{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9d66efc2-3c21-4f0c-ab77-2924c9c9e3ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "import numpy as np\n",
    "from astropy.io import fits\n",
    "import healpy as hp\n",
    "from healpy.newvisufunc import projview, newprojplot\n",
    "from astropy.wcs import WCS\n",
    "from astropy.coordinates import SkyCoord\n",
    "import astropy.units as u\n",
    "import h5py\n",
    "from scipy.ndimage import map_coordinates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "93b33db6-0f85-4e7f-ae82-2d625628df32",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.colors as mcolors\n",
    "original_cmap = plt.get_cmap('Set1')\n",
    "\n",
    "colors = original_cmap(np.arange(1,6,1))\n",
    "Set11 = mcolors.ListedColormap(colors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b7f0ad0d-f505-48c3-adb9-42fbb602d7b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "fn = '~/Research/DRAGONS/DRAGONS_faraday_depth_cubes/faraday_synthesis/dragons_Npeaks_8sigma.hpx.fits' \n",
    "eightsigmapeaksdat = fits.getdata(fn)\n",
    "eightsigmapeakdshdr = fits.getheader(fn)\n",
    "fn1 = '~/Research/DRAGONS/DRAGONS_faraday_depth_cubes/faraday_synthesis/dragons_Npeaks_8sigma.car.fits' \n",
    "eightsigmapeakscardat = fits.getdata(fn1)\n",
    "eightsigmapeakscarhdr = fits.getheader(fn1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "fbbaef11-5510-4bd8-9e1f-5271dc2be404",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(70.0, 90.0)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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(1,1) \n",
    "im = ax.imshow(eightsigmapeakscardat, \n",
    "               cmap = Set11, vmin =1, vmax = 5,\n",
    "               origin = 'lower'\n",
    "              )\n",
    "plt.colorbar(im)\n",
    "ax.set_xlim(240,250)\n",
    "ax.set_ylim(70,90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "de6621ff-f808-4598-9d8c-f8ec45c82825",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7f134208c1d0>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 850x535.5 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "projview(\n",
    "    eightsigmapeaksdat,\n",
    "    projection_type = 'mollweide', \n",
    "    graticule = True, \n",
    "    graticule_labels=True, \n",
    "    # xlabel=\"Longitude\", \n",
    "    # ylabel=\"Latitude\", \n",
    "    cb_orientation = \"horizontal\",\n",
    "    cmap = Set11,\n",
    "    min = 1, \n",
    "    max = 5,\n",
    "    unit = r'Number of Peaks',\n",
    "    title = r'Number of Faraday Depth Peaks Above 8$\\sigma$', \n",
    "    fontsize={\n",
    "    \"title\":10,\n",
    "    \"xtick_label\":10,\n",
    "    \"ytick_label\":10, \n",
    "    \"cbar_label\":10\n",
    "    }\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3dbfe36b-646f-4ff4-9d54-a953d6362b0d",
   "metadata": {},
   "outputs": [],
   "source": [
    "fn = '~/Research/DRAGONS/DRAGONS_faraday_depth_cubes/faraday_synthesis/dragons_Npeaks_8sigma.car.fits'\n",
    "Npeakscardat = fits.getdata(fn)\n",
    "Npeakscarhdr = fits.getheader(fn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "55691ff3-bab9-4012-ae00-904ef15e6f6f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#loading dirty Faraday Depth Cube for spectra\n",
    "fn = '~/Research/DRAGONS/DRAGONS_faraday_depth_cubes/faraday_synthesis/dragons_FDF_tot_dirty_Kgal.car.fits'\n",
    "FDdirtydat = fits.getdata(fn)\n",
    "FDdirtyhdr = fits.getheader(fn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ddbebc6b-8bf6-4d7b-89c5-35773839c037",
   "metadata": {},
   "outputs": [],
   "source": [
    "#loading clean Faraday Depth Cube for spectra from DRAGONS\n",
    "fn = '~/Research/DRAGONS/DRAGONS_faraday_depth_cubes/faraday_synthesis/dragons_FDF_clean_tot_Kgal.car.fits'\n",
    "FDcleandat = fits.getdata(fn) \n",
    "FDcleanhdr = fits.getheader(fn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "41b13547-d5d4-4ba3-95cf-b20dbfff705b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "WCS Keywords\n",
       "\n",
       "Number of WCS axes: 3\n",
       "CTYPE : 'GLON-CAR' 'GLAT-CAR' 'FDEP'\n",
       "CUNIT : 'deg' 'deg' 'rad / m2'\n",
       "CRVAL : 0.0 0.0 0.0\n",
       "CRPIX : 360.5 180.5 401.0\n",
       "PC1_1 PC1_2 PC1_3  : 1.0 0.0 0.0\n",
       "PC2_1 PC2_2 PC2_3  : 0.0 1.0 0.0\n",
       "PC3_1 PC3_2 PC3_3  : 0.0 0.0 1.0\n",
       "CDELT : -0.5 0.5 0.5\n",
       "NAXIS : 720  360  801"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wcs = WCS(FDcleanhdr)\n",
    "wcs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6f6942eb-26b8-47c2-bd14-c4a5c63eae99",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SIMPLE  =                    T / conforms to FITS standard                      \n",
       "BITPIX  =                  -64 / array data type                                \n",
       "NAXIS   =                    3 / number of array dimensions                     \n",
       "NAXIS1  =                  720                                                  \n",
       "NAXIS2  =                  360                                                  \n",
       "NAXIS3  =                  801                                                  \n",
       "COORDSYS= 'Galactic '          / Ecliptic, Galactic or Celestial (equatorial)   \n",
       "CRPIX2  =                180.5                                                  \n",
       "CDELT2  =                  0.5                                                  \n",
       "CUNIT2  = 'deg     '                                                            \n",
       "BUNIT   = 'K/RMSF  '                                                            \n",
       "CTYPE2  = 'GLAT-CAR'                                                            \n",
       "CRVAL2  =                    0                                                  \n",
       "CTYPE3  = 'FDEP    '           / Faraday depth (linear)                         \n",
       "CRPIX3  =                  401                                                  \n",
       "CRVAL3  =                  0.0 / [rad/m^2] Coordinate value at reference point  \n",
       "CDELT3  =                  0.5 / [rad/m^2] Coordinate increment at reference poi\n",
       "CUNIT3  = 'rad/m^2 '                                                            \n",
       "CTYPE1  = 'GLON-CAR'                                                            \n",
       "CRPIX1  =                360.5                                                  \n",
       "CRVAL1  =                    0                                                  \n",
       "CDELT1  =                 -0.5                                                  \n",
       "CUNIT1  = 'deg     '                                                            \n",
       "INSTRUME= 'DRAO-15 '                                                            "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "FDcleanhdr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b3726cf9-20ba-471d-bc07-851db707426b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SIMPLE  =                    T / conforms to FITS standard                      \n",
       "BITPIX  =                  -64 / array data type                                \n",
       "NAXIS   =                    2 / number of array dimensions                     \n",
       "NAXIS1  =                  720                                                  \n",
       "NAXIS2  =                  360                                                  \n",
       "COORDSYS= 'galactic'           / Ecliptic, Galactic or Celestial (equatorial)   \n",
       "CRPIX2  =                180.5                                                  \n",
       "CDELT2  =                  0.5                                                  \n",
       "CUNIT2  = 'deg     '                                                            \n",
       "BUNIT   = 'NUMBER  '                                                            \n",
       "CTYPE2  = 'GLAT-CAR'                                                            \n",
       "CRVAL2  =                    0                                                  \n",
       "CTYPE1  = 'GLON-CAR'                                                            \n",
       "CRPIX1  =                360.5                                                  \n",
       "CRVAL1  =                    0                                                  \n",
       "CDELT1  =                 -0.5                                                  \n",
       "CUNIT1  = 'deg     '                                                            "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Npeakscarhdr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ccdbe6bf-ccb5-4c82-aebd-3f56f889f191",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figsize = (10, 6)\n",
    "fig, ax = plt.subplots(1,1, figsize = figsize)#, subplot_kw={'projection'})#: wcs.celestial})\n",
    "im0 = ax.imshow(Npeakscardat, origin='lower', cmap = Set11, vmin=1, vmax=5)\n",
    "\n",
    "plt.colorbar(im0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "fa2ada02-6dfd-4c1e-b079-e46451aaf78d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 []\n"
     ]
    }
   ],
   "source": [
    "mask = (Npeakscardat == 0)\n",
    "\n",
    "y_indices, x_indices = np.where(mask)\n",
    "\n",
    "zero_comp_pix_coord = list(zip(x_indices, y_indices)) \n",
    "\n",
    "print(len(zero_comp_pix_coord), zero_comp_pix_coord[:5])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c28ee90a-cdd9-4269-9752-04b9f6eef48b",
   "metadata": {},
   "source": [
    "# Finding lines of sight with given faraday complexity, as well as the regions with this complexity. Region here, defined as an area greater than or equal to 59 square pixels. Based on the beam area of the 15 m telescope at DRAO"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9f0aa84-a305-4f89-b4b6-66dd826e5559",
   "metadata": {},
   "source": [
    "## faraday complexity: 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "2253d2cb-a71f-4baf-8618-e45e00148aee",
   "metadata": {},
   "outputs": [],
   "source": [
    "wcs = WCS(Npeakscarhdr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "2b424aee-c8be-4387-8dfb-d22a6682d752",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "37695 [[113  12]\n",
      " [114  12]\n",
      " [115  12]\n",
      " [116  12]\n",
      " [133  12]]\n"
     ]
    }
   ],
   "source": [
    "mask = (0 < Npeakscardat) & (Npeakscardat < 2)\n",
    "\n",
    "yIndices, xIndices = np.where(mask)\n",
    "\n",
    "oneCompPixCoord = np.column_stack((xIndices, yIndices)) \n",
    "\n",
    "print(len(oneCompPixCoord), oneCompPixCoord[:5]) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "dd823be2-3f7b-410a-ad86-ded7a11673bd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of regions: 36726, distinct lines of sight: 37684\n"
     ]
    }
   ],
   "source": [
    "wcs = WCS(Npeakscarhdr, naxis =2)\n",
    "\n",
    "beamLimit = 7.2 #pixels \n",
    "group = list(range(len(oneCompPixCoord)))\n",
    "newLoS = []\n",
    "while group: \n",
    "    currentIndex = group.pop(0)\n",
    "    currentRegion = [tuple(oneCompPixCoord[currentIndex])]\n",
    "    queue = [oneCompPixCoord[currentIndex]] \n",
    "\n",
    "    while queue: \n",
    "        point = queue.pop(0)\n",
    "\n",
    "        if not group: \n",
    "            break \n",
    "\n",
    "        groupCoords = oneCompPixCoord[group] \n",
    "        distances = np.linalg.norm(groupCoords - point, axis=1)\n",
    "\n",
    "        closeIndices = np.where(distances <= beamLimit)[0]\n",
    "\n",
    "        for idx in sorted(closeIndices, reverse=True):\n",
    "            actualIdx = group.pop(idx)\n",
    "            pixel = tuple(oneCompPixCoord[actualIdx])\n",
    "            currentRegion.append(pixel)\n",
    "            queue.append(oneCompPixCoord[actualIdx])\n",
    "        \n",
    "        newLoS.append(currentRegion)\n",
    "\n",
    "minimumPixArea = 59 \n",
    "resolvedRegions = [r for r in newLoS if len(r) >= minimumPixArea]\n",
    "\n",
    "print(f\"Number of regions: {len(resolvedRegions)}, distinct lines of sight: {len(newLoS)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "69a30ed0-00d2-45e2-b299-2fd0e3703a7e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Centroids shape: (37684, 2)\n",
      "Example centroid: [111.15854 -82.05285]\n"
     ]
    }
   ],
   "source": [
    "#try doing the same thing with centroids instead \n",
    "compOneLoSCentroids = np.zeros((len(newLoS), 2), dtype=np.float32)\n",
    "\n",
    "for i, region in enumerate(newLoS):\n",
    "    regionArray = np.array(region,dtype=np.int32)\n",
    "    skyCoords = wcs.pixel_to_world(regionArray[:, 0], regionArray[:,1])\n",
    "    galCoords = skyCoords.galactic \n",
    "    compOneLoSCentroids[i, 0] = np.mean(galCoords.l.deg)\n",
    "    compOneLoSCentroids[i, 1] = np.mean(galCoords.b.deg)\n",
    "\n",
    "print(f\"Centroids shape: {compOneLoSCentroids.shape}\")\n",
    "print(f\"Example centroid: {compOneLoSCentroids[0]}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d457c58-2c61-44c5-ab39-90bc2362c5cf",
   "metadata": {},
   "source": [
    "## complexity = 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a9c0f60f-6b31-4f2d-b67d-12e4305656e1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "60185 [[69 12]\n",
      " [70 12]\n",
      " [71 12]\n",
      " [72 12]\n",
      " [73 12]]\n"
     ]
    }
   ],
   "source": [
    "mask = (1 < Npeakscardat) & (Npeakscardat < 3)\n",
    "\n",
    "yIndices, xIndices = np.where(mask)\n",
    "\n",
    "twoCompPixCoord = np.column_stack((xIndices, yIndices)) \n",
    "\n",
    "print(len(twoCompPixCoord), twoCompPixCoord[:5]) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "20d667ae-12a9-49cd-9c8c-b5e91f044ca2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of regions: 59933, distinct lines of sight: 60153\n"
     ]
    }
   ],
   "source": [
    "wcs = WCS(Npeakscarhdr, naxis =2)\n",
    "\n",
    "beamLimit = 7.2 #pixels \n",
    "group = list(range(len(twoCompPixCoord)))\n",
    "compTwoNewLoS = []\n",
    "while group: \n",
    "    currentIndex = group.pop(0)\n",
    "    currentRegion = [tuple(twoCompPixCoord[currentIndex])]\n",
    "    queue = [twoCompPixCoord[currentIndex]] \n",
    "\n",
    "    while queue: \n",
    "        point = queue.pop(0)\n",
    "\n",
    "        if not group: \n",
    "            break \n",
    "\n",
    "        groupCoords = twoCompPixCoord[group] \n",
    "        distances = np.linalg.norm(groupCoords - point, axis=1)\n",
    "\n",
    "        closeIndices = np.where(distances <= beamLimit)[0]\n",
    "\n",
    "        for idx in sorted(closeIndices, reverse=True):\n",
    "            actualIdx = group.pop(idx)\n",
    "            pixel = tuple(twoCompPixCoord[actualIdx])\n",
    "            currentRegion.append(pixel)\n",
    "            queue.append(twoCompPixCoord[actualIdx])\n",
    "        \n",
    "        compTwoNewLoS.append(currentRegion.copy())\n",
    "\n",
    "minimumPixArea = 59 \n",
    "compTwoResolvedRegions = [r for r in compTwoNewLoS if len(r) >= minimumPixArea]\n",
    "\n",
    "print(f\"Number of regions: {len(compTwoResolvedRegions)}, distinct lines of sight: {len(compTwoNewLoS)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "ac4446f0-3bb4-47be-84c5-fda14eb943bc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Centroids shape: (60153, 2)\n",
      "Example centroid: [144.16667  -83.354164]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([141.88725, -83.19118], dtype=float32)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#try doing the same thing with centroids instead \n",
    "wcs = WCS(Npeakscarhdr, naxis=2)\n",
    "compTwoLoSCentroids = np.zeros((len(compTwoNewLoS), 2), dtype=np.float32)\n",
    "\n",
    "for i, region in enumerate(compTwoNewLoS):\n",
    "    regionArray = np.array(region,dtype=np.int32)\n",
    "    skyCoords = wcs.pixel_to_world(regionArray[:, 0], regionArray[:,1])\n",
    "    galCoords = skyCoords.galactic \n",
    "    compTwoLoSCentroids[i, 0] = np.mean(galCoords.l.deg)\n",
    "    compTwoLoSCentroids[i, 1] = np.mean(galCoords.b.deg)\n",
    "\n",
    "print(f\"Centroids shape: {compTwoLoSCentroids.shape}\")\n",
    "print(f\"Example centroid: {compTwoLoSCentroids[0]}\")\n",
    "\n",
    "compTwoLoSCentroids[5]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "439187fd-4de4-4ffd-8f0f-202270ab7574",
   "metadata": {},
   "source": [
    "## complexity = 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "ead88ab2-f932-44f0-84b1-a4cae12dd149",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "45373 [[55 13]\n",
      " [57 13]\n",
      " [58 13]\n",
      " [59 13]\n",
      " [60 13]]\n"
     ]
    }
   ],
   "source": [
    "mask = (2 < Npeakscardat) & (Npeakscardat < 4)\n",
    "\n",
    "yIndices, xIndices = np.where(mask)\n",
    "\n",
    "threeCompPixCoord = np.column_stack((xIndices, yIndices)) \n",
    "\n",
    "print(len(threeCompPixCoord), threeCompPixCoord[:5]) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "b0d5148d-d524-444f-9409-f15447fc4da2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of regions: 45125, distinct lines of sight: 45371\n"
     ]
    }
   ],
   "source": [
    "wcs = WCS(Npeakscarhdr, naxis =2)\n",
    "\n",
    "beamLimit = 7.2 #pixels \n",
    "group = list(range(len(threeCompPixCoord)))\n",
    "compThreeNewLoS = []\n",
    "while group: \n",
    "    currentIndex = group.pop(0)\n",
    "    currentRegion = [tuple(threeCompPixCoord[currentIndex])]\n",
    "    queue = [threeCompPixCoord[currentIndex]] \n",
    "\n",
    "    while queue: \n",
    "        point = queue.pop(0)\n",
    "\n",
    "        if not group: \n",
    "            break \n",
    "\n",
    "        groupCoords = threeCompPixCoord[group] \n",
    "        distances = np.linalg.norm(groupCoords - point, axis=1)\n",
    "\n",
    "        closeIndices = np.where(distances <= beamLimit)[0]\n",
    "\n",
    "        for idx in sorted(closeIndices, reverse=True):\n",
    "            actualIdx = group.pop(idx)\n",
    "            pixel = tuple(threeCompPixCoord[actualIdx])\n",
    "            currentRegion.append(pixel)\n",
    "            queue.append(threeCompPixCoord[actualIdx])\n",
    "        \n",
    "        compThreeNewLoS.append(currentRegion)\n",
    "\n",
    "minimumPixArea = 59 \n",
    "compThreeResolvedRegions = [r for r in compThreeNewLoS if len(r) >= minimumPixArea]\n",
    "\n",
    "print(f\"Number of regions: {len(compThreeResolvedRegions)}, distinct lines of sight: {len(compThreeNewLoS)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "9cad20f5-e860-4527-a597-59a2a5777e07",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Centroids shape: (45371, 2)\n",
      "Example centroid: [125.26684 -75.33873]\n"
     ]
    }
   ],
   "source": [
    "#try doing the same thing with centroids instead \n",
    "compThreeLoSCentroids = np.zeros((len(compThreeNewLoS), 2), dtype=np.float32)\n",
    "\n",
    "for i, region in enumerate(compThreeNewLoS):\n",
    "    regionArray = np.array(region,dtype=np.int32)\n",
    "    skyCoords = wcs.pixel_to_world(regionArray[:, 0], regionArray[:,1])\n",
    "    galCoords = skyCoords.galactic \n",
    "    compThreeLoSCentroids[i, 0] = np.mean(galCoords.l.deg)\n",
    "    compThreeLoSCentroids[i, 1] = np.mean(galCoords.b.deg)\n",
    "\n",
    "print(f\"Centroids shape: {compThreeLoSCentroids.shape}\")\n",
    "print(f\"Example centroid: {compThreeLoSCentroids[0]}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "032473e5-dbfb-4c1d-a19e-4d904bfc97cb",
   "metadata": {},
   "source": [
    "## complexity= 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "c430a48b-1ee0-4875-ac26-21394d5d628f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "18940 [[32 25]\n",
      " [33 25]\n",
      " [34 25]\n",
      " [35 25]\n",
      " [36 25]]\n"
     ]
    }
   ],
   "source": [
    "mask = (3 < Npeakscardat) & (Npeakscardat < 5)\n",
    "\n",
    "yIndices, xIndices = np.where(mask)\n",
    "\n",
    "fourCompPixCoord = np.column_stack((xIndices, yIndices)) \n",
    "\n",
    "print(len(fourCompPixCoord), fourCompPixCoord[:5]) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "fd4f6de6-a5de-4b07-91f9-8edcc345e7fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of regions: 18227, distinct lines of sight: 18920\n"
     ]
    }
   ],
   "source": [
    "wcs = WCS(Npeakscarhdr, naxis =2)\n",
    "\n",
    "beamLimit = 7.2 #pixels \n",
    "group = list(range(len(fourCompPixCoord)))\n",
    "compFourNewLoS = []\n",
    "while group: \n",
    "    currentIndex = group.pop(0)\n",
    "    currentRegion = [tuple(fourCompPixCoord[currentIndex])]\n",
    "    queue = [fourCompPixCoord[currentIndex]] \n",
    "\n",
    "    while queue: \n",
    "        point = queue.pop(0)\n",
    "\n",
    "        if not group: \n",
    "            break \n",
    "\n",
    "        groupCoords = fourCompPixCoord[group] \n",
    "        distances = np.linalg.norm(groupCoords - point, axis=1)\n",
    "\n",
    "        closeIndices = np.where(distances <= beamLimit)[0]\n",
    "\n",
    "        for idx in sorted(closeIndices, reverse=True):\n",
    "            actualIdx = group.pop(idx)\n",
    "            pixel = tuple(fourCompPixCoord[actualIdx])\n",
    "            currentRegion.append(pixel)\n",
    "            queue.append(fourCompPixCoord[actualIdx])\n",
    "        \n",
    "        compFourNewLoS.append(currentRegion)\n",
    "\n",
    "minimumPixArea = 59 \n",
    "compFourResolvedRegions = [r for r in compFourNewLoS if len(r) >= minimumPixArea]\n",
    "\n",
    "print(f\"Number of regions: {len(compFourResolvedRegions)}, distinct lines of sight: {len(compFourNewLoS)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "4c3634ec-1c8b-4029-ba7c-66f63c0d5917",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Centroids shape: (18920, 2)\n",
      "Example centroid: [162.   -77.25]\n"
     ]
    }
   ],
   "source": [
    "#try doing the same thing with centroids instead \n",
    "compFourLoSCentroids = np.zeros((len(compFourNewLoS), 2), dtype=np.float32)\n",
    "\n",
    "for i, region in enumerate(compFourNewLoS):\n",
    "    regionArray = np.array(region,dtype=np.int32)\n",
    "    skyCoords = wcs.pixel_to_world(regionArray[:, 0], regionArray[:,1])\n",
    "    galCoords = skyCoords.galactic \n",
    "    compFourLoSCentroids[i, 0] = np.mean(galCoords.l.deg)\n",
    "    compFourLoSCentroids[i, 1] = np.mean(galCoords.b.deg)\n",
    "\n",
    "print(f\"Centroids shape: {compFourLoSCentroids.shape}\")\n",
    "print(f\"Example centroid: {compFourLoSCentroids[0]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "1914d01f-12bd-4f9b-a15c-03f67c169f2a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of LoS: 18920\n",
      "Example LoS galactic coords \n",
      "[[163.75 -77.25]\n",
      " [160.25 -77.25]\n",
      " [160.75 -77.25]\n",
      " [161.25 -77.25]\n",
      " [161.75 -77.25]\n",
      " [162.25 -77.25]\n",
      " [162.75 -77.25]\n",
      " [163.25 -77.25]],\n",
      "[[181.25 -73.75]\n",
      " [180.75 -70.25]\n",
      " [181.25 -70.25]\n",
      " [181.75 -70.25]\n",
      " [180.25 -70.75]\n",
      " [180.75 -70.75]\n",
      " [181.25 -70.75]\n",
      " [181.75 -70.75]\n",
      " [182.25 -70.75]\n",
      " [182.75 -70.75]\n",
      " [180.25 -71.25]\n",
      " [180.75 -71.25]\n",
      " [181.25 -71.25]\n",
      " [181.75 -71.25]\n",
      " [182.25 -71.25]\n",
      " [182.75 -71.25]\n",
      " [183.25 -71.25]\n",
      " [180.25 -71.75]\n",
      " [180.75 -71.75]\n",
      " [181.25 -71.75]\n",
      " [181.75 -71.75]\n",
      " [182.25 -71.75]\n",
      " [181.75 -72.25]\n",
      " [181.25 -73.25]\n",
      " [181.75 -73.25]\n",
      " [180.75 -73.75]\n",
      " [181.25 -66.75]\n",
      " [181.75 -67.25]\n",
      " [182.25 -67.25]\n",
      " [182.25 -67.75]\n",
      " [182.75 -67.75]\n",
      " [183.25 -67.75]\n",
      " [180.25 -68.25]\n",
      " [180.75 -68.25]\n",
      " [181.75 -68.25]\n",
      " [182.25 -68.25]\n",
      " [182.75 -68.25]\n",
      " [183.25 -68.25]\n",
      " [180.25 -68.75]\n",
      " [180.75 -68.75]\n",
      " [181.25 -68.75]\n",
      " [181.75 -68.75]\n",
      " [182.25 -68.75]\n",
      " [182.75 -68.75]\n",
      " [183.25 -68.75]\n",
      " [183.75 -68.75]\n",
      " [180.25 -69.25]\n",
      " [180.75 -69.25]\n",
      " [181.25 -69.25]\n",
      " [181.75 -69.25]\n",
      " [182.25 -69.25]\n",
      " [182.75 -69.25]\n",
      " [183.25 -69.25]\n",
      " [183.75 -69.25]\n",
      " [180.25 -69.75]\n",
      " [180.75 -69.75]\n",
      " [181.25 -69.75]\n",
      " [181.75 -69.75]\n",
      " [182.25 -69.75]\n",
      " [182.75 -69.75]\n",
      " [183.25 -69.75]\n",
      " [183.75 -69.75]\n",
      " [184.25 -69.75]\n",
      " [180.25 -70.25]\n",
      " [182.25 -70.25]\n",
      " [182.75 -70.25]\n",
      " [183.25 -70.25]\n",
      " [183.75 -70.25]\n",
      " [184.25 -70.25]\n",
      " [183.25 -70.75]\n",
      " [183.75 -70.75]\n",
      " [184.25 -70.75]\n",
      " [181.75 -66.75]\n",
      " [182.75 -67.25]\n",
      " [183.75 -67.75]\n",
      " [183.75 -68.25]\n",
      " [184.25 -68.75]\n",
      " [184.25 -69.25]\n",
      " [184.75 -69.75]\n",
      " [184.75 -70.25]\n",
      " [182.25 -66.75]\n",
      " [183.25 -67.25]\n",
      " [184.25 -67.75]\n",
      " [184.25 -68.25]\n",
      " [184.75 -68.75]\n",
      " [184.75 -69.25]\n",
      " [185.25 -69.75]\n",
      " [184.75 -68.25]\n",
      " [185.25 -69.25]\n",
      " [185.25 -68.25]\n",
      " [185.25 -68.75]\n",
      " [185.75 -69.25]\n",
      " [185.75 -68.75]\n",
      " [180.25 -65.25]\n",
      " [180.75 -65.25]\n",
      " [180.25 -65.75]\n",
      " [180.75 -65.75]\n",
      " [181.25 -65.75]\n",
      " [180.25 -66.25]\n",
      " [180.75 -66.25]\n",
      " [181.25 -66.25]\n",
      " [181.75 -66.25]\n",
      " [182.25 -66.25]\n",
      " [182.75 -66.25]\n",
      " [182.75 -66.75]\n",
      " [183.25 -66.75]\n",
      " [183.75 -66.75]\n",
      " [184.25 -66.75]\n",
      " [184.75 -66.75]\n",
      " [183.75 -67.25]\n",
      " [184.25 -67.25]\n",
      " [184.75 -67.25]\n",
      " [185.25 -66.75]\n",
      " [185.25 -67.25]\n",
      " [184.75 -67.75]\n",
      " [185.25 -67.75]\n",
      " [185.75 -67.25]\n",
      " [185.75 -67.75]\n",
      " [185.75 -68.25]\n",
      " [186.25 -67.25]\n",
      " [186.25 -67.75]\n",
      " [186.25 -68.25]\n",
      " [186.75 -67.25]\n",
      " [186.75 -67.75]\n",
      " [186.75 -68.25]\n",
      " [186.25 -68.75]\n",
      " [187.25 -68.25]\n",
      " [187.25 -67.75]\n",
      " [187.75 -68.25]\n",
      " [187.75 -67.75]\n",
      " [188.25 -68.25]\n",
      " [188.25 -67.75]\n",
      " [188.75 -67.75]\n",
      " [189.25 -67.75]\n",
      " [189.75 -67.75]\n",
      " [190.25 -67.25]\n",
      " [190.25 -67.75]\n",
      " [190.75 -67.75]\n",
      " [190.75 -67.25]\n",
      " [190.75 -69.75]\n",
      " [190.25 -70.25]\n",
      " [191.25 -69.75]\n",
      " [190.75 -70.25]\n",
      " [190.75 -70.75]\n",
      " [191.75 -67.25]\n",
      " [192.25 -67.25]\n",
      " [191.25 -70.25]\n",
      " [191.75 -69.75]\n",
      " [191.75 -70.25]\n",
      " [192.25 -69.75]\n",
      " [192.25 -70.25]\n",
      " [191.25 -70.75]\n",
      " [192.75 -69.75]\n",
      " [192.75 -70.25]\n",
      " [191.75 -70.75]\n",
      " [193.25 -70.25]\n",
      " [192.25 -70.75]\n",
      " [192.75 -70.75]\n",
      " [193.25 -70.75]\n",
      " [193.75 -70.75]\n",
      " [192.25 -71.25]\n",
      " [192.75 -71.25]\n",
      " [193.25 -71.25]\n",
      " [193.75 -71.25]\n",
      " [192.25 -71.75]\n",
      " [192.75 -71.75]\n",
      " [193.25 -71.75]\n",
      " [191.75 -72.25]\n",
      " [192.25 -72.25]\n",
      " [192.75 -72.25]\n",
      " [193.25 -72.25]\n",
      " [194.25 -70.75]\n",
      " [194.25 -71.25]\n",
      " [193.75 -71.75]\n",
      " [193.75 -72.25]\n",
      " [194.25 -71.75]\n",
      " [193.75 -72.75]\n",
      " [194.75 -71.25]\n",
      " [194.25 -72.25]\n",
      " [194.75 -71.75]\n",
      " [194.25 -72.75]\n",
      " [195.25 -71.25]\n",
      " [194.75 -72.25]\n",
      " [195.25 -71.75]\n",
      " [194.75 -72.75]\n",
      " [195.75 -71.25]\n",
      " [195.25 -72.25]\n",
      " [195.75 -71.75]\n",
      " [195.25 -72.75]\n",
      " [196.25 -71.75]\n",
      " [195.75 -72.25]\n",
      " [195.75 -72.75]\n",
      " [196.25 -72.25]\n",
      " [196.75 -71.75]\n",
      " [196.75 -72.25]\n",
      " [196.25 -72.75]]\n"
     ]
    }
   ],
   "source": [
    "wcs = WCS(Npeakscarhdr)\n",
    "compFourLoSGalCoords = []\n",
    "for region in compFourNewLoS:\n",
    "    regionArray = np.array(region)\n",
    "    skyCoords = wcs.pixel_to_world(regionArray[:, 0], regionArray[:, 1])\n",
    "    galCoords = skyCoords.galactic\n",
    "    galArray = np.column_stack((galCoords.l.deg, galCoords.b.deg))\n",
    "    compFourLoSGalCoords.append(galArray)\n",
    "\n",
    "print(f\"Number of LoS: {len(compFourLoSGalCoords)}\")\n",
    "print(f\"Example LoS galactic coords \\n{compFourLoSGalCoords[0]},\\n{compFourLoSGalCoords[17]}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2ec33f4-d812-4aac-9cda-e3dd029120b9",
   "metadata": {},
   "source": [
    "## complexity = 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "fd2d8a22-2a28-47f9-b804-58a4d9b1dfb5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5602 [[ 95  42]\n",
      " [ 97  43]\n",
      " [697  45]\n",
      " [272  53]\n",
      " [273  53]]\n"
     ]
    }
   ],
   "source": [
    "mask = (4 < Npeakscardat) & (Npeakscardat < 6)\n",
    "\n",
    "yIndices, xIndices = np.where(mask)\n",
    "\n",
    "fiveCompPixCoord = np.column_stack((xIndices, yIndices)) \n",
    "\n",
    "print(len(fiveCompPixCoord), fiveCompPixCoord[:5]) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "cdb82f65-def6-4377-898f-209e99569568",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of regions: 4449, distinct lines of sight: 5593\n"
     ]
    }
   ],
   "source": [
    "wcs = WCS(Npeakscarhdr, naxis =2)\n",
    "\n",
    "beamLimit = 7.2 #pixels \n",
    "group = list(range(len(fiveCompPixCoord)))\n",
    "compFiveNewLoS = []\n",
    "while group: \n",
    "    currentIndex = group.pop(0)\n",
    "    currentRegion = [tuple(fiveCompPixCoord[currentIndex])]\n",
    "    queue = [fiveCompPixCoord[currentIndex]] \n",
    "\n",
    "    while queue: \n",
    "        point = queue.pop(0)\n",
    "\n",
    "        if not group: \n",
    "            break \n",
    "\n",
    "        groupCoords = fiveCompPixCoord[group] \n",
    "        distances = np.linalg.norm(groupCoords - point, axis=1)\n",
    "\n",
    "        closeIndices = np.where(distances <= beamLimit)[0]\n",
    "\n",
    "        for idx in sorted(closeIndices, reverse=True):\n",
    "            actualIdx = group.pop(idx)\n",
    "            pixel = tuple(fiveCompPixCoord[actualIdx])\n",
    "            currentRegion.append(pixel)\n",
    "            queue.append(fiveCompPixCoord[actualIdx])\n",
    "        \n",
    "        compFiveNewLoS.append(currentRegion)\n",
    "\n",
    "minimumPixArea = 59 \n",
    "compFiveResolvedRegions = [r for r in compFiveNewLoS if len(r) >= minimumPixArea]\n",
    "\n",
    "print(f\"Number of regions: {len(compFiveResolvedRegions)}, distinct lines of sight: {len(compFiveNewLoS)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "afbcb552-84b7-4e5d-a078-a16e144b2b13",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Centroids shape: (5593, 2)\n",
      "Example centroid: [131.75 -68.5 ]\n"
     ]
    }
   ],
   "source": [
    "#try doing the same thing with centroids instead \n",
    "compFiveLoSCentroids = np.zeros((len(compFiveNewLoS), 2), dtype=np.float32)\n",
    "\n",
    "for i, region in enumerate(compFiveNewLoS):\n",
    "    regionArray = np.array(region,dtype=np.int32)\n",
    "    skyCoords = wcs.pixel_to_world(regionArray[:, 0], regionArray[:,1])\n",
    "    galCoords = skyCoords.galactic \n",
    "    compFiveLoSCentroids[i, 0] = np.mean(galCoords.l.deg)\n",
    "    compFiveLoSCentroids[i, 1] = np.mean(galCoords.b.deg)\n",
    "\n",
    "print(f\"Centroids shape: {compFiveLoSCentroids.shape}\")\n",
    "print(f\"Example centroid: {compFiveLoSCentroids[0]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "d12e7976-796c-4899-ac46-9631f94511f1",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of LoS: 5593\n",
      "Example LoS galactic coords \n",
      "[[132.25 -68.75]\n",
      " [131.25 -68.25]],\n",
      "[[ 43.75 -63.25]\n",
      " [ 43.75 -59.75]\n",
      " [ 44.25 -59.75]\n",
      " [ 42.25 -60.25]\n",
      " [ 42.75 -60.25]\n",
      " [ 43.75 -60.25]\n",
      " [ 41.25 -60.75]\n",
      " [ 41.75 -60.75]\n",
      " [ 42.25 -60.75]\n",
      " [ 42.75 -60.75]\n",
      " [ 43.25 -60.75]\n",
      " [ 43.75 -60.75]\n",
      " [ 44.25 -60.75]\n",
      " [ 42.25 -61.25]\n",
      " [ 42.75 -61.25]\n",
      " [ 42.25 -61.75]\n",
      " [ 42.75 -61.75]\n",
      " [ 43.25 -61.75]\n",
      " [ 43.75 -61.75]\n",
      " [ 44.25 -61.75]\n",
      " [ 44.75 -61.75]\n",
      " [ 40.75 -62.25]\n",
      " [ 41.25 -62.25]\n",
      " [ 41.75 -62.25]\n",
      " [ 42.25 -62.25]\n",
      " [ 42.75 -62.25]\n",
      " [ 43.25 -62.25]\n",
      " [ 43.75 -62.25]\n",
      " [ 40.75 -62.75]\n",
      " [ 41.25 -62.75]\n",
      " [ 41.75 -62.75]\n",
      " [ 42.25 -62.75]\n",
      " [ 42.75 -62.75]\n",
      " [ 43.25 -62.75]\n",
      " [ 43.75 -62.75]\n",
      " [ 40.75 -63.25]\n",
      " [ 41.25 -63.25]\n",
      " [ 41.75 -63.25]\n",
      " [ 42.25 -63.25]\n",
      " [ 42.75 -63.25]\n",
      " [ 43.25 -63.25]\n",
      " [ 42.25 -56.75]\n",
      " [ 42.75 -56.75]\n",
      " [ 45.25 -56.75]\n",
      " [ 41.75 -57.25]\n",
      " [ 45.75 -57.25]\n",
      " [ 46.25 -57.25]\n",
      " [ 46.25 -57.75]\n",
      " [ 42.25 -58.25]\n",
      " [ 42.75 -58.25]\n",
      " [ 43.25 -58.25]\n",
      " [ 43.75 -58.25]\n",
      " [ 44.25 -58.25]\n",
      " [ 41.25 -58.75]\n",
      " [ 41.75 -58.75]\n",
      " [ 42.25 -58.75]\n",
      " [ 42.75 -58.75]\n",
      " [ 43.25 -58.75]\n",
      " [ 43.75 -58.75]\n",
      " [ 44.25 -58.75]\n",
      " [ 44.75 -58.75]\n",
      " [ 42.25 -59.25]\n",
      " [ 42.75 -59.25]\n",
      " [ 43.25 -59.25]\n",
      " [ 45.25 -59.25]\n",
      " [ 41.25 -59.75]\n",
      " [ 41.75 -59.75]\n",
      " [ 42.25 -59.75]\n",
      " [ 44.75 -59.75]\n",
      " [ 45.25 -59.75]\n",
      " [ 40.25 -60.25]\n",
      " [ 40.75 -60.25]\n",
      " [ 41.25 -60.25]\n",
      " [ 41.75 -60.25]\n",
      " [ 40.75 -60.75]\n",
      " [ 45.75 -56.75]\n",
      " [ 41.75 -56.75]\n",
      " [ 39.25 -58.75]\n",
      " [ 39.25 -59.25]\n",
      " [ 38.75 -59.75]\n",
      " [ 38.75 -60.75]\n",
      " [ 39.25 -60.75]\n",
      " [ 39.75 -60.75]\n",
      " [ 40.25 -60.75]\n",
      " [ 39.75 -62.25]\n",
      " [ 40.25 -62.25]\n",
      " [ 38.75 -58.25]\n",
      " [ 38.75 -58.75]\n",
      " [ 38.75 -59.25]\n",
      " [ 38.25 -59.75]\n",
      " [ 38.25 -60.25]\n",
      " [ 41.75 -53.25]\n",
      " [ 41.25 -53.75]\n",
      " [ 41.75 -53.75]\n",
      " [ 41.25 -54.25]\n",
      " [ 41.75 -54.25]\n",
      " [ 40.75 -54.75]\n",
      " [ 41.75 -54.75]\n",
      " [ 42.25 -54.75]\n",
      " [ 42.75 -54.75]\n",
      " [ 39.25 -55.25]\n",
      " [ 39.75 -55.25]\n",
      " [ 40.25 -55.25]\n",
      " [ 40.75 -55.25]\n",
      " [ 41.75 -55.25]\n",
      " [ 42.25 -55.25]\n",
      " [ 42.75 -55.25]\n",
      " [ 43.25 -55.25]\n",
      " [ 39.25 -55.75]\n",
      " [ 39.75 -55.75]\n",
      " [ 40.25 -55.75]\n",
      " [ 41.75 -55.75]\n",
      " [ 42.25 -55.75]\n",
      " [ 42.75 -55.75]\n",
      " [ 43.25 -55.75]\n",
      " [ 38.75 -56.25]\n",
      " [ 39.25 -56.25]\n",
      " [ 41.75 -56.25]\n",
      " [ 42.25 -56.25]\n",
      " [ 42.75 -56.25]\n",
      " [ 45.25 -56.25]\n",
      " [ 38.75 -56.75]\n",
      " [ 38.75 -55.75]\n",
      " [ 38.25 -56.75]\n",
      " [ 38.25 -57.25]\n",
      " [ 38.25 -57.75]\n",
      " [ 37.75 -57.75]\n",
      " [ 41.25 -53.25]\n",
      " [ 38.25 -56.25]\n",
      " [ 37.25 -56.25]\n",
      " [ 36.75 -56.75]\n",
      " [ 37.25 -56.75]\n",
      " [ 37.75 -56.75]\n",
      " [ 36.75 -57.25]\n",
      " [ 37.25 -57.25]\n",
      " [ 37.75 -57.25]\n",
      " [ 40.25 -50.25]\n",
      " [ 41.25 -50.25]\n",
      " [ 41.75 -50.25]\n",
      " [ 39.25 -50.75]\n",
      " [ 39.75 -50.75]\n",
      " [ 40.25 -50.75]\n",
      " [ 40.75 -50.75]\n",
      " [ 41.25 -50.75]\n",
      " [ 41.75 -50.75]\n",
      " [ 42.25 -50.75]\n",
      " [ 39.25 -51.25]\n",
      " [ 39.75 -51.25]\n",
      " [ 40.25 -51.25]\n",
      " [ 40.75 -51.25]\n",
      " [ 41.75 -51.25]\n",
      " [ 42.25 -51.25]\n",
      " [ 42.75 -51.25]\n",
      " [ 39.25 -51.75]\n",
      " [ 39.75 -51.75]\n",
      " [ 40.25 -51.75]\n",
      " [ 40.75 -51.75]\n",
      " [ 42.25 -51.75]\n",
      " [ 42.75 -51.75]\n",
      " [ 43.25 -51.75]\n",
      " [ 39.75 -52.25]\n",
      " [ 40.25 -52.25]\n",
      " [ 40.75 -52.25]\n",
      " [ 41.25 -52.25]\n",
      " [ 41.75 -52.25]\n",
      " [ 40.25 -52.75]\n",
      " [ 40.75 -52.75]\n",
      " [ 41.25 -52.75]\n",
      " [ 41.75 -52.75]\n",
      " [ 40.25 -53.25]\n",
      " [ 40.75 -53.25]\n",
      " [ 38.75 -51.25]\n",
      " [ 39.75 -50.25]\n",
      " [ 38.75 -50.75]\n",
      " [ 37.25 -48.75]\n",
      " [ 37.75 -48.75]\n",
      " [ 37.25 -49.25]\n",
      " [ 37.75 -49.25]\n",
      " [ 38.25 -49.25]\n",
      " [ 38.75 -49.25]\n",
      " [ 36.75 -49.75]\n",
      " [ 37.25 -49.75]\n",
      " [ 37.75 -49.75]\n",
      " [ 38.25 -49.75]\n",
      " [ 38.75 -49.75]\n",
      " [ 39.25 -49.75]\n",
      " [ 39.75 -49.75]\n",
      " [ 37.25 -50.25]\n",
      " [ 37.75 -50.25]\n",
      " [ 38.25 -50.25]\n",
      " [ 38.75 -50.25]\n",
      " [ 39.25 -50.25]\n",
      " [ 38.25 -50.75]\n",
      " [ 36.25 -49.25]\n",
      " [ 36.75 -49.25]\n",
      " [ 36.25 -49.75]\n",
      " [ 35.75 -49.75]\n",
      " [ 37.25 -47.75]\n",
      " [ 35.75 -49.25]\n",
      " [ 35.25 -46.25]\n",
      " [ 35.75 -46.25]\n",
      " [ 34.75 -46.75]\n",
      " [ 35.25 -46.75]\n",
      " [ 35.75 -46.75]\n",
      " [ 36.75 -46.75]\n",
      " [ 37.25 -46.75]\n",
      " [ 34.75 -47.25]\n",
      " [ 35.25 -47.25]\n",
      " [ 35.75 -47.25]\n",
      " [ 36.75 -47.25]\n",
      " [ 37.25 -47.25]\n",
      " [ 37.75 -47.25]\n",
      " [ 33.75 -48.25]\n",
      " [ 33.75 -48.75]\n",
      " [ 34.25 -48.75]\n",
      " [ 34.25 -49.25]\n",
      " [ 34.75 -49.25]\n",
      " [ 35.25 -49.25]\n",
      " [ 33.25 -47.75]\n",
      " [ 33.75 -47.75]\n",
      " [ 33.25 -48.25]\n",
      " [ 32.75 -48.25]\n",
      " [ 33.25 -46.75]\n",
      " [ 32.75 -47.75]\n",
      " [ 32.75 -44.25]\n",
      " [ 33.25 -44.25]\n",
      " [ 33.75 -45.75]\n",
      " [ 32.75 -46.25]\n",
      " [ 33.25 -46.25]\n",
      " [ 31.75 -46.75]\n",
      " [ 32.25 -46.75]\n",
      " [ 32.75 -46.75]\n",
      " [ 32.25 -47.25]\n",
      " [ 32.75 -47.25]\n",
      " [ 32.25 -47.75]\n",
      " [ 32.25 -44.25]\n",
      " [ 31.75 -47.25]\n",
      " [ 31.75 -47.75]\n",
      " [ 32.25 -48.25]\n",
      " [ 31.25 -44.25]\n",
      " [ 31.75 -44.25]]\n"
     ]
    }
   ],
   "source": [
    "wcs = WCS(Npeakscarhdr)\n",
    "compFiveLoSGalCoords = []\n",
    "for region in compFiveNewLoS:\n",
    "    regionArray = np.array(region)\n",
    "    skyCoords = wcs.pixel_to_world(regionArray[:, 0], regionArray[:, 1])\n",
    "    galCoords = skyCoords.galactic\n",
    "    galArray = np.column_stack((galCoords.l.deg, galCoords.b.deg))\n",
    "    compFiveLoSGalCoords.append(galArray)\n",
    "\n",
    "print(f\"Number of LoS: {len(compFiveLoSGalCoords)}\")\n",
    "print(f\"Example LoS galactic coords \\n{compFiveLoSGalCoords[0]},\\n{compFiveLoSGalCoords[17]}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e15d0e5-2313-400f-92d8-f9b00c427ca5",
   "metadata": {},
   "source": [
    "## complexity = 6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "2057e164-b351-4074-a39b-20663870d578",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1250 [[276  56]\n",
      " [277  56]\n",
      " [278  56]\n",
      " [279  56]\n",
      " [280  56]]\n"
     ]
    }
   ],
   "source": [
    "mask = (5 < Npeakscardat) & (Npeakscardat < 7)\n",
    "yIndices, xIndices = np.where(mask)\n",
    "\n",
    "sixCompPixCoord = np.column_stack((xIndices, yIndices)) \n",
    "\n",
    "print(len(sixCompPixCoord), sixCompPixCoord[:5]) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "edcf9da8-ac52-4936-b8de-04c51ec75581",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of regions: 737, distinct lines of sight: 1217\n"
     ]
    }
   ],
   "source": [
    "wcs = WCS(Npeakscarhdr, naxis =2)\n",
    "\n",
    "beamLimit = 7.2 #pixels \n",
    "group = list(range(len(sixCompPixCoord)))\n",
    "compSixNewLoS = []\n",
    "while group: \n",
    "    currentIndex = group.pop(0)\n",
    "    currentRegion = [tuple(sixCompPixCoord[currentIndex])]\n",
    "    queue = [sixCompPixCoord[currentIndex]] \n",
    "\n",
    "    while queue: \n",
    "        point = queue.pop(0)\n",
    "\n",
    "        if not group: \n",
    "            break \n",
    "\n",
    "        groupCoords = sixCompPixCoord[group] \n",
    "        distances = np.linalg.norm(groupCoords - point, axis=1)\n",
    "\n",
    "        closeIndices = np.where(distances <= beamLimit)[0]\n",
    "\n",
    "        for idx in sorted(closeIndices, reverse=True):\n",
    "            actualIdx = group.pop(idx)\n",
    "            pixel = tuple(sixCompPixCoord[actualIdx])\n",
    "            currentRegion.append(pixel)\n",
    "            queue.append(sixCompPixCoord[actualIdx])\n",
    "        \n",
    "        compSixNewLoS.append(currentRegion)\n",
    "\n",
    "minimumPixArea = 59 \n",
    "compSixResolvedRegions = [r for r in compSixNewLoS if len(r) >= minimumPixArea]\n",
    "\n",
    "print(f\"Number of regions: {len(compSixResolvedRegions)}, distinct lines of sight: {len(compSixNewLoS)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "78386d35-c713-4989-a368-45aab76193ab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Centroids shape: (1217, 2)\n",
      "Example centroid: [ 40.619232 -58.42692 ]\n"
     ]
    }
   ],
   "source": [
    "#try doing the same thing with centroids instead \n",
    "compSixLoSCentroids = np.zeros((len(compSixNewLoS), 2), dtype=np.float32)\n",
    "\n",
    "for i, region in enumerate(compSixNewLoS):\n",
    "    regionArray = np.array(region,dtype=np.int32)\n",
    "    skyCoords = wcs.pixel_to_world(regionArray[:, 0], regionArray[:,1])\n",
    "    galCoords = skyCoords.galactic \n",
    "    compSixLoSCentroids[i, 0] = np.mean(galCoords.l.deg)\n",
    "    compSixLoSCentroids[i, 1] = np.mean(galCoords.b.deg)\n",
    "\n",
    "print(f\"Centroids shape: {compSixLoSCentroids.shape}\")\n",
    "print(f\"Example centroid: {compSixLoSCentroids[0]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "5cfc8368-ebfa-442e-9a1a-e1494f54e23f",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of LoS: 1217\n",
      "Example LoS galactic coords \n",
      "[[ 41.75 -61.75]\n",
      " [ 41.25 -58.25]\n",
      " [ 41.75 -58.25]\n",
      " [ 40.25 -58.75]\n",
      " [ 40.75 -58.75]\n",
      " [ 39.75 -59.25]\n",
      " [ 40.25 -59.25]\n",
      " [ 40.75 -59.25]\n",
      " [ 41.25 -59.25]\n",
      " [ 43.75 -59.25]\n",
      " [ 44.25 -59.25]\n",
      " [ 39.25 -59.75]\n",
      " [ 39.75 -59.75]\n",
      " [ 40.25 -59.75]\n",
      " [ 40.75 -59.75]\n",
      " [ 42.75 -59.75]\n",
      " [ 43.25 -59.75]\n",
      " [ 38.75 -60.25]\n",
      " [ 39.25 -60.25]\n",
      " [ 39.75 -60.25]\n",
      " [ 43.25 -60.25]\n",
      " [ 39.25 -61.25]\n",
      " [ 39.75 -61.25]\n",
      " [ 40.25 -61.25]\n",
      " [ 40.75 -61.25]\n",
      " [ 41.25 -61.25]\n",
      " [ 41.75 -61.25]\n",
      " [ 39.25 -61.75]\n",
      " [ 39.75 -61.75]\n",
      " [ 40.25 -61.75]\n",
      " [ 40.75 -61.75]\n",
      " [ 41.25 -61.75]\n",
      " [ 41.25 -54.75]\n",
      " [ 41.25 -55.25]\n",
      " [ 40.75 -55.75]\n",
      " [ 41.25 -55.75]\n",
      " [ 39.75 -56.25]\n",
      " [ 40.25 -56.25]\n",
      " [ 40.75 -56.25]\n",
      " [ 41.25 -56.25]\n",
      " [ 39.25 -56.75]\n",
      " [ 39.75 -56.75]\n",
      " [ 41.25 -56.75]\n",
      " [ 38.75 -57.25]\n",
      " [ 39.25 -57.25]\n",
      " [ 39.75 -57.25]\n",
      " [ 40.25 -57.25]\n",
      " [ 40.75 -57.25]\n",
      " [ 38.75 -57.75]\n",
      " [ 39.25 -57.75]\n",
      " [ 39.75 -57.75]\n",
      " [ 40.25 -57.75]\n",
      " [ 40.75 -57.75]\n",
      " [ 41.25 -57.75]\n",
      " [ 41.75 -57.75]\n",
      " [ 39.25 -58.25]\n",
      " [ 39.75 -58.25]\n",
      " [ 40.25 -58.25]\n",
      " [ 40.75 -58.25]\n",
      " [ 39.75 -58.75]\n",
      " [ 44.75 -59.25]\n",
      " [ 37.25 -57.75]\n",
      " [ 41.25 -51.25]\n",
      " [ 41.25 -51.75]\n",
      " [ 41.75 -51.75]],\n",
      "[[ 41.75 -61.75]\n",
      " [ 41.25 -58.25]\n",
      " [ 41.75 -58.25]\n",
      " [ 40.25 -58.75]\n",
      " [ 40.75 -58.75]\n",
      " [ 39.75 -59.25]\n",
      " [ 40.25 -59.25]\n",
      " [ 40.75 -59.25]\n",
      " [ 41.25 -59.25]\n",
      " [ 43.75 -59.25]\n",
      " [ 44.25 -59.25]\n",
      " [ 39.25 -59.75]\n",
      " [ 39.75 -59.75]\n",
      " [ 40.25 -59.75]\n",
      " [ 40.75 -59.75]\n",
      " [ 42.75 -59.75]\n",
      " [ 43.25 -59.75]\n",
      " [ 38.75 -60.25]\n",
      " [ 39.25 -60.25]\n",
      " [ 39.75 -60.25]\n",
      " [ 43.25 -60.25]\n",
      " [ 39.25 -61.25]\n",
      " [ 39.75 -61.25]\n",
      " [ 40.25 -61.25]\n",
      " [ 40.75 -61.25]\n",
      " [ 41.25 -61.25]\n",
      " [ 41.75 -61.25]\n",
      " [ 39.25 -61.75]\n",
      " [ 39.75 -61.75]\n",
      " [ 40.25 -61.75]\n",
      " [ 40.75 -61.75]\n",
      " [ 41.25 -61.75]\n",
      " [ 41.25 -54.75]\n",
      " [ 41.25 -55.25]\n",
      " [ 40.75 -55.75]\n",
      " [ 41.25 -55.75]\n",
      " [ 39.75 -56.25]\n",
      " [ 40.25 -56.25]\n",
      " [ 40.75 -56.25]\n",
      " [ 41.25 -56.25]\n",
      " [ 39.25 -56.75]\n",
      " [ 39.75 -56.75]\n",
      " [ 41.25 -56.75]\n",
      " [ 38.75 -57.25]\n",
      " [ 39.25 -57.25]\n",
      " [ 39.75 -57.25]\n",
      " [ 40.25 -57.25]\n",
      " [ 40.75 -57.25]\n",
      " [ 38.75 -57.75]\n",
      " [ 39.25 -57.75]\n",
      " [ 39.75 -57.75]\n",
      " [ 40.25 -57.75]\n",
      " [ 40.75 -57.75]\n",
      " [ 41.25 -57.75]\n",
      " [ 41.75 -57.75]\n",
      " [ 39.25 -58.25]\n",
      " [ 39.75 -58.25]\n",
      " [ 40.25 -58.25]\n",
      " [ 40.75 -58.25]\n",
      " [ 39.75 -58.75]\n",
      " [ 44.75 -59.25]\n",
      " [ 37.25 -57.75]\n",
      " [ 41.25 -51.25]\n",
      " [ 41.25 -51.75]\n",
      " [ 41.75 -51.75]]\n"
     ]
    }
   ],
   "source": [
    "wcs = WCS(Npeakscarhdr)\n",
    "compSixLoSGalCoords = []\n",
    "for region in compSixNewLoS:\n",
    "    regionArray = np.array(region)\n",
    "    skyCoords = wcs.pixel_to_world(regionArray[:, 0], regionArray[:, 1])\n",
    "    galCoords = skyCoords.galactic\n",
    "    galArray = np.column_stack((galCoords.l.deg, galCoords.b.deg))\n",
    "    compSixLoSGalCoords.append(galArray)\n",
    "\n",
    "print(f\"Number of LoS: {len(compSixLoSGalCoords)}\")\n",
    "print(f\"Example LoS galactic coords \\n{compSixLoSGalCoords[0]},\\n{compSixLoSGalCoords[17]}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ecde162c-62f3-4282-b85f-e91af7908431",
   "metadata": {},
   "source": [
    "## complexity = 7 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "7934ad42-d3fb-40f5-b742-2a12476c19a5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "242 [[277  65]\n",
      " [278  66]\n",
      " [279  66]\n",
      " [641 103]\n",
      " [642 103]]\n"
     ]
    }
   ],
   "source": [
    "mask = (6 < Npeakscardat) & (Npeakscardat < 8)\n",
    "\n",
    "yIndices, xIndices = np.where(mask)\n",
    "\n",
    "sevenCompPixCoord = np.column_stack((xIndices, yIndices)) \n",
    "\n",
    "print(len(sevenCompPixCoord), sevenCompPixCoord[:5]) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "6a7c863b-7059-4dc5-b0c7-bf0acce1a7de",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of regions: 68, distinct lines of sight: 241\n"
     ]
    }
   ],
   "source": [
    "wcs = WCS(Npeakscarhdr, naxis =2)\n",
    "\n",
    "beamLimit = 7.2 #pixels \n",
    "group = list(range(len(sevenCompPixCoord)))\n",
    "compSevenNewLoS = []\n",
    "while group: \n",
    "    currentIndex = group.pop(0)\n",
    "    currentRegion = [tuple(sevenCompPixCoord[currentIndex])]\n",
    "    queue = [sevenCompPixCoord[currentIndex]] \n",
    "\n",
    "    while queue: \n",
    "        point = queue.pop(0)\n",
    "\n",
    "        if not group: \n",
    "            break \n",
    "\n",
    "        groupCoords = sevenCompPixCoord[group] \n",
    "        distances = np.linalg.norm(groupCoords - point, axis=1)\n",
    "\n",
    "        closeIndices = np.where(distances <= beamLimit)[0]\n",
    "\n",
    "        for idx in sorted(closeIndices, reverse=True):\n",
    "            actualIdx = group.pop(idx)\n",
    "            pixel = tuple(sevenCompPixCoord[actualIdx])\n",
    "            currentRegion.append(pixel)\n",
    "            queue.append(sevenCompPixCoord[actualIdx])\n",
    "        \n",
    "        compSevenNewLoS.append(currentRegion)\n",
    "\n",
    "minimumPixArea = 59 \n",
    "compSevenResolvedRegions = [r for r in compSevenNewLoS if len(r) >= minimumPixArea]\n",
    "\n",
    "print(f\"Number of regions: {len(compSevenResolvedRegions)}, distinct lines of sight: {len(compSevenNewLoS)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "cae71486-1bd0-427e-9ba0-f267abdc47c6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Centroids shape: (241, 2)\n",
      "Example centroid: [ 40.75     -56.916668]\n"
     ]
    }
   ],
   "source": [
    "#try doing the same thing with centroids instead \n",
    "compSevenLoSCentroids = np.zeros((len(compSevenNewLoS), 2), dtype=np.float32)\n",
    "\n",
    "for i, region in enumerate(compSevenNewLoS):\n",
    "    regionArray = np.array(region,dtype=np.int32)\n",
    "    skyCoords = wcs.pixel_to_world(regionArray[:, 0], regionArray[:,1])\n",
    "    galCoords = skyCoords.galactic \n",
    "    compSevenLoSCentroids[i, 0] = np.mean(galCoords.l.deg)\n",
    "    compSevenLoSCentroids[i, 1] = np.mean(galCoords.b.deg)\n",
    "\n",
    "print(f\"Centroids shape: {compSevenLoSCentroids.shape}\")\n",
    "print(f\"Example centroid: {compSevenLoSCentroids[0]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "7a534d10-5ff5-4660-a4ec-3eb57187cf48",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of LoS: 241\n",
      "Example LoS galactic coords \n",
      "[[ 41.25 -57.25]\n",
      " [ 40.25 -56.75]\n",
      " [ 40.75 -56.75]],\n",
      "[[228.25 -12.75]\n",
      " [226.75  -9.75]\n",
      " [227.25  -9.75]\n",
      " [227.75  -9.75]\n",
      " [225.75 -10.25]\n",
      " [226.25 -10.25]\n",
      " [227.25 -10.25]\n",
      " [227.75 -10.75]\n",
      " [227.25 -11.25]\n",
      " [227.75 -11.25]\n",
      " [228.25 -11.25]\n",
      " [227.25 -11.75]\n",
      " [227.75 -11.75]\n",
      " [228.25 -11.75]\n",
      " [227.25 -12.25]\n",
      " [227.75 -12.25]\n",
      " [228.25 -12.25]\n",
      " [228.75 -12.25]\n",
      " [226.75 -12.75]\n",
      " [227.25 -12.75]\n",
      " [227.75 -12.75]\n",
      " [224.25  -7.75]\n",
      " [223.75  -8.25]\n",
      " [224.25  -8.25]\n",
      " [223.75  -4.25]\n",
      " [222.75  -4.75]\n",
      " [223.25  -7.25]\n",
      " [222.75  -3.75]\n",
      " [223.25  -3.75]\n",
      " [223.75  -3.75]\n",
      " [222.75  -4.25]\n",
      " [223.25  -4.25]]\n"
     ]
    }
   ],
   "source": [
    "wcs = WCS(Npeakscarhdr)\n",
    "compSevenLoSGalCoords = []\n",
    "for region in compSevenNewLoS:\n",
    "    regionArray = np.array(region)\n",
    "    skyCoords = wcs.pixel_to_world(regionArray[:, 0], regionArray[:, 1])\n",
    "    galCoords = skyCoords.galactic\n",
    "    galArray = np.column_stack((galCoords.l.deg, galCoords.b.deg))\n",
    "    compSevenLoSGalCoords.append(galArray)\n",
    "\n",
    "print(f\"Number of LoS: {len(compSevenLoSGalCoords)}\")\n",
    "print(f\"Example LoS galactic coords \\n{compSevenLoSGalCoords[0]},\\n{compSevenLoSGalCoords[17]}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af21b717-3c45-4ae7-ac67-dadf37b28e10",
   "metadata": {},
   "source": [
    "## complexity = 8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "3c8c2550-5b19-4646-a5c5-c5113bcececd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19 [[704 213]\n",
      " [705 214]\n",
      " [705 215]\n",
      " [675 216]\n",
      " [676 216]]\n"
     ]
    }
   ],
   "source": [
    "mask = (7 < Npeakscardat) & (Npeakscardat < 9)\n",
    "\n",
    "yIndices, xIndices = np.where(mask)\n",
    "\n",
    "eightCompPixCoord = np.column_stack((xIndices, yIndices)) \n",
    "\n",
    "print(len(eightCompPixCoord), eightCompPixCoord[:5]) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "70c2729d-778a-46bd-8314-aabed2b7f481",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of regions: 0, distinct lines of sight: 18\n"
     ]
    }
   ],
   "source": [
    "wcs = WCS(Npeakscarhdr, naxis =2)\n",
    "\n",
    "beamLimit = 7.2 #pixels \n",
    "group = list(range(len(eightCompPixCoord)))\n",
    "compEightNewLoS = []\n",
    "while group: \n",
    "    currentIndex = group.pop(0)\n",
    "    currentRegion = [tuple(eightCompPixCoord[currentIndex])]\n",
    "    queue = [eightCompPixCoord[currentIndex]] \n",
    "\n",
    "    while queue: \n",
    "        point = queue.pop(0)\n",
    "\n",
    "        if not group: \n",
    "            break \n",
    "\n",
    "        groupCoords = eightCompPixCoord[group] \n",
    "        distances = np.linalg.norm(groupCoords - point, axis=1)\n",
    "\n",
    "        closeIndices = np.where(distances <= beamLimit)[0]\n",
    "\n",
    "        for idx in sorted(closeIndices, reverse=True):\n",
    "            actualIdx = group.pop(idx)\n",
    "            pixel = tuple(eightCompPixCoord[actualIdx])\n",
    "            currentRegion.append(pixel)\n",
    "            queue.append(eightCompPixCoord[actualIdx])\n",
    "        \n",
    "        compEightNewLoS.append(currentRegion)\n",
    "\n",
    "minimumPixArea = 59 \n",
    "compEightResolvedRegions = [r for r in compEightNewLoS if len(r) >= minimumPixArea]\n",
    "\n",
    "print(f\"Number of regions: {len(compEightResolvedRegions)}, distinct lines of sight: {len(compEightNewLoS)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "f35b56e3-db61-4b8c-9431-2ae032d1bbe9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Centroids shape: (18, 2)\n",
      "Example centroid: [187.25  17.5 ]\n"
     ]
    }
   ],
   "source": [
    "#try doing the same thing with centroids instead \n",
    "compEightLoSCentroids = np.zeros((len(compEightNewLoS), 2), dtype=np.float32)\n",
    "\n",
    "for i, region in enumerate(compEightNewLoS):\n",
    "    regionArray = np.array(region,dtype=np.int32)\n",
    "    skyCoords = wcs.pixel_to_world(regionArray[:, 0], regionArray[:,1])\n",
    "    galCoords = skyCoords.galactic \n",
    "    compEightLoSCentroids[i, 0] = np.mean(galCoords.l.deg)\n",
    "    compEightLoSCentroids[i, 1] = np.mean(galCoords.b.deg)\n",
    "\n",
    "print(f\"Centroids shape: {compEightLoSCentroids.shape}\")\n",
    "print(f\"Example centroid: {compEightLoSCentroids[0]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "e4c1b98c-1f55-4e52-8253-2507c1ddeb7f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of LoS: 18\n",
      "Example LoS galactic coords \n",
      "[[187.75  16.75]\n",
      " [186.75  18.25]\n",
      " [187.25  17.75]\n",
      " [187.25  17.25]],\n",
      "[[342.75  32.75]]\n"
     ]
    }
   ],
   "source": [
    "compEightLoSGalCoords = []\n",
    "for region in compEightNewLoS:\n",
    "    regionArray = np.array(region)\n",
    "    skyCoords = wcs.pixel_to_world(regionArray[:, 0], regionArray[:, 1])\n",
    "    galCoords = skyCoords.galactic\n",
    "    galArray = np.column_stack((galCoords.l.deg, galCoords.b.deg))\n",
    "    compEightLoSGalCoords.append(galArray)\n",
    "\n",
    "print(f\"Number of LoS: {len(compEightLoSGalCoords)}\")\n",
    "print(f\"Example LoS galactic coords \\n{compEightLoSGalCoords[0]},\\n{compEightLoSGalCoords[17]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eca1dcc7-6411-42d3-b7c7-80bc48e7e2de",
   "metadata": {},
   "outputs": [],
   "source": [
    "for name, losList in [('compThreeNewLoS', compThreeNewLoS),\n",
    "                       ('compFourNewLoS', compFourNewLoS),\n",
    "                       ('compFiveNewLoS', compFiveNewLoS)]:\n",
    "    print(f\"{name}: region 0 length={len(losList[0])}, region 1 length={len(losList[1])}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "507a7b15-5555-4cf2-93ea-06f71a239da9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# check if all regions are identical or just same length\n",
    "print(f\"compFourNewLoS region 0 == region 1: {compFourNewLoS[0] == compFourNewLoS[1]}\")\n",
    "print(f\"compFiveNewLoS region 0 == region 1: {compFiveNewLoS[0] == compFiveNewLoS[1]}\")\n",
    "print(f\"compThreeNewLoS region 0 == region 1: {compThreeNewLoS[0] == compThreeNewLoS[1]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a6774059-4fa4-41b0-9a5d-a905adbc59ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "# for each complexity class, get pixel coords and convert to galactic centroids\n",
    "wcs = WCS(Npeakscarhdr, naxis=2)\n",
    "\n",
    "complexityClasses = {\n",
    "    'one':   masks['one'],\n",
    "    'two':   masks['two'],\n",
    "    'three': masks['three'],\n",
    "    'four':  masks['four'],\n",
    "    'five':  masks['five'],\n",
    "    'six':   masks['six'],\n",
    "    'seven': masks['seven'],\n",
    "    'eight': masks['eight'],\n",
    "}\n",
    "\n",
    "pixelGalCoords = {}\n",
    "for name, mask in complexityClasses.items():\n",
    "    yIndices, xIndices = np.where(mask)\n",
    "    pixCoord = np.column_stack((xIndices, yIndices))\n",
    "    skyCoords = wcs.pixel_to_world(pixCoord[:, 0], pixCoord[:, 1])\n",
    "    galCoords = skyCoords.galactic\n",
    "    pixelGalCoords[name] = np.column_stack((galCoords.l.deg, galCoords.b.deg)).astype(np.float32)\n",
    "    print(f\"{name}: {len(pixelGalCoords[name])} pixels\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8879af7d-e80b-4336-8c13-49ead1c5339a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# mask = masks['one']\n",
    "# yIndices, xIndices = np.where(mask)\n",
    "# pixCoord = np.column_stack((xIndices, yIndices))\n",
    "# print(f\"pixCoord shape: {pixCoord.shape}\")\n",
    "# print(f\"pixCoord sample:\\n{pixCoord[:5]}\")\n",
    "# print(f\"x range: {pixCoord[:, 0].min()} to {pixCoord[:, 0].max()}\")\n",
    "# print(f\"y range: {pixCoord[:, 1].min()} to {pixCoord[:, 1].max()}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "99488761-8cdd-40ee-8cc1-17a98c672427",
   "metadata": {},
   "outputs": [],
   "source": [
    "# def clusterLoS(pixCoord, beamLimit=7.2):\n",
    "#     group = list(range(len(pixCoord)))\n",
    "#     losList = []\n",
    "#     while group:\n",
    "#         currentIndex = group.pop(0)\n",
    "#         currentRegion = [tuple(pixCoord[currentIndex])]\n",
    "#         queue = [pixCoord[currentIndex]]\n",
    "#         while queue:\n",
    "#             point = queue.pop(0)\n",
    "#             if not group:\n",
    "#                 break\n",
    "#             groupCoords = pixCoord[group]\n",
    "#             distances = np.linalg.norm(groupCoords - point, axis=1)\n",
    "#             closeIndices = np.where(distances <= beamLimit)[0]\n",
    "#             for idx in sorted(closeIndices, reverse=True):\n",
    "#                 actualIdx = group.pop(idx)\n",
    "#                 pixel = tuple(pixCoord[actualIdx])\n",
    "#                 currentRegion.append(pixel)\n",
    "#                 queue.append(pixCoord[actualIdx])\n",
    "#         losList.append(list(currentRegion))\n",
    "#     return losList\n",
    "\n",
    "# masks = {\n",
    "#     'one':   Npeakscardat == 1,\n",
    "#     'two':   Npeakscardat == 2,\n",
    "#     'three': Npeakscardat == 3,\n",
    "#     'four':  Npeakscardat == 4,\n",
    "#     'five':  Npeakscardat == 5,\n",
    "#     'six':   Npeakscardat == 6,\n",
    "#     'seven': Npeakscardat == 7,\n",
    "#     'eight': Npeakscardat == 8,\n",
    "# }\n",
    "\n",
    "# for name, mask in masks.items():\n",
    "#     yIndices, xIndices = np.where(mask)\n",
    "#     pixCoord = np.column_stack((xIndices, yIndices))\n",
    "#     losList = clusterLoS(pixCoord)\n",
    "#     globals()[f'comp{name.capitalize()}NewLoS'] = losList\n",
    "#     print(f\"comp{name.capitalize()}NewLoS: {len(losList)} LoS\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3e54ffff-8eb8-4c4d-a642-e4686ad5fde8",
   "metadata": {},
   "outputs": [],
   "source": [
    "for name, mask in masks.items():\n",
    "    print(f\"{name}: {np.sum(mask)} pixels\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "273befc3-6e57-4152-9b74-886b93ebff7d",
   "metadata": {},
   "outputs": [],
   "source": [
    "totalPixels = sum(len(pixelGalCoords[name]) for name in ['two', 'three', 'four', 'five', 'six', 'seven', 'eight'])\n",
    "complexPixels = sum(len(pixelGalCoords[name]) for name in ['three', 'four', 'five', 'six', 'seven', 'eight'])\n",
    "print(f\"Percentage complex: {complexPixels/totalPixels * 100:.2f}%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e42e12db-01b6-47e8-ade0-d41fee8e4992",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "c01d5fab-6685-4d78-ba84-1ef2c722bc0d",
   "metadata": {},
   "source": [
    "# Plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b8e5f3d4-8683-4928-935b-f9af91da4241",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = np.array([len(compOneLoSCentroid), len(compTwoLoSCentroid), len(compThreeLoSCentroid), len(compFourLoSCentroid), len(compFiveLoSCentroid), len(compSixLoSCentroid), len(compSevenLoSCentroid), len(compEightLoSCentroid)])\n",
    "x = np.array([1, 2, 3, 4, 5, 6 ,7 ,8])\n",
    "plt.scatter(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ac5976b0-ea30-4c8e-93ff-1149cc81a2c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = np.array([len(newLoS), len(compTwoNewLoS), len(compThreeNewLoS), len(compFourNewLoS), len(compFiveNewLoS), len(compSixNewLoS), len(compSevenNewLoS), len(compEightNewLoS)])\n",
    "x = np.array([1, 2, 3, 4, 5, 6 ,7 ,8])\n",
    "plt.bar(x, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "597219d4-2643-4488-85ea-e2caf11c0044",
   "metadata": {},
   "source": [
    "# working with WHAM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "74bb6eed-c9b2-445c-96c0-b51af766739e",
   "metadata": {},
   "outputs": [],
   "source": [
    "fn = '/home/justin/Research/WHAM/wham-ss-DR1-v161116-170912-grid.fits'\n",
    "whamdat = fits.getdata(fn)\n",
    "whamhdr = fits.getheader(fn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "439e753c-3313-4b26-af07-e358abe24bc4",
   "metadata": {},
   "outputs": [],
   "source": [
    "whamhdr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dae25f9b-ff7d-4bf1-bc21-312ff7b491b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "#putting velo axis in km/s \n",
    "nVel = whamhdr['NAXIS3']\n",
    "crval3 = whamhdr['CRVAL3']\n",
    "crpix3 = whamhdr['CRPIX3']\n",
    "cdelt3 = whamhdr['CDELT3']\n",
    "\n",
    "velAxis = (crval3 + (np.arange(nVel) - (crpix3 -1)) * cdelt3) / 1000\n",
    "print(f\"Velocity range: {velAxis[0]:.1f} to {velAxis[-1]:.1f} km/s\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "187301fa-e0f5-41f2-b5f8-954160da0ed2",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "velMask = (velAxis >= -169.1) & (velAxis <= 157.3)\n",
    "velMask.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aae018b9-d91c-4419-afd4-1b14926127a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "whamIntegrated = np.nansum(whamdat[velMask, :, :] * abs(cdelt3), axis=0) / 1000\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8e2428e9-4ae1-4e62-a5a4-36f8aacb448a",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(f\"NaN count: {np.sum(np.isnan(whamdat))}\")\n",
    "print(f\"Zero count: {np.sum(whamdat == 0)}\")\n",
    "print(f\"Data range: {np.nanmin(whamdat):.4f} to {np.nanmax(whamdat):.4f}\")\n",
    "print(f\"Integrated map range: {np.nanmin(whamIntegrated):.4f} to {np.nanmax(whamIntegrated):.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a6baacb2-99fb-4922-a266-a44f020effc1",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(f\"Channels selected: {velMask.sum()}\")\n",
    "print(f\"Velocity range used: {velAxis[velMask][0]:.1f} to {velAxis[velMask][-1]:.1f} km/s\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3e359ebc-cd51-4b52-9020-ec93bbb76e0f",
   "metadata": {},
   "outputs": [],
   "source": [
    "vmin = np.nanpercentile(whamIntegrated, 5)\n",
    "vmax = np.nanpercentile(whamIntegrated, 95)\n",
    "\n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.imshow(whamIntegrated, origin='lower', vmin=vmin, vmax=vmax, cmap='inferno')\n",
    "plt.colorbar(label='H-alpha intensity (R)')\n",
    "plt.title('WHAM Integrated H-alpha')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "15fa9056-4fcd-435d-9f31-ba2ed4813a33",
   "metadata": {},
   "outputs": [],
   "source": [
    "lon = np.linspace(-180,180, whamIntegrated.shape[1])\n",
    "lat = np.linspace(-90, 90, whamIntegrated.shape[0])\n",
    "lonGrid, latGrid = np.meshgrid(np.deg2rad(lon), np.deg2rad(lat))\n",
    "\n",
    "vmin = np.nanpercentile(whamIntegrated, 5)\n",
    "vmax = np.nanpercentile(whamIntegrated, 95)\n",
    "\n",
    "fig = plt.figure(figsize=(12, 6))\n",
    "ax = fig.add_subplot(111, projection='mollweide')\n",
    "img = ax.pcolormesh(lonGrid, latGrid, whamIntegrated, cmap='inferno', vmin=vmin, vmax=vmax)\n",
    "plt.colorbar(img, ax=ax, label='H-alpha Intensity (R)', shrink=0.5)\n",
    "ax.set_xlabel('Galactic Longitude (deg)')\n",
    "ax.set_ylabel('Galactic Latitude (deg)')\n",
    "ax.grid(True, alpha=0.3)\n",
    "plt.title('WHAM Integrated H-alpha Emission')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1974dfa7-5858-454f-962c-f6bac2bbfeb2",
   "metadata": {},
   "source": [
    "# stats with WHAM and DRAGONS looking for corellation between h-alpha and faraday complexity "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "153c16d2-9e8e-4504-9cbd-ced4841c977c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#mean h-alpha intensity in LoS with complexity 8 \n",
    "whamWCS = WCS(whamhdr, naxis =2) \n",
    "\n",
    "allCoords = np.vstack(compEightLoSCentroids) \n",
    "\n",
    "xPix, yPix = whamWCS.world_to_pixel_values(allCoords[:,0], allCoords[:,1])\n",
    "\n",
    "compEightWhamSampled = map_coordinates(whamIntegrated, [yPix, xPix], order=1, mode='nearest')\n",
    "\n",
    "print(f\"Sampled {len(compEightWhamSampled)} LoS\")\n",
    "print(f\"H-alpha intensity range: {compEightWhamSampled.min():.2f} to {compEightWhamSampled.max():.2f}\")\n",
    "print(f\"Mean H-alpha intensity: {compEightWhamSampled.mean():.2f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "321e49dc-2a83-4f96-b3a5-d077f66554f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "#mean h-alpha intensity in LoS with complexity 7\n",
    "whamWCS = WCS(whamhdr, naxis =2) \n",
    "\n",
    "allCoords = np.vstack(compSevenLoSCentroids) \n",
    "\n",
    "xPix, yPix = whamWCS.world_to_pixel_values(allCoords[:,0], allCoords[:,1])\n",
    "\n",
    "compSevenWhamSampled = map_coordinates(whamIntegrated, [yPix, xPix], order=1, mode='nearest')\n",
    "\n",
    "print(f\"Sampled {len(compSevenWhamSampled)} LoS\")\n",
    "print(f\"H-alpha intensity range: {compSevenWhamSampled.min():.2f} to {compSevenWhamSampled.max():.2f}\")\n",
    "print(f\"Mean H-alpha intensity: {compSevenWhamSampled.mean():.2f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a110f2a0-3fd4-4da8-9902-36a04b14e6a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "#mean h-alpha intensity in LoS with complexity 6 \n",
    "whamWCS = WCS(whamhdr, naxis =2) \n",
    "\n",
    "allCoords = np.vstack(compSixLoSCentroids) \n",
    "\n",
    "xPix, yPix = whamWCS.world_to_pixel_values(allCoords[:,0], allCoords[:,1])\n",
    "\n",
    "compSixWhamSampled = map_coordinates(whamIntegrated, [yPix, xPix], order=1, mode='nearest')\n",
    "\n",
    "print(f\"Sampled {len(compSixWhamSampled)} LoS\")\n",
    "print(f\"H-alpha intensity range: {compSixWhamSampled.min():.2f} to {compSixWhamSampled.max():.2f}\")\n",
    "print(f\"Mean H-alpha intensity: {compSixWhamSampled.mean():.2f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9c950313-e518-4c3f-bbdd-8d202dd21e02",
   "metadata": {},
   "outputs": [],
   "source": [
    "#mean h-alpha intensity in LoS with complexity 5\n",
    "whamWCS = WCS(whamhdr, naxis =2) \n",
    "\n",
    "allCoords = np.vstack(compFiveLoSCentroids) \n",
    "\n",
    "xPix, yPix = whamWCS.world_to_pixel_values(allCoords[:,0], allCoords[:,1])\n",
    "\n",
    "compFiveWhamSampled = map_coordinates(whamIntegrated, [yPix, xPix], order=1, mode='nearest')\n",
    "\n",
    "print(f\"Sampled {len(compFiveWhamSampled)} LoS\")\n",
    "print(f\"H-alpha intensity range: {compFiveWhamSampled.min():.2f} to {compFiveWhamSampled.max():.2f}\")\n",
    "print(f\"Mean H-alpha intensity: {compFiveWhamSampled.mean():.2f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e27ba808-3218-4a66-af39-6d8cd5541dc8",
   "metadata": {},
   "outputs": [],
   "source": [
    "#mean h-alpha in LoS with complexity 4\n",
    "whamWCS = WCS(whamhdr, naxis =2) \n",
    "\n",
    "allCoords = np.vstack(compFourLoSCentroids) \n",
    "\n",
    "xPix, yPix = whamWCS.world_to_pixel_values(allCoords[:,0], allCoords[:,1])\n",
    "\n",
    "compFourWhamSampled = map_coordinates(whamIntegrated, [yPix, xPix], order=1, mode='nearest')\n",
    "\n",
    "print(f\"Sampled {len(compFourWhamSampled)} LoS\")\n",
    "print(f\"H-alpha intensity range: {compFourWhamSampled.min():.2f} to {compFourWhamSampled.max():.2f}\")\n",
    "print(f\"Mean H-alpha intensity: {compFourWhamSampled.mean():.2f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ff86d50a-d21f-4c8d-8380-4f94a7bc8d72",
   "metadata": {},
   "outputs": [],
   "source": [
    "#mean h-alpha in LoS with complexity 3 \n",
    "whamWCS = WCS(whamhdr, naxis=2) \n",
    "\n",
    "allCoords = np.vstack(compThreeLoSCentroids)\n",
    "\n",
    "xPix, yPix = whamWCS.world_to_pixel_values(allCoords[:,0], allCoords[:,1])\n",
    "\n",
    "compThreeWhamSampled = map_coordinates(whamIntegrated, [yPix, xPix], order=1, mode ='nearest')\n",
    "\n",
    "print(f\"Sampled {len(compThreeWhamSampled)} LoS\")\n",
    "print(f\"H-alpha intensity range: {compThreeWhamSampled.min():.2f} to {compThreeWhamSampled.max():.2f}\")\n",
    "print(f\"Mean H-alpha intensity: {compThreeWhamSampled.mean():.2f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a88ce21b-e6f8-4c89-9178-120dc0f25088",
   "metadata": {},
   "outputs": [],
   "source": [
    "#mean h-alpha in LoS with complexity 2\n",
    "whamWCS = WCS(whamhdr, naxis=2) \n",
    "\n",
    "allCoords = np.vstack(compTwoLoSCentroids)\n",
    "\n",
    "xPix, yPix = whamWCS.world_to_pixel_values(allCoords[:,0], allCoords[:,1])\n",
    "\n",
    "compTwoWhamSampled = map_coordinates(whamIntegrated, [yPix, xPix], order=1, mode ='nearest')\n",
    "\n",
    "print(f\"Sampled {len(compTwoWhamSampled)} LoS\")\n",
    "print(f\"H-alpha intensity range: {compTwoWhamSampled.min():.2f} to {compTwoWhamSampled.max():.2f}\")\n",
    "print(f\"Mean H-alpha intensity: {compTwoWhamSampled.mean():.2f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0da97f61-685e-4533-8471-ff556c1c870d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#mean h-alpha in LoS with complexity 1\n",
    "whamWCS = WCS(whamhdr, naxis=2) \n",
    "\n",
    "allCoords = np.vstack(compOneLoSCentroids)\n",
    "\n",
    "xPix, yPix = whamWCS.world_to_pixel_values(allCoords[:,0], allCoords[:,1])\n",
    "\n",
    "compOneWhamSampled = map_coordinates(whamIntegrated, [yPix, xPix], order=1, mode ='nearest')\n",
    "\n",
    "print(f\"Sampled {len(compOneWhamSampled)} LoS\")\n",
    "print(f\"H-alpha intensity range: {compOneWhamSampled.min():.2f} to {compOneWhamSampled.max():.2f}\")\n",
    "print(f\"Mean H-alpha intensity: {compOneWhamSampled.mean():.2f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1f214b53-c6a9-490e-8d0a-c407bdb534d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "#mean h-alpha in LoS not displaying complexity\n",
    "whamWCS = WCS(whamhdr, naxis=2) \n",
    "simpleLoSCentroids = np.concatenate((compOneLoSCentroids, compTwoLoSCentroids), axis=0)\n",
    "\n",
    "xPix, yPix = whamWCS.world_to_pixel_values(simpleLoSCentroids[:,0], simpleLoSCentroids[:,1])\n",
    "\n",
    "simpleWhamSampled = map_coordinates(whamIntegrated, [yPix, xPix], order=1, mode ='nearest')\n",
    "\n",
    "print(f\"Sampled {len(simpleWhamSampled)} LoS\")\n",
    "print(f\"H-alpha intensity range: {simpleWhamSampled.min():.2f} to {simpleWhamSampled.max():.2f}\")\n",
    "print(f\"Mean H-alpha intensity: {np.mean(simpleWhamSampled):.2f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c1dc2648-2e08-4de4-bdc9-e01c5552f141",
   "metadata": {},
   "outputs": [],
   "source": [
    "#mean h-alpha in LoS displaying complexity\n",
    "whamWCS = WCS(whamhdr, naxis=2)\n",
    "complexLoSCentroids = np.concatenate((compEightLoSCentroids, compSevenLoSCentroids, compSixLoSCentroids, compFiveLoSCentroids, compFourLoSCentroids, compThreeLoSCentroids), axis = 0)\n",
    "allCoords = np.vstack(complexLoSCentroids)\n",
    "xPix, yPix = whamWCS.world_to_pixel_values(allCoords[:,0], allCoords[:,1])\n",
    "\n",
    "complexWhamSampled = map_coordinates(whamIntegrated, [yPix, xPix], order=1, mode='nearest')\n",
    "\n",
    "print(f\"Sampled {len(complexWhamSampled)} LoS\")\n",
    "print(f\"H-alpha intensity range: {complexWhamSampled.min():.2f} to {complexWhamSampled.max():.2f}\")\n",
    "print(f\"Median H-alpha intensity: {np.mean(complexWhamSampled):.2f}\")                     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e1066646-a97f-437e-833e-32e3232988c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(f\"WHAM integrated range: {np.nanmin(whamIntegrated):.2f} to {np.nanmax(whamIntegrated):.2f} R\")\n",
    "print(f\"WHAM integrated mean: {np.nanmean(whamIntegrated):.2f} R\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "252e115e-f578-465d-8cd5-a9d94e6232ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "# compare spatial distributions\n",
    "print(f\"Complex centroid lat range: {threeToEightLoSCentroids[:, 1].min():.1f} to {threeToEightLoSCentroids[:, 1].max():.1f}\")\n",
    "print(f\"Simple centroid lat range: {simpleLoSCentroids[:, 1].min():.1f} to {simpleLoSCentroids[:, 1].max():.1f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "740ed5af-0c5e-474b-96d1-30e7f1437d32",
   "metadata": {},
   "outputs": [],
   "source": [
    "maxPos = np.unravel_index(np.nanargmax(whamIntegrated), whamIntegrated.shape)\n",
    "print(f\"Max position (y, x): {maxPos}\")\n",
    "print(f\"Max value: {whamIntegrated[maxPos]:.2f} R\")\n",
    "\n",
    "# check how many pixels are above a reasonable threshold\n",
    "print(f\"Pixels above 100R: {np.sum(whamIntegrated > 100)}\")\n",
    "print(f\"Pixels above 500R: {np.sum(whamIntegrated > 500)}\")\n",
    "print(f\"99th percentile: {np.nanpercentile(whamIntegrated, 99):.2f} R\")\n",
    "print(f\"95th percentile: {np.nanpercentile(whamIntegrated, 95):.2f} R\")\n",
    "print(f\"Median: {np.nanpercentile(whamIntegrated, 50):.2f} R\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c8125fec-ad6b-4f70-9cca-2fdd7d33ae95",
   "metadata": {},
   "outputs": [],
   "source": [
    "whamWCS2D = WCS(whamhdr, naxis=2)\n",
    "maxL, maxB = whamWCS2D.pixel_to_world_values(1009, 356)\n",
    "print(f\"Max pixel galactic coords: l={maxL:.2f}, b={maxB:.2f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "87972a11-1ab5-4493-bfd1-ec88b336003a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import stats\n",
    "\n",
    "rho, pval = stats.spearmanr(complexWhamSampled, threeToEightLoSCentroids[:, 1])\n",
    "print(f\"Spearman rho: {rho}, p-value: {pval}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "449ba7c7-163e-4f0c-b3c1-6a62936a5c86",
   "metadata": {},
   "outputs": [],
   "source": [
    "allLats = np.concatenate([simpleLoSCentroids[:, 1], complexLoSCentroids[:, 1]])\n",
    "allWhamSampled = np.concatenate([simpleWhamSampled, complexWhamSampled])\n",
    "allNpeaks = np.concatenate([\n",
    "    np.full(len(simpleLoSCentroids), 0, dtype=np.int8),\n",
    "    np.full(len(complexLoSCentroids), 1, dtype=np.int8)\n",
    "])\n",
    "\n",
    "print(f\"allLats: {allLats.shape}\")\n",
    "print(f\"allWhamSampled: {allWhamSampled.shape}\")\n",
    "print(f\"allNpeaks: {allNpeaks.shape}\")\n",
    "\n",
    "\n",
    "\n",
    "rhoLatNpeaks = stats.spearmanr(allLats.flatten(), allNpeaks.flatten()).statistic\n",
    "pvalLatNpeaks = stats.spearmanr(allLats.flatten(), allNpeaks.flatten()).pvalue\n",
    "\n",
    "rhoNpeaksHa = stats.spearmanr(allNpeaks, allWhamSampled).statistic\n",
    "pvalNpeaksHa = stats.spearmanr(allNpeaks, allWhamSampled).pvalue\n",
    "\n",
    "rhoLatHa  = stats.spearmanr(allLats, allWhamSampled).statistic\n",
    "pvalLatHa = stats.spearmanr(allLats, allWhamSampled).pvalue\n",
    "\n",
    "\n",
    "print(f\"Npeaks vs H-alpha: {rhoNpeaksHa:.4f}, p-value: {pvalNpeaksHa:.20e}\")\n",
    "print(f\"Latitude vs Npeaks: {rhoLatNpeaks:.4f}, p-value: {pvalLatNpeaks:.20e}\")\n",
    "print(f\"Latitude vs H-alpha: {rhoLatHa:.4f}, p-value: {pvalLatHa:.20e}\")\n",
    "\n",
    "#p-values are zero because of large sample sizes?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b8c3ec3-8119-4105-9c8d-ba9ee7d0f6a1",
   "metadata": {},
   "source": [
    "# currently have a negative correlation between faraday complexity and h-alpha emission. complexity up h-alpha intensity go down "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4bced203-18d9-4f46-960c-136175aac606",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pingouin as pg \n",
    "import pandas as pd \n",
    "\n",
    "df = pd.DataFrame({\n",
    "    'npeaks':allNpeaks,\n",
    "    'halpha':allWhamSampled,\n",
    "    'lat':allLats\n",
    "})\n",
    "\n",
    "result = pg.partial_corr(data=df, x = 'npeaks', y ='halpha', covar ='lat', method ='spearman')\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f7eddb5b-088d-43ee-b1ef-e510fcc452a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"Non-complex LoS H-alpha:\")\n",
    "print(f\"  mean:   {np.mean(simpleWhamSampled):.4f} R\")\n",
    "print(f\"  median: {np.median(simpleWhamSampled):.4f} R\")\n",
    "print(f\"  std:    {np.std(simpleWhamSampled):.4f} R\")\n",
    "print(f\"  min:    {np.min(simpleWhamSampled):.4f} R\")\n",
    "print(f\"  max:    {np.max(simpleWhamSampled):.4f} R\")\n",
    "print(f\"  above 100R: {np.sum(simpleWhamSampled > 100)}\")\n",
    "\n",
    "print(\"\\nComplex LoS H-alpha:\")\n",
    "print(f\"  mean:   {np.mean(complexWhamSampled):.4f} R\")\n",
    "print(f\"  median: {np.median(complexWhamSampled):.4f} R\")\n",
    "print(f\"  std:    {np.std(complexWhamSampled):.4f} R\")\n",
    "print(f\"  min:    {np.min(complexWhamSampled):.4f} R\")\n",
    "print(f\"  max:    {np.max(complexWhamSampled):.4f} R\")\n",
    "print(f\"  above 100R: {np.sum(complexWhamSampled > 100)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f5ed1f46-4b0d-4b14-a55f-67efbeb8eec6",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"Non-complex centroids:\")\n",
    "print(f\"  longitude range: {simpleLoSCentroids[:, 0].min():.4f} to {simpleLoSCentroids[:, 0].max():.4f}\")\n",
    "print(f\"  latitude range:  {simpleLoSCentroids[:, 1].min():.4f} to {simpleLoSCentroids[:, 1].max():.4f}\")\n",
    "\n",
    "print(\"\\nComplex centroids:\")\n",
    "print(f\"  longitude range: {complexLoSCentroids[:, 0].min():.4f} to {complexLoSCentroids[:, 0].max():.4f}\")\n",
    "print(f\"  latitude range:  {complexLoSCentroids[:, 1].min():.4f} to {complexLoSCentroids[:, 1].max():.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "10d1af00-3212-4899-8452-52aa8b506b68",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(f\"Simple LoS median latitude: {np.median(simpleLoSCentroids[:, 1]):.4f} deg\")\n",
    "print(f\"Complex LoS median latitude: {np.median(complexLoSCentroids[:, 1]):.4f} deg\")\n",
    "print(f\"Simple LoS mean H-alpha: {np.mean(simpleWhamSampled):.4f} R\")\n",
    "print(f\"Complex LoS mean H-alpha: {np.mean(complexWhamSampled):.4f} R\")\n",
    "print(f\"Simple LoS median H-alpha: {np.median(simpleWhamSampled):.4f} R\")\n",
    "print(f\"Complex LoS median H-alpha: {np.median(complexWhamSampled):.4f} R\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e774ef6e-4b7f-408e-b979-af8fce44489d",
   "metadata": {},
   "source": [
    "## LoS with high complexity tend to have a latitude further from the galactic plane so they will  have less h-alpha, what if I filter the galactic plane out from these results entirely"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5c1f21da-9051-4a1c-ad2f-9663cd85a2e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "latThresh = 10 #deg\n",
    "\n",
    "simpleMask = np.abs(simpleLoSCentroids[:,1]) > latThresh\n",
    "complexMask = np.abs(complexLoSCentroids[:,1]) > latThresh\n",
    "\n",
    "simpleWhamFiltered = simpleWhamSampled[simpleMask] \n",
    "complexWhamFiltered = complexWhamSampled[complexMask] \n",
    "\n",
    "simpleLoSCentroidsFiltered = simpleLoSCentroids[simpleMask]\n",
    "complexLoSCentroidsFiltered = complexLoSCentroids[complexMask]\n",
    "\n",
    "print(f\"Simple LoS remaining: {len(simpleWhamFiltered)} of {len(simpleWhamSampled)}\")\n",
    "\n",
    "print(f\"Complex LoS remaining: {len(complexWhamFiltered)} of {len(complexWhamSampled)}\")\n",
    "\n",
    "print(f\"Simple median H-alpha: {np.median(simpleLoSCentroidsFiltered):.2f} R\")\n",
    "\n",
    "print(f\"Complex median H-alpha: {np.median(complexLoSCentroidsFiltered):.2f} R\")\n",
    "\n",
    "print(f\"Simple median latitude: {np.median(simpleLoSCentroidsFiltered[:, 1]):.2f} deg\")\n",
    "\n",
    "print(f\"Complex median latitude: {np.median(complexLoSCentroidsFiltered[:, 1]):.2f} deg\")\n",
    "\n",
    "print(f\"Simple maximum intensity: {np.max(simpleLoSCentroidsFiltered)}\")\n",
    "\n",
    "print(f\"Complex maximum intensity: {np.max(complexLoSCentroidsFiltered)}\")\n",
    "\n",
    "print(f\"Simple minimum intensity: {np.min(simpleLoSCentroidsFiltered)}\")\n",
    "\n",
    "print(f\"Complex minimum intensity: {np.min(complexLoSCentroidsFiltered)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "297a0096-514e-431c-af00-3740ede06494",
   "metadata": {},
   "outputs": [],
   "source": [
    "allLatsFiltered = np.concatenate([simpleLoSCentroidsFiltered[:, 1], complexLoSCentroidsFiltered[:, 1]])\n",
    "allWhamFiltered = np.concatenate([simpleWhamFiltered, complexWhamFiltered])\n",
    "allNpeaksFiltered = np.concatenate([\n",
    "    np.zeros(len(simpleWhamFiltered), dtype=np.int8),\n",
    "    np.ones(len(complexWhamFiltered), dtype=np.int8)\n",
    "])\n",
    "\n",
    "dfFiltered = pd.DataFrame({\n",
    "    'npeaks': allNpeaksFiltered,\n",
    "    'halpha': allWhamFiltered,\n",
    "    'lat': allLatsFiltered\n",
    "})\n",
    "\n",
    "result = pg.partial_corr(data=dfFiltered, x='npeaks', y='halpha', covar='lat', method='spearman')\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df470995-d717-485a-8e71-31dfe8c4d0e6",
   "metadata": {},
   "source": [
    "# Now need to make plots of h-alpha intensity versus number of LoS in the various complexities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "afc3a9c2-6f53-4420-bce6-faa80d6515be",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = (compOneWhamSampled.mean(), compTwoWhamSampled.mean(), compThreeWhamSampled.mean(), compFourWhamSampled.mean(),\n",
    "     compFiveWhamSampled.mean(), compSixWhamSampled.mean(), compSevenWhamSampled.mean(), compEightWhamSampled.mean())\n",
    "x = 1, 2, 3 , 4 ,5 ,6 ,7 ,8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7192f56c-cd19-4a6a-8a8d-04e1bfa8fdf1",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.bar(x, y)\n",
    "plt.xlabel(\"Faraday depth components\")\n",
    "plt.ylabel(\"Mean WHAM intensity (R)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ec17f451-cd96-4a97-b5d1-7c57b3b2eae8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.ticker as ticker \n",
    "\n",
    "classLabels = ['1\\n(simple)', '2', '3', '4', '5', '6', '7', '8\\n(complex)']\n",
    "classData = [compOneWhamSampled, compTwoWhamSampled, compThreeWhamSampled, compFourWhamSampled, \n",
    "             compFiveWhamSampled, compSixWhamSampled, compSevenWhamSampled, compEightWhamSampled]\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(12,6))\n",
    "parts = ax.violinplot(classData, positions=range(1,9), showmedians=True, showextrema=False)\n",
    "\n",
    "ax.set_xticks(range(1,9))\n",
    "ax.set_xticklabels(classLabels)\n",
    "ax.set_xlabel('Faraday Depth Peaks')\n",
    "ax.set_ylabel('H-alpha Intensity (R)')\n",
    "ax.set_title('H-alpha Intensity vs Faraday Complexity') #need a better title later \n",
    "ax.set_yscale('log')\n",
    "ax.yaxis.set_major_formatter(ticker.ScalarFormatter())\n",
    "ax.grid(True, axis='y')\n",
    "plt.tight_layout()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5b9d8271-b2ec-435a-8374-a594f7ec1e1d",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(f\"compTwoWhamSampled unique values: {len(np.unique(compTwoWhamSampled))}\")\n",
    "print(f\"compTwoWhamSampled range: {compTwoWhamSampled.min():.2f} to {compTwoWhamSampled.max():.2f}\")\n",
    "print(f\"compTwoWhamSampled median: {np.median(compTwoWhamSampled):.2f}\")\n",
    "print(f\"compTwoWhamSampled std: {np.std(compTwoWhamSampled):.2f}\")\n",
    "print(f\"compTwoWhamSampled shape: {compTwoWhamSampled.shape}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "154bf089-e567-4638-866c-e092d813f21b",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(f\"compTwoLoSCentroids shape: {compTwoLoSCentroids.shape}\")\n",
    "print(f\"compTwoLoSCentroids unique rows: {len(np.unique(compTwoLoSCentroids, axis=0))}\")\n",
    "print(f\"compTwoLoSCentroids sample:\\n{compTwoLoSCentroids[:5]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a8ca3fa2-ebd0-4ba5-afac-789a163c68a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(wcs)\n",
    "print(wcs.naxis)\n",
    "print(f\"compTwoNewLoS length: {len(compTwoNewLoS)}\")\n",
    "print(f\"First region of compTwoNewLoS: {compTwoNewLoS[0][:3]}\")\n",
    "print(f\"First region as array: {np.array(compTwoNewLoS[0], dtype=np.int32)[:3]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a6ed7eca-e095-4708-9521-aa06b91c030e",
   "metadata": {},
   "outputs": [],
   "source": [
    "testRegion = np.array(compTwoNewLoS[0], dtype=np.int32)\n",
    "testSky = wcs.pixel_to_world(testRegion[:, 0], testRegion[:, 1])\n",
    "testGal = testSky.galactic\n",
    "print(f\"Test l values: {testGal.l.deg[:5]}\")\n",
    "print(f\"Test b values: {testGal.b.deg[:5]}\")\n",
    "print(f\"Test mean l: {np.mean(testGal.l.deg):.4f}\")\n",
    "print(f\"Test mean b: {np.mean(testGal.b.deg):.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "68ea5b56-8d2f-4a38-af12-54f12e759a4c",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(f\"compTwoLoSCentroids unique rows: {len(np.unique(compTwoLoSCentroids, axis=0))}\")\n",
    "print(f\"compTwoLoSCentroids first 5:\\n{compTwoLoSCentroids[:5]}\")\n",
    "print(f\"compTwoLoSCentroids last 5:\\n{compTwoLoSCentroids[-5:]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aeea00ea-0f87-4ba7-80ad-b2ce7b67c300",
   "metadata": {},
   "outputs": [],
   "source": [
    "wcs = WCS(Npeakscarhdr, naxis=2)\n",
    "\n",
    "compTwoLoSCentroids = np.zeros((len(compTwoNewLoS), 2), dtype=np.float32)\n",
    "for i, region in enumerate(compTwoNewLoS):\n",
    "    regionArray = np.array(region, dtype=np.int32)\n",
    "    skyCoords = wcs.pixel_to_world(regionArray[:, 0], regionArray[:, 1])\n",
    "    galCoords = skyCoords.galactic\n",
    "    lMean = float(np.mean(galCoords.l.deg))\n",
    "    bMean = float(np.mean(galCoords.b.deg))\n",
    "    compTwoLoSCentroids[i, 0] = lMean\n",
    "    compTwoLoSCentroids[i, 1] = bMean\n",
    "    if i < 3:\n",
    "        print(f\"i={i}, l={lMean:.4f}, b={bMean:.4f}\")\n",
    "\n",
    "print(f\"Unique rows: {len(np.unique(compTwoLoSCentroids, axis=0))}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d6b89f0a-3c3e-4d0c-bf8e-639796a09831",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(300):\n",
    "    region = compTwoNewLoS[i]\n",
    "    print(f\"Region {i}: length={len(region)}, first pixel={region[0]}\")"
   ]
  }
 ],
 "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": "3.11.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}