Statista - Advanced Statistical Analysis Package

Overview

Statista is a comprehensive Python package for statistical analysis, focusing on probability distributions, extreme value analysis, and sensitivity analysis. It provides robust tools for researchers, engineers, and data scientists working with statistical models, particularly in hydrology, climate science, and risk assessment.

Current release info

Name	Downloads	Version	Platforms

conda-forge feedstock

Conda-forge feedstock

Installation

Conda (Recommended)

conda install -c conda-forge statista

PyPI

pip install statista

Development Version

pip install git+https://github.com/Serapieum-of-alex/statista

Main Features

Statistical Distributions

• Probability Distributions: GEV, Gumbel, Normal, Exponential, and more
• Parameter Estimation Methods: Maximum Likelihood (ML), L-moments, Method of Moments (MOM)
• Goodness-of-fit Tests: Kolmogorov-Smirnov, Chi-square
• Truncated Distributions: Focus analysis on values above a threshold

Extreme Value Analysis

• Return Period Calculation: Estimate extreme events for different return periods
• Confidence Intervals: Calculate confidence bounds using various methods
• Plotting Positions: Weibull, Gringorten, and other empirical distribution functions

Sensitivity Analysis

• One-at-a-time (OAT): Analyze parameter sensitivity individually
• Sobol Visualization: Visualize parameter interactions and importance

Statistical Tools

• Descriptive Statistics: Comprehensive statistical descriptors
• Time Series Analysis: Auto-correlation and other time series tools
• Visualization: Publication-quality plots for statistical analysis

Quick Start

Basic Usage

import pandas as pd
from statista.distributions import Distributions

# Load your time series data
data = pd.read_csv("your_data.csv", header=None)[0].tolist()

# Create a distribution object (e.g., Gumbel)
dist = Distributions("Gumbel", data)

# Fit the distribution using maximum likelihood
params = dist.fit_model(method="mle")
print(params)

# Calculate and plot the PDF and CDF
pdf = dist.pdf(plot_figure=True)
cdf, _, _ = dist.cdf(plot_figure=True)

# Perform goodness-of-fit tests
ks_test = dist.ks()
chi2_test = dist.chisquare()

# Create a probability plot with confidence intervals
fig, ax = dist.plot()

Extreme Value Analysis

from statista.distributions import GEV, PlottingPosition

# Create a GEV distribution
gev_dist = Distributions("GEV", data)

# Fit using L-moments
params = gev_dist.fit_model(method="lmoments")

# Calculate non-exceedance probabilities
cdf_weibul = PlottingPosition.weibul(data)

# Calculate confidence intervals
lower_bound, upper_bound, fig, ax = gev_dist.confidence_interval(plot_figure=True)

For more examples and detailed documentation, visit Statista Documentation

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

This project is licensed under the GPL-3.0 License - see the LICENSE file for details.

Citation

If you use Statista in your research, please cite it as:

Farrag, M. (2023). Statista: A Python package for statistical analysis, extreme value analysis, and sensitivity analysis.
https://github.com/Serapieum-of-alex/statista

BibTeX:

@software{statista2023,
  author = {Farrag, Mostafa},
  title = {Statista: A Python package for statistical analysis, extreme value analysis, and sensitivity analysis},
  url = {https://github.com/Serapieum-of-alex/statista},
  year = {2023}
}