def ttv():
"""Processes and executes command paramters"""
# Set up logging
logging.basicConfig(filename='log.log', level=logging.INFO)
# Load request keys
keys = json.load(open('./keys.json'))
# Set up Helix api
api = Helix(user_name=keys['username'],
client_id=keys['client_id'],
access_token=keys['access_token'])
# Param info (temp)
"""
Required Parameters:
list - Lists active streamers ranked (aplhabetically? by num viewers?), then lists non active streamers
open [name] - Opens streamer in browser, active or not
follow - Follow a streamer
check - check if a specific streamer is live
"""
# Process params
command_length = len(sys.argv)
command_tag = str(sys.argv[1])
if command_length < 2:
print("No paramters given. Showing usage")
sys.exit(1)
elif command_tag == "list":
if command_length > 2:
print("Incorrect usage of list. Showing usage")
sys.exit(1)
else:
print(api.get_streams())
sys.exit(0)
elif command_tag == "open":
if command_length != 3:
print("Incorrect usage of open. Showing usage")
sys.exit(1)
else:
api.open_stream(str(sys.argv[2]))
sys.exit(0)
elif command_tag == "check":
if command_length != 3:
print("Incorrect usage of check. Showing usage")
sys.exit(1)
else:
status = api.check_stream(str(sys.argv[2]))
print(status)
sys.exit(0)
else:
print("Parameter not recognized")
sys.exit(1)
import numpy as np import numpy.linalg as LA from scipy.special import erf, erfc import nlopt from helix import Helix from config import * # setup the data N = 11 Y = Helix(radius=2, slope=1, num_points=N).coords.T p = 3 q = 2 # initialize a set of means and standard deviations for the latent variables latent_means = [np.random.randn(q, 1) for _ in range(N)] latent_variances = [np.random.randn(q)**2 for _ in range(N)] latent_Sigmas = [np.diag(var) for var in latent_variances] ss = [-mi[-1]*si[-1] for mi, si in zip(latent_means, latent_variances)] # set starting values for sigma2, mu, B_1, B_2, g_1, and g_2 mu = np.mean(Y, axis=1).reshape(1, -1) # the optimal value for mu is the empirical mean of the data sigma2 = np.random.rand() # set to random positive number B1 = np.random.randn(p, q) B2 = np.random.randn(p, q) g1 = 1. g2 = 1. # I want the observations to be 1xp arrays for later computations Y = [yi.reshape(1, -1) for yi in Y.T]
import time
import serial
from helix import Helix
import json
import requests
import sys
serialcomm = serial.Serial('COM5', 115200)
serialcomm.timeout = 1
keys = json.load(open('./keys.json'))
api = Helix(
user_name = keys['username'],
client_id = keys['client_id'],
access_token = keys['access_token'])
while True:
# Get list of followed streamers
url = 'https://api.twitch.tv/helix/users/follows?from_id=' #TODO Change to get id dynamically
# sys.exit(1)
r = requests.get(url, headers=api.headers)
followed = []
for streamer in r.json()['data']:
followed.append(streamer['to_name'])
url2 = 'https://api.twitch.tv/helix/streams?user_login='
for streamer in followed:
r2 = requests.get(url=url2 + streamer, headers=api.headers)
