{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "\n# Fit Variogram with automatic binning\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "import numpy as np\n\nimport gstools as gs"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Generate a synthetic field with an exponential model.\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "x = np.random.RandomState(19970221).rand(1000) * 100.0\ny = np.random.RandomState(20011012).rand(1000) * 100.0\nmodel = gs.Exponential(dim=2, var=2, len_scale=8)\nsrf = gs.SRF(model, mean=0, seed=19970221)\nfield = srf((x, y))\nprint(field.var())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Estimate the variogram of the field with automatic binning.\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "bin_center, gamma = gs.vario_estimate((x, y), field)\nprint(\"estimated bin number:\", len(bin_center))\nprint(\"maximal bin distance:\", max(bin_center))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Fit the variogram with a stable model (no nugget fitted).\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "fit_model = gs.Stable(dim=2)\nfit_model.fit_variogram(bin_center, gamma, nugget=False)\nprint(fit_model)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Plot the fitting result.\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "ax = fit_model.plot(x_max=max(bin_center))\nax.scatter(bin_center, gamma)"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.9"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}