Ex. 5 - 2D parameter space scan.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "467d52da-e7f6-4285-9ce4-c4eb9144c34f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from netCDF4 import Dataset\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib as mpl\n",
    "from DLScommonTools import *\n",
    "import os\n",
    "import pickle as pkl\n",
    "from scipy import interpolate\n",
    "import scipy as sp\n",
    "set_matplotlib_defaults()\n",
    "\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "# Here are some useful labels to use for plotting throughout notebook\n",
    "store_labels = dict()\n",
    "store_labels[\"cvar\"] = {\"density\" : \"$n_{u}\\ (m^{-3})$\",\n",
    "                        \"power\" : \"$q_{\\parallel}\\ (Wm^{-2})$\",\n",
    "                        \"impurity_frac\" : \"$f_{z}$\"}\n",
    "store_labels[\"crel\"] = {\"density\" : \"$n_{u}\\ /\\ n_{u0}$ $\\ (m^{-3})$\",\n",
    "                        \"power\" : \"$q_{\\parallel}\\ /\\ q_{\\parallel0} (Wm^{-2})$\",\n",
    "                        \"impurity_frac\" : \"$f_{z}\\ /\\ f_{z0}$\"}\n",
    "\n",
    "store_names =         {\"density\" : \"$n_{u}\\ /\\ n_{u0}$\",\n",
    "                        \"power\" : \"$q_{\\parallel0}\\ /\\ q_{\\parallel}$\",\n",
    "                        \"impurity_frac\" : \"$f_{z}\\ /\\ f_{z0}$\"}\n",
    "\n",
    "store_cnames =         {\"density\" : \"$[n_{u}\\ /\\ n_{u0}]^{1}$\",\n",
    "                        \"power\" : \"$[q_{\\parallel0}\\ /\\ q_{\\parallel}]^{5/7}$\",\n",
    "                        \"impurity_frac\" : \"$[f_{z}\\ /\\ f_{z0}]^{0.5}$\"}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9131e86-89c5-47b0-a27d-ccfa6f86f763",
   "metadata": {},
   "source": [
    "# Overview\n",
    "This notebook gives an example calculation and visualisation of detachment front location sensitivity, i.e. how fast will the front move through different regions of the domain depending on the magnetic topology. Front movement is proportional to the fractional B field gradient: the sharper the gradient as a proportion of the B field at that point (see Cowley 2022 for more details). This means the sharper the gradient and the lower the B field, the slower the front movement."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4476f1e-c489-4825-8ab9-b1f874a088dd",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Read and visualise equilibrium\n",
    "- V10 inner and outer, visualised."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "0f9d8767-caf5-4cfa-9fa4-d6e7cd1927a0",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "eqb = file_read(\"eqb_store.pkl\")\n",
    "data = eqb[\"V10\"] # Pick design to use for study"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "7e729991-12bd-4dc7-86ab-7483a3316faa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2cb439ae670>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "colors = mike_cmap(2)\n",
    "labels = {\"ou\":\"Outer\", \"iu\":\"Inner\"}\n",
    "size = 100\n",
    "\n",
    "for i, side in enumerate([\"iu\", \"ou\"]):\n",
    "    d = data[side]\n",
    "    Xpoint = d[\"Xpoint\"]\n",
    "    ax.plot(d[\"S\"], d[\"Btot\"], color = colors[i], label = labels[side])\n",
    "    ax.scatter(d[\"S\"][Xpoint], d[\"Btot\"][Xpoint], color = colors[i], marker = \"x\", s = size)\n",
    "    ax.scatter(d[\"S\"][0], d[\"Btot\"][0], color = colors[i], marker = \"o\", s = size)\n",
    "    ax.scatter(d[\"S\"][-1], d[\"Btot\"][-1], color = colors[i], marker = \"d\", s = size)\n",
    "\n",
    "ax.set_xlabel(\"$S_{\\parallel}$ (m from target)\")\n",
    "ax.set_ylabel(\"$B_{tot}$ (T)\")\n",
    "ax.legend()\n",
    "\n",
    "h,l = ax.get_legend_handles_labels()\n",
    "extra_handles = [\n",
    "    mpl.lines.Line2D([0], [0], color = \"grey\", marker = \"x\", linewidth=0, label = \"X-point\", markersize = 10),\n",
    "    mpl.lines.Line2D([0], [0], color = \"grey\", marker = \"o\", linewidth=0, label = \"Target\"),\n",
    "    mpl.lines.Line2D([0], [0], color = \"grey\", marker = \"d\", linewidth=0, label = \"Midplane\")]\n",
    "\n",
    "ax.legend(fontsize = 12, handles = h+extra_handles)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7cd80bc9-82a4-43fd-87aa-f273dbd9a0b9",
   "metadata": {},
   "source": [
    "# Run basic density scan on both the inner and the outer"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f146001-551a-4a70-b859-fa211e15568f",
   "metadata": {},
   "source": [
    "## Set DLS defaults"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "746d156d-17b2-4832-92ef-0fcf29e75a0b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# results = dict()\n",
    "\n",
    "#set switches\n",
    "radios_default = {\n",
    "    \"ionisation\": False,  # WIP - do not use. in development\n",
    "    \"upstreamGrid\": True, # SET TRUE if including domain above xpoint. if true, source of divertor heat flux comes from radial transport upstream, and Tu is at the midplane. If false, heat flux simply enters at the x point as qi, and Tu is located at the x point. \n",
    "    \"fluxlim\": False,  # WIP - do not use. if true, turns on a flux limiter with coefficient alpha\n",
    "}\n",
    "\n",
    "#set general run parameters\n",
    "constants_default = {\n",
    "    \"gamma_sheath\": 7, #sheath transmittion coefficient for virtual target. Choice does not matter if Tt is low\n",
    "    \"Tt\": 0.5, # requested virtual target temp for calculating heat flux at detachment front. Aim for low (sub 1eV) Tt\n",
    "    \n",
    "    \"qpllu0\": 1e9, # heat flux density at the x point\n",
    "    \"nu\" : 1e20, # Upstream density (if constant)\n",
    "    \"nu0\" : 1e20, # Upstream density (if constant)\n",
    "    \"cz0\" : 0.02, # Impurity fraction (if constant)\n",
    "    \n",
    "    \"Lfunc\": LfuncKallenbachAr, # impurity cooling function. More are available in AnalyticCoolingCurves.py\n",
    "    \"alpha\": 1000, # WIP - do not use. flux limiting alpha. Only matters if fluxlim is true\n",
    "}\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7f19368-92c3-4ebf-988a-c2168f424281",
   "metadata": {},
   "source": [
    "## Run\n",
    "- Running a grid of flux expansion and connection length combinations between Xpoint and target. \n",
    "- Note that the inner is set to the same power as the outer here. \n",
    "- Reducing the inner power to 10% of the outer like the previous studies leads to numerical issues which are still being investigated.\n",
    "- Also note that this is using the STEP V10 profiles.\n",
    "\n",
    "If you don't want to do the run, you can read in the results in the next cell."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "a28db6c1-6bc1-4c4a-8cb1-c86316de1d74",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running side: iu\n",
      "Running BxBt scale factor: 0.25 -- Running Lc scale factor: 0.25 -- Solving...0...110..."
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Mike\\OneDrive\\Project\\DLS\\DLS-mk-git\\LRBv21.py:62: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  dtds = qoverB*fieldValue/(kappa0*T**(5/2))\n",
      "C:\\Apps\\Anaconda3\\envs\\py38\\lib\\site-packages\\scipy\\integrate\\odepack.py:247: ODEintWarning: Excess accuracy requested (tolerances too small). Run with full_output = 1 to get quantitative information.\n",
      "  warnings.warn(warning_msg, ODEintWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Complete in 5.0 seconds\n",
      "Running Lc scale factor: 0.38 -- Solving...0...110...Complete in 5.3 seconds\n",
      "Running Lc scale factor: 0.50 -- Solving...0...110...Complete in 5.4 seconds\n",
      "Running Lc scale factor: 0.62 -- Solving...0...110...Complete in 5.1 seconds\n",
      "Running Lc scale factor: 0.75 -- Solving...0...110...Complete in 4.8 seconds\n",
      "Running Lc scale factor: 0.88 -- Solving...0...110...Complete in 5.2 seconds\n",
      "Running Lc scale factor: 1.00 -- Solving...0...110...Complete in 5.2 seconds\n",
      "Running Lc scale factor: 1.25 -- Solving...0...110...Complete in 4.3 seconds\n",
      "Running Lc scale factor: 1.50 -- Solving...0...110...Complete in 4.8 seconds\n",
      "Running Lc scale factor: 1.75 -- Solving...0...110...Complete in 4.9 seconds\n",
      "Running Lc scale factor: 2.00 -- Solving...0...110...Complete in 3.8 seconds\n",
      "Running Lc scale factor: 2.25 -- Solving...0...110...Complete in 4.7 seconds\n",
      "Running Lc scale factor: 2.50 -- Solving...0...110...Complete in 4.1 seconds\n",
      "Running BxBt scale factor: 0.38 -- Running Lc scale factor: 0.25 -- Solving...0...110...Complete in 4.4 seconds\n",
      "Running Lc scale factor: 0.38 -- Solving...0...110...Complete in 4.5 seconds\n",
      "Running Lc scale factor: 0.50 -- Solving...0...110...Complete in 3.9 seconds\n",
      "Running Lc scale factor: 0.62 -- Solving...0...110...Complete in 4.6 seconds\n",
      "Running Lc scale factor: 0.75 -- Solving...0...110...Complete in 4.3 seconds\n",
      "Running Lc scale factor: 0.88 -- Solving...0...110...Complete in 4.5 seconds\n",
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      "Running Lc scale factor: 2.00 -- Solving...0...110...Complete in 4.6 seconds\n",
      "Running Lc scale factor: 2.25 -- Solving...0...110...Complete in 9.2 seconds\n",
      "Running Lc scale factor: 2.50 -- Solving...0...110...Complete in 10.4 seconds\n",
      "Running BxBt scale factor: 0.50 -- Running Lc scale factor: 0.25 -- Solving...0...110...Complete in 5.4 seconds\n",
      "Running Lc scale factor: 0.38 -- Solving...0...110...Complete in 5.8 seconds\n",
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      "Running Lc scale factor: 0.75 -- Solving...0...110...Complete in 4.9 seconds\n",
      "Running Lc scale factor: 0.88 -- Solving...0...110...Complete in 4.8 seconds\n",
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      "Running Lc scale factor: 1.25 -- Solving...0...110...Complete in 6.2 seconds\n",
      "Running Lc scale factor: 1.50 -- Solving...0...110...Complete in 5.1 seconds\n",
      "Running Lc scale factor: 1.75 -- Solving...0...110...Complete in 4.9 seconds\n",
      "Running Lc scale factor: 2.00 -- Solving...0...110...Complete in 4.6 seconds\n",
      "Running Lc scale factor: 2.25 -- Solving...0...110...Complete in 4.7 seconds\n",
      "Running Lc scale factor: 2.50 -- Solving...0...110...Complete in 3.9 seconds\n",
      "Running BxBt scale factor: 0.62 -- Running Lc scale factor: 0.25 -- Solving...0...110...Complete in 5.4 seconds\n",
      "Running Lc scale factor: 0.38 -- Solving...0...110...Complete in 5.6 seconds\n",
      "Running Lc scale factor: 0.50 -- Solving...0...110...Complete in 5.2 seconds\n",
      "Running Lc scale factor: 0.62 -- Solving...0...110...Complete in 5.3 seconds\n",
      "Running Lc scale factor: 0.75 -- Solving...0...110...Complete in 5.1 seconds\n",
      "Running Lc scale factor: 0.88 -- Solving...0...110...Complete in 3.7 seconds\n",
      "Running Lc scale factor: 1.00 -- Solving...0...110...Complete in 6.0 seconds\n",
      "Running Lc scale factor: 1.25 -- Solving...0...110...Complete in 4.2 seconds\n",
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      "Running Lc scale factor: 1.75 -- Solving...0...110...Complete in 5.2 seconds\n",
      "Running Lc scale factor: 2.00 -- Solving...0...110...Complete in 4.2 seconds\n",
      "Running Lc scale factor: 2.25 -- Solving...0...110...Complete in 4.1 seconds\n",
      "Running Lc scale factor: 2.50 -- Solving...0...110...Complete in 3.2 seconds\n",
      "Running BxBt scale factor: 0.75 -- Running Lc scale factor: 0.25 -- Solving...0...110...Complete in 4.1 seconds\n",
      "Running Lc scale factor: 0.38 -- Solving...0...110...Complete in 4.5 seconds\n",
      "Running Lc scale factor: 0.50 -- Solving...0...110...Complete in 4.4 seconds\n",
      "Running Lc scale factor: 0.62 -- Solving...0...110...Complete in 4.9 seconds\n",
      "Running Lc scale factor: 0.75 -- Solving...0...110...Complete in 4.5 seconds\n",
      "Running Lc scale factor: 0.88 -- Solving...0...110...Complete in 4.1 seconds\n",
      "Running Lc scale factor: 1.00 -- Solving...0...110...Complete in 5.7 seconds\n",
      "Running Lc scale factor: 1.25 -- Solving...0...110...Complete in 5.7 seconds\n",
      "Running Lc scale factor: 1.50 -- Solving...0...110...Complete in 7.1 seconds\n",
      "Running Lc scale factor: 1.75 -- Solving...0...110...Complete in 6.1 seconds\n",
      "Running Lc scale factor: 2.00 -- Solving...0...110...Complete in 3.7 seconds\n",
      "Running Lc scale factor: 2.25 -- Solving...0...110...Complete in 4.3 seconds\n",
      "Running Lc scale factor: 2.50 -- Solving...0...110...Complete in 4.5 seconds\n",
      "Running BxBt scale factor: 0.88 -- Running Lc scale factor: 0.25 -- Solving...0...110...Complete in 6.3 seconds\n",
      "Running Lc scale factor: 0.38 -- Solving...0...110...Complete in 5.8 seconds\n",
      "Running Lc scale factor: 0.50 -- Solving...0...110...Complete in 5.5 seconds\n",
      "Running Lc scale factor: 0.62 -- Solving...0...110...Complete in 7.0 seconds\n",
      "Running Lc scale factor: 0.75 -- Solving...0...110...Complete in 5.6 seconds\n",
      "Running Lc scale factor: 0.88 -- Solving...0...110...Complete in 4.6 seconds\n",
      "Running Lc scale factor: 1.00 -- Solving...0...110...Complete in 5.9 seconds\n",
      "Running Lc scale factor: 1.25 -- Solving...0...110...Complete in 5.6 seconds\n",
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      "Running Lc scale factor: 1.75 -- Solving...0...110...Complete in 5.0 seconds\n",
      "Running Lc scale factor: 2.00 -- Solving...0...110...Complete in 4.1 seconds\n",
      "Running Lc scale factor: 2.25 -- Solving...0...110...Complete in 4.5 seconds\n",
      "Running Lc scale factor: 2.50 -- Solving...0...110...Complete in 3.7 seconds\n",
      "Running BxBt scale factor: 1.00 -- Running Lc scale factor: 0.25 -- Solving...0...110...Complete in 4.7 seconds\n",
      "Running Lc scale factor: 0.38 -- Solving...0...110...Complete in 4.9 seconds\n",
      "Running Lc scale factor: 0.50 -- Solving...0...110...Complete in 4.0 seconds\n",
      "Running Lc scale factor: 0.62 -- Solving...0...110...Complete in 4.1 seconds\n",
      "Running Lc scale factor: 0.75 -- Solving...0...110...Complete in 5.0 seconds\n",
      "Running Lc scale factor: 0.88 -- Solving...0...110...Complete in 4.3 seconds\n",
      "Running Lc scale factor: 1.00 -- Solving...0...110...Complete in 5.0 seconds\n",
      "Running Lc scale factor: 1.25 -- Solving...0...110...Complete in 5.1 seconds\n",
      "Running Lc scale factor: 1.50 -- Solving...0...110...Complete in 5.7 seconds\n",
      "Running Lc scale factor: 1.75 -- Solving...0...110...Complete in 8.5 seconds\n",
      "Running Lc scale factor: 2.00 -- Solving...0...110...Complete in 5.8 seconds\n",
      "Running Lc scale factor: 2.25 -- Solving...0...110...Complete in 5.2 seconds\n",
      "Running Lc scale factor: 2.50 -- Solving...0...110...Complete in 4.6 seconds\n",
      "Running BxBt scale factor: 1.25 -- Running Lc scale factor: 0.25 -- Solving...0...110...Complete in 5.7 seconds\n",
      "Running Lc scale factor: 0.38 -- Solving...0...110...Complete in 7.4 seconds\n",
      "Running Lc scale factor: 0.50 -- Solving...0...110...Complete in 7.3 seconds\n",
      "Running Lc scale factor: 0.62 -- Solving...0...110...Complete in 5.8 seconds\n",
      "Running Lc scale factor: 0.75 -- Solving...0...110...Complete in 5.3 seconds\n",
      "Running Lc scale factor: 0.88 -- Solving...0...110...Complete in 4.9 seconds\n",
      "Running Lc scale factor: 1.00 -- Solving...0...110...Complete in 6.2 seconds\n",
      "Running Lc scale factor: 1.25 -- Solving...0...110...Complete in 9.2 seconds\n",
      "Running Lc scale factor: 1.50 -- Solving...0...110...Complete in 7.2 seconds\n",
      "Running Lc scale factor: 1.75 -- Solving...0...110...Complete in 7.4 seconds\n",
      "Running Lc scale factor: 2.00 -- Solving...0...110...Complete in 8.2 seconds\n",
      "Running Lc scale factor: 2.25 -- Solving...0...110...Complete in 7.1 seconds\n",
      "Running Lc scale factor: 2.50 -- Solving...0...110...Complete in 6.5 seconds\n",
      "Running BxBt scale factor: 1.50 -- Running Lc scale factor: 0.25 -- Solving...0...110...Complete in 7.7 seconds\n",
      "Running Lc scale factor: 0.38 -- Solving...0...110...Complete in 7.9 seconds\n",
      "Running Lc scale factor: 0.50 -- Solving...0...110...Complete in 8.6 seconds\n",
      "Running Lc scale factor: 0.62 -- Solving...0...110...Complete in 10.0 seconds\n",
      "Running Lc scale factor: 0.75 -- Solving...0...110...Complete in 9.5 seconds\n",
      "Running Lc scale factor: 0.88 -- Solving...0...110...Complete in 9.1 seconds\n",
      "Running Lc scale factor: 1.00 -- Solving...0...110...Complete in 8.8 seconds\n",
      "Running Lc scale factor: 1.25 -- Solving...0...110...Complete in 9.9 seconds\n",
      "Running Lc scale factor: 1.50 -- Solving...0...110...Complete in 9.2 seconds\n",
      "Running Lc scale factor: 1.75 -- Solving...0...110...Complete in 7.6 seconds\n",
      "Running Lc scale factor: 2.00 -- Solving...0...110...Complete in 8.4 seconds\n",
      "Running Lc scale factor: 2.25 -- Solving...0...110...Complete in 7.6 seconds\n",
      "Running Lc scale factor: 2.50 -- Solving...0...110...Complete in 6.6 seconds\n",
      "Running BxBt scale factor: 1.75 -- Running Lc scale factor: 0.25 -- Solving...0...110...Complete in 7.9 seconds\n",
      "Running Lc scale factor: 0.38 -- Solving...0...110...Complete in 7.7 seconds\n",
      "Running Lc scale factor: 0.50 -- Solving...0...110...Complete in 7.0 seconds\n",
      "Running Lc scale factor: 0.62 -- Solving...0...110...Complete in 10.1 seconds\n",
      "Running Lc scale factor: 0.75 -- Solving...0...110...Complete in 9.1 seconds\n",
      "Running Lc scale factor: 0.88 -- Solving...0...110...Complete in 7.7 seconds\n",
      "Running Lc scale factor: 1.00 -- Solving...0...110...Complete in 9.2 seconds\n",
      "Running Lc scale factor: 1.25 -- Solving...0...110...Complete in 9.2 seconds\n",
      "Running Lc scale factor: 1.50 -- Solving...0...110...Complete in 9.7 seconds\n",
      "Running Lc scale factor: 1.75 -- Solving...0...110...Complete in 9.1 seconds\n",
      "Running Lc scale factor: 2.00 -- Solving...0...110...Complete in 8.2 seconds\n",
      "Running Lc scale factor: 2.25 -- Solving...0...110...Complete in 8.2 seconds\n",
      "Running Lc scale factor: 2.50 -- Solving...0...110...Complete in 6.4 seconds\n",
      "Running BxBt scale factor: 2.00 -- Running Lc scale factor: 0.25 -- Solving...0...110...Complete in 8.0 seconds\n",
      "Running Lc scale factor: 0.38 -- Solving...0...110...Complete in 8.3 seconds\n",
      "Running Lc scale factor: 0.50 -- Solving...0...110...Complete in 7.5 seconds\n",
      "Running Lc scale factor: 0.62 -- Solving...0...110...Complete in 5.8 seconds\n",
      "Running Lc scale factor: 0.75 -- Solving...0...110...Complete in 7.3 seconds\n",
      "Running Lc scale factor: 0.88 -- Solving...0...110...Complete in 7.0 seconds\n",
      "Running Lc scale factor: 1.00 -- Solving...0...110...Complete in 8.8 seconds\n",
      "Running Lc scale factor: 1.25 -- Solving...0...110...Complete in 9.7 seconds\n",
      "Running Lc scale factor: 1.50 -- Solving...0...110...Complete in 9.5 seconds\n",
      "Running Lc scale factor: 1.75 -- Solving...0...110...Complete in 9.7 seconds\n",
      "Running Lc scale factor: 2.00 -- Solving...0...110...Complete in 10.6 seconds\n",
      "Running Lc scale factor: 2.25 -- Solving...0...110...Complete in 9.8 seconds\n",
      "Running Lc scale factor: 2.50 -- Solving...0...110...Complete in 8.5 seconds\n",
      "Running BxBt scale factor: 2.25 -- Running Lc scale factor: 0.25 -- Solving...0...110...Complete in 8.6 seconds\n",
      "Running Lc scale factor: 0.38 -- Solving...0...110...Complete in 9.6 seconds\n",
      "Running Lc scale factor: 0.50 -- Solving...0...110...Complete in 8.0 seconds\n",
      "Running Lc scale factor: 0.62 -- Solving...0...110...Complete in 8.5 seconds\n",
      "Running Lc scale factor: 0.75 -- Solving...0...110...Complete in 8.6 seconds\n",
      "Running Lc scale factor: 0.88 -- Solving...0...110...Complete in 6.8 seconds\n",
      "Running Lc scale factor: 1.00 -- Solving...0...110...Complete in 10.7 seconds\n",
      "Running Lc scale factor: 1.25 -- Solving...0...110...Complete in 9.5 seconds\n",
      "Running Lc scale factor: 1.50 -- Solving...0...110...Complete in 10.6 seconds\n",
      "Running Lc scale factor: 1.75 -- Solving...0...110...Complete in 9.6 seconds\n",
      "Running Lc scale factor: 2.00 -- Solving...0...110...Complete in 10.4 seconds\n",
      "Running Lc scale factor: 2.25 -- Solving...0...110...Complete in 7.0 seconds\n",
      "Running Lc scale factor: 2.50 -- Solving...0...110...Complete in 8.0 seconds\n",
      "Running BxBt scale factor: 2.50 -- Running Lc scale factor: 0.25 -- Solving...0...110...Complete in 9.6 seconds\n",
      "Running Lc scale factor: 0.38 -- Solving...0...110...Complete in 10.0 seconds\n",
      "Running Lc scale factor: 0.50 -- Solving...0...110...Complete in 8.4 seconds\n",
      "Running Lc scale factor: 0.62 -- Solving...0...110...Complete in 7.7 seconds\n",
      "Running Lc scale factor: 0.75 -- Solving...0...110...Complete in 6.4 seconds\n",
      "Running Lc scale factor: 0.88 -- Solving...0...110...Complete in 6.2 seconds\n",
      "Running Lc scale factor: 1.00 -- Solving...0...110...Complete in 5.7 seconds\n",
      "Running Lc scale factor: 1.25 -- Solving...0...110...Complete in 10.3 seconds\n",
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      "Running Lc scale factor: 1.75 -- Solving...0...110...Complete in 9.8 seconds\n",
      "Running Lc scale factor: 2.00 -- Solving...0...110...Complete in 5.9 seconds\n",
      "Running Lc scale factor: 2.25 -- Solving...0...110...Complete in 4.3 seconds\n",
      "Running Lc scale factor: 2.50 -- Solving...0...110...Complete in 6.3 seconds\n",
      "Running side: ou\n",
      "Running BxBt scale factor: 0.25 -- Running Lc scale factor: 0.25 -- Solving...0...366...Complete in 8.2 seconds\n",
      "Running Lc scale factor: 0.38 -- Solving...0...366...Complete in 6.6 seconds\n",
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      "Running Lc scale factor: 0.75 -- Solving...0...366...Complete in 5.6 seconds\n",
      "Running Lc scale factor: 0.88 -- Solving...0...366...Complete in 7.4 seconds\n",
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      "Running Lc scale factor: 1.75 -- Solving...0...366...Complete in 5.3 seconds\n",
      "Running Lc scale factor: 2.00 -- Solving...0...366...Complete in 7.6 seconds\n",
      "Running Lc scale factor: 2.25 -- Solving...0...366...Complete in 9.1 seconds\n",
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      "Running BxBt scale factor: 0.38 -- Running Lc scale factor: 0.25 -- Solving...0...366...Complete in 11.8 seconds\n",
      "Running Lc scale factor: 0.38 -- Solving...0...366...Complete in 10.1 seconds\n",
      "Running Lc scale factor: 0.50 -- Solving...0...366...Complete in 6.2 seconds\n",
      "Running Lc scale factor: 0.62 -- Solving...0...366...Complete in 4.9 seconds\n",
      "Running Lc scale factor: 0.75 -- Solving...0...366...Complete in 7.7 seconds\n",
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      "Running Lc scale factor: 2.50 -- Solving...0...366...Complete in 4.4 seconds\n",
      "Running BxBt scale factor: 0.50 -- Running Lc scale factor: 0.25 -- Solving...0...366...Complete in 4.3 seconds\n",
      "Running Lc scale factor: 0.38 -- Solving...0...366...Complete in 3.8 seconds\n",
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      "Running Lc scale factor: 0.75 -- Solving...0...366...Complete in 3.9 seconds\n",
      "Running Lc scale factor: 0.88 -- Solving...0...366...Complete in 3.8 seconds\n",
      "Running Lc scale factor: 1.00 -- Solving...0...366...Complete in 4.2 seconds\n",
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      "Running Lc scale factor: 1.75 -- Solving...0...366...Complete in 5.3 seconds\n",
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      "Running Lc scale factor: 2.50 -- Solving...0...366...Complete in 4.8 seconds\n",
      "Running BxBt scale factor: 0.62 -- Running Lc scale factor: 0.25 -- Solving...0...366...Complete in 4.7 seconds\n",
      "Running Lc scale factor: 0.38 -- Solving...0...366...Complete in 4.8 seconds\n",
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      "Running Lc scale factor: 0.62 -- Solving...0...366...Complete in 4.3 seconds\n",
      "Running Lc scale factor: 0.75 -- Solving...0...366...Complete in 3.2 seconds\n",
      "Running Lc scale factor: 0.88 -- Solving...0...366...Complete in 3.8 seconds\n",
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      "Running Lc scale factor: 1.25 -- Solving...0...366...Complete in 3.9 seconds\n",
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      "Running Lc scale factor: 2.00 -- Solving...0...366...Complete in 4.6 seconds\n",
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      "Running BxBt scale factor: 0.75 -- Running Lc scale factor: 0.25 -- Solving...0...366...Complete in 4.2 seconds\n",
      "Running Lc scale factor: 0.38 -- Solving...0...366...Complete in 4.3 seconds\n",
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      "Running Lc scale factor: 0.62 -- Solving...0...366...Complete in 4.9 seconds\n",
      "Running Lc scale factor: 0.75 -- Solving...0...366...Complete in 3.8 seconds\n",
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      "Running BxBt scale factor: 0.88 -- Running Lc scale factor: 0.25 -- Solving...0...366...Complete in 4.5 seconds\n",
      "Running Lc scale factor: 0.38 -- Solving...0...366...Complete in 4.4 seconds\n",
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      "Running Lc scale factor: 0.88 -- Solving...0...366...Complete in 3.9 seconds\n",
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      "Running BxBt scale factor: 1.25 -- Running Lc scale factor: 0.25 -- Solving...0...366...Complete in 4.9 seconds\n",
      "Running Lc scale factor: 0.38 -- Solving...0...366...Complete in 5.2 seconds\n",
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      "Running BxBt scale factor: 1.50 -- Running Lc scale factor: 0.25 -- Solving...0...366...Complete in 4.4 seconds\n",
      "Running Lc scale factor: 0.38 -- Solving...0...366...Complete in 4.3 seconds\n",
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      "Running Lc scale factor: 1.00 -- Solving...0...366...Complete in 4.3 seconds\n",
      "Running Lc scale factor: 1.25 -- Solving...0...366...Complete in 4.7 seconds\n",
      "Running Lc scale factor: 1.50 -- Solving...0...366...Complete in 5.4 seconds\n",
      "Running Lc scale factor: 1.75 -- Solving...0...366...Complete in 5.1 seconds\n",
      "Running Lc scale factor: 2.00 -- Solving...0...366...Complete in 5.1 seconds\n",
      "Running Lc scale factor: 2.25 -- Solving...0...366...Complete in 4.8 seconds\n",
      "Running Lc scale factor: 2.50 -- Solving...0...366...Complete in 5.4 seconds\n",
      "Running BxBt scale factor: 1.75 -- Running Lc scale factor: 0.25 -- Solving...0...366...Complete in 5.5 seconds\n",
      "Running Lc scale factor: 0.38 -- Solving...0...366...Complete in 4.4 seconds\n",
      "Running Lc scale factor: 0.50 -- Solving...0...366...Complete in 5.5 seconds\n",
      "Running Lc scale factor: 0.62 -- Solving...0...366...Complete in 4.9 seconds\n",
      "Running Lc scale factor: 0.75 -- Solving...0...366...Complete in 3.7 seconds\n",
      "Running Lc scale factor: 0.88 -- Solving...0...366...Complete in 4.2 seconds\n",
      "Running Lc scale factor: 1.00 -- Solving...0...366...Complete in 4.7 seconds\n",
      "Running Lc scale factor: 1.25 -- Solving...0...366...Complete in 4.2 seconds\n",
      "Running Lc scale factor: 1.50 -- Solving...0...366...Complete in 3.4 seconds\n",
      "Running Lc scale factor: 1.75 -- Solving...0...366...Complete in 4.8 seconds\n",
      "Running Lc scale factor: 2.00 -- Solving...0...366...Complete in 4.6 seconds\n",
      "Running Lc scale factor: 2.25 -- Solving...0...366...Complete in 3.5 seconds\n",
      "Running Lc scale factor: 2.50 -- Solving...0...366...Complete in 4.7 seconds\n",
      "Running BxBt scale factor: 2.00 -- Running Lc scale factor: 0.25 -- Solving...0...366...Complete in 4.4 seconds\n",
      "Running Lc scale factor: 0.38 -- Solving...0...366...Complete in 4.4 seconds\n",
      "Running Lc scale factor: 0.50 -- Solving...0...366...Complete in 4.6 seconds\n",
      "Running Lc scale factor: 0.62 -- Solving...0...366...Complete in 4.6 seconds\n",
      "Running Lc scale factor: 0.75 -- Solving...0...366...Complete in 3.7 seconds\n",
      "Running Lc scale factor: 0.88 -- Solving...0...366...Complete in 4.4 seconds\n",
      "Running Lc scale factor: 1.00 -- Solving...0...366...Complete in 4.5 seconds\n",
      "Running Lc scale factor: 1.25 -- Solving...0...366...Complete in 4.7 seconds\n",
      "Running Lc scale factor: 1.50 -- Solving...0...366...Complete in 4.5 seconds\n",
      "Running Lc scale factor: 1.75 -- Solving...0...366...Complete in 4.1 seconds\n",
      "Running Lc scale factor: 2.00 -- Solving...0...366...Complete in 4.9 seconds\n",
      "Running Lc scale factor: 2.25 -- Solving...0...366...Complete in 5.7 seconds\n",
      "Running Lc scale factor: 2.50 -- Solving...0...366...Complete in 5.6 seconds\n",
      "Running BxBt scale factor: 2.25 -- Running Lc scale factor: 0.25 -- Solving...0...366...Complete in 5.2 seconds\n",
      "Running Lc scale factor: 0.38 -- Solving...0...366...Complete in 5.2 seconds\n",
      "Running Lc scale factor: 0.50 -- Solving...0...366...Complete in 5.5 seconds\n",
      "Running Lc scale factor: 0.62 -- Solving...0...366...Complete in 5.5 seconds\n",
      "Running Lc scale factor: 0.75 -- Solving...0...366...Complete in 4.4 seconds\n",
      "Running Lc scale factor: 0.88 -- Solving...0...366...Complete in 4.4 seconds\n",
      "Running Lc scale factor: 1.00 -- Solving...0...366...Complete in 4.9 seconds\n",
      "Running Lc scale factor: 1.25 -- Solving...0...366...Complete in 4.3 seconds\n",
      "Running Lc scale factor: 1.50 -- Solving...0...366...Complete in 4.6 seconds\n",
      "Running Lc scale factor: 1.75 -- Solving...0...366...Complete in 5.2 seconds\n",
      "Running Lc scale factor: 2.00 -- Solving...0...366...Complete in 4.7 seconds\n",
      "Running Lc scale factor: 2.25 -- Solving...0...366...Complete in 4.4 seconds\n",
      "Running Lc scale factor: 2.50 -- Solving...0...366...Complete in 4.7 seconds\n",
      "Running BxBt scale factor: 2.50 -- Running Lc scale factor: 0.25 -- Solving...0...366...Complete in 4.0 seconds\n",
      "Running Lc scale factor: 0.38 -- Solving...0...366...Complete in 4.2 seconds\n",
      "Running Lc scale factor: 0.50 -- Solving...0...366...Complete in 3.6 seconds\n",
      "Running Lc scale factor: 0.62 -- Solving...0...366...Complete in 4.8 seconds\n",
      "Running Lc scale factor: 0.75 -- Solving...0...366...Complete in 4.0 seconds\n",
      "Running Lc scale factor: 0.88 -- Solving...0...366...Complete in 4.0 seconds\n",
      "Running Lc scale factor: 1.00 -- Solving...0...366...Complete in 4.4 seconds\n",
      "Running Lc scale factor: 1.25 -- Solving...0...366...Complete in 4.4 seconds\n",
      "Running Lc scale factor: 1.50 -- Solving...0...366...Complete in 4.4 seconds\n",
      "Running Lc scale factor: 1.75 -- Solving...0...366...Complete in 5.7 seconds\n",
      "Running Lc scale factor: 2.00 -- Solving...0...366...Complete in 5.6 seconds\n",
      "Running Lc scale factor: 2.25 -- Solving...0...366...Complete in 5.3 seconds\n",
      "Running Lc scale factor: 2.50 -- Solving...0...366...Complete in 5.4 seconds\n"
     ]
    }
   ],
   "source": [
    "store = dict()\n",
    "\n",
    "design = \"V10\"\n",
    "\n",
    "# \n",
    "list_BxBt_scales = np.append(np.linspace(0.25, 1.0, 7), np.linspace(1.0, 2.5, 7)[1:])\n",
    "list_Lc_scales = np.append(np.linspace(0.25, 1.0, 7), np.linspace(1.0, 2.5, 7)[1:])\n",
    "\n",
    "for side in [\"iu\", \"ou\"]:\n",
    "    print(\"Running side: {}\".format(side))\n",
    "    result_array = []\n",
    "    \n",
    "    for i, BxBt_scale in enumerate(list_BxBt_scales):\n",
    "        print(\"Running BxBt scale factor: {:.2f} -- \".format(BxBt_scale), end=\"\")\n",
    "        result_row = []\n",
    "        \n",
    "        for j, Lc_scale in enumerate(list_Lc_scales):\n",
    "            print(\"Running Lc scale factor: {:.2f} -- \".format(Lc_scale), end=\"\")\n",
    "                \n",
    "            d = copy.deepcopy(eqb[design][side])\n",
    "            constants = constants_default.copy()\n",
    "            radios = radios_default.copy()\n",
    "                              \n",
    "            d[\"Btot\"] = scale_BxBt(d[\"Btot\"], d[\"Xpoint\"], scale_factor = BxBt_scale)\n",
    "            d[\"S\"], d[\"Spol\"] = scale_Lc(d[\"S\"], d[\"Spol\"], d[\"Xpoint\"], scale_factor = Lc_scale)\n",
    "                              \n",
    "            radios[\"upstreamGrid\"] = True\n",
    "            constants[\"XpointIndex\"] = d[\"Xpoint\"], # Index of Xpoint\n",
    "            \n",
    "            # Inner has 10% of the power.\n",
    "            # if side == \"iu\":\n",
    "            #     constants[\"qpllu0\"] = constants_default[\"qpllu0\"] * 0.1\n",
    "            constants[\"cz0\"] = 0.05\n",
    "            SparRange = [0, d[\"S\"][d[\"Xpoint\"]-1]]    \n",
    "            out = LRBv21(constants, radios, d, SparRange, verbosity = 0, control_variable = \"density\")\n",
    "            result_row.append(out)\n",
    "\n",
    "        result_array.append(result_row)\n",
    "                              \n",
    "    store[side] = result_array\n",
    "\n",
    "store[\"list_BxBt_scales\"] = list_BxBt_scales\n",
    "store[\"list_Lc_scales\"] = list_Lc_scales"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4aaab445-849c-4a55-9fb8-1888f91dda22",
   "metadata": {},
   "source": [
    "## Read/write"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "208bbfde-baf6-4c9f-92ca-80c8db221e48",
   "metadata": {},
   "outputs": [],
   "source": [
    "file_write(store, \"2dscan_v10_density_kAr.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "c75f2397-d29e-44ae-a637-1e2cbce05df8",
   "metadata": {},
   "outputs": [],
   "source": [
    "store = file_read(\"2dscan_v10_density_kAr.pkl\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3b39bb3-92b7-4506-ade0-7da9af2aef94",
   "metadata": {},
   "source": [
    "# 2D Contour plot\n",
    "- X axis: change in connection length\n",
    "- Y axis: change in flux expansion\n",
    "- The colour scale indicates threshold improvement (i.e. threshold reduction\n",
    "- Illustrates nonlinearity in impact of both parameters\n",
    "- On the inner, there is a \"blank\" space at the bottom. This illustrates the region of unstable solutions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "53457d05-b427-43e7-a0bc-02edad3c5897",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x648 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "arr = dict()\n",
    "scan = store\n",
    "arr[\"ou\"] = make_arrays(scan[\"ou\"], list_BxBt_scales, list_Lc_scales, cut = False)\n",
    "arr[\"iu\"] = make_arrays(scan[\"iu\"], list_BxBt_scales, list_Lc_scales, cut = False)\n",
    "\n",
    "windowmin_outer = (-(arr[\"ou\"][\"window_ratio_base\"] - 1) / arr[\"ou\"][\"window_ratio_base\"])[0]\n",
    "windowmin_inner = (-(arr[\"iu\"][\"window_ratio_base\"] - 1) / arr[\"iu\"][\"window_ratio_base\"])[0]\n",
    "\n",
    "vmin_outer = ((arr[\"ou\"][\"threshold_base\"] / arr[\"ou\"][\"window_ratio_base\"]) / arr[\"ou\"][\"threshold_base\"] -1)[0]\n",
    "\n",
    "vmin_inner = ((arr[\"iu\"][\"threshold_base\"] / arr[\"iu\"][\"window_ratio_base\"])  / arr[\"iu\"][\"threshold_base\"] -1)[0]\n",
    "\n",
    "fig, axes = plt.subplots(1,2, figsize = (18,9))\n",
    "fig.tight_layout(pad=5)\n",
    "\n",
    "cmap = copy.copy(plt.get_cmap(\"gist_ncar\"))\n",
    "\n",
    "# levels = np.linspace(0, 3.0e19,22)\n",
    "# levels = np.arange(-0.5, 0.55, 0.05)\n",
    "\n",
    "ax = axes[0]\n",
    "y = 1/(arr[\"ou\"][\"threshold_norm\"]+1)-1\n",
    "\n",
    "levels = np.linspace(-0.6, 0.5, 28)\n",
    "vmin = levels[0]\n",
    "cs = ax.contourf(list_Lc_scales-1, list_BxBt_scales-1, y, levels = levels, cmap = cmap, vmin = vmin_outer, zorder = 0,  extend = \"max\")\n",
    "cb = fig.colorbar(cs, ax=ax)\n",
    "cb.set_label(label = \"Threshold improvement\", size = 15)\n",
    "cb.ax.set_yticklabels([\"{:.0%}\".format(i) for i in cb.get_ticks()]) \n",
    "ax.set_title(\"Outer divertor\", fontsize = 15)\n",
    "\n",
    "# [Lx][BxBt]\n",
    "ax.scatter([1.213-1], [1.037-1], marker = \"o\", facecolor = \"None\", edgecolor = \"black\", s = 250, linewidth = 4, label = \"V10X\", zorder = 100)\n",
    "ax.scatter([1.959-1], [1.080-1], marker = \"s\", facecolor = \"None\", edgecolor = \"black\", s = 250, linewidth = 4, label = \"SPR38\", zorder =100)\n",
    "ax.scatter([0], [0], marker = \"+\", facecolor = \"black\", edgecolor = \"black\", s = 250, linewidth = 4, label = \"V10\", zorder = 100)\n",
    "\n",
    "\n",
    "norm = mpl.colors.Normalize(vmin=-1, vmax = 2)\n",
    "\n",
    "ax = axes[1]\n",
    "# levels = np.linspace(-0.05, 0.5, 29)\n",
    "levels = np.linspace(-0.05, 0.5, 27)\n",
    "y = 1/(arr[\"iu\"][\"threshold_norm\"]+1)-1\n",
    "cs = ax.contourf(list_Lc_scales-1, list_BxBt_scales-1, y, levels = levels, cmap = cmap, zorder = 0, vmin = vmin_inner,  extend = \"max\")\n",
    "# cs.set_clim(-0.5,2)\n",
    "# cs = ax.contourf(list_Lc_scales-1, list_BxBt_scales-1, patch_array(arr[\"iu\"][\"window\"],percentile_threshold = 94), levels = 22, cmap = cmap, zorder = 0, vmin = 1)\n",
    "cb = fig.colorbar(cs, ax=ax)\n",
    "cb.set_label(label = \"Threshold improvement\", size = 15)\n",
    "cb.ax.set_yticklabels([\"{:.0%}\".format(i) for i in cb.get_ticks()]) \n",
    "ax.set_title(\"Inner divertor\", fontsize = 15)\n",
    "    \n",
    "# [Lx][BxBt]\n",
    "ax.scatter([2.226-1], [0.889-1], marker = \"o\", facecolor = \"None\", edgecolor = \"deeppink\", s = 250, linewidth = 4, label = \"V10X\", zorder = 100)\n",
    "ax.scatter([1.501-1], [1-1], marker = \"s\", facecolor = \"None\", edgecolor = \"deeppink\", s = 250, linewidth = 4, label = \"SPR38\", zorder =100)\n",
    "ax.scatter([0], [0], marker = \"+\", facecolor = \"black\", edgecolor = \"black\", s = 250, linewidth = 4, label = \"V10\", zorder = 0)\n",
    "\n",
    "crosshair_color = \"black\"\n",
    "for ax in axes:\n",
    "\n",
    "    ax.set_xlim(-0.75,1.5)\n",
    "    ax.set_ylim(-0.75,1.5)\n",
    "    # ax.scatter(0,0, marker = \"o\", s = 200, facecolor = \"black\", linewidth = 3, color = crosshair_color)\n",
    "    ax.hlines(0, -0.75, 1.5, color = crosshair_color, zorder = 2)\n",
    "    ax.vlines(0, -0.75, 1.5, color = crosshair_color, zorder = 2)\n",
    "    ax.grid(color = \"black\", alpha = 0.3, which = \"major\")\n",
    "    ax.grid(color = \"black\", alpha = 0.1, which = \"minor\")\n",
    "    ax.set_xlabel(\"$L_x$ change\", fontsize = 15)\n",
    "    ax.set_ylabel(\"$B_x/B_t$ change\", fontsize = 15)\n",
    "    ax.yaxis.set_major_formatter(StrMethodFormatter(\"{x:.0%}\"))\n",
    "    ax.xaxis.set_major_formatter(StrMethodFormatter(\"{x:.0%}\"))\n",
    "    ax.xaxis.set_minor_locator(MultipleLocator(0.05))\n",
    "    ax.yaxis.set_minor_locator(MultipleLocator(0.05))\n",
    "    ax.xaxis.set_major_locator(MultipleLocator(0.25))\n",
    "    ax.yaxis.set_major_locator(MultipleLocator(0.25))\n",
    "    ax.legend(loc=\"upper right\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d27dcf21-ea7d-451e-a7d6-e1dc8adf58bc",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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