Robert Blackwell RobBlackwell

Keybase proof

I hereby claim:

I am robblackwell on github.
I am robblackwell (https://keybase.io/robblackwell) on keybase.
I have a public key ASCKTiK-mr2e19Jzs6EwX2MserBJYFoa0_EY1j7gEhXNUAo

To claim this, I am signing this object:

Patch Julia

find /nix -name '*ld-linux-x86-64.so.2'
/nix/store/68sa3m89shpfaqq1b9xp5p1360vqhwx6-glibc-2.25/lib/ld-linux-x86-64.so.2

set -x the_interpreter "/nix/store/68sa3m89shpfaqq1b9xp5p1360vqhwx6-glibc-2.25/lib/ld-linux-x86-64.so.2"

patchelf --set-interpreter $the_interpreter julia

	/*
	Si5351 VFO

	By LA3PNA 27 March 2015
	Modified by NT7S 25 April 2015

	Modified to be Si5351 Arduino v2 compliant by NT7S 21 Nov 2016

	Added LiquidCrystal_I2C support, M0NIL, December 2020.

	using Glob
	using CSVFiles
	using DataFrames

	filenames = glob("*.csv")

	function load_dataframes(filenames)
	df = DataFrame(CSVFiles.load(filenames[1]))
	for filename in filenames[2:end]
	df2 = DataFrame(CSVFiles.load(filename))

	"""
	column_vectors(A)

	Given a two dimensional array `A` of size `m` x `n`, return an array
	of `n` vectors being the columns in `A`. Each vector is of length `m`.

	"""
	function column_vectors(A)
	m,n = size(A)
	[A[:,i] for i in 1:n]

	using PyCall
	@pyimport pickle

	# This works for complex objects such as Scikit learn models. REB
	# 20171129

	function mypickle(filename, obj)
	out = open(filename,"w")
	pickle.dump(obj, out)
	close(out)

	"""
	freedman_diaconis(x)

	Estimates the required bin width for the distribution x.

	Freedman, D. and Diaconis, P., 1981. On the histogram as a density
	estimator: L 2 theory. Zeitschrift für Wahrscheinlichkeitstheorie und
	verwandte Gebiete, 57(4), pp.453-476.

	"""

	const MATLAB_EPOCH = Dates.DateTime(-0001,12,31)

	"""
	datenum(d::Dates.DateTime)

	Converts a Julia DateTime to a MATLAB style DateNumber.

	MATLAB represents time as DateNumber, a double precision floating
	point number being the the number of days since January 0, 0000

	using MAT
	function mymatread(filename, varname)
	file = matopen(filename)
	x = read(file, varname)
	close(file)
	return x
	end

	using PerceptualColourMaps
	using Images
	function myimagesc(A)
	x = minimum(A)
	y = maximum(A)
	B = (A .- x) ./ (y - x)
	imgc = applycolormap(B, cmap("R3")) # outputs a 3-dimensional array
	imgc2 = colorview(RGB, permuteddimsview(imgc, (3,1,2)))
	end