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# ----------------------------------------------------------------------------
# Copyright (c) 2013--, scikit-bio development team.
#
# Distributed under the terms of the Modified BSD License.
#
# The full license is in the file LICENSE.txt, distributed with this software.
# ----------------------------------------------------------------------------
import numpy as np
from ._cutils import is_symmetric_and_hollow_cy
from ._cutils import distmat_reorder_cy, distmat_reorder_condensed_cy
def is_symmetric_and_hollow(mat):
"""Check if a Distance Matrix is symmetric and hollow.
Equivalent to [not (mat.T != mat).any(), np.trace(mat) == 0]
Parameters
----------
mat : 2D array_like
Distance matrix.
Result:
-------
is_symmetric: Boolean
not (mat.T != mat).any()
is_hollow: Boolean
np.trace(mat) == 0
"""
# is_symmetric_and_hollow_cy is optimized
# for the common cas of c_contiguous.
# For all other cases, make a copy.
if not mat.flags.c_contiguous:
mat = np.asarray(mat, order="C")
return is_symmetric_and_hollow_cy(mat)
def is_symmetric(mat):
"""Check if a Distance Matrix is symmetric.
Equivalent to not (mat.T != mat).any()
Parameters
----------
mat : 2D array_like
Distance matrix.
Result:
-------
is_symmetric: Boolean
not (mat.T != mat).any()
"""
# the is_hollow check is really cheap,
# so can reuse is_symmetric_and_hollow
return is_symmetric_and_hollow(mat)[0]
def is_hollow(mat):
"""Check if a Distance Matrix is hollow.
Equivalent to np.trace(mat) == 0
Parameters
----------
mat : 2D array_like
Distance matrix.
Result:
-------
is_hollow: Boolean
np.trace(mat) == 0
"""
# is_symmetric_and_hollow_cy spends most
# of its time in symetry check, just use numpy
return np.trace(mat) == 0
def distmat_reorder_buf(in_mat, reorder_vec, out_mat, validate=False):
"""Reorder the rows and columns of a distance matrix given a reorder vector.
Not all of the columns need to be used.
For example:
[ [0, 1, 2, 3] ,
[1, 0, 4, 5] ,
[2, 4, 0, 6] ,
[3, 5, 6, 0] ]
with
[1,0,3,2]
will result in
[ [0, 1, 5, 4] ,
[1, 0, 3, 2] ,
[5, 3, 0, 6] ,
[4, 2, 6, 0] ]
Parameters
----------
in_mat : 2D array_like
Distance matrix
reorder_vec : 1D_array_like
List of permutation indexes
out_mat : 2D array_like
Output, Distance matrix,
must be in c_order and same size as reorder_vec
validate: boolean
Optional, if True, validate reorder_vec content, detaults to False
"""
np_reorder = np.asarray(reorder_vec, dtype=np.intp)
if validate:
maxsize = in_mat.shape[0]
bad_cnt = np.where((np_reorder < 0) or (np_reorder >= maxsize))[0].size
if bad_cnt > 0:
raise ValueError("Invalid reorder_vec")
if not in_mat.flags.c_contiguous:
in_mat = np.asarray(in_mat, order="C")
distmat_reorder_cy(in_mat, np_reorder, out_mat)
def distmat_reorder(in_mat, reorder_vec, validate=False):
"""Reorder the rows and columns of a distance matrix given a reorder vector.
Not all of the columns need to be used.
For example:
[ [0, 1, 2, 3] ,
[1, 0, 4, 5] ,
[2, 4, 0, 6] ,
[3, 5, 6, 0] ]
with
[1,0,3,2]
will result in
[ [0, 1, 5, 4] ,
[1, 0, 3, 2] ,
[5, 3, 0, 6] ,
[4, 2, 6, 0] ]
Parameters
----------
in_mat : 2D array_like
Distance matrix, must be in c_order
reorder_vec : 1D_array_like
List of permutation indexes
validate: boolean
Optional, if True, validate reorder_vec content, detaults to False
Returns
-------
out_mat : 2D array_like
Distance matrix
"""
np_reorder = np.asarray(reorder_vec, dtype=np.intp)
if validate:
maxsize = in_mat.shape[0]
bad_cnt = np.where((np_reorder < 0) or (np_reorder >= maxsize))[0].size
if bad_cnt > 0:
raise ValueError("Invalid reorder_vec")
if not in_mat.flags.c_contiguous:
in_mat = np.asarray(in_mat, order="C")
out_mat = np.empty([np_reorder.size, np_reorder.size], in_mat.dtype)
distmat_reorder_cy(in_mat, np_reorder, out_mat)
return out_mat
def distmat_reorder_condensed(in_mat, reorder_vec, validate=False):
"""Reorder the rows and columns of a distance matrix given a reorder vector.
Not all of the columns need to be used.
For example:
[ [0, 1, 2, 3] ,
[1, 0, 4, 5] ,
[2, 4, 0, 6] ,
[3, 5, 6, 0] ]
with
[1,0,3,2]
will result in
[ 1, 5, 4 , 3, 2, 6 ]
Parameters
----------
in_mat : 2D array_like
Distance matrix, must be in c_order
reorder_vec : 1D_array_like
List of permutation indexes
validate: boolean
Optional, if True, validate reorder_vec content, detaults to False
Returns
-------
out_mat_condensed : 1D array_like
Condensed distance matrix
"""
np_reorder = np.asarray(reorder_vec, dtype=np.intp)
if validate:
maxsize = in_mat.shape[0]
bad_cnt = np.where((np_reorder < 0) or (np_reorder >= maxsize))[0].size
if bad_cnt > 0:
raise ValueError("Invalid reorder_vec")
if not in_mat.flags.c_contiguous:
in_mat = np.asarray(in_mat, order="C")
csize = ((np_reorder.size - 1) * np_reorder.size) // 2
out_mat_condensed = np.empty([csize], in_mat.dtype)
distmat_reorder_condensed_cy(in_mat, np_reorder, out_mat_condensed)
return out_mat_condensed
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