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Repository: seninp/saxpy
Branch: master
Commit: 93da33f16594
Files: 56
Total size: 2.9 MB
Directory structure:
gitextract_qiq5bcio/
├── .github/
│ └── workflows/
│ └── build.yml
├── .gitignore
├── .travis.yml
├── AUTHORS
├── LICENSE
├── MANIFEST.in
├── README.md
├── README.rst
├── conda.recipe/
│ └── meta.yaml
├── data/
│ ├── ecg0606_1.csv
│ └── insect.txt
├── jupyter/
│ ├── discord.ipynb
│ ├── distance.ipynb
│ ├── hotsax.ipynb
│ ├── insect.ipynb
│ ├── paa.ipynb
│ ├── sax.ipynb
│ ├── str_func.ipynb
│ ├── tinkah.ipynb
│ ├── visit_registry.ipynb
│ ├── vsm.ipynb
│ └── znorm.ipynb
├── requirements.txt
├── resources/
│ └── data/
│ ├── Gun_Point/
│ │ ├── Gun_Point_TEST
│ │ └── Gun_Point_TRAIN
│ └── cbf/
│ ├── CBF_TEST
│ └── CBF_TRAIN
├── saxpy/
│ ├── __init__.py
│ ├── alphabet.py
│ ├── discord.py
│ ├── distance.py
│ ├── hotsax.py
│ ├── paa.py
│ ├── sax.py
│ ├── saxvsm.py
│ ├── strfunc.py
│ ├── util.py
│ ├── visit_registry.py
│ └── znorm.py
├── setup.cfg
├── setup.py
├── site/
│ └── citation.bib
├── tests/
│ ├── test_cuts.py
│ ├── test_discord_bruteforce.py
│ ├── test_discord_hotsax.py
│ ├── test_distance.py
│ ├── test_doctests.py
│ ├── test_paa.py
│ ├── test_registry.py
│ ├── test_sax_chunking.py
│ ├── test_sax_window.py
│ ├── test_str.py
│ ├── test_ts2string.py
│ ├── test_vsm.py
│ └── test_znorm.py
└── tox.ini
================================================
FILE CONTENTS
================================================
================================================
FILE: .github/workflows/build.yml
================================================
name: Python package
on:
- push
- pull_request
jobs:
build:
runs-on: ${{ matrix.platform }}
strategy:
matrix:
platform: [ubuntu-latest, macOS-15, windows-latest]
python-version: ['3.10', '3.11', '3.12']
steps:
- uses: actions/checkout@v2
- name: Set up Python ${{ matrix.python-version }}
uses: actions/setup-python@v2
with:
python-version: ${{ matrix.python-version }}
- name: Install dependencies
run: |
python -m pip install --upgrade pip
python -m pip install tox tox-gh-actions
- name: Test with tox
run: tox
env:
PLATFORM: ${{ matrix.platform }}
================================================
FILE: .gitignore
================================================
.idea/workspace.xml
build/
dist/
_build/
_generate/
.cache*
.tox*
.eggs*
ChangeLog
*.so
*.py[cod]
*.egg-info
.coverage
.pypirc
jupyter/.ipynb*
/.venv/
/.vscode/
.idea/
================================================
FILE: .travis.yml
================================================
sudo: false
language: python
python:
- "3.7"
install: pip install tox-travis numpy pytest-cov codecov sklearn scipy
script:
tox
after_success:
- codecov
#deploy:
# provider: pypi
# user: "seninp"
# password:
# secure: "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"
notifications:
email:
on_success: never # default: change
on_failure: always # default: always
================================================
FILE: AUTHORS
================================================
Ameya Daigavane <ameya.d.98@gmail.com>
Ameya Daigavane <ameya.s.daigavane@jpl.nasa.gov>
Pavel Senin <ps1000258515@c02tt2vrhtd5.hgst.com>
Pavel Senin <seninp@gmail.com>
seninp <seninp@gmail.com>
================================================
FILE: LICENSE
================================================
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================================================
FILE: MANIFEST.in
================================================
include LICENSE
================================================
FILE: README.md
================================================
Time series symbolic discretization with SAX
====
[](https://pypi.python.org/pypi/saxpy)
[](https://travis-ci.org/seninp/saxpy)
[](https://codecov.io/gh/seninp/saxpy)
[](http://www.gnu.org/licenses/gpl-2.0.html)
This code is released under [GPL v.2.0](https://www.gnu.org/licenses/old-licenses/gpl-2.0.en.html) and implements in Python:
* Symbolic Aggregate approXimation (SAX) (with z-normalization and PAA) [1]
* EMMA -- an algorithm for time series motif discovery [2]
* HOT-SAX - a time series anomaly (discord) discovery algorithm [3]
* SAX-ZSCORE - an extension to SAX for multi-dimensional time-series SAX that modifies the z-normalization to multi-dimensional sequences and PAA aggregation with the average along data-dimensions. [4]
* SAX-REPEAT - an extension to SAX for multi-dimensional time-series that performs standard SAX on individual dimensions, then clusters to map multi-dimensional words into strings from the required alphabet size. [4]
Note that all of the library's functionality is also available in [R](https://github.com/jMotif/jmotif-R) and [Java](https://github.com/jMotif/SAX).
## References
[1] Lin, J., Keogh, E., Patel, P., and Lonardi, S.,
[*Finding Motifs in Time Series*](http://cs.gmu.edu/~jessica/Lin_motif.pdf),
The 2nd Workshop on Temporal Data Mining, the 8th ACM Int'l Conference on KDD (2002)
[2] Patel, P., Keogh, E., Lin, J., Lonardi, S.,
[*Mining Motifs in Massive Time Series Databases*](http://www.cs.gmu.edu/~jessica/publications/motif_icdm02.pdf),
In Proc. ICDM (2002)
[3] Keogh, E., Lin, J., Fu, A.,
[*HOT SAX: Efficiently finding the most unusual time series subsequence*](http://www.cs.ucr.edu/~eamonn/HOT%20SAX%20%20long-ver.pdf),
In Proc. ICDM (2005)
[4] Mohammad, Y., Nishida T.,
[*Robust learning from demonstrations using multidimensional SAX*](https://ieeexplore.ieee.org/document/6987960),
2014 14th International Conference on Control, Automation and Systems (ICCAS 2014)
## Citing this work
If you are using this implementation for you academic work, please cite our [Grammarviz 2.0 paper](http://link.springer.com/chapter/10.1007/978-3-662-44845-8_37):
[[Citation]](https://raw.githubusercontent.com/jMotif/SAX/master/citation.bib) Senin, P., Lin, J., Wang, X., Oates, T., Gandhi, S., Boedihardjo, A.P., Chen, C., Frankenstein, S., Lerner, M., [*GrammarViz 2.0: a tool for grammar-based pattern discovery in time series*](http://csdl.ics.hawaii.edu/techreports/2014/14-06/14-06.pdf), ECML/PKDD Conference, 2014.
SAX in a nutshell
------------
SAX is used to transform a sequence of rational numbers (i.e., a time series) into a sequence of letters (i.e., a string). An illustration of a time series of 128 points converted into the word of 8 letters:
As discretization is probably the most used transformation in data mining, SAX has been widely used throughout the field. Find more information about SAX at its authors pages: [SAX overview by Jessica Lin](http://cs.gmu.edu/~jessica/sax.htm), [Eamonn Keogh's SAX page](http://www.cs.ucr.edu/~eamonn/SAX.htm), or at [sax-vsm wiki page](http://jmotif.github.io/sax-vsm_site/morea/algorithm/SAX.html).
Building
------------
The code is written in Python and hosted on PyPi, so use `pip` to install it. This is what happens in my clean test environment:
$ pip install saxpy
Collecting saxpy
Downloading saxpy-1.0.0.dev154.tar.gz (180kB)
100% |████████████████████████████████| 184kB 778kB/s
Requirement already satisfied: numpy in /home/psenin/anaconda3/lib/python3.6/site-packages (from saxpy)
Requirement already satisfied: pytest in /home/psenin/anaconda3/lib/python3.6/site-packages (from saxpy)
...
Installing collected packages: coverage, pytest-cov, codecov, saxpy
Successfully installed codecov-2.0.15 coverage-4.5.1 pytest-cov-2.5.1 saxpy-1.0.0.dev154
Simple time series to SAX conversion
------------
To convert a time series of an arbitrary length to SAX we need to define the alphabet cuts. Saxpy retrieves cuts for a normal alphabet (we use size 3 here) via `cuts_for_asize` function:
from saxpy.alphabet import cuts_for_asize
cuts_for_asize(3)
which yields an array:
array([ -inf, -0.4307273, 0.4307273])
To convert a time series to letters with SAX we use `ts_to_string` function but not forgetting to z-normalize the input time series (we use Normal alphabet):
import numpy as np
from saxpy.znorm import znorm
from saxpy.sax import ts_to_string
ts_to_string(znorm(np.array([-2, 0, 2, 0, -1])), cuts_for_asize(3))
this produces a string:
'abcba'
Time series to SAX conversion with PAA aggregation (by "chunking")
------------
In order to reduce dimensionality further, the PAA (Piecewise Aggregate Approximation) is usually applied prior to SAX:
import numpy as np
from saxpy.znorm import znorm
from saxpy.paa import paa
from saxpy.sax import ts_to_string
dat = np.array([-2, 0, 2, 0, -1])
dat_znorm = znorm(dat)
dat_paa_3 = paa(dat_znorm, 3)
ts_to_string(dat_paa_3, cuts_for_asize(3))
and a string with three letters is produced:
'acb'
Time series to SAX conversion via sliding window
------------
Typically, in order to investigate the input time series structure in order to discover anomalous (i.e., discords) and recurrent (i.e., motifs) patterns we employ time series to SAX conversion via sliding window. Saxpy implements this workflow:
import numpy as np
from saxpy.sax import sax_via_window
dat = np.array([0., 0., 0., 0., 0., -0.270340178359072, -0.367828308500142,
0.666980581124872, 1.87088147328446, 2.14548907684624,
-0.480859313143032, -0.72911654245842, -0.490308602315934,
-0.66152028906509, -0.221049033806403, 0.367003418871239,
0.631073992586373, 0.0487728723414486, 0.762655178750436,
0.78574757843331, 0.338239686422963, 0.784206454089066,
-2.14265084073625, 2.11325193044223, 0.186018356196443,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.519132472499234,
-2.604783141655, -0.244519550114012, -1.6570790528784,
3.34184602886343, 2.10361226260999, 1.9796808733979,
-0.822247322003058, 1.06850578033292, -0.678811824405992,
0.804225748913681, 0.57363964388698, 0.437113583759113,
0.437208643628268, 0.989892093383503, 1.76545983424176,
0.119483882364649, -0.222311941138971, -0.74669456611669,
-0.0663660879732063, 0., 0., 0., 0., 0.,])
sax_none = sax_via_window(dat, win_size=6, paa_size=3, alphabet_size=3, nr_strategy=None, znorm_threshold=0.01)
sax1
the result is represented as a data structure of resulting words and their respective positions on time series:
defaultdict(list,
{'aac': [4, 10, 11, 30, 35],
'abc': [12, 14, 36, 44],
'acb': [5, 16, 21, 37, 43],
'acc': [13, 52, 53],
'bac': [3, 19, 34, 45, 51],
'bba': [31],
'bbb': [15, 18, 20, 22, 25, 26, 27, 28, 29, 41, 42, 46],
'bbc': [2],
'bca': [6, 17, 32, 38, 47, 48],
'caa': [8, 23, 24, 40],
'cab': [9, 50],
'cba': [7, 39, 49],
'cbb': [33],
'cca': [0, 1]})
`sax_via_window` is parameterised with a sliding window size, desired PAA aggregation, alphabet size, a numerosity reduction strategy, z-normalization threshold, and a SAX type ('unidim' for unidimensional SAX (default), 'zscore' for SAX-ZSCORE, 'repeat' for SAX-REPEAT):
def sax_via_window(series, win_size, paa_size, alphabet_size=3,
nr_strategy='exact', z_threshold=0.01, sax_type='unidim')
Time series discord discovery with HOT-SAX
------------
Saxpy implements HOT-SAX discord discovery algorithm in `find_discords_hotsax` function which can be used as follows:
import numpy as np
from saxpy.hotsax import find_discords_hotsax
from numpy import genfromtxt
dd = genfromtxt("data/ecg0606_1.csv", delimiter=',')
discords = find_discords_hotsax(dd)
discords
and discovers anomalies easily:
[(430, 5.2790800061718386), (318, 4.1757563573086953)]
The function has a similar parameterization: sliding window size, PAA and alphabet sizes, z-normalization threshold, and a parameter specifying how many discords are desired to be found:
def find_discords_hotsax(series, win_size=100, num_discords=2, a_size=3,
paa_size=3, z_threshold=0.01)
Saxpy also provides a brute-force implementation of the discord search if you'd like to verify discords or evaluate the speed-up:
find_discords_brute_force(series, win_size, num_discords=2,
z_threshold=0.01)
which can be called as follows:
discords = find_discords_brute_force(dd[100:500], 100, 4)
discords
[(73, 6.198555329625453), (219, 5.5636923991016136)]
Time series motif discovery with EMMA
------------
ToDo...
## Made with Aloha!
================================================
FILE: README.rst
================================================
Time series symbolic discretization with SAX
=============================================
.. image:: https://img.shields.io/pypi/v/saxpy.svg
:target: https://pypi.python.org/pypi/saxpy
:alt: Latest PyPI version
.. image:: https://travis-ci.org/seninp/saxpy.png
:target: https://travis-ci.org/seninp/saxpy
:alt: Latest Travis CI build status
.. image:: https://codecov.io/gh/seninp/saxpy/branch/master/graph/badge.svg
:target: https://codecov.io/gh/seninp/saxpy
.. image:: http://img.shields.io/:license-gpl2-green.svg
:target: http://www.gnu.org/licenses/gpl-2.0.html
This code is released under `GPL v.2.0 <https://www.gnu.org/licenses/old-licenses/gpl-2.0.en.html>`_ and implements in Python:
* Symbolic Aggregate approXimation (i.e., SAX) stack [LIN2002]
* a simple function for time series motif discovery [PATEL2001]
* HOT-SAX - a time series anomaly (discord) discovery algorithm [KEOGH2005]
.. [LIN2002] Lin, J., Keogh, E., Patel, P., and Lonardi, S., `Finding Motifs in Time Series <http://cs.gmu.edu/~jessica/Lin_motif.pdf>`_, The 2nd Workshop on Temporal Data Mining, the 8th ACM Int'l Conference on KDD (2002)
.. [PATEL2001] Patel, P., Keogh, E., Lin, J., Lonardi, S., `Mining Motifs in Massive Time Series Databases <http://www.cs.gmu.edu/~jessica/publications/motif_icdm02.pdf>`__, In Proc. ICDM (2002)
.. [KEOGH2005] Keogh, E., Lin, J., Fu, A., `HOT SAX: Efficiently finding the most unusual time series subsequence <http://www.cs.ucr.edu/~eamonn/HOT%20SAX%20%20long-ver.pdf>`__, In Proc. ICDM (2005)
Note that the most of the library's functionality is also available in `R <https://github.com/jMotif/jmotif-R>`__ and `Java <https://github.com/jMotif/SAX>`__
Citing this work:
------------------
If you are using this implementation for you academic work, please cite our `Grammarviz 2.0
paper <http://link.springer.com/chapter/10.1007/978-3-662-44845-8_37>`__:
.. [SENIN2014] Senin, P., Lin, J., Wang, X., Oates, T., Gandhi, S., Boedihardjo, A.P., Chen, C., Frankenstein, S., Lerner, M., `GrammarViz 2.0: a tool for grammar-based pattern discovery in time series <http://csdl.ics.hawaii.edu/techreports/2014/14-06/14-06.pdf>`__, ECML/PKDD, 2014.
In a nutshell
--------------
SAX is used to transform a sequence of rational numbers (i.e., a time series) into a sequence of letters (i.e., a string) which is (typically) much shorterthan the input time series. Thus, SAX transform addresses a chief problem in time-series analysis -- the dimensionality curse.
This is an illustration of a time series of 128 points converted into the word of 8 letters:
.. figure:: https://raw.githubusercontent.com/jMotif/SAX/master/src/resources/sax_transform.png
:alt: SAX in a nutshell
SAX in a nutshell
As discretization is probably the most used transformation in data
mining, SAX has been widely used throughout the field. Find more
information about SAX at its authors pages: `SAX overview by Jessica
Lin <http://cs.gmu.edu/~jessica/sax.htm>`__, `Eamonn Keogh's SAX
page <http://www.cs.ucr.edu/~eamonn/SAX.htm>`__, or at `sax-vsm wiki
page <http://jmotif.github.io/sax-vsm_site/morea/algorithm/SAX.html>`__.
Installation
-------------
::
$ pip install saxpy
Requirements
^^^^^^^^^^^^
Compatibility
-------------
Licence
-------
GNU General Public License v2.0
Authors
-------
`saxpy` was written by `Pavel Senin <senin@hawaii.edu>`_.
================================================
FILE: conda.recipe/meta.yaml
================================================
package:
name: saxpy
version: 0.1.0
build:
# If the installation is complex, or different between Unix and Windows, use
# separate bld.bat and build.sh files instead of this key. Add the line
# "skip: True # [py<35]" (for example) to limit to Python 3.5 and newer, or
# "skip: True # [not win]" to limit to Windows.
script: python setup.py install --single-version-externally-managed --record=record.txt
source:
path: ..
requirements:
build:
- python
- setuptools
- numpy
run:
- python
test:
imports:
- saxpy
about:
summary: SAX: time series symbolic discretization stack
license_file: LICENSE
================================================
FILE: data/ecg0606_1.csv
================================================
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FILE: data/insect.txt
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21
gitextract_qiq5bcio/ ├── .github/ │ └── workflows/ │ └── build.yml ├── .gitignore ├── .travis.yml ├── AUTHORS ├── LICENSE ├── MANIFEST.in ├── README.md ├── README.rst ├── conda.recipe/ │ └── meta.yaml ├── data/ │ ├── ecg0606_1.csv │ └── insect.txt ├── jupyter/ │ ├── discord.ipynb │ ├── distance.ipynb │ ├── hotsax.ipynb │ ├── insect.ipynb │ ├── paa.ipynb │ ├── sax.ipynb │ ├── str_func.ipynb │ ├── tinkah.ipynb │ ├── visit_registry.ipynb │ ├── vsm.ipynb │ └── znorm.ipynb ├── requirements.txt ├── resources/ │ └── data/ │ ├── Gun_Point/ │ │ ├── Gun_Point_TEST │ │ └── Gun_Point_TRAIN │ └── cbf/ │ ├── CBF_TEST │ └── CBF_TRAIN ├── saxpy/ │ ├── __init__.py │ ├── alphabet.py │ ├── discord.py │ ├── distance.py │ ├── hotsax.py │ ├── paa.py │ ├── sax.py │ ├── saxvsm.py │ ├── strfunc.py │ ├── util.py │ ├── visit_registry.py │ └── znorm.py ├── setup.cfg ├── setup.py ├── site/ │ └── citation.bib ├── tests/ │ ├── test_cuts.py │ ├── test_discord_bruteforce.py │ ├── test_discord_hotsax.py │ ├── test_distance.py │ ├── test_doctests.py │ ├── test_paa.py │ ├── test_registry.py │ ├── test_sax_chunking.py │ ├── test_sax_window.py │ ├── test_str.py │ ├── test_ts2string.py │ ├── test_vsm.py │ └── test_znorm.py └── tox.ini
SYMBOL INDEX (52 symbols across 24 files)
FILE: saxpy/alphabet.py
function cuts_for_asize (line 5) | def cuts_for_asize(a_size):
FILE: saxpy/discord.py
function find_discords_brute_force (line 8) | def find_discords_brute_force(series, win_size, num_discords=2, znorm_th...
function find_best_discord_brute_force (line 34) | def find_best_discord_brute_force(series, win_size, global_registry, zno...
FILE: saxpy/distance.py
function euclidean (line 5) | def euclidean(a, b):
function early_abandoned_euclidean (line 10) | def early_abandoned_euclidean(a, b, upper_limit):
FILE: saxpy/hotsax.py
function find_discords_hotsax (line 8) | def find_discords_hotsax(series, win_size=100, num_discords=2, alphabet_...
function find_best_discord_hotsax (line 48) | def find_best_discord_hotsax(series, win_size, global_registry, sax_data...
FILE: saxpy/paa.py
function paa (line 6) | def paa(series, paa_segment_size, sax_type='unidim'):
FILE: saxpy/sax.py
function get_sax_list (line 12) | def get_sax_list(series, cuts):
function ts_to_string (line 45) | def ts_to_string(series, cuts):
function is_mindist_zero (line 78) | def is_mindist_zero(a, b):
function sax_by_chunking (line 89) | def sax_by_chunking(series, paa_size, alphabet_size=3, znorm_threshold=0...
function sax_via_window (line 96) | def sax_via_window(series, win_size, paa_size, alphabet_size=3,
FILE: saxpy/saxvsm.py
function series_to_wordbag (line 7) | def series_to_wordbag(series, win_size, paa_size, alphabet_size=3,
function manyseries_to_wordbag (line 20) | def manyseries_to_wordbag(series_npmatrix, win_size, paa_size, alphabet_...
function bags_to_tfidf (line 38) | def bags_to_tfidf(bags_dict):
function tfidf_to_vector (line 91) | def tfidf_to_vector(tfidf, vector_label):
function cosine_measure (line 103) | def cosine_measure(weight_vec, test_bag):
function cosine_similarity (line 118) | def cosine_similarity(tfidf, test_bag):
function class_for_bag (line 127) | def class_for_bag(similarity_dict):
FILE: saxpy/strfunc.py
function idx2letter (line 4) | def idx2letter(idx):
FILE: saxpy/util.py
function read_ucr_data (line 5) | def read_ucr_data(fname):
FILE: saxpy/visit_registry.py
class VisitRegistry (line 6) | class VisitRegistry:
method __init__ (line 9) | def __init__(self, capacity=0):
method get_unvisited_count (line 15) | def get_unvisited_count(self):
method mark_visited (line 19) | def mark_visited(self, index):
method mark_visited_range (line 23) | def mark_visited_range(self, start, stop):
method get_next_unvisited (line 28) | def get_next_unvisited(self):
method clone (line 35) | def clone(self):
FILE: saxpy/znorm.py
function l2norm (line 9) | def l2norm(array):
function znorm (line 25) | def znorm(series, znorm_threshold=0.01):
FILE: tests/test_cuts.py
function test_sizing (line 5) | def test_sizing():
FILE: tests/test_discord_bruteforce.py
function test_brute_force (line 6) | def test_brute_force():
FILE: tests/test_discord_hotsax.py
function test_brute_force (line 6) | def test_brute_force():
FILE: tests/test_distance.py
function test_euclidean (line 7) | def test_euclidean():
function test_early_abandoned (line 17) | def test_early_abandoned():
FILE: tests/test_doctests.py
function run_doctests (line 6) | def run_doctests(module):
function test_paa_doctests (line 21) | def test_paa_doctests():
function test_sax_doctests (line 25) | def test_sax_doctests():
function test_znorm_doctests (line 29) | def test_znorm_doctests():
FILE: tests/test_paa.py
function test_paa1 (line 6) | def test_paa1():
function test_paa2 (line 15) | def test_paa2():
function test_paa3 (line 27) | def test_paa3():
FILE: tests/test_registry.py
function test_sizing (line 6) | def test_sizing():
FILE: tests/test_sax_chunking.py
function test_chunking (line 6) | def test_chunking():
FILE: tests/test_sax_window.py
function test_via_window (line 6) | def test_via_window():
FILE: tests/test_str.py
function test_sizing (line 6) | def test_sizing():
FILE: tests/test_ts2string.py
function test_stringing (line 7) | def test_stringing():
function test_mindist (line 22) | def test_mindist():
FILE: tests/test_vsm.py
function test_series_to_wordbag (line 9) | def test_series_to_wordbag():
function test_vsm (line 44) | def test_vsm():
FILE: tests/test_znorm.py
function test_znorm (line 7) | def test_znorm():
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"preview": "Ameya Daigavane <ameya.d.98@gmail.com>\nAmeya Daigavane <ameya.s.daigavane@jpl.nasa.gov>\nPavel Senin <ps1000258515@c02tt2"
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"preview": "Time series symbolic discretization with SAX\n=============================================\n\n.. image:: https://img.shiel"
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"path": "saxpy/hotsax.py",
"chars": 4857,
"preview": "\"\"\"Implements HOT-SAX.\"\"\"\nimport numpy as np\nfrom saxpy.znorm import znorm\nfrom saxpy.sax import sax_via_window\nfrom sax"
},
{
"path": "saxpy/paa.py",
"chars": 1646,
"preview": "\"\"\"Implements PAA.\"\"\"\nfrom __future__ import division\nimport numpy as np\n\n\ndef paa(series, paa_segment_size, sax_type='u"
},
{
"path": "saxpy/sax.py",
"chars": 8892,
"preview": "\"\"\"Converts a normlized timeseries to SAX symbols.\"\"\"\nfrom collections import defaultdict\nfrom saxpy.strfunc import idx2"
},
{
"path": "saxpy/saxvsm.py",
"chars": 3567,
"preview": "\"\"\"Implements VSM.\"\"\"\nimport math\nimport numpy as np\nfrom saxpy.sax import sax_via_window\n\n\ndef series_to_wordbag(series"
},
{
"path": "saxpy/strfunc.py",
"chars": 240,
"preview": "\"\"\"Convert a normlized timeseries to SAX symbols.\"\"\"\n\n\ndef idx2letter(idx):\n \"\"\"Convert a numerical index to a char.\""
},
{
"path": "saxpy/util.py",
"chars": 766,
"preview": "\"\"\"Some helpers.\"\"\"\nimport re\n\n\ndef read_ucr_data(fname):\n data = []\n\n with open(fname, 'r') as fp:\n read_l"
},
{
"path": "saxpy/visit_registry.py",
"chars": 1058,
"preview": "\"\"\"Keeps visited indexes in check.\"\"\"\nimport numpy as np\nimport random\n\n\nclass VisitRegistry:\n \"\"\"A straightforward v"
},
{
"path": "saxpy/znorm.py",
"chars": 1808,
"preview": "\"\"\"Implements znorm.\"\"\"\n\nfrom __future__ import division\nimport numpy as np\nfrom scipy.linalg import sqrtm\nfrom scipy.li"
},
{
"path": "setup.cfg",
"chars": 522,
"preview": "[metadata]\nname = saxpy\nversion=1.0.1\nauthor = Pavel Senin\nauthor-email = seninp@gmail.org\nsummary = SAX, HOTSAX, EMMA i"
},
{
"path": "setup.py",
"chars": 157,
"preview": "# -*- coding: utf-8 -*-\n\"\"\"building saxpy.\"\"\"\n\nfrom setuptools import setup\n\nsetup(\n setup_requires=['pbr>=5.8.0', 's"
},
{
"path": "site/citation.bib",
"chars": 683,
"preview": "@article{senin2018grammarviz,\n author = {Senin, Pavel and Lin, Jessica and Wang, Xing and Oates, Tim and Gandhi, Sunil a"
},
{
"path": "tests/test_cuts.py",
"chars": 192,
"preview": "\"\"\"Testing PAA implementation.\"\"\"\nfrom saxpy import alphabet\n\n\ndef test_sizing():\n \"\"\"Test alphabet sizes.\"\"\"\n for"
},
{
"path": "tests/test_discord_bruteforce.py",
"chars": 383,
"preview": "\"\"\"Testing brute force discord search implementation.\"\"\"\nimport numpy as np\nfrom saxpy.discord import find_discords_brut"
},
{
"path": "tests/test_discord_hotsax.py",
"chars": 471,
"preview": "\"\"\"Testing HOTSAX discord search implementation.\"\"\"\nimport numpy as np\nfrom saxpy.hotsax import find_discords_hotsax\n\n\nd"
},
{
"path": "tests/test_distance.py",
"chars": 937,
"preview": "\"\"\"Testing PAA implementation.\"\"\"\nimport pytest\nimport numpy as np\nfrom saxpy import distance\n\n\ndef test_euclidean():\n "
},
{
"path": "tests/test_doctests.py",
"chars": 887,
"preview": "import doctest\nimport warnings\nfrom sklearn.exceptions import ConvergenceWarning\nfrom saxpy import paa, sax, znorm\n\ndef "
},
{
"path": "tests/test_paa.py",
"chars": 1611,
"preview": "\"\"\"Testing PAA implementation.\"\"\"\nimport numpy as np\nfrom saxpy import paa\n\n\ndef test_paa1():\n \"\"\"Test points to poin"
},
{
"path": "tests/test_registry.py",
"chars": 675,
"preview": "\"\"\"Testing PAA implementation.\"\"\"\nimport numpy as np\nfrom saxpy.visit_registry import VisitRegistry\n\n\ndef test_sizing():"
},
{
"path": "tests/test_sax_chunking.py",
"chars": 925,
"preview": "\"\"\"Testing SAX implementation.\"\"\"\nimport numpy as np\nfrom saxpy.sax import sax_by_chunking\n\n\ndef test_chunking():\n \"\""
},
{
"path": "tests/test_sax_window.py",
"chars": 2001,
"preview": "\"\"\"Testing SAX implementation.\"\"\"\nimport numpy as np\nfrom saxpy.sax import sax_via_window\n\n\ndef test_via_window():\n \""
},
{
"path": "tests/test_str.py",
"chars": 383,
"preview": "\"\"\"Testing STR functions.\"\"\"\nimport pytest\nfrom saxpy.strfunc import idx2letter\n\n\ndef test_sizing():\n \"\"\"Test idx to "
},
{
"path": "tests/test_ts2string.py",
"chars": 1020,
"preview": "\"\"\"Testing PAA implementation.\"\"\"\nimport numpy as np\nfrom saxpy import alphabet\nfrom saxpy import sax\n\n\ndef test_stringi"
},
{
"path": "tests/test_vsm.py",
"chars": 2420,
"preview": "\"\"\"Testing SAX implementation.\"\"\"\nimport numpy as np\nfrom saxpy.sax import sax_via_window\nfrom saxpy.saxvsm import serie"
},
{
"path": "tests/test_znorm.py",
"chars": 733,
"preview": "\"\"\"Testing znorm implementation.\"\"\"\nimport pytest\nfrom numpy import array, std, mean\nfrom saxpy import znorm\n\n\ndef test_"
},
{
"path": "tox.ini",
"chars": 337,
"preview": "[tox]\nenvlist = py{310,311,312}-{linux,macos,windows}\n\n[gh-actions]\npython =\n 3.10: py310\n 3.11: py311, mypy\n 3"
}
]
About this extraction
This page contains the full source code of the seninp/saxpy GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 56 files (2.9 MB), approximately 760.0k tokens, and a symbol index with 52 extracted functions, classes, methods, constants, and types. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.
Extracted by GitExtract — free GitHub repo to text converter for AI. Built by Nikandr Surkov.