Issue #144 - update variable est/prediction names

I erroneously named the prediction of x x_est, which is confusing at best.

There were also issues with plotsize. The %matplotlib notebook magic
is resetting the size of the plots. Not sure if this has something to do
with some of my plotting code or not. Anyway, I wrote a context manager
to reset the plot size after exiting interactive mode.

01-g-h-filter.ipynb

@@ -382,7 +382,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1fc4fabb320>"
+       "<matplotlib.figure.Figure at 0x1d7aaf8f1d0>"
       ]
      },
      "metadata": {},
@@ -418,7 +418,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1fc4fabb2b0>"
+       "<matplotlib.figure.Figure at 0x1d7aaf8f0f0>"
       ]
      },
      "metadata": {},
@@ -451,7 +451,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1fc5045eeb8>"
+       "<matplotlib.figure.Figure at 0x1d7ab4ab710>"
       ]
      },
      "metadata": {},
@@ -488,7 +488,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Average of measurements is 164.9963\n"
+      "Average of measurements is 164.9863\n"
      ]
     }
    ],
@@ -521,7 +521,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Average of measurements is 165.0785\n"
+      "Average of measurements is 165.0777\n"
      ]
     }
    ],
@@ -554,9 +554,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1fc4dbfbbe0>"
+       "<matplotlib.figure.Figure at 0x1d7a905fc18>"
       ]
      },
      "metadata": {},
@@ -588,7 +588,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1fc5056c3c8>"
+       "<matplotlib.figure.Figure at 0x1d7ab544668>"
       ]
      },
      "metadata": {},
@@ -617,7 +617,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1fc505f60b8>"
+       "<matplotlib.figure.Figure at 0x1d7ab434a58>"
       ]
      },
      "metadata": {},
@@ -648,7 +648,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1fc50581b70>"
+       "<matplotlib.figure.Figure at 0x1d7ab47fba8>"
       ]
      },
      "metadata": {},
@@ -679,7 +679,7 @@
      "data": {
       "image/png": 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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1fc504dc6d8>"
+       "<matplotlib.figure.Figure at 0x1d7ab4558d0>"
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      "metadata": {},
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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1fc5048d198>"
+       "<matplotlib.figure.Figure at 0x1d7ab4d2ac8>"
       ]
      },
      "metadata": {},
@@ -743,7 +743,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1fc4fedc3c8>"
+       "<matplotlib.figure.Figure at 0x1d7ab7072b0>"
       ]
      },
      "metadata": {},
@@ -785,7 +785,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1fc506c1978>"
+       "<matplotlib.figure.Figure at 0x1d7ab7b1198>"
       ]
      },
      "metadata": {},
@@ -841,9 +841,9 @@
     },
     {
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1fc5044e4e0>"
+       "<matplotlib.figure.Figure at 0x1d7ab687ac8>"
       ]
      },
      "metadata": {},
@@ -851,7 +851,6 @@
     }
    ],
    "source": [
-    "from book_format import set_figsize\n",
     "from kf_book import book_plots\n",
     "import kf_book.gh_internal as gh\n",
     "import matplotlib.pyplot as plt\n",
@@ -864,29 +863,30 @@
     "scale_factor = 4.0/10\n",
     "\n",
     "def predict_using_gain_guess(weight, gain_rate, do_print=True, sim_rate=0): \n",
-    "    set_figsize(y=4)\n",
-    "    # store the filtered results\n",
-    "    estimates, predictions = [weight], []\n",
-    "\n",
-    "    # most filter literature uses 'z' for measurements\n",
-    "    for z in weights: \n",
-    "        # predict new position\n",
-    "        prediction = weight + gain_rate * time_step\n",
-    "\n",
-    "        # update filter \n",
-    "        weight = prediction + scale_factor * (z - prediction)\n",
-    "\n",
-    "        # save\n",
-    "        estimates.append(weight)\n",
-    "        predictions.append(prediction)\n",
-    "        if do_print:\n",
-    "            gh.print_results(estimates, prediction, weight)\n",
-    "\n",
-    "    # plot results\n",
-    "    gh.plot_gh_results(weights, estimates, predictions, sim_rate)\n",
+    "    \"\"\" sim_rate allows for interactive plots in the notebook\"\"\"\n",
     "    \n",
+    "    with figsize(y=4):\n",
+    "        # store the filtered results\n",
+    "        estimates, predictions = [weight], []\n",
+    "\n",
+    "        # most filter literature uses 'z' for measurements\n",
+    "        for z in weights: \n",
+    "            # predict new position\n",
+    "            prediction = weight + gain_rate * time_step\n",
+    "\n",
+    "            # update filter \n",
+    "            weight = prediction + scale_factor * (z - prediction)\n",
+    "\n",
+    "            # save\n",
+    "            estimates.append(weight)\n",
+    "            predictions.append(prediction)\n",
+    "            if do_print:\n",
+    "                gh.print_results(estimates, prediction, weight)\n",
+    "\n",
+    "        # plot results\n",
+    "        gh.plot_gh_results(weights, estimates, predictions, sim_rate)\n",
     "initial_guess = 160.\n",
-    "predict_using_gain_guess(weight=initial_guess, gain_rate=1)    "
+    "predict_using_gain_guess(weight=initial_guess, gain_rate=1)     "
    ]
   },
   {
@@ -897,12 +897,38 @@
     "\n",
     "The estimates are not a straight line, but they are straighter than the measurements and somewhat close to the trend line we created. Also, it seems to get better over time. \n",
     "\n",
-    "Before I go on, let me show this plot interactively so you get a better feel for how the filter works. Put your cursor inside the next cell and press CTRL+Enter to execute the cell. This will cause the plot to be drawn slowly, step by step."
+    "Before I go on, let me show this plot interactively so you get a better feel for how the filter works. First I need to write some boilerplate code to enable plotting interactively. The magic `%matplotlib notebook` enables the interactive plotting module, and the magic `%matplotlib inline` returns us to the non-interactive plotting. Unfortunately `%matplotlib notebook` permanently alters the plot sizes, so I reset it to the book's default with `reset_figsize()`."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 16,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from contextlib import contextmanager\n",
+    "from kf_book.book_plots import reset_figsize\n",
+    "\n",
+    "@contextmanager\n",
+    "def interactive_plot():\n",
+    "    %matplotlib notebook\n",
+    "    yield\n",
+    "    %matplotlib inline\n",
+    "    reset_figsize()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can run the plot interactively. Put your cursor inside the next cell and press CTRL+Enter to execute the cell. This will cause the plot to be drawn slowly, step by step."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
    "metadata": {
     "collapsed": false,
     "scrolled": true
@@ -1675,7 +1701,7 @@
     {
      "data": {
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+       "<img 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       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -1686,9 +1712,8 @@
     }
    ],
    "source": [
-    "%matplotlib notebook\n",
-    "predict_using_gain_guess(weight=initial_guess, gain_rate=1., do_print=False, sim_rate=.4)\n",
-    "%matplotlib inline"
+    "with interactive_plot():\n",
+    "    predict_using_gain_guess(weight=initial_guess, gain_rate=1., do_print=False, sim_rate=.25)"
    ]
   },
   {
@@ -1699,24 +1724,12 @@
     "\n",
     "I will use this technique many times in the book. You can control the rate of drawing with the `sim_rate` parameter, which specifies the delay in seconds between subsequent draws. You may change it to whatever value you prefer. If you make it too large the drawing might take a very long time to finish. Just select `Kernel\\Interrupt` from the menu if it is taking too long.\n",
     "\n",
-    "While we are discussing this code, let's talk about interactive plots. You can create interactive plots in Jupyter notebooks with the\n",
-    "    \n",
-    "    %matplotlib notebook\n",
-    "    \n",
-    "magic that I put at the top of this file. Interactive plots don't just allow animations as in the above plots, they also allow you to pan and zoom the plots, and display the coordinates of the mouse. Once the plot is done I put the line\n",
-    "\n",
-    "    %matplotlib inline\n",
-    "\n",
-    "to return to the static form of plotting. You can remove this line if you want. I always do it because the plot must be in static form to be converted to PDF format, which I do for the printed version of this book.\n",
-    "\n",
-    "If you are reading this in a PDF than you will see the interactive code commented out. I wrote a batch file that does this automatically. \n",
-    "\n",
-    "To get the animation to work you have to call `plt.gcf().canvas.draw()` each time you want an update, and call `time.sleep()` to slow down the animation. If you look at the code in `gh.plot_gh_results()` you'll see it using this to create the animation. For example, the following code draws a line once a second:"
+    "To get the animation to work you have to call `plt.gcf().canvas.draw()` each time you want an update, and call `time.sleep()` to slow down the animation. If you look at the code in `gh.plot_gh_results()` you'll see it using this to create the animation. For example, the following code draws a line once per half-second:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 18,
    "metadata": {
     "collapsed": false
    },
@@ -2488,7 +2501,7 @@
     {
      "data": {
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truncated
+       "<img src=\"data:image/png;base64,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 truncated
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -2500,14 +2513,12 @@
    ],
    "source": [
     "import time\n",
-    "%matplotlib notebook\n",
     "\n",
-    "for x in range(2,6):\n",
-    "    plt.plot([0, 1], [1, x])\n",
-    "    plt.gcf().canvas.draw()\n",
-    "    time.sleep(1)\n",
-    "\n",
-    "%matplotlib inline "
+    "with interactive_plot():\n",
+    "    for x in range(2,6):\n",
+    "        plt.plot([0, 1], [1, x])\n",
+    "        plt.gcf().canvas.draw()\n",
+    "        time.sleep(0.5)"
    ]
   },
   {
@@ -2519,16 +2530,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 19,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1fc5062eba8>"
+       "<matplotlib.figure.Figure at 0x1d7ab5db0b8>"
       ]
      },
      "metadata": {},
@@ -2561,7 +2572,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 20,
    "metadata": {
     "collapsed": false,
     "scrolled": false
@@ -3334,7 +3345,7 @@
     {
      "data": {
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       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -3368,9 +3379,8 @@
     "  \n",
     "    estimates.append(weight)\n",
     "\n",
-    "%matplotlib notebook\n",
-    "gh.plot_gh_results(weights, estimates, predictions, time_step=0.1)\n",
-    "%matplotlib inline"
+    "with interactive_plot():\n",
+    "    gh.plot_gh_results(weights, estimates, predictions, time_step=0.1)"
    ]
   },
   {
@@ -3418,16 +3428,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 21,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1fc4fef8f98>"
+       "<matplotlib.figure.Figure at 0x1d7ab390080>"
       ]
      },
      "metadata": {},
@@ -3511,7 +3521,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 22,
    "metadata": {
     "collapsed": false
    },
@@ -3529,7 +3539,7 @@
        "array([1, 2, 3])"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3550,7 +3560,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 23,
    "metadata": {
     "collapsed": false
    },
@@ -3561,7 +3571,7 @@
        "array([4, 5, 6])"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3573,7 +3583,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 24,
    "metadata": {
     "collapsed": false
    },
@@ -3604,7 +3614,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 25,
    "metadata": {
     "collapsed": false
    },
@@ -3631,7 +3641,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 26,
    "metadata": {
     "collapsed": false
    },
@@ -3660,7 +3670,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 27,
    "metadata": {
     "collapsed": false
    },
@@ -3671,7 +3681,7 @@
        "array([1, 4])"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3689,7 +3699,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 28,
    "metadata": {
     "collapsed": false
    },
@@ -3700,7 +3710,7 @@
        "array([4, 5, 6])"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 28,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3718,7 +3728,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 29,
    "metadata": {
     "collapsed": false
    },
@@ -3763,7 +3773,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 30,
    "metadata": {
     "collapsed": false
    },
@@ -3796,7 +3806,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 31,
    "metadata": {
     "collapsed": false
    },
@@ -3846,7 +3856,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 32,
    "metadata": {
     "collapsed": true
    },
@@ -3866,7 +3876,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 33,
    "metadata": {
     "collapsed": false
    },
@@ -3896,7 +3906,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 34,
    "metadata": {
     "collapsed": false
    },
@@ -3951,7 +3961,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 35,
    "metadata": {
     "collapsed": false
    },
@@ -3971,16 +3981,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 36,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAl8AAAD7CAYAAABUrfHMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1fc50622c88>"
+       "<matplotlib.figure.Figure at 0x1d7ab59fa20>"
       ]
      },
      "metadata": {},
@@ -3989,20 +3999,21 @@
    ],
    "source": [
     "from kf_book.gh_internal import plot_g_h_results\n",
+    "import matplotlib.pylab as pylab\n",
     "\n",
     "def g_h_filter(data, x0, dx, g, h, dt=1.):\n",
-    "    x = x0\n",
+    "    x_est = x0\n",
     "    results = []\n",
     "    for z in data:\n",
     "        #prediction step\n",
-    "        x_est = x + (dx*dt)\n",
+    "        x_pred = x_est + (dx*dt)\n",
     "        dx = dx\n",
     "\n",
     "        # update step\n",
-    "        residual = z - x_est\n",
+    "        residual = z - x_pred\n",
     "        dx = dx    + h * (residual) / dt\n",
-    "        x  = x_est + g * residual     \n",
-    "        results.append(x)  \n",
+    "        x_est  = x_pred + g * residual     \n",
+    "        results.append(x_est)  \n",
     "    return np.array(results)\n",
     "\n",
     "book_plots.plot_track([0, 11], [160, 172], label='Actual weight')\n",
@@ -4046,7 +4057,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 37,
    "metadata": {
     "collapsed": false
    },
@@ -4064,7 +4075,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 38,
    "metadata": {
     "collapsed": false,
     "scrolled": false
@@ -4072,9 +4083,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlkAAAEACAYAAACAkWPlAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1fc50408400>"
+       "<matplotlib.figure.Figure at 0x1d7ab59fa58>"
       ]
      },
      "metadata": {},
@@ -4102,16 +4113,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 39,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1fc5066cf98>"
+       "<matplotlib.figure.Figure at 0x1d7ab687748>"
       ]
      },
      "metadata": {},
@@ -4138,7 +4149,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 40,
    "metadata": {
     "collapsed": false
    },
@@ -4156,16 +4167,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 41,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1fc4fe9d780>"
+       "<matplotlib.figure.Figure at 0x1d7ab564048>"
       ]
      },
      "metadata": {},
@@ -4201,7 +4212,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 42,
    "metadata": {
     "collapsed": false
    },
@@ -4219,16 +4230,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 43,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1fc4fef4668>"
+       "<matplotlib.figure.Figure at 0x1d7ab54fe80>"
       ]
      },
      "metadata": {},
@@ -4266,7 +4277,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 44,
    "metadata": {
     "collapsed": false
    },
@@ -4284,16 +4295,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 45,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1fc4feed780>"
+       "<matplotlib.figure.Figure at 0x1d7ab58a470>"
       ]
      },
      "metadata": {},
@@ -4343,7 +4354,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 46,
    "metadata": {
     "collapsed": false
    },
@@ -4361,7 +4372,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 47,
    "metadata": {
     "collapsed": false,
     "scrolled": false
@@ -4371,7 +4382,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1fc506c6358>"
+       "<matplotlib.figure.Figure at 0x1d7ab76f5f8>"
       ]
      },
      "metadata": {},
@@ -4403,7 +4414,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 48,
    "metadata": {
     "collapsed": false
    },
@@ -4414,15 +4425,15 @@
        "(6, 20)"
       ]
      },
-     "execution_count": 47,
+     "execution_count": 48,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1fc506df2b0>"
+       "<matplotlib.figure.Figure at 0x1d7ab4d44a8>"
       ]
      },
      "metadata": {},
@@ -4457,16 +4468,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 49,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlIAAAD7CAYAAAChU3p8AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1fc50665a90>"
+       "<matplotlib.figure.Figure at 0x1d7ab5fdba8>"
       ]
      },
      "metadata": {},
@@ -4503,16 +4514,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 50,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlQAAAD7CAYAAACsTQo7AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1fc505c0518>"
+       "<matplotlib.figure.Figure at 0x1d7ab64e898>"
       ]
      },
      "metadata": {},
@@ -4550,16 +4561,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 51,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmsAAAEACAYAAADslzHgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1fc50578cf8>"
+       "<matplotlib.figure.Figure at 0x1d7ab49c710>"
       ]
      },
      "metadata": {},
@@ -4599,16 +4610,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 52,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmEAAAD7CAYAAAAil0NHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1fc50686cc0>"
+       "<matplotlib.figure.Figure at 0x1d7ab666780>"
       ]
      },
      "metadata": {},
@@ -4635,16 +4646,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 53,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAaUAAAD9CAYAAAAceGiAAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmEAAAD9CAYAAAD0zqBaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1fc51a28da0>"
+       "<matplotlib.figure.Figure at 0x1d7ac8796a0>"
       ]
      },
      "metadata": {},
@@ -4700,16 +4711,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 54,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAngAAAEdCAYAAACfRhpOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1fc5196bc18>"
+       "<matplotlib.figure.Figure at 0x1d7ac8a7240>"
       ]
      },
      "metadata": {},
@@ -4751,7 +4762,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 55,
    "metadata": {
     "collapsed": false,
     "scrolled": true
@@ -4759,9 +4770,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAaoAAAELCAYAAACIz8K9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmYAAAELCAYAAABzrkqTAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1fc51a28e10>"
+       "<matplotlib.figure.Figure at 0x1d7ac8f3470>"
       ]
      },
      "metadata": {},
@@ -4783,7 +4794,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 56,
    "metadata": {
     "collapsed": false,
     "scrolled": true
@@ -4791,9 +4802,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAaAAAAELCAYAAACf7VJ0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlwAAAELCAYAAAAMf1hiAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1fc506d41d0>"
+       "<matplotlib.figure.Figure at 0x1d7ab509160>"
       ]
      },
      "metadata": {},
@@ -4817,16 +4828,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 57,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmYAAAELCAYAAABzrkqTAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1fc506b7978>"
+       "<matplotlib.figure.Figure at 0x1d7ab444e10>"
       ]
      },
      "metadata": {},
@@ -4850,7 +4861,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 58,
    "metadata": {
     "collapsed": false
    },
@@ -4867,16 +4878,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 59,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmYAAAELCAYAAABzrkqTAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1fc505c4e10>"
+       "<matplotlib.figure.Figure at 0x1d7ab71b470>"
       ]
      },
      "metadata": {},
@@ -4898,16 +4909,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 60,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAaoAAAELCAYAAACIz8K9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1fc504f7f28>"
+       "<matplotlib.figure.Figure at 0x1d7ab3b0278>"
       ]
      },
      "metadata": {},
@@ -4953,7 +4964,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 61,
    "metadata": {
     "collapsed": true
    },
@@ -4974,7 +4985,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 62,
    "metadata": {
     "collapsed": false
    },
@@ -4985,7 +4996,7 @@
        "(0.96, 0.24)"
       ]
      },
-     "execution_count": 61,
+     "execution_count": 62,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5003,7 +5014,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 63,
    "metadata": {
     "collapsed": false
    },
@@ -5029,7 +5040,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 64,
    "metadata": {
     "collapsed": false
    },
@@ -5055,7 +5066,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 65,
    "metadata": {
     "collapsed": false
    },
@@ -5084,7 +5095,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": 66,
    "metadata": {
     "collapsed": false
    },
@@ -5153,7 +5164,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.1"
+   "version": "3.5.3"
   },
   "widgets": {
    "state": {

book_format.py

@@ -56,7 +56,7 @@ def test_filterpy_version():
 test_filterpy_version()
 
 pylab.rcParams['figure.max_open_warning'] = 50
-
+pylab.rcParams['figure.figsize'] = 9, 4
 
 
 

kf_book/book_plots.py

@@ -41,6 +41,11 @@ def equal_axis():
 def reset_axis():
     pylab.rcParams['figure.figsize'] = 9, 3
 
+    
+def reset_figsize():
+    pylab.rcParams['figure.figsize'] = 9, 4
+
+
 def set_figsize(x=9, y=4):
     pylab.rcParams['figure.figsize'] = x, y
 
@@ -131,7 +136,7 @@ def plot_errorbar3():
 
 
 def plot_hypothesis1():
-    with figsize(y=2.5):
+    with figsize(y=3.5):
         plt.figure()
         plt.errorbar([1, 2, 3], [170, 161, 169],
                      xerr=0, yerr=10, fmt='bo', capthick=2, capsize=10)
@@ -270,7 +275,7 @@ def plot_estimate_chart_3():
 def create_predict_update_chart(box_bg = '#CCCCCC',
                 arrow1 = '#88CCFF',
                 arrow2 = '#88FF88'):
-    plt.figure(figsize=(4, 3.), facecolor='w')
+    plt.figure(figsize=(4,4), facecolor='w')
     ax = plt.axes((0, 0, 1, 1),
                   xticks=[], yticks=[], frameon=False)