{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_33881/2491384496.py:20: MatplotlibDeprecationWarning: Auto-removal of overlapping axes is deprecated since 3.6 and will be removed two minor releases later; explicitly call ax.remove() as needed.\n",
      "  sbp1=subplot(511)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from matplotlib.pyplot import *\n",
    "from pylab import *\n",
    "import matplotlib.image as mpimg\n",
    "#from urllib  import urlopen #python 2.7 \n",
    "from urllib.request  import urlopen #python 3.0 \n",
    "\n",
    "ShotNo = 39187\n",
    "\n",
    "\n",
    "#Create a path to data\n",
    "baseURL = \"http://golem.fjfi.cvut.cz/shots/\" #remote access\n",
    "#baseURL = \"file:///golem/database/operation/shots/\" #local access\n",
    "#dataURL = urlopen(baseURL+ str(ShotNo) + '/' + diagnSPEC + '.npz')\n",
    "\n",
    "\n",
    "fig=figure(1);subplots_adjust(hspace=0.001)\n",
    "xticks(arange(10, 15, 2));xlim(0,20)\n",
    "\n",
    "sbp1=subplot(511)\n",
    "dataURL = urlopen(baseURL+ str(ShotNo) + '/Diagnostics/BasicDiagnostics/U_Loop.csv')\n",
    "obj1=np.loadtxt(dataURL, delimiter=',')\n",
    "ylim(0,20);yticks(arange(0, 30, 5))\n",
    "title('#'+str(ShotNo));ylabel('$U_l$ [V]')\n",
    "setp(xticklabels, visible=False)\n",
    "plt.plot(obj1[:,0]*1000, obj1[:,1], 'k-',label='Loop voltage $U_l$' );legend(loc=0)\n",
    "\n",
    "sbp1=subplot(512, sharex=sbp1)\n",
    "dataURL = urlopen(baseURL+ str(ShotNo) + '/Diagnostics/BasicDiagnostics/U_IntBtCoil.csv')\n",
    "obj1=np.loadtxt(dataURL, delimiter=',')\n",
    "yticks(arange(0, 0.6 , 0.2));ylim(0,0.6)\n",
    "ylabel('$B_t$ [T]')\n",
    "setp(xticklabels, visible=False)\n",
    "plt.plot(obj1[:,0]*1000, obj1[:,1], 'k-',label='Toroidal mag. field $B_t$');legend(loc=0)\n",
    "\n",
    "sbp1=subplot(513, sharex=sbp1)\n",
    "dataURL = urlopen(baseURL+ str(ShotNo) + '/Diagnostics/BasicDiagnostics/U_IntRogCoil.csv')\n",
    "obj1=np.loadtxt(dataURL, delimiter=',')\n",
    "yticks(arange(0, 4.5, 1));ylim(0,4.5)\n",
    "ylabel('$I_p$ [kA]')\n",
    "setp(xticklabels, visible=False)\n",
    "plt.plot(obj1[:,0]*1000, obj1[:,1]/1000, 'k-',label='Plasma current $I_p$');legend(loc=0)\n",
    "\n",
    "sbp1=subplot(514, sharex=sbp1)\n",
    "dataURL = urlopen(baseURL+ str(ShotNo) + '/Diagnostics/BasicDiagnostics/U_LeybPhot.csv')\n",
    "obj1=np.loadtxt(dataURL, delimiter=',')\n",
    "yticks(arange(0, 0.14 , 0.03));ylim(0,0.14)\n",
    "ylabel('Intensity [a.u.]')\n",
    "setp(xticklabels, visible=False)\n",
    "plt.plot(obj1[:,0]*1000, obj1[:,1], 'k-',label='Whole spectrum radiation');legend(loc=0)\n",
    "\n",
    "sbp1=subplot(515, sharex=sbp1)\n",
    "dataURL = urlopen(baseURL+ str(ShotNo) + '/Diagnostics/Interferometry/ne_lav.csv')\n",
    "obj2=np.loadtxt(dataURL, delimiter=',')\n",
    "yticks(arange(0, 0.8 , 0.2));ylim(0,0.8)\n",
    "ylabel('$n_e$')\n",
    "xticks(arange(0, 30, 5));xlim(0,20)\n",
    "xlabel('Time [ms]')\n",
    "plt.plot(obj2[:,0], obj2[:,1]*1e-19, 'k-',label='electron density $n_e$');legend(loc=0)\n",
    "\n",
    "xticklabels = sbp1.get_xticklabels()\n",
    "setp(xticklabels, visible=True)\n",
    "\n",
    "savefig('basicgraph.pdf')\n",
    "savefig('basicgraph.jpg')\n",
    "\n",
    "show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
