Reran with tight_layout for interactive plots

This is just so everything looks nice in nbviewer. I added
plt.tight_layout() to the interactive_plot context manager,
which makes plots fill the output cell better.

01-g-h-filter.ipynb

@@ -403,7 +403,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Average of measurements is 164.9735\n"
+      "Average of measurements is 164.9896\n"
      ]
     }
    ],
@@ -436,7 +436,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Average of measurements is 165.0335\n"
+      "Average of measurements is 164.9863\n"
      ]
     }
    ],
@@ -1349,7 +1349,7 @@
     {
      "data": {
       "text/html": [
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       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -2201,7 +2201,7 @@
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@@ -7682,7 +7682,7 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x1c843ff4438>]"
+       "[<matplotlib.lines.Line2D at 0x29b72490e10>]"
       ]
      },
      "execution_count": 26,
@@ -8501,7 +8501,7 @@
     {
      "data": {
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03-Gaussians.ipynb

@@ -1415,7 +1415,7 @@
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       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -2231,8 +2231,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "mean = 1.807\n",
-      "std  = 0.132\n"
+      "mean = 1.814\n",
+      "std  = 0.136\n"
      ]
     }
    ],
@@ -3116,7 +3116,7 @@
     {
      "data": {
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       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -3979,8 +3979,8 @@
    "source": [
     "from filterpy.stats import plot_gaussian_pdf\n",
     "plt.figure()\n",
-    "plot_gaussian_pdf(mean=1.8, variance=0.1414**2, \n",
-    "                  xlabel='Student Height', ylabel='pdf');"
+    "ax = plot_gaussian_pdf(mean=1.8, variance=0.1414**2, \n",
+    "                  xlabel='Student Height', ylabel='pdf')"
    ]
   },
   {
@@ -4773,7 +4773,7 @@
     {
      "data": {
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truncated
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -5585,7 +5585,7 @@
    ],
    "source": [
     "with interactive_plot():\n",
-    "    plot_gaussian_pdf(mean=120, variance=17**2, xlabel='speed(kph)')"
+    "    ax = plot_gaussian_pdf(mean=120, variance=17**2, xlabel='speed(kph)')"
    ]
   },
   {
@@ -6407,7 +6407,7 @@
     {
      "data": {
       "text/html": [
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       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -6421,7 +6421,7 @@
     "from filterpy.stats import gaussian, norm_cdf\n",
     "\n",
     "with interactive_plot():\n",
-    "    plot_gaussian_pdf(22, 4, mean_line=True, xlabel='$^{\\circ}C$');"
+    "    ax = plot_gaussian_pdf(22, 4, mean_line=True, xlabel='$^{\\circ}C$')"
    ]
   },
   {
@@ -7293,7 +7293,7 @@
     {
      "data": {
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       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -7312,7 +7312,7 @@
     "    plt.plot(xs, gaussian(xs, 23, 0.05), label='$\\sigma^2$=0.05', c='b')\n",
     "    plt.plot(xs, gaussian(xs, 23, 1), label='$\\sigma^2$=1', ls=':', c='b')\n",
     "    plt.plot(xs, gaussian(xs, 23, 5), label='$\\sigma^2$=5', ls='--', c='b')\n",
-    "    plt.legend();"
+    "    plt.legend()"
    ]
   },
   {
@@ -8115,7 +8115,7 @@
     {
      "data": {
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-      "  1.915  4.536  4.068  1.288  2.493  0.271  2.668\n",
-      "  5.318]\n"
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+      "  2.875  2.746  3.229  2.298 -6.525  3.393 -1.366\n",
+      "  1.262]\n"
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04-One-Dimensional-Kalman-Filters.ipynb

@@ -1107,7 +1107,7 @@
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       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -1923,7 +1923,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Mean of readings is 9.995\n"
+      "Mean of readings is 10.003\n"
      ]
     }
    ],
@@ -2742,7 +2742,7 @@
     {
      "data": {
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       "text/plain": [
        "<IPython.core.display.HTML object>"

05-Multivariate-Gaussians.ipynb

@@ -1136,7 +1136,7 @@
     {
      "data": {
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      "output_type": "stream",
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-      "[[ 10.1904   2.9346]\n",
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+      "[[ 10.4337   2.8677]\n",
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      "name": "stdout",
      "output_type": "stream",
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-      "[[ 1.0004 -0.0045]\n",
-      " [-0.0045  1.0013]]\n"
+      "[[ 0.9956 -0.0028]\n",
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       "text/plain": [
        "<IPython.core.display.HTML object>"

06-Multivariate-Kalman-Filters.ipynb

@@ -1700,7 +1700,7 @@
     {
      "data": {
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       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"

07-Kalman-Filter-Math.ipynb

@@ -50,7 +50,7 @@
        "    font-family: 'Source Code Pro', Consolas, monocco, monospace;\n",
        "}\n",
        "    div.cell{\n",
-       "        width: 850px;\n",
+       "        //width: 950px;\n",
        "        margin-left: 0% !important;\n",
        "        margin-right: auto;\n",
        "    }\n",
@@ -64,7 +64,7 @@
        "        //font-family: 'Chivo',verdana,arial,sans-serif;\n",
        "        //font-family: 'Cardo',verdana,arial,sans-serif;\n",
        "        //font-family: 'Arvo',verdana,arial,sans-serif;\n",
-       "        //font-family: 'Poppins',verdana,arial,sans-serif;   \n",
+       "        //font-family: 'Poppins',verdana,arial,sans-serif;\n",
        "        //font-family: 'Ubuntu',verdana,arial,sans-serif;\n",
        "        //font-family: 'Fontin',verdana,arial,sans-serif;\n",
        "        //font-family: 'Raleway',verdana,arial,sans-serif;\n",
@@ -88,10 +88,10 @@
        "    h1 {\n",
        "        font-family: 'Open sans',verdana,arial,sans-serif;\n",
        "\t}\n",
-       "\t\n",
+       "\n",
        "    div.input_area {\n",
        "        background: #F6F6F9;\n",
-       "        border: 1px solid #586e75;      \n",
+       "        border: 1px solid #586e75;\n",
        "    }\n",
        "\n",
        "    .text_cell_render h1 {\n",
@@ -104,7 +104,7 @@
        "        display: block;\n",
        "        white-space: wrap;\n",
        "        text-align: left;\n",
-       "    } \n",
+       "    }\n",
        "    h2 {\n",
        "        font-family: 'Open sans',verdana,arial,sans-serif;\n",
        "        text-align: left;\n",
@@ -120,7 +120,7 @@
        "        display: block;\n",
        "        white-space: wrap;\n",
        "        text-align: left;\n",
-       "    } \n",
+       "    }\n",
        "    h3 {\n",
        "        font-family: 'Open sans',verdana,arial,sans-serif;\n",
        "    }\n",
@@ -187,27 +187,27 @@
        "        margin: 2em;\n",
        "    }\n",
        "    ul li{\n",
-       "        padding-left: 0.5em; \n",
-       "        margin-bottom: 0.5em; \n",
-       "        margin-top: 0.5em; \n",
+       "        padding-left: 0.5em;\n",
+       "        margin-bottom: 0.5em;\n",
+       "        margin-top: 0.5em;\n",
        "    }\n",
        "    ul li li{\n",
-       "        padding-left: 0.2em; \n",
-       "        margin-bottom: 0.2em; \n",
-       "        margin-top: 0.2em; \n",
+       "        padding-left: 0.2em;\n",
+       "        margin-bottom: 0.2em;\n",
+       "        margin-top: 0.2em;\n",
        "    }\n",
        "    ol{\n",
        "        margin: 2em;\n",
        "    }\n",
        "    ol li{\n",
-       "        padding-left: 0.5em; \n",
-       "        margin-bottom: 0.5em; \n",
-       "        margin-top: 0.5em; \n",
+       "        padding-left: 0.5em;\n",
+       "        margin-bottom: 0.5em;\n",
+       "        margin-top: 0.5em;\n",
        "    }\n",
        "    ul li{\n",
-       "        padding-left: 0.5em; \n",
-       "        margin-bottom: 0.5em; \n",
-       "        margin-top: 0.2em; \n",
+       "        padding-left: 0.5em;\n",
+       "        margin-bottom: 0.5em;\n",
+       "        margin-top: 0.2em;\n",
        "    }\n",
        "    a:link{\n",
        "       color:#447adb;\n",
@@ -226,10 +226,10 @@
        "       color:#447adb;\n",
        "    }\n",
        "    .rendered_html :link {\n",
-       "       text-decoration: underline; \n",
+       "       text-decoration: underline;\n",
        "    }\n",
        "    .rendered_html :hover {\n",
-       "       text-decoration: none; \n",
+       "       text-decoration: none;\n",
        "    }\n",
        "    .rendered_html :visited {\n",
        "      text-decoration: none;\n",
@@ -242,7 +242,7 @@
        "    }\n",
        "    .warning{\n",
        "        color: rgb( 240, 20, 20 )\n",
-       "    } \n",
+       "    }\n",
        "    hr {\n",
        "      color: #f3f3f3;\n",
        "      background-color: #f3f3f3;\n",
@@ -1302,9 +1302,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAiAAAAECCAYAAADHBWy8AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9163eb8>"
+       "<matplotlib.figure.Figure at 0x1b5cdedb550>"
       ]
      },
      "metadata": {},
@@ -1336,9 +1336,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAu4AAADxCAYAAACUAjKtAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAicAAAERCAYAAACgmwfwAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x943e908>"
+       "<matplotlib.figure.Figure at 0x1b5cdedb470>"
       ]
      },
      "metadata": {},
@@ -1417,9 +1417,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9425908>"
+       "<matplotlib.figure.Figure at 0x1b5d09777b8>"
       ]
      },
      "metadata": {},
@@ -1574,9 +1574,9 @@
     },
     {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xb2aa240>"
+       "<matplotlib.figure.Figure at 0x1b5d1359198>"
       ]
      },
      "metadata": {},

08-Designing-Kalman-Filters.ipynb

@@ -1144,7 +1144,7 @@
     {
      "data": {
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09-Nonlinear-Filtering.ipynb

@@ -1153,7 +1153,7 @@
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       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -4423,8 +4423,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "input  mean, variance: -0.0025, 0.9994\n",
-      "output mean, variance: -0.1208, 2.4038\n"
+      "input  mean, variance: -0.0004, 0.9975\n",
+      "output mean, variance: -0.1242, 2.3965\n"
      ]
     }
    ],
@@ -5218,7 +5218,7 @@
     {
      "data": {
       "text/html": [
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truncated
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -5231,7 +5231,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "output mean, variance: 0.0038, 2.2487\n"
+      "output mean, variance: 0.0006, 2.2445\n"
      ]
     }
    ],
@@ -5264,8 +5264,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "linear    output mean, variance: 0.2167, 7477.4907\n",
-      "nonlinear output mean, variance: -8.6964, 30492.4095\n"
+      "linear    output mean, variance: 0.0324, 7463.4503\n",
+      "nonlinear output mean, variance: -9.1138, 30411.9742\n"
      ]
     }
    ],
@@ -6066,7 +6066,7 @@
     {
      "data": {
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10-Unscented-Kalman-Filter.ipynb

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       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -2745,7 +2745,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Difference in mean x=0.129, y=43.192\n"
+      "Difference in mean x=0.052, y=43.878\n"
      ]
     }
    ],
@@ -3569,7 +3569,7 @@
     {
      "data": {
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       "text/plain": [
        "<IPython.core.display.HTML object>"

13-Smoothing.ipynb

@@ -327,7 +327,7 @@
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x2d9deb0e278>"
+       "<matplotlib.figure.Figure at 0x1e5249ac160>"
       ]
      },
      "metadata": {},
@@ -376,7 +376,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x2d9db2becc0>"
+       "<matplotlib.figure.Figure at 0x1e524999c18>"
       ]
      },
      "metadata": {},
@@ -408,7 +408,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x2d9e045fb70>"
+       "<matplotlib.figure.Figure at 0x1e5262ece48>"
       ]
      },
      "metadata": {},
@@ -539,7 +539,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x2d9e04d54e0>"
+       "<matplotlib.figure.Figure at 0x1e5263624a8>"
       ]
      },
      "metadata": {},
@@ -614,7 +614,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x2d9e045d438>"
+       "<matplotlib.figure.Figure at 0x1e52638ff60>"
       ]
      },
      "metadata": {},
@@ -643,7 +643,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x2d9e05e2390>"
+       "<matplotlib.figure.Figure at 0x1e52638ff28>"
       ]
      },
      "metadata": {},
@@ -682,7 +682,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x2d9e03f3cf8>"
+       "<matplotlib.figure.Figure at 0x1e524df90f0>"
       ]
      },
      "metadata": {},
@@ -721,7 +721,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAhwAAACSCAYAAAAO2xP9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAAcwQHAAAwR3AAAABzBAcAADD3H7HiXZn+RRc2AAAAAElFTkSuQmCC\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x2d9e04714e0>"
+       "<matplotlib.figure.Figure at 0x1e5264df390>"
       ]
      },
      "metadata": {},
@@ -770,7 +770,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x2d9e068f7f0>"
+       "<matplotlib.figure.Figure at 0x1e5263d9438>"
       ]
      },
      "metadata": {},

14-Adaptive-Filtering.ipynb

@@ -1207,7 +1207,7 @@
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       "text/plain": [
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       "text/plain": [
        "<IPython.core.display.HTML object>"
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truncated
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -7859,7 +7859,7 @@
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       "text/plain": [
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       "text/plain": [
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       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -11942,7 +11942,7 @@
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       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
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       "text/plain": [
        "<IPython.core.display.HTML object>"
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       "text/plain": [
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       "text/plain": [
        "<IPython.core.display.HTML object>"
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       "text/plain": [
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       "text/plain": [
        "<IPython.core.display.HTML object>"
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       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"

code/book_plots.py

@@ -17,28 +17,27 @@ for more information.
 from __future__ import (absolute_import, division, print_function,
                         unicode_literals)
 
+
+from book_format import figsize
+from contextlib import contextmanager
 import matplotlib.pyplot as plt
 import numpy as np
-from contextlib import contextmanager
-
-
 import sys
-sys.path.insert(0, '..')
-from book_format import figsize
+import time
 
 try:
     import seaborn
 except:
     pass
 
+sys.path.insert(0, '..')
 
 
 
 def end_interactive(fig):
     """ end interaction in a plot created with %matplotlib notebook """
-    import time
     plt.gcf().canvas.draw()
-    time.sleep(0.1)
+    time.sleep(0.2)
     plt.close(fig)
 
 

code/gaussian_internal.py

@@ -34,17 +34,16 @@ def plot_height_std(x, lw=10):
                      facecolor='yellow', alpha=0.4)
     plt.xlabel('student')
     plt.ylabel('height (m)')
-    plt.show()
 
 
-def plot_correlated_data(X, Y, xlabel=None, 
+def plot_correlated_data(X, Y, xlabel=None,
                          ylabel=None, equal=True):
 
     plt.scatter(X, Y)
-    
+
     if xlabel is not None:
-        plt.xlabel('Height (in)'); 
-    
+        plt.xlabel('Height (in)');
+
     if ylabel is not None:
         plt.ylabel('Weight (lbs)')
 
@@ -112,7 +111,6 @@ def display_stddev_plot():
     ax.xaxis.set_ticklabels(['$-2\sigma$', '$-1\sigma$','$\mu$','$1\sigma$', '$2\sigma$'])
     ax.yaxis.set_ticks([])
     ax.grid(None, 'both', lw=0)
-    plt.show()
 
 if __name__ == '__main__':
     display_stddev_plot()

code/pf_internal.py

@@ -17,6 +17,7 @@ from __future__ import (absolute_import, division, print_function,
                         unicode_literals)
 
 
+from book_plots import figsize, end_interactive
 from filterpy.monte_carlo import stratified_resample, residual_resample
 import matplotlib as mpl
 import matplotlib.pyplot as plt
@@ -309,31 +310,32 @@ def test_pf2():
     plt.show()
 
 
-from book_plots import figsize, interactive_plot
+
 def plot_cumsum(a):
 
     with figsize(y=2):
-        with interactive_plot():
-            N = len(a)
+        fig = plt.figure()
+        N = len(a)
 
-            cmap = mpl.colors.ListedColormap([[0., .4, 1.],
-                                              [0., .8, 1.],
-                                              [1., .8, 0.],
-                                              [1., .4, 0.]]*(int(N/4) + 1))
-            cumsum = np.cumsum(np.asarray(a) / np.sum(a))
-            cumsum = np.insert(cumsum, 0, 0)
+        cmap = mpl.colors.ListedColormap([[0., .4, 1.],
+                                          [0., .8, 1.],
+                                          [1., .8, 0.],
+                                          [1., .4, 0.]]*(int(N/4) + 1))
+        cumsum = np.cumsum(np.asarray(a) / np.sum(a))
+        cumsum = np.insert(cumsum, 0, 0)
 
-            #fig = plt.figure(figsize=(6,3))
-            fig=plt.gcf()
-            ax = fig.add_axes([0.05, 0.475, 0.9, 0.15])
-            norm = mpl.colors.BoundaryNorm(cumsum, cmap.N)
-            bar = mpl.colorbar.ColorbarBase(ax, cmap=cmap,
-                                             norm=norm,
-                                             drawedges=False,
-                                             spacing='proportional',
-                                             orientation='horizontal')
-            if N > 10:
-                bar.set_ticks([])
+        #fig = plt.figure(figsize=(6,3))
+        fig=plt.gcf()
+        ax = fig.add_axes([0.05, 0.475, 0.9, 0.15])
+        norm = mpl.colors.BoundaryNorm(cumsum, cmap.N)
+        bar = mpl.colorbar.ColorbarBase(ax, cmap=cmap,
+                                         norm=norm,
+                                         drawedges=False,
+                                         spacing='proportional',
+                                         orientation='horizontal')
+        if N > 10:
+            bar.set_ticks([])
+        end_interactive(fig)
 
 
 def plot_stratified_resample(a):
@@ -347,22 +349,23 @@ def plot_stratified_resample(a):
     cumsum = np.insert(cumsum, 0, 0)
 
     with figsize(y=2):
-        with interactive_plot():
-            ax = plt.gcf().add_axes([0.05, 0.475, 0.9, 0.15])
-            norm = mpl.colors.BoundaryNorm(cumsum, cmap.N)
-            bar = mpl.colorbar.ColorbarBase(ax, cmap=cmap,
-                                             norm=norm,
-                                             drawedges=False,
-                                             spacing='proportional',
-                                             orientation='horizontal')
-            xs = np.linspace(0., 1.-1./N, N)
-            ax.vlines(xs, 0, 1, lw=2)
+        fig = plt.figure()
+        ax = plt.gcf().add_axes([0.05, 0.475, 0.9, 0.15])
+        norm = mpl.colors.BoundaryNorm(cumsum, cmap.N)
+        bar = mpl.colorbar.ColorbarBase(ax, cmap=cmap,
+                                         norm=norm,
+                                         drawedges=False,
+                                         spacing='proportional',
+                                         orientation='horizontal')
+        xs = np.linspace(0., 1.-1./N, N)
+        ax.vlines(xs, 0, 1, lw=2)
 
-            # make N subdivisions, and chose a random position within each one
-            b = (random(N) + range(N)) / N
-            plt.scatter(b, [.5]*len(b), s=60, facecolor='k', edgecolor='k')
-            bar.set_ticks([])
-            plt.title('stratified resampling')
+        # make N subdivisions, and chose a random position within each one
+        b = (random(N) + range(N)) / N
+        plt.scatter(b, [.5]*len(b), s=60, facecolor='k', edgecolor='k')
+        bar.set_ticks([])
+        plt.title('stratified resampling')
+        end_interactive(fig)
 
 
 def plot_systematic_resample(a):
@@ -376,22 +379,23 @@ def plot_systematic_resample(a):
     cumsum = np.insert(cumsum, 0, 0)
 
     with figsize(y=2):
-        with interactive_plot():
-            ax = plt.gcf().add_axes([0.05, 0.475, 0.9, 0.15])
-            norm = mpl.colors.BoundaryNorm(cumsum, cmap.N)
-            bar = mpl.colorbar.ColorbarBase(ax, cmap=cmap,
-                                             norm=norm,
-                                             drawedges=False,
-                                             spacing='proportional',
-                                             orientation='horizontal')
-            xs = np.linspace(0., 1.-1./N, N)
-            ax.vlines(xs, 0, 1, lw=2)
+        fig = plt.figure()
+        ax = plt.gcf().add_axes([0.05, 0.475, 0.9, 0.15])
+        norm = mpl.colors.BoundaryNorm(cumsum, cmap.N)
+        bar = mpl.colorbar.ColorbarBase(ax, cmap=cmap,
+                                         norm=norm,
+                                         drawedges=False,
+                                         spacing='proportional',
+                                         orientation='horizontal')
+        xs = np.linspace(0., 1.-1./N, N)
+        ax.vlines(xs, 0, 1, lw=2)
 
-            # make N subdivisions, and chose a random position within each one
-            b = (random() + np.array(range(N))) / N
-            plt.scatter(b, [.5]*len(b), s=60, facecolor='k', edgecolor='k')
-            bar.set_ticks([])
-            plt.title('systematic resampling')
+        # make N subdivisions, and chose a random position within each one
+        b = (random() + np.array(range(N))) / N
+        plt.scatter(b, [.5]*len(b), s=60, facecolor='k', edgecolor='k')
+        bar.set_ticks([])
+        plt.title('systematic resampling')
+        end_interactive(fig)
 
 
 def plot_multinomial_resample(a):
@@ -405,20 +409,21 @@ def plot_multinomial_resample(a):
     cumsum = np.insert(cumsum, 0, 0)
 
     with figsize(y=2):
-        with interactive_plot():
-            ax = plt.gcf().add_axes([0.05, 0.475, 0.9, 0.15])
-            norm = mpl.colors.BoundaryNorm(cumsum, cmap.N)
-            bar = mpl.colorbar.ColorbarBase(ax, cmap=cmap,
-                                             norm=norm,
-                                             drawedges=False,
-                                             spacing='proportional',
-                                             orientation='horizontal')
+        fig = plt.figure()
+        ax = plt.gcf().add_axes([0.05, 0.475, 0.9, 0.15])
+        norm = mpl.colors.BoundaryNorm(cumsum, cmap.N)
+        bar = mpl.colorbar.ColorbarBase(ax, cmap=cmap,
+                                         norm=norm,
+                                         drawedges=False,
+                                         spacing='proportional',
+                                         orientation='horizontal')
 
-            # make N subdivisions, and chose a random position within each one
-            b = random(N)
-            plt.scatter(b, [.5]*len(b), s=60, facecolor='k', edgecolor='k')
-            bar.set_ticks([])
-            plt.title('multinomial resampling')
+        # make N subdivisions, and chose a random position within each one
+        b = random(N)
+        plt.scatter(b, [.5]*len(b), s=60, facecolor='k', edgecolor='k')
+        bar.set_ticks([])
+        plt.title('multinomial resampling')
+        end_interactive(fig)
 
 
 def plot_residual_resample(a):
@@ -434,24 +439,25 @@ def plot_residual_resample(a):
                                       [1., .4, 0.]]*(int(N/4) + 1))
 
     with figsize(y=2):
-        with interactive_plot():
-            ax = plt.gcf().add_axes([0.05, 0.475, 0.9, 0.15])
-            norm = mpl.colors.BoundaryNorm(cumsum, cmap.N)
-            bar = mpl.colorbar.ColorbarBase(ax, cmap=cmap,
-                                             norm=norm,
-                                             drawedges=False,
-                                             spacing='proportional',
-                                             orientation='horizontal')
+        fig = plt.figure()
+        ax = plt.gcf().add_axes([0.05, 0.475, 0.9, 0.15])
+        norm = mpl.colors.BoundaryNorm(cumsum, cmap.N)
+        bar = mpl.colorbar.ColorbarBase(ax, cmap=cmap,
+                                         norm=norm,
+                                         drawedges=False,
+                                         spacing='proportional',
+                                         orientation='horizontal')
 
-            indexes = residual_resample(a_norm)
-            bins = np.bincount(indexes)
-            for i in range(1, N):
-                n =  bins[i-1] # number particles in this sample
-                if n > 0:
-                    b = np.linspace(cumsum[i-1], cumsum[i], n+2)[1:-1]
-                    plt.scatter(b, [.5]*len(b), s=60, facecolor='k', edgecolor='k')
-            bar.set_ticks([])
-            plt.title('residual resampling')
+        indexes = residual_resample(a_norm)
+        bins = np.bincount(indexes)
+        for i in range(1, N):
+            n =  bins[i-1] # number particles in this sample
+            if n > 0:
+                b = np.linspace(cumsum[i-1], cumsum[i], n+2)[1:-1]
+                plt.scatter(b, [.5]*len(b), s=60, facecolor='k', edgecolor='k')
+        bar.set_ticks([])
+        plt.title('residual resampling')
+        end_interactive(fig)
 
 
 if __name__ == '__main__':