merge matplotlib notebook with scipy-tutorial one

matplotlib-quickstart.ipynb

-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "%matplotlib inline"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "import numpy as np"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "a=np.linspace(1,10,100)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([  1.        ,   1.09090909,   1.18181818,   1.27272727,\n",
-       "         1.36363636,   1.45454545,   1.54545455,   1.63636364,\n",
-       "         1.72727273,   1.81818182,   1.90909091,   2.        ,\n",
-       "         2.09090909,   2.18181818,   2.27272727,   2.36363636,\n",
-       "         2.45454545,   2.54545455,   2.63636364,   2.72727273,\n",
-       "         2.81818182,   2.90909091,   3.        ,   3.09090909,\n",
-       "         3.18181818,   3.27272727,   3.36363636,   3.45454545,\n",
-       "         3.54545455,   3.63636364,   3.72727273,   3.81818182,\n",
-       "         3.90909091,   4.        ,   4.09090909,   4.18181818,\n",
-       "         4.27272727,   4.36363636,   4.45454545,   4.54545455,\n",
-       "         4.63636364,   4.72727273,   4.81818182,   4.90909091,\n",
-       "         5.        ,   5.09090909,   5.18181818,   5.27272727,\n",
-       "         5.36363636,   5.45454545,   5.54545455,   5.63636364,\n",
-       "         5.72727273,   5.81818182,   5.90909091,   6.        ,\n",
-       "         6.09090909,   6.18181818,   6.27272727,   6.36363636,\n",
-       "         6.45454545,   6.54545455,   6.63636364,   6.72727273,\n",
-       "         6.81818182,   6.90909091,   7.        ,   7.09090909,\n",
-       "         7.18181818,   7.27272727,   7.36363636,   7.45454545,\n",
-       "         7.54545455,   7.63636364,   7.72727273,   7.81818182,\n",
-       "         7.90909091,   8.        ,   8.09090909,   8.18181818,\n",
-       "         8.27272727,   8.36363636,   8.45454545,   8.54545455,\n",
-       "         8.63636364,   8.72727273,   8.81818182,   8.90909091,\n",
-       "         9.        ,   9.09090909,   9.18181818,   9.27272727,\n",
-       "         9.36363636,   9.45454545,   9.54545455,   9.63636364,\n",
-       "         9.72727273,   9.81818182,   9.90909091,  10.        ])"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a"
-   ]
-  },
-  {
-   "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": "3.4.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}