{
 "metadata": {
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3-final"
  },
  "orig_nbformat": 2,
  "kernelspec": {
   "name": "python_defaultSpec_1600068448644",
   "display_name": "Python 3.8.3 64-bit ('base': conda)"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2,
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# pip install mikeio xarray netcdf4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "<xarray.core.options.set_options at 0x1be03c23850>"
     },
     "metadata": {},
     "execution_count": 30
    }
   ],
   "source": [
    "import xarray as xr\n",
    "import numpy as np\n",
    "\n",
    "# remove this line if you like more advanced display of xarray datasets\n",
    "xr.set_options(display_style=\"text\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "from mikeio import Dfs2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfs = Dfs2(\"../tests/testdata/gebco_sound.dfs2\")\n",
    "ds = dfs.read()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "nx = ds[0].shape[2]\n",
    "ny = ds[0].shape[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "x0 = dfs.longitude\n",
    "y0 = dfs.latitude\n",
    "x = [x0 + dfs.dx*i for i in range(nx)]\n",
    "y = [y0 + dfs.dy*i for i in range(ny)]\n",
    "\n",
    "# Either flip the y coordinates, or flip the data\n",
    "y = list(reversed(y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "res = {}\n",
    "\n",
    "spdims = [\"lat\", \"lon\"]\n",
    "\n",
    "if len(ds.time) > 1:\n",
    "    dims = [\"t\"] + spdims\n",
    "    coords = {\"t\": ds.time}\n",
    "else:\n",
    "    dims = spdims\n",
    "    coords = {}\n",
    "    \n",
    "coords[\"lon\"] = xr.DataArray(x, dims=\"lon\", attrs={\"standard_name\" : \"longitude\", \"units\" : \"degrees_east\"})\n",
    "coords[\"lat\"] = xr.DataArray(y, dims=\"lat\", attrs={\"standard_name\" : \"latitude\", \"units\" : \"degrees_north\"})\n",
    "\n",
    "for item in ds.items:\n",
    "    v = item.name\n",
    "    res[v] = xr.DataArray(np.squeeze(ds[v]), dims=dims, \n",
    "    attrs={'name': v,\n",
    "            # TODO add standard name from https://cfconventions.org/standard-names.html\n",
    "            'units': item.unit.name,\n",
    "            'eumType' : item.type,\n",
    "            'eumUnit' : item.unit})        \n",
    "\n",
    "xr_ds = xr.Dataset(res, coords=coords)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "<xarray.Dataset>\nDimensions:    (lat: 264, lon: 216)\nCoordinates:\n  * lon        (lon) float64 12.2 12.21 12.21 12.21 ... 13.09 13.09 13.09 13.1\n  * lat        (lat) float64 56.3 56.29 56.29 56.29 ... 55.21 55.21 55.21 55.2\nData variables:\n    Elevation  (lat, lon) float64 -31.0 -30.0 -31.0 -31.0 ... -37.0 -38.0 -38.0",
      "text/html": "<pre>&lt;xarray.Dataset&gt;\nDimensions:    (lat: 264, lon: 216)\nCoordinates:\n  * lon        (lon) float64 12.2 12.21 12.21 12.21 ... 13.09 13.09 13.09 13.1\n  * lat        (lat) float64 56.3 56.29 56.29 56.29 ... 55.21 55.21 55.21 55.2\nData variables:\n    Elevation  (lat, lon) float64 -31.0 -30.0 -31.0 -31.0 ... -37.0 -38.0 -38.0</pre>"
     },
     "metadata": {},
     "execution_count": 52
    }
   ],
   "source": [
    "xr_ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "xr_ds.to_netcdf(\"gebco.nc\")"
   ]
  },
  {
   "source": [
    "# Clean up"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "os.remove(\"gebco.nc\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}