Max in a sliding window in NumPy array

Question

I want to create an array which holds all the max()es of a window moving through a given numpy array. I\'m sorry if this sounds confusing. I\'ll give an examp

Answer 1

Approach #1 : You could use 1D max filter from Scipy -

from scipy.ndimage.filters import maximum_filter1d

def max_filter1d_valid(a, W):
    hW = (W-1)//2 # Half window size
    return maximum_filter1d(a,size=W)[hW:-hW]

Approach #2 : Here's another approach with strides : strided_app to create a 2D shifted version as view into the array pretty efficiently and that should let us use any custom reduction operation along the second axis afterwards -

def max_filter1d_valid_strided(a, W):
    return strided_app(a, W, S=1).max(axis=1)

Runtime test -

In [55]: a = np.random.randint(0,10,(10000))

# @Abdou's solution using pandas rolling
In [56]: %timeit pd.Series(a).rolling(5).max().dropna().tolist()
1000 loops, best of 3: 999 µs per loop

In [57]: %timeit max_filter1d_valid(a, W=5)
    ...: %timeit max_filter1d_valid_strided(a, W=5)
    ...: 
10000 loops, best of 3: 90.5 µs per loop
10000 loops, best of 3: 87.9 µs per loop