jtg567/Race Cache 2 via ilana workflow.ipynb Secret

## Race Cache 2 via ilana workflow.ipynb
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This script is intended for variables not yet available in the [Experiments Viewer](https://moz-experiments-viewer.herokuapp.com/). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/mnt/anaconda2/lib/python2.7/site-packages/IPython/core/magics/pylab.py:161: UserWarning:\n",
      "\n",
      "pylab import has clobbered these variables: ['rand', 'cbrt', 'rank', 'cosh', 'hypot', 'array', 'tan', 'size', 'isnan', 'randn', 'log', 'floor', 'sum', 'sqrt', 'split', 'rint', 'log10', 'sin', 'log2', 'cos', 'ceil', 'broadcast', 'sinh', 'repeat', 'trunc', 'expm1', 'tanh', 'exp', 'log1p', 'mean']\n",
      "`%matplotlib` prevents importing * from pylab and numpy\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import plotly.plotly as py\n",
    "from scipy.stats import chi2_contingency\n",
    "from scipy.stats import ttest_ind\n",
    "from collections import defaultdict as dd\n",
    "\n",
    "from pyspark.sql import Row\n",
    "from pyspark.sql import SQLContext\n",
    "from pyspark.sql.types import *\n",
    "from pyspark.sql.functions import *\n",
    "import itertools\n",
    "\n",
    "from moztelemetry.dataset import Dataset\n",
    "\n",
    "from scipy.stats import mannwhitneyu\n",
    "\n",
    "py_max = __builtin__.max\n",
    "py_map = __builtin__.map\n",
    "\n",
    "%pylab inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "experiment_probes = {}\n",
    "experiment_probes[\"payload/histograms\"] =  [   \n",
    "                \"NETWORK_RACE_CACHE_WITH_NETWORK_OCEC_ON_START_DIFF\", \n",
    "                \"NETWORK_RACE_CACHE_WITH_NETWORK_SAVED_TIME\",\n",
    "                \"NETWORK_RACE_CACHE_WITH_NETWORK_USAGE_2\",\n",
    "                \"NETWORK_RACE_CACHE_BANDWIDTH_RACE_NETWORK_WIN\",\n",
    "                \"NETWORK_RACE_CACHE_BANDWIDTH_RACE_CACHE_WIN\",\n",
    "                \"NETWORK_RACE_CACHE_BANDWIDTH_NOT_RACE\",\n",
    "                \"NETWORK_RACE_CACHE_VALIDATION\",\n",
    "                \"HTTP_PAGE_COMPLETE_LOAD_V2\",\n",
    "                \"HTTP_PAGE_COMPLETE_LOAD_NET_V2\",\n",
    "                \"HTTP_PAGE_COMPLETE_LOAD_CACHED_V2\",\n",
    "                \"HTTP_SUB_COMPLETE_LOAD_V2\",\n",
    "                \"HTTP_SUB_COMPLETE_LOAD_CACHED_V2\",\n",
    "                \"HTTP_SUB_COMPLETE_LOAD_NET_V2\",\n",
    "                \"TOTAL_CONTENT_PAGE_LOAD_TIME\",\n",
    "                ]\n",
    "\n",
    "probe_names = list(itertools.chain(*experiment_probes.values()))\n",
    "\n",
    "EXPERIMENT_SLUG = \"pref-flip-rcwn2-1381816\"\n",
    "START_DATE = \"20170726\" #YYYYMMDD\n",
    "END_DATE = \"20170802\" #YYYYMMDD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fetching 749.71418MB in 165 files...\n"
     ]
    }
   ],
   "source": [
    "cohorts = Dataset.from_source(\"telemetry-cohorts\")\n",
    "pings = cohorts.where(submissionDate = lambda x: x >= START_DATE and x <= END_DATE) \\\n",
    "               .where(experimentId=EXPERIMENT_SLUG) \\\n",
    "               .records(sc, sample=0.01)\n",
    "        \n",
    "#pings.cache()\n",
    "#pings.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PythonRDD[3] at RDD at PythonRDD.scala:48"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "main_pings = pings.filter(lambda x: x[\"meta\"][\"docType\"]==\"main\")\n",
    "main_pings.cache()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# haven't run this yet - trying to aggregate histograms per Client instead of collapsing over them\n",
    "\n",
    "byClient = main_pings.map(lambda p: (p['clientId'], [p])).reduceByKey(lambda x,y: x+y)\n",
    "\n",
    "#modified from https://stackoverflow.com/questions/13852896/using-a-single-variable-to-index-into-nested-dictionaries\n",
    "def recursive_get(d, keys):\n",
    "    if len(keys) == 1:\n",
    "        return d.get(keys[0],{})\n",
    "    return recursive_get(d.get(keys[0],{}), keys[1:])\n",
    "\n",
    "def extract_probes(i):\n",
    "    clientId, pinglist = i\n",
    "    \n",
    "    # determine which branch this client was in and note if it ended up in both\n",
    "    branch = []\n",
    "    for p in pinglist:\n",
    "        branch.extend(p.get(\"environment\",{}).get(\"experiments\",{}).get(EXPERIMENT_SLUG, {}).get(\"branch\", \"warning\"))\n",
    "    if len(np.unique(branch)) > 1:\n",
    "        branch = \"both\"\n",
    "    else:\n",
    "        branch = np.unique(branch)[0]\n",
    "    \n",
    "    # if you input by client, pinglist instead of ping add another layer of iteration over the list here\n",
    "    output = []\n",
    "    for p in pinglist:\n",
    "        for path, probe_names in experiment_probes.iteritems():\n",
    "            for probe_name in probe_names:\n",
    "                probe = recursive_get(p, path.split(\"/\")).get(probe_name, {})\n",
    "                for k,v in probe.get(\"values\",{}).iteritems():\n",
    "                    output.extend([{\"probe\": probe_name, \"branch\": branch, \"val\": float(k)}] * int(v))\n",
    "    # by this point you should have all this clients pings in an aggregate histogram, right? what then?\n",
    "    \n",
    "                    \n",
    "#    return output\n",
    "\n",
    "fin = byClient.flatMap(extract_probes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#modified from https://stackoverflow.com/questions/13852896/using-a-single-variable-to-index-into-nested-dictionaries\n",
    "def recursive_get(d, keys):\n",
    "    if len(keys) == 1:\n",
    "        return d.get(keys[0],{})\n",
    "    return recursive_get(d.get(keys[0],{}), keys[1:])\n",
    "\n",
    "def extract_probes(p):\n",
    "    branch = p.get(\"environment\",{}).get(\"experiments\",{}).get(EXPERIMENT_SLUG, {}).get(\"branch\", \"warning\")\n",
    "    \n",
    "    output = []\n",
    "    for path, probe_names in experiment_probes.iteritems():\n",
    "        for probe_name in probe_names:\n",
    "            probe = recursive_get(p, path.split(\"/\")).get(probe_name, {})\n",
    "            #take all entries in histogram right now. this is probably problematic. inspect more\n",
    "            for k,v in probe.get(\"values\",{}).iteritems():\n",
    "                output.extend([{\"probe\": probe_name, \"branch\": branch, \"val\": float(k)}] * int(v))\n",
    "    return output\n",
    "\n",
    "probe_dicts = main_pings.flatMap(extract_probes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df = sqlContext.createDataFrame(probe_dicts.map(lambda d: Row(**d)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "control : 4667\n",
      "rcwn-enabled : 4244\n"
     ]
    }
   ],
   "source": [
    "#check on branch distribution\n",
    "for b,v in main_pings.map(lambda x: x.get(\"environment\",{}).get(\"experiments\",{}).get(EXPERIMENT_SLUG, {}).get(\"branch\", \"warning\")).countByValue().iteritems():\n",
    "    print b, \":\", v"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "branches = [i.branch for i in df.select(\"branch\").distinct().collect()]\n",
    "n_branches = len(branches)\n",
    "\n",
    "#for this one - set order\n",
    "branches = [\"control\", \"rcwn-enabled\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NETWORK_RACE_CACHE_WITH_NETWORK_OCEC_ON_START_DIFF (logscale=False):\n",
      "len(branch_vals) = 5105, len(control_vals) = 0\n",
      "branch with no values\n"
     ]
    },
    {
     "data": {
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V9/iTJEna2QYaCUvyziTfSLK+3b6W5HV9NR9Ocn/7OJMvJzm4b//sJBckWZvk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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3e773b93d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NETWORK_RACE_CACHE_WITH_NETWORK_OCEC_ON_START_DIFF (logscale=True):\n",
      "len(branch_vals) = 5105, len(control_vals) = 0\n",
      "branch with no values\n"
     ]
    },
    {
     "data": {
      "image/png": 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cVU/S3OEy/aLQk2juFOytuZ/meTS9LxOVJEk64M3nrr2307xGYfqJvatoLvNt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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3e8b36dad0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "NETWORK_RACE_CACHE_WITH_NETWORK_SAVED_TIME (logscale=False):\n",
      "len(branch_vals) = 213331, len(control_vals) = 0\n",
      "branch with no values\n"
     ]
    },
    {
     "data": {
      "image/png": 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Zsx9/F3d0dJTR0eH5IpIkaWc1NjbG2NjY47aNj4/PUG8eM+0BL8lzgKcDP243LQPW08yO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\nG2MAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3e7545dfd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NETWORK_RACE_CACHE_WITH_NETWORK_SAVED_TIME (logscale=True):\n",
      "len(branch_vals) = 213331, len(control_vals) = 0\n",
      "branch with no values\n"
     ]
    },
    {
     "data": {
      "image/png": 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RhLDlNOHrz2j+HPuvTdI0SJXr5UmSJE21gUa6krwvyYokj7Wvl7guyW/21Xy6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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3e753b0590>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "15471430 values for pref NETWORK_RACE_CACHE_WITH_NETWORK_USAGE_2\n",
      "\n",
      "NETWORK_RACE_CACHE_BANDWIDTH_RACE_NETWORK_WIN (logscale=False):\n",
      "len(branch_vals) = 1145932, len(control_vals) = 0\n",
      "branch with no values\n"
     ]
    },
    {
     "data": {
      "image/png": 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ZjnGf3e7j/Z3Mq2lK+wZg9lj/78Yff/wZnR9PH0saa0UzGwhA+x7ytwNXVdU/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\nHesG5KMAAAAASURBVDWvBh4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3e7737c0d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NETWORK_RACE_CACHE_BANDWIDTH_RACE_NETWORK_WIN (logscale=True):\n",
      "len(branch_vals) = 1145932, len(control_vals) = 0\n",
      "branch with no values\n"
     ]
    },
    {
     "data": {
      "image/png": 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0oemMqup/ufXzznNr+11VP6cJYUtpwteHaa5j/7lJmgKpcr08SZKkYRtopCvJ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\nswAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3e7543f190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "NETWORK_RACE_CACHE_BANDWIDTH_RACE_CACHE_WIN (logscale=False):\n",
      "len(branch_vals) = 3350917, len(control_vals) = 0\n",
      "branch with no values\n"
     ]
    },
    {
     "data": {
      "image/png": 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ZeaX+Nxf6YmZmZtajdLjAkyTSrdZfRMTzhaafAmcCnwMuBo4F5uY8pFu2GyJiTWmXK3Nb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\nwKcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3e74b06210>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NETWORK_RACE_CACHE_BANDWIDTH_RACE_CACHE_WIN (logscale=True):\n",
      "len(branch_vals) = 3350917, len(control_vals) = 0\n",
      "branch with no values\n"
     ]
    },
    {
     "data": {
      "image/png": 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FvpiZmZn1eDtc1EkS6TbqYxHxfKHpZ8BZwGeAS4ETgLk5D+l27IaIWFPa5crcVsm8UWyM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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3e7533cc50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "11584422 values for pref NETWORK_RACE_CACHE_BANDWIDTH_NOT_RACE\n",
      "\n",
      "NETWORK_RACE_CACHE_VALIDATION (logscale=False):\n",
      "len(branch_vals) = 802134, len(control_vals) = 0\n",
      "branch with no values\n"
     ]
    },
    {
     "data": {
      "image/png": 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S021V59+DIPN/tvGZa4DnAIcmuRt4frvt1/h5YPsNmnD2s6HP3jnB/tYBzwVI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\nkqSOs2CTJEnqOAs2SZKkjvsPvmh3dBJD6k8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3e75404d90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NETWORK_RACE_CACHE_VALIDATION (logscale=True):\n",
      "len(branch_vals) = 802134, len(control_vals) = 0\n",
      "branch with no values\n"
     ]
    },
    {
     "data": {
      "image/png": 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DNQcNdW2s0zb4O7admq1atmwZ8+c/9srp+Pg44+Pj2/uoJEl6ipiYmGBiYuIx2zZs2DDt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\nZtZx/wAVIAcHutJltgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3e77398910>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "HTTP_PAGE_COMPLETE_LOAD_V2 (logscale=False):\n",
      "len(branch_vals) = 13618, len(control_vals) = 9808\n",
      "rcwn-enabled ***2.40188683613e-24\n"
     ]
    },
    {
     "data": {
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Akt3a7fV6gibkzAXeC/zuZrZRwAF9bd/ezH6Y2Fe7v5cD5wG/2tvebm+fvt9v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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3e76f2c650>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "HTTP_PAGE_COMPLETE_LOAD_V2 (logscale=True):\n",
      "len(branch_vals) = 13618, len(control_vals) = 9808\n",
      "rcwn-enabled ***2.40188683613e-24\n"
     ]
    },
    {
     "data": {
      "image/png": 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43x+T3Q1sbo/nVNXNfZx3Lr9CM/n9X1fVp7cVJtnRY7r9aEbtOkeNlrd/3r+D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\n6AES98w16XYyewAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3e75544790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "HTTP_PAGE_COMPLETE_LOAD_NET_V2 (logscale=False):\n",
      "len(branch_vals) = 13618, len(control_vals) = 9808\n",
      "rcwn-enabled ***2.40188683613e-24\n"
     ]
    },
    {
     "data": {
      "image/png": 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Akt3a7fV6gibkzAXeC/zuZrZRwAF9bd/ezH6Y2Fe7v5cD5wG/2tvebm+fvt9v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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3e348329d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "HTTP_PAGE_COMPLETE_LOAD_NET_V2 (logscale=True):\n",
      "len(branch_vals) = 13618, len(control_vals) = 9808\n",
      "rcwn-enabled ***2.40188683613e-24\n"
     ]
    },
    {
     "data": {
      "image/png": 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43x+T3Q1sbo/nVNXNfZx3Lr9CM/n9X1fVp7cVJtnRY7r9aEbtOkeNlrd/3r+D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\n6AES98w16XYyewAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3e46edc390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "HTTP_PAGE_COMPLETE_LOAD_CACHED_V2 (logscale=False):\n",
      "len(branch_vals) = 4158, len(control_vals) = 4602\n",
      "rcwn-enabled ***1.05749063491e-16\n"
     ]
    },
    {
     "data": {
      "image/png": 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s1Xpny5YttLa2NnoYlR1++OEcffTRjR6GmZnZkDPokzBJFwAXkYp3PwnMjoj/\n0dhR9a+IHSxa9N+4+eabGz2Uyg488CDWrFm1XyRi9Xr9TbsrW/d8v6rx/eo936tqfL8Gr0GdhEn6\nW+AHwLnACtImrkslvT8iNjZ0cP1qJ7t27QBuAZoH8LpzSbU2+2oV27bNZOPGjUMyCVu3bh0bN/b+\nP6N/+Zd/Yfz48ftwRL0zVGYf/T/81fh+9Z7vVTW+X4PXoE7CSFnCNRFxM4Ck84BpwDnAFY0c2L7R\nDJwwgNc7ZICvN3isW7eO8eOb2bbttUrfG8iaYt3Zn2YfzcyGs0GbhEl6G9ACfKezLSJC0v3A5IYN\nzN5k1apVjR5CZatWrcoJWJXZx72dOewPQ3v20czM/mTQJmHA4cAIoK3U3gY0/pmQAS8DBzBz5sxG\nD2QvVJl9HDwzh0Mh8e3o6NjjZZPt27fT1NTUwBH13VB5BGxmQ8tgTsKqOjD9+RGpZvjQsXPn+vyv\nJcBA/h/XF4FFe/H9x4DdwFeAd/fLiAbO08BdVLvne3u/+sMTgIZM4rvn49sDSP+9DD1ve1sT3//+\n5Rx++OH77Bovvvgiixb1739fBxxwALt3D717/lbj3hf3qr8Mxnvem/t1+OGH8653vWuARjR4Ff4f\n3AMH4nqKiIG4TmX5ceRrwPSI+HGh/UbgkIj4Qin+TBr/fyHNzMxs6PtSRNy6ry8yaGfCIuJ1SSuB\nqcCPASQpf76yi68sBb4EvABsG6BhmpmZ2fBxIPAXpJxinxu0M2EAkv4GuBE4jz9tUfFF4AMR8UoD\nh2ZmZma2VwbtTBhARNwm6XDgW8BY4N+BU52AmZmZ2VA3qGfCzMzMzIYrF/A2MzMzawAnYWZmZmYN\n4CTMzMzMrAGchJmZ7SVJN0ra0svY3ZK+0Y/XPjmfc0p/ndPMBoaTMDOzvRf5aOT1zWyIcRJmZmZm\n1gBOwsysVyQd1OgxmJkNJ07CzOxNJF2a1xk1S7pV0ibgkdz3AUm3Sdog6TVJqyV9O/d9OH/vM4Vz\nnZDbflm6xr2SlhU+vyDpx5I+IWm5pD9K+rWksyqM+0hJP5K0XtI2Sb+SNKsU07mG6q8l/YOk3+Vr\n3S/p2FLsJ/Nv/W0+3zpJ8yR1WdxX0jGSlkr6D0m/l/SP/TXuHPceSf+Wz98maR7QBKi398jMBo9B\nvWO+mTVM5xqj24HngK+Tyrd+GHgU2A5cA/wWOBb4DHAJ8CvgVWAKcHc+x0nAbuCjkt4REf+R68BO\nBv6ldM2CXQfSAAAgAElEQVT/lK95Palk2TnADZJ+GRGrehqwpCOA5cAuUn3ZjcDpwPWS3hkR5Zqz\nX8ux3wcOAS4Gbsnj6vTXwNuBq4E/ABOA2cB7gL8tne/PgPuAZcD/BZwG/JOkERFx6d6OOyd+DwJH\nAf8MvAycBfwlXhNmNjRFhA8fPnzscQDfJCVO/7XU/jApyXpPD9/9CbCs8HkxKbHaAXw6tx2fz/+Z\nQtxaUiLyPxfaDgf+CFzRizFfB7wIjC613wpsApry55PztX8FjCjEzc7X/2ChramL61wM7ASOKrTd\nkL87v4t78UdgTKFtN/CNPox7Tr7GXxViDiQlybuAKY3+78aHDx/VDj+ONLPuBGm2C4Bcx/Uk4PqI\n+H0P33sEOEHS2/PnTwJLgCfz9+FPs2OPlr77bEQ8/sYAIjYCa4D39WK8f0VKekZIOqzzAH5Kmuk6\noRT/o4jYVRq3iteKiO2d/5Z0UD7fMtJSjuO7GMPC0uergJHAp/o47tGFcZ8OvBwR/70wvm3Av/Zw\nbjMbxPw40sx6srbw787k5Jm3+M4jwNuAyZJeBN6V2z7En5KwT5ISrldL313XxfnagUMBJB2Qz1e0\niZSsjAbOBf5LF+cI4IhS2++6uA6d18rX+3PgMuA/F9vz+Q4pfX838JtS23OkxO4vuhgTkt5VYdzv\nBZ7vImZNV+c2s8HPSZiZ9eSPffjOL4FtpHVhvwM2RMTzkh4Bzpc0kpSM/fcuvrurizb408LzPycl\nhpHbAvhf+FMicgtwUzfneKrKtXLCdz8pSfpuvsZW0nqwm+ifF5s6z1Fl3GY2TDgJM7Pe6pzl+VBP\nQRHxuqQVpCRsHfmtyvy3CfgSMBb4RR/GsJ43P9p7EtiSjxER8WAfztuVD5NeFDgrIhZ1Nkrq7tHi\nAaTZwuJs1fj894VuvvMKvR/3b4H/qYv2D7zF98xskPKaMDPrlbw+6xfAOfkxXU8eASYCp+R/ExF/\nAFaTFrYHf0rOqoxhe0Q8WDo6ImI3cAcwXdKbEpW8nq2qzpmy8v9O/j3dv414YRefdwAPdBVccdxL\ngCMlTS/0HwT87939ADMb3DwTZmZV/B+k5KlV0r+SHg0eA5wREcWF6o8A/0B6fFhMtn5BWvu0NiJe\n6uexfY2U9C2XdC3wLDAGaCFt41A1EVsN/Br4gaSjgM3AdNLjya5sB06TdCNpy4kzSIvp/9+cgO7t\nuK8lJXX/VdKJ/GmLiq0Vf5eZDRKeCTOzXouIp4BJpK0qziPtV/UF4N9KoY+TZpI2kx4XdnqENIvU\n1aPInuovvuU+WBGxgbSP14/ymH5IShpHA/93L8/3RntE7CTtf/YEKVH6Bmld2NndfPd10t5g44Ar\nSEnUpRFRLta9x+/s7bgj4o+kpGwpKRn7B9J9LP82MxsiFOE9/szMzMwG2l7NhEn6Wi7/Ma/QdkNu\nKx5LSt9rkrRQ0kZJWyQtzrtGF2MOlbRIUoekdknXSTp4b8ZrZmZmNlj0OQmT9HHS3jZPdtF9L+nt\np3H5qJX6FwDTSOsrpgBHkhanFt0KNANTc+wUChtHmpmZmQ1lfUrCJL2DtK/NV0klTMq2R8QrEbEh\nHx2F744i1YObGxEPR8QTwCzgE5Im5Jhm4FTgKxHxy7yD9mxghqRxfRmzmZmZ2WDS15mwhcBPetjX\n5hRJbZJWS7pa0phCXwvprcw3XtmOiDWk/YQ6C+dOAtpzgtbpftJi1ol9HLOZmZnZoFF5iwpJM4CP\nASd2E3Iv6dHiWuBY0k7TSyRNjvQWwDhgR0RsLn2vLfeR/24odkbELkmbCjFmZmZmQ1alJCzvlbMA\n+FREvN5VTETcVvj4jKSnSXvtnAI81Mdx9mZsh5EeYb5AKpliZmZmVsWBpFqvS99if79+UXUmrIVU\nPLdVUmcttxHAFEkXAk1R2vMiItZK2ggcR0rC1gMjJY0qzYaNzX3kv+W3JUeQNjBcT9dOBRZ102dm\nZmbWW18ivSC4T1VNwu4n1VMruhFYBXyvnIDBG7Nnh5F2dwZYCewkvfV4Z44ZDxwNLMsxy4DRko4v\nrAubSiqsu7ybsb0AcMstt9Dc3FzxZ+2f5s6dy/z58xs9jCHD96sa369qfL96z/eqGt+v3lu1ahUz\nZ86E7uu99qtKSVhEbCWV1HiDpK3AHyJiVd7H65ukNWHrSbNflwPPkXZ5JiI2S7oemCepnVS89krg\nsYhYkWNWS1oKXCvpfGAkaRfpekR0NxO2DaC5uZkTTjihys/abx1yyCG+VxX4flXj+1WN71fv+V5V\n4/vVJwOyrKk/akcWZ792AR8hlfUYDbxESr6+UVpDNjfHLgaagPuAC0rnPRO4ijT7tjvHzumH8ZqZ\nmZk13F4nYRHxl4V/byPVTnur72wn7fs1u4eYV4GZezs+MzMzs8HIBbzNzMzMGsBJ2H6sVitXk7Ke\n+H5V4/tVje9X7/leVeP7NXipixcahyRJJwArV65c6QWIZmZmVllraystLS0ALRHRuq+v55kwMzMz\nswZwEmZmZmbWAE7CzMzMzBrASZiZmZlZA+xVEibpa5J2S5pXav+WpJckvSbpZ5KOK/U3SVooaaOk\nLZIWSyrXijxU0iJJHZLaJV2Xd+Q3MzMzG/L6nIRJ+jhwLvBkqf1i4MLcNwHYCiyVNLIQtgCYBkwH\npgBHkkodFd0KNJNqRk7Lcdf0dbxmZmZmg0mfkjBJ7wBuAb4KvFrqngNcFhF3R8SvSCWMjgQ+n787\nCjgHmBsRD+cC3bOAT0iakGOagVOBr0TELyPicdLu+jMkjevLmM3MzMwGk77OhC0EfhIRDxYbJR0D\njAMe6GyLiM3AcmBybjqRVC6pGLMGWFeImQS05wSt0/2kOpUT+zhmMzMzs0Gjcu1ISTOAj5GSqbJx\npESprdTelvsAxgI7cnLWXcw4YEOxMyJ2SdpUiDEzMzMbsiolYZKOIq3n+lREvL5vhmRmZmY2/FWd\nCWsB3gW0SlJuGwFMkXQh8AFApNmu4mzYWKDz0eJ6YKSkUaXZsLG5rzOm/LbkCGBMIaZLc+fO5ZBD\nDtmjrVaruXaWmZmZvaFer1Ov1/do6+joGNAxVKodmbeIeG+p+UZgFfC9iFgl6SXg+xExP39nFCkh\nOzsibs+fXwFmRMSdOWZ8PsekiFgh6QPAM8CJnevCJH0aWAIcFRFvSsRcO9LMzMz2xkDXjqw0ExYR\nW4Fni22StgJ/iIhVuWkBcImk54EXgMuAF4G78jk2S7oemCepHdgCXAk8FhErcsxqSUuBayWdD4wE\nfgjUu0rAzMzMzIaaygvzu7DHVFpEXCHpINKeXqOBR4DTI2JHIWwusAtYDDQB9wEXlM57JnAV6a3I\n3Tl2Tj+M18zMzKzhKj2OHMw6H0decsklvPe95Semg5skarUaBx10UKOHYmZmtt8a1I8jh4Jvf/vb\njR5Cnzz33HNcfvnljR6GmZmZDZBhWMD7ZtIT0qFzvO1tH2Pr1q375naYmZnZoDQMkzAzMzOzwc9J\nmJmZmVkDOAkzMzMzawAnYWZmZmYNUCkJk3SepCcldeTjcUmnFfpvkLS7dCwpnaNJ0kJJGyVtkbRY\nUrlE0aGSFuVrtEu6Lu/Wb2ZmZjYsVJ0J+x1wMXACqY7kg8BdkpoLMfeS6kCOy0e5aOMCYBowHZgC\nHAncUYq5FWgGpubYKaTNX83MzMyGhapli+4pNV2SywpNItV+BNgeEa909f1cN/IcUt3Ih3PbLGCV\npAm5bmQzcCppo7TOupGzgXskXeSyRWZmZjYc9HlNmKQDJM0ADgIeL3SdIqlN0mpJV0saU+hrISV+\nD3Q2RMQaYB0wOTdNAto7E7DsftKmWhP7Ol4zMzOzwaTyjvmSPgQsAw4kFd/+Qk6kID2KvANYCxwL\nfBdYImlypPpI44AdEbG5dNq23Ef+u6HYGRG7JG0qxJiZmZkNaX0pW7Qa+ChwCPBF4GZJUyJidUTc\nVoh7RtLTwK+BU4CH9nawZmZmZsNF5SQsInYCv8kfn5A0AZgDnN9F7FpJG4HjSEnYemCkpFGl2bCx\nuY/8t/y25AhgTCGmB/OB20ttNd78foCZmZntr+r1OvV6fY+2jo6OAR1DfxTwPgBo6qpD0lHAYcDL\nuWklsJP01uOdOWY8cDTpESf572hJxxfWhU0FBCx/6+HMBc7qw88wMzOz/UWtVqNW23OCprW1lZaW\nlgEbQ6UkTNJ3SOu+1gHvBL4EnAx8Ou/j9U3SmrD1pNmvy4HngKUAEbFZ0vXAPEntpDVlVwKPRcSK\nHLNa0lLg2vzm5Ujgh0Ddb0aamZnZcFF1JuwI4Cbg3UAH8BTw6Yh4UNKBwEeAs4HRwEuk5OsbEfF6\n4RxzgV3AYtIM2n3ABaXrnAlcRXorcneOnVNxrGZmZmaDVtV9wr7aQ9824LTu+gtx24HZ+egu5lVg\nZpWxmZmZmQ0lrh1pZmZm1gBOwszMzMwawEmYmZmZWQM4CTMzMzNrACdhZmZmZg3gJMzMzMysAZyE\nmZmZmTVApSRM0nmSnpTUkY/HJZ1WivmWpJckvSbpZ5KOK/U3SVooaaOkLZIWSyrXijxU0qJ8jXZJ\n1+Ud+c3MzMyGhaozYb8DLgZOAFqAB4G7JDUDSLoYuBA4F5gAbAWWShpZOMcCYBowHZgCHEkqdVR0\nK9BMqhk5LcddU3GsZmZmZoNW1R3z7yk1XZLrO04CVpFKC10WEXcDSDobaAM+D9wmaRRwDjAjIh7O\nMbOAVZImRMSKnNCdCrR0FvCWNBu4R9JFrh9pZmZmw0Gf14RJOkDSDOAg4HFJxwDjgAc6YyJiM7Ac\nmJybTiQlfsWYNaSC4J0xk4D2zgQsux8IYGJfx2tmZmY2mFQt4I2kDwHLgAOBLcAXImKNpMmkRKmt\n9JU2UnIGMBbYkZOz7mLGARuKnRGxS9KmQoyZmZnZkFY5CQNWAx8FDgG+CNwsaUq/jsrMzMxsmKuc\nhEXETuA3+eMTkiaQ1oJdAYg021WcDRsLdD5aXA+MlDSqNBs2Nvd1xpTflhwBjCnE9GA+cHuprZYP\nMzMzM6jX69Tr9T3aOjo6BnQMfZkJKzsAaIqItZLWk95ofAogL8SfCCzMsSuBnTnmzhwzHjia9IiT\n/He0pOML68KmkhK85W89nLnAWXv9o8zMzGz4qtVq1Gp7TtC0trbS0tIyYGOolIRJ+g5wL2kh/TuB\nLwEnA5/OIQtIb0w+D7wAXAa8CNwFaaG+pOuBeZLaSWvKrgQei4gVOWa1pKXAtfnNy5HAD4G634w0\nMzOz4aLqTNgRwE3Au4EO0ozXpyPiQYCIuELSQaQ9vUYDjwCnR8SOwjnmAruAxUATcB9wQek6ZwJX\nkd6K3J1j51Qcq5mZmdmgVXWfsK/2IuZS4NIe+rcDs/PRXcyrwMwqYzMzMzMbSlw70szMzKwBnISZ\nmZmZNYCTMDMzM7MGcBJmZmZm1gBOwszMzMwawEmYmZmZWQNUSsIkfV3SCkmbJbVJulPS+0sxN0ja\nXTqWlGKaJC2UtFHSFkmLJZVLFR0qaZGkDkntkq6TdHDff6qZmZnZ4FF1Juwk0u71E4FPAW8Dfirp\n7aW4e0n1IMflo1y4cQEwDZgOTAGOBO4oxdwKNJNKFk3LcddUHK+ZmZnZoFR1s9Yzip8lfRnYALQA\njxa6tkfEK12dI9eTPAeYEREP57ZZwCpJEyJihaRm4FSgpbN+pKTZwD2SLnL5IjMzMxvq9nZN2Ggg\ngE2l9lPy48rVkq6WNKbQ10JK/h7obIiINaR6lJNz0ySgvVDAG1IJoyDNwpmZmZkNaVVrR75BkkiP\nFR+NiGcLXfeSHi2uBY4FvgsskTQ5IoL0eHJHRGwunbIt95H/bih2RsQuSZsKMWZmZmZDVp+TMOBq\n4IPAJ4qNEXFb4eMzkp4Gfg2cAjy0F9czMzMzGzb6lIRJugo4AzgpIl7uKTYi1kraCBxHSsLWAyMl\njSrNho3NfeS/5bclRwBjCjHdmA/cXmqr8eZ3A8zMzGx/Va/Xqdfre7R1dHQM6BgqJ2E5AfsccHJE\nrOtF/FHAYUBnsrYS2El66/HOHDMeOBpYlmOWAaMlHV9YFzYVELC85yvOBc7q/Q8yMzOz/U6tVqNW\n23OCprW1lZaWlgEbQ6UkTNLVpCmlzwJbJY3NXR0RsS3v4/VN0pqw9aTZr8uB54ClABGxWdL1wDxJ\n7cAW4ErgsYhYkWNWS1oKXCvpfGAkaWuMut+MNDMzs+Gg6kzYeaQ3FH9eap8F3AzsAj4CnE16c/Il\nUvL1jYh4vRA/N8cuBpqA+4ALSuc8E7iK9Fbk7hw7p+J4zczMzAalqvuE9bilRURsA07rxXm2A7Pz\n0V3Mq8DMKuMzMzMzGypcO9LMzMysAZyEmZmZmTWAkzAzMzOzBnASZmZmZtYATsLMzMzMGsBJmJmZ\nmVkDOAkzMzMza4BKSZikr0taIWmzpDZJd0p6fxdx35L0kqTXJP1M0nGl/iZJCyVtlLRF0mJJ5VqR\nh0paJKlDUruk6/KO/GZmZmZDXtWZsJNI5YMmAp8C3gb8VNLbOwMkXQxcCJwLTAC2AksljSycZwEw\nDZgOTAGOJJU6KroVaCbVjJyW466pOF4zMzOzQanqjvlnFD9L+jKwAWgBHs3Nc4DLIuLuHHM20AZ8\nHrhN0ijgHGBGRDycY2YBqyRNiIgVkpqBU4GWzgLekmYD90i6yPUjzczMbKjb2zVho0m1JDcBSDoG\nGAc80BkQEZuB5cDk3HQiKfkrxqwB1hViJgHtnQlYdn++1sS9HLOZmZlZw/U5CZMk0mPFRyPi2dw8\njpQotZXC23IfwFhgR07OuosZR5phe0NE7CIle+MwMzMzG+IqPY4suRr4IPCJfhqLmZmZ2X6jT0mY\npKuAM4CTIuLlQtd6QKTZruJs2FjgiULMSEmjSrNhY3NfZ0z5bckRwJhCTDfmA7eX2mr5MDMzM4N6\nvU69Xt+jraOjY0DHUDkJywnY54CTI2JdsS8i1kpaT3qj8akcP4q0jmthDlsJ7Mwxd+aY8cDRwLIc\nswwYLen4wrqwqaQEb3nPI5wLnFX1Z5mZmdl+pFarUavtOUHT2tpKS0vLgI2hUhIm6WrSlNJnga2S\nxuaujojYlv+9ALhE0vPAC8BlwIvAXZAW6ku6HpgnqR3YAlwJPBYRK3LMaklLgWslnQ+MJG2NUfeb\nkWZmZjYcVJ0JO4+08P7npfZZwM0AEXGFpINIe3qNBh4BTo+IHYX4ucAuYDHQBNwHXFA655nAVaS3\nInfn2DkVx2tmZmY2KFXdJ6xXb1NGxKXApT30bwdm56O7mFeBmVXGZ2ZmZjZUuHakmZmZWQM4CTMz\nMzNrACdhZmZmZg3gJMzMzMysAZyEmZmZmTWAkzAzMzOzBqichEk6SdKPJf1e0m5Jny3135Dbi8eS\nUkyTpIWSNkraImmxpHKZokMlLZLUIald0nWSDu7bzzQzMzMbXPoyE3Yw8O/A35E2bu3KvaRakOPy\nUS7cuACYBkwHpgBHAneUYm4FmknliqbluGv6MF4zMzOzQady7ciIuI+0wz2S1E3Y9oh4pauOXEvy\nHGBGRDyc22YBqyRNiIgVkpqBU4GWztqRkmYD90i6yKWLzMzMbKjbV2vCTpHUJmm1pKsljSn0tZCS\nvwc6GyJiDbAOmJybJgHtheLdkMoXBakYuJmZmdmQVnkmrBfuJT1aXAscC3wXWCJpckQE6fHkjojY\nXPpeW+4j/91Q7IyIXZI2FWLMzMzMhqx+T8Ii4rbCx2ckPQ38GjgFeKi/r2dmZmY2FO2LmbA9RMRa\nSRuB40hJ2HpgpKRRpdmwsbmP/Lf8tuQIYEwhphvzgdtLbTXe/G6AmZmZ7a/q9Tr1en2Pto6OjgEd\nwz5PwiQdBRwGvJybVgI7SW893pljxgNHA8tyzDJgtKTjC+vCpgIClvd8xbnAWf33A8zMzGzYqdVq\n1Gp7TtC0trbS0tIyYGOonITlvbqOIyVEAO+T9FFgUz6+SVoTtj7HXQ48BywF+P/bu/8gq8r7juPv\nD44sRYcSXWC1kZSUhNKxVbOIkkaShrSaYEwy6RhXkYm0k9EShjKTibVNC9U2bWQkBsWOo2YigptB\nnPyyRqomNQb50bDEjA1iohiLCBHFZScgv/bbP56z4exl77K77L3n3uXzmrkD95zv3vuc75y9+73P\nOc/zRMReSfcBSyTtATqApcDaiNiYxTwvaQ1wj6QbgOHAHUCrR0aamZnZUDCQnrAppMuKkT1uy7bf\nT5o77E+A2cBoYAep+PqniDiUe40FwBFgNdBAmvJibsn7XA3cSRoV2ZnFzh9Ae83MzMxqzkDmCXuK\n3qe2uKwPr3EAmJc9ysW8Bczqb/vMzMzM6oHXjjQzMzMrgIswMzMzswK4CDMzMzMrgIswMzMzswK4\nCDMzMzMrgIswMzMzswK4CDMzMzMrQL+LMEmXSPqupFcldUq6ooeYmyXtkLRP0uOSJpbsb5C0TNJu\nSR2SVksqXSvyHZJWSmqXtEfSvdls/WZmZmZ1byA9YacBPyXNjh+lOyXdCHwe+BwwFfgNsEbS8FzY\n7cBM4NPAdOBs0lJHeQ8Ck0lrRs7M4u4eQHvNzMzMas5AZsx/jLTMEJLUQ8h84JaIeCSLmQ3sAj4J\nrJI0CpgDXJXNvo+k64AtkqZGxEZJk4FLgeauBbwlzQP+U9IXvH6kmZmZ1btBvSdM0gSgCXiya1tE\n7AU2ANOyTVNIxV8+ZivwSi7mYmBPVwGWeYLU83bRYLbZzMzMrAiDfWN+E6lQ2lWyfVe2D2AccDAr\nzsrFNAG/zu+MiCPAm7kYMzMzs7rV78uRte+rwEMl21qyh5mZmRm0trbS2trabVt7e3tV2zDYRdhO\nQKTernxv2Dhgcy5muKRRJb1h47J9XTGloyVPAc7IxZSxALh2YK03MzOzk0JLSwstLd07aNra2mhu\nbq5aGwb1cmREbCMVSTO6tmU34l8EPJNt2gQcLomZBIwH1mWb1gGjJV2Qe/kZpAJvw2C22czMzKwI\n/e4Jy+bqmkgqiADeLek84M2I+D/S9BNfkvRL4GXgFmA78B1IN+pLug9YImkP0AEsBdZGxMYs5nlJ\na4B7JN0ADAfuAFo9MtLMzMyGgoFcjpwC/JB0A34At2Xb7wfmRMStkkaS5vQaDTwNfDQiDuZeYwFw\nBFgNNJCmvJhb8j5XA3eSRkV2ZrHzB9BeMzMzs5ozkHnCnuI4lzEjYhGwqJf9B4B52aNczFvArP62\nz8zMzKweeO1IMzMzswK4CDMzMzMrgIswMzMzswK4CDMzMzMrgIswMzMzswK4CDMzMzMrwKAXYZIW\nSuosefy8JOZmSTsk7ZP0uKSJJfsbJC2TtFtSh6TVksZiZmZmNkRUqifsOdJakE3Z4wNdOyTdCHwe\n+BwwFfgNsEbS8NzP3w7MBD4NTAfOBh6uUFvNzMzMqm6wF/DucjgiXi+zbz5wS0Q8AiBpNmmx708C\nq7K1JucAV2UTwyLpOmCLpKldSxuZmZmZ1bNK9YS9R9Krkl6UtELSOQCSJpB6xp7sCoyIvaRFuadl\nm6aQisN8zFbglVyMmZmZWV2rRBG2HvgscClwPTAB+FG28HcTab3JXSU/syvbB+ky5sGsOCsXY2Zm\nZlbXBv1yZESsyT19TtJG4FfAlcDzg/1+ZmZmZvWoUveE/VZEtEt6AZgI/DcgUm9XvjdsHLA5+/9O\nYLikUSW9YeOyfcfxVeChkm0t2cPMzMwMWltbaW1t7batvb29qm2oeBEm6XRSAXZ/RGyTtBOYAfws\n2z8KuAhYlv3IJuBwFvOtLGYSMB5Yd/x3XABcO6jHYGZmZkNLS0sLLS3dO2ja2tpobm6uWhsGvQiT\ntBj4HukS5O8B/wwcAr6ZhdwOfEnSL4GXgVuA7cB3IN2oL+k+YImkPUAHsBRY65GRZmZmNlRUoifs\nncCDwJnA68CPgYsj4g2AiLhV0kjgbmA08DTw0Yg4mHuNBcARYDXQADwGzK1AW83MzMwKUYkb8497\n81VELAIW9bL/ADAve5iZmZkNOV470szMzKwALsLMzMzMCuAizMzMzKwALsLMzMzMClDxecKsbzo6\nOmhrayu6Gf3W2NjI+PHji26GmZlZ3XERVgMiDrJy5TdZvnx50U3ptxEjRrJ16xYXYmZmZv3kIqwm\nHObIkYPACmByFd/3MeCyE/j5Lbz99ix27959UhRhra2tx8yubOU5X/3jfPWdc9U/zlftqvkiTNJc\n4AtAE/AsMC8i/qfYVlXKZOB9VXy/RcDfV/H96ps/yPrH+eof56vvnKv+cb5qV03fmC/pM8BtwELg\nAlIRtkZSY6ENMzMzMztBtd4TtgC4OyKWA0i6HpgJzAFuLbJhdtSWLVuKbsKAeFCBmZkVqWaLMEmn\nAs3Al7u2RURIegKYVljDLOc1YBizZs0quiED0tAwgocfXs1ZZ53Vp/j29vaaGMHq4tHMbGio2SIM\naAROAXaVbN8FTOohfkT654fA/kq2a9B1dr6V/e9RoJq9StuBlSfw82uBTuCvgL4VMrXjFxw4sIrL\nL7+8Xz/V3Nxcofb03amnNrB48VdobKztq/Lbt29n5cqj59ewYcPo7OwssEUDV422l+ZrMNRrzo/X\n7krkarDUYs77kq/GxkbGjBlTpRbVrtyVnRHVeD9FRDXep98knQW8CkyLiA257V8BpkfEtJL4qzmx\nisLMzMwM4JqIeLDSb1LLPWG7gSPAuJLt44CdPcSvAa4BXgbermjLzMzMbCgaAfw+qaaouJrtCQOQ\ntB7YEBHzs+cCXgGWRsTiQhtnZmZmdgJquScMYAnwDUmbgI2k0ZIjgW8U2SgzMzOzE1XTRVhErMrm\nBLuZdBnyp8ClEfF6sS0zMzMzOzE1fTnSzMzMbKiq6RnzzczMzIaqIVGESZoraZuk/ZLWS7qw6DZV\ng6RLJH1X0quSOiVd0UPMzZJ2SNon6XFJE0v2N0haJmm3pA5JqyWNLYl5h6SVktol7ZF0r6TTKn18\ng7QhRhEAAAWZSURBVEnSTZI2StoraZekb0l6bw9xzhdpdQpJz2bH0C7pGUmXlcQ4Vz2Q9HfZ7+OS\nku3OV0bSwixH+cfPS2KcrxxJZ0t6IDvefdnv5/tKYpwzQKkeKD2/OiXdkYupjVxFRF0/gM+QpqSY\nDfwhcDfwJtBYdNuqcOyXke6X+wRpOo8rSvbfmOXicuBc4NvAi8DwXMx/kKb1+CBpfc5ngKdLXuf7\nQBswBXg/8AKwoujj72euHgWuJa2S/sfAI9lx/47z1WO+Zmbn1x8AE4F/AQ4Ak52rXvN2IfASsBlY\n4nOrbJ4WAj8DxgBjs8cZzlfZfI0GtgH3klaSeRfwEWCCc9Zjvs7MnVdjgRmkv5GX1FquCk/WICR7\nPfC13HORpoL/YtFtq3IeOjm2CNsBLMg9H0VaTuDK3PMDwKdyMZOy15qaPZ+cPb8gF3MpcBhoKvq4\nTyBfjdlxfcD56nPO3gCuc67K5ud0YCvwYdLSHfkizPnqnquFQFsv+52v7vn4d+Cp48Q4Z+Vzczvw\nQi3mqq4vR+ro+pJPdm2LlImTfn1JSROAJrrnZi+wgaO5mUIaIZuP2Uqai60r5mJgT0Rszr38E0AA\nF1Wq/VUwmnQMb4Lz1RtJwyRdRZoe5hnnqqxlwPci4gf5jc5XWe9RupXiRUkrJJ0DzlcZHwd+ImmV\n0u0UbZL+umunc1ZeVidcA9yXPa+pXNV1EUbv60s2Vb85NaWJdDL0lptxwMHsBCwX0wT8Or8zIo6Q\nipe6zLEkkb4Z/Tgiuu5Dcb5KSDpXUgfpG+FdpG+FW3GujpEVqecDN/Ww2/k61nrgs6Seg+uBCcCP\nsvtpnK9jvRu4gdTT+hekS2VLJV2b7XfOyvsU8LvA/dnzmspVTc8TZlYhdwF/BPxp0Q2pcc8D55E+\nwP4SWC5perFNqj2S3kkq6j8SEYeKbk89iIj8kjDPSdoI/Aq4knTeWXfDgI0R8Y/Z82clnUsqYB8o\nrll1YQ7w/YjoabnDwtV7T1h/15c8mewk3R/XW252AsMljTpOTOmIkFOAM6jDHEu6E/gY8KGIeC23\ny/kqERGHI+KliNgcEf8APAvMx7kq1Uy6wbxN0iFJh0g3886XdJD07dn56kVEtJNuap6Iz6+evAZs\nKdm2BRif/d8564Gk8aQBDPfkNtdUruq6CMu+dW4ijXwAfnupaQZpJMNJKyK2kU6EfG5Gka5Vd+Vm\nE+kmwnzMJNIv9rps0zpgtKQLci8/g3QSb6hU+yshK8A+AfxZRLyS3+d89ckwoMG5OsYTpBG355N6\nDs8DfgKsAM6LiJdwvnol6XRSAbbD51eP1pJuDM+bROo99OdXeXNIX4Ie7dpQc7kqetTCIIx6uBLY\nR/cpKt4AxhTdtioc+2mkD/zzSaM0/jZ7fk62/4tZLj5O+iPxbeAXdB+Gexdp6POHSN/o13LsMNxH\nSX9ULiRdwtsKPFD08fczV3cBe4BLSN9muh4jcjHO19Fj+HKWq3eRhnD/G+lD6cPOVZ/yVzo60vnq\nfhyLgenZ+fV+4HHSH8szna8e8zWFdG/mTaRpY64GOoCrfI6VzZlIU0z8aw/7aiZXhSdqkJL9N1my\n95Oq0ylFt6lKx/1BUvF1pOTx9VzMItJw3H3AGmBiyWs0AHeQLu12AA8BY0tiRpO+1beTCpl7gJFF\nH38/c9VTno4As0vinK90DPeS5rvaT/rW+F9kBZhz1af8/YBcEeZ8HZOfVtJUQvtJI84eJDfnlfPV\nY84+RppbbR/wv8CcHmKcs6PH8eekz/iJZfbXRK68dqSZmZlZAer6njAzMzOzeuUizMzMzKwALsLM\nzMzMCuAizMzMzKwALsLMzMzMCuAizMzMzKwALsLMzMzMCuAizMzMzKwALsLMzMzMCuAizMzMzKwA\nLsLMzMzMCuAizMzMzKwA/w9H04Po2tZR3QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3e74fd6450>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "HTTP_PAGE_COMPLETE_LOAD_CACHED_V2 (logscale=True):\n",
      "len(branch_vals) = 4158, len(control_vals) = 4602\n",
      "rcwn-enabled ***1.05749063491e-16\n"
     ]
    },
    {
     "data": {
      "image/png": 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fX9Ne+6vA8zrGXdBe/6UdbUtmuc5FwNPAsR1tH22/u2aWe/Ek8IKOtmeB9/YR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\np7FVtTHJNHACTSK1CTg0ydK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uBD4F1ncY25N81X5G6gzW3ROwV9LPkrZIurW/M53QbiHV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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3e7740d110>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "HTTP_SUB_COMPLETE_LOAD_V2 (logscale=False):\n",
      "len(branch_vals) = 113560, len(control_vals) = 107399\n",
      "rcwn-enabled ***3.06151305637e-50\n"
     ]
    },
    {
     "data": {
      "image/png": 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jiz2YBYzZ0XM2MzOzPVBzczPNzVsP/rS1tdHU1FSnHmVqEfB+CVwu6UXg92QpaSpwY65m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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3e74f369d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "HTTP_SUB_COMPLETE_LOAD_V2 (logscale=True):\n",
      "len(branch_vals) = 113560, len(control_vals) = 107399\n",
      "rcwn-enabled ***3.06151305637e-50\n"
     ]
    },
    {
     "data": {
      "image/png": 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oL9TMB4ZzEGtYBwQwb4xjNjMzMzvhTi37A0mXA/+GFJqadZIC0WBT+2DuA+gADuYQNlJN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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3e34848290>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "HTTP_SUB_COMPLETE_LOAD_CACHED_V2 (logscale=False):\n",
      "len(branch_vals) = 144758, len(control_vals) = 82628\n",
      "rcwn-enabled ***0.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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S1i8Dnm9rPt/WHAjsByxpa5YAs5Ic1Llv7kiaoHhHp+ZjSWZ37ps7ClgH3Lf1w7gAmL/1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\nSdKIWcxJkiSNmMWcJEnSiP0DS7JnSr4hSw0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3e7741d810>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "HTTP_SUB_COMPLETE_LOAD_CACHED_V2 (logscale=True):\n",
      "len(branch_vals) = 144758, len(control_vals) = 82628\n",
      "rcwn-enabled ***0.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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CqK/mO325D9Ljx0O5CBsuZiawu9gZEYeBlwoxZmZmZpNCqceIEbGmcPgNSZuA7wK/A2wd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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3e756ad650>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "HTTP_SUB_COMPLETE_LOAD_NET_V2 (logscale=False):\n",
      "len(branch_vals) = 113560, len(control_vals) = 107399\n",
      "rcwn-enabled ***3.06151305637e-50\n"
     ]
    },
    {
     "data": {
      "image/png": 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jiz2YBYzZ0XM2MzOzPVBzczPNzVsP/rS1tdHU1FSnHmVqEfB+CVwu6UXg92QpaSpwY65m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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3e753e5510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "HTTP_SUB_COMPLETE_LOAD_NET_V2 (logscale=True):\n",
      "len(branch_vals) = 113560, len(control_vals) = 107399\n",
      "rcwn-enabled ***3.06151305637e-50\n"
     ]
    },
    {
     "data": {
      "image/png": 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oL9TMB4ZzEGtYBwQwb4xjNjMzMzvhTi37A0mXA/+GFJqadZIC0WBT+2DuA+gADuYQNlJN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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3de3cc6c90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "TOTAL_CONTENT_PAGE_LOAD_TIME (logscale=False):\n",
      "len(branch_vals) = 23036, len(control_vals) = 18786\n",
      "rcwn-enabled ***6.59745599064e-156\n"
     ]
    },
    {
     "data": {
      "image/png": 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lx91c7cxJ2gOkveomAB8DzgOu7sU9mZmZmTWE3mzSu5hUEQEVqs/nvq3A5GKbpEuAFZLe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\nMzMzsxpx8WZmZmZWIy7ezMzMzGrk/wGjj6Po5LV4FgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3e7736f290>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TOTAL_CONTENT_PAGE_LOAD_TIME (logscale=True):\n",
      "len(branch_vals) = 23036, len(control_vals) = 18786\n",
      "rcwn-enabled ***6.59745599064e-156\n"
     ]
    },
    {
     "data": {
      "image/png": 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FOL6UckNbczKwNslhpZTVldosSZLUd7VGtn4lyf1JvpfksiQvBUhyAM1I1/UThaWUR4Gb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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3e753e5910>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# get the bins we use for the histogram for a probe by looking at all branches\n",
    "def get_bins(probe_name, logscale=False):\n",
    "    \n",
    "    all_branches = [r.val for r in df.where(df.probe == probe_name)\\\n",
    "                                     .collect()]\n",
    "\n",
    "    #remove top 0.5%, bottom 0.5% for easy outlier\n",
    "    trim = int(len(all_branches)/200.0)\n",
    "    all_branches_trimmed = sorted(all_branches)\n",
    "    all_branches_trimmed = all_branches_trimmed[trim:-1*trim]\n",
    "    \n",
    "    if logscale:\n",
    "        if all_branches_trimmed[0] < 1:\n",
    "            all_branches_trimmed = py_map(lambda d: d+1, all_branches_trimmed)\n",
    "        return list(np.linspace(np.log10(all_branches_trimmed[1]), np.log10(all_branches_trimmed[-1]), 10))\n",
    "    \n",
    "    n,b = np.histogram(all_branches_trimmed,10)\n",
    "    return b\n",
    "\n",
    "# get values for branch of experiment for pref, and trim off outliers\n",
    "def get_vals(pref_name, branch):\n",
    "    x_vals = [r.val for r in df.where(df.probe == pref_name)\\\n",
    "                               .where(df.branch == branch)\\\n",
    "                               .collect()] \n",
    "    trim = int(len(x_vals)/200.0)\n",
    "    x_trimmed = sorted(x_vals)[trim:-1*trim]\n",
    "    return x_trimmed\n",
    "\n",
    "def median(lst):\n",
    "    return lst[(len(lst))/2]\n",
    "\n",
    "# return (pval, direction) if significant p value for mannwhitneyu vs control\n",
    "def test_unequal(branch_vals, control_vals, p_threshold=.05):\n",
    "    \n",
    "    try:\n",
    "        r = mannwhitneyu(branch_vals, control_vals)\n",
    "    except:\n",
    "        return None, None\n",
    "    \n",
    "    prefix = \"\"\n",
    "    if r.pvalue < p_threshold:\n",
    "        prefix = \"***\"\n",
    "    \n",
    "    if median(branch_vals) > median(control_vals):\n",
    "        return (prefix + str(r.pvalue), \"> control\")\n",
    "   \n",
    "    return (prefix + str(r.pvalue), \"< control\")\n",
    "\n",
    "# return (proceed bool, reason)\n",
    "def can_chart_pref(pref_name):\n",
    "    n = df.where(df.probe == pref_name).count()\n",
    "    if n==0:\n",
    "        return (False, \"0 entries for pref %s\"%pref_name)\n",
    "    elif n>10000000:\n",
    "        return (False, \"%i values for pref %s\"%(n,pref_name))\n",
    "    return (True, None)   \n",
    "\n",
    "# chart histograms for all branches of a probe, log/std, and calculate if any branches vary from the mean\n",
    "def chart_pref(pref_name, logscale):\n",
    "   \n",
    "    sig_branches = []    \n",
    "    fig, axarr = plt.subplots(n_branches, 1, sharex=True, sharey= 'col')\n",
    "\n",
    "    b = get_bins(pref_name, logscale)\n",
    "    plt.tight_layout()\n",
    "    \n",
    "    print pref_name + \" (logscale=\" + str(logscale) + \")\" + \":\"\n",
    "\n",
    "    control_vals = get_vals(pref_name, \"control\")\n",
    "    \n",
    "    for i in range(n_branches):\n",
    "        \n",
    "        if branches[i] == \"control\":\n",
    "            x = control_vals\n",
    "            \n",
    "        else:\n",
    "            x = get_vals(pref_name, branches[i]) \n",
    "        \n",
    "        if logscale: #always assume 0 as lowest val for now\n",
    "            x_trans = py_map(lambda d: d+1, x)\n",
    "            ap,bp,cp = axarr[i].hist(np.log(x_trans), bins=b)\n",
    "            \n",
    "        else: \n",
    "            axarr[i].hist(x, bins=b)\n",
    "            \n",
    "        axarr[i].set_title(branches[i])\n",
    "        \n",
    "        if branches[i] == \"control\": continue\n",
    "        \n",
    "        print \"len(branch_vals) = \" + str(len(x)) + \", len(control_vals) = \" + str(len(control_vals))\n",
    "        if len(x) != 0 | len(control_vals) != 0:\n",
    "            p, direction = test_unequal(x, control_vals)\n",
    "            if p is not None and p.startswith(\"***\"):\n",
    "                print branches[i], p #, direction\n",
    "        else:\n",
    "            print \"branch with no values\"\n",
    "            continue\n",
    "            \n",
    "    plt.show()\n",
    "    \n",
    "for p in probe_names:\n",
    "    can_chart, reason = can_chart_pref(p)\n",
    "    if can_chart:\n",
    "        chart_pref(p, False)\n",
    "        chart_pref(p, True)\n",
    "    else:\n",
    "        print reason\n",
    "    print"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[]\n"
     ]
    }
   ],
   "source": [
    "print get_vals('NETWORK_RACE_CACHE_BANDWIDTH_RACE_NETWORK_WIN', \"control\")\n",
    "# there is seriously NOTHING there in the control branch for probes starting with N_R_C_"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda root]",
   "language": "python",
   "name": "conda-root-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}

## Race Cache 2 via ilana workflow.py

# coding: utf-8

# This script is intended for variables not yet available in the [Experiments Viewer](https://moz-experiments-viewer.herokuapp.com/).

# In[1]:

import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import plotly.plotly as py
from scipy.stats import chi2_contingency
from scipy.stats import ttest_ind
from collections import defaultdict as dd

from pyspark.sql import Row
from pyspark.sql import SQLContext
from pyspark.sql.types import *
from pyspark.sql.functions import *
import itertools

from moztelemetry.dataset import Dataset

from scipy.stats import mannwhitneyu

py_max = __builtin__.max
py_map = __builtin__.map

get_ipython().magic(u'pylab inline')


# In[2]:

experiment_probes = {}
experiment_probes["payload/histograms"] =  [
                "NETWORK_RACE_CACHE_WITH_NETWORK_OCEC_ON_START_DIFF",
                "NETWORK_RACE_CACHE_WITH_NETWORK_SAVED_TIME",
                "NETWORK_RACE_CACHE_WITH_NETWORK_USAGE_2",
                "NETWORK_RACE_CACHE_BANDWIDTH_RACE_NETWORK_WIN",
                "NETWORK_RACE_CACHE_BANDWIDTH_RACE_CACHE_WIN",
                "NETWORK_RACE_CACHE_BANDWIDTH_NOT_RACE",
                "NETWORK_RACE_CACHE_VALIDATION",
                "HTTP_PAGE_COMPLETE_LOAD_V2",
                "HTTP_PAGE_COMPLETE_LOAD_NET_V2",
                "HTTP_PAGE_COMPLETE_LOAD_CACHED_V2",
                "HTTP_SUB_COMPLETE_LOAD_V2",
                "HTTP_SUB_COMPLETE_LOAD_CACHED_V2",
                "HTTP_SUB_COMPLETE_LOAD_NET_V2",
                "TOTAL_CONTENT_PAGE_LOAD_TIME",
                ]

probe_names = list(itertools.chain(*experiment_probes.values()))

EXPERIMENT_SLUG = "pref-flip-rcwn2-1381816"
START_DATE = "20170726" #YYYYMMDD
END_DATE = "20170802" #YYYYMMDD


# In[3]:

cohorts = Dataset.from_source("telemetry-cohorts")
pings = cohorts.where(submissionDate = lambda x: x >= START_DATE and x <= END_DATE)                .where(experimentId=EXPERIMENT_SLUG)                .records(sc, sample=0.01)

#pings.cache()
#pings.count()


# In[4]:

main_pings = pings.filter(lambda x: x["meta"]["docType"]=="main")
main_pings.cache()


# In[ ]:

# haven't run this yet - trying to aggregate histograms per Client instead of collapsing over them

byClient = main_pings.map(lambda p: (p['clientId'], [p])).reduceByKey(lambda x,y: x+y)

#modified from https://stackoverflow.com/questions/13852896/using-a-single-variable-to-index-into-nested-dictionaries
def recursive_get(d, keys):
    if len(keys) == 1:
        return d.get(keys[0],{})
    return recursive_get(d.get(keys[0],{}), keys[1:])

def extract_probes(i):
    clientId, pinglist = i

    # determine which branch this client was in and note if it ended up in both
    branch = []
    for p in pinglist:
        branch.extend(p.get("environment",{}).get("experiments",{}).get(EXPERIMENT_SLUG, {}).get("branch", "warning"))
    if len(np.unique(branch)) > 1:
        branch = "both"
    else:
        branch = np.unique(branch)[0]

    # if you input by client, pinglist instead of ping add another layer of iteration over the list here
    output = []
    for p in pinglist:
        for path, probe_names in experiment_probes.iteritems():
            for probe_name in probe_names:
                probe = recursive_get(p, path.split("/")).get(probe_name, {})
                for k,v in probe.get("values",{}).iteritems():
                    output.extend([{"probe": probe_name, "branch": branch, "val": float(k)}] * int(v))
    # by this point you should have all this clients pings in an aggregate histogram, right? what then?


#    return output

fin = byClient.flatMap(extract_probes)


# In[5]:

#modified from https://stackoverflow.com/questions/13852896/using-a-single-variable-to-index-into-nested-dictionaries
def recursive_get(d, keys):
    if len(keys) == 1:
        return d.get(keys[0],{})
    return recursive_get(d.get(keys[0],{}), keys[1:])

def extract_probes(p):
    branch = p.get("environment",{}).get("experiments",{}).get(EXPERIMENT_SLUG, {}).get("branch", "warning")

    output = []
    for path, probe_names in experiment_probes.iteritems():
        for probe_name in probe_names:
            probe = recursive_get(p, path.split("/")).get(probe_name, {})
            #take all entries in histogram right now. this is probably problematic. inspect more
            for k,v in probe.get("values",{}).iteritems():
                output.extend([{"probe": probe_name, "branch": branch, "val": float(k)}] * int(v))
    return output

probe_dicts = main_pings.flatMap(extract_probes)


# In[6]:

df = sqlContext.createDataFrame(probe_dicts.map(lambda d: Row(**d)))


# In[7]:

#check on branch distribution
for b,v in main_pings.map(lambda x: x.get("environment",{}).get("experiments",{}).get(EXPERIMENT_SLUG, {}).get("branch", "warning")).countByValue().iteritems():
    print b, ":", v


# In[8]:

branches = [i.branch for i in df.select("branch").distinct().collect()]
n_branches = len(branches)

#for this one - set order
branches = ["control", "rcwn-enabled"]


# In[9]:

# get the bins we use for the histogram for a probe by looking at all branches
def get_bins(probe_name, logscale=False):

    all_branches = [r.val for r in df.where(df.probe == probe_name)                                     .collect()]

    #remove top 0.5%, bottom 0.5% for easy outlier
    trim = int(len(all_branches)/200.0)
    all_branches_trimmed = sorted(all_branches)
    all_branches_trimmed = all_branches_trimmed[trim:-1*trim]

    if logscale:
        if all_branches_trimmed[0] < 1:
            all_branches_trimmed = py_map(lambda d: d+1, all_branches_trimmed)
        return list(np.linspace(np.log10(all_branches_trimmed[1]), np.log10(all_branches_trimmed[-1]), 10))

    n,b = np.histogram(all_branches_trimmed,10)
    return b

# get values for branch of experiment for pref, and trim off outliers
def get_vals(pref_name, branch):
    x_vals = [r.val for r in df.where(df.probe == pref_name)                               .where(df.branch == branch)                               .collect()]
    trim = int(len(x_vals)/200.0)
    x_trimmed = sorted(x_vals)[trim:-1*trim]
    return x_trimmed

def median(lst):
    return lst[(len(lst))/2]

# return (pval, direction) if significant p value for mannwhitneyu vs control
def test_unequal(branch_vals, control_vals, p_threshold=.05):

    try:
        r = mannwhitneyu(branch_vals, control_vals)
    except:
        return None, None

    prefix = ""
    if r.pvalue < p_threshold:
        prefix = "***"

    if median(branch_vals) > median(control_vals):
        return (prefix + str(r.pvalue), "> control")

    return (prefix + str(r.pvalue), "< control")

# return (proceed bool, reason)
def can_chart_pref(pref_name):
    n = df.where(df.probe == pref_name).count()
    if n==0:
        return (False, "0 entries for pref %s"%pref_name)
    elif n>10000000:
        return (False, "%i values for pref %s"%(n,pref_name))
    return (True, None)

# chart histograms for all branches of a probe, log/std, and calculate if any branches vary from the mean
def chart_pref(pref_name, logscale):

    sig_branches = []
    fig, axarr = plt.subplots(n_branches, 1, sharex=True, sharey= 'col')

    b = get_bins(pref_name, logscale)
    plt.tight_layout()

    print pref_name + " (logscale=" + str(logscale) + ")" + ":"

    control_vals = get_vals(pref_name, "control")

    for i in range(n_branches):

        if branches[i] == "control":
            x = control_vals

        else:
            x = get_vals(pref_name, branches[i])

        if logscale: #always assume 0 as lowest val for now
            x_trans = py_map(lambda d: d+1, x)
            ap,bp,cp = axarr[i].hist(np.log(x_trans), bins=b)

        else:
            axarr[i].hist(x, bins=b)

        axarr[i].set_title(branches[i])

        if branches[i] == "control": continue

        print "len(branch_vals) = " + str(len(x)) + ", len(control_vals) = " + str(len(control_vals))
        if len(x) != 0 | len(control_vals) != 0:
            p, direction = test_unequal(x, control_vals)
            if p is not None and p.startswith("***"):
                print branches[i], p #, direction
        else:
            print "branch with no values"
            continue

    plt.show()

for p in probe_names:
    can_chart, reason = can_chart_pref(p)
    if can_chart:
        chart_pref(p, False)
        chart_pref(p, True)
    else:
        print reason
    print


# In[17]:

print get_vals('NETWORK_RACE_CACHE_BANDWIDTH_RACE_NETWORK_WIN', "control")
# there is seriously NOTHING there in the control branch for probes starting with N_R_C_

	# coding: utf-8

	# This script is intended for variables not yet available in the [Experiments Viewer](https://moz-experiments-viewer.herokuapp.com/).

	# In[1]:

	import matplotlib.pyplot as plt
	import pandas as pd
	import numpy as np
	import plotly.plotly as py
	from scipy.stats import chi2_contingency
	from scipy.stats import ttest_ind
	from collections import defaultdict as dd

	from pyspark.sql import Row
	from pyspark.sql import SQLContext
	from pyspark.sql.types import *
	from pyspark.sql.functions import *
	import itertools

	from moztelemetry.dataset import Dataset

	from scipy.stats import mannwhitneyu

	py_max = __builtin__.max
	py_map = __builtin__.map

	get_ipython().magic(u'pylab inline')


	# In[2]:

	experiment_probes = {}
	experiment_probes["payload/histograms"] = [
	"NETWORK_RACE_CACHE_WITH_NETWORK_OCEC_ON_START_DIFF",
	"NETWORK_RACE_CACHE_WITH_NETWORK_SAVED_TIME",
	"NETWORK_RACE_CACHE_WITH_NETWORK_USAGE_2",
	"NETWORK_RACE_CACHE_BANDWIDTH_RACE_NETWORK_WIN",
	"NETWORK_RACE_CACHE_BANDWIDTH_RACE_CACHE_WIN",
	"NETWORK_RACE_CACHE_BANDWIDTH_NOT_RACE",
	"NETWORK_RACE_CACHE_VALIDATION",
	"HTTP_PAGE_COMPLETE_LOAD_V2",
	"HTTP_PAGE_COMPLETE_LOAD_NET_V2",
	"HTTP_PAGE_COMPLETE_LOAD_CACHED_V2",
	"HTTP_SUB_COMPLETE_LOAD_V2",
	"HTTP_SUB_COMPLETE_LOAD_CACHED_V2",
	"HTTP_SUB_COMPLETE_LOAD_NET_V2",
	"TOTAL_CONTENT_PAGE_LOAD_TIME",
	]

	probe_names = list(itertools.chain(*experiment_probes.values()))

	EXPERIMENT_SLUG = "pref-flip-rcwn2-1381816"
	START_DATE = "20170726" #YYYYMMDD
	END_DATE = "20170802" #YYYYMMDD


	# In[3]:

	cohorts = Dataset.from_source("telemetry-cohorts")
	pings = cohorts.where(submissionDate = lambda x: x >= START_DATE and x <= END_DATE) .where(experimentId=EXPERIMENT_SLUG) .records(sc, sample=0.01)

	#pings.cache()
	#pings.count()


	# In[4]:

	main_pings = pings.filter(lambda x: x["meta"]["docType"]=="main")
	main_pings.cache()


	# In[ ]:

	# haven't run this yet - trying to aggregate histograms per Client instead of collapsing over them

	byClient = main_pings.map(lambda p: (p['clientId'], [p])).reduceByKey(lambda x,y: x+y)

	#modified from https://stackoverflow.com/questions/13852896/using-a-single-variable-to-index-into-nested-dictionaries
	def recursive_get(d, keys):
	if len(keys) == 1:
	return d.get(keys[0],{})
	return recursive_get(d.get(keys[0],{}), keys[1:])

	def extract_probes(i):
	clientId, pinglist = i

	# determine which branch this client was in and note if it ended up in both
	branch = []
	for p in pinglist:
	branch.extend(p.get("environment",{}).get("experiments",{}).get(EXPERIMENT_SLUG, {}).get("branch", "warning"))
	if len(np.unique(branch)) > 1:
	branch = "both"
	else:
	branch = np.unique(branch)[0]

	# if you input by client, pinglist instead of ping add another layer of iteration over the list here
	output = []
	for p in pinglist:
	for path, probe_names in experiment_probes.iteritems():
	for probe_name in probe_names:
	probe = recursive_get(p, path.split("/")).get(probe_name, {})
	for k,v in probe.get("values",{}).iteritems():
	output.extend([{"probe": probe_name, "branch": branch, "val": float(k)}] * int(v))
	# by this point you should have all this clients pings in an aggregate histogram, right? what then?


	# return output

	fin = byClient.flatMap(extract_probes)


	# In[5]:

	#modified from https://stackoverflow.com/questions/13852896/using-a-single-variable-to-index-into-nested-dictionaries
	def recursive_get(d, keys):
	if len(keys) == 1:
	return d.get(keys[0],{})
	return recursive_get(d.get(keys[0],{}), keys[1:])

	def extract_probes(p):
	branch = p.get("environment",{}).get("experiments",{}).get(EXPERIMENT_SLUG, {}).get("branch", "warning")

	output = []
	for path, probe_names in experiment_probes.iteritems():
	for probe_name in probe_names:
	probe = recursive_get(p, path.split("/")).get(probe_name, {})
	#take all entries in histogram right now. this is probably problematic. inspect more
	for k,v in probe.get("values",{}).iteritems():
	output.extend([{"probe": probe_name, "branch": branch, "val": float(k)}] * int(v))
	return output

	probe_dicts = main_pings.flatMap(extract_probes)


	# In[6]:

	df = sqlContext.createDataFrame(probe_dicts.map(lambda d: Row(**d)))


	# In[7]:

	#check on branch distribution
	for b,v in main_pings.map(lambda x: x.get("environment",{}).get("experiments",{}).get(EXPERIMENT_SLUG, {}).get("branch", "warning")).countByValue().iteritems():
	print b, ":", v


	# In[8]:

	branches = [i.branch for i in df.select("branch").distinct().collect()]
	n_branches = len(branches)

	#for this one - set order
	branches = ["control", "rcwn-enabled"]


	# In[9]:

	# get the bins we use for the histogram for a probe by looking at all branches
	def get_bins(probe_name, logscale=False):

	all_branches = [r.val for r in df.where(df.probe == probe_name) .collect()]

	#remove top 0.5%, bottom 0.5% for easy outlier
	trim = int(len(all_branches)/200.0)
	all_branches_trimmed = sorted(all_branches)
	all_branches_trimmed = all_branches_trimmed[trim:-1*trim]

	if logscale:
	if all_branches_trimmed[0] < 1:
	all_branches_trimmed = py_map(lambda d: d+1, all_branches_trimmed)
	return list(np.linspace(np.log10(all_branches_trimmed[1]), np.log10(all_branches_trimmed[-1]), 10))

	n,b = np.histogram(all_branches_trimmed,10)
	return b

	# get values for branch of experiment for pref, and trim off outliers
	def get_vals(pref_name, branch):
	x_vals = [r.val for r in df.where(df.probe == pref_name) .where(df.branch == branch) .collect()]
	trim = int(len(x_vals)/200.0)
	x_trimmed = sorted(x_vals)[trim:-1*trim]
	return x_trimmed

	def median(lst):
	return lst[(len(lst))/2]

	# return (pval, direction) if significant p value for mannwhitneyu vs control
	def test_unequal(branch_vals, control_vals, p_threshold=.05):

	try:
	r = mannwhitneyu(branch_vals, control_vals)
	except:
	return None, None

	prefix = ""
	if r.pvalue < p_threshold:
	prefix = "***"

	if median(branch_vals) > median(control_vals):
	return (prefix + str(r.pvalue), "> control")

	return (prefix + str(r.pvalue), "< control")

	# return (proceed bool, reason)
	def can_chart_pref(pref_name):
	n = df.where(df.probe == pref_name).count()
	if n==0:
	return (False, "0 entries for pref %s"%pref_name)
	elif n>10000000:
	return (False, "%i values for pref %s"%(n,pref_name))
	return (True, None)

	# chart histograms for all branches of a probe, log/std, and calculate if any branches vary from the mean
	def chart_pref(pref_name, logscale):

	sig_branches = []
	fig, axarr = plt.subplots(n_branches, 1, sharex=True, sharey= 'col')

	b = get_bins(pref_name, logscale)
	plt.tight_layout()

	print pref_name + " (logscale=" + str(logscale) + ")" + ":"

	control_vals = get_vals(pref_name, "control")

	for i in range(n_branches):

	if branches[i] == "control":
	x = control_vals

	else:
	x = get_vals(pref_name, branches[i])

	if logscale: #always assume 0 as lowest val for now
	x_trans = py_map(lambda d: d+1, x)
	ap,bp,cp = axarr[i].hist(np.log(x_trans), bins=b)

	else:
	axarr[i].hist(x, bins=b)

	axarr[i].set_title(branches[i])

	if branches[i] == "control": continue

	print "len(branch_vals) = " + str(len(x)) + ", len(control_vals) = " + str(len(control_vals))
	if len(x) != 0 \| len(control_vals) != 0:
	p, direction = test_unequal(x, control_vals)
	if p is not None and p.startswith("***"):
	print branches[i], p #, direction
	else:
	print "branch with no values"
	continue

	plt.show()

	for p in probe_names:
	can_chart, reason = can_chart_pref(p)
	if can_chart:
	chart_pref(p, False)
	chart_pref(p, True)
	else:
	print reason
	print


	# In[17]:

	print get_vals('NETWORK_RACE_CACHE_BANDWIDTH_RACE_NETWORK_WIN', "control")
	# there is seriously NOTHING there in the control branch for probes starting with N_R_C_