Repository: FergM/fattails (language: python)

Issue:
Added MS plot function to metrics.py



Discussion / hints:

Fix patch:
diff --git a/README.md b/README.md
index 3d02abd..8c5d270 100644
--- a/README.md
+++ b/README.md
@@ -1,27 +1,28 @@
 # Fat Tails
 Python package and Jupyter notebooks for fat-tailed statistics. Inspired by Nassim Taleb's Technical Incerto.
 
-<div>
-  <a href="https://pypi.org/project/fattails">
-      <img src="https://badge.fury.io/py/fattails.svg" alt="package version"/>
-  </a>
-</div>
+[![PyPI](https://img.shields.io/pypi/v/fattails)](https://pypi.org/project/fattails/)
+[![PyPI - License](https://img.shields.io/pypi/l/fattails)](https://github.com/FergM/fattails/blob/main/LICENSE)
 
 ## Content
 
 ### Notebooks
-See the [notebooks/README.md](./notebooks/) for more detail.
+See the [notebooks/README.md](https://github.com/FergM/fattails/blob/main/notebooks/README.md) for more detail.
 
 My favourite notebooks so far:
-* [Central Limit Theorem: How the sum of Uniform values is Gaussian](./notebooks/NB-22%20-%20Visual%20Central%20Limit%20Theorem.ipynb)
-* [S&P500: How geometric average return is impossible](./notebooks/Notebook-11%20-%20Ergodicity%20and%20S%26P500.ipynb)
-* [GameStop: January 2021 was not an outlier if you assume Power Law tails.](./notebooks/NB-25%20-%20Survival%20Plot%20-%20Gamestop.ipynb)
+* [Central Limit Theorem: How the sum of Uniform values is Gaussian](https://github.com/FergM/fattails/blob/main/notebooks/NB-22%20-%20Visual%20Central%20Limit%20Theorem.ipynb)
+* [S&P500: How geometric average return is impossible](https://github.com/FergM/fattails/blob/main/notebooks/Notebook-11%20-%20Ergodicity%20and%20S%26P500.ipynb)
+* [GameStop: January 2021 was not an outlier if you assume Power Law tails.](https://github.com/FergM/fattails/blob/main/notebooks/NB-25%20-%20Survival%20Plot%20-%20Gamestop.ipynb)
 
 ### Functions
 Quick Access:
 * `fattails.mad()`: Calculates mean absolute deviation.
+* `fattails.plot_MS_moments()`: Plots the cumulative max/sum ratio of moments 1 to 4.
+
 Other:
 * `fattails.metrics.get_survival_probability()`: Calculate survival probabilities for a given dataset.
+* `fattails.metrics.calculate_moments()`: Generate dataframe with the chosen moments for each datapoint
+* `fattails.metrics.max_over_sum()`: Calculate the cumulative max/sum ratio
 
 Example:
 ```
@@ -43,13 +44,19 @@ Name: survival_probability, dtype: float64
 ```
 
 ### Derivations
-* [How much data do I need?](/docs/Notes-02%20-%20Derivation%20-%20How%20much%20data%20do%20I%20need.pdf)
+* [How much data do I need?](https://github.com/FergM/fattails/blob/main/docs/Notes-02%20-%20Derivation%20-%20How%20much%20data%20do%20I%20need.pdf)
+
+## Contact
+Jump straight to [discussions](https://github.com/FergM/fattails/discussions) or check out the [contributing guidelines](./docs/CONTRIBUTING.md). Feedback and suggestions are really appreciated!
+
+### Contributors
+Special thanks to:
+* Daniel Reti: [github.com/drettt](https://github.com/drettt)
 
 # External Resources
 Technical Incerto Book One:
-* [PDF on researchers.one](https://researchers.one/articles/20.01.00018)
-* [PDF on arxiv.org](https://arxiv.org/abs/2001.10488)
-* [Errata](https://www.fooledbyrandomness.com/Errata2020FirstEdition.pdf)
+* Free PDF: [researchers.one](https://researchers.one/articles/20.01.00018), [arxiv.org](https://arxiv.org/abs/2001.10488)
+* Errata: [fooledbyrandomness.com](https://www.fooledbyrandomness.com/Errata2020FirstEdition.pdf)
 
 More Links:
 * Incerto Reading Club: [Website](http://www.techincertoreadingclub.com/), [GitHub](https://github.com/Technical-Incerto-Reading-Club/code-examples)
diff --git a/docs/CONTRIBUTING.md b/docs/CONTRIBUTING.md
new file mode 100644
index 0000000..770b397
--- /dev/null
+++ b/docs/CONTRIBUTING.md
@@ -0,0 +1,21 @@
+# How to contribute to `fattails`
+[![PyPI - License](https://img.shields.io/pypi/l/fattails)](https://github.com/FergM/fattails/blob/main/LICENSE)
+
+### Where should I start?
+* Start a conversation on the [discussions page](https://github.com/FergM/fattails/discussions)
+
+Alternatively:
+* Report an [Issue](https://github.com/FergM/fattails/issues)
+* Create a Pull Request
+    * Try start a discussion before doing too much work
+    * Depending how it fits I'll either accept, modify or recommend that you fork the feature
+* Direct message @MFergal on twitter
+    * Sometimes I don't check twitter for a while though
+
+### How can I help?
+* Ask a question
+* Suggest a feature
+* Share what you find useful
+
+### Philosophy
+The goal of `fattails` is to be useful in fat-tailed domains such as Finance and Data Science.
diff --git a/docs/notebooks.md b/docs/notebooks.md
index acba0ef..f976d6c 100644
--- a/docs/notebooks.md
+++ b/docs/notebooks.md
@@ -1,2 +1,2 @@
 # Content
-See [notebooks/README.md](../notebooks) notebook documentation.
\ No newline at end of file
+See [notebooks/README.md](../notebooks/README.md) for a list of the main notebooks.
\ No newline at end of file
diff --git a/fattails/express.py b/fattails/express.py
index 894d231..b845676 100644
--- a/fattails/express.py
+++ b/fattails/express.py
@@ -1,2 +1,35 @@
+import fattails
+
 # Quick access to selected methods in other modules
-from fattails.metrics import mad
\ No newline at end of file
+from fattails.metrics import mad
+
+
+def plot_MS_moments(data):
+    """Plot cumulative max/sum ratio of 1st to 4th moments
+
+    Parameters
+    ----------
+    data : array-like
+        Numeric values whose moments you want to calculate.
+        Must be:
+            * a valid input to `fattails.metrics.calculate_moments`.
+            * 1-dimensional
+
+
+    Returns
+    -------
+    subplots : np.array
+        2*2 array of matplotlib subplots
+    """
+
+    # Calculate Moments
+    moments_df = fattails.metrics.calculate_moments(data)
+
+    # Calculate Max/Sum Ratio
+    max_over_sum_df = moments_df.apply(fattails.metrics.max_over_sum)
+
+    # Plot Max over Sum
+    subplots = max_over_sum_df.plot(subplots=True, layout=[2, 2],
+                                    figsize=[12.0, 8.0])
+
+    return subplots
diff --git a/fattails/metrics.py b/fattails/metrics.py
index fcbebbc..3373471 100644
--- a/fattails/metrics.py
+++ b/fattails/metrics.py
@@ -1,18 +1,20 @@
 from copy import copy
+
 import numpy as np
 import pandas as pd
 
+
 def mad(x):
     """Calculate Mean Absolute Deviation
-    
+
     The average deviation from sample mean.
     Not to be confused with 'Median' Absolute Deviation.
-    
+
     Parameters
     ----------
     x : array_like
         Input array or object that can be converted to an array.
-    
+
     Returns
     -------
     mad : scalar
@@ -29,6 +31,7 @@ def mad(x):
 
     return mad
 
+
 def get_survival_probability(arr):
     """Calculate sample probability of X >= x, for each value in `arr`.
 
@@ -44,45 +47,115 @@ def get_survival_probability(arr):
     -------
     survival_probability_sr : Pandas Series
     """
-    #---------------------------------------------------
+    # ---------------------------------------------------
     # PREPARE
-    ## Sort values from low to high. Keep track of original
-    ## index and order.
+    # Sort values from low to high. Keep track of original
+    # index and order.
 
-    arr = copy(arr)     # Copy to avoid accidental mutation
-    sr = pd.Series(arr) # Ensure we have a pandas series
+    arr = copy(arr)      # Copy to avoid accidental mutation
+    sr = pd.Series(arr)  # Ensure we have a pandas series
 
-    ## Keep a copy of the original index
+    # Keep a copy of the original index
     input_index = sr.index.copy()
 
-    ## Create index of input order
-    df = sr.reset_index(name='input_values') # Keeps the input index as a column
-    df.index.name = 'input_order' # Name the new index
+    # Create index of input order
+    df = sr.reset_index(name='input_values')  # Keeps the input index as a column
+    df.index.name = 'input_order'  # Name the new index
 
-    ## Sort from low to high and reindex
-    df = df.sort_values(by='input_values') # sort from low to high
+    # Sort from low to high and reindex
+    df = df.sort_values(by='input_values')  # sort from low to high
     df = df.reset_index()
-    df.index.name = 'sorted_order' # Name the new index
+    df.index.name = 'sorted_order'  # Name the new index
 
-    #---------------------------------------------------
+    # ---------------------------------------------------
     # CALCULATE
 
     # Label relative positions
     gap_count = len(sr) + 1           # Think of the Posts and Fences analogy
-    df['left_gap_count'] = df.index + 1                   # Count values <= x
-    df['right_gap_count'] = gap_count - df.left_gap_count # Count values >= x
+    df['left_gap_count'] = df.index + 1                    # Count values <= x
+    df['right_gap_count'] = gap_count - df.left_gap_count  # Count values >= x
 
     # Get survival Probability
     df['survival_probability'] = df.right_gap_count / gap_count
 
-    #---------------------------------------------------
+    # ---------------------------------------------------
     # FORMAT THE OUTPUT
 
-    #Reset Input Order and Index
-    df = df.sort_values(by='input_order') # sort from low to high
+    # Reset Input Order and Index
+    df = df.sort_values(by='input_order')  # sort from low to high
     df.index = input_index
 
-    #Extract the output series
+    # Extract the output series
     survival_probability_sr = df.survival_probability
 
-    return survival_probability_sr
\ No newline at end of file
+    return survival_probability_sr
+
+
+def calculate_moments(data, moments=[1, 2, 3, 4], absolute_values=True):
+    """Create dataframe with a column for each moment.
+
+    Parameters
+    ----------
+    data : array-like
+        Numeric values whose moments you want to calculate.
+        Must be 1-dimensional
+
+    moments : list-like of numbers
+        Each moment has its own column in the output.
+
+    absolute_values : , default True
+
+
+    Returns
+    -------
+    moments_df : DataFrame
+        Elementwise evaluation of x**p for each x in data and p in moments
+        Number of rows = len(data)
+        Number of columns = len(moments)
+    """
+
+    # FORMAT THE INPUT
+    sr = pd.Series(data)
+    if absolute_values:
+        # Take absolute values (this matters for odd moments)
+        sr = sr.abs()
+
+    # CALCULATE MOMENTS
+    df = pd.DataFrame()
+    # Add columns with the moments for each datapoint
+    for moment in moments:
+        df[f'moment_{moment}'] = sr**moment
+
+    return df
+
+
+def max_over_sum(data, reset_index=False):
+    """Calculate cumulative max over sum
+
+    Parameters
+    ----------
+    data : array-like
+        Numeric values whose moments you want to calculate.
+        Must be 1-dimensional
+    reset_index : bool
+        Reset the index so it matches the cumulative count of your dataset size
+        Selecting True will give you an output index starting at 1
+
+    Returns
+    -------
+    max_over_sum : Series
+        Series with cumulative maximum divided by cumulative sum.
+        Cumulative sample size is assigned as the series' index.
+    """
+    sr = pd.Series(data)
+
+    if reset_index:
+        # Replace index with Cumulative Sample Size
+        sr.reset_index(drop=True, inplace=True)
+        sr.index += 1
+        sr.index.rename('cumulative_sample_size', inplace=True)
+
+    # Calculate
+    max_over_sum = sr.cummax()/sr.cumsum()
+
+    return max_over_sum
diff --git a/notebooks/NB27 - Max over Sum Plot.ipynb b/notebooks/NB27 - Max over Sum Plot.ipynb
new file mode 100644
index 0000000..5c137ec
--- /dev/null
+++ b/notebooks/NB27 - Max over Sum Plot.ipynb
@@ -0,0 +1,679 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# About\n",
+    "`MS plot` examples for the S&P 500.\n",
+    "\n",
+    "### Summary\n",
+    "1. Load S&P 500 returns\n",
+    "2. MS Plot in one line using `fattails`\n",
+    "3. Validation\n",
+    "4. Detailed Calculation\n",
+    "\n",
+    "### Context\n",
+    "In chapter 10 of the Technical Incerto there is an MS Plot for the SP500. This notebook shows how to replicate that using python.\n",
+    "\n",
+    "An MS plot displays the cumulative max/sum ratio. In the Technical Incerto this plot is used to analyse moments. It can also be applied more broadly to any numeric dataset."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import fattails\n",
+    "import numpy as np\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Helper Function"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_SP500_daily_returns(data_source):\n",
+    "    \"\"\"Load time series of SP500 daily returns from finance.yahoo.com\n",
+    "    \n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    data_source : str\n",
+    "        URL or path to csv file.    \n",
+    "    \n",
+    "    Returns\n",
+    "    -------\n",
+    "    daily_return : Pandas Series\n",
+    "    \"\"\"\n",
+    "\n",
+    "    # Load Dataset\n",
+    "    price_data = pd.read_csv(data_source, index_col=0, parse_dates=True)    \n",
+    "    \n",
+    "    # Select Closing Prices\n",
+    "    close_price = price_data.Close\n",
+    "    \n",
+    "    # Calculate Daily Return\n",
+    "    daily_return = close_price / close_price.shift(1) - 1\n",
+    "    \n",
+    "    # Remove NaN value\n",
+    "    ## The first day has no prior day so return is NaN\n",
+    "    assert daily_return.isna().sum() == 1  \n",
+    "    daily_return.dropna(inplace=True)\n",
+    "\n",
+    "    return daily_return"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Fetch S&P 500 Returns"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Calculate the historic daily returns. For context, a value of 0.01 indicates a 1% increase in value on the specified day."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Date\n",
+       "1960-01-05    0.008012\n",
+       "1960-01-06   -0.004305\n",
+       "1960-01-07   -0.007318\n",
+       "1960-01-08   -0.003183\n",
+       "1960-01-11   -0.012269\n",
+       "                ...   \n",
+       "2019-12-24   -0.000195\n",
+       "2019-12-26    0.005128\n",
+       "2019-12-27    0.000034\n",
+       "2019-12-30   -0.005781\n",
+       "2019-12-31    0.002946\n",
+       "Name: Close, Length: 15101, dtype: float64"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Calculate Returns\n",
+    "# This particular link captures 60 years from 1960 to 2019 end.\n",
+    "data_source = 'https://query1.finance.yahoo.com/v7/finance/download/%5EGSPC?period1=-315619200&period2=1577836800&interval=1d&events=history&includeAdjustedClose=true'\n",
+    "\n",
+    "daily_returns = get_SP500_daily_returns(data_source)\n",
+    "daily_returns"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Express Plot"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can quickly plot the Max/Sum ratio of your dataset's moments using the `fattails.plot_MS_moments()`.\n",
+    "\n",
+    "These plots show the cumulative Max/Sum ratio. As you go right on the x axis, this ratio is recalculated with increasing sample size."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x576 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fattails.plot_MS_moments(daily_returns);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Validate"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Comparison with Nassim Taleb's Plot"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here is a screenshot of the 4th moment MS plot from the Technical Incerto. We see that it matches with the 4th moment plot above. The dataset we are using starts earlier."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![Title](images/NB27-MS_Plot_SP500_4th_Moment.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Eyeball the Historic Returns"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's validate the main jump in the MS plots above. To do this we plot historic returns and look at the most extreme value. The main jump matches with the largest historic loss, a 20% single day loss in 1987."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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Tk+MGF6sBRXSK8SiPiCgKOXrBwtEmEOxIjTdnrMOdXy0OvQAUEE/H6/tF24N+zdnr9iJtxESs3Hk4qOe7fw3XZR7FF39vCbo8RO5CXkIBAFT1NwC/uT020vLzSQCX2jzvBwA/hKMM8SpBgFyEf64BBY+dClRUeN4TGQI5FSYu342eTauhRkqpQiuPJ69OM9Zqe+fK4J5vPefD2TMZKz6ZswnP/roK658fhKTE/H6MtZn2c+Rfn74u6PeavsqYT7dg0wG0qpUS9Os4DHxjNnLzFFd3rh/yaxEBkU+8QiFypP/NC0NtLzs3D0c5bCBsYun2O/LnFXjypxWRLkahOJmdG7PzJ4kon+Ms/3PtXlzx4d8hvVZxGCK865AxTLM4lKW4GDPFSL50OjfP5fGcvDy73YsFx+2H024o3BjkRbswXtvv/uoftHl6avheME7FYuKVcX9tweduw0iWbjsUmcKE0e7DJ9H8yckY91d0DpGx1u0OHDuNR39cjpPZuZErEFExduh4fiPmzhAz0vp7fZ+2yj57ovt5On/jfkxesTugMkxeuTugssSzfYU4bznYalgsxeZ5eYpXp67BnqOcH1qcMMiLcvkBReiv5bhhUGjiZZ7k0HfmRroIIdtqpt6PhXTKoyetxtcLtmJCDPwtgYqGupKqYsLSnWytj6DphZCu3lcv2s3jMmwff2OG6zDByz/4G7d9scjj60xblYnFWw/a3lfWc7kepxMeGrlO5/juyftmwdag3pNnNLBo60G8+ft6PDCe6wMXJwzyolyCM6DgZaa4CGfgTYUrnC2p/2w9iJvGLkRObtENC3J8xxSKInzbYm1t5lH8u/tIpItRwA+Ld+Cer//BJ3M2RbooVAwcORHY1Iibx2Xgonfn4Y4vCyZtcZ+uceJ0btw3Juw9eirg57w0+d+A9g/XMNlYqL85vm+nsnkjKk4Y5BUTf67di8/mBn7zd1xjZq3ZG+YSUbBiaQiGu1U7i1/lORzCcYu9++t/MH31HuyKwJpHJy031hj++vllwGt/YuDrs10e27zvGOau3xehEhn2ZRmVzr1ZgVc+ybfCblRr/NhvuPebf/zef23mUbz7x/pCLJFnLUZOxv8FUNZYdOl7rusX+jOktahDLUdOhSMncrBgU3Qnlo/3+05xxSCvmLj2kwV4+pdVAT/PcWLd/uViHDwW32tlFTex0DrnbsyUgi2duw+fxKHj0fndK4z5k0XZg2ttUNi0j0O2POn18h+46qP5kS4GAFaGwq2oGtVy8hQ/LSk4FDrrVA5embqmwOMDXvsT/5lc8PHCsn5PFsZnbHP+/mucL5C+J4ievEPHszEliGkrm/YdC/g5QH4d4bL3/8Jl7//lY2+iwDHIi3LW4QLZ5nitZdsPodvo33E4wOEgFB6O1rlgK/s/LNqOB78rnuPaZ9r0GHd+cQY6vTAjAqUpXhynYlEG99bv2OKthwAAD32/LGqD7mAVt97z0zl5ePaXVThsSfTB4dvht3X/8Yh9rta3fev39Wj2xCScOB2ZpEeqwIDXZuHh75dF5P1jSTBzmsf9tSVmR7kEKpoat+MhYRGDvChnV7d5bdpa7Dh0Aou2RHf3f7RyVvaDvH488N3SkBZoDdZvy3fhVZsWaX+c8mNSe6yTYtRHs2pXfFU4itNnDwA//bMDn8zdhP/Y9HxTeCzeehBnj5mJr+YbyTIiXbk8lZOHnYdDy9gZLAUQ51PwfPL345m4bBdy8zTgudWD35ztMZOqJ56uW0eicCmraFzGIw5iPAZ5Uc/LeRULX+BN+44xJXyY7D16CmkjJnrMJHnHl4vx5u+RmUMSKfm9b+FTLM674lCGInL4RDbWZB6NdDFc5Jpfgi/nb3UuNeKrDnTLuAxc9VFoa7fFkw1mRskdIS6F4Mu0VZmYt8G/+ZwJAVR0V+86EraehGJxzSliL/62Gu/P2oD3Zm3Asz6mumzadwx/b9zv92sPeXM2Gj8+KeAyfTxnI9JGTMQ2M2tzsLq++HtIzyf/hGN96eKOQV6Us7upOFP4R/n398TpXPR++Q/cP35JpIsSkOLaoLXOrAh/OT8614QrHOE7VwojYHQYO28zNuwtOOcu0O9abp5iXoAJSHLzFM/8stKlMr0/6xRenboGecWg++CJn1Z43Hb8dA42Bzlfxl1unuK5X1ch80hgiXUcS434+o5NXZWJuev9r4jGm+0Hj2PW2qJPMHbzuAxc+WH+fM6T2blY4mGN0EBOx8VbD+HHxTtCK5zph8VFP/Ij0t7/cyNenPQvRk/6F5/M3eQ1cUnvl//Ap3M3+/3a/+4OrtHo741GGf4JcQ3ZrFM5IT0/kiJZ7zyZnYs56/y/vxWD21ehY5AX5ewqec5kEkVakvA7lWP04AVy0nqiqkVeIS2u473DObRtrZ89KPviIKOgt09VVYMegqOqeGrCSlxgsy6hp6+Yp2/euzPX48qP5gd0Ti3eehCfzt2M+75d4nxsxI/L8ebv6/FXAK3jhcVbKvrhn2Wg18t/hOV9/t64Hx/P2RTyvKe//cyip6rF9hoSCX1fmYXrPlngcfv7szYG9HqOIeZb9x/HqF9XIW3ERL+eN+KHZbjgnbnItMmiG2iji7/Xz0BYvzPx9P0pTotw+/u5h6tB+GR2Lo5FODAszEZOfz336ypc/fF8rNx52K/92ZNHxZ71GhFrX1fH+ReOsd5fL9iGho/9FnArfDASE4zy7jx8Es/9GnjG1MLi7fsR7A1iwGt/+txn+fbDSB81HT9EYJ5hUVm+/TA27/c8ROfL+VvR9umpzixsB4+dRq8xM529q1Y5uXkYO2+zM5GS4zw4etL/Y+Tp3rXRfP9AKkTOtfhUcTI7Fxv3ZjkTTBT1TXL1riP4ftF2fPhnfoXe0+Vhf9apsAahjr81J8/3XB27j8UxZ2zptkNYscN3JeSj2ZvQ4NHfmEDLVFjzfs8eMxMfBbB24XLz2PW3ufYVh7mh1rbMYHukijNVxSib+6qnS9HirQcLuUQFFcZl8fCJbAx4bRbW2BzTvq/MQqunpoT/Ta3vfzzbr3rCoi2un/evy3bii7+3OO9nhWm9OYTb32smgzwq9qJxsqu/HKdfOP7E//1jBBhbvFTEw8U6hPbjYrjwsd3nOWZK6Km+s3Pz8MeaPS6Prdl9FMM+MFJDz/VzXktRcAQp4Wp9/OLv/CGwdq24M1YbE/IdyxzM+HcPNu8/jv/O2lBg3y/nb8VTE1aiyeOTnN/bQI2csAIbbYZ3hmLh5oO466vF6PPKLJw2K9xz1u8LavH3oyezcfy07wrD9oPHceeXi7Ev6xSyTuVg0Buz8eB3S/H8b6t9VhpmrN5T4LGl2w7hf/9sR9qIiV7ncuXmqXMkgYOvCnzaiIl49Ef7Xj73sh7wY7mbrxcYCUWCWdQ51q3ceRh/hGHo5psz1rn8vjbzKGau2YOc3DwcP51j2/seyLw7X1btOoK0ERNdMjN+u3Cryz4b92bh5yX+D+vMc+nJC72Mxc0X87faBuWe/lTrtTlQjgW+D5/IDug6VxhJgB77cTnWZmbh9elrC2wr7HmpANDu2anoOeaPgJ9311f/4ImfVqBJEHMcA+WsM3q5VluDUA7XpKjmTyWqOHPMQQrHLTW/VzD41zidk+fXsEP3tzj3rdlBpWUuLPM27Mfl7//lvIEBrt8V67DWQG4er0xdi+s/XYj5lt6TGz9biGOOXp8IXlH/2XrQWWmf+e8etBg5GYu3Hgxbu3uC5Uoa0F9ps/NRS8Xyy7+3BlVd2Lj3GG7/YnHBtwui1mc9Z2abwzyzzd6s92dtxDszCwaqvrR5eipajpyC7Qe9N7rc+80STFy+C+mjpuOF31YH9B4P/1Aw4Br6zlzc962xPMnUlbs9BsLDxy5Esycm227z9hF+vcBYp8z9OrN4y0GX533w50bcNHahl9LDeSEpytbmY6dyPM43K06GvDkHEz2sA7dix2Hs9PO69eo01wrzgNf+xA2fLkTjxyeh5cgpuOrDgmsregvyHJvs5s/acZxPU1flr832yA/LkTZiImau2YOMzQfQ55VZ+L9vlvj1egDwo2V+3u//ZqL5k5Mw8HWj1/HwiWykjZiIz0MIfMJlx6ETeGvGuoDvC2s99E6ePJ2Lmf+6NuyczM4NqXd12fZDAIB2z0wNKBHLzkMnoar4dO4mr71fgSSVm7jc+L6Hcjn4eM4ml/tzoLzVf0K9l/YcMxNPeplfHaqc3Dx8s2ArLv7vPOdj1mtrXp5iysrdMTfEmUFeIXhv1gaM/NnzlzWccy2sJ5aj0j7DvNCNnhTZ9N2LthxE2oiJ2BVEWul9Wadw6XtGD5C33sqf/tmB3//1nbY4v4UnePd9uwTpo6Z73J51KgfP/boKJ7NdW/xW7DiC//vmnxDeOTysX7n5mw7g8IlsvDNzPTbszXKusQbAmdxgxupMdBv9u99zVRwJLjz1UuRG6Nq57cBxXPjuPIz8aSWA/IrVYuuwEj/Pxzu/WoyvF2zF5BW78NHsjZi+KhNHT2aHtUfdWhT3HqBges0Ccdl7f6HnmJketzuGzFnrZVv2B5/YpPtLM7F8e/7QxXF/bca/u41ejUPHT7sMN3OkyndwNtwE+d7P/LIKfV6ZZbvtD7f1ILcdOI4TIWT5dV/SYs76fZhu09NotXGv8bm+XYQZb+/6ajEueGdusU7h7q13e/vB4zj3rTnoOvr3sCTcWW4zrNbbqe7Y5h5s+GK3vt6EJTuD6qF55Iflzp9fnroWJ7PznOeR4178xV/hCfLy8hRbgxwd023073hl2lr8vDSw5DMJHj7/x/63HDd85tpwcjI7NyKJ0MZMWYNHfliGZ35Zhee9NE55G+LvSZ4qvsvYhp/+MT63QIajPvfrKlz+QWAZfE/n5AV13/kuYxtenOR/w9yW/ccLtfHh/T83YsSPy10eU8uf9eX8Lbj180URWb6qMDHIKwSjJ/2LcR4uonuOnkSDR3/DVwu22m4P1H5Lhfq35a4tm94SEhSFL80Tdl4QGeP8nX9077dLcONnGQG/fjAcLWmeWh7fnbkeH8/ZhLNtKsklk4I71ZZtPxT0TdSX46dzMGbKGlz+/l/OseyAkfDmo9kbC4yt92WRebO5/cuCPUhAfqvZqp1HsL+QErHszzpVYMjocbMCtWjrQSzcfMBlHcNAg7OJy3bh0R+X47YvFmPUxNW4aVwG7vt2CRItr/PG9HWYtioT6/dk4b5vl+CmsQuxfMcR53ta5dh8lw5aFtA+nevaINT48Ul+D91ak3kUaSMmYs3uo3h+4iq/Wo0XbD7gMqQ5JzcPOTbReaCNVJv2HfOYMe68t+c4E8GM/HklBr4+GwDQ/tlpXrPMOb5PRTFkvcd/ZuLmccZ1xvGnbz94HK9PX+vXZ/HML6s8tujn5OZ5Xbze314hq6xTOWj6xCTMXBNYsLHMDLhPZQdeqZv57x7nchGBuubj+S7DFA8cO420ERNtK1yOnlg7fS1Bu3vCHevC9KHw9n3ztSC2p8Dz/T/tk8aUKZHkf8F8sH6PwjWc8L+zNuDsMTOxfk/wc/9mr/V/GP/J7FyM9VC3sruWdn9pZkiVdvc6VSDGZxjv6+3c9uVUTq7LiBvAyML70PfLcO+3SzB73V5c9O48D88Oj6ZPTMIF7xZM+uXNV/O34qHvlwWcDClkXkZs7bHJx2DtydtlJlLKPHIS32VsC3kZjOKCQV4RWL79MBZvPYh9WaecKZPDlTrZyv0iF/FO5wCGGx0+no2WIyc717IZO2+z+8sE7dDx086AJSPAwMXKceHYefhEgQsvULDXxSo5MbhT7fy35+LsMTPx67LwD/d0JIjZl+V6E3pjxnqMmrga7/4R2DA869yhtBETMdytVdURHA9+czYGvTE7mCL7dM3HC3D9pwtdApoSZoC9fk8WLn3vL2cQaK3onMjOdQl0AzF99R6XFsgJS3fi5nEZ6PfqLPzvnx2YvnqPc5jLyJ9XugwlsxvGax3CmGuT5GPS8t3m33MUP/7j+zpyzut/4sPZm1zmpsxau9fl97w8delNeGD8Uoz7azMaPz4JV3xYsOXX5ZT24wTt/fIfGPLmbI8B0X1BLJNi1/sRDtZW61s/z7ANsmaszkT3l2bi9enrsGFvwYq73Z9pdz7l5Ske+n4Z2j87zWN5ghn0sS7zKE7n5OH16et872zhXH7H5u6Rl6fI2Ow5M+gNny10LhfhzfRVmS5DCgGjd93aA+VIUPTl/C0BHWe75Cy/Ld+FtBETMcUyJDIUq3d5DuTW+biGPPa/5V63u/szjEtG9H1lVtgTwzju1zsOFaxAHzmZXWBeK2Bct6y9/76uYfM27HNOJ/g9wB7SUJci+HD2pgIjWbYdOI60EROd86z9cTI7F9NWZfo9jNih2ROTMdzLsO6tQQYiqmpbh3HnSGCyYkf+d37qSvvzyBpY+fqeT1uViT6v/BHWkSnvzFyPBeb1yZ+/DXCtmzrKn2Neky95r3CD56LCIC+MDh47bdtaft7bc3DRu/Mw9O25ziGUni61K3Ycdt7gAuX+mqpA/1dn4cUA57KEi+OG4s/ptnT7IRw/nYt3ZhpDkz6zBHn7/UhS4G7yil3OlhhrRefNGeuCXk7BMRej+0szMdochrBm91FnL4S3G3xenoaUQOGRENO2z1yzB1d/7Dq/xNPcEm+VmEDMcLshWy+8e46ewviMbc7joKqYvGJXyPP2NpqJTawX7+RE179zp6VC4tiyNjML/V6d5ZL1UFWDDvw82XHohMeeTmeZLMVdm5lVYHL4ZPMm2+/VPwOaw2CtAP+8ZKfL+nLP/LISLUbmz0H7YfF2jPx5pcfXsn6+7r0TmUdOOitl01dlOitJW/Yf99hwsPfoKduscd68Pn0tRv26KuCKny+3fr7I+fOUlZkFWsr/2rgfz1gWX3avUASylts7M9fjfzaVXLvspxv3ZmGKhwqWR2qscdj5hRl+7e747tn15H0ydxMuee+vkNaqe/C7pbhpXAbuH++5N85gfKYrdx5Bi5GTMXlFcAHaqp1HcId5vr1UBNMXfvpnhzkdw377vA3+j2oRAN9mbAtPwUyOe9Ch49m49L15LsNB+7zyB76avxUvTf4Xl/x3nkuDzPHTObjzy8W2vSGAfc9+26enotkTk/Hzkh1IGzHRWTfq9+qfBRJ4eMqGuGLHYVz54XyXBoBImrV2rzOA8aeBDQBychXNn5yMm8dl4Py35zgfP+hnveaPNXs9Dv8N9nb55M8r0Oix3wAYAainYOtlm4RsnhuOfDcgOO6nj/64HBv3HvMrAZU/sk7luCSPu+qjgnNpvZUHyK+vOj7TWEl4xSAvjM54bho6PO95zpY/4+vPfWsOeluGmazNPOr3cAP3OntOXh7W7cnyOBSksDnHzvtxIXIEHLsOnwwpG5bDbV8s9thbNHxsBhqaF7hAWD9eR8XynNf/xNUfz8eUlbsLzOOxOnY6Fx2en44JS3fiiZ/8u2FZ58Uc86M1++jJ7AJDFQFgybZDuOHTgq2ByyxzoQqL9Ts/dVUmfrH0XD38/TI0fOw3vDljHX5cvAO3fbEYY//aDMC4uQez3IXje+St9+O029IEVo4lBrqN/h3tnpmKfq/OCvsCzL6GtE1Z6dpC7J5tD4BfKfjdqRbsn3EEYIFWJldahqUt3noIaSMmOnt5Or0wA1eYCSvecMteOGbKGo/nt3UdwOcn+l56JOtUbkCp7z3Jyc1Dq5GTnQ1w7o0TdhVQa2C3ad8xPGsJ+g4dP+33PKAFNj1jqoq5lgXrV+06ghOnc9HnlVm49fNFzoaQn5fs8LgAtCP76dLth/Hp3M3Y7ee55Cj2MJt5O6MmGg1bgfZGOMxdv8/lXjZvwz7M27DPObcIMHqx523Yh0/Mhasdf8fXQU5vGPxm/j0gmMbCQK3bk4UP/twYluGQuw6fdP794bLUTCSy5+gpLNx8EO9bsvtu3HsMj/1vOf77xwZkbDmINebyLuMXbkPLkVMwcfkudHxhhksjtKceE2tj938mG5Vvb5XmvR6WdDn3LSMoWpd5FJv2HXMG7JFy3ScLnHO6d9usk2jHeh/fl3XaOarjpcneGx0utfQkuc81dPA0MmLKyt3Yn3UKwz9baJto5Yu/88+n5k9OxiVm/gN3dqOTVu06ElDCGCvHfFrHENb3Zm3E539vweQVwQ+LBTzP07Syq0Nd/sHfePj7pa7XbPMzzVOjpzpcw7wjJXwDvgmA61yyRVsO4qz6lWz3c8yXGXtjR48Z3ox1UYysWJecVafAdvcT3H0oRnaQmS5OnM5F6RKJQT3XpTwBDNd07Lt+T5ZLD4PVJ3M24dy2NVGtQim/3t9uqMbx07lBt/xbK27u8zJ8zcVwuOdrIwHLqAva+Nz3CreKVk5uHpK8DPu879slmL56D/56tA9qppQGALwydY3HcfGOOUZF6e6vCyagsWa4c1QgHDf35y5ojXNaVnc55ou3HkTF0slomFquwGs5joq3NOKOismPi3egTqUyLttOnM7BoeOnXYLTMVP+RetaFcIe7Plrb1bBymkwSZVenlow9TYAvxPr+PLJ3E3OiqEjkLWbJ+Pp/LYmNvlwtu/gLVzJq46dzsWx07l4b9YGdGtcxa/nWL8ft32xyGXbE/9bgeY1y/v1OrNtFqUf8uacAolarL2sCqDHf37HtgP5Zfjh9i44q35l3DIuAx0bVHYGZFbfZWxD/Spl0bFBZY/lcTSSuDdI2jUqnMzOxeQVu7H/2GkM797A+XjmkZOobnONdm9dv9JsCKhctoTzse4v2Sf9idS5F4wXJ/2LMmG4f4ZzjUcH96VyDh3Pxt6jp2yDtbw8+2vDr0t34u6+TbDr8Alnz6Q1GD1yMtvrsF47/V79E5tHD/G4PTFB/L7HFpVFWw76NdJj8z7XIZXpo6bjt3t64JuF3hvWFm72PbXE/RK47cBx7Dl6Crd+vgj1q5TBlv3HMePfPXhwQFPc1adJgeePMxtVPWXU9XSJbf7kZPRvWR0Hjp3GBWfUxpUd6/nV8JinwIDXZjnvCz/+sx2H3IKoWWv3omfTVKSNmIjL0+vipUva+nxdO9NXZeLspqnO6RqeOkvGZ2xH2ZJJKF8yyVlGh2Ef/I0OaZXw3W1dgypDccAgrxBd/N95eHxwC9ttjmDwuk8W2G7/z+R/XYbTpY2YWOAi6H4CLt56MKRMdw67Dp+wrUAHKpDx/75avjfszcKzv67C1wu2Ytr9Pf1+XVV1Gfpp5akyYmdd5lGXoHn9niw880v+cDb3Hgt/yiUi+GPNHjRKLYe6lctAVbH9YH7laqXbTe3FSf/iyXNbFnitict2YV/WKefcoMwjp5xB3ltFmJkvHHYfPunSc/LkTyvw5E8rMOqC1ri6c30AcA6fm/1wbxw5mY1WtVIAGJ+po7XOcaRy89Q5b8TdmsyjuPMr15bhQ8eznWnwHVbsOIKzvGRVDcaD3+UPVzt8PBvJSYLEBMFHdsGNzZ12znr/kxUUld+W78Zvy12H1YVr6K+dkzbzfYJhnRd5zcf21+NAHD2V41cFzRP3AM/drsMnXAI8ALj4v39h/mN9MXVVJqausp8r9JA55Hvz6CHYcegE/ly7F1d0rIcjJ7MxZ90+DG5T0+U67GhUemfmett1NJs/mR94XtulvvPnTi/McLlXqXofqh6LK70eL6T5ouE2YelOj8v7TPcw5yxPjQRX6zLzA5xbPl+Er2/ujF2HT+DdPza4BD+OBgNf9/ghb85GsxrlcWfvxmjkVv9ITBDk2MxPjjR/Ft2260W39jCHwn1OXo//5DeSWBNovTx1LX6wyQNhHZKfnZvnzB2wL+sUpq/K9HqNnWZeZxZtOej3tIHVu45greV7k2vTEXHdJwuc149vM7b5FeTZDfW/aVwGruhYFy9e5Pv532Vsx41mQ5V7L7xdht1owiCvkHlLn+uNp7kr+7JOYV/WKTSvUaHAgJBJK3Zjkoe5C/uzTiEpIQEpZZJtt1uHYIRrXaZAFpr2FRA6sqat25OFQW/Mtq08rthxGPePX4If7+jmfOy0l4m9nV6YgXeuPBND2tYssO1kdi7yVJ3ZzfqbPapWn5pDioLR4NHfMPneHrjeHEa5efQQLNx8EJe9bz9sAjDWuLm7T2NULGO0fP+z9SCaVC/vDFQaVC0LwBj2tunFwVE5pnzbwRPONZ2snvhphTPIc3Dc0Bw3BGsL3MnsXJQrkYTnfl3lMci382IElh1p9+xUr9szj0TfcQTsb7zh5B5QBqsw12YqDJ56u9x7/j356Z8deG36WmzZfxyD29TE/d8uwYx/92DWQ71cGhbPe3su1mYeLdDL8+iPy/Gx2zBZ94WOtx04jpoppZCUmIBxf23BUxM8z+8siuyoFDhP5+/x0zm2jV52CZqspq/KxBwvmbZX7jyClTuP4MfFOwo0aC/bfjigtQLjhft56I2vXA9NHp+E6ff3RIOqZb0uFRUK9yktdiM93KWNmOjsiVRVDPvgb8zfdAAfXpuOzg0rY21mFu7+yn4Yb4afjW1Zp3Lwr1mnnOv2HT2ZnecSAEcbBnlh4u/47FCMz9iGhy2tsYG0bJ01ajpEgNXPDsSRE9nYefgkVu48jF7NqqF2xdIu8wCt511enuLJn1fghm5paFzNvyFIDo75TQfdUgj/tnwXEkQwsHUNLN56EDeNzQhojLddgPfipNWYsmI3Nu8/7jIvzVfweOdXizGkbcFhIn1e/gM7D59E10ZVcG2XNL/LFgj3db9mr/M9JGnAa3/i5UvboWq5krjw3XloWj2/xdO6LEGDRwOfc1gceOv5yc1TzNtQsAfrnZnr8caMdfj65s7Oxzo+71+iiWgQ7uQLReUcm2CdCs9GPxN23fvtEufPqopN5uiPfVmnXQI6b+eir2FqjgaY9nUr+lxc3dsCyxQ5Ez0sHxDsHP+nf/E9z9YhbcREjLuxY1DvQ8Hr9+osfHDNWUX2fp7WHnVP5vXh7E3IOpWLk9m5mG/OQ/55yQ68PGWNc4qAnXV7spA2YiIm/V8Pn2VxjICwu141eXwSOjaojPG3dvH5OsWNROPq7unp6ZqRUfTzibzp+uIM7CyCQM/hhQvbYMv+Y2FJqvLc0FZ40tJtP+Xes9GshhHQbdibhb6vzEKDqmXx7NBWaFytHGqmlMbJ7Fz8u/so2tVJgYhAVfHqtLW4qlN91Egphbw8dUlu8vejfbF61xH0bJrqfHzDC4OdGZ7C6eYeDZxzeh4d1Nxn78y9/Zrgrd/X467ejXHJWXVw5GQ2hrw5x+tzwu2p81q6ZOyjgiqXLRG2bFxEBNzasyHGzdsS0kLvRETxwNu80UgSkUWqmm67jUFeeLQcOTlqxuH747d7eqBlrQoY9esqrxnsnr+wNVrUrIApK3Y7A84hbWri6KmcsK7xQ0REREQUCRteGOxcX7g48RbkcbhmmMRSgAcYE4MvT6/rc6jY4/8rOJ/F0zAPIiIiIqJo896sDbizd+NIFyMgYZlJKCIDRWSNiKwXkRE220uKyLfm9vkikmbZ9qj5+BoROScc5aHwiNa5QERERERE4WKXZbi4CznIE5FEAO8AGASgJYArRMQ9z/twAAdVtTGA1wC8ZD63JYBhAFoBGAjgXfP1iIiIiIiIKAjh6MnrCGC9qm5U1dMAvgEw1G2foQDGmj9/D6CvGHmThwL4RlVPqeomAOvN1yMiIiIiIqIghCPIqw3AOq5vu/mY7T6qmgPgMIAqfj4XACAit4hIhohk7N3LhB5ERERERER2omZ1P1X9QFXTVTU9NTU10sUhIiIiIqI4UKFU9OWqDEeQtwNAXcvvdczHbPcRkSQAKQD2+/lcIiIiIiKiiMjOjb4l58IR5C0E0EREGohICRiJVCa47TMBwHXmz5cA+F2NBfomABhmZt9sAKAJgAVhKBMVM/f2axLpIhARERERBSwnLy/SRQhYyEGeOcfuLgBTAKwGMF5VV4rIsyJyvrnbxwCqiMh6APcDGGE+dyWA8QBWAZgM4E5Vja0F56LY97d1QffGVW23dW1UJaDXurdfU2wePQSPDGwejqIVcH67WkE/99Kz6oSxJERE8e3sppxSQUSFZ/ytXXBdl/p4Y1j7InvPp89vVWTvFS5hmZOnqr+palNVbaSqz5uPjVTVCebPJ1X1UlVtrKodVXWj5bnPm89rpqqTwlGeWPTmFWcE/dwrOuaPiH1kYHO0rZPi/H3qfWcX2P/3B3pi6VMDkJ5WGV08BHNf3tQJ397S2fn78xe2dv5cpoTnVTBu7tEgoLL7680rzsDm0UNcHiuR5Pvr/en1HfDEkJbo27waxlzStlDK5suH16b73KdimeQiKAlR4ejUoHKkixDz0qqUwcVn+m6wujy9rsvvT57rvuKRq8cHt7B9fPytXZw/D2xVw2WbquK/V53psyx2GCBS9QolI12EiGtQtWyki1DoPrjmLNvHm9co7/G641C3cmk8M7Q1hravjeY1yhdG8Qq4qlP9InmfcIqaxCvRylcPk6eJnL2bud7orK9TpWwJfHVzJ7/L8OJF+cHL7b0aYcJd3QEYlYKm1cujWXXXE6RhajmklDaCilvPboivbir4XiKCTg3zA8AqZUsAAAa0rI5Vzw702AOYlJiADS8MxsYXBmPuiD5+/w0OwzrU9brd+jkZI4K9a1K9HFLKJOPj6zugarnI3Fj6t6zuc59nPLQg1a5YGi1qVgh3kSIm44l+kS4CBal3s1SUK2l/PXsnyAp/URGJdAnsnVmvot/73tG7sV/D4h8/twVuPbuh8/cbu6V53f/G7vYNcy1rGdedp85radsINahNTZ9lsTPuRq6iFK8aVysHAEhKYNX0oXOaReR9r+1SdIFMn+bVXH7/7Z4eqFquBN67+iyf1+SaKaWdP4uXna/pXB8LHw+tXlG3cmmMuqC17x2LIZ5JhSgpQTy2SP1wexeMu7Ejlj41AAse61tge58W1XHRmbarSaBxtXLo2sg+iPLXymfOweR7jV68sTd29NjrlZSYgK5mwGYXjDha6OtUKgMAaG4GHN560RITBAkJgtoVS3vcx5M2ll5IO9YezzwPMd7T5+W3XFsvDtUrlCqw7+bRQ3DRGQWPw493dPVVVI8cAbQvTauXc/58XlsjeB3UugZusVTQHh3cHJP+r0fQZXn1snZBP7cwJCd6vyS591Tc2buR1/0/8tBLOvvh3n6XafK9PTDhrm5RmVnLH+XNwKxquRJ+7e9+3k68pzs2jx6CT2/o6DHIiFQDir82veg6CsBX71ZRubdfU6/by1pGTQiAupXLFBjRYPXJ9emoUCoZj1payb1VkAAgwcPmciWTsHn0ENzQrQESPez0woVtvFZWkzy9OBW6Fy5sg00vDi709/mfH/fK67um4Y1h7THWDPDz3Bpo/RmVU1y1qhVcI6y3M2P6/T2DK4wPb195BlrX9l7HKkwta1VAxhP9kWbTi3lN5/ou1zsrb/euxARBann/7z92dfbZD/fB1Z2jrxcPYJBXqO7q0xgPDGiG/1zctkDAdlb9yji7aSpEBNVsgourO9VDsltrluOG+PaVgbeKj76oDX67Jz8YKFsyCaWSjROmRkoprB01yOvzN74w2LZr/ZtbOmPTi4PRunYKfrmrO/6vr1HJq1e5TMBl9McVHerhlUv9C0w8VSCu79bA2WKYdTLH+XhLt4tx69rG7w1TXS84G18YjDPrVcK7AfRO/GL2nlYqk4y/Hu2Dd648E0ufGuC158o6XCMhQbD86QF464oz8NjgFji3rdFKnmtGsr/c1R1vXnEGHujfFL8/0BMjBvk397F+lcI5ToFwVALrVCqNCqWScEcv18CtiXmsXr60Ha7vmuayTSy3QuvwMYeezVJxWXod3NAtDb8/kH9jrFu5DBqleh4OUzo5/2bSvEYFtK1T0WdluKi4D7cLVYtaFbB59BD0bFrN634fX5eO/151Jibe0x1f3dwJL17UBp/d0AGtauVXChyNPQ4vXJh/3fnpzm5hLXdhOq9dcL1Q4WCteHezGRFRwtIQYm3U8lUR/uaWzujT3PeoAXf+fO8duzgaAh119Cs71cOdvRt7fF6nhvnDeB86pxkm32t8V6oFUCmzk1I6OaCKXTyqWbGUx2M755GCjWCf3dABdSvbN8wO794Ab195RoEhuiUSE3BGvUrO31c+c45tXePp81thaPvaSDTLk5unBa710epyt9FHoQ4t/L++TdC4Wjnn6CnACJKHBNlzbnVu21poXSuwIM/bfdTOd7fl36e9XVvcR4VUKlsCXTx0btzey3Njrz8juqzObRt8fofiiEFemPz+QE+X3p03hrXHnb0bo1RyIi7rUDfgrl4RQWKi6wngaA2v5Of8rIfOaeYMzIZ1rFcgiAlEQoJARPDBNWc5W9sc5XScqG3qpDhbdK0nlnVOYCgWPNYXCQmCi8+qg/evOQvvXW0/ntvhpzu7FUj08trlRoD4+uXt0aNJVY/j3hc90Q8/3G4czxoprje2BPNvHNympkur+RvD2uMMD8OralY0AvkBLWugTIkkDGlbEymlk732cJQr6Xqcy5dKRpJZwXMEeY4Kdps6KTi/XS3c3bcJGqaWw209G2HpUwM8vjZg9AqeabkBFxXrTa5DWiWXIbYigocHNscXw/OHCN/QrQG+uqkTLj6zNtrUScFzlnPJ8bkObV8LHRtULtCLkZyYgP9c0g5PndcKDVPL4fZejZxzRmc80Cukv8N9mHMwPLVMevOSzdzRdnUrBvw6js/B0bPsq1Olb4vqGNSmJiqWKYGujariio710KuZa2B4TqvqLsO7q5Qr4bzutA+ijN5cll54CZMqlynhtUfM3ayHeoXUo25lrfgkJggynujnmlzA3Pz44BYu56+nykntiqXx1U2d0Lmh6/zqZU8P8HiNeP3y9raPO9zlFrglmGV29PJ6msvtztpIc2fvxmhew/iuhHKvAoClTw3AvCCmA5ChctmCPSO9mlXD7If7YPPoIeiY5jrH9taeDXFu21oY1KYm1j2fH8SVLel6fStbMqlAY4S10aqMuX/7uhVdklwEc50sbJ5GibhzjzEcI6h88dW2Yu0cOK9dLb+GxD831PhMLzyjNga3qWG7TyDn3vzH+uIxH3PnHF68qA1mPdQLHdLs52c76lsOl6bXxchzW7o0+nr6TErYjAC6zxwFYdeJ4k1hdVBECoO8MGmYWs7lhju0fW2XoWdlSgQ+1MvRUuMYEvndbV3w8qXtnBV9O9YKdI8mVTGglf2JHKwBrWqgpx8T463XtSs61vO437KnB2C+Zbiq3YWzSbVy6JBWyeVkPadVjQLjud21qFkBt/dq5Gw5TEoQXHiGUTFsXTsFnw/vVOCG09H8rKuUK4mSScaN5eIza3tNHDHl3rMxvHsDDG1fG/1aFGwpf3xwC1QtVxLzRvTBqAv9D/afOt/zkLGBrWti4wuDnT2SdlJKJ+OxwfY9em9dcQb+e/VZhdY7Nbx7A5fEPFbWoTjiYVBK9yZVncNky5ZMRNfGVZ1lvaZzfWx4YTA2jx6C8qWMACXHZmyuXSXvkYHNserZgR7L7V55tXL/qNLTQguQLz2rjksvWCg+vNZ7g8eN3YyWdqvnhhrfxVopxnnl/vc9fV5L/Hp394DKISLo2riq83zp3cz+HA3kdT8f3hFf2swL/s8l7dClYWBZfv2VEMB58dA5zVC/Slm/58ba9ZJ4U7VcSQxtnz8S5HmzkaN5zfKoVLYEfr27O56/sLXHIZOD29RwDrm3qlAq2ePQ8fN8zCV37524vVcjdG5YGff2a4I5j/TG7T3tW9btKmN2SiVZe9KDa0xJ9HEMg+ktLG7D20ORajYwOkaZWJUpkYTOZi/rDd3SCoziucFtHme18vn35uTEBDQ0G0/9adixNlpVKJWMX+/ujtfdMiY+O7T4zYeq4Oe0i2CHqpdMsg9sHXe6lpbrjb9zd3s3r4bNo4fgtcvb46z6wSXDsgb4lcuWcDZ6+9IhrRLqV3FtVLc+86z6rvfTxATBjd0b5H8Oqh4zodud6vf0bYw3hrV3mX/szm50WnGfWhAoBnlh9uvd3QO+iXvi6K52ZBurW7kMLrF8ye/pW3D+i7VyEmAvdaHJ9TQ5DsZF3ToXrm3dgpXeEYOa47vbCo7rT04UpNevVKDV+YH+TXGNZfy04yPx5+MYf2uXAi34IoLPbvCcDKBZjfLOOTzX2Exavtm8yNSqWNrjnLPFT/bH+Fu74KubO+GL4Z2w9KkBqFAqGV/d3Mnj/DF/Lq63nN0Im14cjIn35N/Iv7uti89KXCDeu7pgC+LdfRq7JOZ55vxWzh67PDUquue3q4VXL/dcaRoxuDmu6lQPA1sXbKhwVGgdQ0WsQfgtZzfEsA51USuIOZ939WmMptXL4RWbypz7p+34XtkNKxrWoa6zwcCTMZe2w3vXnIWLzqyNdc8PCiiD7kPnNEPr2hVwa0/ju1W1rOcb0+iL2mDkeS0L9NpecEZt3N+/KR4ye7vdA+4+zaujde0UfHp9B8x8sJffZQOAb83zyNMQwibVPTdOWNWoUAo9mqTaDlsEgOcuCC6ldffGVb321LlXGm7v1QhXdTIaq4a0dR0W5W04op06lcpg7og+QfdEXppeF7Mf7o0eTYz7QuvaKV6zvgUSsDokJgim3ne2s3X9SvNv93Q8a6aUxje3dEHFMiVQp1KZAtem/7O5V3ljzdZsPb/Obev/kDRf10dvWaA9qVTGv3mrY4tZ8piX3aY4DO/ewDn3qk2dFCx8vB/eufJMXN81DdPvN3qaHMO3Lz2rboHG4kFuo1jc/XJ3d7x39Zk+p5Y0tBlJ07p2SoFG8Yplkgv0Hkaatx4f6+ilpMTAz797+jRGz6apeHBAwTm5nc37Soua+Y0fdo217Sz5Cxx1JGsAE+gwRgdrT5+vOfRWnoJWX6z1twGtauChc5ph1kO93PeyeZ5gaPvaXjtF7DpBYm2aMIO8MGtdO6XAvJRgXd8tDff2a4LhHrKb3d+/4AWgZHL+IY1kjGetWPhT2XYMwatYuuBN1NO1SETw/e1dcYFbYpS7+zZxGdLnKEmwFzUAKO1nhaBCKdfWvV7N/EsHXrlsCXRsUBldG1VF9yZVnS3sXRtVRd0Qhw+ICFrVSnHOUWzjNrHaWxKZ1rUr+KwMDWztWvHaPHoIKrpVhq7rmuZslMjLU9SpVAZvXnEG6lQq45wb2tRt+GO18qXw/IVtvN4cWtVKwbwRfVyC+scGt8Doi4NbDsOo3PbE4DY18edDvV2GkIy5xD4gda9Ebx49BKMvbovKflQIK5ctgVcva4/kxIQCmXifOq+lx2Hed/ZujF/v7oFHB7XA5tFDPFZoh7av5ex1qVWxND4f3hEppZPx0bXpSEwQ3NO3ibMxqWJZ4zv32uXtsHn0ENQz52v2bl4t7Om8SyYl4ofbC86h9MbuT/T3lHYEJyWTElC1XAnc179g0GHtXXSvND0ysLmzUufo+bTzx4O9PAYi9/RpjBpmg1btiqWDCr4cArkmBNJbv/jJ/s5EYE2rl3e2rr9wYRtsHj0Efcye2VLJgVXYHBn73Oc3P3Gu/VCvKuVKok/zamiUWtYlsLQLMt2Xb/BXMKMY1M+7qj8VRbseNKtwzpfu7zbCxH14d2r5khjStiaePr8VGlczrsO3nt0Qvz/Q0+vwvRkP9LSdNlG2ZBIGtq6Jsh6y7TpGmAQy0si9cSUSnhiS/32t4eU68OJFbfHvcwOx8plzUNPLfp4kmMnp7urTBFd2qucypNWRlOSGbt6Xo7K2rV9wRm1sHj3E5bz159rpaGC5r19TZ16JkkkJ+L++TZzfX3/PImvdNBCO+5Pju3Rn78YFewTDFJiVK5lU4F5amNMCikJsposr5rzdAMqUSMSN5slbMinRZ3Y1q//d0RWp5Uui+0szQy1iyO7r3xSqivv6Ny1Q4bdzTef6zor6E0NaYNTE1c5toQarjl6faM2OFC4JCWKbcvQMmyE1XRpWQeeGVXBP38ZYufMIzn1rTsjv78hO6T6sLbV8SXx1UyefmVM9CabHzh/1qpRxBjoA0K9lddzTtwnenLEOgP1QU2sldsSg5jh6Khtz1+/3+z0rly2BA8dOAzASvnRpVAVP/LTCuT3QhV9HXdDapTLbo0mqx3lY9/VripTSyUU28dxuWEzDqmWxcd8x1K9SBlv2H3fZlpSYgNM5eS6PlfFQiXQ3vHuDAvNz3dlllStbIhGXdyg43Pz8drUwYenOAo+nVS2LVy5rh1+X7XJ5fOI93dGqVgruH5CfadLRkHN3n8YY3KYmBr0x22v57u/fFDm5eV73sVMigJ4Eu7lYVq9e3g4372oYcFKTKuVK4vPhHVG7Ymn0eWUWAODf5wZ6DRY/ub4DACAnNw/3j1/qcT/3uev+8vWsro2qYN6G/Ti7aSr+XLsXgP+NCv4E8G3qpKBimWQcOp6NHk2qYva6fc5td/VujPV7sgqcA8FKKZOMRU/0w6QVu/HETyvQONV3T3pCgqChj/0apZZDIz9e69XL2jkbOABjhEmvZtVse/I8ubZLfVyWXhctRk72+znhdml6XZe6iR1HdkbHd7ttnYpoV7cilm475Pf7WL9nL1zYBnPW7cO3Gdtc9rEbnv3uVWdi075jGDNlTYEMpQXew4+aVUlH41hyAkZf1Ba1K5bGbT0buQTvnhpL3K+RdqNN/AnOrulSH3mquLZLmsd9Qo3x6lUug60HjkPEdZj3uBs7+hyRU9yxJy8CvGWy/PnObngwyPVRzqhXCXUqlXHppo+UlNLJeGZoa78CPHc39XAdQx1KDxxgXITWjhqEp88LbmiXw2ODm6NHk9CWroikNDNgcf847S7SZ9aviP/r1wQiUqCHLRBNq5dzVhyrVSiF72/rgjGXFuxl69q4qnN+XVFyzPV66eI2uKdvE5/DTxyf1LAOdXGp2cLXu3l+b+1Yy7DetKpl8eVN9vMSfb3+s0Nb2SavsM7N8ibZrPgG0ltRKjkRd/RqHNAQnFDYtfI7emqeOq/gfNRkm0qNI6mIHU+JBQKx8tmBGOlWFhHB3X08D9G0mwtmN/fyvv5N8cz5rXBfv6Z+zee7p28TlyDRHzd2a4BbPcyPC0aZEkkF5s74q0eTVOc5XrVcCb97A70NtwLyzxm7jMLT7++Jvx7tYz+Xx8ep4ahEW4eC+7oVOXp6Ak3e4P66nkbvhKJKuZK4qlM9ZDzRL+TENoG66Mw6BeaFNq1e3uexdWhcrRxExO8RNYUl2LrILWad5h4v1w2rYHu9Brep6cyZ4KuorvMoXU+Gxwe3wG09G6G5OSS0SbVyKJGUgAcGNCtw3faUCHD0xW1cfrcbbeLP/Sk5MQE39WjoNXtwKLkFNrwwGBPuMjM/q2vwnJ5WKeBRC8UNe/IiwNuFrUkYMvY5hBocFRfBZA50F451dm45uxFuOTt8Faai9sVNnbBo80G/bpTWimEon93U+1zX80kvZvMquvmYm+VJ9Qql0LZORWwePQR7jpx0Pl7Sw2dVKjkBJ7Pze2Hs1sa0GtQ6+KFJ654fhNZPTUFkB2z7VrVcSfx6d3fbXmJHL6m1tdlT74hdUhHAGHozrENdfLNwm+12wGj5tva+JicKsnPtPzd/P01PCVDclUpOxHWFnCbePUCNVtXKl8Seo6dstzkqeHbD4hyJqcZc2g7fLdruutE8oINa18CkFbtdNqWWL4lru6Rh9rp9LolfvH0HPr2+A3o1S8UlZ9UJuHHTPQlMYa3WIiJRmViiZkrgozX+frQvOr84I6zlCPZeOKRtTXRu2A9V/Pzsb3Qbimm9Dnq6xzj4m4NgaPtauPfbJQCAWQ/1xq7D+fcxRx4BVUWrWileG3rb1qmIT6/vgGXbD+O16Wudj1vnVXpLXBcOoZwujnWbna9lebFQhtQXF+zJK0JPndcy4HkoQRFHBSn6bR49xHaRcgpctfKlMMiPtXTObppaYG6hvyr6ubxHtLOeW9asr3bn3JR7z8afD+Unz/nlru4BpXV2LABvXVbCm+TEBOfNqbjfolrXTrHvGbIp+NAzAhtGqup77trgNjWdafsBI725e5KKQBWX9RQD8ctd3fG4n6nQI+Fhy1DbPx7s5bLmYv6c68Be09HY9cCAZi7LX4y9sSMWPt4P/VtWx+bRQ9DVsjaXt4bT3s2rQUQCCvAcGaIfceuF9LehwOrnKFqHMlr5kyXdU8Zoa4D36KDmHtfZva5LfY+9RyWSElxex+570ii1HOpWLu0yf9C2nCK4tWdDvHnFGahVsbTtddjfkTy9m1dD6RKew4kzg+z995f7JXfFM+cE9nzz/6RECercK87Yk1eEfE2UDZdgb3rFyfDuDfDxnE2RLkbI3FvkYtXSkQOw7eDxQpsfV1yIj5PL7uFmZk9ASulkHD6RjWoVPLfm2sUHr1zWzjbbpzeOG1VuFFwEfri9K9JGTPS53zPnt8Y1ndPw4qTVXjP2hsLfOUbVg0im4Mvsh3uHZcQBYCxc776YsDdt6qQEPSe2qKVVtU+64G9SFIf3rzkLPy7egUapZSEicExZ7u7WM1y6RCL6taiO6aszPb5DsMdt9EVt8cCAZi5LWXx0bTrKl0pGJXOYe/mSSZhwd3ckiuDsMfbz7Zc9PSDohrloValMMg4ez/a43TpvVCS4+tBVnerhy/lbPW6vUaEUdltGcjjeyxdvQ6gr2cyLdazJ6t4ANW9EH+zLcu3hLpWciNkP+7dG5KODwtewYxfcjr6oDUb8uNx23r9D9QolcVl6aGspu793INc+x/7/17cJzm1bE3s9jBiIVgzyYoQ1+1LNlFJYsg0oHcVjiZ88t6VzWYJo0zGtMhZsPoBujas4l7+IJsEM800pk4yUMtFRSQyFp1Zaf0y4qxt+XbbL6/pc4YrJypZMRNapHGjgeTqKhTLmtcu6Dl5igqBZjfJelzOxKleqcG5vAiOL7rVd6uNUdvg+4FCz6Fr1tVmvs7j6/YGeWJt5NODn9W6WitqVSiPrZE5Q71unUhmXZYgqlSmB/cdOm9c/1/PcV8XdbvMn16cjO1fxw6LtmLoq0/Z5JZISULtiaZzKyXU+1q+lcezKm9/fO/s0dma3Xf3sQNvEI6U8ZCBeOnIAJEbHazWoWhY1svOweteRAtva1kkJuUemRc0KeP7CNjh0PBsTl++y3SfYuXOBqlahlO20guoVSnkd6fT3o32DWsIhGNZzxLFG7rCO9ZCeVsmZsdXO/Mf6he29a6WUwltu68H693zBfWa2+v1m4jPA/3U9i7Po/wsIgOvaJS9d0havXd6uyCdXk8Ex4bioshSGm7fK5uR7e2CQZd26a+I0Y6mnWMxbb0L9KmVxZ+/GRTKc7+ubO+PBAU2RUsyGz3ZqUBljLvG9vEWF0smY8UBPl4WSA/VQkAmsPHEPwJ8d2tpn+e7u09hlGQ4qqGFquQLLsLjr3SwV1SuUxC2WhY0/vaEjRl2Qn9wh1AaSH27vilEXtLadM+8IsvxdJw8w1pk8x88lArw1Hlm3eJpPneQhoEkpkxxzPXzPDTUSqDVMLedczLq22wiSN4a5VvRDueK+Yw6rrO5lBIZVcRrNUiOlVJHNwbTOX7OukestwAu3lDIl/F7k/aNr020ft/4d/i70XpyxJy8GVSiVjAvPCHxtjz8f6o2jpzwPfyD/NEwth+VPDwh4yEBx8OG16V4ziDavUQEvXNjGmaigbuXic0MrClXLG5W8Kh5SzSclhNZu9tA5zTDix+WoUDq0707D1HK4q09gC1AXhW9v9T4n2VpR92fYpDf+zJ8pbM1rVAg6GyXlq1KupMcW/3A1mqRVLVtgKKjDgwOaoUvDKkGlU3cU7+nzWuLpX1bZ7mNXl7ypewMs3XYIl/oxlC0WKqP+cgQtlcuWcGZ6PKt+JaRVLeNcrsZ9Xc/ODY3lMALRv0U1588/3N4F9Sr7Xu6hXZ0UfOghePBXKKNFIimSX0HnkO0AWnocPebuwjVkvriI/F2Qio16YVx8Nd5FYjmAUPVulor+Hi58AFCnkhHQVSpbAjd1b4CPYmDOZKCu6FAPZUok4vx2rksZ3NO3CSYs2RHw+mHuhnWsh2EdC67LRpEXSCxRrmQSsk7loK+lokhGllnAWO4n3FSN5CMnsnN97xygEkkJ6N284LG8pnN9fP73Fq/PLWkOpazhJUOkXRa/ahVK+WwUcecYntn26akBPS+aDGxdA89f2BoXn1kHpZIT8ds9PdAwtSySExPQ6LHfbJ/z4bXpaPXUFL9e3zEP747e+csdeOodcj9qP/tY4D6WORpbejcr+ikq4QyM29VJwfDuDXBFx9DmCRYXDPKICOueH+Q1XfD8x/qijGWoUH7LWWGXrHhJSBDbXvL7+zfF/eaYfgrcP0/2R3ZeHq75aEGki+JRIN/1QLO7xYvypZLx693dXZatCJUzFxL8X25nzaiBIb/v5tFDcOxUjs8g7+nzW6FmSin08xLwh9IZWbVc/qiC4jY8uzCICK7qlD9NwH1aivVznv1wb5zOzbNdk9OTZ4e2xsPnNPeY4XLafWdjw95jAIye+s3mgvUjozSHQLg4evJqVyr60T3hrI+ISNTmg7DDIC9GxMqaeBQZvhbAdp/cHY1p4qn4cs8oV6y/XsW5bFGgde0wJ2gKYqhWSQ+JSgJ+az++C5XLlsCjPpanCOV6Os1tLdJ4tvrZgS4LeweTzCgxQbwGy02ql3euZ/zKZe0w+Slj6oK3aQ6+dGlYBelplfDW7+uDfo1Ic3yHCynxsVeOBmpvc+J/vKMrJizZidt7NUKmW0bUWMYgL0aUjOJMmhS92LRA4RRoGvyiVJzLFs8cQ7Wi6ejc2dtzCn1fWtWqgJU78zNK2qXcjxWlkxPRuaH/8yA9JaYBgG6Nqzjn7IWLtYewfpXw9E4X6wYuL4KZFxf+9/a8z5n1KuFMc5h4PK29zCCvmLi6cz18l7E96OdfclbgiVaIglXLXCcstYgydxER2RHreE0vfK15VpTu6xf80O5GqeVcgjw7sx7qhezcaAp77a1+LvRhtQ7VyxduxT7UhB01zTmbNaI0AHH2pvnxtXvqvJb4Y83esJch+r/x4ccgr5gYdUEbl3TQgfI13I4onK7tkoaaFUtjgJdELUQUnL8e7RORYU/RKD/G8/6BjbqgdViCvNLJic7kLsEmfAhleKY/6z+Gq1cplhT302lYh7qoVr5k1CZrcszJy/MjyruhWwPc0K1B2N7beQ3gtKUCGOQRUcASEsTvNaCIAlWc04gXRdlqesnESK78TboQrnnEk+/tgSXbDtmWwReR0JNDPDa4BRpWLYtRE1eH9kIUFsmJghu7hxawKBQJCeIxrX80cDQsNK9R9Oszi0TfkO2iwiCPiCgG/P5AT+yOownlRY2NxMVTUTcI1K9SNuieMoFRETV6HIIrd7mSSbipR0MGeQEqrG/JuucHh/waxblRy1+dG1bBxHu6o2XNog/yHGsSN69RdAuvRwsGeUREMaBhajk0DHEBcSIqPGJ25YWjveC5oa1iYt4dxY5WtcKcOddPNVJKYfytXdC6dtEHmMUdgzwiIooJnRpUxvxNB5y/n2Gum5ZeP/TFt6M16128iIZwZ/RFbTBmyhokhuHLdE2XtNALRE539W6MJtXZSBatOjbwPwtrPAkpyBORygC+BZAGYDOAy1T1oM1+1wF4wvx1lKqONR//A0BNACfMbQNUdU8oZSIiougU7iGRXRtXxeIn+6NyGNLMc7hm8RTOhZAL26XpdXFpet1IF4NsdGxQGWc3TY3Ie9/dt3FE3pdiX6gpGUcAmKGqTQDMMH93YQaCTwHoBKAjgKdExNqsepWqtjf/McAjIopzwXZ02NXzwxHgWbFHr3hxBnlR0ZdHkVIy2Xt1t1qFyC0H1CGNvVBUOEIN8oYCGGv+PBbABTb7nANgmqoeMHv5pgEI3+InREQUExyL1Zb3I008kcH/9bnCjYFl9Hj6/FZet0ciKyRRYQv1TlpdVXeZP+8GYJf/tTaAbZbft5uPOXwqIrkAfoAxlJNXTSKiOPTsBa1wbdf6XEKA/Jbfkxf9BrXmsjSFpWRSosdtTwxpUYQlISo6PoM8EZkOwO7K87j1F1VVEQn0OnuVqu4QkfIwgrxrAIzzUI5bANwCAPXq1QvwbYiIqLgrmZQYsQxtFJ2co2ejvH14/fODkMCxwBFxU4+GEXnfcKybSOSNzyBPVft52iYimSJSU1V3iUhNAHZz6nYA6GX5vQ6AP8zX3mH+f1REvoIxZ882yFPVDwB8AADp6ek8LYiIqMixGl68FIeevHCsc5aUGOrsGSIiV6FeVSYAuM78+ToAP9vsMwXAABGpZCZcGQBgiogkiUhVABCRZADnAlgRYnmIiIgoTkgE5+Q5cG5edPrk+nR8eVOniL0/G4yosIU6J280gPEiMhzAFgCXAYCIpAO4TVVvUtUDIvIcgIXmc541HysLI9hLBpAIYDqAD0MsDxERxSvWtYnIT32a26WRKHoM9qiwhBTkqep+AH1tHs8AcJPl908AfOK2zzEAZ4Xy/kRERESRFI7hmkRE4cZB4EREFBsKsa7NxM/FU/5i6EV/fByJUlrWYvp9Cpww0Q4VMi5GRERE5CfWy4oXx+GIRAheKjkR393WBU2rl4/Au1OsYPMRFRYGeUREFBsKsbZUw1y7rwbX8CtWHL0hkepo7ZBWOTJvTFGP7UVU2BjkERER+XDxmbVRqUwyejerFumikA32hlC04khwKiwM8oiIiHwQEfRtUTyy8VG+SM7JIwpF/hqP/O5S4WDiFSIiIopKzGxJ0YrfXSpsDPKiXHr9SpEuAhERUUTk9+RFthxEgTq3bU0AQCKzOVEh4XDNKDdueEfsPXoq0sUgIioW7unTGFNXZUa6GFREypRIBACUTPbdZt29cVXMWb+vsItE5JeXLmmLx4e0QFIi+1uocDDIi3JlSiShfhUeRiIiALh/QDPcP6BZpItBReSOXo2RmCAY1qGez30/vaEDsnPziqBURL4lJyagSrmSkS4GxTBGB0REFBOYwCD+lC6RiHv7NfVr3+TEBCSz14SI4gSvdkRERERERDGEQR4REcUEZqsjIiIyMMgjIiIiIiKKIQzyiIgoJnBOHhERkYFBHhERERERUQxhkEdERERERBRDGOQRERERERHFEAZ5REQUE+pUKhPpIhARERULDPKIiCiqVSyTDAC4v79/i2ITERHFOgZ5REQU1Ryr4yUlcp08IiIigEEeERERERFRTGGQR0REREREFEMY5BERERFRTPvk+nTnz5XLlohgSYiKRlKkC0BERBQOAs7JIyJ7PZtWwy1nN8QtZzdE1XIlI10cokLHII+IiIiIYlpiguCxwS0iXQyiIsPhmkREFNVE2INHRERkFVKQJyKVRWSaiKwz/6/kYb/JInJIRH51e7yBiMwXkfUi8q2IcJA0ERERERFRCELtyRsBYIaqNgEww/zdzhgA19g8/hKA11S1MYCDAIaHWB4iIooz7McjIiJyFWqQNxTAWPPnsQAusNtJVWcAOGp9TIzxNX0AfO/r+UREREREROSfUIO86qq6y/x5N4DqATy3CoBDqppj/r4dQO0Qy0NERERERBTXfGbXFJHpAGrYbHrc+ouqqohouApmU45bANwCAPXq1SustyEioijDvCtERESufAZ5qtrP0zYRyRSRmqq6S0RqAtgTwHvvB1BRRJLM3rw6AHZ4KccHAD4AgPT09EILJomIiIiIiKJZqMM1JwC4zvz5OgA/+/tEVVUAMwFcEszziYiIiIiIqKBQg7zRAPqLyDoA/czfISLpIvKRYycRmQ3gOwB9RWS7iJxjbnoEwP0ish7GHL2PQywPERERERFRXPM5XNMbVd0PoK/N4xkAbrL83sPD8zcC6BhKGYiIiADOzSMiInIItSePiIiIiIiIihEGeUREFOXYhUdERGTFII+IiIiIiCiGMMgjIiIiIiKKIQzyiIgoqjHhChERkSsGeURERERERDGEQR4REREREVEMYZBHRERRjaM1iYiIXDHIIyIiIiIiiiEM8oiIKCaoRroERERExQODPCIiIiIiohjCII+IiGICl1IgIiIyMMgjIqKYwOGaREREBgZ5REQU1diDR0RE5IpBHhERERERUQxhkEdERDFBwfGaREREAIM8IiKKcsLl0ImIiFwwyCMiIiIiIoohDPKIiCgmMLsmERGRgUEeERFFNWbXJCIicsUgj4iIYgI78oiIiAwM8oiIiIiIiGIIgzwiIooJHLVJRERkYJBHREQxgcM1iYiIDAzyiIgoqrEHj4iIyBWDPCIiIiIiohgSUpAnIpVFZJqIrDP/r+Rhv8kickhEfnV7/DMR2SQiS8x/7UMpDxERxS/lQnlEREQAQu/JGwFghqo2ATDD/N3OGADXeNj2kKq2N/8tCbE8REQUZ4QL5REREbkINcgbCmCs+fNYABfY7aSqMwAcDfG9iIiIiIiIyIdQg7zqqrrL/Hk3gOpBvMbzIrJMRF4TkZKedhKRW0QkQ0Qy9u7dG1RhiYgodnG0JhERkcFnkCci00Vkhc2/odb91JgMEegt9lEAzQF0AFAZwCOedlTVD1Q1XVXTU1NTA3wbIiIiIiKi+JDkawdV7edpm4hkikhNVd0lIjUB7AnkzS29gKdE5FMADwbyfCIiIiIiInIV6nDNCQCuM3++DsDPgTzZDAwhxqz5CwCsCLE8REREREREcS3UIG80gP4isg5AP/N3iEi6iHzk2ElEZgP4DkBfEdkuIueYm74UkeUAlgOoCmBUiOUhIiIiIiKKaz6Ha3qjqvsB9LV5PAPATZbfe3h4fp9Q3p+IiIiIiIhchdqTR0RERERERMUIgzwiIopqXAudiIjIFYM8IiIiIiKiGMIgj4iIYgIXQyciIjIwyCMioqjG4ZpERESuGOQRERERERHFEAZ5REQUExQcr0lERAQwyCMioign4HhNIiIiKwZ5REREREREMYRBHhERERERUQxhkEdERERERBRDGOQRERERERHFEAZ5REQUE7gYOhERkYFBHhERRTUuhk5EROSKQR4REcUEduQREREZGOQREVFUY0ceERGRKwZ5REREREREMYRBHhERxQRl5hUiIiIADPKIiCjKCTOvEBERuWCQR0REREREFEMY5BEREREREcUQBnlEREREREQxhEEeERERERFRDGGQR0REMYG5NYmIiAwM8oiIKKoxtyYREZErBnlEREREREQxJKQgT0Qqi8g0EVln/l/JZp/2IvKXiKwUkWUicrllWwMRmS8i60XkWxEpEUp5iIgofnEtdCIiIkOoPXkjAMxQ1SYAZpi/uzsO4FpVbQVgIIDXRaSiue0lAK+pamMABwEMD7E8REQUbzhek4iIyEWoQd5QAGPNn8cCuMB9B1Vdq6rrzJ93AtgDIFVEBEAfAN97ez4RERERERH5L9Qgr7qq7jJ/3g2guredRaQjgBIANgCoAuCQquaYm7cDqO3lubeISIaIZOzduzfEYhMRUezheE0iIiIASPK1g4hMB1DDZtPj1l9UVUXE4x1WRGoC+BzAdaqaZ3Tk+U9VPwDwAQCkp6fzTk5ERAA4WpOIiMidzyBPVft52iYimSJSU1V3mUHcHg/7VQAwEcDjqvq3+fB+ABVFJMnszasDYEfAfwERERERERE5hTpccwKA68yfrwPws/sOZsbM/wEYp6qO+XdQVQUwE8Al3p5PRETkzbVd0gAA1SqUimxBiIiIignREHJOi0gVAOMB1AOwBcBlqnpARNIB3KaqN4nI1QA+BbDS8tTrVXWJiDQE8A2AygD+AXC1qp7y9b7p6emakZERdLmJiIiIiIiimYgsUtV0222hBHmRwiCPiIiIiIjimbcgL9ThmkRERERERFSMMMgjIiIiIiKKIQzyiIiIiIiIYgiDPCIiIiIiohjCII+IiIiIiCiGMMgjIiIiIiKKIQzyiIiIiIiIYgiDPCIiIiIiohgSlYuhi8heAFsiXQ4bVQHsi3QhKCJ47OMTj3v84rGPXzz28YvHPn4V12NfX1VT7TZEZZBXXIlIhqdV5ym28djHJx73+MVjH7947OMXj338isZjz+GaREREREREMYRBHhERERERUQxhkBdeH0S6ABQxPPbxicc9fvHYxy8e+/jFYx+/ou7Yc04eERERERFRDGFPHhERERERUQxhkEdERERERBRDGOQRERER2RARiXQZiIiCwSAvCLzoxy8e+/jFYx+feNzji4h0F5H/isgdAKBMXBB3RCTR/J/nfpyJtWPPIM9PItJKRHoBvOjHGx77+CUizUSkDcBjH094zscnETkTwH8BLAIwWEReE5H2kS0VFRUR6SYiYwE8ISKVee7Hj1g99syu6YOIJAB4G0AfAFsBzAfws6pmiEiCquZFtIBUaHjs45eIJAF4H0B3ALsA/AJgvKpuExGJlRsAueI5H99E5DYAHVX1RhGpAeBuAMcAfKCq+yJbOipMItIQwP8AvAbgbAAnAPymqhMjWjAqdLF87NmT51slAOVUtTmAqwDsB/CAiJTjDT/mpQAoz2Mfl+rDOPbNANwOIBXAHSJSmgFeTKsIXu/jhohcJiL3i0hX86HFAMqJSA1V3Q3gdxjnfveIFZKKylkAVqvqZwAeALAEwLkiUjeShaIi0QExeuwZ5NkQkUsc4/EBVADQVUTKqupeAD8AOAjgLnPfmBi3SwYRuUhEXjN/rQKgC499fBCRM0WkqflrMoB0EUlW1dUAJgAoC+CSiBWQCoWINBCRUuavlcHrfcwTkUQRGQngEfOh90XkPBi9dpsB9DQfnwXgEIA65vN4/GOEiHS2XO8BYCGAOiJSV1UPApgL49hfFInyUeERkfNE5C4R6Ww+tBBA3Vg89gzyLESknIj8AOBBAAdFJElVN8E44Peau+2CceNvLyI12aofG0SkpYh8BeBJAPeISC1VXQ/gL/DYxzSzkj8RwDsAPheR/qr6L4AZAK42d1sK4B8A7USkYmRKSuEkImkiMgnARwC+FJGW5jn/J4D7zd14zscgVc0F0AzAA6r6KoBnYATySQB2wjjeLVU1B8AaABeaz+Pxj3IiUtG83k8DcJmIlDM3nQQwB8Bl5u9rAKwCUNnSCERRTERqisgvAB6GMUrvUxE5R1U3wqjrxdyxj/sgz61lri6ATFXtrKpfA8g1H/8MQDcRaWhe9DNhXBDKFGlhKawcx15EzgbwIYC/VfUMAG8A6GTu9jGMY9+Axz52uJ33DwJYoqpdAPwM4Frz8dkwenJrqeoxANsB1IYxXp+ikM1xn6+qfQHMBPCMiLSEcb3vzOt9bBGRa0Wkp6WRJhNAJbMx93sAGwD0hzFE8ySAUeZ+tQEsNOfpUvQrC2AKjPmWZWHMwQKAvQD+BtBGRDqaDQE7AHRT1ZMRKSmFWzqA2araQ1Wfg1HXu9ncNhsxeOzjPsgDYI3S2yJ/WMYdAJ4Ske4AVsLozXsZAFR1BYw5O6eKtqgUZqXN/1cBGKCqb4pICQBNADjm3yyBMU/jPwCPfQwpBTgr/ccAZJuPVwCwTkTSYPTo7AHwkLltBowKX4UiLSmFk+O4OyrsqwBAVd8G0BHAFTB6chaA53zUE0NNEZkJ4DoY8yzfMXtv9gFoA8DRk/MmjJ77TFV9BsAhs8dnGICPzICfopAlwK+gqjsAfABgPIxgvqOI1DYr9n/BGLHxmvkdaQVgq4iwgSdKmce+l4iUhHEP/9yyeT+AdebP8xGDxz5ugzwR6S8i0wD8R0SuMB9eDGCXiHwCoAuMMbmPA7gARtadVBF5W0RWANgC4DDH6Ecft2M/TFX3qeoxESmlqqcBLIdRGYCqHgLwLIDaIvIWj310sxz7MSJymTn8ag6AJiLyD4CBMIZsfQOgOYye3H7mPM3lMIZtHo1M6SlYNsc9B8ABAGeISDsRaQdgBYA0AIkAXgDP+agmIonm+V0ewA6zx/Z2AEdgBHTvAugKoK2IlDGHaK8FcKX5ErcCuF5VO5jDeCmKeAjw/ysiVVX1pKoeBzAdxrC9PgCgqpmq+gaMCv8nMIL+l8x9KUrYHPsrYRzPMqq6S0SSzV1rwjj+UNXdsXjs43L4gYg0hjEU4wUYabIfFJFaMLpus2BMuu6iqtkish9AD1X9QEQuBtAYwFRVnRCh4lMIbI79A+awrBeQPzx3CoB6IpKqqntV9bSIDIXRwzeNxz462Rz7h0Sknqq+LCJrALyoqheZ++YAGKyqk8VIyNAGxjCPHyNVfgqOzXF/WESqAhgDY77t8zCyat4LYy7eIFV9XUQuANAIPOejihiLGT8HIFFEfoPR854LGHPxROQuGHMtXwHwFYyeupoAvoXRoz/P3DcbxhA+ijJmgJ8rIo4A/2rze/E6jF68iwBAVeeKSEcAzUQkBUCeqh6FMXqjjPkzRRE/j71jpFZ/GPV+iEg1Vd0DY75e6Vg59nHTkyciCWKsgQQY860WqerPqvoPjDH4T8IYtvEzjJY+xwTMpQCqibFG0h5VnccbfnTx49g/bJ7gjiF7yTAqfQcdr2EGezz2UcbHsZ8OY+HT6jB6dbaJSAtz398B1DLP+82q+gsDvOjhx3F/AUApc17GParaXVUzYAzLzzKfx+t9lBGRnjAWMq8EYD2MYC8bQG+zMu9IuvIMgDGqOg7AVADXmj35STB67CkKiZE19QUAL5jfhWawBPgA/g9G9tyelqd9CKPuNw3AejHmYOfGSiU/XgRy7M0gsASMRpy1IvI8gGkiUklVc2Lp2MdFkCciN8BImvCc+dByAMNEpIH5exKATQD+o6p/wojs7xeRR2AM25pjvg6H6kQZP459MowJ9y87nqOq02FM0O0Kilp+HvuN5vajMNLn3yMi/wdjIfTpAJhNL8r4eb3fAGMIPmBc+yEitwAYDmPYPjMpRqc8AK+o6u2q+iGMIbgNAIwE8F/AueD9DwCOi5Ey/ScYx/1iVb082odnxSs/A/w8AE+b/xyGALgDRoN+G1XdWXSlpnAI8Ng/Yz6tFIDrYczTKw+gnxrLJ8SUmA/yxJhAORTASwAGiUhzVV0GYByMiH8ugB4wMurVEWMR1J8B3AZjTt5wVX1ZVfN4048uARz76wFUEZEa5vOSYVQKtkak4BSyAI79dTCy6jrmYa0CcCaM8/49nvPRJcBzvpKIVFdVFZF7YWRZu1VVF0em9BQGiwCMN4dnAUbPbD01FjlOFJG7zcpeHQDZqroNcM7H2RiRElO4+Bvg/wRgrxjJtQAj+Uo/Vb3ZHK5H0SeQY79HROrAmHP/BYBLVfUeNdZFjTkSD3UYc97NVhEZDaCBql5u3gRSALRU1TlirGz/HIDbNMpTplK+AI79szCOPTPoxYgAjv0oADerkXSHolyA1/tbVfWUGIk32IMTY0TkMwDLVPVVETkDRiBfD8ZQrg9UdUwky0fhI0YWxFwAOeZwvKsAtFbVR0VkCYCPVfUtEUmHsT7iFd5ej6JHgMf+QVUdFsnyFqWY78kDAFV19Mi8DqCBGIsf5gI4rKpzzG23ATiO/FTqFAMCOPYnADBFdgwJ4NgfQ37SHYpyAV7vc8znMMCLIeb8nAQA1QE45lQeBfAYgNEAejHAiy2qelxVT5nnOmAk1XD0ztwAoIWI/Arga5hDsjkFJzYEeOwXAfFz7OOiJ89KRG4FcKWq9jR/7whjmYRkADeq6u5Ilo8KD499/OKxj0887vHJrMCVAPARgP8BuBHGmlh3q+qRSJaNCpfZa68AJsI43uvFyLC7D0BrAJvUWCuPYgyPfUFxFeSZmfLyROR7GCmUT8FIrrBOVTdEtnRUmHjs4xePfXzicY9vItIZxnII8wB8qqofR7hIVAQY4McvHvuC4mqdPPOGXwZANQC9ADyrqpMjWyoqCjz28YvHPj7xuMe97TB6bV/lXOv4YSZSOgPG4ucNwAA/bvDYFxRXPXkAICIPwsis9Qgv/PGFxz5+8djHJx53ovhjZk+8Bgzw4w6Pvat4DPISzBTKFGd47OMXj3184nEnIqJ4FXdBHhERERERUSyLiyUUiIiIiIiI4gWDPCIiIiIiohjCII+IiIiIiCiGMMgjIiICICK5IrJERFaKyFIReUBEvN4nRSRNRK4sqjISERH5g0EeERGR4YSqtlfVVgD6AxgE4Ckfz0kDwCCPiIiKFWbXJCIiAiAiWapazvJ7QwALAVQFUB/A5wDKmpvvUtV5IvI3gBYANgEYC+BNAKNhLMBeEsA7qvp+kf0RREREYJBHREQEoGCQZz52CEAzAEcB5KnqSRFpAuBrVU0XkV4AHlTVc839bwFQTVVHiUhJAHMBXKqqm4rwTyEiojiXFOkCEBERRYFkAG+LSHsAuQCaethvAIC2InKJ+XsKgCYwevqIiIiKBIM8IiIiG+ZwzVwAe2DMzcsE0A7GfPaTnp4G4G5VnVIkhSQiIrLBxCtERERuRCQVwHsA3lZjXkMKgF2qmgfgGgCJ5q5HAZS3PHUKgNtFJNl8naYiUhZERERFiD15REREhtIisgTG0MwcGIlWXjW3vQvgBxG5FsBkAMfMx5cByBWRpQA+A/AGjIybi0VEAOwFcEHRFJ+IiMjAxCtEREREREQxhMM1iYiIiIiIYgiDPCIiIiIiohjCII+IiIiIiCiGMMgjIiIiIiKKIQzyiIiIiIiIYgiDPCIiIiIiohjCII+IiIiIiCiGMMgjIiIiIiKKIf8PxRAxXbvZq7YAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 1080x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot SP500 daily returns\n",
+    "daily_returns.plot(figsize=[15,5], title='SP500 Daily Returns');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Detailed Calculation\n",
+    "This section is a more detailed breakdown of the MS moment plot calculations."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "First we calculate the moments from daily returns. Moments 1,2,3 and 4 correspond to return, variance, skew and kurtosis respectively. There's a slight difference in that these moments are not centered. I impose absolute values so that all moments are positive."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>moment_1</th>\n",
+       "      <th>moment_2</th>\n",
+       "      <th>moment_3</th>\n",
+       "      <th>moment_4</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Date</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1960-01-05</th>\n",
+       "      <td>0.008012</td>\n",
+       "      <td>6.419217e-05</td>\n",
+       "      <td>5.143077e-07</td>\n",
+       "      <td>4.120634e-09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1960-01-06</th>\n",
+       "      <td>0.004305</td>\n",
+       "      <td>1.853574e-05</td>\n",
+       "      <td>7.980222e-08</td>\n",
+       "      <td>3.435737e-10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1960-01-07</th>\n",
+       "      <td>0.007318</td>\n",
+       "      <td>5.354598e-05</td>\n",
+       "      <td>3.918234e-07</td>\n",
+       "      <td>2.867172e-09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1960-01-08</th>\n",
+       "      <td>0.003183</td>\n",
+       "      <td>1.013210e-05</td>\n",
+       "      <td>3.225145e-08</td>\n",
+       "      <td>1.026595e-10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1960-01-11</th>\n",
+       "      <td>0.012269</td>\n",
+       "      <td>1.505261e-04</td>\n",
+       "      <td>1.846791e-06</td>\n",
+       "      <td>2.265810e-08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2019-12-24</th>\n",
+       "      <td>0.000195</td>\n",
+       "      <td>3.820000e-08</td>\n",
+       "      <td>7.466123e-12</td>\n",
+       "      <td>1.459240e-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2019-12-26</th>\n",
+       "      <td>0.005128</td>\n",
+       "      <td>2.629809e-05</td>\n",
+       "      <td>1.348610e-07</td>\n",
+       "      <td>6.915897e-10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2019-12-27</th>\n",
+       "      <td>0.000034</td>\n",
+       "      <td>1.154973e-09</td>\n",
+       "      <td>3.925164e-14</td>\n",
+       "      <td>1.333963e-18</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2019-12-30</th>\n",
+       "      <td>0.005781</td>\n",
+       "      <td>3.341791e-05</td>\n",
+       "      <td>1.931830e-07</td>\n",
+       "      <td>1.116757e-09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2019-12-31</th>\n",
+       "      <td>0.002946</td>\n",
+       "      <td>8.679043e-06</td>\n",
+       "      <td>2.556865e-08</td>\n",
+       "      <td>7.532579e-11</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>15101 rows × 4 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            moment_1      moment_2      moment_3      moment_4\n",
+       "Date                                                          \n",
+       "1960-01-05  0.008012  6.419217e-05  5.143077e-07  4.120634e-09\n",
+       "1960-01-06  0.004305  1.853574e-05  7.980222e-08  3.435737e-10\n",
+       "1960-01-07  0.007318  5.354598e-05  3.918234e-07  2.867172e-09\n",
+       "1960-01-08  0.003183  1.013210e-05  3.225145e-08  1.026595e-10\n",
+       "1960-01-11  0.012269  1.505261e-04  1.846791e-06  2.265810e-08\n",
+       "...              ...           ...           ...           ...\n",
+       "2019-12-24  0.000195  3.820000e-08  7.466123e-12  1.459240e-15\n",
+       "2019-12-26  0.005128  2.629809e-05  1.348610e-07  6.915897e-10\n",
+       "2019-12-27  0.000034  1.154973e-09  3.925164e-14  1.333963e-18\n",
+       "2019-12-30  0.005781  3.341791e-05  1.931830e-07  1.116757e-09\n",
+       "2019-12-31  0.002946  8.679043e-06  2.556865e-08  7.532579e-11\n",
+       "\n",
+       "[15101 rows x 4 columns]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Calculate Moments\n",
+    "moments_df = fattails.metrics.calculate_moments(daily_returns)\n",
+    "moments_df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we calculate the ratio of Max/Sum. A value near Zero indicates that the maximum value doesn't have much effect on the sample average. A value above 0.5 indicates that >50% of the sample average's magnitude is due to one single datapoint. Values close to 1 indicate that your sample average is very sensitive and likely to vary between samples."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>moment_1</th>\n",
+       "      <th>moment_2</th>\n",
+       "      <th>moment_3</th>\n",
+       "      <th>moment_4</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Date</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1960-01-05</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1960-01-06</th>\n",
+       "      <td>0.650466</td>\n",
+       "      <td>0.775943</td>\n",
+       "      <td>0.865678</td>\n",
+       "      <td>0.923038</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1960-01-07</th>\n",
+       "      <td>0.408050</td>\n",
+       "      <td>0.471053</td>\n",
+       "      <td>0.521646</td>\n",
+       "      <td>0.562054</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1960-01-08</th>\n",
+       "      <td>0.351128</td>\n",
+       "      <td>0.438453</td>\n",
+       "      <td>0.505122</td>\n",
+       "      <td>0.554293</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1960-01-11</th>\n",
+       "      <td>0.349673</td>\n",
+       "      <td>0.506938</td>\n",
+       "      <td>0.644610</td>\n",
+       "      <td>0.752957</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2019-12-24</th>\n",
+       "      <td>0.002019</td>\n",
+       "      <td>0.028031</td>\n",
+       "      <td>0.183397</td>\n",
+       "      <td>0.508021</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2019-12-26</th>\n",
+       "      <td>0.002019</td>\n",
+       "      <td>0.028031</td>\n",
+       "      <td>0.183397</td>\n",
+       "      <td>0.508021</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2019-12-27</th>\n",
+       "      <td>0.002019</td>\n",
+       "      <td>0.028031</td>\n",
+       "      <td>0.183397</td>\n",
+       "      <td>0.508021</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2019-12-30</th>\n",
+       "      <td>0.002019</td>\n",
+       "      <td>0.028030</td>\n",
+       "      <td>0.183396</td>\n",
+       "      <td>0.508021</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2019-12-31</th>\n",
+       "      <td>0.002019</td>\n",
+       "      <td>0.028030</td>\n",
+       "      <td>0.183396</td>\n",
+       "      <td>0.508021</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>15101 rows × 4 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            moment_1  moment_2  moment_3  moment_4\n",
+       "Date                                              \n",
+       "1960-01-05  1.000000  1.000000  1.000000  1.000000\n",
+       "1960-01-06  0.650466  0.775943  0.865678  0.923038\n",
+       "1960-01-07  0.408050  0.471053  0.521646  0.562054\n",
+       "1960-01-08  0.351128  0.438453  0.505122  0.554293\n",
+       "1960-01-11  0.349673  0.506938  0.644610  0.752957\n",
+       "...              ...       ...       ...       ...\n",
+       "2019-12-24  0.002019  0.028031  0.183397  0.508021\n",
+       "2019-12-26  0.002019  0.028031  0.183397  0.508021\n",
+       "2019-12-27  0.002019  0.028031  0.183397  0.508021\n",
+       "2019-12-30  0.002019  0.028030  0.183396  0.508021\n",
+       "2019-12-31  0.002019  0.028030  0.183396  0.508021\n",
+       "\n",
+       "[15101 rows x 4 columns]"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Calculate Max/Sum Ratio\n",
+    "max_over_sum_df = moments_df.apply(fattails.metrics.max_over_sum)\n",
+    "max_over_sum_df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plot the MS plot once more. This time we plot from a dataframe which has all the calculated values. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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UObfMOdcE/A04uYt2PwKuBxqSGN+O1J+KiPQki/psDY0QkeyWSCI8EljV7v7q+LY2ZrYPMNo591hPT2Rm55nZAjNbUFVV1edgTZ2qiEhvktZni4j0d7s8Wc7MAsAvgEt6a+ucu805V+mcq6yoqNjVlxYRkT7qS5+9q8WL7XQoT0SyUyKJ8BpgdLv7o+LbWpUAewH/NrMVwGxgjiZfiIhkRNL67F0uXuggnohkuUQS4fnARDMbZ2YR4HRgTutO51yNc26wc26sc24s8DJwknNuQSoCdqosiIj0JKv6bBGRbNZrIuyciwIXAk8CS4D7nHPvmNm1ZnZSqgNsT0tSioj0LJv67LaSsGtJ78uKiCQolEgj59xcYG6nbVd20/bwXQ9LRER2Vtb02a3VCy2fJiJZyldnlhMRER+x1j8xSoRFJDv5LhFWYUFExC80NEJEspuvEmGNERYR8ZHWirAqGCKSpXyVCIuIiI+YKsIikt2UCIuISGpojLCIZDnfJcLqTkVE/EIVYRHJbr5KhE2nKRIR8Q+NERaRLOerRFhERHxE6wiLSJZTIiwiIqmhMcIikuV8lwg7VRZERPxFY4RFJEv5KhHWOsIiIj6iMcIikuV8lQiLiIiPaB1hEclySoRFRCQ1NEZYRLKc7xJhdaciIn6hirCIZDffJcIiIuITGiMsIllOibCIiKSGxgiLSJZTIiwiIqmhMcIikuV8lwjrCJuIiF+oIiwi2c1XibBpIWEREf/QGGERyXK+SoRFRMRH2sYIKxEWkeykRFhERFJDY4RFJMv5KhE2wKmyICLiLxojLCJZyleJcMCgRXmwiIg/aIywiGS5hBJhMzvWzN4zs6VmdnkX+y82s8Vm9qaZ/cvMdkt+qBAMGDFlwiIi/qB1hEUky/WaCJtZELgFOA6YCpxhZlM7NXsdqHTOTQceAH6a7EABAma0qLIgItKjbCleaIywiGS7RCrC+wNLnXPLnHNNwN+Ak9s3cM4965yri999GRiV3DA9SoRFRHqWTcULrSMsItkukUR4JLCq3f3V8W3d+Srw+K4E1Z1gwDRGWESkZ1lTvNAYYRHJdqFkPpmZfQmoBA7rZv95wHkAY8aM2YnnhxZlwiIiPemqeDGrh/YpK17QHM+1G7em5OlFRHZVIhXhNcDodvdHxbd1YGZHAT8ATnLONXb1RM6525xzlc65yoqKij4HGwwYMVUWRESSol3x4mfd7D/PzBaY2YKqqqq+v8Arv/WuX/z1zgcpIpJCiSTC84GJZjbOzCLA6cCc9g3MbCbwO7wkeH3yw/QENUZYRKQ3WVO8wILedbSh748VEUmDXhNh51wUuBB4ElgC3Oece8fMrjWzk+LNfgYUA/eb2SIzm9PN0+0SM6NFcy5ERHqSNcULgmHvOtacspcQEdkVCY0Rds7NBeZ22nZlu9tHJTmuLgUDqCIsItID51zUzFqLF0HgjtbiBbDAOTeHjsULgI+ccyd1+6Q7qy0Rbkr6U4uIJENSJ8ulWsB0Qg0Rkd5kS/GCYMS7blFFWESyk79Osazl00RE/KM1EdbQCBHJUv5KhE1DI0REfGPgWO+6sDyjYYiIdMdXiXBQQyNERPxj1je8671O7b1tzWpY/p/UxiMi0om/xggHtHyaiIhvtI0Rjvbe9tYDobEGrq5JbUwiIu34qiIcMNOZ5URE/KJtjHACq0Y0KgEWkfTzVSIc1GQ5ERH/CBd6162nWk5ESyw1sYiIdMFXibAZOsWyiIhfhPK86xdugkT77vpNqYtHRKQTXyXCQQ2NEBHxD+9kHdBQAx+91HPbsjHe9cblqY1JRKQdXyXCMeeIKhEWEfGfret63l8+1rtevzjloYiItPJVIrxhqzfhor5JY8hERHyltyEPA8d518/8GKo/TH08IiL4LBGeMrwEgC0NOkuRiIivbFrZ8/5AfDXPxq3wxOWpj0dEBJ8lwoOLvYkXtY0JrEkpIiLZY8kjvTRwUDgYZn4JPngK3nkImuvTEpqI5C5fnVCjMBIEoLZRQyNERHxl03Jv0lx+GayYBw1bYI/jt+93zptcd8AF8P4TcP9XIJQPux0IYw+BGWdCydCMhS87wTlv6bxIUaYjEemWryrCxXle3l7bpIqwiIjvLH/eu77rBPjbGRDtfKINg/Lx8O2FcOaDUHkubFkH/7oGfjUTXr8HVrwANWugpSXt4UsfvfhruG4ErOxlxRCRDPJXRbg1EdbQCBERf9jjRHj3Ue/2x2/BlBO373vuJ3DklfE77VYECuXBxKO8C8CLN8M/r4B/XLC9TTAPKibBgN1g7MFexTivZPuSbZJ5rSuA3HkslI70/skpHgoFA6FgAOQP8H5mheXevvwBUDgIwvkZDFpyja8S4eI8b2jENiXCIiL+8Pm7oeYjr6L73E9g1jdgwtGw9J/w/M8hrxQOvmj70IiuHHgh7H8e1KyCTSu8y8ZlUPUebHjfS7SfuBwixTBkKkw8Bvb9ChRXpO99yo6G7uVdH/597+e1aTmsWeitINJQQ4d/ftorGQ4Dx8KAMVBU4SXNBQMhr8wbZhHO985aGMqHcIF3CeZBKBK/zoNAME1vUvzOV4lwaUEYgCXrtnLyjMzGIiIiCQiGvGpfq3f+DjgvSSosh39dCw2bIdoA9FDNDUVg0O7epbPlz8Pa12DLWlj7Ojz7Y5j3Czj5Ftjrs0l+Q9Jns7/pjQ1vr6UFmrZ6Y8U3rYC6DVC/GWqr4v/oLPeGVNRt6NspulsFQp2S4/h1MLL9dqTIq0jnFUOkxLudX+ZVpUuHw6CJ3v1IkY409GO+SoTLCyMA/Pa5D7ns2MmYvpgiIv7wudvhwa/Cuje96m8oz9s291KYd+OuPfe4Q7xLq08WwyP/DQ+c461LPGKGVykeMgUq9vCqjaoYpkFrxbeLv9WBgJdk5pfBgNE9P0200UuSGzZDU633T1Nz/fbr1tuxJq/tDteN3nj0WCPEmr1t0QavMl2zChq3ecv2NW2jyyq1Bb3vzMh9vVhLhnvJctFgb6WTkmHeP3XiS75KhEPB7XP7Pt7SwPCyggxGIyIiCZt2Ksy/3VsabeBYwKB4CHzhblj6NPz5c8l7raFT4ew58OrvYdUrsOpVePvB7fuDeTD+cC+xKR3hHXYvHgoDd/MOxavIkhwunlTu6ucZyvNWDEn1qiEtLdC4BeqqYcMH3tkQG7d4Sfja12HVy94RjZYuhmcWD41fhkDRECgb5d3OH+DFnT/AG+JRVOEN5ZCs4atEGGDikGI+WL+NP7+8kks/vUemwxERkUTtezY89A0vwRg6bfv2CUfBZ/8An7yVvNcKF8BB39l+v2GLN554/RL4+E147wn44MkdH1c2Gobv7VWPh+4Fk47V5K2d1kNFOBsFAvHxyAO6HoID0BKDuo3eEI66DVC7wasqb3gftq33LuuXeN9x18XKJoGw9/0qGea9TqTYG+8cKfT2BcPx65A3vKNtW6jdJRi/dNoWzItX2Uu959VRj4T4LhH+58WH8Z2/vs7vn1/O6fuNYXR5YaZDEhGRROx9undY+pHvgOu0Hvz0zwOfT91r55fCqErvAnD8z7xD6rVV3iHyLWvj41Jf8JKa9x73Yhy+N+xztjdxq6A8Poa0XIfCE5GsinA2CQS9SZi9TcSMNnoTAus3wbZP4kM7aqD6A1jzmnca8YbN0FTnDcno/PuwqyzgTUTNL4W9z4D9vqajHd3wXSIMcPlxezDnjbUc8tNnee/Hx/LBJ9sYWBRh5IDthxveXlNDXijAxKElGYxUREQ62PdsGDzJm6CUaeECL8EdMMZLeME7oQdAc4N3NrynfgCPXbzjYwsGeolF6zjX1ktBOZSN9MYkDxgDxcO8SmNO8llFOJlCed7QiOIhUDG557bOeWOXW5rj11Hv0rYtun1bS9SrSre/72LetuZ6byhHw5btK3NUL4XnrvcukWLv+xnO9+IL5W+/BMPxKnM4Pomw1Ps+hyLeGGkLePst6H2fd9jW1f3220Nd7Itv77zNgu0q3/HquLV7zdZYk/WjSqSRmR0L3AQEgT84537SaX8e8EdgX6AaOM05tyJpUXYyYkABJ04fzqNvruPLf3iVV1dsBODoqUM5ds9hzBwzgBN/PQ+AgycM5oDdB7HboEJGDyykoiSP8qII+WEdMhARyYjdDsh0BL0L53tV6mmneoe+t6zzkovGLd7h740feofIG2q8643LvQpf/aaOh8SLh8GY2V5SPGImjJ7lJcq5wHUx8Ux2ZOYlnESS/9zOeePk170R/47WeKtwtE4YjDZ4Y6Jbmr1kOtbkTUhsmzyYhSrPhRN3cYJtO70mwmYWBG4BjgZWA/PNbI5zbnG7Zl8FNjnnJpjZ6cD1wGlJi7ILN39xHw6Z+BE/feK9tm1vrNrMPxd/0qHd2pp6fvbke50fTmEkyMDCCIOKI5TmhymMBCnOC1GYF6QoL0RxJORd54UoLQhRXpTHgMIwBeEghZEg+WHvEgzk4H+6IiK5wmx71TgRLS2weaVXidu0ApY/551I5L3HvVULwBuHPGSqN7wiv2z7GNFwkXcdjHiXQCh+Oz5OtMNyYPkdlwYL5Xu3s6r63A+HRviNmfeP2JjZfX9sLOolxi7m/XPX0um6tRLtYt73vq1ddMftrVXszttcrF37lnbbYjtWwVtfc9jeSf2IEqkI7w8sdc4tAzCzvwEnA+0T4ZOBq+O3HwBuNjNzLrX/Dp623xhO2nskr67YyN6jyijND/Pux1t5a81m3l6zhU9NGcIRk4ewrTHKqo11rN5Uz4ZtjWysbWq7VNc2sa2hmaqtjWxrjFLXFKW2KUZTNLHTd4YCRl4oQKTdJRwMEAnG7wfj9+Pb80IBwkEjEgoQCgYImhEMGAEzggEIBgLetRmBgLVdhwLt27Xb1touAAEzQvHHd9/Oe46AgZlheG1b+6nW22bx27T2Yd0/xtptb7tP/HlaP6gutlm75297tNHWpnV5vNY2S9Zt4ZE31rW1D7SPMx5PoEPc1hZnW+zx/YF28ewYy/a2Fn9DnePc3i6+rf3j2j6Tnl7DOjwHHe7v+Bztn3+H2Nt9xvFnaoux/f62z3OH7R2foPPPqKu2nV+DXva3357I63cZdwIxtP+sE3mM4a1P3t+OEGXbUbycEghA+TjvArD/173rWLOXEK96BT562asor1/iVeiSOUY0XOgd2m6dhNV6wolQXsekue3QeHfX+e0OT7c7fN3hfih+eLs1UW+fvEe8qiPQrpcSPwmGvEs/l8g7HAmsand/NTCruzbOuaiZ1QCDgA3JCLInBZEgh03aPmh96ohSpo4o5bT9trcpzgsxZXgpU4aXJvy8TdEW6pqibGuMUlPfzKbaZjbXN1HXFKO+KUZ9s5csN0ZjNDR7103RFppjjqZYS/z29uu6uihNMddhW3OshViLI9biaHF4t51r2yZdK8kP4Rw4531uLc7h8O67+H19fJKom06fwckz+s+h6mw9ipfzgmEYuY93mX1+x33ObT8k3Vzv3W6JV+Ni8XGjsaaO6+FGGzuukdt6mLup1kusW5+rud47FF5X22ld3dZD4/HrlDFvXKdIlkprqm9m5wHnAYwZk+BhpgzxqrsRBhRGGDUwMzG0dEqMY85529puQ7SlhZYW2tq1OEc05l23f0y0xXV4vvaJo+uQTMa303l7vC07Pob4dlof33bdcRut7bt4nfjT0HoQYfs2t/3xQOVuA6kcm9hs7faJsSN+HY895lzH13cdX6v9Puft7BRrp7at23t4Hjps7/hZtt5O9Dla3Pb32PrZdf7c2u/obr/bYf/2nxHdte3iMV29Bl28RiKv31637Xcm7m6ec/qoAfQzWXsUT7phFq/E5mXm9VsT8faJcXPDjpOxOhzejnqHuFui2yd5tU3uatp+WL1sVHz8q0h2SiQRXgO0P+3LqPi2rtqsNrMQUIZ3uK0D59xtwG0AlZWV6nB7EQgYAYx+dtQ2bdqGQuiwnOSWpB3F81PxQnZBphNxkQxK5HjFfGCimY0zswhwOjCnU5s5wNnx26cCz6iyICLib86525xzlc65yoqKXtZNFRHxoV4TYedcFLgQeBJYAtznnHvHzK41s5PizW4HBpnZUuBi4PJUBSwiIj3qy1E8ejqKJyLS3yU0Rtg5NxeY22nble1uN5DSUwKJiEiC2o7i4SW8pwNf7NSm9SjeS+gonojksP6/LoaISA6Jj/ltPYoXBO5oPYoHLHDOzcE7iven+FG8jXjJsohIzlEiLCLSz+gonohIYrS4n4iIiIjkJMvUsDAzqwJW7sRDB5OGE3WkQC7HXQbUJCGWvsiWz7uv7z1b4u6rruLOxM99Z+zMZ76bcy6nllFQn+0b6rN3nfrt7Lazn3eX/XbGEuGdZWYLnHOVmY6jr3I5bjO7zTl3XrJiSvA1s+Lz7ut7z5a4+6qruDPxc98Zfv3M/cKvn28ux53LfTao3872fjvZn7eGRkg6PJLpADJI711E/CbXf3dz+f3n3HtXIiwp55zLuV+sVnrvIuI3uf67m8vvPxffux8T4dsyHcBOUtzppbjTy69xg79j9wO/fr6KO738Gjf4N3bFjQ/HCIuIiIiIJIMfK8IiIiIiIrtMibCIiIiI5CQlwiIiIiKSk5QIi4iIiEhOUiIsIiIiIjlJibCIiIiI5CQlwiIiIiKSk5QIi4iIiEhOUiIsIiIiIjlJibCIiIiI5CQlwiIiIiKSk5QIi4iIiEhOUiIsIiIiIjlJibCIiIiI5CQlwiIiIiKSk5QIi4iIiEhOUiIsIiIiIjlJibCIiIiI5CQlwiIiIiKSk5QIi4iIiEhOUiIsIiIiIjlJibCIiIiI5CQlwiIiIiKSk5QIi4iIiEhOUiIsIiIiIjlJibCIiIiI5CQlwiIiIiKSk5QIi4iIiEhOUiIsIiIiIjkplKkXHjx4sBs7dmymXl5EZKctXLhwg3OuItNxpJP6bBHxs+767YwlwmPHjmXBggWZenkRkZ1mZiszHUO6qc8WET/rrt/W0AgRERERyUlKhEVEREQkJykRFhEREZGc1OsYYTO7AzgRWO+c26uL/QbcBBwP1AFfcc69luxARaRrzc3NrF69moaGhkyH0u/k5+czatQowuFwpkPpE/XbItlLfXZq9bXfTmSy3F3AzcAfu9l/HDAxfpkF/CZ+LSJpsHr1akpKShg7dixefiPJ4Jyjurqa1atXM27cuEyH01d3oX5bJCupz06dnem3ex0a4Zz7D7CxhyYnA390npeBAWY2PKFX76O3N7zNss3LUvHUIr7V0NDAoEGD1KEmmZkxaNAgX1ZtsqXfbly+nPpFi5L9tCK+pj47dXam307GGOGRwKp291fHtyXd9+d9n1vfuDUVTy3ia+pQU6Mff65p6bc33nEHq7/z38l+WhHf68d9S8b19bNN62Q5MzvPzBaY2YKqqqp0vrSIiPRRUvps55IblIhIEiUjEV4DjG53f1R82w6cc7c55yqdc5UVFTl1UiYRkWySUL+96322ql4ikt2SkQjPAc4yz2ygxjm3LgnPKyKyUxYtWsTcuXN7bPOPf/yD6dOnM2PGDCorK5k3b16aossKaeu3HaoIi0jvEum3W82fP59QKMQDDzywy6+byPJpfwUOBwab2WrgKiAM4Jz7LTAXbwmepXjL8Jyzy1GJiOyCRYsWsWDBAo4//vhu2xx55JGcdNJJmBlvvvkmX/jCF3j33XfTGGXqZE2/rXGQIpKgRPptgFgsxmWXXcYxxxyTlNftNRF2zp3Ry34HfCsp0YjILrn+1et5d2Nyk7k9yvfgsv0v63b/ihUrOPbYY5k9ezYvvvgi++23H+eccw5XXXUV69ev55577mHChAmce+65LFu2jMLCQm677TamT5/O1VdfzfLly1m2bBkfffQRN954Iy+//DKPP/44I0eO5JFHHiEcDrNw4UIuvvhitm3bxuDBg7nrrrsYPnw4hx9+OLNmzeLZZ59l8+bN3H777cyaNYsrr7yS+vp65s2bx/e+9z1OO+20HeIuLi5uu11bW9uvJq9kVb+tgrBItz6+7joalyS3z86bsgfDvv/9Htv4td8G+PWvf83nPvc55s+fn5TPS2eWE5FdtnTpUi655BLeffdd3n33Xf7yl78wb948brjhBq677jquuuoqZs6cyZtvvsl1113HWWed1fbYDz/8kGeeeYY5c+bwpS99iSOOOIK33nqLgoICHnvsMZqbm/n2t7/NAw88wMKFCzn33HP5wQ9+0Pb4aDTKq6++yi9/+UuuueYaIpEI1157LaeddhqLFi3qtjMFeOihh9hjjz044YQTuOOOO1L6GeWkfvTPhUh/48d+e82aNTz00EOcf/75SfscEjmhhoj4RE+V21QaN24c06ZNA2DPPffkyCOPxMyYNm0aK1asYOXKlTz44IMAfOpTn6K6upotW7YAcNxxxxEOh5k2bRqxWIxjjz0WoO2x7733Hm+//TZHH3004B0WGz58+5K3n/3sZwHYd999WbFiRZ/iPuWUUzjllFP4z3/+wxVXXMHTTz+9S5+DdEGrRoh0q7fKbSr5sd++6KKLuP766wkEklfH9V0i7NSpimSdvLy8ttuBQKDtfiAQIBqN9niqy/Ztw+Fw2xCF1sc659hzzz156aWXenx8MBgkGo3uVPyHHnooy5YtY8OGDQwePHinnkO6oIKwSNbyY7+9YMECTj/9dAA2bNjA3LlzCYVC/Nd//VfCz9GZr4ZGmHpVEV865JBDuOeeewD497//zeDBgyktLU3osZMnT6aqqqqtQ21ubuadd97p8TElJSVs3bq1xzZLly5t+8f6tddeo7GxkUGDBiUUk/SBihcivpSN/fby5ctZsWIFK1as4NRTT+XWW2/dpSQYfJYIi4g/XX311SxcuJDp06dz+eWXc/fddyf82EgkwgMPPMBll13G3nvvzYwZM3jxxRd7fMwRRxzB4sWLmTFjBvfee2+XbR588EH22msvZsyYwbe+9S3uvffefjVhLivo8xTxrWzst1PBMjXUoLKy0i1YsKBPjzn54ZOZMGACPz/85ymKSsR/lixZwpQpUzIdRr/V1edrZgudc5UZCikjdqbPXnfNNWx94kkmvdTzH0CRXKI+O/X60m+rIiwiIimhCruIZDvfTZYTEemLO++8k5tuuqnDtoMOOohbbrklQxHlGI0RFpE+Sme/rURYpB9wzqn61o1zzjmHc87ZuROnaZWaXaXvpEhX1Gf3LJ39toZGiPhcfn4+1dXVStqSzDlHdXU1+fn5mQ7F3/S9FOlAfXbq7Ey/7buKsNP5OkU6GDVqFKtXr6aqqirTofQ7+fn5jBo1KtNh+JcqXiI7UJ+dWn3tt32VCGsdYZEdhcNhxo0bl+kwRLqk0kXucs3NRNevJzxyZKZDySrqs7OLhkaIiEhqqCKc0z6+7jqWHnkUsc2bMx2KSLeUCIuISOpoHGTOqp33AgCxXs4WJpJJSoRFRCQ1VBHObYH4zz8Wy2wcIj1QIiwiIqmjinDOskAQANfSkuFIRLqnRFhERFJDBeHcFoinGEqEJYspERYRkdRRRThnWTwRdjElwpK9lAiLiEhK6MxZOa6tIqwxwpK9fJUIq1MVEfEZVYRzVzBeEdbQCMlivkqERUTET1S8yGVmGiMs2U+JsIiIpI4qwrkr6K0aoeXTJJspERYRkdTQcLacZvFEuG7RoswGItIDJcIiIpIyqgfnruLDDwdg/U+up6W+PrPBiHRDibCIiKSGKsI5LZCf13Z745/+rElzkpVCiTQys2OBm4Ag8Afn3E867R8D3A0MiLe53Dk3N7mhioiI72iMsABVv/gFVTfdRHjECIKlpVhBPoH8Aiw/j9DAcoZ+/3sECgoyHabkoF4TYTMLArcARwOrgflmNsc5t7hdsx8C9znnfmNmU4G5wNgUxCsiIn6hinBOc/F/gia++AK1L7xA/RtvEqupIbalBlffQGzrFhpeegdiMWI1NUTGjSM8bCiR3XajYOZMJcaSFolUhPcHljrnlgGY2d+Ak4H2ibADSuO3y4C1yQyyPafqgohIj7LqKJ767Jxn4TBln/kMZZ/5zA77ops28dE551I3fz5bn3kGotG2fcHBg8mbOIFgSSmBkmKCxcUEiooJFBd794uKIBzGWi+h1tuhdtu23yYUwsIRLBLfHtDoUEksER4JrGp3fzUwq1Obq4GnzOzbQBFwVFdPZGbnAecBjBkzpq+xiohIL7LqKJ4KwtKL0MCBjH/4IcA78Ua0agMNSxbT8OabNH6wlGhVFY1VVbRsq6Vl61ZaamuT9+LBIBaJECwuxgoKCOTnE6qoIDx6FPlTpzLglFO8BFr6tYTGCCfgDOAu59zPzewA4E9mtpdzrsPIeOfcbcBtAJWVlSoTiIgkX1YdxVNFWBJlgQDhoUMIDx1CSXzFic5cSwstdXVtSbFrbsZFo951U+vtJlw0Cs3N3exvxkXj+xoaaamtpaWhgZa6OqLr1lH7wgsAREaPpuiAA9L4CUgmJJIIrwFGt7s/Kr6tva8CxwI4514ys3xgMLA+GUGKiEjCknYUb1eZxghLklkgQLDYGyaRKo0ffsiyE06k+vY7aKlvIFhaQmT33QkNHJiy15TMSSQRng9MNLNxeAnw6cAXO7X5CDgSuMvMpgD5QFUyAxURkaRJ6CheUoazqSIsPhMeNoxAWRm18+ZRO28eAJafz4Rnn1Ey3A/1OlLcORcFLgSeBJbgjSt7x8yuNbOT4s0uAb5uZm8AfwW+4jSrTUQkExI9incfeEfx8IoXgzs/kXPuNudcpXOusqKiou+RqCIsPhQoKmLi8/9h9yceZ+z991F+9lm4hgaaPvww06FJCiQ0Rjg+m3hup21Xtru9GDgouaGJiMhOyK6jeKqJiA8FIhEiY8cCECwpYePdf6Tm0UcJFBWRN2GCJtH1I8maLCciIlnAORc1s9ajeEHgjtajeMAC59wcvKN4vzez/8GbOJeio3imUyyL74XHjKHowAPY/Ld72fy3eyEYJDRoEJHx4ynYa09KTzyR/D32yHSYspN8lwg7dasiIj3KmqN4Ghoh/YAFAoy+/Xaali2jYcm7NC79gOj6KhreeovqP9xO9R9uZ9jVVzHw9NMzHarsBF8lwpqBLCLiMxoaIf2AmZG3++7k7b57h+3NH3/M0sOP4OMf/z/q336bvHHjGfD5UwmWlnbzTJJtdFoVERFJDRUvpJ8LDxvG6N/9lvwpU9j6z6dZ/7Of8f6BB7H13//OdGiSIF9VhEVExGdUEZZ+rviwwyg+7DAANj/0MOu+9z1Wf/N8io88ksL9Kin/8pexYDDDUUp3lAiLiEhq9KEg3NLUhKuvJ1hWlrp4RFJswCn/RfGhh7D20kupe/VVtv3rX2z49c3k7bEHBXvvTdnJJ5M/eVKmw5R2NDRCRERSJ8GK8KqvfZ33Z81OcTAiqRcaNIgxd9zBpFdeZugPf0jxkZ8CYONdd7HiC18gumlThiOU9lQRFhGR1OjDGOG6V19NYSCSETk+KsYCAcq/dCZwJgBbnnyKNf/939QvWkTJEUdkNjhpo4qwiIikjsYIiyZNAlA0a3+Cgwez+sJv07RqVabDkThVhEVEJCW05KXIdsEBAxjx/37Mqm98kw+PPoa8SZMorKwkOHAglpeHhULeJRLGQiEIhbBw2LuEwlgoCMEgFgxtvx0KYZE8wsOGEigpwQKqb/aV7xLhlJz8SEREUkN9tkib4sMOY9w/Hmbbs/9myxNPUPPoo7Rs2ZKcJw+FiOy2G4X7zKTk6KMpPvTQ5DxvP+erRNj6MgVZREQyTH22SGf5kyeTP3kyg7/5DQBcLIaLRnHNUYg245qbvfvRqHe7qQkXjUEs2taWWAwXjeFiUVpqaohu3ER0/XoaP/iAmsfmsvn+BwgNH07hPvsw5LL/JTxkSIbfdfbyVSIsIiIi0p9YMOitM5yXl5Tna2lqYuOdd9Hw7hK2PPYY4RHDGXLJJUl57v5IibCIiKSGxgiLpF0gEmHwN84DYOnixTSvWZvhiLKbRlWLiEhKaW6HSGZYOOwNpZBuKREWEZHUUEVYJKMspES4N0qERUQktVQRFskIC4Vw0eZMh5HVlAiLiEhqqCAsklEWCoEqwj3yXSLscv2cjSIifqOKsEhmhILesmzSLV8lwlpHWETERzRGWCSjNEa4d75KhEVExIdUERbJCAuFcLFYpsPIakqERUQkJUwV4dymf4AyTpPleqdEWEREUksJUY7TP0SZYuEQaIxwj5QIi4hIaqgiLJJZoZDGCPcioUTYzI41s/fMbKmZXd5Nmy+Y2WIze8fM/pLcMEVExLdUERbJCE2W612otwZmFgRuAY4GVgPzzWyOc25xuzYTge8BBznnNpnZkFQFLCIifqGKsEgmWTCoRLgXiVSE9weWOueWOeeagL8BJ3dq83XgFufcJgDn3Prkhrmd1hEWEfEX9doimWFhnVCjN4kkwiOBVe3ur45va28SMMnMXjCzl83s2GQF2J5mIIuI+MhO9NlOwyhEkkdjhHvV69CIPjzPROBwYBTwHzOb5pzb3L6RmZ0HnAcwZsyYJL20iIhktb4kt85pkp1IkmiMcO8SqQivAUa3uz8qvq291cAc51yzc2458D5eYtyBc+4251ylc66yoqJiZ2MWEZEeZM0EZyW0Ihllqgj3KpFEeD4w0czGmVkEOB2Y06nNw3jVYMxsMN5QiWXJC1NERBLRboLzccBU4Awzm9qpTfsJznsCF6U0qL5WhEUkKSwvgmts1JCjHvSaCDvnosCFwJPAEuA+59w7ZnatmZ0Ub/YkUG1mi4FngUudc9WpClpERLqVPROcd6YgrD/YIkkTKCiElhZcs84u152Exgg75+YCczttu7LdbQdcHL+IiEjmdDXBeVanNpMAzOwFIAhc7Zx7ImURqSIskhGBgnwAXH09RCIZjiY76cxyIiK5p/0E5zOA35vZgM6NzOw8M1tgZguqqqr6/CI7s9KPa2np82NEpGuW7yXCLfX1GY4keykRFhHpX7JvgnMfqrwtdXU7/zoi0kGgoBBQItwT/yXCOmomItKT7Jng3IeKsOXlAdBSq0RYJFk6DI2QLvkqETadrlNEpEdZOcE5gYpwoKDAa9qgP9j9hsZ7Z1yguASA2JatGY4keyXrhBppsblxM+FAONNhiIhkteyZ4NyHinDrWMaGxlQFIxmi5aQzJzigDIDYlpoMR5K9fJUIr6tdx7radZkOQ0RE+iCRwqDleTPao+vXA3umNiCRHBEsLQUgVqNEuDu+GhohIiI+0odSYCAvPpZR652KJE2wzKsIt2zZkuFIspcSYfGN+R/P58kVT2Y6DBHps95LwsFB5V5LJcIiSWOFhRAKEdusinB3lAiLb5z75Ll897nvZjoMEUlUX1aNCHvzP1xTU6qiEck5ZkawtJStzz6j0yx3Q4mwiIikVgJ/gC3sjRF2jQ2pjkYkpxQfcThNSz+k5sEHMx1KVlIiLCIiqdGH1QICRd7C/9V33Enz+vUpCkgk9wy/5hqssJDNDz2c6VCykq9WjSjPL2dLowZ8i4j4SiLrCBcVAdC8ahXVv/8Dw37w/VRHJZITLBSi7KTPsPlv9/LJ//0foSFDCZQUEywpITxmDAV75vYqLb5KhKdXTGfdNi2fJiLiB9bHBWSD5eWEhw2jbuGCFEUkkpsGfe3rNK1cyaZ778M1dBx+FKwYTGhgORYOe5dIhJKjjqT8rLMyFG16+SoRNgyncyyLiPhLHybphEePpvHDpSkMRiT3REaNZLc778Q5h6urI7Z1K7GaGmpfeonGDz6gZcsWXFMzrrmJxqUf8sl1/0fRwYeQN35cpkNPOSXCIiKSGjtxSjHLi+AatXKESCqYGVZURKCoiPCwYeRPnrxDm5o5c1j7v5ex7PjjIRwmEIlgkUhbtdhCIQgFsWAICwYhFL8OBtptC2KhMMHygUTG7EawtJRA/HUtHMZCQe95giEsvP15giUlXpv8fK9dGk5L6K9E2EzLfwjOubT8cohIkvSh37ZIZIdDtyKSPmUnnUThrNlsffJJolXrcU1NuOZmWpqavOUNo1FcrAUXi0I0hovFIBbFRWO4aBTX0IBracE1NVG3cCEtO3lWO8vLI2/CBPIm7A6twzbCYQr32ZfSYz+dtPfrr0S4L1OQpd96ff3r7DN0n0yHISK96nufHSwtI1pVxborryJvj8mEKioIDx9B3sQJBPLyUhCjiHQWHjqE8rO+vMvP45yjpbaOltpa71JXh2tuTaZjuOZoPKGO4pqbidXU0NLQgGtopHntWupff426ha/hmpvbLkAOJ8KqCOe02cNn8/K6l7n97duVCIv4SF/67UFfPZfGD5ey+f77O1SSiz/1KUbfeksqwpOU0d/rXGdmBIuLCBYXZTqUbvkqEQYSHiN89uNnM6xoGNcfen2KI5J0KQp7v0hvb3ibplgTkWAkwxGJSI/6MoQpnvSGBg1izO9+h4tGiW3aRPP69az65jeJfvJJioKUlNNQNslivjqhRl8my722/jXmLp+b4ogkEzY2bOToB45m3pp5xFpimQ5HRJKlXcJkoRChigoK9tyTwhkz2g6Jiogkk68qwhoaIbuX7c6Xp36Z6+dfz/lPn09BqICpg6YyrGgYo4pHMa5sHFMHTWVcWf9f8kUk67UmtrvYb1s4rERYRFLCX4mwJsvlvEAgwOcmfY5jxh7Df1b/h9fXv84Hmz7g9U9e5/Hlj9PiWigKF3H/Z+5ndMnoTIcrktuS1GVbOOzNVhcRSTLfJcJaR1gASiIlnDD+BE4Yf0LbtsZYI79+7dfcvfhujv/78ZSESxg3YBynTDiFUyedmsFoRXLcrlaEIxFVhEUkJXyVCGN9m30suSUvmMfFlRdzzNhjWLR+ER9t/YjX17/ONS9dw9MfPc3x447n6N2OpiBUkOlQRXJDkiZJWThMdP16av7xDywvn2BZKcGyMqyggGBxMcEBA7BwOCmvJSK5xVeJsCrCuS2Rf4ICFmB6xXSmV0wHoLmlmd+98TvuWXIPL6x5gSteuILRJaPZvWx3Zg2fxcSBExlcMJiSSAklkRLyglqnVCTpdrGAER4xAoC1l13e5f68iRPY7a9/9c5IpRUKRKQPEkqEzexY4CYgCPzBOfeTbtp9DngA2M85tyBpUW5/flWEc1xfx4mHA2EunHkh39z7m7y49kXerHqTdze+yysfv8Izq57psn1rUlwcLqY4UkxppLTtdkmkhJJwSYfbpXmlDC4YzKD8QfojLNJOsn4fyr/6VUo/8xlcQwMtDY3EqjfQUl9PS20t2+bNY8ucR3i/cj8IhwmVl1N28slU/Pd3vNO2ioj0oNdE2MyCwC3A0cBqYL6ZzXHOLe7UrgT4b+CVVAQKqgjLzgsFQhw66lAOHXUoALGWGCu3ruTj2o/Z2LCRbU3b2Nq0la3NW9nWtI1tTdvY0ryFbU3b2FC3oW1ffbS+29cYUjiEUcWjGFUyioqCCsaUjuHIMUdSlleWrrcpkp12dYywGeGhQ9ttmdR2q/TEEyk++GCi69cTq9lC/aJFVN92G9vmPc9ud9xBcMCAXXptEenfEqkI7w8sdc4tAzCzvwEnA4s7tfsRcD1waVIjbEerRkiyBANBxpeNZ3zZ+D49LtoS9ZLm1oS5eRs1jTWs2baG9ze9z4otK3j141fZUL+BaEuUH738I2ZUzOCMPc7gmLHHpOjdiGSpPp1QYydfIhCg7KSTtj+Nc2y8/XbW3/BzNv/9IQade87OPbGI5IREEuGRwKp291cDs9o3MLN9gNHOucfMrNtE2MzOA84DGDNmTJ+D1dAIybRQIMSA/AEMyB/QY7tYS4zF1Yv558p/cuc7d7LgkwW8MvIVCsOF6QlUJIu4lo79dt38+cS2bqPkU0d0bJiEoRRmxqCvfY3qO+6kdt48JcIi0qNdnixnZgHgF8BXemvrnLsNuA2gsrJypzJaDY0QPwgGgkyrmMa0imlMKp/E957/Hhf/+2KmV0xn9wG7c8jIQ5QUS//XOka30xkgV375LACmvLskZS+dN2ECDe+8g3NOY/dFpFuJJMJrgPZnJhgV39aqBNgL+He8sxkGzDGzk5I9YU5jhMWPDhl5CF+Y9AVe+fgVXlz7Ig7H1EFTuffEezMdmkhKWZLOLLczSo4+mrpXX6XulVcpnLW/kmER6VIiifB8YKKZjcNLgE8Hvti60zlXAwxuvW9m/wa+q1UjRDxleWVcccAVgHfSj0ufu5RnVz1Lc0sz4YDWPpV+LIOJcMG0vQD46CtfIVBSQtEBBzD4gvMJjxxJsKQk7fHkJP29Fh8I9NbAORcFLgSeBJYA9znn3jGza83spJ4fnVyqCOe2/vCzzwvmcfz44wG4+sWr+bj24wxHJP2RmR1rZu+Z2VIz63rxXa/d58zMmVlliiIBMpMPFcyYwe5PPM6wq6+iYOYMtj71FMv/6xQ+OPAgPvra12lYkrphGdKJqvGSxRIaI+ycmwvM7bTtym7aHr7rYXXNzHZ6ZrH0D/1h5ZAjRx/JEaOP4JEPH+GRDx/hoJEHcfDIg/n02E8zuGBw708g0oNsWvJyewKUmY47MnYskbFjGXDaadTNn0+0qoq6V15ly+OP89G5X2X8nH8QqqjISGwikh10ZjmRNAsHw/zqU79i1dZV3PvuvTy76ll+suYn/HT+T9lv2H5cWnkpk8snZzpM8a+sWfIyk0MjOoZhFO2/PwBlJ5xA/rS9+PiKK/ngkEMJFBYSGjKEyPjxhEeOZMBnTyF/ypSMxisi6dPr0Ihso0RY+ovRJaP57n7f5bHPPsY//usffH7S53mz6k1OfeRUFld3zllEEtbVkpcj2zdov+RlKgNxjQ0AtNR3fyKaTBhw6qmMvf9+hlx2GQM+fyp5kybRvOojNt97L8tP+Sy1r7yKi0YzHaaIpIG/KsKaLCf91Piy8fxw9g85a+pZnPDQCZz/9Pkcs9sxHDvuWPYdum+mw5N+pC9LXu7q2u8b7/6jd33nXYz4v+t6bpzGvt3MKJi2V9uEulbb5r3Aqq99jY/OPpvQ0KGUn/Vlys85Bwv4rmYkIgny1W+3hkZIfzemdAw/P+znTBo4iXvfu5evPPEVfvLqT2iONWc6NPGPvix5uQKYjbfk5Q4T5pxztznnKp1zlRU7MZbWxdcPdk1NiT0gw1MAig8+iAn/eY4RP7+B4IABrP/ZDWx59NHMBiUiKeW7RLivlECI3xwz9hh+f8zvmXfGPA4ZeQj3LLmHs584mw31GzIdmvhD25KXZhbBW/JyTutO51yNc26wc26sc24s8DKQ9HXfASzgnVDDdTqhRjYLDxlC2QknMO7vDwLQsFirS4j0Z/5KhHdiaERtc22KohFJrdJIKTd96ibO2OMM3trwFkfcdwRfmvsl3qx6k7rmukyHJ1kqm5a8JBj/ExNrSevLJoMFg+RNmULjhx9mOhQRSSFfjRGGvk+Wa4g1pCgSSbdcHBYTDoT53v7f4/hxx/P0yqf505I/cebcMwlZiMphlXy38rtaYUJ2kDVLXvqwItxeaEgFsSodiRHpz3yVCO/MGOGGqBLh/iQXT5NqZswYMoMZQ2ZwxpQzeLf6XRauX8jfP/g7lz9/OQ+d/FCmQxTpWtBLhP1YEQawcFirR4j0c/5KhHdiaMSKLSsYWzY2NQGJpNnI4pGMLB7JkbsdSXl+OTe9dhPzP57PfsP2y3RoIjtoXW3BrxVhC4ZwMX/GLiKJ8dcY4T5UhEcUjQDg8ucvp6axJpVhiWTE5yd9nrGlY7no2Yv418p/0RRLcGa+SLrEK8K1z/0n8ZUjsogFA6BEWKRf811FONGREYXhQsCbLPfi2hc5btxxKYxMJP3K8sq4/tDrueDpC7jo3xcxIG8Ah406jKFFQymLlFESKaEkUkJhqJDCcCEFoQIKw4UUhgrJD+WTF8wjFPBVFyA+034oU+38+RQfdFD3jbNxjXhVhEX6PV/9FVyxZQVbm7cm3P7wUYfz4toXeX3960qEpV+aOmgq/zz1nzy58knmLJ3DC2tfoLq+OuEjJyELkR/KZ1DBIApCBeQF8xiQN4Dy/HKGFw9n9vDZ7F2xNwHz1cEjyRatY4SBltreV/DZmSUyU8mCQVWERfo5XyXCL6x5AYCmWBORYKTX9qFAiAH5AzRhTvq1cDDMieNP5MTxJwLQ4lrY1ryNrU1b2dq0lfpoPfXN9dRF66htrqUuWkdjtJGGWANNsSbqonVsqN9AY7SR+lg9H9d+zJKNS9hQv4FbF93K0MKhnDzhZE4cfyLjysZl+N2KrwS2J7auwYf9cDCgivAu0JlgxQ98lQi3irnEO6bCUCH10ew6z71IKgUsQGmklNJI6S49z8aGjcxbM485H87htjdv47Y3b2NK+RR+f8zvKcsrS1K00p+1Lp8G0OLDRNiCId9O9MsqObjaj/iHL493trjEl+IZWjiUF9e+yL8++pdOQuB3Ki6kVXl+OSftfhJ/OOYPzPmvOXx+0udZsnEJ72x4J9OhiV8Et/+J8WNF2EIhYlUbaKlXMUWkv/JlIhxtSXxdx6sOuIqgBbno2YuY9ZdZnP342fx58Z/Z2pT4WGPJHtk2hjBXjCsbx/l7nw94Y/VFEtGxIty4w/6G995LZzh9Vjh7FgDrf3aDDvOL9FO+HBqRaEXYzBhdOponPvcEr6x7hbc2vMWzq57l+vnXs7FhI9/Z5zspjlSk/yjPL8cwNjZszHQo4heh7Ylw+4pwoKiIltpatj33H/InZ++ZEUuOPJKCmTPZ9Je/UPPYY4RHjCA8bBiRceMY9PWvERo4MNMhisgu8mVFuC9jhMFbSu2IMUfwnX2+w0MnP0RhqJAtTVtSFJ1I/xQMBCnNK9W63JKwQF5+2+2Wxu2JcME++wDQvGZN2mPqCwsG2e3uuxj2o2spPf44wkOG0LTqIzbecQdr/ufihFbCEJHs1q8rwt0ZmD9Q44VFdkJZpIyaJiXCkphAwfZEuG7+gh32b773XgqmT2PA5z6XzrD6xCIRBn7+8x22fXjiidS9/DIfHHY44TGjCQ0sJzhwIOGRI4mMGUPRgQcQHj48QxGLSF/kZCJcECrgmVXP8KW5X2J40XDGl41nVMkoxpaOZVjRMCoKK5IUqUj/UpZXxpZGHU2RxFhk+zKX0fXrO+wL7zYGV9/ApvvuiyfC/hmDO/7vf2fbiy+y9al/Eq3eQGzzZppWrmTL449DLIbl5TH6d7+laPbsTIcqIr3wZSLcl8lyXfnm3t/kmY+eobqhmjeq3uDJFU92OAHB16d9XeOHRbpQmlfK5obNmQ5D/KL9CTW2dpygHCwbQGTv3djy6GPbT7/sk2W2LBKh5PDDKTn88A7bXXMzTStWsPKcc/n42h8x8pc3kj9pUmaCFJGE+DIRTqQi3NMM30+P/TSfHvvptvuNsUbWbFvDipoV3P3O3fzhrT/wyLJHGFE0gj0H78mXpnyJEcUjkhK7iJ8NyBvAypqVmQ5DfKLDqhG1tbTU1xMoKGjbVnLkUWyZ8wgfHHkk+RMnZiLEpLJwmLyJExl66XdZ+8MrWH7SyQz6+tcYcsklmQ5NRLrhy8ly6+s6HmL73+f+l0eXPbrTz5cXzGN82Xg+NeZT/Oao33D+3uez/7D9ibkYf1r8J86ceyYrt+iPf6YletpgSZ3y/HKqG6q1lJQkpt2qEQANb7/d4X7pp49h6JVXEKveSO2LL+0wfMKvyk4+md0fe5SCvfdm80MPZzocEemBLxPhc548p8P9x1c8zvee/15SnrswXMj5M87n/x38//jz8X/mN0f9hi2NWzjr8bO00oTkvNElo6mP1rOudl2mQxEfsGDHg47RjZt2aFP+xS8y7oH70xVS2kTGjKH4iCOIbdhAbKvWrRfJVgklwmZ2rJm9Z2ZLzezyLvZfbGaLzexNM/uXme2W/FA7iqXptJcHjzyYXx7xSzY2bOS5Vc+l5TVFstWIIm+I0Lw18zIcifiBBTv+iYlt3uzd6HREIX/q1DRFlF4FM2cCUP/aaxmORES602sibGZB4BbgOGAqcIaZde61XgcqnXPTgQeAnyY70M4aYuk7XedBIw+iPL+c/6z+T9peUyQb7T98f0aXjOb/Xvk/3qnWqZalZ4WzZnW4H9vSbuk9f8yL2yX5U6eAGVueeDLToYhINxKpCO8PLHXOLXPONQF/A05u38A596xzrnVh3peBUckN03Np5aVtt+uj6Tv3e8ACHDjiQJ5Y8QSPLntUJxSQnFUQKuCPx/2RkkgJ//vc/3Lz6zfz4PsP8vTKp3ll3StsqN+Q6RAlixQfckiH+22rQ+SIYEkJBTNnUvPQQ6z57qW01OXY+vWaSiA+kMiqESOBVe3urwZmddMW4KvA47sSVHcmDtw+q7i3RDjZE6u+PPXLLFq/iO89/z1CgRAnjDuB48Ydx8whMykMFyb1tUSy2eCCwfz44B9z48Ib+f1bv++wiksoEGJs6VjK8sooCBW0Xc7Y4wz2GrxXBqOWrBBLz5C2bDLm9j9QdfPNbLz9DgBG3vCzDEeUAT5ZFk9yU1KXTzOzLwGVwGHd7D8POA9gzJgxfX7+UGB7uDWNNYwuGd1hv3MOa/cLZ0k89jZ10FQePeVR3trwFvcsuYd/fPgP/vHhP8gL5rHv0H255sBrGFY0LGmvJ5LNDh11KIeOOpSmWBMb6jewtWkrmxo38dLal1hes9y737CJtdG1LKtZxtzlczl98ukMKxrWlhyHg2HyAnnkBfMIB8NEghHCgTChQIhwINx2iQQj5AXziAQjHfoA8R8Xzb1EOFBQwNBLL6Vp+Qrq33oz0+GISCeJ/FVZA7TPOEfFt3VgZkcBPwAOc841dvVEzrnbgNsAKisr+1yybf9H8I2qN3aoML1T/U5Kq07BQJAZQ2YwY8gMLqm8hBVbVvDUiqe4//37+da/vsUFe1/ApPJJjCgaQTAQ7P0JRXwuEox0WGN79vAdz6T1ZtWb3PrGrdz//v00xrrsGhJWECpg97LdGVQwiMJwIXnBvLYkORKIEA6GOyTRReEiSiOl5IfyKQoXMWHABIrCRR3+YZb0cbHuT4Y0fu5cmteuTWM06RUZN5ba55/HNTdj4XCmwxGRuEQS4fnARDMbh5cAnw58sX0DM5sJ/A441jmXsoUgQ7Y93A83f7jD/g83f5i2w6/DioYxrGgYs4fPZvcBu3Pz6zdz0b8vAiAcCLNb6W5MHTSVb+79zR0q17JztI6wP02vmM5vj/ot0ZYo9dF66qP1NEQbaIo10dTSRFOsicZYI02xJqItUZpbmmluaSbaEm3b33pZtXUV6+vW83Htx9RH69se1xBraHtsbwxrS57zgnl8f9b3OWq3o9LwScjG2++g/Oyzd1g1AiBv/Djyxo/LQFTpUbhvJRtvv4MtTz1F2QknZDocEYnrNRF2zkXN7ELgSSAI3OGce8fMrgUWOOfmAD8DioH745WWj5xzJyU92HYV4er66rbbecE8GmON/GLhL5hWMY3xZeOT/dI9OnPKmXxh8hd4e8PbLK9ZzootK1i6aSlPr3yaeWvmce5e53LC+BMYXDA4rXH1R6rk+VcoEKIkUkJJpCRlr+GcI+qiNMe8ZHpr01Zqm2upj9ZT01jDB5s/aEvCG2ONNMYaGVI4JGXxyI7qFy0Ckjt0zQ+KD/dGDNa98qoSYZEsktCAO+fcXGBup21XtrudlnJK++EGb1S90TYmOGhB9hu2Hws+XsDDHzzMxZUXpyOcDsKBMDOHzGTmkJlt215a+xLXvXIdNyy4gZtfv5mz9zybb834lpI5kRQxM8LmDYsAKMsr67D/sNFdTl+QNHJNvVft+yMLBAiWlW1fS1lEsoKvzizXoSLcUN3hTG9Tyqew+4Ddefqjp9ncsDkrDqMfMOIAHjnlER4++WEOHHEgv3vzdzy/5vlMhyUikjGxjdW9N+qn8qdNo2n1qt4bikja+CoRDlvHCQad1yw9d69zWb11Nec8eQ4rt6zMmsrr7gN252eH/YyQhVi0flGmwxGRfi4bzwbaqqVx1yZM+ll4xAiin6RsGo2I7ARfJcKdl07a2LAR2D6J6jO7f4afHPITlm5eSotr6XJCXaZEghHK88vbYhYRSYVsPRtoyac/7cWXwysmBAcOJLZ5M66LyYIikhn9IhGG7RMvjh9/PHd82lu4fOnmpekLLgFFkSJWblmZ6TBEpH/LmrOBtlfxnW97N6LRLleNyAXBkmKIxXD16Tszqoj0zFeJcFG4qMP97z733S7b7TNkn3SE02fTBk9jwScL+M0bv+GDTR+wrWlbpkMSkf6nq7OBjuyhfcrOBtpeaOgwAoWFNK9d523IkqFr6RQsHwRA87p1GY5ERFr56jRNBaGCbve1Hw+crSezuHjfi/mk7hNuXXQrty66FYDSSCn7DNmH/9n3fxg/IL3LvolIbkv12UA7PhcEiopwzU279Dx+Fhk3FoDaF14kb/fdMxuMiAA+S4S7mvxW01jTZduRxSPZu2LvVIfUJ4MKBvH7o3/P+5veZ3nNctbWrmVJ9RKeXPEk/179b/YctCejSkYxsngko0pGMap4VNt1tkz8yySNqxNJSNacDXSH18zLwzXlbiJcsPfeBIqKqL7zTso+ewrB4uJMhySS83yVCHdl1dZVXSZIT3zuiQxE0zszY3L5ZCaXT27b9o3p32Du8rm8teEt3t34Lv/66F9EW7afinTSwEnc/KmbGV48PBMhZ5VcW4RfZCdkzdlAO7NIhJbG3E2ELRBg6A9+wLrvf58PP30sg875CoX770/B9OmZDi01VLwQH/BdInza5NPID+Zz4MgD+cY/v9E2+czPCdKEgRP4zsDvtN2PtcRYX7ee1dtW8/6m97lh/g2c/I+TOWTkIVw480LGlfXf05CKyK7JprOBdmaRSE5XhAEGfPYUgmWlrL/xRtbf8HMA8iZOIH/6dMIjRhCqqCBYWkagqIhAfh6Wn4/l5REsKyNUXu7PVTd0RFOymO8S4R/O/iGwfQ3hbU3bsuLkGckUDAQZXjyc4cXD2W/Yfhww/AD+vOTPzF0+l82Nm7n907dnOkQRyWLZcjbQzgKFhcQ2bSJQkJ+Jl88aJUceScmRR9L8yXo2P3A/dfMXsO3fzxGr7v1kI8EBAwgNGUJk7FgGnn4aRQcemIaIRfov3yXCrUoiJQAs37Lc29CP/+EcP2A8Vx5wJRUFFdz6xq3c9NpNfHGPL1JRWJHp0EREelRxycVsvv8BLC+P0ODBNC1f5iXCqhISHjqEim99q+1+S1MTsepqYlu20FJbh2tsoKWhAdfQQKxmC9ENG4hWVdG8bi1bn3qKrU89ReHs2QRLSwkOGEDe5EmUHn88oYEDM/iuRPzFt4lwXjCPSCDCqq25c7rKs/c8m9fWv8Yf3voDf1r8J35+2M85bHSXk71FRLLC4K9/ncFf/zoAgYICops2E6rQP/FdCUQiBIYPJzy89/kg2154gU33/IXYxo00rl9P87p1uPvq+eRHP6Zg5kzG3HE7gYLuV1oSEY9vE2GAmUNm8p/V/8l0GGlTGC7k98f8ng83f8gFT1/AVS9excMnP8yA/AGZDk1EpFeBkhJiGzZQV1dH/uTJvT9AulV80EEUH3RQ233nHPULF7L16X+x8a67eH/WbEqOOpKSYz5NyTFHYwFfnTZAJG18/Zvx44N/vP1O/xom3KPdB+zOjUfcSHVDNfe8e0+mwxERScigc74CgKur67mh9JmZUVhZydDLL2PoFT+k6OCDqX11PmsuuogPDjiQj849l9X/8z9suv9+mj/+WMtRisT5uiI8rGgY3638LjcsuIGGWEOmw0mrqYOmsv+w/Xnkw0e4YO8LcmKd4f42KVIk14RHjmTgF89g01/+Sv2iRZkOp98qP/NMys88ExeLseXxJ6h9/nkaVyyn4YMP2Pq4t7RoZPx48iZPIm/cOAaff74/V6MQSQJfJ8IAZ009i0EFg5g5ZGamQ0m7E8afwFUvXsVXnvgKp046lb0G78XoktGEAr7/sXbLz8vkiQgMvvBCNv3lr+RNmJDpUPo9CwYpO/EEyk48AYgPn3h9EbUvv0Td/PnUv/EGWx9/goKZ+1B8yMEZjlYkM3yfMZkZJ44/MdNhZMQpE06hPlrP79/8Pd+f933AOw31uLJxlEXKKMtrd4mUURIpoTBcSEGogMJQIYXhQgbmDWRg/kDyQ7m9nJGIpEeovJzxj8/NqeFs2cLMKNxnJoX7eIWjxuXLWXbc8ay74gom/OtpLBjMcIQi6ef7RDiXmRlnTjmTM/Y4g/c2vsf7m97nrQ1vsXbbWmqaalhbu5aaxhq2NG2hxbX0+FwFoQL2GrwXI4tHUhAqoCRS0naK58nlkymNlKbpXYlIf5c3TicFygZ548Yx6JvfoPq3v2Ptpf9LydFHERo6lFBFhXfyjsLCnBh2J7lNiXA/ELAAUwZNYcqgKZw84eQd9re4FrY1b2Nr01bqm+upj9ZTF61jW/M2NjdsZlPjJpZuXsrSTUtZuWUl9dF6aptr25LnUCDElPIpjCgewe5luzOmdAzDioYxpHAIQwqHkBfMS/dbFhGRJKi44AKi6z6m5tFH2TJ3bsed4TDBkhLCw4aRv9dehIYMIVg+kEBBIYGCfAIlJYQGVxAeMZxgSUlm3oDILlIinAMCFqA0Utqnqm5zSzMfb/uYVVtX8dzq51hWs4wl1Ut4asVTO0xaK8srY3D+YErzSpk0cBLn7HUOI4tHJvttiIhIklkkwojrf8LQK66gec1qouvXE11fRWzTRmI1W4jV1NDwzjtsfeopYps3d/s84REjKD78MIZecYWqyOIrSoSlS+FAmNGloxldOpoDR24/hWddcx3ratfxSe0nfFL3CVX1VayvW8+G+g18tPUj7n3vXu59716GFg5lUMEgisPFFIYKKQhvH5fcOn65MFxIJBAhHAwTDoSJBCMUh4spzy+nPL9c45ZFRNIkWFxEcPJk6GF955b6elpqa73r+npatmwhWlVF06rVbPzjH9n0l7+y+e8PkTd+PEUHHsDWfz6dxncgsnOUCEufFIYL2X3A7uw+YPcu96+oWcFTK59i5ZaVbGzYSF1zHZ/UfUJdtI66Zm84Rn20PrHXChUyqGBQW2I8b828ZL4VERHpg0BBQbdnqxt4+mnUPPwwTR+tomHJEqpvvwNa1yrWmsWSxZQIS1KNLRvLedPP67FNc6yZmqYa6prraG5p9i6xZppamtjatJVNDZuobqhmY8NGquu96zXb1gAwuGBwOt6GiIj0QbC0lPKzzmq731Jby7qrrmbLo4/qrHaS1ZQIS9qFg2Evoe26sNCtWEuMgKlDFRHJdoGiIkb87KeM+On1SoQlqykRFt8IBrTGpYiIX5gZaOKcZLmE/k0zs2PN7D0zW2pml3exP8/M7o3vf8XMxiY9UhERERGRJOo1ETazIHALcBwwFTjDzKZ2avZVYJNzbgJwI3B9sgMVEREREUmmRCrC+wNLnXPLnHNNwN+AzmdtOBm4O377AeBI00KCIiIiIpLFEkmERwKr2t1fHd/WZRvnXBSoAQYlI0ARERERkVRI61ROMzvPzBaY2YKqqqp0vrSIiIiISAeJrBqxBhjd7v6o+Lau2qw2sxBQBlR3fiLn3G3AbQBmVmVmK3ci5sHAhp14XKblctxleEcJ0ilbPu++vvdsibuvuoo7Ez/3nbEzn/luqQgkmy1cuHCD+mxfUJ+969RvZ7ed/by77LcTSYTnAxPNbBxewns68MVObeYAZwMvAacCzzjX86lknHMVCbz2DsxsgXOucmcem0m5HLeZ3eac6/ksG0mWLZ93X997tsTdV13FnYmf+87w62eebuqz/UF99q5Tv53d/XayP+9eE2HnXNTMLgSeBILAHc65d8zsWmCBc24OcDvwJzNbCmzES5ZFWj2S6QAySO9dRPwm1393c/n959x7T+iEGs65ucDcTtuubHe7Afh8ckOT/sI5l3O/WK303kXEb3L9dzeX338uvnc/nvfwtkwHsJMUd3op7vTya9zg79j9wK+fr+JOL7/GDf6NXXED1stQXhERERGRfsmPFWERERERkV2mRFhEREREclLWJsJ+PUWz4k4vv8YN/ovdzILxa8UtO/Dr5+vXuMG/sfs1bvBf7H7t/9IZd1Ylwma2p5kdDtDbOsTZxMdxTzazaeC7uP36eR9sZr8xswvAP7Gb2UFmdjfwQzMrV9zSyse/i76MG9Rvp5v67fTKRNxZMVnOzALAzcCngI+AV4B/OOcWmFnAOdeS0QC74eO4Q8DvgIOBdXjrBt7nnFtlZpatvzB+/bwBzGwf4G7gJuC/gA+Au51zizIYVq/MbDzwEHAjcChQD8x1zj2W0cB64de4/cKvv4t+jRvUb2eC+u30ylTc2VIRHggUO+f2AM7EOz3zJWZWnM2/JHinIizxYdy74cU9GTgfqAAuMLOCbO1M4wbgz+8JwP7AfOfcH4CvAXXA8WY2OLNh9WpfYIlz7i7gEmARcKKZje7pQVlgP/wZt1+oz04/9dvpp347vTLSb2csETazU1sPNQClwIFmVuScqwIeBDYBF8bbZs3YFjP7rJndGL87CDjAJ3HvY2aT4nfDQKWZhZ1zS/BOkV2Ed3rsrGJm48wsP363HP98T75gZheb2YHxTa8BxWY2zDn3MfAM3h+ygzMWZBfMbHa77wl4p1gfZWajnXObgBeAzcBnMxFfd8zsM2Z2oZnNjm+aD4zO9rj9RH12+qnfTi/12+mVLf122hNhMys2sweB7wKbzCzknFuO94Yvijdbh/fLMsPMhmfDf7tmNtXM/gJcAXzHzEY455YCL5HdcY8zs8eAW/BOg320c+5d4F/Al+LN3gBeB/Y2swGZibQjMxtrZo8DfwDuMbOp8c/7P8DF8WbZ+HkHzexK4LL4pt+Z2WeAWmAFcFh8+3N4v+Cj4o/L6B8DMxsQ/578E/iCmRXHdzUA84AvxO+/BywGytv9ocsYMxtuZo8A/4tXpbzTzD7tnFuG97uZlXH7ifrs9FO/nV7qt9Mr2/rttCTCnb4so4FPnHOznXN/BWLx7XcBB5nZeOdcFPgE74dZmI4Yu9Iat5kdCvweeNk5NxNvvNCseLPb8eIel21xx30XWOScOwD4B3BWfPvzeJWREc65WmA1MBJvTE5GdBH3K865I4FngWvMbCre92R2Nn1P2nPOxYDJwCXOuV8A1+BVPkLAWrzOf2o89veAU+KPy/QfgyLgSeDb8duHxrdXAS8D08xs//j7WwMc5LxTq2daJfC8c+4Q59yP8H43vx7f9zzZG3dWU5+dfuq3M0f9dtplVb+dropw+0x+Otv/m7oAuMrMDgbewasw3ADgnHsbb0xUY5pi7EpB/HoxcIxz7ldmFgEmAq1jmxbhHT75KWRN3PnQ1kHVAs3x7aXAB2Y2Fu8/9PXApfF9/8LrUEvTGmlHrXGH4vcXAzjnbsYbq3UGXqf0Kln0eZvZWWZ2WLuqzCfAwHjl7AHgQ+BovMNqDcCP4+1GAvPbvd+0ahd3qXNuDd5pK++Lx7i/mY2Md0Qv4VWeboxXHPYEPjKzjPwRi8d9uJnl4X1v/9RudzXehBbwJuVkTdw+oz47/dRvp5H67YzEnZX9dkoTYTM72sz+CfzUzM6Ib34NWGdmdwAH4B1m+AHejMwbgQozu9nM3gZWAjXpPvzQKe7TnXMbnHO1ZpbvnGsC3sIb9I9zbjNwLTDSzH6dJXH/zMy+EP9vdR4w0cxeB47F+w/3b8AeeJWRo8wbP/cW3qG2remMuZu4o8BGYKaZ7W1mewNvA2OBIHAdGf68zTPczJ4Fzsb7PtwS/8XdAEwDWg9T/QrvcOYnzrlrgM3mHc46HfhD/P1mMu7fmNlg51yDc64OeBrvcNWnAJxznzjnbsLroO6Iv5fr420zFfcX47EUOufWmVk43nR4PHaccx9nOm6/UZ+d/kPd6rfVb+9k3Oq3k8U5l5ILMCH+Rk4GZgL34M0CDAE/BxYA4XjbLwO3xW8PAQ4ETkpVbH2M+8/A9+P7WuM9LL69ot3jKrIs7r8A343vmwz8vV3bK4FfxW+PBT4DfDZL4v4rcAFQgje271G8PwqV8fd0UfxxQzP1eQPB+PUk4M+t24Df4P3iDgCewDtMVRjffx/wP63fo/bfnSyI+9ftvx/x7f+DVwFpnWXf2rYkG+Nu1+YR4Kj47SHx61Am4vbbRX12VsSufjt1MavfzrK4s6XfTvYbDwCB+O0zgVvb7fsqXiVhQPyL9gxwZnzfdODh1sdm4AfWU9znxuMe0m7bUfFf9FAm4u1j3EPxOvybgCnxfQcDD2Tp5936PamI3x/fbt+3gK/Fb1sG4m6talyP94f1M3hrSrbfvx7vUM5ZwK3AafF99wCzMvR59xZ3APgYOKzdtmLgl3iHMz8BRmR73EAE7w/aGOD/4VXKBmbiM/fLRX121saufjt5cavfzuK4s6HfTtrQCDM7B2/g/o/im94CTjezcfH7IWA58FPn3H/wfsEvNrPL8A75zIs/T7oPqfUWdxhvrNANrY9xzj2N95/ugWRIgnEvi+/fireEzXfM7L/xFmV/GnBpDZqEvycf4h1yBe87g5mdh9fZvgbpn6RgZocBC/EO3yzFi78ZOMLM9o/HFMObZPEz59wfgaeAs+KHN0N47zWtEoy7Bbg6fml1Al6F5w1gmnNubfqi7nPc18Qflg98BW/8WQlehWFTOuP2E/XZ6ad+W/12EuNWv50sSfoPoBivOvDfeF/4PeLbf4l3yOQFvMNS04C5wLD4/v2AbwAHpPu/lp2I+7F2cYeB84CxPoj7cbzZpFPwZpbeDcz2QdyPAUPj+y/CW19wv0zEHY/hEODL7e7fireo/VeAhfFtAWAYXtVmdHzbMNpVR7I87vtav9N4hzwP9VHco/Am5vwRmJGpuP1y6ePvovrs9Meufjs5savfzu64s6LfTuYHMCZ+/RPg3vjtIN5/tAfH74/GW0YlP1NveBfivhPIy3S8OxH33UAk0/Hu5PckL36/MAviLgTy2D6m6Uzg/+K3FwHfjt+uBP6a6XhzLO6/ZTpeP17UZ2d17Oq3kxN3LvR/fo07a/rtpA2NcM59FL/5S2CceYsjx4Aa59y8+L5v4p2isLmLp8iIPsRdD6Rtlmhv+hB3LdvX/cy4Pn5PovHHZHyWv3OuzjnXGI8VvGV1quK3zwGmmNmjeBWS1zIRY1d2Ju5MzJzvrI9xL4TsiNtP1Genn/rt9FK/nV6+7bdT9F/BN4Dn2t3fH29R8LZDbNl4UdyKO4GYg3iHdh4HJsS3TcCbUHQwMDLTMSpuXXbic/bd76Kf4/Zz7H6M26/9iOJOz8XiASaNmQWccy1m9gDeqRQb8Qb4f+Cc+zCpL5ZEiju9fBy34c1y/QPwEN4s72q8Qz5bMhlbTxS3dMfHv4u+jBv8G7uP4/ZlP6K40yPpZ0aJ/5IU4q0teThwrXPuiWS/TrIp7vTycdzOzGbijX0aB9zpnLs9w2H1SnFLd3z8u+jLuMG/sfs4bl/2I4o7PVJ1isAL8MatHO2cy/RpK/tCcaeXX+NejXdmrV8o7rTwa9x+4tffRb/GDf6N3a9x+7UfUdwplvShEbD98EnSnzjFFHd6+TVukf7Gr7+Lfo0b/Bu7X+MW6U5KEmERERERkWyXtOXTRERERET8RImwiIiIiOQkJcIiIiIikpOUCIuIiIhITlIiLL5hZjEzW2Rm75jZG2Z2iZn1+B02s7Fm9sV0xSgiItup35Zsp0RY/KTeOTfDObcn3jnMjwOu6uUxYwF1qCIimaF+W7Kalk8T3zCzbc654nb3xwPzgcHAbsCfgKL47gudcy+a2cvAFGA5cDfwK+AneGdFygNucc79Lm1vQkQkh6jflmynRFh8o3OHGt+2GZgMbAVanHMNZjYR+KtzrtLMDge+65w7Md7+PGCIc+7HZpYHvAB83jm3PI1vRUQkJ6jflmyXqlMsi6RbGLjZzGYAMWBSN+2OAaab2anx+2XARLzKg4iIpI/6bck4JcLiW/FDbDFgPd6Ys0+AvfHGvjd09zDg2865J9MSpIiItFG/LdlGk+XEl8ysAvgtcLPzxveUAeuccy3Al4FgvOlWoKTdQ58EzjezcPx5JplZESIiklLqtyUbqSIsflJgZovwDqdF8SZZ/CK+71bgQTM7C3gCqI1vfxOImdkbwF3ATXgzkl8zMwOqgP9KT/giIjlH/bZkNU2WExEREZGcpKERIiIiIpKTlAiLiIiISE5SIiwiIiIiOUmJsIiIiIjkJCXCIiIiIpKTlAiLiIiISE5SIiwiIiIiOUmJsIiIiIjkpP8P9lbdg1LRv20AAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 864x576 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot Max over Sum\n",
+    "max_over_sum_df.plot(subplots=True, layout=[2,2], figsize=[12.0, 8.0]);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Comment\n",
+    "Why not center the moments?\n",
+    "\n",
+    "My guess is that the book doesn't center the moments because the average return $\\bar{x}$ is near zero."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.0003137842852293536"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Calculate the average return\n",
+    "daily_returns.mean()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Average daily return is 5% of mean deviation:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.04681360997613759"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Calculate Mean over Mean Deviation\n",
+    "daily_returns.mean() / fattails.mad(daily_returns)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "fattails",
+   "language": "python",
+   "name": "fattails"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.5"
+  },
+  "widgets": {
+   "application/vnd.jupyter.widget-state+json": {
+    "state": {},
+    "version_major": 2,
+    "version_minor": 0
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/notebooks/README.md b/notebooks/README.md
index 16e2c3e..b13b32a 100644
--- a/notebooks/README.md
+++ b/notebooks/README.md
@@ -26,4 +26,7 @@ Below is a list of the main notebooks.
 * [Beware, the __Lognormal__ is fat and thin](../notebooks/NB-23%20-%20Lognormal%20can%20be%20both%20Thin%20or%20Fat.ipynb)
 
 ##### Chapter 9:
-* [The __Frechet Distribution__ preserves the tail exponent](../notebooks/NB26%20-%20Frechet%20Distribution.ipynb)
\ No newline at end of file
+* [The __Frechet Distribution__ preserves the tail exponent](../notebooks/NB26%20-%20Frechet%20Distribution.ipynb)
+
+##### Chapter 10:
+* [The __MS Plot__: Percentage impact of the sample max](../notebooks/NB27%20-%20Max%20over%20Sum%20Plot.ipynb)
diff --git a/notebooks/images/NB27-MS_Plot_SP500_4th_Moment.png b/notebooks/images/NB27-MS_Plot_SP500_4th_Moment.png
new file mode 100644
index 0000000..392868a
Binary files /dev/null and b/notebooks/images/NB27-MS_Plot_SP500_4th_Moment.png differ
diff --git a/setup.py b/setup.py
index f6222a8..08d0953 100644
--- a/setup.py
+++ b/setup.py
@@ -5,7 +5,7 @@
 
 setuptools.setup(
     name="fattails", # Replace with your own username
-    version="0.0.3",
+    version="0.0.4",
     author="FergM",
     description="A package for fat-tailed statistics",
     long_description=long_description,
@@ -21,5 +21,5 @@
         'pandas>=1.2.0',
         'matplotlib>=3.3.3'
     ],
-    python_requires='>=3.8.5',
+    python_requires='>=3.7',
 )


Test patch:
diff --git a/tests/test_express.py b/tests/test_express.py
index f5a3cef..b12d81e 100644
--- a/tests/test_express.py
+++ b/tests/test_express.py
@@ -1,4 +1,6 @@
 from fattails import express
+from matplotlib.axes._subplots import SubplotBase
+import pandas as pd
 import pytest
 
 
@@ -6,7 +8,8 @@ class TestImports:
     """ Check that no imports are missing.
     """
 
-    def test_mad_method(self):
+    def test_mad_method_exists(self):
+        """Check mad exists as expected in this namespace"""
 
         # Check that the attribute exists
         try:
@@ -15,3 +18,18 @@ def test_mad_method(self):
             # Fail test if `express` doesn't have a `mad` attribute
             msg = 'The method called `mad` is missing from the express module.'
             pytest.fail(msg)
+
+
+class TestPlotMSMoments:
+
+    @pytest.mark.parametrize("description, input_data", [
+                            ("list_data",  [1, 2, 3]),
+                            ("series_data",  pd.Series([1, 2, 3])),
+                            ])
+    def testplot_MS_moments(self, description, input_data):
+
+        axes = express.plot_MS_moments(input_data)
+        first_axis = axes[0, 0]
+
+        assert axes.shape == (2, 2)
+        assert isinstance(first_axis, SubplotBase)
diff --git a/tests/test_metrics.py b/tests/test_metrics.py
index e1c6560..1520017 100644
--- a/tests/test_metrics.py
+++ b/tests/test_metrics.py
@@ -1,48 +1,51 @@
+from fattails import metrics
 import numpy as np
 import pandas as pd
-from pandas.testing import assert_series_equal
+from pandas.testing import assert_frame_equal, assert_series_equal
 import pytest
 
 
-from fattails.metrics import mad, get_survival_probability
-
 class TestMad:
     """Test the mean absolute deviation method"""
 
     def test_example(self):
 
-        x = [0,5,-5,0,0]
+        x = [0, 5, -5, 0, 0]
 
-        mad_ = mad(x)
+        mad_ = metrics.mad(x)
 
         expected_mad = 2
         assert mad_ == expected_mad
 
     def test_handles_mad_of_zero(self):
 
-        x = [1,1,1,1,1]
+        x = [1, 1, 1, 1, 1]
         x = np.array(x)
 
-        mad_ = mad(x)
+        mad_ = metrics.mad(x)
 
         assert mad_ == 0
 
+
 @pytest.mark.parametrize("description, input_data, expected_output", [
-    ("duplicate_values",  [ 2,   2, 3], [0.75, 0.5,  0.25]),
-    ("negative_values",   [-1,-0.3, 7], [0.75, 0.5,  0.25]),
-    ("not_sorted_values", [ 2,   3, 2], [0.75, 0.25,  0.5]),
+    ("duplicate_values",    [2,   2, 3], [0.75, 0.5,  0.25]),
+    ("negative_values",   [-1, -0.3, 7], [0.75, 0.5,  0.25]),
+    ("not_sorted_values",   [2,   3, 2], [0.75, 0.25,  0.5]),
 ])
 class TestGetSurvivalProbability:
 
-    def test_accepts_list_input(self, description, input_data, expected_output):
-        """List input data should be accepted even though output is always a pandas series."""
+    def test_accepts_list_input(self, description,
+                                input_data, expected_output):
+        """List input data should be accepted even though
+        output is always a pandas series."""
 
-        output = get_survival_probability(input_data)
+        output = metrics.get_survival_probability(input_data)
 
         assert output.name == 'survival_probability'
-        assert output.to_list() ==  expected_output
+        assert output.to_list() == expected_output
 
-    def test_accepts_series_input(self, description, input_data, expected_output):
+    def test_accepts_series_input(self, description,
+                                  input_data, expected_output):
 
         # Setup
         index = pd.date_range('2000-01-01', periods=len(input_data))
@@ -53,6 +56,52 @@ def test_accepts_series_input(self, description, input_data, expected_output):
         expected_name = 'survival_probability'
         expected = pd.Series(expected_output, index, name=expected_name)
 
-        output = get_survival_probability(input_data)
+        output = metrics.get_survival_probability(input_data)
+
+        assert_series_equal(output, expected)
+
+
+class TestCalculateMoments:
+
+    @pytest.mark.parametrize("description, input_data, expected_output", [
+        ("simple_values",  [1, 2, 3], {'moment_1': [1, 2, 3],
+                                       'moment_2': [1, 4, 9],
+                                       'moment_3': [1, 8, 27]}),
+        ("negative_values",  [-1, 2, -3], {'moment_1': [1, 2, 3],
+                                           'moment_2': [1, 4, 9],
+                                           'moment_3': [1, 8, 27]}),
+    ])
+    def test_gives_expected_output(self, description,
+                                   input_data, expected_output):
+
+        # Set up
+        index = pd.date_range('2000-01-01', periods=3)
+        input_data = pd.Series(input_data, index=index)
+        expected_output = pd.DataFrame(expected_output, index=index)
+
+        # Run
+        moments_df = metrics.calculate_moments(input_data, moments=[1, 2, 3])
+
+        # Check
+        assert_frame_equal(moments_df, expected_output)
+
+
+class TestMaxOverSum:
+
+    @pytest.mark.parametrize("description, input_data, \
+                              expected_output, reset_index", [
+        ("simple_values",  [1, 2, 3], pd.Series([1, 2/3, 3/6]), False),
+        ("reset_index",  [1, 2, 3], pd.Series([1, 2/3, 3/6]), True)
+    ])
+    def test_gives_expected_output(self, description,
+                                   input_data, expected_output, reset_index):
+        expected_output = pd.Series(expected_output)
+        if reset_index:
+            expected_output.index += 1
+            expected_output.index.rename('cumulative_sample_size',
+                                         inplace=True)
+
+        max_over_sum = metrics.max_over_sum(input_data,
+                                            reset_index=reset_index)
 
-        assert_series_equal(output, expected)
\ No newline at end of file
+        assert_series_equal(max_over_sum, expected_output)