evmcheb’s gists

evmcheb / extract_spectrogram.py

Created October 11, 2019 13:34

leverage the librosa python library to extract a spectrogram

	import librosa.display, os, gc
	import numpy as np
	import matplotlib.pyplot as plt

	def extract_spectrogram(fname, iname):
	audio, sr = librosa.load(fname, res_type='kaiser_fast')
	S = librosa.feature.melspectrogram(audio, sr=sr, n_mels=128)
	log_S = librosa.power_to_db(S, ref=np.max)
	fig = plt.figure(figsize=[1, 1])
	ax = fig.add_subplot(111)

evmcheb / simulate_rainforest.py

Created October 11, 2019 10:22

using scaper to simulate chainsaw noises in a forest

	import scaper, os
	soundscape_duration = 30.0
	outfolder = 'soundscapes/'
	foreground = os.path.expanduser("foreground")
	background = os.path.expanduser("background")
	sc = scaper.Scaper(soundscape_duration, foreground, background)
	sc.add_background(label=("const", "rainforest"),
	source_file=("choose", []),
	source_time=("uniform", 0, 300-soundscape_duration))

evmcheb / machine_learning.ipynb

Created July 18, 2019 06:58

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evmcheb / alljson.json

Created July 10, 2019 10:40

speed cameras to july 18

evmcheb / app.js

Last active July 6, 2019 17:18

Nodejs functions for sending static mapbox images

	const apiEndpoint = "https://api.mapbox.com";
	const accessToken="?access_token=[redacted]";



	function sendPostcodeCameras(recipientID, postcode){
	const no_cache = (Math.random() * (0.0005 - 0.0001) + 0.0001)
	const suburbLat = (postcodes[postcode]["lat"] + no_cache).toFixed(4)
	const suburbLon = (postcodes[postcode]["lon"] + no_cache).toFixed(4)
	const allUrl = `/styles/v1/cheb/cjxmvvcys26wx1cogeo3ybh7j/static/${suburbLon},${suburbLat},12,0,0/1000x1000@2X`;

evmcheb / overpass.py

Created July 4, 2019 13:28

Creates an overpass ql query to find roads in suburb boundaries

	for street in data[todays_key]:
	for passing_suburb in data[todays_key][street]:
	query += """node["name"="{}"](-32.398,115.64,-31.6248,116.2796)({}, {}, {}, {});\n""".format(street,
	suburb_min_max[passing_suburb]["lat"][0], suburb_min_max[passing_suburb]["lon"][0],
	suburb_min_max[passing_suburb]["lat"][1], suburb_min_max[passing_suburb]["lon"][1])
	query += """way["name"="{}"](-32.398,115.64,-31.6248,116.2796)({}, {}, {}, {});\n""".format(street,
	suburb_min_max[passing_suburb]["lat"][0], suburb_min_max[passing_suburb]["lon"][0],
	suburb_min_max[passing_suburb]["lat"][1], suburb_min_max[passing_suburb]["lon"][1])

	query += """relation["name"="{}"](-32.398,115.64,-31.6248,116.2796)({}, {}, {}, {});\n""".format(street,

evmcheb / suburbs.py

Created July 4, 2019 13:13

estimates suburbs to a square

	import json

	with open('wa.json') as f:
	data = json.load(f)
	out = {}
	for feature in data['features']:
	out[feature["properties"]["wa_local_2"].lower()] = {}
	out[feature["properties"]["wa_local_2"].lower()]["lat"] = [min([i[1] for i in feature["geometry"]["coordinates"][0][0]]), max([i[1] for i in feature["geometry"]["coordinates"][0][0]])]
	out[feature["properties"]["wa_local_2"].lower()]["lon"] = [min([i[0] for i in feature["geometry"]["coordinates"][0][0]]), max([i[0] for i in feature["geometry"]["coordinates"][0][0]])]