calebmadrigal

import ast

class StringWrapper(ast.NodeTransformer):
    """Wraps all strings in 'START ' + string + ' END'. """
    def visit_Str(self, node):
        return ast.Call(func=ast.Name(id='wrap_string', ctx=ast.Load()),
                        args=[node], keywords=[])

def wrap_string(s):
    return 'START ' + s + ' END'

calebmadrigal

import sys
from urllib.request import urlopen

CAM_RANGE = range(188)
CAM_URL_TEMPLATE = 'http://content.dot.wi.gov/travel/milwaukee/cameras/cam{}.jpg'


def get_camera_pic(cam_num, verbose=True):
    if type(cam_num) != str or len(cam_num) != 3:
        cam_num = str(cam_num).zfill(3)

calebmadrigal

import sys
from datetime import date,timedelta


def is_xkcd_day(d):
    return d.weekday() in {0, 2, 4}


def find_date_by_xkcd_number(xkcd_num, current_xkcd=1964, start_date=date(2018, 3, 7)):
    if not is_xkcd_day(start_date):

calebmadrigal

import sys
import yaml

def parse_wifi_map(map_path):
    with open(map_path, 'r') as f:
        data = f.read()

    wifi_map = yaml.load(data)
    devices = set()

calebmadrigal

Prediction for [1, 1, 1, 1]: [[0.16937022 0.83062978]]
Prediction for [1, 2, 3, 4]: [[0.25071094 0.74928906]]
The maximum opset needed by this model is only 6.
The maximum opset needed by this model is only 1.
INPUT NAME: float_input
OUTPUTS: ['output_label', 'output_probability']
Prediction with ONNX for [1. 1. 1. 1.]: [[{0: 0.16937017440795898, 1: 0.830629825592041}]]
Prediction with ONNX for [1. 2. 3. 4.]: [[{0: 0.2507109045982361, 1: 0.7492890954017639}]]