{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "291c27ef-e798-46bd-b828-a0d369b576a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from math import cos, sin\n",
    "import sys\n",
    "from PIL import Image, ImageDraw, ImageColor\n",
    "from matplotlib import pyplot as plt\n",
    "import matplotlib as mpl\n",
    "import matplotlib.ticker as ticker"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "52605338-6294-4657-b344-1fa21fac03bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "from util_functions import cutoffPercentile, createXZGrid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0b248282-ff8d-46c1-adb9-d584b0bd372d",
   "metadata": {},
   "outputs": [],
   "source": [
    "I = 1 # 1 amp of current flowing through coil\n",
    "mu0 = 4 *np.pi*10**(-7)  # magnetic permeability constant\n",
    "radius = 1  # radius of coil in meters\n",
    "#bbox = (-.05,-.05, -.05, .05, .05, .05)  # bounding box of visual area in meters\n",
    "bbox = (0,0, 10, 10)  # bounding box of visual area in meters\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1bcce8f0-015d-44e9-8498-f773fb6420dc",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "with open(\"cellCanvas-jl-100.npy\",\"rb\") as f:\n",
    "    bField = np.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5f60aada-d48b-472f-a857-e209c6063d30",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(100, 100, 100, 3)\n"
     ]
    }
   ],
   "source": [
    "print (bField.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b1537b03-d4fd-46b4-854f-87679004b770",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(200, 200, 200, 3)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "doubleShape = tuple( x*2 for x in bField.shape[: - 1]) + (bField.shape[-1],)\n",
    "cellsCanvas = np.zeros(doubleShape)\n",
    "meshsize = bField.shape[0]\n",
    "rgridsize = int(radius/(bbox[2] - bbox[0])*meshsize)+1\n",
    "cellsCanvas.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ef5a8cb6-79c5-4241-9417-bd7aed2c6a80",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11\n",
      "(100, 100, 100, 3)\n"
     ]
    }
   ],
   "source": [
    "print (rgridsize)\n",
    "print (bField.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6045e8ef-7ace-42ef-98d3-34a995b6b35e",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "cellsCanvas[meshsize:2*meshsize, meshsize:2*meshsize, meshsize:2*meshsize] = bField\n",
    "cellsCanvas[meshsize-1::-1, meshsize:2*meshsize, meshsize:2*meshsize] = bField\n",
    "cellsCanvas[meshsize-1::-1, meshsize-1::-1, meshsize:2*meshsize] = bField\n",
    "cellsCanvas[meshsize:2*meshsize, meshsize-1::-1, meshsize:2*meshsize] = bField\n",
    "cellsCanvas[meshsize:2*meshsize, meshsize:2*meshsize, 0:meshsize] = bField[:,:,::-1]\n",
    "cellsCanvas[meshsize-1::-1, meshsize:2*meshsize, 0:meshsize] = bField[:,:,::-1]\n",
    "cellsCanvas[meshsize-1::-1, meshsize-1::-1, 0:meshsize] = bField[:,:,::-1]\n",
    "cellsCanvas[meshsize:2*meshsize, meshsize-1::-1, 0:meshsize] = bField[:,:,::-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2786eb0c-107b-4f4f-b7a9-0d9fb2d481ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(200, 200, 200, 3)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cellsCanvas.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f4118567-619f-4c17-ba9b-2ce10a94c364",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0. 0. 0.]\n",
      " [0. 0. 0.]\n",
      " [0. 0. 0.]\n",
      " [0. 0. 0.]\n",
      " [0. 0. 0.]\n",
      " [0. 0. 0.]\n",
      " [0. 0. 0.]\n",
      " [0. 0. 0.]\n",
      " [0. 0. 0.]\n",
      " [0. 0. 0.]]\n",
      "[[ 0.          0.          1.56642803]\n",
      " [ 0.          0.          1.78215368]\n",
      " [ 0.          0.          2.02867004]\n",
      " [ 0.          0.          2.31750561]\n",
      " [ 0.          0.          2.67008588]\n",
      " [ 0.          0.          3.12711506]\n",
      " [ 0.          0.          3.77194321]\n",
      " [ 0.          0.          4.80203391]\n",
      " [ 0.          0.          6.82623291]\n",
      " [ 0.          0.         13.16453276]]\n"
     ]
    }
   ],
   "source": [
    "print (cellsCanvas[0:10,0,0])\n",
    "print (bField[0:10,0,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "942f3078-874a-4c03-b734-ee7f47bdef20",
   "metadata": {},
   "outputs": [],
   "source": [
    "bFieldCanvas = cellsCanvas.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "90c9608c-7f29-4a65-abea-bf45a8c742b8",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 299 ms, sys: 493 ms, total: 792 ms\n",
      "Wall time: 825 ms\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(200, 200, 200)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%time magnitudes = np.linalg.norm( cellsCanvas , axis=-1)* mu0/(4*np.pi)\n",
    "magnitudes.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4378ca0e-b611-42f9-9c48-c46001bbd7ee",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "%time magnitudes = np.linalg.norm( cellsCanvas , axis=-1)* mu0/(4*np.pi)\n",
    "magnitudes.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7597acfd-de93-404a-9961-a4ffe3e64275",
   "metadata": {},
   "outputs": [],
   "source": [
    "magnitudes[:,:,meshsize].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8b9edd0f-d08a-456f-864f-875f17ed4b1f",
   "metadata": {},
   "outputs": [],
   "source": [
    "mags = cutoffPercentile(magnitudes[:,:,meshsize], 99.999)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ec7ca578-2f8a-4972-8059-95f23932a31f",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig,ax = plt.subplots()\n",
    "plt.title(\"1A Current Circular Coil and Magnetic Field (G)\")\n",
    "\n",
    "ax.xaxis.set_major_locator(ticker.NullLocator())\n",
    "ax.yaxis.set_major_locator(ticker.NullLocator())\n",
    "\n",
    "im = ax.imshow(mags*10000)  # Convert to Gauss units\n",
    "cbar = ax.figure.colorbar(im, ax=ax)\n",
    "cbar.ax.set_ylabel(\"Gauss (G)\", rotation=-90, va=\"bottom\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "13d18ffd-d7a2-46ff-921b-1435d3235bf3",
   "metadata": {},
   "outputs": [],
   "source": [
    "cellsCanvas.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9ba1e7ea-df79-4b52-b18f-673e0d126669",
   "metadata": {},
   "outputs": [],
   "source": [
    "mags = cutoffPercentile(magnitudes[meshsize-rgridsize:meshsize+rgridsize,meshsize-rgridsize:meshsize+rgridsize,meshsize], 99.999)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4150cb98-0c95-41a3-8886-da77c07a11b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig,ax = plt.subplots()\n",
    "plt.title(\"1A Current Circular Coil and Magnetic Field (G)\")\n",
    "\n",
    "ax.xaxis.set_major_locator(ticker.NullLocator())\n",
    "ax.yaxis.set_major_locator(ticker.NullLocator())\n",
    "\n",
    "im = ax.imshow(mags*10000)  # Convert to Gauss units\n",
    "cbar = ax.figure.colorbar(im, ax=ax)\n",
    "cbar.ax.set_ylabel(\"Gauss (G)\", rotation=-90, va=\"bottom\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7d54476b-51a5-402e-952f-86c29c358e0d",
   "metadata": {},
   "outputs": [],
   "source": [
    "cellsCanvas = np.zeros(doubleShape)\n",
    "print (\"cells Canvas shape \", cellsCanvas.shape)\n",
    "print (\"bField shape \", bField.shape)\n",
    "xmax, ymax, zmax, _ = cellsCanvas.shape\n",
    "\n",
    "xbmax, ybmax, zbmax, _ = bField.shape\n",
    "\n",
    "# rgridsize is half the coil diameter\n",
    "print (\"coil diameter = \", rgridsize*2)\n",
    "numxfits = xmax//(2*rgridsize)\n",
    "print(\"X axis number of fits, fit width total = \", numxfits, numxfits*rgridsize*2 )\n",
    "# spare is the available space outside the central coil\n",
    "\n",
    "lmargin = (xmax - numxfits*rgridsize*2)//2\n",
    "rmargin = (xmax - lmargin - numxfits*rgridsize*2)\n",
    "print (\"lmargin, rmargin = \", lmargin, rmargin)\n",
    "\n",
    "coilstart = xbmax//2-rgridsize\n",
    "coilend = xbmax//2+rgridsize\n",
    "print (\"coilstart:coilend {}:{}\".format( coilstart, coilend))\n",
    "print (\"prespace = \",coilstart - rgridsize)\n",
    "for ii in range(0,numxfits):\n",
    "    \n",
    "    # Calculate whereabouts the coil should align in the destination array\n",
    "    unadjcellstart = lmargin + 2*rgridsize*ii # Normal unadjusted cell start\n",
    "    unadjcellend = lmargin + 2*rgridsize*(ii+1) # Normal unadjusted cell end\n",
    "\n",
    "    xs1 = max(coilstart - unadjcellstart, 0)  # The first source cell array index position\n",
    "    \n",
    "    # This is the destination array index positions\n",
    "    xd1 = unadjcellstart if xs1 > 0 else unadjcellstart - coilstart\n",
    "    xd2 = min(xd1+(xbmax-xs1), xmax-rmargin)\n",
    "\n",
    "    xs2 = min(xbmax, xs1 + (xd2 - xd1)) # The last source cell array index position\n",
    "    \n",
    "    print (\"{} {}:{} {} {}:{} {}:{}\".format(ii, unadjcellstart, unadjcellend, xs2-xs1 , xs1, xs2, xd1, xd2))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7900186-28db-44fd-b840-b57aae33883b",
   "metadata": {},
   "source": [
    "newCanvas = np.zeros(cellsCanvas.shape)\n",
    "\n",
    "for ii in range(0,5):\n",
    "    for jj in range(0,5):\n",
    "        startcell = meshsize-rgridsize\n",
    "        leftmargin = startcell + ( 2 * ii -1 )*rgridsize\n",
    "        rightmargin = 2*meshsize - leftmargin - 2 * rgridsize\n",
    "        topmargin = startcell + ( 2 * jj -1 )*rgridsize\n",
    "        bottommargin = 2*meshsize - topmargin - 2 * rgridsize\n",
    "        print (\"margin = \", ii,jj, leftmargin, rightmargin, topmargin, bottommargin)\n",
    "\n",
    "        newCanvas[startcell+ii*2*rgridsize:startcell+(ii+1)*2*rgridsize, \\\n",
    "                  startcell+jj*2*rgridsize:startcell+(jj+1)*2*rgridsize] += \\\n",
    "            cellsCanvas[meshsize-rgridsize:meshsize+rgridsize,meshsize-rgridsize:meshsize+rgridsize]\n",
    "        if ii > 0:\n",
    "            newCanvas[startcell-(ii)*2*rgridsize:startcell-(ii-1)*2*rgridsize, \\\n",
    "                      startcell+jj*2*rgridsize:startcell+(jj+1)*2*rgridsize] += \\\n",
    "                cellsCanvas[meshsize-rgridsize:meshsize+rgridsize,meshsize-rgridsize:meshsize+rgridsize]\n",
    "        if ii>0 and jj > 0:    \n",
    "            newCanvas[startcell-(ii)*2*rgridsize:startcell-(ii-1)*2*rgridsize, \\\n",
    "                      startcell-(jj)*2*rgridsize:startcell-(jj-1)*2*rgridsize] += \\\n",
    "                cellsCanvas[meshsize-rgridsize:meshsize+rgridsize,meshsize-rgridsize:meshsize+rgridsize]\n",
    "        if jj > 0:\n",
    "            newCanvas[startcell+(ii)*2*rgridsize:startcell+(ii+1)*2*rgridsize, \\\n",
    "                      startcell-(jj)*2*rgridsize:startcell-(jj-1)*2*rgridsize] += \\\n",
    "                cellsCanvas[meshsize-rgridsize:meshsize+rgridsize,meshsize-rgridsize:meshsize+rgridsize]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "45271554-a869-41ce-90c5-4562847a978f",
   "metadata": {},
   "outputs": [],
   "source": [
    "cellsCanvas.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4734bf53-5392-4914-a22d-fe0a63fc7856",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(bField.shape)\n",
    "print (2*rgridsize)\n",
    "print (meshsize)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1a7211a4-30aa-46f7-a732-b16f22f1ec49",
   "metadata": {},
   "outputs": [],
   "source": [
    "newCanvas = np.zeros(doubleShape)\n",
    "numcells = meshsize // (2*rgridsize)\n",
    "margin = meshsize % rgridsize\n",
    "print(\"num cells = \", numcells, \" remaining margin = \", margin)\n",
    "lmargin = margin // 2\n",
    "rmargin = margin - lmargin\n",
    "\n",
    "for ii in range(0,numcells):\n",
    "    for jj in range(0,numcells):\n",
    "\n",
    "        leftmargin = lmargin + ( 2 * ii )*rgridsize\n",
    "        rightmargin = 2*meshsize - leftmargin - 2 * rgridsize\n",
    "        topmargin = lmargin + ( 2 * jj )*rgridsize\n",
    "        bottommargin = 2*meshsize - topmargin - 2 * rgridsize\n",
    "        #print (\"margins = \", ii,jj, leftmargin, rightmargin, topmargin, bottommargin)\n",
    "        #print (newCanvas[leftmargin:leftmargin+2*rgridsize, topmargin:topmargin+2*rgridsize].shape)\n",
    "        #print (bField[meshsize//2-rgridsize:meshsize//2+rgridsize,meshsize//2-rgridsize:meshsize//2+rgridsize].shape)\n",
    "        newCanvas[leftmargin:leftmargin+2*rgridsize, \\\n",
    "                  topmargin:topmargin+2*rgridsize] += \\\n",
    "            bFieldCanvas[meshsize-rgridsize:meshsize+rgridsize,meshsize-rgridsize:meshsize+rgridsize]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7b16c28f-48d0-4cab-961e-d8797db2d335",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#%time magnitudes = np.linalg.norm( newCanvas * mu0/(4*np.pi), axis=-1)\n",
    "%time magnitudes = np.linalg.norm( newCanvas, axis=-1)\n",
    "\n",
    "magnitudes.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "25e5efea-855c-4b93-9b8e-dcae156caebf",
   "metadata": {},
   "outputs": [],
   "source": [
    "mags = cutoffPercentile(magnitudes[:,:,meshsize//2], 99.999)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "70d977eb-61f1-451e-9c65-eb82f00c121e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots()\n",
    "plt.title(\"1A Current Circular Coil and Magnetic Field (G)\")\n",
    "\n",
    "ax.xaxis.set_major_locator(ticker.NullLocator())\n",
    "ax.yaxis.set_major_locator(ticker.NullLocator())\n",
    "\n",
    "im = ax.imshow(mags*10000)  # Convert to Gauss units\n",
    "cbar = ax.figure.colorbar(im, ax=ax)\n",
    "cbar.ax.set_ylabel(\"Gauss (G)\", rotation=-90, va=\"bottom\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "5c6c9114-7660-4da8-8fe5-98d2c89a31df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(200, 200)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mags = cutoffPercentile(magnitudes[ :, meshsize, :], 99.999)\n",
    "mags.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "be3be80b-41e5-45f9-a978-e2bf1d9a9388",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots()\n",
    "plt.title(\"1A Current Circular Coil and Magnetic Field (G)\")\n",
    "\n",
    "ax.xaxis.set_major_locator(ticker.NullLocator())\n",
    "ax.yaxis.set_major_locator(ticker.NullLocator())\n",
    "\n",
    "im = ax.imshow(mags.T*10000)  # Convert to Gauss units\n",
    "cbar = ax.figure.colorbar(im, ax=ax)\n",
    "cbar.ax.set_ylabel(\"Gauss (G)\", rotation=-90, va=\"bottom\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "daa29b90-4b95-49db-839f-9c78ccf8d580",
   "metadata": {},
   "outputs": [],
   "source": [
    "xgrid, zgrid, _ = createXZGrid(bbox, meshsize*2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "915b89b7-1e8d-47de-82d8-f367b784f8c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots()\n",
    "CS = ax.contour(xgrid*100, zgrid*100, mags.T*10000, levels=(0.0001,0.0005, 0.003))\n",
    "ax.clabel(CS, inline=True, fontsize=8)\n",
    "ax.grid()\n",
    "ax.set_title(\"Magnetic field strength through cross sectional plane of coils in Gauss\", fontsize=10)\n",
    "ax.set(ylabel='x position across coil(cm)', xlabel='z position through middle of coil (cm)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2559ba6f-bee8-42ae-a387-7e847b0470c6",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}