PySATL Time Series Processing subproject (abbreviated pysatl-tsp) is a module designed for adaptive processing of time series data with a focus on streaming architecture. It implements a chain of responsibility pattern that enables building complex data processing pipelines with minimal boilerplate code, making it suitable for real-time applications and large dataset analysis.
- Python 3.10+
- Poetry 1.8.0+
Clone the repository:
git clone https://github.com/PySATL/pysatl-tspInstall dependencies:
poetry installfrom pysatl_tsp.core.data_providers import SimpleDataProvider
from pysatl_tsp.core.processor import MappingHandler
from pysatl_tsp.core.scrubber import LinearScrubber
# Create a data source
data = [i for i in range(100)]
provider = SimpleDataProvider(data)
# Define a simple processing pipeline:
# 1. Create windows of 10 elements with 50% overlap
# 2. Calculate the average of each window
pipeline = (
provider
| LinearScrubber(window_length=10, shift_factor=0.5)
| MappingHandler(map_func=lambda window: sum(window.values) / len(window))
)
# Process the data
results = []
for avg in pipeline:
results.append(avg)
print(f"Number of windows: {len(results)}")
print(f"First 3 window averages: {results[:3]}")
# Visualize results
import matplotlib.pyplot as plt
plt.figure(figsize=(10, 5))
plt.plot(results)
plt.title('Window Averages')
plt.xlabel('Window Index')
plt.ylabel('Average Value')
plt.grid(True)
plt.show()Install requirements
poetry install --with devInstall pre-commit hooks:
poetry run pre-commit installStarting manually:
poetry run pre-commit run --all-files --color always --verbose --show-diff-on-failureThis project is licensed under the terms of the MIT license. See the LICENSE for more information.