Chapter1_Python_introduction_answers.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Introduction to Python With ANSWERS\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "Python is a popular general purpose, free language. As opposed to other systems that are focused towards a particular task (e.g. R for statistics), Python has a strong following on the web, on systems operations and in data analysis. For scientific computing, a large number of useful add-ons (\"libraries\") are available to help you analyse and process data. This is an invaluable resource.\n",
    "\n",
    "In addition to being free, Python is also very portable, and easy to pick up. \n",
    "\n",
    "\n",
    "The aim of this Chapter is to introduce you to some of the fundamental concepts in Python. Mainly, this is based around fundamental data types in Python (`int`, `float`, `str`, `bool` etc.) and ways to group them (`tuple`, `list`, array, string and `dict`).\n",
    "\n",
    "Although some of the examples we use are very simple to explain a concept, the more developed ones should be directly applicable to the sort of programming you are likely to need to do.\n",
    "\n",
    "Further, a more advanced section of the chapter is available, that goes into some more detail and complkications. This too has a set of exercises with worked examples.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this session, you will be introduced to some of the basic concepts in Python.\n",
    "\n",
    "**The session should last 4 hours (one week).**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.1 Getting Started"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.1.1 Comments and print function\n",
    "\n",
    "Comments are statements ignored by the language interpreter.\n",
    "\n",
    "Any text after a # in a *code block* is a comment.\n",
    "\n",
    "You can 'run' the code in a code block using the 'run' widget (above) or hitting the keys ('typing') <shift> and <return> at the same time. \n",
    "   \n",
    "**E1.1.1 Exercise**\n",
    "\n",
    "* Try running the code block below\n",
    "* Explain what happened ('what the computer did')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Hello world"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<mark>**ANSWER**\n",
    "    \n",
    "<mark>Nothing 'apparently' happened, but really, the code block was interpreted as a set of Python commands and executed. As there is only a comment, there was no output."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also use text blocks (contained in quotes) to contain comments, but note that if it is the last statement in the code, the text block may be printed out to the terminal. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\nHello \\nworld\\nis printed\\n'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# single line text\n",
    "'hello world is not printed'\n",
    "\n",
    "# multi-line text\n",
    "'''\n",
    "Hello \n",
    "world\n",
    "is printed\n",
    "'''"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " \n",
    "**E1.1.2 Exercise**\n",
    " \n",
    "* Copy the text from above in the window below\n",
    "\n",
    "* Then put a new text block at the end and note down what happens\n",
    "        \n",
    "* What does the `\\n` mean/do?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\nAnother text block\\n'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# do the exercise here\n",
    "# ANSWER\n",
    "\n",
    "# single line text\n",
    "'hello world is not printed'\n",
    "\n",
    "# multi-line text\n",
    "'''\n",
    "Hello \n",
    "world\n",
    "is printed\n",
    "'''\n",
    "\n",
    "'''\n",
    "Another text block\n",
    "'''"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<mark>ANSWER\n",
    "\n",
    "<mark>The last output is printed. In this case, it is the string `'\\nAnother text block\\n'`.\n",
    "    \n",
    "<mark>The `\\n` is a newline character. It setrs the text cursor to the start of a new line."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "To print some statement (by default, to the screen you are using), use the `print` method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hello world\n"
     ]
    }
   ],
   "source": [
    "print('hello world')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hello world\n"
     ]
    }
   ],
   "source": [
    "print('hello','world')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In Python 3.X, `print` is a function with the *argument(s)* (here, the string you want printed) enclosed in the function's (round) brackets.\n",
    "\n",
    "\n",
    "**E1.1.3 Exercise**\n",
    "\n",
    "* Copy the print statement from above code block.\n",
    "\n",
    "* Change the words in the quotes and print them out.\n",
    "\n",
    "* Add some comments to the code block explaining what you have done and seen.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "good morning\n"
     ]
    }
   ],
   "source": [
    "# do the exercise here\n",
    "# ANSWER\n",
    "\n",
    "\n",
    "'''\n",
    "The print statement below sends text to the output channel (the 'screen' as it were). \n",
    "In this case, it prints the string 'good', followed by a space, then the string 'morning'\n",
    "'''\n",
    "print('good','morning')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.1.2 Variables, Values and Data types"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The idea of **variables** is fundamental to any programming. You can think of this as the *name* of *something*, so it is a way of allowing us to refer to some object in the language.\n",
    "\n",
    "What the variable *is* set to is called its **value**.\n",
    "\n",
    "So let's start with a variable we will call (*declare to be*) `x`.\n",
    "\n",
    "We will give a *value* of the string `'one'` to this variable:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "one\n"
     ]
    }
   ],
   "source": [
    "x = 'one'\n",
    "\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**E1.1.4 Exercise**\n",
    "\n",
    "* set the a variable called x to some different string (e.g. 'hello world')\n",
    "    \n",
    "* print the value of the variable `x`\n",
    "    \n",
    "* Try this again, putting some 'newlines' (`\\n`) in the string"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hello world\n",
      "hello world \n",
      " hello world\n"
     ]
    }
   ],
   "source": [
    "# do the exercise here\n",
    "# ANSWER\n",
    "\n",
    "x = 'hello world'\n",
    "\n",
    "print(x)\n",
    "'''\n",
    "Note the space before the final\n",
    "hello world, as there is a comma \n",
    "between '\\n' and x\n",
    "'''\n",
    "print(x,'\\n',x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 is type <class 'int'>\n"
     ]
    }
   ],
   "source": [
    "# Now we set x to the value 1\n",
    "\n",
    "x = 1\n",
    "\n",
    "print(x,'is type',type(x))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In a computing language, the *sort of thing* the variable can be set to is called its **data type**. \n",
    "\n",
    "In python, we can access this with the method `type()` as in the example above.\n",
    "\n",
    "In the example above, the datatype is an **integer** number (e.g. `1, 2, 3, 4`).\n",
    "\n",
    "In 'natural language', we might read the example above as 'x is one'.\n",
    "\n",
    "\n",
    "**E1.1.5 Exercise**\n",
    "\n",
    "* set the a variable called `x` to the integer `5`\n",
    "    \n",
    "* print the value and type of the variable `x`\n",
    "    \n",
    "* change the data type used for `x` to something else (e.g. a string)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5 <class 'int'> 5\n"
     ]
    }
   ],
   "source": [
    "# do the exercise here\n",
    "# ANSWER\n",
    "\n",
    "x = 5\n",
    "'''\n",
    "We use the methods type() and str() here\n",
    "'''\n",
    "print(x,type(x),str(x))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Setting `x = 1` is different to:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = 'one'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "because here we have set value of the variable `x` to a **string** (i.e. some text).\n",
    "\n",
    "A string is enclosed in quotes, e.g. `\"one\"` or `'one'`, or even `\"'one'\"` or `'\"one\"'`.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "one\n",
      "one\n",
      "'one'\n",
      "\"one\"\n"
     ]
    }
   ],
   "source": [
    "print (\"one\")\n",
    "print ('one')\n",
    "print (\"'one'\")\n",
    "print ('\"one\"')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**E1.1.6 Exercise**\n",
    "\n",
    "* create a variable `name` containing your name, as a string.\n",
    "        \n",
    "* using this variable and the print function, print out a statement such as `my name is Fred` (if your name were `Fred`)\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "My name is Professor Philip Lewis\n"
     ]
    }
   ],
   "source": [
    "# do the exercise here\n",
    "# ANSWER\n",
    "\n",
    "name = 'Professor Philip Lewis'\n",
    "print('My name is',name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Setting `x = 1` or `x = 'one'` is different to:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = 1.0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "because here we have set value of the variable `x` to a **floating point** number (these are treated and stored differently to integers in computing).\n",
    "\n",
    "This in turn is different to:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = True"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "where `True` is a **logical** or **boolean** datatype (something is `True` or `False`).\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**E1.1.7 Exercise**\n",
    "\n",
    "* in the code block below, create a variable called `my_var` and set it to some value (your choice of value, but be clear about the data type you intend)\n",
    "           \n",
    "* print the value of the variable to the screen, along with the data type."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100 <class 'int'>\n"
     ]
    }
   ],
   "source": [
    "# do the exercise here\n",
    "# ANSWER\n",
    "\n",
    "# set to integer\n",
    "my_var = 100\n",
    "print(my_var,type(my_var))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have so far seen four datatypes:\n",
    "\n",
    "- integer (`int`): 32 bits long on most machines\n",
    "- (double-precision) floating point (`float`): (64 bits long)\n",
    "- Boolean (`bool`)\n",
    "- string (`str`)\n",
    "\n",
    "but we will come across more (and even create our own!) as we go through the course.\n",
    "\n",
    "In each of these cases above, we have used the variable `x` to contain these different data types.\n",
    "\n",
    "As we saw above, if you want to know what the data type of a variable is, you can use the method `type()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'int'>\n",
      "<class 'float'>\n",
      "<class 'str'>\n",
      "<class 'bool'>\n"
     ]
    }
   ],
   "source": [
    "print (type(1));\n",
    "print (type(1.0));\n",
    "print (type('one'));\n",
    "print (type(True));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can explicitly convert between data types, e.g.:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "int(1.1) =  1\n",
      "float(1) =  1.0\n",
      "str(1) =  1\n",
      "bool(1) =  True\n"
     ]
    }
   ],
   "source": [
    "print ('int(1.1) = ',int(1.1))\n",
    "print ('float(1) = ',float(1))\n",
    "print ('str(1) = ',str(1))\n",
    "print ('bool(1) = ',bool(1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "but only when it makes sense:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "converting the string '1' to an integer makes sense: 1\n"
     ]
    }
   ],
   "source": [
    "print (\"converting the string '1' to an integer makes sense:\",int('1'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "invalid literal for int() with base 10: 'one'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-23-11bdc0c0e878>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprint\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m\"converting the string 'one' to an integer doesn't:\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'one'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m: invalid literal for int() with base 10: 'one'"
     ]
    }
   ],
   "source": [
    "print (\"converting the string 'one' to an integer doesn't:\",int('one'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When you get an error (such as above), you will need to learn to *read* the error message to work out what you did wrong.\n",
    "\n",
    "**E1.1.8 Exercise**\n",
    "\n",
    "* why did the statement above not work?\n",
    "    \n",
    "* type some other data conversions below that *do* work."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 1 2 3\n",
      "1\n"
     ]
    }
   ],
   "source": [
    "# do the exercise here\n",
    "# ANSWER\n",
    "\n",
    "print(str(1),str('1 2 3'))\n",
    "print(int('1'))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<mark>We tried to convert a string ('one') to an integer, that doesn't make sense. It *is* ok e.g. for `int('1')` though."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.1.3 Arithmetic"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Often we will want to do some [arithmetic](http://www.tutorialspoint.com/python/python_basic_operators.htm) with numbers in a program, and we use the 'normal' (derived from C) operators for this.\n",
    "\n",
    "Note the way this works for integers and floating point representations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "110\n",
      "-90.0\n",
      "0.5\n",
      "0.5\n",
      "200.0\n",
      "8.0\n",
      "1\n",
      "32\n"
     ]
    }
   ],
   "source": [
    "'''\n",
    "    Some examples of arithmetic operations in Python\n",
    "\n",
    "    Note how, if we mix float and int, the result is raised to float\n",
    "    (as the more general form)\n",
    "'''\n",
    "\n",
    "print (10 + 100)     # int addition\n",
    "print (10. - 100)    # float subtraction\n",
    "print (1./2.)        # float division\n",
    "print (1/2)          # int division\n",
    "print (10.*20.)      # float multiplication\n",
    "print (2 ** 3.)      # float exponent\n",
    "\n",
    "print (65%2)         # int remainder\n",
    "print (65//2)        # floor operation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**E1.1.9 Exercise**\n",
    "\n",
    "* change the numbers in the examples above to make sure you understand these basic operations.\n",
    "\n",
    "* try combining operations and use brackets `()` to check that that works as expected.\n",
    "\n",
    "* see what happens when you add (i.e. use `+`) strings together"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11\n",
      "-9.0\n",
      "1.5\n",
      "1.5\n",
      "2.0\n",
      "256.0\n",
      "1\n",
      "64\n"
     ]
    }
   ],
   "source": [
    "# do the exercise here\n",
    "# ANSWER\n",
    "\n",
    "print (1 + 10)     # int addition\n",
    "print (1. - 10)    # float subtraction\n",
    "print (3./2.)        # float division\n",
    "print (3/2)          # int division\n",
    "print (1.*2.)      # float multiplication\n",
    "print (2 ** 8.)      # float exponent\n",
    "\n",
    "print (129%2)         # int remainder\n",
    "print (129//2)        # floor operation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<mark>Most of these are obvious. The integer division in this case returns a float, which is a change from Pythjon 2.\n",
    "    \n",
    "<mark>The remainder and floor terms mean that `129 = 64 * 2 + 1`, i.e. `129 = floor * 2 + remainder`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.1.4 Assignment Operators"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "42.0\n"
     ]
    }
   ],
   "source": [
    "'''\n",
    "    Assignment operators\n",
    "\n",
    "    x = 3   assigns the value 3 to the variable x\n",
    "    x += 2  adds 2 onto the value of x\n",
    "            so is the same as x = x + 2\n",
    "            similarly /=, *=, -=\n",
    "    x %= 2  is the same as x = x % 2\n",
    "    x **= 2 is the same as x = x ** 2\n",
    "    x //= 2 is the same as x = x // 2\n",
    "\n",
    "    A 'magic' trick\n",
    "    ===============\n",
    "\n",
    "    based on\n",
    "    https://www.wikihow.com/Read-Someone%27s-Mind-With-Math-(Math-Trick)\n",
    "\n",
    "    whatever you put as myNumber, the answer is 42\n",
    "\n",
    "    Try this with integers or floating point numbers ...\n",
    "'''\n",
    "\n",
    "# pick a number \n",
    "myNumber = 34.67\n",
    "\n",
    "x = myNumber\n",
    "\n",
    "x *= 2\n",
    "\n",
    "x *= 5\n",
    "\n",
    "x /= myNumber\n",
    "\n",
    "x -= 7\n",
    "\n",
    "x += 39\n",
    "\n",
    "# The answer will always be 42\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**E1.1.10 Exercise**\n",
    "\n",
    "* change the number assigned to `myNumber` and check if `42` is still returned\n",
    "* copy and edit the code to print the value of `x` each time you change it, and add comments explaining what is happening for each line of code. This should allow you to follow more carefully what has happened with the arithmetic and also to simplify the code (use fewer statements to achieve the same thing)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "27.6\n",
      "55.2\n",
      "276.0\n",
      "10.0\n",
      "3.0\n",
      "42.0\n"
     ]
    }
   ],
   "source": [
    "# do the exercise here\n",
    "# ANSWER\n",
    "\n",
    "myNumber = 27.6\n",
    "x = myNumber\n",
    "print(x)\n",
    "x *= 2\n",
    "print(x)\n",
    "x *= 5\n",
    "print(x)\n",
    "x /= myNumber\n",
    "print(x)\n",
    "x -= 7\n",
    "print(x)\n",
    "x += 39\n",
    "# The answer will always be 42\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10\n",
      "42\n"
     ]
    }
   ],
   "source": [
    "# do the exercise here\n",
    "# ANSWER ... not so magic after all\n",
    "\n",
    "myNumber = 27.6\n",
    "\n",
    "x = 10\n",
    "print(x)\n",
    "x += 32\n",
    "# The answer will always be 42\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.1.5 Logical Operators"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Logical operators combine boolean variables. Recall from above:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'bool'> <class 'bool'>\n"
     ]
    }
   ],
   "source": [
    "print (type(True),type(False));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The three main logical operators you will use are:\n",
    "\n",
    "    not, and, or\n",
    "    \n",
    "The impact of the `not` opeartor should be straightforward to understand, though we can first write it in a 'truth table':   \n",
    "\n",
    "\n",
    "\n",
    "| A  | not A  | \n",
    "|:---:|:---:|\n",
    "|  T | F | \n",
    "|   F |  T | "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "not True is False\n",
      "not False is True\n"
     ]
    }
   ],
   "source": [
    "print('not True is',not True)\n",
    "print('not False is',not False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**E1.1.11 Exercise:**\n",
    "    \n",
    "* write a statement to set a variable `x` to `True` and print the value of `x` and `not x`\n",
    "       \n",
    "* what does `not not x` give? Make sure you understand why \n",
    "    \n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n",
      "False\n",
      "True\n"
     ]
    }
   ],
   "source": [
    "# do the exercise here\n",
    "# ANSWER\n",
    "\n",
    "x = True\n",
    "print(x)\n",
    "print(not x)\n",
    "print(not not x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<mark>not not cancels out."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The operators `and` and `or` should also be quite straightforward to understand: they have the same meaning as in normal english. Note that `or` is 'inclusive' (so, read `A or B` as 'either A or B or both of them')."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True and True is True\n",
      "True and False is False\n",
      "False and True is False\n",
      "False and False is False\n"
     ]
    }
   ],
   "source": [
    "print ('True and True is',True and True)\n",
    "print ('True and False is',True and False)\n",
    "print ('False and True is',False and True)\n",
    "print ('False and False is',False and False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So, `A and B` is `True`, if and only if both `A` is `True` and `B` is `True`. Otherwise, it is `False`\n",
    "\n",
    "We can represent this in a 'truth table':\n",
    "\n",
    "\n",
    "| A  | B  | A and B  | \n",
    "|:---:|:---:|:---:|\n",
    "|  T |  T |  T | \n",
    "|  T |  F |  F | \n",
    "|  F |  T |  F | \n",
    "|  F |  F |  F | \n",
    "\n",
    "\n",
    "**E1.1.12 Exercise:**\n",
    "\n",
    "* draw a truth table *on some paper*, label the columns `A`, `B` and `A and B` and fill in the columns `A` and `B` as above\n",
    "* without looking at the example above, write the value of `A and B` in the third column.\n",
    "* draw another truth table *on some paper*, label the columns `A`, `B` and `A and B` and fill in the columns `A` and `B` as above\n",
    "* write the value of `A or B` in the third column.\n",
    "\n",
    "If you are unsure, test the response using code, below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    }
   ],
   "source": [
    "# do the testing here e.g.\n",
    "print (True or False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "<mark>ANSWER\n",
    "    \n",
    "![](images/tt.pdf)\n",
    "\n",
    "![](images/tt2.pdf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**E1.1.13 Exercise**\n",
    "\n",
    "* Copy the following truth table onto paper and fill in the final column:\n",
    "\n",
    "| A  | B  | C | ((A and B) or C)  | \n",
    "|:---:|:---:|:---:|:---:|\n",
    "| T|  T |  T |   | \n",
    "| T|  T |  F |   | \n",
    "| T|  F |  T |   | \n",
    "| T |  F |  F |   | \n",
    "| F|  T |  T |   | \n",
    "| F|  T |  F |   | \n",
    "| F|  F |  T |   | \n",
    "| F |  F |  F |   | \n",
    "\n",
    "* Try some other compound statements\n",
    "\n",
    "If you are unsure, or to check your answers, test the response using code, below.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    }
   ],
   "source": [
    "# do the testing here e.g.\n",
    "print ((True and False) or True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<mark>ANSWER\n",
    "    \n",
    "Do it one statement at a time:\n",
    "    \n",
    "| A  | B  | C | A and B  | (A and B) or C\n",
    "|:---:|:---:|:---:|:---:|:---:|\n",
    "| T|  T |  T |  T |   T | \n",
    "| T|  T |  F |  T |   T | \n",
    "| T|  F |  T |  F |   T | \n",
    "| T |  F |  F | F  |  F  | \n",
    "| F|  T |  T |  F |   T | \n",
    "| F|  T |  F | F  |   F | \n",
    "| F|  F |  T |  F |   T | \n",
    "| F |  F |  F |  F |  F  | \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.1.6 Comparison Operators and `if`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A comparison operator 'compares' two terms (e.g. variables) and returns a boolean data type (`True` or `False`).\n",
    "\n",
    "For example, to see if the value of some variable `a` is 'the same value as' ('equivalent to') the value of some variable `b`, we use the equivalence operator (`==`). To test for non equivalence, we use the not equivalent operator `!=` (read the `!` as 'not'):\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a is 100 and\n",
      "b is 10 \n",
      "\n",
      "\ta is equivalent to b? False\n"
     ]
    }
   ],
   "source": [
    "a = 100\n",
    "b = 10\n",
    "\n",
    "# Note the use of \\n and \\t in here\n",
    "# \n",
    "print ('a is',a,'and\\nb is',b,'\\n')\n",
    "print ('\\ta is equivalent to b?',a == b)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**E1.1.14 Exercise**\n",
    "\n",
    "* copy the code above and change the values (or type) of the variables `a` and `b` to test their equivalence.\n",
    "* what does the `\\t` in the print statement do?\n",
    "* add a `print` statement to your code that tests for 'non equivalence'\n",
    "* write some code to see if `(a or b)` is equivalent to `(b or a)` or not"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a is True and\n",
      "b is False \n",
      "\n",
      "\ta is equivalent to b? False\n",
      "\ta is not equivalent to b? True\n",
      "\t(a or b) == (b or a)? True\n",
      "a is 10 and\n",
      "b is 20 \n",
      "\n",
      "\ta is equivalent to b? False\n",
      "\ta is not equivalent to b? True\n",
      "\t(a or b) == (b or a)? False\n",
      "10 20\n"
     ]
    }
   ],
   "source": [
    "# do the exercise here\n",
    "# ANSWER\n",
    "\n",
    "# for logical operators\n",
    "a = True\n",
    "b = False\n",
    "\n",
    "# Note the use of \\n and \\t in here\n",
    "# \n",
    "print ('a is',a,'and\\nb is',b,'\\n')\n",
    "print ('\\ta is equivalent to b?',a == b)\n",
    "print ('\\ta is not equivalent to b?',a != b)\n",
    "print ('\\t(a or b) == (b or a)?',(a or b) == (b or a))\n",
    "\n",
    "# for other comparisons ... the or works differently\n",
    "a = 10\n",
    "b = 20\n",
    "\n",
    "# Note the use of \\n and \\t in here\n",
    "# \n",
    "print ('a is',a,'and\\nb is',b,'\\n')\n",
    "print ('\\ta is equivalent to b?',a == b)\n",
    "print ('\\ta is not equivalent to b?',a != b)\n",
    "print ('\\t(a or b) == (b or a)?',(a or b) == (b or a))\n",
    "\n",
    "# this just sets to a and b respectively, since True\n",
    "print((a or b),(b or a))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A full set of comparison operators is:\n",
    "\n",
    "|symbol| meaning|\n",
    "|:---:|:---:|\n",
    "| is | [is identical to](https://www.geeksforgeeks.org/difference-operator-python/) |\n",
    "| is not | is not identical to |\n",
    "| == | is equivalent to |\n",
    "| != | is not equivalent to |\n",
    "| > | greater than |\n",
    "|>= | greater than or equal to|\n",
    "|<  | less than|\n",
    "|<=  | less than or equal to    |\n",
    "\n",
    "so that, for example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False\n",
      "True\n",
      "False\n",
      "False\n",
      "True\n",
      "False\n"
     ]
    }
   ],
   "source": [
    "# Comparison examples\n",
    "\n",
    "# is one plus one list identical to two list?\n",
    "print ([1 + 1] is [2])\n",
    "\n",
    "# is one plus one list equal to two list?\n",
    "print ([1 + 1] == [2])\n",
    "\n",
    "# is one less than or equal to 0.999?\n",
    "print (1 <= 0.999)\n",
    "\n",
    "# is one plus one not equal to two?\n",
    "print (1 + 1 != 2)\n",
    "\n",
    "# note the use of single quotes inside a double quoted string here\n",
    "# is 'more' greater than 'less'?\n",
    "print (\"more\" > \"less\")\n",
    "\n",
    "# \"is 100 less than 2?\"\n",
    "print (100 < 2)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Aside on string comparisons**\n",
    "\n",
    "In the case of string comparisons, the [ASCII](http://www.asciitable.com) codes of the string characters are compared. So for example the statement \"more\" > \"less\" returns True.\n",
    "\n",
    "Here, the comparison is effectively\n",
    "\n",
    "    m > l\n",
    "\n",
    "Since `m` comes after `l` in the alphabet, the ASCII\n",
    "code for `m` (109) is greater than the ASCII code \n",
    "for `l` (108) (see http://www.asciitable.com) so\n",
    "\n",
    "    109 > 108\n",
    "\n",
    "returns True. Note that ASCII capital letters come before the lower case letters. \n",
    "\n",
    "In practice, we mainly avoid string comparisons (other than to confirm equivalence). So there is little direct use of string comparisons other than `==`. It is useful to know how this works however, in case it crops up or happens 'by accident'. It is also worth understanding what ASCII codes are.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Conditional test**\n",
    "\n",
    "One common use of comparisons is for program control, using an `if` statement:\n",
    "\n",
    "    if condition1 is True:\n",
    "        doit1()\n",
    "    elif condition2 is True:\n",
    "        doit2()\n",
    "    else:\n",
    "        doit3()\n",
    "        \n",
    "where `is` compares identity. This allows us to run blocks of code (e.g. the method `doit1()`) only under a particular condition (or set of conditions).\n",
    "\n",
    "In Python, the statement(s) we run on condition (here `doit1()` etc.) are *indented*. \n",
    "\n",
    "The indent can be one or more spaces or a `<tab>` character, the choice is up to the programmer. However, it **must be consistent**.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test is [2]\n",
      "passed test 2: \"if test == [2]\"\n",
      "retval is True\n"
     ]
    }
   ],
   "source": [
    "test = [1+1]\n",
    "print('test is {}'.format(test))\n",
    "\n",
    "# initialise retval\n",
    "retval = None\n",
    "\n",
    "# conduct some tests, and set the \n",
    "# variable retval to True if we pass\n",
    "# any test\n",
    "\n",
    "if test is [2]:\n",
    "    retval = True\n",
    "    print('passed test 1: \"if test is [2]\"')\n",
    "elif test == [2]:\n",
    "    retval = True\n",
    "    print('passed test 2: \"if test == [2]\"')\n",
    "else:\n",
    "    retval = False\n",
    "    print('failed both tests')\n",
    "    \n",
    "print('retval is',retval)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**E1.1.15 Exercise**\n",
    "\n",
    "* copy the example above, and change it to use other examples from the 'Comparison examples' code block. Change the value of `test` to get different responses and make notes as to why you get the result you do.\n",
    "* try out some more complicated conditions, e.g. multipler tests, combined with an `and` operator."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test is [1.5]\n",
      "failed both tests\n",
      "retval is False\n"
     ]
    }
   ],
   "source": [
    "# do the exercise here\n",
    "# ANSWER\n",
    "\n",
    "test = [3 / 2]\n",
    "print('test is {}'.format(test))\n",
    "\n",
    "# initialise retval\n",
    "retval = None\n",
    "\n",
    "# conduct some tests, and set the \n",
    "# variable retval to True if we pass\n",
    "# any test\n",
    "\n",
    "if test is [1.5]:\n",
    "    retval = True\n",
    "    print('passed test 1: \"if test is [1.5]\"')\n",
    "elif test == [1]:\n",
    "    retval = True\n",
    "    print('passed test 2: \"if test == [1]\"')\n",
    "else:\n",
    "    retval = False\n",
    "    print('failed both tests')\n",
    "    \n",
    "print('retval is',retval)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<mark> So be careful how you use `is`. It is not the same as `==` which only tests for equivalence. `is` is really a pointer comparison, so are they the same object?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.1.7 Summary\n",
    "\n",
    "In this section, you have had an introduction to the Python programming language, running in a [`jupyter notebook`](http://jupyter.org) environment.\n",
    "\n",
    "You have seen how to write comments in code, how to form `print` statements and basic concepts of variables, values, and data types. You have seen how to maniputae data with arithmetic and assignment operators, as well as the basics in dealing with logic and tests returning logical values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "## 1.2  Text and looping\n",
    "In Python, collections of characters (`a`, `b`, `1`, ...) are called strings. Strings and characters are input by surrounding the relevant text in either double (`\"`) or single (`'`) quotes. There are a number of special characters that can be encoded in a string provided they're \"escaped\". For example, some we have come across are:\n",
    "\n",
    "* `\\n`: the carriage return\n",
    "* `\\t`: a tabulator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I'm a happy string\n",
      "I'm a happy string\n",
      "\tI'm a happy string\n",
      "I'm\n",
      "a\n",
      "happy\n",
      "string\n"
     ]
    }
   ],
   "source": [
    "print (\"I'm a happy string\")\n",
    "print ('I\\'m a happy string') # the apostrophe has been escaped as not to be confused by end of string\n",
    "print (\"\\tI'm a happy string\")\n",
    "print (\"I'm\\na\\nhappy\\nstring\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can do a number of things with strings, which are very useful. These so-called string methods are defined on all strings by Python by default, and can be used with every string.\n",
    "For one, we can concatenate strings using the `+` symbol as we saw above.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.2.1  `len`\n",
    "\n",
    "Gives the length of the string as number of characters:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the length of  is 0\n",
      "the length of Hellothereeveryone is 18\n"
     ]
    }
   ],
   "source": [
    "t = ''\n",
    "print ('the length of',t,'is',len(t))\n",
    "\n",
    "\n",
    "s = \"Hello\" + \"there\" + \"everyone\"\n",
    "\n",
    "print ('the length of',s,'is',len(s))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise E1.2.1**\n",
    "\n",
    "* what does a zero-length string look like?\n",
    "* The `Hello there everyone` example above has no spaces between the words. Copy the code to the block below and modify it to have spaces.\n",
    "* confirm that you get the expected increase in length."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello there everyone\n",
      "the length of Hello there everyone is 20\n"
     ]
    }
   ],
   "source": [
    "# do exercise here\n",
    "# ANSWER\n",
    "\n",
    "zero = ''\n",
    "s = \"Hello\" + \" \" + \"there\" + \" \" +  \"everyone\"\n",
    "print(s)\n",
    "print ('the length of',s,'is',len(s))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.2.2 `for ... in ...` and `enumerate`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Very commonly, we need to iterate or 'loop' over some set of items.\n",
    "\n",
    "The basic stucture for doing this (in Python, and many other languages) is `for item in group:`, where `item` is the name of some variable and `group` is a set of values. \n",
    "\n",
    "The loop is run so that `item` takes on the first value in `group`, then the second, etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4\n",
      "3\n",
      "2\n",
      "1\n",
      "blast off!\n"
     ]
    }
   ],
   "source": [
    "# for loop\n",
    "group = [4,3,2,1]\n",
    "\n",
    "for item in group:\n",
    "    '''print counter in loop'''\n",
    "    print(item)\n",
    "    \n",
    "print ('blast off!')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `group` in this example is the list of integer numbers `[4,3,2,1]`. A `list` is a group of comma-separated items contained in square brackets `[]`.\n",
    "\n",
    "In Python, the statement(s) we run whilst looping (here `print(item)`) are *indented*. \n",
    "\n",
    "The indent can be one or more spaces or a `<tab>` character, the choice is up to the programmer. However, it **must be consistent**.\n",
    "\n",
    "**Exercise 1.2.1**\n",
    "\n",
    "* generate a list of strings called `group` with the names of (some of) the items in your pocket or bag (or make some up!)\n",
    "* set up a `for` loop to go through and print each item"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I have cat in my pocket\n",
      "I have fish in my pocket\n",
      "I have egg in my pocket\n"
     ]
    }
   ],
   "source": [
    "# do exercise here\n",
    "# ANSWER\n",
    "\n",
    "group = ['cat','fish','egg']\n",
    "for item in group:\n",
    "    print('I have',item,'in my pocket')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Quite often, we want to keep track of the 'index' of the item in the loop (the 'item number').\n",
    "\n",
    "One way to do this would be to use a variable (called `count` here). \n",
    "\n",
    "Before we enter the loop, we initialise the value to zero."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "item 0 is hat\n",
      "item 1 is dog\n",
      "item 2 is keys\n"
     ]
    }
   ],
   "source": [
    "# for loop\n",
    "group = ['hat','dog','keys']\n",
    "\n",
    "# initialise a variable count\n",
    "count = 0\n",
    "\n",
    "for item in group:\n",
    "    '''print counter in loop'''\n",
    "    \n",
    "    print('item',count,'is',item)\n",
    "    \n",
    "    # add 1 onto count\n",
    "    count += 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise 1.2.2**\n",
    "\n",
    "* copy the code above, and check to see if the value of `count` at the end of the loop is the same as the length of the list. Why should this be so?\n",
    "* change the code so that the counting starts at 1, rather than 0. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "item 0 is hat\n",
      "item 1 is dog\n",
      "item 2 is keys\n",
      "3 3\n"
     ]
    }
   ],
   "source": [
    "# do exercise here\n",
    "# ANSWER\n",
    "\n",
    "# for loop\n",
    "group = ['hat','dog','keys']\n",
    "\n",
    "# initialise a variable count\n",
    "count = 0\n",
    "\n",
    "for item in group:\n",
    "    '''print counter in loop'''\n",
    "    \n",
    "    print('item',count,'is',item)\n",
    "    \n",
    "    # add 1 onto count\n",
    "    count += 1\n",
    "'''\n",
    "Expect the same as we have incremented\n",
    "count once for each item in group, so it\n",
    "should be the same as the length of the group!\n",
    "'''\n",
    "print(count,len(group))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since counting in loops is a common task, we can use the built in method `enumerate()` to achieve the same thing as above. The syntax is then:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "item 0 is hat\n",
      "item 1 is dog\n",
      "item 2 is keys\n"
     ]
    }
   ],
   "source": [
    "# for loop\n",
    "group = ['hat','dog','keys']\n",
    "\n",
    "for count,item in enumerate(group):\n",
    "    '''print counter in loop'''\n",
    "    print('item',count,'is',item)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise 1.2.3**\n",
    "\n",
    "* copy the code above, and check to see if the value of `count` at the end of the loop is the same as the length of the list.\n",
    "* change the code so that the printed count starts at 1, rather than 0. \n",
    "\n",
    "Hint: how can you make it print `count+1` rather than `count`? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "item 0 is hat\n",
      "item 1 is dog\n",
      "item 2 is keys\n",
      "2 3\n",
      "item 3 is keys\n"
     ]
    }
   ],
   "source": [
    "# do exercise here\n",
    "# ANSWER\n",
    "\n",
    "# for loop\n",
    "group = ['hat','dog','keys']\n",
    "\n",
    "for count,item in enumerate(group):\n",
    "    '''print counter in loop'''\n",
    "    print('item',count,'is',item)\n",
    "\n",
    "'''\n",
    "In this case, count starts at 0 and takes the\n",
    "values 0, 1 and 2 as we loop over the 3 items.\n",
    "'''\n",
    "print(count,len(group))\n",
    "'''\n",
    "print count + 1 !!\n",
    "'''\n",
    "print('item',count+1,'is',item)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.2.3 `slice` \n",
    "\n",
    "A string can be thought of as an ordered 'array' of characters. \n",
    "\n",
    "So, for example the string `hello` can be thought of as a construct containing `h` then `e`, `l`, `l`, and `o`. \n",
    "\n",
    "We can index a string, so that e.g. `'hello'[0]` is `h`, `'hello'[1]` is `e` etc.\n",
    "\n",
    "We have seen above the idea of the 'length' of a string. In this example, the length of the string `hello` is 5."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "length of hello is 5\n",
      "character 0 of hello is h\n",
      "character 1 of hello is e\n",
      "character 3 of hello is l\n"
     ]
    }
   ],
   "source": [
    "string = 'hello'\n",
    "\n",
    "# length\n",
    "slen = len(string)\n",
    "print('length of {} is {}'.format(string,slen))\n",
    "\n",
    "# select these indices\n",
    "indices = 0,1,3\n",
    "\n",
    "# loop over each item in indices\n",
    "for index in indices:\n",
    "    print('character {} of {} is {}'.format(index,string,string[index]))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise E1.2.4**\n",
    "\n",
    "* copy the code above, and see what happens if you set a value in `indices` that is the value of length of the string. Why does it respond so?\n",
    "* make the code robust to this issue, but using an `if` statement to test if `index` is in the required range."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "length of hello is 5\n",
      "character 0 of hello is h\n",
      "character 1 of hello is e\n",
      "character 3 of hello is l\n"
     ]
    },
    {
     "ename": "IndexError",
     "evalue": "string index out of range",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-68-88baf263801d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     18\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mindex\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mindices\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 20\u001b[0;31m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'character {} of {} is {}'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mstring\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mstring\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mindex\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[0m\u001b[1;32m     21\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mIndexError\u001b[0m: string index out of range"
     ]
    }
   ],
   "source": [
    "# do exercise here\n",
    "# ANSWER\n",
    "\n",
    "string = 'hello'\n",
    "\n",
    "# length\n",
    "slen = len(string)\n",
    "print('length of {} is {}'.format(string,slen))\n",
    "\n",
    "# select these indices\n",
    "indices = 0,1,3,6\n",
    "\n",
    "# loop over each item in indices\n",
    "'''\n",
    "WE expect a failure as we have\n",
    "asked for an out of range index\n",
    "'''\n",
    "\n",
    "for index in indices:\n",
    "    print('character {} of {} is {}'.format(index,string,string[index]))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "length of hello is 5\n",
      "character 0 of hello is h\n",
      "character 1 of hello is e\n",
      "character 3 of hello is l\n",
      "index 6 beyond the string length 5\n"
     ]
    }
   ],
   "source": [
    "# do exercise here\n",
    "# ANSWER\n",
    "\n",
    "string = 'hello'\n",
    "\n",
    "# length\n",
    "slen = len(string)\n",
    "print('length of {} is {}'.format(string,slen))\n",
    "\n",
    "# select these indices\n",
    "indices = 0,1,3,6\n",
    "\n",
    "# loop over each item in indices\n",
    "'''\n",
    "Fix by using condition\n",
    "'''\n",
    "\n",
    "for index in indices:\n",
    "    if index < slen:\n",
    "        print('character {} of {} is {}'.format(index,string,string[index]))\n",
    "    else:\n",
    "        print('index {} beyond the string length {}'.format(index,slen))\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "We can use the idea of a 'slice' to access particular elements within the string.\n",
    "\n",
    "For a slice, we can specify:\n",
    "\n",
    "* start index (0 is the first)\n",
    "* stop index (not including this)\n",
    "* skip (do every 'skip' character)\n",
    "\n",
    "When specifying this as array access, this is given as, e.g.:\n",
    "\n",
    "`array[start:stop:skip]`\n",
    "\n",
    "* The default start is 0\n",
    "* The default stop is the length of the array\n",
    "* The default skip is 1\n",
    "\n",
    "You can specify a slice with the default values by leaving the terms out:\n",
    "\n",
    "`array[::2]`\n",
    "\n",
    "would give values in the array `array` from 0 to the end, in steps of 2.\n",
    "\n",
    "This idea is fundamental to array processing in Python. We will see later that the same mechanism applies to all ordered groups.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello World 11\n",
      "HloWrd\n",
      "rld\n"
     ]
    }
   ],
   "source": [
    "s = \"Hello World\"\n",
    "print (s,len(s))\n",
    "\n",
    "start = 0\n",
    "stop  = 11\n",
    "skip  = 2\n",
    "print (s[start:stop:skip])\n",
    "\n",
    "# use -ve numbers to specify from the end\n",
    "# use None to take the default value\n",
    "\n",
    "start = -3\n",
    "stop  = None\n",
    "skip  = 1\n",
    "print (s[start:stop:skip])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise E1.2.5**\n",
    "\n",
    "The example above allows us to access an individual character(s) of the array.\n",
    "\n",
    "* copy the example above, and print the string starting from the default start value, up to the default stop value, in steps of `2`.\n",
    "* write code to print out the 4$^{th}$ letter (character) of the string `s`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "HloWrd\n",
      "l\n"
     ]
    }
   ],
   "source": [
    "# do exercise here\n",
    "# ANSWER\n",
    "\n",
    "s = \"Hello World\"\n",
    "\n",
    "# take the default start by setting to None\n",
    "start = None\n",
    "stop  = None\n",
    "skip  = 2\n",
    "print (s[start:stop:skip])\n",
    "\n",
    "# 4th char ... start at 0 remember!\n",
    "print(s[3])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.2.4 `replace`\n",
    "\n",
    "We can replace all occurrences of a string within a string by some other string. We can also replace a string by an empty string, thus in effect removing it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I'm a very unhappy string\n"
     ]
    }
   ],
   "source": [
    "print (\"I'm a very happy string\".replace(\"happy\", \"unhappy\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise E1.2.6**\n",
    "\n",
    "* copy the statement above, and use the `replace` method to make it print out `\"I'm a happy string\"`. \n",
    "\n",
    "Hint: you want to replace the string `very ` with, effectively, nothing, i.e. a zero-length string."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I'm a happy string\n"
     ]
    }
   ],
   "source": [
    "# do exercise here\n",
    "# ANSWER\n",
    "\n",
    "# note that we replace 'very '\n",
    "print (\"I'm a very happy string\".replace(\"very \", \"\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.2.5 `find`\n",
    "\n",
    "Quite often, we might want to find a string inside another string, and potentially give the location (as in characters from the start of the string) where this string occurs. We can use the `find` method, which will return either a `-1` if the string isn't found, or an integer giving the index of where the string starts (for the first time)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4\n",
      "11\n"
     ]
    }
   ],
   "source": [
    "print (\"I'm a very happy string\".find(\"a\"))\n",
    "print (\"I'm a very happy string\".find(\"happy\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's use the idea of `find()` to sort out a messy table of data that we get from a web page.\n",
    "\n",
    "First, we need to import the package `requests` to access some information from a [URL](https://en.wikipedia.org/wiki/URL) (from a web page). The data we get will be in [html](https://en.wikipedia.org/wiki/HTML).\n",
    "\n",
    "The data we will examine is a dataset of [ENSO](https://en.wikipedia.org/wiki/ENSO) values for each month of the year from January 1950 to present, made available by [NOAA](https://en.wikipedia.org/wiki/NOAA)/\n",
    "\n",
    "If you visit  you will see the data table we are interested in. So, how do we 'grab' this?\n",
    "\n",
    "The [URL](https://en.wikipedia.org/wiki/URL) points to [html](https://en.wikipedia.org/wiki/HTML) code. When you display this in a browser, it is rendered appropriately. \n",
    "\n",
    "If you access the html directly, you will get the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<html>\n",
      "<head><title>MEI timeseries from Dec/Jan 1940/50 up to the present</title></head>\n",
      "<body>\n",
      "<pre>\n",
      "MEI Index (updated: 13 October 2018)\n",
      "\n",
      "Bimonthly MEI values (in 1/1000 of standard deviations), starting with Dec1949/Jan1950, thru last\n",
      "month.  More information on the MEI can be found on the <a href=\"mei.html\">MEI homepage</a>.\n",
      "Missing values are left blank.  Note that values can still change with each monthly update, even \n",
      "though such changes are typically smaller than +/-0.1.  All values are normalized for each bimonthly\n",
      "season so that the 44 values from 1950 to 1993 have an average of zero and a standard deviation of \"1\".  \n",
      "Responses to 'FAQs' can be found below this table:\n",
      "\n",
      "YEAR    DECJAN  JANFEB  FEBMAR  MARAPR  APRMAY  MAYJUN  JUNJUL  JULAUG  AUGSEP  SEPOCT  OCTNOV  NOVDEC\n",
      "1950\t-1.03\t-1.133\t-1.283\t-1.071\t-1.434\t-1.412\t-1.269\t-1.042\t-.631\t-.406\t-1.138\t-1.235\n",
      "1951\t-1.049\t-1.152\t-1.178\t-.511\t-.374\t.288\t.679\t.818\t.726\t.768\t.726\t.504\n",
      "1952\t.433\t.138\t.071\t.224\t-.307\t-.756\t-.305\t-.374\t.\n"
     ]
    }
   ],
   "source": [
    "# Web scraping example\n",
    "\n",
    "import requests\n",
    "\n",
    "url = \"http://www.esrl.noaa.gov/psd/enso/mei.old/table.html\"\n",
    "\n",
    "# This line will pull the URL data as a string\n",
    "txt = requests.get(url).text\n",
    "\n",
    "# show the first 1000 characters (see 'slice' above: this is the same as [None:1000:None])\n",
    "print(txt[:1000])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We notice the presence of html codes in the text string (e.g. `<html>`, `<pre>`). There are particular packages for neatly parsing html (scraping information from web pages), one of the most common being [BeautifulSoup](https://www.crummy.com/software/BeautifulSoup/bs4/doc/). This will tend to be more useful if the html is well fomatted, and the data contained in `<table>` sections, or similar structures. Here, we just have a block of text in the `<pre>` section.\n",
    "\n",
    "If we want to *just* access the dataset here then, we might notice that the data we want to access starts when we see the string `YEAR`.\n",
    "\n",
    "We can use `find()` to discover the index of this in the string:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "start of useful data at index 688\n",
      "---------------------------------\n",
      "YEAR    DECJAN  JANFEB  FEBMAR  MARAPR  APRMAY  MAYJUN  JUNJUL  JULAUG  AUGSEP  SEPOCT  OCTNOV  NOVDEC\n",
      "1950\t-1.03\t-1.133\t-1.283\t-1.071\t-1.434\t-1.412\t-1.269\t-1.042\t-.631\t-.406\t-1.138\t-1.235\n",
      "1951\t-1.049\t-1.152\t-1.178\t-.511\t-.374\t.288\t.679\t.818\t.726\t.768\t.726\t.504\n",
      "1952\t.433\t.138\t.071\t.224\t-.307\t-.756\t-.305\t-.374\t.31\t.306\t-.328\t-.098\n",
      "1953\t.044\t.401\t.277\t.687\t.756\t.191\t.382\t.209\t.483\t.124\t.099\t.351\n",
      "1954\t-.036\t-.027\t.154\t-.616\t-1.465\t-1.558\t-1.355\t-1.456\t-1.159\t-1.32\t-1.113\t-1.088\n",
      "1955\t-.74\t-.669\t-1.117\t-1.621\t-1.653\t-2.247\t-1.976\t-2.05\t-1.829\t-1.725\t-1.813\t-1.846\n",
      "1956\t-1.408\t-1.275\t-1.371\t-1.216\t-1.304\t-1.523\t-1.244\t-1.118\t-1.35\t-1.461\t-1.014\t-.993\n",
      "1957\t-.915\t-.348\t.108\t.383\t.813\t.73\t.926\t1.132\t1.117\t1.114\t1.167\t1.268\n",
      "1958\t1.473\t1.454\t1.313\t.991\t.673\t.812\t.7\t.421\t.171\t.237\t.501\t.691\n",
      "1959\t.553\t.81\t.502\t.202\t-.025\t-.062\t-.112\t.111\t.092\t-.038\t-.151\t-.247\n",
      "1960\t-.287\t-.253\t-.082\t.007\t-.322\t-.287\t-.318\t-.25\t-.47\t-.332\t-.308\t-.39\n",
      "1961\t-.15\t-.235\t-.073\t.017\t-.302\t-.185\t-.208\t-.3\t-.3\t-.51\t-.416\t-.60\n"
     ]
    }
   ],
   "source": [
    "start = txt.find('YEAR')\n",
    "\n",
    "print('start of useful data at index {}\\n---------------------------------'.format(start))\n",
    "print(txt[start:start+1000])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we look again at the web page [http://www.esrl.noaa.gov/psd/enso/mei.old/table.html](http://www.esrl.noaa.gov/psd/enso/mei/table.html), we might notice that the end of the useful data is delimited by two newlines and the string `(1)`, i.e., as a string `\\n\\n(1)`. So we should be able to use `find()` again to get the location of the end of the data (i.e. `stop`, in the sense of a slice).\n",
    "\n",
    "**Exercise 1.2.6**\n",
    "\n",
    "* use this observation to form a string called `data_table`, containing all of the useful data (i.e. `txt[start:stop]`).\n",
    "* print the string `data_table`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "start of useful data at index 688\n",
      "---------------------------------\n",
      "YEAR    DECJAN  JANFEB  FEBMAR  MARAPR  APRMAY  MAYJUN  JUNJUL  JULAUG  AUGSEP  SEPOCT  OCTNOV  NOVDEC\n",
      "1950\t-1.03\t-1.133\t-1.283\t-1.071\t-1.434\t-1.412\t-1.269\t-1.042\t-.631\t-.406\t-1.138\t-1.235\n",
      "1951\t-1.049\t-1.152\t-1.178\t-.511\t-.374\t.288\t.679\t.818\t.726\t.768\t.726\t.504\n",
      "1952\t.433\t.138\t.071\t.224\t-.307\t-.756\t-.305\t-.374\t.31\t.306\t-.328\t-.098\n",
      "1953\t.044\t.401\t.277\t.687\t.756\t.191\t.382\t.209\t.483\t.124\t.099\t.351\n",
      "1954\t-.036\t-.027\t.154\t-.616\t-1.465\t-1.558\t-1.355\t-1.456\t-1.159\t-1.32\t-1.113\t-1.088\n",
      "1955\t-.74\t-.669\t-1.117\t-1.621\t-1.653\t-2.247\t-1.976\t-2.05\t-1.829\t-1.725\t-1.813\t-1.846\n",
      "1956\t-1.408\t-1.275\t-1.371\t-1.216\t-1.304\t-1.523\t-1.244\t-1.118\t-1.35\t-1.461\t-1.014\t-.993\n",
      "1957\t-.915\t-.348\t.108\t.383\t.813\t.73\t.926\t1.132\t1.117\t1.114\t1.167\t1.268\n",
      "1958\t1.473\t1.454\t1.313\t.991\t.673\t.812\t.7\t.421\t.171\t.237\t.501\t.691\n",
      "1959\t.553\t.81\t.502\t.202\t-.025\t-.062\t-.112\t.111\t.092\t-.038\t-.151\t-.247\n",
      "1960\t-.287\t-.253\t-.082\t.007\t-.322\t-.287\t-.318\t-.25\t-.47\t-.332\t-.308\t-.39\n",
      "1961\t-.15\t-.235\t-.073\t.017\t-.302\t-.185\t-.208\t-.3\t-.3\t-.51\t-.416\t-.608\n",
      "1962\t-1.065\t-.963\t-.692\t-1.04\t-.89\t-.87\t-.683\t-.538\t-.56\t-.642\t-.598\t-.482\n",
      "1963\t-.718\t-.837\t-.675\t-.761\t-.473\t-.144\t.404\t.59\t.734\t.848\t.866\t.765\n",
      "1964\t.878\t.481\t-.256\t-.545\t-1.234\t-1.15\t-1.384\t-1.486\t-1.309\t-1.196\t-1.211\t-.907\n",
      "1965\t-.536\t-.329\t-.259\t.086\t.464\t.867\t1.367\t1.425\t1.379\t1.25\t1.378\t1.27\n",
      "1966\t1.307\t1.186\t.689\t.515\t-.178\t-.193\t-.116\t.152\t-.094\t-.018\t.022\t-.181\n",
      "1967\t-.462\t-.898\t-1.05\t-1.03\t-.448\t-.236\t-.492\t-.391\t-.627\t-.655\t-.407\t-.357\n",
      "1968\t-.602\t-.727\t-.635\t-.944\t-1.093\t-.812\t-.503\t-.104\t.213\t.46\t.607\t.367\n",
      "1969\t.67\t.849\t.458\t.622\t.674\t.801\t.49\t.212\t.162\t.537\t.683\t.417\n",
      "1970\t.38\t.432\t.228\t.014\t-.099\t-.636\t-1.055\t-1.007\t-1.251\t-1.068\t-1.063\t-1.203\n",
      "1971\t-1.204\t-1.507\t-1.79\t-1.839\t-1.429\t-1.42\t-1.207\t-1.213\t-1.467\t-1.399\t-1.301\t-.969\n",
      "1972\t-.575\t-.398\t-.256\t-.166\t.423\t.966\t1.826\t1.8\t1.522\t1.667\t1.74\t1.787\n",
      "1973\t1.723\t1.515\t.87\t.491\t-.099\t-.758\t-1.056\t-1.334\t-1.734\t-1.65\t-1.482\t-1.826\n",
      "1974\t-1.912\t-1.768\t-1.743\t-1.62\t-1.048\t-.694\t-.75\t-.664\t-.628\t-1.031\t-1.23\t-.886\n",
      "1975\t-.522\t-.576\t-.85\t-.927\t-.838\t-1.148\t-1.497\t-1.712\t-1.876\t-1.968\t-1.748\t-1.732\n",
      "1976\t-1.587\t-1.366\t-1.213\t-1.157\t-.483\t.276\t.633\t.654\t1.018\t.978\t.511\t.565\n",
      "1977\t.529\t.29\t.145\t.552\t.31\t.414\t.888\t.684\t.783\t1.016\t.99\t.877\n",
      "1978\t.777\t.912\t.935\t.199\t-.378\t-.605\t-.42\t-.199\t-.391\t.003\t.203\t.406\n",
      "1979\t.608\t.379\t.002\t.301\t.374\t.429\t.396\t.615\t.759\t.694\t.759\t1.004\n",
      "1980\t.677\t.601\t.684\t.912\t.958\t.911\t.769\t.329\t.263\t.223\t.27\t.111\n",
      "1981\t-.25\t-.14\t.455\t.669\t.189\t-.024\t-.026\t-.09\t.174\t.132\t-.021\t-.126\n",
      "1982\t-.258\t-.125\t.1\t.008\t.443\t.93\t1.612\t1.778\t1.783\t2.061\t2.441\t2.425\n",
      "1983\t2.677\t2.931\t3.008\t2.812\t2.498\t2.235\t1.793\t1.159\t.459\t.056\t-.115\t-.17\n",
      "1984\t-.314\t-.509\t.151\t.39\t.18\t-.049\t-.054\t-.155\t-.12\t.026\t-.332\t-.585\n",
      "1985\t-.546\t-.576\t-.696\t-.468\t-.706\t-.166\t-.126\t-.365\t-.535\t-.119\t-.042\t-.279\n",
      "1986\t-.293\t-.183\t.028\t-.107\t.353\t.279\t.401\t.765\t1.077\t1.002\t.89\t1.202\n",
      "1987\t1.249\t1.218\t1.716\t1.855\t2.107\t1.958\t1.878\t1.973\t1.851\t1.671\t1.286\t1.293\n",
      "1988\t1.115\t.716\t.495\t.389\t.193\t-.582\t-1.109\t-1.295\t-1.526\t-1.313\t-1.447\t-1.311\n",
      "1989\t-1.103\t-1.241\t-1.035\t-.76\t-.393\t-.242\t-.43\t-.494\t-.315\t-.319\t-.058\t.131\n",
      "1990\t.243\t.573\t.951\t.46\t.652\t.511\t.147\t.127\t.366\t.303\t.4\t.362\n",
      "1991\t.319\t.323\t.399\t.449\t.741\t1.051\t1.044\t1.008\t.739\t1.031\t1.202\t1.34\n",
      "1992\t1.747\t1.886\t1.985\t2.247\t2.085\t1.735\t1.045\t.559\t.488\t.663\t.595\t.664\n",
      "1993\t.692\t.99\t.987\t1.408\t1.993\t1.616\t1.204\t1.026\t.966\t1.09\t.848\t.595\n",
      "1994\t.352\t.193\t.159\t.465\t.58\t.803\t.904\t.762\t.893\t1.437\t1.312\t1.251\n",
      "1995\t1.219\t.959\t.845\t.453\t.57\t.507\t.234\t-.147\t-.445\t-.461\t-.463\t-.537\n",
      "1996\t-.597\t-.566\t-.236\t-.391\t-.041\t.087\t-.173\t-.374\t-.457\t-.338\t-.134\t-.325\n",
      "1997\t-.48\t-.605\t-.248\t.527\t1.132\t2.275\t2.825\t3.002\t2.99\t2.423\t2.551\t2.344\n",
      "1998\t2.455\t2.777\t2.751\t2.658\t2.206\t1.336\t.392\t-.336\t-.636\t-.789\t-1.069\t-.908\n",
      "1999\t-1.039\t-1.123\t-.95\t-.88\t-.596\t-.339\t-.478\t-.739\t-.967\t-.954\t-1.031\t-1.142\n",
      "2000\t-1.122\t-1.189\t-1.09\t-.397\t.251\t.001\t-.159\t-.145\t-.238\t-.367\t-.701\t-.55\n",
      "2001\t-.496\t-.649\t-.548\t-.05\t.282\t.052\t.297\t.332\t-.174\t-.255\t-.138\t.033\n",
      "2002\t.017\t-.16\t-.118\t.401\t.886\t.932\t.712\t1.002\t.881\t1.02\t1.101\t1.156\n",
      "2003\t1.214\t.944\t.83\t.413\t.214\t.107\t.177\t.309\t.46\t.534\t.584\t.362\n",
      "2004\t.332\t.37\t-.036\t.358\t.558\t.315\t.571\t.617\t.558\t.524\t.818\t.684\n",
      "2005\t.325\t.816\t1.057\t.626\t.885\t.589\t.519\t.343\t.296\t-.152\t-.374\t-.55\n",
      "2006\t-.428\t-.414\t-.521\t-.571\t.045\t.526\t.716\t.748\t.8\t.976\t1.297\t.965\n",
      "2007\t.985\t.537\t.125\t.026\t.348\t-.155\t-.248\t-.442\t-1.197\t-1.204\t-1.149\t-1.178\n",
      "2008\t-1.006\t-1.371\t-1.552\t-.858\t-.345\t.142\t.088\t-.269\t-.58\t-.681\t-.584\t-.646\n",
      "2009\t-.714\t-.69\t-.705\t-.106\t.326\t.751\t1.06\t1.05\t.707\t.924\t1.134\t1.059\n",
      "2010\t1.066\t1.526\t1.462\t.978\t.658\t-.228\t-1.103\t-1.671\t-1.879\t-1.888\t-1.472\t-1.558\n",
      "2011\t-1.719\t-1.544\t-1.554\t-1.387\t-.199\t-.003\t-.193\t-.517\t-.778\t-.917\t-.933\t-.945\n",
      "2012\t-.98\t-.675\t-.382\t.11\t.757\t.842\t1.126\t.607\t.316\t.097\t.141\t.111\n",
      "2013\t.103\t-.068\t-.026\t.09\t.205\t-.094\t-.314\t-.481\t-.155\t.148\t-.042\t-.234\n",
      "2014\t-.27\t-.259\t.018\t.295\t1.001\t1.046\t.915\t.937\t.557\t.45\t.773\t.566\n",
      "2015\t.417\t.464\t.614\t.916\t1.583\t2.097\t1.981\t2.334\t2.479\t2.256\t2.3\t2.12\n",
      "2016\t2.216\t2.17\t1.963\t2.094\t1.752\t1.053\t.352\t.167\t-.118\t-.363\t-.197\t-.11\n",
      "2017\t-.052\t-.043\t-.08\t.744\t1.445\t1.039\t.456\t.009\t-.478\t-.551\t-.277\t-.576\n",
      "2018\t-.623\t-.731\t-.502\t-.432\t.465\t.469\t.076\t.132\t.509\n"
     ]
    }
   ],
   "source": [
    "# do exercise here\n",
    "# ANSWER\n",
    "\n",
    "start = txt.find('YEAR')\n",
    "end = txt.find('\\n\\n(1)')\n",
    "\n",
    "print('start of useful data at index {}\\n---------------------------------'.format(start))\n",
    "data_table = txt[start:end]\n",
    "\n",
    "print(data_table)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This exercise is a very good example of [web scraping](https://en.wikipedia.org/wiki/Web_scraping). Web scraping is often rather messy (you have to work out some 'key' to reliably delimit the information you want) but can be extreemely valuable for accessing datasets that are not cleanly presented. We have only gonbe part of the way to extracting a useful dataset here, because the dataset we are interested in (the ENSO data) are still represented as a string, whereas we really want them to be a set of floating point numbers. We will deal with this later.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.2.5 `split` and `splitlines`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first 'line' of `` should contain the 'header' information, i.e. the title of the data columns (`YEAR`, `DECJAN` etc.). We want to separate the header from the numbers in the data table, so we want to 'split' the string called `data_table` into a header string and data string. \n",
    "\n",
    "One approach to this would be split the string into 'lines' of text (rather than one block). Effectively that means splitting into multiple strings whenever we hit a `\\n` character. Rather than do that explicitly, we use the `splitlines()` method:\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'list'> 70\n",
      "line 0 <class 'str'>\n",
      "\tYEAR    DECJAN  JANFEB  FEBMAR  MARAPR  APRMAY  MAYJUN  JUNJUL  JULAUG  AUGSEP  SEPOCT  OCTNOV  NOVDEC\n",
      "line 1 <class 'str'>\n",
      "\t1950\t-1.03\t-1.133\t-1.283\t-1.071\t-1.434\t-1.412\t-1.269\t-1.042\t-.631\t-.406\t-1.138\t-1.235\n",
      "line 69 <class 'str'>\n",
      "\t2018\t-.623\t-.731\t-.502\t-.432\t.465\t.469\t.076\t.132\t.509\n"
     ]
    }
   ],
   "source": [
    "import requests\n",
    "url = \"http://www.esrl.noaa.gov/psd/enso/mei/table.html\"\n",
    "txt = requests.get(url).text\n",
    "\n",
    "# copy the useful data\n",
    "start = txt.find('YEAR')\n",
    "stop  = txt.find('\\n\\n(1)')\n",
    "data_table = txt[start:stop]\n",
    "\n",
    "# split into a list of strings\n",
    "data_lines = data_table.splitlines()\n",
    "\n",
    "# tell me something useful\n",
    "print(type(data_lines),len(data_lines))\n",
    "\n",
    "# loop over some examples\n",
    "for i in 0,1,len(data_lines)-1:\n",
    "    print('line {} {}\\n\\t{}'.format(i,type(data_lines[i]),data_lines[i]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This splits each 'line' of text into an entry in a `list`, so that the header data is now given in the first entry (`data_lines[0]`) and the lines containinmg data, after that.\n",
    "\n",
    "\n",
    "From the print out above, we notice that the final 'data line' (index `-1`) is shorter than (has fewer entries than) the other lines. This is because we are only part way through this year!.\n",
    "\n",
    "In 'real' datasets, we quite often have 'messy' lines of data such as this (or data missing for other reasons). How you want to deal with the 'messy bits' depends on the sort of analysis you want to do. \n",
    "\n",
    "One option (the simplest) would be to simply remove the last line (ignore this year's data):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "header: YEAR    DECJAN  JANFEB  FEBMAR  MARAPR  APRMAY  MAYJUN  JUNJUL  JULAUG  AUGSEP  SEPOCT  OCTNOV  NOVDEC\n",
      "line 0 <class 'str'>\n",
      "\t1950\t-1.03\t-1.133\t-1.283\t-1.071\t-1.434\t-1.412\t-1.269\t-1.042\t-.631\t-.406\t-1.138\t-1.235\n",
      "line 1 <class 'str'>\n",
      "\t1951\t-1.049\t-1.152\t-1.178\t-.511\t-.374\t.288\t.679\t.818\t.726\t.768\t.726\t.504\n",
      "line 67 <class 'str'>\n",
      "\t2017\t-.052\t-.043\t-.08\t.744\t1.445\t1.039\t.456\t.009\t-.478\t-.551\t-.277\t-.576\n"
     ]
    }
   ],
   "source": [
    "header = data_lines[0]\n",
    "\n",
    "# select the data block as being from entry 1 to -1\n",
    "# so, **not including the last row**\n",
    "data = data_lines[1:-1]\n",
    "\n",
    "print('header:',header)\n",
    "\n",
    "for i in 0,1,len(data)-1:\n",
    "    print('line {} {}\\n\\t{}'.format(i,type(data[i]),data[i]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise 1.2.7**\n",
    "\n",
    "* copy the code from above and explore the response using line indices `-1` and `-2`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "header: YEAR    DECJAN  JANFEB  FEBMAR  MARAPR  APRMAY  MAYJUN  JUNJUL  JULAUG  AUGSEP  SEPOCT  OCTNOV  NOVDEC\n",
      "line -1 <class 'str'>\n",
      "\t2017\t-.052\t-.043\t-.08\t.744\t1.445\t1.039\t.456\t.009\t-.478\t-.551\t-.277\t-.576\n",
      "line -2 <class 'str'>\n",
      "\t2016\t2.216\t2.17\t1.963\t2.094\t1.752\t1.053\t.352\t.167\t-.118\t-.363\t-.197\t-.11\n"
     ]
    }
   ],
   "source": [
    "# do exercise here\n",
    "# ANSWER\n",
    "\n",
    "header = data_lines[0]\n",
    "\n",
    "# select the data block as being from entry 1 to -1\n",
    "# so, **not including the last row**\n",
    "data = data_lines[1:-1]\n",
    "\n",
    "print('header:',header)\n",
    "\n",
    "# THE LAST TWO LINES\n",
    "for i in -1,-2:\n",
    "    print('line {} {}\\n\\t{}'.format(i,type(data[i]),data[i]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we want to manipulate or plot the information contained in this (the numbers), we need to convert each of the string representations to a floating point number, e.g. the number `-1.03` rather than the string `'-1.03'`.\n",
    "\n",
    "Each entry in the list `data` is a string, as we saw above.\n",
    "\n",
    "We can split an individual string (such as `data[0]` into a list of strings, using the string method `split()`. By default, this splits on 'white space' (i.e. spaces or tab characters), so, e.g.:\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1950\t-1.03\t-1.133\t-1.283\t-1.071\t-1.434\t-1.412\t-1.269\t-1.042\t-.631\t-.406\t-1.138\t-1.235\n",
      "['1950', '-1.03', '-1.133', '-1.283', '-1.071', '-1.434', '-1.412', '-1.269', '-1.042', '-.631', '-.406', '-1.138', '-1.235'] 13\n"
     ]
    }
   ],
   "source": [
    "line = data[0].split()\n",
    "print(data[0])\n",
    "print(line,len(line))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So, we have split the long string into 13 strings in a list. \n",
    "\n",
    "We want to generate a new list with 13 corresponding floating point values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['1950', '-1.03', '-1.133', '-1.283', '-1.071', '-1.434', '-1.412', '-1.269', '-1.042', '-.631', '-.406', '-1.138', '-1.235']\n",
      "[1950.0, -1.03, -1.133, -1.283, -1.071, -1.434, -1.412, -1.269, -1.042, -0.631, -0.406, -1.138, -1.235]\n"
     ]
    }
   ],
   "source": [
    "# split the line on whitespace\n",
    "line = data[0].split()\n",
    "\n",
    "# make a new list of the same length\n",
    "# by copying the variable line\n",
    "float_data = line.copy()\n",
    "\n",
    "for index,line_data in enumerate(line):\n",
    "    # insert the cast float into the list\n",
    "    # in the right order (use index)\n",
    "    float_data[index] = float(line_data)\n",
    "    \n",
    "# this is the string list\n",
    "print(line)\n",
    "\n",
    "# this is the float list\n",
    "print(float_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise 1.2.8**\n",
    "\n",
    "* set a variable to be the string `\"2, 3, 5, 7, 11, 13, 17, 19, 23, 29\"`\n",
    "* use the approach above to generate a **list of integers** of the first 10 prime numbers. \n",
    "* print the list with syntax of the pattern of 'prime number 3 is 7'\n",
    "\n",
    "Make sure you convert each prime number to an integer, rather than leaving it as a string!\n",
    "\n",
    "Hint: We can still use the method `split()` to do split the string into a list of strings, but this time the [separator](https://python-reference.readthedocs.io/en/latest/docs/str/split.html) is a comma, rather than whitespace. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2, 3, 5, 7, 11, 13, 17, 19, 23, 29]\n",
      "[2, 3, 5, 7, 11, 13, 17, 19, 23, 29]\n",
      "[2, 3, 5, 7, 11, 13, 17, 19, 23, 29]\n"
     ]
    }
   ],
   "source": [
    "# do exercise here\n",
    "# ANSWER\n",
    "\n",
    "'''\n",
    "We recognise these numbers as the first 10 prime numbers!!\n",
    "'''\n",
    "pstring = \"2, 3, 5, 7, 11, 13, 17, 19, 23, 29\"\n",
    "\n",
    "d = [int(p) for p in pstring.split(',')]\n",
    "print(d)\n",
    "\n",
    "# or\n",
    "\n",
    "d = []\n",
    "for p in pstring.split(','):\n",
    "    d.append(int(p))\n",
    "print(d)\n",
    "\n",
    "# or, perhaps the neatest, when we know some numpy (later!)\n",
    "import numpy as np\n",
    "\n",
    "d = np.array(pstring.split(',')).astype(int)\n",
    "print(list(d))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Normally, we wouldn't go to the trouble of first copying the list. \n",
    "\n",
    "Instead, **where the contents of the loop are simple** (e.g. a single statement) we would use a different way of using a `for` loop, called an **implicit loop**.\n",
    "\n",
    "In this case:\n",
    "\n",
    "    for item in group:\n",
    "        doit(group)\n",
    "        \n",
    "becomes:\n",
    "\n",
    "    [doit(group) for item in group]\n",
    "    \n",
    "with the additional feature that everything returned by `doit(group)` for each item of `group` is put in a list."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['1950', '-1.03', '-1.133', '-1.283', '-1.071', '-1.434', '-1.412', '-1.269', '-1.042', '-.631', '-.406', '-1.138', '-1.235']\n",
      "[1950.0, -1.03, -1.133, -1.283, -1.071, -1.434, -1.412, -1.269, -1.042, -0.631, -0.406, -1.138, -1.235]\n"
     ]
    }
   ],
   "source": [
    "# split the line on whitespace\n",
    "\n",
    "# implicit for loop\n",
    "float_data = [float(line_data) for line_data in data[0].split()]\n",
    "    \n",
    "# this is the string list\n",
    "print(line)\n",
    "# this is the float list\n",
    "print(float_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The statement:\n",
    "\n",
    "    float_data = [float(line_data) for line_data in line]\n",
    "\n",
    "is much more [Pythonic](https://docs.python-guide.org/writing/style/) than the code above. It is simple, elegant and neat."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can *nest* for statements, i.e. put one for loop inside another. This allows us to treat data of multiple dimensions.\n",
    "\n",
    "In the examples above, we converted only the data in `data[0]` to a list of floating point numbers.\n",
    "If we wanted to process *all* lines of data, we would have to loop over them as well, in an 'outer' loop.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "line 0 is [1950.0, -1.03, -1.133, -1.283, -1.071, -1.434, -1.412, -1.269, -1.042, -0.631, -0.406, -1.138, -1.235]\n",
      "line 10 is [1960.0, -0.287, -0.253, -0.082, 0.007, -0.322, -0.287, -0.318, -0.25, -0.47, -0.332, -0.308, -0.39]\n",
      "line 20 is [1970.0, 0.38, 0.432, 0.228, 0.014, -0.099, -0.636, -1.055, -1.007, -1.251, -1.068, -1.063, -1.203]\n",
      "line 30 is [1980.0, 0.677, 0.601, 0.684, 0.912, 0.958, 0.911, 0.769, 0.329, 0.263, 0.223, 0.27, 0.111]\n",
      "line 40 is [1990.0, 0.243, 0.573, 0.951, 0.46, 0.652, 0.511, 0.147, 0.127, 0.366, 0.303, 0.4, 0.362]\n",
      "line 50 is [2000.0, -1.122, -1.189, -1.09, -0.397, 0.251, 0.001, -0.159, -0.145, -0.238, -0.367, -0.701, -0.55]\n",
      "line 60 is [2010.0, 1.066, 1.526, 1.462, 0.978, 0.658, -0.228, -1.103, -1.671, -1.879, -1.888, -1.472, -1.558]\n"
     ]
    }
   ],
   "source": [
    "# use a step of 10 for illustration purposes\n",
    "# to save space when printing\n",
    "\n",
    "step = 10\n",
    "\n",
    "for index,line in enumerate(data_table.splitlines()[1:-1:step]):\n",
    "    # convert each line to list of floats\n",
    "    float_data = [float(line_data) for line_data in line.split()]\n",
    "    print('line {} is {}'.format(index*step,float_data))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that whilst we have calculated `float_data` in the loop for each line, it gets over-written with each new line as things stand.\n",
    "\n",
    "We can do the same thing, and generate a list of the responses more neatly, using an implicit loop inside another implicit loop:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_float_data = [[float(line_data) for line_data in line.split()] for line in data_table.splitlines()[1:-1]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The variable `all_float_data` is now a sort of 'two dimensional' list, within which we can refer to individual items as e.g. `all_float_data[10][3]` for row `10`, column `3`.\n",
    "\n",
    "Let's use this idea to print out column 0 of each row (containing the `YEAR` data). We will use the method `range(nrows)` that (implicitly) generates a list `[0,1,2,3, ..., nrows-1]`. \n",
    "\n",
    "Notice the use of `end=' '` in the `print` statement. This replaces the usual newline by whetever is specified by the keyword `end`. Note also that we have used `{:.0f}` to specify the format term. This indicates that the term is to be printed as a floating point number (the `f`) with zero numbers after the decimal point (`.0`)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "column 0 of the data gives:\n",
      "\n",
      "1950 1951 1952 1953 1954 1955 1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 "
     ]
    }
   ],
   "source": [
    "nrows = len(all_float_data)\n",
    "i = 0\n",
    "\n",
    "print('column {} of the data gives:\\n'.format(i))\n",
    "for row in range(nrows):\n",
    "    print('{:.0f}'.format(all_float_data[row][i]),end=' ')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise 1.2.9**\n",
    "\n",
    "* use an implicit loop to create a list of ENSO values in a variable `enso` for the years 1950 up to last year for the period `DECJAN`.\n",
    "* produce a plot of ENSO for `DECJAN` as a function of year (see below on how to do that).\n",
    "\n",
    "Hint: check which column in the header is `DECJAN`. To start you off on this, we give you the implicit loop code for extracting the column containing the `YEAR` data (column 0). We also give you the code to achieve the plotting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'ENSO')"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# do exercise here\n",
    "# ANSWER\n",
    "\n",
    "# generate a list called years of column 0 data\n",
    "years = [all_float_data[row][0] for row in range(nrows)]\n",
    "\n",
    "# very similar to aboive, but using column 1\n",
    "enso = [all_float_data[row][1] for row in range(nrows)]\n",
    "\n",
    "# for plotting\n",
    "import pylab as plt\n",
    "%matplotlib inline\n",
    "\n",
    "# \n",
    "plt.figure(0,figsize=(12,3))\n",
    "plt.plot(years,enso)\n",
    "plt.xlabel('year')\n",
    "plt.ylabel('ENSO')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.2.6 Summary\n",
    "\n",
    "In section 1.2 you have been introduced to text representation in Python, as strings (type `str`), and shown that this sort of variable can be thought of an an 'array', and that it has a length attribute that can be accessed with `len()`.\n",
    "\n",
    "Other useful string manipulation methods you were introduced to are: `replace()`, `find()`, `split()` and `splitlines()`, though of course there are [many more](https://docs.python.org/3/library/string.html).\n",
    "\n",
    "In an 'array', we can use an index to refer to a particular item (e.g. index 0 for the first item, 1 for the second, -1 for the last). We can use this idea to manipulate strings. \n",
    "\n",
    "In a more general sense, we can take a 'slice' of an array, with the syntax `[start:stop:skip]` giving access to a regularly spaced part of an array. We can use this, for example, to print out every 10th value (`skip=10`).\n",
    "\n",
    "You were also introduced to the idea of looping control structures, using a `for ... in ...:` statement, and the equivalent implicit form. This introduced the idea of [indented code blocks](https://wiki.python.org/moin/Why%20separate%20sections%20by%20indentation%20instead%20of%20by%20brackets%20or%20%27end%27) and (related) nested structures (loops within loops).\n",
    "\n",
    "In passing, you have also been shown how to pull html data from a URL (scraping) using the [`requests`](http://docs.python-requests.org/en/master/) package, and also how to produce a simple data plot, using [`pylab`](https://matplotlib.org/index.html)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.3. Groups of things\n",
    "Very often, we will want to group items together. There are several main mechanisms for doing this in Python, known as:\n",
    "\n",
    "* string e.g. `hello`\n",
    "* tuple, e.g. `(1, 2, 3)`\n",
    "* list, e.g. `[1, 2, 3]`\n",
    "* numpy array e.g. `np.array([1, 2, 3])`\n",
    "\n",
    "A slightly different form of group is a dictionary:\n",
    "\n",
    "* dict, e.g. `{1:'one', 2:'two', 3:'three'}`\n",
    "\n",
    "You will notice that each of the grouping structures tuple, list and dict use a different form of bracket. The numpy array is fundamental to much work that we will do later.\n",
    "\n",
    "We have dealt with the idea of a string as an ordered collection in the material above, so will deal with the others here.\n",
    "\n",
    "We noted the concept of length (`len()`), that elements of the ordered collection could be accessed via an index, and came across the concept of a slice. All of these same ideas apply to the first set of groups (string, tuple, list, numpy array) as they are all ordered collections.\n",
    "\n",
    "A dictionary is not (by default) ordered, however, so indices have no role. Instead, we use 'keys'."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.3.1 `tuple`\n",
    "A tuple is a group of items separated by commas. In the case of a tuple, the brackets are optional.\n",
    "You can have a group of differnt types in a tuple (e.g. int, int, str, bool)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 2, 'three', False)\n",
      "1 2 three False\n"
     ]
    }
   ],
   "source": [
    "# load into the tuple\n",
    "t = (1, 2, 'three', False)\n",
    "\n",
    "# unload from the tuple\n",
    "a,b,c,d = t\n",
    "\n",
    "print(t)\n",
    "print(a,b,c,d)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If there is only one element in a tuple, you must put a comma , at the end, otherwise it is not interpreted as a tuple:\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 <class 'int'>\n",
      "(1,) <class 'tuple'>\n"
     ]
    }
   ],
   "source": [
    "t = (1)\n",
    "print (t,type(t))\n",
    "t = (1,)\n",
    "print (t,type(t))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can have an empty tuple though:\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "() <class 'tuple'>\n"
     ]
    }
   ],
   "source": [
    "t = ()\n",
    "print (t,type(t))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**E1.3.1 Exercise**\n",
    "\n",
    "* create a tuple called t that contains the integers 1 to 5 inclusive\n",
    "* print out the value of t\n",
    "* use the tuple to set variables a1,a2,a3,a4,a5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 2, 3, 4, 5)\n",
      "1 2 3 4 5\n"
     ]
    }
   ],
   "source": [
    "# do exercise here\n",
    "# ANSWER\n",
    "\n",
    "t = (1,2,3,4,5)\n",
    "print(t)\n",
    "a1,a2,a3,a4,a5 = t\n",
    "print(a1,a2,a3,a4,a5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### 1.3.2  `list`\n",
    "A `list` is similar to a `tuple`. One main difference is that you can change individual elements in a list but not in a tuple.\n",
    "To convert between a list and tuple, use the 'casting' methods `list()` and `tuple()`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "type of (1, 2, 3) is <class 'tuple'>\n",
      "type of [1, 2, 3] is <class 'list'>\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# a tuple\n",
    "t0 = (1,2,3)\n",
    "\n",
    "# cast to a list\n",
    "l = list(t0)\n",
    "\n",
    "# cast to a tuple\n",
    "t = tuple(l)\n",
    "\n",
    "print('type of {} is {}'.format(t,type(t)))\n",
    "print('type of {} is {}'.format(l,type(l)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can concatenate (join) lists or tuples with the `+` operator:\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "joint list: [1, 2, 3, 4, 5, 6]\n"
     ]
    }
   ],
   "source": [
    "l0 = [1,2,3]\n",
    "l1 = [4,5,6]\n",
    "\n",
    "l = l0 + l1\n",
    "print ('joint list:',l)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**E1.3.2 Exercise**\n",
    "* copy the code from the cell above, but instead of lists, use tuples\n",
    "* loop over each element in the tuple and print out the data type and value of the element\n",
    "\n",
    "Hint: use a `for ... in ...` construct."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "joint tuple: (1, 2, 3, 4, 5, 6)\n",
      "<class 'int'> 1\n",
      "<class 'int'> 2\n",
      "<class 'int'> 3\n",
      "<class 'int'> 4\n",
      "<class 'int'> 5\n",
      "<class 'int'> 6\n"
     ]
    }
   ],
   "source": [
    "# do exercise here\n",
    "# ANSWER\n",
    "\n",
    "l0 = (1,2,3)\n",
    "l1 = (4,5,6)\n",
    "\n",
    "l = l0 + l1\n",
    "print ('joint tuple:',l)\n",
    "\n",
    "for t in l:\n",
    "    print(type(t),t)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A common method associated with lists or tuples is:\n",
    "* `index()`\n",
    "\n",
    "Some useful methods that will operate on lists and tuples are:\n",
    "* `len()`\n",
    "* `sort()`\n",
    "* `min(),max()`\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the index of 4 in (2, 8, 4, 32, 16) is 2\n",
      "the length of the <class 'tuple'> (2, 8, 4, 32, 16) is 5\n"
     ]
    }
   ],
   "source": [
    "l0 = (2,8,4,32,16)\n",
    "\n",
    "# print the index of the item integer 4 \n",
    "# in the tuple / list\n",
    "\n",
    "item_number = 4\n",
    "\n",
    "# Note the dot . here\n",
    "# as index is a method of the class list\n",
    "ind  = l0.index(item_number)\n",
    "\n",
    "# notice that this is different\n",
    "# as len() is not a list method, but \n",
    "# does operatate on lists/tuples\n",
    "# Note: do not use len as a variable name!\n",
    "llen = len(l0)\n",
    "\n",
    "# note the use of integers in the braces e.g. {0}\n",
    "# rather than empty braces as before. This allows us to\n",
    "# refer to particular items in the format argument list\n",
    "print('the index of {0} in {1} is {2}'.format(item_number,l0,ind))\n",
    "print('the length of the {0} {1} is {2}'.format(type(l0),l0,llen))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "**E1.3.3 Exercise**\n",
    "\n",
    "* copy the code to the block below, and test that this works with lists, as well as tuples\n",
    "* find the index of the integer 16 in the tuple/list\n",
    "* what is the index of the first item?\n",
    "* what is the length of the tuple/list?\n",
    "* what is the index of the last item?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the index of 4 in [2, 8, 4, 32, 16] is 2\n",
      "the length of the <class 'list'> [2, 8, 4, 32, 16] is 5\n",
      "16 is item number 4\n",
      "the first item has index 0: 0\n",
      "list length 5\n",
      "the last item has index 4 (len - 1): 4\n"
     ]
    }
   ],
   "source": [
    "# do exercise here\n",
    "# ANSWER\n",
    "\n",
    "# change this to a list\n",
    "l0 = [2,8,4,32,16]\n",
    "\n",
    "# print the index of the item integer 4 \n",
    "# in the tuple / list\n",
    "\n",
    "item_number = 4\n",
    "\n",
    "# Note the dot . here\n",
    "# as index is a method of the class list\n",
    "ind  = l0.index(item_number)\n",
    "\n",
    "# notice that this is different\n",
    "# as len() is not a list method, but \n",
    "# does operatate on lists/tuples\n",
    "# Note: do not use len as a variable name!\n",
    "llen = len(l0)\n",
    "\n",
    "# note the use of integers in the braces e.g. {0}\n",
    "# rather than empty braces as before. This allows us to\n",
    "# refer to particular items in the format argument list\n",
    "print('the index of {0} in {1} is {2}'.format(item_number,l0,ind))\n",
    "print('the length of the {0} {1} is {2}'.format(type(l0),l0,llen))\n",
    "\n",
    "'''\n",
    "find the index of the integer 16 in the tuple/list\n",
    "what is the index of the first item?\n",
    "what is the length of the tuple/list?\n",
    "what is the index of the last item?\n",
    "'''\n",
    "\n",
    "print('16 is item number',l0.index(16))\n",
    "print('the first item has index 0:',l0.index(2))\n",
    "print('list length',len(l0))\n",
    "print('the last item has index 4 (len - 1):',l0.index(16))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A list has a much richer set of methods than a tuple. This is because we can add or remove list items (but not tuple).\n",
    "\n",
    "* `insert(i,j)` : insert `j` beore item `i` in the list\n",
    "* `append(j)` : append `j` to the end of the list\n",
    "* `sort()` : sort the list\n",
    "\n",
    "This shows that tuples and lists are 'ordered' (i.e. they maintain the order they are loaded in) so that indiviual elements may be accessed through an 'index'. The index values start at 0 as we saw above. The index of the last element in a list/tuple is the length of the group, minus 1. This can also be referred to an index `-1`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the list [64, 2, 8, 4, 32, 128, 16, 256] once sorted is [2, 4, 8, 16, 32, 64, 128, 256]\n"
     ]
    }
   ],
   "source": [
    "l0 = [2,8,4,32,16]\n",
    "\n",
    "# insert 64 at the begining (before item 0)\n",
    "# Note that this inserts 'in place'\n",
    "# i.e. the list is changed by calling this\n",
    "l0.insert(0,64)\n",
    "\n",
    "\n",
    "# insert 128 *before* the last item (item -1)\n",
    "l0.insert(-1,128)\n",
    "\n",
    "# append 256 on the end\n",
    "l0.append(256)\n",
    "\n",
    "# copy the list \n",
    "# and sort the copy\n",
    "# Note the use of the copy() method here\n",
    "# to create a copy\n",
    "l1 = l0.copy()\n",
    "\n",
    "# Note that this sorts 'in place'\n",
    "# i.e. the list is changed by calling this\n",
    "l1.sort()\n",
    "\n",
    "print('the list {0} once sorted is {1}'.format(l0,l1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**E1.3.4 Exercise**\n",
    "\n",
    "* copy the above code and try out some different locations for inserting values (e.g. what does index `-2` mean?)\n",
    "* what happens if you take off the `.copy()` statement in the line `l1 = l0.copy()`, i.e. just use `l1 = l0`?  [Why is this?](https://www.afternerd.com/blog/python-copy-list/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2, 8, 4, 64, 32, 16]\n",
      "the list [2, 8, 4, 64, 32, 16] once sorted is [2, 4, 8, 16, 32, 64]\n"
     ]
    }
   ],
   "source": [
    "# do exercise here\n",
    "# ANSWER\n",
    "\n",
    "l0 = [2,8,4,32,16]\n",
    "\n",
    "# insert 64 at index -2 (2 from end)\n",
    "# Note that this inserts 'in place'\n",
    "# i.e. the list is changed by calling this\n",
    "l0.insert(-2,64)\n",
    "print(l0)\n",
    "\n",
    "# copy the list \n",
    "# and sort the copy\n",
    "# Note the use of the copy() method here\n",
    "# to create a copy\n",
    "l1 = l0.copy()\n",
    "\n",
    "# Note that this sorts 'in place'\n",
    "# i.e. the list is changed by calling this\n",
    "l1.sort()\n",
    "\n",
    "print('the list {0} once sorted is {1}'.format(l0,l1))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2, 8, 4, 64, 32, 16]\n",
      "the list [2, 4, 8, 16, 32, 64] once sorted is [2, 4, 8, 16, 32, 64]\n",
      "the list [2, 8, 4, 32, 16] once sorted is [2, 4, 8, 16, 32]\n"
     ]
    }
   ],
   "source": [
    "# do exercise here\n",
    "# ANSWER\n",
    "\n",
    "l0 = [2,8,4,32,16]\n",
    "\n",
    "# insert 64 at index -2 (2 from end)\n",
    "# Note that this inserts 'in place'\n",
    "# i.e. the list is changed by calling this\n",
    "l0.insert(-2,64)\n",
    "print(l0)\n",
    "\n",
    "# copy the list \n",
    "# and sort the copy\n",
    "# Note the use of the copy() method here\n",
    "# to create a copy\n",
    "l1 = l0\n",
    "\n",
    "# Note that this sorts 'in place'\n",
    "# i.e. the list is changed by calling this\n",
    "l1.sort()\n",
    "\n",
    "'''\n",
    "Note that without the copy, we have altered l0\n",
    "even though we did l1.sort()\n",
    "'''\n",
    "\n",
    "print('the list {0} once sorted is {1}'.format(l0,l1))\n",
    "\n",
    "'''\n",
    "For not in place sort, use sorted()\n",
    "'''\n",
    "\n",
    "l0 = [2,8,4,32,16]\n",
    "print('the list {0} once sorted is {1}'.format(l0,sorted(l0)))\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.3.3 `np.array`\n",
    "\n",
    "An array is a group of objects of the same type. Because they are of the same type, they can be stored efficiently in compter memory, and also accessed efficiently.\n",
    "\n",
    "Whilst there are different ways of forming arrays, the most common is to use numpy arrays, using the package `numpy`. To use this, we must first import the package into the current workspace. We do this with the `import` method. Using the optional `as` statement allows us to use a shorter (or more suitable) name for the package. We will generally call numpy `np`, so we use:\n",
    "\n",
    "`import numpy as np`\n",
    "\n",
    "to import ('load') the numpy package. \n",
    "\n",
    "Often, we will read data from a file/URL as we did above for the ENSO dataset. In that case, we had to step through each item to convert from string form to floating point number.\n",
    "\n",
    "This sort of thing is much more simply done using methods associated with numpy arrays. \n",
    "\n",
    "A particularly useful numpy method is `np.loadtxt(file)` that loads an ASCII table of data straight into a numpy array.\n",
    "\n",
    "Whilst this is designed to load data from a file, we can use `io.StringIO()` from the `io` package to make data that we already have as a string seem to `np.loadtxt` as if it were a file. This is a useful 'trick' for using methods that expect data in a file. The `unpack=True` option makes sure the data array is compoised the way we would expect it. The `usecols` option lets us select only those data columns we wish to read (0 and 1 here).\n",
    "\n",
    "\n",
    "An alternative to `np.loadtxt()` is `np.genfromtxt()`. This has some additional features, such the `invalid_raise` flag. If this is set `False`, the loading is made somewhat tolerant to data errors (e.g. inconsistent number of columns). Further, we can explicitly set what will indicate `missing_values` in the input and what we would like to replace them with (`filling_values`) which can be useful for tidying up datasets.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "array type <class 'numpy.ndarray'>\n",
      "data type float64\n",
      "number of dimensions 2\n",
      "data shape (2, 68)\n",
      "data size 136\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x11b8b2668>"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import requests\n",
    "import numpy as np\n",
    "import io\n",
    "\n",
    "# access dataset as above\n",
    "url = \"http://www.esrl.noaa.gov/psd/enso/mei.old/table.html\"\n",
    "txt = requests.get(url).text\n",
    "\n",
    "# copy the useful data\n",
    "start_head = txt.find('YEAR')\n",
    "start_data = txt.find('1950\\t')\n",
    "stop_data  = txt.find('2018\\t')\n",
    "\n",
    "# select a data column\n",
    "data_column = 1\n",
    "\n",
    "header = txt[start_head:start_data].split()\n",
    "data = np.loadtxt(io.StringIO(txt[start_data:stop_data]),unpack=True,usecols=[0,data_column])\n",
    "\n",
    "# so data[0] is the year data\n",
    "#    data[1] is the enso data for column data_column\n",
    "# print some attributes of the data array\n",
    "\n",
    "print('array type',type(data))\n",
    "print('data type',data.dtype)\n",
    "print('number of dimensions',data.ndim)\n",
    "print('data shape',data.shape)\n",
    "print('data size',data.size)\n",
    "\n",
    "# for plotting\n",
    "import pylab as plt\n",
    "%matplotlib inline\n",
    "\n",
    "# \n",
    "plt.figure(0,figsize=(12,3))\n",
    "plt.plot(data[0],data[1],label=header[data_column])\n",
    "plt.xlabel('year')\n",
    "plt.ylabel('ENSO')\n",
    "plt.title('ENSO data from {0}'.format(url))\n",
    "plt.legend(loc='best')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We saw in the example above that a numpy array (`<class 'numpy.ndarray'>`) has a set of attributes that include `shape`, `ndim`, `dtype` and `size` that we can use to query information about the array. We will learn morre about processing data with numpy arrays later in the course, but you should already see that they are a useful construct for manipulating multi-dimensional datasets.\n",
    "\n",
    "**Exercise 1.3.4**\n",
    "\n",
    "* copy the code from the block above and modify it to plot the ENSO data for the period `FEBMAR`. Check this by looking at the data in the [original table](http://www.esrl.noaa.gov/psd/enso/mei/table.html).\n",
    "* modify the code to produce a plot of *all* periods (so the graph should have 12 lines, correctly labelled)\n",
    "\n",
    "Hint: You will need to consider what, if anything to set of `usecols` (what happends if you don't set `usecols`?) and provide a looping structure for the plotting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "array type <class 'numpy.ndarray'>\n",
      "data type float64\n",
      "number of dimensions 2\n",
      "data shape (2, 68)\n",
      "data size 136\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x11bcb0a20>"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# do exercise here\n",
    "#ANSWER\n",
    "\n",
    "import requests\n",
    "import numpy as np\n",
    "import io\n",
    "\n",
    "# access dataset as above\n",
    "url = \"http://www.esrl.noaa.gov/psd/enso/mei.old/table.html\"\n",
    "txt = requests.get(url).text\n",
    "\n",
    "# copy the useful data\n",
    "start_head = txt.find('YEAR')\n",
    "start_data = txt.find('1950\\t')\n",
    "stop_data  = txt.find('2018\\t')\n",
    "\n",
    "# select a data column\n",
    "'''\n",
    "Use column 3 for FEBMAR\n",
    "\n",
    "The code is written so that we only need\n",
    "change this one variable\n",
    "'''\n",
    "data_column = 3\n",
    "\n",
    "header = txt[start_head:start_data].split()\n",
    "data = np.loadtxt(io.StringIO(txt[start_data:stop_data]),unpack=True,usecols=[0,data_column])\n",
    "\n",
    "# so data[0] is the year data\n",
    "#    data[1] is the enso data for column data_column\n",
    "# print some attributes of the data array\n",
    "\n",
    "print('array type',type(data))\n",
    "print('data type',data.dtype)\n",
    "print('number of dimensions',data.ndim)\n",
    "print('data shape',data.shape)\n",
    "print('data size',data.size)\n",
    "\n",
    "# for plotting\n",
    "import pylab as plt\n",
    "%matplotlib inline\n",
    "\n",
    "# \n",
    "plt.figure(0,figsize=(12,3))\n",
    "\n",
    "plt.plot(data[0],data[1],label=header[data_column])\n",
    "plt.xlabel('year')\n",
    "plt.ylabel('ENSO')\n",
    "plt.title('ENSO data from {0}'.format(url))\n",
    "plt.legend(loc='best')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.3.4 `dict`\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The collections we have used so far have all been ordered. This means that we can refer to a particular element in the group by an index, e.g. `array[10]`.\n",
    "\n",
    "A dictionary is not (by default) ordered. Instead of indices, we use 'keys' to refer to elements: each element has a key associated with it. It can be very useful for data organisation (e.g. databases) to have a key to refer to, rather than e.g. some arbitrary column number in a gridded dataset.\n",
    "\n",
    "A dictionary is defined as a group in braces (curley brackets). For each elerment, we specify the key and then the value, separated by `:`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a:\n",
      "\t {'one': 1, 'two': 2, 'three': 3}\n",
      "a.keys():\n",
      "\t dict_keys(['one', 'two', 'three'])\n",
      "a.values():\n",
      "\t dict_values([1, 2, 3])\n",
      "a.items():\n",
      "\t dict_items([('one', 1), ('two', 2), ('three', 3)])\n"
     ]
    }
   ],
   "source": [
    "a = {'one': 1, 'two': 2, 'three': 3}\n",
    "\n",
    "# we then refer to the keys and values in the dict as:\n",
    "\n",
    "print ('a:\\n\\t',a)\n",
    "print ('a.keys():\\n\\t',a.keys())     # the keys\n",
    "print ('a.values():\\n\\t',a.values()) # returns the values\n",
    "print ('a.items():\\n\\t',a.items())   # returns a list of tuples"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because dictionaries are not ordered, we cannot guarantee the order they will come out in a `for` loop, but we will often use such a loop to iterate over the items in a dictionary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "one 1\n",
      "two 2\n",
      "three 3\n"
     ]
    }
   ],
   "source": [
    "for key,value in a.items():\n",
    "    print(key,value)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We refer to specific items using the key e.g.:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n"
     ]
    }
   ],
   "source": [
    "print(a['one'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can add to a dictionary:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'one': 1, 'two': 2, 'three': 3, 'four': 4, 'five': 5}\n",
      "{'one': 1, 'two': 2, 'three': 3, 'four': 4, 'five': 5, 'six': 6}\n"
     ]
    }
   ],
   "source": [
    "a.update({'four':4,'five':5})\n",
    "print(a)\n",
    "\n",
    "# or for a single value\n",
    "a['six'] = 6\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Quite often, you find that you have the keys you want to use in a dictionary as a list or array, and the values in another list.\n",
    "\n",
    "In such a case, we can use the method `zip(keys,values)` to load into the dictionary. For example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'one': 1, 'two': 2, 'three': 3, 'four': 4}\n"
     ]
    }
   ],
   "source": [
    "values = [1,2,3,4]\n",
    "keys = ['one','two','three','four']\n",
    "\n",
    "a = dict(zip(keys,values))\n",
    "\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will use this idea to make a dictionary of our ENSO dataset, using the items in the header for the keys. In this way, we obtain a  more elegant representation of the dataset, and can refer to items by names (keys) instead of column numbers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x11bdc31d0>"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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/p923AGlJmIMsPP6ndjUCRPT7RHRU+/siAEkhxHS12zruhLQITOtuE3RFsVZo3AfgqBAip939c8jJr2d06x2HfC/cpd2OQ06m/KnuUjWI6BgRvVJb5xDkRKz/qz2v49hICnFD+sSXtecdgZwPsC1a0ZckoisbWR9y4uy52n6+CeA9Qogfa8v+AsDrIAvef4BUlBX/Cal2PgppcYpDvnfUOF5HRA/o1n815BWBFcjPwNchTy6hnby8DvJYLEGeoLxUCJHXPf4myKs9ehX7+ZD2nhw28zXIz+c85FWAt2v3j0FevYpDTm6+HcDN2rJXQSq4q5Ce66/ptrfJPqGdfL4c8jOzBDlZ9i/Q2G9ovTFUfS1IWtXOgXbS0SJ/APk99xDk++0bkHMSKqlmFfkAgJu0z+NvQ05ADkG+lj/D5hMQRa3XoOFxaSdd0QafX7VxMkxXQRUnmgzDMJZDMr7uRiHEeAPrvhPAsBDindVuM90BEX0LMkXnf7WxjS8CuEcI8UXdfXdDTha8of1RAiSjKecgJ9Qlt1vfDIjoNZCT817TgX0d1/a1o/LkGaYbYCWbYZhe5yS07O8atxkLIKIrSGaU24joRZB2n39rc7P3GbCN7RgE8F6rCmyNVcgrPqai2eO+zAU2w5gDK9kMw3QdzSjZTHdCRL8FmQE9CJkc8hEhhOEtyo1WshmGYYyCi2yGYRiGYRiGMRi2izAMwzAMwzCMwTisHkAzDA8Pi8nJSauHwTAMwzAMw+xw7r333mUhxEirj++pIntychL33HOP1cNgGIZhGIZhdjhEVNkJtinYLsIwDMMwDMMwBsNFNsMwDMMwDMMYDBfZDMMwDMMwDGMwPeXJZhiGYRiGYbYnn89jZmYGmUzG6qF0PR6PB+Pj43A6nYZul4tshmEYhmGYHcbMzAyCwSAmJydBRFYPp2sRQmBlZQUzMzOYmpoydNtsF2EYhmEYhtlhZDIZDA0NcYG9DUSEoaEhUxR/LrIZhmEYhmF2IFxgN4ZZx4mLbIZhGIZhGIYxGC6yGYZhGIZhGMMhIrz2ta8t3y4UChgZGcF11123ab2XvexluPLKK8u3f/CDH+DKK6+EEAIAUCwW8ZSnPAU/+9nP8MEPfhCf/OQnAQAHDx7c1KTw5MmTuOiiiwAAd9xxB4gI3/3ud8vLr7vuOtxxxx2GP89acJHNMAzDMAzDGI7f78eRI0eQTqcBALfddhv27t27aZ1oNIr77rsP0WgUJ06cAABce+212L9/P7785S8DAD7/+c/j8ssvxzOf+cym9j8+Po6PfOQjBjyT1uAim2EYhmEYhjGFF77whfje974HALj55pvx6le/etPyb33rW3jJS16CV73qVbjlllvK93/mM5/B3/7t3+Lo0aP4whe+gI997GNN7/uSSy5BKBTCbbfd1t6TaBGO8GMYhmEYhtnBfOi7R/HQXNzQbV44NoAPvORJ2673qle9Cn/zN3+D6667Dr/+9a9x/fXX46677iovv/nmm/GBD3wAu3fvxite8Qq8+93vBgCMjo7i7W9/O6688kp87nOfw+DgYEvjfN/73of3ve99eMELXtDS49uBlWyGYRiGYRjGFJ785Cfj5MmTuPnmm/GiF71o07LFxUU89thjuPrqq3HgwAE4HA4cOXKkvPwtb3kLisUiXv/611fddrVUkMr7nvWsZwHApsK+U7CSzTAMwzAMs4NpRHE2k5e+9KV4xzvegTvuuAMrKyvl+7/+9a9jbW2t3AQmHo/jlltuwYc//GEAgM1mqxuvNzQ0hLW1tfLt1dVVDA8Pb1nvve99Lz7ykY/A4ehs2ctKNsMwDMP0OEIIxFJ5q4fBMFW5/vrr8f73vx8XX3zxpvtvvvlmfP/738fJkydx8uRJ3HvvvZt82dtx8OBB3HjjjeUUkq9+9at4znOes2W9a6+9Fmtra3jggQfaeyJNwkU2wzAMw/Q4P31sBZd95DbMx9JWD4VhtjA+Po63ve1tm+47efIkpqen8YxnPKN839TUFAYGBnDo0KGa2yoUCnC73QCAN7/5zQgGg7jkkktwySWXIJlM4h3veEfVx733ve/FzMyMAc+mcdguwjAMwzA9zvRqCvmiwPHFJEZDXquHwzAAgGQyueW+gwcP4uDBgwCA2dnZLcvvu+++uts4evRoOcrP5XLhC1/4QtV96/cDSMuKUrw7BSvZDMMwO4RSSaBQLFk9DMYCkllpFZlZS1k8EoYxj4svvhg2mw3XXnut1UNpCFayGYZhdgj/9NMT+JdD0/jxOw5aPRSmwyQzBQDAzBrbRZidy4MPPmj1EJqClWyGYZgdwkPzcZxYXkep1NlLooz1JLKyyD69yko2s0Gn7RG9ilnHiYtshmGYHcJyMgcAWM8VLB4J02lYyWYq8Xg8WFlZ4UJ7G4QQWFlZgcfjMXzbbBdhGIbZISwnsgCA9WwRQY/T4tEwnUSdWLEnm1GMj49jZmYGS0tLVg+l6/F4PBgfHzd8u1xkMwzD7BCWkrLIlpPgjFdlmO4loSnZy8kc0rkivC67xSNirMbpdJabvDDWwHYRhmGYHUCpJLC6Lu0iyWzR4tEwnSaZ3bAIzUZZzWaYboCLbIZhmB3AWiqHojbhcT3Lnux+I5kpYPeAbNBxepV92QzTDXCRzTAMswNQkx6BDesA0z8kswWcv2cAAPuyGaZb4CKbYRhmB7CkTXoEWMnuR5KZAqaG/XA5bJwwwjBdAhfZDMMwO4DlpK7I5gi/vqJUEkjmChjwODAe9uI0K9kM0xVwkc0wDLMD0BfZbBfpL1L5IoQAAh4Hxgd9rGQzlnP3o8s4PL1m9TAsh4tshmGYHcBSIguX3QaHjdgu0meoRjQBtxPjES93fWQs5/3/fgSf+sFxq4dhOZyTzTAMswNYSmYxEnQjmS1wkd1nyFx0qWRPRHxYS+WRzBYQcPNPPNN5hBCYi/LVFICVbIZhmB3BcjKH4YALAbcDCS6y+wqVix50OzAe8QIAZtkywlhENJVHJl/CXDTd9y3duchmGIbZASwlshgOuBFwO1jJ7jPKdhHPRpHNlhHGKuZi8gQvky8hmspbPBpr4SKbYRhmB7CclEW2323HOnd87CvKdhG3AxODPgCclc1Yx3w0U/5bFdz9imVFNhFNENGPiehhIjpKRG+zaiwMwzC9jGqpPhJ0w892kb4jUZ746MCQ3wWPk7OyGeuY1xXW+oK7H7FSyS4A+AshxAUAngHgLUR0oYXjYRiG6UlUS/XhgAtBD9tF+o1kdqPIJiKMR3yclc1Yxlxso7CeZyXbGoQQ80KI+7S/EwAeBrDXqvEwDMP0KktaRvZw0A2/i4vsfkN5sv1amshExMtKNmMZ89E09oa9cNppU8Hdj3SFJ5uIJgFcCuBQlWVvJqJ7iOiepaWlTg+NYRim61lO5AAAIwFpF0lykd1XJLMFuB02uBzyJ308wg1pGOuYi2WwN+zF7gEP5vs8ys/yIpuIAgC+BeDtQoh45XIhxBeFEJcJIS4bGRnp/AAZhmG6nGWdkq3sIv0endVPJLIFBD0bmdgTg17E0nnEM/2d7MBYw3wsjdGwB2MhLyvZVu6ciJyQBfa/CCG+beVYGIZhepWlhFZka0p2SQDpvPUJI6vrObzk83fj1Mq61UPZ0SQzmxvPjEe0hJHV/lYRmc5TKgksxDIYDXkxGvb0fVMaK9NFCMCXATwshPi0VeNgGIbpdZaTWbgcNgx4HGVfbjdYRh5ZiOPB2RiOzm25SMkYSDJbQMCjL7K1rGye/Mh0mJX1HPJFgbGwB6MhLxbjGZRK/XtVzUol+yoArwXwXCK6X/v3IgvHwzAM05MsJbMYCbhBRAi47QA2JsNZSUxrRMETMc2lsoX6hFKy2ZfNdBiVJrJnwIO9YQ/yRVG2s/Ujju1XMQchxN0AyKr9MwzD7BRkt0cXACDgdgJAVzSkiaZlkZ3KWT+WnUwyU8BY2Fu+HfY54XfZuSEN03HmtFzssbAXNpIl3lwsg10DHiuHZRmWT3xkGIZh2mM5mcNwwA0A8CsluwvUY9VSeT1n/Vh2MsmKiY/lrGz2ZDMdRinZoyEPRsOysO7nhBEushmGYXqc5WQWI0FZZAe6yJMdTctowVQXqOo7mUq7CCATRljJZjrNfCwDt8OGQb8LYyF5daWfE0a4yGYYhulhilpLdaVkq2KrG3zQMVayO0IyUyhPeFWMR3yYXUtzlCPTUeaiaYyGPCAihH1OeJw2VrIZhmGY3kTfUh3oMiVbK7JZyTaPbKGIXLG0yS4CyISRRLaAWJqzspnOMa/F9wHStjQW8mKelWyGYRimF1Ez90eC0v/YTRF+yi7CSrZ5qBSZSrvIOCeMMBYwH02XvdgAMBr2YJaVbIZhGKYXUS3VlZLtc9lB1B12kbKSzekipqFOprYW2VpW9ir7spnOUCwJLCayZS82AIyGvOXJkP0IF9kMwzA9zFJSXood1iY+EhECLkd3KNmck206CaVkeyonPrKSzXSWM4kMiiWxSckeC3lwJpFFvliycGTWwUU2wzBMD6OUbJUuAkjLSDc0oymni7CSbRrqZCpYoWSHvE4EPQ5OGGE6RjkjW6dkj4W9EAJYjPenL5uLbIZhmB5GtVTXF1kBj8NyH3QmX0QmL9Urq8eyk1FXCSqVbED6sk+zks10iHJG9iZPtldbxkU2wzAM02MsJTZaqiv8bgeSFid6qFQLIk4XMZNanmwAmIhwVjbTOeY1JXtUr2SHZME916eTH7nIZhiG6WGWkhst1RUBt91yH7TyY48E3Kxkm0gtTzYglewZzspmOsRcLA2/y44B3XuRlWyGYRimZ1lO5jb5sQGpalrtyY6mpB97b8SLVK7IhZ5JbHiynVuWTQzKY7+6nuv0sJg+ZD6awWjYu+mqWsDtQNDj6NuGNFxkMwzD9DDLyWy526NC2kUsLrI1u8hY2ItiSSBb6M90AbNJZgqwEeBxbv0556xsppPMxzMYDXm23D8W8mI2yko2wzAM00MUSwIrVYrsgNv6iY+qpfpe7XJxLyWMfOK/H8Fff+cIiqXuV9+T2QICbscm9VBRzspmXzbTAea1luqVjIY9fZuVvdXExTAMw/QEa6kcSgJb7CIqwk8IUbX46tTYgI2JT+vZAgb9rnoP6Rq+ee8MFuNZJDJ5fOr3ngK7zZpj2AiJTAFBz1arCLBRZLOSzZhNrlDCUjK7adKjYjTkxa9nYhaMynpYyWYYhulRVEv1akp2wWKLRjSdh9NO5SY56XxvKNnRVA6L8SzO3xPEd+6fw1/cen9XK9rJbL5qsggABD1OhH1OThhhTGcxnoEQwFi4ml3Eg9X1HDI98h1gJFxkMwzD9ChLCVVkV6aLyKLLyoSRaCqPkNcFfxeMpRmOLSQAAO964fl4x7UH8J375/CObzzQtYV2MluomiyiGI94cXqVlWzGXFR6SDUle6yPE0bYLsIwDNOjKCW7ml0EkAXYUIXK3Sli6RzCPif8LjmWXvFkH1+URfb5e4J4znm7AACf/MFxEIBP/O4lXWcdSWYKCPtq23AmIr7yc2IYs1Ce62pKtmpOMx9NY2rY39FxWQ0X2QzDMD2Kaqk+XCXCD4ClCSPRVB5hrxM+lx1A7yjZjywkEPQ4sGdAFgZ/+txzIQTwqduOA+i+QjuZLWB80Fdz+XjEix89csZSfz6z85mr0ohGodqsz7GSzTAMw/QKS1VaqgN6u4h16nE0lcdY2FNW1XtJyT5/T3BTQfpnzzsXgFZoE/CJV3RPoZ3MFra8/nomBn3IapPSdgW3qowMYwTzsTQGPI7y513Pnj7u+siebIZhmB5luUpLdQDwu6V6nMzmrRgWANlWPeR1wa+U7B7o+iiEwLGFBA7sDm5Z9mfPOxd//oID+PZ9s/jLb3aPRzuZKdSc+AhwwgjTGeaimbL3uhKP044hv6svY/xYyWYYhulRlpLZLVYRAAh6lF3ESiU7h4jPCZ9Ssi0cS6MsxDOIZwo4f8/WIhsA3qop2p++7TgIhI+/4smWKtrFksB6rrjNxMeNhjRP3Rfp1NCYPmM+Vj0jWzEa9pQtJf0EF9kMwzA9ynIyh71VJhpZneiRK5Swnisi7HPC6+wdJVsli1RTshVvfZ70aH/m9uMIeZ14/0t8ii83AAAgAElEQVQu7NTwtqCOaSNK9ulVjvFjzGM+lsElE+Gay0dDXpxaWe/giLoDtoswDMP0KEuJrd0eAV26SMaawjaalhMyQz4X7DaC12nvCU+2KrLPq6FkK972/HPxm0/ag+89ONeJYdVEvb71imyfy4Ehv4vtIoxpZPJFrK7nyo2nqjEW8mC+D5VsLrIZhmF6kGJJYHU9uyW+D0A5Ns+qdBHVUj3slZ0I/W57T6SLHFtMYPeAu24knuKivQNYjGctfV7q9a1nFwGkms0NaRizqJeRrRgLe5HIFpDIWDdPxAq4yGYYhulBVEv1akq23UbwuawrbKNprcj2ySLb53L0jJJdzyqiZ2o4AAA4aeEl8EQDSjYAjA/6WMlmTGNeSw0ZrWJdU4z2aUMaLrIZhmF6kI1uj9WbzfjdDst80NGyki0VYSsL/kYplgQePZOsOemxkslhOaHw5LJ1CrFSsoMNKNmza2mUuiQRhdlZqMJ5rJ6S3acxflxkMwzD9CC1uj0qAm5HWensNNGU9GQrJdvv7n4l++TKOnKFUsNK9uSQ7Fx3Yjlp5rDqsuHJdtZdbyLiQ65YwhntxIxhjERF8+2pmy6iNaTpM182F9kMwzA9iCqyhwPV/cMBt8My9Tim2UVCZbuIvevTRY4vqHbqAw2t73c7sHvAjRMWKtnrTXiyAbAvmzGFuVgGg34XPFqSUDV2B92wEfouK5uLbIZhmB6kbBepoWTLyYbWqMfRVB52G5U7Efpdjq7PyT62mAARcM6uQMOPmRzyW+vJzjboydZlZTOM0cxH62dkA4DDbsOuYP9lZXORzTAM04MsJ3NwV2mprgi4HeUirNNE0zmEvc5yJ0qfu/uV7GMLCUwO+eF11VbjKjlrxI8Ty9YV2Y1E+AGclc2Yy3wsUzdZRDEa9rCSzTAMw3Q/y1pGdmVLdYWVdpG1VL5sFQE0JbvLPdnHFhM4sLtxFRuQSvbqeq4cWdhpktk8fC77tl0nPU47RoJuVrIZU5iLpjFWJ1lEMRbycroIwzAM0/3Uaqmu8FvpyU7lyxnZgKZkd3G6SCZfxMnldZzX4KRHxdSwNvnRIstIMlsoNx7ajvGIF6fZk80YzHq2gHim0JiSHfJgLpqGEP2TcsNFNsMwTA+ylMhipMakR6AL7CK6hi4+pwPZQgmFYsmS8WzHY2eSKAngvAYnPSpUkX3SIstIIlOoaReqZCLCWdmM8Sj7R0NKdtiLbKGENYuu/FgBF9kMwzA9yHIyVzO+D5BFdq5QQt6CwjZaoWT73dLnnMp3p2Vko516c3aRiUEfiGCZLzuZLWybLKIYj3gxF02jyFnZjIGoiYyNKNmqEO+nrGwushmGYXoM1VK9ViMaAGUbgRU2jViFJ9untXnv1oSR44sJuOy2cvZ1o3icduwNe60rsjOFbSc9KiYGfSiUBBbi/eWJZcxFKdnbpYvIdVRWNhfZDMMwTJeyul67pbpCFV+dbkiTL5aQyBbK3R6BDSW7WxNGHllI4OxdATjszf8kTg1bF+OXzDZeZJezsjlhhDGQuWgGRPUb0ShU2/V+mvzIRTbDMEyPsV23R2CjQUmnC9u41ogm3GNKdqPt1CuZGpYxflZM5kpkmrGLcFY2YzzzsTRGAm44GzhBHfa74bQT5vooxo+LbIZhmB5jo9tj99lFolWKbL+re5XsWDqP+Vim4XbqlUwO+ZHIFLCynjN4ZNuznmt84uNY2AMicMIIYyjzsUy5Zfp22GyEPSEP5vuoIQ0X2QzDMD1Gudtj3XQRWdgmO6weR1OqyNali2iFYKoLi+zji6qdeotK9og1CSNCCOnJblDJdjvs2B30sJLNGMpcNI2xBqwiitGQt68a0nCRzTAM02M0YhdRSnayw57saEoqupvSRZSS3YV2EZUscqDVIlubLPlEh4vsbKGEQkkg4HZuv7LGeMTLXR8ZwxBCNNztUTEW6q/W6lxkMwzD9BiqpXq9SW8Bq+wiqSqe7C5Wso8tJBB0O5pS4/SMR7xw2KjjSraa0Nqokg3IhBFWshmjiGcKSOWKDWVkK8bCXizGM30TJclFNsMwTI+xtE1LdWCjyE5a5cnWp4t0s5K9mMCBPcG6x7IeDrsN+wZ9HU8YUa+rsgU1wnjEi4V4pmubAjG9xUZ8X+NK9mjYi0JJlK/G7XS4yGYYhukxlrdpqQ7o7CIdLrJjqRyIgKBOYS2ni3SZki2EwLGFRMuTHhWTw348sdThIlsp2U3YRSYiPhRLoq8i1BjzUBMYG4nvU6grRrN9kpXNRTbDMEyPIVuq1y+ynXYb3A6bJekiIa8TNtuGMuxy2OC0E9Zz3aVkn0lkEUvnW570qJga9uPUSgqlDl4CT2TlFYNGc7IBeake6J8ChzGXuSZaqiuU6t0vCSNcZDMMw/QYsqV67WQRRcDt6LxdpKKlusLnciBlQffJejyiJj0aoGSn80UsJjpXOCglO9iEJzvil6+L8s0zTDvMRzOw2wi7gs14slVDmv440eMim2EYpodopKW6wm9FkZ3OI+TbegLgd9m7Tsk+rhXZ57WrZGsJI51sr77hyW68yFaxirF05zO9mZ3HXCyN3UE37LbG5zOEvE54nfa+SRjhIpthGKaHUC3V68X3KQJuR8ftIrFUrrqS7XZ0nSf72GICI0E3Bv3bXxWox0ZWdufi8cpFdhNKtnpdWMlmjGA+2ngjGgURYTTsYSWbYRiG6T4a6faosMQuks5viu9T+F32rksXObbQejt1PaMDHrgdNpxYThowqsZoRcn2uexw2qmcAMMw7TAfS2O0hejLsZAXc30y+ZaLbIZhmB5io9tjI3YRe8eL7LX1HCJV7CI+V3cp2cWSwKNn2k8WAWS76MkhP050UsnOFOC0E9yOxn/GiQhhn4uVbKZtVCOasSaVbAAYDXkw3yeTb7nIZhiG6SEa6faoCHicHVWPiyWBeKaAUBW7iN/dXUr29GoKmXypbT+2YnLY13ElO+B2NJ3vHfY62ZPNtM3qeg7ZQqk1JTvsxVIyi1xh5+e1c5HNMAzTQ2zYRRpJF+mskh1Pb+32qPC5HEjnu6fIVu3UzzNAyQZkwsjp1XTHOtklM4VyFnozhH1OrK2zks20h8pab6YRjWIs7IEQwGJ851tGuMhmGIbpIZYS2W1bqiv8Lkc56q0TROsU2VLJ7h67yLGFBIiAc3cHDNneWcN+5IolzHXoMnhCU7KbJeR1sSebaRv1Pm8mI1uhCvNOfVashItshmGYHmI5mdu2pboi4JHqcafU1WhK2hD0LdUV0pPdPUr28cUE9g36yt0o22VSi/F7okMxfslMoamMbEXY50QsxXYRpj3aVbL129jJcJHNMAzTQywnsw35sYGN5In1Dk04VAppqFa6SK4AITrXFbEejyzEDZn0qNiI8etQkd2ikh32OlnJZtpmPpaBy27DUAvxl2Uluw9i/LjIZhiG6SGWEo01ogFQ9ux2yqYR01IrauVkCwFk8tZPdsrkizi5kjIkvk8xEnDD77J3rCFNMltAwLP1OG9H2OdEKldEttA9VxWY3mM+lsaekAe2JhrRKPxuBwY8jr5orc5FNsMwTA8hlezG1CNVZHfKl122i9To+Ah0TlWvx+NLSRRLwlAlm4gwOezvWJGdyLSoZJe7PrKazbTOfDTTUrKIYizs7YuGNFxkMwzD9AiypXoOIw0q2UFVZHdIyVY2hIEqXmHlfU51QYzf8UWZLGKkkg0AU8N+nFzplJKdb9mTDXDXR6Y95mLpljKyFaMhT1+0Vucim2EYpkdQLdWHG/Rkb9hFOlPYRlN5DHgccNi3/rT43d2jZB9bSMJpl8qzkUwN+3F6NWV6/m+hWEImX2rRky2VbC6ymVYplQQW4xnsaUPJHmUlm2EYhukmmun2CGwUtslsZwqqaCpX1SoC6JTsriiy4zh7JABnlZOBdpga9qMkgNNr5nZ+VCdNrdlFlJLNCSNMaywns8gXBcbaKLL3hr1YS+WR7qLEITPgIpthGKZHaKbbIwAE3bKgSnZKyU7nq2ZkAzoluyvsIknDOj3qUcq42QkjCe2kKdCCXUR14+SEEaZV5tqI71MoP/dOTxjhIpthGKZH2Oj22JyS3al0kWgqX7WlOtA9SnY8k8dsNG3opEfFlJaVbfbkR+Wxb0fJjrFdhGmRea2JzGgLjWgUqkDf6QkjXGQzDMP0CBt2kSbTRToV4ZfO17SL+F2d9YfX4lGTJj0CQMTvQtjnNL/IzrReZAfcDjhshGia7SJMaygle6wNJVs1pGElm2EYhukKlpONt1QHALfDBqedOpcukspVzcgGAJ+mqlutZD+yIItsM5RsQHZ+NLvITigluwW7CBEh7HNijZVspkXmo2l4nLaa1rBGUJMmWclmGIZhuoLlZA4jwcZaqgOyoPK7HR2xi5RKQlOya3iylZJt8USn4wsJ+F12jEdaV+Hqcdaw33RPtlKygy0o2YD0ZbNdhGmV+VgGYyFvw99D1XA77BgOuHZ8wkhrn1CGYRim4ywnG+/2qPC7HB1pRpPIFlASqOnJ9jhtIAJSHVLVa/HIQgIH9gTbKhDqMTnsx7cPzyKTL8LjtJuyj2QbSjYgG9KwXYRplblYui0/tmI05MVdjy7jY99/BHvDXuwNezEW9mIs7EGwhW6m3QgX2QzDMD3CUiKL8YivqccEPY6O2EXKLdVreLKJCH6Xw1Ilu1QSeGg+jpdcMmbaPsoJIyvrOH/PgCn7aMeTDci29wvxnX2ZnjGPhVgGV50z3PZ2fuvSvfjqz07iS3c9gXxRbFoW9DjKhfc5uwJ494suaHt/VmBpkU1E/wTgOgBnhBAXWTkWhmGYbmc5mcWl+yJNPcbvdnSkAcyalrscqePT9Lnslnqyn1hOIpEp4NKJsGn7OEsX42dWka082cqC0ywhn7PsTWeYZiiWBM4kstgz0L6S/carp/DGq6dQLAksJ7OYjaYxF01jdk37P5rBXDSNeKZ3rU1WK9k3APgCgH+2eBwMwzBdzUZL9caSRRR+twOxDjQeUbnL9SZDSX+4dUr2faeiAND0iUozKCX7xLJ5DWnWswUE3A7YbK1ZXiI+F2Kck820wEoyi2JJYHcbjWgqsdsIuwc82D3gwVNN/GxagaUTH4UQPwGwauUYGIZheoGV9WxTLdUVAbe9I3YR1UEw5K19EmC1kn349BoGPI6y2mwGAbcDI0E3TiwnTdtHMlNo2SoCSLtIMltAvmhu+3dm56FsRrub/B7qV7o+XYSI3kxE9xDRPUtLS1YPh2EYxhKWE7KIHWly4mOgQ+pxrBEl22Wtkn14OopL90VaVoAbZWrIj5MmKtnJbKHlSY+AvrU6q9lMcyzGZVb/HgOV7J1M1xfZQogvCiEuE0JcNjIyYvVwGIZhLKHc7bFJBalTEX6qYKuVLgLIrGyrlOxEJo9jiwlcus88P7ZictiHJ0yM8UtkC+VGQ60Q0ianxjhhhGkSpWQb4cnuB7q+yGYYpnv57gNzeP6n70SxJLZfmWmLjW6PzSvZyVwBQpj7GkVTeQTcDjjttX9WfC67Zekiv56JQQhz/diKqeEAlpNZJEyasJXM5FvOyAZQbhjESjbTLIuxDOw2wlCT30P9ChfZDMO0zMPzcTx2JmlaMcFsUFaym5z4GHA7IASQMrm4jaZzdVVsAPC5HJblZB+eXgMAPMXEZBHF1LCMWTy1Yo5lJJlt05PNdhGmRRbjGYwE3LCbbLnaKVhaZBPRzQB+DuA8IpohojdaOR6GYZpD2RD4x9p8lpNZeJyNt1RXKFuB2ZaRWKp2t8fyWCxUsu+bjuKcXYFtTwSMYGo4AACmWUaSmTY92drk1GgXJIzce2oVCzHO7O4VFuIZQ5NFdjpWp4u8WggxKoRwCiHGhRBftnI8DMM0R1KbxNYNP9Y7nTOJbFMt1RWqKE+YXGRH67RUV/jcDks82UIIHJ5eMzUfW8/+Ialkm9VePdGuku1XSra1nmwhBF7/lV/hs7cft3QcTOMsxjOcLNIEbBdhGKZllDrKmbu1+eztx/G1X5xqezuza2nsDXubflygQ0r2WipXVkhr4XfZkS8K5AqdjY47tZLCWiqPp+7vTAavx2nHWMiDEyYU2UIIJLMFBNtQsoNuB+w2svwK1Mp6DolMAY8vmRd3yBjLYjzLySJNwEU2wzAtozoJWq2IdSv5Yglf/MkT+Na9M21va2Yt3XRLdWDDLmJ2VnYjdhGf1qGw02r2fZofuxPJIoqpEb8pRXYqV4QQrbdUB2SL+5DXiajF6SLKs37SJO86YyyZfBGxdB67OVmkYbjIZhimZVjJrs+R2RhSuSJm1tJtbSdbKGIxkcF4pHUlO5kxr7AVQjRkF/G77QDQcV/24ekoAm4Hzt0V7Ng+J4f8OLlifJGtPnPteLIBmTBitZJ9elUW10uJbEcaJjHtobzzXGQ3DhfZDNMmPzi60LfpGqqxSIwnPlbl0AnZ0HY5mUW6jcJyPpqBEGjNLqIVY+smqsfJbAHFktjWLlJWsjtcUB0+vYZLJkIdTUSYGvYjmspjbd1YtVh569tRsgEg5HNafnKsT18xy7/OGMciZ2Q3DRfZDNMGy8ks3vy1e3HrPe3bAXoRpT7xxMfqHHpipfz3bLT1S+JKCW/NLiLV46SJnRbLjWi6UMlO5Qp4eD6BSyc648dWTGmt208YrGarKxLteLKB7lCyp1dTUPN4zVD9GWMpN6IJ8cTHRuEim2HaQHmRZ9u0A/QqqRxH+NWiWBK45+Qazt8jLQqnV1t/j6gCvVvtIuWW6g3kZAOdVbIfnImhWBId9WMDwKQqspcMLrK1Y+d3tVdkR3wuyz3Z06vruGgsBICV7E7w9V9N40yi9bhEpWTvYiW7YbjIZpg2iGuFy3ysP4vssl2ElewtPDQXRyJbwCueNg4AmFlrT8m22wijLczq9zrtsJG56SLqJCvs2y5dRFlXOqdkHz4dBdCZJjR6JiI+2G1kuEKbyBjjyQ75nIiuW28XOX9PELsH3DixzJMfzWQumsa7vvUgvtHGVdfFeBY+l72tbqP9BhfZTNvE0nmsaN3o+o1Eucjuv2YKuUIJuaKMYotZrIh1I4dOSKvIdU8eg8thw+k2rnbMrKWxZ8ADR52W5bUgIvjdDlMnlilFdPucbGkX6WS6yH2n1jA55Ot4G2iXw4bxiNfwhBH1Ogbd7TXVCXtdSGQLyBc7G6eoSOeKOJPIYv+Qz7RJoswG6viqyaatsBDPYM+Ap+ms/n6Gi2ymLb77wByu+fiP8cav3mP1UCxBTXjsJiX7iaUkHjtjfu6sXhllJXsrv3hiFZNDPuwJeTAe8bapZKdasoooAmYX2anG7CJlJdtEf7geIQQOn47i0n2d9WMrpoaNj/FLat85baeLaCdEcYs+u6e1z8PEoA9Tw36c4iLbVNQk09NtfA8txjLYNcB+7GbgIptpiVgqj7fefBh/dvNhpHNFPLIQhxDC6mF1HKVkn0lkLVOEKvnrfzuC93z7QdP3o9IqHF3Q1KLbKJUEfnVyFU+fGgIgJyy248luNSNb4Xc7TLaLSCV7u4mPnVayZ6NpLCWyHfdjKy4aC+GRhUTZy2oEZU+2dixbRRXZVk1antaKvv1Dfuwf8mM5mevblKZOoJTsduJEFxMZThZpEi6ymaa569El/MZnf4L/fHAef/6CA/irF56PTL6EpUT/WUbUj4IQstDuBs7Es1hsY3JLoyg1cjTs4XSRCh5eiCOWzuPpZw0CACbaULJzhRIW4hns7XIl2+eyw+2oX/j5nFq6SIeU7PumpR/7qRYp2a942jiKJYFbf3XasG0msgW4HLZtj/V2hLyqtbo1n91Tmm1h36APU8OqDT37ss3ilHZs56JpFEvNC2JCCCzGs9jN3R6bgotspmHSuSLe/29H8Nov/xJ+tx3/+idX4a3POxdnjchZ9Kfa8Hr1KgldYsN8tDssI2upPFaT5nukVdE2FvIiVyghk+9sg5Fu5tATMh/76WdtKNlrqXxLhe58LA0hWksWUQTMVrLT+W2tIgDgsNvgdtiQyndGyT48vQaP04bz9nSuCY2eyWE/rj5nGLf86nRLhU01kpmCIRPP1CRVq+ZTnF5NIeh2IOJzbiSxsGXENJSSnS+Klq6srKXyyBVK2B3kIrsZuMhmGuLw9Bpe/Lm78M8/P4Xrr5rC9976LFw8LqOX9g9pRXYftsbVF9lzXTD5UQiBaCqHRLaAbMHcolcVbUphZcvIBodOrGA84i03j5kYlP+3omZvZGS3XmT73XbTlezQNskiG2NxINUhJfvwdBRP3huGs4UJo0bx6iv2YTaaxk8eXTJke+vZQtt+bACI+CxWslfWMTHoAxFh/6D8Dem3GL94Jo//c8djhp2A1UIIgVMrKRzYHQDQmmWk3IiGleym4CKbqUu+WMKnf3AMr/j7nyOTL+KmNz0d73/JhfA4Ny5V7g17YSNgug9ViHg6jwHtB68blOz1XBEF7Qt7zeR4LuWrVYWk1Zm73UKpJPDLExt+bGCjiUwrvmyVwT7Rhic74HaaatGIpXMNKdkA4HPZTe0+qcgWinhoLo5L91vjx1a84MLdGA64cNOhaUO2l8wW2u72CKDcnXPNoiJ7ejWF/UPyPe112TEa8vRdkf3dB+bw8e8fw/2n10zdz1Iii3S+iKvPGQHQWsKIakTDLdWbg4tspi7v/dcH8bkfPYaXXTKG/3r7NXjmOcNb1nE5bBgNeTHdh3aReKaAsbAXAbejK2L89C2cV9bN9YirDoJjWpHNrdUlj55JYi214ccGpCcbaFXJTsFG7SlIgQ4o2dvF9yn8rs4o2Udm48gVSx3v9FiJy2HD7142gR89cgYLBnxHJDLGFNlBjwNEQCzV+ZPjUkng9Foa+wY3Thz7McbvyGwcQHuTERvhpHaV+ZlnD4GoRSU7popsThdpBi6ymbocmY3jmgMj+PQrn1KeKFON/UO+PvVk5xH0ODAa8nRFjJ/+0u+Kyb7ssl2krGRzkQ1s5GM/Q6dkD/pd8DrtLSnZM2tpjIa8bVkeVE62WQlA0XTjRbbP3Rkl+/C0VAefalGyiJ5XXT4hJ0De0/4ESKOUbJuNEPI6LfncLsQzyBVK2DekK7KH/eVisF84OhcDIFNwzESdvJy7O4DdQU9LMX6LcSna7GJPdlNwkc3UJZktlL179dg/5CtHMvUTiUwBQY8To2FvdyjZOlVqdd3cIrs88ZGV7E0cemIVoyFP2YcNyIYwE4OtJYzMrKXbShYBZKZysSSQLRgfMymEQCyVR8jboCfb5UCqAx0fD5+OYm/Y2xUtoPcP+fGsc4dxyy+n2/bfJg3yZAMy19wKT/a0LllEMTnkw+p6rm8y9/PFEh6ZTwDYsISZxamVdThshL1hb8uZ/QvxDIYDLrgcXDY2Ax8tpi7rDaom+wb9WFnPmXpJuhtJZDUle8DTdUX2islFdipXgMNG5eYE/fLjWA8hBA6dWMEzzhra0hVtPOJrqetju41oAJQ/w2Z8PtP5InLFUuNKtstuatKJ4vCpNcvysavxmiv2YS6WwZ3Hz7S1naRBdhEACPlclijZ5YxsbcIjgHLCSL/4sh87kyx3zO2EXWQ84oXDLruQtnJFbTGeYRW7BbjIZuqSaLDIVhNY+q1rl1SyHRgNe7CczCJnglLYDHpVatVkT/Z6tgify46g2wG7jXjiI4DHl9axnMzh6VODW5a1kpWtMrLHw+0V2arTYjJjfHG71mC3x/JY3OYr2QuxDOZiGcs6PVbj+RfuxnDAjZsOtWcZSRioZEd8Tks82dOrKdhthLHwRtE2pYrsPvkNOTIrrSLn7gqYbhc5tbJeTgGbGPRhIZ5BocnmaYvxDCeLtAAX2UxNcoUScoVSg0q2Sk/oH8uIEAKJTAEDHifGQl4IAUM7u7WCKrIjPmdH7CIBtwNE0tvJSvaGH1vlY+sZj/iQyBSastUsxDIoCbTV7RGQhS1gjpKtuj02o2Sb3fFRpTV0gx9b4bTb8HuXjeNHjyy2PH8jWygiVygZkpMNyBMjK9JFTq2msDcslVXFvkEfiGB4G/pu5ehcHH6XHVedM4zZtbRp8yWEEDi1nMKkJoSNR7wolkTTV14X4xlOFmkBLrKZmqyX2/c2UGSXlez+KbLT+SKKJYGgx1k+w7faMrKWyiHodmD3gMf0iY+pXKH83rDK29ltHHpiFbuC7vIPmh7l0W5m0tFMVK7brl0kqCmfZtg01ElDuImcbLM7Pt43HYXLbsOFYwOm7qdZXn3FPggAX2+xA6Q6bkbZRcI+V/kkqZPo4/sUHqcdYyFv39hFjszGcOHYACYGfUjni6ad7Kyuy74JZSVbxYk28T2UK5SwnMxxskgLcJHN1ESpXo1cmhzwOBHxOfsqYUQ1ogl6HOXLnlYnjERTOYT9Tgz6XaZ7spPZYrnIHmAlu+zHfnoVPzawoUY3YxnZaETTxUp2WhXZjSvZ6gTVLA5Pr+FJewfabj1uNBODPjzr3BF8/Venm75cD2zYfQKexo71doS8TsQzBdOboVQyrTWiqWRy2NcXCSPFksBD83E8aSxUTmcya/KjOp6Tw0rJ1r6HmvBlLyWl9XAPK9lNw0U2U5Nykd2garJvyN9XCSOJjCwugh4H9oTkF+Vc1GolO4+Iz4VBv8t0u8h6tgC/WxYxYR8X2SdXUliMZ6v6sYENBamZSU4za+m2M7IBmZMNmGUXUZ7sxtNFAHklyAzyxRJ+PRPDU7vIj63nNVdMYD6WwZ3Hm+8A2ex38naoE6N4Bz+78Uwea6k89lcrsvskK/vE8jpSuSIu2hsqX6WajZrz26nmSSklezTsgY2aO9lX+e672ZPdNFxkMzVZb/ILff+gD6dWd/4XpCKuqUoDHicCbgeCHgcWukHJ9rkw5HdhJWn2xMdCuWAKsV0Eh57Q8rHPql5kD3gdCLodTc1bmFlLYc+Ap+3YrIBbFlNm2DTUhNdmctHhTboAACAASURBVLIBIGVSwsjD83FkC6WuShbR87wLdmMk6G6pA6QqsoNGRfip1uodLLKVELOvRpEdTeUtsbB0EpWPfdHegbKSbVbCyMkV2cxKFfNOu2we10zSkZprtJvTRZqGi2ymJokmPNmA/NKci2aQb+EyaC+it4sAwFjIiznLPdl5hL1ODPrdiGcKpr4W67mN5Bnpyd7ZP4zbcejEKoYDLpw9Eqi6nIgwPuhrWslu1yoCoHzFIZk1vpiKpfJwO2zwOBuzZqgTs3WTEkYOT0cBoKuSRfQ47Ta88rIJ/PjYGcw1mSqhXj/DlGzt6kMnP7vljOwq8xZUjJ9Zkx/TuWJXfE8dmY3B7bDhnJEAwj4nfC67aQkjp1bWMRb2brJONZuVrYpsThdpHi6ymZqsN6ma7BvyoVgSTf9w9CobdhGpBo2Gre/6uJbKIeJzYjAgfzzXTLSMrOs82SGfC4ls572d3YIQAoeeWMEVU4NV/diK8Yi3qQlHswY0ogF0EX5mKNlNtFQHpCcbMGcSJiD92LuCbox1cUHwyssnIADc0uQESHVi36jwsR2WKNlVGtEopjTfsFmWkT+7+TCu+OgP8ZHvPWTqd+N2HJmN4/zRATjsNhDJJjFmerInh/yb7huP+JrKyl6IZ+Cy2xpqTMdshotspibJJr/QlceuXxJGKpXs0ZAH8xZ6sgvFEhKZAsI+F4b9ssg2c/JjMlsoX/oPeZ0QYuPEo9+YWUtjLpbB06e2RvfpmYhIJbuRuK58sYT5WLrtZBFAttD2m9QEJprONezHBja+T8zKyr5vOoqn7ovUPdmxmolBH645dwS3NjkB0ni7SOeV7FMrKQz6XWVxQs/EoA82Ak4sG/8b8vhSErc/vIipIT++fPcJXPPxH+PzP3y0I42R9AghcGQuhot0yTd7I15TlezKJJeJQS8WExlkC419BhdjGewacHf1Z6pb4SKbqUmzk2zUxIp+SRhRk4U2imwvVtZzyJg0oWs71MTDiE+miwAwLcYvX9Qy1F0bdhH9GPqNXzyh8rGr+7EV4xEvUrliQ5NSNzKy2y+yARWdZ04zmlArSrYJWdnLySymV1Nd68fW85qn78NCPIMfH2t8AmQ5XcTAnGwAHZ1PcXo1VVXFBgC3w46xsDkxfl/92Um47Dbc+Kan4/tvvwZXnj2ET912HM/+xI9xw09PNFxwtsvp1TQSmQKeNBYq37c3bE6RHU3lEE3lqyrZQjQ+UX8xnuVkkRbhIpupiSqy/a7GvJa7gm64HTZM98HscEAq2UQbl+JHtcvTVjWkUTmrEb8LQwGlZJsz+TGl2Q7KOdm+zv9YdxOHTqwi7HPiwK5g3fVUbFkjvmyj4vsUAbejPM/CSGLaPIBGKSvZJlhX7u9yP7ae556/C7uCbtx06FTDj0lm5XeOr8Hv5O0YsKDIPrW6XrPIBmTnR6M7B8fSeXzz3hlcd8koRoJuHNgdxBf/8DJ8+0+eiXN2BfDB7z6E533qTnz7vhnTLW9HdJMeFXsjXkRTecNPgtVV5S1KdkRNtmxMEFuMZzhZpEW4yGZqsp4twOu0b+rKVQ+bjTAx6Ct77nY6iUweAbcDNpu8hDYWtjbGb6PznguDftk0wKwYv2Rus6IW8nbe29lNHDqxgqdPDZbfC7VQqnQjvmz1A2iUkh3wmKNkR9M5RBpsRAOYq2TfN70Gh41w8d7Q9itbjNNuwysvn8Adx5caLnYSmY0uq0ZgtxEGPI6OXYHKF0uYi2a2FH16Jof8OLG8bmgHxG/ccxqpXBHXXzW16f6n7ovg5j96Bv75+isQ9jnx57c+gBf93V342WPLhu27kiOzMThshAO7N07Iy1nZBqvZytuuJpQqxssdmrffnxACC/EMJ4u0CBfZTE2S2ULTE2z2D/r6ypM9oPMVqpnXC3FrJj+u6Vqqh71O2Mi8IlsVaz5dTjbQn3aRuWgap1fT2/qxgeaVbCJpQzICv8ukIrvJiY/qyo8ZEX6Hp6O4YHQAXoOUXrN55eUTAIBbG5wAmcwWDGupruhk18e5aBrFkqjaiEaxf8iHeKZgWAfEYknghp+dxOWTEVxU5eSLiHDNgRH8+1uuxhdecylS+QL+x433omSSon1kLo5zdwc3pfGUs7INnvx4qkZc4p4BDxw2aujkLpktIJUrYk+Iuz22AhfZTE2S2WLTE2z2DUkl20gVoluJZwqbjs+YxQ1p1pSS7XXBZiNEfOZ1fUxWxDuGtIlvsS6Ix+o0h0405scGpPIf8TkbysqeWUsbkpGt8Lsd5cm6RpHJF5EtlJrzZLuVkm2sXUQIgQdnY3jKRPf7sRXjER8OHhjB1+853VBRl8wUGurA2wwRn7NjV6DK9oVt7CKAcTF+tz+8iJm1NN5QoWJXYrMRrnvyGP7k4DlIZAqm5FYLIXB0dvOkRwDYG9ZOvk1QskdDni3xmnYbYSzcWFZ2OSObPdktwUU2U5NkJl/O122U/YM+pHJFLJs04a6bSGTym5Rsr8uOsM9pWYxfTHXe88sxDfpdWDXpdVB+2i12kT70ZB96YhUDHgfO3zOw/cqQhVVjSnbKMKsIICfoGm3RaLbbIwC47DY4bISUwWOZj2WQzBZwYE99X3y38dKnjGExnsXRufi26+qz6Y0i5HMZphpvR72MbIWyNhg1+fErPz2BvWEvrr1wd0PrKxvHIwvbvx7NshDPYGU9t0VR3xV0w2knU5TsWtacicHGsrIXYnJeDxfZrcFFNlOT9Wyx6S909eU53QedHxMVSjYgL8MtWNSQZi2Vg8NG5cvJQwHzWqtvTIqV+3I5bPC57H1pFzl0YhVXTA3Cvo0fWzEx2FhW9sxauuzVNAK/2254x8dmuz0C8vK8z2X8WB5fSgIAzqnRDKhbuebcERABdxw7s+26iUwBgSrRd+0Q9jo7dgVqejUFl8NW1987EZExfkZkZT88H8cvnljFa6/c3/DcovO0k7Tji4m291/J0VlZuOsnPQJSRR8NGZ8wcmplfUuyiGI83FhWdrkRDRfZLcFFNlOTRLZ51WTfoPxA98Pkx0Q2v6XIHgt7LbSLSG+smhQ15Hdj2aR0kfWyXWTjSkfY27nLzt3CYjyDE8vrDfmxFUrJrmcPKBRLWIhnDEsWAaRdJGmwXWRDyW6u8PO7HYYr2Y+dkUX22buqFxXdylDAjYv3hnDn8e2j/JLZAgJNXl3cjnAH7SLTKylMRLx1Jwi7HDaMR3w4acDcnht+ehIepw2v0rzvjRBwOzAe8eKRBeOL7CNzMRABF4xuveq1N9xcF8btSGTyWE7mytG6lUwMerGczG4bObvAdpG24CKbqcl6C0X2xKAXRP3RkEYq2ZuLi9GQdV0fo6lcubkEoNlFzJr4mNvaqGjA6+w7u0ij+dh6JiJe5AolLCdrnwDNxzIoloShdpGAy4Gclm9uFOr1bsaTDUhrldGe7MeXkgh6HBgJ9N4ErYMHRnDf9FrZ8lWLZMZ4u0jY60QsnTdtop+eU6upmkWfnslhf9t2kdX1HL5z/yx++6njm74XG+G83UFTlOwjs3GcPRKAz7X1NdwbMbbro/oNnqxhF1En8NsV9ovxDAY8jp6ZTNxtcJHN1KSVdBG3w47RAQ+md3iRLYSoahcZDXmwlspb0pBGtVRXDPpdiKbyTXWUa5T1Ck82IBWxeJ8p2YdOrCLgduDCKspULdSPWz3LiLpsbKSSrSbMGZkwoo+NbAa/y2F4usjjZ9Zxzq5AT3ale/Z5IygJ4O5touOkkm2sXSTkc2ndWs3tfCiEqNuIRs/UkA8n24zxu/mX08gWSnjDMyebfux5e4J4Ymnd0BNSADg6t3XSo2I84sWZRNawpjgbGdm1lWwA205+XIhlyslZTPNwkc3UJJltbSb7viHfju/6mM4XUSyJKkq2/OKat8CXLaPUNood1ZDGjElN69kC7DaCW5d8EfI6yx7dfuFXJ1Zx2WSkYb8nsPHjVm/y40YjGiM92fKznDSyyE63ZhfxmaBkP7aUxNk95sdWXDIexoDHgTuP1/Zll0qi5e/kepS7Ppr82V1dzyGZLTRUZO8f8iORLbScjpQvlvC1n5/Cs84dxrm7m58Ie96eIAolUfb5G8FyMov5WKZqjCCwkZU9b5DdUHnaa018LCvZ2/xWLyaybBVpAy6ymarkCpvbZjfDvj7IylaqzxYlOyy/jOZNaJG7HWup3KZiR7VWN8MykswW4HPZN6mGYa+rJyY+RlM5/I+v3YN7T621tZ1SSeDUagrnNfkjruK66sX4zaylZEZ22Lgft4AZRXYqD5fd1nQHQqM92bF0HkuJbM8W2Q67Dc86MII7jy/VVG+VRcvonOyIlkZkdsKImqdTrxGNYqrNhJH/OrKAhXgGb7hqsqXHmzH5UaXH6Nup69kbMbYhzamVdYwE3TWvRo8E3HA5bNsmHS3GMlxktwEX2UxV1CXlVlST/UN+LCezhk9s6iYSGfmDtNUuYq2SHfHrlGyt66MZrdWr+fXDvu73ZGcLRbz5a/fiv48uNpTmUI+V9RxyhVK502ejeF12DAfc2yrZu4MeuB3G+SDV62WkXSSWziGkm2zbKD6X3dC26k+oZJFdvVlkA8CzD4xgMZ6tOeEu2cZ3cj1Uxr3ZDWnK8X0NKNmTbWZlf+WnJzA17MfBA7taevxZwwE4bGTo5Mcjs7Kd+oW17CLaybdRvuyTK6mafmxAJpqMh+snHRVLAkvJLCeLtAEX2UxVKpuNNIP6Et3JCSNxTckeqDLxEUDHJz+mc7IpiD5KTdlFVkzIyk7lilveGwNeJ7KFkiV+9EYQQuCvvvUgfnliFS6HrW3FSL3Goy34Fccj9X/cZtZSZWXLKEyxi6TyTVtFAK37pIEn4eVkkZHeShbRc/DACADUTBlRyTCGT3zsULdWdXWzXrdHxXjEC7uNWorxu/90FIeno3jdlfvrppjUw+Ww4awRP44ZWGQfnYth36Cv3FOgkj0hD4iMa0hzamV920mm44P1M/tXklkUSwK72ZPdMlxkM1VRP8StXJpUlwN3smVETfCrVLI9TjsG/S7MdVjJVt0eIxXpIoB5dhF/hUWg21urf/b2R/Gvh2fxjmsP4Ml7Q217H1VUY7NKNiALje2UbCP92IB5dpFmMrIVPrexSvbjS+tw2qkhlbRb2TXgwQWjAzWvsCRMUrLDHWokNb2awu4B95bug9Vw2m2YiHhbivH7yk9PIOh24BWXNR7bV43z9gwYWmQfmY1vycfWo/LDjVCyU7kCFuPZuko2oJ3s1xHDyvF9wd5L7OkWuMhmqrLehpK9X2Vl7+Aie8OTvbXA2DPg6bgne6PI3hhPxOcCEUxprb5eJXkmXL7s3H1F9rfuncHf/fBRvOJp43jLc86ReeZtXm2Y017jVors8YgXc9E0ilVi0wrFEuZjGeOLbDPSRdL5st2gGZSS3U56hJ7HziQxOeRvagJqN/LsAyO45+Ra1ROh9TaEj3p0qlvr9Eqq/NvQCK3E+C3GM/jer+fxu5dNtK34n78niNloumwNbIdYKo/p1VRNP7Zib8SL2Wj7v5sb/vf6x3si4sP/Y++9oyRLzzLP5wtvMyJdpa2qrKyqzGpXrW61N1JLahlkQYIBhFYSDNLgVjowEiwjYBgYaUGwWqQFtGg4AywMArQjVo0Malpe3VIbmaq2VZlVXSYrvQlvbtx7v/3jxhcRmRnm2nD5/s6pc8pERtzKDPPc9z7v8+zkSg1PvEWxGqWLmKe335EIx7AyNYmFvBgIeHC5j1sfGy0+AsBkPNB2T3alFKRmku12McSDXmw74MmuF+9Y/bDuroSR71zYwv/22bO45/gwPvJjN4Exhsl4ECuJgqVs4JVkHn6Pa9eJjV4OD4ZQUnilTa2W1ZTIyLZ3KiuWmDM2TpCTOcn0JFvlQNGmiLSLPZwsUssD86OQVY5H60T5VewiNk+yPW4Xon6P4+kiV7ZzuqwigpnhsOEYv7/77mUonONd9xw1c4i7EPXqdiw/Prui+bEbJYsIpuL2tD5e2hQZ2S3sIoMi6ai+sF9La58d5Mk2D4lsoi6VxUeT04Cjw2Fc0VHZ2qs0WnwEtOXHdovsenYRwLlCmpyk1F18BLrLLrK4nsF/+NuncHQ4jE++46XwlSMHp+IBSIpqqRFzOVHAVDxoKpdZfLjVu1R7zYH4PqDazmln62Mib96TDdgzVZdkFZe3cz299Ci49cggIn5PXV+2GHyETSQ+tSIe9rYswrFCoaRgNVXQlSwimBkOISsp2GhS2rT3Mf7+8St41alDugpvWnGqnDBybtV6jJ+oU7+hwdKjYGpQO/mvd4XLCJfLXvYjLb7f4qRnqcFn9VqyALeLYbgHC566BRLZRF3EB7EZuwigvbivmFha6RXSBRkuVv8DbzwWQDJfamu6SnWSvVvwDEf8jiw+ZssRfrVUJtldIrK3MkX83F8/CZ/Hhb969+27Fo6ExWPZgi97OZk3HbFX+XCr47+sZmTbO8n2uF0IeF22LRwWZQU5STE3yS4/d3I2ZGVf3spCUXnP1anXw+dx4Z7jw/jGuf1RfpkmV8+sEg/6KifqTiAmpYZEdiXGT5994qEzy9jKSvjZe48ZP8A6TMWDCPvcOLeasnxfzywnMRELYKSFWJ2KByGrHOtpa0OaS1s5DIV9DZcsBZWT/QaT7NVUAaMRP9wmF0gJEtlEAzJWJ9nlxS4n2ga7gXShhIjfU3d7fVJkZbdxml1t3tsjsh2aZGfqRPiJau1uaH0slBS85/95CmupAv7bO2/bd5m6KrLNX21ZSRQqkY1GmYxrSQL1PtyEyJ60MSNbEPF7bFt8TFYq1U14skWcoA2CXxSGnBg1XjrSjTwwfwjXEvl9RShWEp9aEQ95HT05NpIsIjCSlV1SVPzFNy5gfiyKe44PmzvIPbhcDCfHojhng13kmWvJln5soCYr2+Lyo5Ys0vp7PRz2Ieh1N1zCXksVKFnEIiSybcLJS22doPKGbrBkQnB0OARZ5R3Ji24HWqV6/SlBJSvbpuYuPezkSgj53PtylYfCPtsXH2VFRVFW933YR/0euF2s44uPqsrxHz9zBj+4msCf/ORLcMuRwX23mYxZE9klRcVaumBq6REA/B43xqKBBpNsLYXBzoxsQdjvsc0uYrbtEahOsrM2+MMvbGgibLaH4/tqefm8FuX39XO7LSOZooyA1wWvA8udsaCzdpFKxbcBkT0VD8LjYnhRxxXR//Hdy7iwkcUHXztvyr7ViFPjUZxbTVta0M0WZVzczDZNFhFMx+0ppLm8lWvpxwYAxljThJG1VIGSRSxCItsGzi4lcMvvP4zFdfvifjpNtigj6HWb3tYXE4t+jfFLFeSGl21FbrLV9Aoj7OSkfX5sQJtU7OQkyx6/WkQd9l6RzRjDQMD5BapW/NHD5/CFsyv4zR85hR+5aaLubQaCHoR9btN2kbVUAZwDkxamPI0+3JZ28pWKZbsJ+zy2pYuIk6l6z7uWx1F+7uRtsIssrmcwEQs4MuHtBFPxIE4eiuzzZacLMiJ+4yc0enB6kn1lO4ewz12JFdWDx+0qtwc3F9mJnIT/85EF3HdiBK+6zlz5TCPmx6PYyWltomZ5YTUFzoEbDUyyW7UwNqNQUrCczOu25kwPBhs+3mqyQMkiFiGRbQMvbmahcnsWJLqFeukRRhCLJ/1aSJMulPYV0QjEm9JqW+0i9fOKh8I+cG5v4ke2yVWOeMiHZL5zTZ/XEnl88usX8JO3HcZ77p9teDuRMGJ2km0lI1vQKCt7KZGz3Y8tiATss4uIy/hjA8YnXZVJtk12kX5Yeqzl5XOjePzi9q69jkyx8Ym9VeJBHxI5yVLaTjOubOdwZDhseMo8MxLGiy082X/yyALShRJ+643X2TrFBoD5csKIFcvIM+Wlx1bJIgAQ8nkwGPJammQv7eTAeetkEcHhoVBd21peUpAqyFSpbhES2TYg0hTa3fLnJJmiYukNfXwgAJ/b1bcxfukmk2y/x42RiK+tz4dGk+yh8qKNnb7sZhnqsaC3oxF+Ip/89acnWn7gWsnKFj9bK77p6cEgVpJ5lGr2FhSVYyVhf0a2wE5P9qMXNjES8ZsSuGJh2OpyMOccF9b7I76vlpfPj0JSVDx+cbvyd5nyHogTxENeqBzIOLSsfWU7hyNDxp/TM8NhXN5qHOO3uJ7G3373Mt5+5xGcGm9txzDKfCVhxIrITmIk4tN9Mjo1GLTkyRaLokYm2emCvC8VSsSLksi2BolsGxCXTa0kFXQbmUKpEvllBreLYXoo2LeFNOliqelJyHgs0NbnQ7LBJHu4fHnWTl92s6XYWNDb0Qg/8f8c1nFZ2o5JttnFR0DLylb57isea6kCZAcysgVhvz12EbWc5XzfiWFT08OQ3x5P9mqqgKyk9HSdej1unxlC0Ove1f5Yb9nYLkS+fiJr/2tXVTmubOdMxerNjISQkxSsN7Br/NcvPI+Qz41ffXDO6mHWZTjix0jEjxesiOzlFG6YjOl+nVjNyhZV9Lon2eX3mr3WNdH2SBnZ1iCRbQNVkd0/k+xscX8OslGODoX61pPdbPER0MRXO+0iOw1KQYYj9ler5xp4sgFtItZJkS3+n+L/3YypeACbGQmFknGht5zIIxb0WrJU1cvKXnIoI1ugTbKt+6DPraWxmZFw74kRU19v1yT7wromKI73mV0k4HXj7uPDu3zZmaJiexGNoFKt7sA+xXq6CElWDSWLCIRQfLFOwsjXzq3j6+c28P5XnXQ0x3l+PGK6kKZQUrCwlta19CiYiofKlg9z1p3LWzkMBDy6ozXFCf1e65qYZI/HaPHRCiSybaAf7SJpG6YmR4ZCuLJt/s2iW+GcN7WLANpCXLsWH1WVI5kv1beLiEm2zkIHPYhJ9t6cbED7sO5kuoj4f+pZsBJ+ajMJOCvJfGXB1Sz1srJFnrBzItuNTNH6z0c0Et530pzIDnrtmWSLZfMTfWYXAbT2x0tbuYr3PVMs2V6pLhCCzInXrlhcNJIsImgU41dSVHz4C89jZjiEd949Y/kYmzE/NoDza2lTy+Pn19KQVa4rvk8wNRhEoaSaHoxc2spiZkS///3wUP3WRyGyD9Ek2xIksm0gWT77X+6juLqsHSJ7OIxMUcZOn8Ub5ksKFJU3nWSPxzSfm13+12akCiWofHelukAIbzvtIs3aQGNBr3Y8Di1QtWIrKyHq9+iKv6tGLRo/GbpWbnu0wngsANeerOxqRrZD6SJ+Dwol1XJ+/bcWNnF8NGzaLuNyMYR8buuT7I0sogEPRvswZuzlc1qUn5hmZwrWltGbURHZDlyFEsvvR0yI7Ml4ED63C5f2XBH9+8evYHE9gw+94fpKi6tTnBqPolBSTS3xV5YejYhsizF+l7eMWXNiQS8ifs++SfZqsoiQz+3Yid1BgUS2DYiz/82MdlnMLCVFxW9+9mzdS2Ptxmq6CFCdXLSKYOo1UvnWzWtiIW61DdPsnUqU2n7R73W7EAt627f4GNLSTNI2VncbYTsrYUiHVQSw9mG2YqHtUeB1uzARC+6bZI9G/Qh47c/IBqonRlkL0XlFWcETL27jPpNWEUHI57F0HICWLHJ8NGJ7qkQ3cHQ4jJnhEL5xXmt/zBRlx+wisaD2mkk6sLR8ZTsHt4tV4umM4HYxHB4K7ppka5F953HfiRE8aHNkXz3mLCw/PrOcRDTgqUyL9TBtoZBGklUs7eQwY6BZs1FW9lq6gPGBQF++ttoJiWwbEGf/nFcvsZhhcT2DTz9xFd+oWXbpFHa8oYvt5n6L8UsXtJ93M5E9EbNe260XUYfcKK942OZCGiGM6k2ynfR26mErI+nO4h2L+cGY8Z9RTpKRyJVsmTYfHgru82Q7ZRUBakS2hSssP7iSQL6kmPZjC8J+N3IWr/Qs9mGySC0PzB/CYxc2kSrIKCncscVHUb/tjF0kh8l4wHSJzrGRcGWZD9Ai+1J5ZyL76jE3FgFj5kT2s9eSuNHA0iNg7eT/WiIPlcPwkun04P440bVkAYdMxHMSuyGRbQOJXKnywWhl+VE8yZ0sBdCDJKuQZBURn7U39H4tpEmVp7SNcrKBaiFNO3z6jSrVBUNhH7Yz9k6yXQwIePe/fYgP604tP25lJQyH9X0w+D1ujEb8hl+zlYxsC8kigr0fbprIdiZZBKhefbBiY/r2wibcLoa7LNZXW51kpwolrKeLfZeRXcvL50ZRKKn42gva4MWpnGyfx4WI3+OYXcSMVUQwM6yJbFXllci+n77Dmci+eoR8HhwZChlefiwpKp5fNbb0CGjv42Ff46rzZlSTRYx9vw8PBXF1z7LlaqpAySI2oEtkM8YCjLEbGWM3MMbou14D5xzJvITrJrQXkpUacbF40Ml0BqDGc2vxDT3gdWNswN93IltMsgeCjb8/YwMBU1NSM7Rq3huO+Gy1i2SKMsI+T93pjJMLVHrYzhZ1xfcJzGRlixMnq4uPgBaftZYuoChrPv/lhMOT7IANIntxEzdPx5qeZOohbNGTfWFdK//qt/i+Wu6aHYbP48Lnzy4DqH/1yC5iQW/lqpidaCLb/M/o6EgYhZKKtXQBH/7C8wh53fi1VzsT2deIubEoXlhNGfqaxfUMJFnVVUJTC2OatcbMJPty2VZjZpKdk5SK9ZBzjvVUkTKybaCpyGaMeRhjHwWwBOBvAPwdgKuMsY8yxpzpd+0xcpKCksIrIttKooQ4c012eFEw08Rza5SjQ+G61dG9jPAbN1t89HlcGIn42xLjJ94YG0+y/bYvPjZ6bji5QNUKzrkhTzZgLpNWTL7tsItMDwbBuXYytp4WGdndaxdJ5ks4u5Sw7McGgJDfYyld5MJGf8b31RL0uXHnsSF887yW5uKkyI6HvLZ/9qQLJWxnJUuT7GNlwfjXj13C185t4H0OR/bV49R4FJe2cobiPn9wJQEAuGHS+MR9Km6ukObSllZfP2LgPRAAd6/M/wAAIABJREFUDg/uThjZyZUgKSqJbBtoNcn+IwBDAI5xzl/KOb8FwHEAcQB/7PTB9QJCTEzGAogFvVixMLnslkm2ENl2bBUfGQ71XetjVWQ3//5MtCnGL5GT4GKN7SvDYR92bKxMzkpKw6KigQ7aRYRv1cgkeyIWwEqiYChmcjlRAGNaOohVhKXq6nauJiPbQbtI2QKWMbmY+p0LW1A5cN/JURuOxeIkeyMDr5tZEnC9wMvntPZHwPrVxWbEQ17bT47FPo7e9sF6zIxoX/upb17EzHAI77pnxo5DM8TcWBSKynFhI6P7az77/SXMjoRN7QyYnmRvZXHURH39dKWQRntMMRyy4z3uoNNKZL8RwHs45xUzEuc8BeAXAbzeyQPrFWr9sBOxgC2e7G4R2fZMskNYSxVNFX50K9XFx+YXcyZiAUv2Ib3s5CTEgl64XPXfWIfCPijlLG07aDbJrniyO1CtbqSIRjAZDyJfUgzZW5YTeRyK+k0vctUyXZkg5R3PyAaqk1CzdpFHFzcR8rnxksNxy8cS8lmbZC+uZ3B0OGzLz6GbeWC+mqAR9Tt3ATke9FU+z+ziqoX4PsFkLAifxwXO0ZbIvnqcMpgwcn4tjacu7+Cn7zhiajlzKh5CMl8y/Dq9vJWrnJQYYXpPVvZaWlSq0+KjVVo9WzmvM+LhnCsA+qthxCRCuMSCvrK/08okuzsWHzM2ebIBbZIN9FfCSLqgLf6F65Sx1NKu1sedXP0iGoEQnXZZRrJlT3Y9/B43gl53RzzZ1SIa/R8MkyY2+VeSBUt16rWMDQTgdTNc3clhqTxFspq/3QzxmjZrF3l0cRN3HhuyReiE/dYn2f1YQrOX46PhynPCyUl2zIG2VrGPc8TCJNvlYrhhcgAvmxttS2RfPWZGwvC5XbpF9t8/fgU+twtve+m0qcebMhHjJysqru6Yq68fCHgRC3ormf1rSSGyaZJtlVbvlM8xxt659y8ZY+8A8IIzh9RbJGv8sJPxgOk0iVShVHmD6/gku9C4bMQoR/owYSRdKCHir7/4V8tkPIBMUUaq4OzPM9GgUl0gIu3sWn7MFpWmVzk6Va0uTiKM2EWEeDFyBWo5ma/koFvF7WKYjAfLk+w8RiLOZWQDqNh8zEyyryXyuLiZtcUqAlhLFykpKq5s5XD8UP8uPQoYY3hgXvueN7Jp2cFgSGtrtbOh98p2DvGQ1/KS7N/9+zvxqf/lpR3LbPa6XZgdDeOcjoSRQknBZ7+/hNfdOK47TnQv1Rg//Z+bK8kCSgo3nCwiODxUzexfFW2PURLZVmmlon4ZwGcZYz8H4HvQpte3AwgC+DGHj60nSFQm2V5MxIJI5ErISwqCLaacexFnrIeHglhLFcE579gbSrNGP6OIs+p+m2S3sooAWusjAKwkChgYd+4ybyJXahq1JCLttrP2VKtnJRmRJh/2saD93k49bGXM2EW075tekc25lgDyynn7JmqHB0O4up1D2O921CoCaFcavG6GjAmbxqML5Sp1G5YeAe1KkCSrKCmqYcvH5a0sZJX3dUZ2LT977zGEfFrkpFPEgz7IqlZ6o+f9TQ9XtnOm6tT34lTTpRFOjUfx+IvbLW/3hbMrSBVk/PQdR0w/lplCGhHfZ2aSDQDT8RAW1rWTiLWUltLUCWtOv9H0O8g5v8Y5vxPA7wG4BOAKgN/jnN/BOb/WhuPrehJ7JtmAuYQR4V27cTIGSVZRKFmrPbaCnZ7swZAXUb8HV/qo9TFVkHXl1U62KSs7kSsh1mSS7YRdJNTkuRELdmaSLU4ijEyPhsI++D0u3TavRK6EQknFhI2WjunB6iTbaZENaCfPZuwi317cxGjUj7kxe4SteA7lTEyzF9e195N+zsiu5cShCD70husdHbzEHIjfvLyVwxGToq/bmB8fwEqy0PK97dNPXMHsSBh3zQ6ZfqzRiB8+twtLBq6wier5GZPfbzHJ5pxjLVUgq4hNtIrwCzHGvJzzr3LO/y8AXwZwmjFGU+wyibwEn9uFoNdd8WmaSRgRl2lE3E8nLSMZGyfZjLFywkg/TbJLui5/CiHm9PLjTk5q6skW/7ZlUyFNpig3fW44EQWmh62shKjfA79H/1UkxjS7hl5PtrjdlE12EUBLGNnMFHHN4SIaQdjvMWwXUVWORxc3cd+JEduEnthpMOPLFikPswdkkt0O4jYlA3HO8b3LO3jfp3+Aqzs5zI70i8jWnmvNSmmsLjwKXC6GiXjA0CT78mYWAa8Lh6LmrnZMD4ZQlFVsZIpYTRYoWcQmWl0L+FcAMwDAGDsB4DsAZgH8CmPsf3f20HqDZHmKyBirNMCZmWQv7eQR9LpxbER7IXeqlhrQPNlBrxvuBmkVRjkyFMKVvvJk65tkH4pqtd0rFhJnWlGUFeQkBYNNJtk+jwvRgMcWT7aichRKasPFR6CcUtCB5+9WxlhGtmAyHtD9MxInTHYtPgLVS8NOZ2QLIiZE9guraWxlJctV6rWEKpndxifZF9YzGB8IOJobfdCIl0/GzU6yi7LmRX7Lnz2Kt33yMXzt3Dr+/b3H8PP3H7PzMDvGfLlh8oUmy49WFx5rMZrhf2krh6ND4YYpU604PFRNOlpPFyhZxCZavUMNcs4Xyr9/F4BPc87/V8aYD5pH+zetPDhj7HUAPg7ADeAvOed/YOX+OkEiV6pMAMZi2pPS3CQ7h+nBYGWBrZOFNFlJtnWL/chwCF95fh2Kym0T7p0kXSxhLtB6guZ1a1MFJyfZVbtSc3E5HPbZYhfJSsJK1MST3aHFx+2sZGrRaDIWxLfKfuNWVNoebZxk106vu9Uu8uiivX5swPok+6BYRdpFtUjK2PvEeqqAv3v8Cv7+8cvYzEg4PhrG7//ojXjrLVNd4aW2i8lYAFG/B+cbiGyx8PhaCwuPtUzFg/jG+Q3dt7+8lcUxC1cNxPvQixtZbGYksovYRKtXQO2a8SuhldOAcy4xxiyZhhljbgB/BuDV0Boln2SMPcQ5f87K/babRL6a7OD3uDES8ZvKyl7ayePwUKiaM9xBu0i60NwOYJSjQ2FIiorVVMHReLJ2kcrrXwyaiAUdFdmiBrmZXQTQvMd2LD5mdfj1Y0EvCiUVhZLiaFLGXraykqnn12Q8iLV0QdcC3rVEHj63CyMGYgJbcbhGWLfLLmK0Pvvbi5s4cShi6yXkkM/cJJtzjgsbWbzt1inbjoWo2kX0TrKfXkriL799EV84uwKFc7xy/hDefe+MrZaiboIxhrnxaMMYP7Hw+HYLC4+1TA0GsZ4u6nofVVWOy9u5SgqNGcQJ/veu7ABA02V6Qj+tlNRZxtgfA7gG4ASAhwGAMWa9iQC4A8Ai5/xi+T7/AcBbAPSUyE7m5V0f7JNxcy1/Szs53DYzWBHZnczKzrbw3BpFtH1d3sr2vMjmXGzf6/v+TMQCumKfzLKT1Z4nzewigJYdLYoGrKBHZFeuxuRL7RXZmSJOT8UMf91UXKs2X00WKg2MjVhJaF5Fs5dk6zEa9cPvcaEoq215fUT8nkoerh6KsoLHX9zCT91uj3gQiKshRifZa6kiMkWZJtk2U118bH0CdnU7h7d+8lEEPG688+4ZvPPuo5jpE+91M+bHo/j8meW66V+ffuIKjllceKxlqmanp9WE+vJ2DpKsmk4WAbST3uGwD9+7pIlsmmTbQytP9nsAbELzZb+Gcy7ema+H9Vr1KQBXa/68VP67XTDG3ssYe4ox9tTGhv5LJ+0iuSejeNLE5DKZLyFVkDE9GKy80aU6vPhoZx6ryMruB192TlKgqNzQJHs1aay22wjJvGgcbT7JHon4bPFki6ljqwg/7dja9xzmnGMnJxmK7xNMGsjKXk7Yl5EtYIxhajCIkYjPcPSnGYzaRb5/OYFCSbXVjw3UTLINpouIpceDEt/XLvweN0I+fUVSD51ZRknh+OL778fvvOn6AyGwAS3GL1WQsZbafVWwuvB42LYpvpFCmk998wK8boaXz1nLsJ8eCuF8OcaPRLY9tIrwy3PO/4Bz/n7O+Zmav3+Mc/63Fh+73jOxXrvkpzjnt3HObxsdtacEwU4S+aonG9C8miuJvCFRJV5E04MhRHweuFin00UURGys752IBeBxsb5IGEmXi3oGgvom2ZPxAHKSglTefLNdM3ZqIiSbodlFJMtivzLJbrH4CNgbBdaKVEFGSeGmvJATBqI3V5KFyoKznVw/MYDrJgZsv996hP3G6swfXdyE28Vwp00TuupxlCfZBv3hi+tlkU2TbNuJ68y4f+iHy7jt6GDLKz/9xtyYVq/+wmpq19+Lhccff+lh2x5rOq59b1sV0lzcyOCfnlrC2+84YvnnMT2oXdUDQOkiNtFUKTDGvobG9emcc/4qC4+9BKD2GTkNYNnC/bUdkewQC+6eZGclBamCvOvvmyEu408PBuFyMa3Mo4OLj5liCRG/fR9gHrcL04PBviikSZfbG/VOssUb1XIy3zTL2ixGPNmyypHKy5aOQ0+GesXyZND3awVRqW5qki1SgVosLCsqx2qqYOvSo+CPfvxmqA5d7dhLxO9GpihDVbku28u3FjfxksNxy619e7EyyY76PaajyojGxEK+lp8951bTOLeWxu+95YY2HVX3cGpcE9nnVtN4oFxIZffCo2A8FgBjrSfZH/u38/C5XfiVV560/JiHyzshPrerpQWR0Ecru8gHAHxwz6//CeA4AKtjlycBnGSMHSunlfwUgIcs3mdbEdPm2imi+AA2UkCyVDPJBjpX5iHIFhVb00UA4MhwuC/sIqnyJFu/J1v46pyJ8UvkSvB7XC1tBtVCGmvLj9V0EX2e7HaxXalUNy68gj43hsK+lnaR9XQBisor9hI7CfrcbUtimC5Pu/7kKwstr2wkcyU8vZSw3SoCACGf+Un27KFIXy7XdZp40FuxoDXioTPX4HYxvP6miTYdVfcQD/kwNuDftWdTbXi0b4oNaNGrY9FA00KaZ64l8fmzK/i5+2YwasNJp1h+PDTgp9eXTbSyi3xP/AIQAfCH0MTwL3DOb7fywJxzGcCvQCu4eR7AP3HOn7Vyn+1GxOzFaqaIZgppru7kEPK5K2eOsZCvo4uPmYJs+wf+/FgE51bTppJXugkxyR7QKbInKyddziSM7GSbF9EIhirV6tamy8Jm0CrCD2ivyBbxhGYnSZPxQMvnpph0O2EXaSdvu3Ua/+62aXziKwv4Px4+31Rof+fiFlRub3SfwOt2wed2mZpkHx89GB7gdhMPNb+KyjnHQ2eWce+JEYw4WPHezcyN7U4YEQuPd88O2/5YU4PBppPsP374HGJBL977suO2PJ4Q2ZQsYh8ti+kZY69ljH0bwG8D+DDn/H7O+ZfseHDO+Rc553Oc8+Oc8w/bcZ/tRAjhWk+2mWp1Uacszhw7OckuygokRUXUZpH97nu1QoKPP7LQ4pbdTboyydZ3KW004oeLmctO18NOrtTSjw1oOdmA9Wp1PZ7sTuwViDZLM3YRQBPOrewiQoQ7McluJ24Xwx+89TR++o7D+NOvLeIP//VcQ6H97cUNhH1u3HLEjkCp/YT8bkPpIulCCWupIiWLOES8xYDnB1cTuLqdx1tunmzjUXUXp8ajWFjPQFG5IwuPtTQrpHnixW18/dwGfvGB47qtqa0Qnm5aerSPVrXqTwL4CwCfBvDrAJKMsVvFr3YcYDeTqLN0digagNvFDImqpT11yrGgt2PpItVJpb0ieyoexM/cdQSf+d7VSjpAL5I2aBfxuF0YGzAX66iHZF7fJFuIT+uTbBmMVS/116MTewUiA9z8JDvYcpLtRBFNp3C5GD78ozfhHXcdwf/9jQv4yBefryu0H13cwp2zwy3zw80S9hlbwrywkQVAySJOoU2yGy9IP/TDZfg9LrzmhrE2H1n3MD8+AElWcWkrW214vNV6w2M9pge1dCpF3f3z4Jzjo//6Ag5F/XjX3TO2Pd5UPAjGaOnRTlophSyADIAfL/+qhUMrqDmwiMUukaYAaFOisaixQpqlnRzumBms/Dke9LZ1aawWMal0oq74l19xAv/45FV87OHz+LOf6c1zNKOLj4CWrrLqlF0kV8LcWGvBIcSnVZGdKSoI+zwtpzatJmJ2s5mREPV74PeYi8CbjAeQLspIFUoNF/yWEwVE/R7bFwA7hcvF8PtvuREelwv/7VsvQlY5fueN11d+tks7Oby4mcU77jrq2DGEfMYm2RfKySI0yXaGeNCLksKRk5R9gxZZUfH5syt45alDht7/+o35csLImauJysLjsEPWmanBIGSVYy1V2HUF7Wvn1vHU5R381x+90dbYz4DXjY//1C14ybQzV64OIk2VFOf8gTYdR08iLofvTWuYiAd1Ty6T+RLSBXnfJDuZL+ne/rcTMal1QmSPRPz4+fuO4RNfXcQvLCVx07Tx4pBOky7IcLFqJbQeJmJBPLeSan1DEyRyEmLB1tNbv8eNiN+DzYy1xcecpC9DfaDNlqftrIQhk1YRoGoBWUkUMDDeSGTn+2KKXQtjDP/5TdfDxRj++6MvQlU5fvfNN4AxVqlSv/+k/X5sQcjvMeTJvrCRgcfFKtn7hL1Uq9VL+0T2dy5uYTNTxFtecnCtIgBwciwCFwP+9KuLjiw81iIKaa4l8pX3KFXl+KMvn8fR4RB+8nb7H/vNB9gK5ASt7CK/XvP7n9jzbx9x6qB6hWS+BBfDPv/yZFx/IU1tfJ8gHvJC5UDGYBOaHYj0CLvTRQQ//7JZxENefPTLLzhy/0YolIwtXAHaJDvibz3JrWUipi3V2V1IwzlHIlfSHbUksrKtkCnKTf3YgnjQi2Qbr8ZsZ6WK79wMegppVpKFymJzP8EYw2+/8Tq85/5j+JvvXMZvf+4ZqCrHtxe3MBr146SDU+Owz20oXWRxPYOjwyHH7CsHnVgl437/a/ehHy4j6vdUousOKgGvGzPDYVzczDq28CiYrlNI8y9nl/H8Sgq/9uo5eh30AK1+Qj9V8/vf3PNvr7P5WHqORK6EWNC7b9o8GQtgRWfL3974PkCbAgLV9JJ2kim0jmizwkDAi1964Di+tbCJ71zYcuQx9PDccgo3/ucv47zByvN0QTZ8qXQiHkRRVm33KKeLMmSV6/JkA/aI7GxRX/JMPKSv1MIuNjPFSoKKGWonRo1Yrpkm9RuMMfyn11+HX3j5cfzdd6/gQ//f03hscRP3nRhxNMor5DM+ySariHNU4jf3vFcVSgr+9dlVvPbGcQS8zreSdjuilMaphUfB5J73pZKi4mP/dh6nxqN402maOPcCrUQ2a/D7en8+cCTypbpbvROxACRZ1ZXkUBXZ1Q/vTtRSC0TZiN3pIrW88+4ZjA8E8NEvv+BY3Xgrvn9lB7LK8dyyMRtHqlDSvfQomIgZT5zRQyKrr+1RMBz2VVI4zJItKrrsIu1OyLE6yR6N+OF1s4aT7EJJwVZWwmQfLwQxxvAbr5vHr7ziBD79xFVsZSVHovtqCRtIFykpKi5v5Wjp0UFq7SK1fP3cBtIFmawEZW45EkfI53Zs4VEQ8nkwFPZVdMI/PnkVl7dy+OBr59tuJSXM0Upk8wa/r/fnA0ciJ+3KyBZMxPVnZS/t5BD2uXcJpXgXiGwnizECXjfe/+BJ/OBKAo88v+7Y4zRDVDM3m1zWI1WQDS++CZFtd4xfIq+v7VEwHLFhki3Juvz68Zq9AqfhnGsi24In2+ViGI81zsoW9q9+nWQLGGP4j6+Zw68+OIdDUT9eNjfq6OOFDKSLXN7KQVY5iWwHEe8lO3vsIv9yZhkjER/uOe6cNaKX+Ln7juHrH3zAsYXHWkSMX15S8ImvLOC2o4N45amDbdnpJVqJ7JsZYynGWBrA6fLvxZ9vasPxdTXJfGlXRragUtOsY3J5dVuL76u95NSJMg9BJV3EIU+24CdeOo1jI2H88ZfP7YsnaoSqcnzs4XN4/ce/hbzBAou9LKxrNhGj5TiaXcTY90YIs4ub9kYX7pQv6Q6G9Xqy/djONo7n0kO2KFfqsJsRC/nAuWZpcZpUXrPNWK00nmiSlb2S6J/4vlYwxvD+B0/i8f/0Klta5JoR9rmR1znJFtGfZBdxDnEVtdbali6U8Mjza3jDTRPwkAcYgFakdCjanveCqXgQ13Zy+JvvXMJ6uohff90pamPsIVo1Pro55wOc8yjn3FP+vfjzwc3wKZNoUARSqVbXIeCWdnK7rCJAZ+0iIl1Ez3KbFTxuF37t1XM4t5bGQ2eutbx9oaTgff/wA3ziq4t4biWFF1atpXUsrGkf2MZFdqnimdfLSMSPeMiLj3zxBbz5T7+Nv3r0RcspH0BNhKTeSXbYB0lRLQnfTHF/tFc9Ym3cKxBV8VYm2UDz4oflZH+0PRqhHR/kIb8HuZKi64qHuPo0S22PjhHwuhHwunZ99vzbc2soyirefMBTRTrF1GAQSzt5fPLrF/DA/CjuODbU6UMiDECnpRZI5KS6k+zhsA8+j6vywdwIzjmuldsea4lXNrw7M8kO+dxwt8Hv9YabJnD9xAA+9m/nIclqw9ttZYr4mb98HJ8/u4J33zMDAIYXFmtJ5kpYT2vCzKhdxMwk2+1iePhXX4bfesN1UFSO//Ivz+HOj3wFP/tXT+ChM8ump/I7WZHTrj9dBAC2Lfiys0UZER2ebHFMwtLiJMICM2xh8RHQsrLXUvuLH4DqyRiVNNhL2OcG50BBbv0auLCRwfhA4EBnNLeDeNC3K13kcz9cxvRgELceGWzyVYRTTJUX55P5Ej7wmvlOHw5hEBLZJlFUjnRRruvJZoxVYtuakcrLSBflSpWpIOB1wed2dcyT7aQfuxaXi+GDr5vH1e08/vHJK3Vvc2Ejgx/788fwzLUk/vxnbsXvvPF6BL1uvLBqXmQvbmhfe2QohGs7+qP1OOfIFI2LbEBrAv35+2fxhffdj4d/9WV478tmcW41jfd9+ge4/cOP4AOfOYPHFjcNeZiFXURvpa7IkTZbra6oHPmSzkl2Gy1Pm+WTBqt2kcm4Vvywkd5/lWElmcdIxEfJCjYTKj+X9PiyL2xkcfwQTbGdRmt91F63W5kivr24iTfdPEkWhQ4xVR7CvfH0BG6c6r1uiYMOiWyTpAslcN54ijgZa52VfbVORjagifRYyItkG6aAe8kUZUeTRfbywNwo7pgZwie+urgvZeA7F7bw1j9/DDlJxj+89y68/qYJuFwMc2MRS5NsYRV5+dwospKCVEGffSInKVBUbnmSNjcWxW+87hS+/RuvxN+/5068/qZx/Oszq3j7Xz6OP/vaou77SeQkDAQ8un2SwxZbH8XPR29OtnaMzots8f8ZsbiEtDcuq5ZriULfLz12AlHq1CphhHOOi+sZWnpsA7FgNX7zi0+vQFH5gS+g6SS3zwzhwesO4Tded6rTh0KYgES2SYR4aBSfNhEPtPRk18vIFrQ7Ak3Qzkk2oJ1Q/Prr5rGRLuKvHr1U+fv/93tLeOd/fxyjUT/++ZfuxS01lyrnxqI4Z2WSvZ5BwOuqeNv0+rKFX93MJLseLhfDPcdH8NEfvxlP/daDuPlwHF89pz9tZSdXwqCB6W21Wt2cH1xMG41MstuRlS3+P3oXQBsx1aSQZiWRr6TEEPYhlmhbTbLPLCWRLsq4iSZ5jjMYqtpFHjqzjLmxCE6ND3T4qA4uQ2Ef/vJdt++74k30BiSyTSLEQyORPRkLYi1dbJqcUa/tUdApka15btsnsgHgtpkhvPLUIfzFNy4gkZPwsYfP4QOfOYPbZ4bwP3/xnn1vLvPjUWxmJNPLgwvliZi4X/0iW/t5OOEJDXjduP3oIJ5bTqGkNPan15LIl3QvPQJVz7JZu0g13lFfTjYApNpkF4n6PfB7rFk5Knnme54PnHOtUv0ALT22C/FcajXJfuiHy/C5XXjNDePtOKwDjbCLXEvk8eSlHcrGJggLkMg2iTjTb+SHnYgHoKgc6+nGlpGlnTwifk/d+4gHvR1ZfEwX2jvJFnzgNfNIFWS84RPfxie+uoh/d9s0/vpn76j7vZkf19q2zFpGFtczOHkogslyCoze5ceUzZPsvZw+HEdRVnVP6RM5SXelOgAEfW6EfG7Ti49CCOk5CfN73Ah63XXrme3Gaka2IBrwIhrw7BPZqYKMrKRUJt2EfVQm2U2WfxWV41/OLuOB+VHd+weEeWLlttZ/ObMMAHjzzVMdPiKC6F1IZJtETJljwfof7pWs7CYFJEvlZJF6CyUdm2RL5hb7rHL95ADe8pJJXEvk8cHXzuMP33YaPk/9p+d8udLWjGUkU5RxLZHHiUMRjIT98LldukW2mGQPOPT9uXlauxT+9LWkrtvv5CTdRTSCobDP8iRbT042sHuBykm2s5LlpUfBVDy4LxVoJXlwMrLbTWWS3SRW8vGLW9hIFylCrk3Egz5Isop/euoqXnI4jiPDZFMgCLOQyDaJHk82UP2Arke9jGzBQNDblozhvWQKsi47gBP8wVtP40vvvx+//IoTTTfZR6N+DIa8pibZF9ZFoUUULhfDZDzQ9ESolqon25lp2pGhEGJBL84uJXTdPpEtGZ7sDVsQ2cI3q9dO1K4Txc1MEUMW4/sEk/Hgvkm2+DMtPtpPWMck+6Ezywj73HjVqbF2HdaBRnymXdzI0sIjQViERLZJEi3i04R/s1GVNue8PMmuPyWIh7xIF2XdbYh2kS0qiPg7c0k26HPjuonWCzaMMdPLjwtlkX1yTEspqCeqGmH34uNeGGM4PR3DmautJ9mlcqmMmUm2+cVH/Z5sYHdKgZNsZyWM2GAXAVA+6dorsg9eEU27CLVIFynKCr749Apec8M4gj6KT2wHIhnIxYA3nJ7o8NEQRG9DItskibyEiN8Db4P4tIGAB2Gfu6EVIZkvIVOUG06y27k4JijKCiRF1VU20mnmx6M4v5YxXBG+sJ6G181wtLz0OBkP4tpO5xcfBaenYzi3lkah1DxcO33+AAAgAElEQVRtIWGwUl0wFPab9mRnDXiyAe1E0ennL+fcVrvIZDyInVxpl+hbTuThcTHHK8YPIuEWOdnfPL+JVEGm5bs2IpKB7jk+0rbqcILoV0hkmySZb36pnjGGiXiwoV2kWXwfUL1k145JoMCoHaCTzI9HK/5qIyyuZTA7EqlkS0/Gg1hLF3QleqQKJbhYNdvXCU5Px6GoHM8uN6+NFxnqRtJFAGAkotlFjJ6cANVJdsiAXcRpT3YqL0NWuX0iu84uxUqygLGBQFtaUA8afo8LLtZ4kv25H17DYMiL+06OtPnIDi7iKuyP3kILjwRhFRLZJknmSg392ILJeONCmmbxfUB1kt3O5cdMQdgBekBkm1x+XNzI4MRYtdBiOh4E58Bqi+IgQLOLRPweR5vPbp6OAwCebuHLFm2PRtJFAM0uUpRV5ExUuWfKJ2Ehna2H8ZDP8Vr1rbL1xWoRjWCyTlb2ciJfSaIh7IUxhrDPU3eSnS3KeOT5Nbz+pomGVwwJ+zk2EsaX3n8/3nYriWyCsAq9c5lEyyhuIbJjjZfqxCT7cINJdkdEdtFZz7GdzJVj/M4ZWH4slBRc2c7h5KGqyK4nqhqRLsgYcDhCbDwWwKGoH2eXmvuyd8rLi2Y82QCwZcIyki3KCPvccOmc6MaCXhRKakvrixXEEqd9dpH9C8vLyTwtPTpIyO+uO8l+5Pk1FEoq3vISEnvt5rqJAapRJwgbIJFtkkROQrxBfJ9gIhbEZqaIorxfZCzt5BH1ezAQrC9oRTRgO3KGBdWyke4X2QMBLyZjAZw3MMm+sJEB58DJQ9HK3wlRtdwkBUaQLpQc9WMLTk/HcKbFJLtVuk0jRJ70lonlx5xkLEO9HXsF4mTBLpE9NhCAi2k16gCgqhyryQIV0ThI2Oepmy7yuR8uYyIWwG1HB+t8FUEQRPdDItskyXyp5VRTxPitJfcLmqWdHKYaZGQDnVl8FJ7bXvBkA9o0+wUDIntxT7IIUJ1k61l+TBXakyF+ejqOi5vZyqJlPXZy5jzZIupu20SMX6aoGBLZ7dgrEP8Pu+wiXrcLYwPVhJHNTBElhWOK7CKOEfK79+Vk72QlfPP8Bt5886TuKycEQRDdBolsE3DOkdDjyRZLVHWmpM3i+4CqyG5n62O6x0T2/HgUFzeyumvIF9YycLsYZobDlb8LeN0YDvsqk8tmpAuyY0U0tZyejoHz5qU0O7kSvG5meAlzWNhFTIjsbNFYhno7LE9bGe0E1mjKSjNqYx1FMQ1Nsp0j5PNUkmsEX3xmBbLK8SZKFSEIoochkW2CrKRAVnklT7QRjQppqhnZjT+4fR4XQj53Wz3ZlUl2D3iyAW35UVJUXN7K6rr9wnoaR4dD+5okpwb1ZWW3zy4ilh8bi+xETkI85DPsmxR2ETOTbM2TbWCSXbE8OSiysxKiAQ/8HvsSXyZi1Un2SoLaHp0m7HPvW8R96IfLmB0N44bJ1rn5BEEQ3QqJbBMkKpfqdU6y90xJW2VkC9pdrd5L6SKANskGoNsysrCe2bX0KJiM6RXZ7bGLDIV9mB4MNl1+TORKhpNFAG1qGPC6zIlsSTZ0laNiF3Fwr2A7K1Wm83YhqtU555WIyClafHSMkN9TOcEHtKHEE5e28Zabp2j5jiCInoZEtgmqbY/NP9yDPjfiIe8+AXd1u5wsMtTYLqLdf3sa8wSVxUcD08pOcnw0AheDruVHSVZxeSu3a+lRIOwBzbKjOefIFNsjsgEtyq/Z8uNOeZJthuGw32S6iKI7IxtAZWfBUbtItmjb0qNgMh6EJKvYykpYSRYQ9LoN19cT+tk7yf78mRVwDryZKr0JguhxSGSbQCwj6kl2mIztz8pulZEtaPskuygj5HP3TOlGwOvGzEhY1yT70lYWisp3LT0KJuMBZCWl6fc6JylQVN4Wuwig+bKXdvIVz/FezE6yAW1SbiZdJFOUDbWBRv0euJjTnmwJwzYtPQpqYx1FRjZNVJ0j5Ns9yX7ozDJOT8dwbCTc5KsIgiC6HxLZJkgYEdnxwL5Jdqu2R0Es6HwtdS3ZojE7QDdwajyK8zqyshfWtGSRE3XsIuJkp1l7ZLrQ3gxx4cs+22D5cScnGc7IFgyFfabsIjmDnmyXizl+ouiEXaQS65jIYzlZoIxshwn7tUk25xwXNzJ4+lqSatQJgugLSGSboJJR3MIuAmipBPUm2dGAp+Ul6HjI+VrqWtI9KLLnxqK4vJ1DvkWD4cJ6GoxpFpO9VCeXjRNGRJxeuybZN04NgLH6y48i3SZmcpI9HPYZtouoKkdWMmYXAZytVuecYzsr2W4XEf7ra4kCVhJ5TMRo6dFJQj4PZJVDUlQ8dGYZjAFvPE0imyCI3odEtglEVbSeSfZEPIBkvrSr0axVfJ+g3XaRbFHumWQRwfxYFJxrIroZC+sZHBkKIVCnElxP62OqzZPsaMCL2ZEwztbxZeckBZKitnWSnSu3NhqxiwBALORzbK8glZchq9x2u0gs6EXQ68blrSw2MkWaZDuMiKHMFhU89MNl3HlsCON0YkMQRB9AItsEyVwJPo+rrmDbS72EkVbxfYJ4yId8SanbGOkEmYIxO0A3IBJGzrXwZS+u1U8WAbTJrs/jamEX0YRiO3KyBdryY3LfQqYQrWY92cMRP/IlpeX0v5asyTbQuIMnisJXbrddhDGGyXgA37+yA86rr2HCGcTVkSde3MbFzSzefDPVqBME0R+QyDZBIldqmZEtEJeaRVa2lpGd0yWy25HOUEumByfZR4fD8HtcTUW2rKi4uJnB8QYimzGGqXiwqciuTrLblzJxejqGjXQRq6ndNpadrLm2R0G1kEb/8qPZNtBY0IukQxF+olDHbrsIoF3deG45Vfk94RzixP4fnrwCr5vhR24c7/AREQRB2AOJbBMk8pIuqwhQ/YBeKU+yE7kSspKi2y4CtK9aPdODnmy3i+HkWATnmiw/Xt7OoaTwuvF9gql486zs6iS7jSL7sLb8eObqbl+28DhbsYsAMOTLzha1qXfI4JWOeMi5GEpx/KJgx06m4kGo5QsIVETjLKGyBekb5zfwspOjGHTgpIkgCKITkMg2gTbJ1vdBMDYQAGPV5IpqsogOu0ibq9V7MV0E0JYfmyWMiGSRRnYRoH4KTC3tThcBgOsnBuBxMTx9bbcve6c8GTYd4Wei9bGSoW7Uk11OyFHVxhnkZqnaRez1ZAO7p9dkF3EWMcmmbGyCIPoNEtkmSOb1Jzv4PC6MRPwVu8jVckb2YQOT7HbZRbJFpWfaHmuZH4tiLVVs2Cx4YUMT2Y3sIoAmqtbTRUiyWvff04US3C6GkM+++u5WBLxuzI1F9zU/VhtHrdpFjEyyzdtFVK4l19jNdnmSPRi2/+qCENmDIS+CbfyZH0TEayrodePV1491+GgIgiDsg0S2CZJ5/Z5sQPvAFjF+oohmStfiY/sm2UVZS6xo56TWLlotPy6spTEVDzYViJPxIDgHVpP1Y/zSBW3K3+5SkpsPx3B2z/LjTqVx1HwZDQBsG/FkSyYXH8snAk5YnrayEqIBD/we+0XwZHmXYoKm2I4jRParrx8zbEciCILoZkhkmyCRK+n2ZAPaB/ZyjV1kQEdGNtDeSbbw3IZ7cGonRHYjy8jCeqZuCU0t0/HmhTTpQvsq1Ws5PR1HMl/C5a1c5e8SuRIifg98HnMvX/G1xibZ4vlhfJINOHOiuOVAEY1ATLJp6dF5JuNB3HlsCD9770ynD4UgCMJWSGQbpFBSkC8phi7Vi0IaLVlEX0Y2UE2yaIfIzpQ9x5E2LvbZxfhAANGAp269uqJyLK43ju8TtMrKThdKbU0WEZyejgHY3fyYyOlfvK0HYwzDYV/FbqGHrElPduVqTN7+hJHtbNGRZBEAlZzmSVp6dJyA141//A9345Yjg50+FIIgCFshkW0QcdnbyKX6yXgAOUlBKi/rju8DtOSMgYCnPSK74rntvUk2Y6xhvfq1nTyKsoqTY81FthBVjUR2qkOT7LmxKPweF85erS4/WqlUFxgtpKksPhpNF3HwasxWRrK9iEYQ8Lrxu2+6Hm+/84gj908QBEH0PySyDSLiyIxMEoWvczmZNzTJBoBYqD2tj1WR3XuTbEAToy+spvcVt4gmyBNN4vsATVSNRPxN7SLtLKIReN0uXD85sGv5ccegXakeQ2EfNg2I7JwkI+Rzw+Uy5knvVbsIALz73mM4NT7g2P0TBEEQ/Q2JbIMkTCydiZzdZ5dTyEmK7km2eJz2eLLN2QG6hVPjUaQL8r7iloV1LVmklScbAKbigSYiuzN2EUBrfnxmOQmlHIOXsGGSfSgawFqDJc96ZIqKqaU0pwqVOOfYyUqOZGQTBEEQhB2QyDZIJT5NZ042UM3ZfeLFLQD6MrIF8aCvYTSdnYiItV5MFwG0STawP2FkYS2DQ1G/rpOiqcHGhTSdWnwENF92TlKwWD5h2MmVTGdkC2ZHw1hNFZCT9EXraRnqxk/AAl43gl637SI7lZchqxxDDmRkEwRBEIQdkMg2iBm7yGjUD4+L4clLOwBgzC7S9kl2b4rsRjF+i+vpln5swWQsiOVEYZ/lhHOOTLGTIltrfjy7lICicqQKJcQsTrKPjYQBABc3srpuny3Kpp8bsaDX9hPFzUoRDU2yCYIgiO6ERLZBKouPBkS228UwNhDAi5uaoJkeMmAXaZcnu2CubKRbiId8GBvw76pX51wkizT3Ywsm40HkS8o+/3BOUqCovGN2kdmRMCJ+D84uJZHKl8C5+bbHyn2OlkX2pk6RLZkX2fGQ13ZPtljaJLsIQRAE0a2QyDZIIqc1/0UNCg4RBRYLejFgQKyJSfbe6ardmE2P6Cb21quvJAvISoouPzZQjfHb68vuRKV6LS4Xw41TAzi7lKipVLcmLmeGw2AMuFhuw2xFtqiYzlBvdTXm8lYW7/v0D3DT734ZV7dzDW9Xy1Y5ftCpCD+CIAiCsAqJbIMk8hJiQa/h5j+RMGLEjw1oAqWkcORLiqGvM0qmKCNsIj2imzg1HsXCWqayICiWHltlZAvEz2avyE4VNIFo5OTIbm6ejuP5lTTW05pNwmq6SMDrxlQ8WLm60gqrdpF6InszU8TvPvQsHvzYN/DlZ1eRLsj46gvruu5zq2IXIU82QRAE0Z2QyDZIImesUl0gEkaMiuy4gxFotVgRUd3C3FgURVnF5S1NOC6Up9onx/TbRYD9Wdnpssju5FLo6ek4JEXFdy9qy7NWJ9kAMDsa0e3JzhRl01ai+B7LU7Yo4+OPLODlH/0a/va7l/ETtx3Gt379FZgeDOLRxU1d97lNk2yCIAiiy+ltVdUBkvmSIT+2YLIyyda/9AjsrlZ3suI5XZQR6dFkEUHt8uPsaASL6xkMh326hdhgyIuA17VPZKcqdpHOTbJF8+M3zm8AsElkj4TxmUvb4Jy3vDKTkxSLi48llBQV//DEFXz8KwvYzEj4kRvH8YHXzuP4qHal4d7jI/jSMytQVA53iysqW1kJ0YD5anmCIAiCcBr6hDKI6Ul2zNwk28kyj1qyFiaV3cLJQ1Ewhsry48J6RrcfG9CaIyfjwYae7E6U0QimB4MYDHlxptz8aOZEby+zo2FkJaViQWkE51xbfDTpyY6HfMiXFDz4sW/gtz/3LGZHI/jsL92DT77jpRWBDQD3nBhGqiDjmZoK+UY4XURDEARBEFYhkW0Q4ck2ysmxKFwMuG7CWIOcEFNOJ4xkCr0vsoM+N44OhXB+TWt+XFhLGxLZADAVD+JaYndJS9Uu0rlJNmMMp6fjULmWVmOH4J8d0b43F1osP+YkBZybj3ccG9BOMINeN/7q3bfjH997F249MrjvdvccHwEAPHqhtWVkO1t0rFKdIAiCIOyARLZBErkS4iYu1R8bCeOJDz2Iu2aHDX2dEPQpp0V2H3iygWq9+ka6iFRB1r30KJiK7y+k6XS6iODmsmUkbmLxth4ixq/V8qPVDPU33zyJz/zC3fjC++7HK04danjso1E/5seieGxxq+V9bmUk8mMTBEEQXQ2JbAMoKke6IJuaZAPAiInJmxD0ibyzrY+Zomw4lrAbOTUexaXNLJ5dTgHQv/QomIwHsZEuoihX01zSBS22MWTSLmEXopTGarKIYHwggIDX1XL5MStp3wuzVzp8Hhdunxlq6bMGNMvIk5e2UWiRpkN2EYIgCKLbIZFtgJSJtkerhH1uuF3McbtIP6SLAMDceBQqB770zAoA/fF9ArFcupqsWkbSZSuNHdNjK4jlRzuWHgEtf/vYSKRlVraYZLfjJOPe4yMoyiq+f2Wn4W1UlWMnK1ERDUEQBNHVkMg2gJlKdaswxtpSrZ7pg3QRQJtkA8CXn13DQMCD0aixqweiNOjaTtUyki50rlK9lkMDAUzEAraKy9nRcMvWR1FU1A7P/p2z2sS7mWUkVShBVjmGKCObIAiC6GJIZBsgUW7biwfbO0GLB+2vpa6lKCsoKbznFx8B4OhwGD63C8l8CSfHooanz9NxLWKxNmEkXSh1dOmxlj99+634wGvmbbu/4yNhXN3O7bLH7MWqJ9sI0YAXp6djTZcft0SlOtlFCIIgiC6GRLYBxCTbjvg0Iww4PMnOFNo3qXQar9tVWegzahUBgLGYH4wByzUJI6kumWQDwEuPDhr2mTdjdjQClaNpnbnwZLfLTnTv8RGcXUpWUl32si1ENtlFCIIgiC6GRLYBkuVpspmcbCvsbcyzm2yxvSLKaYRlxGh8HwD4PW6MRvy7EkbSBbmjGdlOcmxEOyG50GT5sTrJbs/i5z0nhqGoHI9f3K7771sZLdeb0kUIgiCIboZEtgEqdhGbFs/04rQnO13U7rsfJtmAtvwIGE8WEewtpOkmu4jdiKl/s4SRdtpFAODWI4Pwe1wNLSPCLmImrYcgCIIg2gWJbAMIu0i7p5pOi2wxye4Xkf2K+UO4eTqGl5Qj74yyNyu7nyfZ0YAXo1F/04QRsfgY9rXnexDwunH7zFDD5cftjCay7UpZIQiCIAgnIJFtgESuhKjfA4+7vd+2eFlkqyp35P4zYpLdJ0LyuokBfO5X7jPtnZ8a1CbZnHNwzvt6kg0AsyPNE0ZykoKg160r59ou7jkxjHNrWqnQXrayEqIBD3weevsiCIIguhf6lDJAKl9q+9IjoC0+cg6kyxNFu8lUJtmdLVvpFiZjARRlFdtZCVlJgco73/boJLOjkaatj1obaHufG/eWK9Yfq2MZ2cpKZBUhCIIguh4S2QZI5EttzcgWOF2tXk0X6d9prRFEIc1yolBJuOj3SfZ2VqrsHOylE0VFN07FEA146lpGtrNFWnokCIIguh4S2QZI5KS2Z2QDNdXqDmVltzs9otsRIvtaIod0+QSkvyfZzRNGskW5bX5sgdvFcNfscN3lx62MRCKbIAiC6HpIZBsg0SG7iJhkO7X8mG7zYlu3Mz0oRHbtJLt/vzezo1rUYaPlx2xR6cgJ2L3Hh7G0k8eVrd0Z3ppdhEQ2QRAE0d2QyDZAMldqe0Y2UK1xT+TrX863ijapdMPVxsW2biYW9CLkc2M5kUeqMsnuX7vI4cEgPC7W0JedldpvFwGAe09ovuzaabaqcmxnaZJNEARBdD8dEdmMsZ9gjD3LGFMZY7d14hiMwjnvuCfbqUl2piD3TbKIHTDGMFmO8RN2kX6N8AMAj9uFI8OhhlnZmQ54sgGtTOhQ1I9HF6siO1UoQVE5hsK0+EgQBEF0N52aZD8D4K0AvtmhxzdMpihDUXlHPNmOi+wOTSq7marI7v/FRwCYHYng4mZ9u0iuqCDSASsRYwz3HB/Gdy5sgXMtvrJaREOTbIIgCKK76YjI5pw/zzk/14nHNotYOox1wC4S8Lrh97gqte52kynIiJLI3sVUufXxICw+AsDx0TAubeWg1MlizxZlhDq0FHvPiRFsZSWcW0sD0JYeAapUJwiCILqfrvdkM8beyxh7ijH21MbGRseOQ0yRO7H4CDjb+tiJiLZuZyoewGZGwma6CLeLIeTr7+SV2dEwJFnFtZ38rr/nnCMryR1rA634sstRfttZrZyGRDZBEATR7TgmshljjzDGnqnz6y1G7odz/inO+W2c89tGR0edOtyWCIHbicVHQBPZTkX4ZYqdE1HdiojxO7eWRsTvAWP9vRRaSRjZYxnJl7Qynk6dhE3Fg5gZDuGxsi+7ahchTzZBEATR3Tj2yck5f9Cp++4EQuCKzOp2Ew85N8kmkb2fisheTfe9VQQAjo1oWdkXN7J4YL7699lyG2gnr3Tcc2IED/1wGbKiVuwigx16HRIEQRCEXrreLtItiPi8TqSLAM7aRTJFShfZy1RZZK+ni32/9AgAw2EfBgKefZPsSlFRB+0y9x4fQaYo48xSEttZCQMBD3weeusiCIIguptORfj9GGNsCcDdAL7AGPtyJ47DCJ1cfNQe1+eIyOackye7DuOxAIRDpJ/j+wSMMcyORvbF+GUqbaCd+x7cfXwYAPDY4ia2shKGySpCEARB9AAd+eTknP8zgH/uxGObJZkvIeB1IeDtzETPqUl2UVZRUjjZRfbgdbswFg1gNVU4EJNsQFt+fKy8YCgQk+xOPj+Gwj5cPzGARy9swsUYLT0SBEEQPQFdc9VJIid1JCNbEAt6kSnKKCmqrffbDSKqW5mMBwAcjEk2AMyOhLGaKlSeEwCQkzrvyQaAe08M4/uXE1hO5DFMIpsgCILoAUhk6ySR60zbo0A8dsrmaXaGRHZDxPLjQVh8BKoJI7X16pku8GQD2vKjpKi4tJXDMBXREARBED0AiWydJPIlDHTIjw041/rYDZ7bbmVqUIjsg2MXAYCLNSI72yXPjztmhuBxaSZ5sosQBEEQvQCJbJ2k8qWOZWQD1RKchN0i+4A0Gpph6oBNsmeGw2AMuLhRTRjplpOwsN+DW47EAQDDYVp8JAiCILofEtk66bRdxKlJdlbqDhHVjUzGDtYkO+B1Yyoe3JUwUvFkd0Hj5T3HtfZHsosQBEEQvQCJbJ0k8lLHimiAqsi225OdLpAnuxEz5YKWQ9GDMzk9NhLe5cnOFmX4PS543J1/q3jVdYfAWLU4hyAIgiC6mc5/cvYAhZKCQkntWEY2UK1zt7taXTT6kcjez4lDEXzul+/FK04d6vShtI3joxFc3MiAcw6gu9pAT0/H8b3fejVOT8c7fSgEQRAE0RIS2ToQFo1O2kUGHFt81O6PGh/rc/PhONzlhbuDwOxoGFlJwXq6CABdV1RES48EQRBEr0AiWwdietzJnGyv24Wwz237JDtTnmSHOlSyQ3QXsyNajN+F8vJjVlK6SmQTBEEQRK9AIlsHiZwEoLOTbO3x7a9WzxRkhH1uuA7QtJZoTCXGr7z8mC3KXbH0SBAEQRC9BolsHYjYvE56sgHNMmJ7ukhRJqsIUWF8IICA11VZfuw2uwhBEARB9AoksnWQ7BKRHQ96kcxLtt5nhkQUUYPLxXBsJFLJyu6mxUeCIAiC6CVIZOsgmev84iOgiXwnGh+jJKKIGmZHw5XWx5ykIOwnuwhBEARBGIVEtg4SeQluF+v4RM8pkU2TbKKW4yNhXN3OoSgryBRlhHz0/CAIgiAIo5DI1kEip1WqM9bZ5cB4yOtATjbZAYjdHBsNQ+XAla0cPT8IgiAIwiQksnWQyJcQ67BVBNAWH4uyikJJse0+0wUSUcRuRIzfcyspqBx0pYMgCIIgTEAiWwfJ8iS70whPuJ2WkaxE6SLEbkSM39NLSQBAhDzZBEEQBGEYEtk6SOQlxEOdb5qL2dz6yDnXcrJpUknUEA14MRr14+lrmsgmTzZBEARBGIdEtg4SXTLJtltkF2UVssrJLkLsY3YkjGeXUwDILkIQBEEQZiCRrYNkrjs82aLW3a7lx0xRBgAS2cQ+ZkfD9PwgCIIgCAuQyG6BrKhIF+WOF9EA9k+ysySiiAaI5UcAlJNNEARBECYgkd2CVEETov1oF0mX/29kByD2IpYfAXp+EARBEIQZSGS3IJHTasy7YfExGvCAMSCZs6daXUyyo5QuQuxhdrR2kk3PD4IgCIIwConsFiTKU+Nu8GS7XAwDAftaH4XnlkQUsZfDg0F4XFr5UoTSRQiCIAjCMCSyW5AsLxl2g10EKLc+2iyyyZNN7MXjduHIcAgAECJPNkEQBEEYhkR2CxL57rGLAJov2+5JNolsoh6zIxH4PC543fQ2QRAEQRBGIXXVglsOD+IP33YTxgb8nT4UAPaK7Eq6CHmyiTo8MD+Koqx0+jAIgiAIoichddWCmZEwZkbCrW/YJmJBL67t5G25r0w5XSTkJTsAsZ933HUU77jraKcPgyAIgiB6EroO3GPYaxdREPF74CovuBEEQRAEQRD2QCK7x4gFtcVHzrnl+8oUS1Q0QhAEQRAE4QAksnuMeMgLReXISta9stnyJJsgCIIgCIKwFxLZPYadrY+ZokwimyAIgiAIwgFIZPcYsaAWJZiwofUxU5QpWYQgCIIgCMIBSGT3GHZOsrNFGWFq8yMIgiAIgrAdEtk9hhDZKYsim3OO1VQBw5HuyP8mCIIgCILoJ0hk9xjxkCayEzlrInszIyGRK+HkoYgdh0UQBEEQBEHUQCK7xxCT7B2LInthPQ0AODlGIpsgCIIgCMJuSGT3GGG/B8NhHy5tZi3dz+J6BgBw8lDUjsMiCIIgCIIgaiCR3YPMjUVxbi1t6T4W1jKI+j0YGyBPNkEQBEEQhN2QyO5B5sejWFhLQ1XNtz4urKdxYiwCxqhSnSAIgiAIwm5IZPcgc2NRZCUF1xJ50/exuJ6hpUeCIAiCIAiHIJHdg8yPa+L4vEnLyHZWwmZGIj82QRAEQRCEQ5DI7kHmxjRxbNaXLZYeT1CyCEEQBEEQhCOQyO5BogEvpuJBnF81J7Ir8X1kFyEIgiAIgnAEEtk9ytxYBC+YFdlrGYR8bkzGgjYfFaSpJCcAAA6ESURBVEEQBEEQBAGQyO5Z5sajuLiRRUlRDX/t4noGJw5F4HJRsghBEARBEIQTkMjuUebHopAUFZe3jJfSLKyncYKsIgRBEARBEI5BIrtHqSw/rmYMfV0yX8JaqkjJIgRBEARBEA5CIrtHOXEoAhcznjBSrVOnSTZBEARBEIRTkMjuUQJeN2aGw4YTRhZFsgjF9xEEQRAEQTgGieweZn48ariQZmEtA7/HhenBkENHRRAEQRAEQZDI7mHmxqK4tJVFoaTo/pqF9QyOj0bgpmQRgiAIgiAIxyCR3cPMj0eh8qrPWg+L6xmyihAEQRAEQTgMieweppowos8ykinKuJbI09IjQRAEQRCEw5DI7mFmhkPwuV26fdkXyhPvExTfRxAEQRAE4SgksnsYj9uF44ciumP8FkR8H9lFCIIgCIIgHIVEdo8zPxbRHeO3sJ6G181wdIiSRQiCIAiCIJyERHaPMzcexXKygFSh1PK2i2sZzI5E4HHTj50gCIIgCMJJSG31OKfGNX+1nmn2wnoGJ8gqQhAEQRAE4TgksnucSsJIC192XlJwdSdHySIEQRAEQRBtgER2jzMVDyLsc7ecZF/YyIBz4CQlixAEQRAEQTgOiewehzGGufFoy0n2IiWLEARBEARBtA0S2X3A/FgU51bT4Jw3vM3CehpuF8PMcLiNR0YQBEEQBHEwIZHdB8yNRbGTK2EzIzW8zcJaRiuv8dCPnCAIgiAIwmlIcfUB8yJhpIllZHE9Q35sgiAIgiCINkEiuw8QIvuFBsuPRVnBpa0s+bEJgiAIgiDaBInsPmAk4sdw2NcwYeTFzSxUDpyg+D6CIAiCIIi2QCK7T5gba5wwsrBWThYhuwhBEARBEERb6IjIZoz9EWPsBcbYWcbYPzPG4p04jn5ifjyKhbU0VHV/wsjiegYuBsyOUrIIQRAEQRBEO+jUJPvfANzIOT8N4DyA3+zQcfQNc2NRZCUF1xL5ff+2uJ7BkaEQAl53B46MIAiCIAji4NERkc05f5hzLpf/+F0A0504jn5iflzzW9dLGFlYT+MEWUUIgiAIgiDaRjd4sn8OwJca/SNj7L2MsacYY09tbGy08bB6i5Njmoje68suKSpe3KRkEYIgCIIgiHbiceqOGWOPABiv808f4px/rnybDwGQAfyPRvfDOf8UgE8BwG233da40vCAMxDwYjIWwLk9CSOXt3IoKRwnKVmEIAiCIAiibTgmsjnnDzb7d8bYuwC8EcCreLM+cEI38+PRfSJ7cV37MyWLEARBEP9/e/cfY1lZ33H8/dldtsuy47ogu0sBXZBdKIkgsCI0/qC1pYhRagypFSvtNmmamEp/aKVp/2mNaWuapmlsQhvEYFo1abWVNlZKqUpafpQfwrrIj9klqJuuDC5YGFB2mX36x3lm53ayMzv3crhn5s77lZzMuc8998xzPvPk7nfPfc65koanq7uLXAZ8FHhXKeX5LvowirZtHuOxJ5/j4NShw23Tt+977UbvLCJJkjQsXc3J/iQwBtyS5P4k13XUj5Fy5qYxDkwd4tv7nzvcNj4xySkbjmXt6pftQwtJkiTN0knlVUo5o4vfO+q2TV/8+L3Jw3cTGZ+YdD62JEnSkC2Gu4uoJWdsXMeKzNxhZOpQYc+Tk4fvPCJJkqThsMgeIWuOWcmWE47j0Xrx43efep4DLx7iDM9kS5IkDZVF9ojZtmns8Jns8Ynmokeni0iSJA2XRfaI2bZ5jMf3P8ePDk4xXm/f55lsSZKk4bLIHjFnbR6jFNg9McnuJyY5af0axtYc03W3JEmSlhWL7BEzc4eRZxmfmPQstiRJUgcsskfMlhPWsnrlCh7+3jPsnpj0mx4lSZI6YJE9YlatXMFrN67jPx6e4IcHp9i6yTPZkiRJw2aRPYLO3LSOPU823/ronUUkSZKGzyJ7BG3bPDNFxDnZkiRJw2eRPYLOrBc/njj2Y7xy7eqOeyNJkrT8WGSPoOk7jDhVRJIkqRsW2SPolA3H8qp1q3ndKeu77ookSdKytKrrDqh9SfiX33gz64/1S2gkSZK6YJE9ojavX9N1FyRJkpYtp4tIkiRJLbPIliRJklpmkS1JkiS1zCJbkiRJaplFtiRJktQyi2xJkiSpZRbZkiRJUssssiVJkqSWWWRLkiRJLbPIliRJklpmkS1JkiS1zCJbkiRJaplFtiRJktQyi2xJkiSpZRbZkiRJUstSSum6DwuW5Eng2x386lcB3+/g9y5HZj08Zj08Zj08Zj08Zj08Zj0cs3N+TSnlxEF3tqSK7K4kuaeUsr3rfiwHZj08Zj08Zj08Zj08Zj08Zj0cbefsdBFJkiSpZRbZkiRJUsssshfmb7ruwDJi1sNj1sNj1sNj1sNj1sNj1sPRas7OyZYkSZJa5plsSZIkqWUW2ZIkSVLLlm2RneSGJBNJdvW0nZvkjiTfTPLPSV5R27ck+WGS++tyXc9rLqjb707yl0nSxfEsZv1kXZ87pz73YH1+TW0366Poc1xf1TOm709yKMnr63NmPY8+cz4myY21/aEkv9fzmsuSPFJzvraLY1ns+sx6dZJP1/YHklzS8xrH9FEkOTXJV+s4fTDJNbX9+CS3JBmvPzfU9tQsdyfZmeT8nn1dXbcfT3J1V8e0WA2Q9Vl1zL+Q5MOz9uX7yBwGyPmqOpZ3Jrk9ybk9++o/51LKslyAtwDnA7t62u4G3lrXdwAfq+tberebtZ//Bi4GAvwr8Pauj22xLX1mvQrYCZxbH58ArDTr9rOe9brXAY/1PDbrlnIG3gd8vq6vBR6v7ykrgT3A6cBq4AHg7K6PbbEtfWb9QeDTdX0jcC+woj52TB8965OA8+v6GPAocDbwCeDa2n4t8Kd1/fKaZYCLgLtq+/HAY/Xnhrq+oevjW0zLAFlvBN4AfBz4cM9+fB9pN+efnB6rwNt7xvRAOS/bM9mllNuAp2Y1nwncVtdvAd4z3z6SnAS8opRyR2n+Cp8Bfr7tvi51fWZ9KbCzlPJAfe3+UsqUWS/MSxjXvwh8DhzXC9FnzgU4Lskq4FjgAPAMcCGwu5TyWCnlAPB54IqXu+9LTZ9Znw3cWl83AfwA2O6YXphSyr5Syn11/VngIeBkmnF5Y93sRmayuwL4TGncCbyyZv1zwC2llKdKKU/T/I0uG+KhLHr9Zl1KmSil3A0cnLUr30fmMUDOt9cxC3AncEpdHyjnZVtkz2EX8K66fiVwas9zpyX5RpKvJ3lzbTsZ2Nuzzd7apqObK+ttQElyc5L7kvxubTfrwc03rqf9ArXIxqwHNVfO/wA8B+wDvgP8WSnlKZpMv9vzenNeuLmyfgC4IsmqJKcBF9TnHNN9SrIFOA+4C9hUStkHTdFCc1YV5h7Dju0+LDDruZj1Ag2Q86/SfFIDA+Zskf3/7QA+mORemo8VDtT2fcCrSynnAb8NfLbOATzSnD7vibgwc2W9CngTcFX9+e4kb8OsX4q5sgYgyRuB50sp03NezXowc+V8ITAF/DhwGvA7SU7HnF+KubK+geYfv3uAvwBuB17ErPuSZB3wBeA3SynPzLfpEdrKPO2apY+s59zFEdrMepZ+c07yUzRF9kenm46w2VFzXtVPJ0ddKeVhmukKJNkGvKO2vwC8UNfvTbKH5ozrXmY+SqCu/88w+7xUzZU1TaZfL6V8vz73ZZr5mH+LWQ9knqynvZeZs9jguB7IPDm/D/hKKeUgMJHkv4DtNGdFej9VMOcFmue9+kXgt6a3S3I7MA48jWN6QZIcQ1OM/F0p5Yu1+YkkJ5VS9tXpIBO1fS9HHsN7gUtmtX/t5ez3UtRn1nOZ62+gqt+ck5wDXE9z3cb+2jxQzp7J7pFkY/25AvgD4Lr6+MQkK+v66cBWmovE9gHPJrmoXqn+AeBLnXR+iZkra+Bm4Jwka+sc1rcC3zLrwc2T9XTblTTzy4DDH52ZdZ/myfk7wE/XOzEcR3OB2MM0F+9tTXJaktU0/9m5afg9X3rmea9eWzMmyc8CL5ZSfP9YoJrNp4CHSil/3vPUTcD0HUKuZia7m4AP1LF9EfC/NeubgUuTbKh3bbi0tqkaIOu5+D4yj35zTvJq4IvAL5VSHu3ZfrCcu7jaczEsNGfu9tFcRLCX5mOBa2iuPH0U+BNmvhHzPcCDNPP97gPe2bOf7TTzA/cAn5x+jctgWdft31/z3gV8wqxf1qwvAe48wn7MuqWcgXXA39cx/S3gIz37ubxuvwf4/a6PazEufWa9BXiE5uKmfwde07Mfx/TRs34TzUfgO4H763I5zV2ebqX5VOBW4Pi6fYC/qpl+E9jes68dwO66/ErXx7bYlgGy3lzH/zM0F/TupbmY1/eRdnO+nuaTr+lt7+nZV985+7XqkiRJUsucLiJJkiS1zCJbkiRJaplFtiRJktQyi2xJkiSpZRbZkiRJUssssiVJkqSWWWRLkgCY/tItSdJLZ5EtSUtQko8luabn8ceTfCjJR5LcnWRnkj/sef6fktyb5MEkv9bTPpnkj5LcBVw85MOQpJFlkS1JS9OnqF8LXL9e/L3AE8BW4ELg9cAFSd5St99RSrmA5psPP5TkhNp+HLCrlPLGUsp/DvMAJGmUreq6A5Kk/pVSHk+yP8l5wCbgG8AbgEvrOjRf6b4VuI2msH53bT+1tu8HpoAvDLPvkrQcWGRL0tJ1PfDLwGbgBuBtwB+XUv66d6MklwA/A1xcSnk+ydeANfXpH5VSpobVYUlaLpwuIklL1z8Cl9Gcwb65LjuSrANIcnKSjcB64OlaYJ8FXNRVhyVpufBMtiQtUaWUA0m+Cvygno3+tyQ/AdyRBGASeD/wFeDXk+wEHgHu7KrPkrRcpJTSdR8kSQOoFzzeB1xZShnvuj+SpBlOF5GkJSjJ2cBu4FYLbElafDyTLUmSJLXMM9mSJElSyyyyJUmSpJZZZEuSJEkts8iWJEmSWmaRLUmSJLXs/wCBWGj908nF6AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import requests\n",
    "import numpy as np\n",
    "import io\n",
    "\n",
    "# access dataset as above\n",
    "url = \"http://www.esrl.noaa.gov/psd/enso/mei.old/table.html\"\n",
    "txt = requests.get(url).text\n",
    "\n",
    "# copy the useful data\n",
    "start_head = txt.find('YEAR')\n",
    "start_data = txt.find('1950\\t')\n",
    "stop_data  = txt.find('2018\\t')\n",
    "\n",
    "header = txt[start_head:start_data].split()\n",
    "data = np.loadtxt(io.StringIO(txt[start_data:stop_data]),unpack=True)\n",
    "\n",
    "# use zip to load into a dictionary\n",
    "data_dict = dict(zip(header, data))\n",
    "\n",
    "key = 'MAYJUN'\n",
    "# plot data\n",
    "plt.figure(0,figsize=(12,7))\n",
    "plt.title('ENSO data from {0}'.format(url))\n",
    "plt.plot(data_dict['YEAR'],data_dict[key],label=key)\n",
    "plt.xlabel('year')\n",
    "plt.ylabel('ENSO')\n",
    "plt.legend(loc='best')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise 1.3.5**\n",
    "\n",
    "* copy the code above, and modify so that datasets for months `['MAYJUN','JUNJUL','JULAUG']` are plotted on the graph\n",
    "\n",
    "Hint: use a for loop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# do exercise here\n",
    "# ANSWER\n",
    "\n",
    "import requests\n",
    "import numpy as np\n",
    "import io\n",
    "\n",
    "# access dataset as above\n",
    "url = \"http://www.esrl.noaa.gov/psd/enso/mei.old/table.html\"\n",
    "txt = requests.get(url).text\n",
    "\n",
    "# copy the useful data\n",
    "start_head = txt.find('YEAR')\n",
    "start_data = txt.find('1950\\t')\n",
    "stop_data  = txt.find('2018\\t')\n",
    "\n",
    "header = txt[start_head:start_data].split()\n",
    "data = np.loadtxt(io.StringIO(txt[start_data:stop_data]),unpack=True)\n",
    "\n",
    "# use zip to load into a dictionary\n",
    "data_dict = dict(zip(header, data))\n",
    "\n",
    "\n",
    "'''\n",
    "Do the loop here\n",
    "'''\n",
    "for i,key in enumerate(['MAYJUN','JUNJUL','JULAUG']):\n",
    "    # plot data\n",
    "    '''\n",
    "    Use enumeration i as figure number\n",
    "    '''\n",
    "    plt.figure(i,figsize=(12,7))\n",
    "    plt.title('ENSO data from {0}'.format(url))\n",
    "    plt.plot(data_dict['YEAR'],data_dict[key],label=key)\n",
    "    plt.xlabel('year')\n",
    "    plt.ylabel('ENSO')\n",
    "    plt.legend(loc='best')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also usefully use a dictionary with a printing format statement. In that case, we refer directly to the key in ther format string. This can make printing statements much easier to read. We don;'t directly pass the dictionary to the `fortmat` staterment, but rather `**dict`, where `**dict` means \"treat the key-value pairs in the dictionary as additional named arguments to this function call\".\n",
    "\n",
    "So, in the example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['YEAR', 'DECJAN', 'JANFEB', 'FEBMAR', 'MARAPR', 'APRMAY', 'MAYJUN', 'JUNJUL', 'JULAUG', 'AUGSEP', 'SEPOCT', 'OCTNOV', 'NOVDEC'])\n",
      "data for MAYJUN: [-1.412e+00  2.880e-01 -7.560e-01  1.910e-01 -1.558e+00 -2.247e+00\n",
      " -1.523e+00  7.300e-01  8.120e-01 -6.200e-02 -2.870e-01 -1.850e-01\n",
      " -8.700e-01 -1.440e-01 -1.150e+00  8.670e-01 -1.930e-01 -2.360e-01\n",
      " -8.120e-01  8.010e-01 -6.360e-01 -1.420e+00  9.660e-01 -7.580e-01\n",
      " -6.940e-01 -1.148e+00  2.760e-01  4.140e-01 -6.050e-01  4.290e-01\n",
      "  9.110e-01 -2.400e-02  9.300e-01  2.235e+00 -4.900e-02 -1.660e-01\n",
      "  2.790e-01  1.958e+00 -5.820e-01 -2.420e-01  5.110e-01  1.051e+00\n",
      "  1.735e+00  1.616e+00  8.030e-01  5.070e-01  8.700e-02  2.275e+00\n",
      "  1.336e+00 -3.390e-01  1.000e-03  5.200e-02  9.320e-01  1.070e-01\n",
      "  3.150e-01  5.890e-01  5.260e-01 -1.550e-01  1.420e-01  7.510e-01\n",
      " -2.280e-01 -3.000e-03  8.420e-01 -9.400e-02  1.046e+00  2.097e+00\n",
      "  1.053e+00  1.039e+00]\n"
     ]
    }
   ],
   "source": [
    "import requests\n",
    "import numpy as np\n",
    "import io\n",
    "\n",
    "# access dataset as above\n",
    "url = \"http://www.esrl.noaa.gov/psd/enso/mei/table.html\"\n",
    "txt = requests.get(url).text\n",
    "\n",
    "# copy the useful data\n",
    "start_head = txt.find('YEAR')\n",
    "start_data = txt.find('1950\\t')\n",
    "stop_data  = txt.find('2018\\t')\n",
    "\n",
    "header = txt[start_head:start_data].split()\n",
    "data = np.loadtxt(io.StringIO(txt[start_data:stop_data]),unpack=True)\n",
    "\n",
    "# use zip to load into a dictionary\n",
    "data_dict = dict(zip(header, data))\n",
    "print(data_dict.keys())\n",
    "\n",
    "# print the data for MAYJUN\n",
    "print('data for MAYJUN: {MAYJUN}'.format(**data_dict))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The line `print('data for MAYJUN: {MAYJUN}'.format(**data_dict))` is equivalent to writing:\n",
    "\n",
    "    print('data for {MAYJUN}'.format(YEAR=data_dict[YEAR],DECJAN=data_dict[DECJAN], ...))\n",
    "    \n",
    "In this way, we use the keys in the dictionary as keywords to pass to a method."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another useful example of such a use of a dictionary is in saving a numpy dataset to file.\n",
    "\n",
    "If the data are numpy arrays in a dictionary as above, we can store the dataset using:\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "import numpy as np\n",
    "import io\n",
    "\n",
    "# access dataset as above\n",
    "url = \"http://www.esrl.noaa.gov/psd/enso/mei/table.html\"\n",
    "txt = requests.get(url).text\n",
    "\n",
    "# copy the useful data\n",
    "start_head = txt.find('YEAR')\n",
    "start_data = txt.find('1950\\t')\n",
    "stop_data  = txt.find('2018\\t')\n",
    "\n",
    "header = txt[start_head:start_data].split()\n",
    "data = np.loadtxt(io.StringIO(txt[start_data:stop_data]),unpack=True)\n",
    "\n",
    "# use zip to load into a dictionary\n",
    "data_dict = dict(zip(header, data))\n",
    "\n",
    "filename = 'enso_mei.npz'\n",
    "\n",
    "# save the dataset\n",
    "np.savez_compressed(filename,**data_dict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What we load from the file is a dictionary-like object `<class 'numpy.lib.npyio.NpzFile'>`.\n",
    "\n",
    "If needed, we can cast this to a dictionary with `dict()`, but it is generally more efficient to keep the original type."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.lib.npyio.NpzFile'>\n",
      "\t YEAR True\n",
      "\t DECJAN True\n",
      "\t JANFEB True\n",
      "\t FEBMAR True\n",
      "\t MARAPR True\n",
      "\t APRMAY True\n",
      "\t MAYJUN True\n",
      "\t JUNJUL True\n",
      "\t JULAUG True\n",
      "\t AUGSEP True\n",
      "\t SEPOCT True\n",
      "\t OCTNOV True\n",
      "\t NOVDEC True\n"
     ]
    }
   ],
   "source": [
    "# load the dataset\n",
    "\n",
    "filename = 'enso_mei.npz'\n",
    "\n",
    "loaded_data = np.load(filename)\n",
    "\n",
    "print(type(loaded_data))\n",
    "\n",
    "# test they are the same using np.array_equal\n",
    "for k in loaded_data.keys():\n",
    "    print('\\t',k,np.array_equal(data_dict[k], loaded_data[k]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise 1.3.6**\n",
    "\n",
    "* Using what you have learned above, access the Met Office data file [`https://www.metoffice.gov.uk/hadobs/hadukp/data/monthly/HadSEEP_monthly_qc.txt`](https://www.metoffice.gov.uk/hadobs/hadukp/data/monthly/HadSEEP_monthly_qc.txt) and create a 'data package' in a numpy`.npz` file that has keys of `YEAR` and each month in the year, with associated datasets of Monthly Southeast England precipitation (mm).\n",
    "* confirm that tha data in your `npz` file is the same as in your original dictionary\n",
    "* produce a plot of October rainfall using these data for the years 1900 onwards"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [],
   "source": [
    "# do exercise here\n",
    "# ANSWER\n",
    "\n",
    "'''\n",
    "Exploration of dataset shows:\n",
    "\n",
    "\n",
    "Monthly Southeast England precipitation (mm). Daily automated values used after 1996.\n",
    "Wigley & Jones (J.Climatol.,1987), Gregory et al. (Int.J.Clim.,1991)\n",
    "Jones & Conway (Int.J.Climatol.,1997), Alexander & Jones (ASL,2001). Values may change after QC.\n",
    "YEAR   JAN   FEB   MAR   APR   MAY   JUN   JUL   AUG   SEP   OCT   NOV   DEC   ANN\n",
    " 1873  87.1  50.4  52.9  19.9  41.1  63.6  53.2  56.4  62.0  86.0  59.4  15.7  647.7\n",
    " 1874  46.8  44.9  15.8  48.4  24.1  49.9  28.3  43.6  79.4  96.1  63.9  52.3  593.5\n",
    "\n",
    "so we have 3 lines of header\n",
    "then the column titles\n",
    "then the data\n",
    "\n",
    "we can define these as before using\n",
    "\n",
    "txt.find('YEAR')\n",
    "start_data = txt.find('1873')\n",
    "stop_data = None\n",
    "\n",
    "\n",
    "Other than the filenames then, the code\n",
    "is identical\n",
    "'''\n",
    "\n",
    "import requests\n",
    "import numpy as np\n",
    "import io\n",
    "\n",
    "# access dataset as above\n",
    "url = \"https://www.metoffice.gov.uk/hadobs/hadukp/data/monthly/HadSEEP_monthly_qc.txt\"\n",
    "txt = requests.get(url).text\n",
    "\n",
    "# copy the useful data\n",
    "start_head = txt.find('YEAR')\n",
    "start_data = txt.find('1873')\n",
    "stop_data  = None\n",
    "\n",
    "header = txt[start_head:start_data].split()\n",
    "data = np.loadtxt(io.StringIO(txt[start_data:stop_data]),unpack=True)\n",
    "\n",
    "# use zip to load into a dictionary\n",
    "data_dict = dict(zip(header, data))\n",
    "\n",
    "filename = 'HadSEEP_monthly_qc.npz'\n",
    "\n",
    "# save the dataset\n",
    "np.savez_compressed(filename,**data_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.lib.npyio.NpzFile'>\n",
      "\t YEAR True\n",
      "\t JAN True\n",
      "\t FEB True\n",
      "\t MAR True\n",
      "\t APR True\n",
      "\t MAY True\n",
      "\t JUN True\n",
      "\t JUL True\n",
      "\t AUG True\n",
      "\t SEP True\n",
      "\t OCT True\n",
      "\t NOV True\n",
      "\t DEC True\n",
      "\t ANN True\n"
     ]
    }
   ],
   "source": [
    "# ANSWER\n",
    "\n",
    "loaded_data = np.load(filename)\n",
    "\n",
    "print(type(loaded_data))\n",
    "\n",
    "# test they are the same using np.array_equal\n",
    "for k in loaded_data.keys():\n",
    "    print('\\t',k,np.array_equal(data_dict[k], loaded_data[k]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x11c76a9e8>]"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
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   "source": [
    "# ANSWER\n",
    "\n",
    "'''\n",
    "October rainfall, 1900+\n",
    "'''\n",
    "\n",
    "year = loaded_data['YEAR']\n",
    "\n",
    "# mask where years match\n",
    "mask = year  >= 1900\n",
    "\n",
    "oct = loaded_data['OCT']\n",
    "\n",
    "# set invalid data points to nan\n",
    "oct[oct<0] = np.nan\n",
    "\n",
    "plt.plot(year[mask],oct[mask])"
   ]
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    "### 1.3.5 Summary\n",
    "\n",
    "In this section, we have extended the types of data we might come across to include groups . We dealt with ordered groups of various types (`tuple`, `list`), and introduced the numpy package for numpy arrays (`np.array`). We saw dictionaries as collections with which we refer to individual items with a key.\n",
    "\n",
    "We learned in the previous section how to pull apart a dataset presented as a string using loops and various using methods and to construct a useful dataset 'by hand' in a list or similar structure. It is useful, when learning to program, to know how to do this.\n",
    "\n",
    "Here, we saw that packages such as numpy provide higher level routines that make reading data easier, and we would generally use these in practice. We saw how we can use `zip()` to help load a dataset from arrays into a dictionary, and also the value of using a dictionary representation when saving numpy files."
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