{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Calculating the RDF atom-to-atom\n",
    "\n",
    "We calculate the site-specific radial distribution functions of solvent around certain atoms.\n",
    "\n",
    "**Last updated:** December 2022 with MDAnalysis 2.4.0-dev0\n",
    "\n",
    "**Minimum version of MDAnalysis:** 0.19.0\n",
    "\n",
    "**Packages required:**\n",
    "    \n",
    "* MDAnalysis (<a data-cite=\"michaud-agrawal_mdanalysis_2011\" href=\"https://doi.org/10.1002/jcc.21787\">Michaud-Agrawal *et al.*, 2011</a>, <a data-cite=\"gowers_mdanalysis_2016\" href=\"https://doi.org/10.25080/Majora-629e541a-00e\">Gowers *et al.*, 2016</a>)\n",
    "* MDAnalysisTests\n",
    "\n",
    "**Optional packages for visualisation:**\n",
    "\n",
    "* [matplotlib](https://matplotlib.org)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-19T05:58:29.353011Z",
     "iopub.status.busy": "2021-05-19T05:58:29.352352Z",
     "iopub.status.idle": "2021-05-19T05:58:30.289550Z",
     "shell.execute_reply": "2021-05-19T05:58:30.290032Z"
    }
   },
   "outputs": [],
   "source": [
    "import MDAnalysis as mda\n",
    "from MDAnalysis.tests.datafiles import TPR, XTC\n",
    "from MDAnalysis.analysis import rdf\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading files"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The test files we will be working with here feature adenylate kinase (AdK), a phosophotransferase enzyme. (<a data-cite=\"beckstein_zipping_2009\" href=\"https://doi.org/10.1016/j.jmb.2009.09.009\">Beckstein *et al.*, 2009</a>)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-19T05:58:30.294493Z",
     "iopub.status.busy": "2021-05-19T05:58:30.293625Z",
     "iopub.status.idle": "2021-05-19T05:58:31.229943Z",
     "shell.execute_reply": "2021-05-19T05:58:31.229178Z"
    }
   },
   "outputs": [],
   "source": [
    "u = mda.Universe(TPR, XTC)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculating the site-specific radial distribution function"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A radial distribution function $g_{ab}(r)$ describes the time-averaged density of particles in $b$ from the reference group $a$ at distance $r$. It is normalised so that it becomes 1 for large separations in a homogenous system. See [the tutorial on averaged RDFs](average_rdf.ipynb) for more information. The `InterRDF_s` class ([API docs](https://docs.mdanalysis.org/stable/documentation_pages/analysis/rdf.html#MDAnalysis.analysis.rdf.InterRDF_s)) allows you to compute RDFs on an atom-to-atom basis, rather than simply giving the averaged RDF as in `InterRDF`.\n",
    "\n",
    "Below, I calculate the RDF between selected alpha-carbons and the water atoms within 15 angstroms of CA60, *in the first frame of the trajectory*. The water group does not update over the trajectory as the water moves towards and away from the alpha-carbon. \n",
    "\n",
    "The RDF is limited to a spherical shell around each atom by `range`. Note that the range is defined around *each atom*, rather than the center-of-mass of the entire group.\n",
    "\n",
    "If `density=True`, the final RDF is over the average density of the selected atoms in the trajectory box, making it comparable to the output of `rdf.InterRDF`. If `density=False`, the density is not taken into account. This can make it difficult to compare RDFs between AtomGroups that contain different numbers of atoms."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-19T05:58:31.235366Z",
     "iopub.status.busy": "2021-05-19T05:58:31.234786Z",
     "iopub.status.idle": "2021-05-19T05:58:31.854793Z",
     "shell.execute_reply": "2021-05-19T05:58:31.855177Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/lily/micromamba/envs/mda-user-guide-dev/lib/python3.10/site-packages/MDAnalysis/analysis/rdf.py:580: DeprecationWarning: The `u` attribute is superflous and will be removed in MDAnalysis 3.0.0.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<MDAnalysis.analysis.rdf.InterRDF_s at 0x11169f940>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ca60 = u.select_atoms('resid 61 and name CA')\n",
    "ca61 = u.select_atoms('resid 62 and name CA')\n",
    "ca62 = u.select_atoms('resid 63 and name CA')\n",
    "water = u.select_atoms('resname SOL and sphzone 15 group sel_a', sel_a=ca60)\n",
    "\n",
    "ags = [[ca60+ca61, water], [ca62, water]]\n",
    "\n",
    "ss_rdf = rdf.InterRDF_s(u, ags,\n",
    "                    nbins=75,  # default\n",
    "                    range=(0.0, 15.0),  # distance\n",
    "                    norm='density',\n",
    "                   )\n",
    "ss_rdf.run()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Like `rdf.InterRDF`, the distance bins are available at `ss_rdf.bins`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-19T05:58:31.859860Z",
     "iopub.status.busy": "2021-05-19T05:58:31.859307Z",
     "iopub.status.idle": "2021-05-19T05:58:31.861752Z",
     "shell.execute_reply": "2021-05-19T05:58:31.862184Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.1,  0.3,  0.5,  0.7,  0.9,  1.1,  1.3,  1.5,  1.7,  1.9,  2.1,\n",
       "        2.3,  2.5,  2.7,  2.9,  3.1,  3.3,  3.5,  3.7,  3.9,  4.1,  4.3,\n",
       "        4.5,  4.7,  4.9,  5.1,  5.3,  5.5,  5.7,  5.9,  6.1,  6.3,  6.5,\n",
       "        6.7,  6.9,  7.1,  7.3,  7.5,  7.7,  7.9,  8.1,  8.3,  8.5,  8.7,\n",
       "        8.9,  9.1,  9.3,  9.5,  9.7,  9.9, 10.1, 10.3, 10.5, 10.7, 10.9,\n",
       "       11.1, 11.3, 11.5, 11.7, 11.9, 12.1, 12.3, 12.5, 12.7, 12.9, 13.1,\n",
       "       13.3, 13.5, 13.7, 13.9, 14.1, 14.3, 14.5, 14.7, 14.9])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ss_rdf.results.bins"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`ss_rdf.rdf` contains the atom-pairwise RDF for each of your pairs of AtomGroups. It is a list with the same length as your list of pairs `ags`. A result array has the shape `(len(ag1), len(ag2), nbins)` for the AtomGroup pair `(ag1, ag2)`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-19T05:58:31.867176Z",
     "iopub.status.busy": "2021-05-19T05:58:31.866041Z",
     "iopub.status.idle": "2021-05-19T05:58:31.868917Z",
     "shell.execute_reply": "2021-05-19T05:58:31.868467Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 1041 water atoms\n",
      "The first result array has shape: (2, 1041, 75)\n",
      "The second result array has shape: (1, 1041, 75)\n"
     ]
    }
   ],
   "source": [
    "print('There are {} water atoms'.format(len(water)))\n",
    "print('The first result array has shape: {}'.format(ss_rdf.results.rdf[0].shape))\n",
    "print('The second result array has shape: {}'.format(ss_rdf.results.rdf[1].shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Index the results array to get the RDF for a particular pair of atoms. `ss_rdf.rdf[i][j][k]` will return the RDF between atoms $j$ and $k$ in the $i$-th pair of atom groups. For example, below we get the RDF between the alpha-carbon in residue 61 (i.e. the second atom of the first atom group) and the 571st atom of water."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-19T05:58:31.872815Z",
     "iopub.status.busy": "2021-05-19T05:58:31.872315Z",
     "iopub.status.idle": "2021-05-19T05:58:31.874585Z",
     "shell.execute_reply": "2021-05-19T05:58:31.874939Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.0023665 , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.00114292,\n",
       "       0.00106921, 0.        , 0.00094167, 0.        , 0.        ,\n",
       "       0.        , 0.0007466 , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.00055068, 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.0003116 , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.00025464, 0.00024669, 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ca61_h2o_571 = ss_rdf.results.rdf[0][1][570]\n",
    "ca61_h2o_571"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-19T05:58:31.889621Z",
     "iopub.status.busy": "2021-05-19T05:58:31.888908Z",
     "iopub.status.idle": "2021-05-19T05:58:31.994719Z",
     "shell.execute_reply": "2021-05-19T05:58:31.995133Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'RDF between CA61 and MW6365')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(ss_rdf.results.bins, ca61_h2o_571)\n",
    "w570 = water[570]\n",
    "plt.xlabel('Radius (angstrom)')\n",
    "plt.ylabel('Radial distribution')\n",
    "plt.title('RDF between CA61 and {}{}'.format(w570.name, w570.resid))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you are having trouble finding pairs of atoms where the results are not simply 0, you can use Numpy functions to find the indices of the nonzero values. Below we count the nonzero entries in the first `rdf` array."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-19T05:58:31.999184Z",
     "iopub.status.busy": "2021-05-19T05:58:31.998414Z",
     "iopub.status.idle": "2021-05-19T05:58:32.002173Z",
     "shell.execute_reply": "2021-05-19T05:58:32.002636Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4374 4374 4374\n"
     ]
    }
   ],
   "source": [
    "j, k, nbin = np.nonzero(ss_rdf.results.rdf[0])\n",
    "print(len(j), len(k), len(nbin))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Each triplet of `[j, k, nbin]` indices is a nonzero value, corresponding to the `nbin`th bin between atoms $j$ and $k$. For example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-19T05:58:32.007000Z",
     "iopub.status.busy": "2021-05-19T05:58:32.006227Z",
     "iopub.status.idle": "2021-05-19T05:58:32.009198Z",
     "shell.execute_reply": "2021-05-19T05:58:32.009731Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 0.00028\n"
     ]
    }
   ],
   "source": [
    "print(f\"{ss_rdf.results.rdf[0][j[0], k[0], nbin[0]]: .5f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Right now, we don't care which particular bin has a nonzero value. Let's find which water atom is the most present around the alpha-carbon of residue 60, i.e. the first atom."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-19T05:58:32.014116Z",
     "iopub.status.busy": "2021-05-19T05:58:32.013437Z",
     "iopub.status.idle": "2021-05-19T05:58:32.016651Z",
     "shell.execute_reply": "2021-05-19T05:58:32.017050Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The water atom with the highest distribution around CA60 has index 568\n"
     ]
    }
   ],
   "source": [
    "# where j == 0, representing the first atom\n",
    "water_for_ca60 = k[j==0]\n",
    "# count how many of each atom index are in array\n",
    "k_values, k_counts = np.unique(water_for_ca60, \n",
    "                               return_counts=True)\n",
    "# get the first k value with the most counts\n",
    "k_max = k_values[np.argmax(k_counts)]\n",
    "print('The water atom with the highest distribution '\n",
    "      'around CA60 has index {}'.format(k_max))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also calculate a cumulative distribution function for each of your results with `ss_rdf.get_cdf()`. This is the actual count of atoms within the given range, averaged over the trajectory; the volume of each radial shell is not taken into account. The result then gets saved into `ss_rdf.cdf`. The CDF has the same shape as the corresponding RDF array."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-19T05:58:32.020554Z",
     "iopub.status.busy": "2021-05-19T05:58:32.019986Z",
     "iopub.status.idle": "2021-05-19T05:58:32.023642Z",
     "shell.execute_reply": "2021-05-19T05:58:32.024163Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2, 1041, 75)\n"
     ]
    }
   ],
   "source": [
    "cdf = ss_rdf.get_cdf()\n",
    "print(cdf[0].shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-19T05:58:32.037826Z",
     "iopub.status.busy": "2021-05-19T05:58:32.037243Z",
     "iopub.status.idle": "2021-05-19T05:58:32.136373Z",
     "shell.execute_reply": "2021-05-19T05:58:32.137071Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'RDF between CA60 and HW16365')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(ss_rdf.results.bins, ss_rdf.results.cdf[0][0][568])\n",
    "w568 = water[568]\n",
    "plt.xlabel('Radius (angstrom)')\n",
    "plt.ylabel('Radial cumulative distribution')\n",
    "plt.title('RDF between CA60 and {}{}'.format(w568.name, w568.resid))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The site-specific RDF without densities"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When the `density` of the selected atom groups over the box volume is not accounted for, your distribution values will be proportionally lower."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([[[0. , 0. , 0. , ..., 0.1, 0.1, 0.1],\n",
       "         [0. , 0. , 0. , ..., 0.1, 0.1, 0.1],\n",
       "         [0. , 0. , 0. , ..., 0.1, 0.1, 0.1],\n",
       "         ...,\n",
       "         [0. , 0. , 0. , ..., 0.1, 0.1, 0.1],\n",
       "         [0. , 0. , 0. , ..., 0.1, 0.1, 0.1],\n",
       "         [0. , 0. , 0. , ..., 0.1, 0.1, 0.1]],\n",
       " \n",
       "        [[0. , 0. , 0. , ..., 0. , 0.1, 0.1],\n",
       "         [0. , 0. , 0. , ..., 0. , 0. , 0. ],\n",
       "         [0. , 0. , 0. , ..., 0.1, 0.1, 0.1],\n",
       "         ...,\n",
       "         [0. , 0. , 0. , ..., 0.1, 0.1, 0.1],\n",
       "         [0. , 0. , 0. , ..., 0.1, 0.1, 0.1],\n",
       "         [0. , 0. , 0. , ..., 0.1, 0.1, 0.1]]], shape=(2, 1041, 75)),\n",
       " array([[[0. , 0. , 0. , ..., 0. , 0.1, 0.1],\n",
       "         [0. , 0. , 0. , ..., 0. , 0. , 0. ],\n",
       "         [0. , 0. , 0. , ..., 0.1, 0.1, 0.1],\n",
       "         ...,\n",
       "         [0. , 0. , 0. , ..., 0. , 0. , 0.1],\n",
       "         [0. , 0. , 0. , ..., 0. , 0. , 0. ],\n",
       "         [0. , 0. , 0. , ..., 0. , 0. , 0. ]]], shape=(1, 1041, 75))]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ss_rdf_nodensity = rdf.InterRDF_s(u, ags,\n",
    "                    nbins=75,  # default\n",
    "                    range=(0.0, 15.0),  # distance\n",
    "                    density=False,\n",
    "                   )\n",
    "ss_rdf_nodensity.run()\n",
    "ss_rdf_nodensity.get_cdf()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-19T05:58:32.717827Z",
     "iopub.status.busy": "2021-05-19T05:58:32.717256Z",
     "iopub.status.idle": "2021-05-19T05:58:32.823568Z",
     "shell.execute_reply": "2021-05-19T05:58:32.823941Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'RDF between CA61 and MW6365')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(ss_rdf_nodensity.results.bins, \n",
    "         ss_rdf_nodensity.results.rdf[0][1][570])\n",
    "plt.xlabel('Radius (angstrom)')\n",
    "plt.ylabel('Radial distribution')\n",
    "plt.title('RDF between CA61 and {}{}'.format(w570.name, w570.resid))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## References\n",
    "\n",
    "[1] Oliver Beckstein, Elizabeth&nbsp;J. Denning, Juan&nbsp;R. Perilla, and Thomas&nbsp;B. Woolf.\n",
    "Zipping and <span class=\"bibtex-protected\">Unzipping</span> of <span class=\"bibtex-protected\">Adenylate</span> <span class=\"bibtex-protected\">Kinase</span>: <span class=\"bibtex-protected\">Atomistic</span> <span class=\"bibtex-protected\">Insights</span> into the <span class=\"bibtex-protected\">Ensemble</span> of <span class=\"bibtex-protected\">Open</span>↔<span class=\"bibtex-protected\">Closed</span> <span class=\"bibtex-protected\">Transitions</span>.\n",
    "<em>Journal of Molecular Biology</em>, 394(1):160–176, November 2009.\n",
    "00107.\n",
    "URL: <a href=\"https://linkinghub.elsevier.com/retrieve/pii/S0022283609011164\">https://linkinghub.elsevier.com/retrieve/pii/S0022283609011164</a>, <a href=\"https://doi.org/10.1016/j.jmb.2009.09.009\">doi:10.1016/j.jmb.2009.09.009</a>.\n",
    "\n",
    "[2] Richard&nbsp;J. Gowers, Max Linke, Jonathan Barnoud, Tyler J.&nbsp;E. Reddy, Manuel&nbsp;N. Melo, Sean&nbsp;L. Seyler, Jan Domański, David&nbsp;L. Dotson, Sébastien Buchoux, Ian&nbsp;M. Kenney, and Oliver Beckstein.\n",
    "<span class=\"bibtex-protected\">MDAnalysis</span>: <span class=\"bibtex-protected\">A</span> <span class=\"bibtex-protected\">Python</span> <span class=\"bibtex-protected\">Package</span> for the <span class=\"bibtex-protected\">Rapid</span> <span class=\"bibtex-protected\">Analysis</span> of <span class=\"bibtex-protected\">Molecular</span> <span class=\"bibtex-protected\">Dynamics</span> <span class=\"bibtex-protected\">Simulations</span>.\n",
    "<em>Proceedings of the 15th Python in Science Conference</em>, pages 98–105, 2016.\n",
    "00152.\n",
    "URL: <a href=\"https://conference.scipy.org/proceedings/scipy2016/oliver_beckstein.html\">https://conference.scipy.org/proceedings/scipy2016/oliver_beckstein.html</a>, <a href=\"https://doi.org/10.25080/Majora-629e541a-00e\">doi:10.25080/Majora-629e541a-00e</a>.\n",
    "\n",
    "[3] Naveen Michaud-Agrawal, Elizabeth&nbsp;J. Denning, Thomas&nbsp;B. Woolf, and Oliver Beckstein.\n",
    "<span class=\"bibtex-protected\">MDAnalysis</span>: <span class=\"bibtex-protected\">A</span> toolkit for the analysis of molecular dynamics simulations.\n",
    "<em>Journal of Computational Chemistry</em>, 32(10):2319–2327, July 2011.\n",
    "00778.\n",
    "URL: <a href=\"http://doi.wiley.com/10.1002/jcc.21787\">http://doi.wiley.com/10.1002/jcc.21787</a>, <a href=\"https://doi.org/10.1002/jcc.21787\">doi:10.1002/jcc.21787</a>."
   ]
  }
 ],
 "metadata": {
  "celltoolbar": "Raw Cell Format",
  "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": "3.10.16"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": false,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  },
  "vscode": {
   "interpreter": {
    "hash": "5edc5d8d8cbc0935a054a8e44024f729bc376180aae27775d15f2ff38c68f892"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}