docs/examples/Tutorials/Prep_Rest_Post_Ana/analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Analysis\n",
    "=======\n",
    "\n",
    "<hr style=\"height:2.5px\">\n",
    "\n",
    "In this tutorial, we show the user how to instantiate the `biceps.Analysis` class which uses [MBAR](https://pymbar.readthedocs.io/en/master/index.html) to get predicted populations of each conformational states and compute a BICePs score.  We also provide a short description of the output data from analysis and embed the figures of posterior distribution of populations & nuisance parameters.  Please refer to the [documentation of Analysis](https://biceps.readthedocs.io/en/latest/biceps.html#analysis) for more specific details. \n",
    "<hr style=\"height:2.5px\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import biceps\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\", category=UserWarning)\n",
    "warnings.filterwarnings('ignore', category=FutureWarning)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading results/traj_lambda0.00.npz ...\n",
      "Loading results/traj_lambda1.00.npz ...\n",
      "not all state sampled, these states [ 0  3  4  5  8  9 11 13 14 15 16 18 19 20 21 22 23 24 25 26 27 28 29 31\n",
      " 34 40 41 42 43 44 48 49 51 52 53 54 55 57 60 61 62 64 69 71 72 73 74 76\n",
      " 77 78 81 82 83 86 87 88 89 95 96 97 98 99] are not sampled\n",
      "Loading results/traj_lambda0.00.pkl ...\n",
      "Loading results/traj_lambda1.00.pkl ...\n",
      "lam = [0.0, 1.0]\n",
      "nstates 100\n",
      "Time for MBAR: 0.077 s\n",
      "Writing results/BS.dat...\n",
      "Writing results/populations.dat...\n",
      "Top 10 states: [46, 85, 92, 45, 39, 80, 65, 90, 59, 38]\n",
      "Top 10 populations: [0.01001833 0.0212999  0.02792732 0.03482791 0.07746092 0.07924925\n",
      " 0.08826894 0.09715671 0.20440265 0.32912632]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "A = biceps.Analysis(outdir=\"results\", nstates=100, verbose=True)\n",
    "fig = A.plot(plottype=\"hist\") # plottype=\"step\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!--<img src=\"\" height=\"300\" width=\"300\" align=\"center\">\n",
    "<img src=\"\" height=\"300\" width=\"300\" align=\"center\">\n",
    "<img src=\"\" height=\"300\" width=\"300\" align=\"center\">\n",
    "<img src=\"\" height=\"300\" width=\"300\" align=\"center\">\n",
    "<img src=\"\" height=\"300\" width=\"300\" align=\"center\">-->"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The output files include: population information (\"populations.dat\"), figure of sampled parameters distribution (\"BICePs.pdf\"), BICePs score information (\"BS.dat\"), which are shown above.\n",
    "\n",
    "Now, let's take a look at the populations file:\n",
    "\n",
    "There are 100 rows corresponding to 100 clustered states. 4 columns corresponding to populations of each state (row) for 2 lambda values (first 2 columns) and population change (last 2 columns). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.017975</td>\n",
       "      <td>2.496320e-05</td>\n",
       "      <td>0.004195</td>\n",
       "      <td>5.931122e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.009977</td>\n",
       "      <td>2.292886e-05</td>\n",
       "      <td>0.003138</td>\n",
       "      <td>7.283074e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.002693</td>\n",
       "      <td>3.068112e-04</td>\n",
       "      <td>0.001553</td>\n",
       "      <td>1.773218e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.000990</td>\n",
       "      <td>1.008111e-05</td>\n",
       "      <td>0.000989</td>\n",
       "      <td>1.008553e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>0.006000</td>\n",
       "      <td>1.247277e-11</td>\n",
       "      <td>0.002441</td>\n",
       "      <td>5.105694e-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>0.000993</td>\n",
       "      <td>6.847524e-06</td>\n",
       "      <td>0.000993</td>\n",
       "      <td>6.850553e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>0.004988</td>\n",
       "      <td>1.150700e-05</td>\n",
       "      <td>0.002225</td>\n",
       "      <td>5.157574e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>0.004000</td>\n",
       "      <td>4.474545e-11</td>\n",
       "      <td>0.001996</td>\n",
       "      <td>2.241289e-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>0.002999</td>\n",
       "      <td>5.949074e-07</td>\n",
       "      <td>0.001729</td>\n",
       "      <td>3.439323e-07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           0             1         2             3\n",
       "0   0.017975  2.496320e-05  0.004195  5.931122e-06\n",
       "1   0.009977  2.292886e-05  0.003138  7.283074e-06\n",
       "2   0.002693  3.068112e-04  0.001553  1.773218e-04\n",
       "3   0.000990  1.008111e-05  0.000989  1.008553e-05\n",
       "4   0.000000  0.000000e+00       NaN           NaN\n",
       "..       ...           ...       ...           ...\n",
       "95  0.006000  1.247277e-11  0.002441  5.105694e-12\n",
       "96  0.000993  6.847524e-06  0.000993  6.850553e-06\n",
       "97  0.004988  1.150700e-05  0.002225  5.157574e-06\n",
       "98  0.004000  4.474545e-11  0.001996  2.241289e-11\n",
       "99  0.002999  5.949074e-07  0.001729  3.439323e-07\n",
       "\n",
       "[100 rows x 4 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "pops = np.loadtxt('results/populations.dat')\n",
    "df = pd.DataFrame(pops)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!--There are 100 rows corresponding to 100 clustered states. 6 columns corresponding to populations of each state (row) for 3 lambda values (first 3 columns) and population change (last 3 columns). -->"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1 style=\"align: center;font-size: 18pt;\">Conclusion</h1>\n",
    "    \n",
    "    \n",
    "In this tutorial, the user learned how to call on the `biceps.Analysis` class in order to analyze the trajectory data, which automatically plots the posterior distribution of populations & nuisance parameters.\n",
    "\n",
    "We now conclude the series of tutorials: [Preparation](https://biceps.readthedocs.io/en/latest/examples/Tutorials/Prep_Rest_Post_Ana/preparation.html), [Restraint](https://biceps.readthedocs.io/en/latest/examples/Tutorials/Prep_Rest_Post_Ana/restraint.html), [PosteriorSampler](https://biceps.readthedocs.io/en/latest/examples/Tutorials/Prep_Rest_Post_Ana/posteriorsampler.html) and [Analysis](https://biceps.readthedocs.io/en/latest/examples/Tutorials/Prep_Rest_Post_Ana/analysis.html)\n",
    "**To learn more about BICePs, please check out our other examples & tutorials** [here](https://biceps.readthedocs.io/en/latest/examples/index.html)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h6 style=\"align: justify;font-size: 12pt\"># <span style=\"color:red;\">NOTE</span>: The following cell is for pretty notebook rendering</h6>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        font-family: \"Computer Modern\";\n",
       "        src: url('http://mirrors.ctan.org/fonts/cm-unicode/fonts/otf/cmunss.otf');\n",
       "    }\n",
       "    #notebook { /* centers the page content */\n",
       "    padding-left:8%;\n",
       "    padding-right:9%;\n",
       "    }\n",
       "    #ipython-main-app {\n",
       "        position: relative;\n",
       "    }\n",
       "    #jupyter-main-app {\n",
       "        position: relative;\n",
       "    }\n",
       "    div.cell{\n",
       "        width:98%;\n",
       "        margin-left:4% !important;*/\n",
       "        margin-right:4%;\n",
       "    }\n",
       "    h1 {\n",
       "        font-family: \"Charis SIL\", Palatino, serif;\n",
       "        text-align: center;\n",
       "    }\n",
       "    h4{\n",
       "        margin-top:12px;\n",
       "        margin-bottom: 3px;\n",
       "    }\n",
       "    p {\n",
       "        display: block;\n",
       "        text-align: justify;\n",
       "        word-wrap: break-word;\n",
       "    }\n",
       "    div.text_cell_render{\n",
       "        font-family: Computer Modern, \"Helvetica Neue\", Arial, Helvetica, Geneva, sans-serif;\n",
       "        line-height: 145%;\n",
       "        font-size: 12pt;\n",
       "        width:99%;\n",
       "        margin-left:auto;\n",
       "        margin-right:auto;\n",
       "\ttext-align: justify;\n",
       "        word-wrap: break-word;\n",
       "    }\n",
       "    .output_png {\n",
       "        display: block;/*table-cell;*/\n",
       "        text-align: center;\n",
       "        align: center;\n",
       "        vertical-align: middle;\n",
       "        margin-left: auto;\n",
       "        margin-right: auto;\n",
       "        width: auto; /* width: 100%;*/\n",
       "    }\n",
       "    div.output_area pre {font-weight: normal; text-align: justify; }\n",
       "    .warning{\n",
       "        color: rgb( 240, 20, 20 )\n",
       "        }\n",
       "    .CodeMirror{\n",
       "            font-family: \"Source Code Pro\", source-code-pro,Consolas, monospace;\n",
       "    }\n",
       "    .prompt{\n",
       "        display: None;\n",
       "    }\n",
       "    .text_cell_render p {\n",
       "        display: block;\n",
       "        text-align: justify;\n",
       "        word-wrap: break-word;\n",
       "    }\n",
       "    .text_cell_render h5 {\n",
       "        font-weight: 300;\n",
       "        font-size: 16pt;\n",
       "        color: #4057A1;\n",
       "        font-style: italic;\n",
       "        margin-bottom: .5em;\n",
       "        margin-top: 0.5em;\n",
       "        display: block;\n",
       "    }\n",
       "    /* override table width restrictions as found on https://github.com/getpelican/pelican/issues/1311 */\n",
       "    .wy-table-responsive table td, .wy-table-responsive table th {\n",
       "        /* !important prevents the common CSS stylesheets from\n",
       "           overriding this as on RTD they are loaded after this stylesheet */\n",
       "        white-space: normal !important;\n",
       "    }\n",
       "\n",
       "    .wy-table-responsive {\n",
       "        overflow: visible !important;\n",
       "    }\n",
       "</style>\n",
       "\n",
       "\n",
       "<!-- MathJax configuration -->\n",
       "<script type=\"text/x-mathjax-config\">\n",
       "    src=\"http://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML\">\n",
       "    MathJax.Hub.Config({\n",
       "                        TeX: {\n",
       "                           extensions: [\"AMSmath.js\"]\n",
       "                           },\n",
       "                tex2jax: {\n",
       "                    inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ],\n",
       "                    displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ]\n",
       "                },\n",
       "                displayAlign: 'center', // Change this to 'center' to center equations.\n",
       "                \"HTML-CSS\": {\n",
       "                    styles: {'.MathJax_Display': {\"margin\": 4}},\n",
       "                    linebreaks: { automatic: true }}\n",
       "        });\n",
       "</script>\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.core.display import HTML\n",
    "def css_styling():\n",
    "    styles = open(\"../../../theme.css\", \"r\").read()\n",
    "    return HTML(styles)\n",
    "css_styling()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}