{
  "cells": [
    {
      "cell_type": "markdown",
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      "source": [
        "\n# Vertical diffusivity\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Plot the depth dependence of vertical diffusivity from the various analytical methods\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "import numpy as np\nimport matplotlib.pyplot as plt\nfrom opendrift.models.physics_methods import verticaldiffusivity_Sundby1983, verticaldiffusivity_Large1994, verticaldiffusivity_stepfunction\n\ndepth = np.linspace(0, 60, 100)\nwindspeed = np.arange(0, 20, 5)\ncolors = ['r', 'g', 'b', 'k']\n\nfor w, c in zip(windspeed, colors):\n    plt.plot(np.ones(depth.shape)*verticaldiffusivity_Sundby1983(w, depth, 50),\n             depth, c + '-', label='Sundby, wind = %sm MLD=50' % w)\n    plt.plot(verticaldiffusivity_Large1994(w, depth, 50), depth,\n             c + '--', label='Large, wind = %s, MLD=50' % w)\n    plt.plot(verticaldiffusivity_Large1994(w, depth, 20), depth,\n             c + '-.', label='Large, wind = %s, MLD=20' % w)\n\nplt.plot(verticaldiffusivity_stepfunction(depth), depth,\n         '-m', label='Stepfunction')\n\nplt.xlabel('Vertical diffusivity [m2/s]')\nplt.ylabel('Depth [m]')\nplt.gca().set_ylim([0, depth.max()])\nplt.gca().set_xlim([0, None])\nplt.gca().invert_yaxis()\nplt.legend()\nplt.show()"
      ]
    }
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