block(arrays)
Blocks in the innermost lists are concatenated (see concatenate
) along the last dimension (-1), then these are concatenated along the second-last dimension (-2), and so on until the outermost list is reached.
Blocks can be of any dimension, but will not be broadcasted using the normal rules. Instead, leading axes of size 1 are inserted, to make block.ndim
the same for all blocks. This is primarily useful for working with scalars, and means that code like np.block([v, 1])
is valid, where v.ndim == 1
.
When the nested list is two levels deep, this allows block matrices to be constructed from their components.
When called with only scalars, np.block
is equivalent to an ndarray call. So np.block([[1, 2], [3, 4]])
is equivalent to np.array([[1, 2], [3, 4]])
.
This function does not enforce that the blocks lie on a fixed grid. np.block([[a, b], [c, d]])
is not restricted to arrays of the form:
AAAbb AAAbb cccDD
But is also allowed to produce, for some a, b, c, d
:
AAAbb AAAbb cDDDD
Since concatenation happens along the last axis first, block
is _not_ capable of producing the following directly:
AAAbb cccbb cccDD
Matlab's "square bracket stacking", [A, B, ...; p, q, ...]
, is equivalent to np.block([[A, B, ...], [p, q, ...]])
.
If passed a single ndarray or scalar (a nested list of depth 0), this is returned unmodified (and not copied).
Elements shapes must match along the appropriate axes (without broadcasting), but leading 1s will be prepended to the shape as necessary to make the dimensions match.
If list depths are mismatched - for instance, [[a, b], c]
is illegal, and should be spelt [[a, b], [c]]
If lists are empty - for instance, [[a, b], []]
The array assembled from the given blocks.
The dimensionality of the output is equal to the greatest of: * the dimensionality of all the inputs * the depth to which the input list is nested
Assemble an nd-array from nested lists of blocks.
column_stack
Stack 1-D arrays as columns into a 2-D array.
concatenate
Join a sequence of arrays along an existing axis.
dstack
Stack arrays in sequence depth wise (along third axis).
hstack
Stack arrays in sequence horizontally (column wise).
stack
Join a sequence of arrays along a new axis.
vsplit
Split an array into multiple sub-arrays vertically (row-wise).
vstack
Stack arrays in sequence vertically (row wise).
The most common use of this function is to build a block matrix
>>> A = np.eye(2) * 2
... B = np.eye(3) * 3
... np.block([
... [A, np.zeros((2, 3))],
... [np.ones((3, 2)), B ]
... ]) array([[2., 0., 0., 0., 0.], [0., 2., 0., 0., 0.], [1., 1., 3., 0., 0.], [1., 1., 0., 3., 0.], [1., 1., 0., 0., 3.]])
With a list of depth 1, block
can be used as hstack
>>> np.block([1, 2, 3]) # hstack([1, 2, 3]) array([1, 2, 3])
>>> a = np.array([1, 2, 3])
... b = np.array([4, 5, 6])
... np.block([a, b, 10]) # hstack([a, b, 10]) array([ 1, 2, 3, 4, 5, 6, 10])
>>> A = np.ones((2, 2), int)
... B = 2 * A
... np.block([A, B]) # hstack([A, B]) array([[1, 1, 2, 2], [1, 1, 2, 2]])
With a list of depth 2, block
can be used in place of vstack
:
>>> a = np.array([1, 2, 3])
... b = np.array([4, 5, 6])
... np.block([[a], [b]]) # vstack([a, b]) array([[1, 2, 3], [4, 5, 6]])
>>> A = np.ones((2, 2), int)
... B = 2 * A
... np.block([[A], [B]]) # vstack([A, B]) array([[1, 1], [1, 1], [2, 2], [2, 2]])
It can also be used in places of atleast_1d
and atleast_2d
>>> a = np.array(0)
... b = np.array([1])
... np.block([a]) # atleast_1d(a) array([0])
>>> np.block([b]) # atleast_1d(b) array([1])
>>> np.block([[a]]) # atleast_2d(a) array([[0]])
>>> np.block([[b]]) # atleast_2d(b) array([[1]])See :
The following pages refer to to this document either explicitly or contain code examples using this.
numpy.hstack
numpy.bmat
numpy.stack
numpy.ma.extras.dstack
numpy.dstack
numpy.ma.extras.hstack
numpy.block
numpy.ma.extras.stack
numpy.concatenate
numpy.core._multiarray_umath.concatenate
numpy.vstack
numpy.ma.extras.vstack
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