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# STUMPY
# Copyright 2019 TD Ameritrade. Released under the terms of the 3-Clause BSD license.
# STUMPY is a trademark of TD Ameritrade IP Company, Inc. All rights reserved.
import
math
import
multiprocessing
as
mp
import
os
import
numpy
as
np
from
numba
import
cuda
from
.
import
config
,
core
from
.
mparray
import
mparray
@
cuda
.
jit
(
"(i8, f8[:], f8[:], i8, f8, f8[:], f8[:], f8[:], b1[:], b1[:],"
"i8, b1, i8, f8[:, :], f8[:], f8[:], i8[:, :], i8[:], i8[:], b1,"
"i8[:], i8, i8)"
)
def
_compute_and_update_PI_kernel
(
idx
,
T_A
,
T_B
,
m
,
p
,
p_norm_even
,
p_norm_odd
,
p_norm_first
,
T_A_subseq_isfinite
,
T_B_subseq_isfinite
,
w
,
ignore_trivial
,
excl_zone
,
profile
,
profile_L
,
profile_R
,
indices
,
indices_L
,
indices_R
,
compute_p_norm
,
bfs
,
nlevel
,
k
,
):
"""
A Numba CUDA kernel to update the non-normalized (i.e., without z-normalization)
matrix profile and matrix profile indices
Parameters
----------
idx : int
The index for sliding window `j` (in `T_B`)
T_A : numpy.ndarray
The time series or sequence for which to compute the dot product
T_B : numpy.ndarray
The time series or sequence that will be used to annotate T_A. For every
subsequence in T_A, its nearest neighbor in T_B will be recorded.
m : int
Window size
p : float
The p-norm to apply for computing the Minkowski distance. Minkowski distance is
typically used with `p` being 1 or 2, which correspond to the Manhattan distance
and the Euclidean distance, respectively.
p_norm_even : numpy.ndarray
The input p-norm array to use when `i` is even
p_norm_odd : numpy.ndarray
The input p-norm array to use when `i` is odd
p_norm_first : numpy.ndarray
The p-norm between the first query sequence,`Q`, and time series, `T`
T_A_subseq_isfinite : numpy.ndarray
A boolean array that indicates whether a subsequence in `T_A` contains a
`np.nan`/`np.inf` value (False)
T_B_subseq_isfinite : numpy.ndarray
A boolean array that indicates whether a subsequence in `T_B` contains a
`np.nan`/`np.inf` value (False)
w : int
The total number of sliding windows to iterate over
ignore_trivial : bool
Set to `True` if this is a self-join. Otherwise, for AB-join, set this to
`False`.
excl_zone : int
The half width for the exclusion zone relative to the current
sliding window
profile : numpy.ndarray
The (top-k) matrix profile, sorted in ascending order per row
profile_L : numpy.ndarray
The (top-1) left matrix profile
profile_R : numpy.ndarray
The (top-1) right matrix profile
indices : numpy.ndarray
The (top-k) matrix profile indices
indices_L : numpy.ndarray
The (top-1) left matrix profile indices
indices_R : numpy.ndarray
The (top-1) right matrix profile indices
compute_p_norm : bool
A boolean flag for whether or not to compute the p-norm
bfs : numpy.ndarray
The breadth-first-search indices where the missing leaves of its corresponding
binary search tree are filled with -1.
nlevel : int
The number of levels in the binary search tree from which the array
`bfs` is obtained.
k : int
The number of top `k` smallest distances used to construct the matrix profile.
Note that this will increase the total computational time and memory usage
when k > 1.
Returns
-------
None
Notes
-----
`arXiv:1901.05708
\
<https://arxiv.org/pdf/1901.05708.pdf>`__
See Algorithm 1
Note that we have extended this algorithm for AB-joins as well.
`DOI: 10.1109/ICDM.2016.0085
\
<https://www.cs.ucr.edu/~eamonn/STOMP_GPU_final_submission_camera_ready.pdf>`__
See Table II, Figure 5, and Figure 6
"""
start
=
cuda
.
grid
(
1
)
stride
=
cuda
.
gridsize
(
1
)
j
=
idx
# The name `i` is reserved to be used as an index for `T_A`
if
j
%
2
==
0
:
p_norm_out
=
p_norm_even
p_norm_in
=
p_norm_odd
else
:
p_norm_out
=
p_norm_odd
p_norm_in
=
p_norm_even
for
i
in
range
(
start
,
p_norm_out
.
shape
[
0
],
stride
):
zone_start
=
max
(
0
,
i
-
excl_zone
)
zone_stop
=
min
(
w
,
i
+
excl_zone
)
if
compute_p_norm
:
p_norm_out
[
i
]
=
(
p_norm_in
[
i
-
1
]
-
abs
(
T_A
[
i
-
1
]
-
T_B
[
j
-
1
])
**
p
+
abs
(
T_A
[
i
+
m
-
1
]
-
T_B
[
j
+
m
-
1
])
**
p
)
p_norm_out
[
0
]
=
p_norm_first
[
j
]
if
not
T_B_subseq_isfinite
[
j
]
or
not
T_A_subseq_isfinite
[
i
]:
p_norm
=
np
.
inf
else
:
p_norm
=
p_norm_out
[
i
]
if
p_norm
<
config
.
STUMPY_P_NORM_THRESHOLD
:
p_norm
=
0
if
ignore_trivial
:
if
j
<=
zone_stop
and
j
>=
zone_start
:
p_norm
=
np
.
inf
if
p_norm
<
profile_L
[
i
]
and
j
<
i
:
profile_L
[
i
]
=
p_norm
indices_L
[
i
]
=
j
if
p_norm
<
profile_R
[
i
]
and
j
>
i
:
profile_R
[
i
]
=
p_norm
indices_R
[
i
]
=
j
if
p_norm
<
profile
[
i
,
-
1
]:
idx
=
core
.
_gpu_searchsorted_right
(
profile
[
i
],
p_norm
,
bfs
,
nlevel
)
for
g
in
range
(
k
-
1
,
idx
,
-
1
):
profile
[
i
,
g
]
=
profile
[
i
,
g
-
1
]
indices
[
i
,
g
]
=
indices
[
i
,
g
-
1
]
profile
[
i
,
idx
]
=
p_norm
indices
[
i
,
idx
]
=
j
def
_gpu_aamp
(
T_A_fname
,
T_B_fname
,
m
,
range_stop
,
excl_zone
,
T_A_subseq_isfinite_fname
,
T_B_subseq_isfinite_fname
,
p
,
p_norm_fname
,
p_norm_first_fname
,
w
,
ignore_trivial
=
True
,
range_start
=
1
,
device_id
=
0
,
k
=
1
,
):
"""
A Numba CUDA version of AAMP for parallel computation of the non-normalized (i.e.,
without z-normalization) matrix profile, matrix profile indices, left matrix profile
indices, and right matrix profile indices.
Parameters
----------
T_A_fname : str
The file name for the time series or sequence for which to compute
the matrix profile
T_B_fname : str
The file name for the time series or sequence that will be used to annotate T_A.
For every subsequence in T_A, its nearest neighbor in T_B will be recorded.
m : int
Window size
range_stop : int
The index value along T_B for which to stop the matrix profile
calculation. This parameter is here for consistency with the
distributed `stumped` algorithm.
excl_zone : int
The half width for the exclusion zone relative to the current
sliding window
T_A_subseq_isfinite_fname : str
The file name for the boolean array that indicates whether a subsequence in
`T_A` contains a `np.nan`/`np.inf` value (False)
T_B_subseq_isfinite_fname : str
The file name for the boolean array that indicates whether a subsequence in
`T_B` contains a `np.nan`/`np.inf` value (False)
p : float
The p-norm to apply for computing the Minkowski distance. Minkowski distance is
typically used with `p` being 1 or 2, which correspond to the Manhattan distance
and the Euclidean distance, respectively.
p_norm_fname : str
The file name for the p-norm between some query sequence,`Q`,
and time series, `T`
p_norm_first_fname : str
The file name for the p-norm for the first window relative to the current
sliding window
w : int
The total number of sliding windows to iterate over
ignore_trivial : bool, default True
Set to `True` if this is a self-join. Otherwise, for AB-join, set this to
`False`. Default is `True`.
range_start : int, default 1
The starting index value along T_B for which to start the matrix
profile calculation. Default is 1.
device_id : int, default 0
The (GPU) device number to use. The default value is `0`.
k : int
The number of top `k` smallest distances used to construct the matrix profile.
Note that this will increase the total computational time and memory usage
when k > 1.
Returns
-------
profile_fname : str
The file name for the (top-k) matrix profile
profile_L_fname : str
The file name for the (top-1) left matrix profile
profile_R_fname : str
The file name for the (top-1) right matrix profile
indices_fname : str
The file name for the (top-k) matrix profile indices
indices_L_fname : str
The file name for the (top-1) left matrix profile indices
indices_R_fname : str
The file name for the (top-1) right matrix profile indices
Notes
-----
`arXiv:1901.05708
\
<https://arxiv.org/pdf/1901.05708.pdf>`__
See Algorithm 1
Note that we have extended this algorithm for AB-joins as well.
`DOI: 10.1109/ICDM.2016.0085
\
<https://www.cs.ucr.edu/~eamonn/STOMP_GPU_final_submission_camera_ready.pdf>`__
See Table II, Figure 5, and Figure 6
"""
threads_per_block
=
config
.
STUMPY_THREADS_PER_BLOCK
blocks_per_grid
=
math
.
ceil
(
w
/
threads_per_block
)
T_A
=
np
.
load
(
T_A_fname
,
allow_pickle
=
False
)
T_B
=
np
.
load
(
T_B_fname
,
allow_pickle
=
False
)
p_norm
=
np
.
load
(
p_norm_fname
,
allow_pickle
=
False
)
p_norm_first
=
np
.
load
(
p_norm_first_fname
,
allow_pickle
=
False
)
T_A_subseq_isfinite
=
np
.
load
(
T_A_subseq_isfinite_fname
,
allow_pickle
=
False
)
T_B_subseq_isfinite
=
np
.
load
(
T_B_subseq_isfinite_fname
,
allow_pickle
=
False
)
nlevel
=
np
.
floor
(
np
.
log2
(
k
)
+
1
).
astype
(
np
.
int64
)
# number of levels in binary search tree from which `bfs` is constructed.
with
cuda
.
gpus
[
device_id
]:
device_T_A
=
cuda
.
to_device
(
T_A
)
device_T_A_subseq_isfinite
=
cuda
.
to_device
(
T_A_subseq_isfinite
)
device_p_norm_odd
=
cuda
.
to_device
(
p_norm
)
device_p_norm_even
=
cuda
.
to_device
(
p_norm
)
device_p_norm_first
=
cuda
.
to_device
(
p_norm_first
)
if
ignore_trivial
:
device_T_B
=
device_T_A
device_T_B_subseq_isfinite
=
device_T_A_subseq_isfinite
else
:
device_T_B
=
cuda
.
to_device
(
T_B
)
device_T_B_subseq_isfinite
=
cuda
.
to_device
(
T_B_subseq_isfinite
)
profile
=
np
.
full
((
w
,
k
),
np
.
inf
,
dtype
=
np
.
float64
)
indices
=
np
.
full
((
w
,
k
),
-
1
,
dtype
=
np
.
int64
)
profile_L
=
np
.
full
(
w
,
np
.
inf
,
dtype
=
np
.
float64
)
indices_L
=
np
.
full
(
w
,
-
1
,
dtype
=
np
.
int64
)
profile_R
=
np
.
full
(
w
,
np
.
inf
,
dtype
=
np
.
float64
)
indices_R
=
np
.
full
(
w
,
-
1
,
dtype
=
np
.
int64
)
device_profile
=
cuda
.
to_device
(
profile
)
device_profile_L
=
cuda
.
to_device
(
profile_L
)
device_profile_R
=
cuda
.
to_device
(
profile_R
)
device_indices
=
cuda
.
to_device
(
indices
)
device_indices_L
=
cuda
.
to_device
(
indices_L
)
device_indices_R
=
cuda
.
to_device
(
indices_R
)
device_bfs
=
cuda
.
to_device
(
core
.
_bfs_indices
(
k
,
fill_value
=
-
1
))
_compute_and_update_PI_kernel
[
blocks_per_grid
,
threads_per_block
](
range_start
-
1
,
device_T_A
,
device_T_B
,
m
,
p
,
device_p_norm_even
,
device_p_norm_odd
,
device_p_norm_first
,
device_T_A_subseq_isfinite
,
device_T_B_subseq_isfinite
,
w
,
ignore_trivial
,
excl_zone
,
device_profile
,
device_profile_L
,
device_profile_R
,
device_indices
,
device_indices_L
,
device_indices_R
,
False
,
device_bfs
,
nlevel
,
k
,
)
for
i
in
range
(
range_start
,
range_stop
):
_compute_and_update_PI_kernel
[
blocks_per_grid
,
threads_per_block
](
i
,
device_T_A
,
device_T_B
,
m
,
p
,
device_p_norm_even
,
device_p_norm_odd
,
device_p_norm_first
,
device_T_A_subseq_isfinite
,
device_T_B_subseq_isfinite
,
w
,
ignore_trivial
,
excl_zone
,
device_profile
,
device_profile_L
,
device_profile_R
,
device_indices
,
device_indices_L
,
device_indices_R
,
True
,
device_bfs
,
nlevel
,
k
,
)
profile
=
device_profile
.
copy_to_host
()
profile_L
=
device_profile_L
.
copy_to_host
()
profile_R
=
device_profile_R
.
copy_to_host
()
indices
=
device_indices
.
copy_to_host
()
indices_L
=
device_indices_L
.
copy_to_host
()
indices_R
=
device_indices_R
.
copy_to_host
()
profile
[:, :]
=
np
.
power
(
profile
,
1.0
/
p
)
profile_L
[:]
=
np
.
power
(
profile_L
,
1.0
/
p
)
profile_R
[:]
=
np
.
power
(
profile_R
,
1.0
/
p
)
profile_fname
=
core
.
array_to_temp_file
(
profile
)
profile_L_fname
=
core
.
array_to_temp_file
(
profile_L
)
profile_R_fname
=
core
.
array_to_temp_file
(
profile_R
)
indices_fname
=
core
.
array_to_temp_file
(
indices
)
indices_L_fname
=
core
.
array_to_temp_file
(
indices_L
)
indices_R_fname
=
core
.
array_to_temp_file
(
indices_R
)
return
(
profile_fname
,
profile_L_fname
,
profile_R_fname
,
indices_fname
,
indices_L_fname
,
indices_R_fname
,
)
def
gpu_aamp
(
T_A
,
m
,
T_B
=
None
,
ignore_trivial
=
True
,
device_id
=
0
,
p
=
2.0
,
k
=
1
):
"""
Compute the non-normalized (i.e., without z-normalization) matrix profile with
one or more GPU devices
This is a convenience wrapper around the Numba `cuda.jit` `_gpu_aamp` function
which computes the non-normalized (top-k) matrix profile according to modified
version GPU-STOMP. The default number of threads-per-block is set to `512` and
may be changed by setting the global parameter `config.STUMPY_THREADS_PER_BLOCK`
to an appropriate number based on your GPU hardware.
Parameters
----------
T_A : numpy.ndarray
The time series or sequence for which to compute the matrix profile
m : int
Window size
T_B : numpy.ndarray, default None
The time series or sequence that contain your query subsequences
of interest. Default is `None` which corresponds to a self-join.
ignore_trivial : bool, default True
Set to `True` if this is a self-join. Otherwise, for AB-join, set this
to `False`. Default is `True`.
device_id : int or list, default 0
The (GPU) device number to use. The default value is `0`. A list of
valid device ids (int) may also be provided for parallel GPU-STUMP
computation. A list of all valid device ids can be obtained by
executing `[device.id for device in numba.cuda.list_devices()]`.
p : float, default 2.0
The p-norm to apply for computing the Minkowski distance. Minkowski distance is
typically used with `p` being 1 or 2, which correspond to the Manhattan distance
and the Euclidean distance, respectively.
k : int, default 1
The number of top `k` smallest distances used to construct the matrix profile.
Note that this will increase the total computational time and memory usage
when k > 1.
Returns
-------
out : numpy.ndarray
When k = 1 (default), the first column consists of the matrix profile,
the second column consists of the matrix profile indices, the third column
consists of the left matrix profile indices, and the fourth column consists
of the right matrix profile indices. However, when k > 1, the output array
will contain exactly 2 * k + 2 columns. The first k columns (i.e., out[:, :k])
consists of the top-k matrix profile, the next set of k columns
(i.e., out[:, k:2k]) consists of the corresponding top-k matrix profile
indices, and the last two columns (i.e., out[:, 2k] and out[:, 2k+1] or,
equivalently, out[:, -2] and out[:, -1]) correspond to the top-1 left
matrix profile indices and the top-1 right matrix profile indices, respectively.
For convenience, the matrix profile (distances) and matrix profile indices can
also be accessed via their corresponding named array attributes, `.P_` and
`.I_`,respectively. Similarly, the corresponding left matrix profile indices
and right matrix profile indices may also be accessed via the `.left_I_` and
`.right_I_` array attributes.
Notes
-----
`arXiv:1901.05708
\
<https://arxiv.org/pdf/1901.05708.pdf>`__
See Algorithm 1
Note that we have extended this algorithm for AB-joins as well.
`DOI: 10.1109/ICDM.2016.0085
\
<https://www.cs.ucr.edu/~eamonn/STOMP_GPU_final_submission_camera_ready.pdf>`__
See Table II, Figure 5, and Figure 6
"""
if
T_B
is
None
:
# Self join!
T_B
=
T_A
core
.
check_self_join
(
ignore_trivial
)
ignore_trivial
=
True
T_A
,
T_A_subseq_isfinite
=
core
.
preprocess_non_normalized
(
T_A
,
m
)
T_B
,
T_B_subseq_isfinite
=
core
.
preprocess_non_normalized
(
T_B
,
m
)
if
T_A
.
ndim
!=
1
:
# pragma: no cover
raise
ValueError
(
f"T_A is
{
T_A
.
ndim
}
-dimensional and must be 1-dimensional. "
"For multidimensional STUMP use `stumpy.mstump` or `stumpy.mstumped`"
)
if
T_B
.
ndim
!=
1
:
# pragma: no cover
raise
ValueError
(
f"T_B is
{
T_B
.
ndim
}
-dimensional and must be 1-dimensional. "
"For multidimensional STUMP use `stumpy.mstump` or `stumpy.mstumped`"
)
ignore_trivial
=
core
.
check_ignore_trivial
(
T_A
,
T_B
,
ignore_trivial
)
if
ignore_trivial
:
# self-join
core
.
check_window_size
(
m
,
max_size
=
min
(
T_A
.
shape
[
0
],
T_B
.
shape
[
0
]),
n
=
T_A
.
shape
[
0
]
)
else
:
# AB-join
core
.
check_window_size
(
m
,
max_size
=
min
(
T_A
.
shape
[
0
],
T_B
.
shape
[
0
]))
n
=
T_B
.
shape
[
0
]
w
=
T_A
.
shape
[
0
]
-
m
+
1
l
=
n
-
m
+
1
excl_zone
=
int
(
np
.
ceil
(
m
/
config
.
STUMPY_EXCL_ZONE_DENOM
)
)
# See Definition 3 and Figure 3
T_A_fname
=
core
.
array_to_temp_file
(
T_A
)
T_B_fname
=
core
.
array_to_temp_file
(
T_B
)
T_A_subseq_isfinite_fname
=
core
.
array_to_temp_file
(
T_A_subseq_isfinite
)
T_B_subseq_isfinite_fname
=
core
.
array_to_temp_file
(
T_B_subseq_isfinite
)
if
isinstance
(
device_id
,
int
):
device_ids
=
[
device_id
]
else
:
device_ids
=
device_id
profile
=
[
None
]
*
len
(
device_ids
)
indices
=
[
None
]
*
len
(
device_ids
)
profile_L
=
[
None
]
*
len
(
device_ids
)
indices_L
=
[
None
]
*
len
(
device_ids
)
profile_R
=
[
None
]
*
len
(
device_ids
)
indices_R
=
[
None
]
*
len
(
device_ids
)
for
_id
in
device_ids
:
with
cuda
.
gpus
[
_id
]:
if
(
cuda
.
current_context
().
__class__
.
__name__
!=
"FakeCUDAContext"
):
# pragma: no cover
cuda
.
current_context
().
deallocations
.
clear
()
step
=
1
+
l
//
len
(
device_ids
)
# Start process pool for multi-GPU request
if
len
(
device_ids
)
>
1
:
# pragma: no cover
mp
.
set_start_method
(
"spawn"
,
force
=
True
)
pool
=
mp
.
Pool
(
processes
=
len
(
device_ids
))
results
=
[
None
]
*
len
(
device_ids
)
p_norm_fnames
=
[]
p_norm_first_fnames
=
[]
for
idx
,
start
in
enumerate
(
range
(
0
,
l
,
step
)):
stop
=
min
(
l
,
start
+
step
)
p_norm
=
np
.
power
(
core
.
mass_absolute
(
T_B
[
start
:
start
+
m
],
T_A
,
p
=
p
),
p
)
p_norm_first
=
np
.
power
(
core
.
mass_absolute
(
T_A
[:
m
],
T_B
,
p
=
p
),
p
)
p_norm_fname
=
core
.
array_to_temp_file
(
p_norm
)
p_norm_first_fname
=
core
.
array_to_temp_file
(
p_norm_first
)
p_norm_fnames
.
append
(
p_norm_fname
)
p_norm_first_fnames
.
append
(
p_norm_first_fname
)
if
len
(
device_ids
)
>
1
and
idx
<
len
(
device_ids
)
-
1
:
# pragma: no cover
# Spawn and execute in child process for multi-GPU request
results
[
idx
]
=
pool
.
apply_async
(
_gpu_aamp
,
(
T_A_fname
,
T_B_fname
,
m
,
stop
,
excl_zone
,
T_A_subseq_isfinite_fname
,
T_B_subseq_isfinite_fname
,
p
,
p_norm_fname
,
p_norm_first_fname
,
w
,
ignore_trivial
,
start
+
1
,
device_ids
[
idx
],
k
,
),
)
else
:
# Execute last chunk in parent process
# Only parent process is executed when a single GPU is requested
(
profile
[
idx
],
profile_L
[
idx
],
profile_R
[
idx
],
indices
[
idx
],
indices_L
[
idx
],
indices_R
[
idx
],
)
=
_gpu_aamp
(
T_A_fname
,
T_B_fname
,
m
,
stop
,
excl_zone
,
T_A_subseq_isfinite_fname
,
T_B_subseq_isfinite_fname
,
p
,
p_norm_fname
,
p_norm_first_fname
,
w
,
ignore_trivial
,
start
+
1
,
device_ids
[
idx
],
k
,
)
# Clean up process pool for multi-GPU request
if
len
(
device_ids
)
>
1
:
# pragma: no cover
pool
.
close
()
pool
.
join
()
# Collect results from spawned child processes if they exist
for
idx
,
result
in
enumerate
(
results
):
if
result
is
not
None
:
(
profile
[
idx
],
profile_L
[
idx
],
profile_R
[
idx
],
indices
[
idx
],
indices_L
[
idx
],
indices_R
[
idx
],
)
=
result
.
get
()
os
.
remove
(
T_A_fname
)
os
.
remove
(
T_B_fname
)
os
.
remove
(
T_A_subseq_isfinite_fname
)
os
.
remove
(
T_B_subseq_isfinite_fname
)
for
p_norm_fname
in
p_norm_fnames
:
os
.
remove
(
p_norm_fname
)
for
p_norm_first_fname
in
p_norm_first_fnames
:
os
.
remove
(
p_norm_first_fname
)
for
idx
in
range
(
len
(
device_ids
)):
profile_fname
=
profile
[
idx
]
profile_L_fname
=
profile_L
[
idx
]
profile_R_fname
=
profile_R
[
idx
]
indices_fname
=
indices
[
idx
]
indices_L_fname
=
indices_L
[
idx
]
indices_R_fname
=
indices_R
[
idx
]
profile
[
idx
]
=
np
.
load
(
profile_fname
,
allow_pickle
=
False
)
profile_L
[
idx
]
=
np
.
load
(
profile_L_fname
,
allow_pickle
=
False
)
profile_R
[
idx
]
=
np
.
load
(
profile_R_fname
,
allow_pickle
=
False
)
indices
[
idx
]
=
np
.
load
(
indices_fname
,
allow_pickle
=
False
)
indices_L
[
idx
]
=
np
.
load
(
indices_L_fname
,
allow_pickle
=
False
)
indices_R
[
idx
]
=
np
.
load
(
indices_R_fname
,
allow_pickle
=
False
)
os
.
remove
(
profile_fname
)
os
.
remove
(
profile_L_fname
)
os
.
remove
(
profile_R_fname
)
os
.
remove
(
indices_fname
)
os
.
remove
(
indices_L_fname
)
os
.
remove
(
indices_R_fname
)
for
i
in
range
(
1
,
len
(
device_ids
)):
# pragma: no cover
# Update (top-k) matrix profile and matrix profile indices
core
.
_merge_topk_PI
(
profile
[
0
],
profile
[
i
],
indices
[
0
],
indices
[
i
])
# Update (top-1) left matrix profile and matrix profile indices
mask
=
profile_L
[
0
]
>
profile_L
[
i
]
profile_L
[
0
][
mask
]
=
profile_L
[
i
][
mask
]
indices_L
[
0
][
mask
]
=
indices_L
[
i
][
mask
]
# Update (top-1) right matrix profile and matrix profile indices
mask
=
profile_R
[
0
]
>
profile_R
[
i
]
profile_R
[
0
][
mask
]
=
profile_R
[
i
][
mask
]
indices_R
[
0
][
mask
]
=
indices_R
[
i
][
mask
]
out
=
np
.
empty
((
w
,
2
*
k
+
2
),
dtype
=
object
)
# last two columns are to store
# (top-1) left/right matrix profile indices
out
[:, :
k
]
=
profile
[
0
]
out
[:,
k
:]
=
np
.
column_stack
((
indices
[
0
],
indices_L
[
0
],
indices_R
[
0
]))
core
.
_check_P
(
out
[:,
0
])
for
_id
in
device_ids
:
with
cuda
.
gpus
[
_id
]:
if
(
cuda
.
current_context
().
__class__
.
__name__
!=
"FakeCUDAContext"
):
# pragma: no cover
cuda
.
current_context
().
deallocations
.
clear
()
return
mparray
(
out
,
m
,
k
,
config
.
STUMPY_EXCL_ZONE_DENOM
)
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