| Name | d3d4.pyavg JSON |
| Version |
0.1.2
JSON |
| download |
| home_page | None |
| Summary | Average value calculation |
| upload_time | 2024-11-25 07:16:00 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.8 |
| license | MIT License |
| keywords |
average
avg
stat
pyavg
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# 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.
---
## Installation
You can install the library via PyPI:
```bash
pip install d3d4.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
```python
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
```python
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
```python
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](https://github.com/ehles/ehles.pyAvg/issues).
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"description": "# pyavg\n\n`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.\n\n---\n\n## Installation\n\nYou can install the library via PyPI:\n\n```bash\npip install d3d4.pyavg\n```\n\n## Key Features\n\nThe library offers classes for different average calculation methods, including:\n\n1. Basic Moving Average (bi_avg.Stat)\n2. Cumulative Average (cumulative.Stat)\n3. Exponential Smoothing (exp_smooth.Stat)\n4. PID Controller-Based Average (pid.Stat)\n5. Ring Buffer for Averaging (ring_buff.Stat)\n6. Advanced Smoothing Algorithms (smooth.Stat)\n\nEach class implements a common interface, making it easy to switch between methods as needed.\n\n## Usage Examples\n\n### Basic Moving Average\n\n```python\nfrom pyavg import BiAvgStat\n\n# Create an object for moving average calculation\nstat = BiAvgStat(window_size=5)\n\n# Add values\nstat.add(10)\nstat.add(20)\nstat.add(30)\n\n# Get the current average\nprint(stat.get_average()) # -> 20.0\n```\n\n### Cumulative Average\n\n```python\nfrom pyavg import CumulativeStat\n\n# Create an object for cumulative average calculation\nstat = CumulativeStat()\n\n# Add values\nstat.add(10)\nstat.add(20)\nstat.add(30)\n\n# Get the current average\nprint(stat.get_average()) # -> 20.0\n```\n\n### Exponential Smoothing\n\n```python\nfrom pyavg import ExpSmoothStat\n\n# Create an object for exponential smoothing\nstat = ExpSmoothStat(alpha=0.5)\n\n# Add values\nstat.add(10)\nstat.add(20)\nstat.add(30)\n\n# Get the current smoothed value\nprint(stat.get_average()) # -> smoothed value\n```\n\n## Documentation\n\nEach class provides the following key methods:\n\n- `add(value: float):` adds a new value to the calculation.\n- `get_average() -> float:` returns the current average.\n\nFor details on implementation and additional parameters, refer to the source code or library documentation.\n\n## Requirements\n\n- Python 3.6 or higher.\n\n## License\n\nThis project is licensed under the MIT License. See the LICENSE file for more details.\n\n## Contribution\n\nIf you\u2019d like to contribute or add a new average calculation method, feel free to submit a Pull Request or reach out through [GitHub Issues](https://github.com/ehles/ehles.pyAvg/issues).\n",
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