evmcheb

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]])]

evmcheb

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

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

{
  "8 July 2019": {
    "Beechboro Road North": [
      "beechboro"
    ],
    "Berwick Street": [
      "east victoria park"
    ],
    "Brook Road": [
      "kenwick"

evmcheb

evmcheb

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

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

import os
samples = []
labels = []
images_folder = "images/"
for image in os.listdir(images_folder):
    samples.append(img_to_array(load_img(images_folder+image, target_size=(100, 100))))
    if "normal" in image:
        labels.append((0))
    else:
        labels.append((1))

evmcheb

import numpy as np
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Convolution2D, Conv2D, MaxPooling2D, GlobalAveragePooling2D

model = Sequential()
model.add(Conv2D(filters=16, kernel_size=(3, 3), input_shape=(100, 100, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=2))
model.add(Dropout(0.2))
model.add(Conv2D(filters=32, kernel_size=(3, 3), activation='relu'))

evmcheb

# Grabs the best comments and splits into parts
import praw, re

r = praw.Reddit()
thread = r.submission(url=URL)  
thread.comment_sort = 'best' # get the best comments from the thread

for comment in thread.comments:
	punctuation_reg = re.compile('(?<=[.!,?:;-]) +')
	split_parts = punctuation_reg.split(comment.body)