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I2of5_decode.py
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I2of5_decode.py
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import scipy
import math
#defining the 10 possible codes.
code0 = ['n','n','w','w','n']
code1 = ['w','n','n','n','w']
code2 = ['n','w','n','n','w']
code3 = ['w','w','n','n','n']
code4 = ['n','n','w','n','w']
code5 = ['w','n','w','n','n']
code6 = ['n','w','w','n','n']
code7 = ['n','n','n','w','w']
code8 = ['w','n','n','w','n']
code9 = ['n','w','n','w','n']
codes = [code0, code1, code2, code3, code4, code5, code6, code7, code8, code9 ]
def value_map( i, j ):
# maps from code index to the corresponding value.
# 0 <= i < 10
# 0 <= j < 10
return i*10+j
def pnorm( x, p=2 ):
# computes the p-norm of x
# http://en.wikipedia.org/wiki/Norm_%28mathematics%29
#
sum = 0.0
for k in range(len(x)):
sum += abs(x[k])**p
return (sum/len(x))**(1.0/p)
class I2of5_decode:
# decode one code of interleaved 2 of 5,
# http://en.wikipedia.org/wiki/Interleaved_2_of_5
#
#
signal_group = []
value_group = []
def load_shapes( self, narrow_shape, wide_shape ):
self.signal_group = []
for i in range(10):
for j in range(10):
signal = scipy.array([])
for k in range(5):
if ( codes[i][k] == 'n' ):
signal = scipy.append( signal, narrow_shape )
else:
signal = scipy.append( signal, wide_shape )
if ( codes[j][k] == 'n' ):
signal = scipy.append( signal, scipy.zeros(len(narrow_shape)) )
else:
signal = scipy.append( signal, scipy.zeros(len(wide_shape)) )
signal /= pnorm(signal);
self.signal_group.append( signal )
self.value_group.append( value_map(i,j) )
def decode( self, signal_in ):
# return data
certainty = 0
value = 0
# parameters
p = 2
# condition signal_in
signal_in = scipy.array(signal_in)
signal_in = signal_in[0:len(self.signal_group[0])]
signal_in /= pnorm(signal_in,p)
# compute signal distances
distances = []
for k in range(100):
distances.append( pnorm(self.signal_group[k] - signal_in,p) )
#check that there's only one minimum
minimum = min(distances)
min_count = 0
for k in range(100):
if (distances[k] == minimum ):
min_count += 1
if ( min_count > 1 ):
raise Exception("Error: more than one minimum found.")
#get value
min_index = distances.index( min(distances) )
value = self.value_group[ min_index ]
# some output for debugging
output_distances = [ (self.value_group[i],distances[i]) for i in range(len(distances)) ]
def compare(x,y):
return cmp( x[1], y[1] )
output_distances.sort(compare)
"""
for i in range( len(out) ):
print out[ len(out) -i-1 ]
"""
#statistical wizardry
distances.sort()
#distances = distances[0:20]
minimum = distances[0]
distances = [ d - minimum for d in distances ]
stdev = scipy.std(distances)
distances = [ d/stdev for d in distances ]
mean = scipy.mean(distances)
a = mean
b = distances[1]
c = distances[2]-distances[1]
d = distances[3]-distances[2]
certainty = ( a/2.5 + b/1.0 )/2
return [ value, certainty, [a,b,c,d ], distances, output_distances ]