Skip to main content

Test data generators and Visualization for Apache Otava change point detection

Project description

Otava Test Data

Test data generators and Visualization for Apache Otava change point detection.

Web Visualizer

The package includes an interactive web visualizer for exploring test patterns and comparing change point detection results against ground truth.

Step Function Detection

Features:

  • Generate and visualize 17 different test patterns
  • Compare three analysis methods: Otava (statistical), Moving Average, and Boundary detection
  • View accuracy metrics (precision, recall, F1 score)
  • Adjust parameters in real-time and see results instantly

Multiple Change Points

Detect multiple successive changes in your data:

Multiple Changes Detection

Variance Changes

Detect changes in data volatility even when the mean stays constant:

Variance Change Detection

Starting the Visualizer

pip install otava-test-data[web]

# Start the web server
otava-web
# Or with invoke tasks
inv web-start

Then open http://127.0.0.1:8100 in your browser.

Installation

pip install otava-test-data

Or with all optional dependencies:

pip install otava-test-data[all]

Quick Start

from otava_test_data import step_function, noise_normal, combine

# Generate a step function (single change point) with realistic noise
step = step_function(length=500, value_before=100, value_after=120)
noise = noise_normal(length=500, mean=0, sigma=5)
combined = combine(step, noise)

# Export to CSV for Otava analysis
combined.to_csv("test_data.csv")

# Access ground truth change point information
for cp in combined.change_points:
    print(f"Change at index {cp.index}: {cp.description}")

Available Generators

Basic Building Blocks

Generator Description
constant Constant value: S = x, x, x, x...
noise_normal Normal distribution: S ~ N(mean, sigma)
noise_uniform Uniform distribution: S ~ U(min, max)
outlier Single anomaly: S = x, x, x', x, x...
step_function Single change point: S = x1, x1, x2, x2...
regression_fix Temporary regression: S = x1, x2, x1...

Advanced Patterns

Generator Description
banding Oscillation between two values
variance_change Constant mean, changing variance
phase_change Phase shift in periodic signal
multiple_changes Multiple consecutive step changes

CLI Tool

# Generate test suite
otava-gen generate --output-dir ./test_data --lengths 50 500 --seed 42

# List available generators
otava-gen list

# Get info about a generator
otava-gen info step_function

Documentation

Full documentation available at Read the Docs.

License

Apache License 2.0

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

otava_test_data-0.1.9.tar.gz (16.6 MB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

otava_test_data-0.1.9-py3-none-any.whl (1.3 MB view details)

Uploaded Python 3

File details

Details for the file otava_test_data-0.1.9.tar.gz.

File metadata

  • Download URL: otava_test_data-0.1.9.tar.gz
  • Upload date:
  • Size: 16.6 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.13.7

File hashes

Hashes for otava_test_data-0.1.9.tar.gz
Algorithm Hash digest
SHA256 ad8d981dd13f4f8549f8a258b8120bc8a7412faadf658e0e1074b20c4d9a1bb2
MD5 7271878fed7e9440de319d4d6e6a864c
BLAKE2b-256 a8026bafec44d3854e08eb5f125206f6d36f1197442ee02c8cb802e9ab9e921a

See more details on using hashes here.

Provenance

The following attestation bundles were made for otava_test_data-0.1.9.tar.gz:

Publisher: publish.yml on jdrumgoole/otava-test-data

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file otava_test_data-0.1.9-py3-none-any.whl.

File metadata

File hashes

Hashes for otava_test_data-0.1.9-py3-none-any.whl
Algorithm Hash digest
SHA256 cf535f0126013f40ca721a53b9ad359a616b279ffde42da1c5a4fb663f0b2a36
MD5 bca3a4d0d399da5e799db36c67b967bd
BLAKE2b-256 a2089d25215c9579f64a7122d6792f028fc66be1082836600cc4e71731d0e820

See more details on using hashes here.

Provenance

The following attestation bundles were made for otava_test_data-0.1.9-py3-none-any.whl:

Publisher: publish.yml on jdrumgoole/otava-test-data

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page