{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PBxplore API cookbook --- Visualize protein deformability"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Protein Blocks are great tools to study protein deformability. Indeed, if the block assigned to a residue changes between two frames of a trajectory, it represents a local deformation of the protein rather than the displacement of the residue.\n",
    "\n",
    "The PBxplore API allows to visualize Protein Block variability throughout a molecular dynamics simulation trajectory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/jon/dev/PBxplore\n"
     ]
    }
   ],
   "source": [
    "from __future__ import print_function, division\n",
    "from pprint import pprint\n",
    "from IPython.display import Image, display\n",
    "import matplotlib.pyplot as plt\n",
    "%cd ../../../\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pbxplore as pbx"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we will look at a molecular dynamics simulation of the barstar. As we will analyse Protein Block sequences, we first need to assign these sequences for each frame of the trajectory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Assign PB sequences for all frames of a trajectory\n",
    "trajectory = 'demo2/barstar_md_traj.xtc'\n",
    "topology = 'demo2/barstar_md_traj.gro'\n",
    "sequences = []\n",
    "for chain_name, chain in pbx.chains_from_trajectory(trajectory, topology):\n",
    "    dihedrals = chain.get_phi_psi_angles()\n",
    "    pb_seq = pbx.assign(dihedrals)\n",
    "    sequences.append(pb_seq)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Block occurences per position\n",
    "\n",
    "The basic information we need to analyse protein deformability is the count of occurences of each PB for each position throughout the trajectory. This occurence matrix can be calculated with the :func:`pbxplore.analysis.count_matrix` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "count_matrix = pbx.analysis.count_matrix(sequences)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "``count_matrix`` is a numpy array with one row per PB and one column per position. In each cell is the number of time a position was assigned to a PB.\n",
    "\n",
    "We can visualize ``count_matrix`` using Matplotlib as any 2D numpy array."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x10ac7bb50>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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hNG4bctkbYk+K2vAHgLOB/wKWlMebiojzImL/iDgAeBdwV0S8h60jV6CFkStmZp2QQy+L\nZh8Jg2y7iJKA3y83gAPavNdIWRfTxsgVM7NOyLoNOSL6qrpJRNxNMZt+2yNXLBfTpblh+jS9nPZv\nS5OuX7xl/+QYftD7h8llcNKFade/Frg6PYwcEvKE3d4knSZpj4b9PSR9sN6wzMw6axM7trzVRTHB\nksSSHoyIQ0Yde6BcqqS+wKSAp9IKOXPXtOuv+F7a9QDcUEEZOcilZpkaRx612ypIb0y6PuLOCoJI\nL+JDw2kP017Lobxzu7cQEZOORlJcGotbPv/PdGXS/cbTym9iO0nblZMvU85mtH3VgZiZdVMOTRat\nJOTbgeWS/o7i8/B/Av9Ua1RmZh02Vfohnw38EfDH5f4AxVDo2n1n+Dmz2bXl4N73JF2/3eMfSroe\nYMs+yUVkIpev+rnE0X2VNDkkB5FexCd6Dky6/tQPzEkPgjz6IU8YQUQMS1oKfL089HBEDNcblplZ\nZ02JJoty2PMy4D/KQy+UtKjsylarQ3pPSitgggeWE/nzfXeb+KQJXMT5yWWYTWuxOrGAah5pTYmE\nDPwt8HsR8QiApHnAcsaYHd/MbKra1GRNvYlI2h/4DLA3RUPO30fEJxve/xDwcWCvcizGmFpJyL0j\nyRggItZK6n5ji5lZhRLbkDcDZ0XEA5J2AVZKGoiINWWyPoatrQzjaiWClZKuAa6n6GXxbuDbCYG3\nLLHFIdnH4qPJZZzPRclluNnDrH4pTRYRsQHYUL5+WtIaYB9gDUUrw58B/zhROa0k5D8GTgPOKPfv\nAT49iZjNzLJVVRuypD7gUOCbko4H1kfEd9TCJNit9LJ4BvhEuc0oUcEwJFXRL8jMaldFP+SyueIW\niqXrtgDnUTRX/OaUZtePm5AlNXv0GRFxcBtxmpllrVkb8o9XrOXHK5pPpSBpe+ALwPURcaukV1JM\n2vxgWTvej6IJ+LCIGHMe+GY15Lc2vbuZ2TTSrMli7wUHsveCrQNYVi356jbvq8i41wIPRcTlABGx\nmobJVyT9EJg/qV4WEbFu9DFJewE/iYlmJDIzm2KeTej2BrwBOAn4jqRV5bHzIqIxc0+YN5s1WbwO\n+Gvgp8BfUPSx2wvokfTeUTcyM5vSUtqQI+LrTDCdcUS8eKJymjVZXAWcC+wO3AUsjIj7JL2cYmBI\nSwlZ0jqKpZ+Ggc0RcVi5BNRNwIsoVw2JiJ+3Up6ZWR1ymMuiWUbviYg7IuLzwI8j4j6AiHiY9qYU\nCWBBRBwaEYeVx84BBiJiHnBnuW9m1jU5rKnXLCE3Jt1nEu8zuqvHcRTzY1D+PCGxfDOzJDkk5GZ1\n9IMl/aJ8vVPDayhWom5VAP8saRj4u4i4GpgdESPzKG6kmuUozMwmLev5kCOiqujeEBE/lvRbwICk\nh0fdJ4rlmsayouF1X7mZma0rN1i58pFmJ7Yshzbk2iOIiB+XP5+U9CXgMGCjpDkRsUHSXGDMTtKw\noO7wzGxK6mOkgjZ/fj+Dg8uTS0zs9laJCVedTiFpZ0m7lq+fD/wesBq4DVhUnrYIuLXOOMzMJjJE\nT8tbXequIc8GvlQOG+wFPhcRd0j6NnCzpFMou73VHIeZWVPTvskiIn4IvGqM4z8Fjq7z3rmoYurM\n1Ck8PX2n2cSmyoohZmbTnhOymVkmnJBngCrmVJ4uTQ5ePcVytokdux2CE7KZGbiGbDNMDrVb8YfJ\nZazh5cllvDyD34VtywnZzCwTWQ+dNjObSaZ9P2Sz3AQ3JJdxIB9NLsMPOPOTQ5NFrUOnzcymipTp\nNyUtlbSxcXFoSa+SdJ+kVZLul/SaiWJwDdmsTVFBPeZjJw0llzE8N+1/356Pu4bdaNOzSZMLXQdc\nSbHU3YhLgQsi4nZJby73j2xWiBOymRkwPDT5dBgR90jqG3V4C8USeACzgMcmKscJ2cwMGB6qvA35\nT4DbJf0NRfPw6ya6wAnZrG3tLCk5TgnXV/FQLz0O26qGhPxB4E8i4kuS3gEsBY5pdoETspkZMLR5\n/IQc9/4L8Y172i3yvRFxRvn6FuCaiS5wQjYzA7YMN0mHhx9VbCM+8detFPm4pCMi4m7gKGDtRBc4\nIZuZASQ0WUi6ETgC2EvSo8D5wKnAFZJ6gV8DfzRRObUnZEmzKKrqB1E0vr0f+B5wE/AiyhVDIuLn\ndcdiZjauZ5J6WZw4zluvbqecTgwMuQL4SkQcCBwMPAycAwxExDzgznLfzKx7htrYalL3Iqe7A78T\nEUsBImIoIp4CjgOWlactA06oMw4zswllkJDrbrI4AHhS0nXAIcBKir55syNiY3nORorFUM1mjPRl\nC+CsHdNKWbLprAqiuKyCMjJRY6JtVd0JuRfoB06PiPslXc6o5omICEnjdKhc0fC6r9zMzNaVG6xc\n+Ug1RW6uppgUdSfk9cD6iLi/3L8FOBfYIGlORGyQNBd4YuzLF9QcnplNTX2MVNDmz+9ncHB5epHD\n6UWkqjUhlwn3UUnzImItcDTw3XJbBFxS/rx17BISv9jtkjh5ytP/J+16oGiRMdtWFWst7rEpNYOk\njxbUARckl/GP3z866foXshtXX50cxoxosgBYDHxO0g7A9ym6vfUAN0s6hbLbWwfiMDMb3zPdDgAU\nked4+KJdOfXTN7UWksvvJu2ZpyqopV9VQW3qNE+oXrEc/r7Ta/rjPkJq0Qc+0M/VVx9PREw6GEnB\n/2sjjt9X0v3G45F6ZmYwY5oszMzy54Rct1yaHFKlNTlU8Vs4vYJ15D7lZo+K5fD3vXdyCVuHJHTZ\nDOj2ZmY2NUz3bm82fVTRTet0pdeyfzR0VdL1L+wdp4dlO+56Q3oZRy5JLyMLmdRuq+AmCzOzTGTQ\n7c0J2cwMXEOe2CsTr19dSRRWjSq6bb6o9/S0An4vvblBR6U3NwxdnP6As+ccP+CslBOymVkmnJAn\n4hqubSt5YOnt6TXT2CV9/obtz31FchnDJ6TNONBzq2vY28ig21snVgwxM8vfcBvbKJKWStooaXXD\nsY9LWiPpQUlfLBfsaMoJ2cwMil4WrW7PdR2wcNSxO4CDIuIQihWnz50ohMybLMyqVsHotqcraPZY\nmN5coL1yGKk3jSS0IUfEPZL6Rh0baNj9JvC2icpxQjYzg7rbkE8GbpzoJCdks7al10zjn9K7zk2X\nsX7ZaDZ0+tEVsH7FpIqV9BHg2Yi4YaJznZDNzKB5k8XcBcU24r7WPg4lvQ84FnhjK+fX+lBP0ssk\nrWrYnpJ0hqQ9JQ1IWivpDkmz6ozDzGxCQ21sLZC0EPgwcHxEtDQwu9aEHBGPRMShEXEoMB/4FfAl\nipWnByJiHnAno1aiNjPruM1tbKNIuhH4BvCych3Rk4ErgV2AgbJC+umJQuhkk8XRwL9HxKOSjgOO\nKI8vA1bgpGxm3bRp8pdGxIljHF7abjmdTMjvYutTxtmxdVbqjaQuGmdmlmqmDJ0uV5x+K3D26Pci\nIjTuKocrGl73lZuZ2bpyg5UrH6mmyAyGTneqhvxmYGVEPFnub5Q0JyI2SJoLPDH2ZQs6E52ZTTF9\njFTQ5s/vZ3BweXqRGawY0qmh0yeybafo24BF5etFQAXLOJiZJai4l8Vk1F5DlvR8igd6pzYcvhi4\nWdIpFN870qatMjNLNRPakCPil8Beo479lCJJm5nlYQa1IZuZ5S2h21tVnJDNzGBmNFmYmU0JbrIw\nM8tEBt3enJDNzMBNFmZm2XBCNjPLhNuQzcwy4W5vZmaZcJOFmVkm3GRhZpaJDLq9dWq2NzOzvCXO\n9iZplqRbJK2R9JCkw9sNwTVkMzOoog35CuArEfF2Sb3A89stwAnZzAyS2pAl7Q78TkQsAoiIIeCp\ndstxQjaboi7i/KTrz+eirseQlbQa8gHAk5KuAw4BVgJnRsSv2inEbchmZul6gX7g0xHRD/wSOGcy\nhdRG0lnAKUAAq4H3U7Sr3AS8iHK1kIj4eZ1xmJmlWcG2iy4/x3pgfUTcX+7fQk4JWdK+wGLgwIjY\nJOkm4F3AQcBARFwq6WyKoNsO3GymC5R0/cf4aHIMM6fZYwHbLrq8ZJt3ywWbH5U0LyLWUqyI9N12\n71J3G3IvsLOkYWBn4HHgXOCI8v1lFB87Tshm1mXJI0MWA5+TtAPwfYoWgbbUlpAj4jFJnwB+BPwa\nuD0iBiTNjoiN5Wkbgdl1xWBm40utYUM1tewLRtU229XPqVydHAWkPtWLiAeB16SUUWeTxR7AcUAf\nRfePz0s6qfGciAhJMX4pKxpe95Wbmc1068oN4JGVKysqtftjp+tssjga+GFE/ARA0heB1wEbJM0p\n21zmAk+MX8SCGsMzs6mqj63Vs/7581k+OFhBqb+uoIw0dSbk/wAOl7QT8AxFgv4WRXeQRcAl5c9b\nxyvgU4kPDE5LflhQRWvKxolPMZuiqmj2WMIFSdefSj9U0mgxjWvIEfEtSbcAgxSNM4PA3wO7AjdL\nOoWy21tdMZiZta7782/W2ssiIi4ELhx1+KcUteUJnZ5Yw0397F7DaYklwMt5OLkMuKGCMsysuWlc\nQzYzm1qmeQ3ZzGzqcA25qSb94TriwEpGMr08uYypMZLJbKqb3r0szMymEDdZZK2KLj3qej3fzFrj\nJgszs0y4hmxmlgnXkM3MMuEasplZJlxDNjPLhLu9mZllwjVkM7NMuA152kudWtDMOiWthixpIXA5\n0ANcExGXtFvGdkkRdNW6bgdQWtftAMgjBsgjjnXdDqC0rtsB4BjaNdTGti1JPcBVwELgt4ETJR3Y\nbgROyMnWdTsA8ogB8ohjXbcDKK3rdgA4hnZtbmN7jsOAf4+IdRGxGVgOHN9uBG6yMDMDEtuQ9wUe\nbdhfD7y23UKyTsj9/XPHfe/xx3dhn33Gf79TcogjhxhyiSOHGHKJY6bE8MIX7l5RSUnd3iqZtEYR\neU5+03w1ajOzbUXEpGcDm0y+abyfpMOBCyNiYbl/LrCl3Qd72SZkM7OpQlIv8AjwRuBxigWdT4yI\nNe2Uk3WThZnZVBARQ5JOB26n6PZ2bbvJGFxDNjPLxpTs9iZpoaSHJX1P0tlduP/+kr4m6buS/k3S\nGZ2OoSGWHkmrJH25izHMknSLpDWSHirb07oRx1nlv8dqSTdI2rED91wqaaOk1Q3H9pQ0IGmtpDsk\nzepSHB8v/00elPRFSVU9/Wo5hob3PiRpi6Q964xhqptyCbmqDtiJNgNnRcRBwOHAaV2IYcSZwEN0\ndwnCK4CvRMSBwMFA21/VUknaF1gMzI+IV1J8bXxXB259HcXfYqNzgIGImAfcWe53I447gIMi4hBg\nLXBuF2JA0v7AMcB/1Hz/KW/KJWQq6oCdIiI2RMQD5eunKRLQPp2MAUDSfsCxwDVQwXpTk4thd+B3\nImIpFG1pEfFUN2KheCayc/mAZWfgsbpvGBH3AD8bdfg4YFn5ehlwQjfiiIiBiNhS7n4T2K/TMZT+\nFvizOu89XUzFhDxWB+x9uxQLkvqAQyn+4DvtMuDDwJaJTqzRAcCTkq6TNCjpakk7dzqIiHgM+ATw\nI4qn3D+PiH/udByl2RGxsXy9EZjdpTganQx8pdM3lXQ8sD4ivtPpe09FUzEhZ/MUUtIuwC3AmWVN\nuZP3fgvwRESsoku141Iv0A98OiL6gV/Sma/o25C0B0XNtI/i28oukt7d6ThGi+KpeVf/ZiV9BHg2\nIm7o8H13Bs6DbWbY6ubfavamYkJ+DNi/YX9/ilpyR0naHvgCcH1E3Nrp+wOvB46T9EPgRuAoSZ/p\nQhzrKWpA95f7t1Ak6E47GvhhRPwkIoaAL1L8jrpho6Q5AJLmAk90KQ4kvY+iWasbH04vofiAfLD8\nO90PWClp7y7EMiVMxYT8beClkvok7QD8AXBbJwOQJOBa4KGIuLyT9x4REedFxP4RcQDFw6u7IuK9\nXYhjA/CopHnloaOB73Y6DooHRodL2qn89zma4mFnN9wGLCpfLwK68YE9Mh3kh4HjI+KZTt8/IlZH\nxOyIOKD8O10P9EdE1z6gcjflEnJZ+xnpgP0QcNNkOmAnegNwEnBk2eVsVfnH303d/Fq8GPicpAcp\neln8VacDiIhvUdTOB4GR9sq/r/u+km4EvgG8TNKjkt4PXAwcI2ktcFS53+k4TgauBHYBBsq/0U93\nKIZ5Db/jVcSfAAACHklEQVSLRtk0N+bKA0PMzDIx5WrIZmbTlROymVkmnJDNzDLhhGxmlgknZDOz\nTDghm5llwgnZaidpuOwHu1rSzZJ2avP6fSR9vnx9iKQ3N7z31m5MwWpWB/dDttpJ+kVE7Fq+vh5Y\nGRGXTbKs91FMsbm4whDNsuAasnXa14H/JmkPSbeWk6f/q6RXAkg6omH046Ck55fD5FeX84dcBPxB\n+f47Jb1P0pXltX2S7irL/OdyHl4k/YOkKyTdK+n7kt7Wtf96syackK1jynmKF1IMbb6IoqZ8CMWM\nYCMTI30I+GBEHAr8d+A3czCU819/FFgeEYdGxM1sOxz3SuC6sszPAZ9seG9ORLwBeAsdGMpsNhlO\nyNYJO0laBdxPMQnQUor5QD4LEBFfA14gaVfgXuAySYuBPSJieFRZYvwpHA8HRqaYvJ4ioUORtG8t\n77WGPOYnNnsOrzptnfDrssb7G8WEbM9JrBERl0j6v8DvA/dKehOwqY17jZesn23hHLOucg3ZuuUe\nyjl6JS0AnoyIpyW9JCK+GxGXUtSoXzbquv8Cdm3Yb0yu32DrOnrvBv6ljsDN6uKEbJ0wVleeC4H5\n5ZSdf8XW+YPPLB/gPUhRq/3qqDK+Bvz2yEO98vjIe4uB95fXvptiAdixYnDXIsuSu72ZmWXCNWQz\ns0w4IZuZZcIJ2cwsE07IZmaZcEI2M8uEE7KZWSackM3MMuGEbGaWif8PwnnS+HmZHLcAAAAASUVO\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1063e6350>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "im = plt.imshow(count_matrix, interpolation='none', aspect='auto')\n",
    "plt.colorbar(im)\n",
    "plt.xlabel('Position')\n",
    "plt.ylabel('Block')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "PBxplore provides the :func:`pbxplore.analysis.plot_map` function to ease the visualization of the occurence matrix."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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I3f69iogFeZSztnn8RudRiZmZmVmRXooh7W5CR2h05Q4zMzOz0ipseYku48DP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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x108eb2e90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pbx.analysis.plot_map('map.png', count_matrix)\n",
    "!rm map.png"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The :func:`pbxplore.analysis.plot_map` helper has a ``residue_min`` and a ``residue_max`` optional arguments to display only part of the matrix. These two arguments can be pass to all PBxplore functions that produce a figure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Y3oTsX5R0JLCweKriU1QpF4SZWScaJ0H0+dprAx8H1i+eFa7Zs8zxDsBmVm1tZkPrkrOB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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10add5b90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pbx.analysis.plot_map('map.png', count_matrix,\n",
    "                      residue_min=60, residue_max=70)\n",
    "!rm map.png"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that matrix in the the figure produced by :func:`pbxplore.analysis.plot_map` is normalized so as the sum of each column is 1. The matrix can be normalized with the :func:`pbxplore.analysis.compute_freq_matrix`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "freq_matrix = pbx.analysis.compute_freq_matrix(count_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x10adeadd0>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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18vmukpZIekDSLSOlrMzMuil14aO6dWJVutOB+3jpDpqzgCURsR9wa/nczKyrhuipvHVC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\njqsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10afdf3d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "im = plt.imshow(freq_matrix, interpolation='none', aspect='auto')\n",
    "plt.colorbar(im)\n",
    "plt.xlabel('Position')\n",
    "plt.ylabel('Block')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Protein Block entropy\n",
    "\n",
    "The $N_{eq}$ is a measure of variability based on the count matrix calculated above. It can be computed with the :func:`pbxplore.analysis.compute_neq` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "neq_by_position = pbx.analysis.compute_neq(count_matrix)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "``neq_by_position`` is a 1D numpy array with the $N_{eq}$ for each residue."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x10a8c4590>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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7jE4O8FeNRKI91N5AX3fddam277l0k5ktMgsj+5vZOYDVE4husH9/uPg3er51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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ab951d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(neq_by_position)\n",
    "plt.xlabel('Position')\n",
    "plt.ylabel('$N_{eq}$')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The :func:`pbxplore.analysis.plot_neq` helper ease the plotting of the $N_{eq}$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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2Mial1WNcLV1dOo5JbQopMUuikrJoERx11O63H3VUuE/i18lKStZDSidm9kSG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\nREREJJMUUkRERCSTFFJEREQkk/4/TC1hNyjSV+kAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10acaf710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pbx.analysis.plot_neq('neq.png', neq_by_position)\n",
    "!rm neq.png"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The ``residue_min`` and ``residue_max`` arguments are available."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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w5S8XHU2xxo8vJkm6A3nnGDo09Tf+7GfTRDGtqKNatxcsSHWQkyfD5z7XgMCa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\nZpbDSdLMLMf/B2FXFOLM9X/tAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10addfad0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pbx.analysis.plot_neq('neq.png', neq_by_position,\n",
    "                      residue_min=60, residue_max=70)\n",
    "!rm neq.png"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Display PB variability as a logo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pbx.analysis.generate_weblogo('logo.png', count_matrix)\n",
    "display(Image('logo.png'))\n",
    "!rm logo.png"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Wt3YJ9wTAro8W0o7d0ogxS/Wt3ILtwcsLvOA21t2E3ZiyXC6jZWgmZxgJ96TgsUzH2KfC\nmAWWYLuX4SWwfI7TFvzW8eheyrqu05ikLMv5fN6+Ijc+Pu4eQIaScE8ASBQ9c1Pcjw/voBGzYMe9\nmxcTPpfrevwML5vNmqaZz+dFUaSr4ZlMZmZmJvjVWy/hHgE+xXsuMVxiVOm4dGCT6PBy578yHIYX\ncoLH6SY8DY9mad64ccNycYqi7Nixg66kh3nLNtTjxY/b8NwZKp2yzxanBXjO8ejKm9BcvNhhU7UW\n4t4UwS042z3az5zQeBoeTWW2V77SYrzwSdJJx2cvIddObtZODMxkLrulvX+zHD7UyekaGIZnrXQD\nQLFY7Ovry2QyfX19uVxO6HYNBaDpYF6bYLhtcvkLYc7K3rhWpE6vvohfucqL9zkNGHM8RVEst2ZP\nCosK0zTpknr0Eu5RUZ5kFh8wdnhbXmBgLrvV5s09lzIjPTJ3dxwHDMOLO/pPJdwhMYWwbkjxBHIb\nnllzbnZpK6n22nGyetfgJXMcNwmTcE8GxDShcGypDp2C6yS7z3GaPcElnRLcgU0Ox4uuErSNEFJ8\nnWT3gT4NuA7lSZJ5hjHObM4g44tvnOB0yN4J7YCUJ/021si/5Ehw2XW/FDAxhcPhHs+J1w7AznNc\n5QuJU3TmJJg2GB5NPctkMnGETCNAHHAblR0kCGjiXXc+p1d8ZW2ncbqbNhieqqq08sihK5Eg8kdQ\n/iXmESSKqHLWnbQJAOmUsKJRDT4guWuIOOsQPtRkg8aPpypn7XFLJA2lSifR3B+ZVkfx0nu24MNR\nDoUHV7yRR5E8yijC92Zvr/Lyl34ZKs/1KrwkjwPc40XL9o2iz2hzYJMYXuWW0x20weMlTcI9Wl4Q\n1Bfv5piH3AK1nHVLGzwerXuQZXloqAt3sdifVr1W0h2S7Jz1TDSGR6XaHVDliGKx6BDVtAxvcNAz\nStHR2HcasTjcn+f6RRyLsIaHMS4UClTKwdHeqRLuodnbqzic28Am8XB/IhctOW0i7Bwvm806tjGh\ndK6EeyS8ubWU3iC8gzV8D49ult/cWnKXqEfIyJc7bl9p3k5ZGlrFdmJXpiO/JY4/YQ2vUqnouu5e\nCu8CCfeQvPrgOHPMGQdEO+3YaCVVOduuzTQ5QWhFVNOScKdqf3/605+uXr0KAAsLCz/60Y9acAP+\nIGXMvlkp8lEZ43AiohWGl3QJd/VA0yo5roM+DRiTZoUVJA2CIHA3womEuAxPURRLKKkzJNzNGtFO\ngz7FqLsDAABLvB5JQ0gZA/Xg2vZk5sWyHIjD8BBChBBLwt00zQ6QcNdOk8JRYpuIInk3SEOWZjvR\npy2bJMYsMWZR8QQaf81/X1gm3PA4EInhWVIOlM6TcNenF3M/tje4d1pG8ijAcaSdts4kGJPcj1Py\nKIgDFxfMt3HTSuZIj8zMh3YobbYas8bYyFbqwjSG5BPvHK8jJNwdZeZI3u3px9QDSDtlH4sS7TQa\nP16bNx0bU7r12ylu4aPWbfJqVEnmGdIcXk6VTvJZa1toQ65m0iXc41z5cJcmtGiTV32aZPc1jaUF\nAZXe8toXiRM3vDoBGLveFU8wzyS5Q3Z3hwSBqgC6KxK8xFfaM8HTTi82+7qlcl7L6uTRFPl6zf/a\n8Ik6H16PByANpipnSe4QMZesYrFwFGmnm7wBxlCedPbdiXfp1kLMrJTzXxnuIiD39gkjPXK8m5nY\n5qUUJA2hytkVQ7Ln7uh7LgWaCBzuz/+Md6RVwr8vAACQR9HcH5F2muhToE8TjIlpQnMuCAUJAihj\nSB5dcZB27o7uMLxzd3R8r6XBFVI45shoQepBNP7a2hZCYgWNH28SMrWQR72cqr2dFI7FdGMx0QbD\nS5qE+69+W7v4+R0AAPRdyHx3+/OvPf/XAnvrEu8EyMEHJEfg5L2b5X/5VlNi9DvYWcMxujlGJQiS\nO+SUvs6/xO7cLAYfkH7zaCXImQObRIC3V31/dowqKRxd42uloeAfKjm0x/ASVQh75oPLHxhfWr8+\nLT30/D8MrDbW55Ywqt413r9Ztopfz93R33ONM+MrFCKFo45MACTvXlUHvThv/uJ6IDWqvb3KfvVA\n0z7V+vTSQyqYvwJ9muhTzNNWZFXaHMmBS7gz+L/bC+c/xR8aV92HntiZ3r518xM7nX6PqWb7k89z\n9UW8t1d5/2b5FZYWS3zi0+78G6JPOXXpfcH3cEB93l33SzxLe7XwOZ6T859icc+Z9JZNu3YKjz7S\nk96yqX5r/tKVO3avmN6y6dmnt/30B489urWHtjBNCN/DL17JvXiFrQQBAC2WYCHF15E8yu0hCXDD\nc1K/Nf+v6l/95AePuQ+9+esLP9P+h57zy9+YZz64/Pa/PfnU0IOwVt2+uNX+kHrQMc0j2X1o7pO2\nBVeMqnOZVBB85BK7GL6O52T7I5uZVgcA/7ynya3Vb81bw9HtG8XVjhtXFOEMAxKEVOkkKr2FmqOv\nBGP3tketQDtNMn+/OPzkYuYZ+o/kDoFZg/VahNUGj5e0lDEHF6/cHjvy38/vGXhaeuibmzfSxkuf\n3/nQuPrL3zYtfz8tPfRTm4mO9Mi1eYb2jBexarlbWSmodBKMqrVECauf7IUC10nxBJQn7TeA1INI\nGVvnI96OTxkLuf7jZvsjm7+5ZePhn8/Wb80zT3haeuiJx4Rnn9rmCLGM9MhvuxYMfNgfq+iYJcYh\npFHpLZJ5xn6QFF9H0mC8+WJmjeQO2bMOkCgi9cCay6m6jM6f49E6OhtL8QOjCvoUwfWmFTkhDaKI\nxAGQR61BzuTfLZD7zi+9lqZH69Pk8RoA/Nfiw7BzJwztAgD4/Itd+LPe/60CAMwBGhiFv2ha1tvb\nqwTfm/K5XiVWFZYm5FGUf8kRdSS5Q0gaim+k50iXAbrdJ653aPQ/crrC8BwpJsYsuFax7NCyJSTv\nppv+EH26cYXmQdHf0DNFEcQButBklcOS4utIEBwleXt7FUeZghfxujsXaPy4o8aXYAzZfWjmoxjf\nVBoCeTdopyynR4qvQ3kSld5a5+NM6MrgCtGnQBBS46+lKmcb/0onkbzbcZpjAAYAII+mbny5lP47\n8e7SmaYJZi0198lS+9wnSBSBBiqa8y0O9+eDbAY0upldrRcrqHTSse8fMWZJ7lBMb5cqnUQzH6Hx\n42juE3uAh5jmYuYZkt0HuB7TW3cEXSjhjpQxtGwzTe3qAYK+YW8hxqxj5IOUscbo0T4FUsYaozJx\nAMQBME0AIBjbr5BOCW9uLf3wsp+OqLBBeHNrKeBniRJpEOWPOJ4URDuFpCFQD1xcMO2D5IsL5rmQ\nu9taX5eQRhPvovIkyR1quL7yJOjTayvh7w7aYHhUSRoAqtXq9evX43iD6K8ZGKpm6xNl+dW2icgz\nxTwjTA7yR1Iee26+jbWAg2Q7wgZhb69yuD8P8PMVTlXGkDQEuUNWahgt4Ud05Ln+CGt4GGOq366q\nqkPW1uuQKIqWBEsyU8ZCQtVs3f1Y2CD8attEawaZbjUKL6j456ruavB+Kb1BGNgkNp4gQSxfHECV\n37GDK25Bim4nlOFRnfZ8Pm+aZjabrVQqQQ6tB159cJz6PavkfKRH3i+oLYtkutUovLAMb6RH/vjT\nev32PAB8fAHXby94vSS1M53asukSwHZLriVMeQEAeu4f1/zaDiWU4fnotHeQhLvPOM1rxc/ryX3p\n8ztLFUYAAJt3w+FGPOcafHxpAeDqrp2CtS5PGemRbz++HC7Vl9XUP2XLqt8WzZjWABrlUQAA8PEF\n/MRjzsfEmQ8uX7xy295y/dw/AQBgvObyAgBAf7vuNsoNZXg+Ou2dJeF+5sPLH3/aFGRLb9747NPb\nrBxoC0fvBIBnn9p25sPLH1/A5z/FdM1912PCU9JDjhfSTGv6M829fkp6kOZ5gj5NiicAYxruR9IQ\nUg+6rYvo0zQ0jwQBpCEQB6yS1vNfGfYSWxopCVLsZ2Vpv/nrC2c+vHz+QuMiP933HcfJ27f2PLEz\n7TC8D2evLn0KzmpAlhrfGqCStVQiulAo2HX+3Idefvnlzz77DAC+/vrr+fmlpJBsNvvFF1/4vMWK\nrnLbtm2XL1/2Oooxxhj7qHreunVry5YtYW7g4YcfDvkR/O+BPsLc28JY3HfffX/+85/D3AD/DsN/\nh+Pj46sa00UW1TRNz4wNeuijj9ayVpvJZMLMD+nShVs9vmU3EP4Kjida62+Af4fhb4ABCcHMzIws\ny/RnURTn5uaCHOKsinw+X6lU2n0XnU0Cv8NQHo+p096REu4JRhRFnzESJwgJ/A5DzfEoPjrtnSHh\nzuG0nAgMj8PhrJbOr07oaorForUSEya8sW7RNI0uJoNvllXr6cLqhG7Cvcc1JyAY40KhQLUOYDmV\nShAEjHE265fF3hq4x0suGGNJkrijWxvZbBZjbHm2pKVScY+XXAzDMAxjx44dfX193PWtlkqlYn9m\nJS2VihtechFFMZ/Pz83Nzc3NaZpGQ8Sc8LR9ggfc8JIMNTwAEARBURRrusIJiU+WVcvghpdcNE2z\nRpimafLl0DAoimINGQzDaHtGBw+uJBdZljOZDN1cSRRFRYlRALfrSVoqFV9ATzq6rguCwN1dJCQn\nlYobHofTBvgcj8NpA9zwOJw2wA2Pw2kD/w/walSxRipPNwAAAABJRU5ErkJgggo=\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pbx.analysis.generate_weblogo('logo.png', count_matrix,\n",
    "                              residue_min=60, residue_max=70)\n",
    "display(Image('logo.png'))\n",
    "!rm logo.png"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
