Clase 6.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introducción a Python para ciencias e ingenierías (clase 6)\n",
    "\n",
    "\n",
    "Ing. Martín Gaitán\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "## Acelerando Python\n",
    "\n",
    "\n",
    "Algunas cosas que hay que saber:\n",
    "\n",
    "- Python es un lenguaje de programación estandarizado que tiene **múltiples implementaciones**\n",
    "- La implementación típica y oficial se llama estrictamente **CPython**, porque está programada en **C**\n",
    "- Existen **otras implementaciones** del estándar: IronPython (.Net), Jython (Java) y hasta una implementación en una versión reducida Python llama **PyPy** (que, de paso, es rapidísima)\n",
    "- Cada versión tiene sus propias ventajas, pero el hecho de que CPython sea C **permite extender con código muy eficiente hecho en C** (o C++)\n",
    "- Pero **programar** en lenguajes de bajo nivel como **C es un *perno* **. \n",
    "\n",
    "\n",
    "\n",
    "\n",
    "![](img/chart.png)\n",
    "\n",
    "## Repaso express de f2py\n",
    "\n",
    "\n",
    "Ya vimos la herramienta F2py que aprovecha que Fortran y CPython se basan en C, y analizando el código fortran genera el \"pegamento\" (cabecesas y código C) para comunicar Python con Fortran. El resultado es un módulo (una extensión) importable desde python, donde las subrutinas fortran se convierten en funciones Python. \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overwriting suma.f90\n"
     ]
    }
   ],
   "source": [
    "%%file suma.f90 \n",
    "\n",
    "subroutine suma2(n, v)\n",
    "    ! Compute the dot product between u and v (length n) and put the result in product\n",
    "     \n",
    "    integer, intent(in) :: n\n",
    "    integer, intent(out) :: v\n",
    "    v = n + 2\n",
    "    \n",
    "end subroutine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[39mrunning build\u001b[0m\n",
      "\u001b[39mrunning config_cc\u001b[0m\n",
      "\u001b[39munifing config_cc, config, build_clib, build_ext, build commands --compiler options\u001b[0m\n",
      "\u001b[39mrunning config_fc\u001b[0m\n",
      "\u001b[39munifing config_fc, config, build_clib, build_ext, build commands --fcompiler options\u001b[0m\n",
      "\u001b[39mrunning build_src\u001b[0m\n",
      "\u001b[39mbuild_src\u001b[0m\n",
      "\u001b[39mbuilding extension \"suma\" sources\u001b[0m\n",
      "\u001b[39mf2py options: []\u001b[0m\n",
      "\u001b[39mf2py:> /tmp/tmpq1jzn2hb/src.linux-x86_64-3.4/sumamodule.c\u001b[0m\n",
      "\u001b[39mcreating /tmp/tmpq1jzn2hb/src.linux-x86_64-3.4\u001b[0m\n",
      "Reading fortran codes...\n",
      "\tReading file 'suma.f90' (format:free)\n",
      "Post-processing...\n",
      "\tBlock: suma\n",
      "\t\t\tBlock: suma2\n",
      "Post-processing (stage 2)...\n",
      "Building modules...\n",
      "\tBuilding module \"suma\"...\n",
      "\t\tConstructing wrapper function \"suma2\"...\n",
      "\t\t  v = suma2(n)\n",
      "\tWrote C/API module \"suma\" to file \"/tmp/tmpq1jzn2hb/src.linux-x86_64-3.4/sumamodule.c\"\n",
      "\u001b[39m  adding '/tmp/tmpq1jzn2hb/src.linux-x86_64-3.4/fortranobject.c' to sources.\u001b[0m\n",
      "\u001b[39m  adding '/tmp/tmpq1jzn2hb/src.linux-x86_64-3.4' to include_dirs.\u001b[0m\n",
      "\u001b[39mcopying /home/tin/.virtualenvs/curso/lib/python3.4/site-packages/numpy/f2py/src/fortranobject.c -> /tmp/tmpq1jzn2hb/src.linux-x86_64-3.4\u001b[0m\n",
      "\u001b[39mcopying /home/tin/.virtualenvs/curso/lib/python3.4/site-packages/numpy/f2py/src/fortranobject.h -> /tmp/tmpq1jzn2hb/src.linux-x86_64-3.4\u001b[0m\n",
      "\u001b[39mbuild_src: building npy-pkg config files\u001b[0m\n",
      "\u001b[39mrunning build_ext\u001b[0m\n",
      "\u001b[39mcustomize UnixCCompiler\u001b[0m\n",
      "\u001b[39mcustomize UnixCCompiler using build_ext\u001b[0m\n",
      "\u001b[39mcustomize Gnu95FCompiler\u001b[0m\n",
      "\u001b[39mFound executable /usr/bin/gfortran\u001b[0m\n",
      "\u001b[39mcustomize Gnu95FCompiler\u001b[0m\n",
      "\u001b[39mcustomize Gnu95FCompiler using build_ext\u001b[0m\n",
      "\u001b[39mbuilding 'suma' extension\u001b[0m\n",
      "\u001b[39mcompiling C sources\u001b[0m\n",
      "\u001b[39mC compiler: x86_64-linux-gnu-gcc -pthread -DNDEBUG -g -fwrapv -O2 -Wall -Wstrict-prototypes -g -fstack-protector-strong -Wformat -Werror=format-security -D_FORTIFY_SOURCE=2 -fPIC\n",
      "\u001b[0m\n",
      "\u001b[39mcreating /tmp/tmpq1jzn2hb/tmp\u001b[0m\n",
      "\u001b[39mcreating /tmp/tmpq1jzn2hb/tmp/tmpq1jzn2hb\u001b[0m\n",
      "\u001b[39mcreating /tmp/tmpq1jzn2hb/tmp/tmpq1jzn2hb/src.linux-x86_64-3.4\u001b[0m\n",
      "\u001b[39mcompile options: '-I/tmp/tmpq1jzn2hb/src.linux-x86_64-3.4 -I/home/tin/.virtualenvs/curso/lib/python3.4/site-packages/numpy/core/include -I/usr/include/python3.4m -I/home/tin/.virtualenvs/curso/include/python3.4m -c'\u001b[0m\n",
      "\u001b[39mx86_64-linux-gnu-gcc: /tmp/tmpq1jzn2hb/src.linux-x86_64-3.4/fortranobject.c\u001b[0m\n",
      "In file included from /home/tin/.virtualenvs/curso/lib/python3.4/site-packages/numpy/core/include/numpy/ndarraytypes.h:1804:0,\n",
      "                 from /home/tin/.virtualenvs/curso/lib/python3.4/site-packages/numpy/core/include/numpy/ndarrayobject.h:17,\n",
      "                 from /home/tin/.virtualenvs/curso/lib/python3.4/site-packages/numpy/core/include/numpy/arrayobject.h:4,\n",
      "                 from /tmp/tmpq1jzn2hb/src.linux-x86_64-3.4/fortranobject.h:13,\n",
      "                 from /tmp/tmpq1jzn2hb/src.linux-x86_64-3.4/fortranobject.c:2:\n",
      "/home/tin/.virtualenvs/curso/lib/python3.4/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:15:2: warning: #warning \"Using deprecated NumPy API, disable it by \" \"#defining NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION\" [-Wcpp]\n",
      " #warning \"Using deprecated NumPy API, disable it by \" \\\n",
      "  ^\n",
      "\u001b[39mx86_64-linux-gnu-gcc: /tmp/tmpq1jzn2hb/src.linux-x86_64-3.4/sumamodule.c\u001b[0m\n",
      "In file included from /home/tin/.virtualenvs/curso/lib/python3.4/site-packages/numpy/core/include/numpy/ndarraytypes.h:1804:0,\n",
      "                 from /home/tin/.virtualenvs/curso/lib/python3.4/site-packages/numpy/core/include/numpy/ndarrayobject.h:17,\n",
      "                 from /home/tin/.virtualenvs/curso/lib/python3.4/site-packages/numpy/core/include/numpy/arrayobject.h:4,\n",
      "                 from /tmp/tmpq1jzn2hb/src.linux-x86_64-3.4/fortranobject.h:13,\n",
      "                 from /tmp/tmpq1jzn2hb/src.linux-x86_64-3.4/sumamodule.c:17:\n",
      "/home/tin/.virtualenvs/curso/lib/python3.4/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:15:2: warning: #warning \"Using deprecated NumPy API, disable it by \" \"#defining NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION\" [-Wcpp]\n",
      " #warning \"Using deprecated NumPy API, disable it by \" \\\n",
      "  ^\n",
      "\u001b[39mcompiling Fortran sources\u001b[0m\n",
      "\u001b[39mFortran f77 compiler: /usr/bin/gfortran -Wall -g -ffixed-form -fno-second-underscore -fPIC -O3 -funroll-loops\n",
      "Fortran f90 compiler: /usr/bin/gfortran -Wall -g -fno-second-underscore -fPIC -O3 -funroll-loops\n",
      "Fortran fix compiler: /usr/bin/gfortran -Wall -g -ffixed-form -fno-second-underscore -Wall -g -fno-second-underscore -fPIC -O3 -funroll-loops\u001b[0m\n",
      "\u001b[39mcompile options: '-I/tmp/tmpq1jzn2hb/src.linux-x86_64-3.4 -I/home/tin/.virtualenvs/curso/lib/python3.4/site-packages/numpy/core/include -I/usr/include/python3.4m -I/home/tin/.virtualenvs/curso/include/python3.4m -c'\u001b[0m\n",
      "\u001b[39mgfortran:f90: suma.f90\u001b[0m\n",
      "\u001b[39m/usr/bin/gfortran -Wall -g -Wall -g -shared /tmp/tmpq1jzn2hb/tmp/tmpq1jzn2hb/src.linux-x86_64-3.4/sumamodule.o /tmp/tmpq1jzn2hb/tmp/tmpq1jzn2hb/src.linux-x86_64-3.4/fortranobject.o /tmp/tmpq1jzn2hb/suma.o -lgfortran -o ./suma.cpython-34m.so\u001b[0m\n",
      "Removing build directory /tmp/tmpq1jzn2hb\n"
     ]
    }
   ],
   "source": [
    "# Si queres evitar el output podés guardar en una variable\n",
    "# _ = !f2py -m dot_product -c dot_product.f90   \n",
    "!/home/tin/.virtualenvs/curso/bin/f2py3.4 -m suma -c suma.f90 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import suma\n",
    "\n",
    "\n",
    "suma.suma2(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Installed fortranmagic.py. To use it, type:\n",
      "  %load_ext fortranmagic\n"
     ]
    }
   ],
   "source": [
    "%install_ext https://raw.githubusercontent.com/mgaitan/fortran_magic/b25685d10a2680e97e4804da260fc261d16f006c/fortranmagic.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The fortranmagic extension is already loaded. To reload it, use:\n",
      "  %reload_ext fortranmagic\n"
     ]
    }
   ],
   "source": [
    "%load_ext fortranmagic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\tBuilding module \"_fortran_magic_498116afcbdbd56648d783b3503286f3\"...\n",
      "\t\tConstructing wrapper function \"f1\"...\n",
      "\t\t  z = f1(x,y)\n",
      "\tWrote C/API module \"_fortran_magic_498116afcbdbd56648d783b3503286f3\" to file \"/tmp/tmpjfxu2rlj/src.linux-x86_64-3.4/_fortran_magic_498116afcbdbd56648d783b3503286f3module.c\"\n"
     ]
    }
   ],
   "source": [
    "%%fortran\n",
    "\n",
    "subroutine f1(x, y, z)\n",
    "    real, intent(in) :: x,y\n",
    "    real, intent(out) :: z\n",
    "\n",
    "    z = sin(x+y)\n",
    "\n",
    "end subroutine f1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.279415488243103"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1(4, 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cython\n",
    "\n",
    "Cython es conceptualmente similar: **es un lenguaje** (con su correspondiente compilador), que extiende la sintaxis de Python (python con sabor a C). El **compilador Cython produce código C estándar** listo para ser compilado y usado como una extensión.\n",
    "\n",
    "* código python válido es código cython válido\n",
    "* Se pueden **llamar funciones en C**, o funciones/métodos de C++, **directamente** desde el código en Cython.   \n",
    "* Es posible usar declarar tipos explícitamente (enteros, flotantes, o cualquier tipo de dato).\n",
    "\n",
    "### Instalación\n",
    "\n",
    "\n",
    "    conda install cython\n",
    "\n",
    "o \n",
    "\n",
    "    pip install cython\n",
    "\n",
    "\n",
    "### Hola mundo (versión larga)\n",
    "\n",
    "Por convención, los archivos Cython se guardan con extensión .pyx  (viene de Pyrex, que es el paquete precedente)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overwriting helloworld.pyx\n"
     ]
    }
   ],
   "source": [
    "%%file helloworld.pyx\n",
    "\n",
    "print('Hola Curso, soy Cython. #NiUnaMenos')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Podemos compilar este código Cython para que genere el código C necesario para crear nuestra extensión Python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "!cython -a helloworld.pyx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/* Generated by Cython 0.22 */\r\n",
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      "#define CYTHON_USE_PYLONG_INTERNALS 0\r\n",
      "#endif\r\n",
      "#endif\r\n",
      "#endif\r\n",
      "#include \"Python.h\"\r\n",
      "#ifndef Py_PYTHON_H\r\n",
      "    #error Python headers needed to compile C extensions, please install development version of Python.\r\n",
      "#elif PY_VERSION_HEX < 0x02060000 || (0x03000000 <= PY_VERSION_HEX && PY_VERSION_HEX < 0x03020000)\r\n",
      "    #error Cython requires Python 2.6+ or Python 3.2+.\r\n",
      "#else\r\n",
      "#define CYTHON_ABI \"0_22\"\r\n",
      "#include <stddef.h>\r\n",
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      "  #define __Pyx_BUILTIN_MODULE_NAME \"builtins\"\r\n",
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      "          PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)\r\n",
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      "  #define Py_TPFLAGS_HAVE_NEWBUFFER 0\r\n",
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      "  #define Py_TPFLAGS_HAVE_FINALIZE 0\r\n",
      "#endif\r\n",
      "#if PY_VERSION_HEX > 0x03030000 && defined(PyUnicode_KIND)\r\n",
      "  #define CYTHON_PEP393_ENABLED 1\r\n",
      "  #define __Pyx_PyUnicode_READY(op)       (likely(PyUnicode_IS_READY(op)) ? \\\r\n",
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      "  #define __Pyx_PyUnicode_DATA(u)         PyUnicode_DATA(u)\r\n",
      "  #define __Pyx_PyUnicode_READ(k, d, i)   PyUnicode_READ(k, d, i)\r\n",
      "#else\r\n",
      "  #define CYTHON_PEP393_ENABLED 0\r\n",
      "  #define __Pyx_PyUnicode_READY(op)       (0)\r\n",
      "  #define __Pyx_PyUnicode_GET_LENGTH(u)   PyUnicode_GET_SIZE(u)\r\n",
      "  #define __Pyx_PyUnicode_READ_CHAR(u, i) ((Py_UCS4)(PyUnicode_AS_UNICODE(u)[i]))\r\n",
      "  #define __Pyx_PyUnicode_KIND(u)         (sizeof(Py_UNICODE))\r\n",
      "  #define __Pyx_PyUnicode_DATA(u)         ((void*)PyUnicode_AS_UNICODE(u))\r\n",
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      "  #define __Pyx_PyUnicode_Concat(a, b)      PyUnicode_Concat(a, b)\r\n",
      "  #define __Pyx_PyUnicode_ConcatSafe(a, b)  ((unlikely((a) == Py_None) || unlikely((b) == Py_None)) ? \\\r\n",
      "      PyNumber_Add(a, b) : __Pyx_PyUnicode_Concat(a, b))\r\n",
      "  #define __Pyx_PyFrozenSet_Size(s)         PySet_Size(s)\r\n",
      "#endif\r\n",
      "#define __Pyx_PyString_FormatSafe(a, b)   ((unlikely((a) == Py_None)) ? PyNumber_Remainder(a, b) : __Pyx_PyString_Format(a, b))\r\n",
      "#define __Pyx_PyUnicode_FormatSafe(a, b)  ((unlikely((a) == Py_None)) ? PyNumber_Remainder(a, b) : PyUnicode_Format(a, b))\r\n",
      "#if PY_MAJOR_VERSION >= 3\r\n",
      "  #define __Pyx_PyString_Format(a, b)  PyUnicode_Format(a, b)\r\n",
      "#else\r\n",
      "  #define __Pyx_PyString_Format(a, b)  PyString_Format(a, b)\r\n",
      "#endif\r\n",
      "#if PY_MAJOR_VERSION >= 3\r\n",
      "  #define PyBaseString_Type            PyUnicode_Type\r\n",
      "  #define PyStringObject               PyUnicodeObject\r\n",
      "  #define PyString_Type                PyUnicode_Type\r\n",
      "  #define PyString_Check               PyUnicode_Check\r\n",
      "  #define PyString_CheckExact          PyUnicode_CheckExact\r\n",
      "#endif\r\n",
      "#if PY_MAJOR_VERSION >= 3\r\n",
      "  #define __Pyx_PyBaseString_Check(obj) PyUnicode_Check(obj)\r\n",
      "  #define __Pyx_PyBaseString_CheckExact(obj) PyUnicode_CheckExact(obj)\r\n",
      "#else\r\n",
      "  #define __Pyx_PyBaseString_Check(obj) (PyString_Check(obj) || PyUnicode_Check(obj))\r\n",
      "  #define __Pyx_PyBaseString_CheckExact(obj) (PyString_CheckExact(obj) || PyUnicode_CheckExact(obj))\r\n",
      "#endif\r\n",
      "#ifndef PySet_CheckExact\r\n",
      "  #define PySet_CheckExact(obj)        (Py_TYPE(obj) == &PySet_Type)\r\n",
      "#endif\r\n",
      "#define __Pyx_TypeCheck(obj, type) PyObject_TypeCheck(obj, (PyTypeObject *)type)\r\n",
      "#if PY_MAJOR_VERSION >= 3\r\n",
      "  #define PyIntObject                  PyLongObject\r\n",
      "  #define PyInt_Type                   PyLong_Type\r\n",
      "  #define PyInt_Check(op)              PyLong_Check(op)\r\n",
      "  #define PyInt_CheckExact(op)         PyLong_CheckExact(op)\r\n",
      "  #define PyInt_FromString             PyLong_FromString\r\n",
      "  #define PyInt_FromUnicode            PyLong_FromUnicode\r\n",
      "  #define PyInt_FromLong               PyLong_FromLong\r\n",
      "  #define PyInt_FromSize_t             PyLong_FromSize_t\r\n",
      "  #define PyInt_FromSsize_t            PyLong_FromSsize_t\r\n",
      "  #define PyInt_AsLong                 PyLong_AsLong\r\n",
      "  #define PyInt_AS_LONG                PyLong_AS_LONG\r\n",
      "  #define PyInt_AsSsize_t              PyLong_AsSsize_t\r\n",
      "  #define PyInt_AsUnsignedLongMask     PyLong_AsUnsignedLongMask\r\n",
      "  #define PyInt_AsUnsignedLongLongMask PyLong_AsUnsignedLongLongMask\r\n",
      "  #define PyNumber_Int                 PyNumber_Long\r\n",
      "#endif\r\n",
      "#if PY_MAJOR_VERSION >= 3\r\n",
      "  #define PyBoolObject                 PyLongObject\r\n",
      "#endif\r\n",
      "#if PY_MAJOR_VERSION >= 3 && CYTHON_COMPILING_IN_PYPY\r\n",
      "  #ifndef PyUnicode_InternFromString\r\n",
      "    #define PyUnicode_InternFromString(s) PyUnicode_FromString(s)\r\n",
      "  #endif\r\n",
      "#endif\r\n",
      "#if PY_VERSION_HEX < 0x030200A4\r\n",
      "  typedef long Py_hash_t;\r\n",
      "  #define __Pyx_PyInt_FromHash_t PyInt_FromLong\r\n",
      "  #define __Pyx_PyInt_AsHash_t   PyInt_AsLong\r\n",
      "#else\r\n",
      "  #define __Pyx_PyInt_FromHash_t PyInt_FromSsize_t\r\n",
      "  #define __Pyx_PyInt_AsHash_t   PyInt_AsSsize_t\r\n",
      "#endif\r\n",
      "#if PY_MAJOR_VERSION >= 3\r\n",
      "  #define __Pyx_PyMethod_New(func, self, klass) ((self) ? PyMethod_New(func, self) : PyInstanceMethod_New(func))\r\n",
      "#else\r\n",
      "  #define __Pyx_PyMethod_New(func, self, klass) PyMethod_New(func, self, klass)\r\n",
      "#endif\r\n",
      "#ifndef CYTHON_INLINE\r\n",
      "  #if defined(__GNUC__)\r\n",
      "    #define CYTHON_INLINE __inline__\r\n",
      "  #elif defined(_MSC_VER)\r\n",
      "    #define CYTHON_INLINE __inline\r\n",
      "  #elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L\r\n",
      "    #define CYTHON_INLINE inline\r\n",
      "  #else\r\n",
      "    #define CYTHON_INLINE\r\n",
      "  #endif\r\n",
      "#endif\r\n",
      "#ifndef CYTHON_RESTRICT\r\n",
      "  #if defined(__GNUC__)\r\n",
      "    #define CYTHON_RESTRICT __restrict__\r\n",
      "  #elif defined(_MSC_VER) && _MSC_VER >= 1400\r\n",
      "    #define CYTHON_RESTRICT __restrict\r\n",
      "  #elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L\r\n",
      "    #define CYTHON_RESTRICT restrict\r\n",
      "  #else\r\n",
      "    #define CYTHON_RESTRICT\r\n",
      "  #endif\r\n",
      "#endif\r\n",
      "#ifdef NAN\r\n",
      "#define __PYX_NAN() ((float) NAN)\r\n",
      "#else\r\n",
      "static CYTHON_INLINE float __PYX_NAN() {\r\n",
      "  /* Initialize NaN. The sign is irrelevant, an exponent with all bits 1 and\r\n",
      "   a nonzero mantissa means NaN. If the first bit in the mantissa is 1, it is\r\n",
      "   a quiet NaN. */\r\n",
      "  float value;\r\n",
      "  memset(&value, 0xFF, sizeof(value));\r\n",
      "  return value;\r\n",
      "}\r\n",
      "#endif\r\n",
      "#define __Pyx_void_to_None(void_result) (void_result, Py_INCREF(Py_None), Py_None)\r\n",
      "#ifdef __cplusplus\r\n",
      "template<typename T>\r\n",
      "void __Pyx_call_destructor(T* x) {\r\n",
      "    x->~T();\r\n",
      "}\r\n",
      "template<typename T>\r\n",
      "class __Pyx_FakeReference {\r\n",
      "  public:\r\n",
      "    __Pyx_FakeReference() : ptr(NULL) { }\r\n",
      "    __Pyx_FakeReference(T& ref) : ptr(&ref) { }\r\n",
      "    T *operator->() { return ptr; }\r\n",
      "    operator T&() { return *ptr; }\r\n",
      "  private:\r\n",
      "    T *ptr;\r\n",
      "};\r\n",
      "#endif\r\n",
      "\r\n",
      "\r\n",
      "#if PY_MAJOR_VERSION >= 3\r\n",
      "  #define __Pyx_PyNumber_Divide(x,y)         PyNumber_TrueDivide(x,y)\r\n",
      "  #define __Pyx_PyNumber_InPlaceDivide(x,y)  PyNumber_InPlaceTrueDivide(x,y)\r\n",
      "#else\r\n",
      "  #define __Pyx_PyNumber_Divide(x,y)         PyNumber_Divide(x,y)\r\n",
      "  #define __Pyx_PyNumber_InPlaceDivide(x,y)  PyNumber_InPlaceDivide(x,y)\r\n",
      "#endif\r\n",
      "\r\n",
      "#ifndef __PYX_EXTERN_C\r\n",
      "  #ifdef __cplusplus\r\n",
      "    #define __PYX_EXTERN_C extern \"C\"\r\n",
      "  #else\r\n",
      "    #define __PYX_EXTERN_C extern\r\n",
      "  #endif\r\n",
      "#endif\r\n",
      "\r\n",
      "#if defined(WIN32) || defined(MS_WINDOWS)\r\n",
      "#define _USE_MATH_DEFINES\r\n",
      "#endif\r\n",
      "#include <math.h>\r\n",
      "#define __PYX_HAVE__helloworld\r\n",
      "#define __PYX_HAVE_API__helloworld\r\n",
      "#ifdef _OPENMP\r\n",
      "#include <omp.h>\r\n",
      "#endif /* _OPENMP */\r\n",
      "\r\n",
      "#ifdef PYREX_WITHOUT_ASSERTIONS\r\n",
      "#define CYTHON_WITHOUT_ASSERTIONS\r\n",
      "#endif\r\n",
      "\r\n",
      "#ifndef CYTHON_UNUSED\r\n",
      "# if defined(__GNUC__)\r\n",
      "#   if !(defined(__cplusplus)) || (__GNUC__ > 3 || (__GNUC__ == 3 && __GNUC_MINOR__ >= 4))\r\n",
      "#     define CYTHON_UNUSED __attribute__ ((__unused__))\r\n",
      "#   else\r\n",
      "#     define CYTHON_UNUSED\r\n",
      "#   endif\r\n",
      "# elif defined(__ICC) || (defined(__INTEL_COMPILER) && !defined(_MSC_VER))\r\n",
      "#   define CYTHON_UNUSED __attribute__ ((__unused__))\r\n",
      "# else\r\n",
      "#   define CYTHON_UNUSED\r\n",
      "# endif\r\n",
      "#endif\r\n",
      "typedef struct {PyObject **p; char *s; const Py_ssize_t n; const char* encoding;\r\n",
      "                const char is_unicode; const char is_str; const char intern; } __Pyx_StringTabEntry;\r\n",
      "\r\n",
      "#define __PYX_DEFAULT_STRING_ENCODING_IS_ASCII 0\r\n",
      "#define __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT 0\r\n",
      "#define __PYX_DEFAULT_STRING_ENCODING \"\"\r\n",
      "#define __Pyx_PyObject_FromString __Pyx_PyBytes_FromString\r\n",
      "#define __Pyx_PyObject_FromStringAndSize __Pyx_PyBytes_FromStringAndSize\r\n",
      "#define __Pyx_fits_Py_ssize_t(v, type, is_signed)  (    \\\r\n",
      "    (sizeof(type) < sizeof(Py_ssize_t))  ||             \\\r\n",
      "    (sizeof(type) > sizeof(Py_ssize_t) &&               \\\r\n",
      "          likely(v < (type)PY_SSIZE_T_MAX ||            \\\r\n",
      "                 v == (type)PY_SSIZE_T_MAX)  &&         \\\r\n",
      "          (!is_signed || likely(v > (type)PY_SSIZE_T_MIN ||       \\\r\n",
      "                                v == (type)PY_SSIZE_T_MIN)))  ||  \\\r\n",
      "    (sizeof(type) == sizeof(Py_ssize_t) &&              \\\r\n",
      "          (is_signed || likely(v < (type)PY_SSIZE_T_MAX ||        \\\r\n",
      "                               v == (type)PY_SSIZE_T_MAX)))  )\r\n",
      "static CYTHON_INLINE char* __Pyx_PyObject_AsString(PyObject*);\r\n",
      "static CYTHON_INLINE char* __Pyx_PyObject_AsStringAndSize(PyObject*, Py_ssize_t* length);\r\n",
      "#define __Pyx_PyByteArray_FromString(s) PyByteArray_FromStringAndSize((const char*)s, strlen((const char*)s))\r\n",
      "#define __Pyx_PyByteArray_FromStringAndSize(s, l) PyByteArray_FromStringAndSize((const char*)s, l)\r\n",
      "#define __Pyx_PyBytes_FromString        PyBytes_FromString\r\n",
      "#define __Pyx_PyBytes_FromStringAndSize PyBytes_FromStringAndSize\r\n",
      "static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(const char*);\r\n",
      "#if PY_MAJOR_VERSION < 3\r\n",
      "    #define __Pyx_PyStr_FromString        __Pyx_PyBytes_FromString\r\n",
      "    #define __Pyx_PyStr_FromStringAndSize __Pyx_PyBytes_FromStringAndSize\r\n",
      "#else\r\n",
      "    #define __Pyx_PyStr_FromString        __Pyx_PyUnicode_FromString\r\n",
      "    #define __Pyx_PyStr_FromStringAndSize __Pyx_PyUnicode_FromStringAndSize\r\n",
      "#endif\r\n",
      "#define __Pyx_PyObject_AsSString(s)    ((signed char*) __Pyx_PyObject_AsString(s))\r\n",
      "#define __Pyx_PyObject_AsUString(s)    ((unsigned char*) __Pyx_PyObject_AsString(s))\r\n",
      "#define __Pyx_PyObject_FromCString(s)  __Pyx_PyObject_FromString((const char*)s)\r\n",
      "#define __Pyx_PyBytes_FromCString(s)   __Pyx_PyBytes_FromString((const char*)s)\r\n",
      "#define __Pyx_PyByteArray_FromCString(s)   __Pyx_PyByteArray_FromString((const char*)s)\r\n",
      "#define __Pyx_PyStr_FromCString(s)     __Pyx_PyStr_FromString((const char*)s)\r\n",
      "#define __Pyx_PyUnicode_FromCString(s) __Pyx_PyUnicode_FromString((const char*)s)\r\n",
      "#if PY_MAJOR_VERSION < 3\r\n",
      "static CYTHON_INLINE size_t __Pyx_Py_UNICODE_strlen(const Py_UNICODE *u)\r\n",
      "{\r\n",
      "    const Py_UNICODE *u_end = u;\r\n",
      "    while (*u_end++) ;\r\n",
      "    return (size_t)(u_end - u - 1);\r\n",
      "}\r\n",
      "#else\r\n",
      "#define __Pyx_Py_UNICODE_strlen Py_UNICODE_strlen\r\n",
      "#endif\r\n",
      "#define __Pyx_PyUnicode_FromUnicode(u)       PyUnicode_FromUnicode(u, __Pyx_Py_UNICODE_strlen(u))\r\n",
      "#define __Pyx_PyUnicode_FromUnicodeAndLength PyUnicode_FromUnicode\r\n",
      "#define __Pyx_PyUnicode_AsUnicode            PyUnicode_AsUnicode\r\n",
      "#define __Pyx_Owned_Py_None(b) (Py_INCREF(Py_None), Py_None)\r\n",
      "#define __Pyx_PyBool_FromLong(b) ((b) ? (Py_INCREF(Py_True), Py_True) : (Py_INCREF(Py_False), Py_False))\r\n",
      "static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject*);\r\n",
      "static CYTHON_INLINE PyObject* __Pyx_PyNumber_Int(PyObject* x);\r\n",
      "static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject*);\r\n",
      "static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t);\r\n",
      "#if CYTHON_COMPILING_IN_CPYTHON\r\n",
      "#define __pyx_PyFloat_AsDouble(x) (PyFloat_CheckExact(x) ? PyFloat_AS_DOUBLE(x) : PyFloat_AsDouble(x))\r\n",
      "#else\r\n",
      "#define __pyx_PyFloat_AsDouble(x) PyFloat_AsDouble(x)\r\n",
      "#endif\r\n",
      "#define __pyx_PyFloat_AsFloat(x) ((float) __pyx_PyFloat_AsDouble(x))\r\n",
      "#if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII\r\n",
      "static int __Pyx_sys_getdefaultencoding_not_ascii;\r\n",
      "static int __Pyx_init_sys_getdefaultencoding_params(void) {\r\n",
      "    PyObject* sys;\r\n",
      "    PyObject* default_encoding = NULL;\r\n",
      "    PyObject* ascii_chars_u = NULL;\r\n",
      "    PyObject* ascii_chars_b = NULL;\r\n",
      "    const char* default_encoding_c;\r\n",
      "    sys = PyImport_ImportModule(\"sys\");\r\n",
      "    if (!sys) goto bad;\r\n",
      "    default_encoding = PyObject_CallMethod(sys, (char*) \"getdefaultencoding\", NULL);\r\n",
      "    Py_DECREF(sys);\r\n",
      "    if (!default_encoding) goto bad;\r\n",
      "    default_encoding_c = PyBytes_AsString(default_encoding);\r\n",
      "    if (!default_encoding_c) goto bad;\r\n",
      "    if (strcmp(default_encoding_c, \"ascii\") == 0) {\r\n",
      "        __Pyx_sys_getdefaultencoding_not_ascii = 0;\r\n",
      "    } else {\r\n",
      "        char ascii_chars[128];\r\n",
      "        int c;\r\n",
      "        for (c = 0; c < 128; c++) {\r\n",
      "            ascii_chars[c] = c;\r\n",
      "        }\r\n",
      "        __Pyx_sys_getdefaultencoding_not_ascii = 1;\r\n",
      "        ascii_chars_u = PyUnicode_DecodeASCII(ascii_chars, 128, NULL);\r\n",
      "        if (!ascii_chars_u) goto bad;\r\n",
      "        ascii_chars_b = PyUnicode_AsEncodedString(ascii_chars_u, default_encoding_c, NULL);\r\n",
      "        if (!ascii_chars_b || !PyBytes_Check(ascii_chars_b) || memcmp(ascii_chars, PyBytes_AS_STRING(ascii_chars_b), 128) != 0) {\r\n",
      "            PyErr_Format(\r\n",
      "                PyExc_ValueError,\r\n",
      "                \"This module compiled with c_string_encoding=ascii, but default encoding '%.200s' is not a superset of ascii.\",\r\n",
      "                default_encoding_c);\r\n",
      "            goto bad;\r\n",
      "        }\r\n",
      "        Py_DECREF(ascii_chars_u);\r\n",
      "        Py_DECREF(ascii_chars_b);\r\n",
      "    }\r\n",
      "    Py_DECREF(default_encoding);\r\n",
      "    return 0;\r\n",
      "bad:\r\n",
      "    Py_XDECREF(default_encoding);\r\n",
      "    Py_XDECREF(ascii_chars_u);\r\n",
      "    Py_XDECREF(ascii_chars_b);\r\n",
      "    return -1;\r\n",
      "}\r\n",
      "#endif\r\n",
      "#if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT && PY_MAJOR_VERSION >= 3\r\n",
      "#define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_DecodeUTF8(c_str, size, NULL)\r\n",
      "#else\r\n",
      "#define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_Decode(c_str, size, __PYX_DEFAULT_STRING_ENCODING, NULL)\r\n",
      "#if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT\r\n",
      "static char* __PYX_DEFAULT_STRING_ENCODING;\r\n",
      "static int __Pyx_init_sys_getdefaultencoding_params(void) {\r\n",
      "    PyObject* sys;\r\n",
      "    PyObject* default_encoding = NULL;\r\n",
      "    char* default_encoding_c;\r\n",
      "    sys = PyImport_ImportModule(\"sys\");\r\n",
      "    if (!sys) goto bad;\r\n",
      "    default_encoding = PyObject_CallMethod(sys, (char*) (const char*) \"getdefaultencoding\", NULL);\r\n",
      "    Py_DECREF(sys);\r\n",
      "    if (!default_encoding) goto bad;\r\n",
      "    default_encoding_c = PyBytes_AsString(default_encoding);\r\n",
      "    if (!default_encoding_c) goto bad;\r\n",
      "    __PYX_DEFAULT_STRING_ENCODING = (char*) malloc(strlen(default_encoding_c));\r\n",
      "    if (!__PYX_DEFAULT_STRING_ENCODING) goto bad;\r\n",
      "    strcpy(__PYX_DEFAULT_STRING_ENCODING, default_encoding_c);\r\n",
      "    Py_DECREF(default_encoding);\r\n",
      "    return 0;\r\n",
      "bad:\r\n",
      "    Py_XDECREF(default_encoding);\r\n",
      "    return -1;\r\n",
      "}\r\n",
      "#endif\r\n",
      "#endif\r\n",
      "\r\n",
      "\r\n",
      "/* Test for GCC > 2.95 */\r\n",
      "#if defined(__GNUC__)     && (__GNUC__ > 2 || (__GNUC__ == 2 && (__GNUC_MINOR__ > 95)))\r\n",
      "  #define likely(x)   __builtin_expect(!!(x), 1)\r\n",
      "  #define unlikely(x) __builtin_expect(!!(x), 0)\r\n",
      "#else /* !__GNUC__ or GCC < 2.95 */\r\n",
      "  #define likely(x)   (x)\r\n",
      "  #define unlikely(x) (x)\r\n",
      "#endif /* __GNUC__ */\r\n",
      "\r\n",
      "static PyObject *__pyx_m;\r\n",
      "static PyObject *__pyx_d;\r\n",
      "static PyObject *__pyx_b;\r\n",
      "static PyObject *__pyx_empty_tuple;\r\n",
      "static PyObject *__pyx_empty_bytes;\r\n",
      "static int __pyx_lineno;\r\n",
      "static int __pyx_clineno = 0;\r\n",
      "static const char * __pyx_cfilenm= __FILE__;\r\n",
      "static const char *__pyx_filename;\r\n",
      "\r\n",
      "\r\n",
      "static const char *__pyx_f[] = {\r\n",
      "  \"helloworld.pyx\",\r\n",
      "};\r\n",
      "\r\n",
      "/*--- Type declarations ---*/\r\n",
      "\r\n",
      "/* --- Runtime support code (head) --- */\r\n",
      "#ifndef CYTHON_REFNANNY\r\n",
      "  #define CYTHON_REFNANNY 0\r\n",
      "#endif\r\n",
      "#if CYTHON_REFNANNY\r\n",
      "  typedef struct {\r\n",
      "    void (*INCREF)(void*, PyObject*, int);\r\n",
      "    void (*DECREF)(void*, PyObject*, int);\r\n",
      "    void (*GOTREF)(void*, PyObject*, int);\r\n",
      "    void (*GIVEREF)(void*, PyObject*, int);\r\n",
      "    void* (*SetupContext)(const char*, int, const char*);\r\n",
      "    void (*FinishContext)(void**);\r\n",
      "  } __Pyx_RefNannyAPIStruct;\r\n",
      "  static __Pyx_RefNannyAPIStruct *__Pyx_RefNanny = NULL;\r\n",
      "  static __Pyx_RefNannyAPIStruct *__Pyx_RefNannyImportAPI(const char *modname);\r\n",
      "  #define __Pyx_RefNannyDeclarations void *__pyx_refnanny = NULL;\r\n",
      "#ifdef WITH_THREAD\r\n",
      "  #define __Pyx_RefNannySetupContext(name, acquire_gil) \\\r\n",
      "          if (acquire_gil) { \\\r\n",
      "              PyGILState_STATE __pyx_gilstate_save = PyGILState_Ensure(); \\\r\n",
      "              __pyx_refnanny = __Pyx_RefNanny->SetupContext((name), __LINE__, __FILE__); \\\r\n",
      "              PyGILState_Release(__pyx_gilstate_save); \\\r\n",
      "          } else { \\\r\n",
      "              __pyx_refnanny = __Pyx_RefNanny->SetupContext((name), __LINE__, __FILE__); \\\r\n",
      "          }\r\n",
      "#else\r\n",
      "  #define __Pyx_RefNannySetupContext(name, acquire_gil) \\\r\n",
      "          __pyx_refnanny = __Pyx_RefNanny->SetupContext((name), __LINE__, __FILE__)\r\n",
      "#endif\r\n",
      "  #define __Pyx_RefNannyFinishContext() \\\r\n",
      "          __Pyx_RefNanny->FinishContext(&__pyx_refnanny)\r\n",
      "  #define __Pyx_INCREF(r)  __Pyx_RefNanny->INCREF(__pyx_refnanny, (PyObject *)(r), __LINE__)\r\n",
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      "        }\r\n",
      "        __pyx_code_cache.entries = entries;\r\n",
      "        __pyx_code_cache.max_count = new_max;\r\n",
      "    }\r\n",
      "    for (i=__pyx_code_cache.count; i>pos; i--) {\r\n",
      "        entries[i] = entries[i-1];\r\n",
      "    }\r\n",
      "    entries[pos].code_line = code_line;\r\n",
      "    entries[pos].code_object = code_object;\r\n",
      "    __pyx_code_cache.count++;\r\n",
      "    Py_INCREF(code_object);\r\n",
      "}\r\n",
      "\r\n",
      "#include \"compile.h\"\r\n",
      "#include \"frameobject.h\"\r\n",
      "#include \"traceback.h\"\r\n",
      "static PyCodeObject* __Pyx_CreateCodeObjectForTraceback(\r\n",
      "            const char *funcname, int c_line,\r\n",
      "            int py_line, const char *filename) {\r\n",
      "    PyCodeObject *py_code = 0;\r\n",
      "    PyObject *py_srcfile = 0;\r\n",
      "    PyObject *py_funcname = 0;\r\n",
      "    #if PY_MAJOR_VERSION < 3\r\n",
      "    py_srcfile = PyString_FromString(filename);\r\n",
      "    #else\r\n",
      "    py_srcfile = PyUnicode_FromString(filename);\r\n",
      "    #endif\r\n",
      "    if (!py_srcfile) goto bad;\r\n",
      "    if (c_line) {\r\n",
      "        #if PY_MAJOR_VERSION < 3\r\n",
      "        py_funcname = PyString_FromFormat( \"%s (%s:%d)\", funcname, __pyx_cfilenm, c_line);\r\n",
      "        #else\r\n",
      "        py_funcname = PyUnicode_FromFormat( \"%s (%s:%d)\", funcname, __pyx_cfilenm, c_line);\r\n",
      "        #endif\r\n",
      "    }\r\n",
      "    else {\r\n",
      "        #if PY_MAJOR_VERSION < 3\r\n",
      "        py_funcname = PyString_FromString(funcname);\r\n",
      "        #else\r\n",
      "        py_funcname = PyUnicode_FromString(funcname);\r\n",
      "        #endif\r\n",
      "    }\r\n",
      "    if (!py_funcname) goto bad;\r\n",
      "    py_code = __Pyx_PyCode_New(\r\n",
      "        0,\r\n",
      "        0,\r\n",
      "        0,\r\n",
      "        0,\r\n",
      "        0,\r\n",
      "        __pyx_empty_bytes, /*PyObject *code,*/\r\n",
      "        __pyx_empty_tuple, /*PyObject *consts,*/\r\n",
      "        __pyx_empty_tuple, /*PyObject *names,*/\r\n",
      "        __pyx_empty_tuple, /*PyObject *varnames,*/\r\n",
      "        __pyx_empty_tuple, /*PyObject *freevars,*/\r\n",
      "        __pyx_empty_tuple, /*PyObject *cellvars,*/\r\n",
      "        py_srcfile,   /*PyObject *filename,*/\r\n",
      "        py_funcname,  /*PyObject *name,*/\r\n",
      "        py_line,\r\n",
      "        __pyx_empty_bytes  /*PyObject *lnotab*/\r\n",
      "    );\r\n",
      "    Py_DECREF(py_srcfile);\r\n",
      "    Py_DECREF(py_funcname);\r\n",
      "    return py_code;\r\n",
      "bad:\r\n",
      "    Py_XDECREF(py_srcfile);\r\n",
      "    Py_XDECREF(py_funcname);\r\n",
      "    return NULL;\r\n",
      "}\r\n",
      "static void __Pyx_AddTraceback(const char *funcname, int c_line,\r\n",
      "                               int py_line, const char *filename) {\r\n",
      "    PyCodeObject *py_code = 0;\r\n",
      "    PyFrameObject *py_frame = 0;\r\n",
      "    py_code = __pyx_find_code_object(c_line ? c_line : py_line);\r\n",
      "    if (!py_code) {\r\n",
      "        py_code = __Pyx_CreateCodeObjectForTraceback(\r\n",
      "            funcname, c_line, py_line, filename);\r\n",
      "        if (!py_code) goto bad;\r\n",
      "        __pyx_insert_code_object(c_line ? c_line : py_line, py_code);\r\n",
      "    }\r\n",
      "    py_frame = PyFrame_New(\r\n",
      "        PyThreadState_GET(), /*PyThreadState *tstate,*/\r\n",
      "        py_code,             /*PyCodeObject *code,*/\r\n",
      "        __pyx_d,      /*PyObject *globals,*/\r\n",
      "        0                    /*PyObject *locals*/\r\n",
      "    );\r\n",
      "    if (!py_frame) goto bad;\r\n",
      "    py_frame->f_lineno = py_line;\r\n",
      "    PyTraceBack_Here(py_frame);\r\n",
      "bad:\r\n",
      "    Py_XDECREF(py_code);\r\n",
      "    Py_XDECREF(py_frame);\r\n",
      "}\r\n",
      "\r\n",
      "#if !CYTHON_COMPILING_IN_PYPY && PY_MAJOR_VERSION < 3\r\n",
      "static PyObject *__Pyx_GetStdout(void) {\r\n",
      "    PyObject *f = PySys_GetObject((char *)\"stdout\");\r\n",
      "    if (!f) {\r\n",
      "        PyErr_SetString(PyExc_RuntimeError, \"lost sys.stdout\");\r\n",
      "    }\r\n",
      "    return f;\r\n",
      "}\r\n",
      "static int __Pyx_Print(PyObject* f, PyObject *arg_tuple, int newline) {\r\n",
      "    int i;\r\n",
      "    if (!f) {\r\n",
      "        if (!(f = __Pyx_GetStdout()))\r\n",
      "            return -1;\r\n",
      "    }\r\n",
      "    Py_INCREF(f);\r\n",
      "    for (i=0; i < PyTuple_GET_SIZE(arg_tuple); i++) {\r\n",
      "        PyObject* v;\r\n",
      "        if (PyFile_SoftSpace(f, 1)) {\r\n",
      "            if (PyFile_WriteString(\" \", f) < 0)\r\n",
      "                goto error;\r\n",
      "        }\r\n",
      "        v = PyTuple_GET_ITEM(arg_tuple, i);\r\n",
      "        if (PyFile_WriteObject(v, f, Py_PRINT_RAW) < 0)\r\n",
      "            goto error;\r\n",
      "        if (PyString_Check(v)) {\r\n",
      "            char *s = PyString_AsString(v);\r\n",
      "            Py_ssize_t len = PyString_Size(v);\r\n",
      "            if (len > 0) {\r\n",
      "                switch (s[len-1]) {\r\n",
      "                    case ' ': break;\r\n",
      "                    case '\\f': case '\\r': case '\\n': case '\\t': case '\\v':\r\n",
      "                        PyFile_SoftSpace(f, 0);\r\n",
      "                        break;\r\n",
      "                    default:  break;\r\n",
      "                }\r\n",
      "            }\r\n",
      "        }\r\n",
      "    }\r\n",
      "    if (newline) {\r\n",
      "        if (PyFile_WriteString(\"\\n\", f) < 0)\r\n",
      "            goto error;\r\n",
      "        PyFile_SoftSpace(f, 0);\r\n",
      "    }\r\n",
      "    Py_DECREF(f);\r\n",
      "    return 0;\r\n",
      "error:\r\n",
      "    Py_DECREF(f);\r\n",
      "    return -1;\r\n",
      "}\r\n",
      "#else\r\n",
      "static int __Pyx_Print(PyObject* stream, PyObject *arg_tuple, int newline) {\r\n",
      "    PyObject* kwargs = 0;\r\n",
      "    PyObject* result = 0;\r\n",
      "    PyObject* end_string;\r\n",
      "    if (unlikely(!__pyx_print)) {\r\n",
      "        __pyx_print = PyObject_GetAttr(__pyx_b, __pyx_n_s_print);\r\n",
      "        if (!__pyx_print)\r\n",
      "            return -1;\r\n",
      "    }\r\n",
      "    if (stream) {\r\n",
      "        kwargs = PyDict_New();\r\n",
      "        if (unlikely(!kwargs))\r\n",
      "            return -1;\r\n",
      "        if (unlikely(PyDict_SetItem(kwargs, __pyx_n_s_file, stream) < 0))\r\n",
      "            goto bad;\r\n",
      "        if (!newline) {\r\n",
      "            end_string = PyUnicode_FromStringAndSize(\" \", 1);\r\n",
      "            if (unlikely(!end_string))\r\n",
      "                goto bad;\r\n",
      "            if (PyDict_SetItem(kwargs, __pyx_n_s_end, end_string) < 0) {\r\n",
      "                Py_DECREF(end_string);\r\n",
      "                goto bad;\r\n",
      "            }\r\n",
      "            Py_DECREF(end_string);\r\n",
      "        }\r\n",
      "    } else if (!newline) {\r\n",
      "        if (unlikely(!__pyx_print_kwargs)) {\r\n",
      "            __pyx_print_kwargs = PyDict_New();\r\n",
      "            if (unlikely(!__pyx_print_kwargs))\r\n",
      "                return -1;\r\n",
      "            end_string = PyUnicode_FromStringAndSize(\" \", 1);\r\n",
      "            if (unlikely(!end_string))\r\n",
      "                return -1;\r\n",
      "            if (PyDict_SetItem(__pyx_print_kwargs, __pyx_n_s_end, end_string) < 0) {\r\n",
      "                Py_DECREF(end_string);\r\n",
      "                return -1;\r\n",
      "            }\r\n",
      "            Py_DECREF(end_string);\r\n",
      "        }\r\n",
      "        kwargs = __pyx_print_kwargs;\r\n",
      "    }\r\n",
      "    result = PyObject_Call(__pyx_print, arg_tuple, kwargs);\r\n",
      "    if (unlikely(kwargs) && (kwargs != __pyx_print_kwargs))\r\n",
      "        Py_DECREF(kwargs);\r\n",
      "    if (!result)\r\n",
      "        return -1;\r\n",
      "    Py_DECREF(result);\r\n",
      "    return 0;\r\n",
      "bad:\r\n",
      "    if (kwargs != __pyx_print_kwargs)\r\n",
      "        Py_XDECREF(kwargs);\r\n",
      "    return -1;\r\n",
      "}\r\n",
      "#endif\r\n",
      "\r\n",
      "#if !CYTHON_COMPILING_IN_PYPY && PY_MAJOR_VERSION < 3\r\n",
      "static int __Pyx_PrintOne(PyObject* f, PyObject *o) {\r\n",
      "    if (!f) {\r\n",
      "        if (!(f = __Pyx_GetStdout()))\r\n",
      "            return -1;\r\n",
      "    }\r\n",
      "    Py_INCREF(f);\r\n",
      "    if (PyFile_SoftSpace(f, 0)) {\r\n",
      "        if (PyFile_WriteString(\" \", f) < 0)\r\n",
      "            goto error;\r\n",
      "    }\r\n",
      "    if (PyFile_WriteObject(o, f, Py_PRINT_RAW) < 0)\r\n",
      "        goto error;\r\n",
      "    if (PyFile_WriteString(\"\\n\", f) < 0)\r\n",
      "        goto error;\r\n",
      "    Py_DECREF(f);\r\n",
      "    return 0;\r\n",
      "error:\r\n",
      "    Py_DECREF(f);\r\n",
      "    return -1;\r\n",
      "    /* the line below is just to avoid C compiler\r\n",
      "     * warnings about unused functions */\r\n",
      "    return __Pyx_Print(f, NULL, 0);\r\n",
      "}\r\n",
      "#else\r\n",
      "static int __Pyx_PrintOne(PyObject* stream, PyObject *o) {\r\n",
      "    int res;\r\n",
      "    PyObject* arg_tuple = PyTuple_Pack(1, o);\r\n",
      "    if (unlikely(!arg_tuple))\r\n",
      "        return -1;\r\n",
      "    res = __Pyx_Print(stream, arg_tuple, 1);\r\n",
      "    Py_DECREF(arg_tuple);\r\n",
      "    return res;\r\n",
      "}\r\n",
      "#endif\r\n",
      "\r\n",
      "static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value) {\r\n",
      "    const long neg_one = (long) -1, const_zero = 0;\r\n",
      "    const int is_unsigned = neg_one > const_zero;\r\n",
      "    if (is_unsigned) {\r\n",
      "        if (sizeof(long) < sizeof(long)) {\r\n",
      "            return PyInt_FromLong((long) value);\r\n",
      "        } else if (sizeof(long) <= sizeof(unsigned long)) {\r\n",
      "            return PyLong_FromUnsignedLong((unsigned long) value);\r\n",
      "        } else if (sizeof(long) <= sizeof(unsigned long long)) {\r\n",
      "            return PyLong_FromUnsignedLongLong((unsigned long long) value);\r\n",
      "        }\r\n",
      "    } else {\r\n",
      "        if (sizeof(long) <= sizeof(long)) {\r\n",
      "            return PyInt_FromLong((long) value);\r\n",
      "        } else if (sizeof(long) <= sizeof(long long)) {\r\n",
      "            return PyLong_FromLongLong((long long) value);\r\n",
      "        }\r\n",
      "    }\r\n",
      "    {\r\n",
      "        int one = 1; int little = (int)*(unsigned char *)&one;\r\n",
      "        unsigned char *bytes = (unsigned char *)&value;\r\n",
      "        return _PyLong_FromByteArray(bytes, sizeof(long),\r\n",
      "                                     little, !is_unsigned);\r\n",
      "    }\r\n",
      "}\r\n",
      "\r\n",
      "#define __PYX_VERIFY_RETURN_INT(target_type, func_type, func_value)       \\\r\n",
      "    {                                                                     \\\r\n",
      "        func_type value = func_value;                                     \\\r\n",
      "        if (sizeof(target_type) < sizeof(func_type)) {                    \\\r\n",
      "            if (unlikely(value != (func_type) (target_type) value)) {     \\\r\n",
      "                func_type zero = 0;                                       \\\r\n",
      "                if (is_unsigned && unlikely(value < zero))                \\\r\n",
      "                    goto raise_neg_overflow;                              \\\r\n",
      "                else                                                      \\\r\n",
      "                    goto raise_overflow;                                  \\\r\n",
      "            }                                                             \\\r\n",
      "        }                                                                 \\\r\n",
      "        return (target_type) value;                                       \\\r\n",
      "    }\r\n",
      "\r\n",
      "#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3\r\n",
      " #if CYTHON_USE_PYLONG_INTERNALS\r\n",
      "  #include \"longintrepr.h\"\r\n",
      " #endif\r\n",
      "#endif\r\n",
      "\r\n",
      "static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *x) {\r\n",
      "    const long neg_one = (long) -1, const_zero = 0;\r\n",
      "    const int is_unsigned = neg_one > const_zero;\r\n",
      "#if PY_MAJOR_VERSION < 3\r\n",
      "    if (likely(PyInt_Check(x))) {\r\n",
      "        if (sizeof(long) < sizeof(long)) {\r\n",
      "            __PYX_VERIFY_RETURN_INT(long, long, PyInt_AS_LONG(x))\r\n",
      "        } else {\r\n",
      "            long val = PyInt_AS_LONG(x);\r\n",
      "            if (is_unsigned && unlikely(val < 0)) {\r\n",
      "                goto raise_neg_overflow;\r\n",
      "            }\r\n",
      "            return (long) val;\r\n",
      "        }\r\n",
      "    } else\r\n",
      "#endif\r\n",
      "    if (likely(PyLong_Check(x))) {\r\n",
      "        if (is_unsigned) {\r\n",
      "#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3\r\n",
      " #if CYTHON_USE_PYLONG_INTERNALS\r\n",
      "            switch (Py_SIZE(x)) {\r\n",
      "                case  0: return 0;\r\n",
      "                case  1: __PYX_VERIFY_RETURN_INT(long, digit, ((PyLongObject*)x)->ob_digit[0]);\r\n",
      "            }\r\n",
      " #endif\r\n",
      "#endif\r\n",
      "            if (unlikely(Py_SIZE(x) < 0)) {\r\n",
      "                goto raise_neg_overflow;\r\n",
      "            }\r\n",
      "            if (sizeof(long) <= sizeof(unsigned long)) {\r\n",
      "                __PYX_VERIFY_RETURN_INT(long, unsigned long, PyLong_AsUnsignedLong(x))\r\n",
      "            } else if (sizeof(long) <= sizeof(unsigned long long)) {\r\n",
      "                __PYX_VERIFY_RETURN_INT(long, unsigned long long, PyLong_AsUnsignedLongLong(x))\r\n",
      "            }\r\n",
      "        } else {\r\n",
      "#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3\r\n",
      " #if CYTHON_USE_PYLONG_INTERNALS\r\n",
      "            switch (Py_SIZE(x)) {\r\n",
      "                case  0: return 0;\r\n",
      "                case  1: __PYX_VERIFY_RETURN_INT(long,  digit, +(((PyLongObject*)x)->ob_digit[0]));\r\n",
      "                case -1: __PYX_VERIFY_RETURN_INT(long, sdigit, -(sdigit) ((PyLongObject*)x)->ob_digit[0]);\r\n",
      "            }\r\n",
      " #endif\r\n",
      "#endif\r\n",
      "            if (sizeof(long) <= sizeof(long)) {\r\n",
      "                __PYX_VERIFY_RETURN_INT(long, long, PyLong_AsLong(x))\r\n",
      "            } else if (sizeof(long) <= sizeof(long long)) {\r\n",
      "                __PYX_VERIFY_RETURN_INT(long, long long, PyLong_AsLongLong(x))\r\n",
      "            }\r\n",
      "        }\r\n",
      "        {\r\n",
      "#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray)\r\n",
      "            PyErr_SetString(PyExc_RuntimeError,\r\n",
      "                            \"_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers\");\r\n",
      "#else\r\n",
      "            long val;\r\n",
      "            PyObject *v = __Pyx_PyNumber_Int(x);\r\n",
      " #if PY_MAJOR_VERSION < 3\r\n",
      "            if (likely(v) && !PyLong_Check(v)) {\r\n",
      "                PyObject *tmp = v;\r\n",
      "                v = PyNumber_Long(tmp);\r\n",
      "                Py_DECREF(tmp);\r\n",
      "            }\r\n",
      " #endif\r\n",
      "            if (likely(v)) {\r\n",
      "                int one = 1; int is_little = (int)*(unsigned char *)&one;\r\n",
      "                unsigned char *bytes = (unsigned char *)&val;\r\n",
      "                int ret = _PyLong_AsByteArray((PyLongObject *)v,\r\n",
      "                                              bytes, sizeof(val),\r\n",
      "                                              is_little, !is_unsigned);\r\n",
      "                Py_DECREF(v);\r\n",
      "                if (likely(!ret))\r\n",
      "                    return val;\r\n",
      "            }\r\n",
      "#endif\r\n",
      "            return (long) -1;\r\n",
      "        }\r\n",
      "    } else {\r\n",
      "        long val;\r\n",
      "        PyObject *tmp = __Pyx_PyNumber_Int(x);\r\n",
      "        if (!tmp) return (long) -1;\r\n",
      "        val = __Pyx_PyInt_As_long(tmp);\r\n",
      "        Py_DECREF(tmp);\r\n",
      "        return val;\r\n",
      "    }\r\n",
      "raise_overflow:\r\n",
      "    PyErr_SetString(PyExc_OverflowError,\r\n",
      "        \"value too large to convert to long\");\r\n",
      "    return (long) -1;\r\n",
      "raise_neg_overflow:\r\n",
      "    PyErr_SetString(PyExc_OverflowError,\r\n",
      "        \"can't convert negative value to long\");\r\n",
      "    return (long) -1;\r\n",
      "}\r\n",
      "\r\n",
      "static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *x) {\r\n",
      "    const int neg_one = (int) -1, const_zero = 0;\r\n",
      "    const int is_unsigned = neg_one > const_zero;\r\n",
      "#if PY_MAJOR_VERSION < 3\r\n",
      "    if (likely(PyInt_Check(x))) {\r\n",
      "        if (sizeof(int) < sizeof(long)) {\r\n",
      "            __PYX_VERIFY_RETURN_INT(int, long, PyInt_AS_LONG(x))\r\n",
      "        } else {\r\n",
      "            long val = PyInt_AS_LONG(x);\r\n",
      "            if (is_unsigned && unlikely(val < 0)) {\r\n",
      "                goto raise_neg_overflow;\r\n",
      "            }\r\n",
      "            return (int) val;\r\n",
      "        }\r\n",
      "    } else\r\n",
      "#endif\r\n",
      "    if (likely(PyLong_Check(x))) {\r\n",
      "        if (is_unsigned) {\r\n",
      "#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3\r\n",
      " #if CYTHON_USE_PYLONG_INTERNALS\r\n",
      "            switch (Py_SIZE(x)) {\r\n",
      "                case  0: return 0;\r\n",
      "                case  1: __PYX_VERIFY_RETURN_INT(int, digit, ((PyLongObject*)x)->ob_digit[0]);\r\n",
      "            }\r\n",
      " #endif\r\n",
      "#endif\r\n",
      "            if (unlikely(Py_SIZE(x) < 0)) {\r\n",
      "                goto raise_neg_overflow;\r\n",
      "            }\r\n",
      "            if (sizeof(int) <= sizeof(unsigned long)) {\r\n",
      "                __PYX_VERIFY_RETURN_INT(int, unsigned long, PyLong_AsUnsignedLong(x))\r\n",
      "            } else if (sizeof(int) <= sizeof(unsigned long long)) {\r\n",
      "                __PYX_VERIFY_RETURN_INT(int, unsigned long long, PyLong_AsUnsignedLongLong(x))\r\n",
      "            }\r\n",
      "        } else {\r\n",
      "#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3\r\n",
      " #if CYTHON_USE_PYLONG_INTERNALS\r\n",
      "            switch (Py_SIZE(x)) {\r\n",
      "                case  0: return 0;\r\n",
      "                case  1: __PYX_VERIFY_RETURN_INT(int,  digit, +(((PyLongObject*)x)->ob_digit[0]));\r\n",
      "                case -1: __PYX_VERIFY_RETURN_INT(int, sdigit, -(sdigit) ((PyLongObject*)x)->ob_digit[0]);\r\n",
      "            }\r\n",
      " #endif\r\n",
      "#endif\r\n",
      "            if (sizeof(int) <= sizeof(long)) {\r\n",
      "                __PYX_VERIFY_RETURN_INT(int, long, PyLong_AsLong(x))\r\n",
      "            } else if (sizeof(int) <= sizeof(long long)) {\r\n",
      "                __PYX_VERIFY_RETURN_INT(int, long long, PyLong_AsLongLong(x))\r\n",
      "            }\r\n",
      "        }\r\n",
      "        {\r\n",
      "#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray)\r\n",
      "            PyErr_SetString(PyExc_RuntimeError,\r\n",
      "                            \"_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers\");\r\n",
      "#else\r\n",
      "            int val;\r\n",
      "            PyObject *v = __Pyx_PyNumber_Int(x);\r\n",
      " #if PY_MAJOR_VERSION < 3\r\n",
      "            if (likely(v) && !PyLong_Check(v)) {\r\n",
      "                PyObject *tmp = v;\r\n",
      "                v = PyNumber_Long(tmp);\r\n",
      "                Py_DECREF(tmp);\r\n",
      "            }\r\n",
      " #endif\r\n",
      "            if (likely(v)) {\r\n",
      "                int one = 1; int is_little = (int)*(unsigned char *)&one;\r\n",
      "                unsigned char *bytes = (unsigned char *)&val;\r\n",
      "                int ret = _PyLong_AsByteArray((PyLongObject *)v,\r\n",
      "                                              bytes, sizeof(val),\r\n",
      "                                              is_little, !is_unsigned);\r\n",
      "                Py_DECREF(v);\r\n",
      "                if (likely(!ret))\r\n",
      "                    return val;\r\n",
      "            }\r\n",
      "#endif\r\n",
      "            return (int) -1;\r\n",
      "        }\r\n",
      "    } else {\r\n",
      "        int val;\r\n",
      "        PyObject *tmp = __Pyx_PyNumber_Int(x);\r\n",
      "        if (!tmp) return (int) -1;\r\n",
      "        val = __Pyx_PyInt_As_int(tmp);\r\n",
      "        Py_DECREF(tmp);\r\n",
      "        return val;\r\n",
      "    }\r\n",
      "raise_overflow:\r\n",
      "    PyErr_SetString(PyExc_OverflowError,\r\n",
      "        \"value too large to convert to int\");\r\n",
      "    return (int) -1;\r\n",
      "raise_neg_overflow:\r\n",
      "    PyErr_SetString(PyExc_OverflowError,\r\n",
      "        \"can't convert negative value to int\");\r\n",
      "    return (int) -1;\r\n",
      "}\r\n",
      "\r\n",
      "static int __Pyx_check_binary_version(void) {\r\n",
      "    char ctversion[4], rtversion[4];\r\n",
      "    PyOS_snprintf(ctversion, 4, \"%d.%d\", PY_MAJOR_VERSION, PY_MINOR_VERSION);\r\n",
      "    PyOS_snprintf(rtversion, 4, \"%s\", Py_GetVersion());\r\n",
      "    if (ctversion[0] != rtversion[0] || ctversion[2] != rtversion[2]) {\r\n",
      "        char message[200];\r\n",
      "        PyOS_snprintf(message, sizeof(message),\r\n",
      "                      \"compiletime version %s of module '%.100s' \"\r\n",
      "                      \"does not match runtime version %s\",\r\n",
      "                      ctversion, __Pyx_MODULE_NAME, rtversion);\r\n",
      "        return PyErr_WarnEx(NULL, message, 1);\r\n",
      "    }\r\n",
      "    return 0;\r\n",
      "}\r\n",
      "\r\n",
      "static int __Pyx_InitStrings(__Pyx_StringTabEntry *t) {\r\n",
      "    while (t->p) {\r\n",
      "        #if PY_MAJOR_VERSION < 3\r\n",
      "        if (t->is_unicode) {\r\n",
      "            *t->p = PyUnicode_DecodeUTF8(t->s, t->n - 1, NULL);\r\n",
      "        } else if (t->intern) {\r\n",
      "            *t->p = PyString_InternFromString(t->s);\r\n",
      "        } else {\r\n",
      "            *t->p = PyString_FromStringAndSize(t->s, t->n - 1);\r\n",
      "        }\r\n",
      "        #else\r\n",
      "        if (t->is_unicode | t->is_str) {\r\n",
      "            if (t->intern) {\r\n",
      "                *t->p = PyUnicode_InternFromString(t->s);\r\n",
      "            } else if (t->encoding) {\r\n",
      "                *t->p = PyUnicode_Decode(t->s, t->n - 1, t->encoding, NULL);\r\n",
      "            } else {\r\n",
      "                *t->p = PyUnicode_FromStringAndSize(t->s, t->n - 1);\r\n",
      "            }\r\n",
      "        } else {\r\n",
      "            *t->p = PyBytes_FromStringAndSize(t->s, t->n - 1);\r\n",
      "        }\r\n",
      "        #endif\r\n",
      "        if (!*t->p)\r\n",
      "            return -1;\r\n",
      "        ++t;\r\n",
      "    }\r\n",
      "    return 0;\r\n",
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      "static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(const char* c_str) {\r\n",
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      "    Py_ssize_t ignore;\r\n",
      "    return __Pyx_PyObject_AsStringAndSize(o, &ignore);\r\n",
      "}\r\n",
      "static CYTHON_INLINE char* __Pyx_PyObject_AsStringAndSize(PyObject* o, Py_ssize_t *length) {\r\n",
      "#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT\r\n",
      "    if (\r\n",
      "#if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII\r\n",
      "            __Pyx_sys_getdefaultencoding_not_ascii &&\r\n",
      "#endif\r\n",
      "            PyUnicode_Check(o)) {\r\n",
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      "        char* defenc_c;\r\n",
      "        PyObject* defenc = _PyUnicode_AsDefaultEncodedString(o, NULL);\r\n",
      "        if (!defenc) return NULL;\r\n",
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      "        {\r\n",
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      "            char* c;\r\n",
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      "                    PyUnicode_AsASCIIString(o);\r\n",
      "                    return NULL;\r\n",
      "                }\r\n",
      "            }\r\n",
      "        }\r\n",
      "#endif\r\n",
      "        *length = PyBytes_GET_SIZE(defenc);\r\n",
      "        return defenc_c;\r\n",
      "#else\r\n",
      "        if (__Pyx_PyUnicode_READY(o) == -1) return NULL;\r\n",
      "#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII\r\n",
      "        if (PyUnicode_IS_ASCII(o)) {\r\n",
      "            *length = PyUnicode_GET_LENGTH(o);\r\n",
      "            return PyUnicode_AsUTF8(o);\r\n",
      "        } else {\r\n",
      "            PyUnicode_AsASCIIString(o);\r\n",
      "            return NULL;\r\n",
      "        }\r\n",
      "#else\r\n",
      "        return PyUnicode_AsUTF8AndSize(o, length);\r\n",
      "#endif\r\n",
      "#endif\r\n",
      "    } else\r\n",
      "#endif\r\n",
      "#if !CYTHON_COMPILING_IN_PYPY\r\n",
      "    if (PyByteArray_Check(o)) {\r\n",
      "        *length = PyByteArray_GET_SIZE(o);\r\n",
      "        return PyByteArray_AS_STRING(o);\r\n",
      "    } else\r\n",
      "#endif\r\n",
      "    {\r\n",
      "        char* result;\r\n",
      "        int r = PyBytes_AsStringAndSize(o, &result, length);\r\n",
      "        if (unlikely(r < 0)) {\r\n",
      "            return NULL;\r\n",
      "        } else {\r\n",
      "            return result;\r\n",
      "        }\r\n",
      "    }\r\n",
      "}\r\n",
      "static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject* x) {\r\n",
      "   int is_true = x == Py_True;\r\n",
      "   if (is_true | (x == Py_False) | (x == Py_None)) return is_true;\r\n",
      "   else return PyObject_IsTrue(x);\r\n",
      "}\r\n",
      "static CYTHON_INLINE PyObject* __Pyx_PyNumber_Int(PyObject* x) {\r\n",
      "  PyNumberMethods *m;\r\n",
      "  const char *name = NULL;\r\n",
      "  PyObject *res = NULL;\r\n",
      "#if PY_MAJOR_VERSION < 3\r\n",
      "  if (PyInt_Check(x) || PyLong_Check(x))\r\n",
      "#else\r\n",
      "  if (PyLong_Check(x))\r\n",
      "#endif\r\n",
      "    return Py_INCREF(x), x;\r\n",
      "  m = Py_TYPE(x)->tp_as_number;\r\n",
      "#if PY_MAJOR_VERSION < 3\r\n",
      "  if (m && m->nb_int) {\r\n",
      "    name = \"int\";\r\n",
      "    res = PyNumber_Int(x);\r\n",
      "  }\r\n",
      "  else if (m && m->nb_long) {\r\n",
      "    name = \"long\";\r\n",
      "    res = PyNumber_Long(x);\r\n",
      "  }\r\n",
      "#else\r\n",
      "  if (m && m->nb_int) {\r\n",
      "    name = \"int\";\r\n",
      "    res = PyNumber_Long(x);\r\n",
      "  }\r\n",
      "#endif\r\n",
      "  if (res) {\r\n",
      "#if PY_MAJOR_VERSION < 3\r\n",
      "    if (!PyInt_Check(res) && !PyLong_Check(res)) {\r\n",
      "#else\r\n",
      "    if (!PyLong_Check(res)) {\r\n",
      "#endif\r\n",
      "      PyErr_Format(PyExc_TypeError,\r\n",
      "                   \"__%.4s__ returned non-%.4s (type %.200s)\",\r\n",
      "                   name, name, Py_TYPE(res)->tp_name);\r\n",
      "      Py_DECREF(res);\r\n",
      "      return NULL;\r\n",
      "    }\r\n",
      "  }\r\n",
      "  else if (!PyErr_Occurred()) {\r\n",
      "    PyErr_SetString(PyExc_TypeError,\r\n",
      "                    \"an integer is required\");\r\n",
      "  }\r\n",
      "  return res;\r\n",
      "}\r\n",
      "static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject* b) {\r\n",
      "  Py_ssize_t ival;\r\n",
      "  PyObject *x;\r\n",
      "#if PY_MAJOR_VERSION < 3\r\n",
      "  if (likely(PyInt_CheckExact(b)))\r\n",
      "      return PyInt_AS_LONG(b);\r\n",
      "#endif\r\n",
      "  if (likely(PyLong_CheckExact(b))) {\r\n",
      "    #if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3\r\n",
      "     #if CYTHON_USE_PYLONG_INTERNALS\r\n",
      "       switch (Py_SIZE(b)) {\r\n",
      "       case -1: return -(sdigit)((PyLongObject*)b)->ob_digit[0];\r\n",
      "       case  0: return 0;\r\n",
      "       case  1: return ((PyLongObject*)b)->ob_digit[0];\r\n",
      "       }\r\n",
      "     #endif\r\n",
      "    #endif\r\n",
      "    return PyLong_AsSsize_t(b);\r\n",
      "  }\r\n",
      "  x = PyNumber_Index(b);\r\n",
      "  if (!x) return -1;\r\n",
      "  ival = PyInt_AsSsize_t(x);\r\n",
      "  Py_DECREF(x);\r\n",
      "  return ival;\r\n",
      "}\r\n",
      "static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t ival) {\r\n",
      "    return PyInt_FromSize_t(ival);\r\n",
      "}\r\n",
      "\r\n",
      "\r\n",
      "#endif /* Py_PYTHON_H */\r\n"
     ]
    }
   ],
   "source": [
    "!cat helloworld.c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
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       ".cython.score-254 {background-color: #FFFF09;}.cython .hll { background-color: #ffffcc }\n",
       ".cython  { background: #f8f8f8; }\n",
       ".cython .c { color: #408080; font-style: italic } /* Comment */\n",
       ".cython .err { border: 1px solid #FF0000 } /* Error */\n",
       ".cython .k { color: #008000; font-weight: bold } /* Keyword */\n",
       ".cython .o { color: #666666 } /* Operator */\n",
       ".cython .cm { color: #408080; font-style: italic } /* Comment.Multiline */\n",
       ".cython .cp { color: #BC7A00 } /* Comment.Preproc */\n",
       ".cython .c1 { color: #408080; font-style: italic } /* Comment.Single */\n",
       ".cython .cs { color: #408080; font-style: italic } /* Comment.Special */\n",
       ".cython .gd { color: #A00000 } /* Generic.Deleted */\n",
       ".cython .ge { font-style: italic } /* Generic.Emph */\n",
       ".cython .gr { color: #FF0000 } /* Generic.Error */\n",
       ".cython .gh { color: #000080; font-weight: bold } /* Generic.Heading */\n",
       ".cython .gi { color: #00A000 } /* Generic.Inserted */\n",
       ".cython .go { color: #888888 } /* Generic.Output */\n",
       ".cython .gp { color: #000080; font-weight: bold } /* Generic.Prompt */\n",
       ".cython .gs { font-weight: bold } /* Generic.Strong */\n",
       ".cython .gu { color: #800080; font-weight: bold } /* Generic.Subheading */\n",
       ".cython .gt { color: #0044DD } /* Generic.Traceback */\n",
       ".cython .kc { color: #008000; font-weight: bold } /* Keyword.Constant */\n",
       ".cython .kd { color: #008000; font-weight: bold } /* Keyword.Declaration */\n",
       ".cython .kn { color: #008000; font-weight: bold } /* Keyword.Namespace */\n",
       ".cython .kp { color: #008000 } /* Keyword.Pseudo */\n",
       ".cython .kr { color: #008000; font-weight: bold } /* Keyword.Reserved */\n",
       ".cython .kt { color: #B00040 } /* Keyword.Type */\n",
       ".cython .m { color: #666666 } /* Literal.Number */\n",
       ".cython .s { color: #BA2121 } /* Literal.String */\n",
       ".cython .na { color: #7D9029 } /* Name.Attribute */\n",
       ".cython .nb { color: #008000 } /* Name.Builtin */\n",
       ".cython .nc { color: #0000FF; font-weight: bold } /* Name.Class */\n",
       ".cython .no { color: #880000 } /* Name.Constant */\n",
       ".cython .nd { color: #AA22FF } /* Name.Decorator */\n",
       ".cython .ni { color: #999999; font-weight: bold } /* Name.Entity */\n",
       ".cython .ne { color: #D2413A; font-weight: bold } /* Name.Exception */\n",
       ".cython .nf { color: #0000FF } /* Name.Function */\n",
       ".cython .nl { color: #A0A000 } /* Name.Label */\n",
       ".cython .nn { color: #0000FF; font-weight: bold } /* Name.Namespace */\n",
       ".cython .nt { color: #008000; font-weight: bold } /* Name.Tag */\n",
       ".cython .nv { color: #19177C } /* Name.Variable */\n",
       ".cython .ow { color: #AA22FF; font-weight: bold } /* Operator.Word */\n",
       ".cython .w { color: #bbbbbb } /* Text.Whitespace */\n",
       ".cython .mb { color: #666666 } /* Literal.Number.Bin */\n",
       ".cython .mf { color: #666666 } /* Literal.Number.Float */\n",
       ".cython .mh { color: #666666 } /* Literal.Number.Hex */\n",
       ".cython .mi { color: #666666 } /* Literal.Number.Integer */\n",
       ".cython .mo { color: #666666 } /* Literal.Number.Oct */\n",
       ".cython .sb { color: #BA2121 } /* Literal.String.Backtick */\n",
       ".cython .sc { color: #BA2121 } /* Literal.String.Char */\n",
       ".cython .sd { color: #BA2121; font-style: italic } /* Literal.String.Doc */\n",
       ".cython .s2 { color: #BA2121 } /* Literal.String.Double */\n",
       ".cython .se { color: #BB6622; font-weight: bold } /* Literal.String.Escape */\n",
       ".cython .sh { color: #BA2121 } /* Literal.String.Heredoc */\n",
       ".cython .si { color: #BB6688; font-weight: bold } /* Literal.String.Interpol */\n",
       ".cython .sx { color: #008000 } /* Literal.String.Other */\n",
       ".cython .sr { color: #BB6688 } /* Literal.String.Regex */\n",
       ".cython .s1 { color: #BA2121 } /* Literal.String.Single */\n",
       ".cython .ss { color: #19177C } /* Literal.String.Symbol */\n",
       ".cython .bp { color: #008000 } /* Name.Builtin.Pseudo */\n",
       ".cython .vc { color: #19177C } /* Name.Variable.Class */\n",
       ".cython .vg { color: #19177C } /* Name.Variable.Global */\n",
       ".cython .vi { color: #19177C } /* Name.Variable.Instance */\n",
       ".cython .il { color: #666666 } /* Literal.Number.Integer.Long */\n",
       "    </style>\n",
       "    <script>\n",
       "    function toggleDiv(id) {\n",
       "        theDiv = id.nextElementSibling\n",
       "        if (theDiv.style.display != 'block') theDiv.style.display = 'block';\n",
       "        else theDiv.style.display = 'none';\n",
       "    }\n",
       "    </script>\n",
       "</head>\n",
       "<body class=\"cython\">\n",
       "<p>Generated by Cython 0.22</p>\n",
       "<p>Raw output: <a href=\"helloworld.c\">helloworld.c</a></p>\n",
       "<div class=\"cython\"><pre class='cython line score-0'>&#xA0;1: </pre>\n",
       "<pre class='cython line score-2' onclick='toggleDiv(this)'>+2: <span class=\"k\">print</span><span class=\"p\">(</span><span class=\"s\">&#39;Hola Curso, soy Cython&#39;</span><span class=\"p\">)</span></pre>\n",
       "<pre class='cython code score-2'>  if (<span class='pyx_c_api'>__Pyx_PrintOne</span>(0, __pyx_kp_s_Hola_Curso_soy_Cython) &lt; 0) <span class='error_goto'>{__pyx_filename = __pyx_f[0]; __pyx_lineno = 2; __pyx_clineno = __LINE__; goto __pyx_L1_error;}</span>\n",
       "</pre></div></body></html>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.core.display import HTML\n",
    "HTML(filename='helloworld.html')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Con el código C necesario, podemos compilar nuestra extensión"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "!gcc -shared -pthread -fPIC -fwrapv -O2 -Wall -fno-strict-aliasing -I /usr/include/python3.4 -o helloworld.so helloworld.c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hola Curso, soy Cython. #NiUnaMenos\n"
     ]
    }
   ],
   "source": [
    "import helloworld"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Buenísimo, pero un poquito trabajoso, no?\n",
    "\n",
    "### Hola Mundo, usando distutils\n",
    "\n",
    "`distutils` es un módulo de la biblioteca estándar (es decir, ya viene con python) que sirve para definir archivos de instalación de paquetes Python, sean python puro o con extensiones en C/C++. \n",
    "\n",
    "Cython agrega una función ``cythonize`` que llama al compilador cython y le dice a `distutils` cual es el código C que debe compilar\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overwriting helloworld2.pyx\n"
     ]
    }
   ],
   "source": [
    "%%file helloworld2.pyx\n",
    "\n",
    "print('Hola Curso, sigo siendo Cython')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overwriting setup.py\n"
     ]
    }
   ],
   "source": [
    "%%file setup.py\n",
    "\n",
    "from distutils.core import setup\n",
    "from Cython.Build import cythonize\n",
    "\n",
    "setup(\n",
    "    ext_modules = cythonize(\"helloworld2.pyx\")\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "El archivo `setup.py`, por el sólo hecho de usar `distutils.setup`, se convierte en un script de instalación y tiene muchos subcomandos. En particular nos interesa `build_ext`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Compiling helloworld2.pyx because it changed.\n",
      "Cythonizing helloworld2.pyx\n",
      "running build_ext\n",
      "building 'helloworld2' extension\n",
      "x86_64-linux-gnu-gcc -pthread -DNDEBUG -g -fwrapv -O2 -Wall -Wstrict-prototypes -g -fstack-protector-strong -Wformat -Werror=format-security -D_FORTIFY_SOURCE=2 -fPIC -I/usr/include/python3.4m -I/home/tin/.virtualenvs/curso/include/python3.4m -c helloworld2.c -o build/temp.linux-x86_64-3.4/helloworld2.o\n",
      "x86_64-linux-gnu-gcc -pthread -shared -Wl,-O1 -Wl,-Bsymbolic-functions -Wl,-Bsymbolic-functions -Wl,-z,relro -Wl,-Bsymbolic-functions -Wl,-z,relro -g -fstack-protector-strong -Wformat -Werror=format-security -D_FORTIFY_SOURCE=2 build/temp.linux-x86_64-3.4/helloworld2.o -o /home/tin/lab/curso-python-cientifico/helloworld2.cpython-34m.so\n"
     ]
    }
   ],
   "source": [
    "!python setup.py build_ext --inplace"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hola Curso, sigo siendo Cython\n"
     ]
    }
   ],
   "source": [
    "import helloworld2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Obviamente, no debemos compilar el código cython cada vez que lo queremos usar, sino cada vez que lo modificamos. \n",
    "\n",
    "## Hola mundo, the magic way\n",
    "\n",
    "Pero si lo usamos en el notebook y lo modificamos seguido, es mejor usa el comándo magic **cython** "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# %load_ext cythonmagic    en versiones anteriores a 0.21\n",
    "%load_ext cython"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hola, soy cython, a través de un magic.\n"
     ]
    }
   ],
   "source": [
    "%%cython \n",
    "\n",
    "def hola():\n",
    "    print('hola, soy una funcion cython')\n",
    "\n",
    "print('Hola, soy cython, a través de un magic.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hola, soy una funcion cython\n"
     ]
    }
   ],
   "source": [
    "hola()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "De paso, permitanmé el autobombo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r\n",
      "Code moved from IPython and adapted by:\r\n",
      "* Martín Gaitán\r\n"
     ]
    }
   ],
   "source": [
    "!cat ~/.virtualenvs/curso/lib/python3.4/site-packages/Cython/Build/IpythonMagic.py | grep -B 2 \"Gaitán\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Paréntesis: funciones generadoras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def fib(n):\n",
    "    \"\"\"cálcula la serie hasta n y devuelve una lista\"\"\"\n",
    "    serie = []\n",
    "    a, b = 0, 1\n",
    "    while b < n:\n",
    "        serie.append(b)\n",
    "        a, b = b, a + b\n",
    "    return serie\n",
    "\n",
    "fib(120)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100000 loops, best of 3: 3.57 µs per loop\n"
     ]
    }
   ],
   "source": [
    "%timeit fib(1000000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def fib_2(n):\n",
    "    \"\"\"cálcula la serie hasta n y devuelve una lista\"\"\"\n",
    "    serie = []\n",
    "    a, b = 0, 1\n",
    "    while b < n:\n",
    "        yield b            # devuelve el valor actual y marca el punto de entrada para la proxima llamada\n",
    "        a, b = b, a + b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<generator object fib_2 at 0x7ff9f92e0ea0>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fib_2(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 1, 2, 3, 5, 8, 13, 21]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(fib_2(30))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100000 loops, best of 3: 3.25 µs per loop\n"
     ]
    }
   ],
   "source": [
    "%timeit list(fib(100000))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ) \n",
    "\n",
    "(Fin del paréntesis)\n",
    "\n",
    "### El mismo generador en Cython"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%%cython\n",
    "\n",
    "def fib_cython(n):\n",
    "    \"\"\"Print the Fibonacci series up to n.\"\"\"\n",
    "    a, b = 0, 1\n",
    "    while b < n:\n",
    "        yield b\n",
    "        a, b = b, a + b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The slowest run took 8.16 times longer than the fastest. This could mean that an intermediate result is being cached \n",
      "1000000 loops, best of 3: 1.63 µs per loop\n"
     ]
    }
   ],
   "source": [
    "%timeit list(fib_cython(100000))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Cython on steroids\n",
    "\n",
    "Pero veamos un código Cython que no sea Python puro. Supongamos que queremos calcular $\\sum_{i=0}^n \\alpha^i$ para $\\alpha, n$ dados. \n",
    "\n",
    "Obviamente, antes de optimizar un algoritmo podriamos aplicar la formula de la progresión geométrica \n",
    "\n",
    "$$\n",
    "\\sum_{i=0}^n \\alpha^i = \\frac{1 - \\alpha^{n+1}}{1 - \\alpha}\n",
    "$$\n",
    "\n",
    "pero para \"medir músculos\" (?) lo haremos iterativamente"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def geo_prog(alpha, n):\n",
    "    current = 1.0\n",
    "    sum = current\n",
    "    for i in range(n):\n",
    "        current = current * alpha\n",
    "        sum = sum + current\n",
    "    return sum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000 loops, best of 3: 70.2 µs per loop\n"
     ]
    }
   ],
   "source": [
    "%timeit geo_prog(-4.0, 1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%%cython \n",
    "def geo_prog_cython(double alpha, int n):\n",
    "    cdef double current = 1.0\n",
    "    cdef double sum = current          # declaracion estática de tipo!\n",
    "    cdef int i\n",
    "    for i in range(n):\n",
    "        current = current * alpha\n",
    "        sum = sum + current\n",
    "    return sum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1000000 loops, best of 3: 1.71 µs per loop\n"
     ]
    }
   ],
   "source": [
    "%timeit geo_prog_cython(-4.0, 1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "41.17647058823529"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "70e-6 / 1.7e-6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "geo_prog_cython(-4.0, 500) == geo_prog(-4.0, 500)  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lindo, no?\n",
    "\n",
    "### Funciones Cython internas y públicas\n",
    "\n",
    "la directiva `cdef` tambien permite definir funciones a nivel C/C++, pero son de uso interno a nivel Cython, y no se exponen a Python\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%cython \n",
    "\n",
    "cdef double priv_geo_prog_cython(double alpha, int n):\n",
    "    cdef double current = 1.0\n",
    "    cdef double sum = current          # declaracion estática de tipo!\n",
    "    cdef int i\n",
    "    for i in range(n):\n",
    "        current = current * alpha\n",
    "        sum = sum + current\n",
    "    return sum\n",
    "\n",
    "def public_geo_prog_cython(double alpha, int n):\n",
    "    return priv_geo_prog_cython(alpha, n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'priv_geo_prog_cython' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-35-450fec72db4f>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mpriv_geo_prog_cython\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m2.\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m10\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'priv_geo_prog_cython' is not defined"
     ]
    }
   ],
   "source": [
    "priv_geo_prog_cython(2., 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2047.0"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "public_geo_prog_cython(2., 10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cython y numpy\n",
    "\n",
    "\n",
    "La reglas de broadcasting y vectorización aplican al código Cython"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%%cython\n",
    "\n",
    "def f_plain(x):\n",
    "    return x * (x - 1)\n",
    "\n",
    "def integrate_f_plain(a, b, N):\n",
    "    s = 0\n",
    "    dx = (b - a) / N\n",
    "    for i in range(N):\n",
    "        s += f_plain(a + i * dx)\n",
    "    return s * dx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "328085.875"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "integrate_f_plain(0, 100, 2000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  261.25,   940.31,  2298.33])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "integrate_f_plain(np.array([0, 1, 2]), np.array([10, 15, 20]), 20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pero más aún, se puede usar numpy internamente"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%cython\n",
    "\n",
    "import numpy as np\n",
    "cimport cython\n",
    "from libc.math cimport sqrt\n",
    "\n",
    "def pairwise_cython(double[:,:] X):\n",
    "    cdef int M = X.shape[0]\n",
    "    cdef int N = X.shape[1]\n",
    "    cdef double tmp, d\n",
    "    cdef double[:, :] D = np.empty((M, M), dtype=np.float64)\n",
    "    for i in range(M):\n",
    "        for j in range(M):\n",
    "            d = 0.0\n",
    "            for k in range(N):\n",
    "                tmp = X[i, k] - X[j, k]\n",
    "                d += tmp * tmp\n",
    "            D[i, j] = sqrt(d)\n",
    "    return np.asarray(D)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "X = np.random.random((100, 3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.        ,  0.48449438,  0.36120024, ...,  0.42444183,\n",
       "         0.36412224,  0.61787909],\n",
       "       [ 0.48449438,  0.        ,  0.47334739, ...,  0.45666607,\n",
       "         0.33364929,  0.43178433],\n",
       "       [ 0.36120024,  0.47334739,  0.        , ...,  0.70041621,\n",
       "         0.50892343,  0.80848887],\n",
       "       ..., \n",
       "       [ 0.42444183,  0.45666607,  0.70041621, ...,  0.        ,\n",
       "         0.32684235,  0.28355982],\n",
       "       [ 0.36412224,  0.33364929,  0.50892343, ...,  0.32684235,\n",
       "         0.        ,  0.49831402],\n",
       "       [ 0.61787909,  0.43178433,  0.80848887, ...,  0.28355982,\n",
       "         0.49831402,  0.        ]])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pairwise_cython(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Numba\n",
    "\n",
    "Numba es otra manera de optimizar código Python a través de un proceso de compilación. A diferencia de Cython, el proceso de compilación no produce código C válido para ser compilado como una extensión, sino que directamente se compila a \"código maquina\", pero *justo cuando se lo necesita* (ver [Just in Time compilation](http://en.wikipedia.org/wiki/Just-in-time_compilation))\n",
    "\n",
    "Numba utiliza como tecnología subyacente la infraestructura de compilación [LLVM](http://en.wikipedia.org/wiki/LLVM) que es la encargada de realizar estas optimizaciones al vuelo \n",
    "\n",
    "### Instalación\n",
    "\n",
    "Usando conda\n",
    "\n",
    "    conda install numba\n",
    "    \n",
    "Via pip/virtualenv (linux)\n",
    "\n",
    "    sudo apt-get install libllvm build-essential libllvm-dev libedit-dev\n",
    "    pip install llvmlite\n",
    "    pip install numba\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sum2d(arr):\n",
    "    M, N = arr.shape\n",
    "    result = 0.0\n",
    "    for i in range(M):\n",
    "        for j in range(N):\n",
    "            result += arr[i,j]\n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.ones((10, 20))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "200.0"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum2d(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "m_random = np.random.rand(1000, 1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 loops, best of 3: 215 ms per loop\n"
     ]
    }
   ],
   "source": [
    "%timeit sum2d(m_random)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Así se aplica la magia de Numba"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from numba import jit\n",
    "\n",
    "sum2d = jit(sum2d)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para quienes desconfian, comprobemos que la funcion \"numbizada\" produce el mismo resultado"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "500372.0924206339"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum2d_numba(m_random)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pero anda un par de cientos de veces más rápido"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1000 loops, best of 3: 1.03 ms per loop\n"
     ]
    }
   ],
   "source": [
    "%timeit sum2d_numba(m_random)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Una forma forma más elegante de usar usar `jit` es como un **decorador**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "@jit\n",
    "def sum2d_(arr):\n",
    "    M, N = arr.shape\n",
    "    result = 0.0\n",
    "    for i in range(M):\n",
    "        for j in range(N):\n",
    "            result += arr[i,j]\n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The slowest run took 96.07 times longer than the fastest. This could mean that an intermediate result is being cached \n",
      "1000 loops, best of 3: 1.04 ms per loop\n"
     ]
    }
   ],
   "source": [
    "%timeit sum2d_(m_random)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "¡Más de 200 veces más rápido con una línea de código!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[162, 162, 162, ..., 170, 155, 128],\n",
       "       [162, 162, 162, ..., 170, 155, 128],\n",
       "       [162, 162, 162, ..., 170, 155, 128],\n",
       "       ..., \n",
       "       [ 43,  43,  50, ..., 104, 100,  98],\n",
       "       [ 44,  44,  55, ..., 104, 105, 108],\n",
       "       [ 44,  44,  55, ..., 104, 105, 108]])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy.misc import lena\n",
    "image =  lena()\n",
    "image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f2ba3c4ef28>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "\n",
    "plt.gray()\n",
    "plt.imshow(image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f2ba294b320>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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FCxboLSOkLFu2TG8JhqS1tTXs9xRziFCeeeaZqCg1DA0NeS3HFxcnQ2/Gsnv3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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2ba29f7438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(image < 200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "def filter2d_simple(image, filt):\n",
    "    M, N = image.shape\n",
    "    Mf, Nf = filt.shape\n",
    "    Mf2 = Mf // 2\n",
    "    Nf2 = Nf // 2\n",
    "    result = np.zeros(image.shape)\n",
    "    for i in range(Mf2, M - Mf2):\n",
    "        for j in range(Nf2, N - Nf2):\n",
    "            num = 0.0\n",
    "            for ii in range(Mf):\n",
    "                for jj in range(Nf):\n",
    "                    num += (filt[Mf-1-ii, Nf-1-jj] * image[i-Mf2+ii, j-Nf2+jj])\n",
    "            result[i, j] = num\n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "filter = np.ones((5,5), dtype=np.int32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 loops, best of 3: 25.6 s per loop\n"
     ]
    },
    {
     "data": {
      "image/png": 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qlUKhUMDs7KzbE5FCxPZV69A/Gwlgf/L5PF588UU0Gg3U63Xs7OxEzBYVZjpf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\nVoTRAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff9e6ca3f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%timeit\n",
    "plt.imshow(filter2d_simple(image, filter))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "from numba import int32\n",
    "\n",
    "@jit(int32[:,:](int32[:,:], int32[:,:]))\n",
    "def filter2d_numba(image, filt):\n",
    "    M, N = image.shape\n",
    "    Mf, Nf = filt.shape\n",
    "    Mf2 = Mf // 2\n",
    "    Nf2 = Nf // 2\n",
    "    result = np.zeros_like(image)\n",
    "    for i in range(Mf2, M - Mf2):\n",
    "        for j in range(Nf2, N - Nf2):\n",
    "            num = 0.0\n",
    "            for ii in range(Mf):\n",
    "                for jj in range(Nf):\n",
    "                    num += (filt[Mf-1-ii, Nf-1-jj] * image[i-Mf2+ii, j-Nf2+jj])\n",
    "            result[i, j] = num\n",
    "    return result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La sintaxis de parámetros para `jit()`  es `salida(*entrada)`. En este caso todas matrices de enteros de 32bits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The slowest run took 7.16 times longer than the fastest. This could mean that an intermediate result is being cached \n",
      "10 loops, best of 3: 19.4 ms per loop\n"
     ]
    },
    {
     "data": {
      "image/png": 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qlUKhUMDs7KzbE5FCxPZV69A/Gwlgf/L5PF588UU0Gg3U63Xs7OxEzBYVZjpf\nPm3O8bfmhtrzuuBPd4qemZmJMJkKpjiaizM9FK32+308efIEjx8/RrvddrBfNzHSw2hYN0P9amqp\n70H7pe9Lp9OYn5/H2toaFhcXkc/nLwmV65YrhUMQBP8rgP8SwFEYhm89u7YA4A8A3AXwGMA/DcOw\n/uy33wbwzwGMAfyrMAz/k69edUJSCwKIONnUf2ChpEUZysjqnPMhA7vois/HMTzrjbtHGdFHNHyW\nERq2P46hgShKYD+4es/mMJDglRm0TRQ26gBjny0CUgGWSCRQKBQwPz8fOebNZvJpffY9FrFYNBAE\nAebm5vDaa6/h5OTE7YhE5tBFYHyfRW76Li0+35I+Q0TU6XTQ6XTcSVX2tCqLyGyx46hC+PT0FD/5\nyU9wcHDgBK22z/qIgAt/Auc8nU5HBIP65YCLbQC58G1lZQWVSsUJBt3853nKdfKG/zcAv2Ku/RaA\nvwjD8BUA/8+z7wiC4IsAfh3AF5898z8HQeB9R6vVwtnZ2SVzIg7+a7Fw3xZL8D5Cmha6ijNVlGFt\ne/m7wnPtG0NOfEYZ2Mc01B6qYVSoWQ1lTQ71fNN21U17fX3gkmHmHpRKJSwvL6NUKrlVhBq+VEGm\n+00Mh0PvGGifdFOf8XiMcrmMN998E2+88QbK5bLT7Lq5LcfW0oCll2lmpUUeNJ14PF2/33fvs0yv\nCMm+wyqfMLw4z3Jvbw/dbteF7il4NbeD9XJsrNLUBVacWzVFstksSqUSVlZW3D4bKhiSySTy+fyl\ncZlWrkQOYRj+v0EQ3DOX/wmAX3z2+T8A+M84FxC/BuD3wzAcAngcBMFDAF8F8K6tlyf9WCL1aQK1\nja354BMOyoSWeX0EFOfMimuP1dAWwmp/mKasmYBqNmm92nati/XY3ZV8SIPXFV7bHaKskAzD89x7\n2uDz8/Mol8vu3AVfvcAFwWrffEykcNYKCxJ8KpXCxsYGvv71r2N+fh4ffvghtre3XXafbojrQ0A+\nhWERjM6dlsFggEaj4bz9ut+jTwnoO60mV3Nib28Pjx8/RqPRiPhk1IfA/ljloj4l7qdqEZTmphQK\nBSwuLjrBoGiDiVFc4Xzd8ml9DithGB4++3wIYOXZ53VEBcEugA1fBRwsq50tsytDa7EM5iv2d4W9\n/OxzBPG9Fl1YYaOEYoWJMgudcMCFnegTdD4ho+sP1Neg7VH/g9ZHptS1Gmqa8X+v13NrOyqVitub\nwTpFlTlg4ttuAAAgAElEQVQ5f4pYOJ4+NGZRhBVqQXAe3mTYbXV1Fd/5znfwox/9CJ1OB2dnZ5ds\n72m+Bx0ja4IquppMJjg7O0Oz2XRmG/fmtGHiaSaG1jkej505wRWpViBbk0JpMwgCZ9qoOaFtYZ/S\n6TRmZmawsLCAhYUF5PP5S5muKvCep/yDHZJhGIZBEEyL8Xl/+/73vw/gvPFra2vY2LiQIWpXsvgI\n4CoNrxMWx4hW4/g0TJyN69PG6k9IJpNOMGjqK4lcnUvKsErMFDK0PflO61X3wV8bjbBmBmF+InGx\nVdn8/LzzXygTUsip5p6GrKwj1He/MoaGOYvFIt566y3kcjmMx2P84Ac/cGFOjluc+WDnTtulApnt\no3lD/0Y+n3e5GxTIzN+Yhj5UibTbbXempXW461oVn6MzCIJI2FJ3/LK0kkgkMDMzg5WVFSwvL2Nm\nZsaNOcfo4cOH+Pjjj587xwH49MLhMAiC1TAMD4IgWANw9Oz6UwBbct/ms2uXyttvvx3JBrNIgL/5\n0nmByygjjiBYrIDgMxo6JAGoYLqOZlKm5F+hUEAymUS9Xne7aLM+zU/wISMbu7dCwNqrSiz6nf9t\nf8n0FDjUPPPz88hmsxHi8m1aq/21zkYrmO1YWThuBQ37kcvl8Morr6DVauH4+Bgff/wxwjB02lTH\n2iIUW/Qei2B0zJmMlcvlnIDQvS+0Pj5j/UBnZ2c4ODhwh90qglQ/gZ1zRbJ0HFJo6fhybuiAXF5e\nxvr6Oubn550pwbZls1m8/fbb+NrXvub6+nu/93txpHypfFrh8CcA/hmAf/vs/x/L9f8YBMG/w7k5\n8TKAb/sq8Gk6vW4/W/hlnXfTEICPuH1miRU2ikz0epxpwbbxwBLmNOhmoUB06znfe3lNIxpKiGRq\nEqNvD0g+o2aUCo1MJoP5+XkHRXX3KRYruBQ9qL1rfSEsbJcdX2UuX5/5zmw2i1dffRU7Ozs4PDxE\ns9l0TlU62mwbffNp61eBpkLq7OwMjUYDmUwGuVzO+TlYVBgrItKxqdVqePz4MY6Oji4xtr5Xiwov\nzrFmQKrTmYorl8thcXERGxsbqFQqyGazbmxoomlkKw5lTSvXCWX+Ps6dj4tBEDwB8N8D+DcA/jAI\ngn+BZ6HMZ538URAEfwjgRwBGAP5lGNOqOG1sIbHeb+1Vn6aYpuX5uzKnajhFDL4BVTvOCgjWG4Yh\nisUi5ufncXJyEkn40TrUpNCixERNo8SjTilCYq1XhQCZR5eFM5FpaWkJS0tLKBaL7jlfkowKX2tC\nWH8N7/fBX9/Y2vepn4djUKlU8KUvfQkHBwf44Q9/GNlo12cq+pAcf9f/ep/OPw/BJWOpAKA9HzdW\n3W4XOzs7ePLkiXPu6pyqWWdRiyJKFT6a9UoHcKFQQKVSwebmJhYXFyNJThT8FG7ABa1dxRu2XCda\n8RsxP/1SzP2/C+B3r6pXzQnzvBt4DgiLz/us332S2SdILLFbDa6ohNdtEo+PCZi2SoceV/8pU7Af\nSnTUSGpC6WRq+jfvpQdbx5IMqAKBPo/hcIh8Po+FhQUsLi6iWCy6lGFqPYYZ1fHFkJ6m91rhSFRj\n58WOvd3CjP9VoNloTTqdxhe+8AV89atfRa1WczsmWb+DRY+2KK3p+CqK5DhwH0eiokKhgFKp5Bhx\nNBq5zF4mNfFcy8ePH6NWq11yaGuykzK/DzXpWgqOk85NsVjE+vo6VldXUSgU3HwTtXIDWx/CfZ5y\no+dWWMnP/4oefLaZFh+cZP0Waeh9Pu0WZ2rougfer/dxQieTCWZnZ1GpVBAEgVtMpozrcwwpfLQm\nlQoF+07tBz8zz4B5Afyfy+WwtLSEtbU1lEqliEay46f7SyiT0JSx86Pjrf1Rxlf04xtXn4+ETtRS\nqYQ33ngDDx8+xJMnTxwjqICOg86KDqdpThUUZ2dnaLVaKJVKKBaLePnll3Hv3j03bsysHI1GTgnc\nv38fOzs7ODg4cDkKVjjYd9FhreaghjKJInR9RbFYxMbGBtbX1zE7O+vmi2NFX4nSq53f65YbPysz\njvmtrexjhmn1snDCFd7yPrWJ+Q6Fi9bBp+/XdulzlUoFpVLJHa2ubVBkYu1XDVnxj1pdQ5ij0egS\nk6qpwmXgyrilUglLS0sud4HJMMPhEN1uF0EQDZ31+/3IrkJX7bPJ/msf2R8dR1+egg+BsWiWZLlc\nxosvvoj79+9jf38/gh50rp8XOseZscViEa+//jp+4Rd+Aa+99hpmZmbcOzOZjFvMlEql0Ov18OTJ\nE5crYvtkBRMd0boil/+tT4eCPgxDlEolbG5u4u7du1hYWHBLzhnqzufzzvSxffypFw4Kj/U+O3H6\nvH62woRSV0+KioNtljBZGDpTh2AcEc/MzKBSqSCTyaDT6VwKF1pvvr6D7WFR5GDNGd3gQ7UOM/3o\nAGXq8+LiIsrlsnOwsf26uo/94LvtYiAN53K81Heg6Mg3p2pGsD4bldF54PPcSj+ZTGJrawtbW1s4\nPj6OmE2W+O0YW1rRLE9+13Tl9fV1/NzP/Ry++c1v4qWXXkIymXQbETFKoJGMbDaLu3fvYnl5GQ8e\nPIi8SyMUNgpm06F1Plk4TplMBpVKBVtbW1haWkImk3ECmG1gLgPfZRXr85YbFQ7W9iNh+eLiPgKw\nA27rt4JF0QhwedUi6/UJHhUKPj8GAHfYLIDYk5G1Hfb9Cjf1WRVuuv8h76M9qou6CoVCJJWWp4bR\n/8D+ENoyUYdJQJZh7RzECQOOqzWHeJ8KI2uW8B26vob0QG396quvYnd3122vF0cX2m4tFAIUCPwD\ngFwuhxdffBFf//rX8bWvfQ1ra2sOXWWzWRfV4fhw/FOpFLa2tvDiiy/ie9/7ngth2nbY+ed1VVza\nfiKLTCaDhYUFrK+vY3l52R30RLrmysy4MKmdx+uWG/U56ED4fAMWyrLT+qzatbb4TBZNBrLvViLm\ndftevZ+antJ7YWEBc3NzkXUAqmG1XkVJeo/a+EEQRFCChiHVzNB1CsD5hq5LS0uoVCqYnZ11SU0M\nrVLTEN5qm8jAmojDPrIPyvDaPhVsvqQdZXxe52f10ut8EeEkEueLwCgcHjx4EEEONjxpaUDr5qbG\nGsWZn5/HF7/4RXzjG9/Al7/8ZczOzqLVajk/0tzcXCRDk1qbqdCE/JVKBbVaLUKH6rDlGOi4q3/H\njlsQBJiZmcH6+jo2NzdRKpWQSCQip5YxYcuHuv8h5UaFg362gsKnufR+RRc+c4DPKUrw1WPfFSft\nfc/o51QqhVKphFwu5/bDpO1vcxU04sA6yXS0Y4ELD7WmmtM3oElK3FUrDM+3BVtfX3dmBJEF8yHs\nmJGh0+m0c2ACFxu+Wt8Px4a/sU7dpFUdaSowfM5P39haFMH541b3b731FqrVqtskSP0d+qfaWZ19\nRFncjWl5eRlf+cpX8M1vfhOvvPIKALidqebn5zE/P+/6pCdf09ZnH5l6rvRn/6ugDILoGRRqUvBe\nprRvbGxgcXERmUwmsggrTjDEIe7nKTcmHKzpoEyo9qnPZPAxLACXDuszVzj4agPqu3z1xSELfT+Z\nJ5fLYXZ2Fslk8tJu0lbQ6OpIMjDDdj6fAJk3m82651knV/olk0nMz8+7FGgmNRFVkJAVDZCoSaRB\nEPU1qJa3Apjmi7aNBKpmAfupy4V948k++8ZdhVsqlcLdu3fx6quv4sc//vElhuNz9rOaXr1eD8Ph\nELlcDhsbG/j5n/95fP3rX8fW1hbOzs7QbreRTCZRLpcxNzcXMd18mpnIMZfLRVY+WgVmzVxtt9Ik\nxw2AS49m2JK/U1HoSexXCQEfL00rnxvhoJqdk+GDiVqUiNS55QsjqTDw5UPY+vTaNMhKZqCnGECE\niOwz1HScXF0jwX5r3cDFen1qa44VEUMikUC5XMbKygrm5uacUFBtqaYNBRHTbXUJMSMhCnVZeK+m\nXqu2U2bWsVMIru2w5hbnRx2zbK9GJhhJSCTOzxdl23Rs7PyMx2PnsD07O3NLxH/2Z38Wb731FiqV\nihMcuVwOpVLJ7eLM5ylkFS1aJzLnWBGFoizbTkWhfJb0wyzIzc1NFItFF70AENkejn3lf+UHvT4t\n4uQrnwvhMM2EsEW1gtqsrNM696zpYO+JQyn6Hm2nT+MBcHsqcldt7lVhtSL/6xJahb4kDF1booKD\nfebO3YSdCwsLLqmJOywHQeBgJwmcRM2wJYAIgfHdKhzYB2YmUpDQl6HmA8dKmYj18p7JZIJut+s2\nP9Hl2Fr4DNvJ/qfTaayvryOXy2FnZwdHR0eR9up/Mhv3hgCAjY0N3L17F2+++Sa2traQSqXcATvF\nYjHip9E6NEKkjM/x0X0YrIKytKSKUDcVViRYKpWwvr6OxcVFZ/axbxx/FbDWjFGeUhq8bvlcCAcf\nUfA3FsuMqnnsvZaJ9Xff++x3Pq9OOFuXfd/s7CwKhYJjWhKihY1kTDIntQjrVYep9oMam/f0ej0H\nfZm5Nx6PHZEzH4LEqsQMwJkryrDKABxjQnI1I+wu07qozIZe+V0h+GAwcElEMzMzkf0oLbLwaUDa\n4rTBU6kUjo649i8K19kvhpoXFxextbWFu3fvYm5uDqPR+QGzQRC4rFFGcDTk6RMMVuNrGFnbobkL\nFs3pPLOtHJfV1VWsra1FohNEnJp7Yp24ceV5fQ837pC0AgC4cB5e5X32MXqcL0NRhn0+TmPpvWq2\n6HV69hniqtfrLiaujKHMaR1bXIrN++xOxLqpiia9lMtlt+gGuDgoiNqIQiSbzbot0BSlsH71jdjE\nJdW4jO+rI5Jzpd91Hu0c8AAjZhcy/9/Oj51rnQf+cZ3IysoK+v0+Wq2We04jCul0GisrKy4Fmidc\nM9mLKcf0m5BB7bvZF7sRDHAuHBqNBlqtVgSR+sbFanBFRpPJ+WrK5eVlbGxsOGeoKhnShx0nW55X\nGNjyuRAOOvgsvjg5cHkgrPT0oQX+bjW/1uUTQPpZMyeB6KGmjIEDcMlPtMkJB3XBDjUSfQnqa9Cl\n2SoQtC2ZTAbFYhGlUimyYSnNhslk4oQEfQh8nuNKNKDvJSJQ34KG2HRthSIGXlOnpoaYFe3wkGQy\nI9urfdb5sFqR48ExTiaTmJmZwdzcnDtnleORSJzvd0AhOjs761AYhTNzFyig1ZmsxfqwlNlpDtTr\ndTSbzQjdWuWigsCn/BgWvXPnDlZWVtxOXDTddIPcOKFgS5y5cVW5MeHAEtdB1bRxfgQrCOLQhq3X\nChv9zbYtjkiBC+cihYM6G9VnoPVSI2kbVFiQqWi/sk98px5rTweVMgM132QycclPZFzfIisdOwAu\nHZhpwKrddRGW7Yv1IbFtzL2gwAqCAPl8/pLZZpGcjzlZD3+jz4IHuqRSKZfMlc/nMTs76/apyGQy\nkY1ddOWijpmaVfpeRUNKLxzzXq+HRqPhtrXjb9oPFfY+vwRToNfW1rC+vh6JlADRfT3iig99fdpy\no8IhTijEwSVr66n0VuHgExD8rt51XmfxmRO2fv1NbVkuglEvts/JSbtTTQ511gGIbCmnewFms1m3\nEzQJj0e304xg5EFX5pHZ6G23iInMPhgMnDOVcFwFChAVCIqIqMmtY00Zwa6tsM5KnRcVtFaYU6gl\nEgnH1NlsFjMzM24+5ufnMTc358ZAc06414FFC5YeLTpgu6wwBM53f6rX6w6xKZ1Zk4jPWcHAFb1r\na2tYWFiIOCFtdMSnVH2m8z+k3KhwsFDed933WxxysL/rd9bj+/O1wwdvLfzl3+zsLGZmZiKeZLuS\nk8RiBRgnXaE5gIjTkppubm7OZTaSqOlYVC1Nwtcx0JAlHVqaIUmPPhEG8/TVEcdx0bCi9kXDrZqm\nTSGnjk9FhmrmqRBS56wKZt5DphkMBi7PQ8/XIAKjf0JDxxxrH1qwikcZ27cPYxAEaDabqNVqboxt\n1EIVj1VgFAxzc3Nun42ZmZmIUFZzwleuEgSWzq9TbtysIOHazsXB/zjhoMyudfC/NQWUGJVAbVtU\ngCiRKrHMzs4in887TzU1LnPeWTehvBUMVliwDnr2uVSXcXc9QBW42PSFS3bpbWfiEdGF+gH47slk\n4rIF2R/dXNZHVGQWvp/amAxH4aE7LPO96pS1JgTnQzMA7ZzoPUQjFHTFYtG9g/cFwcV6EYYn+ZvS\njAp+i/goWIFo2Ffbc3JyguPjY7cRLp/T7FAVtNaflk6n3VEA5XLZzT/faZ2gceja8hDveV7BAHyO\nVmXGIQdLiMBlyeur0xIU/8chDIskfM/wOf2NdqLau9TO2WzWCR/CcUUTwIWH2goJmghBELgwKXMY\nlDFUgFAwsF+667G2SU0SmgBhGLrEGkVGloHteJOAmQOhQk/zDTi2JHTLpJpPQLNBHZuKWOhQZLSC\nbSYDj0YjhxLokyBqIZJQE8b6PHTeOU7cnZtmnjI8t6E/Pj6eSutKu5qFyrlhvorNf1EnqLbXzofS\np08Y/NQgB1/nlDFJOBol8AmSaXDKCg+fqTHNtPEJLPs+RhyAqCnCCSch6QYl1nPNeoDoeotkMumS\nq0ioGkHo9XpOU/FEKuAiw5TrLZhwpUR2dnbmvPs8GJcMRASkkRNCZWo+RUNkEk3Y0vg+iVoda9of\nNVN0XHSnK93AhszPiAcAJ+CsQNDojK5TsUpABbPONw/6GQwGzo/D/vD+4+Nj7O7uotFoOBrRceK4\nqqBW5JpOp1EsFlGpVJwTUgWk7t/ho2cf7frKT41wYFEhodJatSyLZXY+bxnZagGFipYI4urldVs4\nyZYA+BsAZ0OqA0rvJVGoFiEB0QRgeE41vY4VF3ZxXQPHSvMo6D8A4NJ/2Vddq6DOOUVdJGxtP30T\nOj+WuZWpNdGK5gKRj4ZMeQ9RB5mZ7aDplEgknANYtS+Rj+6zwDnRnAelBctQKsgAuPMsGo2Ge5Zo\nUM2LnZ0dbG9vOzRjUa0PyRJRptNpzM3NRaIqaq6pU9jSpu0Dy3WU2nXKje/nYCGS7YS1A605wLos\nqvDV7XuWv/naZj/bdwHRQ2o06qCr51RDUDOTwHQdPrUwnY+FQiECvck0zMTLZDLuYFsgmsxEQia8\npwmhTMIQLJGE9slCfp+mV5uazKxRGnXeWWivpova//QfECnoBrpEOEQ56pugaadmEMfTzrU1GX0m\n6mQyQbvdRqPRQK/Xcz6AarWKMAzdlnHdbhePHj3CkydPAMAhC6VjHVvgAkHl83kUCgWsrq5ieXnZ\n5coowlJhpfRn+6G/a19/aoWDz873MbBqfH0+rj4fs/sG0oab9H16zSco1DzwmUDUyExj5v0kFOts\notmhiEGZGYBL9WXuPRnCpvtqFIDtp/Zlm3XbcmuuqMDzncbNe4hQgKhg0jM12C4KPbaDfVKhaNEH\nnaTMWaAfge2lI1Tfp34FK7B9CsJeIz10Oh0nGCjsu90uWq2WE6qTyQT7+/t49OiRWwDGsWJbVEBp\nViNNwaWlJbe9vC7FJ2LwCQdLr+xHnACIe+aqcuMbzNqGW6geB/mvgk5XIQxfsRpFryuMU2FAGzcO\nvfAvnU47H4B1MBE+c1s3CgbNdxiPx5Fj4WZmZi7l6ts9CYEL4QNcbLnGjEDdJo79UGShfVWUoKtO\nrcCwcXj1SbAORQvqn1CkRdODSIF2N8eczlXNFmT4U4W0pTllNv2sJhQ3kOUu1Nxxul6vYzKZYG5u\nzvVjb28P29vbkfwG9RFolINoJ5FIuLyMSqUSCV2qMPNlB1s6Z9vjfvs0iIHlRn0OFvLpdU6cz+li\nNTqLTyP4PmuZNpj2GRK8ErtuzKqMT0Fg20tnmWrfMAzd8em6+Sv9EtzQ5ezsDJlMJuKhV2TBujSa\nEQRBJMKhe0Zogo1GBuwJ09aBSLNImZP9sVENHS9lWmVoMj3RQiJxsTeCZjDSfKCTTheRKULReVNf\nk81WVdpQP0S320W9Xnd9HI3Ot6IH4MY+lUrh7OwMe3t7ODo6iqBQFa4cE3Vi0pxkBieFjZoT1t+m\nisf28aryaQXEjSIHakRfBxXeThsIKyh8MJFE7zNXtA5e43edVIs4SOzMtiMhKBykMNA1+PSu6xF5\nPD6defTUqmQKnhNJbaP5AxoF0T0H2QYKKeZA6GpQAJF6iGDouOSGMRwne+YnGU5NCm2/Cj8VRgAi\nZgdwweAUfjSZKDxoPuhWbRSgivR86EARjTURLZ0NBgPUajVnTnD82Qb2JZvNolqtYnt7G+12OyJ8\nrOmqQok0UigU3OG3egQhacYiBgoFpcM42oyj7ectN+6QVOeNMq4O6jTpGCdYrDZQhldi9U2o1mMH\n3l6jb4H/STiEmQAued11IxYemMLl1apd6bwcjUYuHKmEphqZvgzdAFUjFppZqYyqSIJto0DRhUy6\nSa0uwtJdqZRJNcWZRaMa6tQMw3NHJJ2wHEeaIew7EQZ9ERat+ZzWinziBIMmKrVaLbRaLTdGRGx0\njnIDllwuh6dPn+Lx48eRRWDWVFFkqeFJogaaE7qwypfTYM1V/sbiQ8DXQRXTyudCOADRrc/tZOsz\ngN9MmOZHUChmHV8WDahWjJPG6rVnuEnTcknQACKJM/TCdzod9yyZDoieqExTYjI53xGIdrdqGE15\nVvOA72FylG7M0u/3XX4Ex4TJT4S2/C2RSDjH3MLCAu7cueNMIo6lCgH1S6jvQsO2HFPNOcjn8xGT\nSu1ytp1aXJES32sViTUn4sxKixr6/b47ySoMQxfG7Ha76Pf7yGQyuHfvHhYWFjAcDvH48WPs7+/H\nmscWrfJd+Xwe5XI5cvitT8jF0fU0NKA8Yun8eVHEjW/2YiGSz5x4nk7pZKizzieBrfDRa1ZSa50s\nZFQ+59MeiUTCacLxeOz2RGBKLx2DfCeZgPsc6nbo2icAbit6MhIRgB5Kw+f6/T663S6azWYEytNU\noKBi/7k+IZ1Oo9frod1u4/j4OHJmg4YmaSKq/2I4HKLX66FWq2EwGLh4Pv0oZBQ6YYkmiBTYduAi\n6kGGUzPBhzJ9gsHSCemMbW02m2i1Wq7NR0dHbjEVnYc80frhw4d49OhRZA8JC/+BC0HP71ySvbi4\n6JaQT0uTZt227T6kYIsiuk+DJG48CYrlqkm0Et/nWFJJrX+2WMb3CSh+j5sY+8c2alaefT+JmCnX\n1NR8nqGsfr+P4XDoDlDRUCj/aAIokVOwMIOQwocONXrbac+zrsFg4PYjGI1GLo2XadtBEKBaraJW\nq0UIWB2DjMbQPwJchF4bjQaazSaCIMArr7yClZUVJJNJJ4CCIHC7MTE64dtMF4g/+CdOEPhsd/sb\nIxHHx8c4OjrCwcEBjo6O3O7W3EHq7t272NjYwHA4xIMHD/Do0aNL5isZfTKJLtLiHOfzeVQqFe8m\nLnF+C1+5jmmh5dP4Hm48lOnT3PrZ2pK2DmtH+t5jf1cJr/XE+T+0vSxqO2t+gCZAaV4C4TsZibAc\nuCCk0WjkfAzco0C99WEYOmhNzUtGVRveRiqYTZnNZh3T8RrRA7fTJ3xmn4l8mPFJwUXThM7DIDh3\n5rXb7UtLv4MgQLvddofDrK2tucVF9LPQe68IyDpOrYNT26hzpnNllYp+Zn+q1Sp2d3exu7uLg4MD\nnJ6eurUU+Xwei4uLuHPnDra2tpDNZnH//n1897vfxcnJSSRl3CoOFqK9dDrtTiCzqy51LpW+LA1f\nFwHovZ/W/3CjyGHaQMQ5l3iPj3lZrDkQZxbwu2qeaXaaRRmEo9xGje+hxucp2zwzkxvCUFOT8Ugk\numW6dcwB0WXV6l+gEKCDkcxNZ1qv10MikXB7JtZqNacVuSJTU5jD8Hw7NwqZTqfjwnvMtWAOBn0Y\ndBLS1OE+mtSY6tSk/4RLrbnMWrdet7kQvjmJMx2uYqLJ5HyD22q1isPDQxwcHOD4+BjVatU5IykY\nKpUK7t69izt37qBUKuHk5ATvvfceHj58GPGxaPRF0YxNAiuXy5FVl6xDQ7NKzz7TNm4s7G/8/2kE\nA/A5OfFKPfQKsawGUFtT748TFBZZ+HwMvEch4TSfg7YjCALHOCyaTkzHIH0AdOAxlVmJmNui097W\npB8yi54/QeIDotuya6hPd3SanZ3FaDRCq9VyjkoALjwahqE7qWk4HGJvb8/5O8hIJycnaDabjqFz\nuRwGgwE6nY5DM81m06EGjkcul8PKygpeeeUVvPrqq87RmcvlIrtmA9GTtaY5mVVg8Jpv7nSeh8Mh\n6vW6Mx0ODw9xenrqdsKmmUYBVqlUcOfOHdy9exfz8/NotVr4zne+g/fffx+tVsuhBpuNSuFCs5Fp\n5YxQMC2ec2/XTyhja50WBV/XyfhpTArgcyAcgAtPt5X4cbYir9n64pwytk4LRVVAsLBN2g7Wp3W1\nWi00m02nAYALNMI61ekXBMEl5yJNEMa/dU0EEHUcqvdenXQAIgKF0H88HrsoACE/Q51c5EV4TUE3\nHo/RbrcxNzeHUqnk9n6k9mfYdjweo9VqOXNB8w80M3BzcxM/8zM/gzt37iCTyeDs7Ayzs7ORQ2PU\nkWcFp49ZdD400mXnmAK6Wq3iyZMn2N3dxeHhIWq1musThS19NNxXYX19Haurq5iZmcHOzg7u37+P\n9957D9vb2wCiyUrqeyL9cGzDMHSCsFwuR3wRVsCwL+o/izMxrCms132C5nkRxI0nQVnvv+8+/va8\nDhefeXHVc9MGUB2CvLfX67nwF5NZgIt0WX6nBiUxJRIJZ15Qs3AJMhdShWEY2fdQ0QLrJfHp2Zc8\nvIVhUEL4Xq/n2t7r9dBqtZxA4GIuPs8dmhluDILzMxspnMbjMer1unuGbaYPIQgCdzDtV77yFWxt\nbbn+LCwsuNwOJlxpv9hGftZxtHQQxxicx263i/39fXz88cd49OgR6vV6ZO0F0RYXQc3NzWFxcRFr\na2tYXFxEPp/Hzs4O/uqv/grvv/8+qtWqmy9dm6J0rCYC69cIBQU7UYNmp/pMW9Kaz8RVARKHon31\nXmOuZvcAACAASURBVKfcmHDghFt4pJ+tf4DQfxrcjLPB+Lt+ttLVXre/sZCQOcmNRgPtdttlOPL9\n1CRM4KEzUJ2ChN+E8DZRiT4GzaMALpiQGk8TbdrttnMWMtLR7/cdcqCw4ZJk9Q9oFIFOSO6PyAxL\nHYcgCCJ7U9LnMD8/j9dffx1vv/02lpeXXTRifX0dlUrFPQdEQ3jqpFUEMc1ZzMIxp0nFCMvp6Sm2\nt7ddtIbtpglXKpUcipmbm8P8/DzK5TLy+TyOj4/xzjvv4Nvf/jYajYZzHKoZYB2n/M/PuVwOS0tL\n7pwN9onOa1/4fFr/fP4VHZc4QfG85UrhEATBFoD/HcAygBDA/xKG4f8UBMECgD8AcBfAYwD/NAzD\n+rNnfhvAPwcwBvCvwjD8T7ZehZL8zqLZb5pHzuLTHr6BUHgZJ5GtdrIDbGGamiOE0MfHxzg9PcXi\n4mJEgDEzkYkuhLBc6msFg/oLNG3aJviEYeiiF9Q8HDcKFOY+nJ2dodVq4eTkBI1Gw2k9AM4ZqadN\nz8zMuK3KwjDEyckJ9vb20Ol0sLS05NaNcP7YTkUAc3NzeOutt/D222+jUqm4CMjy8rITDLqPA8fM\nokiLIJR2dDz4R9RzeHiI/f19HB0dYTgcOiFAvwtDijMzMygWiy4zM5fLIZfLOdNpf38f77zzDt57\n7z00m00nEDgfnCuNFnF+SU+JRALFYhFLS0vuSDtet3uN+swES69xxZobeo208VkghyGA/y4Mw/eD\nIJgF8N0gCP4CwH8N4C/CMPwfgiD41wB+C8BvBUHwRQC/DuCLADYA/GUQBK+EYRiBAVbiqeQksSi8\ntuU6Gp5123fGMb611Wy7bFtJpNVqFcfHx3jhhRcckRLqF4vFiGOQDKVbpJMwaRYw/KjbnZFo1LGp\niUjARTiMfej3+y52z4VDRAM0J5gaTMIuFouYn59HIpHAyckJDg8P0Wg0HORmvclkEs1mE6enp25s\nstks1tfXcefOHbz11luYnZ1Fr9fD7Ows1tbW3PO6FZwuAvPN5zTtp8J/MBjg6OgIe3t72NnZce3i\nNv7z8/Nu01kuuea4UyjTtMvn8zg9PcW3vvUtvPPOO277N95j/QzaD9IGTRcmPenhQ74IhY/efJmX\nPrSr333j9rxCgeVK4RCG4QGAg2ef20EQ/D3Omf6fAPjFZ7f9BwD/GecC4tcA/H4YhkMAj4MgeAjg\nqwDe1XpVk1Mb21V7iiCuIznjTAX7XQdOnUA2Xj7N3gMuHFK9Xg+np6fodDruIFtdecfNX8Pw3DlJ\nxqStSwckcB4p4IlQCjnZHjoheU3v1d2R2u02Tk9PcXh46MKS2WzWLUfm4St8ZyKRcEvGR6MRjo+P\ncXx8jFqthrm5OWxsbGBubg6dTgeTycQlVQ2HQxdtqFQqeO2111w+wHA4RKlUcgfB0pTR2D4QRYpW\nWFsHn85B8Mz5enJygu3tbXz44Yc4Pj6OMDHRFM0EanXNKeA1Rm4ajQbeffdd/O3f/i0ODg4iOQga\nLbNMrAiVyI5ZoVx5STryRTl8tGfp+zrmwj+GYACe0+cQBME9AG8D+DsAK2EYHj776RDAyrPP64gK\ngl2cCxNbV2QgbEISr+t/LerA0Tr1v/3M5yzDscTZc77J4PtTqRQGgwFOT09RrVbdDkFqDhAV0LZn\nIhEjE6yfSVDcbkwTnNgXwljmRzCPgQInm82i2+06M4JhSy4xZrITBQ8RCs96CILAJQL1ej3k83ks\nLS25qAKdmwxFMuNybW0NX/jCF1AqlZBInKdHLywsYHV11QlM3WLOmm8Ke4nK7M5OACIM1e/3sb29\njR/84AfY399HvV4HAKyvr2NmZsb5QuhU1YxOFToU0rlcDs1mE++++y7eeecdHB0dRcxSVUA+utFE\nLQBuMVm5XHabxlrUECcMlHZ9aEDbECcsfP6I5ynXFg7PTIr/C8B/G4ZhyzBRGATBNBF16bdpEk2J\nRbWKtOVKCWpTUW2dVrjQuafaygoH/qbag/6EarWKvb09d/CpZhEC534FZWQSJNvH387Ozpyfgkyh\ni5uUmKk5a7Way1HgEnE6JelE1AN+Cac7nQ76/T4SiYTLdGw0Gjg5OXEnNzH01m63kc1mXRZlpVJB\nOp3G3t4eKpUKXnrpJZTLZefYXF1ddUzK8fWhOZ0bzgGvKfPYZ7rdLnZ2dvDee+/hwYMHSKVSbt0G\nBTIzMBmq1VWlvrF88uQJvve97+Hv/u7vsLu7G6Eju3ZDhbYWRaGFQgHlcvnSysu4pKc4ZlbhyDGw\nwsqOka98Fj4HBEGQxrlg+D/CMPzjZ5cPgyBYDcPwIAiCNQA85vgpgC15fPPZtUh55513XIO3traw\nsbFxyXzQRVM+x2Wc88U3CITicWaKL5TGen0ToBCSNvyjR4+wsbGBF198MQIbSRBMqwbgNi1hngOT\npYgKGAmxcJuE3e/3MRqN0O12XR6CrlgkgwRB4AQD07gXFhYQBAFOT09dHgSzN2u1GjqdjnvXaDTC\n6emp22fh5OQEQRBgbW0NrVYL+Xwe6+vrSKfT7vva2hpWV1fdKWC6FN03T3Tsqv1u59fSRLvdxtOn\nT3FwcIAgCCK+GTpayfTM2KSgJDLhkXjj8RiPHj3Cu+++iwcPHjh/BQWDzYmxeRk+JMFl2cVi0c17\nGIYul8KX9MSiKDrOHLZ0Gzde9+/fx/379y89d51ynWhFAODfA/hRGIb/o/z0JwD+GYB/++z/H8v1\n/xgEwb/DuTnxMoBv23q/8Y1vALiMEhRaqt9B75W2Rf77hAIFgU2z9dl0VnqrFuPvVqJTC0wmExwc\nHODHP/6xW7hEZyMdeGQ4phGzXRQO9Avo1mcarSHU1sVZuo08HX3hM6cbMxG5F2IYhlhcXES5XHYm\nh3rNGXmgMzgMQ7RaLWcipFLn52hyPwMAzrt/cnKCxcVFbGxsYG1tzYV1NTfEJropaiNqoJbW+dd5\nUT8BMw4VcXAcmG3KVaV0vAJwjsdMJoNOp4MPP/wQ77zzDj7++GN30K+Oi863+sdszgvpgeHrUqnk\nTCo9qdzWa81na0KxPZrspeMY5xsDgDfeeANvvPGGu/+P/uiPLvFIXLkOcvgvAPxXAD4IguD7z679\nNoB/A+APgyD4F3gWynzWwR8FQfCHAH4EYATgX4ZT8IxCS7Uv+X1aOm2cjyEORSiEVfvdJhZpHSoU\nVHPwd82K7Pf7ePjwIdbW1jA7O+tCZkz2oTOvUCg4xJBIJJzW1DMKgGisXN/BdjKngYhDD1zJ5/PO\nhGF6M4UV/QbcbCaTybhwJAULITg1Pn0UXPfB49sY2kwmk1hdXcXi4qJDRGwz+6pzyfG2OQIWvtsQ\npgqP5eVlHB8fOxOKe1cAcPkczNdQ4UFEtbu7iwcPHuD999/H9va2yxglk2tIkjTJftnQq5qbyWTS\nhUlpUnCVrS9HwgoH63y1xZoe08yKqxD1tHKdaMW3AMS19pdinvldAL97nQb4mJ7XddFK3H0scQOl\nAof1xrVjWn0qGPhdJ5EavVqt4vHjx9jc3HSbeRAdqO+Aqc0A3E5CFAy6J2IQXBxpr+nSTPKpVqtu\nERfvJfETRVgHKQUBIxyJRALtdtuNgW7+og49RTZMzGJYkGgpkUhENsYNw4uVpL5sWCoBu1TZCmJF\naTSZ5ufnsbKy4tZL9Ho9x3x0EjPPQBFjp9PByckJ3n//fXzwwQc4PT11+R/aHgpnjh37pEhI/VSq\nbLiLuO4vqjtyWTrz0S5wdX6C9suiCR+SeJ5y4/s5WCjFaxZO2mesRPShB6vl4xCIFh+c53X9bBEH\nNdrZ2ZmLFKysrLjwHTMPmU3JPqufgQSmR8vpdS4MCoLzBV/0D5CAuCCKDKmLn7h/JR2lzPUH4HY6\n0mQwhehM3iI64crO8XiMxcVFFIvFSJRCGYzt5vVp/gTeq8LJJgjxP3fgXlxcxPHxsTvIttPpRASe\nop0gCJxQffToET766CMcHx9HkrFUMKhQsmauCgfeT5rJ5/OYm5tzm+dotMQXobD06RMSACLv8D03\nTfF9GiFxo8LBx/RarJ2q8M5KSBafX8A3CfpOH+qImyQdaH2OMJRrIQhzaXuT2NS/AFzkShB6quNK\nD39l+JD30yxgViAFAZmZ46DrPWhOcF+BXC6Hw8PDyKpSohmaOhRI6XQ6ciw8AGda6ME6umSZbdNU\nbxsW1Hmk8OD+EXGChP2hmTQ3N+ciLe12G51Ox22m0m630Ww2I9GdbrfrTC3VzBSCKpSA6KY/RLRW\nqfE/fSGlUskteKPJ6DvwxkeXFj2pqR1XfAJnmqC5Trkx4aCdVcefHSB+9g2O+il8121RNGG1gzK6\nlbT2u82VYPs1zq3anEKjWq26bdp0hSYjE3QQ0qanKcH8h2az6VAFEQNwvm8DhQU1Pk0RJihxJyZG\nOFZWVlzCj46PEj/9BdwirVwuu34tLCxgcXHRpQXT3NDkIgo7PYZPx0uP5CO6Yb4B67Fjr4KFQou7\nRzGRjKiG7eUeFPl83j1HB6U1Jaz5qHNuobvSB0s6fX68XbFYdIKU86yrcW2UQYsKd/bVJ1Tss3HK\nzcdT1ymfm4N0qWl8QiKuU0pw0waAk63CyJoc2hb7Wb/77rFCB7jQJCSCer2O/f19NJtNlzwEwCED\nany7lwO1br/fx+npKWq1mvNR5HI5lx9Bbc4kKnumowq9TqeDVqvlIgtMjrIb1lIw3LlzB5ubmwiC\nwOUTlMtllx5OGK8rFSkYdNcqFcYUGhRm7I8eD6haU+eR14gyuN+krpJsNptOcDBaoYim3++7SI99\nH4v1L3GOSKdqWlLg5/N5lEolzMzMOLOKJp3mRqgQVXqyCEXboorH0rpFDP8Y5UbNCiVYjYP70ICv\nxNls9h5b31XwzKIHTowVJrxfkQNDgrrdW7fbxfHxsdujkRrL2ugMr9FEABCJ/1NAMF+faIKOTgoU\nJvwAcHXpFvT9fh8HBwduB2SmctPrT7idy+Xwwgsv4LXXXnMRl5WVFaysrER2j7ImEoUaUY7a5mQY\nJktxbwgN4frGV+1/visIAudPUAEBwG2Ky/R2bu/PnAOaFXFmqCYz2WQ60pAyLNtSLBZdbgPrY9to\npimqtHSlAkyLz/lp6dmHIqyJ8jzlxpGDSkIgSgz6/bpFhYsdeDuI08yPuO++wVYiSiaTbsehbrfr\ndhCqVqtO22mYD4BLjCHz6hJqrrJk/gHDlLlczuUqANFDV/UsTatl6ZUfDAZotVoO+lKY8S+bzeKF\nF17AG2+8gVKphOFwiLW1NaysrLhUbM08JAKiA5a/c3x0bNgXeu913YJdb2Dn02p5rracnZ2NnNmh\nbWOiGJEPcOGbUSa3JoUym0aQVHBQIDN/hXtVsK80lZg3wjGyzkzW9Txaf5pPwQqKq3wWvnLjB+kC\nl6GQz98Qp7XtZ3uPnXj7Dp8w8tXDdjDqkM1mHUMpUZFxTk9PUa/XUSqVHHFq3JzaVQ+rYRISD1bh\nPdR8dHLylGlutKLp1rqxLTUz205fBLUpswZ7vR6Ojo7cs6lUCltbW3jzzTcxPz+PyWSCpaUlrK6u\nuq30NbuT6IS7Q+nGspqRqHtJsB3qALSCwM6vNQeJOrgHJZPLeOgtnbjpdNrtWzE7O+vGQcOMPvPC\nMq0KBt6viobb7DNBjI5I+lA0FK0+HYuOfPRKBlde8QmGOH/ZTxVysPYki4Vu9ve4euw1dQoq9Lf3\nWack/6jdrQTmSssguMh607qomZrNJo6OjrC0tOQIQp2gqs2V2JnkNJlMXFo0cxCo7ShIKCxop6t5\nQUecIpLxeIyFhQUkk0mcnJyg1Wo5ZyIhbyqVwsbGBt566y0sLy8jDEPMz89jbW0N+Xw+sj5A286+\nqzNTITCdpmEYOvhvV0jGCWY7N3qduRYzMzPOkct2Mf8jn8+j1Wqh0WhgbW0NmUzGaXNN7fbRhKII\npUU1oYCLU6wYvlRkxEVy6k/TPysk+E7r82Ad05jch475/6cGOSjUnybt1MF3VQfjkIVOssI4ZVa9\n3yeQ+Bs1J7WsTiBwgVQGgwH29vawvr7uMg6543MQBBHNabURf6OjkDCd26Wrf0YzKjk26s8AomtU\nyExcfp3P510uwGAwwNLSEt58802sr68jDM+XHK+urrpzNjiW1mzQvSV0TokkmLpMrz3HnUzmQ4Yc\nT50D+0chRcGgORJheLEzFXM5iGI0Zdw3hzqX6oTU+4gEALg9MZlPQVrhuHFeSF/WqajKzPoT1JxS\npzd/tzxk+eQ6vOMrN54EBSAyUDpI/M3mNvjMCS1xg0Dtpva51jtNKFCz05ZVxlDmVMLhZim0iWu1\nmtPOeu4ln+EBMNVqFaenp26n53w+76IIANyiopmZmchp3qpZ6CVntiDHkIuwqMmZVs2Iw+uvv+4i\nE5lMBgsLC8jlco6h2DeeoKVZmBRIdESGYeg+c+x1gZSdx7jvcZpShQNP5dZ9ODkeuuJV8zboLJ7m\ncLaMZ7U7+14ul92aE86Bbgasz5Ombb6Ej6GpODRLVZey+9qq3/8h5cZ9DhzIOBgUZ1ZY4rEDowxr\n67CCgZ+t5tDfeU2dWDZcads2GAxcVIAlmUy6kJ22k/s5HhwcRM5PmJubi5glwHlGI730zL606xnC\nMHSQliFDJjuF4Xn6Nfc/WFlZwdLSEvL5PL7whS84yM8ToOkjUAYieiATJBKJyOpPbQvRAc0Jm5oc\nZ0NbCG3nV5EWx4MCIp/Pu5WrFIRMftJNXXUOLcPxN+trYNspAOlr4FoTJrlREGqbrcniU4jst64k\n1joU7fiErNbxaXwNLDcmHHxr4e3kK2PGddCiCZ+AUORhJbSt1xfd4H2cMLvclvCd36lNUqkUjo6O\nHGKgvU2vuuYxNJtNt6Ub1z4oURD6M8PPwmnNrmR7rQnFfIhyuewWgu3v7yOfz+Pu3buoVCoOJfB8\nBTVNWC/boqYRw4NEJRb6xzkeOT/qdI7T1pw/DR/6/A7cC1JXqk4mE+cY5poHjqsWNUHJmOqX0FRp\n5jaUy2W3Cpf0pRvLkC4UIVt/hkXKihhUifpoTulTUQ2v+0z365TPhVlhmVDz+1VI+IrPz2CZ22qf\nTxPW0XrVeWSRDtvMuHa1Wo2kQpNYaK9Ss3GtBPML9NwJ2tEkFDr4mDrMzV0YwqQQonanBmu325EF\nXkQ34/HYCYXJZIJKpRI55JWmhzIkcy3IQNyJirBdTTcVUFaT0S+h0BuICnT+pqacjncikXAH0RAt\nqVnBd9DvwIiCzoe2l9/ZNx8qpbDgGhUu7lKzxedjsPSoyELpwqLpOMe9T4Fx/NUH9GnKjQsHnxMl\nzm6K0+pa4jSPXovzX7DYifAJKBKlFUYkZm5T1uv13PJdhg+JIqiRuPS62+26FF9dKs0NT3VPACbb\nJJNJd7CM9olmS7fbRalUcqFXRkS4VRwPcKGgWFpacns3sM8agmNJJpPO5On3+5H9ISiYSPi0k61Z\nQqJXb71PsKspx2vWlKQ/Q/MtdK654Qudur7NZbWPfK+aFOyLCiWuvmT4ksJc078tDVkUYB2i/My8\nFvq6SJdxprb2l223EY/nKZ8L4WBRgk9K2nt9RX0QcQ4ba1Nq0g2v+drIenWArYBJJM6XBzMC0Gg0\n3KGszDMgc1Ajj0ajSJLUzMyMi1DQ2w3AOdNI0IyacLNY5hqwLbR9qfVVc1Nozc7O4qWXXsLGxvkW\nnzxinns5BEHg7HaFwuzDaDRyJ0k1Gg2XCKQanH981pdtSOhuEYcyuc03sBBbU5t7vV7kAB+9bjdd\nUfOCtMc2qPDSvmvyWqlUcvuGqnLgXE8L1bNtcSaz0hvDo3GK0/IGBarNPH6ecuMZksBlU4CT4tP4\nWnwQ1darn33IwyITX1qq2sZKwHawc7kc1tbWAAAfffQRHjx4gL29PSQSCWxtbeH11193R7jziPdO\np+M2LGE+PqML5XIZxWIR9XrdxeyB82w9LqQio7P9YXhxTuNkMonE2ClQuF0ct5FPpc4Pjd3c3HR7\nPtJ+5slZClWBc2F1fHyMR48eYW9vzwkhnu9AxytXKHIJsxUOFo2ottPwq86DOjIZpjw5OXHnh/C8\nTs1VCcPQhYNHo5FDDpaZFNkwQqT3EFFwbHkAMMOi9AWpr8HSGetin1TwKr1ahMt3816lVSsMtN9x\nbbiq3Dhy8MFIn4Sz16/67EMNV73XZ07EPa8ohARFR1iz2cTu7i729vZQrVaRSqWwvLyMQqHgmJ0H\nzbRaLdRqNSQS54fB0KueyWRQLBZRKBTcKkMlKGUc9UeopmDGoD5Hj/3y8jLu3bvnUA73fKQ/IZvN\notPpOGZVgu/1eu7gmLOzM7cnJdOw2+22216Oi7OWlpawvLzsoiIWCajG1SiBb/zpc+n3+6jX6zg8\nPMTR0REODw9xfHzs7H6rDGhacOdvzWRU5uS9dqdspUEVfkwi0/kgImF9Pvqx9Of7rFES0hqL+mk0\nNBqXQ+ELAkwrN54EZYu1x2xSjU/66eT6BtIH3Xzf49AJ36Hv4wSQoBlbbzabLgxJU4KaOZFI4ODg\nACcnJy5cyfAiV2q2Wi0Mh0N3TLseGUdTgxpvNBq5yAePuqOp0u12XZ6/Lqjq9/vI5/N4+eWXsbq6\nimQy6ZZfa1gwDEOHYLgkOggCNJtN7O/v4/j4GKPRCIuLixHbmqZGGIYuX6NareLg4ACVSgVbW1tY\nX1/H/Px8ZMUoEN2X0Y77ZDJxe2a22213kNDJyQlqtZpz3jLnwNIPF7wx74KCPJ1Ou+3hGA7m/fys\n2p0ICjjfJJjp2KQHXbYeZwLod61f32FRq0W49jele6vorjLH48qNIwdlYOCyU8Vei5O0LJbpNbTD\nAfQ5aXyDqe3jPbS1SSDUHoVCAQCc029hYQFra2vo9/tut6d+v49qtYr9/X3UajV3hgVTsnnYbalU\nwvz8PPr9Po6Ojtx6jlqt5vaJpE3Nd+selQDcEfG6bqPdbmMymWB9fR3r6+vI5XIuA5J+CQo5Rh6Y\nFpxOp1Gv17G9ve3WYeRyuchaDjpeNXu01Wq53aApUMh0PITHOt2stub1wWCARqOB3d1dt/xdF3Ex\nAYo5CFz7omd5UkhwTQs3mVUlpBDeMh6/ZzIZd4we36cIa5oSU8Hgo+FpSCNOyZFOOZ667F6ff55y\noz4HawKoUNCUX95jhYAdUK1PHWe2fjvhttj7ec1CPHq95+bmHHwn4b300ktYWFhwi7DOzs5Qr9fd\nqdxcah0EAUqlEjKZjNu+rVwuO5TRbDadSUI/QxiGTlPy0NrZ2VnU63Wn2cLwfOdo2saMUCwsLODe\nvXsOVi8vLzu/BFd7Ahf7E3BDWu6sfXBw4GA7YfRgMEC9XnfmA9ETt3Kbm5uLrPvodDouAUuPAWS7\n1XFJQTIYDFCr1XBwcIDDw0PUajWXD8KzPogMuFcDUR33bSAT201xfMrHOv+UBtjuUqkUMU2IvDSi\nEWf3WwHgEwY+X4HSuaVXLerzUHPpecqNmxVx6MAXX9bn7GfW5btf30Wi18Qba8poUWJJJBIRgqdt\nrh5lRhIYZiSRPXz4ELu7u5HUYnq7uax7NBq5VOtWq+Uchwp7ScwaAi2XyyiVSm4bevZrPB6jXq+7\n9mWzWdy7dw8rKyvIZDKYn593e04yOYumDMN0QRDg6dOn+OEPf4idnR0XedGkKzpFc7kcdnd33cE3\nc3NzqFQqWF5edntMMtTHZ4Ig8G4xp4KdTsfDw0N3Dma9Xnend6kg0Hm3ZorSQRAEkTR2pRVNTlM6\nI/IIw9CtT9FMV90jMk44+Og2rlgBYdvvu9eX5EcatzkVV5XPRfq0/a6xb51gn6S1sD9OsGi9ZC56\nfH0oRNtEIqFz7e7duy5vgfkFZMgguNjE9PDwEOPx+Y7U29vbODk5QSaTcUfEMfTJRVaEper8SyQS\nzi/B5B6Fiq1WC6enp1hbW8Pdu3edE5QpvGEYOrNmY2MDW1tbKBQKWFhYwPz8vCMa1s16yWi7u7t4\n77338PDhw0sH7xK5cAx1Wzju3dhsNhGGoUMrOie6hZz11JPJRqMR6vU6dnZ28ODBA+zu7joExrkk\nMiBzUrjo3po6l3adAhnc3qNaV2mMtMCt6Sjw9MxTS9M+rW1NFR99+8wardsqvuv46K5bbjR9Whuv\nnaJQ0FwATQJRRiZTsvjqs+aE3qvowQoVNXHCMMTKygpefPFF3Lt3z2kzhZ9sLw/Wffz4sUtuogZf\nXV3F8vKyCz/2+31nYjA/gEuw2RZlIPoX6MAcjc5PpMrn81hZWcHa2hoSiYQLf7JPKtQKhQIqlYpL\n4daVkUqoT58+xXe/+138+Mc/dj4S9eBTkwJAoVDA+vo67t27h/H4/ASpp0+fumP6stks7ty5g3w+\nHxHQ9N9QMAEXCU0A0Gg08OGHH+Lb3/62G0+mS7MPR0dHTiDSPOHYkF4obIkIlKl0ZSzbZZWRjg19\nNfQ3TCaTSxvIxtGf0q/Pl3EdhrZ1WTr1RS18qPqqcuPIQVNFKQDsrjtxUEnrss5GXtd36T2a/GRD\nPyyKYACgVCq5szB5erWWfr/vIgPMYahWq+54ueXlZZTLZbcGIZfLoVarod1uO78CGZaONu0DABce\n5IpMamw6Cak9Z2ZmHPzPZDLY2NjA8vKyQz90oNK7rjsoTSYTHB4e4v79+9je3o4cAEwNCcAJH64c\nXVlZwZe//GXMz89jY2MD3//+97Gzs4Nk8vwQYaZvUyNbdKgohhu2PH78GO+99x4+/PBDJ1C5Jf3L\nL7+M2dlZfPLJJ25nKzt3pBfgIpFIszFpXiiNKWMpjbCvpVLJLUoDLrb+t7kNSnusy86nj+5831Vw\nWKet5RX90zbYUP1V5caEg0J64PIk+AaBxfddGT/OoQhceHPtIiBtF9vB36i9O50Oms0mJpPzZds8\nuYragrY2oxK1Wg3dbhep1Pnx9Pl83m0fl8lk3B6QXMZNAmPIDojuGRCGIRqNBpaXl1EsFt2zGUVl\naAAAIABJREFUwEVSErUYCTWbzaJcLmNjY8NFVri7E4BLm7OG4XkI8uHDhzg8PIxoeQDOJKEA+//Y\ne7PYSLPsTOy7EdwjGIyFa26V2amqxlQ1hFYb3QJkGZoxNANjYEt6kvxiCNC86UEGBrbVmjc/uK3R\nw+jRMAwYEAYeWXqxLNswrAWWNO5F3arNWVWZycoiM5NMkhGMhbExSCYjfj8Ev8MvDm8wmT2SWAXX\nBQIM/vEv97/33LN8Z7l8PjNNC4WCgbH0HhwfH2N+fn4EZ+CiVMZANZ9BVNVq1UBQeiMGg4FFX2Yy\nGaytreHly5dWYp+ajS4G3pd4AueMjGh2dtZyTNgfb07wnpOTk1hYWEAulxtxxfrITqVLrw14z5g/\nPg5P0PfyGq+Oo2e4vMdVsQ62ay8wq4lIusiV+3mtQb8rU+B53n6lDcoBUsBLNQgdQBIP7dZGo4Hn\nz59btN/MzAxWV1cBwJKmKpWKRT7u7Ozg4OAAk5OTKJVKmJ+fNzvcV16iuaBVm8Y1RizS9ODi5BjS\ntAGGxJbJZLCysmJh2KwreXR0ZIyBSVf9fh+1Wg1PnjxBrVbD3NzciPThTtuFQgEhBBwcHFg9iZmZ\nGduHc3Z2FsvLy3jzzTfRaDRQLpcNxWf1Ki20S8ZACcz9NeiuzOfz5o3gnJJ5z87OYm5uDplMxio7\n0ZzQ+STT4ULSXA/N3tTYBqUrxRpoUrAfvDef6f+OMxX0OV6I6Tme2elf/1FzTdcD41dep12rt8Iv\nRo/CejVKj/Ee+okxEQWeeH8ShWoP+kyex4Qi+tOZ8svaAUThd3Z2sLm5ifX1dezs7JgaPjExYXn+\nlLReBeWEsWIQ+6P9IrPr9/tW94HvoIE7HgdgQBC35ctms1Y4hkxB4wLIGMrlMlKpFPL5vKnQ2WwW\nOzs7BuAxZJimws2bN/H222+jVCpZP/h81o1kUwmrWoOCes1mE81mEyEE3Lx501zAlJirq6u2u7dq\nCLyvChmaK3xPApIq+RkM5bVV9WAAsPgGrS6t2Z0xnMEzhhgDiEl1vVbNBD92qjmoGUymxZgOmpJX\nbddeQzLGUT0y61uMOfB/dYF5LcBrDfo/r2d0YavVMk9ENpvFjRs3sLy8jNXVVdvIhbUJ19fX8ejR\nI9TrdQPtyDyWl5fR7/exv79vzAGALUoyBOYyEPDiJqy0sznpIQRbNMD5vhc6blSbc7kcbt++bftY\n3rhxwxY1cx0okSuVCjY2NsyUoOo+PT1tuzdNT09bf+h16ff7mJ2dxd27d7G8vIzBYJiPwfTo2dlZ\n5HK5ETOJzIDvRaLX6EJ1lTJIjDhKNpvFrVu3UCwW0Wq1LF2d/fKqN++tmAPngAtbM0+VQWiYN7Ec\n3ZOCgkcZUwwAVCnuU7mVfmNMJfZdw+n5PI4X6YnVsSgEiJFctV0rc9BgEeDyIKVx6tY4M2McI1Am\noeogMJTiDM3d2dnBzMwM7t27h7t371rxE9qa/X4fjx49wkcffYT33nsP1WoV2WzWNonRVN5Go2GB\nQQxwol9eS6jxGBN6isUi6vW62fasDE2/vjI/SjcS3MTEBJaWlrC4uIhMJoPl5WUUi0Vb+Ixp6Pf7\nqFQqePTokWkMNDMUB6EEYjwGJSbL1QFApVIxU4ptYWEBACy8m1WpPC6ki5nzw41ymY2ay+WQJMPK\nSxMTE9jd3cXu7i62t7fR7XYNP+B4ac4GzRfdTIf34aL0AJ42MtRisWjvS4DSaw38HqNpnS/ewzdv\nSihD4O8+14bvyUzeTCZjDJ2ucaX1q7TPBSCpbs2Y/aRqs2+68Pk/mYIyBt4/FjmpoadcdNlsFrdv\n38Zbb72FpaUls5drtZq5Jx8/foxHjx5ZYtTc3BzW1tYwMTFhngy6KjVaUSeJhEqwi0FO09PTWFpa\nstBkACOErGo6CZ8MBBguSoZIz83NIZ/P24LWTVbK5TIePnyI7e1ti4j0Wo3GDrACNRcgg7oAoFar\nIUkSwwCIA7C2BYO6fEo0CV3djHRVDgYDdDodS4RiiHmtVrMand1u196LDEA3jyHeQVpT96sGlcVy\nKVTz8F4KHuf7KHPRe8Skvpo8eo02zxi821y1EWp5TJnnBkns3+uCkcDnIGXbM4VxmsRldpu3D715\n4Z/juTtwXlNgbm4O9+7dw2AwwMrKCkqlEiYmJqy+Y6/XQzqdxuHhIba3t23vy35/WOqNoJz2g3tR\nsPo0n0tOnySJ7TrFYi80KZgfoFF8MXNL7U1GQi4uLpqNTNtci69Wq1V89NFH2NzcHAFFtcaiSjkA\n1l8mglGLIAHzLxcyF6uOBzULD6Cpr574w/T0tGEWxAuoFdDdSwbIgDEyHu/317ECYAsKGN20hufS\nXNBNkWlSENT06dkxmvUL0/dHBZenU10TMW2CDDuTyRimpFWuPJ72Ou1amQMJWglDiZyLwsc7jFsg\nMQYSs+M8uMPvJycnyOVyWFxctIXCnIhyuYytrS10u92RKku9Xs+k7NHREarVqqnDIQRLOspms+h2\nu+j1erbwGGadJIlJVQCWOk2NgXtiehVUF4G64FZWViwSksVPaSJQohwcHOCjjz7C+vo6BoMBFhYW\njGnQ3NIqxyrpFBtRvIDvpNqRqt48h2aZpwMlfCaN0V2s5hKjNPle6r5krIdiBew7//KjKeKkQS90\n+BvRfppcNAcVO/FCzJvI/C1mSug9PENQgaYMlOanetC07P+/a7s25kBOrYzAq5gMAY4htdpiKlns\nnFjzxzWwh7kLlUrFqh2RMTCQibarZjWyzgAjG6kKq4ux3+9bJeRisQhguC9ms9m02AiaIV4SaZ8J\nsnFBcePb+fl5DAYD5HI55PN5A6i4sewnn3yCTz75BMfHx8YYFJPhwuF3D6J5wE2xCV2Yg8FgZMs+\n3tMzZxUKZHLT09MjZfnpWuZcU1vgnDE6Vd2Uakr6vqsdrtJc+84Pi/nyfH2XmGY7TmvwzMMLusu0\nBB0fAsWsX0ltQTXly9bMVdq1hk97WzPGLF7FSYFXJ7J4DYLNTyAJiAVBiLwfHR3ZbtKDwcBqMVCF\nZ8QhpXSSJNjf38fx8bEVa2FCFDCUsgwKarVaFvHHGochDAu1TExMWIi22r8qvVUaJ0mCtbU1rK2t\nmWQnYyA4dXJygocPH+L9999Hq9Wy3bJ9vIeaPgrckllQSvMYmQswGnGqi9EzBn2ejzNQ0Jbl9b2m\nyUZJDpwzKc65BifR3OHcUssBMFLlWftPzw9xFI3L0GcpTV1GZzEGzzHT3/z/avIwYW9xcdFoT+Ms\nvGYSE55XadfGHLyk0EH10V0xScV2mSoXO5d/9Z5cXD7piYivBskwEo/SRN1sLIpydHRk96JZwuQn\ngmKUREw77nQ6CCEYGk6TisVEmPTkwSx1v9F1mc1mkSTJSIwDNZDPPvsMP/jBD7Czs2OMI4a468d7\nfogZEH9QbIdaF4ARZu9deH4uKBA8c+Czp6amzHzQOBV15VK70LlXFTtJzis8EQfhuLB8HHBR2ExO\nTmJ+ft7qT6jW4V2YbPpMbTEh5WldmYOuE88YWEVb9+DwzOfHZQzAK5hDCGEGwF8CmAYwBeB/TZLk\nt0IIRQB/AOANAE8B/HKSJAdn1/wWgF8D0AfwG0mS/Ens3moLelTWH/MSLcapx3HvmGqmZgpdVDQN\nSMDkxAyDZvxDuVzG8fGxLZBsNjtiY9M+ZQ4FIyY5Waz/yEhD9Q5QDd7f38fh4aHVhTg4OMDe3h5q\ntRrnJeorX1xcxNLSEoBhUJWGME9MTGBrawvf//73sb6+PvK+fnHHxhC4uCmxeog4tiqpvXTnQiaz\n8BKaIcxcfLrw9Pna9L509aomqExGvRVkBJw7b7rqs1m7gdW8iH14TWic6j6OUeh147Ax9eRR4yyV\nSmZKaAl+fZ7iLTpXr9MuZQ5JkhyFEP5RkiSHIYQJAP9PCOFnAfwCgD9NkuR3Qgi/CeDbAL4dQngb\nwK8AeBvATQB/FkJ4K0mSCwiMSjy/eOX5IyqZEjEHN5ajrt4LvdfIiwv6rMEw7BuLltIjQSCMWoMm\nCHHwdc8EYhUsmabp0HSJnp6eolAomMRkyHCz2USxWMTbb79teES9XrdwaZ18jh1jLBjyzBoP1G5a\nrRbeffddfPDBBzg+PjatYVzor2eqfh4YBAScm2M6F97Vpki8uppjTFu1Fd6LOEQsH4bnes3Eazwc\nM7pgaRYSWCVI6pkb6YQmiEa5erBxnOngBVhM9ffn+/GYm5uzosNahyL2PPaF/VWBctX2SrMiSZLD\ns69TANIAGhgyh587O/57AP4CQwbxiwB+P0mSlwCehhCeAPgWgB/4+6oNSVXZEyVf2A+cvrieE/uu\n5+sEkpgGg4ElTFF1JdjI3AEtIEoprUE5JycnthhpRrAKsm7yov3hRjTAObGdnJygUqlYqTgyLLqn\nPJAHnKesM3IzlUpZ0BOjIY+Pj/HRRx/hRz/6Eer1upkansD9eHoJ5r0XvNbPjV6j+IL/8FzVWrSO\ngt5f76NxFx6/UKCb9yTj8sxBdynntX4zGo4/zTsNnvOeChVKeszTsD/ux86POzAEX1kGn+Cjp3Ft\nHoxVTeyq7ZXMIYSQAvAegPsA/rskST4OIawkSVI+O6UMYOXs+w2MMoJtDDWIC03dT+rWUkLwf3mO\n/mWLqcNn/Y+9k6Hovp5Ap9MxM4IIP+3UEIKVJGPF5UajgXQ6baAeNQaaEir1VNUDYMg7mYCXePw/\nl8uhUChgZ2cn6rPP5XJYWVmxYiqlUsliHADg6dOn+Ou//mtsbW1dIG4vgb0UV2mq+AEJTxeE9k2Z\ngwcQdS55H16jFZj8/bWfdHF7GqBng/1nX3k9QUk+hxokGYJ3b/pakZwvaoyqXXka1XeMHVda1ONe\ni5qYmDCXJc1BnStP48qAlTG8LvZwFc1hAODrIYQFAP9XCOEfud+TEMJlfpLob96cAEa3ilfGEJNk\nOvHjJiNmimhkHkEpHh8Mht6JEAJKpRKSJDHprlvQs3xatVpFq9Wy7eu1BoOChzFbmX3U3a+A8yhN\nmhDdbhcLCwu4ffu21ZRkVCXHgHUiBoOBqZ5EsGu1Gj744AM8evTIakFwbLSxn4oD6RjR7PGMSSWy\nfmKuaRUGir57bwLfzxO0apSeafC+Gh3JhaOCQ5kVGQE33m02m0iS83JqDDBiOTj+xngHZZ4eV4hp\nDONM5nHmBPtPrYGmjX+W11q8dvXj4A3Aa3grkiRphhD+DwD/HoByCGE1SZK9EMIagMrZaS8A3JbL\nbp0du9AePHhgL7e0tGRAmg6MBw9jg+InRwlI+j4ijbSoKRdwCMFy9XkNox+1iChND63VwPgEJjOx\n0CsljxKpumhDON8jgosCGHXHMSIzn8/jnXfewcnJCZ4/f24xFPPz86Y19PvDTXILhYIVjfn000/x\n3nvvWYyGRieq+q3jqgyV39l3JThKb50XNQtVNeZH5zjG9Jl/QdBPNZbLGD8ZEs0Fb4bwnMFgYOfw\nd1Z2YtKZYimZTMbiQHhM+8V7xEwHbVfBGLSPfF+ar2RQHqTVppoCP9/73vfw/e9//8K5V2mv8lYs\nAjhNkuQghDAL4B8D+K8B/DGAXwXwL8/+/tHZJX8M4N+EEP4VhubEmwB+GLv3O++8M1YVU4ICcOmA\ncOF5VXwc8U1MDLdsY/jtxMSExTCk02k0m02Uy2VUq1UAMJCPcQ1HR0col8sIISCXy5kqy+IjR0dH\nyGaztnkMkXQuLq25yA8BUUomBjOxKGu1WsXJyQmWl5dx//59VKtV25eyWCwaaMl8DGIKW1tbePfd\nd7G5uWnqsKrwlLREvP24ekInk6Bp4Rcnr9M50OeoG3bcOfzoWCiNeAakv3FfDjIHH5jFxvM03oER\nsbwXGaFmYap25U2lGPbi27jfPZ3yw1yXhYUF0wRjjMibEJrv8bM/+7P4mZ/5GRuv3/3d3720j9pe\npTmsAfi9M9whBeBfJ0ny5yGE9wH8YQjhn+HMlXn2kp+EEP4QwCcATgH8ejJmRLz9HZM6nhNfpor5\ngdbj/t70bXNxZbNZizSs1WpYX19HkiRWc5ELmwk/3W4Xi4uLKJVKFsufy+Vsl6i5uTlks1nb/1IX\nFYmKvnumNLOdnp4ahrC2toaZmRnUajXLf+DEUythTYkkSZDP501rqFar+OEPf4gHDx5Y1SrdG1Kl\nky401abUA6DjqMVUFH+4bA5VBee13nzhdRwnen80ujGmmbDfut0daUsTx0hrZA58Tx/uzedrZi2Z\ni7ovPa15Rhmj+XH06t8NOC9ozH1DfEyFmsSeKZCZxTTDq7ZXuTIfAPhG5HgdwM+PueY7AL5zlYfr\nYHlJRSLxyTC8znNq3oNEzfP8wBOEZFAL94ugxCKQyBoIrE3AHIl+v2/mA8uWEeAjwEh3Jl2ainPw\n3ej6pErL+IfZ2VksLS1Z/AODXT799FOsr68bDpEkCYrFIpaWloywFxcXMT8/j263iw8++AA/+MEP\nUC6XbSwYDOWDzJToVIPwkl3HXpmcMgZP7D5uJTbfyiR04dKTQIDQS0rtJ0vQcX7Z/EIhcyOuwZwT\n1RpIb0zR11qRfoHqu4wDBkmX2n+vKfuxofZIc8J7J9Ts84zBa9qqWbxOu/aUbbXxgNFQUS9RYtxZ\nGQGbHwhV4U9OTmxH6NnZWRweHpoUJiFxEbHCNAkJGAKTzBhkf9RWnpmZsYhIACOx/lTtyVDo4SCx\n5vN55PN55HI5dDodPHnyBM1m0/ZooBfl5OQEs7Oztr8lGQW3pnv06BG++93v4tmzZyMAlUZteo1N\npSCPK2NQhsz35jjznfx1GhavZoWPO1AmwffTCEZqdTqffnGxxifnU7NKgdG4C6ac8zzOBet50Eyk\nEODC5LvGxs1LZQ+We9r19Oo1IZajGwdC+vFXzULP47kal3LVdu2VoBQrUFDRZ2jGgEYvQZRReFBN\nG4OZeA1/JyZAJtJoNMxtxX0SFJiipsFFfnR0hFKpZFmDBOwoocjdNRLw9PTUqlOvrq5idXUV3W4X\n5XLZ8A96KeimY/1K1o4AgEKhgHQ6je3tbbz//vt4/PjxSMg2mQPrLHKc9cNYjNhHcRISty48jjPP\n1VwJTd/WrEFqILqAVFtkvznWXroqE+IGxEzy8jSii6rf76PX6+Hk5MTMLS5EYkvcyYzMIUlGoza9\nWcFj7J9nDuMWrjadB2oNDN/ndZ7evVtaGYyn/y+M5gBcrAblX8yro6+6l/8AF33lLHzCmAACewBs\nByUuEhIJ4xdSqZRlVqbTafR6PRwcHGBlZcVUW/WXM6uQuRRcDMD5QkinhxvZ8t2Pjo6MCNrttoVg\n93o9JMmwCMxXvvIVvPPOO5a8xfdoNpvY3d3F1tbWSJl2tUlZK1GJNqZFsHnm4IO59Dc9psz95OTE\nqlf5zExqTmSUHqjkeVo3QoUGmU+327VNiHUB+3fj85hQR3OLwCOfR8lNF7aaUPqeem/9rl6cGNbg\nj6k5Q4CUWqrXsPycqhmhmngsjuV12rUxBw64MgAuCp9V55NpdPHHVDQ/CHRfTk1NoVAomLdCpZlK\nE17D/SSJbPP3YrGIVCpl9RnI5KgRULtglibDr4mAMxqSi4H3brfbaLVaWFpaQjqdRqPRQK1Ws8Kn\ng8EAq6ur+NrXvoa3334bSZKMMI3Dw0PbxZvErD57Bed4LKZdqd0LjLrhlPnqQvBMRhvn18+lYgF0\n5VI7ourPmpx6H49XcK9OboirKdtemvIa4hM8TzelAWBVlRiZyoWo/dCmdPgqYFJ/8xoaaZVh/bF7\nxebyVQLUm2JXadeqOaiqp35zTgQXmk+N1YG97L7AeQkt3W262WyaKkzfMQnIx0NQ0p6cnKDdbltC\nUwjBmAtTsyktc7kcKpWKMQmaBEmSmCQiss6ajCxmy2CcSqViW8vznZIkwerqKu7du4dSqTRiBhwd\nHWFjYwMfffQRdnZ2RuxuSiS1UT3DvQx78P/H1NdxJp8+k+o5K0uTAXP8QwgGAHNvD1b+HqfNcD5Z\na1OfqfSi80qmqgV2NF+CktvjDeMCvi6jx5iaH7uOHw3KioWna4wJ38WPO+nCaw6v2659Ozw2DyBS\nnWJegd9RiJLRN6/iciA1Z4IMhwFKCp7p4lFpwdL0S0tLyGQyts0bw6uB852NmWOvATwATBPhcxg8\nxZ2klpaWcOvWLXS7XduZm0VgT0+Hm+yurq5aDQYytl6vh/X1dXzve9/D+vq61a9UTwLHVT0AMULX\nptpcTCoqw9HmGYPiHMRy9vb2kE6nLcCIYcwTE8OSfN1uF6lUyiJCKTi8m5T7krbbbVsQqhlpWDrn\niFoik+h4LvvBtOhMJjPiqh3HGGLMM9bGna9/WXHKb8rLcfXMPaY1x0yJ19UagM+B5uAHR0FFtb1I\n0DHvhP6vYBcHloAYvQl6HidfY+25qLvdrhVk6fV6yGazKJVKI0CkLgziEEzUIVFTy+D7MKJxd3fX\ndmkqlUpWmZr7UlKl5fisrKxYmXeCjel0Gnt7e3jvvffw6NEjnJycWE6FvqNKbzK9yxjEVRmGP0eZ\nCPs3PT1tACXteZpmNOkIBtO84L4fdA+rtkIm0e/30W63cXBwYDkyygQ8gKhSlQCm1ohg3sLMzAyK\nxeLI+Ctz8OMRGwelaT92MU2DZhcjZr33QcdzXK5EjDlc1p9XtWsvMMvvXosgEXP/SPXjKqHoAvct\nJuW8FOSzGC3HvSVSqRTa7bap/lTpWX9R7WxqFwAsF4KxC0kyDIFtt9sIISCfz+P+/fumrWxvbxvC\nniSJeSeIjzBQigwHAHZ3d3F0dIS1tTUcHR3h3XffNe8Ed4DiuxDI43tOTU2NMBbPTP0c+XFW9dwv\nPI9h8JnT09OGDfT7feRyOeRyOTufsQksx88KzxotyfsqTsVtB7mTty5kXUghnG9Kw0aPFTMzycRY\nhIcaDe8bU/FfZ7Fdxig4xrGCQ15r0HfSd44xaM/ovzCApOeinrgUKyBz8HYkTQs/MJchtDoZqlJO\nT0+j3+9bDgJ99+12GycnJxZgxIxLglepVMrUT2DoDm2320ilUpbSXSgUUK/X7R1nZmZQKBSwtbWF\nvb09e1c+ixWllpaWLNCJTKndbqPRaFiNyM8++wwffvghGo3GiLlEFJ/v621VNl3knhlw/HScSWRq\nR3sXKOfA4xqsiUGGpYwDAEqlkqWaA7ASeR60ppei2Wyi0WhcwCTYL7puOS9kVtQm6fqkqcG5yWaz\nZhZyjLyGdVm7DIvxTTEJ0oEyB97HM3O9Xs+7zIz4wjAH4PwF1HVIwiM4Q9trnCoFxIu6xKRY7Nok\nGXUdacVrALbXZaFQQLvdxosXL5BKpazeAm1EBdwajYb5qbPZLFZWVlCv1y3mv91u23txg5STkxNU\nq1UUi0XcvHnTNrzlZjQHBwfY2dnBYDDA/Pw8lpaW0Gw28cknn6DRaFgUHwmfaqq6/Diuqi0oqu/H\nJ8aMPfbg/8bmhvPrtQ9mkKZSwyKxCwsLyOfzBth6ZqAMndgFy+spE1PzydMAtQEyB4aW892Y06Im\n41UXnQ8y0oUfo1H+zvdUeo/NhTJy/xw9rgI3pjFftV17DUllBvyov1vVqHE2FjDeJvST4n/nvo8c\nRAY0DQYDc0VmMhl0u13b0k7rGYYQDFnnh6YJ0fClpSXU63V0Oh3Mzs7aVnudTgczMzPmrcjlcrhz\n5w7u3LljpckKhQJ6vR7K5TLq9brVDzw+PsaPfvQjrK+vj2RL8n09nsBxJhrvmeerPl7L45j7MfWq\nLOdRtQkuYG4DkEql0Ol0DITWedXMUd57MBig1WqZh0J/A86zWrlgfGo4zycoWSwWjaHMz89fMCli\n0trjBp6ulFF4lX6ceTKOOShj8K7LcRiIPiv2/SrtWkvTs3EgVcqpSucHRFtMnfW/63eVPiwOS1Sc\nIBjtz1KphFQqhVqthmazaW7FEIItyJcvX6LVallWZgjD6MpUahgHQe2B2+hlMhlL+WaobpIMXZg3\nb97ErVu3sLy8bKZHKpXCixcvUKlULDqy3+/jwYMHePfdd3FwcDAyPhqEFUIwTwrtd6rz3uZVTSI2\nhp6gfaq2Z9I8X71SOqeMI9B9J8k8vFfKp5a/fPkS1WoV9Xp9hI44N55R8nr9P0mGoCRjI8hMuDmM\n4kgxzUC1gnGBej6gj9/HMVrPuL05MU478Md0HmLnXbV9LgBJb07oQPg8/lfdUyWPEojarYylIFZA\n25MExIWZzWaxt7eHra0tYxiqJrNpijAAc5Hlcjnri0a87e3toVKpWI5DLpdDsVjEysqKncPdi/b3\n97G7u4ter2cq74sXL2x/TkoaVcHJGJRAmLPgbXd/zWXz5P/3Jd04zv7ZZPYKNDM/he5Djf33c6af\nfn+4s1itVrOQdmCU8fAe+j6qqaqGqlsIAjBA199P39+r7f77VZrOgUZfqsajzCEW53OZKffjMAPf\nPheApKq9qspx8HzyFVtM7fUDo7+p+pbNZi2qUAlPUWtuTkO7NEbEDKwi+k2J0mw2TTKmUikUCgXM\nzMygXC6bJrKwsIC1tTUUi0VzYdH2ZBLQ5uYm9vf3MTExgeXlZasHub29bbEO9HwAFxeJMll1JzIq\nke5bzkmMyGP/67mKKbCpJ4N/ldg1ilFdmh5g1j4TK6jVaqjX6xciM/17j1PflWbopVJzh5oV7xMz\nn/xY6PhpXy4bS9WKPHOIaQ2eGcVwkB8XX4i1azUrlGB8UxPAc2t9+cvU4VgLYbhJLvMSut2ulfzi\nh372JEkMPwghGPMgcVPysbIQCZnmxu7urlWXokuWu3hPTU1hZWUFb775pm1Ck06nUSqVUCqV0O/3\n8fz5c2xvb2MwGGBpaQkzMzN4/PgxNjc3rXI13aQMBdZxmZycHCntpoyW3gxmII5jAPq//43j7gvz\nUJuI2bu6KOne5Qa4IYSRvUeZJq+7XHU6HVSrVUut58LRzNHY4tCiOxREjFIlQ9YoyRgX8GL2AAAg\nAElEQVTdxMZFxya2WPV6L6T0Go6d0rN+PKPz7xhjGvrcL5RZocEnlD7jJsDbkTEiUG1Dm/+fZbfo\nChsMBgZIsiJ0kiRWLg44z+OnhCZHZ/QmA5bIJJju2+12sbu7i5cvX2Jpacnek+bBzZs3bV/N5eVl\n83P3+308efIEm5ub6Ha7yOfzKBaL2NnZwfr6Og4PD42QNYlJ7VcdEyVMjrsGEvloVY6bAoGxOeJ9\nGb9APCOGrFONV3yk0WigWq3aOAIwHIYBZY1GA0kyrK1wenqKarWKWq12IRJW313fmR4KACPALWmQ\nfec8MwtS+x5bhF5j4Hn6XA+kjrtWtRSPNXgv3biFrkL2C685+JBjtpjrx//vNYVXcXV+GPk2OTlp\nMfusz6d1BBgdqSCZqskkXnoUWJyk1+vZlnYsFEJkPZVKGe5w//59rK6uGkjJiMFMJoPj42Osr69j\nc3PTYi5WVlZwcnKCTz/9FLVazZggvSG+otM4/IYLVLEXqtUaHaoSVsfcmxCp1HAH8Vqthk6nYyaZ\nVnDiczUVW3M9GOA1MTGBo6MjK/JKLw5dt+l02nJOGPSkOAf/1/fVADD+D2CkmhbzZmjW0S2tC83T\nk/+fjMDHg3ja1LHU47xOmUEMa9B54LVeQMbaj8sortWVSSL2Ep8T6qMivXTgBProNf5Vs2RiYgLF\nYhELCwvodrtotVojlZqr1SpOT08tZLZSqdj/lNC0TXnPqakpAx0ZnUgvBSP+8vm8pVNXq1XDHxYX\nF20hadBNvV7HxsaG4Qxra2uYnZ01nIHMisxI7VM21Qw4fmQCas4B56aGbtjL9/OM2msQPIcaU5Ik\nVjbPR7VqwRfelxKdc8r8FZWcMzMzlvZeqVSwv79/AV9RjYT4CfEIpbOXL1+OAKEaVk8gkiXhLjNR\ndExIuzGsRudDf1MGokxEc3p8+PernnFZH8dd86p2rYBkzA4j8THSb1xQCJu32WLA5dTUFFZXV5HP\n5y3kdjAY2KJkaTJVQTVVWEEq3Y2a7stOp2N5FCweQqnO9+RO28wTUWlDfKDX62FrawuVSgVJMixV\nt7Kygq2tLTx58sQSsbgoudj0/RUfIeNl4hMXCM8FMFLGnZKW88DxVaahzIhZs4VCAbOzs8Yk2KfB\nYDBShs0vOA0NZx5FNps1HETjFarVKl68eIFWq3VBzecYq4mlHhTVMnkOs37Z51TqPAuX760a6jh3\npZoSylC1f55OleZVCyOWpcwhhjVoU+ak/fHP/3EYxLVWgmKLvYAP8/W/+2Pettb7FAoFTExMWIWl\nXq+HQqFg5gSLhJBwWPyDGZMkJhZs4cRRDT06OrLdsmk6hDCMd9jf3zfNhXscLi8vI5vNWhUohkVX\nKhU8f/4c/X4fa2truH37NjqdDh4/foxarWb9U/euqs5ezWS0p8YO8Fr+T1CSFbCoJntVVhOb+Eml\nUubeZWCYz5wkc6L5xNqbZLIsiMNszHK5bFW7yZA7nY6FmusOYsBFM5TMT0vwaV+44Hg9maYPtPOq\nv6dbb/t770yMzsfROvvGd46l1F92z3Eajhe8r9s+F3EO+hJ+cIFz84DnKhf36jSAkcXMhXpwcIDt\n7W2ryZDL5TA5OWmRikx+4o7Zuh0epdjMzIzVhNRMUaaDAxiRlCx6yoK2BBa12jUndHd3F5999hk6\nnQ6Wl5exurqKly9f4uOPP8bW1taIhFFTjItW1fwY4abTact4VO1Cd3/SKlV+YagUVnchtbxxdrYS\nLbUt5szwXDIU3a+CzOP4+BiVSgUvXrxAu922hexpgc9WLIvMTM1PPlPHjMzbq+5Xsef9uZ6Bqnag\njCH2OzUHZQ6XaQ2egft+fWGZg7oxdYI9g/BxDsB4cEePkRAnJydRqVSwtbWFer2OyclJ5HI5LCws\noN/vW2Yei39QqrPCM6Wb1kCgx4OgmXotWC+C25eRefAetGv5btxRqdFooNFooFQq4fbt2zg+PsaH\nH35o3gngXDsiAXoJrWOjC5hNCZEMQT0sHtPRRcP58qaH9knjA9S1mCTnu2iTEfN56inRYDLSRafT\nsdgQZZBqsnlw2jNHtefZPPjqNU9/D72Xpz9lgjHGGtOSdT54TDWHmHvf03vsXRUv+kIzByBu2/J/\nnucz89g84ZNQ9LqjoyM0Gg20Wi0MBgOLfmSWI9VOloBjTj/Lr6VS53n+inazZDhzApaWlgyMYwmy\n5eVlC8HWqEoCirTZ2We6N9PpNB4+fIhHjx6Zp4Pvq94Cxl1wvFQ1VgL1C0MXLH39xAQomXVnLB1X\nNWe0L2o3+8WhBHt6emqagTIpPpPjkSSJmYGVSsVyKPyip9ai7+ddf5xHMhxvz6tXSjGcmFkR046u\n0vxiVUGotKveihiT8vPrGZo363wE6eu0a2cOJHJFlmPAmvrkVbOIcfckSawuI0uV8xpGLVLKh3Be\nmiyEYFGKDKemBpIkicVFaMblzs6OFYKhZ4L++KmpKdy6dcvwhU6nY0yKmYC899zcnO2u9ejRI3zw\nwQeoVqsjUYDKPGNEqeOmC5O/0YzQ3Aoi9lyUXGwADMj0BEjG4OdAsQ/VOKgdMFxZYxH4HGpW7PPL\nly/NnGBgFOmDH0pZLmg+jynPeq6X8GoejXOfjzvG47pwVZj540qv7KN+9N18XEPsOTEt5zIB+irT\naFy79urTfrDopVB/M1/+MhNjHCf3UoREq9umaYj0YDCwgrJ6bwXujo6ODFxjqbkkGe6GfXh4aFpI\nOp029J5ajG68QkLWWo9bW1v4m7/5G7x48WJkYSqRewapjMCbF0qsXJyK6gPnwUDUBNQ8mJycHPGq\n6LzFxpsErs/V8Gw1N8jMCOxynFnhaWdnxxikn1P1mqRSKSsMrMyQJfZiUlP7yXHz2tFli0lpQ82x\ny5i3P1/pXs3ByxZ0DFvwuIvXLGLXXaVdez0H/yFjYMyAotZ+ELytqE0Jx0+Iouq6uQmDo3z5MAUP\n/cLQqDq6Msk0SOQhBPOMcPNdViBicZHT01NsbW3hvffeM7dlzNTS7zGJpFGA/J1SlIFeXLAkTAUp\nlUi5sHRx+/lTqauag7fvVZXXcVSvFGMlTk9PLaaBlbfZp6uYL7Fz1DTS4xQ4Si8apKV9jS14ZSQx\n82McvcekPPsZ+6vz7zUGnTN/zb9Lu1bmwKYvx0g1TYTxyLJeo9/9gmEjWEapr3tVcNETQzg8PESr\n1TIsQhcWowgHg4EFUtHtxmfTq5FKpdBsNq0YCbe3Uy2C/09PT6PdbuPBgwd4/Pixlbu/TELHiIoq\nKt/X26dkHuoG1ACi2KKgHa5aifZBiVg/KonVS0FtkItSTQ9ey/oVDJ2m5uAXOJmFmhU8T9+f/dRY\nC2/68F11Yen7xjRTXaQ6zuMYg17jcQEvzPwzPWPw7+u1uhgzG6fNjGvXWn3aE69yfE+I49QjlSrj\nJoDXhRAse1KLuhCo5Ea4SmzKHBicMxgMcHh4iIODAyvPptvMUWuguUBTgmnYjIuYnp5Go9FAuVzG\nJ598gg8//BCtVmukv14SUiNQNV/HTs0hjRbUsfPqLLUpBn7pb/yQcJWxKBiqtjABR3UPKnPgfHFh\n698QgoVJM0ydQDCfoSUDeX/tF5+tnhSfGKagq6dDP6axxaZaE/+PSf3YWHpTmGOmQpH39HTu6Tmm\nsYxjIl8Y5hCTfDrJMVXNnz/unn6idSKpRgOwjD+q25wcagjjdjam9sCNaFjV+vT01KoWq+RUsBMY\nLtpOp2NuupOTE2xsbJhnIvZuyiC0CpUSoKrFlMZ6P11Q3o4n4+z1ehYPQWbGe6u01kXjUXaOHfMr\ntK86R1rrko0p2X4slBGp+uwjPnWcNARZF5MWvfHb7PmF5rW3cZprTLMd9/HabSp17i5Xcyy20JW+\ntW/eTIn1M6ZVX9auNUKSUlClIwmXNrlW5OF1saacm827Otm4cLi4CUDOz8+b90CDe9TbQSmmUrxe\nr9t5k5OTVgeRoCpjJHhPqrjHx8col8vY3d21ArT88L1VM/JErnkL+r58Px7zNinHw+M1qVTKalhw\n96mXL19ibm7ugtrtIwpjc6TvofOjzAY4j2+gucbS/Lq7tWoJ/l15f30n/q7vT2ahuIvObex9dEHF\nNDZvWvhjukh18cZAeHpZdF0opuBNN72vzrlnQIotvU679o10gdGFSxckBypmP6vU4r1i91ViVNs2\nSRLbUIZqvJc8xD9IQJQwWuuBJcXq9brt8MyybcQeiDVonkYIwaItGTrNEG6V6DGtyUtO/q+mA7UJ\nYNR88/fVa5MksRBoluNn9CQlrYYcc28M7aOGd3NMlYkpc+Ii0yxNhpMz6MtrLErwpAF9dx5nP7TF\nTCPf9F2o0fAav9A9jV1Fa9Dn6DEC04qJ+Hvq3MaYUIzx0BTmfNZqteh7j2vXGufgB0yDcrLZ7Igd\nC4xPKokd0/9JeMQBGORE+xqAgYME6CjdFazjoPNaBlTV63Vb/N1uF+l02pK81JzgQs3lcuh2uzg+\nPrZtz6jFaNKSNi4ytUn9+PhjHFMSuJpIHEuVNvQUcRyOj48tLmMwGJg0n5yctIQpumIp+WjvA+fe\nB81LIdEqw2aUppo12lQL0PfkMZ1z9YrwPH1fTUIbDAYj3jAyKgoATwN8XmyRenq7yodMUVPFlbF7\nxuDpWrUP/TAVvdlsWpnBvb09lMtlvE67dlcmMLoQfLw/J4rFW8dx6th9NTYCGG5Td+/ePUxPT1ts\nQ6/Xs3wJEqeqjmQsaptqJh/zJfgO3W7XFgLvr3suUqPI5/O2kOiu456aJBA1JRgoxKZuXH1v1QZ0\nMRKgIwirUp3vp54WjgMxGOaI0Jukqd5kKMpAgVHknN+9K5lMiPPMsdBFr+MRm2+Oh7fFyUj5Xb0S\nvB8Zmo7V4eGh5cr41HOPu3gNIta/mAar48SNc9WUjLk5Y9oJx51rhd62crmM58+f49mzZ3jx4gXq\n9bppZFdt1+7K1Bemm00ZAwALXY4RBe+hUpDmAxlNkiRYWlrCm2++iVKpZPUX8/m8AYIMAtL4Bt6T\nxKwTQmR/dnYWy8vLOD09tSxO7pQdQjD7PZvN2kLTUm2pVAqLi4sYDIYxF1tbWzaJJBYSIqMB2QeP\ngtONGTO51M0ZW2T0DLAaNBcMy9vpfpYKntHsU3tZFxGluL4vr9MFTc1jfn7eqmhx/mNSWgFmLrhx\n5sI4qQtgJJ2cLmaaNsQDtAiufzc+03vZxmkTupjJoBgbo2C8zu9l9yOY3uv10Gq1sLOzg2fPnmFj\nYwPb29uoVqsjSYGv066NOdCNpDanDopunspiH8ptgdGgIC/tSYwhBCwvL1tJthcvXuDFixfIZDIm\nKYhBqCTV5CaCV7wfCUAzOfP5vFWPajab2Nvbs+KyDIpqtVo4ODjA7OwsAKBer6PZbGJlZQVra2v4\n5je/iUwmg8ePH9vu22ojUyMgE1TJ6BeNl2Rq4yvDAEbTlgeDgWkDrHRF3EQXCZmBLkz21ScQ8bnK\ndMnseB9iGCyEs729jb29Pezv79u46mJRDcu7ar1N75kD+6CYBmng6OjIAuPUblfNi8zKe0TopdE+\n6DizqeagyXgcf10TvnlGwzQAbnr06NEjPHr0CDs7OzZu1JC4k9hV25WYQwghDeBvAGwnSfKfhBCK\nAP4AwBsAngL45SRJDs7O/S0AvwagD+A3kiT5k9g9SQxapkwRWiLKlNxeJRsHEOkAplIpq6EwOTmJ\njY0NrK+vo9lsAoCp/ZwM4gOK6ibJcE8JPoOEQnWYKiuL1rJkWr1ex8zMDJaWlrCwsIBer2f1KbPZ\nrE1ar9fDxsYGZmZmcO/ePQtX1mzMceCa2sO6MGKmBhsXslf1uRAo3WdnZ7GwsGDuTL+fAu8NxO1w\nJXI9l+eQeTAaNpPJYHp6GsvLy7hz5w7a7Tb29vbw7NkzPH36FC9evLCMTmUI7DvHwUtvNc+44Lig\niYdoLIsGv6kWy74TD9H3JdPQ+Ao/Lzr+yuR0V20VasrEPdPjOYxN4YZLm5ubWF9fx4sXLyyrmPhQ\nJpOxbOCrtqtqDv85gE8AzJ/9/20Af5okye+EEH7z7P9vhxDeBvArAN4GcBPAn4UQ3kqS5IKDlf5z\nvrhOAgmVA0K1X5mElw4KwnHxMGX68PAQH3/8MTY2NtBsNq2eQrvdtsQsgmdExRXt15qIVNHa7Tbq\n9TqKxaIRZLFYNEByfn4exWIRy8vLODo6wv7+vkVm7u3tGQDFEvTtdhudTgdzc3N46623cHp6amHU\nPiiMxKueAB5nvzUhyWtUPuLUq8JkPD6qUTUQHXM1ZZSAuZD0uGpeqlmwn8RWWLb//v372NnZwebm\nJjY3N7G1tYVarWYMXKWoLlTtL2lKFybP1cAszXfxdMl76V8dP32297DpO3Jhx6qvK8353zg+fD6B\n+263i1qthq2tLXz66ae2ybIyX1Y8G2d2jWuvZA4hhFsA/imA/wbAPz87/AsAfu7s++8B+AsMGcQv\nAvj9JEleAngaQngC4FsAfuDvu7KyYrZdKpWy4q7AaAYhA4w6nY4VYvGgjCK8wJDoCGC2Wi2sr6/j\n4cOHODk5QSaTwerqKvr9/kg0JLdk1wQgqpr9ft8GlxPXbrexsbGBEAIKhYLFBRQKBSwsLCCXy2Fp\naQnpdNp2Z+L1DKmmi5Cb5Q4GA1SrVQDAnTt30O128fz58xHCVns1BoZ5KerjHRSlJ5GracJFpqi/\nN9n4HCX8mEmnc8R+6rnqRfH3J6NYXFxEoVDAnTt38NWvfhWbm5t4/PgxNjY2UKvVRoKovGbJv7rA\nvXRWLUK1R2WY1CgU4FZt11eb8sAl39WD3Go+aL99jUxvXpA5tNtt7O7u4vHjx3j8+DGeP39+odAv\na2Nqvc6rtqtoDr8L4L8EkJNjK0mS0C9SBrBy9v0GRhnBNoYaxIV269YttNttNJtNk+DM2Wc2nW4r\nR4DKc22vtrHu4MnJCVqtFvb29lCtVpEkw6zJmzdvYnp6Gp9++qnF7gOwfRRSqZS5GpvNpi16mg7s\nA6MbyZ0ZPs0t9TjJrVYL1Wp1pMIPVUjiESweMxgMsL+/j16vZyXrG42GMTFlDHx3Lt6YRqVSR3EK\n4GLSGu+hsRYeVVdi1UXPY/qbB9OUKait7t2zer0yuXw+b/Uu7t+/j0ePHuGjjz6yitxcoFx0WoJf\n+6jnaVi2ApRKUzSBdDGry5v38ViBek04F5z/wWAwUtiWmbveQ6Hzp/NBj8re3h4ePHiABw8eYH9/\n3wBydS8Ty+C7vE67lDmEEP5jAJUkSd4PIfzD2DlJkiQhhMuCtqO//ehHPzK7vVgsolQqWRmwJDmv\np8CBpHvT4w1n/bywWMhZWbOBO1avra3hxYsX2NraMkCRkrPX6xlOkE6nTX0l02LFJ0qaRqOBnZ0d\nZLNZ3LlzB/Pz84Z4dzodpFIpHBwcoNvtWkl89ldV2nQ6PbKNfLVatWIxt27dwtOnTwGMFr5RE8HN\nGefFCFLBWSU4LkoF9zQGQM07DSzSwKCY+su++n6pRqImhQdIVZtRzYKuYzKJN954A++++y7ef/99\nbG1t2djo4gXOE8oU5CMNsT9kIjQfvfnB9+Z40vVL7ZbP1JJ7ynQVqCXIyRB60rrStQebydCOj49N\nY3j06BE+/vhj7O3tmVAjNsS+1ut1VCqV12YMwKs1h58B8AshhH8KYAZALoTwrwGUQwirSZLshRDW\nAFTOzn8B4LZcf+vs2IX2S7/0S7aQqEFMTU0ZIyCDUC+Fl1Re3VIzgHtJpNNp2626WCxaxl+z2RyZ\n+CRJrJp0JpMxQBSAlX3zko6Sg8EmZB40aQhMUkpwSz0ypUwmM2KmhBCwsLCASqVieMbKygparRYa\njYaFM3vGSGblx4aquUp/4LxsO7EF4NzeBi6GJKskV8bAc4DRdGqVxGqT6wLxwUpq1tDsid2fC7pY\nLOKnfuqnsLKygpWVFXz3u9/F5uYm+v1h6T/W+NSFyvFS5qcFY3hv/Z/XqRkGwDQAnk/GQPzKmw1U\n9akpkAF5XEgZqDIprodarYZnz57h4cOHePjwIcrlstEnY1R4n6mpKRNcnMMPP/wwthyj7VLmkCTJ\nvwDwL84e9nMA/oskSf6zEMLvAPhVAP/y7O8fnV3yxwD+TQjhX2FoTrwJ4Iexe9O2U+CL28UxSo5x\nB54x6GD6/5VbDwbDbMvbt28bsruzs4O9vT27xgNMjPxT9a9UKlmtB0bOMVGLiD7t37W1NRQKBRwe\nHpobLp1Oo9vtWmwFn0H0+OTkxHZ/Yo3Lw8ND7O7u4u7du1hdXbUaE2oOKLPyC5tjQSJTk0QBRs4D\nmaqCYx4j0DHzi14XOp+tf73W4c/V5/H5ajaqdsjnT01N4Y033rB6nX/1V3+F9fV12+KQ2JEuQGpa\nGvjl3dQ6Bl7N13dXrUmZBL0cDL9mNi9jRACYeTk3N2dCYxxGAcCY3vPnz/H+++/j4cOHqFarRoOk\nJdKHlvan0PTa3Kva68Y5cKR+G8AfhhD+Gc5cmQCQJMknIYQ/xNCzcQrg1xO/qnmj5NyNpOo11Vdu\n2ELuqoSioA+bJ2I+tlQqoVAo4MWLF9je3jZJzonU61lFulKpIJ1OI5fL2WQeHBxYQBMH/8aNG7bj\n1cHBgWkMtVoNlUrFNtald4SuU27iyz5zY5vnz5+j0Wjg/v37uH37Nra3t9FqtZDP55HP57G7uzvi\nxYkNLcdMx0FNLhIzx5m+eeIlfkGoOaCSjNdp4I5n2MoY+CzFF6ieqzbi3YAeLFRGQua3vLyMn/7p\nnzbs56OPPrL30a36iGXxfhqUpSaEB3q9p0j7w3EkQ9DxpdnBzXTUbcqAM7raY54bNprJBwcHePbs\nGdbX11GpVDA1NYV8Po/5+XkDuIHzsnsMF9B+vU67MnNIkuQvAfzl2fc6gJ8fc953AHznVfcjkZEh\naHDJYDCsl6DFRs/uPTJ4/mU9c0in0zb4u7u7Fp7LWAU9F4BtLNPpdDAYDFAqlZDNZi2cVu1vEuLR\n0RHK5TLa7TbW1tYwPT1tla6JelOC0NQAYFoTFxgrYu/s7ODo6Aj37983m5EmEUvH0VxSc8HbxxwP\n2tO6UDXblSopa1zSho4xFC4oH+TEBQtc3GRF3XuqYehCV4xAmb5fKAqqegaWz+fxjW98w0y9Z8+e\nGWOgFuqTxcgw9H7KdNlHT1t8Lx13FWAazERGoUwvlUpZPsXi4qKZrF7AcZ1ww+dqtYq9vT1L7ltY\nWLBMYo6/mi9kWGxfmAhJ+mK5QHSn5yRJTDXkYlL7F4hrCl79perGsvMMUdWy88qxueB5PsOhNZyZ\nKhqzOglkKrErAEibni5ZYBgjwQkEzn3p+Xwet27dMlv21q1btqfn7OwsstmsaVSe2HxVIx0nf666\n2Rg5R+yk2+0aM9MgK2VK1PR0DvS74gQxpuClIjUILiq9HhgtVqPMiJoM3y+fz+PrX/86Xr58iT//\n8z/H06dPDcOanp62cVUcgQzcpzN7DYzP198pyLzwiplianal02nL2OVuYfq7mlaMgjw4OEClUjEP\nG93lpC8y1HQ6bRGXBDGVab1OuzbmUK/Xjai1sAq5W6vVGglGAUYzDmMToHYgFwhdpIwx1zJxBJl4\nPXCeOUkPAkNpyaWZPUkMIJfLYW5uDkdHR4Ya046kxCIIeXx8jPn5ecM/SFhM5JqbmzMXb7lcRrFY\nNLfqwsICVlZWbKs8b5uSUD34qO9GYlGtjRF6DPai1qSETqnuk5AuYwyxOdLmAdLYeV7dV3DUaw98\nZqFQwDe+8Q1Lg9/c3DRvjAKNZBiadKXv5Be9x1D0mZ4Rs/njqrERK1hYWBiJq/ACr98fJr3V63Xs\n7+8b2LqwsGDChddRyOpuX2y6tq7aro050Gbiy5B70k5jPL8S3WWSStVf/Y31IZiUopujqETisxjX\nwO+UaJTczHCkZyWEgEwmAwBoNBqYnZ1FqVTC/Pw8ms2m2Zs0ldTvTAYBjHL8VCqF7e1twyeAYf7G\n8vLyCLhE80iZgqq36vJUNyWv49gT1afazXOIr3B8iL/4hazzEJOeuqgV+CMjZ/9i57M/esxjD+wD\ntcHFxUV885vfRK1WQ61Ws02S6Q4nQ/bVw9liDECZgB8D1WD0etXaPHMIYVi3NJPJXMBu+Jdz1ul0\nUKlUrBL3/Py8bbys/WOoNJkgq5zxt9cNhHr9nS7+llqlUsHBwcFIajBfQFFc2rqeEPhXGcK4ppPr\nI/dIgBxUtbk5+JOTkyOERNU6hGAb8NJupBmSTqetaO3ExATm5ubQ7/fN4wEAnU7HGAgwlCa0QTud\nDj799FMcHh4ik8kgl8shn89bhqoG5MTsVA3+UXVWsQPavmQUCmBxMSVJYhLJYwy6qK46F3qdzoEe\n9z5/4DykWK/z9+D5NMm+9a1v4a233jLmTGyDzJXv5LGbGND7qneLaVIxTZcLlZmYlPJeY+A7nZyc\nWCIfS+dRkPK5yugZYEdNlUxL62xctV0bc6CaxGQkqvK0A2n/052pfng2/X/cpLHGIzdwVc7NDxcI\nuS05NvvFPpF5KNBErWRiYgK53DCIlObIxMSEJS7R3RZCMExD05K5YBmE8/LlS5TLZWxvb6Pf76NQ\nKNg7AKN5FD7CUFvMPeZ9/Po7+6LmmS9ColrJOCmrcxLTCLzKHlv8+rt+98+NHZ+amsJP/MRP4Otf\n/7qFpmvpO461mpaxxe8ludfGxuFg+s7KfJiWzrBwNSli1/V6PdRqNYtzIR2y0STnuzDGh/U3yIxe\n11MBXKNZ0el00Gg00Ol0RuL/gfMXJshGm/wyglF7T33pBwcHI/YmcQAlfFYHYuCULiAAIwNMtZ2q\nIN2YdE15k4FuJjIKBn11Op2R6kmU2kzookbC7fYWFxfRarVGiteq9KA5xqZqqqrzwHlmJp9JxsTr\nVNNQEEv9+ry3/o2ZG3quNwnYVBvxjMSDj+y7fw7vq0wjl8vha1/7Gj7++GPs7xBpU9YAACAASURB\nVO/bYuFf1ZZU/fdmhrYYU4r9z3uQXiYmhpsv0/VIbZDP9/egoGq1WkYPis3p/KhZops3+zl83TiH\na9McTk9PTYJqjDsXEl1wVG1joJAyAb2eWgiJjiAbAEPceZ0Oqqbj6jN1t27mVigxMZlKgU5V7TSR\nRt2bdKvSnUg/fKfTsf0riXdkMpmRClnsJwBTK9UGVSLjNew3CYo4CxkZTTovyfi/ApXqARkn6T3u\nwOYlvbpa9Xcl5hguEFvAqplMTEzgjTfewDvvvINisWhAMAUNVfFxJoB/B87hOE1Ivyv+Q8FQLBax\nuLhoWkM2m7V582PF/IlqtYpGo4F+vz+SEs45VICY2pEyemW84xjeuHZtmoO+jPrV9YV0IQBxrIH3\n0vt6+47nqqQlpkC/uCdQJXxWJ2o0GmaPMwefAGMIwWIheE+6yLQEu9q2av8mSWJazcLCAqamptBu\nt23hMghG3aokLGUKfDc2T7TKGOg+ZuyC7jHpxyg2fyqxeX+P+PMcVd19vzwz1uu0XYXIPdPIZDL4\n6le/irt371qciNrhSkPK9JU5+ed5JqDHlCmQpufm5rC0tITV1dURHMoHhbHpfO/v76PdbgMYLc9P\nxqDJXAq4eo2CfXmddq2ag+4/6fPmPWF5CRNTI729B8AyHpMksYhFNg6eSlaP/E9OTlp4KtVvMjRO\nEqUPpX69XreAIi15x0nT5B2+Aze+IRq9tLRkmZ6DwcCIhNer6aVmw7gwZj3Oa8kEtMCpN1mA0byJ\nccyCz4qBjV6rGKcJ+uuBixqDPzbO1FQT5M6dO3jzzTeNDmgWqqvcawRqUnpNwffVA6p63uTkJAqF\nAlZXV7G4uGhBSzqWftH2+32r08CIXu0DaUA1PTIH1Ro4n2yvyxyuTXOgV4Bqt1YhInGqOsvFC4yv\nesSmE8TJbTabaDQaF0wCReX1ekqYlZUVlEolMwXIBHq9HgCYtPXpukmSWKGWmZkZdLtdAxS73a7Z\nhaVSaWThkfEsLy+byTI3N4dms4mFhQXcu3fPamBwPDR2gfcisWpJfKq4dB1rrr+Oi2pwqiF4xuAX\nKL+rW09/Vy2D3/1xYHRPCgoHz5DGaQ8xDSSXy+H+/ftYW1tDuVw2E0oL9nq8QeknxtS8uaQeImUO\n09PTKBQK5t4mwO69THyOYg3VanXEJFZ6JpbAPtN8JEP0eNHrMgbgGjUHLh61AdUc8AxCCcnbiHrc\nN6K3jP5LpUb3o2CpN11cZETz8/O4ceMGZmdn0Wq1rKQc+058hCYBbVriDjQlONmqHmooNnBeLiyV\nSuH4+BgzMzO4ceMGvvKVryCfz5tGUSgURqQFsRLFE9SfTubHZzHzlAljWhKd70+NxC8AHXsPAsZU\nb/6NmTn+WCz79rJrL9NA9DsZwZ07d3Dz5k3rM6Wufzd/33FeGX8Nm38Haq7qttTQ83FYQ71et1we\n/xylf9IeNUxlHJ75qIl+lXbt1ae1WIaXXETyvdoGxAEpb2JwQBglyYkJIRgQqgOutjsZQzqdtuAT\nagtsXESM1aCpQDcZtQlKCmoLc3NzWFxcBABL1kqSBEdHR5btGcLQxZrL5XB6eoqNjQ1Uq9WRTXO0\nYIlPcQbOK2rxnRgkk81mLfmLBMZ+8l5qWnjJHTPf/LjzOo1y5DEl9HESWr0SMSahx1Vb0e/6/FKp\nhDt37iCfz6NWq43EBFz2LjFNRE0Rb37o+TTZWINTf1fhxmPUGljCgPSmnijOCxc/5029ExrToGvH\n7wfyqva52bdC1UpgNByU0tlLCu+aUQL0bjEGIlFtV8+E/qUtNz8/j8FggJ2dHWQyGczPD8tnMoOR\nCzSWD+BtSU4ki6YuLCygVCqZF4KE0Ww20Ww2bbOcfD6PbDaL/f19tFotbG9vI0mSkVgH1Xa4gH22\nKc9h2jCrBSlxKmBKAvSmhI6pl3jj5ldNA46PLl7+pgyD83cZ0u4Zgl94nmlkMhncvn0by8vLqNVq\nI65yvUZpKqY5eJMihkdwfJigx8hENY9V++L11BoODg7MnNA6En78/T05vn4daFDb67Rr3UhXawzo\nBxgtcqoTN0511KaIMQBzRU5MDIvNqhlDDs8FwISYwWCASqVi4dKMgGRiFvEKJXwPCJKLc5s5bn23\nuLiIe/fu2cY2ah5w27J6vY7FxUUD0dbW1gwUPTw8NG2Im8AkSWIh3RrARaIl06PE9Ik+qnZ6F1nM\nZFOtIsaUFfvwzMV7LGKLWW149cDoOfq/9snTGd//xo0buHHjBj777DObGz2P7xrDDvw9ddxUQ9Pj\nHG+ONd+J7nRtBK25fQG9Uuqh8ICw3o994rO8xqNr66rtc2VWsKnt7DnhONvPTxjPZworB1n9/QBG\nmAP7QJyC9h5xAz6DNqOaQ/yfz6Yk56YwXPxkLIzao8Qgcc3OzqJcLmNjY8MCZhQnGQwGVlGYrk9g\nmKhGxueJg2Op+1GoCaWEG/Of60JnU6mpzTPvy4Awr67z/l6ziKn1MS2STEQZifZjeXkZN27cMHei\nBiCpWevd2tpXb8fr756GNbVdz+GzdOypNTSbTcMaYk3nggyFZoWCyLynMoYvTFYmAANTGAykEkAZ\nQwypVjXe241cEJTktPd9zoTXPAaDgWERvJaDrBmaymj4LFXpVS0nY6LngpoE3VVq3pyenlrG3f7+\nvrkv6YKljaxMgwg4kXdu76fvRQnGDwFNBbUogdg/akkaTarMwqvQ+jc2V7FF7nEBnRMv+TRhi7/r\nfWLqvT4XGCav3bx5E8vLyyPvFqMpZUyqFYxjEMq0dOGr1qsMUK8lHsCt7IhNaX+8UKMZqEGEOj8e\np4uto1e1aw2CAmBSWbecY1OXjKpSXt3zYJkSjCK6GiDCY7xGTQOClmQmnDgyDS5kAo8MJqJLNGZH\nhxBGkm0AjNSVYN/m5uZw9+5dnJ6eYnd31xhKu90eAT65MQ+DsBi+zYXP96XWwZqKfsGzrzRHFAjW\nmg1+XEnYigvwPX0bxyBedT4/1ML8cWXuymC8Zsgxnpqawurqqm1NoLhKTBtRmrpKI+1oOz4+tsLF\n9GApnfL9SGNaZV3HSLUOagQsp8h3I46keSq8XhnoVdu1MwdFW5XT8Rwl0hhhABeBIT2eSqUsiWty\ncnKkjoLatFyE3MLu8PBwJCiLzMADXx4IIoEkSTKS5chissQvJicncXR0NFLWvtVqodvtWtXpJEnQ\nbDZtXPr9/khxEpVITO3VsHPgHPeg9qAuSjYSJkube1zCA6y+jVO1dU50wfKYEry/tzKGV8293ssv\ndPaD87W2tobbt29bRWb9zdvlMe3UazbaYq5JBsVxAROL8GOj6QQUlPx4s5tmL13zFDhqTsR25vrC\nMAd2lCq71xy4yDQ5BriaC9OriFozgotIA0aYQqs7TDNa0YNJmunGRUkJTcAQgLkNWY2YZevb7TZm\nZmawuro6kjgDwPbnGAyGwVc3b960FG26VIHR2otqs+r+ohwLvjsxC0+YdJ/1ej1jaN4P70N8Y4tc\n53TcPPn592q3aiF8Lz2mQXIeYxjHYLwJVCqVcPfuXTufdruX+orJjHsf9ZTo4lONq9froVKp4Pj4\n2MoQsECQ0hVLGLKGiEbW0uRWrZXmBIvWqmtbg6x8vMPrtGtjDiRiEjUXow42F5juJeAbj1FLiNms\nVJVnZmZsvwAS18zMjIW0ktNTXQNwIf6d7iWVwKenp8ZgiA9QxWcYNbWSdHq4HwZTuE9PT02i0J0J\nDOMfFhcXkc/nMRgMA3k0t4P9V49PkiQjUkY9Pnx/H28/GAxMnfValhKXl7K6qD1z8CZETHX3c6Sm\nBn/z/fTahjII37SPeozVtrifycnJiZUiJJ1xzjUuRrEFZUTelvcBSEmSGI7A+BYWHFazgvuj0KRQ\njVoxMGUMdJVSUyV2QXrwEZJfmCAoBQG1eAklotqJ6jbzkXSeK+qkhXBempxSQRc6zYkkOc9toFfC\n29i8J7UZDjaZFieZjILaDrdyT5LzKtQ7OzvY2NiwnIbV1VXk83lMTEwYsZKBrK2t4fDw0ABVmhSc\nfFU7dcHwXWlSkDF48I7u016vN7Ljs8/OjNnhyiB4T13EMYzCMwRvmsXMBKUZD7TF1H3ts392Op3G\n0tKSbfbSbrctrJ4mLDUtjbNRJqn99OCj9074ICUvXDgHZNC6uNU84DNJowCsjwAurAt1efJ3H8T3\nqnatcQ7+/xhRAfE0WT031rzZotKNA6eLAxh1lVIlV88E0XzuxM0YA7oxGULNfSC4aKkFcUPT4+Nj\nS8WlmfPWW2+NFG7t9/u2j0Wj0cDe3l4UeONfJQ5qDNQCqHZ6sIxMsdVqGbhKV6d3e8VMB/4fk6Lj\n5neceeJxBX2WYgE6d4pXeC1hHD2EMKwzmcvl8Pz5c5sH4HwrPI6b31rOe9G8EFE3MGlL+0TGo9GS\nNCk6nY6BkeodI30RZCZ4z3limj+FBPukAXoMr2bsxFXbtTMHqsIE2Di4JFZfmsyrmMogvNbAwdFy\n9JqsotxV76fquNqF7JPiEGrfaWQi8zAoifku8/Pzph1wQbfbbWxvb5smQKKkvVoul9Fqtex91CZX\nsJJjwcIi3DhHdwnXRXh6Oqw4zfJjc3Nz9mwvcRWUjKnr+lfnV6/3C9kDiP6+HnMAzutxeKwhZlKO\nY2QMdGOVJS4a0gIXsN90ibTJhacmCP+SPigYaC6T2bDcIOfv+PgY3W7X4lR6vd6IFkFNlZL/+PjY\nXNc0hTXxDjjXGClk+JwvnOagqhIHm9yQfnkd7BiD8LYuMCrRCOjopA4GgwsZlbxO4wZ0IQKjVZNI\n2OqDVybB/jOXgVJgeXnZNkKllOHOWGQgg8HACKTZbNoztTCO4i2Kr5CwCYCxWK/a8ARHufkOi48Q\n9dYx9ePpTQj9f9xcexyCC1+vUUbhtaQY3sD3UKbitQ/fKFW5Ixb3QdVFRgZ0eHg4EsikDIC0RG1L\nt7j3GASfyblQMLvX6+Hg4ADNZtO0Bxb7ofZKLYTAKbUG3kMjOlW74TgxwXBcYNW4du1BUNQalNC5\n0AhIMvEkZm/GzAo9RnULOFcb9eNjzjnhtO3VI6DqPLUcACM4BQOVOGGUSgsLC6baFQoF3Lt3D5OT\nkxaiTalNTl+v1y0HgNKC704i9DkU1BpIICRaDeFV4Gp/fx/VahXpdNoK2mhlJJ2ncfa9nwNVtT1+\nwH4qY7gsfiGmJXJsdRHH+ukZhPYxhGDvq1KWi1C1MS4ovpdqCcTE5ubmkM/nbT7o9cpkMrZFgXrE\nyPTovmaV7IODAzQaDfMczc7OmvCkicLoWApMPlO1Xt5fQWre43XatXordLHp4uPvajcrhx+HMwCj\nAR8hDF1JGmykC0ylBZ9JP7/iB+wj78+/xAe42PT5AMzrwWIdy8vLmJ6exunpcB8Mqqzci4CAJLfT\nY+FaSgJKSTUpdCxIuAQ6qbn4jM0kGe4Qvru7i6OjIywtLWFhYeFCmXS+j9ciPFofs/f9NWzaB2Ug\nPKamB5ui7N7E5HV6Lc/zZos2pr8zGY+qPl3HZBQUHloQSPtMmmk2myiVSjg8PESpVMLy8jIWFhYw\nGAws/iSTyYyk0lNrIHOo1+toNpvmogwhjJityuxjdUj8fKnWoBGXV23Xxhy0UCqRfdUaKMEp9VRV\nu6wpQATA8IYQwkjxTfUyqKaiHgAlODIU/q6E6GMhyDgUi2AgDFOmp6enLS2c8Q0sKqvP8Cq0agAA\nRkwcmmVkDpoRqEyVW7g3Gg3MzMzY5jmqNXjAjffw8+TNDY4Dr/emg5foqjmM01bU5GPTsaZEV4ag\nf2PvMD09jeXlZRQKBVQqFWPCqjVQOqsb2+czUJVnUl2/38fy8jKKxaLto8o4FdIytYZut2s7WVWr\nVbRaLZycnJhmQuGVJImZiaxl6rEmxT/0d4YJaJWoq7ZrYw6lUskkFKMS2XlV2TQox0sa4KKE8hLH\nq+EMQea51FD4fC5Q/k7Gov5rr+6SwNUVS2ajWEW9XrdFxapQs7OzJl0AWLo2iQ0Yzb5TME7HQFVc\nMoaYS5J7ezJCsFQqYXFx0WIwFKD1i0oZRqx5qX3Z+TFTg9fGzom5Ob3JoYw8xmiUTujSXFxcRK1W\ns2cog5iYmLDwZBU4dE/yb5IkZgrcunXLxpRZsgAMmyDj5xZ3Ozs7xqiZlKfl/xjPoPibVlBTDUrN\nSl6rBWdft10bc1hcXLSJZCCHuv44Gd79A8QZQQww81iDBg0Bo6W9aRqQmfjoO56r6DjjHHxmKVVS\nYhBc0J1Ox8qUTU9P49atW7ZxD4t88F2z2exIwQ4FJP3CImOgjctoUK91HR8fY29vD7u7uzg5OUGh\nUMDS0pJtlhNz0fmF/ar//Vwoc/B/vRnhg+DYPDNWxsNx8J4Nftfz1KMRwtClubi4iM3NzQsaGJm+\nMjyVyrynMgoW6uFWdVygSXIeeUraaDQaePbsGTY2NrC3t4dOp3NhcVMzoiZIoF7dnNo/ja+hS5S/\naV7HVdu1MYe5uTkAw0kiIas6xMmgOqUb6vrm1Vr+r8Sm5/L+/F/NCD5DVUmqhSQ09WXzHiqRyAwY\nF0E7lpPb7Xaxvb2N09NTrK6uIpfLIZfLmbtJ1VpqP9q8zatZlzTFiDUoY9jd3cXTp09t783V1VWU\nSqWRbQf9uOpCHrdw/fneA+EXdcxM0YXMcfempKrRfJ6ah54J6DjFMJC5uTkrusMcFvZZ51WD8Kam\npi48g7+RJlQAscgQN1Fm5uWTJ0/w8ccf4+nTpxYVyznT+Z+ZmTH3JzVfJgFyzHwsBoWVmhua3n/V\ndq2JVz6vwQfpUCL6wBG/WGJSTsFH7/ZS9VBVUSUkJTxFfTkJ1DQYUwBghJtzX00SCqUC/d29Xg87\nOzs4PT1FLpczH/hgMMDe3h7q9frIe3oVnRNPBkoGq0yC79ntdrG3t4etrS20Wi1jDCsrK2bS+fuO\nMx3YVKPi/+ynjiEw6vL0Gp4uYj2f9/QEzXuplkANTq9X08b3m21ychKlUgkLCwuo1+s2n4ql0KRM\nkiQqEFTT4J6jxBm4uxXdw3RVbm1t4cGDB3jy5IntScGFreNKwTg9PW04BUFu9oMaA9cIcO669yaY\nX1+vatfqyqSrkoTtg3SAc5XIE6Fv49RgHRyCk7yvqqT+/jQbvCRjODJtPsYxALBALqqR8/PzaLfb\nFtFGNyeZCZ/f6XRsN6vBYGDBMF5aK1quJhfHUMcSgBW2LZfLKJfLZkqwTDq9E7FxjZkPHuvw48y/\nMfPPmxT+Gv2rKr7XBtTTRK2OjFw1lxjm4XGTEIJtMvPs2bMLeQx6Hseb/7OvnIMkSSzWQc1Z0lW7\n3TaM4bPPPsNnn32GWq1mjIHMgf0mfsR7au6FJlXxWmp/PsNZ3+mqoD7btTEHlcJqY/m/KiG1qcSJ\nESyvU7tP3T8cXKrulACKjCtBhhCs/iLtSWo2zMZktiefzUQvBtMwdoIqIwkrl8uhWq1ie3vbuLvG\ndijjinlY1MU1MTFhW+4xb6Db7WJiYgJra2uWzDU3N2fPipkKXs1X4tJFo33hefzr58UvVP3dm3p+\nfpVJ8HwtckIgWPNdVGB485Itk8mgWCxiZmbGolo9A+H9NL9C559NvWvUFFmQZWdnB5ubm9ja2sLe\n3h4ajYZpGxquToyBofY0MzRrk++sWiOjKH2eDemN9P067dqYA8NKidymUqmR1G3va49pFfwtRtjA\nef09SnS1xRR41O+KU9DO0wlhcBEjDHleKpWyHa+Ai4j2YDAsdU/gUXf1np2dRbFYxM7ODprNpvWb\nhAqc29sKtNEsI9hEDYSAFSPq8vn8SMFan4TlxzA2zjxXgVB/TQxP4Hdvo/MaP2/6vtoPXbTehNHF\noKagxx48g6CGVywWMT8/b8xdmRTfm5oEf6MJ4QFMCjoyhcPDQ2xvb2N9fR3Pnz9HrVazuBuaAqq9\nUePkVnmaZ8E5pRajJjfHgU0rrAEwJvM67UrMIYTwFEALQB/AyyRJvhVCKAL4AwBvAHgK4JeTJDk4\nO/+3APza2fm/kSTJn/h7drtdK+RKCeajuFSdU6nFydfzYvYsFwB3HWYClko1NSPI8VVtowQnkaip\nwcaYBS1Bf3JyYqArA6GWlpYwPT09UgaM98rn81hdXcXm5qYxTjb2WdPEOTaaOKYYytTUFPL5vIGd\nxHUUqLxsMXutIWYC+HMczVy6mPUcfa5fxHq9Zxi+/8okPLPw8RSq8RSLRRQKBdsuj2PtXZj+/TTP\nhvc+PT1Fq9XC/v4+ms0mqtUq1tfX8fTpUxwcHIwAjcD5lgI0LzKZjO3MTsHJGBhiWKRt5s74Qrla\nBkHH4O8qQjIB8A+TJKnLsW8D+NMkSX4nhPCbZ/9/O4TwNoBfAfA2gJsA/iyE8FaSJCNsi7tMM4eC\n0YEcAI1TV+4YMyM8U+B3YhVHR0doNBoXthWjROJ1AEYkhKpiqlVo1BwAm9RSqWQ1HIkOUyJ1u120\n223k83nk83lDxyn1JyeHO2lzf0RlDuq9UaCJGgkDZzRQhvkcJCBNPfYqplfvVWLqObqo/XV6nrfL\n9Rp+94tbrx/3m/ZDF33sd2UW1HhiDC+EgIWFBRSLRSuY4t+fY6/Mi8c0gI/MuVKpjGxjuLOzg3a7\njSRJzBxg0/gVmq106/d6PXQ6HSsczL7Rg6HuagVxfXyDYg+v017HrPAz9QsAfu7s++8B+AsMGcQv\nAvj9JEleAngaQngC4FsAfqAXv3z50pKNAFgadKwiFNFYH0c+jjitw+G8poPakyoN+v2+SQmqXpxA\nLQDD+/IYJ0ttXy5GEmc6nUYul0Ov18Pe3h4ODg4Ms1DfOKX/y5cvkc/ncXh4iHa7bYs4hDDy7hwT\n4g20O4k7aPi0Jgl5M2Kc1qBj7I+Ps1110Ximos9RDfBVTTUGBSG5sJUJ+MXLufbYg39+CMMCMAyl\nbjQapjXyeh07vZ+mwasrk0lU1CKOjo5GCsjwfC3aozU3KOXJFMhA6ErVBC4fmKeMQYWf5hhdtb2O\n5vBnIYQ+gP8+SZL/AcBKkiTls9/LAFbOvt/AKCPYxlCDGGlUiZmYxJqKtOP44lox+VVYgyd42l3E\nDCgZ+PyYZOM91LQgaElGMhgMjNkwW+7o6MjAxenpafNmcFPcWq1mmMDy8jJmZ2ctCpK7WtEcUcnr\n1VoyMYKQ8/PzI+nZmlasKe9qR+u7ejNBpabXsvS4HtPvPF/vyWdxIfC7zh3fbRzTUo1G/1fNz88h\ncRp9Ly52vd/k5HCz2/n5eUtEU9e23pMag35UaiveQC2Y+ACZjCZCqTvaBzmpeUkmQJe3Fgvmwud9\ntdycMom/E8wBwL+fJMluCGEJwJ+GEB7pj0mSJCGEy0TBhd84eVT10+m0+Zlpd7F1Oh1Uq1UDi7zd\nze/66ff7qNVq2N/ft2pMSiAe9fdSiFKd6jq1BwJRGoQCwMrDFYtFLC0tYWtry+IeuDdluVzGwcEB\n8vk8ZmdnreIT/e3pdNqkjpeMauZQeuRyOWMOiifwnfRzmcag4wmMd+3y/9jC1u/jmPhVnj/O1IiZ\nEPyNnxjDUPNDpT4bj5E58JgySz2foLQHzP276vlkQDSjle5COA9iAjCCj+k4k4HQi6H7bjAdW6/V\nCEl1gb9OuxJzSJJk9+zvfgjhf8HQTCiHEFaTJNkLIawBqJyd/gLAbbn81tmxkbaxsQFgOKBLS0u4\ne/cukuQ8JTWTyQAYEuXh4SEqlYrluL+irwAw4sahFqHaAI+TI/NaThpNB2UODGDS/AkFLXu9Hkql\nEtbW1lCr1WynZDI7blbSbDaxtraGXC6H3d1dVCqVC4VEGJevgTn8nYyBQKNWetL3YRtnu8eIn8eV\nMD0+4XEFr2F4ZhPrxzjNjd+9a9QzksvmX5mBN5OUiejzcrkcisUipqamDE/ieGrwHOdcNRjew9OP\nmhIaxMdzOK+cc5oEii+wn6xBSmHALFJeQ88IC8KQMTQaDdTr9QvzeJX2yrNDCHMhhPmz7xkA/wTA\nAwB/DOBXz077VQB/dPb9jwH8pyGEqRDCPQBvAvihv+/t27dx+/Zt3Llzx9RqgjIMiOLA0bXjUdkY\n+KQgFG1J3Y9SiZeTrFKAkoFRkP3+sOirxrefjcWIKxGAlflaWFhAPp9Hr9dDtVq1mPvZ2VkDSDud\njsVC0L1JzePGjRsoFotGMESZyRjm5+eRyWRGzAj1wT99+vTCovKSPybd/SIfh+m86tzLFr1/5qNH\njy7gFGx+IWpTLcfTgDYFcPUc/26zs7MolUpWlEexC7X5nz59OtIHBXjVu0HaoBngI4CpMdANrtXQ\nlK40ilg1RY1/oCnBClIUKvV6HYVCAV/5ylfw1ltv4Sd/8icvjONl7SqsZAXAvw0hfADgrwH878nQ\nNfnbAP5xCGEdwH949j+SJPkEwB8C+ATA/wng15MIlXGhcgI0oAUY5cgEJL20YYups1zYShhJkoyA\nQForQVU8zWg8Pj5GrVazRQnAMjcpyfk+3W4XzWYTqdRwuzsGQdFUmp2dtXegTcl+sshrv9+3TV+5\nP6bHGWLeB13gT58+HTnum6q0lGZ+LPV+nhFftuD1Wt7PS3Cek06n8fDhwwt9iPV3nKkR+8TO83Tg\nz5mYmECpVEI+n7djvFbNyKdPn46YVrqQSUcauUrvUSyJju/NnBrFxmgiMjw/m82aUNACx7y20+mM\nVE7v9/sGrhIL47tdtb3SrEiSZBPA1yPH6wB+fsw13wHwnSvc25gDgT31/3tb6TKb1du2DApqNpsj\n5bFoEuiipurnFw3NBWDoiSiVSgghWGqtItrk3gcHBwCGxURWV1dxeHg4ki1HEIqRmnzPVqtlKi2z\nMm/evGnvQXOLEkillEowjy2o1PXqfkzCj1PjdVFxYcTu7b/rdTHzhcf8vfS54zwkei9lCsoM+LsC\nkxr3wJZKpVAoFLC8vIxyuWzagt6b9MjoXjIG7Te1VmqfNG/IYBQoJHhJMXYCIQAAA5JJREFUmicz\n4NhQi6D2QXOC78iaEIyG1d2yOF40R2iCvk4LrwtS/G20cDl4+WX7sn3Z/g5bkiSXAzdn7fUQii/b\nl+3L9v+bdi2aw5fty/Zl+/y3LzWHL9uX7csWbV8yhy/bl+3LFm1/78whhPAfhRAehRA+DcOErWtt\nIYT/MYRQDiE8kGPFEMKfhhDWQwh/EkLIy2+/ddb3RyGEf3IN/b0dQvi/QwgfhxA+CiH8xue1zyGE\nmRDCX4cQPgghfBJC+G8/r311/U6HEN4PIfxvn/f+hhCehhD+37P+/vBvtb/e//t3+QGQBvAEwF0A\nkwA+APAP/j77EOnTfwDgpwA8kGO/A+C/Ovv+mwB+++z722d9njx7hycAUn/P/V0F8PWz71kAjwH8\ng89rnwHMnf2dwDDn5mc/r32VPv9zAP8TgD/+AtDDJoCiO/a30t+/b83hWwCeJEnyNBlmbf7PGGZx\nXltLkuTfAmi4w7+AYaYpzv7+0tl3yzhNkuQphoP7rb+PfrIlSbKXJMkHZ987AB5imNj2uexzkiTc\nvXUKQ+HQ+Lz2FQBC+P/aOXvWKKIwCj9HMCAIEUkhaCApLAUhnQh+gPiBWKexsLC2SmH+RPwBWqVI\nZSNYKKK1ENwgRkQEC0VIumBn81rcGTNZLqLu7uwrnKfbmYF5WJjDnTv3XJ0CbgAP2W8ip/VtqDWm\nR/btOxxOAl86v6uNzQT8rnH6tXPdVP0lLVBGPa9J6izpkMrq2h3gVURsZ3VtWANWgO5Sy8y+bWN6\nU9Ld5thYfPveJu6/+24a8feN0z6QdBR4DNyLiO9Dqw/TOEfZ5OespFngmaRLQ+fTuEq6CexGxEDS\nxapMIt+GsTemW/oeOQw3Nuc5mGRZ2JF0AkD/0DidNJIOU4JhPSLawltq54jYA54CS+R1PQfckvQZ\n2AAuS1pP7Et0GtPAgcY0jObbdzhsAqclLUiaoWwn96Rnhz9hpMbpJFEZIjwC3kfEg86pdM6S5tqZ\ncklHgCvAIKMrQESsRsR8RCwCy8DLiLid1VcTakz/YgozwdcpM+yfgPt937/iswF8A35Q5kPuAMeB\nF8BH4DlwrHP9auP+Abg6Bd/zlPfhLcqDNgCuZXQGzgBvGte3wEpzPJ1rxf0C+18rUvoCi81/uwW8\na5+ncfl6+bQxpopXSBpjqjgcjDFVHA7GmCoOB2NMFYeDMaaKw8EYU8XhYIyp4nAwxlT5CQSZ/DRn\nVoTRAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff9e40d44a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%timeit\n",
    "plt.imshow(filter2d_numba(image, filter))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ True,  True,  True, ...,  True,  True,  True],\n",
       "       [ True,  True,  True, ...,  True,  True,  True],\n",
       "       [ True,  True,  True, ...,  True,  True,  True],\n",
       "       ..., \n",
       "       [ True,  True,  True, ...,  True,  True,  True],\n",
       "       [ True,  True,  True, ...,  True,  True,  True],\n",
       "       [ True,  True,  True, ...,  True,  True,  True]], dtype=bool)"
      ]
     },
     "execution_count": 262,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filter2d_numba(image, filter) == filter2d_simple(image, filter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 253,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.all(_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1400 veces más rápido casi sin modificar el código!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "De paso, Scipy tiene esta función definida"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.ndimage import convolve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7ff9e41cc908>"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Vkz3HhFTqupqhbIXuTdo2uDsSjbhcoKfg4MAXqacsQy7pTtEEBgeI6H7vezVC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5K/DtqulT2MWfMLRJkgsWx3d7dR2ZuwIaAM0/TK6K6kIjjyuS9W+uUFZvwJ4S5DFML1hzoB2NGk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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff9e404e7b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(convolve(image, np.ones((10,10))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The slowest run took 8.73 times longer than the fastest. This could mean that an intermediate result is being cached \n",
      "100 loops, best of 3: 13.5 ms per loop\n"
     ]
    },
    {
     "data": {
      "image/png": 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Y3/U5+2c9I2QCFn2QKBWG+1CMbQPrJ2PQvQeKKFSa6LuA85uHFGX4UINlOspA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wSoxwVCLoZiRl4oqfBXi1GtBS90faQk5Ia+blw5IKkvReHgO+EAwyWofhby7/zisXGp36CVFos2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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff9e41c07b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%timeit \n",
    "plt.imshow(convolve(image, filter, mode='constant'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A todo esto, les gustó [Lena](http://en.wikipedia.org/wiki/Lenna), cierto?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7ff9df687710>"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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eBJh79KXXnIsFkcYIA8NHWAHC8AbEV/VdvHhRq6ur2t3dVbPZDLHi5Ly9Z+c+\n2DHaarXMy3B/KGEikbA+jxgG4OXU1JQRoNls1mAwCg3bPRwOlU6nzVjzerd+v2+Qm/li/q5fv67P\nf/7zVi/hQwxv0KKt+/jdE8asPccAwwlNvUHGqEjjrI7f6MYcSLKske9sLo0NuA//+LtXXp/J8t4c\ngwXhCHLgtYNra2vGpdXrdf35n/+5/sN/+A9mXEAAnjD1KVsK9tCfWCxmITLyIB0ZyWKxaCEhjuZJ\nx7HWOXS7XRO40Wiker2uSqWira0ti31ZKBQCEpAQwJM03pMjCAgXQuDjRwyMT3F6WOn5CFCGb2Xu\n4SReQjpaBO9t8PDAcmncXHQ4HNpmmpMnT5oH4t/BwUGoZRpxsveWlHtTuzAxMWEVdY1GwzIQXBui\nqlaraXFxUfV6XblcTs1m0wjJIDjaCTk5OWmNYjEmvtKT1wOmUikz8MViUb1eT4uLi/rSl76kb33r\nW/r+978furZXJo/svAHgeAbzhfFA6KWxIcDg8rsPUzgO7oVzIgceNXj+w5PYDF/TEu2/4J0ehsOH\nCaDCbrerV155Rb/5m7+per1ucwLSgVsjNcr9QzBLUj6fD/EurVYr9EYtzgd/9zTj2JCDL8XtdDqW\nuqxUKgaByeX6Cffw0C+kh3k+zJD0yBZpaRzDRrsw+Zyxj3O9cHkj5e+D83gPQ6ztjRmfDYdDnTp1\nSrVazY7n/PAG1CZ4ohVI7OPhRCJhHp3QDEMyHA4t8wD6AFlQO9BqtVQoFLS/v2/ELduJeS9kq9UK\nvYAYrwgSICSDXed+P/e5z+knfuIn1G63zYDAR0RDPAYhkecQMMS+nZonHz0M53sejnvim2uDUDg/\nRs+nO5EJzz2w1r6gyn+XufGhqkcE9+/f19e//nX95m/+pra3tw21cQ2K2nAI8Ew4VUJVdASOh2vH\n43EVi0XlcjmTpyjP8oPGsfaQxED0ej01Gg1Vq1XV63WbcH6yUJ7R9t7Fpyu9h/VhR9SaY8WjG2xA\nEt5IeA4hikwYhCAIJcdFDQX3GQSBzpw5o0ajEbpXXy/gMw3x+FHfi263a9mGnZ0dY7NBE7Dh+Xze\nFJc59nUO3hjjSTudjjKZjClgoVCwuYObAE3wHB5aU0wFh+EV9yd/8if1N//m37TaDIxfs9k0g+G3\nKnvEhcGMEoMYd7+PwhsHr1QoN9uYMVTcp+/yxXr7UMLLj5c1jFQ0Y4KMe+X2zubb3/62/tt/+2+q\nVqtKJpMZZcb8AAAgAElEQVQW3vH8GHRS2ITevG4BmeHaZEhAMRMTE5qcnFS9XtfW1pa63a69XeyJ\ndfSpjv4hDgglLPf+/r52dnbU6/WUy+VCZc0eCUTzu34hpMe/8dqz4B62QuxJ4VJYH8txLlJ3nivw\n2RDORerS5+qlMTwGYuKdHz58aPDx4ODA3pmBx/FVf9Vq1Tx+u93W9PS0eUP4GkqpCUWAma1WS9PT\n02Z06vW6MpmMZRAooyamZX6z2azq9XrIoBLykbobDo8qP733qlarxszTnv1v/I2/oX/zb/6N/tW/\n+lf65Cc/aVuafUoTHgDZwLh5EpB5jnINGEMQIGlv5IY5giAFGbFeXo68DOCk+JvnhKL1MXzXZ1q4\nPiXUe3t7+u53v6vd3V2TuXw+b4aKdSd7x0tpQCvpdFr5fN4+4ziuHYvFVCwWtb+/r93dXQ2HQ01P\nT+vzn//8U+nosZZP00K7Xq+r0WjYBhMfNmCpWRxgE1b5cZCPCQLqEjfDC+D14Q/8Fm1IPMgu7ymS\nyWQop+1DBSw4HAGcAs/jS3nxCmtra6HiGgxGr9dTOp02IYMfmJ6eDqUnE4mEhR6EGnhgdliWSiU7\nH4VLnU7Hsg+lUkmdTkfZbFa5XE7tdtsqJSnDRtAymYxV59VqNYt32QAmyeDryZMnDRp7xj2TyahU\nKukf/sN/qPX1dd27d89qSUAoZK9YR79Hw3MUyBG8lEcIyBAhC8o+GAxUr9dDpKVXfl9WHc1Q4P19\nubfPsmDUQVM+3ckc1Go1vfbaa3rw4IHdG4gvn88bgvDhKbuUkXnmlHAIY4WRmZycVDqdVqVS0WAw\nULlc1vXr13X27Nmn0tFjQw6U8B4cHBgRKY3bugGTUUQf3/sKRDyOh5LSuFgkyvT7BaGACCgPCYSX\njC6+j6994xm8vN/Z6IttJIU4C4zU1taWxdYenvu2XxidYrFozwrJ6reI+47LeF3CgP39fdvCjmFg\nP0ej0bANPIPBUcEMGaNSqaSDgwO1Wi0Vi0V7dkg4QgLQTj6f18rKipaWlpROp00pfX9K+IJCoaC/\n+3f/rj0P73zw8T/rybqB3Hz87Yk6EJZPcSI33pgfHh5qd3fXiEFkgLXC2GNkvLFhXTxHgRPwpKMP\nI5Chg4MD3b59W2+++aatE4a9WCwaAvMl+swDZDaZMK9D0vh9L2wFZ63y+bxefPFF/fiP/3iIwH2S\ncawVkoeHh2o2m6pWq2Yx/eYqFgVLyvf8pAGtfMjgF9WTdp6NZlGBaZyPvD5w0yMZFjNaz+9Tbz71\n6a/j02WTk5PWiHQwGG+igVT0efpCoaBEImE9FH26cnt7264Dadjv9w01gBJKpZIajYaFE8PhUFtb\nW5a9oUYhmUwa8onH49rf39f09LRSqZT29vZMMHd3d1Wr1bSzs6N4PK65uTktLi5qamrK0BMGijXx\nRsuncH/hF37BDAwG1yu4V1rSklEDECWbffmyV17WrdPpqNFoqF6va2Njw9rJw7t4BOERKzKELPiw\nketEZRHUKElbW1v68z//8xAhCtLiLWQYbYwFoRoIEuNBSEYdBxvx4vG4Go2GVaVeuXJFV65csY1f\nTzOOzTjgnUANWE0W1KeXou+l9Arrme5oUROfAdGB+ggrC49AQEjS/8ArI0bF1zqgxL61nTTuJeA5\nDJABRpGuPn4QukAQEu4QAuGxJicnVa1WNT09bSEQ90aYMDU1pUajoXw+r3q9rkKhoHa7rUKhoIOD\nA83Pzxu6AGGgEI1Gw5q7wkmkUiltbm7ai1uKxaIuXbqkhYUF5fN5C2c6nY7xLhhNjw5YewT6c5/7\nnK5cuRIq1kJhfGUqCMF7VdbfZzV8utPvtuVe4A12dna0s7NjBXcQlD5ExdhCfHqH5ElR+AEvjxhb\nvlepVPSd73xHGxsbFgr6UKZcLocKvgjVMKiJxFH3KAy4Nxb5fN6IYLbpDwYDffzjH7cNXaPRyELS\nJx3HZhzYG7C3t2epNR+fEtt5IyGNN5z4WNQbBk8aRfPPnpjke2QpIK8QKA9RfYmsRybeCIEyuEdP\njAKtIfB4JTpCjIGJxWJGZGYyGbXbbR0cHFjWolarWSiUz+dDjD1ZBB9q0NWbLdvZbNZIS3a64sEm\nJia0u7tr/MTe3p6RXJRyc64LFy7o5MmTymaztg7UP0hj4wiRCAlKlmI4HJqhmJiY0C//8i9reXnZ\nvDcKz9xitP26eBIwyv+wDt7LY/SB/rFYTNVq1fakcF88g08t+8IpzuflkufEEHk0gaG/efOmbt++\nHSJOpTC5GkUeyBk7cFlnHypT5FQqlWx+k8mknnvuOZ07d07z8/NGMP/IVEi2Wi3t7u4aURjNwVJ/\nIIXbrGHd8Qw+dPDwEoFFuFhAvLw3EChflJzyGQ8vcAiHrzUglURu3R+LYRiNRqbgHir7bAyxK8qG\ncPr3VXANNkpJshbukI54pGw2G7r/qakp1Wo1i/+poDw8PFQ+nzf+olAoqNfr6d69e1pbW9P09LSu\nXLmi2dlZK8Wl4rHf71ua1RthXz3oG/cwp6zrwsKCfumXfknJZNJ4BZ7ZKx7PFEVzzLPPYHCNaCqa\ndfOGem9vz7Ir/sXKUYSAM/AGCmfl0QKGHifx/vvv67vf/a663a6mpqY0Oztrf0+n0/YqQ9Atck8o\nVigUQnwSqJXw1/eInJiY0MLCgs6cOaOFhQUjkLvdrmq12lPp6A80DkEQ/G9BEGwFQfC2+2wqCII/\nDILgThAE3wyCoOT+9htBELwbBMGtIAg+9z86b7PZDJV5ptPpoxtyHifKM7AYUrhdl09xIiBkFjw0\nxTKzOYoBhCWG85AYBeW+vKB5Yor75LtcD+iXSBy92xBFSSaT1p8BA0Z6kOvw7guIQxZ/cnJSmUzG\nKhsHg0EoXelDrlgsZueNx+OqVqu2CxGSsFQqqVQqqVarWan3xsaG7t27p+npab344otaWVkxj9ho\nNEy5iXPZnUjKlpAKOOurOSVZug5lunz5sv7O3/k76vf7ltKl3sOThsB2v348mw81vJHG6HvjwWdc\nj9oPPDMGAJ7Fo1C/q5MsSZSb4PzValWvv/66ms2mguDonZq1Ws2egTSqRyEoM9eKx+OhojFfzxCP\nxy2tiaHx/A8yD9n8NONJkMP/LunnIp/9z5L+cDQaPSfp//7w/wqC4HlJ/0DS8x9+538NguCx19je\n3g7tFvNMLxVleGVgvY8nfSrTC0q0qtEXQn14j6F0pN+rgXcgrGGrrEcCHIMweo/E/bEgfB8Ciefz\nwgafwDP41CgvLoGgwjsTd2IYQBAUvZDnx/j0ej3t7u6GuBD4jMnJSVNEQou1tTVtbW1paWlJFy5c\nCCky5OVoNFKxWAw1gcGLgUyY65mZGXsnqUcFNLBFub/4xS/q7NmzlqIFKSIfvhbFZyPIWiEjHgn6\nzA+8BRwWz91qtbS3t2fKB3eCoSe9KoVDCkkh4+4dBSHi7du39d5774XewO09P7JIKhpuhn0Qvtyf\nZ/a9OL3BLBaLOn36tE6dOmUb6EajkT0vBVVPOn6gcRiNRi9LqkY+/oKk/+PD3/8PST//4e9flPS1\n0WjUG41Gq5Lek/Tjjztvs9kMWXYIFm/tMARe6aLxJIKJl0RhCR8gOREavAULyue+rfzjDASQDhTC\nIvt9GZ4oQ1nwCPxLJBKWIpRkbeP4XjKZtAIxSCaKwhAg5iOazYHEpG6Az6vVqsrlss0DSkqpNfUN\n6+vrunv3rgqFgq5fv64zZ86YF2WuiF/j8bg1nK3X66Z8GDGepdPpqFar2aYywiUQHN/HOP3Lf/kv\n9cUvflGnT5+20AUkIcmuw/3gDFg3PpPG8J86AIwQ80L2QjrquXlwcGCZM7/r058XufChoTTewo3C\nS9L6+rr+4i/+wtZeGqM5lNyjENABPBKIgGpS33U7k8moUChYdiOXy2lpaUkzMzNWv5NIJGyjG30s\nn2b8VTmH+dFotPXh71uS5j/8/aSkNXfcmqTFx53As7peGSWFPpfGvQqkcQqJBfbEIJ4B1MBPFoAM\nic82IPi+sMaX8QJnfSaE60HocU2QATzCxMSEkX94Du8lvKeTxvl18tWp1NG7FUFAQRCYIpdKJUuD\nYgi5VjabNUa7Wq2qUChYQQypTpQzl8vZ6+PX19f1qU99SqdOnbJ9FtJYychs9Pt9NZtNuw5rx6Yt\nvBXpNL6HQnjmH6PLvS0sLOjLX/6y/sW/+BcGjeGOICs9SkPZfS0CKId7Rym8h/cyVq1Wtb29rfv3\n71v2AsOEbKJsPjPGOX1Ii4Oq1+t64403VK1WjS8B5WJAQMisH4YMZMdnGHtCCp7LF/nNzs5qamoq\nFDowV9PT06Gt+E86/l9XSI5Go1EQBH/Zq3Qe+zf/NuLp6WlNT0+b9UcZmEziUg/peHBPgOGppHEZ\nKRMCB0AqzKcuWXCyJhgusgbwECg9QulDGLwxRBf3RA7fM9DcE4oWrY+A4fbv0iiXy4awgN3sfuT6\nvrqO+4PrgETMZrPa2tpSuVxWt9tVo9HQ5uamisWiPvvZz1qjFjpQHx4eKp1O285MTzbu7+9bJ22u\nT50I3YdYTx8CSI8iP3gL0qbnzp3Tv/7X/1q/9Vu/pVdffdWqNUFknm/iPMgPvI7nhrgm6xiPx21z\nGQ6gVqupXC4rk8nYunt0Gw1rIM1BY9LYkN66dUu3bt2y7AGIE+fA29I9+kDeqYWBj4h2mspms+Zs\nyEwsLy9bBy/mut1ua2dnR6+99tpTavXR+Ksah60gCBZGo9FmEAQnJG1/+Pm6pGV33NKHnz0yLl26\nZFAeISZOw6vi2T1T7NlaKfwOzGjdAN/zsT4QE1iKUPqKRiy6NF5sD1u958BAsXiQdrDG9+7dM4OF\nYHIvPJs0fqmOD20wPlQnAsfz+bwZOkhLadzxyYcpg8FAhULBXoZD1dz29rZSqZTq9brOnj2rc+fO\nmUH0G88kqVar2XsyQAgoI12hCN+oxOx2u6F3Y3hDDkHJXBJSotSklk+dOqW/9/f+nt566y3t7e2p\nXC5bs1XmnhDDV7Ri9P1akeKVxnsdeNktx7ZaLW1vb9u2d5+5QkGRC5QcWUFuksmkVldX9eqrr1pj\nYoyIX2vQFSECSA/D5QlywgnPSRB2rqys6Ny5cyqXyyEiutlsKp1O6/r163rppZfMaH79619/Ev0+\n0qknPjI8flfSP/7w938s6evu8y8HQZAKguCMpAuSvvu4E+AtPRJggrF8wEPvcTyMwwv7OvMoE81x\nXnGlMQGKYPE3fuf+PELBILGAvjuTh7uDwUAvvfRSKNXnPQQKAOHqsxw+vsWQ4PGCILD6ewSMe/WC\nxbMxMCqgmM3NTXU6Hc3Ozuqnf/qnNTMzo3q9HlJYag1YEzgiWsRTOJROp0PGotVqGYTlORF2DA9s\nO6ES2RMyOKx9o9HQ5cuX9Yu/+IsWwkXnyaeBkSFpXGviwwCfffCokiI5+Jnd3V01Gg3LYpA1QVZR\nUkkhGR6NRtra2tLrr7+u7e3tEJrxhXisJztneS7pyHB5o0poORwOQ8elUinNzMxoZWVFMzMzhni5\nV2ofPGEeLRf4QeNJUplfk/Rnki4GQfAgCIJflvS/SPpsEAR3JP3Mh//XaDR6R9L/KekdSd+Q9D+N\nMGeRgaVnQUkpIZi+EAZL78MOHhrlwGtiODyz62M9adz3D4/lhQ3vDpLxlZQoMgJBjA15RRwfBIHO\nnDkTUhBJxg8gXNwj3hgCFCMAw1yr1UIeilLY0Whk5dDZbFa1Ws3ODRlFOg0BwVu++OKLOnXqlGU0\nyuWyZQgQRAwqNRAHBwe2xTyXy9lOTk8KoujpdFr7+/uP1I4goAg6BojuU6wFihSPx/UzP/MzOnv2\nrGZnZy3DgFfFKHwof6H6FCm8UYv15Xfm1FdRHhwcWPUkhWA+lSqNX3voOQ5+Pnz4UG+//bbJrSea\nQRHewCAbnc7Ri43y+bzd++TkpHE0oGxQ2fLysi5duqRTp06F3rXSarVs4xXX9uHX04wfGFaMRqNf\n/B/86Wf/B8d/VdJXf9B5KU0lTQR0Y8JAA56klMasMIIgjUML2PdYLBZqROI5AxTWE4EoqTTuFIQB\nARL6+gnvpTgGwnMwGOjs2bMKgkAPHz40hWAXJAQbKUYgOf0TGF6wpqamrFcCgsW2Y8g2+ARY7Uql\nokRi/G7N7e1tJRIJTU9P6yd+4iesL0QsFtP09LR5Sa4LEQrPMRwe7SRFyX2nLpAZ80aoQUt7BBMl\njBKxGBU/1+wsjceP9m7883/+z/XVr37VDDAFXJ4Y5F4IH1lDjxrx8MiRJzRxPs1mUx988IEpFTxE\nuVy2d4Ksrq4a5GdtHzx4oG9961umqKlUykrUCV9Yf+pJpHGDXkIVvu95BhoGk7o+efKklpeXba4G\ng4FVvxaLRXs+5vuvMo5tyzYehUnDcgNj2SLsQwEpXBYdZY197byH4l7RMCpc05NleALffAOoTbjh\nMysol/eeyWRSP/3TPx3qAEx6kfvHaEVDpna7bR2lgZfcXzabtWdgy3E2m7W4Fg/J1mxJVnr98OFD\nTU5O6sqVKzp79qzFuSg5fR0QYp4foSwUCoZWMKz+VYG+05EkI9v8XgfmmvMhA34re7vdtsI1Unhs\nRHvxxRf18z//8/qt3/qtUFs8lBo+hfX2PIBHe9yjLyX2dRPcZ7PZ1NramgqFgj7/+c/r+eefV7lc\nNpldXFy01n6bm5t644039Bd/8Reh3cU4DmlcSs2zgYx80R33jIywHhQLxmJHfRpYR1AGPTASiaOe\nkXzPGwZPhD+xjj7V0T/EQd4az05IAUOLAjFhnpgE6vN3vBP/PJTzDDE8gWe6PZnIvUD4EBIAs7kW\nw9c3AI2LxaJKpZK9ZwKj5VOgGAXizdFoZFVyeHqEcHJy0jwOu/NyuZx5Hu8tR6OjFm23bt2yeoBa\nraZCoaBPf/rTttMvl8sZwUicTA8HWtevra1pf39fMzMzj+xSZfjX+LEnBCEk3CPzAfLhBb8IKkiP\ncMSHlT7kk6RPfepT5hH5nlc6jE10eMLTo0zW0Ee+XlmXlpb0G7/xG/rUpz6lpaUlDYdDffDBB9ra\n2lKpVNLFixeVz+d15coVvfDCC7pz546hQx8++Uwa1yet7utjuD/kA+NNRqVQKOjSpUu6ePGiSqWS\nGVScZLFYtLoc9MeH4E87jrX7NIvmFxUPCVsrhV+iKo2r0nyo4Ukhz4KjAFS9AR1RZkkGVTEgPuXl\n90Ug8PxkcSH7ut2uXnrpJfV6Pe3t7dm5fIaCMCqdThs6Ia0GGoE7wJhhJCAj2aC0t7dn/EgQHDVg\nWVtbC+2fWFpa0vXr15XL5TQ9PW2wFGPEXg+a4q6trWliYkJLS0s6efKkpTBRVISNCj5QBj9RbozC\nYDCwwigQjS8LBzYHQWBhijRGfpxTkmZmZnT58mWbE8I4aUxA+iIjT3TCY/jwAaPu7xku4jOf+Yz+\n03/6Tzp16pSGw6FWV1e1vr6ucrmsmZkZFQoFa+M2Go1skxPGBqMFYetT4eymlcbby/kev0PqUg8R\njx+1CTx//rzm5uYMcfm5ozbGE6Y4Pa8/TzqO9XV40nhTk09DQcJ4pcfCRg2AZ6i9EnsegWIR/oZA\ncX5fPcfnDOJGz1RzH97CE6dfu3ZNu7u7hgyi8bV0JMgHBwfKZrOGACA3gfw+9JBkpNjExIQ2NzfV\nbDat9mE0Gmlzc9Paw1WrVY1GI12/fl3nzp2zrb6ghUqlYp4LtLC7u6t79+6pXC4bt0EKk81uKFe/\n31c+n7dehhjf0ejo5Sz7+/u26YtaDmA9BtnzSz5bg6El9h4MBrY5L5vN6ktf+pIkhdbZZ6Q88cZn\nvrqVe/VGnvsAtv/ar/2a/u2//bdaWFhQrVbTm2++aQZzampKpVLJZIfvzM7O6otf/KJd16dNJYW8\nNwVpviSb/6MLdH+CtC0Wi7p8+bKWlpZCbx+TZAQkpCWf+1DZk8FPOo7NOERTQHhQrB4hgbd23vJD\n5HEcUBbLj8fGMvvWY1FjwkIHQfBITwkfpnjmm+MlmWCzbZZY3RdV8bwsNkU2h4eHVkLNllwyFtxL\nvV5Xp9NRtVrVe++9Zxul8Ij7+/tKJpOqVCra399XNpvVJz/5SaXTaRWLRSOoGo2GDg8PtbOzo36/\nr6mpKSUSCW1vbyuTyWh+fl7VatXSYaTUuFcUF0QDv0HIU6/XFYvFVCgUQuuHUuKlo30imVsgPXMI\nacucxWIxfeITn9AnPvEJKwv2qWY8LGvrU8ygHhAFpcp43+FwqNOnT+s//sf/qC9/+cvK5XJ69913\ndevWLeMbyCj5Biz+5UCf+cxnQnLK82KAIJ49EsZok2b1mRdkNpvN6ty5czp//rzxDNx/Op22d45w\nTS/nHlE97Tg244C3xOtihVFuaZxuQiH9xP1lyAHlZtEkWVrM91H0qMTHaIQ53gAgXH5TjzTeJhsE\ngS5dumTpsH6/HxKEWGz8Dk7CDIwFvRZbrZalCFGMSqWiTCajWq2mRCKhlZUVi/V3dnYMAezs7KjZ\nbCqTyeiv/bW/Zh2cgNbsf0gmk2o0GioUCtra2tLe3p5KpZK9CDeVSqlardra+K5SVESCKDjf+vq6\nJicnVSgUDCkMh0Or8cDLgxLJKrHmGAqcQyIx3qUrybgLiNCvfOUrisfHHacwUMgLhsHXe+BQer2j\nZsZkZ+AqXnrpJf2X//Jf9PGPf1yj0Uivvfaa1tbWdOXKFXtdICXVGBP2PIAsZ2ZmzKv7Ogoftng+\nSZJ1QvONciAjQaZzc3NW6OTRF2Eqzx7NTHjUEE3xPsk4tmwFQoTF88UaWHafivIPiPJ6ZZXC+Vyf\nXuO8ZAAIAVBaDAOpMc9444k8emBjkV+MRCKhs2fPGpnKYsNKgyKIQ3u9nra2tjQ7O2scA0JInI2R\no7CoVCppNBqFGofWajUTzrm5OV28eNEKyfi7bwCbz+dVLpe1ublpwoago6jT09NW20CbezgGDARd\nsweDgU6fPm3z5itcY7FYqN+DJEvtRfeMYMRAA6wjSg2/k0gkdPLkSf3tv/239Z3vfMcMtTfYPoMF\nadzv963bkzTuRpXNZvULv/AL+qf/9J9qdnZWW1tbeuutt7S0tKSrV68qm82aY0DO4DvYm4Lysx8C\n+ZNkmSRJlp3wYSrcF0YFpzEajSx7df78eS0tLdmuXIwpdTM4uMehA4xzlHh9knGsb7xi4CVIf0Ga\noaDeuzDRCINPT+E5yOHzshhIQM9X+HCBc/m6CSaSc3rmHI/pFWAwGOjMmTOqVCq2qH4fB16MzVDt\ndltzc3OhunmEDKWpVCrW6QePQY6cUISqx5WVFSPrEIa9vT2b11gsppMnT2p7e1vZbFYzMzO2Dghs\nKpWymgc8fzSEY5MW3p09FZ5QZR8EBq/VatmeC+YAEg1OAahOURaOATQJymI9PvvZz9ouSo8saW6C\ncfFZAzw9xVaXL1/WP/kn/0Sf+cxnFI/H9cEHH2h1dVUXLlzQ7OysyQ2yQqEWXbh8Pw6uQ4EW8wVS\nYHcqCuyb8Pi3jJGhoIaCxi2kfyHP0+m0XZ8QGif7OPJVGrfdf9JxbMbBv7ILwfQWn9iJuJVy4eiD\nQkZJ4ziTEl9pzA2Q8/bCjuJ6QhRv4KvgvCfzHap88QxNaqWjvPPm5maoloJQhrTf4uKiLSjzAe+A\n95ybmwu9gHY0GunkyZPWm4EKvhdeeEH5fN7eZdFsNrW+vq5S6agHz8mTJ1Wv13X37l3Nzs5aRghv\nQwg1OTlpZKJPGVJv0W63Va1WNTU1ZdmXer1u1/bt7/GI8Xjcvt/v981gUm25tbWllZUVuybEsfd2\n/POhXDab1d//+39fb7zxhm7evGnr4bkNFMSz9TQZ+qVf+iV96Utf0okTJ7S7u6uNjQ0dHh7q6tWr\noQY2XsYIATk38iaNS9R9BoTwMRaLheSXuQTdggAIRff399XvH/XwuHDhgk6cOBEqRpMUQg1ePrkG\nBDsZIkKWpxnHZhz87jEvBH5yfZqr1xs3WfU9GqTxXge/X4HhBQXL7EMEvkf8BulIKMB3EV5QgydL\nR6ORxexBENhbpKgWhGgajY42S9E6nJqFRqOhweCo52OlUrGUVKfTsRZisVjMmqOk02mtr6+r2+3q\n8uXLWlxcNKEjNRkER6XSZ8+etcY6HMdLcpl3jOHe3p4ZbUIEFJ59ImzwoaUcZdr5fF6NRsO4Cs+M\nE1p5J7C5ualaraaPfexjj+wDIUzwKIO5pt8FGa1PfvKTGg6HWl9ff2w4QY0KBN6JEyf0K7/yK7p6\n9aoGg4Hee+89W98LFy6oUCiYovtUN7KJAYryGdK4tQB/Yx6RL49MUV46j4N6yNRMTBy9eZ2iNbI3\nhELIn894ILvRDA1k7dOOYzMOXiBQVrIG0W3XPCCLKIVb1Hs21sewLIA3PtI4tekHrDULz+++Yg1P\nyLEsdqfT0aVLlzQajezlPLR4J2sRBEe7K+FLGo2GisWiHjx4YBtkarWaLTwIiIXd2dmx8GR7e1vx\neFwvvPCCoQuUenNzU8lk0voOvv3225qZmdHCwoI9F3zPxMSEstmsdnZ2VCqV7OU/vCMDw0CTFsrC\nyUyUSiWbRxQffgNURS9KhL/b7eqdd97RxMSELly4oFarZbsOH7cF2vM/KDvKi7xcv35dW1tbZuyj\nG+L6/aMt0n/9r/91Xbt2zbp3w4dkMhktLCyEKkB9Gb8vnMLAe8eA/MEF+SyNLwrDaOLsGD7lStoW\nEtJ3dCJso7qVc/pr+CwF9+f15al09KmO/iEOHiCaHpTGWQf+7iGlr1nwxsNzCFhrjz7wKpwTOA08\nJv4jLUeJNsdjsHyLLt/XEE9KmnB2dla7u7um6MBR4tZ4/KiTEkorjXtOeEQDicZ7KyEOl5eXNTMz\nYyEBG6LwqLwTpFAomPLxXBhOCqUwEAsLC7blmvqLer2uVCplBVHULsAV9Ho9u7dGo2FcEXPnazVa\nrcZR2SsAACAASURBVJY2NzctxQqiwJhgRMkyUTjkobJPbSYSCdtefebMGa2trZmc+LDp4sWLeu65\n56x/w/37963mY3Z21gypr9BEPn2hHusvyQy3D2Vu374tSaFnRmajiolMeYQkHYUL2WxWV69e1YkT\nJ4zToFTd7xuRxsrvSX0/PHr4kSEkmXCUUxpzDexQ82XGfAdlwwN7I+IrxphIIB25Zt+rAFSAYUGY\n/Q5ID48xDCh5Mpm0Rifnzp3TcDjU7u6uyuWyvfAFISL9BffBHoNaraZSqaTd3V0rYeb5KTuGR+C7\nZ8+eNYFhwYHa09PT2tzcVL1e18rKiu2m3Nra0tzcnBF2IBF2c05PT1sYIx29gKXZbGplZcUUdzQa\naXd3VwsLC6GXy1QqFcvX03cBkq3RaFhzkvX1dS0vL4eYet6RQXNa7o/yaN7xwHckGeHsd5GWy2Wt\nrq6aguBlr169qrm5OfV6PT148MAMe6/X0+nTpw0ZIAuejEbGOB/H+lDUK+Tbb79tYRqvFvD3nUwm\njbyGR8Ox+VqH06dP68KFCyGHghOEL0PuPTphbgi7eQbu/0eGkERRgEMQK1hGvL2HQxgHbwk9svAp\nzygh5UMTb0WZSH8/eCoMDeeWxhAQI0a6sVAo2ItmVldXQ+XXeEdfa0GtQj6ft67RZABg4X0FJZD3\nueeeMwPmS7hTqaN3G9y9e9eQBSjIZxpQAub58PBQ8/PzqtVqVinZ7R61MV9cXDQPx/d4B6Pf6JRO\np61RLB2gIBwxlolEQgsLC2YweCY/39I4tOx2u9awBN7B77KN1rzkcjnbiDY5OamLFy9qenpauVxO\nu7u7Zgjr9bqKxaK955MwIppe9OGsh+ae6/KyJkk3btywQinCA08KemPOC4O8Q8rlckqn01paWrL2\n9TgV7h8uwzst7pnMHI7S820YtKcZx1o+7fkBaRxfekLHQ3omAGMB1Iymazg3SsACMKGkTokt+Z5f\nfI9IvGHgOxiPbrer2dlZOx+Wm629sNSUIPsqTl8RSaVko9Gw91SCnijaOXXqVKh+A3IRjuG9996z\n3gw+hw6RyyvsUCyKrbje2tqaarWa9vf3de7cOcViMWuJz7MAvXke6Ujop6amJI2LzXq9nqVrS6WS\nhSNUFWazWWWzWRUKhRAipMgIyM31uA7P79+sRdNaDMK1a9d05swZjUYjg/pkr06cOGHZEwwD/JHP\nkrD2PmT12QLmH1nr9/tGEnunRtjg+2x4xAtawICTuvTtBKTwRrFo+MB6eu7BO0T+hhw/6Tg248Dw\nBJ8UVlJPzI1Go1C85cum+cki+myFRxj8Dajqd0VKYfLGZykokcWK+23A8Xhc58+fNw6CUtZoDI3y\n+J4Jo9HI9j2QMYADQJi2t7eN8KQgB9hNz4Ner6cbN26oUCjYvgg81/T0tLa3ty1Fi0Fio8709LRa\nrZYqlYr6/b4KhYLm5+dtUxjpSj+/EJuQj3jgqakpdbvdUJEW6Il5jsfjmpqaChGMsVjMGsNAdoK8\nEHRQDgVI7XZb+/v7Fu5AbF6+fFnZbFYbGxvq9XrWdXlqaso2SbGWGB8MgE8H+hRlLBazoidfj8J3\nguCopmRnZ8dqRkA4hEfD4binhy/j5qXGzItvK881yKT5DIg3Yp6r8kWDPmRGt55mHJtxwAr3+33t\n7++blSO+8pVmVLUR50XhP5OAUWGxsZ5YU/4uhXsARv+GBeZ+PGmG9/TpLfL+8fhRgxOEMp/PK5PJ\n6MyZM/Yd3miNoqBYIAcExbPyvDqdBecNVaTBtre3LSzxYUu1WlWtVtNwOLQ5TqVSKpfLajab2tnZ\nCe3LmJqasvsIgsA6SVHIhHIgsJRa4/3W1tbsVX3MryQLQehmDWqKxvjVatWyC61Wy6pNR6Ojjlfx\n+LgTNXtN/Lwnk0ltbGzo7t27tsOTvhcUXCFPXrG5vpcNX6vAm+C5Vx+eQhq/9dZboQwX8sqzU79A\nVoYwgeKocrmspaUlnThxwrgGzgOJzDVBOjg75pm/I784TSmcHXzScWycAw+fTCYfaerCw+EZpfGC\nkZbCQDCBWHRpXNvgYRThAQoUDUX4HschONESbjwqRkca9zU4ODiwrdqEC754RpJxCTQyaTab2t/f\n18mTJ0OZGKrpLl26FIKNhAqUPt+/f18nTpyw0urRaGSVc/QCKBaL5nloB5dIHHWJunXrlqanp62t\nHe+vgBzlmpRQ4wG5J3ZX8hZu5o0QgpQgIQAokWpJnzrEs7J5zO+6TCaPXuQDImo0GlpaWtLCwoIk\nWdetTCZjLfaoF/FogUIzP3AivhYBmev1etrY2LDOUz7M9HL0B3/wB0Yy4pgI56iWxcDxHd7Jwflm\nZ2ct20P2wr8Bnnvi2iAQ1t2jCc/hRMPxJx3Hhhyi/QrwijwUsRu5W28IOM6XhwLh8SRSuDN01Nr7\nbATnAEX4ykYfs0WJTz6bnp6WJCtrpZswLyXJZDLK5XJWjuwRC+3HguBov8f29ra2to5eCXLhwgXz\nfmQ4er2eZmZmVK1WTbHpykT8Kx0ZIa7jid+trS2lUkcNYxuNhnK5nObm5kKVdhMTE1ZshYJ0u11T\nOraPI7DwEihZv9/X5uamlWFLCnEsPs9Pcx0gfiKRsBLudDpt6dZ+v28vnEkkEvqxH/sxLS0tqV6v\na21tzYwCr/7zCAx4DwGInLBm3mlI4V6UW1tbNpf1et0MDVuqQX1/+qd/GiKvoxkxX9buQxic4/Ly\nsm2WYx1AGb6zkzTeHfw4zs4T+Mi756ieZhzrrkxPGEbDAaw00A4BRwg9m+zJI87NpCCE8BUQQpzL\n1xh4I+VjOS9I/POpIWJfXipDd6BWq6VTp06Zl6DfYDwe18bGhiEMeBW/l+HUqVNGXPly24WFBa2u\nrloYgIcHacGR9Ho9265dLBZtB2en09H777+varWq+fl5nTx50rITviFsOp3W9va2tevr9Xra2dkx\n708KMwgCe+374eGhtra2tLm5afwIGRNCMghQwgwMMBWwrANsfqPRsFfsSUcG88qVKzo8PNTDhw+N\njMUAw7lg0NinABRHrlCWqPzBs/R6Ry/ZrVQqln0hQ/X666/bNvrhcKg/+qM/MudEbwXmiNCPykfk\nC/S0uLioM2fOaGlpKRRy+uyMD2W8bILeWHcQFqhDCnN4PsPxJONYCUlu2it7lFhkAoCq0rhSDcXx\nde0MbzBAGQihjyk5Dl6D4bMWWF6IUP7GvdFIlY1e7XZbGxsbBsWJe2nOUqvVNDMzo93dXY1GIwsz\neJYLFy4oFhu/wAYBL5fLWl9ft12a5XLZvApNWdg7UC6XrTR7e3vbUqQYEVKOzC3zjYEAoktHnEYQ\nBBZCMP+FQkHNZtO8P12yqZXwxhmvC7Jglydz2e12tbe3F+IFHjx4YHxDuVzWlStXlEqldO/ePSNu\nE4mE9biE/IQ8ZN4IXZAz74iQQ+9oCJ1WV1dtTen+9Cd/8idm7EAi//k//+fQ7mJCCfgfekBiuGKx\nmKWe8/m8zp8/b2Xp/f64mzqogTXyPBQyjnx61OaRhOcjfmQISSYi2qKcBfOwC2H1vRE8ZGN4hZbG\n7eWYPI7hn6+4IyPiU5We8PGoRArv3KSq0L+J+cSJE6HwAwNycHBgL6/FU9ZqNSPgPvaxj5kSsusS\nodrY2FC1WlU+n7eX2CIMvHfi8PBQpVLJUomJRML6DJBJOH36tILgqH8ESo1n97l/1kmSdnZ2lMlk\nrJYAz0XPiYmJCQtxUEwEkn4FICNviDx5J8l2aB4cHFhnqwsXLmh5eVkPHz5UvV633aCZTMb4FNDB\nYDAwyA+J56G+VxKPTqMI4uHDh6pUKtanIp/P69vf/rYRunAKlUpF29vbNicQv7lcLiSj1Ln4AqhE\nIqHZ2Vl7RYB3RpDCnivAQRFu+1oPHxZyzWh9z48McuDlsCCCKITyhS9YfX7H+zNZHiX4Ckr+5olG\nPpPGk0pqjmN9bAiEY4EwHIPBwJSTlB0GJ5VKaXV1NUR4kvZDCMj9e2LpwoULBl0lWQ/GVCqlBw8e\n2L2Vy2XbFwGBx8Lncjndu3fPFNK/8o202d7engnf5uamgiAwJp2X/MJJSEfGb2pqylJ1qVRKd+/e\nterOcrlsyAtDw/zSSi4ej9vbrKkcBGWhFMPhUDs7O4aoTp8+rYsXL6per+v27dvWTKZQKNhLZDEK\nnlegjoX5YrOXJ635yfpzP1S03r17V5OTkyqVSsrn8/qzP/szlUolLS8vWwYik8no93//9022IDUl\nhbIbhCo4nmQyaVuul5eXLXUJavBZBm88OR9Oj8Fz+YyERwnwLk+bsTi2bAXNQT1BiCeHrASieYX2\nrK1HGSg2E+XTVBgVBMR7FWkMKz1rzeA+IEh9uS3CJI0ZbxpyAPdzuZy2trbsXaC+LRwelvLrbDZr\nHpdahHg8bnsG2u22VlZWTJnL5bJBZ7IDW1tbmp+fN9Z8f39fW1tbVr8AAsPDzc3NWQnz3t6eeV+2\ndSO08CVkBaanp60tHi/VhXxllyben+fh2mQmMMp4wPX1dateXFxc1GAw0P379w1B8N4Mj6x8NS2y\ng9NATvjpY3UvK1GS+d133zUCNggCfe9739P8/LzK5bLVnnziE59QIpHQ7/3e71mYgHxiBLhPzk95\nOMZqYWHBHAv3BH+E7PrKRo+MPcGIvHvi0Wcsov9/0nFsxsHXLKDAoANpnM3wjTCASiieRwYc4xUf\nVEJKjPNHayI4dzQLIYXLvGGQ+S5FQyxoJpOxFmvZbFaxWEyVSkUrKyva29szwaYBLF7u/Pnz6vf7\n2t3dVS6Xs9AjnU7r1q1boZe7gHCI96UjDmZra0uDwcBayXe7XRUKBa2vr2tubs62IntDy27O6elp\na0fXbDYtBAClxONHjWfi8bg1oIUkxWiyIYwGL9VqVcViUZVKJVR8NRwe9USkvgJvicGh4xHvrEwk\njtrwzc8fvcidlneEpJKsmAiy2SNLjyaRAb+2fIfjKpWK1tbWrEblvffe0+zsrCYmJnTz5k0FQaDL\nly9rampK7777riE6QlPkGAeQyWQsRQuiTaVSyufz9jZzTxgis97JSQoptkcFnkD3x/hzIrNPO461\nNb2vdmTRWEjy8ngXn17yYQPGQBpXNkpj4wJsBa5DJvl3FnJOLD4IgUnlXqXx6/e4NnAPprlcLqvX\n62l+fl7dbldzc3P2lut8Pq9cLme1Bu12W2fOnLE02mAwsI1cyWTSUpV4GVK/ZAYgBzc3Nw360yUq\nmUzqzp079ix8BsQkjNjZ2dH6+rpdWzqCxBQKvfHGG/r617+u3d1dK/aCh/EcEZ4aApACKQhY0GEs\ndtQ5K5FIaHNz08jIhYUFXbt2TYPBQBsbGxqNjnpGsD+CEATWH9TGFma/hr5eIdqpiXsFRSA30pGx\nePfddy2F+8EHHyidTuutt97S7/zO79jLgU6dOqVMJqPf/d3fNYcVreaVZLKLo5DGpc7z8/OamZkx\nEhU55r0TPqyIKj3ENefj+aI/QVCEwX6b+JOMY+3ngJB5i8/fWFQaoviMg2eeUVxf38Bkew8ihbe3\njkYjg30sDEbITzL5am+0IPsIU9LptHVE4vX27777rs6fP69bt27p3LlzJmievZ6fnzdFRzA6naPX\n4t27d88EjVZx3CvPnkqldOvWLS0uLtqz8wxra2s6ceKEhV+0anvvvff08OFDY8d5Q9PU1JTm5uas\na9T169dVr9d19epVJRIJ3b9/X2fPnjVDzoYoKi0h7TwPJMnO//LLL+tv/a2/ZW3a/NvCnn/+eWUy\nGSMcaf8+Go3sbVw8m99Zi+z4a/qMgw81/YgaCpwALfulI1S1urqqd955x2oRTp48qbNnz+rEiRM6\nPDzU7//+71tFL0aXmgbkjBS1D4/n5ua0vLxslZAoLkbQF975cNsjIc5PiXm0tscT98j0j0xYwSJ6\nxfWL6GM1f3y02oufUQ+B5/fkGAKDF8JDeE+CZYZ0isZ4rVbLvBbemxqHhw8famFhQe+9954WFxf1\n/vvvq1gsanV1VUtLS9re3rZdgj7+xwtDCFKGTMgC3KfqkZfU1ut1LS4uqts9ek8mL6uhHRzpsMFg\noN3dXd24cUPJ5FEL9UajYYZXOiISX331Vc3MzJhRPnnypIbDob3N6eWXX7b6iVQqpdnZWa2srBgK\nq9Vqun37tj72sY9pc3NTFy5c0O3bt5XP57WxsaGvfvWr+tVf/VXl83k9fPhQ586d06lTp9Rut3Xv\n3j3F43EtLy9bSEXogJf0Rsf/36NJ1tqT1D5U5DPP/MOJ3Llzx3iOV1991UhH2vZfunRJzz//vGKx\nmP7wD/9Q1WrV+CmMNbwBihtNsWcyGU1PT6tcLksa93pgkx3oTnqUY+CZo6GuHz40pys3c/K03aCO\nzTiwWN7j+diQSWYRmRzvKSj6YAEQiCgRA/TDUhPjesvLuaMWlvOBUNg6TVESMXelUtHs7Ky2t7e1\nuLio1dVVnThxQrdv39bc3Jzef/99nThxwqoS8VC8NRlDsL6+rna7rWKxqHQ6rUKhYGleWs/zendS\natQdEKqw03E4HFpmYG9vT6dOnVKxWLQt4Z1Oxxre9vtHHaIfPHhgjW9pyQZJe+bMGSsGarVa+ta3\nvqU//uM/1tLSkr3VajAY6L//9/+uSqWib3zjG4aIZmdn9frrr+tP/uRP9JnPfEZXrlyxeeH5aXoL\nmoKs9srMOvv9BTgRr0QYc09Ie6/K77FYTBsbG7p//75WV1d1584dPXjwwHas8gq/S5cumbc/ODjQ\nv/t3/87mxe9pwXCjjLFYzEJBjP/y8rKFGlROPo505zv8JAz2nBrzEdUt/u55pqdNZR6bcfCxEJML\nKvATTjbA56S98iIkUU8B8pDGJartdttCFEifaHMMzuktrk9TspggB0KjUqmkDz74QDMzM9Y0dXV1\nVYVCQXfv3lU6ndbGxobOnz+v73znO6pWq1Y6De+yu7tr3Ztg6KlIDIKj6rutrS0Nh0OrwUcAIQTJ\nnQPnU6mU3nnnHRUKBS0vL1tT13v37qlYLCoej2t3d1eSrKCpXC5bZWC32w21tOPlsrdu3dLU1JRO\nnDihra0tvfnmm2bUO52OstlsqH39zZs3FY8fdXi+dOmSYrGYXn/9dWWzWc3Pz6tUKimXyxmKglyW\nwmXwcBdRFOCP83G6f1FM1MkcHBzozTfftHb0PL9/fX2pVNLKyopeeOEFKxz79V//dSso85yZFG4Z\nQMiJ8Y3H4zp9+rShRow+4ZN3Tj6d7kNpb9wYPCvXxhn61D3z9TTjWJu9ELcSQ/uSZIwH26C9EHhv\nzgR6uCWF36eJQvuCKmn8nkImnY1AQESfg5fGTUAxCJOTk9aYFdJwa2tLp06d0rvvvmtFSel0Wg8f\nPtSFCxf09ttvWxu20Whkire/v6/t7W3Nz8/bFmO/XTmZTFpbeZ9e9Qvebrc1Pz+vg4MDU4q3335b\nxWJRU1NTqtfrajQaunfvnhVCvf/++8Yr0M06n8/rzp07SiQSRqzeuHFDh4eH2tvb0zvvvKPl5WVd\nuHBBzWZTq6urlkFZXV21fRCDwVEPyUqlopmZGV26dElf+cpXtLm5aZ+dO3fO4C58ijcCrFe02M0j\nBeQCL4oieWPA78PhUFtbW3r77bd148YNI0VBShzPW7s+9rGPaWVlxTpdvfLKK3r55ZdDcuozaCBK\n7ptakV6vpxMnTlg/CVDAaDSyugyfUYmm2T25iIwzTwz/vB4pgLB/ZDgHoKH3wFGFl8aTH81YRPPX\nXhhQaAyGFI7FOCfxHeQW3gBPEM03E75gVBDCGzdu6NOf/rRxCTs7O1peXtaNGzckSXt7e0YSUrzD\nYIErlYoWFhY0HA6tZRtCEo/HrbSYOcIrcc+j0cgITtAOL8VFcJvNpqEX4PT09LTm5uZ09+5dPXjw\nwO53OBxa+/yNjQ3VajWbfyoWv/3tb2tmZkZTU1OWZcnn89rb2zODvLW1pUwmo1/91V/VhQsX9PDh\nQ+3t7Wl5eVnPPfecJJlRlhTykFJ4p6wPMXwK2ocZHulhdPDIb7/9tl555RXduXMnlKWCXyIEIs24\nuLioc+fOWX3F1772Nf32b/+2yRNpStLSyWTSUu8+A0U3rqWlJWsmjFLzHb+5CnIc+fQyDDoEYfmt\nBD60hsNizggPn2Ycm3Fg+P54eGR2/3l2Nmo0/AR7TsCz0x6ieniGEAHpEBSMiYetUriwBYGC7InF\nYtrZ2bF3Ruzu7qpYLKrZbFroEosdFTC99NJLWl9f17lz5+ydl3t7e3r48KFmZ2fNMPB8IA96GEAe\nIgCETrydirlkI1W/39fc3JwdQ8u2XC5n6dXV1VV9//vft2pMQi7QGlxFqVRSu93WqVOn9M4776jV\nauny5ctG3JXLZU1NTen27dtmiA8PDzU3N6df+ZVf0bVr14zQ/PSnP23kKfcrKUTGIRu+oa8nFr0i\neOTnqwWRq/39fX3ta1/Te++9Z4rDqwdwEiAHai1WVlY0OztrZdD/7J/9M62trZmssB2ce+ce2ffi\n1yeZTKpUKmlxcdHQCaiQNKznQxi+TsGHyThCz7UhM8i5FA65fWHfk45jMw7sdUepfSGHTyMCJ4lB\nM5nMIxCTYhrvbRAWX9zE38hGMLlsMOKaGAaEh/4FLLgkC4na7bZef/11/aN/9I90+/ZtnThxQpVK\nRdPT00YuTkxM6OMf/7gqlYoVABUKBbVaLasuTKWO3nYtyXo9DIdHDViDILB4nFfUMUd0UCamhSMA\nveCVB4OBZTzeeOMN6/yEx6KzNHN1/vx57e/v6+Mf/7heffVVeydkv99XLpfT9PS0vvnNb2pxcVG1\nWs16RHgy7pOf/KS+8pWv6MSJE/rGN/4f6t48uM37PBd9PgAEuADEThDgLpKSKJGSaO2SnbiSl+zb\nTJxM2ua4Of+503PnTjuTNpnO9M7UaXrSnGk76UzTpE6aNsvJUrvO6jW15ViSba0kJZEiCe4EV6xc\nsXz3D+h58QJ2Gunc3uH0m9FIIgjg+36/d3ne511+P8fW1hYeffRReDweKT+nApBfAUrhG8NKcgDs\n+dDwn2k+vb9EpJlMBiMjIxgYGEAsFisjLulQHA4HPB6PpHHZO8FispqaGjz55JNS/6Ara5mqJGri\nYCAqPY2bw+FAe3s7GhoaBDUahiGv8floMCpTl9pZadnX5L0Os/h/HWrrkP1ur99oHAzDaAHwLQAN\nAEwA/2Ca5t8ahuED8L8BtAGYAPCYaZqJO+/5EwCfAZAH8D9M03z+nT6bMbuuXKRX5iYQYnFB9cby\nDwlEbSDo6SnsJPY4iJT5cn6WHkGuy61prLj5GsXwvSsrK1hYWEBTU5M0IXHGAQfILi8vl1VTFgql\nMyY4SJZ1DUQavGeOvSfKcTgcMuGJ4+lZtJTNZpFOp9HS0iKxbSaTwdzcHObm5oRLaGxslF4Elk0z\nQ9Db2wufzwefz4fLly9jeHhYhpI0NTVhfn4ei4uLOHjwoFQJejwezM7OinB2dXXh8ccfh9VqxWuv\nvYa6ujqcOXNGGtDYD6Jjbh02ra+vl/FAurBJp7MriTcayPPnz+PcuXPw+XwIhUJoamqSYi8qb1NT\nE1wuF8LhMGw2m2QQqMB2ux1f/vKX8fzzz5dNYwIg6Vt6eoZzLLtmmETE1dzcDL/fX4Z+9XF6vC8+\nH2VQc3FcD75fc2f8mc7U8dIo416uu0EOWQD/t2maVw3DcAK4ZBjGCwB+D8ALpmn+T8MwPgvgjwH8\nsWEY+wB8AsA+AE0AXjQMY7dpmmV3RisLlIqe6Kl1772u9NKhA//ocEO/DpROIdaW1DRNgfxcOI0k\nGB8yfNBxmo4juXH0UIODgzhy5IikOgGgsbFReIjBwUE0NDRgYWEBgUAAsVhMvD7TZbohid2SPp9P\njAn/JpSlAPFULcaghJD5fB7RaBSxWKzsuLe6ujpcu3ZNDAqVqlAooLGxEd3d3XL03cDAgIQ1p06d\nkl6O9vZ2TE1NYXJyEoVCAb29vYLizpw5g8ceewyZTAbRaBQtLS3o6+uTcI9FYITnAMo4Ay0jLHiq\nZO81H8Tn3d7eRjQaxTPPPINkMonm5mbZI6fTie7ubuTzxb4RzpTUhp6KTUL4L//yL/H888+XcVeU\nS6DEh1A+6dRo5Dm3IhKJyBBicmdER7qYS/NgfFYdMuiqSN16oJGF1olKZ/qfjhxM04wBiN35d8Yw\njJsoKv2HALz7zq/9E4B/R9FAfBjAd03TzAKYMAxjFMAxABcqP5uemjl/HTvyITkqTgsxvQQRAeNS\nbpKGjppjkIe+U06sF1KnsBiz6k2h0SEcJJHFuPmFF17A2bNnpTTZ4/FgdXUVXV1duHHjBo4dO4aR\nkRG0trZibGxMJjZns1k0NDSId+SouY2NDTnwRqMcFmElEgm0tLRIdSUbfZi2ZWfj1atXBUHdf//9\nuH79OpaWlmRtfD6fTIfK5XLYtWsXUqkUkskkLl++LGHVoUOH0NjYiHPnziEcDiMajWJoaAg1NTXY\n2NhAT08Pnn76aXz0ox/FRz/6UQwMDGBlZQWdnZ3o7e2VeRO6AUsXCREtcX35PDTERAjcEx1eWCwW\nxGIxfPnLX8bq6ir2798vSjc3NyekKasugVI2i/9nPwxDjH/8x3+UpiqiOnYPA6UqXN4760TIm7E7\n1W63o7u7WypSAUh4XDmWrtL58NJchE6x6/SpRjB6tkQlH3Ev1z1xDoZhtAPoB3ARQMg0zYU7Ly0A\nCN35dwTlhmAGRWNSdumyWA4D1QQKgDKPr1lbneclWtCkjV5Yzd7qijh96ZF0lVwHUCq80ghDI4dC\noSDQPJfLwefzSVHU1NQUgsEgNjc35QQjnorldDqFqSYvUCgUT5kKBAJljPTa2po0YGWzWXR1dcnc\nA46UP3DggMDY6elpTE5OIhgMYn19HZFIBJcuXSqDquFwGLFYTFq7Gxoa0NjYiJdfflkQUKFQOVaO\nHQAAIABJREFUwIkTJxAIBBCNRtHX14dUKoWBgQHs3r0byWQS8XgcXq8X73//+3Hy5EkMDg5iaWkJ\nBw4cQGNjo6A0IpC1tTWZjcj909OLaCS08daj2nXh3Pb2Nv7lX/4FFy5cQCQSQT6fR09Pj5CHnFJF\n2aDXpexQJpxOJ1wuF+rr6/HUU0/hG9/4hjgZoMRt0ONbLKUBseQHtre34Xa7xaBaLBbppSFHxZCB\nMzp0SM1nI0lb2dujDQIdmM7g8Gf6/5Whyr1cd40z7oQUPwLwf5mmmdavmcUV/I+SqG97TcNIbh4Z\nZiozFYZKo3O8jK91/lrHVtx4oDSYlXULutIOKMWPuuCIXAf/UFHJ/DIu5YyEQqGAf/3Xf0UwGEQ2\nm0UwGMTKyoqMN/N6vXC73RgcHJQDZ+12OzKZjJCO6XQaU1NTUolHboQkH++PxUskMcPhMBoaGjA1\nNYVz585hfn4eCwsL4tUbGxulqIkeZffu3TLsxWot9hdwNiXnJrJj1DAMXL16Fe3t7UilUjh//jya\nm5tx9uxZzM3NoampCQ0NDTh58iQuXLiA+fl59PX1oaenB263W9aQ607EQMIRKK9d0aPjNctOA8Lf\nnZ+fx5//+Z/jmWeekSnfLpcLExMTaGlpQT6fl9QyyV32xDD84uxJksHf+ta38M1vflO+hzwE5Uan\nEXV8T7lg49kdnUFXV5esAWWRCE/LIOVeIxulXxLOaE5NZ+50KTl/pn9fG7q7ve7KlBiGUYWiYfhn\n0zSfufPjBcMwGk3TjBmGEQaweOfnswBa1Nub7/ys7Lp69arceCAQQCgUkoclDK7smOQCUekrEQat\nJX9fx6n0wEQAXFCm3AhhCW/5mt4AWnB+PqdOE0q++uqrePTRR+H3+7GysgKXyyVMMYlEDntpbW3F\nxsaGZC1M05RTpxg+kQSkYBqGUXamBQuXRkdHcfPmTUmDUgFra2tlSIuexXjgwAH4fD4MDAzIOrS2\ntmJ2dlYGujB1arfbha9YWFjArVu3kM/n8Qd/8Ad46aWXYLPZ8NGPfhSpVArDw8Ow2Ww4evSoNINp\nfogKwH0nYqOhYDcnB+by4hrwuTOZDJaWlvCzn/0MGxsbuO+++zA/P4+VlRUcPHhQ0FpDQ4PwIIlE\nQry5Li+224tHCMbjcXz2s5/F3Nyc7JfT6RSkoSsh3W63nPDF+9KemXLodrvh9XqFMCZZ6vf7Rd4o\nH/wOjQyIBCrRgu6urNSBSv7h1q1buH379t2o+duuu8lWGAD+EcAN0zT/Wr30LID/BuAv7/z9jPr5\ndwzD+F8ohhPdAN6o/NzDhw+XMf70ivQwlYUwfGgiCR2fachJi8rPplCyq1GnLym8nAVAwdUGRce6\nlXEg74XEWiqVwre//W18+tOfhs/nw/LyMsLhMOLxuBTXZLNZ7N69u4xcAoqNT6y913MDOUqddRWc\nHM25ji6XS87A5AAZtnAHAgHcvn0bq6urYvQaGxuxZ88e4T347MwcLS4uSrPR9vY2RkZGpEHL4/FI\nDL+xsYHXX38d73nPe2C1WjEyMgLDMPC+971PirG4p9ybyjZjGspCoSAj5MgFMDuglYeNWHzm+++/\nH/F4HCsrKzhy5AimpqaEqxgYGMDp06cxNTUFwzCwuLiIcDgsslFTUyMcw8DAAJ588kkkEglRWsoU\nlVY/B5vWMpmMTBWv5LmCwaCEKXz2QqEgJ5lRzvg+ygH/1qhXyx1lv9IIVIbSlM+enh709PSI7P/8\n5z//TSov190gh9MAfgfAdcMwrtz52Z8A+CKA7xuG8d9xJ5UJAKZp3jAM4/sAbgDIAXjCfAc8ox9I\n1zJoph2AnCCkF16/VxM678TKkkvQMSfDAC4goayus+B9UKA1F6JjOpJKLJO+cuUKTp8+LcfVV1dX\niwcbGxtDNBrF2bNnMTAwIPX0GsXw+ynkJE4pJJxVGQ6HYZom5ubmZD5CPl+cycBQg41V+Xxe0pSE\n07OzsxISWSwWOU27rq5OahbYJEYDlUqlYLfbcfjwYUxNTeHxxx+H2+3Gz3/+cxw4cAB79+6F0+ks\nC8+ICKgA3AcdE3M6ljbQfF6NzJgaJrLyeDwyjHdwcBC9vb3CG/HkcL/fL0NpaFhITmazWXznO9/B\n008/Ld2flB+S0yQ9GeZQGblXGmWSK+BMhlAoJEVU5Ft4QBLlV5OElDsaEp2l0XU2fI9Of2oSn68R\nsVS2DdztdTfZitfw67mJh37Ne74A4Av/0edWwnQNsTR3QCGhoNC7aMVmbKrrx7X3192eQHme/J2g\nHH9GK03F0zUQAMrqEoCSQfvxj3+Mz33uc8hkMmKceETbfffdJzEwZztyhgOfyzRNGbSaTqfhdDol\n/KAwFgoFGW6aSqXg8XjEM/G8jFgsJlkYkqkejwdLS0tlXAMrNgGI0jU3N6OzsxPmndQvT+PetWuX\nHNi7Z88eZDIZdHR04MyZM5Km48SnykndWsBpUHVmSMfdmqFn+MaiIRpUl8uFxsZGxGIxhEIhDA8P\n49ixY4jH41heXsbk5CTe+973FgX9DhxnwdvVq1fx/e9/HyMjI7KmNJSal2B4y7XRCqirZDURyPmo\nPIWMxWNEDTos1kS7llE+/zuR7FpOKee6F4XrzD4hzY3cy7Wj8xx0qKAJFJ0poLHg63pheGliU8dq\nGurRs+vPYAhDb63jX34fF5nfR6HXuWQA0t5bKBQwPT0tp1ixv8FqLZ6FmMlkpFCKMJnCw5/xMF2g\nWCtRU1ODtbU1FAoFQQSxWAxjY2Ni/BYWFtDY2Chj3HUDGQ0EU6VLS0sIBoNi/G7cuCHryOe/fv26\nlLFnMhkEAgFsbm7C6/UikUhIs1mhUMCpU6ewsrIiPRaVoR5HxXPfrFZr2YAbcksAJK1dXV0tXpf7\nQa9Ko1ZfX4/m5mYsLCxIBuTf/u3f8Nhjj2F+fh61tbU4f/48Tp06JWT0xsYGLl68iPn5eczOzkqx\nlcPhEI6HxlRnAHgPQNFI6EpQyolOIzJ9SjTB3yOqIN/Cz9PyrmVa17jwdynrGlloxMD36AIrGr17\nuXb0lG1uvM4d05szbUQeQnMSXBxeuu6cSq1nNACl8ENP5OF3M5fN32f2gsiF79eWWaMSCis3iAZl\nfX1dzqowDAPhcFjOhWDhF4/HI9PNwa81NTVoa2uTMnMAmJ+fRzqdlnSaNobZbBYTExNIpVLI5XKi\npOQ5qEy5XHFAand3N1pbW7GwsFCW4uIz0yCx6CmdTiMYDGL//v0yaGZ1dVXOetRko0Z0hPQ0OlQ0\ntuVrw0AUxYN49DwPXduiey14Hkhtba0MoPnGN74Bt9sNwzBw69YtpFKpMt7CNM2yE6gMw5ASaKvV\nKuPnuB6aFC8UCmK4NZfAsIOkc2NjIxwOhxh+ANKkpWVXe3MdVlUaBO1MuY6aY+PnaIOh+Tc2ct3L\ntWPGYX19/W2ZBQASX/LBAAi80/GZJi35Gfr3dFyrDQK/gwuoF5gKrkMRvSFA+QG85AMoJDQSRDv0\nFpzEfOvWLcRiMRGk7e1tNDc3y4lSy8vLQpByxBtQNH6cNZBOpzE8PIyBgQGZr5hKpbCxsYFMJoPt\n7W3Mzc2JMDidTrS2tgKArAUPtGXpNSsRK9PBVG6LxYJAIICTJ09ieXkZVVVV6OrqwsMPP4zm5mb0\n9vbC7XZLR6LuPGXVKVA6g4TKvrW1hdXVVUEXVqtVlEp7aq00vDQ/wO/mPtntdly6dAkejwfBYBBj\nY2NlPAZrDvShwVwf/dlUynw+L/yQrrNgyKDliU1o9fX1ZXUMJEDJf1XW0nA9NP+mw93KYibNe2nu\niwaOr3PtKvXnbq4dPfFKKxRQirm0IGjl06GFjsXeqUhKX7qQih6HHAVJPwoaLS/vgZwDUCJ99AAR\nLr6uhSdyKBSKpc61tbVYWlqSqUc630/Yx2nP7P2goK2urgqptLS0hHPnzmFwcBCxWAxerxeLi4vy\nXnpGVo1SKUOhkIQlVVVVSKfTSKfT8Hq9OHjwoKTuNEOeSqWwvr6OXC6H1tZWgeZra2vo7+/HsWPH\nYLVaZXoTjQE5AYZL5B+2t7eFR2EjGxEH0YLP5xMjTrng3tARaHjO72PrtI6vNzc3EY1GYbMVW845\n9p+GfX5+XgwKKyA1b8B9ZvpYyyXRAFGGRqoWiwWRSARer1fk0WazyXxIOhe+/53IbZ3+1eSlRlGa\njGSYwn3UpDqdH9f0Xq4dP0iXKEGfOMWLD0lPzM2vTC/qNJDmEajwhPAairEIS280Y0b9fq3M/Dzd\n4KUbtrT15vkOeugKFZSVfWTxGesGg0FRlmQyWZZFyeWKMx8WFhZgmsUc+uTkpMTSQInkXVpaEqUh\nienxeCQ02NrawuDgIObm5nDw4EE89NBDOHHihLRyU7ENw0BTUxM++clPoqqqCqOjozh9+jROnToF\ni8WCsbExmY5NfoPnYTK+570xncwp3TRgdXV1wslwb2jgNOyn8nOdyQ04HA7pnSBaYwg6NTUFm82G\neDxehqQAYHl5WSZZUwY1WtCdnwzreG8aPZAzofI1NTVJbQMRBUMkpkFZRq5RKQ2ERsLcdwBlMkcD\nwT3XVZL8HcMwyuZvUj/u5dox40CSBIAc8EoB039rBl+TK5og5OfomFcTj0Cp1LZy4SmU3GBdqUmI\nqv+mx8/n80L0aYaY4cYbb7xR1hjFadJMEfLiZ9Jr0tpPT09Lf0QikUAymYTb7RZv2tHRgbm5Oelq\nJQfBz+R5FwxX6urqhJR0uVyoqqrC+Pg4DMPAuXPncOvWLTGO9EJerxePP/44stniobKnT5/G6dOn\nZYoTD9/N5XIybJWnTpPz0GiO2YZ4PC7ne9Bj6nQg95eKQmXlHy0j1dXVchAOT5Gi9zXN4jmbnLvJ\nk7I0ouG+Wizlcyl5kpgmJUms8qIxYeUmS9DZkk4jwj4KLZc8f4OyzzWnQakMaXXqV5O0mpzXP+P6\naD25V+Swo5OgNFQEIHG4hoe60EgTgnoj+eBUXJvNJjMV6dUoYDpVpOEclZTsPkMKnf6kABmGgQMH\nDuDGjRuymXwflWtqakqaqagITKURZpNL6OjoEE9L/mBiYkJaoDlrob6+Hk6nUyCq9sqc92Cz2cqK\noZxOpxxjH4lEpBqUxBrjZiId3ld9fT0+85nPwGq14saNG+jt7UV/fz8WFortNDSCHLZLsiudTovS\n654UIsOBgQHU1dXJ7AgWYFHRgNJUae0tdboPKPW7cFI1Z1BmMhnU1NQIl0G0dPXqVezbt09QqsVi\nKRvXVldXJ2gFgMgFjYceZ8jv1qnzQqFYWNfQ0CAhDJvrWC69sbEh3ceUd35mZScygLIwQyNqro1G\nO5RDzX/QWVS+/6519J7f8Z90aaZdx0+VC6JjLv6f3kUTMBolVPIQ2rDoBdQcATMWNEQ6XiOUZEvu\n8ePHhUCjISEHwnjYMAy89dZbsNvt0pRFz8jBJvl8Hs3NzeKp2LlXW1uL5uZmMS5zc3NYXFyUuQoM\nEahMZOZ1qo+dmUtLSyKQ6+vraGpqQl9fH+x2OxKJBFZXVxEKhSSEsduLp2Q//vjj8Pl8GB0dRXd3\nN3p7e6VPhNkUkq08WIZ1Fgx1eE80ZuPj41J9qbkdKlmlU6jcT12tqONofWAQURTToKurqxKOZTIZ\nSXk2NjaK8jBLxLUjQuGUJv6OJix1vQNliQ1WDIVra2sle0HZ1mlxoJQd4nkmJJWJTCuzdlqmaRQ0\nOUvnxd/R3Ma9GogdMw61tbWilFpRKwuSKhlWenf9f63slYhD8xWsXqMBIswHSqlKhjEaDvM+Wfcf\njUYxPT0t7+f76GkYQ58/fx5bW1tobm4WodvY2JCiInYR8uTofD4Pj8cjjVkejweTk5MS1+rKxUwm\nI54oGAwiHo+XVTPSwNLwsJs0mUyivr4e7e3t8Hq9GB0dRX9/fxmZ9bGPfQwtLS2IRqPw+/04duwY\nEomEDKUhaiDUZocpURsNJj8znU5jdnZW4LUmHdmdSDRDIece6Rhcx9MM7Yge+Mdut0v6lvMqtra2\npE6D98V0JdOhVGBerC7U3pnyqIlrev76+nopmaZc8rBjXaejuROd/tVGkoaAJ3x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BAAAg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\nBLWEcwiCoJZwDkEQ1BLOIQiCWv4Fwntt4qHym3IAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff9df6cb828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lena_img = image\n",
    "\n",
    "plt.imshow(lena_img )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.ndimage.interpolation import rotate\n",
    "rotate??"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Si les interesa esta área deberian ver los paquetes especializados [Scikit-image](http://scikit-image.org/), [Pillow](http://pillow.readthedocs.org) y [OpenCV](http://opencv.org/)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Los límites de la magia de Numba y como maximizarla\n",
    "\n",
    "Cuando no se definen tipos en la signatura, Numba hace una compilación *\"lazy\"* (vaga, que no se produce hasta el momento de se invoncada la función), e intentará operar sobre cualquier tipo de datos que reciba"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from numba import jit\n",
    "\n",
    "@jit\n",
    "def plus(x, y):\n",
    "    # A somewhat trivial example\n",
    "    return x + y\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.4"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plus(3.4, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'ab'"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plus('a', 'b')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cuando especificamos la signatura, la optimización es *\"eager\"*, y obviamente esta especialización aumenta la performance sacrificando versatilidad"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "@jit(int32(int32, int32))\n",
    "def plus32(x, y):\n",
    "    # A somewhat trivial example\n",
    "    return x + y\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "347"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plus32(2, 345)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Si pasamos parámetros \"convertibles\" al tipo esperado, por defecto Numba le consultará a Python si hay una regla de  *casting* (conversión de tipos) que se pueda aplicar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plus32(2.5, 345)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pero cuando el *cast* falla, se produce un error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "No matching definition for argument type(s) str, str",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-97-249352eefaa0>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mplus32\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'a'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'b'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m/home/tin/.virtualenvs/curso/lib/python3.4/site-packages/numba/dispatcher.py\u001b[0m in \u001b[0;36m_explain_matching_error\u001b[1;34m(self, *args, **kws)\u001b[0m\n\u001b[0;32m    198\u001b[0m         msg = (\"No matching definition for argument type(s) %s\"\n\u001b[0;32m    199\u001b[0m                % ', '.join(map(str, args)))\n\u001b[1;32m--> 200\u001b[1;33m         \u001b[1;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    201\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    202\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__repr__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: No matching definition for argument type(s) str, str"
     ]
    }
   ],
   "source": [
    "plus32('a', 'b')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Numba tiene **dos estrategias de compilación**: una optimización directa y de máxima perfomance denominado **modo nopython**, que genera código maquina que \"no requiere consultar a la API C de Python\" logrando inferir los tipos de datos con los que está trabajando. Cuando esta inferencia falla (cuando Numba no logró inferir cuál es el tipo de dato \"dinámico\" de alguna operación), automáticamente conmuta al **modo object** que sí utiliza la API C de Python. En este modo las únicas optimizaciones que se producen son con los *loops*. \n",
    "\n",
    "\n",
    "\n",
    "Pero la técnica de optimización es **factorizar el código \"nopython\"**. Para evitar que implicitamente se pase a modo object, se puede forzar el modo con `nopython=True`\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 387,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "unsupported operand type(s) for +: 'int' and 'tuple'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-387-2aa65a89f04c>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0msuma\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m: unsupported operand type(s) for +: 'int' and 'tuple'"
     ]
    }
   ],
   "source": [
    "suma(1, (1,))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 389,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "@jit(nopython=True)\n",
    "def suma_nopy(x, y):\n",
    "    return x + y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 391,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.0"
      ]
     },
     "execution_count": 391,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "suma_nopy(1, (1,))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Un ejemplo: minimos locales en un grid 2D\n",
    "\n",
    "Por ejemplo, imaginemos que tenemos que detectar valores mínimos locales dentro de una malla. Los valores mínimos deberán ser simplemente valores más bajos que los que haya en los 8 nodos de su entorno inmediato\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = np.random.randn(1000, 1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def busca_min_np(malla):\n",
    "    minimos = np.zeros_like(malla, dtype=bool)\n",
    "    for i in range(1, malla.shape[1]-1):\n",
    "        for j in range(1, malla.shape[0]-1):\n",
    "            if all((malla[j, i] < malla[j-1, i-1],\n",
    "                    malla[j, i] < malla[j-1, i],\n",
    "                    malla[j, i] < malla[j-1, i+1],\n",
    "                    malla[j, i] < malla[j, i-1],\n",
    "                    malla[j, i] < malla[j, i+1],\n",
    "                    malla[j, i] < malla[j+1, i-1],\n",
    "                    malla[j, i] < malla[j+1, i],\n",
    "                    malla[j, i] < malla[j+1, i+1])):\n",
    "                minimos[i, j] = True\n",
    "\n",
    "    return np.nonzero(minimos)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  1,   1,   1, ..., 998, 998, 998]),\n",
       " array([ 13,  17,  22, ..., 955, 957, 959]))"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "busca_min_np(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 loops, best of 3: 2.79 s per loop\n"
     ]
    }
   ],
   "source": [
    "%timeit busca_min_np(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "busca_min_np_numba_object = jit(busca_min_np)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "404",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-106-fdca13922cdf>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mall\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbusca_min_np_numba_object\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mOut\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m404\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m: 404"
     ]
    }
   ],
   "source": [
    "np.all(busca_min_np_numba_object(data)[0] == Out[404][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 loops, best of 3: 4.04 s per loop\n"
     ]
    }
   ],
   "source": [
    "%timeit busca_min_np_numba_object(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "@jit(nopython=True)\n",
    "def busca_min_np_numba_nopy(malla):\n",
    "    minimos = np.zeros_likes(malla, dtype=bool)\n",
    "    for i in range(1, malla.shape[1]-1):\n",
    "        for j in range(1, malla.shape[0]-1):\n",
    "            if all((malla[j, i] < malla[j-1, i-1],\n",
    "                    malla[j, i] < malla[j-1, i],\n",
    "                    malla[j, i] < malla[j-1, i+1],\n",
    "                    malla[j, i] < malla[j, i-1],\n",
    "                    malla[j, i] < malla[j, i+1],\n",
    "                    malla[j, i] < malla[j+1, i-1],\n",
    "                    malla[j, i] < malla[j+1, i],\n",
    "                    malla[j, i] < malla[j+1, i+1])):\n",
    "                minimos[i, j] = True\n",
    "\n",
    "    return np.nonzero(minimos)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "Failed at nopython (analyzing bytecode)\n'module' object has no attribute 'zeros_likes'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-110-4eced6d8c6b2>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mbusca_min_np_numba_nopy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m/home/tin/.virtualenvs/curso/lib/python3.4/site-packages/numba/dispatcher.py\u001b[0m in \u001b[0;36m_compile_for_args\u001b[1;34m(self, *args, **kws)\u001b[0m\n\u001b[0;32m    160\u001b[0m         \u001b[1;32massert\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mkws\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    161\u001b[0m         \u001b[0msig\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtuple\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtypeof_pyval\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[1;32min\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 162\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcompile\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msig\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    163\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    164\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0minspect_llvm\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msignature\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/home/tin/.virtualenvs/curso/lib/python3.4/site-packages/numba/dispatcher.py\u001b[0m in \u001b[0;36mcompile\u001b[1;34m(self, sig)\u001b[0m\n\u001b[0;32m    303\u001b[0m                                           \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpy_func\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    304\u001b[0m                                           \u001b[0margs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreturn_type\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mreturn_type\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 305\u001b[1;33m                                           flags=flags, locals=self.locals)\n\u001b[0m\u001b[0;32m    306\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    307\u001b[0m             \u001b[1;31m# Check typing error if object mode is used\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/home/tin/.virtualenvs/curso/lib/python3.4/site-packages/numba/compiler.py\u001b[0m in \u001b[0;36mcompile_extra\u001b[1;34m(typingctx, targetctx, func, args, return_type, flags, locals, library)\u001b[0m\n\u001b[0;32m    561\u001b[0m     pipeline = Pipeline(typingctx, targetctx, library,\n\u001b[0;32m    562\u001b[0m                         args, return_type, flags, locals)\n\u001b[1;32m--> 563\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0mpipeline\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcompile_extra\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    564\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    565\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/home/tin/.virtualenvs/curso/lib/python3.4/site-packages/numba/compiler.py\u001b[0m in \u001b[0;36mcompile_extra\u001b[1;34m(self, func)\u001b[0m\n\u001b[0;32m    299\u001b[0m                 \u001b[1;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    300\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 301\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcompile_bytecode\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfunc_attr\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfunc_attr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    302\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    303\u001b[0m     def compile_bytecode(self, bc, lifted=(), lifted_from=None,\n",
      "\u001b[1;32m/home/tin/.virtualenvs/curso/lib/python3.4/site-packages/numba/compiler.py\u001b[0m in \u001b[0;36mcompile_bytecode\u001b[1;34m(self, bc, lifted, lifted_from, func_attr)\u001b[0m\n\u001b[0;32m    308\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlifted_from\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlifted_from\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    309\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfunc_attr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfunc_attr\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 310\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_compile_bytecode\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    311\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    312\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mcompile_internal\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfunc_attr\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mDEFAULT_FUNCTION_ATTRIBUTES\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/home/tin/.virtualenvs/curso/lib/python3.4/site-packages/numba/compiler.py\u001b[0m in \u001b[0;36m_compile_bytecode\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    548\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    549\u001b[0m         \u001b[0mpm\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfinalize\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 550\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mpm\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstatus\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    551\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    552\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/home/tin/.virtualenvs/curso/lib/python3.4/site-packages/numba/compiler.py\u001b[0m in \u001b[0;36mrun\u001b[1;34m(self, status)\u001b[0m\n\u001b[0;32m    197\u001b[0m                     \u001b[1;31m# No more fallback pipelines?\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    198\u001b[0m                     \u001b[1;32mif\u001b[0m \u001b[0mis_final_pipeline\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 199\u001b[1;33m                         \u001b[1;32mraise\u001b[0m \u001b[0mpatched_exception\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    200\u001b[0m                     \u001b[1;31m# Go to next fallback pipeline\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    201\u001b[0m                     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/home/tin/.virtualenvs/curso/lib/python3.4/site-packages/numba/compiler.py\u001b[0m in \u001b[0;36mrun\u001b[1;34m(self, status)\u001b[0m\n\u001b[0;32m    189\u001b[0m             \u001b[1;32mfor\u001b[0m \u001b[0mstage\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstage_name\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpipeline_stages\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mpipeline_name\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    190\u001b[0m                 \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 191\u001b[1;33m                     \u001b[0mres\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mstage\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    192\u001b[0m                 \u001b[1;32mexcept\u001b[0m \u001b[0m_EarlyPipelineCompletion\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    193\u001b[0m                     \u001b[1;32mreturn\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresult\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/home/tin/.virtualenvs/curso/lib/python3.4/site-packages/numba/compiler.py\u001b[0m in \u001b[0;36mstage_analyze_bytecode\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    323\u001b[0m         \u001b[0mAnalyze\u001b[0m \u001b[0mbytecode\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mtranslating\u001b[0m \u001b[0mto\u001b[0m \u001b[0mNumba\u001b[0m \u001b[0mIR\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    324\u001b[0m         \"\"\"\n\u001b[1;32m--> 325\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minterp\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtranslate_stage\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    326\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnargs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minterp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0margspec\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    327\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0margs\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mflags\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mforce_pyobject\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/home/tin/.virtualenvs/curso/lib/python3.4/site-packages/numba/compiler.py\u001b[0m in \u001b[0;36mtranslate_stage\u001b[1;34m(bytecode)\u001b[0m\n\u001b[0;32m    655\u001b[0m             \u001b[0minterp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdump_generator_info\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    656\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 657\u001b[1;33m     \u001b[0mexpanded\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmacro\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexpand_macros\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minterp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mblocks\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    658\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    659\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mconfig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDUMP_IR\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mexpanded\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/home/tin/.virtualenvs/curso/lib/python3.4/site-packages/numba/macro.py\u001b[0m in \u001b[0;36mexpand_macros\u001b[1;34m(blocks)\u001b[0m\n\u001b[0;32m     29\u001b[0m     \u001b[0mexpanded\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     30\u001b[0m     \u001b[1;32mfor\u001b[0m \u001b[0mblk\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mblocks\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 31\u001b[1;33m         \u001b[0mmodule_getattr_folding\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mconstants\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mblk\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     32\u001b[0m         \u001b[0mblock_expanded\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mexpand_macros_in_block\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mconstants\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mblk\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     33\u001b[0m         \u001b[0mexpanded\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mexpanded\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mblock_expanded\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/home/tin/.virtualenvs/curso/lib/python3.4/site-packages/numba/macro.py\u001b[0m in \u001b[0;36mmodule_getattr_folding\u001b[1;34m(constants, block)\u001b[0m\n\u001b[0;32m     58\u001b[0m                     \u001b[1;32mif\u001b[0m \u001b[0mrhs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mname\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mconstants\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     59\u001b[0m                         \u001b[0mbase\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mconstants\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mrhs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 60\u001b[1;33m                         \u001b[0mconstants\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0minst\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtarget\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbase\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrhs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mattr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     61\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     62\u001b[0m                 \u001b[1;32melif\u001b[0m \u001b[0mrhs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mop\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'build_tuple'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: Failed at nopython (analyzing bytecode)\n'module' object has no attribute 'zeros_likes'"
     ]
    }
   ],
   "source": [
    "busca_min_np_numba_nopy(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nos dice que no conoce la función `zeros_like` de Numpy, porque efectivamente,  no está entre las [funcionalidades de Numpy soportadas](http://numba.pydata.org/numba-doc/0.18.2/reference/numpysupported.html) por Numba"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  1,   1,   1, ..., 998, 998, 998]),\n",
       " array([ 13,  17,  22, ..., 955, 957, 959]))"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def busca_min_np_jit(malla):\n",
    "    minimos = np.zeros_like(malla, dtype=bool)\n",
    "    _busca_min(malla, minimos)\n",
    "    return np.nonzero(minimos)\n",
    "\n",
    "@jit(nopython=True)\n",
    "def _busca_min(malla, minimos):\n",
    "    for i in range(1, malla.shape[1]-1):\n",
    "        for j in range(1, malla.shape[0]-1):\n",
    "            if (malla[j, i] < malla[j-1, i-1] and\n",
    "                malla[j, i] < malla[j-1, i] and\n",
    "                malla[j, i] < malla[j-1, i+1] and\n",
    "                malla[j, i] < malla[j, i-1] and\n",
    "                malla[j, i] < malla[j, i+1] and\n",
    "                malla[j, i] < malla[j+1, i-1] and\n",
    "                malla[j, i] < malla[j+1, i] and\n",
    "                malla[j, i] < malla[j+1, i+1]):\n",
    "                minimos[i, j] = True\n",
    "                \n",
    "busca_min_np_jit(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10 loops, best of 3: 20.8 ms per loop\n"
     ]
    }
   ],
   "source": [
    "%timeit busca_min_np_jit(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Entonces, entre Cython y Numba ¿Cuál elegir?\n",
    "\n",
    "Un excelente artículo: \n",
    "\n",
    "http://stephanhoyer.com/2015/04/09/numba-vs-cython-how-to-choose/\n",
    "\n",
    "- Cython está más consolidado y es más fácil de distribuir\n",
    "- Numba no es trivial de instalar si no se usa `conda` (Anaconda)\n",
    "- Numba sólo acelera tipos escalaras y arrays (soporte numpy siempre en crecimiento). Pero no se pueden usar estructuras arbitrarias de Python\n",
    "- Cython es mucho más invasivo del código: ajustes finos requieren definición de tipos y muchas otros \"tun el código resultando\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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