easy-fir-filter 0.1.0

A simple python package to design and apply FIR filters.

Easy FIR Filter

A Python library for simplified design of symmetric FIR (Finite Impulse Response) digital filters.

Description

easy_fir_filter provides a high-level interface for designing and calculating FIR filter coefficients based on a user-defined filter configuration. This library focuses on simplicity and usability, allowing researchers, engineers, and signal processing enthusiasts to create optimized FIR filters with ease.

Features

• Simplified FIR filter design using clear and concise configuration
• Support for different window types, including Kaiser, Hamming, and Blackman
• Automatic calculation of critical parameters such as passband/stopband attenuation
• Optimal filter order determination
• Precise results with configurable rounding control
• Implementation based on solid mathematical principles

Installation

pip install easy-fir-filter

Quick Usage

from easy_fir_filter import EasyFirFilter
from easy_fir_filter.types import FilterConf

# Filter configuration
filter_conf: FilterConf = {
    "filter_type": "lowpass",       # Lowpass filter
    "window_type": "kaiser",        # Kaiser window
    "passband_freq_hz": 1000,       # Passband edge at 1000 Hz
    "stopband_freq_hz": 1100,       # Stopband edge at 1100 Hz
    "sampling_freq_hz": 8000,       # 8 kHz sampling rate
    "passband_ripple_db": 1,        # 1 dB ripple in passband
    "stopband_attenuation_db": 60   # 60 dB attenuation in stopband
}

# Create and initialize the filter
fir_filter = EasyFirFilter(filter_conf, round_to=6)

# Calculate filter coefficients
coefficients = fir_filter.calculate_filter()

print(f"FIR filter coefficients: {coefficients}")

Filter Configuration

The filter configuration is defined through a FilterConf TypedDict with the following parameters:

Parameter	Description	Required
filter_type	Filter type: "lowpass", "highpass", "bandpass", or "bandstop"	Yes
window_type	Window type: "kaiser", "hamming", or "blackman"	Yes
passband_freq_hz	Passband edge frequency in Hz (for lowpass/highpass) or lower passband edge (for bandpass/bandstop)	Yes
stopband_freq_hz	Stopband edge frequency in Hz (for lowpass/highpass) or lower stopband edge (for bandpass/bandstop)	Yes
sampling_freq_hz	Sampling frequency of the signal in Hz	Yes
passband_ripple_db	Maximum allowable passband ripple in decibels (dB)	Yes
stopband_attenuation_db	Minimum required stopband attenuation in decibels (dB)	Yes
passband_freq2_hz	Upper passband edge frequency in Hz (required for bandpass/bandstop filters)	For bandpass/bandstop only
stopband_freq2_hz	Upper stopband edge frequency in Hz (required for bandpass/bandstop filters)	For bandpass/bandstop only

Example Configurations

Lowpass Filter

lowpass_conf = {
    "filter_type": "lowpass",
    "window_type": "kaiser",
    "passband_freq_hz": 1000,       # Frequencies below 1000 Hz pass
    "stopband_freq_hz": 1200,       # Frequencies above 1200 Hz stop
    "sampling_freq_hz": 8000,       # 8 kHz sampling rate
    "passband_ripple_db": 1,        # 1 dB ripple in passband
    "stopband_attenuation_db": 60   # 60 dB attenuation in stopband
}

Bandpass Filter

bandpass_conf = {
    "filter_type": "bandpass",
    "window_type": "hamming",
    "passband_freq_hz": 300,        # Lower passband edge
    "passband_freq2_hz": 3400,      # Upper passband edge
    "stopband_freq_hz": 150,        # Lower stopband edge
    "stopband_freq2_hz": 3600,      # Upper stopband edge
    "sampling_freq_hz": 16000,      # 16 kHz sampling rate
    "passband_ripple_db": 1,        # 1 dB ripple in passband
    "stopband_attenuation_db": 50   # 50 dB attenuation in stopband
}

Design Process

The EasyFirFilter class implements the following design process:

1. Validation of the filter configuration
2. Calculation of the minimum tolerance (delta) between passband and stopband
3. Calculation of passband ripple and stopband attenuation
4. Determination of the D parameter for the Kaiser window (if used)
5. Calculation of the optimal filter order
6. Generation of impulse response coefficients
7. Application of the selected window
8. Calculation of the final FIR filter coefficients

Advanced Examples

Lowpass Filter with Kaiser Window

filter_conf = {
    "filter_type": "lowpass",
    "window_type": "kaiser",
    "passband_freq_hz": 2000,
    "stopband_freq_hz": 2200,
    "sampling_freq_hz": 44100,
    "passband_ripple_db": 0.5,
    "stopband_attenuation_db": 80
}

fir_filter = EasyFirFilter(filter_conf)
coefficients = fir_filter.calculate_filter()

Bandpass Filter with Hamming Window

filter_conf = {
    "filter_type": "bandpass",
    "window_type": "hamming",
    "passband_freq_hz": 300,
    "passband_freq2_hz": 3400,
    "stopband_freq_hz": 150,
    "stopband_freq2_hz": 3600,
    "sampling_freq_hz": 16000,
    "passband_ripple_db": 1,
    "stopband_attenuation_db": 50
}

fir_filter = EasyFirFilter(filter_conf)
coefficients = fir_filter.calculate_filter()

Architecture

The package uses a factory design pattern to create the appropriate filter and window objects:

• EasyFirFilter: Main class providing the user interface
• FilterFactory: Creates filters and windows based on configuration
• FilterConfValidator: Validates the filter configuration
• Specific interfaces for different types of filters and windows

Contributing

Contributions are welcome. Please feel free to submit a Pull Request or open an Issue to discuss proposed changes.

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgements

Some of the filter design techniques used in this package were inspired by:

DeFatta, D. J., Lucas, J. G., & Hodgkiss, W. S. (1988). Digital Signal Processing: A System Design Approach. John Wiley & Sons. ISBN: 9780471837886, 0471837881