Fixed typos and bugs.

2015-08-08 18:09:13 -07:00
1 parent 2ad95fa0e1
commit f7fc01bf68
@@ -364,9 +364,9 @@
    },
    {
     "data": {
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      "text/plain": [
-       "<matplotlib.figure.Figure at 0x6e37518>"
+       "<matplotlib.figure.Figure at 0x6dde4e0>"
      ]
     },
     "metadata": {},
@@ -710,9 +710,9 @@
    },
    {
     "data": {
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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f2e5f8>"
+       "<matplotlib.figure.Figure at 0x7ec82e8>"
      ]
     },
     "metadata": {},
@@ -786,9 +786,9 @@
    },
    {
     "data": {
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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f9eda0>"
+       "<matplotlib.figure.Figure at 0x7f397b8>"
      ]
     },
     "metadata": {},
@@ -884,9 +884,9 @@
    },
    {
     "data": {
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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f30470>"
+       "<matplotlib.figure.Figure at 0x7eaaf60>"
      ]
     },
     "metadata": {},
@@ -996,7 +996,7 @@
     "data": {
      "image/png": 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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f607f0>"
+       "<matplotlib.figure.Figure at 0x7eaac88>"
      ]
     },
     "metadata": {},
@@ -1201,7 +1201,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 87,
+   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
@@ -1312,7 +1312,7 @@
     "data": {
      "image/png": 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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fda898>"
+       "<matplotlib.figure.Figure at 0x8050dd8>"
      ]
     },
     "metadata": {},
@@ -1364,7 +1364,7 @@
     "data": {
      "image/png": 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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x808c780>"
+       "<matplotlib.figure.Figure at 0x7facb38>"
      ]
     },
     "metadata": {},
@@ -1397,7 +1397,7 @@
     "data": {
      "image/png": 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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fb1f60>"
+       "<matplotlib.figure.Figure at 0x7f05dd8>"
      ]
     },
     "metadata": {},
@@ -1430,7 +1430,7 @@
     "data": {
      "image/png": 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      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7ff9b38>"
+       "<matplotlib.figure.Figure at 0x7f99fd0>"
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     },
     "metadata": {},
@@ -1467,7 +1467,7 @@
     "data": {
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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x8128198>"
+       "<matplotlib.figure.Figure at 0x7fa3358>"
      ]
     },
     "metadata": {},
@@ -1508,7 +1508,7 @@
     "data": {
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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x91dabe0>"
+       "<matplotlib.figure.Figure at 0x914c128>"
      ]
     },
     "metadata": {},
@@ -1543,7 +1543,7 @@
     "data": {
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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x91e0550>"
+       "<matplotlib.figure.Figure at 0x90efd68>"
      ]
     },
     "metadata": {},
@@ -1582,7 +1582,7 @@
     "data": {
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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x91d44a8>"
+       "<matplotlib.figure.Figure at 0x91c48d0>"
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     },
     "metadata": {},
@@ -1625,7 +1625,7 @@
     "data": {
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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x919cc18>"
+       "<matplotlib.figure.Figure at 0x90e4a90>"
      ]
     },
     "metadata": {},
@@ -1658,7 +1658,7 @@
     "data": {
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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x919c208>"
+       "<matplotlib.figure.Figure at 0x912cf60>"
      ]
     },
     "metadata": {},
@@ -1668,7 +1668,7 @@
     "data": {
      "image/png": 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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x81025f8>"
+       "<matplotlib.figure.Figure at 0x7f05080>"
      ]
     },
     "metadata": {},
@@ -1710,7 +1710,7 @@
     "data": {
      "image/png": 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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x8102c88>"
+       "<matplotlib.figure.Figure at 0x7f05a58>"
      ]
     },
     "metadata": {},
@@ -1720,7 +1720,7 @@
     "data": {
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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f930f0>"
+       "<matplotlib.figure.Figure at 0x80382e8>"
      ]
     },
     "metadata": {},
@@ -1762,7 +1762,7 @@
     "data": {
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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x91e75f8>"
+       "<matplotlib.figure.Figure at 0x8058358>"
      ]
     },
     "metadata": {},
@@ -1803,7 +1803,7 @@
     "data": {
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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x91e7278>"
+       "<matplotlib.figure.Figure at 0x807f860>"
      ]
     },
     "metadata": {},
@@ -1859,7 +1859,7 @@
     "data": {
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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f93400>"
+       "<matplotlib.figure.Figure at 0x80a3ba8>"
      ]
     },
     "metadata": {},
@@ -1869,7 +1869,7 @@
     "data": {
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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x9107dd8>"
+       "<matplotlib.figure.Figure at 0x6e67e48>"
      ]
     },
     "metadata": {},
@@ -1909,7 +1909,7 @@
     "data": {
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      "text/plain": [
-       "<matplotlib.figure.Figure at 0x92ed400>"
+       "<matplotlib.figure.Figure at 0x7fd0b70>"
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     "metadata": {},
@@ -1919,7 +1919,7 @@
     "data": {
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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x92eda20>"
+       "<matplotlib.figure.Figure at 0x92939b0>"
      ]
     },
     "metadata": {},
@@ -1954,7 +1954,7 @@
     "data": {
      "image/png": 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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f85e10>"
+       "<matplotlib.figure.Figure at 0x91ffac8>"
      ]
     },
     "metadata": {},
@@ -1964,7 +1964,7 @@
     "data": {
      "image/png": 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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f85748>"
+       "<matplotlib.figure.Figure at 0x91ff518>"
      ]
     },
     "metadata": {},
@@ -2025,7 +2025,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
@@ -2043,7 +2043,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 89,
+   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
@@ -2064,7 +2064,7 @@
    "est_xs, ps, _, _ = kf2.batch_filter(zs)\n",
    "\n",
    "nees = NEES (xs, est_xs, ps)\n",
-    "eps = np.mean(ness)\n",
+    "eps = np.mean(nees)\n",
    "\n",
    "print('mean NEES is: ', eps)\n",
    "if eps < kf2.dim_x:\n",
@@ -2117,16 +2117,16 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 106,
+   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
-      "image/png": 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truncated
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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x9a15320>"
+       "<matplotlib.figure.Figure at 0x7380b70>"
      ]
     },
     "metadata": {},
@@ -2158,16 +2158,16 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 109,
+   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
-      "image/png": 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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x922be80>"
+       "<matplotlib.figure.Figure at 0x9174550>"
      ]
     },
     "metadata": {},
@@ -2249,11 +2249,29 @@
  },
  {
   "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "fusion std: 0.391\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtsAAAEWCAYAAAC37ltfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x956a518>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
   "source": [
    "from numpy import array, asarray\n",
    "import numpy.random as random\n",
@@ -2318,12 +2336,23 @@
  },
  {
   "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 43,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtsAAAEWCAYAAAC37ltfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x91ba8d0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
   "source": [
    "from kf_design_internal import sensor_fusion_kf, set_radar_pos\n",
    "\n",
@@ -2343,12 +2372,23 @@
  },
  {
   "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 44,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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truncated
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x91ba748>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
   "source": [
    "from math import radians\n",
    "\n",
@@ -2385,11 +2425,22 @@
  },
  {
   "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtsAAAEWCAYAAAC37ltfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x91e0be0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
   "source": [
    "# compute position and covariance from first radar station\n",
    "set_radar_pos((150, 50))\n",
@@ -2459,14 +2510,56 @@
  },
  {
   "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 46,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x6e15940>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
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truncated
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x91ffbe0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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truncated
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x919b710>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(5.0, 0.02)"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
   "source": [
    "np.random.seed(124)\n",
+    "R=5.\n",
    "xs, zs = simulate_acc_system(R=R, Q=Q, count=30)\n",
    "\n",
    "kf0 = SecondOrderKF(R, Q, dt=1)\n",
@@ -2477,12 +2570,13 @@
    "fxs0, ps0 = filter_data(kf0, zs)\n",
    "\n",
    "# filter twice more, using P as error\n",
-    "fxs1, ps1, _, _ = kf1.batch_filter(fxs0[:, 0], ps[:, 0])\n",
+    "fxs1, ps1, _, _ = kf1.batch_filter(fxs0[:, 0], ps0[:, 0])\n",
    "fxs2, _, _, _ = kf2.batch_filter(fxs1[:, 0], ps1[:, 0, 0])\n",
    "\n",
    "plot_kf_output(xs, fxs0, zs, 'KF', False)\n",
    "plot_kf_output(xs, fxs1, zs, '1 iteration', False)\n",
-    "plot_kf_output(xs, fxs2, zs, '2 iterations', False)"
+    "plot_kf_output(xs, fxs2, zs, '2 iterations', False)\n",
+    "R,Q"
   ]
  },
  {
@@ -2496,11 +2590,42 @@
  },
  {
   "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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truncated
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x92d9358>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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truncated
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x90b3e10>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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truncated
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f53dd8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
   "source": [
    "plot_kf_output(xs, fxs0, zs, title='KF', aspect_equal=False)\n",
    "plot_kf_output(xs, fxs1, fxs0[:, 0], '1 iteration', False)\n",
@@ -2544,14 +2669,23 @@
  },
  {
   "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "fusion std: 0.391\n",
+      "fusion std: 0.438\n"
+     ]
+    }
+   ],
   "source": [
    "fusion_test(1.5, 3.0, do_plot=False)\n",
-    "fusion_test(1.5, 1e80, do_plot=False)"
+    "fusion_test(1.5, 1.e40, do_plot=False)"
   ]
  },
  {
@@ -2577,12 +2711,30 @@
  },
  {
   "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 49,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "std: 40.386\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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truncated
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x91ba5f8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
   "source": [
    "dt = 0.1\n",
    "wheel_sigma = 1.5\n",
@@ -2633,7 +2785,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
@@ -2682,11 +2834,29 @@
  },
  {
   "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "fusion std: 0.452\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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truncated
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x91f9470>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
   "source": [
    "def fusion_test(wheel_sigma, ps_sigma, do_plot=True):\n",
    "    dt = 0.25\n",
@@ -2792,7 +2962,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
@@ -2862,16 +3032,16 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
-      "image/png": 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truncated
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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x801fbe0>"
+       "<matplotlib.figure.Figure at 0x8058518>"
      ]
     },
     "metadata": {},
@@ -3128,7 +3298,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
@@ -3173,16 +3343,16 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
-      "image/png": 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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x812bf60>"
+       "<matplotlib.figure.Figure at 0x930c780>"
      ]
     },
     "metadata": {},
@@ -3298,7 +3468,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
@@ -3307,7 +3477,7 @@
     "data": {
      "image/png": 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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x801f438>"
+       "<matplotlib.figure.Figure at 0x92eb6a0>"
      ]
     },
     "metadata": {},
@@ -3377,7 +3547,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
@@ -3449,16 +3619,16 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
-      "image/png": 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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAuoAAAEWCAYAAAAw37JZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x812b278>"
+       "<matplotlib.figure.Figure at 0x806ebe0>"
      ]
     },
     "metadata": {},
@@ -3520,16 +3690,16 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x820cd68>"
+       "<matplotlib.figure.Figure at 0x7f4fd68>"
      ]
     },
     "metadata": {},
@@ -3537,9 +3707,9 @@
    },
    {
     "data": {
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truncated
+      "image/png": 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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x82d5f28>"
+       "<matplotlib.figure.Figure at 0x801c5c0>"
      ]
     },
     "metadata": {},
@@ -3595,16 +3765,16 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x6e50080>"
+       "<matplotlib.figure.Figure at 0x90d1c88>"
      ]
     },
     "metadata": {},
@@ -3612,9 +3782,9 @@
    },
    {
     "data": {
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truncated
+      "image/png": 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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x9471358>"
+       "<matplotlib.figure.Figure at 0x98040b8>"
      ]
     },
     "metadata": {},