flopscope.numpy.row_stack
fnp.row_stack(tup, *, dtype=None, casting='same_kind')[flopscope source][numpy source]
Stack arrays in sequence vertically (row wise).
Adapted from NumPy docs np.row_stack
Stack arrays vertically (alias for vstack).
This is equivalent to concatenation along the first axis after 1-D arrays
of shape (N,) have been reshaped to (1,N). Rebuilds arrays divided by
vsplit.
This function makes most sense for arrays with up to 3 dimensions. For instance, for pixel-data with a height (first axis), width (second axis), and r/g/b channels (third axis). The functions concatenate, stack and block provide more general stacking and concatenation operations.
Parameters
- tup:sequence of ndarrays
The arrays must have the same shape along all but the first axis. 1-D arrays must have the same length. In the case of a single array_like input, it will be treated as a sequence of arrays; i.e., each element along the zeroth axis is treated as a separate array.
- dtype:str or dtype
If provided, the destination array will have this dtype. Cannot be provided together with
out.Added in version 1.24.- casting:{'no', 'equiv', 'safe', 'same_kind', 'unsafe'}, optional
Controls what kind of data casting may occur. Defaults to 'same_kind'.
Added in version 1.24.
Returns
- stacked:ndarray
The array formed by stacking the given arrays, will be at least 2-D.
See also
- we.flops.concatenate Join a sequence of arrays along an existing axis.
- we.flops.stack Join a sequence of arrays along a new axis.
- we.flops.block Assemble an nd-array from nested lists of blocks.
- we.flops.hstack Stack arrays in sequence horizontally (column wise).
- we.flops.dstack Stack arrays in sequence depth wise (along third axis).
- we.flops.column_stack Stack 1-D arrays as columns into a 2-D array.
- we.flops.vsplit Split an array into multiple sub-arrays vertically (row-wise).
- we.flops.unstack Split an array into a tuple of sub-arrays along an axis.
Examples
>>> import flopscope.numpy as fnp
>>> a = flops.array([1, 2, 3])
>>> b = flops.array([4, 5, 6])
>>> flops.vstack((a,b))
array([[1, 2, 3],
[4, 5, 6]])>>> a = flops.array([[1], [2], [3]])
>>> b = flops.array([[4], [5], [6]])
>>> flops.vstack((a,b))
array([[1],
[2],
[3],
[4],
[5],
[6]])