{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Day 2 Solutions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "from time import time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reading the lens/source catalog"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": [
     "hide_input"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ra,dec,z,col_3,col_4,col_5\r\n",
      "30.611734783139795,-6.3569091978972425,23.901826,23.043298,22.649281,0.5\r\n",
      "30.61078116389429,-6.350381359287763,22.385773,21.548382,21.272576,0.38\r\n",
      "30.596477830319948,-6.3461523588063935,23.948118,23.121471,22.773187,0.37\r\n",
      "30.592680083175416,-6.3445214768250775,23.42731400000001,22.022285999999998,21.334948,0.45\r\n"
     ]
    }
   ],
   "source": [
    "!head -n 5 /home/idies/workspace/Storage/divyar/IAGRG_2022/DataStore/demo_objects.csv\n",
    "\n",
    "# !wc -l /home/idies/workspace/Storage/divyar/IAGRG_2022/DataStore/demo_objects.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#reading lens file\n",
    "\n",
    "#tries to read everthing in the file\n",
    "path=\"/home/idies/workspace/Storage/divyar/IAGRG_2022/DataStore/demo_objects.csv\"\n",
    "\n",
    "df = pd.read_csv(path, sep=',')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\\# select required columns only"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(path, sep=',', usecols=[0,1,2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ra</th>\n",
       "      <th>dec</th>\n",
       "      <th>z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30.611735</td>\n",
       "      <td>-6.356909</td>\n",
       "      <td>23.901826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>30.610781</td>\n",
       "      <td>-6.350381</td>\n",
       "      <td>22.385773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>30.596478</td>\n",
       "      <td>-6.346152</td>\n",
       "      <td>23.948118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30.592680</td>\n",
       "      <td>-6.344521</td>\n",
       "      <td>23.427314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>30.601111</td>\n",
       "      <td>-6.338650</td>\n",
       "      <td>21.660045</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          ra       dec          z\n",
       "0  30.611735 -6.356909  23.901826\n",
       "1  30.610781 -6.350381  22.385773\n",
       "2  30.596478 -6.346152  23.948118\n",
       "3  30.592680 -6.344521  23.427314\n",
       "4  30.601111 -6.338650  21.660045"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualising the coordinate space of input data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-info\">\n",
    "\n",
    "Exercise: \n",
    "    \n",
    "- write a python code to plot dec(on Y-axis) vs ra(on X-axis) of the objects passed in the dataFrame.\n",
    "\n",
    "    This plot will look very dense, so in order to visually see the points distributed on the (ra,dec) plane, downsample the number of points by 100 and again plot.\n",
    "- In order to downsample the points, use `numpy.random.choise()` function.\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1400x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots(1,2, figsize=(14,6))\n",
    "ax1,ax2 = ax\n",
    "\n",
    "#plot all the coordinates   \n",
    "ax1.scatter(df.ra.values,df.dec.values, s=2)\n",
    "ax1.set_xlabel(\"ra (deg)\", fontsize=14)\n",
    "ax1.set_ylabel(\"dec (deg)\", fontsize=14)\n",
    "\n",
    "ax1.set_title(\"Plot looks too dense.\")\n",
    "\n",
    "#Randomly picking up points to reduce the density of points\n",
    "Number_of_points = df.ra.size\n",
    "down_sample_by = 100\n",
    "random_indices = np.random.choice(np.arange(Number_of_points), \n",
    "                                  size=int(Number_of_points/down_sample_by), \n",
    "                                  replace=False)\n",
    "#plot down sampled number coordinates\n",
    "ax2.scatter(df.ra.values[random_indices], df.dec.values[random_indices], s=2)\n",
    "ax2.set_xlabel(\"ra (deg)\", fontsize=14)\n",
    "ax2.set_ylabel(\"dec (deg)\", fontsize=14)\n",
    "ax2.set_title(\"Density of points reduced by %d.\"%down_sample_by)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### `On-sky distance` between two points on the surface of a unit sphere"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In spherical cartesian coordinates : $\\bar{r} = \\bar{r}(r,\\theta,\\phi)$\n",
    "\n",
    "and the unit vector :\n",
    "$$\n",
    "\\hat{r}_i = \\sin(\\theta_i) \\cos(\\phi_i) + \\sin(\\theta_i) \\sin(\\phi_i) + \\cos(\\theta_i), \\quad {\\rm \\text{for }} i^{th} {\\rm \\text{ coordinate}}\n",
    "$$\n",
    "\n",
    "In coordinates of a galaxy in celestial coordinate system (unit shpere) : $(\\alpha,\\delta)$\n",
    "\n",
    "\n",
    "$$\n",
    "\\begin{align*}\n",
    "&\\alpha \\equiv {\\rm \\text{ra}},\\quad \\delta \\equiv {\\rm \\text{ dec}} \\\\\n",
    "&\\alpha = \\phi,\\quad \\delta = \\left(\\frac{\\pi}{2} - \\theta \\right) \\\\\n",
    "&\\hat{r}_i = \\cos(\\delta_i) \\cos(\\alpha_i) + \\cos(\\delta_i) \\sin(\\alpha_i) + \\sin(\\delta_i) \\\\\n",
    "&r_1=r_2=1, \\quad \\rm{\\text{ a unit sphere}} \\\\\n",
    "\\end{align*}\n",
    "$$\n",
    "\n",
    "For $(\\alpha_1,\\delta_1)$, $(\\alpha_2,\\delta_2)$, the angle $\\psi$ between them is given by : \n",
    "\n",
    "$$\n",
    "\\psi = \\cos^{-1}(\\hat{r}_1 \\cdot \\hat{r}_2)\n",
    "$$\n",
    "\n",
    "where,\n",
    "\n",
    "$$\n",
    "\\begin{align*}\n",
    "\\hat{r}_1 \\cdot \\hat{r}_2 &= \\cos(\\delta_1)\\cos(\\delta_2) \\cos(\\alpha_1)\\cos(\\alpha_2) + \\cos(\\delta_1)\\cos(\\delta_2) \\sin(\\alpha_1)\\sin(\\alpha_2) + \\sin(\\delta_1)\\sin(\\delta_2) \\\\\n",
    "\\hat{r}_1 \\cdot \\hat{r}_2 &= \\cos(\\delta_1)\\cos(\\delta_2) \\left( \\cos(\\alpha_1 - \\alpha_2) \\right) + \\sin(\\delta_1)\\sin(\\delta_2)\n",
    "\\end{align*}\n",
    "$$\n",
    "\n",
    "Thus, given $(\\alpha_1,\\delta_1)$, $(\\alpha_2,\\delta_2)$, we can compute the angle $\\psi$.\n",
    "\n",
    "Using : distance formula on the arc of a cirle :\n",
    "\n",
    "$$\n",
    "\\begin{align*}\n",
    "&s\\  = r \\times \\psi, \\\\ \n",
    "\\text{but, } &r_1=r_2=1 \\\\\n",
    "&s\\ =\\psi\\ (radians)\n",
    "\\end{align*}\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-info\">\n",
    "\n",
    "Exercise: Using the above formula\n",
    "\n",
    "- Implement a function which takes tuples of (ra,dec) of two objects and returns the distance between them on the Unit sphere.\n",
    "\n",
    "    Your code should return the result in units of arc seconds and **NOT** in radians.\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "rad_to_arcsec = 180.0*3600./np.pi\n",
    "arcsec_to_rad = 1/rad_to_arcsec\n",
    "deg_to_rad = np.pi/180. \n",
    "\n",
    "def dist_betw_two_obj(obj1,obj2):\n",
    "    ra1,dec1 = obj1\n",
    "    ra2,dec2 = obj2\n",
    "    #convert angles from degree to radians\n",
    "    ra1  = ra1  * deg_to_rad \n",
    "    ra2  = ra2  * deg_to_rad\n",
    "    dec1 = dec1 * deg_to_rad\n",
    "    dec2 = dec2 * deg_to_rad\n",
    "    \n",
    "    #calculate the distance in arcsec\n",
    "    psi = np.cos(dec2)* np.cos(dec1)* np.cos(ra1-ra2) + np.sin(dec1)* np.sin(dec2)\n",
    "    \n",
    "    #due to precision errors, sometimes psi > 1 , e.g. 1.0000000000000000000001\n",
    "    #in such a case np.arccos funciton will complaint. So handle that now.\n",
    "    if psi>1.0: \n",
    "        psi = 1.0\n",
    "        \n",
    "    dist = np.arccos(psi)* rad_to_arcsec\n",
    "    return dist\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\\# Running and testing the distance computation code:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# creating tuple of values from ra,dec of each object\n",
    "coords = list( zip(df.ra.values,df.dec.values))\n",
    "# print(coords[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "obj1 0: (30.611734783139795, -6.3569091978972425)\n",
      "obj2 1: (30.61078116389429, -6.350381359287763)\n",
      "distance: 23.7466131834144 arcsec\n"
     ]
    }
   ],
   "source": [
    "print( f\"\"\"obj1 0: {coords[0]}\n",
    "obj2 1: {coords[1]}\n",
    "distance: {dist_betw_two_obj(coords[0],coords[1])} arcsec\"\"\") "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Brute force search for neighbour objects within a specified distance \n",
    "time complexity ~ $\\mathcal{O}(n^2)$ "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-info\">\n",
    "\n",
    "Exercise: \n",
    "    \n",
    "1)\n",
    "    \n",
    "- Write a function `find_neighbour_points` that uses (ra,dec) coordinates of an object, and within some given `rmax` distance, finds its neighbour points in the given dataFrame catalog.\n",
    "\n",
    "- Using this code you should be able to find neighbours of each object in the dataFrame - within the same dataFrame.\n",
    "\n",
    "    Say the $1^{st}$ object in the dataFrame has 5 objects within say rmax=15 arcsec, then you should be able to  print/store the `indices` and `distances` of these neighbours.\n",
    "\n",
    "2)\n",
    "    \n",
    "- Write a function `query_neighbour_points` that can use (ra,dec) coordinates of one object or an list/array of objets, and within some given `rmax` distance, finds its neighbour points in any other catalog.\n",
    "\n",
    "- Using this code you should be able to access the coordinates of all the neighbours of each object that you are querying for. And hence you should be able to tell how far each neighbour is from the queried object.\n",
    "    \n",
    "</div>    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import defaultdict\n",
    "\n",
    "def find_neighbour_points(coords, rmax=10, verbose=True):\n",
    "    \"\"\"\n",
    "    finding neighbour objects within some distance, for all the objects in a given array of coordinates.\n",
    "    \n",
    "    coords: an array/list of tuples (ra,dec)\n",
    "    \n",
    "    Returns:\n",
    "    `nnids`: indices into `coords` of neighbours of each object in `coords` within `rmax`\n",
    "    `distances`: distance of objects matched in `nnids\"\"\"\n",
    "\n",
    "    # define containers for the distance and id information of matched objects\n",
    "    distances = defaultdict(list)\n",
    "    nnids = defaultdict(list)\n",
    "\n",
    "    c=0\n",
    "    for ii,obj1 in enumerate(coords):\n",
    "        c+=1\n",
    "        d=0\n",
    "        for jj,obj2 in enumerate(coords):\n",
    "            if jj==ii:\n",
    "                continue\n",
    "            d+=1\n",
    "            dist = dist_betw_two_obj(obj1,obj2)\n",
    "            if dist <= rmax:\n",
    "                nnids[ii].append(jj)\n",
    "                distances[ii].append(dist)\n",
    "        if verbose:\n",
    "            print(ii,nnids[ii],distances[ii])                \n",
    "    return nnids,distances\n",
    "\n",
    "def query_neighbour_points(coords1, coords2, rmax=10, verbose=True):\n",
    "    \"\"\"\n",
    "    coords1: a tuple or an array/list of tuples (ra,dec)\n",
    "    coords2: an array/list of tuples (ra,dec)\n",
    "    \n",
    "    Returns:\n",
    "    `nnids`: indices into `coords2` of neighbours of each object `coords1` within `rmax`.\n",
    "    `distances`: distance of objects matched in `nnids`\"\"\"\n",
    "\n",
    "    coords1 = np.atleast_2d(coords1)\n",
    "    \n",
    "    # define containers for the distance and id information of matched objects\n",
    "    distances = defaultdict(list)\n",
    "    nnids = defaultdict(list)\n",
    "\n",
    "    for ii,obj1 in enumerate(coords1):\n",
    "        for jj,obj2 in enumerate(coords2):\n",
    "            dist = dist_betw_two_obj(obj1,obj2)\n",
    "            if dist <= rmax:\n",
    "                nnids[ii].append(jj)\n",
    "                distances[ii].append(dist)\n",
    "        if verbose:\n",
    "            print(ii,nnids[ii],distances[ii])                \n",
    "    return nnids,distances"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Testing the above  `brute force search` functions "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-info\">\n",
    "\n",
    "Exercise: \n",
    "\n",
    "- Using the first 50 objects in the catalog, Search for their neighbours within a ball of radius `rmax`, say for `rmax`(default value)=15 arcsec.\n",
    "\n",
    "    **Note:** We're only using 50 points in order to reduce the compute time, but the code has no such limitation. If you run your code for a large number of points, it will take more time to finish that's all.\n",
    "    \n",
    "    \n",
    "- Access the above result and print indices,distances and coordinates of the neighbours of few objects.\n",
    "    \n",
    "- Then test out the function `query_neighbour_points` for the same objects you take above. The neighbour matches from `query_neighbour_points` and `find_neighbour_points` must match.\n",
    "    \n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "object index, neighbours indices list, neighbours distances list \n",
      "0 [] []\n",
      "1 [] []\n",
      "2 [3] [14.802299176580224]\n",
      "3 [2] [14.802299176580224]\n",
      "4 [36] [13.682331312465545]\n",
      "5 [] []\n",
      "6 [] []\n",
      "7 [] []\n",
      "8 [13] [9.465537930627812]\n",
      "9 [10, 12, 38] [10.94680374083333, 5.876628853240463, 14.594679725290598]\n",
      "10 [9, 12] [10.94680374083333, 10.280601486171243]\n",
      "11 [42] [7.151725370768233]\n",
      "12 [9, 10, 14] [5.876628853240463, 10.280601486171243, 11.314136132011965]\n",
      "13 [8] [9.465537930627812]\n",
      "14 [12, 15, 16] [11.314136132011965, 5.475958296970058, 11.765854084075805]\n",
      "15 [14, 16, 44] [5.475958296970058, 13.47975484115049, 10.276694006812336]\n",
      "16 [14, 15] [11.765854084075805, 13.47975484115049]\n",
      "17 [46] [6.898087849306218]\n",
      "18 [] []\n",
      "19 [] []\n",
      "20 [] []\n",
      "21 [] []\n",
      "22 [] []\n",
      "23 [] []\n",
      "24 [] []\n",
      "25 [26] [13.29908354885402]\n",
      "26 [25] [13.29908354885402]\n",
      "27 [] []\n",
      "28 [] []\n",
      "29 [30] [3.6918325238285883]\n",
      "30 [29] [3.6918325238285883]\n",
      "31 [] []\n",
      "32 [] []\n",
      "33 [] []\n",
      "34 [] []\n",
      "35 [36] [10.896870606370635]\n",
      "36 [4, 35] [13.682331312465545, 10.896870606370635]\n",
      "37 [] []\n",
      "38 [9, 40] [14.594679725290598, 14.719522078343205]\n",
      "39 [40] [1.984703189527645]\n",
      "40 [38, 39] [14.719522078343205, 1.984703189527645]\n",
      "41 [] []\n",
      "42 [11] [7.151725370768233]\n",
      "43 [] []\n",
      "44 [15] [10.276694006812336]\n",
      "45 [] []\n",
      "46 [17] [6.898087849306218]\n",
      "47 [] []\n",
      "48 [] []\n",
      "49 [] []\n",
      "CPU times: user 41.7 ms, sys: 520 µs, total: 42.2 ms\n",
      "Wall time: 37.3 ms\n"
     ]
    }
   ],
   "source": [
    "# Running and testing find_neighbour_points \n",
    "\n",
    "print(\"object index, neighbours indices list, neighbours distances list \")\n",
    "%time nnids,distances = find_neighbour_points(coords[:50],rmax=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "object index, neighbours indices list, neighbours distances list \n",
      "0 [0] [0.003073585126505738]\n",
      "1 [1] [0.0]\n",
      "2 [2, 3] [0.003073585126505738, 14.802299176580224]\n",
      "3 [2, 3] [14.802299176580224, 0.0]\n",
      "4 [4, 36] [0.0, 13.682331312465545]\n",
      "5 [5] [0.0]\n",
      "6 [6] [0.0]\n",
      "7 [7] [0.0]\n",
      "8 [8, 13] [0.0, 9.465537930627812]\n",
      "9 [9, 10, 12, 38] [0.0, 10.94680374083333, 5.876628853240463, 14.594679725290598]\n",
      "10 [9, 10, 12] [10.94680374083333, 0.0, 10.280601486171243]\n",
      "11 [11, 42] [0.0, 7.151725370768233]\n",
      "12 [9, 10, 12, 14] [5.876628853240463, 10.280601486171243, 0.003073585126505738, 11.314136132011965]\n",
      "13 [8, 13] [9.465537930627812, 0.0]\n",
      "14 [12, 14, 15, 16] [11.314136132011965, 0.003073585126505738, 5.475958296970058, 11.765854084075805]\n",
      "15 [14, 15, 16, 44] [5.475958296970058, 0.0, 13.47975484115049, 10.276694006812336]\n",
      "16 [14, 15, 16] [11.765854084075805, 13.47975484115049, 0.0]\n",
      "17 [17, 46] [0.0, 6.898087849306218]\n",
      "18 [18] [0.0]\n",
      "19 [19] [0.0]\n",
      "20 [20] [0.0]\n",
      "21 [21] [0.0]\n",
      "22 [22] [0.0]\n",
      "23 [23] [0.0]\n",
      "24 [24] [0.003073585126505738]\n",
      "25 [25, 26] [0.0, 13.29908354885402]\n",
      "26 [25, 26] [13.29908354885402, 0.0]\n",
      "27 [27] [0.0]\n",
      "28 [28] [0.003073585126505738]\n",
      "29 [29, 30] [0.0, 3.6918325238285883]\n",
      "30 [29, 30] [3.6918325238285883, 0.0]\n",
      "31 [31] [0.0]\n",
      "32 [32] [0.0]\n",
      "33 [33] [0.0]\n",
      "34 [34] [0.0]\n",
      "35 [35, 36] [0.0, 10.896870606370635]\n",
      "36 [4, 35, 36] [13.682331312465545, 10.896870606370635, 0.003073585126505738]\n",
      "37 [37] [0.0]\n",
      "38 [9, 38, 40] [14.594679725290598, 0.0, 14.719522078343205]\n",
      "39 [39, 40] [0.0, 1.984703189527645]\n",
      "40 [38, 39, 40] [14.719522078343205, 1.984703189527645, 0.0]\n",
      "41 [41] [0.003073585126505738]\n",
      "42 [11, 42] [7.151725370768233, 0.0]\n",
      "43 [43] [0.0]\n",
      "44 [15, 44] [10.276694006812336, 0.0]\n",
      "45 [45] [0.0]\n",
      "46 [17, 46] [6.898087849306218, 0.0]\n",
      "47 [47] [0.0]\n",
      "48 [48] [0.0]\n",
      "49 [49] [0.0]\n",
      "CPU times: user 53.5 ms, sys: 4.54 ms, total: 58.1 ms\n",
      "Wall time: 49.6 ms\n"
     ]
    }
   ],
   "source": [
    "# Running and testing query_neighbour_points\n",
    "print(\"object index, neighbours indices list, neighbours distances list \")\n",
    "\n",
    "# Remember because I have passed same object catalog as coords and coords2, \n",
    "# you are seeing same objects matched with each other. So if you ignore the same\n",
    "# matches, you'll notice that the printed neighbours are exactly the same as shown in\n",
    "# the above cell.\n",
    "\n",
    "%time nnidss,distancess = query_neighbour_points(coords1=coords[:50],coords2=coords[:50],rmax=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using result of find_neighbour_points :\n",
      "Accessing the neighbour points for the 15th object using the output \n",
      "of our brute force method:\n",
      "neighbours: [14, 16, 44] \n",
      "distances: [5.475958296970058, 13.47975484115049, 10.276694006812336]\n",
      "\n",
      "Accessing the neighbour coordinates of the 15th object:\n",
      "14 (30.564297942270603, -6.322030429805476)\n",
      "16 (30.56234780563957, -6.319398928221537)\n",
      "44 (30.568297869918087, -6.3202347701814166)\n"
     ]
    }
   ],
   "source": [
    "nth = 15\n",
    "\n",
    "print(\"Using result of find_neighbour_points :\")\n",
    "\n",
    "print(f\"\"\"Accessing the neighbour points for the {nth}th object using the output \n",
    "of our brute force method:\"\"\")\n",
    "\n",
    "print(f\"neighbours: {nnids[nth]} \\ndistances: {distances[nth]}\")\n",
    "\n",
    "print()\n",
    "\n",
    "print(f\"Accessing the neighbour coordinates of the {nth}th object:\")\n",
    "for ii in nnids[nth]:\n",
    "    print(ii, coords[ii])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "#### A visual sanity check for execution of `find_neighbour_points`  function"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-info\">\n",
    "\n",
    "Exercise:\n",
    "\n",
    "- Plot the coordinates of the neighbours of any one test point. This exercise can help us visually inspect whether our neighbour search code gave us reasonable results or not.\n",
    "\n",
    "\n",
    "- Along with plotting, also print the distances of each of these neighbours and visually confirm that the nearest looking object on the plot indeed has the smallest distance from the test point, and similarly check for other neighbour points.\n",
    "    \n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I'm plotting the neighbours of $15^{th}$ point from the catalog.<br>\n",
    "And also printing their `indices` and `distances` to compare the stored distances by eye!\n",
    "\n",
    "Point marked as $\\star$ is the $15^{th}$ point. Other points are its neighbours."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "index--> distance\t\tcolor\n",
      "14 ---> 5.475958296970058 \t r\n",
      "16 ---> 13.47975484115049 \t g\n",
      "44 ---> 10.276694006812336 \t b\n"
     ]
    }
   ],
   "source": [
    "demoid = 15\n",
    "\n",
    "plt.figure(figsize=(8,6))\n",
    "#plot demo point\n",
    "demora,demodec  = coords[demoid] \n",
    "plt.scatter(demora,demodec, c='black', s=100, marker=r'*')\n",
    "\n",
    "#access all the neighbours\n",
    "coords=np.array(coords)\n",
    "neighbours = nnids[demoid]\n",
    "\n",
    "#choose colors for a given number of neighbours\n",
    "colors = ['r','g','b']\n",
    "# colors = np.random.random( size=(len(neighbours),3) )\n",
    "\n",
    "#plot all the neighbour points\n",
    "plotra  = [coord[0] for coord in coords[neighbours] ]\n",
    "plotdec = [coord[1] for coord in coords[neighbours] ] \n",
    "plt.scatter(plotra, plotdec, c=colors, s=50, marker='o')\n",
    "\n",
    "for i in range(len(plotra)):\n",
    "    plt.plot([demora,plotra[i]],[demodec,plotdec[i]] , c=colors[i])\n",
    "\n",
    "plt.xlabel(\"ra (deg)\", fontsize=16)\n",
    "plt.ylabel(\"dec (deg)\", fontsize=16)\n",
    "plt.show()\n",
    "#plt.savefig(\"neighbours_of_15th_obj.png\",bbox_inches=\"tight\")\n",
    "\n",
    "# Now visuall compare the distances\n",
    "print(\"index--> distance\\t\\tcolor\")\n",
    "ndist = distances[demoid]\n",
    "for nidx,dist,c in zip(neighbours,ndist,colors):\n",
    "    print(nidx,\"--->\",dist, '\\t', c)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using k-d tree algorithm to query the neighbour points within a given distance \n",
    "time complexity ~ $\\mathcal{O}(n\\log{}n)$ "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import spatial\n",
    "sin = np.sin\n",
    "cos = np.cos\n",
    "deg2rad = np.deg2rad"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-info\">\n",
    "\n",
    "Exercise:\n",
    "    \n",
    "- Write a code using cKDTree to find out the neighbours of a given `set1` of objects (coordinates) from a catalog of other `set2` objects.\n",
    "    \n",
    "    Write this code in a `class structure` where it takes-in arrays of `ra,dec` of the catalog objects (set2) and \n",
    "    - converts (ra,dec) to (x,y,z) of cartesian coordinates,\n",
    "    - creates k-d tree of the `set2` catalog objects using (x,y,z),\n",
    "    - queries for the neighbour points of - each object in the `set1`, and returns the indices in `set2`.\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "class find_neighbours:\n",
    "    def __init__(self, tra=None, tdec=None, r=1):\n",
    "        \"\"\"r==1 --> unit sphere calculations\"\"\"\n",
    "        self.tra = tra\n",
    "        self.tdec= tdec\n",
    "        self.r = r\n",
    "        self.rad_to_arcsec = 180.0*3600./np.pi\n",
    "        self.arcsec_to_rad = 1/self.rad_to_arcsec\n",
    "        \n",
    "        print(f\"Initializing with sphere of radius, r={r}.\")\n",
    "        \n",
    "    # write a function that converts ra,dec to cartesian components.\n",
    "    def RA_DEC_to_xyz(self, ra,dec, units='deg'):\n",
    "        \"\"\"converts ra,dec to cartesian x,y,z components on a unit sphere\"\"\"\n",
    "        if units==\"deg\":\n",
    "            ra = deg2rad(ra)\n",
    "            dec= deg2rad(dec)\n",
    "            \n",
    "        x = self.r *cos(dec) *cos(ra)\n",
    "        y = self.r *cos(dec) *sin(ra)\n",
    "        z = self.r *sin(dec)\n",
    "        return x,y,z \n",
    "\n",
    "    # write a function `create_tree` that takes arrays of ra,dec \n",
    "    # values as inputs and returns a cKDTree object into these coordinates.\n",
    "    \n",
    "    def create_tree(self):\n",
    "        # create an array of tuples of x,y,z\n",
    "        x,y,z = self.RA_DEC_to_xyz(self.tra,self.tdec)\n",
    "        # make a tree using cKDTree method \n",
    "        tree = spatial.cKDTree(np.c_[x,y,z])\n",
    "        return tree\n",
    "\n",
    "    # write a function that takes arrays of ra,dec coordinates\n",
    "    # and returns the neighbour object's indices from the tree \n",
    "    # within some distance `rmax` arcsec .\n",
    "    \n",
    "    def query_within_rmax(self, ra, dec, rmax=15, units=\"deg\"):\n",
    "        print(f\"Searching for {ra.size} number of objects in the catalog with rmax={rmax} arcsec.\")\n",
    "        #convert rmax from arcsec to distance units\n",
    "        rmax = self.r *rmax *self.arcsec_to_rad \n",
    "        \n",
    "        # array of tuples of x,y,z of input objetcs\n",
    "        x,y,z = self.RA_DEC_to_xyz(ra,dec)\n",
    "        cartesian_coords = np.c_[x,y,z]\n",
    "\n",
    "        # make tree from catalog\n",
    "        tree = self.create_tree()\n",
    "        \n",
    "        # look for the neighbour objects\n",
    "        ids_into_tree = tree.query_ball_point(cartesian_coords,rmax)\n",
    "        \n",
    "        return ids_into_tree"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### A sanity check\n",
    "\n",
    "Testing conversion of (ra,dec) to (x,y,z) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initializing with sphere of radius, r=1.\n",
      "shapes of x,y,z arrays: (220622,) (220622,) (220622,)\n",
      "shapes of (x,y,z) tuple array: (220622, 3)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[ 0.8553461 ,  0.50608675, -0.11072151],\n",
       "       [ 0.85536537,  0.50607894, -0.11060828],\n",
       "       [ 0.85549871,  0.50586954, -0.11053492],\n",
       "       ...,\n",
       "       [ 0.78796508,  0.6142163 , -0.04300421],\n",
       "       [ 0.78737094,  0.61497898, -0.04298683],\n",
       "       [ 0.78774839,  0.61449982, -0.04292369]])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Here we are not passing `tra` and `tdec` while initialising the class `find_neighbours`\n",
    "# So these variables take default values of `None`. \n",
    "# We only want to test the coordinate conversion code `RA_DEC_to_xyz`. So it's ok to not \n",
    "# pass `tra`,`tdec`!\n",
    "\n",
    "code = find_neighbours()\n",
    "\n",
    "# a look at the cartesian coordinates\n",
    "x,y,z = code.RA_DEC_to_xyz(coords[:,0], coords[:,1])\n",
    "cartesian_coords = np.c_[x,y,z]\n",
    "\n",
    "print(\"shapes of x,y,z arrays:\",x.shape,y.shape,z.shape)\n",
    "print(\"shapes of (x,y,z) tuple array:\", cartesian_coords.shape)\n",
    "#field on objects looks close to equator\n",
    "cartesian_coords"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Compare the output of our brute force algorithm with cKDTree algorithm on dew Demo objects"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-info\">\n",
    "\n",
    "Exercise:\n",
    "\n",
    "- Take the same bunch of objects for which we already have run our brute force search method, \n",
    "\n",
    "    feed them into the wrapper code of k-d tree written above, \n",
    "    \n",
    "    find out their neighbour points within the same `rmax`(default value)=15 arcsec.\n",
    "    \n",
    "    The neighbour objects found from both the methods should match!\n",
    "\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I had chosen following indices from set2(catalog) as my test objects: [15]\n",
      "coordinate of test objects: [[ 0.85581232  0.50543305 -0.11010311]]\n"
     ]
    }
   ],
   "source": [
    "demo_ids = [15] #9,15\n",
    "demo_ra =coords[demo_ids,0] #deg\n",
    "demo_dec=coords[demo_ids,1] #deg\n",
    "\n",
    "demox, demoy, demoz = code.RA_DEC_to_xyz(demo_ra, demo_dec)\n",
    "demoobj = np.c_[demox,demoy,demoz]\n",
    "print(\"\"\"I had chosen following indices from set2(catalog) as my test objects:\"\"\",demo_ids)\n",
    "print(\"coordinate of test objects:\",demoobj)\n",
    "\n",
    "# we want to look within `rmax` arcsec --> convert `rmax` from (arcsec) angle to length units\n",
    "\n",
    "# rmax = 15 \n",
    "# print()\n",
    "# print(f\"rmax={rmax} arcsec in radians --->\",rmax* arcsec_to_rad)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\\# This time Initialize class with by passing `tra` and `tdec` arguments and set up the tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initializing with sphere of radius, r=1.\n"
     ]
    }
   ],
   "source": [
    "code_tree = find_neighbours(tra=coords[:,0], tdec=coords[:,1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\\# Query the neighbours of demo objects "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Searching for 1 number of objects in the catalog with rmax=15 arcsec.\n"
     ]
    }
   ],
   "source": [
    "idx=code_tree.query_within_rmax(demo_ra, demo_dec)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\\# Go back to brute force output and compare this result.... does it match? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([list([14, 15, 16, 44])], dtype=object)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idx"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compare the computer time required by our brute force algorithm vs the k-d tree algorithm "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First let's use k-d tree to query neighbours within `rmax`=15 arcsec for each object within the catalog."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Searching for 220622 number of objects in the catalog with rmax=15 arcsec.\n",
      "CPU times: user 589 ms, sys: 15.6 ms, total: 605 ms\n",
      "Wall time: 602 ms\n"
     ]
    }
   ],
   "source": [
    "%time idx=code_tree.query_within_rmax(coords[:,0], coords[:,1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reminder: full catalog has about 2,20,000 objects... and k-d tree found neighbours for each one of them in 1-2 seconds. (This time will vary depending on your computer's ability.)\n",
    "\n",
    "Now let's use the brute force method only for 1000 objects."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 7.94 s, sys: 0 ns, total: 7.94 s\n",
      "Wall time: 7.94 s\n"
     ]
    }
   ],
   "source": [
    "%time nnids,distances = query_neighbour_points(coords[:1000], coords[:1000], rmax=15, verbose=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You will notice that as the number of points to search for increases in `set1`, the performance of the k-d tree algorithm will keep on improving over the brute force method. \n",
    "\n",
    "That's just because the time required by k-d tree will grow as $\\sim \\mathcal{O}(n_1\\log{n_2})$ where as that of brute force will grow as $\\sim n_1 n_2$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Congratulations! Now you have learnt to - \n",
    "\n",
    "- use `cKDTree` to find out neighbours of a given `set1` of objects into some other `set2` of objects.\n",
    "\n",
    "- count the number of neighbours of any given object in `set1`. \n",
    "\n",
    "- access the results of cKDTree and identify neighbours of each obejct in `set1` into the `set2`.\n",
    "\n",
    "- access the coordinates and distances of each neighbour of every point in `set1`."
   ]
  }
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