====== Entropy measurements at key locations ======

{{:summit2011:rng2.jpg|}}

Using a self-made RNG (random number generator) based on avalanche transistor white noise. Analysed for strings, entropy and cumulative deviation against p=0.05 probability.

===== Rendlesham forest =====

{{:summit2011:rendleshamrng.png|}}

Note: After the visit to Rendlesham the RNG results appear skewed and a simple cat /dev/ttyUSB0 would not run continuously. This anomaly later vanished on return to London

===== Bawdsey radar =====

{{:summit2011:bawdseyrng.png|}}

===== Ipswich ghost walk =====

{{:summit2011:csv1rng.png|}}

{{:summit2011:csv2rng.png|}}

{{:summit2011:csv3rng.png|}}

{{:summit2011:csv4rng.png|}}

(referring to map locations)

===== Grimes Graves =====

{{:summit2011:grimesrng.png|}}

===== Woolpit =====

{{:summit2011:woolpitrng.png|}}

===== Orford Ness =====

{{:summit2011:cobrarng.png|}}

====== software for plotting ======

<file python rngplot.py>
#!/usr/bin/python

import Gnuplot, Gnuplot.funcutils, math, sys
g = Gnuplot.Gnuplot(debug=1)

# read in random number file 200 bits at time
# calculate deviation for say each 200*8 bits from say 100 1s
# accumulate deviation to be plotted
# plot also with p=0.05 distribution

def calc_limit_high_005(range):
        var = (1.96 / 2) * math.sqrt(range)
        limit_high = range/2 + var
        return limit_high

def unpack_byte(data):
    byte = []
    for x in range(8):
        byte.append((ord(data)>>x)& 0x01)
    return byte

def read_rng_data(file):
    data = []
    bytes_read = open(file, "rb").read()
    for b in bytes_read:
        data.append(unpack_byte(b))
    return data

def cum_dev(data):
    cum = []
    dev = []
    cummd=0
    for y in range(len(data)/200):
            cumd=0 
        cumz=0
        for x in range(200):
            a=data[x+(y*200)]
            if (a==0): 
                cumd+=1
            else:
                cumz+=1
        cummd+=cumz-100 # mean is 100
        cum.append(cummd) 
    return cum

file=sys.argv[1]
l=(read_rng_data(file))
flatty = [item for sublist in l for item in sublist] # flattened array 
cumdevv=cum_dev(flatty)
cumdev=[]
cumd=0
for x in range(len(flatty)/200):
    cumd = (calc_limit_high_005(x*200)-x*100) # 200*100=20000
    cumdev.append(cumd)

g.title("31st August CSV map 4 cumulative RNG deviation and p=0.05")
g('set term png size 1024,768') # example
g('set output "/root/collect2011/psych/summit2011/logimages/csv4RNG.png"')

g.plot(Gnuplot.Data(cumdev, with='lines'),Gnuplot.Data(cumdevv, with='lines'))
</file>

with thanks to Petr Kazil