{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "\n# Drifter\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "from datetime import timedelta\nimport numpy as np\nfrom opendrift.readers import reader_netCDF_CF_generic\nfrom opendrift.models.oceandrift import OceanDrift\n\no = OceanDrift(loglevel=20)  # Basic drift model suitable for passive tracers or drifters"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Preparing Readers\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "reader_current = reader_netCDF_CF_generic.Reader(o.test_data_folder() +\n    '16Nov2015_NorKyst_z_surface/norkyst800_subset_16Nov2015.nc')\nreader_wind = reader_netCDF_CF_generic.Reader(o.test_data_folder() +\n    '16Nov2015_NorKyst_z_surface/arome_subset_16Nov2015.nc')\n\no.add_reader([reader_current, reader_wind])\n\n# Prevent mixing elements downwards\no.set_config('drift:vertical_mixing', False)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Seeding elements\n\n Elements are moved with the ocean current, in addition to a fraction of\n the wind speed (wind_drift_factor). This factor depends on the properties\n of the elements. Typical empirical values are:\n - 0.035 (3.5 %) for oil and iSphere driftes\n - 0.01  (1 %) for CODE drifters partly submerged ~0.5 m\n As there are large uncertainties, it makes sense to provide a statistical\n distribution of wind_drift_factors\n\n Using a constant value for all elements:\nwind_drift_factor = 0.03\n\n Giving each element a unique (random) wind_drift_factor\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "wind_drift_factor = np.random.uniform(0, 0.06, 2000)\no.seed_elements(4.7, 59.9, radius=3000, number=2000,\n                time=reader_current.start_time,\n                wind_drift_factor=wind_drift_factor)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Running model\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "o.run(time_step=timedelta(minutes=15),\n      time_step_output=timedelta(minutes=60))"
      ]
    },
    {
      "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(color='wind_drift_factor', fast=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "<img src=\"file://gallery/animations/example_drifter_0.gif\">\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "# Plot trajectories, colored by the wind_drift_factor of each element\no.plot(linecolor='wind_drift_factor', fast=True)"
      ]
    }
  ],
  "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
}