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https://github.com/borchehq/incstatspy
NumPy based Python library for online calculation of moments .
https://github.com/borchehq/incstatspy
central-moments mean numpy numpy-arrays numpy-library online-algorithms rolling-statistics running-statistics statistics variance
Last synced: 27 days ago
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NumPy based Python library for online calculation of moments .
- Host: GitHub
- URL: https://github.com/borchehq/incstatspy
- Owner: borchehq
- License: apache-2.0
- Created: 2024-08-06T12:06:11.000Z (3 months ago)
- Default Branch: master
- Last Pushed: 2024-09-09T12:49:24.000Z (about 2 months ago)
- Last Synced: 2024-10-10T14:07:06.708Z (28 days ago)
- Topics: central-moments, mean, numpy, numpy-arrays, numpy-library, online-algorithms, rolling-statistics, running-statistics, statistics, variance
- Language: C
- Homepage:
- Size: 124 KB
- Stars: 1
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE.txt
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README
# incstatspy
A Python C extension for efficiently calculating statistics iteratively on NumPy arrays. This module provides functions for computing mean, variance, skewness, kurtosis, and central moments.
## Status
[](https://codecov.io/gh/borchehq/incstatsPy)
[](https://pypi.org/project/incstatspy/)
## Features
- Compute running mean, variance, skewness, and kurtosis.
- Calculate central moments up to a specified order.
- Calculate standardized central moments up to a specified order.
- Determine minimum and maximum values.
- Designed for efficiency and simplicity.
- Handles weighted data points.
- Iterative update functionality when calling on multiple ndarrays of compatible shape.
## Installation
You can install this project using `pip`:
### Using pip
To install the latest version from PyPI:
```bash
pip install incstatspy
```
### From Source
If you want to install the project from the source code:
```bash
git clone https://github.com/borchehq/incstatsPy.git
cd incstatspy
pip install .
```
## Usage
```python
import incstatspy
import numpy as np
# Calculate mean
ndarray = np.array(np.random.rand())
mean, buffer = incstatspy.mean(ndarray)
print(mean)
# Update mean with new data
ndarray = np.array(np.random.rand())
print(mean)
mean, buffer = incstatspy.mean(ndarray, buffer=buffer)
# Calculate variance
ndarray = np.array(np.random.rand())
# Mean will be automatically calculated with variance
mean, variance, buffer = incstatspy.variance(ndarray)
print(variance)
ndarray = np.array(np.random.rand())
# Update mean and variance
mean, variance, buffer = incstatspy.variance(ndarray, buffer=buffer)
print(variance)
ndarray = np.random.rand(3, 3)
# Calculate skewness (variance and mean will be automatically calculated as well)
*_, skewness, buffer = incstatspy.skewness(ndarray) # discard mean and variance
print(skewness)
ndarray = np.random.rand(3, 3)
# Update skewness
*_, skewness, buffer = incstatspy.skewness(ndarray, buffer=buffer)
print(skewness)
ndarray = np.random.rand(3, 3)
# Calculate kurtosis (skewness, variance and mean will be automatically calculated as well)
*_, kurtosis, buffer = incstatspy.kurtosis(ndarray) # discard mean, variance and skewness
print(kurtosis)
ndarray = np.random.rand(3, 3)
# Update kurtosis
*_, kurtosis, buffer = incstatspy.kurtosis(ndarray, buffer=buffer)
print(kurtosis)
# Calculate 8th moment and discard lower moments. Mean will be calculated as well.
# Calculate along axis 1 (default: axis 0)
p = 8
ndarray = np.random.rand(3, 3)
*_, pth_moment, mean, buffer = incstatspy.central_moment(ndarray, p, axis=1)
print(pth_moment)
ndarray = np.random.rand(3, 3)
# Update 8th moment
*_, pth_moment, mean, buffer = incstatspy.central_moment(ndarray, p, axis=1, buffer=buffer)
print(pth_moment)
```
## License
This project is licensed under the Apache License, Version 2.0. You may obtain a copy of the License at:
http://www.apache.org/licenses/LICENSE-2.0
The full text of the license is available in the `LICENSE.txt` file in this repository.