SciPy.ipynb

{
 "metadata": {
  "name": "SciPy"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np\n",
      "import scipy as sp"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import scipy.stats as spstats\n",
      "import scipy.optimize as spopt\n",
      "import scipy.linalg as splinalg\n",
      "import scipy.ndimage as spndimage\n",
      "import scipy.sparse as spsparse\n",
      "import scipy.interpolate as spinterp\n",
      "import scipy.special as spspecial\n",
      "import scipy.integrate as spinteg\n",
      "import scipy.spatial as spspatial\n",
      "import scipy.signal as spsignal\n",
      "import scipy.odr as spodr\n",
      "import scipy.fftpack as spfft\n",
      "import scipy.cluster as spcluster\n",
      "import scipy.io as spio\n",
      "import scipy.constants as spconst\n",
      "import scipy.misc as spmisc\n",
      "import scipy.lib as splib"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "frozen=spstats.burr(0.5, 1.0, loc=-10, scale=10)\n",
      "%pylab inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Welcome to pylab, a matplotlib-based Python environment [backend: module://IPython.zmq.pylab.backend_inline].\n",
        "For more information, type 'help(pylab)'.\n"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x = np.linspace(-20, 10)\n",
      "plot(x, frozen.pdf(x))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "[<matplotlib.lines.Line2D at 0x107455d90>]"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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1Wr3GWK1W7N+/HwBQX1+PxMREpKSkQBAEFBUVISsrC1u3bg3dFlDYReMx9nIb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\n"
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "hist(frozen.rvs(size=1000), bins=50, range=(-15, 10))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "(array([  0,   0,   0,   0,   0,   0,   0,   0,   0,   0, 193,  60,  38,\n",
        "        25,  24,  24,  21,  16,  11,  17,  18,  10,  14,   4,   8,   8,\n",
        "        10,   9,   2,   8,   8,   9,   4,   2,   4,   5,   7,   5,   3,\n",
        "         4,   1,   0,   4,   3,   4,   8,   4,   4,   3,   4]),\n",
        " array([-15. , -14.5, -14. , -13.5, -13. , -12.5, -12. , -11.5, -11. ,\n",
        "       -10.5, -10. ,  -9.5,  -9. ,  -8.5,  -8. ,  -7.5,  -7. ,  -6.5,\n",
        "        -6. ,  -5.5,  -5. ,  -4.5,  -4. ,  -3.5,  -3. ,  -2.5,  -2. ,\n",
        "        -1.5,  -1. ,  -0.5,   0. ,   0.5,   1. ,   1.5,   2. ,   2.5,\n",
        "         3. ,   3.5,   4. ,   4.5,   5. ,   5.5,   6. ,   6.5,   7. ,\n",
        "         7.5,   8. ,   8.5,   9. ,   9.5,  10. ]),\n",
        " <a list of 50 Patch objects>)"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}