Traffic Map | Levi B.

├── cleanData └── .gitignore ├── rawData └── .gitignore ├── map ├── css │ └── style.css ├── map.html ├── js │ └── app.js └── assets │ └── locations.js ├── requirements.txt ├── README.md └── Logation.py /cleanData/.gitignore: -------------------------------------------------------------------------------- 1 | * 2 | !.gitignore -------------------------------------------------------------------------------- /rawData/.gitignore: -------------------------------------------------------------------------------- 1 | * 2 | !.gitignore -------------------------------------------------------------------------------- /map/css/style.css: -------------------------------------------------------------------------------- 1 | body { 2 | margin: 0; 3 | } 4 | 5 | div.container { 6 | height: 100vh; 7 | width: 100vw; 8 | background-color: #505050; 9 | margin: 0; 10 | overflow: hidden; 11 | } 12 | 13 | .segment { 14 | fill: #9B9B9B; 15 | stroke: darkgray; 16 | stroke-width: 1px; 17 | opacity: 0.9; 18 | } 19 | 20 | .graticule { 21 | opacity: 0.2; 22 | } -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | # This file may be used to create an environment using: 2 | # $ conda create --name --file 3 | # platform: linux-64 4 | 5 | ca-certificates=2019.1.23=0 6 | certifi=2019.3.9=py37_0 7 | libedit=3.1.20181209=hc058e9b_0 8 | libffi=3.2.1=hd88cf55_4 9 | libgcc-ng=8.2.0=hdf63c60_1 10 | libstdcxx-ng=8.2.0=hdf63c60_1 11 | maxminddb=1.4.1=pypi_0 12 | maxminddb-geolite2=2018.703=pypi_0 13 | ncurses=6.1=he6710b0_1 14 | openssl=1.1.1b=h7b6447c_1 15 | pip=19.0.3=py37_0 16 | python=3.7.2=h0371630_0 17 | python-geoip=1.2=pypi_0 18 | python-geoip-geolite2=2015.303=pypi_0 19 | readline=7.0=h7b6447c_5 20 | setuptools=40.8.0=py37_0 21 | sqlite=3.27.2=h7b6447c_0 22 | tk=8.6.8=hbc83047_0 23 | wheel=0.33.1=py37_0 24 | xz=5.2.4=h14c3975_4 25 | zlib=1.2.11=h7b6447c_3 26 | -------------------------------------------------------------------------------- /map/map.html: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | Traffic Map | Levi B. 15 | 16 | 17 |

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19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Logation [NGINX] 2 | _Analyse your NGINX access logs and create beautiful maps of the locations from which people access your service._ 3 | 4 | ![result](https://i.imgur.com/PQKeerX.png) 5 | ____ 6 | [![Build Status](https://travis-ci.org/joemccann/dillinger.svg?branch=master)](https://travis-ci.org/joemccann/dillinger) 7 | 8 | ## How to use 9 | 10 | First of all, make sure you have python3.x and *geolite2* installed. The later can be installed via pip with `pip install maxminddb-geolite2 python-geoip-geolite2`. 11 | 12 | Then simply clone this repository, put a *copy* of your NGINX access.log in the 13 | `./rawData/` folder and run Logation.py. 14 | 15 | The map can then be found at `./map/map.html`. 16 | 17 | The `./cleanData/` folder contains the .json and .txt files with the geolocations of all unique ips and some other statistics (like what OS has been used etc.). 18 | 19 | On an ordinary laptop 100mb of access.log size takes about 30s to analyse. 20 | -------------------------------------------------------------------------------- /map/js/app.js: -------------------------------------------------------------------------------- 1 | const WORLD_DATA_URL = 'https://gist.githubusercontent.com/mbostock/4090846/raw/d534aba169207548a8a3d670c9c2cc719ff05c47/world-110m.json' 2 | 3 | class App { 4 | constructor(div) { 5 | this.div = div 6 | } 7 | 8 | async fetchData() { 9 | // using the fetch api to load all the initial data 10 | 11 | // fetching world data for the globe 12 | let worldDataResponse = await fetch(WORLD_DATA_URL); 13 | let topo = await worldDataResponse.json(); 14 | 15 | // converting topojson to geojson 16 | // MIGHT WANNA PRERENDER THAT FOR RELEASE 17 | this.worldData = topojson.feature(topo, topo.objects.countries) 18 | 19 | // loading initial response 20 | // extracting raster 21 | this.raster = LOCATIONS["raster"] 22 | 23 | // setting raster information object 24 | this.rasterInfo = LOCATIONS["information"] 25 | 26 | // deriving some constants from the info 27 | this.dx = this.rasterInfo["dx"] 28 | this.dy = this.rasterInfo["dy"] 29 | 30 | // JITTER DATA 31 | this.raster = this.raster.map((e) => { 32 | return([ 33 | [e[0][0] + d3.randomUniform(-this.dx, this.dx)()/2, 34 | e[0][1] + d3.randomUniform(-this.dy, this.dy)()/2], 35 | e[1] 36 | ]) 37 | }) 38 | 39 | } 40 | 41 | async initiateEnvironment() { 42 | // intiating all things that we'll need throughout 43 | // (and that won't change after fetching data) 44 | 45 | // loading base data 46 | await this.fetchData() 47 | 48 | // creating graticules 49 | this.graticule = d3.geoGraticule() 50 | .step([10, 10]); 51 | 52 | 53 | 54 | } 55 | 56 | setDimensions() { 57 | // Setting all the dimensions for the graph 58 | 59 | this.boundingRect = this.div.getBoundingClientRect(); 60 | this.height = this.boundingRect.height; 61 | this.width = this.boundingRect.width; 62 | 63 | // derived quantities 64 | this.center = [this.width / 2, this.height / 2] 65 | this.dim = [this.width, this.height] 66 | 67 | 68 | 69 | } 70 | 71 | 72 | createGraph() { 73 | // Defining all needed operators and quantities 74 | 75 | // defining projection 76 | this.projection = d3.geoNaturalEarth1().scale(1).rotate(90) 77 | .fitSize([d3.max(this.dim), d3.min(this.dim)], this.worldData) 78 | 79 | // defining geoPath generator 80 | this.geoPath = d3.geoPath().projection(this.projection) 81 | 82 | // defining scales 83 | 84 | // most number of visits of a square 85 | this.maxVisit = d3.max(this.raster.map((e) => { return e[1] })) 86 | 87 | this.radiusFromVisits = d3.scalePow() 88 | .domain([0, this.maxVisit]) 89 | .range([0, 2]).exponent(0.2) 90 | 91 | this.opacityFromVisits = d3.scalePow() 92 | .domain([0, this.maxVisit]) 93 | .range([0, 1]).exponent(0.2) 94 | 95 | // creating circle path constructor 96 | this.circle = d3.geoCircle() 97 | .radius((d) => { 98 | return this.radiusFromVisits(d[1]) 99 | }) 100 | .center((d) => { 101 | return d[0] 102 | }) 103 | 104 | // plotting the data 105 | 106 | // create svg container 107 | this.svg = d3.select(this.div).append("svg") 108 | .attr("height", d3.min(this.dim)) 109 | .attr("width", d3.max(this.dim)) 110 | 111 | // if height bigger then width, then rotate to display sideways 112 | if( this.height > this.width) { 113 | this.svg 114 | .attr("transform", "translate(" + (this.width - this.height 115 | )/2 +", " + (this.height - this.width 116 | )/2 + ") rotate(90)") 117 | } 118 | 119 | // plot graticules 120 | this.svg.append("path") 121 | .attr("class", "graticule") 122 | .datum(this.graticule) 123 | .attr("d", this.geoPath) 124 | .style("fill", "none") 125 | .style("stroke", "#ccc"); 126 | 127 | // plot globe 128 | this.svg.selectAll(".segment") 129 | .data(this.worldData.features) 130 | .enter().append("path") 131 | .attr("class", "segment") 132 | .attr("d", this.geoPath) 133 | .style("stroke-width", "1px") 134 | .style("opacity", ".6"); 135 | 136 | // plot raster data 137 | this.svg.selectAll("circle") 138 | .data(this.raster) 139 | .enter().append("path") 140 | .attr("class", "marker") 141 | .attr("d", (d) => { 142 | return this.geoPath(this.circle(d)) 143 | }) 144 | .attr("stroke","red") 145 | .attr("fill","orangered") 146 | .attr("opacity", (d) => { 147 | return this.opacityFromVisits(d[1]) 148 | }) 149 | } 150 | 151 | async updateData() { 152 | // updating the data 153 | } 154 | 155 | 156 | handleResize() { 157 | // handling resize 158 | 159 | // just redraw the graph with the new dimensions 160 | this.setDimensions() 161 | // remove old graph 162 | this.svg.remove() 163 | // draw new 164 | this.createGraph() 165 | } 166 | 167 | addEvents() { 168 | // adding event handlers 169 | 170 | // binding this 171 | this.handleResize = this.handleResize.bind(this) 172 | 173 | // adding debounced resize handler 174 | window.addEventListener("resize", _.debounce(this.handleResize, 100)) 175 | } 176 | 177 | async run() { 178 | // putting it all together 179 | await this.initiateEnvironment() 180 | this.setDimensions() 181 | this.createGraph() 182 | this.addEvents() 183 | } 184 | } 185 | 186 | 187 | let container = document.querySelector("div.container") 188 | 189 | let app = new App(container) 190 | 191 | app.run() -------------------------------------------------------------------------------- /Logation.py: -------------------------------------------------------------------------------- 1 | import random 2 | import json 3 | from geolite2 import geolite2 4 | import itertools 5 | 6 | 7 | class Log(object): 8 | """ 9 | Class for analysing your nginx log. 10 | 11 | """ 12 | 13 | def __init__(self, access_log_file="access.log", 14 | clean_dir="./cleanData/", raw_dir="./rawData/", 15 | asset_dir="./map/assets/"): 16 | super(Log, self).__init__() 17 | 18 | # creating all file and directory names 19 | 20 | # dir with data that we can read & write 21 | self.clean_dir = clean_dir 22 | 23 | # dir for source data. Read only! 24 | self.raw_dir = raw_dir 25 | 26 | # "location.js" in this dir will be loaded by the website 27 | # in ./map/ 28 | self.asset_dir = asset_dir 29 | 30 | # path to acces.log 31 | self.data_file = raw_dir + access_log_file 32 | 33 | # this json will contain the geolocation and OS info for 34 | # each IP 35 | self.data = self.clean_dir + "analysis.json" 36 | 37 | # contains general information and rasterised location data 38 | self.responseJson = self.clean_dir + "locations.json" 39 | 40 | # js file that loads the information and raster from 41 | # locations.json intot the website. 42 | self.locationsJS = self.asset_dir + "locations.js" 43 | 44 | # object that gathers miscellaneous information 45 | self.information = {"total": 0, 46 | "OS": { 47 | "Windows": 0, 48 | "Android": 0, 49 | "iOS": 0, 50 | "Linux": 0, 51 | "Other": 0} 52 | } 53 | 54 | def getIP(self, line): 55 | """Get ip from nginx log line""" 56 | return line.split(" ")[0] 57 | 58 | def removeDublicates(self): 59 | """Scans the log file for visits by the same ip and removes them.""" 60 | with open(self.data_file, "r") as f: 61 | 62 | # storing all already added IPs 63 | addedIPs = [] 64 | 65 | # creating file that just stores the ips 66 | self.ip_file = self.clean_dir + "ip.txt" 67 | 68 | # file that stores the log lines without doublicate ips 69 | self.unique_data_file = self.clean_dir + "noDublicatesLog.txt" 70 | with open(self.ip_file, "w") as dump: 71 | with open(self.unique_data_file, "w") as clean: 72 | 73 | # check if we already saw the ip (within the last 1000 ips) 74 | # and ignore it if we did. 75 | # Otherwise, write it down. 76 | for line in f: 77 | IP = self.getIP(line) 78 | if IP not in addedIPs[max(-len(addedIPs), -1000):]: 79 | addedIPs.append(IP) 80 | clean.write(line) 81 | else: 82 | pass 83 | 84 | dump.write("\n".join(addedIPs)) 85 | print("Removed Dublicates.") 86 | 87 | def getLine(self): 88 | """For Debugging. 89 | Gets a random line from the log file.""" 90 | index = random.randint(0, 20000) 91 | with open(self.data_file, "r") as data_file: 92 | for i, line in enumerate(data_file): 93 | if i == index: 94 | return(line) 95 | 96 | def getContext(self, line): 97 | """Method to isolate OS data from a log line""" 98 | return(line.rsplit("\"")[5]) 99 | 100 | def getOS(self, line): 101 | """Gets the OS from a log file entry""" 102 | context = self.getContext(line).rsplit("(")[1] 103 | rawOS = context.rsplit(";")[1].lower() 104 | 105 | if "win" in rawOS: 106 | return("Windows") 107 | elif "android" in rawOS: 108 | return("Android") 109 | elif "mac" in rawOS: 110 | if "ipad" or "iphone" in context: 111 | return("iOS") 112 | else: 113 | return("Mac") 114 | elif "linux" or "ubuntu" in rawOS: 115 | return("Linux") 116 | else: 117 | return("Other") 118 | 119 | def getData(self): 120 | """Removes all dublicates and and creates a file where 121 | each entry is an ip and its OS""" 122 | self.removeDublicates() 123 | 124 | with open(self.unique_data_file, "r") as data_file: 125 | with open(self.data, "w") as json_file: 126 | result = [] 127 | for line in data_file: 128 | try: 129 | entry = {} 130 | entry["ip"] = self.getIP(line) 131 | entry["OS"] = self.getOS(line) 132 | result.append(entry) 133 | 134 | # updating the information Object 135 | self.information["total"] += 1 136 | 137 | if entry["OS"] == "Windows": 138 | self.information["OS"]["Windows"] += 1 139 | elif entry["OS"] == "iOS": 140 | self.information["OS"]["iOS"] += 1 141 | elif entry["OS"] == "Linux": 142 | self.information["OS"]["Linux"] += 1 143 | elif entry["OS"] == "Android": 144 | self.information["OS"]["Android"] += 1 145 | elif entry["OS"] == "Other": 146 | self.information["OS"]["Other"] += 1 147 | 148 | except Exception as e: 149 | pass 150 | 151 | json.dump(result, json_file) 152 | print("Cleaned Data.") 153 | 154 | def addLocation(self): 155 | """ 156 | Scans all ips for their geolocation 157 | """ 158 | self.getData() 159 | with open(self.data, "r") as json_file: 160 | data = json.load(json_file) 161 | reader = geolite2.reader() 162 | result = [] 163 | for item in data: 164 | ip_info = reader.get(item["ip"]) 165 | if ip_info is not None: 166 | try: 167 | item["latitude"] = ip_info["location"]["latitude"] 168 | item["longitude"] = ip_info["location"]["longitude"] 169 | result.append(item) 170 | except Exception as e: 171 | pass 172 | 173 | with open(self.data, "w") as json_file: 174 | json.dump(result, json_file) 175 | 176 | print("Added locations") 177 | 178 | def analyseLog(self): 179 | self.addLocation() 180 | self.rasterizeData() 181 | print("Rasterised Data") 182 | self.createJs() 183 | print("Done!") 184 | 185 | def rasterizeData(self, resLat=200, resLong=250): 186 | """ 187 | Splits the map into equal resLat*resLong chunks and 188 | counts how many visits came from each chunk. 189 | 190 | Returns 191 | { 192 | "information": Object with stats about what 193 | OS your users use and how many unique visitors came, 194 | 195 | "raster": list with entries: 196 | [[center of square], # of visitors to that square] 197 | } 198 | 199 | """ 200 | 201 | latStep, longStep = 180 / resLat, 360 / resLong 202 | # Build the rasterised coord. system 203 | gridX, gridY = [], [] 204 | x = -180 205 | y = -90 206 | 207 | for i in range(resLong): 208 | gridX.append(x) 209 | x += longStep 210 | gridX.reverse() 211 | 212 | for i in range(resLat): 213 | gridY.append(y + i * latStep) 214 | gridY.reverse() 215 | 216 | gridItems = itertools.product(gridX, gridY) 217 | grid = {i: 0 for i in gridItems} 218 | 219 | # Now assign each data point to its grid square 220 | with open(self.data, "r") as json_file: 221 | data = json.load(json_file) 222 | 223 | for point in data: 224 | lat, lon = point["latitude"], point["longitude"] 225 | for x in gridX: 226 | if lon >= x: 227 | coordX = x 228 | break 229 | for y in gridY: 230 | if lat >= y: 231 | coordY = y 232 | break 233 | grid[(coordX, coordY)] += 1 234 | 235 | # remove squres with 0 entries 236 | for key in list(grid.keys()): 237 | if grid[key] == 0: 238 | del grid[key] 239 | 240 | # center squares 241 | raster = [] 242 | 243 | # creating raster and information object 244 | for key in grid: 245 | x = round(key[0] + longStep / 2, 5) 246 | y = round(key[1] + latStep / 2, 5) 247 | raster.append([[x, y], grid[key]]) 248 | 249 | # noting the size of the grid squares 250 | self.information["dx"] = round(longStep / 2, 5) 251 | self.information["dy"] = round(latStep / 2, 5) 252 | 253 | # generating responseJson 254 | with open(self.responseJson, "w") as json_dump: 255 | json.dump({"information": self.information, 256 | "raster": raster}, json_dump) 257 | 258 | def createJs(self): 259 | """creating the js-file that we feed to the website inside the 260 | ./map/ folder to create the map.""" 261 | with open(self.responseJson, "r") as response: 262 | 263 | dataString = "const LOCATIONS = " + str(json.load(response)) 264 | 265 | with open(self.locationsJS, "w") as f: 266 | f.write(dataString) 267 | 268 | 269 | if __name__ == '__main__': 270 | log = Log() 271 | log.analyseLog() 272 | -------------------------------------------------------------------------------- /map/assets/locations.js: -------------------------------------------------------------------------------- 1 | const LOCATIONS = {'information': {'total': 149427, 'OS': {'Windows': 29176, 'Android': 63809, 'iOS': 50939, 'Linux': 5503, 'Other': 0}, 'dx': 0.72, 'dy': 0.45}, 'raster': [[[177.84, -18.45], 1], [[177.84, -38.25], 1], [[176.4, -37.35], 23], [[176.4, -38.25], 13], [[176.4, -39.15], 8], [[176.4, -40.05], 7], [[174.96, -35.55], 3], [[174.96, -36.45], 245], [[174.96, -37.35], 69], [[174.96, -38.25], 13], [[174.96, -39.15], 2], [[174.96, -40.05], 9], [[174.96, -40.95], 106], [[173.52, -35.55], 3], [[173.52, -39.15], 4], [[173.52, -40.95], 95], [[173.52, -41.85], 2], [[172.08, -43.65], 98], [[170.64, -45.45], 29], [[170.64, -46.35], 1], [[169.2, -44.55], 2], [[169.2, -45.45], 4], [[169.2, -46.35], 1], [[167.76, 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