3774 lines
203 KiB
Plaintext
3774 lines
203 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import json\n",
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"import pandas as pd\n",
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"import seaborn as sns\n",
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"import plotly.express as px\n",
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"import plotly.graph_objects as pgo\n",
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"import scipy as sp\n",
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"from pymongo import MongoClient\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"client = MongoClient(\"mongodb://stats_user:%40z%5EVFhN7q%25vzit@192.168.86.120:27017/?authSource=statistics\")\n",
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"db = client.statistics\n",
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"\n",
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"formatted_date = lambda date: {\n",
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" \"unix\": {\"$toLong\": date},\n",
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" \"iso\": {\"$toString\": date},\n",
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"}\n",
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"\n",
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"pipeline = [\n",
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" {\"$sort\": {\"timestamp\": 1}},\n",
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" {\n",
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" \"$group\": {\n",
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" \"_id\": \"$tags.session\",\n",
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" \"host\": {\"$first\": \"$tags.host\"},\n",
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" \"firstTimestamp\": {\"$first\": \"$timestamp\"},\n",
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" \"lastTimestamp\": {\"$last\": \"$timestamp\"},\n",
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" \"firstTimestampWithPeers\": {\n",
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" \"$min\": {\n",
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" \"$cond\": {\n",
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" \"if\": {\"$gt\": [{\"$size\": {\"$ifNull\": [\"$peers\", []]}}, 0]},\n",
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" \"then\": \"$timestamp\",\n",
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" \"else\": None,\n",
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" }\n",
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" }\n",
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" },\n",
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" \"maxNumberOfPeers\": {\n",
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" \"$max\": {\"$size\": {\"$ifNull\": [\"$peers\", []]}},\n",
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" },\n",
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" \"minNumberOfPeers\": {\n",
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" \"$min\": {\"$size\": {\"$ifNull\": [\"$peers\", []]}},\n",
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" },\n",
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" }\n",
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" },\n",
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" {\n",
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" \"$lookup\": {\n",
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" \"from\": \"peertube_ts\",\n",
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" \"let\": {\"currentSession\": \"$_id\", \"ftp\": \"$firstTimestampWithPeers\", \"fst\": \"$firstTimestamp\"},\n",
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" \"pipeline\": [\n",
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" {\n",
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" \"$match\": {\n",
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" \"$expr\": {\n",
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" \"$and\": [\n",
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" {\"$ne\": [\"$tags.session\", \"$$currentSession\"]},\n",
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" {\"$lt\": [\"$timestamp\", \"$$ftp\"]},\n",
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" {\"$gte\": [\"$timestamp\", \"$$fst\"]},\n",
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" {\"$gt\": [{\"$size\": {\"$ifNull\": [\"$peers\", []]}}, 0]},\n",
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" ]\n",
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" }\n",
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" }\n",
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" }\n",
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" ],\n",
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" \"as\": \"concurrentSessions\",\n",
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" }\n",
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" },\n",
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" {\n",
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" \"$addFields\": {\n",
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" \"concurrentSessions\": {\"$gt\": [{\"$size\": \"$concurrentSessions\"}, 0]}\n",
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" }\n",
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" },\n",
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" {\n",
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" \"$group\": {\n",
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" \"_id\": \"$host\",\n",
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" \"sessions\": {\n",
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" \"$push\": {\n",
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" \"id\": \"$_id\",\n",
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" \"startTime\": formatted_date(\"$firstTimestamp\"),\n",
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" \"endTime\": formatted_date(\"$lastTimestamp\"),\n",
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" \"duration\": {\n",
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" \"$divide\": [\n",
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" {\"$subtract\": [\"$lastTimestamp\", \"$firstTimestamp\"]},\n",
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" 1000,\n",
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" ]\n",
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" },\n",
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" \"firstPeerConnection\": {\n",
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" \"$cond\": {\n",
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" \"if\": {\"$eq\": [\"$firstTimestampWithPeers\", None]},\n",
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" \"then\": None,\n",
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" \"else\": {\n",
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" \"time\": {\n",
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" \"date\": formatted_date(\"$firstTimestampWithPeers\"),\n",
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" \"elapsedFromStart\": {\n",
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" \"$divide\": [\n",
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" {\"$subtract\": [\"$firstTimestampWithPeers\", \"$firstTimestamp\"]},\n",
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" 1000,\n",
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" ]\n",
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" }\n",
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" },\n",
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" \"concurrentSessions\": \"$concurrentSessions\",\n",
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" }\n",
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" }\n",
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" },\n",
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" \"maxPeers\": {\"$max\": \"$maxNumberOfPeers\"},\n",
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" \"minPeers\": {\"$min\": \"$minNumberOfPeers\"},\n",
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" }\n",
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" }\n",
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" }\n",
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" },\n",
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" {\n",
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" \"$set\": {\n",
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" \"sessions\": {\n",
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" \"$sortArray\": {\n",
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" \"input\": \"$sessions\",\n",
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" \"sortBy\": {\"id\": 1},\n",
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" }\n",
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" }\n",
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" }\n",
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" },\n",
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" {\n",
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" \"$project\": {\n",
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" \"_id\": 0,\n",
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" \"host\": \"$_id\",\n",
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" \"sessions\": \"$sessions\",\n",
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" }\n",
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" },\n",
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" {\"$sort\": {\"host\": 1}},\n",
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"]\n",
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"\n",
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"result = db.peertube_ts.aggregate(pipeline)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Mean time until first peer connection: 157.86s\n",
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"Median time until first peer connection: 4.02s\n",
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"Number of sessions with concurrent sessions: 3\n",
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"Number of sessions without concurrent sessions: 2\n"
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]
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},
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{
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"data": {
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"application/vnd.plotly.v1+json": {
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"config": {
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"plotlyServerURL": "https://plot.ly"
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"hovertemplate": "ConcurrentSessions=True<br>Elapsed=%{x}<br>count=%{y}<extra></extra>",
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"legendgroup": "True",
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"marker": {
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"yaxis": "y"
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"layout": {
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}
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"# Extract data from the result cursor\n",
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|
"data = []\n",
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|
"for host in result:\n",
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|
" for session in host['sessions']:\n",
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|
" if session['firstPeerConnection'] and session['firstPeerConnection']['time']:\n",
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|
" elapsed = session['firstPeerConnection']['time']['elapsedFromStart']\n",
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" concurrent_sessions = session['firstPeerConnection']['concurrentSessions']\n",
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|
" data.append((elapsed, concurrent_sessions))\n",
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|
"\n",
|
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"# Convert to a DataFrame for easier plotting\n",
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"df = pd.DataFrame(data, columns=['Elapsed', 'ConcurrentSessions'])\n",
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|
"\n",
|
|
"# Convert boolean column to integers\n",
|
|
"df['ConcurrentSessions'] = df['ConcurrentSessions'].astype(int)\n",
|
|
"\n",
|
|
"# Print some statistics\n",
|
|
"print(\"Mean time until first peer connection: {:.2f}s\".format(df['Elapsed'].mean()))\n",
|
|
"print(\"Median time until first peer connection: {:.2f}s\".format(df['Elapsed'].median()))\n",
|
|
"print(\"Number of sessions with concurrent sessions: {}\".format(df['ConcurrentSessions'].sum()))\n",
|
|
"print(\"Number of sessions without concurrent sessions: {}\".format(df['ConcurrentSessions'].count() - df['ConcurrentSessions'].sum()))\n",
|
|
"\n",
|
|
"# Revert concurrent sessions column to boolean for plotting\n",
|
|
"df['ConcurrentSessions'] = df['ConcurrentSessions'].astype(bool)\n",
|
|
"\n",
|
|
"# Plot the histogram of the elapsed time until first peer connection\n",
|
|
"# Color the bars based on the number of concurrent sessions and add a legend\n",
|
|
"fig = px.histogram(df, x='Elapsed', color='ConcurrentSessions', barmode='overlay', nbins=100)\n",
|
|
"fig.update_layout(\n",
|
|
" title='Elapsed time until first peer connection',\n",
|
|
" xaxis_title='Elapsed time (s)',\n",
|
|
" yaxis_title='Count',\n",
|
|
" legend_title='Had concurrent sessions',\n",
|
|
")\n",
|
|
"fig.show()\n",
|
|
"\n",
|
|
"# Plot the line chart of the elapsed time until first peer connection\n",
|
|
"fig = px.line(df, x=df.index, y='Elapsed')\n",
|
|
"fig.update_layout(\n",
|
|
" title='Elapsed time until first peer connection',\n",
|
|
" xaxis_title='Session index',\n",
|
|
" yaxis_title='Elapsed time (s)',\n",
|
|
")\n",
|
|
"fig.show()\n",
|
|
"\n",
|
|
"# Plot the cumulative distribution of the elapsed time until first peer connection\n",
|
|
"# Color the lines based on the number of concurrent sessions and add a legend\n",
|
|
"fig = px.ecdf(df, x='Elapsed', color='ConcurrentSessions')\n",
|
|
"fig.update_layout(\n",
|
|
" title='Cumulative distribution of elapsed time until first peer connection',\n",
|
|
" xaxis_title='Elapsed time (s)',\n",
|
|
" yaxis_title='Cumulative probability',\n",
|
|
" legend_title='Had concurrent sessions',\n",
|
|
")\n",
|
|
"fig.show()\n",
|
|
"\n",
|
|
"# Plot the histogram of the number of concurrent sessions\n",
|
|
"fig = px.histogram(df, x='ConcurrentSessions', histnorm='percent')\n",
|
|
"fig.update_layout(\n",
|
|
" title='Number of concurrent sessions',\n",
|
|
" xaxis_title='Had concurrent sessions',\n",
|
|
" yaxis_title='Percentage',\n",
|
|
")\n",
|
|
"fig.show()"
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|
]
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|
},
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|
{
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|
"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"image/png": 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",
|
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"text/plain": [
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"<Figure size 1000x600 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
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"data": {
|
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"image/png": 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",
|
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"text/plain": [
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"<Figure size 1000x600 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
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"data": {
|
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"image/png": 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",
|
|
"text/plain": [
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"<Figure size 1000x600 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
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"data": {
|
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"image/png": 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",
|
|
"text/plain": [
|
|
"<Figure size 1000x600 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"# Plot the histogram of the elapsed time until first peer connection using seaborn\n",
|
|
"plt.figure(figsize=(10, 6))\n",
|
|
"sns.histplot(df, x='Elapsed', hue='ConcurrentSessions', multiple='stack', bins=100)\n",
|
|
"plt.title('Elapsed time until first peer connection')\n",
|
|
"plt.xlabel('Elapsed time (s)')\n",
|
|
"plt.ylabel('Count')\n",
|
|
"plt.legend(title='Had concurrent sessions', labels=['True', 'False'])\n",
|
|
"plt.show()\n",
|
|
"\n",
|
|
"# Plot the line chart of the elapsed time until first peer connection using seaborn\n",
|
|
"plt.figure(figsize=(10, 6))\n",
|
|
"sns.lineplot(data=df, x=df.index, y='Elapsed')\n",
|
|
"plt.title('Elapsed time until first peer connection')\n",
|
|
"plt.xlabel('Session index')\n",
|
|
"plt.ylabel('Elapsed time (s)')\n",
|
|
"plt.show()\n",
|
|
"\n",
|
|
"# Plot the cumulative distribution of the elapsed time until first peer connection using seaborn\n",
|
|
"plt.figure(figsize=(10, 6))\n",
|
|
"sns.ecdfplot(df, x='Elapsed', hue='ConcurrentSessions')\n",
|
|
"plt.title('Cumulative distribution of elapsed time until first peer connection')\n",
|
|
"plt.xlabel('Elapsed time (s)')\n",
|
|
"plt.ylabel('Cumulative probability')\n",
|
|
"plt.legend(title='Had concurrent sessions', labels=['True', 'False'])\n",
|
|
"plt.show()\n",
|
|
"\n",
|
|
"# Plot the histogram of the number of concurrent sessions using seaborn\n",
|
|
"plt.figure(figsize=(10, 6))\n",
|
|
"sns.histplot(df, x='ConcurrentSessions', stat='percent', discrete=True)\n",
|
|
"plt.title('Number of concurrent sessions')\n",
|
|
"plt.xlabel('Had concurrent sessions')\n",
|
|
"plt.ylabel('Percentage')\n",
|
|
"plt.legend(title='Had concurrent sessions', labels=['True', 'False'])\n",
|
|
"plt.show()"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": ".venv",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.13.1"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 2
|
|
}
|