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  "name": "",
  "signature": "sha256:33e511bb280309527be6f4d9115d599e9df031398c126728bfa1e1292c040305"
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  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Video analysis\n",
      "=============="
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import os\n",
      "import collections\n",
      "import itertools\n",
      "import concurrent.futures\n",
      "\n",
      "import numpy as np\n",
      "import matplotlib.pyplot as plt\n",
      "\n",
      "from utils import utils, video, movement, data, beacons, clustering\n",
      "\n",
      "%matplotlib inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Movement analysis\n",
      "The mean distance each participant did at different blocks (normalized by the length of the block, so actually its the mean participant speed in meters / second)."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dists_futures = {}\n",
      "with concurrent.futures.ProcessPoolExecutor() as executor:\n",
      "    for group, block in itertools.product(['A', 'B'], [1, 2, 3]):\n",
      "        dists_futures[(group, block)] = executor.submit(movement.distance, group, block)\n",
      "# extracting the values from the futures\n",
      "dists = {k: v.result() for k, v in dists_futures.items()}"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "utils.two_groups_three_bars(dists, ylim=[0, 0.5], ylabel='Mean velocity')\n",
      "data.save_to_graphics('analyze-movement')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "A/1 -- mean: 0.258, stderr: 0.0354\n",
        "A/2 -- mean: 0.278, stderr: 0.0434\n",
        "A/3 -- mean: 0.37, stderr: 0.0277\n",
        "B/1 -- mean: 0.269, stderr: 0.0478\n",
        "B/2 -- mean: 0.176, stderr: 0.0252\n",
        "B/3 -- mean: 0.206, stderr: 0.051\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f80d4e6f908>"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "No difference between groups in the first block:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "utils.independent_one_sided_ttest_summary(dists[('A', 1)], dists[('B', 1)])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "mean (a vs b): 0.258\t0.269\n",
        "stderr  (a vs b): 0.035\t0.048\n",
        "t: -0.1848587720687531\tp: 0.42760769582198077\tdf: 21\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x7f809c901f60>"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data.save_to_cooked(dists, 'movement')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## \"Sitting on the bench\" analysis\n",
      "Calculate for each participant the percentage of the time he / she stayed at the bench area (for every block). Apply t-tests."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "bench_pos = data.get_bench()\n",
      "bench_radius = video.BENCH_RADIUS\n",
      "dist_func = utils.euclidean_distance\n",
      "\n",
      "def at_the_bench(point):\n",
      "    '''Returns a boolean indicates if the point is in the bench radius or not.'''\n",
      "    return dist_func(point, bench_pos) <= bench_radius\n",
      "\n",
      "def at_the_bench_percentage(group, block):\n",
      "    '''Returns a list of percentages (value for each participant) of time at the bench.'''\n",
      "    d = data.participants_data(group, block)\n",
      "    return d.apply(utils.divide_apply_unpack, axis=1, args=(at_the_bench, 2)).mean(axis=0)\n",
      "\n",
      "percentages = {}\n",
      "with concurrent.futures.ProcessPoolExecutor() as executor:\n",
      "    for group, block in itertools.product(['A', 'B'], [1, 2, 3]):\n",
      "        percentages[(group, block)] = executor.submit(at_the_bench_percentage, group, block)\n",
      "# extracting the values from the futures\n",
      "percentages = {k: v.result() for k, v in percentages.items()}"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "utils.two_groups_three_bars(percentages, ylim=[0, 0.7])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "A/1 -- mean: 0.204, stderr: 0.0514\n",
        "A/2 -- mean: 0.291, stderr: 0.0756\n",
        "A/3 -- mean: 0.0093, stderr: 0.00509\n",
        "B/1 -- mean: 0.0806, stderr: 0.0461\n",
        "B/2 -- mean: 0.21, stderr: 0.0306\n",
        "B/3 -- mean: 0.383, stderr: 0.112\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x7f809c3eee80>"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data.save_to_cooked(percentages, 'sitting_on_the_bench')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Beacons interaction\n",
      "Expectancy of the number of participants arround a beacon, excluding beacons with 0 participants around."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "close_to = {}\n",
      "with concurrent.futures.ProcessPoolExecutor() as executor:\n",
      "    for group, block in itertools.product(['A', 'B'], range(1, 4)):\n",
      "        close_to[(group, block)] = executor.submit(beacons.close_to_beacon, group, block)\n",
      "        \n",
      "# extract the futures results\n",
      "for key, val in close_to.items():\n",
      "    close_to[key] = val.result()\n",
      "    \n",
      "# calculate vectors of column means (excluding beacons with 0 participants around them)\n",
      "close_to_vecs = {}\n",
      "for key, mat in close_to.items():\n",
      "    # calculate the mean of all the cells with positive numbers\n",
      "    # (do not include beacons with 0 close participants to the mean)\n",
      "    close_to_vecs[key] = np.array([col[col > 0].mean() for col in mat.transpose()])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "/usr/lib/python3/dist-packages/numpy/core/_methods.py:55: RuntimeWarning: Mean of empty slice.\n",
        "  warnings.warn(\"Mean of empty slice.\", RuntimeWarning)\n"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "utils.two_groups_three_bars(close_to_vecs,\n",
      "                            title='Expectancy of participants close to a beacon',\n",
      "                            ylabel='Participants')\n",
      "data.save_to_graphics('analyze-expectancy_of_participants_close_to_a_beacon')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "A/1 -- mean: 1.64, stderr: 0.207\n",
        "A/2 -- mean: 1.63, stderr: 0.151\n",
        "A/3 -- mean: 1.84, stderr: 0.156\n",
        "B/1 -- mean: 1.77, stderr: 0.0714\n",
        "B/2 -- mean: 2.5, stderr: 0.146\n",
        "B/3 -- mean: 2.15, stderr: 0.187\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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qtgjq+IPUvusZmzfvsOVfuPIOVMefLlz5V4g6/onADcDNWcY5rPpHRERCprHA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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f809c74d128>"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "utils.independent_one_sided_ttest_summary(close_to_vecs[('A', 1)], close_to_vecs[('B', 1)])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "mean (a vs b): 1.639\t1.775\n",
        "stderr  (a vs b): 0.207\t0.071\n",
        "t: -0.5655034231641781\tp: 0.29583467952978815\tdf: 10\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
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       "text": [
        "<matplotlib.figure.Figure at 0x7f80cda7fe80>"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "0.29583467952978815 / 2"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "0.14791733976489407"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data.save_to_cooked(close_to_vecs, 'expectancy_of_participants_close_to_a_beacon')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 13
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "---\n",
      "# Video clustering\n",
      "\n",
      "## Participants positioning based clustering\n",
      "\n",
      "Cluster (with MeanShift) every frame in the video and score by:\n",
      "\n",
      "1. _Algorithm score_: the mean of the distances of each participant from his centroid. Consider normalizing by the distances between the centroids.\n",
      "\n",
      "2. _Social score_: The mean of the social score each participant gave to each other participant __in his cluster__.\n",
      "\n",
      "Generate graphs over time of the above data."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "part_clusters = {}\n",
      "with concurrent.futures.ProcessPoolExecutor() as executor:\n",
      "    for group, block in itertools.product(['A', 'B'], [1, 2, 3]):\n",
      "        part_clusters[(group, block)] = executor.submit(clustering.participants_clustering, group, block)\n",
      "        \n",
      "# extract the futures results\n",
      "for key, val in part_clusters.items():\n",
      "    part_clusters[key] = val.result()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "part_scores = collections.defaultdict(dict)\n",
      "with concurrent.futures.ProcessPoolExecutor() as executor:\n",
      "    for key, (clusters, mapings) in part_clusters.items():\n",
      "        participants_data = data.participants_data(*key)\n",
      "        part_scores['algorithm'][key] = executor.submit(clustering.score, 'algorithm', clusters, mapings, participants_data)\n",
      "        part_scores['social'][key] = executor.submit(clustering.score, 'social', clusters, mapings, participants_data)\n",
      "\n",
      "# extract the futures results\n",
      "for kind, inner_dict in part_scores.items():\n",
      "    for key, val in inner_dict.items():\n",
      "        part_scores[kind][key] = val.result()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "d = part_scores['algorithm']\n",
      "utils.two_groups_three_bars(d, title='Participant based clustering - algorithm score', ylim=[0.5, 1.2])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "A/1 -- mean: 0.866, stderr: 0.053\n",
        "A/2 -- mean: 1.01, stderr: 0.0464\n",
        "A/3 -- mean: 0.899, stderr: 0.0772\n",
        "B/1 -- mean: 0.94, stderr: 0.044\n",
        "B/2 -- mean: 0.68, stderr: 0.034\n",
        "B/3 -- mean: 0.737, stderr: 0.0575\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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avNty0nAy8AKttHU3NjZSV1cHQGVlJfX19fE2wFgbYaJNMOr/dWUYolGIZd9f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\nQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f809c3ffb70>"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "utils.independent_one_sided_ttest_summary(d[('A', 1)], d[('B', 1)])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "mean (a vs b): 0.866\t0.940\n",
        "stderr  (a vs b): 0.053\t0.044\n",
        "t: -1.0166109370684138\tp: 0.16076982324511818\tdf: 21\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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XAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f809c8fbcf8>"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "0.16076982324511818 / 2"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 18,
       "text": [
        "0.08038491162255909"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data.save_to_cooked(d, 'participant_based_clustering_algorithm_score')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "d = part_scores['social']\n",
      "utils.two_groups_three_bars(d, title='Participant based clustering - social score', ylim=[0.4, 0.7])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "A/1 -- mean: 0.563, stderr: 0.0378\n",
        "A/2 -- mean: 0.545, stderr: 0.0421\n",
        "A/3 -- mean: 0.531, stderr: 0.0433\n",
        "B/1 -- mean: 0.565, stderr: 0.0274\n",
        "B/2 -- mean: 0.598, stderr: 0.0356\n",
        "B/3 -- mean: 0.598, stderr: 0.0282\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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BXUQkYhTYRUQiRoFdRCRiFNhFRCJGgV1EJGIU2EVEIkaBXUQkYhTYRUQiRoFd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\nIiIiIiIiIiIiIiIiIiIiIiIiUrj+P0s+qtYCQRnRAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f8097fd8b70>"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data.save_to_cooked(d, 'participant_based_clustering_social_score')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 21
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## The two plots on the same line, for paper"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.figure(figsize=(10, 4))\n",
      "plt.subplot(1, 2, 1)\n",
      "utils.two_groups_three_bars(part_scores['algorithm'], title='Clustering algorithm score', ylim=[0.5, 1.2])\n",
      "plt.subplot(1, 2, 2)\n",
      "utils.two_groups_three_bars(part_scores['social'], title='Clustering social score', ylim=[0.4, 0.7])\n",
      "data.save_to_graphics('analyze-clustering_scores')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "A/1 -- mean: 0.866, stderr: 0.053\n",
        "A/2 -- mean: 1.01, stderr: 0.0464\n",
        "A/3 -- mean: 0.899, stderr: 0.0772\n",
        "B/1 -- mean: 0.94, stderr: 0.044\n",
        "B/2 -- mean: 0.68, stderr: 0.034\n",
        "B/3 -- mean: 0.737, stderr: 0.0575\n",
        "A/1 -- mean: 0.563, stderr: 0.0378"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "A/2 -- mean: 0.545, stderr: 0.0421\n",
        "A/3 -- mean: 0.531, stderr: 0.0433\n",
        "B/1 -- mean: 0.565, stderr: 0.0274\n",
        "B/2 -- mean: 0.598, stderr: 0.0356\n",
        "B/3 -- mean: 0.598, stderr: 0.0282\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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MwY55ApgB7AHm5nnOm8BIYGfG4zrbzvc+dLZdmNTXN7Fjx/uVLgZ1dY1s3761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\nGcYXxgAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f809c741080>"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Beacon positioning based clustering\n",
      "\n",
      "Similarly to the participants positioning based clustering, use clustering based on beacons with at least two participants around them. Merge close beacons and ignore timestamps where there is no such cluster."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "beacon_clusters = {}\n",
      "with concurrent.futures.ProcessPoolExecutor() as executor:\n",
      "    for group, block in itertools.product(['A', 'B'], [1, 2, 3]):\n",
      "        beacon_clusters[(group, block)] = executor.submit(clustering.beacons_clustering, group, block)\n",
      "\n",
      "# extract the futures results\n",
      "for key, val in beacon_clusters.items():\n",
      "    beacon_clusters[key] = val.result()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 23
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "beacon_scores = collections.defaultdict(dict)\n",
      "with concurrent.futures.ProcessPoolExecutor() as executor:\n",
      "    for key, (clusters, mapings) in beacon_clusters.items():\n",
      "        participants_data = data.participants_data(*key)\n",
      "        beacon_scores['algorithm'][key] = executor.submit(clustering.score, 'algorithm', clusters, mapings, participants_data)\n",
      "        beacon_scores['social'][key] = executor.submit(clustering.score, 'social', clusters, mapings, participants_data)\n",
      "\n",
      "# extract the futures results\n",
      "for kind, inner_dict in beacon_scores.items():\n",
      "    for key, val in inner_dict.items():\n",
      "        beacon_scores[kind][key] = val.result()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 24
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "d = beacon_scores['algorithm']\n",
      "utils.two_groups_three_bars(d, title='Beacon based clustering - algorithm score', ylim=[0.4, 0.7])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "A/1 -- mean: 0.614, stderr: 0.0438\n",
        "A/2 -- mean: 0.601, stderr: 0.0264\n",
        "A/3 -- mean: 0.604, stderr: 0.021\n",
        "B/1 -- mean: 0.611, stderr: 0.0317\n",
        "B/2 -- mean: 0.624, stderr: 0.0189\n",
        "B/3 -- mean: 0.639, stderr: 0.0386\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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UVPD000+zZcsWunbtys6d8d94Xb58OYcccgh1dXUA/P73v2fQoEGUlZUxatQo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\nXbRnjmBjfgAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f80ce3e1390>"
       ]
      }
     ],
     "prompt_number": 25
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data.save_to_cooked(d, 'beacon_based_clustering_algorithm_score')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 26
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "d = beacon_scores['social']\n",
      "utils.two_groups_three_bars(d, title='Beacon based clustering - social score', ylim=[0.4, 0.7])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "A/1 -- mean: 0.557, stderr: 0.0532\n",
        "A/2 -- mean: 0.591, stderr: 0.0466\n",
        "A/3 -- mean: 0.591, stderr: 0.0467\n",
        "B/1 -- mean: 0.562, stderr: 0.0357\n",
        "B/2 -- mean: 0.611, stderr: 0.0334\n",
        "B/3 -- mean: 0.562, stderr: 0.0379\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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STHcX8GLceANEgMauFlRERDKTrsV+KvAhtuW9F1gMXJxguhuAJ4G/JkgLdaF8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\nQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f8097e914e0>"
       ]
      }
     ],
     "prompt_number": 27
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data.save_to_cooked(d, 'beacon_based_clustering_social_score')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 28
    }
   ],
   "metadata": {}
  }
 ]
}