How can I get the row of the first True find in a numpy matrix?-CodePudding

I have the following matrix defined:

d = np.array(
      [[False, False, False, False, False,  True],
       [False, False, False, False, False,  True],
       [False, False, False, False,  True,  True],
       [False, False, False, False,  True,  True],
       [False, False, False,  True,  True,  True],
       [False, False, False,  True,  True,  True],
       [False, False,  True,  True,  True,  True],
       [False, False,  True,  True,  True,  True],
       [False,  True,  True,  True,  True,  True],
       [False,  True,  True,  True,  True,  True],
       [ True,  True,  True,  True,  True,  True],
       [ True,  True,  True,  True,  True,  True],
       [ True,  True,  True,  True,  True,  True],
       [False,  True,  True,  True,  True,  True],
       [False, False,  True,  True,  True,  True],
       [False, False, False,  True,  True,  True],
       [False, False, False, False,  True,  True],
       [False, False, False, False, False,  True],
       [False, False, False, False,  True,  True],
       [False, False, False,  True,  True,  True],
       [False, False,  True,  True,  True,  True],
       [False,  True,  True,  True,  True,  True],
       [ True,  True,  True,  True,  True,  True]])

And I would like to get a vector of length 6 containing the index of the first True occurrence in each column.

So the expected output would be:

fo = np.array([10, 8, 6, 4, 2, 0])

If there would be no True values in a given column ideally it shall return NaN for that column.

I have tried:

np.sum(d, axis=0) 
array([ 4,  8, 12, 16, 20, 23])

which together with the length of the columns would give the index, but that would work only if there would be only two continuous regions, one with False and another one with True.

CodePudding user response：

You can do this using argmax which find the first true, and then find columns which all is False to cure the result as needed for columns contain only False. e.g. if the first column all is False:

# if first column be all False, so it show 0, too; which need additional work using mask
ini = np.argmax(d == 1, 0)  # [0    8    6    4    2    0]   # if we want to fill with nans so convert it to object using ".astype(object)"
sec = (d == 0).all(0)       # find column with all False
ini[sec] = 1000
# [1000    8    6    4    2    0]

CodePudding user response：

First, we can iterate through the Numpy array. Then, we can check if True is in the nested array we are looking at. If so, we use .index() to find what the index is.

index_list = []
for nested_list in d:
    if True in nested_list:
        index_list.append(nested_list.index(True))