{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hole module: Basic trajectory analysis\n",
    "\n",
    "[MDAnalysis](https://www.mdanalysis.org) contains the [MDAnalysis.analysis.hole](https://www.mdanalysis.org/docs/documentation_pages/analysis/hole.html) module that uses Oliver Smart's [HOLE](http://www.holeprogram.org/) program. This notebook shows how to make use of the basic functionality in `MDAnalysis.analysis.hole`. We will analyze the fluctuations in the pore radius profile of an ion channel (gramicidin A) from a simulation trajectory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "plt.style.use('ggplot')\n",
    "\n",
    "import MDAnalysis as mda\n",
    "import MDAnalysis.analysis.hole"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "MDAnalysis  : INFO     MDAnalysis 0.17.1-dev STARTED logging to 'MDAnalysis.log'\n"
     ]
    }
   ],
   "source": [
    "mda.start_logging()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I am testing HOLE on a simple \"trajectory\": I submitted gramicidin A (1grm, test file `1gmr_single.pdb`) to the [ElNemo](http://www.sciences.univ-nantes.fr/elnemo/) elastic network server. From an ENM it computed a number of modes and provided multi-frame PDB files showing these modes. I just picked the first mode (which is labelled mode 7), which shows a twist motion along the pore axis. Note that this \"trajectory\" is exaggerated and does not correspond to real protein motion.\n",
    "\n",
    "The trajectory is included with the MDAnalysis test files."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Volumes/Data/oliver/Biop/Projects/Methods/MDAnalysis/mdanalysis/testsuite/MDAnalysisTests/__init__.py:109: UserWarning: \n",
      "This call to matplotlib.use() has no effect because the backend has already\n",
      "been chosen; matplotlib.use() must be called *before* pylab, matplotlib.pyplot,\n",
      "or matplotlib.backends is imported for the first time.\n",
      "\n",
      "The backend was *originally* set to 'module://ipykernel.pylab.backend_inline' by the following code:\n",
      "  File \"/Users/oliver/anaconda3/envs/mda_develop/lib/python3.6/runpy.py\", line 193, in _run_module_as_main\n",
      "    \"__main__\", mod_spec)\n",
      "  File \"/Users/oliver/anaconda3/envs/mda_develop/lib/python3.6/runpy.py\", line 85, in _run_code\n",
      "    exec(code, run_globals)\n",
      "  File \"/Users/oliver/anaconda3/envs/mda_develop/lib/python3.6/site-packages/ipykernel_launcher.py\", line 16, in <module>\n",
      "    app.launch_new_instance()\n",
      "  File \"/Users/oliver/anaconda3/envs/mda_develop/lib/python3.6/site-packages/traitlets/config/application.py\", line 658, in launch_instance\n",
      "    app.start()\n",
      "  File \"/Users/oliver/anaconda3/envs/mda_develop/lib/python3.6/site-packages/ipykernel/kernelapp.py\", line 486, in start\n",
      "    self.io_loop.start()\n",
      "  File \"/Users/oliver/anaconda3/envs/mda_develop/lib/python3.6/site-packages/tornado/ioloop.py\", line 888, in start\n",
      "    handler_func(fd_obj, events)\n",
      "  File \"/Users/oliver/anaconda3/envs/mda_develop/lib/python3.6/site-packages/tornado/stack_context.py\", line 277, in null_wrapper\n",
      "    return fn(*args, **kwargs)\n",
      "  File \"/Users/oliver/anaconda3/envs/mda_develop/lib/python3.6/site-packages/zmq/eventloop/zmqstream.py\", line 450, in _handle_events\n",
      "    self._handle_recv()\n",
      "  File \"/Users/oliver/anaconda3/envs/mda_develop/lib/python3.6/site-packages/zmq/eventloop/zmqstream.py\", line 480, in _handle_recv\n",
      "    self._run_callback(callback, msg)\n",
      "  File \"/Users/oliver/anaconda3/envs/mda_develop/lib/python3.6/site-packages/zmq/eventloop/zmqstream.py\", line 432, in _run_callback\n",
      "    callback(*args, **kwargs)\n",
      "  File \"/Users/oliver/anaconda3/envs/mda_develop/lib/python3.6/site-packages/tornado/stack_context.py\", line 277, in null_wrapper\n",
      "    return fn(*args, **kwargs)\n",
      "  File \"/Users/oliver/anaconda3/envs/mda_develop/lib/python3.6/site-packages/ipykernel/kernelbase.py\", line 283, in dispatcher\n",
      "    return self.dispatch_shell(stream, msg)\n",
      "  File \"/Users/oliver/anaconda3/envs/mda_develop/lib/python3.6/site-packages/ipykernel/kernelbase.py\", line 233, in dispatch_shell\n",
      "    handler(stream, idents, msg)\n",
      "  File \"/Users/oliver/anaconda3/envs/mda_develop/lib/python3.6/site-packages/ipykernel/kernelbase.py\", line 399, in execute_request\n",
      "    user_expressions, allow_stdin)\n",
      "  File \"/Users/oliver/anaconda3/envs/mda_develop/lib/python3.6/site-packages/ipykernel/ipkernel.py\", line 208, in do_execute\n",
      "    res = shell.run_cell(code, store_history=store_history, silent=silent)\n",
      "  File \"/Users/oliver/anaconda3/envs/mda_develop/lib/python3.6/site-packages/ipykernel/zmqshell.py\", line 537, in run_cell\n",
      "    return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)\n",
      "  File \"/Users/oliver/anaconda3/envs/mda_develop/lib/python3.6/site-packages/IPython/core/interactiveshell.py\", line 2739, in run_cell\n",
      "    self.events.trigger('post_run_cell')\n",
      "  File \"/Users/oliver/anaconda3/envs/mda_develop/lib/python3.6/site-packages/IPython/core/events.py\", line 73, in trigger\n",
      "    func(*args, **kwargs)\n",
      "  File \"/Users/oliver/anaconda3/envs/mda_develop/lib/python3.6/site-packages/ipykernel/pylab/backend_inline.py\", line 160, in configure_once\n",
      "    activate_matplotlib(backend)\n",
      "  File \"/Users/oliver/anaconda3/envs/mda_develop/lib/python3.6/site-packages/IPython/core/pylabtools.py\", line 308, in activate_matplotlib\n",
      "    matplotlib.pyplot.switch_backend(backend)\n",
      "  File \"/Users/oliver/anaconda3/envs/mda_develop/lib/python3.6/site-packages/matplotlib/pyplot.py\", line 229, in switch_backend\n",
      "    matplotlib.use(newbackend, warn=False, force=True)\n",
      "  File \"/Users/oliver/anaconda3/envs/mda_develop/lib/python3.6/site-packages/matplotlib/__init__.py\", line 1305, in use\n",
      "    reload(sys.modules['matplotlib.backends'])\n",
      "  File \"/Users/oliver/anaconda3/envs/mda_develop/lib/python3.6/importlib/__init__.py\", line 166, in reload\n",
      "    _bootstrap._exec(spec, module)\n",
      "  File \"/Users/oliver/anaconda3/envs/mda_develop/lib/python3.6/site-packages/matplotlib/backends/__init__.py\", line 14, in <module>\n",
      "    line for line in traceback.format_stack()\n",
      "\n",
      "\n",
      "  matplotlib.use('agg')\n"
     ]
    }
   ],
   "source": [
    "from MDAnalysis.tests.datafiles import MULTIPDB_HOLE\n",
    "u = mda.Universe(MULTIPDB_HOLE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Running HOLE\n",
    "You need to install `HOLE` first, which is available from http://www.holeprogram.org/ under the Apache open source license.\n",
    "\n",
    "Set up the `HOLEtraj` class; provide a path to the `hole` executable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "H = MDAnalysis.analysis.hole.HOLEtraj(u, executable=\"~/hole2/exe/hole\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then run `hole` on the individual frames (this takes a while...)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "MDAnalysis.analysis.hole: INFO     HOLE analysis frame    0 (orderparameter 0)\n",
      "MDAnalysis.analysis.hole: INFO     Setting up HOLE analysis for '/var/folders/ds/z7mbf7nx1gn31308g31t9tcc0000gp/T/tmpc54h20_6.pdb'\n",
      "MDAnalysis.analysis.hole: INFO     Using radius file 'simple2.rad'\n",
      "MDAnalysis.analysis.hole: INFO     HOLE will guess CPOINT\n",
      "MDAnalysis.analysis.hole: INFO     HOLE will guess CVECT\n",
      "MDAnalysis.analysis.hole: INFO     Starting HOLE on '/var/folders/ds/z7mbf7nx1gn31308g31t9tcc0000gp/T/tmpc54h20_6.pdb' (trajectory: None)\n",
      "MDAnalysis.analysis.hole: INFO     HOLE finished: output file 'hole.out'\n",
      "MDAnalysis.analysis.hole: INFO     Collecting HOLE profiles for run with id 1\n",
      "MDAnalysis.analysis.hole: INFO     Run 1: Reading 1 HOLE profiles from 'hole.out'\n",
      "MDAnalysis.analysis.hole: INFO     Collected HOLE radius profiles for 1 frames\n",
      "MDAnalysis.analysis.hole: INFO     HOLE analysis frame    1 (orderparameter 1)\n",
      "MDAnalysis.analysis.hole: INFO     Setting up HOLE analysis for '/var/folders/ds/z7mbf7nx1gn31308g31t9tcc0000gp/T/tmpd2ksqofz.pdb'\n",
      "MDAnalysis.analysis.hole: INFO     Using radius file 'simple2.rad'\n",
      "MDAnalysis.analysis.hole: INFO     HOLE will guess CPOINT\n",
      "MDAnalysis.analysis.hole: INFO     HOLE will guess CVECT\n",
      "MDAnalysis.analysis.hole: INFO     Starting HOLE on '/var/folders/ds/z7mbf7nx1gn31308g31t9tcc0000gp/T/tmpd2ksqofz.pdb' (trajectory: None)\n",
      "MDAnalysis.analysis.hole: INFO     HOLE finished: output file 'hole.out'\n",
      "MDAnalysis.analysis.hole: INFO     Collecting HOLE profiles for run with id 1\n",
      "MDAnalysis.analysis.hole: INFO     Run 1: Reading 1 HOLE profiles from 'hole.out'\n",
      "MDAnalysis.analysis.hole: INFO     Collected HOLE radius profiles for 1 frames\n",
      "MDAnalysis.analysis.hole: INFO     HOLE analysis frame    2 (orderparameter 2)\n",
      "MDAnalysis.analysis.hole: INFO     Setting up HOLE analysis for '/var/folders/ds/z7mbf7nx1gn31308g31t9tcc0000gp/T/tmpxkp9n4md.pdb'\n",
      "MDAnalysis.analysis.hole: INFO     Using radius file 'simple2.rad'\n",
      "MDAnalysis.analysis.hole: INFO     HOLE will guess CPOINT\n",
      "MDAnalysis.analysis.hole: INFO     HOLE will guess CVECT\n",
      "MDAnalysis.analysis.hole: INFO     Starting HOLE on '/var/folders/ds/z7mbf7nx1gn31308g31t9tcc0000gp/T/tmpxkp9n4md.pdb' (trajectory: None)\n",
      "MDAnalysis.analysis.hole: INFO     HOLE finished: output file 'hole.out'\n",
      "MDAnalysis.analysis.hole: INFO     Collecting HOLE profiles for run with id 1\n",
      "MDAnalysis.analysis.hole: INFO     Run 1: Reading 1 HOLE profiles from 'hole.out'\n",
      "MDAnalysis.analysis.hole: INFO     Collected HOLE radius profiles for 1 frames\n",
      "MDAnalysis.analysis.hole: INFO     HOLE analysis frame    3 (orderparameter 3)\n",
      "MDAnalysis.analysis.hole: INFO     Setting up HOLE analysis for '/var/folders/ds/z7mbf7nx1gn31308g31t9tcc0000gp/T/tmp4rezxrfz.pdb'\n",
      "MDAnalysis.analysis.hole: INFO     Using radius file 'simple2.rad'\n",
      "MDAnalysis.analysis.hole: INFO     HOLE will guess CPOINT\n",
      "MDAnalysis.analysis.hole: INFO     HOLE will guess CVECT\n",
      "MDAnalysis.analysis.hole: INFO     Starting HOLE on '/var/folders/ds/z7mbf7nx1gn31308g31t9tcc0000gp/T/tmp4rezxrfz.pdb' (trajectory: None)\n",
      "MDAnalysis.analysis.hole: INFO     HOLE finished: output file 'hole.out'\n",
      "MDAnalysis.analysis.hole: INFO     Collecting HOLE profiles for run with id 1\n",
      "MDAnalysis.analysis.hole: INFO     Run 1: Reading 1 HOLE profiles from 'hole.out'\n",
      "MDAnalysis.analysis.hole: INFO     Collected HOLE radius profiles for 1 frames\n",
      "MDAnalysis.analysis.hole: INFO     HOLE analysis frame    4 (orderparameter 4)\n",
      "MDAnalysis.analysis.hole: INFO     Setting up HOLE analysis for '/var/folders/ds/z7mbf7nx1gn31308g31t9tcc0000gp/T/tmpgy7fauh3.pdb'\n",
      "MDAnalysis.analysis.hole: INFO     Using radius file 'simple2.rad'\n",
      "MDAnalysis.analysis.hole: INFO     HOLE will guess CPOINT\n",
      "MDAnalysis.analysis.hole: INFO     HOLE will guess CVECT\n",
      "MDAnalysis.analysis.hole: INFO     Starting HOLE on '/var/folders/ds/z7mbf7nx1gn31308g31t9tcc0000gp/T/tmpgy7fauh3.pdb' (trajectory: None)\n",
      "MDAnalysis.analysis.hole: INFO     HOLE finished: output file 'hole.out'\n",
      "MDAnalysis.analysis.hole: INFO     Collecting HOLE profiles for run with id 1\n",
      "MDAnalysis.analysis.hole: INFO     Run 1: Reading 1 HOLE profiles from 'hole.out'\n",
      "MDAnalysis.analysis.hole: INFO     Collected HOLE radius profiles for 1 frames\n",
      "MDAnalysis.analysis.hole: INFO     HOLE analysis frame    5 (orderparameter 5)\n",
      "MDAnalysis.analysis.hole: INFO     Setting up HOLE analysis for '/var/folders/ds/z7mbf7nx1gn31308g31t9tcc0000gp/T/tmpou9fm86u.pdb'\n",
      "MDAnalysis.analysis.hole: INFO     Using radius file 'simple2.rad'\n",
      "MDAnalysis.analysis.hole: INFO     HOLE will guess CPOINT\n",
      "MDAnalysis.analysis.hole: INFO     HOLE will guess CVECT\n",
      "MDAnalysis.analysis.hole: INFO     Starting HOLE on '/var/folders/ds/z7mbf7nx1gn31308g31t9tcc0000gp/T/tmpou9fm86u.pdb' (trajectory: None)\n",
      "MDAnalysis.analysis.hole: INFO     HOLE finished: output file 'hole.out'\n",
      "MDAnalysis.analysis.hole: INFO     Collecting HOLE profiles for run with id 1\n",
      "MDAnalysis.analysis.hole: INFO     Run 1: Reading 1 HOLE profiles from 'hole.out'\n",
      "MDAnalysis.analysis.hole: INFO     Collected HOLE radius profiles for 1 frames\n",
      "MDAnalysis.analysis.hole: INFO     HOLE analysis frame    6 (orderparameter 6)\n",
      "MDAnalysis.analysis.hole: INFO     Setting up HOLE analysis for '/var/folders/ds/z7mbf7nx1gn31308g31t9tcc0000gp/T/tmph9gqfrqx.pdb'\n",
      "MDAnalysis.analysis.hole: INFO     Using radius file 'simple2.rad'\n",
      "MDAnalysis.analysis.hole: INFO     HOLE will guess CPOINT\n",
      "MDAnalysis.analysis.hole: INFO     HOLE will guess CVECT\n",
      "MDAnalysis.analysis.hole: INFO     Starting HOLE on '/var/folders/ds/z7mbf7nx1gn31308g31t9tcc0000gp/T/tmph9gqfrqx.pdb' (trajectory: None)\n",
      "MDAnalysis.analysis.hole: INFO     HOLE finished: output file 'hole.out'\n",
      "MDAnalysis.analysis.hole: INFO     Collecting HOLE profiles for run with id 1\n",
      "MDAnalysis.analysis.hole: INFO     Run 1: Reading 1 HOLE profiles from 'hole.out'\n",
      "MDAnalysis.analysis.hole: INFO     Collected HOLE radius profiles for 1 frames\n",
      "MDAnalysis.analysis.hole: INFO     HOLE analysis frame    7 (orderparameter 7)\n",
      "MDAnalysis.analysis.hole: INFO     Setting up HOLE analysis for '/var/folders/ds/z7mbf7nx1gn31308g31t9tcc0000gp/T/tmpzx_kni6p.pdb'\n",
      "MDAnalysis.analysis.hole: INFO     Using radius file 'simple2.rad'\n",
      "MDAnalysis.analysis.hole: INFO     HOLE will guess CPOINT\n",
      "MDAnalysis.analysis.hole: INFO     HOLE will guess CVECT\n",
      "MDAnalysis.analysis.hole: INFO     Starting HOLE on '/var/folders/ds/z7mbf7nx1gn31308g31t9tcc0000gp/T/tmpzx_kni6p.pdb' (trajectory: None)\n",
      "MDAnalysis.analysis.hole: INFO     HOLE finished: output file 'hole.out'\n",
      "MDAnalysis.analysis.hole: INFO     Collecting HOLE profiles for run with id 1\n",
      "MDAnalysis.analysis.hole: INFO     Run 1: Reading 1 HOLE profiles from 'hole.out'\n",
      "MDAnalysis.analysis.hole: INFO     Collected HOLE radius profiles for 1 frames\n",
      "MDAnalysis.analysis.hole: INFO     HOLE analysis frame    8 (orderparameter 8)\n",
      "MDAnalysis.analysis.hole: INFO     Setting up HOLE analysis for '/var/folders/ds/z7mbf7nx1gn31308g31t9tcc0000gp/T/tmpk5c9fu7b.pdb'\n",
      "MDAnalysis.analysis.hole: INFO     Using radius file 'simple2.rad'\n",
      "MDAnalysis.analysis.hole: INFO     HOLE will guess CPOINT\n",
      "MDAnalysis.analysis.hole: INFO     HOLE will guess CVECT\n",
      "MDAnalysis.analysis.hole: INFO     Starting HOLE on '/var/folders/ds/z7mbf7nx1gn31308g31t9tcc0000gp/T/tmpk5c9fu7b.pdb' (trajectory: None)\n",
      "MDAnalysis.analysis.hole: INFO     HOLE finished: output file 'hole.out'\n",
      "MDAnalysis.analysis.hole: INFO     Collecting HOLE profiles for run with id 1\n",
      "MDAnalysis.analysis.hole: INFO     Run 1: Reading 1 HOLE profiles from 'hole.out'\n",
      "MDAnalysis.analysis.hole: INFO     Collected HOLE radius profiles for 1 frames\n",
      "MDAnalysis.analysis.hole: INFO     HOLE analysis frame    9 (orderparameter 9)\n",
      "MDAnalysis.analysis.hole: INFO     Setting up HOLE analysis for '/var/folders/ds/z7mbf7nx1gn31308g31t9tcc0000gp/T/tmp6u9jackb.pdb'\n",
      "MDAnalysis.analysis.hole: INFO     Using radius file 'simple2.rad'\n",
      "MDAnalysis.analysis.hole: INFO     HOLE will guess CPOINT\n",
      "MDAnalysis.analysis.hole: INFO     HOLE will guess CVECT\n",
      "MDAnalysis.analysis.hole: INFO     Starting HOLE on '/var/folders/ds/z7mbf7nx1gn31308g31t9tcc0000gp/T/tmp6u9jackb.pdb' (trajectory: None)\n",
      "MDAnalysis.analysis.hole: INFO     HOLE finished: output file 'hole.out'\n",
      "MDAnalysis.analysis.hole: INFO     Collecting HOLE profiles for run with id 1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "MDAnalysis.analysis.hole: INFO     Run 1: Reading 1 HOLE profiles from 'hole.out'\n",
      "MDAnalysis.analysis.hole: INFO     Collected HOLE radius profiles for 1 frames\n",
      "MDAnalysis.analysis.hole: INFO     HOLE analysis frame   10 (orderparameter 10)\n",
      "MDAnalysis.analysis.hole: INFO     Setting up HOLE analysis for '/var/folders/ds/z7mbf7nx1gn31308g31t9tcc0000gp/T/tmpdwimmp5j.pdb'\n",
      "MDAnalysis.analysis.hole: INFO     Using radius file 'simple2.rad'\n",
      "MDAnalysis.analysis.hole: INFO     HOLE will guess CPOINT\n",
      "MDAnalysis.analysis.hole: INFO     HOLE will guess CVECT\n",
      "MDAnalysis.analysis.hole: INFO     Starting HOLE on '/var/folders/ds/z7mbf7nx1gn31308g31t9tcc0000gp/T/tmpdwimmp5j.pdb' (trajectory: None)\n",
      "MDAnalysis.analysis.hole: INFO     HOLE finished: output file 'hole.out'\n",
      "MDAnalysis.analysis.hole: INFO     Collecting HOLE profiles for run with id 1\n",
      "MDAnalysis.analysis.hole: INFO     Run 1: Reading 1 HOLE profiles from 'hole.out'\n",
      "MDAnalysis.analysis.hole: INFO     Collected HOLE radius profiles for 1 frames\n"
     ]
    }
   ],
   "source": [
    "H.run()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The trajectory contains 11 frames. We are typically interested in the *HOLE profile*, i.e., a plot of the pore radius as determined by HOLE against the reaction coordinate (often close to $z$ along the pore axis for simple channels).\n",
    "\n",
    "All profiles are stored as\n",
    "```python\n",
    "H.profiles\n",
    "```\n",
    "which is a `dict`. The keys are the frame numbers (or a user supplied reaction coordinate for the conformation). The values are record arrays with columns `frame` (the reaction coordinate for the conformation), `rxncoord` (the HOLE pore reaction coordinate), and `radius` (radius in Angstrom).\n",
    "\n",
    "\n",
    "The `H.plot()` method plots all the profiles together, the `H.plot3D()` stacks them:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a5f8470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = H.plot()\n",
    "ax.set_ylim(1, 4)\n",
    "ax.set_xlim(-15, 25)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.Axes3DSubplot at 0x11deb7668>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11bdfdd30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "H.plot3D(rmax=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Working with HOLE profiles\n",
    "The output from HOLE is a pore profile $R(\\zeta)$ (radius $R$ vs pore coordinate $\\zeta$).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pore profile data structure\n",
    "All pore profiles are stored in a `dict` named `H.profiles` that is indexed by the frame number. Each pore profile itself is a array containig rows with *frame number* (all identical), *pore coordinate*, and *pore radius*. For more details, see the docs on  [HOLE data structures](http://pythonhosted.org/MDAnalysis/documentation_pages/analysis/hole.html#data-structures)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "H.profiles"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Accessing profiles on a per-frame basis\n",
    "To get at individual profiles in we can iterate over `H.profiles`. However, as a convenience, just iterating over the `HOLEtraj` instance itself will return the profiles in *frame-sorted order* (thanks to [HOLEtraj.sorted_profiles_iter()](http://pythonhosted.org/MDAnalysis/documentation_pages/analysis/hole.html#MDAnalysis.analysis.hole.HOLEtraj.sorted_profiles_iter)).\n",
    "\n",
    "For example, getting the minimum radius "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "frame 0: min(R) = 1.61259\n",
      "frame 1: min(R) = 1.56638\n",
      "frame 2: min(R) = 1.5328\n",
      "frame 3: min(R) = 1.42548\n",
      "frame 4: min(R) = 1.24337\n",
      "frame 5: min(R) = 1.19749\n",
      "frame 6: min(R) = 1.2947\n",
      "frame 7: min(R) = 1.44292\n",
      "frame 8: min(R) = 1.51135\n",
      "frame 9: min(R) = 1.52775\n",
      "frame 10: min(R) = 1.55973\n"
     ]
    }
   ],
   "source": [
    "for frame, profile in H:\n",
    "    print(\"frame {}: min(R) = {}\".format(frame, profile.radius.min()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(For a slightly more advanced [analysis of minimum pore radius as function of a order parameter](http://nbviewer.jupyter.org/gist/orbeckst/a0c856f58059112538591af108df6d59#Analysis-of-the-minimum-pore-radius) see the [Advanced HOLE Hacking Notebook](http://nbviewer.jupyter.org/gist/orbeckst/a0c856f58059112538591af108df6d59).)\n",
    "\n",
    "TODO: Change these links to Binder notebooks."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Extracting data to a file\n",
    "Sometimes you want to have the pore profiles in a simple data file. Here we are using the popular [XMGRACE](http://plasma-gate.weizmann.ac.il/Grace/) XY format where each row will contain\n",
    "```\n",
    "   pore_coordinate   pore_radius\n",
    "```\n",
    "and datasets for frames are separated by\n",
    "```\n",
    "&\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"profiles.xvg\", \"w\") as xvg:\n",
    "    for frame, profile in H:\n",
    "        for _, zeta, radius in profile:\n",
    "            xvg.write(\"{0} {1}\\n\".format(zeta, radius))\n",
    "        xvg.write(\"&\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The corresponding output file looks like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "23.287 12.19109\n",
      "23.387 12.30165\n",
      "23.487 12.3973\n",
      "23.587 12.49308\n",
      "23.687 12.5897\n",
      "23.787 12.68495\n",
      "23.887 12.79422\n",
      "23.987 13.12993\n",
      "24.087 14.08415\n",
      "24.187 15.57652\n",
      "24.287 16.7467\n",
      "24.387 18.19425\n",
      "&\n",
      "-22.41283 20.68026\n",
      "-22.31283 18.25184\n",
      "-22.21283 16.22098\n",
      "-22.11283 14.19682\n",
      "-22.01283 12.62771\n",
      "-21.91283 11.46761\n",
      "-21.81283 10.71226\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"\".join(open(\"profiles.xvg\").readlines()[465:485]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One can use the above code to write the profiles in any format; for instance, it would be easy to write each frame's profile to a separate file by moving the `with` statement *inside* the outer `for` loop."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating an \"average\" HOLE profile "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At the moment (MDAnalysis 0.17.0) does not have a method to compute an average HOLE profile. However, in the following I demonstrate how this can be accomplished using the [numkit.timeseries.regularized_function](http://gromacswrapper.readthedocs.io/en/latest/numkit/timeseries.html#numkit.timeseries.regularized_function) (a function found in [GromacsWrapper](http://gromacswrapper.readthedocs.io/)) but we can also use it independently (I copied it from [numkit/timeseries.py](https://github.com/Becksteinlab/GromacsWrapper/blob/develop/numkit/timeseries.py#L519)):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import numpy\n",
    "\n",
    "def regularized_function(x, y, func, bins=100, range=None):\n",
    "    \"\"\"Compute *func()* over data aggregated in bins.\n",
    "    ``(x,y) --> (x', func(Y'))``  with ``Y' = {y: y(x) where x in x' bin}``\n",
    "    First the data is collected in bins x' along x and then *func* is\n",
    "    applied to all data points Y' that have been collected in the bin.\n",
    "    .. function:: func(y) -> float\n",
    "       *func* takes exactly one argument, a numpy 1D array *y* (the\n",
    "       values in a single bin of the histogram), and reduces it to one\n",
    "       scalar float.\n",
    "    .. Note:: *x* and *y* must be 1D arrays.\n",
    "    :Arguments:\n",
    "       x\n",
    "          abscissa values (for binning)\n",
    "       y\n",
    "          ordinate values (func is applied)\n",
    "       func\n",
    "          a numpy ufunc that takes one argument, func(Y')\n",
    "       bins\n",
    "          number or array\n",
    "       range\n",
    "          limits (used with number of bins)\n",
    "    :Returns:\n",
    "       F,edges\n",
    "          function and edges (``midpoints = 0.5*(edges[:-1]+edges[1:])``)\n",
    "    (This function originated as\n",
    "    :func:`recsql.sqlfunctions.regularized_function`.)\n",
    "    \"\"\"\n",
    "    _x = numpy.asarray(x)\n",
    "    _y = numpy.asarray(y)\n",
    "\n",
    "    if len(_x.shape) != 1 or len(_y.shape) != 1:\n",
    "        raise TypeError(\"Can only deal with 1D arrays.\")\n",
    "\n",
    "    # setup of bins (taken from numpy.histogram)\n",
    "    if (range is not None):\n",
    "        mn, mx = range\n",
    "        if (mn > mx):\n",
    "            raise AttributeError('max must be larger than min in range parameter.')\n",
    "\n",
    "    if not numpy.iterable(bins):\n",
    "        if range is None:\n",
    "            range = (_x.min(), _x.max())\n",
    "        mn, mx = [float(mi) for mi in range]\n",
    "        if mn == mx:\n",
    "            mn -= 0.5\n",
    "            mx += 0.5\n",
    "        bins = numpy.linspace(mn, mx, bins+1, endpoint=True)\n",
    "    else:\n",
    "        bins = numpy.asarray(bins)\n",
    "        if (numpy.diff(bins) < 0).any():\n",
    "            raise ValueError('bins must increase monotonically.')\n",
    "\n",
    "    sorting_index = numpy.argsort(_x)\n",
    "    sx = _x[sorting_index]\n",
    "    sy = _y[sorting_index]\n",
    "\n",
    "    # boundaries in SORTED data that demarcate bins; position in bin_index is the bin number\n",
    "    bin_index = numpy.r_[sx.searchsorted(bins[:-1], 'left'),\n",
    "                         sx.searchsorted(bins[-1], 'right')]\n",
    "\n",
    "    # naive implementation: apply operator to each chunk = sy[start:stop] separately\n",
    "    #\n",
    "    # It's not clear to me how one could effectively block this procedure (cf\n",
    "    # block = 65536 in numpy.histogram) because there does not seem to be a\n",
    "    # general way to combine the chunks for different blocks, just think of\n",
    "    # func=median\n",
    "    F = numpy.zeros(len(bins)-1)  # final function\n",
    "    F[:] = [func(sy[start:stop]) for start,stop in zip(bin_index[:-1],bin_index[1:])]\n",
    "    return F,bins"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The strategy is to collect all data and the histogram the radii over the HOLE reactioncoordinate:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "rxncoord = np.concatenate([profile.rxncoord for frame, profile in H.sorted_profiles_iter()])\n",
    "radii = np.concatenate([profile.radius for frame, profile in H.sorted_profiles_iter()])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now use `regularized_function`.\n",
    "\n",
    "Normally I would import it as\n",
    "```python\n",
    "from numkit.timeseries import regularized_function\n",
    "```\n",
    "but for this notebook it is not necessary to install [numkit](https://github.com/Becksteinlab/numkit) (`pip install numkit`...)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The function to reduce the data in each bin is either just the mean (`np.mean`) or the standard deviation (`np.std`). (For more ideas what else to use see the numkit notes on [coarse graining timeseries](http://gromacswrapper.readthedocs.io/en/latest/numkit/timeseries.html#coarse-graining-time-series).)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_r, q = regularized_function(rxncoord, radii, np.mean, bins=100)\n",
    "std_r, q = regularized_function(rxncoord, radii, np.std, bins=100)\n",
    "zeta = 0.5*(q[1:] + q[:-1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that `regularized_function()` returns the reduced data in each bin $i$ and the bin edges (the bin $i$ is defined as $\\{q: q_i \\leq q < q_{i+1}\\}$). To plot over the bin midpoints, we compute them as $\\zeta_i = \\frac{1}{2}(q_i + q_{i+1})$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11cb48048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.subplot(111)\n",
    "ax.fill_between(zeta, mean_r - std_r, mean_r + std_r, color=\"blue\", alpha=0.3)\n",
    "ax.plot(zeta, mean_r, color=\"blue\", lw=2)\n",
    "ax.set_xlabel(r\"pore coordinate $\\zeta$ ($\\AA$)\")\n",
    "ax.set_ylabel(r\"HOLE radius $R$ ($\\AA$)\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is a plot of the mean pore radius (solid line) with the bands showing the standard deviation of the radius over the trajectory."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Further reading\n",
    "* [MDAnalysis.analysis.hole](https://www.mdanalysis.org/docs/documentation_pages/analysis/hole.html) documentation\n",
    "* For more advanced uses see the notebook [Using MDAnalysis and HOLE to create pore profiles along structural order parameters](https://nbviewer.jupyter.org/github/MDAnalysis/binder-notebook/blob/master/analysis/hole-orderparameter.ipynb)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.4"
  },
  "widgets": {
   "state": {},
   "version": "1.1.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}