Python Course Lecture 8a Files and Relational Databases.ipynb

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    "from IPython.display import Image, HTML\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "# Introduction to Scientific Programming in Python\n",
    "\n",
    "## Python Course Lecture 8a: Data Files and Relational Databases"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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     "slide_type": "slide"
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   },
   "source": [
    "# Files"
   ]
  },
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   "source": [
    "## Files are Stored on Hard Disks\n",
    "  - Permanent Storage:\n",
    "    - **Magnetic Hard Disks**: Holds Terabytes of Data, Works Slowly\n",
    "    - **Solid-State Drives (SSD)**: Holds 100s of Gigabytes of Data, Works Pretty Fast\n",
    "  - Temporary Storage (Lost When You Turn Your Computer Off, or Quit a Program)\n",
    "    - **Random-Access Memory (RAM)**: Holds Gigabytes of Data, Works Super Fast\n",
    "    \n",
    "When we have \"loaded a file into Python,\" it means we have read the file from our hard drive into RAM. \"Saving data\" is the opposite process."
   ]
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   "source": [
    "## Files are Organized on your Computer by your Operating System's Filesystem\n",
    "Operating System Features:\n",
    "  - **Describing Data Content**: A Good Filename can say everything!\n",
    "  - **Interpret Data Format**: using File Extensions\n",
    "  - **Modification Protection**: Set Files and Folders to Read-only\n",
    "  - **Backup**: save a copy on another folder or computer\n",
    "  - **Security**: Can Lock Access from certain Users"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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     "slide_type": "subslide"
    }
   },
   "source": [
    "## How Fast/Easily Can I Read the File?\n",
    "  - **Human Readable** vs **Machine-Readable**\n",
    "      - ex) **Text Files** are Human-Readable: 5, 6\n",
    "      - ex) **Binary File** are Machine-Readable: \\x05\\x00\\x00\\x00\\x06\\x00\\x00\\x00      \n",
    "  - **Bigger Files are Slower To Read**\n",
    "      - Binary Files are often smaller (more compact) than text files.\n",
    "  - **Note: File Extensions are Just Hints at How the File is Organized**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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     "slide_type": "subslide"
    }
   },
   "source": [
    "## Most Important Text File Format for Numeric Data:  CSV\n",
    "  - **.csv (Comma-Separated Values)**:\n",
    "    - Stores Tabular Data (Rows and Columns)\n",
    "    - Rows are Seperated by lines, \n",
    "    - Columns are seperated by a **Delimiter**, which can be any symbol (traditionally a comma, space, or tab)\n",
    "    - Python has a built-in low-level **csv** package, but my recommendation is to use **Pandas** for reading and writing to CSV Files\n",
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   "source": [
    "## Binary File Formats\n",
    "  - Tabular Data:\n",
    "    - **.xls**, **.xlsx**: Microsoft Excel Spreadsheet files\n",
    "      - Cool Feature: User-Friendly, holds multiple tables!\n",
    "    - **.hdfs**, **.hdf5**, **.h5**: \"Hadoop File System\"\n",
    "      - Cool Feature: Super Fast, holds multiple tables, handles lots of data without problems!\n",
    "    - **.sqlite3**: SQLite3 Database files\n",
    "      - Cool Feature: Fast, holds multiple tables, works with database management software."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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     "slide_type": "subslide"
    }
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   "source": [
    "## Binary File Formats (cont'd)\n",
    "  - Image Data:\n",
    "    - **.jpg**, **.jpeg**, **.tif**, **.RAW**, etc\n",
    "  - Application-Specific Data:\n",
    "    - **.pickle**: Python, **.npz**: NumPy, **.mat**: Matlab, **.stata**: Stata"
   ]
  },
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   "source": [
    "## Pandas Can Read from and Write to Lots of File Formats\n",
    "#### Reading\n",
    "```python\n",
    "['read_clipboard', 'read_csv', 'read_excel', 'read_fwf', 'read_gbq', 'read_hdf', 'read_html', 'read_json', 'read_msgpack', 'read_pickle', 'read_sas', 'read_sql', 'read_sql_query', 'read_sql_table', 'read_stata', 'read_table']\n",
    "```\n",
    "#### Writing:\n",
    "```python\n",
    "['to_clipboard', 'to_csv', 'to_dense', 'to_dict', 'to_excel', 'to_gbq', 'to_hdf', 'to_html', 'to_json', 'to_latex', 'to_msgpack', 'to_panel', 'to_period', 'to_pickle', 'to_records', 'to_sparse', 'to_sql', 'to_stata', 'to_string', 'to_timestamp', 'to_wide', 'to_xarray']\n",
    "```"
   ]
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   "metadata": {
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   "source": [
    "# Pandas: Multi-Table Organization"
   ]
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   "metadata": {
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     "slide_type": "subslide"
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   "source": [
    "## Managing Filesize by Reducing DataFrame Size: DRY\n",
    "  - DataSet Size: NRows \\* NColumns \\* Column Format Size\n",
    "  - Problem: The more rows you have, the more memory you need to store additional columns\n",
    "  - Solution: Take Advantage of Perfectly-Correlating Columns by making new, smaller, tables!\n",
    "  \n",
    "### (Demonstration on Following Slides)"
   ]
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       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>Z</th>\n",
       "      <th>Corner ID</th>\n",
       "      <th>Experiment</th>\n",
       "      <th>Experimenter</th>\n",
       "      <th>Interaction Distance</th>\n",
       "      <th>Interaction Level</th>\n",
       "      <th>Phase Time</th>\n",
       "      <th>Rat</th>\n",
       "      <th>Rat Rigid Body</th>\n",
       "      <th>nPhases</th>\n",
       "      <th>JSON File</th>\n",
       "      <th>Texture Size</th>\n",
       "      <th>CSV File</th>\n",
       "      <th>Video File</th>\n",
       "      <th>HDF File</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>136.783342</td>\n",
       "      <td>-0.168612</td>\n",
       "      <td>0.497566</td>\n",
       "      <td>-0.384482</td>\n",
       "      <td>1.0</td>\n",
       "      <td>VR_Engagement</td>\n",
       "      <td>Nicholas A. Del Grosso</td>\n",
       "      <td>0.15</td>\n",
       "      <td>2.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>FlatWhite</td>\n",
       "      <td>Rat</td>\n",
       "      <td>2.0</td>\n",
       "      <td>VR_Engagement_2015-12-05_11-33-26.json</td>\n",
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       "      <td>VR_Engagement_FlatWhite_2015-11-30_13-29-40.ta...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>137.616675</td>\n",
       "      <td>-0.146270</td>\n",
       "      <td>0.289156</td>\n",
       "      <td>-0.308755</td>\n",
       "      <td>1.0</td>\n",
       "      <td>VR_Engagement</td>\n",
       "      <td>Nicholas A. Del Grosso</td>\n",
       "      <td>0.15</td>\n",
       "      <td>2.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>FlatWhite</td>\n",
       "      <td>Rat</td>\n",
       "      <td>2.0</td>\n",
       "      <td>VR_Engagement_2015-12-05_11-33-26.json</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>138.450009</td>\n",
       "      <td>-0.043644</td>\n",
       "      <td>0.256933</td>\n",
       "      <td>-0.290230</td>\n",
       "      <td>1.0</td>\n",
       "      <td>VR_Engagement</td>\n",
       "      <td>Nicholas A. Del Grosso</td>\n",
       "      <td>0.15</td>\n",
       "      <td>2.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>FlatWhite</td>\n",
       "      <td>Rat</td>\n",
       "      <td>2.0</td>\n",
       "      <td>VR_Engagement_2015-12-05_11-33-26.json</td>\n",
       "      <td>NaN</td>\n",
       "      <td>VR_Engagement_FlatWhite_2015-11-30_13-29-40.ta...</td>\n",
       "      <td>VR_Engagement_FlatWhite_2015-11-30_13-29-40.ta...</td>\n",
       "      <td>VR_Engagement_FlatWhite_2015-11-30_13-29-40.ta...</td>\n",
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       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>139.283342</td>\n",
       "      <td>0.060798</td>\n",
       "      <td>0.259256</td>\n",
       "      <td>-0.454783</td>\n",
       "      <td>1.0</td>\n",
       "      <td>VR_Engagement</td>\n",
       "      <td>Nicholas A. Del Grosso</td>\n",
       "      <td>0.15</td>\n",
       "      <td>2.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>FlatWhite</td>\n",
       "      <td>Rat</td>\n",
       "      <td>2.0</td>\n",
       "      <td>VR_Engagement_2015-12-05_11-33-26.json</td>\n",
       "      <td>NaN</td>\n",
       "      <td>VR_Engagement_FlatWhite_2015-11-30_13-29-40.ta...</td>\n",
       "      <td>VR_Engagement_FlatWhite_2015-11-30_13-29-40.ta...</td>\n",
       "      <td>VR_Engagement_FlatWhite_2015-11-30_13-29-40.ta...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>400</th>\n",
       "      <td>140.116676</td>\n",
       "      <td>-0.051600</td>\n",
       "      <td>0.284099</td>\n",
       "      <td>-0.572804</td>\n",
       "      <td>1.0</td>\n",
       "      <td>VR_Engagement</td>\n",
       "      <td>Nicholas A. Del Grosso</td>\n",
       "      <td>0.15</td>\n",
       "      <td>2.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>FlatWhite</td>\n",
       "      <td>Rat</td>\n",
       "      <td>2.0</td>\n",
       "      <td>VR_Engagement_2015-12-05_11-33-26.json</td>\n",
       "      <td>NaN</td>\n",
       "      <td>VR_Engagement_FlatWhite_2015-11-30_13-29-40.ta...</td>\n",
       "      <td>VR_Engagement_FlatWhite_2015-11-30_13-29-40.ta...</td>\n",
       "      <td>VR_Engagement_FlatWhite_2015-11-30_13-29-40.ta...</td>\n",
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       "           Time         X         Y         Z  Corner ID     Experiment  \\\n",
       "0    136.783342 -0.168612  0.497566 -0.384482        1.0  VR_Engagement   \n",
       "100  137.616675 -0.146270  0.289156 -0.308755        1.0  VR_Engagement   \n",
       "200  138.450009 -0.043644  0.256933 -0.290230        1.0  VR_Engagement   \n",
       "300  139.283342  0.060798  0.259256 -0.454783        1.0  VR_Engagement   \n",
       "400  140.116676 -0.051600  0.284099 -0.572804        1.0  VR_Engagement   \n",
       "\n",
       "               Experimenter  Interaction Distance  Interaction Level  \\\n",
       "0    Nicholas A. Del Grosso                  0.15                2.0   \n",
       "100  Nicholas A. Del Grosso                  0.15                2.0   \n",
       "200  Nicholas A. Del Grosso                  0.15                2.0   \n",
       "300  Nicholas A. Del Grosso                  0.15                2.0   \n",
       "400  Nicholas A. Del Grosso                  0.15                2.0   \n",
       "\n",
       "     Phase Time        Rat Rat Rigid Body  nPhases  \\\n",
       "0         300.0  FlatWhite            Rat      2.0   \n",
       "100       300.0  FlatWhite            Rat      2.0   \n",
       "200       300.0  FlatWhite            Rat      2.0   \n",
       "300       300.0  FlatWhite            Rat      2.0   \n",
       "400       300.0  FlatWhite            Rat      2.0   \n",
       "\n",
       "                                  JSON File  Texture Size  \\\n",
       "0    VR_Engagement_2015-12-05_11-33-26.json           NaN   \n",
       "100  VR_Engagement_2015-12-05_11-33-26.json           NaN   \n",
       "200  VR_Engagement_2015-12-05_11-33-26.json           NaN   \n",
       "300  VR_Engagement_2015-12-05_11-33-26.json           NaN   \n",
       "400  VR_Engagement_2015-12-05_11-33-26.json           NaN   \n",
       "\n",
       "                                              CSV File  \\\n",
       "0    VR_Engagement_FlatWhite_2015-11-30_13-29-40.ta...   \n",
       "100  VR_Engagement_FlatWhite_2015-11-30_13-29-40.ta...   \n",
       "200  VR_Engagement_FlatWhite_2015-11-30_13-29-40.ta...   \n",
       "300  VR_Engagement_FlatWhite_2015-11-30_13-29-40.ta...   \n",
       "400  VR_Engagement_FlatWhite_2015-11-30_13-29-40.ta...   \n",
       "\n",
       "                                            Video File  \\\n",
       "0    VR_Engagement_FlatWhite_2015-11-30_13-29-40.ta...   \n",
       "100  VR_Engagement_FlatWhite_2015-11-30_13-29-40.ta...   \n",
       "200  VR_Engagement_FlatWhite_2015-11-30_13-29-40.ta...   \n",
       "300  VR_Engagement_FlatWhite_2015-11-30_13-29-40.ta...   \n",
       "400  VR_Engagement_FlatWhite_2015-11-30_13-29-40.ta...   \n",
       "\n",
       "                                              HDF File  \n",
       "0    VR_Engagement_FlatWhite_2015-11-30_13-29-40.ta...  \n",
       "100  VR_Engagement_FlatWhite_2015-11-30_13-29-40.ta...  \n",
       "200  VR_Engagement_FlatWhite_2015-11-30_13-29-40.ta...  \n",
       "300  VR_Engagement_FlatWhite_2015-11-30_13-29-40.ta...  \n",
       "400  VR_Engagement_FlatWhite_2015-11-30_13-29-40.ta...  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_hdf('rel_data.h5', 'alldata').head()\n",
    "del data['Session']\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Solution: Make two, smaller tables that \"Relate\" to each other by \"Key\" values"
   ]
  },
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       "      <td>-0.308755</td>\n",
       "      <td>1448890319922000000</td>\n",
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       "      <td>138.450009</td>\n",
       "      <td>-0.043644</td>\n",
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       "      <td>-0.290230</td>\n",
       "      <td>1448890319922000000</td>\n",
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       "           Time         X         Y         Z              Session\n",
       "0    136.783342 -0.168612  0.497566 -0.384482  1448890319922000000\n",
       "100  137.616675 -0.146270  0.289156 -0.308755  1448890319922000000\n",
       "200  138.450009 -0.043644  0.256933 -0.290230  1448890319922000000\n",
       "300  139.283342  0.060798  0.259256 -0.454783  1448890319922000000\n",
       "400  140.116676 -0.051600  0.284099 -0.572804  1448890319922000000"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "behav = pd.read_hdf('rel_data.h5', 'behav').head()\n",
    "behav.head()"
   ]
  },
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       "    <tr>\n",
       "      <th>2015-11-30 13:31:59.922</th>\n",
       "      <td>1.0</td>\n",
       "      <td>VR_Engagement</td>\n",
       "      <td>Nicholas A. Del Grosso</td>\n",
       "      <td>0.15</td>\n",
       "      <td>2.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>FlatWhite</td>\n",
       "      <td>Rat</td>\n",
       "      <td>2.0</td>\n",
       "      <td>VR_Engagement_2015-12-05_11-33-26.json</td>\n",
       "      <td>NaN</td>\n",
       "      <td>VR_Engagement_FlatWhite_2015-11-30_13-29-40.ta...</td>\n",
       "      <td>VR_Engagement_FlatWhite_2015-11-30_13-29-40.ta...</td>\n",
       "      <td>VR_Engagement_FlatWhite_2015-11-30_13-29-40.ta...</td>\n",
       "      <td>1448890319922000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-30 13:48:39.755</th>\n",
       "      <td>2.0</td>\n",
       "      <td>VR_Engagement</td>\n",
       "      <td>Nicholas A. Del Grosso</td>\n",
       "      <td>0.15</td>\n",
       "      <td>2.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>FuzzPatch</td>\n",
       "      <td>Rat</td>\n",
       "      <td>2.0</td>\n",
       "      <td>VR_Engagement_2015-12-05_11-33-26.json</td>\n",
       "      <td>NaN</td>\n",
       "      <td>VR_Engagement_FuzzPatch_2015-11-30_13-48-16.ta...</td>\n",
       "      <td>VR_Engagement_FuzzPatch_2015-11-30_13-48-16.ta...</td>\n",
       "      <td>VR_Engagement_FuzzPatch_2015-11-30_13-48-16.ta...</td>\n",
       "      <td>1448891319755000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-30 14:05:29.038</th>\n",
       "      <td>2.0</td>\n",
       "      <td>VR_Engagement</td>\n",
       "      <td>Nicholas A. Del Grosso</td>\n",
       "      <td>0.15</td>\n",
       "      <td>1.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>Nessie</td>\n",
       "      <td>Rat</td>\n",
       "      <td>2.0</td>\n",
       "      <td>VR_Engagement_2015-12-05_11-33-26.json</td>\n",
       "      <td>NaN</td>\n",
       "      <td>VR_Engagement_Nessie_2015-11-30_14-03-45.takea...</td>\n",
       "      <td>VR_Engagement_Nessie_2015-11-30_14-03-45.takea...</td>\n",
       "      <td>VR_Engagement_Nessie_2015-11-30_14-03-45.takea...</td>\n",
       "      <td>1448892329038000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-30 18:33:49.709</th>\n",
       "      <td>3.0</td>\n",
       "      <td>VR_Engagement</td>\n",
       "      <td>Nicholas A. Del Grosso</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>FuzzPatch</td>\n",
       "      <td>Rat</td>\n",
       "      <td>2.0</td>\n",
       "      <td>VR_Engagement_2015-12-05_11-33-26.json</td>\n",
       "      <td>1024.0</td>\n",
       "      <td>VR_Engagement_FuzzPatch_2015-11-30_18-33-19.ta...</td>\n",
       "      <td>VR_Engagement_FuzzPatch_2015-11-30_18-33-19.ta...</td>\n",
       "      <td>VR_Engagement_FuzzPatch_2015-11-30_18-33-19.ta...</td>\n",
       "      <td>1448908429709000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-30 18:15:52.315</th>\n",
       "      <td>1.0</td>\n",
       "      <td>VR_Engagement</td>\n",
       "      <td>Nicholas A. Del Grosso</td>\n",
       "      <td>0.15</td>\n",
       "      <td>2.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>Nessie</td>\n",
       "      <td>Rat</td>\n",
       "      <td>2.0</td>\n",
       "      <td>VR_Engagement_2015-12-05_11-33-26.json</td>\n",
       "      <td>1024.0</td>\n",
       "      <td>VR_Engagement_Nessie_2015-11-30_18-14-43.take.csv</td>\n",
       "      <td>VR_Engagement_Nessie_2015-11-30_18-14-43.takea...</td>\n",
       "      <td>VR_Engagement_Nessie_2015-11-30_18-14-43.take.h5</td>\n",
       "      <td>1448907352315000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Corner ID     Experiment            Experimenter  \\\n",
       "b'Timestamp'                                                                \n",
       "2015-11-30 13:31:59.922        1.0  VR_Engagement  Nicholas A. Del Grosso   \n",
       "2015-11-30 13:48:39.755        2.0  VR_Engagement  Nicholas A. Del Grosso   \n",
       "2015-11-30 14:05:29.038        2.0  VR_Engagement  Nicholas A. Del Grosso   \n",
       "2015-11-30 18:33:49.709        3.0  VR_Engagement  Nicholas A. Del Grosso   \n",
       "2015-11-30 18:15:52.315        1.0  VR_Engagement  Nicholas A. Del Grosso   \n",
       "\n",
       "                         Interaction Distance  Interaction Level  Phase Time  \\\n",
       "b'Timestamp'                                                                   \n",
       "2015-11-30 13:31:59.922                  0.15                2.0       300.0   \n",
       "2015-11-30 13:48:39.755                  0.15                2.0       300.0   \n",
       "2015-11-30 14:05:29.038                  0.15                1.0       300.0   \n",
       "2015-11-30 18:33:49.709                  0.15                0.0       300.0   \n",
       "2015-11-30 18:15:52.315                  0.15                2.0       300.0   \n",
       "\n",
       "                               Rat Rat Rigid Body  nPhases  \\\n",
       "b'Timestamp'                                                 \n",
       "2015-11-30 13:31:59.922  FlatWhite            Rat      2.0   \n",
       "2015-11-30 13:48:39.755  FuzzPatch            Rat      2.0   \n",
       "2015-11-30 14:05:29.038     Nessie            Rat      2.0   \n",
       "2015-11-30 18:33:49.709  FuzzPatch            Rat      2.0   \n",
       "2015-11-30 18:15:52.315     Nessie            Rat      2.0   \n",
       "\n",
       "                                                      JSON File  Texture Size  \\\n",
       "b'Timestamp'                                                                    \n",
       "2015-11-30 13:31:59.922  VR_Engagement_2015-12-05_11-33-26.json           NaN   \n",
       "2015-11-30 13:48:39.755  VR_Engagement_2015-12-05_11-33-26.json           NaN   \n",
       "2015-11-30 14:05:29.038  VR_Engagement_2015-12-05_11-33-26.json           NaN   \n",
       "2015-11-30 18:33:49.709  VR_Engagement_2015-12-05_11-33-26.json        1024.0   \n",
       "2015-11-30 18:15:52.315  VR_Engagement_2015-12-05_11-33-26.json        1024.0   \n",
       "\n",
       "                                                                  CSV File  \\\n",
       "b'Timestamp'                                                                 \n",
       "2015-11-30 13:31:59.922  VR_Engagement_FlatWhite_2015-11-30_13-29-40.ta...   \n",
       "2015-11-30 13:48:39.755  VR_Engagement_FuzzPatch_2015-11-30_13-48-16.ta...   \n",
       "2015-11-30 14:05:29.038  VR_Engagement_Nessie_2015-11-30_14-03-45.takea...   \n",
       "2015-11-30 18:33:49.709  VR_Engagement_FuzzPatch_2015-11-30_18-33-19.ta...   \n",
       "2015-11-30 18:15:52.315  VR_Engagement_Nessie_2015-11-30_18-14-43.take.csv   \n",
       "\n",
       "                                                                Video File  \\\n",
       "b'Timestamp'                                                                 \n",
       "2015-11-30 13:31:59.922  VR_Engagement_FlatWhite_2015-11-30_13-29-40.ta...   \n",
       "2015-11-30 13:48:39.755  VR_Engagement_FuzzPatch_2015-11-30_13-48-16.ta...   \n",
       "2015-11-30 14:05:29.038  VR_Engagement_Nessie_2015-11-30_14-03-45.takea...   \n",
       "2015-11-30 18:33:49.709  VR_Engagement_FuzzPatch_2015-11-30_18-33-19.ta...   \n",
       "2015-11-30 18:15:52.315  VR_Engagement_Nessie_2015-11-30_18-14-43.takea...   \n",
       "\n",
       "                                                                  HDF File  \\\n",
       "b'Timestamp'                                                                 \n",
       "2015-11-30 13:31:59.922  VR_Engagement_FlatWhite_2015-11-30_13-29-40.ta...   \n",
       "2015-11-30 13:48:39.755  VR_Engagement_FuzzPatch_2015-11-30_13-48-16.ta...   \n",
       "2015-11-30 14:05:29.038  VR_Engagement_Nessie_2015-11-30_14-03-45.takea...   \n",
       "2015-11-30 18:33:49.709  VR_Engagement_FuzzPatch_2015-11-30_18-33-19.ta...   \n",
       "2015-11-30 18:15:52.315   VR_Engagement_Nessie_2015-11-30_18-14-43.take.h5   \n",
       "\n",
       "                                     Session  \n",
       "b'Timestamp'                                  \n",
       "2015-11-30 13:31:59.922  1448890319922000000  \n",
       "2015-11-30 13:48:39.755  1448891319755000000  \n",
       "2015-11-30 14:05:29.038  1448892329038000000  \n",
       "2015-11-30 18:33:49.709  1448908429709000000  \n",
       "2015-11-30 18:15:52.315  1448907352315000000  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sessions = pd.read_hdf('rel_data.h5', 'sessions').head()\n",
    "sessions['Session'] = sessions.index.astype(int)\n",
    "sessions.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## \"Joining\" or \"Merging\" Tables Builds a Single Table from Multiple Tables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Session</th>\n",
       "      <th>Time</th>\n",
       "      <th>X</th>\n",
       "      <th>Rat</th>\n",
       "      <th>Corner ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1448890319922000000</td>\n",
       "      <td>136.783342</td>\n",
       "      <td>-0.168612</td>\n",
       "      <td>FlatWhite</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1448890319922000000</td>\n",
       "      <td>137.616675</td>\n",
       "      <td>-0.146270</td>\n",
       "      <td>FlatWhite</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1448890319922000000</td>\n",
       "      <td>138.450009</td>\n",
       "      <td>-0.043644</td>\n",
       "      <td>FlatWhite</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1448890319922000000</td>\n",
       "      <td>139.283342</td>\n",
       "      <td>0.060798</td>\n",
       "      <td>FlatWhite</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1448890319922000000</td>\n",
       "      <td>140.116676</td>\n",
       "      <td>-0.051600</td>\n",
       "      <td>FlatWhite</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Session        Time         X        Rat  Corner ID\n",
       "0  1448890319922000000  136.783342 -0.168612  FlatWhite        1.0\n",
       "1  1448890319922000000  137.616675 -0.146270  FlatWhite        1.0\n",
       "2  1448890319922000000  138.450009 -0.043644  FlatWhite        1.0\n",
       "3  1448890319922000000  139.283342  0.060798  FlatWhite        1.0\n",
       "4  1448890319922000000  140.116676 -0.051600  FlatWhite        1.0"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(behav[['Session', 'Time', 'X',]], \n",
    "         sessions[['Session', 'Rat', 'Corner ID']],\n",
    "         left_on='Session', right_on='Session').head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Benefits of Multiple Tables: Smaller Filesize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "One, Large Table: \t760.0 kb\n",
      "Two, Small Tables: \t240.64 kb\n"
     ]
    }
   ],
   "source": [
    "def table_split_size_comp(tot_rows=5000):\n",
    "    full_bytes = data.memory_usage(index=True).sum() * tot_rows / 5\n",
    "    split_bytes = sum([df.memory_usage(index=True).sum() * nrows for nrows, df in zip((tot_rows / 5, 1), (behav, sessions))])\n",
    "    return {'full': full_bytes / 1000, 'split': split_bytes / 1000}\n",
    "\n",
    "print('One, Large Table: \\t{full} kb\\nTwo, Small Tables: \\t{split} kb'.format(**table_split_size_comp(5000)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f049a86f518>"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f049a93b860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def table_size_ratio(tot_rows=5000):\n",
    "    sizes = table_split_size_comp(tot_rows)\n",
    "    return sizes['split'] / sizes['full']\n",
    "\n",
    "rownums = [5, 10, 20, 50, 100]\n",
    "kb_sizes = list(map(table_size_ratio, rownums))\n",
    "plt.plot(rownums, kb_sizes); plt.xlabel('Rows of Data'); plt.ylabel('Filesize Reduction Ratio')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Benefits of Multiple Tables: More Natural Data Organization\n",
    "\n",
    "For example, this experiment can be organized into four tables:\n",
    "  - **Recording Session Details** (Keys: Session, Stimulus, Rat)\n",
    "  - **Rat Head Position Tracking Data** (Key: Session)\n",
    "  - **Rat Details** (Key: Rat)\n",
    "  - **Stimulus Details** (Key: Stimulus)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Relational Databases"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Relational Databases\n",
    "Data Organized into Multiple, Related Tables that Relate to Each Other are called **Relational Databases**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<iFrame src=\"https://www.ibm.com/developerworks/library/x-matters8/relat.gif\" width=600 height=350></iFrame>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "HTML('<iFrame src=\"https://www.ibm.com/developerworks/library/x-matters8/relat.gif\" width=600 height=350></iFrame>')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Relational Databases Allow The Organization of Even Very Complex Datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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rKy1Hw54o0Gxnk2y6ldQwyx268/OyxSM5H+6rH2r4K/bFv9T+PP7fviDw7qk9\nvYvL4ki8K29zbwMfLgWcQRyMu753AbJ5AJHYV99ftB/8Eqvhj4u8N+FI/DutaX8K5dKVotR1JbAy\njU3ZIwpbfOgQhkcjkk+YfSgD2j47/t6fD74I/Cjwh8RIo73xl4a8UTGGxuNCeJiCELneJHUqRggq\nfmVgQwBGK8A/4fY/DAnH/CCeLzn2tTn/AMi18/ft1fBkfs+/safDTwJB420/x1p2m+Kbp7S9sbIW\nxhWWKSV45MTSB23uxDfLhSBg4ybf/BPn/gnR8P8A9pX4NXnjjxtq2s75tRksrSz0mVLcQiL75Z2V\n9+7euBhdu3vngA4/9sfV/gR4v/ad8Q694jufiLaa7qkOl6hLbaVZ6e9vH5unW0kaqZJQSQjIGyPv\nbgM8E/e/7SH/AAUw8D/sy/FS98B654V8Q6rqFpbwXBudO8jymWRA6gb5AeAcHjtX5a/t/aJb+Gf2\n0fFui2Zc2emDSLCAytuby4tNtI03EdThRk+tdj/wVnGf20PEIPJ/szTwP+/C0AfbPiL/AILPfCPS\ntZntdP8ADfijW7SPaY763ihiSXKgnCySK4wSV5HJXI4INexXH/BQ34aT/s5ar8YdFi1XXdG0m5tb\nHUNNgtjDc211N5f7kmXajFPNXcyMynsTXzb46/4Js/CzwR+w9qN/NcwJ4ztbBdb/AOEu1KV7dFlZ\nVxCwUPthw2NuCS3ORnj55/4JSW/hTx18RPGHwy8a6JP4i0PxPp0bLYPEz2ay27mbfMQw2H5BtbuQ\nBQB88fsv/GnSvgZ+0n4Z+I+q2F5qGlaVdXM72dmUE7iSGWNQu4heDIM89AcV+13x+/bx+Hn7O3gL\nwp4g14Xd7qPiW0hvrHw9YmNr37PIobzGBYKqjO3JYBiGC52nH47fsU/D7w78Q/2yfBvhTxLpFvq/\nh+8vryK4064z5UipbTsoOCDwyKRz2rX/AOChmnxR/tt+NtGQmLTbOXTrC1gDfLBbrZW4WNAegAPH\n1J7mgD770j/gtN8KNQ1eytbvwj4r020nmSOW9mjt3S3QsA0jKkhYhRkkKCeOATX1Jrf7Vnga1/Z5\n1b4zaFev4s8Jadbm4caZgTnDKpQpIV2ONwJV8EAjjmvjf9v39hj4PfB79j/VfEPhLw0dJ8QeGzYJ\nFqiTkzXm+eG3c3BIIcsJCx2hPmAIwOD8ufshzzP+xD+1vA00jQx6dpDJEznYhMtxuIXoCcDJxzge\nlAH15/w+w+F+cDwL4v4/69f/AI7X1/8Asy/tD6N+0/8ACy38c6Fpt/pOnzXU1oLbUgnmhozgn5GY\nYOfWvxl/4J0fsh+G/wBrT4ieIrLxZqd/ZaNodkly9vpxVJrhpGZFAkYEIFIyflOenHWv2m/Z7+Af\nhr9mz4dReCfCcl/Lo8NzLcq2pTrNMXkOW+ZVUY9Bj8+tAHpdFFFABRRRQAUUUUAFFFFABRRRQBwH\nx6+KMHwV+D/ivxvc2891Foli9yYbYKZGPCrgMQOCwPPYV+DHwr+GXxG/b9/aA1FLjWDd+I9VEt7f\n63qccr21uFQlUYxq3lphRGi4AGFUcDFfsN/wUp0LVdb/AGPPHL6Xr0uhixhW8u0iiEn263U4a2bJ\nG0MWU7hk/IBjmvh3/giVdRRfFb4ixPPEksujQCONmAZ8TEnAPXA64oAwv2r/APgkzrvwc8DQeJvh\nxqOpeP47d9mp6b9kH2xFYgJLDHHnzFDcMo+YbgwBUMR9hf8ABMH4m/ErVPhPq3g74oeHvFWn3/hi\nVP7P1PX9Nlt1lsXUBIFaQBpHjZJOxwjxgdAK+t/iB8Q/DXwq8LXXiTxZrVroGh2pUS3l4+EUsQqj\n1JJPQc/rXxD+3t+1x4V+IP7JHi6X4R/ECLUbu3vbGz1R9KZ0dLa4MilGLKCFfYw49O2eQC342/4L\nKfBvw14gmsNG0fxJ4ssEVSmqWNskEUhIBICTskny5wcqOnGRzXc/s6f8FPPhX+0P43g8I21rrPhj\nX72VIdNttUtxIL1yrswV4S6ptCclyoO4Yzzj49/4JPfsr/DT44eGfHPiHx54dj8S3On3kWn21veO\nfIjRkEjOFGDvyMZz0J4r58/bY+Btl+xx+0/Fpvg3ULl7QQW+vacJ8h7XdLIBEWUjeAYiM/KcNjtk\ngH6vftYf8FAfCH7I3jPR/DniPw5rms3OpWA1CObTPJ8tE8149p3upzlCeBjBFeQ+If8AgtB8JNJ1\ny8s7Hwx4p1myhlMcWoQRQxx3Cg/fVZHVwD6MAfUV8i/8FUfFF5468RfBHxHqPkrqGsfD+xv7gQKV\njEsrvI+0ZOFyxwCemOTXtX7Q3/BPnwD8Fv8Agn5e65e6SF+Jeh29pf3ms2t0ZPOuJJo4pYslQGg/\nekgbQQVU5xnIB97/ALN37S3hf9p74Xf8Jz4YW8tbGK4ltLu0vYsTWs8aq7I2Mq3yOjZUkYcDOcgf\nMfjP/gsp8G/DviC4sdH0jxH4rsI1Rk1Syt44IpSVBIVJ2RxtJ2nKjkEjI5Pwf+yP8QG8C/srftOG\nV9S2Xmm6bp9stjlvJuJjdgSsARsX93hn7cV3H/BK79krwp8ffFfiLxP44srPXPD+ip9ng0eW6KvL\ncuB88kQGWjCscNuHzheDzQB5J/wUC/aB8L/tMfGvSvG3hRrlbGbw/aW9xbXkJSW1uFkmLxN/CxAZ\nTuUlTnrXvf7cMXwkltfgWfiBdeNYNRHw30r7Ovhm2s5bfysPgsZpFYPu3dBjG33r52/b3/Zy0f8A\nZg/aCvfCnh+9nvNFurGLVLVLlQHt0kd18osD8+0xn5sDrjHFejf8FLRm7+AHqfhjpX85KAOl/bu+\nPfhHUf2aPg38G/DtjrbNpGmaRr1vqWqrAu+0ksJFRHWNjiXEqkgDAwcE16r/AMEkP2hdCt/AXiX4\nMXGm6j/a1wdR8RNqMXlmAW4gt4inLBt/yk9MdOa4L9tX4a+GLT9gD9nzx3Fo1snjC9tdI0241gZ8\n6S2TTpisR5xtBRe2flFezf8ABJL4X+FpP2cfFnj5tFtv+Ewj1HUtLTWTnzltvstu/ldcbdzE9M80\nAeNf8E4rb4JJ+1v4UbwVe/ECbxCLe/8AIj160sI7Tb9klD72ilZ87c4wvXGeM194ftKf8FGPhP8A\ns16zJ4f1C6u/E3iqGREuNG0RA726MrnfJIxWMEFQDHu3jep245r8vP8AglH/AMnt+DPez1H/ANI5\na4L9mL4daT8bv2yfDfhTxYbm+0nVdYuftgWYiSURpLKFLHPBMYB9ieR1oA/TLwL/AMFjvg14o8QR\n6frOmeIfCVk6Mf7V1G3SaFWH3VKwM78+u3A7nvXvf7Tf7YPhP9mL4ceHvG2pWd54o0XXLxLW0k0K\nSGUOrwvKsqsWCshVMgg8gg1+dX/BWT9l74cfAmx8Aa54E8Pp4bm1SS4srq1tJD5DiMK6vtbJ3/vC\nCc8gKMcc+d/Ey5nuf+CVXwkM0rz7fHN5GvmOWwojusAZ6AAYwKAPr3/h9h8MCQP+EF8X5x/dteT7\nDzq99/ZW/b7+HP7Vmo6hpOiC98P+IbUeZHpGsbBNdQhcmSIozB8HdlQdwC5IxzXw/wD8E1P2Q/AH\nxN+APxA8f+NtEsvFMz3Eljp9pewuv2FreAyPIrhxu8wzoCCvy+UOTnjxT/gk+oP7bHhTI/5cdRI9\nv9FkoA/eIc//AF6/F/8A4LVAf8NMeE/+xRg/9LLuv2hr8Xv+C1o/4yY8I/8AYowf+ll3QB1Hgb/g\nkLYfFD9nzwr4z8O+P57bxNr+jWGpx2mqWyiyjeZI3kQsmXwAzhTjkgZ618z/ALMfxt+IP7Gv7Q0m\nl2Ilg8+/j0jWtC1BXhhuR5m1WdGGVZdxZXxkBiBwxz+2X7IPH7KfweJ7eE9L/wDSWOvxg/aznhvv\n+CiPiiSKWO4hk8V2YEkbb1YZhHUdfSgD9Xv2p/8AgoN8OP2VPEFj4f1lb7xF4hnTzrjTNF8tpbOM\njKNKXZVUtwQpO7BBxggnyHwj/wAFmvg/r/iKysNV0HxN4asJi3m6pdwRTRQYUkbkid3IJAHyqevT\nANfl9+1vrOt+Ov2sfihLezT6vqZ8TXthAFXdI0cM7QQRKF5O2ONEUYzhRXV/H/W/DfxV0rSo/A/7\nOOp/DHVbGU+fc2FzcXUN1EQQVeJrdSGDbSGD8DcMHIIAP3B8c/tNfDn4dfCW0+JmseJIm8FXnli2\n1SxhkulmaTO1VWNWIOVIOehBBwa+Ph/wWv8AhgB/yIvi4+n/AB6//Hf8a+S9f+G3xZ+M3/BPvwc9\nt4euTZfDjX9Usp9Ojt5YLl7KSK1mSYwn/W7ZHnBYYwMYB+Y188+Efib4d8PaDH4R8Z/C3SNYs4JJ\nBd6hayS6fr28OWCC6YyRxhXwpBgJ2ArwTuAB++P7O37T/gj9pj4eN4s8LaiIYrf5dQsL11S509wC\ncTLnABAJDD5SFbB4OPmn4if8FhvhH4G8a6v4fsNG1/xVbafOYBq+liD7NcMB8xjLyKzKGyu7GGxk\nZBBPzB8FPiB8JPC37M3xt1P4L6x408IePT4Yin1Lw/rV9BdWxT7SiGaF1hUyGMSMm4hOLk/Kcgjl\nP+CUn7OngP4/fEfxnJ460Ua9a6Hp8MltYzORbs8rOpZ1HLEBRjkYPY9gD9Cf2av+ClHwv/aU8Z2/\nhHT7fWPDvia9dl0/T9Utt5uwkTyyFXiLIu1I2J3lc8Yya5j45/8ABVbwD8Bvix4i8Bav4Q8S6hqO\nizJDNc2X2fyXLRq+V3SA4w46gV5rH/wS68H+Hv2h7fxn4e+L2neGYLPxBHqNp4Yt7Bd1uizBhaLJ\n9qDHIymduecbTXw5/wAFAbD+1f27fiLYlvLNzqtrCJMZ2breAZxx0zQB+kXhv9vr4f8A7Xvwj+NO\nhW3h7xPodhpXgzULzUZpFtmma3aF0cQgSEGTBJAbC56kV83/APBM3VPg14W+NXi7WPA03jy+1Ow8\nH311PDr1rZRwtbpNbuwQxSsxkJVAMgL1z2r6Qt/2FPA/7JH7OHxl1Pw5qOr6pr2o+ANRsdRub6VP\nJlK2kjO8cIXKBmBIBZsA4yep+Dv+CWo/4ut8Usf9E41b/wBGW1AH2Ef+C2XwvH/MieLvw+y//Ha6\nL4c/8Fh/hH468b6RoF/omv8AhW31Cb7P/a+qiD7LbsQdhkKSMVUttXdjC7skhQSPzm/4J0/Bnwv8\ndv2ntF8N+MLN9R0SK0uL97NX2pM8QDKknGShPBAwT6ivTf8Agqx+zr4E+APxK8Hv4E0YaBaa1p80\ntzYwuWgR4nRVZAeQSGJOSc9qAN7/AIK8/tC6N8UviTofgPT9NvrW+8EXF2l3e3BQwXQuYrV0MW1i\nSMLnnHUYzXuH/BKr9sPw3L4R8D/AFdE1UeI4l1G5bUyYvshHmzXOB82/Owgfd6+1fHH7UHw48N+G\n/wBlL9mvxbp2kxWviPxLZ6o+r6kHd5r1opIRGXLE/dDEDHTOK+gvhb4Z8EfCL/gl5P8AGG08G2k/\nxDv7LU9CGvRXDw3UYu7ueyEu4ZA2RuOABkKOQTkAH0r8Yv8Agrb8JfhP4+1Hwva6brPjA2DeXNqW\niGFrUyj7yIzuu/H95cqexOKu/BX/AIKxfBr4t+J00LUDqfga7uJYobOXXo08i4dyRjzIyyxgYGWk\nKj5hz1x8Of8ABLD9kzwP+0T4u8Ta/wCOWTVbHw4kSReHHOEu3mD/AL2Qg7iiBSNoAyzId3GG5b/g\np/8AArwd+z3+0TpVh4F0w6JpuqaHBqr2KSboYJvOmhIjBGQpECsQSfmZucEAAH6ifta/t5+E/wBk\nHxBoGkeI/D2t61NrFrJdRSaX5O1Aj7SG3upzz2rz34Jf8FXPh/8AHL4qeHfAmleD/E1hqOt3H2aG\n5vPs/lRttZsttkJxhT0FfDH/AAUf8R6n4x8Afsza7rV5JqOr6l4Dguru7mA3TTOsTO5wMZLEnj+t\nfUf/AATk/YC8EWngX4c/G3UNV1a88XSltUtIoZEhtYVIaMRshVi5HznduGdw4GKAP0hWnU0U6gAo\noooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAEbA5OOO5r8u/iL/AMEbfB3hfwb4m8RR/EXX\nLifTtPutQWF7KAK7RxvIFODwCRiv1Frifjfx8F/H3/Yv6h/6TSUAfgX+xN+zdpn7VHxqHgfVdZu9\nCtm02e9F1ZxLI+6MphcNxghj+lfoZofw2+E3/BJKbVPGuo+M9Y8W6v4gtE0218OpBCtzMvnI7SKA\nRhVCnLMQOwJYgV8pf8EfTj9sCM9f+JBfcfjFXb/8FeYfDF58ZrmW9+I1/qXiizsbeOx8HxaQrWun\nIwjL+Zd+cCpcEzBfLbqASAcgA+g7P/gtX8K7i7iil8GeLbWF3CtOy2zLGCcFiBKTgcngE8d6+v7P\n9pf4cX3wT/4W3H4nhh8ANFLMmq3MMkPmCOR42VYnUSMxeNlVQu5jwAcivwl+Ovxc8G/EfwHo0Hgr\n4GaX8Oba2nCX2v29xNdyXcyphYlkZVWMH52ZCGJ+U5ABz9B/CWz8Z/Fn/glL8Q9A02C+8Rf8I/4p\njazsYFDNa2aLDczbVAyVDSSuRycsaAPpNv8Agth8L+g8DeL8ZxnFr/8AHjX0D+zj+2h4M/a+8C+N\nZ/DNlqelX+iQlLzT9TiAdUkiby5A6EoQxSVQN24eWSQAQT+IHwn+I+g/C6/1PS/Gnwx0nxpp11vi\nu4dRaW01K0PluoFvOCRCwZlYlonztxxnNffH/BNC6+BWu+I/F1/4QsvFHhHx7BpFzENC1bWk1C1v\nbMoC0yMlvFl0YYKsPl3ggtuIUA+cP+CVAx+234L7H7LqPTsfsc3tX6IfF3/grB8K/hh8R7zwdp2l\na143urV1ge98PiF7dpySDFGzODIRwMqCCTgE4r8TvCnjLW/Aupy6loGpz6VfyWs9m1xbNtfyZo2j\nlQHtuR2HGDzxg19if8Ek/h74P8b/ALTJvvEOo3EevaBYyalpGn+VH9nuXyInaSRmyWUS5WNUJOS2\n5fLwwB+0vw78UXvjbwVpOu6h4f1DwtdX8PnnR9VMZurZSTtEnlsyhiuG25yu7DAMCB+Vv/Bb4D/h\nYPwt/wCwXefj++jr9c171+Rn/BcD/koPwt/7Bd7/AOjY6AIP2bv+CUHhf46fAXwn49n8e6vpmoa5\nZNcG0itImijcO6gZJyR8or458RaR8Q/2If2irmxg1NdI8aeFrpWju7GZXimjZQ6EgHmOWN1JjcZ2\nvhgORX7a/wDBPLP/AAxb8KuM/wDEsfj/ALbyV+bP/BY3xppHiL9piw0Wwt5ItS0DSIbbUpHhVVlk\nlxOhVgcuBHIgyQCCMdKAP0r+Kf7cHw2+EHwJ8OfEvU9Tl1Sx8QwxSaRYWcZS81EsAXKRPtKqoyWZ\n8AcDOWUH5xT/AILX/C9pcN4H8Xop6nFqdvvjza/O79rlbq1m+D1ldiaL7P8ADvR8202VMTESbvlP\nQnA+vFHjm98MeLvhJ4b8PeF/gLrvh/xLp6hpPFS6hNdtqO75pPNi+zKCCT8m1htHHzdaAP3ltvj1\n4Bu/hF/wtGPxTYnwKbP7d/a5fEax/wB0qRuEm7CeWRv3nbjdxXxfc/8ABa34WRXEscXgrxbPGjlV\nlVbZd4HRgDLkZ9DXyp8APC3iXXf2Bv2gvAMGj6vP4ne50/V7XRZbWVZDaQ3ELzSxq4CnG0khTuOO\nhr5m+EnxC0f4Q+IL618ZfDLR/HNhcFUvLDXBNbXlsqh8iCVWHksS4JLI/wB1ePUA/cr9kv8Abg8E\n/td/27b+G7HVNH1TR9jz2GqRDcYWwFlDoWTG7cuCd3y5xg1yP7S3/BS/4Y/s1eOV8JXltqXivW4k\nJvotDMTLYtxtjkZ2Ubzk/KMkY+bGRXx3/wAE8fG37Pmg+M/EPjHR9H8a+F/GXh3Sry+i0KTWU1SP\nVLRLdnl2KsEO6VVDEI3GQpBycD5x/YY+Atj+1H+1HZaT4imFxoNuJtX1SC7uzHc3cSnAjUkMXYyP\nGWHXYH+YHmgD9Kvg9/wVt+EvxY8fad4XudN1nweb8+XDqWtmFbXzcjajMjtsz/ebC8cmtn9o7/gp\nv4G/Zq+LWp+Atc8KeItU1Cwigme50/yPJYSxq4A3yA8BsHI65ryL49f8EnPhrr3xG/tnwr8RLL4V\n2ksMcn9hTWyXEaSqSPMiLzxsqMAvB3fMGIOCFHxH/wAFIrC60r9p+6sdQ1s+Jr618P6NBPrZQIdR\nkWwhDXG1SQPMIL4BIG7gmgD9HtF/4Ke/Cz41ePbj4ZaHpHiO5j1i2uraHWlgijhZRaySMwR3Dg4V\ngMryQOxr5Z/4JpW/wVj/AGq9GbwReePp9fGn3vlrr9pZR2vl+Sd+4xSs+cdMDr1r6B+Af/BNfwX8\nGvh/onxG1G61ST4j6ZodzeTpb36yaeZpLaRTtTygxXY/r1r4t/4JIn/jM/Qv+wVf/wDok0Aff/jn\n/gq98Mfhv8XPEvgPX9A8Q2U+hXd3ZT6kIopLeSWFX2hQrl8SMqopK4BcFsAEjiIf+C1vwuklRX8E\neLoo2YAystsdoJ6kCXtyePSvh/4l/CY/HL/gpT4u8D/aYLOLV/Gd2k8txcCBRAjvJMA5VsOY0cJw\ncsVHevoX/gpz+wv8Ovgv8KdE+IPgS1bw4bGeDR7zTIwXjvd+4rOWY5SQbWDEZDArwuDkA9C/4Kgf\nte+CPE37Omn+DdBNxrsfjyxtdZ0vW7RkNqkcV2hdJATvWQeWylNuVbhsEEV4h/wR/wD2g9H+G3xJ\n1f4d3unX1zqfjq6tlsrq32eTAbeK5dvM3MG5DYG0Hn078F4Ws9K+IX/BMzxpqutaFptzrfgDWLbS\n9A1ZUZbm3t7q8inmUndhstPIB8vAb2r0j/glB4A8Pap4T+Mnja60qCbxZ4UtIrjQ9VcEy2DvbXm5\noxnGTtHUHpQB9oftAf8ABT/4QfAbxPP4bWW/8Za5ZTNBqFtoKKyWjBVbBlcqjn5sEIzFWVg2CMVx\nfw6/4LDfCTxz420fw/faNr3hW31CcQHV9VEBtrdiDtMhSRmCltq5xhd2SQoJH55/sDfDHRP2mP2t\n7Kw+IUc2uWt1Hd6zeqz7ReTqfMPm8cqzMdwGM+or3D/gqn+xz8PfgRpfhrxv4Fji8PpqV4umT+HY\nTmPiJ3FxHltwA8vawwRlwcjoQD9BP2sv22/Cn7I1p4Tute0XVfEFv4j+0m1k0cxMqiEREkl3XIYT\nLgjPQ+1eC33/AAWg+ElppmmXEHhnxReXFzG7XFokUKtZMJGVVdmcBiygOChYAOAfmBA+Avj58TJf\niR+xh+z7FPavBJ4d1HXdEM8lwZmudi2MokOQNv8Argu0k4Cg55wPpP8AYT/4JofDf9oD9nzSfiB4\nz1bXHv8AV7i58i10qdLdLeOGZ4drblcuSYy2flwCBjjJAP1osbkXtnBcKGVZo1kAbGQCM4OO9WKh\ns7ZbO1ht4yTHEixrnrgDHNTUAFFFFABRRRQAUUUUAFfmx/wW6GfhT8NuM/8AE6uOg/6Yf/qr9J6i\nmtobkATRJKFOQHUHB/GgD4r/AOCQpx+x1YgZ/wCQ3fcf8CSvjT/gtPhv2mvCpPIHhGDp/wBfl3X7\nNyG20y0kkYxWltEpkdzhERQMkk9AAM81xUvxl+GNw4abxz4SkccAvq9qSPzf60AfjN+38P8Ai1H7\nK2OcfDm0zkf7EX5V9mXY2/8ABGgA5OPCacEYx/pQr7Mk+MXwulVVfxx4RdVG1Q2rWpAHoPnqT/hd\nXwz8ryv+E88J+V02f2xa7fy30AfmF/wRGG34ifE/nH/EqtOv/XZ6+ev+CcKBf28vh2DlQLzUMcf9\nOVzxz/nmv3Ag+M3wwts+T468JRZGDs1e1X+T02L4xfC6Bw0fjjwjGw4BXVrUEf8Aj9AH4xftY/Aj\n4jfsZftOX/xE8N6dqY8Pw6qmr6P4nnt0ntxLKzMYpGC+WH3iVRGwzt2nHINJrX7Tv7Tn7b3xVt4f\nBF54h0mREhhXSPBl3c2llZq7qhnndHyFLnLSSEhRxkAV+0cvxr+Gk6bZPHvhSReu19YtiP8A0OmQ\n/GX4YW5Ji8deEoieCU1e1GfyegDk/hj+zfoVp8NfCum/ErR9C+IvjHTNOjs73xDrWnx31xcspY8y\nzqzsBuPU1T+MX7IXw6+IHwt8VeG9E8C+DtB1jVdNntLPVE0C3VrSZ0ISUFEDAqSCCCDxwa77/heP\nw4/6KD4W/wDB1bf/ABdJ/wALx+G5/wCageFv/B1bf/F0Afg54c1f43f8E7vi3JeNpU3hbWJ1eylG\noWSy2uqWsc0byJFI6kNEzImZIyDg8MKs/GD48fGj/goJ480/S30iXXpLAy3Om+H9A07ctjG4iWV8\nqpkKEpGS0jMFz1Ar91Z/jP8ADG52+d478JS7em/V7U4/N6SL4yfC+Bt0XjnwjG3TKavag/8AodAH\nwN8ef2fLv9mr/gk5qXhDWI9PPiMXVldapPYxqPMml1GOQKzjmQxqwj3c5CDGBXC/Bzj/AII0fE0Y\nP/IRn/8ASq0r9N5fjZ8NJkKSePvCkiHqraxbEf8AodMHxn+GKwmEeO/CQiP/ACzGr2u38t9AH5Rf\n8Emvh3Z/FrQvj/4Mv7i4s7PXtAtdOlnttvmIsjTqSu4EZ5714D4W8QfG3/gnX8XZLptJm8M6pNm1\nnj1GyWS11a1imRnWORlO6JiijzIiDg8EGv3ch+M/wxtiTF478JREjB2avajP5PSTfGb4Y3BUy+O/\nCUpXoX1e1OP/AB+gD8Vf2c/gn8Rv25P2mofHuv6VqB0C91Y32teIrW3EFtEYth8qNyuwyAGIbBli\nuWweTW9+3V+z78Qv2Zf2otd+K3hjTtUPh2TWU8Tad4na3W4htbyWYTMkh2bE23DOqo45UKecmv2O\nh+NPwzt02RePPCcSZztTWLUDP/fdLL8a/hpMhSTx74UkQ9VbWLYg/wDj9AH4t/Ef9uX48/th+N/B\n3hzwpJf+Fdd3S2lvY+C9RvLQ3zybSWmCy4OwRsdxwFUuTxzX7Ifs0+BvFXw1+B3hTw14216fxP4q\nsbdxqGqXE8k7zSPK74MkhLNsDBMk/wAHGBgVfi+MXwugYNH448IxsOhXVrUEf+P1Y/4Xj8OP+ig+\nFv8AwdW3/wAXQB4J/wAFThn9h34gdf8AWab/AOnC3/z/AJNfjJ8Cf2pfib+zWmsr8PPEI0FNZ8k3\nu6xt7nzTFv8AL/10b7ceY/T1r+hOX42fDSdCknj7wpIh6q2sWxH/AKHUH/C3vhX/ANDt4P8A/Bra\nf/F0Afgw/hn4+ft1+Oh4nk0vXPHd/cXEWlSauloUsbNgq4jZkUQwKAwduFHzFjyST+jv7RX7AGs/\n8MG+GPhj4FNvf+IPC9/Hr13bxtIRq1yIZY5/K3sdrMZSyrwDtKgDIr7Qh+NPwztk2xePPCcS/wB1\nNYtQP0en/wDC8fhv/wBFB8Lf+Dq2/wDi6APw++F/7bHxy/ZD8IeJfhXsn0+XG23tfEFu/wBr0CV1\nyWgR/uhgytsYFQfmCgsxb6W/YG+Dn7QvxZt/FHiXxn46+IOiaJdeHbu18P3Gtavdtb3Nzd2rRw3P\nlSPudEWZZUdMfMqkNkV+kkvxi+F0zl5PHHhGRz1ZtWtST/4/Uy/G34aogVfH/hVVAwFGs22APT79\nAH4ka94w+M/7I3xbWf4x+F2+JLRWt1pulQ/EQz6rprKZIHkns2kYqSNsYLJ2fB9vPPA/wj8Yftjf\nGTW5Phr4C0/SnlI1C40zTpHi06wBAzh5Wbyw7KxVScAsVUAAAfvvP8Z/hjc487x34Slx036vatj8\n39h+VJB8ZvhjbbvJ8deEot3XZq9qM/k9AH5Nf8FNv2TvGHw9+Luu/GLwxoM9p4Pubm3urjVLG7aS\naDUJDuknZQd8SmXADcKGZQMZArw3xz8fPjp+29D4Q8BNFqXip9DsuNN0e2Z3u3DYN5cqgO5grRpu\nOFUDIALuW/dqX42fDSaMpJ4+8KSIeqtrNsQfw31HD8Y/hfbtui8c+EYm9U1e1B/9DoA/KD9tT9nu\n8/Zp/YT+Dng/Vksf+EhPiC5vdVm0+MASTyRSMFdx/rGjVliDnqEGOMCvr3/gj/8A8mfoO/8Ab99n\nPriOvqSb40fDK5AEvjzwnKAcgPrFqcfm9LD8avhnbpsi8e+FI067U1i1A/8AQ6APw8/4KQr/AMZ5\nfEQ84+16eec8f6DbVv8A/BWcg/tn6+eo/svT/wD0QK/aCX4x/C6V9z+OPCLserNq1qT/AOh+1fk/\n/wAFEPgZ4o+OX7TmseLfBE3hvXdAuLC0hjvU8V6XCGdIgrjbLcqwwRjkc9s0AeO+L/22/jTpfwGv\nvgN4qRoYgyw3F1q9s39prZsqulq5f+DlWDkb8EANjAr7d/4JA/sz+MPhlomt/ErxHB/ZWneKrGKD\nTdOnRhcSQq+9bhh/Cj/w5GWGGHDDP2Z4Q+Kfw2sPCWi2l/4z8IpeW9jBBOr6xaOQ6xqrAkOQcEYy\nDW+vxv8AhsihV8f+FQoGABrNtgD/AL7oA/n98KeJvG37HX7R2neKtR8JzWHiXR5pry30nxDbSwCS\nOWOWJXZQVbbh2IIODgdq+jf+Ci/7OPjXUfGelfHbQ9C1HU9E8U6FZa3rN1aRedDpd2sCCRSACY4R\nGsTBpCQSXGeMV+t8/wAZfhfO4aXxz4Rlbrl9XtSeP+B10/h3xNoXjDT5bjQ9W0/XLFG8h5tPuI7i\nIMACUJQkZwwOPcUAfgh8Tv28fjh+0p4C0/4X6xfxavBqMqW09vY6ZCtzq05uUkt1IRPlZWVFVYgu\n7HzbiTX078Lv2YNf+Af/AATb+Ouu+LrC/wBF8ReK7OBm0m9AVoLaCQeUzJgOjsZpNytyAq8Dmv1h\nXTbRGDLawKwOQRGMg/lU8kaSoVdQ6nqrDINAH5E/8ERV2fEf4n9R/wASm06j/pq1fruKigs4LXPk\nwRw54PloF/lUoGKAFooooAKKKKACiiigAooooAKKRjjviheaAOW+Kfw90r4r/D3X/CGt24utK1i0\ne2niLumQRkcoQ3BAPB7V+AXxJ+DXxf8A2FPi7b6q8Wo6Fd6TqG3SfFNlE32O+wNwMbkFHDIfmibP\nBZWU4Ir+ieopraG5AEsSSgHIDqDg/jQB/Oj8Rv2gvjf+2Rq2g+F/EGran44vYZZH07RdNsI4zJLs\nJZhBAih3CK3zEEqu/GATX6X/AAL/AOCdkA/YU1f4feJbJNM8deK2OrXE15gmwu1G21RmiCsyRqoc\nxsW2vJJwc4r78i0+1gcPHbQxsOjLGARU+KAPwA+HHxJ+OP8AwTb+KV1DeaJc6Yt2Jop9G1iKQ6dq\nYQlPOiII37W2kSRkEjjO1iDqfBv4OfGX9v8A+PEHjPWYbq+0uW88y/8AEWpW7HTbeOFlY2sfGwnD\ngCJecMWPG41+809nb3JBmgjlI4BdAcfnToYI7dNkUaRLnO1FAFAH41f8Fn7C20744fDyzsraK1s7\nfwqkMNvboEjiRbmYKqqBgKAAAAOg4r7y/wCCjoH/AAwj8Rfmz/oVjg/9vttX1DNZW9wwMsEUhAwC\n6A8U+SJJUKOiuh/hYZFAH4uf8EwPhpd/GL4YftJ+CLC9i06817RLCyiurhSyRszXQywBzj6eteGf\nCr4r/FH/AIJ3fHbXYG0W3ttegifTtS0jVoS8NzFncjB1wxTIWRXRgGAHUGv6FIbWC2z5MMcWeuxQ\nM/l9TTZbC2uGLS28UjHqXQE0AfzQfG/xp48+JXjd/F/xDfUJtZ123W/t5r6MxrJasW8swLgARZDB\ndvy8HvmvpH/gpaM3fwB4Jx8MdK5wfWSv3NfT7WTbutoW2jAzGDgegpZLG2lKl7eJyoAXcgOB2xQB\n+YH7UPwq8T/E/wD4JafBObwzpcur/wDCPWGm6vfwwcyparYyRu4Xq20yAtjooY4wDXA/8Emf2hNZ\ngbxJ8GF02w/sCew1LxEdQO8XQm8qCLZ97bswoP3c+9fsCIkWMRhFEYG0KBxj0xUcVjbQMWit4o2P\ndEANAH4Of8Ephj9tvwbjAP2TUcA/9ectVf2hPgh8Tv2Ef2hrnxn4ctdUsdIstRaTQfFdxax3EEhm\nSTCs2wxeYVEuYyMgDOO9fvbFYW0DBo7eKNhwCqAGnzW8VyoWWNJVBzh1BGfxoA/ns+Lf7Qnxs/bv\n8S6PoFzYz+Jp9PQz2GhaDpis8bCGNJ5fkXed5Te24lVLnaFGBX0v+3D8Cp/2df8Agnl8HfBd9LI+\nqw+Ilu9QjkkSQRXU1pcPNGjIMFVcsqnuO5r9eYbG2t23RW8UbdMogBp81vFcKFliSVQcgOoIz+NA\nH58/8En1x+w34x4x/wATrVM5H/Tnb18S/wDBKAFP21vCmQRix1HPH/TrJ/n/APXX7wRW8UEeyOJI\n067UUAflTIrG2gcPHbxRuP4lQA0ASZOev1A5r8YP+C1A3ftL+EQOSfCUAGO/+mXfSv2hqKeyt7k5\nmgilOMZdAePTmgD+fXwh+3n+0RF4R0f4beGvGNyNNNnFoGnadYaXam58sqIYoopFi83zMFVVg2/O\nCDnmvdf2E/8AgnF4y8X/ABQsfFvxc8KatoPhXTnkuEttRc2l3dXiNG0YkjbEvlnc7bxjJTGetfsk\num2aMGW1gVhyCIwCP0qxQB+Kn/BSn9k/xR8CvjPqfxh8HRXFn4T1jUE1FdQ0yeYT6VqLkNIzvuLI\nXn3SKwIALhFxtGeC8Zft7/tEftL2Pg/wLoN9qltrdqDn/hDPOt9Q1iZYjl5PJIY4QSMUTCdSV+Vc\nfvNLDHOmyRFkQ/wsMioorC2gcPHbxRsOjKgBFAH5SfG34FftN+Cv2S/hpd6R4j+IeteMl1C+1HxJ\nHaa7cXF9aiZIEghURtvaJFgZiCW2NJJ68fG1n+1WsvwX1LwDr/w28I+Kb6+a5nl8YapBMdce4lla\nYTtdLIrsyuwIySGAw2QSD/RXjNVhpVkP+XO3/wC/S/4UAfj9+wt/wT48e+I/CHxN1Txnp174Q03x\nD4bm0CxsruIwXtzI80FysoWQfJGDbou4j5vMOPumvmTwb4p+MP8AwT9+NMN5Lpl94b1iPIn03Uon\nFpqtssjIcjgSxFlcLIpyOqkHmv6JQoAwAAKims7e5IM0EcpHQugOPzoA/CL9kz9nv4rftaftGaT8\nSb6xmh0l9c/t/VPFF1Z+VZyyxTiWSOPaFVpGf5dq9MkngGsT9usZ/wCCgXjk+mt2XOD/AM8Lf2r9\n+4oY4UCRosaDoqjAFRPYWskhd7aFnP8AEyAn86APMf2qct+zH8WvX/hEtWxn1+xy1+Pv/BLoAfFb\n4pEnaP8AhXGr4yccb7fn/P8ASv3WZFdGVlDKwwVIyCK5nx9oLaj4C8TWGm2cb315pd1bwxoFQyO8\nTBVzwOTjqaAP5uvg74o8ceBPG8Xif4ey6hb6/okMmpNPp8ZkMUEQ3StKoBDRAD5ww246ivWPHvxC\n+Nf/AAUR+KlpENJm8R39oXFlp2k2IWHSreaZR88irkRqzIDJKxwOSetfVf8AwTj/AGLPjL8Ev2l7\nHxN448DTaJ4fXSru3kupb21mUO6DapWOVm5xjp9a/WO3tILZmMUMcRPUogH8h7UAflj/AMFFP2S9\nV8D/ALHfwjj0WW51Ox+HMdxb6jEYTLLtuvLd5mZBtWON4mUkj+Nea8w/Yb+Oeu/G3wDH+yZc6F4f\nTw/qmiazHa61eQSTXFvcvHPcRTFSxT5JWBG1QRtBBBGT+0TRo6FGUMp4KkZFQxWFrA4aO2ijYdGV\nACKAP57tHn+N3/BOz4wnUf7Lm8MazMklgJr6yWa11S1SWJ5UidlIdGZIgXjIIyMEVoeD/BPxY/4K\nUftD399PdPcXEzLJfajMGNlo1nuISNQOFUAMFQYLkMeSWav6A57SC5AE0McoHTegbH50sNpBb58q\nGOPPXYoGaAPxj/4K1+ArP4XT/A3wfp0895YaB4VbTIJ7nBkljhZEVmKgDJC84x7V+jn/AAT7GP2M\n/hUPTSQCD/11evf5rO3uf9dBHL/voD/OpI41iQIihEHRVGAKAHUUjHAznFAoAWiiigAooooAKKKK\nACiiigAooooAKKKKACiiigAopGz2ri5fjZ8PIJXil8feGI5EYqyPrFuCpHBBG/rmgDta4j44nHwX\n8fcE/wDFP6hwO/8Ao0lH/C8fhx/0UHwt/wCDq2/+LpG+N/w2cYb4geFSPQ6zbf8AxdAH43f8Efxj\n9r+Md/7Avv5xVf8A+Cu/wq8UaD+0tfeObzS5V8La7b2tvYaknzRPLFbqrxsR91wUY7T1Az0r9fIv\njJ8L7dg0XjnwjGwGAU1e1Bx/33TpfjT8MrhAkvjzwnIo6K+sWpH/AKHQB+FP7QPx38f/AB0+DXhW\nT/hXdp4G+GeiX0tss3hixntdKvb9405m+YxNOqxtg/eAd/WvU/2bfhf8VPiH+wx4iv8A4S67q+la\n54d8ZXV9dWOiXk9tdahCbC1GyMxMC7LtYhOSxOBya/YH/hcnwv8AK8r/AITnwj5ed2z+17XGfXG+\nnw/Gn4ZWy7YfHnhOJeuE1i1A/R6APwRh/aB1/wAFeEvG/wAOfiF8PdD8VaxqF1J9s1LxTYuuu2N3\nuJdvtSlZy/mBSRIx+7tPysVP0b/wSm+Bnjiy+ImufEa98OXtj4MPhm8todVu4jFHdvKcDyCceaAY\nXDFc7eM4yK/V1/jD8LZGLP438IMx6ltWtST/AOP1Knxr+GkcYjTx74USMDAVdYtgAPpvoA/B/wD4\nJ/8Awk8LfG39qPwx4Q8aaZ/a3h+8t715rQTyQ72jtpJE+eNlYYZQevbnio/iZ4G8a/8ABP79qS1+\nx3dk2t6JIup6XdxqJYpreTeo3I2SMrvUg8jqMcGv3gj+MfwvhcPH458Io4GAy6vag/8AodLL8Zfh\nhO4eXx14SkYdGfV7Un/0OgCH4AfFyx+O3wa8K+OtP+WLWLMSyIInjVJ1YxzKqv8ANtWRHAPcAHnO\na/M3/gt583xB+F/U40u8z7fvY/8AP41+ncXxr+GkEYjj8e+FI0Xoq6xagD8N9Mn+M3wxuSpm8deE\npSvQvq9qcfm9AH4Q/Dr/AIKC/Hn4T+CdK8JeF/GyadoGkxGG0tTpNlN5S7i2N7wszcsepPWvQPgn\n+xZ8Z/2zPi8vib4h22v6PomsFNV1HxXrFq8ZubeTDKLXeNrEqQECjai7eAoAr9mv+FufCvj/AIrX\nwfx/1FbT/wCLqyvxv+GyKFXx/wCFVUDAA1m2AA/77oA/OH/grP8Ash+KvEninQfiZ4N0fVvE8IsB\npusRWkfntaLCm6KXYi7tpXzd7n5QQnTdz8w6h+358e/it8O/Bfwo8PX97Z31oUs4bvwsslvqmqbc\nLBETEc/KAFxGF3Y+bJr9vD8b/hsQQfiB4VIPGDrNt/8AF1Xj+MHwtidXTxv4QRl5DLq1qCP/AB+g\nD4C0P9mX9pO5/Yd1q91Px341/wCFlvf2/iHSNHTXbkX0cKoUktZWZywYxu0oiUhvMRVIzwPinwj+\n074z+Br+MfC/j74d+HvGniG+mLXU/wAQtI+0atYztHtYmZ8TElTGcOx27Rtxk5/dv/hePw3/AOig\neFv/AAdW3/xdQSfGH4XSuXfxx4Qdj1ZtWtSf/Q6APyY/4JI/AzxzP+0Do3xHPhy8g8E2+n3iDWbm\nIxwTs6PCFhYjEpEikMFztwc4ryL4ifB/4v8A/BPn48af4zbTWKaVqRbTPEgtGk029MiSZjy2QGeL\nzAUzvUZIIIBr9yY/jZ8NIUCR+PvCiIOirrFsAP8Ax+km+NXwzuF2y+PfCkq5zh9YtSP/AEOgD8QN\nXT4yf8FPvjtdazZ6FbXF3p9hDbvHagW9nptorsyxmV85ZneZxvLMSWxwuBS/4KL+CbX4b/tKyeD7\nGea6sPD3h3RdJt7i4x5jxw2EMas+0AbiFBOAOTxxiv3Kh+M/wxtt3leO/CUW7rs1e1Gfyekl+Mfw\nvmffJ458IyP/AHm1e1J/9DoATVxj4GXoPA/4Rx+vP/LrX4x/8ElFK/tn6FkHP9lah+H7k/5/Kv2j\n/wCF3/DbaF/4T/wrgcY/tm2/+Lqunxk+F9sytF448IxsOAU1e1H4cPQB+Cn7W+uXvhf9tX4ma1pk\n/wBn1PTvF1xd2sxVW2Sxz70bawwcMAcEEcdDW98dv2t/i5+3FrPhTwldW4cRhbe18P6FE6pfXZJz\nPImSXkIAwPuqASoXLE+rfEP9nvxfr37ceo+P7M+GrjwfP42TWE1E+K9J2tZi7WQy7Gud+NgJ2lc9\nsV+u8Xxh+F0Thk8b+EI3XBBXVrUEf+P0AfCXjT9inxF8G/8Agmd4h8CaVoc/iH4i61d2Oo6raaIk\nl08swvIm2KgJz5UShSyAA7C2OSa+X/8AgmP8YdZ8JfFi9+Dw022/sr4iu1hqV3OrrdWQitrrmLna\nG+Zs7geg96/ZX/hePw3/AOigeFv/AAdW3/xdQR/GL4XROHTxx4RRwchl1a1BH/j9AH4ffHr4E/Ez\n/gnd8dbDV9B1K8t7NZvtHh/xNbr+7uoxgmGUcqXXo8bcMMHG1hWT49+Ifxr/AOCiHxUtIv7Jl8Sa\nha71sdP0qwCwaXbTTKpLuq5ESsUBklY47nvX7tTfGj4ZXIAm8eeE5QpyA+sWpx+b0kHxl+GFsxaH\nx14SiY8Epq9qM/k9AH5K/wDBRb4JP+z5+zl+zj4JuVsn1SwXWGv7nT4fLjuLhvshZ27s2NqljyQg\n9gPvL/glZ/yY94D6/wCv1Lqf+n+eveJvjN8Mbjb5vjvwlLt6b9XtTj/x/wBqfF8bPhpAgSPx94Uj\nUfwrrNsB/wCh0Ad1RXEf8Lx+HH/RQfC3/g6tv/i6P+F4/Dj/AKKD4W/8HVt/8XQB29FcOfjj8Occ\nfEDwtn/sNW3/AMXXaQSpPEksbrJG4DK6nIYHoQaAJKKKKACiiigAooooA4z41AH4OePARkHQb8EH\n/r3ev50/gB8AvFP7SfxAXwd4PFk2sNayXg+3z+TH5ceN3zYPPzDtX9Fnxp/5I747/wCwDf8A/pO9\nfjL/AMEfxn9sS3/7AV9/7ToAkP8AwR7/AGgCeI/DA/7ix5/8h0n/AA56/aA/ueF//Bsf/jdfuTS0\nAfhr/wAOev2gP7nhf/wbH/43R/w56/aA/ueF/wDwbH/43X7lUUAfhr/w56/aA/ueF/8AwbH/AON0\nf8Oev2gP7nhf/wAGx/8AjdfuSxwKbuwevtigD8OP+HPX7QH9zwv/AODY/wDxuj/hz1+0B/c8L/8A\ng2P/AMbr9yR3pCccnigD8N/+HPX7QH9zwv8A+DY//G6P+HPX7QH9zwv/AODY/wDxuv3IU9aU0Afh\nt/w56/aA/ueF/wDwbH/43R/w56/aA/ueF/8AwbH/AON1+5CknNLQB+G3/Dnr9oD+54X/APBsf/jd\nH/Dnr9oD+54X/wDBsf8A43X7knNIpzn2oA/Df/hz1+0B/c8L/wDg2P8A8bo/4c9ftAf3PC//AINj\n/wDG6/cqigD8Nf8Ahz1+0B/c8L/+DY//ABuj/hz1+0B/c8L/APg2P/xuv3KooA/DX/hz1+0B/c8L\n/wDg2P8A8bo/4c9ftAf3PC//AINj/wDG6/cqigD8Nf8Ahz1+0B/c8L/+DY//ABuj/hz1+0B/c8L/\nAPg2P/xuv3KpM80Afht/w56/aA/ueF//AAbH/wCN0f8ADnr9oD+54X/8Gx/+N1+5BOP/AK1GcdTQ\nB+G//Dnr9oD+54X/APBsf/jdH/Dnr9oD+54X/wDBsf8A43X7j5OOevt3py9OuaAPw2/4c9ftAf3P\nC/8A4Nj/APG6P+HPX7QH9zwv/wCDY/8Axuv3KooA/DX/AIc9ftAf3PC//g2P/wAbo/4c9ftAf3PC\n/wD4Nj/8br9yqKAPw1/4c9ftAf3PDH/g2P8A8bpf+HPf7QA/g8Mf+DY//G6/cmigD8Nv+HPf7QJ/\ng8Mf+DY//G6T/hz1+0B/c8L/APg2P/xuv3KooA/nk/aI/YM+KH7L3gqz8U+NV0YaXd36adF/Z9/5\n0nnNHI4ypVeNsbc/Sv0a/wCCKwx+zF4qxgf8Vdcf+kdpTv8AgtOcfsv+GOuf+Ettv/SS7pn/AARW\n/wCTYfFP/Y23H/pJaUAfoFRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAHjf7ZF/daV+yp8V7yyuZrO8\ng8OXskVxbyGOSNhESGVhyCPUV+Dnwyl+Ofxk1a70zwTqnjTxPf2sBuZ7fTtSuHeOPcF3H5+BuYD8\na/dz9tj/AJNH+L3/AGLN9/6KavzU/wCCJo/4yA8c/wDYsN/6VwUAeKf8M7/tgH/mXfiV/wCBdz/8\nXR/wzv8Atgf9C78S/wDwLuP/AIuv6AaKAP5/v+Gd/wBsD/oXfiX/AOBdx/8AF0f8M7/tgf8AQu/E\nv/wLuP8A4uv6AaKAP5/v+Gd/2wP+hd+Jf/gXcf8AxdH/AAzv+2B/0LvxL/8AAu4/+Lr+gGigD+f7\n/hnf9sD/AKF34l/+Bdx/8XR/wzv+2B/0LvxL/wDAu4/+Lr+gGigD+f7/AIZ3/bA/6F34l/8AgXcf\n/F0f8M7/ALYH/Qu/Ev8A8C7j/wCLr9/2OCKTORgH2696APwB/wCGd/2wP+hd+Jf/AIF3H/xdH/DO\n/wC2B/0LvxL/APAu4/8Ai6/f8EnNKTQB/P8A/wDDO/7YH/Qu/Ev/AMC7j/4uj/hnf9sD/oXfiX/4\nF3H/AMXX9AAzk0tAH8/3/DO/7YH/AELvxL/8C7j/AOLo/wCGd/2wP+hd+Jf/AIF3H/xdf0AE4poy\nMZ7DpmgD8Af+Gd/2wP8AoXfiX/4F3H/xdH/DO/7YH/Qu/Ev/AMC7j/4uv3/U5Hr7+tOoA/n+/wCG\nd/2wP+hd+Jf/AIF3H/xdH/DO/wC2B/0LvxL/APAu4/8Ai6/oBooA/n+/4Z3/AGwP+hd+Jf8A4F3H\n/wAXR/wzv+2B/wBC78S//Au4/wDi6/oBooA/n+/4Z3/bA/6F34l/+Bdx/wDF0f8ADO/7YH/Qu/Ev\n/wAC7j/4uv6ACcYpoY/j3ANAH4A/8M7/ALYH/Qu/Ev8A8C7j/wCLo/4Z3/bA/wChd+Jf/gXcf/F1\n+/wb3z6kUc+tAH4A/wDDO/7YH/Qu/Ev/AMC7j/4uj/hnf9sD/oXfiX/4F3H/AMXX7/Bv85z+FOFA\nH8//APwzv+2B/wBC78S//Au4/wDi6P8Ahnf9sD/oXfiX/wCBdx/8XX9ANFAH8/3/AAzv+2B/0Lvx\nL/8AAu4/+Lo/4Z3/AGwP+hd+Jf8A4F3H/wAXX9ANFAH8/wB/wzv+2B/0LvxL/wDAu4/+Lo/4Z2/b\nAI/5F34l/wDgXcf/ABdf0A0UAfz/AP8Awzv+2D/0LvxL/wDAu4/+LpP+Gdv2wB/zLvxL/wDAu4/+\nLr+gGigD+f7/AIZ3/bA/6F34l/8AgXcf/F0f8M7/ALYH/Qu/Ev8A8C7j/wCLr+gGigD+f7/hnf8A\nbA/6F34l/wDgXcf/ABdH/DO/7YH/AELvxL/8C7j/AOLr+gGigD+f7/hnf9sD/oXfiX/4F3H/AMXS\n/wDDPH7YA5/4R34lf+Bdx/8AF1/QBRQB/NP458T/ABg+GXii68OeKvEXi7QtdtAhnsbzU7hJY96K\n6kjf3Vgfxr+k/TSTp1qSSSYlJJ78CvwR/wCCpmP+G5fiFj/nnpuf/BfbV+9ul/8AINtP+uSfyFAF\nqiiigAooooAKKKKACiiigAooooAKKKKACiiigBD2r+abSfhhrPxl/aDu/BPhxbc61rGs3UFoLqXy\notweRvmbBwMKa/pZ9K/AH9jf/lIh4Q/7Gi7/AJTUAdqP+CPn7QDdI/C+P+wsf/jdH/Dnr9oD+54X\n/wDBsf8A43X7kgc0tAH4a/8ADnr9oD+54X/8Gx/+N0f8Oev2gP7nhf8A8Gx/+N1+5VFAH4a/8Oev\n2gP7nhf/AMGx/wDjdH/Dnr9oD+54X/8ABsf/AI3X7lUUAfhr/wAOev2gP7nhf/wbH/43R/w56/aA\n/ueF/wDwbH/43X7lUUAfhr/w56/aA/ueF/8AwbH/AON0f8Oev2gP7nhf/wAGx/8AjdfuVSGgD8Nv\n+HPX7QH9zwv/AODY/wDxuj/hz1+0B/c8L/8Ag2P/AMbr9Gv2Sf2xtc/aN+MHxZ8G6noFhpFr4Mu2\nt7e5s5XeS4AuJosuG4BxEDgepr55+KH/AAVQ+Jvhj4/+Lvhn4U+Fmn+KrzSNXu9OtIrRrme6uVgd\nst5cYJJ2IWOBwAaAPmv/AIc9ftAf3PC//g2P/wAbo/4c9ftAf3PC/wD4Nj/8br6V0j/gr7r3gHVb\n3TfjJ8HdY8Mai0MU9jaWcUlvMyEuGaRLnadpKgKVGMhgelfbv7Of7S/gz9qDwU3iTwZdXEsFu8cF\n7b3Nu8T2lw0ayNCSwAcrvALIWU9iRQB+Rv8Aw56/aA/ueF//AAbH/wCN0f8ADnr9oD+54X/8Gx/+\nN1+4+T2P51+cvjn/AIKC/tI+HPGHiDTNP/Zu1PUNMsL+4tra9GlakwniSRlSTKx4IZQDkcc8UAfK\nX/Dnr9oD+54X/wDBsf8A43R/w56/aA/ueF//AAbH/wCN17P4N/4K7/Gb4iX09l4V+Clp4nvII/Ol\nt9HjvLqSOPIG9ljBIGSBnGOa+yP2MP2jfih8fv8AhLv+Fj/C+7+HA0r7J9g+1WlzB9s8zzvMx56j\nOzy4/u/3+ccUAfmh/wAOev2gP7nhf/wbH/43R/w56/aA/ueF/wDwbH/43X7lUUAfhr/w56/aA/ue\nF/8AwbH/AON0f8Oev2gP7nhf/wAGx/8AjdfuVXjH7YHx11D9mz9n/wAR/EHTNMttYvdLe1RLO7dk\njfzbmOI5K88CQn8KAPyc/wCHPX7QH9zwv/4Nj/8AG6P+HPf7QH9zwx/4Nj/8br9gP2bvilefGz4G\n+DfHWoWMOm3muWK3ctpbszRxkswwpbnHAr0qgD8Nf+HPf7QH9zwx/wCDY/8Axuj/AIc9/tAf3PDH\n/g2P/wAbr9yqKAPw1/4c9ftAf3PC/wD4Nj/8bo/4c9ftAf3PC/8A4Nj/APG6/cqigD8Nf+HPX7QH\n9zwv/wCDY/8Axuj/AIc9ftAf3PC//g2P/wAbr9yqKAPw1/4c9ftAf3PC/wD4Nj/8bo/4c9ftAf3P\nC/8A4Nj/APG6/cqigD8Nf+HPX7QH9zwv/wCDY/8AxulH/BHv9oAHlPC//g2P/wAbr9yaOtAH8zPx\n4+BviX9nb4jXngnxatmutWsMU8gsZ/Oi2yIHXDYGeD6V/SP8PxjwH4c/7Btt/wCilr8Pf+CtPH7a\nfiT/ALBun/8ApOtfuF4A/wCRE8Of9g226/8AXJaAN+iiigAooooAKKKKAOM+NPPwc8d/9gG//wDS\nd6/GX/gj9/yeHb/9gG+/9p1+zXxp/wCSO+O/+wDf/wDpO9fjL/wR+/5PDt/+wDff+06AP3NooooA\nKKKKACvzW/4KE6tf2P7d37Mtta3tzbW8+oWAlhimZUkH9pRghlBweOOa/SmvzM/4KKD/AIz3/Ze/\n7CFh/wCnOOgDC/4Lb63qOkan8HxY391YiSLVi/2eZo92Gs8Z2kZ6034ff8EvPHE/gHw58QfCXx21\nK08VT6Zba1ptpcWUkcUdy0SypG0wnfChjgt5Z9dp6VT/AOC5P/IT+DnP/LHVv/QrOv0f/Z/A/wCF\nDfDbj/mWtN/9JY6APzL+G37Zvxz/AGSf2jrbwF+0Lrc2peFbi8K3upXNublhF5bpHPayjZmEuY3b\nKltoPy7vlP60adfwatY217aSrcWlzGs0Mq9HRgGUj6gg1+TH/BcEkeOfhUMnH9m33Hb/AFsVfqX8\nL/8AkmnhL/sEWn/olKAPnP8AbC/YWvP2rvF+g67b/Eu/8EJplibL7LZ2TTrPly+8kTx4PzY6Hp1r\n8sP2lf2ftb/Z8/aS0P4Vp8RdW1xNTSxb+1WV4DH9olMf+q81s7cZ+9z7V/QFX4z/APBSX/lI34J/\n65aJ/wClTUAexy/8EcNUjiZj+0BrOVBb5tIYDj/t7/Xnr0rtf+CXmn2nwu+FXxO1PV/iXoHjTTbW\n6jvbrUNHvLu5jsYo4HZ/M8+GMg7QzfKDwPpX3xfgGxuP+ubfyNfkv/wTQAH7Gv7Tntp91/6bp6AP\nvK5/bz+AVroVlrD/ABQ0cWF5PLbQy4lJaSMRmQbAm4YEsZyQAd3B4Nb/AIZ/a2+D3jHwlrHibSPi\nHo1zomkh2vbhpjG0QSPzGwjgO2F54U56DJr8s/8Agmz+wJ4c/aV0XWvG/j43reG7C9WxsLKxuljW\n8lCMZ1m43qF325UqRk7s5ryf4Dfsp+Gvip+2vrfwa1HVtUtvD9hqWr2cWoWjRi6ZbRpRGSWQplvL\nGfl78YoA/aX4XftZ/CT40+Jn8PeCvHem69rSQNc/YofMSRo1IDEb1GcZGQOcZ4713/jXxxofw58M\nah4i8Tarb6LothGZbi9uiQkaj2HJPsMn61+PH7VH7K+kfsg/tdfCO+8Bareafo3iTWIZLWyWZ/O0\n/ZcQRyoJc7mRlmIGTnG4EkGrP/BUT47z/Ez9p+3+Feqa/e+FvAPhpreDUHWP7TE1xIole78pAHk2\nxyRoEZjgoxXG45AP0p8G/tvfAv4geI7XQtB+JWkXmq3QcwwOZIQ2xC7fO6hR8qseSOle4xuJEDKQ\nykZDDoa/BP8AaJ079kOf4VwyfCbV/FMXjnTrWC3ihu7aQQaq/mxiWa4MgIR/L8xgIti5wNvNfcX7\nLf8AwUM+H/ww/ZK+HM/xIvtYtLwyXmiw3TxSX8l49oIGkkLAZUYuowA3PB9KAP0KckAY/wD11+MC\nal44/wCCon7XOs6PpnjW68JeCtFt7q7sd8TJLa2KyJGpECSYeZnaENlwMAkHgKf0Q+C3/BQP4O/t\nAeP7TwZ4O1fUrnXbqKWaKO502WFNsaFmO5hgcA18Q/F7/gmz8ZPg98UtV8cfALWUh0ySe4vYLDS7\n+S3uLKFXWSO1O92NznA+U5DFQCORQBiftGfsA/FX9krwnbfE7wJ8UtV8VHQZGur9xG1nNYooBWdF\nM0iyKPm3A4xxwwJx3njX9pLV/jr/AMEl/EWt6p4hW98a6VdWOn6pNab4pkK6jEsTSHu8kQVmI4JY\n9Og8+1L/AIKAftPfBTwhHo/xk+G1rrtjrE8m2fxpoMlr9phCxh4Akflxuq9eUJy/ORivWv2gvjD8\nPvjj/wAExPHfifwF4Z0rwakupWMWoaJpyQJJbyrqEKoZhEqjcyAMMj7rCgDxfwZpt748/wCCX+mx\nXfxA07wbcD4iO39seI766jicC0ceSHhjlck5LAFcfIec4B/RP9g6zj8J/sf+CIbrxVpniaG0ivnk\n1+xuZJbSVftlwxIkmVGwuSp3AAFD2xX5a3//ACiT0rkjPxOYf+Skldl8UPiprXgz/glX8EvCukus\nVt4su9Tt72VJXSXy4r+dvLG1gCrkkMGBBHFAH6SeIP28/gF4W1q/0jUviho9vqFlM0FxEomk2SKS\nGG5UKnBHYkV7do2u6f4h0i11XTL2G+026jE0F1C4aN0IyGBFfm/of/BHTwjL8AUXUL3WF+KEtil6\nzxX8JgjvRbv/AKKp2FDA0rKS5Bf5BhgMiuB1jTfjr+xL+wN488J+MILCxt9Q1a2s9GvbbVZJ7iCO\n5ST7SsZRx5QQwxMoHGZZSQc8AH3540/bc+Bnw+8RXGha98StHstUtlRpYFMkwUMoZfmjVlPysDwe\n/NeqeBvHWgfEnwxY+IvDGq22taJeoJLe9tWyjj8eQfYgH2r8Pv2cv+GO4PgjLafFyXxDL8RNR+0L\nJd2kNzjSwS6Qtb+X+7chQkn7xXG4kEYGK99/4JCfEOTQvjf8RPhdoetHxJ4JksH1ey1GeKWB90M0\ncQZIWYrGJFuCWGMkxp6UAfrHRTVOc806gAooooA+AP8AgtMf+MX/AAx6f8Jdbfj/AKJd1+c/7OX7\ne/xO/ZZ8E3vhXwXHobaXeX76lKdTsWmk81444zhhIvy7Yk4x6+tfox/wWn/5Nf8ADHOD/wAJbbcf\n9ul3XLf8Efvhd4M8cfs3+JL3xH4R0HX7yLxTcQpcappkFzKifZbUhQzqTtyScdMk+tAHzKP+Cxnx\n9P8Ayw8J/wDgqk/+PV9P/wDBPL/goD8UP2n/AI63vhDxlFoKaVDolxqCnTbJ4ZfNSWFF+YyMMYkb\nt6V9R/tDfAn4a6X8AfiXeWfw88KWl5b+GdTmhuINEtkkidbWQqysEyCCAQRyCK/Mf/gjP/ydnqn/\nAGKt5/6UWtAH7bL3p1FFABRRRQAUUUUAFFFFABRRRQB4p+2v/wAmj/F7/sWb7/0Ua/NX/giZ/wAl\n/wDHP/YsN/6VwV+lX7a//Jo/xe/7Fm+/9FNX5q/8ETP+TgPHP/YsN/6VwUAfsrRRRQAUUUUAFFFF\nABRRRQAh6V+PH7M2vapcf8FePEljLqV7LZL4k8SqLeS4dowAl3gbScYHGB2xX7DkZr8av2X/APlM\nP4n/AOxl8Tf+gXdAHX/t3eAtT+NH/BSDwb8ObbxRfeGbbXNEtYjd2xdxCQLp9wjDKGJ2AdRXSX//\nAAS++N3wh1TT9d+EPxxa811fMink1dZbARRlRjYQ04fJ6ghccc+mj8dv+UyHwk/7Bdv/AOir2v0w\nb5cYAAoA/NL/AIJ+/tw/EG6+Mlz8BvjJFe6n4qNxcwWupylGntriFHklguNpwVCxvh1zgqBghsr+\nlg57/hX49ftZa7dfDT/gq74T1Twr5GjX8t5pMcsltbR/vPtP7m4LAqQzPHK4LkbvmzkEA1+wyjHH\nQDgUAfBX/BTb9uDXv2dLPSPB3gK8htvFerW8st5dyQrL9ktmRkVozvBSYMQw3KRjB56V4f8AB7/g\nk/8AEPx/4DsPEPjn4s6p4O13UD550iOB71ooyAV8yTz0xIecgAgccntw37W2m2eq/wDBXPQbK9tI\nLyyuvEHhqK4triJZI5kaO1VldSCGBHBB6iv2cWJY1REVURBhVAwAOnA7UAfij4a+Jnxd/wCCa37S\ntl4J8Y+JL3UvAUt6t/cQRkXMWo2MpEZuokdwUl2xFcFgFZD94DJ/ZbxZ4y0TwJoVzrPiHVbXRtLt\nkeSW5vJRGqqqszYz1IVWOBknBwDXCfGbUfgz4eu9M1L4qf8ACF2lxcBrexu/FMVqXcKdxSNpRn5S\n2cDoW96/Ob9uS/1P9sH9u3wh8DdH1S3m8P6ZMsE7WF5jYwUyXrOGJj8+JI5lUbc5+U5JoA++PCX7\nc/wI8b+IbDQdF+Jek32q30ghtrciWPzH543OgA6dzXp3xI+J3hj4Q+EbrxR4w1iHQdBtnRJr24DF\nUZ2CqCFBPLEDgd6/N79sX/gmN8O/hN+zbceLPA95f6X4g8IQveXt9eTtM+rR7h98DCxupxtMaouN\n24E4I5rxZ8dLv43/APBIfVm1rWrrXfE+galZ6Vqd1eR4c7b5GtxvGPMPkGHLnknO4lskgH3prn7d\nvwE8NmxGofE/RovttpHfW+zzZN0MmSjHYh2k4+6cEdxXqfw6+JPhn4teFLXxL4Q1q21/Q7kssV5b\nE7SykqykEAggjoR/Svzd/ZX/AOCW/gD4m/ssQeIPEl7c3Xi3xdYRX+m6nbyPGujgjcirGCFkJPD7\nwePu7TzXnv8AwT/8Ya7+zx4R/au+xXi6rJ4LsfOt7ad5Vs5biJ7mNpdgIKlhEvIwcKoJ4GAD9IfH\n/wC2b8FfhZ4rvPDPir4haZpWuWm3z7OQSO0ZIyAxRCAcdq/ML/goN8WbPx5+2F8O9b8F+KG1Lw7q\nOk6VLBNp126xSf6ZMDlQQVPGMEA9OKt/8E4v2GdO/abvNX+JnxUXUNY8OLcyxxWdzJKh1e5cHzJn\nnVg7BGLE7WBLgbiRuB8b/ai/ZqsP2V/2utA8IaTqcup6TdT2OqWZnXEsEUlyVETngMVMZ+YAZBHf\nNAH6F6P4Udf+Cr2sa63xE0KRW09UHg8Xt3/aCf8AEriXcYjD5IH8fEnQ5xngeZeLP+CgVgv/AAUD\n8NPpvxXI+B62mNREav8AYhMLWcHcuzfnzfK6DrjmtLQuP+C2viDH/QKX/wBNENeTeO/2Vvhdp3/B\nULwj8K7fwwIvAWpWX2i60kX1wRJIbO5lz5pkMg+eNDww6UAfRfx+0SDxZ/wUJ+B/iay+Jfh/TbA6\nbp80Hh26vbuO81GM3Fy6vFEkJjYOGwN7rnaQcADP2n8Svin4U+D3hafxJ4y1618PaJA6RyXV2Tjc\nzBVUAAsxJI6A8ZPQZr87v2oNKtdC/wCCpv7OWmafF5FjY6TpltBEGLCONLq7VVycngADn8TzXhPx\nOsfEX7cv/BRXW/hlrviK40bw9b67eWMdvbzyywQW9isiM8UTMyJNJHCfmAxvcnBHBAP1O+Hf7Ynw\nZ+K+vnRPCvxD0jVNVEJn+ylnhLKGVeDIqgnLqMDJ5PBwa6X4tfHrwD8B7PTrvx94ntfDVtqMrRWs\nl2rsJWUAsBsU9AR19a/MP/gor/wTw8O/A3wTpnxH+FNpqFhYafJHa6rpkTS3PlcEpeiRiWj+YbWy\nduXj2hec8r+2v8S9V+MH7B/7NXifWlQ6pcXF/aSMkjuZfs5NurszkszusKsxJOWY9M8AH6b+OP21\nvgf8ONcfRvEPxK0ax1JI1laFGefCtnB3Rqy546Zr2HQtbsPEmk2uqaXeRX+nXcazW9zAwZJEI4II\nr8vPAH/BLnwfpH7HHiLxN48g1MfES40N9bictJayaM0cRmFv5R4LHbsk8xW6nbsPNdN/wRT+K2s+\nIvAXjzwHfOJ9K8O3NtfWMskjvIn2kSK8QyxVYwbcMFUD5pHJzmgD9LKKQDFLQAUUUUAFFFFABRRR\nQAUUUUAfgT/wVM/5Pl+IX/XPTf8A0329frFYf8FDf2dYbOCN/ippIZI1B/c3Hp/1yr8nf+Cphx+3\nL8Qu/wC703/0329e/wBv/wAERfFFxCkg+KejhXUMMaXKeoz/AM9KAPur/h4j+zn/ANFV0n/vzcf/\nABuj/h4j+zn/ANFV0n/vzcf/ABuvhn/hx94p/wCip6P/AOCqX/45R/w4+8U/9FT0f/wVS/8AxygD\n7m/4eI/s5/8ARVdJ/wC/Nx/8brU8Lft1fAjxt4l0vw/ofxI0zUdZ1O4S0s7SOKcNNK7BVQZjAySQ\nOTXwIf8Agh/4pGP+Lp6Oeen9lS//AByvlH9m/wAJv4D/AG8PAfhiW4S8l0Xx1Bpz3EalVlaG8EZc\nA8gErn8aAP6IF6mnUmMUtABRRRQAUUUUAFFFFABRRRQAnpX4A/sb/wDKRDwh/wBjPd/ymr9/vSvw\nB/Y2/wCUiHg//sZ7v+U9AH7/AApaKKACiiigAooooAKKKKACk9KWkPagD8zv+CXg/wCMrP2nv+wq\n/wD6XXdeNfArj/gsxrH/AGNHiD/0mu69l/4Jef8AJ1v7T3P/ADFH/wDS+6rxn4GsF/4LK60zMEA8\nUeIOScf8u13QB+uPj34beGPiXoGoaN4m0Oy1nT763NtPHcxAlozzgN1GCcjBGDzX5Nf8E7dWuvg/\n/wAFCPG3wv0Bj/wi09/q+mPHeEySrFZvMYSGBA3/ALtQzbeRnpX62eM/GmkeAvCuq+IdZ1C2sdM0\n2Brie4uZliRQo7seBk4H4ivyD/4JuW+ofGX/AIKAeKPiZpVh9l0WOfVdXuobicGS3S8MwiTgDewa\nQAkAdzgdKAP2aA6g4qvqnGm3WOD5T/8AoJqyox9Krar/AMgy7/64v/6CaAPx3/4Img/8L38ecZH/\nAAjX/t1DX0T+3x+1h8X/AA18cfDvwL+ENnbWmt6/p0dwNU3Brp3lkcKIicLFsEDksd2Q/bHPzt/w\nRN4+O/jwZ/5lsf8ApVDXZfte/tN/Ef4gftnW/wAK/hHpOiaB4p02RtJj1u70+zlvryVk8zK3U8TN\nAiIWCiMqcu5LHK4AOf8Aix8a/wBsj9hC8s7zxv4o0rxnpuux+Tb3d0WvrWGVSSYxkQskm3k8EEEc\n8YH1H+1r/wAFC4fhJ+zd4H8aeB4ra58TeN44bnS7PWYyGgtSu+Wd40JVsHZHt8wYMoOSFIP59ftt\nfssfE/4C/D/wVq/xD+J9341l1WYxDSbm7uLkafOIQ7hWkkYNgnbuUDPB719MfFKL4JeK/wBin9nH\nwZ8VfEl74Q1a+0W3u9G1qxsFn8plhETLPIUYrBumjZ1BXOwc8ZABg23i39t/4Y/C6T48XninSvEH\nhua1e+n8PXlwLn7JbOxPmmJAqhVABG2VmCtgjOQPRP2gvjxqf7SX/BJbV/Hes6db6dql5NZW93Fb\nOWieSHU4YmkUEZUOVzsyducbj1Pz9rv7Ln7Tf7K/hDXfEnhTx1Y6/wDDDStLedZnv4rrTrqykRZJ\nCun3HmREEscfKcnJB5rsdd/aLf4+/wDBKf4hw3Xh/TtC1Lw3f6XYXT6TaxWtreSNewP56wxgLGzZ\ny4UBSxJAAOAAdJcft82H7M/7D3wm8I+EZYNX+Jup6CmyNSJI9LiLuPMlwTmQnhI+vBLYwA30l/wT\ny8LfHRfBN14x+NPjLUdUk12GNtM8P6ioMtnCDkTSEAbXkB+5jhdpJySq/k7Z/Bjxv+zb4P8AhF8e\npbWG90jWL9rm3RLVLmO3SPYYxKZFaMPMpn2KRlfJJ6gY/eL4EfGPRf2gPhR4f8e+H0mi0vV4mdYb\nldskLpI0csbepV0dc9CACODQB3qnIpaKKACiiigAooooAKKKKACiiigD8H/+Cs/H7aviT/sHaf8A\n+k61y+n/ALUv7WFpYW8Np4q8craRRKkISycqEAG3B8vkYxXT/wDBWn/k9TxJ/wBg3T//AEnWv3D8\nAsW8DeHWJLE6dbEk9/3S0AfgBqn7c/7SmhXr2Wo/EzxPp93Hjfb3QWORcgEZVkB5GDRXT/8ABVAA\nftyfEDH/ADy03/0329FAH77UUUUAFFFFAHGfGjj4O+O/+wDf/wDpO9fjL/wR+/5PDt/+wDff+06/\nZr408/Bzx3/2Ab//ANJ3r8Zf+CP3/J4lv/2Ab7/2nQB+5tFFFABRRRQAV+Zn/BRM4/b3/Ze/7CFh\n/wCnOOv0ybpXnnjv4A/D/wCJnjXw94t8TeGbbVvEfh+RJNKv5nkD2rJIJFKhWAOHAbkHpQB+cv8A\nwXI51P4OZ4/dat/6FZ11nwx/4LD/AAr8E/Dbwn4dvPCPjCe70jSbTT5pIIbQxu8UKxsVzODjKkjI\nHHYV9z/F/wDZw+G3x+k0qT4geE7XxK+liUWRuZJU8kSbPMxsZevlp1z92vOv+HdP7OI5/wCFV6Vk\netxc4/8ARtAH5uX58W/8FU/2urS80zR76z+HWkOguF1C6KxWNgrDzMsuQk84UDYm75jn7qM4/abR\ndKt9C0iy0y0VktLKCO2hVmLEIihVBJ68Ac1BofhrSPDcMkekaTZaVHIQzpZWyQhiBgEhQMmtSgAr\n8Z/+Ckh/42N+Ccj/AJZaJ/6VNX7Lk4ryfx9+yv8ACv4oePLTxp4p8G2eseJ7QQiDUZpZVdPKbdHg\nK4X5TzyKAPUb/ixuP+ubfyr8mP8Agmf/AMmaftPZxj+z7r/03T1+tUiiVGjOGDAgg9CK80+H/wCz\nP8Mfhb4T8ReGvCvhG00bQvEMbRapZRSSst0rRtGQxZyRlGYcEdaAPmH/AII0/wDJpepf9jTef+iL\navlL9jTj/grP4t/7Dvib/wBCua/WX4UfB7wd8EPDMnh7wPoMHhzR5LlrtrS3d2VpWVVZ8uzHJCKM\nZ7VzfhX9lf4VeCfibcfEHQ/Btlp/jO5muLiXVo5ZWkeSfd5xILlctvbOB3oA+J/+Cq6k/tFfszAD\nP/E0f8f9Ls68K/b28IWnwV/4KF2Pj3x9on/CS+A9dns9YexhtvNS4gijW3kt28wLG8mYdxTcflkT\nJ+av1k+JPwC8AfF/XfD2s+MPDNrrmqeH5DLplxPJIptmLI2QFYA/NGh5B6Vu+PPhz4Y+J/h+50Px\nVodlrumXEbxPBeRBsK4w21uqnpypB6UAflf+0B+01+yZp/wSi1D4T/Dfwjd/EXUFtvJsNQ8KRuum\n5ZWm+0b1EbEIHj/ds43EEZX5q+sP2Qv2f9D+KP7Nvhi/+LXwy8DzXlzc3Gp6fplr4cjtI7KKdYlY\nNCUAWVjCCxA5VY+TivQdH/4J/wD7PmgavY6pY/C/SYr2yuI7mB2lnkCSIwZSVaQqwyBwQQehGK+g\nolCIFUBVAwAOB+A9KAPNvBX7NXwp+GniKDXfCnw88OeHtaiR4477TtOjhmVWGGAZRkAjINfkz+xx\n+0zqP7Avxq8a+Dfixo+rabpWpSLHdW0Vuks1tdLMBHOXLjdCImmP7vduypGa/bEjPXmvLfi1+y98\nLPjtqNlqHjvwXp/iK+so2iguJzJG6ocZUsjKWHAwDnHOMZOQD40/bU/4KQ/BLxd8CPE3gvwlq134\nw1PxJp1xYxSWNjJDHYsQu1pTOsZ2nP8AAGPynOOM/M3gH4feI/B//BKT4vatrek3Gm2HiHW9KvdL\nluAFN1ALm3TzFXO4KSDgkDIwRkHNfpz4c/YK+APhLXbLWNL+GOkW+oWcglgld5pQrjodruVP4g16\nh4++FXhP4oeBbjwb4n0O21TwxOIlfTH3RxERsrRgbCpUKyLgDHQUAfixqJA/4JJaVx/zU1h/5KSV\np/F/4Ran4m/4Ju/ATxvonhs382hNqseq6rBGGmtrQ39w0e/+Lyg5c5wQuSTjNfq3J+x98HZvhnF8\nPW8CWJ8Gxaj/AGsmkmWby1uthTzd2/dnaSMZxg13ngf4YeFPhx4DtfBfhzQ7XT/CtrHLFFpQUyQh\nZHZ5FIcncGZ3JBJ+8aAPz8vv+CvnhzTv2XtMv7INqnxllt0sp9LlszDbw3IXD3bEfJ5PBKorFiSo\nKqMkeQeObr42ftq/sP8AiH4g+LtEsPE1zo+twf2D/Z+mfZ723tURzfzLwPMjctbKPLLEmF8gbTX3\n7/w7q/ZyJz/wqzSwfa4ucf8Ao2vftF0LT/DejWek6XZw2GmWcSwQWlugWONAMBQB2FAH5M/s5/tH\n/sd/8KHtB8SPhn4e07xxoVgLaW0Ogi6l1l4o8LLHKI9okl2jPmsgDsedvzV6P/wTG8d6x8WvjX4y\n8VaX8JfCfgX4ewabLaW2oaHosFrNHO80LR2xuQA8w2JIzAAgEJnGVz9W6z+wD+z54g1i/wBUvvhf\npMt/ezvdTuss8YaR23MQqyBV5J4UADsK9k8CeA/D3wz8K2HhzwvpVvouiWMYjt7O2XCoPx5JPcnk\n0Aby96dRRQAUUUUAfn//AMFqP+TXvDH/AGNtt/6SXdJ/wRW/5Nh8U/8AY23H/pJaU7/gtPn/AIZe\n8Men/CW23/pJd03/AIIrZ/4Zh8U+n/CW3H/pJaUAfXP7Sn/JunxT/wCxV1X/ANI5a/I//gjP/wAn\nZ6p/2Kt5/wClFrX64ftKf8m6fFP/ALFXVf8A0jlr8j/+CM//ACdnqn/Yq3n/AKUWtAH7cUUUUAFF\nFFABRRRQAUUUUAFFFFAHin7a/wDyaP8AF7/sWb7/ANFNX5q/8ETP+S/+Of8AsWG/9K4K/Sr9tf8A\n5NH+L3/Ys33/AKKavzV/4Imf8nAeOf8AsWG/9K4KAP2VooooAKKKKACiiigAooooAQ1+NX7MGP8A\nh8P4mx/0Mvib/wBAu6/ZRhnFeUeHf2WPhV4T+KVx8R9J8G2Vl42nuLm6l1iOSUyNLOGEzYLlctvf\nPHegD87v24/i1pnwK/4KeeBfHms2l3faZoui2s09vYBTO4IukGwMQM5cdSOM16dr3/Ba/wCG66Hf\nNongbxVPrIgc2cV+tslu02PkEjJMzBM4yQCfQV9efFL9kf4RfG3xOPEXjjwPZeINaWBLUXdxNMrC\nJSSq4RwMDce3es7wT+xH8DPh14gj1rw/8NdHs9SjR41mk8y4AVhhvlldl5HfGaAPzn/4Jm/DTxh+\n0B+1bqnx68TQz29jYy3d89/HbKlte386tEYEBOQFWWR8qDgoAcZGf2GHfBzj0qrpOj2GhWS2em2V\nvp9omStvaxLEi5POFUAVcxQB+WP/AAVz+CviPRfHng/4zeCtOkt7q0QJfajo9tL9sgngzJHdyyIu\nFWNEVQ7EFdo7Dj1z4Nf8FdfhD4o8ESzeNpL/AMG+ItPtozJZzW0l0l9LtYsLd4w3dQMyhOXHXkj7\ntu7WG9tZreeGOeCZGjkilQMjqRghgeCCCcivnab/AIJ3fs5zyPI/ws0oO53HFxcAAn2EuB+FAH5t\n/EjxVff8FSf2xPDEfhPw7q2i+FtOtLexub11jee0tFleSS4lG4xqwaRwqhiWCDGTkV2P7ara5+yR\n/wAFBdD+Mul+GP7O8MXU8Nwb21to3ivmeMpfjrtWd1klwX2kkhunNfqn8NPhN4P+D/h+HRPBvh2x\n8PaZECqw2keGILM/zOSWb5nY/MTjdT/ib8LfCnxg8JXHhnxloVr4g0K4dJJLO6BA3IwZWDKQykEd\nQQcZHQmgD89P25f+Cknww8f/AABvvA/w8uJvF2r+LIDZ3Km2mtxpsfyk7t6DfIWIVVTcPlYlh8u7\nhPFnwKu/gj/wSF1dda0W70LxP4g1Sz1XUrS8l3Mu6+RYDsyfLJgEWUPzA53AHIH3t4T/AGE/gL4I\n8R2GvaJ8NdKs9VsZfOtpy80vlv2ba8hU49wa9R+JXww8L/GHwjdeF/GOkQ67oN06STWM7MqsyMHQ\n5Ug8MoPWgDzb9hr/AJNA+EfH/MvWv/oNfBP7Dfw8i+LvjT9snwZcXc1hDrZksWuoYxI8Qe6veVU8\nE+1fqf4O8I6P4C8K6X4d8P2MemaJpsC21naRMzJDGvCqCSSR9TXL/Df4A/D/AOEPiDxDrfhDwzba\nHquvyebqdzBJIxum3s+WDMQPmdzwB1oA/Lf/AIJhftZ/D79mLTvHvg74n3974Svbi/S5SW7sppER\n4x5TwMkatIkgO4kMoGFIJzwfCf2pP2k9P/al/a70HxdpGmS6bpFrcWOlWf2g/vZ4orgsJXHRSxkP\ny9gB3zX7SfE79kv4QfGbV4dU8Y+AtK1nUIgwFyVeFzuOWLGNl3knu2T+dcnY/wDBPf8AZ50y/try\n2+F+mRXNvIssUguLklXUgqeZMcEd6APlfQv+U2/iHj/mFLj/AME8Ncv+0j8SfDfwj/4K4+E/F3i3\nUhpPh/TdJiN1emKSXyt9lcxr8sasxyzqOAetforB8AfAFv8AF2b4oxeGrZPHs8Qhk1vfJ5rJ5Qhw\nV3bf9Wqr93PFZ3xa/Zf+Ffx11Kyv/HXgvT/EN9Zo0cNxMXjkVWxkFkZSw+UYznGOMZNAHwt+1Bqt\nprf/AAVP/Zz1KwlE9jeaTplxbygFQ8b3V2ynBGQCCDg469q8J+Jeo+IP2Gv+CjOt/EzX/D8+s+Hr\njXry+juLaKWOGa3vldmSKR1CtNHHMcrnG5SMgc1+s2pfs2/DTWPHHhnxjd+ErSfxL4atYbPR9QeS\nUPaQwljEigNjCl2xkHrXQ/Ez4V+E/jJ4Un8NeM9CtfEOhzOkj2t0DjejBlZWUhlIIHII4yOhNAH5\noft+f8FIPBnxa+E118NfhfNfaxda6bcX9+9htt2gO4vbKHxIJhIIeQhGAQGOa81/bM+Gmq/CD9hP\n9mbwzrZj/tO3ur+7lWNXTy/tDG5VGV1Vg6rMqsCOGVu2K/Tz4dfsb/Bb4U+ITrnhT4d6TpmpmIwC\n5bfOQu5W+USswU7kX5gAwxgHBrp/i58BPh/8eLPTbTx94ZtvEtvpsjTWiXLyIIXYAMRsZeoA60AO\n+OQH/CgfiBwMf8IzqH/pLJX5w/8ABDb/AJC3xj/64aT/AOhXdfqdrGiWXiDRL7R9Rt0utNvrd7W4\nt2J2yROpV14OcFSR+NcL8H/2b/ht8ApdVk+H/hS18MvqoiW9NtJK3nCPdszvZunmP0x1+lAHpdFF\nFABRRRQAUUUUAFFFFABRRRQB+BH/AAVM/wCT5fiF/wBc9N/9N9tX726Z/wAg21/65L/IV+CX/BUz\n/k+X4hf9c9N/9N9vX726YMaba56+Uv8AIUAWqKKKAEbkV+APw4/5Sb6d/wBlPk/9OLV+/wAa/AH4\nb5/4ea6bnr/ws+T/ANOLUAfv/RRRQAUUUUAFFFFABRRRQAUUUUAJ6V+AP7G//KRDwh/2M93/ACmr\n9/vSvwB/Y3H/ABsQ8If9jPd/ymoA/f4UtFFABRRRQAUUUUAFFFFABSGlooA+Rf2Pf2P/ABR+zr8a\nPjB4x13V9I1DT/GV41zZQac8rSwqbmaXEgeNQDiVR8pbkGvl348/8Elfih8Vfjd448a6V4u8J2Vj\nrus3Wo28N1NdLNGksjOqvthYBgCM4OK/VyigD8i/Av8AwRU8Z3GsyJ44+Iuj2ei+Q2JNCE1zOZcr\ntBWVI1243ZO7OQOOTj9IP2b/ANnXwl+zL8NrLwj4UtQFjAkvtRkQCe/uCPmmkPqegXooAA4Ar1Si\ngAqC9hNxazQggGRGTJ7ZGKnooA+C/wDgnx+wF43/AGR/iT4k8ReJ9f0DVrXU9I/s+KLSJJ2dZPOj\nfLeZEg24Qjjnms39qj/gmz4q8ffHmH4sfCDxrb+D/El27XF/JqNxPG8VyAFWW3kiRyNy5UrwBgYz\nk4/QXrRgelAH5ReKP+CRHxU+JGmax4i8Z/Fqw134jXFxEsMt3JcXFrNAqomZZ3TzA4UEKAhGFXnk\n49euv+CaWrfEP9lLwv8ADjx14uspfGfhi9nl0rX7OJ7lY7QxssdlukVXSHdsJwDjyxgGv0ApMCgD\n8mbr/glv+0lqngWy8D6h8Y9EuPBVq6tForajfPbRfMWykZh25yzEe5r6S1//AIJ5Q6F+xP4g+CPg\nfV7F9d1a7gvJtf1WA232kpeRzAy+Urt8saFF69B0B4+1MUYHpQB82aL+yDputfsc+Hvgr48g07XL\njS9OESXVu7iKK7Xf5c0TlQ643kbsZwzDBzXmP7An7HXxb/ZE1zW7PX/E/h7W/BmrL5j2Vhe3Re2u\nAMCWON4VQlgFViSCAo619xUmMUAIvf606iigAooooAKKKKACiiigAooooA/B7/grT/yep4k/7Bun\n/wDpOtfuF8Px/wAUJ4c/7Btt/wCilr8Pf+CtP/J6niT/ALBun/8ApOtfuF4Az/wgnhzP/QNtv/RS\n0AfhR/wVR/5Pk+IH/XLTf/Tfb0Uf8FUP+T5PiBn/AJ5ab/6b7eigD99qKKKACiiigDjPjT/yR3x3\n/wBgG/8A/Sd6/GX/AII/f8nh2/8A2Ab7/wBp1+zXxp/5I747/wCwDf8A/pO9fjL/AMEfv+Tw7f8A\n7AN9/wC06AP3NooooAKKKKACkxS0UAFFFFABRRRQAUUUUAJgelLXnfxi/aD+HvwCtNMufH/ie38N\nQak8kdo9xFK/nMgBcDYrYwGXr611HgrxponxD8Lab4j8N6nb6xomowrcWt7bNuSRGGR7g+oIBB4I\nBBFAG3ijFee/Fb9oD4ffBC60K28ceJrfw9PrkkkWnJPHI5uGQoHA2K2MGSPrj71egrnvQAuKMUtF\nACUdKWigAooooAKKKKAExS0UUAFJgUtFACUtFFABRRRQAUUUUAfAH/BafH/DL3hj1/4S22/9JLum\n/wDBFbH/AAzD4p9f+EtuP/SS0p3/AAWn/wCTXvDH/Y223/pJd03/AIIrZ/4Zh8U+n/CW3H/pJaUA\nfXP7Sn/JunxT/wCxV1X/ANI5a/I//gjP/wAnZ6p/2Kt5/wClFrX64ftKf8m6fFP/ALFXVf8A0jlr\n8j/+CM3/ACdnqn/Yq3n/AKUWtAH7cUUUUAFFFFABRRRQAUUUUAFFFFAHin7bH/Jo/wAXv+xZvv8A\n0U1fmr/wRM/5OA8c/wDYsN/6VwV+lX7a/wDyaP8AF7/sWb7/ANFNX5q/8ETP+S/+Of8AsWG/9K4K\nAP2VooooAKKKKACiiigAooooAKKKKACiiigAooooASvHfj7+1n8Mv2ZJ9Eg+IOtzaO+sLM1ksNjN\nc+YIim/Plo23HmJ1xnPGcV7HX5P/APBccf8AE5+Dvp9n1X/0K0oA/TL4VfFDw78Z/AOk+M/Cl5Jf\n+H9UWQ2tzLA8DOEkeNso4DDDow5HbI4NcL8ev2vfhf8As1appWn/ABA1yfSbrVInntEh0+e4DohA\nYkxq2OSOuK/Nf/gmT8d9W/Zt+M998I/iJdNoekeIIbe7tYtRu8Q2d1NCksJVVDLmeOSHksoAAzzW\nx/wW9z/wsL4Xcf8AMLvP/RsdAH62aTqNvrOm2moWrmS0uoUnhcgjcjKCpwRkcEVcr5d+P37X/hz9\nkP4A+DdW1O1fV9f1TTYYtH0aNihunSGMyM0mCERQ6ZOCcsoAPJHy1ef8FjPG2naOmr3fwAurbSZE\nV0vptVmSBlfGwhza7SGyMHPORjrQB+o9FfPvjj9rfTNH/ZFvPjr4b0o61Yx2FvfRaVc3KwvmWWOM\nxyOm8Ky+YSRg9Md6+PZv+C0eoX+lwXWg/Bu61M21n52sOdTbybKTzJAAHSFt0exUbewQ7mYYwu5g\nD9RKK/Mi2/4LRQa34a0uLQfhNqeq+OLi4kWfRo73dbrCoYhopUjMkjYALL5SgfMdxAr1T9lX/gpv\np3x4+K0/w88YeDbj4d+JJ8LpkMtw9yLiUZLwyAxRtE+3BXIIIDZIwNwB9x0mB6V8b/tgf8FKvB37\nLviO88IWWj3fizxvaxwyzWKuLe0gWQBwJJjlt/lsrgKjAhgCVOceOeGv+Cw95ZeN9C034ifCG+8D\neH9Ql2TapJeyyyQoRjzFieCPzFDFd2GyFJIBOAQD9K8D0oqrpWpW2s6ba6hZzC4s7uJJ4JQCA6MN\nysM88gg1boAKKKKAEwPSloooAKKKKACiiigAooooAKKKKACiiigD8CP+Cpn/ACfN8Qv+uem/+m+2\nr97dM/5Btp/1yX+Qr8Ev+Cpn/J8vxC/656b/AOm+3r97dMGNOtf+uSfyFAFqiiigBDX4AfDb/lJr\npv8A2U+T/wBOLV+/5r8Afhx/yk207/sp8n/pxagD9/6KKKACiiigAooooAKKKKACiiigBPSvwB/Y\n3/5SIeEP+xnu/wCU1fv96V+AP7G//KRDwh/2NF3/ACmoA/f4UtIKWgAooooAKKKKACiiigAooooA\nK8L/AGuP2rdG/ZH8D6R4l1vRdQ1y31LURpscGnNGro5ieTcd5AxiMjHvXulfnj/wWv8A+TefBOcY\n/wCEpTv/ANOlzQB9bfsyftG+HP2pPhfD418NQ3dpbG5ksrmzvUxJbzoFZkJHDfK6HcuR82OoIHM/\nte/te6H+yD4Y0DW9c0LUddh1e8ezji06SNGjZU37m3kcY9K/JL9iD9oDxF+xf8WtBu/E9jqI8FeN\ntNiuGsorhQk0DzPHDeqhOCUeOZMMVOC3PSvsH/gtdcx3nwa+Gc8Esc8E2syvHLGwZXU25IYEcEEH\nNAH3v8G/iVafGP4XeGfG9jaT2Flr1jHfQ2tyytJErjhWI4Jrs6+ENC/ax0r9kX/gnd8IPE91ph13\nVbzSrOy0/SVuVhMzlGZndjlljVUbLKrfMyDA3ZHgWsf8FOP2kLS8h8e/8Krhsfhi93s+xS2czOUj\niieVTdYBUESBhIYtvzYG7aaAP1sor59+Ev7W2mfGD9lnUvi/pWlmK50zSby8vNDkuULR3NtE7vCX\nXJCsyEKzKCVIbaOlfCmu/wDBYD4q/ETQ3t/hz8Jo7DU4Jo2nvUkm1dEjIYbDGkSbSxAIYt/CeDnI\nAP1tor8oPBv/AAWZ8T6d4NsdI1/4bf8ACRfEgajJaTw2kzWcEsZOIwseySQTbj5ZjxztznJ2ja/Z\ng/4KlePdV+M+k+BPjL4dsNIg1y6SGHUvImsXsdyP5atCyuZfMk8tAcrjcTyOgB+otFfKH7bH7fXh\n/wDZB/sjSE0h/FXjPUoxdR6OJzbRx2pdk855drY3MjqqgHOxs7cDPzlL/wAFifFHhzUdNfxd8B9R\n8PaNPcpHNdy38okWPI8wxrJbIHcLuIXcAeASOtAH6eUVz/gHxzovxL8G6R4o8O3gv9E1W3W5tLnY\ny70PQ4YAjoRz6V0FABRRRQAUUUUAFFFFABRRRQAUUUUAfg9/wVp/5PU8Sf8AYN0//wBJ1r9wvh//\nAMiH4c/7Btt/6KWvw9/4K0/8nqeJP+wbp/8A6TrX7heABjwJ4c/7Btt/6KWgD8KP+CqP/J8nxA/6\n5ab/AOm+3oo/4Ko/8nyfED/rlpv/AKb7eigD99qKKKACiiigDjPjT/yR3x3/ANgG/wD/AEnevxl/\n4I/f8niW/wD2Ab7/ANp1+zXxp/5I747/AOwDf/8ApO9fjL/wR+/5PDt/+wDff+06AP3NooooAKKK\nKACiiigAooooAKKKKACiiigD8yv+C3UTXHhH4SxJt3vqV8oycdY4RXAf8Ez/AI5T/s0/Gzxb8BPi\nLqNvpi3N+9nZvLK8kcWqRvs8lH3eWqSfPghcs+wZ5Fei/wDBa7/kXPg9/wBhW8/9Agq9/wAFZf2b\ndV1PTdD+NfgbT7oeJPDssf8Aak2lQqs6QL80d2zqRIxidYwCAdqksSoTNAGf/wAFiefF37PJJwP7\nVvvvdB+8sea+tv2uf2uvD37Ingew13WdMu9bvNRuRa2WnWrCMysMFy0hBChVyeRyRivyz/aJ/aX/\nAOGlvhf+zTqeqX63Hi/SNZ1DT9YWQxrLI4awKT+WpJVHBwGYDLRv6V9af8FRtb+APhzW9AvPiZ4Y\n1rxf47k0mWPSdP06/ktIFiDuUadwwUKZdykrubGeOlAGNY/8Fr/DTX1qmofC3WrGyklRZbn+0o3M\nUZIy4XyxuwMkDIzjrX0v8Uf24/C3gX9mHTvjhoukX3ifw1qN0lvbWxb7HM2ZZIix3g4AaNu3Iwe9\nfnX+11+198Xfjt8EZfDfiH4CT+EPDsFxDdPrd9pVzKLYLlVMcs0SrCxLhfMBBwSucMc9x8U+P+CL\nvw86/wDIRjx2wft11QB3af8ABbvw/ITs+FGrMFGW26vGcD1/1Vbqf8FpPAEngtb9PBGtN4la98j+\nwhcrjyNhPnifZtPPy7Nue9b3/BJ34KeCh+ywfFEugWt3rXilr3TtWnvEE32i2Wd4xBgg4jKqNy9G\nIyRXg/8AwSY+CXh/xH+0D8T/ABnd28Mkng+cWmmWL2yvHE9xLNiVGPKui25VcDpI3TFAH1L+yh/w\nUy8LftQfExvAw8J6h4U1eW3ee0NxdLcxzlAWkUlVXYQoyM9enB69L+15/wAFA/Bf7JuoaZotxYz+\nLfFd2weTRbGdYXtoCpIlkYq2Cx2hVxk5J7c/Afxk0DTfDX/BYXRLLSbC20u0bxboU7QWsYjQyyx2\n0sr4HG53dmJ7sxrA+Inj3XrL/goj8TPELfDm++Nup+H9TuU03SZRcXC6escwWKUxxK5KR7tqqRtD\nOpGCBQB9bfD7/gst4D8ReKYdL8T+Cda8JWcq4S+EwvD5hYBU8tUU4OTz7dK/QuJxIgZW3qeQwOQf\nevxB/a0+I3xN/a+tNPn1P9mDXdD8UWGI4NfsNMv3nNvks0DqYQHXJJGc7SWx945/VX9i/WrzWP2Y\nPh3FqGgav4av9K0i30e40/XLZre5ElqggZyjchXMZZc87WGaAPbKKKKACiiigAooooAKKKKACiii\ngD4A/wCC04/4xe8M/wDY223/AKSXdN/4Irf8mw+KOf8Ambbj/wBJLSl/4LUf8mveGP8Asbbb/wBJ\nLum/8EVv+TYfFP8A2Ntx/wCklpQB9dftKf8AJunxT/7FXVf/AEjlr8j/APgjP/ydnqn/AGKt5/6U\nWtfrh+0p/wAm6fFP/sVdV/8ASOWvyP8A+CM//J2eqf8AYq3n/pRa0AftxRRRQAUUUUAFFFFABRRR\nQAUUUUAeKftsf8mj/F7/ALFm+/8ARTV+av8AwRM/5OA8c/8AYsN/6VwV+lX7a/8AyaP8Xv8AsWb7\n/wBFNX5q/wDBEz/kv/jn/sWG/wDSuCgD9laKKKACiiigAooooAKKKKACiiigAooooAKKKKACvyf/\nAOC4/GtfB7PP+j6r1/3rSv1fNfmF/wAFmfhv4t8fax8Jz4Y8La14kFrBqYnOkafLdeSWa127/LU7\nc7WxnGcH0NAHPft7fskN4w/Zx+Hnxn8OxzS6zovhTS7XVbC2tFkFzbrAjfaWKrndGpIZmLDYiAYC\n8/KX7Yf7U9t+1D4U+EN3dS3cvi/QdGmsNfkuY0CzXO5P36FAqkSbS20KNucc9a/dL4MWUtr8FvAt\nneQSW08fh+xhmt5kKvGwtkDKynBBB4II+tfiD+1z+wX4/wDgt8YNWsvC/gzV9f8ABt9PLc6NcaLb\nT3/l25b5YpWVSyyICAd3XqCRzQB9Rf8ABR34ofCqC2+C/hrxhpfifWfEGiaEmrW0Gi3dvbWyrOsK\noJjNDL5oZrZvlTbgA5J3DHlX7Tf7UP7RfxU/Zmm0jxf8G7Dwh8Nb2OxlTWLDSbu2hjiEkb24jZ5m\nRUbEYAx0IAxxXc/8FI/2ZfiXq/iH4Z/FHwt4futfs7Xw/p2kTWWnWr3F3Z3MJeRWkgC7ijeZjIBw\nUIbblcx/ECP9rf8A4KB+ArbQLnwTJ4K8NaPaFtRjvydPGs38MXmJmOXax3uFCgL5SOQWYYJABL8J\nQB/wRh+I/wDtajL04yfttrX0p/wTG+D/AIc8OfsUW2pi0j1CfxkLu91QX0EUiuFZ7dYfu5aILDu2\nuW+Z5COGxXzH8FdA+Isn/BNX44/DbU/h3r2majplxazafFLptyLnUWnu0aUJEUywjEIOUzw2TgV9\nx/sD+H9V8NfsS+AdJ1nS73StUgsbxZbC9t3hnjJu5yA0bAMCQQQCOcjHFAH5+f8ABFXT7W7+P3jO\n5ntoZ7i08Ol7eWSMM8LG4iQlGPKkqxXjGQSOlenfGjwjpPhb/gsX8MZtLtPssmrw2+pXx3s3nXDJ\ndIz/ADE4+WNBgYHy9OtYH/BHj4X+M/AXxq8bXfiXwhrvh61n8PeXFcarps1tHI32mI7VZ0AJwCcD\n0r1b48+APE2pf8FXPhJ4jtPDesXfh200y2SfV4LCV7SFh9syHlC7FPzLnJGMj2oA+Cb/AFLxXqn7\neHjjUNG8Fp8WfEdn4n1SS10TVop72OQQ3EgjLIjqzrEqLhSduFAIKjFe2ftUeIf2qv2t/COk6F4q\n/Z7l0pdMvRfQX+l6FdJcj926NHvlkfCNvDFR1KKT0rsv2m/2M/jb8EP2ndf+M3wL0u4utJLvr3mW\nN4klxFIy77uGSB2DzCR/Mby0DAq4Xk8Vy3xf8QftSf8ABQ/WLDwVL8MLrwh4bs54dS8m/sXs4beR\nQYGmNzcKjPxcMTEuWwMhTtzQB+j/AOw3oXibwx+yp8PtJ8Y2uo2XiO0tJYru31Ut9ojIuJdituJO\nAmzAPbHQYr3auE+Bfws0/wCCfwk8MeCNMBFpo9oIc+a0oMjEvIQzckF2cjPQEDtXd0AFFFFABRRR\nQAUUUUAFFFFABRRRQAUUUUAFFFFAH4Ef8FTB/wAZy/ELn/lnpv8A6b7ev3t0z/kG2vf90v8AIV+C\nX/BUz/k+b4hf9c9N/wDTfbV+9ul/8g20/wCuS/yFAFqiiigBDX4A/Df/AJSa6b/2U+T/ANOLV+/x\nr8Afhx/yk207/sp8n/pxagD9/wCiiigAooooAKKKKACiiigAooooAT0r8Af2Nv8AlIh4P/7Gi7/l\nNX7/AHpX4A/sbf8AKRDwh/2M93/KagD9/wCikFLQAUUUUAFFFFABRRRQAUUUUAFfnh/wWx/5N48E\n8/8AM0p/6SXNfofXxp/wVE/Z+8d/tE/BvwtofgDQ/wC3tUstfW9ng+1Q2+2EW8yFsyuoPzOowOaA\nPnnxL+y1b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lfir4yk+HXwx8XeK4rRb+XQtHvNUS0aQxiYwwvIELAHbu24zg4z0NdVXmf7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qpKtg/wCya/TD4RftL/D/AMN/sVeDPiHqutjT/DVloMNm8lwm2Z57dfs7xJHnLOZIXCgfe4Pe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\nw4YjGVYDHNc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voet3kl3/AGS98Ip7RmRWkZnmYB98plYB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\n6d/wUB8TfAvwN+114T8ce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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 100,
     "metadata": {
      "image/jpeg": {
       "width": 650
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image('rel_database_diagram.jpg', width=650)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Relational Database Software Exists to Help Store, Manage, and Get Data\n",
    "  - **Describing Data Content**: All Tables contain correct data type\n",
    "  - **Interpret Data Format**: Optimizes data storage for fast write/read speeds\n",
    "  - **Easy Filtering and Querying**: Can quickly organize your data before you work with it in Python.\n",
    "  - **Modification Protection**\n",
    "  - **Backup**: Can do regular data backups\n",
    "  - **Security**: Assign data ownership to certain Users\n",
    "  - **Remote Access**: Can send data over a network"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Relational Database Software: SQL Programs\n",
    "  - Simple, Single-File Based (Fast and Easy to use)\n",
    "      - **SQLite**, **Microsoft Access**\n",
    "  - Programs that Need to Run in Background (Super-super-fast, more challenging to use):\n",
    "      - **MySQL**, **PostgreSQL**\n",
    "      \n",
    "For a larger (but not-exhaustive) list of relational database software: https://en.wikipedia.org/wiki/Comparison_of_relational_database_management_systems"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## SQL: A Language for Talking to Database Software\n",
    "SQL Example: \"Get X,Y,Z positions and the rat name from my tables, but only FuzzPatch's data\"\n",
    "```sql\n",
    "SELECT X, Y, Z, rat FROM behavior, rats WHERE rat='FuzzPatch';\n",
    "```\n",
    "Using with Python, SQLAlchemy, and Pandas: \n",
    "```python\n",
    "import sqlalchemy \n",
    "connection = sqlalchemy.create_engine('sqllite:///database.db')\n",
    "df = pd.read_sql_query(\"SELECT X, Y, Z, rat FROM behavior, rats WHERE rat='FuzzPatch';\", connection)\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Should I Use Relational Database Software?\n",
    "\n",
    "**As a student, Probably not, because**:\n",
    "  - Using them and setting them up is extra work, \n",
    "  - Read Speed benefits 1/3 of HDF5\n",
    "  - Operating systems give you most of the features you'll want\n",
    "  - Read/Write Times are fast enough most of the time.\n",
    "  - Excel and HDF5 files already support organize data in nice tables.\n",
    "  "
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    "**As a PI, possibly yes, because:**\n",
    "  - Most of your lab's data will be encouraged to be organized into similar formats and in same location, \n",
    "  - New analyses can be done on existing data by new students\n",
    "  - New analyses can be done that join together multiple studies.\n",
    "  - Data Loss and Corruption is more likely over longer time periods.\n",
    "  - Data Reorganiztion is time intensive, so it saves time in the long-run.\n",
    "  - Free papers from reinterpretation of data!"
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    "## Reccomendation: Follow this Sequence to Speed Up Data Read Times\n",
    "\n",
    "  1. Single Table: CSV\n",
    "  2. Single Table: HDF5 (300x Faster)\n",
    "  3. Multiple-Table: HDF5 (3-6x Faster, better-organized data)\n",
    "  4. Read Only the Columns and Rows You Actually Need (Pandas.HDFStore) (~2x Faster)\n",
    "  5. Buy more RAM For your computer (2-3x Faster, ~80 Euros)\n",
    "  6. Buy an SSD For your computer (2-4x Faster, ~120 Euros)\n",
    "  7. No, you don't need voltage measurements every picosecond--the brain doesn't spike *that* fast.  Downsample your data.\n",
    "  8. Query your data with Python's Blaze package.\n",
    "  9. Is your computer more than 4 years old? Okay, buy a new computer. (~3-10x Faster)\n",
    "  "
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    "# Questions/Discussion\n",
    "For more details on data read/write performance:\n",
    "  - http://pandas-docs.github.io/pandas-docs-travis/io.html#io-perf\n",
    "  - https://sqlite.org/speed.html"
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