{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Writing your own trajectory analysis\n",
    "\n",
    "We create our own analysis methods for calculating the radius of gyration of a selection of atoms.\n",
    "\n",
    "This can be done three ways, from least to most flexible:\n",
    "\n",
    " 1. [Running the analysis directly from a function](#Creating-an-analysis-from-a-function)\n",
    " \n",
    " 2. [Turning a function into a class](#Transforming-a-function-into-a-class)\n",
    " \n",
    " 3. [Writing your own class](#Creating-your-own-class)\n",
    "\n",
    "The building blocks and methods shown here are only suitable for analyses that involve iterating over the trajectory once.\n",
    "\n",
    "If you implement your own analysis method, please consider [contributing it to the MDAnalysis codebase!](https://www.mdanalysis.org/UserGuide/contributing.html)\n",
    "\n",
    "**Last updated:** December 2022 with MDAnalysis 2.4.0-dev0\n",
    "\n",
    "**Minimum version of MDAnalysis:** 2.0.0\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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-09T12:13:02.845041605Z",
     "start_time": "2023-06-09T12:13:00.083014779Z"
    },
    "execution": {
     "iopub.execute_input": "2021-05-19T05:45:44.674135Z",
     "iopub.status.busy": "2021-05-19T05:45:44.673007Z",
     "iopub.status.idle": "2021-05-19T05:45:46.290616Z",
     "shell.execute_reply": "2021-05-19T05:45:46.291341Z"
    }
   },
   "outputs": [],
   "source": [
    "import MDAnalysis as mda\n",
    "from MDAnalysis.tests.datafiles import PSF, DCD, DCD2\n",
    "from MDAnalysis.analysis.base import (AnalysisBase,\n",
    "                                      AnalysisFromFunction,\n",
    "                                      analysis_class)\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Radius of gyration\n",
    "\n",
    "Let's start off by defining a standalone analysis function.\n",
    "\n",
    "The radius of gyration of a structure measures how compact it is. In [GROMACS](http://manual.gromacs.org/documentation/2019-rc1/reference-manual/analysis/radius-of-gyration.html), it is calculated as follows: \n",
    "\n",
    "$$ R_g = \\sqrt{\\frac{\\sum_i m_i \\mathbf{r}_i^2}{\\sum_i m_i}}$$\n",
    "\n",
    "where $m_i$ is the mass of atom $i$ and $\\mathbf{r}_i$ is the position of atom $i$, relative to the center-of-mass of the selection.\n",
    "\n",
    "The radius of gyration around each axis can also be determined separately. For example, the radius of gyration around the x-axis:\n",
    "\n",
    "$$ R_{i, x} = \\sqrt{\\frac{\\sum_i m_i [r_{i, y}^2 + r_{i, z}^2]}{\\sum_i m_i}}$$\n",
    "\n",
    "Below, we define a function that takes an AtomGroup and calculates the radii of gyration. We could write this function to only need the AtomGroup. However, we also add in a `masses` argument and a `total_mass` keyword to avoid recomputing the mass and total mass for each frame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-09T12:13:02.913378701Z",
     "start_time": "2023-06-09T12:13:02.846193220Z"
    },
    "execution": {
     "iopub.execute_input": "2021-05-19T05:45:46.297926Z",
     "iopub.status.busy": "2021-05-19T05:45:46.297404Z",
     "iopub.status.idle": "2021-05-19T05:45:46.299247Z",
     "shell.execute_reply": "2021-05-19T05:45:46.299751Z"
    }
   },
   "outputs": [],
   "source": [
    "def radgyr(atomgroup, masses, total_mass=None):\n",
    "    # coordinates change for each frame\n",
    "    coordinates = atomgroup.positions\n",
    "    center_of_mass = atomgroup.center_of_mass()\n",
    "    \n",
    "    # get squared distance from center\n",
    "    ri_sq = (coordinates-center_of_mass)**2\n",
    "    # sum the unweighted positions\n",
    "    sq = np.sum(ri_sq, axis=1)\n",
    "    sq_x = np.sum(ri_sq[:,[1,2]], axis=1) # sum over y and z\n",
    "    sq_y = np.sum(ri_sq[:,[0,2]], axis=1) # sum over x and z\n",
    "    sq_z = np.sum(ri_sq[:,[0,1]], axis=1) # sum over x and y\n",
    "    \n",
    "    # make into array\n",
    "    sq_rs = np.array([sq, sq_x, sq_y, sq_z])\n",
    "    \n",
    "    # weight positions\n",
    "    rog_sq = np.sum(masses*sq_rs, axis=1)/total_mass\n",
    "    # square root and return\n",
    "    return np.sqrt(rog_sq)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading files\n",
    "\n",
    "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": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-09T12:13:04.148426149Z",
     "start_time": "2023-06-09T12:13:02.901815020Z"
    },
    "execution": {
     "iopub.execute_input": "2021-05-19T05:45:46.304382Z",
     "iopub.status.busy": "2021-05-19T05:45:46.303729Z",
     "iopub.status.idle": "2021-05-19T05:45:46.735057Z",
     "shell.execute_reply": "2021-05-19T05:45:46.736379Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/pbarletta/mambaforge/envs/guide/lib/python3.9/site-packages/MDAnalysis/coordinates/DCD.py:165: DeprecationWarning: DCDReader currently makes independent timesteps by copying self.ts while other readers update self.ts inplace. This behavior will be changed in 3.0 to be the same as other readers. Read more at https://github.com/MDAnalysis/mdanalysis/issues/3889 to learn if this change in behavior might affect you.\n",
      "  warnings.warn(\"DCDReader currently makes independent timesteps\"\n"
     ]
    }
   ],
   "source": [
    "u = mda.Universe(PSF, DCD)\n",
    "protein = u.select_atoms('protein')\n",
    "\n",
    "u2 = mda.Universe(PSF, DCD2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating an analysis from a function\n",
    "\n",
    "`MDAnalysis.analysis.base.AnalysisFromFunction` can create an analysis from a function that works on AtomGroups. It requires the function itself, the trajectory to operate on, and then the arguments / keyword arguments necessary for the function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-09T12:13:04.317952076Z",
     "start_time": "2023-06-09T12:13:04.015481912Z"
    },
    "execution": {
     "iopub.execute_input": "2021-05-19T05:45:46.744071Z",
     "iopub.status.busy": "2021-05-19T05:45:46.743156Z",
     "iopub.status.idle": "2021-05-19T05:45:46.820924Z",
     "shell.execute_reply": "2021-05-19T05:45:46.821283Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<MDAnalysis.analysis.base.AnalysisFromFunction at 0x7f901a31bca0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rog = AnalysisFromFunction(radgyr, u.trajectory, \n",
    "                           protein, protein.masses, \n",
    "                           total_mass=np.sum(protein.masses))\n",
    "rog.run()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Running the analysis iterates over the trajectory. The output is saved in `rog.results.timeseries`, which has the same number of rows, as frames in the trajectory. You can access the results both at `rog.results.timeseries` and `rog.results['timeseries']`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-09T12:13:04.471427459Z",
     "start_time": "2023-06-09T12:13:04.314601521Z"
    },
    "execution": {
     "iopub.execute_input": "2021-05-19T05:45:46.827199Z",
     "iopub.status.busy": "2021-05-19T05:45:46.826150Z",
     "iopub.status.idle": "2021-05-19T05:45:46.829723Z",
     "shell.execute_reply": "2021-05-19T05:45:46.830409Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(98, 4)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rog.results['timeseries'].shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "gives the same outputs as:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-09T12:13:04.529356339Z",
     "start_time": "2023-06-09T12:13:04.336002495Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(98, 4)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rog.results.timeseries.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-09T12:13:04.865856492Z",
     "start_time": "2023-06-09T12:13:04.372004616Z"
    },
    "execution": {
     "iopub.execute_input": "2021-05-19T05:45:46.853297Z",
     "iopub.status.busy": "2021-05-19T05:45:46.851794Z",
     "iopub.status.idle": "2021-05-19T05:45:46.991491Z",
     "shell.execute_reply": "2021-05-19T05:45:46.991906Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Frame')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels = ['all', 'x-axis', 'y-axis', 'z-axis']\n",
    "for col, label in zip(rog.results['timeseries'].T, labels):\n",
    "    plt.plot(col, label=label)\n",
    "plt.legend()\n",
    "plt.ylabel('Radius of gyration (Å)')\n",
    "plt.xlabel('Frame')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also re-run the analysis with different frame selections. \n",
    "\n",
    "Below, we start from the 10th frame and take every 8th frame until the 80th. Note that the slice includes the `start` frame, but does not include the `stop` frame index (much like the actual `range()` function)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-09T12:13:04.970771465Z",
     "start_time": "2023-06-09T12:13:04.860485450Z"
    },
    "execution": {
     "iopub.execute_input": "2021-05-19T05:45:46.997610Z",
     "iopub.status.busy": "2021-05-19T05:45:46.996736Z",
     "iopub.status.idle": "2021-05-19T05:45:47.008365Z",
     "shell.execute_reply": "2021-05-19T05:45:47.008817Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10, 4)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rog_10 = AnalysisFromFunction(radgyr, u.trajectory, \n",
    "                              protein, protein.masses, \n",
    "                              total_mass=np.sum(protein.masses))\n",
    "\n",
    "rog_10.run(start=10, stop=80, step=7)\n",
    "rog_10.results['timeseries'].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-09T12:13:05.341820897Z",
     "start_time": "2023-06-09T12:13:04.884418060Z"
    },
    "execution": {
     "iopub.execute_input": "2021-05-19T05:45:47.026036Z",
     "iopub.status.busy": "2021-05-19T05:45:47.021412Z",
     "iopub.status.idle": "2021-05-19T05:45:47.153405Z",
     "shell.execute_reply": "2021-05-19T05:45:47.153774Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Frame')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for col, label in zip(rog_10.results['timeseries'].T, labels):\n",
    "    plt.plot(col, label=label)\n",
    "plt.legend()\n",
    "plt.ylabel('Radius of gyration (Å)')\n",
    "plt.xlabel('Frame')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Transforming a function into a class\n",
    "\n",
    "While the `AnalysisFromFunction` is convenient for quick analyses, you may want to turn your function into a class that can be applied to many different trajectories, much like other MDAnalysis analyses.\n",
    "\n",
    "You can apply `analysis_class` to any function that you can run with `AnalysisFromFunction` to get a class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-09T12:13:05.363999617Z",
     "start_time": "2023-06-09T12:13:05.340762290Z"
    },
    "execution": {
     "iopub.execute_input": "2021-05-19T05:45:47.158522Z",
     "iopub.status.busy": "2021-05-19T05:45:47.157956Z",
     "iopub.status.idle": "2021-05-19T05:45:47.159704Z",
     "shell.execute_reply": "2021-05-19T05:45:47.160090Z"
    }
   },
   "outputs": [],
   "source": [
    "RadiusOfGyration = analysis_class(radgyr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To run the analysis, pass exactly the same arguments as you would for `AnalysisFromFunction`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-09T12:13:05.623603885Z",
     "start_time": "2023-06-09T12:13:05.355806546Z"
    },
    "execution": {
     "iopub.execute_input": "2021-05-19T05:45:47.164528Z",
     "iopub.status.busy": "2021-05-19T05:45:47.163876Z",
     "iopub.status.idle": "2021-05-19T05:45:47.223088Z",
     "shell.execute_reply": "2021-05-19T05:45:47.223864Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<MDAnalysis.analysis.base.analysis_class.<locals>.WrapperClass at 0x7f9000500820>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rog_u1 = RadiusOfGyration(u.trajectory, protein, \n",
    "                          protein.masses,\n",
    "                          total_mass=np.sum(protein.masses))\n",
    "rog_u1.run()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As with `AnalysisFromFunction`, the results are in `results`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-09T12:13:06.016298115Z",
     "start_time": "2023-06-09T12:13:05.619608090Z"
    },
    "execution": {
     "iopub.execute_input": "2021-05-19T05:45:47.235361Z",
     "iopub.status.busy": "2021-05-19T05:45:47.234354Z",
     "iopub.status.idle": "2021-05-19T05:45:47.386475Z",
     "shell.execute_reply": "2021-05-19T05:45:47.386946Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Frame')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for col, label in zip(rog_u1.results['timeseries'].T, labels):\n",
    "    plt.plot(col, label=label)\n",
    "plt.legend()\n",
    "plt.ylabel('Radius of gyration (Å)')\n",
    "plt.xlabel('Frame')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can reuse the class for other trajectories and selections."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-09T12:13:06.154323791Z",
     "start_time": "2023-06-09T12:13:06.012196161Z"
    },
    "execution": {
     "iopub.execute_input": "2021-05-19T05:45:47.392838Z",
     "iopub.status.busy": "2021-05-19T05:45:47.391905Z",
     "iopub.status.idle": "2021-05-19T05:45:47.421066Z",
     "shell.execute_reply": "2021-05-19T05:45:47.421613Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<MDAnalysis.analysis.base.analysis_class.<locals>.WrapperClass at 0x7f9000474df0>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ca = u2.select_atoms('name CA')\n",
    "\n",
    "rog_u2 = RadiusOfGyration(u2.trajectory, ca, \n",
    "                          ca.masses,\n",
    "                          total_mass=np.sum(ca.masses))\n",
    "rog_u2.run()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-09T12:13:06.628710751Z",
     "start_time": "2023-06-09T12:13:06.144197177Z"
    },
    "execution": {
     "iopub.execute_input": "2021-05-19T05:45:47.438047Z",
     "iopub.status.busy": "2021-05-19T05:45:47.431283Z",
     "iopub.status.idle": "2021-05-19T05:45:47.574600Z",
     "shell.execute_reply": "2021-05-19T05:45:47.575412Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Frame')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for col, label in zip(rog_u2.results['timeseries'].T, labels):\n",
    "    plt.plot(col, label=label)\n",
    "plt.legend()\n",
    "plt.ylabel('Radius of gyration (Å)')\n",
    "plt.xlabel('Frame')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating your own class\n",
    "\n",
    "Although `AnalysisFromFunction` and `analysis_class` are convenient, they can be too limited for complex algorithms. You may need to write your own class.\n",
    "\n",
    "MDAnalysis provides the `MDAnalysis.analysis.base.AnalysisBase` class as a template for creating multiframe analyses. This class automatically sets up your trajectory reader for iterating, and includes an optional progress meter. \n",
    "\n",
    "The analysis is always run by calling `run()`. `AnalysisFromFunction` actually subclasses `AnalysisBase`, and `analysis_class` returns a subclass of `AnalysisFromFunction`, so the behaviour of `run()` remains identical.\n",
    "\n",
    "### 1. Define `__init__`\n",
    "You can define a new analysis by subclassing AnalysisBase. Initialise the analysis with the `__init__` method, where you *must* pass the trajectory that you are working with to `AnalysisBase.__init__()`. You can also pass in the `verbose` keyword. If `verbose=True`, the class will set up a progress meter for you.\n",
    "\n",
    "### 2. Define your analysis in `_single_frame()` and other methods\n",
    "Implement your functionality as a function over each frame of the trajectory by defining `_single_frame()`. This function gets called for each frame of your trajectory. \n",
    "\n",
    "You can also define `_prepare()` and `_conclude()` to set your analysis up before looping over the trajectory, and to finalise the results that you have prepared. In order, `run()` calls:\n",
    "\n",
    " - `_prepare()`\n",
    " - `_single_frame()` (for each frame of the trajectory that you are iterating over)\n",
    " - `_conclude()`\n",
    "\n",
    "Class subclassed from AnalysisBase can make use of several properties when defining the methods above:\n",
    "\n",
    " - `self.start`: frame index to start analysing from. Defined in `run()`\n",
    " - `self.stop`: frame index to stop analysis. Defined in `run()`\n",
    " - `self.step`: number of frames to skip in between. Defined in `run()`\n",
    " - `self.n_frames`: number of frames to analyse over. This can be helpful in initialising result arrays.\n",
    " - `self._verbose`: whether to be verbose.\n",
    " - `self._trajectory`: the actual trajectory\n",
    " - `self._ts`: the current timestep object\n",
    " - `self._frame_index`: the index of the currently analysed frame. This is *not* the absolute index of the frame in the trajectory overall, but rather the relative index of the frame within the list of frames to be analysed. You can think of it as the number of times that `self._single_frame()` has already been called.\n",
    " \n",
    "Below, we create the class `RadiusOfGyration2` to run the analysis function that we have defined above, and add extra information such as the time of the corresponding frame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-09T12:13:06.642130526Z",
     "start_time": "2023-06-09T12:13:06.627087381Z"
    },
    "execution": {
     "iopub.execute_input": "2021-05-19T05:45:47.588068Z",
     "iopub.status.busy": "2021-05-19T05:45:47.586645Z",
     "iopub.status.idle": "2021-05-19T05:45:47.589492Z",
     "shell.execute_reply": "2021-05-19T05:45:47.589917Z"
    }
   },
   "outputs": [],
   "source": [
    "class RadiusOfGyration2(AnalysisBase):  # subclass AnalysisBase\n",
    "    \n",
    "    def __init__(self, atomgroup, verbose=True):\n",
    "        \"\"\"\n",
    "        Set up the initial analysis parameters.\n",
    "        \"\"\"\n",
    "        # must first run AnalysisBase.__init__ and pass the trajectory\n",
    "        trajectory = atomgroup.universe.trajectory\n",
    "        super(RadiusOfGyration2, self).__init__(trajectory,\n",
    "                                               verbose=verbose)\n",
    "        # set atomgroup as a property for access in other methods\n",
    "        self.atomgroup = atomgroup\n",
    "        # we can calculate masses now because they do not depend\n",
    "        # on the trajectory frame.\n",
    "        self.masses = self.atomgroup.masses\n",
    "        self.total_mass = np.sum(self.masses)\n",
    "    \n",
    "    def _prepare(self):\n",
    "        \"\"\"\n",
    "        Create array of zeroes as a placeholder for results.\n",
    "        This is run before we begin looping over the trajectory.\n",
    "        \"\"\"\n",
    "        # This must go here, instead of __init__, because\n",
    "        # it depends on the number of frames specified in run().\n",
    "        self.results = np.zeros((self.n_frames, 6))\n",
    "        # We put in 6 columns: 1 for the frame index, \n",
    "        # 1 for the time, 4 for the radii of gyration\n",
    "        \n",
    "    def _single_frame(self):\n",
    "        \"\"\"\n",
    "        This function is called for every frame that we choose\n",
    "        in run().\n",
    "        \"\"\"\n",
    "        # call our earlier function\n",
    "        rogs = radgyr(self.atomgroup, self.masses,\n",
    "                      total_mass=self.total_mass)\n",
    "        # save it into self.results\n",
    "        self.results[self._frame_index, 2:] = rogs\n",
    "        # the current timestep of the trajectory is self._ts\n",
    "        self.results[self._frame_index, 0] = self._ts.frame\n",
    "        # the actual trajectory is at self._trajectory\n",
    "        self.results[self._frame_index, 1] = self._trajectory.time\n",
    "        \n",
    "    def _conclude(self):\n",
    "        \"\"\"\n",
    "        Finish up by calculating an average and transforming our\n",
    "        results into a DataFrame.\n",
    "        \"\"\"\n",
    "        # by now self.result is fully populated\n",
    "        self.average = np.mean(self.results[:, 2:], axis=0)\n",
    "        columns = ['Frame', 'Time (ps)', 'Radius of Gyration',\n",
    "                   'Radius of Gyration (x-axis)',\n",
    "                   'Radius of Gyration (y-axis)',\n",
    "                   'Radius of Gyration (z-axis)',]\n",
    "        self.df = pd.DataFrame(self.results, columns=columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because `RadiusOfGyration2` calculates the masses of the selected AtomGroup itself, we do not need to pass it in ourselves."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-09T12:13:06.897781219Z",
     "start_time": "2023-06-09T12:13:06.645032775Z"
    },
    "execution": {
     "iopub.execute_input": "2021-05-19T05:45:47.598314Z",
     "iopub.status.busy": "2021-05-19T05:45:47.597633Z",
     "iopub.status.idle": "2021-05-19T05:45:47.684015Z",
     "shell.execute_reply": "2021-05-19T05:45:47.683397Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "504e4004c0f94406b0b62f17d4ae0b07",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/98 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rog_base = RadiusOfGyration2(protein, verbose=True).run()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As calculated in `_conclude()`, the average radii of gyrations are at `rog.average`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-09T12:13:06.911349878Z",
     "start_time": "2023-06-09T12:13:06.897010120Z"
    },
    "execution": {
     "iopub.execute_input": "2021-05-19T05:45:47.691297Z",
     "iopub.status.busy": "2021-05-19T05:45:47.690130Z",
     "iopub.status.idle": "2021-05-19T05:45:47.693665Z",
     "shell.execute_reply": "2021-05-19T05:45:47.694259Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([18.26549552, 12.85342131, 15.37359575, 16.29185734])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rog_base.average"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The results are available at `rog.results` as an array or `rog.df` as a DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-09T12:13:07.049282088Z",
     "start_time": "2023-06-09T12:13:06.908827827Z"
    },
    "execution": {
     "iopub.execute_input": "2021-05-19T05:45:47.700493Z",
     "iopub.status.busy": "2021-05-19T05:45:47.699598Z",
     "iopub.status.idle": "2021-05-19T05:45:47.714227Z",
     "shell.execute_reply": "2021-05-19T05:45:47.714684Z"
    }
   },
   "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>Frame</th>\n",
       "      <th>Time (ps)</th>\n",
       "      <th>Radius of Gyration</th>\n",
       "      <th>Radius of Gyration (x-axis)</th>\n",
       "      <th>Radius of Gyration (y-axis)</th>\n",
       "      <th>Radius of Gyration (z-axis)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>16.669018</td>\n",
       "      <td>12.679625</td>\n",
       "      <td>13.749343</td>\n",
       "      <td>14.349043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>16.673217</td>\n",
       "      <td>12.640025</td>\n",
       "      <td>13.760545</td>\n",
       "      <td>14.382960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.0</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>16.731454</td>\n",
       "      <td>12.696454</td>\n",
       "      <td>13.801342</td>\n",
       "      <td>14.429350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.0</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>16.722283</td>\n",
       "      <td>12.677194</td>\n",
       "      <td>13.780732</td>\n",
       "      <td>14.444711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.0</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>16.743961</td>\n",
       "      <td>12.646981</td>\n",
       "      <td>13.814553</td>\n",
       "      <td>14.489046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>93.0</td>\n",
       "      <td>93.999992</td>\n",
       "      <td>19.562034</td>\n",
       "      <td>13.421683</td>\n",
       "      <td>16.539112</td>\n",
       "      <td>17.653968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>94.0</td>\n",
       "      <td>94.999992</td>\n",
       "      <td>19.560575</td>\n",
       "      <td>13.451335</td>\n",
       "      <td>16.508649</td>\n",
       "      <td>17.656678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>95.0</td>\n",
       "      <td>95.999992</td>\n",
       "      <td>19.550571</td>\n",
       "      <td>13.445914</td>\n",
       "      <td>16.500640</td>\n",
       "      <td>17.646130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>96.0</td>\n",
       "      <td>96.999991</td>\n",
       "      <td>19.568381</td>\n",
       "      <td>13.443243</td>\n",
       "      <td>16.507396</td>\n",
       "      <td>17.681294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>97.0</td>\n",
       "      <td>97.999991</td>\n",
       "      <td>19.591575</td>\n",
       "      <td>13.442750</td>\n",
       "      <td>16.537926</td>\n",
       "      <td>17.704494</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>98 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Frame  Time (ps)  Radius of Gyration  Radius of Gyration (x-axis)  \\\n",
       "0     0.0   1.000000           16.669018                    12.679625   \n",
       "1     1.0   2.000000           16.673217                    12.640025   \n",
       "2     2.0   3.000000           16.731454                    12.696454   \n",
       "3     3.0   4.000000           16.722283                    12.677194   \n",
       "4     4.0   5.000000           16.743961                    12.646981   \n",
       "..    ...        ...                 ...                          ...   \n",
       "93   93.0  93.999992           19.562034                    13.421683   \n",
       "94   94.0  94.999992           19.560575                    13.451335   \n",
       "95   95.0  95.999992           19.550571                    13.445914   \n",
       "96   96.0  96.999991           19.568381                    13.443243   \n",
       "97   97.0  97.999991           19.591575                    13.442750   \n",
       "\n",
       "    Radius of Gyration (y-axis)  Radius of Gyration (z-axis)  \n",
       "0                     13.749343                    14.349043  \n",
       "1                     13.760545                    14.382960  \n",
       "2                     13.801342                    14.429350  \n",
       "3                     13.780732                    14.444711  \n",
       "4                     13.814553                    14.489046  \n",
       "..                          ...                          ...  \n",
       "93                    16.539112                    17.653968  \n",
       "94                    16.508649                    17.656678  \n",
       "95                    16.500640                    17.646130  \n",
       "96                    16.507396                    17.681294  \n",
       "97                    16.537926                    17.704494  \n",
       "\n",
       "[98 rows x 6 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rog_base.df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using this DataFrame we can easily plot our results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-09T12:13:07.497042457Z",
     "start_time": "2023-06-09T12:13:06.950725110Z"
    },
    "execution": {
     "iopub.execute_input": "2021-05-19T05:45:47.718968Z",
     "iopub.status.busy": "2021-05-19T05:45:47.718192Z",
     "iopub.status.idle": "2021-05-19T05:45:47.913871Z",
     "shell.execute_reply": "2021-05-19T05:45:47.914306Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Radius of gyration (A)')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = rog_base.df.plot(x='Time (ps)', y=rog_base.df.columns[2:])\n",
    "ax.set_ylabel('Radius of gyration (A)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also run the analysis over a subset of frames, the same as the output from `AnalysisFromFunction` and `analysis_class`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-09T12:13:07.609391687Z",
     "start_time": "2023-06-09T12:13:07.500656081Z"
    },
    "execution": {
     "iopub.execute_input": "2021-05-19T05:45:47.929324Z",
     "iopub.status.busy": "2021-05-19T05:45:47.920891Z",
     "iopub.status.idle": "2021-05-19T05:45:47.958180Z",
     "shell.execute_reply": "2021-05-19T05:45:47.957107Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b8fc6d3e50df475fa82e4bcf1046a117",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/10 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<__main__.RadiusOfGyration2 at 0x7f90006ca9d0>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rog_base_10 = RadiusOfGyration2(protein, verbose=True)\n",
    "rog_base_10.run(start=10, stop=80, step=7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-09T12:13:07.610115092Z",
     "start_time": "2023-06-09T12:13:07.546454574Z"
    },
    "execution": {
     "iopub.execute_input": "2021-05-19T05:45:47.962059Z",
     "iopub.status.busy": "2021-05-19T05:45:47.961505Z",
     "iopub.status.idle": "2021-05-19T05:45:47.963733Z",
     "shell.execute_reply": "2021-05-19T05:45:47.964108Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10, 6)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rog_base_10.results.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-09T12:13:07.611281463Z",
     "start_time": "2023-06-09T12:13:07.587365621Z"
    },
    "execution": {
     "iopub.execute_input": "2021-05-19T05:45:47.976103Z",
     "iopub.status.busy": "2021-05-19T05:45:47.974826Z",
     "iopub.status.idle": "2021-05-19T05:45:47.979238Z",
     "shell.execute_reply": "2021-05-19T05:45:47.979840Z"
    },
    "scrolled": true
   },
   "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>Frame</th>\n",
       "      <th>Time (ps)</th>\n",
       "      <th>Radius of Gyration</th>\n",
       "      <th>Radius of Gyration (x-axis)</th>\n",
       "      <th>Radius of Gyration (y-axis)</th>\n",
       "      <th>Radius of Gyration (z-axis)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10.0</td>\n",
       "      <td>10.999999</td>\n",
       "      <td>16.852127</td>\n",
       "      <td>12.584163</td>\n",
       "      <td>14.001589</td>\n",
       "      <td>14.614469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>17.0</td>\n",
       "      <td>17.999998</td>\n",
       "      <td>17.019587</td>\n",
       "      <td>12.544784</td>\n",
       "      <td>14.163276</td>\n",
       "      <td>14.878262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>24.0</td>\n",
       "      <td>24.999998</td>\n",
       "      <td>17.257429</td>\n",
       "      <td>12.514341</td>\n",
       "      <td>14.487021</td>\n",
       "      <td>15.137873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>31.0</td>\n",
       "      <td>31.999997</td>\n",
       "      <td>17.542565</td>\n",
       "      <td>12.522147</td>\n",
       "      <td>14.747461</td>\n",
       "      <td>15.530339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>38.0</td>\n",
       "      <td>38.999997</td>\n",
       "      <td>17.871241</td>\n",
       "      <td>12.482385</td>\n",
       "      <td>15.088865</td>\n",
       "      <td>15.977444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>45.0</td>\n",
       "      <td>45.999996</td>\n",
       "      <td>18.182243</td>\n",
       "      <td>12.533023</td>\n",
       "      <td>15.451285</td>\n",
       "      <td>16.290153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>52.0</td>\n",
       "      <td>52.999995</td>\n",
       "      <td>18.496493</td>\n",
       "      <td>12.771949</td>\n",
       "      <td>15.667003</td>\n",
       "      <td>16.603098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>59.0</td>\n",
       "      <td>59.999995</td>\n",
       "      <td>18.839346</td>\n",
       "      <td>13.037335</td>\n",
       "      <td>15.900327</td>\n",
       "      <td>16.942533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>66.0</td>\n",
       "      <td>66.999994</td>\n",
       "      <td>19.064333</td>\n",
       "      <td>13.061491</td>\n",
       "      <td>16.114195</td>\n",
       "      <td>17.222884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>73.0</td>\n",
       "      <td>73.999993</td>\n",
       "      <td>19.276639</td>\n",
       "      <td>13.161863</td>\n",
       "      <td>16.298539</td>\n",
       "      <td>17.444213</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Frame  Time (ps)  Radius of Gyration  Radius of Gyration (x-axis)  \\\n",
       "0   10.0  10.999999           16.852127                    12.584163   \n",
       "1   17.0  17.999998           17.019587                    12.544784   \n",
       "2   24.0  24.999998           17.257429                    12.514341   \n",
       "3   31.0  31.999997           17.542565                    12.522147   \n",
       "4   38.0  38.999997           17.871241                    12.482385   \n",
       "5   45.0  45.999996           18.182243                    12.533023   \n",
       "6   52.0  52.999995           18.496493                    12.771949   \n",
       "7   59.0  59.999995           18.839346                    13.037335   \n",
       "8   66.0  66.999994           19.064333                    13.061491   \n",
       "9   73.0  73.999993           19.276639                    13.161863   \n",
       "\n",
       "   Radius of Gyration (y-axis)  Radius of Gyration (z-axis)  \n",
       "0                    14.001589                    14.614469  \n",
       "1                    14.163276                    14.878262  \n",
       "2                    14.487021                    15.137873  \n",
       "3                    14.747461                    15.530339  \n",
       "4                    15.088865                    15.977444  \n",
       "5                    15.451285                    16.290153  \n",
       "6                    15.667003                    16.603098  \n",
       "7                    15.900327                    16.942533  \n",
       "8                    16.114195                    17.222884  \n",
       "9                    16.298539                    17.444213  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rog_base_10.df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-09T12:13:08.097097035Z",
     "start_time": "2023-06-09T12:13:07.588010651Z"
    },
    "execution": {
     "iopub.execute_input": "2021-05-19T05:45:48.013053Z",
     "iopub.status.busy": "2021-05-19T05:45:48.009500Z",
     "iopub.status.idle": "2021-05-19T05:45:48.151311Z",
     "shell.execute_reply": "2021-05-19T05:45:48.151749Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Radius of gyration (A)')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax_10 = rog_base_10.df.plot(x='Time (ps)', \n",
    "                            y=rog_base_10.df.columns[2:])\n",
    "ax_10.set_ylabel('Radius of gyration (A)')"
   ]
  },
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   "source": [
    "## Contributing to MDAnalysis\n",
    "\n",
    "If you think that you will want to reuse your new analysis, or that others might find it helpful, please consider [contributing it to the MDAnalysis codebase.](https://www.mdanalysis.org/UserGuide/contributing.html) Making your code open-source can have many benefits; others may notice an unexpected bug or suggest ways to optimise your code. If you write your analysis for a specific publication, please let us know; we will ask those who use your code to cite your reference in published work."
   ]
  },
  {
   "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>."
   ]
  }
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