| import os | |
| import sys | |
| import pytz | |
| import numpy as np | |
| import pandas as pd | |
| import plotly.express as px | |
| from termcolor import cprint | |
| from datetime import datetime, timedelta | |
| from collections import namedtuple, defaultdict | |
| from trello import TrelloClient |
Traversal is a triple entendre:
| \documentclass{article} | |
| \usepackage{amsmath} | |
| \begin{document} | |
| Let $R$ be the set of identified risks, with each risk denoted as $r_i$ ($i$ ranges from 1 to $|R|$). For each risk $r_i$, let $C_{ij}$ be the cost of implementing control measure $j$ for risk $r_i$ ($j$ ranges from 1 to $|C_i|$, where $|C_i|$ is the number of control measures associated with risk $r_i$). Let $OCC_i$ represent the likelihood of occurrence for risk $r_i$, and let $SEV_i$ represent the severity of risk $r_i$. Define $RR_{ij}$ as the risk reduction achieved by control measure $j$ for risk $r_i$. | |
| Define the indicator function $I_{ij}$ as follows: | |
| \[ |
| # Create class for handling filters with logical operations | |
| class Filter(Generic[T]): | |
| def __init__(self, name: str, value: T, op: str = "=="): | |
| self.name = name | |
| self.value = value | |
| self.op = op | |
| if type(value) == str: | |
| if op == "==": | |
| self.op = "eq" |
| // Very Simple Differential Drive Odometry. Ported from Python into C++ | |
| // Original Python: https://github.com/hbrobotics/ros_arduino_bridge/blob/indigo-devel/ros_arduino_python/src/ros_arduino_python/base_controller.py | |
| double x_; | |
| double y_; | |
| double th_; | |
| double dt; // time between last odometry calculation and this odometry calculation | |
| long prev_left_enc_; | |
| long prev_right_enc_; |
| import gi | |
| import nvsmi | |
| import signal | |
| import time | |
| from threading import Thread | |
| gi.require_version('Gtk', '3.0') | |
| gi.require_version('AppIndicator3', '0.1') | |
| from gi.repository import Gtk, AppIndicator3, GObject | |
| class Indicator(): |
| import re | |
| import time | |
| import serial | |
| import threading | |
| import numpy as np | |
| from pprint import pprint | |
| from functools import partial | |
| import matplotlib.pyplot as plt | |
| import matplotlib.animation as animation | |
| from collections import deque, defaultdict |
| import occupancy_grid_utils_python as gu | |
| # geometry_msgs::Point | |
| pt = [1.0, 0.0, 0.0] | |
| # grid, map_info from topics | |
| # map_info = gu.MapMetaData() | |
| cell = pointCell(map_info, pt) | |
| value = get_cell(grid, cell) |
| #!/bin/bash | |
| # This script configures the ROS environment variables according to the route | |
| # to the ROS_MASTER. ROS_MASTER can either be defined as an evironment variable | |
| # itself or given as first argument to this script. The ROS_IP and ROS_HOSTNAME | |
| # are set according to the IP that is sitting on the route to this master. | |
| # The ROS_MASTER_URI is also set, using port 11311. ROS_MASTER needs to be defined | |
| # as a numeric IP address, not a hostname. | |
| if [ "$1" ]; then |
| ffmpeg -pattern_type glob -i "*.jpg" -filter_complex "[0]reverse[r];[0][r]concat,loop=5:250,setpts=N/25/TB,scale=1920:1080" -vcodec mpeg4 -q:v 1 output_looped.mp4 |