Examples/Physics_applications/pulsar/analysis_pulsar_viz.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Overview\n",
    "\n",
    "This a notebook that inspects the results of a WarpX simulation.\n",
    "\n",
    "# Instruction\n",
    "\n",
    "Enter the path of the data you wish to visualize below. Then execute the cells one by one, by selecting them with your mouse and typing `Shift + Enter`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import statements\n",
    "import yt ; yt.funcs.mylog.setLevel(50)\n",
    "import numpy as np\n",
    "import scipy.constants as scc\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib notebook"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read data in the simulation frame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot data with yt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('all', 'particle_cpu'),\n",
       " ('all', 'particle_id'),\n",
       " ('all', 'particle_momentum_x'),\n",
       " ('all', 'particle_momentum_y'),\n",
       " ('all', 'particle_momentum_z'),\n",
       " ('all', 'particle_position_x'),\n",
       " ('all', 'particle_position_y'),\n",
       " ('all', 'particle_position_z'),\n",
       " ('all', 'particle_weight'),\n",
       " ('boxlib', 'Bx'),\n",
       " ('boxlib', 'By'),\n",
       " ('boxlib', 'Bz'),\n",
       " ('boxlib', 'Ex'),\n",
       " ('boxlib', 'Ey'),\n",
       " ('boxlib', 'Ez'),\n",
       " ('boxlib', 'divE'),\n",
       " ('boxlib', 'jx'),\n",
       " ('boxlib', 'jy'),\n",
       " ('boxlib', 'jz'),\n",
       " ('boxlib', 'part_per_cell'),\n",
       " ('boxlib', 'rho'),\n",
       " ('plasma_e', 'particle_cpu'),\n",
       " ('plasma_e', 'particle_id'),\n",
       " ('plasma_e', 'particle_momentum_x'),\n",
       " ('plasma_e', 'particle_momentum_y'),\n",
       " ('plasma_e', 'particle_momentum_z'),\n",
       " ('plasma_e', 'particle_position_x'),\n",
       " ('plasma_e', 'particle_position_y'),\n",
       " ('plasma_e', 'particle_position_z'),\n",
       " ('plasma_e', 'particle_weight'),\n",
       " ('plasma_p', 'particle_cpu'),\n",
       " ('plasma_p', 'particle_id'),\n",
       " ('plasma_p', 'particle_momentum_x'),\n",
       " ('plasma_p', 'particle_momentum_y'),\n",
       " ('plasma_p', 'particle_momentum_z'),\n",
       " ('plasma_p', 'particle_position_x'),\n",
       " ('plasma_p', 'particle_position_y'),\n",
       " ('plasma_p', 'particle_position_z'),\n",
       " ('plasma_p', 'particle_weight')]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "ds = yt.load( './diags/plt00010/' ) # Create a dataset object\n",
    "filenum = 90\n",
    "ds.field_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/dist-packages/yt-3.7.dev0-py3.7-linux-x86_64.egg/yt/visualization/base_plot_types.py:222: MatplotlibDeprecationWarning: default base may change from np.e to 10.  To suppress this warning specify the base keyword argument.\n",
      "  vmax=float(np.nanmax(data)))\n",
      "/usr/local/lib/python3.7/dist-packages/yt-3.7.dev0-py3.7-linux-x86_64.egg/yt/units/yt_array.py:1417: RuntimeWarning: invalid value encountered in true_divide\n",
      "  out=out, **kwargs)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<img style=\"max-width:100%%;max-height:100%%;\" src=\"data:image/png;base64,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\"><br>"
      ],
      "text/plain": [
       "<yt.visualization.plot_window.AxisAlignedSlicePlot at 0x7f3577f67750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "sl = yt.SlicePlot(ds, 0, 'Ey', aspect=1) # Create a sliceplot object\n",
    "#sl.annotate_particles(width=(2.e3, 'm'),ptype=\"plasma_p\",col='b',p_size=5.0)\n",
    "#sl.annotate_particles(width=(2.e3, 'm'),ptype=\"plasma_e\",col='r',p_size=5.0)\n",
    "sl.annotate_streamlines(\"Ey\", \"Ez\", plot_args={\"color\": \"black\"})\n",
    "sl.annotate_grids() # Show grids\n",
    "sl.annotate_sphere([90000.0, 90000.0, 90000.0], radius=12000.0,\n",
    "                  circle_args={'color':'white', 'linewidth':2})\n",
    "sl.show() # Show the plot\n",
    "#sl.save('./particle_in_ring_plt'+str(filenum)+'.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "ename": "YTFieldNotFound",
     "evalue": "Could not find field '('plasma_p', 'particle_position_x')' in .",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m-----------------------------------------------\u001b[0m",
      "\u001b[0;31mYTFieldNotFound\u001b[0mTraceback (most recent call last)",
      "\u001b[0;32m<ipython-input-26-270f574f7011>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0myt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mParticlePlot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mds\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'plasma_p'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'particle_position_x'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'plasma_p'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'particle_position_y'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'plasma_p'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'particle_momentum_x'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"positron_plt\"\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilenum\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m\".png\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/yt-3.7.dev0-py3.7-linux-x86_64.egg/yt/visualization/particle_plots.py\u001b[0m in \u001b[0;36mParticlePlot\u001b[0;34m(ds, x_field, y_field, z_fields, color, *args, **kwargs)\u001b[0m\n\u001b[1;32m    509\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mdd\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    510\u001b[0m         \u001b[0mdd\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mall_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 511\u001b[0;31m     \u001b[0mx_field\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_determine_fields\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_field\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    512\u001b[0m     \u001b[0my_field\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_determine_fields\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_field\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    513\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/yt-3.7.dev0-py3.7-linux-x86_64.egg/yt/data_objects/data_containers.py\u001b[0m in \u001b[0;36m_determine_fields\u001b[0;34m(self, fields)\u001b[0m\n\u001b[1;32m   1359\u001b[0m                     \u001b[0;32mraise\u001b[0m \u001b[0mYTFieldNotParseable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfield\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1360\u001b[0m                 \u001b[0mftype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfname\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfield\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1361\u001b[0;31m                 \u001b[0mfinfo\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_field_info\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mftype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1362\u001b[0m             \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfield\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mDerivedField\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1363\u001b[0m                 \u001b[0mftype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfname\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfield\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/yt-3.7.dev0-py3.7-linux-x86_64.egg/yt/data_objects/static_output.py\u001b[0m in \u001b[0;36m_get_field_info\u001b[0;34m(self, ftype, fname)\u001b[0m\n\u001b[1;32m    737\u001b[0m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_last_finfo\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfield_info\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mftype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    738\u001b[0m                     \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_last_finfo\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 739\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mYTFieldNotFound\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mftype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    740\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    741\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_setup_classes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mYTFieldNotFound\u001b[0m: Could not find field '('plasma_p', 'particle_position_x')' in ."
     ]
    }
   ],
   "source": [
    "p = yt.ParticlePlot(ds,('plasma_p', 'particle_position_x'),('plasma_p', 'particle_position_y'),('plasma_p', 'particle_momentum_x'))\n",
    "p.show()\n",
    "p.save(\"positron_plt\"+str(filenum)+\".png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img style=\"max-width:100%%;max-height:100%%;\" src=\"data:image/png;base64,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\"><br>"
      ],
      "text/plain": [
       "<yt.visualization.particle_plots.ParticleProjectionPlot at 0x7f35755eb4d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "['electrons_plt90.png']"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p = yt.ParticlePlot(ds,('plasma_e', 'particle_position_y'),('plasma_e', 'particle_position_z'),('plasma_e', 'particle_momentum_z'))\n",
    "#p = yt.ParticlePlot(ds,('plasma_p', 'particle_position_y'),('plasma_p', 'particle_position_z'),('plasma_p', 'particle_momentum_x'))\n",
    "p.show()\n",
    "p.save(\"electrons_plt\"+str(filenum)+\".png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from yt.units.yt_array import YTQuantity\n",
    "from yt.units import kboltz\n",
    "from numpy.random import random\n",
    "import numpy as np\n",
    "from yt.utilities.exceptions import YTUnitOperationError\n",
    "def r_cl(field,data):\n",
    "    xc = YTQuantity(90000, 'm')\n",
    "    yc = YTQuantity(90000, 'm')\n",
    "    zc = YTQuantity(90000, 'm')\n",
    "\n",
    "    r = ((data[\"y\"]-yc)*(data[\"y\"]-yc) + (data[\"x\"]-xc)*(data[\"x\"]-xc))**0.5\n",
    "\n",
    "    return r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds.add_field((\"index\",\"r_cl\"),function=r_cl,units='m',sampling_type=\"cell\")\n",
    "#yt.SlicePlot(ds,'z','r_cl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from yt.units.yt_array import YTQuantity\n",
    "from yt.units import kboltz\n",
    "from numpy.random import random\n",
    "import numpy as np\n",
    "from yt.utilities.exceptions import YTUnitOperationError\n",
    "def rad(field,data):\n",
    "    xc = YTQuantity(90000, 'm')\n",
    "    yc = YTQuantity(90000, 'm')\n",
    "    zc = YTQuantity(90000, 'm')\n",
    "\n",
    "    r = ((data[\"y\"]-yc)*(data[\"y\"]-yc) + (data[\"x\"]-xc)*(data[\"x\"]-xc) + (data[\"z\"]-zc)*(data[\"z\"]-zc))**0.5\n",
    "\n",
    "    return r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds.add_field((\"index\",\"rad\"),function=rad,units='m',sampling_type=\"cell\")\n",
    "#yt.SlicePlot(ds,'z','rad')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Store quantities in numpy arrays, and plot with matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from yt.units.yt_array import YTQuantity\n",
    "from yt.units import kboltz\n",
    "from numpy.random import random\n",
    "import numpy as np\n",
    "from yt.utilities.exceptions import YTUnitOperationError\n",
    "def Ex_external(field,data):\n",
    "    xc = YTQuantity(90000, 'm')\n",
    "    yc = YTQuantity(90000, 'm')\n",
    "    zc = YTQuantity(90000, 'm')\n",
    "    r_star = YTQuantity(12000, 'm')\n",
    "    omega = 6245.8\n",
    "    B = 8.03e-6\n",
    "    r = ((data[\"y\"]-yc)*(data[\"y\"]-yc) + (data[\"x\"]-xc)*(data[\"x\"]-xc) + (data[\"z\"]-zc)*(data[\"z\"]-zc))**0.5\n",
    "    c_theta = (data[\"z\"]-zc)/r\n",
    "    s_theta = data[\"r_cl\"]/r\n",
    "    c_phi   = (data[\"x\"]-xc)/data[\"r_cl\"]\n",
    "    s_phi   = (data[\"y\"]-yc)/data[\"r_cl\"]\n",
    "    r_ratio = r_star/r\n",
    "    r3 = r_ratio*r_ratio*r_ratio\n",
    "    Er = omega*r_star*B*r3*r_ratio*(1.0-3.0*c_theta*c_theta) \n",
    "    + (2.0/3.0)*omega*B*r_star*r_ratio*r_ratio\n",
    "    E_theta = (-1)*omega*B*r_star*r3*r_ratio*(c_theta*s_theta*2.0)\n",
    "    Ex_ext = Er*c_phi*s_theta + E_theta*c_phi*c_theta\n",
    "\n",
    "    outside = np.zeros_like(data[\"r_cl\"])\n",
    "    outside[data[\"rad\"] > 12000] = 1.0\n",
    "\n",
    " \n",
    "    return outside.d*Ex_ext.d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from yt.units.yt_array import YTQuantity\n",
    "from yt.units import kboltz\n",
    "from numpy.random import random\n",
    "import numpy as np\n",
    "from yt.utilities.exceptions import YTUnitOperationError\n",
    "def Ex_internal(field,data):\n",
    "    xc = YTQuantity(90000, 'm')\n",
    "    yc = YTQuantity(90000, 'm')\n",
    "    zc = YTQuantity(90000, 'm')\n",
    "    r_star = YTQuantity(12000, 'm')\n",
    "    omega = 6245.8\n",
    "    B = 8.03e-6\n",
    "    r = ((data[\"y\"]-yc)*(data[\"y\"]-yc) + (data[\"x\"]-xc)*(data[\"x\"]-xc) + (data[\"z\"]-zc)*(data[\"z\"]-zc))**0.5\n",
    "    c_theta = (data[\"z\"]-zc)/r\n",
    "    s_theta = data[\"r_cl\"]/r\n",
    "    c_phi   = (data[\"x\"]-xc)/data[\"r_cl\"]\n",
    "    s_phi   = (data[\"y\"]-yc)/data[\"r_cl\"]\n",
    "    r_ratio = r_star/r\n",
    "    r3 = r_ratio*r_ratio*r_ratio\n",
    "    Er = omega*r*r3*B*s_theta*s_theta\n",
    "    E_theta = (-1)*omega*B*r*r3*(c_theta*s_theta*2.0)\n",
    "    Ex_ext = Er*c_phi*s_theta + E_theta*c_phi*c_theta\n",
    "\n",
    "    outside = np.zeros_like(data[\"r_cl\"])\n",
    "    outside[data[\"rad\"] <= 12000] = 1.0\n",
    "\n",
    " \n",
    "    return outside.d*Ex_ext.d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from yt.units.yt_array import YTQuantity\n",
    "from yt.units import kboltz\n",
    "from numpy.random import random\n",
    "import numpy as np\n",
    "from yt.utilities.exceptions import YTUnitOperationError\n",
    "def Ey_external(field,data):\n",
    "    xc = YTQuantity(90000, 'm')\n",
    "    yc = YTQuantity(90000, 'm')\n",
    "    zc = YTQuantity(90000, 'm')\n",
    "    r_star = YTQuantity(12000, 'm')\n",
    "    omega = 6245.8\n",
    "    B = 8.03e-6\n",
    "\n",
    "    r = ((data[\"y\"]-yc)*(data[\"y\"]-yc) + (data[\"x\"]-xc)*(data[\"x\"]-xc) + (data[\"z\"]-zc)*(data[\"z\"]-zc))**0.5\n",
    "    c_theta = (data[\"z\"]-zc)/r\n",
    "    s_theta = data[\"r_cl\"]/r\n",
    "    c_phi   = (data[\"x\"]-xc)/data[\"r_cl\"]\n",
    "    s_phi   = (data[\"y\"]-yc)/data[\"r_cl\"]\n",
    "    r_ratio = r_star/r\n",
    "    r3 = r_ratio*r_ratio*r_ratio\n",
    "    Er = omega*r_star*B*r3*r_ratio*(1.0-3.0*c_theta*c_theta) \n",
    "    + (2.0/3.0)*omega*B*r_star*r_ratio*r_ratio\n",
    "    E_theta = (-1)*omega*B*r_star*r3*r_ratio*(c_theta*s_theta*2.0)\n",
    "    Ey_ext = Er*s_phi*s_theta + E_theta*s_phi*c_theta\n",
    "\n",
    "    outside = np.zeros_like(data[\"r_cl\"])\n",
    "    outside[data[\"rad\"] > 12000] = 1.0\n",
    "\n",
    " \n",
    "    return outside.d*Ey_ext.d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from yt.units.yt_array import YTQuantity\n",
    "from yt.units import kboltz\n",
    "from numpy.random import random\n",
    "import numpy as np\n",
    "from yt.utilities.exceptions import YTUnitOperationError\n",
    "def Ey_internal(field,data):\n",
    "    xc = YTQuantity(90000, 'm')\n",
    "    yc = YTQuantity(90000, 'm')\n",
    "    zc = YTQuantity(90000, 'm')\n",
    "    r_star = YTQuantity(12000, 'm')\n",
    "    omega = 6245.8\n",
    "    B = 8.03e-6\n",
    "    r = ((data[\"y\"]-yc)*(data[\"y\"]-yc) + (data[\"x\"]-xc)*(data[\"x\"]-xc) + (data[\"z\"]-zc)*(data[\"z\"]-zc))**0.5\n",
    "    c_theta = (data[\"z\"]-zc)/r\n",
    "    s_theta = data[\"r_cl\"]/r\n",
    "    c_phi   = (data[\"x\"]-xc)/data[\"r_cl\"]\n",
    "    s_phi   = (data[\"y\"]-yc)/data[\"r_cl\"]\n",
    "    r_ratio = r_star/r\n",
    "    r3 = r_ratio*r_ratio*r_ratio\n",
    "    Er = omega*r*r3*B*s_theta*s_theta\n",
    "    E_theta = (-1)*omega*B*r*r3*(c_theta*s_theta*2.0)\n",
    "    Ey_ext = Er*s_phi*s_theta + E_theta*s_phi*c_theta\n",
    "    outside = np.zeros_like(data[\"r_cl\"])\n",
    "    outside[data[\"rad\"] <= 12000] = 1.0\n",
    "\n",
    " \n",
    "    return outside.d*Ey_ext.d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from yt.units.yt_array import YTQuantity\n",
    "from yt.units import kboltz\n",
    "from numpy.random import random\n",
    "import numpy as np\n",
    "from yt.utilities.exceptions import YTUnitOperationError\n",
    "def Ez_external(field,data):\n",
    "    xc = YTQuantity(90000, 'm')\n",
    "    yc = YTQuantity(90000, 'm')\n",
    "    zc = YTQuantity(90000, 'm')\n",
    "    r_star = YTQuantity(12000, 'm')\n",
    "    omega = 6245.8\n",
    "    B = 8.03e-6\n",
    "    r = ((data[\"y\"]-yc)*(data[\"y\"]-yc) + (data[\"x\"]-xc)*(data[\"x\"]-xc) + (data[\"z\"]-zc)*(data[\"z\"]-zc))**0.5\n",
    "    c_theta = (data[\"z\"]-zc)/r\n",
    "    s_theta = data[\"r_cl\"]/r\n",
    "    c_phi   = (data[\"x\"]-xc)/data[\"r_cl\"]\n",
    "    s_phi   = (data[\"y\"]-yc)/data[\"r_cl\"]\n",
    "    r_ratio = r_star/r\n",
    "    r3 = r_ratio*r_ratio*r_ratio\n",
    "    Er = omega*r_star*B*r3*r_ratio*(1.0-3.0*c_theta*c_theta) \n",
    "    + (2.0/3.0)*omega*B*r_star*r_ratio*r_ratio\n",
    "    E_theta = (-1)*omega*B*r_star*r3*r_ratio*(c_theta*s_theta*2.0)\n",
    "    Ez_ext = Er*c_theta - E_theta*s_theta\n",
    "\n",
    "    outside = np.zeros_like(data[\"r_cl\"])\n",
    "    outside[data[\"rad\"] > 12000] = 1.0\n",
    "\n",
    " \n",
    "    return outside.d*Ez_ext.d \n",
    "    #return outside.d*Er.d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from yt.units.yt_array import YTQuantity\n",
    "from yt.units import kboltz\n",
    "from numpy.random import random\n",
    "import numpy as np\n",
    "from yt.utilities.exceptions import YTUnitOperationError\n",
    "def Ez_internal(field,data):\n",
    "    xc = YTQuantity(90000, 'm')\n",
    "    yc = YTQuantity(90000, 'm')\n",
    "    zc = YTQuantity(90000, 'm')\n",
    "    r_star = YTQuantity(12000, 'm')\n",
    "    omega = 6245.8\n",
    "    B = 8.03e-6\n",
    "    r = ((data[\"y\"]-yc)*(data[\"y\"]-yc) + (data[\"x\"]-xc)*(data[\"x\"]-xc) + (data[\"z\"]-zc)*(data[\"z\"]-zc))**0.5\n",
    "    c_theta = (data[\"z\"]-zc)/r\n",
    "    s_theta = data[\"r_cl\"]/r\n",
    "    c_phi   = (data[\"x\"]-xc)/data[\"r_cl\"]\n",
    "    s_phi   = (data[\"y\"]-yc)/data[\"r_cl\"]\n",
    "    r_ratio = r_star/r\n",
    "    r3 = r_ratio*r_ratio*r_ratio\n",
    "    Er = omega*r*r3*B*s_theta*s_theta\n",
    "    E_theta = (-1)*omega*B*r*r3*(c_theta*s_theta*2.0)\n",
    "    Ez_ext = Er*c_theta - E_theta*s_theta\n",
    "\n",
    "    outside = np.zeros_like(data[\"r_cl\"])\n",
    "    outside[data[\"rad\"] <= 12000] = 1.0\n",
    "\n",
    " \n",
    "    #return outside.d*Ez_ext.d\n",
    "    return outside.d*Er.d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from yt.units.yt_array import YTQuantity\n",
    "from yt.units import kboltz\n",
    "from numpy.random import random\n",
    "import numpy as np\n",
    "from yt.utilities.exceptions import YTUnitOperationError\n",
    "def Ex_total_ext(field,data):\n",
    "\n",
    "    return data[\"Ex_internal\"] + data[\"Ex_external\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from yt.units.yt_array import YTQuantity\n",
    "from yt.units import kboltz\n",
    "from numpy.random import random\n",
    "import numpy as np\n",
    "from yt.utilities.exceptions import YTUnitOperationError\n",
    "def Ey_total_ext(field,data):\n",
    "\n",
    "    return data[\"Ey_internal\"] + data[\"Ey_external\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from yt.units.yt_array import YTQuantity\n",
    "from yt.units import kboltz\n",
    "from numpy.random import random\n",
    "import numpy as np\n",
    "from yt.utilities.exceptions import YTUnitOperationError\n",
    "def Ez_total_ext(field,data):\n",
    "\n",
    "    return data[\"Ez_internal\"] + data[\"Ez_external\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'Ez_external' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m-----------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m     Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-17-4c97ae35de49>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_field\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Ex_external\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mfunction\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mEx_external\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0munits\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m''\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0msampling_type\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"cell\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_field\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Ey_external\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mfunction\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mEy_external\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0munits\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m''\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0msampling_type\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"cell\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_field\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Ez_external\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mfunction\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mEz_external\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0munits\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m''\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0msampling_type\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"cell\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0mds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_field\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Ex_internal\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mfunction\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mEx_internal\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0munits\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m''\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0msampling_type\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"cell\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_field\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Ey_internal\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mfunction\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mEy_internal\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0munits\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m''\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0msampling_type\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"cell\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'Ez_external' is not defined"
     ]
    }
   ],
   "source": [
    "ds.add_field((\"Ex_external\"),function=Ex_external,units='',sampling_type=\"cell\")\n",
    "ds.add_field((\"Ey_external\"),function=Ey_external,units='',sampling_type=\"cell\")\n",
    "ds.add_field((\"Ez_external\"),function=Ez_external,units='',sampling_type=\"cell\")\n",
    "ds.add_field((\"Ex_internal\"),function=Ex_internal,units='',sampling_type=\"cell\")\n",
    "ds.add_field((\"Ey_internal\"),function=Ey_internal,units='',sampling_type=\"cell\")\n",
    "ds.add_field((\"Ez_internal\"),function=Ez_internal,units='',sampling_type=\"cell\")\n",
    "ds.add_field((\"Ex_total_ext\"),function=Ex_total_ext,units='',sampling_type=\"cell\")\n",
    "ds.add_field((\"Ey_total_ext\"),function=Ey_total_ext,units='',sampling_type=\"cell\")\n",
    "ds.add_field((\"Ez_total_ext\"),function=Ez_total_ext,units='',sampling_type=\"cell\")\n",
    "\n",
    "p = yt.SlicePlot(ds,'x','Ez_external')\n",
    "p.annotate_streamlines(\"Ex_external\", \"Ez_external\", \n",
    "                  plot_args={\"color\": \"black\"})\n",
    "#p.annotate_particles(width=(2.e3, 'm'),ptype=\"plasma_p\",col='b',p_size=5.0)\n",
    "#p.annotate_particles(width=(2.e3, 'm'),ptype=\"plasma_e\",col='r',p_size=5.0)\n",
    "p.show()\n",
    "p = yt.SlicePlot(ds,'x','Ez_internal')\n",
    "p.annotate_streamlines(\"Ex_internal\", \"Ez_internal\", \n",
    "                  plot_args={\"color\": \"black\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read data back-transformed to the lab frame when the simulation runs in the boosted frame (example: 2D run)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# read_raw_data.py is located in warpx/Tools.\n",
    "import os, glob\n",
    "#import read_raw_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#species = 'beam'\n",
    "#iteration = 1\n",
    "#field = 'Ex'\n",
    "\n",
    "#snapshot = './lab_frame_data/' + 'snapshot' + str(iteration).zfill(5)\n",
    "#header   = './lab_frame_data/Header'\n",
    "#allrd, info = read_raw_data.read_lab_snapshot(snapshot, header) # Read field data\n",
    "#F = allrd[field]\n",
    "#print( \"Available info: \", *list(info.keys()) )\n",
    "#print(\"Available fields: \", info['field_names'])\n",
    "#nx = info['nx']\n",
    "#nz = info['nz']\n",
    "#x = info['x']\n",
    "#z = info['z']\n",
    "#xbo  = read_raw_data.get_particle_field(snapshot, species, 'x') # Read particle data\n",
    "#ybo  = read_raw_data.get_particle_field(snapshot, species, 'y')\n",
    "#zbo  = read_raw_data.get_particle_field(snapshot, species, 'z')\n",
    "#uzbo = read_raw_data.get_particle_field(snapshot, species, 'uz')\n",
    "\n",
    "#plt.figure(figsize=(6, 3))\n",
    "#extent = np.array([info['zmin'], info['zmax'], info['xmin'], info['xmax']])\n",
    "#plt.imshow(F, aspect='auto', extent=extent, cmap='seismic')\n",
    "#plt.colorbar()\n",
    "#plt.plot(zbo, xbo, 'g.', markersize=1.)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read back-transformed data with hdf5 format (example: 3D run)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "5e10*1400*1400"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "5e6*1400*1400"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "685708*1400*1400"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.7"
  },
  "widgets": {
   "state": {
    "11d243e9f5074fe1b115949d174d59de": {
     "views": [
      {
       "cell_index": 6
      }
     ]
    }
   },
   "version": "1.2.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}