Orpheon

from pynput import mouse, keyboard
import sys
import time

class ClickRecorder:
  def __init__(self):
    self.clicks = []

  def click(self, x, y, button, press):
    if press and button == mouse.Button.left:

Orpheon

import paho.mqtt.client as mqtt
import json
import os
import datetime

SIMULATION_IP = "82.165.25.152"
SIMULATION_PORT = 1884
CAR_IP = "82.165.25.152"
CAR_PORT = 1883
ON_SIMULATION = False

Orpheon

import os
import json
import csv
import datetime

combine_channels = False

root = os.path.join("discorddata", "messages")
with open(os.path.join(root, "index.json"), "r", encoding="UTF-8") as f:
  indexdata = json.load(f)

Orpheon

import requests
import time

DROP_TIME_IN_SECONDS = 5 * 60
REFRESH_PERIOD = 20

old_data = []

while True:
    req = requests.get("https://api.binance.com/api/v3/ticker/price")

Orpheon

# -*- coding: utf-8 -*-
"""
Spyder Editor

This is a temporary script file.
"""


import json
import datetime

Orpheon

# -*- coding: utf-8 -*-
"""
Spyder Editor

This is a temporary script file.
"""


import json
import datetime

Orpheon

void ServerNetworker::registerlobby(Gamestate &state)
{
    xg::Guid message_type(LOBBY_MESSAGE_TYPE_REGISTER);
    xg::Guid lobbyid(GG2_IDENTIFIER);
    xg::Guid compatibility(COMPATIBILITY_IDENTIFIER);

    WriteBuffer buffer;

    // See https://github.com/Medo42/Faucet-Lobby/blob/master/Protocol%20Spec.txt for reference
    // https://github.com/PyGG2/PyGG2/blob/master/server/lobby.py is also helpful

Orpheon

$ python 1dconv.py 
Traceback (most recent call last):
  File "1dconv.py", line 30, in <module>
    outputs = cnn(inputs)
  File "/usr/local/lib/python3.5/dist-packages/torch/nn/modules/module.py", line 202, in __call__
    result = self.forward(*input, **kwargs)
  File "1dconv.py", line 22, in forward
    out = self.layer(x)
  File "/usr/local/lib/python3.5/dist-packages/torch/nn/modules/module.py", line 202, in __call__
    result = self.forward(*input, **kwargs)

Orpheon

import torch 
import torch.nn as nn
import torch.utils.data
from torch.autograd import Variable

# Minimal dataset
class Dataset(torch.utils.data.Dataset):
  def __len__(self):
    return 12

Orpheon

while true do
  turtle.select(1)
  i = 1
  while i <= 16 do
    successful = turtle.suck(1)
    successful = successful or turtle.suckUp(1)
    successful = successful or turtle.suckDown(1)
    if successful then
      i = i+1
    end