Normalized plot styles across chapters.

I had a lot of problems with lines being visible, and ended up
using different styles in different places. I found a style that
seems to work everywhere and put the code in book_plots.py.

animations/Gaussians_Animations.ipynb

@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:456e95ad9d347f3ae0b9b1ebfb1a51829b89dcde3421170630fc3387515bda9c"
+  "signature": "sha256:67b73f8366759bb2837043e5a461a7ce4ff077967115f36e8017eaa1318f1c0f"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -16,7 +16,7 @@
       "%matplotlib inline\n",
       "import sys\n",
       "sys.path.insert(0,'..') # allow us to format the book\n",
-      "\n",
+      "sys.path.insert(0,'../code') \n",
       "# use same formatting as rest of book so that the plots are\n",
       "# consistant with that look and feel.\n",
       "import book_format\n",
@@ -245,13 +245,13 @@
        ],
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 1,
+       "prompt_number": 3,
        "text": [
-        "<IPython.core.display.HTML at 0x7fde2b0ceb38>"
+        "<IPython.core.display.HTML at 0x7fc8b024ce80>"
        ]
       }
      ],
-     "prompt_number": 1
+     "prompt_number": 3
     },
     {
      "cell_type": "code",
@@ -268,7 +268,7 @@
       "    plt.plot (xs, ys)\n",
       "    plt.ylim((0,1))\n",
       "\n",
-      "    \n",
+      "\n",
       "mu = 2\n",
       "sigma = 0.6\n",
       "def ganimate(frame):\n",
@@ -293,13 +293,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAcEAAAGrCAYAAAC41CzUAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAcEAAAGrCAYAAAC41CzUAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fde0e233e80>"
+        "<matplotlib.figure.Figure at 0x7fc8aff6f9e8>"
        ]
       }
      ],
-     "prompt_number": 2
+     "prompt_number": 4
     },
     {
      "cell_type": "code",
@@ -314,7 +314,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "<img src='gaussian_animate.gif'>"
+      "<img src='04_gaussian_animate.gif'>"
      ]
     }
    ],

animations/Kalman_Filters_Animations.ipynb

@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:8dfeca3728647e0419c6f2c7f141dc89f294f6d749e8e29d6baf3d93643fb2a2"
+  "signature": "sha256:bacbdf37e5f8b4f5a0841d27be72d89c6cfab901e53ba2bc642b422618558c14"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -16,6 +16,7 @@
       "%matplotlib inline\n",
       "import sys\n",
       "sys.path.insert(0,'..') # allow us to format the book\n",
+      "sys.path.insert(0,'../code') \n",
       "\n",
       "# use same formatting as rest of book so that the plots are\n",
       "# consistant with that look and feel.\n",
@@ -35,7 +36,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 3
+     "prompt_number": 1
     },
     {
      "cell_type": "markdown",
@@ -102,7 +103,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 4
+     "prompt_number": 2
     },
     {
      "cell_type": "code",
@@ -171,19 +172,19 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAcYAAAHGCAYAAADuYispAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fa8ae47c7f0>"
+        "<matplotlib.figure.Figure at 0x7f2d5f871b70>"
        ]
       }
      ],
-     "prompt_number": 5
+     "prompt_number": 3
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "<img src='volt_animate.gif'>"
+      "<img src='05_volt_animate.gif'>"
      ]
     },
     {
@@ -247,13 +248,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAcYAAAHFCAYAAABo9lmHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fa8a6b732e8>"
+        "<matplotlib.figure.Figure at 0x7f2d585b7e10>"
        ]
       }
      ],
-     "prompt_number": 6
+     "prompt_number": 4
     },
     {
      "cell_type": "markdown",

animations/discrete_bayes_animations.ipynb

@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:433f3b1a6dada8b107fd2ee6433183f04ca080c6c5ef055d0f83e14f9c714fb3"
+  "signature": "sha256:8f58b707cf76c184d840e41abdb14f1e584995252c4df05a742336b9f07b9b0b"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -24,6 +24,7 @@
       "import matplotlib.pyplot as plt\n",
       "import sys\n",
       "sys.path.insert(0,'..') # allow us to format the book\n",
+      "sys.path.insert(0,'../code') \n",
       "import book_format\n",
       "book_format.load_style(directory='..')"
      ],
@@ -250,13 +251,13 @@
        ],
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 2,
+       "prompt_number": 8,
        "text": [
-        "<IPython.core.display.HTML at 0x7ff8d0752630>"
+        "<IPython.core.display.HTML at 0x7f8ada8a3d30>"
        ]
       }
      ],
-     "prompt_number": 2
+     "prompt_number": 8
     },
     {
      "cell_type": "markdown",
@@ -272,25 +273,13 @@
       "from matplotlib import animation\n",
       "import matplotlib.pyplot as plt\n",
       "import numpy as np\n",
-      "%matplotlib inline\n",
-      "\n",
-      "def barplot(pos, ylim=(0,1)):\n",
-      "    \"\"\" implement plotting of bar plots in a way that plays nicely with the\n",
-      "    animations.\"\"\"\n",
-      "    \n",
-      "    plt.cla()\n",
-      "    ax = plt.gca()\n",
-      "    x = np.arange(len(pos))\n",
-      "    ax.bar(x, pos)\n",
-      "    if ylim:\n",
-      "        plt.ylim(ylim)\n",
-      "    plt.xticks(x+0.4, x)\n",
-      "    plt.grid()"
+      "from book_plots import bar_plot\n",
+      "%matplotlib inline"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 3
+     "prompt_number": 9
     },
     {
      "cell_type": "code",
@@ -327,7 +316,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 4
+     "prompt_number": 10
     },
     {
      "cell_type": "code",
@@ -336,7 +325,7 @@
       "pos = [1.0,0,0,0,0,0,0,0,0,0]\n",
       "def bar_animate(nframe):\n",
       "    global pos\n",
-      "    barplot(pos)\n",
+      "    bar_plot(pos)\n",
       "    pos = predict(pos, 1, .8, .1, .1)\n",
       "\n",
       "\n",
@@ -351,13 +340,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAbgAAACsCAYAAAAJ+rmKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nOElSkyw4SVKTLDhJUpMsOElSk/4Po5u82b9IDysAAAAASUVORK5CYII=\n",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAbgAAACsCAYAAAAJ+rmKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7ff8d0768198>"
+        "<matplotlib.figure.Figure at 0x7f8ada768cf8>"
        ]
       }
      ],
-     "prompt_number": 5
+     "prompt_number": 11
     },
     {
      "cell_type": "markdown",
@@ -378,7 +367,7 @@
       "    if nframe == 0:\n",
       "        return\n",
       "\n",
-      "    barplot(pos, ylim=(0,1.0))\n",
+      "    bar_plot(pos, ylim=(0,1.0))\n",
       "    if nframe % 2 == 0:\n",
       "        pos = predict(pos, 1, .8, .1, .1)\n",
       "    else:\n",
@@ -400,11 +389,11 @@
        "output_type": "display_data",
        "png": "iVBORw0KGgoAAAANSUhEUgAAAbgAAACsCAYAAAAJ+rmKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYII=\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x7ff8d065cfd0>"
+        "<matplotlib.figure.Figure at 0x7f8ada701d30>"
        ]
       }
      ],
-     "prompt_number": 6
+     "prompt_number": 14
     },
     {
      "cell_type": "markdown",