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Hernan Rojas committed 4e60896

added Lesson 3

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lessons/03 - Lesson.ipynb

+{
+ "metadata": {
+  "name": "Lesson 3"
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+  {
+   "cells": [
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "# Lesson 3  \n",
+      "**Get Data** - Our data set will consist of an Excel file containing customer counts per date. We will learn how to read in the excel file for processing.  \n",
+      "**Prepare Data** - The data is an irregular time series having duplicate dates. We will be challenged in compressing the data and comming up with next years forecasted customer count.  \n",
+      "**Analyze Data** - We use graphs to visualize trends and spot outliers. Some built in computatiopnal tools will be used to calculate next years forecasted customer count.  \n",
+      "**Present Data** - The results will be exported to Excel for easy distribution.  \n",
+      "\n",
+      "***NOTE:\n",
+      "Make sure you have looked through all previous lessons as the knowledge learned in previous lessons will be\n",
+      "needed for this exercise.***"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "# Import libraries\n",
+      "from pandas import ExcelFile, DataFrame, concat, date_range\n",
+      "import pandas as pd\n",
+      "import matplotlib.pyplot as plt\n",
+      "import numpy as np"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 1
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "print 'Pandas version: ' + pd.__version__"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Pandas version: 0.10.1\n"
+       ]
+      }
+     ],
+     "prompt_number": 2
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "We will be creating our own test data for analysis."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "# Function to generate test data\n",
+      "def CreateDataSet(Number=1):\n",
+      "    \n",
+      "    Output = []\n",
+      "    \n",
+      "    for i in range(Number):\n",
+      "        \n",
+      "        # Create a weekly (mondays) date range\n",
+      "        rng = date_range(start='1/1/2009', end='12/31/2012', freq='W@MON')\n",
+      "        \n",
+      "        # Create random data\n",
+      "        data = np.random.randint(low=25,high=1000,size=len(rng))\n",
+      "        \n",
+      "        # Status pool\n",
+      "        status = [1,2,3]\n",
+      "        \n",
+      "        # Make a random list of statuses\n",
+      "        seed(i)\n",
+      "        random_status = [status[randint(low=0,high=len(status))] for i in range(len(rng))]\n",
+      "        \n",
+      "        # State pool\n",
+      "        states = ['GA','FL','fl','NY','NJ','TX']\n",
+      "        \n",
+      "        # Make a random list of states \n",
+      "        random_states = [states[randint(low=0,high=len(states))] for i in range(len(rng))]\n",
+      "    \n",
+      "        Output.extend(zip(random_states, random_status, data, rng))\n",
+      "        \n",
+      "    return Output"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 3
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Now that we have a function to generate our test data, lets create some data and stick it into a dataframe."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "dataset = CreateDataSet(4)\n",
+      "df = DataFrame(data=dataset, columns=['State','Status','CustomerCount','StatusDate'])\n",
+      "df"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "pyout",
+       "prompt_number": 4,
+       "text": [
+        "<class 'pandas.core.frame.DataFrame'>\n",
+        "Int64Index: 836 entries, 0 to 835\n",
+        "Data columns:\n",
+        "State            836  non-null values\n",
+        "Status           836  non-null values\n",
+        "CustomerCount    836  non-null values\n",
+        "StatusDate       836  non-null values\n",
+        "dtypes: int64(2), object(2)"
+       ]
+      }
+     ],
+     "prompt_number": 4
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "We are now going to save this dataframe into an Excel file, to then bring it back to a dataframe. We simply do this to show you how to read and write to excel files.  \n",
+      "\n",
+      "We do not write the index values of the dataframe to the excel file since they are no meant to be part of our initial test data set."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "# Save results to excel\n",
+      "df.to_excel('Lesson3.xlsx', index=False)\n",
+      "print 'Done'"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Done\n"
+       ]
+      }
+     ],
+     "prompt_number": 5
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "# Grab Data from Excel  \n",
+      "\n",
+      "We will be using the ***ExcelFile*** function and the ***parse*** function to read in data from an excel file. The *ExcelFile* function creates an object and *parse* will help with the actually parsing of the file. Lets take a loook at both of these.  \n",
+      "\n",
+      "**ExcelFile:**  \n",
+      "Parameters  \n",
+      "path : string or file-like objec, Path to xls file  \n",
+      "kind : {'xls', 'xlsx', None}, default None  \n",
+      "Definition:ExcelFile(self, path_or_buf)  \n",
+      "\n",
+      "**parse:**  \n",
+      "Definition: ExcelFile.parse(self, sheetname, header=0, skiprows=None, skip_footer=0, index_col=None, parse_cols=None, parse_dates=False, date_parser=None,   na_values=None, thousands=None, chunksize=None, **kwds)  \n",
+      "Docstring: Read Excel table into DataFrame  "
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "ExcelFile?"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 6
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "ExcelFile.parse?"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 7
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "**Note: The location on the excel file will be in the same folder as the notebook unless specified otherwise.**"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "# Location of file\n",
+      "Location = r'C:\\Users\\hdrojas\\.xy\\startups\\Lesson3.xlsx'\n",
+      "\n",
+      "# Create ExcelFile object\n",
+      "xlsx = ExcelFile(Location)\n",
+      "\n",
+      "# Parse a specific sheet\n",
+      "df = xlsx.parse('sheet1',index_col='StatusDate')\n",
+      "df.dtypes"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "pyout",
+       "prompt_number": 8,
+       "text": [
+        "State             object\n",
+        "Status           float64\n",
+        "CustomerCount    float64"
+       ]
+      }
+     ],
+     "prompt_number": 8
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "df.index"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "pyout",
+       "prompt_number": 9,
+       "text": [
+        "<class 'pandas.tseries.index.DatetimeIndex'>\n",
+        "[2009-01-05 00:00:00, ..., 2012-12-31 00:00:00]\n",
+        "Length: 836, Freq: None, Timezone: None"
+       ]
+      }
+     ],
+     "prompt_number": 9
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "As you can see the first problem we have here is that the column ***Status*** and the column ***CustomerCount*** are of data type *float64*. We would rather have these columns be an ***int*** data type. Below we will simply solve this issue by casting the columns to their appropriate type."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "# Convert data types \n",
+      "df.Status = df.Status.astype('int')\n",
+      "df.CustomerCount = df.CustomerCount.astype('int')\n",
+      "print 'Data Types'\n",
+      "print df.dtypes"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Data Types\n",
+        "State            object\n",
+        "Status            int64\n",
+        "CustomerCount     int64\n"
+       ]
+      }
+     ],
+     "prompt_number": 10
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "df.head()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "html": [
+        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+        "<table border=\"1\" class=\"dataframe\">\n",
+        "  <thead>\n",
+        "    <tr style=\"text-align: right;\">\n",
+        "      <th></th>\n",
+        "      <th>State</th>\n",
+        "      <th>Status</th>\n",
+        "      <th>CustomerCount</th>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>StatusDate</th>\n",
+        "      <th></th>\n",
+        "      <th></th>\n",
+        "      <th></th>\n",
+        "    </tr>\n",
+        "  </thead>\n",
+        "  <tbody>\n",
+        "    <tr>\n",
+        "      <th>2009-01-05</th>\n",
+        "      <td> NY</td>\n",
+        "      <td> 1</td>\n",
+        "      <td> 590</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2009-01-12</th>\n",
+        "      <td> GA</td>\n",
+        "      <td> 2</td>\n",
+        "      <td> 893</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2009-01-19</th>\n",
+        "      <td> NY</td>\n",
+        "      <td> 1</td>\n",
+        "      <td> 498</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2009-01-26</th>\n",
+        "      <td> GA</td>\n",
+        "      <td> 2</td>\n",
+        "      <td> 953</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2009-02-02</th>\n",
+        "      <td> NJ</td>\n",
+        "      <td> 2</td>\n",
+        "      <td> 518</td>\n",
+        "    </tr>\n",
+        "  </tbody>\n",
+        "</table>\n",
+        "</div>"
+       ],
+       "output_type": "pyout",
+       "prompt_number": 11,
+       "text": [
+        "           State  Status  CustomerCount\n",
+        "StatusDate                             \n",
+        "2009-01-05    NY       1            590\n",
+        "2009-01-12    GA       2            893\n",
+        "2009-01-19    NY       1            498\n",
+        "2009-01-26    GA       2            953\n",
+        "2009-02-02    NJ       2            518"
+       ]
+      }
+     ],
+     "prompt_number": 11
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "# Prepare Data  \n",
+      "\n",
+      "This section attempts to clean up the data for analysis.  \n",
+      "1. Make sure the state column is all in upper case  \n",
+      "2. Only select records where the account status is equal to \"1\"  \n",
+      "3. Merge (NJ and NY) to NY in the state column  \n",
+      "4. Remove any outliers (any odd results in the data set)\n"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Lets take a quick look on how some of the *State* values are upper case and some are lower case"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "df['State'].unique()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "pyout",
+       "prompt_number": 12,
+       "text": [
+        "array([NY, GA, NJ, fl, TX, FL], dtype=object)"
+       ]
+      }
+     ],
+     "prompt_number": 12
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "To convert all the State values to upper case we will use the ***upper()*** function and the dataframe's ***apply*** attribute. The ***lambda*** function simply will apply the upper function to each value in the *State* column."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "# Clean State Column, convert to upper case\n",
+      "df.State = df.State.apply(lambda x: x.upper())"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 13
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "df['State'].unique()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "pyout",
+       "prompt_number": 14,
+       "text": [
+        "array([NY, GA, NJ, FL, TX], dtype=object)"
+       ]
+      }
+     ],
+     "prompt_number": 14
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "# Only grab where Status == 1\n",
+      "df = df[df['Status'] == 1]"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 15
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "To turn the ***NJ*** states to ***NY*** we simply.  \n",
+      "\n",
+      "***[df.State == 'NJ']*** - Find all records in the *State* column where they are equal to *NJ*.  \n",
+      "***df.State[df.State == 'NJ'] = 'NY'*** - For all records in the *State* column where they are equal to *NJ*, replace them with *NY*."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "# Convert NJ to NY\n",
+      "df.State[df.State == 'NJ'] = 'NY'"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 16
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Now we can see we have a much cleaner data set to work with."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "df['State'].unique()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "pyout",
+       "prompt_number": 17,
+       "text": [
+        "array([NY, TX, GA, FL], dtype=object)"
+       ]
+      }
+     ],
+     "prompt_number": 17
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "At this point we may want to graph the data to check for any outliers or inconsistencies in the data. We will be using the ***plot()*** attribute of the dataframe.  \n",
+      "\n",
+      "As you can see from the graph below it is not very conclusive and is probably a sign that we need to perform some more data preparation."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "df['CustomerCount'].plot()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "pyout",
+       "prompt_number": 18,
+       "text": [
+        "<matplotlib.axes.AxesSubplot at 0x6422bb0>"
+       ]
+      },
+      {
+       "output_type": "display_data",
+       "png": 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QhEVDdcoreFUDnDjRO1WfLCtFNVSBqYLnMm9DQ7RoqA1lMn6C58srtiEedD9U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\nCnPwDAaDUU/5f9mI4Ko3mUVYAAAAAElFTkSuQmCC\n"
+      }
+     ],
+     "prompt_number": 18
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "If we take a look at the data, we begin to realize that there are multiple values per State, StatusDate, and Status. It is possible that this means the data you are working with is dirty/bad/inaccurate, but we will assume otherwise. We can assume this data set is a subset of a bigger data set and if we simply add the values in the ***CustomerCount*** column per State, StatusDate, and Status we will get the ***Total Customer Count*** per day.  "
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "sortdf = df[df['State']=='NY'].sort(axis=0)\n",
+      "sortdf.head()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "html": [
+        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+        "<table border=\"1\" class=\"dataframe\">\n",
+        "  <thead>\n",
+        "    <tr style=\"text-align: right;\">\n",
+        "      <th></th>\n",
+        "      <th>State</th>\n",
+        "      <th>Status</th>\n",
+        "      <th>CustomerCount</th>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>StatusDate</th>\n",
+        "      <th></th>\n",
+        "      <th></th>\n",
+        "      <th></th>\n",
+        "    </tr>\n",
+        "  </thead>\n",
+        "  <tbody>\n",
+        "    <tr>\n",
+        "      <th>2009-01-05</th>\n",
+        "      <td> NY</td>\n",
+        "      <td> 1</td>\n",
+        "      <td> 590</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2009-01-12</th>\n",
+        "      <td> NY</td>\n",
+        "      <td> 1</td>\n",
+        "      <td> 368</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2009-01-12</th>\n",
+        "      <td> NY</td>\n",
+        "      <td> 1</td>\n",
+        "      <td> 103</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2009-01-19</th>\n",
+        "      <td> NY</td>\n",
+        "      <td> 1</td>\n",
+        "      <td> 498</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2009-01-26</th>\n",
+        "      <td> NY</td>\n",
+        "      <td> 1</td>\n",
+        "      <td> 408</td>\n",
+        "    </tr>\n",
+        "  </tbody>\n",
+        "</table>\n",
+        "</div>"
+       ],
+       "output_type": "pyout",
+       "prompt_number": 19,
+       "text": [
+        "           State  Status  CustomerCount\n",
+        "StatusDate                             \n",
+        "2009-01-05    NY       1            590\n",
+        "2009-01-12    NY       1            368\n",
+        "2009-01-12    NY       1            103\n",
+        "2009-01-19    NY       1            498\n",
+        "2009-01-26    NY       1            408"
+       ]
+      }
+     ],
+     "prompt_number": 19
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Our task is now to create a new dataframe that compresses the data so we have daily customer counts per State and StatusDate. We can ignore the Status column since all the values in this column are of value *1*. To accomplish this we will use the dataframe's functions ***groupby*** and ***sum()***.  \n",
+      "\n",
+      "Note that we had to use **reset_index** . If we did not, we would not have been able to group by both the State and the StatusDate since the groupby function expects only columns as inputs. The **reset_index** function will bring the index ***StatusDate*** back to a column in the dataframe. "
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "# Group by State and StatusDate\n",
+      "Daily = df.reset_index().groupby(['State','StatusDate']).sum()\n",
+      "Daily.head()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "html": [
+        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+        "<table border=\"1\" class=\"dataframe\">\n",
+        "  <thead>\n",
+        "    <tr style=\"text-align: right;\">\n",
+        "      <th></th>\n",
+        "      <th></th>\n",
+        "      <th>Status</th>\n",
+        "      <th>CustomerCount</th>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>State</th>\n",
+        "      <th>StatusDate</th>\n",
+        "      <th></th>\n",
+        "      <th></th>\n",
+        "    </tr>\n",
+        "  </thead>\n",
+        "  <tbody>\n",
+        "    <tr>\n",
+        "      <th rowspan=\"5\" valign=\"top\">FL</th>\n",
+        "      <th>2009-02-02</th>\n",
+        "      <td> 1</td>\n",
+        "      <td> 385</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2009-02-09</th>\n",
+        "      <td> 1</td>\n",
+        "      <td> 125</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2009-02-16</th>\n",
+        "      <td> 1</td>\n",
+        "      <td> 378</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2009-03-02</th>\n",
+        "      <td> 1</td>\n",
+        "      <td> 722</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2009-05-18</th>\n",
+        "      <td> 1</td>\n",
+        "      <td> 962</td>\n",
+        "    </tr>\n",
+        "  </tbody>\n",
+        "</table>\n",
+        "</div>"
+       ],
+       "output_type": "pyout",
+       "prompt_number": 20,
+       "text": [
+        "                  Status  CustomerCount\n",
+        "State StatusDate                       \n",
+        "FL    2009-02-02       1            385\n",
+        "      2009-02-09       1            125\n",
+        "      2009-02-16       1            378\n",
+        "      2009-03-02       1            722\n",
+        "      2009-05-18       1            962"
+       ]
+      }
+     ],
+     "prompt_number": 20
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "The ***State*** and ***StatusDate*** columns are automatically placed in the index of the ***Daily*** dataframe. You can think of the ***index*** as the primary key of a database table but without the constraint of having unique values. Columns in the index as you will see allow us to easily select, plot, and perform calculations on the data.  \n",
+      "\n",
+      "Below we delete the ***Status*** column since it is all equal to one and no longer necessary."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "del Daily['Status']\n",
+      "Daily.head()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "html": [
+        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+        "<table border=\"1\" class=\"dataframe\">\n",
+        "  <thead>\n",
+        "    <tr style=\"text-align: right;\">\n",
+        "      <th></th>\n",
+        "      <th></th>\n",
+        "      <th>CustomerCount</th>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>State</th>\n",
+        "      <th>StatusDate</th>\n",
+        "      <th></th>\n",
+        "    </tr>\n",
+        "  </thead>\n",
+        "  <tbody>\n",
+        "    <tr>\n",
+        "      <th rowspan=\"5\" valign=\"top\">FL</th>\n",
+        "      <th>2009-02-02</th>\n",
+        "      <td> 385</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2009-02-09</th>\n",
+        "      <td> 125</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2009-02-16</th>\n",
+        "      <td> 378</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2009-03-02</th>\n",
+        "      <td> 722</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2009-05-18</th>\n",
+        "      <td> 962</td>\n",
+        "    </tr>\n",
+        "  </tbody>\n",
+        "</table>\n",
+        "</div>"
+       ],
+       "output_type": "pyout",
+       "prompt_number": 21,
+       "text": [
+        "                  CustomerCount\n",
+        "State StatusDate               \n",
+        "FL    2009-02-02            385\n",
+        "      2009-02-09            125\n",
+        "      2009-02-16            378\n",
+        "      2009-03-02            722\n",
+        "      2009-05-18            962"
+       ]
+      }
+     ],
+     "prompt_number": 21
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "# What is the index of the dataframe\n",
+      "Daily.index"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "pyout",
+       "prompt_number": 22,
+       "text": [
+        "MultiIndex\n",
+        "[(FL, 2009-02-02 00:00:00), (FL, 2009-02-09 00:00:00), (FL, 2009-02-16 00:00:00), (FL, 2009-03-02 00:00:00), (FL, 2009-05-18 00:00:00), (FL, 2009-06-08 00:00:00), (FL, 2009-06-15 00:00:00), (FL, 2009-06-22 00:00:00), (FL, 2009-07-06 00:00:00), (FL, 2009-07-27 00:00:00), (FL, 2009-08-03 00:00:00), (FL, 2009-09-14 00:00:00), (FL, 2009-09-28 00:00:00), (FL, 2009-10-19 00:00:00), (FL, 2009-11-23 00:00:00), (FL, 2009-11-30 00:00:00), (FL, 2010-01-04 00:00:00), (FL, 2010-01-11 00:00:00), (FL, 2010-02-01 00:00:00), (FL, 2010-02-15 00:00:00), (FL, 2010-03-15 00:00:00), (FL, 2010-03-22 00:00:00), (FL, 2010-04-12 00:00:00), (FL, 2010-04-19 00:00:00), (FL, 2010-04-26 00:00:00), (FL, 2010-05-03 00:00:00), (FL, 2010-05-10 00:00:00), (FL, 2010-05-17 00:00:00), (FL, 2010-06-07 00:00:00), (FL, 2010-06-21 00:00:00), (FL, 2010-06-28 00:00:00), (FL, 2010-07-05 00:00:00), (FL, 2010-08-30 00:00:00), (FL, 2010-10-11 00:00:00), (FL, 2010-10-18 00:00:00), (FL, 2010-10-25 00:00:00), (FL, 2010-11-01 00:00:00), (FL, 2010-11-15 00:00:00), (FL, 2010-11-29 00:00:00), (FL, 2010-12-27 00:00:00), (FL, 2011-01-03 00:00:00), (FL, 2011-01-10 00:00:00), (FL, 2011-01-24 00:00:00), (FL, 2011-02-07 00:00:00), (FL, 2011-03-07 00:00:00), (FL, 2011-03-14 00:00:00), (FL, 2011-03-28 00:00:00), (FL, 2011-04-04 00:00:00), (FL, 2011-04-18 00:00:00), (FL, 2011-04-25 00:00:00), (NY, 2012-10-22 00:00:00), (NY, 2012-11-12 00:00:00), (NY, 2012-11-26 00:00:00), (NY, 2012-12-03 00:00:00), (TX, 2009-01-05 00:00:00), (TX, 2009-01-19 00:00:00), (TX, 2009-03-02 00:00:00), (TX, 2009-03-16 00:00:00), (TX, 2009-04-13 00:00:00), (TX, 2009-04-20 00:00:00), (TX, 2009-06-01 00:00:00), (TX, 2009-08-03 00:00:00), (TX, 2009-08-31 00:00:00), (TX, 2009-09-21 00:00:00), (TX, 2009-12-14 00:00:00), (TX, 2010-01-04 00:00:00), (TX, 2010-02-15 00:00:00), (TX, 2010-04-19 00:00:00), (TX, 2010-05-31 00:00:00), (TX, 2010-06-07 00:00:00), (TX, 2010-06-14 00:00:00), (TX, 2010-06-28 00:00:00), (TX, 2010-07-05 00:00:00), (TX, 2010-08-09 00:00:00), (TX, 2010-08-23 00:00:00), (TX, 2010-09-06 00:00:00), (TX, 2010-10-04 00:00:00), (TX, 2010-11-01 00:00:00), (TX, 2010-11-08 00:00:00), (TX, 2010-12-13 00:00:00), (TX, 2011-01-17 00:00:00), (TX, 2011-02-14 00:00:00), (TX, 2011-02-28 00:00:00), (TX, 2011-03-14 00:00:00), (TX, 2011-05-16 00:00:00), (TX, 2011-06-13 00:00:00), (TX, 2011-09-12 00:00:00), (TX, 2011-09-26 00:00:00), (TX, 2011-10-17 00:00:00), (TX, 2011-11-07 00:00:00), (TX, 2011-11-21 00:00:00), (TX, 2011-12-05 00:00:00), (TX, 2012-01-30 00:00:00), (TX, 2012-03-05 00:00:00), (TX, 2012-03-26 00:00:00), (TX, 2012-06-04 00:00:00), (TX, 2012-07-02 00:00:00), (TX, 2012-07-30 00:00:00), (TX, 2012-10-08 00:00:00), (TX, 2012-11-12 00:00:00)]"
+       ]
+      }
+     ],
+     "prompt_number": 22
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "# Select the State index\n",
+      "Daily.index.levels[0]"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "pyout",
+       "prompt_number": 23,
+       "text": [
+        "Index([FL, GA, NY, TX], dtype=object)"
+       ]
+      }
+     ],
+     "prompt_number": 23
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "# Select the StatusDate index\n",
+      "Daily.index.levels[1]"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "pyout",
+       "prompt_number": 24,
+       "text": [
+        "<class 'pandas.tseries.index.DatetimeIndex'>\n",
+        "[2009-01-05 00:00:00, ..., 2012-12-31 00:00:00]\n",
+        "Length: 181, Freq: None, Timezone: None"
+       ]
+      }
+     ],
+     "prompt_number": 24
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Lets now plot the data per State.  \n",
+      "\n",
+      "As you can see by breaking the graph up by the ***State*** column we have a much clearer picture on how the data looks like. Can you spot any outliers?"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "Daily.ix['FL'].plot()\n",
+      "Daily.ix['GA'].plot()\n",
+      "Daily.ix['NY'].plot()\n",
+      "Daily.ix['TX'].plot()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "pyout",
+       "prompt_number": 25,
+       "text": [
+        "<matplotlib.axes.AxesSubplot at 0x5a83c70>"
+       ]
+      },
+      {
+       "output_type": "display_data",
+       "png": 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dM2DbXQoxe7E6sRgA0nkDcWkpoM0ZZGQ4V2klh7M5A6EYALWhIrkbo8pK+l7u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\nxhhj4GLAGGMMXAwYY4wB+A8HNbg9S1tcRAAAAABJRU5ErkJggg==\n"
+      },
+      {
+       "output_type": "display_data",
+       "png": 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AducOTSxt5FRcLKZVP5rMweFd/B0dHSEWi1FdXY2GhgZUV1ejW7duOHjwIObP\nnw8AmD9/Pg4cOAAAiIuLQ1hYGMRiMTw9PeHl5YWzZ8/yHVYzDJn5G0v8ucy/tla/TO/+fXqX0ZJt\n1bEj9S5feskyxUVoDNHeAbBc60fdndO4cXQVMyErnXS1feRZvRr41780/76WltJZ/4poWu/Pu/g7\nOztj9erV8PDwQLdu3dCxY0cEBwejqKgILg9HJlxcXFD0sN9qfn4+pFKp7PVSqRR5StZBi4yMlP1L\nTEzUK0ZO/C05879169EFTh8xvngR6NePlpG1xIwZQM+ewObNuh/PGuFW8dLVNtAGSy33VJf5t29P\nvfCTJ4U7Ph/vX0gIFXRN757Lyh4X/8TEROTlReLbb6lWqsNOtzBVk5GRgU8//RRZWVno0KEDnn/+\neezevbvZc0QiEURqaqKUPdbSH6IpjY3UE+vZ0zADvkKIv6cnkJOj/jmZmfRvdHCg2b8m4q2M5OSW\nLR8OkYguFzhwIDBzJi0FZbTM/fu0MZdQq3jJY6mZf0vzJDjff8oUYY7PxxKctrbAG28AH39M5ye0\nRGnp4xV4QUFB6NQpCDNm0P5bGzZsUPl63jP/8+fPY+TIkejUqRPs7Owwffp0/Pnnn3B1dUVhYSEA\noKCgAF27dgUASCQS5MgpWW5uLiQSCd9hye0f6NyZftmEtn2KiuigjLs7v/vt0oX6faoghGb+PXvS\nclZ9Bn3PnlU/2KuIhwdt/vbKK5Y5sCgEhhjs5bDUcs+WzqHQvj8ftg9AZ86fPq1ZCxdlmT9AnYG8\nvJZXEeNd/H19fZGUlISamhoQQnD06FH4+flh2rRpiImJAQDExMQgNDQUABASEoLY2FjU1dUhMzMT\n6enpGKppqqkD3GAvILztc/my/j38ldG1q/oFou/epRe2Dh0eZf66oslgryLLl9MP3sO3m9EChhR/\na838AwKoQHOt3PmGL9uuTRuaOH3yScvPVZb5A1TXAgJa7mjKu/gHBARg3rx5GDx4MPr37w8AeOml\nlxAeHo4jR47Ax8cHx48fR3h4OADAz88PM2fOhJ+fHyZPnoxt27aptYT0hfP7AeFtHz4nd8nTsSPt\n36HKy+csH0C/zL+oiAqTt7d2r7Ozo7X/a9eqv0MxJO++a5hp/rpgiNYOHNbo+QOPSj6F6vLJh+3D\nsWwZLf1s6bujKvMHNKv3F6TO/6233sK1a9dw9epVxMTEQCwWw9nZGUePHkVaWhoSEhLQUa7wPSIi\nAjdv3sRfDqFUAAAgAElEQVSNGzcwceJEIUKSIS/+Qts+Qvj9AP0gd+6s+sPBWT6Afpn/2bO0pYMu\n1+KBA4G5c2n3QlMgMRGIizN2FMphto/+aHIOhbR++LJ9ADpjd8YM4Kuv1D9PVeYPGFH8TRlD2j58\nrN6lCnW+/61bdCIYoF/m39LkrpbYsAH4/XfgyBHd98EX+fk0FlOE2T76o8ndU3AwTQKEKEXmu1pr\n1Spg2zb1392yMib+WmGozP/+fbpwg6+vMPtX5/vL2z76Zv76DL+0bUs/wK+8onmbWSEghA6ApaYa\nbs1mbTC0+Fuj7QPQZVB9fYWZiMin7QPQQdvBg+kSkKpQZ/v4+FDbVh1WJf6E0Mxf3vMXKvP/+2/6\nBorFwuy/SxfV4q9o++iS+Tc1AefO6Sf+AC2tGzIEeP99/fajD+Xl9H0YNsw0ZyCzzF9/ND2HQlk/\nfNo+HKtX07JPVQ0P1Nk+trYtuw5WJf7FxdQG4YYbhBzwFcrv5+jaVXPbR5fMPz2dnqeHFbl68emn\nwPbt2i0uzSd5eXTqfGCgaVo/hmjtwGGpnr+mg+ZCLe0oxCS9oCD6fv3yi/LH1WX+AL1zUIdVib+8\n5QMIa/sILf6qMv/6eupve3jQ33XN/PW1fORxdQU2baKtH4yx7GN+PiCRAKNHm6b4G6q1A2C5mb+m\n53DIEDpBMj+f3+PzbfsAtNCCa/mgDHWZP8DEvxnyg72AsLaPsTL/nBzaJ5xbMlLXzF/fwV5FXnyR\nxtRSBYMQcJn/8OH0feFrdTO+YJ6/fhBCbRdNzqGdHTB+PF1Ji09aWr9XV55/nt7JKw7eNjXRY6rr\nFszEXw7FzF8o24fr4f9wmoMgqBrwlbd8AN0HfPnM/AHaefDrr2kFUG4uf/vVhLw8mvm3a0eXuRO4\nb6DWsFJP/bh/n37ONR1fE8L3FyLzB+jftHIl9f7lKS+nx1PXEkQ+0VWGVYm/YuYvFguT+aen01pd\nIX1cVaWe8oO9gG6lng8eANeu0Vp9PunTh05gWb6c3/22BGf7ANT3P3XKsMdvCUsf8F28mBYPCIW2\nttnEibT8mE8LUogBX47Fi+mdSnb2o22qOnrKY9OCuluV+Bsq8xeyvp9DVeYvX+YJ6Jb5X75MS8U0\nWYxaW9atox1Jtelbri+c7QOY5qCvJds+TU3A3r30uycU2g6YSyT0n6YrXrVEXR39O3VtntgSjo7A\nokXAZ5892qauxl9TrFr8hRrwFdrvB1QP+PKR+fPt98vj4EDtn9dfb7nxFF/IZ/5PPkl7uwvdzVUb\nDN3ewZCZ/7Vr9O8TcolPXc4fn9YPZ/kI2JUGr78OfPfdo/OoSebfElYj/uXldFFz+cWOhRrwNYT4\nOzrSjKOmpvl2Pjx/vv1+RcaMobfe69cLdwx55DP/zp0BqZTe3ZgKluz5//EH/Smk+Oty/vhc2jEz\n81F1nVC4u9M5M998Q39nmb8WcFm//NVZSNtHaPEXiZT7/oq2jy6Zv9DiD9AFX/bupXcZQtLQQO+Q\n5Be5NjXrx5I9/9On6ffO1DL/J5+kdyWlpfof/9o1unau0KxeTa2furqWa/w1wWrEX3GwFxDG9iks\npPsUcEkCGYrlnuXlVOi7dHm0TdvMv7SU/g19+vAXpzKcnYEtW4AlS4Ttr1RcTKf1y1eCmJL4c2WK\nhljFCzC853/6NDB1qumJv4MDvQM9elT/46ek0CoyoRkwAOjdG9izp+Uaf02wGvFX9PsBYWwfLusX\n0v/jUPT9MzOp5SN/bG0neZ07R9cANcSqUrNnUztmyxbhjsGVecrDib8pLDZz/z69O7PjfU095XC2\njyH+9rw8emEbNkzYnkq63jmNH08bvemLocQfeNTygXn+WqBM/IXI/IXq4a8Mxcxf0fIBtJ/kJeRg\nryIiEZ309dFHwlWDyA/2cnh40Eqm1FRhjqkNhmztANCLjFhsmIlup08DI0fSDNXUMn+AfldaWg5V\nEwxl+wB0oLq+HjhwgGX+GqPM9hEy8zcEipm/YqUPoL3tYwi/X54ePeiiL6++Kkw2Kj/YK4+pWD+G\nbO3AYSjr5/Rp6q137Gia4u/mBhQU6Hfs6mqaYCgmlkJhY0Oz/4wMlvlrjCrbh+/M35Dir1jrr1jp\nA2g34EuIYTN/jjfeoHcw33/P/76VZf6A6Yi/IQd7OQxV8XP6NDBqlPDir+s57NZN/x4/N27QpNJQ\nth0AzJlDqxY7ddJvP4KI/7179zBjxgz06dMHfn5+SE5ORmlpKYKDg+Hj44MJEybgntynISoqCt7e\n3vD19UUC3003QMsh79x5fCF1vm2fqiraukCoHv6KaGL7aJP5Z2XR/juGGKyWh1v2cc0aoKSE332b\neuZvDPE3RMVPVRUVxkGDaFZuipm/iwv9/ugz0zclxXCWD4eDA03SRo3Sbz+CiP+KFSswZcoUXL9+\nHVeuXIGvry+io6MRHByMtLQ0jB8/HtHR0QCAlJQU7NmzBykpKYiPj8fSpUvRpKqBtY5kZgLduz8+\niMm37XP1Kh34MVQWoInto03mf/as4bN+jiFD6ADwmjX87lfZgC9AL9BVVfx4vvpgqeKfnEwXEeda\nqJui+NvZ0exZ1boYmmDIwV55unfXX2d4l6ny8nL8/vvviImJoQews0OHDh1w8OBBnDx5EgAwf/58\nBAUFITo6GnFxcQgLC4NYLIanpye8vLxw9uxZDB8+vNl+IyMjZf8PCgpCUFCQxjEps3xobPxm/oa0\nfIDmmX9TE83cPT2bP0ebzD852bB+vyLvv0+zqOPHgXHj+NmnKttHJKJ+9O+/A//4Bz/H0gVjib/Q\nnj9n+QBA69Y0u37wgF4M+Eafc+jmRj8jbm66vf7aNWDePN1eKwSJiYlI1LCEiXfxz8zMRJcuXbBw\n4UJcvnwZgwYNwqeffoqioiK4PJxe6+LigqKHa4zl5+c3E3qpVIq8vLzH9isv/tqibLAX4N/2MbT4\ny2f+BQV09F+xH4+2mf977/Eboza0bw98+SVd9vHKFX6EQpXtAzzq729M8TdkawcOQ3j+p0/TJn4A\nvdB27Ej/ViHEX59z2K2bfoO+xsr8VaGYGG/YsEHlc3m3fRoaGnDx4kUsXboUFy9eRNu2bWUWD4dI\nJIJITSG8usd0QV3mz6ftY8zMX5nlA2ie+dfX0/hb6gEuNNOm0VbYmzbpv6/qajreo6oqwhR8f0u0\nfRobaf+kkSMfbRPS+tFX/HUd9K2poWN8LbVONlV4F3+pVAqpVIohQ4YAAGbMmIGLFy/C1dUVhYWF\nAICCggJ0fbg+oEQiQY6c8ZqbmwsJzyOOhsj8Gxrour1C9vBXpG1bavfcv6+80gfQPPP/+2/qIxpa\niJTx+efAv/9Nb6n1IT+ffrlV5RJPPAHcvs3/ILM2WKLt8/ff1Ebp3PnRNiHFX1/bR9fMPzWVJpVC\nrdMtNLyLv6urK9zd3ZGWlgYAOHr0KPr27Ytp06bJxgFiYmIQGhoKAAgJCUFsbCzq6uqQmZmJ9PR0\nDFViPOszKcUQmX9aGhUaQ03TB6iocdm/skofQPPM3xglnqro1o3aTy+9pHrxak1Q5fdz2NnR1b2M\nuai7JZZ6/vHH45UoiuL/xx/AmTP6vb8A/f7W1eneflyfzN/ULB9tEaTaZ+vWrZgzZw4CAgJw5coV\nrF+/HuHh4Thy5Ah8fHxw/PhxhIeHAwD8/Pwwc+ZM+Pn5YfLkydi2bZtS2+frr3WLpaGBZnfKsmI+\nB3wNbflwcL6/KttH08zf0JO7WuLll+lProuhLqjz+zmMvbiLJdo+3OQueTjPH6DzSebMARYsoLOt\nV66kFwNdLgTcJDldnWJ9Mv9r18xb/AUpSgwICMA5JUv3HFXRRSkiIgIRERFq96nrrfnt27Sjo7KF\nFvi0fYwl/txEr1u36Dq5imia+Z89a/gVttTBLfs4diwQEqJbNYaqMk95Ro8G3npLtxj5wNDtHQAq\n/kJaXadPA4r1GfKZ/4ULNCm5cYMu7LN3L53hXVICPPccXbd21CjN+kvpe/70zfyNWSygL2Yzw/fu\nXd1ep8ryAfi1fQyxepcyuLbO+tg+FRW0TLRfP0FC1Jl+/egdwIoVur2+JdsHoHc7f/9tvEXNLa29\nQ04OHQj19m6+XX6i1759VORFIpo5v/MOnSNz/DidePX663TNhWXLgBMn1E/C0rdaSp/Mn9k+BkLX\nTEXVYC/AX+ZPiHEzf27QUpnFoYntc/EiHag2xYGr9etps7xDh7R/rSa2T+vW9H1LStItPn2xNM+f\na+amaMNwmT8hVPynT3/8tb6+wNtv0+/SyZP0ArB6NX0PX3kFOHbs8e+rvudP11m+Dx7Q753iRc6c\nsHjxbynz50P8ucxB14ki+tClC7VsPDyU3yZzswDV/Z1paaabwbRuTe2fZcu0FyxNMn/AuCWflub5\nK/P7gUfif+0avRMdNEj9fnx86HrPFy/SgeEePYDwcPode+kluqB5fb3+mb9YTOfHaDvLNy2NxmRv\nr/uxjY1ViL+qzF+Z7TNgAF0lRxsM2cNfka5daaWOMsuHo6XsX9Vgsakwbhz1/v/5T+1ep0nmD1in\n+Atl+8jP7JWHE/+ffqJZvzbflV69aOfXc+doouPjQz8Lbm50Poi+Yya6TPQy98FewIzEX1fP/+ZN\n1Zm/ou3T1ERnlmZlaXcMY1k+ABX/khL14t2S72/q4g8A//oX8OOPwPnzmj2fkEd1/i0xahQVlbo6\n/WLUFkOv4sUhlO1TWUkz4oEDH3+ME3/O79eVHj1o/6fkZDpwPGsWEBam+/6ARy0etMEYDd34xmzE\nX5fMnxAqbJoO+FZU0AtAbq52xzHkAi6KcEs2Kitl5Wgp88/IMH3x79yZLvqyZIlmVl1pKbWMNKn/\n7tiRfkYuXtQ/Tm24f59emA091iKU7ZOURO+clVXWdexIk6SiImDECH6O1707sGoVXSZSH3TJ/M19\nsBcwI/FvaqJVBNpQUEA/6KoyK0XPn7N7tBV/Y2f+gOVn/gDwwgu0C+Nnn7X8XE3KPOUxhvVjDMsH\nEE78Vfn9ABX/ggLg2WcNs0SoNuiS+Rty9S6hMBvx79RJ++xf3WAv8LjtU1pKf2oj/pWV9IPj46Nd\nbHzBZf66ev5lZfTCqu/CEIZAJKJtH6KiWrbmNB3s5bA28RfC81fl9wNU/AHlVT7GRtvMv7aWfv7M\nudIHsALxV9d0ycaGCh83s1AX8b9yhWYAhlzJR542beiFR91FTl3mz2X9xhis1gUvL1r+19Kyj5oO\n9nIEBuo+y1RXjCX+Qnj+DQ3Uh5dv5iZPp05AUBD9Z2pom/mnp9PW6crsLXPCbMS/c2ftB33VDfYC\nVPDks/+yMlo5oKSjtEqMNblLntRU9SKibhF3c7F85Fmzhl6g9+xR/RxtM383N9r9U99mctpgTPG/\nf5/fNZOvXKF1+ao6qLZqRSdsmeJcEm0zf0uwfAAzEn8hbB+g+aBvaSng769d5m9Mv19THBxU2z7m\nKP5iMe35s2qV6rJcbTN/4FF/f0NhjNm9APXcW7XSfgxNHer8flNH2xYPljDYC1i4+Kub3cshP+hb\nVkZnuubmap4VmYv4q8r8zaHSRxnDh1P/WFVfHm0HfAHD+/7G6OvDwbf1o87vN3W0neVrCTX+gIWL\nvyaZv7ztU1pKy8dsbB51IFRHQwPNAvz9tYvL0Kgb8DXHzJ/jgw+AX39VLtja2j7AI/Hn0w5Rh7Fs\nH4Dfih9ClLdxNhe4Wb7cwkgtYQk1/oCZib82nn9pKb2Syy8ooQxF28fZmXqXmlg/qan0ue3aaR6X\nMVCW+VdX0wwmNdV8xd/REdi6lU73V/z7dLF9evWinxltJ/npiqWI/+3b9DvUUqJlymg66FtXR5so\nGqu6j0/MRvw7d9Yu8+ey/paqWBRtH2dnmjFqMuhrzMld2tCqFbBzJ62THzmStrju1AmYMYN2tVRc\n9N2cePZZoHdvQH6l0Pp6eiF/uGS0xohEhrV+jCn+3KAvH3B+v7lUjClD00Hf9HTaR0uItYgNjZEK\nFLVHW9vnxg3aJbAlFG0fJyfNM39z8PsBunBGUhLN8Hv2pLOB3dyovWUJfPEFrbiaNYu+54WFdP6D\nLpOJRo+mi7vMm8d/nIpUVBgvW+Yz8zdnv59D08zfUgZ7AYEy/8bGRgwYMADTpk0DAJSWliI4OBg+\nPj6YMGEC7smt5xYVFQVvb2/4+voiISFB5T61FX9NB2X0sX3MRfwnTaKLa8ybRzM0icRyhB+g79c7\n79De/01Nuvn9HIbM/PXtSKkPTPybo2nmz8S/BT777DP4+fnJlmOMjo5GcHAw0tLSMH78eEQ/vEdP\nSUnBnj17kJKSgvj4eCxduhRNKmbZaOv5a1qLq1jnr2nmb8we/ozHWbqUDmp/+61ufj9Hv360vW9R\nEb/xKcPYnj8ftk95Oa2qM/ZcF33RNPO3lBp/QADxz83NxS+//ILFixeDPCybOHjwIObPnw8AmD9/\nPg4cOAAAiIuLQ1hYGMRiMTw9PeHl5YWzZ88q3e/27ZHIy4tEZGQkEhMTW4xD0yu0rpl/Xh59rTF6\n+DMex9aW1v5zPeB1zfxtbem4yB9/8BufMozt+fOR+Scl0d785tzXHrCczD8xMRGRkZGyf+rg3fN/\n44038NFHH6GiokK2raioCC4PR99cXFxQ9DCtys/Px/Dhw2XPk0qlyFMx0vrBB5H4+GO60k9LrRSq\nq+lVXBM/lRvwra2lF4G2bTUTf5b1mx79+wOLFgGbNz++hqw2cNaPPq2HNcHYmT8f4m/Ok7vk0STz\nr6+nhSS9exsmJl0ICgpCkFwPjQ0bNqh8Lq+Z/6FDh9C1a1cMGDBAlvUrIhKJZHaQqseVYWNDm0Nx\n/XfUceMGbbqkSb8dzvbhLB+RSLNqHyb+psk779CLt4eH7vvgBn2FxlLE39z9fkCzzP/mTfrZat3a\nMDEJDa+Z/5kzZ3Dw4EH88ssvePDgASoqKjB37ly4uLigsLAQrq6uKCgoQNeHfYglEglycnJkr8/N\nzYVEzf06N+jLtTFWhTa+HGf7cJYPd5zqauqJtm2r/HV//UVLJRmmRZs2dGEWfQZSBw+mi5IILc7G\nau8A0M+1ppOaVFFfT881X/35jYmLCx3raWxUXSVm6paPtvCa+X/wwQfIyclBZmYmYmNjMW7cOOza\ntQshISGIiYkBAMTExCA0NBQAEBISgtjYWNTV1SEzMxPp6ekYOnSoyv1r2txNmzdJMfMHaPYvlarP\n/s2lxt8a6dxZvwZi9vb0AnDmDH8xKcKt4mXOmf/ly3RGPPe9MWfs7amzoE5fLGmwFxB4khdn4YSH\nh+PIkSPw8fHB8ePHER4eDgDw8/PDzJkz4efnh8mTJ2Pbtm1qLSFNyz21zfwbGppn/oB637+8nNaS\nm3s/b4ZqhC75PHOGfn6M1eXSxYWuiatukZ+WsBS/n6Ml359l/hoyZswYHDx4EADg7OyMo0ePIi0t\nDQkJCejIrewAICIiAjdv3sSNGzcwceJEtfvUVPy1eZOU2T6AevG/coX28zG1FYkY/CG0+MfEAA8L\n4IzCs88C7u60NYauvYwsxe/naKm7JxN/I6KJ+FdXU7umpW6eHMpsH0C97cMGey2fESNoyai6tY91\npaYG2LsXmDuX/31rio0NsHs3vUv+4APtX0+IZYq/qkHfhgba2kGTrgHmgtmJf0uef2oqFX5NV9ZS\nlflLJKozfyb+lk/79kCfPtQa4ZsDB4AhQ3Sfi8AXbdsCBw8C//mP+oVxlJGVRWdT9+ghSGhGQZ3t\nk5FBLw5t2hg2JiExK/HXpLmbtoMynOfPNXXjUGf7mMLqXQzhEcr6+e47YMEC/verC9260QvA8uXA\nn39q/jpLaOamiLrM31J6+MtjVuKvie2jrS/H2T5cUzcOVeJfXw9cv276PfwZ+iOE+Ofl0buJhwVv\nJkFAAL0gTZ9O2xVrgqVZPoD6zN9SevjLY3Hir0vmr82A740btLzNkm7/GMp58kmaDWu6wpMm7NpF\n54eY2kShKVOA9euBqVMBub6LKrFE8VeX+VvaYC9ggeKva+avOODbtSvdplgKx+r7rYcuXWg2eOUK\nP/sjxPhVPup47TXgqaeA559/1O9KGffu0TsES/seqMv8La3GHzAz8W9pkldNDc3WNa30AVTX+dva\n0g+DYibABnutCz5bPZw9Sz9rI0fysz8h2LKFTnhatkx1Ceiff9IBa2PNURAKV1c6y1exsXBDA53x\nbUmVPoCZib+zM83GVX0oU1NpMzdtPpSqbB9AecUPE3/rgk/fn8v6TXmQ1M4OiI0FkpPphUAZlmj5\nAPSi16HD420vbt2iFwZVrV7MFbMSf7GYeu2qFlfX5dZMLKbrct67R6d3y6Po+7Me/tYHX4u6P3gA\n/N//GWaFMH1p3x44dAj45BNalqqIpYo/oNz3t8TBXsDMxB9Q7/vrMihjZ0fvJtq0efyOQVH8c3Lo\nYujarg3LMF+6d6fveXq6fvv5+WdaVaNPt1FD4u5OhX/JEuDChUfb6+tptZIlNHNThjLf3xIHewEz\nFH91vr+umX9xsfLmVIriz7J+64QP6ycmxnRq+zVl8GC6QM4zz9DEB6AFD716GW/5SaFR1uLBEgd7\nATMUf3WZvy4TMezsqPgr+v3A4y0e2OQu62T0aP3Ev7CQWiXTp/MXk6EIDQVWrgSefpp2IbVkywdQ\nXuTBMn8TQZX419TQ7ETbTpstiT/L/BmBgfpV/Hz/PRVRcx0wXL0aGDYMmD0bOHnSssVfMfNvbKSF\nJH36GC8mobAY8del0gdQb/soVvsw8bdO+vShC7u0tLqbMggxrXYOuiASAV9+SQsj4uIsW/wVM//M\nTDrnp10748UkFBYj/rqOyNvZAUVFyjN/Nzf6WEMDrQa6c0ezdYEZloVIRGf76mL9XLpEF00JDOQ/\nLkMiFgP/+x/w/vt0ENxSUcz8LdXyAcxQ/FUN+OraeMnOjmZ1yjJ/e3t6sSkqoqsWsR7+1ouug77f\nfUdr+23M7pv2OB07Am+/bdrzFPRFMfO3xIZuHLx/JHNycjB27Fj07dsX/fr1w+effw4AKC0tRXBw\nMHx8fDBhwgTck2sgEhUVBW9vb/j6+iIhIUHt/vnO/DmbSFnmDzzy/ZnlY93oIv51dcCPP5pHbT+D\n4upKkz1ulq+l1vgDAoi/WCzGJ598gmvXriEpKQlffvklrl+/jujoaAQHByMtLQ3jx49HdHQ0ACAl\nJQV79uxBSkoK4uPjsXTpUjQpzq+WQ5X465P5A+rFPy+Pib+1M2AA7WFfWqr5aw4fpp/Jnj0FC4vB\nMw4OtIyVcxeY7aMFrq6ueOKhSrZr1w59+vRBXl4eDh48iPkPO1rNnz8fBx5OHYyLi0NYWBjEYjE8\nPT3h5eWFs2fPqty/MvF/8AC4fVu3NXW5zF/VItQs82cA9HMybBgtddQUU27ixlANN9GrqYl28bVU\n8ddwvSvdyMrKwqVLlzBs2DAUFRXB5eHUWBcXFxQVFQEA8vPzMXz4cNlrpFIp8pSUVURGRgLgqi6C\nAATJHktNpdmVvb32MbaU+UsktLdHairQr5/2+2dYDpz1M21ay8+9cwdITKQtnBnmBdfiwdGRJpvt\n2xs7Is1JTExEYmKiRs8VTPyrqqrw3HPP4bPPPkN7hbMnEokgUjNqpOwxTvyrq4Gvvmr+mD6+HCf+\n6jL/XbvocnWsh791ExgIRERo9twffqAXCXMSDgaFy/wbG80v6w8KCkJQUJDs9w0bNqh8riA1CPX1\n9Xjuuecwd+5chD5cssjFxQWFhYUAgIKCAnTt2hUAIJFIkMPNHQeQm5sLiZrFTTkBrq5+tE2fEXlN\nBnxTUpjlw6C2z5UrzT97qjD32n5rhiv3tNS2Dhy8iz8hBC+++CL8/PywcuVK2faQkBDExMQAAGJi\nYmQXhZCQEMTGxqKurg6ZmZlIT0/H0KFD1R5D0ffnI/NXJ/4AE38GTTwCAmi7Y3Vcvkw/n2PHGiYu\nBr9w5Z6WPNgLCCD+p0+fxu7du3HixAkMGDAAAwYMQHx8PMLDw3HkyBH4+Pjg+PHjCA8PBwD4+flh\n5syZ8PPzw+TJk7Ft2za1lhDw+ELu+mb+traqZ/BxNyFM/BmAZq0eYmJoeacl1PZbI/KZvyWLP++e\n/5NPPqmyVPPo0aNKt0dERCBCUzMVNPPnSrFqa4HsbMDHR+tQAdDM39lZ9cSV1q3pqkUDB+q2f4Zl\nERgIfPqp6sfr62kvH74XfmcYDjc3Wt59/bpli79Z5ibytk9qKh2M1aXSB3gk/uo4e5Yek8EYNYra\nPqrWuI2Pp8uI6pqMMIxPt250bMfJyXJbVwMWIP76zsCzt1dd6cNgKOLkRJONS5eUP85q+80fV1c6\nO9uSB3sBCxB/fX25ESOAf/+bn7gY1oGq/v4lJcDRo8DMmYaPicEfDg5UYyzZ8gHMVPzlm7vpm/m3\nakUrOBgMTVHV5yc2Fpg8+fG1oBnmh5sbE3+ThM/Mn8HQFk78FesazHGpRoZyZs+2/FJdsxb/2lra\nbIsNrjEMSbduNLu/fv3RtpQUWiHy1FPGi4vBH+vX04F7S8asxT8tDfD0pB4dg2FIFK2fmBhg7ly2\n3gPDfDBL8ec8f0uffs0wXeTFv6GB9n9iVT4Mc8IsxZ/L/C19+jXDdBk9ms70JYRW+Li7W+Yi3wzL\nxSzFv0MH2lzr8mWW+TOMg5cXneiVnf1oqUYGw5wwS/EXiehkmz/+YJk/wziIRNT6+flnOqt39mxj\nR8RgaIdZij9Aff9794DevY0dCcNaCQwEIiOB4OCWW4QwGKaG2Yp/p0701ptV+jCMRWAgXdOX1fYz\nzBFBl3EUkk6dgC5djB0Fw5rp3x8IDwcmTjR2JAyG9pi1+HfrZuwoGNaMrS0QFWXsKBgM3TBb22f5\ncmDRoubbNF242BQwp1gB84qXxSoc5hSvOcUKGD5ekxD/+Ph4+Pr6wtvbGx9++KFGr3niCdpaVx5z\nehfnbaIAABq+SURBVLPNKVbAvOJlsQqHOcVrTrECVij+jY2NeO211xAfH4+UlBT8+OOPuC7fNIXB\nYDAYvGN08T979iy8vLzg6ekJsViM2bNnIy4uzthhMRgMhkUjIoQQYwawd+9e/Pbbb/jmm28AALt3\n70ZycjK2bt0qe05LC7ozGAwGQzmqJN7o1T6aCLuRr08MBoNhcRjd9pFIJMjJyZH9npOTA6lUasSI\nGAwGw/IxuvgPHjwY6enpyMrKQl1dHfbs2YOQkBBjh8VgMBgWjdFtHzs7O3zxxReYOHEiGhsb8eKL\nL6IP643LYDAYgmL0AV9Lp7a2FjY2NhCLxSCEsMFrHnnw4AFsbW3ZuRUA7nw2NjbC1gyWJ0tKSkK7\ndu3Qr18/Y4fSIrW1tbCzs4Otra1RP7dGt3104cyZM7h8+bKxw2iRd955B8888wzWrVuHqqoqsxCn\nP/74AxcvXjR2GC0SHh6OKVOm4LXXXkN5eblZnNvCwkIAQH19vZEjUc/mzZuxYcMGADB54U9JScGk\nSZPw5ptvYunSpfjyyy9RUlJi7LBUsnHjRoSGhuL11183+ufWrMS/uLgY48ePx7p167Bx40Z88803\nuH37trHDUsrmzZtx/fp1xMbGQiQS4d1330VycrKxw1LJvXv3MG7cOLz55ptYs2YNPv/8c5M9t4cO\nHcK1a9ewZ88eNDU14e2338apU6eMHZZKMjIyMGzYMJmdKRaL0dTUZOSoHqe2thYzZsxATEwMzpw5\ngyNHjgCgEzFNkdraWrz33nsYM2YMfv/9d4SHh+PKlSsoLS01dmiPUVRUhODgYFy9ehXbtm1DQUEB\nIiIiABivmtGsxP/kyZPo378/Tp48iX/+85/IyMjAF198YeywZMi/ifn5+QgMDETHjh0RERGBxMRE\n7Nu3D8XFxUaMUDXZ2dno1asX/vzzT3z44YcoKSnB5s2bjR2WUi5fvgwXFxd06dIFmzdvRpcuXXD8\n+HEUFBQYO7THIITgf//7H2bNmoWhQ4di5cqVsu2mhoODA1asWIH9+/cjLCwMO3bsAACZPWEq1NbW\nAqDxRkZGYvny5QCAKVOmICkpCUVFRcYMTykikQjLli3Dnj170KNHD2zduhWHDx9GSUkJs31UkZ2d\njZqaGtn/U1NTAQD9+/dHVVUVTp06hd9++82YIeL+/ft45ZVXsH79eiQkJAAAPDw8UFhYiMLCQjg5\nOaFHjx6oqqrCX3/9ZdRY5eG+RABw8+ZNXL16FQAwcOBAzJgxAwUFBUafbV1ZWYkdO3Y0uwt58skn\nYWdnh9zcXDg5OWHs2LEoLy83qTsr7tyKRCLMmTMHq1atwvbt27F9+3ZkZ2fD1tbW6Bm1snMbGBgI\nHx8f2Tnevn07AJjEncqhQ4cwfvx4fP3117JtvXv3Rrt27VBXV4fa2lq4u7ujU6dORr9Ycec2Ozsb\nAODk5ITx48cDAOrq6iAWixEQEIC2bdsa7dyarPgnJyejV69eeO211/Dss8+ipqYGoaGhqK2txRdf\nfIEzZ86gpKQEEyZMMKr4V1RUYMaMGbCzs0P//v0RHh6Ow4cPY/bs2aiqqsKCBQswYMAADBw4EMAj\n39eYH05lX6JnnnkGDg4OOHToEGxtbdGzZ09MnToVhw8fNlqsFy5cQN++fbF27VqcOnUK1dXVAIA2\nbdqgXbt2OHnyJAB6MWjVqhXy8vIAmN65dXd3BwBIpVK8+OKLWLJkibHCk6F4brkEixMiDw8PPPPM\nM9i/fz+Ki4tha2tr1AvArVu3sGnTJkilUqSmpsrG/Lj32t7eHmVlZaiqqkLPnj0hEolQV1dnlFjl\nz+3vv/+OmpoaiMVitG/fXhZrSUkJqqurIRKJYGNjHBm2jYyMjDTKkdVQX1+PTZs2Yd68eYiOjsZv\nv/2GixcvYuTIkRg4cCBOnjyJffv2YcmSJejatSvy8/MRHBxslFhra2tx7NgxvP/++xgxYgQkEgnC\nw8Px0ksvYfr06fD29sacOXPw7LPPIjc3F1evXsWkSZOMdqt369YtrFy5Ej169EBlZSWkUilcXV1B\nCAEhBPv27cPMmTNhb2+PoqIipKenY/To0XAwwpJpd+/exeTJkzFs2DCcPXsW7u7ucHNzg5ubG27e\nvInr16/DyckJEokEd+7cweHDhzFr1iyTO7dNTU2yqo6JEydizZo1GDp0KHr06IGKigqTOrfcubOz\ns4OjoyOys7ORkZEBOzs7FBQUQCKRGCzGpqYmWTzcHd7YsWORlpaG69evY+zYsRCJRLJze/ToUdTV\n1WHq1KlYv349bt26hYCAAIMPWqs6t/L8+9//Rvfu3TFu3DicPHkShBA4OTkZNE6TyfzlswqxWIzy\n8nI0NDQAoIOnxcXFiI+Px5AhQ7B161YkJCTg6aefRrt27VBZWWmwONPS0rBp0yYkJiaCEILKykrU\n1dWhuroajY2NePrpp+Hn54fo6GgAwJAhQ+Dr64vU1FQcPHgQzzzzjMFi5ZA/tz179sTu3bsRGRmJ\nzp07Y9++fQCorztlyhSIRCJw+YBUKkVubi7atWtnkDi5c3vixAk0NTXB398fY8aMwaxZs/DgwQOc\nPn0aJSUlsLGxwaRJk+Dm5oalS5fi3Llz2LVrF4KCggye9as7tz/99BMAwMbGBjY2Nqirq4NIJMLX\nX3+NF198EevWrcPmzZub2W9C0dK5/eOPP1BWVgbgUTYtlUrh6+uL8PBwTJ8+3aAZ6jfffINBgwYh\nPDxcdh579eqFHj16YPjw4SgsLJRZrJx9lpWVhZ9//hkjR45Efn4+5syZA7FYLHis2pxbTtMqKyth\nZ2eHBQsW4PXXX8eDBw8Ej1MRk8j8v/nmGyxZsgRZWVmoqKhAnz59kJaWBjs7O/Tu3RudO3dGVVUV\nzpw5gwEDBqBjx46orq7Gf//7X6xfvx6LFy9G3759BY/zyJEjCAkJgY+PD/bt24fbt29jwoQJOHHi\nBG7duoVx48YBAAYNGoTXXnsNS5YsQevWrfH1119jwYIFmD17Nl544QXB45RH2bl1dnaGk5MTHjx4\ngAsXLsDe3h5eXl5o3749/P398f777yMlJQXvvvsuQkND8eSTTwqeTcuf2/379yMnJwd+fn5o06YN\n7OzsIBaLceTIEXTt2hU9e/aEs7MzRo4ciYqKCvz888/w8fFBRESEQbP+ls7t+fPn4eDggF69eqGp\nqQl2dnRO5cWLF7Fjxw50794dGzduFPziqu255c5hXFwcVq5ciYiICOzbtw/dDLR03rlz57BhwwZ8\n9dVXaN++PT766CNIpVJ4eXkBADp27IicnBycO3cOkydPll2UfvjhB+Tn5yMmJgZLliwxyB2VtueW\ni3X16tWIj4/HvHnzsH37dnQxxpq0xMicPXuWDBo0iCQlJZG9e/eSIUOGkNOnT5OTJ0+Sl19+mZw5\nc4YQQkh9fT0ZOXIkOX78OCGEkJ9//pnMmjWLnD171mCxbtmyhXz33XeyuFevXk0++ugjkp+fT/r2\n7Uv+/vtvUltbSwghZN68ebJY79y5Q0pKSmT7aWpqMki8iud22LBh5Ndff5U9XlxcTD766COyfPny\nZq+7ffs2OXjwIElOTjZInIQ8fm7feustsm7dumbPWbNmDdmyZQu5d+8eOX36NCGEkMbGRtLQ0CB7\nTmNjo0Hi1fXcJicnk4ULF5rsuS0vL5d95+7du0dKS0tlz6mvrxcsRvn38NChQ+Stt96S/b57927S\nq1evZs8/f/48iYiIIJs3byZr164lxcXFpKqqSrD4VKHtuU1KSiKEEPLTTz810wQhz60qjCL+6t7o\nnTt3Ej8/P0IIIW+99Rb54IMPyI0bNwghhKxevZr897//NVicSUlJ5NKlS7IvwFtvvUVmzZpFCCGk\nrq6OJCUlkYkTJ5KcnBzyySefkPnz55P9+/eTkydPkhEjRpCCggJCyCOxr6+vF1z49f0ScTELjSbn\ndurUqeTcuXOy1xQUFJBRo0YRqVRKRo8eTaqrq2Vi39jYaPLntri4WND4OPg4t/fv35f9vQ0NDYKe\n23feeYesWbOGHDx4kBBCSEJCAhk+fHiz5wwbNoxs3rxZ9nt1dTUJCgoijo6O5PXXXxcsNkX0ObcS\niYQEBgaSmpoa2WOG0ARVGFz8NXmjBw8eTP7zn/+QoqIiEhERQUaPHk3WrVtHunfvTq5duyZ4jEVF\nRWTu3LnE39+fzJs3jwwaNIgQQkh2djYZNWoUuXDhAiGEkJKSEhIVFUW2bNlCGhsbyd69e8ns2bPJ\n4MGDyc6dOwWPUxF9v0QrVqwQPEZtzu2HH35INm3aRAihX6zly5eTLl26kB9//FHwOBVh55Z/kpKS\nyMCBA8nChQvJzp07yRNPPEGOHDlCCCEkICCAfP7557Lnnjx5kgQFBcnurF977TUSHBxM8vLyDBKr\nuZ1bTTCY+GvzRicmJpKxY8fK3ujdu3eTDRs2kOzsbMHjfPDgAdmyZQtZs2aNbFufPn3Irl27CCGE\nbNq0icyfP1/22ObNm8n7778v+72srEzwGBUxly+RLueW+xJVV1eTxMTEZvszxK0yO7fCkZSURLZv\n3y77fe3ateTll18mhBBy4sQJ4urqKvs+paSkkNdee01m7chnz0JjjudWEww24JuXlwdPT09ERkYi\nICAAWVlZuHjxIp5++mn4+vpi5cqVWLx4MVq1aoWamhpkZ2dj/PjxEIvF6N+/P8aMGYMOHToIHqed\nnR3atm2L559/XjZAV1NTg5qaGowYMQI9evTAjh07UFlZiWHDhuG3335DfX29bLDXwcFB1hDLUNUR\n2pzb6upqZGdnY9y4cbC3t8e4ceOwcOFCWQ2ykOhybmtra2WfA09PTwC0YoKroBEadm6Fo2PHjujf\nv7/seLW1tcjIyMDEiRPRs2dP3Lp1C4cOHUJtbS127NiB+/fvIywsTPb3GgpzPLcaYairTGVlJamu\nrpb5iIcOHSJLly6VXQVfffVVsmDBAhIbG0vmzp1LwsLCDBXaY8h7u4QQMmnSJPLDDz/Ifj99+jQJ\nCQkhI0eOJIMGDTKIFaUOdm6Fg51bYWJTxrJly2QZMyGE3L9/nxw+fJjMmjWLrFmzxqgZsymfW10R\nRPzN5Y1u6Rj19fWkrq6OjB8/XjZYx93SV1dXk8uXLwseoyLs3AoHO7fCID+gGR8fL4uFg/t7pk2b\nJovtypUrpLy8nBBCHnu+kJjbudUH3sXfHN5oxS95cXGxrGpE8bGqqioSFhZGKisrycaNG8nKlSsf\n25+hMhJ2boWDnVthKSwsJCtWrCBjxowhqampzc53Y2MjaWxsJHPnziX/+9//yHPPPUdmzpxJ7ty5\nY7D4zPnc6oogmb+pv9Ecp06dIj4+PiQ0NJS88MILSp8TFxdH2rdvT8aMGUNmz55N0tPTDRxlc9i5\nFQ52bvlBUSwLCwvJm2++SXr37q3yNVeuXCEikYgMGTKEfPnll0KHqBJTP7d8orf4m8Mb3dTU1Kxm\nubKykqxevZosXLiQ/Pbbb+TBgwdkxIgRZOPGjYSQ5hOFdu/eTQIDA8nRo0dl2ww1kYidW+Fg51YY\nFOdCcPXwR48eJYMHD5ZVSinGkpOTQzZt2mSwiVrmeG75Ri/xN4c3Wv7YDx48kP1/3rx5ZNiwYSQz\nM5MQQsjff/9NunfvList4/42xcxOE1+YD9i5FQ52bvklMTGRxMXFyX4/duwYGT16NAkNDSXLly8n\nX331FSGEkI0bN5I1a9aQuro6QojhZrorYk7nVki0LvU8efIkrly5gt69e8PGxgbHjx/HggULcP36\ndVy8eBHZ2dmYOXMm7ty5g2vXrmHs2LGws7Nrtlalo6MjAgMDYW9vL0QBEwDI2qhyx9y6dStWrFiB\nwsJC3L17F4sXL8bevXsRFBSETp06wc3NDceOHYOTkxP69OkjK8dq06YNAMOUabFzy86tuZ3b4uJi\n+Pv748aNG5gyZQocHR2xc+dOLF26VNa35vDhw5g5cyakUinOnDmD+/fvw9/fHwAM2ovJ3M6t0GgV\ndXFxMcaOHYvIyEjk5OSAEIJTp05h8+bN+M9//oP09HR8/PHHKCwsxLRp01BVVYW9e/cKFbtSjh07\nhnHjxuHYsWOybonff/89rly5gp9++glisRgRERFwcnLC6NGjERUVhaNHj+LkyZMoLi7G4MGDle5X\n6Lpidm6Fg51b/uG6mXbu3Pn/27v3kKbePw7g72X7Q1K70AU0K4xKbTadtozMeUfEKMq0q2WlmCbR\nnSCioIvd3ZKspBQiIRUszPSPYpqpJMnSQE3LjMzKKG3mJip+fn+I56tlWf3abO7z+suzPeech/fw\nOTvP2fM8iI6OxrRp06BSqSASibB37160tbXB19cXy5cvR0BAAA4dOoQFCxbAwcEBJSUlwlz2xmBq\n2RrNr9weDJw/JSYmhoKDg4XRbu3t7VRQUEDz58+nlJQUio2Npa1btxJR30i3+Ph46ujo+Ou3LN/S\n6XQUFxdHixYtovT0dNLpdMIt3c6dOyknJ4cOHDhAnp6ewhD91tZW8vf3Fx7e3bp1y+D1/BZnazic\n7d939+5dmjt3Ll2/fp2IiL58+ULbtm2jGzdu0Jo1a4Qus6NHj1JaWhoRESmVSrKwsKCysjJqbW01\nWr++qWVrbD9t/E3pg37x4gWFhIQI2wP7E0+cOEEWFhaDHtJVVlaSXq+njIwMWrFixaAJzYzRF8nZ\nGg5nazjl5eUkEonIw8ODcnNzqaOjg06fPk2xsbF08+ZNWrduHRERrV+/ns6cOUP5+fkUFxdHhw8f\nFiZoNBZTy9bYftrn/+XLF5w6dQpv376Fra0tZs+ejYaGBlRVVSEwMBCZmZlYtWoVUlNTYWNjA61W\ni9LSUgQHB0Mul2P69OkG7R8dSK/XIzMzEzNnzkRdXR0KCgpw79496HQ6eHh4oLGxEUFBQZgzZw6u\nXbuGxMREyGQyBAUFISUlBSKRCK6urrCwsDDK7ShnazicreHY2dmhpaUFdXV1WLp0Kc6fP4/169ej\npaUFXl5eePjwIaytrbFs2TKUlpZCqVQiMjISCQkJmDx5ssHrN5CpZWt0w10d4uLiyMnJibKysigy\nMpI0Gg0dP36cKisrKSIiggoKCqi6upr2799P8+bNGzTk2Zi6urro8uXLZG9vT1KplHbv3k2+vr4U\nERFBZ8+epcLCQvL29iZ/f38KCQmhsrIyYd/Hjx/T8+fPjV5nztZwOFvDaW1tJRsbG6qpqaF9+/aR\nRCIRpjXOyMggLy+vEZng8FummK0xDdv4m8oH3a+mpoZ0Op3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+      },
+      {
+       "output_type": "display_data",
+       "png": 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/BmTk1i3gmWeE7+M7gupqOlNt0UL7cZwQdO1KZ7dyOQIhNwDYtyPgVw1xjqCw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\nORP/+Mc/4OPjg88//xwvv/wyhgwZgu3btyM6Ohq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+      },
+      {
+       "output_type": "display_data",
+       "png": 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N6UsUc7DKIevh5En6v75509KW2A5NTgjc3OgHW1N4yByhIeCREIhRSrphg3h7\nB9QROjYutkeQnU1Xi9yZB3zQlyjmYHkC6yE9nW5M/OcfS1tiOzRJIWjbVrsQiB0aAmg7B6mU2iMk\nd+7QHcwvvSTsdbUh5ORXX09X6h4e4nkEx4/Tnda3bvEXYX2JYg4xhaC+3rL9qmyN9HRg9GjmERhC\nkxUCTZVD5goNAeKEhxITgeefb3y2rlgImTAuLaWH4Tg76/cISktpPN5Qjh8HnnmGXv/uXX7PsQaP\nYO1aYOFCca5tbxBChWDiRCYEhtCkhKCkhOYILB0aAsQRAjFbSmhCyMmPyw8A+j2CxETgo48Mf43j\nx+mhQR4e/PMEfD0Cf3/xhOD2bZrkZujn5k2avxo8mIWGDKFJCYG1hIYAWmWisn/OZK5cocIyfLhw\n19SHkLuLufwAoN8jqKgwfLVXVUV3//bpQyue+OYJ+HoEvr6PqtGEprISOH/eunZbWyvp6cATTwBe\nXnThx0p6+dFkhcDeQkMbNtBjMvmcJyAUMhkdUyHOZzbEI7h3z3AhOHsWCAykh9B7ePAXAr5VQ05O\n9L1pTMhKH/fu0fHIyBD+2vbG8eNUCBwd6f9DrOo8bSQkUA/O1miyQmBpj0BIIaivN2+1EIeDA9C5\nM21wZyqGeASVlUBenmEJVC4sBBguBHxCQ4B4eYJ79+j78swZ4a9tb3AeAUBLqM0ZHsrNpY0lIyMN\n26tiDdicELi6UnfPmFWotZSPAlQIhAoNHTxIcx8PN3CbFaEmP0M9AoWCbizki7oQ8M0R8A0NAeIK\nQZ8+TAj0UVtLD57iWqv4+Jg3YfzDD1QIAOBf/zLf6wqBzQmBRELjf4ZMAhzWFBrq2FE4j8DcSWJV\nhMoTGOIRcIsAQz7kqkJgSI7AWjyCIUOYEOjj/Hnqobq40N87dTKfECgU9ACqN9+kzSU3brStRoQ2\nJwSAceEhhYImGdu0sa7QkKmbyu7fB3bsoPkBS2Apj8DJif+HPC+PjlOXLvR3Q0JD1uIRDB4MnDtn\neyEHc6IaFgLMGxraswdo3x7o3Zt+//ln4LXXbGeToc0KgaEewd27dKXg6Kg9NGROj8DFhb4W33p2\nbezYQVe63CRqboSa/FQ9AqmUegTaRLKykvaQ4SsE6el0jLh+QXyF4MEDGobkuy/D31+cNhOVlfQ9\n7+nJGqnpQl0IzBkaWruWTvwcTzwBREfTzZ1idhoWCpsVAkM9gpISGhYCaDy9tJR6AKqYM0cACFNC\nasmwECAAqltGAAAgAElEQVTcpjJVj8DRkU7a2la/9+7RCiC+H3LVsBDAP0fAp+GcKp070/+n0Lui\n792j3U9ZnkA3XMUQh7lCQ0VFwP79NEmsyuzZNG/35pvmO5nQWJqMEHD5AYCWWLZqRTtnqmLO0BBg\neuVQfj5dBb34onA2GUqnTnQ1b0q9dn09Lbl7eEYRAN15AkOF4OxZoG/fR7/zzREYEhYC6K5oHx8g\nM5P/c/hw7x7ddd23LxMCbZSW0s9D9+6PbuOEQOxJ+McfgfHjH+UmOCQS4H//owns//s/cW0wlSYp\nBIDm8JA5Q0OA6UKwaRN9Az72mHA2GQrXgjk72/hrFBfTD5FqW3BdeQJDhaC0lMZtOVxcqMjoc9kN\nSRRzBAUJX+/PCQHzCLRz4gQVStWFXMuW9EvMun5CgHXrGoaFVHnsMWDbNuCTT+iizVppskKgKWFs\na6EhS+wd0ISpeYLCwsY5Dn0eQVAQfyEoL28Y55dI+OUJDPUIALoiFfr8Yk4Ievem3o16SJPROD/A\nIXZ4KC2NdjrV9Noc/v7AmjXA3Lni2WEqTVoI1EtIbSk0dO4cDW0NGSKsTcZgqhAUFT1KFHPo8ggq\nK+kHvKaGTvL6KC9v7LbzEQK+u4pVEdojUCho2K1FC2qLq6vwJ8PZA1xBgDpiVw5xSWJ9eaRnn7Xu\nCiJRhaCsrAwTJkxAYGAggoKCkJ6ejpKSEoSFhaFr164YMWIEylQC9TExMfD390dAQABSU1O1XpeF\nhmiSeMoU89qrDVMTxqqJYg5tHgEhj1bIPj78PCpNQtChg/6EMZcsNoTu3YUVgnv3aHiBm2hYeKgx\nXMdRTatyMSuHiouB3buBV1/V/1g3N+M3wpoDUaeR+fPnY/To0bh8+TLOnz+PgIAAxMbGIiwsDJmZ\nmRg+fDhiH57ZmJGRgaSkJGRkZCAlJQVz5sxBvRYfuHVr+s+vqOBvC3c6GYe20JA5PQJjQ0N1dTQ/\nMGWK8DYZgxChIU0egSYhqK6meYlmzfi5/XV11HNQz6OI5RF07UpbbghVOcRVDHEwIWjM9evUY/L2\nbnyfmKGhDRuAF15oOK9oQyIxrT2O2IgmBHfv3sXhw4cxY8YMAECzZs3Qpk0b7Ny5E1FRUQCAqKgo\n7NixAwCQnJyMyMhISKVS+Pr6ws/PDydOnNB4bWMGVbV8FNAeGjK3R5CTY3hVw549dKVjLeexmrq7\nWJNHoC00xHkDAL8PeUUFXTiou+58hcBQj6B5cyrwQoUBVP9egFUOaUK9bFQVsUJDhDTeO6APaxYC\n0XpVZmdno3379pg+fTrOnTuHvn37YtWqVSgqKoLHw+Wfh4cHih5+GvPz8zFQJcgnl8uRl5fX6LrR\n0dEA6CSxe3counUL5WWPphyB+mrc3KEhFxc6QZWX0x3PfLGWJDHH44/TCbmuzrjup4WFNLauirbQ\nUGXloxUynw+5prAQQIVA32RtTLIYeJQwVv+bjEFdCDiPgBD++xvsHW1hIUC80NCxY/T9/vTT/J8j\nk9Fd7uYgLS0NaWlpvB8vmhDU1dXhzJkzWLNmDfr3748FCxYow0AcEokEEh3vZk33cUJw9WrDunN9\n8MkRmDs0JJE8Cg/xFYLychqXXL1aXNsMwdmZ9n+6eZNuqjIUUzwCHakkAI0rhjg6dACOHtX9XGM8\nAkDYhLG6EHh60vG+eZNOcgwqBBMmaL5PrNDQ2rXArFmGibG3t/mEIDQ0FKGhocrfly5dqvPxoq1/\n5XI55HI5+vfvDwCYMGECzpw5A09PTxQWFgIACgoK0OHhbC6TyZCjskTPzc2FTCbTen1D3Sw+VUPm\n9ggAwxPGW7fS4xaNmaDExJQ8gaYcgTaPwNDQkC6PQIzyUUDYElJ1IQCoV3D6tDDXt3Wqq4GLFxtu\nGFSlQwf6HhCyzcPdu7S1y8MIN29kMusNDYk27Xl6eqJjx47IfLjNcu/evejevTvGjBmDhIQEAEBC\nQgLGjh0LAAgPD0diYiJqamqQnZ2NrKwsDBgwQOv1hRACS+8jAAwXAku3lNCGKUIgZo7AFCEwJlkM\nCOsRVFZqFgKWJ6D89Ret01cfIw4HB+FPA9y0iZaDGhKRAMwbGjIUUc+zWr16NSZPnoyamhp06dIF\n69evh0KhQEREBOLj4+Hr64stW7YAAIKCghAREYGgoCA0a9YMcXFxOsNG3t50NyFfrDE0BBj2Jv3n\nH7r6GT1aXJuMwdiEsUJB/w+qO38BfjkCboWlUGj/v1VUGCcENTX0eEtNz9VHt260nLa2tuFuaWNQ\nrxoC6Or3f/8z7br2grb9A6pwCwahiivWrgXUoty8aJLJYgAICQnByZMnG92+d+9ejY9fsmQJlixZ\nwuvahgyqQkEnENU4PBcaUk26KRSmf3ANRS6nVQ982LgRiIigMWJrw88POHzY8OfduUMFWj3JzMcj\ncHamgl5QQMdRE9o8And3KhI1NVR01DG04ZwqLVpQe/7+m7bCMAVdoSGWMKZC8Oyzuh8jZOXQ6dN0\nUanvNTVhzR6BFWxHMg5DhKCsjIqAatineXM62VRWPrrNmkNDhFhvWAgwflOZpvwAwC9HAOgPD2kT\nAgcHKiLa+tAYmyjmECo8pEkI5HL6XjXmcCZ7Q1fpKIeQlUNr19JTyIyZJ7j2+dbYidRmhcDLiwqB\nrpOsONT3EHCoh4csFRriIwQnTtA3kL43vaXgzi42tA+OpvwAwM8jAPgJgbbzBHSFh4xNFHMIlTDW\nJAQSCcsTAFTE79wBAgJ0P06oyqHKSmDLFmD6dOOe36IF3dio6SwUS2OzQtCqFTBiBPDOO/ofq54f\n4FBPGFuqaohPjoDzBqw1FNCyJR1jQ11fS3kEgG4hMDZRzCGmRwCwyiGALo7699f/mRUqNLRlCz0p\nTkcxo16sNU9gs0IA0Mlx717aBlYXuoRAtYTUEh5Bmzb0dXU1T6upoW9CPj1NLIkxlUOGegSqyWLA\ndCHQ1m/IWkJDmqqGAOYRALo3kqkiVGjo++8N20msCWvNE9i0ELRpQw+IXrJE9+Ygvh6BJXIE3KYy\nXeGh3bvpxOLrazazjMIYIRDbI9BWNQToPqDG1NBQQAA9trKuzvhrAJqrhgDWagLgLwRcHs6U9t0X\nLtBrjBpl/DUA5hGIRrdu9ISgiRO1K602IVDPEVgiNAToDw8lJlq/NwAYlzA2R47A2NCQKR7BY4/R\nD72pLaO1hYYef5yKHJ8jN+2R+noaGuIjBC1a0PbdD/exGsXatTQ3YEwLFVWYRyAio0cDb70FjBtH\nDxxXx5pDQ4D+yqGTJ63j3AF9WKNHYKwQmOoRAMIkjLUJAZcwPnvWtOvbKpmZdHLX9N7RhCnhoaoq\nuols5kzjnq+KOdtMGIJdCAEALFpEK1c0HRSt3oKawxpCQ4Du0FB5OZ0s/f3Na5MxGLOpzNQcQdu2\ntM2Atpbk+qqGdOUITBUCIfIE2oQAaNoJY75hIQ5TKoe2baOhOCFCs9baZsJuhEAiAeLj6Zbzb75p\neB/f8lFrDA1duEAnFEt4KobCCYEhddKmegQSCf2Qaxs/XR6BvhyBqf2czCEETTVPYIwQGFs5ZGi7\naV2w0JAZaNmSNoOKjQX27Xt0uy2Hhs6fB0JCzGuPsbi60ngs37h1bS3d7Kdp5c03RwDoXu1ZqnwU\nECY0pO4BqcKEgP/jjQ0NXb0KXLkChIcb/lxNsGSxmfD1BTZvBiZPphucAOveRwDoFoJz52xHCADD\n8gS3b9P/gSbx5esRANqFgDvFTltoqH17+v9XKBrfZ2qyGBCmckiXR9C1Kx3D0lLjr2+LVFUBly8D\nvXvzf46xoaF164Bp0zS3ITEGDw8aoeCzEdac2J0QAEBoKPDxx8DYsXRFxbdqyJI5Am2hDXsWAk2H\n1nPoyhFoEgJNbn9VFf0Aa+sfJZXSEuSSkoa319bStsWGHBakiZYtaf6DW5AYgy4hcHAAevVqegnj\nM2do2K1FC/7PMSY0VFND9yrNmmXY83Th6EgXIKZUMImBXQoBAMydS3cdTp+uPUdgLaEhV1e6alTf\nVFZfT3MEwcHmt8lYDEkYFxZqThQDuj0C9VCJttWerrAQh6Y8QXExLS4QYlFgap5AlxAATTM8ZGhY\nCDAuNJScTP9/QhdqWGOewG6FQCIB4uJoyCUnR7MQtGpFJxuu5NRSoSGJhIaH1N8c169TsdJku7Ui\ntkdgSGhIV8UQh6Y8gRCJYo6gIOPzBAoFfX82b679MfZaOaSr4IBPozl12raln3Nt1WWaEDJJrIo1\n5gnsVggA2qb4l1+ASZM0rwwlkobhIUt5BIDmElJbCwsBhm0qM9QjIISGbB57rOHtpngEmoRAiEQx\nR/fuxnsE9+7Rv1VXfyl79AhWrQI++UT7/XzOIFCHqy7j6xVkZ9OQ2/jxhr0OH5hHYAG8vGjyWNtK\nXzVhbKkcAaC5hNRWhUAsjyAzk4q7uljL5Y8OqFGFrxCoVzkJkSjmMCU0pKtiiCMwkC4gdPWqsjUy\nM+kmSk0UFtJVvTHhGkPCQ/HxtOBElzdmLNa4qUz0aU+hUKB3794YM2YMAKCkpARhYWHo2rUrRowY\ngbKyMuVjY2Ji4O/vj4CAAKTqO5VcIFTzBJYKDQGaK4dsUQjatqWCqp6A1YS2zWTAI4/g3j3gyy+B\nAQPo7ur332/8WGdnGtNXT8CZkiMQyiMIDKQliJoqk/ShLz8A0JYHwcH0vWIv5OVpF8/0dPpeMKYL\nL1+PoK4OWL9enLAQYJ2bykSf9r7++msEBQUpj52MjY1FWFgYMjMzMXz4cMQ+PPMtIyMDSUlJyMjI\nQEpKCubMmYN6U7pE8UTdI7C20FDPnpaxx1gkEv4JY22byYBHHsG6dcDWrcBnn9EPz7Jlmh+v6UOu\nq+Ech9ihoVatqNhkZxv+XD5CANhfeCg/n/4vNcXzjUkUc/CtHNq9m3oP3bsb9zr6aHIeQW5uLnbv\n3o1Zs2aBPMz+7Ny5E1FRUQCAqKgo7NixAwCQnJyMyMhISKVS+Pr6ws/PDycMOZTYSNRzBNYSGrp7\nl05IXbpYxh5T4Bse4uMR/P038PLLQFiY7oZfmoTA2ByBkMliwPiEcVMVgrw8+n/R5BWYIgR8Q0Ni\nJYk5hPYIFArgxReBGzeMv4aoZxa/8847WLlyJcpVAphFRUXweLgM9PDwQNHDT2F+fj4GqmSA5HI5\n8jTIZnR0tPLn0NBQhIaGmmSjtYaGzp8HevSwjdYS6vBNGPPxCK5dowcQ6cMUIdCUIxCyZJdLGL/4\nomHPM0QIVq0yzjZro66ObpKbOJGKp+qkr1DQ3MGAAcZdm09oKDcXOHKEdvwVC6GTxdu3Azt3Ak8+\nCSxeTG9LS0tDWloa72uIJgS7du1Chw4d0Lt3b60GSSQSZchI2/3qqAqBELRt+2gCtqbQkC3mBzj8\n/AB978GaGur6a1t5S6XUI7hxg59X1KlTYy+ET/mothyB0B7B/v2GP4+vEHTvTkuNNVVU2RpFRXTs\nQ0Iae1GXL9P/l7FhOz6hofXrqQfKZ9yNpU0bKni6dr3zhRBg+XLg7bdpczxOCNQXyUuXLtV5HdHW\nv0ePHsXOnTvx+OOPIzIyEvv378eUKVPg4eGBwodZvYKCAnTo0AEAIJPJkKMSG8nNzYXMlDPheKIa\nGrKkR+DmRrtoVlbS320xP8DBJ0dw6xYde23j7eRE677/+Yf23teHqR6Bat26kDkCwPieQ3yqhgCa\nLA8MpF6krZOfT1fMmsbMmLJRVeRyeni8tpYf9fW0WkjMsBBA82hChYdSU+mi6vPP6WfF2PCQaNPe\n8uXLkZOTg+zsbCQmJmLYsGHYsGEDwsPDkZCQAABISEjA2LFjAQDh4eFITExETU0NsrOzkZWVhQHG\n+oAGoBoasmSOgNtUxnkFtu4R6BMCXfkBgHoE2dn0/8OnlYCxQtCiBRWdu3cf3Sa0EAQG0sZlhlYO\n8fUIAPvJE+Tl0WSqprJbU/IDAP0/t29PxUATe/bQ91ufPsa/Bl+E2lQWE0O9AKmUttTZts2465ht\n2uPCPIsXL8aePXvQtWtX7N+/H4sf+jJBQUGIiIhAUFAQRo0ahbi4OJ1hI6Gwlqoh4FF4SKGgqyFb\n9Qi8vOhqVtcuTl35AYB+aAsL6RkTfDC2aghonCcQOjTUujUVFkNXa01RCDiPwNeXliCr7o8wVQgA\n3XmCX34x30mAQuQJjh6lXsCkSfT3l16iFXbGYBYhGDp0KHbu3AkAcHd3x969e5GZmYnU1FS4uroq\nH7dkyRL8/fffuHLlCkaOHGkO06wmNAQ8qhz6+286OfGZxKwRroRUV8KYj0cA8K+aateONpnjQmsA\nP48AaJgn4GK3Km9LQTBmh3FTFALOI3BwoN1buTGrrKSfC1O9ZB8f7XmC/fuB4cNNuz5fhPAIYmKA\nDz54VE03bBjdjKfrtENt2P3OYn2oh4Ys6RFwoSFbDgtx6AsP8fEIAP4egaYDavgKgWoJKdegUOgF\ngTElpIYIQc+eNPxUXW24bdYE5xEADfMEp0/TSi5nZ9Our80jyM2lZ2P06GHa9fliqkdw4QJw6hRt\nqskhldJzE375xfDrNXkhcHWlq426OsvmCIBHoSFbThRz6EsYC+0RAI0/5HyqhoCGQiB0foBDbI+g\nRQsqvhcvGm6bNcF5BEBD8TSm0ZwmtAnBgQPA0KHm+/ybuqksNhZYsKBxC4wJE4wLDzV5IXBwoCvA\nkhLrCA3l5trWqWTaEMojMFUIDM0RCNlnSBVjeg7xrRrisIfwUF5eQ4+AGzMh8gOA9tDQgQPAM8+Y\nfn2+mFI1dO0a8McfwOzZje979lnqLWhLiGujyQsB8Cg8ZA2hoZwc+wkNCZEj4BsaAhrXiRuTIxCy\nz5AqQUG0Dt6QrimGeASAfQiBttCQUEKgyyMwpxCY4hGsXAm8+abm97azM/D883STmSEwIcCjyiFr\nCA1lZdET1fjUzlszpnoELVvS8Wjfnv9rqn7I6+pofTWfDVbmCA25uNDGeIackmUPQvD55/xP47p/\nnyb83d3p7z4+9LOQkUH/l4YsCrShSQhu3KCvGxho+vX54u1NV+2GtlMrKAC2bAHmz9f+GGPCQ0wI\n8EgILB0a4j4AwcGWtUMI5HI6qVZVab5fn0fQqhWdNA2pIFb9kHO7Nvk8X1UIhC4dVcXQhLGhQhAS\nQnME1nQe7n//S5u48SE/n06Q3P/MwYFOzuvXU29AiGpyV1e6eVCl6TEOHKDH25qhWl1J8+b0/al6\nQiIfvvwSmDJF9wJp5Ei6ILh9m/91bXy6EQauhNTSoSFuU5mth4UAOo4+PprP633wgE5y+k5eM/SD\nqSoEfMNCQOMcgRgeAWB4wthQIWjdmnpRly8bbpsYFBfTyejQIX6PV00UcwQF0XODhQgLAZoPqDF3\nWIjD0DxBaSnwww/Au+/qflyLFsBzzwEP+3nyggkBHuUILO0RAPSDbA9CAGgPD926RePyQq/AuOM+\nFQr+FUNA4xyBmB6BmEIAAH37Wk946MoVOpaHD/N7vGp+gKN7d/p+EUoIgIZCQIhlhcCQPMGaNbQ8\ntFMn/Y81dHMZEwJYT44AoP32J0ywrA1CoS1hrOuISlNo3px6GUVFhnkELi40nHL/vvgegSGhIUOr\nhgDryhNcvkwTl3fv8pvwNHkE3JkAQnabUa0cunaNfu6FPqCeD4ZsKrt3D1i9Gli0iN/jR40Cjh17\ntFlWH0wIYD2hIQAYPFi8icjcaPMIdB1RaSrcas8QIZBIHuUJxBSCwEDDKoeM8QisTQiCguh7mo9X\noMkj6NcPiIwUdqe3qkfAeQPmzA9wGOIRrF0LPP003W3Nh1at6BkeDxs66IUJAawrNGRPaBMCsTwC\n4NGHnG+fIQ5OCMQMDbm60hbEfCuHjBGC3r1p+bExR2MKzeXLVPyefppfnkB1DwGHhwewaZOwdmkS\nAkvAt4S0pgb44gvgww8Nu/6ECfyb0LFpDw1DQ5b2COwJbbuLrc0jAGie4NYtcT0CgMZuw8OBv/7S\n/bi6OvplaEsFV1f6t2RlGW+jUFy+TFewQ4bw9wjUQ0NiwIWGLJkfAPgnizdupJ5V376GXf/556kA\nq3bW1QYTAjQMDTGPQDh8fOgbvaam4e3m8AgMFQIPD2prebnwDedU+fpr2ihsxAjgP//R3huf8waM\nCVn06UN781iS+/cfdY/t3ZtOvPri1Zo8AjHg3iNXr9Id7Jbas8PHI1AoaDsJQ70BgL7/Q0OBX3/V\n/1g27YGFhsTCyYl+sNVDIebyCAw5/cnDg65gXV3F9QolEloHfuYMXa0NGkSra9QxJizEYQ2VQ5mZ\n1CNs1ox+DRxIj4DUBiHm8wi8van398cftGOnJfIDAD+P4Jdf6Pw0dKhxr8F3cxmb9kA3cpWW0tUZ\nCw0Ji6Y8gbV6BBkZ5kvUy+V0Ipo+nSZTv/qqYRLZmIohDmtIGHP5AQ59eYKSElr/bo6jNps1o2dm\nbNxoubAQQDeFlZVp7xhLCG01/eGHxovVmDH8jkllQgDa16ZlSyoGzCMQFk15AmvMEXBCIFaiWBMS\nCW0clp5OV36qh9ub4hH07g2cPWt4+wIhURcCfXkCc3kDHJ060TbOlhQCR0f6vtPWguOPP2hZ8wsv\nGP8abm7AU0/pf5xo015OTg6eeeYZdO/eHT169MA333wDACgpKUFYWBi6du2KESNGoExlr3dMTAz8\n/f0REBCA1NRUsUzTSLt29MPHhEBYNHkE+tpLmEL79vT/WFhoeLI4P98ypbtdugBpabTum4sZmyIE\n7dvTvz07WzATDebKlYaljgMG0PYXqgcHqWKu/ABHp0503Dt2NN9rakJXnoA7htLUOYnPviTRpj2p\nVIqvvvoKly5dwvHjx/Htt9/i8uXLiI2NRVhYGDIzMzF8+HDExsYCADIyMpCUlISMjAykpKRgzpw5\nqDfjkoZbCbLQkLCobyqrqqKucJs24rwe10Lg0iXDPQLAvB6BKo6ONA584AD93RQhACwfHlL3CFq0\noJ7K8eOaH29uIfD1taw3wKEtT3D0KO1E/PLLpr+GqqepDdGEwNPTE7169QIAtGrVCoGBgcjLy8PO\nnTsRFRUFAIiKisKOhw0xkpOTERkZCalUCl9fX/j5+eHEiRNimdcIbgJgHoGwqHsEXFhIzARdp060\no6QxQmDJzXzDhj2K55oqBH37Wq5yqK6O/s+7dWt4u648gblDQwsXAsuXm+/1tKFtU5n6MZSmwOc9\nLcDL6OfGjRs4e/YsnnjiCRQVFcHj4afOw8MDRQ+bvOTn52PgwIHK58jlcuRpGKHo6Gjlz6GhoQgN\nDRXERm6wmEcgLJ0700lZoaBjq6/9tBBwvVgMqRpyd6f2WVoIPv+c/iyER/D118LYZSjZ2TT0p574\nHTKEdiPVRF6eeU/ls5Tnp46m0ND581TEf/7Z+OumpaUhLS2N9+NFF4LKykq89NJL+Prrr9Fa7ZMp\nkUgg0bE01HSfqhAICfMIxKF5cxqzzsmh7riY+QEOTggM8QgcHKidlpwgAgJoZ9bsbNOqhoBHoSFC\nzF8eqZ4f4Bg0CDh5koYG1TfK5efTjplNDZmscf8pbcdQGoL6Innp0qU6Hy/qtFdbW4uXXnoJU6ZM\nwdixYwFQL6DwYZq8oKAAHTp0AADIZDLkqJw8npubC5kZg4ZMCMRDNTxkTo/AECEAqF2W9AgkEhq3\n3r/fdI/Ay4tWw6l8pMyGen6Ao00boGvXxiGry5eBEyeEOXjG1lD3CK5dA/bsoSeQmRPRpj1CCGbO\nnImgoCAsWLBAeXt4eDgSEhIAAAkJCUqBCA8PR2JiImpqapCdnY2srCwMELLloB5YaEg8VBPG5vQI\nDAkNAbSmv3dv4e0xBC5PYKoQAJZLGGsTAqBxnmDnTpokj4kxb2jIWlBPFv/3v9qPoRQT0YTgyJEj\n2LhxIw4cOIDevXujd+/eSElJweLFi7Fnzx507doV+/fvx+LFiwEAQUFBiIiIQFBQEEaNGoW4uDid\nYSOhYR6BeFjCI2jR4tG5x3yZP59fr3cxEVoILJEw5iME9fXAp58Cc+fSFgjTp5vXRmuB8wi4ndU/\n/wy8/bb57RAtRzB48GCt5Z979+7VePuSJUuwZMkSsUzSCRMC8fDze1Q2WFREJzsxefxxesC3LfL4\n47Q1x+nTwLhxpl2rb1/avlgThAC//UZbqzg703i0ru+qPzs5ac87EEJzBNqEYPBgYMYMYOJEev7u\niRM0jNVU4Vb+FRV0d/nUqYad0y0UZqkasgVYaEg8VHcXm8MjaNaMrjRtEYmECuXGjcCrr5p2LW2h\noWPHgHfeoc0Ag4Np8vbBA/pd9Wf179zPdXVUDDSJhZMT/a4t6d6hA21G6O5O20sb2l3V3pBIqFdw\n8SI9hlJfV1qxYELwEOYRiEeXLvTsYkLMkyOwdYYNA3780bSqIYDumq2tpStvLy/a/G/xYtrq4bPP\naPM7Y97v9fXaRaO6Wn/31pMnqWAwKDIZ7Sf04ouW2+nMhOAhTAjEo3Vr+lVQYB6PwNbhdryamiOQ\nSKhXcOgQcOEC8N13wLx5wLp1pl3bwYHmYFq0MO75TAQa4u0NbN4MfP+95WxgQvCQFi1oHNtSLWnt\nHT8/6vbW1xtezdPUkMtpmaUQlSN9+gCTJwOTJtHxt3RvHUZjOnakBxap78Q2JxJCCLHcyxuGRCKB\nDZnLUCEqik5wmzZZthmarZCTQ1eKpuasOC/M0mWxDO2UltLvbm7ivYa+uZN5BAyz4OcH7NvHwkJ8\nEWrl7uXVtKtybAExBYAvLCLOMAt+frRUkCWKGQzrgwkBwyz4+dEW1MwjYDCsDyYEDLPg50e/M4+A\nwbA+mBAwzIKbG/1iHgGDYX0wIWCYDT8/JgQMhjXCykcZZuPYMaB7d/N3VmQwmjr65k4mBAwGg2Hn\n6Js7WWiIwWAwmjhMCBgMBqOJYzdCYMhBzZaG2SoetmQvs1U8bMlea7DV6oQgJSUFAQEB8Pf3x4oV\nKznH12UAABklSURBVHg/zxoGky/MVvGwJXuZreJhS/Zag61WJQQKhQLz5s1DSkoKMjIysHnzZly+\nfNnSZjEYDIZdY1VCcOLECfj5+cHX1xdSqRSTJk1CcnKypc1iMBgMu8aqyke3bt2KP/74A2sfHrS6\nceNGpKenY/Xq1QBg1sPsGQwGw56wmTbU+iZ6K9IsBoPBsBusKjQkk8mQk5Oj/D0nJwdyudyCFjEY\nDIb9Y1VC0K9fP2RlZeHGjRuoqalBUlISwsPDLW0Wg8Fg2DVWFRpq1qwZ1qxZg5EjR0KhUGDmzJkI\nDAy0tFkMBoNh11hVsthQCCEsgSww3JiysRWH+vp6ODhYlSNuN7D3rPHY1Dvy7NmzmDdvHuLj46FQ\nKKz2n15UVIRffvkFV65csbQpvDl16hTefPNNrFmzBrW1tVY9tlu2bMGFCxcsbQpvLl68iJ9++gkA\nrFoESktLkZ2dDYDu6bF2zp07h/nz5+P7779HVVWV1b5nAeDWrVvYsWOH1e6Lst53pRq7du3CzJkz\n0atXLxw7dgyLFy/GX3/9ZWmzGrFlyxY8+eSTSEtLQ1RUFPbu3YuKigpLm6WTS5cuYebMmejRowcO\nHjyIt99+G6dPn7a0WY3Yv38/goODsXfvXowbNw6///47ysvLLW2WTmJiYvDUU0/hyy+/xJ49ewBY\n5yS7YsUKdO3aFQsWLAAAODo6Wm2VXn19PT766CNMmTIFnTt3xvbt2/H2229b2iytrFixAkOHDsVv\nv/2GsLAwHD161NImNcJmhODKlSsYOXIkZs2ahZUrV+LSpUvYvHkzbt++bWnTGvDbb79h9erV+Oab\nb/Duu+/i22+/tYot5Lo4d+4cunXrhnnz5mHt2rVo164dfvvtNxQUFFjatAakpqbiv//9L77//nss\nW7YMKSkp2L17t6XN0klAQAA2btyIDz74AD/88AMA65tkFQoFsrKysHLlSnh5eSE+Ph6A9ZZr3717\nF+3bt8eOHTswf/58/Pjjj0hLS2tQcWgtXLhwARcvXsQvv/yCtWvXYt68eVi5cqWlzWqE1QrB7t27\n8d133+Hvv/8GALRp0wb19fUoKiqCm5sbvL29kZeXZ3F1vXLlCrKzs/HgwQMAQIsWLZCfnw8AiIiI\nwI0bN7Bv3z5kZWVZ0swGnD9/HkVFRcrfQ0JCUFdXh5s3b8LNzQ0jRoxAWVkZDh8+bEEr6QdedfXc\nsmVLpafyyiuvwM/PD2fPnrWqEJz62I4bNw7PP/88+vfvD0dHR+UkW19fbykTATQcW0dHR3z++eeI\niIjAsGHDsGPHDty+fRsODg4Wt5Pj/PnzKCwsBAC0atUKL7/8Mjp37ozq6mp4eHigZ8+euH//vlWI\n1927d1FXVwcA8PT0xPLly5VFL7NmzcKdO3dw9+5dS5rYCKsTAoVCgUWLFuGjjz7C9evX8frrryM1\nNRVhYWEoKyvDG2+8gVdeeQVlZWVo27atctI19xuAs3PEiBFYunQp5s6dC4CWwO7evRsJCQn497//\njc6dO6OmpgZ37twxq32aKCsrw4svvog+ffrgt99+Q1VVFQD6t3Tp0gUHDx4EAAwZMgTt27fHzZs3\nAZh/bB88eIDJkydjzJgxDcJ/PXr0gJOTEy5dugQAeOaZZ1BTU6OMa1sSbWPLJYd9fHwwduxYbN++\nHQUFBRbzCrSNraurKx577DEMGTIEXbp0wZo1awBYPqehOq67d+9GVVUVpFIpvLy8AADOzs4oKyvD\n5cuX4erqatE8gerYnjt3DgDQvn17dOzYUfmYo0ePwsXFBW3atLGUmRqxOiFwdHREUVER1q5di5Ur\nV2Lu3Ll466234Obmhs8//xyvvPIKnnnmGWzduhVhYWH49ddfAZi//URhYSFOnTqF8+fPY/369fj7\n77/x6aefYtasWZgxYwZOnTqFiooKbNu2DeXl5bh48aJZ7dNETk4Ohg0bhhUrVuDixYvKxFWPHj3g\n4+OD8+fPKyeHkJAQbN26FYB5x7a2tha//vorampq0LFjR5w4cQLFxcUAgG7dukEqlWL//v0ghKBH\njx4oKyvD+fPnzWafNtTHlvNSuInUyckJTzzxBPz8/LBlyxYAMLuXqGlsS0tLATzyUDw8PBAeHo5T\np04pQ4OWzHFpG1dVDh06hJ49e8LDwwPV1dUNPDJzoWtsCSFK7+vvv//G0KFDlc/jPAdLYxVCsG/f\nPuWkVFJSAolEgvr6etTW1uKll15Cz549ERMTg9atW+Oll17Ca6+9BoC6i+PHjzebndwKGQBu374N\nHx8fPHjwABKJBD/88AM2bdqEy5cvY8yYMVixYgW++uorAICPjw86d+5sNjtV2bdvHzIyMgDQifS1\n117DvHnzUFFRgSNHjqC4uBgODg4ICwuDq6sr/vWvf0GhUODmzZsYMGAAqqurzWInN7ZSqRRPPfUU\nkpKSMG3aNBw7dkxZIRQUFIQ+ffrg6tWrWL9+PQCga9eukEqlZrFRHV1j++effzaYCACgY8eOmDJl\nCr788ku0atUKhw4dMoudusaWE1EHBwcQQuDg4IBhw4Zh5MiRmDhxIgYNGoQzZ86YxU4OvuPKTa7l\n5eXo378/Nm7ciJ49e+LIkSNms5XP2KoupHJyctCrVy8cPHgQo0ePxtWrV81mq06IBbl58yYJCQkh\noaGhZPjw4eT7778ndXV1ZO7cueSTTz5RPu7atWukXbt2pLi4mBBCyJ9//kl69OhBRowYQbKyssxi\nZ1hYGBkyZAh57733yJUrV0hxcTEZMmQIycjIUD5u4cKFZPz48YQQQurq6sixY8fImDFjyMCBA0lu\nbq7odqrbrDq2a9euJaWlpcr7d+/eTaKiosi+fftIfX09IYSQmpoa8uabb5IXXniBhISEkAsXLpjF\nTm5s33//fXLu3LkG97///vtk6dKl5MaNG4QQQsrLy8mBAwdIUFAQGTVqFHn88cfJxYsXRbdT3WY+\nY7t//37l2NbW1pLr16+THj16kKFDh5ITJ06YxU4+Y3vz5k1CCH3PEkLI9evXSf/+/UlgYCBJSkoS\n3U5Vew0dV0IICQ8PJxKJhERGRpplXDlbDRnb+vp6UltbS/z8/EifPn3I8OHDydatW81iKx8co6Oj\noy0lQidOnAAhBJs2bYK3tzeOHj2KK1euYM6cOVi0aBGGDx8OFxcXtGvXDhcvXkR1dTV69+4NR0dH\n+Pr6IjY2Fu7u7qLbGR8fj5YtWyI+Ph7Hjh3Drl27MHToUFRWViI5ORnjxo0DAAwePBjLly/Hc889\nh7Zt2+KXX36Bm5sbNmzYABcXF9HtVEV9bNPT03Hy5EkMGzYMAODv748///wTxcXFePrpp1FZWYkW\nLVpgxIgRGDlyJN5991106NBBdDtVx/bo0aNITU1Fx44d4e3tDQDo0KEDdu7ciXbt2qFbt25wdnaG\nr68vRowYgZCQEHz11VdmsVMVQ8e2qqoKzs7OylzMihUrIJPJRLeT79i2bdsW3bp1U4axduzYAW9v\nbyQlJaF79+4AzLNZy9BxvXfvHpycnHDr1i1ERUXhk08+Mcu4AoaPrUQiQW1tLVJSUjBmzBj873//\nQ1BQkFls5YW5laewsJBUV1cTQgiJiYkhL774IiGEkKqqKnL06FEycuRIkpOTQ1asWEFmzJhBjh07\nRggh5KWXXmqkuubihRdeINu3byeEEJKfn09iY2PJnDlzSG1tLRk4cCDZsmULqa2tJXV1dWTGjBnk\nzp07ja5RW1srup36xnb06NENVkxFRUVk8uTJZPTo0cTHx4fk5+eLbqM66mO7cuVKEhUV1eAx33//\nPVm8eDF57733yPTp0xtdwxbGNi8vT3Qb1TF0bKdNm9boGmKPrSnj2qlTJ3L79m1R7dOGIWP77rvv\nktdff50QQsiDBw+U93MemDVgthzBpk2bEBISgrfffhsREREAgJkzZyIvLw9nzpxB8+bNERgYiGee\neQbr1q3Du+++i549e2LlypUIDg6Gg4MDHn/8cdHtPHz4MEaOHIklS5YoE9HDhg3DunXrAABeXl4I\nDw/HrVu3cP78ecTGxiI1NRXTp0/H008/jbKyMrRs2VJ5PUIICCFo1ky8tk6GjC2XAAbobuLNmzfD\nzc0Nhw8fVlZiiAWfsX3++edRXV3dYH9A69at8cUXX+DEiRN4/fXXG13XFsaWWymKhRBj+8YbbwB4\nlNMQ830rxLj++eefaNeunSj2qWLq2J48eRLTpk0DQKuc6uvrQQiBo6Oj6LbzRmylUSgUZMOGDWTw\n4MHkzz//JIQQ0qVLFxIfH08IIeQ///kPmTlzJiGExtE2btxI3nvvPeXzb9y4Qc6fPy+2maS2tpZ8\n9tlnpGfPnmTjxo0kISGBuLq6ktraWnL79m0SHh6uXAHcuXOHfPrpp+THH38khBBSUlJC1q5dS7Zt\n2ya6naoYM7aLFi0i1dXVpKqqiiQkJJA9e/aIbqehY/vZZ5+RH374gRBCV4wTJ04k69atU15PNT4s\nFmxsxcFWxpUQ2xtbUzBLaOjkyZMkJydH+ftPP/2kdPGvXbtGnn32WbJ27VpCCCG//vprIxfLHNy7\nd48kJSWRwsJC5W2jRo1S/mMTEhLI0KFDla7yO++8Q+Li4jRey5wun72PrfpYmiMMxMHGVhxsYVwJ\nsc2xNRazhIaCg4Mhl8uVLue5c+eUO+06d+6MDz/8EN999x3eeOMNzJkzB0OGDDGHWQ147LHHEBoa\nCg8PD9TW1qK2thbu7u7o1asXAGDq1Knw9PTErFmz8N1332H//v3w8PBocA3u7zOny2fvY8uNJVfn\nLmYYSB02tuJgC+MK2ObYGo2QqqJQKIhCodB6P6eKb7zxBvn9998b3Hf9+nWyZcsWcvXqVSFN0gqn\n2JrcNe62oUOHNkhQV1RUkKSkJDJ16lSyd+9es9jJwcZWPNjYioMtjSshtjW2QiOIEOTl5TWolOGq\nAAjRHCZ5+umnSXFxMcnIyCDLly8XwgTeqLpo9+7d0/q4K1eukN69exNCCCkuLianT59u9Jj6+nqd\nb3QhYGMrHmxsxcGWxpUQ2xpbsRAkNDR16lTs2LEDlZWVeO211zB16lTExsYCaBwmycjIQFlZGaKj\nozF58mQ0b95cCBN4w7loBw4cQEREBLZv3w6gcWvgrKwsDB48GGvWrEH//v0b7Vasr6+HRCIRvRcL\nG1vxYGMrDrY0roBtja1YGB24Uu1cOG/ePHz//fe4evUqWrdujdmzZ+P1118HIQQffvhhg1OZysrK\ncPPmTUilUhw+fLhBqaUYELWNMCdOnMCUKVPw1FNPoaSkBFu3bsXzzz8PJycnZcxSIpHg0qVLWLNm\nDaKiorBnz55GLSLE/GezsWVja2tjayvjCtje2JoFQ12I+vp6je7dm2++Sfr06aMs9bxw4QLp3Lkz\nuXXrFiHkkUuYnZ2tbBdgTqqqqgghhHz22Wfkf//7HyGEkLS0NDJjxgyyatUqQghp4NJt27aNHDp0\nSPl7XV2d6C4fG1vxYGMrDrY6roRY/9iaE94tJgoLC+Ho6AipVAoHBwdcv34dCxYsQHZ2NqRSKV58\n8UX8/PPPCA0NRdu2bSGTyXDgwAGUlpZi0KBBSrV0dXWFq6urmNqmdNG47z///DP279+PJ598Ev/3\nf/+H+vp6PPvss2jbti0ePHiAbdu2KdtZ1NbWwtHREYGBgfDx8QEhBPX19XB0dBRtiz0bWza2gG2N\nrS2NK2BbY2sJ9PoyCoUC//73v/HUU08pO+UdP34cEyZMwLBhw+Dl5YVXX30Vzs7OGDFiBOLj45Ut\nl52dnfHUU0+J+xdogHuTcccY1tTU4OLFizh27Bhmz56NixcvIi8vD61bt4azszOqqqqQkJAAAI06\nWUokEtHKQdnYsrFVxRbG1hbHFbCNsbUkOoXgjz/+gJeXF+rq6nDw4EFl/WxGRgY+//xzBAYGYtWq\nVcoWxgsWLMDNmzfx0Ucf4YUXXoCDgwNCQkJE/yP27dvX4HCS6upqfPPNN8rzVyMjI9G+fXscPHgQ\nbm5u6NGjB6KiorBr1y6sW7cOffv2RX5+PsrKykS3lYONrXiwsRUHWxlXwPbG1tLoDA0VFxdj9erV\nOHToEFxcXJCWloby8nLk5+cj8v+3d68hTbZhHMD/RYZLnS20IKkEKTRm6pbSeWolUgtETRGSGIGV\nYkEHP6aCZV8sXVKkFFFRRCMNGRElU7QsUda5mFmJYbODmqeGzK73g/m8SvbOfH3c6fp98pn3vefm\n/+Vie3Zfd1oaOjs7kZ+fj71792JgYADe3t4ARqrtiRMnsGfPHtF7xXd1dSEuLg4NDQ2wWCxQKpVC\nta6uroa/vz+CgoLg6emJyspKBAUFISMjA93d3aiurkZubi6kUina29uRkJAg6lrH4mzFw9mKwxly\nBZwzW7uz9RAhMTGRkpKS6ODBg6RUKunu3bv09u1b2rhxo7CBwmw2k0ajIb1eL+oDjYl0d3eTWq2m\ny5cv07p16+jixYs0PDxMVquVTp06Renp6cJYlUpFKSkpZDKZiGikt31paSmFhITQ1atXZ3ztnK14\nOFtxOHquRM6brT3Z/PnohQsXEBAQgPT0dDQ1NQmv79u3D5mZmYiJicGjR4+wfft2bNu2TdSiNZH5\n8+dDJpPh69evKCkpQVlZGTo6OpCTk4PU1FTcu3cPBQUFUCgUkEgkiI+Px9KlSwEA9fX1MJvNqKmp\nmfGe9gBnKybOVhyOnivgvNna1WSqRW5uLsXGxhLRyClWoz+Zam1tpcrKynENpOzh1q1bVFhYSERE\nWq2WpFIpHTp0iKxWK718+ZKSkpIoLi6Ompqaxs1zhH7gnK14OFtxOHquRM6brb3MIvq1Y8KGZcuW\noaioCMnJyRgaGsLcuXPFrlGTduXKFVRVVWHWrFl48eIFjh49ioqKCkilUuTl5SEgIEDYsUi/zgdw\npM0fnK14OFtxOHKugHNnaxeTrRjXr18nDw+P6S9F06Cnp4dkMhllZWUJr5lMpt+aQDlqtedsxcPZ\nisORcyVy7mztYdIbyuRyOXx9fREZGQkADrWRwtPTE2azGWq1GkFBQRgeHoafn5/TbAHnbMXD2YrD\nkXMFnDtbe/irXkMHDhwQax3/27t372CxWIQdf6NoBg7dng6crXg4W3E4cq6Ac2c70yb9jMDRdXd3\nQyaT2XsZLomzFQ9nKx7OdvJcphCMGtvZkE0vzlY8nK14OFvbXK4QMMYY+ztcJhljzM1xIWCMMTfH\nhYAxxtwcFwLGGHNzXAiYyzt+/DjkcjnCwsIQERGBxsZGlJSU4MePHzbnFhcXT2rcRGpqauDr6wuF\nQoHg4GCoVCro9Xqb82pra9HQ0DClezI2FVM+vJ4xZ9DQ0AC9Xg+j0QgPDw90dXXBYrGguLgYu3bt\ngkQi+c/5JSUlSE9PtznuTzZt2oSqqioAwNOnT5GQkACJRILY2Ng/zjEYDPDx8cHatWundE/G/hZ/\nImAuzWw2w8/PTzgQZcGCBdDpdOjo6EBMTAw2b94MANi/fz8iIyMhl8sx2nVFq9X+Nm70sBUA0Ol0\n0Gg0AICbN28iNDQU4eHhiI6OnnAtYWFhOHbsGEpLSwEAVVVVWLNmDRQKBbZu3YrPnz/jw4cPOH/+\nPE6fPo2IiAg8ePAAX758QXJyMqKiohAVFYWHDx+KERVzZ/ZqcsTYTOjv76fw8HBasWIFZWZmUm1t\nLRERBQYG0rdv34RxXV1dRDTShCw6OpqeP38+4Thvb2/hb51ORxqNhoiIQkNDqaOjg4iIvn//TkRE\nBoOB1Gr1uPUYjUYKCQkhopEDVEaVl5fT4cOHiYgoLy+PioqKhP+lpaVRfX09ERG1tbUJ8xmbLvzV\nEHNpXl5eaG5uRl1dHQwGA1JTU1FYWAhgpOfMqBs3bqC8vBxWqxWfPn3Cq1evIJfLbb7/6HusX78e\nu3fvRkpKChITE22OB4D29nakpKTAbDZjaGhoXEO0sePu37+P169fC9d9fX0YHBzEvHnzJpEAY7Zx\nIWAub/bs2VCpVFCpVAgNDcWlS5cA/Nsx8/379ygqKkJTUxN8fX2h0WhgsVgmfK+xzcrGPkQ+d+4c\nGhsbodfroVQq0dzcPOF8o9GIlStXAgCys7Nx5MgRqNVq1NbW4k+NgIkIjx8/drie/8x18DMC5tJM\nJhNaWlqEa6PRiMDAQPj4+KC3txcA0NvbCy8vL0ilUnR2duLOnTvC+LHjAGDRokV48+YNfv78iYqK\nCuH11tZWREVFIT8/H/7+/vj48eNva3n27BkKCgqQlZUl3Hfx4sUAIBSn0Xv29fUJ13FxcdBqtcL1\nkydPphoHYxPiTwTMpfX39yM7Oxs9PT2YM2cOli9fjrKyMly7dg3x8fEICAhAdXU1IiIiEBwcjCVL\nlmDDhg3C/IyMjHHjTp48CbVaDX9/f6xevRoDAwMAgJycHLS0tICIsGXLFqxatQo1NTWoq6uDQqHA\n4OAgFi5ciDNnziAmJgYAkJeXh507d0ImkyE2NhZtbW0AgB07diA5ORm3b99GaWkptFotsrKyEBYW\nBqvVCpVKhbNnz858mMxlcdM5xhhzc/zVEGOMuTkuBIwx5ua4EDDGmJvjQsAYY26OCwFjjLk5LgSM\nMebm/gGspr6+fRFJnQAAAABJRU5ErkJggg==\n"
+      }
+     ],
+     "prompt_number": 25
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "We can also just plot the data on a specific date, like ***2012***. We can now clearly see that the data for these states is all over the place. since the data consist of weekly customer counts, the variability of the data seems suspect. For this tutorial we will assume bad data and proceed. "
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "Daily.ix['FL']['2012':].plot()\n",
+      "Daily.ix['GA']['2012':].plot()\n",
+      "Daily.ix['NY']['2012':].plot()\n",
+      "Daily.ix['TX']['2012':].plot()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "pyout",
+       "prompt_number": 26,
+       "text": [
+        "<matplotlib.axes.AxesSubplot at 0x6402510>"
+       ]
+      },
+      {
+       "output_type": "display_data",
+       "png": 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5Ro4ciaCgIBQXF8PFxQUA4OLiguLiYgBAYWEhBg4cKJ7v7u6OAhV5cJGRkeK/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\n"
+      },
+      {
+       "output_type": "display_data",
+       "png": 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1mUwGmUxW5jFKe06dGPbvB54tF80V0bMncOMGkJkJmJlJHY32evyYzS+lqcXh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\nYBAQEBAQ4PH/NL3D5MqFJdgAAAAASUVORK5CYII=\n"
+      },
+      {
+       "output_type": "display_data",
+       "png": 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1avn7R482XT98jRX4rCwmFF9/zWqBzp5deqKqfn32SFxJ4ssag1zOkn999pmh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\nhw9JTU2NLCwsKDs7u9HjHz9+TBKJhHx8fMjGxobKy8ubLas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6deswZcoUAMCBAwfg7u7O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\nAgLvKP8H4B0gHB6ZHDEAAAAASUVORK5CYII=\n"
+      },
+      {
+       "output_type": "display_data",
+       "png": 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orhoGDdLLLvPtyRNx9rJokegbYUVfmzaiL4mI+5H0KTkZ6NkTaNBANNvmriIw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\nh8PhcARwx8DhcDgcAf8DvI8pfuKT2DkAAAAASUVORK5CYII=\n"
+      }
+     ],
+     "prompt_number": 26
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "We will assume that per month the customer count should remain relatively steady. Any data outside a specific range in that month will be removed from the data set. The final result should have smooth graphs with no spikes.  \n",
+      "\n",
+      "***StateYearMonth*** - Here we group by State, Year of StatusDate, and Month of StatusDate.  \n",
+      "***Daily['Outlier']*** - A boolean (True or False) value letting us know if the value in the CustomerCount column is ouside the acceptable range.  \n",
+      "\n",
+      "We will be using the attribute ***transform*** instead of ***apply***. The reason is that transform will keep the shape(# of rows and columns) of the dataframe the same and apply will not. We eliminate anything around two standard deviations from the mean instead of three standard deviations (1.96). The reason is that we have to be aggressive in this data set to get smooth graphs and remove all of the spikes. Note that we run the risk of eliminating good data."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "# Calculate Outliers\n",
+      "StateYearMonth = Daily.groupby([Daily.index.get_level_values(0), Daily.index.get_level_values(1).year, Daily.index.get_level_values(1).month])\n",
+      "Daily['Outlier'] = StateYearMonth['CustomerCount'].transform( lambda x: abs(x-x.mean()) > 1.60*x.std() )\n",
+      "\n",
+      "# Remove Ouliers\n",
+      "Daily = Daily[Daily['Outlier'] == False]"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 27
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "The dataframe named ***Daily*** will hold customer counts that have been aggregated per day. The original data (df) has multiple records per day.  We are left with a data set that is indexed by both the state and the StatusDate. The Oulier column should be equal to zero signifying that the record is not an outlier."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "Daily.head()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "html": [
+        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+        "<table border=\"1\" class=\"dataframe\">\n",
+        "  <thead>\n",
+        "    <tr style=\"text-align: right;\">\n",
+        "      <th></th>\n",
+        "      <th></th>\n",
+        "      <th>CustomerCount</th>\n",
+        "      <th>Outlier</th>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>State</th>\n",
+        "      <th>StatusDate</th>\n",
+        "      <th></th>\n",
+        "      <th></th>\n",
+        "    </tr>\n",
+        "  </thead>\n",
+        "  <tbody>\n",
+        "    <tr>\n",
+        "      <th rowspan=\"5\" valign=\"top\">FL</th>\n",
+        "      <th>2009-02-02</th>\n",
+        "      <td> 385</td>\n",
+        "      <td> 0</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2009-02-09</th>\n",
+        "      <td> 125</td>\n",
+        "      <td> 0</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2009-02-16</th>\n",
+        "      <td> 378</td>\n",
+        "      <td> 0</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2009-03-02</th>\n",
+        "      <td> 722</td>\n",
+        "      <td> 0</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2009-05-18</th>\n",
+        "      <td> 962</td>\n",
+        "      <td> 0</td>\n",
+        "    </tr>\n",
+        "  </tbody>\n",
+        "</table>\n",
+        "</div>"
+       ],
+       "output_type": "pyout",
+       "prompt_number": 28,
+       "text": [
+        "                  CustomerCount  Outlier\n",
+        "State StatusDate                        \n",
+        "FL    2009-02-02            385        0\n",
+        "      2009-02-09            125        0\n",
+        "      2009-02-16            378        0\n",
+        "      2009-03-02            722        0\n",
+        "      2009-05-18            962        0"
+       ]
+      }
+     ],
+     "prompt_number": 28
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "We create a seperate dataframe named ***ALL*** which groups the Daily dataframe by StatusDate. We are essentially getting rid of the State column. The ***Max*** column represents the maximum customer count per month. The Max column is used to smooth out the graph."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "# Combine all markets\n",
+      "\n",
+      "# Get the max customer count by Date\n",
+      "ALL = DataFrame(Daily['CustomerCount'].groupby(Daily.index.get_level_values(1)).sum())\n",
+      "ALL.columns = ['CustomerCount'] # rename column\n",
+      "\n",
+      "# Group by Year and Month\n",
+      "YearMonth = ALL.groupby([lambda x: x.year, lambda x: x.month])\n",
+      "\n",
+      "# What is the max customer count per Year and Month\n",
+      "ALL['Max'] = YearMonth['CustomerCount'].transform(lambda x: x.max())\n",
+      "ALL.head()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "html": [
+        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+        "<table border=\"1\" class=\"dataframe\">\n",
+        "  <thead>\n",
+        "    <tr style=\"text-align: right;\">\n",
+        "      <th></th>\n",
+        "      <th>CustomerCount</th>\n",
+        "      <th>Max</th>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>StatusDate</th>\n",
+        "      <th></th>\n",
+        "      <th></th>\n",
+        "    </tr>\n",
+        "  </thead>\n",
+        "  <tbody>\n",
+        "    <tr>\n",
+        "      <th>2009-01-05</th>\n",
+        "      <td> 1394</td>\n",
+        "      <td> 1608</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2009-01-12</th>\n",
+        "      <td>  471</td>\n",
+        "      <td> 1608</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2009-01-19</th>\n",
+        "      <td> 1608</td>\n",
+        "      <td> 1608</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2009-01-26</th>\n",
+        "      <td>  408</td>\n",
+        "      <td> 1608</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2009-02-02</th>\n",
+        "      <td>  385</td>\n",
+        "      <td> 1037</td>\n",
+        "    </tr>\n",
+        "  </tbody>\n",
+        "</table>\n",
+        "</div>"
+       ],
+       "output_type": "pyout",
+       "prompt_number": 29,
+       "text": [
+        "            CustomerCount   Max\n",
+        "StatusDate                     \n",
+        "2009-01-05           1394  1608\n",
+        "2009-01-12            471  1608\n",
+        "2009-01-19           1608  1608\n",
+        "2009-01-26            408  1608\n",
+        "2009-02-02            385  1037"
+       ]
+      }
+     ],
+     "prompt_number": 29
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "As you can see from the ***ALL*** dataframe above, in the month of January 2009, the maximum customer count was 1,585. If we used ***apply***, we would have a dataframe with (Year and Month) as the index and just the *Max* column with the value of 1,585. "
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "----------------------------------  \n",
+      "There is also an interest to gauge if the current customer counts were reaching certain goals the company had established. The task here is to visually show if the current customer counts are meeting the goals listed below. We will call the goals ***BHAG*** (Big Hairy Annual Goal).  \n",
+      "\n",
+      "* 12/31/2011 - 1,000 customers  \n",
+      "* 12/31/2012 - 2,000 customers  \n",
+      "* 12/31/2013 - 3,000 customers  \n",
+      "\n",
+      "We will be using the **date_range** function to create our dates.  \n",
+      "\n",
+      "***Definition:*** date_range(start=None, end=None, periods=None, freq='D', tz=None, normalize=False, name=None)  \n",
+      "***Docstring:*** Return a fixed frequency datetime index, with day (calendar) as the default frequency  \n",
+      "\n",
+      "By choosing the frequency to be ***A*** or annual we will be able to get the three target dates from above."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "date_range?"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 30
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "# Create the BHAG dataframe\n",
+      "data = [1000,2000,3000]\n",
+      "idx = date_range(start='12/31/2011', end='12/31/2013', freq='A')\n",
+      "BHAG = DataFrame(data, index=idx, columns=['BHAG'])\n",
+      "BHAG"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "html": [
+        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+        "<table border=\"1\" class=\"dataframe\">\n",
+        "  <thead>\n",
+        "    <tr style=\"text-align: right;\">\n",
+        "      <th></th>\n",
+        "      <th>BHAG</th>\n",
+        "    </tr>\n",
+        "  </thead>\n",
+        "  <tbody>\n",
+        "    <tr>\n",
+        "      <th>2011-12-31</th>\n",
+        "      <td> 1000</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2012-12-31</th>\n",
+        "      <td> 2000</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2013-12-31</th>\n",
+        "      <td> 3000</td>\n",
+        "    </tr>\n",
+        "  </tbody>\n",
+        "</table>\n",
+        "</div>"
+       ],
+       "output_type": "pyout",
+       "prompt_number": 31,
+       "text": [
+        "            BHAG\n",
+        "2011-12-31  1000\n",
+        "2012-12-31  2000\n",
+        "2013-12-31  3000"
+       ]
+      }
+     ],
+     "prompt_number": 31
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Combining dataframes as we have learned in previous lesson is made simple using the ***concat*** function. Remember when we choose ***axis = 0*** we are appending row wise."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "# Combine the BHAG and the ALL data set \n",
+      "combined = concat([ALL,BHAG], axis=0)\n",
+      "combined = combined.sort(axis=0)\n",
+      "combined.tail()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "html": [
+        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+        "<table border=\"1\" class=\"dataframe\">\n",
+        "  <thead>\n",
+        "    <tr style=\"text-align: right;\">\n",
+        "      <th></th>\n",
+        "      <th>BHAG</th>\n",
+        "      <th>CustomerCount</th>\n",
+        "      <th>Max</th>\n",
+        "    </tr>\n",
+        "  </thead>\n",
+        "  <tbody>\n",
+        "    <tr>\n",
+        "      <th>2012-12-10</th>\n",
+        "      <td>  NaN</td>\n",
+        "      <td>  73</td>\n",
+        "      <td> 633</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2012-12-17</th>\n",
+        "      <td>  NaN</td>\n",
+        "      <td> 138</td>\n",
+        "      <td> 633</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2012-12-31</th>\n",
+        "      <td> 2000</td>\n",
+        "      <td> NaN</td>\n",
+        "      <td> NaN</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2012-12-31</th>\n",
+        "      <td>  NaN</td>\n",
+        "      <td> 633</td>\n",
+        "      <td> 633</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2013-12-31</th>\n",
+        "      <td> 3000</td>\n",
+        "      <td> NaN</td>\n",
+        "      <td> NaN</td>\n",
+        "    </tr>\n",
+        "  </tbody>\n",
+        "</table>\n",
+        "</div>"
+       ],
+       "output_type": "pyout",
+       "prompt_number": 32,
+       "text": [
+        "            BHAG  CustomerCount  Max\n",
+        "2012-12-10   NaN             73  633\n",
+        "2012-12-17   NaN            138  633\n",
+        "2012-12-31  2000            NaN  NaN\n",
+        "2012-12-31   NaN            633  633\n",
+        "2013-12-31  3000            NaN  NaN"
+       ]
+      }
+     ],
+     "prompt_number": 32
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "fig, axes = plt.subplots(figsize=(10, 5))\n",
+      "combined['BHAG'].fillna(method='pad').plot(color='green', label='BHAG')\n",
+      "combined['Max'].plot(color='blue', label='All Markets')\n",
+      "plt.legend(loc='best')"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "pyout",
+       "prompt_number": 33,
+       "text": [
+        "<matplotlib.legend.Legend at 0x5a19050>"
+       ]
+      },
+      {
+       "output_type": "display_data",
+       "png": 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y9/X+W3pgKZLXJWPdfesMF1dWPjZFQBksH5cICWvjdConXZFtGtz5PGTxYJmp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\nRTjw8xN48IvlsAWxBvNP/3QOTjyDqWtShW3/yE/DsK