{
  "id": 7174,
  "title": "Python sample code",
  "url": "/competitions/connectomics/discussion/7174",
  "author_name": "",
  "post_date": "2014-02-23T10:40:16.077Z",
  "votes": null,
  "comment_count": 1,
  "views": 916,
  "content": "<p>Hi</p>\n\n<p>When can we expect a sample code for python? will be released anytime soon?</p>\n\n<p>Regards</p>",
  "messages": [
    {
      "id": "39289",
      "postDate": "02/23/2014 10:40:16",
      "content": "<p>Hi</p>\n\n<p>When can we expect a sample code for python? will be released anytime soon?</p>\n\n<p>Regards</p>",
      "rawMarkdown": "",
      "votes": null
    },
    {
      "id": "39336",
      "postDate": "02/23/2014 21:39:37",
      "content": "<p>If you are interested in correlation benchmark for start.</p>\n<p>It consist of two parts. <br>a) discretization<br> np.diff(data, axis=0)</p>\n<p>Take all above 0.12 as 1, else as 0</p>\n<p>b) correlation<br> scipy.stats.pearsonr(first, second)[0]</p>\n<p>Maybe it is something else in it, but result is (almost) identical.</p>",
      "rawMarkdown": "",
      "votes": null
    }
  ],
  "comments": [
    {
      "id": 39336,
      "author_name": "djajetic",
      "author_url": "",
      "post_date": "02/23/2014 21:39:37",
      "content": "<p>If you are interested in correlation benchmark for start.</p>\n<p>It consist of two parts. <br>a) discretization<br> np.diff(data, axis=0)</p>\n<p>Take all above 0.12 as 1, else as 0</p>\n<p>b) correlation<br> scipy.stats.pearsonr(first, second)[0]</p>\n<p>Maybe it is something else in it, but result is (almost) identical.</p>",
      "votes": null,
      "replies": []
    }
  ],
  "raw_markdown_by_id": {
    "39289": "",
    "39336": ""
  },
  "source": "meta"
}