WMSE-beta-list-fully-connected.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plot\n",
    "\n",
    "from IPython.display import display, clear_output\n",
    "import ipywidgets as widgets\n",
    "from ipywidgets import interact, interactive\n",
    "\n",
    "from moviepy.editor import VideoClip\n",
    "\n",
    "from wmse import *\n",
    "from wmse_sequence import WMSESequencialModel, WMSESequencialModelBuilder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = WMSESequencialModel(WMSESequencialModelBuilder(3.0).add_pike(10)\n",
    "                                                           .add_pike(10)\n",
    "                                                           .add_pike(10)\n",
    "                                                           .add_pike(10)\n",
    "                                                           .add_connection(1, 2, 5, 1.0, 3.0, 1)\n",
    "                                                           .add_connection(2, 2, 5, 1.0, 3.0, 1)\n",
    "                                                           .add_connection(3, 2, 5, 1.0, 3.0, 1)\n",
    "                           )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def see_map(t):\n",
    "    fig = plot.figure(figsize=(6, 6), )\n",
    "    ax = model.visualize(t, False)\n",
    "    plot.title('temperature = %.3f' % t, fontsize='xx-large')\n",
    "    fig.canvas.draw()\n",
    "    image = np.array(fig.canvas.renderer._renderer)[:,:,:-1] \n",
    "    return image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d74c72a044f4432189ea791f67506399",
       "version_major": 2,
       "version_minor": 0
      },
      "text/html": [
       "<p>Failed to display Jupyter Widget of type <code>interactive</code>.</p>\n",
       "<p>\n",
       "  If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
       "  that the widgets JavaScript is still loading. If this message persists, it\n",
       "  likely means that the widgets JavaScript library is either not installed or\n",
       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
       "  Widgets Documentation</a> for setup instructions.\n",
       "</p>\n",
       "<p>\n",
       "  If you're reading this message in another frontend (for example, a static\n",
       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
       "  it may mean that your frontend doesn't currently support widgets.\n",
       "</p>\n"
      ],
      "text/plain": [
       "interactive(children=(FloatSlider(value=0.165, description=u't', max=0.17, min=0.16, step=0.001), Output()), _dom_classes=('widget-interact',))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<function __main__.interactive_plot>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def interactive_plot(t):\n",
    "    see_map(t)\n",
    "    plot.show()\n",
    "\n",
    "interact(interactive_plot, t = (0.16, 0.17, 0.001))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[MoviePy] >>>> Building video melting-fully-connected.mp4\n",
      "[MoviePy] Writing video melting-fully-connected.mp4\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████▉| 240/241 [00:25<00:00,  9.57it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[MoviePy] Done.\n",
      "[MoviePy] >>>> Video ready: melting-fully-connected.mp4 \n",
      "\n",
      "fine\n"
     ]
    }
   ],
   "source": [
    "def render_frame(t):\n",
    "    image = see_map(t)\n",
    "\n",
    "    plot.close()\n",
    "    return image\n",
    "    \n",
    "animation = VideoClip(lambda x: render_frame(0.16 + x*0.001), duration = 10.0)\n",
    "animation.write_videofile('melting-fully-connected.mp4', fps=24)\n",
    "print 'fine'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "temperatures = np.arange(0.165, 0.185, 0.0001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "melting_curve = map(lambda t: model.get_link_probability(t)[1], temperatures) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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jFIBXXSRJkpS3LC5i/PB+TB89iJ97dzFJkiTlKYuLALjsxFHMf3U7r+14PesokiRJ0v9g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\n198AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2cd4af64d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot.figure(figsize=(13, 8))\n",
    "diff = np.diff(melting_curve)\n",
    "plot.plot(-diff)\n",
    "plot.title('differential melting curve for $\\\\beta$ list')   \n",
    "plot.legend(fontsize='large')\n",
    "plot.xlabel('temperature')\n",
    "plot.ylabel('$\\\\frac{dp}{dt}$', fontsize='xx-large')\n",
    "plot.xticks(range(0, len(temperatures), 10), np.round(temperatures[::10], decimals=3))\n",
    "plot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAEgAAADuCAYAAACaqEG0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2cd4af6750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f2cd4d70b90>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.visualize(0.1, auto_display=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}