Howto bin series of float values into histogram in

Question

I have set of value in float (always less than 0). Which I want to bin into histogram, i,e. each bar in histogram contain range of value [0,0.150)

The data I have looks like this:

0.000
0.005
0.124
0.000
0.004
0.000
0.111
0.112

Whith my code below I expect to get result that looks like

[0, 0.005) 5
[0.005, 0.011) 0
...etc.. 

I tried to do do such binning with this code of mine. But it doesn't seem to work. What's the right way to do it?

#! /usr/bin/env python


import fileinput, math

log2 = math.log(2)

def getBin(x):
    return int(math.log(x+1)/log2)

diffCounts = [0] * 5

for line in fileinput.input():
    words = line.split()
    diff = float(words[0]) * 1000;

    diffCounts[ str(getBin(diff)) ] += 1

maxdiff = [i for i, c in enumerate(diffCounts) if c > 0][-1]
print maxdiff
maxBin = max(maxdiff)


for i in range(maxBin+1):
     lo = 2**i - 1
     hi = 2**(i+1) - 1
     binStr = '[' + str(lo) + ',' + str(hi) + ')'
     print binStr + '\t' + '\t'.join(map(str, (diffCounts[i])))

~

Answer 1

The first error is:

Traceback (most recent call last):
  File "C:\foo\foo.py", line 17, in <module>
    diffCounts[ str(getBin(diff)) ] += 1
TypeError: list indices must be integers

Why are you converting an int to a str when a str is needed? Fix that, then we get:

Traceback (most recent call last):
  File "C:\foo\foo.py", line 17, in <module>
    diffCounts[ getBin(diff) ] += 1
IndexError: list index out of range

because you've only made 5 buckets. I don't understand your bucketing scheme, but let's make it 50 buckets and see what happens:

6
Traceback (most recent call last):
  File "C:\foo\foo.py", line 21, in <module>
    maxBin = max(maxdiff)
TypeError: 'int' object is not iterable

maxdiff is a single value out of your list of ints, so what is max doing here? Remove it, now we get:

6
Traceback (most recent call last):
  File "C:\foo\foo.py", line 28, in <module>
    print binStr + '\t' + '\t'.join(map(str, (diffCounts[i])))
TypeError: argument 2 to map() must support iteration

Sure enough, you're using a single value as the second argument to map. Let's simplify the last two lines from this:

 binStr = '[' + str(lo) + ',' + str(hi) + ')'
 print binStr + '\t' + '\t'.join(map(str, (diffCounts[i])))

to this:

 print "[%f, %f)\t%r" % (lo, hi, diffCounts[i])

Now it prints:

6
[0.000000, 1.000000)    3
[1.000000, 3.000000)    0
[3.000000, 7.000000)    2
[7.000000, 15.000000)   0
[15.000000, 31.000000)  0
[31.000000, 63.000000)  0
[63.000000, 127.000000) 3

I'm not sure what else to do here, since I don't really understand the bucketing you are hoping to use. It seems to involve binary powers, but isn't making sense to me...

Answer 2

from pylab import *
data = []
inf = open('pulse_data.txt')
for line in inf:
    data.append(float(line))
inf.close()
#binning
B = 50
minv = min(data)
maxv = max(data)
bincounts = []
for i in range(B+1):
    bincounts.append(0)
for d in data:
    b = int((d - minv) / (maxv - minv) * B)
    bincounts[b] += 1
# plot histogram

plot(bincounts,'o')
show()

Answer 3

When possible, don't reinvent the wheel. NumPy has everything you need:

#!/usr/bin/env python
import numpy as np

a = np.fromfile(open('file', 'r'), sep='\n')
# [ 0.     0.005  0.124  0.     0.004  0.     0.111  0.112]

# You can set arbitrary bin edges:
bins = [0, 0.150]
hist, bin_edges = np.histogram(a, bins=bins)
# hist: [8]
# bin_edges: [ 0.    0.15]

# Or, if bin is an integer, you can set the number of bins:
bins = 4
hist, bin_edges = np.histogram(a, bins=bins)
# hist: [5 0 0 3]
# bin_edges: [ 0.     0.031  0.062  0.093  0.124]