pattern-fill 0.1.2a0

Time series gap-filling using daily diurnal patterns

Pattern Fill

A metadata-aware implementation of pattern fill algorithms for gap-filling time series data using daily diurnal patterns.

🎮 Try it Online

Launch Interactive Pattern Designer →

Design and test patterns directly in your browser using our WASM-powered marimo notebook. No installation required!

Features

• Dual pattern modes: Spline-based (traditional) and Sine wave-based (new)
• Metadata awareness: Track processing steps, preserve data provenance
• Weekday/weekend patterns: Different patterns for different day types
• FFT-based auto-fitting: Automatically extract sine components from data
• Flexible API: Easy-to-use factory methods and serialization

Installation

uv pip install pattern-fill

Or with pip:

pip install pattern-fill

Quick Start

Sine Wave Patterns (NEW)

Define patterns using intuitive sine wave parameters:

from pattern_fill import DailyPattern, pattern_fill
import pandas as pd
import numpy as np

# Create a simple daily pattern peaking at 8 AM
pattern = DailyPattern.from_simple_sine(
    amplitude=0.4,    # Variation strength (0-1)
    frequency=1.0,    # Cycles per day
    phase=8.0,        # Peak time (hours)
    baseline=0.5      # Center value (0-1)
)

# Create time series with gaps
index = pd.date_range("2024-01-01", periods=96, freq="15min")
series = pd.Series(np.random.randn(96) + 10, index=index)
series.iloc[20:30] = np.nan

# Fill gaps using the pattern
results = pattern_fill([series], pattern=pattern)
filled_series, processing_steps = results[0]

Complex Multi-Component Patterns

For wastewater treatment or environmental monitoring:

# Complex pattern: daily + twice-daily variations
pattern = DailyPattern.from_sine_waves(
    components=[
        (0.35, 1.0, 8.0),   # Daily cycle, peak at 8 AM
        (0.15, 2.0, 13.0),  # Twice-daily, peaks at 1 PM and 1 AM
        (0.05, 1/7, 0.0),   # Weekly variation
    ],
    baseline=0.45,
    name="nh4_pattern"
)

Auto-Fit from Data

from pattern_fill import fit_sine_pattern

# Automatically extract sine components using FFT
pattern = fit_sine_pattern(
    clean_series,
    n_components=2,  # Number of components to fit
)

# Or specify frequencies explicitly
pattern = fit_sine_pattern(
    clean_series,
    frequencies=[1.0, 2.0],  # Daily + twice-daily
)

Spline-Based Patterns (Traditional)

The traditional approach using control points:

pattern = DailyPattern(
    hours=[0, 6, 12, 18],
    values=[0.2, 0.8, 0.9, 0.5],
    name="flow_pattern"
)

Sine Wave Parameters

• amplitude: Controls the strength of variation (0-1 range)
• frequency: Cycles per day
  • 1.0 = once per day (24-hour cycle)
  • 2.0 = twice per day (12-hour cycle)
  • 0.5 = once every 2 days (48-hour cycle)
• phase: Time of peak in hours (0-24)
  • 0.0 = peak at midnight
  • 6.0 = peak at 6 AM
  • 12.0 = peak at noon
• baseline: Center value around which the sine oscillates (0-1 range)

Weekday/Weekend Patterns

patterns = {
    "weekday": DailyPattern.from_sine_waves(
        [(0.35, 1.0, 8.0)],
        baseline=0.5,
        day_type="weekday"
    ),
    "weekend": DailyPattern.from_sine_waves(
        [(0.25, 1.0, 10.0)],
        baseline=0.45,
        day_type="weekend"
    ),
}

results = pattern_fill([series], pattern=patterns)

Serialization

Save and load patterns:

# To JSON
json_str = pattern.to_json()

# From JSON
pattern2 = DailyPattern.from_json(json_str)

# To dict
d = pattern.to_dict()

# From dict
pattern3 = DailyPattern.from_dict(d)

Benefits of Sine Patterns over Splines

• Intuitive: Parameters directly map to physical phenomena
• Readable: DailyPattern.from_sine_waves([(0.35, 1.0, 8.0)]) immediately communicates "daily cycle peaking at 8 AM"
• Composable: Easily combine multiple periodicities
• Natural: Perfect for wastewater treatment and environmental monitoring patterns

API Reference

Main Functions

• pattern_fill(input_series, pattern, ...) - Fill gaps in time series
• fit_pattern(series, n_control_points, ...) - Auto-fit spline pattern from data
• fit_sine_pattern(series, n_components, ...) - Auto-fit sine pattern using FFT

Classes

• DailyPattern - Main pattern class supporting both spline and sine modes
• SineComponent - Individual sine wave component

Factory Methods

• DailyPattern.from_sine_waves(components, baseline, ...) - Create from sine components
• DailyPattern.from_simple_sine(amplitude, frequency, phase, baseline) - Create simple sine pattern

Examples

See the notebooks/ directory for interactive examples using the pattern designer.

Development

# Install dependencies
uv sync

# Run tests
pytest tests/

# Run specific test file
pytest tests/test_sine_component.py -v

License

MIT License

Contributing

Contributions welcome! Please open an issue or pull request.