{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7a7c94b6",
   "metadata": {},
   "source": [
    "# Touchstone files\n",
    "\n",
    "This example shows how to use objects in a Touchstone file without opening AEDT.\n",
    "\n",
    "To provide the advanced postprocessing features needed for this example, Matplotlib and NumPy\n",
    "must be installed on your machine.\n",
    "\n",
    "This example runs only on Windows using CPython.\n",
    "\n",
    "Keywords: **Touchstone**, **report**."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95acf1d0",
   "metadata": {},
   "source": [
    "## Perform imports\n",
    "Import the required packages."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "38cedcf8",
   "metadata": {},
   "outputs": [],
   "source": [
    "from ansys.aedt.core import downloads\n",
    "from ansys.aedt.core.visualization.advanced.touchstone_parser import \\\n",
    "    read_touchstone"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62d88325",
   "metadata": {},
   "source": [
    "## Download example data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d5d7610f",
   "metadata": {},
   "outputs": [],
   "source": [
    "example_path = downloads.download_touchstone()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bada8e5a",
   "metadata": {},
   "source": [
    "## Read the Touchstone file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c90255f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = read_touchstone(example_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9e5e8f3",
   "metadata": {},
   "source": [
    "## Plot data\n",
    "\n",
    "Get the curve plot by category. The following code shows how to plot lists of the return losses,\n",
    "insertion losses, next, and fext based on a few inputs and port names."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a3c5845f",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.plot_return_losses()\n",
    "data.plot_insertion_losses()\n",
    "data.plot_next_xtalk_losses(\"U1\")\n",
    "data.plot_fext_xtalk_losses(tx_prefix=\"U1\", rx_prefix=\"U7\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f81a8cbb",
   "metadata": {},
   "source": [
    "## Identify cross-talk\n",
    "\n",
    "Identify the worst case cross-talk."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9114026e",
   "metadata": {},
   "outputs": [],
   "source": [
    "worst_rl, global_mean = data.get_worst_curve(\n",
    "    freq_min=1,\n",
    "    freq_max=20,\n",
    "    worst_is_higher=True,\n",
    "    curve_list=data.get_return_loss_index(),\n",
    ")\n",
    "worst_il, mean2 = data.get_worst_curve(\n",
    "    freq_min=1,\n",
    "    freq_max=20,\n",
    "    worst_is_higher=False,\n",
    "    curve_list=data.get_insertion_loss_index(),\n",
    ")\n",
    "worst_fext, mean3 = data.get_worst_curve(\n",
    "    freq_min=1,\n",
    "    freq_max=20,\n",
    "    worst_is_higher=True,\n",
    "    curve_list=data.get_fext_xtalk_index_from_prefix(tx_prefix=\"U1\", rx_prefix=\"U7\"),\n",
    ")\n",
    "worst_next, mean4 = data.get_worst_curve(\n",
    "    freq_min=1,\n",
    "    freq_max=20,\n",
    "    worst_is_higher=True,\n",
    "    curve_list=data.get_next_xtalk_index(\"U1\"),\n",
    ")"
   ]
  }
 ],
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