docs/examples/full_examples/albocycline/albocycline.ipynb

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   "source": [
    "Albocycline\n",
    "===========\n",
    "\n",
    "<!--<img title=\"https://pubchem.ncbi.nlm.nih.gov\" src=\"https://pubchem.ncbi.nlm.nih.gov/image/imagefly.cgi?cid=6440973&width=500&height=500\" align=\"center\" height=\"400\" width=\"400\">-->\n",
    "\n",
    "\n",
    "<hr style=\"height:2.5px\">\n",
    "\n",
    "<h2 style=\"align: center;font-size: 12pt;\">Summary:</h2> Albocycline is a macrolactone isolated from Streptomyces maizeus.  This macrolactone has recently been identified as a promising lead antibiotic candidate for the treatment of both vancomycin-resistant S. aureus (VRSA) and MRSA.<br>\n",
    "In our previous example we showed in detail how to calculate the consistency of theoretical modeling with experiment. The user gained insight on how to run `biceps` calculations in full. In this example, we will repeat the process parallelizing lambda values with `multiprocessing`.</h2>\n",
    "\n",
    "\n",
    "\n",
    "**To convert this Jupyter Notebook into a script use the following command:**\n",
    "\n",
    "```\n",
    "$ jupyter nbconvert --to python albocycline.ipynb\n",
    "```\n",
    "\n",
    "<hr style=\"height:2.5px\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import biceps\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\", category=UserWarning)\n",
    "warnings.filterwarnings('ignore', category=FutureWarning)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Possible input data extensions: ['H', 'Ca', 'N', 'J', 'noe', 'pf']\n",
      "Input data: ['J', 'noe']\n"
     ]
    }
   ],
   "source": [
    "####### Data and Output Directories #######\n",
    "print(f\"Possible input data extensions: {biceps.toolbox.list_possible_extensions()}\")\n",
    "energies = np.loadtxt('albocycline/albocycline_QMenergies.dat')*627.509  # convert from hartrees to kcal/mol\n",
    "energies = energies/0.5959   # convert to reduced free energies F = f/kT\n",
    "energies -= energies.min()  # set ground state to zero, just in case\n",
    "dataFiles = 'albocycline/J_NOE'\n",
    "input_data = biceps.toolbox.sort_data(dataFiles)\n",
    "print(f\"Input data: {biceps.toolbox.list_extensions(input_data)}\")\n",
    "outdir = 'results'\n",
    "biceps.toolbox.mkdir(outdir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "nSteps of sampling: 100000\n"
     ]
    },
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ref</th>\n",
       "      <th>sigma</th>\n",
       "      <th>gamma</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>uniform</td>\n",
       "      <td>(0.05, 20.0, 1.02)</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>exponential</td>\n",
       "      <td>(0.05, 5.0, 1.02)</td>\n",
       "      <td>(0.2, 5.0, 1.01)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "           ref               sigma             gamma\n",
       "0      uniform  (0.05, 20.0, 1.02)               NaN\n",
       "1  exponential   (0.05, 5.0, 1.02)  (0.2, 5.0, 1.01)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "####### Parameters #######\n",
    "\n",
    "nsteps = 100000      # for testing\n",
    "#nsteps=10000000     # for production\n",
    "\n",
    "print(f\"nSteps of sampling: {nsteps}\")\n",
    "n_lambdas = 3\n",
    "lambda_values = np.linspace(0.0, 1.0, n_lambdas)\n",
    "parameters = [\n",
    "        {\"ref\": 'uniform', \"sigma\": (0.05, 20.0, 1.02)},\n",
    "        {\"ref\": 'exponential', \"sigma\": (0.05, 5.0, 1.02), \"gamma\": (0.2, 5.0, 1.01)}\n",
    "        ]\n",
    "pd.DataFrame(parameters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of CPUs: 10\n",
      "Number of processes: 3\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████████████| 100000/100000 [00:01<00:00, 62547.07it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Accepted 70.378 % \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "100%|███████████████████████████████████████████████████████████| 100000/100000 [00:01<00:00, 62460.76it/s]"
     ]
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     "text": [
      "\n"
     ]
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     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Accepted [ ...Nuisance paramters..., state] %\n",
      "Accepted 67.006 % \n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "100%|███████████████████████████████████████████████████████████| 100000/100000 [00:01<00:00, 62394.44it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Accepted [32.549 31.386 31.386  6.443] % \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Accepted [ ...Nuisance paramters..., state] %\n",
      "\n",
      "Accepted 71.999 % \n",
      "\n",
      "\n",
      "Accepted [32.549 31.725 31.725  2.732] % \n",
      "\n",
      "Accepted [ ...Nuisance paramters..., state] %\n",
      "\n",
      "Accepted [32.643 31.537 31.537  7.819] % \n",
      "\n"
     ]
    }
   ],
   "source": [
    "####### Multiprocessing Lambda values #######\n",
    "@biceps.multiprocess(iterable=lambda_values)\n",
    "def mp_lambdas(lam):\n",
    "    ensemble = biceps.Ensemble(lam, energies)\n",
    "    ensemble.initialize_restraints(input_data, parameters)\n",
    "    sampler = biceps.PosteriorSampler(ensemble)\n",
    "    sampler.sample(nsteps=nsteps, verbose=False)\n",
    "    sampler.traj.process_results(f\"{outdir}/traj_lambda{lam}.npz\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "not all state sampled, these states [86] are not sampled\n",
      "not all state sampled, these states [ 9 15 48] are not sampled\n",
      "not all state sampled, these states [ 4  5  7  9 11 15 16 18 21 24 25 27 28 34 40 42 44 48 51 54 58 62 76 77\n",
      " 81 82 86 87 94 95 98] are not sampled\n"
     ]
    },
    {
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\n",
      "text/plain": [
       "<Figure size 800x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "####### Posterior Analysis #######\n",
    "A = biceps.Analysis(outdir, nstates=len(energies))\n",
    "\n",
    "try:\n",
    "    %matplotlib inline\n",
    "    A.plot()\n",
    "except:\n",
    "    print(ipython_warning)"
   ]
  },
  {
   "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": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "    @font-face {\n",
       "        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": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.core.display import HTML\n",
    "\n",
    "def css_styling():\n",
    "    styles = open(\"../../../theme.css\", \"r\").read()\n",
    "    return HTML(styles)\n",
    "\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
}