Autore Topic: Orbit - Cryptocurrency Wallets Relationship Visualizer  (Letto 121 volte)

0 Utenti e 1 Visitatore stanno visualizzando questo topic.

Offline Flavio58

Orbit - Cryptocurrency Wallets Relationship Visualizer
« Risposta #1 il: Luglio 11, 2018, 08:01:32 pm »
Advertisement
Orbit - Cryptocurrency Wallets Relationship Visualizer


Give it a blockchain based crypto wallet address and it will crawl 3 levels deep in transaction data to plot a graph out of the information.


Usage
Run orbit.py with python3 as follows
python3 orbit.py

Enter the wallet address
  __         
 |  |  _ |  ' _|_
 |__| |  |) |  | 
 
Enter a wallet address: xxxxxxxxxxxxxxx
Now orbit will scrape wallets through blockchain API and once its done, a json file will be generated.
Next thing is to plot a graph for which we will be using quark framework which is also written by me :D
Clone Quark and navigate to the Quark directory and feed the json file to quark.py as follows:
python quark.py /path/to/file.json
And that's it! Your job is done here, open quark.html to see your graph ^_^

Warning!
The size of nodes (dots) and edges (lines) depends on the transactions made by that address to other members of the scope.
So the size of nodes can be ridiculosly big but don't get scared, just click on stabilize option in the sidebar and leave the rest to quark.
Also, if the node lables are getting on the way, click on the Node Lables option to turn that off.
The last thing is that there are going to be a lot of nodes that aren't interesting like a wallet that has made only one transaction. Such nodes will just make your graph ugly. To fix this, click on the clean option which will delete such insignificant nodes. More information about how to interact with the graph can be found on Quark's readme.



Source: Orbit - Cryptocurrency Wallets Relationship Visualizer


Consulente in Informatica dal 1984

Software automazione, progettazione elettronica, computer vision, intelligenza artificiale, IoT, sicurezza informatica, tecnologie di sicurezza militare, SIGINT. 

Facebook:https://www.facebook.com/flaviobernardotti58
Twitter : https://www.twitter.com/Flavio58

Cell:  +39 366 3416556

f.bernardotti@deeplearningitalia.eu

#deeplearning #computervision #embeddedboard #iot #ai

 

Related Topics

  Oggetto / Aperto da Risposte Ultimo post
0 Risposte
119 Visite
Ultimo post Maggio 23, 2018, 01:05:32 am
da Flavio58
0 Risposte
82 Visite
Ultimo post Giugno 05, 2018, 01:14:11 am
da Flavio58
0 Risposte
83 Visite
Ultimo post Giugno 06, 2018, 01:01:46 pm
da Flavio58
0 Risposte
60 Visite
Ultimo post Settembre 19, 2018, 10:04:39 am
da Flavio58
0 Risposte
81 Visite
Ultimo post Ottobre 19, 2018, 08:04:15 pm
da Flavio58

Sitemap 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326