Fixed errorbar plots in gh chapter.

I changed the ZZy-axis earlier, which meant that the bar for
sensor A was not visible.

01-g-h-filter.ipynb

@@ -16,7 +16,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "metadata": {
     "collapsed": false
    },
@@ -256,7 +256,7 @@
        "<IPython.core.display.HTML object>"
       ]
      },
-     "execution_count": 1,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -324,16 +324,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 12,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAxsAAAB6CAYAAADXnk6rAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAxsAAAB6CAYAAADXnk6rAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x492d1d0>"
+       "<matplotlib.figure.Figure at 0x79bc780>"
       ]
      },
      "metadata": {},
@@ -358,16 +358,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 15,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAxsAAAB6CAYAAADXnk6rAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAxsAAAB6CAYAAADXnk6rAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x72f5a58>"
+       "<matplotlib.figure.Figure at 0x6030dd8>"
       ]
      },
      "metadata": {},
@@ -389,16 +389,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 16,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAxsAAAB6CAYAAADXnk6rAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAxsAAAB6CAYAAADXnk6rAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x76ec8d0>"
+       "<matplotlib.figure.Figure at 0x83a2cf8>"
       ]
      },
      "metadata": {},
@@ -424,7 +424,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 18,
    "metadata": {
     "collapsed": false
    },
@@ -433,7 +433,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Average of measurements is 165.0030\n"
+      "Average of measurements is 164.9570\n"
      ]
     }
    ],
@@ -455,7 +455,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 19,
    "metadata": {
     "collapsed": false
    },
@@ -464,7 +464,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Average of measurements is 165.0638\n"
+      "Average of measurements is 165.0718\n"
      ]
     }
    ],
@@ -489,7 +489,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 20,
    "metadata": {
     "collapsed": false
    },
@@ -498,7 +498,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAwkAAAERCAYAAAAwgetTAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f15710>"
+       "<matplotlib.figure.Figure at 0x83944a8>"
       ]
      },
      "metadata": {},
@@ -520,7 +520,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 21,
    "metadata": {
     "collapsed": false
    },
@@ -529,7 +529,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAvIAAAEtCAYAAABqG4QrAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f94748>"
+       "<matplotlib.figure.Figure at 0x77db518>"
       ]
      },
      "metadata": {},
@@ -549,7 +549,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 22,
    "metadata": {
     "collapsed": false
    },
@@ -558,7 +558,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAvIAAAEtCAYAAABqG4QrAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fe66d8>"
+       "<matplotlib.figure.Figure at 0x44dcda0>"
       ]
      },
      "metadata": {},
@@ -580,7 +580,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 23,
    "metadata": {
     "collapsed": false
    },
@@ -589,7 +589,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAA48AAAEtCAYAAACoIlXDAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7ff73c8>"
+       "<matplotlib.figure.Figure at 0x78182e8>"
       ]
      },
      "metadata": {},
@@ -611,7 +611,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 24,
    "metadata": {
     "collapsed": false,
     "scrolled": true
@@ -621,7 +621,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAA48AAAEtCAYAAACoIlXDAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f35710>"
+       "<matplotlib.figure.Figure at 0x79a6a58>"
       ]
      },
      "metadata": {},

code/book_plots.py

@@ -20,14 +20,14 @@ from __future__ import (absolute_import, division, print_function,
 import matplotlib.pyplot as plt
 import numpy as np
 
-def plot_errorbars(bars, xlims):
+def plot_errorbars(bars, xlims, ylims=(0, 2)):
 
     i = 0.0
     for bar in bars:
         plt.errorbar([bar[0]], [i], xerr=[bar[1]], fmt='o', label=bar[2] , capthick=2, capsize=10)
         i += 0.2
 
-    plt.ylim(0, 2)
+    plt.ylim(*ylims)
     plt.xlim(xlims[0], xlims[1])
     show_legend()
     plt.gca().axes.yaxis.set_ticks([])

code/gh_internal.py

@@ -25,16 +25,17 @@ import pylab as plt
 def plot_errorbar1():
     with book_format.figsize(y=1.5):
         book_plots.plot_errorbars([(160, 8, 'A'), (170, 8, 'B')],
-                                   xlims=(145, 185))
+                                   xlims=(145, 185), ylims=(-1, 2))
 
 def plot_errorbar2():
     with book_format.figsize(y=1.5):
         book_plots.plot_errorbars([(160, 3, 'A'), (170, 9, 'B')],
-                                   xlims=(145, 185))
+                                   xlims=(145, 185), ylims=(-1, 2))
 
 def plot_errorbar3():
     with book_format.figsize(y=1.5):
-        book_plots.plot_errorbars([(160, 1, 'A'), (170, 9, 'B')], xlims=(145, 185))
+        book_plots.plot_errorbars([(160, 1, 'A'), (170, 9, 'B')], 
+                                  xlims=(145, 185), ylims=(-1, 2))