{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "e0a24f85", "metadata": { "tags": [ "remove-cell" ] }, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "plt.style.use(\"../styles/hda.mplstyle\")" ] }, { "cell_type": "markdown", "id": "c55c306e", "metadata": {}, "source": [ "(chp-working-with-data)=\n", "# Processing Tabular Data\n", "\n", "\n", "\n", "## Introduction\n", "Data analysis in literary studies tends to involve the analysis of text documents (see chapters {numref}`chp-vector-space-model` and {numref}`chp-getting-data`). This is not the norm. Data-intensive research in the humanities and allied social sciences in general is far more likely to feature the analysis of tabular data than text documents. Tabular datasets organize machine-readable data (numbers and strings) into a sequence of records. Each record is associated with a fixed number of fields. Tabular datasets are often viewed in a spreadsheet program such as LibreOffice Calc or Microsoft Excel. This chapter demonstrates the standard methods for analyzing tabular data in Python in the context of a case study in onomastics, a field devoted to the study of naming practices.\n", "\n", "In this chapter, we review an external library, \"Pandas\" {cite:p}`mckinney:2010`, which was\n", "briefly touched upon in chapter {ref}`chp-introduction-cook-books`. This chapter provides a\n", "detailed account of how scholars can use the library to load, manipulate, and analyze tabular data. Tabular data are ubiquitous and\n", "often complement text datasets like those analyzed in previous chapters. (Metadata\n", "accompanying text documents is often stored in a tabular format.) As example data here,\n", "we focus on a historical dataset consisting of records in the naming of children from the\n", "United States of America. These data are chosen for their connection to existing\n", "historical and sociological research on naming practices---{cite:t}`fischer1989albion`, {cite:t}`sue:2007`,\n", "and {cite:t}`lieberson:2000` are three among countless examples---and because they create a useful context for practicing routines such as column selection or drawing time series. The material presented here should be especially useful for scholars coming from a different scripting language background (e.g., R or Matlab), where similar manipulation routines exist. We will cover in detail a number of high-level functions from the Pandas package, such as the convenient `groupby()` method and methods for splitting and merging datasets. The essentials of data visualization are also introduced, on which subsequent chapters in the book will draw. The reader will, for instance, learn to make histograms and line plots using the Pandas `DataFrame` methods `plot()` or `hist()`.\n", "\n", "As this chapter's case study, we will examine diachronic developments in child naming\n", "practices. In his book *A Matter of Taste*, {cite:t}`lieberson:2000` examines long-term\n", "shifts in child naming practices from a cross-cultural and socio-historical perspective.\n", "One of the key observations he makes concerns an accelerating rate of change in the names\n", "given to children over the past two centuries. While nineteenth century data suggest\n", "rather conservative naming practices, with barely any changes in the most popular names\n", "over long periods of time, more recent data exhibit similarities with fashion trends (see, e.g., {cite:t}`acerbi:2012`), in which the rate\n", "of change in leading names has significantly increased. As explained in depth by\n", "{cite:t}`lieberson:2000,lieberson:2003`, it is extremely difficult to pinpoint the factors\n", "underlying this shift in naming practices, because of its co-occurrence with numerous\n", "sociological, demographic, and cultural changes (e.g., industrialization, spread of\n", "literacy, and decline of the nuclear family). We do not seek to find a conclusive\n", "explanation for the observed shifts in the current chapter. The much more modest goal of\n", "the chapter is, one the one hand, to show how to map out such examples of cultural change\n", "using Python and Pandas, and, on the other hand, to replicate some of the analyses in the\n", "literature that aim to provide explanations for the shift in naming practices.\n", "\n", "The structure of the chapter is as follows. First, in section\n", "{ref}`sec-working-with-data-tabular-data` we introduce the most important third-party\n", "library for manipulating and analyzing tabular data: Pandas.\n", "Subsequently, in section {ref}`sec-working-with-data-mapping-change` we show how this\n", "library can be employed to map out the long-term shift in naming practices as addressed by\n", "{cite:t}`lieberson:2000`. After that, we work on a few small case studies in section\n", "{ref}`sec-working-with-data-case-studies`. These case studies serve, one the one hand, to\n", "further investigate some of the changes of naming practice in the United States, and, on\n", "the other hand, to demonstrate advanced data manipulation techniques. Finally, we conclude\n", "in section {ref}`sec-working-with-data-further-reading` with an overview of some resources\n", "for further reading on the subject of analyzing tabular data with Python.\n", "\n", "(sec-working-with-data-tabular-data)=\n", "## Loading, Inspecting, and Summarizing Tabular Data\n", "\n", "In this section, we will demonstrate how to load, clean and inspect tabular data with\n", "Python. As an example dataset, we will work with the baby name data as provided by the\n", "[United States Social Security\n", "Administration](https://www.ssa.gov/OACT/babynames/limits.html). A dump of this data can\n", "be found in the file `data/names.csv`, which contains records in the naming of children in\n", "the United Sates from the nineteenth century until modern times. For each year, this dataset\n", "provides all names for girls and boys that occur at least five times. Before we can\n", "analyze the dataset, the first task is to load it into Python. To appreciate the loading\n", "functionalities as implemented by the library Pandas, we will first write our own data\n", "loading routines in pure Python. The `csv` module is part of\n", "Python's standard library and can employed to conveniently read comma-separated values\n", "files (cf. chapter {ref}`chp:getting-data`). The following code block implements a short\n", "routine, resulting in a variable `data` which is a list of baby name records. Each record\n", "is represented by a dictionary with the following keys: \"name\", \"frequency\" (an absolute\n", "number), \"sex\" (\"boy\" or \"girl\"), and \"year\"." ] }, { "cell_type": "code", "execution_count": 2, "id": "5c61e7b7", "metadata": {}, "outputs": [], "source": [ "import csv\n", "\n", "with open('data/names.csv') as infile:\n", " data = list(csv.DictReader(infile))" ] }, { "cell_type": "markdown", "id": "ce929993", "metadata": {}, "source": [ "Recall from chapter {ref}`chp-getting-data` that `csv.DictReader` assumes that the supplied\n", "CSV file contains a header row with column names. For each row, then, these column names\n", "are used as keys in a dictionary with the contents of their corresponding cells as\n", "values. `csv.DictReader` returns a generator object, which requires invoking `list` in the\n", "assignment above to give us a list of dictionaries. The following prints the first dictionary:" ] }, { "cell_type": "code", "execution_count": 3, "id": "72ac017d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'year': '1880', 'name': 'Mary', 'sex': 'F', 'frequency': '7065'}\n" ] } ], "source": [ "print(data[0])" ] }, { "cell_type": "markdown", "id": "af8a6778", "metadata": {}, "source": [ "Inspecting the (ordered) dictionaries in `data`, we immediately encounter a problem with\n", "the data types of the values. In the example above the value corresponding to \"frequency\"\n", "has type `str`, whereas it would make more sense to represent it as an integer. Similarly,\n", "the year 1880 is also represented as a string, yet an integer representation would allow\n", "us to more conveniently and efficiently select, manipulate, and sort the data. This\n", "problem can be overcome by recreating the `data` list with dictionaries in which the\n", "values corresponding to \"frequency\" and \"year\" have been changed to `int` type:" ] }, { "cell_type": "code", "execution_count": 4, "id": "4424370a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'year': 1880, 'name': 'Mary', 'sex': 'F', 'frequency': 7065}\n" ] } ], "source": [ "data = []\n", "\n", "with open('data/names.csv') as infile:\n", " for row in csv.DictReader(infile):\n", " row['frequency'] = int(row['frequency'])\n", " row['year'] = int(row['year'])\n", " data.append(row)\n", "\n", "print(data[0])" ] }, { "cell_type": "markdown", "id": "82edf55b", "metadata": {}, "source": [ "With our values changed to more appropriate data types, let us first inspect some general properties of the dataset. First of all, what timespan does the dataset cover? The year of the oldest naming records can be retrieved with the following expression:" ] }, { "cell_type": "code", "execution_count": 5, "id": "0e6b101c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1880\n" ] } ], "source": [ "starting_year = min(row['year'] for row in data)\n", "print(starting_year)" ] }, { "cell_type": "markdown", "id": "6042f9cd", "metadata": {}, "source": [ "The first expression iterates over all rows in `data`, and extracts for each row the value\n", "corresponding to its year. Calling the built-in function `min()` allows us to retrieve the\n", "minimum value, i.e., the earliest year in the collection. Similarly, to fetch the most recent year of data, we employ the function `max()`:" ] }, { "cell_type": "code", "execution_count": 6, "id": "b56142cb", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2015\n" ] } ], "source": [ "ending_year = max(row['year'] for row in data)\n", "print(ending_year)" ] }, { "cell_type": "markdown", "id": "031fad67", "metadata": {}, "source": [ "By adding one to the difference between the starting year and ending year, we get the number of years covered in the dataset:" ] }, { "cell_type": "code", "execution_count": 7, "id": "e0e8d52e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "136\n" ] } ], "source": [ "print(ending_year - starting_year + 1)" ] }, { "cell_type": "markdown", "id": "54d5e442", "metadata": {}, "source": [ "Next, let us verify that the dataset provides at least ten boy and girl names for each\n", "year. The following block of code is intended to illustrate how cumbersome analysis can be without using a purpose-built library for analyzing tabular data." ] }, { "cell_type": "code", "execution_count": 8, "id": "75f61e5c", "metadata": {}, "outputs": [], "source": [ "import collections\n", "\n", "# Step 1: create counter objects to store counts of girl and boy names\n", "# See Chapter 2 for an introduction of Counter objects\n", "name_counts_girls = collections.Counter()\n", "name_counts_boys = collections.Counter()\n", "\n", "# Step 2: iterate over the data and increment the counters\n", "for row in data:\n", " if row['sex'] == 'F':\n", " name_counts_girls[row['year']] += 1\n", " else:\n", " name_counts_boys[row['year']] += 1\n", "\n", "# Step 3: Loop over all years and assert the presence of at least\n", "# 10 girl and boy names\n", "for year in range(starting_year, ending_year + 1):\n", " assert name_counts_girls[year] >= 10\n", " assert name_counts_boys[year] >= 10" ] }, { "cell_type": "markdown", "id": "e5c44e66", "metadata": {}, "source": [ "In this code block, we first create two `Counter` objects, one for girls and one for boys\n", "(step 1). These counters are meant to store the number of girl and boy names in each\n", "year. In step 2, we loop over all rows in `data` and check for each row whether the\n", "associated sex is female (`F`) or male (`M`). If the sex is female, we update the girl\n", "name counter; otherwise we update the boy name counter. Finally, in step 3, we check for\n", "each year whether we have observed at least ten boy names and ten girl names. Although this\n", "particular routine is still *relatively* simple and straightforward, it is easy to see that\n", "the complexity of such routines increases rapidly as we try to implement slightly more\n", "difficult problems. Furthermore, such routines tend to be relatively hard to maintain,\n", "because, on the one hand, from reading the code it is not immediately clear what it ought\n", "to accomplish, and, on the other hand, errors easily creep in because of the relatively\n", "verbose nature of the code.\n", "\n", "How can we make life easier? By using the Python Data Analysis Library \"Pandas\", which is a high-level, third-party package designed to\n", "make working with tabular data easier and more intuitive. The package has excellent support for\n", "analyzing tabular data in various formats, such as SQL tables, Excel spreadsheets, and CSV\n", "files. With only two primary data types at its core (i.e., `Series` for one-dimensional data, and `DataFrame` for two-dimensional data), the library provides a highly\n", "flexible, efficient, and accessible framework for doing data analysis in finance,\n", "statistics, social science, and---as we attempt to show in the current chapter---the\n", "humanities. While a fully comprehensive account of the library is beyond the scope of this\n", "chapter, we are confident that this chapter's exposition of Pandas's most important\n", "functionalities should give sufficient background to work through the rest of this book to\n", "both novice readers and readers coming from other programming languages. \n", "\n", "(sec-working-with-data-tabular-data-with-pandas)=\n", "### Reading tabular data with Pandas\n", "\n", "Let us first import the Pandas library, using the conventional alias `pd`:" ] }, { "cell_type": "code", "execution_count": 9, "id": "4d805832", "metadata": {}, "outputs": [], "source": [ "import pandas as pd" ] }, { "cell_type": "markdown", "id": "816dcad8", "metadata": {}, "source": [ "Pandas provides reading functions for a rich variety of data formats, such as Excel, SQL, HTML, JSON, and even tabular data saved at the clipboard. To load the contents of a CSV file, we use the `pandas.read_csv()` function. In the following code block, we use this function to read and load the baby name data:" ] }, { "cell_type": "code", "execution_count": 10, "id": "e308423d", "metadata": {}, "outputs": [], "source": [ "df = pd.read_csv('data/names.csv')" ] }, { "cell_type": "markdown", "id": "b73d7bb2", "metadata": {}, "source": [ "The return value of `read_csv()` is a `DataFrame` object, which is the primary data type for two-dimensional data in Pandas. To inspect the first *n* rows of this data frame, we can use the method `DataFrame.head()`:" ] }, { "cell_type": "code", "execution_count": 11, "id": "919cb07c", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/org": [ "| | year | name | sex | frequency |\n", "|----+--------+-----------+-------+-------------|\n", "| 0 | 1880 | Mary | F | 7065 |\n", "| 1 | 1880 | Anna | F | 2604 |\n", "| 2 | 1880 | Emma | F | 2003 |\n", "| 3 | 1880 | Elizabeth | F | 1939 |\n", "| 4 | 1880 | Minnie | F | 1746 |" ], "text/plain": [ " year name sex frequency\n", "0 1880 Mary F 7065\n", "1 1880 Anna F 2604\n", "2 1880 Emma F 2003\n", "3 1880 Elizabeth F 1939\n", "4 1880 Minnie F 1746" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head(n=5)" ] }, { "cell_type": "markdown", "id": "e1561b1c", "metadata": {}, "source": [ "In our previous attempt to load the baby name dataset with pure Python, we had to cast the\n", "\"year\" and \"frequency\" column values to have type `int`. With Pandas, there is no need for\n", "this, as Pandas' reading functions are designed to automatically infer the data types of\n", "columns, and thus allow heterogeneously typed columns. The `dtypes` attribute of a `DataFrame` object provides an overview of the data type used for each column:" ] }, { "cell_type": "code", "execution_count": 12, "id": "089bff8a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "year int64\n", "name object\n", "sex object\n", "frequency int64\n", "dtype: object\n" ] } ], "source": [ "print(df.dtypes)" ] }, { "cell_type": "markdown", "id": "8c20fa7e", "metadata": {}, "source": [ "This shows that `read_csv()` has accurately inferred the data types of the different\n", "columns. Because Pandas stores data using NumPy (see chapter {ref}`chp-vector-space-model`), the data types shown above are data types inherited from NumPy. Strings are stored as opaque Python objects (hence ``object``) and integers are stored as 64-bit integers (``int64``). Note that when working with integers, although Python permits us to use arbitrarily large numbers, NumPy and Pandas typically work with 64-bit integers (that is, integers between $-2^{63}$ and $2^{63} - 1$).\n", "\n", "(sec-working-with-data-data-selection)=\n", "#### Data selection\n", "\n", "Columns may be accessed by name using syntax which should be familiar from using Python dictionaries:" ] }, { "cell_type": "code", "execution_count": 13, "id": "ec18e0e8", "metadata": {}, "outputs": [ { "data": { "text/org": [ "| Mary | Anna | Emma | Elizabeth | Minnie |\n", "|--------+--------+--------+-------------+----------|\n", "| 0 | M | a | r | y |\n", "| 1 | A | n | n | a |\n", "| 2 | E | m | m | a |\n", "| 3 | E | l | i | z |\n", "| 4 | M | i | n | n |" ], "text/plain": [ "0 Mary\n", "1 Anna\n", "2 Emma\n", "3 Elizabeth\n", "4 Minnie\n", "Name: name, dtype: object" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['name'].head()" ] }, { "cell_type": "markdown", "id": "4f80a388", "metadata": {}, "source": [ "Note that the ``DataFrame.head()`` method can be called on individual columns as well as on entire\n", "data frames. The method is one of several methods which work on columns as well as on data\n", "frames (just as ``copy()`` is a method of ``list`` and ``dict`` classes). Accessing a column\n", "this way yields a `Series` object, which is the primary one-dimensional data type in\n", "Pandas. `Series` objects are high-level data structures built on top of the NumPy's\n", "`ndarray` object (see chapter {ref}`chp-vector-space-model`). The data underlying a `Series` object can be retrieved through the `Series.values` attribute:" ] }, { "cell_type": "code", "execution_count": 14, "id": "60268e87", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "print(type(df['sex'].values))" ] }, { "cell_type": "markdown", "id": "1655ec89", "metadata": {}, "source": [ "The data type underlying `Series` objects is NumPy's one-dimensional `ndarray` object. Similarly, `DataFrame` objects are built on top of two-dimensional `ndarray` objects. `ndarray` objects can be considered to be the low-level counterpart of Pandas's `Series` and `DataFrame` objects. While NumPy and its data structures are already sufficient for many use cases in scientific computing, Pandas adds a rich variety of functionality specifically designed for the purpose of data analysis.\n", "\n", "Let us continue with exploring ways in which data frames can be accessed. Multiple columns are selected by supplying a list of column names:" ] }, { "cell_type": "code", "execution_count": 15, "id": "e7adfa39", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/org": [ "| | name | sex |\n", "|----+-----------+-------|\n", "| 0 | Mary | F |\n", "| 1 | Anna | F |\n", "| 2 | Emma | F |\n", "| 3 | Elizabeth | F |\n", "| 4 | Minnie | F |" ], "text/plain": [ " name sex\n", "0 Mary F\n", "1 Anna F\n", "2 Emma F\n", "3 Elizabeth F\n", "4 Minnie F" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[['name', 'sex']].head()" ] }, { "cell_type": "markdown", "id": "a90a2f3e", "metadata": {}, "source": [ "To select specific rows of a `DataFrame` object, we employ the same slicing syntax used to retrieve multiple items from lists, strings, or other ordered sequence types. We provide the slice to the `DataFrame.iloc` (\"integer location\") attribute." ] }, { "cell_type": "code", "execution_count": 16, "id": "bdaf2184", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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yearnamesexfrequency
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" ], "text/org": [ "| | year | name | sex | frequency |\n", "|----+--------+-----------+-------+-------------|\n", "| 3 | 1880 | Elizabeth | F | 1939 |\n", "| 4 | 1880 | Minnie | F | 1746 |\n", "| 5 | 1880 | Margaret | F | 1578 |\n", "| 6 | 1880 | Ida | F | 1472 |" ], "text/plain": [ " year name sex frequency\n", "3 1880 Elizabeth F 1939\n", "4 1880 Minnie F 1746\n", "5 1880 Margaret F 1578\n", "6 1880 Ida F 1472" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.iloc[3:7]" ] }, { "cell_type": "markdown", "id": "6645942d", "metadata": {}, "source": [ "Single rows can be retrieved using integer location as well:" ] }, { "cell_type": "code", "execution_count": 17, "id": "e4e959e4", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "year 1880\n", "name Catherine\n", "sex F\n", "frequency 688\n", "Name: 30, dtype: object" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.iloc[30]" ] }, { "cell_type": "markdown", "id": "fdd54662", "metadata": {}, "source": [ "Each `DataFrame` has an `index` and a `columns` attribute, representing the row indexes and column names respectively. The objects associated with these attributes are both called \"indexes\" and are instances of ``Index`` (or one of its subclasses). Like a Python ``tuple``, ``Index`` is designed to represent an immutable sequence. The class is, however, much more capable than an ordinary ``tuple``, as instances of ``Index`` support many operations supported by NumPy arrays." ] }, { "cell_type": "code", "execution_count": 18, "id": "7f91e1ac", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Index(['year', 'name', 'sex', 'frequency'], dtype='object')\n" ] } ], "source": [ "print(df.columns)" ] }, { "cell_type": "code", "execution_count": 19, "id": "76fd4223", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "RangeIndex(start=0, stop=1858436, step=1)\n" ] } ], "source": [ "print(df.index)" ] }, { "cell_type": "markdown", "id": "96a833e0", "metadata": {}, "source": [ "At loading time, we did not specify which column of our dataset should be used as \"index\", i.e., an object that can be used to identify and select specific rows in the table. Therefore, `read_csv()` assumes that an index is lacking and defaults to a `RangeIndex` object. A `RangeIndex` is comparable to the immutable number sequence yielded by `range`, and basically consists of an ordered sequence of numbers.\n", "\n", "Sometimes a dataset stored in a CSV file has a column which contains a natural index,\n", "values by which we would want to refer to rows. That is, sometimes a dataset has\n", "meaningful \"row names\" with which we would like to access rows, just as we access columns\n", "by column names. When this is the case, we can identify this index when loading the\n", "dataset, or, alternatively, set the index after it has been loaded. The function `read_csv()` accepts an argument `index_col` which specifies which column should be used as index in the resulting `DataFrame`. For example, to use the \"year\" column as the `DataFrame`'s index, we write the following:" ] }, { "cell_type": "code", "execution_count": 20, "id": "0f2fce98", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python3.9/site-packages/numpy/lib/arraysetops.py:583: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", " mask |= (ar1 == a)\n" ] }, { "data": { "text/html": [ "
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" ], "text/org": [ "| year | name | sex | frequency |\n", "|--------+-----------+-------+-------------|\n", "| 1880 | Mary | F | 7065 |\n", "| 1880 | Anna | F | 2604 |\n", "| 1880 | Emma | F | 2003 |\n", "| 1880 | Elizabeth | F | 1939 |\n", "| 1880 | Minnie | F | 1746 |" ], "text/plain": [ " name sex frequency\n", "year \n", "1880 Mary F 7065\n", "1880 Anna F 2604\n", "1880 Emma F 2003\n", "1880 Elizabeth F 1939\n", "1880 Minnie F 1746" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.read_csv('data/names.csv', index_col=0)\n", "df.head()" ] }, { "cell_type": "markdown", "id": "baba1006", "metadata": {}, "source": [ "To change the index of a `DataFrame` after it has been constructed, we can use the method\n", "`DataFrame.set_index()`. This method takes a single positional\n", "argument, which represents the name of the column we want as index. For example, to set\n", "the index to the year column, we write the following:" ] }, { "cell_type": "code", "execution_count": 21, "id": "bd772996", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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namesexfrequency
year
1880MaryF7065
1880AnnaF2604
1880EmmaF2003
1880ElizabethF1939
1880MinnieF1746
\n", "
" ], "text/org": [ "| year | name | sex | frequency |\n", "|--------+-----------+-------+-------------|\n", "| 1880 | Mary | F | 7065 |\n", "| 1880 | Anna | F | 2604 |\n", "| 1880 | Emma | F | 2003 |\n", "| 1880 | Elizabeth | F | 1939 |\n", "| 1880 | Minnie | F | 1746 |" ], "text/plain": [ " name sex frequency\n", "year \n", "1880 Mary F 7065\n", "1880 Anna F 2604\n", "1880 Emma F 2003\n", "1880 Elizabeth F 1939\n", "1880 Minnie F 1746" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# first reload the data without index specification\n", "df = pd.read_csv('data/names.csv')\n", "df = df.set_index('year')\n", "df.head()" ] }, { "cell_type": "markdown", "id": "6ad1a3f9", "metadata": {}, "source": [ "By default, this method yields a new version of the `DataFrame` with the new index (the old index is discarded). Setting the index of a `DataFrame` to one of its columns allows us to select rows by their index labels using the `loc` attribute of a data frame. For example, to select all rows from the year 1899, we could write the following:" ] }, { "cell_type": "code", "execution_count": 22, "id": "fd994595", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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namesexfrequency
year
1899MaryF13172
1899AnnaF5115
1899HelenF5048
1899MargaretF4249
1899RuthF3912
\n", "
" ], "text/org": [ "| year | name | sex | frequency |\n", "|--------+----------+-------+-------------|\n", "| 1899 | Mary | F | 13172 |\n", "| 1899 | Anna | F | 5115 |\n", "| 1899 | Helen | F | 5048 |\n", "| 1899 | Margaret | F | 4249 |\n", "| 1899 | Ruth | F | 3912 |" ], "text/plain": [ " name sex frequency\n", "year \n", "1899 Mary F 13172\n", "1899 Anna F 5115\n", "1899 Helen F 5048\n", "1899 Margaret F 4249\n", "1899 Ruth F 3912" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.loc[1899].head()" ] }, { "cell_type": "markdown", "id": "b590cc36", "metadata": {}, "source": [ "We can specify further which value or values we are interested in. We can select a specific column from the row(s) associated with the year 1921 by passing an element from the row index and one of the column names to the ``DataFrame.loc`` attribute. Consider the following example, in which we index for all rows from the year 1921, and subsequently, select the name column:" ] }, { "cell_type": "code", "execution_count": 23, "id": "e1f6f42b", "metadata": {}, "outputs": [ { "data": { "text/org": [ "| year | Mary | Dorothy | Helen | Margaret | Ruth |\n", "|--------+--------+-----------+---------+------------+--------|\n", "| 1921 | M | a | r | y | |\n", "| 1921 | D | o | r | o | t |\n", "| 1921 | H | e | l | e | n |\n", "| 1921 | M | a | r | g | a |\n", "| 1921 | R | u | t | h | |" ], "text/plain": [ "year\n", "1921 Mary\n", "1921 Dorothy\n", "1921 Helen\n", "1921 Margaret\n", "1921 Ruth\n", "Name: name, dtype: object" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.loc[1921, 'name'].head()" ] }, { "cell_type": "markdown", "id": "03930821", "metadata": {}, "source": [ "If we want to treat our entire data frame as a NumPy array we can also access elements of the data frame using the same syntax we would use to access elements from a NumPy array. If this is what we wish to accomplish, we pass the relevant row and column indexes to the ``DataFrame.iloc`` attribute:" ] }, { "cell_type": "code", "execution_count": 24, "id": "eb90caee", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1258\n" ] } ], "source": [ "print(df.iloc[10, 2])" ] }, { "cell_type": "markdown", "id": "688adaa5", "metadata": {}, "source": [ "In the code block above, the first index value `10` specifies the 11th row (containing ``('Annie', 'F', 1258)``), and the second index `2` specifies the third column (`frequency`).\n", "\n", "Indexing NumPy arrays, and hence, `DataFrame` objects can become much fancier, as we try to show in the following examples. First, we can select multiple columns conditioned on a particular row index. For example, to select both the \"name\" and \"sex\" columns for rows from year 1921, we write:" ] }, { "cell_type": "code", "execution_count": 25, "id": "4d3afcbe", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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namesex
year
1921MaryF
1921DorothyF
1921HelenF
1921MargaretF
1921RuthF
\n", "
" ], "text/org": [ "| year | name | sex |\n", "|--------+----------+-------|\n", "| 1921 | Mary | F |\n", "| 1921 | Dorothy | F |\n", "| 1921 | Helen | F |\n", "| 1921 | Margaret | F |\n", "| 1921 | Ruth | F |" ], "text/plain": [ " name sex\n", "year \n", "1921 Mary F\n", "1921 Dorothy F\n", "1921 Helen F\n", "1921 Margaret F\n", "1921 Ruth F" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.loc[1921, ['name', 'sex']].head()" ] }, { "cell_type": "markdown", "id": "84449e2e", "metadata": {}, "source": [ "Similarly, we can employ multiple row selection criteria, as follows:" ] }, { "cell_type": "code", "execution_count": 26, "id": "900f4782", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
namesex
year
1921MaryF
1921DorothyF
1921HelenF
1921MargaretF
1921RuthF
\n", "
" ], "text/org": [ "| year | name | sex |\n", "|--------+----------+-------|\n", "| 1921 | Mary | F |\n", "| 1921 | Dorothy | F |\n", "| 1921 | Helen | F |\n", "| 1921 | Margaret | F |\n", "| 1921 | Ruth | F |" ], "text/plain": [ " name sex\n", "year \n", "1921 Mary F\n", "1921 Dorothy F\n", "1921 Helen F\n", "1921 Margaret F\n", "1921 Ruth F" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.loc[[1921, 1967], ['name', 'sex']].head()" ] }, { "cell_type": "markdown", "id": "e33996ec", "metadata": {}, "source": [ "In this example, we select the name and sex columns for those rows of which the year\n", "is either 1921 or 1967. To see that rows associated with the year 1967 are indeed selected, we can look at the final rows with the ``DataFrame.tail()`` method:" ] }, { "cell_type": "code", "execution_count": 27, "id": "9c26f8f0", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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namesex
year
1967ZbigniewM
1967ZebedeeM
1967ZenoM
1967ZenonM
1967ZevM
\n", "
" ], "text/org": [ "| year | name | sex |\n", "|--------+----------+-------|\n", "| 1967 | Zbigniew | M |\n", "| 1967 | Zebedee | M |\n", "| 1967 | Zeno | M |\n", "| 1967 | Zenon | M |\n", "| 1967 | Zev | M |" ], "text/plain": [ " name sex\n", "year \n", "1967 Zbigniew M\n", "1967 Zebedee M\n", "1967 Zeno M\n", "1967 Zenon M\n", "1967 Zev M" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.loc[[1921, 1967], ['name', 'sex']].tail()" ] }, { "cell_type": "markdown", "id": "98ee60c5", "metadata": {}, "source": [ "The same indexing mechanism can be used for positional indexes with `DataFrame.iloc`, as shown in the following example:" ] }, { "cell_type": "code", "execution_count": 28, "id": "0df532cb", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
namefrequency
year
1880Elizabeth1939
1880Annie1258
\n", "
" ], "text/org": [ "| year | name | frequency |\n", "|--------+-----------+-------------|\n", "| 1880 | Elizabeth | 1939 |\n", "| 1880 | Annie | 1258 |" ], "text/plain": [ " name frequency\n", "year \n", "1880 Elizabeth 1939\n", "1880 Annie 1258" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.iloc[[3, 10], [0, 2]]" ] }, { "cell_type": "markdown", "id": "bcd3c3eb", "metadata": {}, "source": [ "Even fancier is to use integer slices to select a sequence of rows and columns. In the code block below, we select all rows from position 1000 to 1100, as well as the last two columns:" ] }, { "cell_type": "code", "execution_count": 29, "id": "1879a2b4", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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sexfrequency
year
1880M305
1880M301
1880M283
1880M274
1880M271
\n", "
" ], "text/org": [ "| year | sex | frequency |\n", "|--------+-------+-------------|\n", "| 1880 | M | 305 |\n", "| 1880 | M | 301 |\n", "| 1880 | M | 283 |\n", "| 1880 | M | 274 |\n", "| 1880 | M | 271 |" ], "text/plain": [ " sex frequency\n", "year \n", "1880 M 305\n", "1880 M 301\n", "1880 M 283\n", "1880 M 274\n", "1880 M 271" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.iloc[1000:1100, -2:].head()" ] }, { "cell_type": "markdown", "id": "e8111078", "metadata": {}, "source": [ "Remember that the data structure underlying a `DataFrame` object is a two-dimensional NumPy\n", "`ndarray` object. The underlying `ndarray` can be accessed through the `values`\n", "attribute. As such, the same indexing and slicing mechanisms can be employed directly on this more low-level data structure, i.e.:" ] }, { "cell_type": "code", "execution_count": 30, "id": "500ba46b", "metadata": {}, "outputs": [], "source": [ "array = df.values\n", "array_slice = array[1000:1100, -2:]" ] }, { "cell_type": "markdown", "id": "0355114e", "metadata": {}, "source": [ "In what preceded, we have covered the very basics of working with tabular data in Pandas. Naturally, there is a lot more to say about both Pandas's objects. In what follows, we will touch upon various other functionalities provided by Pandas (e.g., more advanced data selection strategies, and plotting techniques). To liven up this chapter's exposition of Pandas's functionalities, we will do so by exploring the long-term shift in naming practices as addressed in {cite:t}`lieberson:2000`.\n", "\n", "(sec-working-with-data-mapping-change)=\n", "## Mapping Cultural Change\n", "\n", "(sec-working-with-data-turnover-in-naming-practices)=\n", "### Turnover in naming practices\n", "\n", "{cite:t}`lieberson:2000` explores cultural changes in naming practices in the past two centuries. As previously mentioned, he describes an acceleration in the rate of change in the leading names given to newborns. Quantitatively mapping cultural changes in naming practices can be realized by considering their \"turnover series\" (cf. {cite:t}`acerbi:2014`). Turnover can be defined as the number of new names that enter a popularity-ranked list at position $n$ at a particular moment in time $t$. The computation of a turnover series involves the following steps: first, we can calculate an annual popularity index, which contains all unique names of a particular year ranked according to their frequency of use in descending order. Subsequently, the popularity indexes can be put in chronological order, allowing us to compare the indexes for each year to the previous year. For each position in the ranked lists, we count the number of \"shifts\" in the ranking that have taken place between two consecutive years. This \"number of shifts\" is called the turnover. Computing the turnover for all time steps in our collections yields a turnover series.\n", "\n", "To illustrate these steps, consider the artificial example in the table below ({numref}`tbl-turnover-ranking`), which consists of five chronologically ordered ranked lists:\n", "\n", "```{table} Artificial example of name popularity rankings at five points in time.\n", ":name: tbl-turnover-ranking\n", "\n", "| Ranking | $t_1$ | $t_2$ | $t_3$ | $t_4$ | $t_5$ |\n", "| ------- | ------- | ------- | ------- | ------- | ------- |\n", "| 1 | John | John | Henry | Henry | Henry |\n", "| 2 | Henry | Henry | John | John | John |\n", "| 3 | Lionel | William | Lionel | Lionel | Lionel |\n", "| 4 | William | Gerard | William | Gerard | Gerard |\n", "| 5 | Gerard | Lionel | Gerard | William | William |\n", "```\n", "\n", "For each two consecutive points in time, for example $t_1$ and $t_2$, the number of new names that have entered the ranked lists at a particular position $n$ is counted. Between $t_1$ and $t_2$, the number of new names at position 1 and 2 equals zero, while at position 3, there is a different name (i.e., William). When we compare $t_2$ and $t_3$, the turnover at the highest rank equals one, as Henry takes over the position of John. In what follows, we revisit these steps, and implement a function in Python, `turnover()`, to compute turnover series for arbitrary time series data.\n", "\n", "The first step is to compute annual popularity indexes, consisting of the leading names in a particular year according to their frequency of use. Since we do not have direct access to the usage frequencies, we will construct these rankings on the basis of the names' \"frequency\" values. Each annual popularity index will be represented by a `Series` object with names, in which the order of the names represents the name ranking. The following code block implements a small function to create annual popularity indexes:" ] }, { "cell_type": "code", "execution_count": 31, "id": "a44bade0", "metadata": {}, "outputs": [], "source": [ "def df2ranking(df, rank_col='frequency', cutoff=20):\n", " \"\"\"Transform a data frame into a popularity index.\"\"\"\n", " df = df.sort_values(by=rank_col, ascending=False)\n", " df = df.reset_index()\n", " return df['name'][:cutoff]" ] }, { "cell_type": "markdown", "id": "b16760c0", "metadata": {}, "source": [ "In this function, we employ the method `DataFrame.sort_values()` to sort the rows of a `DataFrame` object by their `frequency` values in descending order (i.e. `ascending=False`).^[Ties (rows with the same frequency) are not resolved in any particularly meaningful fashion. If the sorting of items with the same frequency is important, provide a list of columns rather than a single column as the ``by`` argument. Keeping track of ties would require a different approach.] Subsequently, we replace the `DataFrame`'s index with the (default) `RangeIndex` by resetting its index. Finally, we return the \"name\" column, and slice this `Series` object up to the supplied cutoff value, thus retrieving the $n$ most popular names.\n", "\n", "The next step is to compute popularity indexes for each year in the collection, and put them in chronological order. This can be achieved in various ways. We will demonstrate two of them. The first solution is implemented in the following code block:" ] }, { "cell_type": "code", "execution_count": 32, "id": "a40670e6", "metadata": {}, "outputs": [], "source": [ "girl_ranks, boy_ranks = [], []\n", "for year in df.index.unique():\n", " for sex in ('F', 'M'):\n", " if sex == 'F':\n", " year_df = df.loc[year]\n", " ranking = df2ranking(year_df.loc[year_df['sex'] == sex])\n", " ranking.name = year\n", " girl_ranks.append(ranking)\n", " else:\n", " year_df = df.loc[year]\n", " ranking = df2ranking(year_df.loc[year_df['sex'] == sex])\n", " ranking.name = year\n", " boy_ranks.append(ranking)\n", "\n", "girl_ranks = pd.DataFrame(girl_ranks)\n", "boy_ranks = pd.DataFrame(boy_ranks)" ] }, { "cell_type": "markdown", "id": "85d9d933", "metadata": {}, "source": [ "Let us go over this implementation line by line. First, we create two lists (`girl_ranks` and `boy_ranks`) that will hold the annual popularity indexes for girl and boy names. Second, we iterate over all years in the collection by calling `unique()` on the index, which yields a NumPy array with unique integers. We want to create separate popularity rankings for boys and girls, which is why we iterate over both sexes in the third step. Next, we construct a popularity index for a particular year and sex by (i) selecting all rows in `df` that are boys' names, (ii) sub-selecting the rows from a particular year, and (iii) appling the function `df2ranking()` to the data frame yielded by step i and ii. `Series` objects feature a `name` attribute, enabling developers to name series. Naming a `Series` object is especially useful when multiple `Series` are combined into a `DataFrame`, with the `Series` becoming columns. (We will demonstrate this shortly.) After naming the annual popularity index in step four, we append it to the gender-specific list of rankings. Finally, we construct two new `DataFrame` objects representing the annual popularity indexes for boy and girl names in the last two lines. In these `DataFrame`s, each row represents an annual popularity index, and each column represents the position of a name in this index. The row indexes of the yielded data frames are constructed on the basis of the `name` attribute we supplied to each individual `Series` object.\n", "\n", "The above-described solution is pretty hard to understand, and also rather verbose. We\n", "will now demonstrate the second solution, which is more easily read, much more concise, and\n", "slightly faster. This approach makes use of the ``DataFrame.groupby()`` method. Using ``groupby()`` typically involves two elements: a column by which we want to aggregate rows and a function which takes the rows as input and produces a single result. The following line of code illustrates how to calculate the median year for each name in the dataset (perhaps interpretable as an estimate of the midpoint of its period of regular use):" ] }, { "cell_type": "code", "execution_count": 33, "id": "90a10558", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "name\n", "Aaban 2011.5\n", "Aabha 2013.0\n", "Aabid 2003.0\n", "Aabriella 2014.0\n", "Aada 2015.0\n", "Name: year, dtype: float64" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# we use reset_index() to make `year` available as a column\n", "df.reset_index().groupby('name')['year'].median().head()" ] }, { "cell_type": "markdown", "id": "38666d28", "metadata": {}, "source": [ "To see names that have long since passed out of fashion (e.g., \"Zilpah\", \"Alwina\", \"Pembroke\"), we need only sort the series in ascending order:" ] }, { "cell_type": "code", "execution_count": 34, "id": "fa40eac4", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "name\n", "Roll 1881.0\n", "Zilpah 1881.0\n", "Crete 1881.5\n", "Sip 1885.0\n", "Ng 1885.0\n", "Name: year, dtype: float64" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.reset_index().groupby('name')['year'].median().sort_values().head()" ] }, { "cell_type": "markdown", "id": "571438c0", "metadata": {}, "source": [ "Equipped with this new tool we simplify the earlier operation considerably, noting that if we want to group by a data frame's index (rather than one of its column) we do this by using the ``level=0`` keyword argument:" ] }, { "cell_type": "code", "execution_count": 35, "id": "aa2977d9", "metadata": {}, "outputs": [], "source": [ "boy_ranks = df.loc[df.sex == 'M'].groupby(level=0).apply(df2ranking)\n", "girl_ranks = df.loc[df.sex == 'F'].groupby(level=0).apply(df2ranking)" ] }, { "cell_type": "markdown", "id": "5376ee6f", "metadata": {}, "source": [ "Constructing each of the data frames now only takes a single line of code! This implementation makes use of two crucial ingredients: `DataFrame.groupby()` and `DataFrame.apply()`. First, let us explain the `DataFrame.groupby()` method in greater detail. `groupby()` is used to split a data set into $n$ groups of rows on the basis of some criteria. Consider the following example dataset:" ] }, { "cell_type": "code", "execution_count": 36, "id": "b6481ccc", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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namesexvalue
0JenniferF0.303954
1ClaireF0.581954
2MatthewM0.403416
3RichardM0.118834
4RichardM0.875597
5ClaireF0.325772
6MatthewM0.399039
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" ], "text/org": [ "| | name | sex | value |\n", "|----+----------+-------+-------------|\n", "| 0 | Jennifer | F | 0.303954 |\n", "| 1 | Claire | F | 0.581954 |\n", "| 2 | Matthew | M | 0.403416 |\n", "| 3 | Richard | M | 0.118834 |\n", "| 4 | Richard | M | 0.875597 |\n", "| 5 | Claire | F | 0.325772 |\n", "| 6 | Matthew | M | 0.399039 |\n", "| 7 | Jennifer | F | 0.000801926 |" ], "text/plain": [ " name sex value\n", "0 Jennifer F 0.303954\n", "1 Claire F 0.581954\n", "2 Matthew M 0.403416\n", "3 Richard M 0.118834\n", "4 Richard M 0.875597\n", "5 Claire F 0.325772\n", "6 Matthew M 0.399039\n", "7 Jennifer F 0.000802" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import numpy as np\n", "\n", "data = pd.DataFrame({'name': ['Jennifer', 'Claire', 'Matthew', 'Richard',\n", " 'Richard', 'Claire', 'Matthew', 'Jennifer'],\n", " 'sex': ['F', 'F', 'M', 'M',\n", " 'M', 'F', 'M', 'F'],\n", " 'value': np.random.rand(8)})\n", "data" ] }, { "cell_type": "markdown", "id": "2d63a336", "metadata": {}, "source": [ "To split this dataset into groups on the basis of the sex column, we can write the following:" ] }, { "cell_type": "code", "execution_count": 37, "id": "216f7f04", "metadata": {}, "outputs": [], "source": [ "grouped = data.groupby('sex')" ] }, { "cell_type": "markdown", "id": "b1bf73a2", "metadata": {}, "source": [ "Each group yielded by `DataFrame.groupby()` is an instance of `DataFrame`. The groups are \"stored\" in a so-called `DataFrameGroupBy` object. Specific groups can be retrieved with the `DataFrameGroupBy.get_group()` method, e.g.:" ] }, { "cell_type": "code", "execution_count": 38, "id": "80a93ece", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/org": [ "| | name | sex | value |\n", "|----+----------+-------+-------------|\n", "| 0 | Jennifer | F | 0.303954 |\n", "| 1 | Claire | F | 0.581954 |\n", "| 5 | Claire | F | 0.325772 |\n", "| 7 | Jennifer | F | 0.000801926 |" ], "text/plain": [ " name sex value\n", "0 Jennifer F 0.303954\n", "1 Claire F 0.581954\n", "5 Claire F 0.325772\n", "7 Jennifer F 0.000802" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "grouped.get_group('F')" ] }, { "cell_type": "markdown", "id": "a63febf9", "metadata": {}, "source": [ "`DataFrameGroupBy` objects are iterable, as exemplified by the following code block:" ] }, { "cell_type": "code", "execution_count": 39, "id": "aa0313e0", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "grouper: F\n", " name sex value\n", "0 Jennifer F 0.303954\n", "1 Claire F 0.581954\n", "5 Claire F 0.325772\n", "7 Jennifer F 0.000802\n", "grouper: M\n", " name sex value\n", "2 Matthew M 0.403416\n", "3 Richard M 0.118834\n", "4 Richard M 0.875597\n", "6 Matthew M 0.399039\n" ] } ], "source": [ "for grouper, group in grouped:\n", " print('grouper:', grouper)\n", " print(group)" ] }, { "cell_type": "markdown", "id": "204995f5", "metadata": {}, "source": [ "Perhaps the most interesting property of `DataFrameGroupBy` objects is that they enable us to conveniently perform subsequent computations on the basis of their groupings. For example, to compute the sum or the mean of the value column for each sex group, we write the following:" ] }, { "cell_type": "code", "execution_count": 40, "id": "3a6cd8b1", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/org": [ "| sex | value |\n", "|-------+---------|\n", "| F | 1.21248 |\n", "| M | 1.79689 |" ], "text/plain": [ " value\n", "sex \n", "F 1.212482\n", "M 1.796885" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "grouped.sum()" ] }, { "cell_type": "code", "execution_count": 41, "id": "804c066d", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "sex\n", "F 0.303121\n", "M 0.449221\n", "Name: value, dtype: float64" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ "grouped['value'].mean()" ] }, { "cell_type": "markdown", "id": "2a80d7f9", "metadata": {}, "source": [ "These two aggregating operations can also be performed using the more general `DataFrameGroupBy.agg()` method, which takes as argument a function to use for aggregating groups. For example, to compute the sum of the groups, we can employ NumPy's sum function:" ] }, { "cell_type": "code", "execution_count": 42, "id": "ae13e92a", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "sex\n", "F 1.212482\n", "M 1.796885\n", "Name: value, dtype: float64" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "grouped['value'].agg(np.sum)" ] }, { "cell_type": "markdown", "id": "f5628ce7", "metadata": {}, "source": [ "Pandas's methods may also be used as aggregating operations. As it is impossible to reference a `DataFrame` or `Series` method in the abstract (i.e., without an attached instance), methods are named with strings:" ] }, { "cell_type": "code", "execution_count": 43, "id": "d8a4b3ad", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/org": [ "| sex | size | mean |\n", "|-------+--------+----------|\n", "| F | 4 | 0.303121 |\n", "| M | 4 | 0.449221 |" ], "text/plain": [ " size mean\n", "sex \n", "F 4 0.303121\n", "M 4 0.449221" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "grouped['value'].agg(['size', 'mean'])" ] }, { "cell_type": "markdown", "id": "72cd46ac", "metadata": {}, "source": [ "Similarly, to construct a new `DataFrame` with all unique names aggregated into a single string, we can write something like:" ] }, { "cell_type": "code", "execution_count": 44, "id": "1c9d0f8c", "metadata": {}, "outputs": [ { "data": { "text/org": [ "| | | | | | | | | | | | | | sex | Claire Jennifer | Matthew Richard |\n", "|----+----+----+----+----+----+----+----+----+----+----+----+----+-------+-------------------+-------------------|\n", "| F | C | l | a | i | r | e | | J | e | n | n | i | f | e | r |\n", "| M | M | a | t | t | h | e | w | | R | i | c | h | a | r | d |" ], "text/plain": [ "sex\n", "F Claire Jennifer\n", "M Matthew Richard\n", "Name: name, dtype: object" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def combine_unique(names):\n", " return ' '.join(set(names))\n", "\n", "grouped['name'].agg(combine_unique)" ] }, { "cell_type": "markdown", "id": "7cd50203", "metadata": {}, "source": [ "Besides aggregating data, we can also *apply* functions to the individual `DataFrame` objects of a `DataFrameGroupBy` object. In the above-described solution to create annual popularity rankings for boys' names and girls' names, we *apply* the function `df2ranking()` to each group yielded by `groupby(level=0)` (recall that ``level=0`` is how we use the index as the grouper). Each application of `df2ranking()` returns a `Series` object. Once the function has been applied to all years (i.e., groups), the annual popularity indexes are combined into a new `DataFrame` object.\n", "\n", "Now that we have discussed the two fundamental methods `DataFrame.groupby()` and\n", "`DataFrame.apply()`, and constructed the yearly popularity rankings, let us move on to the\n", "next step: computing the turnover for the name dataset. Recall that turnover is defined as the number of new items that enter a ranked list of length $n$ at time $t$. In order to compute this number, a procedure is required to compare two rankings $A$ and $B$ and return the size of their difference. The data type `set` is an unordered container-like collection of unique elements. Python's implementation of `set` objects provides a number of methods to compare two sets. `set.intersection()`, for example, returns the intersection of two sets (i.e., all elements that are in both sets). Another comparison method is `set.union()`, which returns a new set consisting of all elements that are found in either set (i.e., their union). A useful method for our purposes is `set.difference()`, which returns all elements that are in set $A$ but not in set $B$. Consider the following example:" ] }, { "cell_type": "code", "execution_count": 45, "id": "38435b36", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Difference of A and B = {'Henry', 'Richard'}\n", "Difference of A and B = {'Henry', 'Richard'}\n" ] } ], "source": [ "A = {'Frank', 'Henry', 'James', 'Richard'}\n", "B = {'Ryan', 'James', 'Logan', 'Frank'}\n", "\n", "diff_1 = A.difference(B)\n", "diff_2 = A - B\n", "\n", "print(f\"Difference of A and B = {diff_1}\")\n", "print(f\"Difference of A and B = {diff_2}\")" ] }, { "cell_type": "markdown", "id": "e9cbcd9e", "metadata": {}, "source": [ "Note that the difference between two sets can be computed by calling the method\n", "`set.difference()` explicitly (`diff_1`) or implicitly by subtracting set $B$ from set $A$ (`diff_2`). By taking the length of the set yielded by `set.difference()`, we obtain the number of new elements in set $A$:" ] }, { "cell_type": "code", "execution_count": 46, "id": "9e20c122", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2\n" ] } ], "source": [ "print(len(A.difference(B)))" ] }, { "cell_type": "markdown", "id": "d51e2899", "metadata": {}, "source": [ "While `set.difference()` provides a convenient method to compute the difference between two containers of unique elements, it is not straightforward how to apply this method to our data frames `boy_ranks` and `girl_ranks`, whose rows are essentially NumPy arrays. However, these rows can be transformed into `set` objects by employing the `DataFrame.apply()` method discussed above:" ] }, { "cell_type": "code", "execution_count": 47, "id": "b9902139", "metadata": {}, "outputs": [ { "data": { "text/org": [ "| | | | | | | | | | | | | | | | year | {'Joe', 'Robert', 'James', 'John', 'George', 'Frank', 'Arthur', 'William', 'Joseph', 'Walter', 'Henry', 'Thomas', 'Edward', 'Samuel', 'Charles', 'Albert', 'Louis', 'Fred', 'Harry', 'David'} | {'Robert', 'James', 'John', 'Charlie', 'George', 'Frank', 'Arthur', 'William', 'Joseph', 'Walter', 'Henry', 'Thomas', 'Edward', 'Samuel', 'Charles', 'Albert', 'Louis', 'Fred', 'Harry', 'David'} | {'Robert', 'James', 'John', 'George', 'Frank', 'Arthur', 'William', 'Joseph', 'Walter', 'Henry', 'Thomas', 'Edward', 'Samuel', 'Charles', 'Albert', 'Clarence', 'Louis', 'Fred', 'Harry', 'David'} | {'Robert', 'James', 'John', 'George', 'Frank', 'Arthur', 'William', 'Joseph', 'Walter', 'Henry', 'Thomas', 'Edward', 'Samuel', 'Charles', 'Albert', 'Clarence', 'Louis', 'Fred', 'Harry', 'David'} | {'Robert', 'James', 'John', 'George', 'Frank', 'Arthur', 'William', 'Joseph', 'Walter', 'Henry', 'Thomas', 'Edward', 'Samuel', 'Charles', 'Albert', 'Grover', 'Clarence', 'Louis', 'Fred', 'Harry'} |\n", "|------+--------+--------+-------+---------+--------+--------+---------+---------+--------+--------+--------+--------+--------+---------+---------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n", "| 1880 | Joe | Robert | James | John | George | Frank | Arthur | William | Joseph | Walter | Henry | Thomas | Edward | Samuel | Charles | Albert | Louis | Fred | Harry | David |\n", "| 1881 | Robert | James | John | Charlie | George | Frank | Arthur | William | Joseph | Walter | Henry | Thomas | Edward | Samuel | Charles | Albert | Louis | Fred | Harry | David |\n", "| 1882 | Robert | James | John | George | Frank | Arthur | William | Joseph | Walter | Henry | Thomas | Edward | Samuel | Charles | Albert | Clarence | Louis | Fred | Harry | David |\n", "| 1883 | Robert | James | John | George | Frank | Arthur | William | Joseph | Walter | Henry | Thomas | Edward | Samuel | Charles | Albert | Clarence | Louis | Fred | Harry | David |\n", "| 1884 | Robert | James | John | George | Frank | Arthur | William | Joseph | Walter | Henry | Thomas | Edward | Samuel | Charles | Albert | Grover | Clarence | Louis | Fred | Harry |" ], "text/plain": [ "year\n", "1880 {Joe, Robert, James, John, George, Frank, Arth...\n", "1881 {Robert, James, John, Charlie, George, Frank, ...\n", "1882 {Robert, James, John, George, Frank, Arthur, W...\n", "1883 {Robert, James, John, George, Frank, Arthur, W...\n", "1884 {Robert, James, John, George, Frank, Arthur, W...\n", "dtype: object" ] }, "execution_count": 47, "metadata": {}, "output_type": "execute_result" } ], "source": [ "boy_ranks.apply(set, axis=1).head()" ] }, { "cell_type": "markdown", "id": "2b447eaf", "metadata": {}, "source": [ "The optional argument `axis=1` indicates that the \"function\" should be applied to each row in the data frame, or, in other words, that a set should be created from each row's elements (cf. section {ref}`sec-vector-space-model-array-broadcasting`). (With `axis=0`, the function will be applied to each column.) Now that we have transformed the rows of our data frames into `set` objects, `set.difference()` can be employed to compute the difference between two adjacent annual popularity indexes. In order to do so, however, we first need to implement a procedure to iterate over all adjacent pairs of years. We will demonstrate two implementations.\n", "\n", "The first implementation employs a simple `for`-loop, which iterates over the index of our data frame (i.e., the years), and compares the name set of a particular year $t$ to that of the prior year ($t-1$). Consider the following code block:" ] }, { "cell_type": "code", "execution_count": 48, "id": "785607ed", "metadata": {}, "outputs": [], "source": [ "def turnover(df):\n", " \"\"\"Compute the 'turnover' for popularity rankings.\"\"\"\n", " df = df.apply(set, axis=1)\n", " turnovers = {}\n", " for year in range(df.index.min() + 1, df.index.max() + 1):\n", " name_set, prior_name_set = df.loc[year], df.loc[year - 1]\n", " turnovers[year] = len(name_set.difference(prior_name_set))\n", " return pd.Series(turnovers)" ] }, { "cell_type": "markdown", "id": "0529ad44", "metadata": {}, "source": [ "Let us go over the function `turnover()` line by line. First, using `df.apply(set, axis=1)` we transform the original data frame into a `Series` object, consisting of the $n$ leading names per year represented as `set` objects. After initializing the dictionary `turnovers` in line four, we iterate over all adjacent pairs of years in line five to seven. For each year, we retrieve its corresponding name set (`name_set`) and the name set of the year prior to the current year (`prior_name_set`). Subsequently, in line seven, we count the number of names in `name_set` not present in `prior_name_set` and add that number to the dictionary `turnovers`. Finally, we construct and return a new `Series` object on the basis of the `turnovers` dictionary, which consists of the computed turnover values with the years as its index. The function is invoked as follows:" ] }, { "cell_type": "code", "execution_count": 49, "id": "dd6439d4", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1881 1\n", "1882 1\n", "1883 0\n", "1884 1\n", "1885 0\n", "dtype: int64" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" } ], "source": [ "boy_turnover = turnover(boy_ranks)\n", "boy_turnover.head()" ] }, { "cell_type": "markdown", "id": "b2730dee", "metadata": {}, "source": [ "Let us now turn to the second `turnover()` implementation. This second implementation more extensively utilizes functionalities provided by the Pandas library, making it less verbose and more concise compared to the previous implementation. Consider the following function definition:" ] }, { "cell_type": "code", "execution_count": 50, "id": "5ec285ad", "metadata": {}, "outputs": [], "source": [ "def turnover(df):\n", " \"\"\"Compute the 'turnover' for popularity rankings.\"\"\"\n", " df = df.apply(set, axis=1)\n", " return (df.iloc[1:] - df.shift(1).iloc[1:]).apply(len)" ] }, { "cell_type": "markdown", "id": "315580cd", "metadata": {}, "source": [ "Similar to before, we first transform the rows of the original data frame `df` into `set`\n", "objects. The return statement is more involved. Before we discuss the details, let us first describe the general idea behind it. Recall that Python allows us to compute the difference between two sets using the arithmetic operator $-$, e.g. $A - B$, where both $A$ and $B$ have type `set`. Furthermore, remember that NumPy arrays (and thus Pandas `Series`) allow for vectorized operations, such as addition, multiplication, and subtraction. The combination of these two provides us with the means to perform vectorized computations of set differences: for two arrays (or `Series`) $A$ and $B$ consisting of $n$ `set` objects, we can compute the pairwise set differences by writing $A - B$. Consider the following example:" ] }, { "cell_type": "code", "execution_count": 51, "id": "5afcaffa", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[{'Isaac'} {'Claire', 'Rose', 'Beth'}]\n" ] } ], "source": [ "A = np.array([{'Isaac', 'John', 'Mark'}, {'Beth', 'Rose', 'Claire'}])\n", "B = np.array([{'John', 'Mark', 'Benjamin'}, {'Sarah', 'Anna', 'Susan'}])\n", "\n", "C = A - B\n", "print(C)" ] }, { "cell_type": "markdown", "id": "26c9e589", "metadata": {}, "source": [ "Computing \"pairwise differences\" between two NumPy arrays using vectorized operations happens through comparing elements with matching indexes, i.e. the elements at position `0` in `A` and `B` are compared to each other, the elements at position `1` in `A` and `B` are compared to each other, and so on and so forth. The same mechanism is used to perform vectorized operations on `Series` objects. However, whereas pairwise comparisons for NumPy arrays are based on matching positional indexes, pairwise comparisons between two `Series` objects can be performed on the basis of other index types, such as labeled indexes. The expression `df.iloc[1:] - df.shift(1).iloc[1:]` in the second function definition of `turnover()` employs this functionality in the following way. First, using `df.shift(1)`, we \"shift\" the index of the `Series` object by 1, resulting in a new `Series` whose index is incremented by 1:" ] }, { "cell_type": "code", "execution_count": 52, "id": "2cbed354", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "year\n", "1880 NaN\n", "1881 {Joe, Robert, James, John, George, Frank, Arth...\n", "1882 {Robert, James, John, Charlie, George, Frank, ...\n", "1883 {Robert, James, John, George, Frank, Arthur, W...\n", "1884 {Robert, James, John, George, Frank, Arthur, W...\n", "dtype: object" ] }, "execution_count": 52, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s = boy_ranks.apply(set, axis=1)\n", "s.shift(1).head()" ] }, { "cell_type": "markdown", "id": "8221a803", "metadata": {}, "source": [ "Second, shifting the index of the `Series` object enables us to compute the differences\n", "between adjacent annual popularity indexes using a vectorized operation,\n", "i.e., `df.iloc[1:] - df.shift(1).iloc[1:]`. In this pairwise comparison, the set\n", "corresponding to, for example, the year 1881 in the original `Series` is compared to the\n", "set with the same year in the shifted `Series`, which, however, actually represents the\n", "set from 1880 in the original data. One final question remains: why do we slice the\n", "`Series` objects to start from the second item? This is because we cannot compute the\n", "turnover for the first year in our data, and so we slice both the original and the shifted `Series` objects to start from the second item, i.e., the second year in the dataset. `df.iloc[1:] - df.shift(1).iloc[1:]` returns a new `Series` object consisting of sets representing all new names in a particular year:" ] }, { "cell_type": "code", "execution_count": 53, "id": "c9aa729e", "metadata": {}, "outputs": [ { "data": { "text/org": [ "| year | {'Charlie'} |\n", "|--------+---------------|\n", "| 1881 | Charlie |\n", "| 1882 | Clarence |\n", "| 1883 | |\n", "| 1884 | Grover |\n", "| 1885 | |" ], "text/plain": [ "year\n", "1881 {Charlie}\n", "1882 {Clarence}\n", "1883 {}\n", "1884 {Grover}\n", "1885 {}\n", "dtype: object" ] }, "execution_count": 53, "metadata": {}, "output_type": "execute_result" } ], "source": [ "differences = (s.iloc[1:] - s.shift(1).iloc[1:])\n", "differences.head()" ] }, { "cell_type": "markdown", "id": "ca9008ad", "metadata": {}, "source": [ "The final step in computing the annual turnover is to apply the function `len()` to each set in this new `Series` object:" ] }, { "cell_type": "code", "execution_count": 54, "id": "e13228ee", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "year\n", "1881 1\n", "1882 1\n", "1883 0\n", "1884 1\n", "1885 0\n", "dtype: int64" ] }, "execution_count": 54, "metadata": {}, "output_type": "execute_result" } ], "source": [ "turnovers = differences.apply(len)\n", "turnovers.head()" ] }, { "cell_type": "markdown", "id": "08a67e52", "metadata": {}, "source": [ "Now that we have explained all lines of code of the second implementation of `turnover()`, let us conclude this section by invoking the function:" ] }, { "cell_type": "code", "execution_count": 55, "id": "def14cca", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "year\n", "1881 1\n", "1882 1\n", "1883 0\n", "1884 1\n", "1885 0\n", "dtype: int64" ] }, "execution_count": 55, "metadata": {}, "output_type": "execute_result" } ], "source": [ "boy_turnover = turnover(boy_ranks)\n", "boy_turnover.head()" ] }, { "cell_type": "code", "execution_count": 56, "id": "8d33cd36", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "year\n", "1881 0\n", "1882 2\n", "1883 1\n", "1884 1\n", "1885 0\n", "dtype: int64" ] }, "execution_count": 56, "metadata": {}, "output_type": "execute_result" } ], "source": [ "girl_turnover = turnover(girl_ranks)\n", "girl_turnover.head()" ] }, { "cell_type": "markdown", "id": "8c4a45f7", "metadata": {}, "source": [ "That was a lot to process. Mastering a rich and complex library such as Pandas requires\n", "time, patience, and practice. As you become more familiar with the library, you will\n", "increasingly see opportunities to make your code simpler, cleaner, and faster. Our advice\n", "is to start with a more verbose solution. That's not always the most efficient solution,\n", "but it ensures that you understand all the individual steps. When such a first draft\n", "solution works, you can try to replace certain parts step by step with the various tools\n", "that Pandas offers.\n", "\n", "(sec-working-with-data-visualizing-turnovers)=\n", "### Visualizing turnovers\n", "\n", "In the previous section, we have shown how to compute annual turnovers. We now provide\n", "more insight into the computed turnover series by creating a\n", "number of visualizations. One of the true selling points of the Pandas library is the ease with which it allows us to plot our data. Following the expression \"show, don't tell\", let us provide a demonstration of Pandas's plotting capabilities. To produce a simple plot of the absolute turnover per year, we write the following:" ] }, { "cell_type": "code", "execution_count": 57, "id": "f95d8115", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "findfont: Generic family 'sans-serif' not found because none of the following families were found: \"Roboto Condensed Regular\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "findfont: Generic family 'sans-serif' not found because none of the following families were found: \"Roboto Condensed Regular\"\n" ] }, { "data": { "image/png": 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bNmnKlClKTU31mfbPf/6zXn/99XrbmDBhgs++895772nfvn2N1nao2gHy+eefX2cfOdRNN92k1157zee5YcOGaebMmcrNzVV5ebn3fLty5UpNmjTJ57Xxiy++0LXXXhtUjZJ09913e8+XRx11lKZPn649e/aotLRUhYWFOnjwoF577TX16tUr6GX78+233+rgwYPex127dg167OOqqipdeuml2r17t8/zZ511lubPn6/y8nIdOHBAlZWV+vrrr3XXXXd5A9D77rvP5+a80bBy5Ur95S9/kSSlpaXp7rvv1pdffuk9JioqKrRmzRpdfPHFYbXT1K9rtR133HE+j1esWBFW/QAQU5r3wmQAgN089dRTdb4W6Ha7/U57wgkn+Exb8xXIQPn7KrdlWeb//b//1+B8ixcvNomJiT7zPfbYY422V7stSSYpKanRIRJeffXVOvO99dZbDc7THMNEPPTQQ0GNubdnzx5z4okn+rQzduzYRueL5jARL730kvnwww8Dnr6iosJMnjy5Tn2BDBfhb/+QZH7/+983OBbhkiVLTHp6us88J5xwQr3HzU8//eTzVd2kpCSzfPnygNfxhx9+aLBPKioqTJ8+fXzqSU9P9zsswKGefPLJOut+2WWXNVpP7XkaEq1hIsKZ3p+HHnqoTn82dg4wxpj//ve/Pucqy7LMsmXLQqqhMcEO0eBPuH0V7jARNf/uueeeeo+f2mp/hb7m36mnnmr2799f73xFRUV1hibo379/SO3deuutDZ5jvv32W9O1a1efedq2bdvg0BS1h1N48sknG62thtvtNt27d/eZ/7333qt3+ldeecVnWqfTaZ5++ulG21m0aJHJyMjwmffVV19tcJ76hjYZP368qaqqCngdQ/XSSy/5tHvWWWcFvYza5wNJ5pFHHmlwnvXr19c75FBTDxNR86979+7mu+++C3g9gzlfN/Xrmj/r16/3qa9Lly5BzQ8AsYwwGAAQVUOGDKnzobw+wQTH/vgLAv74xz8GNO9tt93mM9+wYcMancffh6PGxlCsceGFF/rMd8UVVzQ4fXOEwaHYt2+fz80Ck5KSTH5+foPzRPsGcqE444wzfGqcN29eo/P42z/OP//8gNp7880368z7wQcf+J32/fff95nu8ssvD2rdGjNr1qw6tQQ6JmbtMZoty2o0PAgmQIzHMHjfvn0+Yb9lWWb+/PkBz187ZL/wwguDriEQLSUMvvLKK4Nq018427t3b3Pw4MFG5/3iiy/qzNvQeNnFxcWmbdu2dbZnIK9769evr/NHzHvvvbfe6adPn+4z7cCBAxtto8bHH3/sM29WVla9YXVlZaXp1q2bz/TPPfdcwG3NmzfPZ94BAwY0OL2/MPjUU0+N2s2/ap/jbrzxxqDmr6ioqHOD3euvvz6geT/99FO/Y/ZGIwxOSkoyX331VYBr6RHM+bqpX9f8KS4urrOe+/bta/J2ASAaGCYCABA1GzdurDNOaO3hIA512WWXKTEx0fv4hx9+0Keffhpy+x07dtSDDz4Y0LS1v466fv16v+N4NuSYY47RTTfdFFJ7n3/+eVBtxar27dvr6quv9j6urKz0GWcyXv3xj3/0eVzf2J4NSUxM1D/+8Y+Aph0zZozOOOMMn+eef/55v9PWHlczUl9/rlF7DO3zzjtPF154YUDzTp06Vd27d/c+NsaEPCZ3S/H000+rpKTE+3jcuHEaPXp0wPPfeOONOvzww72P582bpz179kS0xpYiKSlJjz/+eNjLefTRR9WmTZtGpxswYIAGDx7s81xD5/aXX37ZZxzz1NRUPfXUUwGNuXrsscfqd7/7nc9z//rXv+odR/riiy9Wq1atvI/Xrl2rr776qtF2JNUZGmfcuHH1fg3/tdde048//uh9PHz4cE2aNCmgdiTP+eW0007zPl63bl2d8X8b889//tPvWMRN4YcffvB53LVr16DmnzNnjs+wVK1bt9Zf//rXgOYdPny4Lr300qDai5Qbb7xR/fr1a7LlN/Xrmj/p6el1jvPa2xcA4hVhMAAgamqPMThkyJA6Y7AeqmPHjjr77LMbXEYwxo0b5/fGbf7069fP50NAaWmpzwfaQFx33XUBTzts2DCfx5s3bw74ZkOxbujQoT6PV61a1UyVRE4k1mn06NHq2bNnwNNPnjzZ5/EHH3zgM2ZvjbZt2/o8juQfFgoKCuqMm3jLLbcEPH9qamqdP3zMnz8/IrXFq9rjqN58881Bze90On3G4nS73Vq6dGlEamtpzj//fGVmZoa1jE6dOgX8xw+p7rl906ZN9U5b+1i46KKLlJ2dHXBbN998s09wvHfv3nqP//T09DpjuAYy/nlRUZFmz57t89zEiRPrnT7c/Vvy/GH4UMH8UXjw4MF1xn5tSjX3CajRpUuXoOb/6KOPfB7/5je/aXAs5tqCed8RSaGM5xyMpnxda0hWVpbP42DfBwJArPJ/e2wAACLM7XZr1qxZPs/5u3FcbePHj9e8efO8j99880099dRTQd35vcbw4cODmr5Xr15av3699/GhV2xFur327dsrIyPDe+MZY4wKCwvrfACKNQcOHNDXX3+t/Px8FRYWqqyszOdu4pL03Xff+TyO9Q9T5eXl+uabb5Sbm6uioiIVFxc3GsyHsk7nn39+UNOfc845cjqd3ivUy8rKtGHDBh1//PE+0w0ZMkSWZXm3w4IFC3T//ffr3nvv9bnSPhTLly/32b5t2rSpc8VyY37zm99oypQp3sffffed9u3b1+BNu1qq/Px8n5sqtmvXTkOGDAl6ObX3gZUrV2rMmDFh19fSHHqFaahOOumkoG5G1bt3b5/HDb2OLF++3OfxRRddFFRtPXr00KBBg3yunF22bFmdQLrGxIkTfQLgl19+WY888kiD6/fGG2+otLTU+/iEE07QUUcd5Xdat9td55sgv/rVrwJZFR/+9u9ARWKbB6OwsNDncbDvVWqv21lnnRXU/CNHjlRKSorKy8uDmi8cnTp1atKrgqWmfV1rSO3tV3v7AkC8IgwGAETFxx9/rNzcXO/jhIQEXX755Y3Od+6556pdu3berwgWFxdr9uzZAQXJtQVzFabk+XrmoYqKipq8vUPvQl5UVBSTYfDGjRs1bdo0vfnmm9q2bVvQ8wcbqkdDfn6+pk+frtdee03r1q0LekiQUNapdsDRmNTUVB155JH6+uuvvc+tX7++znI6deqk3/zmN3rrrbe8zz3wwAN6/vnndfnll+ucc87RSSedpLS0tKBrrv018gEDBgT99eujjjpKaWlpPoHShg0bNHLkyKDriXerVq3yCddbt26t5557LujlHBooS9KuXbvCrq0l6t+/f9jLaKrXkb1799YZ3mPQoEFBtVUzz6Fh8IYNG+qddvjw4erdu7e+//57SdLu3bv1wQcf6Jxzzql3ntpXD1911VX1Tvvtt9/6vKalp6fr1VdfbWwV6sjLy/N5HMz+HYltHoxDz2uS57wdjNpXjg8YMCCo+Z1Op4455pigh9IIRzT6uClf1xpSe/vV3r4AEK8IgwEAUVF7eIfRo0erU6dOjc6XnJysSy+91Gd81BkzZoQUBmdkZAQ1fe2ro4INCKPdXlOrqKjQHXfcoaeffjqs2oIN1ZvaCy+8oDvvvLPOmITBCGWdevToEfQ83bt39wmD9+3b53e6p556SmvWrPEZ33D37t164okn9MQTTygxMVHHHXecTjnlFI0YMUKnn356QGOg7t+/3+dxsMGYJDkcDnXv3l3ffvttvcu1i9rh344dO3TDDTeEvVy79mdj2rVrF/Yymuq8XnubWZYV0jmi9liqDe0LlmVp/Pjxuv/++73PvfTSS/WGwd9//73PECQpKSkN/lG39v5dUlIS9f07Ets8HLW/KdOQ4uJiVVVV+TwX7DAToc4Tjmj1cVO9rjUkmO0HAPGEMYMBAE2usLBQc+bM8XkumDC39rSffPJJnXH5AhHITXgiKdrtNaWKigpdcMEFevLJJ8MOqWNpLOQHHnhA119/fVhBsBTaB8ZQPqTWDqLquyK5S5cuWrVqVb1DBVRVVWnNmjV64okndNFFF6lz58667LLLGh2HsXY/hfpBu/Z6hNv/8aqpQluuXvPv0Bumhaqpzuu1j4H09PSQbnoW7LE1YcIEn3WaN29evfO89NJLPue6Cy64oMFvr8TC/h2JbR6M2lemBjNcw6FXUdeofWV5IMINQIMVrT5uqte1hpSVlfk8DmWIMgCIRYTBAIAm98Ybb9R5Q33ZZZfJsqyA/p1yyik+87rdbs2cOTOaq2B7f/vb3/Thhx/6PNepUyfdeuut+u9//6u1a9dqz549Ki4ulsvlkjHG+2/hwoXNVHXDPv30U58r4iTPlei//e1v9cILL2jFihXauXOnCgsLVVlZ6bNO8XC1UGZmpt588019+eWX+v3vf68jjjii3mnLy8v13//+V4MHD9YNN9ygioqKKFZqX5WVlU2y3HjYPxEbevbs6TNES0VFRZ2bvkmefWrGjBk+zzU0RIRkz/27dhBbXFwc8LzJycl1ngulD5uq32NBtF/XSkpKfB5HO2gHgKbCMBEAgCZXe4iISC3z7rvvjvhy7SKYq3OLior0yCOP+Dx35ZVX6oUXXghoPMRgPgxH03333efzeMCAAZo7d666d+/e6LyRWKfCwkJ17NgxqHlqXzkWyJjS/fv31+OPP67HH39cu3bt0pIlS7R06VItXbpU69atqxOsPPfcczp48KBeeeWVOsuq/XXgUG+mU3s9mvur3DWifdV6+/btfR6fcMIJQd0cy25i6VsFkVb7GCgpKZHb7Q766uBQjq2JEyf6/NFu+vTpdYZzWLRokbZv3+59nJ2drTPPPLPB5dbevzMzM+sMHdHSdOvWzefx7t27A57X3/n84MGDQY+D6+8K45Ymkq9rDak9PnXt7QsA8YorgwEATar2GIORsmnTJluHJuGOLxzMDc8+/PBDnyu7+/Tpo2nTpgV8Y5z8/PygaouGvLw8LVu2zPvY6XTqrbfeCigIliKzToeOexioHTt2+Dzu0KFDUPNnZWXp0ksv1ZNPPqm1a9dq165devLJJ+uMTfrqq6/qo48+qjN/7XDn0HAoUG63u8561F5uqKJ5XERC7XHT6xsDuqWoHWzG+vaJptrHgDEmpHNE7Zt6BnJsjRkzxuer/qtWrfIZ01uq+0fdcePGNRpU196/9+/fH9NX9UZC7XHUD71xbmMSEhLqnNNr31AuELW3XUsX7utafYqKiurcDyCUcbwBIBZxZTAAoEnNmDHD58Nf3759ddttt4W0rJdfftknWJ4xY4aGDh0abolxqfYYfcFeqVo7jGvIl19+6fP40ksvVWJiYsDzr169OuBpo+Wrr77yucpw2LBh6t27d8DzR2KdvvjiCw0aNCjg6cvKyup8yD/uuOPCqqFz58665ZZbdPXVV+v000/XqlWrvL97+eWX61z5V/uu8evWrQv66sVvv/22zpifkbobfTSPi0iovf22b9+ugwcPBn2TsnjRqlUrn6vJi4uLA17XAwcOxNzNJyOpU6dO6ty5s8+Vs2vXrq1zQ7jGrF271ufxscce2+g86enpuuSSSzRt2jTvc9OnT9ff/vY3SZ7t9Oabb/rMM3HixEaXe+SRRyo5Odn79fzq6mp9/fXXOuaYYxqdN17169fP5/GWLVuCmn/w4ME+QzKtWrXKZxiPxuzevVs//vhjUG22NMG+rtVn69atPo+zsrJi5lssABAuwmAAQJPxN8bgxIkTNXny5JCW16FDB58w+LXXXtPf//53JSUlhVVnPKr9ddLaV4M1ZvHixQFPm5eX5/M4mK9Jut1uzZ8/P+DpoyWcdZKk9957L+wa5s2bp2uvvTaoNg+9kjI1NTViIWp6eroeeOABnX322d7nNmzYUGe6oUOHyrIs7x94CgsLtXDhQp1xxhkBtzV79myfx3369An6Cuf6tGnTxqe+goICFRQUBDSchhTccVHD6XT6bBeXy1XnCuX6HHbYYerZs6f3CmuXy6UPP/xQl156adB1NLWEBN+PDaHcSLJt27Y+YfC2bds0YMCAgOZdsmRJ0O3Fm2HDhuntt9/2Pn777bfrvVmWPzt27NCaNWvqLDMQEydO9AmDZ82apYcfflgOh0Nvvvmmz9ipw4YNU9++fRtdZlpamoYNG6ZFixZ5n3v//fdbdBhc+w98/s6jDRk2bJhPGPz666/rjjvuCHh+f+M921Wgr2v1qT3t4MGDI1YbADQ3hokAADSZJUuW1Akpwwk5zjnnHJ87Oe/fv1/vvPNOyMuLZ0ceeaTP440bNwY8TuBPP/2kd999N+C2al8FHMxXtd96662gg+poCGeddu7cGZEP3B988EFQXwN//vnnfR6fddZZdQK6cNS+ArH2jXMkz/ijJ554os9zTz/9dMBtlJeX68UXX/R57tAP6uFyOp11bii0fPnygOd/4YUXgm6zdevWPo+DHUe5dtj36KOPxuRX6cNdT6nueSuYbfOvf/0r6PbiTe1jYfbs2XXGLG3IM88847PvdOrUKeBvH5x66qk67LDDvI9zc3O9X6mfPn26z7SBXBVco/b+/Y9//EPl5eUBzx9v+vTp43P16J49e4IaJ/nyyy/3ebx27do6N2+tT2lpqf7+978H3JYdBPK6Vp/169f7PLbrN9EAtEyEwQCAJlN7jMEhQ4YE9VX82tLS0nTeeec12IZdZGVlqWvXrt7HlZWVevnllwOa99Zbb1VVVVXAbeXk5Pg8DvSq2Ly8PN1yyy0BtxNNtddp6dKlAYVbbrdbEydODOmu5LVVVVXp1ltvDWjat956Sx9//LHPc5MmTfI7bXV1dUj1bNy40edxly5d/E538803+zyeM2eO3n///YDaePDBB30CcMuydNNNNwVZacNqX7116NWODfnnP/+pr776Kuj2Onfu7PO4dj825g9/+INSUlK8jz///HNNnTo16DpqNFWQHO56SnW3Te2QsT7vvPNORK7Gj3VXXnmlz1XspaWlAQ+r9PXXX9cJAq+//vqAvzljWZYmTJjg89z06dO1fft2nyvmU1NTddlllwW0TEm65pprfM4lP/30U73nrkDE4h9KDuVwODRq1Cif54K5qr1v37467bTTfJ6bPHlyQH8UuPXWW6M+1E20NPXrmj+1t9uvfvWrkGoAgFhEGAwAaBKlpaV64403fJ4L5gNkfWovY/78+dq7d2/Yy41HF198sc/jKVOmNDg+odvt1m233aa33norqHZqj1e4ePFi/fvf/25wnh07dmjkyJExe+f4gQMH+lzpWFRUpFtuuaXBoKGsrEyXXXaZ/ve//0Wsjnnz5ukPf/iDz/jFtS1durROSDN48OB6P5jefffd+u1vfxvUDRbz8/N17733+jxX3ziVl1xyifr06ePz3OWXX67PPvuswTaeffZZPfzwwz7PXXbZZXWu5A1X7ePijTfe0Jw5cxqc57XXXtMf//jHkNo7/vjjfR4/99xzQQVWWVlZdb4G/uCDD+r3v/+9KisrA17Ovn379Ne//lWjR48OeJ5g1F7PF198MeiApva2WbVqlZ544okG51m4cKHGjRsXVDvxKj09vU74+9///ld/+tOfGjxHbN68WaNHj/bZX9q2bVvnDzeNmTBhgizL8j5+++239eSTT/rszxdddFFQY1qnpqbWOe5nzJihK664Iqiry4uLi/X0008HNc56c6l9hfehw2QE4rHHHvP51sf27ds1cuRIrVixwu/0+/fv17hx47yvy4f+camlaOrXtdqKi4t9xt/u2LFjXOx7ABAwAwBAE5g1a5aR5P1nWZbZsWNH2MstLy83bdq08Vn2E0884XfaHj16+Ey3bdu2oNoaMWKEz/wLFy5scPpDpw3lJTbYer/++mvjdDp95unUqZN57rnnzL59+7zT5efnm1deecUMGDDAO92wYcN85hsxYkS97bjdbp95a7bntddea7744gvjcrmMMca4XC6zYcMGc99995n09HSfZQfaljHGbNu2zWf6Hj16BNiDwfnd735XZ5uNGDHCLFiwwJSVlXmn+/HHH80zzzxjunfvXu86BbK9a09/wgkneH8++eSTzTvvvONt1+12mw0bNpjbbrutzjZOTEw069evD2i9evfubf74xz+aOXPmmO3bt3u3lTHGVFdXm2+//db83//9n+natatPG+np6eaHH36ot41Vq1aZhIQEn3mcTqe5+eabffaJiooK8/HHH5vzzjuvzvpnZ2f77KeB9ltjKisrfbZVTZ/dddddZuvWrd7pysrKzEcffWQuvPDCeo+LQNp7+eWX68xz/PHHm7vuusv84x//MM8++6zPv8LCwjrLcLlc5te//rXfPnrwwQfNypUrTUVFhc88+/btM59++ql5/PHHzRlnnOHdHv369Wu05lB89tlnderr06ePuf32280TTzxRZz1zc3P9Lqd2H1uWZa6//nrz1VdfeaepqqoyS5cuNVdddZV3/z/ssMNMZmZmUOfIcF8DjDFm6tSpPsuYOnVqUPNPmzbNZ/4JEyY0OH1VVZUZPHhwnb4+8cQTzZw5c0xJSYkxxnOO2Lx5s5k6dapJS0urM/1rr70W9LoaY8zpp59eZ/sc+njBggUhLXfSpEl1amzfvr258847zeLFi01paanP9IWFhWb58uXmmWeeMeedd55JSUnxnpsaEuzrdlPIz883iYmJ3hoOO+ywoJcxZcqUOv1lWZYZNmyYueeee8w//vEP8+CDD5rLLrvMtGrVyjtN//79zRVXXOEz3/Tp0xtsK9h9PNh9uj7BvNZH43XtULNnz/aZ9/rrrw9pHQEgVhEGAwCaxJlnnunzRvqkk06K2LLHjRtXJ3jxp6WHwcYYc9ddd9Vpt+ZfRkaGTyhb82/UqFFmwYIFPs81FtAuWbLE58Ptof+SkpJMhw4d6oSWkswZZ5wRdFvRCoP37t1runXr5nednE6nad++vUlOTvYb0O3YsSPo7V17+m+//da0bdu2zvNt27atE7QeGgY09sHeX8h96PwZGRmmQ4cO9W7PQNowxpiXXnqp3jqdTqdp165dnSCp5l/Hjh3NqlWrAtpOoRxX8+fPr7cPUlNT/fZ7Tk5OSNu1vLzc9O3bt972av+r77guLCw05557boPztm7d2rRv397vsVbzr6nCYGOMGTlyZMDrWd/5cv369fXue8nJyX73mzZt2ph169YFfY6MxzDYGGO2b99ujjjiiHr7tm3btiYpKane30+ZMiXo9awxY8aMepfbrVs3n+AtGJWVleaaa65pcJ9JS0tr8NwkxUcYbIyp8wewhv6AV58bb7wx4ONNkunatavZunWrGT9+vM/zjf1hIN7C4Nr/Ivm6VmPs2LE+8y9evDikdQSAWMUwEQCAiMvNza3zVfpIDBFR37K++OKLoO/Y3VI8+OCDGjt2rN/fHTx4sM7NUs477zy9/fbbdW6g1phTTjlFM2bM8Pv108rKSu3bt08ul8vn+XPPPTektqKlY8eOeu+999StW7c6v3O5XNq/f3+dsYH79OmjhQsX+p0nWH379tUHH3ygTp06+TxfUFDg9+v3aWlpmjZtWp0hI2o79GvetRljdPDgQe3bt8/vuNHt2rXTa6+91mgbkjR+/HjNnTtXmZmZdX7ncrl04MABv8MlHHPMMVq6dKmGDBnSaBuhGj16tJ555hm/fVFWVlbnhoHhbNfk5GTNnTtX/fr1C7VcSZ4btM2bN08PPvig0tLS/E5TVFSk/fv31znWaliWpeOOOy6sOhry6quv6uSTTw5rGccee6zeeOMNJScn1/ldRUVFnf2mc+fO+uijj5p0vWJNjx499Nlnn9UZO7ZGQUGB3yFE0tPT9fzzz+uBBx4Iue0xY8bUuVlgjfHjx8vhCO3jY2Jiol588UX961//Uvv27f1OU1paWu+5qcaAAQNCaj/arrrqKp/Hb775ZtDLePrpp/XMM88ENCzHKaecomXLlql37951ht84dBzqeBWt1zXJcx469Ca7hx9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" ] }, "metadata": { "filenames": { "image/png": "/Users/folgert/projects/hda/_build/jupyter_execute/working-with-data/notebook_109_4.png" } }, "output_type": "display_data" } ], "source": [ "ax = girl_turnover.plot(\n", " style='o', ylim=(-0.1, 3.1), alpha=0.7,\n", " title='Annual absolute turnover (girls)'\n", ")\n", "ax.set_ylabel(\"Absolute turnover\");" ] }, { "cell_type": "markdown", "id": "4605679e", "metadata": {}, "source": [ "\n", "\n", "Pandas's two central data types (`Series` and `DataFrame`) feature the method `plot()`, which enables us to efficiently and conveniently produce high-quality visualizations of our data. In the example above, calling the method `plot()` on the `Series` object `girl_turnover` produces a simple visualization of the absolute turnover per year. Note that Pandas automatically adds a label to the X-axis, which corresponds to the name of the index of `girl_turnover`. In the method call, we specify three arguments. First, by specifying `style='o'` we tell Pandas to produce a plot with dots. Second, the argument `ylim=(-0.1, 3.1)` sets the Y-limits of the plot. Finally, we assign a title to the plot with `title=\"Annual absolute turnover (girls)\"`.\n", "\n", "The default plot type produced by `plot()` is of kind \"line\". Other kinds include \"bar plots\", \"histograms\", \"pie charts\", and so on and so forth. To create a histogram of the annual turnovers, we could write something like the following:" ] }, { "cell_type": "code", "execution_count": 58, "id": "f5de724b", "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": { "filenames": { "image/png": "/Users/folgert/projects/hda/_build/jupyter_execute/working-with-data/notebook_111_0.png" } }, "output_type": "display_data" } ], "source": [ "girl_turnover.plot(kind='hist');" ] }, { "cell_type": "markdown", "id": "fe1dc908", "metadata": {}, "source": [ "\n", "\n", "Although we can discern a tendency towards a higher turnover rate in modern times, the annual turnover visualization does not provide us with an easily interpretable picture. In order to make such visual intuitions more clear and to test their validity, we can employ a smoothing function, which attempts to capture important patterns in our data, while leaving out noise. A relatively simple smoothing function is called \"moving average\" or \"rolling mean\". Simply put, this smoothing function computes the average of the previous $w$ data points for each data point in the collection. For example, if $w=5$ and the current data point is from the year 2000, we take the average turnover of the previous five years. Pandas implements a variety of \"rolling\" functions through the method `Series.rolling()`. This method's argument `window` allows the user to specify the window size, i.e. the previous $w$ data points. By subsequently calling the method `Series.mean()` on top of the results yielded by `Series.rolling()`, we obtain a rolling average of our data. Consider the following code block, in which we set the window size to 25:" ] }, { "cell_type": "code", "execution_count": 59, "id": "f9378f50", "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": { "filenames": { "image/png": "/Users/folgert/projects/hda/_build/jupyter_execute/working-with-data/notebook_113_0.png" } }, "output_type": "display_data" } ], "source": [ "girl_rm = girl_turnover.rolling(25).mean()\n", "ax = girl_rm.plot(title=\"Moving average turnover (girls; window = 25)\")\n", "ax.set_ylabel(\"Absolute turnover\");" ] }, { "cell_type": "markdown", "id": "9482b71f", "metadata": {}, "source": [ "\n", "\n", "The resulting visualization confirms our intuition, as we can observe a clear increase of the turnover in modern times. Is there a similar accelerating rate of change in the names given to boys?" ] }, { "cell_type": "code", "execution_count": 60, "id": "b5fe49d3", "metadata": {}, "outputs": [ { "data": { "image/png": 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5FStWWI8nXV+/8vJyx/8/7bTToupX6HqLFi2yBnFKS0uDMs0TFTT+6KOPgtrZaaedYt5G6Pvz0ksvmUmTJoU9h+LiYmtQqPN21llnRR04/vTTT82QIUMct1VeXu54dYfH4zH33HNPxO1feeWVQescf/zxMb8uTz31VNA2tt56a+vzW79+fVhwOfR1GzhwoONrl05B49DvzN1lDIfu9zpvW221VbdthR5jH374Ycdlu9vfh0rF98ubbrrJcTt5eXkJP4b/9a9/dWwvNzfXlJWVOf7/uHHjzHfffRdx+6FjrruM4dBjbizjO/R777x586J6DRA9n88X9p3v2GOPjWpdW9B41apV5pVXXjEzZ840Dz74oHnhhRfMhx9+aNra2hLa74aGhqDfwkVFRQlvA4iEoDGQBSIFjWfNmhX0f/fff7/jdp544omgZe+9997A/8UTNP7ggw/Cgglut9v8+te/Nh9//HEgS7Gtrc289tpr5rDDDgv7IjZixIiIGUWh/fr5z38exSv2f/x+vxk9enTQNl588UXH5Xv6pb5rBsj+++9vnn322aBA/sqVK811110Xlm1WXFxsampqum0v9JJVaUvA5oYbbghkjRtjTGNjo3nyySfN7rvvHlhuzz33jOtLfTw6vzyPHz/eXHzxxebFF180y5cvD/qx2tHRYRYvXmyuv/76sMssXS6XmTNnTsQ2kj02ampqzPDhw4OWz8/PN2eccYZ56623THNzc2DZuro68+STT5o99tgj7P3qLrgd+gN2wIABgcCEy+UyJ5xwgnnzzTcDXzj9fr/5/vvvzS233BK2rc4ftJWVlebcc881zzzzjPnuu++Csm38fr9ZunSp+de//mXNMou0T+kq2WMzWe9HrO68805z5513mr/85S9B7fTv3z/wf063UKkOGsc79kJ/nHXdL06YMME88sgjQdl/69evN3fccYcZMGBA0HoejyeqUgvV1dXW4MJxxx1n5s6dG8hY7ujoMO+99545+eSTw5YtLi423377rWMb999/f9Dyu+66a7f9CrXvvvsGbSNStntzc3NYgMjtdptjjjnGvPzyy0ElDjZv3mxefvll62XV0QS3Q9fpWprg0EMPNS+88ILZvHlzYPnVq1ebW2+9NeixnrjjjjuC2o/mqqlQoWOu6z5l1KhR5p577gnKDl+xYoW5+eabg06kd95mzJjRbXt1dXVhx39J5pBDDjEvv/xyoOyJz+czixYtMuedd15YANnj8UQMLK1cuTJonby8vJgz3EPHxI033mhdLrQ8g9vtDuxLQ5MRmpubzUcffWTuvPNO85Of/MTk5+enVdD4oYceCmqnu4zhe++9N+x97LxVV1dHXHerrbYKWn716tWOy4ZuuzvJ/n751ltvhSV5FBYWmj/96U/mu+++C3x/q6urM4888khQCaB4juHPPPNM2GuSn59vLrvsMvPVV18F2tu8ebN55plnzJQpU8KWnzBhQsQSQ9dee23Q8t1lDP/xj3+0joOCggLT0tLiuF5bW1vQ615cXGza29u7fQ1i9eabb3b7PSJRt0gnQFLlueeeC3tvou1n6P4n9PtG6Lg/8MADzaxZs+K64s4mdF/R0zJwQCwIGgNZIFLQuLm5OejM/wEHHOC4nZ/85CdBX4Dq6uoC/xdr0LitrS2sLmW/fv26vdzntttuCzs4RzpLHHpZcF5eXlSXGnZ64403gtoaNmxYxC8APf1SL20Jrvz973+PuN7cuXPDLv+7+eabI65TW1trBg0aFLTONttsY1asWOG4jtfrNb/61a+sX4p6M2h82223xVTjsKGhIezS9X333TfiOskeG4ccckjYa7948eKIbfh8PnPhhReGfVHtrOVr45T1lJ+fH3OA88EHHzSvvvpq1Mu3tbWFjZcxY8Z0+6U5FWMzWe9HvBJRDibVQeN4x17oj7PO23nnnRfx8uCvv/46LHP23HPP7ba9adOmBa3j8XjMrFmzIq7z1FNPhZ303HvvvR2Xb2xsDMvm+/zzz7vtW6fvvvsu7DXdtGmT4/KhQbzBgwebd955p9t2br311rB2up60sbG9Vy6Xy3oiozecfvrpQW1Hcyl4KKcxt99++0WsIbx69Wqz/fbbB61TVlZmVq1aFbG90O9lkro97s+fPz8sg7uysjLoBFeon//850HLOwV9bb777rugY2R+fr416NzW1haWQRpt3VhjtpzAi6Z+brKCxqtWrQpqp7uM4eOPPz6w7KhRo4Jes67JFaGqq6uD2hk/fnzEdkLHS3eS+f2ytbXVbL311kHrDB061Hz99dcR1zniiCPiOoavX78+qCSNJDNkyJCIx3Cfz2f+8Ic/hLV1ySWXOK4TOo9Ldyc3ugbmQ5MK3njjDcf13n777aBle6v8gG2/01u33p73JFatra1h++qBAwdGffLS6RjR3W2rrbYy8+fP73H/Q/flyTq+AsYQNAayQqSgsTHG/OIXvwj6Qrl8+fKwbdTU1AR9iQwN1MYaNH744YfDDqzPPPNMVM8ntM6qy+Uy33zzjePyoQGBWCYQOOmkk4LW/cMf/hBx+UR8qY/2kurQS/knT54ccfnQyR8KCwu7vTTPmC3ZgLZLw9PtC2FbW1vYF8LugjLJGhuh9cgGDx4c02RqoT+s/vGPfzgu6xS46+5S5kQ66KCDgtp+7rnnIi6f7LGZzPcjXn0paBzr2LP9OOuu9Eunf/zjH0HrDRs2LOLy8+bNC2vr1ltvjaqtO++8M2xdp3qvxhhz2mmnBS174YUXRtWOMeEZbEcffbTjsl999VVQ4KqwsNB8+umnUbcVemKku4lCbe95LDWRe2qvvfYKajuaCchC2cbcqFGjgk6OO6murg4rbXLppZc6Lr9s2bKwrOHzzz8/qn6G7ru6+3yFntx0Ki9hE/pdyymD+6uvvgpaLtLJk55IVtDYGBP2XSJSxnDXEiPnn3++2WWXXQL3I5UE+c9//hPURncnuELf9+4k8/vlgw8+GLS82+2OavK31tZWM3HixJiP4aFX4ng8nqgzL4877rigdfv16+f4Oe/o6Ag6URMpY7ihoSHoROJNN90UdCXCZZdd5tin0FIy3QXp45XNQWPbCYPbb7896vXjDRpLMjk5OT0O8l500UUx7S+ARCJoDGSB7oLGoWe4r7vuurBt/POf/wxaJvQy/FiDxqETcfzkJz+J+vk0NzeHncGP9IPrgQceCFp2t912i6qdhoaGsMv0vvrqq4jr9PRL/cCBAyNewtbVF198EbRuUVFRxIzO0B9BsfyoDw1CpOMXQmPCgzjdXVqdrLEROtHafffdF9Pz+v7774OCDDvuuKPjsrbAXTyXwvdEaGDjggsuiLh8ssdmMt+PePWVoHE8Yy/0x1lubm7Ey7a7qq2tDcuSi7Ru1yxBSWbixIndTnbUye/3h11WffjhhzsuH3qsHTJkSFST9dlK4bz00kuOy3c9ESzJXHXVVVE9n0719fVBGdulpaUxXWEzdOjQiJd8J1rohG/x1AK1BQQeeeSRqNf/85//HPbeOl1eftlllwUtO2zYsJiuVjjqqKOC1p8wYULE5UPLBkU6sdGpra0tbCJDp0z10GzMeGonRyOZQePf/va3QW05ZQwvXrw4aLnnnnvOXHDBBUHjwClIf8IJJwSt2122dTp/v9xnn32Clj/zzDOjascYY955552w5xbpWOPz+YImK5RiC6CtXbs27CRPpBOFP/7xj4OWdcoYfv7554OW+/zzz4OyQ/fYYw/HNkJLZyxatCjq5xOLbA0aP//882HfE/faa6+YSkdMnTrVuFwus/fee5trr73WvPLKK2bZsmWmqanJtLW1mTVr1pg5c+aYyy67LCwLXtpyIiXa5Cib0OSKn/70p3FvC4gVQWMgC3QXNPb7/aaysjLw/zvssEPYNrpOCDNkyJCwA20sQePa2tqwg/drr70W03MKrTO27bbbOi7b1NQU9gWxu8vQjQnPAtlzzz27XaenX+q7C66FCr0k1Oky4hUrVgQt53K5zPfffx9TW6H1NNPpC2GnTz75JKiP3U3EloyxUVNTE7RseXl5XBNY7L333kHvn1NtSlvgLpaJbBJh06ZNQe3vs88+jssme2wm+/2IV18JGscz9kKDQ7HWGw+t5etU9sjv94fVpL377rtjaiu0/mlRUZFjINjv95tx48YFLf/8889328acOXOC1hk+fLjjj12v1xt0XHC73WbNmjUxPSdjwrPxPvroI8dlQ9/zWCYg7Kn29vaw7xPRXKUQylavMpaaomvXrg3LHna6JDk0s/Lyyy+Pqa9z584Ne80jnRgJPekfzZwToTXmI5VO+PLLL4OW3X777aM+8ZKuQuvlHnfccdblul7ZkJOTYxoaGsyLL74YtK7TFU9dM5Q9Hk+3We3p+v1y06ZNYZ/Bjz/+OKa2utZB7+5Ys2jRorDXYunSpTG1F3pi7ZBDDnFcNrRkj9NVBF2zs4cMGWKMCa637vF4rJN/hmYoDxo0KOqrAdC9L7/8Mmwsl5aWRpyDwOb++++Pep3Nmzebs846K2ycFhcXm3Xr1sXzNMLmIEp2Mgiym1sAsp7L5dJJJ50UuP/111/ro48+Ctz/8ssv9cknnwTun3DCCfJ4PHG3t2DBAhljAvdLS0t10EEHxbSNI444Iuj+N998o40bN1qX7devn4466qigxx588MFu23jggQeC7p922mkx9TEe+++/f0zLjx07Nuh+XV2ddbmFCxcG3d9mm23C1u3OoYceGtPyidbU1KSFCxdq9uzZevjhhzVjxgzdddddQbeXXnopaJ0VK1ZE3GYyxsbcuXOD7k+bNk15eXndthFq1113DfxtjNEHH3wQ9boHHHBAzO05aW1t1SeffKLnn39e//3vf3X33XeHvQ+PPfZY0DqR3odkj810eD+ySSLGXqz7xXHjxgXdd9ovLlmyRLW1tUGP/fznP4+prZ/97Gdyu//v63Rzc7M+++wz67Iul0unnHJK0GPx7G9OOukkx2PwokWL1NDQELi/yy67aOjQod22Earr+JbCP6eRJHJ/052mpqag7xPSlv16T/3oRz9Sbm5u1MsPGTJEe+21V9BjttesqalJixcvDnos1jG37777atCgQUGPvffee47Ln3rqqUGvyezZs7V27dqIbcyYMSPo/tlnn+247HbbbaeysrLA/SVLluhXv/qVmpqaIraRzqZNmxb0uX7zzTfDxpkkzZkzJ/D3HnvsoZKSEu2///5BY+eNN94IW++LL77QunXrAvd33333oNewN/TW98sPPvgg6LUZMWKEJk2aFFNbhx9+eNTLho71HXfcUdtuu21M7YX+hli4cKH8fr912dDfJ7b3UwoeCwceeGDYuj6fT2+99VbYeu+88468Xm/Qui6Xq5tngGisXr1a06dPDzomut1uPfTQQ9p6661j2tZpp50W9TpFRUWaMWOGLr/88qDHm5qa9Ne//jWmdjuFHte6Piegt+WkugMA0sPJJ5+sv/zlL4H7Dz30kHbffffA36HL9sQXX3wRdH/ixIlBX86jscMOO6ioqEjNzc2BxxYvXqxp06ZZlz/ttNOCfng//PDDuuGGGxx/eH///feaN29e4H5BQYGOP/74mPoYj8rKypiWLykpCbrf2NhoXW7JkiVB9ydOnBhTO5I0YcKEmNfpqeXLl+v+++/X448/rq+//tr6oy0Spx85XfX22AgNHDQ1Nemuu+7qtl+hqqqqgu6vWbMmqvVyc3O1/fbbx9xeVxs2bNADDzygRx99VIsWLZLP54tp/UjvQ7LHZqrfj2ySiLEn9d5+MfRYNHr0aA0cODDmtrbZZhstXbo08NjixYu12267WZc/9dRTdc011wT2Zc8//7w2bdqk/v37W5dvamrS008/HfTY6aef7tif0PHtcrniGt+ff/550P1YxvfOO+8cc3vx6vodoFNhYWGPtxsaNI92na4BLdvJg6+//jpo/5mbm6tddtklpnZcLpcmTZqkV199NfDY4sWLdeSRR1qXLysr0wknnKB77rlHkuT1evWf//xHV1xxhXX5b775Jii4VVhYGHayoyuPx6MzzjhDN998c+Cxe+65R0888YSOOeYY/fjHP9b+++/f60HRRCovL9duu+2mDz/8UJJUU1OjxYsXB71XPp9P77zzTuD+D37wA0lScXGx9tprr8D3hDlz5uiCCy4I2n7XAKMUHpjsDb21Hw3dV8Tz2YklyBy633ba10YSuk59fb2WL19ufY122mknDR48WDU1NZKkTz75RLW1taqoqAgsU1NTE9SvzrGw3XbbaeTIkVq5cqWkLe976EmiVIyFbFBXV6cf/ehHWrZsWdDjd955Z0wnKXriL3/5i+bMmRN0XL7vvvt00003xXRSUgo/rtmOfUBvIWgMQJK07bbbaq+99goc2B599FHdcsstcrvdeuSRRwLL7bTTTnF9Iexq06ZNQfdj/SIrbTlTPHr06KCAU+h2u9p///01btw4ff/995KktWvX6tVXX9X06dOtyz/44INBAcrDDz9c5eXlMfczVrH+qAoNbDoF80Kz6eLJPItnnXgZY/TXv/5V1113nVpaWuLejtOPnK56e2x0zSaStmSpOGWqxCLSeO+qtLQ05pMyXd1zzz265JJLwsZQLCK9D8kem6l+P7JJT8dep97aLybiWCRtycjrGjSONBYqKys1depUvf3225KktrY2Pfroo/rNb35jXf6JJ57Q5s2bA/f32muviIH40PH96aef6te//nU0TyOiWMZ312BKKsR6ctFmzJgxMa8zevTooPu2q59CX8dhw4bFdaVDaBZod+/POeecEwgaS1v265dddpn18xmaZXzsscd2+/3nmmuu0VtvvRV0VVpdXZ3uvvtu3X333XK73dpxxx21zz77aOrUqfrBD34Q8wmaZDvooIMCQWNpy7Gia9D4ww8/DMr26xrsO+iggwJB47lz58rr9Son5/9+dqciUNhb+9HQcZ6Iz04kidhvDx06VAUFBWptbQ3arm1bLpdLBx54oB599FFJkt/v11tvvRWUrRyaiR46FjqvKLF91yBonHjNzc067LDDwq7q+Otf/6qzzjoraf1wuVz64x//qJ/85CeBx5qamvTee+9p6tSpMW0rEcc1IF6UpwAQ0DWTZP369Xr55Zc1Z84crVq1yrpMvEKDRKWlpXFtJ/QLcKSglsvl0qmnnhr0WOglv52MMWHZ1ckoTSGp1y5Jq6+vD7ofmkESjXjfp1gZY/TLX/5SV155ZY8CxpIcLzfsqrfHRm8FE6PNMiguLo67jWuuuUZnnXVWjwLGUuQvu8kem6l+P7JJT8ZeV721X0zFsUgKzxR22t/Y/i/d9zdS4t73aBQVFYU91jUQFK94xkLoOLBdYZGqMTdhwgRNmTIlcH/ZsmV65ZVXwpZra2sLK5nyq1/9qtv+9OvXT3PnztVZZ51lvUrH7/dr8eLFuuuuu3T88cdr2LBhmj59eljALJ10V5aga9+Lioo0efLkwP3OTFNpy0nTrpmGXq83KEO5oKAg6L3pLb21Hw0d54n47ESSis9QLGNh6623Dgqcdx0LS5cuDfpNFZqhXFlZGVZeCbFpb2/XkUceGVbG5Pe//31YuYhk+MEPfqCCgoKgx7qejIpW6O+hRJRhAqJFpjGAgOOOO04XXHCB2tvbJUkzZ85Ufn5+4P89Ho9OPPHEVHWvx0499VRdffXVgQDWc889F3aJmSS9/fbbqq6uDtwfMWKEDj744GR2NeG6vo+SAu9xLOJZJx4PPfSQ7r///qDHSktLdeyxx2r//ffX9ttvr5EjR6q4uFiFhYVBP1Crq6tjrocr9e7Y6K3XrbezDt555x1dffXVQY/l5+fryCOP1AEHHKCdd95ZI0eOVGlpqQoKCsIutYv2B2qyx2amvh/oO4488kidc845gZqvH374ob7++mvtsMMOQct9//33evfddwP3CwoKdNxxx0XcdraN75KSErlcrqD+ZXIt3d50zjnnBAVSZsyYEXZFzZNPPhmUOTpx4sSwWs1O+vXrpxkzZuiSSy7RAw88oNmzZ2vx4sXWseP1evXyyy/r5Zdf1s9//nPdf//9aVe+Yt999w3KRp07d646OjoCx7qugcL99tsvKGN8r732UnFxcWAsvvHGG9pnn30khWco77PPPmFBJaSXroFfKTw7uOv90ACzLeDcmagwZ84cxwzl3vDWW28FXRXTm0pKSpL+m9Hv9+ukk04KOyF2xhln6KabbkpqXzoVFBSosrIy6MrY9evXx7ydrlccSclL5AEkgsYAuujfv78OO+wwPfPMM5K21FrsGpA76KCDNHz48B63ExqIi7eYf2iGYneXxI4ZM0bTpk0L1OrrvCw49NLd0Myuk08+uUcT/6WD0EtLQ1+7aMSzTqyMMbryyiuDHvvBD36gxx57zLHmZ1fxBgt6c2yE9vviiy/W3/72t7j6mUx//OMfg+5PnDhRs2fPjuoy0ljeh2SPzUx9P1Ihmkz9TJaqY1G/fv109NFHB50ce+CBB8LGYWgpnJ/97GfdlgkIHd/HHHNM2MSUfUlOTo6GDh0aVHN57dq1MU9yFCqesRA6DmzvVarGnCQdddRRuuCCCwK1WV988UWtXLlSI0eODCwTWv860gR4TsaNG6drr71W1157rTZt2qR3331X8+fP17x58/Thhx8GTfolSc8884w2bNigN998M6iEQ6p1ZgC/+eabkrYEbd5//33tt99+amlpCQrAhwYVc3Nztf/++wcm550zZ47+9Kc/Bf7uKnTdTBM6zhPx2YkkFZ+hzgzgzjJm33zzjVasWKFRo0apqqoqaI6D0Pdz2LBhGj9+vL766itJW97/rkHjrnp7LDz44INRTb6aCGPGjElq0NgYozPPPFNPPPFE0OPHHHNMWMmdZAs9Lsdz9V7ovAKjRo3qUZ+AWFCeAkCQrpPctba2Bp3Z7OkEeJ1CD55dMzej5ff7tXz58ojbtQm9tDf0y1NTU5OeeuqpiOtkotCar/FkGoROWNYbPv74Y61YsSJwv6KiQk888URU7620ZcK2ePXW2Aid5d5W5zLd1NTUBP0g9ng8euqpp6KuOxjL+5DssZmJ70e8oq1J6SSaiSQzWSKORVL4pIjxHIsefvjhoPcn3jJJ2TS+O4XWIe16+Xe8QidPikbod5IBAwaELRM6NtasWRNXdng8Yy4vL0+//OUvA/d9Pp/uvffewP2vvvoqaJLX4uLiHgd9+vfvr8MPP1w33nij3nvvPa1fv17333+/dtppp6Dl3n333bArjNKBU1mC+fPnq62tzXG50Mfef//9wPfpvlbDNnScJ+KzE0ki9tvr1q0LK2PT3WfIaSx0fT/dbrcOOOCAiOt2Xb7r3521kxGfCy+8UPfdd1/QY9OnT9fDDz+ckLkVeiL0BEU8V1WEHtfiqR0OxIugMYAghx12mPWHTnFxcdCkDz0ROrP6okWLYs5oW7JkSViNxWhmbD/yyCOD6i0uXLgwKOD05JNPBgXKJ0+erO222y6mvqWj3XffPej+okWL1NHREdM24qnBFavQWbgPO+ywmCYg7Ekfe2tsTJgwIej+okWL4u5jsnzxxRdBn8nJkyfHVGcvlvch2WMzE9+PeIXWlo01Ez+WH/KZKPSYsXz58phPPDU1Nenbb78NeqzrZFlO9ttvv6DP1OrVq/X6668H7r/zzjtBwZCRI0dGVSYpdHx/9tlnaVtaIlF23HHHoPvfffddj7f56aef9nid0PdCknbYYYegkzkdHR1hkzXF01Y0Y07aUp+4a/v33ntv4GRFaDbeCSecEFeN+UjKy8t12mmn6dNPP9VPf/rToP/rOulyuggNFHYG+boG+wYOHKiJEyeGrds1a7Sjo0Nz585Vc3OzFixYEHi8vLxckyZNSnCvkyt07MXz2ek6gWJ3Qvfbsazb6eOPPw66X1ZW1m0QLpqxMHHiROtvqK5jYfXq1fr666/1v//9L2gfv9NOO2nw4MFRPwf8n2uuuUb/+Mc/gh6bOnWqnnzyybDSacnW0dERdiIl9ORuNP73v/8F3Q897gG9iaAxgCB5eXk69thjwx4/8sgjrRPOxGOvvfYKqnfa0NAQKAsQraeffjro/rbbbmv9ohaq87LgrrpmlMY66VCm2HXXXYNqx9bX11snwXHS0dERlmXbGzovm+0U6+VXL774Ytxt99bYCL3c8NNPP9XatWvj6mOyJPN9SPbYzJT3I/Qy7VizhKXwy4ZjzciaO3duzG1mku222y7skuTZs2fHtI1nn3026ARLUVFRVAE82wSckfY3J598clTZUqH1UTds2JCUE36ptNtuuwXdjycIG+qVV16J6eTVunXrgiY7k2StBVxcXBwW9Hr22Wdj6tu8efPC9tFdJ2GLZPTo0TrssMMC91etWqUXXnhBLS0tYZnt0UyAF6+cnJywciyJeN8Sbffddw/KCly4cKGampqCAoUHHHCAtYb/zjvvHBQEnDNnjubNmxeUoTxt2rSML3+25557Bj3/VatWxRzIfe6556JeNnSsf/HFFzGfKAr9DRH6u8TmwAMPDFqmsx5xZ/kSyTlrPPR9njNnTkoyzh944AEZY5Jyi/fKnVj985//DJt/Y4899tDzzz+vwsLCpPQhknfffTfshH20J/m6Ct0/hiZcAL2JoDGAMGeffbaOPfbYoNtvfvObhG2/oqJCe++9d9Bjd9xxR9Trt7a2Bl1SKUmHHnpo1OuHzlw/c+ZM+f1+VVVVBQVJCgsLrQH0TJSfnx+WKX799ddHnYF29913xzVxQ6xCMwJiuTz+ww8/DJqRPB69MTbGjBkTFNTw+/268cYbe9TP3taT92HlypV69NFHo14+2WMzU96P0Ay/eOo2jh49OuhHU11dnb7++uuo1m1tbdXMmTNjbjOTuFwu/ehHPwp67M4774x67Bljwo5dBx98cNR1WU899dSgIMSzzz6r+vp6NTU16cknnwxaNtqTVAUFBUFBQUm64YYbolo3U4V+n/jss896vM2NGzeG1caMpGvGriQNHjxYe+yxh3XZ0O8r999/f9iVU5H861//Cro/YcIEDRs2LOr1zznnnKD7d911lx577LGg/fyee+6pXXfdNeptxiN00trQiZ7Sgcfj0dSpUwP3vV6vnn322aCgqFMd2tByA2+88UafK00hbflOP2XKlKDHQmtjRzJ37lx9+eWXUS/fOQlvV//+97+jXr+mpkaPP/540GPR/IYYNGhQ0AmftWvX6tFHHw06geM0FkpLS4P2B311LCTbAw88oAsuuCDosR133FGvvPJKwq+SiNf1118fdL+goCBonxKNhoaGoJJERUVFYSV+gF5lAPR5p556qpEUuP3+979PeBt/+tOfgto48sgjIy7/yCOPBC0vybz44otRtXX55ZcHredyucw333wTU3+32mqroG288sor5uqrrw567IQTTohpm8aYsOfUnTFjxgQtX1VVFVN7U6dODVr/rbfeclz2nXfeCevfdddd120bixcvNmVlZWHrjhkzJqa+RuPRRx8NamPcuHHG6/V2u15zc7PZcccdE9LH3hgbzzzzTND6OTk55rXXXou5b538fr/j/1VVVfX4NXj//feDtlFSUmLq6+u7Xc/n85mDDjoo7H3o7rOQ7LGZzPcjXj6fzxQWFgb1c9WqVTFvZ8qUKUHb+MMf/hDVehdeeGHMr2sixp4xse3XbEKPeffff7/jsvPnzw97nnfccUdU7dx9991h677++usx9fXAAw8MWv+uu+4yDzzwQNBjU6ZMiWmbn3zySVi//vOf/8S0ja66G9+xHvcSze/3myFDhgT1oaamJqZthI45SWbUqFGmrq6u23Wrq6tNcXFx0LqXXHKJ4/LLli0zHo8nrs/la6+9FtbPe+65J+rnacyW12ubbbYJrO92u822224b93jp6OiIqf1On376aUz7i9D3aOrUqXG1G6vbbrstqN2tt9466P53333nuO4999wTWM7lcplx48YFrfvVV19F3Y9YP2fJ/H4Zus9yu93mgw8+6LaN1tZWM3HixJiPNX/961+Dls/NzTWLFi2K6nmdeOKJQev269cvqs+5MeHHxa5jIT8/32zevNlx3SuuuCKwbFlZmRk4cGDQd5CGhoao+oAtnn766bD96FZbbWVWr16d0HZ68v3ulltuCRvbJ510Uszbeemll4K2MX369Lj7BMSDoDGQBdIxaNze3h72I6WkpMTMmzcv4nr//ve/ww7Axx13XMz9vfbaa8O2EfplPp4gUjp/qTfGmCOOOCKsjxdffLFpbm62Lv/888+bwYMHG0mmoKAgpi/18VizZo1xuVxB7fzxj3+MuM6mTZvMfvvtF/a84u1jb4wNv98f9l4VFRVFDGbZLFmyxPzmN78xF198seMyiQjctbe3m5KSkqDtnHLKKRG/PDc3N5ujjjrK+j5E81lI5thM5vvRE3vvvXdMnwWb0B8t+fn55v3334+4zk033RT2OYzmdc3EoLExxkybNi1o+ZycHPPUU09FXGf27NkmNzc3aL299947pn4aY8xDDz0UtI3JkyeH9efuu++OebunnHJK2HO6/vrrjc/ni3obK1asMJdeeqk5+eSTIy4X62e9N4Q+3yeeeCKm9W1BY0lm//33jxjMWb16tdl+++3DvsusXLkyYnunnXZaWFv//Oc/I66zYMGCsJNkY8eOddxHRnLzzTc77qvLy8sjBr9C/fOf/zSHHXaYef3116MOsGzevDlsnJ966qkR10lV0PjLL790fK0qKysjrhu6T+x6Gz58eEz9iPVzlszvly0tLWEn3IcOHWqWLFniuE5ra6s58sgjra9Nd8eODRs2BAVdO1/PSO35/X5z6aWXhrV16aWXdvdSBLz44ouO7+e0adMirvvWW285rhvricFs98Ybb5j8/Pyg13DkyJGmuro64W1VVVWZKVOmmJdeeinq/ZvP5zNXX321cbvdQX3My8uL+XNojDGXXHJJ0HZuu+22mLcB9ARBYyALpGPQ2BhjPvjgA5OTkxO0nsfjMb/97W/Np59+Gvhx29bWZt544w3zk5/8JOyL1ogRI8zGjRtj7u+yZcuCgiKhAZJRo0bF9OO6Uzp/qTdmS1C2M9DW9TZgwABz2mmnmeuvv97ceuut5qKLLjI777xz0DLXX399TD+W4nX44YeH9e/nP/+5mTdvnmlvbzfGbPny/+2335q//e1vQT8cQl+PeIJWvTU21q1bZ0aNGhX23CZNmmRmzJhhlixZErRdn89nVq5caV588UVz1VVXmQkTJgTWOeeccxzbSVTg7vzzzw/r69SpU81rr71mWlpaAsutWLHC/Pvf/zajR492fB+i+Swke2wm6/3oidBsKmlLEOvKK680t99+u7nzzjuDbjbr168POwFQXFxsbrjhhqCMnIaGBjN79uyg927y5MkxjaVMDRpXV1dbM9ZPPPFEM2/evEAmpdfrNQsWLAjbfudr+u2338b8XDdv3hz2/nTd5xQWFkaV5R+qubnZ7LrrrmH93Hbbbc2tt95qPvvss6CrOPx+v1m3bp15/fXXzXXXXWcmT54c6Mdhhx0Wsa1YP+u9Yfbs2UF9+OUvfxnT+qFjbs899wza599zzz1mw4YNgeVXrVplbrnlFtO/f/+w5//vf/+72/bq6urCjv+SzKGHHmpee+0109bWZozZst/57LPPzO9+9zvr96XuTrQ72bRpU9iVDJ23c889N6Zt3XrrrYF1hw0bZn7zm9+Yxx57zHzzzTdBWch+v99UVVWZu+66KyxpIJrM1FQFjY0xZtiwYdbXKppxFnrSufPW3cmYULF+zpL9/XLOnDlhfSwqKjJXX321+d///hdYrr6+3vz3v/81O+ywg/XzFu2xI/SKoc795R//+EezZMmSQICvubnZzJ492+y7775hy0+YMCHwWYtGY2Nj2MnCztuf//zniOu2traaoqIi67pXXnll1H3Idv/73//CruyQZM4///yw70TR3iKdGOz6vWbkyJHmd7/7nXn66afN999/H/Qd0e/3m6+//trcdtttYfu3zpvT97Tu7LLLLkH7yu5OSgKJRtAYyALpGjQ2xpgHH3ww7IdQ1x9EFRUV1ow3SWbgwIFRXf7mJPSy4K63K664Iq5tpvuXemOM+eKLL8ygQYMcn7vtdskll5jvv/8+6LHtt98+pr5Ga+nSpdYAjrQlW27AgAHWL+077bST+eyzz2L+4WHTG2PDGGO+/vprxy+TnV8GKyoqTGlpqeO4l5ITNF6/fr01qNr52ezfv39Ypoe05UTO8uXLY/4sGJP8sZmM96MnampqYno9nNx1112O6xQXF5vS0tKwx3feeWezaNGimMZSpgaNjdly2X+/fv0ijoPQrKHOW2FhoXnhhRfieq7GGPOLX/zC8f058cQT497u6tWrzV577eW4bZfLZcrKykx5ebnjc5MyI2jc3t5uBgwYEOjDoEGDoipt1Cl0zL300ktm0qRJ1s+LU7BV2hJEjDYb7dNPP7WeKOt8byoqKiJ+P4q1LEUop3H3xRdfxLSdrkFj262kpMQMHDjQ5OXlOS5z7bXXdttO6Ht0wAEHxPvUYxZa0qDzNmvWrG7XPeuss6zrPvDAAzH1IdbPWSq+X95www2O73F+fr71WDN69GizcOHCuI4dthOrnbfc3FxTXl7u+P9jx46NWFrEyT777GPd3oIFC7pd95BDDrGu+/bbb8fcj2wVKWM73lukz0akqwVcLpcpKSkx/fv3j3gMdblc3Z5UcPLdd98FbevAAw+M85UD4sdEeABS6pRTTtHs2bODZpju5PP5VFtba52UaKeddtK8efMcJ5qJRqSJhaKddCgT7bjjjlq4cKEOOeSQbpctKirS7bffrhtuuCFsIq7y8vJe6d+2226rZ555RhUVFWH/5/V6tXHjxrBZ7ffaay+98cYbKi0tTUgfemtsbL/99vrwww91/PHHW2fq9vv9qq2tVUNDg+NkXAUFBdp+++3j7kO0Bg4cqBdffFGjRo0K+z+fz6d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ojL5hQ4tfTR22SfDS4zOI3kPQGAAAAAAApKWqhvCgcf8Ct2NZiGSryPdYHyfTGH2F7TMopU+2P3pPeuxlAQAAAAAAuvAbo2WWWqrpFKxyKk9R2+ZLck+A3lFdH/4ZlNKnrjh6D0FjAAAAAACQdlY3+WSLvaZTsKo4zyWPpaxrHeUp0EfYMo0LPC4N7WfPskffQdAYAAAAAACkHcfL4tOolqrb5bKWyqilPAX6CFtd8crSHLmZBK/PI2gMAAAAAADSTpXTZfFpVJ5CkjVoTE1j9AW1rT7rCZB0yvZH7yFoDAAAAAAA0o4tw7E0z6X+BekVyqiw9KeW8hToA6ods/0JGmeD9NrTAgAAAACArOc3xlqeYmxZrlxpdlm8LdO4vt0vvzEp6A2QON9bTtxI0tjS9CkRg95D0BgAAAAAAKSVmmafWrzhQdd0K00hSeX54ROC+Y3U0E7QGJnNlu2f65ZGFDMJXjYgaAwAAAAAANKK8yR46Rc0tpWnkKS6Vl+SewIkVnVDeF3xMaU58rjTK9sfvYOgMQAAAAAASCu2DEdpS3mKdFNhKU8hyTqBGJApGtv9Wt9imQSP0hRZg6AxAAAAAABIK7agcVGOS4ML0y+MYatpLEl1BI2RwTIp2x+9I/32tgAAAAAAIGsZY1RluSx+bFlO2k2CJzmXp6htJWiMzFVVH/4ZlAgaZxOCxgAAAAAAIG1sbPWr0TKJXDpOgidJZXlu2ULZZBojk9my/T0uaVRxen4OkXgEjQEAAAAAQNrIpHrGkuRxu1SaFx42pqYxMlm1pTzFqJIc5XrSL9sfvYOgMQAAAAAASBu20hRSel8WX17gCXusrtWXgp4APdfc4deazeHjN12z/dE7CBoDAAAAAIC0Ycs0zve4NKxfeGA2XVRYJsMj0xiZypZlLKX3iRskHkFjAAAAAACQNmxB4zGlOXKn4SR4ncotQeO6Nr+MCa/NDKS7KsegcXqWiEHvIGgMAAAAAADSQl2rz5qhm+6XxdsyjTv80mYvQWNkHtuJG5ek0SXp/TlEYhE0BgAAAAAAacE5wzG9g1XlBfbwSl0rJSqQearqw+uKjyj2qCAnfbP9kXgEjQEAAAAAQFqwZThK6R80tmUaS9Q1RuZp8xmtarJMgpfmn0EkHkFjAAAAAACQFmyZxjluaWRxegesyvPtk/SRaYxMs6zBK1tRlbGl1DPONgSNAQAAAABAWrBdFj+6JEc57vS+LL7CoTxFbVt4xiaQzmyfQYlM42xE0BgAAAAAAKRcU7tf61ssk+BlQLCq3KE8RR3lKZBhnOqKV6b5ZJRIPILGAAAAAAAg5RwnwcuAy+LzPC71s0wSRk1jZBpbXfGhRR4V5RJCzDa84wAAAAAAIOUy/bL4ckuJCmoaI5N0+IxWNIYHjTPlM4jEImgMAAAAAABSzpZp7HZtqWmcCSosJSrINEYmWdHklc8yCx5B4+xE0BgAAAAAAKSc7bL4kcU5yvOk9yR4nWx1jalpjExi+wxKmVEiBolH0BgAAAAAAKRUc4dfazb7wh7PpAzHigJP2GMtXqNWryV1E0hDTkHjygz6HCJxCBoDAAAAAICUWmapoypJY0szJ1hlyzSWpLq28GA4kI6qGsLrig8sdKs0j/BhNuJdBwAAAAAAKeV4WXwGZTjaahpL1DVGZvD5jZZZ6opn0okbJBZBYwAAAAAAkFLVlmCVS1JlBgWsygscMo1bCRoj/a3a7FOHZaiOLaOecbYiaAwAAAAAAFKqqj78svhh/TwqyMmcsAWZxshk1ZbPoESmcTbLnL0vAAAAAADoc9p9RiubMnsSPClSTWOCxkh/VZZsfynzPodIHILGAAAAAAAgZZY3euU34Y9n2mXxhTku5XvCH6+lPAUygK2ueHm+WxUFlkGNrMDpAgAAAAAAkHB+Y/RyVYsWrW9Xi9c5cNrUYYkYK/Mui3e5XCrP92hdc3DWdF1beBZ1ptjc4dfs/zVr7WafxpXl6CfjiuRxu1LdLcSgqr5Dr1S3aFWTPZM4sByT4CEE7z4AAAAAAEi4h75q0svVLXGvn4mXxVfku8OCxpla09hvjP78fl0gmLhwbZuWN3p13q5lKe4ZorWqyaurF9Sp1Wc/MdOdTPwMInEoTwEAAAAAABKqxevXG8vjDxgPKXKrX27mhSzKC8L7vKnVL2PiC9ql0ufr28OyTxesaVNta+ZmTmebd1a2xh0wljKvRAwSK/P2wAAAAAAAIK1VN3jV0YME263LMzNYNdASNN7cYTIy23hpbUfYY34jrbJMWoj0tKIxckmKSFySti4n0zibETQGAAAAAAAJZZtUK1oel3RoZVECe5M8laX2YHdPXo9UcepzJgbAs1VdD96rKcPz1Z9J8LIapwwAAAAAAEBC2QKO0WQuDi7y6JAxhdqmIjMzjZ1qwH5f79VuQ/KT3JuesU2MJkl1rQSNM0Wt5b0qznVpWD/nYHC+x6WdBubpJ+My88QNEoegMQAAAAAASKiqhvDSBiOKPfrLPv1T0JvkGV7sUZ5bag+J1VVbXo90Vtvqc8xSrW2jPEUm8Buj+tCBKGnvYfk6c+fSFPQImYbyFAAAAAAAIGHafEYrG8MDi05ZuH2J2+VSZWn488y08hSR+tuTkgdInoZ2I79lDrzyfEpOIDoEjQEAAAAAQMIsb/DKEqvSWId6v33N2LLw57mx1a+GDAq2OpWmkOwlD5B+6lrtGeEVlskaARtGCgAAAAAASBhbaQopOzKNJefn6fS6pKOqeue+kmmcGZwmLKzIJxSI6DBSAAAAAABAwjiVNrCVbeiLxjo8z0wqUREx05igcUZwCu6XEzRGlBgpAAAAAAAgYWzB0SFFHhXlZkcIYmRJjnIsTzVSIDadNLT7taHFOTDc4jVq89kKkCCdOJURoTwFosVIAQAAAAAACeH1Gy1vDA+OZktpCknKcbs0uiRzJ8OrjqKftQ71cpE+bJnGLklleYQCER1GCgAAAAAASIgVjV7ZklCdSjb0Vbbnu67Zp80d6V/aIZray9Q1Tn+2MiKleS553K4U9AaZiKAxAAAAAABICKds2mzKNJaksWW51serM6BERTQZ0dQ1Tn91lmzw8gJPCnqCTEXQGAAAAAAAJIRT3d6xpfYgal/lNOlfJpSoiKaPdQ71cpE+bIH9CibBQwwYLQAAAAAAICGq6sNLGwwocKs0y4JVY0pzZKsCYHt90klzh19rm7uvV0ymcXozxlhLiJRn2ecQPcNoAQAAAAAAPebzGy2zZBpnW2kKScrzuDSiOLwUgFMmdrqItnwGNY3T22avka18NpnGiAWjBQAAAAAA9NjqzT61WwJVTvV9+zpbSY7VTT61ei0zBaaJaMtn1FKeIq05lQ8pLyAMiOgxWgAAAAAAQI85lV4Y61Dft6+zZVgbyZqNnS6qGqIrn1HX1n0JC6SOU/kQMo0RC0YLAAAAAADoMcdJ8LKwPIXk/LyjDcymQtSZxpSnSGuOmcb54SVTACcEjQEAAAAAQI/ZAo5l+e6szW6sdMiwjjYwm2xtPqNVTdFlEDe2G3n96VtmI9vVOmSCV1CeAjFgtAAAAAAAgB7xG2OdRG1saY5cLlcKepR6hTluDeuXOZPhLWvwyhYGLnMI+jMZXvpyem/Ks/QEDuLDaAEAAAAAAD2yrtmnFssEb9lamqKT7fmvbPSq3Zd+WbpONal3HZRnfZygcfqylQ/pl+NSnic7T+AgPgSNAQAAAABAjziVXMjWSfA62Z6/z0grGtMv29gpA3riYHvQuNahbi5Sz1bTuJzSFIgRIwYAAAAAAPSIY9C4LDfJPUkvTs8/HUtU2N7DoUUea4kNiUzjdGbLNM7W2uKIHyMGAAAAAAD0SHVDeGmDfrkuDSrM7rCDU6a1UymIVOnwGWv289iyHFXk24PGTpOtIfVsAX3qGSNWjBgAAAAAABA3Y4w1SzWbJ8HrVJzntgbOnTKzU2VFk1e2Mstjy3JUkueSrRSurQQCUq/Va6z1xSsK7MF/wAlBYwAAAAAAELeNrX41dtgmwcvu0hSdbK/D8kavvP70mQzPuSZ1rtwul8osWaq2EghIvTqHDHAyjRErRgwAAAAAAIibcz3j7J4Er5OtREWHX1rVlD7lHZzew8r//x7aAo7UNE5PTsF8ahojVowYAAAAAAAQN6f6vE71fLONU/A8neoaV1lqUg8sdKs0b0vYyBZwrKU8RVpyKhtSXkAIELFhxAAAAAAAgLhVNYRnqRZ4XBrajxqqknOZDtvrlgo+v9EyS1+6Bv1tAcf6dr/8Jn1KbGALMo2RKIwYAAAAAAAQN1tpg8qyHLmzfBK8TuX5bmvALl0mw1u12acOS5yxa7Db1n+/kRraCRqnG6eyIdQ0RqwYMQAAAAAAIC61rT5rZiOlKYLZSlRUN3jTIlM3mvIi5fn2rPG61vSpy4wtbGVD8j1SYQ4ncRAbgsYAAAAAACAu1Q4lFpgEL5jt9WjzGa3ZnPqgazQTGVY41MN1KoWA1KlrCx9T5fkeucj8R4wIGgMAAAAAgLh87xhwtNfxzVZjSx3qGqdBiQpbbeXyfLcqCjxB922cSiEgdWyBfOoZIx6MGgAAAAAAEBdb0DPXLY1gErwgTpnXTqUhksVvjKot72FoeRGnoKOtFAJSq87yntgmMgS6w6gBAAAAAABxqW4ID3qOKc2Rx82l8F0NKHCrJC/8NbFl+SbT2s0+tfrC6yqHBrnL8t2yvaNkGqcXr9+osSP8/STTGPFg1AAAAAAAgJg1tvu1vsU2CR6lKUK5XC7r61JV75VJ4WR4TkHr0PIiOW6XNehNTeP04hTEdyovAkTCqAEAAAAAADFzDjgyCZ6N7XVp9hrVWALvyeI4CV5peF/L88NLjtS1pn4iP/wfp3IhThMZApEwagAAAAAAQMyc6vESNLZLx7rGtraLc10aWBgeLrIFHsk0Ti9kGiORGDUAAAAAACBmtixVj0saVUzQ2MaWvSs5Z/v2NmOMNVt8bFmOXK7wUhS2wGNdmz+l5TUQrLbNnvldYckSB7pD0BgAAAAAAMSs2hJwHFWSo1wPk+DZDC7yqDAnfSbDW9/i12bLpGlONaltk6l1+KXNXoLG6aLOoTxFOeUpEAdGDQAAAAAAiElzh19rNodnNTpl00Jyu1zW16eqviMl2bqxlhdxKnHgFKhE8tnKhXhcUkkuJ3IQO4LGAAAAAAAgJrYsY4l6xt2xvT4N7UabUhB4jXUiQ6fJ1KhrnD5sNY3L893WciNAdwgaAwAAAACAmDgHHO2lDbCFU+mHVJSosNVSLsxxaUiRvf4tmcbpr9byXjgF+4HuMHLi9Nxzz+noo49WZWWlCgoKNHjwYE2ZMkV///vf1dDQkPD2qqurdeWVV2rffffVwIEDlZubq+LiYo0bN05HHHGEHn74YXV0pG7GVQAAAABA9rAFHF2SRpeQaRyJUxavU6mI3mKMsbZZWZojt0NWakWBPZjsNPkaks8p0xiIB3vzGDU1NenEE0/Uc889F/T4+vXrtX79ei1YsEC33367Hn/8ce29994JafOWW27R5Zdfrra2tqDHvV6vqqqqVFVVpWeeeUZ/+ctf9OSTT2qnnXZKSLsAAAAAANjYAo4jij0qsEz0hv8zvNijPLfUHhLbswXhe1Ntm1/17ZZJ8CKUF7FNhCfZA5VIPr8x1veiIt8e7Ae6Q9A4Bj6fT0cffbReeeUVSdKQIUN05plnavz48dq0aZNmzZql+fPna8WKFZo+fbrmz5+vHXbYoUdt/utf/9Lvf//7wP0pU6bopz/9qUaNGqWGhgZ9+eWXeuCBB9TU1KSlS5fqgAMO0OLFizV06NAetQsAAAAAgE2bz2hVk2USPOoZd8vtcqmyNEff1AUHiZ1qRPcWpyB1pIkM8zwuFeW41OwNDjYTNE4PDe1GtukUyylPgTixR4/BvffeGwgYjx8/Xm+++aaGDBkS+P9zzjlHF110kW6++WbV1tbq7LPP1ty5c+Nur6WlRZdffnng/j333KMzzjgjbLmrrrpKBx10kBYvXqwNGzboxhtv1C233BJ3uwAAAAAAOFnW4LUGp5zq9SLY2LLcsKDxxla/6tv8KktSKQHniQwjv4fl+W41e4NPGNjq6CL56lrtZUKcMsSB7jByouTz+XTNNdcE7s+cOTMoYNzpb3/7myZOnChJevfdd/Xaa6/F3eb8+fPV2NgoSdpjjz2sAWNJGjRokK6//vrA/Z4EqgEAAAAAiMSp/i6ZxtFxrGvckLy6xrb3MM8tDe8XuZSBbVI1Mo3TQ63D+0BNY8SLkROluXPnas2aNZKkqVOnatKkSdblPB6PzjvvvMD9WbNmxd1mTU1N4O9tttkm4rJd/7+pqSnuNgEAAAAAiKTKIUu1MkJpA/wfpxIQyaxrbHsPx5TmyOOOXJPaFoB0ClYiuZyC9wSNES/26FF6+eWXA39Pnz494rKHHnqodb1YDR48OPD3N998E3HZrv+/4447xt0mAAAAAGBL3d71zT4V5brUv4CJpLqyBTeHFnlUlEtwKhojS3KU45a8ITG+xRvak5Kt3e6TNrSEBxi7K00h2UsdtHiN2nxG+R4mQUwlpzIhtuxwIBoEjaO0ePHiwN977LFHxGWHDh2qUaNGacWKFVq3bp3Wr1+vQYMGxdzmvvvuq4EDB2rDhg366KOPdO+991pLVKxfvz5Q+9jtduvCCy+MuS0AAAAAwBbf13foH5/Ua12zXx6XdMQ2/XTUNv1S3a200OEzWtEYHjSmNEX0ctwujS7J0fchwfcvN3boy431KepV5EnwOpU7nECpbfVpaD/GQCrZMo1dksryCBojPoycKC1dujTw99ixY7tdvusyXdeNRUFBge666y7l5GzZ8Z555pnad999deONN2rWrFmaMWOGzj33XG211VZavHixiouL9d///lf77LNPXO0BAAAAAKT7v2zSuuYtARifkZ74ZrO+d6jjm21WNHnls8yCR9A4NtEEaJMtmvfQaVI16hqnnq1MSGmeq9uSI4CT9NtLpam6urrA3wMHDux2+QEDBljXjdWRRx6pN954Q+ecc46+/PJLzZ8/X/Pnzw9aJjc3V1dccYXOPvtsjRo1Ku62utPR0aFPPvnE+n/Dhg3TsGHDeq1tAAAAAEiGhja/vqkNDxAvWN2mcVFcvt/XOdXdHVvKaxOLsWW50orWVHcjwOOSRpVEkWnsEDSmrnHq1bX6wh5zygwHokGmcZS6Ti5XUFDQ7fKFhYWBvxsbG3vU9v77769//etf2nXXXa3/39HRoTvuuEO33HKLWlpaetRWJDU1Ndptt92stxkzZvRauwAAAACQLJssgRdJqmog01hyDhpXkmkck10H5ymdSgDvOjhPOVFkpDrVx61zqKeL5LEF7p0yw4FosFdPcxs2bNAxxxyjt956SxUVFbr11lv105/+VKNGjVJzc7M+/vhj3XzzzXrppZf0j3/8Q++9955eeumloEznRBk8eLDjxH5kGQMAAADoC5wyJqvqvTLGyOVKo0hfCtiC5wML3SqlbmpMBhZ6dPqOJbrvy0b5LeU+kmlYP49O3qE4qmWdgpBkGqeWMcZaIsQpMxyIBkHjKBUXF6u2tlaS1NraquLiyDvUrhm/JSUlcbXZ3Nys/fbbT0uWLFFFRYUWLlyobbbZJvD/ZWVlOvDAA3XggQfqt7/9re644w598MEHOvfcc/Xf//43rjYjyc3N1aRJkxK+XQAAAABIF061WZs6jDa0+DWoKHsv9/b5jZY1WCbBS8P6vJng4DGFmjwsX9UNXnlNaiLHpXlujSnJibrubWGOS3luqT3kY0JN49Ta7DXqsLwFZBqjJ9izR6m8vDwQNN6wYUO3QeONGzcGrRuPf//731qyZIkk6aKLLgoKGIf629/+pkceeUR1dXV67LHHdMstt2jo0KFxtQsAAAAA2ao2wmX2VQ3erA4ar9rsswamxlLrOW7FeW7tNDAv1d2ImsvlUkWBOzBRZKdInxv0PqfyIOUO5USAaDB6orTddtsF/q6qqup2+a7LdF03Fi+88ELg70MOOSTisv369dOUKVMkSX6/Xx9++GFcbQIAAABANouUMVlVn911jZ2eP5nG2aU8P/zESV2bvRY4ksOpPAiZxugJRk+Udt5558Df3QVk161bpxUrVkjaUgd40KBBcbW5evXqwN9lZWXdLt81o7nrxH0AAAAAgOhEqs3qNAlctnB6/mOZBC+r2OrkUtM4tRwzjS0BfiBaBI2j9KMf/Sjwt9NkcJ1eeumlwN/Tp0+Pu82utZA7g9CRLFu2LPB3b0yEBwAAAAB9XV2rc8ZklaWebzaxPf/yfLcqCghMZZMKS8mDxnYjb6pn9MtitQ6Z3rb3CogWoydKU6dODdQIfvvtt/XJJ59Yl/P5fLrtttsC94877ri42+ya3fzII49EXPa7777TwoULJUlut1u777573O0CAAAAQLaKlDFZ1+ZXbYSgcl/mN0bVlkxjSlNkH1umscRkeKnk9No7vVdANBg9UfJ4PLrqqqsC90855RTV1NSELXfppZdq0aJFkqR99tlHP/zhD63be+CBB+RyueRyuTRt2jTrMieccELg7/vvv1//+c9/rMutXbtWxxxzjLzeLQfwH//4x+rfv380TwsAAAAA8P8ZY7oNfGVriYq1m31q9YVnklKaIvs41cklaJw6tpNd/XJcyvO4UtAb9BXs3WNw5pln6plnntHrr7+uL7/8UhMmTNCZZ56p8ePHa9OmTZo1a5bmzZsnaUt94RkzZvSovUMOOURHHXWUnnzySRljdMYZZ2jmzJk6/PDDNXLkSLW0tOijjz7SzJkzVVdXJ2lLWYqbb765p08VAAAAALLOZq9RRzdxr6oGryYNyU9Oh9KIU2mOsWW5Se4JUq3coeRBrUNdXfQ+W01jp/cJiBZB4xjk5OToqaee0gknnKAXXnhBa9eu1Z///Oew5UaOHKnHHntMO+64Y4/bfPjhh1VaWqr77rtPkvTOO+/onXfesS673Xbb6dFHH9XWW2/d43YBAAAAINs4TSbVVVV9RxJ6kn5spSkkylNkowqHydXINE4dW6axU0Y4EC1GUIxKSkr0/PPP69lnn9URRxyhUaNGKT8/XwMHDtRee+2lv/3tb/riiy80ZcqUhLSXn5+v//znP/r00091/vnna/fdd1f//v2Vk5OjoqIiVVZW6sgjj9TMmTP1+eefa+LEiQlpFwAAAACyTaR6xp2ydTK8qobwYHlxrksDCwkrZBunYKTTZGzofbaAPfWM0VOcEozT4YcfrsMPPzzu9U877TSddtppUS8/ceJE/eMf/4i7PQAAAABAZNFkGm9o8auh3a/SvOwJyBhjrLWcx5blyOWiZmq2Kc5zyeOSQktcR/P5QeK1eo1avOH1xisK7BnhQLSy5ygHAAAAAEAE0WZKOpVq6Ks2tPjV1GGZBK+UesbZyO1yWbNYo8nUR+LVOey3yDRGTzGCAAAAAABQ9DVZbaUa+jLnSfC4eDlb2QKS1DRODadgPTWN0VOMIAAAAAAAFH2mpK1UQ1/mNPkfQePsVVFgyTSmPEVKOJUFKbe8R0AsGEEAAAAAACj6mqzVWTYZni1IXpjj0pAiaqZmK1umcX27X34TXsYEvYtMY/QWRhAAAAAAAIr+8vo1m31q7sierEpbeYrK0hy5mQQva5Xnh58w8BupoZ2gcbI57beoaYyeYgQBAAAAACB7xp5TWDRbso1rW33WoBSlKbKbrTyFJNW1RjeZJBLHVhYk37PlagCgJwgaAwAAAACyXpvPqMUbniW5bUWudXmnyeH6Gqf6zWNLCRpnM6fSB9HWBUfi1LWFB+rL8z1ycSUAeoigMQAAAAAg69U6ZEhOGJRnzTbOlsnwnILjlWX2YDqyg1Ppg2hLvCBxbIF66hkjERhFAAAAAICs5xTsGlLk0Yji8PqtVfUdvd2ltGB7nrluaUQ/JsHLZk7lKWylEtC7bBN4lju8P0AsGEUAAAAAgKzndFl9eb7bWr93VZNPbb6+P+mXLdN4TGmOPG4ufc9mZXluawY+mcbJ5fUbNXaE74fINEYiMIoAAAAAAFnPlq0nbcmoHFsaXorBSFrWx+saN7T7taHFMgme5fVAdvG4XSrNCw8bU9M4uZyC9E7lQ4BYMIoAAAAAAFnPKdhV4ZBpLPX9EhXVTpPgObweyC7lBeElSuocaoOjdziVA3EqHwLEglEEAAAAAMh6toy9PLdUmONSZalD0LiPZxpXNdiD4gSNIdlLIJBpnFxkGqM3MYoAAAAAAFnPlrFXUeCWy+VSUa5bQ4tsk+H18aCx5fl5XNKoYoLGsAcm69r8Mqbv1/pOF7Vt9szuinwmqkTPETQGAAAAAGS9OkvwpbxL4MWWXbui0auOPjwZni1oPKokR7keJsGDPdO4wy9t9vbdz0S6carFXk55CiQAowgAAAAAkPVsl9V3zaS0BY19RlrR1DezjZs7/FrbHB5IpzQFOjkFJp0CmUg8237L45JKcjmxg54jaAwAAAAAyGpev1Fje3h2ZNfJpMaW5lrX7aslKqod6jWPdajvjOxjyzSWqGucTLaaxuX5W8rqAD1F0BgAAAAAkNWimUyq0iHDtq8GjZ2e19gye/Ac2afcoW4umcbJ41SLHUgERhIAAAAAIKs5BY27ZlKW5rk1sDD8J3RVQ0ev9SuVbM/LJWkMmcb4/5yCk06TsyHxnDKNgURgJAEAAAAAspotW08Kr9lqK82wrMErn7/vTfxlyzQeUexRPpPg4f9zCk46nYRBYvmNsb7WFQ4Z4ECsCBoDAAAAALKac6ZxcPDFVpqhwy+t2ty3MitbvUarmmyT4FGaAv8nz+NSv5zwkwjUNE6Ohja/bKernCYoBGLFSAIAAAAAZDWny+lDJ/pymgSuqr5vlahY3ui1BqPGOtR1RvayBSipaZwcTsF5pwkKgVgxkgAAAAAAWc0W5PK4pOK84CxKp6BpX5sMzykI7hQ0R/ayBSjJNE6OaCbwBHqCkQQAAAAAyGq2IFd5vltuV3DQuKLAYw3IVDX0taCx/flUEjRGCNvngZrGyeFUi91pgkIgVowkAAAAAEBWswW5nLL1bNm21fVe+U3fmQzPFgQf1s+jolxCCAhWURA+6VqL16jV23c+D+nKKaObTGMkCiMJAAAAAJDVbBl7Ttl6thIVrT6jtX1kMrwOn9GKxvCgMVnGsHEKUNY51AlH4thOdrkkleUR6kNiMJIAAAAAAFnLb4zq22PINC7LtT7eV0pUrGjyymdJEmUSPNg4TbpGXePeZzvZVZrvlsftsiwNxI6gMQAAAAAgazW0G/ktQdLy/PDL7iXnjNu+Mhme0/NwCpYju5U7ZOTbJpdEYtmyuZ2C+EA8GE0AAAAAgKxV12q/jN6pPMWgQrf65YZn8lXVdyS0X6niGDSmPAUsyDROnVhqsQPxYDQBAAAAALKWU3DLKRjmcrmsAdSqBq9MH5gMr6ohPPg9qNCtEuqkwsK5pjFB495kjLHuu8g0RiIxmgAAAAAAWcspuBUpY89WqmFzh9H6lswOlHn9RssstZkrSylNAbvCHJdslVxs9XaROJs7jLyWl9ipXAgQD0YTAAAAACBrOQW3nMpTSM6TwmV6iYrVTT51WF4OJsGDE5fLZa3/bau3i8SJ9QoJIB6MJgAAAABA1rJlGrsklUUox+BU37fKkqWbSWylKSRpHEFjRGALVFLTuHc5vb7UNEYiMZoAAAAAAFnLFnwpzXPJ4w6f7K7T0H4eFXjC/7/aYRK5TMEkeIiHrSRCHeUpepXzBJ6WWiFAnAgaAwAAAACyli34Ut5N4MXtcqnSkn2b8ZnGlqBxRb6729cD2c2WadzYYeT1Z/7EkOmKTGMkA6MJAAAAAJC1bMGXaOqC2rJv69r8qnXIAEx3fmOsQW/qGaM7ToFKp0km0XPxTOAJxIq9PwAAAAAgKxljrMGXaAIvTsHUvyysU2GOc2mLRHO5XBpTkqOfbV2kgYXxZwSv3exTmy88M5TSFOiO06SRta3+Ho1JOLNN4Nkv16U8S9kcIF7s/QEAAAAAWWmz16jDkrAXVaZxWa718ZVNyc80/qa2Qx+ua9OtU/urKDe+TEOn0hpOzxPoRKZx8tle22j2W0AsGFEAAAAAgKzkNFmXbWKvUCP6eRRnfLZX1LX5NX91W9zrO06CR3kKdKMi355NvClDS7Vkgk2WfRelKZBojCgAAAAAQFZymkwqmow9j9ulrcvTKwv327qOuNetqg9ftyTXpQFRBNCR3QYU2sfI8sbMnhgyXbV6/appDg/ID6AUCBKMvT8AAAAAICs5Zho7ZE6GOmxsUSK702O2wG80jMMkeJVlOXK5qJGKyPrlujXIEjh2yl5Hz1Q3eBVefZz640g8RhQAAAAAICvVttkvn3ea2CvUHkPzdcnuZXp7ZWvSL8WvafGrPiRTemWTT+0+E/NkWOtb/NrcYZsEL70yqZG+Kktztb4luDzK8kavvH6jHDcnHhKJUjJIFkYUAAAAACArOU3UFUtt0ElD8jVpSH6iuhS1579v1sNfNwU95jdbAnWxls1wylAmCIVojS3L0YfrgoPGHX5pVZNPY8iATSjbVQEuidcZCUd5CgAAAABAVrLVNO6X44o5UzcVnC5Fj6ckgC0IJRE0RvTGOYyVeEumwJntMz6sn0eFOYT4kFiMKAAAAABAVrLVNC7PkInfnAK68QTpbEGowhyXhhQxsRai43QSo9rhhATi0+4zWtUU/ppygge9ITOOhgAAAAAAJJgt07gihtIUqdQv163BtsnHYgzSGWOsgebK0hy5mQQPUSov8Fg/O0yGl1grGr3yWWbBo/44ekNmHA0BAAAAAEgwW03jWOoZp9rYsvBAUefkY9GqbfOrvt0yCR6Zi4iRbcxUNXjlN9GPR0RGKRkkU+YcDQEAAAAASJBWr1GLNzyYVVGQOSUZbIEir19a2Rh9dqdTJqhTuQHAiW3MtPmM1m72paA3fZNT+ZlKgsboBQSNAQAAAABZp67NHsjKqExjp8nwYihR4Zy5yOXuiI3TmKFEReLYXsvBhW4V52bOfguZg1EFAAAAAMg6tnrGUubUNJakygQE6WyZi3luaXi/zMm4RnpwnJyRyfASwus3Wm65ioATPOgtmXM0BAAAAAAgQepa7UHj8oLM+Zlcnu9Wf0t/nS5ht7EFmMeU5sjjZhI8xGZAgVslueHjJpbxCGermnzqsOy2qGeM3pI5R0MAAAAAABKkL2QaS/YSFdVRTj7W0ObXRkvwnMxFxMPlctknw6v3yjAZXo851jOm/jh6SWYdDQEAAAAASIA6h6BxJtU0luxZhu1+aXVT95OPVTXYg1BMgod42U44bPYarW+xf94QPeqPI9ky62gIAAAAAEAC1FoybPM9UmFOZpVlcJx8LIo6sk61j7ncHfFynJyREhU9Zvu8VuS7M+5EFzIHIwsAAAAAkHXq2sIzccvzPXK5Mixo3IMgnS2w7HFJo0oIGiM+jpPhxTA5I8L5jVG15fPKCR70JoLGAAAAAICsY6tpnGn1jCWpf4FbpXm2ycfiyzQeXZKjHCbBQ5wGF3ms2frRZL7D2ZrNPrX5wutCEzRGb8q8IyIAAAAAAD1UZylPUV6QeT+Rt0w+Fl6ioqqbyfA2d/i1rjk825ogFHrC7XJZs9+/r+9gMrwecCwlU0o9Y/SezDsiAgAAAADQA16/UWNHeAArEzONJXuJihavUY0lKNzJdqm7RNAYPWcbQw3txprdj+g4lZvh84relJlHRAAAAAAA4lTnELzK1AmlHOvIRigJQOYieovTGKKucfxsn+WSPJcGZODVEcgcjC4AAAAAQFaptZSmkKSKDA3A2MpTSJGDdLbMRbdLGuMwsR4QLaeTGN8TNI6LMcb6WR5bmptxE3cis2TmEREAAAAAgDj1tUzjwYVuFdkmH4sUNLZkLo4o9ijPQxAKPTO82KM8y0epusFeYgGR1bT41exlEjwkX2YeEQEAAAAAiFNtm73Wb0W+J8k9SYwtk+GFB5CqGuyTj7V6jVY3WSbBozQFEsDtcqnSkrFOeYr4UM8YqULQGAAAAACQVeocylOUZ2h5Csk+GV5ju9FGy3Nd1uBVeCiZIBQSp9JSMmVjq1/1TIYXM+f643xe0bsy94gIAAAAAEAcai2BK49LKsnN3NIMsdQ1rnIoE0DQGIniFNB0GntwZislU5Tj0pCizLwyApmDoDEAAAAAIKvYahqX57szelIpp4CvLUjnlLloKykAxMNxPFKiIiZbJsEL/wxXluZk9P4KmYGgMQAAAAAgq9RaSjZk6iR4nYb18yjfMomdPdM4/LFh/TwqzMns1wDpY1RJjmxzKhI0js2mVr8a2pkED6nBEQEAAAAAkFVsmcYVGVzPWIp+8rF2n9HKxvDAHUEoJFKO26XRJeFjqpryFDGxneCRnMvRAImU2UdFAAAAAABi4DfGsTxFprMFfmvb/Kpr9QXur2j0ymeZBY9JtZBotvG4rtmvpg4mw4uWrTSFxOcVyZH5R0UAAAAAAKLU0OaXJWaq8vzMn1TKefIxr/XvoHXJXESCOWWvV1OiImq2ch75Hml4cebvr5D+CBoDAAAAALJGrSXLWMr88hSSc+C3a+CJzEUky9hSh/HocOIC4Wyv1ZjSXLmZBA9JkPlHRQAAAAAAomQrTSFJFX2gPMWIYo9yLU8jKNPYkrk4qNCt4rzMf/5IL2NKc+S2ToZHXeNo1LX5tckyaScneJAsHBUAAAAAAFmj1hKEkfpGTWOnycc6g3Rev9Fy6yR4lKZA4uV5XBphKaNgO3GBcNVOVwUwaSWSJPOPigAAAAAARKkvl6eQ7AGl9S1+NbX7tarJJ9scZGQuorfYSlSs2exTq5fJ8LrjXH+czyuSo28cFQEAAAAAiIKtPIVLUlkfKc/gWNe4wetcz5ggFHqJbWwZSdXUNe6WLSM7xy2NLObziuToG0dFAAAAAACiYCtPUZrnksdWfDUDOWUNV9V3OGcukmmMXuJ0QoISFd2zneQZXZKjnD6yr0L648gAAAAAAMgadW2+sMfKC8LrrmaqUSVbJh/zm+DHqxq82tgSHjCvyHf3qeeP9FLpdBKDTOOImjr8qrF8XrkqAMlEpjEAAAAAIGvYylNU9IFJ8DrleVzWy9f/V+e1lgQgCIXeVJjj1rB+TIYXq2qH18dWIxroLX3nyAgAAAAAQATGGOtEeOV9KGgs2QPB65p9avOZ8GUpTYFeZhuPK5u8areMR2zhFFTnJA+SqW8dGQEAAAAAcLC5w8gbHjPuU5nGUmyBYKeJ84BEsY1Hv5GWN5Jt7KSqIbyesdu1paYxkCx968gIAAAAAIADW5axJJUX9K2fxrFkI5K5iN7mdGKCEhXObK/NyGKP8jxMgofk6VtHRgAAAAAAHDgFjftapnFlaY6iCS2V5Lo0oI8FzJF+nDLfbdm0kFq8fq3ZHD5hJ1cFINk4OgAAAAAAskJda3ggRpLK88Mn6spkBQ6Tj4UaW5Yjl4vMRfSu4jy3BhWGh5/INLZb1uCVrdoz9ceRbASNAQAAAABZwTHTuA9m20ZTdoLMRSSLbawtb/TK62cyvFBMgod00feOjAAAAAAAWNQ71TTuY+UppOgCwmQuIllsY83rl1Y2kW0cqqoh/DVxSRrD5xVJ1veOjAAAAAAAWNgyjfvluPrk5FLRBITJXESyOI21akpUhLFlGg/r51FhDiE8JBcjDgAAAACQFepaw4PG5X2wNIXUfUC4MMelwUV9q5Yz0pdT5rstqzabtfuMNfuaEzxIhb55dAQAAAAAIIQt07iiD5amkKR+uW4NKXJ+bmNLc+RmEjwkSXm+2/pZYzK8YMsbvbKVeR5bSv1xJF/fPDoCAAAAABCizhI07ov1jDtVRgg0kbmIZLONueoGr/yGyfA6MQke0gmjDgAAAACQ8Ta0+LSqySun8JPPL7V4w/+3oqDvlmgYW5ajhWvb7P9H5iKSbGxZjj6paQ96rM1nNHdla8QyMaV5bo0pyZHHndmZ8Y3tflU3eOWLECRftN7+ea0kaIwUYNQBAAAAADKW3xjd+0Wj5ixvjWv9vpxpHCk7kcxFJJvTiYo7P2/sdt3h/Ty6dM9yDcnQOtwvVzXrwa+aHE9qRTK40K3i3L67n0L6YtQBAAAAADLWx+va4w4YS323prHkHKTLc0vDizMz+IbM1ZMTFas3+/Tw100J7E3yrGv2xR0wlpwnEQR6W989OgIAAAAA+rwP19kv545WRYTL4jNdWb5bAyzPr5JJ8JACAwrcKsmLf9x9UtMmr22WuDT3ybq2uAPGElcFIHX67tERAAAAANDnOU0cFY1+OS5tXd63s/gmD8sPf2x4QQp6gmzncrm0x5Dw8Rgtr19a2RT/5z1Vqhp61ufde/CaAT1B0BgAAAAAkJHafSbuIFKeW/rNxFLlefp2xu3hW/fT+P7/FxjfY0iefjC6MIU9QjY7ett+Glsaf+ZsT04SpUpVfUdc67kkHb9dP40qIdMYqcHIAwAAAABkpOWNXtmuVj94dKF2H5rnuF6e26VxZbkqyOnbAWNJKs1z66q9y7WqySe3SxpeTBgAqdO/wKPr9q3Qikafatt8jsu1eY1u+aQh7PGqeq8OGNWbPUysLSe2wp/nLgPzdNg455M3bpdLlSU5Ku3DNdeR/jhaAAAAAAAyklPW4d7D8rXTQOegcbZxuVwaSbYi0oTb5dKY0hyN6SYkNaCgSRtb/UGP9bTUQ7I5ndiaMChPEwdRdgLpjVMWAAAAAICMVNVgv+y7komjgIxnmwBuWUOH/CZzJsNzOrHF5HbIBASNAQAAAAAZyRaQGVzoVnEuP3WBTFdZGj5JZZtPWm0p95CunOoZV/agrjOQLBxJAQAAAAAZx+s3Wt4YHjQeWxYeaAKQeZyycTOpRIWtr0OK3OrHiS1kAEYpAAAAACDjrGzyyusPf5wMPqBvGOvwWXbK3k03Tie2bBnUQDoiaAwAAAAAyDjUCgX6tv4FbpXmucIed/rsp5uVjfYTW+yjkCkIGgMAAAAAMo5z0JgsPqAvcLlc1s9zVYM3IybDcyqjQdAYmYKgMQAAAAAg49gCMhX5bpXn8zMX6CtsJSpavEY1zek/GZ7jiS3KUyBDcDQFAAAAAGQUvzFa1hBe15QMPqBvyeTJ8Gy1lwcUuFXGiS1kCEYqAAAAACCjrG7yqc2SaEjQGOhbnMrNpHtdY78xqrYEttlHIZMQNAYAAAAAZBTHWqFc9g30KYML3SrKybzJ8FY3+dRumQSvkn0UMghBYwAAAABARrFd9i2RxQf0NVsmwwv/XFc1dMik8WR4TkHtceyjkEEIGgMAAAAAMootIFOS59KAAn7iAn2NbTK8xnajja2WVN40UWWpuS5xYguZhSNqnJ577jkdffTRqqysVEFBgQYPHqwpU6bo73//uxoaGhLSxtVXXy2XyxXzbdq0aQlpHwAAAADSjd8Ya3mKsaW5crnCL2MHkNmc6hrbaganC9uJrbI8lyqYBA8ZhFMcMWpqatKJJ56o5557Lujx9evXa/369VqwYIFuv/12Pf7449p7771T0sdx48alpF0AAAAA6G01zT61eMMvSyeDD+ibnD7bVfUd2n1IfpJ70z3HE1tlnNhCZuGoGgOfz6ejjz5ar7zyiiRpyJAhOvPMMzV+/Hht2rRJs2bN0vz587VixQpNnz5d8+fP1w477BB3e8cdd5wmTpzY7XIdHR066aST1N7eLkn6xS9+EXebAAAAAJDOHCfBI2gM9EnD+nmU73GpzRd8sihdJ8NzPLFlKbMBpDNGbAzuvffeQMB4/PjxevPNNzVkyJDA/59zzjm66KKLdPPNN6u2tlZnn3225s6dG3d722+/vbbffvtul3vmmWcCAePttttO++67b9xtAgAAAEA6cwoUEZAB+ia3y6XK0hwtrQ2uE+x0AinVOLGFvoJiKlHy+Xy65pprAvdnzpwZFDDu9Le//S2QHfzuu+/qtdde6/W+3XfffYG/yTIGAAAA0JfZgsZFOS4NKfKkoDcAksEWcN3U6lddW/pNhud4YsuhNjOQrggaR2nu3Llas2aNJGnq1KmaNGmSdTmPx6PzzjsvcH/WrFm92q81a9bo5ZdfliTl5OTolFNO6dX2AAAAACBVjDGqbugIe7yyNIdaoUAf5nQlQXV9+P4g1WxB4345Lg0qJASHzNKj3PjOrNYJEybo/PPPT0iH0lVnYFaSpk+fHnHZQw891Lpeb3jwwQfl8/kkSYcddpiGDh3aq+0BAAAAQKpsavWroZ1J8IBs45SlW9Xg1cTB6TMZnjFGVZYTW2PLOLGFzNOj0xwPPPCAHnzwQXm96VlHJpEWL14c+HuPPfaIuOzQoUM1atQoSdK6deu0fv36XuvX/fffH/j7l7/8Za+1AwAAAACp5lwrlMu+gb5sRLFHuZYIVrpNhrex1a9G64kt9lHIPD0KGvfv31+SNHr06IR0Jp0tXbo08PfYsWO7Xb7rMl3XTaR3331X33zzjSRp2LBh3WZAAwAAAEAmq3K4FJ1J8IC+Lcft0uiS8M+50z4hVZioE31Jj0bt6NGjVVtbq9ra2kT1J23V1dUF/h44cGC3yw8YMMC6biJ1nQDv1FNPlcfTuxM/dHR06JNPPrH+37BhwzRs2LBebR8AAABAdrMFZPI90vBiJsED+rqxZTn6X8g+oKbFr6YOv4ptacgpYCtNIVFCB5mpR5+qH//4xzLGaM6cOYnqT9pqamoK/F1QUNDt8oWFhYG/GxsbE96fxsZGPfHEE4H7nfWle1NNTY122203623GjBm93j4AAACA7GYrTzGmNFduaoUCfZ5TiYfqNCpRYTuxVeBxaWg/Tmwh8/ToVMevf/1r3XHHHXrqqaf09ttva9q0aQnqFrrz2GOPafPmzZKk/fbbT9tss02vtzl48GDHif3IMgYAAADQm+ra/NrU6g97nMu+gezg9Fmvqvdqp4F5Se6NnS1oXFmaw4ktZKQeHV2HDRumxx57TEcddZR++tOf6q9//at++ctfqqioKFH9SxvFxcWBMhytra0qLi6OuHxLS0vg75KSkoT3p2tpimRNgJebm6tJkyYlpS0AAAAA6KraqZ4xl30DWWFUSY48LskXMs+cU0mIZKtr9am2zXJii30UMlSPRm5nSYSdd95Z8+fP1+9+9ztddtll2nXXXTVy5MigEg02LpdL//nPf3rShaQpLy8PBI03bNjQbdB448aNQesm0pIlS7RgwQJJUmlpqY4++uiEbh8AAAAA0o2tNIVEQAbIFnkel0aW5GhZyL7AafK5ZGMfhb6mRyP3gQcekOv/p9h3/tvc3Kz33nsv6m1kStB4u+22U1VVlSSpqqpKlZWVEZfvXLZz3UTq+podd9xxfTKzGwAAAAC6sgWGctzSyGICMkC2GFsaHjRes9mnFq9fhTmpnQzPKXg9ttReixlIdz3+RBljgm62x5xumWTnnXcO/P3hhx9GXHbdunVasWKFpC11gAcNGpSwfni9Xs2cOTNwP1mlKQAAAAAglaos5SlGl+Qox02tUCBb2LJ2jRQWSE4FW6ZxrlsaUcwkeMhMPTol2zWbtq/70Y9+pL///e+SpJdfflkXX3yx47IvvfRS4O/p06cntB8vvvii1q1bJ0naaaedtOeeeyZ0+wAAAACQbpo6/KppoVYokO2csnar6r3avn9qJ8OzndgaU5ojDye2kKF6dIQdM2ZMovqR9qZOnaqhQ4dq7dq1evvtt/XJJ59YJ4Xz+Xy67bbbAvePO+64hPaja2kKsowBAAAAZINqLvsGoC1BWJe2ZBd35VRPOFma2v1abzmxVVnKiS1krtQWfMkgHo9HV111VeD+KaecopqamrDlLr30Ui1atEiStM8+++iHP/yhdXud9aBdLpemTZsWVR/Wrl2rl19+WZKUl5enk046KbYnAQAAAAAZyLFWKJnGQFYpyHFpuKXcgy3LN5mcJ8HjxBYyF0fYGJx55pl65pln9Prrr+vLL7/UhAkTdOaZZ2r8+PHatGmTZs2apXnz5kmSysvLNWPGjIS2/9BDD8nr3bIjOvzwwzVw4MCEbh8AAAAA0lFVQ3hAyO3aUtMYQHapLM3RqiZf0GMrm3xq9xnleVJTCsIpaD2WTGNksISO3o6ODi1cuFBfffWVNm3apPb29qDs3EyXk5Ojp556SieccIJeeOEFrV27Vn/+85/Dlhs5cqQee+wx7bjjjglt/7777gv8TWkKAAAAANnClmk8sjgnZQEiAKkztixX81e3BT3mN9LyRq+2Lk9NZq8t09jjkkZxYgsZLCGjt729XX/5y1/0r3/9S/X19UH/Fxo0/sMf/qDZs2dr1KhRmjNnTiKaT6qSkhI9//zzmj17th566CF9+OGHqqmpUUlJibbaaisdccQROvvss1VWVpbQdufPn6+lS5dKkkaNGqWDDz44odsHAAAAgHTU4vVrzWZf2OOUpgCyk1P2blV9CoPGthNbJZzYQmZzGWNC64fHZOPGjTr44IP12WefKXRTLpdLPl/wwX3+/Pnab7/95HK5tHDhQu2+++49aR5JMHLkSK1atUojRozQypUrU90dAAAAAFlkyaZ2/WlBXdjjp40v1qFji5LfIQAptbnDr1+8tiHs8YNGFeisXUqT3p/mDr9Ot/Rn2sgC/XpC8vsDJEqPJ8I78sgjtWjRIhljtM8++2jGjBkRS1Lss88+GjlypCQFJnUDAAAAAMCGSfAAdNUv160hReHhLKfJ6Hrbskb2UeibehQ0fvrppzV37tz/x959x0lWV/n/f98KnePkPN0DiMQZkkRhCIqOsgjIb5EwIIiw4sKK8bu7gq6rKyqr4iqyoowDiohEkSASJIywZIlD6p6cp7urc6X7+2Psdnrq8+muqnsrv56Pxzxoqu+te7uruurW+557jhzH0Re+8AU9/vjjuvDCC3XAAQeMu94JJ5wg13W1YsUKL5sHAAAAAJQ5UxDkaMcwLACVqa0ptQ3F6t644klPF9NnxXpiy7CPQCnxFBr/+te/liTtv//++s53vpP2evvvv78kjfboBQAAAADApKMnlnLbzPqgakKeL5wFUKJMVbzxpLTWUvWbS6bQ2JE0nxNbKHGe3mX/7//+T47j6BOf+ERG602fPl2StGXLFi+bBwAAAACUsWjC1do+huABGMv2GlCIFhUdkdQTW7MagqoJMQQPpc1TaDwS+i5YsCCj9cLhHSX60WjUy+YBAAAAAGVsdW9cpqvN25u57BuoZLbWD7ZWEbkynHC1ttdwYosqY5QBT6FxTU2NpMzD35GwubW11cvmAQAAAABlzN4rlEAGqGTN1QFNrjEMwzO0s8ml1ZG4TF2UObGFcuApNJ45c6Yk6fXXX89ovaeeekqS1N7e7mXzAAAAAIAyZguA2mhPAVQ8U4uKVb1xJd38DcMztaaQaKGD8uApNH7/+98v13V16623yk3zj3Lr1q267bbb5DiOjjnmGC+bBwAAAACUMVN/0mm1ATWEGYIHVLo2Q4uK4YS03tAHPVdsV0O0cTUEyoCnd9qzzz5bkvTWW2/pm9/85oTLR6NRnX322RoYGJDjODrvvPO8bB4AAAAAUKbiSVere1MDGS77BiBJC4pgGJ4pNJ5eF1Q9J7ZQBjxXGn/kIx+R67q68sorddFFF+ntt99OWW5gYEB33HGHDj30UD344INyHEdnn3223vve93rZPAAAAACgTK3tjSueTL2dy74BSPbXgnz1Nbaf2OI1CuXB8zP5pptu0hFHHKHXX39d119/va6//vrRAXmSNHXqVHV3dyuZ3PFu77quFi1apJ/85CdeNw0AAABUtOGEq7ve6dfa3oTmNAZ18m71qg46hd4twBe2akGG4AGQpNbqgJqrHPVEx7ZLfXTtkN7ssgfHAcdRW1NIp+xep9aaYNbbX9MbV8LQqZXXKJQLz8/k5uZmPfXUU/r0pz+tW265RZI0ODgox9lxsLpt27Yxy59++un6+c9/rrq6Oq+bBgAAACrad5/t1stbd3wwfnqj9GZXTP9+aGuB9wrwh7VXKO0pAEhyHEftzWG9uCU65vb+mKu3usdvUbGyK6bnNg/r+8dMVlWWJ1ttr1FUGqNc+NJkpbGxUTfffLNeeuklXX755Tr44IM1efJkBYNBtbS0aN9999Ull1yip59+WrfccosaGhr82CwAAABQsd7pjo0GxiNe3hrTu3m6LBfINdMl5pNqAmqpplcogB28VPVuHUzqqQ1DWa9vvxqCE1soD76e/thvv/30ve99z8+7BAAAAGBgu/R25faYFlCJiRKXdF11GgIZLvsGsLM9Wr29373VHdfRc7Jb13Ria3JNQE2c2EKZ4JkMAAAAlCBbhVM+p8YDubK+L6EoQ/AATGDR1CrNaci+L3G2Q/MSSVerTCe2eI1CGfH0bL799tt10kknKRymkgEAAADIJ9sHXVuPRaCU2HuF8tkTwN8FA46+dnir7ni7X+/0xJVIGibT/c2mgYQiuwzNWxXZsU4wkFlf4/X9thNbvEahfHgKjT/+8Y9r0qRJ+sd//Eedc845Ouyww/zaLwAAAAAW0YSrtX0J4/fW9sUVTbhZD/YBikFHxHxShPYUAHbVWBXQ0r0bJ1zud2/169Y3+8fcFk3uCIDnNmb22tJpO7HFaxTKiOf2FF1dXfrpT3+qI488Unvuuae++c1vatWqVX7sGwAAAACD1b1x2Yqpku6O7wOlzFRp3FTlaFINHRYBZMcW6GbTosJ6Yov2FCgjnt5xL7nkEk2ePFmu68p1Xb399tu64oortNtuu2nx4sVatmyZ+vr6/NpXAAAAAJq4BQUtKlDKkq5r7M3d3hyW41BBDyA7tkA3m1kApvfZ5uqAWhmChzLi6dn8ox/9SOvXr9ddd92lj3/846qqqpLrukomk3r88cd1wQUXaMaMGTrrrLP0wAMPyHXtvWUAAAAApGeiqihbBRRQCjYPJDQYT/3syGXfALxorQ6o2RDqZnqi1XpiqynEiS2UFc+nQEKhkE466ST99re/1caNG/XTn/5URx11lCTJdV0NDAzoN7/5jZYsWaI5c+boS1/6kl5++WXPOw4AAABUqomqoqg0RimzPb+57BuAF47jGE8+dUbiSmZQ5Gg9scVrFMqMr3Xzzc3N+vSnP63HHntM77zzjr72ta9pjz32GG1fsWHDBl199dVatGiRDjjgAP3gBz/wc/MAAABA2Ysn3Ql7Fq/ujSs+zgR5oJjZTnq0N4fzvCcAyo0p2B2Mu9o0YB4ua2J9jeJqCJSZnDVbaWtr0xVXXKGVK1dqxYoVuvjiizVp0qTRAPmll17S5z//+VxtHgAAAChLa3vjiifHXyaelNb2UW2M0mQKZOpCjqbV0isUgDf2YXjpv2far4bgxBbKS17edQ877DD95Cc/Ga00rqqqysdmAQAAgLKT7sAeWlSgFLmua+zJ3d5Mr1AA3tmC3YxCY8NcgfqQo6mc2EKZyUvtfG9vr2699VYtX75cTzzxBAPxAAAAgCyl+8G2oyeuY+fmeGcAn20bSqo3yhA8ALkxtTag+rCj/tjY15l0B8i6rmt8H+bEFspRzt55k8mkHnjgAS1fvlx33323hoaGJGk0MK6rq9Opp56aq80DAAAAZclU4WRcLs2KZKCY0M8YQC6NDMN7ZdvY99KOnrhc150w+N02lFRvzDQEj9colB/fQ+MXX3xRy5cv180336zNmzdL+ntQ7DiOFi9erHPPPVcf//jHVV9f7/fmAQAAgLKVdF11phkGr4rElHRdBah8QgmxVfuZhlcBQDbam8MpoXFfzNXWwaSm1gXHXZcheKgkvjyrN2zYoJtuukk33nijXn31VUka04LiPe95j5YuXapzzjlHc+dyjRwAAACQjfV9CUUNQ/Cqg46GE2Mrn4YTO5af08gHWZQOUyBTHXQ0s378IAcA0mUdhheJpxEac2ILlcPTs/qmm27S8uXL9cgjjyiZ3HH0OhIWt7a26owzztDSpUt16KGHet9TAAAAoMLZKpyOmFmtR9YOpS4fiRMao6SYnuNtTSEq5gH4xhbwdvTE9L4Z1eOua2r9VBN0NIMTWyhDno4gly5dKsdxRoPicDisD3/4w1q6dKlOOukkhcP0dAEAAAD8Yrt0/7h5tebQuCem98+uyfVuAb7oHkqoazi1lJ4KPgB+mlEfVE3Q0dAuV+ikM2iWE1uoJJ7ffV3X1UEHHaSlS5fqE5/4hKZMmeLHfgEAAADYhenDalOVoz1aQmqsctQbzfwDMFAsbMMb6RUKwE8Bx1FbU0hvdO0yDG+CmQFdnNhChfH0zP7Sl76kpUuXau+99/ZrfwAAAAAYJF3X+IG2vTn8t2nwYf11a3TM9zoicYbhoWRYB0w1cwUrAH+1N6eGxt3DSXUNJdRaY241YRtES2iMchXwsvK3v/1tAmMAAAAgDzYPJDQYd1NuH6nCNH1oHYy72jyQyPm+AX4wnRQJB6TZDfQKBeAve19je7Xxu7YTW02c2EJ58hQaAwAAAMgPexWmPTSWJr7cFigWHT2pPbvnNYYUClApD8BftqB3vPdM0/swJ7ZQznytod+6dav+8Ic/6KmnntKGDRvU29urxsZGzZo1S4ceeqg+8pGP0PMYAAAAyIK13+vfLt239X3t7Inr8Jk52y3AF33RpLYM0isUQH7MbggqHJBiu7zsmE5ejeg0DKOd3xRSkBNbKFO+vAMPDAzoS1/6kn7xi19oeHjYuMx1112n6upqfepTn9JVV12l2tpaPzYNAAAAVARThVNdyNG02h0XD06vC6ou5GhglxYWVBqjFEx0UgQA/BQMOJrfFNLb3WNfe2yvRb22E1u0pkAZ89yeYuvWrTrkkEN07bXXamhoSK7rWv8NDQ3pxz/+sQ455BBt27bNj/0HAAAAyp7ruuowVDi1N4fk/G3InfO3afC76uiJyXVTeyEDxcRW3WeroAcAr0yB79bBpCLR1HDYfmKL1yiUL8+h8WmnnabXX39druuqtrZW559/vm655Ra98MILeuutt/TCCy/olltu0fnnn6+6ujq5rqvXXntNp512mh/7DwAAAJS9bUNJ9UbtQ/BG/9/w4TUSdbV9KPUDMFBMTIFM0JHmNhLIAMgNW+Dbabiyx3pii9AYZczTs/uOO+7Q448/LsdxtGjRIt1+++2aP39+ynILFy7U6aefrq9+9av6+Mc/rueee06PP/647rrrLp188sledgEAAAAoe/YheGHD/w+mrh+Ja3Itg3pQvEzP8TmNIVUF6RUKIDfsA2Rj2n9q1djbDK9RQUea20BojPLlqdL4N7/5jSRp6tSpevDBB42B8c7mz5+v+++/X9OmTZMk/frXv/ayeQAAAKAimFpTSKkfeG2X8o832AcotIFYUhv6Eym305oCQC7NbQjJdF7KFBB3Gq6GmNsYUpgTWyhjnkLjp59+Wo7j6Pzzz9ekSZPSWmfy5Mm64IIL5Lqunn76aS+bBwAAACqC6QNsddDRzPqx1cOzGoKqNhQU2yqVgWKwqpdeoQDyLxx0jC1wdn3P5MQWKpWn0Hjz5s2SpP333z+j9fbbb78x6wMAAACwM4W+bU0hBZyxFU4Bx9F8w2Af2wAfoBhY268YnssA4CdT8LtxIKGB2N9nAZiqjCVObKH8eQqNq6p29HiJRqMZrTeyfDjMQQAAAAAwnu6hhLqGUwfZ2T6smj4Abx9KqttwH0AxMIXGjqT5VPEByDHrMLydgmLbiddd5woA5cZTaDxr1ixJ0uOPP57Reo899pgkafbs2V42DwAAAJQ964dVS6BmnwZPX2MUJ1PP7lkNQdWE6BUKILdswe/OJ7NsJ7bmGVpbAOXEU2i8ePFiua6rG2+8US+99FJa67z44ou66aab5DiOFi9e7GXzAAAAQNmzXrpv+aBrnwZPiwoUn+GEq7W9qb1C26gyBpAH8xpDMp2e2vlklmmY7GxObKECeAqNP/WpT8lxHMViMZ1wwgm6/fbbx13+9ttv1wc+8AFFo1E5jqMLL7zQy+YBAACAsmcKe8OBHR9YTeY0hBQyHOUzDA/FaHUkLtdwO5d9A8iHmpBjfD8dec8cTrha12cYgkc/Y1QAT8/yAw88UBdffLGuvfZabd++XaeffroWLFigD3zgA3rPe96j+vp69ff366233tKDDz6od955R67rynEcXXzxxTrggAP8+jkAAACAsmSqcJrXGFIoYK5wCgUczWsM6d1dQmLT/QCFZmpNIdnbrwCA39qbQ1q7SzC8ri+hobir1b2WE1sM6kQF8PxO/KMf/UiRSES/+tWvJEnvvvuurrvuOuOyrrvjT+2ss87SNddc43XTAAAAQFnriya1ZTD9IXg7f3/X0HjzYFJ9saQawp4uNgR8ZW+/QmgMID/am8J6fN3wmNtcSat749YTrrxGoRJ4PmIMBAK68cYbdcstt+jAAw+U67rWfwcddJBuvfVWLV++XIEAB6sAAADAeLKd2G6rgOqkRQWKjCk0nl4XUD0nNwDkiXUWQE/M+j5M33VUAt+e5aeffrpOP/10rV69Wk8//bQ2bNig3t5eNTY2aubMmTr00EM1b948vzYHAAAAlD1rhdMEH1btH4Dj2ndKlef9AvwQT+649HtXbVz2DSCPbAFwR0/cGBrPqAuqjhNbqAC+nxqZN28e4TAAAADgA9OH1aAjzW0c/zB+XmNIAUdK7tKI0dY/FiiENb1xJQzNQrnsG0A+1YUDmlEX1MaBsX2N3+qOaUN/6hC8Nl6jUCE4NQIAAAAUKdOl+3MaQ6oKmofgjagKOprTkPqh1tY/FigE+hkDKBam1521fQnziS1aU6BCEBoDAAAARWggljRWOKX7YdX0AXhDf0KD8dTBekAhWHt2054CQJ5lcrKKE1uoFL4+05PJpN555x11dXVpaGgorXWOPvpoP3cBAAAAKAurDL1epfQ/rLY3hfTnXW5zJa2KxPXeSfQ1RuGZenZPrgmouZraJgD5lcnJKk5soVL4EhqvWLFC3/nOd/Tggw+mHRZLkuM4ise5RA4AAADYlfXS/TQ/rI43DI/QGIWWSLpaZag0poIPQCGk26d4ck1ATZzYQoXw/I589dVX68tf/rJc15XrGpq9AAAAAMiYKTR2JM1Psz1FW1NIjnZUF4+5X0tLACCf1vcnFDV0Smmjgg9AATRVBTSlNqCtg+O3cOLEFiqJp2f7448/ri9+8YtyHEeu62r27Nk69thjNWfOHFVXV/u1jwAAAEDF6YikXro/qyGomtD4Q/BG1IQCmlkf1Ppd+iKbWgIA+WZ7Hi4gkAFQIO1NIW0djI6/TDMntlA5PL0j//CHPxz9+hvf+Ib+3//7fwoEKNMHAAAAvBhOuFrbm/0QvNHlm0MpofHavoSiCVdVwfTCZyAXrEPwCI0BFEh7c1jPbJogNM7wfRgoZZ4S3r/85S9yHEcf+9jH9G//9m8ExgAAAIAPVkfiKW0lpMwrnEzLJ11ptWXIHpAvnYb2K81VjlrpFQqgQNIJhDmxhUri6R1527ZtkqSTTjrJl50BAAAAYG5NIWX+YdX2Adg2ZA/Ih6TrGiuN25vDchwq4AEUxkTvsc3VAU5soaJ4erZPmTJFklRfX+/LzgAAAACwh7ptWbSnMN8/fY1ROJsHEhqMp9bSc9k3gEJqrQmqZZxQuL0pxIktVBRPofGiRYskSe+8844f+wIAAABA5tB4el1Q9eHMDt/rwwFNr0tdx9ZPFsgH20kRLvsGUGjjnbziNQqVxtMz/oILLtC9996r3/zmN/rKV77i1z4BAAAAFSuedI09h7P9sNrWFNamgeExt63ujSuedBUKUDFVDLYNJrS2z9zHeldVAUdtTSHVZXgCIV8GYkl1ROKKJe0/zTObho23Z9qzGwD81t4c0gtbzMPwuBoClcbTM/6UU07Raaedpttuu01f+tKX9J3vfMev/QIAAAAq0preuBKGvC3T1hQj2ptDenrj2JAunpTW9sbVRkhXUEnX1Q2v9umPqwYzWi8ckD67qEmHzazJ0Z5l57G1g/rpX3uNz9+J1IccTa0tziAcQOUY7+QVJ7ZQaTy/K990000688wzdfXVV+v444/X73//e23dutWPfQMAAAAqjt+X7luH4dGiouBe3BzNODCWpFhS+ulfezUQS+Zgr7LTF03qupezC4ylHc9veoUCKDTbe2Z9mBNbqDyeKo2DweDo167r6tFHH9Wjjz6a9vqO4yge52AVAAAAGGELc9ubsqtwslUTd/TEdezcrO4SPrG1aUjHYNzVa9tjOnh6tY97lL1Xt0UV95BhU8EHoBhMqQ2oIeyoLzb2DBhD8FCJPJ0mcV139N+u/5/uPwAAAAB/19ETS7ltck1AzeNMdB9PS3VAk2oMw/AM20F+2arK01+/eB7D7UPeqp4Pnl7l054AQPYcx9HhhtY/R8wqrnZAQD54qjQ++uijOdMCAAAA+CSRdLXKUGnsdWJ7e1NI24fGDvbpjMSVdF0FOJ4vCNvAw0x4DZ391D2cfWh88m512rOVSmMAxeG0Peq0KhLTm907XmMPn1mtY+YQGqPyeDr6zKQVBQAAAIDxre9PKGrI3tqybE0xor05pOc2jw2No0lpfV9CcxqZBl8ItoGHx8+t0ftmprac+NXr/SkhczH1pe4yhMbhgPSFg5ut6wTkaF5jUC01QesyAJBvrTVBff2IVq3rSygckGbU8z6JysQzHwAAACgStnYDC7xWGo/T15jQuDBsVcKHzqzWwqmpofHzm6IpofH2oaS6h5NqybJ1iZ9Mlcat1QEtMvwsAFDsAo6jubw/osJ5OrpYsGCBFixYoEsvvdSv/QEAAAAqlnUIng/tKczbK56euJUm04GHtudAZ5H0Ne4y9DSmghgAgNLlKTRevXq1Vq1apX322cev/QEAAAAqlqn6tLnKUavHStJJNQE1VaX2Li6mnriVxjbwsMnyWNtC42JpUdE9nEi5zevzFgAAFI6nd/Fp06ZJkiZNmuTLzgAAAACVKum66jQOwQt7Hj7tOI6xRUXH34bhIb+yGXg4pyGkkOHTWzEE//Gkq0g09XlUDG0zAABAdjy9i++5556SpLVr1/qyMwAAAECl2jyQ0GA8NXiztZbIlOl+BuOuNg+kVogit7IZeBgKOJpn6K9p64OdTz2GfsaS1FpDaAwAQKny9C7+8Y9/XK7r6rbbbvNrfwAAAICKZKsY9drPeKL7KZb2BpUk24GHpuB/82BSfTFzaJsvXZbQmEpjAABKl6d38QsvvFD77LOP/vKXv+h73/ueX/sEAAAAVBz7EDx79WkmbPdTDO0NKk22Aw9tj2FngR/DbkJjAADKjqd38aqqKt1zzz1atGiRvvzlL+u0007To48+qmg06tf+AQAAABXBVH1aH3I0tdaf4G1abUB1IYbhFYNsBx5aq8UL/Bh2DVnaUxAaAwBQsjxd67ZgwQJJ0vDwsFzX1Z133qk777xTwWBQkydPVm1t7bjrO46jd955x8suAAAAACXPdV1j8NfeHPI8BG/EjmF4Ib26bWw43RGJyXVd37aD8XkZeDivMaSAIyV3aX3dESlsX+PuYXNf7JaaYJ73BAAA+MVTaNzZ2Tl6YDPyX9d1FY/HtWnTpgnX58AUAAAAkLYNJdUbMwzB86k1xej9NaWGxr1RV9uGkppSS8CXD5s8DDysCjqa0xDU6t6xIW3BK40N7SkCjtRUxec9AABKlafQeN68eQS/AAAAgEfWIXhpBImZ2BFCDxq3T2icH14HHrY3h1NC4w39CQ3Gk6oNFaYdRLehPUVzVUABPisCAFCyPFcaAwAAAPDG1M9YSj9ITJe1J24kpkNmVPu6LZiZWlNI6VeVtzeF9OddbnMlrYrE9d5JVd52LkumSuPWGvoZAwBQyngnBwAAAAqswxAk1gQdzaj3t/p3Zn1Q1UGG4RWS14GHxTgMr9sQGrcwBA8AgJLGOzkAAABQYKbAr60p5Pvl/QHHUZuh5QWhcX74MfBwflNIpiVNJx7yIem66jFVGhMaAwBQ0ngnBwAAAAqoayhhvLzf79YU491v13BS3UMJw9Lwkx8DD2tDAc00VKAXKvjvi7pKpP5IaqE9BQAAJY13cgAAAKCA7D1ucxQaW4brFapStZL4NfDQ9NxY2xdX1JTe5pjphIcktVYzWBEAgFLm6Uj0/PPP97Rxx3H085//3NN9AAAAAKXsXWuQmH71aSZsVa0dPXEdMI1heLnk18DD9qawnlw/POa2pCut7o1r95bcPG9suobNFer0NAYAoLR5Co2XLVuWdu8tG0JjAAAAVDJT9Wk4IM1uyE2l5uyGoMIBKbZLgSiVxrnn18DD8Ybh5Ts07h6yVBrTngIAgJLm+Z3cdd2s/wEAAACVrjOSWn06vymkYMDfIXgjQgFH8xpNw/DMVbDwj18DD9tsobHhuZRrtvYUVBoDAFDaPFUad3R0TLhMIpHQ1q1btWLFCl177bV66623dPbZZ+s//uM/PFcpAwAAAKWsN5rUlkHDELwctaYYvf/mkN7ZJcDcMphUXzSphirCvlzwc+BhQzigabUBbd7luVOIYXjdhMYAAJQlT6Hx/Pnz01puwYIFet/73qd/+qd/0tKlS/WrX/1KkyZN0g9+8AMvmwcAAABKmq0lRK6G4P39/sOShoz7s9+Uqpxuu1L5PfCwvTmszYNj+xqv7o0rnnQVylGVukmXoT1FY5WT130AAAD+y+vp3+rqat14442aN2+efvSjH+nRRx/N5+YBAACAouLXYLRMtTfZeuLSoiJX/B54aHqOxJPS2r78VhubKo1bqTIGAKDk5f3dvKqqSueee65c19VPfvKTfG8eAAAAKBqmdgJBR5rbkNvQeG5jSEFDISjD8HLH74GH9uA/36FxIuW2lurcDHEEAAD5U5BTwHvttZck6amnnirE5gEAAICiYGpZMLcxpLAp0fVRVdDRHOMwPELjXPF74GFbs7lCOZ+Poeu6xvYUrTVUGgMAUOoK8m4ej+84kNmyZUshNg8AAAAU3EAsqQ39qVWatgpSv5m2s6E/oYGYebAZspeLgYct1QFNMoSz+awWH4y7ihqeLgzBAwCg9BXk3fzhhx+WJDU1NRVi8wAAAEDB+T0YLVO27azqpdrYb7kaeGgK/ldFYkq6rqf7TVeXoZ+xRE9jAADKQd7fzW+77TbdeOONchxHBx98cL43DwAAABQFe5CYffVpJmxVrrSo8F+uBh6a1h9OSOv7UivYc8HUmkKSWmhPAQBAyfN0lLJ8+fK0lotGo1q3bp3+9Kc/acWKFXJdV47j6OKLL/ayeQAAAKBkmcJZR9I8Q6/hXJjfFJIjadeaVEJj/+Vq4KHtBENHJG7sWe23biqNAQAoW56OJM477zw5TmaDG9y/XSp18cUX66STTvKyeQAAAKBkmapPZzcEVRPK7RC8ETUhR7Maglq3S1Vqh2FgG7zJ1cBDW//rjp6Y3j+7xtN9p8PWnoKexgAAlD7P7+au62b0b5999tHy5cv14x//2I/9BwAAAErOcMJNCWslqS1PQ/DG297a3oSGE/npiVsJcjnwcFJNQE1VqcFzvqrFu4fMbTBaa4J52T4AAMgdT0cqN9xwQ1rLVVdXq7W1Vfvss4/mzJnjZZMAAABAyVsViae0hZDy18945+09uX54zG2upNWRuPZoze++lKtcDjx0HEftzWG9tCU65vaOSFxJ11Ugw6tCM2WqNK4NOar2WEENAAAKz9ORyrnnnuvXfgAAAAAVI1eD0TJlbW8QiREa+yTXAw/bm0IpofFg3NXmgYRm1Of2+WTqaUw/YwAAygPv6AAAAECe2YLEfLensIXUDMPzT64HHlofQ8tzzE9dQ6mhMf2MAQAoD57e0R977DE99thj2r59e0brdXd3j64LAAAAVBpTkDi9Lqj6cH4Dt/pwQNPrUrdJaOyfXA88tFUs5+MxNFYa1xAaAwBQDjy9oy9evFjHHnusnnjiiYzWe/rpp7V48WIdd9xxXjYPAAAAlJxYwtWa3tRAL9+tKUa0NaWGjqt744onGYbnlW3goZ+P9bTagOoMAXSuQ+NowtVAPPU5QqUxAADloaDv6K7LgSgAAAAqy5q+uBKGw2Bbf+FcMwWYCVfGYBuZsQ48NAT12doxDC/1MeyIxHL6ecs0BE+SWquDOdsmAADIn4KExiMHL06Op/kCAAAAxcZWAVqoSmP6GudOvgYemk449EZdbTP0HPZL11BqBbUktdCeAgCAslCQd/Tu7m5JUl1dXSE2DwAAABSMNTT2sfo0E7bt5mOQWrnrzNPAw0L0NTb1M5akVtpTAABQFgryjn777bdLkubNm1eIzQMAAAAF0xFJrT6dXBNQU4HCtubqgCYbqkNtVbJInym0nVEXVJ3PAw+t1eKG55pfuixVzPQ0BgCgPKR9ivuuu+7SXXfdZfzeNddcozvvvHPc9V3XVX9/v1588UW98847chxHRx99dEY7W0zuvvtu3XjjjXrmmWe0ceNGNTU1affdd9cpp5yiiy66SE1NTTnZ7gsvvKBf//rX+tOf/qS1a9cqEoloypQpmjlzpg477DAtXrxYp5xyioJBeokBAAAUm0TS1SpD9WmhWlPsvP1tQ9Ext62KxJVIugoGaCmXjXjS1eo8DTycWR9UddDR8C7NsgtSaUx7CgAAykLaRywvvviili1bltKH2HVdPfLIIxlt1HVd1dfX6/LLL89ovWLQ19ens846S3ffffeY27ds2aItW7boL3/5i370ox/pt7/9rQ477DDfthuJRHTZZZfpl7/8ZcpAi/Xr12v9+vV67rnn9OMf/1hdXV1qaWnxbdsAAADwx7r+hGKGrM3WXiBf2prCenbT2NA4mpTW9yc0t7GwgXapWtNrGXiYg9A44DhqawppZdfYymJbeww/mAbhhQNSXYiTDAAAlIOMj1hME3gzmcrb3Nys448/XldeeaV23333TDdfUIlEQqeffrruv/9+SdL06dN14YUXau+999b27dt1880368knn9SaNWu0ZMkSPfnkk9prr708b3f79u068cQT9eyzz0qSZs+erVNPPVULFy5Uc3Ozent79dZbb+nBBx/Uc88953l7AAAAyA3rYDSfe9xmaoF1GF6M0DhL+e5d3d6cGhpvH0qqeziZk5YRpkrj1uoAw84BACgTaR8B/su//IvOO++80f93XVcLFiyQ4zi67rrr9IEPfGDc9QOBgBoaGtTa2pr1zhba9ddfPxoY77333nr44Yc1ffr00e9fcskl+sIXvqCrr75aXV1duuiii/TYY4953u6ZZ545Ghh//vOf13/+53+qpqYmZblvfetbWr9+vRoaGjxvEwAAAP6zVX4WQ3sKk85IXKXbUK6wbIME23L0WNuG63X2xLRoWrXv2zP1NG6poUUeAADlIu0jlubmZjU3N6fc7rqupk2bpvnz5/u6Y8UmkUjo61//+uj/33jjjWMC4xFXXXWVHnroIb344ot6/PHH9cc//lEf/OAHs97usmXL9MADD0iS/umf/knf+973xl1+1qxZWW8LAAAAuWWqPm2uDqi1wMPDWqsDaq5y1BPNX0/ccmeqKp9SG1BTVW4ea/swvHhOQuPu4UTKbYV+HgMAAP94elfv6OhQR0fHhFXG5eCxxx7Thg0bJEnHHHOMDjzwQONywWBQl1566ej/33zzzZ62e9VVV0mSGhoa9O1vf9vTfQEAAKBwkq5rrDRubwoV/JJ+x3GMfZU7InElM2hFhx1sAw9t1cB+mNMQUsjw6S4XwX886SoSTX1e5KINBgAAKAxP7+rz58/X/PnzVVtb69f+FK377rtv9OslS5aMu+yHP/xh43qZevLJJ/XGG29Ikk4++WQ1NTVlfV8AAAAorE0DCQ3GU4O2QremGGHqqzwYd7V5ILWiFONb359QNM8DD0MBR/MM/adtfbS96DH0M5ak1hpCYwAAykVxHKGWgJdffnn060MOOWTcZWfMmKG5c+dqzZo12rRpk7Zs2aKpU6dmvM0///nPo18feuihkqTbb79d119/vZ5//nl1dXVp8uTJOuCAA/Txj39c55xzjkIhHlIAAAA/DMVd3fl2v1Z2xRRLeq+27Y+Z76PQQ/BG2MLr7zzbo7qQvRK6PhzQwdOrdcK8moJXTBeLQg08bG8O6d1dKos3DybVF0uqIexfoNtlCY2pNAYAoHwUxxFqCVi5cuXo1+3t7RMu397erjVr1oyum01oPDL8TpKmT5+u0047TbfffvuYZTZs2KANGzbo3nvv1fe//33dddddae0fAAAA7FzX1VXPdOu17f5Xae4ql9WnmbDtx7q+iSuNX9wSVfdwQqe/h4HMkn0IXq6rytubwpKGUm7v7Ilr3ylVvm2n21ZpTGgMAEDZIDROU3d39+jXU6ZMmXD5yZMnG9fNxEgPZUm64oortHLlSlVVVWnp0qU66qijFA6H9dJLL+n666/X9u3b9fLLL+vYY4/V888/r0mTJmW1zfHEYjE9//zzxu/NnDlTM2fO9H2bAAAAhdARieclMK4PO5paWxxB29TagOpDjvoNLTTScV/HoE7dvV7BANXGnQUaeGgdhudzaNw1ZKk0pj0FAABlg9A4TX19faNf19TUTLj8zn2ee3t7s9pmV1fX6NcrV65Ua2urHnroIR1wwAGjt5955pn63Oc+p+OPP16vvfaaVq1apX/913/VT3/606y2OZ7NmzfroIMOMn7vyiuv1Ne+9jXftwkAAFAIb3blPjCWpD1awkXT0sFxHO3RGtaLW6JZrd8fd7W+P6G5hr66lWbbUGp19rzGYM4f63mNIQUcadduKh0Rf5/P3cPm6vPW6qCv2wEAAIXDEV0RSybHnsH/3ve+NyYwHjFjxgz9+te/1qJFiyRJy5Yt03e+8x3fB+dNmzbNOtiPKmMAAFBOOgyVon5zJH2kvS7n28nEkvZavbQlqmw7OHf0xCo+NHZd11iJO6km94FqVdDRnIaQVveOff76/Xw29TQOOlJjVXGcAAEAAN5V9hFdBhoaGkYrf4eGhtTQMH6/tsHBwdGvGxsbs9rmzuvV19fr7LPPti67cOFCHXbYYXrqqac0PDysJ598Uh/+8Iez2q5NOBzWgQce6Ot9AgAAFCNTT9rqoKN5jf4Ef5Nrgzp+bq32n+pfywA/LJxarX99X4seWjOobYP2XsaupLe7U39HHZG4js7h/pWCwbirqKF7Q76GxLU3p4bGG/oTGownVRvyZx+6DaF4c3VAgSKpmgcAAN4RGqeppaVlNDTeunXrhKHxtm3bxqybjdbW1tGv99tvP1VVjf+h4uCDD9ZTTz0lSXrnnXey2iYAAECliyZcre1NDUQPml6lyw5oLsAe5df+U6vSCrMv+tPWlIFo+ajQLnamKlwpf0Pi2ptC+vMut7mSVkXieu8kf05SmH7GfIXiAAAgP3hnT9Oee+45+nVHR8eEy++8zM7rZuK9733v6NfNzRN/QNl5mUgkktU2AQAAKt2a3rgShv4M7U3UW+zM9PvojMSVdLNtblEeCj0kbrxheH7Z9WSBlL9QHAAA5Afv7Gnab7/9Rr9+5plnxl1206ZNWrNmjaQdfYCnTp2a1TYXLlw4+nVPT8+Ey++8TDohMwAAAFKZWlNIUntzOM97UtxM4eRg3NWmAXtbi0pgClSl/IWqbU0hmZpE2J7XmUq6rnpMlcZ5CsUBAEB+8M6epg996EOjX9uGwY249957R79esmRJ1tv88Ic/PDph+eWXX1Y0Ov4k62effXb062yrmwEAACpdR0/MeDuVxmO1WX4fld6iwtaeIl/tG2pCAc2sT+29bXteZ6o36hor8ak0BgCgvPj6zr5hwwbddNNN+td//VddfPHFuuCCC/y8+4I65phjNGPGDEnSo48+queff964XCKR0DXXXDP6/2eccUbW25wzZ46OOeYYSVJ/f79uuukm67IvvfTSaD/jxsZGHXnkkVlvFwAAoJKZQs+ptQE1VBGK7cxWeV3poXH3kLnSurXGnyGK6TBVga/tSyhqSnsz1DVs/vlaqvP38wEAgNzz5ch327ZtWrp0qebPn69zzz1XV111lf73f/9Xy5YtS1n24osvVlVVlXbbbTc/Np03wWBQV1xxxej/L126VJs3b05Z7itf+YpefPFFSdKRRx6pE0880Xh/y5Ytk+M4chxHixcvtm73W9/61ujXX/jCF/TCCy+kLLNp0yadddZZo/9/6aWXqra2dqIfCQAAALuIJ12tNgzBa2uiNcWuptYGVB9ObYTQEfGnorVUmSqNa0OOqoOmphG5YQr0k66Mz+1MdVt6NrfSngIAgLLi+Rq7jo4OHXPMMVq3bp3cNIZe/NM//ZP+93//V52dnXrsscd09NFHe92FvLnwwgt1xx136MEHH9Srr76qhQsX6sILL9Tee++t7du36+abb9YTTzwhSWppadF1113neZuHH364vvzlL+uqq65SV1eXDjvsMJ177rk66qijFA6H9eKLL+r666/X9u3bJUkHH3yw/v3f/93zdgEAACrRur6EYoZMzDZcrJI5jqP2ppBe2TY2JO7oict13dE2a5WmGIbE2VqpdPTEtXuLtxMghW6/AQAA8sPT0W8ikdA//MM/aO3atZKkM888U+eee67eeustffaznzWus3DhQr3nPe/RW2+9pQceeKCkQuNQKKTbbrtNZ555pu655x5t3LhR3/jGN1KWmzNnjm655Rbts88+vmz329/+toLBoK666ipFo1H97Gc/089+9rOU5U488UTdfPPNqqmp8WW7AAAAlcbW93UBobFRe3M4JTTui7naOpjU1LrKbFfQZajEzXegajvJseP57e2KxEIP+gMAAPnh6Z19+fLlevXVV+U4jn74wx/qpptu0gc+8AHNnj173PWOPfZYua6rp59+2svmC6KxsVG///3vdeedd+rUU0/V3LlzVV1drSlTpujQQw/VVVddpVdeeUVHHHGEr9v95je/qeeee07//M//rPe+971qbGxUTU2N5s2bpzPOOEP33nuv7r//frW2tvq6XQAAgErSETFfvs8QPDNrOGn5PVYCY6Vxnls31IcDml6Xuk0/HhdTKO5IaiY0BgCgrHg6+r3tttskSUcddZT++Z//Oe319t13X0nSm2++6WXzBXXyySfr5JNPznr98847T+edd15G6yxcuHDMkD0AAAD4yzTErbU6oJY8DjErJfY2CDG9b0Z1nvem8KIJVwPx1JZ9hWjd0NYU1qaB4TG3re6NK550FQpk3zrEFIo3Vjme7hMAABQfT0cvL7zwghzH0amnnprRelOmTJG0Y4AeAAAAUAySrqtOQyUm/YztZtQHVWMY8GYK3yuBrd9va3X+TzqYnrfxpLTW4zA808/YUoCfDwAA5Jan0Hgk9J0zZ05mGw3s2GwyaT6oAgAAAPJtQ39Cw4nUKlFaU9gFHEdthnCyUttTdA0ljLe35Lk9hZS71iHdhp8x3+03AABA7nl6d6+vr5ckDQwMZLTehg0bJEmTJk3ysnkAAADAN7bq2PbmcJ73pLSYQvXu4aQ1QC1nxTQkrr3J/Lz1UgXuuq6l0pjQGACAcuPp3X3u3LmSpL/+9a8Zrff4449LkvbYYw8vmwcAAAB809ETM95Oe4rxWStaK7BFhWlInFSYULW5OqDJhgpg2/M8HQNxVzHDj1iIUBwAAOSWp3f3xYsXy3Vd/eY3v1E0Gk1rnY6ODt19991yHEfHHnusl80DAAAAvjH1M24MO8bgDX9nq8SuxBYV1krjAj2HTIF+ZySupJvahiUdxRSKAwCA3PL07v7JT35SjuNo/fr1uuyyyyZcfvv27Tr99NMVi8UUCoV0/vnne9k8AAAA4AvXdY2Vse3NITlO6qA3/N3s+qDChk8VXipaS5WpdUM4INWFCvMcajO0qIgmpfV92bUOKbZQHAAA5I6nd/eFCxfqvPPOk+u6+t///V996EMf0p/+9Cf19vaOLjMwMKA33nhD3//+97Vw4UK98MILchxHl1122Wh7CwAAAKCQtgwm1R83DMGjn/GEggFH8w19jak03qG1OlCwEw8LfB6GZwrFJSqNAQAoR54btF177bV699139ec//1kPPvigHnzwQUkaPTBqbGwcXdb922VQJ554ov7rv/7L66YBAAAAX1j7GRvCUKRqbwrr7e6xQeTWwaQi0aSaqionUDS1b2ipCRZgT3aw95uO6f2zazK+v25Le4rWAv6MAAAgNzwfwVVVVelPf/qTvvzlL6u6ulqu647+kzTm/6urq/XFL35R99xzj4JBDiwAAABQHGxD2xiClx7b76mzwobhdQ+ntn0o5JC41uqAmqtSq5yzHVLYZfj5RrYDAADKiy9HwcFgUP/1X/+lyy+/XLfccosee+wxdXZ2qru7Ww0NDZozZ46OOeYYnXHGGZo9e7YfmwQAAAB8Y7pcvzbkaFodhQ7psFa0RmLaf2pVnvemMOJJV5FoaouTQrZucBxH7c1hvbhl7NDyjr8Nwwtk2DbD1H6jLuSoKkjfbwAAyo2vpRNTp07VZz/7WX32s5/1824BAACAnHFdV+8a2lO0N4UyDtUq1dyGkIKOlNglM822orUU9RTpkLj2plBKaDwYd7V5IKEZ9Zl9HDS236DKGACAssQ7PAAAACpa13DSWCFKa4r0hYOO5jYahuFVUGhcrEPi7FXgmT82xkF/BQ7FAQBAbnh6hz/uuON0/PHHa8WKFRmt98wzz4yuCwAAABSStZ9xUzjPe1LaTOHkxoGEBmLmMLXcmAJVqfD9ftubzc/jbAJ9UzBe6FAcAADkhqfyiUcffVSO42jr1q0Zrbd9+/bRdQEAAIBCepcheL5obwrpEcPtnZG49p5c/n2NTa0bJKmlwJW4U2sDqg856o+PrabPNDQeTrgajKdW5Bc6FAcAALnBOzwAAAAqWmcktZ9xVUCa1cAQvEz4WdFairqHE8bbW6sL+zzaMQzP0DokEpPrpobANl1D5p+vpYa/EwAAylFBQuNYbMeBeTjMJX8AAAAoLFOo2cYQvIzNbwrJ9BvrMITy5cjUuiHoSI1VhX8emQL93qirbZbqaJNibb8BAAByoyDv8CtXrpQktba2FmLzAAAAgCSpZzhpDM5sVbOwqw46mm2ozq6YSmPD86i5OlAUJx/amyzD8DJ4bIp10B8AAMiNtBu1RSIRdXd3G7+3efNmrV69etz1XddVf3+/nn/+eX3ve9+T4zjad999M9pZAAAAwE+2Klj6GWenvTmstX1j2xis60toKO6qJlT48DSXinlInO353BGJ6ZAZ1WndhykUl6TWAvdsBgAAuZH20fD3v/99/cd//EfK7a7r6qKLLspoo67rynEcfeITn8hoPQAAAMBPtkpLW2UmxtfeHNLj68be5kpa3RvXe1rLu3rb1L6hWFo3zKgPqjroaDiR/TA8W6VxsfyMAADAXxm9w7uuO+af7faJ/knS2WefrfPPP9/fnwYAAADIgCk0CwWkOY2Extmwt0Eo777GSddVj6nSuEiqcAOOozbDY5NJaGwKxasCUm2ZV5ADAFCp0j4aXrRokc4999wxt/3yl7+U4zhavHix5s2bN+76gUBADQ0Nam9v1wknnEBrCgAAABRcp6E9xbzGkEIBgrBsmIJJqfz7GvdGXe1SxCupuKpw25tDWtk19vneNZxU91BCLTWpvah31WVoT9FaE5BTBD2bAQCA/9IOjU8++WSdfPLJY2775S9/KUm67LLL9A//8A/+7hkAAACQQ32xpDYNGIbg0Zoia3XhgGbWB7Whf2xf445IeYfGXcMJ4+0t1ROHsflirQKPxHVAGqFxt+FnLKafDwAA+MvTEfHSpUvlOM6EVcYAAABAsem09TNuLu/eu7nW1hRKCY3X9MYVS7gKB8uzKrUUhsTZntcdPXEdMG3iYXjFPOgPAAD4z1NovGzZMp92AwAAAMgvW/WrrcUC0tPeHNJfNgyPuS3hSmv64lpQpoG8bUhcMYWqsxuCCgek2C67mk4VeDzpqjea2n+jmH4+AADgL97lAQAAUJFMw9kCjjSf0NiT9iZ7RWu5Mg2Jk4qrp3Eo4GieYcBjOkMKrT9fEVVSAwAAf/EuDwAAgIpkCjFnNwRVVaYtFPKlrbnyhuGZhsQ5kpqLKDSWdlSB72rLYFJ9UXMoPMIWGlNpDABA+fJURnHcccd52rjjOHrooYc83QcAAACQqaF4MqXvrmSvkkX6mqoCmlIb0NbBsUFjR2TiitZSZQpVG6schQLFdQJiR1/joZTbOyJx7TelyrqeKRSXiquSGgAA+MtTaPzoo4/KcbI7EHJdN+t1AQAAAC86I3Gldmg1V2Iic+1NIW0djI65bVUkrkTSVbDIglQ/mIfEBQuwJ+Nrt7Re6eiJjRsaWyuNaU8BAEDZ8vwu77puRv9G1gEAAAAKxdYqgdDYH+2GgXexpLTOUN1dDrqHUn+uYuz3O7cxJFP3lYmG4XUNmx+31iIMxgEAgD88Hckkk8kJ/8XjcW3cuFG333673v/+98t1XZ199tkaHh5WIlGeB40AAAAobraQrI0heL4Yr6K13Liua6k0Lr7QuCroaI5xGN74oXG3oT1F0NnRggMAAJSnnB/JBAIBTZs2TR/72Mf05z//Wf/yL/+iX/3qVzr77LNzvWkAAADAyBSSzawPqjZUfEFfKbJVbJfjMLyBuKuYoXtDsfb7NQX6G/oTGjD9EH9jCsWbqwMK0G4QAICylfcjmauvvlp77723fve73+mOO+7I9+YBAABQ4aIJV2v7UsNLWlP4p7UmaKy0nagNQimyDYkrxkpjyf48X9Vrf2xMPY2L9ecDAAD+yPs7veM4Ouuss+S6rn72s5/le/MAAACocKt740oaRmzYWiogO6bfZ2dPXMkym29iGxJXjD2NJam9KbXftDR+FbjpZyzWSmoAAOCPgrzT77bbbpKkF198sRCbBwAAQAXrtFS7moa3IXumitahhKuNZTYMzxYaF2sl7vymkExNJWyhcdJ11WOqNC7SUBwAAPijIO/0/f39kqTt27cXYvMAAACoYLZhbFQa+8sWwpdbiwpTv19pR4uOYlQTcjSrIXXfOiPmv4veqKuEoTicSmMAAMpbQd7p//CHP0iSJk+eXIjNAwAAoIKZKiqn1gbUUEUI5idbCF9uw/C6h8yV08Ucqpoem7V9CUUN6XDXsPnna6kuzlAcAAD4I+/lFFdffbVuu+02OY6jI444It+bBwAAQI5tH0pobW9CSU3cuzYccNTeFFJd2HvA5rqu1vUltNUS4u1YaEdP413RmsJ/U2oDagg76ouNfR68ui2qF7cMW9dzJM1tDGlSkVbq7spUaVwXclQVNDWBKA7tzWE9sX7sY5B0pUfWDGp6/djfuy3kL9aezQAAwB+eQuP/+I//SGu5aDSqdevW6ZFHHtGaNWtGb7/sssu8bB4AAABFxHVd3fR6n+7pGMxovVBAuni/Jr1/Tk3W2+6PJXX1cz16dZv5EvuJ0JrCf47jqL05pJe3jn1M3u2J67/+r2fC9Ze012rpXg1ynOINXyVzT+Ni7Wc8wtRvWpJ+8Wpf2vdR7D8jAADwxtPR8de+9rWMD+Jc15XjOPrWt76lo446ysvmAQAAUERe2x7LODCWpHhS+t+XI1o0rUqNWbaI+MO7A1kHxpI9RIM37U3hlNA4Xfd2DOqgadXad0qVz3vlr66h1NC42Ktw23w4SVLM7TcAAIB3nt/pXddN+19VVZU+8pGP6JFHHtGXv/xlP/YfAAAAReKZjfaWAxOJJqW/bo1mv+1N2W9boj1FrngN45/1+LjmQylWGteHA5pel337D0dSc5H/jAAAwBtPR3GPPPJIWstVV1ertbVVu+22m0IhqjgAAADKUUfE24Czjp64jpyV+XrRhKu1feP0MZ7AHi2hog/5StX+U6pUHZQss9QmVOxD86IJVwPx1N7dpVCFe8j0qqyuDJCk/aZUKRQo7rYhAADAG08J7jHHHOPXfgAAAKCEJV1XnR4Dvo6e7NoYrO6NKznxzD2jSTUBfWrfxuxWxoQaqgL69H5N+ulfI4qlFuROqCMSV9J1FSjSvsamIXiS1FICQ/xO3r1eb3bH9WZXZn93U2sD+uQ+DTnaKwAAUCwo+wUAAIBnG/sTGkqkJrdHz67RkbOrU26/9c1+vd09NmTuiMRH519kwlaNes5eDZrTaA/v6kMBtTeHqJjMsaNm1+jAaVXqiMQVGyfdX7F+WH9eOzTmtuGEq439Cc1qKM6PLV1D5hLqUqg0bqoK6OuHt2hdX0LbLD/HrhrDAbU1hRTkbwYAgLJXnEdfAAAAKCm21hTvm1GtRVNTQ+PXtsVSQuP+mKstg0lNy7DXqq1C+di5NaoPF394VwnqwgHtM3n8gXaxhFJCY2nHSYFiDY1N/Yyl4u9pPCLgOJrbGNLcxuL8/QIAgMIpjaMZAAAAFDVbta9tEFp7k/n2bFpUmALr6XUBAuMSY3uueO2VnUtdQ+bQuLWG5x4AAChtaZ1Sfuyxx3K2A0cffXTO7hsAAAD5YQp7G8OOJlvCs/ECwkNnpr/deNLV6t7UULGtKZz+naAoTK4JqDHsqDc2toVFtr2u88FWaVwK7SkAAADGk1ZovHjx4ox7y6XDcRzF48VbOQAAAICJua5rrAZtaw5ZjyGn1QVVG3I0GN81IMzs2HBtb1xxQ25nC6VRvBzHUXtzSH/dOjYk7ujJrtd1PpgG4VUFpNpQ8e0rAABAJtI+Be66bk7+AQAAoLRtGUyqP5Z6XNc+TrVvwHHUZmhR0dETy+gY0da6wNb+AsWtvTn1OdMf39HruhiZKo1bqgNFGXADAABkIq2j6SuvvDLX+wEAAIASZWsfMFG1b3tzSK9vH7tuT9RV13BSk2rSG4Znq0xuM4SPKH7j9brOdEBiPph6Grek+dwFAAAoZoTGAAAA8MRa7TtRaGwNCOMZhMapgfWkmoBa6Clbkqy9rnsy63WdL93DiZTb6GcMAADKAUc0AAAA8MRU7VsbcjR9gspQUysCyR5C7yrpuuo0LEtritI10ut6V+k+J/IpnnQViaa2UuGEBQAAKAcc0QAAACBrrusaq33bmkIKTNDXdVZ9UFWGo1Fbu4tdre9LKMoQvLIScBxj6P9uhr2u86HH0M9Yklpr+IgFAABKH0c0AAAAyFrXcFI9hmrLdILbYMDRfOMwvPSqSm3L2SqYURpMz53I33pdFxPb/lBpDAAAyoGvRzS///3v9clPflJ77bWXWlpaFAqF1NLSor333luf/OQndc899/i5OQAAABSYNbhNs0WEKeDdNpRUJI2AsCNiGcBHe4qS1t5kaVuS5smEfOm2VRoTGgMAgDLgyxH1K6+8orPOOkuvvPLK6G0jl49FIhH19vZq5cqVWr58ufbbbz/96le/0j777OPHpgEAAFBA9iF46VX7WofhRWJaOLV6/G0bQsSmKkeTaA9Q0mxV6u/2xHXQ9PGfE/nUNWSpNOb5BwAAyoDnI5rnnntORxxxhF555RW5rjv6r7m5WbNnz1Zzc/OY2//617/q8MMP1/PPP+/H/gMAAKCATP2HqwI7+hWnwxYQTlRVmnRdY2Dd3hyWM0EvZRS3WQ3mXtedlsryQukeThhvb61O77kPAABQzDyFxkNDQzr11FPV19cn13W13377afny5dq0aZO6urq0Zs0adXV1adOmTfrlL3+p/fffX5LU19enU089VcPDw778EAAAACiMTkNwO78ppGAgveB2bmNIQcOitgrmEZsHEhqMG3op05qi5AUcR20eel3ni6mncdCRGqs4aQEAAEqfp9D4+uuv15o1a+Q4ji644AI9//zzOvvsszV16tQxy02dOlXnnHOOnnvuOX3qU5+SJK1Zs0bXX3+9l80DAACggCLRpLYOpgZnmQyiCwUczWvMPCC0t8UgNC4Htl7XPUU0DK/b0J6iuTqgAJXuAACgDHgKje+66y5J0j777KPrrrtOweD4l2IFg0Fde+212nfffSVJd955p5fNAwAAoIA6PQ7BG13eEPRuGkioP2YPCK0D+DIIrFG8rG1LiqhFhanSuIUheAAAoEx4Oqp59dVX5TiOzjnnHAUC6d1VMBjU0qVL5bquXn31VS+bBwAAQAHZArxMq31ty5taX4xu2xAa14UcTasltCsH1gGJRdSiotsQGrcSGgMAgDLh6ahm+/btkqS2traM1ps/f/6Y9QEAAFB6TAFe0NnRpzgT7U3m6mBbQOi6rjGwbm8OMQSvTMxpDClk+KRSLKFx0nWNrTJaagiNAQBAefB0VNPY2ChJ2rZtW0brjSw/sj4AAABKjynAm9cYUijNIXgj5jeFZFqlo8dcybxtKKneKEPwypmt1/V41ef51Bt1lUh9ClJpDAAAyoano5oFCxZI+ntv43SNLD+yPgAAAErLQCypjQOJlNuzGURXFXQ0uyF1NoZt2B39jCuD6STARL2u86VrOPW5L0kt1ePPeAEAACgVnkLjD37wg3JdV3/84x/1y1/+Mq11li9frgceeECO4+jEE0/0snkAAAAUiK3iM5vQWDK3qFjfl9BQPLWc069eyihutpMAxVBt3D1kDq5baU8BAADKhKejms985jOqq6uTJF1wwQW69NJLtWbNGuOya9as0aWXXqoLLrhAklRXV6fPfOYzXjYPAACAArFW+1r6E0/EFPi6klYZAkLTtquDjmbWU+VZTmwnAYqhr3GXoZ+xJLXQngIAAJQJT+UYM2fO1DXXXKNPfepTkqQf//jH+slPfqL3vOc9es973qP6+nr19/frrbfe0sqVK+W6rlzXleM4uuaaazRjxgxffggAAADkl6naN+Ds6E+cDWtAGIlpz0ljg2hTaNjWFFKAIXhlZV7jjl7XyV2KzW29rvOp2xIa09MYAACUC8/X8J1//vlyXVeXXXaZBgYG5LquVq5cqZUrV45ZznV3HO3V1dXpmmuu0Sc/+UmvmwYAAECBmILb2Q1BVQWzC27bLGHzrtvpHkoYqzxpTVF+qoKO5jQEtbp3bP9gW6/rfOoytKdwJDUTGgMAgDLhy1HNBRdcoNdee02f+9zn1NbWNlpRvPO/trY2ff7zn9drr72m888/34/NAgAAoACG4q7W9RmG4GXZmkKSakMBY3uJXQNCW2BoGpqG0mfqa2zrdZ1PpkrjxipHoQDV7gAAoDz4dnQ9b948XX311br66qu1ZcsWbdiwQb29vWpsbNTMmTM1depUvzYFAACAAlrdG5cpsvNa7dveHNKG/rFh9NreuKIJd7SC2dpL2TI0DaWtvSmkP+9y20iv613bluSTqdq9pZqe2gAAoHzkpCRj6tSphMQAAABlytZT1nNo3BTSivXDY25LuNKa3rh2a9kREJoqjcOBHa0xUH4y6XWdT91DqZX2rTW0pgAAAOWDIxsAAABkxFbta+tLnC5btfDOQbEpsJ7XGKItQJma3xSS6ZG1PQfzwXVdS6UxH60AAED5yEvztzvuuEOPP/644vG4Fi1apDPOOEN1dXX52DQAAAB8Zqr2nVkfVG3IW2hm60u8IyiuVV80qS2DDMGrJCO9rtf3F88wvIG4q1jq01CthMYAAKCMeDrCfuutt/T5z39ekvTVr35VhxxyyJjvR6NRfeQjH9HDDz885vZvf/vbeuCBB9Te3u5l8wAAAMizWMLVmt7UwM6P4LahKqCptYGUYHikqtQ6BI9+xmWtvTmUEhrv2us6n7qGDImxqDQGAADlxdORzS233KJ77rlHTzzxhBYuXJjy/W9+85t66KGH5LrumH9vv/22TjnlFCWT5gMuAAAAFKc1fXElDFPwbFXCmTIFwKt744onXXsvZZ+2jeJkansy0uu6ELoNrSkkehoDAIDy4unI5sknn5QknXDCCaqqqhrzveHhYf3whz+U4zhqbm7W97//fd15551asmSJJOnll1/Wrbfe6mXzAAAAyDNbL1m/qn1NAXAsKa3rSxgrjYOONLeR0LicpdPrOp9M/YwlKo0BAEB58XRks3r1ajmOo4MPPjjle3/84x8ViUQkST//+c912WWX6R/+4R901113abfddpMk/e53v/OyeQAAAOSZNTT2rdLY3tfYtO3ZDaGCtChA/ozf6zr/ui3tKVprgnneEwAAgNzxFBpv3bpVkjRnzpyU7z366KOSpEmTJumUU04ZvT0YDOoTn/iEXNfVCy+84GXzAAAAyLOOSGpQN7U2oIYqf6osbQHha9tj2rBLX1uJIXiVYKTX9a5sJzByrWs49XkoMQgPAACUF09HNl1dXZKU0ppCklasWCHHcXT88cfLccZWfyxYsECStHHjRi+bBwAAQB7Fk65WGVoCtDX5N4iupSZoDN+e2jBsXJ5+xpVhvF7X+WbqaVwXcqh4BwAAZcVTaFxTUyNJ2rJly5jbBwcH9fzzz0uSjjjiiJT1GhoaJEnRaNTL5gEAAJBH6/sSihmuzPe72td0f8Om6Xs52DaK03i9rvOty9Cegn7GAACg3Hg6uhlpS/Hcc8+Nuf2BBx5QLLbj0kVTaDxSodzY2Ohl8wAAAMgjU2sKSVrgd2icZvWwI6mNSuOKMF6v63wzVRq31hAaAwCA8uLp6Obwww+X67r63e9+p7Vr10qS4vG4/vu//1vSjn7GBx54YMp6r7/+uiRp3rx5XjYPAACAPMr1ELzR+zO0IjCZWR9UTYiwrhLYnhMdhnYpudZlCI2pNAYAAOXG09HNJz/5SUlSb2+vFi1apDPOOEMLFy7UE088IcdxtHTpUgUCqZt4/PHH5TiO9t9/fy+bBwAAQB6ZQuPW6oBaaoK+bifdlhO0pqgcLdUBY6/rfA/DG064GoyntkphCB4AACg3no5ujjrqKH3605+W67ravn27br31Vr3xxhuSdrSu+Ld/+7eUdd59993Rdham1hUAAAAoPknXNVZ15iK4nVwTUGN44qFi6VYkozyYnmudkbiSbv6G4XUNmXso+33iBAAAoNA8nxK/9tpr9YMf/ED77LOPqqqq1NraqjPOOENPPPGEJk2alLL8T37yk9GvTzzxRK+bBwAAQB5s7E8Yh9H53ZpCkhzHSSuMzsW2UbxsAxI39OdvGJ6pn7FEpTEAACg/no+0HcfRpZdeqksvvTSt5b/whS/on//5n+U4Dj2NAQAASoS1n3GOqn3bm8P669bxh5zRnqKytDdZ+hr3xDW7IT/PBVM/Y4mexgAAoPzk/Uh7xowZ+d4kAAAAPLINHMtVcDtRFfH0uoDqwwR1lcT2XOvoiemo2TV52YfuIUulcQ3PRQAAUF44ugEAAMCEOnpSq34bw44m5ygsmyiMbrNUnaJ8Ta4JqLEqtde17YRGLtgqjWlPAQAAyo2vpSHxeFxPPPGE/u///k/r169Xb2+vGhsbNWvWLB166KE66qijFAwyJAIAAKCUuOMMwXOciQfWZWNaXVC1IUeDcfOQM1pTVB7HcdTeFNZft0bH3N7RE5frujl7Lu7M1NO4KiDVhnK/bQAAgHzy5Wg7mUzqe9/7nn7wgx9o06ZN1uVmzJihz33uc7r88ssVCHA2HgAAoBRsGUyqP2YYgpejfsaSFHActTeF9Np2c19jQuPK1N4cSgmNB+KuNg8mNb0u98UpXYb2FK01gbwE1gAAAPnk+Wi7u7tbS5Ys0dNPPy1pRyWKzYYNG/TlL39Zd9xxh/7whz+opaXF6+YBAADG9dq2qB5cPagtAwlf7zcUcLRHS1in7F6nuiLrrTuccHXH2/1auT2mWNJ+bJYua7XvBH2HvWpvHic0pj1FRRqvr3E+QuPu4dTXkZZqrqQEAADlx9ORvuu6+uhHP6qnnnpKkhQIBPTBD35QJ5xwgvbYYw/V19erv79fb7/9tv70pz/pwQcfVCKR0FNPPaWTTjpJjz/+uC8/BAAAgMnr26P6xtPd8iE3tdx/TK9tj+o/j2gtqkrD7zzTrVe2mcNWP+W62ndHMDyYcvvkmoCa6SFbkWwnKjp64jpsZu63b+pp3MJzEQAAlCFPR/o33HCDVqxYIcdxtPvuu+uWW27RokWLjMtefvnleumll3TGGWdo5cqVWrFihZYtW6bzzjvPyy4AAABY3dcxmLPAeMTb3XG90RXTXpOqcruhNHVGYnkJjGtDjqbluLLTFkrTmqJyTa8Lqi7kaGCX6vd8DMOLJVz1RlNfUFpzNAwSAACgkDwd4dx0002SpObmZj3yyCPWwHjEwoUL9dBDD422pVi+fLmXzQMAAIzrre7ch6eS9FZXfraTjre6ch+eSdIeLSEFclxdPashqIZw6jbe00prikrlOI7aDNXGHT2xcdvk+WFNn/lvazKhMQAAKEOejnBefvllOY6j888/X7NmzUprnVmzZumCCy6Q67p6+eWXvWweAADAqns4qe2GoVW5kI8qx3R19OQ+wHYkLWmvy/l2Ao6jkxaM3U592NHx82pzvm0UL1OleSTq5vzvvaPH/HfelsOBkAAAAIXi6dq+/v5+SdJBBx2U0XoHHnigJGlgYMDL5gEAAKw6LeHpzHpz9Wq61vYlUgbD2cKkQjAF2NVBR/Ma/WklMakmqOPm1WjR1Gpf7m8iJ+9Wp8k1QT2zaVgNYUen7lGvhiIbPIj8am8297ruiMQ1uTZ3LVNsf+e5HggJAABQCJ6OcGbNmqWOjg4lEplNIx9ZfubMPEyrAAAAFclW/fu5A5s130PI89OXInpk7dCY2zb0JzQQS6quwGFmPOlqdW/qz71oapUuP6i5AHvkneM4ev+cGr1/Tk2hdwVFwj4ML6aDp+fuZEZHJPVE1NTagBqrOIkBAADKj6cjnKOPPlqStGLFiozWGxmed8wxx3jZPAAAgJWpKjAckGY3eKtEtA1hW2UIa/NtbW9cccMV+gyOQzmZ1RBUteHPOJcV/4mkq1WGE1FtTbSmAAAA5clTaHzJJZcoEAho2bJlev3119Na5/XXX9eyZcsUDAZ1ySWXeNk8AACAlam377zGkEIBb8Pb2i39S4uhRYWtuprQGOUk4Diabwhrc9lbfF1/QjFOyAAAgAriKTQ+6KCD9N3vflfDw8M67rjjdO+99467/H333afjjz9e0WhUV1999WhvYwAAAD/1xZLaPJia8LT5EPDMawzJFDsXRWhs7blKNSTKi6lFxfahpHqGczMMzzZgcgGhMQAAKFNpHeUsX77c+r1JkybplFNO0e23366TTjpJ733ve3XCCSdojz32UH19vfr7+/X222/rwQcf1BtvvCFJOvXUU9Xc3Kzly5dr6dKl/vwkAAAAf9OZw/C0JuRoVkNQ6/rGznQw9TvNN1OwNakmoOZqeq6ivNgqfDsisZwMaWQIHgAAqDRpHeWcd955cpzxL+V0HEeu6+qNN94YDYd35bquHMfR7bffrttvv12O4xAaAwAA31kDHp+qAtubQimh8drehIYTrqqD3tpfZCvpuuo0XJ5PqIVyZA2Ne+K5CY0Nf1ut1QG11HjrkQ4AAFCs0i47cV13wn8TLWf6PgAAgN9MVb8BZ0drCT+Y+hq7klbnsKfqRNb3JRSl5yoqxJyGkEKGTzK5aBOTdF3j1Qv8bQEAgHKW1pHODTfckOv9AAAA8I0pOJrTEFKVT1XA410av0drYfoH26ur6WeM8hMKOJrXGNK7uzzvO3PQJmZjf0JDidRiF6r4AQBAOUvrSOfcc8/N9X4AAAD4YjCe1Ib+RMrtflYFtlnCokIOw7P1VCbYQrlqb0oNjTcNJNUXS6oh7F8fb1NrCokTMgAAoLwxFQUAAJSVVZG4TA2w/AxP68MBTa9L7WVa0NDYsO2mKkeTajjcQ3myhba2QZjZsv1d204eAQAAlIOCfYqIRCL66U9/WqjNAwCAMpXrIXjj3d/q3rjiyfzPbEi6rrEasr05POEwY6BU2dvE+B0ap1bxN4QdTanlhAwAAChfeT/SefTRR3XOOedo5syZuuSSS/K9eQAAUOZMgZEj/6sCTZXLCVda05v/auPNAwkNxum5isoyrzGkgOGciCnkzZZrPSET4oQMAAAoa3n5JLFu3TotW7ZMN9xwgzo6OiTtOADjQAsAAPjNFBjNrA+qJuTvuXJrlWNPPO+9TvNVXQ0Uk6qgozkNQa3uHdvD3M82MVsGk+qPmU7I0M8YAACUt5xVGsfjcf3ud7/TkiVL1NbWpiuuuEIdHR1yXVeu6yoUCumjH/1orjafc3fffbdOP/10tbW1qaamRtOmTdMRRxyh7373u4pEIr5tZ/HixXIcJ+1/nZ2dvm0bAIBSE024WtuX2yF4o/dpCY38vjQ+HQzqQqUyPcc39Cc0FE/6cv+2qmVOyAAAgHLn+9HOK6+8op///Of61a9+pW3btknaUVU8YtGiRVq6dKnOOussTZ061e/N51xfX5/OOuss3X333WNu37Jli7Zs2aK//OUv+tGPfqTf/va3Ouywwwq0lwAAVKbVvXGZWgrnIjxtqg5ock1A24bGhlN+XhqfLlNlZV3I0TR6rqLMtTeF9OddbnMldUbieu+kKs/3bz8hQ2gMAADKmy9HO729vfr1r3+tX/ziF3r22WcljQ2KHcfRBz7wAX33u9/Vfvvt58cmCyKRSOj000/X/fffL0maPn26LrzwQu29997avn27br75Zj355JNas2aNlixZoieffFJ77bWXb9u/4447Jlxm2rRpvm0PAIBSY23TkKPevu3NIW0bio65bVUkrkTSVdDUbDUHdvRcTQ2q6bmKSjBemxhfQmPDa0ptyNH0uqDn+wYAAChmnj5B/fnPf9bPf/5z3X777RocHJT097C4qqpKJ510km677TZJ0hFHHFHSgbEkXX/99aOB8d57762HH35Y06dPH/3+JZdcoi984Qu6+uqr1dXVpYsuukiPPfaYb9v/2Mc+5tt9AQBQjvJ9KXl7c1jPbhobGkeT0vr+hOY25qcScdtQUr1RhuChMs1vCsnRjurinfnRJsZ1XeNrSltTSAFOyAAAgDKX8TWL69ev17e+9S3tscceOu644/SrX/1KAwMDo72K3/e+9+nHP/6xNmzYoFtvvTUX+1wQiURCX//610f//8YbbxwTGI+46qqrtGjRIknS448/rj/+8Y/52kUAACqeKSiaXhdQfTg3bRpswWw+W1TYh+DRzxjlrzYU0Mz61KpfP4bhdQ0n1WM6IUNrCgAAUAHSPuK57bbb9Itf/EJ//OMflUzu6N03UlU8e/ZsnXPOOTr33HO155575mZPC+yxxx7Thg0bJEnHHHOMDjzwQONywWBQl156qc4//3xJ0s0336wPfvCDedtPAAAqVTzpanVvalDUZhlY5wfrpfGRuI7O2VZ33RaDulDZ2ptDWt8/dgDm2r64oglXVcHsK4Lz3e4GAACgmKR9xHP66afLcZzRoLi2tlannHKKzj33XJ1wwgll3zPvvvvuG/16yZIl4y774Q9/2LgeAADInbV9ccWTqbfnMjxtrQ6ouTqgnuFdh+F5r3JMl2lb1UHHWH0JlKP2prCeXD885raku2Mw5u4t2Z80sg/Bo4ofAACUv4yv1XQcR+eff742bdqkm266SR/4wAfKPjCWpJdffnn060MOOWTcZWfMmKG5c+dKkjZt2qQtW7b4sg8f/ehHNXv2bFVVVam1tVX77LOPLrzwQj3yyCO+3D8AAKXM3qYhd6Gx4zjGqsPOSFxJN/Wy9lww/dz0XEUlGW8YnhemNjNVAWkWJ2QAAEAFyKrB3w033KBDDjlE3/rWt7R69Wq/96korVy5cvTr9vb2CZffeZmd1/XiD3/4g9avX69YLKbu7m699tpruv7663Xcccfp+OOPH22fAQBAJbJfSp7bqkBTYDUYd7VpIGFY2l/dQwl1DaeWV9OaApWkzdomxltvcdNryvymkIIBTsgAAIDyl/Yniquuuko33HCD3njjDUnSm2++qa9+9au64oordMwxx+i8887Taaedprq6upztbCF1d3ePfj1lypQJl588ebJx3Wy0trbqAx/4gA4++GDNnj1bwWBQ69at00MPPaT77rtPruvq4Ycf1uGHH66nnnpKM2bM8LQ9m1gspueff974vZkzZ2rmzJk52S4AAOkwXUo+uWZH+4hcsg/Di2tmfW7DW+vl8/RcRQVpCAc0rTagzYP+tYmJDCe1bch0QobWFAAAoDKk/Ynii1/8or74xS9qxYoVuv7663Xrrbeqv79fruvq0Ucf1aOPPqrPfOYzOu2007R06VIdd9xxudzvvOvr6xv9uqamZsLla2trR7/u7e3Nerv/9V//pYMOOkhVVVUp37v88sv17LPP6rTTTtPq1au1atUqnX/++br33nuz3t54Nm/erIMOOsj4vSuvvFJf+9rXcrJdAAAmknRdrTJUFeaj4tYWInX0xHXErNxu296Sg2ALlaW9OazNg2P7Gq/ujSuedBXKojLYOmCSEzIAAKBCZHzUc8QRR+iII47QNddco9/85jf6xS9+oaeeekqS1N/frxtvvFE33nij5s6dq7POOsv3Ha40hx9++LjfP/jgg3X//ffrgAMO0PDwsO677z4988wzE/Zdzsa0adOsg/2oMgYAFNL6voSGDd0g2nLcmkKSptYGVB921B8b28PY66Xx6TBVGocD0uwGeq6isrQ3h/T0xrGhcTy5Y0BmNq8DheiRDgAAUEyyvl6zoaFBn/rUp7RixQq9+uqr+tznPqepU6fKdV25rqs1a9bo29/+9ujyr7zyirZt2+bLThdCQ0PD6NdDQ0MTLj84ODj6dWNjY072acRee+2lc845Z/T/77nnnpxsJxwO68ADDzT+IzQGABSSrU3DgjwEPLZheB09cbk5HoZnGtQ1rzGUVWUlUMrGaxOTDdNrStCR5jYSGgMAgMrgS5O/vfbaS1dffbXWrVun3/3ud1qyZIkCgYBc15Xzt8ndt912m2bNmqWPfexjuv322xWL5b76xk8tLS2jX2/dunXC5XcOyHdeN1eOPfbY0a9ff/31nG8PAIBiYgpPpfxVBZraQfTFXG0dTO2J6pe+aFJbDPdPJSQqUds4bWKyYVqPEzIAAKCS+DoZJhQK6dRTT9U999yjVatW6Rvf+Iba29tHq49jsZh+//vf6/TTT9fMmTP12c9+1s/N59See+45+nVHR8eEy++8zM7r5srUqVNHv/Y6eA8AgFJjCniaqxy15ngI3ghbUGurgPaDdQge/YxRgVqqA5pUk/r3ns3fYH8sqU0Dqf1uOCEDAAAqSc4+Sc2aNUv/9m//prffflsPP/ywzjzzTNXU1IwGyNu3b9e1116bq837br/99hv9+plnnhl32U2bNmnNmjWSdvQB3jnQzZWdq5/zUdkMAECxSLquMRhqbw6PXvGUa/ZL43N3ZZW1uppBXahQpuf+qkhMyQzbxHRaT8jwtwUAACpHXspvFi9erJtuukkbNmzQ//zP/+jAAw/Mx2Z99aEPfWj0a9swuBH33nvv6NdLlizJ2T7t7JFHHhn9Oh+VzQAAFIvNAwkNxlNDoXyGpzPqg6oJpgbU2V4anw56rgJjmULd4cSOQZmZsA7By8NgTQAAgGKRn2s2/6a5uVmf+cxn9Oyzz+r555/XJZdcks/Ne3LMMcdoxowZkqRHH31Uzz//vHG5RCKha665ZvT/zzjjjJzv25tvvqkbb7xx9P8/+tGP5nybAAAUC3ubhvyFpwHHUZthezltT2EItuY0hlRlCK+BSmBrzZLp36Gpij/gSPOp4gcAABUkr6HxzhYtWjQmXC12wWBQV1xxxej/L126VJs3b05Z7itf+YpefPFFSdKRRx6pE0880Xh/y5Ytk+M4chxHixcvNi5zzTXXaMWKFePu1wsvvKATTzxRQ0NDkqQPfvCDOvTQQ9P4iQAAKA+2qkDbYKxcMVU2dw8n1TWUWZVjOgZiSW3oN/RcJdRCBfOrTYwpZJ7dEOSEDAAAqCh8ssjAhRdeqDvuuEMPPvigXn31VS1cuFAXXnih9t57b23fvl0333yznnjiCUk7+gpfd911nrb38MMP67LLLtNuu+2mE044Qfvuu68mT56sYDCo9evX66GHHtK9996rZHLH5PT58+frhhtu8PxzAgBQSkyhcV3I0bTa/J4btw7D64mrtSbo67ZW9Ra+uhooNpNqAmqqchSJjm1Xk0mbmKG4a2xnQWsKAABQafhkkYFQKKTbbrtNZ555pu655x5t3LhR3/jGN1KWmzNnjm655Rbts88+vmz3nXfe0TvvvDPuMieeeKJ+8YtfaNasWb5sEwCAUuC6rjoiqVWE7c2hvA3BG92mJVTqiMR14PRqX7dlra6m0hgVzHEctTeH9dKW6JjbOyJxJV1XgTReE1ZF4jKNzeOEDAAAqDQc/WSosbFRv//973XXXXdp+fLleuaZZ7R582Y1NjZqt91206mnnqqLLrpIzc3Nnrd19dVX66STTtLTTz+tl156SZs3b9bWrVs1PDys5uZmtbW16fDDD9dZZ51FSwoAQEXaNpRUb7SwQ/BGzG4IKhyQYsmxt2d6aXw6TKGxI0JjoK0plBIaD8ZdbR5IaEb9xH8fppNQEqExAACoPBz9ZOnkk0/WySefnPX65513ns4777xxl9ltt92022676YILLsh6OwAAlDNbxa1tIFYuBQOO5jeF9Hb32H3KxTA8U7A1sz6omlDBxlUARcHaJiYSTy80trymzG/kYxMAAKgsfLIAAAAlq9iqAk0tKrYOJhWJJg1LZ2c44Wptr6HnKpWQwDjD8NI7eWM6yTOzPqi6MB+bAABAZeHoBwAAlCxTEFQddDSz3t/Bc+myBbedGQzimshqa89VBnUB0+uCqgul9i5OJzSOJlytNQyZpO0LAACoRITGAACgZJmCoLamUFoDr3LBfmm8f32NrdXVBFuAHMcxhrwdkZhc13S65e/W9MaVMCxCFT8AAKhEhMYAAKAkdQ8l1DWc2vahkAHP3IaQgoa8Ot1L49Nh7+NMsAVI5r+F3qirbUPjt4mx9R+nih8AAFQiQmMAAFCSrAFPAStuw0FHcw0Dszp9HIZnCo2n1wVUT89VQJI95J3o5E1HD1X8AAAAI/h0AQAASpK94rawVYGmgGlDf0IDMe/D8OJJV6uNPVephARGWIfhTdAmxvSaMrU2oMYqPjIBAIDKwxEQAAAoSaZK43BAmt1QmCF4I6zD8HyoNqbnKjCxWQ1BVRteBsYbSMkJGQAAgLEIjQEAQEkyXUo+rzGkUKAwQ/BGWC+N9yE0tgXPhMbA3wUcR/MNYe94f4Pr+xIyXQzA3xYAAKhUhMYAAKDk9EWT2jJYXEPwRsxrDMkUW/sxDM/akoNqSGAMU4uK7UNJdRuGZ0r21hULiuA1BQAAoBAIjQEAQMmxDsErcD9jSaoJOcYWGbYhW5kw3cfkmoCaqzmkA3ZmbRNj+Tu0n5AhNAYAAJWJoyBUtL5oUh2RuDp6YuqIxHXOXg2aVFPYXpjYoWc4qVW9cSVdQ/POXQQdR21NIQbVABXEFsAWS8DT3hzS2r7EmNvW9SX0/KZhBbJ9qXLN7SmKoboaKDa2v4unNw7LdCnA69tTX1NaqwNq4bgQAABUKD5loCK9ti2qn7wUSbm0+ahZNYTGReD2t/p1y5v9Ga3jSDpvnwZ9qK0uNzsFoKiYKo2DjjS3sTgObdqbwnp83fCY21xJVz3b4/u2GNQFpJrTEFIoIMV36Ubx8JohPbxmKK374IQMAACoZJTloSI1VQWMvTDf9aHfJLxZFYlnHBhLO8KYX77Wp80DiQmXBVD6TJeSz2kMqSpY2CF4I/IZNtFzFUgVCjia5/EkUrFcuQAAAFAIhMaoSLMagjJ1Mui0DEFB/jy7aXjihSySrvTC5uzXB1AaBmJJbehPPUFUTAFPWx73hWpIwMzr30Yx9EgHAAAoFEJjVKTA33rg7sqPyfbwxuugKB5DoPyt6rUNwSue8LQuHNC+k3MfOC1oDqmVIXiA0SHTq7NetzbkaJ88/A0DAAAUKz5loGKZqke2DSXVM5zatgL5Y+pTms/1ARQ/28mh9iLr7fvJfRo1tTZ3h1qt1QFduF+jHKc4WnIAxWbR1CqdOL824/XCAemi/RpVF+ajEgAAqFzFU5ID5JmtIq0jEtOiqdlXpiB7kWhSWw29pg+ZXqUTDB/67nl3QC9vHVuZvKY3rljCVbhI+poC8J8pNHYkzS+i9hTSjh7LP1g8WZ2RuPpi/p6QrAsF1N4U4rUOGIfjODp/30Z9bPc6remNy01jnaqAo/bmkGpDBMYAAKCyFdenKyCPbL0vO3rihMYF0mmpHjxgWrXxMVkdiaeExglXWtMX1wL6EAJlq8PQf35WQ1A1oeILUEMBR7u38HoEFNKkmqAm1QQLvRsAAAAlhVPoqFhzGkMyFZHQE7dwTEGQZK8Ktw2o4TEEytdwwtXa3uIeggcAAAAApY7QGBUrFHA0rzE1ZOi0BJfIPVPYG3SkuQ2W0HicanEA5Wl1xHyJue0kEgAAAAAgc4TGqGim0HHTQNL33pNIjynsndto79nZUBUwDpmyVSwDKH2ZXpEAAAAAAMgcoTEqmq0yzdZbF7kzEEtq44DhkvMJgqC2ptTHcFUkrngynXE3AEqN7fW5jfYUAAAAAOAbQmNUNFsg2REhNM63TsvvfKI+pabHMJaU1velBtAASp/p9Xl6XVD1YQ5pAAAAAMAvfMJCRZvXGFLA0Pmgo4f2Bvlm60M8UZ/SBdbgn8cQKDfxpKvVvamvFVQZAwAAAIC/CI1R0aqCjuY0BFNuZ5Ba/plCXkfS/IkqjRmGB1SMtb1xxQ0t5+lnDAAAAAD+IjRGxTNVsm7oT2jIlEwgZ0wh7+yGoKotQ/BGtNQE1VptGIZHaAyUHVvrIEJjAAAAAPAXoTEqnqlS1ZW9xy78NxR3tc7QgzjdIMi0XEckrqTLMDygnFjb2BgGYgIAAAAAskdojIpnHYZHpWrerO6NyxTvphsEmYL/4YSrjf0MwwPKianf/OSagJoNVxsAAAAAALLHpyxUvPlNIZkaINgug4b/bIMH0680NofLBP9A+Ui6rvEKEFpTAAAAAID/CI1R8WpDAc2sZxheIdl+120TDMEbYa0WJ/gHysb6voSihlbzbbSmAAAAAADfERoDMoeOa/viiiboiZsPpnB3Rl1QdeH0XqIm1wTUGE6tF7dVMAMoPbaTSwuoNAYAAAAA3xEaAzL3zk26O3rtIrdiCVdrDL/nTC45dxzHOgzPZRgeUBY6It7a2AAAAAAA0kdoDIhheIW0pi8uU0F3pkGQqa9xf8zVlkHD9ewASo7p9bi5ylErQ/AAAAAAwHd80gIktVl74tLeINdswbyp+ns87Zb+x7SoAEpf0nWNbWzam8NyHNMoUwAAAACAF4TGgKSGcEDTalP/HKg0zj3rELyMK40ZhgeUq80DCQ3GUy9JsJ0sAgAAAAB4Q2gM/I2pvcHq3rjiSXri5pKpmntKbUBNVZm9PE2rC6o2ZBqGR2gMlDrrFQn0MwYAAACAnCA0Bv6mzVCxFk9Ka/sIHXMlkXS1ynTJeRbVgwHHMa7X0RNjGB5Q4mxXDJhO9gEAAAAAvCM0Bv6GYXj5t64/oZhhTl22QZDpMeyJuuoaZhgeUMpMvcnrQ46mGtoKAQAAAAC849MW8De2oJLQOHdsQ+qy7VNqG57HYwiULtd1jX/D7c0hhuABAAAAQI4QGgN/01IdUGu1YRgeg9RyHmTu8wAAWgtJREFUxu8+pQzDA8rPtqGkemOGIXi0pgAAAACAnCE0BnZiCh1XRWJK0hM3J0xhbkt1QK01wazub1ZDUKb5ebaKZgDFz3pyKcsrEgAAAAAAEyM0BnZiCo2HE9L6vkQB9qa8JV1XnaZLzj0EQQHHMQ40pD0FULqsbWyyvCIBAAAAADAxQmNgJ7aeuJ20N/Ddxv6EhhKmS869BUGmS9a3DSUVYRgeUJJMVyTUBB3NqM/uigQAAAAAwMQIjYGdWHvi0t7Ad7Y+w177lNr7GvMYAqXIdKVAW1NIAYbgAQAAAEDOEBoDO5lcE1BjVWoQwSA1/+WqT6ltfVpUAKWnayihLsNVArSmAAAAAIDcIjQGduI4jrFFRUdPXC7D8Hxlqt5uCDuaUuvtZWlOY0gh0zA8gn+g5NhaAxEaAwAAAEBuERoDuzCFEQNxV5sH6YnrF9d1jSFue3NIjsdLzkMBR/MaGYYHlIN3rVckeGtjAwAAAAAYH6ExsAv6GufelsGk+mOGIXg+BUGmFhWbBhLqjxH8A6XEdLInHJBmNzAEDwAAAAByidAY2AU9cXPPFsD7dcm5bZie7VJ3AMWp0zDAcn5TSMEAQ/AAAAAAIJcIjYFdTK8Lqi7EMLxcsv0u/QqN2wj+gZLXG01qi6EtEK0pAAAAACD3CI2BXTiOYwwdO3piDMPziSm8rQ05ml7nzyXn85tCMhUi0mIEKB22k0ttDMEDAAAAgJwjNAYMTBWvkair7UP0xPXKdV1jeNvWFFLA4xC8EVVBx9jzlGpxoHRY29hYriQAAAAAAPiH0BgwsPXEJXT0rms4qZ6oYQiez9WDpkvY1/clNBSnWhwoBaYrEoKONLeR0BgAAAAAco3QGDCwD8OjvYFXtr7CflcPmkJoV9Iqgn+gJJgGV85pDKkqyBA8AAAAAMg1QmPAYFZDUNWG9roMUvPOPgTP3+FWtsrljgjBP1DsBmJJbehPpNxOawoAAAAAyA9CY8Ag4Diab2hvQHsK70zV2lUBaVa9P0PwRpiGGe7YPo8hUOxMVcaS/21sAAAAAABmhMaAhamibftQUt3DDMPzwhTazm8KKRjw95Lz2lBAMw1BNME/UPysVyQYTuYBAAAAAPxHaAxY2CraOulrnLXIcFLbhlJDd79bU/z9flMfw7W9cUUTDMMDipnp5JKjHSeYAAAAAAC5R2gMWNh74lKpmi1bP+Fc9Sk13W/Cldb08hgCxczUxmZWQ1A1IYbgAQAAAEA+EBoDFnMaQgoZ/kLoiZs92+8uV31KbRXMBP9A8RpOuFrXxxA8AAAAACgkQmPAIhRwNK8xNaQwVcAhPabhVkFHmmv4PfvBFjJ1EvwDRWt1JC5TA5lctbEBAAAAAKQiNAbGYaqA3TyYVF+MYXjZMFUaz2sMKeTzELwRDVUBTa1NfZmztckAUHjWNjY5uiIBAAAAAJCK0BgYR3uTubKNStXMDcSS2jhguOQ8x0GQqTpxVSSueJJheEAxsrWxaaM9BQAAAADkDZ/AgHFYh+H1xLXvlKo8701+xZOu7npnQK9ti2o44T1gHU7NiyXlITRuCun/Ng6PuS2WlP79yS5jz+oRLdUBHTOnVofMqM7p/gGVYutgQne83a/VvQm5rv01Za2hn/H0uqDqw5znBgAAAIB8ITQGxjGvMaSAI+1alFoJ7Q3+58WI/rJheOIFPbJVc/t2/7bgP41heM9siurSA5p05Kwav3cLqCgDsaT+9cku9Qxn19qH1hQAAAAAkF+U7QDjqAo6mtNgGoZX3u0ptg4m8hIYBxxpfo4vOfc6POuedwd82hOgcj2+bijrwFiyD7UEAAAAAOQGoTEwAVOF24b+hAbj5TsM763u/FRSz28MqSqYmyF4I1qqA5pmGIaXrs5IXFEf2nMAlczra8oerbm9IgEAAAAAMBahMTABU4Wbqx3D1MpVviqpP7KgLi/b+aiH7SRdaXVv+T7WQD54eU1Z0BzS3pMIjQEAAAAgn7jeE5jAeMPw3jupPIfhmQKeUMC/S8RbqgN6/+waHTozP72CT2yrU104oL+sH1Ikaq8QH064Wt2bOoSroyeu3VsIrYBsDMVdrTMMt2upDmjqOFcBhAKOdm8J69Td6+Q4ub0iAQAAAAAwFqExMIH5TSE52lFdvLN0BqmVItd1jYP+9mwN64rDWguwR/54/+wavX/2+CF1XyypC/64NeX2Hb+P2hztGVDeVvfGU14/JekfdqvTR9rzc7UBAAAAACAztKcAJlAbCmhmfTDl9o6e/PT9zbdtQ0n1RlMjnkoYRNUQNvc/LvfBh0Au2V4rK+E1BQAAAABKFaExkAZTi4q1fYmyHJBmC0jbmyujPYPp51zdG1c8WX6PNZAPtteUNkJjAAAAAChahMZAGtqbUoPEch2QZmpNIVVOwGM6QRBPSmv7yu+xBvLB1MpnRl1QdWEOQQAAAACgWPGJDUjDeMPwyo3pZ6oOSrMaUlt0lCPbJfPl+FgDuRZLuFpjOLlme00FAAAAABQHQmMgDW3W0Lj8+hqbwtH5TWEFHKcAe5N/bZY2HITGQObW9MVl6uJDaAwAAAAAxY3QGEiDdUCa4bLrUtY9lFDXcDLl9koaWNVSHdCkmvJ/rIF8sPZIN7T8AQAAAAAUD0JjIE2VMCDNFoxWWlWgKSRfFYkp6ZbPYw3kg3UIXoW9pgAAAABAqSE0BtJUCQPSrFWBFRbwmH7e4YS0vi9RgL0BSpdpsOaU2oCaqjj8AAAAAIBixqc2IE2VMCDNVGkcCkhzGiorNG6zXDpPiwogffGkq1WGv5lKancDAAAAAKWK0BhIUyUMSDMN9pvXGFIoUBlD8EbYKqvLcfAhkCvr+xKKpbZIN7b6AQAAAAAUF0JjIE3lPiCtL5rUlkHDELwKa00hSZNrAmqsSg3Ky+kEAZBrptYUEpXGAAAAAFAKCI2BDJTzgDTrEDxLq4Zy5jiO8efuiMTL4rEG8oEe6QAAAABQugiNgQyU84A0W+uFSg14TD/3YNzV5oHSf6yBfDCFxi3VAbXWBAuwNwAAAACATBAaAxmw9eIshxYVnYafIeDs6GlciWxhuen3BGCspOsa/1ZoTQEAAAAApYHQGMiALfAohwFppqrAOQ0hVQUrawjeCPtjTWgMTGRjf0JDidRWLpV65QIAAAAAlBpCYyADk2oCairDAWmD8aQ29Ke2XajkgGd6XVB1IcNjTaUxMCFrj3TL1RoAAAAAgOJCaAxkwHEcY+hR6gPSVkXiMu19JV9K7jiO2gw/f0dPTG4JP9ZAPliH4FXwawoAAAAAlBJCYyBDptCj1AekWQOeCq40lsw/fyTqavtQsgB7A5QOU8uehrCjKbUcdgAAAABAKeDTG5AhW5Baym0LTPvuSMZK20pSzoMPgVxxXdf4N9LeHJLjVGaPdAAAAAAoNYTGQIasQWIJ9zU2VQXOrA+qJlTZLxHlPPgQyJUtg0n1xwxD8JroZwwAAAAApaKyEyEgC9NqA+YBaSUaGkcTrtb2MQTPZFZDUNXB1NtL9bEG8sF2UoXXFAAAAAAoHYTGQIZ2DMMzDEiLlOaAtNW9cSUNu22rqK4kAcfRfEN1JO0pADvb3wehMQAAAACUDkJjIAumtgW9UVfbSnBAmnUIXoX3Mx5h+j1sH0qqe7j0HmsgH0yvKbUhR9PrDGX7AAAAAICiRGgMZKGc+hpzKfn4bL+HTvoaAylc1zW+prQ1hRRgCB4AAAAAlAxCYyALtiCxI1J6QaLpUvLpdQHVh3l5kMZ7rEvvBAGQa13DSfVEDUPwOAkFAAAAACWFVAjIwsz6oKqDpT8ML550tbo3dZ/bDH18K9WchpBChlfKUnusgXyg3Q0AAAAAlAdCYyALAcdRmyEEKbUgcW1vXHFDa16qAv8uFHA0r9H0WJdeVTmQa/YheJyIAgAAAIBSQmgMZMkUrHYNJ9U9lCjA3mTHHvAQGu/M9PvYPJhUX4xheMDOTCdTqgLSrHqG4AEAAABAKSE0BrJku9y6lHrd2i8lpypwZ7bfR2eJVZYDuWZ6TZnfFFIwwBA8AAAAACglhMZAlmyXW5dSiwpTVeDkmoCaq3lp2Jl1GF4JPdZArkWGk9o2lFp9T2sKAAAAACg9JENAlmY3BBU2DUgrkUrjpOuq07CvtKZINa8xJFOhZEeEvsbACNvfA0PwAAAAAKD0EBoDWQoFHM0t4QFp6/sSihpa8rbRmiJFVdDRnIbSH3wI5JK13Q0nogAAAACg5BAaAx6YwpAtg0n1mdLYIkPAkxnT72VDf0KD8eJ/rIF8MF1lEXRkPLkGAAAAAChuhMaAB7YBaaXQooJLyTNj+r24klaVwGMN5IPpRNTcxpBCDMEDAAAAgJJDaAx4YB+QVvwtKkwBT1OVo0k1vCyYMAwPsOuPJbVpIJFyO1cuAAAAAEBpIh0CPLAPSCvuIDHpusZ9bG8Oy3GoCjRpawrJ9Jsp9scayAfTUE2JKxcAAAAAoFQRGgMelOqAtM0DCQ3G3ZTbCXjsakIBzawPptxeClXlQK7Ze6QzWBMAAAAAShGhMeCRbUDaQKx4B6QxBC87pt/P2r6EoonUAB6oJKaTJ46k+ZyIAgAAAICSRGgMeGSrzl3VW7zVxraWClQFjs/0+0m60uoifqyBfDC9psxuCKo6SLsbAAAAAChFhMZZuvvuu3X66aerra1NNTU1mjZtmo444gh997vfVSQSycs+nHfeeXIcZ/Tf1772tbxsF2OV4oA0U1VgXcjRtFpeEsZjO0FQzI81kGtDcVfr+0xD8DgJBQAAAACliutGM9TX16ezzjpLd99995jbt2zZoi1btugvf/mLfvSjH+m3v/2tDjvssJztx3333adf/vKXObt/pG9kQNquDQqKNUh0Xde4b+3NIYbgTcB+giAmqTa/OwMUiVWReMrrn0S7GwAAAAAoZZQVZiCRSOj0008fDYynT5+uf//3f9evf/1r/c///I+OPPJISdKaNWu0ZMkSvf766znZj0gkoosuukiSVF9fn5NtIH3WAWmR4hyQtm0oqd4YQ/CyUR8OaHpd6sumrd0HUAlsr3W8pgAAAABA6SI0zsD111+v+++/X5K0995766WXXtI3vvENfeITn9All1yiJ554Qp///OclSV1dXaPBrt+++MUvas2aNZo7d27OtoHMmCrq1hXpgDT7EDwuJU9HW1Pq72l1b1zxZPE91kA+dFpeU9oIjQEAAACgZBEapymRSOjrX//66P/feOONmj59espyV111lRYtWiRJevzxx/XHP/7R1/14+OGH9bOf/UyS9JOf/ESNjY2+3j+yU0oD0qxVgVxKnhbT7ymelNb2Fd9jDeSDqdJ+Zn1QdWEOMQAAAACgVPGJLk2PPfaYNmzYIEk65phjdOCBBxqXCwaDuvTSS0f//+abb/ZtHwYGBnThhRfKdV394z/+oz760Y/6dt/wppQGpJn2qTroGFtsIFUpDj4EciWWcLXGcHKMKmMAAAAAKG18qkvTfffdN/r1kiVLxl32wx/+sHE9r/7f//t/evfddzVp0iT98Ic/9O1+4Z0tSHxh87CmGnrgFtI7hnCzrSmkAEPw0tJuaE8hSc9vjqq1Jr+PdWt1UHMbgzx2RWQontS7PXFF02hX4kia3RDSlNr8nrAZjCfVkeY+TmTLQFKmLjxcuQAAAAAApY1PdWl6+eWXR78+5JBDxl12xowZmjt3rtasWaNNmzZpy5Ytmjp1qqftr1ixQv/zP/8jSfre975nbI2BwhkZkLZpIDnm9uc2R/Xc5miB9ip9BDzpa64OaHJNQNuGxj7W/7dxWP+3cTjv+7OgOaQvHdys1hoqxQvt2U3DuuaFiIYz7GV+7JwafXr/xryE/89sHNaPXuzRcCK326FHOgAAAACUtuIqgSxiK1euHP26vb19wuV3XmbndbMxNDSk888/X8lkUscff7w++clPero/5IZpQFqpsLXXgFkxhezv9sT1u7f6C70bFW844eqnL2UeGEvSI2uH9EweTjgMxV1d+9dIzgNjidcUAAAAACh1fKpLU3d39+jXU6ZMmXD5yZMnG9fNxhVXXKGVK1eqtrZW1113naf78iIWi+n55583fm/mzJmaOXNmnveouLQ3h/R0ASpN/UBVYGbamsJ6dlPxVJA/s3FYn9rXlUObioJ5Y3tUvbHs2z08symqQ2fW+LhHqd7YHlW/h31M19TagBqrOCcNAAAAAKWM0DhNfX19o1/X1Ez8wb62tnb0697e3qy3+8wzz+i///u/JUlf//rXtdtuu2V9X15t3rxZBx10kPF7V155pb72ta/ld4eKzCHTq/WblaVX8Tm9bkdfXKTvkOlVRVXd2xN11TWc1CRaVBSM10GIHT0xn/ZknG1E8jOs8eDp1XnZDgAAAAAgdwiNi1g0GtX555+vRCKhAw88UJdffnlB92fatGnWwX6VXmUsSXMaQzrzvfW6+Y1+5b6Wzx8NYUcX56mXajlpaw7r/3tPvX77ZvEExx09cULjAvIayK7rS2g44ao6mLu/Ra/Bdjr2aAnp1N3rc74dAAAAAEBuERqnqaGhQV1dXZJ29BhuaGgYd/nBwcHRrxsbG7Pa5n/+53/qlVdeUTAY1M9+9jMFg4UNhMLhsA488MCC7kOxO3m3eh07p1advXEl3eKOjmuCjhY0h1WVw5CqnJ22R71OmFerVXl+rLuGkvrpX1OvXuiIxHUQFZ4FYwpkZ9UHde4+qe8Vz2+K6oFVg2NucyWtisT1ntbctYoxVTPPaQjqnL3Hfz9L16SaoOY0BDkJBQAAAABlgNA4TS0tLaOh8datWycMjbdt2zZm3Uy99NJL+va3vy1JuvzyywlrS0hTdUD7V1cVejeQB80FeKwTSVe/eKVX0eTY2/PR3gBm/bGkNg2kTpd776SwFk1NDfKrAk5KaCzteAxzFRr3xZLaPJhMud22jwAAAACAykZonKY999xTHR0dkqSOjg61tbWNu/zIsiPrZmrZsmWKxWIKBAIKh8P6z//8T+Nyjz322JivR5bbc889dfrpp2e8XQDFLRhwNL8ppLe6x1a25qP1AMw6La0p2pvNb7FtTebbc9lzuNPy/GAIJgAAAADAhNA4Tfvtt5/uv/9+STuG0x177LHWZTdt2qQ1a9ZI2tEHeOrUqRlvz/3b5e7JZFLf+ta30lrnkUce0SOPPCJJOvnkkwmNgTLV3hxOCY23DSUVGU6qqTpQoL2qXLbAvr3JHMjWhQOaWR/Uhv6x1cm5DP7t+8hhAAAAAAAgFelCmj70oQ+Nfm0bBjfi3nvvHf16yZIlOdsnAJXJFvR1RGhRUQim1iABR5o/TiBrqjZe0xtXLJGb/tim50bAkeY2EhoDAAAAAFIRGqfpmGOO0YwZMyRJjz76qJ5//nnjcolEQtdcc83o/59xxhlZbe8HP/iBXNed8N+VV145us6VV145evudd96Z1XYBFD9b2wNaVBSGqa3E7IbguEMmTY9hwpXW9OXmMTQ9N+Y0hBiECQAAAAAwIjROUzAY1BVXXDH6/0uXLtXmzZtTlvvKV76iF198UZJ05JFH6sQTTzTe37Jly+Q4jhzH0eLFi3OxywDK1NzGkExZXy574sJsKO5qfV/qEDxba4qJvp+L4H8wnkxphSHZTz4AAAAAAMAnxgxceOGFuuOOO/Tggw/q1Vdf1cKFC3XhhRdq77331vbt23XzzTfriSeekCS1tLTouuuuK/AeAyhHoYCjeY2hlJCYSuP8WxWJy9RQYqJAti2P1eLWfaSfMQAAAADAgk+MGQiFQrrtttt05pln6p577tHGjRv1jW98I2W5OXPm6JZbbtE+++xTgL0EUAnam1ND400DCfXHkqoPcxFJvtj6SE8UGjdVBTSlNqCtg8m07s8L6xA8Ko0BAAAAABYkCxlqbGzU73//e91555069dRTNXfuXFVXV2vKlCk69NBDddVVV+mVV17REUccUehdBVDGTIPUJKmTFhV5ZQtk56cxYM5U6bsqElci6e8wPFPbEkf25xAAAAAAAI7rurkZ1Y6yMWfOHK1bt06zZ8/W2rVrC707ACS92RXTV1d0pdx+zl4N+uiCugLsUWX60uPbtWqXUHZmfVA/WDx5wnVve6tfv32zP+X27x49SfPSCJ3T9cXHtml179iexrPqg/p+GvsIAAAAAKhMVBoDQAma3xSSYRaeOnr8b28As2jC1dre1CredCt4bT2F/XwMowlXa02D+mhNAQAAAAAYB6ExAJSg6qCj2Q3BlNtNrQiQG2t640oYrtVJN5C1LefnMLzVvXGZul20N4d92wYAAAAAoPwQGgNAiTIFf+v7EhqK03UoH2wBfbqBbGtNUC3VqW/Dfgb/1iF49DMGAAAAAIyD0BgASpSpUtXVjupS5F6nD4GsbRhe0qdxA7ZWF7SnAAAAAACMh9AYAEpUPnriwq4jkvp7nlobUGNV+m+tpvB2MO5q00BqH+JsmKqWp9cFVB/m7R8AAAAAYMenRgAoUbaBa372xIVZPOlqlSGQbWvKrFewrZWFH49hPOkaq84z3UcAAAAAQOUhNAaAElUXDmhmPcPwCmF9X0KxZOrtmbZ9sFeLe38M1/bGFfdhHwEAAAAAlYfQGABKmKnaeE1vXLEEw/ByydSaQpIWZBjITqkNqCHspH3/mbAP6iM0BgAAAACMj9AYAEqYKQBMuNKaPqqNc8lWCZzJEDxJchzH+Bh29MTlehyGZ99H2lMAAAAAAMZHaAwAJSyXPXFhZ/r9tlYH1FKT2i5kIqYQty/mauugobdEBkwDESfXBNRczVs/AAAAAGB8fHIEgBKWy564MEu6rrH1Q7ZtH2zreelNnXRddfq4jwAAAACAykJoDAAlrLEqoKm1qS/lfvTEhdnG/oSGDT2jM21NMbqeLTQ2VAqna31fQlFDoXIbrSkAAAAAAGkgNAaAEmcKAldF4oonGYaXC9ZewZZWIROZXhdUbcgwDM9Dtbht3UwH9QEAAAAAKhOhMQCUOFOlaiy5o9oU/rO1jci29UPAcdRmqFL20p7CVmlOewoAAAAAQDoIjQGgxNmqR2lRkRumthGNYUeTa7J/SzWFud3DSXUNZRf8myqNm6sctTIEDwAAAACQBj49AkCJYxhe/riua/y9tjeH5DipLSbS5edjaB/UF/a0jwAAAACAykFoDAAlrqUmaKwgJTT235bBpPrjhiF4WfYznmj9bFpUbB5IaNC0j1kO6gMAAAAAVB5CYwAoA6b2Bh2RuJIuw/D8ZGpNIXkPZGfVB1VleEe2bW889kF9hMYAAAAAgPQQGgNAGTCFlsMJVxv7GYbnp1wFssGAo/k+DcOzD+rzVg0NAAAAAKgchMYAUAas7Q1oUeErUyBbG3I0rS7o+b5Nj+HWwaQi0WRG92OqTq4POZpay1s+AAAAACA9fIIEgDJgq3TNplIVZq7r6l1DINveFFLAhwFzthYXnRkE/7ka1AcAAAAAqCyExgBQBibXBNQYTg0Fs+mJC7Ou4aQiUdMQPH96BduD//Qfw21DSfXG/B/UBwAAAACoLITGAFAGHMcxD8PrictlGJ4vrP2Mm/wJZOc2hhQ0FANn0mLEtmybx0F9AAAAAIDKQmgMAGXCVE3aH3e1ZTCznrgwezdHQ/BGhAKO5jaag/902SrL/dpHAAAAAEBlIDQGgDJh64lLiwp/dBraRFQFpFkN3ofgjTCFuxsHEhqIpRf8m3pYVwcdzaz3bx8BAAAAAOWP0BgAyoS1J24GlaqwM/0e23wagjfCOgwvzYGG+dhHAAAAAED5IzQGgDIxrS6o2pBhGF6agSPseoaT2jaUWu3r94A52/2lE/x3DyXUNWzaR1pTAAAAAAAyQ2gMAGUi4DjGStV3e2IMw/Oow9CaQvI/kJ3fFJKpJti2/bHL2Ab1ERoDAAAAADJDaAwAZcQUYkairrECFemzVfr6HchWBx3NNvRITqfS2LqPPldDAwAAAADKH6ExAJSR9qbs2xvAzvT7CwWkOY3+V/GaQt51fQkNxcevFjdVGocDMobQAAAAAACMh9AYAMqIrV3Cu4TGnnQa2kPMawwpFPB/wJzpMXQlre4d/zHs6MnfPgIAAAAAyhuhMQCUkVkNQVUZXtlNoSfS0xdLatOAYcBcjnoF2+7XFAqP6IsmtWWQIXgAAAAAAH8QGgNAGQk4jtoMoSPtKbLXmedewabHTxr/MbQOwaOfMQAAAAAgC4TGAFBmTEHhtqGkIgzDy0qnNZDNTRVvXTigmfWGYXiW/ZDsVci5qoYGAAAAAJQ3QmMAKDO2MLODFhVZMQWyAWdHv+BcMVUbr+mNK5YwD8MzBcpBR5qbw30EAAAAAJQvQmMAKDP2nri0qMiGKZCd0xBUVTB3A+ZMwX/Cldb0mR9D02M7pzGU030EAAAAAJQvSpAAoMzMaQwpFJDiu3SjuLdjQM9uGrauFww42q05pFP3qFdDuPzPKb6xPao/rhrU5oHEuMut70v9fq57Bdvu/5oXImoIpwbBG/oN+0hrCgAAAABAlvhECQBlJhRwNK8xpHd3qT7tibrqiY5fbfzG9phe3hrVVe+fpIBTvlWqb3bF9B9PdcvS7WFCuQ5kbfdvCoet95GjnssAAAAAgPJX/qVkAFCBvISaq3sT+uuWqI97U3zu7xzIOjCWch/INlYFNLXW21t0e1Nuq6EBAAAAAOWL0BgAytAerd4Cw7e6y3to3ltd2f984YA0Pw+tH7w8hvnaRwAAAABAeSI0BoAydPjMGk2qyf4lvpyH5vXFkto8mJx4QYsT5tWqNpT7t88Pt9Up2zl2H5xfq5pQ+bYXAQAAAADkFmVIAFCGakKOvnlkq+54e0CrInElXXsvhvV9CfXHx36/I1K+oXGnJRCfWR80DpkbURsKaNG0Kn24rTZXuzbGe1rD+uphLXowjWF9I+rCAS2aWqUP5WkfAQAAAADlidAYAMrUpJqgLti3ccLlfvFKrx5YNTjmtu1DSXUPJ9VSXX4XpNiqqD+7qEm7txRXH+C9JlVpr0lVhd4NAAAAAECFKb80AACQEdtQt86e8uxr3BFJ/bkCjjSvkfOoAAAAAABIhMYAUPFsoXG5tqgwVRrPaQipKtsGwgAAAAAAlBlCYwCocHMaQjLNdSvHYXiD8aQ29Kf2B7YF5wAAAAAAVCJCYwCocKGAY2zN0FGG7SlWReIyjQRsbyI0BgAAAABgBKExAMAYmm4eTKovlizA3uSOrXqaSmMAAAAAAP6O0BgAoPbmsPH2zjJrUWHq0+xIaqPSGAAAAACAUYTGAAD7MLxyC40NLTdm1gdVY2rqDAAAAABAheJTMgBA8xpDCjipt3dEyqevcTTham0fQ/AAAAAAAJgIoTEAQFVBR3Magim3l1Ol8ereuJKGKXjtTebWHAAAAAAAVCpCYwCAJHNf4w39CQ3Gy2MYHkPwAAAAAABID6ExAECS1G4YBudKWmUYHleKTP2MJamN0BgAAAAAgDEIjQEAksp/GF6HIfyeVhtQQ5i3QgAAAAAAdsYnZQCAJGl+U0iGWXjGsLXUxJOuVvem/hymlhwAAAAAAFQ6QmMAgCSpNhTQzHrTMDxzW4dSsrYvLlNrZvoZAwAAAACQitAYADDKFKKu7UsomnALsDf+sQ7BM/RxBgAAAACg0hEaAwBGtTeltmtIujK2diglttC4jfYUAAAAAACkIDQGAIwq12F4pr7Mk2oCaqnmbRAAAAAAgF3xaRkAMKrNFhpHSrevcdJ1tcqw/7SmAAAAAADAjNAYADCqIRzQtNrUt4ZSrjRe35fQcCL1dobgAQAAAABgRmgMABij3dDnd3VvXPFkaQ7DM7WmkMw/JwAAAAAAIDQGAOzCVIEbT0pr+0qz2rijx9xag/YUAAAAAACYERoDAMawhaml2qLCtN9NVY4m1fAWCAAAAACACZ+YAQBjtFnaNpRiaJx0XWN7ivbmsBzHKcAeAQAAAABQ/AiNAQBjtFQHjFW4tt7AxWzzQEKD8dRezLSmAAAAAADAjtAYAJDCFKquisSUdEtrGJ59CB6hMQAAAAAANoTGAIAUplB1OCGt70sUYG+yZ2up0W5pwQEAAAAAAAiNAQAGtlC11FpUmELjupCjabW8/QEAAAAAYMOnZgBAClvP346eWJ73JHuu66ojkrq/7c0hhuAB/397dx4dVZWvffypVAUyJ0QghBAgjTTILCAiigwCMjigtoiCgCggoq23r9qiNIqKtsvGFrqvgmKrgALS3BZQoaUlAqIgCIrMIgEhMiYhJCFj5bx/8HJuQp2akkpSge9nLdY6VWfXPrtc+VlVT+3aGwAAAAA8IDQGALiIDwtRTB3XYNXdcg/BKKOgVDlFbIIHAAAAAIC/CI0BAC5sNpvlEhVpZ0pqzWZ4rGcMAAAAAEDFEBoDACxZzcjNLzF04mzt2AzPamkKyXqTPwAAAAAA8H8IjQEAltyFq7VlMzyrmcZ17TYlRtprYDQAAAAAANQehMYAAEvulnGoLesaW42zeYxDIWyCBwAAAACAR4TGAABLDcNDFOGonZvhnS5wKquw1OV+lqYAAAAAAMA7QmMAgKVzm+G5hqxpZ4plBPlmeO6W0LBapxkAAAAAAJRHaAwAcMsqZM0pMpRR4DqLN5i4mw3tbskNAAAAAADwfwiNAQBu1dZ1ja1mGoeGSElRbIIHAAAAAIA3hMYAALfcrQGcdqa4mkfin7Rs1/E1jXbIEcImeAAAAAAAeENoDABwKzHSrrr22rUZXm5RqU7mswkeAAAAAAAVRWgMAHArxGZTc4t1jYM5NHa7CR7rGQMAAAAA4BNCYwCAR1YzdLMKS3W6wFkDo/HOamkKyXpTPwAAAAAA4IrQGADgkdVMY8n9jN6aZjUuu01KjiY0BgAAAADAF4TGAACP3G6GF6RLVBy0GFeTaIfqWKzNDAAAAAAAXBEaAwA8ahLlkMPi1SIYZxrnl5TqaJ7rshksTQEAAAAAgO8IjQEAHjlCbGpqsbSDu7WDa9KhMyUyLO53N1saAAAAAAC44lM0AMCrlFiHDlyw7MPJ/FLlFpUqqk71fP9YUFKqA9klKiq1ioXP2XaiyPL+lJjQqhoWAAAAAAAXHUJjAIBX50LXApf7086UqH39OlV+/e+OF2rmtjMqdLoPjN2xSWrG8hQAAAAAAPiM5SkAAF653wyv6peoKHIaenN7xQJjSWocZVeYg03wAAAAAADwFaExAMCrptEOhVjkrtWxGd6erGLlFFUsMJbYBA8AAAAAAH8RGgMAvKpjt6lJlNVmeFUfGld2NnPXRnUDNBIAAAAAAC4NhMYAAJ9YLVFxNM+ps8WlVXrdygTTvZuEqVsCoTEAAAAAAP7gN7sAAJ+kxDi01uL+QzkluiK+6jbDO2ixBEbjSLtGt41y+xibpKQoh+qH26tsXAAAAAAAXKwIjQEAPnG/GV7VhcZni0t1NM/pcn+reqHq1IAZxAAAAAAAVAWWpwAA+KR5jEMWe+FV6brGVrOMJfcBNgAAAAAAqDxCYwCAT8IcIUqMdF3uIe1M5Taq8yTNbWgcWmXXBAAAAADgUkdoDADwmdUM3/Rcp4qcRpVcz2oWs01S02hmGgMAAAAAUFUIjQEAPrOa4VtqSL/kVM0SFWnZrrOYk6LsCnNYLZQBAAAAAAACgdAYAOCzlBj3m+EFWqHTUHqu6yZ4rGcMAAAAAEDVIjQGAPjMXWBrNSO4sg6dKZHVohcpMaxnDAAAAABAVSI0BgD4LDI0RAkRri8d7jasqwx3QTQzjQEAAAAAqFqExgAAvzS3mOn7S06JSkoDuxmeuyC6uZslMgAAAAAAQGAQGgMA/GI107ekVDoS4M3wrNZJToiwKyKUly4AAAAAAKoSn7wBAH5xu65xAJeoKHYaOmwRQrM0BQAAAAAAVY/QGADgF3cb0VnNDK6ow7klclqsdpHC0hQAAAAAAFQ5QmMAgF9i64bosjCLzfDcbFxXEe4CaGYaAwAAAABQ9fj0XUHLly/X/PnztXnzZh07dkwxMTG6/PLLddttt2nChAmKiYkJyHU2b96sb7/9Vps3b9bOnTt18uRJnTp1SsXFxYqLi9MVV1yhPn36aMyYMWrWrFlArgkA3qTEOpRRUFTuvoNnSlRqGAqx2Srd/0E3S124m+UMAAAAAAACx2YYRmC3u7/I5ebmasSIEVq+fLnbNsnJyfroo4/UvXv3Sl8vKipKeXl5XtvVrVtXzz77rCZPnlzpa16oSZMmSk9PV1JSko4cORLw/gHUPkv25emfP7n+v2nG9fFqEl357yOnbMjUT6fLB8eXhYXojRvqV7pvAAAAAADgGTON/eB0OnXnnXdq1apVkqSEhASNGzdObdq0UWZmphYuXKgNGzbo8OHDGjx4sDZs2KArrrii0tdt2LChunXrpo4dOyolJUWxsbEqLi7WwYMH9emnn2rDhg0qLCzU008/reLiYk2dOrXS1wQAT37jbjO87JJKh8bOUkOHLGYaszQFAAAAAADVg5nGfpgzZ44efPBBSVKbNm20Zs0aJSQklGvz+OOPa8aMGZKknj17at26dZW65o4dO9S2bVvZPPzce968eRozZowMw5DD4dChQ4fUuHHjSl23LGYaA7hQZoFTE7/IcLl/SEq4RrWJrlTfh3NK9Pi6TJf77/xtpH7XMrJSfQMAAAAAAO/YCM9HTqdT06ZNM2/Pnz/fJTCWpFdeeUWdOnWSJK1fv16ff/55pa7brl07j4GxJI0aNUo33XSTJKmkpMScCQ0AVaVe3RDF1nH9f5O7Dez84W5DvZQYZhoDAAAAAFAdCI19tG7dOh09elSS1KtXL3Xu3Nmynd1u1+9//3vz9sKFC6tlfG3btjWPjx07Vi3XBHDpstlsSol13ZQu7f9vhlcZae42wWN5CgAAAAAAqgWhsY9WrlxpHg8ePNhj20GDBlk+rirt37/fPG7UqFG1XBPApc1q5m9+iaETZ52V6tdqtnJs3RDVq8tLFgAAAAAA1YFP4D768ccfzeOrrrrKY9tGjRopOTlZknT8+HGdPHmySse2YsUK/etf/5IkhYWFaciQIVV6PQCQ3M/8rcwSFaWGoYNWm+DFOLwu1QMAAAAAAAKD3/r6aO/eveZxSkqK1/YpKSk6fPiw+dgGDRpUegzr1q1TZua5zaGKiop0+PBhff755+a6yQ6HQ7Nnz7ZcaxkAAs1qeQrp3PIS11RwL87jZ53KL3Fd3oKlKQAAAAAAqD58CvfR6dOnzeP69et7bX/ZZZdZPrYynnzySW3atMnlfpvNpl69emnatGm6/vrrA3ItK8XFxdq6davlucTERCUmJlbZtQEEnwbhIYp02JR3QcjrbiM7X7ibpcwmeAAAAAAAVB8+hfsoNzfXPA4LC/PaPjw83DzOycmpkjGdl5SUpP79+6tly5ZVep0TJ06oS5culueeffZZPffcc1V6fQDB5dxmeA7tyCgfEqdll8gwjAotJ+E2NHYzqxkAAAAAAAQeoXEtsnHjRvM4Ly9P+/fv1/LlyzVjxgw988wzeu2117Ro0SL169evSq7fsGFDtxv7McsYuDSlxIa6hMY5xYYyCkpVP9zud39pZ1xnKUeG2tQgnCX4AQAAAACoLoTGPoqKilJWVpYkqaCgQFFRUR7b5+fnm8fR0dEBH09kZKQ6duyojh07auTIkbruuuv066+/asiQIdqyZYvat28f8GuGhoaqc+fOAe8XQO3lbtmItOwSv0NjwzAsZxqzCR4AAAAAANWLqVs+iouLM49PnTrltX1GRoblY6tCSkqK/vznP0s6t0He9OnTq/R6AHCeuw3qKrKu8an8UuUWW22Cx9IUAAAAAABUJ0JjH7Vq1co8TktL89q+bJuyj60qgwYNMo+//PLLKr8eAEhSo0i7wuyus4DTzlivTeyJu8e4C6YBAAAAAEDVIDT2UdnlHjZv3uyx7fHjx3X48GFJ59YBbtCgQZWOTSq/BMb5ZTQAoKqF2GxqZrFEhbsN7TxxNzvZ3RIYAAAAAACgahAa+2jgwIHmsbvN4M777LPPzOPBgwdX2ZjK+umnn8zj6gipAeA8q5nAWYWlyipw+tWPVdAcZrepUaT/G+oBAAAAAICKIzT2Ua9evdSoUSNJ55Z/2Lp1q2U7p9OpWbNmmbeHDx9eLeObPXu2eXzttddWyzUBQHI/E/ign0tUWC1P0TzWoRA2wQMAAAAAoFoRGvvIbrdr6tSp5u1Ro0bpxIkTLu2eeuopff/995LOhbc33nijZX/vvfeebDabbDabevfubdlm9uzZSk1NlWG4bgx1ntPp1J///Ge98cYb5n0PPfSQD88IAALD3UZ1B/xYoiKrwKnThaWufbM0BQAAAAAA1Y5P434YN26c/vWvf2n16tXauXOnOnbsqHHjxqlNmzbKzMzUwoUL9dVXX0mS4uLiNGfOnEpdb+PGjZo4caKSk5PVv39/tW/fXg0bNlSdOnV0+vRp7dixQ8uWLdPBgwfNx0yePFm9evWq1HUBwB9JUXaFhkjFF2S+/qxr7K4tm+ABAAAAAFD9+DTuB4fDoaVLl+qee+7RJ598omPHjumFF15wadekSRMtXrxYbdu2Dch1Dx8+rH/84x8e28TGxurll1/WxIkTA3JNAPCVI8SmptEO/XxB8HvwjPXGdlaslqaQ3M9iBgAAAAAAVYfQ2E/R0dFasWKFli1bpnnz5mnz5s06ceKEoqOj1aJFC91+++2aMGGCYmNjK32tWbNm6dZbb9W6deu0bds2/fzzzzp16pSKi4sVFRWlhIQEdejQQTfeeKPuvPPOgFwTACoiJdY1ND6ZX6qcolJF1/G+ElJatmvAHBoiJbEJHgAAAAAA1c5meFowF9C5mdPp6elKSkrSkSNHano4AILQf37J19s/5rjc/8zVcepQv47Xx09ac0qn8suvb3F5nEPTr40P2BgBAAAAAIBv2AgPAFBp7jass5pBfKEzRaUugfG5PlmaAgAAAACAmkBoDACotORoh+w21/t92QzvIJvgAQAAAAAQVAiNAQCVVsduU5No15D3oJsN7spKc7NhHqExAAAAAAA1g9AYABAQVktUHM1z6myx69ITZVnNRrbbpOQoQmMAAAAAAGoCoTEAICDczQz2NtvYKjROjnYo1Gq9CwAAAAAAUOUIjQEAAeFu47o0D6Hx2eJSHTvrdO2LpSkAAAAAAKgxhMYAgIBoFuOQ1dxgdxvdSe5nIVstdQEAAAAAAKoHoTEAICDCHDY1jrK73O9uo7tz59yExrHWs5YBAAAAAEDVIzQGAASM1QzhIzlOFToNy/ZW6xnbdG7WMgAAAAAAqBmExgCAgLGaIWxI+sXNjOK0bNdZyElRdtVlEzwAAAAAAGoMU7kAAAHjbgO7v39/RtF1XIPg9FyrTfBYmgIAAAAAgJpEaAwACJjmbpaVOHbWqWNnfevDXfAMAAAAAACqB8tTAAACJjI0RAkRrpvh+cNqXWQAAAAAAFB9CI0BAAH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" ] }, "metadata": { "filenames": { "image/png": "/Users/folgert/projects/hda/_build/jupyter_execute/working-with-data/notebook_115_0.png" } }, "output_type": "display_data" } ], "source": [ "boy_rm = boy_turnover.rolling(25).mean()\n", "ax = boy_rm.plot(title=\"Moving average turnover (boys; window = 25)\")\n", "ax.set_ylabel(\"Absolute turnover\");" ] }, { "cell_type": "markdown", "id": "279d7f56", "metadata": {}, "source": [ "\n", "\n", "The rolling average visualization of boy turnovers suggests that there is a similar acceleration. Our analysis thus seems to provide additional evidence for {cite:t}`lieberson:2000`'s claim that the rate of change in the leading names given to children has increased over the past two centuries. In what follows, we will have a closer look at how the practice of naming children in the United States has changed over the course of the past two centuries.\n", "\n", "(sec-working-with-data-case-studies)=\n", "## Changing Naming Practices\n", "\n", "In the previous section, we identified a rapid acceleration of the rate of change in the leading names given to children in the United States. In this section, we shed light on some specific changes the naming practice has undergone, and also on some intriguing patterns of change. We will work our way through three small case studies, and demonstrate some of the more advanced functionality of the Pandas library along the way.\n", "\n", "(sec-working-with-data-name-diversity)=\n", "### Increasing name diversity\n", "\n", "A development related to the rate acceleration in name changes is name\n", "diversification. It has been observed by various scholars that over the course of\n", "the past two centuries more and more names came into use, while at the same time, the most\n", "popular names were given to less and less children {cite:p}`lieberson:2000`. In this\n", "section, we attempt to back up these two claims with some empirical evidence.\n", "\n", "The first claim can be addressed by investigating the type-token ratio of names as it progresses through time. The annual type-token ratio is computed by dividing the number of unique names occurring in a particular year (i.e., the type frequency) by the sum of their frequencies (i.e., the token frequency). For each year, our data set provides the frequency of occurrence of all unique names occurring at least five times in that year. Computing the type-token ratio, then, can be accomplished by dividing the number of rows by the sum of the names' frequencies. The following function implements this computation:" ] }, { "cell_type": "code", "execution_count": 61, "id": "8f8a0c0c", "metadata": {}, "outputs": [], "source": [ "def type_token_ratio(frequencies):\n", " \"\"\"Compute the type-token ratio of the frequencies.\"\"\"\n", " return len(frequencies) / frequencies.sum()" ] }, { "cell_type": "markdown", "id": "fb58998c", "metadata": {}, "source": [ "The next step is to apply the function `type_token_ratio()` to the annual name records for both sexes. Executing the following code block yields the visualization of the type-token ratio for girl names over time." ] }, { "cell_type": "code", "execution_count": 62, "id": "920d994f", "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": { "filenames": { "image/png": "/Users/folgert/projects/hda/_build/jupyter_execute/working-with-data/notebook_119_0.png" } }, "output_type": "display_data" } ], "source": [ "ax = df.loc[df['sex'] == 'F'].groupby(level=0)['frequency'].apply(type_token_ratio).plot();\n", "ax.set_ylabel(\"type-token ratio\");" ] }, { "cell_type": "markdown", "id": "537f45b0", "metadata": {}, "source": [ "\n", "\n", "Let's break up this long and complex line. First, using `df.loc[df['sex'] == 'F']`, we\n", "select all rows with names given to women. Second, we split the yielded data set into\n", "annual groups using `groupby(level=0)` (recall that the first level of the index, level 0,\n", "corresponds to the years of the rows). Subsequently, we apply the function `type_token_ratio()` to each of these annual sub-datasets. Finally, we call the method `plot()` to create a line graph of the computed type-token ratios. Using a simple `for` loop, then, we can create a similar visualization with the type-token ratios for both girls and boys:" ] }, { "cell_type": "code", "execution_count": 63, "id": "c70ebd58", "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": { "filenames": { "image/png": "/Users/folgert/projects/hda/_build/jupyter_execute/working-with-data/notebook_121_0.png" } }, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "\n", "# create an empty plot\n", "fig, ax = plt.subplots()\n", "\n", "for sex in ['F', 'M']:\n", " counts = df.loc[df['sex'] == sex, 'frequency']\n", " tt_ratios = counts.groupby(level=0).apply(type_token_ratio)\n", " # Use the same axis to plot both sexes (i.e. ax=ax)\n", " tt_ratios.plot(label=sex, legend=True, ax=ax)\n", "ax.set_ylabel(\"type-token ratio\");" ] }, { "cell_type": "markdown", "id": "edf42b3d", "metadata": {}, "source": [ "\n", "\n", "At first glance, the visualization above seems to run counter to the hypothesis of name diversification. After all, the type-ratio remains relatively high until the early 1900s, and it is approximately equal to modern times. Were people as creative name givers in the beginning of the twentieth century as they are today? No, they were not. To understand the relatively high ratio in the beginning of the twentieth century, it should be taken into account that the dataset misses many records from before 1935, when the Social Security Number system was introduced in the United States. Thus, the peaks in type-token ratio at the beginning of the twentieth century essentially represent an artifact of the data. After 1935, the data are more complete and more reliable. Starting in the 1960s, we can observe a steady increase of the type-token ratio, which is more in line with the hypothesis of increasing diversity.\n", "\n", "Let us now turn to the second diversity related development: can we observe a decline in the frequency of use of the most popular names? Addressing this question requires us to compute the relative frequency of each name per year, and, subsequently, to take the highest relative frequency from a particular year. Consider the following code block and its corresponding visualization below:" ] }, { "cell_type": "code", "execution_count": 64, "id": "63c8d1de", "metadata": {}, "outputs": [ { "data": { "image/png": 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C9pk9e7b++Mc/atmyZc2e7+yzz9YjjzzSaAPAhqqqqvTggw/qiSeekN/vD9vngKaC2Qekp6cHH1dWVjbbH8ntvPPOCxb3X7p0qW699VbLP/jnzJmje+65p9GxioqKRl9Hu5akptcTa7N9i+XajMTmzZt14YUXBsf44x//qK5du1r2ZW22b/FYmw8//LAWLFggm82mp556qtlsp3BYm+1bLNdmw8CytD/TaOTIkVq5cqXuvfdeXXTRRbriiiv07LPP6pNPPgnWU5wxY0ajDYgOYG22b/G6pl999dV64IEH1LFjR8v2A8k8B4IlVlibbVN9fb3OPffcYLnJs88+23LTsVh+/7kXQiRaY21GIpnvhQguAwd54IEH1KdPHz322GO69NJL9fnnn6uyslJ1dXVauXKl7rrrLrlcLs2aNUsTJ04MW0tZkjZt2qTRo0frzDPPlNfr1T/+8Q9t2bJF9fX12rt3r95++22NGzdOO3fu1J133qmzzjpLdXV1CXy1SAXZ2dl69NFHg18/8MADGjNmjB588EG99tpreuqppzR16lSddNJJqqura1RapamdvoFDlci1WVJSotNOOy34R94555yjG2+8MTYvBG1OrNfm+vXr9dvf/laSdP3112vs2LHxfxFok2K5NgOBQKOv7Xa7Xn755UbZRweMHz9e9957b/DrcGXd0H7F45r+1VdfaeDAgbr00kvVsWNHTZ8+XTt37lR9fb127typ6dOna/DgwdqwYYN+8pOf6Jprrjmk2s9IHQc+DTR//nxJUt++ffXPf/6zlWcFJO/aTPZ7IUdrTwBIJnfccYd+//vfS9q/Yc8tt9zSqH3QoEG68847deKJJ+r4449XVVWVpk6dqrVr14a8G79z506NHTtWu3fvVu/evbVw4cJGfQoLC3X66afr1FNP1TnnnKMZM2bonXfe0e9+9zv94Q9/aHSuhjsiRxJ8brhjaHZ2duT/AEha3//+91VbW6vrr79eHo9HS5Ys0ZIlSxr1sdlsuvvuu1VaWqqHH35YkkI2m4x2LUlNryfWJmK1NptSXl6uk08+ObgBy0knnaSXXnqpyeewNhGrtWmapq6++mrV1NSoR48ejQJ0LcHaRKzW5sHr4fjjjw/uHWLlqquu0i233CKv16tFixapsrKy0TlYm4jlNf2bb77R0UcfrZqaGh155JGaM2eOMjIygu3FxcWaOnWqzjzzTB177LFatGiRnnrqKY0ZM0bXXntto3OxNtsW0zR13XXX6cUXX5Qk9ejRQx999FHYvw1j+f3nXghNac212ZRUuBcipQ34r+3bt+tPf/qTJGnAgAG6+eabw/adMGGCLrvsMkn7f9CfeeaZkD733HNP8F2le+65J+xHwex2u/76178Gd5v/61//Kp/P16hPw40t9u7d2+xr2bdvn+Vzkdp+8IMfaN26dfrVr36lUaNGKS8vT06nU927d9f3v/99ff7557rjjjsaff8P3nQg2rXk8/mCH3V0Op3KzMw8pPOxNtumWKzNcA78MXXg5vb444/XW2+9Jbfb3eTzWJuQYrM2//a3v+mTTz4JPm74x3pLsDYhxWZtZmVlNSrPMmrUqCbHzMrK0oABAyTt32x68+bNjdpZm5Bid02/7bbbghun/eUvf2kUWG4oIyNDf/nLX4JfHwhYN8TabDtM09SPf/xj/eMf/5C0f1O92bNnq1evXmGfE8vvP/dCCKe112Y4qXIvROYy8F8ffvihvF6vJOnEE08MBnvDOfnkk/XUU09JkhYuXBjSPnPmzEZ9m9KtWzcNGjRIK1euVEVFhVatWqVhw4YF2w/cCEjSxo0bm30tDfs0fC5SX7du3fSHP/whJLu9oQPvaErSkUce2aite/fuysjIUE1NjbZu3Sqv19tk3dDvvvsuWAu8X79+IT8XAwYM0Jw5cyTtX3fHHntsk/NnbbZdh7o2rZSXl+uUU07Rl19+KUk67rjj9PbbbzeqCRYOaxMHHOraPHCT0blzZy1dulRLly61PEfDQN0LL7ygTz/9VNL+vwEanpO1iQMOdW0ahqH+/fsH++Tm5jY7ZsM+B9fJZW3igENdmx6PR7NmzZK0/02NcePGNTne+PHjlZmZqerqaq1atSokq557obbBNE395Cc/0d///ndJUteuXTVnzpxGJVasxPL7z70QrCTD2rSSSvdCBJeB/9q+fXvwcSR/nDd8N6dhsfR4nK9hoHnRokXNnqthn6FDhzbbH23Hvn37tHz5ckn7/5gfMWJEo3bDMDRkyBAtWrRIfr9fX331VZNBvubW0sFr88orrwx7Lp/Pp6+++krS/o9UDh48OLIXhTahubV5sAN/TB14827SpEmaOXNm2Myng7E2Eanm1uaB+p87duwI1l1uTsPafFlZWY1+z7I2EalIfm8efvjhwSBfeXl5s+ds2Ofgv09Zm4hUc2tz3759qq+vlyTl5OQ0m7RjGIZyc3NVXV0taf+9UMPgMvdCqe9A8O5vf/ubJKlLly6aM2eODjvssGafG8vvP/dCOFiyrM2Dpdq9EGUxgP9q+AfMli1bmu3fMEPJauOUWJ5v8ODB6tGjhyRp1apV2rRpU9jzVFVVBYvPZ2RkaNKkSc2OjbbjhRdeCGbgX3rppZYfl5k8eXLw8Xvvvdfk+d59993g4ylTpjR5rvfff7/JTVjmz58ffOPkmGOOCflYGdq2SNbmAQf/MTVx4kS98847Ef8xJbE2Eblo1mYssDYRqUjWZsNr88G1cQ9WVVWlb7/9VpLkcrnUu3fvRu2sTUSqubXZ8D5o7969zdb7rK2t1Z49e4JfFxQUNGqfNGlSMFOv4dqzsnHjRq1evVqS1KtXLw0aNCiCV4R4Ojh417lzZ82ZM6fJGvENxfpemHshHJBsa/OAVLwXIrgM/FfDd3ZmzpwZ8lHBgx0o8i5Zf7y74fka9rUyd+5cbdu2TdL+P6as3iW78MILg48feuihsOd68skng+/6n3nmmVH9AkJq2759u373u99J2n/TGG732IZradq0acH1crBt27bp1VdflSSlp6frrLPOCunTp08fjRkzRtL+P+bfeOONsPNrWE9v6tSpzbwatCWRrk1p/8e0D/5j6t133436jxzWJiIRydpctmyZTNNs9r+GNwlz5swJHr/pppsanY+1iUhE+nvzzDPPDAbyZs+erbVr14Y95z//+c9gQPCYY44J+RuRtYlIRLI2s7Oz1bNnT0lSfX29XnvttSbP+corrwTX5uGHHx4SrM7MzNTpp58uaX+A5sknnwx7LtZm8rn++uuDwbtOnTppzpw56t+/f1TniOW9MPdCOCDZ1qaUwvdCJtBGPfPMM6YkU5J5+eWXN9u/vr7e7NGjR/A5U6ZMMauqqkL6BQIB89e//nWwX3p6urlt27aQftOmTQv2cbvd5rvvvms57po1axqNe8MNN1j227Vrl5mdnW1KMm02m/nWW2+F9FmwYIGZkZFhSjIdDoe5atWqZl83Ei/atWmaprlhwwZz06ZNYdu/+eYbc8CAAcHz/vGPf2zyfBdccEGw70UXXWR6vd5G7ZWVleakSZOCfX7zm9+EPde7774b7Ne5c2dz7dq1IX0ee+yxYJ/evXubHo+nmVeM1tDaa7OiosIcN25csO/EiRPNysrKaF9GEGuz7WjttRmJhr8z58yZ02Rf1mbbkQxr8w9/+EOw78iRI819+/aF9Pn888/NrKysYL9Zs2ZZnou12Xa09tr81a9+FexXUFBgLl682LLfwoULzby8vGDfhx56yLLf8uXLTZvNZkoyMzMzzYULF4b0efPNN4N9cnNzzT179jTzihFv119/ffB726lTJ3P16tUtOk+s74W5F0Iyrs1UvhcyTLOJ3GggRWzcuFFPP/10o2Nff/213n77bUnS8OHDdcYZZzRqP/7443X88cc3OjZjxgx973vfUyAQkLS/3s6ll16qYcOGyel0asOGDXrllVe0bNmy4HP+8pe/hGQlSfvr1kycOFELFiyQtL++02mnnabJkyeruLhYFRUV+uyzzzR9+nTV1tZK2v/u0pdffmlZZkOSnnvuOV1xxRWS9tfCmTp1qk466STZ7XZ99tlneu6554IfO7v33nv161//OoJ/PcRTrNbm66+/rgsvvFATJkzQscceq379+iktLU07d+7UnDlz9Pbbb8vn80mSrr76aj355JOy2cJ/OGXbtm066qijtHXr1uA8rrjiCnXp0kUbNmzQU089pQ0bNkiSjjjiCM2fP19ZWVlhz3fVVVfpmWeekbS/huMPfvADjRw5UtXV1ZoxY0Zwg0uXy6X3339fxx13XLP/doivZFybRx99tD777DNJUn5+vh599NEm190BJ598cth3/1mbqScZ12Ykjj32WH3yySeS9mcuN7dxCmsz9STr2vR4PDr55JM1b948SVJRUZGuueYaDR06VB6PR3PnztW//vWv4Dl/8pOf6PHHHw97PtZm6knGtVleXq7Ro0dr3bp1kiSn06lzzz1Xxx13nAoKClRSUqLZs2fr3//+d/CcRx55pObNmxe2PNHvfvc73XnnnZKktLQ0XXHFFRo/frz8fr9mzZqll19+WYFAQIZh6Pnnn9cll1wSzT8jYuz222/XvffeK2n/vfAf/vAHDRw4sNnnjRw5MlhqoKFY3gtzL9S+JevaTOl7oRaHwIEkMmfOnOC7LZH+d+edd1qe69VXXzXz8/Obfb7b7Tb/8pe/NDmvkpIS84wzzohoPmPHjjU3btzY7Gt94oknzLS0tLDnsdvt5h133BH9PyLiIlZr87XXXmv2eRkZGeYf//hHMxAIRDS3FStWmAMHDmzynOPHjzd37NjR7Lm8Xq951VVXNXmu/Px8880334z2nxBxkoxrM9r5HPivqd+drM3Uk4xrMxLRZC6bJmszFSXz2iwvLzdPP/30Js9ps9nMW2+91fT7/U2ei7WZepJ1bW7evNkcP358RPOZMmWKuXfv3mbP+Zvf/Ma02+1hz5Oenm7+/e9/b8k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" ] }, "metadata": { "filenames": { "image/png": "/Users/folgert/projects/hda/_build/jupyter_execute/working-with-data/notebook_123_0.png" } }, "output_type": "display_data" } ], "source": [ "def max_relative_frequency(frequencies):\n", " return (frequencies / frequencies.sum()).max()\n", "\n", "# create an empty plot\n", "fig, ax = plt.subplots()\n", "\n", "for sex in ['F', 'M']:\n", " counts = df.loc[df['sex'] == sex, 'frequency']\n", " div = counts.groupby(level=0).apply(max_relative_frequency)\n", " div.plot(label=sex, legend=True, ax=ax)\n", "ax.set_ylabel(\"Relative frequency\");" ] }, { "cell_type": "markdown", "id": "32433be1", "metadata": {}, "source": [ "\n", "\n", "As before, we construct annual data sets for both sexes. The maximum relative frequency is computed by first calculating relative frequencies (`frequencies / frequencies.sum()`), and, subsequently, finding the maximum in the resulting vector of proportions via the method `Series.max()`. The results unequivocally indicate a decline in the relative frequency of the most popular names, which serves as additional evidence to the diversity hypothesis.\n", "\n", "(sec-working-with-data-name-ending-bias)\n", "### A bias for names ending in *n*?\n", "\n", "It has been noted at various occasions that one of the most striking changes in the practice of name giving is the explosive rise in the popularity of boy names ending with the letter *n*. In this section, we will demonstrate how to employ Python and the Pandas library to visualize this development. Before we discuss the more technical details, let us first pause for a moment and try to come up with a more abstract problem description.\n", "\n", "Essentially, we aim to construct annual frequency distributions of name-final letters. Take the following set of names as an example: *John*, *William*, *James*, *Charles*, *Joseph*, *Thomas*, *Henry*, *Nathan*. To compute a frequency distribution over the final letters for this set of names, we count how often each unique final letter occurs in the set. Thus, the letter *n*, for example, occurs twice, and the letter *y* occurs once. Computing a frequency distribution for all years in the collections, then, allows us to investigate any possible trends in these distributions.\n", "\n", "Computing these frequency distributions involves the following two steps. The first step is to extract the final letter of each name in the dataset. Subsequently, we compute a frequency distribution over these letters per year. Before addressing the first problem, we first create a new `Series` object representing all boy names in the dataset:" ] }, { "cell_type": "code", "execution_count": 65, "id": "3d0d6bdd", "metadata": {}, "outputs": [ { "data": { "text/org": [ "| year | John | William | James | Charles | George |\n", "|--------+--------+-----------+---------+-----------+----------|\n", "| 1880 | J | o | h | n | |\n", "| 1880 | W | i | l | l | i |\n", "| 1880 | J | a | m | e | s |\n", "| 1880 | C | h | a | r | l |\n", "| 1880 | G | e | o | r | g |" ], "text/plain": [ "year\n", "1880 John\n", "1880 William\n", "1880 James\n", "1880 Charles\n", "1880 George\n", "Name: name, dtype: object" ] }, "execution_count": 65, "metadata": {}, "output_type": "execute_result" } ], "source": [ "boys_names = df.loc[df['sex'] == 'M', 'name']\n", "boys_names.head()" ] }, { "cell_type": "markdown", "id": "c323a866", "metadata": {}, "source": [ "The next step is to extract the final character of each name in this `Series` object. In order to do so, we could resort to a simple `for` loop, in which we extract all final letters and ultimately construct a new `Series` object. It must be stressed, however, that iterating over `DataFrame` or `Series` objects with Python `for` loops is rather inefficient and slow. When working with Pandas objects (and NumPy objects alike), a more efficient and faster solution almost always exists. In fact, whenever tempted to employ a `for` loop on a `DataFrame`, `Series`, or NumPy's `ndarray`, one should attempt to reformulate the problem at hand in terms of \"vectorized operations\". Pandas provides a variety of 'vectorized string functions' for `Series` and `Index` objects, including, but not limited to, a capitalization function (`Series.str.capitalize()`), a function for finding substrings (`Series.str.find()`), and a function for splitting strings (`Series.str.split()`). To lowercase all names in `boys_names` we write the following:" ] }, { "cell_type": "code", "execution_count": 66, "id": "77483cc5", "metadata": {}, "outputs": [ { "data": { "text/org": [ "| year | john | william | james | charles | george |\n", "|--------+--------+-----------+---------+-----------+----------|\n", "| 1880 | j | o | h | n | |\n", "| 1880 | w | i | l | l | i |\n", "| 1880 | j | a | m | e | s |\n", "| 1880 | c | h | a | r | l |\n", "| 1880 | g | e | o | r | g |" ], "text/plain": [ "year\n", "1880 john\n", "1880 william\n", "1880 james\n", "1880 charles\n", "1880 george\n", "Name: name, dtype: object" ] }, "execution_count": 66, "metadata": {}, "output_type": "execute_result" } ], "source": [ "boys_names.str.lower().head()" ] }, { "cell_type": "markdown", "id": "0a0a9717", "metadata": {}, "source": [ "Similarly, to extract all names containing an *o* as first vowel, we could use a regular\n", "expression and write something like:" ] }, { "cell_type": "code", "execution_count": 67, "id": "c52541d2", "metadata": {}, "outputs": [ { "data": { "text/org": [ "| year | John | Joseph | Thomas | Robert | Roy |\n", "|--------+--------+----------+----------+----------+-------|\n", "| 1880 | J | o | h | n | |\n", "| 1880 | J | o | s | e | p |\n", "| 1880 | T | h | o | m | a |\n", "| 1880 | R | o | b | e | r |\n", "| 1880 | R | o | y | | |" ], "text/plain": [ "year\n", "1880 John\n", "1880 Joseph\n", "1880 Thomas\n", "1880 Robert\n", "1880 Roy\n", "Name: name, dtype: object" ] }, "execution_count": 67, "metadata": {}, "output_type": "execute_result" } ], "source": [ "boys_names.loc[boys_names.str.match('[^aeiou]+o[^aeiou]', case=False)].head()" ] }, { "cell_type": "markdown", "id": "7696888a", "metadata": {}, "source": [ "The function `Series.str.get(i)` extracts the element at position `i` for each element in a `Series` or `Index` object. To extract the first letter of each name, for example, we write:" ] }, { "cell_type": "code", "execution_count": 68, "id": "fa0dc678", "metadata": {}, "outputs": [ { "data": { "text/org": [ "| year | J |\n", "|--------+-----|\n", "| 1880 | J |\n", "| 1880 | W |\n", "| 1880 | J |\n", "| 1880 | C |\n", "| 1880 | G |" ], "text/plain": [ "year\n", "1880 J\n", "1880 W\n", "1880 J\n", "1880 C\n", "1880 G\n", "Name: name, dtype: object" ] }, "execution_count": 68, "metadata": {}, "output_type": "execute_result" } ], "source": [ "boys_names.str.get(0).head()" ] }, { "cell_type": "markdown", "id": "ef4618a2", "metadata": {}, "source": [ "Similarly, retrieving the final letter of each name involves calling the function with `-1` as argument:" ] }, { "cell_type": "code", "execution_count": 69, "id": "1ea41344", "metadata": {}, "outputs": [ { "data": { "text/org": [ "| year | n |\n", "|--------+-----|\n", "| 1880 | n |\n", "| 1880 | m |\n", "| 1880 | s |\n", "| 1880 | s |\n", "| 1880 | e |" ], "text/plain": [ "year\n", "1880 n\n", "1880 m\n", "1880 s\n", "1880 s\n", "1880 e\n", "Name: name, dtype: object" ] }, "execution_count": 69, "metadata": {}, "output_type": "execute_result" } ], "source": [ "boys_coda = boys_names.str.get(-1)\n", "boys_coda.head()" ] }, { "cell_type": "markdown", "id": "b7e9549e", "metadata": {}, "source": [ "Now that we have extracted the final letter of each name in our dataset, we can move on to the next task, which is to compute a frequency distribution over these letters per year. Similar to before, we can split the data into annual groups by employing the `Series.groupby()` method on the index of `boys_coda`. Subsequently, the method `Series.value_counts()` is called for each of these annual subsets, yielding frequency distributions over their values. By supplying the argument `normalize=True`, the computed frequencies are normalized within the range of 0 to 1. Consider the following code block:" ] }, { "cell_type": "code", "execution_count": 70, "id": "efcaaf83", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "year name\n", "1880 n 0.181646\n", " e 0.156102\n", " s 0.098392\n", " y 0.095553\n", " d 0.080416\n", "Name: name, dtype: float64" ] }, "execution_count": 70, "metadata": {}, "output_type": "execute_result" } ], "source": [ "boys_fd = boys_coda.groupby('year').value_counts(normalize=True)\n", "boys_fd.head()" ] }, { "cell_type": "markdown", "id": "77b6fa1d", "metadata": {}, "source": [ "This final step completes the computation of the annual frequency distributions over the name-final letters. The label-location based indexer `Series.loc` enables us to conveniently select and extract data from these distributions. To select the frequency distribution for the year 1940, for example, we write the following:" ] }, { "cell_type": "code", "execution_count": 71, "id": "f5ffd17a", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "name\n", "a 0.029232\n", "b 0.002288\n", "c 0.004575\n", "d 0.074479\n", "e 0.164718\n", "Name: name, dtype: float64" ] }, "execution_count": 71, "metadata": {}, "output_type": "execute_result" } ], "source": [ "boys_fd.loc[1940].sort_index().head()" ] }, { "cell_type": "markdown", "id": "e73daa93", "metadata": {}, "source": [ "Similarly, to select the relative frequency of the letters *n*, *p*, and *r* in 1960, we write:" ] }, { "cell_type": "code", "execution_count": 72, "id": "5c528c3a", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "year name\n", "1960 n 0.190891\n", " p 0.003705\n", " r 0.046851\n", "Name: name, dtype: float64" ] }, "execution_count": 72, "metadata": {}, "output_type": "execute_result" } ], "source": [ "boys_fd.loc[[(1960, 'n'), (1960, 'p'), (1960, 'r')]]" ] }, { "cell_type": "markdown", "id": "594132f4", "metadata": {}, "source": [ "While convenient for selecting data, being a `Series` object with more than one index (represented in Pandas as a `MultiIndex`) is less convenient for doing time series analyses for each of the letters. A better representation would be a `DataFrame` object with columns identifying unique name-final letters and the index representing the years corresponding to each row. To \"reshape\" the `Series` into this form, we can employ the `Series.unstack()` method:" ] }, { "cell_type": "code", "execution_count": 73, "id": "ee89ba59", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/org": [ "| year | a | b | c | d | e | f | g | h | i | j | k | l | m | n | o | p | q | r | s | t | u | v | w | x | y | z |\n", "|--------+-----------+------------+------------+-----------+----------+------------+------------+-----------+------------+-----+-----------+-----------+-----------+----------+-----------+------------+-----+-----------+-----------+-----------+-------------+-------------+------------+------------+-----------+-------------|\n", "| 1880 | 0.0293283 | 0.00662252 | 0.00662252 | 0.0804163 | 0.156102 | 0.00662252 | 0.00756859 | 0.0321665 | 0.0037843 | nan | 0.0189215 | 0.0700095 | 0.0264901 | 0.181646 | 0.0302744 | 0.0037843 | nan | 0.0681173 | 0.0983917 | 0.0605487 | 0.00283822 | 0.000946074 | 0.00662252 | 0.0037843 | 0.0955535 | 0.00283822 |\n", "| 1881 | 0.0271084 | 0.0060241 | 0.00803213 | 0.0763052 | 0.148594 | 0.00502008 | 0.0120482 | 0.0331325 | 0.00301205 | nan | 0.0170683 | 0.0682731 | 0.0261044 | 0.184739 | 0.0291165 | 0.0060241 | nan | 0.0722892 | 0.0983936 | 0.0682731 | 0.00200803 | 0.00100402 | 0.0060241 | 0.00502008 | 0.0953815 | 0.00100402 |\n", "| 1882 | 0.0255009 | 0.00637523 | 0.00637523 | 0.0801457 | 0.166667 | 0.00728597 | 0.00819672 | 0.0336976 | 0.00273224 | nan | 0.0163934 | 0.0701275 | 0.0255009 | 0.175774 | 0.0309654 | 0.00364299 | nan | 0.0673953 | 0.0938069 | 0.0628415 | 0.000910747 | 0.000910747 | 0.00728597 | 0.00455373 | 0.100182 | 0.00273224 |\n", "| 1883 | 0.0281827 | 0.00485909 | 0.00680272 | 0.0826045 | 0.158406 | 0.00680272 | 0.00874636 | 0.0301263 | 0.00194363 | nan | 0.0165209 | 0.0767736 | 0.0281827 | 0.176871 | 0.0281827 | 0.0058309 | nan | 0.0660836 | 0.0991254 | 0.0621963 | 0.00194363 | 0.000971817 | 0.00874636 | 0.00485909 | 0.0942663 | 0.000971817 |\n", "| 1884 | 0.0284444 | 0.00888889 | 0.00622222 | 0.08 | 0.155556 | 0.00533333 | 0.00711111 | 0.0311111 | 0.00177778 | nan | 0.0177778 | 0.0657778 | 0.0248889 | 0.186667 | 0.0275556 | 0.00444444 | nan | 0.0737778 | 0.0977778 | 0.0613333 | 0.00177778 | 0.000888889 | 0.00622222 | 0.00355556 | 0.100444 | 0.00266667 |" ], "text/plain": [ "name a b c d e f g \\\n", "year \n", "1880 0.029328 0.006623 0.006623 0.080416 0.156102 0.006623 0.007569 \n", "1881 0.027108 0.006024 0.008032 0.076305 0.148594 0.005020 0.012048 \n", "1882 0.025501 0.006375 0.006375 0.080146 0.166667 0.007286 0.008197 \n", "1883 0.028183 0.004859 0.006803 0.082604 0.158406 0.006803 0.008746 \n", "1884 0.028444 0.008889 0.006222 0.080000 0.155556 0.005333 0.007111 \n", "\n", "name h i j ... q r s t u \\\n", "year ... \n", "1880 0.032167 0.003784 NaN ... 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NaN 0.073778 0.097778 0.061333 0.001778 \n", "\n", "name v w x y z \n", "year \n", "1880 0.000946 0.006623 0.003784 0.095553 0.002838 \n", "1881 0.001004 0.006024 0.005020 0.095382 0.001004 \n", "1882 0.000911 0.007286 0.004554 0.100182 0.002732 \n", "1883 0.000972 0.008746 0.004859 0.094266 0.000972 \n", "1884 0.000889 0.006222 0.003556 0.100444 0.002667 \n", "\n", "[5 rows x 26 columns]" ] }, "execution_count": 73, "metadata": {}, "output_type": "execute_result" } ], "source": [ "boys_fd = boys_fd.unstack()\n", "boys_fd.head()" ] }, { "cell_type": "markdown", "id": "6bf0525d", "metadata": {}, "source": [ "As can be observed, `Series.unstack()` unstacks or pivots the name level of the index of `boys_fd` to a column axis. The method thus produces a new `DataFrame` object with the innermost level of the index (i.e., the name-final letters) as column labels and the outermost level (i.e., the years) as row indexes. Note that the new `DataFrame` object contains NaN values, which indicate missing data. These NaN values are the result of transposing the `Series` to a `DataFrame` object. The `Series` representation only stores the frequencies of name-final letters observed in a particular year. However, since a `DataFrame` is essentially a matrix, it is required to specify the contents of each cell, and thus, to fill each combination of year and name-final letter. Pandas uses the default value NaN to fill missing cells. Essentially, the NaN values in our data frame represent name-final letters that do not occur in a particular year. Therefore, it makes more sense to represent these values as zeros. Converting NaN values to a different representation can be accomplished with the `DataFrame.fillna()` method. In the following code block, we fill the NaN values with zeros:" ] }, { "cell_type": "code", "execution_count": 74, "id": "32def018", "metadata": {}, "outputs": [], "source": [ "boys_fd = boys_fd.fillna(0)" ] }, { "cell_type": "markdown", "id": "15f13eb6", "metadata": {}, "source": [ "The goal of converting our `Series` object into a `DataFrame` was to more conveniently plot time series of the individual letters. Executing the following code block yields a time series plot for eight interesting letters (i.e., columns) in `boys_fd` (we leave it as an exercise to the reader to plot the remaining letters):" ] }, { "cell_type": "code", "execution_count": 75, "id": "575e0f83", "metadata": {}, "outputs": [ { "data": { "image/png": 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ZjkM+l03fmpajR9b7taUmosHZDt04MUu5bay3O6HQpW9Ny9YDy+vaDVeTmTXQo69OzNL+xpjeOxDS89saO3SeE4Gp9gPmY+2uj+q+ZXX66qQsnTvAo+21UW2tiWh7bUQHG2PKchn6zMhMjcl3KRQz9dbegHbWRVUXiqsuHFcoZqrc33YiHIo1jyh/cUeT7IZU6LWrJKP5v6IMm7bVRLT8UPuj29OxuSaizTXS2/uST+NeHzZTjnLfk+L9JLPaNkSHAqZyc9OqBgAAAAAAAEgiZAYAAH3Yl0/L0qbqltNm57htunFidmKN3LIchzIchpqibaego/OaRygPynLo7rPyFI2bSUe/Hm9SkVs3TszS79b602q3x27omtGZMgxDA3wOfWakQ2f2c+u/36tVTajlSFaXrTnEPX4K53kTslSUYdef19frUFMa8zsfJ8dtU10odf2SDJvOGeDRp4Zl6jdr6rSiCwJaU9Lv1/r1+zZ+duuravWVCVlasCugPf6215tuT8yUDjXFumWq8N4UM+z667ao7i0xZbfQVwEAAAAAAIBjETIDAIA+K9dt011n5OnP6/3aWR/RsByn5k3MUo776Nq3NsPQ2HynVla0HYweCZmPsBIwH3HeQK+8Dpv++oFfh4Otw9rpJS7FTWnVR9d32qRvTs1W3nEjpPv7HPrRmbm69/1aVRwzZfJNk7MVM5unZD7irP5unT/II5th6BezCrSoPKjntzUmAuoLBnm08XCk3WD1MyMz9NlRPjVE4moIx9UQMVUXiqsqEFNtKC6f06bTCl0anGVPhPb/77Qs7aitaRWGH29krkPRuLSzvmMBsSnpj+vTC++7kqHmflEfjreYQt31UdcKdzzX7zI7/ab+b0eTrhiRoSX7Qnr3QFCZTkNXjMjUgDTXpAYAAAAAAEDfwtMjAADQpw38aPSxaZqJIPR44wtcqUPmfFen2jCj1K0pxS69sy+o+R82aV9Dcyh5wUCP/nNClhw2QztqI6oKxDQsx6mijORTcJdmOvTzWQVasLVaG7bt0qenlem0fh5J0tBsh9ZUhjXQ59C4AqdsH71Xh83QxUO8+thgj/b6Y/K5DOV77Pq/7Y16akvb04RnOAxdXpYhSfI5bfI5bW2WPVa+x64fnZmr/36vNmmoLjVPV/7NqTkKRE09uKJWW2s7PhK5N4zOc+rLp/k0NLv5ywfbayNauj8ou83QBYM8ctsN3fZ2dYsR9L3l2a2Nmr+jqcXa4O8dCOm7M3I1sbC5X8dNM9FfAAAAAAAAAImQGQAAQJLaDJglaXyBs81jJRl25bqtBaypOGyGzh/k1ayBHlUF4nI7DGW7jp53eK5Tw3PbbscRbruh8/vZZWzdq4GZwxP7+/sc6p9idKrNMDQ4++jxS4dmaPG+YCLwPt6nR2Qo02KwfLzSTId+dGae7nmvpsWoa6l5FPXc07Jktxly2w3991l5KvfHtK8hqpc+bNKOuq4PnN126c7T81QXiusf2xv1YZrX8DoM2QypLNuh8wZ6dO4AT4tQdkSuUyOO+7O7+8w8/c/GBu2oi8jfgbB5oM+uAT5H0jXF0xU8bkHsSFy69/1aDctxqCYYV304rklFLl07xqdBWfz6AAAAAAAAAEJmAACAdh0bvh7v+KmyO8swjDZHKvckj8PQXWfkaeGegD6oCqsiENPhQFwuu3TxYK8+OSyjU+cvzrDr7rPy9OgGv5YfCsvnNPSZkZm6bKi3ReBvfBR+D8526Mz+Hr28s0mPb2zo8HUHZdnlc9oUiZuKxqV+mXb9x8hMDfwoPJ1R6lZjJK4dtVHtqIvoUGMssSZzdTCuI3FshsPQJUO9mj00Q9kd+JJBf59Dd8zMTWzvrIvoZyvqWo3u/o+RmfrU8Ay9fyCow8G4+mfaNTbflbjmluqI7nm/psWa25lOQz8/L19LD4T0+p6AHDZDc4ZnqMhr1w/frZHVSPvYsH1VRVhrK6t1ej+3ppe45bYbKs2wa4DPnvILGgAAAAAAADg1ETIDAAC0w2YYGuiza2+SUb2jujhkPpHkuG36j5GZ+o+RmZKap002lHrUdzryPXbdOj1XcbM59rQyJfPlZRkq8Nj0xKYGVQXiynXbNKPErRmlLo3MderBlXXacDjSql6e26b/PitPxRYC/EynTROLXJpY1HIa9EjMVGUgJrvNUJHX1qVTSJflOPWTc/L1+Ea/lh4IyW039B8jM3V5WXPoft5Ab9J6o/Od+t7MXP1hbb0qAs0h9M1TspXvsevysozElOZHfH6sT09s6lhIHzOld/eH9O7+o6Oni7w2zSx1q7/PIZetuc+MynPK6+j86H4AAAAAAACcuAiZAQAALLh4iFd//aB1ODc2/9QNmY/XXevypnveM/p5NKPELVPN04wf67+m5egHS2q0v/HoFwJ8TkPfPz3XUsCcitNupJxyvLNy3TbdMiVHX5tktnpfqYwrcOk3FxaqKRJXRjtTmF9e5tXO6ia9cyj5etjpqgzE9fLOQIt9bruhcwa49fHBXg3N6TufDwAAAAAAgL6EIQYAAAAWnDPAoyxny+DvtAKnBvh6f2rrvshuM5IGsT6nTfecnacLB3lUlu3QGf3cuu+c/JNqLeF0AuZjtRcwS82j0K8ZZldxvK5D17AiFDP1+p6gbnunRr9YWSd/uGsCbQAAAAAAAJw4Tp6nbQAAAL3I57TpO9Nz9T8b/aoKxDQqz6UvjfexHu0JyOe0ad7E7N5uxgnLYTN0bnyrNuXP1Mba5qnK+2faddPkbD23rVGrKsJddq33D4a0rTaimyZna3yBq/0KSTSE47Lb1GIK7kjMVCRuyusw+AwCAAAAAAD0AkJmAAAAi0bnN6+bC5zs3IrqlnEOHTZ8CsdMjc5zym4z9J3pOXp7X1A7aqMa4LPrrP4eHQ7E9NcPGrS5pvVa11ZUB+O6571aXTjYo6tGZirPY230f00wpkc2+LXiUFiGpI8N9uj6cVl6Z19Qz25tVG0orgE+u/5ras5JNVIdAAAAAADgVMDTGAAAAKAPMgxDI3JbrplsMwzNGujVrIFH92W5bLr7rDxVB2M61BSTaUpVgZjWVIa1qiKsQNRs91qmpNf3BPXOvqA+MSxDnx6eKZfd0I7aiFZVhBWLm3LaDblshoozbArHpL9tblBtKJ6o/9qeoF7bE2xx3n0NMd3zXo0eODffcngNAAAAAACAziNkBgAAANCufI9d+ccEuecN9CoWN1UdjCsUM1UVjGn5wZDe3hdSKJY8eA7FpOe3NemdfSEVeGzaWN2x0dHHqgub+u2aen3/9FzZmDobAAAAAACgR9jaLwIAAAAArdlthooy7BqY5dDkIrfmTsjWQxcUaFJR6vWXDzXFuiRgPmLD4Yhe2N7UZecDAAAAAABAaoTMAAAAALpMrtum22fk6AtjfXL04G8b/7u1UX9eX6/qYKznLgoAAAAAANBHETIDAAAA6FI2w9AnhmXol7MKdE5/d49c88i6zbcsOqx/ftgk02x/rWgAAAAAAAB0DCEzAAAAgG5RnGHXzVNydN85eRros7dfIYlZAz0anOWwXD4Sl57Y1KD5HzJ9NgAAAAAAQHex/rQGAAAAADpgWI5TPzknX4994Ncb5cEWx04rcOpL47OU67GpPhRXuT+qPf6oYqY0o8St4blOBaNxvfRhQBsPh1XoteszIzP03oGQntrS2OY1n9zcqDy3TecN9Hb32wMAAAAAAOhzCJkBAAAAdDu33dC8idmaXuLWa3sCipnSuQM8Oqe/W4ZhSJJ8Tpv6+xw6vV/Luh6HTVeNypSUmdg3Z7hdMVN6flujYm3MjP3wOr9qQnFdOMirLBeTOAEAAAAAgFNTLG7KbjN69JqEzAAASZJpmtpUHVFVIKZReU6VZnKLAAB0vWklbk0r6fw6zYZh6DMjM3V2f7ee29akd/YFdXzWHDObRzT/79ZGnT/Qq2vGZCrTSdgMAAAAAABOHesqw/rrB37dMiVbZTnOHrsuCQIAQKZp6ndr6/X2vpAkyW5IX5+crbP7e3q5ZQAApFaa6dBNk7M1Ks+hv2xoSFomEpcW7gloTWVIt0zJ0ai8nvuFCwAAAAAAoLvUBmP67Zo61YVN3flujT4/xqdLh3oTs8Z1J77GDwDQyopwImCWmkd+/XWDX+G25h8FAOAE8/EhGbpieEbKMpWBuO5aWqOnNzfIH4536Dqmyb0RAAAAAAD0jLhpalF5QH9YW68XtjeqNhRvcey3a+tVF25+VhGNS49tbNDPV9apoYPPPdLBSGYAgF7bHWi1zx8xtb4q3CVTmgIA0BOuHp2phoiphXta39eOiJvSCzua9MquJl04yKtPDMtQodfe7rnf3hvUM1sbVBWIa3yBU9eM8WlELiOiAQAAAABA5+xviKrcH1WWy6b+PodyXIYMw1BjJK5frarTuqpIouwL2xt16dAMfWpYhhbuCWj9MceOWHEorKUHQrp4iLdb203IDACnANM0LU9/EYmZ2lkf1YHGqLwOm4ZmO7S6Mpy07IpDIUJmAMBJwzAM/eeELE0pdunlnU364HDrX7SOCMWkBbsC+vfugEoy7Crw2HSoKab6cFySIUNSvsem8QUuba2NaHd9NFF3w+GI7lxSo1kDPfrYYK8GZznkcbS8D+9riOpwMC6f01Ce26Yct022HpiqCgAAAAAAnJgaInGtqwzLkDS+0CWv3dD/bGzQa3sCOnbetEyHoUynoYpA69HIoZj04o4mvbijqc3rTC9x6aLB3b8UJiEzAJwgAtG4ttVGFYqactolr92mQVl2ZTiTr2xgmqYW7wtq4e6ADjbFNKnQpatGZao0s/Vf7XHT1NL9Ib22J6BttRFFLM6UsbIirLhp8lAcAHBSmVbi1rQSt/Y1RPWXDf6UYXPclA40xnSgMXbM3uZf7fY3xrS/MfmoaFPSm3uDenNvUJI0NNuhTwzL0LRilx5e59f7B0Mtyue4DF1WlqFPD8/okXWRAAAAAADAiSEQjeufHwb08s4mBaLtL8PVGDXVaKFcMgUem26cmN0jzx4ImQHgBLDxcFg/XVHX6gbjsEkTC106vdStKcVu5bibA+fKppge2eDXmmNGIL+zP6T3D4b02VGZurwsQ3aboVjc1Lv7Q/rH9kbtb/Hw3Jq6UFzba6Malcd0oACAk88An0N3np6rF7Y36X+3Nqo7V1PeVR/Vb9fUt3m8Lmzq6S2NynLZdNHg7p2uCgAAAAAA9L5wrHlJrxe2N8of7s6nEs0MSTdPyVaWK/nAta5GyAwAvawuFNfPkwTMkhSNS6sqwlpVEZbkV5HXJofNOG601VGRuPT3zY369+6AXDZDNaG4pW9GpbLyUIiQGQBw0rIZhj4zMlOTilx6flvjR/fU3vP0lgad1c/d5kwlAAAAAADg5LTxcFjvHQgpGDPVFDG1sTqsxkj3h8tHXDUqU2PzXT12PUJmAOghcdPUxsMRLdkf1JrKsBrCcc0sdas2FLc89UVlkjUYkqmyWM6KFYdCumaMr8vOBwBAbxiR69RtM3K1pz6q+R82asn+kOI993tegj9s6p87mzR7aIYONcU0wNd6PWcAAAAAAHDyiJumntzcqJc+bHud5M6yGdKQLId21keTHj+twKkrRmR02/WTIWQGgB6wuTqs36/161BTyxHI7+wPtVHjxLG3IaaDjdGkaz0DAHCyGZzt0E2Tc/TZUTG9vLNJi8oDCsWO/rI2ONshhyFVB+PaWB1RKHY0iR6a7dC1YzL16u6AVh4Kd3j67ee3Nen5bc2/eLps0pn9PbposFcjcx2s1wwAAAAAwEkgHDO1oy6ig40xvbijqc3ZR9PxyWEZMiR9WBfRjrqoAlFThqTzB3l0w7gseRyGyv1RLdkf1Lv7Q4m8YUyeU9+YmiNbDz9TIDEAgG5WH4rrZyvq1NCD02KkUpph12mFTr22J2i5zspDYV0+jFsGAODUUZxh15fGZ+m6MT5VBWIq8Nrltrf8ZSwYNbW6MqT9DTH1z7RrRqlbDpuhSUVu1QRj2u2PatPhiBbuCXR4+qtwXHprb1Bv7Q2qwGPTxEKXppa4Nb3E1eO/HAIAAAAAgNTipqkXdzTpnx82deiZf57bpiyXTfsbo4p+NCHp6aVuzZuYpcxjltaKm6ZqgnG57YZ8x6yxPCjLoc+N9unqUZmqDMQVM02VZth75UvrJAYA0M2e3dqY9s1m7oQs1QRjWnYwpD3+1N+AOru/W2f39+jvmxu0r6Htsv0z7bpyZKbO6ueW3Wbo40MydNfSGktrNi/Y1aSPDfYynScA4JTjshvq70v+a5HHYejMfp6kx/I8duV57Jpc5Nbssgz9eb1fyw8dnaFk1kCP/vO0LO1tiOqOd2osteVwMK5Fe4NatDeoYTkO3TotRwVee/pvCgAAAAAAdLnGiKmHl9dpdWU47brDchy6ZoxPEwub10xuiMRV7o8qx2VL+lzCZhgpnwkYhqHijN59ZkDIDADdaI8/qtf2BNKq86XxPl002CtJumqUTwcbo1pfFdHayrDK/VHZDGlojkPDcpyaUuTSwKzmv8onF7m0siKs1/cEtLby6BSeA312fWZkps7o524xImpItkN3np6r366p14HGmLJdhm4Yl6VX9wS0uTrSok2Vgbie2dqgL47L6vgPAwCAU1SO26Zbp+doe21Eu+qjGprt0IhcpyRpWI5TZ/Zza+mB9JbI+LAuqu8vqdHtM3I0NMfZHc0GAAAAAAAW1ShDP1kbUVUav9677dLwHKcuHerVzFJ3i9HGPqdNY/Nd3dDSnkPIDABdwDRNrT8c0eFATAN8Do3IdciQ9Kd19Wmt1zgsx6GPD/G22Fea6VBppkMXH7f/eHaboZmlbs0sdasqENOO2ojyPHaNyHW0Od3miFynfjErXw1hUx6HIZfdUNQ0W4XMkvTKzoCWHwwpx21TXSiuulBcNsPQtBKXrh+XpVy3LckVAADoO0bkOhPh8rE+NzpTyw6GFEtzFq2aUFy3vVMjt91Qgcem4gy7+mXaVZbj0Jn9PHLZmWEEAAAAAIDutrQiplftExSzEDDnffRF9GTPB041hMwA0Elx09SvV9frvWNGKLntUijFLNe/nJWv9w+G9MyWxkQIneO26WuTsrtk/cVCr12FFqfXtBmGst1Hr3nuAI9e3xPUlprWQXNlIK7KQPyYPaaW7A9pc3VEN03OVjQuVQZiKvDaNKnQ1SvrQAAAcKIpzXTo82N9+p+NDR2qH4qZ2t8Y0/7GmNZUNu/7+6YG3TIlR6cVntzfegYAAAAA4ERgmqY+rIvqw7qopOYltKqDMb1/IKQddTHJaH+Q1eQil26cmKU8T99Y+oqQGQA6acm+UIuAWUodMM8ZnqH+PoeuGOHQ6aVuLd4XlPejNR97ew0FqTl0vnFilr77drUi8fbLS81rSN79Xm2LfRMLnfqvqTnKcDLCGQCA2WUZGprt0LrKsIoz7JrZzy2f06ZQzNSqipDWVYb15t6g4hZHO9eFTd27rFafH+vT7KFevtgFAAAAAEAHmKaplRVh/WNbo3Z8FDBb5XMa+vSITE0sdCnfY1OWq289CydkBoBOiJumXtjRaLl8jsvQp4dnJLb7+xz63GhfdzStU/r7HPrMyEw9vcX6ezveuqqIfvx+rf4/e/cdHld1r33/3tM16l2y5SLcewEbU21CN8khkNATMO0hhPKenCeNkzwQTkICJ4EkQBKSkECA0AIhmBrIAYzxwWBjDO692yq2ujR99vuH8GBZo9GMNNKMpO/nuriYsmbvJWvt/bPn3mvtW2blqMVvymqRRmbbZLP0/kvwUNiUNQnbAQCgP00udGhyYceZx05r+4VmJ5S79PVJWbr3o0atPdR5NZFowqb02PoW7WwM6vpp2ZKkNQf9agmENSHfrrJM/rkHAAAAAEBX9rcE9eDqpoTDZUmaWGDXv8/KGTKzlqPhWwcA6IUVVT7ta4kxbfkol0zIGjAze790jFvLD/i0synxAnvYtsag/v2dusjzEdlW/fusXFVkt5cf0zTjnnllmqbe3O3Rc5tb1Ro0NbfMqRumZctlGxh/ngAAdMdtt+i2uXn645pmvbPXG/fn3t3n1bv7Orcfl2fTKcNdOmW4S267RTVtIb2zx6P9rSFNKrBrwYgMObmvMwAAAABgCFpd49OvP25SWzDOJcWOsLAyQ1dMzErKhKqBjJAZAHrINE29sLUt7vZnjszQF0a4+rBHyWWzGLptTq5+/lGjtja0B812i1SeaZXLZtHmKPds7s6e5pB+tLxeZ4/K0KcHA9reGFCZ26q5hVJ3Jekf29o6zKz+3/0+eQKmvjcnlyVCAQCDhs1i6MYZOTpluEvr6/xq9ptqCYRV5w1rY11itXdLQ1BbGlr06PqWTstwv3/Apxe3temyiVk6aZhTFmopAAAAAGCQ29cS1PpDAa2s9ml1rT/hzzss0jdm5OikYQPne/6+RMgMAD20qsavHV3M8i3PtCpsmhqZbdOUQodmFDs0LGvgnXLzXFbddVKBWvxheUOmClyWyJfQobCpv25s0Ss7PAlts9lv6rktn4fze1tC2tsiyTZH737kV7ajTkUZVs0tc+jEYS7ZLIbe2NUWdenuj2v9emuPV6ePzOjVzwkAQLqZWuTQ1KKOS2ubpqnnt7bpuc2tSuQ6667u83zIG9aDq5v09CaLjsm1y2pIe5qDavCGlGmZrMqWsPLyevwjAAAAAACQcvtaglq6z6sPDvi0vzX+VUkPsxrSqCxDGY179PXjKlVZSsB82MBLPIa4sGlqe2NQbpuh8kxr2s3eMyW9uS+kTZvqVeCy6sKx7rS8F5w3aKreF1KOw6LMAbJ0MdLH1oaAXtzWphVVvqjvXzc1W2eOGlyhZ5bDoqPvHG21GLpycrZmlTj19p72oDnLbtE/dyUWOh/poFc66A1qR1NQK6p9+s0nzbIaUijGN+mPrW/RtCKHStxD994XAIChwTAMfXVcpipzbHpgdZM8PVjSK5qDnrAOejr+vabFkqv/XhPUjRYvV2gDAAAAAAac1kBYT21s1b92e+K+ULvQZdHZozM0o8ihkCkZhjQ8yyZPc6OWLNmjfOcxfdrngSb90j906ZAnpLs+bIjc/3VOqVM3z8yRy5YeQbNpmlpuGaMdO0OSQpIC+qjap5+clK/yNAqa1xz064GPG9XoN5VpM3TJhEydPdqdsv60BMJ6e49X2xoC2tMc1CFvWMUZFn11XKaOL++/L/RCYVPWIX7/gO7UeUN6cmOLlu6LHi5LUr7TogUVQ+uL2GlFDk07YqbVv41x664PGnp0VVg0sQJmSfKGTN3y9iEdW+LQxAK7ThuRoWwHF48AAAavY0uduuukfP1iZWPS6m00gbB0/8dN+qjapxHZNhVlWFWcYVFRhlWFLkvaXfAKAAAAABi6djcFtbLap1pPSPXesD496O/2u+UjnTzMqRum58hh7fxv3Z5Pqxrc0if5Q0yhsKl7P2qMBMyStKLap99+0qR/n52TFvdQe6cqrB2Wkg6vtQRM/WpVk35yYr7sUQ7MnmgJhPXytja1BEzNLXdq+lHLCMayuzmoe1Y0KBBuf94aNPXndS1qCZj6yrjMSLuq1qA+rvGrMMOqY0scPQ5f/SFT7+3zamN9QG0BU1l2Q1kOiybk2zWrxKFPav1RZ6Hsbg7pvlVNOq3Cr0VTsjtdSFDdFtLqGp/2tYRks0jZDotyHRaVuK0qcVvV5A9rU117aJ3nsui0ERkq7WKW5yFPSL/9pEkb6gIqdVt1zdTsDoHhUBUImfKGTPlCpnZ9Nqv2/f0+ebupSl88xp20sT5QFWVY9bOTC/TazjYdaA3JaTX0Ri9mN8froxq/Pqrx6+9b27SwMkPnVbpZqQAAMGgNz7LprpPy9ZtPmrSy+vP7SJW5rarItqrRF9aWhui39UjUsv0+SR0vshuWadWF4zJ1yvChdXEdAAAAACC6Om9Ie1tCGpllVZ6rf1adrG4Ladk+r5bt92pvS88uwjYkXTEpS1+szOBi6gQRMg8Qr+7waFtj5y+JPqjy6fktrbpo/NEL2bZrC4TlC5kKmZLNUNQDOxAy9clBv3Y1BVXitur4MmfUKzVi2Vwf0N92RD+AdzYF9cTGFl09JbvLz+9qCurpTS060BrS1EKHvj45S06roQ11fn1S61eGzdAZIzPUEjB15/v1OuRtT4nf3O3RBWPcunRi9J//SN6gqV+vaowEzEd6dnOrgmFT/zbGrec2t+qVHZ8vnzC10K7vHJenDXV+Pbu5VS3+sI4vd+nCsW65PwuwvEFTK6p9WlHl00FPSJl2i/whU7uag1GXMXyp2962e3uvV2/v9ao4w6IMm0WSKU/QVK0nyg8Rw+JtbVpQ4dIFYzNVfETY7AmG9eMPGnTgsxkw+1tD+tmHDfrxifkak2dPaB+p1uwPa19L+zFSlGFVjsOilkBYDb6watpCOtAa0iFPWC6bobF5NuU7rdrWGNAntX4d8oZktxhyWAx5QqbqPCE1BxJffrI806ozuDewJMllM3TB2M8v3Pj6pCz9clWjVtX4Y3wqOTxBU89vadMbOz36xowcHVfq7PN9AgCQCm67Rd85Lk/bGwPa3hjU6BybxuTaIv8o3lIf0D+2tXYIoacU2jUx365393kT/jvlkfa3hvTg6iZtqQ/oyslZsvXxijjBsKlD3rAafWE1+8Nq+uy/QFj66hEXiwIAAAAA+o5pmh2C2LBp6uMav/65y6NPatv/7WmzSIsm990tJcNm++S+f+7yaGsvLq4enmXVpAK7zh7t1shs4tKe4E+tn3mDpnY3BzU+P/4Ab39LUM9sbuny/ee2tMluMbRgRIY21fm1utavvc1B7W8NqeWooKwkw6JbZ+VqXL5dNW0hPb+lVR9W+dR2RBD6TIZF10zJ1uwugpl6b0g7m4JyWg05rYbWHvLrlR2emMsOvL7To7G5dp1S4ZJpmvKF2k8EbrtFNW0h3bm8Xq2f9fVAq0f7W4M6odylP61tjoS9f9/SFnUW6Qvb2lSQYdFZo9qXvG4LhLV4e/sMykkFdn1hRIYcVkOPrmuOeSXL37e26e9b2zq9vvZQQFe/UavwEbt+aXub/rXbo5OHudQWDOujan+3M1x7o/0LwJ5/CRgypf/Z49U7e706cZhLXzwmQ6Nz7HpkXUskYD6y7QOrm3T3yQVpsxR7VzzBsNYeDOidvR59XJPY0hfJZDWkE4e59LWJmWn/Z5YqDquh/3tsrv6xrU3LD3iVYbPo1OEunTbCpVpPSP/a1qBPd9Uoq6BY+z2GGnxdj/dzR2fowypf5GKTrjQHTP18ZaMuGOvWxeMze7Tig2maavCFVd0WUk1bSNVt7RctjMmz6ZwULrMPAMCRjsm165jczv++GJfffrFkTVtI2xoDGp5l04gsqwyj/WKwVTU+rarx6+Navxpj1N5Y/rnLo9W1Ps2vyFDINLWtIajtjQFl2Cw6bYRLCyvdcka5gDVsmvq01q/1dQFl2Q1NKXSoMtfWoV5vrPPrjV0e7WwKqqo1FPXvelZD+spYN1ebAwAAAECSHGgN6sMqnzJsho4rdSrfadHSfV69ucuj/a0hTS1y6KvjMtUaCOsPa5o7rMArScGw9PDaZhmGkj4pyxs09eDqJq2o7vqWll2xW6SZxU4dX+7UzGIHt1xMAkLmfmSapn73aZNWVPl09ZT4ruJo8of120+aos6+PdJTm1r11KbWbrdX4wnrJx806KrJWXpsQ0vUWba1nrDuWdmoOaUOXTYxS8Oz2oeJP2Tq71tatXh7W4/CvAc/adKDnzSpOMOiWk9YNot09qgM7W4ORgLmw9YdCmjdoUCH12KFuH9e26Iil1Wjc2360fsNqm5rP6ktP+DTy9vbVJhh1ca6QJef7044yq49QVNv7h5YK/GHTGnpPq+W7vPGbHegNaTr3qzVReMzZbcYGp9v1zFHfenX3xq8IW1rDGpvS1B7m0Pa3hjQvpaQUpQrS5JcVkPnjs7QWaMzVNBPy38MZDaLoa+Oy+w026g806YvjbQpZ8cWzZ8yTLm5uVpd69erO9q07lBAGTZDY/PsGptn1+wSh8bk2XXiMJfu+qAhros7XtjappXVPs0pdWpGsUOVufaoX3b7PtuW02rIEwzrX7u8+ueutqizvNqCDkJmAMCAcfiWKkeyWw0dX+7S8eUuhU1TrQFThiFl2S1avPGQntwakGnE9w/u6rawnt3c8d8iTf6Qnt7Uqpe2tWlKkUO5DovCpqksh0Ut/rDWHQqoqu3ILyJalWVv/wLjS8e49fYej17e0f3ftUOm1BY0lWknZAYAAACA3vrggFcPrP48k/rT2s4TIJcf8Gn5ge5D3j+uadbrO9t0XIlT+S6LThzm6lWwe9AT0s9XNmpnU+Kzl2cWO3Tt1OxO/zZG7xAy96O/b22LHHgPr23WrqagFk3puLRcKNw+0/mgJ6yNdX69vdfbKYDtLW/I1O/XNHfbbkW1Xyur6zSpwK4St1Urq32dZkb3xOHAJhiWXonji6N4mJLuWdnY5f56sxTgUBUIS09u/PzLwlynRceVOHTeMe7IhQd9rbotpDd2tunTg37tbu7Z/RT6yinDXbp8Yibhch8wDEOzSpyaVeJU2DSjXtwwPt+ue+cXaNl+r/Y0h7SzKaA9McbInuaQ9jS3r1ZgMaSKLKuyHRZZjPar36rbQmryt5/f7BZ1e2FPTVt6jUcAAHrDYhjKdnxeb08ts6pu0yrVlc/S1mapzhvu8YV9rUFTH1bFd4V5S8DUO3vbV99JRJM/rEw7V6ADAAAAQG+sqvbp1x83JXXF0MPfy0rS81vb9J9zcjU6ykpc3dlSH9AvPmqMuQLmkXIdhlw2QyOybVpQkaHjSh2sgNUHCJn7yYoqX6er+9/c7dG+lqC+dWyuchwWbW0I6FerGtMqEDUlra8LaH0vZgEjPpdPzNS/HeNWjSes+z9u7NW9BPpCoy8cWXL7nNEZmlfevsxxMGxqeJZNlTk2bWsM6s1dHm2q9yvbYdGUQoemFzk0sSC+onHk/Rze2ePRw2ubuw37+lO+06LjSp06bYRrwN2zeqCKNXu+KMOq88d8Pit6a0NAz25ujdz7oythU59dtBA9KI5nzFW3hTvdfwQAgMEkX2368nib8vLyFAybqvOGddAT0o7GoJ7e1CJ/Gv0drclnqpzbMgMAAABAj3160K/7VjX26S0pG33tK+3efkJ+1Hsg72sJamW1T20BU6YkQ1K+y6JtDUG9283qrBZDml7k0EnDnDqu1Ck3FyL3C0LmflDdFtKDq5uivre+LqD/fK9O8ysy9MqOtqjLVx+tJMOiY0udem1nei7VPCHX0HfmFuq5La16PU37KEm3zspRps3Qn9e1RJbXTgaHRcq0W9TgC8ttMzSp0K7zKt2aVGCXKemDAz49t6U1cn/obIeha6dk64RhLklSqduqH5+Yr7WHAvq01q8mf1jN/rC8QVOHJ73nu6yaVmTXzGKn3DZDzYGwDnrCqm4NqdoTktWQjsm1Kd9p1as72rRknzfqkt89ETLbZ6B3Nwu9ui2srQ1BvbitTflOi64Z1/VJ/cMqnxZva1VVW0gT8u0anmXT4m1t/bIUtiFpeJZVmXaL/CFTNotU4LKqMMOiLLtFTquhDJuhUTm2lC8ZjtjG5tl125xc/Wu3V4+sa+7TvxD5Qqaa/KZynYwHAMDgZ7MYkSW3Jxc6NKnQrl+sbNQhb3okzS3pdFUiAAAAAAwgB1qDenZzq/53f+L3OO6J5oCp77xbJ4vR/r38xHyHvnhMhj6q9uuJjS0J5xjZdkMXT8jUvHKXcrjHcr8jZO4HxRkWnVeZoee3tkV9v9YT1nNbur+fsiRl2Q1969hcHZNr14xih17Y2qZN9Z1nGRe6LBqdY1N5plU2i6F/bIu+78NyHYZmlzr1YZWvx8tzj80xVF6/UZdPmapsh0WLJmdpZLZNj65r7pOZDvlOi+rjXBrhSA6LdPWUbJ30Waj7q2KHNtUHtHSfV+sPBRQ2Tc2vyFCDL6w3dkUPUq2GOgRYpe72Ga4nD3epMscmwzAUCreHwkfOdDQknTDMpePLndrbEtIhT0hTCh1yHHV/WIthaHpR+yzgeBRYrSpwWTU+v/Ps2m/MyNGF4zL16o42vbXHG7nvbH+q94X1mw1hnSlnh9eDYVOPb2jpcDHCymq/VlbHnokaTXGGRXPLnCp1W9UaMNUaCCvLYVGes/2/8kyrijKsqvWEtKkuoObPAsKZxU7lOCk+g4VhGDpzVIZG5dj0q1V98+W3zSKVZFjVEggrl7EDABiCjsm1679PKdCrO9v0UbVfu5qCfX5x4PQihyYV2FWUYVGOw6Ic52f/d1g6/V0aAAAAABCbaZp6aXubnt7U2uvJOjaLdPH4TH2x0q1nNrfqxW7yKKl9tcn2pbQ9enN3zyYrVmRZ9d05eSrlPsspQ8jcDyyGoYsnZGlEjk2/Xd3U48D1+DKnrpqcpcKM9gPm8D1LtzYEtGSvVwc9IY3KsWlumTMSdB42scCuu1dEv2dxeaZVPz0pX267RZdPDOvvW1r1r92emEvGFrosWlDRfpN2u9XQ2Fyb8sxWLVlSF5npaRiGTh+ZoQn5dj2wuqlHN2OXJKfV0KnDXXp3X3tAardI107N1qnDXXpqU6te3h7/jNdJBXZ9Y3q2yjI/H/oWw9CkAocmFXQMdIPh9vu0Hrn07uwSh/7vsblq8of1wQGfbBZDkwrsGp5l7bRsrtXS9ZddFsPQyGxb1CUh+kKJ26pFU7J10fhMvbfPq49r/Fp3yB8ZizOLHfrW7Fy9vKNNf9sc3wUPifKGpPesE3RawNT+Q35trAvogypfj8aFxZDK3FaNybNpTK5d4/Ltcc8yLs+0qTyTU99gNz7frl8uKNTSfV6tqvZp7SG/fD1csGBSgV0nDnOqIsumUrdV+S4LM9oBAENelsOii8dn6eLxUlsgrJ2fBc0jsm3KthtaVePX4u1t2toQUDDc/qWDJAWP+DfG2DybzhiZoRyHRatqfHpnr7fD+5K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" ] }, "metadata": { "filenames": { "image/png": "/Users/folgert/projects/hda/_build/jupyter_execute/working-with-data/notebook_145_0.png" } }, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "\n", "fig, axes = plt.subplots(\n", " nrows=2, ncols=4, sharey=True, figsize=(12, 6))\n", "\n", "letters = [\"a\", \"d\", \"e\", \"i\", \"n\", \"o\", \"s\", \"t\"]\n", "axes = boys_fd[letters].plot(\n", " subplots=True, ax=axes, title=letters, color='C0', grid=True, legend=False)\n", "\n", "# The X-axis of each subplot is labeled with 'year'.\n", "# We remove those and add one main X-axis label\n", "for ax in axes.flatten():\n", " ax.xaxis.label.set_visible(False)\n", "fig.text(0.5, 0.04, \"year\", ha=\"center\", va=\"center\", fontsize=\"x-large\")\n", "# Reserve some additional height for space between subplots\n", "fig.subplots_adjust(hspace=0.5);" ] }, { "cell_type": "markdown", "id": "c2a1ab69", "metadata": {}, "source": [ "\n", "\n", "Before interpreting these plots, let us first explain some of the parameters used to\n", "create the figure. Using `pyplot.subplots()`, we create a figure and a set of subplots. The subplots are spread over two rows (`nrows=2`) and four columns (`ncols=4`). The parameter `sharey=True` indicates that all subplots should share the Y-axis (i.e., have the same limits) and sets some Y-axis labels to invisible. The subplots are populated by calling the `plot()` method of `boys_fd`. To do so, the parameter `subplots` need to be set to `True`, to ensure that each variable (i.e. column) is visualized in a separate plot. By specifying the `ax` parameter, we tell Pandas to draw the different graphs in the constructed subplots (i.e., `ax=axes`). Finally, each subplot obtains its own title, when a list of titles is provided as argument to the `title` parameter. The remaining lines of code help to clean up the graph.\n", "\n", "Several observations can be made from the figure above. First and foremost, the time series visualization of the usage frequency of the name-final letter *n* confirms the suggested explosive rise in the popularity of boys' names ending with the letter *n*. Over the years, the numbers gradually increase before they suddenly take off in the 1990s and 2000s. A second observation to be made is the steady decrease of the name-final letter *e* as well as the letter *d*. Finally, we note a relatively sudden disposition for the letter *i* in the late 1990s.\n", "\n", "(sec-working-with-data-unisex-names)=\n", "### Unisex names in the United States\n", "\n", "In this last section, we will map out the top unisex names in the dataset (for a\n", "discussion of the evolution of unisex names, see {cite:t}`barry:1982`). Some names, such as Frank or Mike, are and have been predominantly given to boys, while others are given to both men and women. The goal of this section is to create a visualization of the top *n* unisex names, which depicts how the usage ratio between boys and girls changes over time for each of these names. Creating this visualization requires some relatively advanced use of the Pandas library. But before we delve into the technical details, let us first settle down on what is meant by \"the *n* most unisex names\". Arguably, the unisex degree of a name can be defined in terms of its usage ratio between boys and girls. A name with a 50-50 split appears to be more unisex than a name with a 5-95 split. Furthermore, names that retain a 50-50 split over the years are more ambiguous as to whether they refer to boys or girls than names with strong fluctuations in their usage ratio.\n", "\n", "The first step is to determine which names alternate between boys and girls at all. The method `DataFrame.duplicated()` can be employed to filter duplicate rows of a `DataFrame` object. By default, the method searches for exact row duplicates, i.e., two rows are considered to be duplicates if they have all values in common. By supplying a value to the argument `subset`, we instruct the method to only consider one or a subset of columns for identifying duplicate rows. As such, by supplying the value `'name'` to `subset`, we can retrieve the rows that occur multiple times in a particular year. Since names are only listed twice if they occur with both sexes, this method provides us with a list of all unisex names in a particular year. Consider the following lines of code:" ] }, { "cell_type": "code", "execution_count": 76, "id": "0ca98c2f", "metadata": {}, "outputs": [ { "data": { "text/org": [ "| year | John | James | William | Robert | George |\n", "|--------+--------+---------+-----------+----------+----------|\n", "| 1910 | J | o | h | n | |\n", "| 1910 | J | a | m | e | s |\n", "| 1910 | W | i | l | l | i |\n", "| 1910 | R | o | b | e | r |\n", "| 1910 | G | e | o | r | g |" ], "text/plain": [ "year\n", "1910 John\n", "1910 James\n", "1910 William\n", "1910 Robert\n", "1910 George\n", "Name: name, dtype: object" ] }, "execution_count": 76, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d = df.loc[1910]\n", "duplicates = d[d.duplicated(subset='name')]['name']\n", "duplicates.head()" ] }, { "cell_type": "markdown", "id": "db25ddd3", "metadata": {}, "source": [ "Having a way to filter unisex names, we can move on to the next step, which is to compute the usage ratio of the retrieved unisex names between boys and girls. To compute this ratio, we need to retrieve the frequency of use for each name with both sexes. By default, `DataFrame.duplicated()` marks all duplicates as `True` except for the first occurrence. This is expected behavior if we want to construct a list of duplicate items. For our purposes, however, we require *all* duplicates, because we need the usage frequency of both sexes to compute the usage ratio. Fortunately, the method `DataFrame.duplicated()` provides the argument `keep`, which, when set to `False`, ensures that all duplicates are marked `True`:" ] }, { "cell_type": "code", "execution_count": 77, "id": "f044d82b", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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namesexfrequency
year
1910AbbieM8
1910AbbieF79
1910AddieM8
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" ], "text/org": [ "| year | name | sex | frequency |\n", "|--------+--------+-------+-------------|\n", "| 1910 | Abbie | M | 8 |\n", "| 1910 | Abbie | F | 79 |\n", "| 1910 | Addie | M | 8 |\n", "| 1910 | Addie | F | 495 |\n", "| 1910 | Adell | M | 6 |" ], "text/plain": [ " name sex frequency\n", "year \n", "1910 Abbie M 8\n", "1910 Abbie F 79\n", "1910 Addie M 8\n", "1910 Addie F 495\n", "1910 Adell M 6" ] }, "execution_count": 77, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d = d.loc[d.duplicated(subset='name', keep=False)]\n", "d.sort_values('name').head()" ] }, { "cell_type": "markdown", "id": "e7e95313", "metadata": {}, "source": [ "We now have all the necessary data to compute the usage ratio of each unisex name. However, it is not straightforward to compute this number given the way the table is currently structured. This problem can be resolved by pivoting the table using the previously discussed `DataFrame.pivot_table()` method:" ] }, { "cell_type": "code", "execution_count": 78, "id": "baff559d", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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sexFM
name
Abbie798
Addie4958
Adell866
Afton146
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" ], "text/org": [ "| name | F | M |\n", "|--------+------+-----|\n", "| Abbie | 79 | 8 |\n", "| Addie | 495 | 8 |\n", "| Adell | 86 | 6 |\n", "| Afton | 14 | 6 |\n", "| Agnes | 2163 | 13 |" ], "text/plain": [ "sex F M\n", "name \n", "Abbie 79 8\n", "Addie 495 8\n", "Adell 86 6\n", "Afton 14 6\n", "Agnes 2163 13" ] }, "execution_count": 78, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d = d.pivot_table(values='frequency', index='name', columns='sex')\n", "d.head()" ] }, { "cell_type": "markdown", "id": "52999bee", "metadata": {}, "source": [ "Computing the final usage ratios, then, is conveniently done using vectorized operations:" ] }, { "cell_type": "code", "execution_count": 79, "id": "8a45bd51", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "name\n", "Abbie 0.908046\n", "Addie 0.984095\n", "Adell 0.934783\n", "Afton 0.700000\n", "Agnes 0.994026\n", "dtype: float64" ] }, "execution_count": 79, "metadata": {}, "output_type": "execute_result" } ], "source": [ "(d['F'] / (d['F'] + d['M'])).head()" ] }, { "cell_type": "markdown", "id": "d3b00a4f", "metadata": {}, "source": [ "We need to repeat this procedure to compute the unisex usage ratios for each year in the collection. This can be achieved by wrapping the necessary steps into a function, and, subsequently, employ the \"groupby-then-apply\" combination. Consider the following code block:" ] }, { "cell_type": "code", "execution_count": 80, "id": "3f1b5c8d", "metadata": {}, "outputs": [], "source": [ "def usage_ratio(df):\n", " \"\"\"Compute the usage ratio for unisex names.\"\"\"\n", " df = df.loc[df.duplicated(subset='name', keep=False)]\n", " df = df.pivot_table(values='frequency', index='name', columns='sex')\n", " return df['F'] / (df['F'] + df['M'])" ] }, { "cell_type": "code", "execution_count": 81, "id": "05c94c71", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "year name \n", "1880 Addie 0.971631\n", " Allie 0.772059\n", " Alma 0.951890\n", " Alpha 0.812500\n", " Alva 0.195402\n", "dtype: float64" ] }, "execution_count": 81, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d = df.groupby(level=0).apply(usage_ratio)\n", "d.head()" ] }, { "cell_type": "markdown", "id": "99e065dd", "metadata": {}, "source": [ "We can now move on to creating a visualization of the unisex ratios over time. The goal is to create a simple line graph for each name, which reflects the usage ratio between boys and girls throughout the twentieth century. Like before, creating this visualization will be much easier if we convert the current `Series` object into a `DataFrame` with columns identifying all names, and the index representing the years. The `Series.unstack()` method can be used to reshape the series into the desired format:" ] }, { "cell_type": "code", "execution_count": 82, "id": "7886c5b8", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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nameAadenAadiAadynAalijahAaliyahAamariAamirAarenAarianAarin...ZurielZyahZyairZyaireZyanZyianZyienZyionZyonZyree
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2011NaNNaNNaN0.318182NaN0.428571NaNNaNNaNNaN...0.131579NaN0.1875000.1542060.2207790.571429NaN0.1531530.155080NaN
2012NaN0.081967NaN0.3333330.998729NaNNaNNaN0.3333330.187500...0.244186NaN0.1311480.1770830.1612900.470588NaN0.1447370.257426NaN
2013NaN0.078947NaN0.5000000.9977050.3333330.042553NaN0.208333NaN...0.220930NaN0.1388890.1682690.260274NaNNaN0.2253520.1472390.5
2014NaNNaNNaN0.3636360.9989750.400000NaNNaN0.3500000.166667...0.313869NaN0.1081080.1237110.268657NaNNaN0.1978020.209790NaN
2015NaNNaNNaN0.4137930.9989670.562500NaNNaN0.380952NaN...0.2465750.750.0985920.1079810.156250NaNNaN0.0945950.164773NaN
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Brighten | Brighton | Brilee | Briley | Brilyn | Brilynn | Brin | Brina | Brinkley | Brinley | Brinn | Brinson | Brion | Brionne | Bristen | Bristol | Brit | Britain | British | Britney | Briton | Britt | Brittain | Brittan | Brittani | Brittany | Britten | Brittian | Brittin | Brittney | Brittni | Britton | Brixton | Brock | Brodee | Brodi | Brodie | Brody | Brogan | Bronte | Bronx | Brook | Brooke | Brookes | Brookie | Brooklin | Brooklyn | Brooklynn | Brooks | Brown | Brownie | Bruce | Bryan | Bryann | Bryant | Bryar | Bryce | Brycen | Bryden | Bryer | Brylan | Brylee | Brylen | Bryley | Brylie | Brylin | Brylyn | Bryn | Brynlee | Brynn | Bryon | Bryse | Brysen | Bryson | Brystol | Bryten | Bryton | Buddie | Buddy | Buff | Buffy | Buford | Bunnie | Bunny | Burdell | Burdette | Burke | Burkley | Burl | Burley | Burlie | Burnell | Burnett | Burnette | Burney | Burnice | Burnie | Burnis | Burton | Buster | Byrd | Byrl | Byrle | Byron | Cacey | Cache | Cadance | Cade | Caden | Cadence | Cadin | Cady | Cadyn | Caeden | Caedyn | Cael | Caelan | Caelen | Caelin | Caelyn | Caesare | Cagney | Cai | Caiden | Caidence | Caidyn | Cailan | Cailean | Cailen | Cailin | Cairo | Caisyn | Caitlin | Caitlyn | Cal | Calan | Calder | Cale | Caleb | Calen | Caley | Cali | Calin | Callaghan | Callahan | Callan | Callaway | Callen | Callie | Callin | Calloway | Callyn | Calvary | Calvin | Calyn | Calyx | Cam | Camara | Camaren | Camari | Camarie | Camaro | Camaron | Camauri | Cambell | Camber | Cambridge | Cambryn | Camden | Camdyn | Camdynn | Camen | Cameo | Cameran | Cameren | Camerin | Cameron | Cameryn | Camil | Camila | Camile | Camilla | Camille | Cammie | Campbell | Camran | Camren | Camrin | Camron | Camry | Camryn | Camrynn | Canaan | Candace | Candice | Candler | Candy | Cannon | Canon | Cantrell | Canyon | Capri | Caprice | Cara | Cardell | Carden | Cardin | Carel | Carey | Cari | Carina | Carissa | Carl | Carla | Carlas | Carle | Carlee | Carlen | Carley | Carli | Carlie | 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Claudine | Claudio | Claudis | Claudy | Clay | Clayton | Clea | Cleao | Cleaster | Cleatis | Cleatus | Cledith | Clem | Clemence | Clement | Clemmie | Cleo | Cleofas | Cleon | Cleone | Cleophas | Cleophus | Cleotha | Clester | Cletis | Cletus | Cleve | Cleveland | Clevie | Clide | Clifford | Clifton | Climmie | Clinnie | Clint | Clinton | Clio | Cloe | Clois | Clover | Clovis | Cloy | Cloyce | Clyde | Clydell | Clydie | Cobi | Cobie | Coby | Coda | Codee | Codey | Codi | Codie | Cody | Cohen | Coi | Cola | Colbe | Colbee | Colbey | Colbi | Colbie | Colby | Cole | Coleman | Coley | Colie | Colin | Colleen | Collen | Collie | Collier | Collin | Collins | Collis | Collyn | Colon | Colt | Colton | Columbia | Colyn | Concepcion | Concetta | Conda | Conlee | Conley | Connar | Connelly | Conner | Connie | Connor | Conny | Conor | Conrad | Constance | Constant | Constantine | Consuelo | Cooper | Copeland | Copper | Cora | Coral | Corban | Corbin | Corby | Corbyn | Corde | Cordell | Cordero | 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Dai | Daijon | Dail | Dailen | Dailey | Dailin | Daily | Dailyn | Dailynn | Daina | Daine | Daire | Daisy | Dajah | Dajon | Dajour | Dakarai | Dakari | Dakoda | Dakodah | Dakota | Dakotah | Dale | Dalen | Daley | Dali | Dalia | Dalin | Dalis | Dallas | Dallie | Dallis | Dallyn | Dalton | Daly | Dalyn | Dalynn | Damani | Damar | Damarea | Damari | Damaria | Damarie | Damaris | Damian | Damien | Damilola | Damion | Damir | Damon | Damond | Damoni | Damonie | Dan | Dana | Danae | Danari | Dandra | Dandre | Dandrea | Dane | Daneil | Danel | Danell | Danelle | Dang | Dangel | Dani | Dania | Danial | Danie | Daniel | Daniela | Daniele | Daniell | Daniella | Danielle | Danila | Danile | Danis | Dann | Danna | Danne | Dannel | Dannell | Danner | Danni | Dannie | Danniel | Dannon | Danny | Dannye | Danon | Dante | Danuel | Dany | Danya | Danyal | Danyale | Danye | Danyel | Danyell | Danyelle | Dao | Daphne | Daquan | Dara | Daran | Darby | Darcel | Darcell | Darcey | Darcy | Dare | Darean | Darein | Darel | Darell | Darelle | Daren | Dareon | Dari | Daria | Darian | Darice | Dariel | Darien | Darin | Darion | Daris | Darius | Dariyon | Darl | Darla | Darlene | Darlin | Darly | Darlyn | Darnel | Darnell | Darnelle | Daron | Darragh | Darrein | Darrel | Darrell | Darrelle | Darren | Darrian | Darrick | Darrie | Darriel | Darrien | Darrin | Darrion | Darris | Darron | Darryan | Darryl | Darryn | Darshan | Darwin | Dary | Daryan | Daryen | Daryl | Daryle | Daryll | Daryn | Dasani | Dash | Dashae | Dashan | Dashaun | Dashawn | Dashay | Dashiell | Dashon | Dasmine | Datril | Daun | Daune | Davan | Dave | Davelle | Daven | Daveon | Davey | Davi | Davian | David | Davida | Davie | Davin | Davine | Davion | Davione | Davionne | Davis | Davon | Davone | Davonne | Davonte | Davy | Davyn | Dawan | Dawn | Dawsen | Dawson | Dawsyn | Dax | Day | Dayan | Daylan | Dayle | Daylee | Daylen | Daylin | Daylon | Daylyn | Daylynn | Dayna | Dayne | Dayon | Dayshawn | Dayton | Daytona | De | Dea | Dean | Deana | Deandra | Deandre | Deandrea | Deane | Deangelo | Deanie | Deanna | Deanne | Deante | Deari | Deaundra | Deaven | Deaveon | Deavion | Deavon | Debbie | Debora | Deborah | Debra | Decker | Decklyn | Declan | Declyn | Decota | Dedric | Dedrick | Dee | Deegan | Deen | Deena | Deidre | Deion | Deivion | Deja | Dejah | Dejan | Dejon | Dejour | Dejuan | Deklyn | Dekoda | Dekota | Del | Dela | Delaine | Delancey | Delane | Delaney | Delani | Delano | Delayne | Delbert | Deleon | Delia | Delijah | Dell | Della | Dellie | Delma | Delmar | Delmer | Delois | Delon | Delone | Delorean | Delores | Delorian | Deloris | Delphine | Delta | Delvin | Delynn | Demani | Demarcus | Demaree | Demari | Demaria | Demarie | Demario | Demaris | Demetra | Demetre | Demetres | Demetress | Demetri | Demetria | Demetrias | Demetric | Demetrice | Demetrie | Demetrious | Demetris | Demetrius | Demetruis | Demetrus | Demian | Demilade | Demitri | Demitrius | Demond | Demoni | Demonie | Dempsey | Dena | Denali | Denari | Dene | Deneen | Denell | Denelle | Deni | Denim | Denis | Denise | Deniss | Deniz | Denni | Dennie | Dennis | Dennise | Denny | Dennys | Denver | Denym | Denys | Denzel | Denzil | Deon | Deondra | Deondre | Deondrea | Deone | Deonna | Deonne | Deonta | Deonte | Deontra | Deovion | Dequan | Derek | Dereon | Derian | Derick | Derin | Derion | Deron | Derrell | Derrian | Derrick | Derrie | Derrion | Derry | Derryl | Deryl | Deryn | Desani | Desean | Desha | Deshae | Deshan | Deshane | Deshannon | Deshante | Deshaun | Deshawn | Deshay | Deshea | Deshon | Deshone | Deshun | Desi | Desire | Desiree | Desmond | Desmone | Dessie | Destin | Destine | Destiny | Destry | Destyn | Detrick | Deundra | Deva | Devan | Devann | Devante | Deven | Devereaux | Devery | Devian | Devin | Devine | Devinn | Devion | Devlin | Devlyn | Devon | Devone | Devonn | Devonne | Devonta | Devonte | Devry | Devyn | Devyne | Devynn | Dewan | Dewayne | Dewey | Dewie | Dexter | Deylin | Deyon | Deyonce | Dezi | Deziah | Dhani | Di | Dia | Diabolique | Diallo | Diamante | Diamon | Diamond | Diamonte | Dian | Diana | Diandre | Diane | Dianna | Dianne | Diante | Diarra | Diavian | Diavion | Dick | Dickie | Diego | Diem | Diep | Dietrich | Dijon | Dilan | Dillan | Dillen | Dillon | Dillyn | Dilpreet | Dilyn | Dilynn | Dima | Dimitri | Dimon | Dina | Dion | Diondra | Dione | Dioni | Dionna | Dionne | Dionte | Dior | Disney | Divine | Divya | Dixie | Diya | Dj | Djimon | Djuan | Dmani | Doan | Dois | Doll | Dollie | Dolly | Dolores | Domani | Domanic | Domanique | Domenique | Domineque | Dominga | Domingo | Domingue | Dominic | Dominick | Dominie | Dominigue | Dominik | Dominiq | Dominique | Domino | Dominque | Domique | Domnique | Domonic | Domonique | Don | Dona | Donal | Donald | Donavan | Dondi | Dondra | Dondrea | Donel | Donell | Donelle | Doni | Donie | Doniel | Donielle | Doninique | Donique | Donis | Donn | Donna | Donne | Donnel | Donnell | Donnelle | Donni | Donnie | Donnis | Donny | Donovan | Donsha | Donshay | Donta | Dontae | Dontavia | Donte | Dontrell | Donya | Donyae | Donyale | Donye | Donyea | Donyel | Donyell | Donyelle | Donzell | Dora | Doral | Doran | Dorcie | Dore | Dorean | Doreen | Doren | Dores | Dorey | Dorian | Dorie | Dorien | Dorin | Doris | Dorn | Dorothea | Dorothy | Dorrian | Dorris | Dorsey | Dorsie | Dorthy | Dory | Dosie | Dossie | Dot | Doua | Doug | Douglas | Douglass | Dove | Dovie | Doy | Doyce | Doye | Doyle | Doyne | Drake | Draven | Dream | Dresden | Drew | Drexel | Dru | Drue | Duane | Duanne | Dublin | Dudley | Duffy | Dulce | Duncan | Dung | Dushawn | Dusti | Dustie | Dustin | Dustine | Dusty | Dustyn | Dwan | Dwann | Dwayne | Dwight | Dyami | Dyamond | Dyan | Dyani | Dylan | Dylann | Dylen | Dylin | Dyllan | Dylyn | Dymon | Dymond | Dyon | Dyson | Eaden | Earl | Earle | Earlee | Earlene | Earlie | Earline | Early | Earnell | Earnest | Earnestine | Earnie | Earon | Earsel | Earsie | Eartha | Easter | Eastin | Easton | Eastyn | Eathel | Eather | Eavan | Ebba | Ebbie | Eben | Ebenezer | Eboni | Ebony | Echo | Ecko | Ed | Edan | Edd | Edden | Eddie | Eddis | Eddy | Edel | Edell | Eden | Edgar | Edi | Edie | Edin | Edison | Edith | Edlin | Edmond | Edmund | Edna | Eduardo | Edward | Edwin | Edy | Edyn | Effie | Efrain | Egan | Egypt | Eh | Ehren | Ehrin | Eileen | Eisa | Eisley | Ekam | Ekamjot | Ekene | Ekin | Eknoor | El | Elaijah | Elaine | Elan | Elba | Elbert | Elbie | Elda | Eldean | Elden | Elder | Eldon | Eldred | Eleanor | Eleazar | Elena | Elester | Elga | Elgene | Elgie | Elgin | Elham | Eli | Elia | Eliah | Elian | Eliana | Elias | Elie | Elijah | Elim | Elin | Eliot | Elis | Elisa | Elisabeth | Elisah | Elise | Elisha | Elishah | Elison | Eliya | Eliyah | Eliza | Elizabeth | Elizah | Elize | Ell | Ella | Ellen | Ellery | Elli | Ellie | Ellington | Elliot | Elliott | Elliotte | Ellis | Ellison | Elly | Elma | Elmar | Elmer | Elmo | Elmore | Elner | Elnora | Elois | Eloise | Elon | Elouise | Elree | Elroy | Elsa | Elsie | Elton | Elva | Elver | Elvia | Elvie | Elvin | Elvira | Elvis | Elvy | Elvyn | Elwin | Elwood | Elwyn | Ely | Elya | Elyah | Elyn | Elyse | Elysha | Elysia | Elza | Elzia | Elzie | Emaan | Eman | Emani | Emanuel | Emanuelle | Emari | Emauri | Ember | Embry | Emeka | Emely | Emer | Emerald | Emeri | Emerie | Emeril | Emersen | Emerson | Emersyn | Emery | Emiko | Emil | Emile | Emilee | Emilie | Emilio | Emily | Emjay | Emma | Emmanuel | Emmanuelle | Emmaus | Emmer | Emmerson | Emmery | Emmett | Emon | Emoni | Emonie | Emori | Emory | Emrey | Emri | Emry | Emrys | Encarnacion | Endy | England | English | Enid | Eniola | Enis | Ennis | Enrique | Ensley | Envy | Epic | Eppie | Era | Eran | Erbie | Ercel | Ercell | Ercil | Eren | Eri | Erian | Eric | Erica | Erice | Erich | Erick | Ericka | Erie | Eriel | Erik | Erika | Erin | Erinn | Erioluwa | Erion | Eris | Erlin | Erma | Ermal | Ermel | Ermine | Ermon | Erna | Ernest | Ernestine | Ernesto | Ernie | Eron | Errin | Erron | Ersel | Ersie | Ervie | Ervin | Erwin | Eryn | Erza | Esa | Esley | Esli | Esmeralda | Espen | Esperanza | Espn | Essa | Essie | Esta | Esteban | Estee | Estefani | Estefania | Estefany | Estel | Estell | Estella | Estelle | Ester | Estes | Estevan | Esther | Estill | Estle | Ethan | Ethel | Ether | Etienne | Etta | Eudell | Eugene | Eugenia | Eula | Eulice | Eun | Eunice | Eunique | Eura | Eureka | Eva | Evan | Evann | Evans | Evelin | Evelyn | Ever | Everest | Everett | Everette | Everly | Evian | Evie | Evin | Evon | Evone | Evren | Evyn | Evynn | Ewell | Excel | Excell | Exie | Exodus | Eyden | Eyram | Ezariah | Ezekiel | Ezell | Ezelle | Eziah | Ezra | Ezrah | Ezri | Ezzie | Fabian | Fabiola | Fable | Faelan | Faith | Falcon | Fallon | Falon | Fannie | Farah | Faris | Farley | Faron | Farrah | Farrel | Farrell | Farren | Farris | Farron | Fate | Fatima | Favor | Favour | Fay | Faye | Fayette | Fayne | Federico | Felice | Felicia | Felicity | Felipe | Felis | Felix | Feliz | Felton | Female | Femi | Fenix | Fern | Fernanda | Fernando | Fernie | Ferol | Ferran | Ferrel | Ferrell | Ferris | Ferry | Fidelis | Findlay | Findley | Finesse | Finis | Finlay | Finlee | Finley | Finn | Finnegan | Finnlee | Finnley | Finnly | Fiona | Fisher | Fiyinfoluwa | Flay | Fletcher | Flor | Flora | Florence | Florentine | Florian | Florida | Florine | Floris | Flossie | Floy | Floyce | Floyd | Flynn | Fong | Fonnie | Fontaine | Ford | Forest | Forever | Forrest | Fortune | Foster | Fox | Foy | Foye | Fran | France | Frances | Francesc | Francesca | Francie | Francies | Francine | Francis | Francisca | Francisco | Francois | Frank | Franki | Frankie | Franklin | Franklyn | Franky | Fred | Freda | Freddie | Freddy | Frederick | Fredie | Fredrick | Free | Freedom | Freeman | French | Frenchie | Future | Fynn | Fynnley | Gabby | Gaberial | Gable | Gabreal | Gabreil | Gabrial | Gabrieal | Gabriel | Gabriela | Gabriele | Gabriell | Gabriella | Gabrielle | Gabriyel | Gabryel | Gaby | Gael | Gaelen | Gagandeep | Gage | Gaige | Gail | Gaile | Gailen | Gal | Galaxy | Gale | Galen | Galyn | Garcia | Gardner | Garen | Gareth | Gari | Garland | Garnell | Garner | Garnet | Garnett | Garnie | Garren | Garret | Garrett | Garrie | Garrison | Garry | Gary | Garyn | Gates | Gatlin | Gatsby | Gavin | Gavriel | Gavyn | Gay | Gayl | Gayla | Gaylan | Gayle | Gaylen | Gaylin | Gaylon | Gaylord | Gaynell | Gaynor | Gean | Gearl | Gearld | Gearldine | Gemini | Gene | Genesis | Geneva | Genevieve | Genie | Gennie | Gentry | Geo | Geoffrey | Geordan | George | Georgi | Georgia | Georgie | Ger | Geral | Gerald | Geraldine | Gerard | Gerardo | Geri | Germain | Germaine | German | Germany | Gerren | Gerri | Gerrie | Gerry | Gertis | Gertrude | Gervase | Geryl | Getsemani | Gia | Giani | Gianna | Gianni | Gianny | Giavanni | Giavonni | Gibson | Gideon | Gilbert | Gilberto | Gill | Gillian | Gillie | Gina | Ginger | Gino | Gio | Gionni | Giovani | Giovanni | Giovannie | Giovanny | Giselle | Gladis | Glady | Gladys | Glee | Glen | Glenda | Glendal | Glendale | Glendel | Glendell | Glendon | Glenis | Glenn | Glenna | Glennie | Glennis | Gloria | Glory | Glover | Glyn | Glyndon | Glynis | Glynn | Gola | Golden | Goldie | Gordie | Gordon | Grace | Gracen | Graceson | Gracie | Graciela | Gracin | Gracyn | Gradie | Grady | Grae | Graecyn | Graeson | Graesyn | Graham | Graison | Graisyn | Grant | Grasyn | Gray | Graycen | Graycin | Graylin | Graysen | Grayson | Greeley | Greer | Greg | Gregg | Gregoria | Gregorio | Gregory | Greta | Gretchen | Grey | Greysen | Greyson | Grier | Griffin | Griffyn | Grisel | Griselda | Grover | Guadalupe | Guadlupe | Guillermo | Gunnar | Gunner | Gurjot | Gurkirat | Gurman | Gurnoor | Gurpreet | Gursimran | Gus | Gussie | Gustavo | Gustie | Guthrie | Guy | Gwen | Gwendolyn | Gwin | Gwyn | Gwynn | Gwynne | Ha | Hadden | Haddy | Haden | Hadlee | Hadley | Hady | Hadyn | Haeden | Haedyn | Haeven | Hagan | Hagen | Hai | Haidan | Haiden | Haidin | Haidyn | Haile | Hailee | Hailey | Haiven | Hal | Hale | Haleigh | Halen | Haley | Hali | Halle | Halley | Hallie | Halo | Halsey | Halston | Hamdi | Hampton | Han | Hana | Hanan | Hananiah | Hanh | Hani | Haniel | Hanley | Hanna | Hannah | Hannan | Hans | Hansel | Hao | Happy | Harbor | Harbour | Hardeep | Hardy | Hargun | Harjas | Harjot | Harkirat | Harlan | Harlee | Harlem | Harlen | Harley | Harlie | Harlin | Harlo | Harlow | Harly | Harlym | Harlyn | Harman | Harmon | Harmony | Harnoor | Harold | Harper | Harpreet | Harrie | Harriet | Harriett | Harris | Harrison | Harry | Harsha | Harsimran | Hart | Hartley | Haru | Harumi | Harvest | Harvey | Harvie | Harvin | Hasani | Hasel | Haskell | Hassan | Hassie | Hattie | Hau | Haven | Havyn | Haydan | Hayden | Haydin | Haydn | Haydon | Haydyn | Hayes | Haygen | Haylee | Haylen | Hayley | Hayven | Haze | Hazel | Hazell | Hazen | Haziel | Hazle | Heath | Heathe | Heather | Heaven | Hebron | Hector | Heidi | Helen | Heli | Helon | Hemi | Hendrix | Henley | Hennessy | Henri | Henrietta | Henry | Hensley | Herbert | Heriberto | Herman | Hermie | Hermon | Hero | Heron | Hershel | Hessie | Hester | Hezekiah | Hiawatha | Hien | Hieu | Hikaru | Hilary | Hilda | Hildred | Hildreth | Hillary | Hillery | Hillis | Hilton | Hinata | Hiyab | Hoa | Hoai | Hoang | Holden | Holdyn | Hollace | Holland | Holley | Holli | Hollie | Hollis | Holly | Holy | Homer | Honest | Hong | Honor | Honore | Honour | Hoover | Hope | Horace | Hosea | Hosie | Houa | Houston | Howard | Hoyt | Hser | Hubert | Hudsen | Hudson | Hudsyn | Hue | Huey | Hugh | Hugo | Humberto | Hunter | Huntlee | Huntley | Huntyr | Hurley | Hutton | Huxley | Hyatt | Hyun | Ian | Iasiah | Ibukunoluwa | Ida | Idell | Idris | Iesha | Ifeoluwa | Ignacio | Ikea | Ikram | Ila | Ilan | Ilham | Ilhan | Ilia | Ilian | Iliya | Illya | Ilo | Ilya | Imaan | Iman | Imani | Imari | Imogene | Imri | Ina | Indi | India | Indiana | Indie | Indigo | Indra | Indy | Ines | Inez | Infant | Infantof | Ingrid | Iniki | Inioluwa | Io | Iona | Ira | Iram | Iran | Irby | Ireland | Iremide | Irene | Ireoluwa | Iridian | Irie | Iris | Irish | Irma | Irvin | Irvine | Irving | Irwin | Isa | Isaac | Isabel | Isabell | Isabella | Isabelle | Isadore | Isah | Isai | Isaiah | Isamar | Isiah | Isis | Islam | Island | Isley | Ismael | Israel | Isreal | Issa | Issac | Itamar | Itzae | Itzel | Iva | Ival | Ivan | Iver | Iverson | Ivery | Ivette | Ivey | Ivie | Ivis | Ivo | Ivon | Ivonne | Ivor | Ivory | Ivry | Ivy | Iyanuoluwa | Iyari | Izaiah | Izamar | Izel | Izell | Izzy | Ja | Jaalyn | Jaaziah | Jaaziel | Jabre | Jabree | Jabri | Jacari | Jace | Jacee | Jacey | Jachai | Jacie | Jaciel | Jacinth | Jack | Jackey | Jackie | Jacklyn | Jackson | Jacksyn | Jacky | Jaclyn | Jacob | Jacobi | Jacobie | Jacoby | Jacolby | Jacori | Jacquay | Jacque | Jacquel | Jacqueline | Jacquell | Jacquelyn | Jacques | Jacquese | Jacqui | Jacquis | Jacquise | Jacy | Jacyn | Jada | Jadakiss | Jadan | Jadden | Jade | Jaden | Jadence | Jadeyn | Jadiah | Jadian | Jadie | Jadien | Jadin | Jadis | Jadon | Jadore | Jadrian | Jadrien | Jady | Jadyn | Jadyne | Jadynn | Jae | Jaedan | Jaeden | Jaedin | Jaedon | Jaedyn | Jaedynn | Jael | Jaelan | Jaelen | Jaelin | Jaelyn | Jaelynn | Jagger | Jahan | Jahari | Jahdai | Jaheim | Jahlani | Jahnai | Jahnari | Jahni | Jahzeel | Jahziah | Jahziel | Jai | Jaice | Jaid | Jaidan | Jaiden | Jaidence | Jaidin | Jaidon | Jaidyn | Jaidynn | Jaie | Jaiel | Jailan | Jailen | Jailin | Jailyn | Jailynn | Jaime | Jaimee | Jaimeson | Jaimey | Jaimie | Jaimy | Jair | Jaire | Jaise | Jaiyden | Jajuan | Jakai | Jakari | Jakaylin | Jake | Jakel | Jakell | Jakhai | Jaki | Jakiah | Jakie | Jakkia | Jakobi | Jakori | Jalah | Jalan | Jalani | Jaleel | Jaleen | Jaleesa | Jalen | Jalene | Jaliah | Jalien | Jalil | Jalin | Jalisa | Jalon | Jaloni | Jalyn | Jalyne | Jalynn | Jalyric | Jama | Jamaal | Jamai | Jamaica | Jamaine | Jamal | Jamani | Jamar | Jamara | Jamarea | Jamaree | Jamari | Jamaria | Jamarian | Jamarie | Jamaris | Jamauri | Jame | Jamee | Jameel | Jamel | Jamell | Jamelle | Jamere | James | Jamesen | Jameson | Jamesyn | Jamey | Jami | Jamia | Jamiah | Jamie | Jamiel | Jamien | Jamieson | Jamil | Jamilah | Jamile | Jamille | Jamin | Jamine | Jamir | Jamisen | Jamison | Jamisyn | Jammie | Jammy | Jamon | Jamoni | Jamy | Jamye | Jan | Jana | Janae | Janai | Janan | Janari | Jancy | Jane | Janee | Janel | Janell | Janelle | Janet | Janette | Jani | Janice | Janie | Janiel | Janine | Janis | Janiya | Jann | Jannie | Jansen | Jante | Jantzen | Jaqua | Jaqualyn | Jaquan | Jaquay | Jaquel | Jaqueline | Jaques | Jaquese | Jaquin | Jaquis | Jaquise | Jarae | Jarah | Jared | Jaree | Jarel | Jaren | Jaret | Jareth | Jari | Jariah | Jariel | Jarin | Jaris | Jariyah | Jarod | Jaron | Jarrah | Jarred | Jarrell | Jarren | Jarrett | Jarrod | Jarvis | Jaryn | Jas | Jasai | Jasaiah | Jasani | Jasdeep | Jase | Jashan | Jashawn | Jashon | Jasia | Jasiah | Jasiel | Jasiri | Jasiya | Jasiyah | Jaskirat | Jasmaine | Jasman | Jasmeet | Jasmen | Jasmin | Jasmine | Jasmon | Jasmond | Jasnoor | Jason | Jasper | Jaspreet | Jassiah | Jassiel | Jastin | Jasyah | Jasyiah | Jaton | Javae | Javan | Javen | Javian | Javier | Javin | Javion | Javis | Javon | Javonne | Javonni | Javonnie | Javonte | Jawan | Jawanza | Jawara | Jax | Jaxen | Jaxon | Jaxsen | Jaxson | Jaxsyn | Jaxx | Jaxyn | Jay | Jaya | Jayce | Jaycee | Jaycen | Jayceon | Jaycie | Jayd | Jayda | Jaydan | Jayde | Jaydee | Jaydeen | Jayden | Jaydence | Jaydenn | Jaydin | Jaydis | Jaydn | Jaydon | Jaydyn | Jaydynn | Jaye | Jayel | Jayla | Jaylan | Jaylani | Jaylee | Jayleen | Jaylen | Jaylene | Jaylenn | Jaylien | Jaylin | Jayline | Jaylinn | Jayln | Jaylon | Jaylyn | Jaylynn | Jayme | Jaymee | Jaymes | Jaymeson | Jaymi | Jaymie | Jayne | Jayni | Jayse | Jayson | Jaz | Jazari | Jazel | Jaziah | Jaziel | Jaziyah | Jazman | Jazmen | Jazmin | Jazmine | Jazmon | Jazmond | Jazz | Jazzman | Jc | Jd | Jean | Jeane | Jeanette | Jeanine | Jeanne | Jeannette | Jeannie | Jeannine | Jearl | Jedidiah | Jeff | Jeffery | Jeffie | Jeffrey | Jeffrie | Jeffry | Jehan | Jelani | Jemiah | Jemmie | Jen | Jena | Jenay | Jency | Jene | Jenesis | Jenifer | Jenna | Jenner | Jennie | Jennifer | Jennings | Jenny | Jensen | Jenson | Jensy | Jensyn | Jentry | Jerae | Jeral | Jerald | Jerami | Jeramie | Jeramy | Jeran | Jere | Jeree | Jerel | Jerell | Jerelle | Jeremi | Jeremia | Jeremiah | Jeremie | Jeremy | Jeri | Jeriah | Jericho | Jerico | Jerin | Jeris | Jermaine | Jermani | Jermanie | Jermany | Jermel | Jermiah | Jermiyah | Jerney | Jerome | Jerre | Jerrel | Jerrell | Jerri | Jerrie | Jerrin | Jerris | Jerrod | Jerry | Jerryl | Jersey | Jerusalem | Jeryl | Jeryn | Jerzey | Jerzy | Jesenia | Jeshua | Jesi | Jesiah | Jesie | Jess | Jessamyn | Jesse | Jessee | Jessey | Jessi | Jessiah | Jessica | Jessie | Jessy | Jesus | Jet | Jett | Jette | Jettie | Jetty | Jevon | Jewel | Jewell | Jewelz | Jeylan | Jeziah | Jezreel | Jhan | Jhayden | Jhett | Jhonnie | Jhordan | Jhordyn | Ji | Jia | Jian | Jianni | Jiayi | Jie | Jihad | Jihan | Jill | Jillian | Jim | Jimena | Jimi | Jimin | Jimmi | Jimmie | Jimmy | Jimmye | Jin | Jing | Jionni | Jirah | Jireh | Jiwoo | Jlyn | Jlynn | Jo | Joan | Joann | Joanna | Joanne | Joanny | Joany | Joaquin | Jobie | Joby | Jocelyn | Jode | Jodeci | Jodee | Jodey | Jodi | Jodie | Jody | Joe | Joedy | Joee | Joel | Joell | Joelle | Joesph | Joey | Johan | Johana | Johann | Johanna | Johanny | Johany | Johari | John | Johna | Johnathan | Johnathon | Johnell | Johnelle | Johney | Johnie | Johnni | Johnnie | Johnny | Johnnye | Johnson | Johnta | Johny | Joie | Jojo | Jolan | Jolene | Jolin | Jolly | Jomari | Jomarie | Jon | Jona | Jonah | Jonai | Jonas | Jonathan | Jonathon | Jone | Jonel | Jonell | Jonelle | Jones | Joni | Jonie | Jonni | Jonnie | Jonny | Jonquil | Jonta | Jontae | Jontay | Jonte | Jontel | Jontue | Jood | Joon | Jordain | Jordan | Jordane | Jordann | Jorden | Jordi | Jordie | Jordin | Jordis | Jordon | Jordy | Jordyan | Jordyn | Jordynn | Jorey | Jorge | Jori | Jorian | Jorie | Jorryn | Jory | Jose | Josefina | Joselyn | Joseph | Josephine | Josey | Josha | Joshlyn | Joshua | Josia | Josiah | Josie | Joslin | Joslyn | Joss | Jossie | Jostyn | Josue | Joud | Jourdain | Jourdan | Jourden | Jourdin | Jourdon | Jourdyn | Journee | Journey | Journie | Jovan | Jovani | Jovanie | Jovanne | Jovanni | Jovannie | Jovanny | Jovany | Jovi | Jovian | Jovita | Jovon | Jovoni | Jovonne | Joy | Joyce | Joye | Joziah | Jozy | Jream | Jrue | Juan | Juana | Juanita | Juanito | Jubilee | Juda | Judah | Juddie | Jude | Judea | Judith | Judy | Juel | Juelle | Juelz | Jule | Jules | Julez | Julia | Julian | Juliana | Juliani | Juliann | Julianna | Julie | Julien | Julienne | Julio | Julissa | Julius | Jullian | July | Jun | Junah | June | Juneau | Jung | Juni | Junie | Junior | Juniper | Junnie | Juno | Jupiter | Jurnee | Jurney | Justen | Justice | Justin | Justina | Justine | Justis | Justise | Justiss | Justus | Justyce | Justyn | Juwan | Jwan | Ka | Kace | Kacee | Kacey | Kaci | Kacie | Kacy | Kacyn | Kadan | Kadance | Kade | Kaden | Kadence | Kadian | Kadin | Kadrian | Kadyn | Kadynce | Kadynn | Kaedan | Kaede | Kaeden | Kaedence | Kaedin | Kaedyn | Kaedynn | Kaegan | Kael | Kaelan | Kaelen | Kaelin | Kaelyn | Kaena | Kaesyn | Kagan | Kagen | Kahlan | Kahlee | Kahlen | Kahler | Kahli | Kahlin | Kahmani | Kahmari | Kahri | Kai | Kaia | Kaidan | Kaiden | Kaidence | Kaidin | Kaidyn | Kaidynce | Kaidynn | Kaiea | Kaige | Kaigen | Kail | Kailan | Kailani | Kaile | Kailee | Kailen | Kailer | Kailey | Kaili | Kailin | Kailon | Kailyn | Kaimana | Kaimani | Kainoa | Kaire | Kairee | Kairi | Kairo | Kairyn | Kaisen | Kaison | Kaisyn | Kaitlin | Kaitlyn | Kaitlynn | Kaiya | Kaiyden | Kala | Kalai | Kalan | Kalani | Kale | Kaleb | Kalee | Kalei | Kaleigh | Kalen | Kaley | Kali | Kalijah | Kalin | Kalis | Kaliyah | Kallan | Kalle | Kallen | Kallin | Kally | Kalon | Kaloni | Kalyn | Kalynn | Kam | Kamal | Kamalani | Kamalei | Kamali | Kamani | Kamar | Kamara | Kamare | Kamaree | Kamari | Kamarie | Kamarii | Kamarion | Kamaron | Kamarri | Kamauri | Kamaurie | Kamaury | Kambryn | Kamden | Kamdin | Kamdyn | Kameko | Kameran | Kameren | Kamerin | Kameron | Kameryn | Kami | Kamil | Kamoni | Kamori | Kamorie | Kamran | Kamren | Kamrin | Kamron | Kamry | Kamryn | Kamrynn | Kamsiyochukwu | Kamyrn | Kana | Kanai | Kanan | Kanani | Kanari | Kandia | Kandy | Kane | Kang | Kani | Kannon | Kanon | Kansas | Kanya | Kanye | Kanyon | Kao | Kaori | Kaoru | Kapri | Kara | Karan | Karcyn | Karder | Karee | Kareem | Kareen | Karel | Karem | Karen | Karey | Kari | Kariel | Karim | Karin | Karina | Karis | Karl | Karla | Karle | Karlen | Karlin | Karly | Karlyn | Karma | Karman | Karmani | Karmen | Karol | Karon | Karriem | Karrington | Karrol | Karron | Karry | Karsen | Karsin | Karson | Karsten | Karstin | Karsyn | Karter | Kartier | Kary | Karyn | Kasani | Kasen | Kasey | Kash | Kashawn | Kashe | Kashmere | Kashmir | Kashtyn | Kasi | Kasie | Kason | Kassandra | Kassey | Kassidy | Kastle | Kasyn | Kate | Katelyn | Katelynn | Katharine | Katherine | Kathleen | Kathryn | Kathy | Katie | Katina | Katlin | Katlyn | Katrell | Katriel | Katrina | Katy | Kaulana | Kauri | Kavi | Kavon | Kavya | Kawan | Kay | Kaya | Kayan | Kayce | Kaycee | Kaycen | Kaydan | Kayde | Kayden | Kaydence | Kaydin | Kaydn | Kaydon | Kaydyn | Kaye | Kaygen | Kayin | Kayl | Kayla | Kaylan | Kayland | Kayle | Kaylee | Kayleigh | Kaylen | Kayler | Kaylin | Kaylon | Kaylor | Kaylyn | Kaylynn | Kayman | Kaymen | Kayra | Kayse | Kaysen | Kaysin | Kayson | Kayton | Kazi | Kaziah | Kazumi | Kc | Keagan | Keagen | Keaghan | Keagyn | Keahi | Keaire | Keala | Kealoha | Keandra | Keandre | Keani | Keante | Keanu | Kearney | Kearston | Keary | Keaten | Keaton | Keatyn | Kebron | Kedron | Kee | Keegan | Keeghan | Keelan | Keeley | Keelin | Keely | Keelyn | Keelynn | Keena | Keenan | Keerat | Kegan | Kehinde | Kei | Keigan | Keighan | Keilan | Keilani | Keilen | Keilin | Keilyn | Keimani | Keimari | Keimoni | Keion | Keir | Keira | Keiran | Keiren | Keisha | Keishawn | Keita | Keith | Kekeli | Kekoa | Kelani | Kelby | Kelcey | Kelcie | Kelcy | Kele | Kelechi | Kelen | Keli | Kelijah | Kelin | Kelis | Kellan | Kelle | Kellee | Kellen | Keller | Kelley | Kelli | Kellie | Kellin | Kellis | Kelly | Kellyn | Kelsea | Kelsey | Kelsie | Kelsy | Kelton | Kelvin | Kely | Kelyn | Kelynn | Kem | Kemani | Kemari | Kemauri | Kemoni | Kemonie | Kemper | Ken | Kena | Kenai | Kenan | Kendahl | Kendal | Kendale | Kendall | Kendel | Kendell | Kendle | Kendra | Kendre | Kendrell | Kendrick | Kendrix | Kendry | Kendy | Kendyl | Kendyll | Kenedy | Keni | Kenia | Kenisha | Kenji | Kenlee | Kenley | Kenly | Kenlyn | Kenna | Kennan | Kennedi | Kennedy | Kenneth | Kenney | Kenni | Kennie | Kennis | Kennison | Kennith | Kennon | Kenny | Kensey | Kensington | Kensley | Kent | Kenta | Kentlee | Kentley | Kentrell | Keny | Kenya | Kenyada | Kenyan | Kenyata | Kenyatta | Kenyatte | Kenyetta | Kenyon | Kenyotta | Kenzi | Kenzie | Kenzy | Keola | Keon | Keona | Keondra | Keoni | Keonta | Keontae | Keonte | Kerby | Keri | Kerin | Kermit | Keron | Kerri | Kerrie | Kerrigan | Kerrin | Kerrington | Kerrion | Kerry | Kersey | Kery | Kesha | Keshaun | Keshawn | Keshia | Keshon | Keshun | Kesley | Kessler | Kestrel | Kevan | Keven | Kevin | Kevion | Kevon | Kevyn | Key | Keyante | Keyari | Keygan | Keylan | Keylen | Keylin | Keymani | Keymari | Keymoni | Keyon | Keyoni | Keyonta | Keyontae | Keyonte | Keyshawn | Keziah | Khaalis | Khadijah | Khai | Khairi | Khalani | Khalee | Khalen | Khali | Khalia | Khalil | Khalin | Khamani | Khamari | Khamauri | Khamoni | Khamryn | Khanh | Khani | Khari | Kharter | Khayden | Khi | Khilyn | Khiry | Khloe | Khodi | Khoi | Khole | Khori | Khris | Khristian | Khrystian | Khylan | Khyle | Khyler | Khylin | Khymani | Khyree | Khyri | Khyrie | Ki | Kia | Kiah | Kian | Kiana | Kiani | Kiante | Kiara | Kiari | Kie | Kiegan | Kiel | Kielyn | Kier | Kiera | Kieran | Kieren | Kiernan | Kierra | Kierre | Kiersten | Kierston | Kieryn | Kijana | Kiki | Kiko | Kila | Kiley | Kilian | Killian | Kilyn | Kim | Kimani | Kimari | Kimbal | Kimball | Kimber | Kimberley | Kimberly | Kimble | Kimi | Kimm | Kimmie | Kimmy | Kimoni | Kimothy | Kin | Kindall | Kindell | Kindle | Kindred | King | Kingslee | Kingsley | Kingston | Kinley | Kinnick | Kinsey | Kinshasa | Kinsler | Kinsley | Kinta | Kinte | Kionte | Kiowa | Kip | Kipp | Kippy | Kiptyn | Kira | Kiran | Kirby | Kiren | Kirin | Kirk | Kirkland | Kirklyn | Kirsten | Kiryn | Kish | Kisha | Kishara | Kishawn | Kishon | Kit | Kito | Kitt | Kiva | Kiya | Kiyan | Kiyoshi | Kizzy | Kmari | Knightley | Knowledge | Knox | Koa | Kobe | Kobee | Kobi | Kobie | Koby | Koda | Kodee | Kodey | Kodi | Kodie | Kody | Koen | Kofi | Kohen | Koi | Kolbe | Kolbee | Kolbey | Kolbi | Kolbie | Kolby | Kole | Koli | Kollins | Kollyn | Koltyn | Kona | Konner | Konnor | Konstantine | Kooper | Koran | Korbin | Korbyn | Koree | Koren | Korey | Kori | Korie | Korin | Korri | Korrie | Korry | Kortney | Kory | Kosisochukwu | Kota | Koty | Kou | Kourtney | Koy | Kramer | Kree | Krimson | Kris | Krishawn | Krishi | Krishna | Krisna | Kriss | Krist | Krista | Kristain | Kristan | Kristen | Kristi | Kristian | Kristie | Kristien | Kristin | Kristina | Kristine | Kristion | Kriston | Kristopher | Kristy | Krosby | Kruz | Krystal | Krystian | Krystle | Ku | Kumari | Kunga | Kunta | Kurt | Kurtis | Kwame | Kwan | Kwanza | Ky | Kya | Kyah | Kyan | Kyann | Kyden | Kye | Kyel | Kyi | Kyla | Kylan | Kylar | Kyle | Kylee | Kyleigh | Kylen | Kylenn | Kyler | Kyley | Kylie | Kylin | Kylle | Kylor | Kylyn | Kylynn | Kym | Kymani | Kymari | Kymoni | Kyndal | Kyndall | Kyndel | Kyndell | Kyndle | Kyra | Kyran | Kyre | Kyree | Kyren | Kyri | Kyrian | Kyrie | Kyrin | Kyung | La | Laakea | Lacey | Lachlan | Lacie | Lacy | Ladale | Laddie | Ladean | Ladell | Ladon | Lael | Laetyn | Lafayette | Lai | Laiken | Laila | Lain | Laine | Laityn | Lajuan | Lakai | Lake | Lakeisha | Lakelyn | Laken | Lakesha | Lakin | Lakisha | Lakoda | Lakota | Lakshya | Lakyn | Lal | Lam | Lamar | Lamari | Lamarr | Lamoine | Lamoni | Lamonica | Lamont | Lamoyne | Lan | Lana | Lance | Landan | Landen | Landin | Landis | Landon | Landree | Landrey | Landry | Landy | Landyn | Landynn | Lane | Lanell | Laney | Lang | Langdon | Langley | Langston | Lani | Lanie | Lanier | Lanis | Lannie | Lannis | Lanny | Laparis | Laquan | Laquinta | Lara | Laramie | Laray | Laren | Lari | Larin | Lark | Larkin | Larnell | Laron | Laroyce | Larri | Larrie | Larry | Larsen | Larson | Larue | Lasalle | Lasean | Lashan | Lashane | Lashaun | Lashawn | Lashay | Lashon | Lashone | Lashun | Latanya | Latasha | Lataurus | Latisha | Latonia | Latonya | Latoya | Latrell | Latrelle | Latrice | Lattie | Laura | Laurance | Laurel | Laurell | Lauren | Laurence | Lauri | Laurice | Laurie | Laurin | Lauris | Laury | Lauryn | Lavar | Lavaughn | Lavell | Lavelle | Lavere | Lavergne | Laverle | Lavern | Laverne | Lavon | Lavone | Lavonn | Lavonne | Lavonte | Lavoris | Lawan | Lawanda | Lawayne | Lawren | Lawrence | Lawson | Lawsyn | Lay | Layden | Layken | Laykin | Layla | Layne | Layten | Layton | Le | Lea | Leah | Lean | Leander | Leandra | Leandre | Leandrea | Leann | Leanna | Leary | Leaster | Leavy | Ledell | Lee | Leeland | Leelyn | Leelynn | Legacy | Legend | Lei | Leif | Leigh | Leighton | Leila | Leilani | Leith | Lela | Leland | Lemmie | Lemon | Lemoyne | Len | Lena | Lenard | Lenell | Leng | Leni | Lenin | Lenis | Lenix | Lenn | Lennell | Lennex | Lennie | Lennin | Lennis | Lennix | Lennon | Lennox | Lenny | Lennyn | Lennyx | Leno | Lenora | Lenore | Lenox | Leny | Lenyx | Lenzie | Lenzy | Leo | Leola | Leon | Leona | Leonard | Leonardo | Leondra | Leone | Leonel | Leonides | Leonor | Leor | Leora | Leotha | Lerin | Leroy | Les | Lesa | Lesean | Leshaun | Leshawn | Leshon | Leslee | Lesley | Leslie | Lesly | Lessie | Lesslie | Lester | Letha | Lether | Leticia | Lev | Levar | Leven | Levern | Leverne | Levert | Levester | Levi | Levie | Levis | Levon | Levone | Levonne | Levorn | Levy | Lew | Lewellyn | Lewis | Lexani | Lexie | Lexington | Lexis | Lexus | Leyton | Li | Lia | Liam | Lian | Libby | Liberty | Liel | Life | Light | Lila | Lile | Lilia | Liliana | Lillian | Lillie | Lilly | Lily | Limmie | Lin | Lina | Lincoln | Lind | Linda | Lindal | Lindel | Lindell | Linden | Linder | Lindley | Lindsay | Lindsey | Lindy | Linell | Ling | Linh | Linkyn | Linley | Linn | Linnell | Linnie | Lino | Linsey | Linwood | Linzie | Linzy | Lionel | Lior | Liron | Lisa | Lisandro | Lisle | Lissette | Little | Litzy | Livingston | Liza | Lizbeth | Lizeth | Lizette | Lizzie | Llewellyn | Lloyd | Lochlan | Lochlyn | Locklyn | Logan | Logann | Logen | Loghan | Login | Logyn | Lohgan | Lois | Loise | Loki | Lola | Loma | Lon | Lona | Londen | London | Londyn | Lonell | Loney | Loni | Lonie | Lonni | Lonnie | Lonny | Lora | Lorain | Loraine | Loral | Loran | Lorane | Loree | Lorell | Loren | Lorena | Lorence | Lorene | Lorenza | Lorenzo | Loreto | Loretta | Loretto | Lori | Lorie | Lorin | Lorine | Loring | Loris | Lorna | Lorne | Lorrain | Lorraine | Lorren | Lorrie | Lorry | Lory | Lossie | Lottie | Lotus | Lou | Louie | Louis | Louisa | Louise | Lourdes | Love | Lovel | Lovell | Lovelle | Lovie | Lovis | Lowell | Lowen | Lowry | Loxley | Loy | Loyal | Loyalty | Loyce | Loyd | Loye | Loys | Lu | Luann | Lubie | Luby | Luca | Lucas | Lucca | Lucero | Lucia | Lucian | Lucien | Lucile | Lucille | Lucinda | Lucious | Lucky | Lucy | Luddie | Ludie | Ludy | Lue | Luella | Luellen | Lugene | Luis | Luisa | Luka | Lukas | Luke | Lula | Lumen | Lummie | Luna | Lunden | Lundon | Lundy | Lundyn | Lupe | Lura | Luster | Luther | Luvern | Luverne | Lux | Luz | Ly | Lyan | Lyda | Lydia | Lyfe | Lyle | Lyn | Lyncoln | Lynda | Lyndal | Lyndall | Lyndel | Lyndell | Lynden | Lyndon | Lyndsey | Lyne | Lynell | Lynette | Lynix | Lynn | Lynne | Lynnie | Lynnox | Lynwood | Lynx | Lyric | Lyrick | Lyrik | Lyriq | Maayan | Mabel | Mable | Mabry | Mac | Macaiah | Macari | Macaulay | Macauley | Macel | Macen | Macey | Machael | Macheal | Maci | Maciah | Macie | Maciel | Mack | Mackay | Mackenna | Mackensie | Mackenzi | Mackenzie | Mackenzy | Mackie | Mackinley | Mackinzie | Macklyn | Maclain | Maclaine | Maclin | Macon | Macy | Macyn | Maddax | Madden | Maddex | Maddison | Maddix | Maddox | Maddux | Maddyn | Maddyx | Madeleine | Madeline | Madelyn | Madison | Madisyn | Madsen | Madysen | Madyx | Mae | Maeson | Maesyn | Magali | Magdalena | Magdiel | Maggie | Magic | Magin | Maguire | Mahari | Maheen | Mahi | Mahin | Mahlon | Mai | Maika | Maiko | Maira | Maisen | Maison | Maisyn | Maitland | Majd | Majestic | Majesty | Major | Makai | Makaiah | Makala | Makana | Makani | Makari | Makay | Makayden | Makayla | Makel | Makell | Makena | Makenna | Makenzie | Makhari | Makhi | Maki | Makia | Makiah | Makiya | Makiyah | Mako | Makya | Makyah | Malachi | Malak | Malakai | Malaki | Malan | Malcolm | Malek | Mali | Malijah | Malik | Malika | Malikah | Malin | Malique | Malka | Mallie | Mallory | Malon | Malone | Malvin | Mamie | Man | Mana | Manaia | Manar | Manasseh | Mandeep | Mandy | Mang | Manjot | Manmeet | Mannie | Manning | Manon | Manpreet | Manu | Manuel | Manuela | Manvir | Mao | Maple | Mar | Mara | Marc | Marce | Marcedes | Marcel | Marcell | Marcella | Marcelle | Marcello | Marcellus | March | Marche | Marci | Marcia | Marcie | Marco | Marcos | Marcus | Marcy | Mardell | Mardi | Mardy | Marek | Margaret | Margarita | Margene | Margie | Margo | Marguerite | Mari | Maria | Mariah | Mariam | Marian | Mariana | Marianne | Maribel | Marice | Maricela | Marie | Mariel | Mariela | Mariko | Marilyn | Marin | Marina | Marine | Mario | Marion | Maris | Marisa | Marisela | Marisol | Marissa | Maritza | Marjorie | Mark | Marka | Markai | Markee | Markel | Markell | Markelle | Markey | Marki | Markie | Marla | Marlan | Marlee | Marlen | Marlene | Marley | Marlin | Marlo | Marlon | Marlow | Marlowe | Marly | Marlyn | Marlys | Marne | Marnell | Marni | Maron | Marque | Marquee | Marquel | Marquell | Marquelle | Marques | Marquette | Marquez | Marqui | Marquie | Marquis | Marquise | Marquita | Marrion | Marsh | Marsha | Marshal | Marshall | Marshawn | Marshay | Marshell | Marshon | Marta | Martel | Martell | Martez | Martha | Marti | Martice | Martie | Martin | Martina | Martine | Martinez | Martrice | Marty | Marva | Marvel | Marvell | Marvelle | Marvelous | Marvie | Marvin | Marvine | Marvis | Mary | Maryan | Maryann | Maryland | Maryon | Marzell | Masa | Masen | Mashawn | Masiah | Masiya | Masiyah | Mason | Massey | Massiah | Massie | Masyn | Mateja | Mateo | Mathai | Mathew | Mati | Matia | Matilda | Matilde | Matisse | Matt | Matteo | Matteson | Matthew | Matti | Mattia | Mattie | Mattingly | Mattison | Mattox | Matty | Maud | Maude | Maui | Maura | Maureen | Mauri | Maurice | Mauricio | Maurie | Maury | Maven | Maverick | Mavin | Mavis | Max | Maxi | Maxie | Maxim | Maxime | Maximus | Maxine | Maxwell | Maxx | May | Maya | Mayan | Mayar | Maycen | Mayer | Mayes | Maynard | Mayo | Mayra | Mays | Maysen | Maysin | Mayson | Maysun | Mazi | Maziah | Mc | Mcallister | Mccall | Mccartney | Mccauley | Mcclain | Mccoy | Mckale | Mckay | Mckayla | Mckell | Mckenley | Mckenna | Mckenzie | Mckenzy | Mckinley | Mckinney | Mckinnley | Mckinnon | Mckinsey | Mclain | Mclean | Meade | Meagan | Meaghan | Mearl | Mearle | Meba | Mecca | Mechel | Meco | Meena | Meerab | Megan | Meghan | Mehar | Meher | Mekel | Mekhi | Mekiah | Meko | Mel | Melanie | Melany | Melba | Melek | Melinda | Melisa | Melissa | Mell | Melody | Melrose | Melva | Melville | Melvin | Melvine | Melvis | Melvyn | Memory | Memphis | Mena | Mendy | Meng | Merari | Merced | Mercedes | Mercer | Mercury | Mercy | Merdith | Meredith | Merel | Meridith | Meril | Merion | Merit | Merl | Merle | Merlin | Merlon | Merlyn | Meron | Merrel | Merrell | Merrick | Merrik | Merril | Merrill | Merrit | Merritt | Mertis | Mervin | Mervyn | Merwin | Merwyn | Meryl | Meryle | Mesiah | Messiah | Meyer | Mia | Miah | Mica | Micaela | Micah | Micaiah | Mical | Micha | Michae | Michael | Michaela | Michaele | Michaell | Michah | Michaiah | Michal | Michale | Micheal | Michel | Michele | Michell | Michelle | Michiah | Michiel | Michon | Miciah | Mickael | Mickel | Mickell | Mickenzie | Mickey | Micki | Mickie | Micky | Mieko | Migdalia | Miguel | Mika | Mikael | Mikah | Mikai | Mikaiah | Mikail | Mikal | Mikala | Mikale | Mikayla | Mike | Mikeal | Mikel | Mikele | Mikell | Mikelle | Mikhael | Mikhail | Miki | Mikiah | Mikie | Mikkel | Mikki | Miko | Mila | Milagro | Milagros | Milan | Mildred | Milen | Miles | Miley | Milford | Millan | Millard | Miller | Millie | Mills | Milo | Milton | Min | Mina | Mindy | Ming | Minh | Minnie | Minor | Mio | Miquel | Mira | Miracle | Miraj | Miranda | Miriam | Mirl | Mirna | Mirza | Misael | Misbah | Mischa | Misha | Mishael | Misty | Mitchel | Mitchell | Mitchelle | Mitra | Mitsuru | Mizuki | Modell | Moe | Mohammad | Moises | Mollie | Molly | Mona | Monet | Money | Mong | Monica | Monico | Monique | Monnie | Monroe | Monserrat | Monserrate | Monta | Montana | Montanna | Monte | Montez | Montgomery | Monti | Montie | Montoya | Montrell | Montrice | Monty | Monyae | Monzerrat | Moo | Moon | Morgan | Morgen | Morghan | Morgon | Moriah | Morireoluwa | Morley | Morris | Morrison | Morton | Mose | Moses | Moshe | Mosi | Mosie | Mosley | Mossie | Moxley | Mozel | Mozell | Mozelle | Mulan | Munachimso | Munachiso | Murel | Muriel | Muril | Murl | Murle | Murlyn | Murphy | Murray | Murrel | Murrell | Murriel | Murry | Music | Musiq | My | Mya | Myah | Myan | Myca | Mycah | Mychael | Mychal | Myer | Myers | Myka | Mykael | Mykah | Mykal | Mykale | Mykel | Mykell | Mykelle | Mykelti | Mylan | Myles | Mylin | Mylo | Myra | Myrel | Myrl | Myrle | Myrna | Myron | Myrtis | Myrtle | Mystikal | Na | Nabil | Nadia | Nadine | Nael | Nai | Nairobi | Naja | Najae | Najah | Najai | Najay | Naje | Najee | Najja | Nakai | Nakari | Nakia | Nakie | Nakita | Nakiya | Nakoa | Nakoma | Nakota | Nalin | Namari | Name | Nana | Nance | Nancy | Nannie | Nao | Naoki | Naomi | Naphtali | Napoleon | Nara | Narada | Narvie | Naseem | Nash | Nashawn | Nasiah | Nasim | Nasir | Nassim | Natale | Natalia | Natalie | Nataly | Natasha | Nate | Nathalie | Nathan | Nathanael | Nathaniel | Nation | Natividad | Natori | Nature | Nautica | Navdeep | Naveen | Navi | Navid | Navil | Navjot | Navneet | Navy | Nay | Nayan | Nayeli | Naylen | Nazaret | Nazareth | Nazari | Naziah | Neal | Neale | Nealie | Nealy | Neco | Ned | Neely | Neema | Neftali | Neftaly | Nehal | Nehemiah | Neil | Neiman | Nekia | Neko | Nekoda | Nelda | Nell | Nellie | Nello | Nelly | Nelson | Nelvin | Nema | Nemiah | Nereida | Neri | Neriah | Nery | Nesbit | Nesiah | Nessiah | Nesta | Nettie | Neva | Nevada | Nevaeh | Nevan | Neven | Neville | Nevin | Nevyn | Newborn | Newell | Newton | Neylan | Neymar | Neziah | Nghia | Ngoc | Ngozi | Ngun | Nguyen | Nhan | Nhia | Nia | Nichelle | Nichol | Nichola | Nicholas | Nichole | Nick | Nickey | Nicki | Nickie | Nickolas | Nicky | Nico | Nicol | Nicola | Nicolas | Nicole | Nicolette | Nieves | Nigel | Nihal | Nija | Nijae | Nijah | Nijay | Nika | Nikai | Nike | Nikel | Niki | Nikia | Nikita | Nikki | Nikkia | Nikko | Niko | Nikola | Nikolas | Nil | Nilay | Nile | Nima | Nina | Nino | Nira | Nitya | Nix | Nixon | Nkosi | No | Noa | Noah | Noam | Noble | Noe | Noel | Noell | Noelle | Noemi | Noha | Nohea | Nola | Nolan | Nole | Nolen | Nolie | Nollie | Nolyn | Noor | Nora | Norah | Norbert | Nore | Norell | Nori | Noriel | Norma | Normal | Norman | Norris | North | Norvel | Norvell | Norvelle | Notnamed | Nou | Nour | Nova | Novah | Novie | Novis | Nur | Nuri | Nyah | Nyaire | Nyel | Nyjah | Nylan | Nyle | Nyree | Oakland | Oaklee | Oakley | Oaklyn | Oasis | Obie | Ocean | Ocie | Octavia | Octavio | Octavious | Octavius | Oda | Odalis | Oddie | Odean | Odell | Odelle | Odera | Odessa | Odia | Odie | Odis | Odyssey | Ofa | Ofelia | Offie | Ogechukwu | Ogle | Okie | Okla | Ola | Olamide | Olamiposi | Olanda | Olayinka | Olean | Olen | Oley | Olga | Olie | Olin | Olis | Oliva | Olive | Oliver | Olivet | Olivia | Olivier | Ollie | Oluwadamilola | Oluwadara | Oluwadarasimi | Oluwademilade | Oluwaferanmi | Oluwanifemi | Oluwapelumi | Oluwasemilore | Oluwaseun | Oluwaseyi | Oluwatamilore | Oluwateniola | Oluwatobi | Oluwatobiloba | Oluwatofunmi | Oluwatomi | Oluwatomisin | Oluwatoni | Oluwatoniloba | Oluwatosin | Oma | Omani | Omar | Omari | Omarie | Omarion | Omega | Omer | Omie | Omni | Omri | Ona | Ondra | Oneal | Oneil | Onell | Oney | Onie | Onis | Onnie | Onyekachi | Onyx | Opal | Opeyemi | Opha | Ophelia | Opie | Ople | Ora | Oral | Oralia | Orange | Orbie | Ordell | Orea | Orean | Oree | Orel | Orell | Oren | Oreoluwa | Ori | Oria | Oriah | Orian | Orie | Oriel | Orien | Orin | Orion | Oris | Orla | Orland | Orlanda | Orlando | Orlean | Orlee | Orlie | Orra | Orrie | Orris | Orva | Orval | Orville | Osa | Oscar | Osceola | Osha | Oshai | Oshay | Oshea | Osie | Osiris | Ossie | Osvaldo | Oswin | Otha | Othal | Othel | Othell | Othello | Otie | Otis | Ottie | Ottis | Otto | Ova | Oval | Ovie | Owen | Owyn | Owynn | Oza | Ozell | Ozie | Ozzie | Pa | Pablo | Pace | Pacey | Paddy | Paden | Paeton | Paetyn | Page | Paiden | Paige | Paisley | Paiten | Paiton | Paityn | Palak | Palmer | Pam | Pamela | Pang | Paola | Paris | Parish | Parker | Parminder | Parnell | Parris | Parrish | Parys | Pascal | Pasha | Pat | Patience | Patric | Patrica | Patrice | Patricia | Patricio | Patrick | Patsy | Patt | Patterson | Patti | Pattie | Patton | Patty | Paul | Paula | Paulette | Paulina | Pauline | Pax | Paxten | Paxton | Paxtyn | Paycen | Payden | Payne | Paysen | Payson | Paytan | Payten | Paytin | Payton | Paz | Peace | Pearce | Pearl | Pearley | Pearlie | Pearly | Pearson | Pedro | Peggy | Peighton | Peja | Peniel | Penn | Penny | Pepper | Percy | Peregrine | Peri | Perla | Perle | Perley | Perlie | Pernell | Perri | Perrie | Perrin | Perris | Perry | Perseus | Pet | Pete | Peter | Petra | Peyson | Peyten | Peytin | Peyton | Phelan | Phenix | Pheonix | Phi | Phil | Philip | Phillip | Phillis | Phoebe | Phoenix | Phoenyx | Phong | Phuc | Phung | Phuong | Phyllis | Phynix | Pier | Pierce | Pierre | Pierson | Pilar | Pink | Pinkey | Pinkie | Piper | Pistol | Pleasant | Plumer | Poet | Polly | Porsha | Porter | Portia | Portland | Posey | Posie | Prabhjot | Praise | Precious | Preet | Prentice | Prentiss | Prescott | Preslee | Presley | Pressley | Preston | Prestyn | Price | Prince | Princess | Priscilla | Promise | Pryce | Psalm | Psalms | Puneet | Purnell | Qamar | Quan | Quanah | Quanta | Quante | Quashawn | Quay | Quayshawn | Queen | Quentin | Quest | Quetzal | Quilla | Quillie | Quin | Quincee | Quincey | Quinci | Quincie | Quincy | Quinlan | Quinlin | Quinlyn | Quinn | Quinncy | Quinnie | Quinnlan | Quinta | Quintell | Quintin | Quinton | Quran | Quyen | Raahi | Rachael | Rachel | Rachelle | Racine | Racquel | Radie | Radley | Rae | Raeden | Raedyn | Raegan | Raekwon | Rael | Raelan | Raelyn | Raelynn | Raeshawn | Raevon | Rafa | Rafael | Ragan | Ragen | Raheel | Raheem | Rahi | Rahil | Rahsaan | Rai | Raiden | Raidyn | Railey | Rain | Raine | Rainer | Rainey | Raini | Rainier | Rainn | Raja | Rajae | Rajah | Rajdeep | Rakhi | Raleigh | Rally | Ralph | Rama | Ramandeep | Rameen | Ramelle | Ramey | Rami | Ramie | Ramiro | Ramon | Ramona | Ramone | Ramsay | Ramsey | Ramzi | Rana | Rand | Randal | Randall | Rande | Randee | Randel | Randell | Randi | Randie | Randle | Randolph | Randon | Randy | Rane | Ranell | Raney | Rani | Rannie | Ranny | Ransom | Rany | Raphael | Raquel | Rasha | Rashaan | Rashad | Rashan | Rashaun | Rashawn | Rashea | Rasheed | Rasheeda | Rasheen | Rashi | Rashida | Rashon | Rashone | Rashun | Ratha | Rathana | Raul | Raven | Ravi | Ravin | Ravinder | Ravion | Ravon | Ravyn | Ray | Rayaan | Rayan | Rayane | Rayann | Rayce | Rayden | Raye | Rayel | Rayen | Raygan | Raygen | Raylan | Raylee | Raylen | Rayli | Raylin | Raylyn | Raylynn | Raymie | Raymond | Rayn | Rayne | Raynell | Rayon | Rayshawn | Rayven | Rayvin | Rayvon | Rayyan | Raziel | Rea | Reace | Reagan | Reagen | Real | Rease | Reason | Reathel | Reather | Reba | Rebeca | Rebecca | Rebekah | Rebel | Rece | Recie | Reda | Redell | Ree | Reece | Reed | Reef | Reegan | Rees | Reese | Reeve | Reeves | Refugia | Refugio | Refujio | Regan | Regen | Reggie | Regina | Reginal | Reginald | Regis | Rehan | Rei | Reice | Reid | Reign | Reiko | Reiley | Reilley | Reilly | Reily | Rein | Reis | Reise | Reiss | Rema | Remedy | Remi | Remie | Remington | Remmington | Remmy | Remy | Ren | Rena | Renae | Renarda | Renardo | Renata | Renato | Renay | Rendy | Rene | Renee | Renell | Renie | Renley | Renly | Renn | Renne | Rennie | Renny | Reno | Reo | Reshawn | Ressie | Reuben | Reva | Revel | Rex | Reyan | Reyce | Reyes | Reyhan | Reyli | Reyn | Reyna | Reynaldo | Reynolds | Rhea | Rhen | Rhett | Rhian | Rhiannon | Rhiley | Rhoda | Rhodes | Rhonda | Rhyan | Rhyder | Rhyen | Rhylan | Rhyland | Rhylee | Rhylen | Rhyley | Rhylin | Rhyon | Rhys | Rhyse | Rhythm | Rian | Riane | Ricardo | Ricci | Richard | Richie | Rick | Rickey | Ricki | Rickie | Ricky | Rico | Rida | Ridley | Ridwan | Riel | Rieley | Rielly | Riely | Rielyn | Rien | Riese | Rigby | Rigel | Righteous | Rigley | Rigoberto | Rihan | Rihanna | Riki | Rikishi | Rikki | Riko | Rilan | Rilee | Rileigh | Riley | Rilley | Rily | Rilyn | Rilynn | Rio | Rion | Riot | Ripley | Rita | Ritchie | Riven | River | Rivers | Riyaan | Riyan | Rmani | Roan | Robben | Robbi | Robbie | Robbin | Robby | Roben | Robert | Roberta | Roberto | Robi | Robie | Robin | Roby | Robyn | Rocco | Rochell | Rochelle | Rocio | Rocket | Rockie | Rocky | Rodell | Roderick | Rodney | Rodolfo | Rodrick | Rodrigo | Rodriguez | Roe | Roen | Rogan | Rogelio | Rogene | Roger | Rogers | Rogue | Rohan | Roi | Roland | Rolanda | Rolando | Rolla | Rolland | Rollie | Rollins | Roma | Romain | Romaine | Roman | Romani | Romayne | Rome | Romell | Romelle | Romey | Romi | Romie | Romney | Romy | Ron | Ronak | Ronald | Ronan | Ronda | Rondell | Rondi | Ronel | Ronell | Ronelle | Roney | Roni | Ronie | Ronin | Ronique | Ronit | Ronne | Ronnell | Ronni | Ronnie | Ronny | Rooney | Roosevelt | Rorey | Rori | Rory | Rosa | Rosaire | Rosalie | Rosalinda | Rosalio | Rosamond | Rosanna | Rosanne | Rosario | Roscoe | Rose | Rosell | Rosella | Rosemarie | Rosemary | Rosemond | Rosetta | Roshan | Roshaun | Roshawn | Roshell | Roshon | Roshun | Rosie | Rosio | Roslyn | Ross | Rossi | Rossie | Roux | Rowan | Rowdy | Rowe | Rowen | Rowin | Rowyn | Roxana | Roxanna | Roxanne | Roxie | Roxy | Roy | Royal | Royale | Royalty | Royce | Roye | Royel | Rozell | Rozelle | Ruari | Ruben | Rubi | Rubie | Rubin | Ruble | Ruby | Ruddy | Rudell | Rudi | Rudolph | Rudra | Rudy | Rue | Ruel | Rufus | Rui | Rumi | Rune | Rupert | Russel | Russell | Rusty | Ruth | Ruther | Ruthie | Rya | Ryan | Ryane | Ryann | Ryatt | Ryden | Ryder | Rye | Ryelan | Ryelee | Ryelin | Ryen | Ryian | Ryin | Ryken | Ryker | Rylan | Ryland | Rylann | Ryle | Rylee | Rylei | Ryleigh | Rylen | Ryley | Rylie | Rylin | Ryly | Rylyn | Rylynn | Ryn | Ryna | Ryne | Rynn | Ryon | Ryver | Saba | Saber | Sabian | Sabin | Sabre | Sabree | Sabri | Sabriel | Sabrina | Sacha | Sade | Sadi | Sadie | Safari | Safi | Sagan | Sage | Sagen | Sahar | Sahara | Sahej | Saher | Sahib | Sai | Saia | Saige | Sailor | Saint | Saje | Sakae | Sakai | Sakari | Saki | Sakima | Salah | Salam | Salem | Sallie | Sally | Salma | Salome | Salvador | Salvatore | Sam | Samaj | Saman | Samantha | Samar | Samari | Samauri | Samay | Sameen | Samer | Sami | Samie | Samir | Sammi | Sammie | Sammy | Samnang | Samon | Samone | Samuel | Samuela | Samy | San | Sana | Sanai | Sanay | Sanchez | Sande | Sandeep | Sander | Sanders | Sandi | Sandie | Sandra | Sandy | Sang | Sani | Sanjaya | Santa | Santana | Santanna | Santi | Santiago | Santos | Saquan | Sara | Sarah | Sarai | Saran | Sarang | Sarath | Sareth | Sari | Sarin | Saron | Sary | Sascha | Sasha | Satori | Satya | Saul | Saumya | Saundra | Sava | Savanna | Savannah | Savaughn | Savion | Savon | Savoy | Sawyer | Saxon | Say | Sayer | Sayge | Sayler | Saylor | Sayre | Scarlett | Schuyler | Schylar | Schyler | Scot | Scotland | Scott | Scotti | Scottie | Scotty | Scout | Sean | Seann | Searcy | Seattle | Sebastian | Seeley | Seely | Seena | Sehaj | Sekai | Selah | Selby | Selena | Selene | Selma | Selmer | Selwyn | Semaj | Semaja | Semaje | Sena | Senai | Seneca | Seng | Senica | Senna | Sequoia | Sequoya | Sequoyah | Seraiah | Serani | Seraphim | Seraphine | Seren | Serena | Serenity | Sergio | Seth | Seton | Sevan | Seven | Seville | Sevin | Sevon | Sevyn | Seymour | Sha | Shaddai | Shade | Shadee | Shadell | Shaden | Shadi | Shadoe | Shadow | Shady | Shae | Shah | Shahad | Shaheen | Shai | Shain | Shaina | Shaine | Shajuan | Shaka | Shakai | Shakari | Shakel | Shakell | Shakil | Shakir | Shakur | Shalamar | Shalan | Shale | Shalimar | Shalin | Shalom | Shalon | Sham | Shamaine | Shamar | Shamari | Shamarie | Shamarion | Shamauri | Shameek | Shamel | Shamell | Shamere | Shamika | Shamil | Shamir | Shamon | Shamone | Shams | Shan | Shana | Shanan | Shandale | Shandel | Shandell | Shandon | Shandy | Shane | Shanell | Shanen | Shanga | Shani | Shanice | Shanika | Shann | Shanna | Shannan | Shanne | Shannen | Shannon | Shanon | Shanta | Shantae | Shante | Shantel | Shantell | Shantez | Shanti | Shantrell | Shaolin | Shaquan | Shaquel | Shaquell | Shaquelle | Shaquiel | Shaquil | Shaquile | Shaquill | Shaquilla | Shaquille | Shaquita | Shaquon | Shara | Sharan | Sharay | Sharell | Shari | Sharif | Sharmaine | Sharman | Sharmon | Sharon | Sharone | Sharron | Shasta | Shaton | Shaughn | Shaun | Shauna | Shaundell | Shaune | Shaunessy | Shaunta | Shauntae | Shauntay | Shaunte | Shauntel | Shavaughn | Shavell | Shavon | Shavonta | Shavonte | Shaw | Shawan | Shawn | Shawna | Shawndale | Shawndell | Shawne | Shawnee | Shawnell | Shawnn | Shawnta | Shawntae | Shawntay | Shawnte | Shawntel | Shay | Shaya | Shayan | Shayde | Shayden | Shaye | Shayla | Shaylan | Shaylen | Shaylin | Shaylon | Shayna | Shayne | Shea | Shean | Sheehan | Sheena | Sheikh | Sheila | Shelba | Shelby | Shelden | Sheldon | Shelia | Shell | Shelley | Shellie | Shelly | Shelton | Shelva | Shelvy | Shem | Shemaiah | Shemar | Shenandoah | Sheng | Shepard | Shepherd | Sherald | Sheri | Sheridan | Sherill | Sherley | Sherlyn | Shermaine | Sherman | Sheron | Sherrel | Sherrell | Sherri | Sherrick | Sherrie | Sherril | Sherrill | Sherrod | Sherron | Sherry | Sherryl | Sherwin | Sherwood | Sheryl | Shevon | Shevy | Shey | Shi | Shia | Shiah | Shian | Shilo | Shiloh | Shin | Shine | Shion | Shiraz | Shirl | Shirlee | Shirley | Shirly | Shiron | Shiva | Shiya | Shomari | Shon | Shondale | Shondel | Shondell | Shone | Shonn | Shonnon | Shonta | Shontae | Shontay | Shonte | Shontel | Shontell | Shoua | Shown | Shree | Shun | Shunta | Shuron | Shy | Shyah | Shyan | Shye | Shylan | Shyler | Shylo | Shyloh | Shyne | Si | Sia | Siah | Sian | Sid | Sidney | Sienna | Sierra | Silas | Silver | Silvia | Simar | Simcha | Simeon | Simmie | Simon | Simone | Simran | Simranjit | Sina | Sinai | Sinan | Sinceer | Sincere | Sinclair | Sion | Sione | Sivan | Siyah | Skai | Skeeter | Skilar | Skiler | Sky | Skye | Skyelar | Skyeler | Skylan | Skylar | Skylen | Skyler | Skylier | Skylin | Skylor | Skylur | Skyy | Sloan | Sloane | Smith | Snehal | Snow | Socorro | Sofia | Sok | Sokha | Sokhom | Sol | Sola | Solace | Soledad | Solomon | Soma | Somtochukwu | Sonam | Song | Sonia | Sonja | Sonni | Sonnie | Sonny | Sony | Sonya | Soo | Sophanna | Sophea | Sopheap | Sophear | Sophia | Sophie | Sora | Soren | Sorin | Soryn | Soua | Soul | Sparrow | Spencer | Spenser | Spirit | Stace | Stacey | Staci | Stacie | Stacy | Staley | Stanley | Star | Starley | Starlin | Starling | Starr | Stav | Steele | Stefan | Stefanie | Stella | Stellar | Stephan | Stephane | Stephanie | Stephany | Stephen | Stephon | Sterling | Steve | Steven | Stevie | Stewart | Stirling | Stone | Stoney | Storm | Storme | Stormey | Stormy | Story | Stratton | Stuart | Su | Success | Sue | Suhail | Sukhman | Sukhpreet | Sullivan | Sully | Sumeet | Summer | Summit | Sumner | Sun | Sundance | Sunday | Sundeep | Sundown | Sung | Sunni | Sunnie | Sunny | Sunshine | Suriya | Surya | Susan | Susana | Susie | Sutter | Sutton | Suzanne | Suzette | Swan | Sway | Swayze | Syaire | Syan | Sybil | Sydney | Syed | Sylar | Sylas | Syler | Sylva | Sylvan | Sylvania | Sylvesta | Sylvester | Sylvia | Symon | Syncere | Ta | Taaj | Tabby | Taber | Tabitha | Tabor | Tacari | Tacoma | Tacori | Tae | Taedyn | Taegan | Taegen | Taeler | Taelin | Taelor | Taelyn | Taetum | Tagan | Tagen | Tahari | Tahj | Tahja | Tahjae | Tahjai | Tahje | Tahsin | Tai | Taigan | Taige | Taigen | Tailer | Tailor | Tailyn | Tait | Taite | Taitum | Taiwan | Taiwo | Taj | Taja | Tajae | Tajah | Tajai | Tajay | Taje | Tajee | Taji | Takai | Takari | Takaya | Takoda | Takota | Tal | Talan | Talbot | Talen | Taler | Tali | Talia | Talin | Talis | Talley | Tallie | Tallis | Tally | Tallyn | Talmadge | Talon | Talor | Talyn | Talynn | Tam | Tamar | Tamara | Tamari | Tamaris | Tamauri | Tamboura | Tameka | Tameron | Tami | Tamija | Tamika | Tamiko | Tamilore | Tamir | Tammie | Tammy | Tamra | Tamryn | Tamu | Tan | Tana | Tanaka | Tanay | Tandy | Tani | Tania | Tanis | Tanisha | Tannar | Tanner | Tanny | Tanveer | Tanvir | Tanya | Tao | Tara | Tarahji | Taraji | Taran | Taren | Tari | Taria | Tarik | Tarin | Tariq | Taris | Taron | Tarran | Tarrell | Tarren | Tarrin | Tarron | Tarry | Tarryn | Tary | Taryn | Tasean | Tasha | Tashan | Tashaun | Tashawn | Tashi | Tashon | Tasi | Tasnim | Tate | Tatem | Tatiana | Tatum | Tatumn | Tatym | Taurean | Taurus | Tavares | Tavaris | Taven | Tavi | Tavin | Tavion | Tavis | Tavita | Tavon | Tavyn | Tawan | Tawn | Tay | Tayari | Taydem | Tayden | Taye | Tayen | Taygan | Taygen | Taylan | Taylar | Taylen | Tayler | Taylin | Taylon | Taylor | Taylour | Taylyn | Tayon | Tayshaun | Tayte | Taytem | Tayten | Tayton | Taytum | Tayven | Tayvin | Taz | Taziyah | Teagan | Teagen | Teaghan | Teagon | Teague | Teagyn | Teal | Ted | Teddie | Teddy | Tee | Teegan | Tegan | Teghan | Tehran | Teigan | Teigen | Teighan | Teja | Tekoa | Tela | Telly | Temeka | Temiloluwa | Temitayo | Temitope | Temple | Teniola | Tennessee | Tennie | Tennille | Tenny | Tennyson | Tenzin | Tenzing | Teon | Terah | Teran | Tere | Terell | Terelle | Teren | Terence | Teresa | Terez | Teri | Terin | Termaine | Terra | Terrace | Terral | Terran | Terrance | Terre | Terrel | Terrell | Terrelle | Terren | Terrence | Terri | Terrian | Terrie | Terril | Terrill | Terrin | Terrion | Terris | Terron | Terry | Terryl | Terrylee | Terryn | Tery | Teryl | Teryn | Tesla | Tessa | Tessie | Teva | Tevin | Tevis | Tevyn | Tex | Texas | Teygan | Teylor | Thad | Thaddeus | Thai | Thailand | Thailer | Thailyn | Thamar | Thang | Thanh | Thao | Tharon | Thayer | Thea | Thelma | Thelmer | Theo | Theodora | Theodore | Theon | Theory | Theresa | Therese | Theron | Theryn | Thi | Thien | Thienan | Thierry | Thoma | Thomas | Thu | Thuan | Thurman | Thuy | Tia | Tian | Tiana | Tiara | Tiegan | Tieler | Tien | Tiera | Tiernan | Tierney | Tierra | Tierre | Tiffany | Tiger | Tija | Tijah | Tilar | Tilden | Tiler | Tillie | Tim | Timari | Timber | Timi | Timmie | Timmy | Timothy | Tina | Tiney | Ting | Tinsley | Tiny | Tion | Tione | Tionne | Tipton | Tishawn | Titus | Tiwan | Tj | Tkai | Tobbie | Tobe | Tobey | Tobi | Tobie | Tobin | Toby | Tobyn | Toccara | Tochi | Todd | Toi | Tollie | Tolulope | Toluwani | Toluwanimi | Tom | Toma | Tomas | Tomeka | Tomi | Tomie | Tomika | Tomiko | Tommi | Tommie | Tommy | Tommye | Tone | Toney | Toni | Tonia | Tonie | Tonja | Tonnie | Tony | Tonya | Topaz | Tora | Tore | Toree | Toren | Torey | Tori | Torian | Torie | Torii | Torin | Torino | Torrance | Torre | Torren | Torrence | Torrey | Torri | Torrian | Torrie | Torrin | Torris | Torry | Torryn | Tory | Toryn | Toshi | Toshua | Tovia | Toy | Toya | Toye | Trace | Tracee | Tracey | Traci | Tracie | Tracy | Tracye | Trae | Traeh | Tramaine | Tramine | Tran | Tranell | Trang | Trashawn | Travis | Travon | Trayce | Tre | Trea | Treasure | Trellis | Tremaine | Tremayne | Trenell | Trenidad | Trenity | Trent | Trenton | Treshaun | Treshawn | Tressie | Treva | Trevis | Trevon | Trevor | Trew | Trey | Tricia | Trillian | Trina | Trine | Trini | Trinidad | Trinidy | Trinity | Tris | Trisha | Trista | Tristain | Tristan | Tristen | Tristian | Tristin | Tristine | Triston | Tristyn | Troi | Tron | Troy | Troyce | Troye | Tru | Truby | Truc | Trudy | True | Truett | Truman | Trust | Truth | Trystan | Trysten | Trystin | Tryston | Trystyn | Tsering | Tsion | Tu | Tucker | Tully | Turner | Tuyen | Twan | Ty | Tyaire | Tyan | Tye | Tyeler | Tyger | Tyjae | Tyjah | Tyjai | Tyla | Tylan | Tylar | Tylee | Tylen | Tyler | Tylin | Tyller | Tylo | Tylor | Tylyn | Tylynn | Tymber | Tyme | Tyne | Tyonne | Tyquan | Tyra | Tyrae | Tyran | Tyre | Tyrea | Tyree | Tyreese | Tyrell | Tyrelle | Tyren | Tyrese | Tyria | Tyrian | Tyrice | Tyrie | Tyrin | Tyrion | Tyron | Tyrone | Tysen | Tyshaun | Tyshawn | Tyshon | Tyson | Tywan | Tziah | Uchechukwu | Uchenna | Udell | Uganda | Ugonna | Uhuru | Ukiah | Ula | Ulises | Ulysses | Unborn | Undra | Undrea | Unice | Unique | Unk | Unkown | Unnamed | Ura | Uri | Uriah | Uriel | Urijah | 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| Zong | Zora | Zuri | Zuriah | Zuriel | Zyah | Zyair | Zyaire | Zyan | Zyian | Zyien | Zyion | Zyon | Zyree |\n", 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"| 2011 | nan | nan | nan | 0.318182 | nan | 0.428571 | nan | nan | nan | nan | nan | nan | 0.00275193 | 0.878049 | nan | 0.583333 | 0.0328947 | nan | nan | nan | nan | nan | nan | nan | 0.00529101 | nan | nan | nan | nan | nan | 0.998416 | nan | 0.384615 | nan | 0.26087 | nan | nan | nan | 0.581395 | 0.477273 | nan | nan | 0.304348 | 0.0112867 | nan | nan | nan | nan | 0.5 | nan | 0.571429 | 0.210526 | nan | nan | 0.00211132 | 0.585366 | nan | nan | nan | nan | nan | 0.979652 | nan | 0.99596 | nan | 0.298246 | nan | nan | nan | nan | nan | 0.0235581 | nan | nan | 0.603774 | 0.0555556 | nan | nan | nan | nan | 0.86875 | nan | nan | 0.318182 | 0.150376 | 0.846154 | nan | nan | nan | 0.25641 | nan | nan | nan | 0.705882 | nan | nan | nan | nan | 0.0218929 | nan | nan | 0.448276 | nan | nan | 0.0358306 | 0.038806 | nan | nan | 0.979118 | 0.115942 | 0.117647 | 0.466667 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.85 | 0.7 | 0.845361 | 0.52 | nan | nan | nan | nan | nan | nan | nan | 0.631579 | 0.272727 | nan | nan | nan | nan | 0.0140808 | 0.35 | 0.00584532 | nan | 0.118644 | 0.636364 | nan | 0.992163 | nan | 0.294118 | nan | nan | nan | 0.984513 | 0.774194 | 0.277778 | nan | nan | nan | nan | nan | 0.075 | nan | nan | nan | nan | nan | nan | nan | 0.24 | 0.702128 | nan | nan | nan | nan | nan | nan | 0.887097 | 0.097561 | nan | nan | 0.642857 | 0.811321 | nan | nan | nan | 0.00383305 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0617761 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.00161812 | 0.0318471 | 0.766667 | nan | nan | nan | nan | nan | 0.0398568 | 0.998227 | 0.00216257 | nan | nan | nan | nan | nan | 0.147059 | nan | nan | 0.754491 | nan | 0.920635 | 0.814588 | nan | nan | nan | 0.421053 | 0.578947 | nan | nan | nan | 0.533333 | 0.925926 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.326874 | nan | nan | 0.695652 | nan | 0.0397351 | nan | 0.83871 | nan | nan | nan | nan | nan | nan | 0.995301 | nan | nan | 0.690909 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.00498504 | nan | nan | nan | 0.996833 | 0.998172 | nan | nan | 0.814815 | nan | 0.98 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.175 | nan | nan | nan | 0.5 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.766234 | nan | 0.736842 | nan | nan | 0.999168 | nan | nan | nan | 0.887324 | nan | 0.996464 | nan | nan | 0.826433 | nan | nan | nan | 0.0486111 | 0.983936 | 0.0391566 | nan | 0.41 | 0.351818 | nan | 0.748252 | 0.272727 | nan | 0.979424 | 0.371429 | 0.267606 | 0.294118 | nan | nan | nan | nan | nan | nan | 0.72 | 0.998901 | 0.333333 | nan | 0.140351 | nan | nan | 0.777778 | nan | 0.727273 | 0.726027 | nan | 0.272727 | nan | nan | 0.5 | 0.0114367 | nan | nan | 0.173077 | nan | nan | nan | 0.782051 | 0.779661 | 0.605263 | 0.6875 | 0.5 | nan | nan | nan | nan | 0.291667 | 0.997722 | nan | 0.54902 | nan | nan | nan | nan | nan | nan | nan | 0.83871 | nan | nan | 0.122449 | 0.545455 | nan | 0.204082 | 0.0411074 | 0.798913 | nan | nan | 0.580645 | nan | nan | 0.994329 | nan | 0.228571 | nan | nan | nan | 0.00082875 | nan | nan | nan | nan | 0.013986 | 0.588235 | nan | 0.163499 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0053286 | nan | nan | 0.793103 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.16 | nan | 0.363636 | nan | 0.998762 | nan | nan | nan | nan | nan | nan | nan | nan | 0.985325 | 0.0310559 | nan | 0.00168138 | nan | nan | nan | nan | nan | nan | 0.970711 | nan | nan | nan | nan | nan | 0.705882 | nan | nan | nan | nan | nan | nan | nan | nan | 0.736842 | nan | nan | nan | 0.0943396 | nan | nan | nan | nan | nan | nan | 0.0227273 | nan | nan | nan | nan | nan | nan | 0.703557 | nan | nan | nan | nan | nan | nan | 0.135593 | nan | 0.048 | nan | nan | nan | 0.209627 | 0.989005 | nan | 0.736842 | 0.106996 | nan | nan | nan | nan | nan | nan | 0.673469 | 0.791069 | nan | nan | 0.434783 | nan | 0.328767 | 0.393617 | 0.116883 | 0.420455 | nan | nan | nan | 0.545455 | nan | nan | nan | nan | 0.736842 | nan | nan | 0.359375 | nan | nan | nan | 0.341176 | 0.735294 | 0.46875 | nan | 0.461538 | nan | nan | 0.055794 | 0.538462 | 0.88 | nan | nan | nan | nan | 0.423163 | 0.533333 | 0.694444 | nan | 0.454545 | nan | nan | 0.649123 | 0.75 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.391304 | nan | nan | 0.15625 | nan | 0.714286 | nan | 0.8125 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.777108 | nan | 0.0604396 | nan | nan | 0.683333 | nan | nan | 0.0663984 | 0.166667 | 0.7 | nan | 0.736842 | nan | nan | nan | 0.787234 | nan | 0.0847458 | nan | nan | nan | 0.979167 | 0.607143 | nan | nan | 0.0104439 | nan | nan | nan | nan | nan | nan | 0.993192 | nan | nan | nan | nan | nan | nan | 0.240964 | 0.202703 | 0.062069 | 0.784884 | nan | 0.862069 | nan | nan | nan | nan | 0.145833 | 0.907767 | nan | nan | nan | 0.0733333 | 0.5 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.613636 | 0.380282 | 0.0119863 | nan | nan | nan | nan | 0.998297 | 0.98058 | 0.943038 | nan | 0.333333 | 0.461538 | 0.179487 | nan | nan | nan | 0.998657 | nan | nan | nan | 0.141317 | nan | nan | 0.077381 | nan | nan | nan | nan | nan | nan | 0.666667 | nan | nan | 0.5 | 0.158192 | 0.01196 | nan | nan | 0.480263 | nan | 0.538462 | 0.998671 | 0.998903 | nan | 0.0810811 | nan | nan | 0.647059 | 0.808511 | 0.91358 | 0.486486 | nan | nan | 0.56 | 0.962617 | 0.981378 | nan | nan | 0.803945 | nan | 0.179104 | nan | nan | nan | nan | 0.105263 | 0.119048 | nan | 0.26087 | nan | nan | 0.695652 | 0.807692 | 0.75 | nan | nan | nan | nan | 0.583333 | nan | 0.545455 | 0.00328407 | 0.0182927 | 0.118343 | nan | 0.0650407 | 0.0226692 | nan | 0.0299728 | 0.162162 | nan | 0.87931 | 0.991843 | nan | nan | nan | nan | 0.394737 | nan | nan | nan | nan | nan | nan | 0.636364 | nan | 0.8 | 0.970588 | 0.647383 | nan | 0.925373 | nan | nan | nan | 0.352941 | nan | nan | 0.625 | 0.54386 | nan | nan | nan | nan | nan | nan | 0.166667 | 0.286822 | nan | 0.763158 | nan | nan | nan | nan | 0.5 | nan | nan | nan | 0.990619 | 0.951655 | nan | 0.8 | 0.16129 | nan | nan | nan | nan | 0.461538 | 0.5 | nan | 0.5 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0398773 | nan | nan | nan | nan | 0.137255 | 0.285714 | 0.588235 | 0.825 | nan | nan | 0.982301 | 0.134146 | nan | nan | nan | 0.416667 | 0.259067 | nan | nan | nan | 0.0307868 | nan | nan | 0.0290155 | nan | 0.0137741 | nan | nan | nan | nan | nan | nan | nan | 0.99789 | nan | nan | nan | nan | nan | nan | nan | 0.00175922 | nan | nan | 0.125 | 0.0431145 | nan | nan | nan | nan | 0.195604 | 0.416667 | 0.842857 | 0.0490514 | 0.655172 | 0.625 | 0.0311804 | nan | 0.357143 | nan | 0.48 | 0.814815 | nan | 0.864542 | nan | nan | nan | 0.95098 | nan | nan | nan | nan | nan | nan | nan | nan | 0.5 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.538462 | nan | nan | nan | nan | 0.757576 | nan | nan | nan | nan | nan | nan | nan | 0.895833 | 0.0149626 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.428571 | 0.151515 | 0.0309735 | 0.777448 | 0.960938 | 0.044843 | 0.0769231 | 0.0548061 | nan | nan | 0.902597 | nan | nan | nan | nan | nan | 0.547945 | 0.0683761 | 0.111111 | 0.040146 | 0.666667 | 0.5 | 0.902439 | nan | nan | nan | 0.5 | 0.388889 | 0.954955 | 0.0578778 | nan | nan | 0.835821 | nan | nan | nan | nan | 0.0519481 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.307692 | 0.0674847 | 0.672414 | nan | 0.0278638 | 0.25 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.647059 | 0.0674157 | nan | 0.00690926 | nan | 0.00880339 | 0.0253623 | 0.454545 | nan | 0.106742 | 0.388889 | 0.890511 | 0.970223 | 0.0126582 | nan | nan | 0.714286 | nan | nan | nan | 0.230769 | 0.789474 | nan | nan | nan | nan | nan | nan | nan | 0.00204577 | nan | 0.869048 | nan | nan | nan | nan | nan | nan | 0.189873 | nan | 0.0266798 | nan | nan | nan | nan | nan | nan | 0.0110367 | 0.144928 | 0.28 | nan | nan | 0.00464396 | nan | 0.0086741 | 0.0560748 | 0.976812 | 0.0408745 | 0.284314 | 0.00906149 | 0.481013 | 0.867347 | nan | nan | nan | nan | nan | 0.139241 | 0.117647 | 0.833333 | nan | nan | nan | nan | nan | nan | 0.5 | 0.357143 | 0.735294 | nan | 0.4 | nan | nan | nan | nan | 0.893939 | 0.5 | 0.0688836 | nan | nan | 0.47619 | nan | 0.235294 | 0.0843373 | 0.115385 | nan | 0.375 | nan | nan | nan | 0.0779221 | 0.0230326 | nan | 0.294118 | nan | 0.36 | nan | nan | nan | 0.00154416 | nan | nan | nan | 0.722222 | 0.99865 | nan | nan | 0.374545 | nan | nan | nan | nan | nan | nan | 0.285714 | nan | 0.277778 | nan | 0.350711 | nan | 0.851272 | 0.758621 | nan | nan | nan | nan | 0.985586 | nan | nan | nan | nan | nan | 0.983562 | nan | 0.482759 | nan | nan | nan | 0.393939 | 0.6875 | 0.576923 | nan | nan | 0.5 | nan | 0.615385 | nan | nan | 0.476852 | 0.12 | nan | 0.0925926 | 0.226415 | 0.0461847 | 0.00373281 | 0.0801282 | nan | nan | 0.880282 | nan | nan | nan | nan | 0.989221 | nan | 0.0212054 | nan | nan | nan | 0.00198638 | 0.583333 | nan | 0.25 | 0.0328436 | nan | nan | 0.0869565 | 0.0909091 | 0.964956 | 0.148148 | 0.619048 | nan | 0.354839 | nan | 0.955959 | 0.994118 | 0.996407 | nan | nan | nan | 0.00279188 | nan | nan | 0.0972222 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0779221 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.171875 | nan | 0.00574053 | 0.011371 | 0.93617 | nan | nan | 0.164557 | 0.042735 | 0.25 | nan | 0.207207 | nan | 0.608696 | nan | nan | nan | 0.4 | 0.0184739 | 0.909091 | 0.326316 | nan | nan | 0.333333 | nan | 0.115385 | 0.416667 | nan | nan | nan | nan | 0.0722892 | nan | 0.000897756 | 0.0462963 | nan | 0.985419 | 0.0857143 | nan | 0.327273 | 0.139706 | 0.411765 | 0.0300231 | nan | nan | nan | nan | nan | nan | 0.833333 | nan | 0.0888889 | nan | nan | 0.401869 | nan | nan | nan | 0.52381 | 0.6 | nan | nan | nan | 0.0489925 | 0.409548 | nan | nan | 0.454545 | 0.261905 | 0.0769231 | 0.307692 | 0.0859717 | 0.632812 | 0.642857 | 0.998995 | nan | nan | 0.993958 | nan | 0.650943 | nan | 0.0523416 | 0.188679 | nan | nan | 0.804233 | 0.804878 | 0.0363636 | nan | nan | nan | nan | 0.0100604 | 0.0551181 | nan | 0.0642202 | nan | nan | nan | nan | nan | nan | nan | 0.194444 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.464286 | nan | nan | 0.30303 | nan | nan | nan | 0.00119617 | nan | nan | nan | nan | nan | nan | 0.708333 | nan | nan | nan | 0.960168 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.787879 | nan | nan | nan | nan | 0.15625 | 0.0349157 | nan | 0.600733 | 0.0221868 | 0.105263 | nan | 0.236842 | nan | nan | nan | 0.41953 | 0.00380518 | nan | nan | 0.432432 | nan | nan | nan | nan | nan | nan | 0.957374 | nan | 0.428571 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.466667 | nan | 0.0421642 | 0.925795 | 0.212121 | nan | nan | nan | 0.785714 | 0.95283 | nan | nan | 0.205128 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.325581 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0217391 | nan | nan | nan | nan | nan | nan | 0.428571 | nan | nan | 0.181818 | nan | nan | nan | nan | nan | nan | nan | 0.0151149 | nan | nan | nan | nan | nan | nan | 0.185676 | nan | nan | nan | 0.75 | nan | nan | nan | nan | nan | 0.297405 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.588235 | nan | nan | nan | nan | nan | nan | 0.973865 | nan | 0.00128884 | 0.28169 | 0.844322 | 0.987047 | 0.360421 | nan | nan | nan | 0.711712 | nan | nan | nan | nan | nan | 0.0110365 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.157895 | nan | nan | 0.303571 | nan | nan | nan | 0.178571 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.916667 | nan | nan | 0.95122 | nan | nan | nan | 0.185393 | nan | nan | 0.991424 | nan | nan | nan | 0.615385 | nan | nan | nan | 0.454545 | nan | nan | 0.461538 | nan | nan | nan | nan | nan | 0.68 | nan | 0.998999 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.647887 | nan | nan | 0.0157608 | nan | nan | nan | 0.717391 | nan | nan | nan | nan | 0.00130769 | 0.962121 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.821429 | nan | nan | nan | nan | nan | 0.949153 | nan | nan | nan | nan | nan | nan | 0.533333 | nan | nan | 0.910714 | nan | nan | 0.464286 | 0.996635 | nan | nan | nan | nan | nan | 0.0178571 | 0.5 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.00518903 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.925532 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.52381 | 0.44 | 0.0287356 | 0.191489 | nan | nan | 0.595238 | 0.4 | 0.00911993 | 0.0121279 | nan | nan | nan | nan | nan | 0.75 | 0.94709 | 0.052729 | 0.00313329 | 0.0307018 | 0.5 | nan | 0.00213936 | nan | nan | nan | 0.166667 | 0.00321101 | 0.746835 | nan | 0.146341 | nan | nan | 0.001608 | nan | nan | nan | nan | nan | nan | 0.214286 | nan | 0.545455 | 0.00321691 | 0.939759 | 0.00148408 | nan | nan | nan | nan | nan | nan | nan | 0.0202575 | 0.176471 | nan | nan | nan | nan | 0.00812065 | nan | 0.11165 | nan | nan | nan | nan | nan | nan | nan | 0.0191939 | 0.882353 | nan | 0.705882 | 0.342857 | nan | nan | 0.263158 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.75 | nan | 0.695652 | nan | nan | nan | nan | 0.490566 | 0.0397878 | nan | nan | nan | nan | 0.388889 | nan | nan | 0.0769231 | nan | nan | nan | 0.853409 | nan | nan | nan | 0.416667 | 0.0406504 | nan | nan | nan | nan | 0.52381 | nan | nan | nan | 0.819672 | nan | nan | nan | nan | nan | nan | 0.00447094 | nan | nan | nan | nan | nan | 0.0755287 | nan | 0.0257549 | nan | nan | 0.010453 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.764706 | nan | nan | nan | 0.263158 | nan | 0.480769 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.125 | nan | 0.710938 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.728571 | 0.807692 | nan | nan | nan | nan | nan | nan | nan | nan | 0.0941176 | 0.32 | nan | 0.508847 | 0.58 | nan | nan | nan | nan | nan | nan | nan | 0.197987 | nan | 0.782609 | 0.3125 | nan | nan | 0.214286 | 0.454545 | 0.339806 | nan | nan | 0.118943 | nan | nan | 0.94717 | 0.00265604 | nan | nan | nan | nan | nan | nan | 0.325 | 0.611111 | nan | 0.921415 | nan | 0.615385 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.357143 | 0.904762 | nan | nan | nan | 0.0016358 | nan | 0.647059 | 0.4 | nan | 0.996011 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.545455 | nan | nan | 0.00576701 | nan | nan | 0.00915332 | nan | nan | nan | nan | nan | nan | 0.28 | nan | nan | nan | nan | nan | 0.881356 | nan | 0.784884 | nan | nan | nan | 0.904459 | nan | nan | nan | nan | nan | nan | nan | nan | 0.5 | nan | 0.0775623 | nan | 0.040404 | 0.0321285 | 0.046729 | nan | nan | nan | 0.416667 | nan | nan | nan | nan | nan | nan | nan | 0.0206612 | nan | nan | nan | nan | nan | 0.0145773 | nan | nan | nan | nan | nan | 0.384615 | nan | nan | nan | nan | nan | nan | 0.0280374 | 0.136364 | nan | 0.0168919 | nan | nan | nan | 0.0956522 | nan | nan | 0.44898 | 0.721311 | 0.0425532 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.132353 | nan | 0.00143646 | nan | nan | 0.0143266 | 0.6 | nan | nan | nan | 0.0233281 | nan | nan | nan | nan | nan | 0.22 | nan | nan | nan | 0.0231884 | 0.527778 | nan | nan | 0.555556 | nan | nan | nan | 0.0717949 | 0.482517 | nan | 0.464286 | nan | nan | nan | nan | nan | 0.0483092 | 0.714286 | nan | nan | 0.00483481 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.28 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.00597426 | 0.321429 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.177778 | nan | 0.473684 | nan | nan | nan | nan | nan | 0.987445 | 0.907407 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.4375 | nan | nan | 0.116667 | nan | 0.545455 | nan | 0.538462 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.5 | nan | nan | nan | nan | nan | 0.271739 | nan | 0.775862 | nan | nan | nan | nan | nan | 0.5 | 0.44 | 0.1 | nan | nan | 0.173913 | nan | nan | 0.02149 | nan | nan | nan | 0.291457 | 0.4 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.00233427 | 0.394958 | nan | nan | 0.2 | 0.136364 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.583333 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.545455 | nan | nan | nan | nan | 0.361702 | nan | nan | nan | nan | nan | 0.0873786 | 0.466667 | 0.996938 | nan | nan | nan | nan | nan | 0.136929 | nan | nan | 0.0609756 | nan | 0.384615 | nan | 0.0611154 | 0.65625 | nan | nan | nan | 0.464286 | 0.109833 | nan | nan | nan | nan | nan | nan | 0.579327 | nan | 0.833333 | nan | nan | nan | nan | nan | nan | nan | nan | 0.464286 | nan | 0.458333 | nan | 0.88 | nan | nan | nan | nan | 0.927835 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.00182959 | 0.647059 | nan | nan | nan | 0.0378378 | 0.1 | nan | 0.0288568 | 0.291667 | nan | 0.352941 | nan | 0.571429 | nan | nan | nan | 0.101266 | nan | nan | nan | nan | 0.842105 | nan | 0.820988 | nan | 0.697479 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.00257495 | nan | nan | nan | 0.0183673 | nan | 0.433054 | nan | 0.277778 | nan | nan | nan | 0.419355 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0458716 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0412574 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0252366 | 0.86747 | 0.142857 | 0.121255 | nan | 0.457627 | 0.592593 | nan | nan | 0.275 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.276596 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0577053 | 0.671875 | nan | 0.36 | 0.315789 | nan | nan | nan | nan | nan | 0.3125 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0113069 | 0.316327 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.841463 | nan | nan | 0.140351 | nan | 0.545455 | nan | nan | nan | nan | nan | 0.869347 | 0.00340136 | nan | nan | nan | 0.0394737 | nan | nan | nan | nan | nan | nan | nan | nan | 0.352941 | 0.873239 | nan | nan | nan | 0.905512 | 0.333333 | nan | nan | nan | 0.416667 | nan | 0.395349 | 0.454545 | nan | 0.545455 | 0.666667 | nan | nan | nan | 0.105263 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.00525007 | 0.914141 | 0.323529 | nan | nan | nan | 0.275362 | 0.00236136 | 0.4 | nan | 0.118812 | nan | nan | nan | nan | nan | 0.308036 | nan | nan | 0.814815 | 0.639175 | nan | 0.998117 | 0.756757 | nan | nan | 0.999374 | nan | 0.951691 | nan | nan | 0.595238 | 0.188131 | 0.159306 | 0.83871 | 0.373494 | 0.883459 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.255319 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.108434 | 0.333333 | nan | 0.913793 | nan | nan | nan | nan | nan | nan | 0.921875 | 0.855072 | 0.909091 | nan | nan | 0.4 | nan | 0.979118 | 0.57971 | nan | nan | nan | nan | 0.927835 | nan | nan | nan | 0.609756 | 0.985 | 0.784327 | nan | nan | nan | nan | nan | nan | 0.999228 | nan | 0.998246 | 0.00298842 | nan | nan | nan | 0.837209 | nan | 0.00310366 | nan | 0.888889 | nan | nan | 0.589623 | 0.470588 | nan | 0.683824 | nan | nan | nan | nan | nan | nan | 0.73913 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.666667 | 0.5 | nan | nan | nan | 0.0925926 | 0.00387597 | nan | nan | 0.6875 | nan | nan | 0.961566 | nan | nan | 0.368421 | 0.835616 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.454545 | nan | nan | 0.695652 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.00131768 | nan | nan | nan | nan | nan | 0.0187266 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0114299 | 0.32 | nan | nan | 0.998808 | 0.415584 | 0.136364 | 0.0103321 | 0.12 | nan | 0.375 | nan | 0.0849057 | nan | nan | nan | 0.708333 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0544474 | 0.191011 | 0.837838 | nan | nan | nan | nan | nan | 0.997586 | nan | 0.866953 | 0.787879 | nan | nan | nan | nan | nan | nan | nan | nan | 0.666667 | 0.291667 | nan | nan | nan | 0.575758 | 0.814815 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.00483092 | nan | nan | nan | 0.545455 | 0.185185 | nan | nan | 0.00292569 | nan | nan | nan | nan | nan | 0.25 | nan | nan | nan | 0.4 | nan | nan | 0.222222 | nan | 0.634463 | 0.0144665 | 0.0107914 | nan | 0.440415 | nan | nan | nan | 0.428571 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0619469 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.118993 | nan | nan | nan | nan | nan | 0.353562 | 0.00956023 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.685714 | nan | nan | nan | nan | 0.126761 | nan | nan | nan | nan | nan | nan | nan | nan | 0.00676983 | nan | 0.490196 | 0.55814 | nan | 0.993177 | nan | 0.15625 | nan | 0.00720461 | nan | nan | 0.00239425 | 0.0220264 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.222222 | nan | nan | nan | nan | nan | nan | nan | 0.00257202 | nan | nan | nan | nan | nan | nan | nan | nan | 0.00188428 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.625 | 0.0757576 | 0.977406 | nan | nan | nan | nan | 0.536 | nan | nan | 0.272727 | 0.00301464 | nan | nan | 0.783784 | nan | nan | nan | nan | nan | nan | nan | nan | 0.233333 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.333333 | 0.998525 | 0.139655 | 0.6 | 0.5 | nan | 0.0221239 | 0.00723589 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.00510051 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.275862 | nan | nan | nan | 0.998952 | 0.571429 | nan | nan | nan | 0.125 | 0.945813 | nan | 0.00457038 | nan | nan | nan | nan | 0.00513573 | nan | nan | nan | nan | 0.184874 | 0.673077 | nan | nan | 0.110169 | 0.0284021 | nan | 0.825397 | nan | nan | nan | nan | nan | nan | nan | 0.193548 | 0.0942029 | 0.00938708 | 0.583333 | 0.00692259 | 0.214286 | nan | nan | nan | 0.903483 | nan | nan | nan | nan | nan | nan | nan | 0.511628 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.352941 | nan | 0.0566038 | 0.973262 | 0.973422 | nan | 0.363636 | nan | 0.560976 | nan | nan | 0.10687 | nan | nan | 0.294118 | nan | 0.788321 | 0.708333 | nan | 0.998725 | 0.454545 | nan | nan | nan | 0.426829 | 0.997439 | nan | nan | nan | nan | 0.912409 | nan | 0.444444 | 0.724138 | 0.0933333 | 0.322581 | nan | nan | nan | nan | 0.25 | nan | 0.468085 | nan | 0.999086 | nan | nan | nan | nan | nan | 0.481481 | nan | nan | nan | nan | nan | nan | 0.5 | 0.0862069 | 0.951754 | 0.241379 | nan | 0.662326 | nan | nan | 0.793103 | 0.972803 | 0.583333 | nan | 0.829268 | nan | nan | nan | 0.774194 | nan | 0.920741 | nan | nan | nan | nan | 0.072 | 0.00489929 | nan | nan | nan | nan | 0.867257 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.454545 | 0.79257 | 0.857143 | 0.321429 | 0.269427 | 0.325 | 0.194444 | 0.104167 | 0.538462 | 0.0526316 | 0.263158 | nan | 0.882353 | nan | 0.868852 | nan | 0.994616 | nan | nan | nan | nan | nan | nan | nan | 0.986987 | nan | nan | nan | nan | nan | nan | 0.294118 | 0.0645161 | 0.762712 | nan | nan | nan | 0.000967787 | 0.847826 | nan | nan | nan | nan | nan | 0.444444 | 0.384615 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.00713012 | nan | nan | 0.845679 | nan | nan | nan | 0.37037 | nan | nan | nan | nan | nan | 0.673469 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.016862 | 0.612903 | nan | nan | nan | nan | nan | 0.0260962 | nan | 0.342857 | nan | 0.177778 | 0.272727 | 0.0735294 | nan | nan | 0.00248668 | nan | nan | nan | nan | nan | nan | 0.62963 | nan | nan | nan | nan | nan | nan | 0.68 | nan | nan | nan | nan | nan | nan | nan | 0.9 | 0.961814 | 0.623529 | nan | nan | nan | nan | nan | 0.55102 | 0.875 | 0.771084 | nan | 0.483871 | nan | nan | nan | nan | nan | nan | 0.6 | nan | nan | 0.292208 | nan | 0.352941 | nan | nan | nan | nan | nan | nan | 0.886598 | 0.995265 | 0.642857 | nan | nan | nan | nan | nan | 0.466667 | 0.000833594 | nan | nan | 0.998694 | nan | nan | nan | nan | 0.00246103 | nan | nan | nan | 0.238095 | nan | 0.714286 | nan | 0.0218978 | nan | 0.2 | nan | nan | nan | 0.993565 | nan | nan | nan | nan | nan | nan | nan | 0.90411 | nan | nan | nan | nan | nan | nan | 0.856322 | nan | 0.995066 | nan | nan | nan | nan | 0.84252 | nan | 0.75 | nan | nan | nan | nan | nan | nan | nan | 0.0576923 | 0.0101064 | 0.895833 | 0.96732 | nan | nan | nan | nan | 0.000611098 | nan | 0.616438 | nan | 0.00225515 | 0.296296 | 0.296875 | nan | 0.001129 | 0.0414201 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.764706 | nan | nan | nan | 0.111111 | nan | 0.982327 | 0.0642382 | nan | nan | nan | nan | nan | nan | 0.230769 | 0.5 | nan | 0.824561 | nan | nan | nan | 0.625532 | nan | nan | 0.340426 | nan | 0.0957447 | nan | nan | 0.414966 | 0.695652 | 0.571429 | nan | nan | 0.450704 | 0.920925 | nan | 0.0294118 | nan | nan | 0.538462 | nan | nan | nan | nan | nan | nan | nan | 0.166667 | 0.177419 | 0.116279 | nan | 0.19403 | 0.126481 | nan | 0.2 | nan | 0.546667 | 0.8125 | nan | nan | nan | 0.0892857 | nan | 0.823232 | 0.947826 | 0.106557 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.105263 | 0.0503145 | nan | nan | 0.272727 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.716418 | nan | 0.5 | nan | 0.034153 | nan | nan | nan | nan | 0.295455 | nan | nan | 0.375 | 0.670968 | nan | 0.919431 | nan | nan | nan | nan | nan | nan | nan | 0.444444 | nan | nan | nan | nan | 0.0495356 | nan | nan | 0.242424 | 0.333333 | nan | nan | nan | nan | nan | nan | nan | nan | 0.00226142 | nan | 0.0224189 | nan | 0.615385 | 0.641026 | nan | 0.90991 | 0.72093 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.217391 | 0.0790861 | nan | 0.5 | nan | nan | 0.533333 | nan | nan | 0.0569106 | nan | nan | nan | nan | 0.3125 | nan | nan | nan | nan | nan | nan | nan | nan | 0.65 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.862069 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.153846 | nan | nan | nan | 0.714286 | nan | nan | nan | nan | nan | 0.73913 | 0.0949868 | 0.118421 | nan | nan | 0.625 | nan | nan | nan | nan | nan | nan | 0.998376 | nan | nan | 0.666667 | 0.0015403 | 0.0219436 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0120482 | nan | 0.21875 | nan | nan | nan | nan | nan | 0.01001 | 0.025522 | 0.00421496 | 0.048 | 0.00269906 | 0.22 | nan | 0.188406 | nan | 0.962733 | 0.0349819 | 0.93719 | nan | nan | nan | 0.761905 | nan | 0.172691 | 0.964455 | 0.636364 | 0.4 | 0.0592671 | 0.583333 | 0.148936 | 0.199187 | nan | 0.214286 | 0.0366133 | 0.315789 | nan | 0.565217 | 0.416667 | nan | 0.198675 | 0.883333 | 0.985887 | nan | 0.118792 | nan | 0.666667 | nan | 0.573529 | nan | 0.875 | nan | nan | 0.842949 | 0.953237 | 0.754717 | nan | 0.12 | nan | nan | nan | nan | nan | nan | nan | nan | 0.615385 | nan | 0.564103 | 0.046875 | nan | nan | nan | 0.994329 | nan | nan | nan | 0.371429 | nan | nan | nan | 0.217228 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.105691 | 0.538462 | nan | nan | nan | nan | nan | nan | 0.965854 | nan | nan | nan | nan | 0.997372 | 0.235294 | nan | 0.0655738 | nan | nan | 0.7 | 0.730769 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.2 | 0.00210499 | nan | nan | nan | 0.189873 | 0.026178 | nan | nan | nan | nan | 0.75 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.942029 | 0.5625 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.119048 | nan | 0.026636 | nan | 0.3125 | 0.791667 | 0.183099 | 0.996577 | 0.550744 | 0.43299 | 0.00192031 | nan | 0.0128205 | nan | nan | nan | nan | 0.975089 | nan | 0.608696 | nan | 0.116279 | 0.257143 | nan | nan | nan | nan | nan | nan | nan | 0.902174 | nan | 0.357143 | 0.666667 | nan | nan | 0.647059 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.193548 | nan | nan | nan | 0.481013 | nan | 0.588235 | 0.872727 | 0.7 | 0.304348 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.421053 | 0.642857 | nan | 0.894737 | 0.890909 | 0.339286 | nan | nan | nan | 0.00424421 | 0.0609756 | 0.929461 | nan | 0.180162 | nan | nan | nan | nan | 0.565217 | 0.454545 | nan | 0.0019008 | nan | nan | nan | nan | nan | nan | 0.333333 | nan | 0.179012 | 0.00399361 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.35 | 0.00796951 | nan | nan | 0.00205098 | nan | nan | nan | nan | nan | nan | 0.73913 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.168732 | nan | 0.73913 | 0.112821 | 0.118644 | nan | 0.677966 | nan | 0.0773481 | nan | 0.684211 | 0.879636 | 0.961003 | nan | nan | nan | nan | nan | nan | 0.142857 | 0.0016253 | nan | nan | 0.00177483 | nan | 0.941748 | nan | nan | 0.00181633 | nan | 0.00322458 | nan | nan | nan | 0.5 | nan | nan | 0.00486027 | nan | nan | 0.327273 | nan | nan | nan | 0.785714 | 0.987179 | 0.935205 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.781609 | nan | nan | nan | nan | nan | 0.983137 | nan | nan | nan | nan | nan | nan | 0.0012815 | nan | nan | nan | nan | nan | 0.0239044 | nan | 0.0294233 | 0.5 | nan | nan | 0.181818 | nan | 0.0375 | 0.533333 | 0.552 | nan | 0.997932 | 0.00457756 | nan | 0.217391 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.619048 | 0.333333 | nan | 0.976744 | nan | nan | nan | nan | nan | nan | nan | 0.910448 | nan | nan | nan | nan | 0.521905 | 0.0018279 | nan | 0.928962 | 0.352941 | nan | nan | 0.0805861 | 0.8 | nan | nan | nan | nan | nan | 0.791667 | 0.86631 | nan | nan | 0.909091 | 0.294118 | nan | nan | 0.0114943 | 0.0102502 | 0.907914 | nan | 0.0290698 | nan | 0.301653 | nan | nan | nan | nan | 0.0420712 | nan | nan | 0.315789 | nan | 0.354839 | nan | 0.219512 | 0.272727 | 0.791667 | 0.993947 | nan | 0.347826 | 0.321429 | nan | nan | nan | 0.5625 | nan | nan | nan | nan | 0.206897 | nan | 0.141482 | nan | nan | 0.0361301 | 0.920635 | nan | 0.386973 | 0.863636 | 0.5 | nan | nan | nan | nan | 0.680851 | nan | nan | nan | 0.489796 | 0.208333 | nan | nan | 0.85 | nan | 0.992472 | nan | 0.384615 | nan | nan | 0.625 | 0.964029 | nan | 0.5 | nan | nan | 0.333333 | nan | nan | nan | nan | nan | nan | nan | nan | 0.676724 | 0.011655 | nan | nan | 0.769231 | nan | 0.19697 | nan | 0.988449 | nan | 0.347826 | nan | nan | 0.212121 | nan | 0.0753425 | nan | nan | nan | nan | 0.96124 | nan | nan | nan | nan | nan | 0.357143 | 0.615385 | nan | nan | nan | 0.28 | 0.350376 | 0.806452 | 0.535714 | nan | nan | 0.230769 | 0.42623 | nan | nan | nan | 0.0428571 | 0.15625 | 0.376344 | nan | 0.216216 | 0.222222 | nan | 0.0724014 | 0.496124 | nan | 0.230769 | 0.454545 | 0.555556 | nan | nan | 0.0934579 | 0.259259 | nan | nan | 0.804 | nan | nan | nan | 0.666667 | 0.35 | nan | nan | 0.583333 | nan | nan | nan | nan | 0.235294 | nan | 0.111111 | 0.833333 | nan | nan | 0.14 | nan | 0.857143 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.993688 | nan | 0.864078 | nan | 0.0342466 | 0.828571 | nan | nan | nan | nan | nan | nan | 0.3125 | nan | nan | 0.966038 | 0.666667 | nan | nan | 0.727941 | nan | nan | 0.875 | nan | nan | nan | 0.232143 | nan | 0.0742857 | 0.0583333 | nan | 0.840816 | 0.107143 | nan | nan | nan | nan | 0.0243902 | 0.638695 | nan | nan | nan | 0.56 | 0.52381 | nan | nan | nan | 0.00734214 | nan | nan | 0.975728 | nan | 0.197368 | nan | 0.998243 | nan | nan | 0.998806 | nan | nan | nan | nan | nan | nan | nan | nan | 0.722222 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.955112 | nan | 0.763158 | 0.949721 | 0.2 | 0.152542 | 0.421053 | 0.127923 | 0.972067 | 0.218182 | 0.291667 | nan | 0.594595 | nan | nan | 0.238095 | nan | 0.998844 | 0.878505 | nan | 0.913793 | 0.998677 | nan | 0.863128 | nan | 0.97096 | 0.30303 | 0.6 | nan | nan | nan | nan | nan | nan | 0.133758 | 0.263158 | 0.0506667 | 0.357143 | 0.333333 | nan | nan | 0.222222 | 0.137795 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.681818 | nan | nan | nan | nan | nan | nan | 0.0666667 | 0.666667 | 0.461538 | nan | nan | 0.0861723 | nan | 0.131783 | nan | 0.731707 | nan | 0.743243 | nan | nan | 0.0223048 | nan | 0.0928571 | nan | 0.388889 | 0.157895 | 0.454545 | 0.0746269 | nan | nan | 0.642857 | 0.756098 | 0.772727 | 0.421053 | nan | nan | nan | nan | 0.125 | nan | nan | nan | nan | nan | nan | nan | nan | 0.39759 | nan | nan | nan | nan | 0.461538 | 0.416667 | nan | nan | 0.333333 | nan | 0.0250597 | nan | nan | 0.0254989 | 0.0760234 | 0.633333 | nan | nan | 0.25 | nan | 0.897045 | 0.849057 | nan | 0.980575 | nan | nan | nan | nan | nan | 0.642857 | nan | nan | 0.783333 | 0.22 | 0.142857 | 0.5 | nan | 0.213115 | nan | nan | 0.146341 | nan | nan | 0.821853 | 0.28 | 0.864 | 0.634615 | 0.4 | 0.6875 | nan | nan | nan | 0.0140281 | nan | nan | nan | 0.970149 | 0.957265 | nan | nan | nan | nan | nan | 0.965035 | 0.935937 | 0.871795 | 0.705882 | 0.978261 | nan | nan | 0.96315 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.935484 | 0.963746 | nan | nan | nan | 0.588235 | nan | nan | 0.97093 | nan | nan | nan | 0.340909 | nan | nan | nan | nan | 0.968944 | 0.992241 | nan | nan | nan | nan | nan | 0.0638298 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.916667 | nan | 0.364583 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.192308 | nan | nan | nan | 0.00228311 | nan | nan | nan | nan | nan | nan | nan | nan | 0.527778 | 0.761194 | 0.466667 | 0.5 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.13253 | nan | 0.6875 | 0.454545 | nan | 0.566667 | nan | 0.0169779 | nan | 0.32 | 0.259843 | nan | nan | nan | nan | nan | 0.294118 | nan | nan | nan | nan | nan | nan | nan | nan | 0.859375 | nan | nan | 0.115385 | nan | 0.178571 | 0.333333 | 0.135135 | nan | nan | nan | nan | nan | 0.545455 | nan | 0.928571 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0831974 | nan | 0.189189 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.991903 | 0.130435 | 0.0543131 | nan | 0.877193 | 0.730769 | 0.536585 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.571429 | nan | nan | nan | nan | 0.756098 | nan | nan | 0.416667 | 0.144105 | 0.0075662 | 0.984958 | nan | 0.981191 | nan | nan | 0.996582 | nan | nan | nan | nan | nan | nan | nan | 0.0496454 | nan | 0.30137 | 0.328947 | 0.333333 | 0.361702 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.409091 | nan | nan | nan | nan | 0.108108 | nan | nan | 0.5625 | nan | nan | 0.010989 | 0.0736842 | 0.0246914 | 0.294118 | 0.3125 | 0.405405 | nan | 0.0930233 | 0.52381 | nan | 0.624 | 0.72093 | 0.0390625 | 0.0292398 | nan | nan | 0.259259 | 0.14 | 0.384615 | 0.294118 | 0.826087 | 0.746988 | 0.0972222 | nan | nan | nan | nan | nan | 0.409091 | 0.0257353 | nan | nan | 0.0967742 | nan | 0.0144628 | 0.135 | nan | nan | 0.0676692 | 0.912548 | 0.8125 | nan | nan | nan | nan | nan | 0.0947368 | 0.454545 | 0.192308 | nan | nan | 0.953333 | nan | nan | nan | nan | 0.214286 | nan | nan | 0.30303 | nan | nan | nan | nan | nan | 0.6 | 0.981693 | nan | 0.0907298 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.042735 | nan | 0.168831 | nan | nan | nan | 0.533333 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.038835 | nan | nan | 0.0484848 | nan | 0.416667 | nan | 0.0992556 | 0.290076 | 0.017491 | 0.994163 | nan | 0.107595 | nan | 0.057971 | nan | 0.99862 | 0.564885 | nan | nan | 0.647059 | 0.868852 | nan | 0.223377 | 0.333333 | 0.272727 | 0.978648 | 0.96875 | 0.807692 | 0.794872 | nan | nan | 0.0486486 | nan | 0.163636 | 0.0473373 | 0.714286 | nan | 0.558065 | 0.128571 | nan | nan | nan | nan | 0.0430108 | nan | 0.965986 | nan | nan | nan | nan | nan | 0.7125 | 0.5 | nan | nan | 0.695652 | nan | nan | 0.727273 | nan | nan | nan | 0.203125 | nan | nan | 0.670732 | nan | 0.6875 | nan | nan | 0.457831 | 0.466667 | 0.903614 | nan | 0.3125 | 0.107955 | 0.208333 | nan | nan | nan | nan | nan | nan | 0.545455 | nan | nan | nan | 0.00500939 | 0.0352113 | 0.142857 | 0.00296706 | nan | 0.8 | 0.429142 | nan | 0.0764563 | 0.263158 | 0.0542125 | nan | nan | nan | nan | 0.833333 | 0.149038 | nan | nan | 0.333333 | nan | nan | nan | nan | nan | nan | nan | nan | 0.758621 | nan | nan | nan | nan | nan | 0.6 | nan | nan | nan | nan | nan | nan | 0.2 | nan | nan | nan | nan | nan | nan | nan | 0.142857 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.99573 | nan | nan | nan | 0.997808 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0909091 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0248307 | nan | nan | 0.0641026 | 0.745455 | nan | 0.998356 | 0.164729 | nan | 0.100478 | nan | nan | 0.998435 | 0.428571 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0539084 | nan | nan | 0.4 | 0.714286 | 0.0570175 | nan | nan | nan | 0.715405 | nan | nan | nan | nan | 0.00842993 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.423077 | 0.272289 | 0.146104 | 0.0347222 | 0.538462 | nan | nan | nan | nan | 0.192308 | 0.454545 | nan | nan | nan | 0.00266548 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.961538 | 0.959671 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.00494753 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.693333 | nan | nan | 0.113333 | nan | nan | 0.000966974 | 0.211111 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.998699 | nan | nan | 0.999144 | nan | 0.384615 | nan | 0.0109991 | nan | nan | nan | nan | nan | 0.428571 | nan | nan | 0.986333 | 0.990798 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.444444 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0407484 | nan | nan | 0.433962 | nan | 0.375 | nan | nan | nan | 0.0657895 | nan | nan | nan | nan | 0.786885 | 0.875208 | 0.983455 | nan | nan | nan | nan | nan | 0.0666667 | nan | nan | nan | nan | nan | 0.384615 | nan | nan | nan | 0.586735 | nan | nan | nan | 0.740741 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.907407 | 0.372093 | 0.0367647 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.444444 | nan | 0.159091 | 0.780488 | nan | nan | nan | nan | nan | nan | nan | nan | 0.0435714 | 0.00124952 | 0.150943 | nan | nan | nan | nan | nan | nan | nan | nan | 0.236364 | nan | nan | nan | nan | nan | nan | nan | nan | 0.00212811 | nan | 0.0733945 | nan | 0.000919012 | nan | nan | nan | nan | 0.631579 | 0.741935 | nan | nan | nan | nan | nan | nan | nan | nan | 0.810526 | 0.983607 | nan | nan | nan | nan | nan | nan | nan | 0.291667 | nan | nan | nan | nan | nan | 0.458333 | 0.108696 | nan | nan | nan | nan | nan | 0.784173 | nan | nan | nan | nan | nan | 0.79918 | 0.583333 | 0.607143 | 0.854545 | 0.827586 | nan | nan | nan | nan | nan | 0.428571 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.993443 | nan | nan | 0.5 | nan | nan | 0.294118 | nan | nan | nan | nan | 0.878049 | nan | 0.116541 | nan | 0.994908 | 0.0588235 | 0.0267896 | 0.0628931 | 0.857143 | 0.5 | nan | 0.998252 | nan | 0.996055 | nan | 0.416667 | nan | 0.227273 | nan | 0.132075 | nan | nan | nan | nan | nan | nan | nan | 0.192308 | 0.444444 | nan | 0.945652 | nan | 0.122449 | nan | 0.761905 | nan | nan | nan | 0.110577 | 0.73913 | nan | nan | nan | 0.553191 | nan | 0.0558511 | nan | nan | 0.318182 | 0.4 | 0.113208 | nan | 0.454545 | 0.998203 | nan | nan | nan | nan | 0.996383 | 0.538462 | 0.0247934 | nan | nan | 0.888889 | nan | nan | nan | 0.87931 | nan | 0.00383142 | nan | nan | 0.0246305 | nan | nan | 0.0689655 | nan | 0.5 | 0.00705645 | nan | 0.541667 | 0.910448 | nan | nan | nan | nan | 0.666667 | 0.384615 | nan | nan | nan | 0.794118 | 0.6 | nan | nan | nan | nan | 0.4 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.5 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.997827 | 0.99799 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.915493 | nan | nan | nan | 0.521127 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.454545 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.987387 | 0.958678 | nan | 0.871378 | 0.279279 | 0.658333 | nan | 0.671642 | 0.815534 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.454545 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.333333 | 0.545455 | nan | 0.00454499 | nan | 0.222222 | nan | 0.237037 | nan | nan | nan | nan | nan | nan | nan | nan | 0.3125 | nan | nan | nan | 0.00155192 | 0.722222 | nan | nan | 0.793103 | nan | 0.152174 | nan | nan | nan | 0.538462 | nan | nan | nan | nan | nan | nan | nan | 0.555556 | 0.0108696 | nan | nan | 0.0020202 | 0.416667 | nan | nan | nan | nan | nan | nan | nan | nan | 0.99801 | 0.4375 | nan | nan | nan | nan | nan | nan | nan | 0.416667 | 0.473684 | nan | 0.142857 | nan | nan | nan | nan | nan | nan | 0.65 | nan | nan | nan | nan | 0.17284 | nan | nan | nan | nan | 0.988531 | nan | 0.885526 | nan | 0.823529 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.791667 | nan | nan | 0.00912409 | nan | nan | nan | 0.99848 | nan | nan | 0.722222 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.198473 | 0.863636 | nan | nan | 0.875 | nan | nan | 0.15625 | nan | nan | nan | 0.989627 | nan | nan | nan | nan | 0.538462 | nan | nan | 0.27907 | nan | nan | nan | nan | nan | 0.0866142 | nan | nan | 0.45 | nan | 0.487179 | nan | nan | nan | nan | nan | nan | nan | nan | 0.12766 | nan | 0.999393 | nan | 0.477273 | nan | 0.0712695 | 0.335443 | nan | 0.309091 | nan | 0.00214337 | nan | nan | nan | nan | 0.64 | 0.612245 | nan | nan | 0.287879 | 0.852941 | nan | 0.995428 | nan | nan | nan | 0.333333 | nan | nan | nan | nan | 0.117021 | nan | nan | nan | nan | nan | nan | 0.810573 | 0.0362319 | 0.293532 | nan | 0.545455 | nan | 0.157895 | nan | nan | nan | nan | nan | 0.0424242 | nan | nan | nan | nan | nan | nan | 0.6 | nan | nan | nan | 0.322581 | nan | nan | nan | 0.759289 | nan | 0.416667 | 0.00208147 | nan | nan | nan | nan | 0.14902 | nan | nan | 0.0141509 | nan | nan | 0.945869 | nan | nan | nan | nan | nan | nan | nan | nan | 0.974152 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.927711 | nan | nan | nan | 0.0133136 | nan | nan | nan | nan | nan | 0.545455 | nan | nan | nan | 0.998065 | nan | nan | nan | nan | nan | nan | nan | nan | 0.771739 | nan | nan | nan | 0.785714 | nan | nan | nan | 0.135802 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.885293 | 0.72973 | nan | nan | 0.975831 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.204724 | nan | nan | nan | nan | nan | nan | nan | nan | 0.997898 | nan | 0.3 | 0.615385 | 0.369231 | 0.131579 | nan | nan | nan | 0.862069 | nan | 0.583333 | 0.285714 | nan | 0.0925926 | 0.205882 | nan | nan | 0.396825 | 0.00462046 | nan | 0.0793651 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.2 | nan | 0.836735 | nan | nan | nan | nan | nan | 0.529412 | nan | nan | nan | 0.56 | nan | nan | nan | nan | 0.263158 | nan | 0.384615 | nan | nan | nan | nan | nan | nan | nan | 0.653846 | nan | nan | nan | nan | nan | 0.998727 | nan | nan | nan | nan | 0.00104692 | nan | 0.00261044 | nan | nan | nan | nan | nan | nan | 0.340909 | nan | nan | nan | nan | nan | 0.848485 | nan | nan | nan | nan | nan | 0.489796 | nan | 0.724138 | nan | nan | nan | nan | nan | nan | nan | nan | 0.222222 | 0.384615 | 0.75 | 0.0280584 | nan | nan | nan | 0.3 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.189189 | nan | 0.0869565 | nan | nan | nan | nan | nan | nan | 0.814815 | 0.997048 | nan | nan | nan | nan | nan | nan | nan | nan | 0.611111 | nan | 0.5 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.00174439 | nan | nan | nan | nan | nan | nan | 0.5625 | 0.0637168 | nan | 0.526316 | nan | 0.998119 | nan | nan | nan | 0.318182 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.634259 | nan | nan | nan | 0.0383387 | 0.0857143 | nan | nan | nan | 0.15 | 0.45 | nan | nan | nan | nan | nan | 0.0661157 | nan | nan | 0.73817 | 0.00460911 | 0.0909091 | nan | 0.025463 | 0.219415 | 0.533333 | nan | nan | 0.5 | nan | nan | 0.00382684 | nan | nan | nan | nan | 0.147059 | 0.895522 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.4 | nan | 0.774194 | 0.877976 | 0.777778 | nan | nan | nan | 0.4375 | nan | 0.4375 | nan | 0.302326 | 0.24 | nan | 0.75 | nan | 0.752941 | 0.446023 | nan | nan | nan | 0.387387 | 0.416667 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.5 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.00166205 | nan | 0.999019 | nan | 0.434211 | nan | nan | 0.652174 | 0.5 | nan | nan | nan | nan | nan | 0.428571 | nan | nan | nan | nan | nan | nan | nan | 0.461538 | nan | nan | nan | nan | nan | 0.032 | nan | nan | 0.368421 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.22973 | nan | nan | nan | nan | nan | nan | 0.73913 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0531915 | 0.611111 | 0.114754 | nan | nan | nan | nan | 0.357143 | nan | nan | 0.0238908 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.228916 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.00215646 | nan | nan | nan | nan | nan | nan | nan | nan | 0.125 | 0.341463 | nan | 0.139535 | 0.612903 | nan | nan | 0.5625 | nan | 0.993337 | nan | 0.884058 | 0.98533 | nan | 0.416149 | nan | nan | nan | nan | 0.926441 | 0.346154 | 0.141743 | nan | nan | 0.83871 | 0.363636 | nan | nan | 0.416667 | nan | nan | nan | nan | nan | nan | nan | 0.00174459 | nan | nan | nan | nan | nan | 0.122807 | nan | nan | nan | nan | nan | nan | nan | 0.178571 | 0.0404192 | 0.5 | nan | 0.466667 | nan | nan | 0.694611 | nan | 0.885017 | 0.877193 | 0.814767 | nan | 0.814815 | nan | nan | nan | nan | nan | nan | nan | nan | 0.943005 | nan | nan | nan | nan | nan | nan | 0.357143 | nan | nan | nan | nan | nan | nan | nan | nan | 0.12 | nan | 0.213904 | 0.138889 | nan | nan | nan | nan | 0.791667 | 0.775862 | nan | 0.670928 | nan | 0.28 | 0.425 | nan | nan | nan | nan | nan | nan | 0.378309 | 0.694444 | nan | nan | nan | nan | nan | 0.608696 | nan | 0.0174216 | nan | nan | nan | nan | nan | nan | 0.996715 | nan | nan | nan | nan | nan | nan | 0.0429936 | nan | nan | nan | nan | nan | 0.688889 | nan | 0.666667 | nan | nan | nan | 0.97449 | 0.917709 | nan | 0.00689429 | 0.213115 | 0.101695 | nan | nan | nan | 0.960177 | nan | nan | nan | nan | nan | 0.461538 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.337662 | nan | 0.301887 | nan | nan | 0.223265 | 0.22807 | nan | nan | 0.599361 | nan | nan | 0.4 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.923077 | nan | 0.384615 | 0.989362 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.727273 | nan | nan | nan | nan | nan | nan | 0.5 | 0.0306748 | nan | 0.868421 | 0.762238 | 0.867769 | nan | nan | nan | nan | nan | 0.290323 | nan | 0.352941 | nan | nan | 0.529412 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.333333 | nan | 0.803571 | 0.185185 | nan | nan | nan | nan | nan | nan | 0.911111 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.929524 | nan | 0.666667 | nan | nan | nan | nan | nan | 0.166667 | 0.114943 | nan | nan | nan | 0.043956 | nan | nan | 0.538462 | 0.893617 | 0.78125 | 0.0758427 | 0.973913 | 0.271186 | nan | 0.73494 | 0.907407 | 0.953271 | nan | nan | 0.575 | 0.876963 | nan | nan | nan | nan | nan | nan | 0.0534351 | nan | nan | nan | 0.918223 | 0.790698 | nan | 0.5 | nan | nan | nan | nan | nan | nan | nan | 0.296296 | nan | nan | nan | nan | nan | 0.264354 | 0.04038 | nan | 0.811111 | 0.25 | 0.835088 | 0.195122 | nan | nan | nan | nan | 0.819527 | nan | 0.0886076 | nan | nan | nan | nan | nan | 0.6 | nan | 0.0166521 | 0.717172 | nan | 0.694444 | nan | 0.4947 | 0.333333 | nan | 0.357143 | nan | 0.642857 | nan | nan | 0.767918 | nan | 0.182764 | 0.230769 | nan | 0.432692 | 0.338462 | nan | nan | nan | nan | nan | nan | nan | nan | 0.0741688 | 0.97546 | nan | nan | nan | nan | 0.538462 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0819672 | nan | nan | nan | nan | nan | 0.416667 | nan | nan | 0.0154185 | nan | nan | 0.740741 | nan | nan | nan | 0.725714 | 0.133333 | nan | 0.426471 | nan | 0.896226 | nan | nan | nan | nan | 0.0658579 | nan | 0.290323 | 0.42487 | nan | nan | nan | nan | nan | nan | nan | 0.72973 | 0.226415 | 0.010118 | nan | nan | 0.4 | 0.116279 | nan | nan | nan | 0.5 | nan | nan | nan | 0.4 | nan | 0.333333 | nan | nan | nan | nan | nan | nan | 0.898305 | nan | 0.25 | 0.83125 | 0.97549 | 0.592881 | 0.615385 | nan | 0.862385 | 0.959459 | 0.36129 | 0.16129 | nan | 0.775 | nan | nan | nan | 0.310825 | 0.388889 | nan | 0.522388 | 0.666667 | 0.103448 | nan | nan | 0.128205 | nan | nan | nan | 0.00129162 | nan | nan | nan | nan | 0.589431 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.53125 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.00404694 | nan | nan | 0.0847458 | nan | nan | 0.615385 | 0.791667 | 0.625 | nan | nan | nan | nan | nan | 0.0118644 | nan | nan | nan | nan | nan | nan | nan | 0.666667 | nan | 0.0283688 | nan | nan | nan | nan | nan | 0.0985915 | nan | 0.12 | nan | nan | nan | 0.410842 | nan | nan | nan | nan | nan | nan | nan | nan | 0.823944 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.6 | nan | nan | 0.341724 | nan | nan | 0.208202 | nan | 0.76087 | nan | nan | nan | nan | nan | nan | 0.192308 | 0.608696 | 0.710526 | 0.0664062 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.997832 | nan | nan | nan | nan | nan | 0.015873 | nan | nan | nan | nan | 0.45 | 0.294118 | nan | nan | nan | nan | nan | nan | nan | nan | 0.0421913 | 0.782609 | 0.951009 | nan | 0.0630631 | 0.0336903 | nan | 0.416667 | nan | nan | 0.475 | 0.545455 | 0.615385 | 0.1 | 0.0114767 | 0.126189 | 0.0450161 | 0.783784 | nan | 0.903084 | 0.917808 | 0.983254 | 0.125874 | 0.436823 | 0.940314 | 0.610526 | nan | 0.75 | 0.909677 | nan | nan | nan | nan | nan | 0.387097 | nan | nan | nan | nan | nan | nan | nan | 0.4 | nan | 0.5 | nan | nan | nan | nan | nan | 0.27907 | 0.712238 | nan | nan | nan | nan | nan | nan | 0.140625 | nan | 0.941379 | 0.888 | nan | nan | nan | 0.363636 | nan | nan | nan | nan | nan | 0.532787 | nan | nan | nan | nan | nan | nan | 0.0250896 | nan | nan | nan | 0.319149 | 0.72093 | nan | nan | nan | nan | 0.0886076 | nan | 0.0244648 | nan | 0.564516 | 0.0764706 | nan | nan | nan | 0.000529101 | 0.5 | 0.25 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.88 | nan | nan | nan | nan | 0.344828 | nan | nan | nan | nan | nan | nan | 0.999099 | nan | nan | nan | nan | nan | nan | nan | 0.893939 | nan | nan | 0.966216 | nan | 0.645161 | nan | nan | nan | nan | nan | nan | nan | nan | 0.206897 | nan | 0.149941 | 0.15 | nan | nan | 0.78125 | nan | 0.948837 | nan | nan | 0.339286 | nan | nan | nan | 0.545455 | nan | nan | 0.181818 | nan | 0.780591 | 0.00282043 | nan | nan | nan | 0.00167913 | 0.272727 | nan | nan | 0.45 | nan | 0.978682 | 0.5 | nan | nan | nan | nan | nan | 0.203426 | nan | nan | 0.745455 | 0.538462 | 0.659574 | nan | nan | 0.782609 | 0.881579 | nan | nan | nan | nan | 0.647059 | nan | nan | nan | 0.997927 | 0.00394322 | 0.0023912 | nan | nan | 0.296296 | nan | nan | nan | 0.5 | nan | nan | nan | 0.333333 | nan | nan | 0.529412 | nan | nan | 0.32 | nan | 0.75 | nan | nan | nan | 0.435897 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.371429 | nan | nan | nan | nan | 0.636364 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0150602 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.79596 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.967066 | nan | 0.466667 | nan | nan | nan | 0.0138568 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.8 | nan | nan | nan | nan | 0.00837989 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.570621 | 0.302632 | nan | nan | 0.25 | 0.756757 | nan | nan | nan | 0.915254 | nan | nan | 0.282051 | 0.53605 | nan | nan | nan | nan | nan | nan | 0.970647 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0462963 | nan | nan | 0.847458 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.361963 | 0.666667 | nan | 0.727273 | 0.736089 | nan | 0.555556 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.75 | 0.444444 | nan | nan | 0.466667 | nan | nan | 0.640747 | 0.996449 | nan | nan | 0.780488 | nan | 0.666667 | 0.126984 | 0.0282486 | nan | nan | 0.988839 | nan | nan | 0.55 | 0.910891 | nan | nan | 0.13981 | 0.636364 | nan | nan | nan | nan | nan | nan | 0.5 | nan | 0.77381 | 0.917404 | 0.690476 | nan | 0.628571 | 0.819323 | nan | 0.34885 | nan | nan | 0.244898 | nan | 0.932584 | 0.799451 | 0.974619 | 0.0731707 | nan | nan | nan | nan | nan | nan | nan | 0.608 | nan | 0.533333 | nan | nan | nan | 0.333333 | 0.571429 | nan | nan | nan | nan | 0.588235 | 0.0869565 | nan | nan | nan | nan | nan | nan | nan | 0.998947 | 0.998099 | 0.866667 | 0.0493151 | nan | nan | nan | nan | 0.738095 | 0.0589286 | 0.238095 | nan | nan | 0.884211 | nan | nan | 0.908451 | nan | nan | nan | nan | nan | 0.533333 | nan | nan | nan | nan | nan | 0.998376 | nan | nan | nan | 0.997211 | nan | nan | nan | 0.170732 | nan | nan | 0.852679 | nan | nan | nan | 0.277778 | 0.413793 | nan | nan | 0.915254 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0562016 | 0.106383 | nan | 0.997247 | nan | 0.375 | nan | nan | 0.895833 | nan | nan | nan | nan | nan | 0.764444 | nan | nan | 0.241379 | nan | nan | nan | nan | 0.368687 | nan | nan | nan | nan | 0.8125 | 0.352941 | nan | nan | 0.983647 | nan | 0.112903 | 0.0131234 | 0.0943396 | nan | nan | nan | nan | nan | nan | nan | 0.201493 | nan | nan | nan | 0.416667 | nan | nan | 0.384615 | nan | nan | nan | nan | 0.594595 | 0.454545 | nan | 0.454545 | 0.9 | 0.923611 | 0.666667 | nan | 0.615385 | 0.428571 | nan | nan | nan | nan | nan | nan | 0.212389 | 0.545455 | nan | nan | nan | 0.625 | nan | nan | nan | 0.7 | nan | nan | 0.0735294 | nan | nan | nan | nan | nan | nan | nan | 0.368421 | nan | 0.4 | nan | 0.0833333 | nan | 0.333333 | nan | nan | nan | nan | nan | nan | 0.159091 | 0.454545 | nan | nan | 0.5 | nan | nan | nan | 0.031068 | nan | 0.506024 | nan | nan | 0.82 | nan | 0.612903 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.285714 | nan | nan | 0.0251601 | nan | nan | nan | nan | nan | nan | nan | 0.979769 | nan | 0.258065 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.966019 | nan | nan | nan | nan | nan | 0.684211 | nan | nan | nan | 0.0359521 | 0.542857 | nan | 0.747581 | nan | nan | nan | nan | nan | nan | 0.0555556 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.5 | 0.0666667 | nan | nan | nan | 0.621622 | 0.28125 | nan | 0.5 | 0.811321 | 0.854167 | 0.113208 | 0.852284 | nan | nan | nan | nan | 0.290323 | 0.684211 | 0.121951 | nan | 0.777778 | 0.104167 | nan | nan | nan | 0.832766 | 0.575758 | 0.918367 | nan | nan | nan | nan | nan | nan | 0.148148 | nan | 0.540179 | 0.784753 | 0.636364 | nan | 0.521739 | 0.375 | nan | nan | nan | nan | nan | nan | 0.5 | nan | nan | nan | 0.642857 | 0.545455 | nan | nan | nan | 0.306122 | 0.436975 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.384615 | nan | nan | nan | nan | nan | nan | 0.357143 | nan | nan | nan | 0.0365112 | nan | nan | 0.6 | nan | nan | 0.722222 | 0.875 | nan | nan | nan | nan | nan | nan | nan | nan | 0.642857 | nan | nan | nan | 0.290323 | nan | nan | 0.642857 | nan | nan | 0.411765 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.227273 | nan | 0.00144634 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.25 | nan | 0.0875 | nan | nan | nan | nan | 0.1875 | nan | nan | nan | 0.409091 | nan | nan | nan | nan | 0.431373 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.586207 | 0.533333 | nan | 0.173789 | nan | nan | nan | nan | nan | nan | nan | 0.5 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.223881 | nan | nan | nan | nan | 0.914773 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.230769 | 0.976546 | 0.277778 | nan | nan | nan | nan | 0.258621 | nan | nan | 0.137931 | 0.294118 | nan | nan | 0.827586 | 0.309524 | nan | nan | nan | 0.4875 | 0.647059 | nan | nan | nan | nan | nan | nan | nan | nan | 0.615385 | nan | nan | 0.59887 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.969543 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.404255 | nan | 0.990959 | nan | nan | nan | nan | 0.0223649 | 0.0701754 | 0.0325203 | 0.0787172 | nan | nan | 0.402655 | 0.72 | nan | 0.00342231 | nan | nan | 0.557895 | nan | nan | nan | 0.594203 | nan | nan | 0.388889 | 0.4 | 0.0917031 | 0.102273 | 0.205128 | nan | 0.5 | nan | nan | nan | 0.00594916 | nan | 0.0434783 | nan | nan | 0.009 | nan | nan | 0.045045 | nan | nan | nan | nan | nan | nan | 0.104348 | 0.358974 | 0.828947 | nan | 0.0200999 | 0.166667 | nan | nan | 0.0857143 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0280374 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.00361272 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.939394 | nan | nan | nan | nan | nan | 0.0525164 | 0.0130719 | 0.0137363 | 0.258065 | nan | nan | nan | nan | nan | nan | nan | nan | 0.151515 | nan | 0.996542 | 0.5 | 0.99764 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0751634 | nan | nan | nan | 0.997712 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.034188 | nan | nan | nan | nan | nan | nan | 0.810811 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.00172771 | 0.999129 | 0.625 | 0.75 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.277778 | 0.997532 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0145985 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.906667 | nan | nan | nan | nan | nan | nan | nan | 0.119048 | nan | nan | nan | nan | 0.988095 | 0.310345 | nan | 0.0131894 | nan | nan | nan | nan | nan | nan | nan | nan | 0.95092 | 0.991197 | nan | nan | nan | nan | nan | nan | nan | nan | 0.09375 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.00161355 | nan | nan | nan | nan | nan | nan | nan | 0.995553 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.52381 | nan | 0.948617 | nan | 0.430556 | nan | nan | nan | nan | nan | nan | 0.861925 | 0.358974 | 0.00195109 | nan | 0.294118 | 0.228916 | nan | 0.341463 | nan | 0.935897 | nan | nan | nan | nan | nan | nan | nan | 0.473684 | nan | nan | nan | 0.32 | nan | nan | nan | nan | nan | nan | 0.374126 | nan | nan | nan | nan | 0.0441176 | nan | nan | nan | nan | nan | nan | nan | 0.538462 | nan | nan | nan | nan | 0.296296 | 0.571429 | nan | nan | nan | nan | 0.851064 | 0.454545 | 0.391304 | nan | nan | nan | nan | nan | nan | nan | nan | 0.0909091 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.5 | nan | nan | nan | nan | nan | nan | 0.333333 | 0.217391 | nan | nan | nan | nan | nan | nan | nan | nan | 0.5 | nan | nan | 0.657143 | 0.3125 | nan | 0.531746 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.5 | 0.342105 | nan | nan | nan | nan | 0.00102911 | nan | nan | nan | nan | 0.142857 | 0.458333 | nan | nan | nan | 0.571429 | 0.24 | nan | nan | 0.785714 | 0.0159744 | 0.0666667 | 0.128906 | nan | 0.183099 | 0.33908 | 0.485714 | nan | nan | nan | 0.5 | nan | 0.8 | nan | nan | 0.240506 | nan | nan | nan | 0.0049597 | nan | nan | nan | 0.886364 | nan | nan | 0.647059 | 0.428571 | nan | nan | nan | 0.880952 | nan | 0.230769 | 0.00835422 | 0.0769231 | 0.5 | 0.555556 | nan | nan | nan | nan | nan | nan | 0.277778 | nan | nan | nan | nan | nan | 0.171053 | nan | 0.466667 | 0.171429 | 0.0674157 | nan | nan | nan | nan | 0.594595 | nan | 0.4375 | nan | 0.872 | 0.681818 | 0.16129 | 0.282051 | nan | nan | nan | nan | nan | nan | nan | 0.245195 | nan | nan | nan | 0.695652 | nan | 0.19403 | nan | 0.997781 | nan | 0.998127 | nan | nan | nan | nan | nan | 0.93994 | nan | 0.131579 | nan | 0.1875 | 0.154206 | 0.220779 | 0.571429 | nan | 0.153153 | 0.15508 | nan |\n", "| 2012 | nan | 0.0819672 | nan | 0.333333 | 0.998729 | nan | nan | nan | 0.333333 | 0.1875 | nan | nan | 0.00278515 | 0.891304 | nan | 0.581818 | nan | nan | nan | nan | 0.113636 | nan | nan | 0.533333 | nan | nan | 0.782609 | nan | 0.101695 | nan | 0.998976 | nan | 0.538462 | nan | nan | nan | 0.545455 | 0.00255754 | 0.645161 | 0.53125 | nan | nan | nan | nan | 0.5 | nan | nan | nan | nan | nan | nan | 0.258427 | nan | nan | nan | 0.483871 | nan | 0.545455 | nan | 0.538462 | nan | 0.979734 | nan | nan | nan | 0.380952 | nan | nan | nan | nan | nan | 0.0254022 | nan | nan | 0.569231 | 0.0818182 | nan | nan | nan | nan | 0.913043 | nan | nan | 0.222222 | 0.104046 | 0.949495 | nan | nan | nan | 0.170213 | nan | nan | nan | 0.692308 | nan | 0.185185 | nan | nan | 0.0185474 | nan | 0.76 | 0.571429 | nan | 0.942529 | 0.0611285 | 0.042735 | nan | nan | 0.987245 | 0.105263 | 0.0869565 | nan | nan | nan | 0.0833333 | 0.122449 | nan | nan | nan | nan | nan | nan | 0.866667 | nan | 0.798246 | nan | nan | nan | nan | nan | nan | nan | nan | 0.783784 | 0.230769 | nan | nan | nan | nan | 0.0129032 | 0.466667 | 0.00581551 | nan | 0.0958466 | 0.714286 | nan | nan | 0.94382 | nan | 0.55 | nan | nan | 0.98411 | 0.846154 | nan | nan | nan | nan | nan | nan | 0.0659341 | nan | nan | nan | nan | nan | nan | nan | nan | 0.685714 | nan | nan | nan | nan | nan | 0.6875 | 0.907216 | nan | nan | nan | nan | 0.851852 | nan | nan | nan | 0.00523104 | nan | 0.988048 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0783699 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.00183217 | nan | nan | nan | nan | nan | nan | nan | 0.0326715 | nan | 0.00183643 | nan | nan | nan | nan | nan | nan | nan | nan | 0.710938 | nan | nan | 0.773946 | nan | 0.736842 | nan | 0.384615 | 0.588235 | 0.972376 | nan | nan | nan | 0.875 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.260017 | 0.998003 | nan | 0.782609 | nan | 0.0432692 | nan | 0.47619 | nan | nan | nan | nan | nan | nan | 0.995788 | nan | nan | 0.585366 | nan | nan | nan | nan | nan | 0.923077 | nan | nan | nan | 0.00519751 | nan | nan | nan | 0.996774 | 0.998525 | nan | nan | 0.742857 | nan | 0.981073 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.484848 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.758621 | nan | nan | nan | nan | nan | 0.806452 | nan | nan | nan | nan | nan | nan | nan | 0.814696 | nan | nan | nan | 0.0769231 | 0.986564 | 0.0482143 | nan | 0.397727 | 0.370892 | nan | 0.859873 | 0.351351 | nan | 0.974468 | 0.34375 | 0.188976 | 0.5 | nan | nan | 0.275862 | nan | nan | nan | 0.558824 | nan | 0.507042 | nan | nan | nan | nan | 0.73913 | nan | 0.888889 | 0.67 | nan | nan | nan | 0.428571 | nan | 0.0134771 | 0.163636 | 0.375 | 0.147059 | nan | 0.837838 | nan | 0.764706 | 0.788462 | 0.714286 | 0.6875 | 0.581395 | nan | nan | nan | 0.6875 | 0.525 | nan | nan | 0.638298 | nan | nan | nan | nan | nan | nan | nan | nan | 0.888889 | nan | 0.0740741 | nan | nan | 0.195652 | 0.0348297 | 0.840426 | nan | nan | 0.6 | nan | nan | 0.994118 | nan | nan | nan | nan | nan | 0.00158491 | nan | nan | nan | nan | 0.0109204 | 0.625 | nan | 0.166013 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.416667 | nan | nan | nan | 0.860465 | nan | nan | nan | 0.172414 | nan | nan | 0.964497 | nan | nan | 0.255814 | nan | nan | nan | 0.999109 | 0.998363 | nan | nan | nan | nan | nan | nan | nan | 0.980535 | nan | nan | 0.0011374 | nan | nan | nan | nan | nan | nan | 0.973684 | nan | nan | nan | nan | nan | nan | 0.166667 | 0.5 | nan | nan | nan | nan | nan | nan | 0.859375 | nan | nan | 0.6875 | nan | nan | nan | nan | nan | nan | nan | 0.0163934 | nan | nan | nan | nan | nan | nan | 0.745819 | nan | nan | nan | nan | nan | nan | 0.192308 | nan | 0.047619 | nan | nan | nan | 0.217284 | 0.994453 | nan | nan | 0.0469974 | nan | nan | nan | 0.998939 | nan | nan | 0.55102 | 0.853971 | nan | nan | nan | nan | 0.322785 | 0.44 | 0.138298 | 0.44186 | nan | nan | 0.185185 | nan | nan | nan | nan | nan | nan | nan | nan | 0.263158 | nan | nan | nan | 0.168224 | 0.666667 | 0.431818 | nan | 0.47619 | nan | nan | 0.0319149 | nan | 0.846154 | nan | nan | nan | nan | 0.388303 | nan | 0.636364 | nan | nan | nan | nan | 0.6 | 0.741935 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.409091 | nan | nan | 0.37037 | nan | 0.631579 | nan | 0.888889 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.52381 | 0.907895 | 0.861111 | 0.0811518 | nan | nan | 0.824561 | nan | nan | 0.0816733 | 0.21875 | 0.858586 | nan | nan | nan | nan | nan | 0.884615 | nan | nan | nan | nan | nan | nan | 0.533333 | nan | nan | 0.0125035 | nan | nan | 0.7 | nan | nan | nan | 0.996393 | nan | nan | nan | 0.9967 | nan | nan | 0.196429 | 0.161765 | 0.0580732 | 0.782609 | nan | 0.76 | nan | nan | nan | 0.924051 | nan | 0.913636 | 0.95935 | nan | nan | 0.037037 | 0.5 | nan | nan | nan | nan | nan | nan | nan | nan | 0.0555556 | 0.537037 | 0.462687 | 0.0125174 | nan | nan | nan | nan | nan | 0.981443 | 0.963415 | nan | 0.3 | 0.56 | 0.352941 | nan | nan | nan | 0.998115 | nan | nan | nan | 0.121747 | 0.861111 | nan | 0.0875576 | nan | nan | nan | nan | nan | nan | nan | 0.827586 | nan | nan | 0.160494 | 0.0118778 | nan | nan | 0.426434 | nan | nan | 0.997617 | 0.998713 | nan | 0.0510204 | 0.957831 | nan | nan | 0.860465 | 0.934066 | 0.446203 | nan | 0.873016 | 0.583333 | 0.962428 | 0.979769 | nan | nan | 0.805211 | nan | 0.168224 | 0.15942 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.630435 | 0.884615 | 0.535714 | nan | nan | nan | nan | 0.461538 | nan | 0.527778 | nan | 0.0300546 | 0.213333 | nan | 0.0716253 | 0.0181617 | nan | 0.0379009 | 0.225806 | nan | 0.857143 | nan | nan | 0.230769 | nan | nan | 0.382353 | 0.642857 | nan | nan | nan | nan | 0.185185 | nan | 0.175 | 0.634921 | 0.971246 | 0.603687 | nan | nan | 0.5 | 0.73913 | nan | 0.238095 | nan | nan | nan | 0.558824 | nan | nan | 0.943182 | nan | nan | nan | 0.232143 | 0.247706 | nan | 0.744186 | nan | nan | nan | nan | 0.344828 | nan | nan | 0.538462 | nan | 0.965645 | nan | 0.8 | 0.101695 | nan | nan | nan | nan | nan | nan | nan | 0.4375 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0230863 | nan | nan | nan | nan | 0.307692 | nan | nan | nan | nan | nan | 0.987248 | 0.225806 | 0.3 | nan | nan | 0.516129 | 0.298561 | nan | nan | nan | 0.0352941 | nan | 0.129032 | 0.0352178 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.00109196 | nan | nan | nan | 0.0305033 | nan | 0.125 | nan | nan | 0.164151 | nan | 0.852941 | 0.0404388 | 0.708333 | 0.564103 | 0.0310078 | nan | nan | nan | 0.607143 | 0.793814 | nan | 0.83209 | nan | nan | nan | 0.911392 | nan | nan | nan | nan | nan | nan | nan | nan | 0.357143 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.913793 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.703704 | 0.75 | nan | nan | nan | nan | nan | nan | nan | 0.883495 | 0.0166205 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.388889 | nan | 0.0412148 | 0.826558 | nan | 0.0931174 | 0.113924 | 0.0625097 | 0.909091 | 0.926829 | 0.947735 | nan | nan | nan | nan | nan | 0.48 | 0.106383 | nan | 0.0319489 | 0.3 | 0.588235 | 0.929487 | nan | 0.901639 | nan | 0.5625 | 0.322581 | 0.953488 | 0.0618893 | nan | nan | 0.736111 | 0.0178042 | nan | nan | nan | 0.0365854 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.444444 | 0.0932836 | 0.809524 | nan | 0.0298507 | 0.466667 | nan | nan | nan | nan | nan | nan | 0.00613497 | nan | nan | 0.588235 | 0.0775862 | nan | 0.00651042 | nan | 0.0141477 | 0.0420561 | 0.666667 | nan | 0.0628931 | 0.416667 | 0.885787 | 0.97732 | nan | nan | nan | 0.659794 | 0.939759 | nan | nan | 0.255814 | 0.807692 | nan | nan | nan | nan | nan | nan | nan | 0.00213129 | nan | 0.89726 | nan | nan | nan | nan | nan | nan | 0.105263 | nan | 0.0182927 | 0.0520833 | nan | nan | nan | nan | nan | 0.00891972 | 0.162338 | nan | nan | nan | 0.00293531 | nan | nan | 0.0769231 | 0.97695 | 0.040796 | 0.375839 | 0.00873709 | 0.556962 | 0.868687 | nan | nan | nan | nan | 0.106667 | 0.122302 | 0.0729167 | 0.8 | nan | nan | 0.3125 | 0.75 | nan | 0.157895 | nan | 0.454545 | 0.861111 | nan | nan | nan | nan | nan | nan | 0.90566 | nan | 0.0674054 | nan | nan | 0.619048 | nan | 0.176471 | 0.0707692 | 0.0442478 | nan | nan | 0.134615 | nan | 0.518519 | nan | 0.0428894 | nan | 0.285714 | nan | nan | nan | nan | nan | 0.00389794 | nan | nan | nan | 0.782609 | 0.99806 | nan | nan | 0.363636 | nan | nan | nan | nan | nan | nan | 0.243243 | nan | nan | 0.545455 | 0.32618 | 0.955357 | 0.852248 | 0.75 | nan | nan | nan | 0.9375 | 0.983949 | nan | nan | nan | nan | nan | 0.99169 | nan | 0.555556 | nan | nan | nan | 0.368421 | 0.473684 | 0.793103 | nan | nan | 0.388889 | nan | 0.5 | nan | nan | 0.433498 | 0.1875 | nan | nan | 0.169492 | 0.0447761 | 0.00183318 | 0.0565217 | nan | nan | 0.868613 | nan | nan | nan | nan | 0.990788 | nan | 0.0330673 | nan | nan | nan | 0.00324044 | nan | nan | 0.190909 | 0.0275482 | nan | nan | 0.0721649 | 0.114286 | 0.950311 | 0.0961538 | 0.727273 | nan | 0.357143 | nan | 0.954054 | nan | 0.994591 | nan | nan | nan | 0.00198364 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.684211 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.106061 | 0.883721 | 0.00609013 | 0.0106775 | 0.937285 | nan | nan | 0.212121 | nan | nan | nan | 0.217949 | nan | 0.583333 | 0.974747 | nan | nan | 0.298701 | 0.0197694 | 0.871429 | 0.258824 | nan | nan | nan | nan | 0.0869565 | nan | nan | nan | nan | nan | 0.0972222 | nan | 0.00150633 | nan | nan | 0.991597 | nan | nan | 0.338028 | 0.105442 | 0.64 | 0.0259179 | nan | nan | 0.454545 | 0.791667 | nan | nan | 0.740741 | nan | nan | nan | nan | 0.416667 | 0.642857 | nan | nan | 0.5 | nan | nan | 0.642857 | nan | 0.0420819 | 0.359173 | 0.642857 | nan | nan | 0.297297 | nan | nan | 0.0677321 | 0.648855 | nan | 0.998332 | nan | nan | nan | nan | 0.671082 | nan | 0.0471698 | 0.137931 | 0.0147059 | 0.846154 | 0.805458 | nan | 0.0752688 | nan | nan | 0.5 | nan | 0.0179641 | nan | nan | 0.059322 | nan | 0.8 | nan | nan | 0.16129 | nan | nan | 0.282051 | nan | nan | nan | nan | nan | nan | nan | nan | 0.545455 | nan | nan | nan | 0.344828 | nan | nan | 0.175439 | nan | nan | nan | 0.00174607 | nan | nan | nan | nan | nan | nan | 0.72 | nan | nan | nan | 0.971014 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.725191 | nan | nan | nan | nan | 0.154639 | 0.032054 | nan | 0.608696 | 0.0258139 | 0.104167 | nan | 0.15625 | nan | nan | nan | 0.389682 | 0.00454545 | nan | nan | 0.333333 | nan | nan | nan | nan | nan | nan | 0.957806 | nan | nan | nan | nan | 0.275862 | nan | nan | nan | nan | nan | nan | nan | 0.411765 | 0.172414 | 0.0427937 | 0.93633 | nan | 0.705882 | nan | nan | 0.594595 | nan | nan | 0.5625 | 0.206897 | nan | 0.0729167 | 0.0196078 | nan | nan | nan | nan | nan | nan | 0.367347 | nan | nan | nan | nan | nan | nan | nan | nan | 0.00347464 | nan | 0.0185874 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.275 | nan | nan | nan | nan | nan | nan | nan | 0.0195059 | nan | nan | nan | nan | nan | nan | 0.212963 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.250733 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.526316 | nan | nan | nan | nan | nan | nan | 0.984431 | nan | 0.00216138 | 0.342105 | 0.867384 | nan | 0.405825 | nan | 0.999331 | nan | 0.748031 | nan | nan | nan | nan | nan | 0.014739 | nan | nan | nan | 0.4 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.174757 | nan | nan | 0.210884 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.5 | nan | nan | nan | 0.932927 | nan | nan | nan | 0.211823 | nan | nan | 0.991185 | nan | nan | nan | nan | nan | nan | nan | 0.678571 | nan | nan | 0.363636 | nan | nan | nan | nan | nan | 0.758621 | nan | 0.998448 | nan | nan | nan | nan | 0.0182094 | nan | nan | nan | nan | nan | 0.131579 | 0.531915 | nan | nan | 0.0143693 | nan | nan | nan | 0.723404 | 0.994676 | nan | nan | nan | 0.00159771 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.709677 | nan | nan | nan | 0.212121 | nan | nan | nan | 0.391304 | nan | nan | nan | nan | nan | 0.227273 | nan | nan | nan | nan | nan | nan | nan | 0.454545 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.00583942 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.885714 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.391304 | nan | 0.276596 | nan | nan | 0.636364 | 0.564103 | 0.0108588 | 0.00846561 | nan | nan | nan | 0.5 | nan | nan | 0.927461 | 0.0502618 | 0.00316874 | nan | 0.695652 | nan | nan | nan | nan | nan | 0.126984 | 0.00412584 | 0.86039 | nan | 0.27027 | nan | nan | 0.000944436 | nan | nan | nan | nan | nan | nan | 0.166667 | nan | 0.631579 | 0.00575916 | nan | 0.00186166 | nan | nan | nan | nan | nan | nan | nan | 0.019456 | nan | 0.208333 | nan | nan | nan | 0.00553506 | nan | 0.0935673 | nan | nan | nan | nan | nan | nan | nan | 0.0267261 | 0.861702 | nan | nan | 0.2 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.741935 | 0.416667 | 0.761905 | nan | nan | 0.102041 | nan | 0.487179 | 0.0342105 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.823834 | nan | nan | nan | nan | nan | nan | nan | nan | 0.142857 | 0.308824 | nan | nan | nan | 0.648352 | nan | nan | nan | nan | nan | 0.545455 | 0.00417412 | nan | nan | nan | nan | 0.655172 | 0.0588235 | 0.3 | 0.0253767 | nan | nan | 0.0167015 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.6 | nan | nan | nan | nan | nan | 0.52381 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.6 | 0.857143 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.75 | nan | nan | nan | 0.5 | nan | nan | nan | nan | nan | 0.0413793 | 0.322581 | nan | 0.520624 | 0.607477 | nan | nan | nan | nan | nan | 0.3125 | nan | 0.227918 | nan | 0.764706 | nan | 0.00566802 | nan | 0.259259 | nan | 0.20202 | nan | nan | 0.0973783 | nan | nan | 0.965 | nan | nan | 0.5 | nan | nan | nan | nan | 0.164773 | 0.172414 | nan | 0.919192 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.924 | nan | nan | nan | 0.00133146 | nan | nan | 0.263158 | nan | 0.994199 | 0.5 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.409091 | nan | nan | 0.0165394 | nan | nan | 0.00398406 | nan | nan | nan | nan | nan | nan | 0.322581 | nan | nan | nan | nan | nan | 0.925 | nan | 0.751592 | nan | nan | nan | 0.939189 | nan | nan | nan | nan | nan | nan | nan | nan | 0.538462 | nan | 0.0455927 | nan | 0.0363636 | 0.0324074 | nan | 0.0423729 | nan | nan | nan | nan | nan | nan | 0.814815 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0576923 | nan | nan | 0.135135 | 0.847826 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.139785 | nan | 0.00143713 | nan | nan | nan | nan | nan | nan | nan | 0.0433566 | nan | nan | nan | nan | 0.384615 | 0.14 | nan | nan | nan | 0.0300188 | 0.459459 | nan | nan | 0.222222 | nan | nan | nan | 0.103627 | 0.508475 | nan | nan | nan | nan | nan | nan | nan | 0.0366133 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.36 | 0.00643317 | 0.470588 | nan | nan | nan | 0.368421 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.295455 | nan | 0.380952 | nan | nan | nan | nan | nan | 0.987451 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.196429 | nan | nan | 0.0909091 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.179487 | nan | 0.225 | nan | 0.695652 | nan | nan | nan | nan | nan | nan | 0.333333 | 0.0534351 | nan | nan | 0.297872 | nan | nan | nan | nan | nan | nan | 0.325123 | 0.4 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.232143 | nan | nan | 0.16129 | 0.140351 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.380952 | nan | nan | nan | nan | nan | 0.0943396 | 0.705882 | 0.995583 | nan | 0.285714 | nan | nan | nan | 0.151899 | nan | nan | 0.0447761 | nan | nan | nan | 0.0637049 | 0.608696 | nan | nan | 0.0681818 | 0.5 | 0.112864 | nan | nan | nan | nan | nan | nan | 0.596244 | nan | 0.866667 | nan | nan | nan | nan | nan | nan | nan | nan | 0.631579 | nan | nan | nan | nan | nan | nan | nan | nan | 0.953039 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.919355 | nan | nan | nan | 0.0412371 | 0.097561 | nan | 0.0279955 | 0.421053 | nan | 0.538462 | nan | 0.75 | nan | nan | nan | 0.0481928 | nan | 0.533333 | 0.5 | nan | nan | nan | 0.797468 | nan | 0.632353 | 0.938272 | nan | nan | nan | nan | nan | 0.346154 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0011325 | 0.00439239 | nan | nan | nan | nan | 0.435845 | nan | 0.421053 | nan | nan | nan | 0.354839 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0453608 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0353698 | 0.883495 | 0.130952 | 0.159292 | nan | 0.517857 | 0.64 | nan | nan | 0.394737 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.257576 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0560916 | 0.671429 | nan | 0.333333 | 0.231579 | nan | nan | nan | nan | nan | 0.454545 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0130326 | 0.360825 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.876543 | nan | nan | 0.138462 | nan | nan | nan | nan | nan | nan | nan | 0.847502 | nan | nan | nan | 0.151515 | 0.0411392 | nan | nan | nan | nan | nan | nan | nan | nan | 0.263158 | 0.903614 | nan | nan | nan | 0.828125 | 0.467742 | nan | nan | nan | nan | 0.965278 | 0.444444 | nan | nan | nan | nan | nan | nan | nan | 0.144737 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.00383568 | 0.896104 | 0.338983 | 0.0194553 | 0.997778 | 0.00186047 | 0.203704 | 0.00202006 | 0.344828 | 0.971014 | 0.0962343 | nan | 0.994138 | nan | nan | 0.997648 | 0.303297 | nan | nan | 0.865385 | 0.658824 | nan | 0.999175 | 0.789474 | nan | nan | 0.998695 | nan | 0.922374 | 0.940171 | 0.997935 | 0.627451 | 0.171555 | 0.157543 | nan | 0.34895 | 0.845878 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.12766 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.149351 | 0.45 | nan | nan | nan | nan | nan | nan | nan | nan | 0.901639 | 0.857143 | 0.940928 | nan | 0.461538 | 0.460674 | 0.375 | 0.973077 | 0.661765 | nan | nan | nan | 0.960265 | 0.926606 | 0.975124 | nan | 0.823529 | 0.610619 | 0.982796 | 0.802489 | nan | nan | 0.148936 | nan | nan | nan | 0.998462 | nan | 0.999045 | 0.00552017 | 0.873239 | nan | nan | 0.9375 | 0.885246 | 0.00491884 | nan | 0.930693 | nan | 0.794118 | 0.59332 | nan | 0.777778 | 0.722222 | 0.133333 | nan | nan | 0.631579 | nan | nan | 0.761905 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.47619 | 0.384615 | nan | nan | nan | nan | nan | nan | nan | 0.828571 | nan | nan | 0.951938 | nan | nan | nan | 0.819444 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.28 | nan | nan | nan | nan | nan | nan | 0.322581 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.00124533 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0154729 | 0.322581 | nan | nan | 0.998111 | 0.412037 | 0.132075 | 0.0119522 | 0.214286 | nan | nan | nan | 0.133333 | 0.333333 | nan | nan | 0.620253 | nan | nan | nan | nan | nan | nan | nan | nan | 0.384615 | nan | nan | nan | nan | 0.0553288 | 0.282051 | 0.886364 | nan | nan | nan | 0.75 | nan | nan | nan | 0.90393 | 0.833333 | nan | 0.972678 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.8 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.014245 | nan | nan | nan | nan | 0.175439 | nan | nan | 0.00604027 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.4375 | nan | nan | 0.27451 | nan | 0.656813 | 0.00897959 | 0.0126354 | nan | 0.452489 | nan | nan | 0.0223881 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0609418 | nan | nan | nan | nan | nan | nan | nan | 0.705882 | 0.0243902 | nan | nan | nan | nan | nan | 0.98645 | nan | nan | nan | nan | nan | 0.0930233 | nan | nan | nan | nan | nan | 0.417476 | nan | nan | 0.104167 | nan | nan | nan | nan | nan | nan | nan | nan | 0.707317 | nan | nan | nan | nan | 0.114754 | nan | nan | nan | nan | nan | nan | nan | nan | 0.00603032 | nan | 0.659574 | 0.484848 | nan | 0.993013 | nan | 0.333333 | nan | 0.00327035 | nan | nan | 0.00351803 | 0.028436 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.384615 | nan | nan | nan | 0.178571 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.00181554 | nan | 0.0216606 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.571429 | nan | 0.982838 | nan | nan | nan | nan | 0.587963 | nan | nan | nan | 0.00213858 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.193548 | nan | 0.56 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.241379 | nan | 0.0967262 | 0.428571 | nan | nan | 0.0384615 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0315789 | nan | 0.00419626 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.3125 | nan | nan | nan | 0.998779 | 0.531915 | nan | nan | nan | 0.285714 | 0.963636 | nan | 0.00736842 | 0.416667 | nan | nan | nan | 0.00522042 | nan | 0.583333 | nan | nan | 0.124088 | 0.626667 | 0.375 | nan | 0.179283 | 0.0362482 | nan | 0.784615 | nan | nan | nan | nan | 0.00406174 | nan | nan | 0.188596 | 0.139706 | 0.0180141 | 0.75 | 0.0101918 | 0.238095 | nan | nan | nan | 0.872222 | nan | nan | nan | nan | nan | nan | 0.375 | 0.648649 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0714286 | nan | 0.98013 | nan | 0.525 | nan | 0.571429 | nan | 0.12766 | 0.0666667 | nan | nan | 0.332016 | nan | 0.771812 | nan | nan | nan | 0.793103 | nan | nan | nan | 0.364865 | nan | nan | nan | nan | nan | 0.900662 | nan | 0.5 | nan | 0.0779221 | 0.571429 | nan | 0.903226 | 0.384615 | nan | nan | nan | 0.375 | nan | 0.999313 | 0.375 | nan | nan | nan | nan | 0.578947 | nan | nan | nan | 0.615385 | 0.5 | 0.5 | nan | 0.0829016 | 0.967033 | 0.313869 | nan | 0.664908 | nan | nan | 0.727273 | 0.938889 | nan | nan | 0.857143 | 0.333333 | 0.142857 | nan | 0.742857 | nan | 0.945263 | nan | nan | nan | nan | nan | 0.00701919 | nan | nan | nan | nan | 0.863248 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.817768 | 0.915254 | 0.263158 | 0.313375 | 0.451613 | 0.271186 | 0.121951 | 0.464286 | 0.0633609 | nan | nan | 0.86 | nan | 0.777778 | 0.151515 | nan | nan | nan | 0.142857 | nan | nan | nan | nan | 0.985119 | nan | nan | nan | nan | nan | nan | nan | 0.0577617 | 0.771552 | nan | 0.0618557 | nan | 0.001239 | 0.867925 | nan | nan | nan | nan | nan | 0.296296 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.538462 | nan | nan | nan | 0.00759494 | nan | nan | 0.814815 | nan | nan | nan | 0.367816 | nan | nan | nan | nan | nan | 0.680203 | nan | nan | nan | nan | nan | nan | nan | nan | 0.0271493 | nan | nan | nan | nan | nan | 0.016791 | 0.576923 | nan | nan | nan | nan | nan | 0.0249392 | nan | 0.310345 | nan | 0.147059 | nan | 0.0638298 | nan | nan | 0.00158172 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.769231 | 0.769231 | nan | nan | nan | nan | 0.183673 | 0.848485 | 0.837607 | 0.968788 | 0.675676 | nan | nan | nan | 0.714286 | nan | 0.568966 | 0.807018 | 0.721519 | 0.705882 | 0.322581 | nan | nan | nan | nan | nan | nan | 0.631579 | nan | nan | 0.287081 | nan | nan | nan | nan | nan | nan | 0.578947 | nan | 0.874074 | 0.995602 | nan | nan | nan | nan | nan | nan | 0.358824 | 0.00109529 | nan | nan | 0.999058 | nan | nan | nan | 0.0230415 | 0.00147275 | nan | nan | nan | nan | nan | 0.72093 | nan | 0.0223124 | nan | 0.185185 | nan | nan | 0.253521 | 0.991837 | nan | nan | 0.00185644 | nan | nan | nan | nan | nan | nan | nan | nan | 0.583333 | nan | nan | 0.870968 | 0.5 | 0.991794 | nan | nan | nan | nan | 0.865385 | nan | 0.824561 | nan | nan | nan | nan | nan | nan | nan | 0.0480769 | 0.00758374 | nan | 0.963563 | nan | nan | nan | nan | 0.00125992 | nan | 0.459716 | nan | 0.00176141 | 0.290323 | 0.25 | nan | 0.00136333 | 0.0592105 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.758621 | nan | 0.996697 | nan | nan | nan | 0.97957 | 0.0546247 | 0.5 | nan | nan | nan | nan | nan | 0.232558 | nan | nan | 0.876712 | nan | nan | nan | 0.702065 | nan | 0.791667 | 0.4 | nan | 0.0740741 | nan | nan | 0.373333 | nan | 0.506143 | nan | nan | 0.442623 | 0.935252 | 0.976492 | 0.0290456 | nan | 0.114754 | nan | nan | 0.333333 | nan | nan | nan | nan | nan | nan | 0.188571 | nan | nan | 0.118644 | 0.108611 | nan | 0.28 | nan | 0.525581 | 0.73913 | nan | nan | nan | 0.12069 | nan | 0.838542 | nan | 0.101966 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0693069 | 0.0863309 | nan | 0.00223964 | nan | nan | nan | nan | 0.75 | nan | nan | nan | 0.135135 | nan | nan | 0.446809 | nan | nan | nan | 0.0252525 | nan | nan | nan | nan | 0.28 | nan | nan | 0.411765 | 0.711712 | nan | 0.936585 | 0.642857 | nan | nan | nan | nan | nan | nan | 0.228571 | nan | nan | nan | nan | 0.0548523 | nan | nan | 0.189189 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.00208628 | nan | 0.0246914 | nan | 0.4 | nan | nan | nan | 0.73886 | nan | nan | 0.125 | nan | nan | nan | nan | nan | nan | nan | 0.413793 | 0.0823328 | nan | 0.545455 | nan | nan | 0.259259 | nan | nan | 0.0614035 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.130435 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.851351 | nan | nan | nan | 0.928571 | nan | nan | 0.545455 | nan | nan | nan | nan | nan | nan | 0.166667 | nan | nan | 0.357143 | 0.666667 | nan | 0.00881057 | nan | nan | nan | nan | 0.0927835 | 0.102941 | nan | nan | 0.754717 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0012193 | 0.0182216 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0079646 | 0.0173913 | 0.00348101 | nan | 0.00300218 | 0.131148 | nan | 0.22619 | 0.00650195 | nan | 0.0349014 | 0.949123 | nan | nan | nan | 0.684211 | nan | 0.0780488 | 0.961326 | 0.666667 | 0.227273 | 0.0486349 | 0.411765 | nan | 0.219048 | nan | 0.27907 | nan | 0.309524 | 0.5 | 0.5 | 0.611111 | nan | 0.209302 | nan | 0.984283 | 0.993481 | 0.136995 | nan | 0.722222 | nan | 0.557078 | nan | 0.903226 | 0.5 | 0.0386473 | 0.838926 | 0.966135 | 0.735849 | nan | 0.0909091 | 0.16129 | nan | 0.910714 | nan | nan | 0.0434783 | nan | nan | 0.571429 | nan | 0.453704 | nan | 0.940476 | nan | nan | nan | nan | nan | nan | 0.432432 | nan | nan | nan | 0.236948 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.00331345 | nan | nan | nan | 0.13 | 0.75 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.997405 | 0.314286 | nan | 0.0896309 | nan | nan | 0.708333 | 0.698113 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.192308 | 0.0024077 | nan | 0.00211342 | nan | 0.178082 | nan | nan | nan | nan | nan | 0.857143 | 0.5 | nan | nan | 0.0416667 | nan | 0.736842 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.943262 | 0.583333 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0285096 | nan | nan | 0.74026 | 0.144444 | nan | 0.569427 | 0.457143 | 0.00316539 | 0.0322581 | 0.0304679 | nan | nan | nan | nan | 0.97929 | 0.877551 | 0.766667 | nan | nan | 0.34375 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.172414 | nan | nan | nan | 0.625 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.103448 | nan | 0.0241935 | nan | 0.569444 | nan | 0.538462 | 0.868421 | 0.62069 | 0.360731 | 0.901639 | nan | nan | nan | nan | nan | nan | nan | 0.998452 | nan | 0.571429 | nan | nan | 0.808824 | 0.893617 | 0.290698 | nan | nan | nan | nan | 0.0649351 | 0.909465 | nan | 0.170782 | nan | nan | nan | nan | 0.529412 | nan | nan | 0.00103598 | nan | nan | nan | nan | nan | nan | nan | nan | 0.170543 | 0.00404204 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.53125 | 0.00574324 | nan | nan | 0.00106918 | nan | nan | nan | nan | nan | nan | 0.793103 | nan | nan | 0.727273 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.421053 | nan | 0.154164 | nan | nan | 0.116732 | 0.153153 | nan | 0.611354 | 0.727273 | 0.0643275 | 0.0486486 | nan | 0.906936 | 0.985955 | nan | 0.00308483 | 0.821429 | nan | nan | nan | 0.25 | 0.00215196 | nan | nan | 0.00127592 | nan | 0.876033 | nan | nan | 0.00142292 | nan | 0.00326087 | 0.992212 | nan | nan | 0.375 | nan | nan | nan | 0.6 | nan | 0.275862 | nan | nan | nan | 0.767442 | nan | 0.919961 | 0.942857 | nan | nan | nan | nan | nan | nan | nan | nan | 0.868421 | nan | nan | nan | nan | nan | 0.98254 | 0.980066 | nan | 0.0201613 | nan | nan | 0.147541 | 0.00140023 | nan | nan | nan | nan | nan | 0.0189394 | nan | 0.0350195 | nan | nan | nan | 0.113636 | nan | 0.0413223 | nan | 0.507246 | nan | nan | 0.00513375 | 0.997299 | nan | 0.886792 | nan | nan | 0.011236 | nan | nan | nan | nan | 0.0487805 | 0.666667 | nan | nan | 0.983494 | nan | nan | nan | nan | nan | 0.983819 | nan | 0.846154 | 0.384615 | nan | nan | nan | 0.534735 | 0.00271509 | nan | 0.92053 | 0.366667 | nan | nan | 0.0734824 | 0.876106 | nan | nan | nan | nan | nan | 0.794118 | 0.84472 | nan | nan | 0.8 | nan | nan | nan | 0.009375 | 0.00842886 | 0.899725 | 0.5 | 0.0454545 | nan | 0.310757 | nan | 0.461538 | nan | nan | 0.0243056 | 0.804878 | nan | 0.294964 | 0.5 | 0.411765 | nan | 0.195122 | nan | 0.742424 | 0.989488 | nan | 0.388889 | nan | nan | nan | nan | 0.533333 | nan | 0.705882 | nan | nan | 0.375 | nan | 0.126748 | nan | 0.0769231 | 0.0383225 | 0.889908 | nan | 0.336957 | nan | 0.6 | nan | 0.166667 | nan | nan | 0.622222 | nan | nan | nan | 0.411765 | nan | nan | nan | nan | nan | 0.990431 | nan | nan | nan | nan | 0.4 | 0.957929 | nan | nan | nan | 0.0324675 | 0.393939 | nan | nan | nan | nan | nan | nan | nan | 0.142857 | 0.694981 | 0.0327381 | 0.00233879 | nan | 0.893617 | nan | 0.173077 | nan | 0.97903 | nan | 0.392157 | nan | nan | 0.209302 | nan | 0.0546875 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.545455 | 0.555556 | 0.116667 | nan | nan | 0.380952 | 0.362928 | 0.666667 | 0.571429 | nan | nan | 0.391304 | 0.321429 | nan | nan | nan | 0.0410959 | nan | 0.400602 | nan | nan | nan | nan | 0.0516467 | 0.518519 | nan | 0.216867 | 0.666667 | 0.586207 | nan | nan | 0.0507614 | 0.171875 | nan | nan | 0.768412 | 0.892857 | nan | nan | nan | 0.258065 | nan | nan | nan | nan | nan | nan | nan | 0.333333 | nan | 0.205882 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.5 | nan | 0.461538 | nan | nan | 0.994438 | nan | 0.917355 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.526316 | nan | nan | 0.953795 | nan | nan | nan | 0.808642 | nan | nan | 0.90625 | nan | nan | nan | 0.2 | 0.269231 | 0.0701559 | 0.0438596 | nan | 0.780083 | 0.130049 | nan | nan | nan | 0.5 | 0.010274 | 0.6425 | 0.0155642 | nan | nan | 0.40625 | 0.5625 | nan | nan | nan | nan | nan | nan | 0.985185 | nan | 0.16 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.16129 | nan | nan | nan | nan | 0.948276 | nan | 0.710526 | 0.951872 | nan | 0.222222 | 0.37931 | 0.114122 | 0.969206 | 0.270492 | nan | 0.0721649 | 0.382353 | nan | nan | nan | nan | nan | 0.841584 | nan | 0.917431 | 0.998042 | nan | 0.839506 | 0.384615 | 0.956835 | 0.166667 | 0.694444 | nan | nan | nan | nan | nan | nan | 0.0987124 | nan | 0.0494624 | 0.666667 | nan | nan | nan | 0.37931 | 0.146771 | nan | 0.318182 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0555556 | 0.454545 | nan | nan | nan | 0.0714738 | 0.3 | 0.119048 | nan | 0.674419 | nan | 0.716667 | nan | nan | 0.0443548 | nan | 0.0574713 | nan | 0.238095 | 0.142857 | nan | 0.150943 | nan | 0.315789 | 0.642857 | 0.814286 | nan | nan | nan | nan | nan | nan | 0.18 | nan | nan | nan | nan | nan | nan | nan | nan | 0.351648 | nan | nan | nan | nan | 0.363636 | nan | nan | nan | 0.5 | nan | 0.0205066 | nan | nan | 0.0230608 | 0.118557 | 0.75 | nan | nan | nan | nan | 0.894578 | 0.862069 | nan | 0.978191 | nan | nan | nan | nan | nan | 0.5 | nan | nan | 0.477612 | 0.258427 | 0.32 | 0.578947 | 0.6 | 0.0909091 | nan | 0.6 | nan | nan | nan | 0.806701 | nan | 0.876671 | 0.416667 | 0.452991 | 0.625 | nan | nan | nan | 0.00869565 | 0.241379 | 0.333333 | nan | 0.974074 | nan | nan | nan | nan | nan | nan | 0.954887 | 0.94453 | nan | nan | 0.967532 | nan | nan | 0.954276 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.955696 | 0.966837 | nan | nan | 0.454545 | 0.357143 | nan | nan | 0.974271 | nan | nan | nan | 0.324324 | nan | nan | nan | nan | 0.97549 | 0.991756 | nan | nan | nan | nan | nan | 0.0934579 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.910714 | nan | 0.3375 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.304348 | 0.642857 | nan | nan | 0.00112504 | nan | nan | nan | nan | nan | nan | 0.238095 | 0.132075 | 0.310345 | 0.77027 | 0.6 | 0.25 | 0.428571 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.1875 | nan | nan | 0.416667 | nan | 0.711538 | nan | 0.00967742 | nan | 0.354839 | 0.252525 | nan | nan | 0.5 | 0.583333 | nan | 0.404762 | 0.333333 | nan | nan | 0.538462 | nan | 0.99884 | nan | nan | 0.883117 | 0.75 | nan | 0.0840336 | 0.294118 | nan | nan | 0.166667 | 0.3 | nan | 0.0806452 | nan | 0.222222 | nan | nan | nan | nan | nan | nan | nan | nan | 0.83871 | nan | nan | nan | nan | nan | nan | 0.0695825 | nan | 0.211538 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0392857 | nan | 0.91 | 0.68254 | 0.477273 | nan | nan | nan | nan | 0.996989 | nan | 0.761905 | nan | nan | nan | 0.413793 | nan | nan | nan | 0.583333 | 0.758621 | 0.526316 | nan | nan | 0.183946 | 0.00505334 | 0.989802 | nan | nan | nan | 0.168831 | 0.995449 | nan | nan | nan | nan | nan | nan | nan | 0.0769231 | nan | 0.26087 | 0.38961 | nan | 0.305556 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.483871 | nan | nan | nan | nan | 0.0497238 | nan | nan | 0.48 | nan | nan | nan | 0.078125 | 0.0242537 | 0.461538 | 0.37037 | 0.176471 | nan | 0.10274 | nan | nan | 0.664122 | 0.542857 | 0.0314286 | nan | nan | nan | 0.375 | nan | nan | nan | 0.611111 | 0.8 | 0.0594059 | 0.0178042 | nan | nan | nan | nan | 0.25641 | nan | nan | nan | 0.117073 | nan | 0.0106838 | 0.138756 | 0.666667 | nan | 0.084507 | 0.896414 | nan | nan | nan | nan | nan | nan | 0.1 | 0.529412 | nan | nan | nan | 0.969697 | nan | nan | 0.611111 | nan | 0.13253 | nan | nan | 0.254717 | nan | nan | nan | nan | nan | nan | 0.970522 | nan | 0.11245 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.047619 | nan | 0.145455 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.21875 | nan | nan | 0.0451977 | 0.6875 | nan | 0.0552147 | nan | nan | nan | 0.105769 | 0.287879 | 0.0172643 | 0.992248 | nan | 0.108108 | nan | 0.055516 | nan | 0.998083 | 0.423423 | nan | nan | 0.73913 | nan | nan | 0.153439 | 0.375 | 0.4 | 0.973485 | 0.965015 | 0.875 | nan | nan | nan | 0.0309278 | nan | 0.155488 | 0.0352113 | 0.657143 | nan | 0.370466 | nan | nan | nan | nan | nan | 0.0746269 | nan | nan | nan | nan | nan | nan | nan | 0.758242 | nan | nan | nan | 0.786885 | 0.996893 | nan | 0.635514 | nan | nan | 0.243243 | 0.30137 | nan | nan | 0.563452 | nan | 0.631579 | nan | 0.638889 | 0.567568 | 0.461538 | 0.954545 | nan | 0.411765 | 0.0914826 | 0.34 | nan | nan | 0.583333 | nan | nan | nan | nan | nan | nan | nan | 0.00406504 | nan | nan | 0.00245779 | 0.892857 | 0.780488 | 0.417219 | nan | 0.0558723 | nan | 0.0585065 | nan | nan | nan | nan | nan | 0.123711 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.642857 | nan | nan | nan | nan | nan | 0.727273 | nan | nan | nan | nan | nan | nan | nan | nan | 0.538462 | nan | nan | nan | nan | nan | 0.118644 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.996184 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.142857 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0334646 | nan | nan | 0.114583 | 0.738095 | nan | 0.998723 | 0.15748 | 0.269231 | 0.0756881 | nan | nan | 0.999133 | 0.411765 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0586667 | nan | nan | 0.375 | 0.72381 | 0.0742049 | nan | nan | 0.863636 | 0.738265 | nan | nan | nan | nan | 0.00885936 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.444444 | nan | nan | nan | 0.461538 | nan | nan | 0.45 | 0.329208 | 0.242894 | nan | nan | nan | nan | nan | nan | 0.172414 | nan | nan | nan | nan | 0.00277874 | nan | nan | nan | nan | 0.00297398 | nan | 0.3125 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.960599 | 0.986559 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.00647561 | nan | nan | nan | nan | nan | nan | 0.206897 | nan | nan | nan | nan | nan | 0.631579 | 0.933333 | nan | 0.141732 | nan | nan | 0.0013684 | 0.341667 | nan | nan | 0.677419 | nan | nan | nan | nan | nan | nan | 0.999301 | nan | nan | 0.998493 | nan | 0.666667 | nan | 0.011236 | nan | nan | nan | nan | nan | 0.5 | nan | nan | nan | 0.982964 | 0.92 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.228571 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0322581 | nan | nan | 0.0519144 | 0.818182 | nan | 0.470588 | nan | 0.5 | nan | nan | nan | nan | nan | nan | nan | nan | 0.72973 | 0.868242 | 0.986594 | nan | nan | nan | nan | nan | 0.0655738 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.610526 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.703704 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.932432 | 0.5 | nan | nan | nan | nan | nan | 0.925 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.156863 | 0.696429 | nan | nan | nan | nan | nan | nan | nan | nan | 0.0381179 | 0.000654267 | 0.150943 | nan | nan | nan | nan | nan | nan | nan | nan | 0.212766 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0506329 | nan | 0.000888297 | nan | nan | nan | nan | 0.684211 | nan | nan | nan | 0.666667 | nan | nan | nan | nan | nan | 0.815789 | 0.980831 | nan | 0.35 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.508475 | 0.0916031 | nan | nan | nan | nan | nan | 0.771186 | nan | nan | nan | nan | nan | 0.803863 | 0.392857 | 0.551181 | 0.92 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.992332 | nan | nan | 0.621622 | nan | nan | nan | nan | nan | 0.138889 | nan | 0.847458 | nan | 0.124767 | 0.0482759 | nan | 0.124088 | 0.0253271 | 0.0328947 | nan | 0.56 | nan | nan | nan | 0.997283 | nan | nan | nan | 0.333333 | nan | 0.166667 | 0.769231 | nan | nan | nan | nan | 0.25 | nan | nan | 0.421053 | nan | nan | nan | nan | nan | 0.615385 | nan | nan | nan | 0.0818182 | nan | nan | nan | 0.545455 | 0.733333 | nan | 0.0335196 | nan | nan | 0.482759 | nan | 0.115385 | 0.5 | 0.6875 | nan | nan | 0.277778 | nan | nan | nan | 0.357143 | nan | 0.15625 | nan | 0.896104 | nan | nan | nan | 0.887097 | nan | 0.00388601 | 0.931034 | nan | nan | nan | nan | nan | nan | nan | 0.00545852 | nan | 0.6875 | 0.857143 | nan | 0.963235 | nan | nan | nan | 0.571429 | nan | nan | nan | 0.709677 | 0.642857 | nan | nan | nan | nan | nan | nan | nan | nan | 0.176471 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.5 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.00184026 | nan | nan | nan | nan | nan | 0.996627 | nan | nan | nan | nan | nan | nan | 0.998023 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.949309 | nan | nan | nan | 0.5 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.984483 | nan | nan | 0.867647 | 0.271186 | 0.72028 | nan | 0.76087 | 0.87931 | nan | 0.87234 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.615385 | nan | nan | nan | nan | 0.380952 | nan | nan | 0.6 | nan | nan | nan | nan | 0.997667 | nan | nan | nan | nan | nan | nan | 0.0183486 | nan | 0.240741 | nan | 0.6 | 0.00430785 | 0.545455 | nan | nan | 0.190476 | nan | nan | 0.555556 | nan | nan | nan | nan | nan | 0.416667 | nan | nan | nan | 0.0015029 | nan | nan | nan | nan | 0.924051 | nan | nan | nan | nan | nan | nan | nan | 0.583333 | nan | nan | nan | nan | 0.560976 | 0.0138249 | nan | nan | 0.00404749 | nan | nan | nan | nan | 0.00378378 | nan | 0.00247448 | nan | nan | 0.997986 | 0.521739 | nan | nan | 0.0892857 | 0.454545 | nan | nan | nan | nan | 0.466667 | nan | 0.114883 | nan | nan | 0.590909 | nan | nan | nan | 0.740741 | nan | nan | nan | nan | 0.136364 | nan | nan | nan | nan | 0.991557 | nan | 0.890052 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.586207 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.011257 | nan | nan | 0.545455 | nan | nan | nan | 0.791667 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.160665 | 0.756098 | nan | nan | 0.878049 | nan | nan | 0.269231 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.283019 | nan | 0.615385 | nan | nan | nan | 0.0833333 | nan | nan | 0.578947 | nan | 0.505882 | nan | nan | nan | nan | nan | nan | nan | nan | 0.0602706 | 0.0967742 | 0.999168 | nan | 0.531915 | nan | 0.0751047 | 0.322148 | nan | 0.5 | nan | 0.00167172 | nan | nan | nan | nan | 0.736842 | 0.587302 | nan | nan | 0.346154 | 0.861111 | nan | nan | nan | nan | nan | 0.428571 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.83945 | 0.0352941 | 0.294416 | nan | nan | nan | 0.162791 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.75 | nan | nan | nan | 0.4375 | 0.997354 | nan | nan | 0.716698 | nan | nan | 0.00305623 | nan | nan | nan | nan | 0.214912 | nan | nan | 0.011259 | nan | nan | 0.932551 | nan | nan | nan | nan | nan | nan | nan | nan | 0.976834 | 0.333333 | nan | nan | nan | nan | 0.333333 | nan | nan | nan | 0.87156 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.997242 | nan | nan | nan | nan | nan | nan | nan | nan | 0.773913 | nan | nan | nan | 0.654135 | nan | nan | nan | 0.07 | nan | nan | nan | nan | nan | nan | nan | nan | 0.5 | nan | 0.885066 | 0.515152 | nan | nan | nan | nan | nan | nan | 0.133333 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.526316 | nan | nan | nan | nan | nan | nan | nan | 0.247423 | 0.106383 | nan | nan | nan | nan | 0.5 | nan | nan | nan | nan | 0.4 | nan | 0.301887 | 0.236842 | nan | nan | nan | 0.931034 | nan | 0.605769 | nan | nan | 0.127273 | nan | nan | nan | 0.191176 | 0.00798526 | nan | 0.0555556 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.171429 | nan | 0.272727 | nan | 0.8 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.457143 | nan | nan | nan | nan | nan | 0.998538 | nan | nan | nan | nan | nan | nan | nan | nan | 0.583333 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.00153521 | nan | nan | nan | nan | nan | nan | nan | nan | 0.294118 | 0.736842 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.571429 | 0.45 | nan | 0.730769 | nan | nan | nan | nan | nan | nan | nan | nan | 0.16 | nan | nan | 0.02897 | nan | nan | nan | 0.169492 | 0.333333 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.25 | nan | 0.115385 | nan | nan | 0.5 | 0.428571 | nan | nan | 0.742857 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.5 | nan | nan | nan | nan | 0.384615 | nan | nan | nan | nan | nan | nan | 0.00103439 | nan | nan | nan | nan | nan | nan | 0.580645 | 0.045977 | nan | 0.616667 | nan | nan | nan | nan | nan | 0.230769 | nan | nan | 0.521739 | nan | nan | nan | nan | nan | nan | nan | 0.571429 | 0.900709 | nan | 0.0367647 | 0.0391061 | 0.105263 | nan | nan | nan | 0.0958904 | 0.529412 | nan | nan | nan | nan | nan | 0.0319149 | nan | nan | 0.778502 | 0.0044825 | 0.0789474 | 0.0487805 | 0.0248869 | 0.175758 | 0.611111 | 0.993224 | nan | nan | nan | nan | 0.00248027 | nan | nan | nan | nan | 0.152542 | 0.894309 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.862069 | 0.87931 | 0.636364 | nan | nan | 0.6 | 0.291667 | nan | 0.583333 | nan | 0.283019 | 0.333333 | nan | 0.805556 | nan | 0.793814 | 0.441589 | nan | nan | nan | 0.470588 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.00151976 | nan | 0.99896 | nan | 0.340659 | 0.56 | 0.545455 | 0.714286 | 0.533333 | 0.461538 | 0.576923 | nan | 0.545455 | nan | nan | nan | 0.571429 | 0.333333 | nan | nan | nan | 0.454545 | 0.5625 | nan | nan | nan | 0.454545 | nan | 0.0403458 | nan | nan | 0.533333 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.279279 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.666667 | 0.2 | nan | nan | nan | nan | nan | nan | 0.0705882 | 0.0301724 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.202247 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.00229779 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.358974 | nan | 0.183673 | 0.814815 | nan | nan | 0.481481 | nan | nan | nan | 0.873016 | nan | nan | 0.547619 | nan | nan | nan | nan | 0.931442 | 0.571429 | 0.160577 | nan | nan | 0.705882 | 0.323529 | nan | nan | nan | nan | nan | nan | nan | 0.75 | nan | nan | nan | nan | nan | nan | nan | nan | 0.111111 | nan | nan | nan | nan | nan | nan | nan | 0.192308 | 0.0311082 | 0.530612 | nan | 0.413793 | nan | 0.833333 | 0.649123 | nan | 0.864322 | nan | 0.832104 | nan | 0.55 | 0.155556 | nan | nan | nan | nan | 0.157895 | nan | nan | 0.963731 | nan | 0.384615 | nan | nan | 0.950355 | nan | 0.538462 | nan | nan | nan | nan | nan | nan | 0.875 | nan | 0.3125 | nan | 0.300971 | 0.116279 | nan | nan | nan | nan | 0.542857 | 0.790698 | 0.777778 | 0.682778 | nan | 0.461538 | 0.514286 | nan | nan | nan | nan | nan | nan | 0.392172 | 0.619048 | nan | nan | nan | nan | nan | nan | nan | 0.0239726 | nan | 0.0431655 | nan | nan | nan | nan | 0.994798 | nan | nan | nan | nan | nan | nan | 0.0488889 | nan | nan | nan | nan | nan | 0.733333 | nan | nan | nan | 0.526316 | nan | 0.961864 | 0.920264 | 0.864865 | 0.00474898 | 0.353846 | 0.111111 | nan | nan | nan | 0.958904 | 0.08 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.344262 | nan | 0.432836 | nan | 0.615385 | 0.249027 | 0.316239 | nan | nan | 0.681553 | nan | nan | 0.416667 | nan | nan | nan | nan | nan | nan | nan | nan | 0.997103 | nan | nan | nan | nan | 0.0985915 | 0.943182 | 0.454545 | 0.7 | 0.991743 | nan | 0.571429 | nan | nan | nan | nan | nan | 0.470588 | nan | 0.76 | nan | nan | nan | nan | nan | nan | 0.421053 | 0.0392927 | 0.159091 | nan | 0.764331 | 0.882353 | nan | nan | nan | nan | 0.578947 | 0.333333 | nan | nan | nan | nan | 0.571429 | nan | nan | 0.813953 | nan | 0.705882 | nan | 0.538462 | nan | nan | nan | nan | nan | nan | 0.461538 | 0.352564 | 0.16129 | 0.887097 | 0.241379 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.92605 | nan | 0.461538 | nan | nan | nan | nan | 0.0152439 | 0.15942 | 0.123487 | nan | 0.871795 | nan | 0.0566038 | nan | nan | nan | 0.909091 | 0.880952 | 0.0543689 | 0.978495 | 0.310811 | nan | 0.71 | nan | 0.945783 | nan | nan | 0.617647 | 0.893258 | nan | nan | nan | nan | nan | nan | 0.0357143 | 0.109091 | nan | 0.615385 | 0.929497 | 0.871795 | 0.5625 | nan | 0.384615 | nan | nan | nan | nan | nan | nan | 0.26087 | nan | nan | 0.230769 | nan | nan | 0.262599 | 0.0297265 | 0.12069 | 0.79 | nan | 0.838105 | 0.171429 | nan | nan | nan | nan | 0.849351 | nan | 0.0833333 | nan | nan | nan | nan | nan | 0.666667 | nan | 0.0251012 | 0.607143 | nan | 0.52381 | nan | 0.420233 | 0.228571 | nan | nan | nan | 0.461538 | nan | nan | 0.785714 | nan | 0.193237 | 0.454545 | 0.52381 | 0.453704 | 0.2 | nan | nan | nan | nan | nan | nan | nan | nan | 0.0428135 | nan | nan | nan | nan | nan | 0.333333 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.5 | 0.0114286 | 0.864865 | nan | 0.767123 | nan | nan | nan | 0.75 | 0.130841 | nan | 0.345455 | 0.4 | 0.866667 | 0.368421 | nan | 0.676471 | nan | 0.0634146 | 0.478261 | 0.518519 | 0.430556 | nan | nan | nan | 0.00165618 | nan | nan | nan | nan | 0.227273 | nan | nan | 0.857143 | 0.35443 | 0.172414 | 0.5 | nan | nan | 0.615385 | nan | nan | nan | 0.205882 | nan | nan | nan | nan | 0.5 | nan | nan | nan | 0.894737 | 0.5 | nan | 0.85 | 0.963855 | 0.626299 | 0.571429 | nan | 0.833333 | 0.951691 | 0.337748 | 0.113636 | 0.0983607 | 0.835821 | nan | nan | 0.5 | 0.36291 | 0.233766 | nan | 0.458333 | 0.727273 | 0.105882 | nan | nan | 0.21875 | nan | nan | nan | 0.00100923 | nan | nan | nan | nan | 0.62724 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.172414 | 0.115044 | nan | nan | nan | nan | 0.692308 | 0.0141844 | nan | nan | nan | nan | nan | nan | nan | 0.294118 | nan | nan | nan | 0.00387747 | nan | nan | 0.10101 | nan | nan | nan | nan | 0.761905 | nan | nan | nan | nan | nan | 0.0116667 | nan | nan | nan | nan | nan | nan | nan | 0.575758 | nan | 0.0165017 | nan | nan | nan | nan | nan | 0.0936639 | nan | 0.42 | nan | 0.416667 | nan | 0.305419 | nan | nan | nan | nan | nan | nan | nan | nan | 0.803419 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.647059 | nan | nan | 0.373428 | nan | nan | 0.293413 | nan | 0.764706 | nan | nan | nan | nan | nan | nan | 0.142222 | 0.538462 | 0.692308 | 0.0752137 | nan | nan | nan | nan | 0.466667 | nan | nan | nan | nan | nan | 0.9976 | nan | nan | nan | nan | nan | 0.0372881 | nan | nan | nan | nan | 0.478261 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0409721 | nan | 0.939891 | nan | 0.0787402 | 0.0280827 | 0.104167 | nan | 0.619048 | 0.545455 | 0.492308 | nan | nan | nan | 0.0143541 | 0.146735 | 0.0562914 | nan | nan | 0.916339 | nan | 0.982184 | 0.193916 | 0.453488 | 0.940463 | 0.561644 | nan | 0.744186 | 0.973958 | nan | nan | nan | nan | nan | 0.323529 | nan | 0.333333 | nan | nan | nan | nan | nan | 0.625 | nan | 0.5 | nan | nan | nan | nan | 0.461538 | 0.315789 | 0.658228 | nan | nan | nan | 0.545455 | nan | nan | 0.227273 | nan | 0.958042 | 0.844262 | nan | nan | nan | 0.333333 | 0.857143 | nan | nan | 0.27027 | 0.545455 | 0.551948 | nan | nan | 0.985836 | nan | nan | nan | 0.00976562 | 0.205882 | nan | 0.998845 | 0.37931 | 0.848485 | nan | nan | nan | nan | 0.167785 | nan | nan | nan | 0.525641 | 0.0977444 | nan | nan | nan | 0.00122303 | nan | nan | nan | nan | 0.972376 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.907895 | nan | 0.714286 | nan | nan | 0.307692 | 0.533333 | nan | 0.00229283 | nan | nan | nan | 0.998842 | nan | nan | nan | nan | nan | nan | nan | 0.875 | nan | 0.684211 | 0.963889 | nan | 0.833333 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.16514 | nan | nan | nan | 0.48 | nan | 0.920833 | nan | 0.998517 | 0.333333 | nan | 0.470588 | nan | nan | nan | nan | 0.323529 | nan | 0.674641 | 0.00502874 | nan | nan | nan | 0.00192907 | 0.234043 | 0.545455 | nan | 0.416667 | nan | 0.989619 | nan | nan | nan | nan | nan | nan | 0.236364 | nan | nan | 0.888889 | 0.615385 | 0.731707 | nan | nan | 0.83871 | 0.873418 | nan | nan | nan | nan | 0.666667 | nan | 0.910714 | nan | 0.998343 | nan | nan | 0.384615 | nan | 0.384615 | nan | 0.388889 | nan | 0.478261 | nan | nan | nan | 0.16129 | nan | nan | 0.4 | nan | nan | nan | nan | 0.826531 | nan | nan | nan | 0.375 | nan | nan | 0.3 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.4 | nan | nan | nan | nan | 0.571429 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0114247 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.6875 | 0.77619 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.981982 | nan | nan | nan | nan | nan | 0.0153061 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0159696 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.576389 | 0.215385 | nan | 0.416667 | 0.156863 | 0.666667 | nan | nan | nan | nan | nan | nan | 0.273381 | 0.573826 | nan | nan | nan | nan | nan | nan | 0.969128 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.320388 | 0.416667 | nan | 0.771429 | 0.725203 | nan | 0.615385 | nan | nan | nan | nan | nan | nan | nan | nan | 0.5 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.84375 | nan | nan | nan | nan | nan | 0.642857 | nan | nan | nan | nan | 0.8 | 0.571429 | nan | nan | nan | 0.583333 | nan | 0.589744 | nan | nan | 0.00240288 | 0.87234 | nan | nan | 0.0769231 | nan | nan | 0.00688705 | 0.988558 | nan | nan | nan | 0.885714 | nan | nan | 0.16 | nan | nan | nan | nan | nan | nan | nan | nan | 0.416667 | 0.762255 | 0.924901 | 0.709677 | nan | 0.533333 | 0.880802 | 0.565217 | 0.405628 | nan | nan | 0.384615 | nan | nan | 0.776435 | 0.990712 | 0.0918367 | nan | nan | nan | 0.999234 | nan | nan | nan | 0.701754 | nan | 0.4 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.35 | 0.0791667 | nan | nan | nan | nan | nan | nan | nan | 0.999193 | 0.998907 | 0.764706 | 0.044335 | nan | nan | nan | nan | 0.877193 | 0.0864639 | nan | nan | nan | 0.867925 | nan | nan | 0.909747 | nan | nan | nan | nan | nan | nan | nan | nan | 0.0515464 | nan | nan | 0.998251 | nan | nan | nan | nan | nan | nan | nan | 0.157385 | nan | nan | 0.826087 | nan | nan | 0.0347222 | 0.222222 | 0.448598 | nan | nan | 0.902439 | nan | 0.0961538 | nan | nan | nan | nan | nan | 0.5 | nan | 0.0805369 | nan | nan | 0.997186 | 0.24 | 0.315789 | nan | nan | nan | nan | nan | nan | nan | 0.875 | 0.776892 | nan | 0.736842 | 0.185185 | nan | nan | nan | nan | 0.601485 | nan | nan | nan | nan | 0.791045 | nan | nan | nan | 0.98487 | nan | 0.0675676 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.15 | nan | nan | nan | nan | nan | 0.230769 | nan | nan | nan | nan | nan | 0.694915 | nan | nan | 0.428571 | 0.807018 | 0.930556 | nan | nan | 0.333333 | nan | 0.0454545 | nan | nan | nan | nan | nan | 0.172414 | nan | nan | nan | nan | 0.782609 | nan | nan | nan | 0.785714 | nan | nan | 0.0441989 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.4 | nan | nan | nan | nan | 0.868421 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0450281 | nan | 0.402985 | nan | 0.5 | 0.855769 | nan | 0.55814 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0259392 | nan | nan | nan | nan | nan | nan | nan | 0.984177 | 0.140351 | 0.24 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.962644 | nan | nan | nan | nan | nan | 0.454545 | nan | nan | nan | 0.0267176 | 0.321429 | nan | 0.691971 | nan | nan | nan | nan | nan | nan | nan | 0.380952 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.428571 | 0.108844 | 0.2 | nan | nan | 0.4 | 0.369565 | nan | 0.517007 | 0.748387 | 0.856522 | 0.122807 | 0.846194 | nan | nan | nan | nan | 0.209302 | nan | 0.185185 | 0.0769231 | 0.875 | 0.111111 | 0.228571 | nan | nan | 0.80401 | 0.577465 | 0.92 | 0.538462 | nan | nan | nan | nan | nan | 0.1 | nan | 0.494898 | 0.707865 | nan | nan | 0.666667 | nan | nan | nan | nan | nan | nan | nan | 0.565217 | nan | 0.5 | nan | nan | 0.592593 | nan | nan | nan | 0.319149 | 0.394161 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.179487 | nan | nan | nan | nan | nan | 0.35 | nan | nan | nan | nan | nan | nan | 0.5 | nan | nan | nan | 0.0342742 | nan | nan | 0.545455 | nan | nan | 0.75 | 0.952381 | nan | nan | nan | nan | nan | nan | nan | 0.294118 | nan | nan | nan | nan | 0.277778 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0471698 | 0.5 | nan | nan | nan | 0.178571 | nan | 0.00146199 | nan | nan | nan | nan | nan | 0.3125 | nan | nan | 0.73913 | nan | 0.296296 | nan | 0.0819672 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.55 | nan | nan | nan | 0.00160823 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.441176 | 0.473684 | 0.0482759 | 0.217778 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.322581 | nan | nan | nan | nan | 0.951613 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.347826 | 0.988208 | nan | nan | nan | 0.0324675 | nan | 0.34375 | nan | 0.192308 | nan | 0.25 | nan | nan | nan | 0.264706 | nan | nan | 0.6875 | 0.575758 | 0.555556 | nan | nan | nan | nan | nan | nan | nan | nan | 0.738095 | nan | nan | 0.614583 | nan | nan | 0.545455 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.972332 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.147059 | nan | 0.989887 | nan | nan | nan | nan | 0.0193162 | 0.0633333 | 0.0305556 | 0.0974026 | nan | 0.0141643 | 0.412935 | 0.761905 | nan | 0.00502874 | nan | nan | 0.418182 | nan | nan | nan | 0.459184 | nan | nan | 0.545455 | 0.354167 | 0.0969163 | 0.128571 | 0.2 | 0.0819672 | nan | nan | nan | nan | 0.00536585 | 0.357143 | 0.0234742 | nan | nan | 0.0100446 | nan | nan | 0.132075 | nan | nan | 0.6 | nan | nan | nan | nan | 0.649123 | 0.9 | nan | 0.022259 | 0.211538 | nan | nan | nan | 0.533333 | 0.807692 | nan | 0.5 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.00369822 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.954955 | nan | nan | nan | nan | 0.24 | 0.0547368 | 0.0227273 | 0.0406504 | 0.390244 | nan | nan | nan | nan | nan | nan | nan | nan | 0.238095 | nan | nan | 0.528302 | 0.996741 | nan | nan | nan | nan | nan | 0.416667 | nan | nan | 0.142857 | nan | 0.0978261 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.046875 | nan | nan | 0.69697 | 0.227273 | nan | nan | 0.8 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.4 | nan | nan | nan | nan | nan | 0.00278552 | 0.998688 | 0.6875 | 0.773585 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0111888 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0632911 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.179487 | nan | 0.0104908 | nan | nan | nan | nan | nan | 0.0956522 | nan | nan | 0.934524 | 0.990476 | nan | nan | nan | nan | nan | nan | nan | nan | 0.097561 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.00112593 | nan | nan | nan | nan | nan | nan | nan | 0.99183 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.26087 | nan | 0.912596 | nan | 0.466667 | nan | nan | nan | nan | nan | nan | 0.897163 | 0.392857 | 0.00133853 | nan | 0.272727 | 0.15 | 0.545455 | 0.419753 | nan | 0.96347 | nan | nan | nan | 0.0241935 | nan | 0.00167754 | nan | 0.217391 | nan | 0.5625 | nan | 0.269231 | nan | nan | nan | nan | nan | nan | 0.306122 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.473684 | nan | 0.384615 | nan | nan | 0.393939 | 0.45 | nan | nan | nan | 0.147059 | 0.775281 | nan | 0.473684 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.5 | nan | nan | nan | nan | nan | nan | nan | 0.583333 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.526316 | 0.5 | nan | 0.72973 | nan | nan | 0.598039 | nan | nan | nan | 0.416667 | nan | nan | nan | nan | nan | 0.5 | 0.589744 | nan | nan | nan | nan | 0.00127877 | nan | nan | nan | nan | nan | 0.617647 | 0.631579 | nan | 0.454545 | 0.52381 | 0.363636 | nan | 0.0761905 | 0.571429 | 0.0350877 | nan | 0.167331 | 0.0531915 | 0.333333 | 0.251969 | 0.209302 | 0.105263 | 0.352941 | nan | nan | nan | 0.607143 | nan | 0.647059 | 0.26087 | nan | nan | 0.00476515 | 0.00376412 | 0.666667 | nan | 0.545455 | nan | 0.990783 | 0.3 | nan | nan | nan | nan | nan | nan | nan | nan | 0.00766284 | nan | 0.4 | nan | 0.625 | nan | 0.0184758 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.211268 | nan | nan | 0.115385 | 0.240964 | nan | nan | nan | nan | 0.4 | nan | nan | nan | 0.889655 | 0.829268 | nan | 0.162162 | nan | 0.454545 | nan | nan | 0.375 | nan | nan | 0.188098 | nan | nan | nan | 0.529412 | nan | 0.12 | nan | 0.997369 | nan | 0.998795 | 0.4375 | nan | nan | nan | nan | 0.960177 | nan | 0.244186 | nan | 0.131148 | 0.177083 | 0.16129 | 0.470588 | nan | 0.144737 | 0.257426 | nan |\n", "| 2013 | nan | 0.0789474 | nan | 0.5 | 0.997705 | 0.333333 | 0.0425532 | nan | 0.208333 | nan | nan | nan | 0.00150685 | 0.882979 | 0.0535714 | 0.775 | nan | nan | nan | nan | nan | nan | nan | 0.545455 | 0.003125 | nan | nan | nan | 0.0714286 | nan | 0.99871 | nan | 0.307692 | 0.5 | nan | nan | nan | 0.00270636 | 0.631579 | 0.212766 | nan | nan | nan | nan | nan | nan | nan | nan | 0.454545 | nan | 0.7 | 0.402597 | nan | nan | 0.000954745 | 0.46875 | 0.008 | nan | nan | nan | nan | 0.982684 | nan | nan | nan | 0.325581 | nan | 0.996377 | nan | nan | nan | 0.015165 | 0.642857 | nan | 0.566038 | 0.0722892 | nan | 0.0438596 | nan | nan | 0.875 | nan | nan | nan | 0.120253 | 0.916667 | nan | nan | nan | 0.216667 | nan | nan | nan | nan | nan | nan | nan | nan | 0.0181139 | 0.997612 | nan | 0.615385 | nan | nan | 0.0830721 | 0.0562914 | nan | nan | 0.985876 | 0.189655 | 0.0652174 | nan | nan | nan | nan | 0.146341 | 0.384615 | nan | 0.827586 | nan | 0.35 | nan | nan | nan | 0.861538 | 0.277778 | nan | nan | nan | nan | nan | nan | nan | nan | 0.133333 | nan | nan | nan | nan | 0.0104128 | nan | 0.00541529 | nan | 0.097561 | 0.6 | nan | nan | nan | 0.533333 | 0.416667 | nan | nan | 0.988787 | 0.571429 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0819672 | 0.0364964 | nan | nan | nan | 0.597015 | nan | nan | 0.380952 | nan | nan | 0.411765 | 0.905138 | 0.0784314 | nan | nan | nan | 0.844828 | nan | nan | nan | 0.00344564 | nan | 0.9869 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0542857 | nan | 0.181818 | nan | nan | nan | nan | nan | nan | nan | nan | 0.0434783 | 0.722222 | nan | nan | nan | nan | nan | 0.0396201 | 0.998294 | 0.00181232 | nan | 0.997721 | nan | nan | nan | nan | nan | nan | 0.669811 | nan | nan | 0.800436 | nan | nan | nan | nan | nan | nan | 0.3125 | nan | nan | 0.826087 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.216522 | nan | nan | nan | 0.615385 | 0.0437956 | nan | 0.578947 | nan | nan | nan | nan | nan | nan | 0.994403 | nan | nan | 0.8125 | nan | nan | nan | nan | nan | 0.95283 | nan | nan | nan | 0.00760043 | nan | nan | nan | nan | 0.997795 | nan | nan | 0.575758 | nan | 0.980247 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.108696 | nan | nan | nan | 0.481481 | nan | nan | nan | nan | nan | 0.666667 | nan | nan | nan | nan | nan | nan | nan | nan | 0.84507 | nan | nan | nan | nan | 0.998809 | nan | nan | nan | 0.884615 | 0.134615 | nan | nan | nan | 0.860215 | nan | nan | nan | 0.110429 | 0.978261 | 0.0388128 | nan | 0.25 | 0.377285 | nan | 0.875817 | 0.425532 | nan | 0.943548 | 0.307692 | 0.277778 | 0.545455 | nan | 0.99634 | 0.157895 | nan | nan | nan | 0.5625 | 0.999004 | 0.465909 | nan | 0.144737 | nan | nan | 0.863636 | nan | nan | 0.772727 | nan | nan | nan | 0.214286 | 0.333333 | 0.0131805 | 0.136364 | 0.48 | nan | nan | 0.875 | nan | 0.76699 | 0.792453 | 0.62069 | 0.695652 | 0.333333 | nan | 0.722222 | nan | nan | 0.4 | nan | nan | 0.559322 | nan | nan | nan | nan | 0.94958 | nan | nan | 0.8 | 0.892857 | nan | 0.113402 | nan | nan | 0.16 | 0.0303286 | 0.836158 | nan | nan | 0.533333 | nan | nan | 0.9954 | nan | nan | nan | nan | nan | 0.00120151 | nan | nan | nan | nan | 0.0133111 | 0.703704 | nan | 0.160766 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.586207 | nan | nan | nan | nan | nan | nan | 0.952663 | nan | nan | nan | 0.772727 | 0.241379 | nan | 0.998696 | nan | nan | nan | nan | nan | 0.384615 | nan | nan | 0.985816 | nan | nan | 0.00122459 | nan | nan | nan | nan | nan | nan | 0.979675 | nan | 0.00165508 | nan | nan | nan | nan | 0.117647 | nan | nan | nan | nan | nan | 0.5 | nan | 0.807692 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.68 | 0.023913 | nan | nan | nan | nan | nan | nan | 0.777778 | nan | nan | nan | nan | nan | 0.954545 | 0.114754 | nan | 0.0376569 | nan | nan | nan | 0.243534 | 0.995905 | 0.982143 | nan | 0.0902394 | nan | nan | 0.454545 | 0.998998 | nan | nan | 0.5 | 0.877475 | nan | nan | 0.2 | nan | 0.300518 | 0.358025 | 0.133333 | 0.455556 | nan | 0.975265 | 0.386364 | 0.416667 | 0.90411 | nan | nan | nan | nan | nan | nan | 0.207547 | nan | nan | nan | 0.378378 | 0.827586 | 0.432432 | nan | nan | nan | nan | 0.0380435 | 0.647059 | 0.84127 | nan | nan | nan | nan | 0.42284 | nan | 0.658537 | nan | nan | nan | nan | 0.578571 | 0.76 | nan | nan | nan | nan | nan | nan | nan | 0.3125 | nan | nan | nan | nan | nan | 0.538462 | nan | nan | 0.307692 | nan | 0.782609 | nan | 0.8375 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.391304 | 0.936573 | nan | 0.0952381 | nan | 0.642857 | 0.826087 | nan | nan | 0.106942 | nan | nan | nan | nan | nan | nan | nan | 0.872727 | nan | 0.078125 | nan | nan | nan | nan | 0.479167 | nan | nan | 0.0117427 | nan | nan | 0.653846 | nan | nan | nan | 0.990438 | nan | nan | nan | 0.995968 | nan | nan | 0.254545 | 0.12 | 0.0672149 | 0.783394 | nan | 0.673913 | nan | nan | nan | nan | nan | 0.905744 | nan | nan | nan | 0.0519481 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0238095 | 0.583333 | 0.52381 | 0.0121294 | nan | nan | nan | nan | 0.997976 | 0.983181 | 0.951613 | 0.87234 | 0.333333 | 0.647059 | 0.302326 | nan | nan | nan | 0.997679 | nan | nan | nan | 0.109589 | nan | nan | 0.079646 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.294118 | 0.166667 | 0.0162589 | nan | nan | 0.57971 | nan | nan | 0.998481 | 0.998885 | 0.956522 | 0.0704225 | nan | nan | nan | 0.767442 | 0.943396 | 0.477193 | nan | nan | 0.84127 | 0.96124 | 0.990244 | nan | nan | 0.817606 | nan | 0.119617 | 0.0921053 | nan | 0.0806452 | 0.92 | 0.145161 | 0.0561798 | nan | 0.16129 | nan | nan | 0.641026 | nan | 0.666667 | 0.857143 | nan | nan | nan | 0.679245 | nan | 0.612245 | 0.00205761 | 0.0251256 | 0.163462 | nan | 0.0634328 | 0.0163934 | nan | 0.0436047 | 0.225 | nan | 0.857143 | nan | nan | nan | nan | nan | 0.363636 | 0.454545 | nan | 0.6 | nan | nan | 0.255814 | 0.583333 | nan | 0.767857 | 0.962069 | 0.458159 | nan | nan | nan | 0.652174 | nan | nan | nan | nan | 0.5 | 0.466667 | nan | nan | 0.915493 | nan | nan | nan | 0.164557 | 0.184466 | 0.942408 | 0.763158 | nan | nan | nan | nan | 0.545455 | nan | 0.615385 | 0.772727 | 0.991289 | 0.960409 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0222965 | nan | nan | nan | nan | 0.272727 | 0.25 | 0.864198 | 0.777778 | nan | nan | 0.993624 | 0.192771 | 0.384615 | nan | nan | 0.464286 | 0.306818 | nan | nan | nan | 0.033532 | nan | 0.142857 | 0.0379257 | nan | 0.0117878 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0014096 | nan | nan | 0.156863 | 0.0299485 | nan | 0.145161 | nan | nan | 0.169421 | nan | 0.852941 | 0.0381857 | 0.52381 | 0.592593 | 0.0456432 | nan | 0.5 | nan | 0.681818 | 0.8 | nan | 0.823944 | nan | nan | nan | 0.9375 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.892857 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.887097 | nan | nan | nan | nan | nan | nan | nan | nan | 0.909091 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.565217 | nan | 0.0391304 | 0.866213 | nan | 0.0614754 | 0.049505 | 0.086872 | nan | nan | 0.964639 | nan | nan | nan | nan | nan | 0.584416 | 0.133858 | nan | 0.0353982 | 0.290323 | 0.619048 | 0.910828 | nan | 0.895833 | nan | nan | 0.512195 | 0.961326 | 0.0758017 | nan | nan | nan | nan | nan | nan | nan | 0.0391459 | nan | 0.170732 | nan | nan | nan | nan | nan | nan | nan | 0.333333 | 0.0939335 | 0.642857 | 0.25 | 0.0182648 | 0.623188 | 0.428571 | nan | nan | nan | nan | nan | 0.00518672 | nan | nan | nan | 0.195876 | 0.736842 | 0.012372 | nan | 0.0127226 | 0.0520231 | 0.5 | nan | 0.0992366 | 0.448276 | 0.898356 | 0.969432 | nan | nan | nan | 0.723404 | nan | nan | 0.722222 | 0.405405 | 0.604167 | nan | nan | nan | nan | nan | nan | nan | 0.00128659 | nan | 0.869565 | nan | nan | nan | nan | nan | nan | 0.0891473 | nan | 0.0123993 | nan | nan | nan | nan | nan | nan | 0.0118211 | 0.16 | nan | nan | nan | 0.00509383 | nan | nan | nan | 0.98646 | 0.0482986 | 0.377698 | 0.00759734 | 0.569444 | 0.932692 | nan | 0.805556 | nan | nan | 0.0666667 | 0.124542 | 0.0957447 | 0.813559 | nan | nan | nan | 0.789474 | nan | nan | 0.5 | nan | 0.823529 | nan | 0.5625 | nan | nan | nan | nan | nan | nan | 0.0646465 | 0.030303 | nan | 0.538462 | nan | 0.129032 | 0.0365535 | nan | nan | nan | nan | nan | 0.416667 | nan | 0.027027 | nan | nan | nan | nan | nan | nan | nan | 0.00189179 | nan | nan | nan | nan | 0.997691 | nan | nan | 0.401487 | 0.0234375 | nan | nan | nan | nan | nan | 0.352941 | 0.025641 | nan | 0.384615 | 0.392562 | nan | 0.883085 | 0.880952 | nan | nan | nan | 0.947368 | 0.976974 | nan | nan | nan | nan | nan | 0.98374 | nan | 0.465517 | nan | nan | nan | 0.34375 | nan | 0.5 | nan | nan | 0.3 | nan | nan | nan | nan | 0.440678 | 0.116279 | nan | nan | 0.2 | 0.037464 | 0.00230787 | 0.0646766 | nan | nan | 0.863248 | nan | nan | nan | nan | 0.99321 | nan | 0.0161017 | nan | nan | nan | nan | nan | nan | 0.196429 | 0.0247669 | nan | nan | 0.0841121 | 0.0666667 | 0.964088 | 0.122449 | 0.828571 | nan | 0.410256 | 0.722222 | 0.957386 | nan | 0.995302 | nan | nan | nan | 0.00183872 | nan | nan | 0.0614035 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.583333 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.00942549 | 0.927093 | nan | nan | 0.285714 | nan | 0.275862 | nan | 0.17284 | nan | 0.444444 | nan | nan | nan | 0.276316 | 0.018997 | 0.853659 | 0.357143 | nan | nan | 0.4 | nan | 0.037037 | nan | nan | nan | nan | nan | nan | nan | 0.0016691 | nan | nan | 0.989418 | nan | nan | 0.278481 | 0.109034 | 0.5 | 0.0272727 | nan | nan | nan | nan | 0.642857 | 0.00338819 | 0.695652 | nan | nan | nan | nan | 0.389381 | nan | 0.384615 | nan | 0.3125 | nan | 0.677419 | nan | 0.615385 | 0.0301896 | 0.393939 | nan | nan | 0.428571 | 0.166667 | nan | nan | 0.0691251 | 0.625 | nan | 0.998834 | nan | nan | 0.993238 | nan | 0.645161 | nan | 0.064257 | nan | 0.0162866 | nan | 0.79782 | nan | 0.0448718 | nan | nan | nan | nan | 0.0180361 | nan | nan | 0.0701754 | nan | nan | nan | nan | nan | nan | nan | 0.25 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.421875 | nan | nan | 0.382979 | nan | nan | nan | nan | nan | nan | nan | 0.269231 | nan | nan | 0.709677 | nan | nan | nan | 0.971207 | nan | nan | nan | 0.0367647 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.808824 | nan | nan | nan | nan | 0.131818 | 0.0325064 | nan | 0.626298 | 0.0303276 | 0.109756 | nan | 0.214286 | nan | nan | 0.0169014 | 0.404073 | 0.00538358 | nan | nan | 0.490566 | nan | nan | nan | nan | nan | nan | 0.958522 | nan | nan | 0.0233645 | nan | 0.193548 | nan | nan | nan | nan | nan | nan | nan | 0.6 | nan | 0.0344968 | 0.890295 | nan | 0.708333 | 0.538462 | nan | 0.596154 | nan | nan | nan | nan | nan | 0.0566038 | nan | 0.294118 | nan | nan | nan | nan | nan | 0.372727 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.032 | nan | nan | nan | nan | nan | nan | 0.466667 | nan | nan | 0.314286 | nan | nan | nan | nan | nan | nan | nan | 0.0220636 | nan | nan | nan | nan | nan | nan | 0.224396 | nan | nan | nan | nan | nan | nan | 0.3 | nan | nan | 0.221854 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.625 | nan | nan | nan | nan | nan | nan | 0.984158 | nan | 0.00213828 | 0.30137 | 0.866567 | 0.987406 | 0.459056 | nan | 0.998817 | nan | 0.673077 | nan | nan | nan | nan | nan | 0.0135642 | nan | nan | nan | 0.333333 | nan | nan | 0.0714286 | nan | nan | nan | nan | nan | nan | nan | 0.113043 | nan | nan | 0.190083 | nan | nan | nan | nan | nan | nan | nan | 0.416667 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.421053 | 0.93617 | nan | nan | 0.964539 | 0.545455 | nan | nan | 0.165179 | nan | nan | 0.990891 | nan | nan | nan | 0.5 | nan | nan | nan | 0.8 | nan | 0.384615 | nan | nan | nan | nan | nan | nan | 0.666667 | nan | 0.998291 | nan | 0.416667 | nan | 0.454545 | 0.0174326 | nan | nan | nan | nan | nan | nan | 0.618182 | nan | nan | 0.0141964 | nan | nan | nan | 0.744186 | nan | nan | nan | nan | 0.00138325 | nan | nan | nan | nan | 0.15625 | nan | nan | nan | nan | nan | nan | 0.772727 | nan | nan | nan | nan | 0.514286 | nan | nan | nan | 0.2 | nan | nan | nan | 0.32 | nan | nan | nan | nan | nan | 0.321429 | nan | nan | nan | nan | nan | nan | 0.0191693 | 0.372549 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.920455 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.388889 | 0.65625 | nan | 0.142857 | nan | nan | 0.609756 | 0.612903 | 0.0107527 | 0.0140845 | nan | nan | nan | nan | nan | nan | 0.966667 | 0.0425258 | 0.00375614 | nan | 0.625 | nan | nan | nan | nan | nan | 0.115942 | 0.00490463 | 0.849315 | nan | 0.368421 | nan | nan | 0.000926498 | nan | nan | nan | nan | nan | nan | 0.246753 | nan | nan | 0.00529101 | nan | 0.00126618 | nan | nan | nan | nan | nan | 0.0490196 | nan | 0.0214558 | 0.1875 | 0.277778 | nan | nan | nan | 0.008726 | nan | 0.122093 | nan | nan | nan | nan | nan | nan | nan | 0.0179574 | 0.922078 | nan | 0.631579 | 0.34 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.852941 | nan | nan | nan | nan | nan | nan | 0.617647 | 0.0335052 | nan | nan | nan | nan | nan | nan | nan | 0.106383 | nan | nan | nan | 0.819527 | nan | nan | nan | nan | 0.0310559 | nan | nan | nan | nan | 0.461538 | 0.428571 | nan | nan | 0.769231 | nan | nan | nan | nan | nan | nan | 0.00390117 | nan | nan | nan | nan | nan | 0.0597015 | 0.538462 | 0.0219953 | nan | nan | 0.0175824 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.588235 | nan | nan | nan | nan | nan | 0.615385 | 0.294118 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.635294 | 0.903846 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.818182 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0648148 | 0.275862 | nan | 0.545547 | 0.626263 | 0.0709677 | nan | 0.705882 | nan | nan | nan | nan | 0.283241 | nan | 0.84058 | 0.466667 | 0.00454959 | 0.736842 | nan | nan | 0.243902 | nan | nan | 0.165179 | nan | nan | 0.976526 | 0.002267 | 0.00309789 | nan | nan | nan | nan | nan | 0.229008 | 0.145833 | nan | 0.93 | nan | nan | nan | nan | nan | nan | nan | nan | 0.461538 | nan | nan | nan | 0.885057 | nan | nan | nan | 0.00203081 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.638889 | nan | nan | 0.0142857 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.347826 | nan | nan | nan | nan | nan | 0.905405 | nan | 0.788079 | nan | nan | nan | 0.918129 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0586319 | nan | 0.040201 | 0.0341463 | 0.0588235 | nan | nan | nan | nan | nan | nan | nan | 0.666667 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.204082 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.034965 | nan | nan | 0.305556 | 0.790698 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.28 | 0.1375 | nan | 0.00121467 | nan | 0.708333 | 0.0289855 | nan | nan | nan | nan | 0.0285285 | nan | nan | nan | nan | 0.388889 | 0.151515 | nan | nan | nan | 0.0301602 | 0.692308 | nan | nan | nan | 0.0864198 | nan | nan | 0.0827068 | 0.487603 | nan | 0.647059 | 0.5 | nan | nan | nan | nan | 0.0739726 | 0.65 | nan | nan | nan | nan | 0.857143 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.206897 | 0.00702651 | 0.25 | nan | nan | nan | nan | nan | nan | nan | nan | 0.0420168 | nan | nan | nan | nan | nan | nan | nan | 0.280702 | nan | 0.4 | nan | nan | nan | nan | nan | 0.990683 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.391304 | nan | nan | 0.110236 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.184211 | 0.352941 | 0.324561 | nan | 0.849315 | nan | nan | nan | nan | nan | 0.545455 | 0.242188 | 0.0697674 | nan | nan | 0.4 | nan | nan | nan | nan | nan | nan | 0.337449 | 0.428571 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.377049 | nan | nan | 0.388889 | 0.151515 | nan | nan | nan | nan | nan | 0.162162 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.441176 | nan | nan | 0.00508647 | nan | nan | 0.101266 | 0.5 | nan | nan | nan | nan | nan | nan | 0.12426 | nan | nan | 0.0491803 | 0.5 | nan | nan | 0.0560123 | 0.521739 | nan | nan | 0.0882353 | 0.5 | 0.114641 | nan | nan | nan | nan | nan | nan | 0.667532 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.518519 | nan | 0.28 | nan | nan | nan | nan | 0.538462 | nan | 0.94878 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.893617 | nan | nan | nan | 0.0367347 | 0.0785714 | nan | 0.0316687 | 0.703704 | nan | 0.521739 | nan | 0.761905 | nan | nan | nan | 0.047619 | nan | nan | nan | nan | nan | nan | 0.795031 | nan | 0.697479 | 0.914286 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.00253084 | nan | nan | nan | nan | nan | 0.439294 | nan | 0.35 | nan | nan | nan | 0.428571 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0491159 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0215827 | 0.882353 | 0.171875 | 0.160114 | nan | 0.416667 | 0.608696 | nan | nan | 0.24 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.131148 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0570601 | 0.701754 | 0.119048 | 0.35 | 0.225806 | 0.454545 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.135135 | 0.0117171 | 0.401639 | nan | nan | nan | 0.5 | nan | nan | nan | nan | nan | 0.825 | nan | nan | 0.108108 | nan | nan | nan | nan | 0.0322581 | 0.35 | nan | 0.842627 | nan | 0.454545 | nan | 0.121951 | 0.0265957 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.901235 | nan | nan | nan | 0.82 | 0.410714 | nan | nan | nan | 0.206897 | nan | 0.357143 | nan | nan | nan | 0.684211 | nan | nan | nan | 0.153333 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.00452432 | 0.905882 | 0.381818 | nan | nan | nan | 0.28169 | 0.00239547 | 0.421053 | 0.959064 | 0.128889 | 0.352941 | nan | nan | nan | nan | 0.316832 | nan | nan | nan | 0.63 | nan | 0.998729 | nan | nan | nan | 0.998933 | nan | 0.965517 | nan | nan | 0.655172 | 0.171082 | 0.160501 | nan | 0.311295 | 0.766571 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.209302 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.133803 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.877551 | 0.94 | nan | 0.611111 | 0.5625 | nan | 0.983051 | 0.621212 | nan | nan | nan | 0.963158 | 0.929825 | nan | nan | 0.821429 | 0.608713 | 0.973899 | 0.841814 | nan | nan | nan | nan | nan | nan | 0.998476 | nan | 0.99914 | 0.00208507 | 0.866667 | 0.454545 | nan | 0.88601 | nan | 0.00254022 | nan | 0.948276 | nan | 0.846154 | 0.592982 | 0.608696 | 0.852941 | 0.710692 | 0.210526 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.466667 | nan | nan | nan | nan | nan | nan | nan | nan | 0.384615 | 0.578947 | 0.0030656 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.962015 | nan | nan | nan | 0.835616 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.172414 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.995129 | nan | nan | 0.00104725 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.99825 | 0.0142936 | 0.478261 | 0.122807 | nan | 0.998174 | 0.478448 | 0.153846 | 0.0115367 | 0.180328 | nan | 0.5 | nan | 0.126582 | 0.4 | nan | 0.2 | 0.7 | nan | nan | nan | nan | nan | 0.384615 | nan | nan | 0.458333 | 0.0032022 | nan | nan | nan | 0.0522491 | 0.263158 | 0.890909 | nan | nan | nan | nan | nan | 0.996768 | nan | 0.889734 | 0.694444 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.794118 | 0.666667 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.010906 | nan | nan | nan | nan | 0.122449 | nan | nan | nan | nan | nan | nan | nan | nan | 0.15 | nan | nan | 0.357143 | 0.4 | 0.545455 | nan | 0.305085 | 0.84127 | 0.657246 | 0.0109066 | 0.00974026 | 0.5 | 0.388316 | nan | nan | 0.0198413 | 0.5 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0788804 | nan | nan | nan | nan | nan | 0.837838 | nan | nan | 0.0405405 | nan | nan | nan | nan | nan | 0.985437 | nan | nan | nan | nan | nan | 0.0961214 | nan | nan | nan | nan | nan | 0.355231 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.55 | nan | nan | nan | nan | 0.138462 | nan | nan | nan | nan | nan | nan | nan | nan | 0.00470902 | nan | 0.542857 | 0.62963 | nan | 0.995043 | nan | nan | nan | 0.00603708 | nan | nan | 0.00325128 | 0.05 | nan | nan | nan | nan | nan | 0.428571 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.00309023 | nan | nan | nan | nan | 0.357143 | nan | nan | nan | 0.00215372 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.619048 | nan | 0.981702 | nan | nan | nan | nan | 0.533333 | nan | nan | nan | nan | nan | nan | 0.727273 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.777778 | nan | nan | nan | 0.0874317 | 0.434783 | 0.666667 | 0.5625 | 0.0223881 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.00467914 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.902439 | nan | nan | nan | nan | nan | nan | 0.393939 | nan | nan | nan | 0.998235 | 0.52381 | nan | nan | nan | 0.268293 | 0.959091 | nan | nan | nan | nan | nan | nan | 0.00423057 | nan | nan | nan | nan | 0.176871 | 0.546667 | nan | nan | 0.180617 | 0.0285514 | nan | 0.769231 | nan | nan | nan | nan | nan | nan | nan | 0.177419 | 0.108108 | 0.0171067 | nan | 0.0103722 | nan | nan | nan | nan | 0.899729 | nan | nan | nan | 0.00331126 | nan | nan | nan | 0.690476 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0793651 | nan | 0.988799 | nan | 0.48 | nan | 0.65625 | nan | nan | 0.097561 | nan | nan | 0.341667 | nan | 0.793651 | 0.681818 | nan | 0.998405 | nan | nan | nan | nan | 0.3125 | 0.996451 | nan | nan | nan | nan | 0.844262 | nan | 0.25 | nan | nan | 0.59375 | nan | nan | nan | nan | 0.384615 | nan | 0.181818 | 0.991495 | 0.998766 | 0.538462 | nan | nan | nan | 0.5 | 0.705882 | 0.583333 | nan | nan | 0.625 | nan | nan | nan | 0.0740741 | 0.970909 | 0.255814 | nan | 0.686275 | nan | 0.333333 | 0.666667 | 0.946181 | nan | 0.666667 | 0.814815 | 0.3125 | nan | 0.996292 | 0.733333 | nan | 0.957644 | nan | nan | nan | nan | 0.05625 | 0.00595002 | nan | nan | nan | nan | 0.85906 | 0.473684 | nan | 0.583333 | 0.0122549 | nan | 0.5 | nan | nan | nan | nan | nan | nan | nan | 0.861224 | 0.864407 | 0.384615 | 0.362363 | 0.52 | 0.276596 | 0.122449 | 0.606061 | 0.0436782 | nan | nan | nan | nan | 0.833333 | nan | 0.997083 | nan | nan | nan | nan | nan | nan | nan | 0.989 | nan | nan | nan | nan | nan | nan | 0.529412 | 0.0718563 | 0.803526 | nan | nan | nan | 0.001015 | 0.828125 | nan | nan | nan | nan | nan | 0.285714 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.00659522 | nan | nan | 0.826772 | nan | nan | nan | 0.4375 | nan | nan | nan | 0.5625 | nan | 0.673367 | nan | nan | nan | nan | nan | nan | nan | nan | 0.0373832 | nan | nan | nan | nan | nan | 0.0160473 | 0.563636 | nan | nan | nan | nan | nan | 0.0296256 | nan | 0.237288 | nan | 0.294118 | nan | 0.145833 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.64 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.333333 | nan | nan | 0.190476 | nan | 0.780488 | 0.962906 | 0.807692 | nan | nan | nan | nan | nan | 0.445946 | 0.849315 | 0.82 | 0.75 | 0.409091 | nan | nan | nan | nan | nan | nan | 0.64 | nan | nan | 0.334764 | nan | nan | nan | nan | nan | nan | 0.521739 | nan | 0.910959 | nan | nan | nan | nan | nan | nan | nan | 0.397436 | 0.000693275 | 0.997007 | nan | 0.998978 | nan | nan | nan | nan | 0.00165584 | nan | nan | nan | nan | nan | 0.884058 | nan | 0.0303831 | nan | 0.25641 | nan | 0.357143 | 0.161765 | 0.992908 | nan | nan | 0.00209717 | nan | 0.171429 | nan | nan | 0.941667 | nan | nan | nan | nan | nan | nan | 0.899696 | nan | 0.993824 | nan | nan | nan | nan | 0.837607 | nan | 0.730769 | nan | nan | nan | nan | nan | nan | nan | nan | 0.00700935 | nan | 0.960526 | nan | nan | nan | nan | 0.000816708 | nan | 0.605442 | nan | 0.00127146 | 0.3125 | 0.351852 | nan | 0.00121332 | 0.0463576 | nan | 0.008726 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.740741 | nan | nan | nan | nan | nan | 0.980601 | 0.0492645 | 0.545455 | nan | nan | nan | nan | nan | 0.34375 | 0.4 | nan | 0.897059 | nan | nan | nan | 0.645833 | nan | nan | 0.492308 | nan | 0.0471204 | 0.26087 | nan | 0.457516 | nan | 0.520833 | nan | nan | 0.46875 | 0.919776 | 0.989562 | 0.0338983 | nan | 0.112903 | nan | nan | nan | nan | nan | 0.6875 | 0.352941 | 0.294118 | nan | 0.174603 | nan | nan | 0.190476 | 0.0838979 | 0.454545 | nan | nan | 0.534884 | 0.708333 | nan | 0.411765 | nan | 0.2 | nan | 0.888889 | nan | 0.116129 | nan | nan | nan | nan | nan | nan | nan | 0.357143 | nan | nan | 0.0721649 | 0.0697674 | nan | nan | nan | nan | nan | nan | 0.73913 | nan | nan | nan | nan | nan | 0.333333 | 0.583333 | nan | nan | nan | 0.035461 | nan | nan | nan | nan | nan | nan | nan | 0.5 | 0.76699 | nan | 0.924812 | nan | nan | nan | nan | nan | nan | nan | 0.708333 | nan | nan | nan | nan | 0.0555556 | nan | nan | 0.32 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.00206566 | nan | 0.0185185 | 0.647059 | 0.37931 | nan | nan | 0.844444 | 0.674444 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.285714 | 0.0600924 | nan | 0.538462 | nan | nan | 0.407407 | nan | nan | 0.059322 | nan | nan | 0.90411 | nan | 0.647059 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.107143 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.583333 | nan | nan | nan | nan | nan | nan | nan | 0.8 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.25 | nan | nan | nan | 0.619048 | nan | 0.00503062 | nan | nan | nan | nan | 0.0827068 | 0.0549451 | nan | nan | 0.666667 | nan | nan | nan | nan | nan | nan | 0.998362 | nan | nan | nan | 0.00127296 | 0.0279627 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.00533808 | 0.0160142 | 0.00422945 | nan | 0.00224366 | 0.151899 | nan | 0.216981 | 0.00912647 | nan | 0.0292505 | 0.917976 | 0.0704225 | 0.00539374 | nan | nan | nan | 0.0867052 | nan | 0.714286 | 0.24 | 0.0448119 | 0.615385 | 0.181818 | 0.245161 | nan | 0.307692 | nan | 0.348315 | nan | 0.692308 | 0.416667 | nan | 0.207207 | 0.931507 | nan | 0.991206 | 0.133767 | nan | nan | nan | 0.621806 | nan | nan | nan | nan | 0.760684 | 0.969697 | 0.725275 | nan | nan | nan | nan | nan | nan | nan | 0.0373832 | nan | nan | nan | nan | 0.445455 | 0.0601504 | nan | nan | nan | nan | nan | nan | nan | 0.526316 | nan | nan | nan | 0.254032 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.111111 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.21875 | nan | 0.069209 | nan | nan | 0.655172 | 0.791045 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.00100865 | nan | nan | nan | 0.164706 | 0.0267857 | nan | nan | nan | nan | 0.794872 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.905882 | nan | nan | nan | nan | nan | nan | nan | nan | 0.0676329 | nan | 0.2 | nan | 0.0263459 | nan | 0.357143 | 0.886364 | 0.127273 | nan | 0.598002 | 0.376471 | 0.00407609 | nan | 0.0268972 | nan | nan | nan | nan | 0.969072 | 0.769231 | nan | nan | nan | 0.208333 | nan | nan | nan | nan | nan | nan | nan | nan | 0.222222 | 0.461538 | nan | nan | nan | 0.727273 | nan | nan | nan | nan | 0.230769 | nan | nan | nan | nan | nan | nan | nan | 0.026178 | nan | 0.444444 | nan | nan | 0.828571 | nan | 0.301255 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.786885 | 0.735849 | 0.283951 | nan | nan | nan | nan | nan | 0.934156 | nan | 0.161148 | nan | nan | nan | nan | nan | nan | 0.6875 | 0.00149491 | nan | nan | nan | nan | nan | nan | nan | nan | 0.195804 | 0.00527108 | nan | nan | nan | nan | nan | 0.578947 | nan | nan | 0.645161 | nan | nan | nan | nan | 0.4 | 0.00619195 | nan | nan | 0.00140581 | nan | nan | nan | nan | nan | nan | 0.697674 | nan | nan | 0.565217 | nan | nan | nan | nan | nan | nan | nan | nan | 0.68 | 0.3125 | nan | 0.146475 | nan | 0.6 | 0.119522 | 0.0679612 | nan | 0.666667 | nan | 0.100775 | 0.0382775 | nan | 0.903532 | 0.956897 | nan | nan | 0.75 | nan | nan | nan | 0.315789 | 0.000889047 | nan | nan | 0.00163733 | nan | 0.86087 | nan | nan | 0.00110198 | nan | 0.00238057 | nan | nan | nan | 0.238095 | nan | nan | nan | 0.631579 | nan | 0.327586 | nan | 0.416667 | nan | 0.717391 | 0.986644 | 0.936149 | nan | nan | 0.0221239 | nan | nan | 0.0198413 | nan | nan | nan | 0.808219 | nan | nan | nan | nan | nan | 0.978723 | 0.984985 | nan | nan | nan | nan | 0.114286 | 0.0015452 | nan | nan | nan | nan | nan | 0.0218659 | nan | 0.0379601 | 0.5 | nan | nan | 0.151515 | nan | 0.029661 | nan | 0.522388 | 0.375 | nan | 0.00543406 | nan | nan | nan | 0.996471 | nan | nan | nan | nan | nan | nan | nan | 0.695652 | 0.230769 | 0.428571 | 0.987472 | 0.454545 | nan | nan | nan | nan | 0.982063 | nan | 0.794118 | nan | nan | 0.761905 | nan | 0.545807 | 0.00205465 | nan | 0.923077 | 0.459459 | nan | nan | 0.100334 | 0.856209 | nan | nan | nan | nan | nan | 0.757576 | 0.788462 | 0.947712 | nan | 0.8 | 0.444444 | nan | nan | 0.0125348 | 0.0127964 | 0.912791 | nan | 0.0491803 | nan | 0.281553 | nan | nan | nan | nan | nan | nan | nan | 0.345794 | nan | 0.388889 | nan | 0.227273 | 0.290323 | 0.87931 | nan | nan | 0.3 | nan | nan | nan | nan | nan | nan | 0.769231 | nan | nan | 0.576923 | nan | 0.124113 | nan | 0.0625 | 0.0276469 | 0.900709 | nan | 0.348178 | nan | nan | 0.454545 | nan | nan | nan | 0.423077 | nan | nan | nan | 0.428571 | nan | nan | nan | 0.790698 | nan | 0.990196 | nan | nan | nan | nan | 0.294118 | 0.96463 | nan | nan | nan | nan | 0.435897 | nan | nan | nan | nan | nan | nan | nan | nan | 0.688259 | 0.0190114 | nan | nan | 0.857143 | nan | 0.192308 | nan | 0.976378 | nan | 0.438596 | nan | nan | 0.255814 | nan | 0.059322 | nan | nan | nan | nan | 0.915094 | nan | nan | nan | nan | nan | nan | 0.648148 | 0.122807 | nan | nan | 0.423077 | 0.382979 | 0.821429 | nan | nan | nan | 0.590909 | 0.288136 | nan | nan | nan | 0.0271444 | nan | 0.319328 | nan | nan | nan | nan | 0.0637481 | 0.569892 | 0.924242 | 0.328767 | 0.617647 | 0.681818 | nan | nan | 0.0582524 | 0.142857 | nan | nan | 0.776217 | nan | 0.28 | nan | nan | 0.391304 | nan | nan | nan | nan | nan | nan | nan | 0.380952 | 0.0285714 | 0.118421 | nan | nan | nan | 0.195122 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0141643 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.981752 | nan | nan | nan | nan | 0.470588 | nan | nan | 0.973485 | nan | nan | nan | 0.733333 | nan | nan | nan | nan | nan | nan | 0.276699 | nan | 0.0704762 | 0.0654206 | nan | 0.769874 | 0.124534 | nan | nan | nan | nan | nan | 0.674863 | 0.0163488 | nan | nan | 0.4 | 0.348837 | nan | nan | nan | 0.00697674 | nan | nan | 0.984637 | nan | 0.15 | nan | 0.997672 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.642857 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.946809 | nan | 0.690476 | 0.965909 | 0.12069 | 0.159091 | 0.15625 | 0.0748114 | 0.961983 | 0.292683 | 0.294118 | nan | 0.384615 | nan | nan | 0.28 | nan | 0.997852 | 0.805195 | nan | 0.875 | nan | nan | 0.832298 | 0.375 | 0.977778 | 0.227273 | 0.676471 | nan | nan | nan | nan | nan | 0.583333 | 0.118367 | nan | 0.0330097 | 0.4 | nan | nan | nan | 0.3 | 0.190931 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0591398 | 0.545455 | 0.384615 | nan | nan | 0.0895429 | 0.272727 | 0.109375 | nan | 0.727273 | nan | 0.8 | nan | nan | nan | nan | nan | nan | nan | 0.204082 | 0.315789 | nan | nan | nan | nan | 0.822222 | nan | nan | nan | nan | nan | nan | 0.152542 | 0.454545 | nan | nan | nan | nan | nan | nan | nan | 0.338028 | nan | nan | nan | nan | 0.263158 | nan | nan | nan | 0.352941 | nan | 0.0310702 | nan | nan | 0.0244845 | 0.115646 | 0.613636 | nan | nan | 0.0641026 | nan | 0.891579 | 0.703125 | nan | 0.988658 | nan | nan | nan | nan | nan | 0.5 | nan | nan | 0.511111 | 0.225352 | 0.384615 | 0.555556 | nan | 0.264706 | nan | nan | 0.0877193 | nan | nan | 0.809028 | nan | 0.869761 | 0.34375 | 0.423077 | nan | 0.996524 | nan | nan | 0.00666032 | 0.232558 | nan | nan | 0.971074 | nan | nan | nan | nan | nan | nan | 0.959184 | 0.954704 | 0.857143 | nan | 0.976562 | nan | nan | 0.962466 | nan | nan | nan | nan | nan | nan | nan | nan | 0.0173611 | nan | 0.953191 | 0.964989 | nan | nan | 0.545455 | 0.466667 | nan | nan | 0.979079 | nan | nan | nan | 0.26087 | nan | nan | nan | nan | 0.975962 | nan | nan | nan | nan | nan | nan | 0.096 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.941176 | nan | nan | nan | 0.354545 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.290323 | nan | nan | nan | 0.0016852 | nan | nan | 0.0847458 | nan | nan | nan | nan | nan | 0.512821 | 0.716418 | 0.571429 | 0.333333 | 0.615385 | nan | 0.615385 | nan | nan | nan | nan | 0.960265 | nan | nan | 0.211111 | 0.166667 | nan | nan | nan | 0.68 | nan | 0.0147783 | nan | 0.518519 | 0.22619 | nan | nan | nan | 0.384615 | 0.333333 | 0.41791 | 0.194444 | nan | nan | nan | nan | 0.998366 | nan | nan | 0.873239 | 0.675 | nan | 0.103448 | nan | nan | nan | 0.171429 | nan | 0.5 | nan | 0.318182 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0673267 | nan | 0.32 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0517799 | nan | 0.885714 | 0.575342 | 0.692308 | nan | nan | 0.986207 | nan | 0.998387 | nan | 0.477273 | nan | nan | nan | 0.473684 | nan | nan | nan | nan | 0.666667 | nan | nan | 0.333333 | 0.182156 | 0.00562061 | 0.992994 | 0.178571 | nan | nan | 0.142857 | 0.994456 | nan | nan | nan | 0.428571 | nan | nan | nan | nan | nan | 0.245033 | 0.380282 | nan | 0.37931 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.541667 | nan | nan | nan | nan | nan | nan | nan | 0.5625 | 0.583333 | 0.0892857 | 0.00962567 | 0.0894309 | 0.0248668 | nan | 0.45 | 0.30303 | nan | 0.0802469 | 0.5625 | nan | 0.644444 | 0.625 | 0.041791 | nan | nan | nan | 0.433333 | 0.127273 | 0.5 | nan | 0.677419 | 0.817073 | 0.133903 | 0.0183486 | nan | 0.85 | nan | 0.0675676 | 0.3 | nan | 0.0301887 | nan | 0.0935961 | nan | 0.013834 | 0.178571 | 0.636364 | nan | 0.135338 | 0.936842 | 0.955414 | nan | nan | nan | nan | 0.782609 | 0.137931 | nan | nan | nan | nan | 0.961538 | nan | nan | 0.680328 | nan | 0.240506 | nan | nan | 0.304348 | nan | nan | nan | nan | nan | nan | 0.987277 | nan | 0.117517 | nan | nan | 0.958042 | nan | nan | nan | nan | nan | nan | nan | 0.056 | nan | 0.181818 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0382166 | nan | 0.0442478 | 0.0636943 | nan | nan | nan | 0.105793 | 0.286765 | 0.0216401 | 0.995177 | nan | 0.105556 | nan | 0.0562219 | nan | nan | 0.455172 | nan | nan | nan | nan | nan | 0.186495 | 0.241379 | nan | 0.976526 | 0.956386 | nan | nan | 0.860465 | nan | nan | nan | 0.136628 | nan | 0.514286 | nan | 0.305331 | 0.0943396 | nan | nan | nan | 0.990909 | 0.0511628 | nan | 0.968944 | nan | nan | nan | nan | nan | 0.681818 | nan | nan | nan | 0.841463 | 0.997666 | nan | 0.672897 | nan | nan | nan | 0.45679 | nan | nan | 0.615023 | nan | 0.729167 | nan | 0.428571 | 0.567568 | 0.428571 | 0.898305 | 0.454545 | 0.45 | 0.131016 | 0.363636 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.00385208 | nan | nan | 0.00297041 | nan | 0.76 | 0.441509 | nan | 0.0744125 | nan | 0.0567901 | nan | nan | nan | nan | 0.783784 | 0.141079 | nan | nan | 0.5 | nan | nan | nan | nan | nan | nan | nan | nan | 0.612903 | nan | nan | nan | nan | nan | 0.636364 | nan | nan | nan | nan | nan | nan | 0.227273 | 0.178571 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.996718 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0224719 | 0.5 | nan | nan | 0.833333 | nan | 0.999227 | 0.190763 | nan | 0.105556 | nan | nan | 0.999107 | 0.529412 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0725389 | nan | nan | 0.428571 | 0.720721 | 0.0526316 | nan | nan | nan | 0.747945 | nan | nan | nan | nan | 0.00903226 | nan | nan | nan | nan | nan | nan | nan | nan | 0.861111 | 0.116279 | nan | nan | nan | nan | nan | nan | nan | nan | 0.518519 | 0.482332 | 0.302554 | 0.0420168 | nan | nan | nan | nan | nan | 0.215909 | nan | nan | nan | nan | 0.00256264 | nan | nan | nan | nan | nan | nan | 0.277778 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.968614 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.384615 | nan | nan | nan | nan | 0.00750771 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.716981 | nan | 0.936842 | 0.185714 | nan | nan | 0.00121326 | 0.299363 | nan | nan | 0.791667 | nan | nan | nan | nan | nan | nan | 0.999292 | nan | nan | 0.999284 | nan | 0.5 | nan | 0.0149144 | nan | nan | nan | nan | nan | 0.546218 | nan | nan | nan | 0.980469 | nan | nan | nan | nan | 0.263158 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.235294 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0361991 | nan | 0.895833 | 0.0542053 | nan | 0.153846 | 0.480769 | nan | 0.636364 | 0.5 | nan | nan | nan | nan | nan | nan | nan | 0.678571 | 0.892571 | 0.986802 | nan | nan | nan | nan | nan | 0.0491803 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.668831 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.578947 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.949495 | 0.368421 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.278689 | 0.727273 | nan | nan | nan | nan | nan | nan | nan | nan | 0.0385591 | 0.000866927 | 0.160656 | nan | nan | nan | nan | nan | nan | nan | nan | 0.282609 | nan | nan | nan | nan | nan | nan | nan | nan | 0.00124938 | nan | 0.0545852 | nan | 0.000522848 | nan | nan | nan | nan | 0.666667 | 0.62963 | nan | nan | nan | nan | nan | nan | nan | nan | 0.80303 | nan | nan | 0.368421 | nan | 0.998462 | 0.310345 | 0.0561798 | nan | 0.419355 | nan | nan | nan | nan | nan | 0.539683 | 0.111111 | nan | nan | nan | nan | nan | 0.81982 | nan | nan | 0.583333 | nan | nan | 0.809192 | 0.619048 | 0.652439 | 0.870968 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.98964 | nan | nan | 0.733333 | nan | nan | nan | nan | nan | nan | nan | 0.863014 | nan | 0.12069 | 0.0465116 | nan | 0.12782 | 0.0324779 | 0.0828402 | nan | 0.6 | nan | nan | nan | 0.996232 | nan | nan | nan | 0.416667 | nan | 0.122449 | 0.684211 | nan | nan | 0.227273 | nan | nan | nan | nan | 0.675 | nan | nan | nan | nan | nan | 0.692308 | nan | nan | nan | 0.113445 | 0.692308 | nan | nan | nan | 0.615385 | 0.00584795 | 0.0282486 | nan | nan | 0.185185 | nan | 0.230769 | nan | 0.461538 | nan | nan | 0.388889 | nan | nan | nan | nan | nan | nan | nan | 0.849462 | nan | nan | nan | 0.820513 | nan | 0.00430849 | 0.938889 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.705882 | 0.868852 | nan | nan | nan | nan | nan | 0.655172 | nan | nan | nan | 0.666667 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.998222 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.95 | nan | nan | nan | 0.932692 | nan | nan | nan | 0.59322 | nan | nan | nan | nan | nan | nan | nan | 0.00272232 | nan | nan | nan | nan | nan | 0.384615 | nan | nan | nan | nan | nan | nan | 0.898876 | nan | 0.864743 | 0.255319 | 0.661871 | nan | 0.686747 | 0.943878 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.00603015 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.18 | nan | 0.392857 | nan | nan | 0.473684 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0952381 | nan | 0.565217 | 0.00405862 | nan | 0.15942 | nan | 0.240876 | nan | 0.00250766 | nan | nan | 0.642857 | nan | nan | nan | nan | nan | nan | nan | 0.00127618 | nan | nan | nan | 0.772727 | 0.931507 | nan | nan | nan | nan | 0.647059 | nan | nan | 0.4 | nan | nan | nan | nan | 0.740741 | 0.010687 | nan | nan | 0.00537939 | nan | nan | nan | nan | nan | nan | 0.00710771 | 0.0384615 | nan | 0.998168 | 0.473684 | nan | 0.3125 | nan | nan | nan | nan | nan | nan | 0.533333 | nan | 0.133333 | nan | nan | 0.45 | nan | nan | 0.72 | nan | nan | nan | 0.0340909 | nan | 0.123288 | nan | nan | nan | nan | 0.990139 | 0.895833 | 0.926143 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.642857 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0113852 | nan | nan | 0.5 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.175214 | 0.882353 | nan | nan | nan | nan | nan | 0.307692 | nan | 0.980831 | nan | nan | nan | nan | nan | nan | 0.52381 | nan | nan | 0.177778 | nan | 0.454545 | nan | nan | nan | 0.0783133 | nan | nan | 0.571429 | nan | 0.446429 | nan | nan | nan | nan | nan | nan | nan | nan | 0.0375865 | 0.234568 | 0.998783 | nan | 0.526316 | nan | 0.0738593 | 0.243902 | nan | 0.358974 | nan | 0.00251743 | nan | nan | nan | nan | nan | 0.522388 | nan | nan | nan | 0.873239 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.113636 | nan | nan | nan | nan | nan | 0.00207684 | 0.84264 | 0.0357143 | 0.252252 | nan | 0.642857 | nan | 0.240741 | nan | nan | nan | nan | nan | 0.0375 | nan | 0.159091 | nan | nan | nan | nan | nan | nan | nan | nan | 0.386364 | nan | nan | nan | 0.495327 | nan | 0.5 | 0.00379451 | nan | nan | nan | nan | 0.208178 | nan | 0.263158 | 0.016158 | nan | nan | 0.934896 | nan | 0.461538 | 0.206897 | nan | nan | 0.684211 | nan | nan | 0.98743 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.884211 | 0.333333 | nan | nan | nan | nan | nan | nan | nan | nan | 0.416667 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.763441 | nan | nan | nan | 0.715278 | nan | nan | nan | 0.186275 | 0.5 | nan | nan | nan | nan | nan | nan | nan | 0.388889 | 0.615385 | 0.895222 | 0.805556 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.684211 | 0.615385 | nan | nan | nan | nan | nan | nan | 0.231343 | nan | nan | nan | nan | nan | nan | nan | nan | 0.997773 | nan | nan | nan | 0.309524 | nan | nan | 0.178571 | 0.333333 | 0.881579 | nan | 0.557692 | nan | nan | 0.125 | nan | nan | nan | 0.242857 | 0.00488103 | 0.4 | 0.0705882 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.714286 | nan | nan | nan | nan | nan | nan | 0.205882 | nan | 0.742857 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.564103 | nan | nan | nan | nan | nan | nan | nan | nan | 0.814815 | nan | nan | nan | nan | nan | 0.56 | nan | nan | nan | 0.5 | nan | 0.998929 | nan | nan | nan | nan | 0.000927931 | nan | 0.00132802 | nan | nan | nan | nan | nan | nan | 0.317073 | 0.837209 | nan | nan | nan | nan | 0.847826 | nan | nan | nan | nan | nan | 0.4375 | 0.5 | 0.6 | nan | nan | nan | nan | nan | nan | nan | nan | 0.1875 | 0.615385 | nan | 0.0303983 | nan | nan | nan | 0.22449 | 0.25 | nan | nan | nan | nan | nan | nan | nan | nan | 0.461538 | nan | nan | 0.963899 | nan | nan | nan | nan | 0.545455 | nan | nan | nan | 0.996644 | nan | nan | nan | 0.0505051 | nan | nan | nan | nan | 0.545455 | nan | 0.5 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.00182098 | nan | nan | nan | nan | nan | nan | 0.767442 | 0.0487395 | nan | 0.421053 | nan | 0.997611 | nan | nan | nan | 0.339623 | nan | nan | 0.578947 | nan | nan | nan | 0.5 | nan | nan | nan | 0.590164 | 0.959016 | nan | nan | 0.027027 | 0.0909091 | nan | nan | nan | 0.209677 | 0.695652 | nan | nan | nan | nan | nan | 0.0399113 | nan | nan | 0.779874 | 0.00432048 | 0.0840336 | 0.0583333 | 0.0212766 | 0.189331 | 0.615385 | nan | nan | nan | nan | nan | 0.00294551 | nan | nan | nan | nan | 0.175 | 0.920319 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.835165 | 0.876755 | 0.736842 | nan | nan | 0.454545 | 0.35 | nan | 0.454545 | nan | 0.229167 | 0.266667 | 0.102041 | nan | 0.16129 | 0.797101 | 0.484902 | nan | nan | nan | 0.418919 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.416667 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.00165153 | nan | 0.998697 | nan | 0.423423 | nan | nan | 0.611111 | 0.37037 | nan | 0.423077 | nan | nan | nan | nan | 0.703704 | nan | 0.214286 | 0.3 | nan | nan | 0.642857 | 0.647059 | nan | nan | nan | nan | nan | 0.0363636 | nan | nan | 0.533333 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.3 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.608696 | 0.15873 | nan | 0.705882 | 0.238095 | nan | nan | nan | 0.0714286 | 0.0181818 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.121739 | nan | nan | 0.4 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.00171174 | nan | nan | nan | nan | nan | nan | nan | nan | 0.162162 | 0.625 | nan | nan | 0.75 | 0.926829 | 0.764706 | 0.481481 | nan | 0.998058 | nan | nan | 0.983908 | nan | 0.536585 | nan | nan | nan | nan | 0.920149 | 0.444444 | 0.175073 | nan | nan | 0.756098 | 0.3 | nan | nan | 0.388889 | nan | nan | nan | nan | 0.818182 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0385164 | 0.531915 | 0.615385 | 0.450704 | nan | 0.764706 | 0.634615 | nan | 0.862637 | nan | 0.838368 | nan | 0.777778 | nan | nan | nan | nan | nan | 0.128205 | nan | nan | 0.955556 | nan | 0.45 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.22 | nan | 0.313131 | nan | nan | nan | nan | nan | 0.75 | nan | nan | 0.712098 | nan | 0.422222 | 0.461538 | nan | nan | nan | nan | nan | nan | 0.441029 | 0.73913 | nan | nan | nan | nan | nan | nan | nan | 0.0241158 | nan | 0.0546875 | nan | nan | nan | nan | 0.997803 | 0.5 | nan | nan | 0.736842 | nan | nan | 0.0349127 | nan | nan | nan | nan | nan | 0.75 | 0.966443 | nan | nan | nan | 0.192308 | 0.965854 | 0.923907 | nan | 0.00562852 | 0.293103 | 0.12 | nan | nan | nan | 0.931034 | 0.138889 | 0.470588 | 0.461538 | nan | nan | 0.315789 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.471698 | nan | 0.474576 | nan | nan | 0.234973 | 0.325843 | nan | nan | 0.75 | 0.5 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0985915 | nan | 0.352941 | 0.642857 | 0.984891 | nan | 0.52381 | 0.545455 | 0.990515 | 0.993767 | nan | nan | nan | nan | nan | nan | nan | nan | 0.666667 | nan | nan | nan | 0.0346821 | 0.147059 | nan | 0.860759 | 0.895238 | nan | nan | nan | nan | 0.45 | 0.24 | nan | 0.3125 | nan | nan | 0.613402 | nan | nan | 0.846154 | nan | nan | nan | 0.652174 | nan | nan | nan | nan | nan | nan | 0.3125 | 0.287425 | nan | 0.901639 | 0.25 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.805556 | nan | nan | 0.049505 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.93801 | nan | nan | nan | nan | nan | nan | 0.0298913 | 0.265625 | 0.111663 | nan | nan | nan | 0.0560748 | nan | nan | 0.583333 | 0.857143 | 0.789474 | 0.0612613 | 0.979592 | 0.323944 | nan | 0.622449 | 0.857143 | nan | nan | nan | 0.666667 | 0.885621 | nan | nan | nan | nan | 0.7 | nan | 0.0828025 | nan | nan | nan | 0.935553 | nan | nan | 0.461538 | nan | nan | nan | nan | nan | nan | nan | 0.460784 | nan | nan | nan | nan | nan | 0.30654 | 0.0237417 | 0.0925926 | 0.627451 | 0.269231 | 0.83031 | 0.26 | 0.178571 | nan | nan | nan | 0.866864 | nan | nan | nan | nan | nan | nan | nan | 0.647059 | nan | 0.0262784 | 0.795082 | nan | 0.590909 | nan | 0.476852 | 0.26087 | nan | 0.26087 | 0.571429 | 0.5 | nan | nan | 0.825858 | nan | 0.216411 | 0.344828 | 0.5 | 0.419231 | 0.164557 | nan | nan | nan | nan | nan | nan | nan | nan | 0.0734824 | nan | nan | nan | 0.666667 | nan | 0.5 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.466667 | nan | 0.545455 | 0.00864553 | 0.8 | nan | 0.735849 | nan | nan | nan | 0.669355 | 0.100917 | nan | 0.290323 | nan | 0.910345 | 0.315789 | nan | 0.708333 | nan | 0.0443686 | 0.315789 | 0.54 | 0.406863 | nan | nan | nan | nan | nan | nan | nan | nan | 0.1875 | nan | nan | nan | 0.336735 | nan | nan | nan | nan | nan | nan | nan | nan | 0.310345 | nan | nan | nan | nan | nan | nan | 0.470588 | nan | 0.933333 | nan | nan | 0.885135 | nan | 0.658951 | nan | nan | nan | nan | 0.311594 | 0.170732 | 0.203704 | 0.802469 | nan | nan | 0.5625 | 0.369102 | 0.1625 | 0.6875 | 0.5 | 0.791667 | 0.0638298 | nan | nan | 0.166667 | nan | nan | nan | 0.00119439 | nan | nan | nan | nan | 0.546243 | nan | nan | nan | nan | nan | nan | nan | 0.5 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.162162 | 0.0618557 | nan | nan | nan | nan | 0.673913 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.00415656 | 0.315789 | nan | 0.092437 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0103858 | nan | nan | nan | nan | nan | nan | nan | 0.678571 | nan | 0.0169492 | nan | nan | nan | nan | nan | 0.0874636 | 0.0793651 | 0.5 | 0.204082 | 0.411765 | nan | 0.317673 | nan | nan | nan | nan | nan | nan | nan | nan | 0.84466 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.75 | nan | nan | 0.373886 | nan | nan | 0.303754 | nan | 0.798319 | nan | nan | nan | nan | nan | nan | 0.203448 | 0.52381 | 0.814815 | 0.0742754 | nan | nan | nan | nan | 0.5 | nan | nan | nan | nan | nan | 0.996982 | nan | nan | 0.545455 | nan | nan | 0.0290909 | nan | nan | nan | 0.217391 | 0.666667 | 0.227273 | nan | nan | nan | nan | nan | nan | nan | nan | 0.0425367 | nan | 0.940075 | nan | nan | 0.0266081 | 0.142857 | nan | 0.772727 | nan | 0.433962 | nan | nan | nan | 0.012749 | 0.148065 | 0.0358566 | 0.757576 | nan | 0.932646 | 0.895522 | 0.984501 | 0.173913 | 0.494845 | 0.968966 | 0.630058 | nan | 0.672414 | 0.94086 | nan | nan | nan | nan | nan | 0.555556 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.590909 | nan | nan | 0.997851 | nan | nan | 0.274194 | 0.674112 | nan | nan | nan | nan | nan | nan | 0.193548 | nan | 0.937634 | 0.841772 | nan | nan | nan | nan | 0.83871 | nan | nan | nan | 0.6 | 0.494565 | nan | nan | nan | nan | nan | nan | 0.0285714 | nan | nan | 0.999077 | 0.313433 | 0.680556 | nan | nan | nan | nan | 0.135484 | nan | 0.0268456 | nan | 0.368421 | 0.131387 | nan | nan | nan | 0.00126103 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.95 | nan | 0.571429 | nan | nan | 0.303754 | nan | nan | nan | nan | nan | nan | 0.998934 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.5 | 0.969739 | nan | 0.782609 | nan | nan | nan | 0.277778 | nan | nan | nan | nan | nan | nan | 0.178414 | nan | 0.578947 | nan | nan | nan | 0.930464 | nan | nan | 0.4 | nan | nan | nan | 0.8 | nan | nan | 0.386364 | nan | 0.717949 | 0.00634921 | nan | nan | nan | 0.000927767 | 0.173913 | nan | nan | 0.517241 | nan | nan | nan | nan | nan | nan | nan | nan | 0.190323 | nan | nan | 0.757576 | nan | 0.591837 | nan | nan | 0.8 | 0.830986 | nan | nan | 0.727273 | nan | 0.357143 | nan | nan | nan | 0.998647 | nan | 0.0031348 | nan | nan | 0.358333 | nan | 0.290323 | nan | 0.5 | nan | nan | nan | nan | nan | nan | 0.263158 | nan | nan | nan | nan | 0.861842 | nan | nan | nan | 0.279412 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.45045 | nan | nan | nan | nan | 0.428571 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.533333 | nan | nan | nan | nan | nan | nan | nan | nan | 0.00784034 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.742627 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.015015 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0133452 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.680723 | 0.319444 | nan | nan | 0.357143 | 0.861111 | nan | 0.5 | nan | nan | nan | nan | 0.36 | 0.652318 | nan | nan | nan | 0.357143 | nan | nan | 0.96331 | nan | nan | nan | nan | nan | nan | nan | 0.0793651 | nan | nan | nan | nan | nan | nan | nan | 0.078125 | nan | nan | nan | 0.826087 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.371681 | nan | nan | 0.692308 | 0.727124 | nan | 0.470588 | nan | 0.454545 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.545455 | nan | 0.833333 | 0.28 | nan | nan | 0.625 | nan | nan | 0.573171 | nan | nan | 0.00146972 | 0.7 | nan | nan | 0.133333 | nan | nan | nan | nan | nan | nan | 0.53125 | 0.935484 | nan | nan | 0.13449 | 0.526316 | nan | nan | nan | nan | nan | nan | nan | 0.583333 | 0.752577 | 0.928385 | 0.733333 | nan | 0.555556 | 0.885661 | 0.611111 | 0.437783 | nan | 0.857143 | 0.525 | 0.545455 | 0.931373 | 0.818182 | 0.98822 | 0.0615385 | nan | nan | nan | 0.998908 | nan | nan | nan | 0.696721 | nan | 0.588235 | nan | nan | nan | 0.416667 | 0.761905 | nan | nan | nan | nan | 0.45 | 0.0492424 | nan | nan | nan | nan | nan | nan | nan | 0.998821 | nan | 0.654545 | 0.0561224 | 0.217391 | nan | nan | nan | 0.848101 | 0.109547 | nan | nan | nan | 0.885135 | nan | nan | 0.890625 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.136986 | nan | 0.00173974 | 0.846154 | nan | nan | nan | nan | 0.509804 | nan | nan | 0.931624 | 0.923077 | nan | nan | nan | 0.5 | nan | nan | nan | nan | 0.0893761 | 0.24 | nan | nan | 0.166667 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.764925 | nan | nan | nan | nan | nan | nan | nan | 0.632 | nan | nan | nan | 0.705882 | 0.867647 | nan | nan | nan | 0.984166 | nan | nan | nan | nan | nan | 0.147059 | nan | nan | nan | nan | nan | 0.229008 | nan | nan | nan | nan | nan | 0.238095 | 0.304348 | nan | nan | nan | nan | 0.847826 | nan | nan | nan | nan | 0.941176 | 0.666667 | nan | nan | nan | 0.0364964 | nan | nan | nan | nan | nan | 0.125 | nan | nan | nan | nan | nan | nan | 0.2 | nan | 0.678571 | nan | nan | 0.0877193 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.6 | nan | 0.16 | nan | nan | nan | nan | nan | nan | nan | nan | 0.219512 | nan | nan | nan | 0.4375 | nan | nan | nan | 0.0179211 | nan | 0.417722 | nan | 0.611111 | 0.870968 | nan | 0.65 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.151515 | nan | nan | nan | nan | nan | nan | 0.0299145 | nan | 0.357143 | nan | nan | nan | nan | nan | nan | nan | 0.291667 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.971204 | nan | nan | nan | nan | nan | 0.5 | nan | 0.454545 | nan | 0.0266075 | 0.416667 | nan | 0.66985 | 0.6875 | 0.722222 | nan | nan | nan | nan | nan | 0.583333 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0896552 | nan | nan | nan | 0.384615 | 0.3 | nan | 0.411392 | 0.825397 | 0.876652 | nan | 0.83404 | nan | 0.894737 | nan | nan | nan | nan | nan | nan | 0.834483 | nan | nan | nan | nan | 0.846421 | 0.56338 | 0.913793 | nan | nan | nan | 0.653846 | nan | nan | 0.133333 | nan | 0.494318 | 0.755 | 0.814815 | nan | 0.645161 | 0.318182 | nan | nan | nan | nan | nan | nan | 0.5 | nan | nan | 0.827586 | nan | 0.527778 | nan | nan | nan | 0.323077 | 0.403226 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.27907 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0340136 | nan | nan | 0.6 | nan | nan | nan | 0.905405 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.227273 | nan | nan | nan | nan | nan | 0.277778 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.00118116 | nan | nan | nan | nan | nan | nan | nan | nan | 0.588235 | nan | 0.434783 | nan | 0.0862069 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.586207 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.424242 | 0.357143 | nan | 0.128623 | 0.25 | nan | nan | nan | nan | nan | nan | nan | 0.615385 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.316667 | nan | nan | nan | nan | 0.949438 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.384615 | 0.978261 | nan | 0.84375 | nan | 0.0592593 | nan | 0.333333 | nan | 0.147059 | 0.159091 | 0.22449 | nan | nan | 0.73913 | nan | nan | nan | nan | 0.586667 | 0.378378 | nan | nan | nan | nan | nan | nan | nan | nan | 0.710526 | nan | nan | 0.668508 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.212121 | nan | 0.990804 | nan | nan | nan | nan | 0.0184049 | 0.0753323 | 0.0431894 | 0.117647 | nan | nan | 0.403141 | 0.761905 | nan | nan | nan | nan | 0.349515 | nan | nan | nan | 0.450549 | nan | nan | 0.277778 | 0.264151 | 0.105556 | 0.153846 | 0.151515 | nan | nan | nan | nan | nan | 0.00709849 | 0.461538 | 0.0489796 | nan | nan | 0.00909091 | nan | nan | 0.0740741 | nan | nan | nan | 0.5 | nan | nan | 0.0666667 | 0.558824 | 0.858974 | nan | 0.0254225 | 0.272727 | nan | nan | 0.0847458 | nan | nan | 0.76 | 0.545455 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.00755858 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.895062 | nan | nan | nan | nan | nan | 0.0611855 | 0.0172117 | 0.0351438 | 0.344828 | nan | nan | nan | nan | nan | 0.615385 | nan | nan | 0.152174 | nan | nan | 0.477612 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.104167 | nan | 0.0488722 | nan | nan | nan | 0.996659 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0783582 | nan | nan | 0.777778 | 0.5 | nan | nan | 0.8125 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.00219861 | 0.999027 | nan | 0.833333 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.998473 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.997359 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0199531 | 0.0405405 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.00853242 | nan | 0.955752 | nan | 0.0066335 | nan | nan | nan | nan | nan | 0.0909091 | nan | nan | nan | nan | nan | 0.232558 | nan | 0.00944056 | nan | nan | nan | nan | nan | 0.0826446 | nan | nan | 0.918919 | 0.981595 | nan | nan | nan | nan | nan | nan | nan | 0.0378788 | 0.120301 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.000842916 | nan | nan | nan | 0.0149254 | nan | nan | nan | 0.990435 | nan | nan | nan | nan | nan | nan | nan | 0.0151515 | nan | nan | 0.777778 | nan | nan | nan | nan | nan | nan | 0.576923 | nan | 0.957303 | nan | 0.455882 | nan | nan | nan | nan | nan | nan | 0.875325 | 0.285714 | 0.00187047 | nan | 0.15625 | 0.189474 | nan | 0.484211 | nan | 0.98155 | nan | nan | nan | nan | nan | 0.00221194 | nan | 0.26087 | nan | nan | nan | 0.206897 | nan | nan | nan | nan | nan | nan | 0.293605 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.666667 | 0.25 | 0.363636 | 0.277778 | nan | nan | nan | nan | 0.651685 | 0.5 | 0.45 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.461538 | nan | 0.538462 | nan | nan | nan | nan | nan | 0.684211 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.444444 | 0.461538 | nan | 0.708333 | nan | nan | 0.630252 | nan | nan | nan | 0.333333 | nan | 0.333333 | nan | nan | nan | nan | 0.580645 | nan | nan | 0.16129 | nan | 0.00104457 | nan | nan | nan | nan | nan | 0.564103 | 0.62963 | nan | nan | nan | 0.375 | 0.0133607 | 0.0819672 | 0.648649 | 0.0522193 | nan | 0.168582 | nan | 0.27451 | 0.114286 | 0.325 | nan | 0.5 | nan | nan | nan | 0.828571 | nan | nan | 0.303797 | nan | nan | nan | 0.00458716 | nan | nan | 0.533333 | nan | nan | nan | 0.705882 | nan | nan | nan | nan | nan | 0.916667 | 0.24 | 0.00519113 | 0.0379747 | 0.396552 | 0.5 | 0.84375 | nan | 0.00853242 | nan | 0.178571 | nan | nan | nan | nan | nan | nan | nan | 0.128571 | nan | nan | 0.147651 | 0.222222 | 0.102041 | nan | nan | nan | 0.461538 | nan | nan | nan | 0.89404 | 0.647059 | nan | 0.166667 | nan | nan | nan | nan | nan | nan | nan | 0.177044 | nan | nan | 0.884615 | 0.5 | nan | 0.222222 | nan | 0.997492 | 0.357143 | 0.998756 | 0.538462 | nan | nan | nan | nan | 0.975862 | nan | 0.22093 | nan | 0.138889 | 0.168269 | 0.260274 | nan | nan | 0.225352 | 0.147239 | 0.5 |\n", "| 2014 | nan | nan | nan | 0.363636 | 0.998975 | 0.4 | nan | nan | 0.35 | 0.166667 | 0.227273 | 0.5 | 0.00311189 | 0.894472 | nan | 0.642202 | nan | nan | nan | nan | 0.145455 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.998922 | nan | nan | nan | nan | 0.288889 | 0.380952 | 0.00420521 | 0.659091 | 0.311475 | nan | nan | nan | 0.00606796 | nan | nan | 0.878049 | nan | 0.357143 | nan | 0.722222 | 0.514706 | nan | nan | 0.00131455 | 0.5 | 0.0106762 | 0.5 | nan | 0.428571 | nan | 0.9817 | nan | nan | nan | 0.353846 | nan | nan | nan | 0.996422 | 0.454545 | 0.0226155 | nan | nan | 0.5 | 0.0714286 | nan | nan | nan | nan | 0.94186 | nan | nan | 0.473684 | 0.121212 | 0.907692 | nan | nan | nan | 0.2 | 0.5 | nan | nan | nan | nan | nan | 0.7 | nan | 0.0154867 | 0.995742 | nan | 0.5 | nan | nan | 0.0672948 | 0.0338983 | nan | nan | nan | nan | 0.136364 | 0.357143 | nan | nan | nan | 0.15 | 0.357143 | nan | nan | nan | 0.392857 | nan | nan | 0.75 | 0.853448 | 0.4 | nan | nan | nan | nan | nan | nan | nan | 0.76 | 0.240964 | nan | nan | nan | nan | 0.0087184 | 0.583333 | 0.00432094 | nan | 0.119231 | nan | nan | nan | nan | 0.3125 | 0.357143 | nan | nan | 0.988142 | 0.8 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.152174 | nan | nan | nan | nan | 0.641791 | nan | nan | 0.416667 | nan | nan | nan | 0.869403 | nan | 0.5 | nan | nan | 0.909091 | nan | nan | nan | 0.00359569 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.056338 | nan | nan | nan | nan | nan | 0.615385 | nan | nan | 0.990305 | 0.00230238 | 0.0534351 | nan | nan | nan | 0.5 | nan | nan | 0.048048 | 0.998353 | 0.00214355 | nan | nan | nan | nan | nan | nan | nan | nan | 0.693878 | nan | nan | 0.78172 | nan | nan | nan | nan | nan | nan | 0.333333 | nan | 0.666667 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.190209 | 0.998364 | nan | nan | nan | 0.0478548 | 0.263158 | 0.6 | 0.695652 | nan | nan | nan | nan | nan | nan | nan | nan | 0.64 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0058072 | nan | nan | nan | nan | 0.998353 | 0.142857 | nan | 0.692308 | nan | nan | nan | nan | nan | nan | nan | nan | 0.993471 | nan | nan | nan | nan | nan | nan | nan | 0.5625 | nan | nan | nan | nan | nan | 0.666667 | nan | nan | nan | nan | nan | nan | nan | nan | 0.75 | 0.444444 | nan | nan | nan | 0.997908 | 0.785714 | 0.615385 | nan | 0.945055 | nan | nan | nan | nan | 0.854086 | nan | nan | nan | 0.0935673 | 0.98374 | 0.0436364 | nan | 0.285714 | 0.3758 | nan | 0.888889 | 0.410714 | nan | 0.950192 | 0.407407 | 0.280702 | 0.5 | nan | nan | 0.173077 | nan | nan | nan | 0.608696 | 0.999089 | 0.45122 | nan | nan | nan | nan | 0.880952 | nan | 0.910714 | 0.707317 | nan | 0.185185 | nan | nan | 0.4375 | 0.0092947 | 0.130435 | 0.318182 | nan | nan | 0.829787 | nan | 0.84 | 0.855263 | 0.538462 | nan | 0.604651 | nan | 0.818182 | nan | 0.827586 | nan | 0.997718 | nan | 0.507692 | nan | nan | nan | nan | nan | 0.962025 | nan | nan | nan | nan | nan | nan | nan | 0.25 | 0.0421488 | 0.927536 | nan | nan | 0.619048 | nan | nan | 0.99261 | nan | nan | nan | nan | 0.00292227 | 0.000987876 | nan | nan | nan | nan | 0.0124805 | nan | nan | 0.158881 | nan | 0.916667 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.694444 | nan | 0.263158 | nan | nan | nan | nan | nan | nan | nan | 0.15 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.987179 | nan | nan | 0.00155763 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.875 | nan | nan | nan | 0.0793651 | 0.116279 | nan | nan | nan | nan | nan | 0.0150376 | nan | nan | nan | nan | nan | nan | 0.722714 | nan | nan | nan | nan | nan | nan | nan | nan | 0.0483871 | nan | nan | nan | 0.248904 | 0.997638 | 0.980723 | nan | 0.107914 | 0.998178 | nan | nan | 0.99886 | nan | nan | 0.686131 | 0.883958 | nan | nan | 0.351351 | nan | 0.301724 | 0.411765 | 0.135922 | 0.398058 | nan | nan | 0.394737 | 0.545455 | nan | nan | nan | nan | nan | nan | nan | 0.231884 | nan | nan | nan | 0.275862 | 0.630435 | 0.454545 | nan | 0.333333 | nan | nan | 0.0371747 | 0.5 | 0.777778 | nan | 0.538462 | nan | nan | 0.426559 | nan | 0.75 | nan | nan | nan | nan | 0.697674 | 0.814815 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.541667 | nan | nan | 0.325843 | nan | nan | nan | 0.734043 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.454545 | 0.949786 | nan | 0.0676471 | nan | nan | 0.732558 | nan | nan | 0.0837607 | nan | 0.772727 | 0.157895 | nan | nan | nan | nan | 0.807018 | nan | nan | nan | nan | nan | nan | 0.590909 | nan | nan | 0.0171233 | nan | nan | 0.666667 | nan | nan | nan | 0.988849 | nan | nan | nan | nan | nan | nan | 0.126984 | 0.265306 | 0.05621 | 0.735669 | nan | 0.603774 | nan | nan | 0.5 | 0.847222 | 0.125 | 0.899123 | nan | nan | nan | 0.0301205 | 0.454545 | nan | nan | nan | nan | nan | nan | nan | nan | 0.0487239 | 0.536585 | 0.662338 | 0.010453 | nan | nan | nan | nan | nan | 0.98271 | 0.884211 | 0.88 | 0.29927 | 0.6 | 0.393939 | nan | nan | nan | 0.999101 | nan | nan | nan | 0.130608 | nan | nan | 0.0756303 | nan | nan | nan | nan | nan | nan | nan | 0.730769 | nan | nan | 0.255952 | 0.0188591 | nan | nan | 0.644385 | nan | 0.5 | 0.998521 | 0.999043 | nan | 0.101449 | 0.965517 | nan | nan | nan | 0.904762 | 0.473913 | nan | 0.924242 | 0.864865 | 0.953964 | 0.98504 | nan | nan | 0.807439 | 0.0985915 | 0.153846 | 0.119565 | nan | nan | nan | nan | 0.0601504 | 0.45 | 0.416667 | nan | nan | 0.625 | nan | 0.55 | nan | nan | 0.6 | nan | 0.666667 | nan | 0.692308 | 0.00356761 | 0.0281124 | 0.122881 | nan | 0.0286885 | 0.0140746 | nan | 0.0183486 | nan | nan | 0.793103 | nan | 0.0568182 | nan | nan | nan | 0.388889 | nan | nan | nan | nan | nan | 0.25 | nan | nan | 0.671642 | 0.972973 | 0.508227 | nan | nan | nan | 0.619048 | 0.6 | 0.26087 | nan | nan | nan | 0.607843 | nan | 0.428571 | nan | nan | nan | nan | 0.236111 | 0.17 | 0.952663 | 0.487179 | nan | nan | nan | nan | 0.6 | nan | nan | nan | 0.98913 | 0.956635 | nan | nan | 0.0933333 | nan | nan | 0.142857 | nan | nan | nan | 0.416667 | 0.444444 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0214724 | nan | nan | nan | nan | 0.278689 | nan | 0.925926 | nan | nan | nan | 0.987885 | 0.152778 | 0.344828 | 0.901961 | nan | 0.441176 | 0.364431 | nan | nan | nan | 0.0268657 | nan | 0.176471 | 0.049375 | nan | 0.0188034 | nan | nan | nan | 0.880952 | nan | nan | nan | 0.998282 | 0.953704 | nan | 0.384615 | nan | nan | 0.545455 | nan | 0.00123385 | nan | nan | nan | 0.0426094 | nan | 0.148936 | 0.034965 | nan | 0.149144 | nan | nan | 0.0357506 | 0.5625 | 0.5625 | nan | nan | 0.333333 | nan | 0.48 | 0.858586 | nan | 0.868613 | nan | nan | nan | 0.945055 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.853333 | 0.588235 | 0.538462 | nan | nan | nan | nan | nan | nan | 0.910448 | 0.0204778 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.545455 | nan | 0.6875 | nan | 0.0506329 | 0.856863 | 0.975 | 0.105802 | 0.125 | 0.0922482 | 0.897436 | nan | 0.97 | nan | nan | nan | nan | nan | 0.637681 | 0.145833 | 0.0675676 | 0.0261438 | 0.407407 | 0.6 | 0.92268 | 0.352941 | nan | nan | 0.454545 | 0.540541 | 0.949438 | 0.0328638 | nan | nan | 0.875 | nan | nan | nan | 0.208333 | 0.0136674 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.411765 | 0.116071 | 0.719298 | nan | 0.0258449 | 0.337079 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.233645 | nan | 0.0182049 | nan | 0.0173954 | nan | 0.529412 | nan | 0.056 | 0.555556 | 0.903969 | 0.967684 | nan | nan | nan | 0.744898 | nan | nan | nan | 0.26 | 0.76 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.853211 | nan | nan | nan | nan | nan | nan | 0.0782313 | nan | 0.0135974 | nan | nan | 0.333333 | nan | nan | nan | 0.00861301 | 0.1 | nan | nan | nan | 0.0036452 | nan | nan | 0.060241 | 0.987993 | 0.0491184 | 0.33887 | 0.0107527 | 0.589744 | 0.877863 | nan | 0.794118 | nan | nan | 0.0813953 | 0.0979228 | 0.11828 | 0.690476 | nan | nan | nan | 0.724138 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.921875 | nan | 0.0661253 | 0.0318471 | nan | 0.5 | nan | 0.123711 | 0.0529954 | nan | nan | 0.538462 | 0.102041 | nan | 0.411765 | nan | 0.0425 | nan | nan | 0.466667 | 0.294118 | nan | 0.454545 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.439776 | 0.0203252 | nan | nan | nan | nan | nan | 0.421053 | nan | nan | 0.294118 | 0.388 | 0.914634 | 0.855228 | 0.83871 | 0.930556 | nan | nan | nan | 0.98862 | nan | nan | nan | nan | 0.357143 | 0.974127 | nan | 0.442857 | nan | nan | nan | 0.521739 | 0.705882 | 0.636364 | nan | nan | 0.357143 | nan | nan | nan | nan | 0.485149 | 0.0838323 | nan | nan | 0.152174 | 0.0439276 | 0.00277585 | 0.0829493 | nan | 0.0373134 | 0.802632 | 0.99749 | nan | nan | nan | 0.990086 | nan | 0.017487 | nan | nan | nan | 0.00329791 | nan | nan | 0.188119 | 0.0210957 | nan | nan | 0.141593 | 0.0675676 | 0.953191 | nan | nan | nan | 0.435897 | nan | 0.964789 | 0.996429 | 0.994458 | nan | nan | nan | 0.00322108 | nan | 0.333333 | 0.0853659 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0886076 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.00903955 | 0.00678952 | 0.928638 | nan | nan | 0.358491 | nan | nan | nan | 0.194805 | nan | 0.588235 | nan | nan | nan | 0.228261 | 0.0138585 | 0.901961 | 0.185185 | nan | nan | 0.352941 | nan | 0.0752688 | 0.529412 | nan | nan | nan | nan | nan | nan | 0.00152157 | nan | nan | 0.98574 | 0.108696 | nan | 0.214286 | 0.0702179 | 0.447368 | 0.0310881 | nan | nan | 0.259259 | nan | 0.6 | 0.0025729 | 0.631579 | 0.363636 | nan | nan | nan | 0.379747 | nan | nan | nan | 0.470588 | nan | 0.735294 | 0.375 | 0.611111 | 0.0289928 | 0.33253 | nan | nan | 0.6 | 0.44 | nan | nan | 0.0694353 | 0.649485 | nan | 0.998658 | nan | nan | nan | nan | 0.579075 | nan | 0.0641026 | 0.166667 | nan | 0.692308 | 0.815261 | 0.880952 | 0.0628272 | nan | nan | nan | nan | nan | nan | nan | 0.0782609 | nan | nan | nan | nan | nan | nan | nan | 0.416667 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.462963 | nan | nan | 0.285714 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.724138 | nan | nan | nan | 0.973714 | nan | nan | nan | 0.03125 | nan | nan | nan | nan | nan | nan | nan | 0.998618 | nan | nan | nan | nan | nan | nan | nan | 0.754545 | nan | nan | nan | nan | 0.116883 | 0.0362076 | nan | 0.62782 | 0.0322669 | 0.0909091 | nan | 0.25 | nan | nan | nan | 0.403207 | 0.00527506 | nan | nan | 0.461538 | nan | 0.00821918 | nan | nan | nan | nan | 0.966642 | nan | 0.185185 | 0.0193798 | nan | 0.26087 | nan | nan | nan | nan | nan | nan | nan | 0.47619 | nan | 0.032197 | 0.941176 | nan | 0.708333 | nan | nan | nan | 0.909091 | nan | nan | 0.214286 | nan | 0.0384615 | nan | nan | nan | nan | nan | nan | nan | 0.408 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.215686 | nan | nan | nan | nan | nan | nan | nan | 0.0219355 | 0.571429 | nan | nan | nan | nan | nan | 0.23393 | nan | nan | nan | nan | nan | nan | 0.333333 | nan | nan | 0.256729 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.980355 | nan | 0.00204638 | 0.36036 | 0.892157 | 0.98954 | 0.460404 | nan | 0.999307 | nan | 0.794118 | nan | nan | nan | nan | nan | 0.0133038 | nan | nan | 0.263158 | nan | nan | nan | 0.0632911 | nan | nan | nan | nan | nan | nan | nan | 0.0795455 | nan | nan | 0.160714 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.5 | 0.880952 | nan | nan | 0.947674 | 0.642857 | nan | nan | 0.156584 | nan | nan | 0.992316 | nan | nan | nan | nan | nan | nan | nan | 0.7 | nan | 0.642857 | nan | nan | nan | nan | nan | nan | nan | 0.793103 | 0.999295 | nan | nan | nan | nan | 0.0128676 | nan | nan | nan | nan | nan | nan | 0.584906 | nan | nan | 0.0154188 | nan | nan | nan | 0.5 | nan | nan | nan | nan | 0.00154769 | 0.944444 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.545455 | 0.942149 | nan | nan | 0.153846 | nan | nan | nan | 0.333333 | nan | nan | nan | nan | nan | 0.205128 | nan | nan | nan | nan | nan | nan | 0.00855746 | 0.450704 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.934579 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.25 | 0.444444 | nan | 0.121951 | nan | nan | 0.72 | 0.514286 | 0.0153235 | 0.0163265 | nan | nan | nan | nan | nan | nan | 0.960674 | 0.0679739 | 0.0026262 | 0.0220264 | 0.55 | nan | 0.0017337 | nan | nan | nan | 0.238806 | 0.0058651 | 0.861257 | nan | 0.314286 | nan | nan | 0.00141132 | nan | nan | nan | nan | nan | 0.6 | 0.195402 | nan | 0.5 | 0.004884 | nan | 0.00372825 | nan | nan | nan | nan | nan | nan | nan | 0.0246365 | 0.328125 | 0.285714 | 0.998311 | nan | nan | 0.00672878 | nan | 0.110465 | nan | nan | nan | nan | nan | nan | nan | 0.0206813 | 0.882353 | nan | 0.787879 | 0.380952 | nan | nan | 0.178571 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.106383 | 0.761905 | 0.45 | 0.0247253 | nan | nan | nan | nan | 0.35 | nan | nan | nan | nan | nan | nan | 0.758684 | nan | nan | 0.214286 | nan | 0.0320513 | nan | nan | nan | 0.0895522 | 0.385965 | 0.428571 | nan | nan | 0.577778 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0604752 | 0.272727 | 0.0240192 | nan | nan | 0.0110865 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.809524 | nan | nan | nan | 0.322581 | nan | 0.527273 | nan | nan | 0.0078125 | nan | nan | nan | nan | nan | nan | nan | 0.178571 | nan | 0.588235 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.793651 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0782609 | 0.27907 | 0.357143 | 0.563951 | 0.60396 | 0.027027 | nan | nan | 0.461538 | nan | nan | nan | 0.277217 | nan | 0.846154 | nan | 0.0062724 | nan | nan | nan | 0.195804 | nan | nan | 0.0765957 | nan | nan | 0.957547 | 0.00154679 | nan | 0.5 | nan | nan | nan | nan | 0.15873 | 0.375 | nan | 0.905128 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.915441 | nan | nan | nan | 0.00179701 | nan | nan | 0.3125 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.938272 | 0.621622 | nan | nan | 0.00984529 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.227273 | nan | nan | nan | nan | nan | 0.925 | nan | 0.791045 | nan | nan | nan | 0.938776 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0496689 | nan | 0.0223881 | 0.0230415 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.2 | nan | 0.0327869 | nan | nan | nan | nan | nan | nan | 0.37931 | 0.714286 | 0.0220264 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.25 | 0.0909091 | nan | 0.00106802 | nan | 0.666667 | nan | nan | nan | nan | nan | 0.0333817 | nan | nan | nan | nan | 0.321429 | nan | nan | nan | nan | 0.0392157 | 0.702128 | nan | nan | 0.238095 | 0.0645161 | nan | nan | 0.044586 | 0.382114 | nan | 0.428571 | nan | nan | nan | nan | nan | 0.0461957 | 0.642857 | nan | nan | 0.00350672 | nan | 0.84375 | nan | 0.647059 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.027027 | 0.375 | 0.00480769 | 0.411765 | nan | nan | nan | nan | nan | nan | nan | nan | 0.037037 | nan | nan | nan | nan | nan | nan | nan | 0.277778 | nan | 0.526316 | nan | nan | nan | nan | nan | 0.993528 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.188679 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.25 | nan | 0.241935 | nan | 0.733333 | nan | nan | nan | nan | nan | nan | 0.298507 | 0.0555556 | nan | nan | 0.367647 | nan | nan | 0.0103627 | nan | nan | nan | 0.398714 | 0.515152 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.259259 | nan | nan | 0.387097 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.666667 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.540541 | nan | nan | nan | nan | nan | 0.115646 | 0.526316 | 0.996609 | 0.263158 | 0.310345 | nan | nan | nan | 0.162011 | nan | nan | nan | nan | nan | nan | 0.0650253 | 0.777778 | nan | nan | 0.0897436 | 0.7 | 0.135314 | nan | nan | nan | nan | nan | nan | 0.647208 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.52381 | 0.736842 | 0.333333 | nan | nan | nan | nan | nan | nan | 0.932927 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.757576 | nan | nan | nan | 0.0217391 | 0.0844156 | nan | 0.043956 | nan | nan | 0.647059 | nan | nan | nan | nan | nan | 0.0893855 | nan | nan | nan | nan | nan | nan | 0.751351 | nan | 0.6 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.00224287 | nan | nan | nan | 0.0136986 | nan | 0.394521 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0381679 | nan | nan | 0.0039557 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0337711 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.816514 | 0.15873 | 0.165087 | nan | 0.5 | 0.608696 | nan | nan | 0.227273 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.25 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0736185 | 0.6 | nan | nan | 0.204545 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0130849 | 0.349593 | nan | nan | nan | nan | nan | nan | 0.12 | nan | nan | 0.864078 | nan | nan | 0.152542 | nan | nan | 0.014862 | nan | nan | nan | nan | 0.825563 | nan | nan | nan | 0.112903 | 0.0269542 | nan | nan | nan | nan | nan | nan | 0.00193573 | nan | 0.30303 | 0.866667 | nan | nan | nan | 0.804651 | 0.394366 | nan | nan | nan | 0.47619 | nan | 0.325581 | nan | nan | nan | nan | nan | nan | nan | 0.122449 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.00293686 | 0.883838 | 0.253968 | 0.0220994 | nan | 0.00132591 | 0.237288 | 0.0024643 | 0.433333 | 0.977099 | 0.13964 | 0.625 | nan | nan | nan | 0.997462 | 0.299349 | nan | nan | 0.709677 | 0.702128 | nan | 0.999371 | nan | nan | nan | 0.99918 | nan | 0.947761 | nan | 0.997321 | 0.64557 | 0.172139 | 0.172936 | nan | 0.258206 | 0.810667 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0927835 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.195312 | 0.411765 | nan | nan | nan | nan | nan | nan | nan | nan | 0.927536 | nan | 0.957529 | 0.00585366 | nan | 0.439394 | nan | 0.986595 | 0.727273 | nan | nan | nan | 0.947826 | 0.938462 | nan | nan | 0.803571 | 0.614144 | 0.973822 | 0.848813 | nan | nan | 0.16129 | nan | nan | nan | 0.999129 | 0.6 | 0.999426 | 0.00312134 | 0.884058 | nan | nan | 0.83871 | nan | 0.00453858 | nan | 0.949275 | nan | 0.833333 | 0.597774 | nan | nan | 0.754098 | 0.183099 | nan | 0.294118 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.3125 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.548387 | 0.00151103 | nan | nan | nan | nan | nan | nan | 0.78 | nan | nan | 0.952336 | nan | 0.583333 | 0.26087 | 0.915663 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.93662 | 0.722222 | 0.171429 | nan | nan | nan | 0.147059 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.00120942 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.998366 | 0.011226 | 0.285714 | 0.133333 | nan | 0.999428 | 0.448413 | 0.126316 | 0.0132347 | 0.197531 | 0.997323 | 0.461538 | nan | 0.16 | nan | nan | nan | 0.781818 | nan | nan | 0.538462 | nan | nan | 0.26087 | nan | nan | 0.428571 | nan | nan | nan | nan | 0.044064 | 0.150943 | 0.923077 | nan | nan | nan | nan | nan | 0.997302 | nan | 0.906137 | nan | nan | 0.944099 | nan | nan | nan | nan | nan | 0.3125 | 0.736842 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.00363901 | nan | nan | nan | nan | 0.142857 | nan | nan | nan | nan | nan | 0.454545 | nan | nan | nan | nan | nan | 0.217391 | 0.28 | 0.5 | nan | 0.190476 | 0.852941 | 0.644292 | 0.0112641 | 0.0125984 | 0.473684 | 0.349246 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0486618 | nan | nan | nan | nan | nan | nan | 0.0175439 | 0.416667 | 0.05 | 0.0409357 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.097561 | nan | nan | nan | nan | nan | 0.485261 | 0.00757576 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.652174 | nan | nan | nan | nan | 0.0520833 | nan | nan | nan | nan | nan | nan | nan | nan | 0.00557485 | nan | 0.452381 | 0.526316 | nan | 0.99636 | nan | nan | nan | 0.00330943 | nan | nan | 0.00309757 | 0.0584416 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.2 | nan | nan | nan | nan | nan | 0.00383877 | nan | nan | nan | nan | nan | nan | nan | nan | 0.00151217 | nan | 0.0219298 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.730769 | nan | 0.985064 | nan | nan | nan | nan | 0.472477 | nan | nan | nan | nan | nan | nan | 0.875 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.807692 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.121311 | 0.555556 | nan | nan | 0.0299625 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.00308166 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.384615 | nan | nan | nan | 0.998293 | 0.507463 | nan | nan | nan | 0.23913 | 0.970443 | nan | 0.00727651 | nan | nan | nan | 0.357143 | 0.0048852 | nan | nan | 0.00302572 | 0.615385 | 0.270115 | 0.565789 | 0.583333 | 0.521739 | 0.118881 | 0.0244048 | nan | 0.792683 | nan | nan | nan | nan | nan | nan | nan | 0.257143 | 0.0877193 | 0.0181171 | 0.75 | 0.0141297 | 0.3 | nan | nan | nan | 0.889344 | nan | nan | nan | nan | 0.545455 | 0.545455 | nan | 0.688889 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.985256 | nan | 0.6 | 0.333333 | 0.722222 | nan | 0.191176 | 0.0534351 | nan | nan | 0.271493 | nan | 0.788136 | 0.631579 | nan | nan | nan | nan | nan | nan | 0.463768 | nan | nan | nan | nan | nan | 0.858065 | nan | 0.433333 | nan | 0.0987654 | 0.393939 | nan | 0.878788 | nan | nan | 0.25 | nan | 0.396226 | nan | nan | nan | nan | nan | nan | nan | 0.590909 | nan | nan | nan | nan | nan | nan | 0.545455 | 0.0857143 | 0.967846 | 0.262172 | 0.169811 | 0.698519 | nan | 0.208333 | 0.75 | 0.945568 | nan | nan | 0.788462 | 0.2 | nan | nan | 0.814815 | nan | 0.966965 | nan | nan | nan | nan | 0.0276243 | 0.00342786 | nan | nan | nan | nan | 0.818182 | 0.409091 | nan | 0.642857 | 0.00863558 | nan | nan | nan | nan | nan | nan | nan | nan | 0.533333 | 0.865927 | nan | 0.333333 | 0.36727 | 0.472222 | 0.16 | 0.194444 | 0.666667 | 0.0455408 | nan | nan | 0.867925 | nan | 0.858696 | nan | 0.997246 | nan | 0.0757576 | nan | nan | nan | nan | nan | 0.988048 | nan | nan | nan | nan | nan | nan | 0.466667 | 0.0530303 | 0.879285 | nan | nan | nan | 0.000955921 | 0.909091 | nan | nan | nan | nan | nan | 0.555556 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.222222 | nan | 0.736842 | nan | nan | nan | 0.0113914 | 0.138889 | nan | 0.841667 | nan | nan | nan | 0.472574 | nan | nan | nan | 0.484848 | nan | 0.687898 | nan | 0.722222 | nan | nan | nan | nan | nan | nan | 0.0661157 | nan | nan | nan | nan | nan | 0.0209699 | 0.469388 | nan | nan | nan | nan | nan | 0.0395499 | 0.6875 | 0.325 | 0.428571 | 0.24 | nan | 0.125 | nan | nan | 0.00110213 | nan | nan | nan | nan | 0.0328947 | nan | 0.69697 | nan | nan | nan | nan | 0.104167 | nan | 0.708333 | 0.75 | nan | 0.466667 | nan | nan | 0.261905 | nan | 0.840909 | 0.968701 | 0.511628 | nan | 0.5 | nan | nan | nan | 0.538462 | 0.905263 | 0.763889 | nan | 0.619048 | nan | 0.910448 | 0.454545 | nan | nan | nan | 0.6 | 0.631579 | nan | 0.393939 | nan | 0.333333 | nan | nan | nan | nan | 0.727273 | nan | 0.870588 | nan | 0.619048 | nan | nan | nan | nan | nan | 0.489583 | 0.0013064 | 0.997849 | nan | 0.998827 | nan | 0.473684 | nan | nan | 0.00157999 | nan | nan | nan | nan | nan | 0.866667 | nan | 0.03657 | nan | 0.228261 | nan | nan | 0.212121 | nan | nan | nan | nan | nan | nan | nan | nan | 0.93617 | nan | nan | nan | 0.5 | nan | nan | 0.920398 | nan | 0.993037 | 0.538462 | nan | nan | nan | 0.877551 | nan | 0.741935 | nan | nan | nan | nan | nan | nan | nan | nan | 0.00664767 | nan | 0.938776 | nan | nan | nan | nan | 0.00103034 | nan | 0.60101 | nan | 0.00204784 | 0.392857 | 0.173913 | nan | 0.00077266 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.803922 | nan | 0.99552 | nan | nan | nan | 0.97579 | 0.0340055 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.929577 | nan | nan | nan | 0.626794 | nan | nan | 0.476923 | nan | 0.0650888 | nan | nan | 0.401575 | nan | 0.560284 | nan | nan | 0.446809 | 0.926931 | 0.977956 | 0.0310734 | nan | 0.176471 | nan | nan | 0.454545 | nan | nan | nan | nan | nan | 0.16129 | 0.13913 | 0.142857 | nan | 0.108696 | 0.08739 | nan | nan | nan | 0.501742 | nan | nan | 0.428571 | nan | nan | nan | 0.828571 | nan | 0.106936 | nan | nan | nan | nan | nan | nan | nan | 0.171429 | nan | nan | 0.0446429 | 0.124031 | nan | nan | nan | nan | nan | nan | 0.75 | nan | nan | nan | nan | nan | nan | 0.736842 | nan | nan | nan | 0.0397614 | nan | nan | nan | nan | 0.4 | nan | nan | nan | 0.692308 | nan | 0.9375 | nan | nan | nan | nan | nan | nan | nan | 0.478261 | nan | nan | nan | nan | 0.0530452 | nan | nan | 0.35 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.00291161 | nan | 0.01861 | 0.578947 | 0.434783 | 0.761905 | nan | 0.888889 | 0.683531 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.194444 | 0.0653061 | 0.75 | nan | nan | nan | 0.318182 | nan | nan | nan | nan | nan | 0.930556 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.12766 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.00586756 | nan | nan | nan | nan | nan | nan | 0.890909 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.285714 | nan | nan | nan | 0.684211 | nan | 0.00399787 | nan | nan | nan | nan | 0.0503432 | 0.111111 | 0.388889 | nan | 0.782609 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.00108206 | 0.0202475 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.00593312 | 0.0119284 | 0.00259909 | nan | 0.0014219 | 0.162791 | nan | 0.16 | 0.0198366 | nan | 0.0249008 | 0.90927 | nan | 0.00297767 | nan | 0.5625 | nan | 0.0700637 | 0.959259 | 0.433333 | 0.263158 | 0.0422441 | 0.368421 | 0.166667 | 0.176471 | nan | 0.333333 | 0.037037 | 0.360656 | nan | 0.565217 | nan | nan | 0.186047 | nan | nan | nan | 0.126104 | nan | 0.631579 | nan | 0.596311 | nan | nan | 0.333333 | nan | 0.820896 | 0.960739 | 0.758621 | nan | 0.0925926 | 0.214286 | nan | 0.826087 | nan | nan | 0.0208333 | nan | nan | nan | nan | 0.427273 | nan | nan | nan | nan | nan | nan | nan | nan | 0.375 | nan | nan | nan | 0.292308 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.126582 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.208333 | nan | 0.996063 | 0.243243 | nan | 0.078271 | 0.0403226 | 0.454545 | 0.702128 | 0.728814 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.00160819 | nan | 0.00198649 | nan | 0.1875 | 0.0347826 | nan | nan | nan | nan | 0.826087 | nan | 0.75 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.931973 | nan | nan | nan | nan | 0.545455 | nan | nan | nan | 0.156069 | nan | 0.135135 | nan | 0.0282981 | 0.583333 | nan | 0.909091 | 0.113043 | nan | 0.642948 | 0.576471 | 0.00283768 | nan | 0.0277247 | nan | nan | nan | nan | 0.960245 | 0.816667 | 0.758621 | nan | nan | 0.304348 | nan | nan | nan | nan | nan | 0.642857 | nan | nan | 0.208333 | 0.28 | nan | nan | nan | 0.545455 | nan | nan | nan | nan | nan | nan | nan | 0.0588235 | nan | nan | 0.142857 | nan | nan | nan | 0.5625 | 0.615385 | nan | nan | 0.75 | 0.333333 | nan | 0.995098 | nan | nan | nan | nan | nan | nan | nan | nan | 0.416667 | nan | nan | 0.928571 | 0.783333 | 0.274725 | nan | nan | nan | 0.00412448 | nan | 0.928839 | nan | 0.242739 | nan | nan | nan | nan | nan | nan | 0.714286 | 0.00131172 | nan | nan | nan | nan | nan | nan | 0.263158 | nan | 0.24026 | 0.00756939 | nan | nan | nan | nan | nan | nan | nan | nan | 0.571429 | nan | nan | nan | nan | 0.44 | 0.00887372 | nan | nan | 0.00123686 | nan | nan | nan | nan | 0.761905 | nan | 0.819672 | nan | 0.615385 | 0.647059 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.384615 | nan | 0.160442 | nan | 0.730769 | 0.130631 | 0.081761 | nan | 0.625 | nan | 0.0512821 | 0.0376569 | nan | 0.914029 | 0.961424 | nan | nan | nan | nan | nan | nan | 0.409091 | 0.00182582 | nan | nan | 0.000496442 | nan | 0.836538 | nan | nan | 0.00138147 | nan | 0.00155666 | nan | nan | nan | 0.461538 | nan | nan | nan | 0.777778 | nan | 0.529412 | nan | 0.6 | nan | 0.836735 | 0.985401 | 0.937081 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.805556 | nan | nan | nan | nan | nan | 0.967832 | 0.980447 | nan | nan | 0.615385 | nan | 0.242424 | 0.00204813 | nan | nan | nan | nan | nan | 0.0216975 | nan | 0.0426558 | 0.647059 | nan | nan | 0.142857 | nan | 0.047619 | nan | 0.567568 | nan | nan | 0.00390727 | nan | nan | 0.857143 | nan | nan | nan | nan | nan | nan | nan | nan | 0.619048 | 0.178571 | nan | 0.989583 | nan | nan | nan | nan | nan | 0.986154 | nan | 0.765625 | 0.230769 | nan | nan | nan | 0.591087 | 0.0012444 | nan | 0.935185 | 0.241379 | nan | nan | 0.0711864 | 0.806452 | nan | nan | nan | nan | nan | nan | 0.840376 | nan | nan | 0.857143 | 0.3 | nan | nan | 0.00905563 | 0.00807754 | 0.868307 | nan | nan | nan | 0.26087 | nan | nan | nan | nan | 0.0284553 | nan | nan | 0.254545 | nan | 0.4 | nan | 0.266667 | 0.263158 | 0.622951 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.3125 | nan | 0.100563 | nan | 0.0722892 | 0.0248652 | 0.915916 | nan | 0.294776 | nan | nan | nan | nan | nan | nan | 0.394737 | 0.978261 | nan | nan | 0.196078 | 0.138889 | nan | nan | 0.733333 | nan | 0.988525 | 0.193548 | nan | nan | nan | 0.5 | 0.962264 | nan | nan | 0.0638298 | nan | 0.195122 | nan | nan | nan | nan | nan | nan | nan | nan | 0.79 | nan | 0.00218261 | nan | 0.846154 | nan | 0.214286 | nan | 0.981947 | nan | 0.367347 | nan | nan | nan | nan | 0.0481928 | nan | nan | nan | nan | 0.872093 | nan | nan | nan | nan | nan | nan | 0.609375 | nan | nan | nan | 0.642857 | 0.362376 | 0.740741 | 0.583333 | nan | nan | nan | 0.3 | nan | nan | nan | 0.025641 | nan | 0.31768 | nan | nan | nan | 0.352941 | 0.0551065 | 0.651685 | nan | 0.211268 | 0.605263 | 0.689655 | nan | nan | 0.065 | 0.137255 | nan | nan | 0.797753 | nan | 0.470588 | nan | nan | 0.291667 | nan | nan | nan | nan | nan | nan | nan | 0.3 | 0.0210084 | 0.0879121 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.189189 | nan | nan | nan | nan | nan | nan | nan | 0.956897 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.965385 | nan | nan | nan | 0.740385 | nan | nan | 0.833333 | nan | nan | nan | 0.227092 | 0.214286 | 0.048893 | nan | nan | 0.797794 | 0.114658 | 0.131579 | nan | nan | nan | 0.0113269 | 0.635659 | 0.0129717 | nan | nan | 0.482759 | 0.536585 | 0.277778 | nan | nan | nan | nan | nan | 0.983762 | nan | 0.191919 | nan | nan | nan | nan | 0.998097 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.909091 | 0.942652 | nan | 0.695652 | 0.965854 | 0.1 | nan | 0.193548 | 0.0770437 | 0.955556 | 0.265957 | nan | 0.0761905 | 0.294118 | nan | 0.454545 | 0.333333 | nan | nan | 0.685185 | nan | 0.860759 | 0.998899 | nan | 0.82069 | 0.375 | 0.969019 | 0.27027 | 0.516129 | nan | nan | nan | nan | nan | nan | 0.111437 | nan | 0.0249653 | 0.444444 | 0.263158 | nan | nan | nan | 0.184615 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.5 | nan | nan | nan | nan | nan | nan | 0.0508941 | 0.722222 | nan | nan | nan | 0.0701019 | nan | 0.0833333 | nan | 0.612903 | nan | 0.761905 | nan | nan | nan | nan | nan | nan | 0.166667 | 0.176471 | nan | nan | nan | nan | 0.615385 | 0.833333 | 0.642857 | nan | nan | nan | nan | nan | 0.2 | nan | nan | nan | nan | nan | nan | nan | 0.703704 | 0.42029 | nan | nan | nan | nan | nan | nan | nan | nan | 0.363636 | nan | 0.0182704 | nan | nan | 0.0176211 | 0.145729 | 0.8125 | nan | nan | nan | nan | 0.876043 | 0.796296 | nan | 0.980676 | nan | nan | nan | nan | nan | 0.565217 | nan | nan | 0.614035 | 0.292135 | 0.3125 | 0.428571 | nan | 0.353846 | nan | 0.6875 | nan | nan | nan | 0.818182 | nan | 0.88715 | 0.285714 | 0.373737 | 0.454545 | nan | nan | nan | 0.00981354 | nan | nan | nan | 0.973451 | nan | nan | nan | nan | nan | nan | nan | 0.946087 | nan | nan | nan | nan | nan | 0.966156 | nan | nan | nan | nan | 0.538462 | nan | nan | nan | nan | nan | 0.974468 | 0.971602 | nan | nan | nan | 0.695652 | nan | nan | 0.985185 | nan | nan | nan | 0.26087 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.115385 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.920354 | nan | nan | nan | 0.373737 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.00237651 | nan | nan | 0.0892857 | nan | nan | nan | nan | nan | 0.37931 | 0.66129 | 0.571429 | nan | nan | nan | nan | nan | nan | nan | nan | 0.968944 | nan | nan | 0.125 | nan | 0.852941 | nan | nan | 0.779661 | nan | 0.00793651 | nan | 0.52 | 0.15493 | nan | nan | 0.5 | nan | nan | 0.327273 | 0.178571 | nan | 0.212121 | 0.555556 | nan | nan | 0.571429 | 0.230769 | 0.877551 | nan | nan | 0.113402 | nan | nan | 0.466667 | 0.146341 | 0.4 | nan | nan | nan | 0.25 | nan | nan | nan | 0.0122164 | nan | nan | nan | nan | 0.772727 | nan | nan | nan | nan | nan | nan | 0.0639535 | 0.1875 | 0.25 | nan | nan | nan | nan | nan | nan | nan | 0.357143 | nan | nan | nan | 0.0286225 | 0.642857 | 0.784091 | 0.575758 | 0.434783 | nan | 0.192308 | 0.978355 | nan | nan | nan | 0.329114 | nan | nan | nan | 0.5 | nan | nan | 0.736842 | nan | nan | 0.538462 | nan | 0.44 | 0.189474 | 0.00385802 | 0.992015 | nan | nan | nan | 0.179487 | 0.996731 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.283582 | 0.43662 | nan | 0.230769 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.432432 | nan | 0.5 | nan | nan | 0.0454545 | nan | nan | nan | nan | 0.0657895 | 0.00878594 | 0.104839 | 0.026616 | nan | 0.323529 | 0.419355 | nan | 0.0898876 | 0.625 | nan | 0.582734 | 0.622222 | 0.0208955 | nan | nan | 0.0290456 | 0.392857 | 0.0757576 | nan | nan | 0.8 | 0.808989 | 0.144092 | nan | nan | 0.785714 | nan | nan | 0.363636 | 0.021097 | nan | nan | 0.135922 | nan | 0.00915751 | 0.165254 | 0.695652 | nan | 0.134615 | 0.923497 | 0.939597 | 0.5 | nan | nan | nan | nan | 0.16092 | nan | 0.179487 | nan | nan | 0.937037 | nan | nan | 0.625 | 0.722222 | 0.2125 | nan | nan | 0.27957 | nan | nan | nan | nan | nan | nan | 0.971429 | nan | 0.0850575 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0909091 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.176471 | nan | nan | 0.0486111 | nan | nan | 0.0942408 | nan | 0.5 | nan | 0.0849057 | 0.355828 | 0.027016 | 0.993734 | nan | 0.0789474 | nan | 0.0720789 | nan | 0.998735 | 0.431507 | nan | nan | 0.6875 | nan | nan | 0.167273 | 0.290323 | nan | 0.977679 | 0.973384 | nan | 0.740741 | 0.85 | nan | nan | 0.230769 | 0.169753 | 0.0322581 | 0.709677 | nan | 0.231302 | nan | nan | nan | nan | nan | 0.059761 | nan | 0.955128 | nan | nan | nan | nan | nan | 0.728395 | nan | nan | nan | 0.761905 | nan | nan | 0.702703 | nan | nan | 0.230769 | 0.434783 | nan | nan | 0.526829 | nan | 0.568966 | nan | nan | 0.454545 | 0.5 | 0.957627 | nan | 0.615385 | 0.160883 | 0.304348 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.00580431 | nan | 0.128205 | 0.00146039 | nan | 0.714286 | 0.5 | nan | 0.076524 | 0.45 | 0.0493238 | nan | nan | nan | nan | nan | 0.0682493 | nan | nan | 0.416667 | nan | nan | nan | nan | nan | nan | nan | nan | 0.488372 | nan | nan | nan | nan | nan | 0.702479 | nan | nan | nan | nan | nan | nan | 0.241379 | 0.116279 | 0.615385 | nan | nan | nan | nan | nan | 0.1875 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.997737 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.00940439 | 0.0415973 | 0.461538 | 0.583333 | nan | 0.814815 | nan | 0.999069 | 0.180747 | 0.227273 | 0.0909091 | nan | nan | 0.998571 | 0.272727 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0619946 | 0.0434783 | nan | 0.277778 | 0.755396 | 0.0733453 | nan | nan | 0.875 | 0.70297 | nan | nan | nan | nan | 0.0131406 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.625 | nan | nan | 0.625 | 0.4 | nan | nan | 0.527778 | 0.527632 | 0.311499 | 0.0679612 | 0.72 | 0.714286 | nan | nan | nan | 0.261682 | nan | 0.75 | nan | nan | 0.00403226 | nan | nan | nan | nan | 0.00144886 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.942507 | nan | nan | nan | nan | nan | nan | nan | 0.0598291 | nan | nan | nan | nan | nan | nan | 0.00558451 | nan | nan | nan | nan | nan | nan | 0.0943396 | nan | nan | nan | nan | nan | 0.648 | nan | nan | 0.170886 | nan | nan | 0.00173414 | 0.195946 | nan | nan | nan | 0.5 | 0.428571 | nan | nan | nan | nan | nan | nan | nan | 0.998966 | nan | 0.705882 | nan | 0.0257293 | nan | nan | nan | nan | nan | 0.453125 | nan | nan | 0.981859 | 0.990724 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.263158 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0327869 | 0.864865 | 0.910714 | 0.0574593 | nan | 0.166667 | 0.553191 | nan | 0.538462 | 0.5 | nan | nan | 0.0672269 | nan | nan | nan | nan | 0.72093 | 0.88305 | 0.986767 | nan | nan | nan | nan | nan | 0.0629921 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.552326 | nan | nan | nan | 0.684211 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.947368 | 0.4375 | nan | nan | nan | nan | nan | 0.913669 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.311111 | 0.808989 | nan | nan | nan | nan | nan | nan | nan | nan | 0.0287041 | 0.000658979 | 0.111421 | nan | nan | 0.011236 | nan | nan | nan | nan | nan | 0.169231 | 0.998602 | nan | nan | nan | nan | nan | nan | nan | 0.00153807 | nan | 0.0314342 | nan | 0.00123892 | nan | 0.837209 | nan | 0.997859 | 0.375 | 0.705882 | nan | nan | 0.722222 | nan | nan | nan | nan | nan | 0.821192 | 0.979508 | nan | nan | nan | nan | 0.294118 | 0.0549451 | nan | 0.358974 | nan | nan | nan | nan | nan | 0.378788 | 0.125749 | nan | nan | nan | nan | nan | 0.84466 | nan | nan | 0.861111 | nan | nan | 0.826531 | 0.277778 | 0.604478 | 0.842105 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.416667 | nan | nan | nan | nan | nan | nan | nan | 0.992809 | nan | nan | 0.538462 | nan | nan | nan | nan | nan | 0.156863 | nan | 0.847458 | nan | 0.116773 | nan | nan | 0.153226 | 0.034923 | 0.0669643 | nan | 0.3 | 0.995663 | nan | nan | 0.996128 | nan | nan | nan | nan | nan | 0.173077 | 0.774194 | nan | nan | nan | nan | nan | nan | nan | 0.692308 | nan | nan | nan | nan | nan | 0.73913 | nan | nan | 0.25 | 0.089172 | 0.8125 | nan | nan | 0.5 | 0.765217 | 0.00751073 | 0.0369318 | nan | nan | 0.333333 | nan | 0.125 | nan | nan | nan | nan | nan | nan | nan | 0.996063 | nan | 0.0263158 | 0.208333 | nan | 0.838235 | nan | nan | 0.416667 | nan | nan | 0.00378788 | 0.917582 | nan | nan | nan | nan | nan | 0.912281 | nan | 0.0071599 | nan | nan | nan | nan | nan | nan | nan | nan | 0.612903 | nan | nan | nan | 0.714286 | nan | nan | nan | nan | nan | 0.3125 | nan | nan | nan | 0.146341 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.5 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.950495 | 0.997828 | 0.997077 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.926136 | nan | nan | nan | 0.632287 | nan | nan | nan | nan | nan | nan | nan | 0.00367309 | nan | nan | nan | nan | nan | nan | nan | nan | 0.571429 | nan | nan | 0.972504 | nan | nan | 0.855954 | 0.120879 | 0.611111 | nan | 0.78481 | 0.927602 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.00459982 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.00348675 | nan | nan | nan | nan | 0.0980392 | nan | 0.54717 | nan | nan | nan | nan | nan | nan | nan | 0.998098 | nan | nan | nan | nan | nan | nan | 0.027027 | nan | nan | nan | 0.607143 | 0.00452567 | nan | 0.104651 | nan | 0.262712 | nan | 0.00133905 | nan | nan | nan | nan | nan | nan | nan | nan | 0.00595238 | nan | 0.00108696 | 0.761905 | nan | nan | nan | nan | nan | 0.684211 | nan | nan | 0.571429 | nan | nan | 0.583333 | nan | nan | nan | nan | 0.764706 | 0.0105597 | nan | nan | 0.00400458 | nan | nan | nan | nan | nan | nan | 0.00903775 | 0.0412371 | nan | nan | nan | 0.887097 | nan | nan | nan | nan | nan | nan | nan | 0.450704 | 0.3125 | 0.122449 | nan | 0.625 | 0.5 | nan | nan | 0.73913 | nan | nan | 0.368421 | 0.04375 | nan | 0.125 | nan | nan | nan | nan | 0.988879 | nan | 0.92233 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.684211 | nan | nan | nan | nan | 0.994356 | nan | 0.791667 | nan | nan | 0.010917 | nan | nan | 0.454545 | 0.998107 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.220339 | 0.888889 | nan | nan | nan | nan | nan | 0.363636 | nan | 0.969388 | nan | nan | nan | nan | nan | nan | 0.36 | nan | nan | 0.26087 | nan | 0.642857 | nan | nan | nan | 0.0533981 | nan | nan | 0.5 | nan | 0.569536 | nan | nan | nan | nan | nan | nan | nan | nan | 0.031941 | 0.238095 | 0.999037 | nan | 0.571429 | nan | 0.0683564 | 0.230337 | nan | 0.315789 | nan | 0.00226757 | nan | nan | nan | nan | nan | 0.661538 | nan | nan | 0.297297 | nan | nan | nan | nan | nan | nan | 0.428571 | nan | nan | nan | nan | 0.0571429 | nan | nan | nan | nan | nan | nan | 0.838095 | 0.0384615 | 0.263158 | nan | nan | nan | 0.192308 | nan | 0.357143 | nan | nan | nan | nan | nan | nan | nan | nan | 0.0352113 | nan | 0.615385 | nan | nan | nan | 0.268293 | 0.998273 | nan | nan | 0.361466 | nan | nan | 0.00514905 | nan | nan | 0.217391 | nan | 0.178218 | nan | 0.375 | 0.00977778 | nan | nan | 0.957944 | nan | nan | 0.148148 | nan | nan | nan | nan | nan | 0.979721 | 0.357143 | nan | nan | nan | nan | nan | nan | nan | nan | 0.8875 | nan | nan | nan | 0.0106572 | nan | nan | nan | 0.75 | nan | nan | nan | nan | nan | 0.998078 | nan | nan | nan | nan | nan | nan | nan | nan | 0.752101 | nan | nan | nan | 0.718391 | nan | nan | nan | 0.14876 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.881246 | 0.717949 | nan | nan | nan | nan | nan | nan | 0.0833333 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.666667 | nan | nan | nan | nan | nan | nan | 0.256098 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.513514 | nan | nan | nan | 0.3125 | 0.866197 | nan | 0.538462 | 0.225806 | nan | 0.0898876 | nan | nan | nan | 0.289474 | 0.00537956 | 0.642857 | 0.0909091 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.1875 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.466667 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.384615 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.00799087 | nan | 0.454545 | 0.5 | nan | nan | 0.5 | nan | 0.999014 | nan | nan | nan | nan | 0.00100604 | nan | nan | nan | 0.357143 | nan | 0.647059 | nan | nan | 0.410256 | 0.764706 | nan | nan | nan | nan | 0.801724 | 0.5 | nan | nan | nan | nan | 0.520548 | 0.5 | 0.5625 | nan | nan | nan | nan | nan | nan | nan | nan | 0.106383 | nan | nan | 0.0360262 | nan | nan | nan | 0.297872 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.464286 | nan | 0.146341 | nan | nan | nan | nan | nan | nan | 0.758621 | 0.998578 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.00992063 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.00162842 | nan | nan | nan | nan | nan | nan | 0.634146 | 0.0527086 | nan | 0.647059 | nan | nan | nan | nan | nan | 0.240741 | nan | nan | 0.434783 | nan | nan | nan | nan | nan | nan | nan | 0.446328 | nan | nan | nan | 0.0249433 | 0.0977444 | nan | nan | nan | 0.152941 | 0.625 | nan | nan | nan | nan | nan | 0.0448065 | nan | nan | 0.820513 | 0.00553372 | 0.0818182 | 0.0785714 | 0.0163265 | 0.18495 | 0.5 | 0.993841 | nan | nan | nan | nan | 0.00383436 | nan | 0.0609756 | nan | nan | 0.119048 | 0.914286 | 0.998944 | nan | nan | nan | nan | 0.886957 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.819277 | 0.915584 | 0.731707 | nan | nan | 0.65 | 0.516129 | nan | 0.4375 | nan | 0.206897 | nan | nan | nan | 0.236842 | 0.721088 | 0.478318 | 0.808511 | nan | nan | 0.487654 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.529412 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.00137946 | nan | 0.998888 | nan | 0.455224 | 0.740741 | nan | 0.644444 | 0.421053 | nan | 0.4375 | nan | 0.541667 | nan | nan | 0.75 | nan | 0.217391 | 0.333333 | nan | nan | 0.625 | nan | 0.545455 | nan | nan | nan | 0.00276702 | 0.0421348 | nan | nan | 0.428571 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.328467 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.631579 | 0.222222 | nan | nan | nan | nan | 0.428571 | nan | nan | 0.0285375 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.165138 | nan | nan | 0.612903 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.00251147 | 0.0980392 | 0.416667 | nan | nan | nan | nan | nan | nan | nan | 0.555556 | nan | 0.2 | 0.772727 | 0.920635 | 0.8125 | 0.409091 | 0.997925 | 0.998665 | nan | nan | nan | nan | 0.609155 | nan | nan | nan | nan | 0.927218 | 0.473684 | 0.196587 | nan | nan | 0.709091 | 0.375 | nan | nan | nan | nan | 0.981752 | nan | nan | 0.730769 | nan | nan | nan | nan | nan | 0.3125 | nan | nan | 0.12069 | nan | nan | nan | nan | nan | nan | 0.0909091 | 0.26087 | 0.0403769 | 0.56 | 0.5625 | 0.40625 | nan | 0.758621 | 0.493151 | nan | 0.907143 | nan | 0.855212 | 0.681818 | 0.777778 | nan | nan | nan | nan | nan | nan | nan | nan | 0.966887 | nan | 0.5625 | nan | nan | nan | 0.0789474 | 0.538462 | nan | nan | nan | nan | nan | nan | nan | nan | 0.3125 | nan | 0.316514 | nan | nan | nan | nan | nan | 0.391304 | nan | nan | 0.707278 | nan | 0.411765 | 0.366667 | nan | nan | nan | nan | nan | nan | 0.41054 | 0.76087 | nan | nan | nan | nan | nan | nan | nan | 0.0174881 | nan | nan | nan | nan | nan | nan | 0.997154 | 0.384615 | nan | nan | 0.6 | nan | nan | 0.0446429 | nan | nan | nan | nan | nan | 0.685185 | nan | 0.571429 | nan | 0.227273 | nan | 0.9625 | 0.924588 | nan | 0.00729129 | 0.313725 | nan | nan | nan | nan | 0.957692 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.00807754 | nan | nan | nan | nan | 0.409091 | nan | 0.328358 | nan | nan | 0.264348 | 0.317073 | nan | nan | 0.744519 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.997597 | nan | nan | nan | nan | 0.060241 | 0.926316 | 0.333333 | 0.666667 | 0.993217 | nan | 0.428571 | nan | 0.992337 | nan | nan | nan | nan | nan | 0.666667 | nan | nan | nan | nan | nan | nan | nan | 0.0295082 | 0.294118 | nan | 0.744526 | 0.93 | nan | 0.897959 | nan | nan | 0.5 | 0.35 | nan | nan | nan | nan | 0.517241 | nan | nan | nan | nan | nan | nan | nan | nan | 0.545455 | nan | nan | nan | nan | nan | 0.392157 | nan | 0.83871 | 0.419355 | nan | nan | nan | 0.545455 | nan | nan | 0.935065 | nan | nan | nan | nan | nan | nan | nan | nan | 0.674419 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.944444 | nan | 0.5 | nan | nan | nan | nan | 0.0269461 | 0.115385 | 0.107724 | nan | nan | nan | 0.0607595 | nan | 0.454545 | nan | nan | 0.742857 | 0.0691928 | nan | 0.320261 | nan | 0.701754 | 0.833333 | 0.970238 | nan | nan | 0.676471 | 0.921986 | nan | nan | nan | nan | nan | nan | 0.0606061 | nan | nan | nan | 0.933828 | 0.761905 | nan | 0.571429 | 0.384615 | nan | nan | nan | nan | nan | nan | 0.54717 | nan | nan | nan | nan | nan | 0.297668 | 0.023235 | nan | 0.644068 | 0.185185 | 0.829545 | 0.240741 | 0.2 | nan | nan | nan | 0.82069 | nan | nan | nan | nan | nan | nan | nan | 0.357143 | nan | 0.0218878 | 0.738889 | nan | 0.72 | nan | 0.458333 | 0.333333 | 0.555556 | 0.333333 | nan | nan | nan | nan | 0.830443 | nan | 0.271589 | 0.428571 | 0.467742 | 0.397163 | 0.174419 | nan | nan | nan | nan | nan | nan | nan | nan | 0.0681818 | 0.982456 | nan | nan | 0.666667 | 0.238095 | nan | nan | nan | 0.5 | nan | nan | nan | nan | nan | nan | 0.384615 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.5 | nan | 0.416667 | 0.00914634 | nan | nan | 0.714286 | nan | 0.127273 | nan | 0.75 | 0.0851064 | nan | 0.40678 | nan | 0.89 | 0.28 | nan | 0.615385 | nan | 0.0491803 | 0.333333 | 0.386364 | 0.406393 | nan | nan | nan | nan | nan | nan | nan | 0.83871 | 0.206897 | nan | nan | nan | 0.514286 | nan | nan | nan | nan | nan | nan | 0.416667 | nan | 0.206897 | nan | nan | nan | nan | 0.466667 | nan | nan | nan | nan | nan | nan | 0.864865 | nan | 0.662235 | nan | nan | 0.905405 | 0.953975 | 0.304054 | 0.39726 | 0.186667 | 0.78125 | nan | nan | 0.555556 | 0.363402 | 0.31 | nan | 0.482759 | 0.722222 | 0.0543478 | nan | nan | 0.128571 | 0.461538 | nan | nan | 0.00105756 | nan | nan | nan | nan | 0.542169 | nan | 0.971774 | nan | nan | nan | nan | 0.35 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0792079 | nan | nan | nan | nan | 0.702703 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.891304 | nan | nan | 0.00461095 | nan | nan | 0.107143 | nan | nan | nan | 0.84375 | nan | nan | 0.911765 | nan | nan | nan | 0.0103093 | nan | nan | nan | nan | nan | nan | nan | 0.777778 | nan | 0.0178926 | nan | nan | nan | nan | nan | 0.131285 | nan | 0.575758 | nan | nan | nan | 0.306617 | nan | nan | nan | nan | nan | nan | nan | nan | 0.845361 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.823529 | nan | 0.533333 | 0.333621 | nan | nan | 0.261307 | 0.411765 | 0.788991 | nan | nan | nan | nan | nan | nan | 0.342318 | 0.6625 | 0.732558 | 0.0667702 | nan | 0.428571 | nan | nan | 0.3125 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.019305 | 0.837838 | nan | nan | nan | 0.62963 | 0.2 | nan | nan | nan | nan | nan | nan | nan | nan | 0.0626229 | nan | 0.957597 | 0.0853659 | nan | 0.0305977 | 0.225 | nan | 0.666667 | nan | 0.566667 | nan | nan | nan | 0.0176535 | 0.163934 | 0.0597015 | 0.775 | 0.285714 | 0.91794 | 0.901408 | 0.992601 | 0.172691 | 0.455446 | 0.948922 | 0.65625 | nan | 0.642857 | 0.958333 | nan | nan | nan | nan | nan | 0.45283 | 0.827586 | nan | nan | nan | nan | nan | 0.333333 | 0.5 | nan | 0.4375 | 0.917808 | nan | nan | 0.761905 | nan | 0.321429 | 0.675502 | nan | nan | nan | 0.5 | nan | nan | 0.19697 | nan | 0.958 | 0.896396 | nan | nan | nan | nan | 0.652174 | nan | nan | nan | nan | 0.571429 | nan | nan | nan | nan | nan | nan | 0.0225564 | nan | nan | 0.997902 | 0.318182 | 0.73913 | 0.545455 | 0.263158 | nan | nan | 0.125 | nan | nan | nan | 0.54902 | 0.128049 | nan | nan | nan | 0.00127714 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.915493 | nan | nan | nan | nan | 0.29683 | nan | nan | 0.00145943 | nan | nan | 0.996443 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.736842 | 0.964286 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.211868 | 0.102041 | nan | nan | 0.68 | nan | 0.928571 | nan | nan | 0.53125 | nan | 0.4375 | nan | 0.75 | nan | nan | 0.526316 | nan | 0.787879 | nan | nan | nan | nan | 0.000968784 | 0.2 | nan | nan | 0.708333 | nan | 0.989637 | 0.583333 | nan | nan | nan | nan | nan | 0.207273 | nan | nan | 0.867925 | 0.4375 | 0.583333 | nan | nan | nan | 0.839506 | nan | nan | nan | nan | 0.608696 | nan | nan | nan | 0.998378 | nan | 0.00332447 | nan | nan | 0.291045 | nan | nan | nan | 0.669725 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.3 | nan | 0.840336 | nan | nan | nan | 0.42623 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.378151 | nan | nan | nan | nan | 0.566667 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0122231 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.752941 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.962848 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.5 | nan | nan | 0.264706 | nan | 0.0122066 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.726804 | 0.367089 | nan | nan | 0.27027 | 0.642857 | nan | nan | nan | nan | nan | nan | 0.384 | 0.660256 | nan | nan | nan | nan | nan | nan | 0.969352 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.860465 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.642857 | 0.284615 | nan | nan | 0.473684 | 0.712575 | nan | 0.529412 | nan | nan | nan | nan | nan | nan | nan | 0.347826 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.555556 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.705882 | 0.733333 | 0.318182 | nan | nan | 0.619048 | 0.6875 | nan | 0.636957 | nan | nan | 0.0017094 | 0.692308 | nan | nan | nan | nan | nan | nan | 0.97954 | nan | nan | 0.533333 | nan | nan | nan | 0.1417 | nan | nan | nan | nan | nan | 0.863636 | nan | nan | nan | 0.75502 | 0.937234 | 0.807692 | nan | 0.547619 | 0.920472 | 0.541667 | 0.540975 | nan | 0.862745 | 0.416667 | nan | 0.933333 | 0.821951 | 0.993236 | 0.0416667 | nan | 0.927711 | nan | 0.999061 | nan | nan | nan | 0.65493 | nan | 0.5625 | nan | nan | 0.5 | 0.357143 | 0.791667 | nan | nan | nan | nan | nan | 0.0620155 | nan | nan | nan | nan | nan | nan | nan | 0.999085 | nan | 0.728261 | 0.0464037 | nan | 0.5 | nan | 0.428571 | 0.890411 | 0.118436 | 0.333333 | nan | nan | 0.837398 | nan | nan | 0.902222 | nan | nan | nan | nan | nan | 0.6875 | nan | nan | 0.0505051 | nan | nan | 0.998568 | nan | nan | nan | nan | nan | nan | nan | 0.150155 | nan | nan | 0.89172 | nan | nan | 0.0327869 | nan | 0.483333 | nan | nan | 0.945455 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0720588 | 0.225806 | nan | 0.991021 | nan | nan | 0.583333 | nan | nan | nan | nan | nan | nan | nan | 0.817073 | nan | nan | 0.153846 | 0.984802 | nan | nan | 0.157895 | 0.636684 | nan | nan | nan | 0.666667 | 0.85 | nan | nan | nan | 0.982735 | nan | 0.08 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.207692 | nan | nan | nan | 0.5 | nan | 0.318182 | nan | nan | nan | nan | 0.416667 | 0.703704 | nan | nan | nan | nan | 0.90625 | nan | nan | nan | 0.731707 | nan | nan | nan | nan | nan | nan | 0.178571 | nan | nan | nan | nan | nan | nan | nan | nan | 0.666667 | nan | 0.5 | 0.0779221 | nan | nan | nan | nan | nan | nan | nan | 0.454545 | nan | 0.5 | nan | 0.18 | nan | nan | 0.0454545 | nan | 0.0909091 | nan | nan | nan | 0.139535 | nan | nan | nan | nan | nan | nan | nan | 0.0332681 | nan | 0.555556 | nan | 0.6 | 0.890909 | nan | 0.72093 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0303824 | nan | 0.333333 | nan | nan | nan | nan | nan | 0.970874 | 0.127273 | 0.466667 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.955357 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0326087 | 0.318182 | nan | 0.640649 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0613497 | nan | nan | nan | 0.583333 | 0.340909 | nan | 0.449275 | 0.774194 | 0.845238 | 0.166667 | 0.84588 | nan | nan | nan | nan | nan | nan | 0.181818 | nan | 0.819277 | nan | nan | nan | nan | 0.857814 | 0.563218 | nan | nan | nan | nan | 0.807692 | nan | nan | 0.208791 | nan | 0.582888 | 0.805486 | 0.55 | nan | 0.725806 | 0.484848 | 0.615385 | nan | nan | nan | nan | nan | 0.695652 | nan | nan | nan | nan | 0.55814 | nan | nan | nan | 0.347222 | 0.395683 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.171429 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.020979 | nan | nan | 0.8 | nan | nan | nan | 0.92562 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.368421 | nan | nan | nan | nan | 0.166667 | 0.4375 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.000713572 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0925926 | nan | nan | nan | nan | nan | nan | nan | nan | 0.2 | nan | nan | nan | nan | 0.568807 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.30303 | 0.428571 | nan | 0.144509 | 0.259259 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.134615 | nan | nan | nan | nan | 0.94375 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.979487 | 0.2 | nan | nan | 0.0571429 | nan | 0.264151 | nan | nan | 0.171429 | 0.216216 | 0.83871 | nan | 0.730769 | 0.15625 | nan | nan | nan | 0.5625 | 0.377778 | nan | nan | nan | nan | nan | nan | 0.0159744 | nan | 0.829787 | nan | nan | 0.737143 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.953488 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.00377929 | nan | nan | nan | nan | nan | nan | nan | 0.404762 | nan | 0.994302 | nan | nan | nan | nan | 0.0154196 | 0.0757576 | 0.0273438 | 0.0728155 | nan | nan | 0.401274 | nan | nan | 0.00526712 | nan | nan | 0.351351 | nan | nan | nan | 0.441441 | nan | nan | 0.529412 | 0.416667 | 0.0851064 | 0.0980392 | nan | nan | nan | nan | nan | nan | 0.00699588 | nan | 0.0588235 | nan | nan | 0.00692521 | 0.294118 | nan | 0.0930233 | nan | nan | nan | nan | nan | nan | 0.0403226 | 0.634146 | 0.851351 | nan | 0.0280983 | 0.12963 | nan | nan | 0.0877193 | nan | 0.684211 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.2 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.00392465 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.90303 | nan | nan | nan | nan | nan | 0.0544218 | 0.00865052 | 0.0167785 | 0.166667 | nan | nan | nan | nan | nan | nan | nan | 0.571429 | 0.15625 | nan | nan | 0.534884 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.184615 | nan | 0.0566038 | nan | nan | nan | 0.9974 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.103175 | nan | nan | 0.6875 | 0.466667 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.00186706 | 0.998874 | 0.411765 | 0.820896 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.997847 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.15625 | 0.998368 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0179415 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.00715308 | nan | nan | nan | 0.00532623 | nan | nan | nan | nan | nan | 0.0744681 | nan | nan | nan | nan | nan | nan | nan | 0.0138583 | 0.666667 | 0.703704 | nan | nan | 0.00236088 | 0.0676692 | nan | 0.128205 | 0.933649 | 0.968619 | 0.166667 | nan | nan | nan | nan | nan | nan | 0.0704225 | 0.0775194 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.00136913 | nan | nan | nan | nan | nan | nan | 0.538462 | 0.993763 | nan | nan | nan | nan | nan | nan | nan | 0.0107527 | nan | nan | 0.714286 | nan | nan | nan | nan | 0.5 | nan | 0.612245 | nan | 0.940778 | nan | 0.569444 | nan | nan | nan | nan | nan | nan | 0.905192 | 0.4375 | 0.00214302 | nan | nan | 0.192661 | nan | 0.444444 | nan | 0.980892 | nan | nan | nan | nan | nan | 0.00252048 | nan | nan | 0.997434 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.320809 | 0.5 | nan | 0.010101 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.56 | nan | nan | 0.666667 | 0.25 | 0.366667 | 0.434783 | nan | nan | nan | 0.147059 | 0.637931 | nan | 0.15625 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.294118 | nan | 0.571429 | nan | nan | 0.5 | nan | nan | 0.416667 | 0.5 | nan | nan | nan | nan | nan | nan | nan | nan | 0.384615 | nan | 0.4 | 0.741935 | nan | nan | 0.478992 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.454545 | nan | nan | nan | nan | 0.00135423 | nan | nan | nan | nan | nan | 0.517241 | 0.653846 | nan | 0.25 | nan | 0.375 | 0.00521921 | nan | 0.583333 | 0.0532995 | nan | 0.141304 | nan | 0.220339 | 0.0973451 | 0.1875 | nan | nan | nan | nan | nan | 0.761905 | nan | 0.538462 | 0.304878 | nan | nan | nan | 0.00413467 | nan | nan | nan | nan | 0.986348 | nan | nan | nan | 0.5 | nan | nan | nan | nan | 0.285714 | 0.00627803 | nan | 0.345455 | 0.583333 | nan | nan | 0.0244328 | nan | 0.131579 | nan | 0.3125 | nan | nan | nan | nan | nan | 0.111111 | nan | nan | 0.12766 | 0.180328 | 0.203704 | nan | nan | nan | 0.645161 | nan | nan | nan | 0.903704 | 0.837209 | nan | nan | nan | nan | 0.12 | nan | nan | nan | nan | 0.166949 | 0.444444 | nan | 0.888889 | 0.4 | nan | 0.145833 | 0.6875 | 0.996427 | nan | 0.998513 | 0.428571 | nan | nan | nan | nan | 0.982249 | nan | 0.313869 | nan | 0.108108 | 0.123711 | 0.268657 | nan | nan | 0.197802 | 0.20979 | nan |\n", "| 2015 | nan | nan | nan | 0.413793 | 0.998967 | 0.5625 | nan | nan | 0.380952 | nan | 0.318182 | nan | 0.00252419 | 0.9 | nan | 0.613333 | nan | nan | nan | nan | 0.117647 | nan | nan | 0.526316 | 0.00309981 | nan | nan | nan | nan | nan | 0.999351 | nan | 0.473684 | nan | nan | 0.302326 | 0.375 | 0.00247015 | 0.484848 | 0.290909 | nan | nan | nan | 0.00944669 | nan | nan | nan | 0.454545 | nan | nan | nan | 0.263158 | nan | nan | 0.00111815 | 0.615385 | 0.0150943 | nan | nan | nan | nan | 0.977762 | 0.87234 | nan | nan | 0.369231 | nan | nan | nan | nan | nan | 0.0170648 | nan | nan | 0.461538 | 0.0373134 | nan | nan | nan | nan | 0.871429 | nan | nan | nan | 0.125714 | 0.971182 | nan | nan | nan | 0.0724638 | nan | nan | nan | nan | nan | nan | nan | nan | 0.0126293 | nan | nan | 0.5 | nan | nan | 0.0353649 | 0.0441176 | nan | nan | nan | 0.0735294 | nan | nan | 0.955556 | nan | nan | 0.285714 | 0.333333 | nan | nan | nan | 0.227273 | nan | nan | 0.771429 | 0.932039 | 0.307692 | nan | nan | nan | nan | nan | nan | nan | nan | 0.178571 | nan | nan | nan | nan | 0.0105914 | nan | 0.00446495 | nan | 0.142157 | nan | nan | nan | nan | nan | 0.380952 | nan | nan | 0.982618 | 0.722222 | nan | nan | nan | nan | nan | nan | 0.0897436 | nan | nan | 0.428571 | nan | nan | nan | nan | 0.194444 | 0.586957 | nan | nan | 0.375 | 0.583333 | nan | nan | 0.883959 | nan | nan | nan | nan | nan | nan | nan | nan | 0.00484262 | nan | nan | nan | nan | 0.996561 | nan | nan | nan | nan | nan | nan | nan | nan | 0.0422535 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.588235 | nan | nan | nan | nan | nan | 0.0565749 | 0.997683 | 0.00151913 | nan | nan | nan | nan | nan | 0.136364 | nan | nan | 0.721154 | nan | nan | 0.774407 | nan | nan | nan | nan | nan | nan | 0.5 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.150762 | 0.998529 | nan | nan | nan | 0.116809 | nan | 0.518519 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.787234 | 0.997453 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.995242 | 0.998127 | nan | nan | 0.75 | nan | 0.980907 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.419355 | nan | nan | nan | nan | nan | 0.76 | nan | nan | nan | nan | nan | nan | nan | nan | 0.755556 | nan | nan | nan | nan | nan | nan | 0.4 | nan | 0.9 | 0.0983607 | nan | nan | nan | 0.831169 | nan | nan | nan | 0.0764706 | 0.978541 | 0.0664336 | nan | 0.458333 | 0.330713 | nan | 0.831776 | 0.412698 | nan | 0.919481 | 0.309524 | 0.191176 | nan | nan | 0.996726 | nan | 0.736842 | nan | nan | 0.5 | nan | 0.4 | nan | 0.140625 | nan | nan | 0.83871 | nan | nan | 0.787234 | nan | 0.230769 | nan | 0.2 | 0.5 | 0.0063234 | nan | 0.192308 | 0.238095 | nan | 0.741935 | nan | 0.776786 | 0.759259 | 0.604651 | 0.837838 | 0.439024 | nan | 0.75 | nan | nan | 0.421053 | nan | 0.454545 | 0.661972 | nan | nan | nan | nan | nan | nan | nan | 0.62963 | 0.877551 | nan | 0.0930233 | nan | nan | 0.242424 | 0.0591527 | 0.923077 | nan | nan | 0.5 | nan | nan | 0.995941 | nan | nan | nan | nan | nan | 0.000896772 | nan | nan | nan | nan | 0.0319052 | nan | nan | 0.153889 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.6 | nan | nan | nan | 0.638298 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.806452 | 0.24 | nan | 0.999019 | nan | nan | nan | nan | nan | nan | nan | nan | 0.987207 | nan | 0.375 | 0.00104019 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.125 | nan | nan | nan | nan | nan | nan | nan | 0.830508 | nan | nan | nan | 0.0704225 | nan | nan | 0.97191 | nan | nan | 0.806452 | 0.0155642 | nan | nan | nan | nan | nan | nan | 0.765306 | nan | nan | nan | nan | nan | 0.954545 | nan | nan | 0.0146199 | nan | nan | nan | 0.25731 | 0.996715 | 0.986971 | 0.75 | 0.101036 | 0.998583 | nan | nan | nan | nan | nan | 0.624242 | 0.837343 | nan | nan | 0.263158 | nan | 0.298165 | 0.277108 | 0.0989011 | 0.347826 | nan | nan | 0.409091 | nan | nan | nan | nan | nan | 0.8125 | nan | nan | 0.278481 | nan | nan | nan | 0.344086 | 0.823529 | 0.25 | nan | 0.4 | nan | nan | 0.0529801 | nan | 0.846154 | nan | nan | nan | nan | 0.434959 | nan | 0.6 | nan | nan | nan | nan | 0.674847 | 0.758621 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.380952 | nan | nan | 0.38961 | nan | nan | nan | 0.845528 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.529412 | 0.948346 | nan | 0.0747423 | nan | 0.5 | 0.826087 | nan | nan | 0.109244 | 0.101449 | 0.538462 | nan | nan | nan | nan | 0.5 | 0.8125 | nan | nan | nan | nan | nan | 0.968553 | 0.575 | nan | nan | 0.0144165 | nan | 0.571429 | 0.676471 | nan | nan | nan | 0.992431 | nan | nan | nan | nan | nan | nan | 0.130435 | 0.269841 | 0.0593034 | 0.731618 | nan | 0.54902 | nan | nan | nan | nan | 0.130435 | 0.909375 | nan | nan | nan | 0.0531915 | 0.545455 | nan | nan | nan | nan | 0.428571 | nan | nan | nan | 0.048 | 0.473684 | 0.60241 | 0.00519211 | nan | nan | nan | nan | nan | 0.98041 | 0.908163 | 0.912281 | 0.410596 | 0.642857 | 0.42 | nan | nan | nan | 0.997498 | nan | nan | nan | 0.105172 | nan | nan | 0.0869565 | nan | nan | nan | nan | nan | nan | 0.875 | nan | nan | nan | 0.338889 | 0.0255154 | nan | nan | 0.701449 | nan | 0.684211 | nan | 0.998835 | 0.955414 | nan | nan | nan | nan | 0.862069 | nan | 0.389908 | nan | nan | 0.822222 | 0.947619 | 0.987059 | nan | nan | 0.808381 | nan | 0.145098 | 0.1 | nan | nan | nan | 0.111111 | 0.0656934 | 0.7 | 0.424242 | nan | nan | 0.487805 | nan | 0.705882 | 0.887097 | nan | nan | nan | 0.731707 | nan | 0.636364 | 0.00370256 | 0.0211765 | 0.0900901 | nan | 0.0530973 | 0.0143929 | nan | nan | nan | nan | 0.907407 | 0.993311 | nan | nan | nan | nan | 0.459016 | nan | 0.5 | nan | nan | nan | 0.219512 | nan | 0.101852 | 0.734177 | 0.953488 | 0.476608 | nan | 0.893617 | nan | 0.666667 | 0.454545 | 0.28 | nan | 0.5 | 0.5 | 0.391304 | 0.0714286 | nan | nan | nan | nan | nan | 0.171053 | 0.155738 | 0.952381 | 0.686275 | nan | nan | nan | nan | 0.642857 | nan | nan | nan | 0.991119 | 0.962761 | nan | nan | 0.106383 | nan | nan | nan | nan | nan | nan | nan | 0.363636 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0215332 | nan | nan | nan | nan | 0.235294 | 0.219512 | 0.84058 | nan | nan | nan | 0.991139 | 0.188679 | 0.5 | nan | nan | 0.48 | 0.434343 | nan | nan | nan | 0.0218905 | nan | 0.169492 | 0.0516971 | 0.5 | 0.0172043 | 0.666667 | nan | nan | 0.681818 | nan | nan | nan | nan | 0.88835 | nan | 0.545455 | nan | nan | 0.357143 | nan | 0.001321 | nan | nan | nan | 0.0372226 | nan | 0.103448 | 0.0413223 | 0.00789889 | 0.143631 | nan | 0.826087 | 0.0409396 | 0.428571 | 0.5 | nan | nan | 0.391304 | nan | 0.65625 | 0.864 | 0.935897 | 0.842105 | nan | nan | nan | 0.870588 | nan | nan | nan | nan | nan | nan | nan | nan | 0.421053 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.961722 | nan | nan | nan | nan | nan | nan | nan | nan | 0.904762 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.666667 | nan | 0.0338983 | 0.872727 | nan | 0.0781759 | 0.0609756 | 0.150837 | nan | 0.924051 | 0.978655 | nan | nan | nan | nan | nan | 0.767123 | 0.178947 | 0.123077 | 0.0437158 | 0.333333 | 0.681818 | 0.871429 | 0.352941 | 0.867925 | nan | 0.391304 | 0.392157 | 0.961783 | 0.0379464 | nan | nan | 0.807692 | nan | nan | nan | nan | 0.026936 | 0.0229358 | nan | 0.5 | nan | nan | nan | nan | nan | nan | 0.5 | 0.0767635 | 0.714286 | nan | 0.0263158 | 0.43617 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.362069 | nan | 0.020936 | nan | 0.0170316 | nan | nan | nan | 0.0978261 | 0.318182 | 0.896825 | 0.989796 | nan | nan | nan | 0.736111 | nan | nan | nan | 0.282051 | 0.791667 | nan | nan | nan | nan | nan | nan | nan | 0.00157078 | nan | 0.855263 | nan | nan | nan | nan | nan | nan | 0.0504732 | 0.8 | 0.0164819 | nan | 0.447368 | 0.555556 | 0.466667 | nan | nan | 0.00846945 | 0.144928 | 0.1875 | nan | nan | 0.00240577 | nan | nan | 0.084507 | 0.983871 | 0.0542245 | 0.296029 | 0.0181818 | 0.508772 | 0.862903 | nan | 0.684211 | nan | nan | 0.0625 | 0.0696379 | 0.115385 | 0.666667 | nan | nan | nan | 0.8 | nan | nan | 0.65 | nan | nan | nan | 0.388889 | nan | nan | nan | nan | nan | nan | 0.0541667 | nan | nan | nan | nan | 0.09375 | 0.0286396 | nan | nan | nan | nan | nan | 0.625 | nan | 0.0153846 | nan | nan | 0.428571 | nan | nan | nan | nan | 0.00226757 | nan | nan | nan | nan | 0.998268 | nan | nan | 0.619137 | 0.0466926 | 0.0247525 | nan | nan | nan | nan | 0.421053 | 0.0302115 | nan | 0.294118 | 0.409639 | nan | 0.907692 | nan | nan | nan | nan | nan | 0.989886 | nan | nan | nan | nan | nan | 0.98317 | nan | 0.52381 | nan | nan | nan | 0.45 | 0.631579 | 0.695652 | nan | nan | 0.416667 | nan | nan | nan | nan | 0.423913 | 0.103896 | nan | nan | 0.1875 | 0.0358209 | 0.00454545 | 0.0935673 | nan | nan | 0.845238 | nan | nan | nan | nan | 0.98958 | nan | 0.019025 | nan | nan | nan | nan | nan | nan | 0.37594 | 0.0247416 | nan | nan | 0.15493 | 0.0714286 | 0.971182 | 0.151515 | 0.76 | nan | 0.647059 | nan | 0.938326 | nan | 0.995912 | nan | nan | nan | 0.00290604 | nan | nan | 0.0823529 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.107692 | 0.5 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.00786517 | 0.00980392 | 0.923598 | nan | nan | 0.145833 | nan | nan | nan | 0.101695 | nan | 0.611111 | nan | nan | nan | 0.235849 | 0.0123077 | 0.897059 | 0.26 | nan | nan | 0.545455 | nan | 0.030837 | nan | nan | nan | nan | nan | 0.0714286 | nan | 0.00125887 | nan | nan | 0.98 | nan | nan | 0.238532 | 0.0739796 | 0.466667 | 0.026455 | nan | nan | nan | nan | 0.357143 | nan | nan | 0.454545 | 0.0505051 | nan | nan | 0.378378 | nan | nan | nan | 0.454545 | nan | nan | nan | 0.6 | 0.0352457 | 0.317136 | 0.647059 | nan | nan | 0.5 | nan | nan | 0.0801589 | 0.642105 | nan | nan | nan | nan | nan | nan | 0.622222 | nan | 0.0239234 | 0.176471 | nan | nan | 0.813861 | 0.806452 | 0.0439189 | nan | nan | nan | nan | 0.0147368 | nan | nan | 0.129771 | nan | 0.85 | nan | nan | 0.25 | nan | nan | 0.258065 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.522388 | nan | nan | 0.285714 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.974742 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.75 | nan | nan | nan | nan | 0.171717 | 0.0361224 | 0.0568182 | 0.609489 | 0.0512117 | 0.108434 | 0.0536913 | nan | nan | nan | nan | 0.398364 | nan | nan | nan | 0.351852 | nan | nan | nan | nan | nan | nan | 0.968504 | nan | 0.193548 | 0.0179211 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0281837 | 0.909502 | nan | nan | nan | nan | 0.5625 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0420168 | nan | nan | 0.35119 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.15 | nan | nan | nan | nan | nan | nan | nan | 0.0190217 | nan | nan | nan | nan | nan | nan | 0.254882 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.285024 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.984197 | nan | 0.00126156 | 0.744118 | 0.879009 | nan | 0.483209 | nan | 0.999118 | nan | 0.757009 | nan | nan | nan | nan | nan | 0.0114772 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.1 | nan | nan | 0.176471 | nan | nan | nan | nan | nan | 0.994334 | nan | nan | nan | nan | nan | nan | nan | 0.848485 | nan | nan | nan | nan | 0.428571 | 0.931507 | nan | nan | 0.923611 | nan | nan | nan | 0.22963 | nan | nan | 0.98847 | nan | nan | nan | nan | nan | nan | nan | 0.8 | nan | nan | nan | nan | nan | nan | nan | nan | 0.810811 | nan | 0.998733 | nan | 0.5 | nan | nan | 0.0140351 | nan | nan | nan | nan | nan | nan | 0.566667 | nan | nan | 0.0150285 | nan | nan | nan | 0.65625 | nan | nan | nan | nan | 0.00153736 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.567568 | nan | nan | nan | 0.236842 | nan | nan | nan | 0.555556 | nan | nan | nan | nan | nan | 0.25 | nan | nan | nan | nan | nan | nan | 0.0171821 | 0.576471 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.880597 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.307692 | 0.725 | nan | nan | nan | nan | 0.65625 | 0.592593 | 0.0149719 | 0.0116959 | nan | nan | nan | nan | nan | nan | 0.925926 | 0.0609929 | 0.00401261 | nan | 0.652174 | nan | 0.00206697 | nan | nan | nan | 0.126984 | 0.00943396 | 0.887417 | nan | 0.342105 | nan | nan | 0.00127429 | nan | nan | nan | nan | nan | nan | 0.193548 | nan | nan | 0.00568182 | nan | 0.00311111 | nan | nan | nan | nan | nan | nan | nan | 0.0205684 | 0.287671 | 0.322581 | 0.998227 | nan | nan | 0.0095613 | nan | 0.119171 | nan | nan | nan | nan | nan | nan | nan | 0.0248366 | 0.883721 | nan | 0.714286 | 0.28125 | nan | nan | 0.318182 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.829787 | nan | nan | nan | nan | nan | nan | 0.454545 | 0.0317003 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.76971 | nan | nan | 0.0862069 | nan | nan | nan | nan | nan | 0.0895522 | 0.422018 | 0.447368 | nan | 0.0144092 | 0.626263 | nan | nan | nan | nan | nan | nan | 0.00444774 | nan | nan | nan | nan | nan | 0.0379242 | 0.409091 | 0.024839 | nan | nan | 0.010142 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.478261 | nan | nan | 0.138889 | 0.382979 | nan | 0.487805 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.147059 | nan | 0.631068 | nan | nan | nan | nan | 0.5 | nan | nan | nan | nan | nan | nan | nan | 0.736842 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0655738 | 0.261905 | nan | 0.586957 | 0.663636 | nan | nan | 0.625 | nan | nan | nan | nan | 0.280594 | nan | 0.714286 | nan | nan | 0.5 | 0.192308 | nan | 0.25 | nan | nan | 0.0902256 | nan | nan | 0.952663 | nan | nan | nan | nan | 0.0462963 | nan | nan | 0.192308 | 0.24 | nan | 0.89779 | nan | nan | nan | nan | nan | 0.0116822 | nan | nan | nan | nan | nan | nan | 0.893103 | nan | nan | nan | 0.00134068 | nan | 0.315789 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.657895 | nan | nan | 0.0124481 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.310345 | nan | nan | nan | nan | nan | 0.876712 | nan | 0.80292 | nan | nan | nan | 0.932961 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.072327 | nan | 0.0298507 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.631579 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.152174 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.047619 | nan | nan | 0.333333 | 0.7 | 0.0433437 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.266667 | 0.133333 | nan | 0.00153728 | nan | 0.73913 | 0.0401786 | nan | nan | nan | nan | 0.0370943 | nan | nan | nan | nan | 0.434783 | nan | nan | nan | nan | 0.0402194 | 0.675676 | 0.0114943 | nan | 0.28 | nan | nan | nan | nan | 0.487395 | nan | 0.6 | nan | nan | nan | nan | nan | 0.0521472 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.314815 | 0.00586428 | 0.425926 | nan | nan | nan | nan | 0.0284091 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.152941 | nan | 0.722222 | nan | nan | nan | nan | nan | 0.996232 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.151515 | nan | nan | 0.101449 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.236842 | nan | 0.327103 | nan | 0.688889 | nan | nan | nan | nan | nan | nan | 0.333333 | nan | nan | nan | 0.370968 | nan | nan | nan | nan | nan | nan | 0.450801 | 0.65625 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.192308 | 0.2 | nan | 0.375 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.545455 | nan | nan | nan | nan | nan | 0.123457 | 0.611111 | nan | nan | 0.5 | nan | nan | 0.583333 | 0.129496 | nan | nan | 0.0769231 | nan | nan | nan | 0.0736486 | 0.68 | nan | nan | 0.09375 | nan | 0.131624 | nan | nan | nan | nan | nan | nan | 0.70765 | nan | nan | nan | nan | nan | nan | nan | 0.416667 | nan | nan | 0.541667 | nan | 0.428571 | nan | nan | nan | nan | 0.555556 | nan | 0.880546 | nan | 0.384615 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.00278897 | 0.894737 | nan | nan | nan | nan | 0.0634921 | nan | 0.0397727 | 0.481481 | nan | nan | nan | nan | nan | nan | nan | 0.0608108 | nan | nan | nan | nan | nan | nan | 0.82199 | nan | 0.690265 | nan | nan | nan | nan | nan | nan | 0.375 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.00308801 | nan | nan | nan | nan | nan | 0.362162 | nan | nan | nan | nan | nan | 0.25 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0421456 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.92381 | 0.149254 | 0.193254 | nan | 0.488889 | 0.52 | nan | nan | 0.277778 | nan | nan | nan | 0.016835 | nan | nan | nan | nan | nan | nan | 0.241935 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0734402 | 0.61039 | 0.142857 | 0.458333 | 0.308824 | nan | nan | 0.722222 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0136596 | 0.439655 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.903226 | nan | nan | 0.140351 | nan | nan | 0.0155039 | nan | 0.0483871 | nan | nan | 0.782144 | nan | nan | nan | nan | 0.0233645 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.927536 | nan | nan | nan | 0.871681 | 0.361702 | nan | nan | nan | 0.275862 | nan | 0.23913 | nan | nan | nan | nan | nan | nan | nan | 0.132812 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.428571 | 0.00379533 | 0.917431 | 0.30303 | nan | 0.998311 | 0.0024558 | 0.40678 | 0.00192066 | 0.333333 | 0.961039 | 0.130653 | 0.375 | nan | nan | nan | nan | 0.285714 | nan | 0.545455 | 0.82 | 0.692308 | nan | 0.999482 | 0.833333 | nan | nan | nan | nan | 0.951311 | 0.950495 | 0.998761 | 0.772727 | 0.19009 | 0.184649 | nan | 0.27668 | 0.755319 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0618557 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.454545 | 0.192593 | 0.52381 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.90625 | 0.958716 | 0.00510725 | nan | 0.5 | nan | 0.982379 | 0.772727 | nan | nan | nan | 0.969849 | 0.967105 | nan | nan | 0.787234 | 0.599125 | 0.982185 | 0.883388 | nan | nan | 0.140625 | nan | nan | nan | 0.998552 | nan | 0.999509 | 0.00219861 | nan | nan | nan | 0.836957 | 0.929577 | 0.00427772 | nan | 0.938462 | nan | 0.867647 | 0.626907 | nan | nan | 0.788235 | 0.283784 | nan | nan | 0.791667 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0983607 | 0.55 | 0.518519 | 0.00164853 | nan | nan | nan | nan | nan | nan | 0.837209 | nan | nan | 0.943359 | nan | 0.416667 | nan | 0.835443 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.8 | nan | 0.545455 | nan | nan | 0.195652 | nan | 0.529412 | 0.175 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.00113273 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.998129 | 0.0162752 | 0.4 | 0.139535 | nan | 0.998178 | 0.425197 | 0.207143 | 0.0143043 | 0.284091 | 0.997415 | 0.45 | nan | 0.0632911 | nan | nan | nan | 0.75 | nan | nan | nan | nan | nan | 0.172414 | nan | 0.545455 | 0.318182 | nan | nan | nan | nan | 0.0455151 | 0.244094 | 0.916667 | nan | nan | nan | nan | 0.333333 | 0.997573 | nan | 0.862069 | 0.875 | nan | 0.971751 | nan | nan | nan | nan | nan | nan | 0.736842 | 0.3125 | nan | nan | nan | 0.84375 | 0.810811 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0122655 | nan | nan | nan | nan | 0.14 | nan | nan | nan | nan | nan | nan | nan | nan | 0.169811 | nan | nan | 0.214286 | 0.236842 | 0.461538 | nan | 0.135135 | 0.860759 | 0.602337 | 0.013634 | nan | 0.44 | 0.345528 | 0.3125 | nan | nan | nan | nan | 0.0179487 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0551181 | nan | nan | nan | 0.0151976 | 0.0364964 | 0.857143 | nan | 0.411765 | 0.0487805 | 0.0301508 | nan | nan | nan | nan | 0.990196 | nan | nan | nan | nan | nan | 0.0930576 | nan | nan | nan | nan | nan | 0.567511 | nan | 0.214286 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.619048 | nan | nan | nan | nan | 0.0530973 | 0.538462 | nan | nan | nan | nan | nan | nan | nan | 0.0047997 | nan | 0.5 | 0.578947 | 0.998256 | 0.990044 | nan | nan | nan | 0.00393038 | nan | nan | nan | 0.0428571 | nan | nan | nan | nan | 0.6875 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.185185 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0260417 | 0.294118 | 0.000870171 | nan | 0.0446927 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.478261 | 0.0945946 | 0.978302 | nan | nan | nan | nan | 0.535885 | nan | nan | nan | nan | nan | nan | 0.742857 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.555556 | nan | 0.121908 | nan | 0.583333 | 0.65 | 0.0641026 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0384615 | nan | 0.00383387 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.901099 | nan | nan | nan | nan | nan | nan | 0.377778 | nan | nan | nan | 0.998684 | 0.563636 | nan | nan | nan | 0.296296 | 0.931034 | nan | nan | nan | 0.583333 | nan | nan | 0.00420698 | nan | 0.428571 | nan | nan | 0.241935 | 0.647887 | 0.615385 | 0.411765 | 0.109635 | 0.0209476 | nan | 0.778761 | nan | nan | nan | nan | nan | nan | nan | 0.231023 | 0.0918367 | 0.0124206 | 0.454545 | 0.0135551 | 0.307692 | nan | nan | nan | 0.887522 | nan | nan | 0.00605327 | 0.00378788 | nan | nan | nan | 0.677419 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.416667 | nan | 0.0882353 | nan | 0.987642 | nan | 0.5625 | nan | 0.68 | nan | 0.14 | 0.140741 | nan | nan | 0.257143 | nan | 0.803738 | 0.736842 | nan | 0.997872 | nan | nan | nan | nan | 0.405797 | nan | nan | nan | nan | nan | 0.894366 | nan | 0.3 | 0.714286 | 0.155556 | 0.463415 | nan | nan | nan | nan | nan | nan | 0.418605 | 0.988372 | 0.99859 | nan | nan | nan | nan | nan | 0.717647 | 0.5 | nan | nan | 0.7 | nan | nan | nan | 0.108553 | 0.970238 | 0.306897 | 0.078125 | 0.744201 | nan | 0.28 | 0.72 | 0.969152 | nan | nan | 0.894118 | 0.277778 | nan | nan | nan | nan | 0.96805 | nan | nan | nan | nan | 0.0334928 | 0.00180614 | nan | nan | nan | 0.2 | 0.857143 | 0.285714 | nan | 0.75 | 0.0114123 | nan | 0.384615 | nan | nan | nan | nan | nan | nan | nan | 0.877115 | 0.923077 | 0.5 | 0.38573 | 0.5 | 0.263158 | nan | 0.423077 | 0.0472727 | nan | nan | 0.866667 | nan | 0.894231 | 0.146341 | 0.997198 | nan | 0.196721 | nan | nan | nan | nan | nan | 0.983158 | nan | nan | nan | nan | nan | nan | nan | 0.0589849 | 0.842663 | 0.880952 | 0.0560748 | nan | 0.00088924 | 0.92 | nan | nan | nan | nan | nan | 0.153846 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0103257 | nan | nan | 0.87037 | nan | nan | nan | 0.375887 | nan | 0.615385 | nan | 0.642857 | nan | 0.724551 | nan | 0.736842 | nan | 0.996499 | nan | nan | nan | nan | 0.0505415 | nan | nan | 0.571429 | nan | nan | 0.0186214 | 0.576923 | nan | nan | nan | nan | nan | 0.0412037 | 0.461538 | 0.408602 | nan | 0.193548 | 0.189189 | 0.0893617 | nan | nan | 0.00245237 | nan | nan | nan | nan | nan | nan | 0.75 | nan | nan | 0.6 | nan | 0.0471698 | nan | 0.315789 | 0.590909 | nan | 0.384615 | nan | nan | 0.142857 | 0.794872 | 0.792308 | 0.968174 | 0.314286 | nan | nan | nan | nan | nan | 0.5 | 0.947712 | 0.704142 | 0.848485 | 0.621622 | nan | 0.924242 | nan | nan | nan | nan | 0.636364 | 0.454545 | nan | 0.331058 | nan | 0.388889 | nan | 0.981949 | 0.444444 | nan | 0.5 | nan | 0.891089 | 0.994004 | 0.5 | nan | nan | nan | nan | nan | 0.388889 | 0.00121335 | nan | nan | 0.998647 | nan | 0.375 | nan | nan | 0.00238537 | nan | nan | nan | nan | nan | 0.821429 | nan | 0.0363748 | nan | 0.341463 | nan | nan | 0.147541 | nan | nan | nan | 0.00197759 | nan | nan | nan | nan | 0.942623 | nan | nan | nan | nan | nan | nan | 0.915888 | nan | 0.995202 | nan | nan | nan | nan | 0.88172 | nan | 0.80597 | nan | nan | nan | nan | nan | nan | nan | 0.0714286 | 0.00654695 | nan | 0.913978 | nan | nan | nan | nan | 0.000709052 | nan | 0.510989 | nan | 0.00196625 | 0.277778 | 0.186047 | nan | 0.00138906 | 0.0431655 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.791667 | nan | nan | nan | nan | nan | 0.982274 | 0.0384986 | nan | 0.722222 | nan | nan | nan | nan | nan | 0.384615 | nan | nan | nan | nan | nan | 0.605556 | nan | nan | 0.383333 | nan | 0.0681818 | 0.333333 | nan | 0.372093 | nan | 0.539568 | nan | nan | 0.333333 | 0.923954 | nan | 0.0226131 | 0.428571 | 0.0862069 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.236842 | nan | nan | 0.170732 | 0.0735901 | nan | nan | nan | 0.492228 | 0.666667 | nan | nan | nan | 0.333333 | nan | 0.927632 | nan | 0.10104 | nan | nan | nan | nan | nan | nan | nan | 0.294118 | nan | nan | 0.0729927 | 0.0694444 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.609375 | nan | nan | nan | 0.0209205 | nan | nan | nan | nan | 0.294118 | nan | nan | 0.538462 | 0.68254 | nan | nan | nan | nan | nan | 0.454545 | nan | nan | nan | 0.5 | nan | nan | nan | nan | 0.0530165 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.00257749 | nan | 0.0183328 | 0.6 | 0.342857 | nan | nan | 0.896104 | 0.69967 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.318182 | 0.0633609 | nan | nan | nan | nan | 0.368421 | nan | nan | 0.0792079 | nan | nan | nan | nan | 0.454545 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.1 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.846154 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.631579 | nan | 0.00524017 | nan | nan | nan | nan | 0.0630252 | 0.0803571 | nan | nan | 0.52381 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.00120627 | 0.0223285 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.172414 | nan | nan | nan | nan | nan | 0.0201378 | nan | 0.00273734 | nan | 0.00198491 | 0.157303 | 0.0483871 | 0.205128 | 0.00997506 | nan | 0.0201956 | 0.895706 | nan | 0.00868936 | nan | nan | nan | 0.0942029 | nan | 0.545455 | nan | 0.0363621 | 0.545455 | 0.214286 | 0.157895 | nan | 0.411765 | nan | 0.225806 | nan | 0.482759 | 0.5 | nan | 0.2 | nan | 0.986842 | 0.990728 | 0.128748 | nan | 0.611111 | nan | 0.628821 | nan | nan | nan | 0.0294118 | 0.857143 | 0.953545 | 0.74359 | nan | nan | nan | nan | 0.892308 | nan | nan | 0.0365854 | nan | nan | nan | nan | 0.403226 | nan | 0.888889 | nan | nan | nan | nan | nan | nan | 0.482759 | nan | nan | nan | 0.303167 | nan | nan | nan | nan | nan | nan | nan | nan | 0.0144092 | nan | nan | nan | nan | nan | nan | 0.384615 | 0.153846 | 0.545455 | nan | nan | nan | nan | nan | nan | 0.96087 | nan | nan | nan | nan | nan | 0.301587 | nan | 0.0874126 | nan | nan | 0.69697 | 0.569444 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0013367 | nan | nan | nan | 0.185714 | 0.0246305 | nan | nan | nan | nan | 0.810811 | nan | nan | nan | 0.0694444 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.941558 | 0.625 | nan | nan | nan | 0.333333 | nan | nan | nan | 0.285124 | nan | nan | nan | 0.026767 | nan | nan | 0.871429 | 0.190141 | nan | 0.667622 | 0.507246 | 0.00314861 | 0.0454545 | 0.0406654 | nan | nan | nan | nan | 0.968553 | 0.863636 | nan | nan | 0.172414 | nan | nan | nan | 0.0510204 | nan | nan | nan | nan | nan | nan | nan | 0.571429 | nan | nan | 0.5 | nan | nan | nan | nan | nan | nan | nan | 0.113924 | nan | nan | nan | nan | 0.0387097 | nan | 0.461538 | nan | nan | 0.84375 | nan | 0.369403 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.794872 | 0.24 | nan | nan | nan | 0.00193199 | nan | 0.934109 | nan | 0.232274 | nan | nan | nan | nan | nan | nan | 0.571429 | 0.00106651 | nan | nan | nan | nan | nan | nan | nan | nan | 0.226277 | 0.00610288 | nan | nan | nan | nan | nan | nan | nan | nan | 0.6 | nan | nan | nan | nan | 0.551724 | 0.00825877 | nan | nan | 0.00144966 | nan | nan | nan | nan | nan | 0.0619469 | 0.79661 | nan | nan | 0.636364 | nan | nan | nan | nan | nan | nan | nan | nan | 0.714286 | nan | nan | 0.161999 | nan | nan | 0.125 | 0.0924855 | 0.533333 | 0.67033 | nan | 0.0576923 | 0.0561798 | 0.5 | 0.917282 | 0.974603 | nan | nan | nan | nan | nan | nan | 0.37037 | 0.00213302 | nan | nan | 0.000966099 | nan | 0.862069 | nan | nan | 0.00164339 | nan | 0.00226586 | 0.99532 | nan | nan | 0.571429 | nan | nan | nan | 0.42 | nan | 0.311111 | nan | nan | nan | 0.785714 | 0.983982 | 0.9455 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.829545 | nan | nan | nan | nan | nan | 0.983607 | 0.976471 | nan | nan | nan | 0.705882 | 0.166667 | 0.00199033 | nan | nan | nan | nan | nan | 0.0181598 | nan | 0.0487189 | 0.76 | nan | nan | 0.208333 | nan | 0.0504587 | nan | 0.417722 | nan | nan | 0.00323826 | nan | nan | 0.806452 | nan | nan | nan | nan | nan | nan | nan | 0.0909091 | 0.702703 | 0.208333 | nan | 0.986002 | nan | nan | nan | nan | nan | 0.98255 | nan | 0.843137 | 0.621212 | nan | nan | nan | 0.561743 | 0.00135208 | nan | 0.95283 | 0.230769 | 0.4375 | nan | 0.088968 | 0.828571 | nan | nan | nan | nan | 0.0648148 | 0.766667 | 0.890547 | 0.951724 | nan | 0.888889 | nan | nan | nan | 0.0167464 | 0.00938967 | 0.870861 | nan | nan | nan | 0.261438 | nan | nan | nan | 0.583333 | 0.0275229 | 0.861111 | nan | 0.235294 | nan | 0.375 | 0.0444444 | 0.240741 | 0.217391 | 0.813953 | 0.986777 | nan | 0.1875 | nan | nan | nan | nan | nan | nan | 0.83871 | nan | nan | nan | nan | 0.09792 | nan | nan | 0.0211657 | 0.877076 | nan | 0.283721 | nan | nan | nan | nan | nan | nan | 0.485714 | nan | nan | nan | 0.371429 | nan | nan | 0.888889 | 0.807692 | nan | 0.988584 | nan | 0.333333 | nan | nan | 0.352941 | 0.955497 | 0.0588235 | nan | nan | nan | 0.340909 | nan | nan | nan | nan | nan | nan | 0.461538 | nan | 0.853093 | nan | nan | nan | 0.837838 | nan | 0.1875 | nan | 0.975993 | nan | 0.422222 | nan | nan | 0.139535 | nan | 0.0705128 | nan | nan | nan | nan | 0.939024 | nan | nan | nan | nan | nan | nan | 0.588235 | nan | nan | nan | 0.416667 | 0.298851 | 0.652174 | 0.5 | nan | nan | nan | 0.378378 | nan | nan | 0.714286 | 0.0390625 | nan | 0.281984 | nan | nan | nan | 0.230769 | 0.0348387 | 0.647059 | nan | 0.268657 | 0.75 | 0.727273 | nan | nan | 0.0486726 | 0.137931 | 0.040724 | nan | 0.77558 | nan | 0.4 | 0.533333 | nan | 0.25 | nan | nan | nan | nan | nan | nan | nan | 0.346154 | 0.0367647 | 0.0454545 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.652174 | nan | nan | nan | nan | nan | nan | 0.992051 | nan | 0.919355 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.384615 | nan | nan | 0.955157 | nan | 0.5 | nan | 0.785714 | nan | nan | 0.895522 | nan | nan | nan | 0.215517 | nan | 0.0491803 | nan | nan | 0.742998 | 0.152871 | 0.269231 | nan | nan | nan | 0.00944882 | 0.700297 | 0.0141509 | nan | nan | 0.382979 | 0.389831 | 0.185185 | nan | nan | nan | nan | nan | 0.979798 | 0.416667 | 0.213483 | nan | nan | nan | nan | 0.997382 | nan | nan | nan | nan | nan | nan | nan | nan | 0.722222 | nan | nan | nan | nan | nan | nan | nan | nan | 0.843137 | 0.961424 | 0.208333 | 0.571429 | 0.932367 | 0.0952381 | nan | 0.142857 | 0.0691102 | 0.953488 | 0.207317 | nan | nan | 0.25 | nan | nan | nan | nan | nan | 0.703125 | nan | nan | 0.998621 | nan | 0.825397 | nan | 0.970711 | 0.25 | 0.35 | nan | nan | nan | nan | nan | 0.454545 | 0.0870712 | nan | 0.0183357 | 0.466667 | 0.230769 | nan | nan | 0.357143 | 0.193906 | nan | 0.380952 | nan | nan | nan | nan | nan | nan | nan | 0.736842 | nan | nan | nan | nan | nan | nan | 0.0579268 | 0.631579 | nan | nan | nan | 0.087372 | nan | 0.0638298 | nan | 0.695652 | nan | 0.676471 | nan | nan | nan | nan | 0.126984 | nan | 0.175 | nan | nan | nan | 0.965753 | nan | nan | 0.849057 | 0.583333 | nan | nan | nan | nan | nan | 0.14 | nan | nan | nan | nan | nan | 0.5 | nan | 0.844444 | 0.359375 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0153061 | nan | nan | 0.0421585 | 0.102857 | 0.651163 | nan | nan | nan | nan | 0.869001 | 0.816327 | nan | 0.981366 | nan | nan | nan | nan | nan | nan | nan | nan | 0.65625 | 0.25 | 0.538462 | 0.72 | nan | 0.169231 | nan | 0.642857 | 0.0833333 | nan | nan | 0.8 | nan | 0.879678 | 0.521739 | 0.3375 | nan | nan | nan | nan | 0.00798176 | nan | 0.269231 | nan | 0.967914 | nan | nan | nan | nan | nan | nan | 0.962264 | 0.962054 | 0.8 | nan | 0.983568 | nan | nan | 0.965014 | nan | nan | nan | nan | 0.454545 | nan | nan | 0.192308 | nan | nan | 0.95189 | 0.982301 | nan | nan | nan | 0.555556 | nan | nan | 0.977707 | nan | nan | nan | 0.5 | nan | nan | nan | nan | nan | 0.99214 | nan | nan | nan | nan | nan | 0.101124 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.951456 | nan | nan | nan | 0.295455 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.233333 | nan | nan | nan | 0.00251451 | nan | nan | nan | nan | nan | nan | nan | nan | 0.277778 | 0.786885 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.189781 | nan | 0.882353 | 0.642857 | nan | 0.780488 | nan | 0.00955414 | nan | 0.25 | 0.225806 | nan | nan | nan | nan | nan | 0.273973 | 0.129412 | nan | 0.243243 | nan | nan | nan | nan | 0.178571 | nan | nan | nan | 0.123711 | nan | nan | 0.375 | 0.186047 | 0.263158 | nan | nan | nan | 0.162791 | nan | nan | 0.826667 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0494037 | nan | 0.215686 | nan | nan | nan | nan | 0.454545 | nan | nan | nan | nan | nan | nan | 0.0251762 | nan | 0.865854 | 0.69697 | 0.652174 | nan | nan | 0.976087 | nan | 0.997881 | nan | 0.254902 | nan | nan | nan | 0.4 | nan | nan | nan | nan | nan | 0.454545 | nan | 0.35 | 0.267123 | 0.00340252 | 0.993626 | nan | 0.971751 | nan | nan | 0.997472 | nan | nan | nan | 0.357143 | nan | nan | nan | nan | nan | 0.205128 | 0.3375 | nan | 0.305556 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.411765 | nan | nan | nan | nan | nan | nan | nan | 0.368421 | nan | nan | 0.00929235 | 0.0584795 | 0.0148305 | 0.5 | 0.369231 | 0.242424 | nan | 0.0769231 | nan | nan | 0.619718 | 0.634615 | 0.04375 | nan | nan | 0.0166113 | 0.45 | nan | 0.454545 | nan | 0.825 | 0.816092 | 0.114754 | 0.0189873 | nan | nan | 0.375 | nan | 0.333333 | 0.0518135 | nan | nan | 0.087156 | nan | 0.0107527 | 0.247788 | 0.625 | 0.333333 | 0.162393 | 0.943966 | 0.944954 | nan | nan | nan | nan | nan | 0.173469 | 0.428571 | 0.121951 | nan | nan | 0.970464 | nan | nan | 0.513889 | 0.666667 | 0.292683 | nan | nan | 0.268817 | nan | nan | nan | nan | nan | 0.470588 | 0.968553 | nan | 0.0651629 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.304348 | nan | nan | 0.243902 | nan | 0.545455 | nan | nan | nan | nan | nan | nan | nan | nan | 0.172414 | nan | nan | 0.0384615 | nan | nan | 0.0456621 | nan | nan | nan | 0.0964706 | 0.36646 | 0.0246508 | 0.992837 | nan | 0.0833333 | 0.5 | 0.068314 | nan | 0.998794 | 0.465753 | nan | nan | nan | nan | nan | 0.160448 | 0.272727 | 0.421053 | nan | 0.956811 | 0.740741 | nan | nan | nan | nan | nan | 0.0977444 | 0.0375 | 0.512821 | nan | 0.147718 | 0.0619469 | nan | nan | nan | nan | 0.0495356 | nan | 0.968944 | nan | nan | nan | nan | nan | 0.7 | nan | nan | nan | 0.7375 | nan | nan | 0.720339 | 0.791667 | nan | 0.2 | 0.428571 | nan | nan | 0.554167 | nan | 0.561404 | nan | nan | 0.516484 | 0.416667 | 0.949367 | nan | nan | 0.179641 | 0.285714 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.00891089 | nan | nan | 0.00403633 | 0.893617 | nan | 0.533214 | 0.583333 | 0.101604 | 0.238095 | 0.0746154 | nan | nan | nan | nan | 0.844444 | 0.05 | nan | nan | 0.642857 | nan | nan | nan | nan | nan | nan | nan | nan | 0.456522 | nan | nan | nan | nan | nan | 0.658537 | nan | nan | nan | nan | nan | nan | 0.222222 | 0.212766 | 0.5 | nan | nan | nan | nan | nan | 0.15 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.997392 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0401107 | 0.466667 | nan | 0.0759494 | 0.746667 | nan | 0.998888 | 0.204598 | 0.2 | 0.0972222 | nan | nan | nan | 0.333333 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0607595 | nan | 0.384615 | 0.368421 | 0.734234 | 0.0605355 | nan | nan | 0.87037 | 0.721627 | nan | nan | nan | nan | 0.0128505 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.45 | nan | nan | 0.6875 | 0.642857 | 0.375 | nan | 0.557692 | 0.578544 | 0.387064 | 0.0734463 | nan | 0.631579 | nan | nan | nan | 0.385714 | nan | 0.827586 | nan | nan | 0.00240595 | nan | nan | nan | nan | nan | nan | 0.258065 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.940594 | 0.941935 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.00699301 | nan | nan | nan | nan | nan | nan | 0.148148 | nan | nan | nan | nan | nan | 0.692308 | nan | nan | 0.139706 | nan | nan | 0.00114742 | 0.191176 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.999094 | nan | 0.5 | nan | 0.0256704 | nan | nan | nan | nan | nan | 0.47561 | nan | nan | 0.973294 | 0.977178 | nan | nan | nan | nan | 0.52381 | nan | nan | nan | nan | 0.133333 | nan | nan | nan | nan | nan | 0.212121 | nan | nan | nan | nan | nan | nan | nan | 0.45 | nan | nan | nan | nan | nan | 0.5 | nan | 0.0257353 | 0.774194 | nan | 0.0604135 | nan | nan | 0.574074 | nan | 0.678571 | 0.428571 | nan | nan | 0.05 | nan | nan | nan | nan | 0.525 | 0.866607 | 0.980516 | nan | nan | nan | nan | nan | 0.0916031 | nan | nan | nan | nan | nan | 0.583333 | nan | nan | nan | 0.601124 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.73913 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.439394 | 0.0229358 | 0.00414594 | nan | nan | nan | 0.915254 | nan | nan | nan | nan | nan | nan | 0.333333 | nan | 0.769231 | nan | 0.28125 | 0.850877 | nan | nan | nan | nan | nan | nan | nan | nan | 0.025188 | 0.00130484 | 0.131148 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.998893 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0570934 | nan | 0.000782243 | nan | nan | nan | 0.998211 | 0.454545 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.755725 | 0.979094 | nan | 0.214286 | nan | nan | 0.315789 | nan | nan | 0.375 | nan | nan | nan | nan | nan | 0.413793 | 0.135593 | nan | nan | nan | nan | 0.65 | 0.836735 | nan | nan | nan | nan | 0.294118 | 0.818857 | 0.555556 | 0.569231 | 0.8 | 0.8125 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.992501 | nan | nan | 0.727273 | nan | 0.52 | nan | nan | 0.176471 | 0.142857 | nan | 0.849315 | nan | 0.0876827 | nan | nan | 0.123377 | 0.0314465 | 0.0873786 | nan | 0.4 | nan | nan | nan | 0.992878 | nan | nan | nan | nan | nan | 0.133333 | 0.764706 | nan | nan | nan | nan | nan | nan | nan | 0.666667 | nan | nan | nan | nan | nan | 0.607143 | nan | nan | nan | 0.0824742 | nan | nan | nan | 0.545455 | 0.733871 | nan | 0.0314961 | nan | nan | 0.363636 | 0.277778 | 0.163265 | nan | nan | nan | nan | nan | nan | nan | 0.996122 | nan | 0.035 | nan | 0.75 | 0.73913 | nan | nan | 0.315789 | nan | nan | 0.00625244 | 0.942708 | nan | nan | nan | nan | nan | 0.891304 | nan | nan | nan | nan | 0.851064 | nan | nan | nan | nan | nan | 0.692308 | nan | nan | nan | 0.78125 | nan | nan | nan | nan | nan | 0.32 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.016129 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.997864 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.901163 | nan | nan | nan | 0.654709 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.684211 | nan | nan | 0.970506 | nan | nan | 0.851661 | 0.177083 | 0.631148 | nan | 0.813725 | 0.92827 | nan | 0.893617 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.451613 | nan | nan | 0.5625 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0864198 | nan | 0.526316 | 0.00547335 | nan | nan | nan | 0.268519 | nan | 0.00120168 | nan | nan | nan | nan | nan | nan | 0.391304 | nan | nan | nan | 0.00118455 | 0.631579 | nan | nan | nan | nan | nan | 0.7 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.758621 | 0.0101188 | nan | nan | 0.00672318 | nan | nan | nan | nan | 0.00243665 | nan | 0.0112391 | 0.0352941 | nan | nan | 0.4 | 0.935897 | nan | nan | nan | nan | nan | nan | 0.615385 | 0.390625 | nan | 0.127596 | nan | nan | 0.357143 | nan | nan | nan | 0.8 | nan | nan | 0.0357143 | nan | 0.215686 | nan | nan | nan | nan | 0.994364 | nan | 0.921421 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.642857 | 0.403846 | nan | nan | nan | 0.75 | nan | nan | 0.787879 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.996088 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.206827 | 0.75 | nan | nan | nan | nan | nan | 0.181818 | 0.357143 | 0.959707 | nan | nan | nan | nan | nan | nan | 0.545455 | nan | nan | 0.26087 | nan | nan | 0.705882 | nan | nan | 0.0952381 | nan | nan | 0.545455 | nan | 0.529801 | nan | nan | nan | nan | nan | nan | nan | nan | 0.0314506 | 0.162791 | 0.998854 | nan | 0.596154 | nan | 0.0827962 | 0.340278 | nan | 0.52381 | nan | 0.00250749 | nan | nan | nan | nan | nan | 0.666667 | nan | nan | 0.240741 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0973451 | nan | nan | nan | 0.5 | nan | nan | 0.820833 | nan | 0.318182 | nan | nan | nan | 0.228571 | nan | nan | nan | nan | nan | 0.0326797 | nan | nan | nan | nan | nan | nan | 0.611111 | nan | nan | nan | 0.25 | 0.998305 | nan | nan | 0.35961 | nan | nan | 0.00269469 | nan | nan | 0.26087 | nan | 0.207602 | nan | 0.375 | 0.0101246 | nan | nan | 0.948454 | nan | nan | 0.133333 | nan | nan | 0.681818 | nan | nan | 0.983376 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.789474 | 0.416667 | nan | nan | 0.0105042 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.735955 | nan | nan | nan | 0.637755 | nan | nan | nan | 0.177419 | nan | nan | nan | nan | nan | nan | nan | nan | 0.466667 | 0.615385 | 0.876847 | nan | nan | nan | nan | 0.5 | nan | nan | 0.0697674 | nan | nan | nan | nan | nan | nan | 0.416667 | nan | 0.545455 | nan | nan | nan | nan | 0.611111 | 0.416667 | nan | nan | nan | nan | nan | nan | 0.370213 | nan | nan | nan | nan | nan | nan | 0.538462 | nan | nan | nan | 0.384615 | nan | 0.477273 | nan | nan | nan | 0.227273 | 0.837398 | nan | 0.490566 | nan | nan | 0.1 | nan | nan | nan | 0.238095 | 0.00747986 | 0.461538 | 0.0561798 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.295455 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.155556 | 0.5 | 0.652174 | nan | nan | nan | nan | nan | 0.5 | nan | nan | nan | 0.468085 | nan | nan | nan | nan | nan | 0.998662 | nan | nan | nan | nan | nan | nan | 0.00603015 | nan | 0.541667 | nan | nan | nan | nan | nan | 0.998764 | nan | nan | nan | nan | 0.000936988 | nan | nan | 0.416667 | nan | nan | nan | nan | nan | 0.298246 | 0.830508 | nan | nan | nan | nan | 0.763158 | nan | nan | nan | nan | nan | 0.486486 | nan | 0.357143 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0217628 | nan | nan | nan | 0.268293 | 0.263158 | nan | nan | nan | nan | 0.952756 | nan | nan | nan | nan | nan | 0.3 | 0.979522 | nan | nan | nan | nan | nan | nan | nan | nan | 0.997737 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.583333 | nan | nan | nan | nan | nan | nan | nan | 0.00112885 | nan | nan | nan | nan | nan | nan | 0.5 | 0.0539359 | nan | 0.4 | nan | nan | nan | nan | nan | 0.211538 | nan | nan | 0.714286 | nan | nan | nan | 0.357143 | nan | nan | nan | 0.502703 | nan | nan | 0.0318471 | 0.0234192 | 0.0961538 | nan | 0.666667 | nan | 0.225352 | 0.666667 | nan | 0.111111 | nan | nan | 0.5 | 0.0346021 | nan | nan | 0.855814 | 0.00697272 | 0.0491803 | 0.0709677 | 0.0321101 | 0.194366 | 0.533333 | 0.993774 | nan | nan | nan | nan | 0.00314081 | nan | nan | nan | nan | 0.28 | 0.86901 | nan | nan | nan | nan | nan | 0.937063 | 0.5 | nan | nan | nan | nan | 0.375 | nan | nan | nan | nan | nan | 0.815126 | 0.922466 | 0.814815 | nan | nan | 0.48 | 0.526316 | nan | nan | nan | 0.225352 | 0.25 | nan | nan | 0.121951 | 0.762136 | 0.488589 | 0.859375 | nan | nan | 0.448276 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.34375 | 0.428571 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.00172236 | nan | 0.999591 | nan | 0.314607 | 0.48 | nan | 0.622642 | 0.25 | nan | 0.538462 | nan | 0.375 | 0.416667 | nan | 0.678571 | nan | 0.208333 | nan | nan | 0.384615 | 0.684211 | nan | nan | nan | nan | nan | 0.00272331 | 0.026087 | nan | nan | nan | nan | nan | nan | 0.15625 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.23871 | nan | nan | nan | nan | nan | nan | 0.868421 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0518519 | 0.653846 | 0.137931 | nan | nan | nan | nan | nan | nan | nan | 0.0248027 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.00218245 | nan | nan | nan | nan | nan | nan | 0.0990991 | nan | nan | 0.423077 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.00188147 | 0.113208 | 0.55 | nan | nan | nan | 0.0967742 | nan | nan | 0.12766 | 0.483871 | nan | 0.25 | 0.75 | nan | nan | 0.413793 | nan | nan | nan | 0.730769 | nan | nan | 0.5623 | nan | nan | nan | nan | 0.934985 | 0.315789 | 0.210935 | nan | nan | 0.727273 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0909091 | nan | nan | 0.971609 | nan | nan | nan | 0.0869565 | nan | 0.0398614 | 0.46875 | nan | 0.41791 | nan | 0.590909 | 0.468966 | nan | 0.928571 | nan | 0.85349 | nan | 0.730769 | nan | nan | nan | nan | nan | 0.125 | nan | nan | 0.952055 | nan | 0.625 | 0.153846 | nan | nan | nan | 0.347826 | nan | nan | nan | nan | nan | nan | nan | nan | 0.290323 | nan | 0.384956 | nan | nan | nan | nan | nan | 0.538462 | 0.75 | nan | 0.739163 | nan | 0.351351 | 0.5 | nan | nan | nan | nan | nan | nan | 0.374932 | 0.666667 | nan | nan | nan | nan | nan | nan | nan | 0.0300752 | nan | nan | nan | nan | nan | nan | 0.996597 | 0.384615 | nan | nan | 0.578947 | nan | nan | 0.0645514 | nan | 0.5 | nan | nan | 0.545455 | 0.534483 | 0.962687 | nan | nan | nan | 0.217391 | 0.966346 | 0.930397 | nan | 0.00776461 | 0.446809 | 0.139535 | nan | nan | nan | 0.931298 | nan | 0.647059 | nan | nan | nan | 0.681818 | nan | nan | nan | nan | nan | nan | nan | nan | 0.00974026 | nan | nan | nan | nan | 0.4375 | 0.722222 | 0.54717 | nan | nan | 0.24374 | 0.333333 | nan | nan | 0.766376 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.642857 | nan | nan | nan | nan | nan | nan | 0.108108 | nan | 0.192308 | 0.529412 | 0.993162 | nan | 0.461538 | nan | nan | 0.996665 | nan | nan | nan | nan | nan | nan | 0.533333 | nan | nan | 0.2 | nan | 0.5 | 0.0395738 | 0.2 | nan | 0.732283 | 0.864078 | nan | nan | nan | 0.190476 | 0.545455 | 0.347826 | nan | nan | nan | 0.545455 | 0.666667 | 0.416667 | nan | 0.724138 | nan | nan | nan | 0.533333 | nan | nan | nan | nan | nan | nan | 0.454545 | 0.391509 | nan | 0.779661 | 0.37931 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0121212 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.947598 | nan | nan | nan | nan | nan | nan | 0.0253521 | 0.278481 | 0.114232 | nan | nan | nan | 0.0621891 | nan | nan | 0.7 | nan | nan | 0.0819912 | 0.977528 | 0.345912 | nan | 0.655462 | 0.854839 | nan | nan | nan | 0.689655 | 0.905594 | nan | nan | nan | nan | nan | nan | 0.0958904 | nan | nan | nan | 0.936498 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.648438 | nan | nan | nan | nan | nan | 0.268072 | 0.0333952 | nan | 0.827586 | 0.217391 | 0.838095 | 0.244898 | 0.242424 | nan | nan | nan | 0.83871 | nan | 0.146667 | nan | nan | nan | nan | nan | 0.382353 | nan | 0.0249084 | 0.65812 | nan | nan | nan | 0.511628 | nan | 0.571429 | 0.272727 | nan | nan | nan | nan | 0.875 | 0.84375 | 0.277946 | 0.5 | 0.296875 | 0.372727 | 0.144578 | nan | nan | nan | nan | nan | nan | nan | nan | 0.0644068 | nan | nan | nan | 0.558824 | 0.27027 | 0.227273 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.307692 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.473684 | nan | nan | 0.00890869 | nan | nan | 0.857143 | nan | nan | nan | 0.778626 | 0.116279 | nan | 0.283582 | nan | 0.870504 | 0.428571 | nan | 0.571429 | nan | 0.037931 | nan | 0.410714 | 0.417062 | nan | nan | nan | nan | nan | nan | nan | nan | 0.205128 | 0.0166667 | nan | 0.806452 | 0.533835 | nan | nan | nan | nan | nan | nan | nan | nan | 0.225 | 0.15625 | 0.25 | 0.4375 | nan | nan | nan | nan | nan | nan | 0.583333 | nan | 0.884058 | nan | 0.733925 | nan | nan | 0.836957 | 0.975 | 0.269504 | 0.121951 | 0.128571 | 0.769841 | nan | nan | 0.45 | 0.385656 | 0.274336 | 0.122449 | 0.350515 | nan | 0.0925926 | nan | nan | 0.192308 | nan | nan | nan | 0.00131492 | nan | nan | nan | nan | 0.552017 | nan | 0.980392 | nan | nan | nan | nan | 0.305556 | 0.615385 | 0.0255319 | nan | nan | nan | nan | nan | nan | nan | nan | 0.102041 | 0.104167 | nan | nan | nan | nan | 0.568966 | 0.0128866 | nan | nan | nan | nan | nan | nan | nan | 0.529412 | nan | nan | nan | 0.0064317 | 0.526316 | nan | 0.143885 | nan | nan | 0.642857 | nan | nan | nan | 0.938272 | nan | nan | nan | 0.0134875 | nan | nan | nan | nan | nan | nan | nan | 0.806452 | nan | 0.0115132 | nan | nan | nan | nan | nan | 0.0946746 | nan | 0.580645 | 0.107143 | nan | nan | 0.328864 | nan | nan | nan | nan | nan | nan | nan | nan | 0.784091 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0484848 | 0.592593 | nan | 0.75 | 0.35311 | nan | 0.25 | 0.246241 | nan | 0.8125 | nan | nan | nan | nan | nan | 0.0131086 | 0.364798 | 0.666667 | 0.879032 | 0.0797227 | nan | nan | nan | nan | 0.416667 | nan | nan | nan | nan | nan | nan | nan | nan | 0.368421 | nan | nan | 0.0304183 | nan | nan | nan | nan | 0.574468 | 0.241379 | nan | nan | nan | nan | nan | nan | nan | nan | 0.0736773 | nan | 0.976471 | 0.0754717 | 0.0582524 | 0.0311987 | 0.170213 | nan | nan | nan | 0.603448 | nan | nan | nan | 0.0175816 | 0.169801 | 0.0722135 | nan | nan | 0.939106 | 0.923077 | 0.98801 | 0.244813 | 0.474576 | 0.976717 | 0.723077 | nan | 0.805556 | 0.968586 | nan | nan | nan | nan | nan | 0.415094 | nan | nan | nan | nan | nan | nan | nan | 0.722222 | nan | 0.48 | nan | nan | 0.998523 | nan | 0.615385 | 0.3 | 0.687549 | nan | nan | nan | nan | nan | 0.108696 | 0.147541 | 0.5 | 0.952038 | 0.847826 | nan | nan | nan | nan | 0.678571 | nan | nan | nan | 0.583333 | 0.538206 | nan | nan | nan | nan | nan | nan | 0.0228137 | nan | nan | 0.998682 | 0.311111 | 0.686275 | nan | 0.357143 | nan | nan | 0.0769231 | nan | 0.0149254 | nan | 0.586207 | 0.125874 | nan | nan | nan | 0.0011168 | nan | nan | 0.384615 | nan | 0.968553 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.921053 | nan | 0.642857 | nan | nan | 0.318182 | nan | 0.333333 | nan | nan | nan | nan | 0.997771 | nan | nan | nan | nan | nan | 0.852941 | nan | nan | nan | 0.615385 | 0.942339 | nan | 0.5 | nan | nan | nan | nan | nan | 0.998708 | nan | nan | nan | nan | 0.222812 | 0.108696 | 0.571429 | nan | nan | 0.895833 | 0.928155 | nan | nan | 0.409091 | nan | nan | nan | 0.731707 | nan | nan | 0.709677 | 0.152174 | 0.778523 | 0.00319829 | nan | nan | nan | 0.00208572 | 0.294118 | nan | nan | 0.588235 | nan | 0.991124 | nan | nan | nan | nan | nan | nan | 0.222656 | nan | nan | 0.839286 | 0.411765 | 0.64 | nan | nan | 0.795918 | 0.843373 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.346535 | nan | nan | nan | 0.672269 | nan | nan | 0.5625 | 0.185185 | nan | nan | 0.555556 | nan | nan | 0.47619 | nan | 0.900709 | 0.3125 | nan | nan | 0.379747 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.360294 | nan | 0.791667 | nan | nan | 0.472222 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.7 | nan | nan | nan | nan | nan | nan | nan | nan | 0.0139116 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.753799 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.978648 | nan | nan | nan | nan | nan | 0.0160256 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.00830737 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.74026 | 0.287879 | nan | nan | 0.275862 | 0.690476 | nan | nan | nan | nan | nan | nan | 0.310078 | 0.663158 | nan | nan | nan | nan | nan | nan | 0.970988 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.75 | nan | nan | 0.278351 | nan | nan | 0.606061 | 0.663435 | nan | 0.5 | nan | nan | nan | nan | nan | nan | nan | nan | 0.64 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.722222 | nan | 0.4 | nan | nan | 0.432432 | nan | nan | 0.598655 | nan | nan | 0.0028463 | 0.816327 | nan | 0.736842 | 0.0704225 | nan | nan | 0.00487508 | 0.977011 | nan | nan | 0.611111 | nan | 0.25 | nan | 0.113208 | 0.62963 | nan | nan | 0.666667 | 0.758621 | nan | nan | nan | nan | 0.818386 | 0.93577 | 0.848485 | nan | 0.54902 | 0.937756 | 0.6 | 0.551913 | nan | 0.882353 | 0.4375 | 0.588235 | nan | 0.885965 | nan | 0.0666667 | nan | nan | nan | 0.999068 | nan | nan | nan | 0.649254 | nan | 0.695652 | nan | nan | nan | nan | nan | nan | nan | nan | 0.666667 | 0.647059 | 0.0878378 | nan | nan | nan | nan | nan | nan | nan | 0.999193 | nan | 0.630952 | 0.0662768 | nan | nan | nan | 0.5 | 0.870968 | 0.142428 | 0.294118 | nan | nan | 0.863248 | nan | nan | 0.883621 | nan | nan | 0.948454 | nan | nan | 0.526316 | nan | nan | 0.0990991 | nan | nan | 0.998534 | 0.545455 | nan | nan | nan | nan | nan | nan | 0.125373 | nan | nan | 0.882736 | nan | nan | 0.0434783 | 0.333333 | 0.546763 | nan | nan | 0.946565 | nan | nan | nan | nan | 0.642857 | nan | nan | nan | nan | 0.0958904 | 0.291667 | nan | 0.995235 | 0.192308 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.75 | nan | nan | 0.163934 | nan | nan | nan | 0.163265 | 0.660407 | nan | nan | nan | nan | 0.741935 | nan | nan | nan | 0.981825 | nan | 0.1 | nan | nan | nan | 0.242424 | nan | nan | nan | nan | nan | 0.194444 | nan | nan | nan | nan | nan | 0.178571 | nan | nan | nan | nan | nan | 0.762712 | nan | nan | nan | nan | 0.924658 | 0.642857 | nan | nan | 0.6875 | 0.041958 | nan | nan | nan | nan | nan | 0.216216 | nan | nan | nan | nan | nan | 0.864865 | nan | nan | nan | nan | nan | 0.0825688 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.4375 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.214286 | nan | nan | nan | nan | nan | 0.454545 | nan | 0.0245536 | nan | 0.432836 | 0.861111 | 0.473684 | 0.921348 | nan | 0.731707 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.642857 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.212121 | nan | nan | nan | nan | nan | nan | 0.0383964 | nan | 0.5 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.953668 | nan | nan | nan | nan | nan | 0.647059 | nan | nan | nan | 0.0406824 | 0.391304 | nan | 0.624496 | nan | nan | nan | nan | nan | nan | nan | 0.538462 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.708333 | 0.215686 | nan | 0.460938 | 0.715789 | 0.858156 | nan | 0.851083 | nan | 0.866667 | nan | nan | 0.263158 | nan | nan | nan | 0.809859 | nan | nan | nan | nan | 0.878378 | 0.580645 | 0.76087 | nan | nan | 0.722222 | 0.727273 | nan | nan | 0.09 | nan | 0.59799 | 0.716456 | nan | nan | 0.828571 | 0.763158 | nan | nan | nan | nan | nan | nan | 0.551724 | nan | nan | 0.6875 | nan | 0.481481 | nan | nan | nan | 0.328358 | 0.424242 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.25 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0203562 | nan | nan | 0.666667 | nan | nan | 0.703704 | 0.97076 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.272727 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.5 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.00140193 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.333333 | nan | 0.0980392 | nan | nan | nan | nan | nan | nan | nan | nan | 0.310345 | nan | nan | nan | nan | 0.674797 | nan | nan | nan | 0.00180636 | nan | nan | nan | nan | nan | nan | nan | 0.684211 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.4375 | nan | 0.0992908 | 0.178733 | 0.4 | nan | nan | nan | nan | nan | nan | 0.5 | 0.5 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.376812 | nan | nan | nan | nan | 0.941489 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.25 | 0.974286 | nan | nan | nan | 0.0616438 | nan | 0.333333 | nan | nan | nan | 0.171429 | nan | nan | nan | 0.135135 | nan | nan | nan | 0.486842 | 0.348837 | nan | nan | nan | nan | nan | nan | nan | nan | 0.621622 | nan | nan | 0.535484 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.979675 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.545455 | nan | nan | nan | 0.3125 | nan | 0.99162 | nan | nan | nan | nan | 0.0199294 | 0.0380313 | 0.0336538 | 0.115942 | nan | nan | 0.416058 | nan | nan | 0.00666112 | nan | nan | 0.411765 | nan | nan | nan | 0.404255 | 0.042735 | nan | 0.478261 | 0.246154 | 0.0639535 | nan | nan | nan | nan | nan | nan | nan | 0.00626959 | nan | 0.0495868 | nan | nan | 0.0145631 | nan | nan | 0.0666667 | nan | nan | 0.555556 | nan | nan | nan | nan | 0.588235 | 0.828571 | nan | 0.0378774 | 0.171429 | nan | nan | nan | 0.5 | 0.785714 | nan | nan | 0.583333 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.925676 | nan | nan | nan | nan | 0.147059 | 0.055336 | 0.018 | nan | 0.275862 | nan | nan | nan | nan | nan | nan | nan | 0.796296 | 0.213115 | nan | nan | 0.569444 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0722892 | nan | 0.0813559 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0532319 | nan | nan | 0.711111 | nan | nan | nan | 0.865385 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.999209 | nan | 0.762712 | nan | nan | nan | nan | nan | nan | 0.00162031 | nan | 0.357143 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0176298 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.307692 | nan | nan | nan | 0.060241 | nan | nan | 0.952381 | nan | 0.00679887 | 0.454545 | nan | nan | nan | nan | 0.0736842 | nan | nan | nan | nan | nan | 0.255814 | nan | 0.0132064 | nan | 0.823529 | 0.0216718 | 0.0252525 | 0.00151057 | 0.0465116 | nan | nan | 0.932489 | 0.984479 | nan | nan | nan | nan | nan | nan | nan | 0.0893617 | 0.101351 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.000947927 | nan | nan | nan | nan | nan | nan | nan | 0.995339 | nan | nan | nan | nan | nan | nan | nan | 0.0114943 | nan | nan | 0.761905 | nan | nan | nan | nan | nan | nan | 0.533333 | nan | 0.944538 | nan | 0.463415 | 0.0740741 | nan | nan | nan | nan | nan | 0.859307 | 0.405405 | 0.00652174 | 0.160714 | 0.444444 | 0.27451 | nan | 0.431034 | nan | 0.97541 | nan | nan | nan | nan | nan | 0.0010846 | nan | 0.315789 | nan | nan | nan | nan | nan | nan | nan | nan | 0.357143 | nan | 0.368794 | nan | nan | nan | 0.384615 | nan | nan | nan | 0.538462 | nan | nan | 0.217391 | nan | 0.533333 | nan | nan | 0.529412 | 0.171429 | 0.478261 | nan | nan | nan | nan | nan | 0.5625 | nan | 0.289474 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.375 | 0.4 | nan | nan | 0.642857 | nan | 0.615385 | nan | nan | nan | 0.529412 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.636364 | 0.238095 | nan | 0.554745 | 0.642857 | nan | nan | 0.416667 | nan | 0.363636 | nan | nan | nan | 0.5 | 0.217391 | nan | nan | nan | nan | 0.00103864 | nan | nan | nan | 0.060241 | nan | 0.638889 | 0.566667 | nan | nan | 0.625 | 0.454545 | 0.00692841 | 0.0648148 | 0.6875 | 0.0416667 | nan | 0.0833333 | nan | 0.152778 | 0.0791367 | 0.151515 | 0.0413223 | nan | nan | nan | nan | 0.625 | 0.583333 | nan | 0.125 | nan | nan | nan | 0.00722022 | 0.388889 | nan | nan | nan | nan | nan | 0.619048 | nan | nan | nan | nan | 0.917526 | nan | nan | 0.00888681 | 0.0496454 | 0.340426 | nan | nan | nan | 0.00856164 | nan | 0.151515 | nan | nan | nan | nan | nan | nan | nan | 0.237113 | nan | 0.5 | 0.120301 | 0.165563 | 0.126582 | nan | nan | nan | 0.423077 | nan | nan | nan | 0.901408 | 0.775862 | 0.192308 | 0.170732 | nan | nan | nan | 0.615385 | nan | nan | nan | 0.153691 | 0.461538 | 0.885246 | nan | 0.541667 | 0.5 | 0.151515 | nan | 0.997504 | 0.545455 | 0.997975 | 0.736842 | nan | nan | nan | nan | 0.979282 | nan | 0.246575 | 0.75 | 0.0985915 | 0.107981 | 0.15625 | nan | nan | 0.0945946 | 0.164773 | nan |" ], "text/plain": [ "name Aaden Aadi Aadyn Aalijah Aaliyah Aamari Aamir Aaren \\\n", "year \n", "2011 NaN NaN NaN 0.318182 NaN 0.428571 NaN NaN \n", "2012 NaN 0.081967 NaN 0.333333 0.998729 NaN NaN NaN \n", "2013 NaN 0.078947 NaN 0.500000 0.997705 0.333333 0.042553 NaN \n", "2014 NaN NaN NaN 0.363636 0.998975 0.400000 NaN NaN \n", "2015 NaN NaN NaN 0.413793 0.998967 0.562500 NaN NaN \n", "\n", "name Aarian Aarin ... Zuriel Zyah Zyair Zyaire Zyan \\\n", "year ... \n", "2011 NaN NaN ... 0.131579 NaN 0.187500 0.154206 0.220779 \n", "2012 0.333333 0.187500 ... 0.244186 NaN 0.131148 0.177083 0.161290 \n", "2013 0.208333 NaN ... 0.220930 NaN 0.138889 0.168269 0.260274 \n", "2014 0.350000 0.166667 ... 0.313869 NaN 0.108108 0.123711 0.268657 \n", "2015 0.380952 NaN ... 0.246575 0.75 0.098592 0.107981 0.156250 \n", "\n", "name Zyian Zyien Zyion Zyon Zyree \n", "year \n", "2011 0.571429 NaN 0.153153 0.155080 NaN \n", "2012 0.470588 NaN 0.144737 0.257426 NaN \n", "2013 NaN NaN 0.225352 0.147239 0.5 \n", "2014 NaN NaN 0.197802 0.209790 NaN \n", "2015 NaN NaN 0.094595 0.164773 NaN \n", "\n", "[5 rows x 9025 columns]" ] }, "execution_count": 82, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d = d.unstack(level='name')\n", "d.tail()" ] }, { "cell_type": "markdown", "id": "193b607f", "metadata": {}, "source": [ "There are 9,025 unisex names in the collection, but not all of them are ambiguous with respect to sex to the same degree. As stated before, names with usage ratios that stick around a 50-50 split between boys and girls appear to be the most unisex names. Ideally, we construct a ranking of the unisex names, which represents how close each name is to this 50-50 split. Such a ranking can be constructed by taking the following steps. First, we take the mean of the absolute differences between each name's usage ratio for each year and 0.5. Second, we sort the yielded mean differences in ascending order. Finally, taking the index of this sorted list provides us with a ranking of names according to how unisex they are over time. The following code block implements this computation:" ] }, { "cell_type": "code", "execution_count": 83, "id": "48246788", "metadata": {}, "outputs": [], "source": [ "unisex_ranking = abs(d - 0.5).fillna(0.5).mean().sort_values().index" ] }, { "cell_type": "markdown", "id": "136c5dda", "metadata": {}, "source": [ "Note that after computing the absolute differences, remaining NaN values are filled with the value 0.5. These NaN values represent missing data that are penalized with the maximum absolute difference, i.e., 0.5. All that remains is to create our visualization. Given the way we have structured the data frame `d`, this can be accomplished by first indexing for the *n* most unisex names, and subsequently calling `DataFrame.plot()`:" ] }, { "cell_type": "code", "execution_count": 84, "id": "40f33625", "metadata": {}, "outputs": [ { "data": { "image/png": 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6pNavZn9EO48q0NhSr5Y1BXXHJy3a1BmRzy19b/syHTWpJGE7JzqChsIRQ3VdiQNaY0p7g8yJ18H2NMplN/kjihhGzEQvfCutILPX69Wzzz6rs88+Wy+99JI2btyo3//+9wnrbbnllnrqqae0ww479PtEH330UVVUVOiBBx6QJM2ePVuzZ882XXfq1Kl68sknNWXKlH4fFwCQv1r81jcFXSFDd37SqjsOGSEXNwGAJOty2WEymQEAAICs67ToWzOtxqcljd9mOVcUuHToVkUx6yTPZHbWkxkAgEzqDkX0xw+b9VVzT5XJArf0690r9fiSDm3q7AkKByPSQ4vatdvoQo0q+fZ6lUomc313xLRi35jN+ysryEwmc8SQmv0RrqsW0p7SVl5ern/961964YUXdPLJJ2urrbZSYWGhRo4cqb322ks333yzvvjiC+27774ZOdHCwkLdf//9+vjjj3X55Zdr9913V01Njbxer0pKSjRp0iSdcsopeuSRR/TZZ59pxowZGTkuACB/tSfpiVHXFdGGjhQbbQDDED2ZAQAAgOzrMikpNLLYrWv2qtKPdirXrqMLdOiEIt24f41K4jKyRhQn6clcSCYzACD7PtzojwaYJSkQkW5d2KqVrbGtDQ1Jb6/tjlnmNMu4PWhok8WY79gS60zmzpChrjQGwRopmW0prUzmvk444QSdcMIJaW9//vnn6/zzz3e8/owZM/SXv/wl7eMBAIaOjiRBZqmnpPb4sn5f7oAhgZ7MAAAAQP7oNCmXXeJ1yet26dAJxTp0QrHltiOSZFTRkxkAkAuvrexKWGaVPTx7bZdO26406XrxOoIR037MkjR6c5C51CTILKXXY5kgszXuNgAAg1a7g/5VVjccwHBkNVmTnswAAABA9pmVyy7xOhuuLfC4VGFSCrQXZT0BILl17SGtbg0pYjD5PlO+aQklX2kzrzv2OpZSJrPFmO+3PZnNr5EN3amPFTd0Mb5shdQuAMCglaxctkSQGegrZHGzHuJhCgAAAMi6TpNn2hKLQXEzI4o8ag0kDuZ73VK5TQAaAIa7QNjQnZ+06sONfknS1GqffrFrhaqZoJNV8fOquvuZyVzqc0XLZJcXmE/aSicruclPdoYVMpkBAINWeyD5jUctQWYgKmBRFptMZgAAACD7zDKZi73Og8MjLfoyVxe65XIRZAYAK7PXdkcDzJK0tCmof33TmcMzGjo8/bj8BBwO43ZYZDKPKfl2kgCZzNlBkBkAMGg5yWTe2MFNACBJhmFYBpPpyQwAAABkl2EY/SqXLUkjis0z7qyytwAAPV5bmRhQNusljNSVplCRozUuQ9hpT+b2QESbTMZ8Y4LMGcxkpiezNe44AACDVoeDnsy1nWFFDEON3WF1OAhKA4NBezCi5hRnXoYNyeovhkxmAAAAILv8Yclsrmcq5bJHWpR1pRsOhgvDMFTbGXYcmAJ6rW1PHFMJG1KQ36WUGYahus5wtJ+yJ4VKGq0BQ+E+F0Onf8uBiHlpbWeZzASZM4mezACAQctJJnMgIv387QZt6ozI45JOmlKi07Yry8LZAZkXihi697M2zVnXLUnadXSBLt+lUkUOSuoFbbKVw4xCAQAAAFnVaTHTsySFctlWfZe5u8dw0NAV1k3zW7S6LSSPSzptu1KduE0JpeKRVMAmkFnXFdb4MsJmTsX/HZ66bWlKST6GpJZARDWbJ03Z/ds4ERtkNs+xbUmjv3JDd1iGYfD5YoJMZgDAoOWkJ7MkbersuXkIG9Izyzr1eX1gIE8LGDBvre6KBpgl6aPagJ5d1uFoW7t7fDKZAQAAgOzqtKjMlUomc4VFKdAty8wznIGh5N7P27S6LSSpZ7znyaUdWtoUzPFZYTDYaNLLt5dZn19Yi/87fOqrDgVSHGNq7hP07W9VgjGl317/SnwuZSokHIxI7Q4qag5HBJkBAIOWk0xmM3PWdqu5u6eMNjCYPLk0MaD81mpnPYPsSj7RkxkAAADILrN+zFJqPZmn1fhU6EkcQj98YnHa5wUMBhHD0Kd1iQkEzy9P7LMLxNvQHrJ8jyCzc1Z/h6lqyWSQuU8ms9vlSmniVjKUzDZH3j8AYFCKGIajnsxm5qzr1sbOsOo6w9pzbKH2HleoaTU+uSl5gjzXZTIQZTU4Fc+uXLbDXQAAAADIEKty2aUpDIiX+Nw6ZnJxTGBteo1PU6t9/T4/IJ9ZVeP6JAMBLwx9ZDJnRn9LW/fKVCaz1y3VFMVO1CrzudURzMy/aWN3WBMrkodUN3WG9dEmvyoK3dptdIGKUpg8NhgRZEZeMAxDwYjkcUkeN0EeAMl1hYx+9Zn6anMJpddXden1VV2qLHTrpG1KdPTkksycIJBn7MtlE2UGAAAAsqnLYtJ0cQo9mSXpzKll2qbSp2XNQdUUuXXYhGJ6RmLI4xkW/bGhwzrouKwpqIcWtenr5qC2KPfqe9uXWfb27V1/U2dP8HGr8uEVbgtkKB7fN8hsFriuLnSryUEf5dHFnoQEojKfS5v6f4qSnGUyL24I6Kb5zfJv/tlMqvDq2r2rVGrzOzTYDa/feuSN5c1B3bawRYGwoUDEUCDc0+T9V7tVas+xhbk+PQCDQKb7YLT4I2KOC/JZf2eI2mYyU/EHAAAAyCrLctlpDETvMbZQezCehmGEalyQejJLv24OqaLAre2qvY4n2Gy0CzI3h7Ssuaec9lfNIX3dHNItB1Sb7vvhxW16ZUVPCzOPS7pgh/IBbVfQEYxoeXNQ40u9GtWnLHSuBDI02SMmk9lkfKq6yFmQeYzJz6SsIHPB3Ybu5FH1p7/qiAaYJWlla0jvruvWkZOGblITQWbkhGFIDSYzPzJVYgHA0NceyGxUzCXxUI681uzghtqOXSCZWeAAAABAdnVaTJwuSTGTGRiOeIbFF/UB3bqwJTphZ6+xhfrFrhWOAs12mczxVreFtKI1pK0rY9sQrGoNRQPMkhQ2pCeWtuvALYtU6Mn85/hndQH9eeG3GbInTynR6duVply5oj0QkVyyzc52akDKZZvMIKkpcuubluT7GW0WZM5kT+YuB5nMjcGEZQ8saifIDGSa1QctQWZgeGsNRPT++m553C7tOrpANUXWs/LaLWr/ji3x2PZWsTK12md7PCDX7ILMoYghb5JUfLtrLJdfAAAAILs6LGaBEmQGkqMaFx79sj2mIsSHG/16d71fB2xRlLCuYRh6e223ljYGVVHoTnkS//LmYEKQ+Y1VnQnrdQQNfdMS1PY1BSntPxnDMHTPZ60xGbLPLe/UvuOLHJfoDoQN3flJq+Zt9EuS9htfqB/vXJF0LMmOXVu2VPT+exiGYdqTubrQ2Xjt2FKzIHPmMpkbk/zehIfp5BeCzMiJAovPhUyVWAAw+NR3hfX/3m1UW6Dnc6Cq0K1r9qrSlhY3S1blsreu8qYVZN57HFnMGHjzN/rlckkjitwaUexRuc/leNZpk01ZnkA4eZDZbqY3s8ABAACA7DLLZHZJKiLIDCQVNqyfYQNhQwUDkEmK/NEejGhFayhh+T8Xt5kGmf/5ZXtM1nGqXEr8fXpnXbfpurWdYW1fk/ahTK1qDZlWhX1zVZd+sGO5o33M/KZTH24OMEvSu+v9mlTRpf/aJv0M20wlDLZsDt6GIj0tVeOV+Fwq8bos20z0GmcaZM7cZ8GndQEtbw5qSpXP9P3uAcrgiBiGukI9Afjuzf/3hw1tUeZVeQbLgaeLIDNywupCbzZTBcDw8NCitmiAWeqZxfbqyk5dtFOF6fpW5bK3qfTpvfV+0/fs0A8e2fD4knat71OWqcDd87v3010qk25rN9M2EDZUYn6PG2U3w5RZ4AAAAEB2mQ2WF3tdcqdY+hQYjuyeYVsDEY0splLdUNZkEnCVpNaAodrOcEzZ5PZgRG+uSj/ALCVOaogYRkxWcV/9bXVmxqof8ef1Acf7ePqrjoRlzy7v6FeQOZjhnsx+i/0VeFwq9SUPMptlMpdmOAh79dwm7Ta6QKdvV6pJcdntXQPULH5xQ1C//7A5Yfmvd6vMi9aPuQ9zY1gqsMi2CqSefAhgCKjvCmv+psQbo6+aEmcl9rLKZN6mKvX5U9tVeTWCBxAMMMMw1BCXjRyISE7HkKweKiTJyTOM3c0/mcwAAABAdnWZRMlKMphxBQxldmVpWy2SEjB02I2PxGcYr24N9busc3xi3Lp26yBGg4O+vamy+nXvR6VrSf0PimaqKm1XaHOGrsX5FLpdKk1S9trjkkaZjO1mMpO518LagK54t0mvrogtmd5t8/Psz7ibVYWTfEnYJMiMnLDKZM7U7BcAg8tbq81nFK5usw4yd1jcIU6uSD3IvPe4xFI6QKZ1BM1nuo5w2Au82WKmruTsxtI2yMzlFwAAAMgqs3LZ9GMGnLF7hm0dgEzSocofNmTYlB7PV3btxN5Z1x3zPfWtJpeu+ODh0qag5brxyQW9gmFD72/o1otfd+ibFuvtzVgFc/sbZHbKHza0sSOk9rix2GAGEwZbAhHLsa0CT/Jg8egSj2kbuUz2ZO7LJWnnUbG9t+2C9v2Z/JLvVYEpl42csPrbzlQdfwCDRyhi6D8WQWY7ZpnMxV6Xiryp3zzsRT9mZEG9xYPGiGJnv7N2M3WdXD/ty2Vz/QUAAACyyazsZ0kaz7PAcGT3DEsmc3Kt/oj++nGLFjUEVVXo1jnbl5n2Ms5XdiWpN3SEtaw5pO2qe0oZr2+3TmBxKr7X7tJG6yBxvUkmcyhi6OYFzfq8vmc7lzr0453LddCWxY6ObzYpSZJcWWiv8GVjQH/9qFVN/oiKvS6dN71Mh2zVc96ZymSWehIrCi1yMAo9yTOZx5aYb9yfIPPkCq9qO8PqMLleH7hlkcaXxYZXu2zG5pr9EdU4TDKJV2QRZB6oHtCp4s4FOeFyuWRWDp8gMzD8fLjBr5ZA6uVEzDKZe2e1bV+TpDltH9WFbnr1ICusSiY5zmS2K5ftJMhssw49mQEAAID++6I+oKeWtuvfq7tMn2W/agrqgS/a9OTSdm00ya6jXDbgjH1PZsaXk7n701Z90RCUoZ4J7Xd90mpbTTDfWPVk7tW3ZPaGDGQyx5dxtstkru9KPN4X9YFogFmSDElPLu1wnEVuGWR2tHX6DMPQ3z9tjSY9dIUM3f9Fm5o3J1FkMpbT7A9b9rku9LqSZjKPK7MIMhek/1M6ZKsi3fndETp12xIV96k04nFJp25bmrC+Xbnsln5UWCi0CDLnSyyNTGbkTIHHlTDbJZOzXwDkh3fWdWvm153qCkW03/ginTG1VO4+M+3eWGWfxbyiJaSnl3VoQ3tIkyt9Onf7Mo0q8ajd5KGhd1bbjFEF+jJuVqHbZd7D5NjJJWl8V0DqGi0egkYUZSeT2e4h3JAUMYyYv00AAAAAzr38Taf++WV79PWctd26bp+q6D32hxu6ddtHrbb7KKZcNuBIyCY4RyazPX/Y0Md1gZhlhqT/rO7S+TuU5+akUmQ3PiJJc9d36/vbl8nncWm9Tf9kp/pmjDb7I9rUab3P9mBPf+G+fXSfXd6ZsF5jd0S1XRGNscjA7avTYkDHqlx2Q1dY93/RpuXNQU2q9On86WVJj2FmbXtYmzrjSmRHpFdXdumsaWUZbX3a7I+o2CLr2ElP5oHIZC71uVXic+u07cp01KQS/eubTr22slP7b1Gk0SbH67IZeLNLHEnGKshs1cM628hkRs6Y1ZIPZLCOP4Dc+6I+EJ0NWdcV0Qtfd+q5PjdWq9tCWmIz+0+Sfvtekz6tC6i2K6IPN/r1l49bZBhGQh8Q6dtM5sMmFGubym/nUY0sduvG/aoT+lsVuKV9x1MqG9nRYDKbVZJGOMikD0cM275SToLMySZykc0MAAAApKczGNFTX7XHLFvSFNTcdX5JPdlgTyztSLofymUDzoRtM5lz93AbihimlffyidX5vboy9VZ2udKcJJO5I2hoWXNQoYihWouxmFT0zVD9Ksk4ppTYl3mFRQ9mu4pzfXVYZDKbzbUwDEN/nNeshbUBtQQMfVoX0FVzmxwdJ16tRTB91tqeTPFMxnKa/RG1WYx7FXhcKk2ayWyeT5ssA9pO32OWF7h19rQy3XHISJ2xnXnQ3i6TuT9B5gKLYcN8KZdNJjNypsBkqk2+pPgDyIwPN/oV/1f90jed0ZIin9cHEjdKYnlzSMubQ6Y9mcs21+EvK3Drf/at1hf1AQUj0s6jClTgcenCHcv1v5+3qTtsqNAjXbBDuaMAH5AJ9SYPQQVuZze8LYFIwt9SX07KZSfruxyKGKYTwAAAAIDBqq4zrGeXd2hNW0hbV/p0zrRSFQ1AIPej2oBpmc931nfrgC2LtKkz7KhkK+WyAWfsnm/bchBkNgxDzy3v1AvLO6LjUJfvUqGSfmRRDpShMMG8yaquch8rWkKqKHCbVjVMVd9g3gcbum3W7FHfFdYWfYKeVufgZCxHss5kNgsyrmwNaV1c9nZXhjNee0s/ZzKT+TmTbO9eTnoyj7PIZPa4XSr1ukz7KidjdszKQuvzsPs596dctntz69n4jzanvz8DjSAzcsZsBka+/GEAyAyzUth9L7h2mZl2Zq3tss1kliSv26UZo2OzlPfbokg7jixQXVdYo4o9tjcGQKaZZTKPKPbI5aBEdbIZjw6er5RsMvVQeNAEAAAAeoUihm5Z0KzVbT03y8ubQ1rWFNT/7Fud8cmV8zb6TZd/WhdQqz+iRQ3JM98kJVTfAmDOLl6U7lhTf7y6skv/99W31Qo+qQvon4vbdcnOFVk/l2QG+/i7YRhJezJL0jctQdOSxunwhw29u65bL37d6ah3dX1X7PlZ/cSdBn+tejKbbb+wNvWEHsvj2pyfYRhZSxhM1pPZ55ZGFFuP8ZYVuNURSj3tOln2dLyBymSWen4GgbjWkfnyt8zoOnLGtFw2PZmBYaH3JiTdi+GG9rBpQMxJn43KQremVPkIMCPrGkweghz3Y07yAOXkxj7ZDFO7nlYAAADAYPPhRn80wNxrRWtIjy9pt9giPYGwoU/qrAf139/QrUUNzgb9yWQGnAnbPN+2BrL7bLu6NaSHFyd+riyoNZ98kmv5EphKV2fISMjoNPNNS0jrO5IHhL8zskDJPnmXN4d0x+Z2gE7El8u2Gm5xGmS2ysI16wGc6lUkYjMWZNdjuLE7kjSZwUx1oVtVKY7JJstkHlPikdsmgaO8IL1ra7Ls6XhdNn9b/Q0yF5nE0vLlb5kRduSMebnsHJwIgKzrzUK2m+FlZ5XFTZ2TIDOQC4ZhqLHbPJPZiWQ3o04maSUNMpPJDAAAgCHko03mAZ5XV3bp4wwGfz6vD9gO9M5d79cXjjOZeaYFnLB7fs1mT+ZA2NAdn7SYvtcWMJK2rcqFfAlMpctJFrMkbegI6+tm8/HDHUb4NK7Uo0O2LNIvdq3QzqMKMnmKKWQyO/terDKZ/WH7ILETdoFiuyD4mraQ40zmYycX6+xppTp3+zL9+aAabVOZWoHlQrdUZhMoHldqP7aW7nhxqv2cB6pcttQTaI+XL3/LlMtGzphlMmeyjj+A/NUeNFRTZN47xIkOi5sruxsOIJdaA4bpjftIp5nMSctlOwgyJ5nIlY8PvwAAAEA6whH77OK/f9qqWw4ckXI2lRmrUtm9ljY5CzBLlMsGnLJ7BO4M9QR3vSYJTpn25NL2hIoJfbUGIqopykzJ5kzJVonjgZJsfKSXoZ6KFvHGlXp07d7VMcvOnlam9e3Nqu3KzAQFs3ZpZhyXy7YJRneFjJiyzqn+1gcjhmkAU5K6LMZfJWl1W8hxVdqtyr06ZKvi6OtUr70FSTKZx5bahznTzWRO9TNkQMtlmwWZM9xrO11Mj0POmJbLHuQXOQDOtG+eVZrpGVdkMiNfxZdK6lXjNJM5K+WyHZ0KAAAAkPe+ag6q3WZwvCVg6P+W9r9sdjhiaKFFxnQ6KJcNOJNsknQ2spnbgxG9trLL/jxy0B86mXQTPvJFsvGRZMyyXidWePWnA2v0+32rNbXa53hfU6q8KjQZ1jFrl2bGcblsm+uZ031YCdn8Ptj1ZF7dFlbQ4e9SfIA4vXLZ2c9kTpXdv0VnqH89rM2CzBZDjVnHaDxypsDkt48gMzA89D7sp1su24rdDQeQSw0Ws2Ed92T22985OspkTloum2swAAAAhoaPNiXvgfzeBn+/y4wuaQqqzWbwP1WUywacSVZlOBtB5kX1AduM6p7zyL/n7ME+/t6YZHwkmfEWAckir1vbVfu0bZWz4r8zRhXoj/vVaO9xRQnv1XeFZWy+vhg215m2QEQPLWrTz2Y16HfvN2lxQ+K1K2IYtuOn8YHNcIrXtX6Vy3b4ZxZfpaM6hex+r1vyuF22lT6SBZnLzQJRA6A7yQdTf0pmmwWZ8+VvmTsX5IxpJjMD3MCwQCYzhhurTOZM9WR28oyV7CGcnswAAAAYKj5y0HO5K2RofXv/ghXzk5TKThWZzIAzoSSBtGwEd+2qJfRqyWJ/aKfypY9ruqzGR8aWOBtfGV9mH0Qucti2YKvynv2YtUELRr79HbQbr3lpRZdeXdmlTZ1hLW4M6oZ5zdrYEdtHuitkWPZ07n2/r1Rj8HYJCXY9o9e1hxwnD8Vf276TQg/sws0lq90u63+XsUkzmbNzbe1K8rfVn5LZhSa/l/nyt8xoPHLGLMjsD9vP7gEwuFhNFGsLDlCQmZ7MyFNWmcyOezJno1w2E70AAAAwBGzqDGutw+Dx1y3O+yXHMwxDCzJYKluiJzPgVLJJ0m1ZKFPtZEwrGxnVqfKHkq+TD9qDEd39aat+M6dRf17QrE2dPZ/rZuMjZT6XptY4K3OdLOvVqj9xvOLNn9dWyQO9yQZ2/ZTjBSOJfaTtSmVLiYHgVKtGppvJHIz0ZDM7EV8ue0yJR+dNL3PUP9oshhSvOkn57exlMtv/7Psz6cS8XHZ+jOMRZEbOFFg0Trf7YAMwuFjN/uu9Qcr0xZBMZuQrs0zmYq9LJQ5+Zw3DSDrb0VGQOck69GQGAADAUPBRCoHfr5vTj7as7wirzmQy6T7jCi3LsdpxyXkGHTDchfOgJ7OTYF42gt2p8g+SCea3LmjR7LXdWt0W0vxNAf3hgyaFI4aaTH6mVYVuTa5wVuY6U5nMJdEgs/m4Tv3m60Nnii0VHl/SEfM6WZA6/vcw1YQeu4QEu57Mkkz/LcyUmvxMj5lcovuOGKk/7letn+xcYblt3+BquUVykcsmy1mSyrIUZE7WH7s/mcxFpgmbRl4kbDIaj5wpsLjfpmQ2MHRYlTKJlsvOYFTL53Y2uw3IBbNMZqf9mNuCRtI+T856Mtu/n+whHQAAABgMzEplu9TT1zHe8ub0M5k/rjXv+7zX2EL9ZvfKpJlV8Yq8LttyoAC+lWw4KRtBZifP4XlZLtvmh9ffPvWZsrEjpMWNsZ/PtV0RfVIXULNJJnN1kVtbVybPZC7xulSZpAqiWTDPdF+bSzCPtOgvXN/Vk2yQLBM5mWRB6vhAcKpBZruqdsmCpk4VWwTuy3xuTanyaZpNFnrfsd4jJ5YkvH/6dqVJj1+eRrnsSQ4nLfSV7OfVn57MZmPeESM/Wt8RZEbOWAWD8qVhOYD+swpatQ1AJjNZzMhnZpnMjvsxJymVLTmboJW8XLaj0wEAAADyVjhiJAQmJGlKlVe7jCpMWL6qLRQzwP51c1ALN/nVZHL/Hu+TusRgttsl7TSqQOPLvLp276qUAs2UygacS9buKSuZzIO1XLbNeadaanmgLDH5HJekV1Z0qsmk6XB1oUeTKr1Jyy+PL/MkzXotMpuRZKJk83ojLTOZUy+XbSZZkLr/mczW72UiyFzkccljUdG218hit2WWct8Y/pETi7V9n4D09jU+HTmxOOk5pFMu+5Rtkwev+wqGkyeI9Ksns0UsLR8qE6QejgcyxKpcNkFmYOiwulFpD0QUMYyMlsenHzPyVcQw1GgSKK5x2o/Z5AEqXiYymUN5MmMZAAAASFd70DCdPLnjyAIVelyaH1dKOxSRVrWGtE2VT/d/0aY3VnVJ6sm6+umMCu02JjYwXdcZ1qbOsKoK3VrckBgE2a7KF50APb7Mq+v2qdKN81qivUTtlKSRaQUMV8kegVsDA/9866Q6X2selsu2G3v3hw2VOGttPKCsTnFDR1hmQyTVRW4VelzastyjNW3Wn7fbOMh2LnTY7aD3M7vI61apz5UQDG7oTq9cdrxkQer4TOaUezLb/D7E93tOR6mDa5vL5dI2lT59UpdYIcTbJ4ZUUejWtXtXaUVLSBGjZwJZskkDkrPEpFKvSx2bf3Z7ji3UrqMLkm7TV5eDcblMl8uWej6HynL8N0uQGTljmcmcf9deAGmyClq1ByMZnx1JJjPyVbM/YvqANMKipJLZ9skEko9ZJZ3pTSYzAAAABjur4EmZz60JFqUvv24Jyu1SNMAs9WRvPfJlu3YZXRAtYf3mqi49vLjNdvLmjLhB6XGlXt16YI0+2OBXIGJoRJFbN85vMd22xGH2HIDBlMmcf5O57c471SzYgdJu8UHbYFHprbdqxNaVPssgc7HXpSMcZL0WeZxmMn8b2xhZ7FFHMBTz/qrWkPxhQx39HGxJFqSOH19NtWqkVdW7UMQwDeinyukEqm2qvKZB5t6M8F5ul0vbVKUWVS309LRZtLp+VxW6deuBNfqyMbi59Lo35fYVTsa5+1Muu9AikpsPf7MEmZEzlMsGhjbDMJ9BLvXMLs9kqWxJqkij9AmQDWZZzJJ1SaV4zoLMyf+ekpXUTvaQDgAAAOQ7q3veAo+0TaX5MOjy5pDWtSeOpG/oCGt1W0iTKnxq8Uf0yJf2AWZJ2mVUYuaTz+PSAVsWSbIOnEiUywZSkSxul40MYifBnXwsl22fyZzFE7GRasZn1eZKcfuPL9Lstd0J70+v8emCHcu1ZXnycFiRw8/ikj7JLiOK3FrVGvv+ho6wbp7frO2q+5dm2pEkeBlf0jrVoOP6jrD+9nGLVrWGNLXap+9tX6YSnztjyUFOJ1BZZZnXdvX/b8jlcqnM51aTxe9VoUcqK3Brj7GJbTWcclJafCDKZWd6fD0dBJmRM1bxIILMwNBg96fcHohkfKbV7mNSK2MCZEtDl/lTmtNM5jYHM5+d/D0lewgnkxkAAACDnVXZzwK3S6U+t8aVerShI/b+/OvmoOX9dG1nRJMqevovJwu+VBe6NdEiW7pXmc8dU5KzL8plA86Fk7R7astGJrODoFJnyFAwbMhnESDKBbvxg3zIipRSz/jszWTeaaRPZ04t1bPLOhSMSNtWeXXqdqXaeWSBo7LKUgpB5j7rTazw6qPaxCzcRQ1BLTJprZBMKGJEy0R3JpndFF/SOtV/wyeXdkS/Xtse1oaOsK7bpzqhDHe6nJTLlqwnglktT1V5gUtNfvP3rJIhU+GktHi/MpmtymXnwd8sQWbkDJnMwNBmlxUZiDgLnDl16IQi7TOuKGP7AzKp3iKTeYTDTGYnD8dmN5WGYcQ8RFmVQOpFJjMAAAAGO6tb594xqG0qvQlB5nXtYY2yuDfvLdO5vDlk+n5fM0Y7C2KMKvGoozVxf5TLBpxLFs9pDxqKGEbKJW9T4TSDsC0YUY3HYaPfLLALSuVDVqSUesZn9eZJ/C6XSydNKdV/bV2i7rCRVms9q2BevOI+QeYjJhbrrdVdGRvrbA9EVLX5e0pWLjshk7mfweHFjUFt7AhlLKvdaZWOqiKPtizzaG1cZZGDtszMeG/P74L5N+X039yOk0kn3WFD3aGIitK43udzkJm7F+SM1R9GHlYRARBn4Sa/fjW7QT94o05//7TV9IKWrIxYfE8Np3o/OUo391L5437V+tFOFXk1KxToyzqTOXNB5rAhhTcHiedv9Os3cxp00Zv1uuezVgXCPQ/XyR7C8+C+FAAAAOgXq8QF3+aMMLM+joasy3Fu7OgNMifPRDMrlW2mN+MuHpnMgHPJMpkNZTa5wYzTcsL9yV4cCHbBw/4GKDMl3UzmXl63K60AsyQVORhfLPQommksSTVFHv12ryqVF2Tmc7ytT2A51XLZmZgo8N56v6PMXCdKUvh3OHNqmfr+BEcVu7V3hpKKym3aLBa4M5DJ7PDnnm7JbMsgcx78zZLJjJwhkxkYnNa1h/SnBS3q/Ut9e3Ovkx/vXBGzXrKsyIY0empMKPfoij2qFDakmkI3gWUMCg0mmcylXpfjmYtOy3wFIoY2tYd020ct6v3zm7WmWy5JF+xQnnR7MpkBAAAw2FmNKRVsTiKcYhJktrOpM6xA2NAqk8zjvjwuaaeRzoLMZRYD3dyOA845iX+1BiKqtJjUkQlOMwjzrS/zYCiXnUogrtTryki5414Fnp4EF7ufhFnliUkVPl27d7Wuf68paWA4mfY+mTvJy2V/e6xQxMhIAkFrIOKox7ATpQ4zmSVpj7GFumn/ar2zrltlBW4dPqHY8pqZKrsJANnKZJakFr+hsaWp79+qjHs+/M2SyYycsZohQpAZyG8vfdOZcKM1b6NfRtws0mQ3/PXdqWcyVxS4NbLYozElHgLMGDQaTYLMTktlS7EzWO34w9IHG/wJg1P/WdOtgIMRqzyY/AgAAAD0i9V9b28AYlKFV6k8Sm7sCOublmDSQftDtipynK1lNdDdnmeBKCCfOZkkPdAZxE4zRlsHOKM6VXZj7/kQsApFDLU7HAeRpCqHVeKccrtcSYOOxRYBvwnlXtOKGalq7/M7k6w3ct/fw0z9+7UEIhnryexJMUt4UqVP504v10lTSjMWYJakcptrdGZ6MjvNZE6vsqd1uey0dpdRBJmRMwUWrSicDIQDyJ3/rOlOWNYZSpwpl6z/azrlsu1KmwD5qsNk1mlFCr/LTmc9B8KGnl/eafqek2xoMpkBAAAw2FlmMm8e5C7wuDSmxHlv1LqusJY2WZfKLvO5dOiEIp0zrczxPieUmxeW3KKMgpOAU07iObWdAxt9cZq52Jpn5bLtguP50JM51ckB40oz3++6MEn2rV17A6et0ezEZjLb/5v0DQY7/Z1MptWfuUzm+KSkXLELWGcik9l5kDmz5bLz4W+WuxfkDOWygaElFDFi+pEku7amUy47lcAckC/MHgicZjmEI4Y6HGcyW6/XZJJNHS9D7XYAAACAnLGaW9l3DKqmyK31Hc6CT2FDen+9P2G52yU9eMQoy/KVdvYcW6j7v2hT/FzUPccVprwvYLhyMkl6IIPMqZQlzrdy2baZzHlQ4izVINyMUZn/7CzyuNRi875ZuexeNRkIMvdNFOhIMljT3ef9TGYyd6WQTW4nX0I9eVMuO83PA6tzzIdYGkFm5Ixluez8uu4C6MPughn/VrIbfqty2V63dbCLTGYMRmYlhkocDkalUiKq/0Hm3N+YAhi8WvwR+cOGRhW75XLR0gIAkBvWPZm/vTZVF3kkWWcnx1th0o95Qrk3rQCzJJX63Dp9u1I9tqQjuuzoScUpZVgDw13YwfjxpjQq6DmVSvZgukGlgZAsOJ4PAatUM5l3GV2Q8XNI9vlul8lcU9z/z/LesSDDMJJmMvvDPQkKHrcrY1mtLf6IujKUibBFWX5c28psy2X3f/9Wmcx7jytUVaE7+t82lemVU7cul537v1mCzMgZMpmBwWd9R+LDda/4e49k9yJtFj1pRhS5tanTfOMKm1lnQD4KRwzTGz67B5K+nJS57mV3/Wx08JBGJjOAdEQMQ//7eZveXtMtQ9KUKq/+e/cqVRYyMQwAkH1WbZv6zlfORJbZlKr+Dakev02pdhhRoOXNQU2s8Gpqdf97eALDSchBCd5MZDIbhqF17WHVdYU1tdoXrUqWSsZvKs/1Ay1ZQCofSu+mksk8odyjkRkI6sYrSpLZapc4UJOB56D2zb8zgYizTODusKFStytjAcfWgJF2Wee+vG5p9zH5UaXDLnEpI5nMJjNfSn0u/WLXyn7vW5J8bsklKf5fOFMl0vuDIDNyxmryCEFmIH+ta7O+QY/Pgkw3K3JEkccmyMyANQYXq5mMTjOZUymrZXf9bLKoHNCXk4d0AIj3zrpuzVrTHX29vDmkJ5e26+LvVOTwrAAAw5WjTOYMBAC2rep/UHibKp+2ycB+gOHIySTpTf0MMkcMQ//7WZtmre251y3xunTlnlXartqXUjA2n3oyJwtC5kNWZCqZ37uOHpgAZrKgo22QuTgD5bI3Zy93xPdVsNAVMlTqy2y58zVt1olGTrgkXbBDueN2cQOtzCbZwyoZMhVm43/FaVY8MeNyuVTkdSUcxx821BGMKBRRziZ6E2RGzrhcLhW4E8tjE2QG8tfadusbjPg/XasZ5HY8LvsLIuWyMdiYlcqW7Pv39JXKjGe7h0EnM1CdlBsDgHhvre5OWPafNd0EmQEAORGwiCn53LE9mftrCsFhIKfCDsac2gKGOoORtINcXzQEowFmqef5/oEv2nTTATUpBWNbLCr55UKycfdcBZnfW9+tp7/qkD9sqMFBu69eA1EqW3JSLtuuJ3MGymVvHgtKViq7V2/gMZP/fqtsEo3sXPKdcpX63Nq60jsgWebpGvBMZpPxv2QZ8akq8CQGmd9e263Za3uqiu0yqkA/37VCRQ7HHDOFIDN05yct8rpdKvO5VeZzqazArYnlXm2bhVI9BR6XAnE3BfGvAeQPuyBzYiZz6vsv8rhUajOzrILSmxhkOi1mnToul51CT+ZA2Lx0juSwJzOZzADS8FWTeU/LiGHITW9mAECWmY0puRRbTa+6nwGAEq9L4/OkxyQwXDlN2KztCmtSmkHmF5Z3JCxb0RpSRzBiWaK22CTTMJUKZQMtWQb2QAWZm/0RLdzkV3fI0LhSj7Yq92pksVsul0uvr+zUA4vaU95nmc+l7QYoftGfctnlPpd8bslhErKp9s0bWyUuxOv9nctkufN0fxe2KPMO2L9Lf9iNN2ciyDzQmcySVOSRWkyW9x7547qA7v60Tb/cLTMlup0iyDzMRQxD767zJwxIHzmxOGtBZsUNoFvNOgWQe+varf9A47Mg0ymXXeh1qdTm5r/CYWAOyBfWmcyZ78nsjxhyu8z79TTRkxlAlnUEDZUXcN0GAGSXWZaez91TTa9Xf8tlT6nyMpEKyDEnmcySVNsZ0aQ0C+wsajCfTOkPG5YBuFHFbq2OywDtChkKhg35MpzVmI5k4+4D0d91TVtIv/+gKSGje0SRW+NKPfrC4ueczIxRBQP2WZw8k9n6fZfLpZoit2UrQCfaUy6X3bNePvTnzZPq2AnsflfSDTK3ByNa0RLSNy1Bfd2SmJiV6SCzk/P8cKNf8zf6tcfY7PXCJsg8zHWGDNOMp7IsDQgVuBOPQ7lsID8FwoY2dtj0ZDYyk8ls1yOjjHLZGGSsgszFDu+6Uwkyd4UM0wCzJDU6yWSmkgiADGrxR2hzAQDIOrMxpfhei9X9LJedjaQMAPacjjltshnH6s+xrYJ5o0s8CUFmqSebeUQelA7ORSbzi193mpYMb+iOpFQaO96MAerHLDnpyWx/Haku8vQryNyWB+Wy05WJ/sbZlk6Q+aZ5zfq4LmC7Tjplq7tDhpY390y82HFkbDl4p+d5/xdt2mGEL2v9sAkyD3PtFoPXZVn6BSwwubZSLhvITxs6wqaTUnrF3+CnU3q30GOfyew1mZgC5DPLctkOZzO2ptC7qdUmW9nJg0bv33BTd1iPLenQipagxpZ69P3p5RpTkvuHYQCDSz6VBQQADB9ml5/4AW+v26WKAldK99q9XJL2H1+U5tkByBSnY06bOjMfZA6EDctg7SiLQHK+BJkz0ZN5bVtIzf6IJpR7HbW1W9xgH4hLh8fV0392oCTLZE6WoTqin5OZgpGef4tUy2XnQ5DZNwjHbs1iVMk4mVDtNJP541q/Pq8PaEljUCtbQwob0vY1vrSDzE3+iB5f0qELdyp3tH5/EWQe5totZsPYZRJmktnMFjKZgfxk149ZSsyCTKf3SKHXPpMZGGwGolx2ZaFbLSYB5WYHJbHthCKGDMPQXz5u1ZLGnlmTa9vDWtPWrNsPqpFnED4oABh4Vr3gCTIDAHLBNJPZ5D62usij1oD9M66ZvccVanwZw6lArjnNZK7tynyQ2R82bDOZzbRk6d748/qA3lnXrWKvS8dvXZIQ2E4WhPQn+XE9vqRdM7/ulKGe+MFPZ1TYZhQHwoajymqpOmZyyYBWO0zWk9muv68k1RT1f0JBeyCSQrns3iBzvw/bb4OxmFU6mcxbV3o1Z539Osl+j3q9urJLn8ZlRS9vDiaU2U/lPN9c3aX9tyjUtJqBm4zRaxD+kyOT2i0+qLKWyWxyo58PH4YAEq1rSxJkjrtPTaf0blGSTGZgsLEqbeS0ZI1ZkNlqRmx/H1pDhvRNSygaYO61qTOctAQQgOHLanKY2WQYAAAGmnm57MT10u3LfNKU0rS2A5A5hmHdKipe7UBkMkesM5mtgsxt/oFPqnpnXbf+8GGzZq/t1msru/TrOY1q7o79/v1JMmPtgtArWoJ6cXOAWepJXntwUbsiNlnltZ32VRGdOmpSsQ7askg7jfTp/OllOmO7gf0sTt6T2f4aUtPPTGZJagtGUs5kzoeezPlcLtvqzNI5560rk7fOcJrJPNWkDUcwIq1ojR2LL0yxx/O9n7UpmIWETkbyh7kOi9JA2ep7avYHHKRcNpCX1rbb35iH4/520+nJXJikJzMw2AxEJnNVoVtmbV36G9AJRwwt3OQ3fW/eRvPlAGA1OYxMZgBALpiNKZmNPaUTANh9TIEmVpDFDORaKjGT2s6wbRA0Hf6wYRmMHW1TLnugPf1VR8zrzpChN1d3xSxLnsls/f7c9YnjAhs7w1pj0oO6V6bKlW9d6dWlO1fot3tV6+jJJTHZnQMheU/mZJnM/Y+ttAeMQdmTOZ/LZVuVxfa4Uj/nSZVey6B1r2STFXpNqzEPWC9pjE34SDXjen1HWLPXdae0TToIMg9z1pnMlMsGECt5uezY1+lMGCnyksmMoaXTZLaF1+18lmSbyQNFeYFbhSY37f0NMocMaV2H+QNg3QDM/gYwNFg9OKfT5xIYykIRQ182BjR/o9/yORxA/5n2ZDYtl536cydZzEB+SCWpIWwo4+Wag2HrjNGRxQNTeSyZ+q6waUD3P2tiA0zJgpDdoZ42WmZe+qbTdPmq1qDpcknaaDHG8OcDa/S7faq0//hCbV2ZfPJOVZrVJ9KVrMxxsgzVjJTLDkbU4fCXvTtLQebCJN+WSzJNisgXVtnH6cxZKPS4tFW5/Q/EaSbzlCqf6TksbYr923Jafrv32BfuWK7vblXkeJt0Mf1umLPuyZytctmJywgyA7nT7I9oY0dIW1f6YoJgoYhheWMYXcfofyZzT7ls8wvm7mMGvocEkGlms06dZjEHw0Z0Nmpf5T6XCjwudcS91+9y2RFDXzebPxw6mX1sGIbquiJqDUQ0ucJLD2dgmLC6c6dcNvAtf9jQLfOb9UVDz3W2ssClq/eqJiMSGABmY0pmGW/VyUbK48wYVaApVclLYwIYePHjT73GlXq0wWTsalNnWCMtMozTYZXJXOhxqdjrks/dU+q2r4HOZF7WZP4sHx9gTzbubqjn3M0yPq22tKt8aDWWMKbEowKPN9ov9tM6v26Y12K5n6oUP7P7yy4DtdAjeZOMd2SkXHYKmcy9CQ4DHWSuKTL/G+vlc0uuNLKCs+WUbUv1hw+bY5ZVFro1viy936/JlT6ttsnkdxpkLvS4NLnSq+XNsQleSxuDMgwj+jN1mrCy+5gC/XDH8oxMdnCCJ5phzmoGdbLm9ZlimslMuWwgJ55b1qGnNpfWKfG6dOWeVdpuc0+IjR3hpOWIQhFp7rpufVYf0NhST1qDy4Uel0p8bm1b5dWyuAvrMZNLUt4fMNAihqH2oKG2QEQuSePLYm+tzILEJQ6ndbZZXKPLC9ymJXL6e/ms7wrLb3Fv3NAdUVsgonKLdhqhiKF7PmvTO5vL8Iwuduuavast+1EBGDri22X0olw28K2567qjAWZJagkYenJpu67Yoyp3JwUMUaY9mU1uYVMJAIwocuvi75T357QAZFDY4jZzvFWQuSOsHUZk7viBsHlP5iJPT4CtosCthrjgbofDYGG64jMe+wqGjehkGydBSH/YSBizb+q2DqStbg2pIxjR5/UBlRe4Nb3GFw2KbTQJMtcUuRP2n2wST2UeZTI7GdOpKnTLJevAvBPtwYg6HVa/6c5ST+bqQrdtkDmf+zFL0vQan2aMKtAndT1lqF2SzppaKneagfFtKr2avdb6/VQyj6dW+xKCzG1BQ+s7wtpi81ijk3LZJ00p0RnblWY12N/vIPPMmTP1yCOPaP78+dq4caMqKio0ZcoUnXTSSbr44otVUVGRifNM8PHHH+vxxx/XW2+9pbVr16q1tVUjR47UuHHjtPfee+vggw/WSSedJI+HwU077SZl7Iq9rqxlH5mXy1bMDA0AA291aygaYJZ6+rbc9Umr/npIz114XVfyLManlrarrqt/A8qFm2d4XbhTuW74sFktmz+jjp5UrO0t+lMAufDHD5u0ojWk9oARfWjYZVSB/t+eVTHrmT0QlDicyNVmUWq2vCDxgSwTrALMvVa1hrTjSPOKAvM3+qMBZkmq7Yro4cVt+s3uVRk8QwD5yGqciiAz8K3Hl7YnLPuoNmCyJoD+MktcMLt3TlYu++q9qiSjJ9iy86iCvB84B4aTkMUkx/FlXi00ub7WZrj9kz9syG8SzOvNfjULBA10hukyi6pkUk828Zbl3s3nkXxf/rCh+Gk1K1utW+h9XBfQT2c1RAPpu4wq0G92r5TH7TKtijjWZDJ6qc+t8aUerTdZ3yWpoiC7n8GFNhmoTrJTvW6XqgrdaupHdaf2QMSyCm283gQHs8kPmZTs2pnP/ZglyeN26Te7V2rhJr82dYa148gCyxLaTkxOsm1xCrXDp9UU6OUVXQnLlzQGo0FmJ0HrIyYWZz2ulnaQub29Xeecc45mzpwZs7yurk51dXV6//33dccdd+j//u//tPfee/f7RHu1trbq8ssv18MPP5zQH2D9+vVav369Fi5cqLvuuktNTU2qqqrK2LGHIrNM5mz1Y5bML7qGejIifcwPALLmlZWJfVU2doa1vj2k8WVeRzc1/Q0wS99eLCdV+HTXd0fqm5aQRhS7M1rWCMiEjqCREAQ2yzzuNM1kdhpkNv+bqihwm5auGmgrbYLMTy7tSFi2YBOD58iMhq6wZq/tVnfYUHeoJ2vAHzZ07OSSaMUN5E7EolxhK+WygSiriWNMrgYyL2ASQDHryVyTJCtuVLFb40opAAnkI6s4Wk1RT9Wv+IDuJgeJE6kIRMwzmXvHuU2DzAOYYRoIG1rRYh0E3tDRN8ic/DzMsmG/sdm/FJup/XFdQB9u9GvPsYWqN/nZj7GoeDalymcaZPa6lXamabpsM5kdxk5qivoXZG4LGqY/PzNdWerJnKz88mCYkOV1u7TXuMz0KZ5U4ZXbZV1Z0K7serypFmMbS5uCOnRCsST7yQ+9yrPUBrevtO6WwuGwTjvtNL322muSpDFjxuiiiy7S9OnT1djYqCeeeEJz587VmjVrdMwxx2ju3Lnafvvt+32yjY2NOvLII7VgwQJJ0hZbbKGTTz5ZO++8syorK9XW1qZly5bpzTff1MKFC/t9vOHALHCUrX7MkvXsFn/EMO2ZM5i1BSJ6fEm7ljeHNLnSq3OmlWW91Adg5bM682BQSyCi8ZI6HJZn6a++N+I+j0tTyV5GnjIrG20WUDELMjudyWgVZC4vcDkqkZNpdjOXzUpgSQyeIzOa/JGYahu9dh9TSJA5D4QsbhHagobCEYP+7ICNQKSnrx+AzDAMw7xctsm9c0Wh23ZguMKiTQyA3LPKZPa5XRpT4k7okZrpTOaARU/m3sCkWSBoIDNMv2kJ2ra4+2BDt2av7VJrwLAtq93L7HuzC2Kb+bg2oK0rvabnNabUKsjs1Zx1icuzNCQZwy446LQFWk2RW19bt5lOak1bSE6LQ2UryJw8k3lAD593CjwubVXu1SqL8TKnPZmlnpLwZn3llzR++zebbCywxOvKSUwtrSDzfffdFw0wT58+Xf/5z380ZsyY6PuXXXaZfv3rX+vWW29VU1OTLr74Ys2ZM6ffJ3v22WdHA8y/+tWv9Ic//EFFRYmzDm644QatX79eZWVl/T7mUNdu8klVlsXyE1aZWIGwIQ2hMUPDMHTDvOborK/VbSF93RzUnw6syfpMLMCM1a9hb3KS0/Is/ZVKrwogl8wGndpM/k46TZY5nfVqVWrWqifzQFvZmvxhNF4wYn2tB5yyujYMdL8pOGPXD74taKiqkGs7YMUfMnJyTQeGqrBh3v/SLMjsdvWUMm3sTrzn9ricVx8CkH1Wkxw9Lml0iSchyLzJpoeslfgKqn35w+bPIr3BZbPnF7MJMJmyrMk+APzuen9K+zMLVKY6HjBnXbf236LQ9L2xFkHmbfNoAnEmMpmrk2T9JpMse7yvrs3/ZgOZMS/1BM7tDIZM5kzbuiIzQWZJmlbtSwgyb+oMq6k7rOoiT9LnhlxNkEv5qOFwWL/73e+irx955JGYAHOvm2++WTNmzJAkvfPOO3rjjTfSP0tJDz30kF5//XVJ0o9//GP9+c9/Ng0w9xo/fry8XsraJGOWnZjNTGazkkXSwF54c2FdezjhwrC2PZzQzB3IV9nKZE6ljAiQS+UmE7K6QoaCfa5fEcO8hJbjctkWkzsGqidzMuvawylfn8164gGpsro2DPQsbTgTshmAo2Q2YG+g++YBw43VvarVmKvVYHlloZtqPEAes7r/9LqlUSbt1tqChmWLF8tj2NzGJstkNnteH8hnFyfZyamIP9e2QCStFnnr282D+1blsieUm8dyDt4yM6WNU+Fz9/SCNuN0TCfVAKNTW5Yl/vy6No/bDvS9ZU2SEjxWsZ6hbOsq6xhkquPcVhU9e//Gk1VAynbv8l4pRxPnzJmjDRs2SJIOOugg7brrrqbreTwe/exnP4u+fuKJJ9I8xR4333yzJKmsrEw33XRTv/aFHoZh5LxcttUgudNSEIPF/E3mM8beWJXYzB3IJ73xoXaLPnKZRiYHBgur2YF9+zKblcqWpFKHs17NymW7JJX5XDm5cY8Y0tr2xMlRdjO8h9qkMeSGZSYzv195IWJz394y1G7qgQzjcwzILKvLjlXpyGqLFmaUygbym2Ums9tlGdhLNcjrt5kwHQgb5pnMNj2ZB+qabxiGljUPbJB5hU3rLDufWrTmG2sRZPa6XTpkq8SA8l5jzTOiB5LL5ZJVVWynQeaBqtY4qSIxqBmI9PxeDvStZdJy2cOwkt3WldYZ+MUp/g5Mswgy95bMTprJnKPWrCkf9dVXX41+fcwxx9iue/TRR5tul6q5c+dqyZIlkqQTTjhBFRUVae8L3+q2+OApczj4nQmWQeYh9rBtFQsgAwf5Lrj5pjoXPZmBfGbWk1mKLXFtVipbct6/xyzIXOZzye3KTU9mSVppUq6py6YcE9c5ZILV7F/KZecHu38Gq7L/AHoMdElDYLixzGS2CjJblDLN5rgYgNSFrTKZXdbjSqk+O9iNTQci5lXLimzKZQ/Us3FdV0TNGa4elBBkbkkviP2xSZC5vMClEpsEtzO3K9WOI3oCbS5JJ25TohmjC9I6fn9ZzTOwO/++kpWWTkex12XZ0zrTvwfxCj3JkyaGYyazVQa+1DPxJRVjSzyqNMlG7s1kTjZxIVeT5FKuJ/35559Hv95jjz1s1x07dqy22morrVmzRps2bVJdXZ1GjRqV8knOnj07+vVee+0lSXruued033336aOPPlJTU5NGjBihXXbZRaeeeqrOPfdcSmU7YJWZWJrFX0arQ9ldyEMRQ0sag2roDmunkQWq6Wd/g2ywusEZasF0DD29seWs9WSmXDYGCasbt9Y+19ZOi+nV8f17FjcEVNsZVvfm2dD+sKFir8s0yNwb3M5Vn+N7P29Tqc+lnUYWRB+s7IJIBJmRCV63Sx6XEiZHEmTOD3alBymXjcEoYhha3hzSuvaQplb7NL5s4MYW+g5QRwxDS5uC2tgR1vQRBZalJAFYsy6Xbf6caRUA4A4DyG9WmcxetytjrXbsxmy7QobMcjG+zWROfC8UkcIRI+WgUzLLMlwqW0p8zlqRQm/gZKyymHtVFXn0272q1OSPqMhjH5AeaJZBZodjl7uOKZTH1ZbR7OIxJR4VWyQu1Hel3ns8FSVed9Igp1XlkKEsk+3sXC6XtqsuSKiKu7I1pO5QJNr33cqgCTIvXbo0+vXkyZOTrj958mStWbMmum06QeYFCxZEvx4zZoxOOeUUPffcczHrbNiwQRs2bNArr7yi22+/XS+++KKj8xvO2i0yE7M5Y9My+GrxKR4IG7p1YYs+2TwTqtDj0hV7VGqHEbmZ0eSUz+IGgsF35Aurv/rem+rsZTJn5TBAv5n1ZJZis4+tM5ljt535dWfCDN+xJR7Th+PeIHMus/5v+6hVY0o8unbvKo0s9qjFb1dGLIsnhiGtyONSR9xgB/dR+cGuXx3lsjHYGIah+79o01uruyX1VKS65DvlOmjL4gE5Xu/nWMQwdNcnrXp3fc+Akscl/WyXCu09Lvs9CIHBzGosyWqCplW7uDCXLyCvhSz+1j1um1Y7KWcyW79nNdG69xneKhDkDxsqyXSQOcOlsiWzTOYMBpktsnD7crlceZFUZvUbE584YKWiwK0jJhbr1ZXftsvcrtqnr/oxMWBsqUdVFoHETyzKk2dKsdclj9sln1umkyyk4ZnJLEnHTC7WKyti26LuNNK6jLadaTW+hCBzxJCWNYe0hUk/7r5yVS475SBzc3Nz9OuRI0cmXX/EiBGm26aitwe0JF177bVaunSpCgoK9P3vf1/777+/fD6fPv30U913331qbGzU559/rkMOOUQfffSRampq0jqmlWAwqI8++sj0vXHjxmncuHEZPd5AsspMzIeezEGLC/lHtf6YD0x/2NBjX7brhv1j/53bAxGtbQ9pcqUvL8rvWt38MDiKfNdbLjt7mcz0voJzubwmW2Yy98nasyojXRx3nTULJneHjejfX1+9we1MzpRMx6bOsF76plPn71BOJjOyosibGGSml2nuRQzDNtuLctnDy1B4Vl7VFooGmKWeAZ1HFrdrv/FF8g7AoFnvgPdXTcFogFnqqdzwz8Xt2mtsoVyu3D/PAoNFqpnMVrfUVqV4gcFiKFyT7Vg9Bnhd1pnMqT472D3LmlUdk+x7MveeQ0l6cSdL9V2Zv9/u+703dIW1sTNzs8eHQqUWpy3QJOm86WXatsqnpU1BbVnu0cFbFuvc1+rSPvbYEo9l394PNnSbLk9Vkcdl+vfSeykt9roUtKiQOxx7MkvSf21dov+s7o7+3Dwu6ehJJWntK/7f1+3q6cMdjhjJezIPlkzm9vb26NdFRcln1RYXfzvjt62tLdXDSZKampqiXy9dulTV1dX697//rV122SW6/Oyzz9YvfvELHXrooVq8eLFWrVqlq666Svfcc09ax7RSW1ur3XbbzfS96667Ttdff31GjzeQLDOZLbKzBoLVILnVhfz/vupIWPZ1S0ihiBF96H9tZaceWtQuQz0X9d/sXqmdRuY209lq9htlHpHvAmFDhmFkLZM5DyYqYhDJ5TXZ6satLZh6JrPZTWJ3yDAtQfttueyBv1YXuCW7+FBvTxi7TMX+tIXwhw21BSIaWcwHA6z/TpBbViXkelEue3gZCs/Ks9cmDs61BQ0tbgzqO3HPlKGIocbuiEYWu+VOMxDc+9z7r286E95r6I5oQ0d4QMt1A0ON1W2p1b3zttXmA/WHTRiY6gVAtgyFa7Idq2QebyYzmW1udFstAmy9x7Y6h4Fom2h3nuny9xnHfn554j1Kf4wtGfz3NcUptPpzuVzab4si7bdFZqrTjC31aFSJR1uWebS2PTbgsKkzM89e+4wv1Kw1iffEvccr9ros/waGayZzTZFHtx1Uo1dWdMqQtPe4Im1ncY+RzKQKr2aMKtCUKp+m1fi0bZU3mpRl9dnXq3KwBJlzIRKJ/QP585//HBNg7jV27Fg9/vjjmjFjhiTpoYce0i233KKKioqMncvo0aP16quvmr432GaBWfVkzmoms8UHj9VFd127ebS2K2SovMCl+q6wHlz07UQIf9jQHZ+06p5DR6T94J8JVhd8MryQ7wKRnv+yFGPOeXYmBpdcXpNLfC65XYkBltY+paMtezLHPZBYZTKbqchiuewjJ5WYDnr36tgcRG+xCSKl+xD9/PIOPbOsQ6FIzw32f+9eqREEm4e1VP5OkD3J/glaLJ43MDQNhWflbyzKQdaZZPCs7wjrN3Ma5XP3DPiNK/VqfKlH+29RpK3KnQ219A54L2owL5toVRUFgLmgxYXJqkfklmWJA/Vet7TbmMIBOT8gW4bCNdmOXU/mQotLcKZ7MpvpfWaxGtsaiOeXgRhb7t1nbWdY/1nTlfC+Sz1tCPxpJDiPcVAuO985LZc9EHrLje8yulBr2zM7AaDXvuOKTIPMkyp6/rh6Ap7mf4TDeVx3RLFH504v7/d+vG6XrtyzyvI9j8v6Obwii8mjfaUcZC4rK4tmFnd3d6usrMx2/a6ubz+IysvT+yH33a60tFTf+973LNfdeeedtffee+uDDz6Q3+/X3LlzdfTRR6d1XDM+n0+77rprxvaXS/nQk9mqL06qs7B6gszSW6sTL3wt/oiWNYU01aKURDZY3ZgMxAw2IJOC4exlMRd6lNPJIBh8cnlNdrtcKve5EgIoMZnMFg+eJfHlslO4CS/fvG1hFmaHbl/j01bl5XpkcbvaTLKyOzd/r3ZB5nQeeBc3BPTk0m8rl6xsDem+L9p0xR5VKe8LQ4fZ3wmT9XIvnOSe3aqUIIamofCsHD8RrJdZ9tOG9p6AdDAirWkLa01bz0jrdtU+50HmzZ9jZi0yJD7ngFRZl8s2X9/lculXu1Xqrx+3amVr6P+z9+ZxllTl/f/nVN196b17Znr2HQaGZdgZYCCACGJIVIzRb5CYL5p8NSbxS6ImxiUuka9KEjXmp5jEHTXuqCCIIMiiwz7DwMDADLP39N5996Xq98fte/su55w6Vbdu36Wf9+vFi+l7q27V7a6qc87zPJ/Pg/6AhrefFi25BxFEu9IJY7IMkaW9hwG6SwleJzFbKyVz2qK1cSZv4mf7Ezg4m8OKiAe/vy4kLJKp5zytKM4/vv9inJvMunh5ABEfq+lBq8LSjrDLbkw8ZnWXB6/MyC+S4u9v25BPKgqoh819XvQHNIynKtdypw8WXH1kMSzvIlUyLyQBvbaVWJG26cnc09NTSjKPjY1ZJpnHx8cr9nVCb29v6d9bt26Fzye3Pj777LPx6KOPAgBeeuklR8dcDMQFNp7hFujJbHeAtKoAH03msRnNSzKLggakwCFaBdEUIGOYQtcDt2mF/ukEYYeoT8N0VT+E8oRKPXbZIiKlnszKuzgm6tNw1hI/Ll4ewGcen8ZjI5mK9+O5gp2+2z2ZeQVjT5zIIG+Y0GnBsmjhKplJ4dd0LJXMZJdNtBki+0Ne4djROF++MxxRH6SL46RIkSUqWCMIgo9IsCBTVg1HPLjl4j4kcwYCOqM+6ATRBojGTV1jELXLtW2X7UCl6/fIezJbrY8/8/g0nhotrrvT2DuZxd8LFI2qn+mEVM7EsXgODxypVbPqDHj9xhA8GsPDR9OYsjHf7w9oiDZJaekm1cIBtzhtwCdNMvt1oGcuibip14ughzXE9canAX+8OYLPPz1T8drFc5bfMrvwhYhVLXZ8siRzk4rkbB918+bNpX/v37/fcvvybcr3tcNJJ51U+nd3d7fl9uXbzMzMSLZc3MQ4QWG/vrC2BkK7bJvxqGKy1hRUsjV7jSCamGQNqk4nWpts3hS6HrgNJZmJdoOncChPuPLssj1a7TjLS56JCFosWt2k6GyiMYYVnH6QhlkYw+R22faP+9DRNPd1sgxd3HB7MtMcqulYKZkTOVNoXUoQrcBvj6Vwy84p/MsT03h2PCMck3mq/GOcJLPOgMGq9g6GYI0KWAe8EwvVs4YgOgTR3FOlR2TQo1GCmSDaBEc9mW3OSdMOeh0Xj+0XzCdkMeADM9myBHOBp0czOBqTK1sbpWT+8b5ETXswALh0RQBLwx4MBHV85pI+3LAlgj/eHMZ/XN5vadV7+apgWz1nr1wV5L5er5J5x4ra/sznLPHjtEG5sHJJyFP6/Xk0htMG5Ns7wacVXD4uWu7HX2/rwpmDPlw07MdHL+wtOfXIYlikZG48ot9/yMPgadLv33aSeevWraV/79y5U7rtyMgIDh06BKDQC2JwcNDu4QAULLCLTE9PW25fvo1KUnqxwkscLWQ/ZsA9JXNyLpAv2qvZRkcy+2+yESRaAdEtlzHErgduYyfRRhCtAK9CcLZM+c9TH/EWI3bssovbLkRBWHkSXdRzKJ41MS1TMjtYmItYqIIXojUhJXNrojJln6V7l2hRHj6awq1PzOCJExk8eiyNT/xuCnsEzlg8lc4xTtB3SUivCe6I1FaAdcHxQs3DCaJTcKJkJgii/RAtA3TGxCpi20rmOpLMDhLd9x6sVQ0DhUSzDN55rrDhqsIjlTfx2Ina4m+PBrxuY7j0c8Sn4TVrQ/iDDWH0BXSskrQL6fZreM1aftK2VblsZW0y+OwlvroTeVesCqI8BaMz4Ko1QZzU64UsNbO0qp/1mUMNSDLPXbuMMVywLID3nduDvzyzG2u65x1iZTEsGm8bj+j50t0kq2zAQe7t1a9+denfd955p3Tbn//856V/X3PNNXYPVeLqq68uVWns2rULmYz84frYY4+V/u1UPb0YiHEWrAtplQ0AXo1v0+vULlsUy252aEv2fSjJTKjy8nQW//NCDD/fn8CMy/aTIkt3t5XMItsigJTMRPvBs3mazRglVw2eXXaIcxOIqpx5FBNtjZ64MwDhssSyaH4Qz5nS55GbVdVkGbq4EfVklikEicajcouTZTbRqvzqUGV7hpzBVycDgiQzZ9tl4dqgrmieDRSCuLLnGI19BGEPuz2ZCYJoT6RKZsH62raS2cFatri2F/Zklnzm4Vm+YtnqPNKcqcvGHi/Wd9vuklrilZlcRQF9kR3LAxgIihPYKyVJ5tdvCCEgCwq2IOt7vLhpaxRRLwMDcEq/F+/Y2lX3527q9eJD5/fiilUBXLYygH88vwdbB3zw6Qxb+sWJ4+p+1mdaKJ+doBKbldllL3BqaVEi+hvx3BYXCttH3rFjB5YuXQoAuP/++/HEE09wt8vn8/jsZz9b+vlNb3qTw1MEVqxYgR07dgAA4vE4vvGNbwi3ffrpp0v9mKPRKLZv3+74uJ0OX8m8sEkexhj34WNfyVy0y+a/3wjrEDtIk8xUnU4o8PhIGh94aBLfezGBr+6J4R8fnnQ1aCvKI2cMk1uQ4pTqCVE5dtScBNEK8JTMeXM+IMxVMnPGWVtK5gWyyw57GbQyG6uwYBERyxjScczN8ZfUXIsb0X3ixJKdcI+8QpJf5nZAEM1k1xhftcxjqiqCOysY/4Y57SVk9ZrpnMkN4hYhu2yCsIeoRQMpqwiis8gLhkePVljH8nItdl2QnLR8sXIekyWMZwRzZtlpmKbJ/UyfznDz2d04d6kfg0ENSySxOB6iVlWnWyQ1RUrmgM5wucB6utW5YlUQX7pyAP991QA+eH4vulxSi27s9eKmrV3489O6cHLf/O9VZoFdrWTuCehYV0cxAQ+V8VLmREnjbeMRxQOt7Oobie27Qtd1fPCDHyz9fMMNN+DEiRM1273vfe/DU089BQDYvn07rrrqKu7nfeUrXwFjDIwxXHrppcLjfuITnyj9++abb8aTTz5Zs83IyAje8pa3lH5+97vfjWCwPR9gCwEvWLvQSmaA//DhWRzJKrxLSWbB+81OMsuq10nJTKjwvRfjFRPL44k8fnOEb6XjBNE9kskDcReDW9UTonLILptoN0RVgsXFIS8wzLXLtqNktrDfcovq7yayyz4uUHwVcVL9LSpudvNZRLQfIsU/9WVuLqIAXzluu68QRDOYTM07lQBixTNPySxSWwGFZ5iscJSUzARhD1F4hYLeBNFZ5AQx4uKSgdtqx+a6wUm4trhOFyqZBeO6aZqYFMwHRMlnoODCwvtEv87QF9Dxf8/qxud/bwCfvqRPfuKKbOz1St/fJHj/7VujTesV6wYaYwgukApb1peZVyxwhstqZqUks8wuu43/zu2CXxBa726iktlRqcNNN92EH/7wh7jnnnvw7LPP4vTTT8dNN92ELVu2YGJiArfffjt+85vfAAB6enrwxS9+se4TveCCC/De974Xt9xyCyYnJ3H++efjrW99Ky666CJ4vV489dRT+PKXv4yJiQkAwNlnn40PfOADdR+3k4lxBqlIEyoefDoDqhLePFWKbIFdnCiItnAS5HYTmcqG93cgiHIyeRMvT9fa5txzMInXrAvV/fmGaQorI7OGiZhEXWGXoZAOBvEkmCDaCZ6SGSj0ZV4W5lf/8hYmdq79or1UowNl0aqksqgI7WgjksyMIcd5SpCSeXEjWsimcgbQxN5Dix2VW3zGxXkEQTSLjFEY14tFV0c5/ZgB+3bZ6bwpVfvT2EcQ9uAVTzMU+l0SBNE5yJTMQHGNXfk8sLs2dbKWLTmPCQpkRZ85nTGFY74syZxW7EPv0xmiPiZ1T7FiMKihLyBXRK+IenDOEj92jsz3cz5ryIcLhv2Oj7vYWCnpp80T7pw26MMP9iVcO75KnlJql00DbsMRLS1EMcqFwFGS2ePx4Pvf/z7e/OY346c//SmOHz+Oj370ozXbrVixAt/5zndwyimn1H2iAPDJT34Suq7jlltuQSaTwW233YbbbrutZrurrroKt99+OwKB2ubsRIFM3uRWZEWaoWTmVLjwFgZJyQJ73i6bvw2vP8VCwlNmF6HAG2GFSCkher0c0zSxazyLo7EcVnd5KixYisjEgZm8iXjOvUKIgM7Q49e4FZqUZCbaDV5PZmDeoaIRdtnFyXyj546RaiWzYBFxLM4Pshdx4iTi0QDwis0o0L6ocau3GuEuKnbZssAYQbQTU2kDobn1smgePsxNMos/M5UzpWr/hIvzcIJYDPDmnj694J5IEETnIHIJKS6tuUpmm+4gdteyXg2lllM+DVyBhSjJfEjQjxmQuwKJzpGndOwP6JjNyNfvMkQq5Wr+ZlsXfvFKEi9P57C+24PLVwUrWnERchhj2LEigF8frnSvLKjTawNBG3u8CHqY0OLcLiqxWaldNtV/NxzRcyTaxOJ7x6bt0WgUd9xxB3784x/ja1/7Gnbu3IkTJ04gGo1i/fr1eN3rXod3vOMd6O7udvN88fGPfxxvfOMb8Z//+Z+45557cOTIEWSzWQwNDeHCCy/EDTfcgKuvvtrVY3YivH7MwML3ZAbU7bKlSua590S53GbbZcuOL/pbEEQRkVJC5W79z90x3HMwWfr5jZvCeP3GcMU2sj4zbiuZPRpDb4CfZCa7bKLdkNllG6bJneTXY5fNMD9hX2i7bJGS2arYxUmRl1fj+x2QXfbiRmw5t8AnQlSgYpctswImiHZiMm1gOFL4N8/JI+hh6OYEd2R22em8iSlJIQYVWBGEPXixJJ+9VqQEQbQBvBAxA1DUMfHWDnaLU+0qmcvX9Ywx+HRW8xmic5AmmSXzBHGSufb79wU0HJgRfpQlqklmXWO4Zm39rouLmVetDuKBw6mKqMglywPcZL1HYzil34vHRjKuHLtuu2wSEDUcYZK5CXm9InV3Br/uuutw3XXXOd7/xhtvxI033mhrn9NPPx2f/exnHR+TAGKCxWq1cmkh4E34uUpmSRV3cZAWbdJsu2zqyUzUg8iOtj8ov18PzuQqEswA8KN9cVy9JlhSYQBypX3GAAwXEzseTWzfQUpmot0QXcszGUNYRVqPkjngmVdh6FohkO0kebOlz4vfXx/CJ3dOC7epnpyKlczyLLKTIi9RkXOc+lIuasRKZppHNRMVJTMViBCtiKFw7VYzlZq/lnlOHsvCOlctmZUMldZKZhr7CMIOvFZl1B+SIDoP3hLAo6E0DruhZJbFcnlUr+sDem3RtSMls0T4ISrq5iX6+i2srq1QTTIT9bOhx4t3nBbFt/fGkcqZOGepD398Uli4/WkDvgVNMsv6U3tpzG04oudIYIH6hvOoO8lMtCeiPsBNUTLz7LI5pyezfSi+J7rJmp1klvVkrqcfBrE4ECmZoxb29j/dX9uTI2MAL05lcfrgfD8UWew3mzeRcvES9WgM4SZWVhGEm8h6MouURyHOpE9VyVy9aF0d1fGMRZK5x69ham6bsJfhT06O4NIVAcvkcHXRmVdn8Gm147PVultWxCLcRzBmU6JqcSMqRLIbLCLcRWWKTT1liVbEyZBSdOIxTBPHOeMozyq7cCyJI5dFT+YEjX0EYQve/UaqKoLoPHguIXpZoRdv7dDonszVx/Tx+kIL1i6yJPNsxoBpmtxCNtE58p57PKtlVfw6sDpKaaSF5LKVQVy6IqDU7uH0wdrWiEXWd3sQ9jI8M5YFUCjGkHVjUSnMksWwmtCJddEh+vs107mFng6LFFGwpyk9mXl22ZxBUmYVlpq7u0S2I9WD7lQqjwOzOSwJ6VgWbvxtQHbZRD2IlMxWVZW/O57mvl6tfJTZZWcM09UAvlcTP2fctOUmiIXApzP4ORZYMxlDqDziKYI9GoPOrJM11RP5VVFPaaHAo8vHcMvFfXhmNA2NMWwb8pVcDDwWCweeFXjIqyFjUzntpMhLNGaSZejihnoytyYqv36RgxJBNBPZ/FdEsWhrLGlwk9TLIvx1pcwuO28CEynx2EouHgRhD948klRVBNF58IbH8npurl227Z7M9s6per2imug2TROHZsUHy5uF+QBPGGbHLtvKDVHG+m4vdHqWLjgqCWYAWBLSMRTUcCJZO6eM+DT87Vnd2DWWwWgyj9MGffjHhyYxK1ij1d2TmQq7Gs4fbAjhC0/P1ry+rrt5bgOUZF6kNLon80gij398aAJRn4Yun1b6/8XLA9jcV3nBKyeZLZTMpilOhpV/3m+OpPClXTNI5wv9Ot5yUgSvXd/YXhEyJRfZZRMyTNPE0Rh/silL3oj6wRb2q/xZdn2m86arCiQPY4j4+M8ZmSU+QbQqXT6G0WTlPTKbMZAQXM88u2ygsAi2CiRXL5RXdcmncUEPQ49fwyUrgjXvWbno8Hq5hDwMU/zaFSF27bIN0+S6mQCkZF7siGzlScncXPIKbgV07xKtiF0LTACYmptEH+VYZQMyJbP8c08kxIHlnFEYSylgRxBqUE9mglgccJXMZUlQrl22zbWp3bVsjV025xx4cbyxpGF5bjNpAxGvBtM08cuDKeway2BpWMeSEP8Bx1cyO38Ybiar7JaGMYbTBn345cFUzXujiTy8OsO2JfOOlgXRgUgFb328oKwnMxUjNJwzBv0IemIVcf8zB31Ct8WFgJLMixSRYtCtnszTaQPTGRPTmTyA+UXz5l5vbZKZc0h+klm8Ok/mCkFp0ZBcHMSTOQNf2TNbSrKZAL75fAwXrwigx9+YG9EwTWlggeyyCRmTafFkU5ZkPiJITAMFlWU5sj5xbl+fHg3YNuTH91+stfLePhxw9VgEsRBEfRpGq6pFZzKGLbtsoLAAtUwyVy1SreyqZP1YrJTMvPmAE6t7u0pm2XhJaq7FjahautktURY7Knk6sssmWhFHdtlziuNjgnn2MkGSWaZkBuRJZqBgme3TKUtGECrwYkkU8CaIzoMXIi5fLvAKVLNGoUBSVZFrd53hVMl8WNAir5yZjIFhAF9/Loaf7U9abu/nfMf+OuyyN1KSueU5fdDPTTLzCn5liWS1nsykZG4m3X4NHzivB998LoZj8TxOHfDhbadEmnpOlGRepDRaySxS50b9tZ/PVTJzFuKynsypvNzSt7jQePhouiZpZgJ4diyD7csbk+CyEmem8iayeRNeeggTHEQqZkA+4X1+QmyhW31/OlFyOMWrMazv9mB5RK9IhIc9DFv6xT1ECKJV4VUKzmZMsV22YJxVsSSqnsgvj3igMXGSR1ZdatUGmmeXHXbQUqPaOcEKWbU42WUvboRKZkoyNxWr5BlQ+BvlDNOyuIUgFhJnSua5JLOglY0oyWx1LKvHWCJnosfy7Aiis8gbJp4azeBwLIeT+3zYpJjg4NnbUsCbIDqPvFk7eJbPNf2SAtWQ4pzU7lyhek2vmmQ+KOnHXGQ2U4h7//KgdYK5cOza1+rpyUxJ5tbnlH7+3+giTr5Dti6rtyezlWse4Q4berz40AW9zT6NEvRnX6Twksw6Uwtyq1CtlCzCC8bzjpnJF2yCy5ElmZM5UxpkTM0tNJ48wff5fHFKnJCrFxV7lVmyESQEiOz4gELypvo+KfL8REa4nxtJZlnVmgyPxsAYw/vO6cFJfV54NGBtlwd/f16PdJJCEK1KlGP/XlAyC+yyBde5yvVfPV56dSa05rT6TMuezDy7bAeFaJm8KXxO8ZAVz4gK5IjFgWiOSnbZzUV1CkFFIkSr4aQn82QpyVw7P+8LaEIHkXqHL3IDIBYbhmnis0/N4P89No1vPR/HPz48iTv31zph8eDbZdM6kyA6DZ6gp/xWd6NA1baSWSHJnOIUwsj6MReZyRh4eTqrXMTNe+7JnM4uWyEWXm3q8TTVhpdQI+zVsH3YX/P6tqHa17ySeJBKbkgWT9IU+0gTnQUpmRcpMc5CNeLTlBvKW2Enycwb+EwUJgzesti5LDiVypnSfq7FRO9BwcA9KOhh4QayfrdFZjMm+sgpmOAgUzIDQMbgVyjunZQomavuJbt9ZgCgx68hmbMpUcR8RdtQSMdHLuiFYZo0ASHaGt64lsyZmBFYzQuTzAoTeV7SeHWXB4cFzwlZMYhlT2aektlBSSpvPJchex5lqS/lokbXGLxabbKGlMzNRTXHH8sa6GpQaxqCcIKTxG88W3CgOspRMsuKvlQU/9LjWlljEUSH8fJ0Do8eqxQIfPeFOK5cHbQslOTbZbt6egRBtABWSmZRwbWdAlWeM4KMavU0b42f5hz/kIKSeSZjcNfoIuyumd9xWhRnLfHj3oNJPD2WKRWSBnSGt5zcXBteQp0bTo5gJmNg11gWDMAfnxTmKpxloR2KtxBOoCTzIiXGSQK7ZZUNiPu48gZEUfVMxqi0kJYlkfOmuM80UKg+M0wTI4J+V/Uu/GUoKZkFSXkZL09nsXcii5VRD07p97pWIEC0FrwgVjnpvFlTZTaWzNf0iC2nVsls/7y6/ZrQKlBGdVCAEsxEuyNa6PFUTgAQElhOqyiZeYvUVVEPHgLfpUP2mRpjYCgkgXnH4QXwnCiZASBtqLeEsBozEzlKMi9mAjqrcd/gBWqIhcNQdCogJSbRajhtF3MimccYZ569LCwOrdSrZE7auH8OzuawdyKLJWEdp/Z7aa5NtCU/46iWEzkTz01ksXVA3mKJd2/T3JEgOg9uT+aypXa9SmbTNJVEQ+VUH5P37EnPOX0VY7iGaeKIYk/mbhuxY5Ea9fKVAdx7qLJv7+WrAmCM4Zylfpyz1I+ptIFnxzJI5Eycs9SPHioUbRt6Ajo+cF4vptIGIl5+XAeQK5ll/ZoJQgQlmRcpXCWzg16LInhKZr/OH+RED6/JlFHR/1Fmlw0A02m5klmWELNrgWIHlco3u3bZP9ufwNf2xEo/X74qgLdv7bJ7akQbcNRispnOmUDVOlumYgbcscvudlgOTr05iE5DZBvFUxcHPUwY5HLSkxkoJJntbF+Oh6MKBfgW4AAQdphkzuRNQLGFk5X9Vzxr0CJ3ERPwsBo3DlIyNxdVgWWc7O6JFsNpkvn5Cf48e1lEHJFzeqwiccVimgcPp/Afz8yUejyv7/bgr7Z1Y0kDXbsIohGMCGI3VmOJaZrUk5kgFgk8sVD58rfeVjt5U70tTJFQVcCLV/RtorAGL8bCp9OGUjHaTNrApLf+JPM1a0N4bCSN6TmhVtTHcM2aUMU2PX4N2zl9fIn2wSpmIk8y05hJ2IeSzIsU3uTcXSVz7eeLAvF9PK9fAPcdSuJPtkRLPycsJgKTkiRz2jCxT9J32a4Fih1UKt9kKuxq0nkT39kbq3jt3oMpXLMmhBWSZAPRfmTyJlcpUQ6vQGKvIPhVpNppwKldthNkExmCaEdESubDHMsrWXGGU7vsVV31JJlrVaGA+DtVL5pVkRVyZfImfvxSAg8fTaHbr1mqU0gNubjh9jUjJXNTUQ2+0b1LtBpO6x72T/MLQGWJ3HpdsxIKJ2uYJr75fAzlQ+5L0zm898EJvGNrFBcMU7CYaB9ES0arZWvO4Lv00BqUIDoP3vNAxS5bVWTkJE7W5bfuyVw8h2IiT+ZCWM5MxkCA19BZgEhHtiLqwT9f1IedI2nkTeC8pX4MBKkYbbEh0xn6acwkHEBSkEWKqCezW/CUzKKg9dZBH3jj7n2HUhWBQysl85QkyZwz5Ik3J5MHKxJZA1NpQ+mzRT2seeway3CVXr86lLRzekQbcCye5y6Sy+FNkJ+3UDKn8oV+ckWcBNmc9lVUcAQmiLaiS6D65T36uyX3jVO77P6A+DODuvw+FeWMRc4mTpXMsoX8D16M43svxnE0nsdzE1l894W49LNUAu1E58K7T0jJ3FxUk2cxuneJFsOpulhkaRmVROvqvfwTCkUao0mDW3SdzJn41ydn8I3nYsr29gTRbHSBzbvVbSsq8CfrT4LoPHhzUL3CLps/LqsWqDpxvKwWV8mSzEXGkmqJ45mMiYmU2oTCr0PaUrE/qOPVa0J4zdoQJZgXKSIbbUBdybx92F/z2umDctEA0bmQ7HERYpgmThvwIZY1EMuaiGUMxLNm05TMPX4NFyzz4zdHK3tKxnMmfnM0hStWBQFYB5YnLSq6nh0XJ97ctMvOGya+vHsW9x1KwQSg8lu1E3ibEHzPV2ase3gQ7cVRQU/Xcqqv3VTOwEGFa2E2a6BPL0wm7QbZfJq1QlKEbCJDEO2IqICKhyzJrGKXzUuwMcYQ9bEahwLR9uV4GL8rs8gu22lPZpFbSCZvcnvuyRBZhr4yk4Nhmlge8ZC9UwfDK7QgJXNzUZ1C8ApcCaKZZB2u/w4JkswRwdgJuKBkVnjOxSyKlu94OYHxVB7/57QueGmcJFoc0ZLRqlBCVODvozUoQXQcXCVzWWJVtBZWLVC1oQUqUb3eV7HsHlVOMqufEK2HCStkSmbV6+eSFQE8fDRdEVHaQTbrixZKMi9CNMbwnrO6a153s7J5hhPsFgWtAeDVa0I1SWYAuHN/ApevDIAxZrm4limZAeB4Qjxwq1haq3Lf4RR+dShV+lnlk3lJeRFMKW1NdAJHOT1dq6lOMh+atVY/AwXL7L65sd9ukC3i0xwrkqknM9Fp2Eoyy+yyHSqZAWAwqGM2Uxv0thrXRfE20XcKO7yBRQG/fVNZ24v36nYfecPEvzwxjZ0jGQCFHtV/e3Y3hqj/ZEfCu0/cLBQk7JNTXD9QT2ai1XB6SfKKugCxC0g9xyqicv+oFC0/fDSN6fQU3ndODwWgiZZGaJdtcZmL5pV0vRNE52GlZBZ0ZlxQJbNo/V6pZFa3y84rxq5VCtiJxY28J7PaZ5wx6MffbOvCPQeTyBnAZSsD1Mt7EUPhfqKEJrHSsEM2b3KtrUVKZgDY2OvF+u7amofDsTyeHc8iZ5iWi3NZT2Yr3LTL/slL9lRZgDhYwUN0rhRi7TyOxa2TzNXXw0FOH1ge5YUNdpM8YS9zrEgmJTPRaUS86qU/Urtsh0pmAFjf7eW+bhVQE1VxL5Rd9nMW/eN5VPd1/fXhVCnBDBSegT94UW65TbQv3J7MlGRuKtSTmWhXnNpli5CNkfUeS0XJrHqPPTuexXf2xuo6H4JoNLpgzWglDhAqmSnhQhAdR44Tx6rsySywy25kT+aquLcoWefELjtnANOKsWNybyCskLna2ClSOG9ZAB84rxcfvqAXO1YE3Tg1ok2hJDPhOrOCbLCV2uvVa/gPo/sOJZUW1lOKvSl4uKmCGZEopkXYscsW/X6pxVbnoaJwr54gO0ky2w18Rbwat4+6CrJqOYJoRzTGlHuU99Rrly3oKyUaP7cOyPvhiKq4xXbZ7iqZ94xnuK/LqO5L+V/PztZs8+So/c8l2gNuT2ayy24qvAAfD1IyE62Gm5dk0CMvwKzbLlshgWxnPfnYCI2TRGsjmhZbxW3Edtn1nhFBEK0Gz02nfKkgVBErrh3sJpkDOqspaPELisTLn2Wqdtl2oMIawgqpXTbFbQkH0FSLcB1RnwiZkhkALlgWQDcnsP3SdI6rjK5G1KdRhbT7Y7ot7NhlxwSVaxRj7TxUrvvqhbZKP2ag8j61a5ddn5LZ0W4E0dIMBNQubKmSWcUuW7DNiqgH24f9Fa9tH/ZjICj3ORLd+lHBiiPk0CefFxDMGSZemHSiZJ5/dhkm3+XEqn0G0b7wgkVZw92WL4Q9VH/3pGQmWg2nPZl5RCycPuq2y1ao5rBzj9XjAEYQC4FoqWmVZBYVT1PChSA6D97QWO6C4NPBdRxrlJK5ixPPtrLLNk1T2S7bDmSXTVghi+nSmEk4gcL9hOuIrJ+tksxeneEUjuoqljWQaLD6wU27bCfYscsWKZljdj2PiZYnqRBQKi+QME1TWckcKwtE2b10Il7NcbKY7LKJTqTfIplbhLfwLKJilx2UJHnfdUYX/vy0KF6zNoi3b43iXWd0KZ0Tj4jgPH06k1a8iuAFBJ30YwYqC8pkLQVU+1UR7YXoPiE1c/NQnULHKMlMtBj1qovLCVsMjguiZBYMqjy3k3TedDXJThBuI2rlZqVAzAimhqTKIojOg7feK49TaaxWWQyorxvsrlV57maiZG9xfRzP8dtNrumqbSdpB1E/aoIoIpu6UpEC4YT6nloEwUGkZBbZb5YT5mSu4llTyS67HtxKMjtV0iRyJqbShtRKtYgogGBHDU20B3aVzJNpQzmIW49ddtjLoDvs4e7QbZcgWpp+RSWz1C5bRcksmexrjOGyle70wJG1twh5NUzbVEDxAn57xu2rmAFUFJ3tmxJ/RipvItzGAUXTNMEcPmc7GdF9ksqbCPFbkxMNRrWgg+yyiVbDzaVTo5XMTnsy+3Xx3GM2a6BPpyg00ZqI4ipWDnSins2kyiKIzoM3NHqq1k8BD6speFZVMtttq8gTVomSdcVE95ig3eL6bg8OKLoU8qBnHmGFTABEcVvCCXTZEK4zIwg+WymZAb56yjCBiTr6LavgVk9mlaSgiKdH00rbiZKIsaxJdpEdhopqobxAQtUqG6jXLtuZkplBXJVOEO2MlS11EaldtkpPZod21XYR2WUDQNjBOfACfs9NOOsHWT4GvjgpfubVMx43k5xh4su7ZvGnd4/h3feN4b5DyWafUktBSubWQ3UKQXbZRKvhppI5YrHOrVc1nMxZr/N4PZkjXk1YOCZqwUQQrYDI0MsqOSQSD3jbuPCQIIhaDNMEbxivjlPx1g6PHEvjz+4exc0PjON5yZrUvl127XgrWr8XY9CjAqvsdT31Vc+SEpWwQubwQcXuhBMoyUy4jsjOWaaMKiKyGhsVVHe5hWjyMJnK4/NPzeD//noc/7V71rLPYz1WgE+eyCCRNbB7LIMRyfcVKZZNUACvkzBNNQV/MbBu2LDKBiot2kUV3yJCHlbR60YV6sdMdCr9QeuL26fJE8lWCWSGhasolTmPhCzUWjyqC7lyhom9DvoxA5XFNzIlc7smmb/1fAz3HEwimTMxkjDw/z0zi91jzhLynYjoPlFVJBDuo/qrT+VNV5N6BFEvdue/MqyUzG5c+1brAt46MOxliArOTbRmJ4hWQOS0ZZX0Eb3vI9E+QXQUecEQVq3OFK0dYlkTh2bz+Nhvp7hFWoD9JDOvoNzKLnssyY/9ruuuz3iWWgQQVlB8lnAbsssmXGcmXTsQa6ywyLVCtEAfFQy8bsELMqRyBj722ykcjhWOfTiWxOFYDh84r0eoxqynL/Ijx9J44kSmNNl4zdogbtgSrT2GJCAwmzGUkvlE65POFwoHrHjkWAq7xjIYT+W59mEaA1ZHPdhfpXKusMu2eXuFvQxOBJVUQU50Kv0B68hVt1+TVoRaVRsHPWzBKkpl5yLrO9kf0DCRMmqeXdUL9Jenc5Z2hyLic9KWTF5eWNOOSeaptIGf7a9VLu8cSePUAV8Tzqj1ICVz65G34aKTyJro8tNcgGgNREpJJ0QsqsDcyOcmsiYiEmGTSMksUlnXs24liEYjumesHOjILpsgFgc5wfyz+la3WmNnDWDn8TS37ZTdYjReLFacZC78X5RkXhbWEfQwx2taeuYRVlB8lnAbykYRrsOrio56mZJNrmiBLhp43SKdLyhHy7nrQLKUYC7y7HgWz0gURfVWhJcvmn62P1ljoZ3Jm9LA/CzZnnUMScXI10zGxNE4P8EMAMNhHX2cfrHlgSi7k+eCXTYpmQmiyICCkllmlQ1YK5kbYZW9KsqvNZQls0OS8xgK6dwFbXVAcM+4c2VuImvCNE28PJ2VKijbMel470G+NfYhGy4VnY6oJ7NbbU8I+4iUJDxkhZIEsdDYVSjJsE4yu6Bktrh/xEpmUU9mem4SrYtI/W9tl81/nVR9BNFZKCuZFZKtdx7gr8FsK5k5bmAejdUkvoH5tSrPLjvsZQh6NHRJ3MWsILtswgpKMhNuQyF/wnV4PZlV1bUitbOoT4WblK/bE1kDP9yX4G734OGU8DPiLid5f3mw8lhWwTmyPescVKyyVVjV5eHefzNl16rdwFfIyxwljGkSQ3QqPX6Nu3is3kaG1QJYZYFsl1evqa3Y/r2VAek+MleSoZAOP0fUXZ0AfFFic21F3ix83r4peeJVtVCnVcgZJn4pSDLPkNqshFDJTEnmpmHnV09tXYhWwt2ezFZ22fUfw2ptwFsnhr2a8NxIyUy0MqLkTrrsPhhL5vHx307iT38xio8+OokD01mJXTatQwmikxCN4dW3ukqhtmgLu85bvJ7MAD/hK7PLHgwWFtT1uFTSM4+wwkNtJAiXoSQz4Tq8YKhosK1GVAV+osE9mYHKhczP9ieEAcvfHU8Lg9eqCo1ev6ZUWfbYSLoiABCzSGKL+jUT7YdbVq+rovwkczpvlq55u3bZIY8GjwPbXlIyE52KxhjXMaCcbotx0KPJizcaoWS+dEUAFy/3l35e1+3BH22OSPeR2WUPBnXu2FYd8DsSq29Mj2dNaT9moP3ssneOpDGR4o/hKnbsiwVhT+Y2+3t3EnbssuNUDEm0EG5ejm4qmUVLRFmRRlbgdhXxMuG5xajog2hhRIUZxcRMLGvggw9P4pmxLBI5E7vHs/h/j01jiiN4AKgnM0F0GqKpvxMls2h9YVfJ3CUoKrebZC66pKnG0fnHdLwrsUgghw/CbagnM+E6POutepXMCyFQSedNRFBI1PJ6IhbJGIVE844VtQow1cV6yMuwvseDx0bklqGGCTx0NIWr1oQAWCuVKcncObimZI56cDjGV/zNZgz0B3XbSuawlzmyGNQXqJ8sQTSD/oAudd2wsssGCgtQUVV2I5TMusbwrjO68eaT8kjlTCwL65Z9n+V22RrXsaA88J0zTIzUWTgWz3VekvkXAps2wF1L13ZnMfRkPjybw2+OphDxarhsZUBY2HHr49PQtYJLiHfu/6f0+3DOUj93+3r49t4Yjsby8OkMPr3wrBoM6rhmbciWXTYpmYlWwg0L6yIylw/Anmp6XbcHL3LcOhI5A6Zp4pFjaRyJ5bAq6sG5S/1gjAkLOMJeDT6dwa/XKrJo3Ui0MqL7s5iY+cqzsxivKs4bTxn4xSv8+RQF0wmisxCNq9VF2yriHtEQbTvJLIh7BzwMqOyEWBJ9THOEREUlc31JZnrmEXJIBES4DSWZCVcxTJO7YFUdHGUKqUZTXLDc8XLCMjj9wOEUN8msulj3aQxnDPotk8wA8MCR+SSzla0Z9WTuHJIuyStWRT2YFlR0z2YLSWa7PZlDHuao8MNLE12ig+kPasCk+H2VJHNAZ8IkTCOUzEX6bChlHSmZy54xx+N54UJelWOxnGUbjXayT35lJofnJsRJczctXdsdUZK5U3oyP3UijU/unEbx29z9SgIf295XM482TBO/PZ6u2V9jaEiSec94FnsnK6/Rdd2eQpLZhpKZejITrUTWzZ7MFmtdO5f+um4vP8mcNXHbrlnce2i+ndLlqwJ4+9YuYaFz0So74tWQrqoIofuRaGVEc5903sRvj6Xw4JHaMVCEzgqFlQRBdA4it4NqYYPKGprX8hGA7TiZXbtsnooZAAbmksxDIedyZLLLJqygdoaE21CS2SaxWAy33nqrdJtly5bhj//4jyteu/3223Hs2DHLz7/gggtwwQUXlH5Op9P493//d6Vze9Ob3oTh4eHSzy+88AJ++tOfWu7n8/nwrne9q+K1u+++G7t377bcd+PGjXjta19b+jmRM7H62e/Bk63sZzzyLMOt91Q+wK688kps3bq19PPY2Bi+9rWvYaPALrKc/Se/HjlfuPRzz+geDB59zHK/TKAbr2y+ruK15S//EqHZo/jm84WemlNpAxs5c4nJwZMxNnwOAODZ8SzGk3l89T/+rWKbeNbERk7A4sjay5HoWl762Td9BE9/7x5sFExmynnx9BtwNJbDcMSD2ayJgaM70Tv6HHfbl3cz3Hpn4fe8Zs0avO51r6t4/6tf/SrGx8ctj7ljxw6cddZZpZ9nZ2dx2223We4HADfccAMGBgZKP+/atQv33HOP5X6RSARvf/vbK16744478OKLL1rue+qpp+JVr3pVxWuf//znkclYJ/GvvfZabNq0qfTz0aNH8e1vf9tyPwB45zvfCb9/Ppj7yCOP4JFHHrHcT+UZkc6b2MgJGI0vOR0TS08v/azlM1i/m3++jAFf36shawAbMwYObbgaqfBg6f0XXngBP3jgF1iWMrBUcr6G7sVLpxbOl6EwUf/lPfdg49O7LL9rrHs1jq3ZAQAozu+/9KUvIRaLWe4rekaocNNNNyEajZZ+fvzxx/HrX//acr/+/n689a1vrXjtBz/4AQ4cOGC577Zt23DppZdWvCYbL97znvdYfiYhp5XG5GTOxMayAqXq+y1z/GXc+oO7pccbTBvo0ebvtyJDhx5GevdLuPVB+UKgekwG3L/fMoaJjVUFTcUxudCTmdWMyUwDbn1Em9u/8DwC5GOyjLt3Mwz0nVQak4tsfHr+fPc8y7BfEFR43etehzVr1pR+PnDgAH7wgx9Ij1mk+r69//778cQTT1juJxuTq6+daox15wK4pPTzYh6TTYA7T3z2WYar/+pdDRuTRbg9b//h93+CDVWXwucfYxXJdZ/Ph5v+4p0V2wwdehjdE/tw6FmGW+8SPyecPiO0jIGu4fMx0z//t/GlpnDrrV9BgjP3ZQwozz0XnxHFIhoakzuXVhqXq6m+5/In9mPjc/dZ7lc+Dy5SvOeKfPd5DbxYXfGeK1dlrt1Tu1YuZ2QPw8asiZEVlffcgWMncOT+72Fj2bYHnwY+fbcGw5wfW4H5e65YGNYzugd9L/+u4jhxDbj1gcqA+ELec+W02rhsBa2VG79WzvZdUfHayhd/jkBiDADw08fBjdcAgrXys9/Grc9bF3u2QjwNoLUyQGOyW7TTmGz3fsuVTTTLx+TH9jDsKps3l6+zymNTRdbu+R68uQRufbL2GRErm+NWj8m+1CRW772j9DNjwOf38p8zvjPfCMBX+rlndA+w6zF8/SGtYuwusvs5hhc0hnBPH7Dkmor3VNbKAHDYOANYWfkcpTG5lsU8Ju976nfY+PSj3G1vLbuW2/UZUQ6NybU0YkymJLNNDMPA7OysdJvu7u6a1xKJhOV+QOGGq0ZlPwDI5yuroLLZrNK+Pp+v5rVUKqW0byqVqvh5Jm3Ak03Am41XvJ7LAtWfls1WKiKKv1uv5VEBoHJVoeWzNcfkkddrv6ueS8GbjSMxdzr63H812+XnH/gmgN8c5f+OeOfPzMq/jQd5xGOq37WgZn7T5ghmMwb0fEb4XY0sMDv3J0kkaoMX8Xhc6e9aPbiZpql8HRpG5SRJ9Trk4fQ6BAr3jcogXX0d5vN5x+ebTqeV9lV9RvCuj/LrsLSd5Nqfzcx/VvV1GE8V/jZWA0E+P38mIS+Dxhgy6ZTSPafn5/82xUq5WCym9HsSPSNUMKuUVZlMRmnfQCBQ89pCPL8JZ7TamFx+z1bfb35m/WzRAHi02jtfz6dhpmKl57sI3rOwEfdb7RkW7re+QMGWkzcmz5b9Kov7y8ZkGUbW+lnIm3eU3svlan5u9HPfakyWzQdSORqTy+H9rnIcIbjbYzIPN+ft4/E0tEwc1eGpbBYo/3o+n6/GRlTPp+HNxqXXPeD8GcEAaEblfeOFqXT9Fiicb9HSl8bkzqXVxuVyqu+5fC4Hv8r6Mc8fl8vHnLjAjCKVSsE0zQrFFW+tXE4mW7inqu+5J0bSWMHZr3jsyrMs3HORORvvALIwOfvOVv06m3XPteK4LIPWyo1fK2d7qsY5hfkhwJ8fejLx0ppYRivE0wBaKxPu0U5jst37rdyco3xMzmSB6tvdW9qu9n7zZBPwZMXPiOK+1WMyM83a9a7gM/xa5XNfy2fB0nGk0vw5dDpbcNcOBALYOuDFrrH5e131WWjmak+GxuRaFvOYnM+mhdfSbNlh2vUZUQ6NybU0YkymJLNNNE2rqC7gEQqFuK9Z7QegoqqkiMp+AKDrlalRr9ertC/vBgwEAkr7Vl/QMxkDOW/t9494WY1dh9dbOZwWf7fTaUPBirfyswzdi6w3LNh2nry31uI67wkg6w0j6itUoIushauD4Y8cTWOo6nc0kzG4ti0mq/zbeDweBKJRZAwTMQWL6wePpPDGTQUFSF73Cb+rR5u3aOFdh+FwmPvQrKb6mmCMKV+HmlYZHlW9DiORSM1rTq9DoHDfqAzS1dehruvK37Uav9+vtK/KMyKZM7m27bykjOh6KN53ebNwXVdfh2mz8F0nLNwDDH3+dxSeUwYGg0G1e06f/9sUe37w/tY8RM8IFar7yvp8PqV9w+Ha77QQz2/CGa00JmeNypYJ1fdbV8D6GpzNGMix2qVmXvdDD0ak/ZAB/rPQ7fstZxTGukoK56WxQt/H6jFZY0DPnF14PGuWrI1lY7IVVs9Cv86EPTI9Hk/Nz8147hfH5FjWlPb8ylUVHiz2MXkyZaD6t8WzoXNzTBbh5rz9cEI8nvcF5v+OPp+vxuY3r/uR9YYR8jJp/3anz4iptAFDq7xvfB4NnmgUsxmjxgpY1wDDKC8JLZxTUclMY3Ln0krjcjXV91yO6UrjTfk8uEjxngMKV3dvgK9eCgQCyJuV5dG8tXI5fQENk6naey5l8J8RXq1gi1nZbqNwzxWVzAG/HzNV+5aPzUWadc+12rhsBa2VG/+3qY6p1DM/zPvCNdc6j1aIpwG0Vibco53GZLv3W/kzonxMjvhYRQ/2dN4sjY/lsakixTG5x1/rSFIe360ek03GKp5J5bHYmmNUfVdD9yLvC8OvM27MrzeggaFwv124LoRdY9Pz31XxWUhjMo3JKuehsvZs12dEOTQm19KIMZmZ1elygsuKFStw5MgRLF++HIcPH2726bQsO4+n8enHp2te/4dze3DaYO2NzuMfH57EC1V93xaCvzu7G4YJ7vnz0BnwzasHKx4Gf33/OI7F+X01yjl/mR9/s60bpmni7leSuPuVJEIehitWB/H4SIbbZ+9fdvThxy8lcP9h8SA7HNbxL5f2K50/0dp847kY7nhZbKUnY1XUgytWBfCq1UEwxjCTMXDTPWM1271hYxivXRfCW38xqvzZa7o8uOXiPqRypq39AOCMQR/ef26PrX0IgkcrjskHprN472/ETZn/81UDiEj6GQPALTun8MQJ/uLi9RtCeONmtQluIzkSy+E9v57gvved1wzhs09O46GjlWNYl4/htisL1uEfeGiC22vSbc5Z4sPNZ/c0/Dg8coaJ4/E8loZ1eBR6Hcn+7gDQH9DwhcsHhO8vNt5+zyimqwr0Lljmx19vq62ybid49w4A9Po1/H9XVP79TyTy+Mv7au3j3rolgmvWyhNYTuD9zrcP+/HuM7vxoUcm8XxVT/G1XR5MZ4yaIrZzlvhx89nt/Xci+LTiuGzFX9w7ZlloCRSeweOS7foCGv5D8oxO5Qy89Re183Aexftd9dwAYHlExxWrgvjqnlorv3+7tA9Lwx58edcs7jmYrHiPt5YliFYgZ5h4y5321pkyKEZCLDbacUy2y57xDD7y6FTN6zef1Y1zls4nTx48ksLnn5qx/LxPbO/F+p7KJNH7fzOBl6fV1q1nDfnwd+f0cN/73JPT+E3VHD/iZThzyFfTX96nAV979fzYbJom/u7BCRyctY4zl/NPF/Ric5+qdyaxGDkWz+Gv76+N6+gM+NY1Q004I6LdISUz4Sq16qYCXT71xWtEoDxqNOm8WaFCK+fsJT48NlIZAM6bBbVpqOx8Y9VSDgHFyjrGGK5aE8JVa+YDghpj3CTzdMaw/PxZxeMTrU/C4d/yuvUhvPmkykRUxMvAUG0yX7heqy03rSgqKT3WxeA1ONmHINqF/iCv0UIBjzbvAiCDp8Ysvaew/0Jgpaaudi0BgPTcmtg0TRyN2VsgO4VXFb4QPDOawb88MY1EzkRAZ3j3mV04a0ltlWg5PAeUcuw+pzudgIfVJDzT1hY4LY1pmnh2nF9gyRs7RdeEV6GowQlpzm1bnMsanHPRNSDsZZioqouM0zyVaCFUn62DQV2aZLZau9q57JeGC3OJkIeBX85Vy0RKvEYsFrdFOGtx3lqWIFqBnMvzHt7clCCI9ka0fqq+3WUOP+XwYtkyp6lqRCpmgL+OT+dNbjHZQFCvKP5ijOHadSF84Wl7NrZ+cWiCIAAAIskp1R4STqGQP+EqoiRtl4I9URGRvWWjyeRNTAoCCKui/HqMWJktmWGqWV8DgFcy4IsS8omsiVmLz49lTBhkTtARJBwmSHj3j8YYNwBWsLi0d5zi5ztZq6so+giiXYl4mXAx1+3TlJRCAUkCN9giSeaoT+Oey41bCsUtvER5Jm/CNE3MZEzEFyj524wkczZv4ta5BDMApPIm/uWJaaQszsUqmEp5uUr8eu2c0up33OocieUxJWjXEsvWfrdqu+wiFmYJjslwrtFi0J73q9cZK9n0lsP7LgTRLLKKNU+DIfmNZeVSYmeuvSQ0l2S2cTMncyZm0rXHYEApgRwVfN4s3ZNEC+L2vKdV5tAEQbiHaP1UHXOSra/L4bVNtFPEKot58xLdWQPcuT/P2n/7cAC9NmLqABXXENaICiN2LK+1jCYIFSjJTLiKSMksWtjysFqoN4p03uRWqfu0+aryasqT6qmcWaMUFeGTJNt4QTmgmGSWr7jMue2I9sdpgkR0/0Q4E4jZjGGrOhOYD3oxxmwnmml9T3QyjDH0B/hjRbfiolDaS7VFFooejeGSqoVHyMNw4XDhNV6S2USh2vxIrPE22UWakWR+4kS65rhZA/jdcXkvKatTJSVzJbxgUaoFlczTaQM/fimOrzw7i91j8h5buyTvJ3NmTSBNNB30NuA5kTNM8C7BYvCK957O+OpOUjITrYTqs3VI4lQCiNduRazcKsopJZltTpr3TdU6IYQ8DNpcgRtPyQyIC8QJopnYnfe8bkNIOk/eOqDWto0giPZBNPWvrkWVOYWVM81TMtsYImVKZlHCdzRRW+3GS1Z7NIY/Odle2yxKMhNWRHwaNvXWWqq/eo37rZeIxQHZZROuMsNR2gY9zFbQq3lKZmAiVTvI9wV0YeKu3J7aTiW4bMAXBRXiOUMpODeTMbgJRRVm0gZ+8nICx+I5bOnz4eq1wVJwglhYnCeZ+X+vqE+r6Rc+kzFtV4qX358eDcjbcL5tlI0nQbQK/UENR+O1N0W34jNZmmRuIb/5t26JoNuv4ckTaQwEdbxhY7iUSBeNb2nD5P5uGkUzko77BL2mX5jM4ZIV4v2slMw5o2CnTH0zC/BqOVpNyTybMfDBhydxfC54dOeBJP7itCguXRnkbv/suDwJPZsx0Fv2xUXXjKyI0SmiYjSfLj6Xgl127TMrToWQRItgmKYwQF3NYEieZBYlcIvYSZgVC5vtrof3z9SOP+XnJSr4jlGSmWhB7BRmAMD6Hi/+9mwf/ueFOPZOZisK/0/t9+LVa/hjL0EQ7YtQyVy1XqpHyWxHkCFb74sS3bwhWJSs3r48AAPAfYeS8OsM+6dzmBS4IMmOSRDl/OUZXbhl5xQOx/Lw68CfnBzFqi5KFRLOoCuHcBVeNXTU5iLZqhq8UWQMfk+MvoCGqGCgL7fHtrNIlyaZBd8/njWVbAadWhFm8iY+9MhkKQnw2EgGR+I5vH1rl6PPI+ojYXd1PYfo/uHZsE+nHSiZyxJdHsaQVtbvk1020fkUlMy1aiLVlhGyvsutomQGAF1jeP3GMF6/MVzznl9wn2fyJo4KlMzLwnpNEUy9NEPJLGpXYZUbVnncZ435pN5ih2eX3Wo9mR85liolmIv86KUEN8lsmCb2CPoxF5nNmugtMxDg2VcDjbHLFs0T/FIlM+MmyVL5giqb5gNEs7EzzR60UDI3xC7bhcKy8jWBqACZ7LKJVsT++pRhS78Ppw74kDMK7nTjyTx6/BqWhnUqmieIDkS01KsePheqJ3PU7846XqaIvnh5ABfPOYr997OzuOtAUrgtJZkJFYZCOj6zox/jyTy6/Rqt0Yi6aB1ZDNER8AZmO/2YAbESs9Gk8ya3Eqw3oAnPqTypHrMhCZWJ2kRK5vFUXqni3qnt2e6xTI3K7NeHU0g6THYS9eFYySxQU/Amq5NpA89YWHhWUx405sT5pbSQEJMgGoJorFAd1eRK5vaY8IsSoem8iaMxjiWYj2Eg6P7DIZUr9IFeSESjpdVazUrJrLrNYqEd7LK/vidW89qxeJ4brNo/nbPsVV5dyCjqJduIwIBoWllUTec595nO5O1fCKLZiAo1eAwENek4brV2tWWXHS72ZK7/Xi4/r6hgfUBKZqIVsTvnKR9vPBrDkpCOLf0+DEc8lGAmiA5F9JyojlGpK5krPy9nqDueAPLksJ2Eb7ckWV1O0OIzm6TdItqU/qBOCWaibuixQ7gKL8EpG2x5NEvJPJU2uIm9/oAuVjJny5PM6jMQmW2wT2fcCcGIosprZq7P7g/3xfHeByfwz7+bwiscC7VqfvxSoua1nAG8KLD/JBpLknM9qbgCiNQUJ/XV9toAgO+/GLd1XuVFEHYnITRpITod0TWuqmKSLYLbJ8kssMvO8+2yh8MeBBtQgWJi4dWtoj+z1aOPl6SrhlrZzsMrxmg1u2xR3obX9mS3hVU2UDu/FtplN0C1ILqPisfiva0xhrDgmWWnKJMgGkXOxvgQ8GhSS2yrNkV2lMzFebzdnsw8KpTMIrtsuh+JFqSedk4EQSwORAVcNXbZDnsy23VUkNpl2xjTVePnQclzz6+D2iwRBLHgUJKZcBVeT2ZRglZEsxYJoiRuX0BD2Mu4FeyzDbDLBviW2ScSaknmWMbE916M49t74zgwk8NToxl84KEJbr/pcngqdADId4h6KpM3sWc8g8OzuQVXt9nFNE0kOAHznoD1vSRSU1ywLMBVMdhfxM+fg91YNimZiU5nZZTfhWR9N7/IoxqpkrlNLK9EldrxrMkdx4YjOoIWC+9BC6Xzpl7+73ehLbOFSWaL/dTsslt73FpIeAUXebM91N68gsT909bFfNWWtq1gl11KMnOuX48mTmpRX2aiFbAj4A3o8uCxlZLZSR7XjaLr8ntQZS1LEK2C3TkPJZkJYvEhKtKtjjmpDqczVa6Wdo0+ZMlht+yyVT+zEUWnBEEQVlDIn3CNTN7kqh3sKpmt+loBQNDDbCe4rDgmSOL2BbSCIoOzeHGqZLZMMnMCqCeSarOc0WQeP9tfqUrOGMBvj6WVz6+cNojZWnI0lsNf3z+Ojzw6hf/7wAT+7cmZlg5Gp/Mmt9Nxr4X1vM7ECR6fzvDqNaG6z63cvs+uMlmm4CeITuCsJb6ahKnOgO3DfqX9O0HJLHoGHZzJcZ9rw2GPpWLrouUB6fsn1ZlkNkwTjxxN4fbnY9h5PO24EEnUk9kKlfGIkszziK6xVlMz8+CpBlXanFRvI0paNWKcFSW0i9N7XpBPY+KgP0/NTRALjZ11gE9n0vWs1dpVVTV91pCv9G93lMzzn6Gylp3JGBhL5lt6jUQsDuxYzDO0TyEmQRDuIVQyV82FVRW9MxmjYi1nV8nslTyH7NhlKyuZJfME6sdMEEQzoCQz4RoiJayoB5QIlZ7MS0LWyie7THP6MQNAX6DQG4unyK5QMrvUkxmorxr38RNp7oTrd8edJZkXWgnWCL7+XAzjqflfyiPH0vitw9+HHQzTxI/2xfGBhyZw6+PTeHk6q7QfT8UMAD1+QbPTOSJeJp1Ev2p10PLasyJcYZdtb19SMhOdTtCj4S0nRUpFUAzAGzaG0ROQ37tFZAtCq75LrYKoiGq/oG3DcES3TKCfu9QvfHaFPQwrovzfr+r49e9PzeBfn5zBj15K4NOPT+OrnH66KoimAVbTAzUls/3z6VRE10ur9WXmwVPxqhQp1vZkFiiZW8Qu26MxoRKz2o6QIJqBak9mn1ZI0HZJCj2tlcxqx7pqTbD0b3d6Mlees2wt++OX4vg/947hnb8ax3sfnMDhWWqXRDQPO4V1YYv1L0EQnYlQyezwcZA3gUTWeZJZhh1lcbeFsKSIrN2Uj8QdBEE0AQr5E64hSjI3oidzvUnmLhuJ7745i2JeAKFCyWzDbsxayez81hwTKJ6d9txK2CklblGeOFHb7/AHNnsRO+Hbe+O4fW8cL07l8NvjaXzkkSkl23NRYqTXwi7b6t7p8mm4bGVQuo0VoQq7bJs9mSkAQCwCrlwdxKcv6cPNZ3Xjlov78LqNYeV9ZcnWdinSECXKj8TESWar8Tzq1XDGEF8NrmniMVMlyXw0lsNvjlYWHf3ilaRliwkeIiWtVW9olWAqKcvmESmWWkXJLAtK8VqrqCh7q+2yRddMI+yys4JbwS+xy9YY0C+wuR9RbP9CEI1EdXlTvM6ldtkWa13RsTb1ehH1MSwN6Xj71ihOH5wf59ywy64uWhatZV+ZyeFbz8dLxUyHY3l8Zc9s3ccnCKfYmfOQVTZBLE5EY6vOSbBaOQIWmSoTHqkWo6mg6kgmcwKqRrZ+JrtsgiCaAb95IEE4oMun4fpNYcxmDMxkjLn/m+hXVHAV8eoMfh1IS2JQS0I6RhJ5jCpaSFfT49cxk7Gu0GYAeuYmJPzq73K7bPVzsbIzdKN6vZqlYWe3e7v3zhMtUg/HGhvkNE0Tvz6cqngtlTfxyLEUrlsvTzqJEiM9FpNjFReAa9eFcPcrSa5trQrhCrtse/u2S5KMIOplOOLBcMT+M1dm99cuKg2fYMgXjde9ft2ysMqnM5y7xM915LhoOCBcZKskmR8bqf1MwwQePprGtevstRgQHc8qyaySGxUl+hYjra5kFhVdAnzVsoqSual22ZZK5tr3dVYo0vRqted6PE4XM9F8VBVK/rnnDW8dWMSpkvnPTolgdZeHO767YZddnfzmfYdYxsAP9tUW3u4ayyKZM6RKKYJoFHZq490oyCAIov0Qxfl4w9YFw378fH/S8jPL5/Ci9duqqAcHq9w+3rRZHt/rsxCLFIn6Cq0aVSC7bIIgWg1KMhOuMRDU8QYbii0ZYa+GNE8aMcfSsI6M4cUBgf2mjJCHKVeS9fi1UiUcr99WeWCQp04RYVVZFm5A702rYIro3Xa3yxZZTzeanFFZCVnkW8/HLZPMCUHA2TLJrOAaMBTSsb7Hg31T9u8dhsrJrN2ezHa3J4jFRicsCEXfgdeSwqcVEoZWSma/Xuh37dOA6qH25D6vOOmo8PyfSPHHbpHyWsVqm8cAAJlVSURBVEZKUFIvSzIbpgmVQnnqyTxPqyuZZUnmatVyzjCV5lnKdtkNGGeFdtma2C5bZwwaY1gS0muK+ijJTLQCtpXMfufBXFHCzKOJbX7dKDiuVkPxknGzWROPHuO3EIplTAQpWkQ0ATtzHjcKMgiCaD9Eyyue2971G8M4OJPD7vFC+7r+gFbRTq9IeUsX0brijZvCuO9QEo/PuSWes8SHa9bKC5PDc+tdqzl/tw3HTXmSWfljCIIgXIOWDURLEvEyTKTE7y8J6djY68VdB6yr0aoJe5nyoFteccbrLZ3MmcgZJjwaU1KiFBEpvYqEGlCRa6XwEVUCihKe7UKiSY0srZRrMpKCyJdVklm1krs/oGMf7CdQAh5WUVlpd01PheYEIUe1AKqVsdMDqqiqskoy+/TCs+dNmyP42nPz/ZLXd3twzlK/MGmVlBSrFREV51QrR1VICp77siIv1UQHJZnn2dDjwc1ndcPvYQjoc/95mOUYuVDwCiqKVM8VVedYzbTLFtkF+vSCawvvbX3uPJaF+Ulm0zTbxp2B6ExUn6nFIU3W/snqWhatsWQ91OtpnVSkticzfy0rolmFugRhp1sXKZkJYnFiR8kc8mr4x/N7MZrIw+9hmM0YeM+vJ2q2m0lbJ5m7fBr+7pweTKTy0AD0KDh3MsYwGNRwcFZeaGmn1STZZRME0WpQkploSQqLBfEAvCSkYzCk45R+L56dq0az89mqarHyJDNPyQwU1CU9Ad2WXbZVEL4RFblWCh9RoLPdezI3S4mtmmQ2TRMHZ/MIeAoTT40xYVAn5GVcJV8RFbtsABgQ9Em0oloRQUpmgnCXTlYy81BJMns0lIpbXrMuhDXdHjx5IoOBoIbLVwahMbE7yXiy0Guyx6+hW5CAzAsCFLMZ+2OHk57Mqn0H23wodpXegI5zlrZuib7cLtuQ/izcT8EuW2f8PnT1IrPLtlKRFFq1ZCrei+dMxLImN+FFEAuFapK56AZvJ/CreixZbsyvF+7peroA1PZktvcdFmINOJ028LP9CUylDZy71I+zhnxUgELYUzJTT2aCWJTwhiiNQWo3PRgqrB9Eo2F5oahIpFNcd/bZbAs5ENStk8w2CmbJLpsgiFaDksxES1K9KC5HZ0D/XJLs6jUhPDs+beuzI16mXNlVPnEQ9eKazZro9ps2lcwWdtkNWCzJkq2GaQoTm22vZG7hJHMia+Djv5sqWVefvcSHvzyjW/i3Cnk0+D0MGUHyQ1nJHHQWnK9WVdidu1KSmSDkdMI9Yqdyukshyeyv+p2c0u/DKf2+itdE+//opQR+9FICfp3hrVsiuHxVsGYb0fN22omS2UmSWXGIEqlJidZjJi3+W8Wr5lSqc8dY1oRhmqXAGe96aNTzIy2Ih/l0JrR6Lz4Glob5841j8by0xy1BNBrVPvfFS7y+JDP/ddk9yxhDyMNqXAzsoKJkltHoNWAia+C9D05gci6o/+vDKbztlAiuWiO3HSU6HztJ5ka0GSMIovXJmbXPCdVlaNjLuIVcKnbZTp3HBhVicHbmGn6dgYHf9tCOsxhBEIRb0OqeaElkldZLQnopyHbWEh+iNhOyYa+mPOhWKJkFC/PZjIFkTq2nYhFrJXMD7LIl1eipnCnsydzuVmnNUjJb9cAGgK/tiVX0Rn5sJINfHUoKf+dBD5NWJSormQPNUTJTQSVBdD5erdC/XYViwLteuy+rau103sR/PzuLyVRtVkH0vB1L5mFwghciTFPcW1eUpAPsKJnbeyxeTMgKFKoVydU9mkWYqExQ866HRuVsRcF+v8aE12XRLntpiB9QOx6337KDINzErpJZ5IahgtAu2+Ij62mfpLPanoxR20pmZ+POVNrAbbtm8PHfTuHn+xPCNdFdB5KlBHORbz4ft+UORnQmdi4BsssmiMUJL7ypGp9ijHETutNlhaJCJbPDoJbbSWYmcfMiu2yCIJoBzciIlkSm5F1SFrDSGMMVq2uVSVaf7cQuW7Qwj2XtqZgBlZ7Maud34bAfywQqkWpkPZllQQTVAGirIuvJbNpIINhFllQACsUJ9x2ubTz+9GgGScE5WyeZG6tkrk0y29ufciQEYc267lqTmavX2Bvnmglj6m4h80pm8cNE5bM0xiz7OmcN4OFj6ZrXRUqtrAFMptTHv6whtjV1pyez8qkQTUZul12lZLZhy17eJzzDmWPI+rvWA+/6ZSjMAcRK5sK5iOaoxxOKMlKCaBB2+9yL1qYqKkrR89vbwPZJES+rsZ2O2KxEka2hRGTyJv7+NxP45cEUnhnL4Kt7YvjW8zHuto+fqB2T03kT9x5M2j4u0VnkbPjEN8IBjiCI1ofX8sjOsMkrHptpoJJ5IGQ9Btt1TREtockumyCIZkBJZqIlkVWkLqkKWF27NmRLvRH2ao7ssmVKZrsV11bHV00yDwR0vH1rVGnbdB5CVZbMDk2mBD4ez2HPeKalFVay81ftm+yEtOR3YpomHjxSm2AGgKdGM9xz9mmFykxpklnRBq/foZK51i7b3uTVjiqQIBYrl64IVPysM+Ci5QHB1q1JtXpKhEpPZtXPUqkqv/9QbeA6KcnyjswlwgyzEPT+7JPT+N6LcW7xlSgQAbjTkznbwPGKcJfptPiaqr527MwfyxPUvARZo8RcvOvXpxcKSkSXffF27A1o3PM6HqckM9FcVG+94iWuMYaNPbVFYH+0OVzz2vF4Dv/zQhxfemYGn/zdFO54OcH9bKthSyV5JvqMaU4Bi133LydK5idPpDFeVaB114EkxpOV9/xMxqhwcyrnzv3Jll5bEo3HXk9mCmkSxGKEN0TZcdrjJZmtejIzOHcOUlEy23VNEcXjSMlMEEQzoBkZ0ZLIbH+XVFnvRXwa/uzUykTraQOV/RrLCXuZpZK4iJqS2cD+aXXbP52hZPctPEdFiWhvQMOWfh8uX6mWgBAFweOSIDsvwGCYBevRv7p/Ah95dAr/594xHJxpTetDWYCkkVbgMuVaOg/8ipPsAApBYt55BeeuP1mSWdUurNuvObKuJiUzQTSeq9aE8L9PjeKkXi+2Dfnw/nN7sKHH2+zTsoWqjVixWturiQPlqotkKyUzwC86ko0DJ+aSzF/dE8OXds3ioaNp/M8LcfzTo1M1AfCk9Jlff09mu6o7onnIlMzxud7KRew44ZQrmXkJGCtVpFN485nifSkqHisGvTTGuH2Zj1GSmWgydu2yAeANm8IVRRMrozq2D9euwSZSBr73Yhz3HkrhydEM93O9GmqUxtWoJM+u31Sb5AaADZyEuH0ls/1x50VO4tgEcH+Ve9PTgt8LAEymDTx8lF+MSywObNllU09mgliUcJXMNoY5rl22hZI54Kl1CVFlQMku295ni6b+qkXaBEEQblK7+iCIFsCqJ3M1l64MYk2XB0+OZrAkpOPcpX685c5R4WcnFP0pK5LMgoX5t56PK31WEZWAuaqSuXeu0u0tJ0fw+IkMpiTqGaAQYA9xchVWSmbDNCsS489NZHHXgfkk6XTGxL89OY1PXdJnmUBfaJyqtOtFllTYNZbBoVl+gLXHr3HPq2iZ50ZPZo0x9AU0jCbtKfCrbfvsKpkpRUIQaly5OogrbbaCaCV6AjpOKDxfigtpNmd3zUu4+RQTZyrWZVNpo2Y8k40DI4k8UjmjxrrzwEwOu8czOGPQX/Y54u+bNwtJQV51vbKSmeyy2waZktlEobChOF7baUlSYZfN2a1hSWbOsYr3peiy18umzEtDnpo5z/F4HqZpOg7UEUS9KCeZy/59xqAfn7yoD0+cSKPLp+GcpX5ugWefgmOQitpKxS776jVB/Gx/ArNVymXeelm1rU4R1fVyOVOCfkG7xzJ4/cb5hPiTHKvscn66P4mLlwfoGbFIsaNkV42bEATRWfCGKDvxqW5OQjeZM5HJm/DpjKtkdtqPuXg8ryZf09m1yxadDtllEwTRDEjJTLQkMnswniICANZ0e/GHG8K4cDggXbiHvUwpaB3yMATKSuF8OnNsjVKOynCv2oOrdy6IEfZq+PPTopbqVFFfZqtK9eog/DOc6vPDsTyeGRNXpTcLWQKhkUlmmZJZZJsHFP5GXCWzUpJZ/QJVqaSsplpRYadSlAE4VeIwQBBE56ASYAcqi7dESmTVRbLKuJk1gKOxygC4VZL5hckcNxjw06rnuMwuGxCPCaoxfLIObQ9M05QqmYGCmrmIHbvs2XK7bM711CjHUGdK5vl/8+btiZxZ8X0IYqHJKorpq+e6K6Ie/P76MC5dGRQ6CJW3WxKhcr9a2WUH9MJa9Y82RWreu5jTZsOv23tOOHF8EhVT753Mlno8G6bJXUuW88pMDrvHs7aPT3QGdgrr7BZPEATRGeQ4c1BbSmaBNfXdrxSKi0VKZqcwxiwts+3aZYvC2mSXTRBEM6AZGdGSyOy8hhwkx8oJe+V9bYvwguQiNbMd8gp9aQMeppSM7imbhJw55McHzuvBxcv9wu1FwXSrSvV4VSDwgMAa+96DrWdtJvtuTmzgVJEpmfdOioMmqZzJVcSpKJntVHI76ctca5etfryzlvhsV2YSBNGeqD5fuiqSzPx9VBfJqov+6vYWCUkk80Qij4wguTtZ1XPSqmhJNCaQkrmzSOZMy79VrCwJXT2/AsRWeeX78Y7hbVBAiZdkLs5FxErm+XNZJigOpb7MRDNRVTK/UWBHLcOnM8sEsYrzgGhcnH+/8BlXrArgxi0RrIrq2NTjwbvP7MKZQ7XrQcYYVkbVjeycrJNE++TNeYvsfVM5pSKTasvs2YyBH+6L49vPx3A01pptmgh3sNeTmZIpBLEYmU7XlwReEeGPh998PobnJzL8JHOdc22Z0ENn6mKjIiIXSUoyEwTRDMgum2hJZAvzeoNoYa9WYynGg1eFHvFpGE9ZR3oZCkG1o5wAmopqSWMMIQ9D3CJo3Vt1jlv6fdjS78P5y9L41GPTNdsLezJbLPQLidr5Y4kCI4+NpDGRyitV8C8UciWzgROJPMaSeazr9tZVmViNLMksI2sAMc71aaVkDnqYraRvvxMlc9XvR/br2j7sh09nODybw0l9PkdBOoIg2pPqsUlEeeGWaFHtZk9mANg/k8XFKCi8cobJtQIuciKRF/aSr37dSsksTDJTT+aOwkrFDFSql2Oc7QeCOhK5XM18cbYiybyAPZk5xyreuqLL0krJDADH4zls6m2vfvNE56DyTO0LaDjNoQtPX0BDXCKXVpmzWyWqi+MeYwxXrw3h6rUhy8+8aDiAl6djltsBzpTMsn0eG0njguEAnhqVW2UXKS9EmUjl8Q8PTWJibh3+k5cTeP+5PdhKLkkdiR33FpGjAEEQnc14snaM7bcRhzx90Idev4bJqjY3hgn86xMz3H3qjRcOhsTPqy6fZrtFhGiZrNpuiiAIwk0oyUy0JI1cLEQUlcyDQY6SWaFSdnWXBzduiWA0mccXnp6teV819xjyypPMYY/4e4gq7MRKZoskc1USOp3nB1ENE/jVoRTesLF1EoqyKvx/f3q2FPiP+hjed04PNvS4E/B0mmQGUDPRBebVDH7BvNluVaUzJXPlPrpk8joU0vGmzbX2fQRBdD4qzxeGyj7yokW76JlXTUBXe6aVO3FYqY+nMyY3gMHDWsnMfz2vrGSmJHM7MKNQxFhpl127fcTLEPXWBr0q7LIXMMnMm88Uiz94VoVAVZKZ0xsWICUz0Vx4bgB+Hbh2bQi7xrNYFtbx+o3hmlYxqvQF9Jpe5OWofKyVosmJgnPHigBu3xtTcseQOX2IkPWZf/JEBjnDxJMn1Norlbd5+vXhVCnBDBTW0z94MU5J5g5F9dLTGVxpZ0YQRHuRyBrcWCkvhivCozH8n9O78InfTaH6k3jxOKB+JbPMLtuuVTYgtssmCIJoBpRkJlqSsGBRPSxQQ9j6bK8Gn24d2OIpPWU23kVuuagXjDHsPK5WpS0i5NWApHiF1SMJ4ouC9eKezPKVXHXwnBcULfKrg0n84fqQNAG5kMgS6OWB09mMif9+dhYf397nynEzLsdOi4EkUWGBz+at4awnc7VdtnjbRgW7CYJofVR6Moe9rGKcECeZFXsyKwbbD0znYJomGGOWiWFA3B6iek/ndtmWp2Bru4UglTPw38/G8PRoBkMhHW8+KYyT+hZvoD9nmPjGczE8NZrBMYXEaYWSmTP/Cns1RHwGJqumkRV22byezA0ykeHNZ+Z7MvP30cuUGL0BDT4NNa4BxxOUZCaaB69QI+DR8MbNEbzRhc/vswgWqymZ1eyy7RDxabhwOIBfH7ZucZTImZjNGHh5Oou+gK5ktS0r7o3nTPz2eBovT6tZXZePmz/cl6h5f89EFoZpCu1CifZFVckc9jLbyj+CINqfMUGc1G6M67RBH96wKYz/eSGutH29SmbZ+Yna5cgQjX+GQotGgiAIt6G6P6Il0TWG0wZqFaVvPql+ZWTYy5TsNwe4Smb5LXPmoK+00HGy8C9HlGgv0isJXoiOLbLztKtklgUQxlMGnhxVq1BfCHj9jUXsm8pxrSudUI+SmUfJLlvwt7VjlQ24o2T2SBb1Fm3kCILoYFRaJkSriraEdl+qPZkVt4vnTIzOBSYSCuODKBhevXZPWjzzeX1tAfVAqmj/ZvAfz8zi/sMpTKYN7J3M4uO/nVJWfHciX9sTw50HkkoJZmBeyWyaJrddScSrceebs9lyu+zaz22YXbZMySy4fsuNBTTGuJbZqr8vgmgEPCdrN420rIqtVI5ltZa06tks4lWrg0rbTaQM/PX94/jE76Zx8wMT+P+emYFpEbi2WlOqBvKByrWUaF01KinIJtoXVSUzWWUTxOJkVLDuGBS458h43YYQNiu2b6lfySy3y7ZLRFBk3SqCH4IgFhd1z8p+8pOf4Prrr8eaNWsQCAQwNDSECy+8EJ/61KcwM8PvY+A2N954Ixhjpf8+/OEPL8hxicbyR5sjFYnU85b6cdaS+pUyGlOzy+b184hYVJet75mv8HZiYVaO1f6ynpeioIQo4arWk7l8e/nK7+Gj1tXxC4UsIc5DZI1jl0YlmUX9VewGl13pySwZQewmvQmC6BxUlMzVC2ldULSiupa3U9hVVCerjA8HZ/lJ5uoKccc9mZWVzJXB9qdOpPHESBqpBZY4p/MmHj1WKbHNGKh5rR4M08Rf3TeOzzw+jTv3J3BgJtuyFfmmadqe8xSL2VJ5k9tCJeJlNUUYhf3kdtmN6r/GTTJr6kpmAFgSqlVAHo/nLRNWBNEoGm05L1unqR5LtSezXdZ3e6TFyuWUu1fddyiFhznP+tmMUXJXsFojiopLVkRqf19pBbeRYzE1VTTRXqi2CLG6RwiC6EzGBElmnlDICo0xrOtWM3mtuyezTMnswC77DzbUtinUGbClz50WgARBEHZwbJcdi8Xwlre8BT/5yU8qXh8dHcXo6CgeeeQRfO5zn8N3v/tdnH/++XWfqIg777wTX/3qVxv2+UTz2NDjxacu6cP+6RwCHoZNitVlRV67LoQ7Xq601ipOHlQCcU6UzOu758/Rqo+WFSGL6nRZcEBUYSdWMssDAuVJaNM0pb2iAWCkRSwQTdO0rKivZjJtYGW0/mO7rTorXg+iia1dMUOhN7m4Tyj/HCqPLWuBWuflTxBEG+PRGLp9DNOS/rTRqqItUTJZ9VFqZ9F/YDqLc5f6leyyRVSPLVauGcIks2KSrRi3n0ob+OijkzgcKzy8l4Z0/OP5PY5aIDhBFNS5fW8Mr1kXcuUYB2ZyOJ7I43gij9/NtR4JexneuiWCHSvUFHgLRdao7JWsQjFpExPcH2GfhggnUTOTMUpJWV4ep1EOIhlOsL9YrCm6P6vv52UcJXMiZ2IiZTgqeiOIeuGp8BdSyeyGXbbTtSZjDNdvCuNLu2Zt73vHSwlcuMwPxhgSWQNfeHoGO0cKDlbXrA0qK1CrWRn1lMa1IqI2T+Uci+dxhrNDEi2MapLZKl5CEERnInKxkCVxZagKhOpNMvdK5gZOlMwn93mxusuDV8raO12yIlBovUgQBLHAOHry5PN5XH/99aUE85IlS/CBD3wA3/rWt/D5z38e27dvBwAcOnQI11xzDZ577jn3zriMmZkZvOMd7wAAhMO1FTxE+xP1aTht0Gc7wQwAFy33w181x7hyVSE42Sgl87qe8iRzfQO7VWWubIJivyezuvVZKm8KlSuqn7dQpPNmTd9MKyZT7iTI3VYyWwWS7KovGGPca1xGte2OzC6bejITxOLGyjK7eiGtCYY0xTijrWD7/qKSuY4kczxrVqhrG9+TubD/nfsTFYH444k8fvJSAqZpYt9UFr88mMTL01m1D3WAKCfu5oj33Hjt+cezJrodBF8ajZOCsqLST6T4i3gZt6gxbxbmYKJrRtVa3g6GaXKTRr652zsvuEGr7+eVUf7z4CXF3qwE4Ta87jjuKpnrt8u2Gtfqac20fTjgSPG1fyaH5ycLz+jvvRgvJZgB4Of7k47OJexl3KR81rDuK6liu5/Jmzg0m1vUbR3aDdW5ESmZCWJxwit6DXqYYwt91f3qtcsW9VAGnCWZPRrDB8/vwes2hHDuUj/euiWCt291QTFDEAThAEdK5i9/+cu46667AABbtmzBr371KyxZsqT0/jvf+U7cfPPN+MxnPoPJyUm84x3vwAMPPODOGZfxt3/7tzh06BBWrlyJ66+/HrfeeqvrxyDalzVdXnzgvF784kACiZyJ7cMBXLQ8AMA6ENft1+DlbCNTMvcHNPSUqYvrtsuuoydzwRK8VqUqCoJbKZPLlc5W1tqy4yw0ThIIkyl3rEfdVjIXA0miRbeT+XSPX8PROnoiyuooKMlMEIubvoCG/ZKuKbU9mfnPDFESqxo7leX7p3MwTdNSfSzDRGGMKfbCsrLLrrsn89yp/uilRM17v3gliZCH4Ydl7735pDCuW+9+AaYoWe6m6/GeiUzNaxoDNreg9RtP5WtFScksmE9FvBoiPv57sxkTEcGvoRGihYxgilB0BBIrmSvvxw09/JPeN1VwFSCIhYavZHZv7mpVaKWiZLbuyez8fAMehr/Z1o3/eHqmRkFsxc/3J7Gpx4ufOUwqVzMU1IUF4Om8KW2bcTQuL1R5ZSaHf31iGkfjeWgMuG59CG/aHKnndIkFQFnJTElmgliU8Hoyy/odWxFWHE/rVTLL6HZglw0U1g1/ROMaQRAtgO2nWD6fx0c+8pHSz1//+tcrEsxFbrnlFpxxxhkAgAcffBB3332387Pk8Ktf/Qq33XYbAOALX/gColGq1iFq2dTrxV+e2Y33ntNTSjADqFE4VzMgqD6PSKrL1lcF0Dwa4y6KX79RzU7SyuLEqkI+wMkAipK/CQtvs3JlskqS2cp+e6Fwoqhu2Z7Mc4to0aLbSWDMTt8XXq+0amVzOeReRhCLG7tK5nrtsoM2HjqTaQM7RzJ1F0SVj4dWnyVyEnHSk5nHD6uSz9/ZG0fMqW+pBNH3dGvEM0wTz0/UKpnXdnls/Y0XCidjffHvIvr7hL0MXQLnnFjW4CowgcYUd4mS6D6bdtlLwzo3gLdvqnGqe4KQwSv88bro3N7l468DS8dSeJzpGpOqpup9Jm7o8eLTl/ThP181gL/Z1qW8387jaTzK6c3slKGQJMmcM6V9nq2UzF/bM1sqqDVM4If7Enhxkp47rY7q9CVClrAEsSgZ49hl19M6aKGUzIB4zWvVmpEgCKLVsf0Ue+CBB3Ds2DEAwI4dO7Bt2zbudrqu493vfnfp59tvv93hKdaSSCRw0003wTRN/NEf/RGuvfZa1z6bWBxYKZlFE5TqHpLlrO+uNQa4ak1l70CdAecvDdRsx8NSyWwRwOdNgHhKq2yeb4VYTnlQWbbQL5LOq6uzGomTBIJbSmY7vY5VKKoVNgus4y9YpnZdlSOy5OFVgb5qdW0fTNklqqLQIAiic7HqR1k9nq6M8s11RK9XY1fR9d0XYkpFUzJiZdk+q96RQiWzck9me+eaN4FHjrqXBCjSaCXzodk8V+G7pd/nzgFcxkmSOW6lZPZpwsD5TMYQzq8akmQWfL/5JDP//Wols8YY1vfU3ssvT+cs7XAJohHwCnzcvIc0xqQFwarHktkBO+3JXA5jDBGvZqt/owngP5+1389ZhCzJnMqbwmclUEg0iJ5TsxkDuzntF359OOXsRIkFI6c4trpxDxAE0V5k8iamOMIQp/2YgYXryQwApw3w1zRk/08QRLtjO8l85513lv59zTXXSLe9+uqrufvVy/vf/368/PLL6Ovrw7/927+59rnE4kFjTKq0FPWoklWXreNYAf6vkyJ47boQloR0bO714u/O7saqLrWAuWVPZgsVKi/gzguCq1hKlyudVYPyrWCZ7URR7VZPZrftssNzF+y6bk9NEjigM5zjwG5SpJTa0OPFX5wWxZouD1ZEdLzlpDA/yUxKZoIgBPRbWJZVF7lcOOyveW7oDLhgmdqzzW6S+dBsHr86VJ/VZ7kS1bonM/911eRx1sHQdMzCRtQJVrbg9bJnvNYqGwBObkGrbEBsJy2jWJwg7cksKAKLZUzh/MJNFWYRcZK58P+8YJqlc06fZ5mdzJk4YtOqlyDcgPfsdbtQo0+yVlMtxpSNbfXYZVdjN1lXb5FWOYNBTRi4T+fFrg9FjgvUzEcFz5Z7Drpj8000DtW5kdP+qwRBtC/jgnidKIargrJdtgtK5us21DpbDgY1DHOcAwmCINoJ2z2Zd+3aVfr3OeecI9126dKlWLlyJQ4dOoSRkRGMjo5icHDQ/lmW8fDDD+Pzn/88AODTn/4016qbIFTwa0yoBukXqIRlFW48JbOuMfyvkyP4Xyfb75ERlCyawh5mqcbmLdZ5QXAVZXLcppIZKCSvo00WHjlJdE+0qF12ZC4hzBjD+87pweeemsGBmRyWhXX8xWldjqoqRUrm6bSBS1cGcenK2sRyObwgchHqyUwQixsrt43qJFrQo+F/nxrFF5+ZhQmAAXjbqVFldZXsGfhnp0bwn7tjNa/PZtyzy3bek1ntWHaVzIV9bO8CoHCuP34pjn1TOazp8uC69aHS30Gk2HZrxHuOY5XNAJzUsklm+988YxT2iwmuv4hXE15PsxlD+HddSCWz31LJXPuarC+zqmMBQbgF75nqdoFkYRzkF/uoFoUUkmj8gHrQRdVTMxWhQyFduGZL5UzLhPaxeI5bxG3Vr7kRPD+Rwd2vFJLY164LYV13a45drY7q/IV6MhPE4mOUY5UN1Ktkrn+9qcpJvV6cs8SPnSMFxymdAW/cFIbG6HlGEER7Y3tFv3fv3tK/165da7n92rVrcejQodK+9SSZU6kU3va2t8EwDFx++eX40z/9U8efRRA+nVUkT8sRVcHJBn63K2ll1XQ9FjakABBUtMtWUzKXJZkVE7cF9bN71XixjIGJtIGBgLqlm6OezCkDhmnWPclzU8msscqqyRVRD265uA+ZvGlZbCDj5D5+FcAVHNUyD4/kd0RKZoJY3PRbjFO8IpfLVgZx5qAP+2cKyU2rRHU5vDGvyO+tDOKnLycwknC3R3G5uspqLBX3ZFYbK5y0oMg6HIc+8/g0nhotKIqfGs1gz3gG/3RhLxhjDVUym6aJ5yZqlcyruzwtq1ZyOtbHswZXnefVCvNTURFYMmcKCw4akWQWKfB9c8cSK5lrz2W9JMl8mUVRG0G4DS+J5XNbyeyCXbYs+euqkrmJz9ihkC5UI6fzZkVrCh5HbSqZASCVMxBwebHy9Gga//y76VLR1cNH0/j49l7hs4/gkzdM5cI1VfUhQRCdw1hCpGSupyfzwtllM8bwf8/qwhMnMphKG9jY41V2uyQIgmhlbD/JpqamSv8eGBiw3L6/v5+7rxM++MEPYu/evQgGg/jiF79Y12c5JZvN4oknnuC+t2zZMixbtmyBz4hwiqj3EwD0SyYoJ/V58XyV0mbHCvv9cK2QTXSsrLIBgZKZZ5etkIhNOFQyu8UjR1P44q5ZJHMmol6Gm7ZGcZ5CD2In55A3C3aUXf76JpBW/TntEPYyME5Ct54EMwCsjOpY0+XBgZn5Sn+vBmxV7H0pi81QT2ZiIaAxuXWx6skssuvvCeg400ZyuQgvqVXEozG8fmMYX3javR6SAEpKVMM0Ld0r6lcyF5Kwdsg4SEyPJPKlBHORF6ZyODCTw9pur6tjWzVHYnnMcNS9W/pbN0CfdvA7Bgr9mHl9Rou9mENehvds60LUpxX+8zJEfBo8GuMm4oHC+O02omtoviczfz/e9KTHr2EwqNUoUPZNydWGmbyJ5yezCOgM67s90nudaD7tMi7zlcwLl2RWzW/KlJohF5OkzVQyDwZ1TKb4g2HaoiczABwTJZklSuYjsTzW97j70PzFgWRFctQE8LP9Cbz7zG5Xj9Pp2HFhadUCNIJoFdplTLbDWJL/zK9uKWeHoIeBwdqZyQ27bKCQaD5rif12dwRBEK2M7SRzLDZvNxgIWCd5gsH5yvTZWefBvZ07d+LWW28FAHzkIx/B+vXrHX9WPZw4cQJnnXUW970PfehD+PCHP7ywJ0Q4xieJYcuq4F61KliRZGYAfn9dbV+NepEFDlTUXbwkM1/JbL2SS+dN5A0TusaU1cFOVMQ8UjmjlGAGgNmsidt2zWLrgM+y6j7poCczAEyk8+hSSOSLyBmmMPDqhEiDFtCFKspufO7Jabw4lcPSsI63nRpV/u6yYBzZZRMLAY3JrUvQoyHoYVwLTK8mL/RyyqqoBwdnK4PK160vjM8XLw/gh/sSwmC0E4pFVyrqXlESOqeYOM4apnJCuoiTnpkPHUlxX//14VQhySz5rqZpcguiVNkjSJ5uEbhutAJOlcxPnEjj8GxtAqRYYKgxJiymE/XnrrfwjIe4J7N9u2ygYJk9mkxXvHZwNid0ZjmRyOOjj07ixFxiekufFzef3U2JhRamHcZlw+Q/T2VrQyf0SdZrqvNk2bXuppLZpzN4NPXCJ7fo9Wvw6UyoDkvlTcsC52MxfjJZpmQ+HMu5rjB+/ETtGPbQ0TTefaarh+l47Di3qKoPCWKx0g5jsl14dtleDXXF7zTGEPQwS5GK2w4YBEEQnURbeDJkMhm87W1vQz6fx7Zt2/Ce97ynaecyNDSEO++8k/teO1aBLWZEwTiPJlZYAcD25QH4PQwPHC4EYv9gQwgrGtBLTla5rqJk5gUe0nmzxgpaNQidzJmI+JhlNXkRleS1CrvGsjVJitmsiZ0jaexYIbdXdKqmnkwZWNPlaFcA7lplA41dQA+FdHx0ex+yeRNemwFq2eY0/yYWAhqTW5u+gIYjnCBv1KfVlYwU8arVQXx593xBo19nuGxlIVGnMYaT+7yuJpmL46GKuleYZLahZLarmp1O2x+HRd+lqGiVfdeMAfjrSNLsGa/txwy0bj9mAMg4vJy+9Xyc+3pEYJNdjsguuxEOIsIkc9EuW6hk5p/L+h4vHjlWmWQ2TODl6SxO4hQTfPeFWCnBDAB7JrL4j6dn8X/P6mrIM4Son3YYl0XP3YVVMreWXXbxWDw3iUYyGCoMGqLCM6dK5pxhYkRgqQqAOzepB5nTSM4wyeHJBqIxjkczbd4Joh1ohzHZLjwl80BQr7vdXdirkGRuQEEnQRBEp2A7MxaJRDA5OQmg0CM5EolIt08mk6V/R6NRu4cDAHzsYx/D7t27oes6brvtNui6y2XGNvB6vdi2bVvTjk+4h2gx2x+wnqCcvcSPsxtsbyJbjPY67MkMAH9+7zhgmrhgOIA/3hxWTsTGcyYivmKvZWvcUjLvneQHnR85ap1kTjo8B5FlmyruJ5kbv4C2m2AGSMlMNB8ak1ubfkGSWdRvtl6uXB1E0MPwyLEUIl4N16wNYVl4fqpbT68uHsWeujy1djXiJLP6eGF3XJ2x6GPJIy84n2LSUKZkTuZMxwr1Qj/m2vF+VVRHtEHXixuIxvs1XR6cNeTD6YN+fPCRSeXPiygUlYl7MisfRhmxXXbh/8LrRXAuG3v4S8+XpnLcJDOv8GDnSBp3v5LEVWvcdxEi6qcdxuWF6msu78ms9hmiomOv5n5SPOTRMCOpnOkLaJioc41UzZJQ4RchTDLnTG7/+nJmsyZmM0bFWHEikZe6Sh0RqJ+dEpeMjScSeQxH2kLb0RLYsctups07QbQD7TAm22WUm2SufxIc9ta2dCmHwX3HE4IgiE7C9my3p6enlGQeGxuzTDKPj49X7GuXp59+Gp/85CcBAO95z3s6boAkmodPsDB3Y4LSaFSUzH7BoquobLrrQBIepm6vWEgu68rKZ7d6MoviJ+MKQQ7HSmYb6q+8YeJ4Io/BoF76XToQj0lpVSsweU/mhTsPgiBak0Jrh9okkUoizSkXLQ/gouV8m+F6enXxKI6HKknmensyA7AMtFcznTFsW1hb9diVKZmTOQM9Dq3qjifymOIMnie3sFU2IC4e+IfzetDl03BCoqTjoVJUtrB22fzX/ZY9mfnnsqbLC40V1MvlvDhV+5wwTZN7TQDA15+L4aQ+H1Z3UeKGsI/oUep2oYasvZFqQlvUPqkRyTWZixYAvGZtCP/zQlzJvaPIyqiOI7F8zT1fZGiu+EukDivYZVsf71g8X5FktnItOTwrfz+bN3H3wSRGE3mcPujDGYM+6Vg6JVmX3n84hWdGMxhJ5LGu24O/OL3L9aK3TsKOkrkR4x5BEK2LYZrcYic3nqlW46pfZ3WrpQmCIDoZ2yvzzZs3Y//+/QCA/fv3Y82aNdLti9sW97XLV77yFWSzWWiaBq/Xi4997GPc7R544IGKfxe327x5M66//nrbxyU6H9GipB0WfUpKZoXgw72HUrhEEIyvppiwVQ1yqwTdVZhI8YMAI4lcqU+0+BycZXtVlcwvTGbx6cenMZ02ENAZ3nZqBDtWBIVBZ6c0qidzvYiCyEBjbDsJgmgv+gVjVbMeD26P72PJPH55MImnTqQtt00L4tminrY87PZYzhmFsdtOoZKVjayVktkpot6ZJ/e3rlU2YG0nbbegQmV7kbq4EQ4iovmMzyrJLJi2BDwMK6MevDJTqSI8zFEVJnKm8POzBvAvT0zjw+f3oEeSyCMIHlnBheXE1UeGzNlBtRhT9PwONqCa0yrAPhzWcdnKAO48kJRuV07Uq2EwaGIkwR9cSnbZgmOn8yZiCq4c//H0DP7pwt5SolnWjxkARhJ5YS94wzTx4UcnsW+q8Fy680ASb9wUxus3hoWfJytQ/vFLidK/d49n8ZnHp/HPF/VJz28xYyfJTBDE4mIyZXDnhoMurPGs1ktut6ggCILoNGwnmbdu3Yq77roLALBz505cdtllwm1HRkZw6NAhAIVeEIODg7ZPsNjfxjAMfOITn1Da57777sN9990HALjuuusoyUxwEQXOREHxVkJWGV8kKIrwlZHMmcp2YUWbTlV1sFs9mccFljXpPHBoNoc13eIAtHMlc21gYvdYBt99IY5M3sQFw368dl0In3tyuqQMT+VNfPGZWazu8thSpqnQjkrmFs2LEwSxgIhUrTYFua7hdpJ5NGngtl2z1hui/p7MABB38IubThu2Wi6Ikt7F+bhMwSZLQFshSlC7ETRqJFZ20nYDUipFZSKL9YbYZVsk0Q2hvbr4M4fDek2SeSxZq7q3sns/Fs/jn347hQ+eR4lmwh4LaTkvQl3JLEoyN0LJLP8FdPs1vGFjGI+NpKWWopWfyeDRPBhJZLjvD80lmX1awYq0+i+j0pMZAI7G8/jL+8bxf07vwrlL/Tgal69vTQBH4zms6apdR+4ay5QSzEV+uC+Oq1YHERG0bxC5LvB4eTqHY/FcRTsPYh7VedG2odZ2OiEIwn14VtmAO26UVmNggJLMBEEQUmw/iV/96leX/n3nnXdKt/35z39e+vc111xj91AE0VBmBcGrtlAyK9hRqk6C9k8rJpnnVnyqQW63ejLLen+9OCU/d6fnUH3MI7EcPvbbKeydzGL/TA7fej6Of39qBieqAix5E/j6npgtJfPSkPX1thA9mZ3gkSmZyUqIIBY9IseQZqlU+gIamvVkcqMns10lM1CwzLaDKIlcrL1qlJJZtK/IQrVV4P1dvRpKdnqMMdhpKR3xqSiZ+a83QsksTqIXjiX6k8ucTniFA8mcWVMYOJO2vp6OxAqJ5imB6w1B8BDbZbt/D4mm8MpJZsEHNCTJbPGZPX4NEZ+GT17Uhz85OYI3bLTuix72algiWesU7bIZY9w5QzpnKq89kzkTn3tyGrGsgaMWdtkAcERgmf3MaG1CPGsAj59IYzpt4L5DSTw2koZRVpQls8vmHttCab2YUZkj+nXgteusrz+CIDqLMUGB00IomWXuJARBEISDJPOOHTuwdOlSAMD999+PJ554grtdPp/HZz/72dLPb3rTmxyd4L/+67/CNE3L/z70oQ+V9vnQhz5Uev1HP/qRo+MSnY8oydzXQkrm4TB/sqTSf0g1yRxXVSZnTeQMU2j5WY0bdtmmaWJcEjjk9fBz4xyq7bJ/cSBZU1n/m6N8e9Td41m8ZHFe5SwR/I3LaWT/0nqgnswEQchYKni+bR1ojvrEozHLMX7I5b7NRZrRkxkAZmyoqwBxcVYx+ZzOiz+vnnFflNxuddUAr2dx9RzNTvGiWk/mhbH6BSRK5rmvJMoFyE5FpDapVqdYKZmLUKKZsItYyez+PRQVVJmozpNFiV+r/slOsPrM7rki54hPw7XrQrh+UwRb+uQtDUIeJl3r9Jc9D3iGBEnFnsxFMgbw5IkMjio4dYncvPYJCrB/+nIC7/rVGP6/Z2bxqcem8ZFHpkqFYjwXLBlpl9pKtSKmaeJ4PIenTqQdzVtEu7x9axRv3RLBH20K4xPb+7Cln5TMBLHYECuZ3ejJTEpmgiCIerAdSdN1HR/84AdLP99www04ceJEzXbve9/78NRTTwEAtm/fjquuuor7eV/5ylfAGANjDJdeeqnd0yEI1xEFA5rBNWtrK3TPW+pX2tftCvdEzt4iP+GCH2osa0ptVfdZJHOdWnZPpY2K6vRfvKLeewwAvr03rrxtWyuZJcE4Rkpmglj0bOrxloLS5Vy8PNCEsylgVemuUvjjhLzJVy3nGtiTGbCvZBaN3UXFbqOUzCnBeN3qAR2ektlXNTZusdFXWqWobGHtsmtf85QptUXnIsvVie7BanWKapIZmE80q/RuJYiFtMsWrStlrQfKEfdkXlglc9TLuPP+Lgt3rZCXSdc6Wtl6gacSm0oZNYW+Vrw0lcVMxnqvwwI1schB4+BsvsJJ4vnJLH59OFU4T5sFXXaeb+2EaZr42nMx/NX9E/jnndN4573j2D3Gt0oXkRPcG4MhHdesDeF1G8NYESWrcYJYjIxxkswM7giFrJTMre6uRBAE0WwcPYlvuukmXHnllQCAZ599Fqeffjo++MEP4tvf/ja+8IUv4OKLL8anP/1pAEBPTw+++MUvunfGBOESv78+XPMaA7Ai0jqLlktWBHDawHxwciCo4U2ba8+bh9uToETWsNUP0mk/5HJkVtlAIagoOqe8DdV1NSZQ6rVs2kgAlI5tY5chpSRza05oaZ5NEIQMXWO46dRoReD+DzeEsLyJ46xVz66locadWzEhaZomTiTyyOTNBejJbG8ME7mblJLMkgEu6bCwCxAnr4MtPtDwklXViZLXbQhjWVnxwqYe8TWm0pO52XbZ/rLj8HJ1OpMXmg0I5j2jicpJ27QgaSNKhG3o8TZE3Ul0Hgtplz0oGHNUlxdiu2z3M+IyFRevYAwAuiyKs8MeuV12OX7OvS1ytLrh5Ajee3Y3972nOHbXPA4LlMy8JIaIJ04UnK0oyVxg31QOP98/X5ydypv48u5ZW+vpVuiZThBEa8Kzy+4LaFLxgyqWSeYWL3wlCIJoNo4iaR6PB9///vfx5je/GT/96U9x/PhxfPSjH63ZbsWKFfjOd76DU045pe4TJQi3OWuJD998nlUoby5eHmipyYNfZ3j/uT14eTqHRNbEpl4PAopBhWYrmSdTBr71fAyjyTzWdnnwmrUh6DYnfxMK1ocvTeVw2mCtXVa9dt2TaQO9AR3TCpXwTol6mVICWSXo3AwaEIsjCKLDOGepH5//vQG8NJXFcETHsnBzC7mslMwii283SOdNTKVz+NRj0zgWz8Ovw1YxlCMls0t22emcCcOUF2/JVM5WJAW9je3OGxYarpK56hLqD+r4fxf34YXJLHw6w7puD+4/nMJtu2YrttOZ2vXHC8AzNKbwi//95g+U5yQOrM5DlHSrTuyIkjCfuqQPH//tVEXP1R0rAvjz06IVqkiCELGQlvO/vy6Mx0Zqk56qVr8+rVAcVR1YF7VUqgdZkUaPwyRzyMuEBbWnVrk88JTMov6bER/DtiV+DIf1mv7LxxT6MQPA8XgeOcOsSU5YFTmX89hIBqZp1rRasqJTk8w8969j8TxGEnksVZz/ierVGlEEQhBEe/E327owljQwmsyX/u9WqMzSLrvFC18JgiCajePHcTQaxR133IEf/ehHeN3rXoeVK1fC7/djYGAA5513Hm655Rbs3r0bF154oZvnSxCu0RfQ8Q/n9uCMQR+GwzquXhPETVujzT6tGjTGsKHHi9MGfcoJZsD9SrunRjN4eZpvT82rbo/nTPz4pQQePprGN5+P49+fnrF9zHGFBbuoL3O9SupisODwrHVPL6d0+zWlYoBWVTKTJTZBECr0+DWctcTf9AQzUEj4yWhkkjmTN/GFp2dKAXBRwlYUR3WiZLYbyBa1mUjlTW7CsRxeolgVXoK6lYr+RPB6Flf3ZC6+duqAD5t6vfBoDDuWB7ChStF86YqAWk9mTpLZqzVmTOZ/v/l/81p0WxUGBD0ad14zqmCXHfYwDAR1fOj8nlKSjRLMhF0W0nJ+U68HG6vu9QuW+YVJ22oYY7h6TWX7pF6/hguWqbVPsoPMLlucZJbfdyEPQ8DDcAqnbcCrq74XL4AvGlWKBbiqKmkeeRO460BlUjSZM2yvIUcSeQdK5s7syfzUnLK7mlkb31ekZG6AeJ8giDYj4NGwIurBmUN+XLk6iDefFMH1myKufLZVzK0RbSoIgiA6ibqjfddddx2uu+46x/vfeOONuPHGG+s9DXz4wx/Ghz/84bo/h1hcbOz14v3n9jT7NBqC25V2EykD//VsjPveYFCzVEs9ciyNPzk5j96AejBgQsGuTNSX2Wk/5tKx55LMRwRWam7Q7deU/k4qPRoJgiAIa0QqyiKy3pH1cjSWx74p6zElqDOubXWswT2Zc5I2E+m8aalUTjo4vyK8z251q2yAXyjAU+NV49UZ3nt2D+45mMSh2Rw293px1Zqg0jGznGM2QoEJWCfReXUFKqcyGNQRz1beCypK5mL/155AIdH8i1eSuH5TmBLMhC0W0nKeMYYPnNeLO15O4NBsDpt6vXi14r1e5Np1IfQGNDw2kkavX8Or14TQY2M9pYrImhuQ2GVbJMuLhTN/vDmCTz8+XUrGnrPEj7OWVKq5VZ6dRYpJZpW2QzK++XwM67o9JWX5uEA5LePZ8aztxHSnKpk1jYFXGmBVpFbOQtrZEwRBFLEq9CQlM0EQhJzmS0oIgmgIusbg08SBFDcZDOqWgXPDBJ4Zy2DHCvXAioqS+SVBkrluu+w5q+4jMYeNnRWIeDVLpZbO7AVdCIIgCDEDFkrmwZAOfoi0fg7MqBUtBb38JHOj7bJFVtnAXJK5gUpm3pjdtkpmxUB4l1/D6zeGbR+Tp/JSPaZdrL6fE7tsoGD/e6DK4KY6yczrJ15uzdsT0PFHm91RrxCLC7GSuTH3UcDDcP0m+/d6OduHA9g+HHDpjPg4UzJb22UDhcLuz+zow4uTWQQ9DJt7vTXuC7yezCKKirMliu4ja7o8mMkYNVbYhgn86xPTeN+5PVjX7cWYQquman53nK/elTFjU/ncLoj+hHacWBb6/iQIggCslcx2xiiCIIjFCCWZCaKDCXoYMgtgx2UVtC9it1+jSk+s6YxZ0U8rkTXw8wNJ/OzlhK1j1Rx7bvF/uIFKZo9mHUQPexnZUhMEQbiEbLzSWaH/ZZff2p3DCap9HkWV8jEndtk2vkdc4gCSzlkrmevpycxLYNtpEdIsMryEb4MLw0R22Y2AV6hopWRWURXzeqNPZ0xk8mbp82d5SmaLhBZBqMArngAAb+OMLNqChvRkLlvnRLwazhwS23zbUjLPHVfVfWRttwfnL/Pjk7+brikim86Y+PvfTOKKVUEMhew/Y54are25bQXv+dYJiPLAdpxYeOMq0LhxjiAIApAXWgGkZCYIgrCCpmoE0cE4VQFtH7bX58vKfrSISAWVM0z89lgKd7yUqOj7PKFYTV5cuOYNEx95dAr/80LcvZ7MDVQyezWGoEUQXaU/I0EQBKFGwMMQFfSQDM0V9XRb9Jh0ykhCUcksGLudKJnjOVOoCqpGpmRO5U1Lh5B6xl2uXXYbKAZ4FqCNTzLXvuZZQCVzeSIoz7m2VGoDRMUeRTWzaZoCu+zWvyaI1kdUT7PYlZIhyc3b4+ffs1ZJZjvrGDsB/GIrIVW77DVdHpwx6Bcqyk0A9xxM4pvPx5XPoR5iWRMGxwmi3dEFRUZ2iuTo/iQIohkEPAyyp0w7OCwRBEE0E8peEEQH40QFNBzW8boNYeXgrlcT9+mqhqcMM0wTt+ycxq1PzOAbz8fw97+ZxL0HkwDU7LIBIDYXiHxmLKNsR2rFVNrAbMZoiJqtiFdjlgEV6sdMEAThLjwVJTBfwd4oteSxuFrRklXxkV1UxzGeRXeRvGmd5E5JlNBWcO2y20AxwE/CNvaY2QVMbPOS6F7NDSUz/xofnUsyx3Mm97O7SclMuIBYKdn6z5xG4sQuO+qTB+XtFAupPvP9OisV1iyxkWQGgD/cEMJZQz6LrRuPCWB2AdzGFhrRLWSnSI7n1gE0rpiKIAgCKMxfZY4elGQmCIKQQyt1guhggg6CjqcO+LAi6sEntvfimrXW/ZPDXk1a+V7OJCfQ/fRoBs+MzduMmQD+c/csdo9llPsqF6uj3UowA4Wg/JEGWmUDwPoej2XwhZTMBEEQ7iJSURaTu12KhVN2GU2qJWHdVvBOK9pyJiyURlafozpm8+DbZbd2MMcwTa6quNFKZl6CrHF22byE9vy/eYlgtZ7MIiVz4Rcqsnknu2zCDcQ9Xxf4RFoMr+TmFSWZNcYQEbh/lCeDVVDtd1legOvTGXoVxuzVc0lmjTG884wurOtuftc4nltDuyMwLSsVhKsgVjI7OCGCIAgbhCXjUDsUvxIEQTQTmqoRRAfjJEB7ar8XADAc8eCtW6L47GX90u3DXnnFXzlTHGXynvFszWt5E/job6eUPhOYr44uKmDcYDpj4OBs45LMXg04b6kfXg1SBUC4xZXMPDXA760MNOFMCIIg1BgQqCiLsfBmqyXdTjKr9mW2UhpZKaKdJplNk9/vudWDOaKcvL/BaiteAL5RCswMZ1plZZetK9w+IjeB4jxOlHxpVAEIsbgQ3buLXcksQ5RIBsTFH3bXMKo9masLcK3UzEtDeoVDSNir4SMX9OING0O2E5fLwu5ZVXRikjkpyBDb6cnMUzIzADrdnwRBNJiQZFBohzY+BEEQzaT5JZwEQTQMu0lmBmBLf2XScCiooduvCYPLYQ9TnnDxlMxH4/UncotK5nFFlZgKhgk8P1GbAK8HhoJSW2PA/z41WprEBj1M2Msy0uJl25evCuKJExkUz96rAZettFbAEwRBNAuRirLYk7jZaslGKJkN08QdLyfwwmQW/QEdf7AhhL5A5e9B1pMZaFySOWMAvD1bPZjDs5IG5GpAN+BZdDfimKZpco9VrtTmK5mtzyXqY/Bphb99OcWezKJrrdn3JtEZjAmKUl3uVNBRyGzwu3wajqD2dyqz3+ahmmSubiW0JKzj+Unxmq2oYi7HpzNcvykCj8bw7b3yPswXL/fj2nUhZPMFF6oPPzKFvZLjqdKZSWb+uDhr47vykswtvhwmCKJDkBVHtXrxK0EQRLOhJDNBdDB27bJXd3kQrQrgMcawqceDnSMZ7j4Fu2xFJTMnaHgkVr/6OJZxX8kMAM9yVNZOOanPi/ds68YrMzms6vJU2M4FJEnmllcyL/Hjfed049dHUvBqDL+3MoBNvd5mnxZBEIQQkYoyPlewZFctGfQw5Ix56+TlEb2usc31JHPawJd2zeK+Q6nSa8+MZfDpS/oqrEwTFj2Vreyy82ahX7DdhKcoKN3qdtm8BCygnihxykIF4HOC5L/PoiezytdnjGEgqONoVZ/yoqX8jKBXabNdBoj2J5Yx8Lvj6ZrX+wMamEKBBFGLqPhDpgjjoRrAj1Qdb8hCybyGk2QusrHHes3SH9Cxpmt+u029Xssk81BQw9puL/aMZzArKOBSdRlpF7J5fgsJwJ6SeSHdOgiCIMqRtapr9XUJQRBEs6EkM0F0MHYnQlsHaq2PgcJiWpxkZspBhGSuYIlZPK+cYWIk4UKSOWvANM1SLz+34CXFneLXGbr9Gk4brP0dy4Iq7dCT+YwhP84Y8jf7NAiCIJQQ2WXH55KddhNZr14dxJWrg/jd8TSiPg1nL/Hjrb8YdXx+bicpj8XzePBIqua1h46msGPFvPNEvXbZQGGct5tkTgmS262uGGhWknmhAvC8fsxAlZKZa5etdi68JPOYlV22xLKXIFS4/3CKmwg7dynNYwHg2rVB/HR/suK116yVOxQJk8x2lcyK21cX4FrZZa+V9F9e1+0pOU2J6K+aM6gU067u8uA9Z3UDKIydb//lWM02omKadiUpasiM+SI+FXiFVJ4Wnw8QBNEZUE9mgiAI57R+9oIgCMfYVUOd0s9fNMsW02GvBp+mplwBgKn0fEDxeDwPQQyTi+gQsayJWNYUWlcWiQqCk7IKd7fwSYKusmKAaks4giAIoj4GAvyA9AXLCkkGu5a8/UEd/UEdV68N4aLlAQQ8THlM5OHVmKvK1L2TWW5i8hvPxSp+tlQyqySZLcZhHrx+zED72mX73GvZyYWX/G1Eklnl+zlVMgPAIKfYYyJlIG+YwiRztdsOQdjBME388mCS+94Vq6jVCwBcuy6E5ZH5m3x5RMe160LSfdwq/lC3y7bXk5lnl10k5NUwHJHv3181Z9jS57W0Vu/xz+8T9THuGtaOhXQ7IGuZEbOTZObUn7dBzTVBEB1ASGaX3eLrEoIgiGZD0zWC6GDsToRO7uMnk9d1y5LMDIwx5Wr18r7MR2L2+jH3BTREORO/WNaQWmX7deCyFQH88/Y+7vtv2Bi2dR5O8EviF7JAejsomQmCINqJLr+GtZyg844VAQBAt9/e2MkrVPLVkWX2aO4mDUXW3TMZE6Y5HxR2R8lsvU02b2I8mceB6SyeGc3glwdT3O1aPcmcEUw76vnbq5Dl9mR2/zgipXalXTZHyaz49Xm90Q2zkGjmXWsRL1NWSRMEj93jWRyL1964W/q8WBElgzcA6A3o+OeL+vD+c7rx/nO78YntfegTFGYVERV/5OxUEqOOnsySJHOXj6HXogXGBgvL7Golc8SnWSrfewLz+2iMcYuGO60nc1KSSE7nxWNKNbzrhuyyCYJYCGSxt0bP7wmCINodWk0RRAejEOstsbHHg4CgLNunFxbok5ygX9EqO+Rlwp5T5Uyl5j+j2ibRit6AhnjWxGxViXMsY2JcYJX9vnO6sXXAV+o7uX3Yj4eOzvdiO7Xfi9M5FtZuIwucyO2yaTJLEAThNjeeEsH/2zldssi+eHmg1DLCjpJ5OKxjfU/tdNqvM6mqR4bHZSWzjKPxPJZHCuefsDjfuML3EamSy/mLX41hVsEmVNU6tVmkRXbSDQyGmya/52UjjinKf/gr7LJr31dNBIt6o48m81yFn12HAYKo5p5X+CrmV60mFXM5fp3ZaoMjKmq2OwSqWpFWJwG6fAwBnSHFSWKu6fJY9tpe3+3Brw+L369WMgPAlauCePhobW/vItWJ7ahPq1m/toOS2TBNHI/n0RfQhHGCIpZziKwBn25dEcW1y6bHP0EQC4BMOKNZjCUEQRCLHUoyE0QHY8ea6nILm7jlEZ2bZC72WAp6NADWx5uqQ8nc69cB1CamZUrmwaBeSjADwDtP78Lm3iRenMpiddSLq9YE4dMLSmyrxXE9yCof5XbZtKomCIJwm5P6fPi3y/rx4mQWA0EdK6N6KRAtU9C+aXMY330hDsMEevwa/uzUKDfoUE9fXo+GuXGr8f0anx3PzCeZbcwZRKgk1qNeDbMiGXAZQb21xz+eohhobE/mvMm/KjyNSDIL7bLLlcy17ysrmUP8v+9YMo9pXpLZQo1IEDImUnk8NlKbFOz2aziH+jHXhej5Y1vJrFhYFKmy52ZM3KJiTZd1/2SZktmn8VsXndznxXC4tq98kZ6q51WXX6vZttWVzIdnc7hl5xROJA3oDPjjzRG8dr3YOt1q/I9lTfQGrI/LK5InJTNBEAsBCTwIgiCcQ0lmguhg1nV5AdRW7d9wcgR7JjJ4bCQDjQGXrwyWbEJFnLvUj93j2ZrX189ZaTuzy65dmK+I6DhzyI87Xk7UvDcY0rjVzfGsgTFBknmgyuJM1xiuWhPCVVXb9fg1JHL2lNXV+09JrERl1fmyJDNNdAmCIBpD1Kdh25La5IJI9dTj1/CHG8K4eHkAU2kDw2G95OZRjaxFghUexhYsoLp7LItXrS7828ouWwWVJDMv2M6j1XufiXsWN+68eXMgoJAIcRuVJLNRh122WMlsYIajdO8mJTNRB/ceTIF3+/zeykBDijQWEyK76q399pyinPZkBsQqWl47i2pWd3ng0fjJzf6gzp0TMMZwxaogvvZcjPuZNUlmzvOL95xrJf7jmRmcmHMKy5vAN56PYeuAF2sEbbSsirVVi98zZJdNEESToFZ1BEEQzqEnKEF0MGcM+VC9JgvoDJevCuDms7rxn1cO4EtXDOB/b+Urscq5aHmgJuHp1YAt/XNJZsVkaDERa5omjnKSzMsjHrxhYwj9gdrH02kDPkR9tceJZUyMceyyI15mae1VpDoYYJeVUXlGQapkltpl02OaIAhiobmUU3h13ZyCZyCoY0OPV5hgBlzoydyAHrs89oxnSolCN9w8lJLMisnC1u/JLErCNu6YWUFu3tuAxLbK9+MlZVTtsnv9Ws0cFQBGE2SXTbhL3jDxq0O1RbcMwBUWTk6ENRt6PNy+xztWKshWy7Bo/VyCV4B78XL+sVYpJJk9GhMmo3nr0SKyAu3eQHWSufacZzMGTE6hTiswlTawb6rWceyBIynhPkmLPl0xxaQ6vyez0q4EQRB1oRrTJAiCIGqh6RpBdDBRX8HKszhV0hnwv0+NIuDRwBhDxKchqhi0C3s1/Okp0VJC1KMBf3pKtJQEDSkmcyfnejJPpAxu76zhiI6AR8Pfnd1dkVC+bEUApw/6uNXr8ZyJkURt5HVAoJLh0V13klkexJBV58sC6fWo4QiCIAhnXL8pjOHw/AN464AXv7dSPRlRn132wimZZ7MmDs3mYZim4x7S5aglmdW+W7sqmRtply1SMjdCiSlUapcdqx4ls64xbgLnwEyOqzjt8rf29UC0NjecHMGWvkoF5rYhn621AsFHYwzvOC1a4ajwxk1hLAvbM81TLc7q42Sjty+vdSUZCmpYHlH7+64XqHNlj9aIT8PFnOP6dVZTFMMrksmbhTVsKzKR4lc0/fIgv685oGKXraZk5m1GbgMEQSwEYWoATxAE4RiyyyaIDueKVUGcvcSPV2ayWN3lrUuxe/HyAE4b8OFYPIehkF6xyFet+ntxKosf7ovjpala620AWD4XkFjT7cW//94AXpzMoi+gYVm4YFcmUvYenK2ttq62ypZRr5J5aUiHzvj9CQErJbP42CLbVoIgCKJxDAR1fOqSPuybyiLgYVgV9Vg6fpRTT6JR1xY2oLp7PIP+oD3FmQg3lcwyl49WQNRO09fAv92C2mWLvl/Z34X359Zt3CcDQR2jVU40+2dq53MAKZkJ5+gawwXDAVwwHMDh2Rx+eTCJXx9O4crVpGJ2izOH/PiPKwbw8lQOyyM6+h0k7zXG4NPEzx6goGjt5hQqnT7gw7YhH544kSlt979OjiiP20vD/PO1Snxftz6MR4+lKxKjFw77a8Zw0fNrJm1wC6ibjUh17JH8PpMWLTdIyUwQRKtDSmaCIAjnUJKZIBYBPX4NPYO1ldZO6PZr6PbX9thStbVM5kx8e29c+P5wWcW5X2c4daDyWBHBxI8XdxX1++NRr5K5x6+h269hIsWPjMgUya2u1iIIgliMeDSGk/rs9ZQsUpeSeS7QvlA8O5bBOZze1E5IWdhlAmrJQp21flBZZCfdSCWzKPnSCOW7yvfjzb3sfP2CipRfdFgN9WQm3GBF1IMbT4niTZsjDbW2X4xEvBpOG3Q2ZhbxexgykmTkoKRH8t+d3Y0nTmQwnTZwyoBP2Cuax5lDPnx1T+3r5y2Vj40rox68Z1s3/ufFOGYyBk4f9OHGLdGa7YRJ5oyBYeWzXDim0/zBRibyc6snMymZCYJoFqJYI0EQBGENJZkJgnCFkEuJUitbs4iNIKOdKnoVJfPyiI4jnD7SANDl19DlkyWZJUpmwe+O1tMEQRDtSb09mRcyoLpnIsvtgesEt5TMAZ21vJOH0E66gUlmnsILWNgkc/n3y3POR2LOUsOgDccZ1fYuBKECFXi2JgGdYRbicWRIkjhmjOEshwVTy8IenDHow1OjmdJrQQ/DuRZJZgDYtsSPbRbHjQrWmbOK6t6FZkqYZJYomV1LMvOUzHS/EgTReBpZKEoQBNHp0GqdIAhXCLkgOeoPaAhY9EGxU11oJ3ipkmTe1MPv1wUUFDaywLks6CxSgdMclyAIoj2pvyeziydjQTJncltOOPosUc+IMkTB9nLaIQEkSsI28m8nPGYDFJkZBWtu3unYtctWpV7HGYIgWh+rsdOOS5Vd3nFaFNuH/ej1a9jQ48EHz++xXJeq0sWx+AYKSuZWZFpwXrI/T9LCySRmYaedM0w8PZpGnLNdqzubEATRGTDGsLarVot3w8mRJpwNQRBEe0FKZoIgXMENJfOwhYoZgK2+VW4HLzf2enHf4RT3vS6/hm6/+HcgVTIL3rMTqCUIgiBah3rGxHqVzEtCOkYSfNcNESdsbi8ipaRktv5uqi04mgkv4evT0FAFtkgI1gy7bNM0+UlmW0pm9Xka9WQmiM7HMskcatxzoC+g491ndjfks2V22a3IVJo/J5CJkS3tssu+61gyj5enc1jd5cGSkI7xZB4ffmQSJ5L2FdQEQRBuct36EP7tyZmSp8ZgUMOFw+60FSIIguhkKMlMEIQruJJkDls/kiIKwekidpLMVkrmoIdJrbzDHuZYySwKqNgJ1BIEQRCtw2mDPvzopYSjfT2M1WW5vCpqP8k8Jgjs2sUqyAwo2mW3Q5KZo/RttM2e0C67AccV5BhK16bgVGwqmdUmOgxA1Mb8jyCI9qSZSuZG0g5J5nTehGkCfl3ckzmWNWCaJreYytouu/D+nfsT+MqeGIBCa6gbt0Tw6LG0MMEMkJKZIIiF44LhALr9Gh4bSSPq07BjRQC9gfYcewiCIBYSSjITBOEKbthlW/VjBtSVzF4N6LYRkOzyaWCAsAvYYFCXqp0ZY9L3ZUET0THbIMZOEARBcDi5z4vNvV7sncza3regZHZ+7KVhD4CM5XbljCUXUsms1pO51eElYRvZjxlYWItu3rEY5u1SRc7odgRnqsWAfQENGrm7EETHY1VgJOvJ3Mp4NIaQh9UUYs0IkrlAwS3i0GxhoFkR1Rv6DLzrQALf3RtHPGdic68XhwQtNLIGkDEKiehqVHoyH5zJ4atzCWagUKz0X8/GJHsVoJ7MBEEsJFv6fdjS72v2aRAEQbQVVBNIEIQr1Ktk1hhw5pC1DU1YsSfzQFC3ZVnp0ZhUJbMkpKHbIjAuC5zLksyigMlVa0LS4xEEQRCticYY3nVGlyPb50JPZudjqqo6tBy3ksxWQWZgPtguoy2UzDy77AYnmRfULlug1C7OrfIm/29t50/n0+UFekVWc/rjEQTReXSqkhngrxN/czSN3WMZmFXP02TOwCd3TuNvH5zA3z44gY/9dqphqud9U1l85dkY4nPj997JrNSVJCY4D2u7bBM/2BcXFlfL8LRB4RlBEARBEMRihlbsBEG4Qj0WkToDrt8UVqpO1xhD2MNKC2ERToLsPX4NMxl+oH0opFsmC2RJaFngucevYVOvFy+UKd40BuxYEbA4Y4IgCKJVGQrp+NNTIvjC07O29qs3yewkCD/qkl12Mqf2OV0+DYmcOLHdrj2ZG22XnRV4VPsakGTe2u+DT2PIGCYy+cJ/5X0xhUpmm+cyGNSE1qxFKMlMEIsD2TPUpwFdbWyb3+VjOM7povHR305hS58X/3BeT+kZe88rSTw1Ou9I8ux4FncdSOCNmyJ1nYNpmnhsJIPfHk8h5NFwzdogfnkwaSvxG8+a6A/Wvp6UNWwGkMqbeORY2t4Jz0F22QRBEARBEK0NrdgJgnCFvoCGqJdhNqu2TF0R0fGB83pwLJ5HX0Cbs/dUI+JTSTLbD7J3+zVglh/0HpxTRq+K6jhYtc2rVhdW2l0SNY5VG5e/PrML//bkDF6YzKIvoOFtp0YdfQeCIAiidbhkeQBPnsjYCqx6tfoCqv0OiqycKIt4qCiZAXGwvUhAb/2IcpqnZG7waYuSzPXYq4vYvjyA7cvFxW55QT7Bbp59IKhj3xTfmrXIGkoyE8SiQJZkHgzZc6lqNWTrxD0TWdzxcgJ/uCEMAPjBvtoB8pevJOtOMt97KIXbds0Xvj10NFXqlaxKjJNMzhkmGtleuhGFVARBEARBEIR70IqdIAhX0DWGa9eFcPveeOm15REdR2L8pO3ZS/zoDejotcq+cgh7NQDylayTBG2PZPFfVFlftjJY0UtKY8DFc0FYmV22lYVmf1DHP13Yi1TOhF9HWwdRCIIgiAKMMfzF6V0wzBn87ngh0WwVzvVorEIxapdmFihljUKw2er8ZcF2oE3ssjkJ34bbZQvE340+Lg+RXbbdU1FR3lOSmSAWB7JnfztbZQPA8ogHj41khO/fezCJ69aHwMAv2JrO1FcOljdMfGdvZf9juwlmADgSy2P/dALxrIFzl/qxptuLlGKBmVM29nob+vkEQRAEQRBEfdCKnSAI1/iDDWEsC+t4ejSDwZCOK1cH8dFHp3BgplahcvYS6/7LIiIKEq+lCtbb1fT4xfsUk8xXrwmCAfjN0RRCHobXrA1h09zCV5Zk1hSTxu0QWCcIgiDU8esM7zmrG7GsAQbgbx+YwHhKXCjlYfUpmcMehqCHKauK3SaVMxGxsDSVjZdA+9plN74nM/9v2oiezFYIlcw2z8WqvUlAZ0rtVAiCaH+kSuY2TzLvWBHA3a8khWPzaNLA7vEsVkXFIbpkzkDQoXXFcxNZzNSZqAaAL++eV0L/6KUE/u7sbgxH3AsrXrU6iPsOJUvK6CtWBbC+m8KWBEEQBEEQrQzN1giCcJXzlgVw3rJ5e8XTB301SeZev4b1Pc4fP1bBawCOPr9bQcnMGMPVa0O4em2oZhtKEBMEQRAiigVSsiA6Q8Eho56kIWMM/QENhwVOIo0mmTMR8cm3sUoyB5qgzFUhnjXww30JTKbyGEnUZlmb1ZO5Gf0qF0rJvKrLo1yoRxBEeyO3y279Ngoylkc8+KcLevHz/QncdzjF3ea+Q0lcuYrT8HiO8aSBFVFnv4eim4qb5M1CovnGU+qz8S5y2YoA3nZqFK9dF8K+6SyWhnSs6fKQwxdBEARBEESLQ0lmgiAayqUrArhzf6KiT9ObNofrChhaKZnDHuZMySwJejc6cEwQBEEsDmTjiUcrJIm9dY45g0G9qUlmKyyTzC1YtJUzTLz/N5MYSYh/r41XMvNfb4qSWfBn1m3O76zs3ckqmyAWD7LxcajNlcxAoWjmz0/vwk1bo/jze8dqlMU7j6elz7yJlIEVUfvHNUwTO0fcTzIDBYW0E+eUy1cFcO5SP/7nhTim0wXr7TefVEhWD4Z0DJKDBUEQBEEQRNtAq3aCIBrKcMSDj1/Uh5+8FEc8a+Li5QFcsMy5VTYARLzyAOb6Hq+jiufuQHtXyBMEQRCtj1+SQC32MnaqTP1fcwHaZvZlTuTEVuBF2tEu+/GRtDTBDAC+Bid7eRbdQKE4YaERJ5ntfY6VXTYlmQli8SBzseikpKOuMVy6IoifvJyoeD1rAP/zQly433jKWfHYS1M5TEjadNRLwkFv52vXhjAc8eCMwfriAgRBEARBEETzoVU7QRANZ1XUg3ed0e3a51kpmZ1accuUzKrsWBHAr6ss0E4ftPANJQiCIBYNsiB6MbfqRJnq1YBzlhbGG1niLuxliDsICKuiEmyO+uXfr5l22Zm8iecnsphOG5hKG5jOGJhK5/HgEWsVmK/BORBe/t47p35faPIC627d5lQq7NWkPcRXU5KZIBYNsiKsTlAyl3PZykBNkhkQO1YAwLggUXzvwSTuO5RE2KvhVauDOGtJZeK2USrmInYT2Cf3eV3t40wQBEEQBEE0F5rZEQTRdlj1ZN7Q43X0uT0CJfNFw+oV1petDOA3R1IVCp/LVwbEOxAEQRCLCrlddlHJbJ00XB7RccagD4+PZNAf1PCGjWEsDRem9jIl8/KIBy9MZm2etToqCexWtstO5018/HdTjvZtdGuNDCex2wyrbMA9u2wAGAxqODhbq9BjKBQqEgSxOPBL8shRi/VfuzEc8WBzrxd7bYzH48na5+SvDyfxpV2zpZ93jWXwd+d0lxTCpmnit8canWS2p7C+fKW47zRBEARBEATRftCqnSCItsNSydzt7NHW5dO4i/3XrAspf8bJfT78/bk9uOdgEpm8ictWBnHuUrIBIwiCIArIEqhF22MV++Owh+GGLVHcsKX2PZmSeWlIb2yS2QW77GYmmcNeBp2Jk6gyGt+TmZNkbpLqOy/4M9tVMgOFogheknk4ojf8d0oQROsgK9RphmNDo7lsZcBWkrlaMZw3TNz+fKW9dt4E/nt3DJ/Z4YNHYzgUy+O4RauHehEprHmEPQzn1dk6iyAIgiAIgmgtqAEpQRBtR1jSk7k/oKE34NxO7YYtEQzOBecZgD9cH8K6bnvK6FMHfPibbd147zk9lGAmCIIgKnBLySxLvsmUzFZuIKr0+vnLCDeUzM3syawxhm7Bd7Oi4UlmTp7AhU4fjsibArtsB4mgHsHvezhM9dAEsZhoZquEZnDBMr9SUVmRsSol8zNjGUymaxO8xxN53HswCQDYebyxKmbAnpL54hUBKh4iCIIgCILoMGjlThBE2yFTMq93aJVdZEOPF5++pA/HE3mEPBqGQp3V/4sgCIJoLrIkc/EtlSSz7HNECWAACLmUwO0LaNzgdlzWUHIOn84Q0BlSArlwsMkB6G6fZrvHJNB4u2yektnTcnbZ9j9LdMl0mj0uQRBy9CY9z5pFwFNw0Xp2XE3NXD0u3XcoJdz2ey/GccmKAF6aqv1sjwa8dUsEX9sTk/aAVmU8qf4hZJVNEARBEATReVCSmSCItiMike04tcouJ+DRsKaLjB4IgiAI91FRMvsU6ptkSiBRoH5DjwchO7IpCUMhHYdjeaSrso0qSmYA6PIxpJL8bZtplw2IlbVWNFpVzLXLblqSWaRktv9ZZy/x4cEjtcmSs5eQGwxBLCZEBVLXru3cxOTWAZ9ykjmRM5HMGQh6NMxmDDx+QqxSnsmYuOOlBLcYbHXUg1etDuG8pQEcms1haVjHPz486ai4CgD3GDw29HiwqotCkARBEARBEJ0GZVEIgmg7IhK77A11KpkJgiAIopHI7ECL+d8lCi4aVnaT160P1bx21eogQpIx1A6ruzzc9hXKSWZBMoGh8bbTVjhNMjdeyVz7mkpBQiMwhD2Z7f8OTh/01Vik9wU0bB3wOTk1giDalIhPw6aeyiQkA3DVmtrxrFM4fdDec66oGn7oaAo5i9zuT/cncGg2V/N6b6AwxnX7NZw64MNAUMfSOty7kjn+uB8ue657NODPTo06PgZBEARBEATRulCSmSCItkNmDbnOBSUzQRAEQTQKv2SYKo5vIa+GwaB8mm6V0Lx2XQjbhuaD11esCuCCZQHX7LJXR/lJ5oRV1HsOUV9mv86gOejr6yat25O5M+2ygx4Nf3FatHQ99QU0/Plp0aYXGxAEsfD81bbukjNV1Mvw7jO7Orp90ZouD6I2ir/G5/of3y+xyi6SzvOLk3iFVCrFbXZYEdHx8Yt68YaNIbx2XQifvKgP67qpGJwgCIIgCKIToWwMQRAdRUjSr5kgCIIgmo3ULrvsrdVdHowmM44+Bygkcf/u7G5MpAzoGisFlYMKdtkeDZYKqVVdHoQ9GoB8xeuqSuaoIMlcrWhtBo7tspvQk7nRFt0ixHbZzn4H5y0L4PRBH2YzJrr8WsNV4QRBtCYDQR2fuKgP8ayBkIeBNbnoqNFojOHUAR8eOSa2vi5nImXglZkc9s/UKpRV4SaZw+4mmYMehmVhD67fFHH1cwmCIAiCIIjWg5LMBEG0Jecu9eN3xysX41et7tx+XQRBEERnILfLnn9vVdSDx0bESWYVm2TGGPqDlRuq2GV3+zTMZAyuAgooWGD2BzSBXXZ9SuYmCXMrcKxkbvDJv25DGNMZA1nDRCYP5AyzaQq/vNAu2/lnBjwaArQ6JQgCQHgRFQ6fZiPJPJY0MJ5S21ZEj7923FjqepJ58fz9CIIgCIIgFju0jCcIoi25Zm0QT4+mkZ4TUAU9jNt/kiAIgiBaCamSuSwmu6pLPk13mtBUscuO+jRk8iZXOQsUVNaMMa57iHJPZh//PETHXEhatSfz9uWBhn6+HURK5lYoEiAIgmgnTrPRl3k8lQdvmPRohfXwbMZ6DC32ZC7HbbtslYI2giAIgiAIojOgJDNBEG3JyX0+/PNFfaWq7ytXBR0rjwiCIAhiofBLkrzlSubVUfk03WlCUy3JzBDLMswKEsbFBHiEE0RO5kwYpmnZV1mkZM7kuS8vKM57Mrt8Ii2MqCezp8OtbQmCINxmIKg+eEykDPBE3j1+DQNBHc9PZC0/g1dItdTlJHMrtL4gCIIgCIIgFgZKMhME0bYsj3jwho30GCMIgiDaB9WezEvDOnwakBHYEjvt/8tTH1cT8Wrw62Lb62ICnKdUMgEkciY3AV2OKMnczkrmRvdkbiVESWZSMhMEQdjnilUB/PJgynK78WQeEc44HvFq6OcolHnwxjiVuYEdVAraCIIgCIIgiM6AZH8EQRAEQRAEsUCo9mTWGMNKiZrZqZLZqwFWu3b5NOnnF5XMYUHPxYSCZXaXIJErSl4uJCEPg5N2ko22y24l8oJiAGrDSRAEYZ/LVgaVthtPGZjN1haBRbwMfQE1NbKokOrUfq/S/iqQkpkgCIIgCGLxQBJAgiAIgiAIglggAlK77MqfV3d58NJ0jrut04QmYwwhj9gKGyjYZYs+nwGl5HdYoFaOZw0A8mC3SMncCjDG0OPXMJYUq7l5OO2T3Y5sXx7Aln4f8qaJvFEoDsibpnKSgyAIgphnQ48Xb9ocxvdfjCNrAEtCGgaDOnaPV9pfJ3MmRhO1fSUiPjUlc9TLKgraynnV6mDN8ZwSpIojgiAIgiCIRQMlmQmCIAiCIAhigZDaZVcFfldJlMzeOnJ5Ia88yVywy+af57KwXnovLLDXjKsomX38z18ZbY0kZbfPfpJ5McXUI16Na9lKEARBOOMPN4Rx2cogElkDA0Ed9x5McpO+vDYaBSWz9TO5R7LNuUv9ePcZXfjZ/gR0Brx2fRjffC6G45ykthWkZCYIgiAIglg8UJKZIAiCIAiCIBYIWZLZW5VkXt3lvl02UFQYiROoXT4NovzhqrJzkiuZ5QQ8GlZEdByOVQav/3B92HLfhcBJX2bGKKhOEARBOKfHr5XGn/6getFVxKspOUn0SsY2xhi2Lw9g+/JA6bUfvxQHEsqnUXY+NB4SBEEQBEEsFqj8nCAIgiAIgiAWCJFNJQBUC39WNSjJHLJQGEV9GiZS/ETx6jJ1dUiYZFZrrPymzZGK/tCber04a4lfad9G0+0gyUwQBEEQbqFif10k4tPQH7Tevttvzy3EqWMFjaEEQRAEQRCLB1IyEwRBEARBEMQCwgDw0rB6VQJaFtytp/+vKDlcOq6PYURgj1me+BadXzynlmQ+Z6kfn7y4D0+PZtAf0LBtyC/tWb2QOFEyEwRBEIRb2Eoyexl6/Bo0BhiSIVimZOYhciyxostHYyhBEARBEMRigZLMBEEQBEEQBLGA6BqQ4wiF7fT09dVtly2my6fhtEEfHj6arnlvZbmSWZAQVrHLLrIq6pH2nm4WpMIiCIIgmkmXQtK4SMSrQWMMvX4N4wInEkDek5mH07kGJZkJgiAIgiAWDzTzIwiCIAiCIIgFRBSz5VlpnzXk425rpUaWoWKXfdFwoOb14bCOoTI7zoCHgfdJCUW77FbGrpL54uWtYfNNEARBdAYaY+hWTNZGfIXRuM8iiWx3bMvk7Y/nOqtvjkIQBEEQBEG0F5RkJgiCIAiCIIgFRGf84Csv9/tHmyM1r23q8TjukwjIg79erdDv+fRBH07t91a8/paTI2Bl564xxrXStKNkblXsKJmjXobfXx9u4NkQBEEQixHVpHBxTtAXkPdctmuX7STJHPUVVNUEQRAEQRDE4qD1vOkIgiAIgiAIooPRBTFenpJ5dZcHb9ocxvdejCNnAENBDW87NVrX8WVK5qLFpUdj+IfzerDzeBpTaQNnDvkxFKoNXoe9DLEq5XJ8ESiZ/3pbF9I5ExnDxNlL/JaBfYIgCIKwi2rBU3Su4Ks/6K6SeWOvFztHMrb2IatsgiAIgiCIxQUlmQmCIAiCIAhiAfEwBqA2EStqlfyHG8LYsSKAVM5Ef1CHv45+zAAQkqigo2XBYY0xnLes1ja7nLBXA1CpXI7nOj/JvDzSmr2kCYIgiM5BNSkcVlQy200yX7AsgG89H7e1T5ePVMwEQRAEQRCLCSoxJAiCIAiCIIgFxI6SuUhfQMdwxFN3ghkAghIlc8RmH0WeKroT7LIDOoNMjEVKLYIgCKLRqCSF/TqDd25u0C/pyezX5eM/j6GQjtdvCNnax067CYIgCIIgCKL9qXv295Of/ATXX3891qxZg0AggKGhIVx44YX41Kc+hZmZGTfOEQAwOzuL73//+3jXu96FCy+8EIODg/B6vejq6sJJJ52EG264AXfddRdMs/2VEwRBEARBEETnIuzJvEBxWZlddtRm8jTMUUUnOsAumzEmDe5HbSbjCYIgCMIuKknm8uKwPkmSucevgTnolfzGzRF85pI+/PWZXXj/ud2W21MRFkEQBEEQxOLCscdbLBbDW97yFvzkJz+peH10dBSjo6N45JFH8LnPfQ7f/e53cf7559d1krfeeiv+4R/+AalUqua92dlZ7N27F3v37sXXv/51XHzxxfjGN76BVatW1XVMgiAIgiAIgmgEomSyx0Hw1wkhSTbbbnA4zEm2doKSGSiosU4ka79LxMugS1TnBEEQBOEGKqrg8uKwfolddo9fbqUtY0XUgxVRDyZTeVvnQxAEQRAEQXQ+jpLM+Xwe119/Pe666y4AwJIlS3DTTTdhy5YtmJiYwO23346HHnoIhw4dwjXXXIOHHnoIJ598suOTfOGFF0oJ5uXLl+OKK67AWWedhaGhIaRSKTz66KP4xje+gVgshgcffBCXXnopHn30UQwNDTk+JkEQBEEQBEE0ApGSeYFyzAhJVLgRm70UeUrmjAFk82bJvrNdESnISKVFEARBLAR2lcy9AQ0MAM9PxG4/Zh5BBcsVsssmCIIgCIJYXDhKMn/5y18uJZi3bNmCX/3qV1iyZEnp/Xe+8524+eab8ZnPfAaTk5N4xzvegQceeMDxSTLG8KpXvQo333wzLr/8cmha5aT1rW99K973vvfhqquuwt69e7F//368733vw3/91385PiZBEARBEARBNAJRjDZvLIzNtNQum5M0lsFTMgNAPGeip82TzKJAOam0CIIgiIVAJTFcXuzl0RgiXoZZTtsKN5LMfh3CJHYRKsQiCIIgCIJYXNie/eXzeXzkIx8p/fz1r3+9IsFc5JZbbsEZZ5wBAHjwwQdx9913Oz7Jj3/84/jFL36BK6+8sibBXGT16tX4zne+U/r5O9/5DhKJhONjEgRBEARBEEQjEOVecwvUyjgoSTLbDQ6LEtadYJktCsiTUzZBEASxEKiogqsdSETtHHol/ZpVYYxJC9UAoMumIwpBEARBEATR3tieZT7wwAM4duwYAGDHjh3Ytm0bdztd1/Hud7+79PPtt9/u8BSBvr4+pe1OP/10bN68GQCQSCSwb98+x8ckCIIgCIIgiEYgCgAvlJI5IAkQ27XLjgiUz3GOiqrdECXczfb/agRBEEQbEPIwWBmMVDuQiArZ3FAyA0BQ0nIDICUzQRAEQRDEYsP27O/OO+8s/fuaa66Rbnv11Vdz92skXV1dpX8nk8kFOSZBEARBEARBqHJqv4/7+lBIX5Dja5Lmz3atoEX9nTtByewVFAMYlGUmCIIgFgDGmGVyuLrYS+S24VarB5kbCgB0UU9mgiAIgiCIRYXt2d+uXbtK/z7nnHOk2y5duhQrV64EAIyMjGB0dNTu4WyRyWTwwgsvlH5evXp1Q49HEARBEARBEHa5clUA1SHabr+GTb3eppxPOXYVSGGBxCrRAUpmUb/p/uDCFAMQBEEQhJVldrUDyeouD3c7K5trVYIe8fnoDAi7dByCIAiCIAiiPbCdZN67d2/p32vXrrXcvnyb8n0bwbe+9S1MT08DALZt24alS5c29HgEQRD/f3t3Hl1VeS4O+D0ZSAiEQRSDDGIp4qzgrbYq4lCVantbbau0tThXK9Lp3lbt9afSOvXe2lrrUqwDON3WqqvVqniLFVtsK611VsQBpyiKBBSIQEKyf3+wOBKSnOyEkHNCnmct1jr77G9/+zvk3d8beM/+NgC014Dy4jh9j8rskpYVJZn4zth+Oe8w7iqtLX/dmtYKsbVru/+dzLsO6tXisqOfHtG76wcDQI/U3juZj9qholmbPiWZGDWgc77IlqtY3a9XUWQK4HcZAAC6Tstfcczh/fffz77eeuut22w/aNCgFo/tbO+9916cffbZ2e3zzjtvs50LAAA2xcHDe8enhpTHotq1MbyyJEpaW9+yi5W18ybd1u5k3hKeydy3V1EcMrx3zH7jo0fwjOpfErsMyv8d5wD0DG3dybzxl7123qo0dt6qNOYvrc++d9THKqKstYc1t1N5riKzpbIBAHqcdheZV65cmX1dXl7eZvvevT/6pv+KFSvae7pU6urq4otf/GIsXrw4IiK+8IUvxNFHH71ZzlVfXx+PP/54i/uGDBkSQ4YM2SznBQCakpPp7spLMrFD/8IqWLb3DqTW7mjaEorMEREn79Y3RvYviReW1sXQviUxcWTvgrjjHAqRvAydr607mTd+1nImk4n/t++A+NObq2LZ6sbYcWBpjB1c1mnjyfVM5n695EcoFHIyAF2l3UXmQtPY2Bgnn3xyzJ07NyIiRo0aFTfeeONmO9/ixYtj7733bnHfBRdcEBdeeOFmOzcA8BE5GTpu0pg+8ZsFtU3e23+79v8ndK/iTJQWRdRvtDp27cZvdFNFmUx8ekRvS2RDCvIydL62l8tuXtgtLsrE4ds3Xza7M7S1XDZQGORkALpKu4vMffv2jWXLlkVExOrVq6Nv3745269a9dHycpWVle09XU5JksQZZ5wRt912W0REjBgxIh588MEYOHBgp55nQ4MHD45Zs2a1uM+3wACg68jJ0HGHjugdf1+0Jl5fvjYiIgaWFcUxH+/Tob76lhbFsjVNi8pbyp3MQHryMnS+/m0Ublt7bMXmkvtOZkVmKBRyMgBdpd1F5gEDBmSLzEuWLGmzyFxTU9Pk2M6SJEmceeaZcd1110VExLBhw+Khhx6KkSNHdto5WlJaWhrjxo3brOcAANomJ0PH9etVFD/61MB4rqYuVjcksfvWvTr8n8MVpZlYtqbpe1vKncxAevIydL4BZcWt7isrXreiSFfqXdL67wqKzFA45GQAukq7fwMcM2ZM9vWrr77aZvsN22x47KZIkiSmTJkS06dPj4iIoUOHxpw5c2LUqFGd0j8AAGzpyksysfe2ZbH/duWb9B/DLd1F9eFadzIDwKYaUN56fu7bxXcxR+S+k7l/G0t7AwCw5Wn3b4C777579vU///nPnG3ffffdePPNNyNi3TId22yzTXtP18z6AvM111wTERHbbbddzJkzJz7+8Y9vct8AAED79GnhP5wtlw0Amy7Xctl983DnsGcyAwCwoXb/Bjhx4sTs69ae7bDe/fffn3195JFHtvdUzWxcYB4yZEjMmTMnRo8evcl9AwAA7dentKUis+WyAWBTlZdkoryVJbH7tpB/NzfPZAYAYEPt/g1wwoQJUVVVFRERDz/8cDz++OMttmtoaIgrr7wyuz1p0qQODvEjZ511VrbAXFVVFXPmzIkdd9xxk/sFAAA6pqXlsmvrk0gSdzMDwKYa0Moy1PlZLjvHM5nLur7oDQBAfrX7N9Li4uI4//zzs9uTJ0+OxYsXN2t3zjnnxJNPPhkREfvvv38cccQRLfY3c+bMyGQykclk4qCDDmr1vFOnTo2rr746ItYVmB9++OFOe8YzAADQMRUt3EmVRMQqz2UGgE3W2rOO83Inc45z5lraGwCALVNJRw467bTT4ne/+13Mnj07nnvuudhzzz3jtNNOi1122SWWLl0av/71r+ORRx6JiIgBAwbEtddeu0mDPO+88+Kqq66KiIhMJhPf/va3Y/78+TF//vycx40bNy5GjBixSecGAABat+c2vaJ3SSb6lBZFn9JM9CkpiorSTJS1srwnAJBea0tUF9ozmXMtpQ0AwJapQ0XmkpKSuOuuu+KrX/1q3HvvvfHOO+/Ej3/842bthg0bFrfffnvsuuuumzTI9QXriHXPZT733HNTHTdjxow48cQTN+ncAABA63beqlfsvFWvfA8DALZIra1QnY+ibnmOc2YyiswAAD1Nh7/2WFlZGX/4wx/i97//fRxzzDExfPjwKCsri6233jr23Xff+MlPfhLPPvts7Lfffp05XgAAAADoEYpbKd4meXgqRW+rlAAAsIEO3cm8oc9//vPx+c9/vsPHn3jiiW3ebfzwww93uH8AAAAA6I6KW7k9ZG0eqszFRYrMAAB8pOsf4AIAAAAAtOmA7cpbfH+vbcq6eCTr9C1tXmgeN9hjMwAAeiJFZgAAAAAoQHts3SsqNyrsDiovio8P2OTFCTtkwrDmRe/Dt++dh5EAAJBviswAAAAAUIBKizPxw30HxIjK4shExMf6l8QP9xkQRa08q3lz+8qYvjFhWHmUFUf075WJyTv3jbGD83NXNQAA+ZWfrz0CAAAAAG36WP/S+J8DB8XaxiRK8vxc5NLiTJy5Z7/4xu6VUZyJyOSp2A0AQP4pMgMAAABAgct3gXlDhTQWAADyw3LZAAAAAAAAAKSmyAwAAAAAAABAaorMAAAAAAAAAKSmyAwAAAAAAABAaorMAAAAAAAAAKSmyAwAAAAAAABAaorMAAAAAAAAAKSmyAwAAAAAAABAaorMAAAAAAAAAKSmyAwAAAAAAABAaorMAAAAAAAAAKSmyAwAAAAAAABAaorMAAAAAAAAAKSmyAwAAAAAAABAaorMAAAAAAAAAKSmyAwAAAAAAABAaorMAAAAAAAAAKSmyAwAAAAAAABAaorMAAAAAAAAAKSmyAwAAAAAAABAaorMAAAAAAAAAKSmyAwAAAAAAABAaorMAAAAAAAAAKSmyAwAAAAAAABAaorMAAAAAAAAAKSmyAwAAAAAAABAaorMAAAAAAAAAKSmyAwAAAAAAABAaorMAAAAAAAAAKSmyAwAAAAAAABAaorMAAAAAAAAAKSmyAwAAAAAAABAaorMAAAAAAAAAKSmyAwAAAAAAABAaorMAAAAAAAAAKSmyAwAAAAAAABAaorMAAA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" ] }, "metadata": { "filenames": { "image/png": "/Users/folgert/projects/hda/_build/jupyter_execute/working-with-data/notebook_162_0.png" } }, "output_type": "display_data" } ], "source": [ "# Create a figure and subplots\n", "fig, axes = plt.subplots(\n", " nrows=2, ncols=4, sharey=True, sharex=True, figsize=(12, 6))\n", "\n", "# Plot the time series into the subplots\n", "names = unisex_ranking[:8].tolist()\n", "d[names].plot(\n", " subplots=True, color=\"C0\", ax=axes, legend=False, title=names)\n", "\n", "# Clean up some redundant labels and adjust spacing\n", "for ax in axes.flatten():\n", " ax.xaxis.label.set_visible(False)\n", " ax.axhline(0.5, ls='--', color=\"grey\", lw=1)\n", "fig.text(0.5, 0.04, \"year\", ha=\"center\", va=\"center\", fontsize=\"x-large\");\n", "fig.subplots_adjust(hspace=0.5);" ] }, { "cell_type": "markdown", "id": "7154d603", "metadata": {}, "source": [ "\n", "\n", "It is interesting to observe that the four most unisex names in the United States appear to be Alva, Jessie, Marion, and Tommie. As we go further down the list, the ratio curves become increasingly noisy, with curves going up and down. We can smooth these lines a bit by computing rolling averages over the ratios. Have a look at the following code block and its visualization below:" ] }, { "cell_type": "code", "execution_count": 85, "id": "06364ab0", "metadata": {}, "outputs": [ { "data": { "image/png": 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7ACARH+w/vO59PBePydCVEzJltyV/dmyGy655ozIkSReOztBX362M6Sck6YP9fl05MVNZSZiR3RkbKq2rfUwhyNxvMCIPAEgpe+vN6zF7HFKRL7VvygAAAAAAyWN/Q1iljebnz/H5Ls0b6dPADIcGZTiU5bLJkBQIG7LbbPI6kz8AAgDJJGoY+ueOJst9hV67vjotR8cMSM2gZY7brlOHevWvvYGY7c0R6Z3dfn1hbHKtLd1Z6w+Zg8xeh02jcwk99heMyAMAUopVueyhWc6UmMkIAAAAAEgN8bKYzxnh05whXo3NcynbbZfNZpPdZlOGy06AGQC6YNnBoMosJvUMy3boF6cVpGyA+Yh5IzMst7+4rUkHm8zfd6oIhKPaXmsep51YwHrM/QlBZgBAyghFDB2wuPliPWYAAAAAQHdaZrEes90mzRiY2sEOAEgmhmHo5e2NlvsuGZOpzBQvJy1Jw3OcOqbQZdreHDH00Oo6RY3461Ans01V1usxT7H4XpG+Uv8vFADQb5Q2RhS1uHkpyXL0fmMAAAAAAGmppjmqrTXm7KzJBa6UXz8TAJLJxqqQtll83g702XXSYE8ftKhnzB9nXRZ7Q1VIdyyq1tIDzdpXH5aRQgHn9YdClttZj7l/IfULAJAy9lqUypbIZAYAAAAAdJ/lB5tlNcx//KD0CXgAQDJYsN16LeYLR2fIkUYll6cUuvW54V69sydg2re9NqxfLq+VJE0vcuv243LkdSb3hKZQxNCqCvN6zBlOm0axHnO/kty/qQAAtGK1HrNEkBkAAAAA0H3ircdMkBkAuk+lP2IZqMx223T6MF8ftKhnfWlSlop87YfkVlUE9Yc19Umd0byjNqT//ahKeyzGaScVuGS3pc/kAHSMIDMAICXUNkf1icWaWD6nTYVeujMAAAAAwNELhKNaW2kOeozMcaoog6WaAKC7fLg/YFk14twRGfI40i9Q6XPadcuxOR0e90lZs17b6e+FFnXe0gPNumNRtfY1RCz3Uyq7/2FUHgCQ9BqCUf300xqVNZpvYIZmOWRjhhwAAAAAoBusrggqFDVvP34QA+cA0F0Mw9AH+8ylo22SPjfc2/sN6iXHDHDr3BEdZ2k/talBG6vME576UqU/ot+tqlMkTpK13SadWEzFj/6GIDMAIKk1Rwzds7RGu+usS2VPZYYcAAAAAKCbLDtoPahPqWwA6D7ba8MqtUgmmVbkVr43vatGXDs5S2cN86q9ZO2oIf1mRZ1qmy1mPfUBwzD08Np6BeJEmB026T+nZlPxox9iEUsAQFJ7ZUeTttVYB5hLshy6aExGL7cIAAAAAJCOIlFDK8rNyzQN8Nk1ModhVADoLgstspgl6bSh6ZvFfITTbtN/Hpujaydn6VAgqiVlzXpuS6PpuJrmqJ7YUK+vz8jtg1bG+qi02XL9bOnwchJfnZajEfST/RKZzACApBbvBqY4w6E7ZuUpw0VXBgAAAAA4epurQ2oImbO0jh/kYZkmAOgmoYihj0vNQWaf06YT+lG5Za/TrqFZTs0fl6nLx2daHrOotFmrLCY/9aaaQERPrK+33DdniEc/PTmfAHM/xv88ACBpGYahvfXmLOZCr10/OCkv7cvnAAAAAAB6z7KD1gP5lMoGgO6zorzZckLPSYM9crdXQzqNzR+boW3VIa20SLZ5ZF29fnmaW15nz/9sFpcF9P7egOw2aUyeSzWBqBaVBtQUNv9/5Xns+o9jsuW098//MxxGkBkAkLQq/FH5LW5iThvq1QAfAWYAAAAAQPcwDENLD5iDzJlOmyYVuPqgRQCQfnbXhfXkxgbLfXNL0r9Udjx2m003H5utby6sMgV0K/xRvbC1UV+alNWjbVi4z6/fr/4sY3lFuXV1ySO+MiVLWVSY7Pf4DQAAJC2rLGZJGkYJFgAAAABAN9pbH1G5P2raPn2gmywtAOgGS8oC+sHH1aqw+Kwd6LNrYn7/ntCT73XomjiB5Fd2NOmdPf4eu3ZdMKon1lsH/63MKvZo1uD+OykAn2GUHgCQtHbXWQeZh2fTfaF/MwxDUUOy28TacAAAAEA3WBqnVPYJlMoGgKNiGIb+vq1Jz29pjHvM6cN8jG9IOnOYVx/uC2hTdShmuyHp4bX1qg5EdPaIDOW4bbK3+nltrwnptZ1N2lkX1sR8l740KUsZ/84yrgpEtL8hIp/TpjyPXXkeu2ny1PNbGtVoUU3SykCfXV+Z0rNZ1UgdjNIDAJLWHotMZqddGpxJqWz0PztqQ1p+sFlbqsPaVhNSU9jQyBynrpucpcmF7r5uHgAAAJDSrNZjdtikaUXcawNAVwXChv6wpk6flFlP5JGkYdkOfX50Ri+2KnnZbTbdNDVb//NhlSIWMd8Xtjbpha1NctqlkTlOHT/Io/0NES0qDSj67+P3N0RU2hjRnSfl6a+bGvXPHU0x7+Fx2HTyEI9umJItt8OmPXVhvb07sSzp4wa6ddPUbOV5GZvFYQSZAQBJy6pc9tAsJ6XK0O88u7lBL21rMm3fVRfWjz+p0VUTM/X50Rkxs1gBAAAAJGZPfVg7as3Pn1MHuFsywQAAnVMTiOiepbXaFadSoSRNKnDpm8flyu1gPOOIkmynrpyQqac3xc/8DkelbTVhbaux/tlurArp50trtbLCvK5yc8TQv/YG5LTb9JUpWXp8Q706ymHO99h1w5QsnVjsIeMcMQgyAwCSUihiqLQxYto+PJuZcuhfXtnRZBlgPsKQ9NdNjdpUFdI3ZuTI62QQDAAAAOiMeBlcx1MqGwC6pNIf0U+W1KjMYmzviM8N9+qGKdkkk1j4/OgMhaPSc+2UGO+IVYC5tXf3+DUs26H1h0KW+786LVuBsKEMp10nFLsZb4IlgswAgKS0vyHcUualNdZjRn/ycWlAT25sSOjYFeVBPbquQbdOz+nhVgEAAADpwx+O6oN9AdN2t12aM4QgMwB0hmEY2lEb1v0ralXpj1oeY7dJ10/O0rkjKZEdj81m0/xxmcrz2PWntR1nGndFxJAeXWc95vSFMRmaW+Lrgasi3TBSDwBISlbrMUsEmdF/bKwK6sHVdZ0654P9AV00JkPD+DsBAAAAEvLh/oACFgtfnjLUq0xKZQNAQir9Eb2wpVErKoKqbbYOLktSlsum24/L1TEDWO8+EWcO9ynXY9eDq+vUGOqJULNZvseuL4xlAgASwwgkACAp7am3LqczPIeuC+kvEjX0pzX1Csd/Lovr/b1+fXlydvc3CgAAAEgzhmHorTilss8eQQYXACRiw6Gg7l1ao+b4lbElSUMyHfrOCbkqzmRsrzNmDvLowTMLtbU6rAp/RIcCEW2uCmn9oVCPZDhfMT5TPkpjI0H8NQMAkpJVJnOmy6Z8Dzc5SH9bakKWa5JL0rWTs3TBqAytqzz8EBdqE4j+cH9AV03MYk0jAAAAoAObqkPaazHBeVyeU6NzXX3QIgBILdtqQrpvaW2HAeaROU59/8Q85TCu1yU+p13HFsVmf9cEIlp6MKiDTRGNzj0c6vu/ldYV8Rw26aqJWXqqgyXZBmXYdVqJt3sajX7hqP+iFyxYoMsvv1wjR46U1+vVwIEDNWfOHP3iF79QXV3nSjx2xsqVK/X//t//04wZM1RUVCSPx6OhQ4fq+OOP12233aYXXnhBkUgHn2wAgKS1p84cZB6R7ZTNRuAM6W9tZdBy+wWjfLpg1OGSRccMcOukweY14mqDhlaWW58PAAAA4DPxspjPIYsZADq0qy6kny2psVxyoLVxeU7deRIB5u6W53Xo7BE+fWlSluYM8Wr2YI9GxakAec4Iny4Y5VORr/3/g0vGZpK0gE7pciZzQ0ODrrnmGi1YsCBme0VFhSoqKrR48WI98MAD+tvf/qaTTjrpqBt6RF1dnb7xjW/oiSeekGHEfniVlpaqtLRUy5cv14MPPqjq6mrl5eV127UBAL2jPhhVtcX6Lawzi/5ibYU5SOxxSFdNyIrZdnqJTx/ubzYd++zmBpU1hlXdHNVJxV5NKCALAwAAAGitJhDRkjLzvXS2y6aTBpPFBQDtqQpE9LNPa9UYjh9gttukzw336ZqJmfJSfrnH2Ww2XTkhU/csrY3Znu2y6bLxmbLbbDpruE/Pbm60PH+gz65Th9L/oXO6NFofiUR0+eWX64033pAkDRo0SDfddJMmT56sqqoqPfPMM1q0aJH27t2r888/X4sWLdKkSZOOurFVVVU699xztWzZMknS0KFDNX/+fE2bNk25ubmqr6/X1q1b9fbbb2v58uVHfT0AQN+wKpUtsR4z+oemUFTbas1/A5MK3HI5YmeTTi50qchnV4U/dlLGvoaInt50+KHhtZ1+nTnMqxumZMvtYDYqAAAAIEn/2huQVfLdGcN83DcDQDsMw9BDa+pVa5EgIkkzitw6c7hPkwtdynIRXO5N0wd6dOWETD23uVGGpAynTd85Ia/l/+GMYT49v6XRsv+bP44sZnRel0brH3nkkZYA8+TJk/Wvf/1LgwYNatl/66236tvf/rZ+9atfqbq6WjfffLM++OCDo27s1Vdf3RJg/ta3vqWf/OQn8nrNMyt+9rOfqbS0VFlZWaZ9AIDkt9oii1OShpPJjH5g/aGQohY3+1MHuE3b7Dab5pb49MJW61moR/xrb0DbasK6/bgcDcni7wgAAAD9WyRq6J095lLZNkmfo1Q2ALTrrd3+uGN3xw9y6/bjcglW9qFLxmbqrGE+lTVGNCrXGTNxKs9j14nFHi1uU8mDLGZ0VaenkUQiEf3oRz9qef3kk0/GBJiPuO+++zR9+nRJ0ocffqi33nqr662U9Pjjj+vNN9+UJP3Xf/2XfvnLX1oGmI8YMmSInE4GUQEgFRiGoepARHvqwnpiQ71e3t5kedywbEcvtwzofWvirMd8rEWQWZLmliT2ELCnPqzvL6pWaYN1pQAAAACgv1hRHtShgDkDb3qRW4MyeO4EAEmKGoZqm6Mxy5aWNoT11MYGy+OPHeDSf88gwJwMcjx2TShwWVbm+NKkLOW4P9vusku3TMvh/w1d0uko7AcffKCysjJJ0ty5c3XcccdZHudwOPT1r39dX/nKVyRJzzzzjM4555wuN/S+++6TJGVlZenee+/t8vsAAJLLx6UBPbmxQVUWD/itDc50yMf6LegH1loEmXM99riTLAZmOHRMoUvrDoU6fO+msKEHV9fpJ3PyZbPx8AAAAID+6W2LLGZJOocsZgBpqj4Y1Ss7mrS6Iqhcj11nDvPqxGKPIoa05ECzNlWFNCTToZOHeJXjsWvhPr+e39KoCn9UBV67vjEjR+PyXHpwdZ2CFkN4+R67vjEj17TMF5LPAJ9DPz25QB/uDygYMXRisUdj8lx93SykqE4HmV9//fWWr88///x2j503b57leZ21aNEibdq0SZJ08cUXKycnp8vvBQBIHvvqw/rtyjpZVAY2OZeHffQDlf6Iyhojpu1TC13tBoXPGeFLKMgsSdtqwtpYFdLkQuvMaAAAACCdHWgMW5Z5LfLZNX0g98gA0ksoYujN3X69uLVRjeHPRuBWVQQ1o8itg00RlbYah/jblkaNz3dpVavPyapAVD/7tEZzBnu1rca6OtpXp+Uoy01ySKoYmOHQpeMy+7oZSAOd/qtfu3Zty9cnnHBCu8cWFxdr2LBhkqSDBw+qoqKis5eTJC1cuLDl61mzZkmSXnzxRZ1//vkqLi6Wx+PRkCFDdMEFF+ixxx5TOEwZSABIBe/vCyQUYJ4/NkPnjSTIjPS3Js6aRscWtT/YdWKxR2cN+6xstsMmHVPoUqbLOjD98vYmrasM6oGVtfr96jptr0ksQA0AAACkurd3W2cxnz3CJzvVfgCkkapARN9bVKUnNzbEBJiPWFkRjAkwS4croK2yGJtojkjv7QtYXufcEb4Oxy0ApKdOZzJv3ry55etRo0Z1ePyoUaO0d+/elnOLioo6e0ktW7as5etBgwbp0ksv1YsvvhhzTFlZmcrKyvTaa6/p17/+tV5++eWE2gcA6DtWs8dbc9mlW47N0SlDE1tzFkh18dZjnhpnPeYjbDab/vPYHJ0/KkNVgahG5TqV7bYrEI7qtvcOqT4Y+zC5qiIY89C4qDSgu+fka3Qu5ZEAAACQ3j490Gza5rRLZ5QwsRlAenlqY4P21JurpXWnIZkOXTMpq0evASB5dTrIXFNT0/L1gAEDOjy+sLDQ8tzOOLIGtCTdeeed2rx5s9xut6699lqdcsopcrlcWr16tR555BFVVVVp7dq1OuOMM7RixQoVFBR06ZrxhEIhrVixwnLf4MGDNXjw4G69HgCkq+pARHvq41eeKPIdXstlXD5BL1hLtz45ahhad8gcZC7JcqjAa70es+nYbKdKsj977XXaNW9khv62pbHd88JR6emNDfrBSfmdajMAAEekW78MID01BKMq95sXE51V7FGOhzKvSA/0yZCkplBUSywm1XQnu026dXqOPKzDDPRbnQ4yNzQ0tHzt9XacWebzfTYLsL6+vrOXkyRVV1e3fL1582bl5+fr3Xff1YwZM1q2X3311br99tt11llnacOGDdq9e7e+973v6aGHHurSNeMpLy/XzJkzLffddddd+uEPf9it1wOAdBUvY3NakVuXj8vUqFynnHZuUhFfuvXJu+vCpoxjqeMs5o6cO8Knl7c3qrmDycvrDoW0uy6sETmdvj0EACDt+mUA6WlnnfVE5ymFlHlF+qBPhiStKA8qbJ5T063mj83U2DySQ4D+LCVGEaPR2E/DX/7ylzEB5iOKi4v117/+VdOnT5ckPf744/r5z3+unJycbmvLwIED9frrr1vuYxYYACQuXqnsG4/J1sCMxLI20b+lW5/c1VLZHcly23XmMJ9e32W99lxrr+9q0i3Hdt99EwCg/0i3fhlAetpZG7LcPpKJlkgj9MmQFDeL+evTc7Syolkf7v9sf67bpnmjMvRJWbN2tZqMk+G0qcliLWdJGpPr1CVjM7q30QBSTqfvoLKysloyiwOBgLKy2q+37/d/NqCZnZ3dzpHxtT4vMzNTX/rSl+IeO23aNJ100kn65JNP1NzcrEWLFmnevHlduq4Vl8ul4447rtveDwD6o6hhWAbUBmc6CDAjYenWJ+d57BqX59S2mrCOPMI5bNLkwqOfFXzh6Ay9uduvqPWzYYuP9gd09cQs5bgpFQgA6Jx065cBpCerTGaHTRqWTZAZ6YM+GYFwVCvLzUHmIZkOzRni0clDvbpwdEiryoMalOHQtCK3Mlx2XTwmQ2srg9paE9bYXKemDnDr47Jm/W5VXcz7uO3SbdNzqEAIoPNB5ry8vJYgc2VlZYdB5kOHDsWc2xX5+Z+tDzh16lS53e1n9Bx//PH65JNPJEnbt2/v0jUBAD1nV5yywNOKKFGG/mtuiU9zS3xqDEW1/lBIayqCCkQM+ZxHH/Ad4HPolCFefbA/0O5xoaj07h6/LhmbedTXBAAAAJLNzlpzkLkk2yk364kCSCMrK4IKWZTKnjXYI5vt8OfdyByXRubETmq322yaVuTRtCJPy7ZTh3p1yB/RM5sbJR0OMP/3cbkaksXkHABdCDJPmDBBO3fulCTt3LlTI0eObPf4I8ceObcrJk6cqHfffVeSlJub2+HxrY+pq6tr50gAQF+IVyp72lGWBQbSQabLrhOLPTqx2NPxwZ1w7eQsba0Jqayx/cWZ39zl1+dHZzAjGQAAAGmlKRS1vBceRalsAGlmSZl1qexZXRxn+MLYTJ061KuyxohG5TqV6aL6GYDDOv1pMHXq1Javly5d2u6xBw8e1N69eyUdXguiqKios5eTdLgE9hG1tbUdHt/6mESC0gCA3mUVZHbapcmFBJmBnpLttuuXpxXof47P1bdn5urJ84o03aJ6QHVzVJ/GWbsJAAAASFW7681ZzJI0KpcgM4D0EYwYWlFuHncblGE/qvXnC30OHTPATYAZQIxOfyKcd955LV+//vrr7R772muvtXx9/vnnd/ZSLebNm9dSxmHt2rUKBq0z4I5YtmxZy9ddzZ4GAPSMplBUW6pDpu0T813yOsmcBHqS027TzEEenVDskdth07xRPsvjVsWpNgAAAACkKqtS2ZI0qk25WABIVVHD0Ou7mtQcMS9RN6vY2xJjAYDu0ukg89y5c1VcXCxJev/997VixQrL4yKRiH7729+2vL7yyiu72ESppKREc+fOlSQ1Njbqqaeeinvs6tWrW9Zjzs7O1sknn9zl6wIAut/GqpAs7nV1LOsxA71u2gC3inzm28GDHZTUBgAAAFKNVZDZJmkE5bIBpIEt1SF996Nq/XVTo+X+WYO7d0kuAJC6EGR2OBy68847W15fe+21Ki8vNx33v//7v1q1apUk6eSTT9a5555r+X6PP/64bDabbDabTj/99LjX/dnPftby9be//W2tXLnSdMzBgwd1zTXXtLz++te/Lp/POkMHANA3tteYs5glaRpBZqDX2Ww2Dc0yD6odbCLIDAAAgPSys878LDoky0FFLQApb2V5s364uFq76qwrNhR67RrD0gAAekCXPlluuukmvfTSS3r77be1fv16TZs2TTfddJMmT56sqqoqPfPMM/roo48kSXl5efrjH/941A2dPXu2vvOd7+i+++5TdXW1TjrpJF133XU65ZRT5HK5tGrVKj3yyCOqqqqSJB1//PG64447jvq6AIDutdPihtfjsGl4Nje7QF8YlOEwbatujioYMeR2MOAGAACA1NccMbSv3jyRchRZzABS3M7akH69os6yauARp5dQKhtAz+jSnZTT6dTf//53XX311XrllVd04MAB3X333abjSkpK9Nxzz2nKlClH3VBJuvfee+VwOHTfffcpGAzq4Ycf1sMPP2w67txzz9Uzzzwjr9fbLdcFAHQfq1mVI3KcsnOzC/SJgRZBZkkqb4qohMkfAAAASAN76sKyir+MymU9ZgCp65A/ovuW1lquwXzEsQPcumhMRi+2CkB/0uWRw+zsbP3zn//Uyy+/rL/85S9aunSpysvLlZ2drTFjxmj+/Pm6+eablZub253t1U9/+lNdccUVevTRR/X2229r//79CoVCGjhwoObMmaNrr71W8+bN69ZrAgC6R11zVFWBqGk7s8eBvmOVySwRZAYAAED6sCqVLUmjKB8LIEXVBaO6d2mtqpvN42zS4Wf9qyZk6qTBHrKYAfSYo76Tuvjii3XxxRd3+fzrr79e119/fafOmTZtmn772992+ZoAgL4Rb22YEQSZgT4TN5PZz7rMAAAASH3NEUMflzZb7hvJsyiAFFQViOinS2q0r8H6uX1uiVf/OTVbTjvBZQA9izspAECvYfY4kHwGZtgtt5c3EWQGAABAaqttjurny2q0rcY84XlQhkOZLut7YQBIVgebIvrJJ9Uq91tnME8d4CLADKDXcCcFAOg1VpnMDps0LIsgM9BXfE67st3mh8+DBJkBAACQwuqDUd35cbVlgFmSxuezHjOA1NIUiurudgLMJVkOffO4XALMAHoNQWYAQK/ZVWt+uB+a5ZTLwc0v0Jes1mUmkxkAAACp7OXtTToQ557W45A+Pzqjl1sEAEdnwY4mVcQJMA/02fW/J+QpgwoNAHoRnzgAgF4RCBsqazQ/4LMGFtD3rNZlPtgUlWEYfdAaAAAA4OhtrApabs9223THrHyN4FkUQBcYhqFddSFtqgoqGOm9Z+ZgxNA7u/2W+0qyHPrRnHwVWTzbA0BP4m4KANCjyhrD2lMXVkPIkNWt90jWYwb63CCf+UG0OWKoLmgo10OlAQAAAKQWwzBU2mCe5Fzks+uOWXkqzuQ5FEDnBSOGfr2iVivKD09iGZzp0Ddn5mp49uHPlKZQVC67rUcq9n20P6D6kHlkbWSOU3fMylO2m3xCAL2POyoAQI95a3eTHl/foPYmdo5i9jjQ56wymaXDJbNzPTyoAgAAILXUNEfVFDY/iM4Z4iXADKDLntrY0BJglqSyxoh+uqRG35iRozd2+bX0YLMMQ/rccJ+unZwldzcFmw3D0Ou7miz3XT8liwAzgD7Dpw8AoEfUNkf11MbGdgPMEuWygWTQXpAZAAAASDX7LbKYJWloFqVkAXTN2sqg3rQoV13THNWPPqnRkgPNihqSIentPX79anmtQt1UTnv9oZD21FsvQTcx39Ut1wCArmBkHwDQI1ZVNKu5g5vpgT67MlzMdwL62qA4QeaDBJkBAACQgkobw5bbh5DFDKALmkJRPbSmrlPnrKoI6v4VtfrWzFw57Z3PaK5rjurN3U1afyikjVUhy2POH+WTzcYSVwD6DiP7AIAesbYy2OExI3OZbQkkg0KfXVZVvMhkBgAAQCqyWo9ZkoaQyQygC/6yoUGV/minz1tRHtRvVtQpanQuozkYMXTP0hq9sLUpboA5123TnMHeTrcJALoTQWYAQLczDENrK61vglujVDaQHOw2mwb4zANuZDIDAAAgFVmVy87z2JVJJS0AnbSpKqj39gW6fP7Sg816f2/nzn9nj187aq0rMhzxuRE+ubppzWcA6CrurAAA3W5fQ0Q1zR3P8BxFkBlIGoMyzLeF5X6CzAAAAEg9VuWyWY8ZQFe8s6frAeYjXt3ZJCPBbOZw1NArO5raPcZhk84Z7jvqdgHA0SLIDADodomUys522TSpkHLZQLIYaLEu8yF/VOFo58p6AQAAAH0pEDYsy9qyHjOAzgpFDC072Gza7nPa9KPZeTEV+nI9ds0fm6EMpzm7eF9DRFtr2s9MPmJRaUCHAu0nblwzKUt5XibOAOh73F0BALpdR0Fmn9OmrxyTLZ+TuU5AsrAKMhuSKv0RFTMgBwAAgBRhlcUskckMoPPWVAblD5snXp9e4tXEArd+enK+NlSFFI4YmjrALZfDpuJMh36/ut50zrt7/Bqf336yRdQwtGB7/CzmM0q8OqHYo+MGujv/zQBAD2DEEADQrcJRQxsOmddjHpvn1Ldm5qrSH9WgDIdyPQSYgWQyyCLILB1el5kgMwAAAFJFqcV6zJI0hCAzgE76pMycxSxJJw32SJKcdpuOHRAb8J092Ksn1jeosU1wenFZQNdNzlJGO2vDrygPap/FZ9iwbId+fmqB7DbWYAaQXBjhBwB0q201IQUi5lmeUwe4VeB1aHy+iwAzkISsMpklaXFps6IJrh0FAAAA9LXShniZzEycBJC4eKWyC7z2djOS3Q6bTi3xmrY3R6RFpdZBa0kyDEP/2NZoue/i0ZkEmAEkJUb5AQDdam2lOYtZOhxkBpC84mUyv7cvoF8uq5U/3P6aUAAAAEAy2N9ozgL0OA4HhgCgI+GooapARGsqg2qyKJU9q9jTYcD3rGE+y+3v7PHHPWdnXdhy3eYin12zh3g6aDUA9A2m8AEAutU6i/WY3XZpfF77684A6FuZLrsG+uwq95uDycvLg/rZkhr94KR8uR3MngYAAEDysspkHpLpJAsQQLvqglH9c3uT3t7jt1yH+YgjpbLbMzzHqXF5TlPQeFddWDtqQxqdax4ji1ea+8LRGXLa+fwCkJyYwgcA6DaBcFRba8yZzJMK3XIRmAKS3uXjM+Pu21IT1j93NPViawAAAIDOiRqGyiwymVmPGUA8wYih5zY36Gv/OqQFO5raDTDne9ovld3aWcOts5mtgsmGYejTA+btHod0eon1+wBAMiDIDADoNltqwrJYjllTCymVDaSC00p8unVatpxx7hD/sa1RFU3mQTsAAAAgGZQ3RRSyWOWF9ZgBWIkahu5bWqMXtzUpYDWg1caswR2Xyj5i9mCvvBYJF9stkjP2NkQsJ8hML/LI6yRpA0DyIsgMAOhQ1DC0pTqk7TUhGUb8m+4t1dbrMU8qpFQ2kCpOK/Hpzln5ynWbH2SDUenJjQ190CoAAACgY6UN1hMiyWQG0t+6yqB+t6pOj6yt14FGc9l8K4tLm7XukPVYlpVESmUf4XXaNC7fPMFlZ23YNLa2JE6p7FmduB4A9AWm8QEA2lUXjOruT2q0p/7wDfqkApf+94Q8y5mUm6vMN+ZuuzQyh+4GSCUTClz6nxPydMeiarWdVrLkQLPWVAR1bBEVCgAAAJBc9ltkAkrS0EyeSYF0tqQsoPtX1LW8XrjPr/vnFqooI/4Ek0jU0PNbGxO+xpBMhyYkWCr7iNG5Lq2tjB0rawwbOtgUUXGrzyWrUtlOuzSD524ASY5MZgBAu/66saElwCxJG6tCWrDdfBMeNQzL9ZjH5rnktFPaB0g1Y/NcOnOY13LfY+vrFY52XEoMAAAA6E2lDebsRZuk4kwymYF09uzm2HGqYFR6bVdTu+d8VBqwLFEtSccUulTo/Sx0kuex67+m5SRcKvuIUbnWE1x21H72WXWgMRwz7nbEsQPcynARvgGQ3JjGh6RiGIbKGiNaWxnUmsqgRuQ4dcX4rL5uFtBvBcJRLSoNmLZ/sD+gy8dnytbq5npvfUT+sDnoNL6TszwBJI8rJ2bpkwPNagzF/m2XNkb0+k6/Pj8mo49aBgAAAJhZBYyKfHa5LdZFBZAeypsiKrX42/9gX0DXTc62PCccNfT3OFnMN0/N1pnDfTIMQ/sbImoIRTUq1yVPFz5HxuRaj4ntqA1rzpDDXy+xyGKWpBOLKZUNIPkRZEbSeGpjgxaXBVTpj7ZsO9gUIcgM9KHl5UEFo+btFf6odteFNbLVzXK89Zg7W0oIQPLIcdv1xfGZ+vN68zrML2xt1MlDPSrwkhUCAACA5GAVZB6SxfAnkM7WVgYttzeEDIUihlwWweEP9gV0sMk84DUk06HT/13Ry2azqST76D4/inx2ZbpsponbO2s/G0OzKpVtt0nHDyLIDCD5UW8BSaPCH4kJMEuHMyOrA9ZlSwD0vI8tspiPWHow9iZ+c5wg8ziCzEBKO3uEz3Jd9UDE0NMbzcFnAAAAoC/4w1HVNJuDRpTKBtJbvCCzJMsy1FHD0IvbrLOYLxuf2emS2O2x2WwabVEye0dtWIZhqNIf0bYacxsnF7iU7SZ0AyD58UmFpHHsALfl9vZuFAD0nKZQVKsq4v/9LTsYO9NyS7X52CGZDm6KgRRnt9l0wxTrqiIflTZrwyH6aQAAAPS9eGurDibIDKStqGFoXTtjx63XPj5iS3VIFX7zhJRh2Q7NHtz92cOjLUpmN4UNHWyKaHGpdansWT3QDgDoCYz8I2lMjRNkXlNpnR0JoGctPdissEWp7CN21YX13l6//ryuXo+srbcsMzShgCxmIB1MLHDr1KFey32PrW9Q1DCvxw4AAAD0JoLMQP+zqy6s+lD859EdteZx5U1V1mPNl43r3izmI6wymaXDAfCPy8wVBG2STqRUNoAUwaIkSBoDMxwanOkwPRSsrQzKMAzZeqCTBxDfx3FmU7b20Jr6dvezHjOQPq6ZmKllB5vlD8c+wO+pD2vDoZCOiTNZDAAAAOgNB+IGmRn+BNLVmnYq8EnWmcxWy7057dJxA3smsGuVySwdHnezat+UQpfyvEyOAZAayGRGUrHKZq5pjmpvPesyA72pLhjtllL1BJmB9JHvdeiycZmW+9qWzwcAAAB6W1mjOVjjtEsDfAx/Aumqo7GrvfVhBSOfTZSOGoY2W2Qyj851ye3omQSnIp9dWS7zey+N8xw9e4h1FTEASEbcZSGpxFuXeQ3rMgO96tMDzYocZfXbbJeNsmRAmjlvpM/y4Xj5wWYZlMwGAABAH7Iqlz0ow9Ej5W8B9L3miKFNFlnJrUWMw9W3jtjXEFFj2PzsOrEHkyRsNlvcktltOWzSicWUygaQOggyI6lMKXTJbnHv3x0ZlQASEzUMvb6z6ajfZ3y+izL3QJpx2m2aMdA8IazcH9W+BqqOAAAAoG8YhqEyi/tRJj4D6WtTVVDhaMfHtV6X2SqLWZImFvRsJb5RcUpmtzV1gFs5bkI2AFIHn1hIKhkuu8ZazOzacCio0NGmVQJoEY4aWlwa0ILtjSptiC0p9klZs2WwqNDbuS5jQg/foAPoGzPjrFO1nJLZAAAA6CP1IcMyO5H1mIH0taqD9ZiP2FHz2bjXxirrc8b38HJvYxLMZJ4zhCxmAKmFIDOSztQic4ZUMCptrAopHCXQDByt5oihe5fW6Dcr6/T0pkbdvrBKS8oCkg5nMb+wtdHyvCvGZ2p4dmI3xV6HTWcM83VbmwEkj2lFblktVbW8nKojAAAA6BsHLEplS2QyA+lqb31Yb+/2J3Ts9trPgsybLcprl2Q5lN3D2cOJZDI77dIJgwgyA0gtBJmRdOKty/zTT2t0zesV+uHiapU3UZIT6KrntzRqbWXsTfXDa+sVjBhaXNqs/RZZzMUZDp061KsvT8pS69hSpsumH8/O18NnD9AFo3wamuXQtCK3fjQ7j/I+QJrKcNk1yaJSwdbqkGqbE6hVBgAAAHSzssaw5XaCzED6CUcNPbiqTqEEHz/3NYQVjBiq9EdU6Tef1NOlsiWpyGfXiJz2EzdmFLmV4WIsDUBqoWYMks7YPJd8Tpv8FmWOpMMZzb9bVacfzc5jvVegk7bXhPTKDvN6y/UhQyvLm+NmMc8flyGH3aZji9z6+WkF+nh/QB6nTWcN97UEk6+dnK1re7T1AJLF8YM8WncodrKKIWllebNOp4oBAAAd2lMX1oIdTaoPRjWtyK15I3083wJHwWo9ZokgM5COXtrWqJ115oklNkknFnu05EDsUk5RQ9pVF1ZFnKSlifnWCU/dyWaz6cZjsvXLZTWqDVqPeZ881Nvj7QCA7kaQGUnHabdpcoGr3bKbm6tD2lYT1rgeXi8DSAeV/oj2N4Tlstt0//JaxSs6/+f1DaqxyEIcnOnQKUM+u9Ednu3U8IlZPdRaAKlg5iCPHt/QYNr+hzX1WlMZ1ACfQ/keuwb4HDpmgEs+J7OxAQA4orY5qruXVKvu34PMqyqCKm+K6Pop2YoahjZXhxSMGJpU4Jbbao0KACZlFsEjr8OmPA/3oUA62VYT0ovbzMkTknT+KJ9mDjIHmaXDlbcOxAkyT+iFTGbp8LrPv5pbqLd3+/XWbr+qW43BHTvApVnFlMoGkHoIMiMpHVvk7nBtx4X7AgSZgQ68vrNJT25sUCSB5cytAsySNH/s4SxmADhiYIZDJVkO7bPIGFlUGvtA73Pa9D/H52pyYc/PDgcAIBW8sqOpJcB8xOu7/BqT69Kbu5u0teZwdtagDLvumJWvgRlkYgIdKbNYk7k400GFACCNBCOHy2RHLca4hmY5dOWELIWtdkp6a7dfVnOfC7x2Ffl6bzJKttuu+eMyddGYDC072Kw99WENynBozmCv7HxeAUhBNsMwEgg9oKSkRPv371dubq7uvPPOdo8dPHiwrrrqqphtzzzzjMrKyjq8zuzZszV79uyW183NzXrwwQcTauOVV16pIUOGtLzesmWLXnnllQ7Pc7vduu2222K2vfXWW1q3bl2H544bN06f//znY7b96U9/UkODObuprbPPPltTp05teV1ZWam//OUvkg6X3Kxpjireb+fOSZfKk5Glhz43QG6HTcuXL9fChQs7vGZhYaGuu+66mG0vvviidu3a1eG5xx13nE4//fSYbffff3+H50nS/PnzNXLkyJbXu3bt0rPP/13N0cMLo3udNsWboP7Nb34z5vX777+vFStWdHjNkSNHav78+THbnnjiCR06dKjDc+fOnauZM2e2vK6vr9fDDz/c4XmSdO2112rAgAEtr9euXau33367w/OysrL0n//5nzHb/vnPf2rr1q0dnnvMMcfonHPOidn2u9/9TsFg+xMVJOnCCy/U+PHjW16Xlpbq2Wef7fA8Sbr11lvl8Xw2y3Dx4sVavHhxh+f11mdE1IgNHO8dO0+BzKKW11k1uzR49wftXs9hk4qyvUn3GdGRm266SdnZ2S2vk/Ezou3fNhJHn2ytt//e/GFD/rChnZMuVdid2bI9r2KDikqXxRxrs0m5brtaz1dJpj75xRdfTOhc+mT65Nb4jKBPxmH0y9ba+5uraY5aDpAfcbDkJNUVHv48HJXj1O0TInr6qSc7vKaUGn9zrdEv0y+3djSfETvzp6p84LSW1/ZIUJM2PqcsV8dBm2T7jOhIOvfL9MlHJ9375Lyzrtc/Wy0BN3Dvx8qt2iZJyvXYW8ZW64JRhVvlUTTkjlDZyLkx7zdqwwtyhprkdtja/ZxI57+3tuiT6ZNbS8XPCPrkXR2e2xN9MpnMnRSNRlVfX9/uMbm5uaZtTU1NHZ4nHf6DayuR8yQpEomdtRkKhRI61+02ZxYFAoGEzg0EAqZtDQ0NCZ0bCsWu5dj2Z9v+L6ehxrCh5QebNXuIV8FgMKFrer3mtS164/8mHI5dJ+TjfY1qbjr8IRWVZF3kJX47ErluU5P5XRsbGxM6t23nZhhGwt9rNBqbDZvo76GVo/k9rK+vT6iTbvt7GIlEutzeRP9vevMzonWuv82I/YywRSNyhazXYI59/5BpWzJ8RrSn7fypZP+MQNfQJ5uPa6un/94Of8bE/r3ZIyHLz5bGNh/JydInh8PhHv/cp0+mT26rv3xGSPTJ/Qn9svm4to78zTkktZebbI9+1lftrAvrrd2J/Yyk1Pubo1+mX27taD4jjCzz92QEGlRv/hGYJNtnREfol9GRdO2T5w/zamNVUNv+XfHDEWluefZsavVnYVPseJgjYv57c4aa5Ao1ygip3c+J/vT3Rp9Mn9xaKn5GtEWfbNYTfTJB5k6y2+0xswusZGRkWG7r6DxJMbNKjkjkPElyOGIfU10uV0LnWv0Ber3ehM61+oXOykpsrVaXK7bUdXs/23D08Cy0zxyeYfb+voBmD/FKDpfcGVkKRQ8PdXscktuivG9mZqZpW2/83zidn/2pHfJH9N7+oAa5YtvisB2edZdIOxK5rtXvYWZmpuWHZlttfydsNlvC36vdHvs9JPp7aPV7czS/h9nZ2Ql10m1/Dx0OR8Lfa1tH83/TE7+HbTMkDFvsZ4RhdyjkMv9NHHHkdzIVPiPaalsSze12J3RuX31GoGvok83HtdXTf29NYUNH+uQjog5X3M+WDJdN3n9PL0+GPvnI67743KdPNkvnPrmt/vIZIdEn9yf0y+bj2srKylJT2FAg3H5Buag9tq96Z0+zjsnKjlv9qrVU+5ujX6Zfbq2rnxHhqBRxmP9ePRlZCa1rnkyfEYmgX0ZH0rVPHpLl1I9n5+vl7U16YWujIg6PDE+mctzm8dTa5mjL0nERh/nvLew6/P3ne+xqr0p1f/p7o0+mT24tFT8j2qJPNuuJPply2Qk6Um5k6NCh2rdvX183p98xDEO3L6yyXGNnYr5LO2pDCrZZTvaWY7N1xjBfL7UwcX9YXaf391l3lNkumwZlOrSzNqyBGQ59eVKWZg4y/+EDHVlTGdRPl9S0e0y2y6b6UPwu4LbpOTp1qLmTBfoafXJy2VwV0tKDzSprDOtAY0QV/oiazd21JMlpl356cr5G5sTehFcFIlp/KCSHTZpc6FZeApOuAADJgX45cVHD0FffPaTq5mjHB7cxJNOh+04tSChgBvQ37+zx6+G15iycu+fka3y+y+IMID31pz55Z21If1pbr1un5agk25xH9/5ev/6wpv3svKsnZuriMfGTLwAAHSOTGSnBZrNpbolXz242l9/cVG0u5StJj61v0JRCtwZmtFeIrHftrgtrYZwAsyTVhwzV/7vkS1ljRP+3sk4/PzVfxZn8qaJzPojzezY+z6njBnk0Id+lAT6Hvvae9RonQzIdOnkIExwAdGxCgUsTCj4bvDMMQxX+qOXnSzgqPbquQT+enadAxNDCfQEtLmvW5qpQS9Ftm6Rx+S6dWOzRWcO8ynARcAYApIdNVaEuBZglqbQxold3NumSsQyGA22VNYQttw/JTJ7xIADda1SuSz87Od+UCXjEKUO9emFroyr81v3uuDynPj/anKkJAOgcIldIGacN9eq5zY1KNPW+OWLo1ytqNb3IrfWHQrLbpHNH+nRSsSfuDUgiNlUF9dTGBu2qC8uQZJdkt9lkt0l22+ESww67raXU8LED3Jpb4lWRz6F7Pq1JuP1HvofXdvr1lWMoI4TEBcJRfXrAHGTOddv0w9n5crQqJT8+z6ktNeYH8kvHZcp+FH8nAPovm82mgRkO/WBWnn6yxNzvbakO6aPSZj27uUGVFg/8xr+P2VId0pu7mvSTkwvIbAYApKRw1FBT2FAwcrhE9tObGo7q/d7fG9AXxmQc1fMskI4ONJnL6GS6bMqyKKELIH201x867TZdNCZDj64z970uu/TVaTmMewFANyDIjJRR6HNo6gC31lR2vE7BETtqw9pR+1kAbWNVSGcM8+o/pmTL1YUyY3vqwvrpkhpTaW6ZhtAPv67wR7WtJqwXtzV1+lpHfLg/oGsmZclDWTQkaMmBZstStacM9cYEmCXprOE+bamJLR9UkuXQHLKYARylYwa4ddGYDL283dwH/m5VXULvUeGP6i8b6vX1Gbnd3TwAAHrcazub9PQmczWu1jKdNs0e4tG7ewIyJI3Nc+rWaTl6YFVdzLOsdDiQtrc+ouE5DOUArR20CDIPJosZ6PdOL/HppW1NqgrEDuReNTFLQ7LoSwGgOzClDynl3JFHv8bye3sD+tEn1aoKxFkwMo5I1NAf1tRZBJh7VlPY0Cdl8UtsA63tbwjrlR1+y32nWayvPLfEGxNQzvXY9e3jc5nNCaBbXDouU9nuo/s8WVTarE1ViU8wAwAgWSQyUXjWYI9umpqjP31ugH53RqHunpOvIVnxS3guOdDc3c0EUlrUMHSw0Ty+MyiJlk4D0DfcDpu+NTNXPudn/fG5I3ya1w3jywCAw5iyg5Ry/CCPPj86Q6/saGrJHXbaDz88jM936b29iQVjt9aEdfcnNbrnlAJ5nYkNfr+2y2+aSX60slw2jcp1am2l9brSR7yzJ6C5JdwAIb5AOKq/bGzQv/6dAdHW8GyHRlhkPNhsNn1jRq4uHRtWXTCqsXkuucmaB9BNPA6b5o3M0N+2tJ/F1ZHH1jfonlPymQADAEgpidxXzx5yeCJoTpulIWYMdMtpl8JtJjl/eqBZl49nXWbgiJrmqGUyQDFBZgCSxua59NBZA7SpKqiiDIeGZDpYdgIAuhFBZqScL03K0gWjfKpujirPY1eex94y6Dyr2KN7l9Ym9D6ljREt2NGoK8ZndXjsgcaw/rb56NbPsnLF+EydNdynZzc3akV5swq9dq2xCDhvqQ5pT31Yw7P5k4WZYRj69Yo6raqIn+l32lBfuzfRJfxuAegh547w6eXtTWqOWE2BOSzPY9fFYzJkk/Tclkb5w7HH7qoL6909AZ09gglXAIDU4e0gyFzks2tKgctyn89p17ED3FpRHnuPv6c+rAONYRVncv8OSNIBiyxmSRpEuWwA/+Z12jR9IMvCAUBP4KkEKSnf61C+1/zAMGOgR6cN9eqD/YllNH+wL6DLx2W2G3wzDEN/WltvOTO2yGfXsUVuRQ21+mcoHJWawlFtOBSS1Zi6yy5dMCpD54w4HPj70qQsfWnS4WD3m7ua9Of15oD2v/b4df2U7IS+L/Qv22rC7QaYPQ6bTh3KzTSAvpHltutzw716dad1KX9JuuXYbM3490N/OCo9tcncDz63uUGzh3iU5WK1FwBAamgvkznbZdNXp+XIYY9/zInFHlOQWTqczXzRGIZzAMl6PWaJctkAAAC9gacSpJ2bpmarOWK0rFU1ucClU4Z69fL2JtPDR4U/qs3VIU0scMd9vy3VYa0/ZF3O+rbpOe2eWxeMatH+wL8HBgyNyXNpYoFL4/NcyogzSH7qUK+e2thgCmp/sC+gqydmUcoYJm/uboq7z+uw6dbpOcqzmJQBAL3l/FEZemOX33Li1alDvS0BZkmaN8qnd/f6VdYmK6U+ZOjNXX5dOo4SoQCA1DA0y6ErxmfK47D9+9/hCaBZbrvG5Lo6XLpp5iCP7LZ6Rdv0n4eDzPSHgBQ/k5lsfwAAgJ7HHRfSjtth0zdn5qouGJVdhzOoJCnbbdevlptLaX+0v7ndQPHqymbL7eeO8LV7niTluO2aNypD80ZlJNz+DJddc4Z49f6+2GzsxrChFeXNOmmwN+H3QvqrbY5qcZn5d9Rhk84Z4dMlYzOV6yHrD0DfGuBz6JShXi1s07fleuy6fnLsshVOu03XT87SPRbLX7y7x68vjMloN+sLAIBkUZzp1KXjuj7skuO2a3KBS+vaTHreWhPWrrqQRuZYl9oG+hOrTGavw6ZcN/eLAAAAPY3IA9JWjtveEmCWpBlFbmVazBT/uCygcNup4a1stMhi9jhsumpiz80cP2u49ZqTyw/GL4mM/ulfe/0KW5RyP2+kT9dPySbADCBpXDMxS0W+zz6TPA7pa9NzYvrqI6YP9OjYAeaJXIcCUcuyoQAApKsTi62XvfnOh9X62ac1Wl3RLMOI/zwLpDurTOZBmY52l0UDAABA9yCTGf2Gy2HTSUM8endPmwzhkKHFpc0q9Nm1vyGissaw6oOGSrId+txwn7bWmIPM4/Od8jl7Lng3Ls+pAT67Kv2x0cOVFc2KGobsPCxBUiRq6J091mucnj3CeqICAPSVXI9dPzulQB+XBhSMGDpuoEcl2fFvRc8f5dOaSnNA+a3dfp0QZ8AdAIB0c0KxR39e32C5b3VFUKsrghqV49TFYzN0UrGHwBr6FcMwdMAik7mY9ZgBAAB6BUFm9CunDvWagsyS9LvVdZbHP7e50XL9yEkdlMk+WjabTTMHevTm7tgAYn3Q0NbqsCYUUBYN0oryoGkigiRNK3JrMOtPAUhCOW67zhuZ2BIS04rcKvLZVdHmc25NZVAHGsOsswcA6BcKvA5NzHdpU7V58vMRO+vC+s2KOs0e7NFt03PkZFkJ9BP1IUP+sHnQpjiTIDMAAEBvoI4q+pUJ+a6YUp0dsQowS9KkXgjyHjfIOpC9vNx6jWj0P2/ubrLcfi5ZzADSgN1mi1uV4e3d1lUcAABIR1dNzJQrgcfYxWXNenZzY883CEgSVqWyJWkQmcwAAAC9giAz+hW7zaZThniP6j2cdmlsXs8HmacUuOVxmGegryDIDEk1gYjWVZqzGYp8ds0Y2LOZ9gDQW84o8clqdYr39wXUHG8mGAAAaWZigVv3nVqgU4d6LfvF1v65o0mrK3hmRP9wkCAzAABAnyLIjH7nlKFHF2Qem+uS2yL4291cDpuOHWAOFu6tj6jcYs0h9C/LyoOyCq+cPcLHmt0A0kaOx66TLNZfbggZ+uOaOhkGgWYAQP8wNMup26bn6A9nDtCVEzKV54k/nPPg6nrVNJuX1QHSjdV6zBLlsgEAAHoLQWb0OyXZTk0r6nqmZ2+Uyj5iZryS2QeZmd7fLT1g/Ttw8lFm6gNAsjlnhPUazotKm/XiNutlAwAASFc5HrsuGZupB84o1I3HZFuW0a5tjuqBlbUKUvUDae5gU9i0zWWXCrwMdwIAAPQG7rrQL908NVtTBxwOFjvt0jGFLl09MVPfnpnb4bkTezHIPGOgR1Y5qSvKg73WBiSfplBUayvNvwOjc50a4GPGNoD0Mj7fqXF5Tst9f9vSqMWlgV5uEQAAfc/tsOnsET59aVKW5f51h0L66ac1agyR0Yz0ZbUm88AMB9W9AAAAeon1iB2Q5gp9Dt0xK1/BiCGHTXLYP3sAmT3Yo8Vl1lmiNknj83svyJznsWtMnlPbamJn564/FFRjKKpMq2nrSHurKoKySko4YZC5pCwApDqbzab/mpajOxZVqyls/vD7w5p6TShwqcDLJBsAQP9z7gif1lYGteygeRLqpqqQ7lpcre+dmEc/ibR00KJcNusxAwAA9B4iVOjX3A5bTIBZks4a7ot7/KhcpzJ6ObA7c6A5cBgxpDd2+Xu1HUgeS+OUSz/BYt1SAEgHQ7Ocuv24XNktklKaI4Ze20mfCADon2w2m245NidueeC99RHdsaha++rNZYWBVNYUiqouaJ6AyHrMAAAAvYcgM9DGlEKXfE7r0koTejGL+Yh4gcNXdjSpgdJn/U4oYmilRbn04gyHSrJ4mAaQvo4tcuuGKdYlQd/d45c/TJ8IAOifst12fXtmrrJc1s+xhwJR3bW4WluqQ73cMqDnHLDIYpYOPxsDAACgdxBkBtqw22yaPzbDct+UQncvt0Yalu1sWT+6taawodd2NPV6e9C31lcF5bcoF3tCsUc21p0CkObOGZGhWRaTr5rChv61l7WZAQD915g8l340O1+FcTKaG0KG7v6kWmsqzRNWgVRkVSpbolw2AABAbyLIDFg4fZhP2W1mged57Dq2qPeDzJJ0xXjrzK3XdvlVHyRzq79oCkXjloQ9flDf/G4CQG+7aIz1RLDXdzYpErVYsB4AgH6iJNupu+fkx61wFIxKv11RqwaeIZEGDjTGyWSmXDYAAECvIcgMWMhx2/X143JV5Dv8JzLAZ9fXZ+TI4+ibTNHx+S7NsAhw+8OG/kk2c7+wsrxZ31xYpdUV5syDXLdN4/uglDsA9IWxeS5NLDB/5lX4o/r0gPWa9QAA9BeFPod+NCdfE+M8H9SHDL29x3riKpBKyiyCzA6bNMBHkBkAAKC3EGQG4jh2gFv/d3qhHjl7gB44o7BPSmW3dvn4TMvtb+zyq4m1mdPaJ2UB3be0VtXN1v/PMwd5ZKdUNoB+5MJR1tnM/9zRJMMgmxkA0L9luez6/qw8nRCn2tHru/wKRegvkdpKG8KmbQMzHHLaeTYGAADoLQSZgXY47DZlu+1JEcAbk+eyLIncHDG0opx1tdLZ81sa1d4Q0FnDfb3WFgBIBjMHuTXYohTi9tqwXtpGhQ8AANwOm24/LldTCs0ZzbXNUX1UGuiDVgHdwzAMlVpkMg+hVDYAAECvIsgMpJBLx1lnM39SRnnQdHXIH9G+Buu1pjwO6avTsjU2j1LZAPoXu82m80dZT7D525ZGLT9IvwgAgMNu0xfGWj9DvkL1D6Sw+pChxpD593dIlrMPWgMAANB/EWQGUsioHKflzNxVFc0KhCmZnY7WH7LOUj+m0KVfnlaouSVkMQPon+aW+JTvMd/KGpIeWFWn/RYlFAEA6G+mFro0IscceNvXENGqCipiITVZlcqWZFnpBgAAAD2HIDOQQmw2m04a7DFtD0WllZTMTkvrD4Ust99ybI4GZvAADaD/8jhsum16jqyW3fOHDf1mRZ2iZGgBAPo5m82mC+NU/3hlB0tMIDWVWZTKlggyAwAA9DaCzECKmWURZJakxZTMTjuGYWidRSbzoAyHiggwA4COGeDWtZOyLPftqQ9r6QH6RgAA5gzxqsBrHv5Zdyik8ibrYB2QzOIFmSmXDQAA0LsIMgMpZkS203J27uGS2WRspZNyf1SVfnMZ9GMKWYMZAI44b6RPc0u8lvte2+Xv5dYAAJB8nHabzhtpnc28qoIJWUg9VuWyM5w25botStwAAACgxxBkBlKMzWbTrGJzNnNzRFrNAEFaWVdpXQJ9ygB3L7cEAJKXzWbTjcdka1CG+bZ2U1VIO2qtlx0AAKA/mVtiHWRezbrMSEGlFpnMgzMdstkIMgMAAPQmgsxACqJkdv+w3qJUtiRNKSTIDACtuR02zRuZYbnvtZ2sNwkAQJ7HrlE55lLC6ypDCkepiIXUEYkaOmARZB6SxZJSAAAAvY0gM5CCRuU4LTO2VpQ3qykU1aaqoH65rEZ3La7W6zubZBgMGqSaw+sxm7PvSrIcyvPw0Q0AbZ0+zCuf05y98nFps6oCrDcJAMC0IvNk1UDE0OZqqn4gdVT4I4pYDHEMzmQ9ZgAAgN5GpAJIQYdLZpvXn2yOSH9aW6+fLqnR0oNBbaoK6fENDXpuc2MftBJHY39DRLXNFusxUyobACz5nHadOczcN0YM6S3WZgYAwDLILFEyG6mlzCKLWSKTGQAAoC8QZAb+f3t3Hh5Vef5//DNbMtkTEkJCwib7DkFFQcSlKqKt1ValrcWlWq1oF7+2LvUn0rrWpdb6dakbblWrfqu4QKWKFVRwQXYI+xK2kH3PbOf3R8pImDPJTLaZSd6v6+K65sw8c84T8sy5M+c+z/3EqJPyAi+kS00ls11H5Sbf2lanzdydHlOCXegZQ6lsAAhqxsBEma3E9+6OOq0Jss49AAA9xbAMh2nVD5LMiCX7asyTzLlJJJkBAAC6GklmIEYNSLVrdKYjpLaGpMdXV8llVlMKUcVnGHpne51e3lQT8JpF0sgQf+cA0BNlJ9p0fE58wPNun/TAVxXaVMZFdCAWldR79eSaKv3+0zI9t75ade7Aai8AWme3WjTG5PvEziqPKlhaAjEi2ExmymUDAAB0PZLMQAw7Z1BiyG331Xr1xhbKZkczt9fQQ19X6qWNNaZrTA1MtSvZwWkbAFpy7jHmsbHRK937ZaV2VXm6uEcA2qPBY+ieLyr00Z4Gba3waNHOej20sjLS3QJi1vjegTdjSdJqKn4gRuyrDfxbLivBqnibWT0bAAAAdCayFUAMm5gdF1ZJqHe212l7JWWzo9W7O+r05cHgF3cm55pfEAIAfGtYhkMzByWYvlbvMfTU2ioZBpU9gFixZE+9io4qjbq2xK3d1dwwArQF6zIj1pmVy6ZUNgAAQGS0O8m8YMECXXjhhRo4cKCcTqeys7M1ZcoU3X///aqqquqIPrbqsssuk8Vi8f+74447uuS4QKRZLZawZjP7DOkfhcxmjlZL9zYEfW1slkMzw/hdA0BP9tORyTo5z2n62pYKj/YGWcsPQHTx+Aw9vyFwCRFJ2lLOjZNAW2Qn2tTXJCG3qtilahel6BHdGjw+lTcGjtO+lMoGAACIiDYnmWtqanTeeefpvPPO0xtvvKFdu3apsbFRhw4d0ueff67f/e53GjNmjJYvX96R/Q2wcOFCPf/88516DCCanZzvVLIj9LJQa0pcqmEdu6hTXOc1TXpYJP1gaKJuOS6d8l8AECKrxaJrxqVossn6zJL0dXFjF/cIQFss39+oYHUHtlWQZAbaymw2c63H0Pz11RHoDRC6fUHXY2YmMwAAQCS0Kcns9Xp14YUXasGCBZKkPn366LbbbtPf//53Pfroo5o6daokac+ePZo5c6Y2btzYcT0+QlVVla6++mpJUlJSUqccA4h28TaLzhgQWBbUbpVO6ht4cd1rSCsPcnE92qwMkvC4fHSyLhqWLJuVBDMAhMNmtejqcSkyuz9nZQtLEwCR4PJSwv1ohmFowfa6oK9vq6RcNtBWwZbhWbavUV8eCP27YrXLp68PNurtbbX69+56NXg4l6Fz7QmyVELfZJLMAAAAkdCmejJPP/20Fi1aJEkaNWqUPvroI/Xp08f/+pw5c3TjjTfqwQcfVHl5ua6++mp98sknHdPjI/z2t7/Vnj171K9fP1144YV66KGHOvwYQCz43jGJ+vJAY7P16i4blaLRmQ4t2xd4keCLAy6dnG++XiUiY2VxYMLDIumEXPNyrwCA1iU5rBrRy6H1pc1nPBaWu1Xt8iklrt0rxwDttr/Wo1uXlev0/gk6Z1CCMpxcKJek1SUu7aoKnkjeXe2Ry2sojkovQNhGZDhUkB1n+h3k6XXVcvsMjcmKU6pJnDQMQx/uadDCHXUB66W/u71Od0/NUKKD+IrOEWzt8LxkymUDAABEQth/+Xu9Xs2bN8+//eKLLzZLMB923333acKECZKkpUuX6oMPPmh7L0189NFHeuqppyRJjz32mFJSUjp0/0AsSXRYNfeEDP10ZLJmDkrQrcen6YwBCeqbbFe+yR29qw81cpd5FGnwGNpQGvhleXC6XWnxXKABgPaYlB04W8tQ09qTQDRYsK1OdR5D72yv0/VLSvX02moV17Fu+Dvbgs9iliSfIe1gNjPQJhaLRVeNTVGSPfAmjYpGn/7yTZV+vrhEj3xTqaqj1r/91656PbW2OiDBLEn7a716c0ttp/UbPZvPMEyTzDmJNmUlcIMWAABAJISdvfjkk0+0f/9+SdL06dNVUFBg2s5ms+mXv/ylf/uVV15pYxcD1dXV6aqrrpJhGLr44ot17rnndti+gViVGm/Vucck6tJRKRrf+9sL6sebrEfp8jUlmhEd1pW6ZLZMdoFJYgQAEJ6CPoHrTkqsy4zoUFrv1X+KGvzbbp+0eHe9bvykTHVmfxz0ENsq3FpX2vqay9sqWZcZaKteTpsuHZ0c9HVD0qf7GvX7T8tU9N8SxZWNPr1a2HIS+cM9Dar39NzzFzrP5nK3atyBN8tPzDb/Ww8AAACdL+wk88KFC/2PZ86c2WLbs88+2/R97XXLLbdo+/bt6tWrl/7yl7902H6B7sgsySxJX4Sx1hY6V7D1mAv4sgwA7ZabZFffJLOqHi55fFT1QGS9u6NOZssxT8uL79HlZt9pYS3mI22rIMkMtMfJeU5N6N3yd47iep9u+6xcqw816vXNtapvpSJWvcfQx3saWmwDtMU3QarQcHM2AABA5IR95WLt2rX+x8cdd1yLbXNyctSvXz9J0sGDB3Xo0KFwDxfgs88+06OPPipJeuCBB0xLdQP41sBUu3onBH7UVxZzcT0aGIZh+mU5I96qgamsKwUAHcHspp06j6FNZSSoEDlVjT59uLs+4HmLpO8NTur6DkWJRq+hLSEmj7dRLhtoF4vFop+PTVFGK0v01HsM3f1FpRabnLPMLNxZL5/Bd010LLM1xONtFo3s5YhAbwAAACC1IclcWFjofzxo0KBW2x/Z5sj3tkVDQ4OuuOIK+Xw+nX766br88svbtT+gJ7BYLKazmes8htaZrAOMrrWryqOyhsBychOz42SxBK6RBgAI36Q+5jNc/riiQq8V1qiknvVv0fUW7qxTo8nQm9o3Xn0Se+7akvE2i/5ySqZ+MS5F+ckt/z/sr/WqtgeXFQc6QmaCTX+YkqEz+ico09kxFRQO1nlNE4JS07q6jV5DBklohKGk3qvd1YE3Fo3LipPDxvdmAACASAl7mlxFRYX/cVZWVqvtMzMzTd/bFrfffrsKCwuVkJCgJ598sl37aiu3262VK1eavpabm6vc3Nwu7hHQuuNz4vXejsC7zj/b16gJvSktFUnBLr5Q8gtoHTEZoRqe4VCS3aJakxKf/7e1Tu9ur9NtkzM0vJdDHp+hnVUeWS1N1UCs3PCDTlDn9mnRTvMZgecN6bmzmA+zWy06pV+CTs536ptil97bUadhGQ79c2tgGe1tlR6Ny2KJkWhAXI5d2Yk2XTk2RYaRrH21Xj25plqF5a1XFLBbpVnDkvXSppqA197bXqdjj7rJa9WhRj2/vkb7ar3KTrDqmvGpGp3J5xetC14qm/EDmCEmAwC6SthJ5pqab788OJ3OVtsnJCT4H1dXV4d7OL8vv/xSDz30kCRp3rx5Gjx4cJv31R7FxcWaNGmS6Wtz587VHXfc0bUdAkIwLMOhtDiLKl3NL65/uq9Bs4YnqZez586WiTSz2eR2qzQ2i5JfQGuIyQiVzWrRhOw4fbqv0fR1l0/639WVuvX4dD28sko7qppmygzPcOim49KU1IPXxkXn+GBXvepMbno4tk+c+qewXMZhVotFk/rEa1KfeG2tcJsnmSvcJJmjBHE59lksFuUl23Xb5HQ9uaZKy4LEzcNmDkzUzEEJ+teuOh2qb15VYEOZW18eaNRx/62qtXhXvZ5ZV63DZ77iep/u+aJCv5+crpG9+AyjZSuLzcfiRJLMgCliMgCgq8TEFQyXy6UrrrhCXq9XBQUFuuGGGyLWl+zsbC1cuND0Ne4CQ7SyWiw6Idepf+1qPmPG45Pe3V6n2aNSItSzns3lNbTZZIbAiAyHnHYSGkBriMkIx3f6JwRNMkvSwTqffvVxWbPnCsvdenJNtX5TkMoSBugwHp+h94PMYj6fWcxBDUixy25t+vv1SNsqWJc5WhCXu484m0XXTUjVgNQ6vba5NuBzJ0kpcRadPyRRNqtFMwYm6sWNgbOZH/6mUj8bnaL9tV4t2B54k4jbJ93/ZaX+MCVD+dxggyBcXkPrSgJvzh6UalcGN8wDpojJAICuEvZf8cnJySovL5fUtEZycnJyi+3r67+9gJKS0rZE1p133ql169bJZrPpqaeeks0WuT8iHQ6HCgoKInZ8oK1mDkrQB7vqdfScmX/vbtD5Q5KUEkdSs6ttrXDLbBlBSsYBoSEmIxyjMuP005HJeq2wRq4wlnBdcaBRn+1r1NS81iv4AKHYXulRZWPgIByb5dCQdCqZBOOwWTQgxa5tlc2TytsrWy/pi65BXO5eLBaLvjc4SQXZ8Xp8TZW2HnVDx+yRyUr8b6WP0/o59frmWjV4m3/b9PikJ9e2XNGu1mPoni8r9McpGVTYgqkNZS7Tv90olQ0ER0wGAHSVsLNK6enp/sclJSWtti8tLTV9b6hWr16te++9V5J0ww03ECCBNspJsmtK38B1fhu9hv65tVaGEViyEZ1rQ5n5RdFRmVxgBoDOcO4xiXr8O1maMz5FfZNCv5D9zPpqlTd4O7Fn6El2BEmKnjMosYt7EnsGmyThSxt8fD6BTpSfYtcfp2ToyjEpGpHh0KheDs0Zn6KT879dGi3RYdUPh7W9EkNJvU8Pfl0pH99JYWLtIfP1mCdmB17fAAAAQNcKeybz8OHDtWPHDknSjh07NHDgwBbbH257+L3hmj9/vtxut6xWqxwOh+68807Tdp988kmzx4fbDR8+XBdeeGHYxwW6o/MGJ5mWCn1vR73e21Gvvkk2fX9IoqYfccEAnWejyXrMcVZpcBpJZgDoLMkOq07OT1DfZLtu+7Q8oMKHmVq3oSfWVOu3x6bJbqVsNtrn8JrfRxueQfxvzeA086+v2yo9OpYZkECnsVosOmNAgs4YEPx74rmDElRc59UHu8yXA2jN1gqPFu+q11kDueEGza0pCbw5K8lh0eB0SqwDAABEWth/kY0dO1aLFi2SJH355Zc69dRTg7Y9ePCg9uzZI6lpLYjevXuH3cHDsyt9Pp/uvvvukN6zZMkSLVmyRJJ03nnnkWQG/mtAql0F2XFaWWx+J/C+Wq8eW12tHZUe/XRksmxcSO80bq+hQpP1mIdlOOSw8f8OAJ1tSLpDp/Vz6sM9DSG1X3XIpZuXlemacamUNEa77KgMTDLnJNr8ZWcRXLDP3rYKt47tw4w2IJIsFosuH52sRq+h/xSFFluP9kphrY7PiWedXfhVNPq0uzowbo7JjJPVwvdmAACASAv7SsaMGTP8jxcuXNhi2/fff9//eObMmeEeCkAnOH9I62XMFu6s15++qlSd2YLB6BBbK83XYx7FeswA0GVmjUhWkiP0C5R7qr267dNyLdhW24m9Qnfm9hraY3KxfFCQGbporm+yTU6bRfE2i0b2cujcQQn69cTUFmdXAug6VotFV49N0Ym55jd9HJNm19wT0vXMmVnKSQxMJNd7DM3fUNPZ3UQMWVdifoP82Cy+NwMAAESDsJPM06dPV05OjiTp448/1sqVK03beb1ePfLII/7tWbNmtamDDz/8sAzDaPXf3Llz/e+ZO3eu//m33nqrTccFuqthGQ6NDmHN31WHXPr1x6V6d3udGr2sjdXRNpSar8cYyu8GANAxUuOsumREcsDzp/dzKj3e/M9kQ9LLm2q1fH/bZmmhZ9tT45HZn1UDU0kyh8Jqsej+k3tp/llZuuPEDP10VIpO7OtUL2Y9AlHDZrXoVxNTNWd8ik7Nd+rMAQm6bkKq/ve0TN1zUi+NyoxTssOqK8emmL5/+f5GXfxeseZ9Xq6XN9Zo9aFGvo/2YGuCJJnH9SbJDAAAEA3CTjLbbDbdfvvt/u3Zs2eruLg4oN3NN9+sVatWSZKmTp2qs846y3R/8+fPl8VikcVi0SmnnBJudwC0wbXjU5UR5OL5kSpdhl7cWKPrPirRCxuqtbPS7S9hj/bZYLIes4P1mAGgy53WP0FXjE5WbpJNOYk2zRqepCvHpuj6CalqqXrxCxtq1OAJHhPdXkNF1R55fMRNfGunSalsiZnM4chOtFEiFYhyFotFJ+cn6JrxqfrZmBRNy3MqK6H5zSBjs+J0Ut/gZe43lLm1YHud7v6iUld8cEiPr64i2dzDGIahtSZJ5j6JVvUxmQkPAACArtemqxlXXXWV/vnPf2rx4sVav369xo8fr6uuukqjRo1SWVmZXnnlFS1btkySlJ6erieffLJDOw2gfbISbHrk1EytPuTSgVqv1pS4gt4hLElVLkPv7ajXezvqNTDVrtmjkjWass4hMQxDm8rd2lfj1ZB0hwak2uX2Gtpssh7zcNZjBoCIOGtgos4amNjsuTFZcfrDlAw9saZau6oCE4OlDT69tbVWs0xmQq/Y36DHVlerwWso2WHRnPGpKmC9WEjaYTKWJGlQKjeZAeh5fjoqRSuLXapr4aYtSfL4pI+LGlTnMXRDQaos3GjSI+yt8aqsIXCNqTGUygYAAIgabUoy2+12vfnmm/rxj3+sd999VwcOHNAf//jHgHb5+fl67bXXNHr06HZ3FEDHirNZdFxO0wXv7w5O1JZyt+7/ulKVjS2vw7yzyqM/Lq/Q+UMS9cOhSbJZ+YLfkv9dXa2le78tqTouy6H+KXa5WI8ZAKLeMWkO3T01Q29sqdU/t9YFvP7OjjpN7+dUbtK3f1IXlrn18DdVOjyBucZt6IGvK3Xr8elcFIV2VAbeZJbptCo1hAozANDdpMdb9aMRSXpmXWjrMH9xoFFL9jTotP6sw94TmM1ilqRx/D0FAAAQNdp8NSMlJUXvvPOO3nrrLV1wwQXq16+f4uPjlZWVpcmTJ+u+++7TunXrNGXKlI7sL4BOMjTDobumZGhUr9Zn0hiS/m9rne5YXqGSem/ndy5GfVPc2CzBLElrStx6d0e9aftRrMcMAFHHbrXo4mFJpvHR45OeX//thfFql09/+aZSR1fI9hrSg19Xane1+SxW9Axen2E6K55S2QB6su/0T9DUFspmH23+hhrtr21+Lq12+bSvxqMGT8s3TCO2mCWZLZLGcHM2AABA1LAYLLAakvz8fO3du1d5eXkqKiqKdHeATmMYhtaWuvXG5loVmpR0PlpqnEW/KUhjFq6Jh1dW6vP9jSG1dVil587sTblsIATEZETC7iqPblpWFpBAlqS5J6RrRC+H7v+qUiuLgy8/kem06s6pGerlZB3BnmhPtUc3flIW8PyFw5L0w6FJEegR0DGIy2gvn2FoU5lbG8rcKq33qqTeq60VnqBltIek2zXvxAy5vIb+uqqqWexNclg0NN2hmYMSNL43S1XEKo/P0JWLS1R/1BgYnGbX3Sf1ilCvgOhHTAYAdDVumwfQjMVi0bisOI3NdGhDmVtL9tTriwONagwyYbnKZejOFRW6dFSyzhyQwPpY/1Xv8emrg6ElmCVpWp6TBDMARLH+qXadNSBBC3cGVqNYvr9R2yo9LSaYpaZ1nB9dVaX/NzmdeNkDmZXKlqRBqXwlA9CzWS0WjcqMa3bjss8w9Om+Rj26qiqg/dYKj/6xuVa7qjxadah57K11G1p1yKVVh1z67jGJ+tFwlniKRV8fbAxIMEvSWEplAwAARBWuaAAwZbFYNDozTqMz49Tg8elfu+r1WmGtvCY3k3sN6dn1NfIZ0tmDEru+s1HoqwMuuUOs1nZCbrxmj0ru3A4BANrtwmFJ+mRvg2rdzYPhsn0N8phNcTaxvtStrRUeDc1giYSeZqdJqWyJctkAYMZqsWhanlObylz69+6GgNff3lbX6j7e2V6nHZVuXT8hVelUEYkpi0xu6pOkcb1JMgMAAEQTrmgAaJXTbtV5g5M0JjNOj3xTpQN15tOaX95UoxNy45XRjb7A+wxD60rc+mhPvXZWeZTksOjCoUmakN1y6bVl+wIvhBxteIZDPxqRpJG9+KIMALEgyWFVQXa8lu5tfo4/Ouncmg9315Nk7oF2VAYmmdPiLMqIt0agNwAQG346MkUbSt3aVxuktFYr1pW6dd2SUp2U59T0fKf/nLu+1K01JS4dqvNqYKpd3xucqJwkLpFFg52VTaXTj9Yn0aqRvfj7CQAAIJrwFzSAkA1Od+iekzL02OoqfXkwsCSo29dUMjSaZjN7fIa2VLhVWu/TuKw4pYZ4Ibfe49OHuxv0wa56HTwqqf7A15X607Re6ptsfgqtavRpTUng/096vFUXDktSab33v7PEHZRLBYAYM6F3XECSOZgbClL15Jpq1R5V7vGz/Q06c2CC3t9Rr2qXTyfkxmt6vlNWYkK35TMM7TCZyTwojb8FAKAlTrtF109M1f/7rFyeECtFHc3tk5bsadCSPebxe1ulR8v2NWjW8GTNGJhAPI6wYLOYzxqYyO8GAAAgypBkBhCWRIdVN0xK0/9trdPrm2sDXv9sX0NUJJnrPT59tLtB7++sU0l909UIu1W69fh0jc4MnDlc1uDV3hqvql0+7azyaPGuetWZrAElNV2keH9Hva4cmyKp6cLxkV92lx9okFnV1LMGJOg7/RM64KcDAETKuKw4WSS1Nnd5SLpdk3Od2lDmDrhY2uiVbllW7t9edcilGpeh7w4OLX76DEO7qjxKdljVO7H7VA/pznZWeUzXlmQ9ZgBo3TFpDl06KlnPrKtpsV3fJJuK671tSkY3eqXnN9Tow931Gp0Zp0Fpdo3o5VBOok0eX9MNYl8fdKnS5dPJ/50VbWet5w5X1egzrQrmtFl0ar4zAj0CAABAS7iqASBsVotFPxyapLUlLm06qozV5gqPDtV5I3rRe1Vxox5ZVRVQvtTjk/6yslKPnJolp93y3+cMPfJNlVYcaAzrGF8ebNS5xyToyTXV2ljmVk6STT8fm6JRmXH6dJ/5vqbm8aUYAGJdarxVg9Ls2m5S+vhIU3Kbzvmn90sIOiPnSG9urdWMgQly2Fq+YF1c59XdX1Ro/3/Lhn6nv1NXjklhNmyU+6Y4sMKJJMqmA0CIzuifoMIyt5YF+a51zqAEzR6Voq0Vbj30daVKG9o27bmoxquimm/jdlaCVW6fVNn47f42lbn19cFG/bogTXGtxG2E58M99XKb/Oqm5zuV6GB5CQAAgGjDX2gA2mxKrvm6xJ/vD62MaGcoLHPrvq8qg66PWekytGhnnX/71cLasBPMklTR6NOvPi7ThjK3DEn7a726c0WF3tteF5B4l5pmtPVhthkAdAsTegdWxDjaCf+Nkf1T7Rqa3vp9nfUeQxvLA+OHy2uo4YgZsP+7usqfYJakf+9u0PL94ccxdK2VxYG/I4dVptVVAACBLBaLrhqbqvzkwO9U/VPs+tHwZEnSkHSH7jmpl07MjVdHpH9L6n3NEsyHfV3s0v1fVTSL0Wgfn2Hog13mN+bNGEhFMAAAgGhEkhlAm03OdZp+cf88Qhe7Gzw+/e/qStNS1UdasL1OdW6fyhq8zRLO7eU1pBc2mpdwO6kvs5gBoLsY30qSeUQvhzITvr0IfnqISyV8899EpGEYWlvi0p0rynXpvw7pZ4sP6em11dpc7ja9kenjosjd3IXWVTT6tLUicOb76Mw4f2UVAEDrnHaLbjw2TdkJ317KynRa9ZuC1GaVQNLirfp1QZr+fEovnTkgQXGddOVrTYlbd3xeruX7G+Rp7UsoWrWzyqMykxnoE3rHqW8yhRgBAACiEX+lAWiz9HirRmc6tK60+QXv7ZUeHaj1KCep808xm8pc2lHpUXKcVR/urtfButbLotW6Db23o041bsO0FNfRhqbbtcXk4nCo7FbpxCCzvgEAsWdoukOJdovqgsxeOrrSx4m5Tj2/ocZ0Td4jfVPs0nF9XHp5U02zpKTPkBbvrtfi3eaze1YdcqnO7aOMZJT6xmQWsyQVZDOLGQDClZtk173Temn1IZe8hjSxd5ySg2SRc5Ps+tmYFP1oeJK2VnhU0ehVtdtQo8dQ7wSbxmQ5tK/WqyfXVIX0PdLMjiqP/ryySmnxVv1gSKLOHJDAEhZtVGhyI50kfSfEm/UAAADQ9UgyA2iXKX2dAUlmSfrVx2U6f3CiRmXFaXQvh2zWjv+i/Wphjf65tW0zkd/Y0vL7bBapIDte5w1O1NAMh/76TWXQ9b9ac/6QJKU7KZUNAN2FzWrR2Ky4oMstTM5tXr3CabfopL7OoEniw/bXejVveUWb+vTNIZemUjUjKq0Msh5zQTY3oAFAWyQ5rJoSRsxLdFg1LkgVkgynTX+alqn/FNVrTYlL2yvNZ9O2prLRp2fX18hqseiMASRF26LQZNkQi6TRmY6u7wwAAABCQpIZQLtMzonXM+uq5TWZnPXPbXX657Y6pcRZNCk7XsmOpkRzosOqE3Pj21Xyam+NR2+FkGAenuGQwyrTRLiZTKdVtx6frqwEW7MSlsfnxLcpyTyxd5wuGJIY9vsAANFtfG/zJPMxaXalxwfOqDrnmAR9srdejd6AlzrElwcaSTJHIbfX0JpDgUnm/ik29U7kBjQAiAZOu0VnDUzUWQObvrdVNHi1scytNSUubSh1q9rt06hecRqZ6dBbW2tV5QpemeSVwhqdmBsfdHY1zBmGYZpk7pdip1ILAABAFCPJDKBdkuOsGpcVp29MLqAeVu0yAtaLfHNLrX4yMlkzB7atnNh/ihrU2qpXTptF101IVZXLp99/Wt7qPi2Sbpucbpr8npAdrzir5ArjpvY+iTZdNzFVVsqlAUC3M6F3nCxSQCw6I0hJx9wku24oSNPLm2pU3ujTwFS71paEdgNUKFYdcsntNZqtSYnI21jmVoPJnXjMYgaA6JXutOnEvjadaHLz1sTecbrniwoV15t/Max1G3pjS60uG53S2d3sVkrqfaYzyEf0YhYzAABANON2QADtdlJe+DOnvIb0woYaPfh1pWpCWRj5CD7D0LK9Da22u2x0srITbRqS7tCxfVpf9/CkvOCzq+NtFk0I44JwvE268dg0JXPXNQB0S5kJNp3ev3n865di08n5wWPihOx43X9ypp4+o7dum5yhYRkdd+G03mNoXWnwG74QGSuDrsdMkhkAYlHfZLvuP7mXfjIiSTlBKlJ8sKte+2o8Xdyz2LbJZBaz1FSZDAAAANGL7AeAdpvSN16j2niH8ZcHXbplaZkO1Ib+JXxDqVulrayTdVo/p04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" ] }, "metadata": { "filenames": { "image/png": "/Users/folgert/projects/hda/_build/jupyter_execute/working-with-data/notebook_164_0.png" } }, "output_type": "display_data" } ], "source": [ "# Create a figure and subplots\n", "fig, axes = plt.subplots(\n", " nrows=2, ncols=4, sharey=True, sharex=True, figsize=(12, 6))\n", "\n", "# Plot the time series into the subplots\n", "d[names].rolling(window=10).mean().plot(\n", " color='C0', subplots=True, ax=axes, legend=False, title=names);\n", "\n", "# Clean up some redundant labels and adjust spacing\n", "for ax in axes.flatten():\n", " ax.xaxis.label.set_visible(False)\n", " ax.axhline(0.5, ls='--', color=\"grey\", lw=1)\n", "fig.text(0.5, 0.04, \"year\", ha=\"center\", va=\"center\", fontsize=\"x-large\");\n", "fig.subplots_adjust(hspace=0.5);" ] }, { "cell_type": "markdown", "id": "3b1c2514", "metadata": {}, "source": [ "\n", "\n", "(sec-working-with-data-further-reading)=\n", "## Conclusions and Further Reading\n", "\n", "In what precedes, we have introduced the Pandas library for doing data analysis with\n", "Python. On the basis of a case study on naming practices in the United States of America,\n", "we have shown how Pandas can be put to use to manipulate and analyze tabular\n", "data. Additionally, it was demonstrated how the time series and plotting functionality of\n", "the Pandas library can be employed to effectively analyze, visualize, and report long-term\n", "diachronic shifts in historical data. Efficiently manipulating and analyzing tabular data\n", "is a skill required in many quantitative data analyses, and this skill will be called on\n", "extensively in the remaining chapters. Needless to say, this chapter's introduction to the\n", "library only scratches the surface of what Pandas can do. For a more thorough\n", "introduction, we refer the reader to the books *Python for Data Analysis* {cite:p}`mckinney:2012`\n", "and *Python Data Science Handbook* {cite:p}`vanderplas:2016`. These texts describe in greater\n", "detail the underlying data types used by Pandas and offer more examples of common\n", "calculations involving tabular datasets.\n", "\n", "## Exercises\n", "\n", "### Easy\n", "\n", "1. Reload the names dataset (`data/names.csv`) with the year column as index. What is the\n", " total number of rows?\n", "2. Print the number of rows for boy names and the number of rows for girl names.\n", "3. The method `Series.value_counts()` computes a count of the unique values in a `Series`\n", " object. Use this function to find out whether there are more distinct male or more\n", " distinct female names in the dataset.\n", "4. Find out how many distinct female names have a cumulative frequency higher than 100.\n", "\n", "### Moderate\n", "1. In section {ref}`sec-working-with-data-name-ending-bias`, we analyzed a bias in boys'\n", " names ending in the letter *n*. Repeat that analysis for girls' names. Do you observe any\n", " noteworthy trends?\n", "2. Some names have been used for a long time. In this exercise we investigate which names\n", " were used both now and in the past. Write a function called `timespan()`, which takes a\n", " `Series` object as argument and returns the difference between the `Series`' maximum and\n", " minimum value (i.e. `max(x) - min(x)`). Apply this function to each unique name in the\n", " dataset, and print the five names with the longest timespan. (Hint: use `groupby()`,\n", " `apply()`, and `sort_values()`).\n", "3. Compute the mean and maximum female and male name length (in characters) per year. Plot\n", " your results and comment on your findings.\n", "\n", "### Challenging\n", "1. Write a function which counts the number of vowel characters in a string. Which names have eight\n", " vowel characters in them? (Hint: use the function you wrote in combination with the `apply()`\n", " method.) For the sake of simplicity, you may assume that the following characters unambiguously represent vowel characters: `{'e', 'i', 'a', 'o', 'u', 'y'}`.\n", "2. Calculate the mean usage in vowel characters in male names and female names in the entire\n", " dataset. Which gender is associated with higher average vowel usage? Do you\n", " think the difference counts as considerable? Try plotting the mean vowel usage over\n", " time. For this exercise, you do not have to worry about unisex names: if a name, for instance, occurs once as a female name and once as a male name, you may simply count it twice, i.e., once in each category.\n", "3. Some initials are more productive than others and have generated a large number of\n", " distinct first names. In this exercise, we will visualize the differences in name\n", " generating productivity between initials.[^creditsbh] Create a scatter plot with dots for each\n", " distinct initial in the data (give points of girl names a different color than points\n", " of boy names). The Y-axis represents the total number of distinct names, and the X-axis\n", " represents the number of people carrying a name with a particular initial. In addition\n", " to simple dots, we would like to label each point with its corresponding initial. Use the\n", " function `plt.annotate()` to do that. Next, create two subplots using `plt.subplots()` and\n", " draw a similar scatter plot for the period between 1900 and 1920, and one for the\n", " period between 1980 and 2000. Do you observe any differences?\n", "\n", "[^creditsbh]: This exercise was inspired by a blog post by Gerrit Bloothooft and David Onland\n", " (see https://www.neerlandistiek.nl/2018/05/productieve-beginletters-van-voornamen/)." ] } ], "metadata": { "jupytext": { "cell_metadata_filter": "-all", "formats": "md:myst", "text_representation": { "extension": ".md", "format_name": "myst", "format_version": 0.13, "jupytext_version": "1.10.3" } }, "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.13" }, "source_map": [ 16, 20, 89, 94, 102, 104, 114, 124, 128, 131, 137, 140, 144, 146, 151, 171, 204, 206, 210, 212, 216, 218, 225, 227, 237, 239, 248, 250, 256, 258, 262, 264, 268, 270, 274, 278, 280, 290, 293, 300, 305, 309, 311, 315, 317, 321, 323, 329, 331, 335, 337, 342, 344, 348, 350, 354, 356, 362, 365, 395, 401, 407, 424, 432, 435, 439, 441, 445, 448, 452, 461, 465, 467, 471, 473, 477, 481, 485, 489, 491, 495, 497, 501, 503, 507, 512, 520, 529, 534, 536, 540, 542, 548, 557, 561, 564, 568, 573, 578, 584, 588, 591, 602, 605, 609, 612, 616, 621, 624, 641, 647, 655, 657, 663, 667, 673, 677, 699, 703, 707, 710, 719, 731, 739, 751, 766, 769, 773, 775, 780, 782, 786, 788, 792, 795, 799, 802, 806, 808, 812, 814, 818, 821, 825, 827, 831, 848, 865, 869, 873, 876, 880, 883, 887, 889, 893, 901, 904, 908, 911, 915, 917, 921, 937, 943, 958 ] }, "nbformat": 4, "nbformat_minor": 5 }