{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scripts.interneuron_placement import Pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def distance(pickle_1, pickle_2):\n",
    "    return np.sqrt((pickle_1.x - pickle_2.x)**2+(pickle_1.y - pickle_2.y)**2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def contains(pickle, XX, YY):\n",
    "    d1, d2 = distance_to_points(pickle, XX, YY)\n",
    "    return d1+d2 < pickle.c\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def distance_to_points(pickle, XX, YY):\n",
    "    x_p_1, y_p_1 = pickle.get_p1()\n",
    "    x_p_2, y_p_2 = pickle.get_p2()\n",
    "    \n",
    "    d1 = np.sqrt((XX-x_p_1)**2 + (YY-y_p_1)**2)\n",
    "    d2 = np.sqrt((XX-x_p_2)**2 + (YY-y_p_2)**2)\n",
    "    return d1, d2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_overlap(pickle_1, pickle_2, ds = 0.1):\n",
    "    if distance(pickle_1, pickle_2) > 2*(max(pickle_1.a, pickle_1.b)+max(pickle_2.a, pickle_2.b)):\n",
    "        overlap = 0\n",
    "    else:\n",
    "        bb_x_min = pickle_1.x - max(pickle_1.a, pickle_1.b)\n",
    "        bb_x_max = pickle_1.x + max(pickle_1.a, pickle_1.b)\n",
    "        bb_y_min = pickle_1.y - max(pickle_1.a, pickle_1.b)\n",
    "        bb_y_max = pickle_1.y + max(pickle_1.a, pickle_1.b)\n",
    "        bb_X, bb_Y = np.meshgrid(np.arange(bb_x_min, bb_y_max,ds), np.arange(bb_y_min, bb_y_max, ds))\n",
    "        in_1 = contains(pickle_1, bb_X, bb_Y)\n",
    "        in_2 = contains(pickle_2, bb_X, bb_Y)\n",
    "        overlap = np.sum(np.logical_and(in_1, in_2))*ds**2\n",
    "    return overlap\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "e1 = Pickle(25, 50, 40, 20, 0.0)\n",
    "e2 = Pickle(75, 50, 40, 20, np.pi/2.0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 100)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 216x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,1, figsize=(3,3))\n",
    "\n",
    "ax.add_artist(e1.get_ellipse())\n",
    "ax.add_artist(e2.get_ellipse())\n",
    "ax.set_xlim(0, 100)\n",
    "ax.set_ylim(0, 100)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2513.2741228718346"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "e1.get_area()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2507.25"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_overlap(e1, e1, ds = 0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2513.1475000000005"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_overlap(e1, e1, ds = 0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "172.11500000000004"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_overlap(e1, e2, ds=0.05)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}