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sparse_utils.pyx
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/
sparse_utils.pyx
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# This file is part of QuTiP: Quantum Toolbox in Python.
#
# Copyright (c) 2011 and later, Paul D. Nation and Robert J. Johansson.
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are
# met:
#
# 1. Redistributions of source code must retain the above copyright notice,
# this list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright
# notice, this list of conditions and the following disclaimer in the
# documentation and/or other materials provided with the distribution.
#
# 3. Neither the name of the QuTiP: Quantum Toolbox in Python nor the names
# of its contributors may be used to endorse or promote products derived
# from this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A
# PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
# HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
# SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
# LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
# DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
# THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
###############################################################################
import numpy as np
cimport numpy as np
cimport cython
# see scipy/sparse/csgraph/parameters.pxi
ctypedef np.int32_t ITYPE_t
ITYPE = np.int32
@cython.boundscheck(False)
@cython.wraparound(False)
def _sparse_bandwidth(
np.ndarray[ITYPE_t, ndim=1] idx,
np.ndarray[ITYPE_t, ndim=1] ptr,
int nrows):
"""
Calculates the max (mb), lower(lb), and upper(ub) bandwidths of a
csr_matrix.
"""
cdef int lb, ub, mb, ii, jj, ldist
lb = -nrows
ub = -nrows
mb = 0
for ii in range(nrows):
for jj in range(ptr[ii], ptr[ii + 1]):
ldist = ii - idx[jj]
lb = max(lb, ldist)
ub = max(ub, -ldist)
mb = max(mb, ub + lb + 1)
return mb, lb, ub
@cython.boundscheck(False)
@cython.wraparound(False)
def _sparse_permute(
np.ndarray[cython.numeric, ndim=1] data,
np.ndarray[ITYPE_t, ndim=1] idx,
np.ndarray[ITYPE_t, ndim=1] ptr,
int nrows,
int ncols,
np.ndarray[ITYPE_t, ndim=1] rperm,
np.ndarray[ITYPE_t, ndim=1] cperm,
int flag):
"""
Permutes the rows and columns of a sparse CSR or CSC matrix according to
the permutation arrays rperm and cperm, respectively.
Here, the permutation arrays specify the new order of the rows and columns.
i.e. [0,1,2,3,4] -> [3,0,4,1,2].
"""
cdef int ii, jj, kk, k0, nnz
cdef np.ndarray[cython.numeric] new_data = np.zeros_like(data)
cdef np.ndarray[ITYPE_t] new_idx = np.zeros_like(idx)
cdef np.ndarray[ITYPE_t] new_ptr = np.zeros_like(ptr)
cdef np.ndarray[ITYPE_t] perm_r
cdef np.ndarray[ITYPE_t] perm_c
cdef np.ndarray[ITYPE_t] inds
if flag == 0: # CSR matrix
if len(rperm):
inds = np.argsort(rperm).astype(ITYPE)
perm_r = np.arange(len(rperm), dtype=ITYPE)[inds]
for jj in range(nrows):
ii = perm_r[jj]
new_ptr[ii + 1] = ptr[jj + 1] - ptr[jj]
for jj in range(nrows):
new_ptr[jj + 1] = new_ptr[jj+1] + new_ptr[jj]
for jj in range(nrows):
k0 = new_ptr[perm_r[jj]]
for kk in range(ptr[jj], ptr[jj + 1]):
new_idx[k0] = idx[kk]
new_data[k0] = data[kk]
k0 = k0 + 1
if len(cperm):
inds = np.argsort(cperm).astype(ITYPE)
perm_c = np.arange(len(cperm), dtype=ITYPE)[inds]
nnz = new_ptr[len(new_ptr) - 1]
for jj in range(nnz):
new_idx[jj] = perm_c[new_idx[jj]]
elif flag == 1: # CSC matrix
if len(cperm):
inds = np.argsort(cperm).astype(ITYPE)
perm_c = np.arange(len(cperm), dtype=ITYPE)[inds]
for jj in range(ncols):
ii = perm_c[jj]
new_ptr[ii + 1] = ptr[jj + 1] - ptr[jj]
for jj in range(ncols):
new_ptr[jj + 1] = new_ptr[jj + 1] + new_ptr[jj]
for jj in range(ncols):
k0 = new_ptr[perm_c[jj]]
for kk in range(ptr[jj], ptr[jj + 1]):
new_idx[k0] = idx[kk]
new_data[k0] = data[kk]
k0 = k0 + 1
if len(rperm):
inds = np.argsort(rperm).astype(ITYPE)
perm_r = np.arange(len(rperm), dtype=ITYPE)[inds]
nnz = new_ptr[len(new_ptr) - 1]
for jj in range(nnz):
new_idx[jj] = perm_r[new_idx[jj]]
return new_data, new_idx, new_ptr
@cython.boundscheck(False)
@cython.wraparound(False)
def _sparse_reverse_permute(
np.ndarray[cython.numeric, ndim=1] data,
np.ndarray[ITYPE_t, ndim=1] idx,
np.ndarray[ITYPE_t, ndim=1] ptr,
int nrows,
int ncols,
np.ndarray[ITYPE_t, ndim=1] rperm,
np.ndarray[ITYPE_t, ndim=1] cperm,
int flag):
"""
Reverse permutes the rows and columns of a sparse CSR or CSC matrix
according to the original permutation arrays rperm and cperm, respectively.
"""
cdef int ii, jj, kk, k0, nnz
cdef np.ndarray[cython.numeric, ndim=1] new_data = np.zeros_like(data)
cdef np.ndarray[ITYPE_t, ndim=1] new_idx = np.zeros_like(idx)
cdef np.ndarray[ITYPE_t, ndim=1] new_ptr = np.zeros_like(ptr)
if flag == 0: # CSR matrix
if len(rperm):
for jj in range(nrows):
ii = rperm[jj]
new_ptr[ii + 1] = ptr[jj + 1] - ptr[jj]
for jj in range(nrows):
new_ptr[jj + 1] = new_ptr[jj + 1] + new_ptr[jj]
for jj in range(nrows):
k0 = new_ptr[rperm[jj]]
for kk in range(ptr[jj], ptr[jj + 1]):
new_idx[k0] = idx[kk]
new_data[k0] = data[kk]
k0 = k0 + 1
if len(cperm):
nnz = new_ptr[len(new_ptr) - 1]
for jj in range(nnz):
new_idx[jj] = cperm[new_idx[jj]]
if flag == 1: # CSC matrix
if len(cperm):
for jj in range(ncols):
ii = cperm[jj]
new_ptr[ii + 1] = ptr[jj + 1] - ptr[jj]
for jj in range(ncols):
new_ptr[jj + 1] = new_ptr[jj + 1] + new_ptr[jj]
for jj in range(ncols):
k0 = new_ptr[cperm[jj]]
for kk in range(ptr[jj], ptr[jj + 1]):
new_idx[k0] = idx[kk]
new_data[k0] = data[kk]
k0 = k0 + 1
if len(rperm) != 0:
nnz = new_ptr[len(new_ptr) - 1]
for jj in range(nnz):
new_idx[jj] = rperm[new_idx[jj]]
return new_data, new_idx, new_ptr