"""Stack arrays in sequence vertically (row wise)."""
from __future__ import annotations
from typing import Sequence
import numpy
import numpoly
from ..baseclass import ndpoly, PolyLike
from ..dispatch import implements
[docs]@implements(numpy.vstack)
def vstack(tup: Sequence[PolyLike]) -> ndpoly:
"""
Stack arrays in sequence vertically (row wise).
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.
Args:
tup:
The arrays must have the same shape along all but the first axis.
1-D arrays must have the same length.
Return:
The array formed by stacking the given arrays, will be at least 2-D.
Example:
>>> poly1 = numpoly.variable(3)
>>> const1 = numpoly.polynomial([1, 2, 3])
>>> numpoly.vstack([poly1, const1])
polynomial([[q0, q1, q2],
[1, 2, 3]])
>>> const2 = numpoly.polynomial([[1], [2], [3]])
>>> poly2 = poly1.reshape(3, 1)
>>> numpoly.vstack([const2, poly2])
polynomial([[1],
[2],
[3],
[q0],
[q1],
[q2]])
"""
arrays = numpoly.align_exponents(*tup)
coefficients = [
numpy.vstack([array.values[key] for array in arrays]) for key in arrays[0].keys
]
return numpoly.polynomial_from_attributes(
exponents=arrays[0].exponents,
coefficients=coefficients,
names=arrays[0].names,
dtype=coefficients[0].dtype,
)