P5EP 0. Introduction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Python in 5 Easy Pieces"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Guillermo Avendano-Franco (gufranco@mail.wvu.edu)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is a set of lectures about Python for Scientific Computing. These lectures are presented for those who attended the Virtual School of Computational Science and Engineering [VSCSE](http://www.vscse.org/) at [West Virginia University](http://www.wvu.edu).\n",
    "From June 16 to 20 of 2014 and From June 30 to July 2 of 2014\n",
    "\n",
    "The other lectures are accesible using the IPython Notebook Viewer and ther are located on my GitHub account\n",
    "\n",
    "[Python in 5 Easy Pieces](http://nbviewer.ipython.org/github/guilleaf/Lectures/tree/master/Python_in_5_Easy_Pieces)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Installing a Python environment for Scientific Python Computing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before actually starting with the tutorials I will suggest several ways to follow them on your own laptop computer. These are your alternatives acording with the Operating System that you are currently using on your machine"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Windows User"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you are a windows user, you have several alternatives today:\n",
    "\n",
    "<br>\n",
    "\n",
    "#### Installing Canopy\n",
    "\n",
    "   Installing a fairly complete python environment such as [Enthought Canopy](https://www.enthought.com/products/canopy/compare-subscriptions/).\n",
    "   Windows users will have to download around 250 MB and install the software on their own machine.\n",
    "   Enthought Canopy includes all the packages needed to follow this session an most of the next session.\n",
    "\n",
    "<br>\n",
    "\n",
    "#### Using Linux\n",
    "\n",
    "   Use a Linux Live-DVD, put the DVD on your machine and reboot the machine using the DVD. Nothing of your system will be changed, if you are lucky and Linux can detect\n",
    "   your network device, you can connect to internet and follow the tutorials using the IPython notebook present on the live DVD.\n",
    "   Once you finish working with the Live-DVD, remember to save anything to an external USB, otherwise eveything you did during the session will be lost."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mac OsX User"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mac users have also several options to actively follow the tutorials:\n",
    "\n",
    "<br>\n",
    "\n",
    "#### Installing Canopy\n",
    "[Enthought Canopy](https://www.enthought.com/products/canopy/compare-subscriptions/) is also available for Mac users. It is a _dmg_ disk image, it should be very easy to install.\n",
    "\n",
    "#### Installing using MacPorts\n",
    "If you are confident you a Unix-like environment, this is probably also a good option for you. You should already have installed MacPorts on you Mac otherwise you will no have time to do it during the session.\n",
    "You need to install Python (Version 2.7.x is just fine) and a few other packages such as:\n",
    "\n",
    "* numpy\n",
    "* scipy\n",
    "* matplotlib\n",
    "* ipython (Be sure to add the variants _notebook_ and _parallel_)\n",
    "* pandas\n",
    "* mayavi (If you are brave enough! )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Linux Users"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For Linux users, I do not need to say much... Just go and install python, ipython, numpy, scipy, pandas and mayavi.\n",
    "Just do it now, you will have everything in place in just a few minutes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### I do not want to install anything on my laptop (and I do not want to use a Live-DVD)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ok, you can follow passively this tutorials using the [IPython Notebook Viewer](http://nbviewer.ipython.org), a simple way to visualize IPython Notebooks using your web browser your internet connection."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A little bit of motivational Python propaganda."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Just to give some time to install Python on you laptop or lauching the Live-DVD, you can multi-thread your brain to listen some basics of Python and demystified some beliefs about scripting languages."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What is Python?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> [Python](http://www.python.org/) Python is a multiparadigm, general-purpose, interpreted, high-level programming language. \n",
    "> Python allows programmers to use different programming styles to create simple or complex programs, get quicker results and write code almost as if speaking in a human language. \n",
    "> Some of the popular systems and applications that have employed Python during development include Google Search, YouTube, BitTorrent, Google App Engine, Eve Online, Maya and iRobot machines.\n",
    ">> [_from Technopedia_](http://www.techopedia.com/definition/3533/python)\n",
    "\n",
    "\n",
    "Key characteristics of Python:\n",
    "\n",
    "* **clean and simple language:** (KISS principle) Easy-to-read and intuitive code, minimalistic syntax, scales well with projects.\n",
    "* **expressive language:** Fewer lines of code, fewer bugs, easier to maintain.\n",
    "* **multiparadigm:** Including object-oriented, imperative and functional programming or procedural styles. \n",
    "* **standard library:** Large and comprehensive set of functions that runs consistently where Python runs.\n",
    "\n",
    "Technical details:\n",
    "\n",
    "* **dynamically typed:** No need to define the type of variables, function arguments or return types.\n",
    "* **automatic memory management:** No need to explicitly allocate and deallocate memory for variables and data arrays (Like _malloc_ in C). \n",
    "* **interpreted:** No need to compile the code. The Python interpreter reads and executes the python code directly. \n",
    "\n",
    "Advantages:\n",
    "\n",
    "* The main advantage is ease of programming, minimizing the time required to develop, debug and maintain the code.\n",
    "* Well designed language that encourage many good programming practices:\n",
    "* Modular and object-oriented programming, good system for packaging and re-use of code. This often results in more transparent, maintainable and bug-free code.\n",
    "* Documentation tightly integrated with the code (Documentation is usually ).\n",
    "* A large standard library, and a large collection of add-on packages.\n",
    "\n",
    "Disadvantages:\n",
    "\n",
    "* Since Python is an interpreted and dynamically typed programming language, the execution of python code can be slow compared to compiled statically typed programming languages, such as C and Fortran. \n",
    "* Lacks an standard GUI, there are several.\n",
    "* There are two versions currently in use the latest to date (June 2014) are 2.7.7 and 3.4.1. This could be confusing to new users trying to decide wich version they should learn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scripting languages are inefficient, I could do better programming in Fortran, C, or assembler!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ok, what exactly you want to optimize? The time that your code is running on the machine or the time you need to write the code that you need to get your results?\n",
    "If you devote too much time hard programming in a language like C or Fortran, probably someone else will do it faster, the code could run slower but she/he will get\n",
    "the results sooner, like publish first and will receive a new grant to hire more people for her/his group.\n",
    "\n",
    "Now, what its efficiency?\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"https://raw.github.com/jrjohansson/scientific-python-lectures/master/images/optimizing-what.png\" width=\"600\">\n",
    "\n",
    "([from Johansson's Scientific Python Lectures](http://jrjohansson.github.io/) )\n",
    "\n",
    "That is the reason why Python has a strong position in scientific computing.\n",
    "It is an open language (no license costs, you can use your research budget for buying more machines)\n",
    "\n",
    "One important reason why Python is so popular is the set of scientific libraries and environments,\n",
    "among them:\n",
    "\n",
    "* **numpy:**\n",
    "\n",
    "[ http://numpy.scipy.org](http://numpy.scipy.org) - Numerical Python, operations with arrays mostly\n",
    "\n",
    "* **scipy:**\n",
    "\n",
    "[http://www.scipy.org](http://www.scipy.org) -  Scientific Python, advanced linear algebra, FFTs\n",
    "This library uses blas, altas blas, lapack, arpack, Intel MKL internally for performance\n",
    "\n",
    "* **matplotlib:**\n",
    "\n",
    "[http://www.matplotlib.org](http://www.matplotlib.org) - Plotting library (Mostly 2D)\n",
    "\n",
    "* **pandas:** \n",
    "\n",
    "[http://pandas.pydata.org](http://pandas.pydata.org/) - data analysis\n",
    "\n",
    "* **sympy:**\n",
    "\n",
    "[http://sympy.org](http://sympy.org/en/index.html) Symbolic math. Computer algebra system (CAS)\n",
    "\n",
    "* **IPython:**\n",
    "\n",
    "[http://ipython.org/](http://ipython.org) Interactive computing\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Python is also good support HPC"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Python is able to use:\n",
    "\n",
    "    * Parallel processing with processes and threads\n",
    "    * Interprocess communication (MPI)\n",
    "    * GPU computing (OpenCL and CUDA)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Programmer or Scripter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You do not need to be a Python Programmer to actually used and take advantage of Python for your own research.\n",
    "Have you ever found doing the same operation in a computer over and over again, simply because you do not know how to do it differently.\n",
    "\n",
    "Scripts are not bad programs, they are simply quick and dirt, pieces of code that help you save your brain to better purposes.\n",
    "They are dirty because, typically they are not commented, they are not actively maintain, no unitary tests, no continuous integration,\n",
    "no test-farms, nothing of such things that world-class software usually has.\n",
    "\n",
    "For programs, there are those who write programs, integrated pieces of code that are intended to be used independently. There are also those\n",
    "who write libraries, set of functions, classes, routines, methods, as you prefer to call them. Those are the building blocks of larger structures, \n",
    "such as programs or other libraries.\n",
    "\n",
    "As a scientist that uses computing to pursue your research, you could be doing scripts, doing programs or doing libraries. There is nothing\n",
    "pejorative in doing scripts, and there is nothing derogatory in using scripting languages. The important is the science, get the job done and\n",
    "move forward.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### You should be ready now!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I assume that you have now running Ipython Notebook or you will continue to see this presentation using your browser, in any case I hope you will enjoy this time experiment a different way of doing scientific computing."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Testing your Python Environment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will now exploring a little bit about how things work in python. The purpose of this section is two-fold, give you a quick overview of the kind of things that you can do with Python allow to test if everthing works for you, in particular the external libraries that could still not be present in your system."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The most basic thing you can do is use the Python interpreter as a calculator, test for example:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "365"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "31*7 + 30*4 + 28"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Old calculators use to fail with this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11978571669969891796072783721689098736458938142546425857555362864628009582789845319680000000000000000"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import math\n",
    "math.factorial(70)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.1978571669969892e+100"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "float(math.factorial(70))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is called list comprehension, we will see about them later on. I search for a very obfuscating case indeed!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41, 43, 47, 53, 59, 61, 67, 71, 73, 79, 83, 89, 97]\n"
     ]
    }
   ],
   "source": [
    "n = 100 \n",
    "primes = [prime for prime in range(2, n) if prime not in [noprimes for i in range(2, int(n**0.5)) for noprimes in range(i * 2, n, i)]]\n",
    "print(primes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can know wich version of Python you are using"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'3.5.2 (default, Nov 12 2018, 13:43:14) \\n[GCC 5.4.0 20160609]'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import sys\n",
    "sys.version"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Numpy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we will test if numpy is working on your system"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you get an error in the previous input, numpy is not installed on your system. The next inputs will fail also"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Those are the Pauli matrices:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.+0.j, 1.+0.j],\n",
       "       [1.+0.j, 0.+0.j]])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sigmax=np.array([ [0, 1], [1, 0] ], dtype=complex)\n",
    "sigmax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.+0.j, -0.-1.j],\n",
       "       [ 0.+1.j,  0.+0.j]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sigmay=np.array([ [0, -1j], [1j, 0] ], dtype=complex)\n",
    "sigmay"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.+0.j,  0.+0.j],\n",
       "       [ 0.+0.j, -1.+0.j]])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sigmaz=np.array([ [1, 0], [0, -1] ], dtype=complex)\n",
    "sigmaz"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This shows the involutary property of Pauli matrices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.-0.j, 0.-0.j],\n",
       "       [0.-0.j, 1.-0.j]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "-1j* np.dot( sigmax, np.dot(sigmay, sigmaz))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This shows how to produce the Identity matrix with complex entries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.+0.j, 0.+0.j],\n",
       "       [0.+0.j, 1.+0.j]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.eye(2, dtype=complex)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These are the rotation matrices in $\\mathbb{R}^3$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from math import cos, sin, radians\n",
    "def Rotation(alpha, beta, gamma):\n",
    "    a=radians(alpha)\n",
    "    b=radians(beta)\n",
    "    c=radians(gamma)\n",
    "    Rx=np.array([[1, 0, 0],[0, cos(a), -sin(a)],[0, sin(a), cos(a)]])\n",
    "    Ry=np.array([[cos(b),0,sin(beta)],[0, 1, 0],[-sin(b), 0, cos(b)]])\n",
    "    Rz=np.array([[cos(c), -sin(c), 0],[sin(c), cos(c), 0],[0, 0, 1]])\n",
    "    return Rx, Ry, Rz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[ 1.,  0.,  0.],\n",
       "        [ 0.,  1., -0.],\n",
       "        [ 0.,  0.,  1.]]), array([[ 1.,  0.,  0.],\n",
       "        [ 0.,  1.,  0.],\n",
       "        [-0.,  0.,  1.]]), array([[ 0., -1.,  0.],\n",
       "        [ 1.,  0.,  0.],\n",
       "        [ 0.,  0.,  1.]]))"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.set_printoptions(precision=3,suppress=True)\n",
    "Rotation(0,0,90)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This shows that rotations form a group, the so call Special Orthogonal Group $SO(3)$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1.     0.     0.   ]\n",
      " [ 0.     0.707 -0.707]\n",
      " [ 0.     0.707  0.707]]\n",
      "[[ 0.707  0.     0.851]\n",
      " [ 0.     1.     0.   ]\n",
      " [-0.707  0.     0.707]]\n",
      "[[ 1. -0.  0.]\n",
      " [ 0.  1.  0.]\n",
      " [ 0.  0.  1.]]\n"
     ]
    }
   ],
   "source": [
    "Rx,Ry,Rz=Rotation(45,45,0)\n",
    "print(Rx)\n",
    "print(Ry)\n",
    "print(Rz)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that rotate 45 degrees about the y axis followed by a rotation of 45 degrees about the x axis does not produce the same matrix, or same rotation, that operating in the inverse order.\n",
    "Matrix multiplication and rotations do not conmute. The _np.dot()_ is used to multiply matrices."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.707,  0.   ,  0.851],\n",
       "       [ 0.5  ,  0.707, -0.5  ],\n",
       "       [-0.5  ,  0.707,  0.5  ]])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.dot(Rx,Ry)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.707,  0.602,  0.602],\n",
       "       [ 0.   ,  0.707, -0.707],\n",
       "       [-0.707,  0.5  ,  0.5  ]])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.dot(Ry,Rx)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Scipy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We start importing the Bessel function of the first kind of real order v: $J_v(\\rho)$. We also need to import the RBF class for radial basis function interpolation of n-dimensional scattered data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.interpolate import Rbf\n",
    "from scipy.special import jn\n",
    "from math import pi"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we create our random sampling of data. The \"Sombrero\" function is defined as $2 J_1(\\pi \\rho)/(\\pi \\rho)$. This function is very used in optics and image processing. Lets try to see if the RBF algorithm is able to capture the small waves of the \"Sobrero\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.random.rand(100)*8.0-4.0\n",
    "y = np.random.rand(100)*8.0-4.0\n",
    "rho = np.sqrt(x**2 + y**2)\n",
    "z = 2 * jn(1,pi*rho)/(pi*rho)\n",
    "tx = np.linspace(-4.0, 4.0, 100)\n",
    "ty = np.linspace(-4.0, 4.0, 100)\n",
    "XI, YI = np.meshgrid(tx, ty)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we have the sampled data and a mesh grid to work with. The class Rbf returns a callable that we use to fill the interpolated values in the ZI array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "rbf = Rbf(x, y, z, epsilon=2)\n",
    "ZI = rbf(XI, YI)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can used this interpolated data for visualization. Matplotlib is the standard \"de facto\" for plotting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/guilleaf/.local/lib/python3.5/site-packages/IPython/core/magics/pylab.py:160: UserWarning: pylab import has clobbered these variables: ['radians', 'sin', 'pi', 'cos']\n",
      "`%matplotlib` prevents importing * from pylab and numpy\n",
      "  \"\\n`%matplotlib` prevents importing * from pylab and numpy\"\n"
     ]
    }
   ],
   "source": [
    "%pylab inline\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import cm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now plot the interpolated mesh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfsAAAHiCAYAAADvUmWBAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJzsvXvcJVdZ5/t73rdv6UvS6Q5J6EvoCAkSuWobb+OBo+gnIGNmvALCGT46ZpwzOeMccTwyUWRwvOJxdI7MaI8gilF0UDQj8RNgBFGHOAkQLklIDNCkO1c61066093v+z7nj9qVt+rZtfdTa69Vuy779/183k/32lW1atWqVbVqrd+znkdUFYQQQggZLkttF4AQQgghzcLOnhBCCBk47OwJIYSQgcPOnhBCCBk47OwJIYSQgcPOnhBCCBk47OxJa4jIX4rIP2u7HHUQkY+IyD+f8diLROQJEVlOXa55IiJvEJG/nbL9m0XkjnmWqXDuwyLy8inb/52I/PY8y0RIl2BnT0qMXponR53T/SLyLhHZXtj+LhE5Pdp+XEQ+LiIvLWx/g4isjrbnf79RdS5VfYWq/m7Ncs3c2c4b2/Go6t2qul1VV9ssV2pEREXkOXlaVf9GVZ/bZpkmoao/r6q9aD+ENAE7e1LFP1bV7QBeDOAlAN5ktv/yaPvZAP4LgD81o9aPjTq3/O/q+RR7Mn0fVZPZEZENbZeBkLZhZ08moqr3A7gBWadftV0B/AGAXQAuCM2/OFrPp4hF5FdE5BER+aKIvGK07ecAfDOA3yjOFIjIV4rIB0XkYRG5Q0S+r5D3u0Tkv4jI9SLyJID/ffTbb46OOS4ify0izyoc840icpOIPDb69xsnlPvZIvJXIvKQiBwTkWtFZOdo27sBXATgv4/K+hMicmA0Ct4w2mePiFw3KvddIvLDhbzfIiJ/LCK/NyrjrSJyMLRuTR3/BxH5n6Py/HcR2T0q8+Oj6zww2rdUTnuPTL4fHf33U6N8v19EXiYiRwv7vEREPjG6jj8SkfeIyH8YbRuTBIozBSLyHSLyyVEZj4jIW8y+rxeRL43uwTVm21tE5L0i8vsi8jiAN4x++/3CPv9oVCePjvJ/w+j3V4rIbaMy3yMiPx5e64R0D3b2ZCIisg/AKwDcNWH7MoD/A8AXATyQ4JRfB+AOAOcB+GUA7xARUdVrAPwNgKvzmQIR2Qbgg8g+Ns4H8GoA/1lELivk91oAPwdgB4C8Y/kBAD87OsctAK4dXcsuAO8H8J8A7AbwqwDeLyK7qy4dwC8A2APgeQD2A3gLAKjq6wHcjdHsiKr+csXx7wFwdHT89wD4eRH5lsL27xztsxPAdQAqZZAAXg3g9QD2Ang2gI8B+B1kH2m3A/iZ0AxV9X8b/fdFo+v8o+J2EdkE4M8AvHt0nv8G4LsDTvEksra1E8B3APiXIvJPRnlfhmxG6fXI6nA3gH3m+CsBvHd0/LWmbM8C8JcA/j8Az0D2MXvLaPM7APwLVd0B4PkA/iqgzIR0Fnb2pIo/E5HjAI4AeBDjncGPi8ijAJ4A8GsAftro0V8/GjHlf19f87xfUtX/OsrrdwE8E5NnDF4F4LCq/o6qrqjqJwH8CYDvLezz56r6d6q6pqpPjX57v6p+VFVPAbgGwDeIyH5kHco/qOq7R/n9IYDPAfjH9sSqepeqflBVT6nql5F9GLzU7lfF6FzfBOD/UdWnVPUWAL+NrGPL+VtVvX5UD+8G8KI6eU/hd1T186r6GLJO7vOq+iFVXUHWCb8kMv8qvh7ARgC/pqpnVPW9AG6qe7CqfkRVPzO6d58G8IdYr+PvAfAXhfv40wDWTBYfU9U/Gx1/0mx7LYAPqeofjsr20Og+AMAZAJeJyNmq+oiqfiLssgnpJuzsSRX/ZDSyeRmAr0Q2Ci7yK6q6E8BWAAcBvC2fch9xo6ruLPzdWPO89+f/UdUTo/9un7DvswB8XfGjAtmo/cLCPkcqjnv6N1V9AsDDyEaHewB8yez7JWSj4RIicsFoSvqe0TTx72O8jiaxB8DDqnp8ynnuL/z/BIAtUqE7S2ZhnhtB/uaUcxZnXU5WpCfVcQx7ANyj5Uhbtn4nIiJfJyIfFpEvi8hjAH4E63W8B+X7+CSAh0wWVfc+Zz+Az0/Y9t0AXgngSyOZ5xvqlpmQLsPOnkxEVf8awLsA/MqE7aqqnwXwd8hGxo0Wx6SPAPhr81GxXVX/5ZRjgOxFDwCQbJXBLgD3jv6eZfa9CMA9FXn8/CjvF6jq2QBeh2xqf9p5c+4FsEtEdtQ4z1RGFua5EeSPhB5fwZOjf7cWfruwasca3Adgr4gU6+Uic66nzyMi9jx/gEzC2K+q5wD4TazX8X0o38etyKbyi0y7B0eQyRljqOpNqnolMmnozwD88ZR8COkN7OyJx68B+DYRqZxKFpGvBPCPANzacDkeAPAVhfRfALh0ZKi1cfT3tSLyPCefV46MszYh0+5vVNUjAK4f5fdaEdkgIt8P4LLReSw7kEkYj4nIXgD/1inr04zO9T8B/IKIbBGRFwL4IWSzA60ykiTuAfA6EVkWkR/EhE5xxMTrRGYXsALgX4/uzXcBuLyw/VMAvkpEXiwiWzCyeSiwA9kMyFMicjmyqfec9wJ4VeE+vhVh77JrAbxcRL5vdK93j8qxSUR+QETOUdUzAB7HuDxASC9hZ0+mMuoAfg/Amws//8Ro6vhJAB9AZuz1Ww0X5dcBfI9klvr/aTQN/u3IjM/uRTb1/UsANjv5/AEyG4SHAXwNslE5VPUhZHYAb0Q2JfwTAF6lqscq8vj3AL4awGPIjPr+1Gz/BQA/NZIXqqy5XwPgwKjc7wPwM6r6Iafc8+KHkX28PATgq5B9mEziLQB+d3Sd31fcoKqnAXwXgDcgq+vvR6GeVPVOZJ30hwD8A9YNKHP+TwBvHdmOvBmFEbaq3grgXyG7l/cBeASZwWMtVPVuZFP1bxyV7Ras20W8HsDhkTzzI8ikIUJ6j5QlNUKGi4i8C8BRVf2ptsuyiLD+CWkPjuwJIYSQgZOssx9pfJ8UkSqNkxBCCCEtkWwaX0R+DNkyrLNV9VVJMiWEEEJINElG9iNPa9+BzDkIIYQQQjpEqmn8X0NmvcxlKoQQQkjHiI4GJSKvAvCgqn5cRF42Zb+rAFyVpbZ+zfTlu/YbRAK3B2IPj023gVVjYtNzxyvArAWsujmTvkljK6HO8VXlqXveJhpa6jxD8/P2n8PDZV8noa+f0LSNv2i327eyd7yXrhrS2XOM5VFuk7JUfmaWNpTTyyhHb14yz5hNe/t72wHg2MePHFPVZ4xtSMhzRJ525ZmS+4AbVPWKBrKeSLRmLyK/gGxt6gqALcjCnv6pqr5u8jEv1Mw5Vo5teVtN2m4/y6Q31i9wVXb2cLvdezC841Ow4mw/Y9I2cro93qa94xv/GLAFsHgVUMUasptjnds9Vb37TOco4l0DkDUOW55V1KvgwHZei9SNNbSM3vkTXLPtrO3za18n1lODt32HSW8x6W3O/tZZ8U5n+zlOft7xVfuMpcvPwqbt5S5vx87jpfT2pXJ6K8rhCHbg+NT0VpyYuv0s2PAGwDvk6o+r6swRIeuwR0T/RQP5vgVovOyW6Gl8VX2Tqu5T1QPIHJz81bSOnpD5YUcDig5MYRBCeoIg+xxN/dcGXGdPBowCOI31Tj5Pt8ka1j84iuUihJDmSPqRoaofAfCRlHkSEscaJk/bt0He2UshTQjpIoJmxLM2aGlGQVAWwmx1WpHME9lrnG4ankYfyjyk385p8rGaexOafeg5mj4+ljrnD301xV6TPV/sfbIPn9VqPQOZiuu3bdsW8QnnlKdM2mrg9lmy20PtaSyhz7J3fC3K9Xp6pWwYUFbUMa75c864c7QlHxBCCCGdJtfsh8BQroMQQghJypCm8TnZQgghhAyclkb2S5iu2SfW6L118R72eIvV0Gz+obJolQbXOU3eK4AlVrOfhXlr7CnsCqZR53Gd9zWnPp/3cHp1XFVHzvtFzXZP07fb7Tp6u71qnXsIoY+ad/wsx6yUX6qnMV8N3zrZmRdDmsbnyJ4QQggZOEP5aCGEEEKSMiTNnp09IYQQUsGQpvGHch0ZgnW9nk7JCCGEEACdNdALRMyfJTZQjbUNCTXY8/KzVBnUNG6QF2qAF2tw17aDmllo2gAv9Pxd+FZPXSfeNXrtpupd4uVptxunXvZZsk53Qp3mxAatCiWFgd4Y7RrszYshTeP3pMprMKmjJ4QQQhacLgwNCCGEkM5Bzb6rFOOLEEIIIREMaRq/xUA4AVXoOcmp2n+5cJx3lbEaPpAJIsVyKiYHNAt1kFN1zrk7yfG2z8PTh2XezXdY38bdJNaWo6pdeV6vvLZtA3M5TnisYO019VjNPtZGaJZzjpFWw182BWjLqc6QGObba5hXRQghZI4MaWQ/HAO9tlGsj7a57I8QQkiH4Bg4FWr+zw6fEEJ6z1A6yfauY5ohnafJe/MqqTX6uuSdvKd3ha67zfMOYt6afNtr0Geh6Qm6NdQTTJfR3Uk27757a9Zj85+HPwZP07c46/JjA+mE0oRmH81irMPvE0P5aCGkgyxhuqVmDpeQENJFhqTZs7MnpFGWUZ6qUmRDrXydaHHZCCGkS3CdPSFkRob0+iCE9AW+dQghhJAKOI2fguLMZqzBXez+ocQa4FmSOMwZmkHeLDfRa0ipH9s+fiuH3jdbZ6md3qTOv4rUeXp16ATSeSrydLHvlzrnSA4N9tqmj28rQgghpHGGJLoN5ToIIYSQpAxpGp+TI4QQQsjAaWdkv4SyrJWyFMXPl1k92YVq8hbPSU4S73pNa/TzdpLjNYKq7+tYY4/Yb/Y+TozFOr3xnOg0rcGnyK9pRz1OIB01dXTSyS42BkwnNHtLmIa/vKudQDicxu8qNvIcQLe1hBBCFp5hdfZVCNjhE0IICWZImv3wOvvcMVkxTQghhAQypGn8YRno5S7IFYw8RwghhIxoz0Bvc4P5h9pydNJJjqXvBnmhxnJ1Js9snvaYpg30+kjoNXt16LUb7x55EedCjem6aMDnWeA5Bnuh/q1mKX7rQSuNwd6GraX08Q3tGegN5S0xrJE9IYQQQsYYihxBCCGEJGVII3t29oQQQsgEhtJJtnMdsZ9Lnia1bNJW7onV6JNr8nX081CNPTSwTSypNXlP6627T4P0MQx9cFuNDS7kOeHxtnvna0OjD332vDqy+Z01fbP1ODOL3t6OBD4Z2ww2lH84aZzukHCG8tFCnoZLEHoHfUEQ0kkEwMYmeskWDCLZ2Q8KRQfMakkIuYls3uGz0yeENAA7+0HRtbk5MhW6dyak04iMKQppWJiRvQDYUkiHauiebBe7zr51jb5KQwzVCXPWkOYjIHbN+gaUe7bQNfIJ9HjbsVrbDkvqJelFFMBT5rf8uYh5KmNfIqHPkvsseLYasZp+HQ3fe95i21bTb25Hw3+igVPO2+2G1+Yb6XEXC9bgoKkzL6xYdz3YJCsY7/AXGEG1Vk/PF4R0BhFgozco6Ans7AdL3tFP68gV8536t4ELFpxNAE5jvcPfAHb2hHSIxqbxW2Agl0HGqTMPN48RPZmIdRvN76AFhR/BpHnY2ZM5whfaGKwSgqeQvYqH4qttODS29K4F2jPQi9FBQo1H7P6dN8irc4EpnOYsYfxiJ92cUOcqy8iuY22U51koz1EnMMDzDO48WzCv9fdRqwsNMGVv/xaTDjWezWWJSc/M2O+eAZ8xTnOflaqb6hnxhUaaCaXqeGu/slLYzzufqRNbp3UM9mKNmmNxDfIC9ycurMKFJu/YVwvpVKKxdZNIMXohoK8AMiRiB6YdIrqzF5EtAD6KbEyxAcB7VfVnYvMl80LAbz6SDHb0ZEgM6PWY4jJOAfgWVX1CRDYC+FsR+UtVvTFB3oQQMkBWsT5kpOEGaZ7ozl5VFesq0cbRX9j3vRe4JpbGNfpZnOTE7F93nxBCNfmGneLY95/VkqtOYdOehu/lF7q9i8Tat1hss6u6zTZQy1lYvxc2f+tYKLmTnqqTem1zXhr+GWTyVpXElf+mAE6abbZ8VsM311Ol4cdeQqzmHpueFwMa2ScRUkVkWURuAfAggA+q6t9X7HOViNwsIjdj9cspTksI6RrLAM5G9nG2GcBWDEbzbIaq5a+5w4VlsPJIKpJ09qq6qqovBrAPwOUi8vyKfQ6p6kFVPYjlZ6Q4LSGkiywh6+ztAgxSAzu1zyn+1tnQwJ+DiFwhIneIyF0i8pMV2y8SkQ+LyCdF5NMi8kovz6SPoqo+CuDDAK5ImS8hhBAyd3Jr/NR/004psgzg7QBeAeAyAK8RkcvMbj8F4I9V9SUAXg3gP3uXEt3Zi8gzRGTn6P9nAfg2AJ+LzZcQQhaPfFpfzb9kgbgcwF2q+gVVPQ3gPQCuNPsoMsEMAM4BcK+XaQrTg2cC+N3R18gSsq+Nv5h6hF2CHWtrZg3sPOOTuRvkzRqxLnSfafTMAK+OgU6sE53UUe+6SGizsU55Qp3ueI+Cd0+ijWmrbprXMKwBXCwprCI3IXtI6lgrO+W3BnvAuGGkR6jpQKjDqthntSnaMdDbC+BIIX0UwNeZfd4C4AMi8n8B2Abg5V6mKazxPw3gJbH5EEIIAbIvmFOF9BC+OonhPBG5uZA+pKqHAo5/DYB3qer/KyLfAODdIvJ8VZ0Y8GQgiwoIIYSQxDQ3sj+mqgcnbLsHwP5Cet/otyI/hJFtnKp+bOTc7jxkK+Iqoa0sIYQQ0h1uAnCJiFwsIpuQGeBdZ/a5G8C3AoCIPA+ZwDZ1TXs/R/axPmrmrtF7BU7hICdWk7fYACSRmrzV3Gz2nkZn969zTKwTnSEQeo226Xp16GnysRr/KZNOEUPKvai2nfCE2vjMMM1vdXxPw/dOYZ+90HRVHK0ibT6rc7YXUNUVEbkawA2js79TVW8VkbcCuFlVrwPwRgD/VUT+b2Q92htGDu4msgivO0IIISScljzoqer1AK43v7258P/bAHxTSJ6cxieEEEIGDkf2hBBCSBUD8o3fzmXYCoyVvELX5rqk1uhTB60BOrdu3jvcarexGn7VOWKDZ4Rqc6kDNnUBb9177P6h6+49edzT9KuOGdsnNLhO7Lr81Bp+AjwNvyqYThGvCr209/xX2eyQIAbyzUIIIYQ0wEBiEbGzJ4QQQqoY0DQ+DfQIIYSQgTOQbxZCCCEkMQMa2ffzMrzAN8GBbjwLHmuQMw8nOZZYg7zETnK8wDU2oEqowV4dH0ChwTZSY/NfBIO91HgGeKEGfFXOYTwjQntMreA60zKM9WQUSgMGfNZgzzPQ8+6Tl/beJ3Y7CaafnT0hhBDSNBzZE0IIIQsArfEJmYElZK1OMEOMAkIIIbPQTmevmC4zxfqwCe5EQp3ipA58McttCPVK0REnOUso6//LWP8AmHb+Kub9xe1p8k1Lryme1qafeC9/Tz/3todq+sC4yY1XxmgnPLHMQ8O3ZbaVZLbbd6pXp6Fpa+Njn+22NPsBTeNz6R2ZH7ajB4C1NgpCCCGLxUC+WQghpA80sVKHNAZH9oTMgGJ8OpAtkCwMq5iLn3tCKujGN0vounlvu4unwXtCoSX1A1ylAYZGfXF0RDud7mnuqdbNryKbus+/mPNi1wl80zReO7OEBn0JPb9lljqx9ynWLiD1GyM2P2+dftU57Lp6W69ecB13Xf6kqC2TbnDoOv5Qqo4/YdJbTdqK8uaavDoJTT85/XTuOv+mENAan5CZWB79seWRhaNqGovLUjoNp/EJIYSEsRHjr9xNbRSELCAD+WYhhJCuIwC2IdMJFNkUl9XTSOcYSC85kMsghJA+MCARmPSK9pzqpFyBEh3oZt4GeZ4xXZXBTmRgGy+QzaxOcmbdv0hRF6uy2J+VUCc4XjOIzW8e2Hq2xmahwYM8A7+mDfo8O9RZ6tgLulInuM607a7BXmylNNGwIiMQeQZ41sAuNDCONRCeFwP6NuPInrSLYF22zG2VVkGbJUJI+9BAj5BE5F/NUviXrZIQQpIykG8W0musxECbJUJIFxjQyL4blxEb+Cb6BKFOdix2/9DAGJ7DHKBxjd7TMT2nOqGBbIo6mGK9vIr6/vI9JzihGnzq4y2ppdY6T2+sRu/d17Y1/lnqIDS4TijBgXRimaVhecdEBsrxHBfZ94lNe053SDDd6OzJ4pJr9PnLYA0MjkMI6Q4D6SUHchmk13ghjwkhpA0GZI1PUyhCCCFk4HBkTwghhFRBA71Icp02J3mUu1CnOE0b5HleO+pErGvYIK9u1LqcGIO8KkId1lT95hnQWaOf1Pt30TeAF90wtUFe7PEeTTjxCXXU412TdWRkbds6abBn8d6hTlQ8++x4zp5s2jrhse8nEsxAvlkIIYSQxAxoZE/NnhBCCBk4A/lmIYQQklF0Q7mGbupLPWIg1vjtafZFWSnUiU5w2/Wc5oQSq9F7aetAp4KuafSpNfk6jpQ8jd2mbZ6uluoR6/0pVByug3V2YvJYMdu9IlRp/oLMCcoSMq21mEdoQCV7D0M1/RQavYe9ptjXR6MavlehE09q8OyS7HZzY+072l6z51THpo+jHQY0jT+Qy+gTA/lMJIvLVmQvwTxwEUA/CZ2Bo3hSDTv7uZIPiwjpKRuw3tED605H2NmTITKgkT0N9OZK8S1JCCGp4SudVNPeN0txJBC9rn5a5lXpeTNNg1tCrdvQtEZvtdTU6+a9W2A1PU+Pr/rNS7sznCsoX9gJUxBbyKY1e0udxzXUh4NjL2I1/6c2ruup+TT+aaxXja1z2+7semqrzXqavrfeuk4VdU1JC9bwvXZTbLeTpl1CX/1etCBH47f30Vtn76XnxYBG9gO5jD7DkX53sDLLVrRnGdRhHkfWKS8he8e39SImExhQD9UFuvZxOCNsEa2QdyhLYGffdZbAMHwGRTZ4JIT0Bnb2rTCQT8WFgNbNhCwsA5okibbmEJH9IvJhEblNRG4VkR9NUTBC5k/ugCT/ewrs7AkhQyDFN8sKgDeq6idEZAeAj4vIB1X1tolHKMr2HNFOdEKd5sR6xbCEBrqxVkg1nGjYyYC+GeTZdKjDmypd2AbLcNuJzdSmTyCriA3I2og9gWdJFWuw51HH2Urqtmj33zr9eDVpW2VWtfKMcT2DPK9dA+FvOZunTYcGgQrFbdee0606hp+xbdF7wK1zJ2f3UKc782JAI/voy1DV+wDcN/r/cRG5HcBeAJM7e0I6ywraX71BCOkMA1Fdky7KFJEDAF4C4O9T5ksIIYSQ2Uk2QSEi2wH8CYB/o6qPV2y/CsBVAICli1KdlhBCCGkGTuOXEZGNyDr6a1X1T6v2UdVDAA4BgGw4qCVtLrkTHcu8NXpPB3W016q7YrP0dMSua/Re2i5vrzWzboU/+83pafah6ViN3tvuafQpNHyvYdjt9sbY7U5azy6nbZXadrDdpJ34K5V4+4RO03r71ynTNLxnJ1jDr0PoO9JzsuO0O3tNdveuONUZENGdvYgIgHcAuF1VfzW+SIQQQkgHGNDIPoVm/00AXg/gW0TkltHfKxPkSwghhJAEpLDG/1v0xg2cYgaNgBBCyCKSR3UcAAOZoKjLKugkhRBCSC0GNI3fzmUoykYo0U50LJOsuWI6+uInnmdw5xlBVWTt7e5FB/O2ewZ8Hp5Bnhe1ztp1WYMb15FI1T23mVqDvNQGerYMdrsl1HC0jsFdkSai4HnGpYEGeWPpk8ga/GZURny0TnlmMtR0CDXYC70tltDjvclHr9kl8frjOSrzIvE17GSHBDOQb5bU5LE7izBONCFp2IbsGeuJ+kcWm4H0kgvWg9Udzi5jPSJdPqLni4mQeDaCHT0h82cg3yx1yTvwOnOBC/YdRAghpAw1+0isZh9tIB/iEGKWEYWnyQdqZLYINthHHac6VsPyitS0Rm81eLv9SZN2HYV4+jvga/a2EHZ/G5S97UA3rhhraCIwji1DqMbvpU8hC52Rf3jr6Le8gRinOzbtVdEs7xLPYZUnV3u3wQvmExr8x2uGtdpFaFtL7XTHcbJj66Atpzq0xieEkFlYQxY361xkr59VhHc8hJBQ2NkTQubMCoAvj/6/o82CEDKdAU3jU5gmhBBCBk57mv0Zk56KK1IlJlSj99YmG6wGZPWpKs0uVHoNJTSwjd3f0+jdmVqrtz/sbAfCNXu73TNEaFqj9/C01zrPgafJh67D9/LzNHuvzj27CTMTcNKUb5Zb4lVz6BpvLz9bhaEava1S10dFVYG8B7xOHkVCfZ846+6tht+mbj6Qkf1ALoMQQghJDA30CCGkZ2xCeVTMMBlkgWBnTwgZPsvIOvp82auOfmOHT6ZBAz1CCOkRdiqWDvzIgtHeN0uUjV2s4VRswJFArxpeoBv7Iqq6K94+sU50LLZKrcGMtY0LNsjzDPCaMNBLbYBn9499nGIjrlQR+qB5wX48gz3veFse+zB4dW637zLZV9Th8dFptmD8WTwJ32DOu63ebQu9rfbZtVXkOeGp5bYgNFhOaGAcz6lOpJOdeTGgkf1ALmMB2YD1eRlORRIynZPIvLDllvUC+vIhCwU7+z6yBeveRoFsJKCIi+BLyNA5hqyz34HsA5nPC6kDrfFJa+SBw3KqIvISQsY5BT+ePSE5nMZvmhWUe6+mnehYQr1iOHgaYB0HOXYfRdmyONTU0k79W43MS3uBb8awGvxDJm319zqafWqnOaF4NzJ2/yYItUPwtttr8rbb89vAN7Hl2zX+k9XxrX1JKJ5+nNokyNPoQ29J5UlCnexYPA3fyz/QyQ4JpqOdPYepU8lHJ6wmQghpjgGN7Ln0ro8osuBhWvg/IYQQMoGBfLMsKBzZE0JIc3BkT8jQUWRi6Qpouk3IArPcwJ+DiFwhIneIyF0i8pMT9vk+EblNRG4VkT/w8uzIN0uoc5PU0cdCI385FjihDm/qOMRpOqqdrUJrEGMja9n0GNZYzqZDDfLqGOh5DlxC2kmukwDrHf82lL+PYyPGxYYurGNElfrZ8gytQh0NOVHt3P3rYIz2bKQ877bM8rxOI9SOM9ShlmcbV+uknmGlh7d/pJOdASMiywDeDuDbABwFcJOIXKeqtxX2uQTAmwDdInhXAAAgAElEQVR8k6o+IiLne/l2pLNfYDZivf+g9t4RJjktWEEWTYUQshC0M41/OYC7VPULACAi7wFwJYDbCvv8MIC3q+ojAKCqD3qZchq/TTaP/nIHOQNx3kAIIWRm9gI4UkgfHf1W5FIAl4rI34nIjSJyhZcpR/ZtUnSOkzvGYYCODiCo9lTEx4WQhaK5kf15InJzIX1IVQ8FHL8BwCUAXgZgH4CPisgLVPXRaQd0gFCdsGkitdRQqTYnH91XbQ+9U55Gb+Vt6yTHamR2/7FZbi+QTagm7wW1qfrNayeeWFp0rabI2uEpZDdlG4DtU/YHgK3O9nlo9l4gGs+xkLd/aJ17DS/22a4T0MXWs9Hwrf1JqCYeGyjHktrJTtUAItje1At04+E53XECLrVpH9vMjOsxVT04Yds9APYX0vtGvxU5CuDvVfUMgC+KyJ3IOv+bJp2Q0/htQiPvDiPIOu9zkXl5o8ZCCJkLNwG4REQuFpFNAF4N4Dqzz58hG9VDRM5DNq3/hWmZdmRkvwAI1vV5IDPGy70Ct+E1lRBCyHRaMNBT1RURuRrADchGGe9U1VtF5K0AblbV60bbvl1EbkM2bPy3qmp9kJdgZz8vipHqFNktXB39sbMnhBAyQlWvB3C9+e3Nhf8rgB8b/dWipc5+DWWNJlSjb3pdvbe/wWpiVZpfMSStmP28dbR18DR6m7b7W8nMavZjt8QLQhO6zj5UK64slCFUbLVBWazmbrfbNeHe8V7a++qr85ycMGlbb169x9632HX588DcdzX3zV5i6PMZGiin6XX3VQrUWLVHBq5x73voun3brloKVUgPeiQI6vKEkIWlqpuhU5F5M5Bvlh6wiuwLm0vrCCELRdVLbw29GGsOaGQ/kMvoAWew3r5Z64SQhaY/ox4dyEKcHnxaDYhVxJsbEEJIr6iasmfXM29aGmPmDktyQgOWeNtjzdsDq8X78pvVyU4Inj2NNbizaetUZ8wezta5Z7jlpT1DsFkMt2zFegZx1qBuV+B2Z387eLE+eTabtGufZ8pv7xkAnDRlcg0r7Wod6/zIcxxkj28yOFEdqirR/uYYRq6Y7U862aUOlGMJdbJjz1dVJXO1i9TRCaWQ9qjjLKl5VIDVgczEDuQyCCGEdBtaKrcJO3tCCCGkCo7sCSGEkGGjAqwsN2FfMP+lhy1q9kXRyOp2sTqeF3TBEiiaWy02Np5JHULjjXg+Lqxmb4OBjBGqudu05+xlFi031GmNp7lfMD0tJr/dZvedgWkvTo7FiykDjOvLNgbWl81JH9pXTqutgwdM2obNtoW2mr+lqtBFQp99e37b7qr28ZwZme2evUvbgXI8jb7q/PYdNjbD7mnmse/o0KBVbThfGhYc2RNCyNNYr4hkkVERrG5oops83UCe0+H6B0IIAZCtNGjJLSshDcORPSGEAMic2vfH2QuZD6vLw/Cqw86eEEIAZEZTCnb4JEchWI12lNANknT2IvJOAK8C8KCqPt8/Yg3jBltFQo0xQi8jscVMaFSqFG3Hs2fznORYe7sxAx0vGlqo05zQaGmWqntip1yt3mot6M43aWOcZg32zjOb95v0hU7aM9CzTna8aGmeoRgAPGLS1ufN/U76iKnnY7aOrFOdWA9RXjuwaXs+e3zV+b22vKGw336sq5sK4ElATbuy9W7vm1fkUCdcltBgj1WvO1sGNwqeZ1iZGq8SSSipavBdAH4DwO8lyo8QQubMGQBfAvDMUXoFdASz2CgEKwMZ2Scx0FPVj8Jfc9MgfCAJISnIA1icAd8rZEgMZG5kFZnONowvMEII6T5rWIS49KsD6SbndhUichWAq7LUHqR3klB01JNawzc0/U1Rp2qspGWDYdg8rIY/dg4v0I2ne3oBUGIdJ1UtifI0+r0mfaCc3HDW1M1u+mKT9jR8Wzxb/C3OSPIpYzhmzSAA4DGTtpr8UZP+oknbMh62aRtox3NQ4+FFcPK8Q9XR7G1b9QLj2Dytkx1z/Lyd7MQGxqk6xn3n2ANOIewZjnXCkyJaWDhDMtCb2zp7VT2kqgdV9eD4G4UQQkh/oEe7vjGM+QlCiI8ge+L5niakFhzZG0TkDwF8DMBzReSoiPxQinxJW2jhj/QeAbAJ2UzofnBijSSA48S+keSOqeprAo9AvIZTpB0952liNbpZ8DR6qyO6y2TzHVaRicH5CXIjnNQavi2wF6ykSrMP1OjPMnk8x+z+XJO+xKS/0qTt8QfKhhG7LzxWTi+VF71vNb4mNjn+sk9jUyl9YmzNO/CoWcz/4L0XADduAs6M9H4BcC6ya9uCcVcDzzBpL3jPXeaHk/YeeLYaoe8Bb/+qhu6tzffasrlGdTR7b929508hlNDAOFXHBAfGWQawGeWLK5449fRRyv4ijKGM7Pl5RgzHkXXu+dO/BI7we8waqt+TnMon0SxjvYNvrzMm9WBnTwooqpfS0H1ob1lCNoVvJw1angwjpA8MyakOO3tSQEZ/diTPkX2v+aozwGc2ZrdxVTI3vZvbLhQh3Scz0BtGNzmMqyAJ2Y5sKj/v4Gmo13u2K3D5aeCkAA9sou8pQhaQljr7ogOcWQgtduScpZ3FbmMKNNS3iDUaCnKis4x17T6f1vcC3XgGeN799hydnF1xjHHwYg30PIM8G7LJTZc/ep75FYdL6QPGA80FeKCU3m2i0uw0UWs2OwZ6p4yB3qM4d2yfh4zR4gN7LiilD196oJS+78JyGjtNY7fBery2f7u5TyvWAjDUcDPUwK/q3WDPYYNw2bbtOdkx1+gVyTOmnXdgnKrfggPj2AxCA9eEGmq2pzvRQI8MGAEtuAghZDiwsyeEEEIqGJJTHXb2pAZryKY+89E+rbvIENiAbP76FLJp4rl5Dyc9IROc2dlHsIbpXl5iixUaaSJQD/L0rlDqLFH1dD+73Wr2Y1id0t6P4vbHzQnzIBjF3zwt1sNzqmP1eQA436SNlnrAbLZOc6wmf7Cc3HSwbJdw6a47THZ3mtOVo8rswX2ltNXwd+LR8vnGohWVOW0+sqwDHQB4AGWN/t6nY7NXl+Guryg7/rlzZ7mSTm+pspUoYNuuve132ft2gUl77SZU06+Sn6oMXLYie+4F2fvoFLIHWcLLZIMBec+q50/KkjowDuA73gkOjOPdl1i4jj8WjuxJDao6oWVQ1yf9ZQuq3cbRpwQpMpyld5y3IjWoegFyOR4hhPSFYXyykIY5GzDLxMZdshHSJ04iG90Xxzsc1ZMyNNAjC8YmZE0lX3PPCSHSd04ia8+bkBmcLoGdPamCnX0UoVHvWv4mCb3XTRTXs3+xBnljs+z2AOtYJLWhlHd/rQGeNfixEd12VORhjL2eaTYfMOnnmfSLy0lrkPeCXZ8pp1FOPxdlg71LTXo/jpTS1mBv56nybMnWJ6viEqxzYlv5I+vRzeNOdaxB3hHsL6XPG3PsUzYS3LyrPGPzmYMvKKVPP2UM9p4wBXjUSR+zUfGscybPcNQz/Kxqd/aBXCn8+yTGjT9D27Zpy14UvFhCDfaq3l+ej5zgKHjeCWyd2u00wGsajuwJIaQpNmC946SZS+/gND4hhJDpbEbZ3fCkoJKEzAF29oQQ0gTbMT4dTrOAXsEQt9HEBsKxDOybpcoJhrePK3l5mrvd7nlL8fLz8ALfWI2+wqmOmDwuNNsPmPRYIJxyma3THKvRvxifLKVfaLZfapzs7D1S1sdxtzn/wyb9JKaybVt5WLht10Nj++z9ivJve55ZthM414joZ43ZbpQ5tascfOezzzeehx41vdeXTQa2iA+Ze6b2vtpKsZq+F4ClilANPtR+pSIwzqROfbXidKk1/TrYagwOjONl6AXG8TR8kpqB9ZKEENIB8vFMUbMHOI3fQ4biVGcYV9E0dKxFCAnlYQDnYn3Qeho00usZNNBbBOzXOJfhEkJCWEMmY4yHMCBk7nQkEE5oMTz9pwGf7Ruw7kvGCzyROlAO4F+Sq/N7Gn3odk/3DI32YTV7G4Bl9/gh9ier2e8zaaPZ77vocCltA9tchttKaavRvxi3lNLnfsrU2e3m/F806QdM2tHssc2kbUwZAGZpP/Y+ryyab31RucyWk8a/wXFjO/HoV5TX9h+931TqUZPhPSZt79kx+8ODJm0rybaTWQKwhNqrBLb9VUd/9gLlxL4vvPcTMB7uIjgwjrdD04Fx5sOQRvZ0hVYXjuoJIYT0FE7jT6KoreWRMAkhhCwUXHq3SLCWCCFk4VCGuCWEEEJIX+hpIJw5OGRYQlbMri6VcW0QPQM6z5FIqAGeVyDPitFzsmPTAJ5h0p6B3oGyVZINVHPAWNA9G3eV0tZpzphB3s3mfJ8xaWugd69Jhxro7anYxzH6O9fc50tfVL6mR4zp+DFjUXevOenRA+VAO9i3uZy298Tes2PefbYBkWygnDqEOnjxjMuc/dW05TpOsmIIDYwz6bdp293AOB5eIRswqk7AkAz0hjE/kZr83hYDWFCzJ4QQ0lPY2VvsenpF9mXOmiKEkIWDI/uhwsAVHWMDKqfwCSGkYRgIJwkhGs0cgyZYb3mKcYcTNh1ai7b4KfxNuBpaqKOQpp1k1NHoN2PMc8wy1u+N9Uy2e7T9UmQ+ebaW9z9/T1nQ3mNEc6vhX4zDpfRYYBvrNMdq9NZ/jdHsHzaa/ePOI3G2aWe7rP8ZAHhseh5W999rgulcvP9wKX0EZU3e1pmt0wcvvKh8Auszx96zMfncBkCy7cKL4JLiYYp0qmPL7D1KNm3MHsbwTA7q4DnRsU53XOZxX0gMHNlbrFGegmsWWuM8jFW+Zz/xImShRfOX2SrKHwiEEFKTIS29G8ZVpKbLVvgLReD02SaUO/ocGlgSQhYcdvakw5xGNqdZs6deq78rIYTUgQZ6UcSus/fSVudzNLXQ03t4ethciF232rTGVuceHkdWmaOg4JtQ1jPtuvMdAB5Bpgvn92AZwDnZ4TvxaGn33ThWSl9ggrCcb4Oy3G3OZ9fN/8P07YfN8YfN7g9jOrvMLT1gywPgAJApH/k30kcBnI/1+rDBc8wy+fP3l6/Z1omtM1unD55jNPty3Jzxe2YfxeN2XX2dqC6hhGrwnobfceq8v7zH0b7T3MA41m+HZ6iUwhCBTIMje9Jh1pB1gQJsP7/eqP0IgBPIpvP3IOtMFmm0LwC2YP2anwJwP4C9aU+jANae9jy1SBVMFgk61SFkrmhYf/LQ6O853o7NoQqcXgGWBNg44V1xAtlkwSoyR3NJuswqY9LTWF9lkoDj2I6HcB4AYC+O4gFciJV2pq8IaRR29oSQiaycBu68ETj1JAAFzt0KPMssQXscwCew7kn1YWQTEXbWOxmJBt+nsAkPY/fTGW7AKi7AA7hnzDcxIaRLsLMnJDFf+jTw1KijB4BHTgLbjY/6O1B2mb4G4EEk6OxXUR7FL+Fpm4UUnMamUloAbMAKOJ1Phgqd6kQRaqAXGnQl0iBv3tgZ0EYCZ8Q6xYk12PMMrRwnO1WORuxt3WLSO2zyeFB656lHyhlYJzY26Mxo+4lHUFq6qQo8/Eg57k3ZrC3jDMaN9oo8PmVbztmjk2w+G1jaAJy1Z5RxXvYJZc6x17xj83gdPYJdxhWFjOpOxr9Wtpu0vWf2vh6fh0FeasnBeR81bWvmvcWr3iehTnTs/u41eQeEGuyRWFijhCRm02bgjHl32VfbBQDuw/o3gVTsE8Op0ZfBWRdN3y+U7XgCO3Acj2MHBNmypCewHRzVkyFCpzqEkIkcOADc8TlgdTSY2QTg7CWURlhnI1sRl08CbEc/HIoKgP24GyewFSvYiE/hRVgbyDQnIRYa6BFCJrJ5C/BVLwDu/xQgMlryXjHw3QLgmYW0t86+KwiAbTgBAOzoCekJLWr200Sf0AnNOTtgsMULrUXPf0SS96fnJWMAS6XsJVnNfkvZ5/EmnC6lN5v01lEH9nT6ybVyflbXfHJyehnAmdHh+VlsZ27TxxHG2RW/2WA6u6aUEcDYNdlr3rq5XCe2zmydYoPxM73BfOW4zTD04fKcuaQg8fulD/5i+A33NEMZ2TPECyGEEDJwOI1PCCGEVMB49gYRuQLAryOb/PltVf3FFPkSQgbAKjItg/OIpGcMyRo/+vETkWUAbwfwCgCXAXiNiFwWmy8hZACcQuab/2EAx5DcRz8hpB4pPlkuB3CXqn4BAETkPQCuBHDb5EM8pzqegwUvyl3LhNoY2e1VVeNVgbX2NnZSvWcA9oQLyUMot8VtyNYZPpHqBCkWLIY67WoY6wRnHm2/8cFrnZdcN2nDQK/ubLmIfDeA9wL4WlW9eVqeKSbW9iKLNZZzFBXf7yJylYjcLCI3h9sdE0J6yVrFb/xwI2QidWfLRWQHgB8F8Pd18p2biqaqh1T1oKoeHPNjSggZJlUjxqfmXgpCZiJ3qpP6z+Hp2XJVPQ0gny23/CyAX0LNJypFZ38PgP2F9L7Rbz1h0/hPQ5sCJ6QtzkN5zfYxNLMUnpDh4M6Wi8hXA9ivqu+vm2kKpeYmAJeIyMXIOvlXA3jt9EM8pzqhDmCs/lM36IugFRNhz6lO1eV6gSrG8ALNhDormYfzkilU3UJbb/b79qmyIYON2HbKpE9gazm9rdw2tm0zc9LbMDV9tqniXaa8dQLblI530lXn9MpoA9GcNo6JTpkdbJ3ZOsVKhavA87Aeie+zZtvYffWe5SbwDGIaPp1lGCu9BkNDmv15maT9NIdU9VCdA0VkCcCvAnhDyAmjW7WqrojI1QBuQNZM36mqt8bmSwgZCAJ2YKSXNLjO/lgmaVfizZbvAPB8AB+RzA/3hQCuE5HvnGakl+QTVlWvB3B9irzmi4JxuAkhhHSIqbPlqvoYsvkyAICIfATAj3vW+MPwFhDFKYwt3WPfTwghC08bTnUmzZaLyFsB3Kyq182Sb4uBcIranCdiW2J1PKsLRq7Tt7M8oRJgncu3+9hz2vRYHp7dg6fpe3YUoffE3gObNjYBpyrCvlizAavZH7fJHUHpRzefW0pv2/VQOcMLzPn2lJO7HiynD9yNqVQFtinlZ9IHqvYxZcD5Jm3LbLYf27q7lH4UO0tpr87G1s/btL1nY7YodgevncyD0Ac48PAu0IfgPAtE1Wy5qr55wr4vq5NnH5ohIaQmqoCeyf5d2tjBSaozWP8oq1gIQ0jXGErUO3b2hAyEJQFWjuPppaNrTwHLx4Glrri1eBjlELsnAewE8Gg7xSHEI19nPwQYmoKQgbBtE8Z8RKx9oZWiVHO44redFb8RQpLDkT0hA2G54tNdu+SCnEML0jMY4jY5nlcZz6CurhOdjmDbjg18UXVX7DGez5sxgxvPwC7UkZGXvxdcxEsbQ62qwClPmvQjJv1YOWmNzR5aX70CAHjAWKs9aNJ7LzIGeheb8z0wvXwHzOaz7y2nH3eMpMac9BhjvLUzwOpJrI/uBZCLAVxS2Gl/+RhcVE7aa7Z1YuvM1imOmfyL9+Q8AF802++DudWeU53Q9CzEOqSaM028/uw7KRpaALZNx1otIfNnN45hNx7CMlZG+lznzNpqsbQR0HOAtQcAKCDnAMtf23apCmxD9sbJO5IlUK8nnWco8eyHcRWEzMh+fAnPweexjDUogCWs4Qw2oq8d/vIF2d/6D60VpRoB3zqkN9BAj5BBoE939EDWD2XREqrishJCSH9p0alOUcMJ1ehDneJ0XC+q8+EY6vPGzcDWmU2H6pb2nngFcpzo2LQiC9qyGVlQlZMYnwI2kjruLycfvLfsUebePXsgxnxdIXgE5+JxnIMvGpX9/P1lLzl7n2dOaG0ILCYIzS7j0GZX4PHWiQ+AcTuCF0xP37//nFLaXvMRI/Lfa05q69TW+dg9sfds7NE0npBcJzt1CHUQ5eE9G4Gnb/otXPX6C63GjptBNQVH9oTMmy3IvkeWkb1bdyDBC0hwD/ZixTwGNvodIYT0HapnpB9sxriM/hTCB2WGv8E34+twI/bgPixhFV/G+ViJzZQQMhiGMrJnZ0/6SwIbuhVsxN/hmwEA34i/i8+QEEI6CDt70g9OAtiKrIPPIxNHxi8ihJBp0KlOcjzjrsRR6uZNaFup2j/UyMeOejXUSMlut3VuDadCPXvY/U+Y9OPl5OmTgC4BmzZkUV6eWgE2binvY43Djpr04c2l5JE9ZeOzw8a67QKUDfLONdZlW190i9lu6sQa1NmIc44TnjG8/IBxpznGIO+RF5Xv4+24rJT+PJ5TSts6sQZ7tk7H6tzeky+b9Fg7Mvd9rF004UHGa+s27TxLTa/abMLeOLlvomFY9LUR4rYphnEVJJJVlD2ddHSN+Zk14MzptktBCCG9g539wrMK4EHg6bXla+ieJxZCCGmHoRjocendwnMcGHMiQ6cyhBAyJAYysg/VhxxBSqdvjsZb2VV1OfZO2Y/NLU76pD1pvpbcRIsBkE3jR3vxMXh2GY5TnTHvLAAe2ldOW33YBl0xux/dd6CU3nlROZLODuPgZRNOjZehwKUvurOU3rvLlNnq6Q+btKfZG3ncxKjJMIFtrNMcq9F/2oj6t41p+M8upY/efaB8grvM+T3Nfuw22h+acKrjOcEJdSDl7G+fTS8di60SG8Sm6nVn9/HiD7mBcbz7Yrf3Q9MfklOdgXT2ZHZ2IOtlil84bBaEEDIka3xO4y8825HFHs2N8jZifAhJCCGkz3AIRwDsBJwpalKfJV2FQLG2BCzR/IGQXsOld50mdC2u3W40OG/NqatnBVKl6dtzREvq3gGpA+NYbKVabdZqt1bgBqBmofkRU6bdZv9nmvR55TLfuf25pfTmXdOX+Z02MyCPYCe+cvVzeCbuwxoEj1+4GQ8sXYATki2Qt4F0dp4q2whsfXL6l8FpY4fxyNZzxva5zwSqsevi70D5Gq1GP5Z+uJzGZ819tpr9YZM+YtJq24W9r3advefPwaarGn7sOnpvf0NgnJzkb+E6r79QTd61Y0q+UJ8kZqCdPSHzZ6c+ggtxP5ax9rTKd8HaA/ji0sWAdNR3QWdR9MWIiwwXGugRQsY4S58a+02gECi0q46KOskqshF/08tiCJnOkDp7GugRkojjsh1S6KAUmUMOFT5mYTyOzNcDO3tCUsGRPSGJeEJ24E65BJfqncjH9PctWUMB4pPaCIaQ2RnK0ruOdPY9ix9upUTrwKYJbHuLDozjGR2FpkMD43hePKyhVoWBHu4pJ48dKKcPm913mrS5b6c3nF1Kf+Zg2eHMqV2bSunj2FFKP4DzgSVgg57BVpzATjxaGtXbwDo7N5cD62zePH1FxCljEPjo2AWNylDAGuzdZQLdWKc5dzxeNuA7fUu5TvAZc8LbTfofTPqYLaG5Z2P39SQqGiuy9rKKNE51PCc5ocapBvs+8E4XiudEx1K13bOnm7u5RB7KMjdS7UjXNCBYo4QkZkU24nGcg3PGPlhIPc5C5ugpn8ovBmoiw6V761QZ9Y4QQhpDkMXzVVTExCWDhB9zTcPOnhDSQQSdDbVMFoYhWeO31NmLc+pQrxShBApSsXF2Umh29hj7IZwsME5OrOOR0MAXVvO3U+BVjkzsb0ZfPrxr+u7OfTi9Us7vs89/SSn96EXnltL3Gn18D+4tpXcbAdsG2tk6VgemPJhuMwAAx4wnIVsm62TnS3eXNfwxpzlWo7/VpD9v0odtiawm/4Cz3d53zzlLnYcr1ElOYNoLUuXZ03h9Sez7p6oKkwe+CU33h6F09lwTRAghhAwcTuMTQgghFQxpGp8je0IIIWTgcGRPCCGEVKCgU51IBGVDmra/Oax1imO5ZY1XQgM8OUH2KqvDO0d0FLxQIyVr0GeNy2yBQp3s2PyqnOo4zlBWbIQ2Y8BnsUWwru6PlfM7+pyycdu9zy57yzvvgodK6d0op62B3iYnzLCNsldloGcd7TzwQNnJztrhbeUDrIHd50zaRrWz2216xd63oyYdGuUu1LCrjiGnbbu2HhNHuUvdV3jvH8/4rioPe0zjUe5C92/L8dpw1tlzGp8QQggZOMP4ZCGEEEISMyQDPXb2ZM7krk/XkE0s0XEKIYQ0TUec6tQRrVMSqAN6GpmnmYU60agjZ4U67ths0mNxa5oOjLMBWcWcLvy2iuxCBOMX7dkAADAauNtuTh4op283Gr495ZMmbYO63FdOWj38wQtNeudF5QO2m/y2ODd+xdz0Jyo+lGwZ7zfpIyZt49JYjf6wkz5pK83u8KBJW83+uEmHarmes6eqfby0te0waVvtoYFvYl9v3vvH0+PrHDNGqJOc/jrRsXBkT0gwVQZoa0hvwUQIIfEoZDDW+DTQI4QQQgYOR/ZkjmxEeRof4PcmIUOnuI6vXzY6DHE7QkS+F8BbADwPwOWqenPNIzF9nb23cDU0UI6nA1p9yWh4ds2pp9F7Gr9X67MsKfXsArwqG6siT8e0Wq2XXkFWyCVkHb6OCrFc2D6tQNODxGQEVtzKvnL6LhM4x2r2Vv+2S8gvMOlnmPQ5Jm2Xd29wGoatEit3A8BjJm0jxFozB0/DN3YJ4+viD5u01eht4JvYdfVeQ65aE594Xb0XZMrax8T2FZ6e7vmHqDreVnv0uvpJ9221TuZo5qVIisQ2w88C+C4Av5WgLGTwCLIX71bU67wJiUUAbML6DNIKss6nXyPMfqKo19F3GxroAVDV2wFAhA8OIaSLnIPyEs98VcimiUeQVPS/ox8SwxAjCCGkks0oj+IFNdaZkST0fxC4UE51RORDAC6s2HSNqv553ROJyFUArspSu+seRgghEawie83lHQ+n8OeHjP76O8JXCFbXFqSzV9WXpziRqh4CcAgARJ6t053qzNvJjmdssnH65tDAFKFOdqqwVWTLYLd7RkXWqEc9gzxrxGSNnrxoHF6lzGKgZx22eGWweRoLu/v2ltP3m0q1BnvnmfROkx5zouOkLfZyqtwWPBqYtgZ4Y4aa1grRM8DzAt1Yq+dlJ8cAAB7rSURBVMJQg7xQhzgrKFesILtRxVUgc3ai4z3voU68PCc6dQLhjJHKic4yss4+dDaFBnmp4TQ+IWTA5E6b8tGlgMs9502PZ1IUWLGeK3tKVKsXkX8qIkcBfAOA94vIDWmKRQghqcg7eMZiIItLrDX++wC8L1FZCCGEkM6gKlhdGcYEeIuBcIqajCdyNR1Jwttuzu9palUxYKbtX+dyPAnLC4xjt3t68ZhE7mn4XiVYbbWOsOht9xyyhNoN2PyM3qzG6c59xtD0PnON9pK9OvfaQR0/JtYR0Nh9tAdZjT00bb302BPGavS2UjxbEdtOq36bpPMvoXJJ3ryd6Fi8++450akyd2nMic6k/S2hTnTa6aqyzp7T+IQQMiA2gdP8ZKgMY36C1EORfXDnNktr6POqGEISwnEPqUDBkT3pIXlHD6zbLHEgQwj41UuGDkf2i8SaSfff5wUhicjXgi+DX8AkR1WwcmYYI/sWDfSmRZaat8Ge5+DFiYLnGeRZAxrPmC4FVVV2BuMdfr4iyXWy4zkv8YzdZjHA84iNlOeV2RqXWeM062DGGIudNHV00tZhaDuuYyTlRR/0nNzY7d7+3vliDfLqGtflWIO9qt+MoaXNM5/xyvv8tp3oeM6UPDvUWv5sUjnRmRXPArktJzuCtdVhjIk5jb9IWKthQsg4lLfIABnGJwupxzKy6LIryELKE0IImYwCoIEe6SUTlhITQggZLh1xqhPqYMEj9LKs/uQ5gHGc7HgafVUAk2mnryL0Y9NWiZ3StzqfDdpipVo38E2ow5sqrTWUUA3f2271ak8v9oK0hNqieNTR7L1rPOFs99KhzlZCA9vYtNXbnSA2dfaxU/aeM6R5O9HxNH3PiU4tA9yuO9FpqatS4cieEEIIIf2Amj0hhBBShQJYGYa1Jjt7QgghZBKzrAruIC119kuoJ0znpNb0vXX1nn5lzmc1MW9drCdPzdK4PFnJk4etLumt3T1pM/DW3cdS1QbGDAkM3n30IhJ5erdNx/qH8NpxnTr1tFbvmkIDFDn2LO41hga2sWmr4dv0pN8K2LZv7VXs9nmvq48NhFOJZ0MTGlQqlNB19W2tsx8OHNkTQgghVeTxRAYADfQIIYSQgcORPSGEEFLFgEb27OwJIT1mC9ZfYyvI3s7DsJ4mHUCR3vyoJXpioOcRexmeBYzn4MEYj3h2YNagpg7WSCgWz6jI3h5r9DNmr+M5nAnFGuRYBzdVhDp8iTXgizXQs4S24zpDjlhnKJ4BnnfNoU5zPAO84vZN5vwbkXm8sRZ2poz2lNtM2j5rXiAcD89gLtaJjr1FSZzoxNJTJzoDhjVKCOkpVWbvDPpAEpJHPh4ANNAjhPSUqiEsX2mk/4jIFSJyh4jcJSI/WbH9x0TkNhH5tIj8DxF5lpcnnwxCSE+pCjKRIsZCD1EAa6g5hU+CWGngbwoisgzg7QBeAeAyAK8RkcvMbp8EcFBVXwjgvQB+2buMFgPhpDx1aocLnk7pONmxeBq9nY2sqhpPeg2tTk9atXiS3qMmrak1+yrsRVgnO1ZT95zmhGr6Hl5woDYchYQGqol1khOq0dvO2gt8sxPZ1L0i0+ttlBqMF9lq8LZI3vMY6kTH0qQTncrOvuqehzrRiaWnTnTasca/HMBdqvoFABCR9wC4EsBtTxdL9cOF/W8E8DovU47sCUkGh1XzZwlZ730WFvJ1xiY3RPYCOFJIHx39NokfAvCXXqY00CMkGVz2RcigaG5kf56I3FxIH1LVQ6GZiMjrABwE8FJvX3b2hBDSV/ht2VeOqerBCdvuAbC/kN43+q2EiLwcwDUAXqqqVQYsJdjZE5IMvnkJmY3cjkvQqbVu7Wj2NwG4REQuRtbJvxrAa4s7iMhLAPwWgCtU9cE6mQ7EqY7n/CSWUGMVc22hUfFmIbVPCi8qnsX6MXnCpMcM9kINdupEvbPHeFHqYg34FOs3VxDe2TfhmitFvU7b3z63nqWnve8xTnSAcQM+x2EO4Ee1szZ93iXGRrULdaJj3w/BTnSqeqvQHszbP8aJjmD9JggqbS+qHq152Cu00Nmr6oqIXA3gBmSt7Z2qequIvBXAzap6HYC3IWvJ/01EAOBuVf3OaflyZE/IzMzSwRNCyuQfAmL+XVxU9XoA15vf3lz4/8tD82RnTwghhExiIIFwFnCtCiGEkO6Q6xJq/iUpadGpzjTNvu1PKe/8Vn/yAuc4gXI86bjqGE9etnh32tseGojH83czFjhnt0nX0Za9fUI1e5sO9XZimXdwkSpCg++EavRbTdpq6qFOdOx2xzmTF9Smap/UGn0ooc3K0/jdE9SxDQm1HwnV6L3tMuH/I6ruwTy6iQFFvePInhBCCBk41OwJIYSQKgYU9Y6dPSGEEFJFO+vsG6HFdfZW6yviiSSeRt703YnVao1eZb8cvcA5dfA09tQafqiu6Wr4XrrOPo+bdNOavXff5x1cBPA1+dSBbWLX2ZvtVr4NXSNfZ59Yjb4X6+otTWv0Xjtz8rP3vaqpD6QTnhcc2U9lgoMHQgghw2dAI3v2ZFNZAh08EEII6Tsc2U+EHT0hhCw0AxrZs7OfCkOWEkLIQsPOPgYbCGfeBnd18ltDZqmTd/g6+m0W707WIshYm9gs6xjoWSMiW2Wp7W9CA+1Y5yaeEdSTJm3rYCyQDjButWONPm29xxro2e2hTnbaMNCLNciLDXTj7e+czvrcsYahXroqz6ad5thmYduyl452ouMVaB547cxpu977jQTDKpzKKrIPE0U5whkhhJDBw2n8RWKt7QIQMgeWwLZOyHBhZ5+UJXCBA+kXG1FeiD4Qd2GEpIAj+1jEnNrT6D3dMfZuxB5/BlknvwFlg768pXjVbHTNKrXAynLW8UYooXfeami2T7BaaagNgL3FT5i0lcuBGRzx2ExOONu9iEV9d6pT9SbbCGAn1ttxrJMdc7y1d7Xtxtp6eEFsbPGqNPvUmrznFCfWF1OwEx3LPCK3eBp8nUBWAdm3NSxlIJwMEXmbiHxORD4tIu8TkZ2pCtY/aLVP+kZVLzIH2xQ+KoTMndg55w8CeL6qvhDAnQDeFF+kvkK9k/SNSb1ug71x/h1BtYv0gTwQTuq/Foh65FT1A6qaT0LdCGBffJH6igI4jbLl/kDEHjJQrJwGZM7nU3X2E/KR6ZsJIelJqYT8IIA/qrerYLreaLe1LZp4kS3yalxFpuva8nudvidgV2RRpWFPy8Kewh7vLa+2X6OhOqjNP7RKrFYLjNstjEnunp5sF3GHrqP3bkKoph9Knce3zjr7M8jKthHjdeJp9gZrsjJtv9w8oIgXYClFEJvQ14un0dtm4O0fut0lRbuqY/8xbf/QQDkG++HXtC+EEAYyZnPfFiLyIQAXVmy6RlX/fLTPNciq5Nop+VwF4KosddEMRSWENMNGhL/sayDIXtLFl2XRC3WbL3BCFgy3s1fVl0/bLiJvAPAqAN+qqhO/5VX1EIBD2TEH6Z2GkEVgI7K3zBrKZi0NfV8QkpQBqbFR0/gicgWAnwDwUlW165gIIWR9hL+MdTmGej3pAwPq7GNtYn8Dmcj3QRG5RUR+M0GZCCFDRcCOnpAWiBrZq+pzUhVkOqEOG0IDlFhiA/OEnq/Op6O5Zs9m0AumYw2hUhvseXqsF5zElqeqpdpmYI34PAO+VRuQKHReOdQ7yjxIbDhl8ZzixBrYVTlnys+pFfvPovvHGuR5BnX22UvuRKeL7SrSC44X+KYtyWdATnXoLpcQ0l1oxEdIEtjZE0K6Sd7Rc9qftEXuVGcAsLMn6+TLonKfQHzJkjZh+yNdYCAGeu119tMe5OCFeU0LOp6GH3q8R5XjEucaPY3eYzvKnf0Kyo18ktaa38dYDd9SxwmPF5zHC6ISqs3a2+hq/o4DmnngdZi2Dj3ptY7GPi3tafhFJhnzzTuwjWcP47UT13ZkctGaI/SdGarRBzrR8YrDYWk0rEKSsYyyG1Mt/DuJ4loOAcMDkLRUtT361CfzZEBL79jZV5ILNfT9M5GiJzSg3scBaYdiBzmHoHaNwql9QmaCnX0lC9jRr6E8jQ8sXBUMEvtRBvC+ElIXLr0bOgv4NjyDrDUsIbt8b0o+r6K6gU9Id+AMDCH1oDV+AqYZ2XiV676oYg32qt6GS1gvdOynnnd8ndtiLJtscWMN9jynOysANiErav5ATPtA8IyqQp3wVP3mGUqF+kZyDfQCj58HxWtUAE+i3DYEmc/LSU0s1EDPu2+h+VlSGON5BniW0Ch2dn9rkOc54Qn+8JpHw0pskGfxnOjQv0JyOLKvZAPG3+y0DBrj9OgPYEvqIoLsm7AYtWILeK8IqQsN9IbORmSd+wrKJuqE9IxlZMsqcwNKPvGELCR89CeSh+kCBmOhQRYTBp8hZHY4so9AMF3isds8rTS5hu/dXa+AsVgRsA6JNXx7SU37h4l1wgOE68Gh8YxCDXXa1uzrEKq5h54v9g3TRJ2H2mJ4gW08jd4eH6zRx75fZrkJiTV6z4lO4rg6ZBxWYS2WMRiTTEIIIfXg0rtFQ8AOnxBCFgwuvVs0KHgSQgjpL+1p9inPnFzDX8L4ovGioUHovE4b4q2j4VuzgFiN3guEE5pfHc0+dO2+LWPTenMf6fo1h/pGAHxNPjbwTXKNPpYUgcESa/TeuvquwqV3Q6crI/klrLu0I4SQnpK74yatwc6+klyjzzvZNsK55R09100RQnpOX8crHNkvAsVOtq3Onp08IYS0xoCs8TmxQgghpFk4bmmdbhro2WmT0FJGG+zFztuERmCpuuBVZFJCMSjPtHLZbcvIrsuGpcv3M9fsBfew2Dq2gXO8e+bZ+8zSBmKD7cTuT9I7wfEc4NQ5v/e4xTrViQ5sYwkNlOVVYgqnOoEUbZwFWdCsaR1+l581Lr0jzZIv8JxVSuCnNCGkJXKzp2KatAo7+06jmH2YsIrxeKd9tZKZQHG0MBAjGkJIh6CBHuk+iiz+7KZCeiDzUcC6QpF39rnyMbDvGUJIi7Czb5jUpQrW1FIXIIUTHlumunk+OfrX82rjaPhPmLTV6O3+m53t9nir2XmS4WZUTw1O+56J1QVT+Cop0sbTF/rimkUjn3a+2OBDofp7ijySa/KpaUGTt3jT9KFBqrrZM/UaVinpJ2sY77w79xImhPQaLr0jpGVOYd0MQZF1/gNSKQghJCUc2ZN+sgbgBNYdHVp7REIIiWVApk58PZL+MiDjGUIIaZL2nOqEGEuFRivzcDuIUOOV0B4n1CnGLOcILYPF1IEXNc+7JGuQ50XZ86LoAb4vpNh2Ys9py+w1k9jze8fP0iRmaXpFPGM3S6jxW6zxXFV5Om+AFysKp7YcrYEX1S60SC1cQm0GMqDgyJ4QQgipYkCzhzTQI4QQQgYOR/aEEEJIFQNaereYnb3Vm4ID5VhCq7GJ1hPqrWSrSVsR3hPAHSc8VgeN1eirboHnqCNUR4x9GkLjKTWhyXvEBpbxNPV8GWQeyiG1Jh8alKaKxjX51M93BzR5i/dshT6bpHEWs7MnhDSDIBMHBYNZskQWGC69I4SQCTDCGRkKNNAjhBBCSF9oZ2Rvp0Y8/cZuT73uPnqdbWpNLcWnpJfHCZOOXbTurMv3Aut4GqBdp1/nGFvkU8720Pw8O4Uu6pJNrXPPww0L1kdDpyqOt5p7J4PSpAhcVaQHE6jeunlLqEYfSpdG010qSwQ9aIWEkM5zGutvk1UMxoKZkKHAzp4QkoYVDGYURAgALr0jhBBCBs+ArPFpoEcIIYQMnG6M7Js2bPKuMvjLbd6BcurkERqlJXa+NdIJj2fA5znlqcgy2slO0wZ9Fq/dzfIceHmmDjTjHW/rsBUDPHuS0LbvzeOGPmuBxq6x1FkK2bQDqi4aq9ZhQEvvutHZE0IIUO7cuV6fkGSwsyeEtE/uZtf+RkibDGhkH6XZi8jPisinReQWEfmAiOxJVTBCyAKxBnbuhDRI7Mj+bar60wAgIv8awJsB/Ih7lP1asqWItX70nJ9YPKc90YFybNQXjyqN0LuoWJ0v1ibA0yUTa/pVv3mOQbqu8XvUeVpDb2PbgWqCO/hZ9HdPc2966DbnQDYpNHqvraWeEw59vcyLlpbeicgVAH4d2Z36bVX9RbN9M4DfA/A1AB4C8P2qenhanlEje1V9vJDcBn6bE0IIGRKrDfxNQUSWAbwdwCsAXAbgNSJymdnthwA8oqrPAfAfAfySdxnRS+9E5OdE5AiAH0A2sp+031UicrOI3Az9cuxpCSGE9AEu8A7lcgB3qeoXVPU0gPcAuNLscyWA3x39/70AvlVEps7puLdBRD4kIp+t+LsSAFT1GlXdD+BaAFdPykdVD6nqQVU9CHmGd1pCmiE3BOMcFCGkDtrA33T2AjhSSB8d/Va5j6quAHgMwO5pmbrKi6q+3C1axrUArgfwMzX3J2S+rGB8aRfXoxBC5s95InJzIX1IVQ81ecKoV52IXKKq/zBKXgngczNlFOpzIpbQ/Fs32KtDagsW76Z4ViueIVWoFWXVTQs06ltG2XjpNMoGZakN+ixtGEF51RrrVMf6VmrcAK8JA71QYiNEevkFEhqxruqU8zbI8xiIP/opHFPVgxO23QNgfyG9b/Rb1T5HRWQDgHOQGepNJPYW/qKIPBfZxOiXUMcSn5A2oIMWQtphDf31oNcONwG4REQuRtapvxrAa80+1wH4ZwA+BuB7APyVqk79tI7q7FX1u2OOJ2RuUKMnhPQAVV0RkasB3IDsM+mdqnqriLwVwM2qeh2AdwB4t4jcBeBhZB8EU6FiSRaHNZRNUq3HNkII6QCqej0yG7jib28u/P8pAN8bkmc7nb3nVCe1hh8qD3sSWuMa/iyBcEIJza+uhr80+svnzSfFiAzVXqvqxArGjhCpG6evcfUcyliaduLTBJ4Gb+mcJh9qK5ICeyNDg04lJlSjryrevDX6gYSJ7TMc2ZPEFDt6jP7PhbaEkD7Skgu9BmBnTxKTD/WKI3sK5oSQPjKcSDgccpHE5PN1xU6e4jghhLRJe5r9tJmRpjX80PNZGg+cUwcnkEzyqacQQXsF69+RdTt6r7x16ixU9/fyjAzWExuoxytOHWID40RPytgTWCOAWE2+iVGX51Nizhp9KHXW0Ie2Jc8uILUm35nBNKfxCXHgaJ4QQroCO3tCCCGkEmr2hBBCCOkJHNkTQgghlVCzj6dJJwuhDiNSGwBGG+zV8YIRaowWOhVlG7hXKaERV0Kpc7w1/vLKXKfep+XnWUJFGvRZUvj3T74KMrWTHO/FGmpxWMW8DexCDUMNqZ031aFpB09Nvy6SMZzOntP4hBBCyMDhND4hhBAykc5MM0TBkT0hhBAycNob2Re1Q0+/b1pim3ctzOS4xNOXQzV1T9MPcdJTdQGxNgIpCNV/Pc3f1klonYbaEBg64XW4jxp9LKHPXsMvrNBHvw6hGr33zo7V5Dsjkw9Hs+c0PolkOOtQCSGkzHDeb5zGJ5F0YrhJCCFkChzZk0jY2RNChspwpvE5sieRsAkR0hqC7BFcBh9FMpVujOzt4DDU4U7TTnJCj4+1fau6/uhIep5Vj2dINW3/JQCnR/8XVFv7hOpes+hkoV/goY6JrAFfqKFW6D3wylNFqHFYagO5IRjkxdKwE53ifpvM/ksY7/Tz7cV3SNsGed5tTB59cVaGo9l3o7MnPWcDOJ1PyJyZ1KlP2kfAYJTBcBqfEEJIl5HCv/kf3/gLC0f2hBDSR1axPqkmo3/XUJ6iTxFPYaHhNH6zpNbwPWKDPtjjbXlDbQqq2lZyjSu1nhxKivxi9V/vRtj8Y53qhOZnqaMFp75PoXXc9JRnEw5rYm0tWnSis4pstK6Fv5w8LSZdh6Y1+s5o8otDNzv7vpN/ZRNCSNNM0+GrOn8SwHA0e3b2TVBl/UoIIfOmKx38Emgc2DLs7JuAnT0hhKyTGwb2rsOnZt9vPI0+VIJbw7gGJQA2o9pAxpsVqrorW0zatr/k61jb1vQtdfKPXaMdq9F75YnV9L3865Qplqbvc2y7muX6YzX6Oa2rn8QslxxqBxWq0a9i3FBwCetNvDcDoeFM43MhRgqqOnSZ8DshhCwCtkPvTQc/TBZzZJ8aQVaTq4U0P6MIIYvKGtZXCeQGy72dDe9twUuws09F3uETQgjJ+sh80LOKHur1w6LF7qm4AJQQQsjgWDP/9o7haPYtdfZ2TscxaGla6/HsaVLXUqhdVxW2/W02aWuAE2rQF23AZ4mtxDam0obxkE8nRWMs4kV58tqJF4Ap9PyzHNOwQV4f6JzTnLaexeF09lSWCSGEkIFDlZkQQgippNeWhSXY2RNCCJlOHmSHpla9pWUDvVzYCdS8PIcQsVflHe9tjw2sY/V3YPyaQ53sWKwMdco53u7vfuye5e0QyCw3tekv8lDvSKm1v6rnJtYxjy1zrKYfqmd7mn9s/oB/TQ1r9J4THe/0TTDtNhZf1XnaOhKL1ui9Fww1+1haNNBbAyPGEEJIx6l6RS/M6J7T+JEUvc7YOIyEEEIISUmL0/jFzp2dPSFhcCENmRNVr+aFmZAdzjQ+3xiE9BI+umROVHX2oYF0SOt0xBp/FeXW0zEnFbaWPIMa7/hZCJWNPCcXns1RqAHfU6N/J37xW4O9FJVyMvL4pr/YQ6Pk1WXSUGsF4c9OE9as1qJLMLvVaoo6i31AGzbIiyWFpBxq0Nu4QZ59tts00KNmH0neOk6j2vyc9JL8xbYw03xt0PXKtZ07JTpC2qalzn4N60NBQkg4NmB410Yf7ODJEBiOZt+RaXxCSBjDmV4kpLsM5zlL0tmLyBsB/AqAZ6jqsfAcbGU2rNl7TitCNfpQPySWOhqeF+jGYsvg+agIvQZvu524cQPppCBWw7fEOnRp+lt6Hg5lus4sddzwNYZq9G1UeesavX1WvRfUMEbXbRL9NhKR/QC+HcDd8cUhhBBCusJwpvFTrN/5jwB+At23GiKEEEIWkqiRvYhcCeAeVf2UCA1yCCGEDIkF0uxF5EMALqzYdA2Af4dsCt9FRK4CcNUoeQo457N1CzmGnUOw9+L4zDmHch6AGWwUOsdQrgMYzrXwOrpF/evw3k+W+fYlQ7kfAPDc5k9x3w3AW85rIOO53wNRnW32XUReAOB/ADgx+mkfgHsBXK6q9zvH3qyqB2c6cYfgdXSPoVwLr6Nb8Dq6x5CuZR7MPI2vqp8BcH6eFpHDAA7OZo1PCCGEkKagg21CCCFk4CRbCKyqBwJ2P5TqvC3D6+geQ7kWXke34HV0jyFdS+PMrNkTQgghpB9wGp8QQggZOK139iLyRhFREWlieUPjiMjPisinReQWEfmAiOxpu0yzICJvE5HPja7lfSKys+0yzYKIfK+I3CoiayLSO0tdEblCRO4QkbtE5CfbLs+siMg7ReRBEZl9iW0HEJH9IvJhEblt1K5+tO0yzYKIbBGR/yUinxpdx79vu0wxiMiyiHxSRP6i7bL0hVY7+4G42n2bqr5QVV8M4C8AvLntAs3IBwE8X1VfCOBOAG9quTyz8lkA3wXgo20XJBQRWQbwdgCvAHAZgNeIyGXtlmpm3gXgirYLkYAVAG9U1csAfD2Af9XTe3IKwLeo6osAvBjAFSLy9S2XKYYfBXB724XoE22P7HvvaldVHy8kt6Gn16KqH1DV3L3Hjcj8JvQOVb1dVe9ouxwzcjmAu1T1C6p6GsB7AFzZcplmQlU/CuDhtssRi6rep6qfGP3/OLIOZm+7pQpHM54YJTeO/nr5rhKRfQC+A8Bvt12WPtFaZ190tdtWGVIhIj8nIkcA/AD6O7Iv8oMA/rLtQiwgewEcKaSPoocdy1ARkQMAXgLg79styWyMpr5vAfAggA+qai+vA8CvIRskrrVdkD7RaAzOVK5222badajqn6vqNQCuEZE3AbgawM/MtYA18a5jtM81yKYur51n2UKocx2EpEREtgP4EwD/xszm9QZVXQXw4pE9zvtE5Pmq2iubChF5FYAHVfXjIvKytsvTJxrt7FX15VW/j1ztXgwgD6CzD8AnRMR1tdsGk66jgmsBXI+OdvbedYjIGwC8CsC3aofXZAbcj75xD4D9hfS+0W+kRURkI7KO/lpV/dO2yxOLqj4qIh9GZlPRq84ewDcB+E4ReSWALQDOFpHfV9XXtVyuztPKNL6qfkZVz1fVAyNnPEcBfHUXO3oPEbmkkLwSwOfaKksMInIFsqmx71TVE97+pBFuAnCJiFwsIpsAvBrAdS2XaaGRbDTyDgC3q+qvtl2eWRGRZ+QrbETkLADfhh6+q1T1Taq6b9RvvBrAX7Gjr0fbBnpD4BdF5LMi8mlkskQvl+YA+A0AOwB8cLSM8DfbLtAsiMg/FZGjAL4BwPtF5Ia2y1SXkYHk1QBuQGYI9seqemu7pZoNEflDAB/7/9u5YyIEYiiKou/LQAEitsMHFUIRQIOIFUJBFNDs7OMcBWmSO38ySZLrzOwz8zh6TT/aktyT3Na+eK+p8mwuSZ7rnHrle2fv2dof8YMeAJQz2QNAObEHgHJiDwDlxB4Ayok9AJQTewAoJ/YAUE7sAaDcB1F/HEQ3W5jtAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 648x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure(figsize=(9, 8))\n",
    "#n = plt.normalize(-1., 1.)\n",
    "plt.subplot(1, 1, 1)\n",
    "plt.pcolor(XI, YI, ZI, cmap=cm.jet)\n",
    "plt.scatter(x, y, 20, z, cmap=cm.jet)\n",
    "plt.title('RBF interpolation - multiquadrics')\n",
    "plt.xlim(-4, 4)\n",
    "plt.ylim(-4, 4)\n",
    "plt.colorbar();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that the Rbf algorithm was able to capture, even if not completely the first and second ring in the \"Sombrero\" function.\n",
    "We can try now to plot the surface of the function using also Matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "fig = plt.figure(figsize=(16,8))\n",
    "ax = fig.gca(projection='3d')\n",
    "surf = ax.plot_surface(XI, YI, ZI, rstride=1, cstride=1, cmap=cm.coolwarm,\n",
    "        linewidth=0, antialiased=False)\n",
    "\n",
    "ax.set_zlim(-1.01, 1.01)\n",
    "ax.zaxis.set_major_locator(LinearLocator(10))\n",
    "ax.zaxis.set_major_formatter(FormatStrFormatter('%.02f'))\n",
    "\n",
    "fig.colorbar(surf, shrink=0.5, aspect=5)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### SymPy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is a library for symbolic math, different of most of the libraries presented untit now that are basically numeric. Lets do some simple examples to see how SymPy works.\n",
    "This is one of the libraries that you have not installed. If you are not really intrested in computer algebra, just move on to the next library. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/guilleaf/.local/lib/python3.5/site-packages/IPython/extensions/sympyprinting.py:31: UserWarning: The sympyprinting extension has moved to `sympy`, use `from sympy import init_printing; init_printing()`\n",
      "  warnings.warn(\"The sympyprinting extension has moved to `sympy`, \"\n"
     ]
    }
   ],
   "source": [
    "%load_ext sympyprinting\n",
    "import sympy as sym"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Deprecated\n",
    "#sym.interactive.ipythonprinting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will inform Sympy that treat 'x', 'y', 'z' and 't' as symbols. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "x, y, z, t = sym.symbols('x y z t')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Equality is given by the function Eq, not by '=' (or ':=')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle x = y$"
      ],
      "text/plain": [
       "Eq(x, y)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sym.Eq(x,y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The imaginary unit is I, not 1j like use to be in Python "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle 4.71238898038469 + \\frac{e^{i x}}{x^{2} + y}$"
      ],
      "text/plain": [
       "4.71238898038469 + exp(I*x)/(x**2 + y)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sym.Rational(3,2)*pi + sym.exp(sym.I*x) / (x**2 + y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\left(x + 1\\right) \\left(x + y\\right)^{3}$"
      ],
      "text/plain": [
       "(x + 1)*(x + y)**3"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eq = ((x+y)**3 * (x+1))\n",
    "eq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle x^{4} + 3 x^{3} y + x^{3} + 3 x^{2} y^{2} + 3 x^{2} y + x y^{3} + 3 x y^{2} + y^{3}$"
      ],
      "text/plain": [
       "x**4 + 3*x**3*y + x**3 + 3*x**2*y**2 + 3*x**2*y + x*y**3 + 3*x*y**2 + y**3"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sym.expand(eq)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets try to solve a differential equation, we start defing two symbols that are actually functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "f, g = sym.symbols('f g', cls=sym.Function)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now a differential equation for $f(x)$ could be defined as: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle f{\\left(x \\right)} - 2 \\frac{d}{d x} f{\\left(x \\right)} + \\frac{d^{2}}{d x^{2}} f{\\left(x \\right)} = \\sin{\\left(x \\right)}$"
      ],
      "text/plain": [
       "Eq(f(x) - 2*Derivative(f(x), x) + Derivative(f(x), (x, 2)), sin(x))"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diffeq = sym.Eq(f(x).diff(x, x) - 2*f(x).diff(x) + f(x), sym.sin(x))\n",
    "diffeq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle f{\\left(x \\right)} = \\left(C_{1} + C_{2} x\\right) e^{x} + \\frac{\\cos{\\left(x \\right)}}{2}$"
      ],
      "text/plain": [
       "Eq(f(x), (C1 + C2*x)*exp(x) + cos(x)/2)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sym.dsolve(diffeq, f(x))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As expected, there are two arbitrary constants in the solution to the differential equation above"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas is the last library that we will test in for the introduction. Pandas is a library for Data Analysis, it is robust enough to suppport data from csv, "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "#education=pd.read_json('http://data.undp.org/resource/mvtz-nsye.json')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "#education"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "#education.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "#education.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "#education['name'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "#education[['name','_2012_hdi_rank']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "#education[['name','_2012_hdi_rank']].tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "#education._2005_2010_adult_literacy_rate.hist(bins=15)\n",
    "#plt.title(\"2005 2010 Adult Literacy Rate\")\n",
    "#plt.ylabel('count of countries')\n",
    "#plt.xlabel('%');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This concludes an quick overview of the most important libraries for scientific computing with Python. For the next lesson we will explore step by step the basics of Python language."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}