{"cells":[
 {
  "cell_type": "code",
  "execution_count": null,
  "metadata": {
   "collapsed": false
  },
  "outputs": [],
  "source": "# This R environment comes with all of CRAN preinstalled, as well as many other helpful packages\n# The environment is defined by the kaggle/rstats docker image: https://github.com/kaggle/docker-rstats\n# For example, here's several helpful packages to load in \n\n#library(ggplot2) # Data visualization\n#library(readr) # CSV file I/O, e.g. the read_csv function\n\n# Input data files are available in the \"../input/\" directory.\n# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n\n#system(\"ls ../input\")\n\n# Any results you write to the current directory are saved as output.\nimport numpy as np\n\nsize = 10.0;\n\nx_step = 0.2\ny_step = 0.2\n\nx_ranges = zip(np.arange(0, size, x_step), np.arange(x_step, size + x_step, x_step))\ny_ranges = zip(np.arange(0, size, y_step), np.arange(y_step, size + y_step, y_step))\n\nfor x,y in x_ranges:\n    print (\"hel\")\n    print (x)\n    print (y)\n    for a,b in y_ranges:\n        print (a)\n        print (b)\n"
 },
 {
  "cell_type": "code",
  "execution_count": null,
  "metadata": {
   "collapsed": false
  },
  "outputs": [],
  "source": ""
 }
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