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Mossop/Signed Dashboard.ipynb

Created December 9, 2015 20:23
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Signed Dashboard.ipynb
{"nbformat_minor": 0, "cells": [{"execution_count": 1, "cell_type": "code", "source": "import matplotlib.pyplot as plt\nimport pandas as pd\nimport numpy as np\n%pylab inline\nfrom IPython.display import display, display_html\n\nimport re\nimport ujson as json\nfrom datetime import datetime, timedelta\nfrom urllib2 import Request, urlopen\n\nfrom moztelemetry.spark import get_pings, get_one_ping_per_client", "outputs": [{"output_type": "stream", "name": "stdout", "text": "Populating the interactive namespace from numpy and matplotlib\n"}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 2, "cell_type": "code", "source": "def load_data():\n global bc_amo_extensions \n amo_extensions = dict()\n req = Request(\"http://www.oxymoronical.com/files/allextensions.txt\", headers={'User-Agent' : \"Firefox\"})\n lines = urlopen(req).readlines()[3:-1]\n for line in lines:\n start, id, uid, status, disabled, end = re.sub(r'\\s*\\|\\s*', \"|\", line).split(\"|\")\n amo_extensions[id] = {\n \"status\": status,\n \"disabled\": disabled == 1,\n }\n bc_amo_extensions = sc.broadcast(amo_extensions)\n\nload_data()\n\ndef is_amo(addon):\n return addon[\"id\"] in bc_amo_extensions.value", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 3, "cell_type": "code", "source": "def extract(pingstr):\n data = json.loads(pingstr)\n\n ping = {\n \"clientID\": data[\"clientID\"],\n \"os\": data[\"environment\"][\"system\"][\"os\"],\n \"addons\": []\n }\n\n if \"addons\" in data[\"environment\"]:\n activeAddons = data[\"environment\"][\"addons\"][\"activeAddons\"]\n for (id, addon) in activeAddons.iteritems():\n addon = activeAddons[id]\n if \"signedState\" in addon:\n addon[\"id\"] = id\n addon[\"isAMO\"] = is_amo(addon)\n ping[\"addons\"].append(addon)\n\n return ping\n\n# Dates are inclusive so count from one week till yesterday\nnow = datetime.now()\nstart = now - timedelta(7)\nend = now - timedelta(1)\n\npings = get_pings(sc, \n app=\"Firefox\",\n channel=\"nightly\",\n submission_date=(start.strftime(\"%Y%m%d\"), end.strftime(\"%Y%m%d\")),\n build_id=(\"20150603000000\", \"99999999999999\"),\n fraction=1)\n\n# Extracts fields\npings = pings.map(extract)\n\n# Reduce to one per client\npings = get_one_ping_per_client(pings).cache()\nostypes = pings.map(lambda p: p[\"os\"][\"name\"]).distinct().collect()", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 4, "cell_type": "code", "source": "SIGNED_STATES = {\n \"Broken\": -2,\n \"Unknown\": -1,\n \"Missing\": 0,\n \"Preliminary\": 1,\n \"Fully Signed\": 2\n}\n\ndef is_foreign(addon):\n if not \"foreignInstall\" in addon:\n return False\n return addon[\"foreignInstall\"]\n\ndef is_valid(addon):\n if addon[\"signedState\"] <= 0:\n return False\n if is_foreign(addon) and addon[\"signedState\"] < 2:\n return False\n return True\n\ndef reduce_addons(check):\n def mapper(ping):\n ping[\"addons\"] = list(filter(check, ping[\"addons\"]))\n return ping\n\n return mapper\n\ndef check_any_addon(check):\n def reducer(ping):\n for addon in ping[\"addons\"]:\n if check(addon):\n return True\n return False\n\n return reducer\n\ndef check_all_addons(check):\n def reducer(ping):\n for addon in ping[\"addons\"]:\n if not check(addon):\n return False\n return True\n\n return reducer\n\ndef reduce_addon_state(check):\n def mapper(addons):\n return list(filter(check, addons))\n\n return mapper\n\ndef check_any_unsigned(ping):\n for addon in ping[\"addons\"]:\n if not is_valid(addon):\n return True\n return False\n\ndef check_any_signed(ping):\n for addon in ping[\"addons\"]:\n if is_valid(addon):\n return True\n return False\n\ndef check_some_signed(ping): \n return check_any_signed(ping) and check_any_unsigned(ping)", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 5, "cell_type": "code", "source": "noaddons = pings.filter(lambda p: len(p[\"addons\"]) == 0)\nanyaddons = pings.filter(lambda p: len(p[\"addons\"]) > 0).cache()\n\nnonesigned = anyaddons.filter(check_all_addons(lambda a: not is_valid(a)))\nsomesigned = anyaddons.filter(check_some_signed)\nallsigned = anyaddons.filter(check_all_addons(is_valid))", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 6, "cell_type": "code", "source": "def count_by_os(rdd):\n base = { \"All\": 0 }\n for os in ostypes:\n base[os] = 0\n\n def apply(b, p):\n b[\"All\"] += 1\n b[p[\"os\"][\"name\"]] += 1\n return b\n\n def combine(b1, b2):\n for os in base:\n b1[os] += b2[os]\n return b1\n\n return pd.Series(rdd.aggregate(base, apply, combine))\n\noscounts = pd.DataFrame({\n \"No Add-ons\": count_by_os(noaddons),\n \"None Signed\": count_by_os(nonesigned),\n \"Some Signed\": count_by_os(somesigned),\n \"All Signed\": count_by_os(allsigned),\n})\n\ntotals = oscounts.sum(1)\nfor index in oscounts:\n oscounts[index] = 100.0 * oscounts[index] / totals\n\nplt.figure(figsize=(30, 7))\noscounts.plot(kind='bar', stacked=True)\nplt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\nplt.show()", "outputs": [{"output_type": "display_data", "data": {"text/plain": "<matplotlib.figure.Figure at 0x7f4bbc92e150>"}, "metadata": {}}, {"output_type": "display_data", "data": {"image/png": 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ROoD68GfTGsIF38zMrAFc8Ltxv15ezmdmrdIB1Ic/m9YQLvhmZmYN4ILfjfv1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\nb2Zm1gD/HwzhEiPI0zq7AAAAAElFTkSuQmCC\n", "text/plain": "<matplotlib.figure.Figure at 0x7f4b9c940550>"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 7, "cell_type": "code", "source": "alladdons = anyaddons.flatMap(lambda p: p[\"addons\"]).cache()\ndisplay(alladdons.count())", "outputs": [{"output_type": "display_data", "data": {"text/plain": "12392"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 8, "cell_type": "code", "source": "amostates = alladdons.filter(lambda a: a[\"isAMO\"]).map(lambda a: a[\"signedState\"]).countByValue()\nnonamostates = alladdons.filter(lambda a: not a[\"isAMO\"]).map(lambda a: a[\"signedState\"]).countByValue()\n\ndef get_state_counts(state):\n return pd.Series({ \"AMO\": amostates[state], \"Non AMO\": nonamostates[state] })\n\nstatecounts = {}\nfor (name, state) in SIGNED_STATES.iteritems():\n statecounts[name] = get_state_counts(state)\n\nstatecounts = pd.DataFrame(statecounts)\n\nplt.figure(figsize=(30, 7))\nstatecounts.plot(kind='bar', stacked=True)\nplt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\nplt.show()", "outputs": [{"output_type": "display_data", "data": {"text/plain": "<matplotlib.figure.Figure at 0x7f4b9cf99dd0>"}, "metadata": {}}, {"output_type": "display_data", "data": {"image/png": 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I3ksgEBER0WIJBNooOQLRUMkRiOi9BAIREREtlkCgjZIjEA2VHIGI3ksgEBER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\n", "text/plain": "<matplotlib.figure.Figure at 0x7f4b98127a10>"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 9, "cell_type": "code", "source": "foreignstates = alladdons.filter(is_foreign).map(lambda a: a[\"signedState\"]).countByValue()\nfor (name, state) in SIGNED_STATES.iteritems():\n foreignstates[name] = foreignstates.pop(state)\n\nplt.figure(figsize=(10, 7))\npd.Series(foreignstates).plot(kind='bar')\nplt.show()", "outputs": [{"output_type": "display_data", "data": {"image/png": 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8dn6STya5Kcmz+wpckiSptS17pJJ8G/Al4M1V9bhu388Cn6uqn03yKuB+VbUv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\ntKD/H2PgrJqNDBb/AAAAAElFTkSuQmCC\n", "text/plain": "<matplotlib.figure.Figure at 0x7f4b9cf999d0>"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 10, "cell_type": "code", "source": "unsigned = anyaddons.filter(check_any_unsigned).map(reduce_addons(lambda a: not is_valid(a)))", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 11, "cell_type": "code", "source": "def sorted_table(map):\n return sorted(map.items(), key = lambda i: i[1], reverse = True)\n\ndef ordered_ids(pings):\n total = pings.count()\n allids = pings.flatMap(lambda p: [a[\"id\"] for a in p[\"addons\"]])\n return (100.0 / total) * pd.Series(allids.countByValue())\n\ndisplay(ordered_ids(unsigned).nlargest(20))", "outputs": [{"output_type": "display_data", "data": {"text/plain": "firefox@mega.co.nz 6.294086\nadbhelper@mozilla.org 5.100380\nhttps-everywhere@eff.org 3.852415\nfirebug@software.joehewitt.com 3.309821\nabs@avira.com 2.984265\n{d10d0bf8-f5b5-c8b4-a8b2-2b9879e08c5d} 2.604449\nhelper@savefrom.net 2.550190\nwrc@avast.com 2.495931\niobitascsurfingprotection@iobit.com 2.278893\nsweetsearch@gmail.com 2.170374\n9tffxtbr@free.internetspeedtracker.com 2.007596\nuBlock0@raymondhill.net 1.899078\nvb@yandex.ru 1.736300\nyasearch@yandex.ru 1.627781\n{195A3098-0BD5-4e90-AE22-BA1C540AFD1E} 1.519262\n{e4f94d1e-2f53-401e-8885-681602c0ddd8} 1.519262\nsupport@lastpass.com 1.465003\n{73a6fe31-595d-460b-a920-fcc0f8843232} 1.356484\n{4ED1F68A-5463-4931-9384-8FFF5ED91D92} 1.302225\nanti_banner@kaspersky.com 1.247965\ndtype: float64"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 12, "cell_type": "code", "source": "for os in ostypes:\n display_html(\"<h2>\" + os + \"</h2>\", raw=True)\n idcounts = unsigned.filter(lambda p: p[\"os\"][\"name\"] == os)\n display(ordered_ids(idcounts).nlargest(20))", "outputs": [{"output_type": "display_data", "data": {"text/html": "<h2>Darwin</h2>"}, "metadata": {}}, {"output_type": "display_data", "data": {"text/plain": "adbhelper@mozilla.org 21.666667\nfirebug@software.joehewitt.com 16.666667\nhttps-everywhere@eff.org 11.666667\nonepassword4@agilebits.com 8.333333\nsupport@lastpass.com 8.333333\njid1-MnnxcxisBPnSXQ@jetpack 6.666667\n{6AC85730-7D0F-4de0-B3FA-21142DD85326} 6.666667\nfxos_2_1_simulator@mozilla.org 5.000000\n{d10d0bf8-f5b5-c8b4-a8b2-2b9879e08c5d} 5.000000\nfirefox@mega.co.nz 3.333333\nfxos_1_3_simulator@mozilla.org 3.333333\nfxos_2_0_simulator@mozilla.org 3.333333\nfxos_2_2_simulator@mozilla.org 3.333333\njid0-edalmuivkozlouyij0lpdx548bc@jetpack 3.333333\njid1-AQqSMBYb0a8ADg@jetpack 3.333333\njid1-cwbvBTE216jjpg@jetpack 3.333333\nreadability@readability.com 3.333333\nshumway@research.mozilla.org 3.333333\nuriloader@pdf.js 3.333333\n{1280606b-2510-4fe0-97ef-9b5a22eafe30} 3.333333\ndtype: float64"}, "metadata": {}}, {"output_type": "display_data", "data": {"text/html": "<h2>Linux</h2>"}, "metadata": {}}, {"output_type": "display_data", "data": {"text/plain": "https-everywhere@eff.org 18.918919\nadbhelper@mozilla.org 16.216216\nfirebug@software.joehewitt.com 8.108108\nfxos_2_0_simulator@mozilla.org 8.108108\njid1-MnnxcxisBPnSXQ@jetpack 8.108108\n{73a6fe31-595d-460b-a920-fcc0f8843232} 8.108108\npl@dictionaries.addons.mozilla.org 5.405405\nr2d2b2g@mozilla.org 5.405405\nshumway@research.mozilla.org 5.405405\nuBlock0@raymondhill.net 5.405405\n@mystart-newtab 2.702703\nanti_tumblr_infinite_scroll@davidk.se 2.702703\nbetterkongregate@matthewammann.com 2.702703\ncitizenex@jetpack 2.702703\ndta@downthemall.net 2.702703\nel-GR@dictionaries.addons.mozilla.org 2.702703\nelemhidehelper@adblockplus.org 2.702703\nen-GB@dictionaries.addons.mozilla.org 2.702703\neo-EO@dictionaries.addons.mozilla.org 2.702703\nes-MX@dictionaries.addons.mozilla.org 2.702703\ndtype: float64"}, "metadata": {}}, {"output_type": "display_data", "data": {"text/html": "<h2>Windows_NT</h2>"}, "metadata": {}}, {"output_type": "display_data", "data": {"text/plain": "firefox@mega.co.nz 6.471936\nadbhelper@mozilla.org 4.295533\nhttps-everywhere@eff.org 3.264605\nabs@avira.com 3.150057\nfirebug@software.joehewitt.com 2.749141\nhelper@savefrom.net 2.691867\nwrc@avast.com 2.577320\n{d10d0bf8-f5b5-c8b4-a8b2-2b9879e08c5d} 2.520046\niobitascsurfingprotection@iobit.com 2.405498\nsweetsearch@gmail.com 2.290951\n9tffxtbr@free.internetspeedtracker.com 2.119129\nuBlock0@raymondhill.net 1.832761\nvb@yandex.ru 1.832761\nyasearch@yandex.ru 1.718213\n{e4f94d1e-2f53-401e-8885-681602c0ddd8} 1.603666\n{195A3098-0BD5-4e90-AE22-BA1C540AFD1E} 1.489118\n{4ED1F68A-5463-4931-9384-8FFF5ED91D92} 1.374570\nanti_banner@kaspersky.com 1.317297\n{B64D9B05-48E1-4CEB-BF58-E0643994E900} 1.260023\nsupport@lastpass.com 1.202749\ndtype: float64"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}], "nbformat": 4, "metadata": {"kernelspec": {"display_name": "Python 2", "name": "python2", "language": "python"}, "language_info": {"mimetype": "text/x-python", "nbconvert_exporter": "python", "version": "2.7.9", "name": "python", "file_extension": ".py", "pygments_lexer": "ipython2", "codemirror_mode": {"version": 2, "name": "ipython"}}}}
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