data_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "import os\n",
    "job_ids = [re.search('(\\d){7}', dir).group() for dir in os.listdir(\"./shared/NORTH/\") if re.search('data_(\\d){7}', dir)]\n",
    "job_ids.sort()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The last (highest id) job id is the lates job\n",
    "job_id = job_ids[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'5145562'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "job_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/niflheim/s173965/.local/lib/python3.9/site-packages/boutdata/data.py:732: AlwaysWarning: While building x, y, z coordinate arrays, an exception occured: name 'mxg' is not defined\n",
      "Evaluating non-scalar options not available\n",
      "  alwayswarn(\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "from boutdata import collect\n",
    "import matplotlib.cm as cm\n",
    "import matplotlib.animation as animation\n",
    "from matplotlib.animation import FuncAnimation\n",
    "from boutdata.data import BoutData\n",
    "\n",
    "path        = './shared/NORTH/data_' + job_id + '/'\n",
    "bdata = BoutData(path)\n",
    "outputs = bdata['outputs']\n",
    "options  =bdata['options']\n",
    "\n",
    "field_keys = bdata['outputs'].keys()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import rcParams\n",
    "rcParams.update({\n",
    "    \"font.size\": 16})\n",
    "rcParams['axes.titlepad'] = 20"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read Options"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_option(*keys):\n",
    "    from numpy import sqrt\n",
    "    val = options\n",
    "    try:\n",
    "        for key in keys:\n",
    "            val = val[key]\n",
    "        return eval(str(val))\n",
    "    except:\n",
    "        print('Error reading option for keys: ', keys)\n",
    "        return 0\n",
    "\n",
    "def get_options(keys_list):\n",
    "    vals = []\n",
    "    for keys in keys_list:\n",
    "        vals.append(get_option(*keys))\n",
    "    return vals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.25, 10.0, 2, 1.67e-27, 0.077, 1.6e-19, 2, 0)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "R, Te0, N_nuclei, mp, B0, e, mxg, myg = get_options([\n",
    "    ('north', 'R'), ('north', 'Te0'), ('north', 'N_nuclei'),\n",
    "    ('north', 'mp'), ('north', 'B0'), ('north', 'e'),\n",
    "    (['mxg']), (['myg'])])\n",
    "R, Te0, N_nuclei, mp, B0, e, mxg, myg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "nx:  80+2*mxg\n",
      "ny:  1\n",
      "nz:  256\n",
      "Lx:  north:a/north:rho_s\n",
      "rho_s:  sqrt(2*e*Te0/(N_nuclei*mp))/oci\n",
      "oci:  e*B0/(N_nuclei*mp)\n"
     ]
    }
   ],
   "source": [
    "print('nx: ', options['mesh']['nx'])\n",
    "print('ny: ', options['mesh']['ny'])\n",
    "print('nz: ', options['mesh']['nz'])\n",
    "print('Lx: ', options['mesh']['Lx'])\n",
    "print('rho_s: ', options['north']['rho_s'])\n",
    "print('oci: ', options['north']['oci'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "nx_all, ny_all, nz_all = get_options([('mesh', 'nx'),('mesh', 'ny'),('mesh', 'nz')])\n",
    "oci = get_option('north', 'oci')\n",
    "rho_s = get_option('north', 'rho_s')\n",
    "Lx = R/rho_s * rho_s\n",
    "nx_inner = nx_all - 2*mxg\n",
    "ny_inner = ny_all - 2*myg\n",
    "nz_inner = nz_all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mxsub = 2 mysub = 1 mz = 256\n",
      "\n",
      "nxpe = 40, nype = 1, npes = 40\n",
      "\n",
      "Reading from 0: [2-3][0-0] -> [0-1][0-0]\n",
      "\n",
      "Reading from 1: [2-3][0-0] -> [2-3][0-0]\n",
      "\n",
      "Reading from 2: [2-3][0-0] -> [4-5][0-0]\n",
      "\n",
      "Reading from 3: [2-3][0-0] -> [6-7][0-0]\n",
      "\n",
      "Reading from 4: [2-3][0-0] -> [8-9][0-0]\n",
      "\n",
      "Reading from 5: [2-3][0-0] -> [10-11][0-0]\n",
      "\n",
      "Reading from 6: [2-3][0-0] -> [12-13][0-0]\n",
      "\n",
      "Reading from 7: [2-3][0-0] -> [14-15][0-0]\n",
      "\n",
      "Reading from 8: [2-3][0-0] -> [16-17][0-0]\n",
      "\n",
      "Reading from 9: [2-3][0-0] -> [18-19][0-0]\n",
      "\n",
      "Reading from 10: [2-3][0-0] -> [20-21][0-0]\n",
      "\n",
      "Reading from 11: [2-3][0-0] -> [22-23][0-0]\n",
      "\n",
      "Reading from 12: [2-3][0-0] -> [24-25][0-0]\n",
      "\n",
      "Reading from 13: [2-3][0-0] -> [26-27][0-0]\n",
      "\n",
      "Reading from 14: [2-3][0-0] -> [28-29][0-0]\n",
      "\n",
      "Reading from 15: [2-3][0-0] -> [30-31][0-0]\n",
      "\n",
      "Reading from 16: [2-3][0-0] -> [32-33][0-0]\n",
      "\n",
      "Reading from 17: [2-3][0-0] -> [34-35][0-0]\n",
      "\n",
      "Reading from 18: [2-3][0-0] -> [36-37][0-0]\n",
      "\n",
      "Reading from 19: [2-3][0-0] -> [38-39][0-0]\n",
      "\n",
      "Reading from 20: [2-3][0-0] -> [40-41][0-0]\n",
      "\n",
      "Reading from 21: [2-3][0-0] -> [42-43][0-0]\n",
      "\n",
      "Reading from 22: [2-3][0-0] -> [44-45][0-0]\n",
      "\n",
      "Reading from 23: [2-3][0-0] -> [46-47][0-0]\n",
      "\n",
      "Reading from 24: [2-3][0-0] -> [48-49][0-0]\n",
      "\n",
      "Reading from 25: [2-3][0-0] -> [50-51][0-0]\n",
      "\n",
      "Reading from 26: [2-3][0-0] -> [52-53][0-0]\n",
      "\n",
      "Reading from 27: [2-3][0-0] -> [54-55][0-0]\n",
      "\n",
      "Reading from 28: [2-3][0-0] -> [56-57][0-0]\n",
      "\n",
      "Reading from 29: [2-3][0-0] -> [58-59][0-0]\n",
      "\n",
      "Reading from 30: [2-3][0-0] -> [60-61][0-0]\n",
      "\n",
      "Reading from 31: [2-3][0-0] -> [62-63][0-0]\n",
      "\n",
      "Reading from 32: [2-3][0-0] -> [64-65][0-0]\n",
      "\n",
      "Reading from 33: [2-3][0-0] -> [66-67][0-0]\n",
      "\n",
      "Reading from 34: [2-3][0-0] -> [68-69][0-0]\n",
      "\n",
      "Reading from 35: [2-3][0-0] -> [70-71][0-0]\n",
      "\n",
      "Reading from 36: [2-3][0-0] -> [72-73][0-0]\n",
      "\n",
      "Reading from 37: [2-3][0-0] -> [74-75][0-0]\n",
      "\n",
      "Reading from 38: [2-3][0-0] -> [76-77][0-0]\n",
      "\n",
      "Reading from 39: [2-3][0-0] -> [78-79][0-0]\n",
      "\n",
      "\n",
      "mxsub = 2 mysub = 1 mz = 256\n",
      "\n",
      "nxpe = 40, nype = 1, npes = 40\n",
      "\n",
      "Reading from 0: [2-3][0-0] -> [0-1][0-0]\n",
      "\n",
      "Reading from 1: [2-3][0-0] -> [2-3][0-0]\n",
      "\n",
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      "\n",
      "Reading from 3: [2-3][0-0] -> [6-7][0-0]\n",
      "\n",
      "Reading from 4: [2-3][0-0] -> [8-9][0-0]\n",
      "\n",
      "Reading from 5: [2-3][0-0] -> [10-11][0-0]\n",
      "\n",
      "Reading from 6: [2-3][0-0] -> [12-13][0-0]\n",
      "\n",
      "Reading from 7: [2-3][0-0] -> [14-15][0-0]\n",
      "\n",
      "Reading from 8: [2-3][0-0] -> [16-17][0-0]\n",
      "\n",
      "Reading from 9: [2-3][0-0] -> [18-19][0-0]\n",
      "\n",
      "Reading from 10: [2-3][0-0] -> [20-21][0-0]\n",
      "\n",
      "Reading from 11: [2-3][0-0] -> [22-23][0-0]\n",
      "\n",
      "Reading from 12: [2-3][0-0] -> [24-25][0-0]\n",
      "\n",
      "Reading from 13: [2-3][0-0] -> [26-27][0-0]\n",
      "\n",
      "Reading from 14: [2-3][0-0] -> [28-29][0-0]\n",
      "\n",
      "Reading from 15: [2-3][0-0] -> [30-31][0-0]\n",
      "\n",
      "Reading from 16: [2-3][0-0] -> [32-33][0-0]\n",
      "\n",
      "Reading from 17: [2-3][0-0] -> [34-35][0-0]\n",
      "\n",
      "Reading from 18: [2-3][0-0] -> [36-37][0-0]\n",
      "\n",
      "Reading from 19: [2-3][0-0] -> [38-39][0-0]\n",
      "\n",
      "Reading from 20: [2-3][0-0] -> [40-41][0-0]\n",
      "\n",
      "Reading from 21: [2-3][0-0] -> [42-43][0-0]\n",
      "\n",
      "Reading from 22: [2-3][0-0] -> [44-45][0-0]\n",
      "\n",
      "Reading from 23: [2-3][0-0] -> [46-47][0-0]\n",
      "\n",
      "Reading from 24: [2-3][0-0] -> [48-49][0-0]\n",
      "\n",
      "Reading from 25: [2-3][0-0] -> [50-51][0-0]\n",
      "\n",
      "Reading from 26: [2-3][0-0] -> [52-53][0-0]\n",
      "\n",
      "Reading from 27: [2-3][0-0] -> [54-55][0-0]\n",
      "\n",
      "Reading from 28: [2-3][0-0] -> [56-57][0-0]\n",
      "\n",
      "Reading from 29: [2-3][0-0] -> [58-59][0-0]\n",
      "\n",
      "Reading from 30: [2-3][0-0] -> [60-61][0-0]\n",
      "\n",
      "Reading from 31: [2-3][0-0] -> [62-63][0-0]\n",
      "\n",
      "Reading from 32: [2-3][0-0] -> [64-65][0-0]\n",
      "\n",
      "Reading from 33: [2-3][0-0] -> [66-67][0-0]\n",
      "\n",
      "Reading from 34: [2-3][0-0] -> [68-69][0-0]\n",
      "\n",
      "Reading from 35: [2-3][0-0] -> [70-71][0-0]\n",
      "\n",
      "Reading from 36: [2-3][0-0] -> [72-73][0-0]\n",
      "\n",
      "Reading from 37: [2-3][0-0] -> [74-75][0-0]\n",
      "\n",
      "Reading from 38: [2-3][0-0] -> [76-77][0-0]\n",
      "\n",
      "Reading from 39: [2-3][0-0] -> [78-79][0-0]\n",
      "\n",
      "\n",
      "mxsub = 2 mysub = 1 mz = 256\n",
      "\n",
      "nxpe = 40, nype = 1, npes = 40\n",
      "\n",
      "Reading from 0: [2-3][0-0] -> [0-1][0-0]\n",
      "\n",
      "Reading from 1: [2-3][0-0] -> [2-3][0-0]\n",
      "\n",
      "Reading from 2: [2-3][0-0] -> [4-5][0-0]\n",
      "\n",
      "Reading from 3: [2-3][0-0] -> [6-7][0-0]\n",
      "\n",
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      "\n",
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      "\n",
      "Reading from 7: [2-3][0-0] -> [14-15][0-0]\n",
      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
      "Reading from 14: [2-3][0-0] -> [28-29][0-0]\n",
      "\n",
      "Reading from 15: [2-3][0-0] -> [30-31][0-0]\n",
      "\n",
      "Reading from 16: [2-3][0-0] -> [32-33][0-0]\n",
      "\n",
      "Reading from 17: [2-3][0-0] -> [34-35][0-0]\n",
      "\n",
      "Reading from 18: [2-3][0-0] -> [36-37][0-0]\n",
      "\n",
      "Reading from 19: [2-3][0-0] -> [38-39][0-0]\n",
      "\n",
      "Reading from 20: [2-3][0-0] -> [40-41][0-0]\n",
      "\n",
      "Reading from 21: [2-3][0-0] -> [42-43][0-0]\n",
      "\n",
      "Reading from 22: [2-3][0-0] -> [44-45][0-0]\n",
      "\n",
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      "\n",
      "Reading from 24: [2-3][0-0] -> [48-49][0-0]\n",
      "\n",
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      "\n",
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      "\n",
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      "\n",
      "Reading from 28: [2-3][0-0] -> [56-57][0-0]\n",
      "\n",
      "Reading from 29: [2-3][0-0] -> [58-59][0-0]\n",
      "\n",
      "Reading from 30: [2-3][0-0] -> [60-61][0-0]\n",
      "\n",
      "Reading from 31: [2-3][0-0] -> [62-63][0-0]\n",
      "\n",
      "Reading from 32: [2-3][0-0] -> [64-65][0-0]\n",
      "\n",
      "Reading from 33: [2-3][0-0] -> [66-67][0-0]\n",
      "\n",
      "Reading from 34: [2-3][0-0] -> [68-69][0-0]\n",
      "\n",
      "Reading from 35: [2-3][0-0] -> [70-71][0-0]\n",
      "\n",
      "Reading from 36: [2-3][0-0] -> [72-73][0-0]\n",
      "\n",
      "Reading from 37: [2-3][0-0] -> [74-75][0-0]\n",
      "\n",
      "Reading from 38: [2-3][0-0] -> [76-77][0-0]\n",
      "\n",
      "Reading from 39: [2-3][0-0] -> [78-79][0-0]\n",
      "\n",
      "\n",
      "mxsub = 2 mysub = 1 mz = 256\n",
      "\n",
      "nxpe = 40, nype = 1, npes = 40\n",
      "\n",
      "Reading from 0: [2-3][0-0] -> [0-1][0-0]\n",
      "\n",
      "Reading from 1: [2-3][0-0] -> [2-3][0-0]\n",
      "\n",
      "Reading from 2: [2-3][0-0] -> [4-5][0-0]\n",
      "\n",
      "Reading from 3: [2-3][0-0] -> [6-7][0-0]\n",
      "\n",
      "Reading from 4: [2-3][0-0] -> [8-9][0-0]\n",
      "\n",
      "Reading from 5: [2-3][0-0] -> [10-11][0-0]\n",
      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
      "Reading from 11: [2-3][0-0] -> [22-23][0-0]\n",
      "\n",
      "Reading from 12: [2-3][0-0] -> [24-25][0-0]\n",
      "\n",
      "Reading from 13: [2-3][0-0] -> [26-27][0-0]\n",
      "\n",
      "Reading from 14: [2-3][0-0] -> [28-29][0-0]\n",
      "\n",
      "Reading from 15: [2-3][0-0] -> [30-31][0-0]\n",
      "\n",
      "Reading from 16: [2-3][0-0] -> [32-33][0-0]\n",
      "\n",
      "Reading from 17: [2-3][0-0] -> [34-35][0-0]\n",
      "\n",
      "Reading from 18: [2-3][0-0] -> [36-37][0-0]\n",
      "\n",
      "Reading from 19: [2-3][0-0] -> [38-39][0-0]\n",
      "\n",
      "Reading from 20: [2-3][0-0] -> [40-41][0-0]\n",
      "\n",
      "Reading from 21: [2-3][0-0] -> [42-43][0-0]\n",
      "\n",
      "Reading from 22: [2-3][0-0] -> [44-45][0-0]\n",
      "\n",
      "Reading from 23: [2-3][0-0] -> [46-47][0-0]\n",
      "\n",
      "Reading from 24: [2-3][0-0] -> [48-49][0-0]\n",
      "\n",
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      "\n",
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      "\n",
      "Reading from 28: [2-3][0-0] -> [56-57][0-0]\n",
      "\n",
      "Reading from 29: [2-3][0-0] -> [58-59][0-0]\n",
      "\n",
      "Reading from 30: [2-3][0-0] -> [60-61][0-0]\n",
      "\n",
      "Reading from 31: [2-3][0-0] -> [62-63][0-0]\n",
      "\n",
      "Reading from 32: [2-3][0-0] -> [64-65][0-0]\n",
      "\n",
      "Reading from 33: [2-3][0-0] -> [66-67][0-0]\n",
      "\n",
      "Reading from 34: [2-3][0-0] -> [68-69][0-0]\n",
      "\n",
      "Reading from 35: [2-3][0-0] -> [70-71][0-0]\n",
      "\n",
      "Reading from 36: [2-3][0-0] -> [72-73][0-0]\n",
      "\n",
      "Reading from 37: [2-3][0-0] -> [74-75][0-0]\n",
      "\n",
      "Reading from 38: [2-3][0-0] -> [76-77][0-0]\n",
      "\n",
      "Reading from 39: [2-3][0-0] -> [78-79][0-0]\n",
      "\n",
      "\n",
      "mxsub = 2 mysub = 1 mz = 256\n",
      "\n",
      "nxpe = 40, nype = 1, npes = 40\n",
      "\n",
      "Reading from 0: [2-3][0-0] -> [0-1][0-0]\n",
      "\n",
      "Reading from 1: [2-3][0-0] -> [2-3][0-0]\n",
      "\n",
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      "\n",
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      "\n",
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      "\n",
      "Reading from 6: [2-3][0-0] -> [12-13][0-0]\n",
      "\n",
      "Reading from 7: [2-3][0-0] -> [14-15][0-0]\n",
      "\n",
      "Reading from 8: [2-3][0-0] -> [16-17][0-0]\n",
      "\n",
      "Reading from 9: [2-3][0-0] -> [18-19][0-0]\n",
      "\n",
      "Reading from 10: [2-3][0-0] -> [20-21][0-0]\n",
      "\n",
      "Reading from 11: [2-3][0-0] -> [22-23][0-0]\n",
      "\n",
      "Reading from 12: [2-3][0-0] -> [24-25][0-0]\n",
      "\n",
      "Reading from 13: [2-3][0-0] -> [26-27][0-0]\n",
      "\n",
      "Reading from 14: [2-3][0-0] -> [28-29][0-0]\n",
      "\n",
      "Reading from 15: [2-3][0-0] -> [30-31][0-0]\n",
      "\n",
      "Reading from 16: [2-3][0-0] -> [32-33][0-0]\n",
      "\n",
      "Reading from 17: [2-3][0-0] -> [34-35][0-0]\n",
      "\n",
      "Reading from 18: [2-3][0-0] -> [36-37][0-0]\n",
      "\n",
      "Reading from 19: [2-3][0-0] -> [38-39][0-0]\n",
      "\n",
      "Reading from 20: [2-3][0-0] -> [40-41][0-0]\n",
      "\n",
      "Reading from 21: [2-3][0-0] -> [42-43][0-0]\n",
      "\n",
      "Reading from 22: [2-3][0-0] -> [44-45][0-0]\n",
      "\n",
      "Reading from 23: [2-3][0-0] -> [46-47][0-0]\n",
      "\n",
      "Reading from 24: [2-3][0-0] -> [48-49][0-0]\n",
      "\n",
      "Reading from 25: [2-3][0-0] -> [50-51][0-0]\n",
      "\n",
      "Reading from 26: [2-3][0-0] -> [52-53][0-0]\n",
      "\n",
      "Reading from 27: [2-3][0-0] -> [54-55][0-0]\n",
      "\n",
      "Reading from 28: [2-3][0-0] -> [56-57][0-0]\n",
      "\n",
      "Reading from 29: [2-3][0-0] -> [58-59][0-0]\n",
      "\n",
      "Reading from 30: [2-3][0-0] -> [60-61][0-0]\n",
      "\n",
      "Reading from 31: [2-3][0-0] -> [62-63][0-0]\n",
      "\n",
      "Reading from 32: [2-3][0-0] -> [64-65][0-0]\n",
      "\n",
      "Reading from 33: [2-3][0-0] -> [66-67][0-0]\n",
      "\n",
      "Reading from 34: [2-3][0-0] -> [68-69][0-0]\n",
      "\n",
      "Reading from 35: [2-3][0-0] -> [70-71][0-0]\n",
      "\n",
      "Reading from 36: [2-3][0-0] -> [72-73][0-0]\n",
      "\n",
      "Reading from 37: [2-3][0-0] -> [74-75][0-0]\n",
      "\n",
      "Reading from 38: [2-3][0-0] -> [76-77][0-0]\n",
      "\n",
      "Reading from 39: [2-3][0-0] -> [78-79][0-0]\n",
      "\n",
      "\n",
      "mxsub = 2 mysub = 1 mz = 256\n",
      "\n",
      "nxpe = 40, nype = 1, npes = 40\n",
      "\n",
      "Reading from 0: [2-3][0-0] -> [0-1][0-0]\n",
      "\n",
      "Reading from 1: [2-3][0-0] -> [2-3][0-0]\n",
      "\n",
      "Reading from 2: [2-3][0-0] -> [4-5][0-0]\n",
      "\n",
      "Reading from 3: [2-3][0-0] -> [6-7][0-0]\n",
      "\n",
      "Reading from 4: [2-3][0-0] -> [8-9][0-0]\n",
      "\n",
      "Reading from 5: [2-3][0-0] -> [10-11][0-0]\n",
      "\n",
      "Reading from 6: [2-3][0-0] -> [12-13][0-0]\n",
      "\n",
      "Reading from 7: [2-3][0-0] -> [14-15][0-0]\n",
      "\n",
      "Reading from 8: [2-3][0-0] -> [16-17][0-0]\n",
      "\n",
      "Reading from 9: [2-3][0-0] -> [18-19][0-0]\n",
      "\n",
      "Reading from 10: [2-3][0-0] -> [20-21][0-0]\n",
      "\n",
      "Reading from 11: [2-3][0-0] -> [22-23][0-0]\n",
      "\n",
      "Reading from 12: [2-3][0-0] -> [24-25][0-0]\n",
      "\n",
      "Reading from 13: [2-3][0-0] -> [26-27][0-0]\n",
      "\n",
      "Reading from 14: [2-3][0-0] -> [28-29][0-0]\n",
      "\n",
      "Reading from 15: [2-3][0-0] -> [30-31][0-0]\n",
      "\n",
      "Reading from 16: [2-3][0-0] -> [32-33][0-0]\n",
      "\n",
      "Reading from 17: [2-3][0-0] -> [34-35][0-0]\n",
      "\n",
      "Reading from 18: [2-3][0-0] -> [36-37][0-0]\n",
      "\n",
      "Reading from 19: [2-3][0-0] -> [38-39][0-0]\n",
      "\n",
      "Reading from 20: [2-3][0-0] -> [40-41][0-0]\n",
      "\n",
      "Reading from 21: [2-3][0-0] -> [42-43][0-0]\n",
      "\n",
      "Reading from 22: [2-3][0-0] -> [44-45][0-0]\n",
      "\n",
      "Reading from 23: [2-3][0-0] -> [46-47][0-0]\n",
      "\n",
      "Reading from 24: [2-3][0-0] -> [48-49][0-0]\n",
      "\n",
      "Reading from 25: [2-3][0-0] -> [50-51][0-0]\n",
      "\n",
      "Reading from 26: [2-3][0-0] -> [52-53][0-0]\n",
      "\n",
      "Reading from 27: [2-3][0-0] -> [54-55][0-0]\n",
      "\n",
      "Reading from 28: [2-3][0-0] -> [56-57][0-0]\n",
      "\n",
      "Reading from 29: [2-3][0-0] -> [58-59][0-0]\n",
      "\n",
      "Reading from 30: [2-3][0-0] -> [60-61][0-0]\n",
      "\n",
      "Reading from 31: [2-3][0-0] -> [62-63][0-0]\n",
      "\n",
      "Reading from 32: [2-3][0-0] -> [64-65][0-0]\n",
      "\n",
      "Reading from 33: [2-3][0-0] -> [66-67][0-0]\n",
      "\n",
      "Reading from 34: [2-3][0-0] -> [68-69][0-0]\n",
      "\n",
      "Reading from 35: [2-3][0-0] -> [70-71][0-0]\n",
      "\n",
      "Reading from 36: [2-3][0-0] -> [72-73][0-0]\n",
      "\n",
      "Reading from 37: [2-3][0-0] -> [74-75][0-0]\n",
      "\n",
      "Reading from 38: [2-3][0-0] -> [76-77][0-0]\n",
      "\n",
      "Reading from 39: [2-3][0-0] -> [78-79][0-0]\n",
      "\n",
      "\n",
      "mxsub = 2 mysub = 1 mz = 256\n",
      "\n",
      "nxpe = 40, nype = 1, npes = 40\n",
      "\n",
      "Reading from 0: [2-3][0-0] -> [0-1][0-0]\n",
      "\n",
      "Reading from 1: [2-3][0-0] -> [2-3][0-0]\n",
      "\n",
      "Reading from 2: [2-3][0-0] -> [4-5][0-0]\n",
      "\n",
      "Reading from 3: [2-3][0-0] -> [6-7][0-0]\n",
      "\n",
      "Reading from 4: [2-3][0-0] -> [8-9][0-0]\n",
      "\n",
      "Reading from 5: [2-3][0-0] -> [10-11][0-0]\n",
      "\n",
      "Reading from 6: [2-3][0-0] -> [12-13][0-0]\n",
      "\n",
      "Reading from 7: [2-3][0-0] -> [14-15][0-0]\n",
      "\n",
      "Reading from 8: [2-3][0-0] -> [16-17][0-0]\n",
      "\n",
      "Reading from 9: [2-3][0-0] -> [18-19][0-0]\n",
      "\n",
      "Reading from 10: [2-3][0-0] -> [20-21][0-0]\n",
      "\n",
      "Reading from 11: [2-3][0-0] -> [22-23][0-0]\n",
      "\n",
      "Reading from 12: [2-3][0-0] -> [24-25][0-0]\n",
      "\n",
      "Reading from 13: [2-3][0-0] -> [26-27][0-0]\n",
      "\n",
      "Reading from 14: [2-3][0-0] -> [28-29][0-0]\n",
      "\n",
      "Reading from 15: [2-3][0-0] -> [30-31][0-0]\n",
      "\n",
      "Reading from 16: [2-3][0-0] -> [32-33][0-0]\n",
      "\n",
      "Reading from 17: [2-3][0-0] -> [34-35][0-0]\n",
      "\n",
      "Reading from 18: [2-3][0-0] -> [36-37][0-0]\n",
      "\n",
      "Reading from 19: [2-3][0-0] -> [38-39][0-0]\n",
      "\n",
      "Reading from 20: [2-3][0-0] -> [40-41][0-0]\n",
      "\n",
      "Reading from 21: [2-3][0-0] -> [42-43][0-0]\n",
      "\n",
      "Reading from 22: [2-3][0-0] -> [44-45][0-0]\n",
      "\n",
      "Reading from 23: [2-3][0-0] -> [46-47][0-0]\n",
      "\n",
      "Reading from 24: [2-3][0-0] -> [48-49][0-0]\n",
      "\n",
      "Reading from 25: [2-3][0-0] -> [50-51][0-0]\n",
      "\n",
      "Reading from 26: [2-3][0-0] -> [52-53][0-0]\n",
      "\n",
      "Reading from 27: [2-3][0-0] -> [54-55][0-0]\n",
      "\n",
      "Reading from 28: [2-3][0-0] -> [56-57][0-0]\n",
      "\n",
      "Reading from 29: [2-3][0-0] -> [58-59][0-0]\n",
      "\n",
      "Reading from 30: [2-3][0-0] -> [60-61][0-0]\n",
      "\n",
      "Reading from 31: [2-3][0-0] -> [62-63][0-0]\n",
      "\n",
      "Reading from 32: [2-3][0-0] -> [64-65][0-0]\n",
      "\n",
      "Reading from 33: [2-3][0-0] -> [66-67][0-0]\n",
      "\n",
      "Reading from 34: [2-3][0-0] -> [68-69][0-0]\n",
      "\n",
      "Reading from 35: [2-3][0-0] -> [70-71][0-0]\n",
      "\n",
      "Reading from 36: [2-3][0-0] -> [72-73][0-0]\n",
      "\n",
      "Reading from 37: [2-3][0-0] -> [74-75][0-0]\n",
      "\n",
      "Reading from 38: [2-3][0-0] -> [76-77][0-0]\n",
      "\n",
      "Reading from 39: [2-3][0-0] -> [78-79][0-0]\n",
      "\n",
      "\n",
      "mxsub = 2 mysub = 1 mz = 256\n",
      "\n",
      "nxpe = 40, nype = 1, npes = 40\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#%% Read data\n",
    "\n",
    "field_list  = ['T', 'n', 'phi', 'vort','source_n', 'source_T','wall_shadow']\n",
    "par_list    = ['t_array']\n",
    "# fast_list   = ['t_array', 'n0', 'phi0', 'n1', 'phi1', 'n2', 'phi2', 'T0', 'T1', 'T2']\n",
    "#%%\n",
    "data, par, fast = {}, {}, {}\n",
    "\n",
    "for _field in field_list:\n",
    "    data[_field] = collect(_field, path = path, xguards = False)\n",
    "\n",
    "for _par in par_list:\n",
    "    par[_par] = collect(_par, path = path, xguards = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "dmp_nr = 4\n",
    "\n",
    "thetas = np.linspace(0, 2 * np.pi, nz_inner)\n",
    "rhos = np.linspace(1/(2*nx_inner), Lx+1/(2*nx_inner), nx_inner)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7fb2eee4c4f0>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(subplot_kw={'projection': 'polar'})\n",
    "\n",
    "plot0 = ax.contourf(thetas, rhos, data['n'][dmp_nr, :, :, :].squeeze(), 100, cmap = 'inferno')\n",
    "ax.set_rticks([])\n",
    "ax.set_xticks([])\n",
    "plt.colorbar(plot0, ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fig, ax = plt.subplots(subplot_kw={'projection': 'polar'})\n",
    "\n",
    "# plot0 = ax.contourf(thetas, rhos, data['n_source'][:, :, :].squeeze(), 100, cmap = 'inferno')\n",
    "# plt.colorbar(plot0, ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "vmax = data['n'][data['n'].shape[0]-1, :, :, :].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_cyl_data(data, cbarlabel):\n",
    "    fig = plt.figure(figsize=(5,5))\n",
    "\n",
    "    # the carthesian axis:\n",
    "    rect = [0, 0, 1, 1]\n",
    "    ax_carthesian  = fig.add_axes(rect,zorder=2)\n",
    "    ax_carthesian.patch.set_alpha(0)\n",
    "    ax_polar = fig.add_axes(rect, polar=True, frameon=False,zorder=1)\n",
    "\n",
    "    # the polar axis:\n",
    "    contour_set = ax_polar.contourf(thetas, rhos, data, 100, cmap = 'inferno', vmin=0, vmax=vmax)\n",
    "    ax_polar.grid(False)\n",
    "    ax_polar.set_xticks([])\n",
    "    ax_polar.set_rticks([])\n",
    "    # im_ratio = data.shape[1]/data.shape[0]\n",
    "    # # plt.colorbar(label=cbarlabel,fraction=0.046*im_ratio, pad=0.04)\n",
    "    # plt.colorbar(contour_set, ax=ax_polar, label=cbarlabel, fraction=0.046*im_ratio, pad=0.04)\n",
    "    # plt.colorbar(contour_set, ax=ax_carthesian, label=cbarlabel, fraction=0.046*im_ratio, pad=0.04, alpha=0)\n",
    "\n",
    "    ticks = np.round(np.linspace(0,Lx,11),3)\n",
    "    ax_carthesian.set_xlim(0,Lx)\n",
    "    ax_carthesian.set_ylim(0,Lx)\n",
    "    ax_carthesian.set_xticks(ticks)\n",
    "    ax_carthesian.set_yticks(ticks)\n",
    "    ax_carthesian.set_xticklabels(ticks,rotation = 90)\n",
    "    ax_carthesian.set_xlabel(r'$x$ [m]')\n",
    "    ax_carthesian.set_ylabel(r'$y$ [m]')\n",
    "    ax_carthesian.grid()\n",
    "\n",
    "plot_cyl_data(data['n'][dmp_nr, :, :, :].squeeze(), cbarlabel=r'$n \\,\\, [\\mathrm{m^{-3}}]$')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7fb2eeab1940>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(subplot_kw={'projection': 'polar'})\n",
    "\n",
    "plot0 = ax.contourf(thetas, rhos, data['T'][dmp_nr, :, :, :].squeeze(), 100, cmap = 'inferno', vmin=0,vmax=data['T'].max())\n",
    "ax.set_rticks([])\n",
    "ax.set_xticks([])\n",
    "plt.colorbar(plot0, ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Method to change the contour data points\n",
    "def plot_field(field, i, cmap='inferno'):\n",
    "    vmin = data[field].min()\n",
    "    vmax = data[field].max()\n",
    "    # ax.clear()\n",
    "    contour_set = ax.contourf(thetas, rhos, data[field][i, :, :, :].squeeze(), 100, cmap = cmap, vmin=vmin, vmax=vmax)\n",
    "    ax.set_rticks([])\n",
    "    ax.set_xticks([])\n",
    "    fig.tight_layout()\n",
    "    return contour_set\n",
    "\n",
    "def animate_n(i):\n",
    "    plot_field('n',i)\n",
    "def animate_T(i):\n",
    "    plot_field('T',i)\n",
    "def animate_vort(i):\n",
    "    plot_field('vort',i, cmap='jet')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "MovieWriter ffmpeg unavailable; using Pillow instead.\n"
     ]
    }
   ],
   "source": [
    "# Call animate method\n",
    "fig, ax = plt.subplots(subplot_kw={'projection': 'polar'})\n",
    "\n",
    "plt.colorbar(plot_field('n',0), ax=ax)\n",
    "ani = animation.FuncAnimation(fig, animate_n, frames=data['n'].shape[0], interval=100, blit=False)\n",
    "ani.save('plots/' + job_id + '_animation_n.gif', fps = 4)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "MovieWriter ffmpeg unavailable; using Pillow instead.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Call animate method\n",
    "fig, ax = plt.subplots(subplot_kw={'projection': 'polar'})\n",
    "\n",
    "plt.colorbar(plot_field('T',-1), ax=ax)\n",
    "ani = animation.FuncAnimation(fig, animate_T, frames=data['T'].shape[0], interval=100, blit=False)\n",
    "ani.save('plots/' + job_id + 'animation_T.gif', fps = 4)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "MovieWriter ffmpeg unavailable; using Pillow instead.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Call animate method\n",
    "fig, ax = plt.subplots(subplot_kw={'projection': 'polar'})\n",
    "\n",
    "plt.colorbar(plot_field('vort',-1), ax=ax)\n",
    "ani = animation.FuncAnimation(fig, animate_vort, frames=data['vort'].shape[0], interval=100, blit=False)\n",
    "ani.save('plots/' + job_id + 'animation_vort.gif', fps = 4)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.9.6 ('north-simulation': venv)",
   "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": "3.9.6"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "224ae1986ea58ff94007dbe32974f2e348ab1cb1b1c3796c4332e053e2c649e7"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}