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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analysing traces\n",
    "\n",
    "Often the first step after running the simulation is to ensure that the simulation is converged.\n",
    "\n",
    "xemc3 can be used to do this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xarray as xr\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import xemc3\n",
    "import glob\n",
    "\n",
    "# Matplotlib setup\n",
    "import setup_plt\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use local helper function to get some data\n",
    "from get_data import load_example_data\n",
    "\n",
    "path = load_example_data(get_path=True)\n",
    "# If you want to use your own data use something like\n",
    "# path = \"path/to/mydata/\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reading some files\n",
    "\n",
    "The fastest way is to load just a single iteration trace `*_INFO` file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds = xemc3.load.file(path + \"/ENERGY_INFO\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "nbsphinx-thumbnail"
    ]
   },
   "outputs": [],
   "source": [
    "plt.figure()\n",
    "ds[\"Te_upstream\"].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "However in most cases we are only interrested in the last points of the INFO file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure()\n",
    "ds.Te_upstream[-50:].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reading all INFO files\n",
    "\n",
    "Besides using `xemc3.load.all(path)` it is also simple to read just the `*_INFO` files:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds = xr.Dataset()\n",
    "for file in glob.iglob(f\"{path}/*_INFO\"):\n",
    "    ds = xemc3.load.file(file, ds)\n",
    "ds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again, the different traces can be plotted, to get an estimate of whether the simulation is converged:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure()\n",
    "ds.dens_change[-60:].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Checking how much data\n",
    "\n",
    "As numpy arrays need to be blocks, xemc3 uses nan-padding. `np.isfinite` can be used to check how much data we have"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.isfinite(ds).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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