yunoooo111/resume1.txt

## resume1.txt
# METADATA

# Metadata is data about data.Metadata are pieces of information that have some meaning in relation to another piece of information, that can be created, managed, stored, and preserved like any other data.
#  Six types of metadata :
#• Structural metadata (Structural metadata provides valuable information that helps to establish the relationship between objects.)
#• Descriptive metadata (Descriptive metadata provides helpful information for discovering and identifying a data resource.)
#• Preservation metadata (Preservation metadata refers to the information related to the preservation management of collections and information resources.)
#• Administrative metadata (Administrative metadata provides information that is useful in managing resources.)
#• Provenance metadata (Provenance metadata provides helpful information on the origins of a data resource.)
#• Definitional metadata (Definitional metadata refers to the metadata that provides a common vocabulary that facilitates a shared understanding of the meaning of the data.)


# RESUME 2
# DATA SCIENCE

# Data Science is scientific process of transforming data into insight for making better decisions,
# that the goal is to turn data into actionable value

# Data :
# Data refers to raw facts, figures, and statistics that are collected
# and stored for analysis. It can be in various forms, such as numbers, text, images,
# or any other format that represents information.

# Data Science:
# Data science is a multidisciplinary field that uses scientific methods, processes, algorithms,
# and systems to extract insights and knowledge from structured and unstructured data.
# It involves a combination of skills from statistics, mathematics, computer science,
# and domain-specific knowledge to analyze and interpret complex data sets. The goal of data science is to uncover patterns,
# trends, and valuable insights from data that can be used to inform business decisions, solve problems, or gain a competitive advantage.

# Data Scientist:
# A data scientist is a professional who possesses a combination of skills in statistics, mathematics, programming, and domain expertise.
# Data scientists use their skills to analyze large and complex data sets, develop algorithms, and create predictive models to extract meaningful insights.
# They work on identifying patterns, trends, and correlations in data to help organizations make data-driven decisions.
# Data scientists also play a crucial role in designing and implementing machine learning models, building data pipelines, and communicating findings to non-technical stakeholders.

# Foundational aspects of data science
#• Mathematics :
    #• It will cover foundational mathematical concepts, such as functions, relations, assumptions, conclusions, and abstraction, so that the concepts can be used to define and understand many aspects of data manipulation.
    #• Other mathematics and statistics courses have also connections to data science, including graphs for social network analysis, matrices for finding themes in relations, and supervised machine learning.

#• Technology :
    #Python knowledge will be extended from the prerequisite with more advanced table manipulation functions, extended practice with data cleaning and manipulation tasks, computational notebooks (such as Jupyter), and GitHub for version control and project publishing.

#• Visualization :
    #• New types of plots will be learnt for a wide variety of data types and what you intend to communicate about them.
    #• The general principles that govern when and how to use visualizations will be studied.
    #• How to build and publish interactive online visualizations (dashboards) will also be learnt.

#• Communication :
    #• How to write comments in code, documentation for code, motivations in computational notebooks, interpretation of results in computational notebooks, and technical reports about the results of analyses.
    #• Clarity, brevity (concise), and knowing the target audience will be prioritized.

## resume3.ipynb

      
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## resume4.ipynb

      
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## resume5.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "89838292",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "64ac8d8a",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = {\n",
    "    \"month\" : [1,2,3,4,5],\n",
    "    \"weather\" : [\"rainy\", \"sunny\", \"cloudy\", \"windy\", \"cloudy\"]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a9e3e196",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'month': [1, 2, 3, 4, 5], 'weather': ['rainy', 'sunny', 'cloudy', 'windy', 'cloudy']}\n"
     ]
    }
   ],
   "source": [
    "print(data\n",
    "     )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3f580539",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "39730056",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   month weather\n",
      "0      1   rainy\n",
      "1      2   sunny\n",
      "2      3  cloudy\n",
      "3      4   windy\n",
      "4      5  cloudy\n"
     ]
    }
   ],
   "source": [
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0887d72f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 2, 3, 4, 5]\n"
     ]
    }
   ],
   "source": [
    "m = list(df['month'])\n",
    "print(m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "46351399",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['rainy', 'sunny', 'cloudy', 'windy', 'cloudy']\n"
     ]
    }
   ],
   "source": [
    "m = list(df['weather'])\n",
    "print(m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "40de65a1",
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'other'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\indexes\\base.py:3802\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   3801\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m-> 3802\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine\u001b[38;5;241m.\u001b[39mget_loc(casted_key)\n\u001b[0;32m   3803\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\_libs\\index.pyx:138\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\_libs\\index.pyx:165\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "File \u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi:5745\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "File \u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi:5753\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 'other'",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[10], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mother\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[0;32m      2\u001b[0m \u001b[38;5;28mprint\u001b[39m(m)\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\frame.py:3807\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   3805\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39mnlevels \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[0;32m   3806\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_getitem_multilevel(key)\n\u001b[1;32m-> 3807\u001b[0m indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39mget_loc(key)\n\u001b[0;32m   3808\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_integer(indexer):\n\u001b[0;32m   3809\u001b[0m     indexer \u001b[38;5;241m=\u001b[39m [indexer]\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\indexes\\base.py:3804\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   3802\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine\u001b[38;5;241m.\u001b[39mget_loc(casted_key)\n\u001b[0;32m   3803\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[1;32m-> 3804\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[0;32m   3805\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[0;32m   3806\u001b[0m     \u001b[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[0;32m   3807\u001b[0m     \u001b[38;5;66;03m#  InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[0;32m   3808\u001b[0m     \u001b[38;5;66;03m#  the TypeError.\u001b[39;00m\n\u001b[0;32m   3809\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_indexing_error(key)\n",
      "\u001b[1;31mKeyError\u001b[0m: 'other'"
     ]
    }
   ],
   "source": [
    "m = list(df['other'])\n",
    "print(m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "5267c604",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   month weather\n",
      "4      5  cloudy\n"
     ]
    }
   ],
   "source": [
    "m = df.loc[4:5]\n",
    "print(m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "230e7d73",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   month weather\n",
      "a      1   rainy\n",
      "b      2   sunny\n",
      "c      3  cloudy\n",
      "d      4   windy\n",
      "e      5  cloudy\n"
     ]
    }
   ],
   "source": [
    "idx = ['a','b','c','d','e']\n",
    "df = pd.DataFrame(data, index=idx)\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "ff1a4a41",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   month weather\n",
      "c      3  cloudy\n",
      "d      4   windy\n",
      "e      5  cloudy\n"
     ]
    }
   ],
   "source": [
    "print(df.loc['c':'e'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "9164b7a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['a']['month']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "c99907e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'cloudy'"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['e']['weather']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "802c56e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv(\"data_no_idx_sep_comma.csv\", sep=\",\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "c00653b8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       t         x         y\n",
      "0    0.0  2.000000  0.000000\n",
      "1    0.2  1.984229  0.250666\n",
      "2    0.4  1.937166  0.497380\n",
      "3    0.6  1.859553  0.736249\n",
      "4    0.8  1.752613  0.963507\n",
      "..   ...       ...       ...\n",
      "95  19.0  1.618034 -1.175571\n",
      "96  19.2  1.752613 -0.963507\n",
      "97  19.4  1.859553 -0.736249\n",
      "98  19.6  1.937166 -0.497380\n",
      "99  19.8  1.984229 -0.250666\n",
      "\n",
      "[100 rows x 3 columns]\n"
     ]
    }
   ],
   "source": [
    "lissa = pd.read_excel(\"Downloads/lissajous_circle.xlsx\")\n",
    "print(lissa)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "feb36162",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>t</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.2</td>\n",
       "      <td>1.984229</td>\n",
       "      <td>0.250666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.4</td>\n",
       "      <td>1.937166</td>\n",
       "      <td>0.497380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.6</td>\n",
       "      <td>1.859553</td>\n",
       "      <td>0.736249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.8</td>\n",
       "      <td>1.752613</td>\n",
       "      <td>0.963507</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     t         x         y\n",
       "0  0.0  2.000000  0.000000\n",
       "1  0.2  1.984229  0.250666\n",
       "2  0.4  1.937166  0.497380\n",
       "3  0.6  1.859553  0.736249\n",
       "4  0.8  1.752613  0.963507"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lissa.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "ec935845",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "d1c83670",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "12644d12",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot t vs x\n",
    "plt.plot(df['x'], df['y'], label = 'x(y)', marker='o')\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('y')\n",
    "plt.title('x & y')\n",
    "plt.grid(True)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "2119f33a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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zz9eaNWv6vlAAAAAAUc1RQev48eO65JJL9Nhjj/Vo++rqal199dUaN26c9uzZo2XLlum2227Txo0b+7hSAAAAANHMURfDmDx5siZPntzj7desWaNhw4Zp9erVkqSRI0dq9+7dWrVqlWbMmNFHVQIAAACIdo4KWqGqrKxUcXFxm3VXXXWV1q5dK7/fL7fb3W6Mz+eTz+cLLjc0NEiS/H6//H5/3xbcA55YK/QxMVab31BEvJZor/V14fWJTL3pP/0VfbU93reRib4a2eirX+iur0bKHA6lDpdlWY58hV0ulzZt2qRp06Z1us2IESM0Z84cLVu2LLhu586d+uY3v6mDBw8qIyOj3Riv16vly5e3W79+/XolJiYaqR0AAACA8zQ1NWnWrFmqr69XUlJSl9v26yNa0ulAdqbWXPnl9a2WLl2qkpKS4HJDQ4OysrJUXFzc7ZMZDnneLSGP8cRYuqcgoLt2x8gX6Hi/o81e71V2l4AO+P1+lZeXq6ioqMMjzrBXb/pPf0VfbY++Gpnoq5GNvvqF7vpqpPSY1rPdeqJfB6309HTV1dW1WXf48GHFxcXpvPPO63CMx+ORx+Npt97tdkdEg/K19P4Pui/gOqvx/UkkvJboXKS839AW/aM9+uoXeM9GNvpqZKJ/tNdZX42U+RtKHY666mCoxo4dq/Ly8jbrtm7dqoKCgoh5sQAAAAD0P44KWo2NjaqqqlJVVZWk05dvr6qqUm1traTTp/3dcMMNwe3nzZun/fv3q6SkRB988IGeeeYZrV27VosWLbKjfAAAAABRwlGnDu7evVsTJ04MLrd+lurGG29UaWmpDh06FAxdkpSbm6vNmzfr9ttv1+OPP67MzEw98sgjXNodAAAAQJ9yVNAqLCxUVxdJLC0tbbduwoQJ+stf/tKHVQEAAABAW446dRAAAAAAnICgBQAAAACGEbQAAAAAwDCCFgAAAAAYRtACAAAAAMMIWgAAAABgmKMu7w6YkrOkrNPbalZOCWMlQGTp6r0BdIW+CnSMvhq9OKIFAAAAAIYRtAAAAADAMIIWAAAAABhG0AIAAAAAwwhaAAAAAGAYQQsAAAAADCNoAQAAAIBhBC0AAAAAMIygBQAAAACGEbQAAAAAwDCCFgAAAAAYRtACAAAAAMMIWgAAAABgGEELAAAAAAwjaAEAAACAYQQtAAAAADCMoAUAAAAAhhG0AAAAAMAwghYAAAAAGBZndwFApMlZUtbpbTUrp4SxEqBvdDXHgb5AX0V/R19FRziiBQAAAACGEbQAAAAAwDCCFgAAAAAYRtACAAAAAMMIWgAAAABgGEELAAAAAAxzXNB64oknlJubq/j4eOXn5+uNN97odNuKigq5XK52Px9++GEYKwYAAAAQbRwVtDZs2KCFCxfqzjvv1J49ezRu3DhNnjxZtbW1XY7bt2+fDh06FPwZPnx4mCoGAAAAEI0c9YXFDz74oH74wx9q7ty5kqTVq1dry5YtevLJJ7VixYpOx6Wmpio5OblHj+Hz+eTz+YLLDQ0NkiS/3y+/39/74g3xxFqhj4mx2vxG70XCHOjPWp9fnue+1Zs+grboq+bwfu9b9NXwoK+eve76aqTM4VDqcFmW5YiZ0dzcrMTERP3Hf/yHrr322uD6f/u3f1NVVZW2b9/ebkxFRYUmTpyonJwcnTx5UhdeeKF+9rOfaeLEiZ0+jtfr1fLly9utX79+vRITE83sDAAAAADHaWpq0qxZs1RfX6+kpKQut3XMEa0jR46opaVFaWlpbdanpaWprq6uwzEZGRl66qmnlJ+fL5/Pp+eff17f+ta3VFFRofHjx3c4ZunSpSopKQkuNzQ0KCsrS8XFxd0+meGQ590S8hhPjKV7CgK6a3eMfAFXH1QVPfZ6r7K7hH7N7/ervLxcRUVFcrvddpfTb/Wmj6At+qo59NW+RV8ND/rq2euur0ZKr2g9260nHBO0WrlcbZ94y7LarWv19a9/XV//+teDy2PHjtWBAwe0atWqToOWx+ORx+Npt97tdkdEg/K19P4Pui/gOqvxUETMgWgQKe+3/oo+YA599ezxXg8P+mrfog+Y01lfjZT5G0odjrkYRkpKimJjY9sdvTp8+HC7o1xdufzyy/XRRx+ZLg8AAAAAghwTtAYMGKD8/HyVl5e3WV9eXq4rrriix/ezZ88eZWRkmC4PAAAAAIIcdepgSUmJZs+erYKCAo0dO1ZPPfWUamtrNW/ePEmnP1/1ySef6LnnnpN0+qqEOTk5GjVqlJqbm/XCCy9o48aN2rhxo527AQAAAKCfc1TQmjlzpo4ePaq7775bhw4dUl5enjZv3qzs7GxJ0qFDh9p8p1Zzc7MWLVqkTz75RAkJCRo1apTKysp09dVX27ULAAAAAKKAo4KWJM2fP1/z58/v8LbS0tI2y4sXL9bixYvDUBUAAAAAfMExn9ECAAAAAKcgaAEAAACAYQQtAAAAADDMcZ/RAuyUs6Ss09tqVk4JYyVA17qaq0Akoa/CKeirCBVHtAAAAADAMIIWAAAAABhG0AIAAAAAwwhaAAAAAGAYQQsAAAAADCNoAQAAAIBhBC0AAAAAMIygBQAAAACGEbQAAAAAwDCCFgAAAAAYRtACAAAAAMMIWgAAAABgGEELAAAAAAwjaAEAAACAYQQtAAAAADCMoAUAAAAAhhG0AAAAAMAwghYAAAAAGEbQAgAAAADDCFoAAAAAYBhBCwAAAAAMi7O7AKC/yFlS1ultNSunhLESRIuu5hzQH9BXEW70VZjEES0AAAAAMIygBQAAAACGEbQAAAAAwDCCFgAAAAAYRtACAAAAAMMIWgAAAABgmOOC1hNPPKHc3FzFx8crPz9fb7zxRpfbb9++Xfn5+YqPj9f555+vNWvWhKlSAAAAANHKUUFrw4YNWrhwoe68807t2bNH48aN0+TJk1VbW9vh9tXV1br66qs1btw47dmzR8uWLdNtt92mjRs3hrlyAAAAANEk5KD12muvdXrbb37zm7MqpjsPPvigfvjDH2ru3LkaOXKkVq9eraysLD355JMdbr9mzRoNGzZMq1ev1siRIzV37lzddNNNWrVqVZ/WCQAAACC6xYU6YMqUKVqwYIFWrFihAQMGSJI+/fRT3XTTTfrTn/6kW265xXiRktTc3Kx33nlHS5YsabO+uLhYO3fu7HBMZWWliouL26y76qqrtHbtWvn9frnd7nZjfD6ffD5fcLmhoUGS5Pf75ff7z3Y3zpon1gp9TIzV5jfCLxLmjhO0Pk88Xz3Tm34AM+ir9qNP9Ax9NTT0Vft011cjZQ6HUkfIQWvHjh2aPXu2XnvtNa1fv141NTW66aabdOGFF+q///u/Q727Hjty5IhaWlqUlpbWZn1aWprq6uo6HFNXV9fh9qdOndKRI0eUkZHRbsyKFSu0fPnyduu3bt2qxMTEs9gDMx74Ru/H3lMQMFcIQrJ582a7S3CU8vJyu0twhLPpBzCDvmof+mpo6Ks9Q1+1X2d9NVLe801NTT3eNuSgNWbMGO3Zs0fz5s1Tfn6+AoGA7r33Xt1xxx1yuVyh3l3IvvwYlmV1+bgdbd/R+lZLly5VSUlJcLmhoUFZWVkqLi5WUlJSb8u2ld/vV3l5uYqKijo8ioe28rxb7C4hanliLN1TENBdu2PkC/R9P0F7e71X2V2CI9BXQ0NftQ991X701Z5xSl9tPdutJ0IOWpK0b98+7dq1S0OHDtXBgwf14YcfqqmpSeecc05v7q5HUlJSFBsb2+7o1eHDh9sdtWqVnp7e4fZxcXE677zzOhzj8Xjk8XjarXe73RH9ovdEf9iHcPC18IfIbr6Ai9fBJvSI0NBXe4b3s/3oq/ahR4Qm0vtqKLWFfDGMlStXauzYsSoqKtLevXu1a9cu7dmzRxdffLEqKytDvbseGzBggPLz89sd+i4vL9cVV1zR4ZixY8e2237r1q0qKCiI6BcQAAAAgLOFHLQefvhhvfTSS3r00UcVHx+vUaNG6e2339b06dNVWFjYByV+oaSkRE8//bSeeeYZffDBB7r99ttVW1urefPmSTp92t8NN9wQ3H7evHnav3+/SkpK9MEHH+iZZ57R2rVrtWjRoj6tEwAAAEB0C/nUwffee08pKSlt1rndbv3qV7/Sd77zHWOFdWTmzJk6evSo7r77bh06dEh5eXnavHmzsrOzJUmHDh1q851aubm52rx5s26//XY9/vjjyszM1COPPKIZM2b0aZ0AAAAAolvIQevLIetMEyZMOKtiemL+/PmaP39+h7eVlpa2WzdhwgT95S9/6eOqAAAAAOALIZ86CAAAAADoGkELAAAAAAwjaAEAAACAYQQtAAAAADCsV19YDPRnNSundHpbzpKyMFYC9I2u5jjQF+ir6O/oq+gIR7QAAAAAwDCCFgAAAAAYRtACAAAAAMMIWgAAAABgGEELAAAAAAwjaAEAAACAYQQtAAAAADCMoAUAAAAAhhG0AAAAAMAwghYAAAAAGEbQAgAAAADDCFoAAAAAYBhBCwAAAAAMI2gBAAAAgGEELQAAAAAwjKAFAAAAAIYRtAAAAADAMIIWAAAAABhG0AIAAAAAwwhaAAAAAGAYQQsAAAAADIuzuwDASWpWTun0tpwlZWGsBOhaV3MViCT0VTgFfRWh4ogWAAAAABhG0AIAAAAAwwhaAAAAAGAYQQsAAAAADCNoAQAAAIBhBC0AAAAAMMwxQeuzzz7T7NmzNWjQIA0aNEizZ8/W559/3uWYOXPmyOVytfm5/PLLw1MwAAAAgKjlmO/RmjVrlv7nf/5Hr776qiTpRz/6kWbPnq1XXnmly3GTJk3Ss88+G1weMGBAn9YJAAAAAI4IWh988IFeffVVvfXWWxozZowk6be//a3Gjh2rffv26etf/3qnYz0ej9LT08NVKgAAAAA4I2hVVlZq0KBBwZAlSZdffrkGDRqknTt3dhm0KioqlJqaquTkZE2YMEH33XefUlNTO93e5/PJ5/MFlxsaGiRJfr9ffr/fwN6EX2vdTq3fKTyxlt0lOJ4nxmrzG73H+71v0VfDg7569uir5vB+71tO6auh1OeyLCvi33n333+/SktL9de//rXN+hEjRugHP/iBli5d2uG4DRs26Nxzz1V2draqq6t111136dSpU3rnnXfk8Xg6HOP1erV8+fJ269evX6/ExMSz3xkAAAAAjtTU1KRZs2apvr5eSUlJXW5r6xGtzkLNmXbt2iVJcrlc7W6zLKvD9a1mzpwZ/HdeXp4KCgqUnZ2tsrIyTZ8+vcMxS5cuVUlJSXC5oaFBWVlZKi4u7vbJjFR+v1/l5eUqKiqS2+22u5x+K8+7xe4SHM8TY+megoDu2h0jX6Dz9za6t9d7ld0l9Gv01fCgr549+qo59NW+5ZS+2nq2W0/YGrQWLFig66+/vsttcnJy9O677+p///d/29326aefKi0trcePl5GRoezsbH300UedbuPxeDo82uV2uyP6Re+J/rAPkczXwh8wU3wBF8/nWeK9Hh701b5FHzCHvnr2eK+HR6T31VBqszVopaSkKCUlpdvtxo4dq/r6er399tv6xje+IUn685//rPr6el1xxRU9fryjR4/qwIEDysjI6HXNAAAAANAdR3yP1siRIzVp0iTdfPPNeuutt/TWW2/p5ptv1ne+8502F8K44IILtGnTJklSY2OjFi1apMrKStXU1KiiokJTp05VSkqKrr32Wrt2BQAAAEAUcETQkqTf/e53uuiii1RcXKzi4mJdfPHFev7559tss2/fPtXX10uSYmNj9d577+m73/2uRowYoRtvvFEjRoxQZWWlBg4caMcuAAAAAIgSjri8uyQNHjxYL7zwQpfbnHkBxYSEBG3ZwodoAQAAAISfY45oAQAAAIBTELQAAAAAwDCCFgAAAAAY5pjPaAGRrmbllE5vy1lSFsZKEC26mnNAf0BfRbjRV2ESR7QAAAAAwDCCFgAAAAAYRtACAAAAAMMIWgAAAABgGEELAAAAAAwjaAEAAACAYQQtAAAAADCMoAUAAAAAhhG0AAAAAMAwghYAAAAAGEbQAgAAAADDCFoAAAAAYBhBCwAAAAAMI2gBAAAAgGEELQAAAAAwjKAFAAAAAIYRtAAAAADAMIIWAAAAABhG0AIAAAAAwwhaAAAAAGBYnN0FANGgZuWUTm/LWVIWxkrgNF3NHSCa0VfRW/RVhAtHtAAAAADAMIIWAAAAABhG0AIAAAAAwwhaAAAAAGAYQQsAAAAADCNoAQAAAIBhBC0AAAAAMIygBQAAAACGOSZo3XfffbriiiuUmJio5OTkHo2xLEter1eZmZlKSEhQYWGh3n///b4tFAAAAEDUc0zQam5u1nXXXacf//jHPR7zwAMP6MEHH9Rjjz2mXbt2KT09XUVFRTp27FgfVgoAAAAg2jkmaC1fvly33367Lrrooh5tb1mWVq9erTvvvFPTp09XXl6e1q1bp6amJq1fv76PqwUAAAAQzeLsLqCvVFdXq66uTsXFxcF1Ho9HEyZM0M6dO3XLLbd0OM7n88nn8wWXGxoaJEl+v19+v79vi+4jrXU7tf7+zhNr2V1CxPDEWG1+g/dtpKKvRjb66hfoq+3xvo1MTumrodTXb4NWXV2dJCktLa3N+rS0NO3fv7/TcStWrNDy5cvbrd+6dasSExPNFhlm5eXldpeADjzwDbsriDz3FATsLiFibN682e4S0AX6amSir7ZHX/0CfTWyRXpfbWpq6vG2tgYtr9fbYag5065du1RQUNDrx3C5XG2WLctqt+5MS5cuVUlJSXC5oaFBWVlZKi4uVlJSUq/rsJPf71d5ebmKiorkdrvtLgdfkufdYncJEcMTY+megoDu2h0jX6Dz92k02eu9yu4S0AH6amSjr36BvtoefTUyOaWvtp7t1hO2Bq0FCxbo+uuv73KbnJycXt13enq6pNNHtjIyMoLrDx8+3O4o15k8Ho88Hk+79W63O6Jf9J7oD/vQH/la+MP3Zb6Ai+fl//GejWz01chE/2iPvvoF3rORLdL7aii12Rq0UlJSlJKS0if3nZubq/T0dJWXl2v06NGSTl+5cPv27frlL3/ZJ48JAAAAAJKDrjpYW1urqqoq1dbWqqWlRVVVVaqqqlJjY2NwmwsuuECbNm2SdPqUwYULF+r+++/Xpk2btHfvXs2ZM0eJiYmaNWuWXbsBAAAAIAo45mIYP//5z7Vu3brgcutRqm3btqmwsFCStG/fPtXX1we3Wbx4sU6cOKH58+frs88+05gxY7R161YNHDgwrLUDAAAAiC6OCVqlpaUqLS3tchvLanvpUpfLJa/XK6/X23eFAWepZuWUDtfnLCkLcyWwU2fzAEDo6KuQ6Kuwn2NOHQQAAAAApyBoAQAAAIBhBC0AAAAAMIygBQAAAACGEbQAAAAAwDCCFgAAAAAYRtACAAAAAMMIWgAAAABgGEELAAAAAAwjaAEAAACAYQQtAAAAADCMoAUAAAAAhhG0AAAAAMAwghYAAAAAGEbQAgAAAADDCFoAAAAAYBhBCwAAAAAMi7O7AAAdq1k5pdPbcpaUhbESmNLVawqg79FX+x/6KiIZR7QAAAAAwDCCFgAAAAAYRtACAAAAAMMIWgAAAABgGEELAAAAAAwjaAEAAACAYQQtAAAAADCMoAUAAAAAhhG0AAAAAMAwghYAAAAAGEbQAgAAAADDCFoAAAAAYBhBCwAAAAAMI2gBAAAAgGEELQAAAAAwLM7uAnrqvvvuU1lZmaqqqjRgwAB9/vnn3Y6ZM2eO1q1b12bdmDFj9NZbb/VRlUB41Kyc0ultOUvKwlgJvqyr1wZA5KKvRi76KpzKMUe0mpubdd111+nHP/5xSOMmTZqkQ4cOBX82b97cRxUCAAAAwGmOOaK1fPlySVJpaWlI4zwej9LT03u8vc/nk8/nCy43NDRIkvx+v/x+f0iPHSla63Zq/QiNJ9ayu4Re88RYbX47Ee+z6EBfjS70VXvxPosOTumrodTnsizLUe+80tJSLVy4sMenDr700ksaMGCAkpOTNWHCBN13331KTU3tdIzX6w2GujOtX79eiYmJZ1M6AAAAAAdramrSrFmzVF9fr6SkpC637ddBa8OGDTr33HOVnZ2t6upq3XXXXTp16pTeeecdeTyeDsd0dEQrKytLR44c6fbJjFR+v1/l5eUqKiqS2+22uxz0sTzvFrtL6DVPjKV7CgK6a3eMfAGX3eX0yl7vVXaXgDCgr0YX+qq96KvRwSl9taGhQSkpKT0KWraeOtjZ0aMz7dq1SwUFBb26/5kzZwb/nZeXp4KCAmVnZ6usrEzTp0/vcIzH4+kwhLnd7oh+0XuiP+wDuudrceYf0jP5Ai7H7gfvsehCX40OTu1HZ6Kvwikiva+GUputQWvBggW6/vrru9wmJyfH2ONlZGQoOztbH330kbH7BAAAAIAvszVopaSkKCUlJWyPd/ToUR04cEAZGRlhe0wAAAAA0ccxl3evra1VVVWVamtr1dLSoqqqKlVVVamxsTG4zQUXXKBNmzZJkhobG7Vo0SJVVlaqpqZGFRUVmjp1qlJSUnTttdfatRsAAAAAooBjLu/+85//vM2XD48ePVqStG3bNhUWFkqS9u3bp/r6eklSbGys3nvvPT333HP6/PPPlZGRoYkTJ2rDhg0aOHBg2OsHAAAAED0cE7RKS0u7/Q6tMy+gmJCQoC1bnHuVIAAAAADO5ZhTBwEAAADAKQhaAAAAAGAYQQsAAAAADHPMZ7QA9EzNyimd3pazpCyMlfRfXT3HAPof+mrfo6+iP+KIFgAAAAAYRtACAAAAAMMIWgAAAABgGEELAAAAAAwjaAEAAACAYQQtAAAAADCMoAUAAAAAhhG0AAAAAMAwghYAAAAAGEbQAgAAAADDCFoAAAAAYFic3QUACJ+alVM6vS1nSVkYK4l8XT1XANCKvtpz9FVEG45oAQAAAIBhBC0AAAAAMIygBQAAAACGEbQAAAAAwDCCFgAAAAAYxlUHDbAsS6dOnVJLS4vdpXTI7/crLi5OJ0+etKXG2NhYxcXFyeVyhf2xAQAAADsQtM5Sc3OzDh06pKamJrtL6ZRlWUpPT9eBAwdsCzuJiYnKyMjQgAEDbHl8AAAAIJwIWmchEAiourpasbGxyszM1IABAyLyqE0gEFBjY6POPfdcxcSE92xRy7LU3NysTz/9VNXV1Ro+fHjYawAAAADCjaB1FpqbmxUIBJSVlaXExES7y+lUIBBQc3Oz4uPjbQk5CQkJcrvd2r9/f7AORJ5o/NJNvjwTQF+irwLRjUMLBnCEpns8RwAAAIgm/NcvAAAAABhG0AIAAAAAwwha6NT48eO1fv36Lrd57LHHdM0114SpIgAAAMAZuBhGHwnnh1z74oOnf/zjH1VXV6frr78+uM7lcmnTpk2aNm1acN3NN9+s++67T2+++aauvPJK43UAAAAATsQRLXTokUce0Q9+8INuL2Lh8Xg0a9YsPfroo2GqDAAAAIh8HNGKQp9++qkuuugi3XbbbVq2bJkk6c9//rPGjRunP/7xj7r00kv12muv6aGHHgqOycnJkSRde+21kqTs7GzV1NRIkq655hoVFxfrxIkTSkhICOu+IDw6O2rqhMsTc6lhAJGIvgr0f444olVTU6Mf/vCHys3NVUJCgr761a/qF7/4hZqbm7scZ1mWvF6vMjMzlZCQoMLCQr3//vthqjpyDRkyRM8884y8Xq92796txsZG/eu//qvmz5+v4uJivfnmm0pMTNTIkSODY3bt2iVJevbZZ3Xo0KHgsiQVFBTI7/fr7bffDvu+AAAAAJHIEUHrww8/VCAQ0G9+8xu9//77euihh7RmzZrg0ZjOPPDAA3rwwQf12GOPadeuXUpPT1dRUZGOHTsWpsoj19VXX62bb75Z3//+9zVv3jzFx8dr5cqVkk4H27S0tDanDQ4ZMkSSlJycrPT09OCyJJ1zzjlKTk4OHuECAAAAop0jTh2cNGmSJk2aFFw+//zztW/fPj355JNatWpVh2Msy9Lq1at15513avr06ZKkdevWKS0tTevXr9ctt9wSltoj2apVq5SXl6c//OEP2r17t+Lj4yVJJ06cCP67pxISEtTU1NQXZQIAAACO44ig1ZH6+noNHjy409urq6tVV1en4uLi4DqPx6MJEyZo586dnQYtn88nn88XXG5oaJAk+f1++f3+Ntv6/X5ZlqVAIKBAIHA2u3NWuntsy7KCv8/c9uOPP9bBgwcVCARUXV2tvLw8SdLgwYP12WefdXi/ne3rP/7xD5133nmd1hIIBGRZlvx+v2JjY3u8b4hsH91T3Olted4tId+fJ8Zq8zsUe71Xdbj+y+9bwITWecX8gmn0VUQrp/TVUOpzZND629/+pkcffVS//vWvO92mrq5OkpSWltZmfVpamvbv39/puBUrVmj58uXt1m/dulWJiYlt1sXFxSk9PV2NjY3dfl6sL7WGwe6cecpkc3OzZs2apWuvvVbDhw/X3Llz9ac//UmpqakaMWKE6urqVFtbq+Tk5OAYt9utxsbGdo9XXV2tkydPavjw4Z3W0tzcrBMnTmjHjh06depU6DsJx3ngG70fe09B6P/jYvPmzb1/QKCXysvL7S4BUYS+imgQ6X01lDO4bA1aXq+3w1Bzpl27dqmgoCC4fPDgQU2aNEnXXXed5s6d2+1juFyuNsuWZbVbd6alS5eqpKQkuNzQ0KCsrCwVFxcrKSmpzbYnT57UgQMHdO6554Z8qp1JX67ryyzL0rFjxzRw4MDgvi9evFiNjY164okndO6556qiokK33367XnnlFV155ZUaMmSI3n33XX3nO98J3k9OTo4qKyv17W9/Wx6PR//0T/8kSdqzZ4/OP/98XXLJJZ3WcPLkSSUkJGj8+PG2PleIbH6/X+Xl5SoqKpLb7ba7HKBTzFU4BXMVTuGUudrTAxySzUFrwYIFbb4QtyOtlxWXToesiRMnauzYsXrqqae6HJeeni7p9JGtjIyM4PrDhw+3O8p1Jo/HI4/H02692+1u96K3tLTI5XIpJiam3fdNRdKlT1tP52uttaKiQg8//LC2bdsWPGL1/PPP6+KLL9ZvfvMb/fjHP9ZNN92k3//+97rmmmuC9/PrX/9aJSUlevrpp/WVr3wlePGLDRs26Oabb+7yO7diYmLkcrk6fB6BL2OewCmYq3AK5iqcItLnaii12Rq0UlJSlJKS0qNtP/nkE02cOFH5+fl69tlnu/0i3dzcXKWnp6u8vFyjR4+WdPr0te3bt+uXv/zlWdfuZIWFhe3OLx02bJg+//zz4PLChQs1atQo7d+/X9nZ2ZKkqVOnaurUqW3G7d27V1VVVfrDH/7Q53UDAAAATuGIy7sfPHhQhYWFysrK0qpVq/Tpp5+qrq4u+DmsVhdccIE2bdok6fTRm4ULF+r+++/Xpk2btHfvXs2ZM0eJiYmaNWuWHbvhKGlpaVq7dq1qa2u73O7gwYN67rnnNGjQoDBVBgAAAEQ+R1wMY+vWrfr444/18ccfa+jQoW1ua72iniTt27dP9fX1weXFixfrxIkTmj9/vj777DONGTNGW7du1cCBA8NWu5N997vf7XabM6/qCAAAAOA0RwStOXPmaM6cOd1ud2bokk4f1fJ6vfJ6vX1TGAAAAAB0wBGnDgIAAACAkxC0DPjykTS0x3MEAACAaELQOgutl3cM5YvLolXrcxTJl+sEAAAATHHEZ7QiVWxsrJKTk3X48GFJUmJiYpdfhmyXQCCg5uZmnTx5stvL4ptmWZaampp0+PBhJScnKzY2NqyPDwAAANiBoHWWWr8YuTVsRSLLsnTixAklJCTYFgSTk5ODzxUAAADQ3xG0zpLL5VJGRoZSU1PbfQlwpPD7/dqxY4fGjx9vy6l7brebI1kAAACIKgQtQ2JjYyM2TMTGxurUqVOKj4/nM1IAAABAGHAxDAAAAAAwjKAFAAAAAIYRtAAAAADAMD6j1Y3WL9ptaGiwuZLe8/v9ampqUkNDA5/RQkRjrsIpmKtwCuYqnMIpc7U1E7RmhK4QtLpx7NgxSVJWVpbNlQAAAACIBMeOHdOgQYO63MZl9SSORbFAIKCDBw9q4MCBEfllxD3R0NCgrKwsHThwQElJSXaXA3SKuQqnYK7CKZircAqnzFXLsnTs2DFlZmYqJqbrT2FxRKsbMTExGjp0qN1lGJGUlBTRExdoxVyFUzBX4RTMVTiFE+Zqd0eyWnExDAAAAAAwjKAFAAAAAIYRtKKAx+PRL37xC3k8HrtLAbrEXIVTMFfhFMxVOEV/nKtcDAMAAAAADOOIFgAAAAAYRtACAAAAAMMIWgAAAABgGEELAAAAAAwjaPVzTzzxhHJzcxUfH6/8/Hy98cYbdpcEtOP1euVyudr8pKen210WoB07dmjq1KnKzMyUy+XSSy+91OZ2y7Lk9XqVmZmphIQEFRYW6v3337enWES17ubqnDlz2vXZyy+/3J5iEbVWrFihyy67TAMHDlRqaqqmTZumffv2tdmmP/VVglY/tmHDBi1cuFB33nmn9uzZo3Hjxmny5Mmqra21uzSgnVGjRunQoUPBn/fee8/ukgAdP35cl1xyiR577LEOb3/ggQf04IMP6rHHHtOuXbuUnp6uoqIiHTt2LMyVItp1N1cladKkSW367ObNm8NYISBt375dt956q9566y2Vl5fr1KlTKi4u1vHjx4Pb9Ke+yuXd+7ExY8bo0ksv1ZNPPhlcN3LkSE2bNk0rVqywsTKgLa/Xq5deeklVVVV2lwJ0yuVyadOmTZo2bZqk0//XNTMzUwsXLtRPf/pTSZLP51NaWpp++ctf6pZbbrGxWkSzL89V6fQRrc8//7zdkS7ATp9++qlSU1O1fft2jR8/vt/1VY5o9VPNzc165513VFxc3GZ9cXGxdu7caVNVQOc++ugjZWZmKjc3V9dff73+/ve/210S0KXq6mrV1dW16bMej0cTJkygzyIiVVRUKDU1VSNGjNDNN9+sw4cP210Solx9fb0kafDgwZL6X18laPVTR44cUUtLi9LS0tqsT0tLU11dnU1VAR0bM2aMnnvuOW3ZskW//e1vVVdXpyuuuEJHjx61uzSgU629lD4LJ5g8ebJ+97vf6fXXX9evf/1r7dq1S//yL/8in89nd2mIUpZlqaSkRFdeeaXy8vIk9b++Gmd3AehbLperzbJlWe3WAXabPHly8N8XXXSRxo4dq69+9atat26dSkpKbKwM6B59Fk4wc+bM4L/z8vJUUFCg7OxslZWVafr06TZWhmi1YMECvfvuu3rzzTfb3dZf+ipHtPqplJQUxcbGtkv/hw8fbvd/CYBIc8455+iiiy7SRx99ZHcpQKdar4xJn4UTZWRkKDs7mz4LW/zkJz/Ryy+/rG3btmno0KHB9f2trxK0+qkBAwYoPz9f5eXlbdaXl5friiuusKkqoGd8Pp8++OADZWRk2F0K0Knc3Fylp6e36bPNzc3avn07fRYR7+jRozpw4AB9FmFlWZYWLFigF198Ua+//rpyc3Pb3N7f+iqnDvZjJSUlmj17tgoKCjR27Fg99dRTqq2t1bx58+wuDWhj0aJFmjp1qoYNG6bDhw/r3nvvVUNDg2688Ua7S0OUa2xs1Mcffxxcrq6uVlVVlQYPHqxhw4Zp4cKFuv/++zV8+HANHz5c999/vxITEzVr1iwbq0Y06mquDh48WF6vVzNmzFBGRoZqamq0bNkypaSk6Nprr7WxakSbW2+9VevXr9d//ud/auDAgcEjV4MGDVJCQoJcLlf/6qsW+rXHH3/cys7OtgYMGGBdeuml1vbt2+0uCWhn5syZVkZGhuV2u63MzExr+vTp1vvvv293WYC1bds2S1K7nxtvvNGyLMsKBALWL37xCys9Pd3yeDzW+PHjrffee8/eohGVupqrTU1NVnFxsTVkyBDL7XZbw4YNs2688UartrbW7rIRZTqao5KsZ599NrhNf+qrfI8WAAAAABjGZ7QAAAAAwDCCFgAAAAAYRtACAAAAAMMIWgAAAABgGEELAAAAAAwjaAEAAACAYQQtAAAAADCMoAUAAAAAhhG0AAAAAMAwghYAAN0oLCzUwoUL7S4DAOAgBC0AAAAAMMxlWZZldxEAAESqOXPmaN26dW3WVVdXKycnx56CAACOQNACAKAL9fX1mjx5svLy8nT33XdLkoYMGaLY2FibKwMARLI4uwsAACCSDRo0SAMGDFBiYqLS09PtLgcA4BB8RgsAAAAADCNoAQAAAIBhBC0AALoxYMAAtbS02F0GAMBBCFoAAHQjJydHf/7zn1VTU6MjR44oEAjYXRIAIMIRtAAA6MaiRYsUGxurCy+8UEOGDFFtba3dJQEAIhyXdwcAAAAAwziiBQAAAACGEbQAAAAAwDCCFgAAAAAYRtACAAAAAMMIWgAAAABgGEELAAAAAAwjaAEAAACAYQQtAAAAADCMoAUAAAAAhhG0AAAAAMAwghYAAAAAGPZ/DvX+YRiW/ZUAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot t vs x\n",
    "plt.figure(figsize=(10,5))\n",
    "plt.bar(df['t'], df['x'], label='x(t)')\n",
    "plt.xlabel('t')\n",
    "plt.ylabel('x')\n",
    "plt.title('t & x')\n",
    "plt.grid()\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "1fa851a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 6))\n",
    "plt.scatter(df['x'], df['y'], marker='o')\n",
    "plt.xlabel('t')\n",
    "plt.ylabel('y')\n",
    "plt.title('Scatter Plot')\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "a4d9795c",
   "metadata": {},
   "outputs": [],
   "source": [
    "bf = pd.read_excel(\"Downloads/lissajous_butterfly.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "f2a5a92a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       t         x         y\n",
      "0    0.0  2.000000  0.000000\n",
      "1    0.2  1.996053  0.250666\n",
      "2    0.4  1.984229  0.497380\n",
      "3    0.6  1.964575  0.736249\n",
      "4    0.8  1.937166  0.963507\n",
      "..   ...       ...       ...\n",
      "95  19.0  1.902113 -1.175571\n",
      "96  19.2  1.937166 -0.963507\n",
      "97  19.4  1.964575 -0.736249\n",
      "98  19.6  1.984229 -0.497380\n",
      "99  19.8  1.996053 -0.250666\n",
      "\n",
      "[100 rows x 3 columns]\n"
     ]
    }
   ],
   "source": [
    "print(bf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "9e74029c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 64,
     "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": [
    "\n",
    "bf.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "2c9d271f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 6))\n",
    "plt.scatter(bf['x'], bf['y'], marker='o')\n",
    "plt.xlabel('t')\n",
    "plt.ylabel('y')\n",
    "plt.title('Scatter Plot')\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "b33449bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "db = pd.read_excel(\"Downloads/distribution.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "20cb0668",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          x\n",
      "0        36\n",
      "1        31\n",
      "2        34\n",
      "3        29\n",
      "4         5\n",
      "...     ...\n",
      "249995  997\n",
      "249996  997\n",
      "249997  998\n",
      "249998  998\n",
      "249999  999\n",
      "\n",
      "[250000 rows x 1 columns]\n"
     ]
    }
   ],
   "source": [
    "print(db)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "e3d4cea2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 6))\n",
    "plt.scatter(db.index, db['x'], marker='o', color='blue')\n",
    "plt.xlabel('Index')\n",
    "plt.ylabel('x')\n",
    "plt.title('Scatter Plot of Distribution')\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "eda316d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "559705ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 6))\n",
    "sns.countplot(x='x', data=db, color='skyblue')\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Count Plot of x Values')\n",
    "plt.grid(axis='y', linestyle='--', alpha=0.7)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "a8cd4159",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fruit = pd.read_excel('Downloads/fruit_pie_chart.xlsx')\n",
    "\n",
    "# Membuat plot pie chart\n",
    "plt.figure(figsize=(8, 8))\n",
    "plt.pie(fruit['amount'], labels=fruit['fruit'], autopct='%1.1f%%', startangle=140)\n",
    "plt.title('Pie Chart of Fruit Distribution')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "32aa9c6a",
   "metadata": {},
   "outputs": [],
   "source": [
    "contour = pd.read_excel('Downloads/contour.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "71b480d7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       x    y          z\n",
      "0      0    0  82.462113\n",
      "1      5    0  77.620873\n",
      "2     10    0  72.801099\n",
      "3     15    0  68.007353\n",
      "4     20    0  63.245553\n",
      "..   ...  ...        ...\n",
      "436   80  100  80.000000\n",
      "437   85  100  80.156098\n",
      "438   90  100  80.622577\n",
      "439   95  100  81.394103\n",
      "440  100  100  82.462113\n",
      "\n",
      "[441 rows x 3 columns]\n"
     ]
    }
   ],
   "source": [
    "print(contour)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "87b474e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "plt.tricontourf(contour['x'], contour['y'], contour['z'], cmap='cubehelix')\n",
    "plt.colorbar(label='z')\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('y')\n",
    "plt.title('Contour Plot')\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1cfb4155",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}

## resume61.ipynb

      
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	# METADATA

	# Metadata is data about data.Metadata are pieces of information that have some meaning in relation to another piece of information, that can be created, managed, stored, and preserved like any other data.
	# Six types of metadata :
	#• Structural metadata (Structural metadata provides valuable information that helps to establish the relationship between objects.)
	#• Descriptive metadata (Descriptive metadata provides helpful information for discovering and identifying a data resource.)
	#• Preservation metadata (Preservation metadata refers to the information related to the preservation management of collections and information resources.)
	#• Administrative metadata (Administrative metadata provides information that is useful in managing resources.)
	#• Provenance metadata (Provenance metadata provides helpful information on the origins of a data resource.)
	#• Definitional metadata (Definitional metadata refers to the metadata that provides a common vocabulary that facilitates a shared understanding of the meaning of the data.)


	# RESUME 2
	# DATA SCIENCE

	# Data Science is scientific process of transforming data into insight for making better decisions,
	# that the goal is to turn data into actionable value

	# Data :
	# Data refers to raw facts, figures, and statistics that are collected
	# and stored for analysis. It can be in various forms, such as numbers, text, images,
	# or any other format that represents information.

	# Data Science:
	# Data science is a multidisciplinary field that uses scientific methods, processes, algorithms,
	# and systems to extract insights and knowledge from structured and unstructured data.
	# It involves a combination of skills from statistics, mathematics, computer science,
	# and domain-specific knowledge to analyze and interpret complex data sets. The goal of data science is to uncover patterns,
	# trends, and valuable insights from data that can be used to inform business decisions, solve problems, or gain a competitive advantage.

	# Data Scientist:
	# A data scientist is a professional who possesses a combination of skills in statistics, mathematics, programming, and domain expertise.
	# Data scientists use their skills to analyze large and complex data sets, develop algorithms, and create predictive models to extract meaningful insights.
	# They work on identifying patterns, trends, and correlations in data to help organizations make data-driven decisions.
	# Data scientists also play a crucial role in designing and implementing machine learning models, building data pipelines, and communicating findings to non-technical stakeholders.

	# Foundational aspects of data science
	#• Mathematics :
	#• It will cover foundational mathematical concepts, such as functions, relations, assumptions, conclusions, and abstraction, so that the concepts can be used to define and understand many aspects of data manipulation.
	#• Other mathematics and statistics courses have also connections to data science, including graphs for social network analysis, matrices for finding themes in relations, and supervised machine learning.

	#• Technology :
	#Python knowledge will be extended from the prerequisite with more advanced table manipulation functions, extended practice with data cleaning and manipulation tasks, computational notebooks (such as Jupyter), and GitHub for version control and project publishing.

	#• Visualization :
	#• New types of plots will be learnt for a wide variety of data types and what you intend to communicate about them.
	#• The general principles that govern when and how to use visualizations will be studied.
	#• How to build and publish interactive online visualizations (dashboards) will also be learnt.

	#• Communication :
	#• How to write comments in code, documentation for code, motivations in computational notebooks, interpretation of results in computational notebooks, and technical reports about the results of analyses.
	#• Clarity, brevity (concise), and knowing the target audience will be prioritized.