filter-pipe 1.0.0

Build and expose flexible calculation pipelines that give the end user total control over dynamic variables and data logic.

🔗 filter_pipe

A powerful and flexible Python library for building data processing pipelines with filters and mathematical operations. Chain multiple filters together to process numerical data in a clean, intuitive way.

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✨ Features

• ⛓️ Chainable Pipelines - Combine filters seamlessly using pipe syntax
• 📊 Signal Processing Filters - Moving average, low-pass, high-pass, band-pass, notch filters
• 🧮 Mathematical Operations - Add, subtract, multiply, divide operations
• 🎯 Flexible Configuration - Easy-to-read pipeline syntax with customizable parameters
• 🔍 Regex-based Validation - Robust pattern matching for pipeline chunks

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🚀 Quick Start

Installation

pip install -r requirements.txt

Basic Usage

from src.pipeline.pipeline import Pipeline

# Create a pipeline with multiple filters
pipeline = Pipeline('mavg(n=10) | lpass(alpha=0.2) | str(ndigits=2)')

# Process a value through the pipeline
result = pipeline.calc(2134.1231231)
print(result)  # Output: '2064.17'

Mathematical Operations

from src.pipeline.math_ops import Divide, Multiply, Add

# Use mathematical operations
divider = Divide(' / 2')
result = divider.calc(10)
print(result)  # Output: 5.0

# Combine in a pipeline
pipeline = Pipeline('*2 | +5 | /3')
result = pipeline.calc(10)  # (10 * 2 + 5) / 3 = 25/3 ≈ 8.33

Advanced Pipeline Example

# Signal smoothing pipeline: moving average → low-pass filter → round to 2 decimals
pipeline = Pipeline('mavg(n=5) | lpass(alpha=0.3) | str(ndigits=2)')
values = [100.2, 101.5, 99.8, 102.1, 100.9]

for value in values:
    smoothed = pipeline.calc(value)
    print(f"Input: {value:6.1f} → Output: {smoothed}")

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📦 Available Pipeline Chunks

🎯 Filters

Chunk	Syntax	Description	Parameters
Moving Average	mavg(n=N)	Calculates the average of the last N values	n - window size (integer)
Low-Pass Filter	lpass(alpha=A)	Smooths data by attenuating high frequencies	alpha - smoothing factor (0-1)
High-Pass Filter	hpass(alpha=A)	Attenuates low frequencies, preserves high frequencies	alpha - smoothing factor (0-1)
Band-Pass Filter	bpass(low_alpha=A,high_alpha=B)	Passes frequencies within a band	low_alpha, high_alpha - bounds (0-1)
Notch Filter	notch(low_alpha=A,high_alpha=B)	Removes frequencies within a band	low_alpha, high_alpha - bounds (0-1)
High Cut	hcut(cut=C)	Caps maximum value	cut - maximum threshold
Low Cut	lcut(cut=C)	Caps minimum value	cut - minimum threshold
To String	str(ndigits=N)	Converts to formatted string	ndigits - decimal places (optional, default=0)

🧮 Math Operations

Chunk	Syntax	Description	Parameters
Divide	/ D	Divides value by D	D - divisor (number)
Multiply	* M	Multiplies value by M	M - multiplier (number)
Add	+ V	Adds V to value	V - value to add (number)
Subtract	- S	Subtracts S from value	S - value to subtract (number)

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💡 Examples

Example 1: Temperature Smoothing

Smooth noisy temperature sensor readings:

from src.pipeline.pipeline import Pipeline

# Combine moving average with low-pass filter
pipeline = Pipeline('mavg(n=3) | lpass(alpha=0.5) | str(ndigits=1)')

temperatures = [20.1, 25.3, 19.8, 21.2, 20.9]
for temp in temperatures:
    smoothed = pipeline.calc(temp)
    print(f"Temperature: {temp}°C → Smoothed: {smoothed}°C")

Example 2: Financial Data Processing

Process stock price data:

# 10-period moving average → normalize → round to 2 decimals
pipeline = Pipeline('mavg(n=10) | str(ndigits=2)')

stock_prices = [150.25, 149.80, 151.10, 150.95, 149.50]
for price in stock_prices:
    avg = pipeline.calc(price)
    print(f"${price} → MA: ${avg}")

Example 3: Signal Processing

Apply advanced filtering:

# Remove noise with band-pass → apply notch → convert to string
pipeline = Pipeline('bpass(low_alpha=0.2,high_alpha=0.8) | notch(low_alpha=0.3,high_alpha=0.7) | str(ndigits=3)')

signal_data = [0.5, 0.6, 0.55, 0.58, 0.52]
for signal in signal_data:
    filtered = pipeline.calc(signal)
    print(f"Signal: {signal} → Filtered: {filtered}")

Example 4: Value Constraints

Bound values within a range:

# Scale value, then apply min/max constraints
pipeline = Pipeline('*10 | lcut(cut=0) | hcut(cut=100)')

raw_values = [-1.5, 5.3, 12.8, -0.2]
for val in raw_values:
    bounded = pipeline.calc(val)
    print(f"{val} → {bounded} (0-100 range)")

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🧪 Running Tests

Execute the test suite to verify functionality:

python3 -m pytest

Tests are located in the tests/ directory:

• test_filters.py - Filter functionality tests
• test_math.py - Math operations tests
• test_pipeline.py - Pipeline chaining tests

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🔗 Resources

• 📦 PyPI Package: Find this project on PyPI
• �💼 Regex Patterns: Learn and test regex patterns used in this project at regex101.com
• 📚 Tools: See tools/index.html for playing around with the filters and learn how to calibrate the gains.

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📝 License

This project is open-source and available for educational and commercial use.

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Built with ❤️ for data processing pipelines