{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "\n# Checkerboard\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "import numpy as np\nfrom opendrift.readers import reader_global_landmask\nfrom opendrift.readers import reader_netCDF_CF_generic\nfrom opendrift.models.oceandrift import OceanDrift\n\no = OceanDrift(loglevel=20)  # Set loglevel to 0 for debug information\n\n# Norkyst\n#reader_norkyst = reader_netCDF_CF_generic.Reader('https://thredds.met.no/thredds/dodsC/sea/norkyst800m/1h/aggregate_be')\nreader_norkyst = reader_netCDF_CF_generic.Reader(o.test_data_folder() + '16Nov2015_NorKyst_z_surface/norkyst800_subset_16Nov2015.nc')\n\no.add_reader([reader_norkyst])\n\no.set_config('drift:vertical_mixing', False)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Seeding particles in a checkerboard pattern\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "di = 5 # Horizontal number of particles per square\ndj = 5 # Vertical number of particles per square\nlons = np.linspace(3.5, 5.0, 100)\nlats = np.linspace(60, 61, 100)\n\nii = np.arange(len(lons))//di\njj = np.arange(len(lats))//dj\nii, jj = np.meshgrid(ii, jj)\nboard = (ii+jj)%2 > 0\n\nlons, lats = np.meshgrid(lons, lats)\nlons = lons[board].ravel()\nlats = lats[board].ravel()\n\no.seed_elements(lons, lats, radius=0, number=5000,\n                time=reader_norkyst.start_time)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Running model\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "o.run(steps=66*2, time_step=1800)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Print and plot results\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "print(o)\no.animation(fast=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "<img src=\"file://gallery/animations/example_checkerboard_0.gif\">\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "o.plot()"
      ]
    }
  ],
  "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": "3.11.6"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}