Os)\n", + " rcrdf.run()\n", + " \n", + " bins_all_Os[key] = rcrdf.bins\n", + " rdfs_all_Os[key] = rcrdf.rdf\n", + " \n", + " ax.plot(rcrdf.bins, rcrdf.rdf, label=key)\n", + "\n", + "ax.legend()\n", + "fig" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": { + "collapsed": true, + "hidden": true + }, + "outputs": [], + "source": [ + "fig.savefig('g-of-r-Os-rjm-PT-comb-0.pdf')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "heading_collapsed": true, + "hidden": true + }, + "source": [ + "### CV oxygen $g(R)$" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": { + "hidden": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "univ.select_atoms('(resname is 3HT) and (name is O or name is OH)')" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": { + "hidden": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "univ.select_atoms('resname in TAD and (name is OH or name is O1)')" + ] + }, + { + "cell_type": "code", + "execution_count": 111, + "metadata": { + "hidden": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Now starting on MaEn 0...\n", + "Now starting on MaEx 0...\n", + "Now starting on MiEn 0...\n", + "Now starting on MiEx 0...\n" + ] + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAD8CAYAAAB5Pm/hAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQmCC\n", + "text/plain": [ + "" + ] + }, + "execution_count": 111, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAD8CAYAAAB5Pm/hAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "name_gro = 'solutes.gro'\n", + "\n", + "name_gro = os.path.abspath(name_gro)\n", + "\n", + "fig, ax = plt.subplots()\n", + "\n", + "bins_CV_Os = {}\n", + "rdfs_CV_Os = {}\n", + "\n", + "for key in sorted(configs):\n", + " with cd(configs[key]):\n", + " i = 0\n", + " print 'Now starting on {} {}...'.format(key, i)\n", + " name_xtc = 'npt-PT-{}-out{}.xtc'.format(key, i)\n", + " univ = ca.Taddol(name_gro, name_xtc)\n", + " temp = 205\n", + " final_time = int(univ.data['Time'].iat[-1]/1000)\n", + " if final_time > 1000:\n", + " final_time = str(final_time/1000)+'us'\n", + " else:\n", + " final_time = str(final_time) + 'ns'\n", + " file_name_end = '-rjm-PT-{}-{}-{}.pdf'.format(key, i, final_time)\n", + " \n", + " reactant_CV_Os = univ.select_atoms('(resname is 3HT) and (name is O or name is OH)')\n", + " catalyst_CV_Os = univ.select_atoms('resname in TAD and (name is OH or name is O1)')\n", + " \n", + " rcrdf = mdardf.InterRDF(reactant_CV_Os, catalyst_CV_Os, range=(2.0, 12.0))\n", + " rcrdf.run()\n", + " \n", + " bins_CV_Os[key] = rcrdf.bins\n", + " rdfs_CV_Os[key] = rcrdf.rdf\n", + " \n", + " ax.plot(rcrdf.bins, rcrdf.rdf, label=key)\n", + "\n", + "ax.legend()\n", + "fig" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": { + "collapsed": true, + "hidden": true + }, + "outputs": [], + "source": [ + "fig.savefig('g-of-r-CV-Os-rjm-PT-comb-0.pdf')" + ] + }, + { + "cell_type": "code", + "execution_count": 113, + "metadata": { + "hidden": true + }, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYwAAAEPCAYAAABRHfM8AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots()\n", + "for key in sorted(rdfs_CV_Os):\n", + " bins = bins_CV_Os[key]\n", + " rdfs = rdfs_CV_Os[key]\n", + "\n", + " ax.plot(bins, rdfs, label=key)\n", + "\n", + "ax.legend()\n", + "ax.set_ylabel(r'$g(r)$')\n", + "ax.set_xlabel('distance / $\\mathrm{\\AA}$')\n", + "ax.set_xlim([2.2,9.5])\n", + "ax.set_ylim([-0.2,150])\n", + "fig.savefig('g-of-r-CV-Os-rjm-PT-comb-0.pdf')" + ] + }, + { + "cell_type": "code", + "execution_count": 116, + "metadata": { + "hidden": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 116, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYgAAAEPCAYAAABY9lNGAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "r = 0.0019872\n", + "temp = 205\n", + "\n", + "g_CV_Os = {}\n", + "for key in rdfs_CV_Os:\n", + " rdfs = rdfs_CV_Os[key]\n", + " g_CV_Os[key] = - r * temp * np.log(rdfs + 1e-40)\n", + "min_g = min([min(gs) for gs in g_CV_Os.values()])\n", + "for key in g_CV_Os:\n", + " g_CV_Os[key] = g_CV_Os[key] - min_g\n", + "\n", + "fig, ax = plt.subplots()\n", + "\n", + "for key in sorted(g_CV_Os):\n", + " ax.plot(bins_CV_Os[key], g_CV_Os[key], label=key)\n", + " ax.set_xlim([2.4,9.9])\n", + " ax.set_ylim([-0.1,2.7])\n", + "ax.legend()\n", + "ax.set_ylabel(r'$\\Delta G$ / (kcal / mol)')\n", + "ax.set_xlabel('distance / $\\mathrm{\\AA}$')" + ] + }, + { + "cell_type": "code", + "execution_count": 117, + "metadata": { + "collapsed": true, + "hidden": true + }, + "outputs": [], + "source": [ + "fig.savefig('fes-g-of-r-CV-Os-rjm-PT-comb-0.pdf')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# CVs: h-bonds in/out" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "All below in VMD numbering:\n", + "Looking at a \"single open\" structure\n", + "The out-of-pocket OH\n", + "* O: 8\n", + "* H: 9\n", + "\n", + "The in-pocket TAD OH\n", + "* O: 6\n", + "* H: 7\n", + "\n", + "3-HTMF\n", + "* H: 159\n", + "* ketone O: 132\n", + "\n", + "Based on the plots [in this notebook](/notebooks/implicit_test/pt_impl/taddol/7_pt1_16walkers_wider1_longer/PCA_on_internal_coords_work.ipynb#oxygen-distances-correlations), it seems that $4 \\,\\mathrm{\\AA}$ is a reasonable cutoff for in-pocket vs. out-of-pocket for O(l or r)-Cy distances (less than 4 is in-pocket)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "name_gro = 'solutes.gro'\n", + "name_xtc = 'major-endo/13-3htmf-etc/05/npt-PT-MaEn-out0.xtc'\n", + "\n", + "dict_dists = {'OlH': (9, 160), 'HlO': (10, 133), 'OrH': (7, 160), 'HrO': (8, 133)}\n", + "\n", + "univ = ca.Taddol(name_gro, name_xtc, format='xtc')\n", + "try:\n", + " univ.read_data()\n", + " univ.calculate_distances(**dict_dists)\n", + "except IOError:\n", + " univ.calculate_distances('all', **dict_dists)\n", + " univ.calc_open_closed() # Good to have this saved if I can anyway\n", + " univ.save_data()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "olo = univ.data[lambda x: x['O(l)-Cy'] < 4][['OlH', 'HlO']].min(axis=1)\n", + "oli = univ.data[lambda x: x['O(l)-Cy'] > 4][['OlH', 'HlO']].min(axis=1)\n", + "oro = univ.data[lambda x: x['O(r)-Cy'] < 4][['OrH', 'HrO']].min(axis=1)\n", + "ori = univ.data[lambda x: x['O(r)-Cy'] > 4][['OrH', 'HrO']].min(axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYcAAAEICAYAAAC0+DhzAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nIElqMRwkSS3/D9pHiabHXSO/AAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dists_to_plot = (olo, oli, oro, ori)\n", + "dict_dists_to_plot = {'olo': olo, 'oli': oli, 'oro': oro, 'ori': ori}\n", + "\n", + "fig, axes = plt.subplots(2,2, sharex=True, sharey=True)\n", + "for i, key in enumerate(dict_dists_to_plot):\n", + " ax = axes.flat[i]\n", + " ax.hist(dict_dists_to_plot[key])\n", + " ax.set_title(key)\n", + "fig" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "fig.savefig('rough-hist-new-CVs-rjm-PT-MaEn-0.pdf')" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAagAAAEYCAYAAAAJeGK1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n/w8v9VXXfmgNMgAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "temp = 205\n", + "dict_data = dict_dists_to_plot\n", + "\n", + "fig, axes = plt.subplots(2, 2, sharex=True, sharey=True)\n", + "r = 0.0019872 # kcal_th/(K mol)\n", + "delta_gs = []\n", + "handles = []\n", + "# Use whatever the default colors for the system are\n", + "# TODO find a more elegant way to do this\n", + "colors = mpl.rcParams['axes.prop_cycle'].by_key().values()[0]\n", + "for i, key in enumerate(dict_data):\n", + " if len(dict_data[key]) > 50:\n", + " n, bins = np.histogram(dict_data[key])\n", + " n = [float(j) for j in n]\n", + " # TODO find better way to account for zeros here rather than\n", + " # just adding a small amount to each.\n", + " prob = np.array([j / max(n) for j in n]) + 1e-40\n", + " delta_g = np.array([-r * temp * np.log(p) for p in prob])\n", + " delta_gs.append(delta_g)\n", + " ax = axes.flat[i]\n", + " _bins = ca.Taddol._running_mean(bins)\n", + " line, = ax.plot(bins[:-1], delta_g, colors[i])\n", + " handles.append(line)\n", + " ax.set_ylabel(r'$\\Delta G$ / (kcal / mol)')\n", + " ax.set_xlabel(r'distance / $\\mathrm{\\AA}$')\n", + " ax.set_ylim(-0.1, 1.6)\n", + " ax.set_title(key)\n", + "# axes.flat[3].axis('off')\n", + "# axes.flat[3].legend(handles, ['O-O', 'O(l)-Cy', 'O(r)-Cy'],\n", + "# loc='center')\n", + "fig.tight_layout()\n", + "fig" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# cont" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python [conda env:py2.7]", + "language": "python", + "name": "conda-env-py2.7-py" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}