matplotlib-gc.ipynb

matplotlib-gc2.ipynb

matplotlib-gc3.ipynb

{
 "metadata": {
  "name": "",
  "signature": "sha256:43c8644bcc937e095965445f0fc7f19d01ac99ee8e14303d135f59b6ccd69fb7"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline\n",
      "import gc\n",
      "import matplotlib.pyplot as plt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "len(gc.get_objects())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 2,
       "text": [
        "40604"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.hist([1,2])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 3,
       "text": [
        "(array([ 1.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  1.]),\n",
        " array([ 1. ,  1.1,  1.2,  1.3,  1.4,  1.5,  1.6,  1.7,  1.8,  1.9,  2. ]),\n",
        " <a list of 10 Patch objects>)"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAEACAYAAABI5zaHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x109820cd0>"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "len(gc.get_objects())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "44801"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "gc.collect()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "0"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}