minigst (Python)

The companion Python package for gstlearn.

This Python package wraps gstlearn functions to offer access to some basic geostatistical methods (mainly variography and kriging).

Installation

Prerequisites

You need Python 3.8 or higher. You will also need to install the gstlearn Python package. Please refer to the gstlearn documentation for installation instructions.

Installing minigst from our pypi (official release)

You can install the official release of minigst package using pip:

cd python
pip install minigst

Installing minigst from sources

After cloning the github repo, you can install the minigst package from source using pip:

git clone https://github.com/gstlearn/minigst
cd minigst
cd python
pip install .

Or for development:

git clone https://github.com/gstlearn/minigst
cd minigst
cd python
pip install -e .

Usage

import minigst as mg
import gstlearn as gl
import pandas as pd

# Load data from a pandas DataFrame
df = pd.read_csv("data.csv")
db = mg.df_to_db(df, coord_names=["x", "y"])

# Compute experimental variogram
vario_exp = mg.vario_exp(db, vname="variable", nlag=20, dlag=10.0)

# Fit a model
model = mg.model_fit(vario_exp, struct=["NUGGET", "SPHERICAL"])

# Perform kriging
target_db = mg.create_db_grid(nx=[100, 100], dx=[1.0, 1.0])
mg.minikriging(db, target_db, vname="variable", model=model)

# Plot results
mg.dbplot_grid(target_db, color="K.variable.estim")

Features

The minigst Python package provides wrapper functions for:

• Database operations: Convert pandas DataFrames to gstlearn Db objects, create grids, manipulate variables
• Plotting: Visualize spatial data and grids using matplotlib
• Variography: Compute experimental variograms and fit models
• Kriging: Perform simple, ordinary, and universal kriging
• Simulation: Generate Gaussian random fields

Documentation

For more information about the underlying gstlearn library, please visit gstlearn.org.

License

This package is distributed under the BSD-3 license.