"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "e_TK3BqFJ-jc"
},
"source": [
"## Scope of this tutorial\n",
"\n",
"This will be a very brief tutorial on Python. \n",
"For a more complete tutorial about Python see [A Whirlwind Tour of Python](https://github.com/jakevdp/WhirlwindTourOfPython) and [Python Data Science Handbook](https://jakevdp.github.io/PythonDataScienceHandbook/) for a specific tutorial about Python for scientific computing.\n",
"\n",
"To use Python for scientific computing we need the Python program itself with its main modules and specific packages for scientific computing. [See this notebook on how to install Python for scientific computing](http://nbviewer.ipython.org/github/demotu/BMC/blob/master/notebooks/PythonInstallation.ipynb). \n",
"Once you get Python and the necessary packages for scientific computing ready to work, there are different ways to run Python, the main ones are:\n",
"\n",
"- open a terminal window in your computer and type `python` or `ipython` that the Python interpreter will start\n",
"- run the `Jupyter notebook` and start working with Python in a browser\n",
"- run `Spyder`, an interactive development environment (IDE)\n",
"- run the `Jupyter qtconsole`, a more featured terminal\n",
"- run Python online in a website such as [https://www.pythonanywhere.com/](https://www.pythonanywhere.com/) or [Colaboratory](https://colab.research.google.com/notebooks/welcome.ipynb)\n",
"- run Python using any other Python editor or IDE\n",
"\n",
"We will use the Jupyter Notebook for this tutorial but you can run almost all the things we will see here using the other forms listed above."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "w0h03lJZJ-jd"
},
"source": [
"## Python as a calculator\n",
"\n",
"Once in the Jupyter notebook, if you type a simple mathematical expression and press `Shift+Enter` it will give the result of the expression:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "O929Q6kWJ-jd"
},
"outputs": [],
"source": [
"1 + 2 - 25"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "3RFHjK6dJ-jf"
},
"outputs": [],
"source": [
"4/7"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "iC0k0CfpJ-jf"
},
"source": [
"Using the `print` function, let's explore the mathematical operations available in Python:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"sl_translate": "sl_none",
"id": "PhucMAT_J-jg"
},
"outputs": [],
"source": [
"print('1+2 = ', 1+2, '\\n', '4*5 = ', 4*5, '\\n', '6/7 = ', 6/7, '\\n', '8**2 = ', 8**2, sep='')"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "eoNO8gRFJ-jh"
},
"source": [
"And if we want the square-root of a number:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "E9eAB3cyJ-jh"
},
"outputs": [],
"source": [
"sqrt(9)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Yketd7ESJ-ji"
},
"source": [
"We get an error message saying that the `sqrt` function if not defined. This is because `sqrt` and other mathematical functions are available with the `math` module:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "NxeP6yKCJ-ji"
},
"outputs": [],
"source": [
"import math"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "7WIoDCnuJ-ji"
},
"outputs": [],
"source": [
"math.sqrt(9)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "IDuxpN8aJ-jj"
},
"outputs": [],
"source": [
"from math import sqrt"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "BpgX9M1ZJ-jj"
},
"outputs": [],
"source": [
"sqrt(9)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "OALnH4rzJ-jj"
},
"source": [
"## The import function\n",
"\n",
"We used the command '`import`' to be able to call certain functions. In Python functions are organized in modules and packages and they have to be imported in order to be used. \n",
"\n",
"A module is a file containing Python definitions (e.g., functions) and statements. Packages are a way of structuring Python’s module namespace by using “dotted module names”. For example, the module name A.B designates a submodule named B in a package named A. To be used, modules and packages have to be imported in Python with the import function. \n",
"\n",
"Namespace is a container for a set of identifiers (names), and allows the disambiguation of homonym identifiers residing in different namespaces. For example, with the command import math, we will have all the functions and statements defined in this module in the namespace '`math.`', for example, '`math.pi`' is the π constant and '`math.cos()`', the cosine function."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "AjaHEYf-J-jj"
},
"source": [
"By the way, to know which Python version you are running, we can use one of the following modules:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "UEV6FOjyJ-jk"
},
"outputs": [],
"source": [
"import sys\n",
"sys.version"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "lUh9kXnxJ-jk"
},
"source": [
"And if you are in an IPython session:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "FjfKs6fvJ-jk"
},
"outputs": [],
"source": [
"from IPython import sys_info\n",
"print(sys_info())"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "DMKpOSGCJ-jk"
},
"source": [
"The first option gives information about the Python version; the latter also includes the IPython version, operating system, etc."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "yidKiSGnJ-jk"
},
"source": [
"## Object-oriented programming\n",
"\n",
"Python is designed as an object-oriented programming (OOP) language. OOP is a paradigm that represents concepts as \"objects\" that have data fields (attributes that describe the object) and associated procedures known as methods.\n",
"\n",
"This means that all elements in Python are objects and they have attributes which can be acessed with the dot (.) operator after the name of the object. We already experimented with that when we imported the module `sys`, it became an object, and we acessed one of its attribute: `sys.version`.\n",
"\n",
"OOP as a paradigm is much more than defining objects, attributes, and methods, but for now this is enough to get going with Python."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "s565ADyzJ-jl"
},
"source": [
"## Python and IPython help\n",
"\n",
"To get help about any Python command, use `help()`:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "H_Q3xeiJJ-jl"
},
"outputs": [],
"source": [
"help(math.degrees)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "q8pGEGC4J-jl"
},
"source": [
"Or if you are in the IPython environment, simply add '?' to the function that a window will open at the bottom of your browser with the same help content:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "kEpPV01UJ-jl"
},
"outputs": [],
"source": [
"math.degrees?"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Ju-_lp7EJ-jl"
},
"source": [
"And if you add a second '?' to the statement you get access to the original script file of the function (an advantage of an open source language), unless that function is a built-in function that does not have a script file, which is the case of the standard modules in Python (but you can access the Python source code if you want; it just does not come with the standard program for installation).\n",
"\n",
"So, let's see this feature with another function:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "Hydjdqv3J-jl"
},
"outputs": [],
"source": [
"import scipy.fftpack\n",
"scipy.fftpack.fft??"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "1Ch7y4WFJ-jm"
},
"source": [
"To know all the attributes of an object, for example all the functions available in `math`, we can use the function `dir`:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "Jajaqp8SJ-jm"
},
"outputs": [],
"source": [
"print(dir(math))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "zIdrR4gnJ-jm"
},
"source": [
"### Tab completion in IPython\n",
"\n",
"IPython has tab completion: start typing the name of the command (object) and press `tab` to see the names of objects available with these initials letters. When the name of the object is typed followed by a dot (`math.`), pressing `tab` will show all available attribites, scroll down to the desired attribute and press `Enter` to select it."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "i2lDjy-FJ-jm"
},
"source": [
"### The four most helpful commands in IPython\n",
"\n",
"These are the most helpful commands in IPython (from [IPython tutorial](http://ipython.org/ipython-doc/dev/interactive/tutorial.html)):\n",
"\n",
" - `?` : Introduction and overview of IPython’s features.\n",
" - `%quickref` : Quick reference.\n",
" - `help` : Python’s own help system.\n",
" - `object?` : Details about ‘object’, use ‘object??’ for extra details."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ZTa4z_A9J-jn"
},
"source": [
"### Comments\n",
"\n",
"Comments in Python start with the hash character, #, and extend to the end of the physical line:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "yJTQAW-bJ-jn"
},
"outputs": [],
"source": [
"# Import the math library to access more math stuff\n",
"import math\n",
"math.pi # this is the pi constant; a useless comment since this is obvious"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "8mVf67v8J-jn"
},
"source": [
"To insert comments spanning more than one line, use a multi-line string with a pair of matching triple-quotes: `\"\"\"` or `'''` (we will see the string data type later). A typical use of a multi-line comment is as documentation strings and are meant for anyone reading the code:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "rfq-YVu5J-jn"
},
"outputs": [],
"source": [
"\"\"\"Documentation strings are typically written like that.\n",
"\n",
"A docstring is a string literal that occurs as the first statement\n",
"in a module, function, class, or method definition.\n",
"\n",
"\"\"\""
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "RB_Xo6UZJ-jn"
},
"source": [
"A docstring like above is useless and its output as a standalone statement looks uggly in IPython Notebook, but you will see its real importance when reading and writting codes.\n",
"\n",
"Commenting a programming code is an important step to make the code more readable, which Python cares a lot. \n",
"There is a style guide for writting Python code ([PEP 8](https://www.python.org/dev/peps/pep-0008/)) with a session about [how to write comments](https://www.python.org/dev/peps/pep-0008/#comments)."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Pd3V5NCCJ-jn"
},
"source": [
"### Magic functions\n",
"\n",
"IPython has a set of predefined ‘magic functions’ that you can call with a command line style syntax. \n",
"There are two kinds of magics, line-oriented and cell-oriented. \n",
"Line magics are prefixed with the % character and work much like OS command-line calls: they get as an argument the rest of the line, where arguments are passed without parentheses or quotes. \n",
"Cell magics are prefixed with a double %%, and they are functions that get as an argument not only the rest of the line, but also the lines below it in a separate argument."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "oo6M7TfzJ-jo"
},
"source": [
"## Assignment and expressions\n",
"\n",
"The equal sign ('=') is used to assign a value to a variable. Afterwards, no result is displayed before the next interactive prompt:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "Lx3wU9_DJ-js"
},
"outputs": [],
"source": [
"x = 1"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "84GWjS04J-js"
},
"source": [
"Spaces between the statements are optional but it helps for readability.\n",
"\n",
"To see the value of the variable, call it again or use the print function:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "WEVrbDM9J-js"
},
"outputs": [],
"source": [
"x"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "wBMXVo-vJ-js"
},
"outputs": [],
"source": [
"print(x)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "a6o94obQJ-js"
},
"source": [
"Of course, the last assignment is that holds:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "aM5maAHsJ-js"
},
"outputs": [],
"source": [
"x = 2\n",
"x = 3\n",
"x"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "75TQLtwnJ-jt"
},
"source": [
"In mathematics '=' is the symbol for identity, but in computer programming '=' is used for assignment, it means that the right part of the expresssion is assigned to its left part. \n",
"For example, 'x=x+1' does not make sense in mathematics but it does in computer programming:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "6P6NRtVAJ-jt"
},
"outputs": [],
"source": [
"x = x + 1\n",
"print(x)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ce8TtA_VJ-jt"
},
"source": [
"A value can be assigned to several variables simultaneously:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "PRAqYC_jJ-jt"
},
"outputs": [],
"source": [
"x = y = 4\n",
"print(x)\n",
"print(y)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "XNJfh-0ZJ-ju"
},
"source": [
"Several values can be assigned to several variables at once:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "-uOVXQxoJ-ju"
},
"outputs": [],
"source": [
"x, y = 5, 6\n",
"print(x)\n",
"print(y)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "cXpFVnO4J-ju"
},
"source": [
"And with that, you can do (!):"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "_591RmdUJ-ju"
},
"outputs": [],
"source": [
"x, y = y, x\n",
"print(x)\n",
"print(y)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Awl_RsUwJ-ju"
},
"source": [
"Variables must be “defined” (assigned a value) before they can be used, or an error will occur:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "Dw3nMjgoJ-jv"
},
"outputs": [],
"source": [
"x = z"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "cBL8rgrVJ-jv"
},
"source": [
"## Variables and types\n",
"\n",
"There are different types of built-in objects in Python (and remember that everything in Python is an object):"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "lCkdPzf9J-jv"
},
"outputs": [],
"source": [
"import types\n",
"print(dir(types))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "75Yz67jqJ-jv"
},
"source": [
"Let's see some of them now."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "TyIiDpsBJ-jw"
},
"source": [
"### Numbers: int, float, complex\n",
"\n",
"Numbers can an integer (int), float, and complex (with imaginary part). \n",
"Let's use the function `type` to show the type of number (and later for any other object):"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "y9gueVJDJ-jw"
},
"outputs": [],
"source": [
"type(6)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "j2F6KUDSJ-jw"
},
"source": [
"A float is a non-integer number:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "cB3p0yU6J-jx"
},
"outputs": [],
"source": [
"math.pi"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "HbXSM3rJJ-jx"
},
"outputs": [],
"source": [
"type(math.pi)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "OvRt2ldZJ-jx"
},
"source": [
"Python (IPython) is showing `math.pi` with only 15 decimal cases, but internally a float is represented with higher precision. \n",
"Floating point numbers in Python are implemented using a double (eight bytes) word; the precison and internal representation of floating point numbers are machine specific and are available in:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "8WaiBZ5yJ-jx"
},
"outputs": [],
"source": [
"sys.float_info"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "fJmPOtbBJ-jy"
},
"source": [
"Be aware that floating-point numbers can be trick in computers:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "rI9tmyhWJ-jy"
},
"outputs": [],
"source": [
"0.1 + 0.2"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "ZK7g53aoJ-jy"
},
"outputs": [],
"source": [
"0.1 + 0.2 - 0.3"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "oYumutYqJ-jy"
},
"source": [
"These results are not correct (and the problem is not due to Python). The error arises from the fact that floating-point numbers are represented in computer hardware as base 2 (binary) fractions and most decimal fractions cannot be represented exactly as binary fractions. As consequence, decimal floating-point numbers are only approximated by the binary floating-point numbers actually stored in the machine. [See here for more on this issue](http://docs.python.org/2/tutorial/floatingpoint.html)."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "g285thNSJ-jy"
},
"source": [
"A complex number has real and imaginary parts:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "asEYEkf9J-jz"
},
"outputs": [],
"source": [
"1 + 2j"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "jujEJ-_TJ-jz"
},
"outputs": [],
"source": [
"print(type(1+2j))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "qdKHJ1xmJ-jz"
},
"source": [
"Each part of a complex number is represented as a floating-point number. We can see them using the attributes `.real` and `.imag`:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "dhGLHQCsJ-jz"
},
"outputs": [],
"source": [
"print((1 + 2j).real)\n",
"print((1 + 2j).imag)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "7_6kKm_GJ-jz"
},
"source": [
"### Strings\n",
"\n",
"Strings can be enclosed in single quotes or double quotes:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "LUMx2Zu2J-j0"
},
"outputs": [],
"source": [
"s = 'string (str) is a built-in type in Python'\n",
"s"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "YhlmEt37J-j0"
},
"outputs": [],
"source": [
"type(s)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "yYlBCWamJ-j0"
},
"source": [
"String enclosed with single and double quotes are equal, but it may be easier to use one instead of the other:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "3zdO1oNOJ-j0"
},
"outputs": [],
"source": [
"'string (str) is a Python's built-in type'"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "r2tnlXgsJ-j0"
},
"outputs": [],
"source": [
"\"string (str) is a Python's built-in type\""
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "x5bXJADuJ-j1"
},
"source": [
"But you could have done that using the Python escape character '\\':"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "KWfrIOY5J-j1"
},
"outputs": [],
"source": [
"'string (str) is a Python\\'s built-in type'"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "JoexutopJ-j1"
},
"source": [
"Strings can be concatenated (glued together) with the + operator, and repeated with *:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "3bEmCN7KJ-j1"
},
"outputs": [],
"source": [
"s = 'P' + 'y' + 't' + 'h' + 'o' + 'n'\n",
"print(s)\n",
"print(s*5)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "j5CSHGZSJ-j2"
},
"source": [
"Strings can be subscripted (indexed); like in C, the first character of a string has subscript (index) 0:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "UF_3j2WWJ-j2"
},
"outputs": [],
"source": [
"print('s[0] = ', s[0], ' (s[index], start at 0)')\n",
"print('s[5] = ', s[5])\n",
"print('s[-1] = ', s[-1], ' (last element)')\n",
"print('s[:] = ', s[:], ' (all elements)')\n",
"print('s[1:] = ', s[1:], ' (from this index (inclusive) till the last (inclusive))')\n",
"print('s[2:4] = ', s[2:4], ' (from first index (inclusive) till second index (exclusive))')\n",
"print('s[:2] = ', s[:2], ' (till this index, exclusive)')\n",
"print('s[:10] = ', s[:10], ' (Python handles the index if it is larger than the string length)')\n",
"print('s[-10:] = ', s[-10:])\n",
"print('s[0:5:2] = ', s[0:5:2], ' (s[ini:end:step])')\n",
"print('s[::2] = ', s[::2], ' (s[::step], initial and final indexes can be omitted)')\n",
"print('s[0:5:-1] = ', s[::-1], ' (s[::-step] reverses the string)')\n",
"print('s[:2] + s[2:] = ', s[:2] + s[2:], ' (because of Python indexing, this sounds natural)')"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "dhOoJQ_QJ-j2"
},
"source": [
"### len()\n",
"\n",
"Python has a built-in functon to get the number of itens of a sequence:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "7E4V8-ZsJ-j2"
},
"outputs": [],
"source": [
"help(len)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "QiR6izU2J-j2"
},
"outputs": [],
"source": [
"s = 'Python'\n",
"len(s)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "oT9_aBdJJ-j3"
},
"source": [
"The function len() helps to understand how the backward indexing works in Python. \n",
"The index s[-i] should be understood as s[len(s) - i] rather than accessing directly the i-th element from back to front. This is why the last element of a string is s[-1]:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "xQ0nBuZkJ-j3"
},
"outputs": [],
"source": [
"print('s = ', s)\n",
"print('len(s) = ', len(s))\n",
"print('len(s)-1 = ',len(s) - 1)\n",
"print('s[-1] = ', s[-1])\n",
"print('s[len(s) - 1] = ', s[len(s) - 1])"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "rixKZTjFJ-j3"
},
"source": [
"Or, strings can be surrounded in a pair of matching triple-quotes: \"\"\" or '''. End of lines do not need to be escaped when using triple-quotes, but they will be included in the string. This is how we created a multi-line comment earlier:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "G0w79b8aJ-j3"
},
"outputs": [],
"source": [
"\"\"\"Strings can be surrounded in a pair of matching triple-quotes: \\\"\"\" or '''.\n",
"\n",
"End of lines do not need to be escaped when using triple-quotes,\n",
"but they will be included in the string.\n",
"\n",
"\"\"\""
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "0B5GvtDBJ-j4"
},
"source": [
"### Lists\n",
"\n",
"Values can be grouped together using different types, one of them is list, which can be written as a list of comma-separated values between square brackets. List items need not all have the same type:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "KddUMaR4J-j4"
},
"outputs": [],
"source": [
"x = ['spam', 'eggs', 100, 1234]\n",
"x"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "kygtFy5MJ-j4"
},
"source": [
"Lists can be indexed and the same indexing rules we saw for strings are applied:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "FwTM274lJ-j4"
},
"outputs": [],
"source": [
"x[0]"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "AugDvMZ8J-j8"
},
"source": [
"The function len() works for lists:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "zpfBpU0IJ-j8"
},
"outputs": [],
"source": [
"len(x)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "_qWHUQFyJ-j8"
},
"source": [
"### Tuples\n",
"\n",
"A tuple consists of a number of values separated by commas, for instance:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "4clrjeSKJ-j8"
},
"outputs": [],
"source": [
"t = ('spam', 'eggs', 100, 1234)\n",
"t"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "hLPcKYd5J-j9"
},
"source": [
"The type tuple is why multiple assignments in a single line works; elements separated by commas (with or without surrounding parentheses) are a tuple and in an expression with an '=', the right-side tuple is attributed to the left-side tuple:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "tpjBdIHuJ-j9"
},
"outputs": [],
"source": [
"a, b = 1, 2\n",
"print('a = ', a, '\\nb = ', b)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "r5nDmCgvJ-j9"
},
"source": [
"Is the same as:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "8_8r_WyEJ-j9"
},
"outputs": [],
"source": [
"(a, b) = (1, 2)\n",
"print('a = ', a, '\\nb = ', b)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "S5L3jmzuJ-j-"
},
"source": [
"### Sets\n",
"\n",
"Python also includes a data type for sets. A set is an unordered collection with no duplicate elements."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "A2U8aOJ0J-j-"
},
"outputs": [],
"source": [
"basket = ['apple', 'orange', 'apple', 'pear', 'orange', 'banana']\n",
"fruit = set(basket) # create a set without duplicates\n",
"fruit"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "N0gB9s7PJ-j-"
},
"source": [
"As set is an unordered collection, it can not be indexed as lists and tuples."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "6bewLW3cJ-j-"
},
"outputs": [],
"source": [
"set(['orange', 'pear', 'apple', 'banana'])\n",
"'orange' in fruit # fast membership testing"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "zqOcpgGrJ-j-"
},
"source": [
"### Dictionaries\n",
"\n",
"Dictionary is a collection of elements organized keys and values. Unlike lists and tuples, which are indexed by a range of numbers, dictionaries are indexed by their keys:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "DQSfxQRqJ-j-"
},
"outputs": [],
"source": [
"tel = {'jack': 4098, 'sape': 4139}\n",
"tel"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "_NRjD2K7J-j_"
},
"outputs": [],
"source": [
"tel['guido'] = 4127\n",
"tel"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "lEf5h4DPJ-j_"
},
"outputs": [],
"source": [
"tel['jack']"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "4CpWZ-OZJ-j_"
},
"outputs": [],
"source": [
"del tel['sape']\n",
"tel['irv'] = 4127\n",
"tel"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "nM8evyuMJ-j_"
},
"outputs": [],
"source": [
"tel.keys()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "0BBg6PqpJ-kG"
},
"outputs": [],
"source": [
"'guido' in tel"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "hY2SNyKhJ-kG"
},
"source": [
"The dict() constructor builds dictionaries directly from sequences of key-value pairs:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "2IGkxuZKJ-kG"
},
"outputs": [],
"source": [
"tel = dict([('sape', 4139), ('guido', 4127), ('jack', 4098)])\n",
"tel"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "s4rpOSKLJ-kH"
},
"source": [
"## Built-in Constants\n",
"\n",
"- **False** : false value of the bool type\n",
"- **True** : true value of the bool type\n",
"- **None** : sole value of types.NoneType. None is frequently used to represent the absence of a value."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "fvHgbaWCJ-kH"
},
"source": [
"In computer science, the Boolean or logical data type is composed by two values, true and false, intended to represent the values of logic and Boolean algebra. In Python, 1 and 0 can also be used in most situations as equivalent to the Boolean values."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "GTP8711jJ-kH"
},
"source": [
"## Logical (Boolean) operators"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "f6UUFypXJ-kH"
},
"source": [
"### and, or, not"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "wzg1GO1IJ-kH"
},
"source": [
"- **and** : logical AND operator. If both the operands are true then condition becomes true.\t (a and b) is true.\n",
"- **or** : logical OR Operator. If any of the two operands are non zero then condition becomes true.\t (a or b) is true.\n",
"- **not** : logical NOT Operator. Reverses the logical state of its operand. If a condition is true then logical NOT operator will make false."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "qNtqkWUzJ-kI"
},
"source": [
"### Comparisons\n",
"\n",
"The following comparison operations are supported by objects in Python:\n",
"\n",
"- **==** : equal\n",
"- **!=** : not equal\n",
"- **<**\t: strictly less than\n",
"- **<=** : less than or equal\n",
"- **\\>** : strictly greater than\n",
"- **\\>=** : greater than or equal\n",
"- **is** : object identity\n",
"- **is not** : negated object identity"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "Tq1NRMjpJ-kI"
},
"outputs": [],
"source": [
"True == False"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "qpq2t_sYJ-kI"
},
"outputs": [],
"source": [
"not True == False"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "fXcAuhtAJ-kJ"
},
"outputs": [],
"source": [
"1 < 2 > 1"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "tC-LBptmJ-kJ"
},
"outputs": [],
"source": [
"True != (False or True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "I1MyDi1EJ-kJ"
},
"outputs": [],
"source": [
"True != False or True"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "brfbgWegJ-kJ"
},
"source": [
"## Indentation and whitespace\n",
"\n",
"In Python, statement grouping is done by indentation (this is mandatory), which are done by inserting whitespaces, not tabs. Indentation is also recommended for alignment of function calling that span more than one line for better clarity. \n",
"We will see examples of indentation in the next session."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "qVQvJ-2NJ-kJ"
},
"source": [
"## Control of flow\n",
"\n",
"### `if`...`elif`...`else`\n",
"\n",
"Conditional statements (to peform something if another thing is True or False) can be implemmented using the `if` statement:\n",
"```\n",
"if expression:\n",
" statement\n",
"elif:\n",
" statement \n",
"else:\n",
" statement\n",
"```\n",
"`elif` (one or more) and `else` are optionals. \n",
"The indentation is obligatory. \n",
"For example:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "HdFqqxvQJ-kK"
},
"outputs": [],
"source": [
"if True:\n",
" pass"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "3W0M6VghJ-kK"
},
"source": [
"Which does nothing useful. \n",
"\n",
"Let's use the `if`...`elif`...`else` statements to categorize the [body mass index](http://en.wikipedia.org/wiki/Body_mass_index) of a person:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "R5ipBPsKJ-kK"
},
"outputs": [],
"source": [
"# body mass index\n",
"weight = 100 # kg\n",
"height = 1.70 # m\n",
"bmi = weight / height**2"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "OOn4CQzGJ-kK"
},
"outputs": [],
"source": [
"if bmi < 15:\n",
" c = 'very severely underweight'\n",
"elif 15 <= bmi < 16:\n",
" c = 'severely underweight'\n",
"elif 16 <= bmi < 18.5:\n",
" c = 'underweight'\n",
"elif 18.5 <= bmi < 25:\n",
" c = 'normal'\n",
"elif 25 <= bmi < 30:\n",
" c = 'overweight'\n",
"elif 30 <= bmi < 35:\n",
" c = 'moderately obese'\n",
"elif 35 <= bmi < 40:\n",
" c = 'severely obese'\n",
"else:\n",
" c = 'very severely obese'\n",
"\n",
"print('For a weight of {0:.1f} kg and a height of {1:.2f} m,\\n\\\n",
"the body mass index (bmi) is {2:.1f} kg/m2,\\nwhich is considered {3:s}.'\\\n",
" .format(weight, height, bmi, c))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "pBbe6tnzJ-kL"
},
"source": [
"### for\n",
"\n",
"The `for` statement iterates over a sequence to perform operations (a loop event).\n",
"```\n",
"for iterating_var in sequence:\n",
" statements\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "vfexpJO_J-kL"
},
"outputs": [],
"source": [
"for i in [3, 2, 1, 'go!']:\n",
" print(i, end=', ')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "lq6w8PFjJ-kL"
},
"outputs": [],
"source": [
"for letter in 'Python':\n",
" print(letter),"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "PMDViF9AJ-kL"
},
"source": [
"#### The `range()` function\n",
"\n",
"The built-in function range() is useful if we need to create a sequence of numbers, for example, to iterate over this list. It generates lists containing arithmetic progressions:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "Mi-F2buvJ-kL"
},
"outputs": [],
"source": [
"help(range)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "JmMNoPTxJ-kM"
},
"outputs": [],
"source": [
"range(10)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "ChXXinjIJ-kM"
},
"outputs": [],
"source": [
"range(1, 10, 2)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "6ewd8VQtJ-kM"
},
"outputs": [],
"source": [
"for i in range(10):\n",
" n2 = i**2\n",
" print(n2),"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "eRwOEdQ0J-kM"
},
"source": [
"### while\n",
"\n",
"The `while` statement is used for repeating sections of code in a loop until a condition is met (this different than the `for` statement which executes n times):\n",
"```\n",
"while expression:\n",
" statement\n",
"```\n",
"Let's generate the Fibonacci series using a `while` loop:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "duDEtnO4J-kN"
},
"outputs": [],
"source": [
"# Fibonacci series: the sum of two elements defines the next\n",
"a, b = 0, 1\n",
"while b < 1000:\n",
" print(b, end=' ')\n",
" a, b = b, a+b"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "FiBpNcEmJ-kN"
},
"source": [
"## Function definition\n",
"\n",
"A function in a programming language is a piece of code that performs a specific task. Functions are used to reduce duplication of code making easier to reuse it and to decompose complex problems into simpler parts. The use of functions contribute to the clarity of the code.\n",
"\n",
"A function is created with the `def` keyword and the statements in the block of the function must be indented:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "MokTs8jOJ-kN"
},
"outputs": [],
"source": [
"def function():\n",
" pass"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "sEkkjcZQJ-kN"
},
"source": [
"As per construction, this function does nothing when called:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "LHl7xfZbJ-kO"
},
"outputs": [],
"source": [
"function()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "mSbnYbHSJ-kO"
},
"source": [
"The general syntax of a function definition is:\n",
"```\n",
"def function_name( parameters ):\n",
" \"\"\"Function docstring.\n",
"\n",
" The help for the function\n",
"\n",
" \"\"\"\n",
"\n",
" function body\n",
"\n",
" return variables\n",
"```\n",
"A more useful function:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "hZ1PlhwTJ-kO"
},
"outputs": [],
"source": [
"def fibo(N):\n",
" \"\"\"Fibonacci series: the sum of two elements defines the next.\n",
"\n",
" The series is calculated till the input parameter N and\n",
" returned as an ouput variable.\n",
"\n",
" \"\"\"\n",
"\n",
" a, b, c = 0, 1, []\n",
" while b < N:\n",
" c.append(b)\n",
" a, b = b, a + b\n",
"\n",
" return c"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "HivE1p0lJ-kO"
},
"outputs": [],
"source": [
"fibo(100)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "W5LFIvkCJ-kP"
},
"outputs": [],
"source": [
"if 3 > 2:\n",
" print('teste')"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "fnURIaQAJ-kP"
},
"source": [
"Let's implemment the body mass index calculus and categorization as a function:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "H5LoWWJFJ-kP"
},
"outputs": [],
"source": [
"def bmi(weight, height):\n",
" \"\"\"Body mass index calculus and categorization.\n",
"\n",
" Enter the weight in kg and the height in m.\n",
" See http://en.wikipedia.org/wiki/Body_mass_index\n",
"\n",
" \"\"\"\n",
"\n",
" bmi = weight / height**2\n",
"\n",
" if bmi < 15:\n",
" c = 'very severely underweight'\n",
" elif 15 <= bmi < 16:\n",
" c = 'severely underweight'\n",
" elif 16 <= bmi < 18.5:\n",
" c = 'underweight'\n",
" elif 18.5 <= bmi < 25:\n",
" c = 'normal'\n",
" elif 25 <= bmi < 30:\n",
" c = 'overweight'\n",
" elif 30 <= bmi < 35:\n",
" c = 'moderately obese'\n",
" elif 35 <= bmi < 40:\n",
" c = 'severely obese'\n",
" else:\n",
" c = 'very severely obese'\n",
"\n",
" s = 'For a weight of {0:.1f} kg and a height of {1:.2f} m,\\\n",
" the body mass index (bmi) is {2:.1f} kg/m2,\\\n",
" which is considered {3:s}.'\\\n",
" .format(weight, height, bmi, c)\n",
"\n",
" print(s)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "DslqRKfuJ-kP"
},
"outputs": [],
"source": [
"bmi(73, 1.70)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "4UvAAHfDJ-kQ"
},
"source": [
"## Numeric data manipulation with Numpy\n",
"\n",
"Numpy is the fundamental package for scientific computing in Python and has a N-dimensional array package convenient to work with numerical data. With Numpy it's much easier and faster to work with numbers grouped as 1-D arrays (a vector), 2-D arrays (like a table or matrix), or higher dimensions. Let's create 1-D and 2-D arrays in Numpy:"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"run_control": {
"breakpoint": false
},
"id": "fARXA0xrJ-kQ"
},
"outputs": [],
"source": [
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"run_control": {
"breakpoint": false
},
"id": "Y1btT-j4J-kQ",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "568938e4-9d35-453e-aa8b-c7ab12b2082d"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([1, 2, 3, 4, 5, 6])"
]
},
"metadata": {},
"execution_count": 3
}
],
"source": [
"x1d = np.array([1, 2, 3, 4, 5, 6])\n",
"print(type(x1d))\n",
"x1d"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"run_control": {
"breakpoint": false
},
"id": "H7yd75cRJ-kQ",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "8403f286-5651-45ac-ef15-355d6754d953"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([[1, 2, 3],\n",
" [4, 5, 6]])"
]
},
"metadata": {},
"execution_count": 4
}
],
"source": [
"x2d = np.array([[1, 2, 3], [4, 5, 6]])\n",
"x2d"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "QucDdCQGJ-kR"
},
"source": [
"len() and the Numpy functions size() and shape() give information aboout the number of elements and the structure of the Numpy array:"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"run_control": {
"breakpoint": false
},
"id": "EMWLUEqxJ-kR",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "c05d914d-8485-4b31-b676-b7550366d5af"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"1-d array:\n",
"[1 2 3 4 5 6]\n",
"len(x1d) = 6\n",
"np.size(x1d) = 6\n",
"np.shape(x1d) = (6,)\n",
"np.ndim(x1d) = 1\n",
"\n",
"2-d array:\n",
"[[1 2 3]\n",
" [4 5 6]]\n",
"len(x2d) = 2\n",
"np.size(x2d) = 6\n",
"np.shape(x2d) = (2, 3)\n",
"np.ndim(x2d) = 2\n"
]
}
],
"source": [
"print('1-d array:')\n",
"print(x1d)\n",
"print('len(x1d) = ', len(x1d))\n",
"print('np.size(x1d) = ', np.size(x1d))\n",
"print('np.shape(x1d) = ', np.shape(x1d))\n",
"print('np.ndim(x1d) = ', np.ndim(x1d))\n",
"print('\\n2-d array:')\n",
"print(x2d)\n",
"print('len(x2d) = ', len(x2d))\n",
"print('np.size(x2d) = ', np.size(x2d))\n",
"print('np.shape(x2d) = ', np.shape(x2d))\n",
"print('np.ndim(x2d) = ', np.ndim(x2d))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "0yxM3Nm8J-kR"
},
"source": [
"Create random data"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"run_control": {
"breakpoint": false
},
"id": "gtvPAjAtJ-kR",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "283b2126-56e5-4766-e087-218c53874ff5"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([[ 0.64904804, 0.18555854, -1.04376783],\n",
" [-1.61315003, -0.36981362, 0.56190893],\n",
" [ 1.54122048, -0.12391706, 0.19090342],\n",
" [ 0.07474882, 0.22379742, 0.26319027]])"
]
},
"metadata": {},
"execution_count": 6
}
],
"source": [
"x = np.random.randn(4,3)\n",
"x"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "fxtQQG6lJ-kR"
},
"source": [
"Joining (stacking together) arrays"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"run_control": {
"breakpoint": false
},
"id": "ETZVPSdSJ-kR",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "f35b7bdf-7e06-4ed3-8681-e4f0fba8e37a"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"[[3 0 3]\n",
" [1 3 4]]\n",
"[[6 6 6]\n",
" [9 7 7]]\n"
]
}
],
"source": [
"x = np.random.randint(0, 5, size=(2, 3))\n",
"print(x)\n",
"y = np.random.randint(5, 10, size=(2, 3))\n",
"print(y)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"run_control": {
"breakpoint": false
},
"id": "3_WauBUwJ-kS",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "98d35a92-d247-4926-e2c2-6ee4edcc96df"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([[3, 0, 3],\n",
" [1, 3, 4],\n",
" [6, 6, 6],\n",
" [9, 7, 7]])"
]
},
"metadata": {},
"execution_count": 8
}
],
"source": [
"np.vstack((x,y))"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"run_control": {
"breakpoint": false
},
"id": "clIJj3RXJ-kS",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "2822cf68-bb00-4996-98ad-10e2f10c0afa"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([[3, 0, 3, 6, 6, 6],\n",
" [1, 3, 4, 9, 7, 7]])"
]
},
"metadata": {},
"execution_count": 9
}
],
"source": [
"np.hstack((x,y))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "J_yek0YrJ-kS"
},
"source": [
"Create equally spaced data"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"run_control": {
"breakpoint": false
},
"id": "s91rbxhqJ-kS"
},
"outputs": [],
"source": [
"np.arange(start = 1, stop = 10, step = 2)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"run_control": {
"breakpoint": false
},
"id": "jbpYUmDKJ-kS"
},
"outputs": [],
"source": [
"np.linspace(start = 0, stop = 1, num = 11)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "F22L1NdkJ-kS"
},
"source": [
"### Interpolation\n",
"\n",
"Consider the following data:"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"run_control": {
"breakpoint": false
},
"id": "NDh30VOqJ-kT"
},
"outputs": [],
"source": [
"y = [5, 4, 10, 8, 1, 10, 2, 7, 1, 3]"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "WOkEk73PJ-kT"
},
"source": [
"Suppose we want to create data in between the given data points (interpolation); for instance, let's try to double the resolution of the data by generating twice as many data:"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"run_control": {
"breakpoint": false
},
"id": "rgXDq-k-J-kV",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "21ad14a8-839d-4e27-83ee-7317f243efa7"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([5. , 4.52631579, 4.05263158, 6.52631579, 9.36842105,\n",
" 9.26315789, 8.31578947, 5.78947368, 2.47368421, 3.36842105,\n",
" 7.63157895, 8.31578947, 4.52631579, 2.78947368, 5.15789474,\n",
" 6.36842105, 3.52631579, 1.10526316, 2.05263158, 3. ])"
]
},
"metadata": {},
"execution_count": 11
}
],
"source": [
"t = np.linspace(0, len(y), len(y)) # time vector for the original data\n",
"tn = np.linspace(0, len(y), 2 * len(y)) # new time vector for the new time-normalized data\n",
"yn = np.interp(tn, t, y) # new time-normalized data\n",
"yn"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "N1XzNCyAJ-kV"
},
"source": [
"The key is the Numpy `interp` function, from its help: \n",
"\n",
" interp(x, xp, fp, left=None, right=None) \n",
" One-dimensional linear interpolation. \n",
" Returns the one-dimensional piecewise linear interpolant to a function with given values at discrete data-points.\n",
"\n",
"A plot of the data will show what we have done:"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"run_control": {
"breakpoint": false
},
"id": "Xhf8zzCkJ-kV",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 445
},
"outputId": "302596b3-b63f-46b2-ad93-4cd94c0f7da1"
},
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"
"
],
"image/png": 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\n"
},
"metadata": {}
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"plt.figure(figsize=(10,5))\n",
"plt.plot(t, y, 'bo-', lw=2, label='original data')\n",
"plt.plot(tn, yn, '.-', color=[1, 0, 0, .5], lw=2, label='interpolated')\n",
"plt.legend(loc='best', framealpha=.5)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "tt3qzQVcJ-kV"
},
"source": [
"For more about Numpy, see [http://www.numpy.org/](http://www.numpy.org/)."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "-1viUs3-J-kW"
},
"source": [
"## Read and save files\n",
"\n",
"There are two kinds of computer files: text files and binary files:\n",
"> Text file: computer file where the content is structured as a sequence of lines of electronic text. Text files can contain plain text (letters, numbers, and symbols) but they are not limited to such. The type of content in the text file is defined by the Unicode encoding (a computing industry standard for the consistent encoding, representation and handling of text expressed in most of the world's writing systems). \n",
">\n",
"> Binary file: computer file where the content is encoded in binary form, a sequence of integers representing byte values.\n",
"\n",
"Let's see how to save and read numeric data stored in a text file:\n",
"\n",
"**Using plain Python**"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"run_control": {
"breakpoint": false
},
"id": "BgSl0GHYJ-kW"
},
"outputs": [],
"source": [
"f = open(\"newfile.txt\", \"w\") # open file for writing\n",
"f.write(\"This is a test\\n\") # save to file\n",
"f.write(\"And here is another line\\n\") # save to file\n",
"f.close()\n",
"f = open('newfile.txt', 'r') # open file for reading\n",
"f = f.read() # read from file\n",
"print(f)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"run_control": {
"breakpoint": false
},
"id": "G27mTms_J-kW"
},
"outputs": [],
"source": [
"help(open)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "s9kbyRARJ-kW"
},
"source": [
"**Using Numpy**"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"run_control": {
"breakpoint": false
},
"id": "dlB8I05VJ-kW",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "caf25349-f5b5-46a4-8f73-887e670d4da6"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([[ 1.07597 , -0.922058 , 0.94503 ],\n",
" [-1.03802 , -0.373469 , 2.03777 ],\n",
" [-0.0813479, -0.142094 , -0.0509791]])"
]
},
"metadata": {},
"execution_count": 13
}
],
"source": [
"import numpy as np\n",
"data = np.random.randn(3,3)\n",
"np.savetxt('myfile.txt', data, fmt=\"%12.6G\") # save to file\n",
"data = np.genfromtxt('myfile.txt', unpack=True) # read from file\n",
"data"
]
},
{
"cell_type": "markdown",
"source": [
"### Mount the Google Drive"
],
"metadata": {
"id": "N5gFCECRld0v"
}
},
{
"cell_type": "code",
"source": [
"from google.colab import drive\n",
"drive.mount('/content/drive')"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "xUMde8QWleMb",
"outputId": "e55e7615-53e6-4292-d824-c8c5fb4e7610"
},
"execution_count": 13,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Mounted at /content/drive\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"For example, using pandas to read a csv file in the Google Drive:"
],
"metadata": {
"id": "g2TsnoJSmMgf"
}
},
{
"cell_type": "code",
"source": [
"import pandas as pd\n",
"tabela = pd.read_csv('/content/sample_data/california_housing_test.csv')\n",
"tabela"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 423
},
"id": "H9lmgsiQleSH",
"outputId": "4e156b4a-bc17-4597-864f-d5c5f7a44893"
},
"execution_count": 18,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" longitude latitude housing_median_age total_rooms total_bedrooms \\\n",
"0 -122.05 37.37 27.0 3885.0 661.0 \n",
"1 -118.30 34.26 43.0 1510.0 310.0 \n",
"2 -117.81 33.78 27.0 3589.0 507.0 \n",
"3 -118.36 33.82 28.0 67.0 15.0 \n",
"4 -119.67 36.33 19.0 1241.0 244.0 \n",
"... ... ... ... ... ... \n",
"2995 -119.86 34.42 23.0 1450.0 642.0 \n",
"2996 -118.14 34.06 27.0 5257.0 1082.0 \n",
"2997 -119.70 36.30 10.0 956.0 201.0 \n",
"2998 -117.12 34.10 40.0 96.0 14.0 \n",
"2999 -119.63 34.42 42.0 1765.0 263.0 \n",
"\n",
" population households median_income median_house_value \n",
"0 1537.0 606.0 6.6085 344700.0 \n",
"1 809.0 277.0 3.5990 176500.0 \n",
"2 1484.0 495.0 5.7934 270500.0 \n",
"3 49.0 11.0 6.1359 330000.0 \n",
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}
},
"metadata": {},
"execution_count": 18
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "JsKAyNU4J-kW"
},
"source": [
"## Ploting with matplotlib\n",
"\n",
"Matplotlib is the most-widely used packge for plotting data in Python. Let's see some examples of it."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"run_control": {
"breakpoint": false
},
"id": "vVHvlSY-J-kX"
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"id": "_lo2xubeJ-kX"
},
"outputs": [],
"source": [
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"run_control": {
"breakpoint": false
},
"id": "nYsv5ivWJ-kX",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 472
},
"outputId": "0bbb2e71-4b36-41ab-819a-709fe860c4a4"
},
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
""
],
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\n"
},
"metadata": {}
}
],
"source": [
"t = np.linspace(0, 0.99, 100)\n",
"x = np.sin(2 * np.pi * 2 * t)\n",
"n = np.random.randn(100) / 5\n",
"plt.Figure(figsize=(12,8))\n",
"plt.plot(t, x, label='sine', linewidth=2)\n",
"plt.plot(t, x + n, label='noisy sine', linewidth=2)\n",
"plt.annotate(text='$sin(4 \\pi t)$', xy=(.2, 1), fontsize=20, color=[0, 0, 1])\n",
"plt.legend(loc='best', framealpha=.5)\n",
"plt.xlabel('Time [s]')\n",
"plt.ylabel('Amplitude')\n",
"plt.title('Data plotting using matplotlib')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "oM5eFtmnJ-kX"
},
"source": [
"Use the IPython magic `%matplotlib qt` to plot a figure in a separate window (from where you will be able to change some of the figure proprerties), **but it doesn't work in Google Colab**:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"run_control": {
"breakpoint": false
},
"id": "IlGE6M7pJ-kX"
},
"outputs": [],
"source": [
"%matplotlib qt"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"run_control": {
"breakpoint": false
},
"id": "vg7YBnlyJ-kY"
},
"outputs": [],
"source": [
"mu, sigma = 10, 2\n",
"x = mu + sigma * np.random.randn(1000)\n",
"fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 4))\n",
"ax1.plot(x, 'ro')\n",
"ax1.set_title('Data')\n",
"ax1.grid()\n",
"\n",
"n, bins, patches = ax2.hist(x, 25, density=True, facecolor='r') # histogram\n",
"ax2.set_xlabel('Bins')\n",
"ax2.set_ylabel('Probability')\n",
"ax2.set_title('Histogram')\n",
"fig.suptitle('Another example using matplotlib', fontsize=18, y=1)\n",
"ax2.grid()\n",
"\n",
"plt.tight_layout()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "0QoYKIFmJ-kY"
},
"source": [
"And a window with the following figure should appear (**it doesn't work in Google Colab**):"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"run_control": {
"breakpoint": false
},
"id": "5-_35c4YJ-kY"
},
"outputs": [],
"source": [
"from IPython.display import Image\n",
"Image(url=\"./../images/plot.png\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "kncaZ4ZtJ-kY"
},
"source": [
"You can switch back and forth between inline and separate figure using the `%matplotlib` magic commands used above. There are plenty more examples with the source code in the [matplotlib gallery](http://matplotlib.org/gallery.html)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"run_control": {
"breakpoint": false
},
"id": "pWaEk7OIJ-kY"
},
"outputs": [],
"source": [
"# get back the inline plot\n",
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "OzIA2nMfJ-kY"
},
"source": [
"## Signal processing with Scipy\n",
"\n",
"The Scipy package has a lot of functions for signal processing, among them: Integration (scipy.integrate), Optimization (scipy.optimize), Interpolation (scipy.interpolate), Fourier Transforms (scipy.fftpack), Signal Processing (scipy.signal), Linear Algebra (scipy.linalg), and Statistics (scipy.stats). As an example, let's see how to use a low-pass Butterworth filter to attenuate high-frequency noise and how the differentiation process of a signal affects the signal-to-noise content. We will also calculate the Fourier transform of these data to look at their frequencies content."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"run_control": {
"breakpoint": false
},
"id": "XCVdvLO_J-kY"
},
"outputs": [],
"source": [
"from scipy.signal import butter, filtfilt\n",
"import scipy.fftpack\n",
"freq = 100.\n",
"t = np.arange(0,1,.01);\n",
"w = 2*np.pi*1 # 1 Hz\n",
"y = np.sin(w*t)+0.1*np.sin(10*w*t)\n",
"# Butterworth filter\n",
"b, a = butter(4, (5/(freq/2)), btype = 'low')\n",
"y2 = filtfilt(b, a, y)\n",
"# 2nd derivative of the data\n",
"ydd = np.diff(y,2)*freq*freq # raw data\n",
"y2dd = np.diff(y2,2)*freq*freq # filtered data\n",
"# frequency content\n",
"yfft = np.abs(scipy.fftpack.fft(y))/(y.size/2); # raw data\n",
"y2fft = np.abs(scipy.fftpack.fft(y2))/(y.size/2); # filtered data\n",
"freqs = scipy.fftpack.fftfreq(y.size, 1./freq)\n",
"yddfft = np.abs(scipy.fftpack.fft(ydd))/(ydd.size/2);\n",
"y2ddfft = np.abs(scipy.fftpack.fft(y2dd))/(ydd.size/2);\n",
"freqs2 = scipy.fftpack.fftfreq(ydd.size, 1./freq)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "_4-Y6jA5J-kZ"
},
"source": [
"And the plots:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"run_control": {
"breakpoint": false
},
"id": "kb_2DbNCJ-kZ"
},
"outputs": [],
"source": [
"fig, ((ax1,ax2),(ax3,ax4)) = plt.subplots(2, 2, figsize=(12, 6))\n",
"\n",
"ax1.set_title('Temporal domain', fontsize=14)\n",
"ax1.plot(t, y, 'r', linewidth=2, label = 'raw data')\n",
"ax1.plot(t, y2, 'b', linewidth=2, label = 'filtered @ 5 Hz')\n",
"ax1.set_ylabel('f')\n",
"ax1.legend(frameon=False, fontsize=12)\n",
"\n",
"ax2.set_title('Frequency domain', fontsize=14)\n",
"ax2.plot(freqs[:int(yfft.size/4)], yfft[:int(yfft.size/4)],'r', lw=2,label='raw data')\n",
"ax2.plot(freqs[:int(yfft.size/4)],y2fft[:int(yfft.size/4)],'b--',lw=2,label='filtered @ 5 Hz')\n",
"ax2.set_ylabel('FFT(f)')\n",
"ax2.legend(frameon=False, fontsize=12)\n",
"\n",
"ax3.plot(t[:-2], ydd, 'r', linewidth=2, label = 'raw')\n",
"ax3.plot(t[:-2], y2dd, 'b', linewidth=2, label = 'filtered @ 5 Hz')\n",
"ax3.set_xlabel('Time [s]'); ax3.set_ylabel(\"f ''\")\n",
"\n",
"ax4.plot(freqs[:int(yddfft.size/4)], yddfft[:int(yddfft.size/4)], 'r', lw=2, label = 'raw')\n",
"ax4.plot(freqs[:int(yddfft.size/4)],y2ddfft[:int(yddfft.size/4)],'b--',lw=2, label='filtered @ 5 Hz')\n",
"ax4.set_xlabel('Frequency [Hz]'); ax4.set_ylabel(\"FFT(f '')\")\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "BJoM-sUpJ-kZ"
},
"source": [
"For more about Scipy, see [https://docs.scipy.org/doc/scipy/reference/tutorial/](https://docs.scipy.org/doc/scipy/reference/tutorial/)."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "1LmplijsJ-kZ"
},
"source": [
"## Symbolic mathematics with Sympy\n",
"\n",
"Sympy is a package to perform symbolic mathematics in Python. Let's see some of its features:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"run_control": {
"breakpoint": false
},
"id": "6xiX8jjZJ-ka"
},
"outputs": [],
"source": [
"from IPython.display import display\n",
"import sympy as sym\n",
"from sympy.interactive import printing\n",
"printing.init_printing()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "kzmYkX7dJ-ka"
},
"source": [
"Define some symbols and the create a second-order polynomial function (a.k.a., parabola):"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"run_control": {
"breakpoint": false
},
"id": "DmCPkW02J-ka"
},
"outputs": [],
"source": [
"x, y = sym.symbols('x y')\n",
"y = x**2 - 2*x - 3\n",
"y"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "7KXUw2MrJ-ka"
},
"source": [
"Plot the parabola at some given range:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"run_control": {
"breakpoint": false
},
"id": "pTDVzdRqJ-ka"
},
"outputs": [],
"source": [
"from sympy.plotting import plot\n",
"%matplotlib inline\n",
"plot(y, (x, -3, 5));"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "zS8UVz10J-ka"
},
"source": [
"And the roots of the parabola are given by:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"run_control": {
"breakpoint": false
},
"id": "0vo-XGM3J-kb"
},
"outputs": [],
"source": [
"sym.solve(y, x)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ltAhU7pmJ-kb"
},
"source": [
"We can also do symbolic differentiation and integration:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"run_control": {
"breakpoint": false
},
"id": "3JmF9oWWJ-kb"
},
"outputs": [],
"source": [
"dy = sym.diff(y, x)\n",
"dy"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"run_control": {
"breakpoint": false
},
"id": "C7efFPgcJ-kb"
},
"outputs": [],
"source": [
"sym.integrate(dy, x)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "-XjFy6DtJ-kc"
},
"source": [
"For example, let's use Sympy to represent three-dimensional rotations. Consider the problem of a coordinate system xyz rotated in relation to other coordinate system XYZ. The single rotations around each axis are illustrated by:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"run_control": {
"breakpoint": false
},
"id": "WJzGeWQJJ-kc"
},
"outputs": [],
"source": [
"from IPython.display import Image\n",
"Image(url=\"./../images/rotations.png\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "H4pkQ9C2J-kc"
},
"source": [
"The single 3D rotation matrices around Z, Y, and X axes can be expressed in Sympy:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"run_control": {
"breakpoint": false
},
"id": "-wemSeWrJ-kc"
},
"outputs": [],
"source": [
"from IPython.core.display import Math\n",
"from sympy import symbols, cos, sin, Matrix, latex\n",
"a, b, g = symbols('alpha beta gamma')\n",
"\n",
"RX = Matrix([[1, 0, 0], [0, cos(a), -sin(a)], [0, sin(a), cos(a)]])\n",
"display(Math(r'\\mathbf{R_{X}}=' + latex(RX, mat_str = 'matrix')))\n",
"\n",
"RY = Matrix([[cos(b), 0, sin(b)], [0, 1, 0], [-sin(b), 0, cos(b)]])\n",
"display(Math(r'\\mathbf{R_{Y}}=' + latex(RY, mat_str = 'matrix')))\n",
"\n",
"RZ = Matrix([[cos(g), -sin(g), 0], [sin(g), cos(g), 0], [0, 0, 1]])\n",
"display(Math(r'\\mathbf{R_{Z}}=' + latex(RZ, mat_str = 'matrix')))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "iE6PranTJ-kd"
},
"source": [
"And using Sympy, a sequence of elementary rotations around X, Y, Z axes is given by:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"run_control": {
"breakpoint": false
},
"id": "HCJHFpQUJ-kd"
},
"outputs": [],
"source": [
"RXYZ = RZ*RY*RX\n",
"display(Math(r'\\mathbf{R_{XYZ}}=' + latex(RXYZ, mat_str = 'matrix')))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "SUSM3dfiJ-kd"
},
"source": [
"Suppose there is a rotation only around X ($\\alpha$) by $\\pi/2$; we can get the numerical value of the rotation matrix by substituing the angle values:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"run_control": {
"breakpoint": false
},
"id": "TDU6ziKNJ-kd"
},
"outputs": [],
"source": [
"r = RXYZ.subs({a: np.pi/2, b: 0, g: 0})\n",
"r"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "NM0s0wkPJ-kd"
},
"source": [
"And we can prettify this result:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"run_control": {
"breakpoint": false
},
"id": "w6Ocmn1yJ-ke"
},
"outputs": [],
"source": [
"display(Math(r'\\mathbf{R_{(\\alpha=\\pi/2)}}=' + latex(r.n(3, chop=True), mat_str = 'matrix')))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "AMQNTcAfJ-ke"
},
"source": [
"For more about Sympy, see [http://docs.sympy.org/latest/tutorial/](http://docs.sympy.org/latest/tutorial/)."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "lG5jNvauJ-ke"
},
"source": [
"## Data analysis with pandas\n",
"\n",
"> \"[pandas](http://pandas.pydata.org/) is a Python package providing fast, flexible, and expressive data structures designed to make working with “relational” or “labeled” data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real world data analysis in Python.\"\n",
"\n",
"To work with labellled data, pandas has a type called DataFrame (basically, a matrix where columns and rows have may names and may be of different types) and it is also the main type of the software [R](http://www.r-project.org/). Fo ezample:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"run_control": {
"breakpoint": false
},
"id": "yUOT_qHpJ-ke"
},
"outputs": [],
"source": [
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"run_control": {
"breakpoint": false
},
"id": "Ev9PCYg8J-kf"
},
"outputs": [],
"source": [
"x = 5*['A'] + 5*['B']\n",
"x"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"run_control": {
"breakpoint": false
},
"id": "jBKcCHZjJ-kf"
},
"outputs": [],
"source": [
"df = pd.DataFrame(np.random.rand(10,2), columns=['Level 1', 'Level 2'] )\n",
"df['Group'] = pd.Series(['A']*5 + ['B']*5)\n",
"plot = df.boxplot(by='Group')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"run_control": {
"breakpoint": false
},
"id": "kCCK1XrwJ-kf"
},
"outputs": [],
"source": [
"from pandas.plotting import scatter_matrix\n",
"df = pd.DataFrame(np.random.randn(100, 3), columns=['A', 'B', 'C'])\n",
"plot = scatter_matrix(df, alpha=0.5, figsize=(8, 6), diagonal='kde')"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "8F0vKjj-J-kf"
},
"source": [
"pandas is aware the data is structured and give you basic statistics considerint that and nicely formatted:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"run_control": {
"breakpoint": false
},
"id": "75i6pAewJ-kg"
},
"outputs": [],
"source": [
"df.describe()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "o7KxQQxeJ-kg"
},
"source": [
"For more on pandas, see this tutorial: [http://pandas.pydata.org/pandas-docs/stable/10min.html](http://pandas.pydata.org/pandas-docs/stable/10min.html)."
]
},
{
"cell_type": "markdown",
"source": [
"## Plot with some interactive features of matplotlib"
],
"metadata": {
"id": "GDnKkGeMgoIu"
}
},
{
"cell_type": "code",
"source": [
"!pip install -q ipympl\n",
"from google.colab import output\n",
"output.enable_custom_widget_manager()\n",
"%matplotlib widget"
],
"metadata": {
"id": "HTmfZTvtgncx"
},
"execution_count": 6,
"outputs": []
},
{
"cell_type": "code",
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt"
],
"metadata": {
"id": "b9gMM5U2hw8S"
},
"execution_count": 4,
"outputs": []
},
{
"cell_type": "code",
"source": [
"t = np.linspace(0, 0.99, 100)\n",
"x = np.sin(2 * np.pi * 2 * t)\n",
"n = np.random.randn(100) / 5\n",
"plt.Figure(figsize=(12,8))\n",
"plt.plot(t, x, label='sine', linewidth=2)\n",
"plt.plot(t, x + n, label='noisy sine', linewidth=2)\n",
"plt.annotate(text='$sin(4 \\pi t)$', xy=(.2, 1), fontsize=20, color=[0, 0, 1])\n",
"plt.legend(loc='best', framealpha=.5)\n",
"plt.xlabel('Time [s]')\n",
"plt.ylabel('Amplitude')\n",
"plt.title('Data plotting using matplotlib')\n",
"plt.show()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 569,
"referenced_widgets": [
"c14b527ae2e54e829d8217113837e84a",
"24e1ba3e514946b1acc89a798e756bf5",
"a3df35ecbfe54463992e3b93f4cfbabb",
"7a1a12340e0a456295c07da2e89cae19"
]
},
"id": "7F-jsDE-ghLB",
"outputId": "83fb395f-548d-47af-a61c-90704ab6e27d"
},
"execution_count": 5,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
],
"image/png": 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iIjj//PO55ZZbqK2t5ZVXXiEjI4Njx451aL5RUVEsXLiQa6+9lsmTJ/Pll1/y+eef89vf/tYtTk3PH//4R2cdvJ///OeEhYXx0ksv0dTUZKjNN27cOCwWC3/5y1+oqqoiMjLSWbdwwoQJvPDCC/zxj39k0KBBZGRkMHv27M583G7ccsstPPfcc1x55ZX88pe/JDs7m3//+9/Oz709i9EVV1zBvffey0UXXcQdd9xBfX09L7zwAkOGDDEkPzz88MOsWLGCc889l379+lFSUsLzzz9P3759DYkLXeXCCy9k0qRJ3H333ezdu5dhw4bx6aefOoVnd7sozz33XJ544gnmz5/PVVddRUlJCX//+98ZNGiQQRyD+v/59ddf88QTT9CnTx/69+/P5MmTnfv379/PggULmD9/PmvXruVf//oXV111FWPHjvV6/t/85je88847nH322dxxxx2kpKTw5ptvsn//fv773/9iNqt2ioEDB5KUlMSLL75IfHw8sbGxTJ482WDN6whdmaMgBCV+zEAWhIDEUXbD8RMREaFkZWUpZ555pvL0008r1dXVbsccOXJEueiii5SkpCQlMTFRufTSS5WjR4+6lapQFEX5wx/+oOTk5Chms9lQEubTTz9VxowZo0RFRSn5+fnKX/7yF+W1117zWjZGz7XXXqvExsYq+/btU8466ywlJiZGyczMVB544AFnmQ0Hnua0adMmZd68eUpcXJwSExOjnHHGGcqaNWvczvPKK68oAwYMUCwWi6EUS3FxsXLuuecq8fHxCuAsQ+KtDMzIkSM9vgfXUh+FhYXKueeeq0RHRyvp6enK3Xff7Syr8u2337b5mSiKoixatEgZNWqUEhERoQwdOlT517/+5VYGZsmSJcoFF1yg9OnTR4mIiFD69OmjXHnllcru3budY7yVgYmNjXU7p+vrK4qilJaWKldddZUSHx+vJCYmKtddd52yevVqBVDefffdNt+Dt7JEjvO4lq/xNK9//OMfyuDBg5XIyEhl2LBhyuuvv+5xnjt37lRmzJihREdHK4CznItj7I4dO5RLLrlEiY+PV5KTk5XbbrtNaWhoMLyGaxkYRVGUffv2KZdccomSlJSkREVFKZMmTVI+++wzt/f6ySefKCNGjFDCwsIMn3dnysB0ZI6CEAqYFMWHUdWCIPRKrrvuOj744ANqa2v9PZVu56mnnuLOO+/kyJEj5OTk+Hs6Xebjjz/moosuYtWqVZx++un+nk6bPPjggzz00EOUlpY6C5gLgtC7kRhAQRACloaGBsN6Y2MjL730EoMHDw4o8ef6PqxWK88++ywJCQmMHz/eT7MSBCGYkRhAQRAClosvvpi8vDzGjRtHVVUV//rXv9i5cyf//ve//T21TnH77bfT0NDA1KlTaWpq4sMPP2TNmjU88sgjhiLNgiAIvkIEoCAIAcu8efN49dVX+fe//43VamXEiBG8++67XH755f6eWqeYPXs2jz/+OJ999hmNjY0MGjSIZ5991tC5RRAEwZdIDKAgCIIgCEKIITGAgiAIgiAIIYYIQEEQBEEQhBBDBKAgCIIgCEKIIUkgJ4HNZuPo0aPEx8dLQ3FBEARBCBAURaGmpoY+ffo4u82EGiIAT4KjR4+Sm5vr72kIgiAIgtAFDh8+TN++ff09Db8gAvAkiI+PB9R/oISEBD/PRhAEQRCEjlBdXU1ubq7zPh6KiAA8CRxu34SEBBGAgiAIghBghHL4Vmg6vgVBEARBEEIYEYCCIAiCIAghhghAQRAEQRCEEENiAAVBEISQR1EUbDYbNpvN31MRfIDZbMZsNod0jF97iAAUBEEQQprW1lYqKytpbm7291QEHxIREUFSUhJhYSJ1PCGfiiAIghCyKIpCaWkpZrOZ5ORkLBaLWI0CHEVRsFqtVFdXU1paSlZWlvxNPSACUBAEQQhZWltbURSF5ORkIiIi/D0dwYdYLBbKyspobW0lPDzc39PpdUgSiCAIghDyiIUo+JC/aduIABQEQRAEQQgxRAAKgiAIQhBx3XXXceGFF/p7GkIvR2IABUEQBCGIePrpp1EUxd/TEHo5IgAFQRAEIYhITEz09xSEAEBcwII7q56Ex4fD1vf9PRNBEATBCx988AGjR48mOjqa1NRU5s6dS11dnZsLeNasWdxxxx3cc889pKSkkJWVxYMPPmh4rcrKSm688UbS09NJSEhg9uzZbNmypWffkNCjiAAUjCgKLP8r1ByFNc/4ezaCIAiCB44dO8aVV17JT3/6UwoKCli2bBkXX3yxV9fvm2++SWxsLN999x2PPfYYDz/8MIsXL3buv/TSSykpKeHLL79k48aNjB8/njlz5lBeXt5Tb0noYcQFLBhpbYSWOnW5odKvUxEEQfAH5z+7itKaph4/b3p8JP+7fVqHxh47dozW1lYuvvhi+vXrB8Do0aO9jh8zZgwPPPAAAIMHD+a5555jyZIlnHnmmaxatYp169ZRUlJCZGQkAH/729/4+OOP+eCDD7j55ptP8p0JvRERgIKRxiptubnWf/MQBEHwE6U1TRRXN/p7Gm0yduxY5syZw+jRo5k3bx5nnXUWl1xyCcnJyR7HjxkzxrCenZ1NSUkJAFu2bKG2tpbU1FTDmIaGBvbt29c9b0DwOyIABSMiAAVBCHHS4yN7/XktFguLFy9mzZo1LFq0iGeffZbf/e53fPfddx7Hu3bCMJlM2Gw2AGpra8nOzmbZsmVuxyUlJXV4TkJgIQJQMKIXgNZmaG2GMGmPJAhC6NBRN6y/MZlMnH766Zx++uncf//99OvXj48++qjTrzN+/HiKi4sJCwsjPz/f9xMVeiWSBCIY0QtA6L1WwKoi2PIeNNX4eyaCIAg9znfffccjjzzChg0bOHToEB9++CGlpaUMHz680681d+5cpk6dyoUXXsiiRYs4cOAAa9as4Xe/+x0bNmzohtkLvQERgIKRQBCArU3wxrnw0c3w6R3+no0gCEKPk5CQwIoVKzjnnHMYMmQIv//973n88cc5++yzO/1aJpOJL774ghkzZnD99dczZMgQrrjiCg4ePEhmZmY3zF7oDZgUKRfeZaqrq0lMTKSqqoqEhAR/T8c3rH8VPr9bW791LWSO8N98PLHuFfji/9TllAFwx/f+nY8gCAFLS0sLpaWlpKenu8XJCYFNW3/boLx/dxKxAApG3CyAdf6ZhzdaGmHl49p6/Qn/zUUQBEEQAhQRgIIRNwHYy2LsNr4BNce09cYqsLb4bTqeaGiAsDAwmeCRR7rvPM3NMHiwep4PPui+8/Qkv/iF+n6uvdbfMxEEQQhuRAAKRlwFYFMvigFsaYBVT7hvr+9dleq//x6sVnV54sTuO8/TT8PevTBqFPzoR5079t57VaHl+PFQ/cEv3HsvRETAP/8JGzf6ezYCNpvaGeibP/a6By1BEE4OEYCCEdfuH73JBbzhNag97r69l7mB9Ulz3SUAa2rgL39Rl3//e1XEdZTNm+EJDzq6u6mv1yyjjz7qeUxenmr9UxT4f/+vZ+cneGDfN7D0j7Dir7D2OX/PRhAEHyICUDDSW7OAm+tg1ZPa+qAzteVeJgDvuEMVMIoCKSndc44XXoATJ1TBdOmlHT/OZoObb4bWVsjI6J65eWPjRs0yeuqp3sfdbc9B+vJLsQL6nZLt2vJ3L4sVUBCCCBGAghE3F3AviQFc/w+oK1WXR14E/adr++rL/DMnP2G1wnN2Y8yVV4K5E1fxM8/A+vUwbBjccEP3zM8b69apv02mti2jQ4fC+PHq8rPPdv+8hDaoPKQt1xyFHZ/4by6CIPgU6QQiGOmNWcBNtbD6afuKCWb+Bop0ftZeZgHsbhYvhsOH1eWrr+74cYcOaW7VF1+EpUt9P7e2cAjAQYOgve5SV18NmzbB+++rIjA+vtunF1zUl8Ph79SHppYG9TpuaYCIWBh3NcSld+x19AIQ4NsXYPQlvp+vIAg9jlgABSO90QW89V3NyjfqYsgYBjG6puXdlASiKPDJJ3D55TBgAMTGQnQ05OfDtGnw29/CqlXGY3bu1BIr3n3X/TW3b9f2f/yxum3RIvUceXkQGQk5OfCzn6kuXk/85z/q78GDYfTojr+fX/wCamvVGLuZMzt3nD5hpKM/8fGqtTI9XV13zHvPHvexrkLWkdRSX6/+DYR2aG2C3Yvgq9/Bi9PhsQHwzhXw6e3w5T2w5CFY8Rh8/QB89duOv66rACzaAIfX+3bugiD4BbEAChqK0juzgEt2asun3qj+jknTtnWDBbC8HC6+GJYvd9938KD6s3o1rF1rtKRt2aItjxvnfqx+f36+eg7X1p1Hj8JLL8HKlaq7NibGuN9xvilTOv5+/vMf+OwzNSbxb3/r+HGgvseuMGkSHD8OZR3w0I8aZVzv1w+ysqC4WI0F/PGPuzaHkMBmhVfnQPG2jo0/sq5j4xQFKg+7b//2ech9vePzEwShVyICUNBoaQCbS5B3b7AA6mP8Evqov2N02RV1vo0BVBS44ALVuhcRoSZZXHCBapmLjFQF2nffqbX3HLFqDhwCLzpatdC54tgfHg4//7maMXzzzerrZ2aq1rE//AF27FB/3npLtQY6OHIEDhxQl9tKpNBTWQm//KW6/Je/QFpam8Pd+Pe/oaQEZs1S1+++G667zn3c5Mmqxe7SS+H++1WxmZwM27bBrl1wid1z+MwzcMYZxmP79nV/vUmT4NNPPYtwQUfZHnfxlzUa+s+E1EGq2zc8GhbfD+WFUHVEFY1mS9uvW18OLfYQkH7ToHSnei3u+ER9jUQPfzQhoHjwwQf5+OOP2bx5c4+fe9asWYwbN46nnnqqx88tqIgAFDRcrX/QOwSgXuA5LH8GF7BvLYBffqm5dhcudBcrEybA+eerQq2y0rjPIfBGjwaLh/urY39LiyqKVq5UhZP+tadPh4EDoalJtTLqBeCaNdryKad07P3cc49qSTv99K4lfgwfrh7v4Iwz3C12Bw6o4g/U8+j3jxqllp5xcM456vtrjwkTVAFYVKRaEqUlqReqi7TlMZfDvEchNtV93JZ3VQFoa1WPScpr+3UrD2rLaYMg/3RY/hdQrGo7xjMf8s38Bb/xf//3f9x+++1+OfeHH34orff8jMQAChqeBGBvcAE7BF5YlGrNAIhKBHOYcb+PWLlS/Z2f7y7+9JhMqoVLj0PojB3r+Ri9C/i994ziz0FOjmY9rHX5+I8c0ZY7UsZl5Up49VW1/t6LL3auXqCerVu15TFj3PfrBV5b+xMS1HjKjqB/f4WFHTsmJKk+qi3nTvIs/gCS+mnLFQc9j9Gjj/9LyoOJN4DZfsPe+EbvSBATToq4uDhSU738v3QzKSkpxEt2l18RAShoeLQA9oIveYcFMCZNUzAmk2YF9HESSFOT+vvwYaPFrT3KylT3MHiO/ysp0Sxp554Lc+d6fy2H8HP9bi4t1ZZdxacrzc2qe1lR4M473a12nWGb3cOYlAS5ue779cK2LQE4ZkzHRai+hqLeAim4oBeACTnexyXna8uVHRCAVbr4v6R+EJ+pZQA3VqoWRcFvzJo1izvuuIN77rmHlJQUsrKyePDBBw1jDh06xAUXXEBcXBwJCQlcdtllHD+uFdN/8MEHGaf7slq2bBmTJk0iNjaWpKQkTj/9dA4ePMiBAwcwm81s0Fe5B5566in69euHzWbzOMfnn3+ewYMHExUVRWZmJpdcomWQz5o1i1/96lfO9fz8fB555BF++tOfEh8fT15eHi+//LLh9Q4fPsxll11GUlISKSkpXHDBBRxwxMQInUYEoKDRWOm+zd+9gG02zcLnatlwCsAyVeX4CIcws1rVbNnrr1czdR3C0Bt6EeTJAqjf/5OfeH+dhga1ZAu4u0rLdVq3PQH4yCNqVnJeHjzwQNtj28NhAfSWdewQeNnZ7qIVtPfeUbc1GN9fXS94Dum1VOvMwo4YWU8kn6QFEGCyLh5h3Ssdm5/Qbbz55pvExsby3Xff8dhjj/Hwww+zePFiAGw2GxdccAHl5eUsX76cxYsXU1hYyOWXX+7xtVpbW7nwwguZOXMmW7duZe3atdx8882YTCby8/OZO3cur79uTP55/fXXue666zB7KEa6YcMG7rjjDh5++GF27drFwoULmTFjRpvv5/HHH2fixIl8//33/PznP+fWW29l165dALS0tDBv3jzi4+NZuXIlq1evJi4ujvnz59Pc3NyVjy/kkRhAQaM3uoAbK9WYIzBm/oImAFsboaVecw+fJOeco7ZXe/RRtWPGG2+oP3FxcPbZagLEOee4H+cQOSaTZyuYXgBOn+6+38HWraruBffXiYrSlhsavNfH27lTa7f27LNqCZuuYrOp5Ws8zceB3sLnSlGRlgnsyTLqjYYGbVlChdqgoxZAgwv4QPuvqxeAiXazb59xkD0Wjm2B0gI1cSw8ujOzDQxemgm1JT1/3rgMuKXjWU9jxozhAfvT3eDBg3nuuedYsmQJZ555JkuWLGHbtm3s37+fXLvZ/q233mLkyJGsX7+eU12yyKqrq6mqquK8885joP3Jc/jw4c79N954Iz/72c944okniIyMZNOmTWzbto1PvNRpOnToELGxsZx33nnEx8fTr18/TmnnCfCcc87h5z//OQD33nsvTz75JEuXLmXo0KG899572Gw2Xn31VUx2N8Lrr79OUlISy5Yt46yzzurw5yaoiAAUNHqjC1ifABLrRQCCaiX0kQAENcHjuuvULNzPP4fvv1fdsu+/r/7MnKkmi0Tr7n0OETRggGdh5hCAmZnQpw1DjT6ezvX7Ml1Xv7e83LsAfPJJ1QU8YICanOGpJuEPP2jL33yjuVnPP98oGPfu1RI8PAm8qiotM9mThVD/fjojAPXWzvYKR4c0DgFoiTReE67okz464gJ2CEBLBMTpMnBSBqoCEKDmGKR0MKgzkKgtUTuf9HLGuFyQ2dnZlJSowrWgoIDc3Fyn+AMYMWIESUlJFBQUuAnAlJQUrrvuOubNm8eZZ57J3Llzueyyy8jOzgbgwgsv5Be/+AUfffQRV1xxBW+88QZnnHEG+fn5Hud25pln0q9fPwYMGMD8+fOZP38+F110ETGuda28vB+TyURWVpbz/WzZsoW9e/e6xQ02Njayb9++dj4pwRMiAAUNTy7g1gawtoLFT/8q9R4ygJ3ruptdXVn7WY2dZOBAeOgh9ef4cVVE/fnPqlBavhyeegruu08b7xB43kROR92g33+v/s7IcBeKegFYUaHWy/OEw11dWKi2i2uPP/xBW96/3ygAHfF/0L5ls634v/DwzsUhVlRoy3m+/dMGF44s4IQ+bQdYRsRAbAbUlbTvAlYUTQAm5hr7DerdzNVHg1MAxvVwo+wuntc1i9ZkMnmNx+sIr7/+OnfccQcLFy7kvffe4/e//z2LFy9mypQpREREcM011/D6669z8cUX8/bbb/P00097fa34+Hg2bdrEsmXLWLRoEffffz8PPvgg69evJ8nLE11b76e2tpYJEybw73//2+24dP0Xo9BhRAAKGnoLYFi0Kv5ALQUTneSXKRktgF5iAKHbuoE4yMxUa+ldfDEMGQKNjZpQA9XaVlCgLnuK/2tuVt2y0HEB6ElI6i1su3d3zqLWVRzxfyaTZwHXXgawQyAOH67WVewou3ervyMj1fZxggeaarXrti33r4PkfqoArC1u233bUKGVgHJ9sIrP1parj3V+zoFAJ9ywvZXhw4dz+PBhDh8+7LQC7tixg8rKSkaMGOH1uFNOOYVTTjmF++67j6lTp/L2228zxV51/sYbb2TUqFE8//zztLa2cvHFF7c5h7CwMObOncvcuXN54IEHSEpK4ptvvmn3OE+MHz+e9957j4yMDBISEjp9vOBO0CSBrFixgvPPP58+ffpgMpn42NFnqw2WLVvG+PHjiYyMZNCgQbzxxhvdPs9ejV4A6p/y/ekG1lsAY12e8rqxFqA3+vbV3K76ZIeCArW2H3gWZTt2aPtdi0frsVo1i5un15k4UYsDXN9GR6433lCNOG396BNDli7Vtrt6dBwCsH9/NQ7SFcf+sDBV5Llij+Fm2DDv8/WE4/2dcorEAHqlRifA2koAcWDIBPbQ5cO5T58A4pL2naATgAHgJg1V5s6dy+jRo7n66qvZtGkT69at45prrmHmzJlMnDjRbfz+/fu57777WLt2LQcPHmTRokXs2bPHEAc4fPhwpkyZwr333suVV15JdLT3+M/PPvuMZ555hs2bN3Pw4EHeeustbDYbQ4cO7dL7ufrqq0lLS+OCCy5g5cqV7N+/n2XLlnHHHXdwRF8fS+gwQSMA6+rqGDt2LH//+987NH7//v2ce+65nHHGGWzevJlf/epX3HjjjXz11VfdPNNejF4AJuqsCf4sBl2nE3auLmB9TKAPBOCePWq2b1v8/e9aKZZzz9W2661g7WUAt2UB3LVLS37wJAAjIrTages62NHrZHEIUm8JIA7L5oABni181dXqb4cA7ghNTZqwlNjuNqjqYAawA30iSFtxgJ4ygB3E613AQWoBDAJMJhOffPIJycnJzJgxg7lz5zJgwADee+89j+NjYmLYuXMnP/rRjxgyZAg333wzv/jFL7jlllsM42644Qaam5v56U9/2ub5k5KS+PDDD5k9ezbDhw/nxRdf5J133mHkyJFdej8xMTGsWLGCvLw8Lr74YoYPH84NN9xAY2OjWAS7SNC4gM8++2zOPvvsDo9/8cUX6d+/P48//jigPtmsWrWKJ598knnz5nXXNHs3BgugTgD6OBPYZnMp2WJrxRzmxcRT31YSSIrncV1kyRK49VY19u+ii9Reu3l5aiZwYSH885/geD445xw47zztWIfAS072HK/m2N9eIWS9W9mbULzgAjUGcd06qKnxngjiC+rqtCLM3krAOASxt0o8AwaoSSKffQbPPQdTp6puXVBjGD3Nf8UKTTBedFGXpx/8dDQD2IGhFMwBFEXx+HczGwSgS6CpXmiKBdBvLFu2zG2bq+crLy/Pa5YuqHUAHbUDMzMz+ci1MbkHioqKGD16tFsSiSvTpk3zOEcHrvs81fNzbVGXlZXFm2++2e4chY4RNAKws6xdu5a5LpV4582bZyhM6UpTUxNNumJw1Q7TRrDgEIAmszHrzwcWwBarjU83H+Ufq/az45j2uU0zb+OF8KfYEzGcI+f+i3NGZxNm0RmmDW3g2ooBPHkLoOO7Zt8++NvfvI+7/HJwKYflFHjtdQAZN67tOH3HHGJi1FhDT1xzjZp80tgIH32krncX27Zpws6bBdARz71njyru5syBP/5R23/nnaqLuaUFXLtObdzo2SX+9tvq75EjeybOMWAxCMDOWQA3bdnMzxYvoaTGWOAy3GLixdQNzHEeE4IxgIIbtbW1HDhwgOeee44/6i9wIWAJGhdwZykuLibTpbloZmYm1dXVNOgLkOl49NFHSUxMdP7kemqJEMg4BGBUIkTqzDInIQBrm1p5dWUhMx9byt3vbzGIP4DrLQuJNzUwvmUTz773GWc8voy31h6godle+69NC6BvBeAf/6ha+W68ESZNUjteREaqGbFDh6plYZYvV7OBXUNffJ0BPHq0MfFST2qqmowCmlDqLtrLAAY1lrBfPzUG8OBByMoy7j/vPLVkzplnqhZShwD2lhXc2Agffqgu20uCCd7Q9wHugAA8Zta+844f2uUm/gBarAq2Cs0CuKo02mi1D4vQwjFqRACGCrfddhsTJkxg1qxZ7bp/hcAgZC2AXeG+++7jrrvucq5XV1cHlwjUC8AIXbR/F13An245yv/7+AeqGozBX4My4kiICgNFYWJZIdjvLTmmMpaV9+X+T7bz1Nd7ePTi0cxzxACawyHSJc7Dx1nAaWnw4x+rP52lrB0PdHv7HXz9dcfG/fKX8M476viDB72Xg2mLBx9Uf9rippvUn7Y45xytDqA35s1TfzrCBx+ocYOpqd1r3QwK9BbAxL5eh1ltCo9+UcBba/axI9xMmMlGrqkUswlG5yRiMWtm6UPlDfRtVv36zYqFa94/xNBV1fz9qlMYkG7/XkjIVh/Oao6plcK9Pa0IQcMbb7whiZJBRsgKwKysLENPRIDjx4+TkJDgNbMpMjKSSEfwUrChKNBQqS5HJUKkTgB2wQL42qr9PPzZDsO2OcMy+NmsgUzsl6xWcq88BE9pcYczslpYZr+fldc1c+u/NvJDwnFiQLX+ufpOw6MhPBZa6oyu4hBg8mTVCvjhh2rHjxdf9PeMfIPNprawA/j1rz1nHQs6HALQHO6eJGWnqdXKne9t5ottxYCZo0oqeaZSBoafYPmvziA3xViYt6mlFdNfyqEVjipp2DBTcKyaS19cyxvXT2J030Q1EaR4G9haoa5U7RMsCEJAEbKPbVOnTmXJkiWGbYsXL2bq1Kl+mpGfaa7TWq5FJRktgN4EoEMw6lAUhb9+tdMg/s4dnc2iO2fwj+tO5dT8FGcbH45+bzj2p6PC+eKO6cwdrt5MbIpCWKNqAVS8dThw9gPumTIwvYlHHlHdrq+/DsFSBeH999WSOnl5cMcd/p5NAOAsAp3t0QpX29TK9a+vt4s/CDObnIkg0dYacqPdXcCRrTVEtKrXfHzWAAZnqN8FJ+qaueLltazZWyalYAQhCAgaAVhbW8vmzZudWUP79+9n8+bNHDqkxrLcd999XKPzJ/3sZz+jsLCQe+65h507d/L888/zn//8hzvvvNMf0/c/+gxgVwugJxfw0kfgL/3g87udm6w2hd9+tI2/L9Xa8vxyzmCeu+oUhmR6SPUs2mRcry5iRJ8EXv7JBG6a3p8E6okwqaJ0f320e/YwaJnADeVaA90QYehQeO01NSHk0KH2xwcCVqsaU/jPf7rHWQouNNer//fgMQP4RG0TV778LWv2qQ9H0eEWXr12InkDdMUaPXUE0dUHTM0ZyAe3nsakfPU6q2u2ct3r69nToLuegyQRRPGWxi4ELPI3bZugcQFv2LCBM844w7nuiNW79tpreeONNzh27JhTDAL079+fzz//nDvvvJOnn36avn378uqrr0oJGHCPAfRUCHrzO+rvjW/C/D/TioXb3/meL39QLQ0mEzx4/kiuPS3f+zmPugtAALPZxO/OHcEA83H4Tt21tTKcZ9/fwuOXjsWsi1dyJoYoNrWVnb40TAjwk5/4ewa+5aqr/D2DAKKNItDFVY1c9cq3FJap125STDivXXcq4/OS4bhLLcA+44yv61ICJjE6nLdumMRtb2/i64ISmq02Xt3SyF8clZsC3AJosVgAaG5uJqIzrWqEXk9zczOg/Y0FI0EjAGfNmtWm2vcUvDpr1iy+//5798GhSJsCsMZ9fJ29+JutBcoLeXKT4hR/4RYTj182jgV5LbDwtzDkLBgwy3i8zQZHNxu3VRUZVq8cGeMUgOVKAh99X8SgjDh+cYauL5hrJnCICUAhhPGSAdxitfGLtzc5xV9WQhT/vGESgx1W+KR87TiPFkD3ItBR4RZe/PEEfvPhNj7YeIRjiu46C3ALoNlsJiYmxlnWKyIiQgtTEQISRVFobm6murqamJgYzJKk5JGgEYDCSWIQgEltu4Cb67U+wUDB1nU8v0y9IYSZTbxyzURmDc2Ad6+GnZ/Bxjfg7p0QpcviLd8HTS51FKuPqskoji9fXQmYctRjn1i8m8n9U5hod0m5l4IZ3Ik3LQgBjKEGoJYB/OTi3Ww8WAFATlI0790yhb7JukQPQzu4jglAgDCLmb9eMoa4yDDWrNXcxM0VRQS63SwxMREIwtquIU5MTIzzbyu4IwJQUOmMC9il68aK1StRlAsA+L95Q1XxB3DYbr5rqYO9X8MoXQNw1/g/x7jGSohOVtd1mb1jhwyEHWqc4S/f3cznd0wjKSbCL/2ABaFX4MECuHJPKS8sV2Nww8wmnrvqFKP4A5duIB4EYJWuR3CiscyVyWTi9+cO58YjR6BE3bZ3326GK0pAW81MJhNJSUkkJCRgtVr9PR3BB1gsFrH8tYMIQEGlTQHoYgF0EVo5LQcAmDEknZun2/uc1RzX3MQAOz83CkB9/F9CjnYzqz6qCUCd0Jw9YQST6lNYd6CcosoG7vlgKy/9ZAImEYBCqOLSBq60pok739vi7Nzy63lDOSUv2f242HQIj4GWeqg44L7fYRU0hxm7ftgJs5h55MrpNDwdQTTNhNcV869vD/KTqfkn/Zb8jdlsFtEghAzyny6ouArAsEj1BgDQ5BID6CK0hpiOkB4fyROX6RI0ircZj9mzCFqbtXW9BXDoOdqyPg6wTjuPJS6dp68cR1KMGnm+aMdx/vntQaMFMMRqAQohju5ascVnc9d/NlNWq5Z1mTkknZscD2OumEyaa7fykHv2vMMFnJADFs82gj7JMShxqjjMNJXzh88L2HFU3KeCEEiIABRUGiu15ahE9SYREauuu7qA64wCsL+pmKcvGUFanK5IdvFW4zFN1XBgpbpsbdH2J/eHzJHaOL1by6UNXHZiNH+7RGu2+8fPCtjfEKUbLxZAIYRwXCsmCy9tqmHlHvV6yYiP5PHLXLLlXXH0BLY2Qa2uIH5jlfYw6NoD2IWYVNU9nGBqIKy1ntve2URdU2uX3oogCD2PCEBBxdUCCBBhzxp0cQFbXSxt4SYrpyVVGl/v+A/u59j5mfq7pABaG9XlnPHGFlZ6Aag/j93SN3dEJtefnq9Oy2rj0eW6MT5oBycIAYPdBdwSm8nji9W4P5MJnrp8nPFhzBPJLqVgnMu6+L92BKC+GHSWqZzC0jqeWLy7Q1MXBMH/iAAUVFwtgKBlArtkAf+wZx9ulBYY1x0uYEsEWOw3o51f2Mu/6Ny/fcYba5jp45ocMYQmi5qZbOc3Zw9jeLaaFbyhRGflqBcXsBAitDQ6/98PtCTTai+SfuvMgZw2yHNLOAP6TGB9IoiXDGCP6K7b3LBKAN5ae4D9ZR7qhgqC0OsQASioeLQA2l3ALXXOOKHK+mb27PeQOViyU1turocTe9XljOEw0F6gu7ZYFX/6+L+c8cYuBlW6nmYOl25smqHNVWSYhQfOH6EOJxYbJuN4QQh2dEWgd9WrlvqshChumz3I2xFGkvSZwAe05c4IwHhNAF4+VI0VbLEq/PnLAm9HCILQixABKKg4BKDJogk/fSZwi/pU/+w3e4mzVuGG3gJYUqB25gDIGg3DztX27fxMswCazJA9VhWc4fZzOlzAiqK5gD00uZ8yIJV5IzOxYqFKsR8rAlAIFXSWckdR5nvmDyUmooOFHby6gDtjAdRcwHP7WkmPVy39X20/zreFci0KQm9HBKCg4hCA0UlaIeZIXb/Pplr2l9Xx1toDJJt0WcEWewlYvQXwuC4DOHM0DDkbHFa67R+pAhEgfZgqNk0mSLRbAR3FoJtr1QB1gFhdpq+O35w9nHCLiXJFnaetTm46QhDy3cvwxa+hUZdlqxOAxUoKo3MSuXCcez9gryR5qQWoF4OdsABG1BXz67OGOtf/+PkOz727BUHoNYgAFFQcAjBKVzXdYQkEaK7jz18W0GJVSMEuAMNjId3+pV++T41LAmMJmKzREJcOeVPU9YoDYLNnCvYZr41zxBO11ENDhUsCiOeYpv5psVwzNZ9yVAFobq6B1qYOvmFBCACKNsKXv4Z1L8OSh52bm8o1S90xJYXfnzu87axfV6IStHqbDtFXVaRduyaLQeB5RGcBpOYYP5rQ1xmb+0NRNR99X+TlQEEQegMiAAXV4uZRAGou4K2FR/hqu1ouIs1sF4CxqZAxwv4aNjixR10u1mUAO0q86N3ADnJO0ZYT9JnAR43u3FjvQe13zB5MrVmb8/a9B7yOFYSAY89ibXnLO04r4PadWshFv/6DmTzAs5W8TRxWwOoi+OZP8OwETQwm9/NaA9BJXCZOy371USxmtUuIg8e+2kl9s5SFEYTeighAQXW3OmL29AJQ1w/47ZXbATBjIxF7VnBMqurGdVCyU00WcZSAScpTXcpgLPbswJMFENQbUgcsgACJMeHk9NHE42uLN6Ao4noSgoR9S7Xl5lrY+h5FlQ2cOHrAufmqM6d27bUdmcCKDVY8pvX3jk6Gs/7Y/vGWcIizt320J6WcPiiNucPVbcerm3h5RaE2fsXf4K0LoFRKxQhCb0AEoOA5AxgMFsCSE2qNvUmZJszYxWJMmprl66C0ACr2a3UDM0dr+1IHatZCUGMHM0dp64m6+KXqIpci0G1bNwbma/FMR48VsaSgpM3xghAQNFbDkfXGbetf5alFu8hAtZDbMJOb279rr69PBAG188+Un8Ptmzxb7D3haBVXexysqrXvvnOGE2Z3R7+0vJATtU1QUwzf/AEKl8HSP3VtvoIg+BQRgEKHBGAcanzfr6ena/s9WQD1BaCzdAIQjDeVzFEQFqGtG0rBdNwCCGCJ0/anUs2Lyz3UKRSEQOPASlCsxm2lOzm69WuyTfai53GZ7btqvZE/Q1secjb8/FuY/yjEpHT8NRzXrWKDOvXBa2B6HFdNVhNIGlqsvLX2oLHbyME1IFZ6QfA7IgAF7wJQ5wKONTUwNjeJ8em6G1JMqhpHFB6jrpfscE8A0TN8gbbc7zTjPr0ArD6qFYGGNmMAnfOwk2yqYcPBCjYcKFfrEe5fof4WhEBD7/4dfalz8VrTF6ShXrPmxE5k/royeC78dBHcvByuehfSBnf+NfSJINVabcKbZwzAYrcCvrX2AI21Fdq4uhLVUyAIgl8RASi0YQHUsoBjaORnMwZgMiRnpKoFmtOGqOsVB+DwOm1/ls7FC5A9Bs57CibeANPuNO4zuICPuCSBpNMmOgHoyFB+aUUh/PdGePN8eO/qto8XhN5IoV0AmsPg7MewxaqxdWdZNmI22S1oCe1k6rZH3mToM67rx8frM4G10jR9k2M4f4y6r6K+hTXbC43HHfqu6+cUBMEniAAUoKFSW9a1XDtQo/179I21cdbILKMwcwgvZ2yforqtACITjLXGHEy8Hs57wt2qF5mguZw76QLW7+8bqVr7jhSsg12fqxv3LTXWUBOE3k7lYa2bTt9TISaFTWkL3MclnIQF0Bd4a+MI3DJzoHPZTQAe/rY7ZyUIQgcQASh4tQB+tF3bPiUnQnXpeBJmGbo4QEc2ceYoraB0RzCZtJtZ9VEtCcRk1uqVeUMXszQ+TXVRX29ZqBugwLHNHZ+LIPibQp37d8AZNLVaeaBoEq2Ky1f2yVoATxa9BdBFAA7PTmDmENV6b62vNB53SASgIPgbEYCCRwF46EQ9Swq1pu6Dkxz9dsu1sQ4LYLouE9iBa/xfR3DczFob4IQ9kSM6xdAH2CM6F3C/6Eb6RdVxgWW1ccyRDZ2fjyD4C33838Az+Pj7IrbXxrHYNsE47mRiAH2BXoDq+hM7uGXmAHUYLnG4pTuN3yWCIPQ4IgAFjwLwlZWF1ChRzs1hrXYx6NEFrLMAOnCN/+sI+ptZk91l214CCKgt6+wt6cIaK/hT33VEmlwK0BZt9HxsYxVs+ieUS1C60Euw2WD/cnU5MhFb9ilqTCvwlvUs41h/u4DbsAACTB2Qypi+icSbPCRiuZa4EQShRxEBKLgIwCRO1Dbxnw2HqVOite1N9tp+hvp8dnGWmGsoGQN00QLo4WbWXvwfqO5jhxitOcbUik8AaFXM1Ctqg3qvAvDL38Cnt8FbC9QbryD4m+Kt2oNW/+ks3nWCwlL1AcyWNw3StJ67JPb18AI9SFQCRNh7hnuwAJpMJm6ZMdDdAgjiBhYEPyMCUIDGSm05KpE31x6kqdVGHZHadkdxZ8eNyWTWEkZMJmM9QJPFs1u4PTwJwHaKQDtxCMD6Miz2emQLbaeyyTZI3V5zTE0u0WNtgYJP1eXKQ9AgLimhF6CL/1MGzDLUtfzZGYPUWn2x6TDuav8LQNBKwVQf81jfb/6oLDIjPfToPiyZwILgT0QACgYLYGNYHP9cewCAFnMUisn+L+IQgHV2Aegam6d3A6cNgXDNfdxhumoBBI/Fa9+wzmeLomUiUuQSB3h4nfa+wFh7UBD8hS7+b0f0BL4/VAnA0Mx4Zg1Jh0Fz4Nd74cLn/TRBFxxu4JY6LXRDh8VsYkiiZl2vttiv1aKN0NrcEzMUBMEDIgAFTQCaw/lqVxUV9S0AnDumDyaHa7fJxQIY42KZ01v8uuL+Bc8B7R2JAQR3odjnFJKGTGOzwwII7m7gfUuM6/oMZ0HwBy0Nmms0MY83CrSv6Bum98fUmcz6nqKNUjAOMiNUodeqmFnaYi8b1doIx7Z09+wEQfCCCEBBE4BRiby7/ohz85WT8rTYvuY69ebUYk8GcRWAuZO05bwpXZuHp5IW7RWBduA6nyk/56op/dhs01kAj7gKwG+M62IBFPzNwTVgVd2lzfkz+GxbMQDxUWGcP8bPJV+80U4iCIC5Sf2OqSGGddYh2g6pBygIfkMEoOAUgC3hCawtVC18A9Jimdw/ResG0lzr0p3DgwA87yk443dwyk+6No+oRC2g3IGrsPOGflxcFoy4kJlDMghLzKZIUffZjm4Cm72VXd0JOLrZ+Br69ycI/kAX//ctY2loUf9fLxyXQ3SExV+zapt2SsEATtdwtRLDepsuiUUSQQTBb4gADHVsNqcALG3V4vaumJSrupsc/YCba12KQHsQZhOvh5n3QFhE1+fjagXsqAtY35P01BshTC1cfdnEXKcV0NxSr9YfA/uN1iVgXSyAgr/Z67BKm3j+oBYSceWkPP/MpyPEe+4H7ERRnN8x1oh49ig5VCn2/uGHv/OYOCIIQvcjAjDUaa7BIYQO1YUBEG4x8aPx9uxChwtYsUGV5h7ucHJGZ3GNA+zoeUZcAIPPguHnw5RbnZsvOzWXLYoWB2g7bE8EcXX/gghAwb9UHYGS7QDUp4/lW9X7y9i+iYzok+DHibVDfJa2bM/AN9BSDza1LmdcYioKZjbZBtvHl0J5ofsxgiB0OyIAQx1dBnCZVa37d9bILFLj7CVg9PX9Kg9qyx11zXaWrloAo5Ph6vfh8n9pVksgJykaU47WPaG4YLVqcdinWVqcSBKI4E/2LHIurjZr/7O92voHxgx8T909dH24U1PTiY8KY4PeDSzlYATBL4gADHV0ArDa7pa58lTdDUcnpqjoCQHoUtcs2r28S2c59bTZzh6qtsProWSHFquUP00bKAJQ8Ce7NQH48jHVah0bYeH8sb00+cOB/hr1FEer+46xxCRx0Sk5bFR0iSCH1nbj5ARB8IYIwFBHLwCJJS8lhtMG6sSdNwtgRws0dxa9Czg6GSxhJ/2SM0flU2hSRW12035qN3+k7Rx2rpZ4Ii5gwV+0NDrbvzVEpLKhOReABeNyiI08+WugW4lKVIu/g+di6vragJEJXHFqHpttA2lR1GOUQ2IBFAR/IAIw1HGxAF5+ai5ms84t6sgChh6yAOqsHT6KMwy3mGnIGAuAxaRgWf+ytnPgHE3M1osFUPATB1epsXLAavN4FPtX85WTcv05q45hMmlu4PoK9/0uvcZH9ElgaG4m25V89fCyXZ5dx4IgdCsiAEOc1vpK53KtKY5LJ7q4YCN1ZVkMMYDdlASidwF3NP6vA+SOnuFcjm6t0s6VNlirNdhQobaHE4SeRuf+/aB6JAAj+yQwOifRXzPqHA43sCcLoEEAqsksV03KZZ1N1z1or0tRdkEQuh0RgCHOnkNaZm9en2wy4l1auOldwC26hu7dZQFM7qe6fgEyR/nsZVOGnOa+cdBsu/VCJzTFEiH0NIoCe74CwIqF1Tb1//6KSXm9s/OHJxwWwOZaaHXp++tiAQQ4b0wfVpsnOje37vi0u2coCIILIgBDmZICEra/7Vw9dXi++xi9C9hBWDRExHTPnMKj4er/wpl/gDN+67vXTR9Ka5jLnAfOUX/rLY0SByj0NCf2QsUBADYyjBpiiAo3c8G4Xp78oSe6jUxgfQygXQDGRoaRM3Y2JxS7h2HPYrXTkCAIPYYIwFDEZoNvX0R5aSY5LQcAqCeKkaec7j42Mt59mw9dsx7pOwFOv8NYXuJkMVsw5Yx3rloVE42509UVfbs5EYBCT7P7K+fiopZxAJw1IouEqHA/TagL6K9VVzew3gIYqdUzvHhCHoutarmbMGsD7FuKIAg9hwjAUKOmGP59CSy8F5O95+guW1/eHPYSYUk57uP1LmAHvhRmPYilr1ZbbYsykCUH1Ab1BkEr7eCEnkZX/2+pbRxAYFn/wKUWoMs11OhuAQQYn5fMhmjtobNx28fdNDlBEDwhAjCUsLbCPy+CfVrA9Wut81nQ/EemnDbT8zGeXMDdlQDS3eROcS4us47jk81F6opYAAV/0VQDB9cAcFjJYJ/Sh6SYcKYPTm/nwF5GWy5gD0kgAGaziaxT5lGrqHHHpt0L1e8oQRB6BBGAoUTRBrUIMmCNzeDa5nt5uPUaMlOSGJeb5PmYSE8WwG5KAOluhszDNulmFplO5zXrfJbtKqWqvkViAAX/sW8p2NTM82+sYwET54zOJiIswL6a9d8JbbmAo5IMu84b399p9YxsqYKDq7tnfoIguBFg3zLCSaHrf7u63y9YblNr410wro/3bMMIDzGAgSoAzRbM5/yVb8f/jVpiaLbaWLj9mNGiKd1AhJ7E4P49BYALenvnD0+05QI2FII2fp8My0pgW5xWoqlGX6RdEIRuRQRgKKETgK8fy3cutxlv5MkF3F1dQHoI/fv9ZPNRFxewCEChh1AUNfsVaFAiWGsbQZ/EKE7ND8AYW4ML2KUYtMMCGB4LFvfElrTx59GkqN1OTDs/V5PUBEHodkQAhgoNFVC0EYDmlKEsPaZ+EY/sk8CgDA9WPgfB5AK2M6ZvIv3TVGG7tvAExa06kSsuYKGnKN4KtcUArLGNpIkIzh/Xx9iJJ1BoMwvYbgHUxf/pOXvCYFbZRgMQ11yCYv+eEgShexEBGCrsXwGK+mT9Q5SWDdtutmF4ECWB2DGZTCywu9kUBT7bXqZlJ0o7OKGnOPq9c3GVvfjzBWM9ZOIHAvqHQrcsYLsFMMpzV5PclBh2JWtJaCc2fujr2QmC4AERgKGCzv37XsVgQG2CcX578UZms7sIDHALIMACb25gcQELPYWut3ah0ofBGXEMz27DGt+b0Sd36LOAra3QUmcf472tXdqEC7AqquXTvPN/6pOZIAjdigjAUEBRYK8qAG3mCD6pyAdgUn4K2YnR7R/v6gYOAgE4MD3O2Wd1W1EVDRH29nNN1e6trAShO9D11j6spLedjNXbsYRpAk/vAjYkgHh2AQPMmTCS9YraGzil8TC24wXdMUtBEHSIAAwFyguh6hAAB+PG0EgkABeM66C7ybUYdHd3Aukh9O7vI036OECxAgo9gM4CWKSksSBQ3b8OHA+Gegughz7AnkiNi2Rv6hnO9aPf/cfXsxMEwQURgIGKzQaHvjV+wXpD5/79vG44AOEWE2ePyurYuQyZwCaITu7ERHsv543pg8PgUlAdqe2QRBChB2gtPwBAsZLMiLwM8lK7qb92T+HIBG6s1Ao6eykC7Ym0iRc5l5t3L/Pt3ARBcEMEYKCy9I/w2jx47ez2yyboBOAX9aoAnDE4neTYiI6dS1+7KzoZzJbOzrZXkpUYxZT+qtXiQKPOFS6JIEJ301xHWIP6f3ZYSXcmJQU0+kzgxkr1d5PnNnCemDZxPLWKeh1a6o5jtUkcoCB0JyIAA5XC5ervku1Qsd/7uNZmNQMYqA1LpkDJA+Cc0dkdP5feBRwE8X96zhmtWkFPKLqbk7iAhe6m8pBz8bCSwdmjOnE99lYMmcB2N7DeAthGDCBAXGQYdRGqiExSKtl4sKLN8YIgnBwiAAOVuhJt+fh27+OOrIfmWgBWK6NRMBNmNjFneEbHz6V3AQdJ/J+Ds0aqArBc0Vk5xQUsdDPFB3c5l1vjc8lKjPLjbHxEtIduIB2MAXQQFq9m4yea6lm89VA7owVBOBlEAAYqeiuVvb+vR3Tu368aRgAwdWAqSTEddP+CMQs4yCyAmQlRjM9LogyxAAo9x55d2kNbRt4QP87Eh8ToYoMdmcCNHXcBAySkaYkwG3bsRpFyMILQbYgADESaaqGlXls//oP3sToBuNJebX9+R5M/HOj7AccEYJuqdjh7VDYnFJ17SgSg0M1UFO12Lg8bPtqPM/EhBgugBxdwBwRgeEKmc7ml+jjbijqQ5CYIQpcQARiIuLooj3uxANaXO7sNFJr7UUoyJhOcOSLT83hv6F3AAd4FxBPzRmZRrheAkgQidCNHKxuIqj3sXM/qN9SPs/EhnrqBdLAOoBNdX+40UxULfyj20eQEQXBFBGAg4mqhKi+E5nr3cYXLANWFsrhZbTU1sV8yGfGdjDcKYhcwQF5qDFlZfbDZOxE0Vx3384yEYOar7cXkmtSHOKvJAgkBXv/Pgad+wJ20AOoFYLpdAIobWBC6h6ASgH//+9/Jz88nKiqKyZMns27dOq9j33jjDUwmk+EnKipAArH1CSAAKFDqoXJ+4VLn4krbGADmdyXbUH+DSs7v/PEBwLzROVSgCt0mEYBCN7Jw2zH6OgRgfN+gKavkCxcwcVpyWirVFJbVsbek1kcTFARBT9AIwPfee4+77rqLBx54gE2bNjF27FjmzZtHSYmrWNJISEjg2LFjzp+DBw96Hdur8JSl6skNfHANAC2EscGmBprPG9lJ9y/A8PPh1Jtgys9hyPzOHx8AzB+luYHDm8rbGS0IXaOstok9Bw8Rb2oAIDytv59n5EPaKwPTTiFowM0FDPCluIEFoVsIGgH4xBNPcNNNN3H99dczYsQIXnzxRWJiYnjttde8HmMymcjKynL+ZGZ2QRz5g1oPAtA1E7i2BE7sBWCrrT+NRDI6J5G+yV3oNhAWCef+DeY/qvb8DEIGZcRRF65mMUYpjZSWiwgUfM/iHcfJQbt+TUn9/DgbH+PJBeyIATSHQXgHvntiNQugQwBKHKAgdA9BIQCbm5vZuHEjc+fOdW4zm83MnTuXtWvXej2utraWfv36kZubywUXXMD27W3U0+tNeLQAumQCH9Le93qb2mS909m/IYTJZCIqSXsAWLNlVxujBaFrLPyhmFyTziuRHEQCMCwSwu0JY64WwMgEnH0X2yJOswDmR6lxzTuOVXPohIcYZ0EQToqgEIBlZWVYrVY3C15mZibFxZ6fHocOHcprr73GJ598wr/+9S9sNhunnXYaR44c8XqepqYmqqurDT9+QS8ATfY/oasL+KAmAL8TAdgh0jO0WMdNO/f6cSZCMFLV0MKafWXOBBAAgskCCJoV0DUJpCPxf6AKRYtaozQ3Qov9+2q7WAEFwdcEhQDsClOnTuWaa65h3LhxzJw5kw8//JD09HReeuklr8c8+uijJCYmOn9yc3N7cMY69AIwZ4L6u75Mdfs6OLgaAJtiYqNtCIMz4hiYrsvmFdxIydD6sRYdOUhVQ4sfZyMEG0t3ltBiVVwsgPl+m0+34BCA9eVqj3JHIeiOxP+BaiW0u4ETbZXOzV/+cMyHkxQEAYJEAKalpWGxWDh+3Ji9efz4cbKyOmb1Cg8P55RTTmHvXu+Wn/vuu4+qqirnz+HDh72O7VYcAjA8Bvqeqm13uIEbq53Lu5RcqonlbLH+tYtJ1+YumSqWFEg2sOA7HCImqC2AjkxgxQq1xepv6LgFEJxu4LDGcoZnqHGDmw5VUlzV6MuZCkLIExQCMCIiggkTJrBkyRLnNpvNxpIlS5g6dWqHXsNqtbJt2zays72XSYmMjCQhIcHw4xcclr7YNMgcqW13uIEPrwPFBsA6m1pk1tHzVmgDXQZiCjUs3iECUPANjS1WVuxW63fmW3QPcEHWW9uQCFK+X1vuSBFoB45EEMXGgqHRzs1Ldsr1KAi+JCgEIMBdd93FK6+8wptvvklBQQG33nordXV1XH/99QBcc8013Hfffc7xDz/8MIsWLaKwsJBNmzbx4x//mIMHD3LjjTf66y10DGurFl8Tmw4ZI7R9jkzgQ2ucm9bZhtMnMYqRffwkVgMJnQBMNVWzYncpTa1WP05ICBbW7jtBQ4sVEzZyTPZC7sn5HUuMCCT0pWAqdAIwKqnjr6G7Dufkap/PkgLvJb0EQeg8QVPT4/LLL6e0tJT777+f4uJixo0bx8KFC52JIYcOHcJs1vRuRUUFN910E8XFxSQnJzNhwgTWrFnDiBEjvJ2id+BosQTqk3L6MMAEKHDcnsWsSwBZZxvK/OGZmILtRtMd6KwxqaZq6pqtfFdYzowh6W0cJAjt87U9nCCTCsIUe2xpsLl/wVgMWm8B7IILGGBwbAOZCZEcr25i9d4yGpqtREcESeFsQfAzQSMAAW677TZuu+02j/uWLVtmWH/yySd58skne2BWPkbfBSQ2DSJiIGUAlO+D0p3QXAdFGwA4YMuklGTmDM/w8mKCAb0FEDV4fUnBcRGAwkmhKArf7FSv2wFhujaOwVQCxoHeBWywAHbBBQyY6kqZPWwY76w7RFOrjVV7yzrfy1wQBI8EjQs4ZNBnADvaJjniAFsb4Yf/grUZgPW2ocREWJgyIPj693YLUUlgUq0LaWZVAH5dUCK9SIWTYsexao7ZExhmZTRoO8QC6Bndgxh1JcwZpglCScwSBN8hAjDQ0HcBcXxR6hNB1r2iLSrDmDYojahwcZl0CLPZGcOUFabWICuqbGD3celFKnQdfezapOQabUfQWwAPaMudSQKJ0wvAUk4flEZkmHqr+mZnCTabPJAJgi8QARho1HkQgPpEkOKtzsV1tmHMHS7ukk5h/0yTlCpAvdF8LVYH4STQW60GR+hieIPRAuipHRx00gKoC1mpLSU6wsK0QWp8bklNEz8crfJyoCAInUEEYKBhiAH0YAG0U6IkcVDJ5IxhEv/XKWJVC2CYrZk4VHeduJ2ErlJS08iWI6pgGZYVT2x9kbYzGC2Aehewnk7FABpdwABzdA+yX3ckG1ixJ8W1Nnf8vIIQYogADDTqdEHkji/K5P5ujdbX2YYyNjeZ9PjIHpxcEKC7+YxPU0vAfH+4krLaJn/NSAhglu7UxMrc4ZlQcVBdiU6ByHg/zaobifESb9wZC2BMitbi0u7xmN3ZOMBFv4cXToO3L+34eQUhxBABGGh4SgIxm+3lYDTW2YYxV6x/nUcnAOfmqbGTimK8kQtCR9Fbq+YOSYJquwUwGK1/ABGxzl6+BjoTA2i2QIy9JJM95jkrMYpROeprbD9azbGqBm9HqxT8T/1duExtSycIghsiAAMNRxcQkxmik7Xtmcb6hettwwxuE6GDxGi1AKdm2ZzL34gAFDpJY4uVVXtUi31aXARj4mtxxJUGZfwfqIWtPbmBO2MBBO1BrK5EfQID5gzTvs/avB6tLVB1RFs/tqVz5xaEEEEEYKDhcAHHpKpPyg4yRzkXq5UYauIHMTw7CF1M3Y2uGPTAmEaSY8IBpCuI0GnWFqrdPwDOGJqBufKAtjM53y9z6hFiPAjAzlgAQcsEtjZDk1qSSZ/Q1mZXkKrDWg9igOJtnTu3IIQIIgADCUXRkkBiXdy7ukzg9bahzBqRLd0/uoLOBWyuL+WMoern7OgKIggdRR+rNmd4JlQe1HYGqwsY3OMAI+LA0smeAy6ZwAAj+ySQYY9pdnQF8Yi+/AwYKiMIgqAhAjCQaKp2Fnl2ayKfO5mSiDysiol3rLPF/dtV9J9rXZnhc5RsYKGjKIrCN3YrVYTFzPTBaVoCCASvCxiMoSnQefcveMwENptNzq5Gjq4gHtEXoAY4JgJQEDwhAjCQ0GcAxxktgFZLJOe3PsbEphdYEzaJqdL9o2voYgBpKGfGkDTCLaolVbqCCB1lx7Fqjtq7f0wdmEpsZJiLBTDfPxPrCVxdwJ11/4JbMWgH+jhArw9krhbAE3ugub7zcxCEIEcEYCBR66EGoJ2tRyo5Xm+jggROl+4fXUd/86o/QXxUOJP7q2K6qLKBPSXSFURoH33WuLMXt9MCaILEvj0/qZ7C1QXcJQug3gWsfZauXUE8PpC5CkDFBiU7Oj8HQQhyRAAGEp66gNhZtkvbN1vKv3SdqCStBlm92rVh1lDts162S7KBhfbRX4+OOFJnZmp8NoQFcX1O1yzgzhSBdhDr2QIYHWFh6kBVYJbUNFFwrMb1SKjY775NMoEFwQ0RgIGEpy4gdpbt1r4k9YJF6CRms3YDcwpATVDrb+yC4Imq+hY2HaoAYGB6LLkpMWCzQr09hCM+yONzXV3AXbEAenEBA8waonsg2+3yQKYoxlhLB5IIIghuiAAMJDx1AQFO1Dax9UglAEMz48lOjO7hiQUZDheWvYDswPRY+iarn+n6A+XUNrX6a2ZCALBybyk2u2fS+fBQf0J1RYJ7Bn+w4WoB7EoMoBcXMLTzQFZf7iwbQ99TAXslBEkEEQQ3RAAGEoYuIJoAXLmnzFErVax/vsBhwWiuhZZGTCaT83NtsSqs8ZZ9KAgYRYnzevRy7QYlPokB1GfjG0Veflos+alq68uNByuobmzRdurj/zJHQcoAdblkB1jlwU0Q9IgADCS8JIHo49JmigA8efQ3sAbVCjhriM7qsFvcwIJnbDaF5fb/j+hwC5P62x8m2kjgCjrcXMBdsACGRWrCsc79enNYAa02hdV7dA9k+vi/lP6QNVpdbm1Us4EFQXAiAjCQ8OACttkUVti/AGMjLEzs56EKv9A5XDKBAU4blEqERb1clu8qlXIwgkd2HKumtKYJgNMGphIZZs/GNyRwBbsL2Ad1AEH7nGrdBeBMQ2KWbr9eACbnQ/YYbV3cwIJgQARgIOFIAomIh3A1Jm1rURXldWpx6GmD04gIkz/pSaO3ANoFYExEGJMHqMKwqLKBvVIORvDAcn0y1jAvcWxxQS4A9Zn00HUB6PicmmugpcGwa+qAVGc5mOW7dQ9kehdwcj5kjdXWJRFEEAyIWggkHFYEXXyM3v2rD44WTgIPAhBg5hAvVgdBsGO4Hoe4d7MAgt8FbDYbrYCRXbUAeo8DjArXysEUVzeys9heDqb8gDbI1QIoAlAQDIgADBRam6CxSl2O85wFJwkgPsIgALX+v4bsQ9fyE0LIo5Z/qQR05V8c6N2YwW4BBGMm8Mm6gMGjG3iWpwcyhwUwOkU9b1wGxNnL7hzbChK6IQhORAAGCh7i/07UNrFFyr/4Hi8CUF8OZt1+KQcjGFm5txSrvf6LmzU+lGIAwXgNdSUJBIxCuY1EEIClu0rUh+TqInVDSn9tYJbdCthYCVWHuzYXQQhCRAAGCh66gEj5l27CiwtYysEIbdGmNd7hAjZZ3JMkgpGEbPuCydhfuzMYXMDuFnfXcjC1x/cB9i9Efa9lgxt4W9fmIghBiAjAQMGDAJTyL92EhyxgB1IORvCE1/IvDmp18bvmEPjaPe126DMeZv0GYlPbH+8Jb8WgN74Bz50KW/9jKAdTsEMn7pI9WABBMoEFQUcIfBMFCS4CUMq/dCNeLIAg5WAEz3gt/wJq3JkzgSsE3L8AORPg5qWqAOwqBhew3dp+Yh98dheU7YYv/o9Zg5OcQ4oKd2jj9RZARy1AkEQQQdAhAjBQMJSRSDeUfzl9kJR/8SmRCWAOU5ddBGBMRJjTuiPlYAQHy9vqxd1QATZ7t4pg7wLiSzy5gL/5IyhWdbmxitMsu5zlYBqO79PG62MAk/urpbNALICCoENUQ6DgYgGU8i/diMmkZTHqkkAczPJWhFYIWdq8HkMtAcRXuLqAj34P2z80DInY+yVTBqgW+7SWo9oOvQXQbNasgNVHPF7TghCKiAAMFAxZwBkGi4PE/3UDDjewiwUQjAJwucQBhjzVjVr5lwGu5V/AzXovdJDIOAi3f5Z1ZbDkYfcxOz9n1hDVUphrsn/OlgiIzzaOk3qAguCGCMBAQZcFV21OYsvhSgAGZ8SRkyTlX3yOQwC2NkBzvWHXwHTtM193oJyGZmtPz07oRazZW+Ys/6IvFu5ELIBdx+EGLtsF+75Rl5PyoP9Mdbm6iDOTjwEKeQ4BmNQPzBbj6+jjAMUNLAiACMDAwXETMYexqqgV+/2G6YPFotAttJEJbDKZmD5YvTE1t9r4br+7lVAIHRzJWAAzPF2PdSFWBNqXOASzYtO2nfF7GHmRczWn+BtGJzYRY1KTcKxJ/dxfJ3WQtlxT3B0zFYSAQwRgoOAsI5FuvOEM6WKNLaFt2sgEBpihs/Ss2C31AEMVRVFYYQ8DiLCYnf2iDdSGUBs4X+MqmDNGwuhLYOg5gAkA087POadvk3PIMbOL+xcgIk5bbpbELUEAEYCBgc0G9arIUGLTWGkXgBFhZib372KNLaFt9AKwwT1o/PSBaZjV+w8r90gcYKiyv6yOIxUNAJzaP5mYiDD3QaHUB9jXxLo84M65X3XvxmdC7iR1W2kBZ0ZoNQB/qPdQaDsiVlturuuGiQpC4OFzAVhZWcmrr77KfffdR3m5euPctGkTRUVFvj5V6NBYCTa17Vh9eCpFleoNZ1J+CtERljYOFLqMl3ZwDhJjwhmbmwTAnpJajtr/JkJosVJnjfcajhFqfYB9iT5mMncKDJmnrQ8717k44MB7zuVV5TprnwODBVAEoCCAjwXg1q1bGTJkCH/5y1/429/+RmVlJQAffvgh9913ny9PFVroYoiOtWhfZOL+7UbacQGDMd5LrIChicP9e5PlM67bfAXsWew+yGkBPIm2aKFK7mT1tzkMzvqDWqLJwbDznIvmBu0aXVeZ6HxIdmKwAIoLWBDAxwLwrrvu4rrrrmPPnj1ERUU5t59zzjmsWLHCl6cKLXQCcE+9lvE7w1PGoeAbOiIA9XGAeyQOMNRobrWxtvAEEbRwT/h/iKrYDcsedR/ouH5jUsDiwUUseGfIWXD9Qrh5mebydZA6ENKHuR1yWEl3CnMn4dE4YgbFAigIKj4VgOvXr+eWW25x256Tk0NxsWRedZnKw87FLZWqAMyIj2RoZry/ZhT8tJEF7GBs30Tio9Qb+qo9WikQITTYcLCc+mYrfUxlhKOGaFCyU2395kBRdAlc4v7tEv2mGsu46NG5gQFKlCQaiHIXgCaT5gYWASgIgI8FYGRkJNXV1W7bd+/eTXq6WKu6zIm9zsVdrZmAGm9k0rtDBN/SAQEYZjEzbZDq0qtqaGHrkcoemJjQW3DE//U16ay/LXVQretI0Vyr1pIEKQLdHejcwABFJvX7cfXeMlqtNuNYhxtYBKAgAD4WgAsWLODhhx+mpUXte2kymTh06BD33nsvP/rRj3x5qtBCJwD3K1mAxP91Ox1wAYPRDbxS3MAhhcPK1NfkYm0q26UtG0rAiAXQ5/Q5BeL7OFeb4/MAqG5sZcuRKuNYpwCUGEBBAB8LwMcff5za2loyMjJoaGhg5syZDBo0iPj4eP70pz/58lShxQm1yXkrFo4o6ZhMOC1PQjcREae2lII2e4c6CkID7m4nIWgprWli+1HV2zEuvsa4s2yPtuzSw1vwMSaTwQ0cm6UVfHa7HsUCKAgGfBqRnJiYyOLFi1m1ahVbt26ltraW8ePHM3fuXF+eJrSw2aBcFYAHbRm0EsboPomkxkX6eWJBjsmkWgFrjrVpAeybHMOA9FgKS+v4/nAl1Y0tJESF9+BEBX+waq8mLkbHVUGjbmfZbm1Z+gB3P+Ougg2vgWIla9w82Ka2blyxp5Q7zxyijXPEANpaoLUZwiL8MFlB6D10S0ratGnTmDZtWne8dOhRcwxa1C80cf/2MHoBqCjGEhQ6ZgxOp7C0DqtNYc3eE8wfldXDExV6mpW67i+5ri7gUp0LuE5cwN1Ozni4ZTlYm0nLmcDgjOXsKally+FKqupbSIyxP5C5loIJ89C1RRBCiJMWgM8880yHx95xxx0ne7rQwxD/p7Y4kv6/PYQjEcTarLqNIj0UmEUV5G+sOQCoVgcRgEFGwWdwcA2cfgfEZ2GzKc6yPzERFuIbjxnHG1zAurhQKQLdfeiyhKcPTmdPSS02BVbvK+Oc0fbWcK7dQGJEAAqhzUkLwCeffNKwXlpaSn19PUlJSYDaGSQmJoaMjAwRgF3BRQDGRlgYn+eh1ZHge1wTQbwIwCkDUomwmGm22lixuxRFUSRDO1ioLYUPrlcfAlrq4PynKSiupqxW7T07vX88poMuArC2GBqrICpR+gD7gRlD0nht9X5AjQPUBKDu+rV7VQQhlDnpJJD9+/c7f/70pz8xbtw4CgoKKC8vp7y8nIKCAsaPH88f/vAHX8w39LAngIDqAp46MI2IMGnh3CN0MBM4JiKMifmqKD9S0cD+MgkyDxqOrFPFH0DRRsCY7T0v1wp4qP9YZn9w07uAxQLYI0zun+r8jnQ8kAHSDUQQXPCpkvh//+//8eyzzzJ06FDntqFDh/Lkk0/y+9//3penCh10FsBCW7bE//Uk7fQD1qMvB7Nqr5SDCRqObNCWy/aCzWZo+zclRSf2w2N0Y+1xgLWSBdzTREdYmJSvunePVjVS6Hggc3UBC0KI41MBeOzYMVpbW922W61Wjh8/7stThQ52AVivRHKcZIn/60k6aAEEY1meFbtFAAYNdqsfAK0NNJYdYP3+CgBykqLJVnQWvv4ztGVHJrDDAhiZCGGSud9T6MszrXSUgxEBKAgGfCoA58yZwy233MKmTZuc2zZu3Mitt94qpWC6grUFpeIAoLp/+ybHkJ8a0/Yxgu+Ibr8biIMR2QmkxqplJb4tPEGLaxcCIfCw2eDo94ZNe7ZvpNn+t50xJA1TldamkUG67zhHIojDAiglYHoU/YOy0yKvjwEUASgIvhWAr732GllZWUycOJHIyEgiIyOZNGkSmZmZvPrqq748VWhQcRCTYgXUBBBp/9bDdKAdnAOz2cTpditgbVMrmw9XduPEhB7hxB5oMra2LN63xbk8bVA6VB7SdvY7DcKi1OXSXdDSAM32ItFSAqZHGZYVT1qc+kC2dp/9gUxiAAXBgE8FYHp6Ol988QU7d+7k/fff5/3336egoIAvvviCjAz5Auw0+vg/Jcvg1hB6gLZcwPXlcGSjWh/QjsHtJG3hAh+9+9eOtWQnoJaEPH1QqlEAJudDqr0TRcV+Y09gsQD2KGazyRmWUdds5ftDleICFgQXuiWddMiQISxYsIAFCxYwZMiQ9g8QPKMTgAeUbE4bmNrGYMHneBOArU3w2jx4dTZ8/YBzs97tpE8UEAIUfQKInbTGgwCM6ZtEUkwEVNpdwDFpqsBIs3/f2Vrh8DrtQLEA9jhu16O4gAXBgE87gfz0pz9tc/9rr73my9MFPfXFu3BE/FnSB6s3HKHn8JYFfGSDFuS/5lkYfRlkjSIrMYrBGXGeuxAIgYfTAmiC6GRoKGeQqQhQmD4oTW0nVmO38iXlqr/TdA+8B1dpy1ICpseZpu/TvaeMu4eIC1gQ9PjUAlhRUWH4KSkp4ZtvvuHDDz+ksrLSl6cKCWqLdjqX8wePbmOk0C1ExEBYtLqstwAeXK0tKzb48l6nK9hhdbApsLZQ3MABS0sjHP9BXU4fCtljAUgy1ZFGterury5S//4ASXnq77TB2msc0P2fSAmYHiczIYqhmfEAbDtSSY1N9wAtFkBB8K0F8KOPPnLbZrPZuPXWWxk4cKAvTxUSRFQVAnBCiefUEYP8PJsQJSYVqo9Ag84CeGClcczBVbD9Ixh1MdMH67oQ7Clj/qjsHpys4DOKt6puXICcCSgRcZgKlwIwMuIYp+Qlw6EftPEOAZiu1UClYr+2LALQL0wbnMau4zXYFNhU3MJMxw4RgILQPTGAhhOYzdx1111uLeOEtrE11pLUqlqQDpHNKXlJ/p1QqOLIBK4/oVr5Wpvg8Hp1m0VX123R/4PmeiYPSCHcomZqr5JEkMBFnwCSM4HiyH7O1blplWqnCX0CSJJ9f8pAwEOmvriA/YI+MWv14SZth7iABaH7BSDAvn37PBaIFryzf49mXaiP70+4Rdq/+QVHHKCtVS0JUrQJWhvUbSMvgkFnqsvVR2D1U2pbuH6qaDxUXs/BE2JpCEj0CSA5E9hQq1nwJsTaE3wMAtBuAYyI0eIB9YgF0C9M7q/26QZYtl93LYoFUBB86wK+6667DOuKonDs2DE+//xzrr32Wl+eKugp3LkZh9M8Kmtom2OFbsQ1E1gf2J8/DfKmwPNLVYG46ikYdxXTBqextlCNGVy5p4x+qbF0ig2vqy7nqbdJ9wh/4bAAhkVB5kgWHi/mfPuufMWe+asvAp2oE31pQ4ziEMQC6CeiIyxMzE9mzb4T7K1UwF6mUQSgIPjYAvj9998bfrZu3QrA448/zlNPPeXLU3nk73//O/n5+URFRTF58mTWrVvX5vj333+fYcOGERUVxejRo/niiy+6fY4dpeJwgXM5Z9AoP84kxDEUgy6HAy4CMG0wTLlVXbc2waLfM+NkysEc+hY++xUseRh++LDr8xa6Tn25Fr+XPZZGm5mvD9moUNQyItFV+9R9BgugXgC6PLCFxxpr0Ak9ijMxCzOtZrsCFAEoCL61AC5dutSXL9cp3nvvPe666y5efPFFJk+ezFNPPcW8efPYtWuXxyLUa9as4corr+TRRx/lvPPO4+233+bCCy9k06ZNjBrlX8HV2GIlvLLQKc8z80f6dT4hjd4CWHNMq+2WkKMW/gWYcQ9seU/t+1rwP0bO/C3JMeFU1LewZu8JWq02wjrqwte7Hst2+eQtCJ3EJf5v/YFymloV9phzmGTahanmGDRWaQIwOgUi47Vj9JnAIEWg/cz0wWn8ZaG63EAk8TRKDKAg4GML4OzZsz2We6murmb27Nm+PJUbTzzxBDfddBPXX389I0aM4MUXXyQmJsZr7cGnn36a+fPn8+tf/5rhw4fzhz/8gfHjx/Pcc8916zw7wrr95fRD6yJgShngx9mEOHoBuPdraKlXl/Onqe0gAKISYOrPncPMx753toWraWply5Gqjp/vxB5tuU6SSPyCiwB0JPPstfXRth/foZaBAS3+z0GaS/F7KQLtV/R9uqus9lIwYgEUBN8KwGXLltHc3Oy2vbGxkZUrV3o4wjc0NzezceNG5s7VmrGbzWbmzp3L2rVrPR6zdu1aw3iAefPmeR3fk6zaW0Z/UzEADdHZamC54B/0LuCdn2vL+dOM47LGaMslO7ruBi7Tur8Yik8LPYeLAFxhF4D7yNG2Fy5zrwHoIN3FBSwJIH7F0KdbERewIDjwiQvYEesHsGPHDoqLi53rVquVhQsXkpOT4+lQn1BWVobVaiUzM9OwPTMzk507d3o8pri42ON4/dxdaWpqoqlJKyVQXV3tdezJ8P3OfSSbVBdFWPrgdkYL3YreAlinE3L9TjeOyxihLZfuZNpkY1/gX83tYEtEvQWwXiyAPY6iaG746BRKw7IpOLYDgNbkwVBjH7d3sXaMqwCMSbV3DqlQ18UF7HemDU7j0y1HqceeVNVSDzYrmC3+nZgg+BGfCMBx48ZhMpkwmUweXb3R0dE8++yzvjiVX3n00Ud56KGHuvUcNY0tRFZrBWTDM0QA+pUYD/2X4/uAq1s+PguiEtXYsJIC+iRFMzA9ln2ldWw+XEl1YwsJUe20hWushtrj2rq++4jQM1Qc0Ip+50xg9T7tb5A1aCx8b18p2qQd4yoATSbVDXz4O3VdXMB+x1EPsM5hAQRVBOpjNwUhxPCJC3j//v3s27cPRVFYt24d+/fvd/4UFRVRXV3dbp/gkyEtLQ2LxcLx48cN248fP05WVpbHY7Kysjo1HuC+++6jqqrK+XP48GGvY7tKfFQ4by7QuR1TpQOIX/EkAPXxfw5MJs0KWF0EjVXO7EOrTWHtvg6IuRN7jesSA9jz6N2/fSeyQue+HzdyFIQ7wjEUbZyrAARjIoiUgPE72YnRDM6Iox6dABQ3sBDi+EQA9uvXj/z8fGw2GxMnTqRfv37On+zsbCyW7jWzR0REMGHCBJYsWeLcZrPZWLJkCVOnTvV4zNSpUw3jARYvXux1PEBkZCQJCQmGn+7AUrFPWxEB6F+iU9y35Z/uvg0gfZi2XLLT0IWgQ11BXAVgYyVYpYB6j6ITgEqf8c6/W3S4hfH5Ke4ZvuBZAOpjQpP6ue8Xepxpg9OoEwEoCE5O2gX86aefcvbZZxMeHs6nn37a5tgFCxac7Om8ctddd3HttdcyceJEJk2axFNPPUVdXR3XX389ANdccw05OTk8+uijAPzyl79k5syZPP7445x77rm8++67bNiwgZdffrnb5thh9EIgVXoo+5XwKIiIM5aNyJ/ueawhDrCAKaMmEG4x0WJVOpYIUrbHfVtDuViQepLyQufiPkt/Smp2AzBlQAqRYRa1xt+xLcZjEj10/hh3tVoyKDIeBs3pzhkLHWTG4HSKvtMVVpdSMEKIc9IC8MILL6S4uJiMjAwuvPBCr+NMJhNWq/VkT+eVyy+/nNLSUu6//36Ki4sZN24cCxcudCZ6HDp0CLNZM3iedtppvP322/z+97/nt7/9LYMHD+bjjz/2ew1AAE7YLYDmMLEe9AZiUrSbRXy2e/yfgwy9BbCA2MgwTslLZt3+cg6cqOdweT25KW1kdJ/wIADrT4gA7El0mdcrjticy9McWd3pLsk8UUlqGSBXIuPgkn90wwSFrjJ5QApvm6K1DWIBFEKckxaANpvN47I/uO2227jttts87lu2bJnbtksvvZRLL720m2fVSRRFs0Ik9weLT2t1C10hOkUr+tvvdPf4Pwd6C2CJ2sllxuA01u1XRcXKPWVcNdmDu9BB2V73bRIH2LM4EkAi4lm+T6vfOMPhznft8uHJ/Sv0SmIiwkhMSnJmch8vO0GmPF8LIYxP6wAKPsBkgv/bA7esgPOf9vdsBDAmgrjW/9MTmwYxdqFgF4DTOloP0GaD8n3u2yUTuGexWwBt0cl8t1/97DMTIhmUobaBc6vxJwIwoOiTrsXl7jrsveSXIIQCJ21eeuaZZzo89o477jjZ04UGkXGQPdbfsxAc6G/y/We0PTZjOBxYqbaFqzvB6JwUEqPDqWpoYfXeMqw2BYvZgwWx5qjWZUSP1ALsOWw2NfEGqLck0NiiejSmD07H5LD6pgxQQzNs9uQcCdEIKPplZ4DdwbK/6DjtXM2CENSctAB88sknOzTOZDKJABQCk9NuV4VY3mntJ+U4BCBAaQGW/GlMG5TG59uOUd3YytYjlZySl+x+nD4BJHWwFg8o3UB6jsZKZ3ePEmusc7M+mxtLOKQM1Po0iwUwoOiTof0tj5aWda5PtyAEGSctAPfv39/+IEEIZFIHwuX/6tjYjOHackkB5E9j2mBVAIJaDsajANRnfudN1gSgxAD2HI7OHcChBq1ciKONmJP0IToB6CEDWOi1mCPjnMuWlnq2FlUx3tP1KAghQLc9+iiKgqIo7Q8UhGAi3UUAAtMGGdvCeURvAczT1aKUGMCeQ2dtPVCvlgsZ2SeBtLhI4zhHGSBzOGSP66HJCT4hQrPsxpgaWblbHrCE0MXnAvAf//gHo0aNIioqiqioKEaNGsWrr77q69MIQu9EXwqmVO1DnZsSQ/809caz6VAFtU0eijvrS8DkTtGWJQaw52jQBGAlqqVo2uA093ETrocLX4Br/weJ3dfjXOgGIjQLYCyNHavPKQhBik9rjNx///088cQT3H777c6OGmvXruXOO+/k0KFDPPzww748nSD0PqKT1VqBNcegZIda1sdkYvrgNPaX1dFqU/h23wnmjsg0HucoAROZqLqczeFgaxELYE+iswBWKGqP2Bm6LG4nYREw7qqempXgS/QWQJr4/nAlNY0txLfXp1sQghCfWgBfeOEFXnnlFR599FEWLFjAggULePTRR3n55Zd5/vnnfXkqQei9OOIAGyqgtgTQ3MD9Tcf44YfNxvEtDVBl7yudNkgtBeQoPVMnArDHaNALwDgiw8xM6CfxYUGFTgDGmho73qdbEIIQnwrAlpYWJk6c6LZ9woQJtLZKT1MhRDDEAe4AYOrAVGZatrEo4h5u3fFjYzux8kLAHi/r6P3sEID1J1QrotD91BtdwJMHpBIV3r19zIUeRucCjqERgFV7Jcyit1Lb1Cq5BN2ITwXgT37yE1544QW37S+//DJXX321L08lCL0XfSawPQ4wPsLMH6LfJtxkJZJm6pc+ro1xLQEDEGsXgNYm6VnaUzQYXcAzPMX/CYGNzgIYZ1IFoNfELMHv/PKd75n2l6X85r9bPcdOCyeFz/uM/eMf/2DRokVMmaIGsn/33XccOnSIa665hrvuuss57oknnvD1qQWhd5DhbgFk63/Iaz3o3By153OoPgYJ2cYEkDQXCyCoVsDI+G6csAAYYwCJ85wAIgQ2lghnIe+0iFZohv1ldVqf7p2fw+F1au3PWPn7+5PmVhtrC09Q32zl64ISHrlotL+nFHT4VAD+8MMPjB8/HoB9+9S2VmlpaaSlpfHDDz84x5m89VIVhGBA3y6sZCe0NsHSRwxDzEorbHgNZv/O2APYYQGM0d186k5Acn73zVcAwFZf7nSJhMemMjRTRHfQYTKpVsDGKpLDm52bV+wp5epRcfD+dWBtBsUKZ/3Rf/MU2HCwnPpmK6D24jZ76qAknBQ+FYBLly715csJQmASGQ+JeVB1SHUBb3hNXQa2MpjhSiHhJivKxtcxzfg/nQXQpHUacbUACt1OfVUpcUCLYmH84Dx5UA1WIuKgsYo4U5Nz04rdpVzdp0QVf2AMyxD8gt41P32IWGO7A+mBIwjdgcMN3FQN3/zJufnz3F/zle1UAEx1pbD9I80CmJgL4dHqst79JLUAewSbPeO6kjhmDPVQ/kUIDuxxgGHWBpJj1PIva/aewFpdrI2RFozdz/aP4L83Qekuj7tX7NZqNE73VI5JOGl8agFsbGzk2WefZenSpZSUlGCz2Qz7N23a5MvTCULvJWMY7PlKXW6uUX+PvJgB+VN5Y+8RzrN8q25b/hg0VanLjvg/gJgUbVksgD1CRLP6d6hQ4uSGE8zYBaCpuZbTB6Xy2bZiappaOXJ4P/0cYxpEAHYrLY3w8S+gpU5NcrvyHcPu0pomth+tBrx04xF8gk8F4A033MCiRYu45JJLmDRpkrhQhNAlY4Rx3WSB2b9nuiWde5WhbLf1Y6T5IJTv08ak6gWgPgZQLIDdTUl5BRmoLsHmiCRSYiP8PCOh23CUglFsnDEogc+2qZa/o0cOagJQLIDdS12JKv7AWBLLzmpdaZ4ZQ+RhrLvwqQD87LPP+OKLLzj99NN9+bKCEHikDzOuj78GUgfSBxiUEc8bJ+bxV/PLxjGOBBCQGMAeZmPBPs62L0fGS7xRUKMrBTMtL9q5XFN2RBvTUAE2K5ilDmS3oP9Oqy6CphpDpQOj+1eux+7CpzGAOTk5xMdL5pwgkD4UTPbLKywKZt7r3DVjcDqfWk+jXIkzHqN3ARtiAEUAdjfb9ux3LiemZrYxUgh4dAIwM6qVIZnqdWiuK9ENUqCxqocnFkK4djjSJd3YbAor7AkgMREWJvZLQegefCoAH3/8ce69914OHjzY/mBBCGbCo2HKz1Xxd9Yf1Xp/dmYMSaOJCN6znmE8Rm8BjJYYwJ7CZlMoPHTYuZ6anuXH2Qjdjk4A0lzn7Pecbqo0jhM3cPfh+p2mE4AFxdWU1arhGOfnNhFhre/JmYUUPhWAEydOpLGxkQEDBhAfH09KSorhRxBCinl/gt8Vw6SbDJsn908lIszMv1rnYnVcgmHRkJCjDQqLgMhEdVliALuVHceqMTdWONctsaltjBYCHl07OJrrnDFmGa4CUBJBug/XygZlWiawvvzLr2v+DI/2hb9PAWtLT80uZPBpDOCVV15JUVERjzzyCJmZmZIEIggeroHoCAuT8lNYtdfGm61n8dOwhTD+J2B2eR6LSVEzhMUC2K2s2FNKsknXbi9GHlaDGoMFsJZJ/VOICoM0XFy+YgHsPlwfast2Oxcd8X9htJJauxdQwNYKlvAenGBo4FMBuGbNGtauXcvYsWN9+bKCEHTMGJLGqr1lPNx6DRFz7uPHZ4xzHxSbBhX7obFSffqVL8BuYcXuUiagE4DRIgCDGhcXcFS4hdl5YYQftRrHiQWw+3B9qC1VBWB9cysbDqjW+GmJZZia7IW5s0VTdAc+dQEPGzaMhoYGX76kIAQl+jpzSw40ex6kzwRuqPA8Rjgp6ppa2XiwgmRTjbZRLIDBjYsLGGBOrodxYgHsPlwFYHkhWFv4rrCcZqtaP/j8dF1STp9xPTe3EMKnAvDPf/4zd999N8uWLePEiRNUV1cbfgRBUBmWFU9GvFrc9NvCcppare6DYiQTuLv5tvAELVbF6AIWC2Bw4+ICBpiU5iG+7GQtgDYP17Sg4vp9ZmuBigMs15V/mRh5SNsvFsBuwacCcP78+axdu5Y5c+aQkZFBcnIyycnJJCUlkZyc7MtTCUJAYzKZnFbAhhar0+1hQG+JkkSQbsERb5SExACGDC4uYIC+4R4MFCdjAVz+V3g0F759oeuvEcx4+j4r282KPer1aDGbyGnQ4gLJGtNDEwstfBoDuHTpUq/7tm3b5stTCULAM2NIGv/dpBafXbG7lNMHuRQ8lVqA3Y6j3liKwQIoD6tBjQcBaKo97j7uZCyA372gdrr47iWYcmvXXydY8dDfvOrwdgpLRwIwoW88YSXb1R3J/SE6qQcnFzr4VADOnDnTsF5TU8M777zDq6++ysaNG7ntttt8eTpBCGimDUrDZAJFgeW7S7nvnOHGAYZuILovTJsNPr0djm+DH71mLCAtdJjD5fXsL1MFQGZ4PViByARJtgl2PMQAUlviPq6rFkBF0WJ2pZi0O9ZWaKhUl8NjoEWt83fiwDZAFYAL+tZCiT2fQOL/ug2fuoAdrFixgmuvvZbs7Gz+9re/MXv2bL799tvuOJUgBCypcZGMzlFr/e0sruF4daNxgCEGUHczOrgaNv9L7aG5/C89MNPgZJku3shpARTrX/DjIQaQmmL3cV1NvGquBcVmfH1Bo6ECUNTlnAnadl0x6OmxurZ8Ev/XbfhMABYXF/PnP/+ZwYMHc+mll5KQkEBTUxMff/wxf/7znzn11FN9dSpBCBpm6hqdL99VatyptwDqY2YOrtGW9y6WYPMusnyXavUxYyOy1R4DJvF/wY8HFzA6F3C1EgOA1bVdWUfRW/2szdDqJcs/VNGHsyTlQWIeAOmNBwCF5Jhwcps0MUj2uJ6cXUjhEwF4/vnnM3ToULZu3cpTTz3F0aNHefbZZ33x0oIQ1MwaqgnAZbtd3FD6jhT6L81DOgHYUAFH1nfT7IKXplYra/apn2n/2FZMDouEZAAHPx5dwKoAbLLEclRRrzulqy7gRpeEErECGtGHs8SkQJraAjPe1EA6lcwYko752BZtjFgAuw2fCMAvv/ySG264gYceeohzzz0Xi8Xii5cVhKBnXG4yidFqzNnKPWW02mtgAZ5jAK2tcNhF8O3+qptnGSS0NKqfVV0Z6/dXUN+sWk7n5uti/sQCGPx4dAGrAlCJzaASVSCG2ZqguQt9aJtEALaJ3psRkwbpQ52rg8xHmTUkBYrtSaNJeXJNdiM+EYCrVq2ipqaGCRMmMHnyZJ577jnKyqRshSC0h8VsYvpgNdavprGVTYcqtZ2RCWC2ixOHBbB4q5pdqEcEYMf46j54+zJ46wKW7dRcftNzdO36xAIY/IRFA/a/eXOd/UctBB6ZlE2dOcE5tLmmC/cx18SPJhGABvTejNg0pwUQYJDpKGekVmvfcWL961Z8IgCnTJnCK6+8wrFjx7jlllt499136dOnDzabjcWLF1NTU9P+iwhCiDJraIZzedkunRvYZNKsgI54pENr3V+gZDtUHu7GGQYJhcvU38d/YNsutfm82QTj0hRtjFgbgh+zWbMCNtcZ4v9M8VlEJ2phGTsKD3T+9cUF3DZ6ARiTyonofOfq5PhSkqoKtP0S/9et+DQLODY2lp/+9KesWrWKbdu2cffdd/PnP/+ZjIwMFixY4MtTCULQoE8EWeaaCOKoBVh/Qi0voU8AGXGBtrxHrIBtYrNCpdZZILJ8JwCn5CUTZ9PdsMUCGBoYBKDuoSsuk7SMLOfqjr0HOv/ajZXG9SYxgBgwCMA0VpRrmfejI0vg6GZtvwjAbqVbysAADB06lMcee4wjR47wzjvvdNdpBCHgSY+PZFSO6nbacayaEn05GIdFytqk3kgO2cspRSXCtDu1ceIGbpuqI2Brda4OMallJmYNSTeW2BELYGjgFIC1xhIw8Zn07dPXubr/UBcs624xgHWex4UqdcYkkK/2W6lQ1LjL7NbDankrB+IC7la6TQA6sFgsXHjhhXz66afdfSpBCFjO0LuBdfXpDLUAD6/TkkFyp6hPx/HZ6vr+FV0LWA8VKg4YVoea1Bv7GcMyjB0fpA5gaNCGBTAmSbPIN1SXcrSyoXOvHWou4JYG+OG/UF7YsfG6LOCWqBRW7zvBPqUPABF1x+DoJnVnQg7EpXt6BcFHdLsAFAShffTlYAz1APWZwDv/py33m6rGCA4+S11vbVRFoOAZFwE4xHyEtLhIRmQniAUwFHGUgrE2Q5XOyheXaQgDSKKW5btdwjLaI9SSQJY+Ah/8FN44r2M1Dx0uYHM4G4ut1DS1stfWR9tv7wwi7t/uRwSgIPQCjOVgSrVyMPp+wDs/15bzTlN/D5mvbZM4QO9U7DesDjYVMWtwKmazycUCKAIwJNCXgtFbruIyDQ8ByaZaY2JWR3BzAQd5DKDDZVtdBCf2tD0WtIS2mFSW7VatgQ4LoAFpAdftiAAUhF6AvhxMdWMr3x+uVHcYuoHYLRFhUdqX44CZYIlUl3d/pSaKCO64WABjTE2cnWu3VogFMPTwJgDjswzXXJKphtV7T9DcqqvP2R6uLuBgtwC26mKWS3e2PVZRNAtgbJpTXO8jx32sxP91OyIABaGX4LEcjF4AOsiZAGF20RcRC/2nq8vVRXD8h26eZYDiIgABJsfay384er6aw41dIoTgRf93Lrdbh81hqgVYZwVOppbaplY2HuxEX2BXF3CwJ4G06GIkS3e1Pba5Vk1oA5oikthZrFpHwzOGuo8VF3C3IwJQEHoJHsvBeBKAeVON63o3sGQDe6Z8v9umuKrd6oLDAhiTosZVCsGP3gLYahcwsRlqjcDoJByFolNMqkBxa9PYFqHWCaQzFkBdBvDxVk2EDxs+SvNkAMRlQXymr2YoeEEEoCD0EvTlYLYfraakptEYA+ign4sAdCSCgAhATzRUOGuzlShJ2vYSe8FZRwygxP+FDnoB6CDOboE3W9QyS0CSSRVvy13rc7aFmws4yGMAO2MB1IVb7KuPci7PHJYFqYO0cRL/1yOIABSEXsSsIXo3cKm7BdBkhr6TjNuS+0H6cHX5yHpoqOz4CVc+AU+PhYL/tT82UKk46FxcaRtNs2LvVV5SoJbOcVgwJP4vdPAkAOO1AtCO/4U0s+q+3Vlc0/FyMG4u4CC3AOoF4Im9YG3xPlZXAmZHpZr0lhwTzti+SZA+RBsn8X89gghAQehFnDFMcwN/U1DiLgCzRkNUAm44vzAVLVmkPaytsOxRNT5u9TNdmm9AoMsA3mfrwyGTPeC8bLehDZjUAAwhPMV6xmkPXw5rcJxSiwUrAN/s7IAb2Nrq3qs7lJJAbK1t1wPUdQE5ZncBTx+cjsVsgoyR2ricCb6epeABEYCC0IsYl5tMSmwEoJaDaVLMEJmoDXCUf3ElPFpbbumgpaKuRK2DBoYn86BDlwByUMmkNtHefN7WAkc2aOPEAhg6eHQBu1sAARJRBV2HBKBr/B8EdxKIorh/37TlBtbFAFYo8QDMGW4X3hOvV+OZx18Lg+b6eqaCB0QACkIvwmI2OYtC1zVb+a6wHGJ1VsC8KZ4PDI/RljsqAPUtsFzjloIJnQA8pGQQmzta23dwlbYsMYChQ1sxgGD4XxgUr2atrt5bRkOzte3XdXX/QnDXAbS2gOLymbQlAHUPmuXEq993jrCX2DS46j1Y8Iwahyl0OyIABaGXMXe4lv22pOC4mp3owDUD2EG4FlDtzGpsD73705PlIkhQdAKw2JxF7lCde+nAam1ZLIChgycXsIcYQIDZeWqsWlOrjVV727GUe7qOgtkF7Om7pq1MYJ0L+ISSwKn5ySTGhHfDxISOIAJQEHoZ0wenEW5Ry1B8XVCCMulmNT5tys+9l0boigu45pi2bG2G1qYuzrh301K6D4AqJYYRA/sRlTNK26nvXCAWwNDBowVQd23p/hemZGmlgZYUHKdNPFoAg1gAtjS6b2vTBawJwAolnjnDpNSLPxEBKAi9jPiocCb3V92+RZUN7E4/C+7ZD/Mf9X5QWFcEoMvNLBjdwNYWwmqOAqr7d+7wDEjKN35eDsQCGDq0JwBjtISgEUmtRIapt8pvdpZgs7XRbcfTNWRt7liP3EDEkwWwbDfYvLjKdRbACuK0+D/BL4gAFIReiP6L8euC4+0XKNa7gLtiAYTAdwN7uulUHcFsz+I8pGRwxjB7sd+MYe5jxQIYOnjMAvZsAYxormTaILUeZ0lNEz8c9WDlc+DtGgpWK6Cn7xprk8fOOwDWWrVCQZUSQ15aIgPSpfOOPxEBKAi9EL1rpF23ExiTQLoSAwie3VeBwsrH4U/ZsNRoJa0o2u1cro3JpW+y/XNy1E3UIxbA0MHVAhiVaHyI0v8vNJQzRxeX+3VBG9nAhmtI99AWSgIQvLqBbbVqDOUJJUGsf70AEYCC0AvJS41hcIb6dPz94UrKatuJzwvTWwA9xOV4IpgsgOteUS0Pq540BN3v27XNuZycoys0m+FBAIoFMHTQPzCB0foHxv+F+nJmD9PESpsPZHoXsD6rOFgTQfQ1AOP7aMueEkGsLYS3qhnR5SQwW+L//I4IQEHopTisDoqi6w3sjS6VgXG5kQVqyypFV/za2gT7ljh3lR3WLIADBusKzWaMcH8dKQQdOrhaAF0FoIsFMCsxytCmsbjKy0OW/iEqQSeIgtYCWK8t9zlFWy7b7T60RvsOqzEnMDFfrjd/IwJQEHopc4d30OoAnS8DY7OqhaD1BGoSSGOV2oHAwc7P1c0tVkyVB5ybBwzRZf+6WgAjE8ES1o2TFHoVlnCwRGrrbVoAKwCXsIydXq5He89pABJytOVAfbhqD723IWu02qoSPFoAt+/VOvJEJWYQbhH54W/kLyAIvZRT8pJJttfIWrG7lKbWNorQdtYCWFcKis24LVBdwLrMQgB2fwXWFtbuO0GOot6orVgwJ+VqYxL6GDusxIg1IuTQWwH1NQABImK0sIqGcsC1PqeXOED9Q5ReAAarBVDvAo5OguR8dbl0N9iM3y/bdu9zLqdl9EHwPyIABaGXYjGbOGOoagV0dgXxRlgns4D1XUAcBKoFsN7lc2mshINrWFJQTJ5JFYBNsX2MFj6TyWgFlPi/0EOfCRznISHB8T9h//8a2SeBjHjVaui1K4g+CcTgAg7SdnD675qwKEgbat9eB9VHnLsURaHw4EHnet8c3cOY4DeCQgCWl5dz9dVXk5CQQFJSEjfccAO1tW0/cc2aNQuTyWT4+dnPftZDMxaEjqHPPmyzF2lnC0F7EoDBYgEElJ2fs6GgkAST+llEpg9wP04vACUDOPTQWwDjstz3O/4nGspBUTCbTc7M1aZWG6s9dQVxXEMms9GtHKxJIPrvmvAYSB+qresygQvL6mit1T6v6CRJAOkNBIUAvPrqq9m+fTuLFy/ms88+Y8WKFdx8883tHnfTTTdx7Ngx589jjz3WA7MVhI4zY4i+K8hxFMVLEVq9AOxIDGCtJwtggJaBqXe/Ebfs+IzIGs3iYEn1JAB1iSBiAQw9DALQkwXQHhZgbXZa8NqNA3RY0SPj1R8HwdoPWP9dEx4F6br6mro4wCUFx0k16R4wY3T9zQW/EfACsKCggIULF/Lqq68yefJkpk2bxrPPPsu7777L0aNH2zw2JiaGrKws509CQkIPzVoQOoa+K8iRigYKjnm5kRg6gXSgDIxHC2CA3qT0FkCzGjMZUVvEPMsGbbsjNklPli4pxFuLPSF4aSsGENwygQFOH5RGVLh621y84zhW164gjoeoyESI1LmYg9YCqPuuCYt2sQBqAvCr7cdJRvf9EisCsDcQ8AJw7dq1JCUlMXHiROe2uXPnYjab+e6779o89t///jdpaWmMGjWK++67j/r6+jbHC4I/mDdKuzkt/OGY50EGF3AH/o+D1QU87Fzn4lUWrRyMRwGYOwXGXKGWr5hwfffNT+idOP4nwmMhsa/7fpdagADRERZmDkkHoKy2mY0HK4zHOK6hqESI0FsAg1QAuloA03S1Nu0u4JLqRjYerCDVpBOAYgHsFQR83YPi4mIyMozm+7CwMFJSUigu9nCTs3PVVVfRr18/+vTpw9atW7n33nvZtWsXH374oddjmpqaaGrSCvJWVwfoDVMIKOaNyOT+T35AUWDh9mLuOmuo+yB9EkhrByyArl1AIICTQHQC8JSfwI6PAUgy6QLvU/q7H2c2w8Uvde/chN7LzHvVRJABM43uWgceLIAA80dl8dV29fpZ+EMxk/rbx7U0qu5igKiEELEAusQARsZBYh5UHVIFoKLw1Q71s0pB7wJO6+GJCp7otRbA3/zmN25JGq4/O3d6qDbeQW6++WbmzZvH6NGjufrqq3nrrbf46KOP2Ldvn9djHn30URITE50/ubmSySR0PxkJUYzPU+ORdh+vZV+ph5uJ2ayJwA4lgdgtiSazVkImYC2AuizgrNGUxHso8uzJAiiENok5MP8RGDLP834PFkCA2cMynXG5X20v1uJy9TG0UYnGLONgtQC6ZgGD5gZuqoaaYr76QTXEJDssgGFR7oW4Bb/QawXg3XffTUFBQZs/AwYMICsri5ISY3Zka2sr5eXlZGV5iOvwwuTJkwHYu3ev1zH33XcfVVVVzp/Dhw937c0JQieZP1L7X/5quxfLdqcEoN0CGJsOUUnqcjBYAGNSWGSdYNwfnazekAWhMxgsgJqrNzE6nNMGqhasosoGthXZhZ/+ASoywShyglUA6r0NjjAUXRxgzeEfWFuoXp8ZZvtnEJOqlmES/E6vdQGnp6eTnp7e7ripU6dSWVnJxo0bmTBB/eL/5ptvsNlsTlHXETZv3gxAdna21zGRkZFERkZ63S8I3cX8UVn86YsCQHU7/XzWIPdB4TFqDbz2BKDNqrmA47OgtQlqjgZuEkidPQs4KpGjNa28VTGKH+sv02QP7l9BaA8vFkBQr8flu9XWZgt/KGZM3yTjA1RUgtECGAouYKcFUMsE3rtjA1bbGEAhyVQNChL/14votRbAjjJ8+HDmz5/PTTfdxLp161i9ejW33XYbV1xxBX36qIU4i4qKGDZsGOvWrQNg3759/OEPf2Djxo0cOHCATz/9lGuuuYYZM2YwZswYf74dQfBIbkoMI/uoWepbj1RRVOlB5DnawbVXBqb+BCj2IrZxWaq1AtTirdZW78f1VhwWwJhUvtpezG6lLwdturhgcf8KXUFvAXSpNXnmiEynEWvhD3Y3sL4NXJS9taAjOz9YLYCGGECHBVATgNH7vsSEjQTqsTi+c0QA9hoCXgCCms07bNgw5syZwznnnMO0adN4+eWXnftbWlrYtWuXM8s3IiKCr7/+mrPOOothw4Zx991386Mf/Yj//e9//noLgtAuBjfwDx7cwI6bTXtlYGp0mcTxWcYA+ECLA7S2ajfemFQW/lAMmFhk06oCiAAUukS05yQQgLS4SE7NV/cXltWxp6TW3QUMWiJIsFoAPbmAs8dAvGp8Gda4hestXzEkXjcuVhJAegu91gXcGVJSUnj77be97s/PzzcU0M3NzWX58uU9MTVB8BnzR2Xx+OLdgJoN/NNpLq5NxxdwawMoivc4mxpdBnB8lvHG1VQdWF0xdLFZzRHJrC9Qb9TfxZ/FjU2LMNla1SxPQegsMd5dwABnj8pi3X51+8IfihmSpE8CsQvAiDi173YoWAAdLuCwSLjw7/DPiwC4N+wdvsrKAkfIvGQA9xqCwgIoCKHA4Mx4BqargeXrD5RTWtNkHGDoBtKGFVDfBSQuU7NWQOAlguhcc0eaonHU5R08diqmW1bAjUtgwCz/zE0IbKKS1Cx5cLMAAszTWeQX/lDsEgNoTzpyxAEGqwB0fM+ERRsfOAfOZknSJQBEmlo59+gz2j5xAfcaRAAKQgAx314UWlHUTgQGOtoPWF8EOj5bs1ZA4CWC6NrA7ayOcC7PH5kFmSOh70RPRwlC+5jNWoa8Bwtgn6RoxvZVhd6OY9VUVeriBF1dwNZmaG3uxsn6CUfReUf8sZ3GFit3l19AgU0tlWax6h5WpQtIr0EEoCAEEPNHalnqC13LweiLQXdYAGaqbascBFoMoM4CuL1SjWjpkxjFmL5S9kXwAQ43cEOFx93zR2nX4+FjuthaVwsgBKcVsEVnAdSxck8Zlc0WftlyGy2mCOMxYgHsNYgAFIQAYlROAjlJ6pftmr1lVDW0aDsdBZ2hcxZAfRJIALuAS2zq+5g3KguT1BkTfIEjEaSpGqwtbrvnjdR6SJeVlWo7HALQ0A0kwKzrHcFRccDFArjQnqS2W8ll/yn3GI+RGMBegwhAQQggTCaTM/ao1aawpEDnBtZ/CbdVCsYZA2iC2AwXF3DgCsAKRRWA+mxpQTgpvBSDdjAgPY6hmer/XUudLgkkUpcE4iAoLYAOAag9fLZYbXxt/16Kiwyj39l3wsA56k5LBKR6qGEq+AURgIIQYJw9WhM4n2/VuZ3COhoDqOsCYglzSQKp8nxMb0UXm1WuxJMWF8HE/ADKYhZ6N20Ug3bgiMtN0PeedjxU6a3rzbr9wYCi6JJAtIfPVTrPxBnDMogMD4PL3oLZ/w8u/5cadiL0CkQACkKAMSEvmexE9Qt3+e5SKursweUdSQKx2TQLoOOLOKCTQHQWQOI5Z3Q2FrO4fwUf0UYxaAfnj1XjAOOxX3OWCE0Q6dvBBdq11R6eagACn24+6lw+f4w9RjIyDmb8n/e+y4JfEAEoCAGG2WxiwVi10GqrTeHzbXYrYEfKwDSUg83e7SPObkmMDGAXcJ2WBVyuxHPBuBw/TkYIOvRFi+tKPQ4ZlBHPyD4JxKNmxFoj4rWSKMHsAvZQA7C+udXZqzwhKoyZQ9tv5yr4DxGAghCALBjXx7nsfOI2WADrPR/o2gUEAjoJpKVWFYCtipmEpFTG5yX5d0JCcBGrEzC6kkOuXDCuj9MFXK3orH6RQdwP2EMbuK8LSqhvVlu+nTsmm8gwiz9mJnQQEYCCEICMyE5gUIZ6c1l3oFztDWwoA+PFAujaBQS0jEUIOAtgU1UJABXEseCUvpL9K/gWfcZqnWcXMMD5Y7KIs7uAjzdHaJ2nIvQxgEEmAD24gD/dXOTctGCsWON7OyIABSEAMZlMXDBWswL+b8tRlzIwnbEABm4nEEuTmplZIe5foTvogAsYIDvKisWkir6ylki2H7VfR5Gh4wKuqGtm2S71M8pKiGJSf0nG6u2IABSEAEXvBv5k81GXMjBeLICGNnB2ARimC1oPIAvg4eMniFbU99kYnsSQzPh2jhCETqIXgG24gPXXTTWxfOKwhBmSQIJMALpYAL/8oZhWey/G88dKMlYgIAJQEAKUfqmxjMtNAqDgWDVFdbovXK8WQA8uYNCsgAGUqbhk4w7nclyylJYQugGDC7gNAaiznNcoMXy65ShWmxLcLmD9d0x4tCZ6QazxAYIIQEEIYC7QWQFXH9DVGfMaA+jBBQxaKZgAcQErisK32/c41zOy+rQxWhC6SESsVl+zTQGo1c+sIZrj1U2s218e+Ekgx3fAZ3fCgdXu+3TfMdWtYaw7oNZJHJAey8g+Ce7jhV6HCEBBCGDOHZONw9OyrFAn3ry6gHUWwNgMbdmRCdxUrdYK7OXsOFZNbUWJc10sgEK3YDJpbuCOuoDtWcCfbC5yKQMTONZ1J5/dCRteg49+5r5P121oR2kzjryXC8bmSDJWgCACUBACmIz4KE4fpN6gDuqNd15dwPYYwJg0NfbPgTMRRAkIV9Wnm4+Sgu6GKg3mhe7C8b9Vf8L7w5HOct5oUQXgF9uO0WTRJWYFmgWwpRGKNqjLVYfAZnXfb2fT0Sbnsj42WejdiAAUhADHEW/TSLi20ZMLWFE0Aah3/0JA9QO22RQ+3XKUZJNeAEqDeaGbcNQCVGwe+wED0FjpXMzPUbtfVDe2smK/Liwj0FrBlWzXisaDe3yw7iFzf7UqjMf2TaR/WixCYCACUBACnHkjM4kIM9OgRGobPVkA68vBpvbodBOAkfpagL3bVbX+QDnHqhpJMYkFUOgBOlIKRvfQNHpQnnP5460lWgxhAFjWDRzdbFx3/V7QhZk0Kao3YYEkfwQUIgAFIcCJjwpn7vAMGtG5dD3FAHoqAeMgKnBqAf5nwxEAUtDNM0ZqjgndREdKweiumeH5fUmNVa/FxTuOY3PEAQaaC/jYZuO6q2dAVwewkXDMJl3vXyEgEAEoCEHApRNzadALQE8WwBqdAIx3SZrQt4PrxS7gqoYWPt+mtr7LCNPdUMUCKHQXHSkFo8sCDotO5KJTVEtYs9VGjWKvsRloSSBHvzeuu7mANQHYQCSzhmaQkRCFEDiIABSEIGDG4HRSEzU3bnNjewLQ5Und0A2kit7KJ5uLaGxR440GxzVrO0QACt1FJ13ARCVyxaRc52ppU5h9TABZAFsaoaTAuM3NBayzACoRXDkpDyGwEAEoCEGAxWzi0lP70aSoN5vKag9WPIML2MUCGABJIIqi8PZ3h5zrfSLsItcSaey4IAi+xJEEAmomsCf0D01RiQzKiGdSvhqWUN5qt8zbWqC12cPBvRDXBBBwezCsr9OSWmJiYzljaDpCYCECUBCChMtO7euMA2yor1U7EejRu6/iMoz79BbAXpoEsuVIFTuL1bmNy00iqrlS3RGbptZrE4TuoEMuYN1Dk/1aclgB6xSdWzRQEkFcE0DA7XvhQLH2WZwxKo8wi8iJQEP+YoIQJGQnRqPYMw7DbE2s2O3irmo0uqkMBEASyLvrNOvfVafmatYYSQARupPOuIDDY8GiWuHPGZ1NQlQYdUTrxvXOhys3XOP/wOAZsNkUjpaVO9fPGjugJ2Yl+BgRgIIQREREq67QaJp4WyeYAEOtMoPFz3W9F7qAa5ta+XSLmvwRFxnGecPitJI2Ev8ndCeGLOB2XMC6B6mocAsXj+9LbSBaAF0zgMEgXlfvK8PWrMUA9klL7oFJCb5GBKAgBBHRdgEYRQvf7CzheLWuHIwhUL0NAdgLLYCfbj5KfbPaieCCcX2Iaa3UdooAFLoTQz9gLxZAxzXjYlm/YlKuwQKoBIIFUJ8AYtJJBN3c31l3iCh08Yzhkv0biIgAFIQgwhShtp6KMTVhtdl4f8NhbafjJmUyG3uUQq9PAnlHZ828clKeWtTagQhAobtxJIJ4igG0tkKLPSHCxbI+LCuB+ARNFO47UkyvR58A0me8tt3+/VFW28TiHceJNLVo+8J0bm4hYBABKAjBhO6LOJIW3l1/GJsjGcQh7CIT3JMmerEF8IeiKrYVqS620TmJjMpJNLriRAAK3U2s/X+sody9J25blnVgaJ7WG3dtwSG3/b0Offxf/+nasv19/nfjEVqsCtHY+/+aLGDRtaEUAgYRgIIQTITrBWAzRyoaWLXXbrVwuqncb1KER4PZUa+sdwlAvfXPWV9NBKDQk7TVD9ilBIwrw/tpNTd3HDxKTWOL25hehT4DuP8MbbmpGkVReHe96lVwuoDDoyULP0ARASgIwYQuFifa/gX9z28PqhucFkD3mxQmk9YNpBcJwOrGFj7ZrCZ/xERYWDDWbk3Ru+JEAArdTVulYPTXi2tyFRARo11vEdZ6PtxU5OvZ+RZHAojJDLmTtTjAphpW7S1jf5nq7k4Kt1tCwyT+L1ARASgIwYTOBdzXrue+LjjOvqNlYLU/sXuyAIJ28+pFLuC3vztEbZMaj3TBuBzio+yuJrEACj1JrO5/zDURxGAB9HBtRWrxtrE08OqqQlqtNh9P0EfoE0DSh6kJMM4HwxpeXlHoHJrgEIDhMT08ScFXiAAUhGBC5wK+8hTVaqEo8PaKH7QxHqwUgHbzaqpWD/IzTa1WXlu1H1ANlDdN76/tFAEo9CSGbiAuFsC26muCIeEqztTI4fIGvvyhlyaDHNclgGSPU3/bvy9a6qtYuUd97/1SY4hU7DGAkgEcsIgAFIRgQicAzx6WTHykGte3ats+bYxXC6D95mVrNTR69xcff19ESY16k5k3IosB6brMZckCFnqStlzAegugp4crnQCMQS3L9NKKfSjeHrJsfrQOHtMlgPQZp/62vydFJ3Rvmj4Ak+M7QlzAAYsIQEEIJnQCMNbUzNVT+gEQZdMVoG3PAgh+71hgsym8pHM33TLTpdOAwQIonUCEbia2gzGAUUnux+pcwLmxqtv0h6Jq1uxzKSrdXAcvz4K/DYbibSc33w2vw1sXwJGNnTtOnwDitACqLuAIpYkwWkmNjeCSU7K0QuzhUgImUBEBKAjBhL4eV0sjPz09nwiLmXiTzqLnyU0FWqwP+D0R5OuC4xSWqsHmk/qncEqeS6cBhxsuIh7CInt4dkLIYegG4iIAq49qy54eRnQWwBGp2i33xeX7jON2fKKWYKkvg/X/6Ppca4rh87ugcBkse7RzxzoEoMkMWaPVZd2DYRwNXHdavksRaBGAgYoIQEEIJvRfxi31ZCREcfH4HOKp17a3lwQCfk8E0Vv/bp050H2AwwIYK+5foQeIaaMfcMkObTljuPuxOgtgVlQreSlq0sTKPWX8UKRzH+/8XFs+sr7rc935uVquBqCyE3UHWxqhVJ8Aos6zyRLrHJIR0cRPpvZTxzqQItABiwhAQQgm9AKwVf2SvmnGABJMmgBsDY93PUrF4AKu8jymB9hwoJyNB9Vaa0Mz45mVFwZ7l0CrPejc2goNleqyxP8JPYHBBeziunVkzUYlQnw2bugsgKbmWm6aoYUzOLNqWxpg3ze619zR9TCMgv9py7XHO36cpwQQYHelNuRHIxNJiomAVp1HQZJAAhYRgIIQTOgDsu1B2gPT45iQaXFuXl/c6vlYbxZAmw1O7Oux4HS9a+zm6f35/+3deXxTZf4v8E+SNuneQkvT1hZKgbassgkUZEAscEVRxiswOK8KXJVRUS8ydxAHpQ5ujD9/jivyc0VnvOAKP64gLiw6IIssRQQsS1mK0NZSutM1z/3j5OScpGmTFJo0J5/369WXJ8k5ydMjPeeb7/M830f3z2nAv24H3s4GqkuAunIA1gH0DADJG4zhSrkTdRfw5UtApbWuX3w/5wWR9QYlS1ZfjenDkhEbbgQAbDh0AYVltVJ3baMqSy8swK/7PW9nbRlw+t/K47py5YuTK0UHlW3rBJDahibsK1JWPrm9v/UawQygJjAAJNISuy5g5Vv69d2Ntu31v1Qry8OptTYJZP2DwKtDgbXzrmZLnTpeXIVvj5YAABKjQzC1jxG4YL0xFf0EvDPRvnuMASB5i9wNrO4CLvlF2Y7v1/qxcjdwQw1Cgg2YPToVANBsEXhn+yn77l9Ze7qBj21Ssniy6hL3jpUzmQBg7g8A+GTvOZQ2KmNsuwVbg0l1sMoxgH6LASCRlth1ASsBYJJJWX4q/5IO/++n82hBnQGUJ4HUVQI/fSRt//y50vXaQV769rht++7re8JY7jBQ/tJp4KMc5TEDQPIWuRu4VrUecMlh5XVn4/9kcjdwg/TFKmdUD4QGS1n5j/acRnP+l9LrOtUtuT0BoLr7V9aeALBbX9Q2NOGNbSdRBVWhZ/mLYZMqA8gA0G8xACTSklYygOpaZZUIw/Ob8lHX6LCovbMu4FPfKRkF0Qyc+t755148CXyx0H4ck4f2ni7DhkMXAABxEUb8YUR3oPSYsoPBmomwqNZSZQkY8hbbOECh1KF0kjVzSs4A1kvlmLqEG3FXllSiqW/zMRjkbuWMKUCodcb7uR89K8heXy2NlXXkzjhAIaQxgAAQYQbCY/Hm9wUoqqxDtVBdU+TriPrawjqAfosBIJGWBLUSAKrKulSJMPxaflnqelILcZIBPPGt/T4nndxgAGD9Q8Ded4BP5kqTNDxksQg8tUG5mS6cmIEIUxBQqmQEcfubQPcs+wOZASRvcbYaSLGLGcAyo3XilaXRNibvgRt6o0tYMCYZVLX6Mm8Gkq+zfsZFoKwAbjvxDdBs7aJVr/dd7caqIzW/AZetQW18XxRV1OG/vpM+u1bnKgPIANBfMQAk0hI3MoA11gv6iq0nUFKlupA7ZgCFAI47BIAntrTMSlSeB87ssB5XDlR4UHrCav3B8zhYWA5Amvk7Y3iy9MLFE8pOydcBOWuBjJuV5xKv9fiziNpF/WWjplT6O5BLwEQmKZk7Z4xKKRU5CxgdGoxHJqZjon4vAKAZeog+k5QAEPCsG1jd/Tv4TmXbnS5gu1I2/fDC1/m4bO0hGNUvVdV2awBoNwaQawH7KwaARFripAwMACWjpzPgtuv6AABqGprxj29UXayOZWB++wWoPGf//hVn7YMyoOW4I0+yFgAuNzTj75uUwfRLbu6LIIP10iR3AQeHA1FJ0u834wPgf74DzFoDJA3x6LOI2i3coRZgVZF1Rjrazv4BdrUA0aCsyjMrrQ699NKwh72WdGw82di+ALCxDjj2lbQdEgMMuF15zZ0uYFVX9q/Bqfhsv/R3HxUShN+PUv1u8nXEbhYwM4D+igEgkZa0mgG0XrhDovDIpAzbGsEf/ViIoxesr5kcZgGru39jeijbjuP8jqy3f1zm0LXswjvbC3ChQrqhjM/oht+lW7vamhqAS2ek7dheSokNQxAw8A4g4yaPPofoith1AV+0nwBibmMGMGBXC1AdAAYf/9K2/XXzMDz35VHUmQcDsP5bL9zjXttOfae8b8YUIOoa5TUPM4ArjhhtSf6Hb+yDqBhV5tPWBayuA8hJIP6KASCRlrgaA2iKQlyECfMn9AYAWATwzIaj0sL0xgjYbjx1lcDxb5Tjs59UttUDzatLlO5f2UWHmbttKKmqw4pt0v4GvQ5LpqiyDZdOSRNPACCuj9vvSdQhHFcDUU8AaasEDOCwzKJqXe5fNto2v7EMx7lLl7FqX5mSUSw+LK0R7Ir6S1jfqfbBqocZwHW/Sm1NjQ3DXVmp9m2vc5IBZADotxgAEmmJekC2/C1dCLsMIADMGZ2K5C7ShXv7iVJszS8B9HrlYl9VBJzdKW3HdAf6/x4Ij5cen/63Ulz2ly9gK8os86AL+MWvj6G2QQry7hzRHX3MqpuNegJIXLrb70nUIey6gEvdnwACOGQArVm06hJbF29d1wycgxkA8NqWE6hLGCbtI5ql9YHb0twE5FvrCAaHA71uAIKMQKh1hryrAFAIWwB4QRePGkjXhcem9IUxSO8QvMpjANWzgBkA+isGgERa4iwD2FSnlE6xzg4MCTZg8U2Ztl2fWHcYFbWNSjdw1Xmg2brge++JUvdrrwnW960FCndL20f+u2UbytzLAG4/XoqP9hYCACJNQViQ7ZDlU5eAie3t1nsSdRh1AFhbquo21Ulr57ZFPQmk+Ahwdhew6w3IX55C+k/FzOtSAADV9U346EKCsr+rcYBndkgrkgBA+iQlIxchBZSoLmm7nExFoa37+HCT1HU8smdXTOpnPT4oBNAHS9vyEpFcCk4TGAASaYkhSLlYywGgagaweqLHzQMTMSpNyhL8Wn4Ziz47CKGeCCLrM1H6b+8bledObJZqoZ2yLjsV0wMwD5C2L51xWQrmt6p6LPgoz3ZfWjAxHbERJvud1JNN2AVMvqbuAq4qliZJAUDXNNfdoOpJIN88Abw7Gdj+ovJc5hQsnJiB6FDpb/eDc/HKa4UuAkB58gcgdf/KIqzv0VRnVwaqBVX37zGRgiC9Dk/c0g86ecytTqdkAW0ZQC4FpwUMAIm0Ri7LYAsAVRd/1UQPnU6Hf8wcjJgw6abz1eFi/NagLBkHADAYgdSx0nbaDcrzJzdLy1fJY/T63SbdCAEp21hR2GrzLBaBhR/nobRa6kYen9ENc61LY9lRdwEzA0i+ZgyTulgBaXlCeZa9qwkggPK34UxcOpA4BN0iTXhhulTWqEAkokJYP8tVQeiin5TtnuOUbTkDCLQ5EaS6UDk+35KMRf8jAwOuibbfqUUAyKXgtIABIJHWyF0y8g1K/e3fIcOXGB2KF+5QaukdLXN4r+5ZSvYiohuQMEjaLjokFX6WqQNAoM1xgCu/P4l/H5cK6cZbb3p6vc5+JyGULuCoZPsuNCJfCbfOiFV3gbqaAAJIX54mPQ0MuAMYNgfIehAY9ygw+TmptqVeuhVP7GfGnNGpENAjz9JLOramBCg/4/x91St4hMfbd1NHqLKIrYwDtFgE8vbttD2O6jEI91zvJFiVrxtyfVAuBacJQb5uABFdZfIF2WkXcHSL3bP7mTF3TCre23EaFcLhYi53/8p636hkHOTB6VHJwDXD7IvJlhUAuBGO9p0pw39+LQV2Oh3w0szBiHPs+gWkMhtyjbU4Zv+okwjvBpQ7FDp3JwDU6YDRD7n1EY9NycSPp8uwv7gPxhmsf2uFPwJdUlvuXF2srODhmIm0ywA6DwDf+O4kxlefAPRAE/RYMHNKyy9jgNJzIK9kwqXgNIEZQCKtCXIIAOuddwGrLb4pEwOuiUKVcKjq39shAOzVMqhDv1ulG5yLDGBFbSMeXp2HZovUnfXQDb0xundci/0AOHT/cvwfdRJhTv69uhMAesAUZMBrdw7FEUOG7bmTB7Y637lYVYsw3mEtYhddwPvOlOHlb46it+48AKAhqidiY1p+QZQa5bBMJDOAmsAAkEhr5Aty02X7EjBAiy5gmSnIgNdmDUWdQelqrTAmQDiWX0kZqYyDkvW9VfqvOgB0qAVYVtOAu97bg1/LpaB0RGpXPHxjG4HdRXUJGAaA1EmEOwSABlPb4/vaqWdcOKbdcqvtcW3BTnx92Mmavuqse4sMYOtdwAcLy3HvB/uQLIpg0kkVAsKSB7TeIMdSMBwDqAkMAIm0Rr4gC4tUysWNDCAApMaFI6tfT9vjDbX98JfPDqGp2aLsFGQEeo5VHkckSEEhAEQmKtlHVQbwfPllTF/5g22t3y5hwXh51mBluTdn1CVgGABSZ+EYAHZLl2bed4CbR/RFkSkVAJCJM/jf/9qJj/c6TK4qtl/D104rGcDtx0sx661dKKtpQLpOtdRjW5lMuwCwkrOANYIBIJHWqMfkNF52KwMo6zdwuG37S8sIfLrvHO77137UWReGB2DfDdx3qm0Au1038KXTQHMTTpRU4443fsDJ36TVDOIjTVg9bxQSo13cNEpVJWDYBUydhWMX8FXu/nUU31f6shWsa8ZAnMSiT3/Cyu9U2XXbcnROahE6GQP4xU/nMXfVHlvx9Ru7lqo/rPWGqK8bdZXKJBiDUfn7J7+jif9zzzzzDEaPHo2wsDDExMS4dYwQAkuXLkViYiJCQ0ORnZ2N48ePuz6QqLNzXA9YPQnE1MoYH1nGFODm/8TB4X/Hbt1gAMC3R4tx1zt7UHHZWkx60HTpZhOZCIy63/74WKUUzJH8I5i+8gect67zmxobhs/uH43MhLaDUABKF3BQqP26pkS+pF5iDejwAFDfY5Rte7heyoov//IXPLvxKCxNTcBv+dKLXdOkMjVqoV0AvZSdFNXF+OeuM3ho9QE0NktjcCf1M+P2ZNWXQ7czgFVKBpDZP7+miQCwoaEB06dPx/333+96Z6vnn38er7zyClauXIndu3cjPDwckydPRl1dneuDiTozdQDYdLnNMjAtGIKA6+7Btbfch1Vzr0O40QAA2HO6DOP+Yyte/Dofpc1hwAO7gEeOALG97I9XjYda/q8NuFQrBY39k6Lw6f2jkdLV4SblTFMDUHZK2o7tzQwDdR6OXcAdHAAiRQkAZ5h/tW2/+X0B5v5jTdu1CPV6CGsWsLzkHJ5Y97OtnODM4SlY8cehMJRai1kbTECXni3fQ+Y4CUSeYMbxf35NE2Vg/va3vwEAVq1a5db+Qgi89NJLePzxx3HbbbcBAD744AOYzWasW7cOf/jDHzqqqUQdzy4DWNdqIWhXRveOw5p5WZjz3h5crGlAeW0jXtlyAv/1fQFmDE/B7UOvQUiwwbZ/UUUdTh7R4x7r4+46qdspKy0Wb941DJEhwe598KXTSoFpjv+jziQs1v6xO0Wgr0RsL6nbubYUqbU/47nf98eSdYdhEUDYpWOAtW773suJCDtfCZ2qgsvBwnIMrg5FJoCo5nLoYYEFetw/vhcWTc6ArqlemazlaiyjXQBYpXQBcxk4v6aJANBTp06dQlFREbKzs23PRUdHY+TIkdi5c2erAWB9fT3q6+ttjysr21heh8hXHNcDtssAuugCdjAwORrr5o/BP749hvV559FkEahvsuCfu87gn7taFqcdpQ/HPdabUl9jCf7P2HTcMzbNLlB0iTOAqbNSdwGbojt+eIJOJ02yyt8A1JVjVs86pN4zCv/49hgyC5UJIW8fC8OmX/7d4vC3gyORaQAMOoGR8Rb8MXsYbhmUJL148bjyRctVJlPdBVxXyS5gjQjIvpWiImk6vdlstnvebDbbXnPmueeeQ3R0tO0nJSWlQ9tJ1C6OXcDyGEB9ULu6bFK6huHFGYPx/aIbcPf1PRFmbD2YO2VRFrH/Q68mPDihj2fBH2A/A5gTQKgzCe8mTXwAgIQBsEu5dZTuI5Xtwl3I6hWLj/+Uhdm9amxP5wvn96LfRIxt+//OSlWCP8BuDeA2J4AA9kNH6itUGUAGgP6s02YAFy9ejL///e9t7nP06FFkZma2uc/V9Nhjj2HhwoW2x5WVlQwCqfNxnAQiZwBNUVd0w0qKCcUTt/TDwxP64JN9hbaZvbIgvQ5ZaYMh1odC13QZhkutLwfXJvUMYK4CQp1JcIi0pNvPnwETnvDOZ6rGAeLsbmkpOQAxVVKm3BIUgikjs1B22WJ3WJjRgBuaBgB5UhFpXfVv9u9b0kYJGUfqDGBtmVRiCmAA6Oc6bQD45z//GXPmzGlzn7S09hXgTEiQshTFxcVITEy0PV9cXIzBgwe3epzJZILJ5GTZKqLOpLUyMK4mgLgpOiwY94xt429ve5pUnuLSacDSDOg9zABe5Cog1ImN/JP04y1Jg6VJGs31QOEu6bmGGttEKX23TPzlpv7Oj93TA8izbjsuB+dJBlA9BlC9qgiXgfNrnTYA7NatG7p16+Z6x3bo2bMnEhISsHnzZlvAV1lZid27d3s0k5ioUwpWzbR1zAB6Q9eeUgBoaQQqCp2vYdoWeRm4yCTAFHHVm0fkV4JMQNIQKfgrK5ACsIpCANYpveZWgj+gzdVAbEWkjRFAtIueLLsAUPU+zAD6NU2MATx79izy8vJw9uxZNDc3Iy8vD3l5eaiurrbtk5mZibVr1wIAdDodFixYgKeffhrr16/HoUOHcNdddyEpKQnTpk3z0W9BdJWoZ+bVXgQsTdK2hxNA2k1dGsbJmsBtqrmoLG7P7l8iid04wN1trwCi1tp6wFVFQMVZaTthoOuhIeouYPX7MAD0a502A+iJpUuX4v3337c9HjJkCABg69atGD9+PAAgPz8fFRVKQdxFixahpqYG8+bNQ3l5Oa6//nps2rQJISFMaZOfU2cA1d/WvZYBdFgTuNcE949l9y9RSymjALwsbZ/dpYzBA9ouRdNaBvDMD8p29yzXnx9klLp7m+qAGtVYQnYB+zVNBICrVq1yWQNQyBUwrXQ6HZYtW4Zly5Z1YMuIfEB9UVZ/W79KYwBd6qrOAJ7y7NhSdQmY9KvTHiJ/l6LKAJ7dBRjDlcfxbXQBh6sDQNW1QB0A9hjjXhtMkVIAKFTLQjID6Nc00QVMRCrqi3K1qqyRLzKAZSdb38+Zgm3KNruAiSThsUpG/MJBoOgnaTss1j7L58gUIY3xA+wzgGd3Sv/V6YGUEe61Qd0NLGMG0K8xACTSGrsAUJ0B9NIYwMhEpUCsJ2MALxwEfv5U2g7tYp/1IAp08jhASyNw+ZK0Hd/P9fg9OUCUrwWXy4Hiw9K2eYD7PQPOvkAGu7G0I3VaDACJtEYdAFapMoDe6gLW66WZwIBSCsYVIYCvVXXVfrfIecaBKFCp6wHK2poBLJMngtRXSFUBCnfDNoO4x2j3P9/Z3yOXgvNrDACJtEa9PFPtRWXbW13AgNIN3NwAVJxzvf+JzcCp76TtmB7AdXd3XNuI/FF3JwGgqwLOgMNEkBLPJ4DInPUgcCk4v8YAkEhr7L6VqyY/eSsDCDiMA3TRDWxpBr5RZf+yc6XaZ0SkiO0tjflT8yQDCEgBoDz+D2AGMMAxACTSmtbG5XgzA2hXC9DFRJCDq5VlqZKGAv1+33HtIvJXOl3LcbHd3FgKVZ0BLD8D/Lpf2o7t3fYEEkdOA0COAfRnDACJtKa1mXnemgQC2GcA1aVdHDXUAlueVh5PekoaQ0hELakDwC6p7q2Uo84A5m+UJpEAnnX/As6/QHIWsF/jlZZIa1qrzeXNDKB5gLItZxyc2bUCqLogbaffBKRe37HtIvJn6nGAbdX/U7MLADcp2550/wKtZAA5BtCfMQAk0hpDMKB3UuPdm2MAw7oqWcALB4Gmhpb7NNQCO6yrG+j0QPaTXmsekV9Kvg5IGw8EhwPX/S/3jlF38zbWKNueZgCdXT8YAPo1TawEQkQOgsOA+kr757zZBQxIN6uyAqC5Hij+GbhmqP3r5/YobRw4HYh3YzwTUSDTG4C7/htobpS+6LlDnQGURSZKXciecNoFzADQnzEDSKRFjmNz9MHeH69zzXBl+9d9LV9Xl6PoPbHj20OkFe4GfwAQ3g2AQ7Ho7lmuC0g74ixgzWEASKRFjhfmkCjPL/hXKnmYsn1ub8vX7dYj9bA7iojcYwhuWT7G0/F/ACeBaBADQCItcizP4M0JIDLzQMBgref3q0MA2FQPnPtR2o7pDkQne7dtRIHEsRu4XQEgy8BoDQNAIi1y/GbuzQkgtjYYgcRB0vbFE0BtmfLa+QNAU5203WOM99tGFEjUE0FCYoBufT1/D6eTQJgB9GcMAIm0qDNkAAGHcYCqcjBndijb7clGEJH71BnA7qPaV2vTWQaQk0D8GgNAIi1qMQbQyzOAZcnqAFDVDWw3/o8ZQKIOpc4Aelr+RWZ0DAB1XLLRzzEAJNKiTpMBdDIRpLkJOLtb2o4w268aQkRXn1xAWh8EZExp33sYgqT6g7LgUO9PLKOrinUAibSoxRhAH2UAu6RKMxBrL0qlYIQAig8BDVXS6z1G8yZC1NEypgB3rQdCY4Bu6e1/H1OkUkyaM4D9HjOARFrkWKHfF5NAACm4k8cBXi6TCkOz+5fIu3Q6IG0ckHjtlb2PehwgVwHxewwAibTI8eLsqy5gwGEc4D6HAJATQIj8hvqLJDOAfo8BIJEWdYYyMDJ1AHjuRyUAbG85CiLyDbsMIGsA+jsGgERa1FkmgQBAkmoN4J8/k7qCASn7155yFETkG+rrCGsA+j1efYm0yNlScL4SGgPEWQee115Unmf3L5F/MbELWEsYABJpUYsMoI9mAcvUBaFl3RkAEvkVdgFrCgNAIi3qTGMAASB5mP3j4HBlmTgi8g8h7ALWEgaARFrk+O3cV3UAZY4ZwJQRgCHYN20hovZRZwC5DJzfYwBIpEWO3859OQkEAMz97bOSrP9H5H84CURTGAASaZH627nB6PuLtSEYSBysPOYEECL/wzGAmsIAkEiL1IWgfZ39kw2eJf03LgNIvs63bSEiz4XGKNvG8FZ3I//AtYCJtEid8fP1BBDZsDlA2nggMhEIMvq6NUTkqR5jAPNAoOoC0P/3vm4NXSEGgERapO6e6SwZQADokurrFhBRewWZgPv+DViaOIlLA9gFTKRFQZ0wA0hE/k+nY/CnEQwAibQorCugt16ko5J92xYiIup02AVMpEUh0cCU/wBObgHGLvR1a4iIqJNhAEikVcPnSj9EREQO2AVMREREFGAYABIREREFGAaARERERAGGASARERFRgGEASERERBRgGAASERERBRgGgEREREQBhgEgERERUYBhAEhEREQUYBgAEhEREQUYBoBEREREAYYBIBEREVGAYQBIREREFGCCfN0AfyaEAABUVlb6uCVERETkLvm+Ld/HAxEDwCtQVVUFAEhJSfFxS4iIiMhTVVVViI6O9nUzfEInAjn8vUIWiwXnz59HZGQkdDrdVX3vyspKpKSkoLCwEFFRUVf1vckez7X38Fx7D8+19/Bce8/VOtdCCFRVVSEpKQl6fWCOhmMG8Aro9XokJyd36GdERUXxguIlPNfew3PtPTzX3sNz7T1X41wHauZPFphhLxEREVEAYwBIREREFGAYAHZSJpMJubm5MJlMvm6K5vFcew/PtffwXHsPz7X38FxfPZwEQkRERBRgmAEkIiIiCjAMAImIiIgCDANAIiIiogDDAJCIiIgowDAA9JHXX38dqampCAkJwciRI7Fnz5429//kk0+QmZmJkJAQDBw4EBs3bvRSS7XBk/P91ltvYezYsejSpQu6dOmC7Oxsl/9/SOHpv23ZmjVroNPpMG3atI5toIZ4eq7Ly8sxf/58JCYmwmQyIT09ndcSN3l6rl966SVkZGQgNDQUKSkpeOSRR1BXV+el1vqv77//HlOnTkVSUhJ0Oh3WrVvn8pht27Zh6NChMJlM6N27N1atWtXh7dQEQV63Zs0aYTQaxbvvvisOHz4s7r33XhETEyOKi4ud7r9jxw5hMBjE888/L44cOSIef/xxERwcLA4dOuTllvsnT8/3nXfeKV5//XVx4MABcfToUTFnzhwRHR0tzp075+WW+x9Pz7Xs1KlT4pprrhFjx44Vt912m3ca6+c8Pdf19fVi+PDhYsqUKWL79u3i1KlTYtu2bSIvL8/LLfc/np7rDz/8UJhMJvHhhx+KU6dOia+++kokJiaKRx55xMst9z8bN24US5YsEZ9//rkAINauXdvm/gUFBSIsLEwsXLhQHDlyRLz66qvCYDCITZs2eafBfowBoA+MGDFCzJ8/3/a4ublZJCUlieeee87p/jNmzBA333yz3XMjR44Uf/rTnzq0nVrh6fl21NTUJCIjI8X777/fUU3UjPac66amJjF69Gjx9ttvi9mzZzMAdJOn5/qNN94QaWlpoqGhwVtN1AxPz/X8+fPFhAkT7J5buHChGDNmTIe2U2vcCQAXLVok+vfvb/fczJkzxeTJkzuwZdrALmAva2howL59+5CdnW17Tq/XIzs7Gzt37nR6zM6dO+32B4DJkye3uj8p2nO+HdXW1qKxsRFdu3btqGZqQnvP9bJlyxAfH4+7777bG83UhPac6/Xr1yMrKwvz58+H2WzGgAED8Oyzz6K5udlbzfZL7TnXo0ePxr59+2zdxAUFBdi4cSOmTJnilTYHEt4f2y/I1w0INKWlpWhubobZbLZ73mw245dffnF6TFFRkdP9i4qKOqydWtGe8+3o0UcfRVJSUouLDNlrz7nevn073nnnHeTl5XmhhdrRnnNdUFCALVu24I9//CM2btyIEydO4IEHHkBjYyNyc3O90Wy/1J5zfeedd6K0tBTXX389hBBoamrCfffdh7/+9a/eaHJAae3+WFlZicuXLyM0NNRHLev8mAEkasPy5cuxZs0arF27FiEhIb5ujqZUVVUhJycHb731FuLi4nzdHM2zWCyIj4/Hm2++iWHDhmHmzJlYsmQJVq5c6eumac62bdvw7LPPYsWKFdi/fz8+//xzbNiwAU899ZSvm0Zkwwygl8XFxcFgMKC4uNju+eLiYiQkJDg9JiEhwaP9SdGe8y174YUXsHz5cnz77bcYNGhQRzZTEzw91ydPnsTp06cxdepU23MWiwUAEBQUhPz8fPTq1atjG+2n2vPvOjExEcHBwTAYDLbn+vbti6KiIjQ0NMBoNHZom/1Ve871E088gZycHNxzzz0AgIEDB6Kmpgbz5s3DkiVLoNcz93K1tHZ/jIqKYvbPBf4r9DKj0Yhhw4Zh8+bNtucsFgs2b96MrKwsp8dkZWXZ7Q8A33zzTav7k6I95xsAnn/+eTz11FPYtGkThg8f7o2m+j1Pz3VmZiYOHTqEvLw828+tt96KG264AXl5eUhJSfFm8/1Ke/5djxkzBidOnLAF2QBw7NgxJCYmMvhrQ3vOdW1tbYsgTw68hRAd19gAxPvjFfD1LJRAtGbNGmEymcSqVavEkSNHxLx580RMTIwoKioSQgiRk5MjFi9ebNt/x44dIigoSLzwwgvi6NGjIjc3l2VgPODp+V6+fLkwGo3i008/FRcuXLD9VFVV+epX8BuenmtHnAXsPk/P9dmzZ0VkZKR48MEHRX5+vvjiiy9EfHy8ePrpp331K/gNT891bm6uiIyMFKtXrxYFBQXi66+/Fr169RIzZszw1a/gN6qqqsSBAwfEgQMHBADx4osvigMHDogzZ84IIYRYvHixyMnJse0vl4H5y1/+Io4ePSpef/11loFxEwNAH3n11VdF9+7dhdFoFCNGjBC7du2yvTZu3Dgxe/Zsu/0//vhjkZ6eLoxGo+jfv7/YsGGDl1vs3zw53z169BAAWvzk5uZ6v+F+yNN/22oMAD3j6bn+4YcfxMiRI4XJZBJpaWnimWeeEU1NTV5utX/y5Fw3NjaKJ598UvTq1UuEhISIlJQU8cADD4hLly55v+F+ZuvWrU6vv/L5nT17thg3blyLYwYPHiyMRqNIS0sT7733ntfb7Y90QjAfTURERBRIOAaQiIiIKMAwACQiIiIKMAwAiYiIiAIMA0AiIiKiAMMAkIiIiCjAMAAkIiIiCjAMAImIiIgCDANAIiIiogDDAJCINGXOnDmYNm2a1z931apV0Ol00Ol0WLBggVvHzJkzx3bMunXrOrR9RERqQb5uABGRu3Q6XZuv5+bm4uWXX4avFjiKiopCfn4+wsPD3dr/5ZdfxvLly5GYmNjBLSMisscAkIj8xoULF2zbH330EZYuXYr8/HzbcxEREYiIiPBF0wBIAWpCQoLb+0dHRyM6OroDW0RE5By7gInIbyQkJNh+oqOjbQGX/BMREdGiC3j8+PF46KGHsGDBAnTp0gVmsxlvvfUWampqMHfuXERGRqJ379748ssv7T7r559/xk033YSIiAiYzWbk5OSgtLTU4zavWLECffr0QUhICMxmM+64444rPQ1ERFeMASARad7777+PuLg47NmzBw899BDuv/9+TJ8+HaNHj8b+/fsxadIk5OTkoLa2FgBQXl6OCRMmYMiQIdi7dy82bdqE4uJizJgxw6PP3bt3Lx5++GEsW7YM+fn52LRpE373u991xK9IROQRdgETkeZde+21ePzxxwEAjz32GJYvX464uDjce++9AIClS5fijTfewE8//YRRo0bhtddew5AhQ/Dss8/a3uPdd99FSkoKjh07hvT0dLc+9+zZswgPD8ctt9yCyMhI9OjRA0OGDLn6vyARkYeYASQizRs0aJBt22AwIDY2FgMHDrQ9ZzabAQAlJSUAgIMHD2Lr1q22MYURERHIzMwEAJw8edLtz504cSJ69OiBtLQ05OTk4MMPP7RlGYmIfIkBIBFpXnBwsN1jnU5n95w8u9hisQAAqqurMXXqVOTl5dn9HD9+3KMu3MjISOzfvx+rV69GYmIili5dimuvvRbl5eVX/ksREV0BdgETETkYOnQoPvvsM6SmpiIo6Mouk0FBQcjOzkZ2djZyc3MRExODLVu24Pbbb79KrSUi8hwzgEREDubPn4+ysjLMmjULP/74I06ePImvvvoKc+fORXNzs9vv88UXX+CVV15BXl4ezpw5gw8++AAWiwUZGRkd2HoiItcYABIROUhKSsKOHTvQ3NyMSZMmYeDAgViwYAFiYmKg17t/2YyJicHnn3+OCRMmoG/fvli5ciVWr16N/v37d2DriYhc0wlflcwnItKQVatWYcGCBe0a36fT6bB27VqfLGFHRIGJGUAioqukoqICERERePTRR93a/7777vPpyiVEFLiYASQiugqqqqpQXFwMQOr6jYuLc3lMSUkJKisrAQCJiYluryFMRHSlGAASERERBRh2ARMREREFGAaARERERAGGASARERFRgGEASERERBRgGAASERERBRgGgEREREQBhgEgERERUYBhAEhEREQUYP4/i0THb1eHb50AAAAASUVORK5CYII=",
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