channel_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import openpathsampling as paths\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Channel Analysis Example\n",
    "\n",
    "First, let's load up the storage and load things from the storage. Before running this example, you'll need to run the preparatory script, `channel_analysis_setup.py`, which will generate the appropriate file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "storage = paths.Storage('channel_analysis_sim.nc', 'r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cv_x = storage.cvs['xval']\n",
    "cv_y = storage.cvs['yval']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's quickly look at the path density, just in order to see what our channels look like."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 4min 51s, sys: 475 ms, total: 4min 52s\n",
      "Wall time: 4min 52s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "path_dens = paths.PathDensityHistogram([cv_x, cv_y], (0.0, 0.0), (0.05, 0.05))\n",
    "hist = path_dens.histogram([s.active[0].trajectory for s in storage.steps])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXMAAAD8CAYAAACFK0QrAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n9TthIxI0AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x106df73d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotter = paths.numerics.HistogramPlotter2D(path_dens)\n",
    "plotter.plot(cmap=\"Blues\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The hill in the potential causes the white gap centered around $(0.1, 0.0)$. Based on the path density plot, we'll take two regions that define the two channels (`hi` and `lo`, above and below that gap, respectively)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x_range = paths.CVDefinedVolume(collectivevariable=cv_x,\n",
    "                                lambda_min=-0.2,\n",
    "                                lambda_max=0.2)\n",
    "y_range_hi = paths.CVDefinedVolume(collectivevariable=cv_y,\n",
    "                                   lambda_min=0.05,\n",
    "                                   lambda_max=1.0)\n",
    "y_range_lo = paths.CVDefinedVolume(collectivevariable=cv_y,\n",
    "                                   lambda_min=-1.0,\n",
    "                                   lambda_max=-0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "hi_channel_vol = x_range & y_range_hi\n",
    "lo_channel_vol = x_range & y_range_lo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The channels themselves are ensembles. Trajectories in the `hi` channel must have a segment with all frames in `hi_channel_vol`, and `lo` is defined analogously. With the `channels` dictionary defined, we do the analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "channels = {'hi': paths.AllInXEnsemble(hi_channel_vol),\n",
    "            'lo': paths.AllInXEnsemble(lo_channel_vol)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1min 59s, sys: 338 ms, total: 1min 59s\n",
      "Wall time: 2min\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "channel_analysis = paths.ChannelAnalysis(steps=storage.steps, channels=channels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that it is theoretically possible for a path to enter the volumes for both the `hi` and `lo` channels. More complicated ensembles could make that more difficult (e.g., requiring a segment that starts left of the `hi_channel_vol`, enters `hi_channel_vol`, then goes to the right of it). That modification is left as an exercise to the reader. However, the channel analysis code also allows us to explicitly consider the case that multiple channels are visited. We can change that after the primary analysis by changing the `treat_multiples` attribute. Valid options are `'all'`, `'newest'`, `'oldest'`, or `'multiple'`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "channel_analysis.treat_multiples = 'multiple'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hi</th>\n",
       "      <th>lo</th>\n",
       "      <th>hi,lo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>hi</th>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lo</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hi,lo</th>\n",
       "      <td>56</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       hi  lo  hi,lo\n",
       "hi      0   5     55\n",
       "lo      3   0     46\n",
       "hi,lo  56  45      0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "channel_analysis.switching_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most switching occurs through cases with the combination of `hi` and `lo` channels. Now let's plot the residence time histograms for each."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX4AAAD8CAYAAABw1c+bAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1127fc350>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(channel_analysis.residence_times['hi']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nB2ji/wC8gvvpoLO1jAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11dc7c9d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(channel_analysis.residence_times['lo']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nuiQ1wkKXpEZY6JLUCAtdkhrxv+jESwdoH5bwAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x106aea910>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(channel_analysis.residence_times['hi,lo']);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that all of these are at least somewhat Poissonian. What's particularly interesting, however, is that the residence times for the combination `hi,lo` are much shorter than either of the other channels, even though most of the switching involves `hi,lo`. This means that we switch into `hi,lo`, and then rapidly switch out to either `hi` or `lo`, and that's the primary mechanism of switching with these channel definitions."
   ]
  }
 ],
 "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": "3.7.10"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": true,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": true
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}