error with kdeplot

index.ipynb

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   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline\n",
      "\n",
      "import numpy as np\n",
      "import matplotlib.pyplot as plt\n",
      "import seaborn as sns\n",
      "import statsmodels\n",
      "import scipy"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sns.__version__"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 2,
       "text": [
        "'0.5.1'"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "statsmodels.__version__"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 3,
       "text": [
        "'0.6.0'"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "scipy.__version__"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "'0.14.0'"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "X = np.random.randn(100)\n",
      "Y = np.random.randn(100)\n",
      "\n",
      "sns.kdeplot(X, Y, bw=1.);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "ename": "UnboundLocalError",
       "evalue": "local variable 'bw_x' referenced before assignment",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mUnboundLocalError\u001b[0m                         Traceback (most recent call last)",
        "\u001b[0;32m<ipython-input-5-eb14a1a4356c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mY\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkdeplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mY\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbw\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1.\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
        "\u001b[0;32m/usr/local/lib/python2.7/site-packages/seaborn/distributions.pyc\u001b[0m in \u001b[0;36mkdeplot\u001b[0;34m(data, data2, shade, vertical, kernel, bw, gridsize, cut, clip, legend, ax, cumulative, **kwargs)\u001b[0m\n\u001b[1;32m    860\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mbivariate\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    861\u001b[0m         ax = _bivariate_kdeplot(x, y, shade, kernel, bw, gridsize,\n\u001b[0;32m--> 862\u001b[0;31m                                 cut, clip, legend, ax, **kwargs)\n\u001b[0m\u001b[1;32m    863\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    864\u001b[0m         ax = _univariate_kdeplot(data, shade, vertical, kernel, bw,\n",
        "\u001b[0;32m/usr/local/lib/python2.7/site-packages/seaborn/distributions.pyc\u001b[0m in \u001b[0;36m_bivariate_kdeplot\u001b[0;34m(x, y, filled, kernel, bw, gridsize, cut, clip, axlabel, ax, **kwargs)\u001b[0m\n\u001b[1;32m    727\u001b[0m         \u001b[0mxx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0myy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mz\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_statsmodels_bivariate_kde\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbw\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgridsize\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcut\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclip\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    728\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 729\u001b[0;31m         \u001b[0mxx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0myy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mz\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_scipy_bivariate_kde\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbw\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgridsize\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcut\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclip\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    730\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    731\u001b[0m     \u001b[0;31m# Plot the contours\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/usr/local/lib/python2.7/site-packages/seaborn/distributions.pyc\u001b[0m in \u001b[0;36m_scipy_bivariate_kde\u001b[0;34m(x, y, bw, gridsize, cut, clip)\u001b[0m\n\u001b[1;32m    783\u001b[0m         \u001b[0mbw_x\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkde\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"%s_factor\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mbw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mdata_std\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    784\u001b[0m         \u001b[0mbw_y\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkde\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"%s_factor\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mbw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mdata_std\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 785\u001b[0;31m     \u001b[0mx_support\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_kde_support\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbw_x\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgridsize\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcut\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclip\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    786\u001b[0m     \u001b[0my_support\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_kde_support\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbw_y\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgridsize\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcut\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclip\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    787\u001b[0m     \u001b[0mxx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0myy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmeshgrid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_support\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_support\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;31mUnboundLocalError\u001b[0m: local variable 'bw_x' referenced before assignment"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x10bdca690>"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}