py4ds-Session10-drafts.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "11a546d4",
   "metadata": {},
   "source": [
    "## Session 10 (numpy part f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "db16a4be",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6eb85e85",
   "metadata": {},
   "outputs": [],
   "source": [
    "a = np.array([1, 4, 7, 3, 1, 2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "bd0eab8f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.max(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7c84d108",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.argmax(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d0bc9450",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.argmin(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c8f6f0cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[18, 16, 10, 15],\n",
       "       [11, 15, 17, 10],\n",
       "       [13, 12, 11, 15]])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b = np.random.randint(10, 20, size=(3, 4))\n",
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "991290ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.argmax(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b01a0806",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 2, 3])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.argmax(b, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "298f1c3a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 1, 0])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.argmax(b, axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8f31a31",
   "metadata": {},
   "source": [
    "### sorting arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6e1b748e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 4, 7, 3, 1, 2])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "45adf063",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 2, 3, 4, 7])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sort(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "69bc4276",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 4, 5, 3, 1, 2])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.argsort(a)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b22eb58f",
   "metadata": {},
   "source": [
    "### applying a function over array elements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "8bf22cda",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 5, 8, 4, 2, 3])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "94cfd18b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2., 5., 8., 4., 2., 3.])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result = np.zeros(a.shape)\n",
    "for i, x in enumerate(a):\n",
    "    result[i] = x + 1\n",
    "    \n",
    "result    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "b5656103",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 5, 8, 4, 2, 3])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def f(x):\n",
    "    return x+1\n",
    "\n",
    "np.apply_along_axis(f, 0, a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "12504018",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2,  8, 14,  6,  2,  4])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.apply_along_axis(lambda x: x*2, 0, a)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48aa42f8",
   "metadata": {},
   "source": [
    "### rolling (sliding) window operations"
   ]
  },
  {
   "attachments": {
    "Screenshot%20from%202022-06-14%2010-04-13.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "b7906812",
   "metadata": {},
   "source": [
    "![Screenshot%20from%202022-06-14%2010-04-13.png](attachment:Screenshot%20from%202022-06-14%2010-04-13.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "300fc8d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "from numpy.lib.stride_tricks import sliding_window_view"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "cbb4fd60",
   "metadata": {},
   "outputs": [],
   "source": [
    "d = np.array([1, 3, 1, 5, 2, 7, 3, 4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "e920ff3c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 3, 1],\n",
       "       [3, 1, 5],\n",
       "       [1, 5, 2],\n",
       "       [5, 2, 7],\n",
       "       [2, 7, 3],\n",
       "       [7, 3, 4]])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wd = sliding_window_view(d, window_shape=3)\n",
    "wd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "f591a429",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 5,  9,  8, 14, 12, 14])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(wd, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "3c05f9bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([19, 21, 22])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(wd, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "2bc655e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "62"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(wd)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "404594d4",
   "metadata": {},
   "source": [
    "### brent oil daily price example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "69f34afc",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib notebook\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "659454c0",
   "metadata": {},
   "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>Date</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1987-05-20</td>\n",
       "      <td>18.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1987-05-21</td>\n",
       "      <td>18.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1987-05-22</td>\n",
       "      <td>18.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1987-05-25</td>\n",
       "      <td>18.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1987-05-26</td>\n",
       "      <td>18.63</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date  Price\n",
       "0  1987-05-20  18.63\n",
       "1  1987-05-21  18.45\n",
       "2  1987-05-22  18.55\n",
       "3  1987-05-25  18.60\n",
       "4  1987-05-26  18.63"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('brent-daily.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "637ad739",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df.Price.values[:20]\n",
    "x = np.arange(len(y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "49616c2b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19]\n",
      "[18.63 18.45 18.55 18.6  18.63 18.6  18.6  18.58 18.65 18.68 18.75 18.78\n",
      " 18.65 18.75 18.78 18.78 18.68 18.78 18.9  19.03]\n"
     ]
    }
   ],
   "source": [
    "print(x)\n",
    "print(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "0ec59f8b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fc023533700>]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6282f7e7",
   "metadata": {},
   "source": [
    "### peak detection using sliding window"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "2a63db8e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[18.63, 18.45, 18.55, 18.6 , 18.63],\n",
       "       [18.45, 18.55, 18.6 , 18.63, 18.6 ],\n",
       "       [18.55, 18.6 , 18.63, 18.6 , 18.6 ],\n",
       "       [18.6 , 18.63, 18.6 , 18.6 , 18.58],\n",
       "       [18.63, 18.6 , 18.6 , 18.58, 18.65],\n",
       "       [18.6 , 18.6 , 18.58, 18.65, 18.68],\n",
       "       [18.6 , 18.58, 18.65, 18.68, 18.75],\n",
       "       [18.58, 18.65, 18.68, 18.75, 18.78],\n",
       "       [18.65, 18.68, 18.75, 18.78, 18.65],\n",
       "       [18.68, 18.75, 18.78, 18.65, 18.75],\n",
       "       [18.75, 18.78, 18.65, 18.75, 18.78],\n",
       "       [18.78, 18.65, 18.75, 18.78, 18.78],\n",
       "       [18.65, 18.75, 18.78, 18.78, 18.68],\n",
       "       [18.75, 18.78, 18.78, 18.68, 18.78],\n",
       "       [18.78, 18.78, 18.68, 18.78, 18.9 ],\n",
       "       [18.78, 18.68, 18.78, 18.9 , 19.03]])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w = sliding_window_view(y, window_shape=5)\n",
    "w"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "db53e183",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x is : [18.63 18.45 18.55 18.6  18.63]\n",
      "[False  True  True False False]\n",
      "   ------------------------------   \n",
      "x is : [18.45 18.55 18.6  18.63 18.6 ]\n",
      "[ True  True  True False  True]\n",
      "   ------------------------------   \n",
      "x is : [18.55 18.6  18.63 18.6  18.6 ]\n",
      "[ True  True  True  True  True]\n",
      "   ------------------------------   \n",
      "x is : [18.6  18.63 18.6  18.6  18.58]\n",
      "[ True False  True  True  True]\n",
      "   ------------------------------   \n",
      "x is : [18.63 18.6  18.6  18.58 18.65]\n",
      "[False  True  True  True False]\n",
      "   ------------------------------   \n",
      "x is : [18.6  18.6  18.58 18.65 18.68]\n",
      "[False False  True False False]\n",
      "   ------------------------------   \n",
      "x is : [18.6  18.58 18.65 18.68 18.75]\n",
      "[ True  True  True False False]\n",
      "   ------------------------------   \n",
      "x is : [18.58 18.65 18.68 18.75 18.78]\n",
      "[ True  True  True False False]\n",
      "   ------------------------------   \n",
      "x is : [18.65 18.68 18.75 18.78 18.65]\n",
      "[ True  True  True False  True]\n",
      "   ------------------------------   \n",
      "x is : [18.68 18.75 18.78 18.65 18.75]\n",
      "[ True  True  True  True  True]\n",
      "   ------------------------------   \n",
      "x is : [18.75 18.78 18.65 18.75 18.78]\n",
      "[False False  True False False]\n",
      "   ------------------------------   \n",
      "x is : [18.78 18.65 18.75 18.78 18.78]\n",
      "[False  True  True False False]\n",
      "   ------------------------------   \n",
      "x is : [18.65 18.75 18.78 18.78 18.68]\n",
      "[ True  True  True  True  True]\n",
      "   ------------------------------   \n",
      "x is : [18.75 18.78 18.78 18.68 18.78]\n",
      "[ True  True  True  True  True]\n",
      "   ------------------------------   \n",
      "x is : [18.78 18.78 18.68 18.78 18.9 ]\n",
      "[False False  True False False]\n",
      "   ------------------------------   \n",
      "x is : [18.78 18.68 18.78 18.9  19.03]\n",
      "[ True  True  True False False]\n",
      "   ------------------------------   \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([False, False,  True, False, False, False, False, False, False,\n",
       "        True, False, False,  True,  True, False, False])"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def f(x):\n",
    "    print(f'x is : {x}', end='\\n')\n",
    "    mid = len(x)//2\n",
    "    print(x[mid] >= x, end='\\n   ------------------------------   \\n')\n",
    "    return np.all(x[mid] >= x)\n",
    "\n",
    "peak_loc = np.apply_along_axis(f, 1, w)\n",
    "peak_loc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "6783933a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(16,)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "peak_loc.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "c0eb3ba3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# as you see our peak_loc array shape doesn't match the y and x (peak_loc has 16 elements while y, x had 20),\n",
    "# that's because applying sliding window reduces the size of result array because of it's operation on array.\n",
    "# so we can concatenate the peak_loc array with false values to match the shape of y, x; note that\n",
    "# this doesn't make our data inaccurate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "12478bb2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False, False, False, False,  True, False, False, False, False,\n",
       "       False, False,  True, False, False,  True,  True, False, False,\n",
       "       False, False])"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "peak_loc = np.concatenate([[False]*2, peak_loc, [False]*2])\n",
    "peak_loc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "66515f90",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([18.63, 18.45, 18.55, 18.6 , 18.63, 18.6 , 18.6 , 18.58, 18.65,\n",
       "       18.68, 18.75, 18.78, 18.65, 18.75, 18.78, 18.78, 18.68, 18.78,\n",
       "       18.9 , 19.03])"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "4d68ed19",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([18.63, 18.78, 18.78, 18.78])"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y[peak_loc]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "574937ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 4, 11, 14, 15])"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x[peak_loc]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "2d576e09",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fc0232f75e0>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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pBcyf0t+lk3xzNNErZUNJmYXc+5fd/ObTU0wdFMrWH03j0Yn9mhyxqFegH3FRwWxMyMQZvm2fvlDMA8v38MtNJxjTrztbFk9l/pQBNu3s64m4SDp5e7J8R6rN9nHVsu0pBHby5qHxzn/juz000StlA2WVNbyy+ST3/Pkr8oorWP7oGFY8HkuvwOYHsZgV04e0i6UcTrtkh0gbV15Vw++2nOau/97FufwSfj93NO88NZ6IIH+b77t7Zx8eHt+XDxMzybxcZrP9JOcWs+VEDvPiIuni6963KzXRK2Vlu87kMeMPO1ixM5WHxvdl64+mMXNE7xavP3NEL/y8PVh/ONOGUTbtwNmL3PGnXfzpy2TuGhXGv340jVkxfbDnAHHzp/QH4K87bXdVv3xHKn7eHjwRF2mzfTgLTfRKWcnFkkp+tC6Rx1YdwNvTg3ULJ/LKrJEEdvJu1Xa6+HoxY3gvPj6aTWV1rY2ivVZhWRUvrj/Gg2/upbK6lrefGs/v50YT3MXXbjFcFdatE/fFhPPuwTQullRaffuZl8vYmJDJQ+P6EtTZx+rbdzaa6JWyguLyKu79y1d8dCSLf795IJt/OIUJA4LbvL1ZY8IpLKti2+lcK0bZtNpaw/f/uo91B9OYP7k/WxZPZdpgx3YZ/uy0AVRU17J691mrb3vlrrpvCgumDrD6tp2RJnqlrOCVzSfJvFTGmvkT+PGMIe3uFGvKwBBCuviwMcE+1TdfnMrleFYRv7l/FD+/azj+Po6vsx7YI4AZw3vy9t7zXKmottp2L5ZU8u6BdO6NDie8WyerbdeZaaJXqp12fJ3H2gPpLJwa1a6r+Pq8PD24e3QYX5zMpbC0yirbbIoxhv/Znkyf7p2YHRNu03211qLpAyksq+LdA9br92b1nnOUVdXw7LSOcTUPmuiVapfCsir+84OjDOrRhedvHWTVbc+KCaeyppbNSdlW3W5D+89eJCHtMs9MtW2zybaIjuhGXFQwf92VSkV1Tbu3V1JRzdt7zjFjeE8G9XS/zsua4lx/VaVczK8+PkHelQpenzPa6n2YjwwPJCq0Mxts3Ppm2fYUgjv7MMdJx0hdND2KnKIKq1RjrT2QRmFZFc9Oj7JCZK5DE71SbbTtVC7vH8rg2WkDGB3RzerbFxFmxYRz4NxF0i+WWn37AMezCtnxdR5PTe7vtINtTB4Ywojwrry5I5Wa2rY/RFZRXcPKXWeZOCCIMX27WzFC56eJXqk2KCyt4oX1RxnSM4Af3mLdKpv67o2uqzP/MNE2V/XLd6TSxdeLRyf2s8n2rUFEeG76QFLzS/j8+IU2b+fDhCwuFJXz3PSBVozONWiiV6oNXvr4OPlXKnnjwdE27SMlIsif8ZFBrLdBlwjn8kv45GgWj0zs2+q2/vZ22w296B/SmWXbU9p0HGpqDct3pnBDWFemDAqxQYTOTRO9Uq209UQO6w9n8m83DWREuPU79mpo1phwUvNKOJZZaNXtrtiVipeHB09P6m/V7dqCp4fwzNQBHMssZHdyQavX33L8Aql5JSyaHmXXJ3ydhSZ6pVrhUkklP9twjGG9u/KDm+xTBXDHyN74eFq3S4TconI+iM/g/rF96NG1+f53nMGsMeH07OrLsh3JrVqvbmCRFCKD/bm9FV1RuBNN9Eq1wi83HedSSSVvzBmNj5d9Pj6Bnby5ZVgPNh3JoqrGOl0irNp9luraWp5xoSdDfb08mT95ALuTCziSfrnF6+1OLuBoRiHPTIty24FFmqOJXqkW+iwpmw8Ts/jhLYMYHtbVrvueFRNOQUklX53Jb/e2CsuqWLMvjTtG9iYypLMVorOfhyfU3U9Ytj2lxess25FMjwBfZo9xrofB7EkTvVItUHClgiUbkhgR3pVFDmiDPX1ID7r5e7PBCm3J/7GvrkuBZ6e5XlvyLr5ezLuxH5+fuEBy7pVmyx9Jv8zuZPcfWKQ5muiVaoGlHx2nqLyK1+eMxtsBT4/6eHlw16jebDlxoV39vpRX1fC33WeZNjjULjeSbWFeXCS+Xh68uaP5q/pl21Po6ufFw24+sEhzNNEr1YyPj2bxydFsnr91MEN72bfKpr5ZMeGUV9XyWVLb25K/H59O/pVKh3wrsZbgLr48NK4vGxMzybrOwCTJuVf4/MQFHr8xkgA/524+amvNJnoReUtEckUkqd68aBHZJyKJIhIvIuMbWe8my/KrP+Uicp+V41fKpvKKK/jFxiRG9Ql0+I3LMX270zfInw0JGW1av7qmljd3phLTtxsT+gdZOTr7mj+lP8bAqq+a7sJ4xc4UfL08eHJSpP0Cc1ItuaJfDcxsMO9V4CVjTDSw1PL6O4wx24wx0ZYyNwOlwJb2BKuUPRlj+MXGJEoqanhjzmiHd/glItwXE86elAKyC1s/xN7HR7PJuFTGc9MHunxb8j7d/bknOoy1B9K41MjAJNmFZWxIyGRubIRDBk5xNs2eucaYncDFhrOBq99hA4Hmhqx/APjUGGObDjuUsoFNR7P57PgFfjRjsNP0dDgrJhxj4KPE5j5y32WMYdn2FAb16MItQ3vYKDr7enZaFKWVNby999w1y1buOkutgflTXKf5qC219RLleeA1EUkHXgdebKb8Q8DaphaKyEJLFVB8Xl5eG0NSynpyi8tZ+mESMX27scCJkkX/kM5ER3RrdeubbadzOZ1TzLPTovBwk7bkg3sG8L3hPVm95xylld/eoL5UUsnaA2ncOzrMLoOZu4K2JvpFwGJjTASwGFjVVEER6Q2MBD5vqowxZoUxJtYYExsa6tjhy5QyxrBkQxJllTW8Pme00z1kM3tMOKcuFHMyu6jF6/zPthTCu3XinugwG0Zmf4umR3G5tIq1B9K/mff23nOUVtbwjAs2H7WVtib6ecB6y/T7wDU3Y+t5ENhgjLHtMDlKWcnGxEy2nsjhJzOGEBXaxdHhXOOuUWF4eUiLr+oPnrtI/PlLLJjS3yFNQ21pTN/uTOgfxMpdqVRW11JaWc3qPee4dVgPhvRyjuo2Z9DWv3oWMM0yfTNw5jplH+Y61TZKOZOconL+74fHGduvO09Nds7OvoI6+zB9SCgfJma2qH/2ZdtTCOrsw9xx7tmW/LmbBpJdWM6HiZm8eyCdy6VVLOqAXRFfT0uaV64F9gJDRCRDRJ4GFgBviMgR4BVgoaVsrIisrLduJBAB7LBB7EpZlTGGF9cfo7KmltceGOV0VTb1zYrpQ05RBXtTrt+T48nsIr48lcuTcZF08nHPJ0OnDgpheO+uLNuRwspdqYzvH8TYfh1rYJHmNDvUuzHm4SYWjW2kbDwwv97rc0DH7WBCuZQPDmXw5alclt41nAFOWGVT3y3DehDg68WGhEwmX6d/9eU7Uujs48njN0baLzg7ExEWTY/i39cmAPDy7JEOjsj5uFeFnVJtUF5Vw++2nOZnG44xPjKIJ+IiHR1Ss/y8PbljZG8+S8qmrLLxQbPTCkrZdCSL70/oS6C/ez8ZesfI3vQP6czw3l2ZPlgbdDTU7BW9Uu7swNmLvLD+KKl5JcyOCecXdw13meaH98WEsy4+nS0nLnwz5GB9K3al4OkhPD3ZeZqH2oqnh7Bu4UQ8PMTlHwazBU30qkMqLKviN5+eYu2BNPp078Q7T41nqotdCU7oH0RYoB8bEjKvSfR5xRW8F5/B/WP60CvQNQYWaS9XGUDFETTRqw7ns6Rsln54nPwrFSyY0p/F3xuMv4/rfRQ8PIR7Y8JZsTOVvOIKQgO+fdT/b7vPUlVTy0IXGlhE2Y7W0asOI6eonGf+Hs+z/zhMSBdfNv7bJJbcOdwlk/xVs2PCqak1bDrybZcIReVV/H3veW4f0cvpbyor+3DdM1ypFqqtNfzvgTR+++kpKmtqeeH2oTw92T0eHhrUM4AbwrqyISHzm3b/a/alUVxRzaJp2pZc1dFEr9xacu4VXlx/lIPnLhEXFcwrs0a63PB5zZkVE86vPjlJcm4xfbr7s+qrs0wZFMLIPq45sIiyPk30yi1VVteyfEcKf/4ymU4+nrz2wCgeGNvHLVtk3BMdxiubT7IhIZPegZ3Iv1LBomnRjg5LORFN9MrtHDp/iRfXH+XrnCvcPTqMpXcN/86NSnfTI8CPyYNC2ZiQhaeHMDqiGzdGBTs6LOVENNErt3GloprXPjvFO/vO07urH289EcvNQ3s6Oiy7mB0TzvPrEgH42R3D3PKbi2o7TfTKLZRUVHPPn7/ibH4J826M5Ce3DaGLb8c5vWfc0BN/H096B/oxY3jH+OemWq7jfBKUW/v1pyc5m1/C20+63oNP1uDv48XyR8cS0sXXZZ7sVfajiV65vK/O5POPfWksmNK/Qyb5qzrye1fX5/oNiVWHVlxexX/+8ygDQjvz4xlDHB2OUk5Jr+iVS3tl8ymyC8v4YFEcft7u2d+6Uu2lV/TuYs0aiIwED4+632vWODoim9v5dR5rD6SxYOoAxvTVgSaUaorbJHpjDGdyiim4UuHoUOxvzRpYuJC8vEKqxAPOn4eFC9062RdZqmwG9ujC4lsHOzocpZya2yT6jEtlfO/3O/n4aLajQ7G78qW/5Dfj5jDx397m7nl/IKH3YCgthSVLHB2azfzq4xPkFlfwxpzRWmWjVDPcJtFHBPkTEdSJ3cn5jg7FrvYk53PbrT9l+cQ5zDy9m8udApj92Ou8dMsCSrJzHR2eTWw7lct78Rk8O20AoyO6OTocpZyeW92MnRQVwuZj2dTUGqce2NkaLpdW8vInJ3n/UAb9vLz537U/Iy7tKMU+nXh12jxWj72bLcOm8KtTudw0tIejw7WawtIqXlh/lCE9A/jhLYMcHY5SLsFtrugB4gaGUFReTVJmoaNDsRlj6voev/V3O1ifkMmz06L4fKI3cfnJAARUlvH/ti7ngw+W0ikokCdXH+SHaxPId5N7Fy99fJz8K5W88eBofL20ykaplnCrK/obB9R15LQ7Jd8tv9JnXi7jFxuT+PJULqP6BPL2U+O5ISwQGFr3L3vJEkhLg759GftfP+GTuXeybHsKf9mWzM4zefz8zuHcPybcZftB2Xoih/WHM/nhzQMZEa5d8CrVUmKMcXQM3xEbG2vi4+PbvP5tv99JaIAv/5g/wYpROVZNreHve8/x2uenqTXw4xmDeSIuEq8WDpxxJqeYF9Yf49D5S0weGMLLs0bQL9i1+mS/VFLJjD/sJLizDx/9YDI+Xm71ZVSpdhORQ8aY2MaWud2nJW5gMAfPXaSiusbRoVjF6QvF3L9sD7/cdIKxkUFsWTyV+VMGtDjJQ90oRO8/cyP/774RJKZf5rY/7GT5jhSqa2ptGLl1/XLTcS6V1FXZaJJXqnXc7hMzKSqEiupaDp+/7OhQ2qW8qoY3tpzmzj/t4nxBCb+fO5q3nxxHRJB/m7bn4SE8NrEfW380lSmDQvnNp6e49y+7XeJ+xmdJ2XyYmMW/3zzIUlWllGqNZhO9iLwlIrkiklRvXrSI7BORRBGJF5HxTazbV0S2iMhJETkhIpFWjL1REwYE4ekh7Elx3WaW+1MLuONPu/jvL5O5Z3QYX/x4OrNirDM6Uu/ATqx4bCzLHhlDbnEF9/z5K17ZfJKySuf8BlRwpYIlG5K4Iawrz90U5ehwlHJJLbmiXw3MbDDvVeAlY0w0sNTyujHvAK8ZY4YB4wGbN+wO8PNmVJ9Al2xPX1hWxYvrjzF3xT4qq2t556nx/G5uNEGdfay6HxHh9pG9+dfiacwdF8GKnanM+MMOdp3Js+p+rGHpR8cpKq/ijQdHu8Vg3ko5QrOtbowxOxu5EjdAV8t0IJDVcD0RGQ54GWO2WrZzpX2httykqBCW7UihuLyKAD9ve+22XdIKSnloxV4uFJWzYEp/Fn9vMP4+tm0UFejvza9nj+Le6HB+tv4Yj606QKd2PGUqArGRQTwR14/pg3u0u1/0j49m8cnRbP7PbUMY2qtr8ysopRrVolY3lkT/sTFmhOX1MOBzQKj7VhBnjDnfYJ37gPlAJdAf+BfwgjHmmjoCEVkILATo27fv2PPnzzcs0ip7kvP5/sr9rJoXyy3DnH+0nQuF5cx5cw/F5dWsfnI80Q5oGlpeVcOa/WnkFJW3eRsVVTV8mnSB3OIK+gX789jEfsyJjSCwU+v/2eYVVzDj9zuICPJn/aK4Vt18Vqojul6rm7ZeMi4CFhtj/ikiDwKrgFsb2fYUIAZIA9YBT1jKfocxZgWwAuqaV7Yxpm+M6dcdXy8PdicXOH2iv1hSyaOr9nOppIr/XTCBUX26OSQOP29Pnp7cv93b+fldw/ks6QJv7znHrz45yRtbvmb2mHDmxUUyuGdAi7ZhjOEXG5MoqajhjTmjNckr1U5tTfTzgP+wTL8PrGykTAaQaIxJBRCRjcBEGkn01ubn7UlsZHenvyFbVF7FvLcOkH6xlLefGu+wJG9N3p4e3D06jLtHh5GUWcjbe87x/qEM1uxPIy4qmHlxkdw6rOd1u6jYdDSbz45f4D9nDmVQC/85KKWa1tZLpSxgmmX6ZuBMI2UOAt1EJLReuRNt3F+rxUWFcOpCsdM++l9WWcP81fGczC5i+aNjmWh5qtedjAgP5LU5o9n34i38dOYQzuWX8MzfDzH11W0s257CpZLKa9bJLS5n6YdJREd0Y8GU9n/DUEq1rHnlWmAvMEREMkTkaWAB8IaIHAFewVK/LiKxIrISwFIX/xPgCxE5Rl19/l9t8zauNWlgCAB7UwrstcsWq6yuZdGaQxw8f5Hfz412q07HGhPU2Yfnpg9k509vYvmjY+gb5M9vPzvFxF9/wU8/OMLxrLq2/MYYlmxIorSyhte1ykYpq2lJq5uHm1g0tpGy8dTdgL36eiswqs3RtcOIsK4E+HmxJyWfu0eHOSKERtXUGhavS2T76Tx+PXukU8Vma16eHswc0ZuZI3pz+kIxb+89x4bDmbwXn8G4yO6M6tONrSdyWHLHMAb26OLocJVyG257yeTl6cGE/sHsTnaeK3pjDD9bf4xPjmWz5I5hPDy+r6NDcpghvQJ4ZdZI9r14Cz+/cxg5RRWs+uosY/t15ykr3BRWSn3LrXqvbGjSwGD+dTKH9Iulbe46wFqMMbz8yUnWxafz7zcPZMHUAQ6Nx1kE+nszf8oAnpzUn/1nCxjSM8DtxxJQyt7c9ooevq2nd4bWN3/6IpmVX53libhIfvQ9HeO0IU8PIS4qhOAuvo4ORSm349aJflCPLoQG+Dq8+uatr87y+399zf1j+rD0ruEu2x+8Uso1uXWiFxHiooLZk1KAo/rdfy8+nf/6+AQzb+jFb+8f2e5uAZRSqrXcOtFDXb83+VcqOJNrt652vrH5WDYv/PMoUwaF8MeHo7W5oFLKIdw+88QNtAwvaOfeLHd8ncd/vJtATN/uvPnYWB3fVCnlMG6f6Pt096dvkL9d6+kPnrvIM3+PZ1CPAN56YpzNe6FUSqnrcftED3XNLPenFthl6LykzEKe+ttBwgI78c7T49vUc6NSSllTh0j0cVEhFFdUc8zGw+Yl517h8bcO0LWTN/+YP4EQbSqolHICHSTR19XT77FhvzclFdU8vmo/HiL8Y/4Ewrp1stm+lFKqNTpEog/u4svQXgE2fXDq3YPpZBWW85fvx9A/pLPN9qOUUq3VIRI91D0lG3/uEuVV1h8Eu7K6lpW7UhnfP4gJbtjdsFLKtXWYRB8XFUxFdS2Hz1+y+rY3JmaSXVjOc9OjrL5tpZRqrw6T6Mf3D8LTQ9ht5eqb2lrD8h0pDOvdlWmDQ5tfQSml7KzDJPoAP29G9wm0env6LSdySM0rYdH0KO3DRinllDpMooe6evqjGZcpKq+yyvaMMSzbnky/YH/uGNHLKttUSilr61CJPi4qhFoD+1MvWmV7e1MKOJJRyMKpA7QfG6WU0+pQ2WlMv274eXtYrZnlsh0phAb4cv+YPlbZnlJK2UKHSvS+Xp6MiwxijxXq6Y9lFLLrTD5PT+6Pn7d2WKaUcl4dKtED3BgVzOmcYvKKK9q1nWU7kgnw8+KRCR133FellGvocIl+UlT7hxdMzbvCp0kXeGxiPwL8tNMypZRz63CJfkR4IF39vNpVffPmjlR8PD14clJ/K0amlFK20eESvaeHMHFAcJsfnLpQWM76hAwejI0gNEB7p1RKOb8Ol+ihrj19xqUy0gpKW73uqq9SqTWwcOoAG0SmlFLW12yiF5G3RCRXRJLqzYsWkX0ikigi8SIyvol1ayxlEkXkI2sG3h6TBl7ttrh1V/WXSytZsz+Nu0f1JiLI3xahKaWU1bXkin41MLPBvFeBl4wx0cBSy+vGlBljoi0/97Q5SiuLCu1CjwBfdreyf/p39p6ntLKGZ7XzMqWUC2k20RtjdgINHyU1QFfLdCCQZeW4bEpEiIsKZm9KPsaYFq1TWlnN33af5eahPRjaq2vzKyillJNoax3988BrIpIOvA682EQ5P0vVzj4Rua+pjYnIQku5+Ly8vDaG1DpxA0PIv1LJ6ZziFpV/72A6l0qrtCtipZTLaWuiXwQsNsZEAIuBVU2U62eMiQW+D/xBRBrNksaYFcaYWGNMbGiofbr6nTSwrj19S3qzrKqp5a+7zjIusjuxkUG2Dk0ppayqrYl+HrDeMv0+0OjNWGNMpuV3KrAdiGnj/qwuvFsnIoP92ZPc/A3ZjxKzyLxcxiK9mldKuaC2JvosYJpl+mbgTMMCItJdRHwt0yHAJOBEG/dnE3EDQ9h/9iLVNbVNlrk6sMjQXgHcNKSHHaNTSinraEnzyrXAXmCIiGSIyNPAAuANETkCvAIstJSNFZGVllWHAfGWMtuA3xhjnCvRRwVzpaKao5mFTZb518kczuRe0YFFlFIuy6u5AsaYh5tYNLaRsvHAfMv0HmBku6KzsRstA3nvSc5nTN/u1yw3xvA/21OICOrEnSN72zs8pZSyig75ZOxVwV18Gda7a5M3ZPefvUhi+mUWTo3SgUWUUi6rw2evSVHBHEq7RHlVzTXL/md7CiFdfJgzVgcWUUq5Lk30A0OorK4l/tyl78xPyixk59d5PDlJBxZRSrm2Dp/ox/cPwstDrunNcvmOFAJ8vXjsxn4Oikwppayjwyf6zr5eREd0+057+nP5JWw+ls0jE/vRVQcWUUq5uA6f6KGumeWxzEIKy6oAeHNnKl6eHjw1OdKxgSmllBVooqfuwalaA/tTC8gtKuefhzKYM7YPPQL8HB2aUkq1W7Pt6DuCmL7d8PP2YE9KAYfOX6K6tlYHFlFKuQ1N9ICvlyfjIoP44lQOl0qquHNUGP2COzs6LKWUsgqturGYNDCE9ItlXKmoZtE07bxMKeU+NNFbTIqq67Z4+pBQhofpwCJKKfehVTcWN4R15bnpUcweE+7oUJRSyqo00Vt4eAg/nTnU0WEopZTVadWNUkq5OU30Sinl5jTRK6WUm9NEr5RSbk4TvVJKuTlN9Eop5eY00SullJvTRK+UUm5OjDGOjuE7RCQPON+OTYQA+c2WchyNr300vvbR+NrHmePrZ4wJbWyB0yX69hKReGNMrKPjaIrG1z4aX/tofO3j7PE1RatulFLKzWmiV0opN+eOiX6FowNohsbXPhpf+2h87ePs8TXK7erolVJKfZc7XtErpZSqRxO9Ukq5OZdM9CIyU0ROi0iyiLzQyHJfEVlnWb5fRCLtGFuEiGwTkRMiclxE/qORMtNFpFBEEi0/S+0VX70YzonIMcv+4xtZLiLyJ8sxPCoiY+wY25B6xyZRRIpE5PkGZex6DEXkLRHJFZGkevOCRGSriJyx/O7exLrzLGXOiMg8O8b3moicsvz9NohItybWve65YMP4fikimfX+hnc0se51P+82jG9dvdjOiUhiE+va/Pi1mzHGpX4ATyAFGAD4AEeA4Q3KPAcst0w/BKyzY3y9gTGW6QDg60bimw587ODjeA4Iuc7yO4BPAQEmAvsd+Pe+QN3DIA47hsBUYAyQVG/eq8ALlukXgN82sl4QkGr53d0y3d1O8c0AvCzTv20svpacCzaM75fAT1rw97/u591W8TVY/gaw1FHHr70/rnhFPx5INsakGmMqgXeBexuUuRd42zL9AXCLiIg9gjPGZBtjDlumi4GTgCsORHsv8I6psw/oJiK9HRDHLUCKMaY9T0u3mzFmJ3Cxwez659nbwH2NrHobsNUYc9EYcwnYCsy0R3zGmC3GmGrLy31AH2vvt6WaOH4t0ZLPe7tdLz5L7ngQWGvt/dqLKyb6cCC93usMrk2k35SxnOiFQLBdoqvHUmUUA+xvZPGNInJERD4VkRvsGxkABtgiIodEZGEjy1tynO3hIZr+gDn6GPY0xmRbpi8APRsp4yzH8SnqvqE1prlzwZZ+YKlaequJqi9nOH5TgBxjzJkmljvy+LWIKyZ6lyAiXYB/As8bY4oaLD5MXVXEaOC/gY12Dg9gsjFmDHA78G8iMtUBMVyXiPgA9wDvN7LYGY7hN0zdd3inbKssIkuAamBNE0UcdS4sA6KAaCCbuuoRZ/Qw17+ad/rPkism+kwgot7rPpZ5jZYRES8gECiwS3R1+/SmLsmvMcasb7jcGFNkjLlimd4MeItIiL3is+w30/I7F9hA3Vfk+lpynG3tduCwMSan4QJnOIZAztXqLMvv3EbKOPQ4isgTwF3AI5Z/RtdowblgE8aYHGNMjTGmFvhrE/t19PHzAmYD65oq46jj1xqumOgPAoNEpL/liu8h4KMGZT4CrrZueAD4sqmT3Nos9XmrgJPGmN81UabX1XsGIjKeur+DPf8RdRaRgKvT1N20S2pQ7CPgcUvrm4lAYb1qCntp8krK0cfQov55Ng/4sJEynwMzRKS7pWpihmWezYnITOCnwD3GmNImyrTkXLBVfPXv+cxqYr8t+bzb0q3AKWNMRmMLHXn8WsXRd4Pb8kNdi5Cvqbsbv8Qy77+oO6EB/Kj7up8MHAAG2DG2ydR9hT8KJFp+7gCeBZ61lPkBcJy6FgT7gDg7H78Bln0fscRx9RjWj1GAv1iO8TEg1s4xdqYucQfWm+ewY0jdP5xsoIq6euKnqbvv8wVwBvgXEGQpGwusrLfuU5ZzMRl40o7xJVNXv331PLzaEi0M2Hy9c8FO8f3dcm4dpS55924Yn+X1NZ93e8Rnmb/66jlXr6zdj197f7QLBKWUcnOuWHWjlFKqFTTRK6WUm9NEr5RSbk4TvVJKuTlN9Eop5eY00SullJvTRK+UUm7u/wPeAhiW6gFvRAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, y)\n",
    "plt.scatter(x[peak_loc], y[peak_loc], color='red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "be5773f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# as you see we found the peak locations !"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "a2633e64",
   "metadata": {},
   "outputs": [],
   "source": [
    "# now we scale up our plot to 1000 values from dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "b0a3d069",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fc022e9a730>]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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IENqAIZbgbZkzviSm2239+edYufsI/oDmanPXezBy4FYAj7bMPtlIABdCJJWD5oYK+ZnpPfYc6WkOThlT1Op8sz8YCuCp0BGXHLgQIinlZ8Xe8+6OS6Yb2/nOLCvA4wuEBjE1yR/BJYALIZJS5L6WPeX+L0yl/J7zyHA5aPYH2XbImBOeAhkUCeBCCAHGB4bHF+SD7YcBUqD/LQFcCJGE/nz1jF5/zow0B83mIiJIjUFMCeBCiKQzf+KgXn9Ot8tJs69lK7cUiN8SwIUQAqL0wBPYllhJABdCJJXs9N4ZvIyUkebkcH3L5g7BFOiCSwAXQiSVAZm9M30wkjuiYFYKxG8J4EKI5OJOYA/cTgYxhRCikzJ7af53pD7ZA1dKDVdKLVZKbVRKbVBKfds8f5dSar9Sao35s6DnmyuE6Ivsvd2shPXAIwJ4QlrRObHUQvED39Var1ZK5QKrlFJvm9c9oLW+t+eaJ4ToD/y2wiNlhdkJaUOGK/VSKB0GcK31AeCAefmoUmoTMKynGyaE6D+sAlJjS3L4qVkDvLelYg+8UzlwpVQZMA1YZp76llJqnVLqcaXUwDbuc71SaqVSamVlZWX3WiuE6JN8AWMBzf/MGB62mXFvitzsuE9NI1RK5QAvArdoreuAR4AxwFSMHvp90e6ntX5Uaz1Daz2juLi4+y0WQvQ5/oARLNOciduHUkVsyZYC8Tu2AK6UcmEE72e01i8BaK0Paa0DWusg8GdgZs81UwjRl/mCRg88zZm4iXGRex2nQPyOaRaKAh4DNmmt77edH2K72cXA+vg3TwjRH1g9cFesW8v3gMhNkfvEICYwG7gK+FQptcY897/AFUqpqRgfVOXADT3QPiFEP9CSQkmiHnjyx++YZqF8AET7WHw9/s0RQvRHVgrFlcAceF7EEv4UiN+yElMIkXhWDzxyJkhvmjW6kGH5maHjPjULRQgheorfGsR0JC4kKaVC+2MqlRopFAngQoiECw1iJjCFkookgAshEs6fBNMIoWWwz/o32WeiSAAXQiScLwmmEQJG7oSWRT3B5I7fEsCF6It+t2gb/177WaKbEbNkmEYIrafbJXsPPDFFB4QQPeq+t7cCcMGUoQluSWxaVmImtgdureUJpVAS1pLYSA9cCJFwLSsxkyMkWYE82acSJsdvSwgRNxs/q0t0EzrNb1YjTOQ8cABFahW0kgAuRB/zvRfWJroJnWZt6JAs0wgjA3mykgAuRB9z8uhCIHG7u3dF0kwjjIjbkkIRIgkt21lF2e0L2V5Rn+imxJ0rzYhCpQMzKT/ckODWxMaaRpiW4BTKlSeP5LRxRXxldhkAH26vCu0WlIwkgIt+6VVzit1HOw4nuCXx1+wzerMbPqtjzr1L2PBZbYJb1LGWlZiJDUkF2ek8fd1JFOWkA/D1v67kz+/vTGib2iMBXPRLVu3nZO5ddVWzPxh2XFHXnKCWxM6fJNMILfba4Dsrk/dbmgRw0S85QtPEEtuOntDsD4Qd13l8CWpJ7HxJNo3QLpnT4Mn32xKiFzjMCN7k9Se4JfEX2QM/0uBNUEtiZ00jTJYeuH1/zGT+kJcALvolK8hVHE3+9EJnNfvCe+BV9SkQwIOJrwduZ2+GTuL1mBLARb/k8RpB7rOapgS3JP4ie+CPvr+TN9YfTFBrYpMsg5gW+8eIlULRWvPBtsMEk6hLnhy/LSF6WZPZS1226wi1jcmfI+6M2qbw1+P1B/nG31YlqDWx8QeDKJU8PXB7CsUqaPXh9iqufGwZD76zNVHNaqVfBPBVu4+wbGdVopshkojHDOBHPX5e+zR1qvZ15FCdh3X7akPT4FKFL6CTagDTYfsgWbuvlve2VrLQ/Dt5/MPyBLWqtX5RjfDSRz4GoPye8xLcEpEsPL4gM0YOZO2+GvZV9500yleeWAHAw1dM46jHzz9W7OXdzRUAHPX4cChFUGty3cm1StMfCCbNACZAuq0tuw43cM3jy0PH9c3JM/DdZwP4658eYPaYIgZktfyhBoM67JNV9F8ef4CcjDRKct0cqvMkujlxc6DW+DA6aVQhTofimWV7QtdNuustADJdTj66fS4Ds5Onl+4P6oSvwrTrKBfv8QVwu5y91Jq2Jc93ljjadbiBG59ZzdVPLA87X5UC06lEz2ho9rPfNmDZ5A2QkeakJC+jTwVwBXzppBGhXHJ9lDngTb4AS7dV9nLL2ucLBJNmABM6DuCH65Nj9lKHvzGl1HCl1GKl1Eal1Aal1LfN8wVKqbeVUtvMfwf2fHNjs3p3NQBr99aEcp1g/CcW/dOX/7KM2fe8Gzpu9gdxuxwMznNzKAVWKsbiUJ2H6kYfw/IzQ+fOOKYk7DYTBucChH2YJQN/QCfNACa0HcB/cO4EoPVA8Wc1TSw2U1W9KZaPPD/wXa31ROBk4Cal1ETgdmCR1nocsMg8TgpvbzwUuvzHJTtCl5MpdyV615q9NQChKWDWV+BBeW4O1faNHvhJv1wEGEWsLN+aO5b/u3I6AOlpDt645XRyM9KSbnm9L5hcPfD0tJYPk59cMDE0L7zQTDtZ0x4/2nGYl1bv48rHlnHtkyvwRkzhBLjvrS08u3xPq/Px0GEOXGt9ADhgXj6qlNoEDAMuBOaYN3sKWAL8oEda2UnlVQ3MnVDC+v21PLxoW+j8UY8E8P6u3usny+WkqsFLYXY6eZkujjb7kyan2VX2zok9gDsdijnjS5gwOJeLpw0DYHhBFi+s3MsZxxRz5oSSVo+VCIGgTqpBTPuHybWzRzF+cC6Pf1BOSV4GYKR8AL7052WAMa4ARk+8rCg77LF+9+52AK6YOSLu7ezUR55SqgyYBiwDBpnBHeAgMKiN+1yvlFqplFpZWdnzebdgULP3SCPD8jMZMsAddp30wPun+9/aErp858vrGfvD/+D1BxlbkkOu2+jDpHp6zSq4dOOcMZwwsiDsOrfLyRu3nM4NZ4wB4OzjBtHgDXDtkyuYfNebSVErxR9I7kHMU8YU8ZdrZpCRZgRqbyC8p22tK4ic0dRoK9UQrXfeXTEHcKVUDvAicIvWOmzPJm3MdI+6PElr/ajWeobWekZxcXG3GhuL51fupcEbYPrIfArMrzvTR+QD8On+5C+rKeLvYbMHBPDympY53+MG5ZKVbgTwFeVHqKpvpuz2hbz+6YFWj5HMGpr9fP73HwKEetntGT8oN3S5zuNnzZ6anmpazOqb/WRnJM+kuLbSOVZqxUqhjCzMCrs+8sPQHtDf2hj/1bAxBXCllAsjeD+jtX7JPH1IKTXEvH4I0PsZ/AjBoOaeNzYzpXQAF00dFvqDOHN8CbPHFvL0x+Whrz6if1hlDmhHM6Y4m+x0o0f1jb+tZr25l+Tf/ru7V9oWTUOzn5v+vpr1nehsrNvXctsREQElmhPKwucbrNtXE/Nz9ZTaJl9S7SCU3kYATzMXG1lxxBGxhU99RJp275FGAO66YCLnTx4a72bGNAtFAY8Bm7TW99uuehW4xrx8DfBK3FvXSVc9voyaRh9XzSpDKRUa1R6Ync7F00qpbvQx+a63JIj3I8t3HWl1bsLgXH5z2WRy3S6ybL2+HebuPFnpicuF/2f9QRauO8D5v/uAJm+g4zsAS7YYfacXvjEr9BW/PSW5bv514ynMO7aEwXluVidBD7yuyUdeMgXwtOih0eqZ+wKaT/fVsitix6OjEam4PWYAXzB5SA+0MrYe+GzgKmCuUmqN+bMAuAeYr5TaBswzjxNm75FGPtxexZTh+Zw3yfhlFeUYAw6BoOb0Y4oAI1f1wfa+twuLaE1rza/f2AzAJdOHcb75n+i8SUP4wozhAKEeOMDdr20ESOhgpn3a68tr9gOwfn9tqNyqXZM3wFn3LeFPS3cyflAuJ5YVtLpNW6aNGMhfrjmRs48bxLubK5h691s810MzJWKRbD3wtjZXts77AkHWRPnmcjQihbLl4FEGZrkoNmNRvHUYwLXWH2itldZ6stZ6qvnzuta6Smt9ltZ6nNZ6nta6dVenhwWCmsVbKnh74yFO+81iAB758nQyzf+U3zpzLJedUMrF04dRkutm88/OxeVUUXtlHVm8uYLbX1zX6hNXJC/7e3X/F6aGelVFuS3/mawcuF12lHO9xcqZjirK5oWVe1m+6wjn/+4Dfr5wU9iAGMDSbZXsqDReo7WjTWeNKc4BoKbRx+0vfco7tim4vaG+2c+n+2qpafJRkJU8K0PT2qjLYvXA/cFgaLn9zFEtH5yRKZSdlQ2MK8kNK44VT8kz8bILbnxmFdc+sYKv/3Vl6NxQ2yKGgdnp3Hv5FPLMug9ul5PBA9ydLiG6s7Kea59cwXMr9nLjM6vj03jR485+YCkAV55sTN+aPcb4FjZ9REsOeFBe655RZgJTKOWHGxhTnM3p44rYeqieH7y4DoAnPypn4p1vhs0ntlIso4uzeeiL07r0fAURy+lfX9+7A7i/fWMzF/z+AwJBzbhBOb363O0pMIuB3XPJpLDz1lRHn19T12QE6znjWyZn/OWDXfz2zc2h45omb6vfcTwlz7BvjLz+IEu3VrKtop43N4T3Fm7/3IQO7z90QGYoL9WR1XuqOWZQbthA2KYDdfzPnz7mma+dRFoSLTwQbbtxzljASKPMO3ZQWH2cwihfbSO3JGtLTaOXAZmuuPSuPL4Aq3dXU17VwKiibEYVZVPf7G819fXBd7bidjn4/JRhoZ12XvrmKeR3sfeab/4uBue5OXZIbqcGT+PBGjgGmD22qFefuz05GWns+tWCVu+tNbh5uKGZxuYADgUjC8Lnff9h8Q4unV7K6OIcqht9DMzuudRQSgTwdftqWLu3hsKcDB5ZsiNsOuDdFx7Hna9sYP7EQXzDnOfanpNGF/Lwom0crPWgMT5Fxw9umVbl8QW4+rHlTB2Rz6NLd5KV7mz1NXvZriP8e91nXDytNH4vUsTduEG5DMt3h76VKaXCgrfl+Rtm8YU/fczwgkz8Ac1nNR2vzNx0oI7PPfQ+D31xKhdO7XjqXkeuf3oVS7ca6yRmjy3ic5OGcNe/jZz8j847lrLCbL7215Ucqmvm1n+sxelwUNPoxaEIfcPsCiv43Dp/HAdqPSzeUsnOynpGF/dObzho1tpOT3OExqySRbQPZqvT9ps3jLUFAzJdFOe2bvfOSuODuKq+mQGZ/bwH/o8Ve8OqqoHx1feqk0fy5ZNGUlXv5axjY1tRNmd8MQ8v2saavdXc8dKnVDf62PWrBVQcbeZHL68nGNQsLz/C8nIjT97oDdDoDTB9RD6Pf+VENn5Wx5f+sozlu45IAE9ylUc9TB0+oMPbzRxVECo1PPXut3hva2WouNJPXllPQXYG3543Luw+VonWleXV3Q7gWutQ8AYoK8pmUJ6bopwMDtc3c9KoQiaVDuDFb87iuqdWUtPo4+ZnP6E4N4PSgVndqrA5ojCLTXefS2a6k52V9Tz4zjbm3vceH98xlyEDMjt+gG6qbvAydICbf9wwq8efKx4iBzfzMtMoiRLAd1TWk52RRlBDQX/vgZ8ypohnlu3h81OGsmDSEM49fnDY9bfOPybmx5o4JI80h2LtvlqqzZ1Y3t54iK2HjobVUAFjwcM5xw/G4wvwzTPGkJ+Vzilji5g1upCNn9VFe3iRJPyBIFUNXopz3R3f2ObiacN44sNy9h5pZHRxDk99bMwJv/HMMWGLOz7ZY6TV2ppu1hk1ETsClZlzua87dRS/fmNzKDd8wsgC1tx5Nre9sJYXVu2j8mgzF07t/txiK+c/yrYEfNav3u2V+vl1Hj8LJg1meEHH89eTQeQCnzy3ixERbS/KSWdnZQMenzGwfNkJw3usPSkRwBdMGszCm0/luKEd96Y64nY5mTAkN2yxxv1vb2XzwaOh4/83dyw3nzUOp1JRezeji7NZmGKr9fqKnZX1lBVmd9jrrGrwojVRe0ftOX/yEJ74sJy7X9sYthv5gRoPIwqzqKjz8PWnV7HWLI4Vj1K0kWMyVlrjm3PGcMPpo1u91u+ePZ4XVu0DwuuedFdkyuCJD3fhcjrw+ALMGV/M2JLcNu7ZNVprapt83UoB9bbIBT55bhcOh2Lhzady8R8+whsIMmFwHmv31dDoCzC8ILNHBzFTYhROKRWX4G05YcTAsMJW9uD956tn8I0zjN5WW0FicJ6bmkZf2Jxdy1GPL+lKdfYVT3y4i7n3vRfTPH6r2l60/GR7rHzlki2VYWkNK8i+/umBUPAeXpDJoToPS7ZUcM9/Nsc8+BkIap76qJzvvbCW7RX17DYf+8lrT+RH5x3L8IKWoBztb3DwADd/vnoGAOMH53Xq9XVk+Q/P4munjgLgp//eyI9eXs/PF24K5XzjqdEbIBDUSTX/uyOR70deptEHPm7oAFb9eB7L//csThlbyOaDR3lrw0HG9vBYQkoE8HizqoW5XQ7u/8KU0PlF3z2D+RMHdViTYZBZJOuqx5axvaKeijoPD7y9FY8vwOm/Wczse94NbYQKxqyGWb9aRNntC2NeXSdae/zDXQBUHu24FGplvdEz7mwP3CpuFenKx4yqc1sO1YfOTR8xkN1VjXzliRX833s7OOFn78T0HL98fRM/eXUD/1y1j3n3v8d2cwXoSaMK+dppo2Oa1TJ/4iCWfG8O50+K7wq/klw3o4qzW53f2QPrH6zpvMm0M1Bn2XvXuW4XJXlu5pg12Jv9QcYNiu+3lkj9MoAPNKdcOZQK66GNLmr9hxvNyaMKAVhRXs28+9/jjpc+5aFF25jw4zdCefU/LtkR2u38Fws3ccCsOf2npTuiP6hoV5M3wN4jxn/4F1btbXcu//Mr9vLVJ421ASV5ncuBR354P3ntiWHHdU0+hgxw88mP5zO6KIcK24dJfbM/6opJu8WbK3jsg11h5x5etI2S3IxOzz8vK+o4ldQVJbZxA2tmSCwfmp0RCGoeMks9J9P0wc46yYwFdhOHtnwrmjBYAnjcWXNfnUpRmN0SwGOdzzuiMIvRtl7Koig7cfz2zS389i1jQv8b61uqkDVFSbuIjm091JLm+u/OI9z7Zuuv9Fpr3tpwkO+bi1+ATu/OnhWxjH5yaX5oUBGMJd9DBrgZmJ3OVbNGhs4fZ/6nPdLY/rZ9/91ZBcDS285kzZ3zQ+cjB8IS6bRxRcw7toQfnXcsK380j9vOGU9tk6/VStDueHXtfl5bZ4wj2XcQSgW55of8ry6ZxAVTog8iWwvEZoyMvbxBV/TLAG597Rman8no4mxOP6aYF7/ZuWlMx5s5eauHMmFwLi98Yxb3Xd6SknGaHwj2eebxrHns8QX42WsbqW30sW5fTdScfF8RWQp4e2V92HFtk4/nVuzl+qdXhc5NG5EfU3EnO3uP9q9fnUlBdjqXm3VTPL4AdZ6WoksF2encMm8ck4YN4MqTjWBeVd92ANdas3TbYcYUZzOiMIv8rHQmlxp/R2NLkmcVotvl5C/XnMjXThsNEKqrf7CNnYv2Hmnk4x1VnXqO6gbj2+kdMSy+SzaLvncGH90+lytmjmhzG7g/XTWDn1wwMabqkN2RErNQ4u3YIXncdOYYrjq5DLfLyV+/OrPTj/GzC49n3sRBvLelkhdX7+OS6cM4sayAE8sK2HroKH9auhO3y8nBWk9YvWVnHGsi/Gf9AR77YBc7KutZsqWSq2eN5O4Lj4/b4yeTRZsOUVaYRXmVMeBn1WO2nPiLd8IK5r/6rdlMGtb1ge9TxxZx+jHGEmkrYNd5fNQ1+SgrbPn2dcu8Y7hl3jEsM3vW7W12u2p3NZsO1HHDGaND57539ngeeGcrX5ld1uW29rTBtgAebYHPTX9fzbp9tbz//TNjng7oMQd8rzmlLG7t7C0lMUxNnTo8n6nD83u8Lf2yB+5yOrjtnAmhP8yuGJDl4vNThjI033iMfNtqqzsWHEtRTgZ1Hj+XPvJRWIlJpRQ7KutZurUybKCzK6xaxEu2GLMl7GmGvqbiaHOo8BIQVhJ426GjYcE7O93J5NL8Li9x3/yzc8Ny33nmwGZFXTP7a5rC6u1YSs3AZX3A2G2vqDe293t3OzkZadw8t2VR0OnHFPOvG2czIc6zSeLJWtBzoI0euDWL65/m1MZY1DT6yEhzpPQ2dsmgX/bA4+mbc8bgdjm5KGInlLzMNOqiTCn0B4Ocdd97gFEo54vd2CevoTk8ZRItsPQF97+9lQ2f1YXyzGCkUD7eUcWsMYWhVZGWj+44q1vPFxlUrIHuO19Zjy+gQzs82Q0d4CbPncbmA8YCL60133r2E97ecChs+63/XTAhqXaeicVgcyD4YJ0HfyDI+9sPM+eY4tAHZFF2Op/Venho0TZj/UQMaUKrjozonn7ZA4+nrPQ0bjpzbKsVeXluF0c9/tDg1IvfPAUgNEsFjFks3VFtGzBLT3OEihv1JZ/VNIU2pnY6FJefUMrALBdawxV//i+7Djfwq/+0VH+7ZtbIuAeG0nzjPbQ2Pphmq2ZoUUoxND+TQ+b88zV7a1i47kBY8J45qoCvnza61X2TXWa6k0yXk+oGL88u38O1T6wIDUCCMf5gaS+FZNfgDZDTxpRNETv5DfaQXHcadU0+apt8XDNrJCeMHMikYQN4+ZP9odt0N4WyxbYAad6xJWywLe+vafSSnZHW5t5+qeIT2/jBxdNKmTmqgDc3HOQGc7DyzHuXhK7f/ovP9UiFyCH5Lam29DRHm4uDCnPSOdJgBDCrIFZxbgZlhVn8/kvTKcnN6LG60D2tIDvdrKxnpAM/3H6YC6YMZdfhBhq8AaYOz2fN3hoO1HoYFMPUTY83ENrJXXRdav/vTmJ5mS7qPD6OenzkmkuFJwzOpdEbwOVUjB+US3mVsThi1e4jnc5fH65v5t3NFZw3eQhr7zybcSW57DnSyMbP6rjr1Q1MvfttbnpmdWhesscXSMmt5N7baqRHXvzmKaHC+dEW59x2zvgeK+/rcjpY9aN5APz2sslt3q4wO4Mq81tQxVEjgL95y+m88I1TGJTnTtngDTAw2xX2je+5FXspu30h971lTOe0KoHG+nfc6A0kdOu6vkICeA/Jc6dxpMFLULcUC7KmL86dUML0kQNDO8Zc+sjHoc0HYvXyJ/upb/Zz67xxDMhyUZKXgdaw4OH3efKjcgDe2niIL/15GXUeHxN+/AZf/+vKlFoJ2uj188qaz7hk+jBOGGnfhKF1D++CHtgw1q4wJ4Pye85rt/Lg4AFudlc18s2/reLnCzeR7nSQ30fyvAOz0jnS4KU6Ik332roDlORmcM5xg8jPcoV9Y2pPky8gA5hxICmUHpLndoWqzGVY+XGzA3b80AFkuBxUN/oou31h6D6H65tjrom89dBRinIyQgWG8iNqDpcOzGRfdRPLy48w+a63AGO2yuV/+ojX/t9p3XlpvWJ7xVHm3W98qF12QnjZ3kF5bj4/ZShnHVtCQXY6mw7U9fh821hYdS/+Yy7cmjR8QI+slEyEgVnp7DnSyL7qJsoKsygdmBWqSTNhSB5KKQqz06lr8nXwSIYmbyDqbkiic6QH3kMKbSsArZ6Gx+z9ZmWkhU2Js/x77WcxP/7uqkZGFbUErXzbRgXPfO0k3r71DAptdRqsAknr99cRCHYv994b7MvNIzfrdToUD18xjQunDuO0ccVcf3rHG3n0hounD+MS22ykkUnwoRIvBdlGD3z9Z7VMKs3n6etmhmpjjzMXIeW6jbRhLJp8kgOPBwngPWScrfSmFcCtAcWBWS7OOKaYK2aOYHCem7kTjOI3726uCBvRb09Noy9U0wWMLaDAWDo+a3QhmelOVv14Pp/edTZbfn4u739/Lj+/yFjkYxVPaksyBPjSgS3BL1UGYl1OB/dePoUvnmis3Lzq5JEd3CN1DMxK56jHz8FaD4PzjMFYa+Nfa0wi153G+9sOU3b7wg5rpzT5Agnde7SvkBRKD7HXabZ6GjfPG0d2RhoXTBlKmtPBr2wbpl77xHIWb6lkyk/fareQvi8QpLrRS02TlylZLSsNjx82gJvPGsfVs0aGfW3PtdVaHma26ZwHl7b5HLurGjjnwaXMnVDCZSeUMnfCoE6+8vhoNhfmfCXFVuo5HIp7Lp3MPZe2PdiZiqxdZfxBHfqbsuZ7W2m/bbZKjXuONLSarVN+uIH6Zj9jS3KoafR2eR9P0SI1ujYpyL5Yw+0yfs15bhe3zj8mao/SPkgXuTOQ5Zevb2LcD//DzF8s4lBdc1gP3OlQfGf+Me3m0EttC30q2tiIYNGmCjy+IK9/epCvPrmy1+urHK5v5qF3tnGwtomcjDTu+vxxvfr8Ijp7yVfrb9tKoVjpwmm2BU5ef/i3uPe3VTLn3iWc/7sP+HD7YXwBHZpVJLpOAngPsQfwWKZ72/O4X//rSqoiFkT4AkEeXboz7FxnNysYZvtW8ONX1ocuP7t8D/PvN1aHfhixWcINT6/i6Y/LO/U83XH3vzfywDtbeWn1/lBaSCSefZC8yaxK+K2548hzp4U6H/faCrm9v61lMwyvP8hVjy0PHV/31EoyXU5mj0ndMrLJosMArpR6XClVoZRabzt3l1Jqv1JqjfmzoGebmXrswacghpKm6WkO3rttTuh4fcSem+fYphla82ev7GSONSs9jWe+dhIAS7e2BOo7XvqUbRX1eHwBdlWFF+5/b2slP35lQ4d1ruPFmkfsD2rqm+NXvlR0j8MWKbxmIbHrTh3F2p+cHUqpZGek8VPzG9Mfl+zAHwjy6NIdvLnhYKvHa/IF4rKfaH8Xy2/wSeDcKOcf0FpPNX9ej2+zUp+9HsT0KEuvoxlZmM0/rj8ZoNWGBdaOKL//0jSWfG8O73znjC7No509togLpgylyRfguidX0GALkpVHm6mq93L+5CEsvPnUUOoH4P0YtjHrrve2VoZtb3fp9O7t9i7iZ0ppPuMH5TKzrICvnTYqdD5ycdKXTmqp7bO9sp5fvr6Z//fsJ4Cxofi4JCqb2xd0+B1Va71UKVXWC20RtNTZaGsUf3Cem5I8NyXdKF430JxyuGhzBUu3VpKd7qTBG2D5riPUNvkYUZDFcUMHMDjPHaqu9/CibazeXU1ORho3nBHfaXtef9AoOfC48TV76W1nkpXhTKnNbvu67Iw03rz19A5v53I6+MOXpnPT31dz7oPvh1338BXTGJafybF3vtFTzex3upNk/JZS6mpgJfBdrXXUykxKqeuB6wFGjOh65b1UdP8XpjCyMLZt2izpaQ7ys1yhpdgL1x3guRV7QtdHK6TUWfMnDmL5riNsPniUpdsqycpIo8Eb4A9LtgMt+fus9JY/j0/21IRW2V04dVi3SvFG+smrG3h2ectrTIZFOaLr2tqFPT/LRWa6k5mjCpjWC7Wy+4OuJqEeAcYAU4EDwH1t3VBr/ajWeobWekZxcXEXny41XTK9NGx2SawavQH+9t89HKht4qa/r+b9bUb64s7zJ8ZUqrMjp40r5o1bTuf8yUNYvNnogQPUe4x0yldnG1+Rg+boa+RUvtfWxb7gqCPPLNsdFrz/d0Hq7dAiwtmn0NpZVSKfv2EWdyw4tjeb1Gd1KYBrrQ9prQNa6yDwZ6DzW9qINp082tgoNbKuREmclx4fOyTPqPFsLtypONpMdroztMDC2r/z8hmlvHzTbP523UkMyHSxZEv3N6Ow/PBf68OOI+uqi9QzvCCLJ689kbLCLJ67/mQe/J+p/N+V01NmQVYq6dJvVCk1xHZ4MbC+rduKzvuNuQikKqJwUCxlOjvD2odxX3XLgGmerfiStZJwREEWU4fnc+q4Iq48eQQfbD/MD1/umbe8OMZaMCK5zRlfwpLbzuTk0YVcNG0Y5x4/pOM7iU7rMAeulHoWmAMUKaX2AT8B5iilpgIaKAdu6Lkm9j/FuRk4VOvFNvHOG0bbSNc+cHjdqaO4dvaosLTNd+aPp/xwI698sp+fX3h8XIs13XbO+JQuuSpEb4tlFsoVUU4/1gNtESanQ1GUkxHaiT3XncZbt54e93rXI6JsQDumpGXQVSmFMyKeOh2K048pYuGnB9h5uD5UDbGrhgxwc6DWw3u3zen0gK8Q/Z0sdUtSFUebqTA3K779cxNCG8vGU7ScpDWA2Z7ZY40VdEu3Hibd6eSz2qZQ3r4rLj+hVIK3EF0gATwFTBo2oOMbddF9l0/h+ZV7+eUlk/AFgjHtjl46MIv8LBc7Kuv59RubafYH2fWrBV1Kf3j9QVmRJ0QXSQBPUk9fN5MlWypJcyiOG9pzAfzSE0q5NGLDhFiUFWZTXtUQqhpY0+gLK3gE0OwP0OwPtrsgRwK4EF0nATxJnTaumNPGJe+8+bLCLFaUt6zdKq9qaBXAr/rLcpaXH2HesSX84cvTyUhrvfS/OSABXIiukv85okvKirLZb6vXsudIY6vbLC8/AsA7myo4UNO6fO1fPy7H6w+ikJknQnSFBHDRJZF5+W8/t4YLfvdBm/XDPf7W53/7hrGj+a7D7e8QJISITlIookusmSh2n+6vZcmWiqiLNjy+oPlvgBufWW1s0WVWQjxYG31zCSFE+6QHLrrE7XLywyj1LKxg3BzR424yN3TedqiedzdX8OLqfaHrOlvXXAhhkAAuusw+a/CaWUYQvuvfGwGobQzfnNnjD/DJnmou+P0HYefPnjiIy2cM79mGCtFHSQAXXXbRtGFMLh3A+98/k59eeHzofOXRZo40GnVcbjrTqB1+7RMruPiPH4VuY20H15VNKYQQBgngosuKcjJ49VunMtxckn+xWUlwW8VRlpirSCeX5re639+/fhKDzMqKmRLAhegyCeAibr5h7tRTVe/l+ZV7ObFsIJNLw2erfO74wZwypoiBWcaccat0rRCi8ySAi7ixdmJZv7+WnZUNnD95aKtVmOcePzjsWFIoQnSdBHARN9Zem7vMDZgH5bnJzkjjypNHMGV4Pm/fejoXTjXSLNbWcBdNG5qYxgrRB8g8cBE3aU4HRTnpbK8wFubkuo0/r59fNKnVbW+dN44b54yRHrgQ3SA9cBFXDqXYafbAczLa7h8opSR4C9FNEsBFXA3Jb6lbnuOWL3hC9CQJ4CKufnlxy3zw9nrgQojukwAu4sq+8bJsUCxEz5Iukoirwux0bj5rHOdNGhLXDY+FEK1JABdxpZTiO/OPSXQzhOgXJIUihBApSgK4EEKkKAngQgiRojoM4Eqpx5VSFUqp9bZzBUqpt5VS28x/B/ZsM4UQQkSKpQf+JHBuxLnbgUVa63HAIvNYCCFEL+owgGutlwJHIk5fCDxlXn4KuCi+zRJCCNGRrubAB2mtD5iXDwKD2rqhUup6pdRKpdTKysrKLj6dEEKISN0exNRaa0C3c/2jWusZWusZxcXF3X06IYQQpq4u5DmklBqitT6glBoCVMRyp1WrVh1WSu3u4nMWAYe7eN9UJa+5f5DX3D905zWPjHayqwH8VeAa4B7z31diuZPWustdcKXUSq31jK7ePxXJa+4f5DX3Dz3xmmOZRvgs8DEwXim1Tyl1HUbgnq+U2gbMM4+FEEL0og574FrrK9q46qw4t0UIIUQnpNJKzEcT3YAEkNfcP8hr7h/i/pqVMYlECCFEqkmlHrgQQggbCeBCCJGiUiKAK6XOVUptUUptV0r1iborSqnhSqnFSqmNSqkNSqlvm+ejFgpThofN38E6pdT0xL6CrlNKOZVSnyilXjOPRymllpmv7R9KqXTzfIZ5vN28viyhDe8ipVS+UuqfSqnNSqlNSqlZff19Vkrdav5dr1dKPauUcve197kzhf7ae1+VUteYt9+mlLqmM21I+gCulHICfwA+B0wErlBKTUxsq+LCD3xXaz0ROBm4yXxdbRUK+xwwzvy5Hnik95scN98GNtmOfw08oLUeC1QD15nnrwOqzfMPmLdLRQ8Bb2itJwBTMF57n32flVLDgJuBGVrr4wEn8EX63vv8JLEX+ov6viqlCoCfACcBM4GfdKq6q9Y6qX+AWcCbtuM7gDsS3a4eeJ2vAPOBLcAQ89wQYIt5+U/AFbbbh26XSj9AqfmHPRd4DVAYq9PSIt9v4E1glnk5zbydSvRr6OTrHQDsimx3X36fgWHAXqDAfN9eA87pi+8zUAas7+r7ClwB/Ml2Pux2Hf0kfQ+clj8Gyz7zXJ9hfmWcBiyj7UJhfeX38CDwfSBoHhcCNVprv3lsf12h12xeX2vePpWMAiqBJ8y00V+UUtn04fdZa70fuBfYAxzAeN9W0bffZ0tn39duvd+pEMD7NKVUDvAicIvWus5+nTY+kvvMPE+l1PlAhdZ6VaLb0ovSgOnAI1rraUADEfXz++D7PBCj5PQoYCiQTetUQ5/XG+9rKgTw/cBw23GpeS7lKaVcGMH7Ga31S+bpQ2aBMCIKhfWF38Ns4PNKqXLgOYw0ykNAvlLKWhVsf12h12xePwCo6s0Gx8E+YJ/Wepl5/E+MgN6X3+d5wC6tdaXW2ge8hPHe9+X32dLZ97Vb73cqBPAVwDhzBDsdYzDk1QS3qduUUgp4DNiktb7fdpVVKAzCC4W9ClxtjmafDNTavqqlBK31HVrrUq11Gcb7+K7W+svAYuAy82aRr9n6XVxm3j6leqpa64PAXqXUePPUWcBG+vD7jJE6OVkplWX+nVuvuc++zzadfV/fBM5WSg00v7mcbZ6LTaIHAWIcKFgAbAV2AD9MdHvi9JpOxfh6tQ5YY/4swMj9LQK2Ae8ABebtFcZsnB3Apxgj/Al/Hd14/XOA18zLo4HlwHbgBSDDPO82j7eb149OdLu7+FqnAivN9/plYGBff5+BnwKbgfXA00BGX3ufgWcxcvw+jG9a13XlfQW+ar727cC1nWmDLKUXQogUlQopFCGEEFFIABdCiBQlAVwIIVKUBHAhhEhREsCFECJFSQAXQogUJQFcCCFS1P8HDrDM4jJH5ggAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y = df.Price.values[:1000]\n",
    "x = np.arange(len(y))\n",
    "plt.plot(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "111abb7a",
   "metadata": {},
   "outputs": [],
   "source": [
    "w = sliding_window_view(y, window_shape=51)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "e691c59e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def f(x):\n",
    "    mid = len(x)//2\n",
    "    return np.all(x[mid] >= x)\n",
    "\n",
    "peak_loc = np.apply_along_axis(f, 1, w)\n",
    "peak_loc = np.concatenate([[False]*25, peak_loc, [False]*25])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "8cd729ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "/* global mpl */\n",
       "window.mpl = {};\n",
       "\n",
       "mpl.get_websocket_type = function () {\n",
       "    if (typeof WebSocket !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof MozWebSocket !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert(\n",
       "            'Your browser does not have WebSocket support. ' +\n",
       "                'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "                'Firefox 4 and 5 are also supported but you ' +\n",
       "                'have to enable WebSockets in about:config.'\n",
       "        );\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = this.ws.binaryType !== undefined;\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById('mpl-warnings');\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent =\n",
       "                'This browser does not support binary websocket messages. ' +\n",
       "                'Performance may be slow.';\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = document.createElement('div');\n",
       "    this.root.setAttribute('style', 'display: inline-block');\n",
       "    this._root_extra_style(this.root);\n",
       "\n",
       "    parent_element.appendChild(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen = function () {\n",
       "        fig.send_message('supports_binary', { value: fig.supports_binary });\n",
       "        fig.send_message('send_image_mode', {});\n",
       "        if (fig.ratio !== 1) {\n",
       "            fig.send_message('set_device_pixel_ratio', {\n",
       "                device_pixel_ratio: fig.ratio,\n",
       "            });\n",
       "        }\n",
       "        fig.send_message('refresh', {});\n",
       "    };\n",
       "\n",
       "    this.imageObj.onload = function () {\n",
       "        if (fig.image_mode === 'full') {\n",
       "            // Full images could contain transparency (where diff images\n",
       "            // almost always do), so we need to clear the canvas so that\n",
       "            // there is no ghosting.\n",
       "            fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "        }\n",
       "        fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "    };\n",
       "\n",
       "    this.imageObj.onunload = function () {\n",
       "        fig.ws.close();\n",
       "    };\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_header = function () {\n",
       "    var titlebar = document.createElement('div');\n",
       "    titlebar.classList =\n",
       "        'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n",
       "    var titletext = document.createElement('div');\n",
       "    titletext.classList = 'ui-dialog-title';\n",
       "    titletext.setAttribute(\n",
       "        'style',\n",
       "        'width: 100%; text-align: center; padding: 3px;'\n",
       "    );\n",
       "    titlebar.appendChild(titletext);\n",
       "    this.root.appendChild(titlebar);\n",
       "    this.header = titletext;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = (this.canvas_div = document.createElement('div'));\n",
       "    canvas_div.setAttribute(\n",
       "        'style',\n",
       "        'border: 1px solid #ddd;' +\n",
       "            'box-sizing: content-box;' +\n",
       "            'clear: both;' +\n",
       "            'min-height: 1px;' +\n",
       "            'min-width: 1px;' +\n",
       "            'outline: 0;' +\n",
       "            'overflow: hidden;' +\n",
       "            'position: relative;' +\n",
       "            'resize: both;'\n",
       "    );\n",
       "\n",
       "    function on_keyboard_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.key_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    canvas_div.addEventListener(\n",
       "        'keydown',\n",
       "        on_keyboard_event_closure('key_press')\n",
       "    );\n",
       "    canvas_div.addEventListener(\n",
       "        'keyup',\n",
       "        on_keyboard_event_closure('key_release')\n",
       "    );\n",
       "\n",
       "    this._canvas_extra_style(canvas_div);\n",
       "    this.root.appendChild(canvas_div);\n",
       "\n",
       "    var canvas = (this.canvas = document.createElement('canvas'));\n",
       "    canvas.classList.add('mpl-canvas');\n",
       "    canvas.setAttribute('style', 'box-sizing: content-box;');\n",
       "\n",
       "    this.context = canvas.getContext('2d');\n",
       "\n",
       "    var backingStore =\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        this.context.webkitBackingStorePixelRatio ||\n",
       "        this.context.mozBackingStorePixelRatio ||\n",
       "        this.context.msBackingStorePixelRatio ||\n",
       "        this.context.oBackingStorePixelRatio ||\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        1;\n",
       "\n",
       "    this.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n",
       "        'canvas'\n",
       "    ));\n",
       "    rubberband_canvas.setAttribute(\n",
       "        'style',\n",
       "        'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n",
       "    );\n",
       "\n",
       "    // Apply a ponyfill if ResizeObserver is not implemented by browser.\n",
       "    if (this.ResizeObserver === undefined) {\n",
       "        if (window.ResizeObserver !== undefined) {\n",
       "            this.ResizeObserver = window.ResizeObserver;\n",
       "        } else {\n",
       "            var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n",
       "            this.ResizeObserver = obs.ResizeObserver;\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n",
       "        var nentries = entries.length;\n",
       "        for (var i = 0; i < nentries; i++) {\n",
       "            var entry = entries[i];\n",
       "            var width, height;\n",
       "            if (entry.contentBoxSize) {\n",
       "                if (entry.contentBoxSize instanceof Array) {\n",
       "                    // Chrome 84 implements new version of spec.\n",
       "                    width = entry.contentBoxSize[0].inlineSize;\n",
       "                    height = entry.contentBoxSize[0].blockSize;\n",
       "                } else {\n",
       "                    // Firefox implements old version of spec.\n",
       "                    width = entry.contentBoxSize.inlineSize;\n",
       "                    height = entry.contentBoxSize.blockSize;\n",
       "                }\n",
       "            } else {\n",
       "                // Chrome <84 implements even older version of spec.\n",
       "                width = entry.contentRect.width;\n",
       "                height = entry.contentRect.height;\n",
       "            }\n",
       "\n",
       "            // Keep the size of the canvas and rubber band canvas in sync with\n",
       "            // the canvas container.\n",
       "            if (entry.devicePixelContentBoxSize) {\n",
       "                // Chrome 84 implements new version of spec.\n",
       "                canvas.setAttribute(\n",
       "                    'width',\n",
       "                    entry.devicePixelContentBoxSize[0].inlineSize\n",
       "                );\n",
       "                canvas.setAttribute(\n",
       "                    'height',\n",
       "                    entry.devicePixelContentBoxSize[0].blockSize\n",
       "                );\n",
       "            } else {\n",
       "                canvas.setAttribute('width', width * fig.ratio);\n",
       "                canvas.setAttribute('height', height * fig.ratio);\n",
       "            }\n",
       "            canvas.setAttribute(\n",
       "                'style',\n",
       "                'width: ' + width + 'px; height: ' + height + 'px;'\n",
       "            );\n",
       "\n",
       "            rubberband_canvas.setAttribute('width', width);\n",
       "            rubberband_canvas.setAttribute('height', height);\n",
       "\n",
       "            // And update the size in Python. We ignore the initial 0/0 size\n",
       "            // that occurs as the element is placed into the DOM, which should\n",
       "            // otherwise not happen due to the minimum size styling.\n",
       "            if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n",
       "                fig.request_resize(width, height);\n",
       "            }\n",
       "        }\n",
       "    });\n",
       "    this.resizeObserverInstance.observe(canvas_div);\n",
       "\n",
       "    function on_mouse_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.mouse_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousedown',\n",
       "        on_mouse_event_closure('button_press')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseup',\n",
       "        on_mouse_event_closure('button_release')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'dblclick',\n",
       "        on_mouse_event_closure('dblclick')\n",
       "    );\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousemove',\n",
       "        on_mouse_event_closure('motion_notify')\n",
       "    );\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseenter',\n",
       "        on_mouse_event_closure('figure_enter')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseleave',\n",
       "        on_mouse_event_closure('figure_leave')\n",
       "    );\n",
       "\n",
       "    canvas_div.addEventListener('wheel', function (event) {\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        on_mouse_event_closure('scroll')(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.appendChild(canvas);\n",
       "    canvas_div.appendChild(rubberband_canvas);\n",
       "\n",
       "    this.rubberband_context = rubberband_canvas.getContext('2d');\n",
       "    this.rubberband_context.strokeStyle = '#000000';\n",
       "\n",
       "    this._resize_canvas = function (width, height, forward) {\n",
       "        if (forward) {\n",
       "            canvas_div.style.width = width + 'px';\n",
       "            canvas_div.style.height = height + 'px';\n",
       "        }\n",
       "    };\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n",
       "        event.preventDefault();\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus() {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'mpl-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'mpl-button-group';\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'mpl-button-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        var button = (fig.buttons[name] = document.createElement('button'));\n",
       "        button.classList = 'mpl-widget';\n",
       "        button.setAttribute('role', 'button');\n",
       "        button.setAttribute('aria-disabled', 'false');\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "\n",
       "        var icon_img = document.createElement('img');\n",
       "        icon_img.src = '_images/' + image + '.png';\n",
       "        icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
       "        icon_img.alt = tooltip;\n",
       "        button.appendChild(icon_img);\n",
       "\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    var fmt_picker = document.createElement('select');\n",
       "    fmt_picker.classList = 'mpl-widget';\n",
       "    toolbar.appendChild(fmt_picker);\n",
       "    this.format_dropdown = fmt_picker;\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = document.createElement('option');\n",
       "        option.selected = fmt === mpl.default_extension;\n",
       "        option.innerHTML = fmt;\n",
       "        fmt_picker.appendChild(option);\n",
       "    }\n",
       "\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', { width: x_pixels, height: y_pixels });\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_message = function (type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function () {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function (fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1], msg['forward']);\n",
       "        fig.send_message('refresh', {});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
       "    var x0 = msg['x0'] / fig.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
       "    var x1 = msg['x1'] / fig.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0,\n",
       "        0,\n",
       "        fig.canvas.width / fig.ratio,\n",
       "        fig.canvas.height / fig.ratio\n",
       "    );\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n",
       "    fig.rubberband_canvas.style.cursor = msg['cursor'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_message = function (fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n",
       "    for (var key in msg) {\n",
       "        if (!(key in fig.buttons)) {\n",
       "            continue;\n",
       "        }\n",
       "        fig.buttons[key].disabled = !msg[key];\n",
       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
       "    if (msg['mode'] === 'PAN') {\n",
       "        fig.buttons['Pan'].classList.add('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    } else if (msg['mode'] === 'ZOOM') {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.add('active');\n",
       "    } else {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message('ack', {});\n",
       "};\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function (fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            var img = evt.data;\n",
       "            if (img.type !== 'image/png') {\n",
       "                /* FIXME: We get \"Resource interpreted as Image but\n",
       "                 * transferred with MIME type text/plain:\" errors on\n",
       "                 * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "                 * to be part of the websocket stream */\n",
       "                img.type = 'image/png';\n",
       "            }\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src\n",
       "                );\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                img\n",
       "            );\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        } else if (\n",
       "            typeof evt.data === 'string' &&\n",
       "            evt.data.slice(0, 21) === 'data:image/png;base64'\n",
       "        ) {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig['handle_' + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\n",
       "                \"No handler for the '\" + msg_type + \"' message type: \",\n",
       "                msg\n",
       "            );\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\n",
       "                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
       "                    e,\n",
       "                    e.stack,\n",
       "                    msg\n",
       "                );\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "};\n",
       "\n",
       "// from https://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function (e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e) {\n",
       "        e = window.event;\n",
       "    }\n",
       "    if (e.target) {\n",
       "        targ = e.target;\n",
       "    } else if (e.srcElement) {\n",
       "        targ = e.srcElement;\n",
       "    }\n",
       "    if (targ.nodeType === 3) {\n",
       "        // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "    }\n",
       "\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    var boundingRect = targ.getBoundingClientRect();\n",
       "    var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
       "    var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n",
       "\n",
       "    return { x: x, y: y };\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * https://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys(original) {\n",
       "    return Object.keys(original).reduce(function (obj, key) {\n",
       "        if (typeof original[key] !== 'object') {\n",
       "            obj[key] = original[key];\n",
       "        }\n",
       "        return obj;\n",
       "    }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function (event, name) {\n",
       "    var canvas_pos = mpl.findpos(event);\n",
       "\n",
       "    if (name === 'button_press') {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * this.ratio;\n",
       "    var y = canvas_pos.y * this.ratio;\n",
       "\n",
       "    this.send_message(name, {\n",
       "        x: x,\n",
       "        y: y,\n",
       "        button: event.button,\n",
       "        step: event.step,\n",
       "        guiEvent: simpleKeys(event),\n",
       "    });\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.key_event = function (event, name) {\n",
       "    // Prevent repeat events\n",
       "    if (name === 'key_press') {\n",
       "        if (event.key === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.key;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.key !== 'Control') {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    else if (event.altKey && event.key !== 'Alt') {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    else if (event.shiftKey && event.key !== 'Shift') {\n",
       "        value += 'shift+';\n",
       "    }\n",
       "\n",
       "    value += 'k' + event.key;\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n",
       "    if (name === 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message('toolbar_button', { name: name });\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "\n",
       "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n",
       "// prettier-ignore\n",
       "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";/* global mpl */\n",
       "\n",
       "var comm_websocket_adapter = function (comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.binaryType = comm.kernel.ws.binaryType;\n",
       "    ws.readyState = comm.kernel.ws.readyState;\n",
       "    function updateReadyState(_event) {\n",
       "        if (comm.kernel.ws) {\n",
       "            ws.readyState = comm.kernel.ws.readyState;\n",
       "        } else {\n",
       "            ws.readyState = 3; // Closed state.\n",
       "        }\n",
       "    }\n",
       "    comm.kernel.ws.addEventListener('open', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('close', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('error', updateReadyState);\n",
       "\n",
       "    ws.close = function () {\n",
       "        comm.close();\n",
       "    };\n",
       "    ws.send = function (m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function (msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        var data = msg['content']['data'];\n",
       "        if (data['blob'] !== undefined) {\n",
       "            data = {\n",
       "                data: new Blob(msg['buffers'], { type: data['blob'] }),\n",
       "            };\n",
       "        }\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(data);\n",
       "    });\n",
       "    return ws;\n",
       "};\n",
       "\n",
       "mpl.mpl_figure_comm = function (comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = document.getElementById(id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm);\n",
       "\n",
       "    function ondownload(figure, _format) {\n",
       "        window.open(figure.canvas.toDataURL());\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element;\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error('Failed to find cell for figure', id, fig);\n",
       "        return;\n",
       "    }\n",
       "    fig.cell_info[0].output_area.element.on(\n",
       "        'cleared',\n",
       "        { fig: fig },\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
       "    var width = fig.canvas.width / fig.ratio;\n",
       "    fig.cell_info[0].output_area.element.off(\n",
       "        'cleared',\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "    fig.resizeObserverInstance.unobserve(fig.canvas_div);\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable();\n",
       "    fig.parent_element.innerHTML =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "    fig.close_ws(fig, msg);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.close_ws = function (fig, msg) {\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width / this.ratio;\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message('ack', {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () {\n",
       "        fig.push_to_output();\n",
       "    }, 1000);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'btn-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'btn-group';\n",
       "    var button;\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'btn-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        button = fig.buttons[name] = document.createElement('button');\n",
       "        button.classList = 'btn btn-default';\n",
       "        button.href = '#';\n",
       "        button.title = name;\n",
       "        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message pull-right';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = document.createElement('div');\n",
       "    buttongrp.classList = 'btn-group inline pull-right';\n",
       "    button = document.createElement('button');\n",
       "    button.classList = 'btn btn-mini btn-primary';\n",
       "    button.href = '#';\n",
       "    button.title = 'Stop Interaction';\n",
       "    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
       "    button.addEventListener('click', function (_evt) {\n",
       "        fig.handle_close(fig, {});\n",
       "    });\n",
       "    button.addEventListener(\n",
       "        'mouseover',\n",
       "        on_mouseover_closure('Stop Interaction')\n",
       "    );\n",
       "    buttongrp.appendChild(button);\n",
       "    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
       "    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
       "    var fig = event.data.fig;\n",
       "    if (event.target !== this) {\n",
       "        // Ignore bubbled events from children.\n",
       "        return;\n",
       "    }\n",
       "    fig.close_ws(fig, {});\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (el) {\n",
       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
       "    // this is important to make the div 'focusable\n",
       "    el.setAttribute('tabindex', 0);\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    } else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (event, _name) {\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which === 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "};\n",
       "\n",
       "mpl.find_output_cell = function (html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i = 0; i < ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code') {\n",
       "            for (var j = 0; j < cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] === html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel !== null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target(\n",
       "        'matplotlib',\n",
       "        mpl.mpl_figure_comm\n",
       "    );\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"432\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fc022da8610>"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.plot(x, y)\n",
    "plt.scatter(x[peak_loc], y[peak_loc], color='red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a96ced2d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}