gh_system_viewer.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import scipy.sparse as sps\n",
    "import scipy.sparse.linalg as spsl\n",
    "import time\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as patches\n",
    "%matplotlib inline  \n",
    "\n",
    "from utils.archiver import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "unarch = Unarchiver('/home/epz/scratch/test_gh_system.arch')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11\n"
     ]
    }
   ],
   "source": [
    "G = unarch.G\n",
    "h = unarch.h\n",
    "G = G.tocsr()\n",
    "(N,N) = G.shape\n",
    "n = int(np.round(np.power(N / 7,1./3.)))\n",
    "print n\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.savez('/home/epz/scratch/test_gh_system_{}.npz'.format(n), \n",
    "         G_data=G.data, G_indices=G.indices, G_indptr=G.indptr, h=h)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.966683864594\n",
      "6.14702999115e-13\n"
     ]
    }
   ],
   "source": [
    "start = time.time()\n",
    "x = spsl.spsolve(G,h)\n",
    "print time.time() - start\n",
    "print np.linalg.norm(G*x - h)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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qEsUgJC2LtCy6yCsM2Xj/daH86SQfmP59FjOx7o9z2Re/m0n5/7n2ZqqCgnxlodCjqEgU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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff21a14a710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig1 = plt.figure()\n",
    "ax1 = fig1.add_subplot(111, aspect='equal')\n",
    "N = n**3 + 1  # 1 for augmentation var\n",
    "\n",
    "# Red is M\n",
    "ax1.add_patch(\n",
    "    patches.Rectangle(\n",
    "        (0, 3*N),   # (x,y)\n",
    "        4*N,          # width\n",
    "        4*N,          # height\n",
    "        alpha=0.2,\n",
    "        fc='r'\n",
    "    ),\n",
    ")\n",
    "\n",
    "ax1.add_patch(\n",
    "    patches.Rectangle(\n",
    "        (N, 0),   # (x,y)\n",
    "        3*N,          # width\n",
    "        3*N,          # height\n",
    "        alpha=0.2,\n",
    "        fc='g'\n",
    "    ),\n",
    ")\n",
    "ax1.add_patch(\n",
    "    patches.Rectangle(\n",
    "        (N, 0),   # (x,y)\n",
    "        3*N,          # width\n",
    "        3*N,          # height\n",
    "        alpha=0.2,\n",
    "        fc='b'\n",
    "    ),\n",
    ")\n",
    "ax1.add_patch(\n",
    "    patches.Rectangle(\n",
    "        (4*N, 0),   # (x,y)\n",
    "        3*N,          # width\n",
    "        3*N,          # height\n",
    "        alpha=0.2,\n",
    "        fc='g'\n",
    "    ),\n",
    ")\n",
    "ax1.add_patch(\n",
    "    patches.Rectangle(\n",
    "        (4*N, 4*N),   # (x,y)\n",
    "        3*N,          # width\n",
    "        3*N,          # height\n",
    "        alpha=0.2,\n",
    "        fc='c'\n",
    "    ),\n",
    ")\n",
    "\n",
    "ax1.spy(G,precision=0.05, markersize=2)\n",
    "plt.savefig('/home/epz/scratch/gh_system_{}.png'.format(n))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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