d3d4.pyavg 0.1.1

Average value calculation

pyavg

pyavg is a Python library that provides a set of classes for calculating averages from input data using various methods. It supports both basic and specialized smoothing and filtering algorithms.

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Installation

You can install the library via PyPI:

pip install ehles.pyavg

Key Features

The library offers classes for different average calculation methods, including:

1. Basic Moving Average (bi_avg.Stat)
2. Cumulative Average (cumulative.Stat)
3. Exponential Smoothing (exp_smooth.Stat)
4. PID Controller-Based Average (pid.Stat)
5. Ring Buffer for Averaging (ring_buff.Stat)
6. Advanced Smoothing Algorithms (smooth.Stat)

Each class implements a common interface, making it easy to switch between methods as needed.

Usage Examples

Basic Moving Average

from pyavg import BiAvgStat

# Create an object for moving average calculation
stat = BiAvgStat(window_size=5)

# Add values
stat.add(10)
stat.add(20)
stat.add(30)

# Get the current average
print(stat.get_average())  # -> 20.0

Cumulative Average

from pyavg import CumulativeStat

# Create an object for cumulative average calculation
stat = CumulativeStat()

# Add values
stat.add(10)
stat.add(20)
stat.add(30)

# Get the current average
print(stat.get_average())  # -> 20.0

Exponential Smoothing

from pyavg import ExpSmoothStat

# Create an object for exponential smoothing
stat = ExpSmoothStat(alpha=0.5)

# Add values
stat.add(10)
stat.add(20)
stat.add(30)

# Get the current smoothed value
print(stat.get_average())  # -> smoothed value

Documentation

Each class provides the following key methods:

• add(value: float): adds a new value to the calculation.
• get_average() -> float: returns the current average.

For details on implementation and additional parameters, refer to the source code or library documentation.

Requirements

• Python 3.6 or higher.

License

This project is licensed under the MIT License. See the LICENSE file for more details.

Contribution

If you’d like to contribute or add a new average calculation method, feel free to submit a Pull Request or reach out through GitHub Issues.