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@chutten
Last active March 9, 2016 21:22
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FX_REFRESH_DRIVER_SYNC_SCROLL_FRAME_DELAY_MS on beta45ex2
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### E10S Experiment 2 Beta 45 (without addons): FX_REFRESH_DRIVER_SYNC_SCROLL_FRAME_DELAY_MS"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Unable to parse whitelist (/home/hadoop/anaconda2/lib/python2.7/site-packages/moztelemetry/bucket-whitelist.json). Assuming all histograms are acceptable.\n",
"Populating the interactive namespace from numpy and matplotlib\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/hadoop/anaconda2/lib/python2.7/site-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.\n",
" warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')\n"
]
}
],
"source": [
"import ujson as json\n",
"import matplotlib.pyplot as plt\n",
"import pandas as pd\n",
"import numpy as np\n",
"import plotly.plotly as py\n",
"import IPython\n",
"\n",
"from __future__ import division\n",
"from moztelemetry.spark import get_pings, get_one_ping_per_client, get_pings_properties\n",
"from montecarlino import grouped_permutation_test\n",
"\n",
"%pylab inline\n",
"IPython.core.pylabtools.figsize(16, 7)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"64"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sc.defaultParallelism"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [],
"source": [
"def chi2_distance(xs, ys, eps = 1e-10, normalize = True):\n",
" histA = xs.sum(axis=0)\n",
" histB = ys.sum(axis=0)\n",
" \n",
" if normalize:\n",
" histA = histA/histA.sum()\n",
" histB = histB/histB.sum()\n",
" \n",
" d = 0.5 * np.sum([((a - b) ** 2) / (a + b + eps)\n",
" for (a, b) in zip(histA, histB)])\n",
"\n",
" return d\n",
"\n",
"def median_diff(xs, ys):\n",
" return np.median(xs) - np.median(ys)\n",
"\n",
"def compare_histogram(histogram, e10s, none10s, threshold):\n",
" # Normalize individual histograms\n",
" e10s = e10s.map(lambda x: x/x.sum())\n",
" e10s = e10s.map(lambda x: x[x.index > threshold])\n",
" none10s = none10s.map(lambda x: x/x.sum())\n",
" none10s = none10s.map(lambda x: x[x.index > threshold])\n",
" \n",
" pvalue = grouped_permutation_test(chi2_distance, [e10s, none10s], num_samples=100)\n",
" \n",
" eTotal = e10s.sum()\n",
" nTotal = none10s.sum()\n",
" \n",
" eTotal = 100*eTotal/eTotal.sum()\n",
" nTotal = 100*nTotal/nTotal.sum()\n",
" \n",
" fig = plt.figure()\n",
" fig.subplots_adjust(hspace=0.3)\n",
" \n",
" ax = fig.add_subplot(1, 1, 1)\n",
" ax2 = ax.twinx()\n",
" width = 0.4\n",
" ylim = max(eTotal.max(), nTotal.max())\n",
" \n",
" eTotal.plot(kind=\"bar\", alpha=0.5, color=\"green\", label=\"e10s\", ax=ax, width=width, position=0, ylim=(0, ylim + 1))\n",
" nTotal.plot(kind=\"bar\", alpha=0.5, color=\"blue\", label=\"non e10s\", ax=ax2, width=width, position=1, grid=False, ylim=ax.get_ylim())\n",
" \n",
" ax.legend(ax.get_legend_handles_labels()[0] + ax2.get_legend_handles_labels()[0],\n",
" [\"e10s ({} samples\".format(len(e10s)), \"non e10s ({} samples)\".format(len(none10s))])\n",
"\n",
" plt.title(histogram)\n",
" plt.xlabel(histogram)\n",
" plt.ylabel(\"Frequency %\")\n",
" plt.show()\n",
" \n",
" print \"The probability that the distributions for {} are differing by chance is {:.2f}.\".format(histogram, pvalue)\n",
"\n",
"def normalize_uptime_hour(frame):\n",
" frame = frame[frame[\"payload/simpleMeasurements/uptime\"] > 0]\n",
" frame = 60 * frame.apply(lambda x: x/frame[\"payload/simpleMeasurements/uptime\"]) # Metric per hour\n",
" frame.drop('payload/simpleMeasurements/uptime', axis=1, inplace=True)\n",
" return frame\n",
" \n",
"def compare_count_histograms(pings, *histograms_names):\n",
" properties = histograms_names + (\"payload/simpleMeasurements/uptime\", \"e10s\")\n",
"\n",
" frame = pd.DataFrame(get_pings_properties(pings, properties).collect())\n",
"\n",
" e10s = frame[frame[\"e10s\"] == True]\n",
" e10s = normalize_uptime_hour(e10s)\n",
" \n",
" none10s = frame[frame[\"e10s\"] == False]\n",
" none10s = normalize_uptime_hour(none10s)\n",
" \n",
" for histogram in e10s.columns:\n",
" if histogram == \"e10s\" or histogram.endswith(\"_parent\") or histogram.endswith(\"_children\"):\n",
" continue\n",
" \n",
" compare_scalars(histogram + \" per hour\", e10s[histogram].dropna(), none10s[histogram].dropna())\n",
"\n",
" \n",
"def compare_histograms(pings, threshold, *histogram_names):\n",
" frame = pd.DataFrame(get_pings_properties(pings, histogram_names + (\"e10s\",) , with_processes=True).collect())\n",
" e10s = frame[frame[\"e10s\"] == True]\n",
" none10s = frame[frame[\"e10s\"] == False]\n",
" for histogram in none10s.columns:\n",
" if histogram == \"e10s\" or histogram.endswith(\"_parent\") or histogram.endswith(\"_children\"):\n",
" continue\n",
" \n",
" has_children = np.sum(e10s[histogram + \"_children\"].notnull()) > 0\n",
" has_parent = np.sum(e10s[histogram + \"_parent\"].notnull()) > 0\n",
" \n",
" if has_children and has_parent:\n",
" compare_histogram(histogram + \" (parent + children)\", e10s[histogram].dropna(), none10s[histogram].dropna(), threshold)\n",
" \n",
" if has_parent:\n",
" compare_histogram(histogram + \" (parent)\", e10s[histogram + \"_parent\"].dropna(), none10s[histogram].dropna(), threshold)\n",
" \n",
" if has_children:\n",
" compare_histogram(histogram + \" (children)\", e10s[histogram + \"_children\"].dropna(), none10s[histogram].dropna(), threshold)\n",
" \n",
"\n",
" \n",
"def compare_scalars(metric, *groups):\n",
" print \"Median difference in {} is {:.2f}, ({:.2f}, {:.2f}).\".format(metric,\n",
" median_diff(*groups), \n",
" np.median(groups[0]),\n",
" np.median(groups[1]))\n",
" print \"The probablity of this effect being purely by chance is {:.2f}.\". \\\n",
" format(grouped_permutation_test(median_diff, groups, num_samples=10000))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Get e10s and non-e10s partitions"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"dataset = sqlContext.read.load(\"s3://telemetry-parquet/e10s-experiment/e10s-beta45-withoutaddons@experiments.mozilla.org/generationDate=20160217\", \"parquet\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Sample by clientId:"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"sampled = dataset.filter(dataset.sampleId <= 30)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Transform Dataframe to RDD of pings"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def row_2_ping(row):\n",
" ping = {\"payload\": {\"simpleMeasurements\": json.loads(row.simpleMeasurements),\n",
" \"histograms\": json.loads(row.histograms),\n",
" \"keyedHistograms\": json.loads(row.keyedHistograms),\n",
" \"childPayloads\": json.loads(row.childPayloads),\n",
" \"threadHangStats\": json.loads(row.threadHangStats)},\n",
" \"e10s\": True if row.experimentBranch == \"experiment-no-addons\" else False}\n",
" return ping"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"subset = sampled.rdd.filter(lambda r: r.experimentBranch in [\"experiment-no-addons\", \"control-no-addons\"]).map(row_2_ping)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Plots"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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pnpjqKPp2C7c/tk017XkfAfhIdhF09cOeN5B3tF6Sbpd3vufxzrvIT8EFdArOLftCUr4/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\nKlWqpCpVqhR5+57zzz9f06d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2PgAAAIA128Amu2Z2WMbfHiXpEklHSrrJzPZr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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fbcd0e2cfd0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"The probability that the distributions for payload/histograms/FX_REFRESH_DRIVER_SYNC_SCROLL_FRAME_DELAY_MS (parent + children) are differing by chance is 0.00.\n"
]
},
{
"data": {
"image/png": 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dAAAAIMv87Gc/U7169XTbbbfp22+/1ffff6+PPvpIc+bMiS+zdetWbd26Ve6u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\nu6pXr14p23vttddKkpYvX66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DwscXSHo2uRAAAAAA4AerInLkZDMbK6mBmf1G0quSHspk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\nrkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fbcc797f910>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"The probability that the distributions for payload/histograms/FX_REFRESH_DRIVER_SYNC_SCROLL_FRAME_DELAY_MS (parent) are differing by chance is 0.00.\n"
]
},
{
"data": {
"image/png": 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0aZNefvnluHUPP/xw/frrr/r+++/DyxYuXKgjjzxSkheGx4wZo++//17Tp0/X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\n1dOnn36q9u3ba/Pmzbriiiv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b6FAyM3u/me2b7u9kZt9Kx5NZOUlqz4fNbFLd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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fbcc52b5c90>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"The probability that the distributions for payload/histograms/FX_REFRESH_DRIVER_SYNC_SCROLL_FRAME_DELAY_MS (children) are differing by chance is 0.00.\n"
]
}
],
"source": [
"compare_histograms(subset,\n",
" 0,\n",
" \"payload/histograms/FX_REFRESH_DRIVER_SYNC_SCROLL_FRAME_DELAY_MS\")"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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GDTVs2FD9+/cPvJ9FxVtZxUIeGU3L7lhJHYInmFm6pI6STnXOnSrpqbIPDQAA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\nevRQp06dtGbNGuXm5qpv374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bKqlRNAVJdgEAAAAAsWq3c25P/gszqybvPrsRkewCAAAA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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fbcd0e7cc50>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"The probability that the distributions for payload/histograms/FX_REFRESH_DRIVER_SYNC_SCROLL_FRAME_DELAY_MS (parent + children) are differing by chance is 0.00.\n"
]
},
{
"data": {
"image/png": 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J7zrn2kqaLWnk/m4YAAAAAKDaqvIcWSveAs65eWaWHTX5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\nbbBdw1G5AAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fbccc233390>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"The probability that the distributions for payload/histograms/FX_REFRESH_DRIVER_SYNC_SCROLL_FRAME_DELAY_MS (parent) are differing by chance is 0.00.\n"
]
},
{
"data": {
"image/png": 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Erlgl2d1S0s1tf79F6T8FAAAAFFU6US+tm6Tu\nhuv/qF3/fddxnzNaIt3tsekmWe81fsnYUndJV8lEfbT4pRP1km3v1oMPPTj663rio7Y0Ild0RPT2\nD+03StojIt6b//5PknaMiA90PK+3HQAAAAAApoSI8Mj9bnPF0qqM7N4qaWbb37fK29bQ/p8GAAAA\nAAy8rnLF0iYufTe2hZK2tv102+tKOkBSM+eJAAAAAAD6pRG5Ys8juxHxsO1DJc3T6nLSS2trGQAA\nAABgymlKrtjzml0AAAAAAJqqyjRmAAAAAAAaiWQXAAAAADBwSHYLsb35ZLehV1O57Zgctjed7Dag\nGtvr2m6/ZMCuto+w/doa9zHT9sb5/pDtN9l+QdNj9yn+Drb3tb2X7efUFTfHnmZ7Wr6/ru3tbD9p\nCsV/3CjbnlxX/I64h5SIi/6x/TnbOxeKve/Ie9v2ZrZPtb3E9hm2t6ppH3vY/rLtc/Lty7ZfU0Nc\n236z7f3y/VfZ/rztQ0Y+vzW1/V22hzq2v7Ni3GKvaRf7Hq4hRrFj34/35FS31iS7tjeyfYzt623f\nYfvPtpfmbRtXjP2kjtumki63vUkdP/i2N7Q91/Z1tu+yfbvtS20fXEPsYm0vecxz/B1sz7f9/2w/\nzfYF+fgstP0PNcRfZPsTtp9VNdYosYu2fYJ9n1fx3x8z0tHM/4/fS7rM9k22d6mlkaPvt0gnNH++\ntqvjPdkW07Z3sv2GfNvJdi2XYSsYe6GkkWTuw5I+JWk9SYfbPrpqcNtHSrpI0qW23y3pfEmvlXSG\n7cObGrt0fNu72L5C0jGSviHpvZJOtr3A9tMqNl2295F0m6Rbbe8t6WJJ/yHpGtuvb3J8pxMut0i6\nzfY8r9mBnlcldo5/eMftCElzR/5eNX7eR5HOf+nYbfFrT7raYpdq+9sknZh/kz5T82/qpyLijnz/\ni5IWK30XnCfplKrBbZ8g6TCl75vP5NtFkj5g+8SK4b8k6c1Kx+fbkt6n9L3/CknHV4wt25+W9HFJ\nL5T037bf3/bwoRXDl3xNJ/LuGmKUPPZF35PtRnKFOmP2RUQ04iZpkaRPSHpWofg/lfRRSVu0bdsi\nb5tXMfYjkm7suD2Y//x9DW0/W9LBStenOlzSv0t6tqRvSfp0U9te8pjnWJcrfaDfIulmSW/K218l\n6Zc1xL9R0mcltfK+PijpqTW9H0u3fbsxbttLuq1i7CVt9+dLekm+v42kK2o6Pod33I6Q9KeRv1eM\nfVLb/Zfl13d+fh32rKHtu0v6rdIPzdfz7fy8bfcGx7627f4VktbL96dLuqaG43KdUvK8qaR7JG2W\nt2/Qvu+mxe5D2xe3xXuGpB/l+6+u6Xtycf7efYakuyX9fd7+9Do+ryXjK3UGn5/vv0nSbyS9dGS/\nNbT9HklnSBqW9Ml8u3Pkfg3xPy3p55JOkPQ7Se9ve2xRU2PnGCdIOlfpUiEvy7cD8rYTG972xfnP\nbZT6S9dJuj6/rttUjH1D2/0rOx67qoa2/3qM7Zb0m4qxl+Q/Hyfpz5LWzX+v6zt+iaTp+f7G+b1y\nfPtr0sTXNMe9e4zbPZIequPYlDr2fXhPzpR0uqTb83fwbyX9MW8bqhq/H7dJb0DbwSyWWHS+GR7L\nY13GPkKpw/nC9v9PjW2/uuPvC/Of0yRd39S2lzzmOcbitvutsR6rEH9R2/2XSzpJ0nKlxOi9DW/7\nw5J+ltvaebu/YuylbT9ol3Y8tqRK7LY4xTqhHa/rfEnb5fvPVD2d/6Wj/QAoJQNLGxz7/0t6Qb5/\nvqRN8v2/Uz0J4zX5z3XyD+W0tseqJozFYver7W3x29+f19XQ9vbvmms7HqstsSgRX4/+7Xu+pBsk\n7VNT22dK+p6kYyWtn7dVPkHdFr9k579Y7ByjaNJVuO2Pem9IepGkoyX9tmLsr0iaq3Ty6zhJ++bt\nu0q6qIa2X6N8Arlj+46q+Pva8Vk9v+OxOpKipR1/X0fSyfkzVum7rORrmmO1JM0Y47Gba4hf7Nj3\n4T35S0n7S1qn47U9QB19wKbemjSN+c6I+FBEzFRKwJ4taVGe6vneGuLfZPsjtmeMbLA9w/ZHlUZ0\nehYRxylNcxh2WlfwBElRrblruNf2yyTJ9l6S7sj7fUTph6dnhdte7Jhnf7W9u+39JEWeTqc8lfbh\nGuKvEhEXR8QhkrZU6hj9Y8WQpdu+VNI/R8SunTelEdIqTpJ0ru1XSjrf9ol5KuZRkq6q2vDs+Uon\nczaQ9B8RcZTSd8RR+X5dNoqIRZIUEb9XPUs7pku6ZZTttyqd1W1q7PdJ+o7tU5USuitsnyLpEqWR\nmKoW2T5N0g8l/bekb9l+q+2TJf2qwbFLx7/C9sm23yrpNEkLJMn2+kodisq8ek3YO9u2rSNp3YbH\nf9D2FiN/iYjrlGa/zFHqI1QSEa2I2E/pRM8Ftt9UNWaH6RHxUN7XSkmvl/RE299T9WNTMraUfqNe\nMsr2l0j6a8XYpdv+qH5RRFwTEbMjYuuKsQ9VmhF3g6T9JP3A9j2S3qM0RbWqgyV90fav8tT9ebaX\nSvp8fqyK5bY3lKSIWDUdPX/GHqgYW5J+57alTBHxcES8S+lYPbdi7JKvqSSdqjQbZTSn1RC/5LEv\n/Z58ckScERGr+qb5tT1dabZT8012tt12lmC0szbrSHqNpFNqiL+JUpJyvdII0R1KCcGxkp5U4/9j\nL0mXSlpeY8wXK41236nU8RyZJraZpA80te2jHPM78zH/TB3HXNK2SlOlz5P0HEkn5n1cJ2nnGuKf\nXtexHeM1Ldn2N428T0Z5bJ8a4s9SGnldpHSW/lxJ/yzpcTUfp70l/SL/f2oZcZF0n9LZ8yVKI8gj\nI5jTVM8o4GylqZ0flXRgvh2Zt80uEPujdcTO8ddRml5/mNJJx/0lbVzTcZ+uNG3/gHx/Z6X1RR+R\ntEHNsf9XXbFLx1c6SXFIjvce5bPnSmfpn15D218i6e9G2T4k6Z+aHF/SbpJePMr2jSV9vI73ZVvM\nDZXWGv+8xpg/lrTLKNv/r6RHmho7x9lO0mVKJ3Pm5dtSpT7C9g1v+4Z1vjfG2c9GkjYtFHsLpWVH\n26ttKVihfW0gafMa4qynvPxllMe2nAqvab9vdR37tni1vyeVpiufJGknSU/Nt53ytjMn+xh2c3P+\nj0w626dHxAGF9/EcpXWvl0bEX9q2vyYizq8h9pZKPw4PK609vraO2Dn+c3P8Em3fUVJExELbL1RK\nMBZFxLmVGj36vr4dEXWcaep7/Dy6vqNSQlSpOIrtnZSmoN9lez2lJGY7pWT30xFxV8X4j1dKVP4Q\nERfaPlCpg75U0lcj4sGK8Z8p6Y2Snqb0fr9B0mkRcXeVuGPsawOlkZydIuIVNcTrPHt7W0Q84FR0\n6xUR8cMa9vFcpc/RlnnTrZLOiYjKo4y2n6d0Yqr22P1me9OI+PNkt6NpbG8eEX+c7Hagmvzdroi4\nf5THtoyIW5sYuyPWFmr7romI5TXE7Evbx9j3cyLi+qbHtr2DVv++/rpEm/NI4zZKJ5JX1hj3cZ19\nDNtPjoiqs8rG2l/l4257XUkPRk6KbO+q1Cf7VURUKurZto+Zku6OiJW5MNsOSv3Aa+uIP8Y+6zo2\n79IofRpJJ0fE36q1srzGJLvjsf2OiDilYowPSPpXpc7+tpIOi4iz82OLImK7JsZui3+I0ghp3W3/\npNJIznRJFyglcwuUiqP8NCI+VSH2OaNsfqXSWlJFxF69xu5T/MsjYsd8/z1Kr/GPlIoE/VdEHFMh\n9nVKIxYP2f6qpHsl/UBpit6LI+INFdv+HaXXdH1JK5VGLX6Y4zsi3l4h9mGSXqdUYGRPpVHFlZL2\nlXRIRCyo0vZBNBWSOttPlPQxpR+zcyPiu22PnRRpGn+V+MdI+mxE/Cl35M5U6sitK+mgiLioSvxx\n9nteRFS6fJLtRUqfn+9GxO/qadmq2J2VLS3pSkn/oPRZvePR/+oxxd9Caa37I0pr4N+vdKJqqdJv\nyW1Njd9+Mtf2RpI+pzSSfK2kD0bEiopt31BpdP6NSifCH1AqmPSfEfHNKrFz/GId6D51zq3UJ2jv\n4F4+ss+Ksfve8c/7bUVaLtfI2Hka8HFKv6nbK81s2kSpcOjbIqLnJWDt3+P55P1pSu/3rZWWPVUa\n4MjvwW8r1XlYpFTbZFl+rHJfeJz91nHcr5Y0KyLudLoawb5KM9Z2USr6dGTF+EcqzX77m1J9og8p\nvbYvVUoYP1cl/jj7LfZ+n0qmSrJbxxt5iaR/jIi/5C/W70v6dkScaHtxRPRcxrxk7D61fVtJj1cq\nvrRVRNydz7xeFhEvqhB7kdIUqK8rrQO2pO8qTQVU1c6t7cVKI6HF4o8cW9sLlSr13p5HGi+NiBdW\niL00Ip6b76/xI2D7qojYtmLbr4mIF9mertRBeWpEPJw7L1dXfF2XSNo2x1tfKTGalTsvZ1d9v3ex\n/0rJSx86/+1J3fZKxTlqSepKds5t/0Cp0uKlSmsvH5R0YET8raaTdktGPjO250v6SJ5Nso3SrIAd\nKsQeq22W9OOIeEqvsXP8G5VORr1Z6Xvyu5LOiIg/VImbYz8i6aaOzVsprc2OiHhmxfjnS/qJ0nS5\nAyV9R6mTu4+k3SJi76bGb3/f2f660rH/mqQ3KE2D3adi289WOoF5odJru4HSlL1PKI1ifqxi/PE6\n0FdExOwmxs7xd1eapvgbpd8QKb0vt1Y6qdnz7KbSHX/bnx/rIUlvj4gnNjF2jr9YqbL+7bafIelz\nEbGv7VdL+nBE7F4hdvvnab6kIyJikdNMrTOrfAfnmAslHRwR1zmtfz9aKUG/tIa+aunjfm1EvCDf\nv0LSyyPi/tyHWlSlz5RjXqd0Qmd9ScskPbOtP3nZyL57jF302OR97KH0nd5+4uvsqGHmaj9Mn+wG\njLB9zVgPSZoxxmOPxbTI038jYpntWZK+7zSlseo1KkvGLh3/oUiLzu+z/bvI01Dzh/yRirF3UFr7\n93GlL+mrbN9f4wjO9oXjT7O9idJaznUi4nZJioh7bT9UMfa1Xj1j4WrbO0TEFbnjX2mKcTYtn/nf\nQOnLdSOldeqPV/VCRlL67ng4xxsputCyXUfsiZKXSicCJH1Tqzvn85U653sqfZH/p9JUnSpe13YW\n+LOS9m9P6pQ+F736jlLnfA91dM5tb1Oxc/6siHhjvn+W7Y9L+plTUbw6TLc9UphmvYhYKEkR8Wun\nafdVLFS6FuVo34d1XD/5zoj4kKQP2X650vrdRU6FY74bEV+tEPvDSjNpPhwRS6SUXEfEMyq3OpkR\nEV/IcQ+JiGPz9i/YftcUiD9ih7aTgMfb7nl2SpuhtpNEn7O9MCL+j+13KJ2orZTsKv1u3Jnv76/V\nHehjlEa+qiSkJWNLqY7EbiMjcyNyAnauqhUcepuk52mMjr/SCH4V71CqOTDa9Mq3NDi21NbXUKoQ\n/HRJiogLnK7BW5c1ijN6dZG5KtaNVEROEfH9/P34Q6eipFVH1kof97ttvyDSzII/KY1O36/U16nj\n2DycP58P5Lh/llb1J6vGLnps8vtuG6UiXiMFMrdSuvbzayPisKr7KK0xya5SQruHUpGedlaqlFjV\nCtvbRsRVkpRHSf+3pG8oXQC7qbFLx3/A9voRcZ9S8ihp1ZSxSslupGrRxztVWDze9grV+J4rHV8p\nQbxS6T0Ytp8SEbfl0bWq307vVrpA+ieUvlh/aftmpSrVdVzA/GSlae/rKJ0M+J7t3yudOT+9Yuyv\nS1po+zKlSzIdK0m2N1OuFF6DkslL6c55yaSuZOf88ban5c+VIuJTtm9Vmq6+YaVWJyNVvI9RruKt\nNDX4lapexXuk+vhvOh/In6vaRMTFki62/X6lJHV/ST0nuxFxnO0zlL7DblaadVDnlKv2jtqp4zzW\nxPib2z5c6XtgI9uOWDUdrY6232v7ZRFxiTuudOAaeqAq24Eu3TkvWfm9ZMdfSr8f10bEo/qOtuc0\nOLaUq7MrLcfaS/VWZ39OHliypCHbm+SZAdNUTxXsB21vEXlddx7hfZVSQbJnVYxd+riPXI3gaq2+\nGsHPlfrYdV6NYAOtruh/vtLvX9V6G6WPzZ4Rsc0osc+Q9GulQadGa1Ky+2OlamuP6vTYXlBD/IMk\nrTEalzujB9n+SoNjl47/isiLy0c6udnjJNVx5lwRcYuk/Wy/Tuki3bUqFT8ihsZ46BGlKWNVYt8l\n6WCndZLPUO5YRMU1aG3xj89fRIqIPzhdTmY3SV+LiMsrxj7R9oVKZ/aPi1z8IJ+NrlxAKiuZvIzX\nOa/jUi8lk7qSnfP/ym28cGRDRHzT9nJJX6gYWxHxBacp8P+idJZ4utLlY85SqsJaxRyN3cF/f8XY\nUvpBX0OeEXN+vlXS9h22t1LthPWrxmxztu0NI+IvEfGJkY22t9Yo/6+Gxf+apCfk+9+U9GRJtzst\nRajjMmf/Iulrtp+ttCTmndKqE3dfqiF+yQ506c75N5ROap6u1ZcKfJrSMqGTK8Yu2fGXUvX+US+P\nVMOMiZKxpTS9+z1Klze8UOl1kNIJsD0qxu4cjb83//kkpSU9VR2pNHC1qohZRNzitA750Iqxix73\niLgmzyjbXen36Wqlkz0fjHqKd71b6bJAobQUcSelUdcbVP27pvR78q+2XzJy4r5NHZch64spsWYX\nwNrFab3Pkoi4YZTH9omIsyrEnivpM9FW1Txv31rSMRFR+VqbTksN2pO6m5WSum/kE1W9xn2R0sj6\nqs55HjHeTNJbImKstTvdxl9VVT7WrPr+2qih6M048euuKv88pcvWXR81VZUvHb9tP+tJOjXS9V9r\n5xqryud4O0laGqtrPRypXChJFSvLOxVn/FFUKMozQfx1lTqct0aBqvV5H+todQd6ZLT0p3V0oEvG\nzvGLVJV3WgfZ3vHfUWm9d0vSlyLi3nH+OYAJuMaK/vkkwJeVTjyOzPZ4mqS7JP1rRFxZx35KItkF\nMKW4hurskxG7dPyqsfO03ENVtqp8qYr4nVXld1Jaj125qnzp+J7CVeVzzM7K8vcpJTCVK8vbvktp\n9Ol3SkXBvher1zNW5tVV69dT6rjVVrUezeTqBQ77VSH8DUoJRZ1FCNvbvrFS1ec62/5EpbXiW0k6\nLyJOa3usUkX/fKxnK9XW2FzpRMkfJZ2tdJK60gkel6/M3t7+GUozBGtpvwtX9G/bT+2XIesXkl0A\nU4obfumIyYpfNbapKj8p8d2HqvVRqKp8jlmssrxTZdrtlZZg7K+0hvFKpePzw4i4p2Lbi1Wtz/GL\nddBd/lJhnUndccozAlQxMXLBS3nl+MWqs3sKVwjvQ9uLVfS3/VOlE4DfGkmycvL1dkmvigpVqnOs\n0pXZx2r/wZJeWaX9LlzRP++j2GXI+qFJa3YBQJKKVmcvGbt0/MJtp6r85MQvXbW+ZFV5qWxl+YhU\nS2KepHlO1d5fqzT1+LOSNqsYv3TV+jOVOrizRumgn6k0ut6rU5QSix9Iemde+nFgpBocL63U6uTT\nWr0e/TilxOj1SonRV5QS+F5tolRocL5TTYDaLuWVla7OPmIqVggfUaLtJSv6D8XqYpKSpPyZOtb2\nO2uK/818v8RxH6v9x+R9VFG0or/HuQyZU5HPysthSiPZBdBEJauzl678PlXbTlX5SYgfU7uqvFS2\nsvwa7Yu0hvYcSec4VaetqmTVeqlsB730pcLa1Z0YlbyUl1S2wOFUrhBeuu0lK/rfZPsjSiOjKyTJ\n9gylkdE61vSXrsxerP1RvqJ/ycuQ9QXJLoAmKlmdvXTl96nadqrKT178KVlVPscvWVl+/3H2e1/V\n4FGwan1WsoNe+lJhpRMjSfVfyiubo3LV2ftdIfxdkuqqEF667SUr+u+vVPzuovwZCkkrlE5+vbli\nbKl8Zfai7Y+yFf1LXoasL1izCwAAULM8ffxIpYrGm+fNIx3cYyKic4bGY4n9GUnzIuLCju2vkfSF\niHh2r7FznE92bDop0lrvLZSq2R9UIfbpEXFAlfZ1sY+Sld+Lxc5xnpvjX1qg7X2pKt+2v1OrvFfG\niftypTWkS0pMo3X9VeuLVZbPSzEOkPSHSFXl3ybpHUpLHCpXlbc9WykhH+0yZGdGxNFV4vcDyS4A\nAEAfucGV2Sczfh2xXbbyez+q1h+iNL2+dNX6HSUtUH1V6zsry1vSrqqhsrzXrCr/bqXX9yzVV1W+\nM/6hqrdqfXtl+dOUKsv/qUrMttgjVeXXl7RSqQbBj1RjVfl8YmQv1XwZsn4h2QUAAOgjN7gy+2TG\nryO2y1d+p2r96PEXK00Brr2yvMtXlS8eX4Uqy7twVflBwJpdAACAmnnqVmaf0m1X2crvVK0f2/Yq\nV1m+dFX50vEjylWWL1pV3oWvcdwPJLsAAAD1m6qV2UvHL932kpXfqVo/hihbWb50VfnS8UtWli9d\nVb7kJdT6gmnMAAAANbN9sqRTIuKSUR47LSIObGLs0vH70PatlEYxl4/y2M4R8Ysmxi4d3/bjI1eV\n79j+ZElPiXyN1ro4VZbfOSLqujbwaPtYX9KMiLixyfFtbxMRv66pWaPFf6q0qqr8xkrTpVtRZJCX\nqQAAAMRJREFUQ1V52zdExN8/1seahGQXAAAAALAG2/OULic12iXUXh0Ru01i87pS27XSAAAAAAAD\nY39JmypdI/gO23coVfF+kqT9JrNh3WJkFwAAAADQtdKXOasLyS4AAAAAoGulL3NWF6oxAwAAAADW\n0IdLhRVHsgsAAAAA6FT6UmHFkewCAAAAADr9WNKGI9d+bmd7Qf+b89ixZhcAAAAAMHC49BAAAAAA\nYOCQ7AIAAAAABg7JLgAAAABg4JDsAgAAAAAGzv8A76hIRfeOCPQAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fbcc7240210>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"The probability that the distributions for payload/histograms/FX_REFRESH_DRIVER_SYNC_SCROLL_FRAME_DELAY_MS (children) are differing by chance is 0.00.\n"
]
}
],
"source": [
"compare_histograms(subset,\n",
" 16,\n",
" \"payload/histograms/FX_REFRESH_DRIVER_SYNC_SCROLL_FRAME_DELAY_MS\")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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