{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import os\n",
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from IPython.display import display\n",
"\n",
"import seaborn as sns\n",
"import statsmodels.api as sm\n",
"import statsmodels.formula.api as smf\n",
"from statsmodels.sandbox.stats.multicomp import multipletests\n",
"from scipy.stats import mannwhitneyu, chi2_contingency\n",
"\n",
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Import cleaned dataset."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"data_dir = os.path.join(os.getcwd(), 'Data')\n",
"output_dir = os.path.join(os.getcwd(), 'Figures')\n",
"\n",
"post_peer_review = pd.read_csv(os.path.join(data_dir, 'Peer_Reviewed_Papers_Updated.csv'),\n",
" index_col=[0, 1], parse_dates=[6, 7])\n",
"\n",
"# Turn this into a category to save memory.\n",
"post_peer_review['Decision_Type'] = post_peer_review['Decision_Type'].astype('category')\n",
"post_peer_review['Decision_Type'].cat.rename_categories(['Accept', 'Reject', 'Revise'],\n",
" inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"2747"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(np.unique(post_peer_review.index.get_level_values(0)))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This reproduces Figure 2 in the paper. "
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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DH/oQn/vc57j99tt5/etfTzab5YILLiCRSMyYqG/bto3BwUEuuOCC8cd6enpYuXIlr3vd\n61i9ejVtbW3sueeeHHzwwXOK/9hjj+WGG26gr6+Pc889lw984AOMjo6yePFiLrnkEgBe+cpXcuaZ\nZ/KJT3xil3Pf+ta3csEFF/DZz36W7du3c9FFFwHwwhe+kN/+9rcA7LPPPuPfHEz1/EceeWROcUqS\nJLWSxFQTIZtVCGE5kAGSMcb7Znl667wxkiRJqqUpq7RW1OvY1772NW688cbxCnsul2PJkiV89rOf\nndP5t956K1ddddUu5ycSCa644oppJ5lKkiSpPlhRn17rvDGSJEmqpSkr6s7gkyRJkuqQibokSZJU\nh0zUJUmSpDrkZNIa+vQnPs7w8HDZXm/p0qW8/exzyvZ6kiRJqh0T9RoaHh7mtBelyvZ6vXemZ33O\nAw88wHHHHcdzn/vc8VVgXvSiF3HGGWc86bnnn38+L3/5yzn00EPLFqMkSZLmxkS9BT3nOc9h7dq1\ntQ5DkiRJMzBRb0GTl+TcuXMn73vf+/i///s/Hn74YY488kjOPvvs8eP33Xcf559/Prvvvju5XI5L\nL72Upz/96Vx22WX88Ic/ZMeOHbzpTW/i6KOPrva/iiRJUtMyUW9Bv/zlLznllFPGW1/+8R//kRe8\n4AW85jWvIZvNcthhh+2SqN9xxx08//nP59xzz+UHP/gBw8PDxBh54IEHuPbaa8lmsxx//PEceuih\n7LnnnjX8N5MkSWoeJuotaHLry2OPPcYNN9zAhg0bWLJkCdu2bdvl+a997Wu58sorOfXUU9lrr704\n55xz+PnPf8699947nvDv2LGD+++/n/3337/a/zqSJElNyeUZW9Dk1pcbbriBZcuWcckll/DmN7+Z\n0dHRXY739/dz8MEHc/XVV/Oyl72Mq666ilQqxYoVK1i7di1r167l6KOPZr/99qvmv4YkSVJTs6Je\nQ0uXLp3TSi3FvN5cJBK77lJ7yCGH8K53vYt77rmHhQsXsnz5ch566KHx4wceeCDnnXcel19+OTt3\n7uSCCy7ggAMOYMOGDZx00kmMjIzQ3d3NHnvsUbZ/F0mSpFaXmFxdbWYhhOVABkjGGO+b5emt88ZI\nkiSplhJTPWjriyRJklSHTNQlSZKkOmSiLkmSJNUhE3VJkiSpDpmoS5IkSXXI5Rlr6BOf/BSPDg+X\n7fX2WrqUs896R9leT5IkSbVjol5Djw4Pc+Axryvb6/345n+f9Tkf+chHuPfee/n973/P6Ogoz3zm\nM9l77735+Mc/XrY4JEmSNH8m6i3mvPPOA/K7kWYyGd75znfWOCJJkiRNxR51cdddd3H88cfzhje8\ngRtvvJEjjzySbDYLwKWXXsq6desAuOyyyzjppJM44YQTuOWWW2oZsiRJUtOzoi4AstksX/3qVwH4\n5Cc/+aTjt912G/fffz/XXnst2WyW448/nkMPPZQ999yz2qFKkiS1BBN1AZBMJsfHuVzuSeOf//zn\n/OQnP+GUU04hl8uxY8cO7r//fvbff/+qxypJktQKbH0RAAsWPPG/QltbGw8//DC5XI5NmzYBkEql\nWLFiBWvXrmXt2rUcffTR7LfffrUKV5IkqelZUa+hvZYundNKLcW8XjmceuqpnHbaaTzjGc/gKU95\nCgBHHHEEGzZs4KSTTmJkZITu7m722GOPslxPkiRJT5aY2ObQ7EIIy4EMkIwx3jfL01vnjZEkSVIt\nJaZ60NYXSZIkqQ6ZqEuSJEl1yERdkiRJqkMm6pIkSVIdMlFXXRocHGRwcLDWYUiSJNWMyzOqLg0M\nDADQ2dlZ40gkSZJqw4q66s7g4CCjo6OMjo5aVZckSS3LRF11p1BNnzyWJElqJSbqkiRJUh0yUVfd\n6erqmnIsSZLUSkzUVXc6Oztpa2ujra3NyaSSJKllueqL6pKVdEmS1OpM1FWXrKRLkqRWZ+uLJEmS\nVIdM1CVJkqQ6ZKIuSZIk1SETdUmSJKkOmahLkiRJdchEXZIkSapDJuqSJElSHTJRlyRJkuqQibok\nSZJUh0zUJUmSpDpkoi5JkiTVIRN1SZIkqQ6ZqEuSJEl1yERdkiRJqkMm6pIkSVIdMlGXJEmS6pCJ\nuiRJklSHTNQlSZKkOmSiLkmSJNUhE3VJkiSpDpmoS5IkSXXIRF2SJEmqQybqkiRJUh0yUZckSZLq\nkIm6JEmSVIdM1CVJkqQ6ZKIuSZIk1aHdax3AZCGE3YFrgOXAduA0YAdwNbATuDfGeGat4lN1pNNp\nAFKpVI0jkSRJqo16rKj3ALvFGF8MfAj4F+Ay4IIY4+HAghDCK2oZoCqvv7+f/v7+WochSZJUM/WY\nqP8c2D2EkACWAduAg2KMg2PHbwa6axWcKi+dTpPJZMhkMuOVdUmSpFZTj4n6Y0AS+BlwBfBJIDHh\n+DD5BF5NamIl3aq6JElqVXXXow78I3BLjPE9IYS/AP4LWDTh+FJg82wvEkK4EHh/JQKUJKkWnL8j\ntZZ6rKj/ARgaG28m/8vEj0IIh489dgwwONWJE8UYL4wxJib+IV+pV53r7u6ecixJrc75O1JrqceK\n+seBz4cQbgMWAquBHwJXhRAWApuAr9UwPlVYKpUimUyOjyVJT8zfKYz9fJSaX90l6jHGLcDrpjj0\nkiqHohqyki5Ju5o8f8dEXWp+9dj6IkmSJLU8E3XVJfswJWlXzt+RWk/dtb5I9mFK0pM5f0dqPSbq\nqjv2YUrS1KykS63FRF2SpAZh4UJqLfaoq+7YhylJkmRFXXXIPkxJkiQTddUpK+mSJKnVmairLllJ\nlyRJrc4edUmSJKkOmahLkiRJdchEXZIkSapDJuqSJElSHTJRlyRJkuqQibrUwNLpNOl0utZhSJKk\nCnB5RqmB9ff3Ay5nKUlSM7KiLjWodDpNJpMhk8lYVZckqQmZqEsNqlBNnzyWJEnNwURddWndunWs\nW7eu6te151tSPfMzSmotJuqqSxs2bGDDhg1Vv25/f3/DVKe7u7unHEtqXo30GSVp/kzUVXfWrVtH\nLpcjl8tVtapuz7ekeuZnlNR6TNRVdyZW0qtZVW+0nu9Gi1fS/HjPS63HRF2SJEmqQybqqjsrVqyY\nclxpjdbz3WjxSpof73mp9bjhkerOypUrx1teVq5cWbXrplIpksnk+LjepVIp2traxseSmlujfUZJ\nmj8TddWlalbSJ2qkKlU6nWZ0dHR87F/cUvNrpM8oSfNnoq66VM1K+kSNlOxOnljWSLFLKo33udRa\n7FFXXRocHGRwcLDWYUiSJNWMibrq0sDAAAMDA7UOo645sUySpOZmoq66Mzg4yOjoKKOjo1bVZ1CY\nWJZMJv06XJKkJmSPuurOxEr6wMAAnZ2dNYymvllJlySpeZmoSw3MSrokSc3L1hfVna6urinHqh9O\n9pUkqfJM1FV3Ojs7aWtro62tzbaXOuVkX0mSKs/WF9UlK+n1qzDZtzD2lylJkirDRF0109fXx8aN\nG6c8NjIyAsAdd9wx5fGOjg56enoqFpum52RfSZKqw9YX1aVsNks2m611GHUvnU6TTqdrHYYkSaoA\nK+qqmZ6enmmr4mvWrAFg9erV1Qyp4fT39wPVXf2lq6uL9evXj48lSVJlWFGXGlQ6nSaTyZDJZKyq\nS5LUhEzUpQZVqKZPHlfa5B51SZJUGSbqkiRJUh0yUZcaVHd395TjSnNDKkmSqsNEXWpQqVSKZDJJ\nMpms6mRSN6SSJKk6XPVFamDVrKRPZCVdkqTKM1GX6txcNoZqb2+f8nilNoayki5JUuXZ+iI1MDeG\nkiSpeVlRl+qcG0NJktSarKhLkiRJdajkinoIYQGwD/DHGKPfvUuSJEllVHSiHkL4S+AjwMuANuCl\nIQSADwPvijHeXtYIJUmSpBZUVOtLCOE5wF3AS4CbgcTYoR3A/sC3QwgvKmeAmr90Ok06na51GA3B\n92pufJ8kSaq8YivqHwYeBw4CdgKvBogx3hpC+CvgduD9wDHlDFLz09/fD1DVTXEale/V3Pg+SZJU\necVOJj0SuDzG+BCQm3ggxvgA8G/AwWWKTWWQTqfJZDJkMhkroLPwvZob3ydJkqqj2ER9MfDHGY5n\ngal3XlFNFCqfk8d6Mt+rufF9kiSpOopN1O8BjpvqQAhhd+ANwNRbKEqSJEmas2IT9Q8DR4UQvkS+\nDQZgeQjhOOC75HvXLy1jfJqn7u7uKcd6Mt+rufF9kiSpOoqaTBpjvCmEcCrwceDEsYd7ya/+Mkp+\necavlzdEzUcqlSKZTI6Ppfny/ylJkqqj6HXUY4xXhxC+DrwUeDawG3Af8O0Y4yPlDU/lYNVzbib3\nXpuETs//pyRJqrySdiaNMQ4DVs4bhAmnys3/pyRJqrxSdiY9mXw1/c+Zusc9F2Psmm9gUrV1d3fT\n29s7Ptb01q1bB8DKlStrHIkkSc2rqEQ9hHAxcD75ZRgfIr8jqdQU7L2euw0bNgAm6pIkVVKxFfU3\nAv8BvDrG+HgF4pFqykr67NatW0culxsfm6xLklQZxS7PuBfwdZN0NatUKmU1fRaFavrksSRJKq9i\nE/VbeGL9dEmSWlI6nSadTtc6DElNrtjWl3cA/SGEa4F15PvUc5OfFGO8rQyxSapDK1as4M477xwf\nS62osJyr38BJqqRiE/X9gGXkNzs6YYrjCfKJ+27zjEtSnTrwwAPHE/UDDzywxtFI1ZdOp8lkMuNj\nk3VJlVJsov4Z4CnAJcDPge1lj0hSXXNjKLU67wFJ1VJsov484MIY479WIhip1go9p/7FK0mSaq3Y\nyaS/AXZWIhCpHvT39+9SLdOTTVzC0uUs1Yq8ByRVS7GJ+r8C54QQ/qoSwUi1VOg7zWQyruYwgx//\n+MdTjqVWUdgcLZlM+u2bpIoqtvXlBeQni24MIaSB3/HkPvVcjLGrHMFJ1WTf6dxMXkfdDY/Uiqyk\nS6qGYhP1Y8kn5r8BFgHPLHtEkiTVOX+Rl1QNRSXqMcZkpQKRaq27u5ve3t7xsabmOuqSJFVHsT3q\nUtNKpVK0tbXR1tZmtWwGE1tdbHuRJKlyZqyohxC+A1wcYxyY8PNs7FFXQ0qn04yOjo6PTdanNjg4\nuMu4s7OzhtFIktS8Zmt9SQJLJvz8bPKTSaWm42TSuRkYGNhlbKIuSVJlzJioT+5JjzEur2g0kiRJ\nkoBZetRDCO8LITyvWsFIteQmJnPT1dU15ViSJJXXbK0vFwK/BO6tfChSbRU2MSmMJUmSaqnYddSl\npmYlfXb2qEuSVB0m6tIEVtIlSVK9mEuivn8I4bBiXjTGeFuJ8QAQQlgNHAcsBP4NuA24GtgJ3Btj\nPHM+ry+pdF1dXaxfv358LEmSKmMuGx69B/hukX9KFkI4HDgkxvh3wEuA/YDLgAtijIcDC0IIr5jP\nNSSVbmKri20vkiRVzlwq6uuAjZUOZIKXAfeGENYBS4F/Av4hxljYZeVm4CjgxirGJGmMGx5JklQd\nc0nUvx5j/HLFI3nCU8lX0Y8lv8HSN9m18j8MLKtiPGoifX19bNw4/e+dIyMjALS3t095vKOjg56e\nnorE1iicTCpJUnXU42TSR4BNMcbtwM9DCKPAMyYcXwpsnu1FQggXAu+vSIRqWtlsFpg+UZckSaqW\nekzUbwfOAj4WQtgXWAIMhBAOjzHeChwDfGe2F4kxXkh+HfhxIYTlQKbM8aqB9PT0zFgRX7NmDQCr\nV6+uVkgNx8mkkiRVx2yJ+q3A76oRSEGMcX0IoTOEcBeQAFYB9wFXhRAWApuAr1UzJklPeOSRR6Yc\nS5Kk8poxUY8xHjGfFw8hnASsjTHuVsx5McapypkvmU8skspjw4YNu4xXrlxZw2gkSWpec1meUZIk\nSVKVmahLKsqKFSumHNe7dDpNOp2udRiSJM2ZibqkoqxcuZJEIkEikWiotpf+/n76+/trHYYkSXNW\nj6u+SKpzjVRJh3w1PZPJjI9TqVSNI5IkaXYm6pKK1kiVdGCXSnp/f7+JuiSpIdj6IkmSJNUhE3VJ\nTa+7u3vKsSRJ9czWF0lNL5VKkUwmx8eSJDUCE3VJU+rr62Pjxo1THhsZGQGgvb19yuMdHR309PRU\nLLZSWEmXJDWaSifqG4EPVvgakqosm80C0yfq9chKuiSp0RSdqIcQ/gR4FfBnwG5TPCUXY/wQQIzx\nx8CP5xUhKR9OAAAgAElEQVShpJro6emZtiq+Zs0aAFavXl3NkCRJailFJeohhJcANwHtQGKap+WA\nD80vLEmSJKm1FVtRXwNsAd4C3ANsLXtEkiRJkopO1J8P/HOM8auVCEaSJElSXrHrqP8e2FaJQCRJ\nkiQ9odhE/RrgtBBCWyWCkSRJkpRXbOvLz4A9gZ+FENYDDwM7Jz1nfNUXSZIkSaUpNlFfO2G8aprn\nuOqLJEmSNE/FJurJikQhSZIkaRdFJeoxxv8tjEMIC4CnAtkY4+ZyByZJUr1Kp9OAO95KqqxSdibd\nD/hX4OXAHmOPbSG/EdL5E5N5SZKaUX9/P2CiLqmyit2Z9FnAXeQr6f8JbCK/csz+wPFAVwjh4Bjj\nb8odqCRJ9SCdTpPJZMbHJuuSKqXYivrF5Kvoh8QY75p4IIRwEPBd4APkdy6VJNWhvr4+Nm7cOOWx\nkZERANrb26c83tHRQU9PT8ViawSFanphbKIuqVKKXUf9pcAnJyfpADHGu4FPA0eXIzBJanTpdHq8\nl7lRZLNZstlsrcOQJFF8RX0p8OAMxx8E/qT0cCSpedRrH3NPT8+0VfE1a9YAsHr16mqG1FC6u7vp\n7e0dH0tSpRRbUf8ZcNwMx1cCPy89HElqDoU+5kwm03BVdUlSfSi2ov5poDeEcD2whieS8v2B84Aj\ngTPLF54kNSb7mJuX/20lVUux66h/LoSwP/BO4FWTDifI969/tlzBSZIkSa2q2NYXYoznAs8Dzgeu\nAK4ELgCeF2M8p7zhSVJjmti7bB9zc/G/raRqKXrDI4AY4ybya6hLkqaQSqVoa2sbH6t5pFIpksnk\n+FiSKmXGRD2E8D7gGzHGeyf8PJtcjPFD5QhOkhpVOp1mdHR0fGxC11yspEuqhtkq6hcCvwTunfDz\nbHKAibqkluaEw+bmf09J1TBbop4EHp70syRJkqQKmzFRjzH+70w/S5Km5qY4kqT5KnoyaQhhOXBQ\njPEbYz+fALwL2EF+ecYvlzVCSWpATjiUJM1XUYl6COHvgG8Dvwa+EULoAL4E/BHYDHwxhLAtxnh9\n2SOVpAZjJf0JfX19bNy4cdrjIyMjALS3t095vKOjg56enorENp2ZYp4tXqhNzJKaS7HrqF8I/B/w\nmrGfTyW/0VEn8P/IJ/HvKldwktTIUqmU1fQ5ymazZLPZWocxZ40Wr6TGVGzry98CF8YYfzL287HA\nxhjjzwBCCDcAl5YxPklSE+jp6ZmxurxmzRoAVq9eXa2QZjVTzPUYr6TmU2xFfQGwBSCEsD/5VWBu\nnnC8DRgtT2iSJElS6yo2Uf8ZUCgvnEF+zfR1ACGEPYA3Aj8tW3SSJElSiyq29eUjwFdCCJuBvYBb\nY4x3hRAOBr4J7AMcV+YYJUmSpJZTVEU9xvh1oBu4FngPTyTlQ8D/AD0xxpunOV2SWko6nSadTtc6\nDElSgyp6HfUY463ArZMe+wVwTLmCkqRm0N/fD7iOuiSpNDMm6iGEw4BNMcaHJ/w8qxjjbWWITZIa\nVjqdJpPJjI9N1iVJxZqtov5fwBuAL0/4OTfD8xNjx3ebb2CS1MgK1fTC2ERdklSs2RL1NwPfn/Sz\nJEnSnBTmafjLqlS8GRP1GOM1M/1cEELYB/h9jHGmarsktYzu7m56e3vHx1Krcq6GVLpi11EnhPD2\nEMIDIYRnT3j4EuB3IYRTyheaJDWuVCpFMpkkmUyaoKhlFeZqZDIZV0CSSlBUoh5COBX4JPBLYNuE\nQ18B7gG+EEJ4VfnCk6TG1d3dbTVdLW3yXA1JxSl2ecazgRtjjK+c+GCM8RbglhDCN4HzgW+UKT5J\nalhW0iVJ81Fs60sK6Jvh+E3AAaWHI0mSmsXEb5T8dkkqXrEV9d8DHTMcD+R3KZUkSS2uMFejMJZU\nnGIT9W8AZ4YQbo8x/vvEAyGEVwBnAJ8rV3CSJKmxWUmXSldsov4+4AjgyyGETwBp8hscPRt4OnAv\n8N6yRihJkhqWlXSpdEX1qMcYh4G/Ad4O/DewDPhT4MfAO4EVMcbN5Q5SkiTllzt0mUOpdRRbUSfG\nuA24fOyPJEmqEjcPklpL0Yk6QAjhQOBY4Jnk11XfAjwvxnhzGWOTJEljCpsHFcYm61LzK2Vn0k+R\n39zoYuCtwL7A3wLrQwjfCiG0lTdESZLk5kFS6yl2Z9KzgDOBNcCLgMTYoQHgY8DLgfPKGaAkSZLU\nioqtqL8NuD7G+B7yK74AEGPcHGN8F/Al4MQyxidJknDzIKkVFZuoPxv4zgzHB4H9Sg9HkiRNJZVK\n0dbWRltbm/3pUosoNlH/PfCMGY4/F/hD6eFIkqSppNNpRkdHGR0ddYlGqUUUm6jfQH5n0r+a8FgO\nIIRwDPnJpd8qU2ySJGmMk0ml1lNsov7PwAPAD4FbyCfpF4YQ7gZuAh4kv3upJEmSpHkodmfSzcAK\n4CPAYmB07Oc9gEuBg2OMD5c7SEmSWp2TSaXWU8rOpI8DF479kSRJVZBKpUgmk+NjSc2vqEQ9hPBU\n4PnAn5Jve3kY2BhjdAKpnuTyyy9naGiopHML561Zs6boc5ctW8aqVatKum6tlPpezed9gsZ8r6RW\nZiVdai1zStRDCK8BVgMvnOJwLoRwB/CvMcb15QxOjW1oaIihoc0sXbyw6HMXLsjvpbVzdEtR5w1v\n3Vb0tepBqe9Vqe8TNO57JbUyK+lSa5k1UQ8hfIb8RkebgS8C9wJ/BBaSr6wfBLwU+GYI4eNjGx9J\nACxdvJDTDz2gate78vZNVbtWufleNZ/CEnomV5KkUsyYqIcQTgJWkd9x9IwY42PTPG9P4BPAOSGE\n78UYv172SCWpwRSW0DNRlySVYraK+unAnTHGU2Z60lgCf+rY+uqnAybqklpaOp0mk8mMj03WJUnF\nmm15xueR3+RortYxdR+7JLUUN6eRJM3XbIn6XsBDRbze/wF/Uno4klQZ6XTabdclSQ1ltkR9N6CY\npSG2z+E1Janq+vv7q1rZdnMaSdJ8Fb3hkSQ1mlr0i7s5jSRpvuaSqL8qhPCXc3y9jvkEI0mVMLlf\nvFqJs5V0laKvr4+NGzdOeWxkZASA9vb2KY93dHTQ09NTsdgkVdecEvWxP3OVKzEWSWoqVtJVbtls\nFpg+UZfUXGZL1I+oShSSVEHd3d309vaOj6vFDY9Uip6enmmr4mvWrAFg9erV1QxpXrwPpNLNmKjH\nGG+dz4uHEA4EXhlj/OB8XkeS5qNW/eJueCR5H0jzUenJpB3A+wETdUk1Ve1+cTc8krwPpPlyKUVJ\nLSGVStWkmj55LLUS7wNpfkzUJbWEwcFBBgcHax2GJElzVreJegjhaSGEX4cQ/l8IIRVCGAwh3BpC\n+EytY5PUeAYGBhgYGKja9dzwSPI+kOarLhP1EMLuwGeBx8ceugy4IMZ4OLAghPCKmgUnqeEMDg4y\nOjrK6OioVXWpigoTuZPJpP3pUgnqdWfSjwKXA+cDCeCgGGPhb9ebgaOAG2sUm6QGM7GSPjAwQGdn\nZ8WvWatNlmrp8ssvZ2hoqKRzC+cVlh8sxrJly1i1alVJ11XlWUmXSld3iXoI4U3AQzHGb4cQLhh7\neGLlfxhYNofXuZD8ijOSpCoYGhpi89AQi9qXFH1uYrf8X0ePZ7cXdV52ZEvR11J1tcIvqVKl1F2i\nDrwZ2BlCOAp4PrAW2GfC8aXA5tleJMZ4IXDhxMdCCMuBTJnilNQgurq6WL9+/fi4Gmq1yVKtLWpf\nwkHHnli1691903VVu5YkVVvd9ajHGA+PMR4RYzwCuAc4Gbg5hHDY2FOOAWwylTRnE1tdqtH2Avbm\nSpLmr9IV9cTYn/l6N9AbQlgIbAK+VobXlNQiJk4gHRwcrFqy3kqVdElS+ZWUqIcQnkp+Qud+wL8D\nW4Cnxhg3TXxejPFLwJdKDS7GeOSEH19S6utUU19fHxs3bpzy2MjICADt7e3Tnt/R0UFPT09FYpNa\nVS0mk4K9uZKk+Sm69SWE8C7g18C1wL8Azwb+Drg3hPCZEEI5KuhNKZvNks1max2GJEmSGkBRFfUQ\nwuuBS4DrgG8A148duhu4AXgbEIFPljHGhtLT0zNtRbyw7Njq1aurGZLU8moxmVSSpPkqtqL+buDb\nMcaTgP8qPBhj/E2M8TVAH3Ba+cKTpPnr7Oykra2Ntra2qrW9SJI0X8X2qB8AXDXD8W8BHys9HJVq\nPr3xzdYXPzw8XNKmKeCmK83MSrokqdEUm6gPA0+Z4fizgMdKD0eVUOiLn2kSazPZuXOnm67oSayk\nS5IaTbGJ+i3AGSGEq4AdEw+EEDqAM4GbyhSbimBv/K7cdEX1IJ1OA67+ItWC95+aQbGJ+vnAXcBP\ngNuAHPDWEMLbgZcDjwLvK2uEktSg+vv7ARMFqRa8/9QMippMGmN8ADgYWA8cSX4zo9cCLwVuBFbE\nGH9V7iAlqdGk02kymQyZTGa8siepOrz/1CxmrKiHEL4IXBVjvHXs5/2Ah2OMbxpbL/2pwG5jj+2Y\n4aUk1ZktW7ezc+tQSRNn5zPpFlpj4m2hmlcYW9WTqsf7T81ittaX1wC3jv0ByABvAK6LMeaAhysY\nm6QK2pnLkUskip44C6VPuoXaTby1X1WS1GhmS9R/C7wzhLCI/GouCeCwEMLCmU6KMa4tU3ySKqja\nk26hdhNvq92v2t3dTW9v7/hYUvV4/6lZzJaovxf4PPDpsZ9zwFvH/kwnB5ioS6obhX7VwrgayXoq\nlSKZTI6PJVWP95+axYyJeozxyyGEW4AALAa+A1wM9M90niTVk1r1q1rJk2rH+0/NYNblGWOMfwC+\nDxBCuAa4Kca4odKBSVKje/DBBwErepKk0hS7POObTdIlNZqJlbVqVtkGBgYYGBio2vUkPaG/v3+X\nb9OkRjTb8ow7gJNjjF8e+3kn+R70meRijMVupCRJTWVwcJDR0dHxcWdnZ40jklpHLealSJUwW0K9\nFkhP+nm2RF0CYGRkhOzWbVx5+6aqXXN4dFtD/g9ai/cqB2zPbq3a9WqpFj3qEyvpAwMDJupSFbmO\nuprFbJNJ3zzp5zdVNBpJkiRJwBwmkxYjhPASoCPG+Mlyvq4aU3t7O4sTOzn90AOqds0rb9/E8Nbi\nN+GptVq8V5f2b2T3RYurdr1aqsWayl1dXaxfv358LKl6XEddzaKoyaRz8DrgY2V+TUmal1QqRVtb\nG21tbVX7Cryzs5NEIkEikbDtRaqywjrqyWTSthc1NCd9Smp66XR6fGJntSaWpdNpcrlcVa8p6QlW\n0tUMyl1Rl6S6M3liWbNeU9ITUqmUvyCr4ZmoS5IkSXXIRF1S06vFhke12mRJktQ8ZtvwaL8iX2/p\nPGKRJEmSNGa2yaT3UdwGR4kiny9JFVeLzU/ccEWSNF9z2ZnUxFuSJEmqstl2Jn3TdMdCCH8O7AdE\nYATYFmPcWdboJKkMarH5iRuuSJLmq+jJpCGEF4cQfgjcD9wBHAQcDvw6hHB8meOTpHmrxYZHtbim\nJKm5FJWohxD+BugnP2n04xPO/wOwDfhyCOGYskYoSfNU2PBodHSUdDrdtNeUJDWXYivqFwEZ4PnA\nhwsPxhj/e+yxTcAFZYtOksrADY8kSY2o2ET9EOALMcYRJk0yjTE+ClwJPK9MsUmSJEktq5QNj7bO\ncKytxNeUpIpxwyNJUiMqNqneALx+qgMhhCXAPwA/mG9QklRODz744JRjSZLqWbGJ+vuAF4YQbgXe\nSL79ZUUI4Szgf4BnAxeXN0RJmp+BgYEpx5Vkj7okab6KStRjjN8HjgWeAXyU/E6kF5NfAaYdOCHG\n+N1yBylJkiS1mqL7yWOM3wb+EjgYeB35Vpi/A54VY/x6ecOTpPnr6uqaclxJ9qhLeel02iVKpRLN\nuDPpdGKMOeDusT+SVNc6OzvHW146Ozurcs1UKkUymRwfS62q0PrlfSAVr6REXZIaTbUq6RNZSVer\nS6fTZDKZ8bHJulQcE3WpReWA7MgW7r7puqpeNzuyhcSORVW9JlSvkj6RSYla3eRJ1d4TUnFM1CWp\nQgYHB4Hq/5Jw+eWXMzQ0VNK5hfPWrFlT0rkL25eUdF1J0pOZqKvp5HK5qleKa1Ulno8EsLB9CQcd\ne2JVr3v3TdfRvqg1Pnqq3RdfMDQ0xNDQZpYuXlj0uQsXJADYObql6HNzudzsT1JL6e7upre3d3ws\nqTit8belJFXZ4OAgo6Oj4+NqJ+tLFy/k9EMPqOo1L+3fWNXrqf45qVqaHxN1NZ1EIlH1SnErVYnr\nVV9fHxs3Tp8ojoyMANDe3j7l8Y6ODnp6esoWz+RNlmrRI19ttZj30IjfZrUaK+lS6cwsJLWEbDYL\nTJ+oS6oMK+lS6UzUJTWFnp6eGSvihcmRq1evrko8XV1drF+/fnzcCmox78FvsyQ1s6J3JpUkzW5i\nq0srtL1IksrPRF2SKqCwNOPksSRJc2WiLkkVMHkyqSRJxTJRlyRJkuqQibokVcDECaStMplUklRe\nJuqSVAGPPPLIlGNJkubKNa1KcPnllzM0NFT0eYVzCsvEFWvZsmWsWrWqpHNV/4a3buPK2zcVdc7o\nth0AtC3crejrudl7ZW3YsGGX8cqVK2sYjabj57mkemaiXoKhoSGGhjazdPHCos5buCABwM7RLUVf\nc3jrtqLPUeNYtmxZSedt25pPFvZoW1L0uYmtxScnUrPx81xSPTNRL9HSxQs5/dADqna9Yiutaiyl\nVtbms4nPmjVreDy7vaTranYrVqzgzjvvHB+rfvl5Lqle2aMuSRUwsdXFthdJUilM1CWpAtLp9JRj\nSZLmytYXVVQpEySh9EmS+d7PRNHXk8qtv79/l3EqlaphNJKkRmSiroopdYIklD5JclkbDA8Pl3xd\nSVJzKHyT5S/JamQm6qqY+Sw95iRJNbru7m56e3vHx5Kqq/Ctlom6GpmJegMZHh4uac1e1/uVqu/H\nP/7xLuNWSRayI1u4+6brij5ve3YrALsvWlz09fZYVPq3d2pO6XSaTCYzPm6V+0/Nx0S9gezcuZPN\nQ0Msai+uHSSxW/4/cylV5uxI8WsES2rNDY8WJBLsBPZYVPxfLUNjnzV7LCru822PRcvm1Wan5uQc\nETULE/UGs6h9CQcde2LVrldKZUxSa1qyeHcWtC0puWUNSmt3k6Rm5fKMklQBEzc5csMjqbomzgtx\njojKIZ1O12SpXSvqklQBK1euHN+ZtBXaXqR6kkqlSCaT42Npvmo1OdlEXZIqYHBwcJdxZ2dnDaOR\nWo+VdJVLLScnm6iXYGRkhGyJG/mUanh0G7mqXU3SfA0MDOwyNlGXqstKusqllpOT7VGXJElNp1Y9\nxVI5WVEvQXt7O4sTOzn90AOqds0rb9/E8FY38ZEaRVdXF+vXrx8fS6ouNzxSudRyAzsr6pJUAZ2d\nnbS1tdHW1mbbi1RlhZ7iTCZjVV3zVpicnEwmnUwqlYO7I85Ntd+nwjUb8b2aTl9fHxs3bpzy2LZt\n24CZdwXu6Oigp6enIrFJrcoNj1RutZqcbKKupjOfXQpbaXfEWrxP+XMa770q1c6dO2sdgiSpDGr1\ny56JuprOqlWrSj63lXZH9H0qj56enmkr4r5PUm3UsqdYKicTdUmS1FTc8EjNwkRdkiQ1HSvpjWum\nuT+Q388G8qvwTaWZ5v6YqEuSpKZjJb15ZbNZYPpEvZmYqEuSpIopLI9Y7cS5VtfV/M009wdaa/6P\nibokSaqYWm085IZHagZueCRJkiqiVhsPueGRmoUVdUmSqmx4eHjGjbCmMzQ0BMy8idZMli1bNq+l\nWYtVq42H3PBIzcJEXZKkKtu5cyebh4ZY1F7cpmGJ3fJ/bT+e3V70NbNjG5VJahx1l6iHEHYHPg8s\nBxYBFwM/Ba4GdgL3xhjPrFV8kiSVw6L2JRx07IlVu97dN11XtWsV1GrjITc8UrOoxx71NwC/jzEe\nBhwNfBq4DLggxng4sCCE8IpaBihJkmZX2HgomUxWtf2kVteVyq3uKurAV4Hrx8a7AduBg2KMg2OP\n3QwcBdxYg9gkSVIRalXRtpKuZlB3iXqM8XGAEMJS8gn7e4CPTnjKMLBsttcJIVwIvL8CIUqSpDmq\nVUXbSrqaQT22vhBCeCbwHeCaGONXyPemFywFNs/2GjHGC2OMiYl/gGRlIpYkSZLKq+4S9RDC04H/\nAP4pxnjN2MM/CiEcNjY+Bhic8mRJkiSpSdRd6wtwPvAU4J9DCO8DcsDZwKdCCAuBTcDXahifJEmS\nVHF1l6jHGM8Bzpni0EuqHIokSZJUM3XX+iJJkppHOp0mnU7XOgypIdVdRV2SND8jIyNkt27jyts3\nVfW6w6PbWJQbqeo1Vf/6+/sBV2GRSmFFXZIkVUQ6nSaTyZDJZKyqSyWwot5Acrkc2ZEtVd0GOjuy\nhcSORVW7nqT5a29vZ3FiJ6cfekBVr3vl7ZtY0NZe1WvOVy2+fRge3UaualerrUI1vTC2qi4Vx4q6\nJEmSVIesqDeQRCLBwvYlHHTsiVW75t03XUf7Iv83kdScavHtw5W3b2J46/aqXa+Wuru76e3tHR9X\nU6HVxiq+GpkZmCRJqohUKkVbW9v4uJqcxKpmYOuLJEmqiHQ6zejoKKOjo1WdTOokVjULK+qSJKki\najWZtJaTWPv6+hgcHJzyWC43v2nEiURi2mOdnZ309PTM6/VbXV9fHxs3bpzy2MhIfunZ9vbpJ8x3\ndHSU/b+BFXVJkiRpBtlslmw2W/XrWlGXJEkVUavJpLWcxNrT02Nlu0HN9N9uzZo1AKxevbqaIVlR\nlyRJlfHggw9OOa60VCpFMpkkmUw6mVQNzYp6gyllw6Pt2a0A7L5ocUnX22PRsqLPkyRpYGBgl3Fn\nZ2fVrl3tSrpUCSbqDWTBggUsXbq06POGRrYAsMeiJUWfu8eiZSxbZqIuSWosVtLVDEzUG8jSpUtL\n6o2qVV+VJKm1dXV1sX79+vFxNRVWXqlmFV8qN3vUJUlSRXR2dpJIJEgkElVPmAcGBnZpvZEakYm6\nJEmqiHQ6TS6XI5fLVXXjocHBwfGNlqZb01xqBCbqkiSpIiZvPFQtkyexSo3KRF2SJEmqQybqkiSp\nIiYukVjN5RInTlyt9iRWqZxM1CVJUlPZd999pxxLjcblGSVJFdfX18fGjRunPT40NAQ8sZzsZB0d\nHW7L3oAm96hXa23zWl1XKjcTdUlSzS1atKjWIUiqossvv3z8F/RizfaL/UyWLVvGqlWrSrpuLZio\nS5Iqrqenx4p4C+ru7qa3t3d83OzX1dwNDQ0xNLSZpYsXFn3uwgUJAHaObinqvOGt24q+Vq2ZqEuS\npIpIpVIkEonxcbNfV8VZunghpx96QNWud+Xtm6p2rXIxUZfUMPyqVGosg4OD5HK58XG1diet1XWl\ncjNRl9Qw/KpUzSKXy5Ed2cLdN11XtWtmR7aQ2FHduQCTNx6qVsJcq+tK5WaiLqmh+FWpJKlVmKhL\nklRliUSChe1LOOjYE6t2zbtvuo72RdX9a7+rq4v169ePj5v9ulK5ueGRJElqKm54pGZhoi5Jkipi\ncq94tUze8EhqVCbqkiRJUh0yUZckSRUxsT+8mr3iEzc5csMjNTInk0pSExreuq2kFWtGt+0AoG3h\nbiVdc1lb0aepiXV2do63vFRzicRUKkVbW9v4WGpUJuqS1GSWLVtW8rnbtuY3htqjbUnx122b37XV\nnGqx6ko6nWZ0dHR8bLKuRmWiXqJSqlVWqiRVw3x2US3s3Lp69epyhaMWV4vNhiZPJjVRV6MyUS9B\nqRUjK1WSJEmaKxP1EpRarbJSJUlS5XV3d9Pb2zs+lhqVq75IkqSm8uCDD045lhqNFXVJmsXw8PD4\nN2LFGBrKt7uVci7k293m02+uuanFnCNIFH2e5m7yRku16JOXysFEXZJmsXPnTjYPDbGovbj5JYnd\n8h+xj2e3F33N7MiWos9R8Wo152h4eLik60pqLSbqkjQHi9qXcNCxJ1btenffdF3VrtXKajXnaM2a\nNSX9Aqe56erqYv369eNjqaDRviE1UZckSU1l3333nXIsNdo3pCbqTaKvr4+NGzdOeWy23wI7Ojro\n6empWGySpOY2099BIyMjALS3t095vBJ/B7mOumbSSN+Qmqi3gEWLFtU6BElSi8pms8D0ibqk6Zmo\nN4menh6r4pKkmpjp76Ba7CHiOupqFibqkiSpqaRSKdra2sbHUqNywyNJktRU0uk0o6OjjI6Okk6n\nax2OVDIr6qoZJ8BKkirByaRqFibqqktOgJUkSa3ORF014wRYSVIlOJlUzcJEXZIkNZVUKkUymRwf\nS43KRL3M5tN3DfZeS6o854fUh+zIlqI3Qtme3QrA7osWl3S9PRYtK/q8ejXT/8cAW7bkd4P0/2U1\nMhP1KrLvWlK983OqOpYtKy1hHhrbinyPRcVtf54/Z1nJ121E27cXv9W7VG9M1MvMvmtJ9c7Pqdpb\ntWpVSefVYvOgejXb/8e+V2oGrqMuSZIk1SETdUmSJKkO2foiqWGMjIyQ3bqNK2/fVLVrDo9uI1e1\nq0mS9AQr6pIkSVIdsqIuqWG0t7ezOLGT0w89oGrXvPL2TQxvdfUISVL1WVGXJEmS6pAVdanOuTmN\nJEmtyURdamBuTiNJUvMyUZfqnJvTSJL+f3v3Hm7XfOdx/H1yv4ijLdUqEdcvVUwJMUoZU0+raFEM\nqu53M9S4DGMQfUwbU9SjKlodmZaWIVW3jFFKKYOahj46M74ZkdRl3DWnEXI9Z/74rp1sx9nnrLVP\nzl5r7fV5PU+e5OxLzi+/7M/v/Nb63aSa1FEXERGRfk2fPn3lVLusBpqi15/Ozs6mT3GVYtN2u+mo\noy4iIiL96urqoqtrARNGj8z83pHDOgDoXrwo0/sWLlmW+XuJtBt11EVEBtDT08PS9xYx++6bWvY9\nl763iI4VWoMgxTFh9MiWb40q7Uvb7aaj7RlFRERERApId9RFRAbQ0dHByLHj2W6fQ1v2PWfffRNj\nR+FsQXMAABOBSURBVKmJFhFZnco2Qqo76iIiIiIiBaTbNVIp/R0eBDpAqJ4OWhKRvC1cuLCp3WJA\nu81I38o2QqqOukgdHSCUjupJRFqhu7ubBV1djBo7PvN7O4ZHF+fdpdkWDy59L9vuNCJDSR11qRQd\nHpSe6kpEimBUDnc/RYpCc9RFRERERApIHXURERERkQLS1BcRERHpl457l3bSzPaMy5cuAWDEqNFN\nfb9xozozvw/UUReRklnYZGdh8bIVAIwZOTzz94OOzN9PRESKZ9iwYUyYMCHz+7qSRcbjRmVf2Dxu\nVCedneqoi0iba7ahA1i2JLZqGzcmWyPbOSa2iBOpMh33Lu1iwoQJnHvuuZnfV9vms5n3DoY66iJS\nGoPZ13gwjey0adMyb/EmIiIyWOqoi4ikUKY5jSLtomzHvUu5DeagPxiaw/7UURcRGUCzU27ymtMo\nIiKrV14H/ZWmo25mHcA1wLbAYuA4d38+31KJSBU0O+UmrzmNIu2ibMe9SzZ5bA7QOabx80U86K9M\nn8T9gNHuvrOZTQGuSB4TERERkRLJa3OAso1Ulqmjvgvw7wDu/oSZTc65PCIiIiLShLw2Byibjp6e\nchwnYGbXATPd/d7k6/nAxu7eneHvmATMAzZy9/kDvLwcFSMiQP+LgGDVQqBGd1OaXQSUZvFRf3dw\nhmLxkaweef3fDub7DtXnadq0aXR1LWDC6JF9Pr942QqWrUj94/h9Rg4f1ucUhtoZBj3AqLEfvHO6\nfOkSulc0vxvTsOEj+lzovfS9RazV2VmJTmBR5dWe56zPAzvK1FG/HHjM3WcmX7/g7hP7ef1U4KIG\nT6fpqIuIiIiI5KZMU18eBfYBZprZTsAz/b3Y3acCU+sfM7MRwPrAS0NTRBERERGR1aNMd9Rru75s\nkzx0tLvPybFIIiIiIiJDpjQddRERERGRKhmWdwFEREREROSD1FEXERERESkgddRFRERERApIHXUR\nERERkQJSR11EREREpIDKtI96pdXtAS+yur3k7s0f79fmlD0ZQsreAJQ/GSKlyZ466uWxPjAv70JI\nW9oImJ93IQpM2ZOhouwNTPmToVCa7KmjXh6101Q3yrUU5TEP1VUa89BJvQNR9rJR9tJR9tJR/tJT\n9tIpVfZ04FGJmFmPu3fkXY4yUF2lo3pKR/WUnuoqHdVTeqqrdFRP6ZStnrSYVERERESkgNRRFxER\nEREpIHXURUREREQKSB31crk47wKUiOoqHdVTOqqn9FRX6aie0lNdpaN6SqdU9aTFpCIiIiIiBaQ7\n6iIiIiIiBaSOuoiIiIhIAamjLiIiIiJSQOqoi4iIiIgUkDrqIiIiIiIFpI66iIiIiEgBqaMuIiIi\nIlJA6qi3ETObaGab510OkapR9kTyoexJu1NHvb0cBnzfzMaZ2ZfMbGLeBSoiMxuddxmk7Sh7KSh7\nMgSUvRSUvfJSR729/AD4JDAHuApYlm9xisfMNiEa9trXY3IsjrQPZW8Ayp4MEWVvAMpeuamj3ibM\nbKS7vw38FFgPeB1Ynm+pCqmbuPtyhZldABxoZspBBqqv91P2UlP2Bkn19X7KXmrK3iDlWV8dPT09\neX1vWQ3MbJi7d9d9/UVgEfATwIFj3X1+TsUrJDPbBngSGAl80t2fzblIhWRmHe7eY2ZbA5sQF/ZP\nuvuLORetEJS97JS9dJS9/il72Sl76RQxe7qiKrH6xsrMtjWzzQB394eAycDmwPVmtmGe5SwCM+tI\nfh8OjCUaqyXASbqz0LeksfoK8G/APwCnAM8nPxQrTdlLT9nLTtlrTNlLT9nLrojZ039USSVXfbXG\n6lLgTuB+4DEzO8vdXwU+DWxGNFpTzOx0M5uUW6FzkjTstaGj8e7+BDAK+CxwHHB1rUGTVczMiDmf\nF7n7ZKLBGg5MMrNxyWsq14Yoe+kpe81R9vqm7KWn7DWniNmrXNDbRS2AZjYF+Gry6xDgQmCamV3u\n7m8Sdxg2Bm4nFpO8kE+J89GrYf8GMMvM3gaOAGYDXwKOBK6ue8+EPMpaQGOAF9z9+mQx0i+B6cBt\nRCM/qX74uSqUvXSUvUFR9vqg7KWj7A1K4bI3opXfTFYvMzsP2B54yN0fSR5+zMxeBW4zs9nu/pNk\nbtp2wCPu3t17fl87q2vYzwROTH6tAfzR3VcADyRDWrPMbF3iyvlOYEZORS6StYCJZrYdcCtwj7uf\nmtTTZ4g7M/NzLF9ulL2BKXuDouw1oOwNTNkblMJlTx31cnsJ+Edgrpmt7e5vmtlwd7/dzK4GDjWz\n2919IfAQxFy1JKiVkQxTfRq41N1v7/XcbsDzwOeAS4n5eze0vJA5s1ULaNYjGqrn3P0hM3sc+E/g\nZnc/IXn5u0Q9deVU3CJQ9lJQ9gam7GWm7KWg7A2sLNnT1JeS6D0nKrk7cAOwD7Ey+TiAusbobWC5\nuy+qf1/VGqvECGBbwGoPmNkoM+skhv72c/fHgc8Dn3f35RaLb9qWmXXYqoVGtcbqAOBXwIPAdDMb\nCXwXuJeYn7eVmW0LnA18GHgqn9K3lrI3KMpeL8peesreoCh7vZQ1e9qesQTs/avcTwQ2BbYCvufu\ns8xsH2LY6gJgFvB/xNXxy+5+TE7FzkWj4U0zO4uYl3eRuz9Y9/gM4F13P7XusY66RThtyWL/4WV1\nX+8K3AOcB9wNDHP3uWa2JrATcBRwADCX+AFwqLvPbnnBW0zZS0/ZS0fZS0fZS0/ZS6es2VNHvUTM\nbBqxAOQGYDRwNDDD3U83swOBW5KX/hz4GLBbcpXc9gGEDzTshwJbJk/dB/wBuB54FfiRu99nZmsQ\nDf0D7n5JHmXOg5mdARxOLLgaRhyGcQ6whbsfbWYfIj5n+wPjiL2JryKGURcCC9z99TzKnhdlr3/K\nXjrKXnbKXv+UvXTKnD111EvCzLYC/pU4yOEJM9sBeAL4IjGv6jkz2xu4i1VbC3VVYW5e77sJZvZP\nxE4A/wG8SWyvdDAxLPo3REP2DnHU9HhgO3evzGl2FivZh7v7nFrdJY3Y5cTw3slEw/Q7Yl7e/sAU\nd6/Uzgk1yl5jyl42yl42yl5jyl42Zc6e5qiXRw9xYMF/m9l+wAPAacSCh+vMbAd3n0UE9TTgdDNb\nqwKN1Tq9GqspwF8BB7r7IcSQKMBi4DHgGOAk4jCD60kaKzOrzMJqd5+bNFY7AM+Z2Xjge8QdqzOJ\n+XrHu/tRwOnAG8A6ORW3CJS9Pih72Sl7mSl7fVD2sitz9tRRL6C+FtAArxEfnDOJLZTOdverPfaM\n3QDYDcDdbyGuoqcCJ1sbH2hgZpcDNyd/rv07O4BX3P03dcOiJxEN+13Apu7+K3e/0N2vqy2gqdKd\nBQAz25z4IfgnoiEf4e5HAlu6+3Gs2n7qYuIHZSWOLlf20lH2mqfs9U3ZS0fZa15Zs6eOesH0mm92\ngpl9G7iGGIr5NXGww3R3vzZ5zVrAImBe8nWHu88kFkDc0eZz9C4FvpD8eY3k9+XAxmb2d8B1wDnu\n/gPiRLZNgA8cK93ud196M7ONgH8mhj/3JersKTMbnwwbHw08a2YPAMcCB1dhXqyyl4my1wRlr2/K\nXibKXhPKnD3NUS8oM/smseL4XuIK8A53v8PMbgD2IhqxRcDuxAKaye6+onaF3eYN1fuY2SHAjcAm\n7v4HM5sOHA9c6e5nJa9Zk5i7d667351faYvBYp/YF939IDPbkLgD00kMib5rZscDC4An3X1+jkVt\nOWUvPWUvO2WvMWUvPWUvu7JmTx31AjKzLYgP0Mnu/mjyWG3Pz/HE3rFfIK6m5xLzqpZVYQENvK8u\nar9vAPwLsV/sDsQw4GXE9krTiHl6hwIfBXasQh3V611fyWPbAD8FznP3u8xsM6LRHwfs7HFYSOUo\ne/1T9rJR9tJT9vqn7GXTTtnT1Jdi6iEWMYyqe6zDzHYE5gA3uvtewF+6+1FJYzWiCkFMhkhrV5fj\nzGyUu78IHAj8npiTt4JY5X4LcBFwAtFo7ZTcfWnrQx16SxqrnYi5mxOSh18i6mv35OvngK8CI4nj\npVceDFExyl4Dyl52yl4myl4Dyl527ZQ93VHPmX1wi6XhwEeI4apvuPuP655bC3gSOMndf1n3eFX2\ni62/Mj4b2BOYALxCbK+0ApgObEPcQXjZzNYljvxdkgR3RJUW0CSfpx7gx8DWxPy8rxMr3LcAHgX2\ndPeHk9dvDHQXadhvqCh76Sl72Sl7jSl76Sl72bVb9nRHPUdJAGsLaE41s2uID9Zy4EfANWa2Z91b\nhgHvER/AlarQWCVqR/9eSOwCMJPYkmt3YsupRcQikGeA2WY20d1fA5bVDYFVorGquyswzN273f1w\nYA9i265vAbcSDdYVwGFmNhbA3Z8vamO1Oil7mSl7KSl7/VP2MlP2UmrX7OmOegGY2SVE0H6WPPSQ\nu9+aLA45EvguscBhV2JocKcqDPfBygMvhgPPJI3O2sDtxDHSNyUN+kzgRGIHgGeJhv1m4g7Ddu7+\nSu87OO2sbm7ensARxGKZN4l5ea8lw4E7EcOj7wBrAlu5+0u5FTonyl5jyl52yl56yl5jyl527Zw9\nddRz0Gsoa2MicGe6+4O9XrcGsB+x5dRaxPyqY6uygMbMbgWmEIuHHNjb3d82syeIuXlbEVfIZ7v7\ntWb2M2JF99fN7BPAD4FNge3d/U/5/Ctaq/a5sDit7xZiSPQpYnuzDuBznpy0ZmYTiQZtP+Agd5+X\nU7FbRtlLR9nLTtnrn7KXjrKXXbtnT1NfWiiZa9d7yG4ZsSp7fN3rhpvZp4gG6mF3P4CYT3WEV2QB\njZk9AkwC/pqYWzaOOFUN4mp4FnH34LSkseoghk7HAbj7y8RQVzfwZy0tfIuZ2Xpm9nGIvXEtdkg4\nAfiOxzZdvwBGEz8YR5vZh5LXvkDU0S5laKwGQ9lLT9lLT9kbmLKXnrKXXpWyp456iyRXyTda7H1a\nbzRxStamtQeSxmge8DIxjLXycIIqzDdLGqsxwB7ufqfHwqKpxIEOGwJnEFfJc9x9Bqz8IfBxoP6A\ngk8Rw1+lCGMzzOx84H7gcTN7wMw+7O6LiFXsD5rZOsDTwC/c/e+Bk4HLk/d2uPsKd1+cV/lbQdlL\nT9lLT9kbmLKXnrKXXtWyNyLvAlSBma0HbEnU9/UWJ2DdSxxM8JyZXQVcbWavATfX3XlY1vvv8jZf\nQGNm9xEr2rdLrpLHJIGaQ6xy7yaGAy8k6uyp5OuJxJyzqXV/3SPAZzy2sWo7ZvYd4GvEccfjgcOA\nB4FtibsvFwHrAz8H/jZ5Ww+wHrT/ZwmUvSyUvfSUvYEpe+kpe+lVMXuao94iZnYZsD0xnDUV2JlY\nrX0lcBvw5eTP1wBLiS2FPgrs0O7DfTVmtinRMJ1HLJp5p+6564g62ovYG3YesZ3XKclLeoAL3X25\nVWArqqSxOgr4C3d/Onnss8AdxKEgfyQaqrHuvnHd+2YQdx2OJLajavsGQNkbmLKXnrKXnrI3MGUv\nvapmTx31IWarViLXhqK+6e6XmdkewDHAQcQQ4BnEiuQe4BPAW8ApVVlAU2NmuxBbdV1LNFqLzOwW\nYH/icII1iPqZQxzy8D/ADz22o6IKdWVxzPapwBR3f9bMRrv7kuQO1r3A0cSw377EzgnzgceII7e/\nDPy5u/9XLoVvIWUvG2VvYMpeOspeNsrewKqcPXXUW8CSLZLM7AJiJfux7j47ee4tonHaEPhfYq7Z\nQe7+VvJ8218l92ZmuwI3AJcSdxI2IK6i5xL19DHgK8AuwELgs+3eSNUki2deJu5CnefuS+qeO4lY\nJLMnsQvAa2ZmxPBfJ3Ewxrfc/fetL3k+lL1slL3GlL1slL1slL3Gqp49ddRbyMwmEyuRz3f36Wb2\nNBG4fQEjQrg1sR1Tt1Xk5LW+JMNZdxIHXezj7r9NHu99ol3tzk2V9ovdFZgBfB+Y4e5vmtk5wDRi\n6O8NYpHWk8Qdhd8SJ/7Nr/DnSdlLSdlrTNnLTtlLT9lrrMrZU0e9xczsYuAs4kM1HzjY3V9Pnhvp\n7suSP1cmgI1YHFBwK/Bt4CZ3fyN5vAOoP92ucnWVNFo3EgtqJhGr2k8GfkfceVmf2HN3MrHgZgcv\nyVZUQ0XZS0/Za0zZy07ZS0/Za6yq2VNHvcXMbEdiEc3LwH7u/krORSq0umBeCdzg7m/mXKTCSO6+\n3EbsoXuUu9/S6/na0PPaqjdlLytlrzFlLxtlLxtlr7EqZk/7qLeYu/8GuIfYVmkBxAcr10IVmLv/\nGjicOADilGRxkgDu/jCwN9AFrGuxdyyw8jNVuwp/K4fiFY6yl42y15iyl42yl42y11gVs6egtFAy\ndAVwCbHA4XyAqg1fZZU0WicS+6RW4kjktNz9CeBgYlj5a7VGy91XbkFV9vl5q4Oy1xxlrzFlLx1l\nrznKXmNVy5466i1U98F5nVjpvk4/L5c67n4/cGCygKZjwDdUSN3dl1OAE8zsIzkXqXCUveYpe40p\newNT9pqn7DVWpexpjnpOLI4Efqkq2yutLlXeEWAgyR7FVwK7u/vbeZenqJS95ih7jSl76Sh7zVH2\nGqtC9tRRz1kVDiqQ1jGzce7+bt7lKANlT1YnZS89ZU9Wp3bPnjrqIiIiIiIFpDnqIiIiIiIFpI66\niIiIiEgBqaMuIiIiIlJA6qiLiIiIiBSQOuoiIiIiIgWkjrqIiIiISAH9P4EVGn1xly8TAAAAAElF\nTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11a4f7470>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"def tweak_xaxis_labels(ax):\n",
" for tick in ax.get_xticklabels():\n",
" tick.set_fontsize(14)\n",
" tick.set_rotation(45)\n",
" ax.set_xlabel('')\n",
" \n",
"\n",
"fig1 = plt.figure(figsize=(12, 8), dpi=250)\n",
"sns.set(style=\"ticks\")\n",
"\n",
"# We do a separate boxplot per each round of revision.\n",
"for n_revision in range(3):\n",
" ax = fig1.add_subplot(1, 3, n_revision+1)\n",
" subset = post_peer_review[post_peer_review['Revision Number'] == n_revision]\n",
" sns.boxplot(x='Decision_Type', y=\"e_Life_Decision_Time\", hue=\"Editor_As_Reviewer\",\n",
" data=subset, palette=\"RdBu\", ax=ax)\n",
" ax.set_ylim(0, 100)\n",
" ax.set_title('Revision {}'.format(n_revision), fontsize=18)\n",
" \n",
" # Format the x and y axis across subplots to make the plot look pretty.\n",
" if n_revision == 0:\n",
" sns.despine(ax=ax, offset=10, trim=True)\n",
" tweak_xaxis_labels(ax)\n",
" ax.set_ylabel('eLife_Decision_Time', fontsize=18)\n",
" \n",
" if n_revision == 1:\n",
" sns.despine(ax=ax, left=True, offset=10, trim=True)\n",
" tweak_xaxis_labels(ax)\n",
" ax.yaxis.set_visible(False)\n",
" ax.legend_.remove()\n",
" \n",
" if n_revision == 2:\n",
" sns.despine(ax=ax, left=True, offset=10, trim=True)\n",
" tweak_xaxis_labels(ax)\n",
" ax.yaxis.set_visible(False)\n",
" ax.legend_.remove()\n",
" \n",
"fig1.subplots_adjust(wspace=0)\n",
"fig1.savefig(os.path.join(output_dir, 'Figure_1.png'),\n",
" dpi=250, bbox_inches='tight')"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Revision Number</th>\n",
" <th>Decision_Type</th>\n",
" <th>Editor_As_Reviewer</th>\n",
" <th>Manuscript_Number</th>\n",
" <th>Received Date</th>\n",
" <th>Decision Date</th>\n",
" <th>e_Life_Decision_Time</th>\n",
" <th>Total_Time</th>\n",
" <th>Author_Time</th>\n",
" </tr>\n",
" <tr>\n",
" <th>MS TYPE</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0</td>\n",
" <td>Revise</td>\n",
" <td>True</td>\n",
" <td>3</td>\n",
" <td>2012-06-27</td>\n",
" <td>2012-07-25</td>\n",
" <td>28.0</td>\n",
" <td>77.0</td>\n",
" <td>6.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>Accept</td>\n",
" <td>True</td>\n",
" <td>3</td>\n",
" <td>2012-09-05</td>\n",
" <td>2012-09-05</td>\n",
" <td>0.0</td>\n",
" <td>77.0</td>\n",
" <td>42.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Revision Number Decision_Type Editor_As_Reviewer Manuscript_Number \\\n",
"MS TYPE \n",
"1 0 Revise True 3 \n",
"1 1 Accept True 3 \n",
"\n",
" Received Date Decision Date e_Life_Decision_Time Total_Time \\\n",
"MS TYPE \n",
"1 2012-06-27 2012-07-25 28.0 77.0 \n",
"1 2012-09-05 2012-09-05 0.0 77.0 \n",
"\n",
" Author_Time \n",
"MS TYPE \n",
"1 6.0 \n",
"1 42.0 "
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"post_peer_review.ix[3]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Reproduce Table 1 from the main paper.\n",
"\n",
"We look at how the mean e_Life_Decision_Time / Author_Time varies across different rounds of revision as well as in function of having an editor as a reviewer.\n",
"\n",
"We then do a Mann-Whitney test to check if having an editor as a reviewer influences the length of the duration process. Since we do multiple comparisons, we correct the p-values using [Benjamini-Hochberg](https://en.wikipedia.org/wiki/False_discovery_rate#Benjamini.E2.80.93Hochberg_procedure)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr>\n",
" <th></th>\n",
" <th></th>\n",
" <th colspan=\"2\" halign=\"left\">counts</th>\n",
" <th colspan=\"3\" halign=\"left\">eLife Time</th>\n",
" <th colspan=\"3\" halign=\"left\">Author Time</th>\n",
" </tr>\n",
" <tr>\n",
" <th></th>\n",
" <th>Editor_As_Reviewer</th>\n",
" <th>False</th>\n",
" <th>True</th>\n",
" <th>False</th>\n",
" <th>True</th>\n",
" <th>M-W</th>\n",
" <th>False</th>\n",
" <th>True</th>\n",
" <th>M-W</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Decision_Type</th>\n",
" <th>Revision Number</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th rowspan=\"4\" valign=\"top\">Accept</th>\n",
" <th>0</th>\n",
" <td>5.000</td>\n",
" <td>12.000</td>\n",
" <td>21.600</td>\n",
" <td>23.333</td>\n",
" <td>0.500</td>\n",
" <td>4.000</td>\n",
" <td>4.833</td>\n",
" <td>0.458</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>440.000</td>\n",
" <td>650.000</td>\n",
" <td>8.802</td>\n",
" <td>6.949</td>\n",
" <td>0.003</td>\n",
" <td>52.209</td>\n",
" <td>51.168</td>\n",
" <td>0.201</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>115.000</td>\n",
" <td>164.000</td>\n",
" <td>4.339</td>\n",
" <td>3.238</td>\n",
" <td>0.088</td>\n",
" <td>16.487</td>\n",
" <td>14.652</td>\n",
" <td>0.201</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>6.000</td>\n",
" <td>11.000</td>\n",
" <td>3.667</td>\n",
" <td>2.455</td>\n",
" <td>0.373</td>\n",
" <td>6.833</td>\n",
" <td>9.909</td>\n",
" <td>0.405</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"4\" valign=\"top\">Reject</th>\n",
" <th>0</th>\n",
" <td>461.000</td>\n",
" <td>616.000</td>\n",
" <td>36.104</td>\n",
" <td>30.981</td>\n",
" <td>0.000</td>\n",
" <td>6.182</td>\n",
" <td>6.430</td>\n",
" <td>0.133</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>10.000</td>\n",
" <td>10.000</td>\n",
" <td>22.200</td>\n",
" <td>31.600</td>\n",
" <td>0.074</td>\n",
" <td>64.900</td>\n",
" <td>101.800</td>\n",
" <td>0.201</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1.000</td>\n",
" <td>0.000</td>\n",
" <td>17.000</td>\n",
" <td>N/A</td>\n",
" <td>0.000</td>\n",
" <td>60.000</td>\n",
" <td>N/A</td>\n",
" <td>0.000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0.000</td>\n",
" <td>0.000</td>\n",
" <td>N/A</td>\n",
" <td>N/A</td>\n",
" <td>N/A</td>\n",
" <td>N/A</td>\n",
" <td>N/A</td>\n",
" <td>N/A</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"4\" valign=\"top\">Revise</th>\n",
" <th>0</th>\n",
" <td>705.000</td>\n",
" <td>946.000</td>\n",
" <td>36.018</td>\n",
" <td>31.053</td>\n",
" <td>0.000</td>\n",
" <td>5.786</td>\n",
" <td>5.744</td>\n",
" <td>0.201</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>129.000</td>\n",
" <td>182.000</td>\n",
" <td>19.651</td>\n",
" <td>15.747</td>\n",
" <td>0.003</td>\n",
" <td>66.930</td>\n",
" <td>64.110</td>\n",
" <td>0.458</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>6.000</td>\n",
" <td>12.000</td>\n",
" <td>7.833</td>\n",
" <td>7.167</td>\n",
" <td>0.373</td>\n",
" <td>21.333</td>\n",
" <td>35.250</td>\n",
" <td>0.365</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0.000</td>\n",
" <td>0.000</td>\n",
" <td>N/A</td>\n",
" <td>N/A</td>\n",
" <td>N/A</td>\n",
" <td>N/A</td>\n",
" <td>N/A</td>\n",
" <td>N/A</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" counts \\\n",
"Editor_As_Reviewer False \n",
"Decision_Type Revision Number \n",
"Accept 0 5.000 \n",
" 1 440.000 \n",
" 2 115.000 \n",
" 3 6.000 \n",
"Reject 0 461.000 \n",
" 1 10.000 \n",
" 2 1.000 \n",
" 3 0.000 \n",
"Revise 0 705.000 \n",
" 1 129.000 \n",
" 2 6.000 \n",
" 3 0.000 \n",
"\n",
" \\\n",
"Editor_As_Reviewer True \n",
"Decision_Type Revision Number \n",
"Accept 0 12.000 \n",
" 1 650.000 \n",
" 2 164.000 \n",
" 3 11.000 \n",
"Reject 0 616.000 \n",
" 1 10.000 \n",
" 2 0.000 \n",
" 3 0.000 \n",
"Revise 0 946.000 \n",
" 1 182.000 \n",
" 2 12.000 \n",
" 3 0.000 \n",
"\n",
" eLife Time \\\n",
"Editor_As_Reviewer False \n",
"Decision_Type Revision Number \n",
"Accept 0 21.600 \n",
" 1 8.802 \n",
" 2 4.339 \n",
" 3 3.667 \n",
"Reject 0 36.104 \n",
" 1 22.200 \n",
" 2 17.000 \n",
" 3 N/A \n",
"Revise 0 36.018 \n",
" 1 19.651 \n",
" 2 7.833 \n",
" 3 N/A \n",
"\n",
" \\\n",
"Editor_As_Reviewer True \n",
"Decision_Type Revision Number \n",
"Accept 0 23.333 \n",
" 1 6.949 \n",
" 2 3.238 \n",
" 3 2.455 \n",
"Reject 0 30.981 \n",
" 1 31.600 \n",
" 2 N/A \n",
" 3 N/A \n",
"Revise 0 31.053 \n",
" 1 15.747 \n",
" 2 7.167 \n",
" 3 N/A \n",
"\n",
" \\\n",
"Editor_As_Reviewer M-W \n",
"Decision_Type Revision Number \n",
"Accept 0 0.500 \n",
" 1 0.003 \n",
" 2 0.088 \n",
" 3 0.373 \n",
"Reject 0 0.000 \n",
" 1 0.074 \n",
" 2 0.000 \n",
" 3 N/A \n",
"Revise 0 0.000 \n",
" 1 0.003 \n",
" 2 0.373 \n",
" 3 N/A \n",
"\n",
" Author Time \\\n",
"Editor_As_Reviewer False \n",
"Decision_Type Revision Number \n",
"Accept 0 4.000 \n",
" 1 52.209 \n",
" 2 16.487 \n",
" 3 6.833 \n",
"Reject 0 6.182 \n",
" 1 64.900 \n",
" 2 60.000 \n",
" 3 N/A \n",
"Revise 0 5.786 \n",
" 1 66.930 \n",
" 2 21.333 \n",
" 3 N/A \n",
"\n",
" \\\n",
"Editor_As_Reviewer True \n",
"Decision_Type Revision Number \n",
"Accept 0 4.833 \n",
" 1 51.168 \n",
" 2 14.652 \n",
" 3 9.909 \n",
"Reject 0 6.430 \n",
" 1 101.800 \n",
" 2 N/A \n",
" 3 N/A \n",
"Revise 0 5.744 \n",
" 1 64.110 \n",
" 2 35.250 \n",
" 3 N/A \n",
"\n",
" \n",
"Editor_As_Reviewer M-W \n",
"Decision_Type Revision Number \n",
"Accept 0 0.458 \n",
" 1 0.201 \n",
" 2 0.201 \n",
" 3 0.405 \n",
"Reject 0 0.133 \n",
" 1 0.201 \n",
" 2 0.000 \n",
" 3 N/A \n",
"Revise 0 0.201 \n",
" 1 0.458 \n",
" 2 0.365 \n",
" 3 N/A "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"means_elife_time = post_peer_review.pivot_table('e_Life_Decision_Time',\n",
" index=['Decision_Type', 'Revision Number'],\n",
" columns='Editor_As_Reviewer')\n",
"means_author_time = post_peer_review.pivot_table('Author_Time',\n",
" index=['Decision_Type', 'Revision Number'],\n",
" columns='Editor_As_Reviewer')\n",
"\n",
"counts = post_peer_review.pivot_table('e_Life_Decision_Time',\n",
" index=['Decision_Type', 'Revision Number'],\n",
" columns='Editor_As_Reviewer',\n",
" aggfunc='count')\n",
"counts.fillna(0, inplace=True)\n",
"\n",
"# Group papers across decision (accept/reject/revise)\n",
"# and revision number (0, 1, 2)\n",
"gb = post_peer_review.groupby(['Decision_Type', 'Revision Number'])\n",
"\n",
"# We have to write our own function to apply a statistical test\n",
"# between the two subgroups\n",
"def apply_mw(x, col_name='e_Life_Decision_Time'):\n",
" # Each chunk (x) now refers to a block, grouped by\n",
" # decision_type and by revision number. We further\n",
" # separate them by which type of editor a paper had.\n",
" re_time = x[x['Editor_As_Reviewer']][col_name].values\n",
" se_time = x[x['Editor_As_Reviewer'] == 0][col_name].values\n",
" \n",
" return mannwhitneyu(re_time, se_time)[1]\n",
"\n",
"\n",
"\n",
"# MW for eLife Review Time (between editor reviewer versus\n",
"# editor supervisor) stratified across review rounds\n",
"# and decision type\n",
"mw_elife_time = gb.apply(apply_mw)\n",
"# Do multiple testing correction using BH method\n",
"_, p_vals, _, _ = multipletests(mw_elife_time, method='fdr_bh')\n",
"mw_elife_time.values[:] = p_vals\n",
"mw_elife_time.name = 'M-W'\n",
"\n",
"# MW for Author Time (between editor reviewer versus\n",
"# editor supervisor) stratified across review rounds\n",
"# and decision type\n",
"mw_author_time = gb.apply(apply_mw, 'Author_Time')\n",
"_, p_vals, _, _ = multipletests(mw_author_time, method='fdr_bh')\n",
"mw_author_time.values[:] = p_vals\n",
"mw_author_time.name = 'M-W'\n",
"\n",
"pd.options.display.float_format = '{:,.3f}'.format\n",
"pd.options.display.expand_frame_repr = True\n",
"pd.options.display.width = 20\n",
"\n",
"bre_reviewer_effect = pd.concat([counts,\n",
" means_elife_time.join(mw_elife_time),\n",
" means_author_time.join(mw_author_time)],\n",
" axis=1, keys=['counts', 'eLife Time', 'Author Time'])\n",
"\n",
"\n",
"#bre_reviewer_effect.index.rename(['Revision', 'Decision'], inplace=True)\n",
"display(bre_reviewer_effect.fillna('N/A'))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Box Plots for Total Time"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(2.1550936592668228,\n",
" 0.34042963401173282,\n",
" 2,\n",
" array([[ 588.00133899, 814.99866101],\n",
" [ 460.17496095, 637.82503905],\n",
" [ 829.82370007, 1150.17629993]]))"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"reviewer_decision_counts = post_peer_review.pivot_table('Manuscript_Number',\n",
" index='Decision_Type', columns='Editor_As_Reviewer', aggfunc='count')\n",
"chi2_contingency(reviewer_decision_counts)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>Editor_As_Reviewer</th>\n",
" <th>False</th>\n",
" <th>True</th>\n",
" <th>Totals</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Accept</th>\n",
" <td>566</td>\n",
" <td>837</td>\n",
" <td>1403</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Reject</th>\n",
" <td>472</td>\n",
" <td>626</td>\n",
" <td>1098</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Revise</th>\n",
" <td>840</td>\n",
" <td>1140</td>\n",
" <td>1980</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Totals</th>\n",
" <td>1878</td>\n",
" <td>2603</td>\n",
" <td>4481</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"Editor_As_Reviewer False \\\n",
"Accept 566 \n",
"Reject 472 \n",
"Revise 840 \n",
"Totals 1878 \n",
"\n",
"Editor_As_Reviewer True \\\n",
"Accept 837 \n",
"Reject 626 \n",
"Revise 1140 \n",
"Totals 2603 \n",
"\n",
"Editor_As_Reviewer Totals \n",
"Accept 1403 \n",
"Reject 1098 \n",
"Revise 1980 \n",
"Totals 4481 "
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"reviewer_decision_counts.index=reviewer_decision_counts.index.tolist() # Get rid of categorical\n",
"reviewer_decision_counts.ix['Totals'] = reviewer_decision_counts.sum(axis=0)\n",
"reviewer_decision_counts['Totals'] = reviewer_decision_counts.sum(axis=1)\n",
"\n",
"reviewer_decision_counts"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Some statistical analysis"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"first_date = post_peer_review.groupby(level=0).first()[['Received Date', 'Editor_As_Reviewer']]\n",
"last_date = post_peer_review.groupby(level=0).last()[['Decision Date', 'Decision_Type']]\n",
"\n",
"final_decision_df = pd.concat([first_date, last_date], axis=1)\n",
"\n",
"\n",
"final_decision_df['MS_NO'] = final_decision_df.index.get_level_values(0)\n",
"\n",
"# Find first date:\n",
"first_pub = final_decision_df['Received Date'].copy()\n",
"first_pub.sort_values(inplace=True)\n",
"last_pub = first_pub.tail(1).values\n",
"first_pub = first_pub.head(1).values\n",
"\n",
"final_decision_df.columns = ['Received_Date', 'Editor_As_Reviewer', 'Decision_Date',\n",
" 'Decision_Type', 'MS_NO']\n",
"\n",
"# NB: Total_Decision_Time differs from Total_Time because it does not include the time\n",
"# spent in the pre-review stages. The difference is minimal \n",
"final_decision_df['Total_Decision_Time'] = (last_date['Decision Date'] - first_date['Received Date']).dt.days\n",
"final_decision_df['Received_Since_Start'] = (first_date['Received Date'] - first_pub).dt.days\n",
"final_decision_df['Publication_Since_Start'] = (last_date['Decision Date'] - first_pub).dt.days\n",
"final_decision_df['Received_Date_Str'] = final_decision_df['Received_Date'].apply(lambda x: x.strftime('%d-%m-%Y'))\n",
"final_decision_df['Decision_Date_Str'] = final_decision_df['Decision_Date'].apply(lambda x: x.strftime('%d-%m-%Y'))\n",
"\n",
"# Unfortunately, we have to give this pre-processed dataset because giving the unique reviewer\n",
"# ids would allow an enterprising researcher to de-anonoymize all editors and reviewers.\n",
"unique_reviewers = pd.read_csv(os.path.join(data_dir, 'Unique_Reviewers.csv'), index_col=0,\n",
" header=None)\n",
"final_decision_df['Unique_Reviewers'] = unique_reviewers\n",
"\n",
"for idx in final_decision_df.index:\n",
" final_decision_df.loc[idx, 'Total_Revisions'] = len(post_peer_review.ix[idx])"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# drop papers that are still in revision\n",
"accept_reject_df = final_decision_df[final_decision_df['Decision_Type'] != 'Revise'].copy() \n",
"accept_reject_df['accept'] = accept_reject_df['Decision_Type'] == 'Accept'"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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cdkZGBrp27VrvX2RkpD2bQ0TkUA3u2l+8eBFBQUEICgrC3LlzAQDnzp3DggUL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\nxjf5s+YE/1QAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11a4b0550>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure(figsize=(5, 7.5), dpi=250)\n",
"ax = fig.add_subplot(111)\n",
"\n",
"sns.set(style=\"ticks\")\n",
"sns.boxplot(x='Decision_Type', y=\"Total_Decision_Time\", hue=\"Editor_As_Reviewer\",\n",
" data=accept_reject_df, palette=\"RdBu\", ax=ax)\n",
"ax.set_ylim((0, 400), emit=True)\n",
"ax.set_xlabel('')\n",
"sns.despine(trim=True)\n",
"\n",
"fig.savefig(os.path.join(output_dir, 'Figure_2.png'),\n",
" dpi=250, bbox_inches='tight')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Linear model to see if the BRE_Reviewer still has an effect on the total decision time after taking into account the number of unique reviewers (Table S1)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<table class=\"simpletable\">\n",
"<caption>Generalized Linear Model Regression Results</caption>\n",
"<tr>\n",
" <th>Dep. Variable:</th> <td>Total_Decision_Time</td> <th> No. Observations: </th> <td> 2501</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Model:</th> <td>GLM</td> <th> Df Residuals: </th> <td> 2497</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Model Family:</th> <td>Gaussian</td> <th> Df Model: </th> <td> 3</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Link Function:</th> <td>identity</td> <th> Scale: </th> <td>1618.00877435</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Method:</th> <td>IRLS</td> <th> Log-Likelihood: </th> <td> -12787.</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Date:</th> <td>Fri, 14 Oct 2016</td> <th> Deviance: </th> <td>4.0402e+06</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Time:</th> <td>15:25:06</td> <th> Pearson chi2: </th> <td>4.04e+06</td> \n",
"</tr>\n",
"<tr>\n",
" <th>No. Iterations:</th> <td>4</td> <th> </th> <td> </td> \n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<tr>\n",
" <td></td> <th>coef</th> <th>std err</th> <th>z</th> <th>P>|z|</th> <th>[95.0% Conf. Int.]</th> \n",
"</tr>\n",
"<tr>\n",
" <th>Intercept</th> <td> 85.7882</td> <td> 4.461</td> <td> 19.229</td> <td> 0.000</td> <td> 77.044 94.532</td>\n",
"</tr>\n",
"<tr>\n",
" <th>C(Editor_As_Reviewer)[T.True]</th> <td> -9.2276</td> <td> 1.700</td> <td> -5.428</td> <td> 0.000</td> <td> -12.560 -5.895</td>\n",
"</tr>\n",
"<tr>\n",
" <th>C(Decision_Type)[T.Reject]</th> <td> -65.3517</td> <td> 1.621</td> <td> -40.305</td> <td> 0.000</td> <td> -68.530 -62.174</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Unique_Reviewers</th> <td> 7.1889</td> <td> 1.620</td> <td> 4.438</td> <td> 0.000</td> <td> 4.014 10.364</td>\n",
"</tr>\n",
"</table>"
],
"text/plain": [
"<class 'statsmodels.iolib.summary.Summary'>\n",
"\"\"\"\n",
" Generalized Linear Model Regression Results \n",
"===============================================================================\n",
"Dep. Variable: Total_Decision_Time No. Observations: 2501\n",
"Model: GLM Df Residuals: 2497\n",
"Model Family: Gaussian Df Model: 3\n",
"Link Function: identity Scale: 1618.00877435\n",
"Method: IRLS Log-Likelihood: -12787.\n",
"Date: Fri, 14 Oct 2016 Deviance: 4.0402e+06\n",
"Time: 15:25:06 Pearson chi2: 4.04e+06\n",
"No. Iterations: 4 \n",
"=================================================================================================\n",
" coef std err z P>|z| [95.0% Conf. Int.]\n",
"-------------------------------------------------------------------------------------------------\n",
"Intercept 85.7882 4.461 19.229 0.000 77.044 94.532\n",
"C(Editor_As_Reviewer)[T.True] -9.2276 1.700 -5.428 0.000 -12.560 -5.895\n",
"C(Decision_Type)[T.Reject] -65.3517 1.621 -40.305 0.000 -68.530 -62.174\n",
"Unique_Reviewers 7.1889 1.620 4.438 0.000 4.014 10.364\n",
"=================================================================================================\n",
"\"\"\""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"accept_reject_df['accept'] = accept_reject_df['accept'].astype('int')\n",
"formula = 'Total_Decision_Time ~ C(Editor_As_Reviewer) + Unique_Reviewers + C(Decision_Type)'\n",
"model = smf.glm(formula=formula, data=accept_reject_df).fit()\n",
"display(model.summary())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Logit model to test whether the odds of a paper being accepted are affected by the kind of editor (reviewing versus supervising) and how many days since eLife started accepting papers. Table S2."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Optimization terminated successfully.\n",
" Current function value: 0.684179\n",
" Iterations 4\n"
]
},
{
"data": {
"text/html": [
"<table class=\"simpletable\">\n",
"<caption>Logit Regression Results</caption>\n",
"<tr>\n",
" <th>Dep. Variable:</th> <td>accept</td> <th> No. Observations: </th> <td> 2501</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Model:</th> <td>Logit</td> <th> Df Residuals: </th> <td> 2497</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Method:</th> <td>MLE</td> <th> Df Model: </th> <td> 3</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Date:</th> <td>Fri, 14 Oct 2016</td> <th> Pseudo R-squ.: </th> <td>0.002208</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Time:</th> <td>15:25:06</td> <th> Log-Likelihood: </th> <td> -1711.1</td>\n",
"</tr>\n",
"<tr>\n",
" <th>converged:</th> <td>True</td> <th> LL-Null: </th> <td> -1714.9</td>\n",
"</tr>\n",
"<tr>\n",
" <th> </th> <td> </td> <th> LLR p-value: </th> <td>0.05573</td>\n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<tr>\n",
" <td></td> <th>coef</th> <th>std err</th> <th>z</th> <th>P>|z|</th> <th>[95.0% Conf. Int.]</th> \n",
"</tr>\n",
"<tr>\n",
" <th>Intercept</th> <td> 0.6015</td> <td> 0.257</td> <td> 2.344</td> <td> 0.019</td> <td> 0.099 1.105</td>\n",
"</tr>\n",
"<tr>\n",
" <th>C(Editor_As_Reviewer)[T.True]</th> <td> 0.0815</td> <td> 0.087</td> <td> 0.939</td> <td> 0.348</td> <td> -0.089 0.252</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Publication_Since_Start</th> <td> -0.0003</td> <td> 0.000</td> <td> -2.338</td> <td> 0.019</td> <td> -0.001 -5.31e-05</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Unique_Reviewers</th> <td> -0.0386</td> <td> 0.081</td> <td> -0.474</td> <td> 0.635</td> <td> -0.198 0.121</td>\n",
"</tr>\n",
"</table>"
],
"text/plain": [
"<class 'statsmodels.iolib.summary.Summary'>\n",
"\"\"\"\n",
" Logit Regression Results \n",
"==============================================================================\n",
"Dep. Variable: accept No. Observations: 2501\n",
"Model: Logit Df Residuals: 2497\n",
"Method: MLE Df Model: 3\n",
"Date: Fri, 14 Oct 2016 Pseudo R-squ.: 0.002208\n",
"Time: 15:25:06 Log-Likelihood: -1711.1\n",
"converged: True LL-Null: -1714.9\n",
" LLR p-value: 0.05573\n",
"=================================================================================================\n",
" coef std err z P>|z| [95.0% Conf. Int.]\n",
"-------------------------------------------------------------------------------------------------\n",
"Intercept 0.6015 0.257 2.344 0.019 0.099 1.105\n",
"C(Editor_As_Reviewer)[T.True] 0.0815 0.087 0.939 0.348 -0.089 0.252\n",
"Publication_Since_Start -0.0003 0.000 -2.338 0.019 -0.001 -5.31e-05\n",
"Unique_Reviewers -0.0386 0.081 -0.474 0.635 -0.198 0.121\n",
"=================================================================================================\n",
"\"\"\""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"accept_reject_df['accept'] = accept_reject_df['accept'].astype('int')\n",
"formula = 'accept ~ C(Editor_As_Reviewer) + Publication_Since_Start + Unique_Reviewers'\n",
"model = smf.logit(formula=formula, data=accept_reject_df).fit()\n",
"display(model.summary())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Logit model to test whether the number of revisions a paper goes through before it is accepted are influenced by the number of days since eLife started accepting papers (the age of the journal) plus the type of reviewer."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<table class=\"simpletable\">\n",
"<caption>Generalized Linear Model Regression Results</caption>\n",
"<tr>\n",
" <th>Dep. Variable:</th> <td>Total_Revisions</td> <th> No. Observations: </th> <td> 2501</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Model:</th> <td>GLM</td> <th> Df Residuals: </th> <td> 2496</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Model Family:</th> <td>Poisson</td> <th> Df Model: </th> <td> 4</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Link Function:</th> <td>log</td> <th> Scale: </th> <td>1.0</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Method:</th> <td>IRLS</td> <th> Log-Likelihood: </th> <td> -3061.5</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Date:</th> <td>Fri, 14 Oct 2016</td> <th> Deviance: </th> <td> 141.82</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Time:</th> <td>15:25:06</td> <th> Pearson chi2: </th> <td> 156.</td> \n",
"</tr>\n",
"<tr>\n",
" <th>No. Iterations:</th> <td>7</td> <th> </th> <td> </td> \n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<tr>\n",
" <td></td> <th>coef</th> <th>std err</th> <th>z</th> <th>P>|z|</th> <th>[95.0% Conf. Int.]</th> \n",
"</tr>\n",
"<tr>\n",
" <th>Intercept</th> <td> 0.6856</td> <td> 0.099</td> <td> 6.952</td> <td> 0.000</td> <td> 0.492 0.879</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Editor_As_Reviewer[T.True]</th> <td> -0.0134</td> <td> 0.033</td> <td> -0.403</td> <td> 0.687</td> <td> -0.078 0.052</td>\n",
"</tr>\n",
"<tr>\n",
" <th>C(Decision_Type)[T.Reject]</th> <td> -0.7762</td> <td> 0.035</td> <td> -22.225</td> <td> 0.000</td> <td> -0.845 -0.708</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Publication_Since_Start</th> <td> 1.06e-05</td> <td> 5.33e-05</td> <td> 0.199</td> <td> 0.842</td> <td>-9.39e-05 0.000</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Unique_Reviewers</th> <td> 0.0382</td> <td> 0.031</td> <td> 1.230</td> <td> 0.219</td> <td> -0.023 0.099</td>\n",
"</tr>\n",
"</table>"
],
"text/plain": [
"<class 'statsmodels.iolib.summary.Summary'>\n",
"\"\"\"\n",
" Generalized Linear Model Regression Results \n",
"==============================================================================\n",
"Dep. Variable: Total_Revisions No. Observations: 2501\n",
"Model: GLM Df Residuals: 2496\n",
"Model Family: Poisson Df Model: 4\n",
"Link Function: log Scale: 1.0\n",
"Method: IRLS Log-Likelihood: -3061.5\n",
"Date: Fri, 14 Oct 2016 Deviance: 141.82\n",
"Time: 15:25:06 Pearson chi2: 156.\n",
"No. Iterations: 7 \n",
"==============================================================================================\n",
" coef std err z P>|z| [95.0% Conf. Int.]\n",
"----------------------------------------------------------------------------------------------\n",
"Intercept 0.6856 0.099 6.952 0.000 0.492 0.879\n",
"Editor_As_Reviewer[T.True] -0.0134 0.033 -0.403 0.687 -0.078 0.052\n",
"C(Decision_Type)[T.Reject] -0.7762 0.035 -22.225 0.000 -0.845 -0.708\n",
"Publication_Since_Start 1.06e-05 5.33e-05 0.199 0.842 -9.39e-05 0.000\n",
"Unique_Reviewers 0.0382 0.031 1.230 0.219 -0.023 0.099\n",
"==============================================================================================\n",
"\"\"\""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"formula = 'Total_Revisions ~ Publication_Since_Start + Editor_As_Reviewer + C(Decision_Type) + Unique_Reviewers'\n",
"model = smf.glm(formula=formula, data=accept_reject_df, family=sm.families.Poisson()).fit()\n",
"display(model.summary())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Analyze Citation Data"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"citations_file = os.path.join(data_dir, 'Citations_29-02-2016.csv')\n",
"citations_date = os.path.basename(citations_file)[10:-4]\n",
"citations_date = pd.to_datetime(citations_date, dayfirst=True)\n",
"\n",
"citations_df = pd.read_csv(citations_file, index_col=0, header=0)\n",
"citations_df.index = citations_df.index.values.astype('int')"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"We look whether the number of citations in SCOPUS is influenced by the kind of reviewer, the days since eLife started publishing papers (age of journal), and the decision time. Table S4."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<table class=\"simpletable\">\n",
"<caption>Generalized Linear Model Regression Results</caption>\n",
"<tr>\n",
" <th>Dep. Variable:</th> <td>log1p_Citation_Rate</td> <th> No. Observations: </th> <td> 1403</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Model:</th> <td>GLM</td> <th> Df Residuals: </th> <td> 1399</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Model Family:</th> <td>Gaussian</td> <th> Df Model: </th> <td> 3</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Link Function:</th> <td>identity</td> <th> Scale: </th> <td>9.88133273137e-05</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Method:</th> <td>IRLS</td> <th> Log-Likelihood: </th> <td> 4480.7</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Date:</th> <td>Fri, 14 Oct 2016</td> <th> Deviance: </th> <td> 0.13824</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Time:</th> <td>15:25:07</td> <th> Pearson chi2: </th> <td> 0.138</td> \n",
"</tr>\n",
"<tr>\n",
" <th>No. Iterations:</th> <td>4</td> <th> </th> <td> </td> \n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<tr>\n",
" <td></td> <th>coef</th> <th>std err</th> <th>z</th> <th>P>|z|</th> <th>[95.0% Conf. Int.]</th> \n",
"</tr>\n",
"<tr>\n",
" <th>Intercept</th> <td> 0.0038</td> <td> 0.002</td> <td> 2.538</td> <td> 0.011</td> <td> 0.001 0.007</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Editor_As_Reviewer[T.True]</th> <td> 0.0025</td> <td> 0.001</td> <td> 4.465</td> <td> 0.000</td> <td> 0.001 0.004</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Total_Decision_Time</th> <td>-2.812e-05</td> <td> 5.27e-06</td> <td> -5.338</td> <td> 0.000</td> <td>-3.84e-05 -1.78e-05</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Unique_Reviewers</th> <td> 0.0007</td> <td> 0.001</td> <td> 1.241</td> <td> 0.215</td> <td> -0.000 0.002</td>\n",
"</tr>\n",
"</table>"
],
"text/plain": [
"<class 'statsmodels.iolib.summary.Summary'>\n",
"\"\"\"\n",
" Generalized Linear Model Regression Results \n",
"===============================================================================\n",
"Dep. Variable: log1p_Citation_Rate No. Observations: 1403\n",
"Model: GLM Df Residuals: 1399\n",
"Model Family: Gaussian Df Model: 3\n",
"Link Function: identity Scale: 9.88133273137e-05\n",
"Method: IRLS Log-Likelihood: 4480.7\n",
"Date: Fri, 14 Oct 2016 Deviance: 0.13824\n",
"Time: 15:25:07 Pearson chi2: 0.138\n",
"No. Iterations: 4 \n",
"==============================================================================================\n",
" coef std err z P>|z| [95.0% Conf. Int.]\n",
"----------------------------------------------------------------------------------------------\n",
"Intercept 0.0038 0.002 2.538 0.011 0.001 0.007\n",
"Editor_As_Reviewer[T.True] 0.0025 0.001 4.465 0.000 0.001 0.004\n",
"Total_Decision_Time -2.812e-05 5.27e-06 -5.338 0.000 -3.84e-05 -1.78e-05\n",
"Unique_Reviewers 0.0007 0.001 1.241 0.215 -0.000 0.002\n",
"==============================================================================================\n",
"\"\"\""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"accepted_papers = pd.concat([accept_reject_df[accept_reject_df['Decision_Type'] == 'Accept'],\n",
" citations_df], axis=1)\n",
"accepted_papers.dropna(subset=['Received_Date'], inplace=True)\n",
"accepted_papers = accepted_papers.rename(columns = {'www.ncbi.nlm.nih.gov': 'Pubmed_Citations',\n",
" 'www.scopus.com': 'Scopus'})\n",
"accepted_papers['Pubmed_Citations'].fillna(0, inplace=True)\n",
"accepted_papers['Scopus'].fillna(0, inplace=True)\n",
"\n",
"accepted_papers['Days_Since_Decision'] = citations_date - accept_reject_df['Decision_Date']\n",
"\n",
"accepted_papers['Days_Since_Decision'] = accepted_papers['Days_Since_Decision'].dt.days.astype('int')\n",
"accepted_papers['log1p_Citation_Rate'] = np.log1p(accepted_papers['Scopus'] / accepted_papers['Days_Since_Decision'])\n",
"\n",
"formula = 'log1p_Citation_Rate ~ Total_Decision_Time + Editor_As_Reviewer + Unique_Reviewers'\n",
"model = smf.glm(formula=formula, data=accepted_papers).fit()\n",
"display(model.summary())\n",
"\n",
"accepted_papers_1 = accepted_papers.copy()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Using new citation data."
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#citations_file = os.path.join(data_dir, 'Citations_29-02-2016.csv')\n",
"citations_file = os.path.join(data_dir, 'Citations_10-07-2016.csv')\n",
"\n",
"citations_date = os.path.basename(citations_file)[10:-4]\n",
"citations_date = pd.to_datetime(citations_date, dayfirst=True)\n",
"\n",
"citations_df = pd.read_csv(citations_file, index_col=0, header=0)\n",
"citations_df.index = citations_df.index.values.astype('int')"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<table class=\"simpletable\">\n",
"<caption>Generalized Linear Model Regression Results</caption>\n",
"<tr>\n",
" <th>Dep. Variable:</th> <td>log1p_Citation_Rate</td> <th> No. Observations: </th> <td> 1403</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Model:</th> <td>GLM</td> <th> Df Residuals: </th> <td> 1399</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Model Family:</th> <td>Gaussian</td> <th> Df Model: </th> <td> 3</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Link Function:</th> <td>identity</td> <th> Scale: </th> <td>0.000195889378811</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Method:</th> <td>IRLS</td> <th> Log-Likelihood: </th> <td> 4000.6</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Date:</th> <td>Fri, 14 Oct 2016</td> <th> Deviance: </th> <td> 0.27405</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Time:</th> <td>15:25:07</td> <th> Pearson chi2: </th> <td> 0.274</td> \n",
"</tr>\n",
"<tr>\n",
" <th>No. Iterations:</th> <td>4</td> <th> </th> <td> </td> \n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<tr>\n",
" <td></td> <th>coef</th> <th>std err</th> <th>z</th> <th>P>|z|</th> <th>[95.0% Conf. Int.]</th> \n",
"</tr>\n",
"<tr>\n",
" <th>Intercept</th> <td> 0.0052</td> <td> 0.002</td> <td> 2.448</td> <td> 0.014</td> <td> 0.001 0.009</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Editor_As_Reviewer[T.True]</th> <td> 0.0024</td> <td> 0.001</td> <td> 3.059</td> <td> 0.002</td> <td> 0.001 0.004</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Total_Decision_Time</th> <td>-3.081e-05</td> <td> 7.42e-06</td> <td> -4.155</td> <td> 0.000</td> <td>-4.54e-05 -1.63e-05</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Unique_Reviewers</th> <td> 0.0017</td> <td> 0.001</td> <td> 2.302</td> <td> 0.021</td> <td> 0.000 0.003</td>\n",
"</tr>\n",
"</table>"
],
"text/plain": [
"<class 'statsmodels.iolib.summary.Summary'>\n",
"\"\"\"\n",
" Generalized Linear Model Regression Results \n",
"===============================================================================\n",
"Dep. Variable: log1p_Citation_Rate No. Observations: 1403\n",
"Model: GLM Df Residuals: 1399\n",
"Model Family: Gaussian Df Model: 3\n",
"Link Function: identity Scale: 0.000195889378811\n",
"Method: IRLS Log-Likelihood: 4000.6\n",
"Date: Fri, 14 Oct 2016 Deviance: 0.27405\n",
"Time: 15:25:07 Pearson chi2: 0.274\n",
"No. Iterations: 4 \n",
"==============================================================================================\n",
" coef std err z P>|z| [95.0% Conf. Int.]\n",
"----------------------------------------------------------------------------------------------\n",
"Intercept 0.0052 0.002 2.448 0.014 0.001 0.009\n",
"Editor_As_Reviewer[T.True] 0.0024 0.001 3.059 0.002 0.001 0.004\n",
"Total_Decision_Time -3.081e-05 7.42e-06 -4.155 0.000 -4.54e-05 -1.63e-05\n",
"Unique_Reviewers 0.0017 0.001 2.302 0.021 0.000 0.003\n",
"==============================================================================================\n",
"\"\"\""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"accepted_papers = pd.concat([accept_reject_df[accept_reject_df['Decision_Type'] == 'Accept'],\n",
" citations_df], axis=1)\n",
"accepted_papers.dropna(subset=['Received_Date'], inplace=True)\n",
"accepted_papers = accepted_papers.rename(columns = {'www.ncbi.nlm.nih.gov': 'Pubmed_Citations',\n",
" 'www.scopus.com': 'Scopus'})\n",
"accepted_papers['Pubmed_Citations'].fillna(0, inplace=True)\n",
"accepted_papers['Scopus'].fillna(0, inplace=True)\n",
"\n",
"accepted_papers['Days_Since_Decision'] = citations_date - accept_reject_df['Decision_Date']\n",
"\n",
"accepted_papers['Days_Since_Decision'] = accepted_papers['Days_Since_Decision'].dt.days.astype('int')\n",
"accepted_papers['log1p_Citation_Rate'] = np.log1p(accepted_papers['Scopus'] / accepted_papers['Days_Since_Decision'])\n",
"\n",
"formula = 'log1p_Citation_Rate ~ Total_Decision_Time + Editor_As_Reviewer + Unique_Reviewers'\n",
"model = smf.glm(formula=formula, data=accepted_papers).fit()\n",
"display(model.summary())\n",
"\n",
"accepted_papers_2 = accepted_papers.copy()"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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rkZmZidraWqhUKsTGxiIyMhKCICA0NBTdunWzOAYA5s+fjyVLlkChUMDe3h7v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\n/IIgCIKwgIjALwiCIAgLyP8AWYf1qZKZANsAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x12106f1d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig3, ax = plt.subplots()\n",
"\n",
"def modified_coefplot(formula, data, ax, jitter=0, intercept=False, ci=95,\n",
" palette=\"husl\"):\n",
" \"\"\"Plot the coefficients from a linear model.\n",
"\n",
" Parameters\n",
" ----------\n",
" formula : string\n",
" patsy formula for ols model\n",
" data : dataframe\n",
" data for the plot; formula terms must appear in columns\n",
" groupby : grouping object, optional\n",
" object to group data with to fit conditional models\n",
" intercept : bool, optional\n",
" if False, strips the intercept term before plotting\n",
" ci : float, optional\n",
" size of confidence intervals\n",
" palette : seaborn color palette, optional\n",
" palette for the horizonal plots\n",
"\n",
" \"\"\"\n",
" import statsmodels.formula.api as sf\n",
" import itertools\n",
"\n",
" alpha = 1 - ci / 100\n",
" coefs = sf.ols(formula, data).fit().params\n",
" cis = sf.ols(formula, data).fit().conf_int(alpha)\n",
"\n",
" # Possibly ignore the intercept\n",
" if not intercept:\n",
" coefs = coefs.ix[1:]\n",
"\n",
" n_terms = len(coefs)\n",
"\n",
" colors = itertools.cycle(sns.color_palette(palette, n_terms))\n",
" for i, term in enumerate(coefs.index):\n",
" color = next(colors)\n",
" low, high = cis.ix[term]\n",
" i += jitter\n",
" ax.plot([i, i], [low, high], c=color,\n",
" solid_capstyle=\"round\", lw=2.5, alpha=0.5)\n",
" ax.plot(i, coefs.ix[term], \"o\", c=color, ms=8)\n",
" ax.set_xlim(-.5, n_terms - .5)\n",
" ax.axhline(0, ls=\"--\", c=\"dimgray\")\n",
" \n",
" if jitter == 0:\n",
" ax.set_xticks(range(n_terms))\n",
" ax.set_xticklabels(coefs.index)\n",
"\n",
"modified_coefplot(formula, data=accepted_papers_1, ax=ax)\n",
"modified_coefplot(formula, data=accepted_papers_2, ax=ax, jitter=0.25)\n",
"\n",
"ax.set_title('Predictors of Citation Rates', fontsize=18)\n",
"fig3.savefig(os.path.join(output_dir, 'Figure_3.png'), dpi=125)"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"Figure 4:"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"final_decision_df['month'] = final_decision_df['Received_Date'].apply(lambda x: x.month + 12 * ((x.year) - 2012))\n",
"papers_by_year_gb = final_decision_df.groupby(['month', 'Editor_As_Reviewer'])\n",
"papers_by_year = papers_by_year_gb.apply(lambda x: len(np.unique(x['MS_NO']))).unstack(-1)\n",
"\n",
"papers_by_year['Total'] = papers_by_year.sum(axis=1)\n",
"papers_by_year['BRE_Fraction'] = papers_by_year[True] / papers_by_year['Total']\n",
"\n",
"papers_by_year.rename(columns={True: 'Papers_with_RE', False: 'Papers_with_SE'},\n",
" inplace=True)\n",
"\n",
"# Add information about how many unique editors were active in a given month\n",
"n_unique_editors = pd.read_csv(os.path.join(data_dir, 'Unique_Editors_Per_Month.csv'), \n",
" index_col=0, header=None)\n",
"papers_by_year['Unique_Editors'] = n_unique_editors\n",
"papers_by_year['Papers_Per_Editor'] = papers_by_year['Total'] / papers_by_year['Unique_Editors']"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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OTc0UmuTeeymAl59pT0pGHh/9dIKth6KrLR9zdWos/e+V9VSAK45NzdgTFsPD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\nC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JfU91i4vF2Xv5uv+h4LFy93Z2dn4+fnp78LTUirZ4mIiPgwXfoWERHxYQpqERERH6ag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\nTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x121077160>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig4, (ax1, ax2) = plt.subplots(2, sharex=True)\n",
"\n",
"# Drop last months for which we don't have review info yet.\n",
"papers_by_year = papers_by_year.ix[:40]\n",
"\n",
"ax1.plot(papers_by_year.index, papers_by_year['Papers_Per_Editor'], marker='o',\n",
" label='Papers per Editor')\n",
"ax1.plot(papers_by_year.index, papers_by_year['BRE_Fraction'], marker='o',\n",
" label='Fraction of Papers with Editor_as_Reviewer')\n",
"ax1.legend(loc='best')\n",
"\n",
"\n",
"ax2.plot(papers_by_year.index, papers_by_year['Total'], marker='o',\n",
" label='Papers per Months')\n",
"ax2.plot(papers_by_year.index, papers_by_year['Unique_Editors'], marker='o',\n",
" label='Active Editors Per Month')\n",
"ax2.legend(loc='best')\n",
"ax2.set_xticks([12, 24, 36, 48])\n",
"ax2.set_xticklabels([2013, 2014, 2015, 2016])\n",
"ax2.set_xlim(4, 45)\n",
"\n",
"ax2.set_xlabel('Year')\n",
"\n",
"fig4.suptitle('eLife Publishing Trends', fontsize=18)\n",
"fig4.savefig(os.path.join(output_dir, 'Figure_4.png'), dpi=125)"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"Let's try some nice data-visualizations now:"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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" <span id=\"30f868c0-e18c-4d69-8f27-5cb695f96a23\">Loading BokehJS ...</span>\n",
" </div>"
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{
"data": {
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" window._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n",
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" }\n",
"\n",
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" function(Bokeh) {\n",
" console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.11.1.min.css\");\n",
" Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.11.1.min.css\");\n",
" console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.11.1.min.css\");\n",
" Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.11.1.min.css\");\n",
" }\n",
" ];\n",
"\n",
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" console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
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" run_inline_js();\n",
" });\n",
" }\n",
"}(this));"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import bokeh\n",
"from bokeh.plotting import figure, output_notebook, show, output_file\n",
"from bokeh.plotting import ColumnDataSource\n",
"from bokeh.models import HoverTool\n",
"from bokeh.models.widgets import Panel, Tabs\n",
"output_notebook()"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"final_decision_df['Color'] = 'red'\n",
"final_decision_df.loc[final_decision_df['Editor_As_Reviewer'] == 1, 'Color'] = 'blue'\n",
"\n",
"for idx in final_decision_df.index:\n",
" final_decision_df.loc[idx, 'Total_Revisions'] = len(post_peer_review.ix[idx])\n",
"\n",
"accepted = final_decision_df[final_decision_df['Decision_Type'] == 'Accept'].copy()\n",
"rejected = final_decision_df[final_decision_df['Decision_Type'] == 'Reject'].copy()\n",
"revised = final_decision_df[final_decision_df['Decision_Type'] == 'Revise'].copy()"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from bokeh.io import output_notebook\n",
"from bokeh.plotting import figure\n",
"from bokeh.models import HoverTool, Panel, ColumnDataSource, Tabs"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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" <span id=\"ddbb9561-d2aa-4fa6-965f-84c13996003e\">Loading BokehJS ...</span>\n",
" </div>"
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},
"metadata": {},
"output_type": "display_data"
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{
"data": {
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"\n",
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" }\n",
"\n",
" function run_callbacks() {\n",
" window._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n",
" delete window._bokeh_onload_callbacks\n",
" console.info(\"Bokeh: all callbacks have finished\");\n",
" }\n",
"\n",
" function load_libs(js_urls, callback) {\n",
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" };\n",
"\n",
" var js_urls = ['https://cdn.pydata.org/bokeh/release/bokeh-0.11.1.min.js', 'https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.11.1.min.js', 'https://cdn.pydata.org/bokeh/release/bokeh-compiler-0.11.1.min.js'];\n",
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" console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.11.1.min.css\");\n",
" Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.11.1.min.css\");\n",
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" console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
" run_inline_js();\n",
" } else {\n",
" load_libs(js_urls, function() {\n",
" console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
" run_inline_js();\n",
" });\n",
" }\n",
" }(this));\n",
"</script>"
]
},
"metadata": {},
"output_type": "display_data"
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{
"data": {
"text/html": [
"<p><code><Bokeh Notebook handle for <strong>In[27]</strong>></code></p>"
],
"text/plain": [
"<bokeh.io._CommsHandle at 0x122902438>"
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"execution_count": 27,
"metadata": {},
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"source": [
"output_notebook()\n",
"all_figs = []\n",
"for title, df in zip(['Accepted', 'Revised', 'Rejected'],\n",
" [accepted, revised, rejected]):\n",
"\n",
" hover = HoverTool(\n",
" tooltips=[\n",
" (\"Manuscript Number\", \"@MS_NO\"),\n",
" (\"Received Date\", \"@Received_Date_Str\"),\n",
" (\"Final Decision Date\", \"@Decision_Date_Str\"),\n",
" (\"Total Revisions:\", \"@Total_Revisions\")\n",
" ]\n",
" )\n",
" \n",
" \n",
" data = ColumnDataSource(df)\n",
" p = figure(width=800, height=800, x_axis_type=\"datetime\",\n",
" tools=[hover], title=title)\n",
" p.circle('Received_Date', 'Total_Decision_Time', color='Color', alpha=0.5, \n",
" source=data)\n",
" \n",
" p.xaxis.axis_label = 'Date Received'\n",
" p.yaxis.axis_label = 'Days Till Decision'\n",
" \n",
" tab = Panel(child=p, title=title)\n",
" all_figs.append(tab)\n",
"\n",
"\n",
"tabs = Tabs(tabs=all_figs)\n",
"show(tabs)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
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"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
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