05-Kinetic profiles.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Kinetic profiles\n",
    "\n",
    "## Electron Density\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "| Diagnostic | Shotfile | Signal | Area-Base space |\n",
    "|---|---|---|---|\n",
    "| Thomson Scattering, core | VTA | Ne_c | R,z |\n",
    "| Thomson Scattering, edge | VTA | Ne_e | R,z |\n",
    "| Lithium Beam | LIN | ne | $\\rho_{pol}$ |\n",
    "| IDA | IDA | ne | $\\rho_{pol}$ |\n",
    "| O-mode Reflectometer, LFS | RPS | neb_LFS | Real space, antenna LOS, Z=0.14m |\n",
    "| O-mode Reflectometer, HFS | RPS | neb_HFS | Real space, antenna LOS, Z=0.07m |\n",
    "| ICRH embedded X-mode Ref., above midplane | RIC | Ne_Ant1 | $\\rho_{pol}$ |\n",
    "| ICRH embedded X-mode Ref., midplane | RIC | Ne_Ant4 | $\\rho_{pol}$ |\n",
    "| ICRH embedded X-mode Ref., below midplane | RIC | Ne_Ant8 | $\\rho_{pol}$ |\n",
    "\n",
    "\n",
    "Don't use the reflectometers if you're a weak individual. As an example we'll use only the diagnostics that feeble-minded people can handle so nobody gets ill while using this notebook, Thomson Scattering (TS), Lithium beam, and IDA. Since TS is mapped in (R,z), a special function is included to map data to $\\rho_{pol}$. Also, note that the edge and core TS systems have diferent time bases."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from ipfnpytools.get_vta import get_vta\n",
    "from ipfnpytools.getsig import getsig\n",
    "from ipfnpytools.closest import closest\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('./Styles/darklab.mplstyle')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/afs/ipp/aug/ads-diags/common/python/lib/dd.py:118: RuntimeWarning: Warning by DDcsgnl (8.1): no calibration of data\n",
      "  warnings.warn(err_txt, RuntimeWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": []
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": []
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": []
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": []
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": []
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": []
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": []
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": []
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": []
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": []
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "trz_to_rhop:1001/1001\n",
      "trz_to_rhop:801/801\n"
     ]
    }
   ],
   "source": [
    "shotnr = 35647\n",
    "time = 2.5\n",
    "lin = getsig(shotnr, 'LIN', 'ne')\n",
    "ida = getsig(shotnr, 'IDA', 'ne')\n",
    "vta = get_vta(shotnr, verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "linind = closest(lin.time, time)\n",
    "idaind = closest(ida.time, time)\n",
    "necind = closest(vta.time_c, time)\n",
    "neeind = closest(vta.time_e, time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc671ff4ad0>"
      ]
     },
     "metadata": {
      "needs_background": "dark"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "multiplier = 1e-19 #Data multiplier\n",
    "plt.plot(lin.area.data[linind,:], lin.data[linind,:]*multiplier, 'C2', label='LIN')\n",
    "plt.plot(ida.area.data[idaind,:], ida.data[idaind,:]*multiplier, 'w', label='IDA')\n",
    "plt.plot(vta.rho_c[necind, :], vta.ne_c[necind, :]*multiplier, 'C1o', label=r'TS $\\mathrm{n_{e,core}}$')\n",
    "plt.plot(vta.rho_e[neeind, :], vta.ne_e[neeind, :]*multiplier, 'C4o', label=r'TS $\\mathrm{n_{e,edge}}$')\n",
    "plt.legend(loc='lower left')\n",
    "plt.ylim(bottom=0)\n",
    "plt.xlabel(r'$\\mathrm{\\rho_{pol}}$')\n",
    "plt.ylabel(r'$\\mathrm{n_{e}\\,[10^{19}\\,m^{-3}]}$')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Electron Temperature "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Carrega, Gil!"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}