AnalyzeOverflow.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import print_function\n",
    "from ipywidgets import interact, interactive, fixed, interact_manual\n",
    "import ipywidgets as widgets\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import xarray as xr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "casepath = 'tmp_wettingdrying/ocean/overflow/10km/default/forward/'\n",
    "caseoutput = 'output.nc'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds = xr.open_dataset(casepath + caseoutput)\n",
    "ds.set_coords(['yCell','xCell','refBottomDepth'], inplace=True)\n",
    "ds['yCell'] = ds.yCell - ds.yCell.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116cc5f90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot(ds, variable, layernum=0, nlayers=4, time=-1):\n",
    "    y = ds.yCell[layernum::nlayers].values\n",
    "    z = ds.refBottomDepth.values\n",
    "    Y,Z = np.meshgrid(y,z)\n",
    "    plt.pcolor(Y, Z, ds[variable].isel(Time=time)[layernum::nlayers,:].T)\n",
    "    #plt.xlim(0,200000)\n",
    "    plt.ylim(0,ds.refBottomDepth.max())\n",
    "    plt.gca().invert_yaxis()\n",
    "    plt.colorbar()\n",
    "plot(ds, 'temperature', time=-3)\n",
    "plt.clim(0,36)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x320741fd0>]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x31761fc50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds.ssh.isel(Time=-1).groupby('yCell').mean().plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ea9bce47c1be44f8b866d57356562c85",
       "version_major": 2,
       "version_minor": 0
      },
      "text/html": [
       "<p>Failed to display Jupyter Widget of type <code>interactive</code>.</p>\n",
       "<p>\n",
       "  If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
       "  that the widgets JavaScript is still loading. If this message persists, it\n",
       "  likely means that the widgets JavaScript library is either not installed or\n",
       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
       "  Widgets Documentation</a> for setup instructions.\n",
       "</p>\n",
       "<p>\n",
       "  If you're reading this message in another frontend (for example, a static\n",
       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
       "  it may mean that your frontend doesn't currently support widgets.\n",
       "</p>\n"
      ],
      "text/plain": [
       "interactive(children=(IntSlider(value=0, description=u'atime', max=4), Output()), _dom_classes=('widget-interact',))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot(atime):\n",
    "    (ds.ssh.isel(Time=atime).groupby('yCell').mean()*10).plot()\n",
    "    bd = ds.bottomDepth.groupby('yCell').mean()\n",
    "    top = bd.values\n",
    "    bottom = np.ones_like(bd.yCell.values)*4000.0\n",
    "    plt.fill_between(bd.yCell.values, bottom, top,\n",
    "                     where=bottom > top, facecolor='orange', interpolate=True)\n",
    "    #plt.legend()\n",
    "    plt.xlim(0,150000)\n",
    "    plt.ylim(-100,2100)\n",
    "    plt.ylabel('Depth (m)')\n",
    "    plt.xlabel('x')\n",
    "    plt.gca().invert_yaxis()\n",
    "interact(plot, atime=widgets.IntSlider(min=0,max=len(ds.Time)-1,step=1,value=0));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}