{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "\n# Trajan demo\n\nFrom OpenDrift 2.0, analysis and plotting of results from OpenDrift simulations\nwill be handled by a new, standalone package: Trajan\nhttps://github.com/OpenDrift/trajan\n\nThis example creates a test dataset, and demonstrates its anlysis using Trajan\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "import os\nfrom datetime import datetime, timedelta\nimport matplotlib.pyplot as plt\nimport xarray as xr\nimport trajan as ta\nfrom opendrift.readers import reader_netCDF_CF_generic\nfrom opendrift.models.openoil import OpenOil"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Create test dataset with OpenDrift\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "o = OpenOil(loglevel=20)\n\n# Add forcing\nreader_arome = reader_netCDF_CF_generic.Reader(o.test_data_folder() +\n    '16Nov2015_NorKyst_z_surface/arome_subset_16Nov2015.nc')\nreader_norkyst = reader_netCDF_CF_generic.Reader(o.test_data_folder() +\n    '16Nov2015_NorKyst_z_surface/norkyst800_subset_16Nov2015.nc')\no.add_reader([reader_norkyst, reader_arome])\n\n# Seeding some particles\no.seed_elements(lon=4.4, lat=60.1, radius=1000, number=1000,\n                time=reader_arome.start_time)\n\n# Running model\no.run(end_time=reader_norkyst.end_time, outfile='openoil.nc')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Demonstrating analysis and visualisation of the output dataset, independently of OpenDrift code\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "if not os.path.exists('openoil.nc'):\n    raise ValueError('Please run create_test_dataset.py first')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Importing a trajectory dataset from a simulation with OpenDrift\ndecode_coords is needed so that lon and lat are not interpreted as coordinate variables\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "d = xr.open_dataset('openoil.nc', decode_coords=False)\n# Requirement that status>=0 is needed since non-valid points are not masked in OpenDrift output\nd = d.where(d.status>=0)  # only active particles"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Displaying a basic plot of trajectories\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "d.traj.plot(land='mask')\nax = plt.gca()\nax.set_title('Adding custom title')\nplt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Demonstrating how the Xarray Dataset can be modified, allowing for\nmore flexibility than can be provided through the plotting method of OpenDrift\n\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Extracting only the first 100 elements, and every 4th output time steps:\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "dsub = d.isel(trajectory=range(0, 100), time=range(0, len(d.time), 4))\ndsub.traj.plot(land='h')\nplt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "With several plots on the same figure\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "d.traj.plot(color='red', alpha=0.01, land='mask')  # Plotting individual trajectories in red\ndmean = d.mean('trajectory', skipna=True) # Overlaying a \"mean\" trajectory in black\ndmean.traj.plot(color='k', linewidth=5)\n# Showing the a sub-period of the mean trajectory in yellow\ndmean.sel(time=slice('2015-11-17', '2015-11-17 12')).traj.plot(color='yellow', linewidth=5)\nplt.tight_layout()\nplt.show()"
      ]
    }
  ],
  "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
}