Calculating cumulative minimum with numpy arrays

Question

I\'d like to calculate the \"cumulative minimum\" array--basically, the minimum value of an array up to each index such as:

import numpy as np
nums = np.array([5         


        

Answer 1

Create a matrix which lower triangle (np.tril) is filled with values of your array nums and your upper triangle (np.triu, with second parameter 1, so the diagonal stays free) is filled with the maximum of the array. (EDIT: instead of the maximum, the first element of the array is the better way. -> comments)

nums = np.array([5.,3.,4.,2.,1.,1.,2.,0.])
oneSquare = np.ones((nums.size, nums.size))

A = nums * np.tril(oneSquare)
B = np.triu(oneSquare, 1) * nums[0]
A, B

Out:

(array([[ 5.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],
       [ 5.,  3.,  0.,  0.,  0.,  0.,  0.,  0.],
       [ 5.,  3.,  4.,  0.,  0.,  0.,  0.,  0.],
       [ 5.,  3.,  4.,  2.,  0.,  0.,  0.,  0.],
       [ 5.,  3.,  4.,  2.,  1.,  0.,  0.,  0.],
       [ 5.,  3.,  4.,  2.,  1.,  1.,  0.,  0.],
       [ 5.,  3.,  4.,  2.,  1.,  1.,  2.,  0.],
       [ 5.,  3.,  4.,  2.,  1.,  1.,  2.,  0.]]),
 array([[ 0.,  5.,  5.,  5.,  5.,  5.,  5.,  5.],
       [ 0.,  0.,  5.,  5.,  5.,  5.,  5.,  5.],
       [ 0.,  0.,  0.,  5.,  5.,  5.,  5.,  5.],
       [ 0.,  0.,  0.,  0.,  5.,  5.,  5.,  5.],
       [ 0.,  0.,  0.,  0.,  0.,  5.,  5.,  5.],
       [ 0.,  0.,  0.,  0.,  0.,  0.,  5.,  5.],
       [ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  5.],
       [ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.]]))

Now take the minimum of each row:

(A+B).min(axis=1)

Out:

array([ 5.,  3.,  3.,  2.,  1.,  1.,  1.,  0.])

Answer 2

For any 2-argument NumPy universal function, its accumulate method is the cumulative version of that function. Thus, numpy.minimum.accumulate is what you're looking for:

>>> numpy.minimum.accumulate([5,4,6,10,3])
array([5, 4, 4, 4, 3])