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// processing script to simulate Brownian random motion

int n_particles = 200; // number of particles

// prepare arrays to store particle coordinates
float[] x = new float[n_particles];
float[] y = new float[n_particles];

void setup() {
  size(512, 512);

dododas

# Estimate per-step and per-trajectory diffusion coefficients from SPT data
#
# Created by: Raibatak Das
# Jan 2016

library(readxl)
library(dplyr)

# Function to compute per-step diffusion coefficients
singlestepDmle = function(frame, x, y, pxl, dt){

dododas

# Monte Carlo simulation to compute the probability that in a random sample of
# 50 individuals, 4 share a birthday and the other 46 have distinct birthdays

bdayOneTrial = function(){
    # Simulate a single trial
    bdays = sample.int(365, size=50, replace=T)
    counts = as.data.frame(table(bdays))
    sorted.counts = sort(counts$Freq, decreasing=T)
    success = sorted.counts[1]==4 && sorted.counts[2]==1
    return(success)

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#!/usr/bin/env python3

'''
A python generator for the sequence 1, 2,.., 9, 10, 20,.., 90, 100, 200,.. up to n_max

Created by: Raibatak Das
'''
from math import floor, log10

def logseq(n_max):