Assume an array of
0 = {ndarray: (4,)} [5 0 3 3]
1 = {ndarray: (4,)} [7 9 3 5]
2 = {ndarray: (4,)} [2 4 7 6]
3 = {ndarray: (4,)} [8 8 1 6]
I would like slice index where epoch_label is equal to zero
[1 1 0 0]
From above, the index will be the second and third index
epoch_label is an integer, and the value can be 0,1,2,...Using masked_where, this will produce something as
[1 1 -- --]
And, the expected output should be
[2 4 7 6]
[8 8 1 6]
However, using the code below
epoch_com = [np.random.randint(10, size=4) for _ in range(Nepochs)]
epoch_com_arr=np.array(epoch_com)
epoch_label=np.random.randint(2, size=Nepochs)
mm=ma.masked_where(epoch_label == 0, epoch_label)
expected_output=np.where(epoch_com_arr[mm,:])
The above snippet code produce
0 = {ndarray: (14,)} [0 0 0 0 1 1 1 1 2 2 2 3 3 3]
1 = {ndarray: (14,)} [0 1 2 3 0 1 2 3 0 2 3 0 2 3]
which is not as per I intend
or
expected_output=epoch_com_arr[mm,:]
which produced
0 = {ndarray: (4,)} [7 9 3 5]
1 = {ndarray: (4,)} [7 9 3 5]
2 = {ndarray: (4,)} [5 0 3 3]
3 = {ndarray: (4,)} [5 0 3 3]
May I know how to solve this
With
In [242]: Nepochs = 4
...: epoch_com = [np.random.randint(10, size=4) for _ in range(Nepochs)]
...: epoch_com_arr=np.array(epoch_com)
...: epoch_label=np.random.randint(2, size=Nepochs)
...: mm=np.ma.masked_where(epoch_label == 0, epoch_label)
...: expected_output=np.where(epoch_com_arr[mm,:])
Looking at the variables:
In [246]: epoch_com_arr # a (4,4) array
Out[246]:
array([[7, 1, 3, 3],
[5, 6, 7, 8],
[5, 6, 3, 8],
[3, 5, 1, 1]])
I don't know why you are using the "0 = {ndarray: (4,)} [5 0 3 3]" style of display. It isn't normal numpy.
I don't think making a masked_array has any benefit:
In [247]: epoch_label
Out[247]: array([0, 0, 1, 0])
In [248]: mm
Out[248]:
masked_array(data=[--, --, 1, --],
mask=[ True, True, False, True],
fill_value=999999)
Instead just convert the 0/1 to boolean. Often when we talk about 'masking' we mean using using a boolean array as index, not the use of np.ma.
In [249]: epoch_label.astype(bool)
Out[249]: array([False, False, True, False])
That boolean can be used to select rows of arr, or alternatively 'deselect' them:
In [250]: epoch_com_arr[epoch_label.astype(bool),:]
Out[250]: array([[5, 6, 3, 8]])
In [251]: epoch_com_arr[~epoch_label.astype(bool),:]
Out[251]:
array([[7, 1, 3, 3],
[5, 6, 7, 8],
[3, 5, 1, 1]])
I don't think the np.where is useful here. That gives the indices of the nonzero terms in epoch_com_arr[mm,:], and indexing with the np.ma` array is questionable.
np.where could be used convert epoch_label into an index:
In [252]: idx = np.nonzero(epoch_label) # aka np.where
In [253]: idx
Out[253]: (array([2]),)
In [254]: epoch_com_arr[idx,:]
Out[254]: array([[[5, 6, 3, 8]]])