drainableWater.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xarray as xr\n",
    "import glob,os\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from jupyterthemes import jtplot\n",
    "\n",
    "jtplot.style(theme='grade3')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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407O0b9+2KvsRERGptAp/zszjdtO0aZOq7EVEROSsVDjMOnS8hc/Wf16VvYiI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\nFYdNoPf0LjcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fff2364cf98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = xr.open_dataset('/glade/p/work/manab/SHARP/PNW_3L/output/test/PNW3L3H_2015-11-01-00_spinup_G00001-00002_1.nc').set_index(hru = 'hruId')\n",
    "df['drainableWater'].plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}