# Source code for stumpy.chains

```# STUMPY
# Copyright 2019 TD Ameritrade. Released under the terms of the 3-Clause BSD license.  # noqa: E501

from collections import deque

import numpy as np

[docs]def atsc(IL, IR, j):
"""
Compute the anchored time series chain (ATSC)

Note that since the matrix profile indices, `IL` and `IR`, are pre-computed,
this function is agnostic to subsequence normalization.

Parameters
----------
IL : numpy.ndarray
Left matrix profile indices

IR : numpy.ndarray
Right matrix profile indices

j : int
The index value for which to compute the ATSC

Returns
-------
out : numpy.ndarray
Anchored time series chain for index, `j`

--------
stumpy.allc : Compute the all-chain set (ALLC)

Notes
-----
`DOI: 10.1109/ICDM.2017.79 <https://www.cs.ucr.edu/~eamonn/chains_ICDM.pdf>`__

See Table I

This is the implementation for the anchored time series chains (ATSC).

Unlike the original paper, we've replaced the while-loop with a more stable
for-loop.

Examples
--------
>>> mp = stumpy.stump(np.array([584., -11., 23., 79., 1001., 0., -19.]), m=3)
>>> stumpy.atsc(mp[:, 2], mp[:, 3], 1)
array([1, 3])
"""
C = deque([j])
for i in range(IL.size):
if IR[j] == -1 or IL[IR[j]] != j:
break
else:
j = IR[j]
C.append(j)

out = np.array(list(C), dtype=np.int64)

return out

[docs]def allc(IL, IR):
"""
Compute the all-chain set (ALLC)

Note that since the matrix profile indices, `IL` and `IR`, are pre-computed,
this function is agnostic to subsequence normalization.

Parameters
----------
IL : numpy.ndarray
Left matrix profile indices

IR : numpy.ndarray
Right matrix profile indices

Returns
-------
S : list(numpy.ndarray)
All-chain set

C : numpy.ndarray
Anchored time series chain for the longest chain (also known as the unanchored
chain). Note that when there are multiple different chains with length equal to
`len(C)`, then only one chain from this set is returned. You may iterate over
the all-chain set, `S`, to find all other possible chains with length `len(C)`.

--------
stumpy.atsc : Compute the anchored time series chain (ATSC)

Notes
-----
`DOI: 10.1109/ICDM.2017.79 <https://www.cs.ucr.edu/~eamonn/chains_ICDM.pdf>`__

See Table II

Unlike the original paper, we've replaced the while-loop with a more stable
for-loop.

This is the implementation for the all-chain set (ALLC) and the unanchored
chain is simply the longest one among the all-chain set. Both the
all-chain set and unanchored chain are returned.

The all-chain set, S, is returned as a list of unique numpy arrays.

Examples
--------
>>> mp = stumpy.stump(np.array([584., -11., 23., 79., 1001., 0., -19.]), m=3)
>>> stumpy.allc(mp[:, 2], mp[:, 3])
([array([1, 3]), array(), array([0, 4])], array([0, 4]))
"""
L = np.ones(IL.size, dtype=np.int64)
S = set()  # type: ignore
for i in range(IL.size):
if L[i] == 1:
j = i
C = deque([j])
for k in range(IL.size):
if IR[j] == -1 or IL[IR[j]] != j:
break
else:
j = IR[j]
L[j] = -1
L[i] = L[i] + 1
C.append(j)
S.update([tuple(C)])
C = atsc(IL, IR, L.argmax())
S = [np.array(s, dtype=np.int64) for s in S]  # type: ignore

return S, C  # type: ignore
```