changed stats module to use more efficient computation methods

Kalman_Filters.ipynb

@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:7ef35bb11ab92ac993eeaa45ddc2bd139cb3a3842240266d90d82e6a0812daff"
+  "signature": "sha256:9cd59481342e5ef50f2f0a6e6cbd72ad8f60d22f9fa94d6e6e2b50793e23f26b"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -185,7 +185,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 3
+     "prompt_number": 2
     },
     {
      "cell_type": "markdown",
@@ -209,11 +209,11 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "-0.8025\t0.4440\t1.6176\t-0.7325\t0.0304\t0.3592\t-0.1916\t1.3645\t0.2563\t-0.4732\t-0.3872\t0.2693\t0.4122\t0.1272\t0.7263\t-0.5945\t0.4633\t-1.5166\t0.5133\t1.1987\t"
+        "-0.2093\t1.0177\t-0.1450\t-2.2557\t0.2699\t0.2095\t-0.4464\t-0.3039\t-0.4437\t-0.3667\t0.7999\t0.6447\t1.9560\t0.1862\t2.2909\t-0.3298\t0.5015\t0.4101\t1.3123\t-0.3362\t"
        ]
       }
      ],
-     "prompt_number": 4
+     "prompt_number": 3
     },
     {
      "cell_type": "markdown",
@@ -253,13 +253,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAArwAAAFyCAYAAAAXsAkaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAArwAAAFyCAYAAAAXsAkaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x28b10d0>"
+        "<matplotlib.figure.Figure at 0xa234128>"
        ]
       }
      ],
-     "prompt_number": 5
+     "prompt_number": 4
     },
     {
      "cell_type": "markdown",
@@ -298,13 +298,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAtoAAAGPCAYAAABiRXfqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAtoAAAGPCAYAAABiRXfqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x28e3f90>"
+        "<matplotlib.figure.Figure at 0xa362748>"
        ]
       }
      ],
-     "prompt_number": 6
+     "prompt_number": 5
     },
     {
      "cell_type": "markdown",
@@ -332,13 +332,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAtoAAAGPCAYAAABiRXfqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAtoAAAGPCAYAAABiRXfqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x28c0410>"
+        "<matplotlib.figure.Figure at 0x3d7ecf8>"
        ]
       }
      ],
-     "prompt_number": 7
+     "prompt_number": 6
     },
     {
      "cell_type": "code",
@@ -352,13 +352,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAtoAAAGPCAYAAABiRXfqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAtoAAAGPCAYAAABiRXfqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x28b1250>"
+        "<matplotlib.figure.Figure at 0xa353da0>"
        ]
       }
      ],
-     "prompt_number": 8
+     "prompt_number": 7
     },
     {
      "cell_type": "markdown",
@@ -395,13 +395,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAswAAAFyCAYAAADh+YyCAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAswAAAFyCAYAAADh+YyCAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x28cfb50>"
+        "<matplotlib.figure.Figure at 0xa3669e8>"
        ]
       }
      ],
-     "prompt_number": 9
+     "prompt_number": 8
     },
     {
      "cell_type": "markdown",
@@ -433,13 +433,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAsIAAAFyCAYAAAD/MLwxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAsIAAAFyCAYAAAD/MLwxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x28b1110>"
+        "<matplotlib.figure.Figure at 0xa76bd30>"
        ]
       }
      ],
-     "prompt_number": 10
+     "prompt_number": 9
     },
     {
      "cell_type": "markdown",
@@ -530,13 +530,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAswAAAFyCAYAAADh+YyCAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAswAAAFyCAYAAADh+YyCAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x2dffb90>"
+        "<matplotlib.figure.Figure at 0xa78ef28>"
        ]
       }
      ],
-     "prompt_number": 11
+     "prompt_number": 10
     },
     {
      "cell_type": "markdown",
@@ -578,13 +578,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAswAAAFyCAYAAADh+YyCAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAswAAAFyCAYAAADh+YyCAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x2e0ba50>"
+        "<matplotlib.figure.Figure at 0xa78e4e0>"
        ]
       }
      ],
-     "prompt_number": 12
+     "prompt_number": 11
     },
     {
      "cell_type": "markdown",
@@ -628,13 +628,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAswAAAFyCAYAAADh+YyCAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAswAAAFyCAYAAADh+YyCAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x2c29cd0>"
+        "<matplotlib.figure.Figure at 0xa96f6d8>"
        ]
       }
      ],
-     "prompt_number": 13
+     "prompt_number": 12
     },
     {
      "cell_type": "markdown",
@@ -705,7 +705,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 14
+     "prompt_number": 13
     },
     {
      "cell_type": "markdown",
@@ -724,7 +724,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 15
+     "prompt_number": 14
     },
     {
      "cell_type": "markdown",
@@ -753,30 +753,30 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "time: 0 \tposition = 0.793 \tvariance = 2.500\n",
-        "time: 1 \tposition = 0.575 \tvariance = 1.667\n",
-        "time: 2 \tposition = 0.277 \tvariance = 1.250\n",
-        "time: 3 \tposition = 0.348 \tvariance = 1.000\n",
-        "time: 4 \tposition = 0.401 \tvariance = 0.833\n",
-        "time: 5 \tposition = 0.231 \tvariance = 0.714\n",
-        "time: 6 \tposition = 0.386 \tvariance = 0.625\n",
-        "time: 7 \tposition = 0.517 \tvariance = 0.556\n",
-        "time: 8 \tposition = 0.452 \tvariance = 0.500\n",
-        "time: 9 \tposition = 0.332 \tvariance = 0.455\n",
-        "time: 10 \tposition = 0.298 \tvariance = 0.417\n",
-        "time: 11 \tposition = 0.280 \tvariance = 0.385\n",
-        "time: 12 \tposition = 0.197 \tvariance = 0.357\n",
-        "time: 13 \tposition = 0.132 \tvariance = 0.333\n",
-        "time: 14 \tposition = 0.116 \tvariance = 0.312\n",
-        "time: 15 \tposition = 0.076 \tvariance = 0.294\n",
-        "time: 16 \tposition = 0.135 \tvariance = 0.278\n",
-        "time: 17 \tposition = 0.040 \tvariance = 0.263\n",
-        "time: 18 \tposition = 0.003 \tvariance = 0.250\n",
-        "time: 19 \tposition = 0.006 \tvariance = 0.238\n"
+        "time: 0 \tposition = 1.524 \tvariance = 2.500\n",
+        "time: 1 \tposition = 0.607 \tvariance = 1.667\n",
+        "time: 2 \tposition = 0.647 \tvariance = 1.250\n",
+        "time: 3 \tposition = 0.667 \tvariance = 1.000\n",
+        "time: 4 \tposition = 0.654 \tvariance = 0.833\n",
+        "time: 5 \tposition = 0.596 \tvariance = 0.714\n",
+        "time: 6 \tposition = 0.619 \tvariance = 0.625\n",
+        "time: 7 \tposition = 0.662 \tvariance = 0.556\n",
+        "time: 8 \tposition = 0.733 \tvariance = 0.500\n",
+        "time: 9 \tposition = 0.865 \tvariance = 0.455\n",
+        "time: 10 \tposition = 0.869 \tvariance = 0.417\n",
+        "time: 11 \tposition = 0.826 \tvariance = 0.385\n",
+        "time: 12 \tposition = 0.676 \tvariance = 0.357\n",
+        "time: 13 \tposition = 0.567 \tvariance = 0.333\n",
+        "time: 14 \tposition = 0.660 \tvariance = 0.312\n",
+        "time: 15 \tposition = 0.712 \tvariance = 0.294\n",
+        "time: 16 \tposition = 0.695 \tvariance = 0.278\n",
+        "time: 17 \tposition = 0.607 \tvariance = 0.263\n",
+        "time: 18 \tposition = 0.545 \tvariance = 0.250\n",
+        "time: 19 \tposition = 0.582 \tvariance = 0.238\n"
        ]
       }
      ],
-     "prompt_number": 16
+     "prompt_number": 15
     },
     {
      "cell_type": "markdown",
@@ -843,7 +843,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 17
+     "prompt_number": 16
     },
     {
      "cell_type": "markdown",
@@ -893,47 +893,47 @@
        "stream": "stdout",
        "text": [
         "UPDATE: 1.0000,\t502.0000\n",
-        "SENSE:  -0.9237,\t9.8047\n",
+        "SENSE:  0.8308,\t9.8047\n",
         "\n",
-        "UPDATE: 0.0763,\t11.8047\n",
-        "SENSE:  0.1727,\t5.4138\n",
+        "UPDATE: 1.8308,\t11.8047\n",
+        "SENSE:  3.0009,\t5.4138\n",
         "\n",
-        "UPDATE: 1.1727,\t7.4138\n",
-        "SENSE:  3.2003,\t4.2574\n",
+        "UPDATE: 4.0009,\t7.4138\n",
+        "SENSE:  4.1303,\t4.2574\n",
         "\n",
-        "UPDATE: 4.2003,\t6.2574\n",
-        "SENSE:  6.5697,\t3.8490\n",
+        "UPDATE: 5.1303,\t6.2574\n",
+        "SENSE:  4.0626,\t3.8490\n",
         "\n",
-        "UPDATE: 7.5697,\t5.8490\n",
-        "SENSE:  7.6101,\t3.6904\n",
+        "UPDATE: 5.0626,\t5.8490\n",
+        "SENSE:  4.5662,\t3.6904\n",
         "\n",
-        "UPDATE: 8.6101,\t5.6904\n",
-        "SENSE:  8.2152,\t3.6267\n",
+        "UPDATE: 5.5662,\t5.6904\n",
+        "SENSE:  5.1264,\t3.6267\n",
         "\n",
-        "UPDATE: 9.2152,\t5.6267\n",
-        "SENSE:  6.5868,\t3.6007\n",
+        "UPDATE: 6.1264,\t5.6267\n",
+        "SENSE:  7.5146,\t3.6007\n",
         "\n",
-        "UPDATE: 7.5868,\t5.6007\n",
-        "SENSE:  6.4438,\t3.5900\n",
+        "UPDATE: 8.5146,\t5.6007\n",
+        "SENSE:  6.9139,\t3.5900\n",
         "\n",
-        "UPDATE: 7.4438,\t5.5900\n",
-        "SENSE:  7.7971,\t3.5856\n",
+        "UPDATE: 7.9139,\t5.5900\n",
+        "SENSE:  6.8968,\t3.5856\n",
         "\n",
-        "UPDATE: 8.7971,\t5.5856\n",
-        "SENSE:  7.7203,\t3.5838\n",
+        "UPDATE: 7.8968,\t5.5856\n",
+        "SENSE:  9.1777,\t3.5838\n",
         "\n"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAr0AAAFyCAYAAAD4cmIkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAArwAAAFyCAYAAAAXsAkaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x2c57890>"
+        "<matplotlib.figure.Figure at 0xa6fb7b8>"
        ]
       }
      ],
-     "prompt_number": 18
+     "prompt_number": 17
     },
     {
      "cell_type": "markdown",
@@ -1065,17 +1065,17 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAsYAAAFyCAYAAAD22xxLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAsAAAAFyCAYAAAD7xWwMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x2e129d0>"
+        "<matplotlib.figure.Figure at 0xa96d828>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAsUAAAF9CAYAAADsuhWdAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAsUAAAF9CAYAAADsuhWdAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x3246b10>"
+        "<matplotlib.figure.Figure at 0xa992f98>"
        ]
       },
       {
@@ -1086,7 +1086,7 @@
        ]
       }
      ],
-     "prompt_number": 19
+     "prompt_number": 18
     },
     {
      "cell_type": "markdown",
@@ -1222,7 +1222,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 20
+     "prompt_number": 19
     },
     {
      "cell_type": "markdown",
@@ -1286,17 +1286,17 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAr8AAAFyCAYAAAD8h7IZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAr8AAAFyCAYAAAD8h7IZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x3214710>"
+        "<matplotlib.figure.Figure at 0xa36da58>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAroAAAF9CAYAAADr+MuIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAroAAAF9CAYAAADr+MuIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x2e2bcd0>"
+        "<matplotlib.figure.Figure at 0xa986f98>"
        ]
       },
       {
@@ -1304,11 +1304,11 @@
        "stream": "stdout",
        "text": [
         "Variance converges to 0.0907297673624\n",
-        "Last voltage is 15.6491261618\n"
+        "Last voltage is 16.3277712404\n"
        ]
       }
      ],
-     "prompt_number": 21
+     "prompt_number": 20
     },
     {
      "cell_type": "markdown",
@@ -1333,7 +1333,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 22
+     "prompt_number": 21
     },
     {
      "cell_type": "markdown",
@@ -1367,13 +1367,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAsUAAAFyCAYAAAAd7KdIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAsUAAAFyCAYAAAAd7KdIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x2bdaa50>"
+        "<matplotlib.figure.Figure at 0xa714b00>"
        ]
       }
      ],
-     "prompt_number": 23
+     "prompt_number": 22
     },
     {
      "cell_type": "markdown",
@@ -1436,21 +1436,21 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAsgAAAFyCAYAAADoEiz4AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAsgAAAFyCAYAAADoEiz4AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x2e1db10>"
+        "<matplotlib.figure.Figure at 0xa96a7b8>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAsIAAAF9CAYAAAAOZg7kAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAsIAAAF9CAYAAAAOZg7kAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x2e22e50>"
+        "<matplotlib.figure.Figure at 0x1520ce10>"
        ]
       }
      ],
-     "prompt_number": 24
+     "prompt_number": 23
     },
     {
      "cell_type": "markdown",
@@ -1522,13 +1522,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAtEAAAFyCAYAAAAzqYbaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAtcAAAFyCAYAAAA+t/adAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x2e1d850>"
+        "<matplotlib.figure.Figure at 0xa963f98>"
        ]
       }
      ],
-     "prompt_number": 25
+     "prompt_number": 24
     },
     {
      "cell_type": "markdown",
@@ -1587,13 +1587,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAsgAAAFyCAYAAADoEiz4AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAsgAAAFyCAYAAADoEiz4AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x3234790>"
+        "<matplotlib.figure.Figure at 0xa977080>"
        ]
       }
      ],
-     "prompt_number": 26
+     "prompt_number": 25
     },
     {
      "cell_type": "markdown",
@@ -1651,13 +1651,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAtEAAAFyCAYAAAAzqYbaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAtEAAAFyCAYAAAAzqYbaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x2dffbd0>"
+        "<matplotlib.figure.Figure at 0xa7718d0>"
        ]
       }
      ],
-     "prompt_number": 27
+     "prompt_number": 26
     },
     {
      "cell_type": "markdown",
@@ -1703,13 +1703,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAtEAAAFyCAYAAAAzqYbaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAtEAAAFyCAYAAAAzqYbaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x2c2bd10>"
+        "<matplotlib.figure.Figure at 0xa23b518>"
        ]
       }
      ],
-     "prompt_number": 28
+     "prompt_number": 27
     },
     {
      "cell_type": "markdown",
@@ -1758,13 +1758,13 @@
       {
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 29,
+       "prompt_number": 28,
        "text": [
         "<function __main__.plot_kalman_filter>"
        ]
       }
      ],
-     "prompt_number": 29
+     "prompt_number": 28
     },
     {
      "cell_type": "markdown",
@@ -1813,21 +1813,21 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAsgAAAFyCAYAAADoEiz4AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAsgAAAFyCAYAAADoEiz4AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x324e950>"
+        "<matplotlib.figure.Figure at 0xa79b048>"
        ]
       },
       {
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 30,
+       "prompt_number": 29,
        "text": [
         "<function __main__.plot_kalman_filter>"
        ]
       }
      ],
-     "prompt_number": 30
+     "prompt_number": 29
     },
     {
      "cell_type": "heading",
@@ -1863,7 +1863,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 31
+     "prompt_number": 30
     },
     {
      "cell_type": "markdown",
@@ -1905,13 +1905,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAsIAAAFyCAYAAAD/MLwxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAsIAAAFyCAYAAAD/MLwxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3Xd4FNX6B/DvbE+nSScqcC2USxHpiPReA4iolEsRKYqI\nqFh/IhfBclUsKEqTItJBAekloakUBTSiKAnSa+r2+f2xJBKYJFtmZ2Z3v5/nuc91s1MO8GbmnTPv\nOUdITU0VQUREREQUYXRqN4CIiIiISA1MhImIiIgoIjERJiIiIqKIxESYiIiIiCISE2EiIiIiikhM\nhImIiIgoIvmdCB8+fBjdunVD586dMW7cODnbREREREQUdAZ/dnK73Zg4cSKmTp2K+vXr48qVK3K3\ni4iIiIgoqPzqET5y5AhKlSqF+vXrAwBKliwpa6OIiIiIiILNr0T4zJkziIuLw7Bhw9CrVy8sWrRI\n7nYREREREQWVX6URNpsNBw4cwDfffIPY2FgkJSWhRYsWqFKlitztIyIiIiIKCr8S4dtuuw3Vq1dH\n+fLlAQC1atXCiRMnCiTCBw8eRJkyZeRpJRERERFRITIzM1GjRg2f9/MrEa5VqxZOnz6Na9euISoq\nCr/99hsSExMLbFOmTJn8GmKiPGvXrkW3bt3UbgZpDOOCpDAuSArjgqQcOHDAr/38SoTj4uIwadIk\nDBo0CE6nE926dcOdd97pVwMosqSlpandBNIgxgVJYVyQFMYFycmvRBgAOnbsiI4dO8rZFiIiIiIi\nxXBlOVJU27Zt1W4CaRDjgqQwLkgK44LkxESYFHX33Xer3QTSIMYFSWFckBTGBcnJ79IIf4miiPPn\nz8Plcil96rCk1+tRtmxZCIKgdlO8kpycjObNm6vdDNIYxgVJYVyQFMYFyUnxRPj8+fOIi4tDdHS0\n0qcOSzk5OTh//jzKlSundlOIiIiIQoripREul4tJsIyio6NDqnedT/EkhXFBUhgXJIVxQXJijTAR\nERERRSQmwqSo5ORktZtAGsS4ICmMC5LCuCA5MRGW0LRpU+zevVvtZhARERFRECk+WC4UKJkEv/nm\nm/jrr78wc+ZMxc6pJtZ2kRTGBUlhXJAUxgXJiT3CRERERBSRmAjf4LXXXkNiYiLKlCmDHTt25P98\n9OjRGDt2LDp37ozExESMHTs2/7tFixahbdu2GDx4MG6//Xb0798fV69eBeBZD7106dJwu90APHVN\ntWrVAgDs2bMHiYmJeO+997By5UokJiYiMTERly5dUvBPrDzWdpEUxgVJYVyQFMYFyYmJ8A1ee+01\npKWloXLlyrcsULF9+3bMmjULu3fvxurVq3Hw4MH87w4ePIg+ffrg+PHjiI2Nxeuvv17suZo0aYK0\ntDQ8/fTT6N27N9LS0vITZyIiIiIKPibCXurUqRMqVaqEypUro0aNGvjjjz/yv0tMTETXrl1hMpkw\nbNgwfPfdd14fVxRFiKIYjCZrEmu7SArjgqQwLkgK44LkpLlE2PLmmyhZqtQt/7O8+abX2xe2bSAS\nEhLy/9tkMsFqteZ/LlOmTIH/vnjxouznJyIiIiJ5aS4Rtj7/PK5cvnzL/6zPP+/19oVtGywXLlzI\n/++LFy/mJ8ZmsxkA4HQ6AQCZmZm37KvX6xVooXawtoukMC5ICuOCpDAuSE6aS4S1wpdyhfT0dKxd\nuxY2mw2zZs1C+/btAXh6h+Pj4/Hjjz8CANasWXPLvmXLlsXx48dDaplkIiIionDARPg6l8uFKlWq\nIDExEX///TcGDBiAxMRELFiwAABuGTx3o3r16mH58uW46667kJ2djVdeeQWAp7f39ddfx4gRI9Cl\nSxeUKVPmluP06tULcXFxqFmzJmrXro3Lly8H7w+pAaztIimMC5LCuCApjAuSk5CamhqUkVrp6emo\nV68+bs4fT58+jYoVKwbjlKpYtGgRFixYgHXr1qnWhnD7OyUiIiLyxYEDB1ClShWf9wtqj3B2djCP\nTqGItV0khXFBUhgXJIVxQXIKaiJ89Wrh5QThQhCEIssmiIiIiEibgloaYbE0QI0a7gI/52t8+fHv\nlIiIiCKZJksjrl3jWDwiIiIi0iaWRpCiWNtFUhgXJIVxQVIYFySnIPcIMxEmIiIiIm1iIkyK4vyP\nJIVxQVIYFySFcUFyYmkEEREREUUk9gjf5PPPP8e//vUvJCYmYufOnfk/f+aZZ/D2228X2HbixIlI\nTExEmTJlsGPHDqWbGpJY20VSGBckhXFBUhgXJCcmwjdwOBx49dVXsXr1aqSlpeGBBx7I/+6dd97B\nhAkTCmw/ffp0pKWloXLlyoXOJdytWzd8+eWXQW03EREREfmOifANzp07B6vVirvvvlu2Y3KxjYJY\n20VSGBckhXFBUhgXJCcmwtc1adIETZo0AQDceeed+aURGzduRGJiIsqVK4cpU6Z4fbx3330XiYmJ\n2LNnD5577jkkJiaiTZs2+d9fuXIFjz/+OO655x7Uq1cP8+fPL7D/6NGj8cILL2DgwIFITExEnTp1\nkJWVJc8floiIiIiYCOfZs2cPdu/eDQD466+/8ksj2rdvj7S0NPTp08en3t3x48cjLS0NTZo0yS+h\n2LJlS/73I0eOhMlkwuHDh7Fy5UpMmzYNhw4dKnCMr7/+Go8++ihOnjyJhQsXwmAwyPOHVRFru0gK\n44KkMC5ICuOC5BTUzCrUZo0QxaJXmy7ue2/3O3v2LLZs2YI//vgDZrMZd9xxB7p164Zvv/0WdevW\nzd+uRYsWaN++PQCgVq1afp2biIiIiKQFNRH2Z4nlUqVKynLuy5evyHIcOdzck/z3338DQIGk1+Vy\noXfv3gW2q1atWvAbpzDWdpEUxgVJYVyQFMYFySmgRPjee+/NH1h2//3348UXXyzwfU4O4HIBer33\nx9RSAnuzwkojTCYTXC6X5Hc63a0PA5UqVYLFYsGJEyeKLLeQ2peIiIiI5BFQpmWxWLBq1SqsWrXq\nliQYAGJjRWRkhFZ5RFEKK42oXr16fn3xzcqWLYtjx44V+Fn58uXRtGlTvPbaa8jOzobD4cC+fftw\n9OhR2dusNaztIimMC5LCuCApjAuSU1C7HBMSxJAaMAfc2uvbu3dvJCYmYtmyZZgxYwYSExMxZsyY\nAtu8+OKLWLt2LapUqYJXXnmlwHejR4/G9u3bUbNmTfTo0SP/559++ikuXryI+++/H3fddRcmT558\nS68yp14jIiIiCh4hNTXVvxFgAGrWrIm7774bZrMZzzzzDBo0aJD/XXp6OsaPb4n3389BnTr/JHin\nT59GxYoVA2s1FcC/UyIiIopkBw4cQJUqVXzeL6Aa4Z07d6J06dL4+eefMWbMGGzatAkmkyn/+4QE\nMeRmjiAiIiKiyBBQIly6dGkAQO3atVG2bFmcOnUKVatWzf/+zz8PYu7cVOzZcwQJCQmoXbt2ge9J\nPnk1U3mjabX6Oe9nWmkPP2vj8yeffILatWtrpj38rI3PeT/TSnv4WRufeb3g5zzJyclIS0sDAAwb\nNgz+8Ls04tq1azCbzbBYLDh16hQGDBiAjRs3wmKxAPCURsyZ0xwNGzrx2GP2/P34Gl9+ofR3mpyc\nnB/MRHkYFySFcUFSGBckRfHSiBMnTuCFF16AyWSCXq/HlClT8pPgPCyNoJvx4kVSGBckhXFBUhgX\nJCe/E+F69ephw4YNRW6TkBBe06cRERERUfgI6vRpJUrcOn2aKIp+L1VMtwq1v88ba3uI8jAuSArj\ngqQwLkhOQZ9H+ObSiISEBFy+fDmYp40oly9fRkJCgtrNICIiIgo5fpdGeMOzoEbBXDs2NhY2mw2n\nT58O5qkjhtlsRmxsrNrN8Bpru0gK44KkMC5ICuOC5BTkRNgtOVgub9o1IiIiIiK1cIllUhRru0gK\n44KkMC5ICuOC5BT0RJizRhARERGRFvm9oEZx0tPTcc899VG9egmcPn01GKcgIiIiIvJ7QY2g9ghH\nRQEuF2C1BvMsRERERES+C2oiLAieuYRZHkF5WNtFUhgXJIVxQVIYFySnoCbCAJdZJiIiIiJtCnoi\nHB/PmSPoH5z/kaQwLkgK44KkMC5ITkFPhKWWWSYiIiIiUpsipRFMhCkPa7tICuOCpDAuSArjguTE\nRJiIiIiIIpICpRFuXLsW9NNQiGBtF0lhXJAUxgVJYVyQnDhrBBERERFFJJZGkKJY20VSGBckhXFB\nUhgXJCcmwkREREQUkVgaQYpibRdJYVyQFMYFSWFckJwUSYS5xDIRERERaQ0X1CBFsbaLpDAuSArj\ngqQwLkhOLI0gIiIioogkpKamisE4cHp6OurXrw+HA6hUqQTOnbsKgfkwEREREcnswIEDqFKlis/7\nBb1H2GgEzGYgOzvYZyIiIiIi8p4iS77Fx7M8gjxY20VSGBckhXFBUhgXJCdFEuESJThzBBERERFp\niyKJcEKCG9euKXIq0jjO/0hSGBckhXFBUhgXJCeFEmGWRhARERGRtihWGsG5hAlgbRdJY1yQFMYF\nSWFckJwU6xFmIkxEREREWsJZI0hRrO0iKYwLksK4ICmMC5ITSyOIiIiIKCKxNIIUxdouksK4ICmM\nC5LCuCA5BZQIZ2VloXnz5pg9e3aR2zERJiIiIiKtCSgRnjlzJmrVqgVBKDrJZWkE5WFtF0lhXJAU\nxgVJYVyQnPxOhE+cOIHLly+jVq1aEEWxyG05jzARERERaY3fifC7776LsWPHerUtSyMoD2u7SArj\ngqQwLkgK44Lk5FcivHXrVtxxxx2oUKFCsb3BAFCiBJdYJiIiIiJtMfiz008//YSNGzdiy5YtuHLl\nCnQ6HcqWLYuuXbsW2G7UqFFITEyEKArIzv4vduxIRsuWntqevCe6vFoffuZnfo7cz3k/00Line truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x2e17990>"
+        "<matplotlib.figure.Figure at 0xa78a630>"
        ]
       }
      ],
-     "prompt_number": 32
+     "prompt_number": 31
     },
     {
      "cell_type": "markdown",
@@ -1950,7 +1950,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 33
+     "prompt_number": 32
     },
     {
      "cell_type": "markdown",
@@ -1992,13 +1992,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAsMAAAFyCAYAAAAQ8tcPAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3Xd4FOX2B/DvlC3pQAKhhNBCkNB7i4AgHQSkiiJcumC7\n14b9WlCuyhXhBygoCFdFUAGlg3SQ3muoIXRIIX3bzPz+2CSmzO7O7s6WJOfzPPe57u7Mzgu82Zx9\n57znMJIkSSCEEEIIIaQcYn09AEIIIYQQQnyFgmFCCCGEEFJuUTBMCCGEEELKLQqGCSGEEEJIuUXB\nMCGEEEIIKbcoGCaEEEIIIeWWy8HwwYMH0bRpU8TFxWHEiBFqjokQQgghhBCvYFypMyyKIho2bIgl\nS5agY8eOSElJQXh4uCfGRwghhBBCiMe4tDJ89OhRVK5cGR07dgQACoQJIYQQQkip5FIwnJSUhLCw\nMPTp0wctW7bEggUL1B4XIYQQQgghHse7cpLBYMC+fftw5swZhIWFoXXr1ujduzfq1Kmj9vgIIYQQ\nQgjxGJeC4apVqyIuLg5RUVEAgFatWuHChQtFguEVK1YgIiJCnVESQgghhBBig8FgQL9+/Vw616Vg\nuHXr1khKSkJaWhqCgoJw+vRp1KtXr8gxERERaNmypUuDImXX2rVrMWDAAF8Pg/gZmhdEDs0LIofm\nBZFz7Ngxl891KRgOCwvD7Nmz0a1bN5jNZjz99NOIjY11eRCk/EhKSvL1EIgfonlB5NC8IHJoXhC1\nuRQMA8DQoUMxdOhQNcdCCCGEEEKIV1EHOuJVjz/+uK+HQPwQzQsih+YFkUPzgqiNgmHiVQ0aNPD1\nEIgfonlB5NC8IHJoXhC1UTBMvGrv3r2+HgLxQzQviByaF0QOzQuiNgqGCSGEEEJIucVIkiR54o23\nbdtGpdUIIYQQQojHHTt2DN27d3fpXFoZJoQQQggh5RYFw8SrKNeLyKF5QeTQvCByaF4QtVEwTAgh\nhBBCyi3KGSaEEEIIIaUa5QwTQgghhBDiAgqGiVdRrheRQ/OCyKF5QeTQvCBqo2CYEEIIIYSUW5Qz\nTAghhBBCSjXKGSaEEEIIIcQFFAwTr6JcLyKH5gWRQ/OCyKF5QdRGwTAhhBBCCCm3KGeYEEIIIYSU\napQzTAghhBBCiAs8Ggzfvct48u1JKUS5XkQOzQsih+YFkUPzgqjNo8HwqVOcJ9+eEEIIIYQQt3g4\nGOY9+fakFIqPj/f1EIgfonlB5NC8IHJoXhC10cowIYQQQggptzwaDJ88ScEwKYpyvYgcmhdEDs0L\nIofmBVGbR4PhtDQWqam0iY4QQgghhPgnjwbDTZtacPo0rQ6Tv1GuF5FD84LIoXlB5NC8IGrzcDAs\nUKoEIYQQQgjxWx4PhqmiBCmMcr2IHJoXRA7NCyKH5gVRm8fTJKiiBCGEEEII8VceDYZjY0Xcvs0i\nM9OTVyGlCeV6ETk0L4gcmhdEDs0LojaPBsM8DzRsKODsWVodJoQQQggh/sejwTCQv4mO8oaJFeV6\nETk0L4gcmhdEDs0LojYvBMOUN0wIIYQQQvyTx4PhZs2ovBr5G+V6ETk0L4gcmhdEDs0LojaPB8MN\nGwq4do2DweDpKxFCCCGEEOIcjwfDOh1Qr56Ac+dodZhQrheRR/OCyKF5QeTQvCBq83gwDOQ336Bg\nmBBCCCGE+BevBMPWvGGqKEEo14vIo3lB5NC8IHJoXhC1eWllmCpKEEIIIYQQ/+OVYLhxYwEJCRzM\nZm9cjfgzyvUicmheEDk0L4gcmhdEbV4JhoOCgBo1RFy8SKvDhBBCCCHEf3glGAaAZs0sVG+YUK4X\nkUXzgsiheUHk0LwgavNaMEwVJQghhBBCiL/x4sowBcOEcr2IPJoXRA7NCyKH5gVRm9eC4SZNBJw5\nw0MUvXVFQgghhBBC7PNaMFyhgoTwcBFXrnjtksQPUa4XkUPzgsiheUHk0LwgavNqZEp5w4QQQggh\nxJ94NRimTnSEcr2IHJoXRA7NCyKH5gVRm1vBcGZmJqpXr45Zs2YpOr5JEwtOn6aVYUIIIYQQ4h/c\nCoZnzJiB1q1bg2EYRcdbV4Y5SJI7VyWlGeV6ETk0L4gcmhdEDs0LojaXg+GEhAQ8ePAArVq1gqQw\nuq1SRUJAAHDjBm2iI4QQQgghvudyVPrmm2/i3//+t9PnNW1qwbFjlCpRXlGuF5FD84LIoXlB5NC8\nIGpzKRheu3YtYmNjUbNmTcWrwvkee8yCrVs1rlyWEEIIIYQQVblU2uHQoUP47bff8PvvvyM5ORks\ny6J69ep46qmnihw3depUREdHAwDCwsLQpEkT9O37KD7/PBS7du0Dx0kFuT/53/ToMT2mx+Xvcf5z\n/jIeekyP6bH/Ps5/zl/GQ4998zj/v5OSkgAAEyZMgKsYydml3WI++OADhISE4F//+leR57dt24aW\nLVvKnvPYYyH4+ONcdOpkcefShBBCCCGE4NixY+jevbtL5/pkJ1vfvmasX0+pEuVR4W90hOTz9bzQ\nrFsHze+/+3QMpCRfzwvin2heELXx7r7B+++/7/Q5/fqZ8NRTwZgxIxcKq7IRQojHaP78E0xaGiyt\nWkGKivL1cAghhHiRT1aGGzYUwXHAmTNUVaK8KZzzRUg+X88L9uJF8Pv3gz940KfjIEX5el4Q/0Tz\ngqjNJ8Eww1CqBCHET0gSuIQEWDp0AJuS4uvREEII8TKfdb/o18+MDRsoGC5vKNeLyPHlvGCSkwFJ\ngtCwofW/id+gzwsih+YFUZvPguG2bS24e5fF9evUjY4Q4jtcQgLEBg0gVa5MK8OEEFIO+SwS5Tig\nd29aHS5v/CHXq0J4OJCZ6ethkEJ8OS+EBg2Q89FHEMPDaWXYz/jD5wXxPzQviNp8uixLqRLE6wQB\njCSBvXHD1yMhfkKqXBlC69YQYmNhadPG18MhhBDiZT4Nhjt3NuPUKR4pKVRfrbzwda4Xk5UFAGBv\n3fLpOEhRvp4XACDGxcH4/PO+HgYpxB/mBfE/NC+I2nwaDAcEAF27mrF5M60OEy/JS48QbHRHJIQQ\nQkj54vPda5QqUb74OtdLiopCWkoKpPBwn46DFOXreUH8E80LIofmBVGbz4Phnj3N2L1bg5wcX4+E\nlBvU9pAQQggheXweDFeoIKFVKwt27KDV4fKAcr2IHF/NC83q1dDNmeOTaxPH6POCyKF5QdTm82AY\nsHajczVVgk1IoMoAhBCX8EePApL09+OtW8EmJflwRIQQQrzNL4LhPn1M2LxZA4vF+XP1CxYgYPp0\n9QdFPIJyvYgcX80L7uJFiA0aFDzW/fADuKNHfTIWUhJ9XhA5NC+I2vwiGI6KkhAdLeLAAd7pc5nU\nVGg3bgR77pwHRkbKHKMREAQEPfssrQASsAkJEGJjCx5LERHUhY4QQsoZvwiGAWtVifXrnU+VYFJS\nYBwzBozR6IFREbX5Otcr4OOPoZs3D0xqKtjERJ+OhfzNJ/MiKwtscjLEWrUKnqIudP7F158XxD/R\nvCBq85tguG9fEzZs0BRO31PEOG0act9+G0KLFp4ZGClTmPR0SGFhEGvWpFzzco67fBlC3brW3vB5\npIgIMLQyTAgh5YrfBMOPPCKCZYHz550bkrlvX0gRER4aFVGbr3O9mPR0SKGhEKOiKBj2I76YF0Jc\nHLKWLy/ynBgRAZZWhv2Grz8viH+ieUHU5jfBMMMA3bpZsH07lVgjnsNkZFhXhqOjKRgu77RaSFFR\nRZ4S4uJgfvRRHw2IEEKIL/hNMAwA3bqZKRgu43yd68VkZFhXhmvWBHvzpk/HQv7m63mRT3zkEZjG\njfP1MEgef5kXxAOysxHaoYNLp9K8IGrzq2D40UfNOHKEd6sbHZOeDmRlqTcoUrZYLJDCwmBp0wY5\nc+f6ejSEEFIucYmJ4BISrBV+CPExvwqGQ0OBJk0s+Osv50us5Qt4913oFy1ScVRETb7O9crctQti\n/fpAUBDE6GifjoX8zdfzgvgnmhdlWG4uAIC9d8/pU2leELX5VTAMOJc3zB0+DF2xwNfw3HPQffNN\nwQ8aIYSU4GzZGkKIqoTWrWFp3RrMnTu+Hgoh/hgMK88b5s6dA3f6dJHnxIYNYWnVCroff/TE8Iib\nKNeLyPH2vNCsWYPAadO8ek3iPPq8KNtMQ4ZACglx+jyaF0RtfhcMN2smICWFwc2bjMNj2dRUSOHh\nJZ43vPwydHPnAmazJ4ZICCnluIQEiNWry76m+eMPsNeve3lExONMJuv/iN8wTp4MMS7OqXP4XbtQ\nY+dOzwyIlFt+FwyzLNC1q7JUCSYlBWKlSiWeF9q0gVirFrSrVnliiMQNlOtF5Hh7XnAXL0Jo0ED2\nNe2vv4I7ccKr4yHy1JwXIf36IXjIENXej/gGk5KCxpcu+XoYpIzxu2AYUJ4qwaSk2Gy4kfvhhxAa\nNlR7aMRDNBs2gPf0t32zGYVLlWh++w0B77zj2WsSv8QlJECMjZV9TQoPpy50ZRB/9CiYtDRfD4O4\nSYyJAXvliq+HQcoYvwyGH3vMjN27eVgs9o9jU1IgyqRJAIDQvDmEpk09MDriDlu5XtzZs+D37PHo\ntfnduxH89NN/PxEcDO7CBY9ekyjj1RxAiwXstWsQYmJkX6YudP5DzXkh1K+PbKo05BeY9HRwBw+6\ndK5Qty6Yy5cBUVR+Uk4OuDNnXLoeKR/8MhiuWlVCjRoijh3j7B5neP55CM2be2lUxJPEyEiXSuw4\nI7/7XD6hZk3qQlcOsTdvWvOFAwNlX6eV4TLIZAKblASxbl1fj4QA4I4fR8CMGc6fd+AAgiZNgik4\nGOytW4rP03/9NUI7d3b6eqT88HowrFuwAPz+/Q6PU1JizdK5M6QqVdQaGvECuRxAft8+aHLine truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAsMAAAFyCAYAAAAQ8tcPAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x2e27990>"
+        "<matplotlib.figure.Figure at 0x2252048>"
        ]
       }
      ],
-     "prompt_number": 34
+     "prompt_number": 33
     },
     {
      "cell_type": "markdown",
@@ -2034,7 +2034,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 34
+     "prompt_number": 33
     },
     {
      "cell_type": "markdown",

Multidimensional_Kalman_Filters.ipynb

@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:9de522896a31234c5502f9ba39e27ca8fe00b307764f4a503b6b7ca27476e2b3"
+  "signature": "sha256:22be69a2e94220518ac46fa32a493f4e6a94a118e580f9527d8d1a8ac217f37a"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -352,16 +352,14 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "%matplotlib inline\n",
       "import matplotlib.pylab as pylab\n",
       "from matplotlib import cm\n",
       "from mpl_toolkits.mplot3d import Axes3D\n",
       "import numpy as np\n",
       "\n",
-      "pylab.rcParams['figure.figsize'] = 12,6\n",
       "pylab.rcParams['axes.color_cycle'] = '348ABD, 7A68A6, A60628, 467821, CF4457, 188487, E24A33'\n",
       "\n",
-      "P = np.array([[8.,0],[0,10.]])\n",
+      "P = np.array([[8.,0],[0,4.]])\n",
       "mu = np.array([2,7])\n",
       "\n",
       "xs, ys = np.arange(-8, 13, .5), np.arange(-8, 20, .5)\n",
@@ -396,9 +394,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAqsAAAFdCAYAAAAkOCRoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAqsAAAFdCAYAAAAkOCRoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x2734110>"
+        "<matplotlib.figure.Figure at 0xa4c6ef0>"
        ]
       }
      ],
@@ -421,15 +419,15 @@
       "\n",
       "P = np.array([[2,0],[0,2]])\n",
       "plt.subplot(131)\n",
-      "stats.plot_covariance_ellipse(P, x=2, y=7, title='|2 0|\\n|0 2|')\n",
+      "stats.plot_covariance_ellipse((2,7), cov=P, title='|2 0|\\n|0 2|')\n",
       "\n",
       "plt.subplot(132)\n",
       "P = np.array([[2,0],[0,9]])\n",
-      "stats.plot_covariance_ellipse(P, x=2, y=7, title='|2 0|\\n|0 9|')\n",
+      "stats.plot_covariance_ellipse((2,7), P, title='|2 0|\\n|0 9|')\n",
       "\n",
       "plt.subplot(133)\n",
       "P = np.array([[2,1.2],[1.2,2]])\n",
-      "stats.plot_covariance_ellipse(P, x=2, y=7, title='|2 1.2|\\n|1.2 2|')\n",
+      "stats.plot_covariance_ellipse((2,7), P, title='|2 1.2|\\n|1.2 2|')\n",
       "\n",
       "plt.tight_layout()\n",
       "plt.show()"
@@ -440,9 +438,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAA1MAAAGpCAYAAABoLmzKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAA1MAAAGpCAYAAABoLmzKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x28ceed0>"
+        "<matplotlib.figure.Figure at 0xa4c6e48>"
        ]
       }
      ],
@@ -530,9 +528,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAsYAAAF/CAYAAABKRQ+VAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAsYAAAF/CAYAAABKRQ+VAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x28d4410>"
+        "<matplotlib.figure.Figure at 0xa4c6358>"
        ]
       }
      ],
@@ -557,9 +555,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAsYAAAF/CAYAAABKRQ+VAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAsYAAAF/CAYAAABKRQ+VAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x28e3650>"
+        "<matplotlib.figure.Figure at 0xa7cef28>"
        ]
       }
      ],
@@ -576,7 +574,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "mkf_internal.show_x_error_char()"
+      "mkf_internal.show_x_error_chart()"
      ],
      "language": "python",
      "metadata": {},
@@ -584,9 +582,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAswAAAF/CAYAAABdZ59cAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3XmUXFd9L/rvOVXV8zxLanVrltVYtiWPsRsMtogxxsEE\n5CSL4XGJCcHJI2RBFsklw0u4N+/hcCHhBhMSQggkTE1WLoNtHGQMpm1j2W4ZSdY8tLqlVs/zVNM5\n74+tUtc5e+9Tp6pLVSXX97OWVlzV1VKRtfv0r37nu38bICIiIiIiIiIiIiIiIiIiIiIiIiIiIiIi\nIiIiIiIiIiIiIiIiIiKiq5iR7zfg5fnnn7cXFhby/TaIiIiI6LXvZ3v27Hmj6gvBHL+RtCwsLGD3\n7t35fhtESj/4wQ9w//335/ttEEm4NqmQcX1Soerr67tT9zUzl2+E6LVkYGAg32+BSIlrkwoZ1ydd\njVgwExERERF5YMFMlKE9e/bk+y0QKXFtUiHj+qSrEQtmogxt374932+BSIlrkwoZ1yddjVgwE2Wo\nt7c332+BSIlrkwoZ1yddjVgwExERERF5YMFMlKHu7u58vwUiJa5NKmRcn3Q1YsFMREREROSBBTNR\nhpjDo0LFtUmFjOuTrkYsmImIiIiIPLBgJsoQc3hUqLg2qZBxfdLViAUzEREREZEHFsxEGWIOjwoV\n1yYVMq5PuhqxYCYiIiIi8sCCmShDzOFRoeLapELG9UlXIxbMREREREQeWDATZYg5PCpUXJtUyLg+\n6WrEgpmIiIiIyAMLZqIMMYdHhYprkwoZ1yddjVgwExERERF5YMFMlCHm8KhQcW1SIeP6pKsRC2Yi\nIiIiIg8smIkyxBweFSquTSpkXJ90NWLBTERERETkIVcF82cADAM4lPTcgwBOADgO4G05eh9EWcMc\nHhUqrk0qZFyfdDXKVcH8HwDuS3pcAuD/A3AHgD0A/jZH74OIiIiIKC25KpifBzCR9PhWAK8CGAMw\neOnP9Tl6L0RZwRweFSquTSpkXJ90NQrm6d9tA3ARwIcATELENdYA+GWe3g8RERERkVK+CuaEL136\nv78OwFa94OGHH0ZHRwcAoLa2Fjt37rz86TSRg+JjPs7H4y9+8Ytcj3xckI+TM6KF8H74mI+5Pvm4\nEB8n/ntgYAAA8NBDD0HH0H4l+zYA+AGAnRDZ5T8GcP+lrz0N4A8AHEz+hn379tm7d+/O4Vsk8q+3\nt/fyDx9RIeHapELG9UmFqq+vD3v27FHWxsFcv5lLXgTwOgDNAMoAtMNVLBMVOl7wqVBxbVKhmZ0F\nPve5cjzzTBA33/xm3HjjEsrL8/2uiPzL1aa/LwB4DsB2iA1+90B0mJ8F8BSAj+bofRAREVEOxWLA\ne99bhYEBE5/61BLGx028611VCIfz/c6I/MtVwfx7ANZCjJNbDxHN+A6AbZf+PJaj90GUNckZKKJC\nwrVJheSzny2DaQL/+I8LuP32GN73vidRXW3jC18oy/dbI/KNJ/0RERHRFTE5aeBLXyrF5z+/iEBA\nPGeawKc/vYRHHy3FhQu53EpFlDkWzEQZYk6UChXXJhWKL36xFPffH8X69dbl57q7u9HZaeFd74rg\nq18tzeO7I/KPBTMRERFlXTQKfO1rpfj9319Wfv0DHwjj618vRSSS4zdGlAEWzEQZYk6UChXXJhWC\nn/40iM5OC1u2WI7nE+tz2zYL27fH8cQToXy8PaK0sGAmIiKirOvpKcGDD3q3j9/xjgh++MOSHL0j\nosyxYCbKEHOiVKi4NinfIhHgySdL8Pa3ywVz8vq8994o9u0LMpZBBY8FMxEREWXV/v1BbN0aR3Oz\n7fm61lYb27dbeOaZfJ2jRuQPC2aiDDEnSoWKa5Py7amnQnjTm6LKr7nX5z33RPHUU8wxU2FjwUxE\nRERZ9ZOfBHH33eqC2a27O4pnn2WHmQobC2aiDDEnSoWKa5PyaXLSwNmzAdx0U1z5dff6vOGGOPr7\nA5ic5CEmVLhYMBMREVHWvPhiEDfeGEPQZ9M4FAJuuSWG555jl5kKFwtmogwxJ0qFimuT8mn//gBu\nuSWm/bpqfTKWQYWOBTMRERFlzf79Qc+CWeXGG+Po62PBTIWLBTNRhpgTpULFtUn5Eo0Cr7wSxM03\n6wtm1fq8/voYXn01gKi/fYJEOceCmYiIiLLi+PEA1q61UFOT3vfV1ADr1lk4dixwZd4Y0SqxYCbK\nEHOiVKi4NilfDh4M4Lrr1NMxEnTr88YbY+jrY8FMhYkFMxEREWXFwYMB7NyZXn45YdeuOF55hTlm\nKkwsmIkyxJwoFSquTcqXw4dTd5h16/Paa+N49VV2mKkwsWAmIiKiVbMs4NChIHbu9C6YdXbsiOPY\nsQAsK8tvjCgLWDATZYg5USpUXJuUDwMDJqqqbDQ22p6v063PujobNTU2BgdZmlDh4aokIiKiVTt+\nPIAdOzLrLifs2BHHkSOMZVDhYcFMlCHmRKlQcW1SPhw/bmLbttQFs9f67OpiwUyFiQUzERERrdrx\n4wFs3766DjMLZipULJiJMsScKBUqrk3KhxMn/BXMXutz69Y4Tp1iaUKFh6uSiIiIVsW2EwXz6kZc\nbNkSx5kzAdje+waJco4FM1GGmBOlQsW1Sbl28aKBsjIb9fWpK12v9VlTA1RW2hgaMrL59ohWjQUz\nERERrcrp0wFs2bK6/HLCli1xnD7NHDMVFhbMRBliTpQKjW0D+/YF8dnPnsTcXL7fDRWTM2dMbNzo\nL46R6tq5ZYvFHDMVHB7aTkT0GhAOA7/1W1UYGTERDG7Cd75Tg+9+dw7t7QyD0pV39mwAmzZl54i+\nzZvjOHWKHWYqLPwIR5Qh5kSpkHzyk+WorLTxzDOz+NnPAviN34jgPe+pQjw7d8mJPIkOs7/Flura\nuWWLxUgGFRwWzEREV7mXXw7gRz8qwRe+sIDApTrjox9dRnW1jX/5l9L8vjkqCmfPmlnrMHd2xnHu\nHMsTKixckUQZYoaZCsVnPlOGj350GTU14nFvby8MA/j0pxfxmc+UIRzO7/uj1zbbBvr7A747zKmu\nnR0dFgYGTI6Wo4LCgpmI6Cp27JiJX/4yiPe8R66Ku7os7NgRxw9/GMrDO6NiMToqRsolPrCtVlUV\nUFVlY2SEo+WocLBgJsoQM8xUCL7znRI8+GAEZWUrzyWvzQ98IIwvf7lM8Z1E2dHfb6Kz038cw8+1\ns7PTQn8/SxQqHFyNRERXKcsCenpK8eCD+szFvfdGcfKkifPn2a2jK+P8eRPr12cnv5zQ2WlhYIAb\n/6hwsGAmyhAzzJRvL7wQRG2tha4uZ7GSvDaDQeCee6J4/PGSXL89KhKDg+kVzH6unZ2dcXaYqaBw\nNRIRXaV+/OMg7r03mvJ1990XxWOPMcdMV8bAQAAdHdnvMHNSBhUSrkaiDDHDTPn21FMh3H23XDC7\n1+ab3hTFgQNBzMwwlkHZl26H2c+1s73dwoULLFGocHA1EhFdhUZGDAwMmLjpptSjvMrLgZtuiuH5\n53m4K2WfKJize0LOunUWzp9niUKFg6uRKEPMMFM+/fSnIbz+9TEEFTWwam12d8fw85+zYKbssm2x\n6a+9PbsZ5nXrLAwNcRYzFQ4WzEREV6Ff/CKI22+P+X79HXdE8eyzLJgpuyYmDIRC2ZvBnFBVBZSV\n2ZiYYIyICgMLZqIMMcNM+bR/fxC33qoumFVrc/fuOM6cCWB6mgUIZc/QkIl169Lb8Of32rluHXPM\nVDi4EomIrjIzMyK/fO21/nOjoRCwa1cML7/M2baUPRcvmliz5srkJrjxjwoJVyJRhphhpnx56aUA\nbrghhpBmUpxube7aFUdfH2MZlD0XLxpYuza9DrPfayc3/lEh4UokIrrKvPRSEDff7D+/nLBrVwwH\nDrDDTNlz4YKJNWuyO4M5Yd06mx1mKhhciUQZYoaZ8uXgwQCuu04fx9Ctzd274zhwIMjJA5Q1IpJx\nZTLMa9ZYGB5m5p4KAwtmIqKrzMGDQc+CWae93UI8DgwNsQih7Lh40Uw7kuFXW5uF4WGWKVQYuBKJ\nMsQMM+XD5KSB2VkDGzboixTd2jQM4Lrr4jh4kDlmyo6hIRNr16Z3y8LvtZMFMxUSrkQioqvIoUMB\nXHttDGaGV++urjiOHmWOmbIjk01/frW12SyYqWBwJRJliBlmyoeDBwPYudM7juG1Nru64jhyhAUz\nrd7iIhAOG6ir89dhtm1gbMzAHXf4u3bW1tqIRoH5+dW8S6LsYMFMRHQVOXo0gK6u9PPLCSyYKVtG\nR020tFgwfETiJyYMvOUt1di9uxZvfnM1xsZSf5NhiFjGyAhLFco/rkKiDDHDTPlw4kQA27d7F8xe\na3Pbtjj6+01EItl+Z1RsRkYMtLam7i7bNvChD1Xixhtj6O+fxoYN/fid36n0Na2ltdVmwUwFoRBW\n4V8AePXSnz/P83shIipYtp0omDPPjJaVAevXWzh5kl1mWp3RUROtranX4k9+EsTQkIm/+qslBALA\ne997DGNjBh5/XHPyTpK2NgsXL3KqC+VfvgvmjQDeC2AngBsA/F8AOvP6joh8YoaZcu3CBQOVlXbK\nzGiqtXnNNXEcP57vyz9d7UQkI3Wb+HOfK8PHPraE4KXhLHfeeQc+/vFlfPGLpSm/l5MyqFDkexXO\nAogCKL/0JwJgJq/viIioQB0/njqO4cfWrXGcOsUOM63OyIiBlhbvDnN/v4mTJwP4tV+LOp6/774o\nzpwJ4OhR7zKkpcXG2Fi+SxWi/BfMEwD+DsAggAEAnwEwndd3ROQTM8yUa37yy0Dqtbl5s4XTp/N9\n+aernZ9IRk9PCR54IIJQUvqit7cXoRDwjndE8P3vl3h+f1OT5WuDINGVlu/p9RsA/C5EDKLine truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAswAAAF/CAYAAABdZ59cAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x2f9a390>"
+        "<matplotlib.figure.Figure at 0xaac6128>"
        ]
       }
      ],
@@ -615,9 +613,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAswAAAF/CAYAAABdZ59cAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAswAAAF/CAYAAABdZ59cAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x27c7950>"
+        "<matplotlib.figure.Figure at 0xabf1ef0>"
        ]
       }
      ],
@@ -668,13 +666,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAsAAAAF9CAYAAAAKk97ZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAsAAAAF9CAYAAAAKk97ZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x27c7dd0>"
+        "<matplotlib.figure.Figure at 0xa5e7208>"
        ]
       }
      ],
-     "prompt_number": 14
+     "prompt_number": 15
     },
     {
      "cell_type": "heading",
@@ -926,11 +924,9 @@
       "\n",
       "$$R = 5$$\n",
       "\n",
-      "In general the matrix will have dimension $m{\\times}1$, where $m$ is the number of sensors. We have only 1 sensor here, so we can get by using a scalar. However, we could equally have written\n",
+      "In general the matrix will have dimension $m{\\times}m$, where $m$ is the number of sensors. It is $m{\\times}m$ because it is a covariance matrix, as there may be correlations between the sensors. We have only 1 sensor here so we write:\n",
       "\n",
-      "$$R = \\begin{bmatrix}5\\end{bmatrix}$$\n",
-      "\n",
-      "but I endevour to keep the nomenclature as simple as possible."
+      "$$R = \\begin{bmatrix}5\\end{bmatrix}$$"
      ]
     },
     {
@@ -985,7 +981,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 15
+     "prompt_number": 16
     },
     {
      "cell_type": "markdown",
@@ -1010,7 +1006,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 16
+     "prompt_number": 17
     },
     {
      "cell_type": "markdown",
@@ -1100,7 +1096,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 17
+     "prompt_number": 18
     },
     {
      "cell_type": "markdown",
@@ -1168,7 +1164,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 18
+     "prompt_number": 19
     },
     {
      "cell_type": "markdown",
@@ -1189,21 +1185,21 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAsgAAAF9CAYAAAAZRJ4tAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAsgAAAF9CAYAAAAZRJ4tAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x112c750>"
+        "<matplotlib.figure.Figure at 0xa5eb588>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAsgAAAFyCAYAAADoEiz4AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAsgAAAFyCAYAAADoEiz4AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3X90ldd95/vPfo4QGIyxJY5GmKAVT0xQYuzEjCfp1Kw4\nuL4IKcFm7P6QnATcXq+uhJqbNOqaJLQdkmnSrLol47smHZL+cNvbzkQ33DpNacGGODQOde9yEmwj\nmoo4wR6gJuhgMAZh6whJ84eQLKOfPJbOc7Q/79daXuZsHY6eT9je+bL1fZ4dDh061C8AAAAAkqQk\n6wsAAAAAygkFMgAAADAMBTIAAAAwDAUyAAAAMAwFMgAAADAMBTIAAAAwzIQF8rPPPqu1a9eqqalJ\nv/7rvy5J2rlzpxoaGtTQ0KC9e/cOvXescQBA6bzjHe/QunXrtG7dOn3hC1+QxLoNAJcjjPcc5L6+\nPjU2NuqLX/yiVqxYodOnT2vevHlqbGzU9u3b1d3drfXr12vPnj0qFoujjgMASuvmm2/W008/PfR6\nrPWZdRsARlcx3hcPHjyoqqoqrVixQpJ0zTXX6Pvf/76WLl2qqqoqSVJtba06Ojp07ty5Ucfr6+un\nOQIAYDwHDhxg3QaAyzBugXz8+HHNnz9f999/v1566SX9wi/8gqqqqpTP59XW1qYFCxYon8+rs7NT\n58+fH3WchRYASqtYLOruu+/W7Nmz1draqpMnT7JuA8BlGLdA7u7u1v79+/V3f/d3uvLKK3XPPffo\n53/+5yVJzc3NkjTix3HDx0MI03HNAIBxPPHEE6qurlZ7e7seeOABffKTn5TEug0AkzVugZzP53X9\n9dertrZWkrR8+XIVi0UVCoWh9xQKBdXU1Kirq2vEeD6fH/GZTz/9tBYuXDhV1w8AJXP27Fm9853v\nzPoyJlRdXS1JuvHGG1VTU6PFixdr165dQ1+/nHX7/28/pMVXzy3NhQPAFEu7bo9bIC9fvlwvvvii\nzpw5oyuuuEI/+tGP9Ku/+qt65JFHdOrUKXV3d+vEiROqr69XsVjUc889N2L8UgsXLhzqaXaxY8cO\nrV27NuvLKCkye3DLvH///qwvYUJnzpzR7NmzNWfOHB07dmyoZWK09Xky6/biq+dqxYoV6jxX1K/v\n+JH+R8vyjJKVjtu8lsjswjFz2nV73AJ5/vz52rx5szZs2KALFy5o7dq1WrZsmVpbW9XS0iJJ2rx5\nsySpsrJy1HFIR44cyfoSSo7MHhwzl7vDhw/rM5/5jCorK5XL5fT5z39eV1555Ztet0OQ+sZ85lFc\nHOc1mT04Zk5r3AJZktasWaM1a9a8YaypqUlNTU0j3jvWOACgNG6++WY9+uijI8bf7LqdKKi/36RC\nBmCPk/RK4I477sj6EkqOzB4cM7tKjHaQHec1mT04Zk6LArkEli1blvUllByZPThmdjXQYuFRITvO\nazJ7cMycFgVyCezbty/rSyg5MntwzOwqCUEe5bHnvCazB8fMaVEgAwAm5NRiAQAUyCWwcuXKrC+h\n5MjswTGzqxCCTYuF47wmswfHzGlRIAMAJsQOMgAnFMgl4NjzQ2YPjpldJUY7yI7zmsweHDOnRYEM\nAJhQCJJJfQwAFMil4NjzQ2YPjpld5Yx2kB3nNZk9OGZOiwIZADAhp6OmAYACuQQce37I7MExs6sk\nBEmyOG7acV6T2YNj5rQokAEAk8KTLAC4oEAuAceeHzJ7cMzsLMjjuGnHeU1mD46Z06JABgBMSpIE\nnmQBwAIFcgk49vyQ2YNjZmeJpF6DCtlxXpPZg2PmtCiQAQCTEgI7yAA8UCCXgGPPD5k9OGZ2lgTJ\noT52nNdk9uCYOS0KZADApCQhqJfHWAAwQIFcAo49P2T24JjZmcsOsuO8JrMHx8xpUSADACYlGB03\nDcAbBXIJOPb8kNmDY2ZnLgeFOM5rMntwzJwWBTIAYFKSECyOmgYACuQScOz5IbMHx8zOgskOsuO8\nJrMHx8xpUSADACZloMXCoEIGYI8CuQQce37I7MExs7PE5KAQx3lNZg+OmdOiQAYATAo7yABcUCCX\ngGPPD5k9OGZ2loRAD3KkyOzBMXNaFMgAgEkJkkWLBQBQIJeAY88PmT04ZnaWhKBegwrZcV6T2YNj\n5rQokAEAk5IEdpABeKBALgHHnh8ye3DM7MzlqGnHeU1mD46Z06JABgBMShKkvqwvAgBKgAK5BBx7\nfsjswTGzM5ejph3nNZk9OGZOiwIZADApLkdNAwAFcgk49vyQ2YNjZmc5epCjRWYPjpnTokAGAExK\n4CkWAExQIJeAY88PmT04ZnYWTI6adpzXZPbgmDktCmQAwKTkTI6aBgAK5BJw7PkhswfHzM5cdpAd\n5zWZPThmTosCGQAwKUHsIAPwQIFcAo49P2T24JjZWS7xuEnPcV6T2YNj5rQokAEAkzKwg2xQIQOw\nR4FcAo49P2T24JjZWWJyUIjjvCazB8fMaVEgAwAmJQlB/TKokAHYo0AuAceeHzJ7cMzsLASpry/r\nq5h+jvOazB4cM6dFgQwAmJQkBPWxgwzAAAVyCTj2/JDZg2NmZ4nJDrLjvCazB8fMaU1YIL/jHe/Q\nunXrtG7dOn3hC1+QJO3cuVMNDQ1qaGjQ3r17h9471jgAYOYLQfQgA7AQDh06NO5qd/PNN+vpp58e\nel0sFtXY2Kjt27eru7tb69ev1549e8Ycv9TRo0e1YsUKHX+lW13FXl2/cO7UpwKAabB//34tWbIk\n68soqcE1W5K+uPcFvWfJVfq566syvioAmJy06/Zlt1gcOHBAS5cuVVVVlRYtWqTa2lp1dHSMOT6a\nQ4Uu/V9/+yP99cHOy75gAEA2EpOjpgFgwgK5WCzq7rvvVktLi77//e/r5MmTyufzamtr065du5TP\n59XZ2Tnm+Gh+67HD+neL51s8T1Py7PkhswfHzM5CCBYn6TnOazJ7cMycVsVEb3jiiSdUXV2t9vZ2\nPfDAA/rkJz8pSWpubpakEW0Uw8dDCKN+5pY7rtPJrh794wsvv6mLBwCUTs7koBAAmLBArq6uliTd\neOONqqmp0eLFi7Vr166hrxcKBdXU1Kirq0uFQuEN4/l8ftTP/O//5T9pzvX/Xidm12rbjx7VjTfe\nOPRsvsG/3cT2elC5XA+vp/71ypUry+p6SvF6cKxcrmeqX2/btk3t7e2qq6uTJK1evVrOXI6adnxW\nLJk9OGZOa9yb9M6cOaPZs2drzpw5OnbsmD70oQ/p7//+73XXXXcN3Yy3YcMG7d69e8RNeoPjlxq8\n4WPfCy/rW8+d0mf/j387rQEBYKq436T30L4jur56rj74joUZXxUATM603KR3+PBhrVu3Tnfeeac2\nbdqkz3/+87ryyivV2tqqlpYW3Xfffdq8ebMkqbKyctTxseSCx06E5NnzQ2YPjpmdJSY7yI7zmswe\nHDOnNW6Lxc0336xHH310xHhTU5OampomPT6ahF42AJhRQpDFTXoAkNlJesHocUGOPT9k9uCY2Vli\n8pM/x3lNZg+OmdPKrEBOQlCvwZGlABALfvIHwEVmBXIuBJsjSx17fsjswTHzTHDu3DmtXLlSDz/8\nsCRp586damhoUENDg/bu3Tv0vrHGxzLQYhH/uu04r8nswTFzWhM+5m26JEHqYwcZAKbcV77yFS1f\nvlwhBBWLRW3dunXoCUPr16/XqlWrxhwfTxKCWLYBOMiuQE48etkkz54fMntwzFzuDh8+rFOnTmn5\n8uXq7+/XgQMHtHTpUlVVVUmSamtr1dHRoXPnzo06Xl9fP+Znuxw17TivyezBMXNaGfYg08sGAFPt\nS1/6kjZt2jT0+uTJk8rn82pra9OuXbuUz+fV2dk55vh4EpOjpgEg25v0TFZax54fMntwzFzOvv3t\nb+utb32rFi1aNKJXuLm5WY2NjSN+z/DxEMK4nx+C1GuwbDvOazJ7cMycVmYtFk4HhQBAKRw4cEC7\nd+/W448/rtOnTytJEt17770qFApD7ykUCqqpqVFXV9eI8Xw+P+rnbty4UXV1dTo893rNmV2pt3ZV\nlc1x4NPxur29vayupxSvB5XL9fB6el63t7eX1fVM13+/Z86ckSQdOXJE999/v9IY96jp6TB4bOmP\nT57X1u8e0bb/OHa/GwCUk5l01PSXv/xlzZs3Tx/+8Ie1Zs2aoZvxNmzYoN27d6tYLKqxsXHE+KWG\nHzX9l/uPq79fWv/vFpU6DgCkknbdzvApFkF9NCEDwLSaNWuWWltb1dLSIknavHmzJKmysnLU8fGE\nEHSBxw8BMJBdD3Lic5OeY88PmT04Zp4pHnjgAf3yL/+yJKmpqUmPPfaYHnvsMb3//e8fes9Y42NJ\n5HHUtOO8JrMHx8xpZVcgy+cmPQCIQZJ4HBQCAOwgl4DjcwfJ7MExs7OBjY2sr2L6Oc5rMntwzJxW\npo95YycCAGaOxOSoaQDgoJAScOz5IbMHx8zOQggW67bjvCazB8fMaXFQCABgUpw2NgB4y6xAdjoo\nxLHnh8weHDM7S0JQv+Jftx3nNZk9OGZOK9sWCx6nCQAzRmDdBmAiw6dY+OwgO/b8kNmDY2ZnSQjq\nM9hBdpzXZPbgmDktbtIDAEzKwFMssr4KAJh+md6k57KD7NjzQ2YPjpmdBZN123Fek9mDY+a0Mt1B\ndnjgPADEIsdP/gCYYAe5BBx7fsjswTGzsxBksW47zmsye3DMnFbGT7GIf6EFgFgEeRwUAgDZHjWd\n1TcvMceeHzJ7cMzsLJd4HDXtOK/J7MExc1qZP8XCYbEFgBiwgwzARWYFcgjB5lFvjj0/ZPbgmNkZ\na3a8yOzBMXNamRXIkteNegAw0yUh8FM/ABYyLpA9diMce37I7MExs7PAmh0tMntwzJwWO8Line truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x28d06d0>"
+        "<matplotlib.figure.Figure at 0xa4b2ba8>"
        ]
       }
      ],
-     "prompt_number": 19
+     "prompt_number": 20
     },
     {
      "cell_type": "markdown",
@@ -1331,7 +1327,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 20
+     "prompt_number": 21
     },
     {
      "cell_type": "heading",
@@ -1368,21 +1364,21 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAsYAAAF9CAYAAAAHja6eAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAsYAAAF9CAYAAAAHja6eAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x29b6b10>"
+        "<matplotlib.figure.Figure at 0xa8a1c50>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAsYAAAF9CAYAAAAHja6eAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAsYAAAF9CAYAAAAHja6eAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x29b5c90>"
+        "<matplotlib.figure.Figure at 0xaaafac8>"
        ]
       }
      ],
-     "prompt_number": 21
+     "prompt_number": 22
     },
     {
      "cell_type": "markdown",
@@ -1491,13 +1487,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAsYAAAF9CAYAAAAHja6eAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3XmcjeX7wPHP2WZfbNkNY8vOyJY1k4wIJUmyfUWEQmj5\nklQjS+lXohIllRaVtWRExNiZsoYvJSJbyuxnfX5/nDFmmGHOmecsz8z1fr3Oa+bZ7ueec3V0zT3X\nc98ghBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEII\nIYQQ/s2R43UZ2AY84KO+PA8kAmlZ/cnLEOAEkAHsBJpfdzwcWAT8m/X6AAi57pyWwO6sNo4Dg1To\ne166AvuATOAkMN5D97mVgrwnt1IPWAr8hjM2L6rZQSGEEEIIf+AAngTK4kx+5gM2oIUP+vIKMA54\nHbDncbxT1v6ngDrAYuASUDLHOZ/iTN5aA22AP3AmhVeVAf4BPsxqY2xWm3ep92MAUB+wAG8DtwPD\nACvwH5XvUxC3ek8KogXOuPQFzgJT1OygEEIIIYQ/cAADc2yHZ+0b65vuADCYvEeMlwNrc2wHAanA\n6KztUjiTz745zhmAM0GNzNoeA1wBTDnO2QB8XdhOX+ctnAlkTkuBvSrf51YK8p646nckMRZCqEjv\n6w4IIUQOuqyvJpylCg5gRwGvjcKZnKbk82qjYj+bAVtybGfiLKe4OrrdFDBcd85PgDHrGDhLL3bh\nTBZznqP2CHkzbkyC9wCNyJ2U38wh8n9fnytgGwV5T4QQwqeMvu6AEELk8B4wFwgGjgIdKXhifAZn\nspef60dNC6MsztKJ+3GWQtyTtV02x3GAv4GvgCpcK5Eoe10blYGfganAxRzH1ezr8ev2XcaZpJYC\nzhegjS7kn0RfdqEfcPP3RAghfEoSYyGEP3kBZ5lCY5y1pzHkHmG8GTvO+lVvSsZZJ5ueta3kcc5Z\n8n+AD5ylBCdxJowlb3Keu/Lq01U361dOp9XoSA63ek+EEMInJDEWQviTCzhnejiBcyT1FZwPbBVk\nVDIKOEz+iWAXYKsKfQRnP8vgfEAwJmvfbVxLIC9kfS2Ns5b4av9yHjsPVMjavjqjxRQKNoLral9L\nX7evFNdm/yiIQ1zr//WmATMK2A+4+XsihBA+JTXGQgh/NR8wcy2JupWrpRSN83mp+bDZbqB9ju1g\nnFOv7cra/hnnCHbOc+7COctGUo42WgAB152zC3XtwVnDq8uxr3nW/rxm3MhLF/J/X+cXsI2CvCdX\nBQLVcP+hPCGEEEIITbt+VgqAeJx1uK7OdVtYUUATnCO4DpwJYBOu1dnmnK6tHs7p2i6SuxTiE5yz\nJrQB2uIsufgwx/HSOEdsP8xqY1xWmx3y6M/UrH60z+PYrdTD+QvGXJzTwj2O84G//m60VVi3ek+u\nuov85yg24YxFE5wlGe9mfV9F/e4KIYQQQvhGXolxBZxJ3VNe7stHXFtsxJ7ja85ygv/gLPm4OiNF\ns+vaCKNgC3zsymrjODf+/FddnU/5dnd+GOBeYD/OemYHMN3NdgqrIO8JOBNjO3lPxVaNG2PjIO8E\nWwghvCoc52/sV1dR6gMcw/k0+X2+6pQQQhQxu3HO5KCGD3GO1NZWqT0hhBBZZgCrgKdx1sn9hvMB\nlCrcOD2QEEII15XEOWpeR6X2dMCzQEWV2hNCiCKjMLNS3I4zCd6L8x/aFjifXL6Ydfw0zrq8fYXp\noBBCFHP/4HwYTS0KMFPF9oQQosgwFOLaBThXPIrBWbdmB8rhHIWojnPU+AQyciyEEEIIITTA3RHj\n7jhriU+TewoguDZ1Ty9uPrG8EEIIIYQQfsPdxLgF8CDQE+ck9w5gHs4nyK8qD/x1/YVffPGFUqZM\nGTdvK4QQQgghRMFcunTpRN++fWsW9Hx3E+MXsl7gnGcyBXgb52wUtwFBOFet2n/9hWXKlKFp06Zu\n3lb42urVq+nevbuvuyHcILHTNomfdknstE3ip21JSUk1XDlfzSWhrThrjq8uuTpWxbaFnzh16pSv\nuyDcJLHTNomfdknstE3iV7yokRi/lOP7pVkvIYQQQgghNEXv6w4IbenUqZOvuyDcJLHTNomfdkns\ntE3iV7xIYixccvvt7q5IK3xNYqdtEj/tkthpm8SveFGzxrhQFEXhwoUL2O12X3dF3MSVK1eIjIx0\n+TqDwUDZsmXR6a6f3U94S2JiIm3btvV1N4SbJH7aJbHTNolf8eI3ifGFCxcIDw8nJCTE110RN1Gx\nonuryKanp3PhwgXKlSunco+EEEIIIdThN6UUdrtdkuIiLCQkRP4a4GMy4qFtEj/tkthpm8SvePGb\nxFgIIYQQQghfksRYiGIiMTHR110QhSDx0y6JnbZJ/IoXSYw1pl27dlSpUoXSpUvjcDh83R0hhBBC\niCJDEmMPK126NCdPnlStvS1btrBt2zbV2hPFh9TJaZvET7skdtom8SteJDH2IEVRcn1Vu10hhBBC\nCKEeSYwLYM6cOTRt2pTKlSvTrFkzVqxYkX1s69atdOrUiWrVqtGyZcvs0dyHHnqIqlWrAtC+fXui\noqKYNGkS4Fx3PWcpRGJiIg0aNCjQ/YRwl9TJaZvET7skdtom8Ste/GYeY39WokQJvvrqK2rUqMG6\ndesYNGgQ7du3JyUlhX79+vH+++/TuXNnTpw4wd9//w3AV199BThLKbZs2UK1atUKfb9SpUp54scT\nQgghhBBIYlwgAwcOzP6+c+fOREZGcvToUbZv305sbCxxcXEA1KxZk5o1a3rkfseOHaNVq1aFblsU\nX1Inp20SP+2S2GmbxK940UxiXGqOOqOll5+67PI1X375JfPmzePMmTM4HA5SUlKwWq2cPXuWKlWq\nqNKvgtxPCCGEEEJ4jmYSY3cSWjWcPn2asWPHsmrVKpo3bw5AjRo1UBSFSpUqsW/fvpter9PpbtgX\nGBgIgM1mIyAggJSUlALd7yqTyQQ4VwvU66VMXBRMYmKijHxomMRPuyR22ibxK14kq7qFtLQ0dDod\nZcqUwWaz8fbbb3PlyhV0Oh0PPvggP/74I2vXrsVut/Pbb7+xc+fOXNeXK1eOw4cP59pXpkwZIiIi\n2Lt3LwCrVq265f1yKlu2LBEREWzdutVDP7UQQgghRPEjifEt1KlTh1GjRtGpUyfq1atHWlpadvlE\nVFQUS5Ys4fXXX6d69eo88sgjNyy6MXnyZJ555hnq169PfHw8AAaDgZdffpnHH3+cbt26UaZMmeyR\n5Zvd7yqDwcBrr73GiBEjiIqK4vvvv/fCOyG0TkY8tE3ip10SO22T+BUvN/6d38PWr1+vNG3a9Ib9\nZ8+epWLFit7ujvAiibEQQgghvCkpKYlOnToVON+VEWMhigmZi1PbJH7aJbHTNolf8SKJsRBCCCGE\nEEhiLESxIXVy2ibx0y6JnbZJ/IoXSYyFEEIIIYRAEmMhig2pk9M2iZ92Sey0TeJXvEhiLIQQQggh\nBJIYC1FsSJ2ctkn8tEtip20Sv+JFEmMhhBBCCCGQxFiIYkPq5LRN4qddEjttk/gVL5IYC1WULl2a\nkydP+robQgghhBBuk8RYFJqiKLm+Cv8kdXLaJvHTLomdtkn8ihdJjG/hs88+IzY2lvr16zNkyBAe\neeQR6taty+HDh3E4HMycOZMmTZpQp04dnnvuOWw2GwB//PEHPXv2pHr16lStWpX//Oc/JCcnZ7eb\nkJBAixYtiIqKonnz5vz444/Zxxo3bsxPP/2UvX39aOyoUaN4/vnnGThwIFFRUTRu3JjU1FQAVq9e\nTevWralevToPP/ww58+fz76me/fu1K5dmylTptCyZUtiY2PJyMgA4J9//mH48OHUqVOHmJgYPv74\n41z3e/LJJ+natStRUVE8+eST2cceeughqlatCkD79u2Jiopi0qRJar39QgghhBBeI4lxAQQGBrJ9\n+3bWrl3LY489Rv/+/Vm+fDlz584lISGBtWvXsmfPHo4ePcr8+fMBsFgsDBo0iIMHD3Lw4EH++ecf\nZs6cmd3m2LFj+e9//8upU6dYtmwZFSpUyD6m0+nQ6XQ37dPSpUvp378/f/zxB0uWLMFoNLJ3717G\njBnDvHnzOH78OI0aNWLcuHHZ17Rs2ZL33nuPBQsWsG7dOoKCgti1axcAI0aMICAggH379rF8+XJm\nzpzJL7/8kn3tpk2bWLBgAdu2bWPlypUkJSUB8NVXX3Hq1CkAtmzZwqlTp5g2bVoh33HhCVInp20S\nP+2S2GmbxK94kcS4AKKjo4mIiKBUqVLUrFmTqKgoLl68yJIlS5g4cSLly5cnLCyMxx57jG+//RaA\nWrVq0atXL0JCQggPD6dHjx4cOnQou029Xs/vv/9OcnIyVapUoW7dui71qV27dnTu3BmdTkeDBg0I\nCgri008/5ZFHHiEmJga9Xs+oUaNYt24dFosl++eoVq0aZcqUITIykqioKC5dusS5c+fYsGED8fHx\nBAYGUq1aNbp37853332Xfb97772XSpUqUblyZerVq8eJEydUeGeFEEIIIfyH0dcdKKigGTMInjXr\nhv0ZzzxD5nPPFej8/M69laujt0ajEYPBgNFoxGazcebMGUaMGIFe7/z9wuFwUL58eQAuXrzIc889\nx44dO0hPT8dqtdKkSZPsNhctWsSbb77JnDlzqFWrFm+99ZZLyXGNGjVu2HfmzBm2bdvGZ599lr0v\nMDAwu5ziat8NBkP2ttVq5ezZswC5+me32+nVq1f2dmRkZPb3AQEBmM3mAvdV+Aepk9M2iZ92Sey0\nTeJXvGgmMc587jmXklpXz3eVoihUqlSJefPm0axZsxuOv/zyyxgMBnbu3ElYWBjz589n5cLine truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAsYAAAF9CAYAAAAHja6eAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x24673d0>"
+        "<matplotlib.figure.Figure at 0xa888550>"
        ]
       }
      ],
-     "prompt_number": 22
+     "prompt_number": 23
     },
     {
      "cell_type": "markdown",
@@ -1552,8 +1548,7 @@
      "collapsed": false,
      "input": [
       "P = np.array([[2,0],[0,6]])\n",
-      "e = stats.sigma_ellipse (P, 0, 0)\n",
-      "stats.plot_sigma_ellipse(e, '|2 0|\\n|0 6|')"
+      "stats.plot_covariance_ellipse ((0,0), P, title='|2 0|\\n|0 6|')"
      ],
      "language": "python",
      "metadata": {},
@@ -1561,13 +1556,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAr0AAAGNCAYAAAActsnoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAr0AAAGNCAYAAAActsnoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x27c2a50>"
+        "<matplotlib.figure.Figure at 0xaabc518>"
        ]
       }
      ],
-     "prompt_number": 23
+     "prompt_number": 26
     },
     {
      "cell_type": "markdown",
@@ -1581,8 +1576,7 @@
      "collapsed": false,
      "input": [
       "P = np.array([[2,2.4],[2.4,6]])\n",
-      "e = stats.sigma_ellipse (P, 0, 0)\n",
-      "stats.plot_sigma_ellipse(e, '|2.0  2.4|\\n|2.4  6.0|')"
+      "stats.plot_covariance_ellipse ((0,0), P, title ='|2.0  2.4|\\n|2.4  6.0|')"
      ],
      "language": "python",
      "metadata": {},
@@ -1590,13 +1584,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAr0AAAGNCAYAAAActsnoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAr0AAAGNCAYAAAActsnoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x28cec50>"
+        "<matplotlib.figure.Figure at 0xabf9128>"
        ]
       }
      ],
-     "prompt_number": 24
+     "prompt_number": 28
     },
     {
      "cell_type": "markdown",
@@ -1658,8 +1652,8 @@
       "    plt.title(title)\n",
       "\n",
       "    for i,p in enumerate(cov):\n",
-      "        e = stats.sigma_ellipse (cov=p, x=i+1, y=ps[i])\n",
-      "        stats.plot_sigma_ellipse(ellipse=e)\n",
+      "        stats.plot_covariance_ellipse ((i+1, ps[i]), cov=p)\n",
+      "\n",
       "        if i == len(cov)-1:\n",
       "            s = ('$\\sigma^2_{pos} = %.2f$' % p[0,0])\n",
       "            plt.text (30,1,s,fontsize=18)\n",
@@ -1681,21 +1675,21 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAsAAAAF9CAYAAAAKk97ZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAsAAAAF9CAYAAAAKk97ZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x29a62d0>"
+        "<matplotlib.figure.Figure at 0xa7ebb70>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAsAAAAF9CAYAAAAKk97ZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAroAAAF9CAYAAADr+MuIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x29a1990>"
+        "<matplotlib.figure.Figure at 0xa7f2e48>"
        ]
       }
      ],
-     "prompt_number": 25
+     "prompt_number": 30
     },
     {
      "cell_type": "markdown",
@@ -1756,13 +1750,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAA3wAAAF9CAYAAABf12quAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAA3wAAAF9CAYAAABf12quAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x24c7e10>"
+        "<matplotlib.figure.Figure at 0xa8a97f0>"
        ]
       }
      ],
-     "prompt_number": 26
+     "prompt_number": 31
     },
     {
      "cell_type": "markdown",
@@ -1779,15 +1773,6 @@
       "\n",
       "> **sidebar**: when plotting covariance ellipses, make sure to always use *plt.axis('equal')* in your code. If the axis use different scales the ellipses will be drawn distorted. For example, the ellipse may be drawn as being taller than it is wide, but it may actually be wider than tall."
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 26
     }
    ],
    "metadata": {}

Unscented_Kalman_Filter.ipynb

@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:66c26b5f12522984f5c0f4f1f89c03cd62b201e37c170c063437cb122ac1b054"
+  "signature": "sha256:5a550f9fc280bc70f84269889df27a4796e358296fd0bd82d4b2b6e09eea2c89"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -157,9 +157,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAr0AAAF9CAYAAAAJJNDxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAr0AAAF9CAYAAAAJJNDxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x1f3af90>"
+        "<matplotlib.figure.Figure at 0xa136b00>"
        ]
       }
      ],
@@ -191,9 +191,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAqwAAAFdCAYAAADG5D8RAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAqwAAAFdCAYAAADG5D8RAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x1fbd4d0>"
+        "<matplotlib.figure.Figure at 0x3e70c18>"
        ]
       }
      ],
@@ -243,9 +243,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAswAAAFyCAYAAADh+YyCAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAswAAAFyCAYAAADh+YyCAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x24d7a10>"
+        "<matplotlib.figure.Figure at 0xa583a58>"
        ]
       }
      ],
@@ -283,9 +283,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAqwAAAFdCAYAAADG5D8RAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAqwAAAFdCAYAAADG5D8RAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x22fdad0>"
+        "<matplotlib.figure.Figure at 0x3e707f0>"
        ]
       }
      ],
@@ -373,9 +373,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAswAAAFyCAYAAADh+YyCAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAswAAAFyCAYAAADh+YyCAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x2189250>"
+        "<matplotlib.figure.Figure at 0xa7d3588>"
        ]
       }
      ],
@@ -477,7 +477,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 9
+     "prompt_number": 7
     },
     {
      "cell_type": "markdown",

bb_test.py

@@ -155,7 +155,7 @@ def plot_ball_filter4 (f1, zs, skip_start=-1, skip_end=-1):
             m[0] = x
             m[2] = y
  
-        f1.update_long_form (m)
+        f1.update (m)
 
         
         '''
@@ -176,8 +176,8 @@ def plot_ball_filter4 (f1, zs, skip_start=-1, skip_end=-1):
         if i > 0 and z[1] < 0:
             break;
 
-    plt.plot (xs, ys, 'r--')
-    plt.plot (pxs, pys)
+    p1, = plt.plot (xs, ys, 'r--')
+    p2, = plt.plot (pxs, pys)
     plt.axis('equal')
     plt.legend([p1,p2], ['filter', 'measurement'], 2)
     plt.xlim([0,xs[-1]])
@@ -191,7 +191,7 @@ def run_6():
     dt = 1/30
     noise = 0.0
     f1 = ball_filter6(dt, R=.16, Q=0.1)
-    #plt.cla()
+    plt.cla()
     
     x,y = baseball.compute_trajectory(v_0_mph = 100., theta=50., dt=dt)
     
@@ -215,4 +215,4 @@ def run_4():
     plot_ball_filter4 (f1, znoise, start_skip, end_skip)
     
     
-run_6()
+run_4()

dog_track_1d.py

@@ -5,6 +5,7 @@ Created on Wed Apr 30 10:35:19 2014
 @author: rlabbe
 """
 import numpy.random as random
+import math
 
 class dog_sensor(object):
     def __init__(self, x0 = 0, motion=1, noise=0.0):

g-h_filter.ipynb

@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:327668f3cfc21e289ec66ce5afcf96d3bdb9e2cc78fc817e634cd292d081ac7a"
+  "signature": "sha256:54ea380f616e75260be91fea63e27687b7f7fe4dfd03d6185542e4746e737014"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -687,7 +687,9 @@
       "\\end{aligned}\n",
       "$$\n",
       "\n",
-      "I'll let you decide which form of the equation is more expressive. One form explicitly uses $g$ and $(1-g)$ to compute the point between the two values, the other finds the difference between the points, and adds a fraction of that to the first value. Both are computing the same thing. You'll see both forms in the literature, so I have used both to expose you to them. "
+      "I'll let you decide which form of the equation is more expressive. One form explicitly uses $g$ and $(1-g)$ to compute the point between the two values, the other finds the difference between the points, and adds a fraction of that to the first value. Both are computing the same thing. You'll see both forms in the literature, so I have used both to expose you to them.\n",
+      "\n",
+      "**author's note: I think residual form is clearer. recast development to use this form**"
      ]
     },
     {

mkf_internal.py

@@ -63,21 +63,24 @@ def show_position_prediction_chart():
     plt.scatter ([4], [4], c='g',s=128)
     ax = plt.axes()
     ax.annotate('', xy=(4,4), xytext=(3,3),
-                arrowprops=dict(arrowstyle='->', ec='g',shrinkA=6, lw=3,shrinkB=5))
+                arrowprops=dict(arrowstyle='->',
+                                ec='g',
+                                shrinkA=6, shrinkB=5,
+                                lw=3))
     plt.show()
 
 
-def show_x_error_char():
+def show_x_error_chart():
     """ displays x=123 with covariances showing error"""
 
     cov = np.array([[0.003,0], [0,12]])
     sigma=[0.5,1.,1.5,2]
+    e = stats.covariance_ellipse (cov)
 
-    e1 = stats.sigma_ellipses(cov, x=1, y=1, sigma=sigma)
-    e2 = stats.sigma_ellipses(cov, x=2, y=2, sigma=sigma)
-    e3 = stats.sigma_ellipses(cov, x=3, y=3, sigma=sigma)
+    stats.plot_covariance_ellipse ((1,1), ellipse=e, variance=sigma, axis_equal=False)
+    stats.plot_covariance_ellipse ((2,1), ellipse=e, variance=sigma, axis_equal=False)
+    stats.plot_covariance_ellipse ((3,1), ellipse=e, variance=sigma, axis_equal=False)
 
-    stats.plot_sigma_ellipses([e1, e2, e3], axis_equal=True,x_lim=[0,4],y_lim=[0,15])
 
     plt.ylim([0,11])
     plt.xticks(np.arange(1,4,1))
@@ -90,23 +93,35 @@ def show_x_error_char():
 def show_x_with_unobserved():
     """ shows x=1,2,3 with velocity superimposed on top """
 
+    # plot velocity
     sigma=[0.5,1.,1.5,2]
     cov = np.array([[1,1],[1,1.1]])
-    ev = stats.sigma_ellipses(cov, x=2, y=2, sigma=sigma)
+    stats.plot_covariance_ellipse ((2,2), cov=cov, variance=sigma, axis_equal=False)
 
+    # plot positions
     cov = np.array([[0.003,0], [0,12]])
-    e1 = stats.sigma_ellipses(cov, x=1, y=1, sigma=sigma)
-    e2 = stats.sigma_ellipses(cov, x=2, y=2, sigma=sigma)
-    e3 = stats.sigma_ellipses(cov, x=3, y=3, sigma=sigma)
+    sigma=[0.5,1.,1.5,2]
+    e = stats.covariance_ellipse (cov)
 
+    stats.plot_covariance_ellipse ((1,1), ellipse=e, variance=sigma, axis_equal=False)
+    stats.plot_covariance_ellipse ((2,1), ellipse=e, variance=sigma, axis_equal=False)
+    stats.plot_covariance_ellipse ((3,1), ellipse=e, variance=sigma, axis_equal=False)
+
+    # plot intersection cirle
     isct = Ellipse(xy=(2,2), width=.2, height=1.2, edgecolor='r', fc='None', lw=4)
-    plt.figure().gca().add_artist(isct)
-    stats.plot_sigma_ellipses([e1, e2, e3, ev], axis_equal=True,x_lim=[0,4],y_lim=[0,15])
+    plt.gca().add_artist(isct)
 
     plt.ylim([0,11])
+    plt.xlim([0,4])
     plt.xticks(np.arange(1,4,1))
 
     plt.xlabel("Position")
     plt.ylabel("Time")
 
-    plt.show()
+    plt.show()
+
+
+
+if __name__ == "__main__":
+
+    show_x_with_unobserved()

stats.py

@@ -1,16 +1,34 @@
+"""
+Author: Roger Labbe
+Copyright: 2014
+
+This code performs various basic statistics functions for the
+Kalman and Bayesian Filters in Python book. Much of this code
+is non-optimal; production code should call the equivalent scipy.stats
+functions. I wrote the code in this form to make explicit how the
+computations are done. The scipy.stats module has many more useful functions
+than what I have written here. In some cases, however, my code is significantly
+faster, at least on my machine. For example, gaussian average 794 ns, whereas
+stats.norm(), using the frozen form, averages 116 microseconds per call.
+"""
+
+
 import math
 import numpy as np
 import numpy.linalg as linalg
 import matplotlib.pyplot as plt
 import scipy.sparse as sp
 import scipy.sparse.linalg as spln
+from matplotlib.patches import Ellipse
 
 _two_pi = 2*math.pi
 
 
 def gaussian(x, mean, var):
     """returns normal distribution for x given a gaussian with the specified
-    mean and variance. All must be scalars
+    mean and variance. All must be scalars.
+
+    gaussian (1,2,3) is equivalent to scipy.stats.norm(2,math.sqrt(3)).pdf(1)
     """
     return math.exp((-0.5*(x-mean)**2)/var) / math.sqrt(_two_pi*var)
 
@@ -24,7 +42,7 @@ def add (a_mu, a_var, b_mu, b_var):
 
 
 def multivariate_gaussian(x, mu, cov):
-    """ This is designed to work the same as scipy.stats.multivariate_normal
+    """ This is designed to replace scipy.stats.multivariate_normal
     which is not available before version 0.14. You may either pass in a
     multivariate set of data:
        multivariate_gaussian (array([1,1]), array([3,4]), eye(2)*1.4)
@@ -36,6 +54,10 @@ def multivariate_gaussian(x, mu, cov):
 
     The function gaussian() implements the 1D (univariate)case, and is much
     faster than this function.
+
+    equivalent calls:
+       multivariate_gaussian(1, 2, 3)
+       scipy.stats.multivariate_normal(2,3).pdf(1)
     """
 
     # force all to numpy.array type
@@ -65,89 +87,71 @@ def norm_plot(mean, var):
     plt.plot(xs,ys)
 
 
-def sigma_ellipse(cov, x=0, y=0, sigma=1, num_pts=100):
-    """ Takes a 2D covariance matrix and generates an ellipse showing the
-    contour plot at the specified sigma value. Ellipse is centered at (x,y).
-    num_pts specifies how many discrete points are used to generate the
-    ellipse.
+def covariance_ellipse(P):
+    """ returns a tuple defining the ellipse representing the 2 dimensional
+    covariance matrix P.
 
-    Returns a tuple containing the ellipse,x, and y, in that order.
-    The ellipse is a 2D numpy array with shape (2, num_pts). Row 0 contains the
-    x components, and row 1 contains the y coordinates
+    Returns (angle_radians, width_radius, height_radius)
     """
-    cov = np.asarray(cov)
+    U,s,v = linalg.svd(P)
+    orientation = math.atan2(U[1,0],U[0,0])
+    width  = math.sqrt(s[0])
+    height = math.sqrt(s[1])
+    return (orientation, width, height)
 
-    L = linalg.cholesky(cov)
-    t = np.linspace(0, _two_pi, num_pts)
-    unit_circle = np.array([np.cos(t), np.sin(t)])
 
-    ellipse = sigma * L.dot(unit_circle)
-    ellipse[0] += x
-    ellipse[1] += y
-    return (ellipse,x,y)
 
-def sigma_ellipses(cov, x=0, y=0, sigma=[1,2], num_pts=100):
-    cov = np.asarray(cov)
+def plot_covariance_ellipse(mean, cov=None, variance = 1.0,
+             ellipse=None, title=None, axis_equal=True,
+             facecolor='none', edgecolor='blue'):
+    """ plots the covariance ellipse where
 
-    L = linalg.cholesky(cov)
-    t = np.linspace(0, _two_pi, num_pts)
-    unit_circle = np.array([np.cos(t), np.sin(t)])
+    mean is a (x,y) tuple for the mean of the covariance (center of ellipse)
 
-    e_list = []
-    for s in sigma:
-        ellipse = s * L.dot(unit_circle)
-        ellipse[0] += x
-        ellipse[1] += y
-        e_list.append (ellipse)
-    return (e_list,x,y)
+    cov is a 2x2 covariance matrix.
 
-def plot_covariance_ellipse (cov, x=0, y=0, sigma=1,title=None, axis_equal=True):
-    """ Plots the ellipse of the provided 2x2 covariance matrix.
+    variance is the normal sigma^2 that we want to plot. If list-like,
+    ellipses for all ellipses will be ploted. E.g. [1,2] will plot the
+    \sigma^2 = 1 and \sigma^2 = 2 ellipses.
+
+    ellipse is a (angle,width,height) tuple containing the angle in radians,
+    and width and height radii.
+
+    You may provide either cov or ellipse, but not both.
+
+    plt.show() is not called, allowing you to plot multiple things on the
+    same figure.
     """
-    e = sigma_ellipse (cov, x, y, sigma)
-    plot_sigma_ellipse(e, title, axis_equal)
 
+    assert cov is None or ellipse is None
+    assert not (cov is None and ellipse is None)
 
-def plot_sigma_ellipse(ellipse, title=None, axis_equal=True):
-    """ plots the ellipse produced from sigma_ellipse."""
+    if cov is not None:
+        ellipse = covariance_ellipse(cov)
 
     if axis_equal:
         plt.axis('equal')
 
-    e = ellipse[0]
-    x = ellipse[1]
-    y = ellipse[2]
-
-    plt.plot(e[0], e[1],c='b')
-    plt.scatter(x,y,marker='+') # mark the center
     if title is not None:
         plt.title (title)
 
-def plot_sigma_ellipses(ellipses,title=None,axis_equal=True,x_lim=None,y_lim=None):
-    """ plots the ellipse produced from sigma_ellipse."""
 
-    if x_lim is not None:
-        axis_equal = False
-        plt.xlim(x_lim)
+    if np.isscalar(variance):
+        variance = [variance]
 
-    if y_lim is not None:
-        axis_equal = False
-        plt.ylim(y_lim)
+    ax = plt.gca()
 
-    if axis_equal:
-        plt.axis('equal')
+    angle = np.degrees(ellipse[0])
+    width = ellipse[1] * 2.
+    height = ellipse[2] * 2.
 
-    for ellipse in ellipses:
-        es = ellipse[0]
-        x = ellipse[1]
-        y = ellipse[2]
-
-        for e in es:
-            plt.plot(e[0], e[1], c='b')
-
-        plt.scatter(x,y,marker='+') # mark the center
-    if title is not None:
-        plt.title (title)
+    for var in variance:
+        e = Ellipse(xy=mean, width=var*width, height=var*height, angle=angle,
+                    facecolor=facecolor,
+                    edgecolor=edgecolor,
+                    lw=1)
+        ax.add_patch(e)
+    plt.scatter(mean[0], mean[1], marker='+') # mark the center
 
 
 def _to_cov(x,n):
@@ -164,9 +168,26 @@ def _to_cov(x,n):
         return np.eye(n) * x
 
 
+
+def test_gaussian():
+   import scipy.stats
+
+   mean = 3.
+   var = 1.5
+   std = var*0.5
+
+   for i in np.arange(-5,5,0.1):
+       p0 = scipy.stats.norm(mean, std).pdf(i)
+       p1 = gaussian(i, mean, var)
+
+       assert abs(p0-p1) < 1.e15
+
+
 if __name__ == '__main__':
     from scipy.stats import norm
 
+    test_gaussian()
+
     # test conversion of scalar to covariance matrix
     x  = multivariate_gaussian(np.array([1,1]), np.array([3,4]), np.eye(2)*1.4)
     x2 = multivariate_gaussian(np.array([1,1]), np.array([3,4]), 1.4)
@@ -180,13 +201,11 @@ if __name__ == '__main__':
     assert rv.pdf(1.2) == x2
     assert abs(x2- x3) < 0.00000001
 
-    cov = np.array([[1,1],
-                    [1,1.1]])
+    cov = np.array([[1.0, 1.0],
+                    [1.0, 1.1]])
 
-    sigma = [1,1]
-    ev = sigma_ellipses(cov, x=2, y=2, sigma=sigma)
-    plot_sigma_ellipses([ev], axis_equal=True,x_lim=[0,4],y_lim=[0,15])
-    #isct = plt.Circle((2,2),1,color='b',fill=False)
-    #plt.figure().gca().add_artist(isct)
+    P = np.array([[2,0],[0,2]])
+    plot_covariance_ellipse((2,7), cov=cov, variance=[1,2], title='my title')
     plt.show()
+
     print "all tests passed"