I would like to replace by zero value all my masked values in 2D array. I saw with np.copyto it was apparently possible to do that as :
test=np.copyto(array, 0, where = mask)
But i have an error message...'module' object has no attribute 'copyto'. Is there an equivalent way to do that?
Try numpy.ma.filled() I think this is exactly what you need
In [29]: a
Out[29]: array([ 1, 0, 25, 0, 1, 4, 0, 2, 3, 0])
In [30]: am = n.ma.MaskedArray(n.ma.log(a),fill_value=0)
In [31]: am
Out[31]:
masked_array(data = [0.0 -- 3.2188758248682006 -- 0.0 1.3862943611198906 -- 0.6931471805599453 1.0986122886681098 --],
mask = [False True False True False False True False False True],
fill_value = 0.0)
In [32]: am.filled()
Out[32]:
array([ 0. , 0. , 3.21887582, 0. , 0. ,
1.38629436, 0. , 0.69314718, 1.09861229, 0. ])
test = np.copyto(array, 0, where=mask) is equivalent to:
array = np.where(mask, 0, array)
test = None
(I'm not sure why you would want to assign a value to the return value of np.copyto; it always returns None if no Exception is raised.)
Why not use array[mask] = 0?
Indeed, that would work (and has nicer syntax) if mask is a boolean array with the same shape as array. If mask doesn't have the same shape then array[mask] = 0 and np.copyto(array, 0, where=mask) may behave differently:
np.copyto (is documented to) and np.where (appears to) broadcast the shape of the mask to match array.
In contrast, array[mask] = 0 does not broadcast mask. This leads to a big difference in behavior when the mask does not have the same shape as array:
In [60]: array = np.arange(12).reshape(3,4)
In [61]: mask = np.array([True, False, False, False], dtype=bool)
In [62]: np.where(mask, 0, array)
Out[62]:
array([[ 0, 1, 2, 3],
[ 0, 5, 6, 7],
[ 0, 9, 10, 11]])
In [63]: array[mask] = 0
In [64]: array
Out[64]:
array([[ 0, 0, 0, 0],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11]])
When array is 2-dimensional and mask is a 1-dimensional boolean array,
array[mask] is selecting rows of array (where mask is True) and
array[mask] = 0 sets those rows to zero.
Surprisingly, array[mask] does not raise an IndexError even though the mask has 4 elements and array only has 3 rows. No IndexError is raised when the fourth value is False, but an IndexError is raised if the fourth value is True:
In [91]: array[np.array([True, False, False, False])]
Out[91]: array([[0, 1, 2, 3]])
In [92]: array[np.array([True, False, False, True])]
IndexError: index 3 is out of bounds for axis 0 with size 3
array[mask] = 0or usingnp.where