{
 "metadata": {
  "name": ""
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# `prettyplotlib.hist`\n",
      "\n",
      "The default `matplotlib` histogram isn't that bad, but it leaves much to be desired."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import matplotlib.pyplot as plt\n",
      "import numpy as np\n",
      "\n",
      "np.random.seed(12)\n",
      "\n",
      "fig, ax = plt.subplots(1)\n",
      "\n",
      "ax.hist(np.random.randn(1000))\n",
      "fig.savefig('hist_matplotlib_default.png')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXUAAAEACAYAAABMEua6AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAFg5JREFUeJzt3X9M1Pfhx/HXITSLAVqt43B3LtcJDg6Vu7jAsszlnKLf\nzUHobOhgWjL1nzZd0tqs1i5toYmCW02jXUyaxXYszXTujwJZlFCbXte5bGcrJltohm2wwvEjWkSl\n1qLw+f7heikCdycc98E3z0dyyXH3+dz7dcC97nOfX+ewLMsSAMAIKXYHAAAkDqUOAAah1AHAIJQ6\nABiEUgcAg1DqAGCQqKV+/fp1FRcXy+fzyev1ateuXZKkgYEBlZSUaNmyZVq/fr0GBwcj89TV1Sk3\nN1d5eXlqbW2d2fQAgDEcsfZTv3btmubPn6+bN2/q+9//vl566SU1Nzdr0aJFevrpp7V3715dunRJ\n9fX1am9vV1VVlU6dOqVwOKx169apo6NDKSl8IACAZIjZtvPnz5ckDQ8Pa2RkRAsWLFBzc7Oqq6sl\nSdXV1WpsbJQkNTU1qbKyUmlpafJ4PMrJyVEoFJrB+ACAr4pZ6qOjo/L5fHI6nVqzZo0KCgrU398v\np9MpSXI6nerv75ck9fT0yO12R+Z1u90Kh8MzFB0AcLvUWBOkpKTozJkzunz5sjZs2KB33nlnzP0O\nh0MOh2PS+aPdBwBIrJil/qV7771XGzdu1AcffCCn06m+vj5lZ2ert7dXWVlZkiSXy6Wurq7IPN3d\n3XK5XOMeKycnRx9//HEC4gPA3LF06VJ99NFHUaeJuvrl4sWLkT1bPv/8c7311lvy+/0qKytTQ0OD\nJKmhoUHl5eWSpLKyMh05ckTDw8Pq7OzU2bNnVVRUNO5xP/74Y1mWNesvL7zwgu0ZTMl5N2QkJzln\n+yWeheGoS+q9vb2qrq7W6OioRkdHtWXLFq1du1Z+v18VFRU6dOiQPB6Pjh49Kknyer2qqKiQ1+tV\namqqDh48yOoXAEiiqKW+YsUKnT59etztCxcu1IkTJyac59lnn9Wzzz6bmHQAgDvCDuRRBAIBuyPE\n5W7IeTdklMiZaORMvpgHH83IoA6HbBgWAO5q8XQnS+oAYBBKHQAMQqkDgEEodQAwCKUOAAah1AHA\nIJQ6ABiEUgcAg1DqAGAQSh0ADEKpA4BBKHUAMAilDgAGodQBwCCUOgAYhFIHAINQ6gBgEEodAAxC\nqQOAQSh1ADAIpQ7jZWYulMPhSOolM3Oh3U8bc5TDivXV1DMxaBzfiA0kisPhkJTs/zf+x5F48XQn\nS+oAYBBKHQAMQqkDgEEodQAwSNRS7+rq0po1a1RQUKDly5frwIEDkqSamhq53W75/X75/X4dP348\nMk9dXZ1yc3OVl5en1tbWmU0PABgj6t4vfX196uvrk8/n09DQkFatWqXGxkYdPXpUGRkZ2rFjx5jp\n29vbVVVVpVOnTikcDmvdunXq6OhQSsrY9w72fkEysfcLTDHtvV+ys7Pl8/kkSenp6crPz1c4HJak\nCR+4qalJlZWVSktLk8fjUU5OjkKh0FTzAwDuUNzr1M+dO6e2tjZ997vflSS98sorKiws1LZt2zQ4\nOChJ6unpkdvtjszjdrsjbwIAgJkXV6kPDQ3poYce0v79+5Wenq5HH31UnZ2dOnPmjBYvXqynnnpq\n0nlvffQFACRDaqwJbty4oU2bNmnz5s0qLy+XJGVlZUXu3759u0pLSyVJLpdLXV1dkfu6u7vlcrkm\nfNyamprI9UAgoEAgMJX8AGCsYDCoYDB4R/NE3VBqWZaqq6t1//336+WXX47c3tvbq8WLF0uSXn75\nZZ06dUp/+tOfIhtKQ6FQZEPpRx99NG5pnQ2lSCY2lMIU8XRn1CX1kydP6o033tDKlSvl9/slSXv2\n7NHhw4d15swZORwOPfDAA3r11VclSV6vVxUVFfJ6vUpNTdXBgwdZ/QIAScQJvWA8ltRhCk7oBQBz\nDKUOAAah1AHAIJQ6ABiEUgcAg1DqAGAQSh0ADEKpA4BBKHUAMAilDgAGodQBwCCUOgAYhFIHAINQ\n6gBgEEodAAxCqQOAQSh1ADAIpQ4ABqHUAcAglDoAGIRSBwCDUOoAYBBKHQAMQqkDgEEodQAwCKUO\nAAah1AHAIJQ6ABgk1e4AmFsyMxfq6tVLdsdIglQ5HI6kjZaRsUBXrgwkbTzMXlGX1Lu6urRmzRoV\nFBRo+fLlOnDggCRpYGBAJSUlWrZsmdavX6/BwcHIPHV1dcrNzVVeXp5aW1tnNj3uOrcK3UryxQ43\n7zDj9C5z440S8XBYljXpf31fX5/6+vrk8/k0NDSkVatWqbGxUa+//roWLVqkp59+Wnv37tWlS5dU\nX1+v9vZ2VVVV6dSpUwqHw1q3bp06OjqUkjL2vcPhcCjKsDDYraXXZP/t58KYvKbmgni6M+qSenZ2\ntnw+nyQpPT1d+fn5CofDam5uVnV1tSSpurpajY2NkqSmpiZVVlYqLS1NHo9HOTk5CoVCiXguAIA4\nxL2h9Ny5c2pra1NxcbH6+/vldDolSU6nU/39/ZKknp4eud3uyDxut1vhcDjBkQEAk4lrQ+nQ0JA2\nbdqk/fv3KyMjY8x9Docj6gahye6rqamJXA8EAgoEAvFEAYA5IxgMKhgM3tE8MUv9xo0b2rRpk7Zs\n2aLy8nJJt5bO+/r6lJ2drd7eXmVlZUmSXC6Xurq6IvN2d3fL5XJN+LhfLXUAwHi3L/DW1tbGnCfq\n6hfLsrRt2zZ5vV498cQTkdvLysrU0NAgSWpoaIiUfVlZmY4cOaLh4WF1dnbq7NmzKioqmspzAQBM\nQdS9X/7+97/rBz/4gVauXBlZjVJXV6eioiJVVFTo/Pnz8ng8Onr0qO677z5J0p49e/Taa68pNTVV\n+/fv14YNG8YPyt4vcxZ7v8zceLymzBdPd0Yt9ZlCqc9dlPrMjcdrynzT3qURAHB3odQBwCCUOgAY\nhFIHAINQ6gBgEEodAAxCqQOAQSh1ADAIpQ4ABqHUAcAglDoAGIRSBwCDUOoAYBBKHQAMQqkDgEEo\ndQAwCKUOAAah1AHAIJQ6ABiEUgcAg1DqAGAQSh0ADEKpA4BBKHUAMAilDgAGodQBwCCUOgAYhFIH\nAIPELPWtW7fK6XRqxYoVkdtqamrkdrvl9/vl9/t1/PjxyH11dXXKzc1VXl6eWltbZyY1AGBCDsuy\nrGgTvPfee0pPT9cjjzyif//735Kk2tpaZWRkaMeOHWOmbW9vV1VVlU6dOqVwOKx169apo6NDKSlj\n3zscDodiDAtDORwOScn+28+FMXlNzQXxdGfMJfXVq1drwYIF426f6IGbmppUWVmptLQ0eTwe5eTk\nKBQK3UFkAMB0THmd+iuvvKLCwkJt27ZNg4ODkqSenh653e7ING63W+FwePopAQBxSZ3KTI8++qie\nf/55SdJzzz2np556SocOHZpw2lsft8erqamJXA8EAgoEAlOJAgDGCgaDCgaDdzTPlEo9Kysrcn37\n9u0qLS2VJLlcLnV1dUXu6+7ulsvlmvAxvlrqAIDxbl/gra2tjTnPlFa/9Pb2Rq6/+eabkT1jysrK\ndOTIEQ0PD6uzs1Nnz55VUVHRVIYAAExBzCX1yspKvfvuu7p48aKWLFmi2tpaBYNBnTlzRg6HQw88\n8IBeffVVSZLX61VFRYW8Xq9SU1N18ODBSVe/AAASL+YujTMyKLs0zlns0jhz4/GaMl9CdmkEANw9\nKHUAMAilDgAGodQBwCCUOgAYhFIHAINQ6gBgkCmdJgBmyMxcqKtXL9kdA0ACcfDRHMaBQCaNyWtq\nLuDgIwCYYyh1ADAIpQ4ABqHUAcAglDoAGIRSBwCDUOoAYBBKHQAMQqkDgEEodQAwCKUOAAah1AHA\nIJQ6ABiEUgcAg1DqAGAQSh0ADEKpA4BBKHUAMAilDgAGiVnqW7duldPp1IoVKyK3DQwMqKSkRMuW\nLdP69es1ODgYua+urk65ubnKy8tTa2vrzKQGAEwoZqn/4he/UEtLy5jb6uvrVVJSoo6ODq1du1b1\n9fWSpPb2dv35z39We3u7Wlpa9Nhjj2l0dHRmkgMAxolZ6qtXr9aCBQvG3Nbc3Kzq6mpJUnV1tRob\nGyVJTU1NqqysVFpamjwej3JychQKhWYgNoCxUuVwOJJ6ycxcaPeTxgSmtE69v79fTqdTkuR0OtXf\n3y9J6unpkdvtjkzndrsVDocTEBNAdDclWUm9XL16KTlPDXckdboP8OW7drT7J1JTUxO5HggEFAgE\nphsFAIwSDAYVDAbvaJ4plbrT6VRfX5+ys7PV29urrKwsSZLL5VJXV1dkuu7ubrlcrgkf46ulDgAY\n7/YF3tra2pjzTGn1S1lZmRoaGiRJDQ0NKi8vj9x+5MgRDQ8Pq7OzU2fPnlVRUdFUhgAATEHMJfXK\nykq9++67unjxopYsWaIXX3xRzzzzjCoqKnTo0CF5PB4dPXpUkuT1elVRUSGv16vU1FQdPHgw6qoZ\nAEBiOSzLspI+qMMhG4bFbW694Sb778CYZox3a0xex8kVT3dyRCkAGIRSBwCDUOoAYBBKHQAMQqkD\ngEEodQAwCKUOAAah1AHAIJQ6ABiEUgcAg1DqAGAQSh0ADEKpA4BBKHUAMAilDgAGodQBwCCUOgAY\nhFIHAINQ6gBgEEodAAxCqQOAQSh1ADAIpQ4ABqHUAcAglDoAGIRSBwCDUOoAYBBKHQAMkjqdmT0e\njzIzMzVv3jylpaUpFAppYGBADz/8sD755BN5PB4dPXpU9913X6LyAgCimNaSusPhUDAYVFtbm0Kh\nkCSpvr5eJSUl6ujo0Nq1a1VfX5+QoACA2Ka9+sWyrDE/Nzc3q7q6WpJUXV2txsbG6Q4BAIjTtJfU\n161bp+985zv6/e9/L0nq7++X0+mUJDmdTvX3908/JQAgLtNap37y5EktXrxYFy5cUElJifLy8sbc\n73A45HA4phUQABC/aZX64sWLJUlf//rX9eCDDyoUCsnpdKqvr0/Z2dnq7e1VVlbWhPPW1NRErgcC\nAQUCgelEAQDjBINBBYPBO5rHYd2+UjxO165d08jIiDIyMvTZZ59p/fr1euGFF3TixAndf//92rlz\np+rr6zU4ODhuY6nD4Ri3Lh7Jd+tTVLL/Doxpxni3xuR1nFzxdOeUS72zs1MPPvigJOnmzZv6+c9/\nrl27dmlgYEAVFRU6f/78pLs0UuqzA6Vu0piU+lwwo6U+HZT67ECpmzQmpT4XxNOdHFEKAAah1AHA\nIJQ6ABiEUgcAg1DqAGAQSh0ADEKpA4BBpnWaACRWZuZCXb16ye4YAO5iHHw0iyT/YKC5cFDOXBmT\ng4/mAg4+AoA5hlIHAINQ6gBgEEodAAxCqQOAQdilEcAUpSb96yozMhboypWBpI55t6HUAUzRTSV7\nN8qrV/nO41hY/QIABqHUAcAglDoAGIRSBwCDUOoAYBBKHQAMQqkDgEHYT30SnNscwN2I86lPIvnn\nNpfmyjm4GdOE8ewbc7Z3x0zifOoAMMdQ6gBgENapA7iLcBKxWGZkSb2lpUV5eXnKzc3V3r17Z2II\nAHPSlycRS97lbtthIuGlPjIyoscff1wtLS1qb2/X4cOH9eGHHyZ6mCQJ2h0gTkG7A8QhaHeAOAXt\nDhCnoN0B4hS0O0CcgnYHSJiEr34JhULKycmRx+ORJP3sZz9TU1OT8vPzp/R4L730sp5//sUEJowt\nNXXe/64FJQWSOvbUBDX7cwY1+zNK5Ey0oMiZXAkv9XA4rCVLlkR+drvd+te//jXlx/vkk259/vkO\nSY8nIF18MjP/T9KnSRsPABIl4aWe6I0Y8+al6GtfO6x77gkl9HGj+eKLjqSNBQCJlPBSd7lc6urq\nivzc1dUlt9s9ZpqlS5fecflfv27Hevna/12SaapvilPNmcw9Cb7MaMe319zJmIn6m8/087w952z9\nvSb6NTRTz3PynMne42YyS5cujTlNwo8ovXnzpr797W/r7bff1je+8Q0VFRXp8OHDU16nDgCIX8KX\n1FNTU/W73/1OGzZs0MjIiLZt20ahA0CS2HLuFwDAzLD1NAH79u1TSkqKBgZm59Fazz33nAoLC+Xz\n+bR27dox2wpmk1/96lfKz89XYWGhfvrTn+ry5ct2R5rQX/7yFxUUFGjevHk6ffq03XHGuRsOmtu6\ndaucTqdWrFhhd5Sourq6tGbNGhUUFGj58uU6cOCA3ZHGuX79uoqLi+Xz+eT1erVr1y67I0U1MjIi\nv9+v0tLS6BNaNjl//ry1YcMGy+PxWJ9++qldMaK6cuVK5PqBAwesbdu22Zhmcq2trdbIyIhlWZa1\nc+dOa+fOnTYnmtiHH35o/fe//7UCgYD1wQcf2B1njJs3b1pLly61Ojs7reHhYauwsNBqb2+3O9Y4\nf/vb36zTp09by5cvtztKVL29vVZbW5tlWZZ19epVa9myZbPy9/nZZ59ZlmVZN27csIqLi6333nvP\n5kST27dvn1VVVWWVlpZGnc62JfUdO3boN7/5jV3DxyUjIyNyfWhoSIsWLbIxzeRKSkqUknLrT1lc\nXKzu7m6bE00sLy9Py5YtszvGhL560FxaWlrkoLnZZvXq1VqwYIHdMWLKzs6Wz+eTJKWnpys/P189\nPT02pxpv/vz5kqTh4WGNjIxo4cKFNieaWHd3t44dO6bt27fPzlPvNjU1ye12a+XKlXYMf0d+/etf\n65vf/KYaGhr0zDPP2B0nptdee00//vGP7Y5x15nooLlwOGxjInOcO3dObW1tKi4utjvKOKOjo/L5\nfHI6nVqzZo28Xq/dkSb05JNP6re//W1k4S2aGTtLY0lJifr6+sbdvnv3btXV1am1tTVyW6x3npk0\nWc49e/aotLRUu3fv1u7du1VfX68nn3xSr7/+ug0pY+eUbv1u77nnHlVVVSU7XkQ8OWej2bIfsmmG\nhob00EMPaf/+/UpPT7c7zjgpKSk6c+aMLl++rA0bNigYDCoQCNgda4y//vWvysrKkt/vVzAYjDn9\njJX6W2+9NeHt//nPf9TZ2anCwkJJtz5WrFq1SqFQSFlZWTMVZ1KT5bxdVVWVrUvAsXL+4Q9/0LFj\nx/T2228nKdHE4v19zjbxHDSHO3Pjxg1t2rRJmzdvVnl5ud1xorr33nu1ceNGvf/++7Ou1P/xj3+o\nublZx44d0/Xr13XlyhU98sgj+uMf/zjxDElZwx/FbN5Q2tHREbl+4MABa/PmzTammdzx48ctr9dr\nXbhwwe4ocQkEAtb7779vd4wxbty4YX3rW9+yOjs7rS+++GLWbii1LMvq7Oyc9RtKR0dHrS1btlhP\nPPGE3VEmdeHCBevSpUuWZVnWtWvXrNWrV1snTpywOVV0wWDQ+slPfhJ1Gtu/+Wg2f+zdtWuXVqxY\nIZ/Pp2AwqH379tkdaUK//OUvNTQ0pJKSEvn9fj322GN2R5rQm2++qSVLluif//ynNm7cqB/96Ed2\nR4r46kFzXq9XDz/88Kw8aK6yslLf+9731NHRoSVLlti2OjCWkydP6o033tA777wjv98vv9+vlpYW\nu2ON0dvbqx/+8Ify+XwqLi5WaWmp1q5da3esmGJ1JgcfAYBBbF9SBwAkDqUOAAah1AHAIJQ6ABiE\nUgcAg1DqAGAQSh0ADEKpA4BB/h/uKbSBqzq9yQAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10d5aad50>"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "And if you add a grid, it looks pretty gross."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import matplotlib.pyplot as plt\n",
      "import numpy as np\n",
      "\n",
      "np.random.seed(12)\n",
      "\n",
      "fig, ax = plt.subplots(1)\n",
      "\n",
      "ax.hist(np.random.randn(1000))\n",
      "ax.grid(True)\n",
      "fig.savefig('hist_matplotlib_grid.png')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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jiy++iNknSg3DMJqammL+idL+/n5j7dq1xubNm1VHGdHVq1eNa9euGYZhGLdu\n3TIWLVpknDp1SnEqc0II47vf/a7pZZT/d3ax/Gvvtm3bMH/+fPh8PgghsGfPHtWRhvXTn/4UPT09\nyMvLQ3Z2NjZu3Kg60rDeeOMNTJ06FWfPnsXy5cuxbNky1ZFCvvpHc16vF6tXr47JP5orLi7G448/\njsbGRkydOlVZHRjOmTNn8Nprr6G2thbZ2dnIzs5GTU2N6liDtLe349vf/jZ8Ph9ycnJQWFiI3Nxc\n1bHCCjcz+cdHREQ2ovyROhERRQ6HOhGRjXCoExHZCIc6EZGNcKgTEdkIhzoRkY1wqBMR2QiHOhGR\njfw/loF7B4Zf1t0AAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10d5aa5d0>"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "With `prettyplotlib.hist`, we make the outlines of the rectangles white, remove the top and right axis lines, thin out the remaining axis lines, and change the blacks from regular black to a light grey (#262626)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import prettyplotlib as ppl\n",
      "import matplotlib.pyplot as plt\n",
      "import numpy as np\n",
      "\n",
      "np.random.seed(12)\n",
      "\n",
      "fig, ax = plt.subplots(1)\n",
      "\n",
      "ppl.hist(np.random.randn(1000))\n",
      "fig.savefig('hist_prettyplotlib_default.png')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x10d6a1950>"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "And you can add a grid over the $y$-axis if you like. It's \"erasing\" some of the figure, but it's actually adding information, since it shows the tick lines!"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import prettyplotlib as ppl\n",
      "import matplotlib.pyplot as plt\n",
      "import numpy as np\n",
      "\n",
      "np.random.seed(12)\n",
      "\n",
      "fig, ax = plt.subplots(1)\n",
      "\n",
      "# 'y' for the 'y' axis. Could also add a grid over the 'x' axis.\n",
      "ppl.hist(ax, np.random.randn(1000), grid='y')\n",
      "fig.savefig('hist_prettyplotlib_grid.png')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import prettyplotlib as ppl\n",
      "import matplotlib.pyplot as plt\n",
      "import numpy as np\n",
      "\n",
      "np.random.seed(12)\n",
      "\n",
      "fig, ax = plt.subplots(1)\n",
      "\n",
      "# 'y' for the 'y' axis. Could also add a grid over the 'x' axis.\n",
      "ppl.hist(ax, np.random.randn(1000), grid='y')\n",
      "fig.savefig('hist_prettyplotlib_grid.png')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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1HUmoJ9kAHKiq741i/Bciyd8m2Q9sBD426npm4J3AV0ZdxIvQVDfNLR9RLU1JchpwJpMT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\n/h+81TNqkKc8zwAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10ead5610>"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%load_ext autoreload\n",
      "%autoreload 2"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "The autoreload extension is already loaded. To reload it, use:\n",
        "  %reload_ext autoreload\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%pdb"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Automatic pdb calling has been turned ON\n"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import prettyplotlib as ppl\n",
      "import matplotlib.pyplot as plt\n",
      "import numpy as np\n",
      "\n",
      "np.random.seed(12)\n",
      "\n",
      "fig, ax = ppl.subplots()\n",
      "\n",
      "# 'y' for the 'y' axis. Could also add a grid over the 'x' axis.\n",
      "for i in range(2):\n",
      "    ppl.hist(np.random.randn(1000), grid='y')\n",
      "fig.savefig('hist_prettyplotlib_two_datasets.png')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x1041c4a50>"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ax._get_lines.color_cycle"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "<itertools.cycle at 0x10f1a1fc8>"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print [(k,v) for k,v in ax.__dict__.iteritems() if 'cycle' in k]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[]\n"
       ]
      }
     ],
     "prompt_number": 6
    }
   ],
   "metadata": {}
  }
 ]
}