Magnetic_Cleanliness_SoftWare.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "How many dipoles will you setting? ex)3:1\n",
      "Include 40uT of Earth Background Field? Enter 1 if you want, or 0 if you don't.1\n",
      "Enter the dipole moment in order. ex)0 0 0.001: 0 0 0.001\n",
      "Enter the position of the dipole in m units in order. At this time, the same position value cannot be input.Also, 0,0,0 cannot be input. ex)0 0 0.001: 0 0 0\n",
      "How many nT or less resolution do you want? ex)1: 1\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 828x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\hyejeong\\anaconda3\\lib\\site-packages\\magpylib\\_lib\\fields\\Moment_Dipole.py:46: RuntimeWarning: Warning: getB Position directly at moment position\n",
      "  warn('Warning: getB Position directly at moment position', RuntimeWarning)\n",
      "C:\\Users\\hyejeong\\anaconda3\\lib\\site-packages\\magpylib\\_lib\\fields\\Moment_Dipole.py:46: RuntimeWarning: Warning: getB Position directly at moment position\n",
      "  warn('Warning: getB Position directly at moment position', RuntimeWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\hyejeong\\anaconda3\\lib\\site-packages\\magpylib\\_lib\\fields\\Moment_Dipole.py:46: RuntimeWarning: Warning: getB Position directly at moment position\n",
      "  warn('Warning: getB Position directly at moment position', RuntimeWarning)\n",
      "."
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0 For resolution below nT, the magnetometer should be installed at least at the following coordinates.\n",
      "X-axis(y=0, z=0): 0.0 0 0 (Unit:m) magnetic field at this point:  nan Unit:T\n",
      "Z-axis(x=0, y=0): 0.0 0 0 (Unit:m) magnetic field at this point:  nan Unit:T\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "----------------------------------------------------------------------\n",
      "Ran 1 test in 12.577s\n",
      "\n",
      "OK\n"
     ]
    }
   ],
   "source": [
    "import unittest\n",
    "import magpylib as magpy\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt,cm\n",
    "from magpylib import Collection, displaySystem\n",
    "\n",
    "'''\n",
    "Reference:\n",
    "Jo, Hye Jeong, et al. \"Analysis of a CubeSat Magnetic Cleanliness for the Space Science Mission.\" Journal of Space Technology and Applications 2.1 (2022): 41-51.\n",
    "\n",
    "This code was created for magnetic field researchers.\n",
    "Made by HyeJeong Jo\n",
    "'''\n",
    "\n",
    "\n",
    "def Cleanliness_Function():\n",
    "    dipm=int(input(\"How many dipoles will you setting? ex)3:\"))\n",
    "    earth=int(input(\"Include 40uT of Earth Background Field? Enter 1 if you want, or 0 if you don't.\"))\n",
    "    smoment=[]\n",
    "\n",
    "    for z in range(dipm):\n",
    "        momentz=list(float(q) for q in input(\"Enter the dipole moment in order. ex)0 0 0.001: \").split())\n",
    "        smoment.append(momentz)\n",
    "    slocate=[]\n",
    "    for i in range(dipm):\n",
    "        locatez=list(float(q) for q in input(\"Enter the position of the dipole in m units in order. At this time, the same position value cannot be input.Also, 0,0,0 cannot be input. ex)0 0 0.001: \").split())\n",
    "        slocate.append(locatez)\n",
    "    moment=np.array(smoment)*np.pi*4*1e-7\n",
    "    i=0\n",
    "    collectionlist=[]\n",
    "    if(earth ==0):\n",
    "        for i in range(dipm):\n",
    "            collectionlist.append(magpy.source.moment.Dipole(moment=moment[i],pos=slocate[i]))\n",
    "    elif(earth ==1):\n",
    "        for i in range(dipm):\n",
    "            collectionlist.append(magpy.source.moment.Dipole(moment=moment[i],pos=slocate[i]))\n",
    "        collectionlist.append(magpy.source.current.Circular(curr=122, dim=128e5, pos=[0,0,0]))\n",
    "    c=Collection(collectionlist)\n",
    "    b=float(input(\"How many nT or less resolution do you want? ex)1: \"))\n",
    "    ts= np.linspace(-1,1,100)\n",
    "    POS=np.array([(x,0,z) for z in ts for x in ts])\n",
    "    Bs=c.getB(POS).reshape(100,100,3)\n",
    "    X,Z=np.meshgrid(ts,ts)\n",
    "    U, V, K=Bs[:,:,0],Bs[:,:,1],Bs[:,:,2]\n",
    "    fig=plt.figure(figsize=(11.5,10))\n",
    "    plt.tight_layout() \n",
    "    CZ=np.sqrt(U**2+V**2+K**2)\n",
    "    CP=np.log10(CZ)\n",
    "    cp=plt.contourf(X,Z,CP,1000, cmap=plt.cm.jet, vmin=-10, vmax=-1)\n",
    "    plt.xlabel('X [m]',fontsize=15)\n",
    "    plt.ylabel('Z [m]',fontsize=15)\n",
    "    plt.tick_params(axis='x', direction='in', length=8, top=True, labelsize=11)\n",
    "    plt.tick_params(axis='y', direction='in', length=8, right=True, labelsize=11)\n",
    "\n",
    "    mynorm = plt.Normalize(vmin=-10, vmax=-1)\n",
    "    sm = plt.cm.ScalarMappable(cmap=cm.jet, norm=mynorm)\n",
    "    sm_=plt.colorbar(sm)\n",
    "    sm_.set_ticks([-1.0,-2.0,-3.0,-4.0,-5.0,-6.0,-7.0,-8.0,-9.0,-10.0])\n",
    "    sm_.set_ticklabels(['$\\mathdefault{10^{-1}}$','$\\mathdefault{10^{-2}}$','$\\mathdefault{10^{-3}}$','$\\mathdefault{10^{-4}}$','$\\mathdefault{10^{-5}}$',\n",
    "                      '$\\mathdefault{10^{-6}}$','$\\mathdefault{10^{-7}}$','$\\mathdefault{10^{-8}}$','$\\mathdefault{10^{-9}}$','$\\mathdefault{10^{-10}}$'])\n",
    "    sm_.ax.tick_params(labelsize=15)\n",
    "    sm_.set_label('$\\mathdefault{lBl}$ [T]', labelpad=-57, y=-0.02, rotation=0, fontsize=15)\n",
    "    sm_.ax.tick_params(size=0)\n",
    "    plt.show()\n",
    "    def find_nearest(array, value, da):\n",
    "        array = np.asarray(array)\n",
    "        idx = (np.abs(array - value)).argmin()\n",
    "        return da[idx]\n",
    "\n",
    "    def scar5(place):\n",
    "        A = c.getB(place)[0]\n",
    "        B = c.getB(place)[1]\n",
    "        C = c.getB(place)[2]\n",
    "        result = np.sqrt(A ** 2 + B ** 2 + C ** 2)\n",
    "        return result\n",
    "\n",
    "    xdata = []\n",
    "    ydata = []\n",
    "    zdata = []\n",
    "    resultdata = []\n",
    "\n",
    "    for x in range(0,4000):\n",
    "        xdata.append(x*0.01-20)\n",
    "    for y in range(0,4000):\n",
    "        ydata.append(0)\n",
    "    for z in range(0,4000):\n",
    "        zdata.append(0)    \n",
    "    for i in range(len(xdata)):\n",
    "        resultdata.append(float(scar5([xdata[i], ydata[i], zdata[i]])))\n",
    "    \n",
    "    res=np.array(resultdata)*1e9 \n",
    "\n",
    "    plt.figure(figsize=(12,5))\n",
    "    plt.subplot(121)\n",
    "    plt.plot(xdata, res, color='k')\n",
    "    plt.xlabel('X-axis [m]',fontsize=11)\n",
    "    plt.ylabel('Magnetic [nT]',fontsize=11)\n",
    "    plt.tick_params(axis='x', direction='in', length=8, top=True, labelsize=11)\n",
    "    plt.tick_params(axis='y', direction='in', length=8, right=True, labelsize=11)\n",
    "\n",
    "    near_x = find_nearest(res, b, xdata)\n",
    "    collectionlist=[]\n",
    "\n",
    "    for i in range(dipm):\n",
    "        collectionlist.append(magpy.source.moment.Dipole(moment=moment[i],pos=slocate[i]))\n",
    "    c=Collection(collectionlist)\n",
    "\n",
    "    xdata = []\n",
    "    ydata = []\n",
    "    zdata = []\n",
    "    resultdata = []\n",
    "    \n",
    "    for z in range(0,4000):\n",
    "        zdata.append(z*0.01-20)\n",
    "    for y in range(0,4000):\n",
    "        ydata.append(0)\n",
    "    for x in range(0,4000):\n",
    "        xdata.append(0)\n",
    "    \n",
    "    for i in range(len(xdata)):\n",
    "        resultdata.append(float(scar5([xdata[i], ydata[i], zdata[i]])))\n",
    "    \n",
    "\n",
    "    res=np.array(resultdata)*1e9 \n",
    "    plt.subplot(122)\n",
    "    plt.plot(zdata, res, color='k')\n",
    "    plt.xlabel('Z-axis [m]',fontsize=11)\n",
    "    plt.ylabel('Magnetic [nT]',fontsize=11)\n",
    "    plt.tick_params(axis='x', direction='in', length=8, top=True, labelsize=11)\n",
    "    plt.tick_params(axis='y', direction='in', length=8, right=True, labelsize=11)\n",
    "    plt.show()\n",
    "\n",
    "    near_z = find_nearest(res, b, zdata)\n",
    "\n",
    "    print(b,\"For resolution below nT, the magnetometer should be installed at least at the following coordinates.\")\n",
    "    xB=scar5([near_x,0,0])\n",
    "    zB=scar5([0,0,near_z])\n",
    "    print(\"X-axis(y=0, z=0):\",near_x,0,0,\"(Unit:m)\",\"magnetic field at this point: \",xB,\"Unit:T\") \n",
    "    print(\"Z-axis(x=0, y=0):\",near_z,0,0,\"(Unit:m)\",\"magnetic field at this point: \",zB,\"Unit:T\")\n",
    "\n",
    "# TestCase를 작성\n",
    "class CustomTests(unittest.TestCase): \n",
    "    def test_runs(self):\n",
    "        Cleanliness_Function()\n",
    "\n",
    "\n",
    "# unittest를 실행\n",
    "if __name__ == '__main__':  \n",
    "    unittest.main (argv = [ 'first-arg-is-ignored'], exit = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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