Add Ocean.ipynb

{
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The idea is to create an empty array on the whole grid and then merge with the land-only array. This creates a dataset with the 2 arrays \"expanded\" with NaNs on the additional points."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xarray as xr\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds = xr.open_dataset(\"/g/data/w35/lt0205/research/lpj_guess/runs/CRUNCEP/agpp_LPJ-GUESS_1901-2015.nc\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Longitude and Latitude resolution\n",
    "dx = ds.Lon - ds.Lon.shift(shifts={'Lon':1})\n",
    "dx = dx.min()\n",
    "dy = ds.Lat - ds.Lat.shift(shifts={'Lat':1})\n",
    "dy = dy.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "newlon = np.arange((-180.+dx/2.),180.,dx)\n",
    "newlon = xr.DataArray(newlon, dims=(\"Lon\"),coords={\"Lon\":newlon},\n",
    "                      attrs=ds.Lon.attrs)\n",
    "newlon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "newlat = np.arange((-90.+dy/2.),90., dy)\n",
    "newlat = xr.DataArray(newlat, dims=(\"Lat\"), coords={\"Lat\":newlat}, \n",
    "                      attrs=ds.Lat.attrs)\n",
    "newlat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "foo = xr.DataArray(np.empty((newlat.size, newlon.size)),\n",
    "                   dims=(\"Lat\", \"Lon\"),\n",
    "                   coords={\"Lat\":newlat, \"Lon\":newlon},\n",
    "                   name=\"foo\")\n",
    "foo[:]=np.NaN\n",
    "foo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds2 = ds.broadcast_like(foo)\n",
    "ds2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds2.Total.sel(Time=\"1910\").plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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