{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. 🐍 Introduction to Python: Basic Libraries for AI\n",
    "\n",
    "![Status](https://img.shields.io/static/v1.svg?label=Status&message=Finished&color=green)\n",
    "\n",
    "**Filled notebook:** \n",
    "[![View filled on Github](https://img.shields.io/static/v1.svg?logo=github&label=Repo&message=View%20On%20Github&color=lightgrey)](https://github.com/bfortuno/Surgical-Phase-Recognition/blob/main/docs/tutorial_notebooks/tutorial2/python_basics_2.ipynb)\n",
    "[![Open filled In Collab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/bfortuno/Surgical-Phase-Recognition/blob/main/docs/tutorial_notebooks/tutorial2/python_basics_2.ipynb)    \n",
    "**Author:** Benjamin I. Fortuno"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ce97q1ZcBiwj"
   },
   "source": [
    "## 1 Introduction\n",
    "\n",
    "### 📌 What You Will Learn:\n",
    "- Fundamental **Python** programming concepts\n",
    "- Introduction to **NumPy** and **Pandas** for data handling\n",
    "- Basics of **data visualization** using Matplotlib\n",
    "- Practical exercises tailored to **surgical task analysis**\n",
    "\n",
    "### 🎯 Goal:\n",
    "By the end of this notebook, you will have a **solid foundation in Python** and be able to **load, manipulate, and analyze surgical motion data**, preparing you for more advanced machine learning tasks.\n",
    "\n",
    "Let's get started! 🚀\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WOyh91ABBlc3"
   },
   "source": [
    "## 2 NumPy\n",
    "\n",
    "NumPy provides an in depth overview of [what exactly NumPy is](https://numpy.org/doc/stable/user/whatisnumpy.html). Quoting them:\n",
    ">*At the core of the NumPy package, is the ndarray object. This encapsulates n-dimensional arrays of homogeneous data types, with many operations being performed in compiled code for performance.*\n",
    "\n",
    "Let's get started by importing it, typically shortened to `np` because of how frequently it's called. This will come standard in most Python distributions such as Anaconda. If you need to install it simply:\n",
    "~~~\n",
    "!pip install numpy\n",
    "~~~"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "VLP6WeFY8UWo"
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WrQ0eAG8CBqG"
   },
   "source": [
    "### Creating Arrays"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_S1VR75hIAUM"
   },
   "source": [
    "#### Manually or from Lists\n",
    "Manually create a list and demonstrate that operations on that list are difficult."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "RFL-_fg5IDeg",
    "outputId": "373c2282-92f1-4dab-b75f-55fcf5764575"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 1, 2, 3, 0, 1, 2, 3]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lst = [0, 1, 2, 3]\n",
    "lst * 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "nXhiXAjgIbdc"
   },
   "source": [
    "That isn't what we expected! For lists, we need to loop through all elements to apply a function to them which can be incredibly time consuming."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "zMQ03IPWIS7S",
    "outputId": "f1ba686c-6261-408a-aae7-ea49e83e9141"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 2, 4, 6]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[i * 2 for i in lst]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "B-kxWUb6Ipf4"
   },
   "source": [
    "Now lets make a numpy array and once in an array, let's show how intuitive operations are now that they are performed element by element."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "qkFeoqrRIn-r",
    "outputId": "c62abf15-989d-4a01-cdb3-8b8af8c8fd5e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1 2 3]\n",
      "[0 2 4 6]\n",
      "[2 3 4 5]\n"
     ]
    }
   ],
   "source": [
    "array = np.array(lst)\n",
    "print(array)\n",
    "print(array * 2)\n",
    "print(array + 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "iL3RZxhbIf4I"
   },
   "source": [
    "Let's create a 2D matrix of 32 bit floats."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "DLQY2HN_JdcB",
    "outputId": "d4a0c081-676a-43a9-b08d-84f33a613170"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 0., 0.],\n",
       "       [0., 1., 0.],\n",
       "       [0., 0., 1.]], dtype=float32)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array([[1, 0, 0],\n",
    "          [0, 1, 0],\n",
    "          [0, 0, 1]], dtype=np.float32)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-Nuet-s2Jd3q"
   },
   "source": [
    "#### Using Functions\n",
    "\n",
    "We'll go through a few here, but for more in depth examples and options see NumPy's [Array Creation Routines](https://numpy.org/doc/stable/reference/routines.array-creation.html). These first few examples are for very basic arrays/matrices."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "CQyecbfVL3F9",
    "outputId": "fb2c7665-6da1-4299-c57d-2cd26348b699"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 0., 0., 0., 0.])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.zeros(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "1TP42y0nL5iq",
    "outputId": "49751d7a-31ca-4205-e2ca-1e4abd980a7a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0.],\n",
       "       [0., 0., 0.]])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.zeros([2, 3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "22P2Xf2aL-T8",
    "outputId": "797a529d-9e08-48e5-cddb-ada72817ed66"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 0., 0.],\n",
       "       [0., 1., 0.],\n",
       "       [0., 0., 1.]])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.eye(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Dhn3g-dkxZY6"
   },
   "source": [
    "Now let's start making sequences."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "vVFX3uuD-E0G",
    "outputId": "37e76848-9dee-4296-f453-e531243fd469"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "-ZkTkdK5EtRg",
    "outputId": "dc486237-4d77-4be8-f2b8-89fad6726b5b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(0,  #start\n",
    "          10) #stop (not included in array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "a5OIfwPN-O48",
    "outputId": "b4768962-0028-469d-c194-af02dd98f1a7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 2, 4, 6, 8])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(0,  #start\n",
    "          10, #stop (not included in array)\n",
    "          2)  #step size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "yAO2opdCKMVE",
    "outputId": "911b0441-5f17-4886-dfeb-f30150a133ac"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.,  2.,  4.,  6.,  8., 10.])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.linspace(0,  #start\n",
    "            10, #stop (default to be included, can pass in endpoint=False)\n",
    "            6)  #number of data points evenly spaced"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "x0xigPrOK4Y1",
    "outputId": "f2f023ee-94f7-44da-a7b1-7b7bc89dc776"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([   1.,   10.,  100., 1000.])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.logspace(0, #output starts being raised to this value\n",
    "            3, #ending raised value\n",
    "            4) #number of data points"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cv0nw1SnLb5x"
   },
   "source": [
    "Logspace is the equivalent of rasing a base by a linspaced array."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "J3txdCX2LTjy",
    "outputId": "de2c4c21-ea5d-4bb3-9b5f-ebc1b138357f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([   1.,   10.,  100., 1000.])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "10 ** np.linspace(0,3,4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Ew2EuHuELa-A",
    "outputId": "30ca9d00-eabb-4826-e6d0-e3d41f262c0d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.,  2.,  4.,  8., 16.])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.logspace(0, 4, 5, base=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wkVckjCtxl_A"
   },
   "source": [
    "If you don't want to have to do the mental math to know what exponent to raise the values to, you can use geomspace but this only helps for base of 10."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ytK6IH_HLnXF",
    "outputId": "bf3d4c46-b54a-4703-e32b-55c3f3d1d4ed"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([   1.,   10.,  100., 1000.])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.geomspace(1, 1000, 4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DQT4Vdrax8IE"
   },
   "source": [
    "Random numbers!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "CNipvh8wMi5t",
    "outputId": "28a12c88-6e3b-4d1f-d571-495db1fcc42b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.10419445, 0.72301424, 0.92475581, 0.82504997, 0.09006971])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.rand(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "YtcgKwEhMosI",
    "outputId": "e632e6a6-5b74-4b64-a0b8-00c4e94fd72b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.91349968, 0.80744317, 0.41486676],\n",
       "       [0.69650334, 0.3682823 , 0.55498108]])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.rand(2, 3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NzxTMPNaCW86"
   },
   "source": [
    "### Indexing\n",
    "\n",
    "Let's first create a simple array."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "qkWur-4ARuBa",
    "outputId": "b2d0a3a6-2c5a-4e24-e849-5ca49ba53aff"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array = np.arange(1, 11, 1)\n",
    "array"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FFs20jVcjF2L"
   },
   "source": [
    "Now index the first item."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "OFCBTk5qTvLp",
    "outputId": "a7f71372-c785-46f5-ea94-3c11437fe9b5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.int64(1)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gJFlP9V2jH_A"
   },
   "source": [
    "Index the last item."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "7Ct2CXINT_HH",
    "outputId": "5571a5f0-e08c-4a1f-cacf-a5158d5691d7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.int64(10)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array[-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "an9Cc7rejJd-"
   },
   "source": [
    "Grab every 2nd item."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Ec6mWL-5UAok",
    "outputId": "5177ffee-2c8d-4b76-b54a-8abdeebca2d3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 3, 5, 7, 9])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array[::2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "H-KRmA9hjMLu"
   },
   "source": [
    "Grab every second item starting at index of 1 (the second value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Kpwn_NT3UCdR",
    "outputId": "1b19b929-d194-491d-c2c9-15f281b86683"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2,  4,  6,  8, 10])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array[1::2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HgCo96qfjQJM"
   },
   "source": [
    "Start from the second item, going to the 7th but skipping every other. (Our first time using the full `array[start (inclusive): stop (exclusive): step]` array 'slicing' notation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "8q-r1nFgUF5Z",
    "outputId": "cc8fd7bf-887b-4674-88ad-d2e5acde32b5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 4, 6])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array[1:6:2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "O5NAi55AjpE-"
   },
   "source": [
    "Reverse the order."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "HLv2rpV5jna1",
    "outputId": "9781f215-afc2-4d28-c1bc-83f106176a34"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([10,  9,  8,  7,  6,  5,  4,  3,  2,  1])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array[::-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "IKIl4jfbk6LZ"
   },
   "source": [
    "Boolean operations to index the array."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "vAbOU4ayUqDD",
    "outputId": "ceece14d-1c76-4610-8dc4-4d652503e9c2"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array[array < 5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "kSxUAD5Vk8p9",
    "outputId": "cd032815-3669-4a27-fee3-80f6bd5baf66"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array[array * 2 < 5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MmlhYRGMhvZg"
   },
   "source": [
    "Integer list can also index arrays."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "1uUNfzc7huzw",
    "outputId": "0a0aa077-08d3-4f75-a720-67d2073182d5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 4, 6])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array[[1,3,5]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DlqWdsqqdJT0"
   },
   "source": [
    "Now let's create a slightly more complicated, 2 dimensional array."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "4Vz5G3PNc2cZ",
    "outputId": "16662c03-c015-4806-8ec3-6be75ea72c6c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1, 2, 3, 4],\n",
       "       [5, 6, 7, 8, 9]])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array_2d = np.arange(10).reshape((2, 5))\n",
    "array_2d"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "k688SAR_iAjf"
   },
   "source": [
    "Indexing the second dimension."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "WbYDyf-KiJzr",
    "outputId": "139e6975-5656-49e4-9adf-3a17ca561e5d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 6])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array_2d[:, 1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "euR2mj3RiRcl"
   },
   "source": [
    "Any combination of these indexing methods works as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "SW9G--6JiQfp",
    "outputId": "001a5226-7cd0-43bd-a3a3-d39effd809f4"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 3]])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array_2d[[True, False], 1::2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "aJqko5CBimsB",
    "outputId": "cb1faf48-1bb1-4220-db95-df40e6972fa3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5, 6, 9])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array_2d[1, [0,1,4]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "n2mS2VIsCzp8"
   },
   "source": [
    "### Operations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "_1Rs1BpJltMH",
    "outputId": "a4a0f90f-df5a-4c50-f184-610bcd66bdb3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "oxc2cNL4lyKw",
    "outputId": "b1e1b39a-64e6-438c-f35b-bbb076fae6c1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([101, 102, 103, 104, 105, 106, 107, 108, 109, 110])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array + 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "-N2RQG6Ilo2X",
    "outputId": "1bea148b-72c4-413c-bf8c-5f2cc17d7a37"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2,  4,  6,  8, 10, 12, 14, 16, 18, 20])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array * 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "EpuHnkodzQmv"
   },
   "source": [
    "To raise all the elements in an array to an exponent we have to use the notation `**` not `^`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "H0pp6Rt-ls7K",
    "outputId": "912abeb5-d74f-4e31-c151-fd449f85feaa"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  1,   4,   9,  16,  25,  36,  49,  64,  81, 100])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array ** 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ipJ0Ss5Rm2f7"
   },
   "source": [
    "Use that shape to create a new array matching it to do operations with."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "wX4Rudonl1Xb",
    "outputId": "00f7b08f-7dfe-4748-dafb-0e7ede76d066"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  5, 10, 15, 20, 25, 30, 35, 40, 45])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array2 = np.arange(array.shape[0]) * 5\n",
    "array2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "IRvsl8qEl9V3",
    "outputId": "d238cdb2-3630-4fb0-e651-712eb9a36c63"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  7, 13, 19, 25, 31, 37, 43, 49, 55])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array2 + array"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Qex1vdxJnFAC"
   },
   "source": [
    "### Stats of an Array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "m6KX660In-f2",
    "outputId": "43020216-077c-4503-fec8-239dff49cff6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 1  2  3  4  5  6  7  8  9 10]\n",
      "[[0 1 2 3 4]\n",
      " [5 6 7 8 9]]\n"
     ]
    }
   ],
   "source": [
    "print(array)\n",
    "print(array_2d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "9NkWPeaknIj1",
    "outputId": "c51538dd-7c08-4926-8445-0b65e729ed30"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10,)\n",
      "(2, 5)\n"
     ]
    }
   ],
   "source": [
    "print(array.shape)\n",
    "print(array_2d.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "nPrYLzs_nxgh",
    "outputId": "65d85450-b74a-4d90-8ac4-25bd7e9b71e7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10\n",
      "2\n"
     ]
    }
   ],
   "source": [
    "print(len(array))\n",
    "print(len(array_2d))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "z8bhAHwFnO_D",
    "outputId": "99546b5c-3606-4713-e29b-7b2a6447e169"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10\n",
      "9\n"
     ]
    }
   ],
   "source": [
    "print(array.max())\n",
    "print(array_2d.max())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "n6SSOUXtnaXc",
    "outputId": "89eb2d31-8edb-45b6-fd94-13ddc0944b20"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "0\n"
     ]
    }
   ],
   "source": [
    "print(array.min())\n",
    "print(array_2d.min())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "zZSRKnF6nbas",
    "outputId": "9cf0ecd3-7abe-4d94-8a64-36c623ff51da"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(2.8722813232690143)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "jdMsJndyndOQ",
    "outputId": "6f3d707e-2963-4388-e8ce-f2297df4b6fb"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  3,  6, 10, 15, 21, 28, 36, 45, 55])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array.cumsum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "VRpZYL4YnfUb",
    "outputId": "dfdb683c-ed16-4f16-f9f8-05b173349f08"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([      1,       2,       6,      24,     120,     720,    5040,\n",
       "         40320,  362880, 3628800])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array.cumprod()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0nDm77jUm7St"
   },
   "source": [
    "### Constants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "aMGMdwaqoXkn",
    "outputId": "68025722-d17c-4c87-e958-40988b6f79ee"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.141592653589793"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.pi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "jLhkYK-zoYo6",
    "outputId": "21e3820a-bb77-41bf-e52e-d648cbc8ff3c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.718281828459045"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.e"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "0H-XpiGNoZgG",
    "outputId": "88623e5a-fcee-4076-81da-b9efda2f3234"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "inf"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.inf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jX-rL5P0ouD6"
   },
   "source": [
    "### Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "eG1GKzVuovNY",
    "outputId": "eecdf4ad-89b7-447a-e4a8-4a2758af972c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(1.0)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sin(np.pi / 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "tWQ3xUExo7Bn",
    "outputId": "ffabcf03-ea44-4f68-dfdc-d3dc16c7ca4b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(-1.0)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.cos(np.pi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "gk6bP-m-o9gC",
    "outputId": "c1b67bd1-1cb5-436c-92db-1ca0263a734f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(1.0)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.log(np.e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "WHZQnDRzpENN",
    "outputId": "0ca8e0ff-f6aa-46ab-c019-057e555ab342"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(2.0)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.log10(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "hAxm918epGJj",
    "outputId": "d2a60bf9-123d-44ca-b875-a0ddd0a25013"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(6.0)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.log2(64)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "p8IvjWHkzvlm"
   },
   "source": [
    "To demonstrate rounding, let's first make a new array with decimals."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "VSJgi2IQpYXa",
    "outputId": "e437c7a9-e4fe-4499-b7ee-b0db39da7eeb"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.         0.33333333 0.66666667 1.        ]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([0.  , 0.33, 0.67, 1.  ])"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array = np.arange(4) / 3\n",
    "print(array)\n",
    "np.around(array, 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "88VwFdWBCFkC"
   },
   "source": [
    "### Looping vs Vectorization\n",
    "\n",
    "As mentioned in the beginning, NumPy uses machine code with their ndarray objects which is what leads to the performance improvements. Let's demonstrate this by constructing a simple sine wave."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "mX9awPKn-ZlU",
    "outputId": "29c7994e-1b18-4885-ceea-4f5e363c2c3c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1001,)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fs = 500 #sampling rate in Hz\n",
    "d_t = 1 / fs #time steps in seconds\n",
    "n_step = 1000 #number of steps (there will be n_step+1 data points)\n",
    "\n",
    "amp = 1 #amplitude of sine wave\n",
    "f = 2 #frequency of sine wave\n",
    "\n",
    "time = np.linspace(0,             #start time\n",
    "                   n_step * d_t,    #end time\n",
    "                   num=n_step + 1)  #number of data points, one more than the steps to include an endpoint\n",
    "time.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "CIKO7e0e0QvP"
   },
   "source": [
    "First we make a function to loop through each element and calculate the amplitude."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "id": "wqVZJKtwTB8l"
   },
   "outputs": [],
   "source": [
    "def sine_wave_with_loop(time, amp, f, phase=0):\n",
    "  length = time.shape[0]\n",
    "  wave = np.zeros(length)\n",
    "\n",
    "  for i in range(length-1):\n",
    "    wave[i] = np.sin(2 * np.pi * f * time[i] + phase * np.pi / 180)*amp\n",
    "  return wave"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8nkt7EDc0c3H"
   },
   "source": [
    "Now let's time how quickly that executes for our time array of 1,001 data points."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "CMwHGKrITpN4",
    "outputId": "950cebd8-3bb5-4062-cbde-15b4b4727eb3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.41 ms ± 306 μs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit sine_wave_with_loop(time, amp, f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "g4s7-D_90nNH"
   },
   "source": [
    "Now let's do the same using NumPy's sine function and vectorization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "id": "89xWQJpzAdal"
   },
   "outputs": [],
   "source": [
    "def sine_wave_with_numpy(time, amp, f, phase=0):\n",
    "  \"\"\"Takes in a time array and sine wave parameters, returns an array of the sine wave amplitude.\"\"\"\n",
    "  return np.sin(2 * np.pi * f * time + phase * np.pi / 180) * amp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6rNJady-0sJp"
   },
   "source": [
    "Notice my docstrings!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "AsVkSRvtV3HI",
    "outputId": "c2da90a6-e914-4964-c53b-2f1b2924a1e8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function sine_wave_with_numpy in module __main__:\n",
      "\n",
      "sine_wave_with_numpy(time, amp, f, phase=0)\n",
      "    Takes in a time array and sine wave parameters, returns an array of the sine wave amplitude.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(sine_wave_with_numpy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Q5S5fTrw--Z3",
    "outputId": "cb83ee47-de30-4215-9624-9c55571f30ce"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "17.2 μs ± 3.68 μs per loop (mean ± std. dev. of 7 runs, 100,000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit sine_wave_with_numpy(time, amp, f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jtS0wmIyppOW"
   },
   "source": [
    "Using vectorization is about **100x faster!** And this increases the longer the loops are."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KQIddnfwp7t5"
   },
   "source": [
    "#### Why Vectorization Works So Much Faster\n",
    "The above example highlights that NumPy is much faster, but why? Because it is using compiled machine code under the hood for it's operations.\n",
    "\n",
    "Python has the [Numba](http://numba.pydata.org/) package which can be used to do this compilation which we will do to highlight just why NumPy is faster (and recommended!)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "id": "cX8PSvkRoJR3"
   },
   "outputs": [],
   "source": [
    "from numba import njit\n",
    "\n",
    "numba_sine_wave_with_loop = njit(sine_wave_with_loop)\n",
    "numba_sine_wave_with_numpy = njit(sine_wave_with_numpy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "T_ckZKtlo1IJ",
    "outputId": "da56b3e6-205c-4da7-8b89-b622903a94e1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The slowest run took 5.39 times longer than the fastest. This could mean that an intermediate result is being cached.\n",
      "21.3 μs ± 19.4 μs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
      "The slowest run took 4.73 times longer than the fastest. This could mean that an intermediate result is being cached.\n",
      "23.7 μs ± 18 μs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%timeit numba_sine_wave_with_loop(time, amp, f)\n",
    "%timeit numba_sine_wave_with_numpy(time, amp, f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Gk0i33QcstPA"
   },
   "source": [
    "Let's combine this into a DataFrame we'll discuss next in more detail, but here's a preview."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 175
    },
    "id": "U2y5ggCUpEHh",
    "outputId": "b87bf24b-7621-4553-e384-5a0b14bd3035"
   },
   "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>Time (us)</th>\n",
       "      <th>Method</th>\n",
       "      <th>Numba?</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>277.0</td>\n",
       "      <td>Loop</td>\n",
       "      <td>w/o</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>27.5</td>\n",
       "      <td>NumPy</td>\n",
       "      <td>w/o</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>23.1</td>\n",
       "      <td>Loop</td>\n",
       "      <td>w</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>22.6</td>\n",
       "      <td>NumPy</td>\n",
       "      <td>w</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Time (us) Method Numba?\n",
       "0      277.0   Loop    w/o\n",
       "1       27.5  NumPy    w/o\n",
       "2       23.1   Loop      w\n",
       "3       22.6  NumPy      w"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "time_data = pd.DataFrame({'Time (us)':[2.77*100, 27.5, 23.1, 22.6],\n",
    "                          'Method':['Loop','NumPy','Loop','NumPy'],\n",
    "                          'Numba?':['w/o','w/o','w','w']}\n",
    ")\n",
    "time_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Y4xT1NK3sqnQ"
   },
   "source": [
    "Now let's plot it and preview Plotly!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 559
    },
    "id": "qxZGaIywpPBr",
    "outputId": "a0fda4c9-0df4-4412-abe0-9e440f820206"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig = plt.bar(time_data['Method'], time_data['Time (us)'])\n",
    "plt.ylabel('Time (us)')\n",
    "plt.title('Time taken for sine wave generation')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "q220YoGquB1h"
   },
   "source": [
    "##  3 Pandas\n",
    "Pandas is built *on top of* NumPy meaning that the data is stored still as NumPy ndarray objects under the hood. But it exposes a much more intuitive labeling/indexing architecture and allows you to link arrays of different types (strings, floats, integers etc.) to one another.\n",
    "\n",
    "To quote Jake VanderPlas:\n",
    ">*At the very basic level, Pandas objects can be thought of as enhanced versions of NumPy structured arrays in which the rows and columns are identified with labels rather than simple integer indices.*\n",
    "\n",
    "To start, import pandas as `pd`, again this will come standard in virtually all Python distributions such as Anaconda. But to install is simply:\n",
    "~~~\n",
    "!pip install pandas\n",
    "~~~"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "id": "zigCIKBhNkNl"
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OtlqVHIwT6wx"
   },
   "source": [
    "There are three types of Pandas objects, we'll only focus on the first two:\n",
    "1. **Series** – 1D labeled homogeneous array, sizeimmutable\n",
    "2. **Data Frames** – 2D labeled, size-mutable tabular structure with heterogenic columns\n",
    "3. **Panel** – 3D labeled size mutable array."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ReBZJAjGuFTx"
   },
   "source": [
    "### Creating a Series\n",
    "First let's create a few numpy arrays."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Slfe4gYJuKZL",
    "outputId": "b14099a3-7fd2-4c23-b9fb-b5eccf322338"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.    0.002 0.004 ... 1.996 1.998 2.   ]\n",
      "[ 1.22464680e-16 -2.51300954e-02 -5.02443182e-02 ...  5.02443182e-02\n",
      "  2.51300954e-02  1.10218212e-15]\n"
     ]
    }
   ],
   "source": [
    "amplitude = sine_wave_with_numpy(time, amp, f, 180)\n",
    "print(time)\n",
    "print(amplitude)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "yH8v1NpUBr0X"
   },
   "source": [
    "Now let's see what a series looks like made from one of the arrays."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Ke_Vt_SmBrl0",
    "outputId": "4e323c4a-ef2d-40a0-c44d-0511336ba3e5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       1.224647e-16\n",
       "1      -2.513010e-02\n",
       "2      -5.024432e-02\n",
       "3      -7.532681e-02\n",
       "4      -1.003617e-01\n",
       "            ...     \n",
       "996     1.003617e-01\n",
       "997     7.532681e-02\n",
       "998     5.024432e-02\n",
       "999     2.513010e-02\n",
       "1000    1.102182e-15\n",
       "Length: 1001, dtype: float64"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(amplitude)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jt0xL0cuB2Ai"
   },
   "source": [
    "This type of series has some value, but you really start to see it when you add in an index."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "OQ11y3klvvLj",
    "outputId": "5dfe9f12-e4a2-4cc4-d234-dd048046d477"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.000    1.224647e-16\n",
       "0.002   -2.513010e-02\n",
       "0.004   -5.024432e-02\n",
       "0.006   -7.532681e-02\n",
       "0.008   -1.003617e-01\n",
       "             ...     \n",
       "1.992    1.003617e-01\n",
       "1.994    7.532681e-02\n",
       "1.996    5.024432e-02\n",
       "1.998    2.513010e-02\n",
       "2.000    1.102182e-15\n",
       "Name: Amplitude, Length: 1001, dtype: float64"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "series = pd.Series(data=amplitude,\n",
    "                   index=time,\n",
    "                   name='Amplitude')\n",
    "series"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YxGmalSBl2_3"
   },
   "source": [
    "Here's where Pandas shines - indexing is much more intuitive (and inclusive) to specify based on labels, not those confusing integer locations. We'll come back to this when we have the dataframe next too."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "VTqpSZl9lyIl",
    "outputId": "3a92856e-0677-4c80-f52f-7d0e2fd9e80a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.000    1.224647e-16\n",
       "0.002   -2.513010e-02\n",
       "0.004   -5.024432e-02\n",
       "0.006   -7.532681e-02\n",
       "0.008   -1.003617e-01\n",
       "0.010   -1.253332e-01\n",
       "Name: Amplitude, dtype: float64"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "series[0: 0.01]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "s38flxoeB8n0"
   },
   "source": [
    "Being able to plot quickly is also a plus!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 283
    },
    "id": "YbluH1OXu-0f",
    "outputId": "f1a137b5-b116-4ef8-e9de-4458e1510bcf"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "series.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "H8I5XnmU4Dp7"
   },
   "source": [
    "Remember we never left the NumPy array, it is still here and can be accessed with the following."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "xtmkmd2l5U4o",
    "outputId": "278f9301-9f68-42b1-d0dd-11657f48e364"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.22464680e-16, -2.51300954e-02, -5.02443182e-02, ...,\n",
       "        5.02443182e-02,  2.51300954e-02,  1.10218212e-15])"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "series.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "-jvWYyW44JUH",
    "outputId": "7ec67c4d-b14b-4dd3-ceda-5d42655636e4"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.22464680e-16, -2.51300954e-02, -5.02443182e-02, ...,\n",
       "        5.02443182e-02,  2.51300954e-02,  1.10218212e-15])"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "series.to_numpy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wzVCQDfNuIOP"
   },
   "source": [
    "### Creating a DataFrame\n",
    "\n",
    "A DataFrame is basically a sequence of aligned series objects, and by aligned I mean they share a common index or label. This let's us mix and match types easily among other benefits.\n",
    "\n",
    "First we'll start creating dataframes using what is called a \"dictionary\" with keys and values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 423
    },
    "id": "Bty17IsDU6B3",
    "outputId": "5434f563-2cdf-4618-ee07-a0056152f58f"
   },
   "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>Phase 0</th>\n",
       "      <th>Phase 90</th>\n",
       "      <th>Phase 180</th>\n",
       "      <th>Phase 270</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.000</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.224647e-16</td>\n",
       "      <td>-1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.002</th>\n",
       "      <td>2.513010e-02</td>\n",
       "      <td>0.999684</td>\n",
       "      <td>-2.513010e-02</td>\n",
       "      <td>-0.999684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.004</th>\n",
       "      <td>5.024432e-02</td>\n",
       "      <td>0.998737</td>\n",
       "      <td>-5.024432e-02</td>\n",
       "      <td>-0.998737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.006</th>\n",
       "      <td>7.532681e-02</td>\n",
       "      <td>0.997159</td>\n",
       "      <td>-7.532681e-02</td>\n",
       "      <td>-0.997159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.008</th>\n",
       "      <td>1.003617e-01</td>\n",
       "      <td>0.994951</td>\n",
       "      <td>-1.003617e-01</td>\n",
       "      <td>-0.994951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.992</th>\n",
       "      <td>-1.003617e-01</td>\n",
       "      <td>0.994951</td>\n",
       "      <td>1.003617e-01</td>\n",
       "      <td>-0.994951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.994</th>\n",
       "      <td>-7.532681e-02</td>\n",
       "      <td>0.997159</td>\n",
       "      <td>7.532681e-02</td>\n",
       "      <td>-0.997159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.996</th>\n",
       "      <td>-5.024432e-02</td>\n",
       "      <td>0.998737</td>\n",
       "      <td>5.024432e-02</td>\n",
       "      <td>-0.998737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.998</th>\n",
       "      <td>-2.513010e-02</td>\n",
       "      <td>0.999684</td>\n",
       "      <td>2.513010e-02</td>\n",
       "      <td>-0.999684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.000</th>\n",
       "      <td>-9.797174e-16</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.102182e-15</td>\n",
       "      <td>-1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1001 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Phase 0  Phase 90     Phase 180  Phase 270\n",
       "0.000  0.000000e+00  1.000000  1.224647e-16  -1.000000\n",
       "0.002  2.513010e-02  0.999684 -2.513010e-02  -0.999684\n",
       "0.004  5.024432e-02  0.998737 -5.024432e-02  -0.998737\n",
       "0.006  7.532681e-02  0.997159 -7.532681e-02  -0.997159\n",
       "0.008  1.003617e-01  0.994951 -1.003617e-01  -0.994951\n",
       "...             ...       ...           ...        ...\n",
       "1.992 -1.003617e-01  0.994951  1.003617e-01  -0.994951\n",
       "1.994 -7.532681e-02  0.997159  7.532681e-02  -0.997159\n",
       "1.996 -5.024432e-02  0.998737  5.024432e-02  -0.998737\n",
       "1.998 -2.513010e-02  0.999684  2.513010e-02  -0.999684\n",
       "2.000 -9.797174e-16  1.000000  1.102182e-15  -1.000000\n",
       "\n",
       "[1001 rows x 4 columns]"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({\"Phase 0\": sine_wave_with_numpy(time, amp, f, 00),\n",
    "                   \"Phase 90\": sine_wave_with_numpy(time, amp, f, 90),\n",
    "                   \"Phase 180\": sine_wave_with_numpy(time, amp, f, 180),\n",
    "                   \"Phase 270\": sine_wave_with_numpy(time, amp, f, 270)},\n",
    "                  index=time)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dGDtiYG-cU9V"
   },
   "source": [
    "### Plotting (Preview)\n",
    "\n",
    "Dataframes also wrap around Matplotlib to allow for plotting directly from the dataframe object itself. This can also be done from the Pandas Series object too like we showed earlier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 283
    },
    "id": "ZkC62XnKVd6X",
    "outputId": "6e92b6eb-a66d-4582-9ea7-7fe80c6cf0f2"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 423
    },
    "id": "goaaXKMXVjUq",
    "outputId": "e3c5e802-dd8e-4fb3-8598-0dd8be598f66"
   },
   "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>Phase 0</th>\n",
       "      <th>Phase 90</th>\n",
       "      <th>Phase 180</th>\n",
       "      <th>Phase 270</th>\n",
       "      <th>Max</th>\n",
       "      <th>Min</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.000</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.224647e-16</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.002</th>\n",
       "      <td>2.513010e-02</td>\n",
       "      <td>0.999684</td>\n",
       "      <td>-2.513010e-02</td>\n",
       "      <td>-0.999684</td>\n",
       "      <td>0.999684</td>\n",
       "      <td>-0.999684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.004</th>\n",
       "      <td>5.024432e-02</td>\n",
       "      <td>0.998737</td>\n",
       "      <td>-5.024432e-02</td>\n",
       "      <td>-0.998737</td>\n",
       "      <td>0.998737</td>\n",
       "      <td>-0.998737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.006</th>\n",
       "      <td>7.532681e-02</td>\n",
       "      <td>0.997159</td>\n",
       "      <td>-7.532681e-02</td>\n",
       "      <td>-0.997159</td>\n",
       "      <td>0.997159</td>\n",
       "      <td>-0.997159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.008</th>\n",
       "      <td>1.003617e-01</td>\n",
       "      <td>0.994951</td>\n",
       "      <td>-1.003617e-01</td>\n",
       "      <td>-0.994951</td>\n",
       "      <td>0.994951</td>\n",
       "      <td>-0.994951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.992</th>\n",
       "      <td>-1.003617e-01</td>\n",
       "      <td>0.994951</td>\n",
       "      <td>1.003617e-01</td>\n",
       "      <td>-0.994951</td>\n",
       "      <td>0.994951</td>\n",
       "      <td>-0.994951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.994</th>\n",
       "      <td>-7.532681e-02</td>\n",
       "      <td>0.997159</td>\n",
       "      <td>7.532681e-02</td>\n",
       "      <td>-0.997159</td>\n",
       "      <td>0.997159</td>\n",
       "      <td>-0.997159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.996</th>\n",
       "      <td>-5.024432e-02</td>\n",
       "      <td>0.998737</td>\n",
       "      <td>5.024432e-02</td>\n",
       "      <td>-0.998737</td>\n",
       "      <td>0.998737</td>\n",
       "      <td>-0.998737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.998</th>\n",
       "      <td>-2.513010e-02</td>\n",
       "      <td>0.999684</td>\n",
       "      <td>2.513010e-02</td>\n",
       "      <td>-0.999684</td>\n",
       "      <td>0.999684</td>\n",
       "      <td>-0.999684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.000</th>\n",
       "      <td>-9.797174e-16</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.102182e-15</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1001 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Phase 0  Phase 90     Phase 180  Phase 270       Max       Min\n",
       "0.000  0.000000e+00  1.000000  1.224647e-16  -1.000000  1.000000 -1.000000\n",
       "0.002  2.513010e-02  0.999684 -2.513010e-02  -0.999684  0.999684 -0.999684\n",
       "0.004  5.024432e-02  0.998737 -5.024432e-02  -0.998737  0.998737 -0.998737\n",
       "0.006  7.532681e-02  0.997159 -7.532681e-02  -0.997159  0.997159 -0.997159\n",
       "0.008  1.003617e-01  0.994951 -1.003617e-01  -0.994951  0.994951 -0.994951\n",
       "...             ...       ...           ...        ...       ...       ...\n",
       "1.992 -1.003617e-01  0.994951  1.003617e-01  -0.994951  0.994951 -0.994951\n",
       "1.994 -7.532681e-02  0.997159  7.532681e-02  -0.997159  0.997159 -0.997159\n",
       "1.996 -5.024432e-02  0.998737  5.024432e-02  -0.998737  0.998737 -0.998737\n",
       "1.998 -2.513010e-02  0.999684  2.513010e-02  -0.999684  0.999684 -0.999684\n",
       "2.000 -9.797174e-16  1.000000  1.102182e-15  -1.000000  1.000000 -1.000000\n",
       "\n",
       "[1001 rows x 6 columns]"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Max'] = df.max(axis=1)\n",
    "df['Min'] = df.min(axis=1)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "I7_Phn2UF7Yp"
   },
   "source": [
    "This will be the topic of the next webinar, plotting with Plotly!\n",
    "\n",
    "Note that I need to install an upgraded version of Plotly in Colab because the default Plotly Express version doesn't work in Colab (but their more advanced graph objects does)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "id": "Ilzy8VvZDgt0"
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 542
    },
    "id": "7XF4YcNIE1Hn",
    "outputId": "6c694dac-a715-472b-a2cb-37b29b113858"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "df.plot(ax=ax)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0_45NbIUDvGI"
   },
   "source": [
    "### Load from CSV\n",
    "\n",
    "This dataset was discussed in a blog on [vibration metrics and used bearing data as an example](https://blog.endaq.com/top-vibration-metrics-to-monitor-how-to-calculate-them).\n",
    "\n",
    "Note you don't *have* to use a CSV. They have a lot of other file formats natively supported ([see full list](https://pandas.pydata.org/docs/user_guide/io.html)):\n",
    "* hdf\n",
    "* feather\n",
    "* pickle\n",
    "\n",
    "But I know everyone likes CSVs!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "colab": {
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    },
    "id": "8Yiph7IODh4E",
    "outputId": "c87e5403-aeb5-4a33-a402-8971ecb52c85"
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   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fault_021</th>\n",
       "      <th>Fault_014</th>\n",
       "      <th>Fault_007</th>\n",
       "      <th>Normal</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.000000</th>\n",
       "      <td>-0.105351</td>\n",
       "      <td>-0.074395</td>\n",
       "      <td>0.053116</td>\n",
       "      <td>0.046104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.000083</th>\n",
       "      <td>0.132888</td>\n",
       "      <td>0.056365</td>\n",
       "      <td>0.116628</td>\n",
       "      <td>-0.037134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.000167</th>\n",
       "      <td>-0.056535</td>\n",
       "      <td>0.201257</td>\n",
       "      <td>0.083654</td>\n",
       "      <td>-0.089496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.000250</th>\n",
       "      <td>-0.193178</td>\n",
       "      <td>-0.024528</td>\n",
       "      <td>-0.026477</td>\n",
       "      <td>-0.084906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.000333</th>\n",
       "      <td>0.064879</td>\n",
       "      <td>-0.072284</td>\n",
       "      <td>0.045319</td>\n",
       "      <td>-0.038594</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "    <tr>\n",
       "      <th>9.999667</th>\n",
       "      <td>0.095754</td>\n",
       "      <td>0.145055</td>\n",
       "      <td>-0.098923</td>\n",
       "      <td>0.064254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9.999750</th>\n",
       "      <td>-0.123083</td>\n",
       "      <td>0.092263</td>\n",
       "      <td>-0.067573</td>\n",
       "      <td>0.070721</td>\n",
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       "    <tr>\n",
       "      <th>9.999833</th>\n",
       "      <td>-0.036508</td>\n",
       "      <td>-0.168120</td>\n",
       "      <td>0.005685</td>\n",
       "      <td>0.103265</td>\n",
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       "      <th>9.999917</th>\n",
       "      <td>0.097006</td>\n",
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       "      <td>0.093400</td>\n",
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       "    <tr>\n",
       "      <th>10.000000</th>\n",
       "      <td>-0.008762</td>\n",
       "      <td>0.165846</td>\n",
       "      <td>0.130923</td>\n",
       "      <td>0.114947</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>120000 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Fault_021  Fault_014  Fault_007    Normal\n",
       "Time                                                \n",
       "0.000000   -0.105351  -0.074395   0.053116  0.046104\n",
       "0.000083    0.132888   0.056365   0.116628 -0.037134\n",
       "0.000167   -0.056535   0.201257   0.083654 -0.089496\n",
       "0.000250   -0.193178  -0.024528  -0.026477 -0.084906\n",
       "0.000333    0.064879  -0.072284   0.045319 -0.038594\n",
       "...              ...        ...        ...       ...\n",
       "9.999667    0.095754   0.145055  -0.098923  0.064254\n",
       "9.999750   -0.123083   0.092263  -0.067573  0.070721\n",
       "9.999833   -0.036508  -0.168120   0.005685  0.103265\n",
       "9.999917    0.097006  -0.035898   0.093400  0.124335\n",
       "10.000000  -0.008762   0.165846   0.130923  0.114947\n",
       "\n",
       "[120000 rows x 4 columns]"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('https://info.endaq.com/hubfs/Plots/bearing_data.csv', index_col=0)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SuYE7enn5-0T"
   },
   "source": [
    "### Save CSV\n",
    "\n",
    "Like reading data, there are a host of native formats we can save data from a dataframe. [See documentation](https://pandas.pydata.org/docs/reference/io.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "id": "rB5uIDRt6CJe"
   },
   "outputs": [],
   "source": [
    "df.to_csv('bearing-data.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "JrAWfnkOJZsk"
   },
   "source": [
    "### Simple Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 300
    },
    "id": "TVD8WFpvHqs-",
    "outputId": "17bbd709-f79a-4d6e-f0e1-bb42f01d3242"
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Fault_021</th>\n",
       "      <th>Fault_014</th>\n",
       "      <th>Fault_007</th>\n",
       "      <th>Normal</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>120000.000000</td>\n",
       "      <td>120000.000000</td>\n",
       "      <td>120000.000000</td>\n",
       "      <td>120000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.012251</td>\n",
       "      <td>0.002729</td>\n",
       "      <td>0.002953</td>\n",
       "      <td>0.010755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.198383</td>\n",
       "      <td>0.157761</td>\n",
       "      <td>0.121272</td>\n",
       "      <td>0.065060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-1.037862</td>\n",
       "      <td>-1.338628</td>\n",
       "      <td>-0.650390</td>\n",
       "      <td>-0.269114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-0.107020</td>\n",
       "      <td>-0.096649</td>\n",
       "      <td>-0.072284</td>\n",
       "      <td>-0.032544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.011682</td>\n",
       "      <td>0.001299</td>\n",
       "      <td>0.004548</td>\n",
       "      <td>0.013351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.132054</td>\n",
       "      <td>0.100872</td>\n",
       "      <td>0.080081</td>\n",
       "      <td>0.056535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.917908</td>\n",
       "      <td>1.124376</td>\n",
       "      <td>0.594025</td>\n",
       "      <td>0.251382</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Fault_021      Fault_014      Fault_007         Normal\n",
       "count  120000.000000  120000.000000  120000.000000  120000.000000\n",
       "mean        0.012251       0.002729       0.002953       0.010755\n",
       "std         0.198383       0.157761       0.121272       0.065060\n",
       "min        -1.037862      -1.338628      -0.650390      -0.269114\n",
       "25%        -0.107020      -0.096649      -0.072284      -0.032544\n",
       "50%         0.011682       0.001299       0.004548       0.013351\n",
       "75%         0.132054       0.100872       0.080081       0.056535\n",
       "max         0.917908       1.124376       0.594025       0.251382"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "6fiVPnBNJb32",
    "outputId": "df0b8b39-f9df-44bf-c236-7695d375cae4"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Fault_021    0.198383\n",
       "Fault_014    0.157761\n",
       "Fault_007    0.121272\n",
       "Normal       0.065060\n",
       "dtype: float64"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "oQgXuQ9VJhTd",
    "outputId": "537e189f-46b5-4945-808d-705f1932e4f0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Fault_021    0.917908\n",
       "Fault_014    1.124376\n",
       "Fault_007    0.594025\n",
       "Normal       0.251382\n",
       "dtype: float64"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.max()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KuwaUNJPth1r"
   },
   "source": [
    "Note that these built in Pandas functions are using NumPy to process and are the equivalent of doing the following."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "TUsz_I4ptfo3",
    "outputId": "bd9f9729-ecf7-4899-8113-ca5a5cc4ffe0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(1.124375968063872)"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.max(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "cEG3SeRAJkyW",
    "outputId": "33ef1359-42c7-41f7-d908-5a753168ac5f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Fault_021   -0.107020\n",
       "Fault_014   -0.096649\n",
       "Fault_007   -0.072284\n",
       "Normal      -0.032544\n",
       "Name: 0.25, dtype: float64"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.quantile(0.25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 455
    },
    "id": "Shx-sdauJllV",
    "outputId": "8f8d4f7b-5071-4df5-c5d2-4c7ef7ae0a49"
   },
   "outputs": [
    {
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       "      <th>Normal</th>\n",
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       "    <tr>\n",
       "      <th>Time</th>\n",
       "      <th></th>\n",
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       "      <th>0.000083</th>\n",
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       "      <th>0.000250</th>\n",
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       "    <tr>\n",
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       "      <td>...</td>\n",
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       "      <th>9.999667</th>\n",
       "      <td>0.095754</td>\n",
       "      <td>0.145055</td>\n",
       "      <td>-0.098923</td>\n",
       "      <td>0.064254</td>\n",
       "      <td>0.145055</td>\n",
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       "    <tr>\n",
       "      <th>9.999750</th>\n",
       "      <td>-0.123083</td>\n",
       "      <td>0.092263</td>\n",
       "      <td>-0.067573</td>\n",
       "      <td>0.070721</td>\n",
       "      <td>0.123083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9.999833</th>\n",
       "      <td>-0.036508</td>\n",
       "      <td>-0.168120</td>\n",
       "      <td>0.005685</td>\n",
       "      <td>0.103265</td>\n",
       "      <td>0.168120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9.999917</th>\n",
       "      <td>0.097006</td>\n",
       "      <td>-0.035898</td>\n",
       "      <td>0.093400</td>\n",
       "      <td>0.124335</td>\n",
       "      <td>0.124335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10.000000</th>\n",
       "      <td>-0.008762</td>\n",
       "      <td>0.165846</td>\n",
       "      <td>0.130923</td>\n",
       "      <td>0.114947</td>\n",
       "      <td>0.165846</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>120000 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Fault_021  Fault_014  Fault_007    Normal  abs(max)\n",
       "Time                                                          \n",
       "0.000000   -0.105351  -0.074395   0.053116  0.046104  0.105351\n",
       "0.000083    0.132888   0.056365   0.116628 -0.037134  0.132888\n",
       "0.000167   -0.056535   0.201257   0.083654 -0.089496  0.201257\n",
       "0.000250   -0.193178  -0.024528  -0.026477 -0.084906  0.193178\n",
       "0.000333    0.064879  -0.072284   0.045319 -0.038594  0.072284\n",
       "...              ...        ...        ...       ...       ...\n",
       "9.999667    0.095754   0.145055  -0.098923  0.064254  0.145055\n",
       "9.999750   -0.123083   0.092263  -0.067573  0.070721  0.123083\n",
       "9.999833   -0.036508  -0.168120   0.005685  0.103265  0.168120\n",
       "9.999917    0.097006  -0.035898   0.093400  0.124335  0.124335\n",
       "10.000000  -0.008762   0.165846   0.130923  0.114947  0.165846\n",
       "\n",
       "[120000 rows x 5 columns]"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['abs(max)'] = df.abs().max(axis=1)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "oilY0XL3MSA_"
   },
   "source": [
    "### Indexing\n",
    "Here is where indexing in Python gets a whole lot more intuitive! A dataframe with an index let's use index values (time in this case) to slice the dataframe, not rely on the nth element in the arrays."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 455
    },
    "id": "-u2YH6_WMxdX",
    "outputId": "6b4dd652-7842-4961-ee70-2c8bb4970ca9"
   },
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Fault_021</th>\n",
       "      <th>Fault_014</th>\n",
       "      <th>Fault_007</th>\n",
       "      <th>Normal</th>\n",
       "      <th>abs(max)</th>\n",
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       "    <tr>\n",
       "      <th>Time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
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       "      <th>0.000000</th>\n",
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       "      <td>-0.074395</td>\n",
       "      <td>0.053116</td>\n",
       "      <td>0.046104</td>\n",
       "      <td>0.105351</td>\n",
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       "    <tr>\n",
       "      <th>0.000083</th>\n",
       "      <td>0.132888</td>\n",
       "      <td>0.056365</td>\n",
       "      <td>0.116628</td>\n",
       "      <td>-0.037134</td>\n",
       "      <td>0.132888</td>\n",
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       "    <tr>\n",
       "      <th>0.000167</th>\n",
       "      <td>-0.056535</td>\n",
       "      <td>0.201257</td>\n",
       "      <td>0.083654</td>\n",
       "      <td>-0.089496</td>\n",
       "      <td>0.201257</td>\n",
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       "    <tr>\n",
       "      <th>0.000250</th>\n",
       "      <td>-0.193178</td>\n",
       "      <td>-0.024528</td>\n",
       "      <td>-0.026477</td>\n",
       "      <td>-0.084906</td>\n",
       "      <td>0.193178</td>\n",
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       "    <tr>\n",
       "      <th>0.000333</th>\n",
       "      <td>0.064879</td>\n",
       "      <td>-0.072284</td>\n",
       "      <td>0.045319</td>\n",
       "      <td>-0.038594</td>\n",
       "      <td>0.072284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.049584</th>\n",
       "      <td>0.131010</td>\n",
       "      <td>0.129136</td>\n",
       "      <td>-0.014619</td>\n",
       "      <td>0.021487</td>\n",
       "      <td>0.131010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.049667</th>\n",
       "      <td>0.437675</td>\n",
       "      <td>-0.221399</td>\n",
       "      <td>-0.025340</td>\n",
       "      <td>0.021070</td>\n",
       "      <td>0.437675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.049750</th>\n",
       "      <td>0.095754</td>\n",
       "      <td>-0.120689</td>\n",
       "      <td>0.033137</td>\n",
       "      <td>0.035256</td>\n",
       "      <td>0.120689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.049834</th>\n",
       "      <td>-0.137269</td>\n",
       "      <td>0.275977</td>\n",
       "      <td>0.023716</td>\n",
       "      <td>0.044226</td>\n",
       "      <td>0.275977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.049917</th>\n",
       "      <td>0.150203</td>\n",
       "      <td>0.019167</td>\n",
       "      <td>-0.044670</td>\n",
       "      <td>0.005424</td>\n",
       "      <td>0.150203</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>600 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Fault_021  Fault_014  Fault_007    Normal  abs(max)\n",
       "Time                                                         \n",
       "0.000000  -0.105351  -0.074395   0.053116  0.046104  0.105351\n",
       "0.000083   0.132888   0.056365   0.116628 -0.037134  0.132888\n",
       "0.000167  -0.056535   0.201257   0.083654 -0.089496  0.201257\n",
       "0.000250  -0.193178  -0.024528  -0.026477 -0.084906  0.193178\n",
       "0.000333   0.064879  -0.072284   0.045319 -0.038594  0.072284\n",
       "...             ...        ...        ...       ...       ...\n",
       "0.049584   0.131010   0.129136  -0.014619  0.021487  0.131010\n",
       "0.049667   0.437675  -0.221399  -0.025340  0.021070  0.437675\n",
       "0.049750   0.095754  -0.120689   0.033137  0.035256  0.120689\n",
       "0.049834  -0.137269   0.275977   0.023716  0.044226  0.275977\n",
       "0.049917   0.150203   0.019167  -0.044670  0.005424  0.150203\n",
       "\n",
       "[600 rows x 5 columns]"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[0: 0.05]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xTQkEsfJuDAR"
   },
   "source": [
    "We can also use the same convention as before by adding in a step definition, in this case we'll grab every 100th point."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 269
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    "id": "aH5m-nHdM6MV",
    "outputId": "97f1fc33-f5aa-42ca-83f5-d1e8b2919da6"
   },
   "outputs": [
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       "          Fault_021  Fault_014  Fault_007    Normal  abs(max)\n",
       "Time                                                         \n",
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       "0.041667  -0.289975  -0.227409   0.088202  0.016898  0.289975"
      ]
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     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df[0: 0.05: 100]"
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  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0xzown4qLWGb"
   },
   "source": [
    "There are ways to use the integer based indexing if you so desire."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
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    "id": "VgZB15g6LQTY",
    "outputId": "23123f3d-7038-4e4c-86b7-b8bba57e093e"
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   "outputs": [
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       "    <tr>\n",
       "      <th>0.000750</th>\n",
       "      <td>0.021487</td>\n",
       "      <td>0.092426</td>\n",
       "      <td>0.027452</td>\n",
       "      <td>0.044018</td>\n",
       "      <td>0.092426</td>\n",
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       "          Fault_021  Fault_014  Fault_007    Normal  abs(max)\n",
       "Time                                                         \n",
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       "0.000667   0.206529  -0.079431   0.045482  0.038177  0.206529\n",
       "0.000750   0.021487   0.092426   0.027452  0.044018  0.092426"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[0:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XxIMZ9-gMSfC"
   },
   "source": [
    "### Rolling\n",
    "I love the [rolling method](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.rolling.html) which allows for easy rolling window calculations, something you'll do frequently with time series data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
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    },
    "id": "QeIgZF4WNo7K",
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       "    <tr>\n",
       "      <th>9.999833</th>\n",
       "      <td>0.748721</td>\n",
       "      <td>0.768318</td>\n",
       "      <td>0.408362</td>\n",
       "      <td>0.18525</td>\n",
       "      <td>0.933137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9.999917</th>\n",
       "      <td>0.748721</td>\n",
       "      <td>0.768318</td>\n",
       "      <td>0.408362</td>\n",
       "      <td>0.18525</td>\n",
       "      <td>0.933137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10.000000</th>\n",
       "      <td>0.748721</td>\n",
       "      <td>0.768318</td>\n",
       "      <td>0.408362</td>\n",
       "      <td>0.18525</td>\n",
       "      <td>0.933137</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>120000 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Fault_021  Fault_014  Fault_007   Normal  abs(max)\n",
       "Time                                                         \n",
       "0.000000         NaN        NaN        NaN      NaN       NaN\n",
       "0.000083         NaN        NaN        NaN      NaN       NaN\n",
       "0.000167         NaN        NaN        NaN      NaN       NaN\n",
       "0.000250         NaN        NaN        NaN      NaN       NaN\n",
       "0.000333         NaN        NaN        NaN      NaN       NaN\n",
       "...              ...        ...        ...      ...       ...\n",
       "9.999667    0.748721   0.768318   0.408362  0.18525  0.933137\n",
       "9.999750    0.748721   0.768318   0.408362  0.18525  0.933137\n",
       "9.999833    0.748721   0.768318   0.408362  0.18525  0.933137\n",
       "9.999917    0.748721   0.768318   0.408362  0.18525  0.933137\n",
       "10.000000   0.748721   0.768318   0.408362  0.18525  0.933137\n",
       "\n",
       "[120000 rows x 5 columns]"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n = int(df.shape[0] / 100)\n",
    "df.rolling(n).max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 455
    },
    "id": "eke1fPqkN_NS",
    "outputId": "e238d753-2fcb-4119-90b4-b8e34c98b900"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\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>Fault_021</th>\n",
       "      <th>Fault_014</th>\n",
       "      <th>Fault_007</th>\n",
       "      <th>Normal</th>\n",
       "      <th>abs(max)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.000000</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.100001</th>\n",
       "      <td>0.726190</td>\n",
       "      <td>0.710166</td>\n",
       "      <td>0.443448</td>\n",
       "      <td>0.184416</td>\n",
       "      <td>0.813809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.200002</th>\n",
       "      <td>0.696150</td>\n",
       "      <td>0.641131</td>\n",
       "      <td>0.411773</td>\n",
       "      <td>0.204443</td>\n",
       "      <td>0.740376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.300003</th>\n",
       "      <td>0.491289</td>\n",
       "      <td>0.846612</td>\n",
       "      <td>0.492178</td>\n",
       "      <td>0.172525</td>\n",
       "      <td>0.846612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.400003</th>\n",
       "      <td>0.578699</td>\n",
       "      <td>0.608482</td>\n",
       "      <td>0.524828</td>\n",
       "      <td>0.204860</td>\n",
       "      <td>0.608482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9.500079</th>\n",
       "      <td>0.731823</td>\n",
       "      <td>0.500950</td>\n",
       "      <td>0.474798</td>\n",
       "      <td>0.206112</td>\n",
       "      <td>0.760820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9.600080</th>\n",
       "      <td>0.697818</td>\n",
       "      <td>0.713253</td>\n",
       "      <td>0.470412</td>\n",
       "      <td>0.190466</td>\n",
       "      <td>0.713253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9.700081</th>\n",
       "      <td>0.677791</td>\n",
       "      <td>0.547244</td>\n",
       "      <td>0.415996</td>\n",
       "      <td>0.229060</td>\n",
       "      <td>0.677791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9.800082</th>\n",
       "      <td>0.625220</td>\n",
       "      <td>0.569498</td>\n",
       "      <td>0.391469</td>\n",
       "      <td>0.176489</td>\n",
       "      <td>0.653801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9.900083</th>\n",
       "      <td>0.749555</td>\n",
       "      <td>0.949271</td>\n",
       "      <td>0.332342</td>\n",
       "      <td>0.176489</td>\n",
       "      <td>0.949271</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Fault_021  Fault_014  Fault_007    Normal  abs(max)\n",
       "Time                                                         \n",
       "0.000000        NaN        NaN        NaN       NaN       NaN\n",
       "0.100001   0.726190   0.710166   0.443448  0.184416  0.813809\n",
       "0.200002   0.696150   0.641131   0.411773  0.204443  0.740376\n",
       "0.300003   0.491289   0.846612   0.492178  0.172525  0.846612\n",
       "0.400003   0.578699   0.608482   0.524828  0.204860  0.608482\n",
       "...             ...        ...        ...       ...       ...\n",
       "9.500079   0.731823   0.500950   0.474798  0.206112  0.760820\n",
       "9.600080   0.697818   0.713253   0.470412  0.190466  0.713253\n",
       "9.700081   0.677791   0.547244   0.415996  0.229060  0.677791\n",
       "9.800082   0.625220   0.569498   0.391469  0.176489  0.653801\n",
       "9.900083   0.749555   0.949271   0.332342  0.176489  0.949271\n",
       "\n",
       "[100 rows x 5 columns]"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.rolling(n).max()[::n]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 542
    },
    "id": "ZX7LWaYlL-eH",
    "outputId": "dcf2ba3c-08ed-46a2-e1b6-8ab978787f0a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Time'>"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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5Gc3NzeJ9TU1NJEijoKCgoKCgiAIibsD60ksvoa6uDpdffrlunBkzZiAlJUX8tebhPBQUbQHUfISC4vjG5MmTceGFF0abjKghoszIRx99hCeffBKffvqp4RaladOmobq6WvwdOHAgglRSUMQISAwI1dJQUEQNkydPBsMwmt/u3bvDXtbIkSNxzz33BJWmuLgYEyZMQHx8PLKysnD//ffD6/Uq4ixfvhyDBg2C0+lEQUEB3nvvPcXz/Px84jvefvvtLXwjY0RMTTN//nzcdNNN+OyzzzBmzBjDuE6nU3HKIAUFBQUFRSzgnHPOwbvvvqsIy8zMjBI1Enw+HyZMmICcnBysWrUKR44cwaRJk2C32/Hss88CAPbt24cJEyZgypQpmDdvHpYuXYqbbroJubm5GDduHADgjz/+gM/nE/P966+/MHbsWFx22WWtSn9EJCMff/wxrr/+enz88ceYMGFCJIqkoGi7oF7PKE4gsCyLBk9DVH6hnBPrdDqRk5Oj+M2aNQt9+/ZFQkICOnbsiNtuuw11dXVimieeeAIDBgxQ5DNz5kzk5+cTy5g8eTJ++eUXzJo1S5RMFBUVGdL1ww8/YNu2bfjwww8xYMAAjB8/Hk899RTmzJkDt9sNAJg7dy66dOmCl19+Gb169cIdd9yBSy+9FP/+97/FfDIzMxXvtmDBAnTr1g1nnnlm0HUVDIKWjNTV1SlEUvv27cOmTZuQnp6OTp06Ydq0aTh06BD+97//AeBUM9dddx1mzZqFYcOGoaSkBAAQFxeHlJSUML0GBQUFBUVbRKO3EcM+GhaVstdctQbx9vgW52OxWDB79mx06dIFe/fuxW233YYHHngAr732Wkj5zZo1Czt37kSfPn0wffp0AIGlL6tXr0bfvn2RnZ0tho0bNw633nortm7dioEDB2L16tUazcS4ceN01UFutxsffvghpk6d2up+j4KWjKxbtw4DBw4Ut+VOnToVAwcOxGOPPQYAOHLkCIqLi8X4b775JrxeL26//Xbk5uaKv7vvvjtMr0BBcXxC0fWpBSsFRUxgwYIFikPqLrvsMtxzzz0YNWoU8vPzMXr0aDz99NP49NNPQy4jJSUFDocD8fHxooTCarUapikpKVEwIgDEe0EIoBenpqYGjY2Nmjy//vprVFVVYfLkySG/i1kELRkZOXKkoWhLbQyzfPnyYIugoKDQAdXgUBxviLPFYc1Va6JWdrAYNWoUXn/9dfE+ISEBP/30E2bMmIHt27ejpqYGXq8XTU1NaGhoQHx8yyUv0cLbb7+N8ePHIy8vr9XLogflUVBQUFBEDQzDhEVVEikkJCSgoKBAvC8qKsJ5552HW2+9Fc888wzS09OxcuVK3HjjjXC73YiPj4fFYtEs4j0eT1jpysnJwdq1axVhpaWl4jPhvxAmj5OcnIy4OCVjtn//fvz000/48ssvw0qnHuhBeRQUMQaS9IMqaSgoYhPr16+H3+/Hyy+/jFNOOQU9evTA4cOHFXEyMzNRUlKiYEg2bdpkmK/D4VDsagmE4cOHY8uWLSgrKxPDfvzxRyQnJ6OwsFCMs3TpUkW6H3/8EcOHD9fk9+677yIrKytim04oM0JB0ZZA9TQUFDGFgoICeDwevPrqq9i7dy8++OADzJ07VxFn5MiROHr0KF544QXs2bMHc+bMweLFiw3zzc/Px5o1a1BUVITy8nL4/X7D+GeffTYKCwtx7bXXYvPmzViyZAkeeeQR3H777aKrjClTpmDv3r144IEHsH37drz22mv49NNPce+99yry8vv9ePfdd3HdddfBZouMAoUyIxQUsQoqDqGgiHn0798fr7zyCp5//nn06dMH8+bNw4wZMxRxevXqhddeew1z5sxB//79sXbtWtx3332G+d53332wWq0oLCxEZmamYmMICVarFQsWLIDVasXw4cNxzTXXYNKkSeJuHADo0qULFi5ciB9//BH9+/fHyy+/jP/+97+ijxEBP/30E4qLi3HDDTcEWRuhg2FD2WgdYdTU1CAlJQXV1dVITk6ONjkUFK2KLcsPYsX8neg2KBPn/F9fAMCHj61GdVkjLrpvEPIKUqNLIAVFC9DU1IR9+/ahS5cucLlc0SaHIgww+qZm528qGaGgoKCgoKCIKigzQkHRBtDaDocoKChiG1OmTFH4N5H/pkyZEm3yWgy6tZeCoi0h5pWqFBQUrYHp06fr2pkcD+YLlBmhoIhZUGkIBQUFh6ysLMPT7ts6qJqGgoKCgoKCIqqgzAgFRZsC1dNQUFAcf6DMCAVFjCH2N9tTUFBQhBfUZoSCog2AbqbRB+v3w1Nbowiz2B2wteEDyigoTjRQZoSCIkZBYkCo1ESLxY++h33H8hVhFngxZlwTul90fnSIoqCgCApUTUNBQdGmcaAiRxPmhw2Hd5RHgRoKitjC8uXLwTAMqqqqok2KISgzQkERczAQf1DJiC6uuisbt7xyCgb3OhBtUiiOU0yePBkMw+C5555ThH/99dfUMWELQZkRCgqK4wJWlxO2+HhYLPykwNLJgSL8cLlceP7551FZWRm2PN1ud9jyaqugzAgFRaxCPpfSVZcBaN20ZbAsC39DQ1R+oZwTO2bMGOTk5GhO5pXjiy++QO/eveF0OpGfn4+XX35Z8Tw/Px9PPfUUJk2ahOTkZPzf//0f3nvvPaSmpmLBggXo2bMn4uPjcemll6KhoQHvv/8+8vPzkZaWhrvuugs+n0/M64MPPsCQIUOQlJSEnJwcXHXVVSgrKwv6vaINasBKQdGGQLU05kHrqm2AbWzEjkGDo1J2zw3rwQS568pqteLZZ5/FVVddhbvuugsdOnRQPF+/fj0uv/xyPPHEE7jiiiuwatUq3HbbbcjIyMDkyZPFeC+99BIee+wxPP744wCAX3/9FQ0NDZg9ezbmz5+P2tpaXHzxxbjooouQmpqKRYsWYe/evbjkkktw2mmn4YorrgAAeDwePPXUU+jZsyfKysowdepUTJ48GYsWLWpZ5UQYlBmhoKA4TkHZEYrWwUUXXYQBAwbg8ccfx9tvv6149sorr+Css87Co48+CgDo0aMHtm3bhhdffFHBjIwePRr//Oc/xftff/0VHo8Hr7/+Orp16wYAuPTSS/HBBx+gtLQUiYmJKCwsxKhRo/Dzzz+LzMgNN9wg5tG1a1fMnj0bJ598Murq6pCYmNhaVRB2UGaEgiLGQJIcUy2NCfCVROuqbYGJi0PPDeujVnaoeP755zF69GjN4XV///03LrjgAkXYaaedhpkzZ8Ln88FqtQIAhgwZoskzPj5eZEQAIDs7G/n5+QqmIjs7W6GGWb9+PZ544gls3rwZlZWV8Pv9AIDi4mIUFhaG/H6RBmVGKCjaEqijEQ1YPUNVasDaJsAwTNCqkljAiBEjMG7cOEybNk0h8TCLhIQETZjdblfcMwxDDBMYjvr6eowbNw7jxo3DvHnzkJmZieLiYowbN67NGcVSZoSCIkZBp1IKitjGc889hwEDBqBnz55iWK9evfDbb78p4v3222/o0aOHKBUJF7Zv345jx47hueeeQ8eOHQEA69atC2sZkQLdTUNBEavwNABVxdzP5wFArSCMoFbP0LqiaG307dsXV199NWbPni2G/fOf/8TSpUvx1FNPYefOnXj//ffxn//8R6POCQc6deoEh8OBV199FXv37sW3336Lp556KuzlRAKUGaGgiDXUlnD/dywGZvblfsd2cWHuxujRFfOgsiSKyGP69Omi2gQABg0ahE8//RTz589Hnz598Nhjj2H69OkhqXICITMzE++99x4+++wzFBYW4rnnnsNLL70U9nIiAYYNZaN1hFFTU4OUlBRUV1cjOTk52uRQULQqNn/wLVb+lojurl9xdrvXAQDzS57FMW8+zr8uFR2HD4oyhbGFuVO+hw8OTHowH0n5XbFuzv+wZksHFHbaj1EPXR9t8ihUaGpqwr59+9ClSxe4XK5ok0MRBhh9U7PzN5WMUFDEKhKzgEdKuJ+FN2Lz+4zTUFBQULRBUGaEgqItQSYOpqCgoDheQJkRCoq2AMEcgqXMiBqsUDnUgpWCos2CMiMUFG0C/ERL1TQUFBTHISgzQkERayCt6BnKjAQC3UtDQdF2QZ2eUVC0IVQdKIVrPefUyGK3Ib1PPzCWE31NQWZDqJaGgqLtgDIjFBQxB+00KghGVqxuB6yuEcMHFLyP0+6j21cVoCISCoo2hxN9SUVB0SZQOMCORFsFEm2VSLRVwmmpAwBUVkSZsFiCcFCecE9FIxQUbQZUMkJBEWMgzaF9r7scfa+T7v+e/wWWLY8URRQUFBStCyoZoaBoy6Crfy2omoYiCigqKgLDMNi0aVOL8nn77bdx9tlnh4eoFmDbtm3o0KED6uvrI1IeZUYoKGINPIOhdptBERwon0bR1tDU1IRHH30Ujz/+eLRJQWFhIU455RS88sorESmPqmkoKNogRB9oUaVCQk15I5bP246meq8iPK97Kk6/rHtEaGAo90bRxvH5558jOTkZp512WrRJAQBcf/31uPnmmzFt2jTYbK3LLlDJCAVFm0RsTbz7NpfjwN+VOFpcq/htXnoATfWeVi2bVdUFQ7iiiF2wLAtPsy8qv2DPif3+++9x+umnIzU1FRkZGTjvvPOwZ88eRZzt27fj1FNPhcvlQp8+ffDLL7+IzyorK3H11VcjMzMTcXFx6N69O959913x+fz58zFx4kRFfpMnT8aFF16IZ599FtnZ2UhNTcX06dPh9Xpx//33Iz09HR06dFDkAwD/+te/0KNHD8THx6Nr16549NFH4fF4xDofM2YMxo0bJ9ZBRUUFOnTogMcee0zMY+zYsaioqFC8Q2uBSkYoKNoiYmye9fu5Aa19z1QMGNMJALBwzp/cM1+syG8oYhFetx9v3t36kx0J/zfrTNidVtPx6+vrMXXqVPTr1w91dXV47LHHcNFFFynsRO6//37MnDkThYWFeOWVVzBx4kTs27cPGRkZePTRR7Ft2zYsXrwY7dq1w+7du9HY2CimXblyJa699lpNucuWLUOHDh2wYsUK/Pbbb7jxxhuxatUqjBgxAmvWrMEnn3yCW265BWPHjkWHDh0AAElJSXjvvfeQl5eHLVu24Oabb0ZSUhIeeOABMAyD999/H3379sXs2bNx9913Y8qUKWjfvr2CGXE4HBgwYAB+/fVXnHXWWSHUsHlQZoSCgqLl4PmNxFQX8vu2424YLjzY1WfIoGoailbGJZdcorh/5513kJmZiW3btiExMREAcMcdd4jxXn/9dXz//fd4++238cADD6C4uBgDBw7EkCFDAAD5+fliXlVVVaiurkZeXp6m3PT0dMyePRsWiwU9e/bECy+8gIaGBjz00EMAgGnTpuG5557DypUr8Y9//AMA8Mgjj4jp8/Pzcd9992H+/Pl44IEHAADt27fHG2+8gUmTJqGkpASLFi3Cxo0bNeqYvLw87N+/vyXVZgqUGaGgiDkEnrzFeZeN3QmYYRiOEYmWYIQKZNoEbA4L/m/WmVErOxjs2rULjz32GNasWYPy8nL4+VO0i4uLUVhYCAAYPny4lL/NhiFDhuDvv/8GANx666245JJLsGHDBpx99tm48MILceqppwKAKCFxuVyacnv37g2LzNNydnY2+vTpI95brVZkZGSgrKxMDPvkk08we/Zs7NmzB3V1dfB6vUhOTlbke9lll+Grr77Cc889h9dffx3du2vtu+Li4tDQ0BBUPYUCajNCQdGmERszLituAZLCBIap9QUjjOEtRWyDYRjYndao/II1ep44cSIqKirw1ltvYc2aNVizZg0AwO12m0o/fvx47N+/H/feey8OHz6Ms846C/fddx8AICMjAwzDoLKyUpPObrdr64wQJjBHq1evxtVXX41zzz0XCxYswMaNG/Hwww9r6GxoaMD69ethtVqxa9cuIs0VFRXIzMw09X4tAWVGKCjaMNTGmzEFkRmJDsMUG2waxfGCY8eOYceOHXjkkUdw1llnoVevXkTG4ffffxevvV4v1q9fj169eolhmZmZuO666/Dhhx9i5syZePPNNwFw9hmFhYXYtm1bi2ldtWoVOnfujIcffhhDhgxB9+7diaqWf/7zn7BYLFi8eDFmz56NZcuWaeL89ddfGDhwYItpCgSqpqGgiDGYm0RjkwmRU8WtOtkIMCPUMQtF6yMtLQ0ZGRl48803kZubi+LiYjz44IOaeHPmzEH37t3Rq1cv/Pvf/0ZlZSVuuOEGAMBjjz2GwYMHo3fv3mhubsaCBQsUjMq4ceOwcuVK3HPPPS2itXv37iguLsb8+fNx8sknY+HChfjqq68UcRYuXIh33nkHq1evxqBBg3D//ffjuuuuw59//om0tDQAnCO3Q4cOYcyYMS2ixwyCloysWLECEydORF5eHhiGwddffx0wzfLlyzFo0CA4nU4UFBTgvffeC4FUCooTBG1xSU+gmYmQMxTN1l7Kk1C0AiwWC+bPn4/169ejT58+uPfee/Hiiy9q4j333HN47rnn0L9/f6xcuRLffvst2rXjjLodDgemTZuGfv36YcSIEbBarZg/f76Y9sYbb8SiRYtQXV3dIlrPP/983HvvvbjjjjswYMAArFq1Co8++qj4/OjRo7jxxhvxxBNPYNCgQQCAJ598EtnZ2ZgyZYoY7+OPP8bZZ5+Nzp07t4geMwhaMlJfX4/+/fvjhhtuwMUXXxww/r59+zBhwgRMmTIF8+bNw9KlS3HTTTchNzcX48aNC4loCooTAW1yUlXYjHA3kVPTtMUKo2hLGDNmjEaNIm/fwvWVV15JTP/II48odrmoUVhYiAkTJuC1117DtGnTAIC4eF++fLkmrKioSHH/wgsv4IUXXlCECRKXzMxMlJSUKJ7Z7XasW7dOvHe73Zg7dy4++ugjXXrDiaCZkfHjx2P8+PGm48+dOxddunTByy+/DADo1asXVq5ciX//+9+UGaGgOI4hGrD6I1te2xQtUVBwePHFF/Hdd99FmwwUFxfjoYceipg32Fa3GVm9erVG3zRu3DhDnVhzczOam5vF+5qamtYij4KiTSJyO1VCB2OJlqRCkMhEqXgKihYgPz8fd955Z7TJQEFBAQoKCiJWXqvvpikpKUF2drYiLDs7GzU1NQrPc3LMmDEDKSkp4q9jx46tTSYFRQyh7c2ioqharluK2G4auimQgqKtIyZ78bRp01BdXS3+Dhw4EG2SKChiC4zmIuYg2oxESE0j1EXs1ggFBYUeWl1Nk5OTg9LSUkVYaWkpkpOTERcXR0zjdDrhdDpbmzQKijaM2JxylVt7uf+RcwcfmWIowgPBQRdF20c4vmWrMyPDhw/HokWLFGE//vijwmUuBQVFcIjQrtmWgYmS7QZlSmIaDocDFosFhw8fRmZmJhwOR9CeUCliAyzLwu124+jRo7BYLHA4HCHnFTQzUldXh927d4v3+/btw6ZNm5Ceno5OnTph2rRpOHToEP73v/8BAKZMmYL//Oc/eOCBB3DDDTdg2bJl+PTTT7Fw4cKQiaagOK4R0xxGAOxYCDx3DgCAqX8FQBo+efp33PhUP7gysyJKCjVgjU1YLBZ06dIFR44cweHDh6NNDkUYEB8fj06dOinOzwkWQTMj69atw6hRo8T7qVOnAgCuu+46vPfeezhy5AiKi4vF5126dMHChQtx7733YtasWejQoQP++9//0m29FBQ6MDWHxphoRJz4G44B9ioAQDvrXhT7BgOwoHTjZnQ+e2yr0kBX120HDocDnTp1gtfrhc/nizY5FC2A1WqFzWZrcf8LmhkZOXKkoQ6Y5KBl5MiR2LhxY7BFUVBQtDEwAJDaGbj6c0zws/jvk3vh8UfW/otpA8a9FNJhb+oD3yhOTMTkbhoKCoo25oFVvj6xOYHMHrBk90Sq8xj3uJUkONE6hI+CgiK8oMwIBUXMwcQE20YYFYbh3yUqPANlVCgo2gooM0JBEWsIwmhEfUhc9MCq/kcBGlFSrNQNBQVFILT61l4KCgBgvT6sfW0eqiu8ivCkZAuG3nElrA7qVyYYxPY0G0HqqPDDEO5GL6rLlZ6uXQl2JKW7okQRBQUZlBmhiAjKNqzDum0dtA9KgI6/rUaHUSMjTRJFGCGZbuhxB5HnGk50PsXn8ePDx1ajsdajeTbxrv7oVJgRBaooKMigahqKiMDbzA2IcZYanH5yKU4/uRSJtgrFM4ogEGNbe2MBsS0tijyaGjwiI5KQ4kBCigNWOzfkV5Y0RJM0CgoNqGSEIqJw2RrR/8YrAQC7pv4Pdd50uiPiOAIDRH0bEEMZNQ78+1ssDCY/fzoA4If//oVd68po3aiw9H9/4+D2CkWYM96OsTcUIiMvMUpUtQ5+feEdbNuXowizMR6MOteBrueNjxJVlBmhiBT8wqmusjC6lCUjiImircwprL91KFXk2qb2Qrc+KI9vDu5GL7avOqIJr6toxv4tx447ZmRnUQa8rNJmyMu6sG/jfnQ9L0pEgTIjEYPX7UNDjVsR5oizwZVwYjj8IY2LDB9KB83gEbvzrvJjMpFkl2K2TqIM0umFFCL8Mkb5kgcGw2Jl8MfCIhT9WX5cS20nXgqkdu2Ev79dgXXb8+HzR7dtUGYkAvC4ffjwkdUaZsRiZXDxfYOR3SU5SpRFF+JEFYUOf7S4FmsX7IPPqzxtsvuQbPQ6NTfi9ChBkCLpIjYmF91P2NrkkQoWTguOkbqJHvTb0fE8ybYEWZ2TYLFaEJd4/C8SE3Ozkdy1APFJvwEA/P7ompBSZiQCqK9sFhkRm9MKAPC5ffD7WBw7XHdiMCPiuKgdBKMxLm755SCK/izXhB87WBcDzEgwiK1Jhfu+kdzaG1vvH0ugVWMS8noSJEcCQ3sc1qGaSbfyPMie0g6or25GQkp03CxQZiQCEMSAzgQbbnp5BABg4Wt/cpPhcdjYSWAJLyp454xGh/d7uUK7n5yNzn0yUF/VjNVf7YHf19Y+SIys/mNi1GZkf3HC9K1AYGRthGpptJCPTSdU9fCNwe6QDiqsPdYUNWaEbu2NAATjPYvlhGrqOogNyYgwAGV2SkLPYTno0r+dIjyaMKNeaGsn1Ea/Vk9gEIzGY4J3jBGwck2tUjByXDdcYQjJb1+L/vHfontucVRtGCkzEgGIHZ8wgZxoululLd2J9e7mYb5eYqX5iGQwULTztsUyHV8wHFtipN3EGrRM/vFYUYziv93G4vTkd3F2vzVIzY6PGlVUTRMBiJKRE3lkJm7t5NU0rbTtMyTEECmGzSWG21Kz14fi0loAMklTa23tJU64MVw5UYBSMELrRg2xDZ2ou45i5F0pMxIBCI2dsVDdrbzDR3TbpxrC+NNmvwOj+h9dFB/jPHoyYLGvvAHn/HsFAOApt88oWVjBnNDcPgGiRFYWJu40iiGuO0bAEG5iRfIYToi8l6a/RPdlKTMSAQgGrCfyYMkSrpgY6vBtzQYj2gOHGsfqmsVrq4VBeoIDFfXuqFApGkZHuNz1m0uxZtUhhbTGZrdiwnnd0CE3KcLUBEBsNZ+oQrAZaXtjQLgQG+9NbUYiALGxE5iRWJiII4I28p4x8T1igYaQwaJ7VhI2PDpWE368Y8k7W8FurgL+rBZ/3vUV+Pjdv6JLGLXhCQCtmub4rifV28XIqpBKRloZfj+L6qOcCPvE3k3DS4dkIdH0wBoTTEcYECuvoV+frUxhDBlp2rycj5WqNBtscVZ4K91IbWThbfRGlhAeJFOI43yWDQnSBgNZID9BHy/jhBz6u/UoM3Jc4+f//Y3tv5cAUNuMnFijAklNI4bEUoePKWL0Ebvth1x/kanV2KiT0Zd0x/AhuXjj7c3w/nEM0ds0pl/wibaLzwxOOONeRnMRVVA1TSuj4kg9AMCVaEfhaW3Js2eYQfLaLTo9i97AyKg8LrY5xMicojCI5OtUwS9Fks5YYdSiTIbRip9CgrCbjyGpaY5npk3dFqia5viG8H3Puq4X8vu2kx4wqggnCIiH9sZQFcQCKaaYs7Y2p7RWxRpqaSJbSerSxLE+yo2KHpRtEqQzfCJPRcQgCUZio1VQZoQCTfUexYFxDMMgLskeVlWAtEqTr6Cjs+uBhBjpj0qYcDQSK4fBEV01cE8iUzBi5xuKdAgSomg1cCNGLRpHMPj8KNlbA59H5vKUAbK7JMPhit5URHRKGSNtqTUgjhkx5uCNMiMRgnpijxXByOZlB7Dy012a8B5DszH2ht6tUKLcsp9/+WjUgV7FxwJnxMNoPJSetT7BRzesx5HNyjbiTHCh23lnwxZP8thIojxyFSv5r4nwx1T5rmEEJXi0O3mMTLJrvtuHDd/v14TnFqTg4vsGR4EiASTjesWj4xx0N80JgVg3FCvZUy3dMBA735Hd1Zq4HrcPRX+Ww+v2K8LzuqciJTPOVHkkR2fLlzmRe3o90vMSTNMdfrTVpVDr0s36/fjmv4fQ7M/SPPM0L0Cfay/nI4opyPm0DnlKaFZ60VHTxJodEtkWIvJ01B5rAgDEpzgQl+iA1+1D9dFGHNldjWfuWy5FtDA45bwuOGtEp4jQJfkZiUhxMYNYM4KnzEikEN1xUhcCrzTiHz3Qd2QHlBbV4PPn1hE9NG5csh9/LCzShKdkxuGap4abK0iGnMRD2FfVAwBweFdldJgRtQ1X5CkIDRFqP6zXh2Z/IgCgS7siWC1AWWUiajzt0FTbbJg2EoMd+XvFRucS3z9Kjcp4IRS9/fSDzu6M/md1RNn+Gnw2Yx0AILVOucBZ88N+DTOy789yLH1/m2Yx1HNoNkZd26vl9Cm4tthoQ60DHT8jUR796G6aCCFmm7bgql6tRiS0y4YaNwAgJSsOnftkoH2PVEW4YTGEsEG5a9HBsQkA4PNGtiOoSzuux54WgIU08I++dyLGTb8BHXLquGeyShSPPIgFdi7KUmdxCz//z1Xvw9L3tqFsf010CJIjig1d2+ckWnyFyWh3dh7qOri4uD7txyvaUo7mei98Hr/it2tdWcvoUo2B3I3y2XEJybgpqmQIoMxIK0O/LceGUx2jE4U1cb0c09GzwyGcN3QtRvXbzOfhN0qmzkW6ZBgkWCoBAD5fMHm0IqL9QWINsgPuGA2nqtNmFHOxYBcUhe00UQYTxwmebR4W238vwXqCvURkCNFeR7WZE5pNn0HZuOLik5DTJZkLMCBwwJiOmPTsqbjovkFc1Nag8TgGy+r0W2ozcoIgROZz/eZS/L7yoCLMYmUw5pyu6J6f2mKy1KsCYcIhtsuSvwDkgNm5GDj8ORhvFoA3AE+TmYIU5QCAx+eHleG8U+7aXYkKJ5CY5MDQAdmwWFqZT1YZG7Y5iJNKK7+AvCHwFpnBGV9HkD6+rGh9UtUmGlizXfgmvhkjExKRctSjUS+0NkjfJ6rN3aDPCWGil2pS2+LDnPE2JKW7pNO++Rf1+/148ZnV8B9Vqg/9Dgsuu60/enRNI5NFOJvmhDJgjZFBkDIjrQ1xy6NqN43J1r7k3a1IUc31fgCfHdqCh546o+XkiQOEUrRMFE96eUIS2gGFFwA1VqAcMNNjpey4i0a3D9sO1sDKcNKW8j8rUP5nBQBg+2nlmHxt3+BfpiWIDbUpB1OzfGQGEHk7MDLMZFljilqdaQJiRdos9nWLhcFOhx+D02xIOepB1Hb3xEjFaJo1wUQjJDsbPu6Bw3VIPESwY3L78esvxURm5K/tx7Dkna1wAahze3H7RxsAAMMqI7IxPaqgp/ZSBAWrh/tflWaDLd4Kb7UHqXV+sM1hWmWpHESYsmXK6QNc/jywdxew7gBCmQUqG9zwsix6uFZgg/dMNLKJcHhZuFgGlWWNQecXNHy8ncv+1cAfS4BaG4BOiHaHVMCgWiO2tVeughP8ZhiWzIoxuOhR2mYbDQSStkWrChRqmlhQD8vbBx/C31itBnoktUtZFd8inI7uB4seV3QDAKxbWITUOr/4TI3FX+9EfA0nna3y+bDwzyNcnl4neh+nVgxa30SxoLujzEgEQPAGRboPgBEXdMMZp7THvE+2oernEoRrZGM1A6j+ikDfNYf5l1HHzHHsxD23+IHeo/HS878D+xpM59USsGXbAeQAW78EihaD8bUD8JZoF9N20MpbexVqGpVkhGDAGnGoyj1c1YjdRzkD29LqBMyeze3WGHxKLk4b2r5VSVGraaItjxB2xMWOm3M1PTK1iEVgUMxLRtRx/fw7sQDGjcoHAKxfdgCo8+u+rp93wOaxMeh4bkfcZQNmL9sNr6gCCkxH20W0W6gSlBlpZQTq84Geqwe4cLcftZpGkozoE6bxpxBUhw1GpdOKECQjSblAz/OBGhY4GoFyw4VI6XkJahqzO2YioR5QU/LyDzuRVVqJRACN7lRYt3E7WH7dVdPqzIgIPfvAyJQelQJ9Xj+OHapT9F2LhUFG+wRYrBYiPSQmyehkczF5EM3KbFTbScm44dyeKD7WgNnLdkdbSNDKiM2tvZQZiRTU3990QknszaULhQEwyl5HckOkhLybwpRkxJBeNQ0R6BRCkR1PBq64Ctj5J7C+vPXLDTNau6ZYv1ZNQypbahEsmVGKwOjOMAyqGz1IcxzByQnzUWzvi532AUg56oGNsFW01eiQCAIge/VYmOHCPHwIWPT6FhRvPab7vHfnIiD9ZH2yBMlIEF5rpbUQq0iiSBloaFJJhtV5RnuCbk1Qp2dtAM0V5ag7qNzB4spoh4T2HULOU/ezq9q6x+1ThFl1Oku4mpG0m0YpJiXu1tWTopighrQZVM3ERNeCPRZ06eYRsXGEJBkh7eTRrbdIVygLMD4MTfoEQ/sBG7pfgdVz/opIyRqJoZqyqNmMtP4ukapS7nTyeGcjrJ5aeFgnmtgU8fneg2nITdeQo6HLUNqqZ5Nj4l10VcxqMxSV+URbGQ9CgqYeqWQkptBcWYH/Pfw73KzyzA0GR3HpTUeQNUSfuw8KhB654uMd2PLLIUWYC0omofXUNAJdfLg5pW3wBZKSaA1XIo7YWiMEg0jajIjLVsU/XUSiUlUDqN54GtHvqzatiVLjiuTcIixezh20Ctn7/gOMuB+eATejbMt2fP2xDwCjbS9yNQ3ftAQD1tQGFkUHapDfMdmgVDNiHmGRoWvwxsXixx5LjEkLWgPSIlDFgVEPrLGFugPFPCPiR5y1GnHWaljgBQsrKosOB51fME7Fiv+u0H2mWW2Fqd3orQxI+bOqK2kRY0ZNY17sGpU+EUPeCGPlJF4AChGZViImixbINiqcNOmB4axZWGVQFBArkhG+n8qDWqlCRAmr0F4cibCnZ8OVlionRUGD3KbIwnMjVqs0JX37zB8oPqTvtVYtRAmpegW6LQIdXKZiqz+OJSNUTdNGEGetxQ1zLgIAfPvguzhQ1Zk4mjTUuLF5aTHcjT5FeE63FPQcliM1djOF8tlfcM8AZHdNwY7fS/DLRzsUUcLegFRqGjP0hUKC2s8IF0a2QTAllQk3YqljBjFrtfYER9xNQyxbaEeSjVM0alS9Ao4Gg6u1PxAQpZmN0V6He/eT4IDM6KxCTZlyyQh/feopeXhj6QGk1vlhBYP9B2rRqT0nHdFQbMLYPpD/ILUBv0WV5/HJi+h8JKqmiS2I3viID7VB21YexoYlxZrwrb8eQpf+7QKXx7Kaa5vDCrvDCqtNS0Ur8SIEmxHty7Ia8Unw0gSdNaPyWVQlI7GDmCBJ7g7eYuUvQsgnQt80msOpZPcQRSJkEIcWvw+o4seo5tpWLYuB8ghciReQ2a2ohxFZxHZpcXj4pZF4/o5lSPSSmSb1wklcxBDGrGA/BaOWjBzPiJWGyoMyIxoE16A9bk4ikt0lGZ16ZwAsiz8WFoFlAZ/Hj9omzqHOrfM2oIw7AwoWBphkSdTPlGAYyrSSQk1vtWI0qItbO82obKWSoK5J3ZVOBKDeRBQrXirNIioiVot6giFBHdq6loCa0tTNOap2SK2jWjUNL+cxkakvA2aez13X3AjgPKDqAIBuYStKMoTXMwwx/g56n0kpfFNLvcx/W11pK5+nsKVY65T0+JONaMfd2Bj7KDOigjQ5y8XT6mfyBNy/nC4pGHpeF7A8M8LFB2qbPIgDUNHgRonsbIr9fgvUe3MkqYxWN2/RWMUF81ZK/L3qCLb+yhnKVhzmrOAFZsfQZkRXyhqYUyKpabQPg+JuWgUxZa8RA5CvNo0mV+URMdr222qflFXSpzd5ReSrqqSMquDIz2tN1RIFtjjFI7auZSfdqiEx92ojNJECQ0bRorfaMlNnIr9LUK3wRdSUNeKDj7cqktkdVtHpmUDJiWDAKkJjh0jVNLEFQxsPM0aYZL361cM64YxTO2DTgSo88jV5q6HoMdFgbg/HCn7Dkv2oKlV6Ok1Mc4klcLQY0CD13KDLNurrIflQayla8C6tjWavH9O+3KJwZc0wwIUD26N9xMiVfQ1TkpEogmH0bUYiUbxYppIZi1o9yZn8R0q4y2dfA7Ra5ZYXJdqMqOqfEJck0VCPeRIDx2oDgwJXVuKhZtQcKtU8TRUuRMmIcvyLuTYeFigPvIyOdZcWlBlRQ2TxCTYTxJapkvfrpMlLcaFP+xRUN3rkqZR56kk4QZBctAA+L7caGH5xN5T6vXj9j/1498M1AIBEL3AZrPB4A2tN5aSwLBu0SFxXChEVDj14+5fWRmWDBx+v1c4cfx6sxhu9IkOvQjIinKQs9A3CPKE0A4hAXaqaCtd05PYJkf+eeiVG2mW+9E0IEqIwk6KRjBhZshIQZHRNGpZlycOGKp/qOAaswwJLgw/JHilcWItoFoLHJzfCQWO8QyUjMQWWIBkJdThjWRbNXh8SYIGN37KmdqqjjM+XpzL+kqcLx/guTDDte6Thq03F+LNeOpjO7WcAWOHzkZgRVeFqObwRbUZqGp3sIwqdSavR7cWCxXvRUKs8s6ZrQRrOOKV13YsLbXFI5zSMOikLBysb8fHaYjR65Du3WnsAMWlDRZKNRxqMgW1AJMkIn0Y1PIgET6hnM2JRcAxKekwMsgoDf3VcxWrIHJ3XPzgUudkJeOOtTfCul7lSOIH8jIiIsVc9oZmRL19aj9K90j52i92CXv34HQNmjCbkQeqOxYfbLIzCNNtY764vZRHtSMKwDUtgRiwWRjxc6ppTOuGaUzqj+EANdr29k5zOoMhAvIgZRJJBlwY2Y6Paj77cgablWvHuxpWl6FmQhqx28Zpn4cZto7ph9EnZ+KOoAh+vLVbtwGptyYjQeBWNGACweWcuVtyzDACQ0sSF1foyW5UeLZRfLJoGrPolaSVJEUGgDtsKRanPLZK6tPZEI+XZNDqKccO1i5TGSMBMgkUlARFoEXgnIY/6Zq+J3NoOlNI5FVcXZZuRkPZozJkzB/n5+XC5XBg2bBjWrl1rGH/mzJno2bMn4uLi0LFjR9x7771oamoKieBwgvWz8Mt+3mYf9u30aCMG+a3kolChwyS6OL5Pauz6xrCMRnwmyzsMY6sgeWcsUqfLTHThpJxk5GckcM8M0ksrP8KqRwcGtr/ynA3zaE3oTVq1Vc0AgDqGRXWaFdVpVvjBwgoGFVXha8Merx+Lf9qH+V9sx97DHC1ulaqM0b1pTQiSQulr1TdJa5iUJokRAYB9zZKHYkWVRmScY4i+T7gnkYNB940oiMb4uiEtgyBJ/bukCgDw/dZSPLNwG0pqzPURUz4dRYZHJXoCAJmaRsHk6HwDRmUjJtw7rBYkuaT2vZc/Afp4hLZq2pia5pNPPsHUqVMxd+5cDBs2DDNnzsS4ceOwY8cOZGVlaeJ/9NFHePDBB/HOO+/g1FNPxc6dOzF58mQwDINXXnklLC8RKs69tR98Xu4D7FhzBL9/vVdcABL1rCSQhBkMozs5ay3t5atcQdRJSGchdMBQIXN0pu7ARsazrHoQCMWA1YAeaRkVCdGIHu1kpbE9y4V/PXkaAGDmrUthYcNL5pff7kT5D4KHX/4QD74xHqnmBnS5QWSk5jnFQXk8fH6pjjpd1BkAUPzVfi4+q6d0b6VvqnYHr7MyjyRiYTfN5r/KsGLOfm4XHsvinJkrAABT6prDXhbrZ8Hy42hxRQOGOYA/iirw9p59sHf1IQGJXCWoxhr5+VcOF3kqCqXOzCRRfyNBPWOzWvDpLcPx7HOrAQBegu+SNg3564hzSmzoa4JmRl555RXcfPPNuP766wEAc+fOxcKFC/HOO+/gwQcf1MRftWoVTjvtNFx11VUAgPz8fFx55ZVYs2ZNC0lvOeKSHOK1M94OQObjKbBiXBeCloYkETNlYU9U0xg8DBJ+v5wZ4VU2In3chRUM5kzhRPAFg7Mw7uY+hra6prusTKesZ8Aaye4vUhDI0QGjDQqnQWLVMWkFaYmrQEf/XjTFbQHYQhytVU4gEZWmCm0F5O82cRznq2KOwIyAEesy0kOcsAZQONiK4IEX6j6qVT1E7sOt/LkYFp7hTrSWY3tJEgCgqpmT/FbXh69imhslVUaXDBaoBbplJQFHgGaPDwng1TRqow+5BENvZ6+8yrx8H9n9I2A9DLitAE7h4kGnP+o0wppjTXDI7l3x0lTYKzcZfdqnAHsbyAeGtmEYtsC2pKZxu91Yv349xowZI2VgsWDMmDFYvXo1Mc2pp56K9evXi6qcvXv3YtGiRTj33HN1y2lubkZNTY3i19pQG5ZqNZwgm5GoM9BJIAxMap2kwmbEwM+IejcNYbOPaYjlWLRGs3FJdlRYlD1wz8ajxHwUq4sA9Byr4owu3V4/5vy8G3N+3i2niM/PHP1hhag2NS6cpJMmCA1ajJosB269ohznpT0Dh42rd79qEGc5/R8AoNEbj/XfF2HDkv2oLKkPOz0kdWIodiqtNs5pJCNkRLJpqe0RoiEZ8fu4wtJtxZiQ8Tw+uHEoF846AQBl1a1jC9G5HefPpEc2x/ywJG5eCDHSO8p13cLV0e3cxb7lwK8vgVn9qhQ/yLr1NMnev28KJozrSqQtUgbR7kYvmmU/T7MvcKIWIpwL3HAgKMlIeXk5fD4fsrOzFeHZ2dnYvn07Mc1VV12F8vJynH766WBZFl6vF1OmTMFDDz2kW86MGTPw5JNPBkNaiyG6QSdy11yY6YYpY2wIXZB/qE2m8TNC6Kxh0dIIq12ZAavQ+Ww2K95JakYCw2D5HSPwydNrxVFUHFZCEI1s+ssDwI5GjwMvLuHO2znHrooUQccMGmM6uTEcaZsy4Z3ZVls2qahTfSOWBWz8UQGN3kROvQhg76ajuPRfQ8JMSiurWVoIDVWqAItBf2t1MJqLiEHo4x0dm+CyNuOM7pn4+OZTsP1VbpxmLG6j5CFD86YEGx6GUC+6gkl5F/PxHmVTOgCDpwC1DcAyUjmBId+yfvvtgzXPpfkguHxDwbIP/sbfvx1RlQ+M+EcP9DlT7RqzhTAS10e5j7e6EHP58uV49tln8dprr2HDhg348ssvsXDhQjz11FO6aaZNm4bq6mrxd+DAgdYmU8FABAWSkzRSx+LDNJIReVZ8x/vzYDXW769AUTlhpduCcY1lWRzZXSVy3RaL3F+ixOywDNDAsIhLsqtoVRl9KQQjxhXnt3ArkWRnCa4Y0hGFucm6apqowJQ/aiVaW50s1I9ocCx7lp3DYmjiRzgpYxPy+2YAABprwz/BSFvdpZf1G0pGGJHSiPv4YBitzUg01DSq1xZoiqRkRHFWDP8dhnfLQE5Ka6+4hfbCt125EzEDFbieQkvxPYXLzJOA8c+DOf1u2SM9PyPB92s5PZHwDXPw70pNGMsCh3ZWhb8w+fvE0PALBCkZadeuHaxWK0pLlVsdS0tLkZOTQ0zz6KOP4tprr8VNN90EAOjbty/q6+vxf//3f3j44YdhUcs0ATidTjidzmBIazGMmEOCxNA4L95qxOiQJ1JWDW4vbADu+WQTKq0sTnJbMZHXbEoW+qGv9PZvOYaFr/0p3luskh5XbTOimGj5a83rhODvOzuhDM9f2g+LtxwBPhXSalf+EYe8Y7IgdFRG8RiIHJ2S9EparTEMcHLiZ0DeIBw952YUbTkGn6cVJDXE7ez6o5iGwWyJPjEEKJ0IRqbsiq1/4feP1oJBPgAgY/H/AXFNKEg4HUDviNMDQLL1YchtorVI0dhgqe1E5HHlauhQGFf1+MNKl/ql8nECvL9wVk1L6mn+F9tx6KdDsKry8BckYup9w2S0cBEuvn8wsjol4a8Vh7Dys12t8o0UWaqdV7Uld/AOhwODBw/G0qVLceGFFwIA/H4/li5dijvuuIOYpqGhQcNwWK2cL49IeyQ0hrLxKZz3iN+KMDCr4miuVVBvQFH4jODHjaxkF5JdDBIqfECDMl1LmNnaCslIst/oDkhIdWp28OguJOTnfJBUFqapCGwfYm32Y/vqI4oyLVYLOvfJgCtBrdtpGRh1h1SDtJrjeE3iKaGtAeKCUlaBVhvXv7wev+jmPy7ZAWdcGNwIEZxZmbUZUfB3LaiqqrIGrPh4B5oblHYOHXqlY9AopV8TAvuvCWmq9+D7N7egvkopSUrNisM5t/QV69Mstn+/DvuO5QMAbEwjko78DDBu9LOuBfB2VITfCsmIQh0SGWq0rLuhYESfaZDxUoEoJz7XydhCOBFdkSwM8/PeTUeRQugr9fuUEm9hHLHZLbDaLbBYW1FtYphlG2JGAGDq1Km47rrrMGTIEAwdOhQzZ85EfX29uLtm0qRJaN++PWbMmAEAmDhxIl555RUMHDgQw4YNw+7du/Hoo49i4sSJIlMSCxDEuUEYZAeG3GZEZCbktikk4SSDpy/sg5P7ZuHDT/9G9TJOl8iE0R98t0GZOOPyHmKJcrp0PRCysroR5+/gJSMS1KIIKcxV7sbS9//WpDhpeA7Ouq4w2ILMUcMoh0/SYKoOabWuq2qEftW9kilmYbVz1DY3eDHv8d8BcAPb1dOHIzGtZRJGkq+KpAR93wvy3TThwp4NZThAEGWX7a9Fv9PSFWEsq1LUEJj4w7uqcGhHlSa/qtIGHDtUh6zOyUHR5+ONRRMdO7AlrgL1ox5H6vKHwbD6RqI791Zg82alYXh8gh1jR3aCw9FyJpKkXlM+bx0ZvXq8I0qqgvCfRFL/Sm4IlL00GGPTcy/piR9mbQK6kU9PF8fpMPRyX68kjDuvG3bsrMCBb4o130Ty+ySoN7n7PRuOYvHcLYq4CWlOnHpxN9jsIc6dikVlGM8YCQOCbvVXXHEFjh49isceewwlJSUYMGAAvv/+e9Gotbi4WCEJeeSRR8AwDB555BEcOnQImZmZmDhxIp555pnwvUUYIPKiBD8jIgzapdKboLl4erAbrMxa0m40W+sAjQGrgkXQ4TUkXkQ2GARaQqjsHoi+VFRhOV2T4Uywo66iGccO1aGhJow2ERpyg5CMCI9aWTKiVgfpaeiSMuLQ4aQ0lO2vBQC4m7yclKSsocXMCIk77915D1xlm7DRkQJgdMvyNwFhZ0inwnT0HdUBPq8f37/BHTap+AaMsZdPAUKajPaJGHElx5QvefMvNNS4Q9rKKVRRna0ZnzMD8M+CXjwzwj1Qf0e/z49vX9qIOL+WuA8O1eLG6/sHT4QOTXKbkchAYIJ4da84qRsbimi2QZNINuqzQXbFPidloOero3THWmGedu1vwOwpS+G2AIOv7I5RZ3QKriAAcQl29OyWjuoaNw6AoLlU+5eSfa+9m7Q7GTsVpiO/b7ug6eDKMnrWxiQjAHDHHXfoqmWWL1+uLMBmw+OPP47HH388lKIiByPraSOjEYP4RCkL/4w45vHxrbyYTm58p9YmhDa8aFUk6q29FgWDAVXEcAxqwmBFsi9Q3p5+WQ9kd0nG9tVHOElJa/QVoUzNGRqB31W+amqocWPtLwewfsl+qNfD/nQH/vXEqUT7KDOQVrnyNirRZ7EwuOCegeL9x9PXoOJwfVgGF9IK22H34JSkj7AZFxNSyButPKMWk4LEDBfy+7aD1y0ZYeqM6xIJhIlYiOOMtyGvIBUAYLW3wNKVVV5IEyv5pd0ev8iIVMUzgIWBvdGHBB+DmmNh8uqrYzMSMb5EGAPFQxXV3nGV30aXLqN2o14MGQ24BBgt+vK7p6H47xpYwMAKIM4PbNlQGhwzIi5kOBpsNvVpuRz8st2NapJdiXYMO5/bdrzpp2JUlzXC626JbZiSeQeRoujghD6bRg6NnxGCpMN4bJcn0A5GpMkeADauOoxlGzhVjIsPs6sO1ZNft8SAVaNmkWVjIbRLVsbCf/HCelQcU3rYVbtUNoYyLkPgiBiVpEGcIEJ/5SBg/l0EOvz8nMiyLD6b8QfqKpvFb6hAmRuHSurRMS8pJMr8hDZphLBuSzTgv4MbwkInRu2gl9FjmDWl6KkotEx5S6BRZAlO3zSSEe7KKzuEcspjpyAjNQ4vvbwG2FUP+MLTykXDdJXNSGsbFDM6rYMFUFnPSTbv/XQTtn/NMV9T+B6jO5YQF3vaSRsIr4r9gnMLcPSU9qirc+OTd/5CUklzCC1Y2XDFRaaGg+ajEYh1xNnQZwR3IOeeDWWoLmuEn3iIaVAUkXFgDdBcCzhDG6daCsqM8NAO4CabXqAOoFkNCMHc6ru5rAnSUWsM/GCRmszvoFEYnoVh5FQN6oBMTcPfKwUE0g2nAuCaS2oyQV0SsLq0hWskI0elfC1WBgkpDmWSMIoRDaWVhBuSit3PP2RZoK6S85Jaw/hxtKMTF4zqAgDY8v4OMJA83YZCncZmRLHdUb8BhsVAnGh7oGwzegSEWTBC1iXKGFgGjKY+DA2/w8WMaPzwCCtg8qQhd5YnnOYtEOrY14BVX+zGqZcUBE1HzbFGbF91BD4vC1uVh0iDuYVVGMB/KzlvVdnggQPcJ/OzgFfWKiw+dRtX/tfeAOpVFTFNC75xZnocMtPjYHUKBoVBZqBiMnQlI15OlsqsmgkkNoLZlwuglyItANGw1d8ShlXW+MS8Lbz9ibsOKNsOdDxZmy4CoMyIAAPVighDkSE5L3XDk07dVYY3FyTAaregU7dU5GQmaPMU0gsN289i76ajyO6SjISUIO0CCIanepIbObKSKzDOMQ3J2XfqZaMPNVNGIks2sVz+0MmSu/5IOCAKhtkTPqHQr2WEvZ/UjDH56ThzOOes6M/3d3AqqVAkWVC+t1kL/3D6MCIdDR9x/zBqXz4KhlkpdmYRmD4pTXhFIxIzopYKSEwroJSMCKo7f4q0S+zP5QdDYkb+WFiE7as4KaswsDuYxqBFQAeP1KKhUals7NYpJahdRlJbldc1dz2qZyb+d1VvHKtowrIZG/h45HyMGGq1AWurI8RChDHfyn9ri1qa520G4ASz4X3AVgo0nAWJGZGpY3nGtfpoI9kxY6jocibQ8RSgrhSwOQLHbyVQZkQHDOGG1C8Mra0Jj4T+0+T1Q65fv/aGfshMj9OlQW1BHucBFs/dgtTseFz95Cn6NMjJIbyAsOomnZmkfjen3Y1kWxmZwgAzJMvHlkTt6qdQrHIz2pOt3FsLuq7tDdQUetGJg0QLRkv1d2MBwwkmnGoa6aA8uTSG+xeUlq4l76+6NypWa5egn2HY1DQaVZGOTIi/9MlWpzZ+BPblujA/oRn/qHeGLNFqruekIe17pmFHUxP2lR7GTXG/gGjHo1PEex9sQf1vWqPJz5ItePiFkQFpMLRC4Mt02q3ISnKB8cgYXM2H09KpJVl6rx/f3ooOo/IC0hcSjLxzG4GP3uHoz8Db9yOrIR7AnbCBwYKH3sWIm85EcteuogSa6TUByEwAs9kHEE5AEdQ86xYVoaqsAeNu6hP0qxDnq6Rs4MYlQecVblBmhIfW/XdwDY8gGCFGSIm3EwdBC2lkJxh4xeXEYZvdi/ZWG1KagLrK4A3eyuuasXrPMQDA6r3HFAUoDFhVhOptEwRMrNalmGJxmiQ6qtBWcYam1hoFsqAjPPcLk4oJqU9LIHlgJTEZ2ko5Wsztqlnwn82QV2rH1IOY+OwkMCpD2rLyBnzy8TZ4mpTeObsVZuCMztr8A8oVRJ2+tBre+kcdire+o4hmdwBDJ41GUud8vZzIBerYjFTUu7HlUDUGy3Y9MgQXrAY7TEOCkJ90nINUpkVkwyUIJ4UDgM0iiO4ZVFq5bxXqKQMCHT1OzsaasnJsq6mG09IAQGLsGcKVHKVFNUgE4AMrWpvYwCChNljPrcp6ZwnWJPJPo1ZFSukI6kFhrLIxSLCUo97fDod2VJKZkQgL8UhoX74SaPgdSawDNuYmeNk47K/ojA9eKALsWwCWs9Fght4IdO8DpvwTYA+XVt5Guw7MFM8JKysKw3ltIZy63pqgzAgPDS9CvDaxTJZlRuKks5JcmHfjMLw3e4MyCUECqpSM8KI+uxULEzwYnOHC6D3BHXZVyh9Fv3J3OR56S3kWwtEaLVPzx74KFUGqUTyI3SHqIVmxlU+oJ70dDWJULp6n2Yf6auVJtg6XDfHJLRExmpeMQM0UyFdvDFl9FZS/Aj7jbu0SEFduxS1nctb0oU2eUp0eqOoET20NHCmpihhffLEdlq01UCv7ivbUYegtnKt54qm9QdBTVp+LMsLpBskLf8PJt+WbyoO46pb1sbNe+QV6Q5pCVqHyaBsuSJTIp3wWWw5VoxecKDtchxl3LgMsgODJRFiEWGTMeaiSETEdA/j8pOk/cBsSu3jfVNxz+2Ds2F+Fn2ZsgJ1l8O3sTQC4raUDxpB3lTAiw8DdK/gqtUpYQbs2J0Dt4l21OGIYXJz+MD4of0O3d2lPTg4eLV0MCXXivfhNbPv4L4xpsKLYPYh76JGMRW2JyUICbeEAegzNQWp2PD6bsS50u5EIOWoMBZQZ4WFmYDJqjD6WRbPX3Orh1IJ2+DEtDij1iGEkyQjRF4f6Ioi2VdngFsvqnsWtlnaVcQ6sjvCMilVGx20fbcC9kFRHanIU98E2ctK79UtF809HUFuQoIoqjAacD40PHl6NpnqPKjEw4bZ+pvffG9oVmFA5yaO11sme/TqmYcsdZ4tGjlJ70K62SXDY3LhqWh/43U343/PFXErCuzXVeeEAUB3HwJUXD5+fRfy+BtjAwN0kTScfrSnGnqN1GHGoCmeaoIDUfk8bUgqbw4KibbXYX9UVW3ZmoGjGHwDDoM+I9uh1am7gDGX5emUu8Ju9foABOqbHA7UAWNZQTRM+A1btJCleg0W1hQULFlaWQbKs2frBwsp/W4vcRwqLkGwCBP8pFgsD/Q0XrOwvAeKJ3lzZdpcVTQwLF8vgwDZucXJweyX6jeog2jAY0iQWpGWIkpOc2GvzwcYCHUypZTXiTIl7YmWFBRRTRwgqRiqu/0W4OO4oRswrQLm3K9xsHLa6TsWBY92QmG1BXO5oPqYslXqRLCx0Q2Qq3n5dOg5kxIvLUclwY22C04bC3GQ8cX4hCrLobproQtNoSR9bG3awshEA8PryPVi5hjuN9o4mF+LAwO+XprxA44qV2LFlHVi1B52wKA8Ioa+mxtvx49QzAQD5Dy5U5Gu3WnDLmV3x8/Yyzp9DtYwa9TkXCgmAWUoY8a9fVemWTCdmpzTh4k7tSEm4XSsVzSIjYndx8nif2w+/n0X5gbrgnQEJ85vcGI71A+DyZkgTl0iPbCCEdEmc/0LkV2yydkFU0xhkbLf6kNC+A3yNDQB4ZoQwiAlbBV3tEzD1jpPQvHUh3norCwwYFP22DUAeABYPfcV5g2xvq8eZNsCpa9Co39h7/+M82BOTYJ/7CvZvAhqbHGjknbVtbPISmRFWNcnIJ+lPXtiqiJufEY8bT+sCfC/E1dIQfsmIaoJXqWmqrCz63VIId40ba7/ag5QmKOODoLY0x28qqRCqiT+NW2RvGFLjNc5E6A9WmwXzEpvRmbHh6Ym9sXzeDrB+nUPpAE2ZynFKmchmteCxF0bC6/fDZVdORWqGn7tWycYYRixPb/wJyxcWF36hdWIGrLhj5cwemQADZNq5k7Y/OeVdzF62G5N6d5Yl0Kda3FETIjNSv78eLgAMfGjw+tHMj+nNXjdW7i5HTVNw0vZwgjIjPIzGJaNnJTVNcIDct2uD+LAkjQfJ14kA0kTYEshpnTa+F6aN74V9pXVY9PhaIj0cTQRVSwBIzJlCcC4+YxntwCIZZErhrgQ7bnz5DADA8nnbsfXXwy3aPiuv4do6N2y8cZ1RlnoMoZGjrZYgaBczhMlbtBX2+7H+zzI0NnrRXOOGC4DVxgArXoRz1Wy4mP+hmU3CX7s5PbyV8QGwI9FpQ7/cFKAEGH2S0u+MKZL4QblHxxIkF09Dc+G1qMiaiNVf7tEfYAkLXjXynWvBMj05myeFZEKbNuybafhJUrZpUnbFFTZiIMdkrVlcBPC2OUpmRLV5mmWDVjGwovMswKtTlwHPphFeQhRCMaiwsmhy+NF9SDaWz9sh0meKJvHKIvk+kTH+KfE6Z02pGX5ZbtLnZaQSWLmkMrxouUE4C73jo4W6kNvMyFuCZswV4oeqbuGTXZw+DRfctQCsMxnbDteg0cO1yfyMBIPErQvKjPBQb1s71kQQFxsIS/p1SMHcOwfDzwJvTV0BgDuzwmbCGM0PFhZCYyU6PRNWx2LxQTRKA8u9DmlxmjC1sEajg9byEwZla5OqVSWhLlRDGyyUkqaNB6rEJx89vEa8FoZKi0xdIda9nzz6BVMtZARQE7GsqcoSJ2KF1IfL+803N8O3qRIAkMI/s9ktQH05AKAg8UsUNZ+CdolOMAyQ3jMZ2AakxNkxJD8DKAFykoku3lTlqxhLwWDTwiDXsR3IrYCzc4omreJ99TLnH2TadmNC2gxMbXpPq+5U9Rt5hkSmMYQv5vaqvZzqqy8sDisAbuD3yoqX24wAyrZcXtmI2lqlb5+8nAQ4VWfYiMwIo5TKErkumWqpqc6DXetK4XX7YW/gVc1WZf9QOyXW7WsqBri1/ZmIJOmUE5fsACucNgrAbVKVTixEVkb9oQNY9PJy1LvjFVHTEmpx3pP/gNXlUnGbOswIIzAjsqgGehrxNOEWHtBtZXzISY0DXPHokBYfOEEEQJkRHnk9UtH3zPbY8ssh3TjkjsUFWi0Mklx2yA/q+v7ljeJkZjR5sDqPTYUF0dmNmGmSfxGrVRmmXlXJdw0EJkPZo5WCEUEywgV+ueEQvt54CBaGwR2jCzAxPdWwEHHAbIFx1qYj1cix/40STy/ic8cxmcGsatWmOHlZ9rzlYAzuzIAkUuDCasoakACgiWHhtTLw2YBzRnQEdnPPf3PE4y1rJ2x8eCzSEhzYWFwJbFtFzh/AFwnNmNjI4vKUpwHo65wZYS+rXPcWSORDkGQwjORITt4uNaZXBmqacKG+WZB0sEhy2ZDoklb7FtUWsbMv6Y6fvt0N1sui20DpxGEGDPyKyZ6jccmyIuz4dA+sqhepsQP3/XukwqW5sFr+bP1BrCo9hlQCraQx5Y9F+/DnsoMAAMEEnFEtzvwsq2Rq9SQvYjA/acrKc1V55Y9MwfBTMQwEp24syM3n2mv74IuknahfybkkYEMw/JS0NFLaw2s2oqyhvSZufXUaKv7ehsyBg2TpWYAhH2wnVKlfhxsJu2QkhkGZER42uxUjruyJml1/Y//hZCRaj0kPjTqPelVM6O1VFj9cBgeW6TcrKS81s0DayREQBuJuEt02lWjEaGtv4CWQkmCSCLpvhxTYrQw8Ppb30shiydZSTByRalyEsFoIZpJRDZqNtmSkp3yC8y3bAQBNE1/DOZ8kYECzFR28FviGZmiyqK3zaMJ0iwvDBEiesw3yJahp1O3G2TcV/7xtsJRmtzILn8q7qN4Gqr12P7Y716K9YyvADJeVq2KohAwIntkC1ZDCIzHkPIowITGqfkJWdbTW1t7s5Dj8cO8IuOxykbsSg/tnY3D/bE0eaiZKyHPPjgqREfEJCx9whrDlxxqRmy0Tq/Npft5RhkqHH2kMqcMrvycA1FdxjHZ2l2TsbmzC35X1OKU9JymVtzmCZlUXesdWuMHCmRLErjetlgZE5RvLEgeIpEQHzpvQDZ/wzEhIkziJoeXzyYo/hJHXcCeJf/f2ATT6kmVeTmWdTUcywoiSETJddc1e9Hl8CerdHCOX7GPwf3DB2xyChEdbehjyCB9OaGbk+7+OoLLBg7MLs5GRyDELp55cjsQfVqNPzyoAlwGQfzIDtkHnu5a5gPedzThXtW21KV66P2YlW/0r1TSMgpa6Zi+CbUxqQ0C9sgRYNX5GjPIOihSin5FTumZgw6Nj0ej2Yd3+Stw2bwN8fj8CHTwWDj8kfha43vsv/JzxIrrUb4Q9jkGZk8VCG8dwTE6TBtAkngexWbUDLifNbq1OrhV9swDmLt+Dg5WSKDpP/lBMxznZCyg9Us3UflH6I+QU7LvpSdOkjxaYKSC0W3nfUOjY1dI8pYpKeThbeL6TIAuNd1mRmxIHeBqJtBnBYlHZjPiVUre6PCf+9dhpYFkWr936MwDOedr/PtqKspUlsLCAnQUsYERpxMuXDQC+VRUkdhZOlfPjO1tRvJXbJdP7jDysOlCGXzZW41S7tMuHf0lNXerVhv4d8FpKE+5L0bETUdBp8IiRXyjbJzG+TMIb0pZYAmMlvL/T4UXmII6Zt75TpHimsOXV4eKtos2ItjiAG+frGMmezy/+D3Ww40aoWMQJzYy8sGQH9h6txx9FFXjl8gEAgPTUZoxMmQtkXiZFNJjs9NdAHDwW8qPyHAd+KK+GnQXKrSweIUSSh1iaK4Ff30Kn0qO4z3YYaIgDcEXYRM4k6tWSEY2fEQDCJGdWRCNMZio5j3iV5LIjyWVHO5459PpYw/oHJNFlS7gRH2G5bLda4PHx+n2Z18xqJ5DSLBWnLpW8gyMIYnQiS0yXNKA0un14/vvtinj389ux1UZxrDxv8XXJ7VYSTYtEyWgwTqMHBrLVnKRbk8L0HF8R1DR6zAjXZPXF3FyRrO4zM/j+h33Ys0vyweNsTAMgP2xS7fTMHBTMiFrySZByff7x32gsqkOyTL/jBYty3t/4wI4pysSQq7RYVByux+51kkfl1OwEsAeEeMr/LJRMo/5uGlU6WTy/FfAwwdX7kZ8O4f3fSpGaHQ+bXz2hM4pvr0eT1WpBPcPCwQLxqUEenQFSX5Az5wTRjYYQfcmIqKaR91VC/Uw+NR+3jyrAkdJ6/PrSJljAwO/3h3wSuG5BUcQJzYw08keRH6sjHPwWJPeoJ8HUHYoYBsesxg1QPvDEbZkHbH4eHQDcYQPqmTS8hysQjCWT0N6zfCXA4gcBAI/Z9gEAeh7sCDQ+AsSlivHVNiM+v1/tWFGa5ELwM2Lk60OQOujtClBkJe69N1+8WuIr+GUQKWJZuOxWNPBt5MPfi/H0hX0VkcQBRGUzErYuriOZkteIIHZul+jENafwjqg+P6iMyW+BZKFlLrTEChM196F94gqdED2AFaM6a8XATVDT6IIoGJEramRnvRhJ89QfJ4QPVVXZiN1f7lVIiJrBbSd3OLTqA9OSEdVumuJDNehekA6/n+W6HGGAsO+sE23SUs7MwYCB2Vi0sxRVq/bpliOvda9bYg7/8dhQZOQlgv2D//7iN5SkccFJRhjFHXetzNMI9U4GaQ0sfE0+1DX5UFfZjCRXqvIl1CXrLg4YzE1uAgPgH0GcryNlIJagKYvI7Kqlgbo2I5JaUa4+UjTRZj/gAhKcVmQmOeFpkFTDPn9Qfic1VMYaTmhm5IFzeuLeTzbr6usEyCcoNQJJJkinvSry1LkHVGoaD+eLAXkDcTipH1ZsKeXyNyxdRQtPa+/mjcCa1wEANwgt4CCALT2BoTeL8V12K8psfmR5eZGtRem1VZF3wMKFF1EOdnqw8UsGr88fUA0jPA/0HYlpoUorq/S7z+qOx7/dSkjETYR+1UStpgeQaY1Vkfx+FrXHJHE+WxfE/n75Co2/yUlx4p4xPQAAc0RmRJtIsyU8wLwgqmmE6C1YTSlPkJV91ABZEr8qo71kwRjSx7Is6ivd2L6aa8dKlY0xDQLq6zxgwMAHFg15LjAM0K1uA9p7t6NXv96EzMwyI8qYP7y0CctPTjec9OqtgDfNDmeyHTdd0gNOhw0bKmu1ZZPek2Xg43cBpeXEIyMvUUGtVjICRZ2bZfyVBxnqv4sa29rb8JO3Bk+cV4iyhQfRUOMm+xlRHOCoL67xMwHiGIDEN4vG0wrZuDJvu98NwV8R2cW21F7lay67Qybpa+KYEYFpkfuj8nr9CgPm4EElIzED4QMHdCIl75B60PmuZhfrpEFUHmQRen/BWOzvPAVvblyES7gnJkuQ5SW8yelT8Z/le3CWZSN6WYqB5lpFPLvVgovuH4xXF2zH7/srMD/9T0DuIV624g6sh9CuGkUQ0gpndhyubsJvu8t143FkCNxIABIMIEgA5JV+3an5RGZEZDAINmTc4lv7jkKaw6X1+OCtzUg8SD5TKLmMJKUTSJPPwMoy5PY9dlcZPE1ZyGt3QIqubr3qWUcMFyQjXPjspbuQ5LKjgTeg42xS9dQ0qhUhIy8IUDjNI22rDdiE5BIHebBcMqJOIgX4WeD7N7egjHeyZrEFPxgLDKgHwD8fHg671QJ89haw9Ssg8QW+0ODVNKcVtENeWhx2NvnQtdkCGxgcO1CHON7wnTQ+ZJ6Sieuv7asIM8uPN9W3w0ezNyIeQI1bYoQ1jDX/3y/Y2/ACqYCSEWFslUtThDzNcCMMg6NWFs7MONHtglqayV0qOPPWAXGeEJ5pmS1hHErzlaMZ2VzkuDRSxsIOasVCKi+vWRtTYEZkbdbn82PdnP9h3x5lfTrsPpx+7SBk9OkX4MViCyc0M3LK6luwwbkR1sNWYNX9wKl3Sg/J8jcDyDqdTIKsJxlRg3xmEWGFJXQMc9kqoBHRn/UYXvppETJt1RwzQsh1QOc0nFSQhqUHKnTsY5QrZxNUcDkw6nyUSHJJTfOdVUW4RHNyigSlLYVZKMsWmBEjKZg6qU/noDwjLF68R8GI+MHCA8Cpq+jTUqx4yvphgR822YScnP0H+lbuh6tDjphS9FTpN8exuexWwA18uk4pZWnJWkrJrEiSEbm7f6/bh29mbkRVqSQ1EjzuNsnVqRr9CweF0bXaOJZlUXuMq/vkdi70H90x6HcQpWEMv5gp3w3UlvA0qYxzAeQmO3Hb6ScFzLdP+xT89iDnDnzumxvh21DJSR9UTsjkIB0hoegDBGObjKQGPsSGeJ4HOeiVMSNCClE0osxK2FKtJxlRLzmKK+rRC9xEbPOq+pgBGMKN2u2+3ICVex64I4ZiVqZlpyDZHZHK4OMJ27oZsMClbxPzFr7hgj+PoKphDT64cSisFqCTY4N0fo2sHLtMMuLxeLF2Sy5YaFVAu37aGJgZiTGbkZbIeNo87J5apDN1SPFXA5vn86Ha1mr4yQji7r0OrhFabAyOmPQnQ5SMKE7+Fg+N4HeiBN+QNH4Z1IyNTk+VW9QDwGvLd2PwUz/izBd/EeO8+eRa1NXrr+qVTIi6TrXldkyPx1MX9FbRr831aG0zDvAu+fcVV2PhD3ux5OciNJr2fsvtKnCLRiNmVm0CPQIjJhcVG2fhdUujePy5eUi9rhveaOdGLdEzpjIjEtOVVL0Te13X4IujE4ElDwMA7PYG9I7/EVarrA7kZ3jI/mvbHffgooHtMeXMbphyZjfYZbZDJM+5WrLJFWBl5PTwjZv1K16z/FAdSvbWoKneI/4ElOyVnVSqkJIY2YxI99+/8Rca+e3YE+8cgKzOyQgWcnG6pfYQMOdkoHg1HyCI5KUyF911Ov5vRLegyhBstViWFVfMZMlp8GNAn451uKrd7Th96CY4z87FvMRmrJXtNJbUD3wZJtogCclx3O6zGo+2HwZzWCypPylpkjMj5DxaPOcKOxk9LCpL6lFT3ihj2ghqGuEfzzwdyT4TyFFKsIR8e+cli/1r5e5yHKt3A2CgPnqDpKZxN3tFRmTc2bWYcIEHXdsVAQB8Ojt/E9gG8oMYwAnNjGwe+hLu9/wfd6Nh9RnCpba1k1TvK5J8eDG1EedNH4b9Zs5/MgWpJM30YZLdJ1uAyxmbAGoQ/nl5nRvH6t2obHCjna0IAODyAOv/LCOm18szENXXDs9H/w4punYXPj+Lc2f/ii83c47qavfUoujLIuz+ZC/+8+8/DPMWsvKxfox+eTk+WlMsUCbEAAA8OJ5b1U45UzahqA1m1aJt+eJcRbvAuNRkO3D9+Sfh2uH5SI6zm9JlK7zvZvXWin53LFKUr/TByZevLkZnoO6ckYAHx5+EB8efBIfifJxgoIw94WqZrxaCIp4FK94mpDpx5WPDcOVjw8TnFfXNePmHHXj5hx3wyE6Ck7ekBKdV+QFkl/v/OgbWz8Jqt4R8wrMoGQHA1BzhGoHFDnQbDXQfp3o3gnrMBCzSFguxjclf6aiFC3R20K50iKWpZuM022H07+rBwMHZOGzzo9HnR4PbiyaPT0yf3FgMLH8eCb89h3ttn+Fe22dgf54hZrXys13Yta5USzsfoX1qHD6bMhwPTyzEhNRnFHSbYaIUTI+oJlGvZJS7aVpPTcP9iytpxkdPrMEHj6zGuvVJSlLkIO2m0cl4cOd0rH90rBgiMp9QMyPcf7tMTeNplhi9LmNOQ/74cUhNE0jQWRDIdcv22PC8KuCEVtM0J3bAQZb3gkjYGaFG2SE3tn/yFVzJ8eh01mhYHHZiOwt2p6mpxbjISaj0p8KNmVlCoaYhCB916LXIBwYeI3pk4rEx/dHl7f6YW/oZAOnANeOyOZhdrRgxLR6fH0drm+GzM+gBO5wst+UWANyVWr0rCXXNXhQd41YLTpsFyXF27nBA/l1vGdEV5/XLRftUrbt89U4TjmDxj3RP0jXL0Ld9CnCwOiCtCkPepGzgnzuxeMMufPL113jP8YJIiMA6SFJ6iQhpy6jImSoRwCOY0XcLNPFaFMZ2MjWNjC8RirfaLUjPU56TUd3gwX+XcV7Z7vK6RNWWUG6P7CQ8cM5JwIHNuvR26p2OUy8ugCOOPPQt/HoXml3KNVrXHmk456wuAAA/ST+R0h649itVIKN8oSAgnobLQkwv9376flIz7ACeTdD662A1A4OaLKmynTZuVX2gohGFjy1RRBu642Wg/BfEA7hbqKpfAJflA9QjEXs3HkXxX8dQMJh8PhHDMDg5Px1IzgZ+XIfL292KUzyv8DSaYby1JGukwaqPq+fQzIQszxBsjgtVf1ch2WKFg2Hg9fhR3yBIwQjxzXL8PP3JLjtsFgZe4QBChtF47hUYVLm7hS8/2iEqaIQD9EzZNwLYN+hBZFI1TeyAm+i4D+JnfThn5go8u/hvAMAXGw+hy7SFGPbsT/DzDWNPWT6W/pyChd/YsevbRarMlPlyeZpr+uZ0qNLkIacbCKKDaZwXqCkIoKaRxUpy2VCQlQQr40WqlZNM+AwdCinz6Jwu48oN6kku0tWLVmFl8cDzI/DQrNFIG50rEWkCQrSMBAf+fOJspMUrV8wMw6BDWrzKeJRPq3sYGaEctRRFFuetSUPQKFf7mp3AbA4021NRwwp1qcNgQC5CVnOF5ooSoGgLgbb2qvK2WBWdRMhEGUmlJpDDYbVg8qn5mHxqvtL6hBdpPzKhF3rlKlUvajlifLIDGQbH1TftqAG7uUrx2/nZXpRXcKpA4Tuysr9EiDNo8FbVQj1xp+NqvynLAG4GKMjSvkcwk23XzAR0Sievjp1ezsjX02U03vOejWW+AQCACQWfYMhEjjHzevx4570t+HCe3MibTEG8tRI+/h22HKohxiGBm5sFyYgQKlciESQjYZ5jmTQn3kpuxpGz2uH0y7srn8nVNIyqjxnz9QoI/WrMy7/g0W/+0khGKnkVuNVqgZsvx1rEtUknUwdBK23hnwVqdrHFhnA4oZkRC8OI4iyP14/tJbUaxzalNc3w9u2Cru2K0CltPxKsVQCAhsp6RV4M4drswGBO98uL10SbERWhAeBu9MJd0ijRp7ByNxbliJIRnbyFjuMz9AmiZISykl0Y1rVdQLqtCm4kYPTg9cP897daGG6lqDdJKgrhY4hjT8CpSSqOEMtqYXD5nQNRlWhB54vzZeWQX6bR40NJdRMOVTVi3f4KzffTvoF0hseO9ZX465eDcNT6xCdqCoU0EhnhGboYK1kyQiqdNFq6bFY8cX5vPHF+b5XwSa/+OZWMV/Zk1x+BVYl1VqA2x4naHCf8YGEFg+oaTtJm3MZlEA2+WqCm8bLw8if8yr/BN7efhlevHIgBHVONMzLyFseyiHfYsPy+kfh7+jl4Z/IQJfl8e3H3n4QnvJPxke8sAEBmXAn+SmZF1/RNa8rRvEHaYscKe2hVbcZpl7jtRoIdiRpyBkTKSq2mATRqPoO8uPyC/x5yiaQlmCMypEgBHwj+QmqbOXXZoMQvAQDb7FxdbSiuFOO2H98R1R1cqOlgR7/4BRib+m80ewUVmCAB1StU6Cuxx46c0GoaCwP4pWUuACDRaQX8wLl9c/FJRTrWFlWgufNJuPBCrjMunf42th9ONXQeFezuDr1mQfbFwXfSICUjX760Ht7SJj4Hv5jP9At6I/e3OKBOPyeGIU8cQh4WhhswfV4jdlw7SKXECWJm/TeQq2mOHarDzrUlBmUED5bvvJqDAg1303BxK5cdwcJUJ0YOUx6YRfpqGlWYKlKfnhno89JI7mYludhk2QFsp8xYKl4PUDFQwp1fVoiV8QAssO7HUgClorOsYPkMbnunjgqHECavRcXuD5nkQLHyFVeTxrn5LBD5czvDMdl/H6nBqT2Up21b7RZ8leDGZfXcbizjNsrnnW7HQ0+cBgD495SlcEByIy4yoAry9PtpKGoawUgxtc4P1HFMkLw6+ndMRX8dRiTYydZiYRDnsHK7p5Q5cf80amEWFQ1u/BLnQV+LA0kuG6zNfiTWSY7KFeAJl++irjA0dOeTEcKECVbiTcgeWENRxRhBaLYV9c04UqPckq8UmAptxKSIRpZYPv4kuuzI8u3BVYPnYuDu6wAAcqHz5ef3AM7vAb+nGZZnzgMACKyKwIwE4pljjxWhkhHZJMGLvvhGEWe3wWHkUEbtDEr2SBxcTdKhNyEkJUkqA4dFOPHSom1IJgqqKuMG7CbGhwLXSrHQScPzMfok3pw+wG4aXWt1E5KRUCWoFoZzIS1g/ff7jROYLEDNHEjzZOAMrPESD7/p+/2alyMZsIrtxWjpHwAp8XYUqtQQANArN0VRGMlm6Yzkt9HNuQpd+qjSawQjxo3JFNWMNGkoTtRVCEbIkgOzk+nAywrQ1CUeni42DE3kdsLVEg4PYxigyB66AxqB6RBUrgrVqwmGNRQ1zZAhOaizca7dvWDRYGHRd0hO4IQamNcTqJlxi2DoaBGYEalRsSzwl9MH18hsPPTMCFx558CgqEp0mjibhsfusloDOwgGcj8fem1H/mYhbe3l62bJ1lI8/8MO1TMz6XWfiFfy+s/kj8JIi5N2mvkIW/JJHo2ldWMgRsj4cTRwQktGGAbw8/wYyw8aVtmKSzKcYhVpuPgQHiofgGzwSSpbvNZpGWcMy8PO7ceQluFCnNAYGYZX0wQHwb5hYVIZ/unYBUA2IARQTWhOFFXRKzAjggGZ3+NB0fc/KFRZlvp2YupgYGEYHLH6kXxyO3i2V6Ox1vikXEZ/5CJC+nxqTkI/gxvvHIR3XtuIxINNSGj047UnVyEe0rho6F3W1PygX/ZpBRnYdoTTuVsYYMGdZ6CQ3QW8JaXTZM0wKHCtQoFrFVj2O7yGZ8VYA/e/Dew4F+h5jiaNeEkONqRTgJWRthI64wKoaVjW9Pw5dmRnjB3ZGWisBJ4/wifX0pPgsKFruwSgKgimQFa2kMrnFXTxra+m6dE1Df/6z+ig0wEmJlud9m1VdXLBYF44FkAuQZZ4b9Kii1H918JhwtmcMDm/9MNO/NOfxJ9+pVLTMKqeFm6RCI/RJ2Xh642HUN3ogf+Ycvwpr2vCoi1HcG5fmUTOz8Ld5IXHIzD+Ou9LmDP04CXY45HUT4E3TwhpYk8OEXsURRBybpRhBQc1QoCOKNoUo2EsSdAm0KHPasHNN/THpRf0lFZYgthUwe4HLkKrVlJMMepISjpESY/quciN88wI32GKf1yKxQvj8MuqduJPgK+Z8LIGFWW1cKeQJg3KQHqeZLAn9z+hpEk3K0MEc8ZDZnocLr6mECxY2FkG8Y2q4diQFwlmxNRmJFd1XHFyJxTmJUP9/US9MSE9c2w7UqySW//2TeuB1f9RUGgEjTqLR7fMBI3+nGGAjIRfMDL5NVyU/jBc8VblQ6E8Gf/Iap7LkgSoui7tEjRhVguD7+8ZYZxQW5J4Ja45BEZbtrXXmHMKXU0TNphVF4A0GSrHGykPNsCGK7IqV45eOYH9u1w6uIN47RZUa2p38GAU5bVWVffKTcaPU8/E2ofH4JlrBiie+dlqvPXrXo4anqymJhb/e2gVfL44PjwwYfJ+Teq3xM0QsrDJ763D6JeXY/NBTmGz40A7PPvIr/DqqCXDZQcWTpzwzIggGSnjdYGkj6TcuhlYMRl4bc2J/NTxDaEYWISfUE7gxi6t6AgDVABpgPRYh1nhB65mXkzewJ+PEW+tRpd2RejCO+IBAK9b7skt8JvLdyZldkpSPGv2tMD/O/8qQg7SJGtuEumen4p+1/VEysgceDoF3q+vlaQFQ6wEuYdR0ZZO5/uxOrPGReMPwN5tPywp36K94y/AT/KQxJAvGVUAjw9vGqZcHYo0NqJ3/I/Ic2yD8nwOnXoWGSoCSSTI0p/fvz0xiqG6NQAEe0yB0TZ9HmQL1DQtQeCxgFzvGmdx4pZiK58v99zj84sqA0MJMMEGa9FdZ+C+s3vg5hFdA9AIXH5yRzx1YR8uqZpkRdMMrDYL57w7dFAuBt3UC30HF+H8tCeQFr9Spp7m/tdU+tHcwKnV06wHkZxcrZMbWU0jQXqfDmnGY8zeo3XYe7QeTf4qLiXrQEq5B7//coCcIPZ4kRNbTdMlMwFWK9fZBO7VSvhI8jaunpdJ7hqECXTRliM4UNGIQDitIPCuEmkVJuymUT7a+FcZvv90J1gVJ9x1cBauuLgnIZ/gJSNued6yqM1+TmLRyJ8oWSfsALAewW3ezgCA+w3fTR/CgOfzs6jrmYCVv3lxegPXbPtP/0ETP2iDTFG8qU4YeNYZMbwDMBz4fsle7CkukvIk5aLJLrTRQC5Ot1lUq1a1nxGdMhJ6DMLWuiw0Vxbx9WXSDgIqqmVxc1PikNs3B9ihSUJOLVw2VoCpKxMIbpGkXXOceqhLZYG2QxuQhDqwSML+z75HxUIPSipTAN69uTaBPCh0NU1LQPQzEoLNiKB6FfybCNnuLKnBW8X7+GxJ0iv99y3MS+YleeYQSD0s958DAA3btgME1+iKPMLwOYYPyeWYzEObsdV3sm4ZNlsdrmp3J3Y4ppAz0jFglWP2lQMxe+kuTBtPOlJAJhUCg4GdUnFBlg/OjXdh/rHZAIDa0sDzT6zghJaMtE+NwzvXDwUgcdjybWRGh9dpN5ZIcZ38SmzmT7vEsJJq8sFocXYr3r5uCPGZAipXjOrdND99uwfJZW6kVHgVvwNLDyn03NKVecmIMAEe5S37WTBYuOWImIeP52nj+N0xwp54tUtjQG9lqT9CyI1nf99XgT/sHlRZ/NhuJ28PNHM8uYIeCKomIYPgmQSbS8nTE6VrwjuakYwY+l0hDGDq3TT8bXldMxb8eRibDlQpM1HvkDCzsg0UToqqesm/S2qxobgSG4orUd3Et42/vwM+vEiiJwiv/K2OHx5BhuUoAGB/WXv8VZSP8mqOEeHOnzKa2UJU03ibAU+T9PMG3nkiR8DSdCpWazPCe3m12XFmj0wI70NYwugVFIiSgJDUwxzE3TRiu7coJCO7N5AZkWDHhGDAQrsgNdppqYS8L5PDz++fh5+mnonu2UqpsJYOBmnxDuSluJBhPwBHHOcvS88RZSyqaU5oyQgApPBnKAinayp2H/L/FVoaRhsmjwtwLsS/2XQYAIuf/uZWfcd0trOlxNnNNQyZB1bleoC78fNSi6p0G3J7pqK+xg3L1hrYWOV4eFJeIlClztx44DyzRyb6dUhBXKVNNO6VI8lahhpfjiiuFdq/wwr89STnIvv9u1fwpJofzgBpQPKzLPx+Fl4GsJyXhxln9cAt5fU471XlPlgT9qcqqCUjwU8iKR0TsMvuQ4UlsEieZYPY4U+IqJCMWMk0C8E7Suvw9EcbAQBFLnm+DB+VVFkmJCN67ZVYZ1Lcez/9E9vZKgBAoSUJX2d1g6PuEHcErli6+dW8PpEGaYPJ1l2H0SmvYnHjpejcvgMaKv3YW94FgAmHhrqrFgMs/Cfwx39V+ViBc18ATr4pCMKhKttYBQBoq0xcnFkseP+GocCeOuADlVqEOBaq3jf4DqnOXpazKsTmhOX0OzFoxS/YWdkXdd70gHkGZ7NllBEr0qTO069S25hhhuSLjDJ+0Rew7aiey/smY+Hy8Os5ooxBZuSElowAkD4e33DiHVo3vwqdqBAuzjta7ndi/zz897oh+O91kghPb/AyrxuXju80smtJ7ZiIG67rh3ETuLNUrCwUkpHbRnbVFhxgwMhvl4Bv7zgdY07KJBIvegsU7DBE/2wsEp02JDolnpfoJMiEB1Yfy8LHx3PZuTyTXARemlH8CwihaK0Bn/lBy2Jl8HWCGyviOGmN4Tdt4VwrZ0b0JCN92qcAAHJSXFJ7loOxKI1FAwx6tbJDB03RrdpmKCA7xYWO6XFwWC3Y5u+M5WMXAQOugsI7rFH9kMj87i4TBAUHedmZ9iIsdVnRYdJlKBgkqVNZ2V8isaFMwrt+1IaxPmDvctNZhKqG0NqM6BuwinGMelkYJju1ZESgodHtw+GqRu538jR0e2AaRozXt6lozXlXLhlR2xOKNWZCypibKq0WahrNHvKppENuzyVIpXW9RMeg0QhlRvjOluy04tHzCjGut7SfnzjG8P81YvcA6K2jK9XTFWqhHPjka3CWMIjbndwkZAWDmnJJRaRZTcuvTY5kelIhoU6EXcgWQuUo9r8Hocv2s5KKR5iQjeou0ApIvcrSTuzmoWYOSR3dr+TXQoacGZGcQSq/X04y56fgvH7tiTtMJDUN4V0DtIHxfULxd8Hh/RtOwa8PjEaf9sl8+YTi1bR8eYt4m+gtB14dDLx9NlBZxAWW86rQpFzp1NwWQs2kCROO/DNbAu3FFOrW28z9fMZb0hUlT/oWmHYQGDdDQUfwIDFL5L4ep3J6ZhH3qVsUeTCKOKQiwyR5kIrUtJMFW0pw6nPLxN/QZ5ZiTZHMC2zYKDCATDIiQFynCgMVwyrCtZCevDXJhKpeS4RufsIC0dPoRW1FE9ymTzGPHk54NY3wAePtFtx4ehfgV22VENU0Jk0Alv3zTBQdq8fgzoFFiIaQ2YxoF/GSLkaYGB2yweXj6WvEa/E4+CAkIyaIU/4Tx0Bjsb0ZCJMvy6tpAGkQNFqQmi3lUDXvIr8FahpjpkgnwIz4hICT89OR4LDCx7IY1iVDlZc2nbS6lHmrZCyqHRCE8gj0vXBpP1w+pCNAWMCTkJPigvLEcmHVJnxTKUykxC9rxzWHgD/nY2CCAxvrL8LwxA+BY7u5364fgaE3S5P8pe8QKNC+l49lcf27a8X7vu1TMPXsnoo40qsr60j/kxk0xLmn8fdWYPzzHM2B4EgAnEmAzRk4rgqhqiE6Z8TjrrO6Y0dJDXLigfhioa0IkmKOKZEf4Eaqj3C6GTfqVzYLAwvDcBJTP4vDNU2QasugP4aNU5Ey0tkQhoCzgyy4XaITJ+UkYXtJLbEMMgkyGsD5n5IGQO47lWypwP+2rILNYVGcgB2LahrKjAicv2YLno4gSz3uixMv+eN2zUxE10z9g7mC3sKos5tG3e7jE+zYZveiwGOFQ/YmNvVqmlSGaShVXJU/l+Dfy4/AzuaB6xct7/lC/fj9rLiFzkKQjOhO+gR43T4crs4DAPx9pBawyZi0EKBeIZK/qbCSCqZOtBkN7pyGzY+fDRaA3UreTSP/jsTVq1oyEogx4WETMzMnFXj7upOx5SWLhgHTfjVWSwbAGXACGJT8OeZaeyJjyD9wUr0XKPpViuznmRGLea+eP+84qrj2+FnkyMqW6kW697NKl+ZkgmXoOgrY+qUsrg/Y87MxMyJmp+K2g+iXyk9JmAx1BhyGYTB1bA/gyJ/AO+MAT4MqvlYyIoyQCWkuHLL64GYAK+NTxA+FuVeTSkr5/g1DcVpBOyzecgS3ztsArx8SMxLReVbhkJ77y7KAgvk3R1CTx8fnEtoLyL+Qw3kIR+tYJFst8PtYeN1+/PTeNrj9yfLIMQWqplGvKmWdhiH0hlDs0kwVb4TaUmDfr0IKzWOWQLPDZsHCBA9WuyTxnBcsEpzCSic4dQlfkN4D8crBMmD4ZtUu6ZhBTMBMjxAYDp9MTSOEyZkRv0oyZPR5irdKIl0/w9XPzWeobWnMf2A120qUXqsvWjAY2KwWGSMCQ5q1nmUBMFalzQjxXbUEqndcBOoE6QkO5KTEafJU9CGGkZhWGVGMrwko3wkA8FhdWGHpgsOpg4D4DGUhPr59W+XrKmNJ1YuX9sOLl/YTw15fvgdVDZKBuYbnAGfwy8gdUwX6fpe+Azx4gPud+xKfiOTPxQjBN5KLB3G+Vk4P6C6Ar+iqA8C2b4AN/wN+nwv8+rLEiKTlA5n8llKVbZ0sCLAAHyW68XmCOywLEAFC/z6WboPdZYXN4kGStRRN9kpJHc33A6+BPxd50w+fYERS4anLMdppqXqguOuoPkE54CRDkIzwsDnK8XpKE858aDBOGsapVo/srhafu5vCo9IMJ6hkRJCMNNcBi/8FrJnL3fulSVzhDl4I01fXBQVDm5G9y4GfngAOb5QlsII7g1Xew+RqGi4owWnDHaMK8Pv2o6hpsMLi9SOreyoyk4T1g+GUaQjJLbMwuUjpPGBx0bm70O6P55Bw0khC4uAkMnLHSgLDYRWZEVkW6vO5DOj3uKVJoclejn9f1h8T++cpUwbBbWp2IhhULckvTcuhpllakYkMhGLU5KQVRMmIyW3FZtEtS7Z7i+9rZDfifPGN/IBZthWY/y8AgJdx8OkANPHPf30ZaK7hfkBQkpHLhnQEALRLcuL6d/8AwPmxEQZDqYnKB3sDNY2evtDFr0Lt/CRDdC4nh6pxhMAY56bEYdv0cZwNyJHN+vQB3LbhuadJdSrHyTdxTJRGMqIdC7U3BmUGASGLw9k2vPjw6cC8y4FdS/CA52axLQoO7Q5VNyOlxSUGA4EZYWSLQT5MtblBtyZUdZTgaNl0zIAR85Tb6w2/uADZXVPg9/nx6yecjVXAphgFUGbEye/f9jVLjAgA7F0OJuNGTXRJ3x0eHtuwy657R8mIdD4dKBgDplYZjeVmFg6yGfq+cT2BcUp9uGjwRxpJQnwn+QBVawM6ZwGwVqoLISQ0IRnh38fPatU08uzF3UpBDIIdHRtRxOgq5ExDsxNBIYUAQacG46oJ9jsY2oyQ4qsNWAnlEepRNHLUq2MC3TYLQYKjaG4yaQNYoPaIFCUuDQCDDcnnArV88r0/c5HrSoBlT0l5O439MAhwy0gf1TMLz1/SF//6Yovh+7D8JaPpMya/k2BYG7RkREmHWcSLk5qBxItlAXedxIgUjOHqkLEA9jjglNsCS08JYZrdci2wRxON11WTOwuJyU7kd9SZzT1c4zbJgFV8RKCXDGX4A+f0xIpdRzG2Zw6wSycJgQadh/xfFvHJDvQZwUnMBGYksGgv8qDMSHIeJ1It3cbd/8qLVN2S5Z3im/PfcPOuDjhd9izUT2u42hRE0ENvAUbcDyTyW2trq5WSETkdpgkJQU2jC3WnCJ4x0INQP80ePzw+pRtqopomhDKUmw2CHzzNnGsjDoLBVI3p+lMxkzJbAZF5ZhjpbBfGAlYu7wswQN81ugB/Ha7ByJ6ZhvHM0i3VsKpcFmCFmcfmBP5VBAD46cstwP5ibV8ZeC33P7sPkNbZsMzPE5oxqtGORfFuhTdg7fZRaBqRqKYJtT0LRqCBlqMaG4/QJ3JTkIsTr/48wHfTSkaMNxSFTzJCsrMSys7j1YDNtmZY4IUfNtSohGSts41Vyahy5cjplakfTbabrpmJ2PLEOFj/quGZEfPfXVLTCN+JD9fLghqwxij6XML9AIkZ0RHLJrg43XK8q1n5INRva7hC5gevnD4SIyJSJ4NfUrYHbGNGjqmC0FHK02lWQ4bOlgx0GAQIg87LP+4Uw0hbe8USFTs1zKGl3giNbEY0bBpLitVCmJCMWOSrckZY1RtNdhJ96t0mElTpSjabolNjMyLPh5+wlZ+EwGg6k4EL5Af8kciT8t1n92OfvVkTxWZouCyl97PK8UDpgTXAt7ToGckHQEsnjEAGrAr/SeakmEo1jcgtyPIMnkw9yLf1c3nLyla1pWbGh8lZN2KLpzfuS31AN89WYuuUZZj9zIQ619hlGZekzU5jJK6XMvaYEWrAagjtqql9ZiUXxioH8laRjGgcD/G3qiSL5m5F8jHe4VbAxkzg1oOUBmgbsnnJiKr7BCxreLcM2S4OIMFhxcCOaQCAZJnTsxTeFb16TRkIrN66KaitvaoAQoYV+2uxf+sxMPwBWuEdCwiTAgAwDLl9Jbfn/YsRnrVEjL3xQ+6/h3z0gcS8qvuV7Er0mCdjNMMnaNPAQmBexWLUoviQFxyCmibQLKV+UbOLhFDAQu5IMSAEex/CsEGkLixSUT5/tU0GGNFuTCjFBwZxlhqk2A9oNBCtIgQgeGAVT8s2OpRUSVmQ4QbkgFGMZALTWNvkRUl1k/iTJYg5UMmICSgGKp4v8LOqnhhiizdMJTIjWstnFgziLRVo8KfjyN4aWX4B6CCu5oIf9MYWZstSByMZIeDgH8CwW4iPLhrYAeP75Ir2InarRTRas1kt+POJs8Gysm2uIXyGlqppzDg92/bdfmzDfqnDGX9402XzBPDJtJOdhhkpGAMkZYNlD8qKM2czIntIDnalAI2VwICryPmoJjRhC6SCDL8fgEUpXRLnaEKbJZKn/0y98iStRFlCG2BZA0dnAaUKwqARrJpGfGCcTj9DPjsd+nQWO2Tw0ki5nxH9aNqAkBgqLu3hqiZ8vv4gLmZZWMAtBCUnhXz2In0sspNcilzkfYDokTgkyJgRneEv4NbeQPUeqM7Uzxnxj/gZrnzrd0WU+8GptXymj5+OHCgzYgBSG5IkruFht0OSjIABGOCS9Adx0N0fP9fcLj0LOK60XDJy56gCpI8aBLAenp4gbEbky5aaQ9x/V4pheS67/gCS7FIqiElF/rXiEP5acUgk69ihOgDAAXd/AD+EtEvEqEy97OKSHahjWByqa0JyXhw5kjKnIAkg2Yyo4qZ0EC9JkomgJj7NKMy316xCKazv5UDJFs6uI6WjklyRSnmWPgA2RSDxKIUQv5man7ARGAwtK8Do7KYxWVehGrAKBZb8BWz9WvksKRfoODSEepAxB8EwIwTxP3nXWPgmOYFxOFTViPs+24xR+W4IG7slXoS7EBaH8XYGc68drMjHamHwzEV90NDsQ26KmX5nAoStvapH4lNNNV30BrDkIeAf88h5h9C2RbZepb4SoPajVNsUe9tpKDOiB1a+10BmtCWTjHzx3S4klfIn1IZYjGG7kw550aRhwSDZdhS9rD9hT/uHUbS9CnXwIyk7QGdriWSEf56d7OR8kQu7n2VbexndMggoGAscWGPSVbY5kOpz44/FqDlKOkpb2GqqyIH71wIPrHpv3fv0PPxsd+OTX/bgxhTzW1EDQ59mNW2bD1ajW7OXN2A1lLMHT4b42WXttfB87qfIWaYaEe1XAHejF2t/qgaQpZCCCNlaiAx0cOicoXSPLxpIe32IU2utZfXpZ4FE2YkO9cF8PrMGrHpqmtrDwGfXaaPfsATodErA7PS/ZTD6L4EZ0dptGMVvCU7pmoE7Rxfgyw2HcKiqEW6v4BRMkmgJJAgG/YlOC1IStZ5rrx7WucX0KCGNcerus3trE4AEpPCHQmrqt/8/gH5XhEF/pLYZ0appAO4Ih9ev4Ri0OVOWAQBSUmPPQiP2KIpxCN/b67Pi0MJiMdwZFxxfd+lgboV691nd9SPJXMAb0TPx9t74c3gy3khphi3ZEaBkA8nI1q+A/47hfmvf0s9CNVgGJRmRw8bT6gvumHRTkNtE8icaj/hHD5x/9wC4EqSZhGVV0oMQBoh2iQ7F2R4d0siHdimqPOhSDKCRbEn1nxqvnDW3HqrGoi1H+FgEiZgZJiyg2N9cctIuicrmLACAwy6pA8g2I8HV4Ne3n4axhdmaM0AK85KR5FT1XTFrfvXLMvhhawlYuxtXt7sVvVLfwB85JlVGQMslIwDnR6XTqdzPyXNFgmQxpDyDlYwo/imycm7/Cqudd2Cdcwrs+39RPgxB7SnAYbPgn2f3xMn5aQCA3WV1fE6SmkZ+3IGavkhBsGlJSeIWVc1+juFNtpYCAJw20mGVZig1r6aRbN+0+cqZ+DHt/4vTkt7BaQP0vYJHCyExI3PmzEF+fj5cLheGDRuGtWvXGsavqqrC7bffjtzcXDidTvTo0QOLFi0KieBIQmH1z0O+jdMqrPD6pOD88wuCyvvFS/th7cNnYXzfXP1IQsGqA8A4yYg6KhcS2H6VILUQRPf1ZZz9xsE/gF+eD5CRItPAZZBg5ZmRkr+A32ZxXiB1jR/NQlumcKZNTrcUdOyVjoz2ytVxS3fTJLnsWHbfmfjopmH49o7TcOXQjuIzec0wFu1x40QEPW4Ly0MvsHsp0CTZED1wzkl44BzlbpiaRg9vwCqUFy41iMA1mBtWJD8j2vJPvVY6R0M0EGwBfQM6puKtSUM0Bwd2SIvHH4+MIdtayUTxry3fg8/XH0SqrQSpjt1gLYx56ZloMxLAgFV3ay+AxCzghsXcL2+AKr5uhppslI+Dtxlpz5TjP/ZZiEOTWGeu7V8hl6lAO4Zvd4wVyNTbgRU8BnbimBFhjEtwWpHHn3IrsTrclc3XAOz+SfrVHdXkFxbsXy2WK3yFsx68EhdeZcPQc9wYmDobZ6W8CgDokE44rLIVoNhNoyNE7Jm+BwMSvgNjso9GEkGraT755BNMnToVc+fOxbBhwzBz5kyMGzcOO3bsQFZWlia+2+3G2LFjkZWVhc8//xzt27fH/v37kZqaGg76IwJ5l7c5lZIHP1jceYdSR2kGDMMgS2VopS3YwGZEEY8NYscBIeLAa4GMAm4SqzkELLovKBd9CskIo1OGWLosTFjhlW4BfhQcTzHAoGtNl62hhdDHWJWzNPWOo5aqaQDO82UgfbRMIxFePyMWvhv73MCHF8szQPvUONw2sgBYzoWwYODm/bWQnZ4F8946NiMBBjr19mv1Ww7svh9pvUZrijHPfwTHqLjsVqTIbcBUyQuyErG2FKhtlh8SF0QZwmKiqQrY9q0Unt0byOhmMhNZeXVl3P8vbgSWTpfCM0/i7BCsJnVIbBDMY1IuF4/14zzrGnzmGwmGGQAAEGxC5zJX4P9uuQeWxEyNK4KW4LpT8zG+IA5Zr3FjxKMTe8PJ24qJkmpwRMQ3lwMfXiIlTu4A3PtXGFQiKhzeAABwQZLqWuPi0H7ECJQfqIJ3/ZOw8kdNBLVbV44gxiD1rkClBEt2F+zmgggiaGbklVdewc0334zrr78eADB37lwsXLgQ77zzDh588EFN/HfeeQcVFRVYtWoV7HauAeXn57eM6giBtFpyDr0CSV//hlo/x3i1qk0yS7YZ4cplVPd8VNOdTi4CtgL5p3PXRwV/HkTTLFUW2snMKnNNT2rwrNyAtddE4OjfQP0xoHg1ULkPaKzQpAkNMn2/j7v2gkWz14d+Z3fCwe2V6On6GXuhqrNW2QfIYcWucrxxpIwvJozlJOcBgycDRSu5E20DYOGfR7D1cA2GW4wYrxDoMznQKVoNqR4YwOvzY0NxFTw+P0prOWmZ0mak9VZ23jhBEsm9z7g+ufioFJBvhGVkzwO2GUECWH0A+FTGaNsTgPt3caf0kvLTa5dHt0vXVfuV15s/Bvpexp1qXPqXjGIVgjVgTc4Fbl0NvMZJrOzwcrZjkMTrN517Giw5hToZBMAfb3PHcfjl9mMMcPo9wJgnkNW4Rwx1dhgoi8G92za2M77znYLTUquQnmDnFlNl24CagyC4z205nElAXSm+852iGRb9rFr+2foTP2d+JbVMfed0QXP2EUNQzIjb7cb69esxbdo0McxisWDMmDFYvXo1Mc23336L4cOH4/bbb8c333yDzMxMXHXVVfjXv/4Fq5W8S6K5uRnNzZKDopqaGmK81oXMel4+WKd2RP/Bfqz8Q4gVZlQflAw6xZMzyQasAuqaPaI+1bCJeRqBd8abo8OQK1eKkeXb/ZK9FgQUDwtwJQNnP81df30bx4wE6xhKhzI5Gpt9sAAYN/NXVFk52t5pNwujrGuwyDNFZ+USfjZzbVGFeLRo5wyyXUlIYBhg4izuxNU3zlCGE7D1sEF/MrUa0/mwpiUjQlEqGxdZ7s8t3o7/rtynCFca44Z3MN2cBnStYvFdghtndMwQCCTGZYg7awzQfgjQ7x9AZZEUduB3wFPPSSMdemJ8E+9740/c/69uASr2AN/eyZ1n1SA7pFJPUhLsKjnrJDTnDoHzyDrcPbobevUTznPi8rFZSd/dpKRx5/cqRoTPd8diYMwTUvqUjkBWL032Xthwp+cuzDijL64c2gloqABe6KKgL6zg23o5m6LJ3c9C6SE72InfbHyDOtVtOa3isyY8CIoZKS8vh8/nQ3Z2tiI8Ozsb27dvJ6bZu3cvli1bhquvvhqLFi3C7t27cdttt8Hj8eDxxx8nppkxYwaefPLJYEiLKOR2I4aftnI/d/6DHOndALuOembZ08CKF7XhGjWNEqNe+hlH+VMYDbeplvwFuPmDbeQdWpF5EB3HagOG3Yqs37ZiR9MoKZwwyBW4fsXupjPQP2EhgBcI5Zr0xRAIgs6UBQ6V1MHr9cPv88MCBnJJvJVpJC5AQ1XTmAEvoMG8m4bh1G4ZBjFbb8AY2CkV2GuiPDPtQFNH5lZdSnkaWTJSXMEx4tnJTqTFO5Aab+d82/zUEvr0sT2NwQ9sIzFv0pZeRmHrEoAWmwO4+A1l2PR2/ORLMh4mSUZ08u54Mvf/tLuB7+7irgVGJC2fU78OuJrwMizMfi85nDZuyuibl8jtqJPT3ZLVtpe3FZvwClB4AbfLbv5Vsjrh/9uVqlB1kcSFhU472Fi2EQdrDyrC8hLzMDjbhNrd4GwaTjISWcmDqKYhFEueE9q4ZCQU+P1+ZGVl4c0334TVasXgwYNx6NAhvPjii7rMyLRp0zB16lTxvqamBh07diTGbU1IVv/kcAD65w1t+gj4+lZteFZv4LZV5DTCoXhWp7SaSW4PdDhZE1VOU22TF4AV3TITMKxrug5BUEodrv5MP56mBAOMfw6zy/5Adt0O5DUpV0ryispJXYjR/jmo7HA6OR9GNbCFCKHI5Gbg6yc4w2ob3/EW9/kZmSmcFKnw0J9SmpasYgJA3j6EN+vbIcWkmqaltGjTd8tKEpkR4qm9LWGEgrQZkQRoWsmIEHL3WT1w1bBOpFxCJpNME+laoIJ7H7/sY3L2P2FgGs0aDwdSpQy+DqgrBX5+Rgrrezkw+mGDsoMxYOUh7gySSzDDYIfg5W0vEtpxP7XvIR0pjrpEhsTIEdr0nqo9mLR4EpGUL87/Aj3SegQgWMpTffie369mRlpr4lerzaWy9LdgHydqmnbt2sFqtaK0tFQRXlpaipycHGKa3Nxc2O12hUqmV69eKCkpgdvthsOh3YrqdDrhdGr3ikcU8m1T6m9uRjJSupX7b48HHImc/UfDMU6PGajMibOAAVfqRtM6jWLRPjUOS/85Uj9vObXp3QyM3Awaqf6pS8p6YKVwAV3u+BoHti5Dl6ETyFm09GRTsUQl/Sxv755r347cPf+FhfFDrdUm98vwSycETbLdzMl6ocDEAKO0ZVPKKFQxgy8naJsRQZevHVQD7g4L82BKbgLKgVv+di22+SGm1yx7dK5N5klkMuQNIARmhCTBNCUZUb3btm+AJY9wp6UDkjTHKoz76rZJLkOz6mc0F8Rx62gjt8smzhaHgVkDAQCbyjahwduA8sbywMwIX3d+WDRfzaeWjATdVkJpW2p38DrFx7ABa1CjosPhwODBg7F06VIxzO/3Y+nSpRg+fDgxzWmnnYbdu3fDL9vWtnPnTuTm5hIZkViCnmt1xYCul1jorMNu4YzUbl0dKAXMc62MRgxobtu6icFHTxxkUJh2q7H2PZLb5aHrmdeAiUvTyTPEw8Q0+Shv8yZ0wu2DZ+DijIdh6XsxcOaDKMqV7Ga40y4Jg34rqGmEud/4cLYwIkCjEN9Q/q4tee+gbUa4/05LvfK57FlI3nFDSEN2NU/uj1Lva8kqk8AIGqppQpCkGaXxNAJ/fxdE3kKeRhJMkkRHJ+8tnwHVxZw0p66U25ZusQGZKiZAKEdPMqJR0xDqjjCmCMxuh6QOeGPsG3hj7BvomMRL3810AVb6R3IHrz5VPSQE6Q5evrVXsT9BQcpxYjMCAFOnTsV1112HIUOGYOjQoZg5cybq6+vF3TWTJk1C+/btMWPGDADArbfeiv/85z+4++67ceedd2LXrl149tlncdddd4X3TVoD4qDJorpRMq7yyhgr3U8r7oThV/xB6biDa8gMTJ72aMKJWqhQSkZCeA/Th4kFyEZ1b2W9wKF13M3Aq4Fuo7G/fh7yjyzWTdNaEGqF5IJcGbH1BgylwNZAMhKSUybzzLQ8tYNp0JQtuoDXlYyEV7pk7m0lEXjYupDpb21GMmIxvpfjj/9K19YgFoUiMyKXYIbAlAmLtTP/BfTiPfQmZktbgvXy0jCGOmobxbsbGXqG+iGDsRkJsowQGxcj/ytrVwqGPhz2Pa2EoJmRK664AkePHsVjjz2GkpISDBgwAN9//71o1FpcXAyLTAzdsWNHLFmyBPfeey/69euH9u3b4+6778a//vWv8L1FK+OJ77bhie8k9cr/eQ8jBZx9hK7NiNDZRIdlqgZhJKYNZADIaDuBqRVkMH4FguCgGQCHXGXo5XYgK24TgGSJUNOZhNeAVUC3ba9KNzbe+E3lRI7sZlzn/fetAFa8pHRhz1iAIdcDfS9VxvV5kIgGANxuiQstK3FzwlYwK7YBZ/xTaQlt4l0CQx0/BMlIi2xGzLUvtWSEpHYUrTV066D1BlP1LroGtw+AFduO1AIOVZwWFyKHQf83U6Amjsl8Rj8aOG8BJJuRUBYfQj9P6Qjk9CFE0FHTqGOpJSNi0zNW05CcDwaleuPz9EPryNCnthlptYmfp4GfhPQEaeTijwNmBADuuOMO3HHHHcRny5cv14QNHz4cv//+uzZyjEPvc2ma8Z+fAVs+VTb63T/ymRAkI3rMiMkjvbVDDmvOsU4wR4YHAYYBvBYvJmXejCJLRwDXBF+OfJA7tofzlyCf8C1WoM8lnKOoALTIEdd4mCMjvh2QN5CLI2NGODWNIgdjOte8Aez7RRveWKllRvb/hnZMKcrRFQBwqm0juvtWAj8vA7qPEemJKGQvaygZMawHnWcmV/lSqVI/4I+iEyMIh4rqUhGS5MYEUYT0e8sbACQpovZtnwKwR2UhocKAxmC3MmskIyRmRBXngjmG9mm66f0EyQg5AR9FrctQL9Z0EEBNoy2NoKYh0Se2L21+5rwkS44D1a/m9YXLgDVINQ0YWX+ShR+PBqwnFrQN4dcHRiEnxYUf5+7BPt5haJ0V3L7+moOa+ABkrHqAzgGELELjmJEgBmdTNiMmBhhC0i7sAWD5DBO0qBPLxL8/PArsWKiNU7wGuJ4QbkAZw/AM2LVfiVuqa5q8hmkAcFsLh03RDpYCgzTkRqDrmcDRHdwOBo2PBADNdRib+m9sbhqB9Ww+jvUegu4lf3PGeh7SwX1hBqFNyAdf4vbDYFREeqf2BikZ4cJY0aCWYRjJgFU3qzAz1KRrVnkh1FePrEQ8e1FfYM+ylpdItNdhVP9hUjJighnpfTFQ9BvnciAhE+h+tlmC+TwNJCOhqGkYHWZEk5eO/Y5aqibpaZT0sSxQc1hiIupb6nyQzBzN/WUPnlu8HW/ZW9I+g0srUsJIaYUtBdmowIijK4EV/C7LxqoW0NW6oMyIAdQN1W61wG61IDmtAZdm3I9fPUPxH5yHJ738xDL6EW4r7t7lwJ+f8JnoSEaMCw5Il3oiiabNCEMwqAVA3JKsnwk/kG5fJHlhPek8ILUzZ+j293dAU3XQtFlAUPvIGAynzYJuWYnaZ9u+4Vxtj31SlZj/dnkDOX8I+/lt2iRbF78X6baDiI/fgP+4z0Nql144pXohx4wYtoEQVSWhftcIn00jntrL34n/xNWq7HgDdbvqeArnMGzgNdBH8LQbTUoqc1U4rIyyfkKpqqDVLmYKUc/MhO+Q2w+46UcTeekVwef53V2c/xKrDXoTsyHMSkZES1FyGZoTs/UMWL+5Hdg0TwqKcwE5WWBqS6S04nc2IxmRqWlk0ZdtLxPDZUQFzi8kEAxYVfin7TOcc/gX4LDqgT2MThfDBMqMGEC7yub+ZyW7kG3fjURLGho9kLj8XudzB0R5myRmhNTZGiuB7d8B3mZZIAOU75Sug6ELZm1GzDAjBqJ7PY+UDJRu3gFg6P8FJ/5N4A3XKiS3zxj1EKeW2fMzx4yYMW5VLw6hfeexvfMA7mgJPHlBb7iSZU7oBlzFqYgApcdMAZpVoIEkyc9JYLyszCFdIJsUFfUtg8n0lftkNybo0ms/Qns2YfPEFSWVpT7fSDBg1WR1zefcQY75IwLTGQSUYm3hSrRc4e90VustAsFeR922gIAMHjlOK0yCKe2l6yObgQ6DTUpGVHXlDyRFU/crst6ONBZqn7BcmwG4048tVrDCItGjMp42C5maRg4Pf+5Tn/apgMjnhHGRQIpGYHyEkHYMv4DrfLp0DlJGQUB1dzRAmZEgIHzgHjnJ/L3AtfMdSziwzCrzkSJKRmSd7pfngT/eMijIuPEmuWyalZwp1xVi2zbDuAQ30GpiJ2oPTTTEkBuA+AygmfcQm9ZZ6jBB+CBRr6Qt0NrJOO1Ss3fZVV2gywjg3Je4wwKJk41qhWbEXAjMCH+Il1yMGi23zPJmcxQp0k1DBRAvd5gXwgAqfh9zzMhn6w/iki5+xEPd5KXVpobJdiYB3UYj3CDbk+p9o3B8O6N2QCAmFAPW1ji/Z8yTwNo3uWuSajIQTQICSUZMGi6rx0GJjyOoaQBg0jdA/mlgf3sO2D0PDHHNZeb7suJfeXwv72bZbmtB3ZuVpKjo7JEt2TW5vT7koRwZwknKJAP7GANlRgygb5zOXXRMi8MLZ/QDflAdaBcvc/Udl6rNrJ43fMvpC7TryZ3mufsn6XmAwTwj0Yk3rh0MfCrEZmENSjJiwmbECIQBQLNqtAbptM4Rry9JacFOG5JkRFdPrQZpUDIjGak5AhSv4uxOIDEjCslI7RFg5xJl3s5koOMwc7QRoZ6MSN9SCjvAyo518PEeMINikmRx5d9G5bJbDQfvRvzPg9VY4TmKcwAwslmBMZKMBIMg3kXpMEpZqKSmUX3rYJh7TYGENBrvikGqaczYjLQUjnjOaWLFngD06qCqGPA0ScdkBOyL6rpWFam6l/yMqLf2qvotv3BkQnUloOMOXpCMWORMVovtUoxhtVrwy/0j0TkjAfiNK2tE/H5MYGXuMwL0yVgAZUb0QBjI1A51CnOTUHhyR2CJissvOItbXfvcnMEYl0iWNx+/3z+AU+/gvLXKmRETjXdkT+X5QOYMsczp9BVxTYCB2qE3gNQwuu9nDCQjnkZuVc8j3a48BM5prQdnNhKEyNtQlaIjGZEPavMulZ2YCjRBftw5H//Lm8llXzAndKmJr1l5T3pPVTPxMzZYWK9WBRbsAOqTjlKHzZgRvfH0rvhhWyka3D7UNfs0ZHHMLYeQnJ6FAHIxrOyvvDWEU6plVk1DkpaoJvJg/Iy0BOo2b3Zr75o3gcX3K8N0bUb01DQ6khANBTqSESGcL5fkNj2Y3TR+MDhc3YSb3l+H2iYPio7VK/LS0BIMilcDv76sDEvIAvr/g/egLVlddc5IEF4CAJBUX8TdW51A3gCg86mh0RBBUGbEAAH1kUIDV1uGW+3AUNVkQzJgFQaLFut62TAasAah91U8kaU79yWg1wWB6TEL4nkY4CzDZw+UDF4BDGaBuNSzsTLj/5DQsT0yi0uABijfOaBOy0CErpaMiN9OLhnhrcXyBmH1ET/e9Z7DJ2G0dZ/bn2s3VcVAQzl3arNIRpDtoEnGiHUYCvSaqImim6NYtyHassiZkQBSsb4dUvDO5JPxjzd/x9G6ZsCmlIwAkug7MqyIElqbFpVkRDdBUKWoygiUr+x6/AvA4n8BE14yV0bYoWbWjWxGZGElm7n/tjhuZ1tyB31JoDovXQ+sDLq2S8Deco4JyG+nnJRlGSjCRZsRbzPw+Q28DyIVM28IpWTkp7+VR6S4HHrHbZiBjPal07WP4zOAk84lx1ejywjOzqoNgDIjBtDqI1WrYVGEaMYyXM6MqJiCkFY0StYoKD8jZtQ0ngZgl8rqfv9vuqQoBuquI00asZiEMHD4VcxIxV6JEbHw0ge/B71dP6D3uVcBvQuB5wmDmE1msOpXbvPlopphyNRqGkKci+bi7jcPoIxtlsVU5X3995zoe8G9wLp3DMoNEro7JlQqCMbCS7HV4mozzC3LbXXePB+o2i+F6557JEHwQuuy2/jqUhqwSjxfmFQgwSQXr3QYBfVqPVwwUvvIg4bdwu1kcSaq4kRATSPPN0gfIGK8kQ8Cp99jsrDADM93d56OnaW1yEp2oX2qoI5QjbdqxpI/iI/x+4C/vuCuu5Ocr+mRpf32I3pk4oohHdEpPR4Jv38hPWjJd7A6gH5XcNd7lgE1h7gNEHIajFTQu1uwcyrCoMxIECBKRvw+2SRvwIzIG4xfZegXyiDCyKnR9zOyvnQ9FuxdwK00q4qBdumwWWrxj8rdKEgrMC5jnp7BE2Nwh/CLhwXGRq2mEeo9tRNwD+/45b3zgKJftSt9ef2kyFRIJGYkKMmI+EAbR21MyzCBvzXr53YptBI060X1WSPBqoh2LAa+lTlAdCabar+JLuXQoxhPGQZ+3uuZKSa7JfA0AY2VyGArkAXOjsHuUxlfi+J7I/VdkDA1QRmI+tWMCCnP1lLTBCUZkcGvsq0zU4ZYhD7Dk+C0YWCnNFVytSRaJRlJ5U6CZlI7AUmDgJ2LCQy5AcStvdK7dMtMwIR+uXw5YTJgdSQCF/yHu553OceMGBny9zyHc0twoO05GaXMiAE0ahpGfcFyE58AQ105STJCcohGujfOb4hlJ3p1GUiM9cIfL2DbMdlJwUmJABpQ8+ebeOHMF4zpBICsQo47rz3CHWilA10RdjggSkZ0mBF5xzezalMcu0wYgExtv1VLRgjMiGpAYhgQPrWqDfw2i9saTkgfFuhZZbM+vn5NTiwCBG+0aV2A/NOA7uNMJbPzRqw+0VBVpruXae1bfDquEZpqgFcHAfVHMQ8AeIGZZ4sTOH0FeeVJRLjUNGpGV6fN6mYZga29AFgG2Oawo6lqF1CaALBNgNOJXj43DL1XmPYtYgCz7UERT2szIrawpGwgZSiwc7G0s8br5nxF+WQLFYbhVEoiE6i0JwJUZ071OIdj1B2JQOfTzNEc6D00h4kSxrb0rsCNS4Dp7cztdoohUGZEF9qJSBoYZQOJ3KOdK1k/OwWnrlbTqFc0JjqcTA0y+4J8xA/rSYzWyDtkuyT3DORVl2Dr0c1YlhAvhgfEVZ9wkodlzwArSMwLVy+s3mQfDgj51ZVwE6ZFxZzIJVJ6p4oql97StdHWSuIzdb6k04alSUVRrDxvdVlCRIERAYA+4d+KpysZeW24smzDTAjtM70LZ3xrEsKOGp8fgAXwyVRwe8rrcJDh2meLJCNl24DvH5Luc/oqd2xV7BF3tnlhBcsCdsYHO9sMlGyB+ltrd9OEWU2jgU6b1Y2u6ndmv2eQeN/uw8vtc4E/ZwJ/gptF8rJRuP2/+KTvZSqaCBJhM7vZNBLHFtQ1STIiGn9qpZXsureBvxZp8+l5LnDlx7I8lYswm1WWT99Lw7SV1ogZOb5AmREj6PEI8pWz0DDyzwiQl3w17tN/RipYDz3PBXYsQrxNp6MeWg+2+hAA4Pz1n2NQczO+SEzAsgSD9YveyllvMudjKCgINzOS1lm6rirmJj6AvNLSXT3o0Ud6biAZ0ahpCHF1Jiml0zM1Larw0Y8CnVqyzdcs+PL5ieugzYovUtPhrtwA/LGPp4zBmM5jMCBrgCotK712Tr+gSrVZuff1+FnAAnj8zQA4fX9xZQ2O2jk7m0RnCEOUk/e3ULUf+F3FIHUdCSTzonRh5ZvaGRda5+CvQzV4x/4CRls3cTuTxAlHjXCoaQh5tXRrb4eTAVcq5yoA4GyqWgHFFq5vpdjikRafBU/1ARxifDjQXGGckA1FTROkXYoiCws/DsjaqUr9I2dGxJ01ggQ4tRMQlwa4G4Bju4Di3zlPrl1HiQbbck+rAU/jNk+47JJwrd7FRNxp1dr6zfCDMiMG0D2eWt5RTLtYN5KMhGh4JkoISHYPAP54G6ynHrDbxY7G5PQFfIfNbV9TEmX4tFVPqXQmcaoin1u5KiCqaVTMCMkoMJCaJiiYELcroupwuJHS9/PSGnGMl5cbl47Xh12Kb/d/D+z7TpHst8O/4asLvtLmJ0wwjoSgyIhXOZtzJP4Mm7cT+jFbUVWQg1s6dUVuigtD8tN1cjBAj/HAuBkAf/4IAOD31zmGq7kWAM+MCGJsq13s6x5hSJTvDhJE+6z6Wwep0lLAQPoW6uSS1Qt4YC8wna8zwcdRmCH09WvyRmLKqOex/7+jcJ69PHBC8STzUNp2KHUtr2NWESTGYBiNpIYV6Dz7ae7Ih6P/3955h0dRrX/8O7M1yab3hIQECL33ekVAQBEpio127XpBQa4NC9ixInbE7lUEkZ8CFhQQUBDpvfeaQnrPtvn9MTuzU3dnk91sEs7neXjYTDlzZubMOe952zkGvN+LdZbf/TX7z4WdcbdjTRGNvqLYtwXIgTrIEGFEEwzMqV9jyPfPso3XaQeaN0O48xTeKj8Pdk7opSH6EtqrdSBS86XgcFjd+UevfhroNAm4tBH4e64HYUTjYCk5JKBmGoC1vVYVigUvj2YaDw6sUHgXIjwMFD5pRiiRQKuoGfFruKi2zkmoyeLMNHYAc6NCsD6bjZga3GwwWkS1wOXKy1h1apXErKekevftnUeGGlw1ZsvSGXIRH3EJ3W1rgfZPY8i/2vlUngiDGej3H/G2XV+xwohQK8kJHLSB9xewcl3irq/4tZCka9MEDskVTAKzL6ft8QatA8Z+CBz4P3ZJhgDgXvlBPCgq9ylKkzAtZhrJd1UrzQgFPlJMzWcEEGhGXHB9jM7I/h+XBQyeLV4ANL0/KuM6IndLDF/F5EhBlJ7fENyvWoqDQPrr1SNEGPGA2yxjgyHiIKqFYz5NowoObC49wQojXjtjhY9SLZpGa+Pi0s+reVczTreLU8vBQFisTNvjHbU6io8IqJkGcN+rUBjh0/B70ox4M9Mo4NGBVdopSlSnwt9SB1bRedJr1ZNmBK7ZIP9c2OscNRqx0qwDrGw6/ns734tO8Z2w//J+rDq1Sj1FNv8OfHdKNAps7KzXkcqz8AdCwd3pBPZ8DZzZxG7TGUC5mtVlJor9cWk3f2qVLhyA0P9C+ixqUV+Pbcy1L70vm7OnLBuQ+mJ4ouvt7L8AwwlwFGfO8iatOX1xYPWHFsp1rLVcdr7YZ0R8LV4z4koXAIoCBj/hFkZoPXDnrwgFsLJHCU5eLkeE2YBBWXE+1M1TtdXMNAKT6um/2Mga6THug/1Tl3qECCOacH9lK8augOnYWry77RX8bAlz79M8wEEwm1Qz02isltIALYRx8rM6WrKCnOrgoupM69l+zXjZX2eUTFKKg75UOFDoxJSciUX4ohlRcphVdmClaag/33oLyxS/HU4zYhNs/HbUt+gYJ865oDjr5ULbAW2zXQk0LX5sdJ3MHt4uJsjie/4fYOWD7n3mSKCCveY79nFIyuyI69pFs/vi26LsQAKAc352YFVoY9LyaJ08eWKA2Xd5H7469BXsgu+MpmiMzxqPgakDAbi/dWl0u1fT73HX8ge1adu10YxwZrjja+SaEeGz5upTVQSYTUDxGfZvtXw5gnM7pkaiY2qk9jr5jIIwsmaO5JDA9RX1CRFG1GAEw6sg7DDVkgqTKRoWl/c/IzW5qCIcACWq7bpqRsrzlPcLNSOSDl67z4ikx1E6QhqzGlDNiEALpKT2VdWM+CCM+KIZ8WKmERULyXPyKMDV08xG0i6aWZqJBBGvobV1CNekOTU6XJqRQN4yJRBmKwvY35ZEoP1YoOttoH5gV28tRjh2JNyI6/q3d597gM1h437DgTLYBFAY08hnBz7DunPrZNsvll8UCCMsfLeo6uirgpbl69W+QV+eTWQaUHIevBOr4HxeM0JRsrbLAKxZOF45QrFefTU8hXdHprF+QR3G1V99AggRRjyg1O5p0KIdjFYHVsUBUG1WrPGDc6nUUZEP7PgcOPKTeEGkw6vgTEtxXYmbzXgrW6MZQUD35tE4ukd4TiCEEVeH8fHVbEcBuLUkSotSefQZEVBXnxEfHFhZmU2lc6k3zYhYW8O4NGa80UZmWlKysQu1fK53UAvNiE4SEq70y2/wmjWn21ckrjVwHReu7s4uLH0VfVvE4put5+DXdPAeBd7gUe1gzVE3tLwBXeK74HTJaXx9+GtYRQ69LG7NiAeHSumzSe4KtNaSi0bFTOMLmf8C9nzDCswqPiNKob3ocjsw+HnlxHKiOgUKlUmKtJ5D5wKdVcx3JJqmqeKePXODuUw16U8zjdbOmFsDZP93wN7FiodINSOKg4tHVAQmAZP7NsfYxH7Al/B6bK2hBU2VW/GTI7mr+7cWzYgITx1obTUjbvORqFuRaZA8qZsCaaahwNXXbrAA1bl8iKKasKpo1ss9wC5UCNRKMyLNwSIPnfcjQjMNF9IrUMN7uuT1nZNh0NHoToUBy1C3AVKKopkmiAOJqwp9kvvghpY3YFv2Nnx9+GvR++cjsARaLe6XV4a/qCnyysE4ka3XsS5NZReA6kKYdTTifHk2onxEkgkC/6dbGOFLDo1RF0SEJ9cHnjQjGpZdaEwQYcQDSs6enMMT7WqPTi3rvQjOhjA3CXeOORIITwHKLrFOU7EttVWQWxlX6EfR+14gvi37++dZgm5CMtioBtOozf48zOIBhJsVTCX+RCiM9Lmf/QewHY4wvbta+JuqZsRfPiNO+TFgV/QUlaqaO6KW2jGlunlDUPTeHi9ioHULGGsBUPCXvM0rVYNb2+fCdvc2lQFmT94efLr/U9gE2SApisJNWTe5QiHdF6AD2clzmpvLR92Of7RAGBEeKj2VojCyYxJw7qxriz/q6dHu6YfyawfXn0n7C+HkRS56eIqmkaCxb7hv5yvYmpbK/vF/17L/pzfDM84K3KypBEDktOyDZoShVYbF0e8Aqx4CblWe+PkNtT5C2i64aJ8mAhFGNCBMVc2qsQVdtmafEYBP8CCdAekMwPTtQNEZIDwZCIvVVjFpYxz0X2CowLlJIIxI1e8+h/Z6m5GIHMIC0Jl2vIlNlW4KBzrd7E58JkU1S6FynYrslXjtr9koqi5ybyzLQYeoSDzoUzSNwjGS59A1LQrYEWwzjfhJlMR0AzpfByZnO/DbXzLNiKImrcutrC3eFfqK0Dig7SjFa3116CtsuLBBtj23Mhc09YDgOsoGG7/BDTCrHnJvE8wsI0PcvyNCVGackqiLOmkyFLVvwTfZOF1aYE/9hdRcxXWP/hRGDpayCdvMDAPKEAqbwwo748AhSm4uUkWoDVMznYICmvcHojNBUa6JQ1xr5fJ6TAW63Abo61EIEHnAS4br+qxHPUCEEVWEDnXuj4yiKMAcyasmVaNSFOE6M4mZBmDVgkk+rBoJyNV0Cku3O6Udh88dp3czjeo5/uSqR9l/Xi/t6uwO/gAUnPCaEntD+Vn8dPkv2fbN0ZG4mbEhUbrDxzwjHP+9pjXSYkIlatfgzI6ViubasTTqShFLAnDd65quxWlExrUah15JvXC65DQ+3v8xbA4baJoSDV8BNdN0mwj8/R7bPmgd61vVdSK/+9kbOqDnvmyYDTrc1L2ZSiH1pbEInmaEbwe86YISbXdtdP3n0i7kHQbSU7Up5jQKI1xZywutSJ91AJ+snYm3L66Dkh5T/VoeNCPCykZnADP2AL9MBi7vYTXVatS3ACD8FrrcDuS4TKORzYD0fvVblwBDhBEJ+TSNp+NjUajX42LlUwjNtILSSXwU4rL47oKbSWjXjEBupqktYfGS8uXlcQmKZDOdOoX2KiBcJDCYtkyuIzn/j3jlSpUBzuYSDNvHtsfEduzg9NzmObAyDlgVuz6V2XBNGfBOd5cJQ64tS48NVTjPk2ASoAGJEidi4y7LtWM1zUitJ+2u87rEd8HolqOxK3cXPt7/MRgw0AmuRfGutOxffqf/g+w/FZrHhmHa1V5WseapQ4SH+yRXUUpCbPAQRZnA3W8ommmcdqDkosB/TgM+93mckMxexSdhxINmxO3rJ6hag3H6VOkj0vsA98gjnbyX0TggwoiEzaEh2BzqikhxnoFOKakebXB/gL6YafgOSBJNU1u4REgrH2TD5TIGyg6R+oy4t9fFgVWh3vFtgb7TWOcvH1OD+5V/PQpEpgL2GjaEc/snrh3Kz5pxdW8pYSm4oeUNAIB5fz8PK+PwrPXinoclATCGs5FNhSdlx7x2U2dsP12IUZ2SxecBnttMAM00GXFhOJxdCgBIj2GFJLV7VfIZ8AWp2p/738k4oaMpd4p1iM2hDRKZv1Ud6uvJSdqPg+K7u98Vr9oNIMYcg8d6PYZIk1wDoOozImwf9mrACFD7lgGbPgN07KDPaKm2xnvjhSJGLIz49MS5b2jfMnc4t5LPiMq1GwQNRkAKPEQYkcBlsehUY8XEIQux4ehlrM5/QXwQra+9zwggWCK+jg3NEAJ0nwJ0mww4bIoqRKnPSCBCe/njRr7stcoBJyKZ9Z0BgPwTbmFELUrEJW0Knwv3W3EWJjXBmMKBh3YBBSfZ0Oot7wkOpnBzzzTc3FPBwVZWp/rzGVl2fz8czi5FYriZ19hwHbBUg1ZXpEIO/2wZJ8Z3T4Xpb/Z6Zj2NZtEmoAoNuAP2SQdQC3wvt6SmBBsvbITNIXYQ7p/SHwzDYNG+RYrn9UjsgfFZ4+U1UDPTCDUj1grAGCbvEZSqX8u0BSKBoDQb1JFfgHADHL40Dc7RuvSC7PrcfYq0hA1Sm9AQ6xQYiDAigfsEYh1OjGp1NUa1AlZ/KRFGdHp+oPpf4S4kRIQD9lzg0NdoFd0KfZP7KhfOxeoXn/NvpSlK1ZbJD6i8nbeWM93GmOVPFG4q+ahHzAPO/Q0mpRuQu0F8GqcSVtQWKGyzJLD/Lh8Rb/dF8KttrhlvdVPAYtKjl2QBOqXOWfi3t/ZSWF2IGnuNaFtCaIJc7c/lNWEY/Hd4G8CYBWwAburRDLBWAJfYqzZIuGqVnGf/r1MoroKZRnYh7yzYtQDfH/tetr17Qnc8P+B5AIBJZ8IzfZ8BAHx/7HvsubwHZ0vP4lQx6yQaZghDYhjrHcVpRjxlbBbV+InzoCovA6vGaqtwbcw0u/8H2loOIBpOXyJIutwGHFvNhqC7KyCujsJ3FnTNiFa/Mk8YQwHRelINHyKMSNCkaqT1+DuElbrtFIVXY6MB63lg+6ugQOGPm/9AXIiXdQrCEoBmPeteYS/wqZsl/2sO7ZU5ajYilJKhcfT7D9DvP2AOs2F6sgXtoNIpeVq2W+oro3SM0iqcrhqoHlcPeNOMeDJZ/XD8B8z5e45se6+kXjC6Bg/u+Sr5IHA1aAjRJB4RmjXsPkR1eERwzwqJxbxRUMWaH1pHt0aKJQUlNSXYnbcbhdWF/Dsz0kaMaTUGALDn8h7subwHnx34DJ8d+Iwv563Bb2FY82G++YwAgDkClLNadowqPrZrigFgreDDvpmkTtpPjmsF3L4UeKuDoED2vjjH6oapDRFSy/rdvgxYficw/CX/VieAEGFEAp/8yZMTps6IIp17oLs29V+AMQxrz66FzWlDaU2pd2HkkWOBH+BjWoKh2Nlq3dXvfpDW6xvKg2bEhbTzZY/UohlRKE+Wn0DhmE4TWA2KwyZezEz6TIW5U/yK8nNwKuZb0caBfHbmqaN00FE6MGBgc9pwIP8Auid0Z6/KJd0TmGlcG9j/84+7M+s21PYVnuT+LcztU6cMrC6slYJ92r9VTuCY2G4ixmeNx67cXZi6eioYMIpOydc0vwabL27mV2GutFXC6rTiaNFRVhjREE0j/So8as/0IeK/NTrpujV1DMA43Xmd9D6ujEtL+3EK83fMx+cHP+f+VL12g0Dw7v688Cee/ftZPksux9hWY/FYr8fE5zXrAczYWx819BtEGJHSYTyQuwF0oocwW70JMIQBrhnBa8PeBwAMWjIIxTXF6jMEztExpkVAOtySmhJU2avw9KansTNvJxDFwM6IOxXvDonSejVmzYigeXupv5JmxOmrZkQqjCgd030y+88TnSawqayDgCwdvAYHVm7ffV3uwwNdHsCl8ksYsXwEGIaRO0RKBzcuV8nZzYISG2hbE2rahBEatUJipqkQrC8lFHq8wAscEs2Tk3EKAkjcz7N/Sn/8ftPv/N+vbHsF3xz+hl8YT0vSM8RmATWXQLW8WnyM0iBuMANTVgBfjXFVWGWFcVVcwgj/F3uNTRc34WzpWdGRCaEJGJo+VNyGFbSVf174k/+zV2IvH+tTHwgnfu57WXt2LS5XXZYd/b9D/8OZkjOgKApXNbsKN7fRnBauQUGEEQlMxkAgdwOoiFR+GwVK1hk7DWagRiyhijoCJcZ/BPzyKLssuJ/568JfePCPB+Fg5B97YmgiEkITANTCZ0SrA2tDREOKciVfCVphNig4w/W/wvOISHH/NkXIZ4UeEZSX0q3ehT/pIOQL0rYk/A7U1P58SLzQfyokGgiJAVoO8bkO9YJQ0+bzoKqG69lxbc1o8end8+9Nco5QEPSUO0ZPsUOAw3U/0vfl1hIK+rTkzsCZS6CyhkvuRKVPSe8vqLDKCuMS+K+MAYSLljqcDpwvPY8H1j6geN5nIz5DrySBgKGg4bYzbB0WDluIAakD+O0NJ7RXiLtO3DuY2n4qbmx9I2ocNZiwio2o/Osimytpy6UtRBhpKiipNmmKlg3yWmaKMtqOUs1UWVcOFhyEg3GAAgU9rUe7mHZ4c/CbANhQPqNWxy+1fBcN8kP1gnCGpDJ4KOcbcA2YxWeB7+9kVb3mCFbAKMuRHc+T3g+YtBwozQZSu/uWIElryK8Eq8OKnbk7WRt44UEgxAwKQDdrOSxGhfU1VAQ0XihTyzPiATXnVycEwohkps0PbsJB7vEzXq8VVGg1YcT3b6OIplBs0AMV2UBJFFB2HgkUhTAfzalSXx+hj4eSCVKK3qXN43woeAFGEn2n6DOita5CgUCzMCII7WWcvM+IE04U1RQBAEL0Ibiq2VUAgK3ZW1FUU4TimmJxQUYLkNoTuLgDiM4EIpvxWqBwY7jHawcNFQdWrl6xIbHIjMwEAHw/+nscKjiEcls5Xtv+GmxOGxiGaaCClWeIMCJBMeSLS1YmQEn7oZitsJ7grjmh9QQ80+8Z9QMp8fFeUXRgbSQN3RwFJHdhF0azJCgeoqgZcc2AnTWlwIHliuftqcrDwrX3i0MqQWFc1jiM8maGUaR2wsj8nfPxzeFv3BuS2Pvsu+FhfDz8Y/f2DuOB478DnW9RLEdt4PKlTUtNBUKfVH6fdJZeB1+Vekf4XkQrwfrGoYJDuD2ahiMmBfj7cX57eFoqVueVIcKHsqTtV/i+tGi7OGGkzFqG3IpcXijh3pOSw7t0sBaWrzgQCv/2+X0zAOOAjlcguYWsGHMMXr+KzQT879X/xs7cnXLNMEUBd60BakrZMHxax9+jXmJWbZjOrHLNiNAM1SamDdrEtEFRdRFe286uQM2AaaD34hkijEhQ6pRHtxiNH078gP4pbnWjUuesHikQeLTMgoBamGn4Rt34GjdoGrhnAwBGXSOg8NxogxmoAZz9pgFF2cC+JewOcxS7LktkGr4t3IvNFzfLysutzMWoFrXQftVSM3KulDVzpFpSEWWMREX+YZyhGWRXZIsPvOkzNlpDL18yAFDXbmhBVbUPD2YaJc2IH3AyTiw/vhw5FTmi7WnhaRjTckzdZowUxZpqGEedzDRHC4/CQVHQMwxCDRaA1qHMWoYyHY2zBh3aOx2gKVpTXdU0GU5BlhxPzuvcgLzi5AqsOLnCvYOfgyhoRqQCkJZn2u4GIP8YkNbb+7GC61EAwLgXtdh4YSP2Xt4ruj4g6HuVBESaBkKi+D85zYiO8twnBA1h0kjBb0/1Er5jJ+P0e76g+oAIIxKUOuXn+j+H6d2miyJklBpGMDUjSlKzEl7rGODQ3rVn12LetnmwCsIYKVC4te2t+E/X//jlGiJobR+lomakw1gg7xRs+5bgkl4PRCUDA9g65mxmQ1nHthqLASkDcLb0LN7b855oddpao+FZ51flw8k4UW5jlyqY2X0mRmaOxJ7c3Zi8eopcc0dRqoII4FbT18qBVWVwUpydu/4LlDCyJ28Pnt/yvOK+djHt0CamTd0uQOsAhwOeFl9To9xajkp7JfKr8gEAV1dWYf6YL4HUHhiz/FqcKr+A2+MswP+6IiUsBctuWIYIo2c9iU/PXoG+yX2x+PBilFnL+G0plhR0iO0gOlfYntSET26f4vVu+R+rSdKa9IxrbgwAxokMm/u74kwxwrbKaXI8RYVdKLuA6eumo7C6EABgkETa7MjdAQB4dOOj+P6oK3cLxU5GudDoeiGtD3DNC0DpJaCz2//DUx8vFAgbVDSQDxBhRIJSmmCKongHUP44hReuNCupL7QKI7UmXOCcGS5bPk4zv535DXmVebLtP574USSMHCw4iPd3vy8SWgBgdEvvHUN2eTb25+8XbQszhKF3cm9ZB6RspmGf4bJjy9Alpi9uTUnCMZMRQAXww/Wi8zvEdsDIzJHYf3k/3tvznjgEkmGw/vx6/HnhTz6UkkNP6zGx3US0j23v2qJdM/L69tfx1aGvRNtM3CKJniIbPFBQzearqLBV+HQeIB+cPPktCJOesT/8+62UWtk09zHmGIzMGAkA+Pn0zyipKUFJTUmtyy23luN82XnAaAIcDPDXK4hI6YFU76cCALbnbMe9a+7lZ+UAYHYyvDzTP7YzTpW7M4VeqriEY4XH0DPJcy4iabp9oeZTbb0hIV0TuuLPW/9U3a9FGJWaaVRln9om8mOc6FpjxW/NxmPEhf+T1Q3QEDwA9h2cLGGXbIg0RSIpTBy1lBCSgLwqtm/amrOV33629Gz9CiO0DhjwkGyzNOxadIrA/BmM8ccfXNHCSE5FjmiwizJHqTrySVHUjMjWrqg/PK21IMTndPDc31nDgSkr2YFSYQ0crXCdxT2d7sGoFqNwovgEHtn4iKwTWXJkCe8hLuRM6RmPHQPDMJj0yyS+UxHyeK/HMan9JPHxCmYabja68uRKzI7p7RJEgDCGAm20oNpRLUuapNRp7728FzPWz1Cta5W9CvMHz4erAPcOL8LI7rzd/LVpikZSWBI6xbPJoLSaCguqCrA7bzd/3D/Z7KKC3Kydr4oGs54s5bt0cBJs4+pXZivDqZJTaOF67wyA7PJLosGaoig0szTzybTCtaNm4c0wu89sAMDO3J0oqSkRle0LNocNN/x4AxtWmRTNbizYCBRsxPshZvxLg2nrYP5BkXnA5HRgcGUluM7i8XZT8cCfH4EJjcOklu1wpvSMJnOBNJpGZK5QCO31FU/9iZKZxn8mDrGZBgBSDOGINcfygrOimcbD9blnlRaehmWjlyHUECra/8mIT/DtkW/ROb4zdJQOuRW5eHPnm7LMwsHCk6ZLaqZpjFzRwsgjGx/h7Y8Aq7bjFkvz9gGH6kNls91ap1oHG7L296W/eU9xjlZRrQSzZ3V8NtP4GtpL00CLq7Sd4wHuuvGh8WgZ1RI1DvZDlw5o3Ax9TMsx6J/SH9kV2Viwa4FXMwgDhhdEOsd3hp7S42L5ReRW5sr8CIT1EfLywJcx+sfRAIAaQTv4jUlB5O2r8eq2V/H14a8BCFTV0igRgFcHA8DgtMHomdgTNEVjf/5+/Hr6V1TbhaHh2oURblB7f+j7GNRskGiflhkiAExbNw0HCw7KtgvrLKyWJ02LVBAWtkHOoVA6WALAmB/H4AMkYRCAt6Kj8PnyEbKyx7QcgxcHvujxXkR1cdVT6A8gjRjxlVJrKZ/fIcEYBVTmo5SmUU3TOG0wQEtGGO4Z3dDyBrw08CVgQSegsgrY9BZw6zcA40SEkwEYCmdKzwAA7vztTnSO78yXMSx9GO7oeIfi/SpqRjSE9npDMemZB/8ifwkjfJuylgPnXFoKilbt35S+P7UyW0W1QphBvphnZmQmnuzzJP/36ZLTeHPnm3wocLDx5BdIzDSNnBB9CCwGNvyx0l4Jm9OGk8WsGs+bhuH9oe/jyU1P4r89/8tv0zoQKPHXRTZPiBQ9rceGmzcorrApxJMKT4hXB0W10F4/o5Qe/FzpOfxy+hc4GAeOFx0HAHRP7I7rWlyHE0UnsGDXAq8fmnD/+0PeR5Q5Cgt2LsCnBz5VVF8qdazNI5rzv+3JgvTTXSd6vR9hZ8y1g67xXfHukHf57StOrMCvp38Vd9w+aEa4QVWn4JSrVdjMrcwFwPpRhOhDsCtvl8fjtaDUSUpnc6mWVFybcS1+PfMrAOA/yEFMeioKBRmNLQYLHIwDVfYqHC487FMdeOFH8D450xwX9uipvkoIha11t/0FLL4FTxVuw8pwC7vHpBBCLUHuO+O63ws7XBfhMtKK3/2+y/v438cKj8mFERWNqFKOl9ogywsDz4NiQLTC+UfZ/ylaURsi/O2pf/A1lw4nxNZWoybE5rRhe/Z2VNorRds7xHZAsiVZUxl8H68gXArviWhGGiHC0Mcpv04Rqa29Deod4jpgxdgVivs8DQTHi47j4/0fy1R/ZTbWgSzGHIN2se0AAP9c+gd2px0lNSVehRG15Edq+Bza6yfUQhGdjBOvb38dGy5sEB0faWTvW6lTVEK4XzWCQ1gfhY6VoijoKT3sjB12Qa4QqsMYxWOF1xA+VzVtldeO00vb4wZcqf+LsD7eOiTu2i8MeAFtYtpg/s75+PzA5/LyfDDTeNSMCFT6r131Gi+MAOAFEQNtwKpxq5BqScXW7K24+/e7fe5YpT4UgHtQeXjDw/y2VEsqvhv9nVcHUUDhPQ5/EdT6WUDlGSxJaIZzyemAwGk2NiQWd3a8EyGCpHeyfmX8x8CnwwTfl9wZ1mKw4OWBL6O4phhz/p6jOENXM9OI9tVlQuE6tcpWhTe2s8ka151bJz4kAGYarhRKJLCLI4zEiQq1TwS19pHc9+UPYWTx4cV4Y4c82WVcSBzWTVinydfPUx8veu/EZ6RpUBepUks0zeIji/Hr6V9V98eYY7Bw2EIAQL/F/VBuK9dmO4bGjod3a1ErM7CaEekHJfS14JwPB6QMQLPwZogPiefNEFoHWbGywbswwh8ruU8drYPdYRf7MEgEKKVtIs2IwsCofi+eNSM2hw2nSthVVjnzoDRPgvBaXjVIkvf/ny7/QYQxAlenXS2uq7vBeEVJGFHrQD8f8Tle2voS7ikuQdYFdvYf99/jiDZHi8pQyijsCSUNYc+knnykBMfF8ouaHEQV7yEuC1RKV+DEGVx0VGDZ+d9l52RFZWF4hjtDqcwXjYtskkYVCeqdEZGBq9Ov5h2+ld6pdLasFP1SF6f2MD1rzrA6rfjy0JeifUrRNACQW5GLbTnbRNuMOiMGpQ6S+Wl4gxrwMLDpbcAcCWQMBJ39k+z6wt+eBmKt2mMOztRnd9rx3dHvALCrIbeKbuXTPQDgTcSJoYlItaTCwTiw9/Je5Fflw+a0uR3QPaDWnwDETNOkkH7EtZlNaHGi4vwErml+Dfql9IPVYcUr217h958oPiGrk5bG5TczjewEP2tGPERXcPsmtJ6Aoc2His7TOsiKNCPwLoyoOSxz5wiFEaldXvhbScBQeyf8LE7YcXox09yz5h7szN0p2sal8haimL7bA1y9zXoz7u50t6ZzpHgK9VQymwCsgPDDmB+AE2uBbyYA3acCLkEEcA8EDh/zeShpo6Z1nYZJ7SbxdZmwagLyKvOw5OgSXsADWKfXzRc3S3x5gLaxbdkyBepx4f2YdWbc2elOAMBPJ3/CubJzMn8yuZmGSwwniSoS1Fv6PJX6Fen9CvsgX7WlSiSGJeKVQa/gaBFrKtl3eR/fDpWEcwYMHt7wsCyaDQAmt58sX9BNBf477/cfYMgcfrta/8Z/4071du+rxsDkEhgZMHjhnxcAsJqM9Tev96kcwP2ebmh5Ax7q/hBqHDXo+TUrCFsdVk3CiCdfHVE0DTHTNA3qEiLrccEoF1yH2C2hGya0ZtcVuL7F9Ri4RCFCxasWw43WaBrp8fJrBlYzopYe3FvnqVUNK3z20g7ao5lGcp/cQK+oGVGYkSk9d7X74f6utFVi+bHlbK6QkkNABJueulvFRXQWF8ULqdGmaOhoHTIiMhRzZmgJxQQ8d2y+lifLyKkwS1MdEFsNAx47zc58BdRWM6L2/QrNnJym4bczv+G3M79pLlvJRwEAQg2heKALu1bK/sv7ca7snKytyWa1vDDCaUa4I9UFXe5ZWh1W3P7z7byAIDyG7zMYhheq6pqNc1SLURgFNpHfFwe+kAnFQhiG4Z9v5/jOsBgsuFR+CWdKz+BypXyRN9VyVNqbLDO2Cy1mXF8T+0UYI/BYr8ewK3cXrE4r/rzwJ/Kr8muVbp1rx5yfl9DEqtWx2pMLgXBb0JO21RIijLiQdrq1mU1omZVyMz2ht7+aP4gvkS++RtOoFmkIY1flLDgOJHbUtNicT3CmcQWNgiftjuZBVrDfF82IFK7TuOv3uzxez9M11KIZuHvZn79fPIOMZTUDllNLsKnPQyIHVa6sr679ChmRGar1keXxUEFzKLiGjlvNDwhQ14yIEGTH5OBMUD77jPioDRiWPowfaDh6JfVCr8ReKLGWiFLtq5VJK2gzZDCSY/k0AFIzDQXBRyIpgt1+oeyCSBCxGCzIispyncKeVOOowX1r7/Ncp1ogbJNqob3cO3im7zNoG9MWiw8vxrxt83wWLIXX4P8WCiAKmqqVJ1biQP4BAED72PaY2M7tdK7VH1DI5PaTMbn9ZJTUlPATRgfjkGklvz70NX488aNom0lnwqyes9AjsYeiBktP62F32nHVUjZKsXtCd3w+8nPV+nlq28JtRDPSyPGHmUbLgMmVr5aKWKk8LYKuZmHEW8dE08ADfwOlF4HIZn4300gTMQkHT0/3oNkXQiFawlcHVoC11++5vEeUKMuTFkRJcFTr/KTCSWZkJjrGdoTNacPqM6tRbq+Ek3FCB7kwovX9elNJ+yp0a9GMKDlRcsK3rwMirxmRmGl25+3GZ/s/E80mKYrCTVk3YWjzoT6Fs3aN74q3rn4LFbYK9F3cl9/eO6k37u9yP86XnRcJI2qaEeG11DR4svfBn8+I/xcII0o+OMLvJMoUhR/H/AiL0cKr+bljuZB5ALg281rVZ+ArQj8lxQysDMMLHVxduL6O+zar7FVYemQpCmvEYeTtY9pjZOZIj9dXE/w4X6MDBQdwoIAVRlaeXIlrml/DJ6ysy5IHwvu2O+0yf63PDnzGh38L+eH4DyJhRNjv90zsyef3AYBdebtQWF0oyvTNsT1nOzZd3ATAs6nKyTjx3dHvEGYIQ1ZUFvqn9lc8tiFChBEJp0tO1/pcLY1cqq7jaB7RHGdLz4okbl80I1qTtWkqU28EYjK9XrM2qKavBuPRCVdr2LTPmhEVDcGi4YtwtPAoduXtwls73xIfI3TvkDri+hBNwzEwdSAe6/UYKmwVWH1mNQC2nRjgVuVqnfFrMRUK92vVjHgsT6rtEpR5ruycx/LV4H1GJLPprw5+JYu4AthQ5aHNh/Lv0xdhP0Qfws9ShfX3JESKnpvwEaqYVmVtgfu/qgjIPQQUnhJsF38HUp8M7jvRUTrEhsSK70nyPmPMMbwJyR8In6tS26myV8kGXpoWm9z+OPcH3tz5puxcChR6J/dGtMntO6S2cKP093+6/gctIlvwSSw/2PsBquxVKLeWu4WROmi9pcKIFG7b3H5zkWJJwR/n/sDSo0thdbL1kQpoALDomkV8Arehy4aKtMNSHvvT7WvDORVLMevMqLRX4sO9HwJgn8+6CesQHxqv+T6DSa3crN9//31kZGTAbDajT58+2LZtm/eTACxZsgQURWHs2LG1uWxA4Rqo2oqOWtAyYPLCiKSzfG/Ie7im+TVYPGqxu05aBgIXvE1a4ysNlse1mgOr8ENUVENymitvob0KUQQe/U0kAylHiD4EXRO68upvYXkeo2kUkkOpRdNwcG3BU64ArQ7Kvppp/OESJH2nelqPXkm9+P0x5hi0jWnrU5ncfRZUF+Dq767mJwlc5z4+azxeHvgy7u9yPwA2ZH7h3oVYf369qC6eEAodi69zf3dK71lappK/AqDe1uTvT1D2h/2AH+5zbXaXxeW7kWoetJgzOa7LvE52TF0QOlpyEyrhNQd/N1i2doy0TZZb2fWU0sPTMbX9VExtPxV6Sg8GDCpt4jwc0neg1v5jzDG4vd3t+HfHf+PfHf+NcCPrf/XpgU8xf+d8zN853525uBbCiLC//r/j/4dlx5ZhT94efhvXL3VP6I7+Kf3RKoqNuOGEFCXNCEVRiAuJQ1xInNexg3tmt7a5VZbokOPZ/s/i+hbX4/oW18NIG8GA4d+FJy6WX8T6c+ux/tz6Oi2ZUFd8HnGXLl2KWbNmYeHChejTpw8WLFiAESNG4OjRo0hIUF6mHQDOnDmDRx55BIMGKT/IYCNt9Ne3uF7lSO+oaR0YhsHRQtbWK9WMZERmuFODc3VSMfswDIODBQdF64j8c4lV92lV46vVsdxaLkvJHWGM8JvdWaaJEMwkebOGgkBVm5BVqfblUsUlrD/HDlahhlD0SOzhdbaklNdAyYlOKZJK1YFVpYP15ISm1bynNR+LmhAmQ2WmLy5K7sD66fBP+YgSo87os3CfaklFQmgC8irzkF+Vjz15e5AZmSlKJDe65WgcyD+AhXvZUPj397zPn+9rCGmEyZ1vRMncJP1bKceF8Fwp8nYvUaeExrKCSNeJWJDeBd8d+w7Tuk6TlenN0TslLAXdE7rjcOFhJIYm4t7O96rec20Y1GwQH/49IGUAAHZG3j+lP/6+9LfoWGm75iZiXNtsE9MGj/R6BAC7DpTdbhdF1SmhZiqTEmOOQV5lHlaeXCnbVxszjY7SwUAbYHPa8PqO1wGwTu5/3PwHos3RMs2XNOuvkmZEiDcHfe6Z3NHxDhh1RsVjrs28ljfJbc3eistVl7366VgdVty08iZ+wc2vr/saXeK7eDwnUPgsjMyfPx/33HMP7rjjDgDAwoUL8fPPP+Ozzz7DE088oXiOw+HAxIkT8dxzz+Gvv/5CcXFxnSodCIQNtH1se3RN6Op7Ga7O4aE/HuLNLWkRafh8xOcINYTif4f+x2e+1KRGlvixcCw7towPNVOrQ2345vA3ojBjDm8puUtqSlBQVQCaoqGjdKBp1/8Um77ZYrDArDezB/OmcckgznjuZKWOrkcKj/B5STjSwtPEaZ5dxXCe6ztzd4oiAR7v9bhXE5iqOl6yX+ld8R0Q1Ac1wN0WhG1C5nOg0ZeJ219hq8Dey3vROa6z4vMMpAMrwL4vXwUCIaGGUKwevxpTfp2CAwUH+Gcp9cMRCs43Zt0ImqJh1Blxc5ub5YV6wJO2i0PNX0ExqkPt/fFRL4I2EN0cmLGX/3MoIAptl0Yneco3oaN1+PLaLxXu0D/EmGPwzpB3RNsoisJH13yEpzc9jRUnV/DbeZ8R18SLq7daewFckxJGPqGQHqe0T8jcfnPx25nf4GScKK4pFgkltY2UfKrPU9h4YSMA4O9Lf6PGUYNfTv+CVEsqv+qxVBjhMklzIcdq/b63SYSvvozcM/cWGl9uK+cFkc5xnRGqr/03W1d8EkasVit27tyJ2bNn89tomsawYcOwZcsW1fOef/55JCQk4K677sJff8kXP5NSU1ODmhq3A1ZpaamHo/2EaMyp3YDeLqYdjhcdF+UYOFRwCEeLjqJbQjdRToPeSb29lsc1XGnHdqGMXd0zyhTFOzudKD4Bs86sWapVGoR35OxQOBKyhFFCssuzcf0P1/PqcyVC9CH4YuQXaB/bXvZRCWcEmhxYweD3s7/jkY2PKF7rvSHvuc9xlT0kfQg2X9qM4upiAEBOZQ7yKvNwofwCvxyAlneuFD3A7+PCde2V+HDPhyiqKeI7Im8+I7xZwINHvKcBSIhQAzHpl0l4f+j7+Fezf8mO80eqcGlZ/sagM7D2/gL385CtxSKo/9x+c326H2EabqUZtyeNlrcZukybKdX6Ccv2ojWS+ox4SgseTAw6cUZgqfmRG5CVHLvVfNk8vQPOUVWJjnEd0TGuIwDgRNEJRQ2Jr9zY+kbc2PpGAMANP96A0yWnZZM3qc/WxfKLmP3XbH65hdrmSfH1e+VTE3hZV0fYz3wz6hsPRwYen4SR/Px8OBwOJCaKl5BPTEzEkSNHFM/ZtGkTPv30U+zZs0fzdebNm4fnnnvOl6r5ldoKIy8OeBH3d76fHzjuX3M/LpRfkNkNH+r2kCanIrXICG6WOC5rHGb1mAWAbfw2p01VhceX6bo3q8OKnIocJIYmyvxl5vSbgxuzbsT+/P2Y9Msk1bIA1uHX6rSCpmiE6cPgYBxwMk7R/1X2KuzO2432se1VOxtRJ6skjAg63vk7WHNWpCkS8SHsc+TycOzMc2s+uLKTwpLw/lC3Cn/RvkV4d/e7uFR+CVGmKNGxas9L+FtpRic8/4O9H4jK4OzXSmUCAkc/SdSEEK3RNElhSbi1za1YcnQJACguDigsX3OeEQ0+Rv4MIeXgZ3icil+iYegY2xH9U/ojLTxN8/Wf7/88vjn8DaZ1mcZvU3rPWs00SkKKV58RYdletKQyB1Y/JDMLBNJwV2k0jVSgVPqOhGvqKNE3uS+OFR3zqV7eJgO14Z5O92Dp0aVgGAbHi90TUO5eW0a15I/96ZQ7a6zU4VhaJ1Xzqlazqgtf19XxxzOpKwGNpikrK8PkyZPx8ccfIy5OHq6kxuzZszFr1iz+79LSUqSlpQWiijxqyXR8KoOikBbhridnmuA6Um92Qyn8R+xUVvlKbdXeBBHuOIAdvK/5/hqMbjEaLw96GYC74Zp0JjYOnvKe64GTvNvFtMOS65fI9j+z+Rn8eOJHvLLtFZGPhtRJUKh+VvrgQg2hsBgsKLeV41LFJQDAA10e4PMIcNdRSlImxaxj3wvn7CisjycUHQYlGh6OlpEtMbT5UBhpI8a0GiM+R4PgI1qYTCGRmxoUReGpvk+hoLoAa86uUbUZB9pM4y+kg7tUK6Cjdfjomo98KnNc1jiMyxqneB1AIGBK7kcaTqx4rsoz4L8hbrfwPXrTjEjNNHVIzBhIpKGt0mia4ppibM/Zjq057Cq8SiYXoe+YEo/2ehSHCw9je852zfXy5rNVG0a3HI3RLdmVvSf/Mhl7Lu8B4H4n3CSHY2yrsRiQOgBD0od4rKNaXytNieAN7tn/79D/8PuZ35FiScHk9pNlbcYfKzv7C5+Ekbi4OOh0OuTm5oq25+bmIikpSXb8yZMncebMGYwePZrfxg2ser0eR48eRcuWLWXnmUwmmEze0+P6E7WZTl2QprT2JccIIHf84qhN8h6ODrEdkBGRwWts9uXv4/dxuQn47ICux5BflS9KCgWwA3q3xG784K/moNg3uS+fDGh33m7VpGfeNCNGnRFLr1+KI4WsBi7MEIbeyW5Tl9JMQO3DvSrtKvx06ifeczxEH4LhzYcrHisy3yloQdRMN3GhcXiwm3wVZsAtDHFsyd6CB7o+AIqiQIESzX4B8fv3WZD1lgBJY1PXYooJpDDCf0MazVW+oiRcSN8pZ1sH1CcvaoOK3DlbaKbxImAKhVSB5qChCSPCSJtWUa343B/cpOZo0VHc+dud/DFCc7aaBk7pG+YWz9SKt2i2uiLKveJ6VwmhCYg2RaOopggUKExpPwVZ0VlqRXj9Xn1dV4dLpClc1LBLfBeZL2RD0rL5JIwYjUb06NED69at48NznU4n1q1bh+nTp8uOb9u2LfbvF69R8PTTT6OsrAxvv/12wLUdviB6GX56L95UzN5QitAQllObzijaHI1V41ZhT94eTP51Mt/JO5wO3jeE+7i4jtPmtGHaummysoY3H44RGSMAqAtYo1qMwuozq7Hh/AbWL0Sa9EwgcHnTHKVHpCM9Il1xn3BRK8DzwNg8ojm+G/2d6n4hWs0YEcYImHVmVDvYFNxbs7eqntM1oSvu6ngXPj3wKQDg/s738/toioaDcYBhGFTYKnCm9IwmAUuKWtIwDn/mGfGn/4kUaSct0zD4CSXthnS22C+5n+LxSvlHpM/Lo5nGB80I4KfVeAPA/V3uh9VhxbiscRiYOpC/115JvdAvuR+fJv5kyUkAEIWRisJ/vci9HeI6YO25tZrrJcuA7OfnJoyM5H6b9Wb8NP4nXCi7gLiQOD7XiWodvYT2+uqX9XTfp3kH3h9O/ID8qnzeyVZUbgC1mr7is5lm1qxZmDp1Knr27InevXtjwYIFqKio4KNrpkyZgtTUVMybNw9msxkdO3YUnR8VFQUAsu3BJhCaEW5GUGfNiGRA8YealhM4OAFAGJnSMZZ9N9JOsG1MW+gpPe84dr7sPH+e0nL2HFyEi8Pp8Nj4z5edr9W9APJQukDOGpXaSqghFEtHL8WYH8eoncajp/WY2WMmZvaYiRpHjWhGSVEUwLBC1dgVY2U+H1rbjteZlkYBQouAEcicNVLtYKCcNxWz6kruXWkGLD3O2wRCMZqmlj4jDU0zkhmZibeufku2PdIUiUXDF/F/d/qyEwBJGL7AP04pPF/I1PZT4XA6MDBVYT0vBWRmGn9rRhQSVQLsBKV9bHtNZXhMzOghukiNrOgsXhOzNXsr8qvyFf1HGpKWzWdh5JZbbsHly5cxZ84c5OTkoGvXrli9ejXv1Hru3DneRthY8ZuZxiUl78zdiavSrvLZZ0RzmGAtUDMBAexKnYD8OXx0zUeIMcdgy6UtuHfNvThceBjPbWEdjT3lkRBm05QOgtGmaLSObs07pcWFxCEzMtPn++GuwSXH8tc79BQ547oQT4vIFj6XL12tkxtkS6wlvCCSFMaaQPsm9xWHLnvA0/sFfJ8R+ZIO3p9w75WL1gpU56klgZiojSuY79jNKpoRqY+OURBCGRqjuW7ekp41JpQGWZmZRqF9GnQG3NflPs3XkZlp/KwFUPMf8gXuu8+vykdUBRslyY0fwm+vNuVLNfRCGq2ZhmP69OmKZhkA2LBhg8dzv/jii9pcMvCodC51gdMWfHnoS7SJaVNrzYjUw9ofTkdafArUMoUq1d/TLEUkjEgGQR2tw7LRy3h7fIg+xKOWRQ1OBbn38l7FuvuTQGjROLh3/sn+TwAARtqINTet8bkcPreDN82IP4WRAPqMfLDnA5wtPRuwzlOpPOn3JZwBC9uo8Lc0Ai6nIgeF1YUoqGLTfvODiSkcmPwjkL0XaO9ZmyZ1bL5QfkG1zo0JJc2IdLs/kA7gg1L9m3hT2C5qK4xw93//WtZk2y2hG7669isAtdOMiOrH+dMphPk2JMGWrE3jIhADzMR2E7Eth02V//Opn7H50mYA0Kw5Uoum8cfsUOpcq4S0M+bzBkg6wdf/9brHBa6E5ialWTRN0YgwRiieqxVhuvEIYwRGZnhecEsriur7AAoj4cZwVFdV80vbx4R4njWr4VUz4k9tho9hh77QI7EHlh9fDgDYcH4DWkayDu/+NtN4yvrLwSfuAzC6xWgcKzyGSnslbmt7G79dOMN/Z9c7+Hj/x6IyRM+75dXsP28ITmEYBkuOsFFr3DosjRWlQVYqPPujfQrb5Zqb1vCaRn8xptUYnCw5iS7xXWrdj43IGIFvj3wLhmFgZ+z8pAoQT0brksq+3FqOMmsZwgxhsoluo/QZaar4I7RXypD0IZjedTre2/MeL4gA2jtSrsHM3zkfnx34DMmWZDzT9xlegKiTMOJBdcchfQ7SvAEc0mRHsmsJtDCBcpia0GYCMiIy0C62He/F7w+0OrD6i9evep1fnROAYsIyLXDPfGfuTny6n3WUpSkaV6ddjYzIDK9Oghzc/duddsz4Y4YsfDMhNIFfuC4QM/XRLUejQ1wHjPlxDKwOa8A0I0qp+IXXiAuJw+3tbuf/zojMwLtD31UthwHDC1EAkBiaiHBjOAY3G+xz3dTy0gxNH6p0eKNBSTPiLQNrbRC+W61aaV8Y1nwYhjUfVqcyHu31KB7t9SiKq4sxaOkgPgkkTdGib7U27Z7TjDy35Tk8t+U5RJoisX7Cehh0Bs0LrNYHRBhxoTXNsK+MbTUW2RXZ2JO3h/ci1+rUlBiaiMOFh3G06Ci/7foW1/tVM1Jhq8Ddv9/NZycVomYzV0tnrnotl+Cz6eImPpTY38KIgTbU33LZIpcR/95Hj8Qe6JHYo87lcGmd/8n+R7RM+YbzG/DltV9qzzMiaAN/nP/D47HSkGV/wc02bU5bwEJ7lRA+mw+GfqBpsT/ueS3at4gPXf121Ld8NtDaILzX17a/xptp+qc0nuXhlVDzGQlURl+gdgug1ifCyByH0wFaR3t16PXGwNSB+PvS37wgX1JTgr8u/oUh6UPq9XvyRsN+M/VIIDQjAOsM+mz/Z/Hd0e/49WS41Ti98eLAF/FP9j9wMk68vettXCy/KJod1qUBRZmj+IWf1MJQ1dbmkAof3oQRbjAR5jRpCJK4FtrFtkO4IZx36gU8dwh3d7obn+z/BHd3urs+qqfKrW1vRbWjmh8Q86vyseniJn4VT61mmghjBMIN4SizucMC3x3CagRmrJ8hUqsPThvsxztwI0zmd6jgEIDA+epIr5scloxyW7nmb7Z1dGsA4hwa3JINtUV4r8K05v7UAAYbtSgkfyBso9IFShsawr7U5rTBoDOI6l+bdj+x3UTc3OZmMAyDHl+zEx3OR68hLS1AhBEFAmE/k0ZNaCHSFMnn8fj60Ne4WH4RJdYSPr1wXeoZYYzAkuuX4EQRm0b9TOkZfLj3Q9ExahoQqc+LN2Hk9na3w6gz4kjhEaw/tx4ZkRlBWxnSV8KN4fhl/C8w6QXhtx58Ru7tfC+6xncVJWQLBimWFDzZ50n+7525O7Hp4iaPKbmVMOvNWDF2BYYsc2eO5ISOdRPW4ervWJ+HOzveWaeF8TwRog9BqD4UlXb38vKpllS/XsOoMyI9PB3ny87zWgyaovHDmB9gd9o139vEdhMxOG0wLpRdwN2/swJpXWfjQoFxRMYINLM0Q3pEOrKi1JNoNQaUZvzCaCHAP5MW4WAuTVnf0BA6Q0sXhwRqPwHlyh2cNhgbzm/g/Y0afTRNUySQTokAOzjUBU6i//X0r/w24bLntaF1dGt+JpdTkSMTRtRWzJQKH94ccmPMMX5fyrw+iTJHif7mEpsBgLSphOhDcFXaVYGvlI/I0qr74MCqto5SXEgcfhn/C349/StuaXOLn2oqx0AbsGj4IuzNY536WkW1Qoso38OoPUFTNJbfsBzltnKRJkNrKLWQVEsqjLRbm+MPFfg7V7+DMlsZbmh5Q53Laiio5RnxNyH6EP53YzLTrDy5EhPbTfRrHh9OKOHyMQUyEs5XGvabqU8old9+oq6qWq5D49TsAPwWMQKwuSye7fcszHqzYjrsZ/o+w/9Wc6i7UhAu1NUqqlUQa6Idb9E1tSUtPK1eBM0u8V0Crk0z682iiJm6EG4MBwUKFEX5ZVn2q9M1RN00MmqbZ8RXosxReK7/czDQBk3rdwUTmqKho3RwMA68t/s9VhhRCYGuDdz9v7z1ZdzW9jYS2tsQCbRmpK4vm1Mv1tjZ9WPGtBzjt46Tg1sem0P4HIQzCm7dA7W/mzrCTIZafQmCDWcTFkY0AQ1jRtQUMevN+G70d2AYxu/faVNBuIIt55Q7+dfJ6BTXid/uL/PB+KzxfimnPvhg2Ae4b819qLRX4osDX4j63rqOI8L+ysk4carkFABipmlQNHRhhFPfcSvW1oe6UVhn4fWSwpLw1uC3cLz4ONLC0xq97dpXtC7L3ZDgTGlOxom/Lv7Fb28InVBTRUv0zZXIc/2fw4oTKzC9q3LizP357HpmMeaYBu/jEQi6xnfltSNv7nxTtK+uY9PkdpPxwZ4PAABnSs5g7t9zATSMnDVX3pvWQCA66DoLIy5TiDTTaCARaUYknYI/YusbK41RGBHmetlyaQu/3dcVUAmEujI+a7yqpmJkxkg+gWL7mPYNPvolEIQaQjG331zsyN2B/fn7+SUugLoLI8JJ5ZgV7sy/t7e9XenweoUIIy4ClWeEQxg6VZvwteEZw7Evfx+/0uWJ4hN+q5sawmRmwmiSKx2ltMoNHWGGS8557Z5O91yRnT2h4TFv0Dz8ef5PPNv/2YBFZTUmxmWNw7iscVh9ZjUe//NxOBknOsV1qrNGXLrURog+BFnRWZjSYUqdyvUHRBhxIRJAAqC5Fgo7ntaDUWNsq7EY22os5u+Yj88Pfo77OmtfKKq2xIXE4b7O9yGnIke0fPqVTmPXjHDCiDDKwButolrhRPEJJIR4XgqdQKgN17e4Hte3uD7Y1WhwjMwYiYEpA1HjqEG0ObrOWnvp5GNm95mizMLBhAgjLgKtGRFGnNRGGOG4v8v96JrQFX2T+/qjWl6Z3k3Zrnslww3mjQnOZ6Sopgg1DtYJ2pcFCZ/t/yze2fVOvQjBBALBjcVogQUWv5XXK6kXtudsBwBcm3mt38qtK0QYUSAQwohoVco6xI2HGkIxJH2I9wMJAaMxakaEPj8/n/oZgPc1hYR0ie+CT0d86vd6EQiE+uWT4Z+gsLoQMeaYBhHSy9FwatKACMQLEuYaaOix7gTPdI7rDACID1FOBtYQaRbeTLatTXSbINSEQCAEE5qiERcS16AEEYBoRngCHdobagjF21e/zf8mNF4e7PYgnIwTN7e5OdhV0QxN0Zg/eD5mbZgFABiUOgg9k3oGuVYEAoHAQoQRFyLHoAClXiDmlaZBYlgiXh70crCr4TPCFOUJocQRlUAgNBwalp4miAhVVldaenPClYHQR4SYCgkEQkOCaEZcjGs1DmdLz4JhmEaVOphA0Er7mPZIDktGUXUR+qf0D3Z1CAQCgYdi/LkkYIAoLS1FZGQkSkpKEBFRt5VqCYQrGYZhwIBpcM5rBAKhaaJ1/CaaEQLhCoKiKLI4HoFAaHCQ6RGBQCAQCISgQoQRAoFAIBAIQYUIIwQCgUAgEIIKEUYIBAKBQCAEFSKMEAgEAoFACCpEGCEQCAQCgRBUiDBCIBAIBAIhqBBhhEAgEAgEQlAhwgiBQCAQCISgQoQRAoFAIBAIQYUIIwQCgUAgEIIKEUYIBAKBQCAEFbJQHoFAIDRQrBcuoObECdE2fXQ0zJ07g6LIgoeEpgMRRggEAqEB4qyowKnRN4CpqpLta/bBBwgfcnUQakUgBAYijBAIBEIDxFFczAoiFAVzx44AAOu5c3CWlMCWfSnItSMQ/AsRRggEAqEBwjDs/5TJhMxl3wEALs6ahdJffgUcziDWrGHDOJ0o+mYxbNnZou3G9DRE3XILMW81UIgwQiAQCA0RxiVw0II4A4oW7yPIqNq9G7kvvaS4L6RLF5jbtavnGhG0QIQRAoFAaIi4VCOiebyOFUYYJ1P/9WkkOEpLAQC6uDhEjrkBAFDy/XI4SkrgKCsLZtUIHiDCCIFAIDREnC7th8CsQHGaEacjCBVqJLiEOENqChIffRQAUPHnn3CUlLifqR9xVlWh5vhx0TbKbIYpK6teTUKM3Q7GbhfXw2RqNGYpIowQrnhsOTko+fFHMFarYCsFy9AhCOnQAQA72ype9j2cFeWic0N790FY3z71WFvClQLDOY0IzTQ0pxkhZho1GAcrqPGCGwDQOtE+f3J24iRUHzok25745GzETJni9+spUbX/AM79+99wVlSItps7dkTGd0tB0Q0/pRgRRggNDsZuR8WWf+AsKxVtN3foAGPz5n6/Xv77H6B42TL59g8+QPTEiQCAmpMnUfnPP7Jj6C++ROsd2xvFx05oZHCWGOHMlnb9JmYadTjnXp3OvY37Pv0sxDE2Gy+I6JOTQdE0HKWlcJaVoeb0ab9eyxNVu3fLBBEAqD5wAI7iYuhjYuqtLrWFCCOEBkfJjz8i++lnZNt1kZHI2rwJlN6/zZazI4f27AlTmzawFxSgbPVqAEDRN9+IjjW3b4+Qbt3AOOwoXrIUzspKMNXVoEJD/VonQv1Q+PU3yHvtNbF6m6YR98D9iJ82LXgVA3gnVaGanXLN8IkDqwe45yaYIHC//a0Zqdy1m//d6rfVoIxGXP7gA+S/8y4Ymw2lv/0OR1GR6Bxzu7YI6dLFr/Xg7jl85EikvPQiAOBoj57svkaiRSPCCKHBYcvJBQDoExJgzMwEnE5Ubt8OR0kJnNXV0Fks/r2g62MNv+5axNx+OwCgbO0oVB08CKayEoVffsUfahk8GPEPPQjG6UTxkqUAgAvTHwRlMCCkWzfE3X+ff+tGCCjlf6yTmOcAOJ0oW7uuAQgjXGyvUDMSmEG1KcEoaUa43/7WjAjaDmU0sv+73lHZ72tQ8v1y2TmUwYCsvzdDFx7uv3q4NGWU0QA6LIzdqNMBDkejMekRYcRPMAwDp8RTmzIYQIeEiI6xZ2e7OxmwDVgfH19v9WwUcMLBsKFImjMHjNOJI+1Z3w3GZvP/9bioBUGnHz5sGMKHDYOjpEQkjFAmE/s/TUOflAR7Tg4q/v4bAFC+cSOibrkZ+uho/9eREBC4Tjxp7hyEDxuGyp07cXHmw4DEETAY8IOIyGfEs5mmav8BXJjxEJyl4r4orF9fpL7zToNwZiz/809Ubt8u2kaHWRB96y3QRUXV/QIu516KFmqUAiTEuTQSZpdvmetibDVcUT36xESEdO4EAChbuw6MzQZnaalfhRGuzxT7ydCAwyEabxoyRBjxE+fvvgcVmzeLN+r1SH3jDUSMHAEAuDjzYZT99pvs3IRHH0XsXXfWRzUbBQwXKeBSSVM0Dej1gN0Oxup/YYThVN6U3O+DDguDLjqaV7UamqXy+5p/8Tkqd+4EAGTPmcvOQmpq/F4/QgBxDU66qCjo4+Ohc9nWrefPI/uZZ2R2eMvgwYi84Yb6qZuCz4g3M03F5k2wX8qWbS9bsxbOsjLoIiL8XUufYGw2XHhoBpjqavlOmkbcvffU/RqcZoRW0Iz4OVmcksBI6cT9SGiPHkid/yYA4EjXbmCqq93OySo4q6pQ/udfYKrFSwGE9OgJo6APElREXg+KYptQI9Gi1UoYef/99/H6668jJycHXbp0wbvvvovevXsrHvvxxx/jq6++woEDBwAAPXr0wMsvv6x6fDBxlJaCsVrZxux08NKmPiWFn1EwDIPqQ4fgLBd3UjJBBADsdlTu2MELI1V79wJwqfNomrVT2+2oOrA/gHfVCHF1GMKPmjIY2NC1gGhGXP/T8lkjpdcj8/tlqDp0CLrISIT27MnvM2ZkwJiRAQDIeeFFMA4HGHvj+PC94Sgrg6O4WLRNHxPjVgE3EaSCKOePxFRXo3jZ97Ljy/5YD31SkkwLamrdGsa0NH/XzlU3uZmm8IsvUfz9cugiI5D61lswtWrFnuFqfxHXXYf4GQ8BDIOTI6917WsA2h6bjRdEoidNAqXXo3L7dlQfPAhnaYl/LsIJCDoln5HaP4OakydR8fcW0TZOKyrqO6STGoG5iKJpTQJC/qJFKPhwoeK+qAkT2EtaLIi98w7o4+PdeWdoBZNeI3F29lkYWbp0KWbNmoWFCxeiT58+WLBgAUaMGIGjR48iISFBdvyGDRtw2223oX///jCbzXj11VcxfPhwHDx4EKmpChJekMhfuBCXF7ytuC9y3DikzHsZAFDyf/+H7KeeVi2n1R/roI+PR8Enn+Dy2++g5sgROGtqQJtM/Kw5c/n3MGVloXDxYuQ+/4JXz/iCL75A4edfiO2dBj0SHn4YkaNH+3ajAaDwq69Q8OlnCvWbhcjR1/teoIKmgjIYwFRVgbFZVU5yncowKFu7ljWHCTCkpyN88GDlkxTyOYjOTU2FwUtbpXQ6tpNpRPkfytavR8nKlW5hDKzNOXzIUFx6/HGZloe2WNDy998ahWe+ZhzigYsS+hkACOvfD5bBV8NZWYnLCxaAqa7GuSlTZcXQoaHI2rxJZJatM7zq3d0uOaHDWVkJZ2Ul7Hl5KPn5ZyTMmCE6RxcV6Y4843wHbA1AGBH0EQmPPgLaZELeG2+g+uBBOEpKUXXgoOh4OsQMY4sWPpmXOM2q0GTBCWL2y5drXfcL06bDeuaM4j6ZeUS4T/i3q32V/bFepGWlaBqhPXtCFxnJ1jM3DwBgaJ4OY7M0OCsqULVnDwCIIv9oSxjr28S1FYnTLgM0Gmdnn4WR+fPn45577sEdd9wBAFi4cCF+/vlnfPbZZ3jiiSdkx38jiUb45JNPsHz5cqxbtw5TVGKwa2pqUCPoCEtLSxWP8yeV2wQ2TJ3OLUnbbKgWaC6sZ86yh0RGQhcfBzicsApCuCijkfUVcc0gK3fswMnhI9Dy1194Zyepo5PaAMZYrTh3z72o3LpVcX/JipW8MFL2xx/IffElOIXOeBQQffMtiH9wutbHUCuKv18Oe26uQv1W1EoYYRwKMxuDAQBw5qYJyFzxI4zNmimeW71vHy4++JDivha//AxTixbyHQofss9wM+o6aEaqjx1D0eLFIu0PRdOIvOEGhPbqVfu6Cch99TUUffstwDCqJqWyNWvZfTQN2mwGwKqNneXlsJ45A31MDBi7HXlvvAnbJfUF2/Tx8Uj47yzQDTnSiHv3nBAiidQyd+mCmCmT4SguxuUFC/jtuthYXhNStXcvnJWVcBQX+1cYUXBgjb7lZoT17QNnZSUKPv8cpStXoeDDhQjr0xdhffvITJwAq+1hHA7ArqxVZGw2VB87JpoUUQY9TK1b1+mbKPvjD1Ru3Sa9mvsaXNk69pkXL1umGGKf8NhjiL3zDs3Xteflucp1PwNunRpHYRFKf/9ddLwuKgqhPXt6vVd7YSEAIOxfg6CzWFBz+gxqDh9md4qEAIngJNGMAEDeq6/Kyg8bOBDpn3zM/uHSnETffDNi77oLAFCxZQuvXa/YtBmVO3bAWebKe6Rkag5QOHOg8EkYsVqt2LlzJ2bPns1vo2kaw4YNw5YtWzyc6aayshI2mw0xHmZX8+bNw3PPPedL1eoMJ7GnvPEGIq8fBQCo2LYN56ZMdQ+OcDtARd54IxIfexSO0lIc6y1IeuVqAGH9+8OQkgLbpUuw5+biaPce/CGcMMI1HDU1WvWx4yJBJP3LL6GLjED5n3/h8vz5IpVj6U8/Kw4Mxd9951UYufTUUyhd9ZNoGx0SgpTXX4PlX//yeC7gfibJL74Ac8eOqNi8GXmvv6E42FXt34+8V1+DU2IzjhwzBjGTJ7F/8MKB+yM2ZmagqqAAzooKlG/YiJhJExXrws18dNHRCOvXDwBQvmEDO1hIQuz4+ruN817vVQ2+I6uDGrhg4UJ2ETQJVQcPosX//V+tyxVS+tNPMnt9wqOPgDKbUbVzF0p/+YVfsj6kWzdkfPM1AODU6NGoOX6C99mp3LEDhV984fV6oX16I2L4cL/UPRAwEq2YNGyccg2UUiEl/JphSH72WQDA0e492BBvlxDprK5G4Zdfse1NR7Nl6GiYWrTwSZPpVr2LB0lO4xF5wxiUrlwFAKjat49Nvqegrqf0ejA1Napmmouz/ouyNWtk22OmTkXibPkEU1PdrVZcnPmwPFJJCC02jfGbIyJAh4bCWV4OZ3k58hcuRMWmTW6TGgBKb0Ds3XcjrI/Y3F+yahXy332PLcdk5Leb2rSGPScHBYsWKVYl9e23ETHCSzt1tZWkJ5+EMSMDhV99hVxeGBGaR8TaNZEPiUAwMTZvDl1MDJyVlag5ehTVR46gdDXrU1i1f7+srLB+/fg+jbFaUbljB/983b4rSmaaJiiM5Ofnw+FwIDExUbQ9MTERR44c0VTG448/jpSUFAwbNkz1mNmzZ2PWrFn836WlpUjzuz1WAj8ACj5iHZe1z/0RM67ZBf8BSaVpV6dmatUKLdeu4aNAOExZWXz0DN9IVRoLY3UP5q23beWdz3hVoWAW7nQdG/efBxA+YiSs587i4oMPafIeL/3pZ1mn4bBaUbLqJ1ZoENZPp0NYv37i8FrXNYwZGTC3bQvbxYuu+ss7ouLly1G5Y4dsuy07mxdGlJzC0j/+GKduGAPb+fPuD9DhgPXsOQhnWzUnTwFgbfic09jJa6+D9fRp9Weh0un7hF4nrrsEhmHgyM8XOa5RBoMo8sZZUQkACB8+HOaOHWG7dBHFS5aCcW1XomTFClRsESdjoy0WxN13r2KUFqc5S//icxjT0qCLiwPtihAqMhhQ+ssv7voJVeMuzdSFadNgbN6cz81izMhAzFS5hrPw629gPXkS1YcPy9q3Lioaob17BTxRHMMwKP7+e7nJLjUVkePHs/fHZet0fetSMw33jcqEFKHmwWgEKiv5dlm2dh0uv/WWYp3Kfv+d7QOSkhA1YYJn84NSaK8Ay8ABiJowgdUmcH0UH0kiuA9ea6csjFQfOwqAXcuFMhrAVFbBUVyMmhMn1OvmBWd1Nf88Yu++C6BoVGzeLM5Uygsj4meetf4P0GFhKPj8C+S9+iqcpaVu3wwBlF4vE0YqNrl99yKudwt+UjNKSLduAADr6dNwFBZ61PBx8N8210aEGg9R+ZL3JXKkdR8X99CDiBw1CjWnTuPUddfBkZ+PizNnik6VOsPy210TWltuLqxnz7r7MNHSAVzkVRMURurKK6+8giVLlmDDhg0wu9S/SphMJphcHWR9oaTe5AcnoQe2SwDgPiBph6rWwTb78AOE9uoFOjTUfQyvGREPkracHNiys1F9kP1wTVlZYi94Xkhiz3MUF6N87ToArG+EuU1r3qyhxWmNm9FlLFsGfVwsCr/4AoVffoXSVatQumqV7PiI669H6huvu8/nhQfXMzGy765q714cbtsOpnbtED99GsKHDgVTxc7KI28cj4jhw2HPz0f2U0/zs3H2hrgBwv0s6ZAQhPbpjZLz5/n6Xpgxg79vKZRgVsR3AGq+OXynr7xbC9wMWu15X3rsccVnKVRBc8/RcvXViBo3FlV797LCiEqZjNWKS089rRiGqo+LRdz998vPcWmrjGlpMj8Y6UAs6mxd7clZUSEaUEL79EH0bbfJrlO2fj2sJ0+qOuGlvvsOIq65RnGfv6javRs5z8xR3Fe0ZCnChw6Bk2t3/Gq4kjbieq9ans2p60ezA7+r/ZqyshA2cCDgdPDh4WVr1qJszVr2RIfDbX6jaRibNxdfRyHpmRTK4Gp3Ln8QRROnSxi58OBD0EVEIPa+e1G8ZClsl1lzhu0iOxCnf/IxzG3bovSXX3Bx1n99dhZnGAa2c+fA2GxuLSRNI/6//wVFUciprna3HZp235dOeRiijAbR33EPToexeQYqd+5A8bdLFOvHfSsJjz6C8CFXu3cInisdHs5r/C7NfhIlP/zATzK5+2AqJRMAg0HgW6bgX6SQYI3/W+RIK283xswMRE+ahOojrJal+uAhd38obXfcua4+tnzdOpSvWwdz587sdkpBC9MUhZG4uDjodDrkSvwDcnNzkZSU5PHcN954A6+88grWrl2Lzq4HF2yKliyFLTsblMkIe3YOu1HhIxYKC/zsmnvRUuFDtNy3uxPRWSzyZF20fJC0XriAkyNGirytKYngxg98rtlQwSefuK/jil3nOykvwgjjcEcNGZqlQh8dDTpcHP5natsWuvBwPjdA6U8/IXzYUESMHMke4LoG99GZWrcGbbHAWc7aM2sOH0b+R4sQPnQor8Ext20Hy1VXweZqS87KSpT8/DN04eG8ZkWm7uQELFcnxHVstMUCSqdjF8JyQQuEWb4DUHMu9YPPCHfumZsmsO/d6YQpqxVCe/WGuUMHd14Fmub3g2F4pzQA/OyW77z496xcb6fVyj/7+JkzQRn0KN/4Jyq3bYNDkmcCcHWyLmGEUhL2PaiXaaP4+LRFH4HS6xHSoweUEM3MKYqPQqo5dQqOggL3O/YzzspKftXWmuPszF6fkIBwl+DDZdSt3r8f1ZwqHAINiEE8APLPQKYZcT8bQ/N0t2Ok4HuLmToFUTfdBACIHDsWxT/8ADicfB1ynnteVBHZ9LoAABzDSURBVKYuKgopr7+GkG7d2L7Ci2aEPUnSRhTasjEtDVUFBbCeYrWGlx59jP82+fsxmWBITmZ/u2bdvgojlxe8jYKPPhKXazbzQoeSIycAVZ8i3pztInzIEJjbtQMYJ4q/XYKKTZtw8bHH3MfTOlRs28pfV1SWQs4RQKDxcr23muPHcWq0PHSbMhrdPn9cm1DK/wLIviM1zQilZ9saRVFIevopfvvpmyag2hV9KhOCXVgGD0bpzz+j5vRpMJWVsJ48qVAnz24AjvJy5MyZ6/axAZtvx5SVpXh8oPFJGDEajejRowfWrVuHsWPHAgCcTifWrVuH6dPV/RJee+01vPTSS/jtt9/QUxAaGWxKfvxRPBhA8sFwv+0O1ibscPBx31xDkptpVAY0hUalZKaxnj3LCiIGAwxJSaBoGtG33iI+j1NrurQ09sv57N96PTsTg/wjU0MorHAdsVQ1mPzcswjp0gX2ggIcH8CWX75+PS+MuNWX7DUNiQnI2vQX7Hl5KPziSxQtXsx3fkyN64N2aS5oQadx6b+PSO5TMgC4nnnhF1+g5ugRvrNs/tWXMLdvD3t+Ps7deRccxcWIGDXKfSIf1qc2Q+A6/doLIyE9erCqXobhB5Ga4yf4AZGjxaqVMLVsiaLvvkPOnLkiQUOaH4EXKFX8UISDRezdd4HS6+GsqETltm2o+Osv5FRXIbRPX7ct3GZzJ3hTEEZkjneCTjR68iQ4q6tBm0yInzlDFOKsiED1ToWEoPn/WM3ApaefRsn3y+EsLRUt505bLJojJi6/+x7yFy4UfTeUXo/oSZNQtHSpbFZratMGSc+wEXAxU6egZNUqlKxcCdvZc4J7VRhgAP67pSiKj0oRbgeAtIULUbVnL8AwMGZmgDIYQZuMogRe5nbtkNSuHQAgpGsX5L32Ov/+uBBqR3Exzt9zL0L79kXzLz7XJIzw34irjUi1lACQ9vEiVO3dh7J1a1H87RL+WzS1bYuE//4XAGDMzOQjObh+wFFYiPKNG2XXtF26hOqjR2XCe9HS79jzQ0NBuwSJyDGCgV3BkRMAIkZdB9v5cyj+cQWibryRd/43CCe4Op3b7Cj4TjmfGflzEQuVijlHINcsFS9X9s0SmZw585JAo+PJTCPSjAjPkQi+ittVJkimFpnIXP49Lj72GEpXrnL3BbTcTFO+cSPMbVrLyqjYtElklgWguL5NfeGzmWbWrFmYOnUqevbsid69e2PBggWoqKjgo2umTJmC1NRUzJs3DwDw6quvYs6cOVi8eDEyMjKQk8NqICwWCyz+TuvtIxHXjkTVvn1iNZYoeQ3baO15eWyef6G9Xy/opATIOnR+u0KjUjLTuIQDc+vWyFwuz3MAgP+YnNXV7KJMLm1D4uOPuzUCnCnHasWZ2ydCHxuLpOefk2UHLVm50l0d7iOQDMqUK0JAHxuLxGeeRu4LL6L0t99R4YpAsrveqWgmbTbDmJ6OyHFjUbR4MaynTuFw23bu/a566iIj3XZvF+YOHUBHhCNc4lDGhzWWl7tV3QA/a9XHxaHFyhWyx2XPZ4W18/fcI5L6GbsdcQ/cD3t+getGa2+nSXntVSQ+9iivfSj96ScwNjvyP/hAdBztavN8pyQUFh0SEyDXadrsvK+JqL1x53IDJQBdXCwAdoZXc/w4ir9fjvChQ1hBRZi6WoNmRNixRQwf7pMjqkgdLZwFm1jhM/+DD5H/wYf8dsuwoUh77z0wDIPz993n1lRy50VEIPmF52Fq0QJla9fK8jQwNhsKP//cfU1OsDYYED7cbQ4ypqcjfto0VP6zVSSM8LNz6fcsmJ3zUSkQt3WdxQLLwAGeHoeIyNGjRY6sjNOJ7KeeRtXevbCeOgXbhQuu7d59mbi2wnBtRKl+4eGwDBwA2/lzonP1sbGwDBooL9P1jqxnz+L8fXJTnzeSn52rmBhONMsX/NZHRyNx9mwkCgIjACBswAA0++AD2AvyYWrZCvq4OLYcQbuMHDMGpjZtUH3wIEp//tl9Len6VSqaEd6fhnM+dgmyEdddh2TXGi+X335H7KzNm5eUw3lNmZmiSxtbtOR/R904HoX/+xqG5GSEdFVen0YojFAqJiz3vbjeP6edFqZDCGX77cvz58OYng7KZGRTTNhsYJxOVO7cBYDtb2PvuRsAYAjAQqRa8VkYueWWW3D58mXMmTMHOTk56Nq1K1avXs07tZ47dw604MV8+OGHsFqtuMmlruSYO3cunnV5oweLmKlTQYWGiuzKwg9GbL91CyJ0WBhCOfW0VOMhyYCntN29TZ7amW9UHhaD4xqo9eRJUSSP0E9CFxEBOiyMjU/fxTY6y9AhiHJptAD2w+PvXadzfwSSunJCAMAuFAewSaGkjoGywQzsjEsfHy+K76dCQ0Xpk5NfeJ4XRgzN01WFsKibJyC0Zw+cue12PtUy4PlZAYDDJYwA7CAt5NJjj7vLUREktUBRlMhhNO6BBwAAplYtUfz992AYBiGdOsPg+k74QUQgjEgdd7n7chQX40g79rlzkQa66GgkPTmbP45ra1FjxgB2O+wFhSj46CMwVivyP/gAZWvWiMKOlWZlUo0YpfA+NaOTC/UAm7ej+PvvZZFWnONh5ZYtqPjzL8Uiy35fA9P99/GaotR33kZo9+4o//MvZD/5JH9caJ8+aP7lFx6rJ5wBxs94COaOHV2VFbcBoV8Ln0sGULXl1waKppEy72VUHTiIMzfdBNuFCzh9401us6OnZumqR+GXX6Lwyy/d2xXeneyd65XvIaRbV4SPHMkLRUowNdUI6d4D+rg4OCsqxIO12rPRqfSNKlA0Lfb74BD0T5ZhQxFxzTVgnE5U7d3L15nTeLjLUtGM6N2+dfaiIhR/x2p3zB068GHalFksuLudnQXXEPQdoT17ouXatXAUFYIOCxOlE4i7/35FXy7R7Qkm6XSYl7B47h0qOP2nvPwyzk5kgwKkjrFCjJmZbpN7EKmVA+v06dNVzTIbNmwQ/X1GJUlMQ0En8Y8QaQUkUmnqggWwDLkaFE27B0DpR6U2i1EwAbjzjAhCh7nERAb1V2Pu0B6mrFYyE4BQrUmbzcj84f9QfeQICj/9DFV79yL7idko/nYJADbDJmdDBoD0Tz/l6yMclKNuvUU0kwjt1g2t1q2FvZB1UKs+sJ+3fSt5fussFrRat1askg8NrVU+BoqiYGrZErTZ7JMwIiT9888AsI6EVfv28bZZ1wV8rpM3Iq67DhHXXSffoeQPIons0Ccnw9CsmWhQcJaWwllaCntODi5yZi3BIEOHhSFmyhQwdjtvvxdqIAA2pFBxMJAOYHUYcEUdteD9hA8dijY7d/CCl6OwECcGXw2muhplf6yHPd8ttHLvqvCr/6F8/Xq34Ob6RvSxsdDHxUEfHye+ttm78zsjiMDiBEdAPEgmPPaYOCJJOGOti6CmgnAArT7oTv5lbKYeSRjarRsKDAbWBMdhMCivCqsWtiyBNpnQbIFyNJASjuJikTAiM5Fw2wXPrE7mAOHsn3MwpmkkP/8czt15l6sOUs2IsoMpJ6BZT5/GuTvu5LfrBBpk2XPizL5CTaOkfzc2SwWU0rZrIP7B6ezSBJGRXlMryNqhUCjq0QMJjz2GUtcK5ExVJRzFJaDMZtaESFGgjUaZG0CwuOLXprH8a5BoYBcOqMbm6Qj71yDUHD4CfXIywvr15W2hHHIzjYowojTrdjWkyj17cHwQ2+g4DYLaBw2watcWq1axOTc2boSzxgp9XCzCBohVxcb0dBjT01H666+AK1kOlzRHSPjw4QgVhsgJPiylj0GYkVToha40GwNYVbc+Nlb1fnxF5mSoURihw8L4OP2wfv1QuX07zk4WhKXWwWfEV3jnaKsV1UeOgHE44Kx0ddCuNkSbTGi5+ldekGOqqmAvKsLld95BxcY/3Y5nCg6ulF7PliMQdFPefAP62DiY27VVrpTU1l0XTZHgOxJqpri68SW7OkUwDC785z8wd2IXFIsYPZp/V2Xr/gDg9p1hJEIb53jJYUgS/+1bxYWh/eL2QIeFwunSVlAh6tGAtb60pB3H3HEHLFcPRojrmShhueoqtPlni8gER5tMik6h+jhxqLcvQrwnaIm5XRqqyyN4nqb27ZSP0XRBYQ4VgZZDaHr04HAs0oy4tMnlgkm0LiqKX8KD3aAcMWlsni6ok//6DnO7dkh+7llNx0qftXT8ib3zDp8SxgWTK14YoUNDkfziizhzy62uDWIzTbpKkhz1AlXiwhVmmaYWmeyHYbPJ0hSreVGLLhUWpjzrlsBUusNmLVddhaibJ7guQiO0Zw/54lnCmYPR8yzTlJkJOiIClNEIQ7LniCp/Ie1odVoFHekz9WBXDjRcJ1K1axdOjx0n2acX/eb9fKKjYUhJgbF5c/DzSp2O1yDIrmEyiUKmw/r29SgUytpcHWb/5k6dUbJipdfjaLMZCY8+irzXXgMAPsJF2CZ5ocBlZuI1JK4Zq6lVKzT/+n+wXrgAymCA5aqrvFdQbQUGkQO7+P6Tn3sOpb+uBmU0IPKGMd6v4SNS4cDctg3CNKzhRYeFaVozKKxfX8n1/KPdofR6mNq147ORqgk5Qv+bMGGiSF+vp/KOhNoMXUSk+CQVzUjkddehatdufq0hOioSyXPnivoYNc0IZQ6Rbat3pHWrxwmVv7nihREAMLZsCX1iIhibDabWdQxrkmhK6NBQOCsr+QXVhJiyspC1YT3sBawD5dnJU/iPgvNs9wchPXqgfONGGFu1RNpHynkfhAhnxMIshkrooqKQtXEDq/LzkDtGK0qJuqTEz3gIF//7CJiaGkRPniwK41XEpSGQOpbJOs36XF5dQdjUJyW5HNu6ejw19q67QJtDENavL689UCLuvntF6y15M40Z09PFf9fBmS1m0kRQJqNqng8hsXfeAWdlJfLfe4/fxiWlAsAPOIzNBuuZM+7wZIFZI7RnT+8RPkLUVk0VtgHJjNgyaBAsgwZpv4aPSDUMtB/7AIAVNmMfuB8FC9mw7DAN2ZW1oo+PQw2XjFRFMIoYORJVO3aCsdsROX6c4jGaEIXmur8jU4sWSH7pRTgrqxDaS9wWhG2fEvw2ZmS4U7CrIBPaXAO+IdG9Fps+wXu/FQhkmvj67MP8DBFG4PZrAMOohlt5ghM4QFEys03Wlr8Bu111oNbHx/MDcPNvvkbpz7+A0tGIHFeHj1VC7D13I6x/fxgzNA4utIrqU+1wP6zHkfTss7j83nt8qKEnwocNQ9u9e2AvKOCXfPdE6ltvofCrr5D42KOi7fKsmvU3qxAlHNPp0GrtGpm5QfXcxEQkzHrY63Fx998POsyCym1bETZggNc1YkxZWWi55nfYc3NBGY1up85aEnXTTayfT1vvKvmYKZOhi4yEs6oKESOGiwQhbjAo/OILsW9CXZxINQgjgfAL8YQ+NhbJL76AqgMHoE9IgKV/f79fI2HGDMQ/xK7d5Mvic97QCxZJFQmSAoxpaZomQ74gbQNRN96oeFz05ElwWmvA1FgRNW6sb9cwCfpumuaFYH18PDKWfw/b+Qt8SoX6xty5E2/mZLNj9/V+UgOFCCMu6mI/bbbwQ1Rs2qzYEGiTCdCYTdbcujXMreXx4HWFoiiEdOzg/UAOoU22njLhRt96i8+OVFr9UCJGDFdcd0LUyQD1Oqswt26NzBUrYM/JhiEtTbMg4isxkye51/zRgDEtjV8Arq5QFMUn/fKGLiJCvZ4KQkFoz56K2sa6QnnQjNQHUTfdpPmZ1RZ/CiEc8Q8+BGNqKkL79q2bkKgBYQZgWmpiVsGUmYmUF1+s1fUiRo5A9YEDcBQXI6x/P5EmNqRDB4R08KFv9TORo0YhrH9/MNXV0MfF1Woy3VAgwogfCOvdW5Ntt7Eg8jY3ejbTNGaMmRkwZWWh5vhxUKGhMAVAEPSEuU1rQCEZEUGMdHBre/hQ3QfUBqgZacwYEhNEUUmBRNgn1cc3q4+LQ8or8wJ+ndoizR3VWCHCCEGGULpu0Mu/1xGKotBi1UrYi4pUIxAIwUcX5fadiL3vPv/M7LX4jATLKZHgkZhJk1C9/wDChw0NiJaHEByIMEKQYRkyBBHX/c2G8EpWaG6KNJWZRVMlcvx4MA4nGLvNbyYMNfMjU13N/xYm+yM0HOiQEDR7523vBxIaFUQYIcjQx8Qgdf78YFeDQADAOpjH3vFvv5YZe+cduPTEbERNmKB6jKlVS9V9BALBvxBhhEAgXHFEXHcdwocPlzmu61NSEPavQaDNIX6JEiMQCNogwgiBQLgiUYqgoyjK90SHBAKhzhAPLQKBQCAQCEGFCCMEAoFAIBCCChFGCAQCgUAgBBUijBAIBAKBQAgqRBghEAgEAoEQVIgwQiAQCAQCIagQYYRAIBAIBEJQIcIIgUAgEAiEoEKEEQKBQCAQCEGFCCMEAoFAIBCCChFGCAQCgUAgBBUijBAIBAKBQAgqRBghEAgEAoEQVIgwQiAQCAQCIajI19BugDAMAwAoLS0Nck0IBAKBQCBohRu3uXFcjUYhjJSVlQEA0tLSglwTAoFAIBAIvlJWVobIyEjV/RTjTVxpADidTly6dAnh4eGgKKrW5ZSWliItLQ3nz59HRESEH2tIkEKedf1BnnX9QZ51/UGedf0RyGfNMAzKysqQkpICmlb3DGkUmhGaptGsWTO/lRcREUEadz1BnnX9QZ51/UGedf1BnnX9Eahn7UkjwkEcWAkEAoFAIAQVIowQCAQCgUAIKleUMGIymTB37lyYTKZgV6XJQ551/UGedf1BnnX9QZ51/dEQnnWjcGAlEAgEAoHQdLmiNCMEAoFAIBAaHkQYIRAIBAKBEFSIMEIgEAgEAiGoEGGEQCAQCARCULlihJH3338fGRkZMJvN6NOnD7Zt2xbsKjU55s2bh169eiE8PBwJCQkYO3Ysjh49GuxqXRG88soroCgKM2fODHZVmiwXL17EpEmTEBsbi5CQEHTq1Ak7duwIdrWaHA6HA8888wwyMzMREhKCli1b4oUXXvC6tgnBO3/++SdGjx6NlJQUUBSFH3/8UbSfYRjMmTMHycnJCAkJwbBhw3D8+PF6qdsVIYwsXboUs2bNwty5c7Fr1y506dIFI0aMQF5eXrCr1qTYuHEjpk2bhn/++Qdr1qyBzWbD8OHDUVFREeyqNWm2b9+Ojz76CJ07dw52VZosRUVFGDBgAAwGA3799VccOnQIb775JqKjo4NdtSbHq6++ig8//BDvvfceDh8+jFdffRWvvfYa3n333WBXrdFTUVGBLl264P3331fc/9prr+Gdd97BwoULsXXrVoSFhWHEiBGorq4OfOWYK4DevXsz06ZN4/92OBxMSkoKM2/evCDWqumTl5fHAGA2btwY7Ko0WcrKypisrCxmzZo1zFVXXcXMmDEj2FVqkjz++OPMwIEDg12NK4JRo0Yxd955p2jb+PHjmYkTJwapRk0TAMwPP/zA/+10OpmkpCTm9ddf57cVFxczJpOJ+fbbbwNenyavGbFardi5cyeGDRvGb6NpGsOGDcOWLVuCWLOmT0lJCQAgJiYmyDVpukybNg2jRo0StW+C/1m5ciV69uyJCRMmICEhAd26dcPHH38c7Go1Sfr3749169bh2LFjAIC9e/di06ZNuPbaa4Ncs6bN6dOnkZOTI+pLIiMj0adPn3oZKxvFQnl1IT8/Hw6HA4mJiaLtiYmJOHLkSJBq1fRxOp2YOXMmBgwYgI4dOwa7Ok2SJUuWYNeuXdi+fXuwq9LkOXXqFD788EPMmjULTz75JLZv346HHnoIRqMRU6dODXb1mhRPPPEESktL0bZtW+h0OjgcDrz00kuYOHFisKvWpMnJyQEAxbGS2xdImrwwQggO06ZNw4EDB7Bp06ZgV6VJcv78ecyYMQNr1qyB2WwOdnWaPE6nEz179sTLL78MAOjWrRsOHDiAhQsXEmHEz3z33Xf45ptvsHjxYnTo0AF79uzBzJkzkZKSQp51E6bJm2ni4uKg0+mQm5sr2p6bm4ukpKQg1appM336dPz0009Yv349mjVrFuzqNEl27tyJvLw8dO/eHXq9Hnq9Hhs3bsQ777wDvV4Ph8MR7Co2KZKTk9G+fXvRtnbt2uHcuXNBqlHT5dFHH8UTTzyBW2+9FZ06dcLkyZPx8MMPY968ecGuWpOGGw+DNVY2eWHEaDSiR48eWLduHb/N6XRi3bp16NevXxBr1vRgGAbTp0/HDz/8gD/++AOZmZnBrlKTZejQodi/fz/27NnD/+vZsycmTpyIPXv2QKfTBbuKTYoBAwbIwtSPHTuG5s2bB6lGTZfKykrQtHho0ul0cDqdQarRlUFmZiaSkpJEY2VpaSm2bt1aL2PlFWGmmTVrFqZOnYqePXuid+/eWLBgASoqKnDHHXcEu2pNimnTpmHx4sVYsWIFwsPDeTtjZGQkQkJCgly7pkV4eLjMFycsLAyxsbHERycAPPzww+jfvz9efvll3Hzzzdi2bRsWLVqERYsWBbtqTY7Ro0fjpZdeQnp6Ojp06IDdu3dj/vz5uPPOO4NdtUZPeXk5Tpw4wf99+vRp7NmzBzExMUhPT8fMmTPx4osvIisrC5mZmXjmmWeQkpKCsWPHBr5yAY/XaSC8++67THp6OmM0GpnevXsz//zzT7Cr1OQAoPjv888/D3bVrghIaG9gWbVqFdOxY0fGZDIxbdu2ZRYtWhTsKjVJSktLmRkzZjDp6emM2WxmWrRowTz11FNMTU1NsKvW6Fm/fr1iHz116lSGYdjw3meeeYZJTExkTCYTM3ToUObo0aP1UjeKYUhaOwKBQCAQCMGjyfuMEAgEAoFAaNgQYYRAIBAIBEJQIcIIgUAgEAiEoEKEEQKBQCAQCEGFCCMEAoFAIBCCChFGCAQCgUAgBBUijBAIBAKBQAgqRBghEAgEAoEQVIgwQiAQAs6///3v+kkpTSAQGiVXxNo0BAIhcFAU5XH/3Llz8fbbb4MkeyYQCGoQYYRAINSJ7Oxs/vfSpUsxZ84c0Qq3FosFFoslGFUjEAiNBGKmIRAIdSIpKYn/FxkZCYqiRNssFovMTDN48GA8+OCDmDlzJqKjo5GYmIiPP/6YX007PDwcrVq1wq+//iq61oEDB3DttdfCYrEgMTERkydPRn5+fj3fMYFA8DdEGCEQCEHhyy+/RFxcHLZt24YHH3wQDzzwACZMmID+/ftj165dGD58OCZPnozKykoAQHFxMYYMGYJu3bphx44dWL16NXJzc3HzzTcH+U4IBEJdIcIIgUAICl26dMHTTz+NrKwszJ49G2azGXFxcbjnnnuQlZWFOXPmoKCgAPv27QMAvPfee+jWrRtefvlltG3bFt26dcNnn32G9evX49ixY0G+GwKBUBeIzwiBQAgKnTt35n/rdDrExsaiU6dO/LbExEQAQF5eHgBg7969WL9+vaL/ycmTJ9G6desA15hAIAQKIowQCISgYDAYRH9TFCXaxkXpOJ1OAEB5eTlGjx6NV199VVZWcnJyAGtKIBACDRFGCARCo6B79+5Yvnw5MjIyoNeTrotAaEoQnxECgdAomDZtGgoLC3Hbbbdh+/btOHnyJH777TfccccdcDgcwa4egUCoA0QYIRAIjYKUlBRs3rwZDocDw4cPR6dOnTBz5kxERUWBpklXRiA0ZiiGpEUkEAgEAoEQRMh0gkAgEAgEQlAhwgiBQCAQCISgQoQRAoFAIBAIQYUIIwQCgUAgEIIKEUYIBAKBQCAEFSKMEAgEAoFACCpEGCEQCAQCgRBUiDBCIBAIBAIhqBBhhEAgEAgEQlAhwgiBQCAQCISgQoQRAoFAIBAIQeX/Abrvmeji/3SVAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.rolling(n).max().plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fEldJh0jO-N8"
   },
   "source": [
    "### Datetime Data (Yay Finance!)\n",
    "\n",
    "Let's use Yahoo Finance and stock data as a relatable example of data with datetimes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "jSQFF4_PMFyX",
    "outputId": "e290bbf0-00e8-46ab-b733-d8652f8a0c3f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "%pip install -q yfinance\n",
    "import yfinance as yf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 697
    },
    "id": "NBd9jY74MOs_",
    "outputId": "34453976-3b01-4b41-ce61-3b936aa5ba08"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "YF.download() has changed argument auto_adjust default to True\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  5 of 5 completed\n"
     ]
    },
    {
     "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>Price</th>\n",
       "      <th colspan=\"5\" halign=\"left\">Close</th>\n",
       "      <th colspan=\"5\" halign=\"left\">High</th>\n",
       "      <th>...</th>\n",
       "      <th colspan=\"5\" halign=\"left\">Open</th>\n",
       "      <th colspan=\"5\" halign=\"left\">Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ticker</th>\n",
       "      <th>AAPL</th>\n",
       "      <th>AMZN</th>\n",
       "      <th>GOOGL</th>\n",
       "      <th>MSFT</th>\n",
       "      <th>SPY</th>\n",
       "      <th>AAPL</th>\n",
       "      <th>AMZN</th>\n",
       "      <th>GOOGL</th>\n",
       "      <th>MSFT</th>\n",
       "      <th>SPY</th>\n",
       "      <th>...</th>\n",
       "      <th>AAPL</th>\n",
       "      <th>AMZN</th>\n",
       "      <th>GOOGL</th>\n",
       "      <th>MSFT</th>\n",
       "      <th>SPY</th>\n",
       "      <th>AAPL</th>\n",
       "      <th>AMZN</th>\n",
       "      <th>GOOGL</th>\n",
       "      <th>MSFT</th>\n",
       "      <th>SPY</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-01-02</th>\n",
       "      <td>37.667171</td>\n",
       "      <td>76.956497</td>\n",
       "      <td>52.543530</td>\n",
       "      <td>95.119812</td>\n",
       "      <td>227.637482</td>\n",
       "      <td>37.888998</td>\n",
       "      <td>77.667999</td>\n",
       "      <td>52.847926</td>\n",
       "      <td>95.712427</td>\n",
       "      <td>228.574686</td>\n",
       "      <td>...</td>\n",
       "      <td>36.944454</td>\n",
       "      <td>73.260002</td>\n",
       "      <td>51.174492</td>\n",
       "      <td>93.642972</td>\n",
       "      <td>223.815926</td>\n",
       "      <td>148158800</td>\n",
       "      <td>159662000</td>\n",
       "      <td>31868000</td>\n",
       "      <td>35329300</td>\n",
       "      <td>126925200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-03</th>\n",
       "      <td>33.915253</td>\n",
       "      <td>75.014000</td>\n",
       "      <td>51.088299</td>\n",
       "      <td>91.620544</td>\n",
       "      <td>222.205368</td>\n",
       "      <td>34.757230</td>\n",
       "      <td>76.900002</td>\n",
       "      <td>53.120433</td>\n",
       "      <td>94.244994</td>\n",
       "      <td>226.172509</td>\n",
       "      <td>...</td>\n",
       "      <td>34.342203</td>\n",
       "      <td>76.000504</td>\n",
       "      <td>52.343750</td>\n",
       "      <td>94.160331</td>\n",
       "      <td>225.863134</td>\n",
       "      <td>365248800</td>\n",
       "      <td>139512000</td>\n",
       "      <td>41960000</td>\n",
       "      <td>42579100</td>\n",
       "      <td>144140700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-04</th>\n",
       "      <td>35.363075</td>\n",
       "      <td>78.769501</td>\n",
       "      <td>53.708801</td>\n",
       "      <td>95.881752</td>\n",
       "      <td>229.648361</td>\n",
       "      <td>35.432248</td>\n",
       "      <td>79.699997</td>\n",
       "      <td>53.804953</td>\n",
       "      <td>96.427338</td>\n",
       "      <td>230.303487</td>\n",
       "      <td>...</td>\n",
       "      <td>34.473394</td>\n",
       "      <td>76.500000</td>\n",
       "      <td>51.939713</td>\n",
       "      <td>93.802888</td>\n",
       "      <td>225.280863</td>\n",
       "      <td>234428400</td>\n",
       "      <td>183652000</td>\n",
       "      <td>46022000</td>\n",
       "      <td>44060600</td>\n",
       "      <td>142628800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-07</th>\n",
       "      <td>35.284363</td>\n",
       "      <td>81.475502</td>\n",
       "      <td>53.601688</td>\n",
       "      <td>96.004036</td>\n",
       "      <td>231.459030</td>\n",
       "      <td>35.499034</td>\n",
       "      <td>81.727997</td>\n",
       "      <td>53.939461</td>\n",
       "      <td>97.142237</td>\n",
       "      <td>232.887558</td>\n",
       "      <td>...</td>\n",
       "      <td>35.468025</td>\n",
       "      <td>80.115501</td>\n",
       "      <td>53.853275</td>\n",
       "      <td>95.608959</td>\n",
       "      <td>229.921306</td>\n",
       "      <td>219111200</td>\n",
       "      <td>159864000</td>\n",
       "      <td>47446000</td>\n",
       "      <td>35656100</td>\n",
       "      <td>103139100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-08</th>\n",
       "      <td>35.956989</td>\n",
       "      <td>82.829002</td>\n",
       "      <td>54.072483</td>\n",
       "      <td>96.700127</td>\n",
       "      <td>233.633682</td>\n",
       "      <td>36.212208</td>\n",
       "      <td>83.830498</td>\n",
       "      <td>54.470040</td>\n",
       "      <td>97.800700</td>\n",
       "      <td>234.125033</td>\n",
       "      <td>...</td>\n",
       "      <td>35.673149</td>\n",
       "      <td>83.234497</td>\n",
       "      <td>54.103867</td>\n",
       "      <td>96.925884</td>\n",
       "      <td>233.679194</td>\n",
       "      <td>164101200</td>\n",
       "      <td>177628000</td>\n",
       "      <td>35414000</td>\n",
       "      <td>31514400</td>\n",
       "      <td>102512600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-17</th>\n",
       "      <td>143.338959</td>\n",
       "      <td>173.126007</td>\n",
       "      <td>140.291443</td>\n",
       "      <td>291.171875</td>\n",
       "      <td>420.953064</td>\n",
       "      <td>146.047551</td>\n",
       "      <td>174.870499</td>\n",
       "      <td>142.931865</td>\n",
       "      <td>295.667581</td>\n",
       "      <td>424.739163</td>\n",
       "      <td>...</td>\n",
       "      <td>146.047551</td>\n",
       "      <td>174.420502</td>\n",
       "      <td>142.513886</td>\n",
       "      <td>295.347166</td>\n",
       "      <td>424.310026</td>\n",
       "      <td>129868800</td>\n",
       "      <td>92332000</td>\n",
       "      <td>53384000</td>\n",
       "      <td>41372500</td>\n",
       "      <td>118425000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-20</th>\n",
       "      <td>140.277100</td>\n",
       "      <td>167.786499</td>\n",
       "      <td>138.218445</td>\n",
       "      <td>285.763367</td>\n",
       "      <td>413.934052</td>\n",
       "      <td>142.141698</td>\n",
       "      <td>170.949997</td>\n",
       "      <td>138.497934</td>\n",
       "      <td>290.055171</td>\n",
       "      <td>416.337308</td>\n",
       "      <td>...</td>\n",
       "      <td>141.121079</td>\n",
       "      <td>169.800003</td>\n",
       "      <td>137.662462</td>\n",
       "      <td>287.734482</td>\n",
       "      <td>414.735137</td>\n",
       "      <td>123478900</td>\n",
       "      <td>93382000</td>\n",
       "      <td>46518000</td>\n",
       "      <td>38278700</td>\n",
       "      <td>166445500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-21</th>\n",
       "      <td>140.757935</td>\n",
       "      <td>167.181503</td>\n",
       "      <td>138.530807</td>\n",
       "      <td>286.248901</td>\n",
       "      <td>413.542877</td>\n",
       "      <td>141.906151</td>\n",
       "      <td>168.985001</td>\n",
       "      <td>139.505773</td>\n",
       "      <td>288.909444</td>\n",
       "      <td>417.624613</td>\n",
       "      <td>...</td>\n",
       "      <td>141.248620</td>\n",
       "      <td>168.750000</td>\n",
       "      <td>139.244711</td>\n",
       "      <td>287.113100</td>\n",
       "      <td>416.308534</td>\n",
       "      <td>75834000</td>\n",
       "      <td>55618000</td>\n",
       "      <td>25332000</td>\n",
       "      <td>22364100</td>\n",
       "      <td>92526100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-22</th>\n",
       "      <td>143.132874</td>\n",
       "      <td>169.002502</td>\n",
       "      <td>139.776794</td>\n",
       "      <td>289.919220</td>\n",
       "      <td>417.577026</td>\n",
       "      <td>143.702055</td>\n",
       "      <td>169.449997</td>\n",
       "      <td>140.378115</td>\n",
       "      <td>291.511664</td>\n",
       "      <td>419.646518</td>\n",
       "      <td>...</td>\n",
       "      <td>141.758946</td>\n",
       "      <td>167.550003</td>\n",
       "      <td>138.800840</td>\n",
       "      <td>288.122906</td>\n",
       "      <td>415.850873</td>\n",
       "      <td>76404300</td>\n",
       "      <td>48228000</td>\n",
       "      <td>25056000</td>\n",
       "      <td>26626300</td>\n",
       "      <td>102350100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-23</th>\n",
       "      <td>144.094620</td>\n",
       "      <td>170.800003</td>\n",
       "      <td>140.705933</td>\n",
       "      <td>290.870819</td>\n",
       "      <td>422.650604</td>\n",
       "      <td>144.339962</td>\n",
       "      <td>171.447998</td>\n",
       "      <td>141.184696</td>\n",
       "      <td>292.171947</td>\n",
       "      <td>424.281413</td>\n",
       "      <td>...</td>\n",
       "      <td>143.917965</td>\n",
       "      <td>169.002502</td>\n",
       "      <td>140.478258</td>\n",
       "      <td>290.181422</td>\n",
       "      <td>419.474872</td>\n",
       "      <td>64838200</td>\n",
       "      <td>47588000</td>\n",
       "      <td>20952000</td>\n",
       "      <td>18604600</td>\n",
       "      <td>76396000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>688 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Price            Close                                                  \\\n",
       "Ticker            AAPL        AMZN       GOOGL        MSFT         SPY   \n",
       "Date                                                                     \n",
       "2019-01-02   37.667171   76.956497   52.543530   95.119812  227.637482   \n",
       "2019-01-03   33.915253   75.014000   51.088299   91.620544  222.205368   \n",
       "2019-01-04   35.363075   78.769501   53.708801   95.881752  229.648361   \n",
       "2019-01-07   35.284363   81.475502   53.601688   96.004036  231.459030   \n",
       "2019-01-08   35.956989   82.829002   54.072483   96.700127  233.633682   \n",
       "...                ...         ...         ...         ...         ...   \n",
       "2021-09-17  143.338959  173.126007  140.291443  291.171875  420.953064   \n",
       "2021-09-20  140.277100  167.786499  138.218445  285.763367  413.934052   \n",
       "2021-09-21  140.757935  167.181503  138.530807  286.248901  413.542877   \n",
       "2021-09-22  143.132874  169.002502  139.776794  289.919220  417.577026   \n",
       "2021-09-23  144.094620  170.800003  140.705933  290.870819  422.650604   \n",
       "\n",
       "Price             High                                                  ...  \\\n",
       "Ticker            AAPL        AMZN       GOOGL        MSFT         SPY  ...   \n",
       "Date                                                                    ...   \n",
       "2019-01-02   37.888998   77.667999   52.847926   95.712427  228.574686  ...   \n",
       "2019-01-03   34.757230   76.900002   53.120433   94.244994  226.172509  ...   \n",
       "2019-01-04   35.432248   79.699997   53.804953   96.427338  230.303487  ...   \n",
       "2019-01-07   35.499034   81.727997   53.939461   97.142237  232.887558  ...   \n",
       "2019-01-08   36.212208   83.830498   54.470040   97.800700  234.125033  ...   \n",
       "...                ...         ...         ...         ...         ...  ...   \n",
       "2021-09-17  146.047551  174.870499  142.931865  295.667581  424.739163  ...   \n",
       "2021-09-20  142.141698  170.949997  138.497934  290.055171  416.337308  ...   \n",
       "2021-09-21  141.906151  168.985001  139.505773  288.909444  417.624613  ...   \n",
       "2021-09-22  143.702055  169.449997  140.378115  291.511664  419.646518  ...   \n",
       "2021-09-23  144.339962  171.447998  141.184696  292.171947  424.281413  ...   \n",
       "\n",
       "Price             Open                                                  \\\n",
       "Ticker            AAPL        AMZN       GOOGL        MSFT         SPY   \n",
       "Date                                                                     \n",
       "2019-01-02   36.944454   73.260002   51.174492   93.642972  223.815926   \n",
       "2019-01-03   34.342203   76.000504   52.343750   94.160331  225.863134   \n",
       "2019-01-04   34.473394   76.500000   51.939713   93.802888  225.280863   \n",
       "2019-01-07   35.468025   80.115501   53.853275   95.608959  229.921306   \n",
       "2019-01-08   35.673149   83.234497   54.103867   96.925884  233.679194   \n",
       "...                ...         ...         ...         ...         ...   \n",
       "2021-09-17  146.047551  174.420502  142.513886  295.347166  424.310026   \n",
       "2021-09-20  141.121079  169.800003  137.662462  287.734482  414.735137   \n",
       "2021-09-21  141.248620  168.750000  139.244711  287.113100  416.308534   \n",
       "2021-09-22  141.758946  167.550003  138.800840  288.122906  415.850873   \n",
       "2021-09-23  143.917965  169.002502  140.478258  290.181422  419.474872   \n",
       "\n",
       "Price          Volume                                            \n",
       "Ticker           AAPL       AMZN     GOOGL      MSFT        SPY  \n",
       "Date                                                             \n",
       "2019-01-02  148158800  159662000  31868000  35329300  126925200  \n",
       "2019-01-03  365248800  139512000  41960000  42579100  144140700  \n",
       "2019-01-04  234428400  183652000  46022000  44060600  142628800  \n",
       "2019-01-07  219111200  159864000  47446000  35656100  103139100  \n",
       "2019-01-08  164101200  177628000  35414000  31514400  102512600  \n",
       "...               ...        ...       ...       ...        ...  \n",
       "2021-09-17  129868800   92332000  53384000  41372500  118425000  \n",
       "2021-09-20  123478900   93382000  46518000  38278700  166445500  \n",
       "2021-09-21   75834000   55618000  25332000  22364100   92526100  \n",
       "2021-09-22   76404300   48228000  25056000  26626300  102350100  \n",
       "2021-09-23   64838200   47588000  20952000  18604600   76396000  \n",
       "\n",
       "[688 rows x 25 columns]"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df =  yf.download([\"SPY\", \"AAPL\", \"MSFT\", \"AMZN\", \"GOOGL\"],\n",
    "                  start='2019-01-01',\n",
    "                  end='2021-09-24')\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DrzB4cQh7zvI"
   },
   "source": [
    "Let's compare not the price, but the relative performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 455
    },
    "id": "KA6yCs-IPqpD",
    "outputId": "5309f784-3917-4125-f1a9-f39b56fb3372"
   },
   "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>Ticker</th>\n",
       "      <th>AAPL</th>\n",
       "      <th>AMZN</th>\n",
       "      <th>GOOGL</th>\n",
       "      <th>MSFT</th>\n",
       "      <th>SPY</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-01-02</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-03</th>\n",
       "      <td>0.900393</td>\n",
       "      <td>0.974759</td>\n",
       "      <td>0.972304</td>\n",
       "      <td>0.963212</td>\n",
       "      <td>0.976137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-04</th>\n",
       "      <td>0.938830</td>\n",
       "      <td>1.023559</td>\n",
       "      <td>1.022177</td>\n",
       "      <td>1.008010</td>\n",
       "      <td>1.008834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-07</th>\n",
       "      <td>0.936740</td>\n",
       "      <td>1.058722</td>\n",
       "      <td>1.020139</td>\n",
       "      <td>1.009296</td>\n",
       "      <td>1.016788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-08</th>\n",
       "      <td>0.954598</td>\n",
       "      <td>1.076309</td>\n",
       "      <td>1.029099</td>\n",
       "      <td>1.016614</td>\n",
       "      <td>1.026341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-17</th>\n",
       "      <td>3.805408</td>\n",
       "      <td>2.249661</td>\n",
       "      <td>2.670004</td>\n",
       "      <td>3.061107</td>\n",
       "      <td>1.849226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-20</th>\n",
       "      <td>3.724121</td>\n",
       "      <td>2.180277</td>\n",
       "      <td>2.630551</td>\n",
       "      <td>3.004247</td>\n",
       "      <td>1.818391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-21</th>\n",
       "      <td>3.736886</td>\n",
       "      <td>2.172416</td>\n",
       "      <td>2.636496</td>\n",
       "      <td>3.009351</td>\n",
       "      <td>1.816673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-22</th>\n",
       "      <td>3.799937</td>\n",
       "      <td>2.196078</td>\n",
       "      <td>2.660209</td>\n",
       "      <td>3.047937</td>\n",
       "      <td>1.834395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-23</th>\n",
       "      <td>3.825470</td>\n",
       "      <td>2.219436</td>\n",
       "      <td>2.677893</td>\n",
       "      <td>3.057941</td>\n",
       "      <td>1.856683</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>688 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Ticker          AAPL      AMZN     GOOGL      MSFT       SPY\n",
       "Date                                                        \n",
       "2019-01-02  1.000000  1.000000  1.000000  1.000000  1.000000\n",
       "2019-01-03  0.900393  0.974759  0.972304  0.963212  0.976137\n",
       "2019-01-04  0.938830  1.023559  1.022177  1.008010  1.008834\n",
       "2019-01-07  0.936740  1.058722  1.020139  1.009296  1.016788\n",
       "2019-01-08  0.954598  1.076309  1.029099  1.016614  1.026341\n",
       "...              ...       ...       ...       ...       ...\n",
       "2021-09-17  3.805408  2.249661  2.670004  3.061107  1.849226\n",
       "2021-09-20  3.724121  2.180277  2.630551  3.004247  1.818391\n",
       "2021-09-21  3.736886  2.172416  2.636496  3.009351  1.816673\n",
       "2021-09-22  3.799937  2.196078  2.660209  3.047937  1.834395\n",
       "2021-09-23  3.825470  2.219436  2.677893  3.057941  1.856683\n",
       "\n",
       "[688 rows x 5 columns]"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df['Close']\n",
    "df = df / df.iloc[0]\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 542
    },
    "id": "GwRWHNPUQj1I",
    "outputId": "4bf45621-d8e6-4720-d389-3f17ebcd1383"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Date'>"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0AN97x-7RGF9"
   },
   "source": [
    "The [rolling function](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.rolling.html) will play very nicely with datetime data as shown here when I get the moving average over a 40 day period. And this can handle unevenly sampled date easily."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 542
    },
    "id": "FL9eZOutQzso",
    "outputId": "36661409-e636-4055-e6b7-7bd68c046329"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Date'>"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.rolling('40D').mean().plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3XzrLgmRR02j"
   },
   "source": [
    "Indexing with datetime data though will require a slightly extra step, but then it is easy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "id": "ZQpQ7EJIRAJP"
   },
   "outputs": [],
   "source": [
    "from datetime import date\n",
    "start = date(2021, 4, 1)\n",
    "end = date(2021, 4, 30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 739
    },
    "id": "1YU6TiEcRzPa",
    "outputId": "96ee05aa-e1a9-4e69-cf17-2d62f5e1c9b9"
   },
   "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>Ticker</th>\n",
       "      <th>AAPL</th>\n",
       "      <th>AMZN</th>\n",
       "      <th>GOOGL</th>\n",
       "      <th>MSFT</th>\n",
       "      <th>SPY</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-04-01</th>\n",
       "      <td>3.194389</td>\n",
       "      <td>2.053758</td>\n",
       "      <td>2.019361</td>\n",
       "      <td>2.463520</td>\n",
       "      <td>1.667523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-05</th>\n",
       "      <td>3.269704</td>\n",
       "      <td>2.096464</td>\n",
       "      <td>2.103918</td>\n",
       "      <td>2.531830</td>\n",
       "      <td>1.691457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-06</th>\n",
       "      <td>3.277755</td>\n",
       "      <td>2.094573</td>\n",
       "      <td>2.094720</td>\n",
       "      <td>2.519530</td>\n",
       "      <td>1.690458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-07</th>\n",
       "      <td>3.321645</td>\n",
       "      <td>2.130678</td>\n",
       "      <td>2.122947</td>\n",
       "      <td>2.540267</td>\n",
       "      <td>1.692414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-08</th>\n",
       "      <td>3.385533</td>\n",
       "      <td>2.143614</td>\n",
       "      <td>2.133756</td>\n",
       "      <td>2.574320</td>\n",
       "      <td>1.700448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-09</th>\n",
       "      <td>3.454095</td>\n",
       "      <td>2.190978</td>\n",
       "      <td>2.152947</td>\n",
       "      <td>2.600749</td>\n",
       "      <td>1.712810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-12</th>\n",
       "      <td>3.408387</td>\n",
       "      <td>2.195650</td>\n",
       "      <td>2.128247</td>\n",
       "      <td>2.601359</td>\n",
       "      <td>1.713435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-13</th>\n",
       "      <td>3.491233</td>\n",
       "      <td>2.209040</td>\n",
       "      <td>2.137549</td>\n",
       "      <td>2.627586</td>\n",
       "      <td>1.718512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-14</th>\n",
       "      <td>3.428904</td>\n",
       "      <td>2.165509</td>\n",
       "      <td>2.125678</td>\n",
       "      <td>2.598107</td>\n",
       "      <td>1.712643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-15</th>\n",
       "      <td>3.493051</td>\n",
       "      <td>2.195455</td>\n",
       "      <td>2.166771</td>\n",
       "      <td>2.637852</td>\n",
       "      <td>1.731042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-16</th>\n",
       "      <td>3.484221</td>\n",
       "      <td>2.208676</td>\n",
       "      <td>2.164400</td>\n",
       "      <td>2.650457</td>\n",
       "      <td>1.736827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-19</th>\n",
       "      <td>3.501882</td>\n",
       "      <td>2.190855</td>\n",
       "      <td>2.171047</td>\n",
       "      <td>2.630127</td>\n",
       "      <td>1.728294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-20</th>\n",
       "      <td>3.456953</td>\n",
       "      <td>2.166607</td>\n",
       "      <td>2.160854</td>\n",
       "      <td>2.625248</td>\n",
       "      <td>1.715641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-21</th>\n",
       "      <td>3.467081</td>\n",
       "      <td>2.184364</td>\n",
       "      <td>2.160229</td>\n",
       "      <td>2.648831</td>\n",
       "      <td>1.731874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-22</th>\n",
       "      <td>3.426567</td>\n",
       "      <td>2.149942</td>\n",
       "      <td>2.135738</td>\n",
       "      <td>2.614168</td>\n",
       "      <td>1.716056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-23</th>\n",
       "      <td>3.488377</td>\n",
       "      <td>2.170629</td>\n",
       "      <td>2.180690</td>\n",
       "      <td>2.654625</td>\n",
       "      <td>1.734663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-26</th>\n",
       "      <td>3.498765</td>\n",
       "      <td>2.214888</td>\n",
       "      <td>2.190171</td>\n",
       "      <td>2.658691</td>\n",
       "      <td>1.738284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-27</th>\n",
       "      <td>3.490195</td>\n",
       "      <td>2.220365</td>\n",
       "      <td>2.172204</td>\n",
       "      <td>2.662960</td>\n",
       "      <td>1.737909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-28</th>\n",
       "      <td>3.469159</td>\n",
       "      <td>2.247049</td>\n",
       "      <td>2.236735</td>\n",
       "      <td>2.587636</td>\n",
       "      <td>1.737410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-29</th>\n",
       "      <td>3.466561</td>\n",
       "      <td>2.255372</td>\n",
       "      <td>2.268707</td>\n",
       "      <td>2.566798</td>\n",
       "      <td>1.748482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-30</th>\n",
       "      <td>3.414101</td>\n",
       "      <td>2.252844</td>\n",
       "      <td>2.231482</td>\n",
       "      <td>2.563443</td>\n",
       "      <td>1.736994</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Ticker          AAPL      AMZN     GOOGL      MSFT       SPY\n",
       "Date                                                        \n",
       "2021-04-01  3.194389  2.053758  2.019361  2.463520  1.667523\n",
       "2021-04-05  3.269704  2.096464  2.103918  2.531830  1.691457\n",
       "2021-04-06  3.277755  2.094573  2.094720  2.519530  1.690458\n",
       "2021-04-07  3.321645  2.130678  2.122947  2.540267  1.692414\n",
       "2021-04-08  3.385533  2.143614  2.133756  2.574320  1.700448\n",
       "2021-04-09  3.454095  2.190978  2.152947  2.600749  1.712810\n",
       "2021-04-12  3.408387  2.195650  2.128247  2.601359  1.713435\n",
       "2021-04-13  3.491233  2.209040  2.137549  2.627586  1.718512\n",
       "2021-04-14  3.428904  2.165509  2.125678  2.598107  1.712643\n",
       "2021-04-15  3.493051  2.195455  2.166771  2.637852  1.731042\n",
       "2021-04-16  3.484221  2.208676  2.164400  2.650457  1.736827\n",
       "2021-04-19  3.501882  2.190855  2.171047  2.630127  1.728294\n",
       "2021-04-20  3.456953  2.166607  2.160854  2.625248  1.715641\n",
       "2021-04-21  3.467081  2.184364  2.160229  2.648831  1.731874\n",
       "2021-04-22  3.426567  2.149942  2.135738  2.614168  1.716056\n",
       "2021-04-23  3.488377  2.170629  2.180690  2.654625  1.734663\n",
       "2021-04-26  3.498765  2.214888  2.190171  2.658691  1.738284\n",
       "2021-04-27  3.490195  2.220365  2.172204  2.662960  1.737909\n",
       "2021-04-28  3.469159  2.247049  2.236735  2.587636  1.737410\n",
       "2021-04-29  3.466561  2.255372  2.268707  2.566798  1.748482\n",
       "2021-04-30  3.414101  2.252844  2.231482  2.563443  1.736994"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[start:end]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "aJWvXHmlcGtV",
    "outputId": "7564cf85-46c9-482b-e30c-895d888a17c3"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_346054/3907196692.py:1: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "  pd.date_range(start='1/1/2019', end='08/31/2021', freq='M')\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2019-01-31', '2019-02-28', '2019-03-31', '2019-04-30',\n",
       "               '2019-05-31', '2019-06-30', '2019-07-31', '2019-08-31',\n",
       "               '2019-09-30', '2019-10-31', '2019-11-30', '2019-12-31',\n",
       "               '2020-01-31', '2020-02-29', '2020-03-31', '2020-04-30',\n",
       "               '2020-05-31', '2020-06-30', '2020-07-31', '2020-08-31',\n",
       "               '2020-09-30', '2020-10-31', '2020-11-30', '2020-12-31',\n",
       "               '2021-01-31', '2021-02-28', '2021-03-31', '2021-04-30',\n",
       "               '2021-05-31', '2021-06-30', '2021-07-31', '2021-08-31'],\n",
       "              dtype='datetime64[ns]', freq='ME')"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.date_range(start='1/1/2019', end='08/31/2021', freq='M')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 425
    },
    "id": "Q1KZmgV4cVGe",
    "outputId": "6c89eb55-ca9f-4df1-ffce-dfb308d16ff4"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_346054/2442371637.py:1: FutureWarning: 'Q' is deprecated and will be removed in a future version, please use 'QE' instead.\n",
      "  df.resample(rule='Q').max()\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Ticker</th>\n",
       "      <th>AAPL</th>\n",
       "      <th>AMZN</th>\n",
       "      <th>GOOGL</th>\n",
       "      <th>MSFT</th>\n",
       "      <th>SPY</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-03-31</th>\n",
       "      <td>1.240671</td>\n",
       "      <td>1.182005</td>\n",
       "      <td>1.172043</td>\n",
       "      <td>1.193962</td>\n",
       "      <td>1.143114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-30</th>\n",
       "      <td>1.346620</td>\n",
       "      <td>1.275045</td>\n",
       "      <td>1.228998</td>\n",
       "      <td>1.373424</td>\n",
       "      <td>1.187798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-30</th>\n",
       "      <td>1.435250</td>\n",
       "      <td>1.313073</td>\n",
       "      <td>1.181344</td>\n",
       "      <td>1.410902</td>\n",
       "      <td>1.218385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-31</th>\n",
       "      <td>1.887425</td>\n",
       "      <td>1.214842</td>\n",
       "      <td>1.291832</td>\n",
       "      <td>1.595238</td>\n",
       "      <td>1.315273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-31</th>\n",
       "      <td>2.108058</td>\n",
       "      <td>1.410030</td>\n",
       "      <td>1.445813</td>\n",
       "      <td>1.893692</td>\n",
       "      <td>1.377995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-30</th>\n",
       "      <td>2.367842</td>\n",
       "      <td>1.796086</td>\n",
       "      <td>1.388762</td>\n",
       "      <td>2.053599</td>\n",
       "      <td>1.324073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-30</th>\n",
       "      <td>3.473548</td>\n",
       "      <td>2.294446</td>\n",
       "      <td>1.628352</td>\n",
       "      <td>2.343208</td>\n",
       "      <td>1.471860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-31</th>\n",
       "      <td>3.544630</td>\n",
       "      <td>2.237387</td>\n",
       "      <td>1.730354</td>\n",
       "      <td>2.281494</td>\n",
       "      <td>1.551180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-03-31</th>\n",
       "      <td>3.712410</td>\n",
       "      <td>2.196046</td>\n",
       "      <td>2.008780</td>\n",
       "      <td>2.484634</td>\n",
       "      <td>1.649707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-30</th>\n",
       "      <td>3.562981</td>\n",
       "      <td>2.277547</td>\n",
       "      <td>2.323662</td>\n",
       "      <td>2.765188</td>\n",
       "      <td>1.787611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-30</th>\n",
       "      <td>4.082359</td>\n",
       "      <td>2.424363</td>\n",
       "      <td>2.753735</td>\n",
       "      <td>3.115720</td>\n",
       "      <td>1.892556</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Ticker          AAPL      AMZN     GOOGL      MSFT       SPY\n",
       "Date                                                        \n",
       "2019-03-31  1.240671  1.182005  1.172043  1.193962  1.143114\n",
       "2019-06-30  1.346620  1.275045  1.228998  1.373424  1.187798\n",
       "2019-09-30  1.435250  1.313073  1.181344  1.410902  1.218385\n",
       "2019-12-31  1.887425  1.214842  1.291832  1.595238  1.315273\n",
       "2020-03-31  2.108058  1.410030  1.445813  1.893692  1.377995\n",
       "2020-06-30  2.367842  1.796086  1.388762  2.053599  1.324073\n",
       "2020-09-30  3.473548  2.294446  1.628352  2.343208  1.471860\n",
       "2020-12-31  3.544630  2.237387  1.730354  2.281494  1.551180\n",
       "2021-03-31  3.712410  2.196046  2.008780  2.484634  1.649707\n",
       "2021-06-30  3.562981  2.277547  2.323662  2.765188  1.787611\n",
       "2021-09-30  4.082359  2.424363  2.753735  3.115720  1.892556"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.resample(rule='Q').max()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "50HsfFt6TFSE"
   },
   "source": [
    "### Sorting & Filtering on Tabular Data\n",
    "To highlight filtering in DataFrames, we'll use a dataset with a bunch of different columns/series of different types. This data was pulled directly from the enDAQ cloud API off some example recording files."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "PI9EYy7JSgeo",
    "outputId": "6936608c-279b-44e3-e0ad-f6faeff55be9"
   },
   "outputs": [
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       "      <td>1632515143</td>\n",
       "      <td>...</td>\n",
       "      <td>[0.    0.    0.    0.    0.001 0.002 0.002 0.0...</td>\n",
       "      <td>[0.13  0.118 0.1   ... 0.105 0.1   0.066]</td>\n",
       "      <td>21.806</td>\n",
       "      <td>4012.0</td>\n",
       "      <td>[0.    0.    0.    0.    0.008 0.01  0.019 0.0...</td>\n",
       "      <td>1.754</td>\n",
       "      <td>1.557</td>\n",
       "      <td>[ 0.854  1.159  1.662  1.815  3.022  6.139 10....</td>\n",
       "      <td>0.079</td>\n",
       "      <td>[ 0.  0.  0. ... nan nan nan]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>['Shock Severity: Very Low', 'Acceleration Sev...</td>\n",
       "      <td>0396df6a-56b0-3e43-8e46-e1d0d7485ff5</td>\n",
       "      <td>9295</td>\n",
       "      <td>Seat-Base_21-1632515142.ide</td>\n",
       "      <td>5248836</td>\n",
       "      <td>83.092255</td>\n",
       "      <td>1575800210</td>\n",
       "      <td>1632515143</td>\n",
       "      <td>1632515143</td>\n",
       "      <td>...</td>\n",
       "      <td>[0.002 0.007 0.008 0.005 0.002 0.005 0.002 0.0...</td>\n",
       "      <td>[0.084 0.137 0.178 ... 0.347 0.324 0.286]</td>\n",
       "      <td>17.820</td>\n",
       "      <td>4046.0</td>\n",
       "      <td>[0.002 0.014 0.016 0.013 0.003 0.031 0.003 0.0...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.666</td>\n",
       "      <td>[ 74.73   79.453 101.006 151.429  73.92   53.4...</td>\n",
       "      <td>0.130</td>\n",
       "      <td>[0.001 0.002 0.002 ...   nan   nan   nan]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>['Drive-Test', 'Vibration Severity: Very Low',...</td>\n",
       "      <td>5e293d65-c9e0-3aa1-9098-c9762e8fbc86</td>\n",
       "      <td>11046</td>\n",
       "      <td>Drive-Home_01-1632515142.ide</td>\n",
       "      <td>3632799</td>\n",
       "      <td>61.755371</td>\n",
       "      <td>1616178955</td>\n",
       "      <td>1632515142</td>\n",
       "      <td>1632515142</td>\n",
       "      <td>...</td>\n",
       "      <td>[0.    0.    0.    0.    0.001 0.    0.    0. ...</td>\n",
       "      <td>[0.001 0.003 0.002 ... 0.011 0.008 0.006]</td>\n",
       "      <td>29.061</td>\n",
       "      <td>4013.0</td>\n",
       "      <td>[0.    0.    0.    0.    0.007 0.    0.    0. ...</td>\n",
       "      <td>16.243</td>\n",
       "      <td>0.363</td>\n",
       "      <td>[ 2.336  7.82  19.078 10.384 16.975 38.326 18....</td>\n",
       "      <td>0.021</td>\n",
       "      <td>[ 0.  0.  0. ... nan nan nan]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>['Acceleration Severity: High', 'Shock Severit...</td>\n",
       "      <td>cd81c850-9e22-3b20-b570-7411e7a144cc</td>\n",
       "      <td>0</td>\n",
       "      <td>HiTest-Shock-1632515141.ide</td>\n",
       "      <td>2655894</td>\n",
       "      <td>20.331848</td>\n",
       "      <td>1543936974</td>\n",
       "      <td>1632515141</td>\n",
       "      <td>1632515141</td>\n",
       "      <td>...</td>\n",
       "      <td>[0.304 0.486 0.399 0.454 0.347 0.178 0.185 0.1...</td>\n",
       "      <td>[ 3.088  3.019  2.893 ... 73.794 40.005 24.303]</td>\n",
       "      <td>9.538</td>\n",
       "      <td>19997.0</td>\n",
       "      <td>[ 0.304  0.537  0.479  0.811  0.624  0.554  0....</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[1378.444 2470.941 4368.78  5033.327 5814.49  ...</td>\n",
       "      <td>11.645</td>\n",
       "      <td>[0.157 0.304 0.42  ... 0.001 0.01  0.013]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>['Vibration Severity: High', 'Acceleration Sev...</td>\n",
       "      <td>0931a23d-3515-3d11-bf97-bb9b2c7863be</td>\n",
       "      <td>11162</td>\n",
       "      <td>Calibration-Shake-1632515140.IDE</td>\n",
       "      <td>2218130</td>\n",
       "      <td>27.882690</td>\n",
       "      <td>1621278970</td>\n",
       "      <td>1632515140</td>\n",
       "      <td>1632515140</td>\n",
       "      <td>...</td>\n",
       "      <td>[2.100e-02 7.400e-02 7.500e-02 4.900e-02 5.500...</td>\n",
       "      <td>[7.486 7.496 7.137 ... 7.997 8.294 7.806]</td>\n",
       "      <td>24.545</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>[2.10000e-02 9.00000e-02 8.60000e-02 6.30000e-...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.166</td>\n",
       "      <td>[ 90.703 198.651 288.078 183.492 126.417 108.4...</td>\n",
       "      <td>2.712</td>\n",
       "      <td>[0.007 0.021 0.055 ...   nan   nan   nan]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>['Shock Severity: Very Low', 'Acceleration Sev...</td>\n",
       "      <td>c1571d10-2329-3aea-aa5d-223733f6336b</td>\n",
       "      <td>10916</td>\n",
       "      <td>FUSE_HSTAB_000005-1632515139.ide</td>\n",
       "      <td>537562</td>\n",
       "      <td>18.491791</td>\n",
       "      <td>1619108004</td>\n",
       "      <td>1632515140</td>\n",
       "      <td>1632515140</td>\n",
       "      <td>...</td>\n",
       "      <td>[ 0.  0.  0.  0.  0.  0.  0. nan nan nan nan n...</td>\n",
       "      <td>[0.001 0.001 0.001 ... 0.002 0.006 0.006]</td>\n",
       "      <td>18.874</td>\n",
       "      <td>504.0</td>\n",
       "      <td>[0.    0.    0.001 0.001 0.    0.    0.      n...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.749</td>\n",
       "      <td>[ 2.617  6.761 17.326 34.067 28.721  9.469 15....</td>\n",
       "      <td>0.011</td>\n",
       "      <td>[ 0.  0.  0. ... nan nan nan]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>['Shock Severity: Medium', 'Acceleration Sever...</td>\n",
       "      <td>8ce137ff-904e-314b-81ff-ff2cea279db2</td>\n",
       "      <td>9874</td>\n",
       "      <td>Coffee_002-1631722736.IDE</td>\n",
       "      <td>60959516</td>\n",
       "      <td>769.299896</td>\n",
       "      <td>952113744</td>\n",
       "      <td>1631722736</td>\n",
       "      <td>1631722736</td>\n",
       "      <td>...</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>24.540</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>[]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.082</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.059</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1</td>\n",
       "      <td>['Vibration Severity: High', 'Shock Severity: ...</td>\n",
       "      <td>9d7383fb-40d6-34c1-9d0c-37f11c29bff5</td>\n",
       "      <td>11456</td>\n",
       "      <td>100_Joules_900_lbs-1629315313.ide</td>\n",
       "      <td>1596714</td>\n",
       "      <td>20.200623</td>\n",
       "      <td>1627327315</td>\n",
       "      <td>1629315313</td>\n",
       "      <td>1629315313</td>\n",
       "      <td>...</td>\n",
       "      <td>[0.071 0.201 0.293 0.686 1.565 1.028 0.856 0.9...</td>\n",
       "      <td>[0.304 0.274 0.296 ... 1.513 1.313 0.652]</td>\n",
       "      <td>24.180</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>[0.071 0.236 0.311 1.148 2.036 1.791 1.498 2.7...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.471</td>\n",
       "      <td>[ 194.741  361.14   563.488  855.34  1712.229 ...</td>\n",
       "      <td>2.877</td>\n",
       "      <td>[0.02  0.071 0.138 ...   nan   nan   nan]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2</td>\n",
       "      <td>['Acceleration Severity: High', 'Shock Severit...</td>\n",
       "      <td>5f35ed7f-ff55-36a3-896d-276f06a0e340</td>\n",
       "      <td>11456</td>\n",
       "      <td>50_Joules_900_lbs-1629315312.ide</td>\n",
       "      <td>1597750</td>\n",
       "      <td>20.201752</td>\n",
       "      <td>1627329399</td>\n",
       "      <td>1629315312</td>\n",
       "      <td>1629315312</td>\n",
       "      <td>...</td>\n",
       "      <td>[0.074 0.196 0.327 0.874 1.521 0.917 0.478 0.7...</td>\n",
       "      <td>[0.304 0.21  0.098 ... 0.693 1.418 0.932]</td>\n",
       "      <td>24.175</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>[0.074 0.232 0.392 1.258 2.045 2.049 1.143 2.3...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.575</td>\n",
       "      <td>[ 242.141  388.517  736.981 1070.284 1843.722 ...</td>\n",
       "      <td>2.423</td>\n",
       "      <td>[0.021 0.074 0.137 ...   nan   nan   nan]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>3</td>\n",
       "      <td>['Shock Severity: Very Low', 'Vibration Severi...</td>\n",
       "      <td>f7240576-47c6-34b8-bdce-3fe11bb5f1c6</td>\n",
       "      <td>11046</td>\n",
       "      <td>Drive-Home_07-1626805222.ide</td>\n",
       "      <td>36225758</td>\n",
       "      <td>634.732056</td>\n",
       "      <td>1616182557</td>\n",
       "      <td>1626805222</td>\n",
       "      <td>1626805222</td>\n",
       "      <td>...</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>28.832</td>\n",
       "      <td>4012.0</td>\n",
       "      <td>[]</td>\n",
       "      <td>16.277</td>\n",
       "      <td>4.185</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.097</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>4</td>\n",
       "      <td>['Big-Mining']</td>\n",
       "      <td>c2c234cc-8055-3fba-ad8b-446c4b6f85dc</td>\n",
       "      <td>5120</td>\n",
       "      <td>Mining-SSX28803_06-1626457584.IDE</td>\n",
       "      <td>402920686</td>\n",
       "      <td>3238.119202</td>\n",
       "      <td>1536953304</td>\n",
       "      <td>1626457585</td>\n",
       "      <td>1626457585</td>\n",
       "      <td>...</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>5</td>\n",
       "      <td>['Shock Severity: Medium', 'Acceleration Sever...</td>\n",
       "      <td>69fd99f8-3d85-38ec-9f5d-67e9d7b7e868</td>\n",
       "      <td>10030</td>\n",
       "      <td>200922_Moto_Max_Run5_Control_Larry-1626297441.ide</td>\n",
       "      <td>4780893</td>\n",
       "      <td>99.325134</td>\n",
       "      <td>1600818455</td>\n",
       "      <td>1626297442</td>\n",
       "      <td>1626297442</td>\n",
       "      <td>...</td>\n",
       "      <td>[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]</td>\n",
       "      <td>[ 3.34  13.586 10.13  ...  7.737  8.978  8.672]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4014.0</td>\n",
       "      <td>[[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\\n [0...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[165.983 278.352 717.331 950.513 697.421 358.6...</td>\n",
       "      <td>3.528</td>\n",
       "      <td>[0.006 0.016 0.04  ...   nan   nan   nan]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>6</td>\n",
       "      <td>['Ford']</td>\n",
       "      <td>f38361de-30a7-3dda-aec4-e87a67d1ecbb</td>\n",
       "      <td>9695</td>\n",
       "      <td>ford_f150-1626296561.ide</td>\n",
       "      <td>96097059</td>\n",
       "      <td>1207.678344</td>\n",
       "      <td>1584142508</td>\n",
       "      <td>1626296561</td>\n",
       "      <td>1626296561</td>\n",
       "      <td>...</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>7</td>\n",
       "      <td>['Shock Severity: High', 'Vibration Severity: ...</td>\n",
       "      <td>40718e01-f422-3926-a22d-acf6daf1182b</td>\n",
       "      <td>7530</td>\n",
       "      <td>Motorcycle-Car-Crash-1626277852.ide</td>\n",
       "      <td>10489262</td>\n",
       "      <td>151.069336</td>\n",
       "      <td>1562173372</td>\n",
       "      <td>1626277852</td>\n",
       "      <td>1626277852</td>\n",
       "      <td>...</td>\n",
       "      <td>[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]</td>\n",
       "      <td>[3.371 3.641 3.649 ... 6.168 7.463 7.04 ]</td>\n",
       "      <td>26.989</td>\n",
       "      <td>10001.0</td>\n",
       "      <td>[[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\\n [0...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.810</td>\n",
       "      <td>[ 2723.153  5977.212 11406.291 12031.397  7337...</td>\n",
       "      <td>1.732</td>\n",
       "      <td>[2.250e-01 8.390e-01 2.025e+00 ... 1.000e-03 1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>8</td>\n",
       "      <td>['Shock Severity: Very Low', 'Surgical', 'Vibr...</td>\n",
       "      <td>ee6dc613-d422-347c-8119-f122a55ac81a</td>\n",
       "      <td>11071</td>\n",
       "      <td>surgical-instrument-1625829182.ide</td>\n",
       "      <td>541994</td>\n",
       "      <td>6.951172</td>\n",
       "      <td>1619110390</td>\n",
       "      <td>1625829183</td>\n",
       "      <td>1625829183</td>\n",
       "      <td>...</td>\n",
       "      <td>[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]</td>\n",
       "      <td>[0.667 0.549 1.05  ... 1.109 2.86  3.99 ]</td>\n",
       "      <td>21.889</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>[[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\\n [0...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.647</td>\n",
       "      <td>[ 88.953 171.73  104.303  71.184  58.883  72.4...</td>\n",
       "      <td>1.568</td>\n",
       "      <td>[0.001 0.001 0.003 ...   nan   nan   nan]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>9</td>\n",
       "      <td>['Acceleration Severity: High', 'Shock Severit...</td>\n",
       "      <td>f642d81c-7fe7-37fd-ab75-d493fc0556db</td>\n",
       "      <td>9680</td>\n",
       "      <td>LOC__3__DAQ41551_11_01_02-1625170795.IDE</td>\n",
       "      <td>2343292</td>\n",
       "      <td>28.456818</td>\n",
       "      <td>1616645179</td>\n",
       "      <td>1625170795</td>\n",
       "      <td>1625170795</td>\n",
       "      <td>...</td>\n",
       "      <td>[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]</td>\n",
       "      <td>[ 32.679  24.925  30.95  ... 106.511  27.715  ...</td>\n",
       "      <td>33.452</td>\n",
       "      <td>20010.0</td>\n",
       "      <td>[[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\\n [0...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>286.217</td>\n",
       "      <td>[2333.993 5629.021 6417.48  5802.895 3692.838 ...</td>\n",
       "      <td>94.197</td>\n",
       "      <td>[ 2.09   8.631 18.196 ... 69.565 59.225 60.801]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>10</td>\n",
       "      <td>['Vibration Severity: High', 'Shock Severity: ...</td>\n",
       "      <td>0c0e92ae-214a-32bd-a5bb-5e1801da06b2</td>\n",
       "      <td>9680</td>\n",
       "      <td>LOC__4__DAQ41551_15_05-1625170794.IDE</td>\n",
       "      <td>6927958</td>\n",
       "      <td>64.486054</td>\n",
       "      <td>1616646130</td>\n",
       "      <td>1625170794</td>\n",
       "      <td>1625170794</td>\n",
       "      <td>...</td>\n",
       "      <td>[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]</td>\n",
       "      <td>[14.223 14.903 24.949 ... 11.482 21.319 32.766]</td>\n",
       "      <td>32.202</td>\n",
       "      <td>19992.0</td>\n",
       "      <td>[[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\\n [0...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>277.654</td>\n",
       "      <td>[ 378.188  748.336 1054.232 1115.369  756.905 ...</td>\n",
       "      <td>46.528</td>\n",
       "      <td>[ 0.072  0.302  0.816 ... 52.226 45.804 52.81 ]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>11</td>\n",
       "      <td>['Vibration Severity: High', 'Mining-Hammer', ...</td>\n",
       "      <td>99ac47a8-63c2-38a4-8d92-508698eb3752</td>\n",
       "      <td>9680</td>\n",
       "      <td>LOC__6__DAQ41551_25_01-1625170793.IDE</td>\n",
       "      <td>8664238</td>\n",
       "      <td>63.878937</td>\n",
       "      <td>1616648007</td>\n",
       "      <td>1625170793</td>\n",
       "      <td>1625170793</td>\n",
       "      <td>...</td>\n",
       "      <td>[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]</td>\n",
       "      <td>[ 66.83   54.723  63.536 ... 109.259  85.341  ...</td>\n",
       "      <td>26.031</td>\n",
       "      <td>19992.0</td>\n",
       "      <td>[[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\\n [0...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>245.929</td>\n",
       "      <td>[ 780.577 1168.663 1851.417 1094.945  793.122 ...</td>\n",
       "      <td>54.408</td>\n",
       "      <td>[ 0.162  0.587  1.495 ... 45.639 43.86  40.177]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>12</td>\n",
       "      <td>['Mining-Hammer', 'Vibration Severity: High', ...</td>\n",
       "      <td>ebf31fb2-9d36-37de-999c-3edb01f17fb2</td>\n",
       "      <td>9680</td>\n",
       "      <td>LOC__2__DAQ38060_06_03_05-1625170793.IDE</td>\n",
       "      <td>1519172</td>\n",
       "      <td>27.057647</td>\n",
       "      <td>1616640862</td>\n",
       "      <td>1625170793</td>\n",
       "      <td>1625170793</td>\n",
       "      <td>...</td>\n",
       "      <td>[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]</td>\n",
       "      <td>[ 33.294  29.392  19.99  ... 175.123 162.043  ...</td>\n",
       "      <td>25.616</td>\n",
       "      <td>19998.0</td>\n",
       "      <td>[[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\\n [0...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>266.815</td>\n",
       "      <td>[1073.684  940.873 1063.151  957.568  975.826 ...</td>\n",
       "      <td>131.087</td>\n",
       "      <td>[ 0.371  0.409  0.787 ... 27.577 24.778 37.822]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>13</td>\n",
       "      <td>['Shock Severity: Very Low', 'Vibration Severi...</td>\n",
       "      <td>e39fe506-f39a-3301-866c-9da6a30f9577</td>\n",
       "      <td>9695</td>\n",
       "      <td>Tilt_000000-1625156721.IDE</td>\n",
       "      <td>719403</td>\n",
       "      <td>23.355163</td>\n",
       "      <td>1625156461</td>\n",
       "      <td>1625156722</td>\n",
       "      <td>1625156722</td>\n",
       "      <td>...</td>\n",
       "      <td>[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]</td>\n",
       "      <td>[0.033 0.029 0.03  ...   nan   nan   nan]</td>\n",
       "      <td>26.410</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>[[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\\n [0...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.933</td>\n",
       "      <td>[142.046 273.02  216.774 129.288  54.011  24.0...</td>\n",
       "      <td>0.044</td>\n",
       "      <td>[0.008 0.02  0.03  ...   nan   nan   nan]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>23 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Unnamed: 0                                               tags  \\\n",
       "0            0  ['Vibration Severity: High', 'Shock Severity: ...   \n",
       "1            1  ['Vibration Severity: Low', 'Acceleration Seve...   \n",
       "2            2  ['Shock Severity: Very Low', 'Acceleration Sev...   \n",
       "3            3  ['Shock Severity: Very Low', 'Acceleration Sev...   \n",
       "4            4  ['Shock Severity: Very Low', 'Acceleration Sev...   \n",
       "5            5  ['Drive-Test', 'Vibration Severity: Very Low',...   \n",
       "6            6  ['Acceleration Severity: High', 'Shock Severit...   \n",
       "7            7  ['Vibration Severity: High', 'Acceleration Sev...   \n",
       "8            8  ['Shock Severity: Very Low', 'Acceleration Sev...   \n",
       "9            0  ['Shock Severity: Medium', 'Acceleration Sever...   \n",
       "10           1  ['Vibration Severity: High', 'Shock Severity: ...   \n",
       "11           2  ['Acceleration Severity: High', 'Shock Severit...   \n",
       "12           3  ['Shock Severity: Very Low', 'Vibration Severi...   \n",
       "13           4                                     ['Big-Mining']   \n",
       "14           5  ['Shock Severity: Medium', 'Acceleration Sever...   \n",
       "15           6                                           ['Ford']   \n",
       "16           7  ['Shock Severity: High', 'Vibration Severity: ...   \n",
       "17           8  ['Shock Severity: Very Low', 'Surgical', 'Vibr...   \n",
       "18           9  ['Acceleration Severity: High', 'Shock Severit...   \n",
       "19          10  ['Vibration Severity: High', 'Shock Severity: ...   \n",
       "20          11  ['Vibration Severity: High', 'Mining-Hammer', ...   \n",
       "21          12  ['Mining-Hammer', 'Vibration Severity: High', ...   \n",
       "22          13  ['Shock Severity: Very Low', 'Vibration Severi...   \n",
       "\n",
       "                                      id  serial_number_id  \\\n",
       "0   6cd65b8f-1187-3bf9-a431-35304a7f84b7              9695   \n",
       "1   342aacc3-cd91-3124-8cf4-0c7fece6dcab              9316   \n",
       "2   5c5592b4-2dbc-3124-be8f-c7179eecda49             10118   \n",
       "3   ef832e45-50fa-38b4-8f2c-91769f7cef6e             10309   \n",
       "4   0396df6a-56b0-3e43-8e46-e1d0d7485ff5              9295   \n",
       "5   5e293d65-c9e0-3aa1-9098-c9762e8fbc86             11046   \n",
       "6   cd81c850-9e22-3b20-b570-7411e7a144cc                 0   \n",
       "7   0931a23d-3515-3d11-bf97-bb9b2c7863be             11162   \n",
       "8   c1571d10-2329-3aea-aa5d-223733f6336b             10916   \n",
       "9   8ce137ff-904e-314b-81ff-ff2cea279db2              9874   \n",
       "10  9d7383fb-40d6-34c1-9d0c-37f11c29bff5             11456   \n",
       "11  5f35ed7f-ff55-36a3-896d-276f06a0e340             11456   \n",
       "12  f7240576-47c6-34b8-bdce-3fe11bb5f1c6             11046   \n",
       "13  c2c234cc-8055-3fba-ad8b-446c4b6f85dc              5120   \n",
       "14  69fd99f8-3d85-38ec-9f5d-67e9d7b7e868             10030   \n",
       "15  f38361de-30a7-3dda-aec4-e87a67d1ecbb              9695   \n",
       "16  40718e01-f422-3926-a22d-acf6daf1182b              7530   \n",
       "17  ee6dc613-d422-347c-8119-f122a55ac81a             11071   \n",
       "18  f642d81c-7fe7-37fd-ab75-d493fc0556db              9680   \n",
       "19  0c0e92ae-214a-32bd-a5bb-5e1801da06b2              9680   \n",
       "20  99ac47a8-63c2-38a4-8d92-508698eb3752              9680   \n",
       "21  ebf31fb2-9d36-37de-999c-3edb01f17fb2              9680   \n",
       "22  e39fe506-f39a-3301-866c-9da6a30f9577              9695   \n",
       "\n",
       "                                            file_name  file_size  \\\n",
       "0                        train-passing-1632515146.ide   10492602   \n",
       "1                          Seat-Top_09-1632515145.ide   10491986   \n",
       "2                               Bolted-1632515144.ide    6149229   \n",
       "3                             RMI-2000-1632515143.ide    5909632   \n",
       "4                         Seat-Base_21-1632515142.ide    5248836   \n",
       "5                        Drive-Home_01-1632515142.ide    3632799   \n",
       "6                         HiTest-Shock-1632515141.ide    2655894   \n",
       "7                    Calibration-Shake-1632515140.IDE    2218130   \n",
       "8                    FUSE_HSTAB_000005-1632515139.ide     537562   \n",
       "9                           Coffee_002-1631722736.IDE   60959516   \n",
       "10                  100_Joules_900_lbs-1629315313.ide    1596714   \n",
       "11                   50_Joules_900_lbs-1629315312.ide    1597750   \n",
       "12                       Drive-Home_07-1626805222.ide   36225758   \n",
       "13                  Mining-SSX28803_06-1626457584.IDE  402920686   \n",
       "14  200922_Moto_Max_Run5_Control_Larry-1626297441.ide    4780893   \n",
       "15                           ford_f150-1626296561.ide   96097059   \n",
       "16                Motorcycle-Car-Crash-1626277852.ide   10489262   \n",
       "17                 surgical-instrument-1625829182.ide     541994   \n",
       "18           LOC__3__DAQ41551_11_01_02-1625170795.IDE    2343292   \n",
       "19              LOC__4__DAQ41551_15_05-1625170794.IDE    6927958   \n",
       "20              LOC__6__DAQ41551_25_01-1625170793.IDE    8664238   \n",
       "21           LOC__2__DAQ38060_06_03_05-1625170793.IDE    1519172   \n",
       "22                         Tilt_000000-1625156721.IDE     719403   \n",
       "\n",
       "    recording_length  recording_ts  created_ts  modified_ts  ...  \\\n",
       "0          73.612335    1588184436  1632515146   1632515146  ...   \n",
       "1         172.704559    1575800071  1632515146   1632515146  ...   \n",
       "2          29.396118    1619041447  1632515144   1632515144  ...   \n",
       "3          60.250855            24  1632515143   1632515143  ...   \n",
       "4          83.092255    1575800210  1632515143   1632515143  ...   \n",
       "5          61.755371    1616178955  1632515142   1632515142  ...   \n",
       "6          20.331848    1543936974  1632515141   1632515141  ...   \n",
       "7          27.882690    1621278970  1632515140   1632515140  ...   \n",
       "8          18.491791    1619108004  1632515140   1632515140  ...   \n",
       "9         769.299896     952113744  1631722736   1631722736  ...   \n",
       "10         20.200623    1627327315  1629315313   1629315313  ...   \n",
       "11         20.201752    1627329399  1629315312   1629315312  ...   \n",
       "12        634.732056    1616182557  1626805222   1626805222  ...   \n",
       "13       3238.119202    1536953304  1626457585   1626457585  ...   \n",
       "14         99.325134    1600818455  1626297442   1626297442  ...   \n",
       "15       1207.678344    1584142508  1626296561   1626296561  ...   \n",
       "16        151.069336    1562173372  1626277852   1626277852  ...   \n",
       "17          6.951172    1619110390  1625829183   1625829183  ...   \n",
       "18         28.456818    1616645179  1625170795   1625170795  ...   \n",
       "19         64.486054    1616646130  1625170794   1625170794  ...   \n",
       "20         63.878937    1616648007  1625170793   1625170793  ...   \n",
       "21         27.057647    1616640862  1625170793   1625170793  ...   \n",
       "22         23.355163    1625156461  1625156722   1625156722  ...   \n",
       "\n",
       "                                   psdResultantOctave  \\\n",
       "0   [0.    0.    0.001 0.024 0.032 0.008 0.008 0.0...   \n",
       "1   [0.    0.    0.    0.    0.    0.    0.    0. ...   \n",
       "2   [0.001 0.003 0.004 0.064 0.106 0.102 0.103 0.1...   \n",
       "3   [0.    0.    0.    0.    0.001 0.002 0.002 0.0...   \n",
       "4   [0.002 0.007 0.008 0.005 0.002 0.005 0.002 0.0...   \n",
       "5   [0.    0.    0.    0.    0.001 0.    0.    0. ...   \n",
       "6   [0.304 0.486 0.399 0.454 0.347 0.178 0.185 0.1...   \n",
       "7   [2.100e-02 7.400e-02 7.500e-02 4.900e-02 5.500...   \n",
       "8   [ 0.  0.  0.  0.  0.  0.  0. nan nan nan nan n...   \n",
       "9                                                  []   \n",
       "10  [0.071 0.201 0.293 0.686 1.565 1.028 0.856 0.9...   \n",
       "11  [0.074 0.196 0.327 0.874 1.521 0.917 0.478 0.7...   \n",
       "12                                                 []   \n",
       "13                                                 []   \n",
       "14           [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]   \n",
       "15                                                 []   \n",
       "16           [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]   \n",
       "17           [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]   \n",
       "18           [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]   \n",
       "19           [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]   \n",
       "20           [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]   \n",
       "21           [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]   \n",
       "22           [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]   \n",
       "\n",
       "                                     samplePeakWindow  temperatureMeanFull  \\\n",
       "0           [1.241 1.682 1.944 ... 1.852 1.372 1.473]               23.432   \n",
       "1           [0.035 0.024 0.007 ... 0.067 0.031 0.022]               20.133   \n",
       "2           [5.283 5.944 5.19  ... 0.356 1.079 1.099]               23.172   \n",
       "3           [0.13  0.118 0.1   ... 0.105 0.1   0.066]               21.806   \n",
       "4           [0.084 0.137 0.178 ... 0.347 0.324 0.286]               17.820   \n",
       "5           [0.001 0.003 0.002 ... 0.011 0.008 0.006]               29.061   \n",
       "6     [ 3.088  3.019  2.893 ... 73.794 40.005 24.303]                9.538   \n",
       "7           [7.486 7.496 7.137 ... 7.997 8.294 7.806]               24.545   \n",
       "8           [0.001 0.001 0.001 ... 0.002 0.006 0.006]               18.874   \n",
       "9                                                  []               24.540   \n",
       "10          [0.304 0.274 0.296 ... 1.513 1.313 0.652]               24.180   \n",
       "11          [0.304 0.21  0.098 ... 0.693 1.418 0.932]               24.175   \n",
       "12                                                 []               28.832   \n",
       "13                                                 []                  NaN   \n",
       "14    [ 3.34  13.586 10.13  ...  7.737  8.978  8.672]                  NaN   \n",
       "15                                                 []                  NaN   \n",
       "16          [3.371 3.641 3.649 ... 6.168 7.463 7.04 ]               26.989   \n",
       "17          [0.667 0.549 1.05  ... 1.109 2.86  3.99 ]               21.889   \n",
       "18  [ 32.679  24.925  30.95  ... 106.511  27.715  ...               33.452   \n",
       "19    [14.223 14.903 24.949 ... 11.482 21.319 32.766]               32.202   \n",
       "20  [ 66.83   54.723  63.536 ... 109.259  85.341  ...               26.031   \n",
       "21  [ 33.294  29.392  19.99  ... 175.123 162.043  ...               25.616   \n",
       "22          [0.033 0.029 0.03  ...   nan   nan   nan]               26.410   \n",
       "\n",
       "    accelerometerSampleRateFull  \\\n",
       "0                       19999.0   \n",
       "1                        3996.0   \n",
       "2                       20000.0   \n",
       "3                        4012.0   \n",
       "4                        4046.0   \n",
       "5                        4013.0   \n",
       "6                       19997.0   \n",
       "7                        5000.0   \n",
       "8                         504.0   \n",
       "9                        5000.0   \n",
       "10                       5000.0   \n",
       "11                       5000.0   \n",
       "12                       4012.0   \n",
       "13                          NaN   \n",
       "14                       4014.0   \n",
       "15                          NaN   \n",
       "16                      10001.0   \n",
       "17                       5000.0   \n",
       "18                      20010.0   \n",
       "19                      19992.0   \n",
       "20                      19992.0   \n",
       "21                      19998.0   \n",
       "22                       5000.0   \n",
       "\n",
       "                                       psdPeakOctaves  microphonoeRMSFull  \\\n",
       "0   [0.    0.    0.001 0.131 0.229 0.045 0.05  0.0...                 NaN   \n",
       "1   [0.    0.    0.001 0.    0.001 0.    0.001 0.0...                 NaN   \n",
       "2   [0.001 0.004 0.004 0.128 0.125 0.119 0.14  0.1...              25.507   \n",
       "3   [0.    0.    0.    0.    0.008 0.01  0.019 0.0...               1.754   \n",
       "4   [0.002 0.014 0.016 0.013 0.003 0.031 0.003 0.0...                 NaN   \n",
       "5   [0.    0.    0.    0.    0.007 0.    0.    0. ...              16.243   \n",
       "6   [ 0.304  0.537  0.479  0.811  0.624  0.554  0....                 NaN   \n",
       "7   [2.10000e-02 9.00000e-02 8.60000e-02 6.30000e-...                 NaN   \n",
       "8   [0.    0.    0.001 0.001 0.    0.    0.      n...                 NaN   \n",
       "9                                                  []                 NaN   \n",
       "10  [0.071 0.236 0.311 1.148 2.036 1.791 1.498 2.7...                 NaN   \n",
       "11  [0.074 0.232 0.392 1.258 2.045 2.049 1.143 2.3...                 NaN   \n",
       "12                                                 []              16.277   \n",
       "13                                                 []                 NaN   \n",
       "14  [[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\\n [0...                 NaN   \n",
       "15                                                 []                 NaN   \n",
       "16  [[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\\n [0...                 NaN   \n",
       "17  [[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\\n [0...                 NaN   \n",
       "18  [[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\\n [0...                 NaN   \n",
       "19  [[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\\n [0...                 NaN   \n",
       "20  [[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\\n [0...                 NaN   \n",
       "21  [[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\\n [0...                 NaN   \n",
       "22  [[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\\n [0...                 NaN   \n",
       "\n",
       "    gyroscopeRMSFull                                pvssResultantOctave  \\\n",
       "0              0.625  [ 10.333  25.952  30.28   52.579 172.174 275.8...   \n",
       "1              0.945  [53.648 76.9   43.779 44.217 39.351 11.396  9....   \n",
       "2                NaN  [ 26.338  27.636  64.097 102.733 107.863 124.6...   \n",
       "3              1.557  [ 0.854  1.159  1.662  1.815  3.022  6.139 10....   \n",
       "4              2.666  [ 74.73   79.453 101.006 151.429  73.92   53.4...   \n",
       "5              0.363  [ 2.336  7.82  19.078 10.384 16.975 38.326 18....   \n",
       "6                NaN  [1378.444 2470.941 4368.78  5033.327 5814.49  ...   \n",
       "7              0.166  [ 90.703 198.651 288.078 183.492 126.417 108.4...   \n",
       "8              0.749  [ 2.617  6.761 17.326 34.067 28.721  9.469 15....   \n",
       "9              0.082                                                 []   \n",
       "10            24.471  [ 194.741  361.14   563.488  855.34  1712.229 ...   \n",
       "11            15.575  [ 242.141  388.517  736.981 1070.284 1843.722 ...   \n",
       "12             4.185                                                 []   \n",
       "13               NaN                                                 []   \n",
       "14               NaN  [165.983 278.352 717.331 950.513 697.421 358.6...   \n",
       "15               NaN                                                 []   \n",
       "16            19.810  [ 2723.153  5977.212 11406.291 12031.397  7337...   \n",
       "17             4.647  [ 88.953 171.73  104.303  71.184  58.883  72.4...   \n",
       "18           286.217  [2333.993 5629.021 6417.48  5802.895 3692.838 ...   \n",
       "19           277.654  [ 378.188  748.336 1054.232 1115.369  756.905 ...   \n",
       "20           245.929  [ 780.577 1168.663 1851.417 1094.945  793.122 ...   \n",
       "21           266.815  [1073.684  940.873 1063.151  957.568  975.826 ...   \n",
       "22            11.933  [142.046 273.02  216.774 129.288  54.011  24.0...   \n",
       "\n",
       "    accelerationRMSFull                                    psdResultant1Hz  \n",
       "0                 0.372                            [0. 0. 0. ... 0. 0. 0.]  \n",
       "1                 0.082                      [ 0.  0.  0. ... nan nan nan]  \n",
       "2                 2.398          [0.    0.001 0.002 ... 0.002 0.001 0.001]  \n",
       "3                 0.079                      [ 0.  0.  0. ... nan nan nan]  \n",
       "4                 0.130          [0.001 0.002 0.002 ...   nan   nan   nan]  \n",
       "5                 0.021                      [ 0.  0.  0. ... nan nan nan]  \n",
       "6                11.645          [0.157 0.304 0.42  ... 0.001 0.01  0.013]  \n",
       "7                 2.712          [0.007 0.021 0.055 ...   nan   nan   nan]  \n",
       "8                 0.011                      [ 0.  0.  0. ... nan nan nan]  \n",
       "9                 0.059                                                 []  \n",
       "10                2.877          [0.02  0.071 0.138 ...   nan   nan   nan]  \n",
       "11                2.423          [0.021 0.074 0.137 ...   nan   nan   nan]  \n",
       "12                0.097                                                 []  \n",
       "13                  NaN                                                 []  \n",
       "14                3.528          [0.006 0.016 0.04  ...   nan   nan   nan]  \n",
       "15                  NaN                                                 []  \n",
       "16                1.732  [2.250e-01 8.390e-01 2.025e+00 ... 1.000e-03 1...  \n",
       "17                1.568          [0.001 0.001 0.003 ...   nan   nan   nan]  \n",
       "18               94.197    [ 2.09   8.631 18.196 ... 69.565 59.225 60.801]  \n",
       "19               46.528    [ 0.072  0.302  0.816 ... 52.226 45.804 52.81 ]  \n",
       "20               54.408    [ 0.162  0.587  1.495 ... 45.639 43.86  40.177]  \n",
       "21              131.087    [ 0.371  0.409  0.787 ... 27.577 24.778 37.822]  \n",
       "22                0.044          [0.008 0.02  0.03  ...   nan   nan   nan]  \n",
       "\n",
       "[23 rows x 29 columns]"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('https://info.endaq.com/hubfs/data/endaq-cloud-table.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "TrjLYbATgLDi",
    "outputId": "7154d956-2df0-4a0b-a04c-b70ac0be95fb"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Unnamed: 0', 'tags', 'id', 'serial_number_id', 'file_name',\n",
       "       'file_size', 'recording_length', 'recording_ts', 'created_ts',\n",
       "       'modified_ts', 'device', 'gpsLocationFull', 'velocityRMSFull',\n",
       "       'gpsSpeedFull', 'pressureMeanFull', 'samplePeakStartTime',\n",
       "       'displacementRMSFull', 'psuedoVelocityPeakFull', 'accelerationPeakFull',\n",
       "       'psdResultantOctave', 'samplePeakWindow', 'temperatureMeanFull',\n",
       "       'accelerometerSampleRateFull', 'psdPeakOctaves', 'microphonoeRMSFull',\n",
       "       'gyroscopeRMSFull', 'pvssResultantOctave', 'accelerationRMSFull',\n",
       "       'psdResultant1Hz'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8y82HS2h8c0G"
   },
   "source": [
    "There's a lot of data here! So we'll focus on just a handful of columns and convert the time in seconds to a datetime object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "6BUQuL9kgQ6z",
    "outputId": "72f73913-1a57-4804-f69d-c92882d80b0b"
   },
   "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>serial_number_id</th>\n",
       "      <th>file_name</th>\n",
       "      <th>file_size</th>\n",
       "      <th>recording_length</th>\n",
       "      <th>recording_ts</th>\n",
       "      <th>accelerationPeakFull</th>\n",
       "      <th>psuedoVelocityPeakFull</th>\n",
       "      <th>accelerationRMSFull</th>\n",
       "      <th>velocityRMSFull</th>\n",
       "      <th>displacementRMSFull</th>\n",
       "      <th>pressureMeanFull</th>\n",
       "      <th>temperatureMeanFull</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11456</td>\n",
       "      <td>50_Joules_900_lbs-1629315312.ide</td>\n",
       "      <td>1597750</td>\n",
       "      <td>20.201752</td>\n",
       "      <td>2021-07-26 19:56:39</td>\n",
       "      <td>231.212</td>\n",
       "      <td>2907.650</td>\n",
       "      <td>2.423</td>\n",
       "      <td>54.507</td>\n",
       "      <td>1.066</td>\n",
       "      <td>98.745</td>\n",
       "      <td>24.175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11456</td>\n",
       "      <td>100_Joules_900_lbs-1629315313.ide</td>\n",
       "      <td>1596714</td>\n",
       "      <td>20.200623</td>\n",
       "      <td>2021-07-26 19:21:55</td>\n",
       "      <td>218.634</td>\n",
       "      <td>2961.256</td>\n",
       "      <td>2.877</td>\n",
       "      <td>53.875</td>\n",
       "      <td>1.053</td>\n",
       "      <td>98.751</td>\n",
       "      <td>24.180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>9695</td>\n",
       "      <td>Tilt_000000-1625156721.IDE</td>\n",
       "      <td>719403</td>\n",
       "      <td>23.355163</td>\n",
       "      <td>2021-07-01 16:21:01</td>\n",
       "      <td>0.378</td>\n",
       "      <td>330.946</td>\n",
       "      <td>0.044</td>\n",
       "      <td>11.042</td>\n",
       "      <td>0.345</td>\n",
       "      <td>99.510</td>\n",
       "      <td>26.410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>11162</td>\n",
       "      <td>Calibration-Shake-1632515140.IDE</td>\n",
       "      <td>2218130</td>\n",
       "      <td>27.882690</td>\n",
       "      <td>2021-05-17 19:16:10</td>\n",
       "      <td>8.783</td>\n",
       "      <td>1142.282</td>\n",
       "      <td>2.712</td>\n",
       "      <td>46.346</td>\n",
       "      <td>0.617</td>\n",
       "      <td>102.251</td>\n",
       "      <td>24.545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>11071</td>\n",
       "      <td>surgical-instrument-1625829182.ide</td>\n",
       "      <td>541994</td>\n",
       "      <td>6.951172</td>\n",
       "      <td>2021-04-22 16:53:10</td>\n",
       "      <td>5.739</td>\n",
       "      <td>387.312</td>\n",
       "      <td>1.568</td>\n",
       "      <td>24.418</td>\n",
       "      <td>0.242</td>\n",
       "      <td>99.879</td>\n",
       "      <td>21.889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>10916</td>\n",
       "      <td>FUSE_HSTAB_000005-1632515139.ide</td>\n",
       "      <td>537562</td>\n",
       "      <td>18.491791</td>\n",
       "      <td>2021-04-22 16:13:24</td>\n",
       "      <td>0.202</td>\n",
       "      <td>53.375</td>\n",
       "      <td>0.011</td>\n",
       "      <td>1.504</td>\n",
       "      <td>0.036</td>\n",
       "      <td>90.706</td>\n",
       "      <td>18.874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10118</td>\n",
       "      <td>Bolted-1632515144.ide</td>\n",
       "      <td>6149229</td>\n",
       "      <td>29.396118</td>\n",
       "      <td>2021-04-21 21:44:07</td>\n",
       "      <td>15.343</td>\n",
       "      <td>148.276</td>\n",
       "      <td>2.398</td>\n",
       "      <td>14.101</td>\n",
       "      <td>0.154</td>\n",
       "      <td>99.652</td>\n",
       "      <td>23.172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>9680</td>\n",
       "      <td>LOC__6__DAQ41551_25_01-1625170793.IDE</td>\n",
       "      <td>8664238</td>\n",
       "      <td>63.878937</td>\n",
       "      <td>2021-03-25 04:53:27</td>\n",
       "      <td>564.966</td>\n",
       "      <td>2357.599</td>\n",
       "      <td>54.408</td>\n",
       "      <td>145.223</td>\n",
       "      <td>3.088</td>\n",
       "      <td>102.875</td>\n",
       "      <td>26.031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>9680</td>\n",
       "      <td>LOC__4__DAQ41551_15_05-1625170794.IDE</td>\n",
       "      <td>6927958</td>\n",
       "      <td>64.486054</td>\n",
       "      <td>2021-03-25 04:22:10</td>\n",
       "      <td>585.863</td>\n",
       "      <td>2153.020</td>\n",
       "      <td>46.528</td>\n",
       "      <td>148.591</td>\n",
       "      <td>2.615</td>\n",
       "      <td>105.750</td>\n",
       "      <td>32.202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>9680</td>\n",
       "      <td>LOC__3__DAQ41551_11_01_02-1625170795.IDE</td>\n",
       "      <td>2343292</td>\n",
       "      <td>28.456818</td>\n",
       "      <td>2021-03-25 04:06:19</td>\n",
       "      <td>622.040</td>\n",
       "      <td>8907.949</td>\n",
       "      <td>94.197</td>\n",
       "      <td>372.049</td>\n",
       "      <td>9.580</td>\n",
       "      <td>105.682</td>\n",
       "      <td>33.452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>9680</td>\n",
       "      <td>LOC__2__DAQ38060_06_03_05-1625170793.IDE</td>\n",
       "      <td>1519172</td>\n",
       "      <td>27.057647</td>\n",
       "      <td>2021-03-25 02:54:22</td>\n",
       "      <td>995.670</td>\n",
       "      <td>5845.241</td>\n",
       "      <td>131.087</td>\n",
       "      <td>323.287</td>\n",
       "      <td>3.144</td>\n",
       "      <td>104.473</td>\n",
       "      <td>25.616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>11046</td>\n",
       "      <td>Drive-Home_07-1626805222.ide</td>\n",
       "      <td>36225758</td>\n",
       "      <td>634.732056</td>\n",
       "      <td>2021-03-19 19:35:57</td>\n",
       "      <td>23.805</td>\n",
       "      <td>356.128</td>\n",
       "      <td>0.097</td>\n",
       "      <td>6.117</td>\n",
       "      <td>0.135</td>\n",
       "      <td>101.988</td>\n",
       "      <td>28.832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>11046</td>\n",
       "      <td>Drive-Home_01-1632515142.ide</td>\n",
       "      <td>3632799</td>\n",
       "      <td>61.755371</td>\n",
       "      <td>2021-03-19 18:35:55</td>\n",
       "      <td>0.479</td>\n",
       "      <td>40.197</td>\n",
       "      <td>0.021</td>\n",
       "      <td>1.081</td>\n",
       "      <td>0.023</td>\n",
       "      <td>100.284</td>\n",
       "      <td>29.061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>10030</td>\n",
       "      <td>200922_Moto_Max_Run5_Control_Larry-1626297441.ide</td>\n",
       "      <td>4780893</td>\n",
       "      <td>99.325134</td>\n",
       "      <td>2020-09-22 23:47:35</td>\n",
       "      <td>29.864</td>\n",
       "      <td>1280.349</td>\n",
       "      <td>3.528</td>\n",
       "      <td>55.569</td>\n",
       "      <td>1.060</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9695</td>\n",
       "      <td>train-passing-1632515146.ide</td>\n",
       "      <td>10492602</td>\n",
       "      <td>73.612335</td>\n",
       "      <td>2020-04-29 18:20:36</td>\n",
       "      <td>7.513</td>\n",
       "      <td>419.944</td>\n",
       "      <td>0.372</td>\n",
       "      <td>6.969</td>\n",
       "      <td>0.061</td>\n",
       "      <td>104.620</td>\n",
       "      <td>23.432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>9695</td>\n",
       "      <td>ford_f150-1626296561.ide</td>\n",
       "      <td>96097059</td>\n",
       "      <td>1207.678344</td>\n",
       "      <td>2020-03-13 23:35:08</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9295</td>\n",
       "      <td>Seat-Base_21-1632515142.ide</td>\n",
       "      <td>5248836</td>\n",
       "      <td>83.092255</td>\n",
       "      <td>2019-12-08 10:16:50</td>\n",
       "      <td>1.085</td>\n",
       "      <td>251.009</td>\n",
       "      <td>0.130</td>\n",
       "      <td>7.318</td>\n",
       "      <td>0.190</td>\n",
       "      <td>98.930</td>\n",
       "      <td>17.820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9316</td>\n",
       "      <td>Seat-Top_09-1632515145.ide</td>\n",
       "      <td>10491986</td>\n",
       "      <td>172.704559</td>\n",
       "      <td>2019-12-08 10:14:31</td>\n",
       "      <td>1.105</td>\n",
       "      <td>86.595</td>\n",
       "      <td>0.082</td>\n",
       "      <td>1.535</td>\n",
       "      <td>0.040</td>\n",
       "      <td>98.733</td>\n",
       "      <td>20.133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>7530</td>\n",
       "      <td>Motorcycle-Car-Crash-1626277852.ide</td>\n",
       "      <td>10489262</td>\n",
       "      <td>151.069336</td>\n",
       "      <td>2019-07-03 17:02:52</td>\n",
       "      <td>480.737</td>\n",
       "      <td>12831.590</td>\n",
       "      <td>1.732</td>\n",
       "      <td>143.437</td>\n",
       "      <td>3.988</td>\n",
       "      <td>100.363</td>\n",
       "      <td>26.989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>HiTest-Shock-1632515141.ide</td>\n",
       "      <td>2655894</td>\n",
       "      <td>20.331848</td>\n",
       "      <td>2018-12-04 15:22:54</td>\n",
       "      <td>619.178</td>\n",
       "      <td>6058.093</td>\n",
       "      <td>11.645</td>\n",
       "      <td>167.835</td>\n",
       "      <td>4.055</td>\n",
       "      <td>101.126</td>\n",
       "      <td>9.538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>5120</td>\n",
       "      <td>Mining-SSX28803_06-1626457584.IDE</td>\n",
       "      <td>402920686</td>\n",
       "      <td>3238.119202</td>\n",
       "      <td>2018-09-14 19:28:24</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9874</td>\n",
       "      <td>Coffee_002-1631722736.IDE</td>\n",
       "      <td>60959516</td>\n",
       "      <td>769.299896</td>\n",
       "      <td>2000-03-03 20:02:24</td>\n",
       "      <td>2.698</td>\n",
       "      <td>1338.396</td>\n",
       "      <td>0.059</td>\n",
       "      <td>5.606</td>\n",
       "      <td>0.104</td>\n",
       "      <td>100.339</td>\n",
       "      <td>24.540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10309</td>\n",
       "      <td>RMI-2000-1632515143.ide</td>\n",
       "      <td>5909632</td>\n",
       "      <td>60.250855</td>\n",
       "      <td>1970-01-01 00:00:24</td>\n",
       "      <td>0.332</td>\n",
       "      <td>17.287</td>\n",
       "      <td>0.079</td>\n",
       "      <td>1.247</td>\n",
       "      <td>0.005</td>\n",
       "      <td>100.467</td>\n",
       "      <td>21.806</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    serial_number_id                                          file_name  \\\n",
       "11             11456                   50_Joules_900_lbs-1629315312.ide   \n",
       "10             11456                  100_Joules_900_lbs-1629315313.ide   \n",
       "22              9695                         Tilt_000000-1625156721.IDE   \n",
       "7              11162                   Calibration-Shake-1632515140.IDE   \n",
       "17             11071                 surgical-instrument-1625829182.ide   \n",
       "8              10916                   FUSE_HSTAB_000005-1632515139.ide   \n",
       "2              10118                              Bolted-1632515144.ide   \n",
       "20              9680              LOC__6__DAQ41551_25_01-1625170793.IDE   \n",
       "19              9680              LOC__4__DAQ41551_15_05-1625170794.IDE   \n",
       "18              9680           LOC__3__DAQ41551_11_01_02-1625170795.IDE   \n",
       "21              9680           LOC__2__DAQ38060_06_03_05-1625170793.IDE   \n",
       "12             11046                       Drive-Home_07-1626805222.ide   \n",
       "5              11046                       Drive-Home_01-1632515142.ide   \n",
       "14             10030  200922_Moto_Max_Run5_Control_Larry-1626297441.ide   \n",
       "0               9695                       train-passing-1632515146.ide   \n",
       "15              9695                           ford_f150-1626296561.ide   \n",
       "4               9295                        Seat-Base_21-1632515142.ide   \n",
       "1               9316                         Seat-Top_09-1632515145.ide   \n",
       "16              7530                Motorcycle-Car-Crash-1626277852.ide   \n",
       "6                  0                        HiTest-Shock-1632515141.ide   \n",
       "13              5120                  Mining-SSX28803_06-1626457584.IDE   \n",
       "9               9874                          Coffee_002-1631722736.IDE   \n",
       "3              10309                            RMI-2000-1632515143.ide   \n",
       "\n",
       "    file_size  recording_length        recording_ts  accelerationPeakFull  \\\n",
       "11    1597750         20.201752 2021-07-26 19:56:39               231.212   \n",
       "10    1596714         20.200623 2021-07-26 19:21:55               218.634   \n",
       "22     719403         23.355163 2021-07-01 16:21:01                 0.378   \n",
       "7     2218130         27.882690 2021-05-17 19:16:10                 8.783   \n",
       "17     541994          6.951172 2021-04-22 16:53:10                 5.739   \n",
       "8      537562         18.491791 2021-04-22 16:13:24                 0.202   \n",
       "2     6149229         29.396118 2021-04-21 21:44:07                15.343   \n",
       "20    8664238         63.878937 2021-03-25 04:53:27               564.966   \n",
       "19    6927958         64.486054 2021-03-25 04:22:10               585.863   \n",
       "18    2343292         28.456818 2021-03-25 04:06:19               622.040   \n",
       "21    1519172         27.057647 2021-03-25 02:54:22               995.670   \n",
       "12   36225758        634.732056 2021-03-19 19:35:57                23.805   \n",
       "5     3632799         61.755371 2021-03-19 18:35:55                 0.479   \n",
       "14    4780893         99.325134 2020-09-22 23:47:35                29.864   \n",
       "0    10492602         73.612335 2020-04-29 18:20:36                 7.513   \n",
       "15   96097059       1207.678344 2020-03-13 23:35:08                   NaN   \n",
       "4     5248836         83.092255 2019-12-08 10:16:50                 1.085   \n",
       "1    10491986        172.704559 2019-12-08 10:14:31                 1.105   \n",
       "16   10489262        151.069336 2019-07-03 17:02:52               480.737   \n",
       "6     2655894         20.331848 2018-12-04 15:22:54               619.178   \n",
       "13  402920686       3238.119202 2018-09-14 19:28:24                   NaN   \n",
       "9    60959516        769.299896 2000-03-03 20:02:24                 2.698   \n",
       "3     5909632         60.250855 1970-01-01 00:00:24                 0.332   \n",
       "\n",
       "    psuedoVelocityPeakFull  accelerationRMSFull  velocityRMSFull  \\\n",
       "11                2907.650                2.423           54.507   \n",
       "10                2961.256                2.877           53.875   \n",
       "22                 330.946                0.044           11.042   \n",
       "7                 1142.282                2.712           46.346   \n",
       "17                 387.312                1.568           24.418   \n",
       "8                   53.375                0.011            1.504   \n",
       "2                  148.276                2.398           14.101   \n",
       "20                2357.599               54.408          145.223   \n",
       "19                2153.020               46.528          148.591   \n",
       "18                8907.949               94.197          372.049   \n",
       "21                5845.241              131.087          323.287   \n",
       "12                 356.128                0.097            6.117   \n",
       "5                   40.197                0.021            1.081   \n",
       "14                1280.349                3.528           55.569   \n",
       "0                  419.944                0.372            6.969   \n",
       "15                     NaN                  NaN              NaN   \n",
       "4                  251.009                0.130            7.318   \n",
       "1                   86.595                0.082            1.535   \n",
       "16               12831.590                1.732          143.437   \n",
       "6                 6058.093               11.645          167.835   \n",
       "13                     NaN                  NaN              NaN   \n",
       "9                 1338.396                0.059            5.606   \n",
       "3                   17.287                0.079            1.247   \n",
       "\n",
       "    displacementRMSFull  pressureMeanFull  temperatureMeanFull  \n",
       "11                1.066            98.745               24.175  \n",
       "10                1.053            98.751               24.180  \n",
       "22                0.345            99.510               26.410  \n",
       "7                 0.617           102.251               24.545  \n",
       "17                0.242            99.879               21.889  \n",
       "8                 0.036            90.706               18.874  \n",
       "2                 0.154            99.652               23.172  \n",
       "20                3.088           102.875               26.031  \n",
       "19                2.615           105.750               32.202  \n",
       "18                9.580           105.682               33.452  \n",
       "21                3.144           104.473               25.616  \n",
       "12                0.135           101.988               28.832  \n",
       "5                 0.023           100.284               29.061  \n",
       "14                1.060               NaN                  NaN  \n",
       "0                 0.061           104.620               23.432  \n",
       "15                  NaN               NaN                  NaN  \n",
       "4                 0.190            98.930               17.820  \n",
       "1                 0.040            98.733               20.133  \n",
       "16                3.988           100.363               26.989  \n",
       "6                 4.055           101.126                9.538  \n",
       "13                  NaN               NaN                  NaN  \n",
       "9                 0.104           100.339               24.540  \n",
       "3                 0.005           100.467               21.806  "
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df[['serial_number_id', 'file_name', 'file_size', 'recording_length', 'recording_ts',\n",
    "         'accelerationPeakFull', 'psuedoVelocityPeakFull', 'accelerationRMSFull',\n",
    "         'velocityRMSFull', 'displacementRMSFull', 'pressureMeanFull', 'temperatureMeanFull']].copy()\n",
    "\n",
    "df['recording_ts'] = pd.to_datetime(df['recording_ts'], unit='s')\n",
    "df = df.sort_values(by=['recording_ts'], ascending=False)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5ZLSvcMZ8t6-"
   },
   "source": [
    "Filtering is made simple with boolean expressions that can be combined. There is also a method to sort_values by columns/series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 702
    },
    "id": "3oe0EgX0gyfz",
    "outputId": "7008ef1a-9396-4183-c7c8-23f08a4b050f"
   },
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
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       "        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>serial_number_id</th>\n",
       "      <th>file_name</th>\n",
       "      <th>file_size</th>\n",
       "      <th>recording_length</th>\n",
       "      <th>recording_ts</th>\n",
       "      <th>accelerationPeakFull</th>\n",
       "      <th>psuedoVelocityPeakFull</th>\n",
       "      <th>accelerationRMSFull</th>\n",
       "      <th>velocityRMSFull</th>\n",
       "      <th>displacementRMSFull</th>\n",
       "      <th>pressureMeanFull</th>\n",
       "      <th>temperatureMeanFull</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11456</td>\n",
       "      <td>50_Joules_900_lbs-1629315312.ide</td>\n",
       "      <td>1597750</td>\n",
       "      <td>20.201752</td>\n",
       "      <td>2021-07-26 19:56:39</td>\n",
       "      <td>231.212</td>\n",
       "      <td>2907.650</td>\n",
       "      <td>2.423</td>\n",
       "      <td>54.507</td>\n",
       "      <td>1.066</td>\n",
       "      <td>98.745</td>\n",
       "      <td>24.175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11456</td>\n",
       "      <td>100_Joules_900_lbs-1629315313.ide</td>\n",
       "      <td>1596714</td>\n",
       "      <td>20.200623</td>\n",
       "      <td>2021-07-26 19:21:55</td>\n",
       "      <td>218.634</td>\n",
       "      <td>2961.256</td>\n",
       "      <td>2.877</td>\n",
       "      <td>53.875</td>\n",
       "      <td>1.053</td>\n",
       "      <td>98.751</td>\n",
       "      <td>24.180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>11162</td>\n",
       "      <td>Calibration-Shake-1632515140.IDE</td>\n",
       "      <td>2218130</td>\n",
       "      <td>27.882690</td>\n",
       "      <td>2021-05-17 19:16:10</td>\n",
       "      <td>8.783</td>\n",
       "      <td>1142.282</td>\n",
       "      <td>2.712</td>\n",
       "      <td>46.346</td>\n",
       "      <td>0.617</td>\n",
       "      <td>102.251</td>\n",
       "      <td>24.545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>11071</td>\n",
       "      <td>surgical-instrument-1625829182.ide</td>\n",
       "      <td>541994</td>\n",
       "      <td>6.951172</td>\n",
       "      <td>2021-04-22 16:53:10</td>\n",
       "      <td>5.739</td>\n",
       "      <td>387.312</td>\n",
       "      <td>1.568</td>\n",
       "      <td>24.418</td>\n",
       "      <td>0.242</td>\n",
       "      <td>99.879</td>\n",
       "      <td>21.889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>11046</td>\n",
       "      <td>Drive-Home_07-1626805222.ide</td>\n",
       "      <td>36225758</td>\n",
       "      <td>634.732056</td>\n",
       "      <td>2021-03-19 19:35:57</td>\n",
       "      <td>23.805</td>\n",
       "      <td>356.128</td>\n",
       "      <td>0.097</td>\n",
       "      <td>6.117</td>\n",
       "      <td>0.135</td>\n",
       "      <td>101.988</td>\n",
       "      <td>28.832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>11046</td>\n",
       "      <td>Drive-Home_01-1632515142.ide</td>\n",
       "      <td>3632799</td>\n",
       "      <td>61.755371</td>\n",
       "      <td>2021-03-19 18:35:55</td>\n",
       "      <td>0.479</td>\n",
       "      <td>40.197</td>\n",
       "      <td>0.021</td>\n",
       "      <td>1.081</td>\n",
       "      <td>0.023</td>\n",
       "      <td>100.284</td>\n",
       "      <td>29.061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>10916</td>\n",
       "      <td>FUSE_HSTAB_000005-1632515139.ide</td>\n",
       "      <td>537562</td>\n",
       "      <td>18.491791</td>\n",
       "      <td>2021-04-22 16:13:24</td>\n",
       "      <td>0.202</td>\n",
       "      <td>53.375</td>\n",
       "      <td>0.011</td>\n",
       "      <td>1.504</td>\n",
       "      <td>0.036</td>\n",
       "      <td>90.706</td>\n",
       "      <td>18.874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10118</td>\n",
       "      <td>Bolted-1632515144.ide</td>\n",
       "      <td>6149229</td>\n",
       "      <td>29.396118</td>\n",
       "      <td>2021-04-21 21:44:07</td>\n",
       "      <td>15.343</td>\n",
       "      <td>148.276</td>\n",
       "      <td>2.398</td>\n",
       "      <td>14.101</td>\n",
       "      <td>0.154</td>\n",
       "      <td>99.652</td>\n",
       "      <td>23.172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>9695</td>\n",
       "      <td>Tilt_000000-1625156721.IDE</td>\n",
       "      <td>719403</td>\n",
       "      <td>23.355163</td>\n",
       "      <td>2021-07-01 16:21:01</td>\n",
       "      <td>0.378</td>\n",
       "      <td>330.946</td>\n",
       "      <td>0.044</td>\n",
       "      <td>11.042</td>\n",
       "      <td>0.345</td>\n",
       "      <td>99.510</td>\n",
       "      <td>26.410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>9680</td>\n",
       "      <td>LOC__4__DAQ41551_15_05-1625170794.IDE</td>\n",
       "      <td>6927958</td>\n",
       "      <td>64.486054</td>\n",
       "      <td>2021-03-25 04:22:10</td>\n",
       "      <td>585.863</td>\n",
       "      <td>2153.020</td>\n",
       "      <td>46.528</td>\n",
       "      <td>148.591</td>\n",
       "      <td>2.615</td>\n",
       "      <td>105.750</td>\n",
       "      <td>32.202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>9680</td>\n",
       "      <td>LOC__6__DAQ41551_25_01-1625170793.IDE</td>\n",
       "      <td>8664238</td>\n",
       "      <td>63.878937</td>\n",
       "      <td>2021-03-25 04:53:27</td>\n",
       "      <td>564.966</td>\n",
       "      <td>2357.599</td>\n",
       "      <td>54.408</td>\n",
       "      <td>145.223</td>\n",
       "      <td>3.088</td>\n",
       "      <td>102.875</td>\n",
       "      <td>26.031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>9680</td>\n",
       "      <td>LOC__2__DAQ38060_06_03_05-1625170793.IDE</td>\n",
       "      <td>1519172</td>\n",
       "      <td>27.057647</td>\n",
       "      <td>2021-03-25 02:54:22</td>\n",
       "      <td>995.670</td>\n",
       "      <td>5845.241</td>\n",
       "      <td>131.087</td>\n",
       "      <td>323.287</td>\n",
       "      <td>3.144</td>\n",
       "      <td>104.473</td>\n",
       "      <td>25.616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>9680</td>\n",
       "      <td>LOC__3__DAQ41551_11_01_02-1625170795.IDE</td>\n",
       "      <td>2343292</td>\n",
       "      <td>28.456818</td>\n",
       "      <td>2021-03-25 04:06:19</td>\n",
       "      <td>622.040</td>\n",
       "      <td>8907.949</td>\n",
       "      <td>94.197</td>\n",
       "      <td>372.049</td>\n",
       "      <td>9.580</td>\n",
       "      <td>105.682</td>\n",
       "      <td>33.452</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    serial_number_id                                 file_name  file_size  \\\n",
       "11             11456          50_Joules_900_lbs-1629315312.ide    1597750   \n",
       "10             11456         100_Joules_900_lbs-1629315313.ide    1596714   \n",
       "7              11162          Calibration-Shake-1632515140.IDE    2218130   \n",
       "17             11071        surgical-instrument-1625829182.ide     541994   \n",
       "12             11046              Drive-Home_07-1626805222.ide   36225758   \n",
       "5              11046              Drive-Home_01-1632515142.ide    3632799   \n",
       "8              10916          FUSE_HSTAB_000005-1632515139.ide     537562   \n",
       "2              10118                     Bolted-1632515144.ide    6149229   \n",
       "22              9695                Tilt_000000-1625156721.IDE     719403   \n",
       "19              9680     LOC__4__DAQ41551_15_05-1625170794.IDE    6927958   \n",
       "20              9680     LOC__6__DAQ41551_25_01-1625170793.IDE    8664238   \n",
       "21              9680  LOC__2__DAQ38060_06_03_05-1625170793.IDE    1519172   \n",
       "18              9680  LOC__3__DAQ41551_11_01_02-1625170795.IDE    2343292   \n",
       "\n",
       "    recording_length        recording_ts  accelerationPeakFull  \\\n",
       "11         20.201752 2021-07-26 19:56:39               231.212   \n",
       "10         20.200623 2021-07-26 19:21:55               218.634   \n",
       "7          27.882690 2021-05-17 19:16:10                 8.783   \n",
       "17          6.951172 2021-04-22 16:53:10                 5.739   \n",
       "12        634.732056 2021-03-19 19:35:57                23.805   \n",
       "5          61.755371 2021-03-19 18:35:55                 0.479   \n",
       "8          18.491791 2021-04-22 16:13:24                 0.202   \n",
       "2          29.396118 2021-04-21 21:44:07                15.343   \n",
       "22         23.355163 2021-07-01 16:21:01                 0.378   \n",
       "19         64.486054 2021-03-25 04:22:10               585.863   \n",
       "20         63.878937 2021-03-25 04:53:27               564.966   \n",
       "21         27.057647 2021-03-25 02:54:22               995.670   \n",
       "18         28.456818 2021-03-25 04:06:19               622.040   \n",
       "\n",
       "    psuedoVelocityPeakFull  accelerationRMSFull  velocityRMSFull  \\\n",
       "11                2907.650                2.423           54.507   \n",
       "10                2961.256                2.877           53.875   \n",
       "7                 1142.282                2.712           46.346   \n",
       "17                 387.312                1.568           24.418   \n",
       "12                 356.128                0.097            6.117   \n",
       "5                   40.197                0.021            1.081   \n",
       "8                   53.375                0.011            1.504   \n",
       "2                  148.276                2.398           14.101   \n",
       "22                 330.946                0.044           11.042   \n",
       "19                2153.020               46.528          148.591   \n",
       "20                2357.599               54.408          145.223   \n",
       "21                5845.241              131.087          323.287   \n",
       "18                8907.949               94.197          372.049   \n",
       "\n",
       "    displacementRMSFull  pressureMeanFull  temperatureMeanFull  \n",
       "11                1.066            98.745               24.175  \n",
       "10                1.053            98.751               24.180  \n",
       "7                 0.617           102.251               24.545  \n",
       "17                0.242            99.879               21.889  \n",
       "12                0.135           101.988               28.832  \n",
       "5                 0.023           100.284               29.061  \n",
       "8                 0.036            90.706               18.874  \n",
       "2                 0.154            99.652               23.172  \n",
       "22                0.345            99.510               26.410  \n",
       "19                2.615           105.750               32.202  \n",
       "20                3.088           102.875               26.031  \n",
       "21                3.144           104.473               25.616  \n",
       "18                9.580           105.682               33.452  "
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mask = df.recording_ts > pd.to_datetime('2021-01-01')\n",
    "df[mask].sort_values(by=['serial_number_id'], ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 361
    },
    "id": "ug9gKCKQhW7K",
    "outputId": "f8b91607-0276-4304-a49c-add75954c180"
   },
   "outputs": [
    {
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       "      <th>serial_number_id</th>\n",
       "      <th>file_name</th>\n",
       "      <th>file_size</th>\n",
       "      <th>recording_length</th>\n",
       "      <th>recording_ts</th>\n",
       "      <th>accelerationPeakFull</th>\n",
       "      <th>psuedoVelocityPeakFull</th>\n",
       "      <th>accelerationRMSFull</th>\n",
       "      <th>velocityRMSFull</th>\n",
       "      <th>displacementRMSFull</th>\n",
       "      <th>pressureMeanFull</th>\n",
       "      <th>temperatureMeanFull</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>9680</td>\n",
       "      <td>LOC__2__DAQ38060_06_03_05-1625170793.IDE</td>\n",
       "      <td>1519172</td>\n",
       "      <td>27.057647</td>\n",
       "      <td>2021-03-25 02:54:22</td>\n",
       "      <td>995.670</td>\n",
       "      <td>5845.241</td>\n",
       "      <td>131.087</td>\n",
       "      <td>323.287</td>\n",
       "      <td>3.144</td>\n",
       "      <td>104.473</td>\n",
       "      <td>25.616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>9680</td>\n",
       "      <td>LOC__3__DAQ41551_11_01_02-1625170795.IDE</td>\n",
       "      <td>2343292</td>\n",
       "      <td>28.456818</td>\n",
       "      <td>2021-03-25 04:06:19</td>\n",
       "      <td>622.040</td>\n",
       "      <td>8907.949</td>\n",
       "      <td>94.197</td>\n",
       "      <td>372.049</td>\n",
       "      <td>9.580</td>\n",
       "      <td>105.682</td>\n",
       "      <td>33.452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>9680</td>\n",
       "      <td>LOC__4__DAQ41551_15_05-1625170794.IDE</td>\n",
       "      <td>6927958</td>\n",
       "      <td>64.486054</td>\n",
       "      <td>2021-03-25 04:22:10</td>\n",
       "      <td>585.863</td>\n",
       "      <td>2153.020</td>\n",
       "      <td>46.528</td>\n",
       "      <td>148.591</td>\n",
       "      <td>2.615</td>\n",
       "      <td>105.750</td>\n",
       "      <td>32.202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>9680</td>\n",
       "      <td>LOC__6__DAQ41551_25_01-1625170793.IDE</td>\n",
       "      <td>8664238</td>\n",
       "      <td>63.878937</td>\n",
       "      <td>2021-03-25 04:53:27</td>\n",
       "      <td>564.966</td>\n",
       "      <td>2357.599</td>\n",
       "      <td>54.408</td>\n",
       "      <td>145.223</td>\n",
       "      <td>3.088</td>\n",
       "      <td>102.875</td>\n",
       "      <td>26.031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11456</td>\n",
       "      <td>50_Joules_900_lbs-1629315312.ide</td>\n",
       "      <td>1597750</td>\n",
       "      <td>20.201752</td>\n",
       "      <td>2021-07-26 19:56:39</td>\n",
       "      <td>231.212</td>\n",
       "      <td>2907.650</td>\n",
       "      <td>2.423</td>\n",
       "      <td>54.507</td>\n",
       "      <td>1.066</td>\n",
       "      <td>98.745</td>\n",
       "      <td>24.175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11456</td>\n",
       "      <td>100_Joules_900_lbs-1629315313.ide</td>\n",
       "      <td>1596714</td>\n",
       "      <td>20.200623</td>\n",
       "      <td>2021-07-26 19:21:55</td>\n",
       "      <td>218.634</td>\n",
       "      <td>2961.256</td>\n",
       "      <td>2.877</td>\n",
       "      <td>53.875</td>\n",
       "      <td>1.053</td>\n",
       "      <td>98.751</td>\n",
       "      <td>24.180</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    serial_number_id                                 file_name  file_size  \\\n",
       "21              9680  LOC__2__DAQ38060_06_03_05-1625170793.IDE    1519172   \n",
       "18              9680  LOC__3__DAQ41551_11_01_02-1625170795.IDE    2343292   \n",
       "19              9680     LOC__4__DAQ41551_15_05-1625170794.IDE    6927958   \n",
       "20              9680     LOC__6__DAQ41551_25_01-1625170793.IDE    8664238   \n",
       "11             11456          50_Joules_900_lbs-1629315312.ide    1597750   \n",
       "10             11456         100_Joules_900_lbs-1629315313.ide    1596714   \n",
       "\n",
       "    recording_length        recording_ts  accelerationPeakFull  \\\n",
       "21         27.057647 2021-03-25 02:54:22               995.670   \n",
       "18         28.456818 2021-03-25 04:06:19               622.040   \n",
       "19         64.486054 2021-03-25 04:22:10               585.863   \n",
       "20         63.878937 2021-03-25 04:53:27               564.966   \n",
       "11         20.201752 2021-07-26 19:56:39               231.212   \n",
       "10         20.200623 2021-07-26 19:21:55               218.634   \n",
       "\n",
       "    psuedoVelocityPeakFull  accelerationRMSFull  velocityRMSFull  \\\n",
       "21                5845.241              131.087          323.287   \n",
       "18                8907.949               94.197          372.049   \n",
       "19                2153.020               46.528          148.591   \n",
       "20                2357.599               54.408          145.223   \n",
       "11                2907.650                2.423           54.507   \n",
       "10                2961.256                2.877           53.875   \n",
       "\n",
       "    displacementRMSFull  pressureMeanFull  temperatureMeanFull  \n",
       "21                3.144           104.473               25.616  \n",
       "18                9.580           105.682               33.452  \n",
       "19                2.615           105.750               32.202  \n",
       "20                3.088           102.875               26.031  \n",
       "11                1.066            98.745               24.175  \n",
       "10                1.053            98.751               24.180  "
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mask = (df.recording_ts > pd.to_datetime('2021-01-01')) & (df.accelerationPeakFull > 100)\n",
    "df[mask].sort_values(by=['accelerationPeakFull'], ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "oi-4q68J9DsC"
   },
   "source": [
    "Another preview to plotly, but visualizing dataframes is made easy, even with mixed types."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 542
    },
    "id": "thTJcMe0kDWT",
    "outputId": "4458ed52-eda1-4d5b-a27d-84d38c0b77b6"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "scatter = ax.scatter(df['recording_ts'],\n",
    "                     df['accelerationRMSFull'],\n",
    "                     s=df['recording_length'],\n",
    "                     c=df['serial_number_id'],\n",
    "                     cmap='viridis')\n",
    "plt.colorbar(scatter)\n",
    "plt.yscale('log')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "LUM38k4F9O3T"
   },
   "source": [
    "Plotly automatically made my colors a colorbar because I specified it based on a numeric value. If instead I change the type to string and replot, we'll see discrete series for each device."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 542
    },
    "id": "HmeGuksDd8PK",
    "outputId": "b895aea4-a574-4f33-e8fd-4ddd0056b7b7"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "df['device'] = df[\"serial_number_id\"].astype(str)\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "scatter = ax.scatter(df['recording_ts'],\n",
    "                     df['accelerationRMSFull'],\n",
    "                     s=df['recording_length'],\n",
    "                     c=df['serial_number_id'],\n",
    "                     cmap='viridis')\n",
    "\n",
    "plt.colorbar(scatter)\n",
    "plt.yscale('log')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "provenance": [],
   "toc_visible": true
  },
  "kernelspec": {
   "display_name": "trien_project",
   "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": "3.10.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}