DomiDre

from dataclasses import dataclass
import re, datetime

LEETTIME = "1337"
LEET_HOUR = "13"
LEET_MINUTE = "37"

datasrc = "./whatsapplog.txt"
scores = {}
class Score:

DomiDre

import numpy as np
import lmfit
from numpy.polynomial.hermite import hermgauss

file_path = "./example_data.xye"
savefile_path = './magnetization_fit.dat'
quadrature_degree = 21 # integer number between 2 and 100

# use kB/muB in Langevin function -> mu is in units of Bohr magneton
kB = 1.380649e-23 # J/K

DomiDre

var LM = require('ml-levenberg-marquardt');
var fs = require('fs');

function gaussianFunction([A, mu, sigma, c]) {
	return (x) => A * Math.exp(-0.5*((x-mu)/sigma)**2) + c;
}



fs.readFile('../gaussianData.xye', 'utf8', async function(err, data) {

DomiDre

use ndarray::{array, Array1};
use rusfun::{curve_fit, func1d, size_distribution};
use std::fs::File;
use std::io::{BufRead, BufReader, Result};

fn main() {
    // read data
    let (x, y, sy) = read_column_file("./gaussianData.xye").unwrap_or_else(|err| {
        eprintln!("Error reading data file: {}", err);
        std::process::exit(1);

DomiDre

import numpy as np
from scipy.optimize import leastsq
import time

# Load data from file
rawdata = np.genfromtxt('./gaussianData.xye')
x = rawdata[:, 0]
y = rawdata[:, 1]
sy = rawdata[:, 2]

DomiDre

import numpy as np

x = np.linspace(0, 10, 51)
A = 42
mu = 4.2
sigma = 0.666
c = 10

sig_y = 3*np.ones(len(x)) #np.sqrt(y)

DomiDre

The introduction to Reactive Programming you've been missing

(by @andrestaltz)

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This tutorial as a series of videos

If you prefer to watch video tutorials with live-coding, then check out this series I recorded with the same contents as in this article: Egghead.io - Introduction to Reactive Programming.

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