Simulating the Golden Balls game show

In this eposide of Golden Balls, an inspired event takes place:

I retold the event at DIKU APL lunch (nice to have a job where game theory is a valid conversation topic), and we had a conversation about it. At first I thought this was prisoners dilemma, but it was quickly revealed that it is a different game. What is cool about it is, that Nick basically forces Abraham to pick split. I don’t think the same approach would work again though. The person in Abrahams position might be tempted to try a counter-steal. The person in Nicks position might be tempted to actually steal the money, which would be a dirty thing to do, but not completely unlikely.

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Creating visually random images

How complicated does a mathematical function, pseudorandom(x), have to be to create something that seems random, and how do you check whether it seems random? Looking at a long list of numbers is not a good way, because our ability to look at long lists of numbers is very limited. Our ability to look at images is much better.

So, to inspect whether a list of numbers is seemingly random, a fun way is to create an image using the numbers for each pixel, and simply look at it.

Given a number x in the series {0, 1, 2, …, huge n}, let’s create an image that plots a random function that’s implemented using the sin() function and a 3rd degree polynomial function:

color_range = 2**8
a = -3.0
b = -5.0
c = 13.0
 
def pseudo_randombit(x):
	color255 = math.sin(a*x**3 + b*x**2 + c*x) % color_range
	# make black/white
	bit = color255 / 127
	return bit

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How to remove extended attributes from a file edited with TextMate

If you run ‘ll -l’ sometimes a file has ‘@’ permission. This means that the file has extended attributes. TextMate may extended attributes to a file. Consider a fictional Python file called xxx.py edited with TextMate:

View extended attributes:

$ xattr xxx.py
com.macromates.caret

Remove extended attribute (com.macromates.caret):

$ xattr -d com.macromates.caret xxx.py

Incrementing part of an array in numpy

given a (2D) array a, and a smaller (2D) array b, how can you add the smaller array to the bigger array at some offset in numpy?

>>> a = zeros((4,4), dtype=int)
>>> a
array([[0, 0, 0, 0],
       [0, 0, 0, 0],
       [0, 0, 0, 0],
       [0, 0, 0, 0]])
>>> b = ones((2,2), dtype=int)
>>> b
array([[1, 1],
       [1, 1]])
>>> a[1:3,1:3] += b
>>> a
array([[0, 0, 0, 0],
       [0, 1, 1, 0],
       [0, 1, 1, 0],
       [0, 0, 0, 0]])

Hello world of raster creation with GDAL and Python

Mostly so I myself can remember how to do it, here is how to create a random geotiff with GDAL in Python

Note: the width and height are given in opposite order in the GDAL raster and numpy arrays!

import osr
import numpy
import gdal
import math
 
width = 4000
height = 3000
 
format = "GTiff"
driver = gdal.GetDriverByName( format )
 
dst_ds = driver.Create( "test.tiff", width, height, 1, gdal.GDT_Byte )
 
dst_ds.SetGeoTransform( [ 444720, 30, 0, 3751320, 0, -30 ] )
 
srs = osr.SpatialReference()
srs.ImportFromEPSG(25832)
dst_ds.SetProjection( srs.ExportToWkt() )
 
raster = numpy.zeros( (height, width), dtype=numpy.uint32 )
color_range = 2**8
seed = math.pi**10
for i in range(height):
	for j in range(width):
		color = (seed*i*j) % color_range
		raster[i][j] = color
 
dst_ds.GetRasterBand(1).WriteArray( raster )

It’s kind of slow, so perhaps the operation can be speeded up somehow? The result looks kind of nice though (image created with width and height both 4000):

So, not completely random.