ROMS_Vertical_Grid_examples.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f3537593-ef42-4f4c-aadf-352580327733",
   "metadata": {},
   "source": [
    "# The ROMS vertical grid\n",
    "I'm using some packages that I've developed over the years as a basis for this. They are [xroms](https://github.com/xoceanmodel/xroms) and [octant.depths](https://github.com/hetland/octant/blob/master/octant/depths.py).\n",
    "\n",
    "Here. I've pulled out the relevant code to simplify the discussion, which hopefully is sufficient for demonstration purposes.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "32321dfe-7b13-41ea-b356-0eed8a5aeb42",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b5681b6-4433-46e5-ad3b-5264b4446614",
   "metadata": {},
   "source": [
    "## Vstretching\n",
    "\n",
    "These have all been simplified -- no limits are checked, and no docs.\n",
    "see https://github.com/hetland/octant/blob/master/octant/depths.py#L74 for \n",
    "full info, docs, etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "db3d9349-86ed-4370-974c-898a6a22b690",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "def get_Vstretching_1(theta_s, theta_b):\n",
    "    def C(s):\n",
    "        a = theta_s\n",
    "        b = theta_b\n",
    "        return (1 - b) * (np.sinh(s * a) / np.sinh(a)) + \\\n",
    "                b * (-0.5 + 0.5 * np.tanh(a * (s + 0.5)) / np.tanh(0.5 * a))\n",
    "    return C\n",
    "\n",
    "def get_Vstretching_2(theta_s, theta_b):\n",
    "    def C(s):\n",
    "        return (1.0 - np.cosh(theta_s * s)) / (np.cosh(theta_s) - 1.0)\n",
    "    return C\n",
    "\n",
    "\n",
    "def get_Vstretching_4(theta_s, theta_b):\n",
    "    def C(s):\n",
    "        C = (1.0 - np.cosh(theta_s * s)) / (np.cosh(theta_s) - 1.0)\n",
    "        return (np.exp(theta_b * C) - 1.0) / (1.0 - np.exp(-theta_b))\n",
    "    return C"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52602311-c72d-4fc6-b21c-31861de3b226",
   "metadata": {},
   "source": [
    "## VTransform\n",
    "\n",
    "These have also all been simplified -- no limits are checked, and no docs.\n",
    "see https://github.com/hetland/octant/blob/master/octant/depths.py#L283 for \n",
    "full info, docs, etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a3a9f876-639d-4764-bfc8-25fffbc36ac2",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_Vtransform_1(C, h, hc):\n",
    "    def Zo(s):\n",
    "        if np.ndim(h) == 1:\n",
    "            s = s[:, np.newaxis]\n",
    "        if np.ndim(h) == 2:\n",
    "            s = s[:, np.newaxis, np.newaxis]\n",
    "\n",
    "        assert hc <= np.min(h), 'hc cannot be larger than the minimum depth'\n",
    "\n",
    "        return hc * (s - C(s)) + C(s) * h\n",
    "    return Zo\n",
    "\n",
    "\n",
    "def get_Vtransform_2(C, h, hc):\n",
    "    def Zo(s):\n",
    "        if np.ndim(h) == 1:\n",
    "            s = s[:, np.newaxis]\n",
    "        if np.ndim(h) == 2:\n",
    "            s = s[:, np.newaxis, np.newaxis]\n",
    "\n",
    "        return (hc * s + C(s) * h) / (hc + h)\n",
    "    return Zo\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f966419b-1622-4584-ac7f-e22304ec9e07",
   "metadata": {},
   "source": [
    "## scoord\n",
    "\n",
    "These are all pretty straightforward. They are just the uniformly spaced s-coordinate (from -1 at the sea floor to 0 at the free surface)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f84ad544-7e78-4f8c-9ab2-19a666fb5165",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_sw(N):\n",
    "    'return N evenly s-coordinate w-points.'\n",
    "    return np.linspace(-1.0, 0.0, N)\n",
    "\n",
    "\n",
    "def get_srho(N):\n",
    "    'return N evenly spaced s-coordinate rho-points'\n",
    "    sr = np.linspace(-1.0, 0.0, N+1)\n",
    "    return 0.5 * (sr[1:] + sr[:-1])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69536f33-229b-49fb-9521-36d5c028229e",
   "metadata": {},
   "source": [
    "## Get the depths\n",
    "\n",
    "Again, see https://github.com/hetland/octant/blob/master/octant/depths.py#L360 for details\n",
    "\n",
    "This walks you through the steps in creating a physical vertical coordinate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "b0218630-147b-4c43-a857-b896a7c50581",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "N = 30        # the number of vertical levels.\n",
    "theta_s = 5.0\n",
    "theta_b = 0.5\n",
    "h = np.linspace(10, 200, 10)\n",
    "hc = 40\n",
    "zeta = 0.0\n",
    "\n",
    "sw = get_sw(N+1)    # need to add an extra layer since s_w has one more layer than s_rho\n",
    "\n",
    "C = get_Vstretching_4(theta_s, theta_b)\n",
    "\n",
    "Zo = get_Vtransform_2(C, h, hc)\n",
    "\n",
    "z_w = zeta + (zeta + h) * Zo(sw)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "1db7732f-b01f-45f5-b03c-5c20dd3b24bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.LineCollection at 0x110ca8eb0>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(10, 6))\n",
    "ax.plot(z_w.T, 'k-', lw=0.5);\n",
    "ax.hlines(-hc, 0, len(h), ls='--', color='r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "0c0d7eae-f4d5-4363-8aaf-a316336abe0d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0;31mSignature:\u001b[0m\n",
       "\u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhlines\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mxmin\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mxmax\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mcolors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mlinestyles\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'solid'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m''\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0;34m*\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mDocstring:\u001b[0m\n",
       "Plot horizontal lines at each *y* from *xmin* to *xmax*.\n",
       "\n",
       "Parameters\n",
       "----------\n",
       "y : float or array-like\n",
       "    y-indexes where to plot the lines.\n",
       "\n",
       "xmin, xmax : float or array-like\n",
       "    Respective beginning and end of each line. If scalars are\n",
       "    provided, all lines will have same length.\n",
       "\n",
       "colors : list of colors, default: :rc:`lines.color`\n",
       "\n",
       "linestyles : {'solid', 'dashed', 'dashdot', 'dotted'}, optional\n",
       "\n",
       "label : str, default: ''\n",
       "\n",
       "Returns\n",
       "-------\n",
       "`~matplotlib.collections.LineCollection`\n",
       "\n",
       "Other Parameters\n",
       "----------------\n",
       "**kwargs :  `~matplotlib.collections.LineCollection` properties.\n",
       "\n",
       "See Also\n",
       "--------\n",
       "vlines : vertical lines\n",
       "axhline : horizontal line across the Axes\n",
       "\n",
       "Notes\n",
       "-----\n",
       "\n",
       "\n",
       ".. note::\n",
       "    In addition to the above described arguments, this function can take\n",
       "    a *data* keyword argument. If such a *data* argument is given,\n",
       "    the following arguments can also be string ``s``, which is\n",
       "    interpreted as ``data[s]`` (unless this raises an exception):\n",
       "    *y*, *xmin*, *xmax*, *colors*.\n",
       "\n",
       "    Objects passed as **data** must support item access (``data[s]``) and\n",
       "    membership test (``s in data``).\n",
       "\u001b[0;31mFile:\u001b[0m      ~/miniconda3/envs/compass/lib/python3.9/site-packages/matplotlib/pyplot.py\n",
       "\u001b[0;31mType:\u001b[0m      function\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hlines?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ee40edc8-6440-4c52-a375-2e6cd96f6777",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}