edxmorgan

// File: video_stream.cpp
// sudo apt-get install libgstreamer1.0-dev libgstreamer-plugins-base1.0-dev
// g++ video_stream.cpp -o video_stream $(pkg-config --cflags --libs opencv4 gstreamer-1.0 gstreamer-app-1.0)

#include <iostream>
#include <gst/gst.h>
#include <gst/app/gstappsink.h>
#include <opencv2/opencv.hpp>

int main(int argc, char* argv[]) {

edxmorgan

import numpy as np
from pytensor.graph.op import Op
from pytensor.graph.basic import Apply
import casadi as cs
import pytensor.tensor as at
import pytensor.compile.function as pt_function
from pytensor import grad

class CasadiJacOp(Op):
    """

edxmorgan

edxmorgan

#include <iostream>
#include <string>
#include <boost/asio.hpp>
#include "json.hpp"  // Changed from <nlohmann/json.hpp> to "json.hpp"

using boost::asio::ip::tcp;
using json = nlohmann::json;

// Function to handle received JSON data
void handle_json(const json& data) {

edxmorgan

import casadi as ca

def qr_eigen(A, iterations=100):
    pQ = ca.SX.eye(A.size1())
    X  = ca.SX(A)  # Make a copy in SX
    for _ in range(iterations):
        Q, R = ca.qr(X)   # QR decomposition in CasADi
        pQ   = ca.mtimes(pQ, Q)
        X    = ca.mtimes(R, Q)
    return ca.diag(X), pQ  # (eigenvalues, eigenvectors)

edxmorgan

import socket
import json

def receive_json_data(host='192.168.2.95', port=16171):
    """
    Connects to a TCP server and receives JSON data.

    Args:
        host (str): The server's hostname or IP address.
        port (int): The port number to connect to.

edxmorgan

import asyncio
import aiohttp
import json
import sys
import signal

async def get_vision_position_delta(session, url):
    try:
        async with session.get(url) as response:
            response.raise_for_status()  # Raise exception for HTTP errors

edxmorgan

import asyncio
import json
from urllib.parse import urlparse, parse_qs
import aiohttp
import websockets
import datetime
import csv
import os
import aiofiles

edxmorgan

import numpy as np
import matplotlib.pyplot as plt

class BayesianParameterEstimator:
    def __init__(self, param_min=0.0, param_max=5.0, num_points=500, known_variance=1.0):
        """
        Initialize the Bayesian estimator.
        
        :param param_min: Minimum value of the parameter space
        :param param_max: Maximum value of the parameter space

edxmorgan

I, i_X_p = rig_dyn.model()
Ic_i = []
for i in range(0, ss.n_joints):
    Ic_i.append(I[i])
for i in range(ss.n_joints-1, -1, -1):
    if i != 0:
        p_X_i_f = pluck.inverse_spatial_transform(i_X_p[i]).T
        Ic_i[i-1] = Ic_i[i-1] + p_X_i_f@Ic_i[i]@i_X_p[i]