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DEP: linalg: remove tri{,u,l} (#19674)
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* DEP: linalg: remove tri{,u,l}

* DOC: fix refguide check
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@j-bowhay
j-bowhay committed Dec 11, 2023
1 parent ba781ee commit 5721702
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3 changes: 0 additions & 3 deletions scipy/linalg/__init__.py
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Expand Up @@ -48,8 +48,6 @@
pinvh - Pseudo-inverse of hermitian matrix
kron - Kronecker product of two arrays
khatri_rao - Khatri-Rao product of two arrays
tril - Construct a lower-triangular matrix from a given matrix
triu - Construct an upper-triangular matrix from a given matrix
orthogonal_procrustes - Solve an orthogonal Procrustes problem
matrix_balance - Balance matrix entries with a similarity transformation
subspace_angles - Compute the subspace angles between two matrices
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pascal - Pascal matrix
invpascal - Inverse Pascal matrix
toeplitz - Toeplitz matrix
tri - Construct a matrix filled with ones at and below a given diagonal
Low-level routines
==================
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143 changes: 1 addition & 142 deletions scipy/linalg/_special_matrices.py
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@@ -1,10 +1,9 @@
import math
import warnings

import numpy as np
from numpy.lib.stride_tricks import as_strided

__all__ = ['tri', 'tril', 'triu', 'toeplitz', 'circulant', 'hankel',
__all__ = ['toeplitz', 'circulant', 'hankel',
'hadamard', 'leslie', 'kron', 'block_diag', 'companion',
'helmert', 'hilbert', 'invhilbert', 'pascal', 'invpascal', 'dft',
'fiedler', 'fiedler_companion', 'convolution_matrix']
Expand All @@ -14,146 +13,6 @@
# matrix construction functions
# -----------------------------------------------------------------------------

#
# *Note*: tri{,u,l} is implemented in NumPy, but an important bug was fixed in
# 2.0.0.dev-1af2f3, the following tri{,u,l} definitions are here for backwards
# compatibility.

def tri(N, M=None, k=0, dtype=None):
"""
.. deprecated:: 1.11.0
`tri` is deprecated in favour of `numpy.tri` and will be removed in
SciPy 1.13.0.
Construct (N, M) matrix filled with ones at and below the kth diagonal.
The matrix has A[i,j] == 1 for j <= i + k
Parameters
----------
N : int
The size of the first dimension of the matrix.
M : int or None, optional
The size of the second dimension of the matrix. If `M` is None,
`M = N` is assumed.
k : int, optional
Number of subdiagonal below which matrix is filled with ones.
`k` = 0 is the main diagonal, `k` < 0 subdiagonal and `k` > 0
superdiagonal.
dtype : dtype, optional
Data type of the matrix.
Returns
-------
tri : (N, M) ndarray
Tri matrix.
Examples
--------
>>> from scipy.linalg import tri
>>> tri(3, 5, 2, dtype=int)
array([[1, 1, 1, 0, 0],
[1, 1, 1, 1, 0],
[1, 1, 1, 1, 1]])
>>> tri(3, 5, -1, dtype=int)
array([[0, 0, 0, 0, 0],
[1, 0, 0, 0, 0],
[1, 1, 0, 0, 0]])
"""
warnings.warn("'tri'/'tril/'triu' are deprecated as of SciPy 1.11.0 and "
"will be removed in v1.13.0. Please use "
"numpy.(tri/tril/triu) instead.",
DeprecationWarning, stacklevel=2)

if M is None:
M = N
if isinstance(M, str):
# pearu: any objections to remove this feature?
# As tri(N,'d') is equivalent to tri(N,dtype='d')
dtype = M
M = N
m = np.greater_equal.outer(np.arange(k, N+k), np.arange(M))
if dtype is None:
return m
else:
return m.astype(dtype)


def tril(m, k=0):
"""
.. deprecated:: 1.11.0
`tril` is deprecated in favour of `numpy.tril` and will be removed in
SciPy 1.13.0.
Make a copy of a matrix with elements above the kth diagonal zeroed.
Parameters
----------
m : array_like
Matrix whose elements to return
k : int, optional
Diagonal above which to zero elements.
`k` == 0 is the main diagonal, `k` < 0 subdiagonal and
`k` > 0 superdiagonal.
Returns
-------
tril : ndarray
Return is the same shape and type as `m`.
Examples
--------
>>> from scipy.linalg import tril
>>> tril([[1,2,3],[4,5,6],[7,8,9],[10,11,12]], -1)
array([[ 0, 0, 0],
[ 4, 0, 0],
[ 7, 8, 0],
[10, 11, 12]])
"""
m = np.asarray(m)
out = tri(m.shape[0], m.shape[1], k=k, dtype=m.dtype.char) * m
return out


def triu(m, k=0):
"""
.. deprecated:: 1.11.0
`tril` is deprecated in favour of `numpy.triu` and will be removed in
SciPy 1.13.0.
Make a copy of a matrix with elements below the kth diagonal zeroed.
Parameters
----------
m : array_like
Matrix whose elements to return
k : int, optional
Diagonal below which to zero elements.
`k` == 0 is the main diagonal, `k` < 0 subdiagonal and
`k` > 0 superdiagonal.
Returns
-------
triu : ndarray
Return matrix with zeroed elements below the kth diagonal and has
same shape and type as `m`.
Examples
--------
>>> from scipy.linalg import triu
>>> triu([[1,2,3],[4,5,6],[7,8,9],[10,11,12]], -1)
array([[ 1, 2, 3],
[ 4, 5, 6],
[ 0, 8, 9],
[ 0, 0, 12]])
"""
m = np.asarray(m)
out = (1 - tri(m.shape[0], m.shape[1], k - 1, m.dtype.char)) * m
return out


def toeplitz(c, r=None):
"""
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2 changes: 1 addition & 1 deletion scipy/linalg/special_matrices.py
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Expand Up @@ -5,7 +5,7 @@
from scipy._lib.deprecation import _sub_module_deprecation

__all__ = [ # noqa: F822
'tri', 'tril', 'triu', 'toeplitz', 'circulant', 'hankel',
'toeplitz', 'circulant', 'hankel',
'hadamard', 'leslie', 'kron', 'block_diag', 'companion',
'helmert', 'hilbert', 'invhilbert', 'pascal', 'invpascal', 'dft',
'fiedler', 'fiedler_companion', 'convolution_matrix', 'as_strided'
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107 changes: 2 additions & 105 deletions scipy/linalg/tests/test_special_matrices.py
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@@ -1,123 +1,20 @@
import pytest
import numpy as np
from numpy import arange, add, array, eye, copy, sqrt
from numpy import arange, array, eye, copy, sqrt
from numpy.testing import (assert_equal, assert_array_equal,
assert_array_almost_equal, assert_allclose)
from pytest import raises as assert_raises

from scipy.fft import fft
from scipy.special import comb
from scipy.linalg import (toeplitz, hankel, circulant, hadamard, leslie, dft,
companion, tri, triu, tril, kron, block_diag,
companion, kron, block_diag,
helmert, hilbert, invhilbert, pascal, invpascal,
fiedler, fiedler_companion, eigvals,
convolution_matrix)
from numpy.linalg import cond


def get_mat(n):
data = arange(n)
data = add.outer(data, data)
return data

dep_filter = np.testing.suppress_warnings()
dep_filter.filter(DeprecationWarning, "'tri'/'tril/'triu'")

@dep_filter
class TestTri:
def test_basic(self):
assert_equal(tri(4), array([[1, 0, 0, 0],
[1, 1, 0, 0],
[1, 1, 1, 0],
[1, 1, 1, 1]]))
assert_equal(tri(4, dtype='f'), array([[1, 0, 0, 0],
[1, 1, 0, 0],
[1, 1, 1, 0],
[1, 1, 1, 1]], 'f'))

def test_diag(self):
assert_equal(tri(4, k=1), array([[1, 1, 0, 0],
[1, 1, 1, 0],
[1, 1, 1, 1],
[1, 1, 1, 1]]))
assert_equal(tri(4, k=-1), array([[0, 0, 0, 0],
[1, 0, 0, 0],
[1, 1, 0, 0],
[1, 1, 1, 0]]))

def test_2d(self):
assert_equal(tri(4, 3), array([[1, 0, 0],
[1, 1, 0],
[1, 1, 1],
[1, 1, 1]]))
assert_equal(tri(3, 4), array([[1, 0, 0, 0],
[1, 1, 0, 0],
[1, 1, 1, 0]]))

def test_diag2d(self):
assert_equal(tri(3, 4, k=2), array([[1, 1, 1, 0],
[1, 1, 1, 1],
[1, 1, 1, 1]]))
assert_equal(tri(4, 3, k=-2), array([[0, 0, 0],
[0, 0, 0],
[1, 0, 0],
[1, 1, 0]]))


@dep_filter
class TestTril:
def test_basic(self):
a = (100*get_mat(5)).astype('l')
b = a.copy()
for k in range(5):
for l in range(k+1, 5):
b[k, l] = 0
assert_equal(tril(a), b)

def test_diag(self):
a = (100*get_mat(5)).astype('f')
b = a.copy()
for k in range(5):
for l in range(k+3, 5):
b[k, l] = 0
assert_equal(tril(a, k=2), b)
b = a.copy()
for k in range(5):
for l in range(max((k-1, 0)), 5):
b[k, l] = 0
assert_equal(tril(a, k=-2), b)


@dep_filter
class TestTriu:
def test_basic(self):
a = (100*get_mat(5)).astype('l')
b = a.copy()
for k in range(5):
for l in range(k+1, 5):
b[l, k] = 0
assert_equal(triu(a), b)

def test_diag(self):
a = (100*get_mat(5)).astype('f')
b = a.copy()
for k in range(5):
for l in range(max((k-1, 0)), 5):
b[l, k] = 0
assert_equal(triu(a, k=2), b)
b = a.copy()
for k in range(5):
for l in range(k+3, 5):
b[l, k] = 0
assert_equal(triu(a, k=-2), b)


@pytest.mark.parametrize("func", [tri, tril, triu])
def test_special_matrices_deprecation(func):
with pytest.warns(DeprecationWarning, match="'tri'/'tril/'triu'"):
func(np.array([[1]]))


class TestToeplitz:

def test_basic(self):
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