{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1.4.3'"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matplotlib.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<module 'matplotlib' from '/nuwa_cluster/home/jackyu/locally_installed_python_pacakges/anaconda3/lib/python3.4/site-packages/matplotlib/__init__.py'>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Say there are the following 1d arrays\n",
    "\n",
    "x = np.array([  3.12199990e-04,   2.90415010e-03,   1.57711500e-02,\n",
    "                8.56452990e-02,   4.65100010e-01,   2.52570000e+00,\n",
    "                1.37158500e+01,   7.38875010e+01,   1.92762500e+02,\n",
    "                3.11637500e+02,   4.30512500e+02,   5.49387510e+02,\n",
    "                6.68262510e+02,   7.87137510e+02,   9.06012510e+02])\n",
    "\n",
    "y = np.array([ -2.43725140e-05,  -2.54052210e-05,  -2.66895420e-05,\n",
    "               -3.07354470e-05,  -9.19818190e-06,   9.97550570e-06,\n",
    "               -1.53637560e-04,  -8.40315400e-04,  -1.99370040e-03,\n",
    "               -2.19048730e-03,  -1.26747160e-03,   6.09407900e-04,\n",
    "                3.31778190e-03,   6.78078280e-03,   1.02557620e-02])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2aab4fa252b0>]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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U1q2DI0dg7dpRj0aLye/kkjo0NdXfcz1ypP88vCYrzYcBKw05vTywe3d/z3X37rPXZKX5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\nGWq1DohCRwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2aab4fa25160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot y against x\n",
    "\n",
    "fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(5, 5))\n",
    "\n",
    "ax.plot(x, y, 'x')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Z5KlurM+sd+6SNE6jXPHuAQ5X1WXAY135DEk2AfcAu4ArgJuSXL5K/z8HfrKq/hFwC3D/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\nDF5JaszglaTG/j9dVwdW9rvBeQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2aab4fa25160>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Say I wanted to zoom in on the y values for the first x values\n",
    "# but keeping the x-axis the same as before\n",
    "ax.set_ylim(bottom=y[range(10)].min(), top=y[range(10)].max())\n",
    "fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2aab4fa25160>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now create some margin at the top and bottom so the markers are \n",
    "# not partially obstructed by the top and bottom frames\n",
    "ax.set_ylim(bottom=y[range(10)].min(), top=y[range(10)].max())\n",
    "ax.margins(x=.2, y=.2)\n",
    "fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# it appears that ax.margins only now works for the x-axis \n",
    "# and not the y-axis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.4.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}