{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dfs1\n",
    "\n",
    "Dfs1 has in addition to the dfs0 file a single spatial dimension.\n",
    "\n",
    "This spatial dimension has information on the grid spacing, but do not contain enough metadata to determine their geographical position, but have a relative distance from the origo.\n",
    "\n",
    "See [Dfs1 in MIKE IO Documentation](https://dhi.github.io/mikeio/user-guide/dfs1.html)\n",
    "\n",
    "See [DFS - Reference manual](https://docs.mikepoweredbydhi.com/core_libraries/dfs/dfs-file-system/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import mikeio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds = mikeio.read(\"data/waterlevel_north.dfs1\")\n",
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds.geometry"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This dfs1 file contains only two nodes (`nx=2`) and has a grid spacing of 8800 m."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dataset has a single item `North WL` which we can access in three different ways.\n",
    "\n",
    "1. As an attribute `ds.North_WL`\n",
    "2. As a key in a dictionary `ds[\"North WL\"]`\n",
    "2. By position `ds[0]`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "da = ds.North_WL\n",
    "da"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dataarray variable `da` contains all the data for the `North WL` item from the dfs1 file."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualization\n",
    "\n",
    "The data can be visualized with the `.plot()` method.\n",
    "\n",
    "The default plot show time on the vertical axis and space on the horizontal axis since dfs1 are often used to represent horizontal variations along an open boundary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "da.plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fort this example, with only a few nodes on the spatial axis, it makes more sense to visualize the data as individual lines in a timeseries plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "da.plot.timeseries();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Inline exercise**\n",
    "\n",
    "*Are there other possible visualizations?*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Spatial and temporal selection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Spatial subsetting can be done in two ways:\n",
    "1. positional index using `.isel()` x=0..(nx-1) (or with negative indexing from the end)\n",
    "2. distance along the axis using `sel()` x=0..8800 (pick closest node)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "da.isel(x=0).plot(title=\"First\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "da.isel(x=-1).plot(title=\"Last\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "da.sel(x=8800).plot(title=\"Last (x=8800)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculated items\n",
    "\n",
    "Creating a new dataarray in the dataset based on an existing dataarray is easy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds[\"North WL calibrated\"] = da + 0.1 # add 0.1 m to the original data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Export to dfs0\n",
    "\n",
    "After subsetting into a single spatial point the result no longer has any spatial dimension and has become a simple timeseries which can be saved as a dfs0 file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds.isel(x=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds.isel(x=0).geometry"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds.isel(x=0).to_dfs(\"random_0.dfs0\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mikeio.read(\"random_0.dfs0\").plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Inline exercise**\n",
    "\n",
    "*Export the timeseries in the last spatial element as a csv file*\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import utils\n",
    "\n",
    "utils.sysinfo()"
   ]
  }
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