I have to count all the values in a matrix (2-d array) that are greater than 200.
The code I wrote down for this is:
za=0
p31 = numpy.asarray(o31)
for i in range(o31.size[0]):
for j in range(o32.size[1]):
if p31[i,j]<200:
za=za+1
print za
o31
is an image and I am converting it into a matrix and then finding the values.
My question is, is there a simpler way to do this?
This question is related to
python
arrays
coding-style
numpy
pixel
To count the number of values larger than x in any numpy array you can use:
n = len(matrix[matrix > x])
The boolean indexing returns an array that contains only the elements where the condition (matrix > x) is met. Then len() counts these values.
Here's a variant that uses fancy indexing and has the actual values as an intermediate:
p31 = numpy.asarray(o31)
values = p31[p31<200]
za = len(values)
This is very straightforward with boolean arrays:
p31 = numpy.asarray(o31)
za = (p31 < 200).sum() # p31<200 is a boolean array, so `sum` counts the number of True elements
There are many ways to achieve this, like flatten-and-filter or simply enumerate, but I think using Boolean/mask array is the easiest one (and iirc a much faster one):
>>> y = np.array([[123,24123,32432], [234,24,23]])
array([[ 123, 24123, 32432],
[ 234, 24, 23]])
>>> b = y > 200
>>> b
array([[False, True, True],
[ True, False, False]], dtype=bool)
>>> y[b]
array([24123, 32432, 234])
>>> len(y[b])
3
>>>> y[b].sum()
56789
Update:
As nneonneo has answered, if all you want is the number of elements that passes threshold, you can simply do:
>>>> (y>200).sum()
3
which is a simpler solution.
Speed comparison with filter
:
### use boolean/mask array ###
b = y > 200
%timeit y[b]
100000 loops, best of 3: 3.31 us per loop
%timeit y[y>200]
100000 loops, best of 3: 7.57 us per loop
### use filter ###
x = y.ravel()
%timeit filter(lambda x:x>200, x)
100000 loops, best of 3: 9.33 us per loop
%timeit np.array(filter(lambda x:x>200, x))
10000 loops, best of 3: 21.7 us per loop
%timeit filter(lambda x:x>200, y.ravel())
100000 loops, best of 3: 11.2 us per loop
%timeit np.array(filter(lambda x:x>200, y.ravel()))
10000 loops, best of 3: 22.9 us per loop
*** use numpy.where ***
nb = np.where(y>200)
%timeit y[nb]
100000 loops, best of 3: 2.42 us per loop
%timeit y[np.where(y>200)]
100000 loops, best of 3: 10.3 us per loop
You can use numpy.count_nonzero, converting the whole into a one-liner:
za = numpy.count_nonzero(numpy.asarray(o31)<200) #as written in the code
Source: Stackoverflow.com