Lesson_01_introduction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
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   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# SOFTWARE AND COMPUTING FOR PHYSICS\n",
    "\n",
    "### Enrico Giampieri, PhD (enrico.giampieri@unibo.it)\n",
    "\n",
    "### Prof. Daniele Bonacorsi, PhD (daniele.bonacorsi@unibo.it)\n",
    "\n",
    "### Prof. Lorenzo Rinaldi, PhD (lorenzo.rinaldi@unibo.it)\n",
    "\n",
    "### https://github.com/UniboDIFABiophysics/programmingCourseDIFA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## about the course\n",
    "\n",
    "This course is designed to refresh general concept of programming, leveraging high level, dinamic programming.\n",
    "\n",
    "We will employ Python as a model language, but all these concepts will be applicable in general"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## program\n",
    "\n",
    "### module 1 topics (more or less 2 hours each)\n",
    "\n",
    "1. introduction to the bash command line\n",
    "1. introduction to python\n",
    "1. version control\n",
    "1. testing\n",
    "1. documentation and collaborative working\n",
    "1. object oriented programming\n",
    "1. functional programming\n",
    "\n",
    "..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "8. numerical calculus and vectorization\n",
    "1. scientific libraries and symbolic algebra\n",
    "1. visualization\n",
    "1. databases and dataframes\n",
    "1. data pipeline and data formats\n",
    "1. introduction to machine learning\n",
    "\n",
    "## module 2 will differ for applied physics and nuclear physics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## The exam\n",
    "\n",
    "* You will be evaluated on a programming project;\n",
    "* this project needs to be hosted on a public repository;\n",
    "* you are free to choose the programming language of the project;\n",
    "* accepted control version systems are git and fossil;\n",
    "* there will be a list of suggested projects, or you can propose your own;\n",
    "* projects in combination for other exams are welcome."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "The evaluation method is the following:\n",
    "\n",
    "* clarity of the repository commit history (6 point)\n",
    "* clarity and completeness of the documentation and source code (12 point)\n",
    "* presence and executability of test routines (12 point)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Optionally, the evaluation will be improved on the basis of the following topics:\n",
    "\n",
    "* Usage of innovative technologies and libraries (up to 3 points)\n",
    "* Contribution to open source projects (up to 6 points)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## The rationale\n",
    "\n",
    "You have received some basic programming.\n",
    "\n",
    "Most of you haven't used it since, as it seemed like a very difficult task for little gain.\n",
    "\n",
    "The main goal of this course is to show you that this is not the case, and to give the instruments to learn yourself how to be proficient with programming."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Is it useful?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[4, 16, 36, 64]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = [1, 2, 3, 4, 5, 6, 7, 8, 9]\n",
    "[x**2 for x in data if x%2==0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[4, 16, 36, 64]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = [1, 2, 3, 4, 5, 6, 7, 8, 9]\n",
    "\n",
    "def is_even(number):\n",
    "    return number%2==0\n",
    "\n",
    "[x**2 for x in data if is_even(x)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2*x"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sympy.abc import x, y, z\n",
    "\n",
    "f = x**2 + y**2\n",
    "f.diff(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 1]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sympy import solve\n",
    "\n",
    "f = x**2 -x\n",
    "solve(f, x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.50+/-0.09\n"
     ]
    }
   ],
   "source": [
    "from uncertainties import ufloat\n",
    "\n",
    "x = ufloat(1, 0.1)  # x = 1+/-0.1\n",
    "y = ufloat(2, 0.3)\n",
    "\n",
    "print( (x+y)/y )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.0 meter / second\n"
     ]
    }
   ],
   "source": [
    "from pint import UnitRegistry\n",
    "ureg = UnitRegistry()\n",
    "\n",
    "distance = 24.0 * ureg.meter\n",
    "time = 8.0 * ureg.second\n",
    "speed = distance / time\n",
    "print(speed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.50+/-0.06 meter / second\n"
     ]
    }
   ],
   "source": [
    "position = ureg.Quantity(ufloat(1, 0.1), 'meter')\n",
    "time = ureg.Quantity(ufloat(2, 0.1), 'second')\n",
    "print(position/time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fbf78a83898>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy.integrate import odeint\n",
    "\n",
    "def harmonic(state, t):\n",
    "    position, speed = state\n",
    "    return (speed, -position)\n",
    "\n",
    "time = plt.linspace(0, 10, 100)\n",
    "y0 = [0, 1]\n",
    "\n",
    "result = odeint(harmonic, y0=y0, t=time)\n",
    "\n",
    "# -----------------------------------------\n",
    "\n",
    "import pylab as plt\n",
    "lines = plt.plot(time, result)\n",
    "plt.legend(lines, ['position', 'velocity'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2**3\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import PySimpleGUI as sg\n",
    "\n",
    "text = sg.PopupGetText('Enter a command:')\n",
    "print(text)\n",
    "eval(text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "import os\n",
    "from glob import glob as list_file\n",
    "\n",
    "for old_name in list_file('*.jpeg'):\n",
    "    new_name = old_name.replace('jpeg', 'jpg')\n",
    "    os.rename(old_name, new_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('Abe',)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from kanren import Relation, facts, var, run\n",
    "parent = Relation()\n",
    "facts(parent, (\"Homer\", \"Bart\"),\n",
    "              (\"Marge\", \"Bart\"),\n",
    "              (\"Homer\", \"Lisa\"),\n",
    "              (\"Marge\", \"Lisa\"),\n",
    "              (\"Homer\", \"Maggie\"),\n",
    "              (\"Marge\", \"Maggie\"),\n",
    "              (\"Abe\",  \"Homer\"))\n",
    "\n",
    "y = var()\n",
    "x = var()\n",
    "run(0, x, parent(x, y), parent(y, 'Bart'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hours_play = 10.0\n",
      "hours_work = 6.0\n"
     ]
    }
   ],
   "source": [
    "from pulp import LpProblem, LpMaximize, LpVariable\n",
    "\n",
    "prob = LpProblem(\"work-life balance\", LpMaximize)\n",
    "\n",
    "x1 = LpVariable(\"hours_work\", lowBound=0, upBound=12)\n",
    "x2 = LpVariable(\"hours_play\", lowBound=0)\n",
    "\n",
    "# The objective function is added to 'prob' first\n",
    "prob += x2\n",
    "\n",
    "# The constraints are entered\n",
    "prob += 10*x1 - 5*x2 -10 >=0, \"Balance cost of life\"\n",
    "prob += x1 + x2 <= 16, \"There is only so much time\"\n",
    "\n",
    "prob.solve()\n",
    "\n",
    "for v in prob.variables():\n",
    "    print(v.name, \"=\", v.varValue)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['R', 'Claudia', 'Python', 'Nico', 'C++']"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import networkx as nx\n",
    "G = nx.Graph()\n",
    "\n",
    "G.add_edge(\"Enrico\", \"Python\")\n",
    "G.add_edge(\"Claudia\", \"Python\")\n",
    "G.add_edge(\"Claudia\", \"R\")\n",
    "G.add_edge(\"Nico\", \"Python\")\n",
    "G.add_edge(\"Nico\", \"C++\")\n",
    "G.add_edge(\"Daniel\", \"Matlab\")\n",
    "G.add_edge(\"Daniel\", \"C++\")\n",
    "G.add_edge(\"Alessandra\", \"Matlab\")\n",
    "G.add_edge(\"Alessandra\", \"Python\")\n",
    "\n",
    "nx.shortest_path(G, \"R\", \"C++\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fbf74f9f048>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import vapory as vp\n",
    "\n",
    "camera = vp.Camera( 'location', [0, 2, -3], 'look_at', [0, 1, 2] )\n",
    "light = vp.LightSource( [2, 4, -3], 'color', [1, 1, 1] )\n",
    "sphere = vp.Sphere( [0, 1, 2], 2, vp.Texture( vp.Pigment( 'color', [1, 0, 1] )))\n",
    "\n",
    "scene = vp.Scene( camera, objects= [light, sphere] )\n",
    "\n",
    "image = scene.render(width=700, height=500)\n",
    "\n",
    "# -----------------------------------------\n",
    "\n",
    "import pylab as plt\n",
    "plt.imshow(image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " * Running on http://localhost:5000/ (Press CTRL+C to quit)\n",
      "127.0.0.1 - - [28/Feb/2019 14:34:53] \"\u001b[37mGET / HTTP/1.1\u001b[0m\" 200 -\n",
      "127.0.0.1 - - [28/Feb/2019 14:35:07] \"\u001b[37mGET /png HTTP/1.1\u001b[0m\" 200 -\n",
      "127.0.0.1 - - [28/Feb/2019 14:35:15] \"\u001b[37mGET /svg HTTP/1.1\u001b[0m\" 200 -\n"
     ]
    }
   ],
   "source": [
    "#%%file hello.py\n",
    "\n",
    "from flask import Flask\n",
    "app = Flask(__name__)\n",
    "\n",
    "@app.route(\"/\")\n",
    "def list_files():\n",
    "    import os\n",
    "    filenames = os.listdir()\n",
    "    text = \" <BR> \".join(filenames)\n",
    "    return text\n",
    "\n",
    "@app.route('/<extension>')\n",
    "def show_user_profile(extension):\n",
    "    from glob import glob\n",
    "    filenames = glob(f\"*.{extension}\")\n",
    "    text = \" <BR> \".join(filenames)\n",
    "    return text\n",
    "\n",
    "\n",
    "from werkzeug.serving import run_simple\n",
    "run_simple('localhost', 5000, app)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-7-b0dd127a8d30>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0mpag\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmoveTo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0msearch_bar\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mduration\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0mpag\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclick\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m \u001b[0mpag\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtypewrite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"let me google that for you\\n\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minterval\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/miniconda3/lib/python3.6/site-packages/pyautogui/__init__.py\u001b[0m in \u001b[0;36mtypewrite\u001b[0;34m(message, interval, pause, _pause)\u001b[0m\n\u001b[1;32m    983\u001b[0m             \u001b[0mc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlower\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    984\u001b[0m         \u001b[0mpress\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_pause\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 985\u001b[0;31m         \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msleep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minterval\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    986\u001b[0m         \u001b[0m_failSafeCheck\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    987\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "import pyautogui as pag\n",
    "\n",
    "new_tab = (1876, 41)\n",
    "search_bar = (768, 83)\n",
    "\n",
    "pag.moveTo(*new_tab, duration=2) \n",
    "pag.click() \n",
    "pag.moveTo(*search_bar, duration=2) \n",
    "pag.click() \n",
    "pag.typewrite(\"let me google that for you\\n\", interval=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<h1>Good boy!<br /><img src=\"https://upload.wikimedia.org/wikipedia/commons/thumb/3/34/Cub_with_trophy.jpg/220px-Cub_with_trophy.jpg\" /></h1>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from yattag import Doc\n",
    "\n",
    "doc, tag, text = Doc().tagtext()\n",
    "\n",
    "img_loc = 'https://upload.wikimedia.org/wikipedia/commons/thumb/3/34/Cub_with_trophy.jpg/220px-Cub_with_trophy.jpg'\n",
    "\n",
    "with tag('h1'):\n",
    "    text('Good boy!')\n",
    "    doc.stag('br')\n",
    "    doc.stag('img', src=img_loc)\n",
    "\n",
    "# *******************************\n",
    "from IPython.display import HTML\n",
    "HTML(doc.getvalue())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "./fractal_wrongness.png PNG 595x496 RGB\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=128x128 at 0x7FDD842ED828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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DkKtULlwIyRXj7AXl8x9Yb7O4aR8/POubnwWAqQWrZppyLX3qWPrmfey9r2QBDE2VFlfsgOklLX9RkuYzFQBnljA0XQSgKMq5jBX1kNPLUC39f30VABYW3U+9L3bNVXj1PWsJxT5+VgQwkWQmp9SOXmzaExVpY7GanZ7OLeYRDwMALSKTg2mgXHXMHG1YCoCy5NpE5INf7nHASU66PmgpCC8LdS+HBrAkgGs/5oQqaGpqf3Z+MdEczk8WAVRMf2+iBgCk6/PRCxUTQAtNJ+IAsFSzvvSBPeWiQdsXv/Rtuz4YZsoMCAeAJpGVkvlvf+MDcOqCkJOyAJqiTKQFZtkEMFsl7ZID4PQSbt/mATC8JJ8b8g9crAL48L1Nt16pAGBU+Z+fsCpKBwAY1lI2CWDgGK68nu3rgE9kZF2emvAAiHj4Hz9SANDW66lkVZ52AFx7M0uzDABDsyuK9pN/MAF4BMY2ALwMcfJvKwCSguPQX3kwsiyXzLL4g5PlDS3e0xO17jgSnVhYRDELgmZYr0ka4Ehi3y7xxu3WwDh1ekrRJbzmlfztO7TzGZTLCJOt//BgkqKwUABp8RzNtQqVLQeCHbxCksbmDX0kWygWCoViWLeNiqG2NnjOztWW8ri1w3l2AXoZ+3fBdQNf+XalrSHcHC7edq3v2r62ZwaWfnBEqmnBoyfLtNnAe4qrq4TrGOFWvqdTW9vBXxjWRC+bLxqFFcRFz8KqvGMHuTBHlWWztYuFIzBcheNRlEGAFnjruW+4vAOa4e161vq3k8FvZYJYRgTw5HlrIpkDMJ6U79gTyVdrABQHZkUEwAhkgjWv2Yar9mDgW+/823tNAFvXOB6WBLCxobdQIz0WB6BiZF5xHQBc2Y1i1vPxdwTvvbf9Y6/Z+uR5AwAtKEuZ0kwRJRSHZ2vXb/QB2BgPfOR6FsBbr6Y7mnDNunUH1zf/zTuY9oYqgELVPjy29PApiWdRk8tN/gCApRUzGPQDaIgZRiE6taQBkGpRw+ABtKyXP/wRcf8u+nWvJTato2TJAFAteRy3VZEZWSZyKfb19zUDMO088X/7Kv7rNP4WGkABtnB2xvPEiTyAE+ewczPGF+NL2eyOtY2nxgoAdM0kSYgkp0jC/e+ntzcWBN5drjGrSiOAudHqlXsZSaoIvoCcx2pFBTCXkVfzEY4qn3zKfuL/eFayoh7If+O77l0HyKTsWSopTYy9awMT4GIDM6kNbV4AFYeeyTjLs/yBvT5eyT85VqtU7IVFZu/eyOxyxlTd8Vm2VOVXl6vpObRuAABKx+QUduyIOG6B9lLJGQ5AIuEevAKNEVPgLB/vL+Xtxw87I7M6xZKSZHuDvGu5Iq+t7+JjCTzw8SkANN3m2PXiwX+xhvNfFB1NAcBjg/bAZKmnO9bUw+/ewl+c9AAIihiazezuEw2H+eObI9dt9wLo6iw/P5gHIIY7mxrjrpyEWVjXR9qGDODU6fzmHvo1B9awLHwiFfEUAHzrgbbRtA5gdNzt6oIiOwACsPevZwGcmc3EQk1NiXUrRfnw6fLRk1UAP35k/vikA2AqiwpB3P/FNICqzldlA4Bs4m3v717fSV23C5/6oPjOt3AAVlM4+6wAlyIdu6OdcB0VAGhaquoAujvclTmHYwgA6SVNVk0Ay1ltcQlv+otugKhaKwRRp/6/WMP5r2gADRbA159yVMcC4FLi8eNKzgIAX41iwgBw6Ix9YFO4pbkMYHTaeePNLIDdm1pFsQogmSrMpMjetmYA2Vz62s1NACaS8wPT4sZtGwF00CuFcgHAkWU1nyYsxhvmzPGUdt+tLQCGZ5ZnshSAa7e3PD+4OJcnAaztjrlmGcC5QWc+ZVOCUyyRpO7Eg0EA8yll/doggGt2Sf0JcXlVA/DwYfnRRwBAENG/Bft3AUA87GeEKoB0Ek88iskLPlBSqIEvSRoAgsbGdSwAOgiKNJ76lxKACB+ynboAzN90TviNNYADCBj7/spYTFsLWaxpFryy4rrY2kJSBkgf0ciFFnPuX70+rLjWUo4/M0mbBjWzSlVVx0U0CLdYrUpVurkhVi6urhQLh5/XZyazmVIhmSXnMspTh8+EsXRmZllTnfMzasAMhAUXQDajFRT87Vdzh8+mfnwU8yt2OmN/+cH8kkQrBmNpzI9+lvnZIT2fDXkDDsUAQHbO2bstdPF0cGRI277VnZvU7rolSrlOqlQTWe3cqGy55K59ZMgTcAHSRk0R29uDM0vV1AqxsECcGSOFUBxALAKa1XiGZhn091DVctAV+GLaSC15N94S5hhRtosUrQP0f0EPflMNYACcmnADDPXDE/qeDv+CZpw4DQATs5pAU7EEQbH29ZuihF0BsCRRh59RHQ9F215aqHz5I7tjkVyqXBk8UwBA0lz3usbvfXvx/jeycwX3qUETgCP4rJq0uQ8C3TKyoo6MFAD0rgmkVioApou8ktWamlGUILJ0WbIArO1GQabSq3Y03iJEq55SAkBOWtq43gIwPWnKZYaPwFbNXVtDqq4AWFzVk8tgHHJ22Vm8SEfbLAA33tC7mptuiKJYgmQmivkCgHLSWLcGAHr7+ybHp5ZXqEDQHpuDx0sAoF0Ph9rIc0UCmkg32b/5fPAbCICiKAf2Dx4Tz64qt+zE0hLGV2ilinzZGl7l3apWU7FjOxsW7akl+55reDEYvrm3Ygl46Fj4LbffAaBqTKfTT45OO7FE4sKscsPO5sNnx958Ve/4ytyJaddHOKeGIVfRs56bLug0y961teOxJ6cSHfz5Ea1vTQCaW6nUnj7nNPoJkhVstzHSNJeeF01a2b8xMlsszE5hTVff9Ozsrm772iuIeE+fllv005pKNJ+fJh57UOrb1HB+fKqnG/kSKwjG8grmzyHkD1owirISbcWO7TTLx1LZdD7rin7OLhl2zd27LyxSBO8paCZcFz/8Pnbt8E4v04Zb3rI2WCkbPOU+8u9zLggv3WjbDEiTcH7dZrtf1wSREAG87pPCD44qAI4MeTkOrkUC6G2L3LDBBnDznsa1YcNj2wB+/Kj2mqtuAqArwg3rCsBpTjufTT2RKzIANjR4X7WRBMBJYLlQQeYAjGX82zd7AUCy/vEtr7z/piYAfIA4flED0NvOJnNVAG//o0QuxZ46pQAYHRYMDgD+7UeFKEF2NgJAlLN7+gBgXZTZ1kMB8NKqLmf719cAzM9iasIH4OIpLA6jsYMEcMvtvh27AeDsGeu5x1a1dAKANMfkVnkAPf2iL2QA8EXQ0oUPfwQAtvbD62EB2LYys6he9doEgKqVoeHg5+twfi1uf00N4ADseJfewADAnv2ioDKTWSvqlSdnEI8BgJclr929+6a9DSU3MzE66WvcD8DMDzgOAAjRuK9WemZxBcBt11w1e+owgMGCuGctffJsdceGyGSG+MmTeQA9a/DaqxMkKflo99iI+Zl/MQDcdbswNqUCcFRUC1jXlzh6DEvpVYYEAIsT/uhO6/U3CENpefCCyxEOgCu2YVt3DIBNkg89mRlZBOHg6GmAogEsnrK6tos1zdm8g6FUPdJoAKAo4fnjasBPDD3jIT01kiUAXHUz0+LjAfD+amOCk2S90Q9fEN/+Nw8AhXbiPr+H4wculLrXsA9//SKAENFlkDoAwvnPU0b/uQAoEoSDq99CXLWXq5IhGquOwc0s6OGIZ6WoxgVHYOm2Dt5Wa3/5x3eCcVFOl4zlZDU/u6Qx3kBUCOQKJW/INzCSduDcsDUxK5mCU51Mc7fu3/Gv33y2vQULtYhAFKBDjDYvp+Tda2sb1vAX5oPPnljuahLOXFBPDWLrDmzpj0V82sKiRCFx83XanjVdJwbztM+vyUumw7RGDA9DPXyilJeZ3p4orWW39dvN8U1PHZ16elADMDqPpgb/iSNuNKIUkjbtA0Hyne247UYmFJaWl9HTgFSBePAQIkJoethcTknbrocLwcN6+rq1hWStox3BAAiSEl1akvSvfxvNjdiyDqbZHo1Zjz+9wnq4oScWLFBhotmqtxj9Zz7RfyYAmgTwls/7lyfL+7fzyaw2mkRYFAEkU+yerWUA29eJU3NqU8QF8K5X7qtlSwBGc1PDWbs1HshknXhEPH8xA6CtBQXV29HGjw1VN6+LtQcYAB/914WoDwBaEq2zi8mtrQDga42OzrkAiuVCYwjfehD/+LkDc4NHqmUA2L1X3LWJA2BX9dW854nBHICtGym56AJ4dNBJLYqvvlVvbV+7OD8yMAkA4wtcSdIBeGksjPprVhXA2nXMnfeYAMIMeiOoyQBwfAxnznkAHPuZ07nTDDYwlYq6fr3gWBqAhihFwAVQKnGrK0pLkAUQjRmKxQB49DFTNfnVoSUSngDhq2dVf3V/0a8WgADa3XxHYsPm+fFJCDwkjW4IWqsSbBl33iI+9Zhyy81EXnZliazJns++L5FOTkXiISmtnZwxNdsqyGAB1RAJW9m8LTp4WtMpIywYV+4PDw6WGdvJq3AcFDRuKQuPyN65T1nTuq5KO4MTRjFnjswWFJ2M+is3XSm2ROj+1pDrUq3R1qefP077EI0Ip4ak8QUEfEQhF1xZ5fduX5UM6sKUq1suoZObt9nlImlbxOic61BcakXVJdAaUlNgwli7B4qEu1+xsb+3ZmolP1NuCXjnstT589Jqjn7+WT6Tq9IcwwXNrl5UCkRrFxUKU4oCgdFlBQtLnhavmmim25riVbUa8DqyIcdi1BOPWKeHoEwZQE0gw677n/R4/VIBkKTXcexbPqTu3cpPzGkRgj8xor3z3tbvPpr0M8TuDf4yYTYn1MPP+Qi+CiA5g7/8yN64cXJBwWoarEXPZMhs3iBIsjnG65a2PO90tnolpbaxVzg/bq7pdgbm/JJpbU7UJpfRGFjv+GcBtHtRM81gQ9f/+Ifpvp6w4LPlSuXVr0DCK16xNVZTtXxG0WrqwKKlmOxK1gFQKBNZyUnw8bnMKi8gHiNzRc/8jB6LYN8eS9VaVivVak0vZY25RTLGmQDUstC5NyDl082J+HXX2q1RBoChSEsripwnzk66U3Nufg6ROFeq2M2dFkEQjNd1HHR2cEtzuuRwPV4dgEOS/rDTlvBrerUhRoo+kiIsoxoemqx978tpC+Uw12UYDED+spr+SwvAD7oK3P5xy64AwDVbyfNDnqHF2tVXcc2ccXLKAXDTFvZ80Th9gRA40bJknhajXvzFW5qGl9IAphZqUs1r1JxCRSmWiPU9jQDWbTbPnsiPpLCxlV0qGJklALjjZjTFe/K5mURi5/xqZmGiFmoki0bl+cMmAM3EDVeTPR0NPT0aZcsGSADJJXclTc2mDdKy4o3NI6OrgpdbWFYpEgC8AT4WEGYmSizvN2WubW0OAEnE5pdyHeGAQlZaG4MTk2WbYNIp8+D1/PKC3tflbtvETy5oYQ87NGykShSAqads3kdqjtO+TjQIpXcjAWBlwa/VzJUTStMm7NnpT+YMn98JiIbXAwDBKOOjzULBo1blUhXf+1oagI9sdFwBAAH1chm8hAAYOixY1Ucuhr/7WHVghL/ugDEyw58fLYY5QrZjsXiJIpnGmFqTKEUPSFoxn3Hb2kG54p4t9sEdiWiD/fjRbHJZF2hWrtF5SXEN1rYIMaiTBFkrY1V23npH76mBZFc3ZZesVFEPxeGhUDUxXxS4kpCyi6UMmhvAuPjCn2//n98aiEUT49OrjU3xRFRbzFanZuHCQ1N6peo4Gmm7jkUwq8s6z3kI0qmUVMJLeFy3qcWfLWoC6/HHa+s6yc527slHiTe8tmFkPP3ksept1zQ+dTRbkpxYjGjv8kmVansrlpYxOwufFyszkEoQDFElFEpEsBWaisYm6EUiO00aFVsIgG1GXw/f0SMoUtXRbW8YJRWkRXVG+AuTcrWGvdvbvvDxCxxFBYiQAe4lZfBiARAkDeAL32ccVwXwsyOEVCRVwwYwOoortgNA0YGr0SQNACJvqTIAbNqAtjgAXLEpuJCzT48ZALx8qFSsAFALqsxCrgDA3n5PPKwDmEoSwQgxMmECsGgh5lMAHDpM929oBXDHQX1NV4q3RQBPXVTOjQFAOEDPJy2CAoBKERvXrgUwPDGtaxYAwmBXZhxvVACgGJKrkAB23BSjncwV2wAgFICHAYCqhqFRCCLxxCG3fycmpwGgqzlIcxaAi6drrINSDgD8op9vsgD4SH52rMgQHAAXesfWOIBAOCtVACDeRBE8IckWACWLhrAHgOFSpYx88tFVAFEibtWLaIT6wiDtPwQLNCjXcd77BdvPqiTVcHoUC+ngJ96z/p6buYAY7e9FNEEd2Ef97duF26+yGyPBQJS6ZZ9w5S7a68FiEi7bzppszZRoR9q6xrthc5NUKxar6sqMmmgX80lmZ18sEaKeHZZVl1uQhIeedh59zkiukssl985b2o+cQnHB85m/8FCYl/PzF2ZSEwvR6bQyntWnlwi75gmEMDxg+YOBqgnC4WyFOPTY+IWhuUSrrSoIegS5bCBvqUtGg0gKWohyxJ4esVTOru33T0/i6j3hu29qWdPZ5WNJkfS0tooA2powcg7dLQ0em15aqS0s1AorNKEHGjrEzrUMI2L9fqOUVyxTWckVOZfWSrqmmQaBtk5Cd7K6Ea9U0NaHhQWnlON4PqDVRLh0YyywOC8TGmVq9voDcQaM5FZoqAAF1/PCYf9/PwrgTZgf+oZ95/61I+PjOQk/PUObOQvAHdcRT54XskvKmnX44B+F5xZKAP79SbIr5m1uNGhSG5hzc1l09ojpaeV//HkUQLLCHj1TyhRtwBmbtpq6AKC0IIQbiOUVJZ2BWsBVt3IAPIbYktBa13R47akTY/a23kAk4HzpAWntHhaAQPumFooMC9vwzY64PetFAHMrsqHVTJXwBNxsllqz2ZkecxuCsZHHcgAIE/AzYtjr6ShJMsQAALzxTly9jerrS9gVaXhRmlzgAIxPq7kUADx9CEJcDDcb5gqdr2i7b/AAYGCs7faOTqmJZk2wIvMZG8DA0TLvQymDhkQ75V+MxuJ8MLs0SdIeBwBhsbpihEO0lLXyOlrCANDdG5lfqV38aZaEzBNNLjyAA6j/QQMowm+Bef83bdHFoWPjHQlhOuljbNEhSDaAHx+hYn7KoNlNHfjhs2UbxBMjzHLKPnG+spxVT0y5uoX5MbZSIU5N46NflLOpUm3VFGjV7zWWlqxbbtrQwbQujVE7t3pS06Ys87293J6rPPkl65V7PZtatIVFVU7N1mr2pjacGKqcH7WZMCLCpkIBjz5VsE12NY9yhtu0ucmxM2OjmJ6oRRpQrrkrE4RZdE4874b8KEnF6HaQBOfC60pmYkt14xru5r0iati2jr84gvlVl5Ehcg0h0ev1qPm8RzER8Lcynoa2Pta2lMqEVStr26+JppY1r1+ORs3kcqm/X2sK+/t6S2vXKLOjZcMCSYrBGAhP1rGFlVR28Di8XjLg9agVgqad7o7Grpbozq3+198WirfSngBmZmQ/J2y5OWCBqrkZv+8/2HwSAEVRAN7xhVp/c2KhSF+1d8OpKbckSwBMwtmxTgSQXpEczXh+2AvgeycDYxMmgKlRPH9C1HV6dAEHriU2tDoAJi+oBmsD2LTd29Mc43wUgJKcBGUDWL8+sKHbc0WfDqBUs2dWigDEEJ4+Y1Ylz0KBP3IaP3lGBXDo+XMXhgwApRxdTLk7twCAzxsKNVUAnPkB9u3H2i3h/de4972paWoWAAyFcwJ6XZ0bm+j1jTqAzjbkihyA8xPEwKwyNJ8CIMm+xYwKoOKWhyYzALoa4zbj4xMA0NgWDXJRAJu3Rzaui+1apwLY0GZcdRAAyklFShGrk2pDl0qwaIiLACoZnXK8AJZW03ywsnMnCSDmZ1ezdZrLixm0X9VQlcxstXRp+NdNEAfgI1/ir7/SD+DchXyoiTp+rAZgNo233eUDoJnSTw6Teg3DYw7lg8gCgGxRZokAwPisfdtw41U8gHROu/1AH4AA5f/Os+fa20JPnixRmufsuAygOcacHTNtm73nJn5+nlJrBoCGVuPhB00A2/czphKencoAuOEWD22RAFby0tkBvP4tCT2TXddlG1w/gO9+ayIUEwAcuEINsrDIPgB/+8klU9IAxJsEzWb88WrnZjSznkdOywD6uoN7dpKLy8Wb9xCpAj8wrAIoVXw1iQDgZbWzx4wQxXTudSam7Y5uANi93b9xbZBjQZqZBo+zUuAB3P8xp7AiA4ABsoGhYyYAukKbrgdAuLlyzU0B26iEfEHXLE/NtQLQ7Nr8dAnAvs0d3/zyiI/y/gcNEGGMTiY64xW/h5kfTd5zDRt09X27/IFQr5+mM2VvZ1iKcVxzmAKweSPuvoXZvy3Y0kbcsp/s3yL4AnSA92xcSzE2Oho9DQEsp0u0NEV51c4WP4A/ujJcq8kdneSf3hS7drfQE6UAzIwZ07Pm0JCaL8iL0wSAlsZA0GcqVlZWcOU+7N4s79om9fdJ114R2rY1AoDg7I7OYFdw1qhUXnVXr2Py11/PBAWuqU3sbFo2C4X/8RddqETEJkQ61X03KgDmLuDhc0ZnAvE4T7rOucEagEdP8UPjqmOSqzl6KS3Jai2/ogydJKgVFsDkkM1z8Pn8ohfTc9Vnnl0CMJMlRxbMIKtenBR1XgYbBInwRsaxTZZg6apXXabNXJXkK/uvasgsVwDkS0qxxnuDqWohuX0tJTL+Bj9+8pXnX8Q+AOJrD9JFmw24yuklvOWa9dniKIAnBiEjUliqGJr1oXt9Dw3oAGYWDc4EgHe9MWiq8kNHQRLcE8/WDuxhAPR3m8E4z5ra6WHcdgAWvAC6421DUxkA//6csaVHKiyLACRbk2sOgEIKt9xEPPoEAzjxBmv7Rj+AJ56Tb72F83FKazPN28yhMwCQzjgHNusAWtuDgRB15FR4ZGx6w5oozUkAehJ0tSwblXWPnB8LBhkvIwJQzYpfbAEwMpE5c9TsWEMBWEra/dfRAJBnHNYslSxCJ4Ph4MLTVQCmYIX7ACDWBJ+Pi0VoAMGYp1LIAnBIplgwh48jHuV9XttwOQCKQS0erxAG7/q1niuwvg8AWI4pKiaAqoIWHolEVJPsr356lCaaXsR+OBwmkwV+Zk5RbXo1hY99dZRig8+PsZkax1KcwlhX7Aw/eo4OB4y5FFHMYyHH33OLX1WV9T3RjT3IFqx4CyanvSqBY6cS4xPak+fpfAWpTCxbrQmuM5fOmC4OTSg3bJNoO1CSlHyNMXVybQKZZTZXJAg6dvUerliwOMHjD5sC5TY1sUeeQzASCvDhxbS6qUclaCG1ok8tCqSXOzddruU1ml2Uq8TA+cLsjJEvG4fOaQ4VyLpTlAVTMjPpiqRUGAEiln3B3LXXmh+7n1w4Yy8M2iLFZS5YSspanSUtg5GzjC9AOabWtdsSW+FEEYoElSI9dg7zM8ZqAfMZ+ciJbF7iCpp3+KKpG+BIdvMG59oDoZaGWjhktjdUGvsQWa9deROu3eUplOE4pOJalgWaQJyjyjpMxfnHTz9zOfsAisUiCSCTQyFPM7wXwPHBWrVKA5hcSFWK+NHTRQCHn8XKkg6gockzmaMA/PBEZiprSZoJIJcsDxwzAVAEhs9abW0AsL2jN9FoAehqIG7aRReW4cqVhSxOnJMAGAQV7zQAfPVfsg8/I2kOC2BkhhmeMwBkVtmxaWZgNg9gehU//l4RwFzaeO6EDuDQEI4PGqs5H4DxOXcxKwD44eHKkTErEhIB0CIIEQAcL9ata2ltYVta6H/6AbH7ZgCAzi6epADUck48wSWnaQCRdq5tiwVgXaeSaGU7N8ITdAcfl0lbADAzoufmZQAXHvVH+oxEAwGA98AfsgFs2cF3d0aXazQAwSQKBgugr50nLS+ANW3BB+4fEYmtL7Y8BCGKIgDitZ/ATTubB4ZzgwuGJiHoB4CwP5zTDcKuTU5i60bvkaM1AJt3ihSrAWhrcngvNb8gUISxbysmBpsAFKTVT75PXEmWHz4qtCSUV9/YBuDi0JLMgGcCDfHWf/7eeKVqpzO48orY2rgL4OnB/IVzqFUhV9C+Do0hL4DTz9Sue2VQ0qobN/OmokxOegCMnpPb1wBAWxtfkLTUtD+Tr25Y01RSUgAa2mE6kCpxRi/0tdBVlwDQEtd27ukZHZvp6QixrgGNMFH7zBdRzXoB2HKtcQuZTTuxBDiCaumwAfhowRclJ6bl5WV09gSTI2Z6VkYMMV8AQG64Et6HYIRsaXUbBbq+VN91KDmtp3P0QtKM9eE1t4UBFIrk6EweAGo4+uCyh2h5Efs8z6fT6UAgEAwGSd5mHzkuAfAReNVNvFnEzCr72GPl80/VALzhLmzp11//xkAgRGmGcvao45i+mVlueswGYGvWtnZj/4GFcOvC7QetgNcE0LXWmZwXTo9kT0wlqzXKR7AAiOJMbxMDoLnF8+TPcoTHV9UsH4lAMADgptuCNx+gKb42NlYLNODCORPAwBn+iUfhgjz1sCzXQLrRlvZAKo3pSQAwFmCimsuC9yOVwkoKAJZX+amUo8tWf6+3qmJoZA7A2ExpOiMDWK3g6gMx29KNghJoJVmBB9DWRl95LePYuOEmrqld3bmrFcDuHaBsG0D7VrR1oCrJuWGNEBHxBAHoemhiySxUzYrMBCLhYBsHINqCgxuiRweKcwvFrR207rBb1jafezB1OfskSaqqWlcCSZJoy6YFWM9dtDeuiQ7Nom+rNv6I4fPTAHlwF9EaZgBcXHKiUQ5Q2ntgOdKaBA+IC0bty+/mAVQ1sqeVvWarAttY0l1L0dauQVX1CbLW2u4AzvGLBtXi39SGlSwxleSjLfzY2FJVtVdzsVgI5Rr2bTcAtjHA06idO8qbsOenHM5TZD1QZDPcEihXK/k0hk5X2DAAxNppXwuxMCrLOnSNdlyHJmBImqnK8zO49jWB4+P5G69YA0CWiqpTqip4vlir5Qkgb5VdOs7173ABi2CJYMzKlqyWZmgFmwU/N7rQ3wWSJALemi/EzU8DQPdWKy9yxao7P66E2x2foBOER9PczKpCuDYAMcRrsvn8xfyWbbhxI3QHf/fWxoPbHicvs/uiKMqyDCAYDAJwHIcs1Oj0inLHbi5flDL5/KHnmeuv8L72Jl/vGsvQmViUW8go1Wqtq8NytSjH4dwZ5/AZJRhyO8L46tc0ssrG/ApvlSsFz0yRaPDFSYYt1KjjQ5I/FHAYN12K3HFlKJHQBmYkSVIFujCfLFy5f19jBBaVm0nlFAmzWZExjXylppQCgl+zHW3/Pu+efYGtm5mD++21u2uOikw+f9Md/t52f3dvQPAUE2vccKsQ9UHNWyL40ryzcka2JdITwMOnpK5o+OTY9Jkzk9UalUlZispNjqFikmeOsv5ujz+mnx8wKI7nBfrseWzpDGzaLciGtXUXdu7quvFgePv69sZGz5Z+b18fW5OJ+VFomsFabrjZU8xAN2CYlKzQPM/nquTUsO4SWkM4QlBoCwizGre+u/TKKw/5vOtfxL7f71cUBYDX663Vah//+Mdd1yWhU1WaA7CxQR8ZgaHYh08pJavKBQAgmuhVbQCoyG5Nzy/PAsCb7ml7220EgN37KY2pVsuIhfD4eNWq6gAow+D4XtKlHjpSIy1c0dcPQLaMlp5WAK+8IXLTFd50ara5oSnmYUIeDgBH9zw9bB09AxMVpYx9ezoArO90b7uaBrBrLd+zmXr1q9sB3HSDcv3VFQA71lM7NyrwEJZDTB1VXJMAwAWd7ZtQzNGHDtd6fVRFB4DUKk6elAEsL7nFrNnWKjfF0N3jHTwnAbjzytCFpYpTIiIB0etvcpxlAJy4YMg1ACEe+7bF1BlUF0yraJOyA6Ca5tRCtTlstSfIzd0EWU/NpgsbewCgmfVcv3eUINbiMhAEEQgE/H5/NpsF8OlPf1oUReIdfxe13fy5EVDANVdGZ2fViTG5qGPjWn50XHvf20UAT51UM3m3WGEJx8iWkXsoAeBkMt0SDVYqEsEHRXDZfG4yZR3o9/6fZ63kijI1gR2bSdty2zrda7ZjfCUBYHhotbWDzOScmUVqedHW7USpsLpmM8IC2puIgUlPagrhmHf4THrPdbjtOhaAUjHODePMQCAYsa7ZITc1AUBLomVxsVaulkdmcOb5httfRT19PKVUodZAk9iwPrhSK6/pwXKuYed67dRABUBJgpGkZMneuhWxNq5oONm0xQTcg3sZwYyDUzray0CY5+TmBrGSqwEYnvYuLBQnZtHSGH/8n7OsE7DICpoRSuDglYGGONXWyUnV1WqF0wknnzEBdEXFv/3QMEN0X86+pmldXV2FQsF1XUEQKpWK3++XJIkEMLYCQ0Z2lQbQFJUdAQBmR7Vr90WfOaI+c5J9+oibzwBAZhFrI+Q/fmf1THp1b9+6aL06v5L3ekwAlA8/PlEtSwqAGw9SjJcF8L2HoeseW0oPjqanhrGntRGAT3BdFgAcEjduD919PQVg93rbG6oB8ASpwZM4cd7QVGYySQ9f8AJwHNkX5ru7mhoSfEtUi4VNAE1xb1tXBsDmftx6E268lQUwO1M+0E0BSMT1oREHKhyInAXZsgHMJ2HSLgBDczf14+JpC4DAl4pSCIDPFymVKjRp8CSzoc0wXQA4cixbT1qSDnhVWNMWAyDrRdWsAbj5itCmDjvEiI3Btk99aOhy9jmOc10XgOM4mqZdOl6pVAAQ3a9CPEJevZY7M+UWkxofAQDB8d22PwBgXl5+9AmyVHYA7N798232erqEuw9aEZEDQAfDP35+CUAny84qtkexA22xz30119sFACNJYk8fCaCn3T47T29vbwHAk/mfHZMBDC+7G9p5jtH+9j3dqfTqg085AEYm2QsnLQDekNLS6U2lawCa46FX3BUAEPInNyV43aYBHD5VOX++BcDaTsMTkKNRR9WZ+VT1Zw/RANZvdE8etQH070ZhBZbkAyApEs8AgNAsbN9AOqTL0Eo84tctCYDf6+7aEgdAo8Sr5rl5AcBjhzHyqAOAhu6YpEM6iODGe4R4pw7g7bdymawKYCyDj79qniA6X8Q+TdN1n4dhmLoY6raIJMkvfOEL73nPe8jtvUiuOABCHuQMTM7RAQE3Xesh6NXG9lxUEA7uJqoa/vgONMaI176y6Z47iQ+9sbO3cR3LexZTtYnxpc1dwo37N11MG4K/AcCjT+Y2rKdZG1M5sLJbqVEBjrww7vNpcdNHPXdi4QeP19paXVd0bz7ARSIUgI/902whq1yzxUgVzKJS7d/Ei0GlbQ38AY7lOdfF1Lj0s0cyS/MLi0n61LShKPq5UWViHoGGdK26LNky4zMA9HYQ1+0WXnOPRZHc9JTrjVG+CJtegIf0yynJVHWrDNfha3PInVKLJRfAwDDm0qZKuDRHjsziwlh5JV25OGYfW+IJWj0+YE2NqoDu9zJEHOGOAABUIBN2oUCFPM580qUtalkhP/6qqcvZJwjinnvuoSjqheyzLFv//P73v5+iKHJsCcEAHvixKrvOxo3s295IR+MAMJEkpnI6HH9rY/SPbsAjRwBgMplaExNY0CTrmpYFVgSgaeGnjyyk8rh4IaXyzFyOnl6ALxDe0dwEIOLlRhbY4XHp7Hiq2a3u2RsBQLr0+17TEvU4bQ2GYeHceXzwy5jIdVZLYQBi2Lrpdu/WDVxvd42i9Prq9NSSWi77JZmeW2Z/elw7OekCOHLUshkk2njL9QtxuiOR6Gppb2sWXVsG0NVJUjXSdlBQq6zAqmkDAMNrnjZf71VUKa9nSkoghHRGLea51bwIoGKEjp3S55eI5XnzyACRzvoARPqFniv5hhbwDSWuGUQMx39qFIpma4wCkHKCb7ruDEH0vYj9YDDIsuxPfvKTcrl8iX2SJCmKEgSh7gIBIN7+lcDZE+KWNcWZFfquG7TFFNMdsOFrnZhTqvmy2KhRdlNqISV6Gho7KkZFcym86ZpGTzBb0xzDCcwk5ZIkLqT1QkGnCGJs1hVa/LsiKFviiXPpm/bQnN9S1ZYnDlkHDhq711K5bOHMjHPvK9d2NsgTSenUkFysGMs5dmrS8Pv9U+PVTVtC+66wehJWOucUUhTJeM6NWZWC6pLE4kX1hjsCq7kKbNYWDNIJlsoQPer29b7mVrlWUxu9cB2MJWkpLz72eNUbYErzJhejo+HgymAeFKJdHBXQ+/owOY29e+OZFF3QUx2dbCFnUCQfatSUMmoqF4mw1axRyLOWbMmqdfVOzi/UBqcg1wjLYdJLxtp9AgstFma27TE++po5gejCZfjOd77zhje8IRKJtLS0DAwMuK5br7tQFFUul73en6dFyZNPVda2rgJ46y0qbfoBBOJOrThXQxqAnoZeTQEgyMw7boqtbWMBRDsONPj6AIzPyiPL1sBwFUBjPDI24QKIa9pY0gaQWoCpeEgDANLJ9NqWMgDOw2oaTh1fAdAZkL1eAwBHu+GIH4BWBhT+qq0JAFetJXu7bQACU8rljJkTADAzxp19BAAGn8LcZHk1WwYgeoPJrAVgVRaHFgkAcxlT8PEeigSgL7vZ0TwAAiwV1Pt6GQC3X+c9dSwLYG4AF08bAOBwqwswlAiA2Slpbk4HkMlg+27a768BaO9iI3EBwGte1RgRVACu4v7VS7HP87xt2wAMw6h7nPfff79t25/+9KcBmKZ5iX0AZHIu/Nhj4VuuYpqaE2t7xN6YllwiG2PhNg8IQXvzHT1vvLmnqU3cvc37wE+SguAaeYha1jIbcyvkyII1OcNJFmolz4XJQiAOj4VTw8TQqHXkbGbzBvInRyoLq8TREyvdbfjWNx1Ty41M2XBwYsmynG7dIm7YyIkMubBomnrVtQWvhy+p1a98bbYzxrkOs6GNI8RcrkyBpFzOaG4PLEl573pStQLxNni5sEjB47VX0iuVAr2wgtVVy7aZ8Vl7YUBTlnXBp8MEKdt2FbQBKghNJSXL3L2ptybrB69ghgcqgkApeUozQBLy8A8wOVUoLOtszR9h/clJqX2Ls6qrOslVWZax7Jaofu8fs4mO7I5t5JZ+6if/vMpcxj5N05IkWZY1NDREkqTruufPn3/kkUcEQfjwhz9cLpeJ/7hHJsFfidcfbHznnwTPj0wwDHTXW8jrjSGfSxXPzcBrMO0t5jMXsJzDvk0YnyfzRedv3tftWgtnh4TBccVhmXTGVioUGGL3Wvr50zSBckVF3IO+rsazE+nlOTS0QNPxvjd1pArlhfky60NmGQUJP/5y7/mx9NNnpFoNF2cRDFIkSUpFNl2W92/BR97eNzA4d3QQzw5ajM14QybLQdEQEMlazVFkiGQ4uVT0xLFpMxeI6IQdM3WlVJI1TaysmKszFkcH5BUdhAWYia2iBt2h3FBMaGtTW/zU+Dw7P6OW550N13hskxg/WYMEwhTcPpWogXfFts2U5pf8IagVbFmLICN0xlU/B9oXWEhWPv2uCZbofxH7FEWNj493dnZyHOc4DkVRpmnWj1MUZVkv8cYC4tY34kN/1hSI+gW+9sOHlru7AODsBabqiAAWZmjKXyhVAMByKYFPALjvbnoxt3BuBACSaRTSHIBSRVcMNEWDpWp54wZkFtoAVGtLjkUBuPMu/9ELUi3tAFBlZ3UIALgeXLcPwTjuun3H9x87l075AJR1Ra0GALhysSajtacDgMUs7N7cD8Dnsc4cm6npMQCsgOSQAYCPMf5ovrEhlk6ZQ4NSZ58NQHCYoUdtAKKXDe/RbrnCB+DRZ/T61OeLEMWMYcpkddFxKiAbASDkYUmbApAzVVTReQUNwBFY1lEAfP79wVKpXK1AYfj3v/ocS2x4EZUkSdZqNQC2bYfDYQCu69q2XTdHFEVdmopFUaznJGiaJo4PBb0EOzmXlUi0+lsePLXM07ROW2oeBAOjhjuv9kua9eefVYgaIr2QqmR3xHn3m1rPLCUvDMLPkjMrDoXA8kplw4a25fyKWY1Va2lLgRDGlbu5VJJoijDpgnRhHKoKL+lRNdnMIRz1NF0hW2pk6/rC/o2QdCykmak5wiXIiubkZgnF0kN+uE6UDOTbI/CE6ZZGqimol0uoaeGT5yuOZus6ajatJq2mfmpuwobFCX4z0Mg0xm3bIGjXPfFDi27ArhsYmjPbouzQiLEwBJenpKQd6qdrC7abI31tYs2xCFblw9Br8DN8WdUCYW8hU4t2kRTjsAJ9037XyzprushkDp9+4yRB9LyI/Xq0Zdu2ruuu6/p8vnrMRdN0XQYk+X9bsARBUFWVIIi3ve1tJICiJm3eulnSWAAl3dfitQDwpsgQNEfjsWNVQ7fecHcMwK5E9L1vCPR24NCzyT6S2rY+AKA9QVCMCcBxNVdxdTMNYOMWlncAwHKE0xftuSTv9/JOGhwjA+i9OpjYTQEIxAunx8D6SQC97VxvswXAw3CNnWQ0FKGoyOJ0viMQBRAOi2sazbAHXS3gmGJrGACEsJA6bgFYnmbtaYKzBQA0yVhwATC8u+FadG/wFVI8WYaXpwG8+g0N+hQBoDRoI+tapA1g+9XO2l0EAH8DnTe1cCNHsSZD8Z6SCECS7LFJG8Djp5z737h4Ofssy2qapus6AIZhANRTngAsy6Io6oXsA1BVlWGYXC4HgDh6glrM8GMrsq6AcDCapgAUVb+XKjV5Gy/Opdf2epby5tpWYmRG39IJjULCi+07tpi6try6MLNk5is8gPFJuVoEK4BjqVqOXUmqAEIRSmZtiuScks54BMJS7TL69jO6SgHo38SvJMuK6nvVK4MbO/Kj8zSAcyP60JgpcBFJzifHABLQcPU9OHiV0Bz2xUQlk6+pMjm3yp8btU48bABwKgBJsD2uMY6GzQFPmwKAsszmCDwMDMSDfiT8tOvLx6P28nwDgK/dnwMsJkLrFfPAq7y9m1QAQ6Ps5LQqijB0lDJkNCyWl2uRLtQXJWw/wD/1T5M80f4i9gVBUBQll8v5/X6O42iadhynbu7rfuflIAjia1/72ve+971/+Id/IJM1LEouAFnD7CpaPMJrdrW2CNINV4kAIhTNMSyAp4/qMzPsE8cFAI+fwo8eHgMQ92ktjV4vL2eycigAhwSA5qa21l7/+l0C78OaTdizgwJgkmhqMQGoMpJzlihaWk0bfL5saRSAf/23pJ+PtgaVSskwNJMXXQClAro3BQB07AzNTOPsIA3AdN0g4wNAUYokEX17GcfBplex/TcTAKIH+MxqpZg2KcK88iBuvRkAvEI2EK8BMGRDqQnx9tS54yUnYjZudAH4u7G6YhYrtlGww369o40EABcu59RvOz0EOwN/hHnin1YvZx9A3Zo3NDTwPA/AMAySJGmaBlC3/pejPhn86Ec/2rBhA/FX/8gAmE6ajgIABhNKLpccKQCga2PFqAFARyv5vZ8611/NL1e0yQGvxdcAvPtVkA0A0EwoyppkaRLA8CDkGgBEokS1SO++ygQwO8LPT2kAwMNcAoBQP1taZcDIACIBzhPXAfz5W9nnzxhTGQqASzi5tAuAYuAJUqZpZ5O44Zb4n97JAhgZWnnicDOAs2cyje0NLX3LAJQSl0/rAFJT2HETs7nPBEA4yC1HAcS7LMoxAOQK6lLa0xWNAJCdpEfwAhgerJJRsDYAyLonk5IBZBZCjc0KgMYm98T3KyLzEvvTGIYBIBgM1gd73QGVZTkajTqOc7kAaJquK8clIRGffADjC/TsguUVEabJs5NOa7MvtSrxAsoSBM5319W1jCSeOSvv3M2vbya/87Q5Omxevzc8vFi85SaPWpH9XpbwG2MX2FgivJoqSYquaXDhUxTJqFGtncTkmCu6IX8s39SMM2fAUNy2K5iZuVohD2OJJMB4/aSvUe3cGBs+mevqp/xNaGmMzyVXs6toaQLJCktJVapBKsDL4i/ete1D7x8kKLgOqABN+KxdV8BDkbbh9LX6QkxIZ5Injrpta70cVduxjuTtwGe+WPqTP9k8Nj9mWJQqawspRANke7tDkbRHRDyCxZTz2E+FSKvsApIMigBDeiwT+YrM0MiekAXa8yIqg8Gg4zitra3T09N+vz+fz18a3dVqVRAElmVfdApBEI2NjaurqwRB8DxvWZbjOMQr/gw5kzElU66Suu5wNAugVDX27vGdPCdFRKRk2EUCwNUHfK6/CkDKioku5bmnICt4/zsxP9sDINE1c+g52kNRoaBrW/6FdB5Aa7Pv7IBtSPrmLdi5jRgcsPJViiK8icYKgLllbnXC1YpEIKzXGJrzWgDkZWy6ERv6ErqRFQXk8zaAiiTmcgqAQoorntfhh6+VpWBINRZAz2Y6Fgmw1OqeLSwfEeJWBUA00v7MhcU1baRD06WMAXfzanY60mhA9gN4/GixuYEOxUAhVK5pSwsugPEJra0rKmlpTYJaFWIJcnlMNtIllgi9iEqe5+v8GoYhSVIikZAkSdd1QRAAVKvV+ujmOK6uIgRBMAwzNTUFoKOjA79whFBvTdz9x3rNFNSq2twcW54tdnQm8rnSYlI2Zf7WW7ixQXksaUX8fq1grd1DJ1eqm9b7+tbWSqrnxOO1UATb9mNiGtu2RFiqduhpPZ0BOEbknDU9hJR1fcHAalo5eJ0GoJDGg4fJ/Tu4cBCuaVZkd3TM3rSm2RNZUWScPClWUjpKaFhD77hNj4qIhVmHZOdmagyNVIGdHDMcE3pZbO9Qy47LgJXKht9H7t3ljM1gIYN3vsMvL1dNHrdsC48typYs1KwgIy4BkJJRg85SYOONxNFn9M6e8NhUkSOh08gXQRLMyGETKryboFfBChSAUMCeet4VX+rlDq7rrq6uNjc38zz/1re+9XOf+5zP59N1nSRJ27brsa7rupeC3k984hOWZSmK8rGPfSwej1+6zpve9CYSwOB3WE43RI8AQAzS584tA1jXjd172KeOVAD0NvPBoAxgfMiuLyVMpXhTc0uqCOD7X+VIOgDg/JC9c0ewvdMDQPDwA2c4AE8/Xayq2uIybdv+s6fQGGAB9MXU7k4AoL3oW59rDooAmtsVX8gGkD1vPPoFKAoAhL3O/g0kgMaQG4kDgGopoWayJQ4Afh/e+XY/gOt2U3/9fu+fbo8BeOsNXXMrGoAbrmJ7Wyozk05LyHfXDXY4GARwcUKvr+3evA4H9pOvvJ4HUFgmfFUPgNoIwUsJs+w4ij32fP5y9uvZBQCf+cxnHMcB8KUvfemjH/1o3esvl8v1yaCuAfUPr3rVqz772c8C8Pl8jY2N9YsAqH8mAIAEQWLrrVAAQwUATRe2rWGHL1RAoObx+00XQHpVautjASwtGTu2McMXTH+AXrnI2pwC4MbXU9WK3dAotsfNMxewNG8CKBURiQEAx9FlhcmfVQG03k694zoewLlpw8u3e70zNIW5JfrsKA1AXzHlSYLwc1RYfveH/eubNACrBeqxkyqAhXkufU5v6muO96+QFL13ZxQAScqdDU3Jxcm7bmh0TSujSQBKc3owSgI4N8yv7eZdUgfwoyfkTB4AIrHgug6rVKx1dFKzqcA3PlcF0Nhj6RkvgHLRVuSUcJnl8Xq9P/3pT6+88kqfz1cf3fV4qv7thg0bqtUqgGQyWTcvXq/Xsiyfz3fnnXd+85vfvDQrUBT1vve9z+fzfexjHyOBny8mvvgYdmxoY3j0doW6W6zBKQ3A1u2BlrBWysvJZa2hkWhtIDiebOtljj9jAsgULcqnQMbtb/H4WMewAaCodAZENhpHVcb6bWjviCWavT19LYqkbrgZm27zdQfIL/5Mfm4IP/mWuZxbBCArXE2zfEGNdnmJsIUuEkCglTx2Vh1JOqkKOZOi+luIQsaXOqtbJgVgcYYplK0TZ8l8KR1SpVb/JICYL+gPMH6eb/OEoxHA5KeW2c525btPFFcq5GMDelFnb7vGs6GvI+onM2UfgKOD9ukhiYxaTduc/u3U7pvd9dtqjjx/OfsURUmSBMAwDIZhLMuq25xLCYaRkZGlpaVcLmdZFk3ToVDIsqz3v//9AL797W/zPF+P1HRdr3uuH/vYx4DLFsu88a/I4UmnIRAAMDBR6evwAShV9WzeEEFs3RjlY7mHf8IDCPk1mWAByEvG294VXk7JAAandJEWAUzPKDfcgHwZoTBqEvIFADiwq5Njl0anyRBjnh+GLwQAkbifdGsA5tMOTyYmR7IAKNLesC8MIO61dbfS1ez91/9ZQ5gI8C6AcJxu3iQAIGTp1TeGD+zkARhW3pIJALppRSONc3MrKQm2ShZlCgAjNKg55ZnxWrStIWEmCxoPwNT9F5JZADs72pIrS6yf4iH85J8XBSLyIvZpmq4TfSmZXCexPvwdx6l/oCjq4MGDTzzxBEVRLMvWbdSPfvSjO++8E0AgECiVSnUbtW7duoceeug/CgAQELIoac/rqEJVb4uzLmdMzlCtcT4cN7N5c/SCe2Cf79DPpNZWrxiq7dyJsQmMjHEtCX3HejQ249QgNztnMQJpFUSGdXZc7Qt78jnZ8ZLUSkGfmxOWJtU/eTOj24RSs3ftYkOhxNTA3IKE4ycQCzREgqWObv65o9X0FGIJGIS/b13VUphYkzU+6s3NSJuvoVIrwY6Wwu6dmFlkVvPm3727Z3wxSep6OIqtPa2SWjYt6Lru2k7NZkbPqxBAUkLIEzt8YqmvE6cGoetIrWLNOrK3z88z5YceRlnjm3xWe5cF1fP1ryT9RPhF7LMsWw+v6ixblvXClDJBEJeU4BL8fn8qlfJ6vYcPH77tttvqlkoQBFmWBUE4ffr0/v3765m7/5ijQMmEdfTbulH1t0UpAE2NzFxKBuD3kIl28dmjOoC5IcLNMgC8vqBc0ru7PSXNM3jef/qMDiB52M7OVwAMz6eae4yusAXA0n2ljA3gkYfNFcVIxAMAgpivL1pc04vycmZNv80x8gf+NPDuDzIASrnq4rgXwMQ0m624N97dDKC1p3TLtc0A1nSbkTBGknM2aRIM0RLzA7AsWrctAILIKape78Xn/KGSqQPQ2faKjrtvjgA4d9KhFRvAO1/LsbYGQDHxg6+cv5x9AHXTUa1W64HVJW2o43L2AXz/+9+vf3XLLbcA8Hg8oVBIEIRoNKqq6rZt2/6v/C4/GSRPw6bbzdf9Cfv8kAggnSzv2Mx5/frUnDA3qlqrJACac9p245Z9oaE5J5uvTAwjEEBlmmThARDYKgfjjlXDO+7lTp6zjo/ZAHb1i7u2urZBzK1617WXK6YFYGnaGR5BY9yfiIfCiaSk051xPDvoPvMDE4CnCTRPkhVvSapeeydblgwA12/kcq4bFzyHzpR2b8btt/kZkyyly5EoD6AhQJfLSrXGJIt22YzMz2UAOA4hUrG52WxBR3cDBs4BwM7tQZ/PBvDkmDn2zSpBvTh04nleVVXLsu6+++5/+7d/AxAOh+s25EV68EI8/vjjb3zjGz/2sY/VS2CXYjSGYU6cOHHw4EGCIOrTNV56vyBHA6DPk4srzS2B8nK23LPG6/d5AFyxR33FXYFwP+kLcGwj3nKPH0BXtMKTBADe9vEBjvLKdLNMy/TsKAD89BmTioiNUVyxjdFspbnRBrBzS60rKtR0x0MIr72VueeVAHB0cLHen/HUcWMxSbdvDnb0hw5u8XW3OwBoiX32mwZMj1LC1x/SoRsA3n4XT7g0gLm5ck9XwjCMoOBGfJGNiW0AHMtamM8IDDiSk2U3k9EAdMYxNYPFGuKdjUdGjIm09Oiz0nOfH76cfZqm69wxDPPwww9Ho9F69HtJDy5njiCI+vGvf/3rH/3oR0ulkqIoPM/XO9EfeuihPXv2ALjEPn7lXhEA0LQ3sGs3JnMVAH4NB68PAnj+TG143rpmIwOgu8k8PUoBWF6xF84AQO8OtLSRFy+yAOJx1pdQAOzdRCXTensbAGzs8OZKBoCFFebgdlQkGcDTJxnRIgB0rne/+5AJoFzi1/Rr+9ZQACZTxI++bAHgWoRwo9a9wQXwoTfwIykNgJfaft3+EoByucxTVpNvB4DJqTOPjtYASDlxNas0tAJAYcXr8coADEtUa/bYCjlXUqSjYx5q3eUMPPjggwDe/va3l0olAPUg61ekOUmS/Id/+AcA9913X10M9VhsZWUFwNmzZ++66y4AL+zN+lUCqH/JuT5qs7R7D+ZKpFx23v/6npGJJE/p6zqDPzlWaY8wZ0aNhgS3smC5ui3XSMJxE83u1m2+fNV68lHVrWDHlfGlclbNYet1uPWqRJtvVWP9jmwfOW+Vc/rm9S2JyMqJUXJlmck52l0bPRYv8wx7//80WgLYuFMIhLTd/a4oek8Nef75SxmYwns/4kPI3tvD61aVIDGxylYkJT2p/ulrSd3y0ly1s6GnslKZl8r5ojUwwZfLhqETrmllV7FuCwhTPHZKoby4cQf9s6Pc+JGUnwi86LkbGhqWlpaampouacClauL+/fvPnj1bTzC8CI2NjaZpvve97/3kJz8JIBQK1Wq1np6excXFpqammZmZS5m4/yC2XyUAFy4rWeNwieCBDj+Ai6NpB4Gbboy19THbe6iSTgEYH9fvvDUE4NqDZLns2hZ57oycrqi+MAAMPiSXp3DjXf643x+mVnMFAFBNc8v6WFu7r1SrED7f+REbwJXN4oV5GcDxi0RTJzZvAqmrLBc6Ow0AtKCCRENC/f6/Z/sayk+dWAFwbkzIFSUAV94Q5ILt7R3NzYm1FxdmJqQqgPGkW5LsqkJqNuHSnjX9fgDjM04sHgXw709aE0cmLmff7/dPTk6qqlooFN7xjnfgFx5nHceOHbuc/UgkAiCdThcKhXrQWwdFUfPz8wCSySSAl64J/yoBAAAYAY7Ov+FTWlBAKktu7RR1b5Q0FqbmAnm5kpz31Bfpbe40v/WknE0x+ZSpa7At+GIojlOhAHZdb2/e1cTTqe3dcdYje0JtTz49mysY6zeEzg+VcuUAG670xLG11QPg4RPUDw5Vr93q8fsMDibjCU8ulsC7sxeZTNZqCPIfvw8GoVYkHBlCIhECcMMVxNJscdOmTZXibKUmW2Bd0lhcombnbN1kaiZTyiiECY5ExSD3bgkB+O5PC7WhWj0XjV/4OfVyiqIoH/jAB771rW/Vi1ZPPvnk/v37RVEkSVJRlHq67YWgKGpmZqZeiDdNk2EYx3E8Ho8kSd///vfvueeelxz4l/CfbyxnqgDwrY+jUMJrbmsDICUXDz8HAE4NLQkce77cFiifn7VqZQBwBezYCQCNfjocZve9xgZAqup1OwIAKnk5tZQUPTSAkXGlLcYB2Brxzw96qq7s2JrPY/U3MAA4mLu2xwA4qm9hMrS6YgJo7eOWqgSA8RXKFT0Ts6XWRi29ZFZkCkChavC+OqcehgbJkQCUimboAKDRhN/HjMwWTl0o6FP5S+xTFOXz+eodg3WmvvzlL7/3ve8FQJLkrbfe6vF4AExPT1/OPkEQf/7nfw7A6/XWpei67ic/+cn3ve99AN7whjf8p/T+2ttWEhxc5+NfollePTEjALhqfXA1VwJwegw0qdXrFafPkls3agASzfbMDKoyWht8pC19+kPdAHixMjNRml+xY03eZI4amq4AKGexrhUA5gpRgcqfuQAAbS3s/h0sgEJBPTnkLVcqmSW2MWY0tQGAL0oWqj4A27rN268mj0/SAJriZKlSBGCZIGy/ZdEzS8X5VeRzJAAKhFFhAZQtPXda4Qj+Rc/num49T5nL5equ/Y033vj0008DqAe0l0MQhI9+9KMASJL8+Mc/fuk4y7Jf/vKXNU378Ic/jF8UbX4Zfu2dc10dwN+/17g4CS+vFhbV1QxaItrEirMwrbUGkcupS4vqjm3UhTF7XQvXHibb28IAnjssnR/AbK7sUlnbUBmeAUDQfK5QiYdivIJ92+IDk3j0NJPO5MslD8syiTZPqIE6PmxMZ+zHT7jBkAfAjTf7etZi03pfrFGkKWd+tjJ4uOKn+nVHZazysQvlc8PF/rUCS3DBQKym/TzVHo+QJOPEW5yabZO8agpa7nT2EvuXPHRBEEiSXFhYKBQK+EWC4dChQ9dddx1+kbx8ESiKWrNmzb333js1NXX//ffv3LmzvupocXERQDAY/PCHP+y67q9m/zcRAACYFjw/+rz/jn293a0+AE8fJ2fHjfXr+KzE/+nd9NV7AaC/yzu9grmsQ5JF22QcBYUMnjykiTQLl2tJiLSIbNlsbQlQrmk6JACbwC27zHWNVCKgbenzt4csACJpP/UEqUrO+Fhq71Z6KVUoVOCCbm8wANx5K6IhXHvdsiBETAIsDwBaUVhaMhaTOcs2mzpDigzSdHZtQyEDALqJqScrHBH9+YhyXQCqqno8HtM0KYoKBoOXiogvlMFLasCjjz7693//93/yJ3/y1FNPATh79uzevXsBtLe3R6PR17zmNYZh1LuyXkYBAKjyhPnGW6Y9vPT06dXd/Z53vyGqVI1r+jS56Fyzp9vv2AuLtWdPKBcuEqfOI5WyCYEPNWDTJoZ0VcuQqorgQFxNVSbGtWyuygepBx/J+j14/DBG5tzWVkZyynPz+pYW68Qx29VlnyDs2YlwA7d3M2oyZpLBYhVbOkW/h776OuL7h42JOfX4OQS9TL4ClpM3bPVoNYETyOefm+3uCRE89IJ/07rY/m2BqSOKj/JfehKCICKRCE3T1Wq1tbXVsizTNLdu3drf3/9CGTz55JP1PymK8nq9dW0QBKFuoIaGhgqFgmVZLMsePnz4da97HYBUKhUKvTiZ+nIJAJqrAvj638OjQ3IcAOEo86+HAOCHTy4OpWVW9ABYmHbzMwDg8xi0H92N/tnlqECZZ4dXAKhlTyGvA1hasrxe2lIAILXs/OQoACxlqceOwR8DgKBPbYoAQDSCA/tZAIOjvvm0klq2AHSEnGcnTQDnJ83+DuTKOoCi6sxOEAA02Z7PAEBTIve9z5z11ZdvvwD1/hwAXV1ddYXI5XJDQ0OX4qxLyYZLRziOa2lpqX/1wAMPACBJsr+//1Of+hSAb3/72xzHAajHbr8O/osvcCBJOC7e9jf0wHkrlSMB0I7DeJDJwuOjKmV7TXMQwOJSubWXIih7ZgQTj75mbPmsYSUPnXCSGQJARUIo7F9e0lZXlOuv910Y0ACMTTguZTMkYxImx2PPVgBY24uAFwC+8i/gw2R2FFffIShl+YYbCACPjzI3buvqFJMQtfEp5unTGoBCAXfcBADfOYT8E5MEsaZ+53VPRlXVundoWRbDMPVSSaFQqNVq0Wi0vozrct+xXgqWZXn9+vWTk5N4waLfnTt3Dg8PvyjK/bWY/M3JB4C6Vfz6X1ohTqiVHEN1DuyJzo0CQHuD96arOCpQqcjlUBQ+rw1g7xW+L/7gEIDxZTNTJWjOWVixZlPW80eLAJpaYk8dkhJNZjTi71lrd7YCwIZu7N2I50+BZoQT59hMnhqfiqllMX+UBXDka2qotUeyWmZnXWXM+Ma3JgBs6WAdTrvl+p7u3l6/D6ur4sQCnXti7BL7mqY98sgjdcrqlr29vX3Tpk11e+31el/xilfUaX1J9uv+KIDR0dF6r6fruvWSyyXD9Zvit3qJDwUaQGCj1dsfEDgFwOCgeesrOAA1k5gY0lo7fABopuaqJIDezbZSBYDpGWE14+quBkCtoKMzDEBZkZkAu2mNCeDYOe2GqwDg+AlhbFHdtokdnTBoT0ibqAGwdZJwdQA7XsfOLqGYAoDH/s9a2ZgB8MRx/fQFyzVRs7Hw4DRB977wng3DcF23TqVlWT9fpkIQl9L6L5nf9/l8l3yhemBcj7nqTbjvete7/vqv/xov6MT66le/+ra3va1euqnn/X8ZfquX+NiwHIjV4UAoaCXT5vOPenSJza0QAcFlTCOdgiLRpiV5fQLrYQzG98ADSOeDuTy2dhvda4logAsFQ+E4q5rOynzR8hJl2bRh1QwuW8Dkoidf8C+tqj6BzWSJeHMonjAC6+GILMHDteH6cf4xw5AML0XBNj74maE2hliZlJWK1RxFXsfID5MvYh8Ay7LxeNw0zXXr1jmOwzCMbduKolwi/XL26+FxpVLp7u6uVCp1l4kgCE3TlpaW/H7/Jz7xiUt2DMADDzzQ3NxcN3F1bftdCQCAi6oF5YmvGazL+tvLAE5dMC8OsxfOO7EwJsZL9bpjvElT9DKAJx8uN8a76JBnfRcTihLNURWAbcmJPsg10bLEp5+1Tp41AVwYN49ckAAIAbu731jXLwMIhGzXlAGQDuE1BZfl1FWO4xmUAOBfnjIHFgEgrYlLjyg+pvUl77lSqRAEsXv3bgCmaUYiEUEQXrKuAuBSX7/P5xscHAQQjUb9fj9JkizLsiw7PT0NQNO0ujYYhhEMBu+++24AlmX9su7ES3iZXmVIAo6PbJKuuDJ29PEc4fgpf9V0SIZxtu1GWwdsAgAOPQZWQDTOsD6zr5PRTMcjNqWTSYJvLJU0hi9Xs1B0cAIYBGuGAiC9YGzZRrY1ekVBMlz3qYcBQF4kwTrhNiglStZsViRdwzE1vP3DADCZjj76xYyHeOnG2BdBlmXbtg8cOBAIBJ5//vkXfcuybC6Xa2hocF23PmeYptnQ0NDd3X3ixIn29vZisagoyiWW8/l8JBJhWdY0zXoh89dh7uWAAwBEir44mqNNADApiuUcAPMLwR/+EKThnxjx2zaiCQ5AZoIOohfAbDa5d0crRacBVGSUTADw8WxXVzkScuyaGaJCM0sOAMobCoboa27jXSXkRJytN4gAthyw995EAXADgARHE8p64MkvDr2Q/V82tOuoTwbvete7LmdfEIQ6g6VSSdd1giAsy+I47p/+6Z8AXLx4MZVK1X/Jsmzd3Nd/BiAej/867OPlfT8uScGxaaqFZGH4WwHAIbhSxgLganawlWY8FoBSGvo4ALzxI80n51eaYt7tffy3f5jftIUCMJ0llGULwBUHfA9+RWraHASQrZa72gBAk7E0QDZvcgDs3U2LPAGgUmaPnVAAFOZcu5infhHrXsILm9R+TRAE4fP5ADAMc6mdhCCIenDb2tq6uroKwDTNF/bB/Ypp/JeS9hvd1q+GYwMAsWyYtBDj+VgD29LiimHb1ciGEO0PWzQDy6WjLTyZYHmS+fevrkyeAoCnBsq5MlaWSY6z1zVbZJguF/DQQxJBIHW8XDXk6w+ERCaQz4LmaKHJKa5iz05/2MuHBVLk0dsmX7HPW5WaasXi5ezXJ8xfn5E6JEmamZmRZdk0zVqtVqvVTNM8e/bs3/zN3zz++OOlUolhmLowjh8/ThBEOBzmeZ6m6Rd2Cv06eLlf6AyLZECuqAs5bW2PAaC5kbF1i2J4a9UbCzJdTRSAti4PkTCdGAueNTRqacHyhbG4aIohL4ANzXaoA6wHQi/IGFHfsPHVd5m3XA2XsBo74Y/AdAQAedv0+UwArFeqTs56L+tp4DiubqB/IxnYti0IQkdHR/2Uel6Boqht27atrKzcdNNNeEGVZt++fSdPniwWi/Xy/S9Lnf4yvJwm6BJoUAT4yD55x0b2mSMGzaCaBc9RjT1O7zpvUZEGHgJoNHTDE6U2bwjl8uWRWZthaCVr9q9HV4LYt6PloScyQxMGRUCvUK96HRX20rmiUy0RI1OqwDQqaqVvrdverAGQbeYbf5GkiMbL76Qe09I0XXfG/1NbVO+mqnc81Jd3AXAc54Mf/OA//uM/1r2aF8oynU63t7eTJKlp2m+qZHW87BoAABZsALkTyElo7WcABBPQdBuAValV85QYZAFkzsMusQBs2gpFXADxdm5qDh09PgBrO42WJgJALG4vzvFFWQGQldRAAwtADNh+T7RQxkoVD/xF6iXZr9M3PDxcLpfrR361Hng8nnvvvRcv6PxxHCcWi9XTO47j1Ef3pSu8853vvO++++oLY1649vo3wu9EA34OkgDhXv0m3+ScVE4BwOYtvpYWrSKbUzNiekoxSQDo2chQIRNAeygwn1Qtx0OypVcf8J0alQAsLqMp1JSrpmIRNLSIExMKAE6AmgcADczAwytBIv6i/8xxnKZpmqZdSukQBHEpofbL9OC1r33tN7/5zfvuu+8rX/nKpVMsyyJJ8nLDUiwWN2/eXC/2/uqi439C0n/ttF8LjgvguX+TeIcTQ6Kax+y4OzsNAErZoDyE6MF1t3j6uk2rSmtFxBs94aABIDeNJ4+70agnJwUMHecHUwB4xjdwUuV4CHagsAxfgK6oOPeD6cvZZximvkhREIR6j6Ysy/fee2+dI5qmL2efIIgvf/nLAO6///56jrOuBFdddRUAj8fzoi4gjuMikUgymaznJ/7L7ON3qwF1MASA/usiuWqeNDkAuZmfv+lv44Hg/l3Wcq5G89RSkoo0GAAinJBcDQBINNc8nJnN6gBmh3DgmhCAkwOl6Tk0N+GGg94H/mKeJGI/fwyCcF23bg3qzommaZdWzXm93uXlZQBer7duT14IjuPqjuby8nJXV1f9Z9Vqtd4L7fP56jbnhTHtpemh3jr329Dzu9SAOkyXgXficfWW6+j2tXpRsskYD4cCRQ+fKR85VeMFwnTtvh77wjlsaaW6O9REQ9qw0h7Gbk+4jSHRVtDcjHi8FImXrt6FPVeircH7wF+sXGIfvxiwhmHUarVrrrmmt7dXEATbtlVV9fl8qqpGIpH777//Evv1VVr4hZskimJzc3N/f79hGARBFIvFuuX59Kc/XS+2sCzL8/ylFuh6Fo8giN+SfQAv0V/3ssM0JUD45l8z936EWo25qxk0bCJNwixOY/PG1ryU3Lc1OLko7d8NLsi4hm3bQiLG+cK1ogqXYqu0IgBdbajkAjVUAgL33a/kyf9YVTdNUxCE+mRbr6TXO/RrtVrdXyyVSpeqVKIoqqoqiiJN03UHyTTNbDbLcRxJkrquf+ELXwDAcVx92qizzPO867rve9/7vva1rymKcklvfkv87jXg51AJQv3W/9Dbe1gvp7V0mZ2d6NwMjye5uY8ZmSgHvHZhFc8f1uYyYDjWgX30uJVNUyOTZb/IBJoJXSV4b7Vo+n74hQUv9eKeBgAcx9UZrP/J8/yhQ4cCgYDruqqqvrBGWM99FovFfD5fq9Vc1zVN0zRNWZZ5nv/KV74Si8XqDeUvNDtvetObAHzpS1/Sdb2jo+NlYR+/RwGg7ryd+Ved91C8EogE0JEITc8CQFOCFFx200YhFgdv8/vWSgBa4mHaiQJYXDBnZt3T592xjPCvfz3GE4nLL1438TzPX/JYarVavRO23pj/ot9zHFe3J3/1V39VN0F1I1Y393fccUe9xHgJ4XD4q1/96qU/FxYWXi5afh8m6BJsgATyJ8n2tmorgaRZAtDaHD93cQUwtnSHMzK1dV0cQH/r3JnzkMrYt53OzlsAXIb+9L1LwmWZBoIg6oxfvHhx48aNAOoyqGfnP/CBD1x+G8FgsG6seJ5nGIZhmLqZqo/3paWlRCJxKQUEwOPxDA0N1WtelmX9F+qOvwK/ey/oMpAU79o+t0Fav117/es4VRJovnz6FF732jWPH1s4fl5/1VWBcxOVqkPs6ghenC5VJLz7Hu/bXjV4+b5s9XZBgiDq/Qq6rjc0NEiSZJqmoigv6XECoGn6vvvu+9znPlfPG9fbnnme/8QnPuHz+d7znvdcfgpFUTRNv1xm54X4AwgAQL10ZDQ0f/Wfs8tpj4Ly8hz61gPAxTPYshbHxuDquPNqfP850B7Ps18cuXxPQvwiIVM32fUxm0gk6j57Op2+vC3H5/PVu9U+8YlP+P1+TdMMw7i0k099b8NfM438cuH3Nwe8EAZgMKBy+YeeshlHjwcaRBFKVjjxDEoSgv6YAPaOq+C6uGo3Dn8h/5L7cda34BcEIZlM+v3++vDUNG1sbCyZTF7OPkEQx44dm5iYqDv7qqrWuWYYxuPx1N3K3zP7+EMJAPj5+0UP/ZPDeJxypQSAEsNijAMwkymN5YxHT0NhhfvfmqboF0+hPp+vHnyyLFv3ET/72c9eClYvD7Xqv6wvjP7iF79YT/hcSuXX8ZL9h78H/GFM0CUwFGvaxie/irmleuYCTa3is88qAPZtEP/XXy5R5Evk9+vsm6ZZD6Zs287lcvV9Ol8yz0OSZF0ql9ZV0zRtmuaf/dmffe1rX/ttEgm/Pf5wGgAAMG0DwF+/jRQ4bNrcrjtoov0eCtfs3vLFj49fzj7DMMViEb/Y/kLX9foa3aNHjwLI5/OX2H/hiF5ZWfnOd75TD75c1617PvUgq97VU1/D9QfBH1gDfgEBwJ8/oEOKJcLEtwfTZ76SJl8qw3wpP3NpqSJ+Udd94ToW/GJDnnoXEH7R+VPf3QpAPdb91Kc+Vb/Cr27d+f8LEPCAxB3vYLe/Da6bvvwHDMPU82IMw9Q3AK4zW49jX2T362tdAHg8nvrqdfyiKdHr9b75zW8Oh8N/+Zd/Wa8g/j4e7/8bIOFFXHeXL//G5/PVt4TZunUrftE6SNN0Pf1w6tSpy0+pB7cej+fw4cMvPB4Khd785jfbtn15kvn/AfX3Kl4O0zTr473OeN2M1FujXrLCRZLkRz/60eHhYQDf+MY3vvGNbwDweDwcxx05ciSTydSngd/hc/x/HfURWjf0tm2/0Kmnafqxxx6rb97wkuxzHMcwzEc/+tHPfOYzoijWd6gCEAgE6imggYGB/zf2fxXqO52+sIj4IgHUf/DL3Mef/exnP/vZzwRBqKtL/aAgCIIg1Ks0def1/+GlcYn9+p/EL/DCPMyLGHzhRkjHjx/ftWvX+Pi41+u91DBb94he2D/7//DSeBH7AN761rdeksFLGpx61bd+SqFQKBQKhmF4PJ56mwLDMJ///Oc///nP113V/4YC+P8BlztSyHmQBgwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=128x128 at 0x7FDD842ED940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from PIL import Image\n",
    "from IPython.display import display\n",
    "filename = \"./fractal_wrongness.png\"\n",
    "\n",
    "with Image.open(filename) as im:\n",
    "    print(filename, im.format, \"%dx%d\" % im.size, im.mode)\n",
    "    resized = im.resize((128, 128))\n",
    "    display(resized)\n",
    "    \n",
    "    display(resized.rotate(45))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fractal_wrongness.png    370765\n",
      "__temp_ipython__.png      28246\n"
     ]
    }
   ],
   "source": [
    "from plumbum import local\n",
    "\n",
    "for file in local.cwd//'*.png':\n",
    "    size = file.stat().st_size\n",
    "    print(f\"{file.name.ljust(21)}{str(size).rjust(10)}\")\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'hsa04014': 'Ras signaling pathway',\n",
       " 'hsa04064': 'NF-kappa B signaling pathway',\n",
       " 'hsa04650': 'Natural killer cell mediated cytotoxicity',\n",
       " 'hsa04658': 'Th1 and Th2 cell differentiation',\n",
       " 'hsa04659': 'Th17 cell differentiation',\n",
       " 'hsa04660': 'T cell receptor signaling pathway',\n",
       " 'hsa05340': 'Primary immunodeficiency'}"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from bioservices import KEGG\n",
    "k = KEGG(verbose=False)\n",
    "k.get_pathway_by_gene(\"7535\", \"hsa\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Introduction to the UNIX shell\n",
    "\n",
    "the most commonly used shell is called **bash**, and we will refer to that one as the standard implementation.\n",
    "\n",
    "A very similar shell is present in OSX, and is possible to install it under window activating the `Windows Subsystem for Linux` option in the control panel."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Why learning the bash shell\n",
    "\n",
    "Graphical User Interfaces (GUI) are useful, but they are no always available in all the systems.\n",
    "\n",
    "Serves, for example, are often only manageable.\n",
    "\n",
    "Also, once you are used to it, it gives you a faster, more reliable way to perform your tasks: you are trading memory for speed (we will come back to this)\n",
    "\n",
    "bash and similar shells are sometimes referred as Command Line Interfaces (CLI)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Structure of a shell\n",
    "\n",
    "The bash is a type of shell.\n",
    "**shells** are interactive systems that take user input in the form of text commands and perform actions based on them\n",
    "\n",
    "most systems in a computer provide a shell interface:\n",
    "\n",
    "* bash: a shell of the operating system\n",
    "* IPython: a shell to interface to a python engine\n",
    "* most databases will provide one\n",
    "* etc ..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "The commands can be given **interactively**, by actually typing each command, or using a **script**, where the commands are pre-written and get executed one command at the time.\n",
    "\n",
    "For all intent and purposes, bash scripts can be used a program.\n",
    "\n",
    "As a matter of fact, big chuncks of unix systems are just a collection of bash scripts.\n",
    "\n",
    "They can also be used to glue together various programs that provide a CLI.\n",
    "Most programs provide one, even if you might not know about it, such as:\n",
    "* firefox\n",
    "* openoffice\n",
    "* vlc\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## CLI\n",
    "\n",
    "Command line interfaces of programs allows automation of work.\n",
    "One does not need to remember and perform the exact sequence of action each time they want to perform the same task, but might save the relevant command on a small bash script and execute it directly.\n",
    "\n",
    "This also allows **batch execution**, i.e. repeat the same task on a (potentially huge) number of files all at the same time.\n",
    "\n",
    "For example, this converts all the JPG images in the current directory into GIF thumbnails in a \"thumbs\" sub-directory\n",
    "\n",
    "    mogrify -format gif -path thumbs -thumbnail 100x100 *.jpg"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "the same process with GUI tools?\n",
    "\n",
    "1. open the GUI of your program\n",
    "2. open image #1\n",
    "3. resize it to thumbnail size\n",
    "4. save it as gif in the right folder\n",
    "5. close the image\n",
    "6. repeat step 2-5 as many time as needed\n",
    "7. close the GUI"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## file system, disks and partitions\n",
    "\n",
    "The file system is the way that the operating system uses to organize the files in your disk.\n",
    "\n",
    "It traditionally organize the files in various directories\n",
    "\n",
    "It can merge the content of various local disks (and remote ones) in a transparent way\n",
    "\n",
    "The location of a file (or a directory) is represented with a string, where the `/` is used to separate between directories.\n",
    "Most programming languages (including Python) follow this convention\n",
    "**note** windows use `\\` to separate directory, just to make things easier for all of us.\n",
    "Get used to this, we will see it often..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "![](https://imgs.xkcd.com/comics/standards.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "![](./directory_structure.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "![](./OS_structure.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## File ownership and permissions\n",
    "\n",
    "each file is owned by a user, and a group (of which the owner usually is part of, but not necessarely).\n",
    "\n",
    "every time a user want to interact with the file, the permissions are given based on:\n",
    "\n",
    "* is it the owner?\n",
    "* if not, does it belong to the group?\n",
    "* anybody else\n",
    "\n",
    "The permission are related to:\n",
    "\n",
    "* **reading**\n",
    "* **writing**\n",
    "* **executing** (or **entering** for directories)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "![](./users_and_groups.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "![](./file_inode_permissions.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### file name and extension\n",
    "\n",
    "by tradition, file names ends with a full stop and 3 (or more) letters, that are used to quickly identify what kind of file it is.\n",
    "\n",
    "Some common extensions are:\n",
    "* `.exe` windows executable\n",
    "* `.sh` bash script\n",
    "* `.txt` raw text files\n",
    "* `.doc`, `.docx` Office Word files\n",
    "* `gif`, `png`, `jpg` image files\n",
    "\n",
    "The reality is that there is no rule linking the content of a file to its extension: you could rename a `jpg` image as a `txt` file, but the content would still just be the previous image.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Some basic bash commands\n",
    "\n",
    "Bash comes with a long list of pre-installed programs that provide several utilities.\n",
    "\n",
    "The ones that are (for our purpose) most important are the ones that allow you to move around and manipulate the file system and the files inside it."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### `ls` --- list content of a directory\n",
    "* `ls` list all the files in the currect directory\n",
    "* `ls <directory_name>` list all the file in the given directory\n",
    "* `ls -l` list the files, including some metadata about them\n",
    "* `ls -l -h` show file sizes in human readable format\n",
    "* `ls -l -h -S` sort them by size (greatest to smallest)\n",
    "* `ls -l -h -S -r` invert the order of sorting\n",
    "* `ls -lhSr` short form of the previous one\n",
    "* `ls -l -t` sort them by the time of last edit (most recent to oldest)\n",
    "* `ls -l -R` show them recursively (this directory and all the subdirectory)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### file name expansion\n",
    "\n",
    "see: https://www.gnu.org/software/bash/manual/html_node/Pattern-Matching.html\n",
    "\n",
    "* `*` \tany number (including none) of characters of any kind\n",
    "* `?` \ta single (one and only one) character of any kind\n",
    "* `[characters]` a single (one and only one) character included in the list of characters specified\n",
    "* `[[:classname:]]` a single (one and only one) character belonging to a specific character class. For example, if you use `[[:alnum:]]`, bash will match a character only if it is alphanumeric. Supported character classes include: `alnum`, `alpha`, `ascii`, `blank`, `cntrl`, `digit`, `graph`, `lower`, `print`, `punct`, `space`, `upper`, `word`, `xdigit`\n",
    "\n",
    "----\n",
    "\n",
    "* `ls *.txt` list all the text file\n",
    "* `ls document.*` list all the file whose name is `document` and have any kinf of extension\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### `cd` --- change directory\n",
    "\n",
    "* `cd <directory_name>` goes to that directory\n",
    "* `cd ~` goes back to the home directory\n",
    "* `cd -` goes back to the previous folder\n",
    "* `cd ..` goes the directory above the current one"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### mv --- move and rename files and directories\n",
    "\n",
    "* `mv <base_file> <new_location>` change position (and rename) a single file\n",
    "* `mv <file1> <file2> <file3> <destination_directory>` move several files to a directory\n",
    "* `mv -i <origin> <destination>` ask for confirmation before moving if it's going to override\n",
    "* `mv -u <origin> <destination>` move only if the origin is newer than the destination\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### rm --- remove a file\n",
    "\n",
    "* `rm <filename>`\n",
    "* `rm -r <>` recursive elimination (remove inside directories that are not empty)\n",
    "* `rm -i <>` ask authorization before all removal\n",
    "* `rm -f <>` remove files without asking permission \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### cp --- copy a file\n",
    "\n",
    "* `cp <base_file> <new_location>`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### mkdir --- create directories\n",
    "\n",
    "* `mkdir <directory_name>` create the directory inside the current one (gives error if it does already exists)\n",
    "* `mkdir -p <directory_name>` same as before, but create it only if it does not exists\n",
    "* `mkdir <dir/subdirectory>` create both the directory and the subdirectory directly (any number of levels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### `chown` and `chmod` change file ownership and permissions\n",
    "\n",
    "**chown** allows to change either the user or the group (or both) that owns a file.\n",
    "\n",
    "**chmod** allows to change the permission of the file"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### `ssh` and `scp` --- secure remote server access and file copy\n",
    "\n",
    "**ssh** open a system shell (typically bash on a unix server) on a remote server, and allow to interact with it as if it was a local shell, but using remote resources\n",
    "\n",
    "**scp** allows to exchange files between the local machine and a remote server, with a syntax similar to the cp command."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### random commands\n",
    "\n",
    "* `man <command>` give the complete help on the command\n",
    "* `pwd` print the current working directory\n",
    "* `top`/`htop` display informations about the system\n",
    "* `nano`/`vim` edit text files\n",
    "* `touch <filename>` create and empty file, or refresh the last modify date of an existing one"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Pipes\n",
    "\n",
    "**Pipes** are a methods of the bash shell that allow to manage the flow of the data inside and outside of a program, and between programs."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Any program have various \"streams\":\n",
    "\n",
    "\n",
    "* STDIN (0) - Standard input (data fed into the program)\n",
    "* STDOUT (1) - Standard output (data printed by the program, defaults to the terminal)\n",
    "* STDERR (2) - Standard error (for error messages, also defaults to the terminal)\n",
    "\n",
    "each one of these can be redirected to other places.\n",
    "The basic redirection is done with the `>` and `<` operators."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### input redirection\n",
    "\n",
    "a program that takes parameters as text input from the user can be automated by passing it a file containing the input.\n",
    "\n",
    "    myprogram < input_file.txt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### output redirection\n",
    "\n",
    "create (or overwrite) a file with all the output of the program\n",
    "\n",
    "    myprogram > all_output_file.txt\n",
    "    \n",
    "append (do not replace) the existing file with the output (good for logging)\n",
    "    \n",
    "    myprogram >> all_output_file.txt\n",
    "\n",
    "redirecting separately standard output and standard error streams\n",
    "\n",
    "    myprogram 1> regular_output_file.txt   2> error_output_file.txt      \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### piping to other programs\n",
    "\n",
    "the `|` (pipe) allows to take the output of a program and use it directly as the input of another, without creating intermediate files.\n",
    "\n",
    "* `ls *.txt | head -3` list all the `.txt` files, shows only the first 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "# IPython\n",
    "\n",
    "the main way we will use to interact with python will be the ipython shell."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "#### ipython special pre-commands\n",
    "\n",
    "* `%<magic function>` use magic functions\n",
    "* `%%<cell magic>` use cell magic mode\n",
    "* `!<function>` call system programs (non interactive)\n",
    "* `/<function>` activate autocalling\n",
    "\n",
    "\n",
    "    /print \"a\"\n",
    "\n",
    "\n",
    "* `,<function>` (comma) activate autocalling and autoquoting (divide by whitespace)\n",
    "* `;<function>` (comma) activate autocalling and autoquoting (ignore whitespaces)\n",
    "\n",
    "\n",
    "    ,my_function a b c    # becomes my_function(\"a\",\"b\",\"c\")\n",
    "    ;my_function a b c    # becomes my_function(\"a b c\")\n",
    "    \n",
    "all of thse should be used as the first letter, the only exception being `!`, that can follow the `=` assignment\n",
    "\n",
    "* `?` general help\n",
    "* `<object>?` help of an object\n",
    "* `pattern?` return all the variables in the namespace that follow the pattern"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Variable   Type        Data/Info\n",
      "--------------------------------\n",
      "a          list        n=2\n",
      "attach     function    <function attach at 0x7f638c121378>\n",
      "b          int         2\n",
      "data       dict        n=2\n",
      "info       SList       ['Filesystem     1K-block<...>584   1% /run/user/1000']\n",
      "l          SList       ['Lesson 1 - introduction<...>ME.md', 'Untitled.ipynb']\n",
      "l1         SList       ['/bin/bash: !ls: command not found']\n",
      "l2         SList       ['Lesson 1 - introduction<...>ME.md', 'Untitled.ipynb']\n",
      "lal        int         1\n",
      "lel        int         3\n",
      "lol        int         2\n"
     ]
    }
   ],
   "source": [
    "%whos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['b', 'lal', 'lel', 'lol']"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%who_ls int"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Variable   Type    Data/Info\n",
      "----------------------------\n",
      "a          list    n=2\n"
     ]
    }
   ],
   "source": [
    "%whos list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "def myfun(n=1000, k=10):\n",
    "    a = 1\n",
    "    a = np.random.randn(1, n)\n",
    "    for i in range(k):\n",
    "        a = np.random.randn(n, n)\n",
    "    a = np.random.randn(n, 1)\n",
    "    a = np.random.randn(n).reshape(n, 1)\n",
    "    return None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " \n",
      "*** Profile printout saved to text file 'out.txt'. \n"
     ]
    }
   ],
   "source": [
    "%prun -T out.txt myfun()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         18 function calls in 0.382 seconds\r\n",
      "\r\n",
      "   Ordered by: internal time\r\n",
      "\r\n",
      "   ncalls  tottime  percall  cumtime  percall filename:lineno(function)\r\n",
      "       13    0.381    0.029    0.381    0.029 {method 'randn' of 'mtrand.RandomState' objects}\r\n",
      "        1    0.001    0.001    0.382    0.382 <ipython-input-218-7ec54cd088a8>:3(myfun)\r\n",
      "        1    0.000    0.000    0.382    0.382 {built-in method builtins.exec}\r\n",
      "        1    0.000    0.000    0.000    0.000 {method 'reshape' of 'numpy.ndarray' objects}\r\n",
      "        1    0.000    0.000    0.382    0.382 <string>:1(<module>)\r\n",
      "        1    0.000    0.000    0.000    0.000 {method 'disable' of '_lsprof.Profiler' objects}"
     ]
    }
   ],
   "source": [
    "!cat out.txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "peak memory: 176.84 MiB, increment: 30.29 MiB\n"
     ]
    }
   ],
   "source": [
    "%memit myfun()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "371 ms ± 2.65 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%timeit myfun()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "*** Profile printout saved to text file 'out.txt'. \n"
     ]
    }
   ],
   "source": [
    "#%load_ext line_profiler\n",
    "%lprun -T out.txt -f myfun myfun()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {
    "scrolled": false,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Timer unit: 1e-06 s\r\n",
      "\r\n",
      "Total time: 0.373999 s\r\n",
      "File: <ipython-input-218-7ec54cd088a8>\r\n",
      "Function: myfun at line 3\r\n",
      "\r\n",
      "Line #      Hits         Time  Per Hit   % Time  Line Contents\r\n",
      "==============================================================\r\n",
      "     3                                           def myfun(n=1000, k=10):\r\n",
      "     4         1          1.0      1.0      0.0      a = 1\r\n",
      "     5         1         83.0     83.0      0.0      a = np.random.randn(1, n)\r\n",
      "     6        11         24.0      2.2      0.0      for i in range(k):\r\n",
      "     7        10     372958.0  37295.8     99.7          a = np.random.randn(n, n)\r\n",
      "     8         1        885.0    885.0      0.2      a = np.random.randn(n, 1)\r\n",
      "     9         1         47.0     47.0      0.0      a = np.random.randn(n).reshape(n, 1)\r\n",
      "    10         1          1.0      1.0      0.0      return None"
     ]
    }
   ],
   "source": [
    "!cat out.txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Name: ZMQbg/1', 'Umask: 0002', 'State: S', 'Tgid: 22797', 'Ngid: 0']"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "ret = !cat /proc/{os.getpid()}/status\n",
    "ret.fields(0, 1)[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "b'ciao'\n",
      "b'ciao'\n",
      "b'ciao'\n",
      "----------\n",
      "b'cia\\xe8'\n",
      "b'cia\\xc3\\xa8'\n",
      "----------\n",
      "b'123'\n",
      "b'123'\n"
     ]
    }
   ],
   "source": [
    "s = \"ciao\"\n",
    "print(bytes(s, encoding='ascii'))\n",
    "print(bytes(s, encoding='utf8'))\n",
    "print(bytes(s, encoding='latin1'))\n",
    "\n",
    "print('-'*10)\n",
    "s = \"ciaè\"\n",
    "print(bytes(s, encoding='latin1'))\n",
    "print(bytes(s, encoding='utf8'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "232"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ord('\\xe8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'è'"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chr(232)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "default language: 'it_IT'\n",
      "default encoding: 'UTF-8'\n",
      "default language: 'it_IT'\n",
      "default encoding: 'UTF-8'\n"
     ]
    }
   ],
   "source": [
    "import locale\n",
    "language, encoding = locale.getdefaultlocale()\n",
    "print(f\"default language: '{language}'\")\n",
    "print(f\"default encoding: '{encoding}'\")\n",
    "language, encoding = locale.getlocale()\n",
    "print(f\"default language: '{language}'\")\n",
    "print(f\"default encoding: '{encoding}'\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'ciao·mondo·!'"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import re\n",
    "regex = re.compile('[ ]+')\n",
    "re.sub(pattern=regex, string=\"ciao     mondo    !\", repl='·') "
   ]
  }
 ],
 "metadata": {
  "celltoolbar": "Slideshow",
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.7"
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