Planet hunter mini course analysis

gistfile1.txt

{
 "metadata": {
  "name": "Minicourse Analysis"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "from  pymongo import MongoClient\nfrom pylab import *\n%matplotlib inline\n",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "client = MongoClient()\ndb = client.ouroboros",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "planet_hunter_proj_id = \"5333063b3ae740228a000001\"\nplanet_hunter_users = db.planet_hunter_users\n",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "#Basic stats \nThese are some basic stats about how many users opted in and out of the minicoruse"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "total_users = planet_hunter_users.count()\nno_opt_in  = planet_hunter_users.find({\"preferences.planet_hunter.course\" : \"yes\"}).count()\nno_opt_out = planet_hunter_users.find({\"preferences.planet_hunter.course\" : \"no\"}).count()\n\nprint str(no_opt_in) +\", \"+ str(no_opt_in*100.0/total_users) + \"% users opted in\"\nprint str(no_opt_out) +\", \"+ str(no_opt_out*100.0/total_users) +\"% users opted out\"",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "1043, 47.0667870036% users opted in\n1160, 52.3465703971% users opted out\n"
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "#Course progress \n\nThese plots show for the users who took the course how much they participated. So for example about 65% of users who opted in to the course viewed only 1 slide with only roughtly 5% completing the coruse"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "course_progress  = [int(ph[\"preferences\"][\"planet_hunter\"][\"curr_course_id\"]) for ph in planet_hunter_users.find({\"preferences.planet_hunter.curr_course_id\" : {\"$exists\" : True} })]",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "fig, axes = plt.subplots(1, 2, figsize=(24,8))\n\naxes[0].set_title(\"Minicourse use\")\naxes[0].set_xlabel(\"No of course slides viewed\")\naxes[0].set_ylabel(\"No of users\")\naxes[0].hist(course_progress,bins=25)\n\n\naxes[1].set_title(\"Minicourse use\")\naxes[1].set_xlabel(\"No of course slides viewed\")\naxes[1].set_ylabel(\"% of  users\")\naxes[1].hist(course_progress, normed=True, bins=25)   ",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": "(array([ 0.65267536,  0.06727711,  0.03538601,  0.02708559,  0.02621186,\n        0.01485339,  0.01529025,  0.01135847,  0.00830042,  0.01092161,\n        0.00567924,  0.00524237,  0.0061161 ,  0.00655297,  0.00349491,\n        0.00131059,  0.00349491,  0.00305805,  0.00087373,  0.00262119,\n        0.00262119,  0.00174746,  0.00131059,  0.00174746,  0.04630762]),\n array([  0.  ,   1.04,   2.08,   3.12,   4.16,   5.2 ,   6.24,   7.28,\n         8.32,   9.36,  10.4 ,  11.44,  12.48,  13.52,  14.56,  15.6 ,\n        16.64,  17.68,  18.72,  19.76,  20.8 ,  21.84,  22.88,  23.92,\n        24.96,  26.  ]),\n <a list of 25 Patch objects>)"
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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8AADQB61evTo1NTWprq5OeXl5ZsyYkYaGhh36XXHFFfnYxz6WoUOH9kCVAAAU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\n5AUAgD6osrIyLS0tnectLS2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J9p69u3HjxgwePDgbNmzIwIEDkySNjY37XAxi7dq1aWxs\nPMDIs7q5cgCA3q2lpSUtLS1d92fPnl27YgAAgCQ1WK6hKIpMnz49w4cPz8yZM7seb21tzfz585Mk\n8+fP72r+tra25r777ktHR0dWr16dVatWZdy4cQe7bAAAAACAunTQz+R9/PHH861vfSsjRozI6NGj\nkyTXX399rr322kyZMiV33nlnmpubs2DBgiTJ8OHDM2XKlAwfPjx9+vTJbbfd9pZLOQAAAAAA9CaV\noiiKWhdRDXsbv9Xblfe//8rceOPxufLKK6s2JgBAT1OpVNJD4iQHUO2cnazP4YePyUsvra/imAAA\nPcu7zdkHfbkGAAAAAACqR5MXAAAAAKDENHkBAAAAAEpMkxcAAAAAoMQ0eQEAAAAASkyTFwAAAACg\nxDR5AQAAAABKTJMXAAAAAKDEKkVRFLUuohoqlUqS6u3K+99/ZZJv5tVXt1dtzCTp169/tm7dXNUx\nAQBqpVKppIfESQ6g2jk7WZ+kOcmuKo65l6wNAPQU7zZn9+nGWkpvb4O3uv9p2batUtXxAACgfHal\n2jk7kbUBgN7Lcg0AAAAAACWmyQsAAAAAUGKavAAAAAAAJabJCwAAAABQYpq8AAAAAAAlpskLAAAA\nAFBimrwAAAAAACWmyQsAAAAAUGKavAAAAAAAJabJCwAAAABQYpq8AAAAAAAlpskLANS9ww77UCqV\nSlVvhx32oVrvFgAA1Jys3TP0qXUBAABvZ9u2F5MUVR6zUtXxAACgjGTtnsGZvAAAAAAAJabJCwAA\nAABQYpq8AAAAAAAlpskLAAAAAFBimrwAAAAAACXWp9YF9D59UqlU9wqD/fr1z9atm6s6JgAAlI+s\nDQD0Tpq8B93uJEVVR9y2rbpBFgAAyknWBgB6J8s1AAAAAACUmCYvAAAAAECJafICAAAAAJSYJi8A\nAAAAQIm58FqPUP2rCCcNSXZVdURXJgYAoHxkbQCg/mny9gjVv4pwUqn6mK5MDABA+cjaAED9s1wD\nAAAAAECJafICAAAAAJSYJi8AAAAAQIlp8gIAAAAAlJgmLwAAAABAifWpdQH0Jn1SqVT7qr8NSXZV\necykX7/+2bp1c9XHBQCA7lGOrC1nA0D30OTlINqdpKjymJVuGDPZtq3aARkAALpTObK2nA0A3cNy\nDQAAAAAAJabJC/u19+tu1bwddtiHql7lYYd9qBR1AgDAXnI2AHQHyzXAflX/627btjV0wzppia/Q\nAQBQHt2Rs6ufX7dtezFlqBMAXqPJCwdNd62TBgAAvVl3XHQOAMqlNMs1LFmyJCeeeGKGDh2aOXPm\n1Loc3pG2WhfAfrW9zfPV/wpdpfK+XjvmO/laXlvb2x0TasFxqUdttS4ASuedZOirrroqQ4cOzciR\nI7N8+fKDXCG/v7ZaF1BHXjuZopq330fb2zxflpwta9O9HJN61VbrAniPStHk7ezszIwZM7JkyZKs\nXLky9957b5555plal8Xbaqt1AexX29s83x0heVevHXPvV/3empBTnxyXetRW6wKgVN5Jhn7ooYfy\n7LPPZtWqVbnjjjtyxRVX1Kha3r22WhfAm7S9zfNlydmyNt3LMalXbbUugPeoFE3eJ598MkOGDElz\nc3MaGhpywQUXZNGiRbUuC+AdePszNmbPnl31Mxbere64uEgtz9ioxv6/2+NS1n0Heq53kqEXL16c\nqVOnJknGjx+fl156KZs2bapFuQC/h96ctcuRNeVsOHhKsSbvunXrcswxx3Tdb2pqyk9/+tM3bXfY\nYZ+q2nt2dKyo2lhAb/ZO1mKe9bvbO1OWi/jtVan6uAdv/2fl3RyXNyvLvjdk75k19T4m8G69kwy9\nv23Wrl2bQYMG7bNdNXN2UbySbduqNhzQq/XmrF2WrJn03pydyNocTKVo8r6TifbhD384v/rVD7rj\n3Y35nszuhjHfqLsuslCWWn+fMd94XKox5tsx5lt7u2NyMJRpLnWH/dX5Xo9LGfa9OwJid4XO2an2\nXOmeMN+7fPjDH651CRzAO/33XRT7/kf5ja8rV87urnHrdcyyZu2ePGZPydndNa6sXf9jdofemrMT\nWbssx6k+vducXYomb2NjY9rb27vut7e3p6mpaZ9tnn322YNdFgAA1K13kqHfuM3atWvT2Ni4zzZy\nNgBA/SvFmrxjxozJqlWrsmbNmnR0dOT+++9Pa2trrcsCAIC69U4ydGtra+6+++4kyRNPPJEjjjji\nTUs1AABQ/0pxJm+fPn1y66235pxzzklnZ2emT5+eYcOG1bosAACoWwfK0LfffnuS5PLLL89f/MVf\n5KGHHsqQIUNy6KGH5q677qpx1QAA/D4qxRsX4QIAAAAAoDRKsVzDW1myZElOPPHEDB06NHPmzKl1\nOfxOc3NzRowYkdGjR2fcuHG1LqdXmjZtWgYNGpRTTjml67HNmzdn0qRJOf7443P22WfnpZdeqmGF\nvWpFUHQAAA4bSURBVNP+jsusWbPS1NSU0aNHZ/To0VmyZEkNK+x92tvb8/GPfzwnnXRSTj755Myb\nNy+J+VJrBzou5kvtvPLKKxk/fnxGjRqV4cOH57rrrktirvR0snb9kbPrg6xdf+Ts+iNn1yc5u/5U\nK2eX+kzezs7OnHDCCXnkkUfS2NiYsWPH5t5777WUQx047rjj8tRTT+VDH/pQrUvptZYtW5a+ffvm\nkksuyYoVK5Ik11xzTf7oj/4o11xzTebMmZMXX3wxN9xwQ40r7V32d1xmz56dfv365R/+4R9qXF3v\ntHHjxmzcuDGjRo3K9u3bc9ppp2XhwoW56667zJcaOtBxWbBggflSQzt37swHP/jB7N69O2eccUbm\nzp2bxYsXmys9lKxdn+Ts+iBr1x85u/7I2fVJzq5P1cjZpT6T98knn8yQIUPS3NychoaGXHDBBVm0\naFGty+J3Svz3gx7hzDPPTP/+/fd5bPHixZk6dWqSZOrUqVm4cGEtSuvV9ndcEvOllgYPHpxRo0Yl\nSfr27Zthw4Zl3bp15kuNHei4JOZLLX3wgx9MknR0dKSzszP9+/c3V3owWbt++RysPVm7/sjZ9UfO\nrk9ydn2qRs4udZN33bp1OeaYY7ruNzU1df3DpLYqlUomTpyYMWPG5N/+7d9qXQ6/s2nTpq4rZg8a\nNCibNm2qcUW85pZbbsnIkSMzffp0X1eqoTVr1mT58uUZP368+VJHXjsuH/nIR5KYL7W0Z8+ejBo1\nKoMGDer6mp+50nPJ2vVJzq5fPg/rk9xQH+Ts+iRn149q5OxSN3krlUqtS+AAHn/88Sxfvjw/+tGP\n8vWvfz3Lli2rdUm8QaVSMYfqxBVXXJHVq1fn6aefzlFHHZUvfvGLtS6pV9q+fXvOO++83HzzzenX\nr98+z5kvtbN9+/acf/75ufnmm9O3b1/zpcYOOeSQPP3001m7dm2WLl2axx57bJ/nzZWexbGsT3J2\nOfg8rA9yQ32Qs+uTnF1fqpGzS93kbWxsTHt7e9f99vb2NDU11bAiXnPUUUclSQYMGJBzzz03Tz75\nZI0rItn7l5+NGzcmSTZs2JCBAwfWuCKSZODAgV0f2Jdeeqn5UgO7du3Keeedl4svvjiTJ09OYr7U\ng9eOy0UXXdR1XMyX+nD44YfnL//yL/PUU0+ZKz2YrF2f5Oz65fOw/sgNtSdn1yc5u369l5xd6ibv\nmDFjsmrVqqxZsyYdHR25//7709raWuuyer2dO3dm27ZtSZIdO3bk4Ycf3ucKp9ROa2tr5s+fnySZ\nP39+14c5tbVhw4aun7/3ve+ZLwdZURSZPn16hg8fnpkzZ3Y9br7U1oGOi/lSOy+88ELX1/Zefvnl\n/Pu//3tGjx5trvRgsnb9kbPrm8/D+iM31JacXZ/k7PpTrZxdKUq+qvKPfvSjzJw5M52dnZk+fXqu\nu+66WpfU661evTrnnntukmT37t3567/+a8elBi688ML853/+Z1544YUMGjQoX/7yl/PpT386U6ZM\nyXPPPZfm5uYsWLAgRxxxRK1L7VXeeFxmz56dtra2PP3006lUKjnuuONy++23d627Q/f78Y9/nAkT\nJmTEiBFdX3+5/vrrM27cOPOlhvZ3XL761a/m3nvvNV9qZMWKFZk6dWr27NmTPXv25OKLL87VV1+d\nzZs3mys9mKxdX+Ts+iFr1x85u/7I2fVJzq4/1crZpW/yAgAAAAD0ZqVergEAAAAAoLfT5AUAAAAA\nKDFNXgAAAACAEtPkBQAAAAAoMU1eAAAAAIAS0+QFAAAAACgxTV6gpg455JD84z/+Y9f9uXPnZvbs\n2e953I6OjkycODGjR4/Od77znfc8Xi00Nzdn8+bNSZI//dM/3e82n/vc5/Lggw92y/t///vfz5w5\nc7pl7Dfqzv0AAOiN5OwDk7OBnqhPrQsAerf3ve99+d73vpfrrrsuRx55ZCqVSlXG/fnPf55KpZLl\ny5dXZby3s2fPnhxySHX/bvb638Xjjz9+wG2q9Tt7o0996lP51Kc+1S1jv1F37gcAQG8kZx+YnA30\nRM7kBWqqoaEhl112WW666aY3PbdmzZqcddZZGTlyZCZOnJj29vY3bbN58+ZMnjw5I0eOzOmnn54V\nK1bk+eefz0UXXZSf/exnGT16dP7v//5vn9c8++yzmThxYkaNGpXTTjstq1evTpJcffXVOeWUUzJi\nxIgsWLAgSdLW1rZPAJsxY0bmz5+fZO8ZANdee21OO+20fOc738m8efNy0kknZeTIkbnwwguTJDt2\n7Mi0adMyfvz4nHrqqVm8ePGb9mHDhg2ZMGFCRo8enVNOOWW/QbNv375JkqIoMmPGjJx44omZNGlS\nfvOb36QoiiTJU089lZaWlowZMyZ//ud/no0bNybJfut6vdNPPz0rV67sut/S0pKnnnoq3/zmN3Pl\nlVcmSZ5//vmcf/75GTduXMaNG5ef/OQnSZIRI0Zk69atKYoiRx55ZO65554kySWXXJL/+I//yJ49\ne3L11Vdn3LhxGTlyZO6444633Q8AAN47OVvOlrOhlykAaqhv377F1q1bi+bm5mLLli3F3Llzi1mz\nZhVFURSf/OQni7vvvrsoiqL4xje+UUyePPlNr58xY0bx5S9/uSiKonj00UeLUaNGFUVRFG1tbcUn\nP/nJ/b7nuHHjioULFxZFURSvvvpqsXPnzuKBBx4oJk2aVOzZs6fYtGlTceyxxxYbNmwoHnvssX3G\nmTFjRjF//vyiKIqiubm5uPHGG7ueO/roo4uOjo6iKIpiy5YtRVEUxXXXXVd861vfKoqiKF588cXi\n+OOPL3bs2LFPPV/72teKr3zlK0VRFEVnZ2exbdu2rvF/+9vfdv2eiqIoHnzwwa46169fXxxxxBHF\ngw8+WHR0dBSnn3568cILLxRFURT33XdfMW3atAPW9Xo33XRT8S//8i9FURTF+vXrixNOOKEoiqK4\n6667ihkzZhRFURQXXnhh8eMf/7goiqL49a9/XQwbNqwoiqL427/92+KHP/xhsWLFimLs2LHFZZdd\nVhRFUQwdOrTYuXNncfvttxf/+q//WhRFUbzyyivFmDFjitWrVx9wPwAAqA45W86Ws6F3sVwDUHP9\n+vXLJZdcknnz5uUDH/hA1+NPPPFEFi5cmCS56KKLcs0117zptY8//ni++93vJkk+/vGP57e//W22\nb99+wL9Wb9u2LevXr8+nP/3pJHu/xvbaOJ/97GdTqVQycODAfOxjH8vPfvazHHbYYW9Z+2c+85mu\nn0eMGJHPfvazmTx5ciZPnpwkefjhh/P9738/c+fOTZK8+uqraW9vzwknnND1urFjx2batGnZtWtX\n19kSB7J06dKuOo866qicddZZSZL//d//zS9+8YtMnDgxSdLZ2Zmjjz76gHW93pQpU3L22Wdn1qxZ\nWbBgQf7qr/7qTds88sgjeeaZZ/b5Pe7YsSNnnnlmli5dmj/+4z/OFVdckTvuuCPr169P//7984EP\nfCAPP/xwVqxYkQceeCBJsnXr1qxatSrLli3b734AAFA9cracLWdD72G5BqAuzJw5M3feeWd27Nix\nz+MHCpHvdpt34o3jVCqV9OnTJ3v27Ol67OWXX95nm0MPPbTr5x/+8If5/Oc/n5///OcZO3ZsOjs7\nkyTf/e53s3z58ixfvjxr1qzZJ3gmyZlnnplly5alsbExn/vc57q+irU/lUrlgPt70kkndb3Pf//3\nf2fJkiVvWddrjj766Bx55JFZsWJFFixY0BWoX792V1EU+elPf9o1fnt7ew499NBMmDAhS5cuzbJl\ny9LS0pIBAwbkgQceyIQJE7pee+utt3a97le/+lUmTZq03983AADVJ2fL2UDvoMkL1IX+/ftnypQp\nufPOO7tCz0c/+tHcd999SZJvf/vb+wSa15x55pn59re/nWTvul4DBgzoWldrf/r165empqYsWrQo\nyd6/+L/88ss588wzc//992fPnj15/vnns3Tp0owbNy7HHntsVq5cmY6Ojrz00kt59NFH9ztuURR5\n7rnn0tLSkhtuuCFbtmzJ9u3bc84552TevHld2+3vAhXPPfdcBgwYkEsvvTTTp09/y4tYTJgwoavO\nDRs25LHHHkuSnHDCCXn++efzxBNPJEl27dqVlStX7reuNwb8ZO+ZEnPmzMnWrVtz8sknd+3Ta84+\n++x99uPpp59OkjQ1NeWFF17Is88+m+OOOy5nnHFG5s6d23WszjnnnNx2223ZvXt3kuSXv/xldu7c\necD9AACguuRsORvoHSzXANTU6/+K/cUvfjG33npr1/1bbrklf/M3f5Mbb7wxAwcOzF133fWm18+a\nNSvTpk3LyJEjc+ihh3ZdrOGtriJ7zz335PLLL8+XvvSlNDQ05IEHHsi5556b//qv/8rIkSNTqVS6\n3jPZ+zWrk08+Occdd1xOPfXU/Y7Z2dmZiy++OFu2bElRFPnCF76Qww8/PP/8z/+cmTNnZsSIEdmz\nZ0/+5E/+5E0XhWhra8uNN96YhoaG9OvXL3ffffcBf0/nnntuHn300QwfPjzHHntsPvrRjyZJ135c\nddVV2bJlS3bv3p2///u/z/HHH/+muvb31bjzzz8/X/jCF/KlL31pn/d87X3nzZuXz3/+8xk5cmR2\n796dj33sY7ntttuSJB/5yEe6zsI444wz8k//9E8544wzkiSXXnpp1qxZk1NPPTVFUWTgwIFZuHDh\nAfcDAIDqkLPlbDkbepdK4Tx+AAAAAIDSslwDAAAAAECJafICAAAAAJSYJi8AAAAAQIlp8gIAAAAA\nlJgmLwAAAABAiWnyAgAAAACUmCYvAAAAAECJ/T8YxKKFnfpv/wAAAABJRU5ErkJggg==\n",
       "text": "<matplotlib.figure.Figure at 0x107fcda50>"
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "\n\nfig = plt.figure()\nax = plt.gca()\nax.set_yscale('log')\nax.set_title(\"Course participation vs classification count\")\nax.set_xlabel(\"No of course slides done\")\nax.set_ylabel(\"Classification count\")\n\nclassification_counts   = [int(ph[\"projects\"][planet_hunter_proj_id].get(\"classification_count\", 0)\n                               ) for ph in planet_hunter_users.find({\"preferences.planet_hunter.curr_course_id\" : {\"$exists\" : True} })]\nax.plot(course_progress, classification_counts, 'o')\n\nmean_fig = plt.figure()\nmean_fig_ax = mean_fig.gca()\ntogether = zip(course_progress, classification_counts) \nmeans = [median([c_count for course_no,c_count in together if course_no == i]) for i in [0,5,10,15,25] ]\nstds  = [std([c_count for course_no,c_count in together if course_no == i]) for i in [0,5,10,15,25] ]\n\nmean_fig_ax.errorbar([0,5,10,15,25], means, fmt=\"o\", yerr=stds)\nmean_fig_ax.set_yscale('log')",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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27hxklxxqBAT44erVZABLAXhDWgncjoCAXxR9ta7cP//8AszmibKxmyJ3w/Md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\nAAAAAElFTkSuQmCC\n",
       "text": "<matplotlib.figure.Figure at 0x1080e0050>"
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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sfD6f6enpMYODg6awsNB0dnaOZwlJl5WVZQYGBpwuI2FaW1vNd999NyIEX3rpJVNbW2uM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\nAcBihDwAWOz/AHdksb467TdwAAAAAElFTkSuQmCC\n",
       "text": "<matplotlib.figure.Figure at 0x10a0110d0>"
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "\n\nhist_fig = plt.figure()\nhist_fig_ax = hist_fig.gca()\n\n\n\n\nfor i in [0,5,10,15,25]:\n    hist_fig_ax.hist( [c_count for course_no,c_count in together if course_no == i], normed=True, range=[0,4000], bins=20, log=True, label=str(i)+\" course views\")\nhist_fig_ax.legend()\n\n",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "text": "<matplotlib.legend.Legend at 0x10a090090>"
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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h8q6h2tpajB07FgcPHjT1SxERUReYPBFs3rxZWpGPiIgsj0GJYPHixfDw8EBo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\nnfc2wM9vDO0AAAAASUVORK5CYII=\n",
       "text": "<matplotlib.figure.Figure at 0x1080d2490>"
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "#Splits "
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "split_in  = [\"a\",\"b\",\"c\",\"g\",\"h\",\"i\"]\nsplit_out = [\"d\",\"e\",\"f\",\"j\",\"k\",\"l\"]\n\nopt_in_split_counts =  [ph[\"projects\"].get(planet_hunter_proj_id,{}).get(\"classification_count\",0)  for ph in planet_hunter_users.find({\"projects.5333063b3ae740228a000001.splits.mini_course_sup_tutorial\" : {\"$in\" : split_in}})]\nopt_out_split_counts =  [ph[\"projects\"].get(planet_hunter_proj_id,{}).get(\"classification_count\",0)  for ph in planet_hunter_users.find({\"projects.5333063b3ae740228a000001.splits.mini_course_sup_tutorial\" : {\"$in\" : split_out}})]\n\nfig, axes = plt.subplots(1, 2, figsize=(24,8))\n\n\naxes[0].hist(opt_in_split_counts, log=True)\naxes[1].hist(opt_out_split_counts, log=True)\nprint \"opt in stats mean: \"+ str(mean(opt_in_split_counts))+ \" median \" + str(median(opt_in_split_counts)) + \" std \" + str(std(opt_in_split_counts))+\" total people \"+ str(len(opt_in_split_counts))\nprint \"opt out stats mean: \"+ str(mean(opt_out_split_counts))+ \" median \" + str(median(opt_out_split_counts)) + \" std \" + str(std(opt_out_split_counts))+ \" total people \"+ str(len(opt_out_split_counts))",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "opt in stats mean: 118.300794552 median 13.0 std 575.508779782 total people 881\nopt out stats mean: 166.900419287 median 11.0 std 1032.0872257 total people 954\n"
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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zAwMDsXTp0liyZEls2bIlIiL27dsXvb29ERHxtre9bZbLZWrKugvoWGVZ1l1C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\nQIb+H2la/NE3CzABAAAAAElFTkSuQmCC\n",
       "text": "<matplotlib.figure.Figure at 0x109996e50>"
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "no_opt_in_in  = planet_hunter_users.find({\"preferences.planet_hunter.course\" : \"yes\",\"projects.5333063b3ae740228a000001.splits.mini_course_sup_tutorial\" : {\"$in\" : split_in}}).count()\nno_opt_out_in = planet_hunter_users.find({\"preferences.planet_hunter.course\" : \"yes\",\"projects.5333063b3ae740228a000001.splits.mini_course_sup_tutorial\" : {\"$in\" : split_out}}).count()\nin_opt_in     = planet_hunter_users.find({\"projects.5333063b3ae740228a000001.splits.mini_course_sup_tutorial\" : {\"$in\" : split_in}}).count()\nin_opt_out     = planet_hunter_users.find({\"projects.5333063b3ae740228a000001.splits.mini_course_sup_tutorial\" : {\"$in\" : split_out}}).count()\nprint \"Farction of users who opt in in the opt in split \" + str(no_opt_in_in*100.0/in_opt_in)\nprint \"Farction of users who opt in in the opt out split \"+ str(no_opt_out_in*100.0/in_opt_out)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "Farction of users who opt in in the opt in split 13.5073779796\nFarction of users who opt in in the opt out split 90.9853249476\n"
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 10
    }
   ],
   "metadata": {}
  }
 ]
}