Douglas Duhaime duhaime
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University of Notre Dame
- New Haven, CT
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- http://douglasduhaime.com/
View socket_stream.py
| import sys, socket, redis, json | |
| # config | |
| stream = {'host': '127.0.0.1', 'port': 6000} # streaming data host / port | |
| r = redis.Redis(host='127.0.0.1', port=6379) # redis instance host / port | |
| # consume data from stdin and publish to redis on localhost | |
| client = socket.socket(socket.AF_INET, socket.SOCK_STREAM) | |
| client.connect((socket.gethostbyname(stream['host']), stream['port'])) # host, port |
View signal.py
| # class to gracefully handle sigint / sigterm (SO 18499497) | |
| class SignalListener: | |
| kill_now = False | |
| def __init__(self): | |
| signal.signal(signal.SIGINT, self.exit_gracefully) | |
| signal.signal(signal.SIGTERM, self.exit_gracefully) | |
| def exit_gracefully(self,signum, frame): | |
| self.kill_now = True |
View server.py
| from flask import Flask, jsonify, request, send_from_directory | |
| import redis, celery, sys, socket, os, json | |
| # config | |
| port = 6000 # port on which data streams | |
| # app | |
| app = Flask(__name__, static_url_path='') | |
| app.config['CELERY_BROKER_URL'] = 'redis://localhost:6379/0' | |
| app.config['CELERY_RESULT_BACKEND'] = 'redis://localhost:6379/0' |
View index.html
| <!DOCTYPE html> | |
| <html> | |
| <head> | |
| <meta charset='UTF-8'> | |
| <title>title</title> | |
| <style> | |
| * { | |
| margin: 0; | |
| padding: 0; | |
| } |
View knn.py
| from annoy import AnnoyIndex | |
| import json | |
| import os | |
| def build_ann_index(model): | |
| '''Build an ANN model and persist to disk for faster vector similarity queries''' | |
| words = list(model.wv.vocab.keys()) # list of strings, one per word | |
| idx_to_word = {str(idx): i for idx, i in enumerate(words)} # d[word] = word_idx in words | |
| dims = model.wv[words[0]].shape[0] # number of dimensions in each input vector |
View unproject.js
| function getWorldCoords(e) { | |
| // identify the x,y coords in canavs that got event | |
| var rect = canvas.getBoundingClientRect(), | |
| x = e.clientX - rect.left, | |
| y = e.clientY - rect.top; | |
| // convert x,y to clip space in canvas: | |
| // canvas coords from top left in clockwise order | |
| // (-1,1), (1,1), (-1,-1), (1, -1) | |
| var mouse = new THREE.Vector3(); | |
| mouse.x = ( (x / canvas.clientWidth ) * 2) - 1; |
View index.html
| <!DOCTYPE html> | |
| <html> | |
| <head> | |
| <meta charset='UTF-8'> | |
| <title>Drawing a Neural Network</title> | |
| <script src='https://cdnjs.cloudflare.com/ajax/libs/d3/5.9.2/d3.min.js'></script> | |
| </head> | |
| <body> | |
| <script> |
View index.html
| <html> | |
| <head> | |
| <style> | |
| html, body { width: 100%; height: 100%; background: #000; } | |
| body { margin: 0; overflow: hidden; } | |
| canvas { width: 100%; height: 100%; } | |
| </style> | |
| <meta charset='UTF-8'> | |
| </head> | |
| <body> |
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