In Numpy 1.4.1, what is the simplest or most efficient way of calculating the histogram of a masked array? numpy.histogram and pyplot.hist do count the masked elements, by default!
The only simple solution I can think of right now involves creating a new array with the non-masked value:
histogram(m_arr[~m_arr.mask])
This is not very efficient, though, as this unnecessarily creates a new array. I'd be happy to read about better ideas!
After running into casting issues by trying Erik's solution (see https://github.com/numpy/numpy/issues/16616), I decided to write a numba function to achieve this behavior.
Some of the code was inspired by https://numba.pydata.org/numba-examples/examples/density_estimation/histogram/results.html. I added the mask bit.
import numpy
import numba
@numba.jit(nopython=True)
def compute_bin(x, bin_edges):
# assuming uniform bins for now
n = bin_edges.shape[0] - 1
a_min = bin_edges[0]
a_max = bin_edges[-1]
# special case to mirror NumPy behavior for last bin
if x == a_max:
return n - 1 # a_max always in last bin
bin = int(n * (x - a_min) / (a_max - a_min))
if bin < 0 or bin >= n:
return None
else:
return bin
@numba.jit(nopython=True)
def masked_histogram(img, bin_edges, mask):
hist = numpy.zeros(len(bin_edges) - 1, dtype=numpy.intp)
for i, value in enumerate(img.flat):
if mask.flat[i]:
bin = compute_bin(value, bin_edges)
if bin is not None:
hist[int(bin)] += 1
return hist # , bin_edges
The speedup is significant. On a (1000, 1000) image:
