CnrLwlss

using VideoIO
using Images

download("https://live.staticflickr.com/4293/35896494470_6e106950d9_o_d.jpg","input.jpg")
img = load("input.jpg")
(h,w) = size(img)

function getframe(img, dx, dy, N, scaleup)
	smimg = img[dy:(dy+(N-1)),dx:(dx+(N-1))]
	bigpix = repeat(smimg, inner=(scaleup,scaleup))

CnrLwlss

using VideoIO
using Images

download("https://live.staticflickr.com/4293/35896494470_6e106950d9_o_d.jpg","input.jpg")
img = load("input.jpg")
(h,w) = size(img)

function getframe(img, dx, dy, N, scaleup)
	smimg = img[dy:(dy+(N-1)),dx:(dx+(N-1))]
	bigpix = repeat(smimg, inner=(scaleup,scaleup))

CnrLwlss

using Images

smallpix = rand(UInt8, 8, 8)
bigpix =  repeat(smallpix, inner=(200,200))

save("greychess.png",bigpix)

CnrLwlss

import fpdf

def makePage(w=297,h=210):
    '''Page width and height in mm.  Default is A4 landscape'''
    pdf = fpdf.FPDF(unit='mm', format=(w, h))
    pdf.add_page()
    return(pdf)

def drawLines(pdf, titlegap = 30, stafflinegap = 4, linegap = 10):
    '''All dimensions in mm.  5 lines for music notation.

CnrLwlss

import numpy as np
from PIL import Image

# Open a multichannel image
# In this example, an RGB image

fname = r"C:\Users\Conor\Downloads\PXL_20240304_174814563.MP.jpg"
im = Image.open(fname)
arr = np.array(im,dtype=np.uint8)

CnrLwlss

seed(99)
N = 100
x = runif(N)
noise = rnorm(N,mean=0,sd=0.1)
y_corr = 1*x + noise
y_nocorr = noise
rsq_corr = cor(x,y_corr)
rsq_nocorr = cor(x,y_nocorr)

plot(x,y_corr,xlim=c(0,1),ylim=c(-0.3,1.0),pch=16,xlab="x",ylab="y",cex.lab=1.55)

CnrLwlss

from fpdf import FPDF

def makeGrid(w=210.0, h=297.0, nrow=5, ncol=4, outf="grid.pdf", grey = 220, dl=3, sl=2, lwd=0.2):
    '''Writes a single page pdf of width w mm and height h mm (defaults are A4) with a nrow x ncol grid
traced out on it, to file outf.  Grid is a thin line (thickness is lwd) coloured grey (default is very
light grey) with dashing specified by dash length dl and space length sl.'''
    pdf = FPDF(orientation="P", unit="mm", format=(w,h))
    deltax = float(w)/ncol
    deltay = float(h)/nrow

CnrLwlss

# Plot wide data
test = data.frame(id=1:4,"A"=1:4,"B"=5:8,"C"=11:14)
# Plot original order
stripchart(test[,2:4])
# Plot updated order
stripchart(test[,c("C","A","B")])

# Plot long data
test2 = reshape(test,direction="long",varying=c("A","B","C"),v.names="Value",idvar=c("id"),timevar=c("Label"),times=c("A","B","C"))
# Plot original order

CnrLwlss

# A possible problem with freezing artefacts and correlations
# Need MASS library to sample from multivariate normal distribution (correlated datasets)
library(MASS)

R_good = matrix(c(0.55, 0.5,
              0.5, 0.55), 
            nrow = 2, ncol = 2, byrow = TRUE)

R_bad = matrix(c(1, 0.5,
              0.5, 1), 

CnrLwlss

import cv2
import time
from pathlib import Path
import os

cam = cv2.VideoCapture(1)
cam.set(3, 1920)
cam.set(4, 1080)

cv2.namedWindow("test")