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    "\n",
    "Langmuir probe data analysis\n",
    "============================\n",
    "\n",
    "Let's analyze a few Langmuir probe characteristics using the\n",
    "`diagnostics.langmuir` subpackage. First we need to import the module and some\n",
    "basics.\n"
   ]
  },
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   "source": [
    "%matplotlib inline\n",
    "\n",
    "import astropy.units as u\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import os\n",
    "\n",
    "from pathlib import Path\n",
    "from pprint import pprint\n",
    "\n",
    "from plasmapy.diagnostics.langmuir import Characteristic, swept_probe_analysis"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
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   "source": [
    "The first characteristic we analyze is a simple single-probe measurement in\n",
    "a low (ion) temperature, low density plasma with a cylindrical probe. This\n",
    "allows us to utilize OML theory implemented in :func:`~plasmapy.diagnostics.langmuir.swept_probe_analysis`.\n",
    "The data has been preprocessed with some smoothing, which allows us to obtain\n",
    "a Electron Energy Distribution Function (EEDF) as well."
   ]
  },
  {
   "cell_type": "code",
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   "source": [
    "# Load the bias and current values stored in the .p pickle file.\n",
    "path = (Path.cwd() / \"..\" / \"langmuir_samples\" / \"Beckers2017.npy\").resolve()\n",
    "bias, current = np.load(path)\n",
    "\n",
    "# Create the Characteristic object, taking into account the correct units\n",
    "characteristic = Characteristic(u.Quantity(bias, u.V), u.Quantity(current, u.A))\n",
    "\n",
    "# Calculate the cylindrical probe surface area\n",
    "probe_length = 1.145 * u.mm\n",
    "probe_diameter = 1.57 * u.mm\n",
    "probe_area = probe_length * np.pi * probe_diameter + np.pi * 0.25 * probe_diameter ** 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can actually perform the analysis. Since the plasma is in Helium an\n",
    "ion mass number of 4 is entered. The results are visualized and the obtained\n",
    "EEDF is also shown.\n",
    "\n"
   ]
  },
  {
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   "execution_count": null,
   "metadata": {
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   "outputs": [],
   "source": [
    "pprint(\n",
    "    swept_probe_analysis(\n",
    "        characteristic, probe_area, \"He-4+\", visualize=True, plot_EEDF=True\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The cyan and yellow lines indicate the fitted electron and ion currents,\n",
    "respectively. The green line is the sum of these and agrees nicely with the\n",
    "data. This indicates a successful analysis.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The next sample probe data is provided by David Pace. It is also obtained\n",
    "from a low relatively ion temperature and density plasma, in Argon.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
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   "source": [
    "# Load the data from a file and create the Characteristic object\n",
    "path = (Path.cwd() / \"..\" / \"langmuir_samples\" / \"Pace2015.npy\").resolve()\n",
    "bias, current = np.load(path)\n",
    "characteristic = Characteristic(u.Quantity(bias, u.V), u.Quantity(current, u.A))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Initially the electrons are assumed to be Maxwellian. To check this the fit\n",
    "of the electron growth region will be plotted.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
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   "metadata": {
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   "source": [
    "swept_probe_analysis(\n",
    "    characteristic,\n",
    "    0.738 * u.cm ** 2,\n",
    "    \"Ar-40 1+\",\n",
    "    bimaxwellian=False,\n",
    "    plot_electron_fit=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It can be seen that this plasma is slightly bi-Maxwellian, as there are two\n",
    "distinct slopes in the exponential section. The analysis is now performed\n",
    "with bimaxwellian set to True, which yields improved results.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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   },
   "outputs": [],
   "source": [
    "pprint(\n",
    "    swept_probe_analysis(\n",
    "        characteristic,\n",
    "        0.738 * u.cm ** 2,\n",
    "        \"Ar-40 1+\",\n",
    "        bimaxwellian=True,\n",
    "        visualize=True,\n",
    "        plot_electron_fit=True,\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The probe current resolution of the raw data is relatively poor, but the\n",
    "analysis still performs well in the ion current region. The bi-Maxwellian\n",
    "properties are not significant but do make a difference. Check this analysis\n",
    "without setting `bimaxwellian` to True!\n",
    "This is reflected in the results, which indicate that the temperatures of\n",
    "the cold and hot electron population are indeed different, but relatively\n",
    "close.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This Helium plasma is fully bi-Maxwellian.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "# Import probe data and calculate probe surface area.\n",
    "path = (Path.cwd() / \"..\" / \"langmuir_samples\" / \"Beckers2017b.npy\").resolve()\n",
    "bias, current = np.load(path)\n",
    "characteristic = Characteristic(u.Quantity(bias, u.V), u.Quantity(current, u.A))\n",
    "probe_length = 1.145 * u.mm\n",
    "probe_diameter = 1.57 * u.mm\n",
    "probe_area = probe_length * np.pi * probe_diameter + np.pi * 0.25 * probe_diameter ** 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`plot_electron_fit` is set to True to check the bi-Maxwellian properties.\n",
    "The fit converges nicely to the two slopes of the electron growth region.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "pprint(\n",
    "    swept_probe_analysis(\n",
    "        characteristic,\n",
    "        probe_area,\n",
    "        \"He-4+\",\n",
    "        bimaxwellian=True,\n",
    "        plot_electron_fit=True,\n",
    "        visualize=True,\n",
    "    )\n",
    ")"
   ]
  }
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