talks/python_intro.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "990b29ec",
   "metadata": {},
   "source": [
    "# Python for Scientific Computing\n",
    "\n",
    "Preamble: Run the cells below to import the necessary Python packages\n",
    "\n",
    "*This notebook created by William Gilpin. Consult the [course website](https://www.wgilpin.com/cphy) for all content and [GitHub repository](https://github.com/williamgilpin/cphy) for raw files and runnable online code.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a7c39f7d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "022cc420",
   "metadata": {},
   "source": [
    "## Imports and preamble\n",
    "\n",
    "These are external libraries, including your own code stored in separate .py files, that you want to import in order to run the notebook. Since our notebook environment is running in Python, we don't need to import the language itself.\n",
    "\n",
    " However, it's worth checking that you are using the right version of Python (usually Python 3)\n",
    "\n",
    " For larger projects, you will want to use separate virtual environments for different projects. It's a good idea to double check your environment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1dbc7a28",
   "metadata": {},
   "outputs": [],
   "source": [
    "# pre-installed library containing a grab bag of system functions\n",
    "import sys\n",
    "print(sys.version)\n",
    "\n",
    "# Import some local code, which I'll use behind the scenes\n",
    "# from ../resources import helper_functions\n",
    "\n",
    "# Wipe all outputs from this notebook\n",
    "from IPython.display import Image, clear_output\n",
    "clear_output(True)\n",
    "\n",
    "# Import local plotting functions and in-notebook display functions\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0bb2eb1a",
   "metadata": {},
   "source": [
    "# Why Python?\n",
    "\n",
    "+ Interpreted language\n",
    "+ Extensible, plenty of external modules that can be imported\n",
    "\n",
    "Other good options for computational physics\n",
    "\n",
    "+ C/C++\n",
    "+ Mathematica\n",
    "\n",
    "Keeping an eye on\n",
    "\n",
    "+ Julia\n",
    "+ Rust\n",
    "+ Haskell\n",
    "\n",
    "Specialized\n",
    "\n",
    "+ R\n",
    "+ MATLAB\n",
    "+ Perl\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "c540427b",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(\"../resources/jupyterkernels.jpg\")\n",
    "# Source: https://anchormen.nl/blog/big-data-services/pyspark-jupyter-kernels/"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38f4ac2d",
   "metadata": {},
   "source": [
    "# Python basic syntax\n",
    "\n",
    "+ See resources on syllabus for more info on Python\n",
    "+ We'll focus on \"sharp edges\" and unique features of the language here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "bbb61266",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 2\n",
      "2 1\n",
      "1 2\n"
     ]
    }
   ],
   "source": [
    "## Assigning variables, pass by value for immutable types\n",
    "\n",
    "a = 1\n",
    "b = 2\n",
    "print(a, b)\n",
    "\n",
    "temp = a\n",
    "a = b\n",
    "b = temp\n",
    "print(a, b)\n",
    "\n",
    "a, b = b, a\n",
    "print(a, b)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08101430",
   "metadata": {},
   "source": [
    "# Python data types\n",
    "\n",
    "+ Everything in Python is an object. Given a variable, how do we know what it is and what it can do?\n",
    "+ There are three functions that you can call to get information about an object: `type()`, `dir()`, and `help()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3eefd65d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'int'>\n",
      "<class 'str'>\n",
      "Help on int object:\n",
      "\n",
      "class int(object)\n",
      " |  int([x]) -> integer\n",
      " |  int(x, base=10) -> integer\n",
      " |  \n",
      " |  Convert a number or string to an integer, or return 0 if no arguments\n",
      " |  are given.  If x is a number, return x.__int__().  For floating point\n",
      " |  numbers, this truncates towards zero.\n",
      " |  \n",
      " |  If x is not a number or if base is given, then x must be a string,\n",
      " |  bytes, or bytearray instance representing an integer literal in the\n",
      " |  given base.  The literal can be preceded by '+' or '-' and be surrounded\n",
      " |  by whitespace.  The base defaults to 10.  Valid bases are 0 and 2-36.\n",
      " |  Base 0 means to interpret the base from the string as an integer literal.\n",
      " |  >>> int('0b100', base=0)\n",
      " |  4\n",
      " |  \n",
      " |  Built-in subclasses:\n",
      " |      bool\n",
      " |  \n",
      " |  Methods defined here:\n",
      " |  \n",
      " |  __abs__(self, /)\n",
      " |      abs(self)\n",
      " |  \n",
      " |  __add__(self, value, /)\n",
      " |      Return self+value.\n",
      " |  \n",
      " |  __and__(self, value, /)\n",
      " |      Return self&value.\n",
      " |  \n",
      " |  __bool__(self, /)\n",
      " |      True if self else False\n",
      " |  \n",
      " |  __ceil__(...)\n",
      " |      Ceiling of an Integral returns itself.\n",
      " |  \n",
      " |  __divmod__(self, value, /)\n",
      " |      Return divmod(self, value).\n",
      " |  \n",
      " |  __eq__(self, value, /)\n",
      " |      Return self==value.\n",
      " |  \n",
      " |  __float__(self, /)\n",
      " |      float(self)\n",
      " |  \n",
      " |  __floor__(...)\n",
      " |      Flooring an Integral returns itself.\n",
      " |  \n",
      " |  __floordiv__(self, value, /)\n",
      " |      Return self//value.\n",
      " |  \n",
      " |  __format__(self, format_spec, /)\n",
      " |      Default object formatter.\n",
      " |  \n",
      " |  __ge__(self, value, /)\n",
      " |      Return self>=value.\n",
      " |  \n",
      " |  __getattribute__(self, name, /)\n",
      " |      Return getattr(self, name).\n",
      " |  \n",
      " |  __getnewargs__(self, /)\n",
      " |  \n",
      " |  __gt__(self, value, /)\n",
      " |      Return self>value.\n",
      " |  \n",
      " |  __hash__(self, /)\n",
      " |      Return hash(self).\n",
      " |  \n",
      " |  __index__(self, /)\n",
      " |      Return self converted to an integer, if self is suitable for use as an index into a list.\n",
      " |  \n",
      " |  __int__(self, /)\n",
      " |      int(self)\n",
      " |  \n",
      " |  __invert__(self, /)\n",
      " |      ~self\n",
      " |  \n",
      " |  __le__(self, value, /)\n",
      " |      Return self<=value.\n",
      " |  \n",
      " |  __lshift__(self, value, /)\n",
      " |      Return self<<value.\n",
      " |  \n",
      " |  __lt__(self, value, /)\n",
      " |      Return self<value.\n",
      " |  \n",
      " |  __mod__(self, value, /)\n",
      " |      Return self%value.\n",
      " |  \n",
      " |  __mul__(self, value, /)\n",
      " |      Return self*value.\n",
      " |  \n",
      " |  __ne__(self, value, /)\n",
      " |      Return self!=value.\n",
      " |  \n",
      " |  __neg__(self, /)\n",
      " |      -self\n",
      " |  \n",
      " |  __or__(self, value, /)\n",
      " |      Return self|value.\n",
      " |  \n",
      " |  __pos__(self, /)\n",
      " |      +self\n",
      " |  \n",
      " |  __pow__(self, value, mod=None, /)\n",
      " |      Return pow(self, value, mod).\n",
      " |  \n",
      " |  __radd__(self, value, /)\n",
      " |      Return value+self.\n",
      " |  \n",
      " |  __rand__(self, value, /)\n",
      " |      Return value&self.\n",
      " |  \n",
      " |  __rdivmod__(self, value, /)\n",
      " |      Return divmod(value, self).\n",
      " |  \n",
      " |  __repr__(self, /)\n",
      " |      Return repr(self).\n",
      " |  \n",
      " |  __rfloordiv__(self, value, /)\n",
      " |      Return value//self.\n",
      " |  \n",
      " |  __rlshift__(self, value, /)\n",
      " |      Return value<<self.\n",
      " |  \n",
      " |  __rmod__(self, value, /)\n",
      " |      Return value%self.\n",
      " |  \n",
      " |  __rmul__(self, value, /)\n",
      " |      Return value*self.\n",
      " |  \n",
      " |  __ror__(self, value, /)\n",
      " |      Return value|self.\n",
      " |  \n",
      " |  __round__(...)\n",
      " |      Rounding an Integral returns itself.\n",
      " |      \n",
      " |      Rounding with an ndigits argument also returns an integer.\n",
      " |  \n",
      " |  __rpow__(self, value, mod=None, /)\n",
      " |      Return pow(value, self, mod).\n",
      " |  \n",
      " |  __rrshift__(self, value, /)\n",
      " |      Return value>>self.\n",
      " |  \n",
      " |  __rshift__(self, value, /)\n",
      " |      Return self>>value.\n",
      " |  \n",
      " |  __rsub__(self, value, /)\n",
      " |      Return value-self.\n",
      " |  \n",
      " |  __rtruediv__(self, value, /)\n",
      " |      Return value/self.\n",
      " |  \n",
      " |  __rxor__(self, value, /)\n",
      " |      Return value^self.\n",
      " |  \n",
      " |  __sizeof__(self, /)\n",
      " |      Returns size in memory, in bytes.\n",
      " |  \n",
      " |  __sub__(self, value, /)\n",
      " |      Return self-value.\n",
      " |  \n",
      " |  __truediv__(self, value, /)\n",
      " |      Return self/value.\n",
      " |  \n",
      " |  __trunc__(...)\n",
      " |      Truncating an Integral returns itself.\n",
      " |  \n",
      " |  __xor__(self, value, /)\n",
      " |      Return self^value.\n",
      " |  \n",
      " |  as_integer_ratio(self, /)\n",
      " |      Return integer ratio.\n",
      " |      \n",
      " |      Return a pair of integers, whose ratio is exactly equal to the original int\n",
      " |      and with a positive denominator.\n",
      " |      \n",
      " |      >>> (10).as_integer_ratio()\n",
      " |      (10, 1)\n",
      " |      >>> (-10).as_integer_ratio()\n",
      " |      (-10, 1)\n",
      " |      >>> (0).as_integer_ratio()\n",
      " |      (0, 1)\n",
      " |  \n",
      " |  bit_count(self, /)\n",
      " |      Number of ones in the binary representation of the absolute value of self.\n",
      " |      \n",
      " |      Also known as the population count.\n",
      " |      \n",
      " |      >>> bin(13)\n",
      " |      '0b1101'\n",
      " |      >>> (13).bit_count()\n",
      " |      3\n",
      " |  \n",
      " |  bit_length(self, /)\n",
      " |      Number of bits necessary to represent self in binary.\n",
      " |      \n",
      " |      >>> bin(37)\n",
      " |      '0b100101'\n",
      " |      >>> (37).bit_length()\n",
      " |      6\n",
      " |  \n",
      " |  conjugate(...)\n",
      " |      Returns self, the complex conjugate of any int.\n",
      " |  \n",
      " |  to_bytes(self, /, length=1, byteorder='big', *, signed=False)\n",
      " |      Return an array of bytes representing an integer.\n",
      " |      \n",
      " |      length\n",
      " |        Length of bytes object to use.  An OverflowError is raised if the\n",
      " |        integer is not representable with the given number of bytes.  Default\n",
      " |        is length 1.\n",
      " |      byteorder\n",
      " |        The byte order used to represent the integer.  If byteorder is 'big',\n",
      " |        the most significant byte is at the beginning of the byte array.  If\n",
      " |        byteorder is 'little', the most significant byte is at the end of the\n",
      " |        byte array.  To request the native byte order of the host system, use\n",
      " |        `sys.byteorder' as the byte order value.  Default is to use 'big'.\n",
      " |      signed\n",
      " |        Determines whether two's complement is used to represent the integer.\n",
      " |        If signed is False and a negative integer is given, an OverflowError\n",
      " |        is raised.\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Class methods defined here:\n",
      " |  \n",
      " |  from_bytes(bytes, byteorder='big', *, signed=False) from builtins.type\n",
      " |      Return the integer represented by the given array of bytes.\n",
      " |      \n",
      " |      bytes\n",
      " |        Holds the array of bytes to convert.  The argument must either\n",
      " |        support the buffer protocol or be an iterable object producing bytes.\n",
      " |        Bytes and bytearray are examples of built-in objects that support the\n",
      " |        buffer protocol.\n",
      " |      byteorder\n",
      " |        The byte order used to represent the integer.  If byteorder is 'big',\n",
      " |        the most significant byte is at the beginning of the byte array.  If\n",
      " |        byteorder is 'little', the most significant byte is at the end of the\n",
      " |        byte array.  To request the native byte order of the host system, use\n",
      " |        `sys.byteorder' as the byte order value.  Default is to use 'big'.\n",
      " |      signed\n",
      " |        Indicates whether two's complement is used to represent the integer.\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Static methods defined here:\n",
      " |  \n",
      " |  __new__(*args, **kwargs) from builtins.type\n",
      " |      Create and return a new object.  See help(type) for accurate signature.\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data descriptors defined here:\n",
      " |  \n",
      " |  denominator\n",
      " |      the denominator of a rational number in lowest terms\n",
      " |  \n",
      " |  imag\n",
      " |      the imaginary part of a complex number\n",
      " |  \n",
      " |  numerator\n",
      " |      the numerator of a rational number in lowest terms\n",
      " |  \n",
      " |  real\n",
      " |      the real part of a complex number\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['__abs__',\n",
       " '__add__',\n",
       " '__and__',\n",
       " '__bool__',\n",
       " '__ceil__',\n",
       " '__class__',\n",
       " '__delattr__',\n",
       " '__dir__',\n",
       " '__divmod__',\n",
       " '__doc__',\n",
       " '__eq__',\n",
       " '__float__',\n",
       " '__floor__',\n",
       " '__floordiv__',\n",
       " '__format__',\n",
       " '__ge__',\n",
       " '__getattribute__',\n",
       " '__getnewargs__',\n",
       " '__getstate__',\n",
       " '__gt__',\n",
       " '__hash__',\n",
       " '__index__',\n",
       " '__init__',\n",
       " '__init_subclass__',\n",
       " '__int__',\n",
       " '__invert__',\n",
       " '__le__',\n",
       " '__lshift__',\n",
       " '__lt__',\n",
       " '__mod__',\n",
       " '__mul__',\n",
       " '__ne__',\n",
       " '__neg__',\n",
       " '__new__',\n",
       " '__or__',\n",
       " '__pos__',\n",
       " '__pow__',\n",
       " '__radd__',\n",
       " '__rand__',\n",
       " '__rdivmod__',\n",
       " '__reduce__',\n",
       " '__reduce_ex__',\n",
       " '__repr__',\n",
       " '__rfloordiv__',\n",
       " '__rlshift__',\n",
       " '__rmod__',\n",
       " '__rmul__',\n",
       " '__ror__',\n",
       " '__round__',\n",
       " '__rpow__',\n",
       " '__rrshift__',\n",
       " '__rshift__',\n",
       " '__rsub__',\n",
       " '__rtruediv__',\n",
       " '__rxor__',\n",
       " '__setattr__',\n",
       " '__sizeof__',\n",
       " '__str__',\n",
       " '__sub__',\n",
       " '__subclasshook__',\n",
       " '__truediv__',\n",
       " '__trunc__',\n",
       " '__xor__',\n",
       " 'as_integer_ratio',\n",
       " 'bit_count',\n",
       " 'bit_length',\n",
       " 'conjugate',\n",
       " 'denominator',\n",
       " 'from_bytes',\n",
       " 'imag',\n",
       " 'numerator',\n",
       " 'real',\n",
       " 'to_bytes']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Getting help with an unknown python object\n",
    "\n",
    "# Check what a variable actually is\n",
    "a = 1\n",
    "print(type(a))\n",
    "\n",
    "b = \"Kolmogorov\"\n",
    "print(type(b))\n",
    "\n",
    "# Get help with a variable\n",
    "help(a)\n",
    "\n",
    "# List all of the functions/properties of a variable\n",
    "dir(a)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fcb5f9f9",
   "metadata": {},
   "source": [
    "# Built-in data types\n",
    "\n",
    "+ Given information: detector counts, velocities, temperatures, how do we store it and access it? Either for data analysis or within a simulation.\n",
    "+ Because Python is object-oriented, we will eventually build more esoteric objects by composing built-in data structures\n",
    "+ In scientific computing: lists, numpy arrays, dicts, and strings are the most common datatypes (lists, strings, and dicts are native). \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "035eaf80",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'float'>\n",
      "<class 'int'>\n",
      "<class 'str'>\n",
      "Strings are immutable and ordered\n",
      "\n",
      "<class 'tuple'>\n",
      "Tuples are immutable and ordered\n",
      "\n",
      "<class 'set'>\n",
      "Sets are mutable and unordered\n",
      "\n",
      "<class 'list'>\n",
      "Lists are mutable and ordered\n",
      "\n",
      "<class 'dict'>\n",
      "{'property': 27, 'dog': 'cat'}\n",
      "Dictionaries are mutable and unordered\n",
      "Dictionaries are indexed by keys\n",
      "Keys must be immutable\n"
     ]
    }
   ],
   "source": [
    "a = 1.0\n",
    "print(type(a))\n",
    "\n",
    "a = 1\n",
    "print(type(a))\n",
    "\n",
    "a = \"1\"\n",
    "print(type(a))\n",
    "print(\"Strings are immutable and ordered\\n\")\n",
    "\n",
    "a = (1, 2, 3)\n",
    "print(type(a))\n",
    "print(\"Tuples are immutable and ordered\\n\")\n",
    "\n",
    "a = {1, 2, 3}\n",
    "print(type(a))\n",
    "print(\"Sets are mutable and unordered\\n\")\n",
    "\n",
    "a = [1, 2, 3, 5, 5, 9]\n",
    "print(type(a))\n",
    "print(\"Lists are mutable and ordered\\n\")\n",
    "\n",
    "a = dict()\n",
    "a[\"property\"] = 27\n",
    "a[\"dog\"] = \"cat\"\n",
    "\n",
    "\n",
    "print(type(a))\n",
    "print(a)\n",
    "print(\"Dictionaries are mutable and unordered\")\n",
    "print(\"Dictionaries are indexed by keys\")\n",
    "print(\"Keys must be immutable\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53de90ca",
   "metadata": {},
   "source": [
    "## Lists are ordered sets of objects\n",
    "\n",
    "+ Can mix different types\n",
    "+ Every object is stored, along with an index\n",
    "+ Lists can contain duplicate entries\n",
    "+ Similar to stacks in other languages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "43899f17",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 1, 2, 3, 4, 5, 12, 6, 7, 8, 9]\n",
      "11\n",
      "[0, 1, 2, 3, 4, 5, 12, 6, 7, 8, 9, 11]\n",
      "[0, 1, 2, 3, 4, 5, 12, 6, 7, 8, 9, 11]\n",
      "[0, 1, 2, 3, 4, 5, 12, 8.5, 6, 7, 8, 9, 11]\n",
      "[0, 1, 2, 3, 4, 5, 6, 7, 8, 8.5, 9, 11, 12]\n"
     ]
    }
   ],
   "source": [
    "xs = [0, 1, 2, 3, 4, 5, 12, 6, 7, 8, 9]\n",
    "print(xs)\n",
    "\n",
    "## Find length of the list\n",
    "print(len(xs))\n",
    "\n",
    "## Add element to back\n",
    "xs.append(11)\n",
    "print(xs)\n",
    "\n",
    "## Count number of occurrences of a value\n",
    "xs.count(6)\n",
    "print(xs)\n",
    "\n",
    "xs.insert(7, 8.5)\n",
    "print(xs)\n",
    "\n",
    "# In-place sort\n",
    "xs.sort() # alternative: sorted(xs) returns a sorted copy\n",
    "# xs = sorted(xs)\n",
    "print(xs)\n",
    "\n",
    "# Remove element based on value\n",
    "xs.remove(8)\n",
    "print(xs)\n",
    "\n",
    "\n",
    "## Remove element from back and return it\n",
    "## Similar to a stack in other languages\n",
    "print(xs.pop()) \n",
    "print(xs)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9a0e6b3d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Slicing syntax.\n",
      "10\n",
      "[20, 3, 4]\n",
      "[20, 3, 4, 5, 6, 7, 8, 9]\n",
      "[10, 1, 20, 3]\n",
      "[10, 1, 20, 3, 4, 5, 6, 7, 8, 9]\n",
      "9\n",
      "[10, 1, 20, 3, 4, 5, 6, 7, 8]\n",
      "[7, 8, 9]\n"
     ]
    }
   ],
   "source": [
    "## Slice notation\n",
    "\n",
    "a = [10, 1, 20, 3, 4, 5, 6, 7, 8, 9]\n",
    "\n",
    "# slicing up lists:\n",
    "print(\"\\n\", \"Slicing syntax.\")\n",
    "print(a[0]) # zero-indexed\n",
    "print(a[2:5]) # returns neq list [a[2], a[3]] (it doesn't include a[4])\n",
    "print(a[2:])  # this notation starts with a[2] and goes to the end\n",
    "print(a[:4])  # this starts at the beginning and goes up until a[3]\n",
    "print(a[:])   # this just returns a copy of the whole list\n",
    "\n",
    "# # print(\"\\n\", \"Negative indices count from the end.\")\n",
    "print(a[-1])    # last element\n",
    "print(a[:-1])   # this goes up until the last element\n",
    "print(a[-3:])   # this goes from the third-to-last element to the end\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1876c48",
   "metadata": {},
   "source": [
    "## List comprehensions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "e1c7ec1d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]\n"
     ]
    }
   ],
   "source": [
    "# Standard loop\n",
    "a = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\n",
    "for i in range(10):\n",
    "    a[i] = a[i] ** 2\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "ab219fb1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]\n"
     ]
    }
   ],
   "source": [
    "# List comprehension; Notice how python does exponentiation\n",
    "a = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\n",
    "a = [i ** 2 for i in a]\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "e2f25a84",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 4, 16, 36, 64]\n"
     ]
    }
   ],
   "source": [
    "# List comprehension with conditionals\n",
    "a = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\n",
    "a = [i ** 2 for i in a if i % 2 == 0]\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "9f696ea4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]\n"
     ]
    }
   ],
   "source": [
    "# Map\n",
    "a = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\n",
    "a = list(map(lambda x: x ** 2, a))\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "2d78b04d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 4, 16, 36, 64]\n"
     ]
    }
   ],
   "source": [
    "# Map with filter\n",
    "a = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\n",
    "a = list(map(\n",
    "    lambda x: x ** 2,\n",
    "    filter(lambda x: x % 2 == 0, a)\n",
    "))\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e71e082f",
   "metadata": {},
   "source": [
    "# Pass by reference vs. pass by value\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "079ea150",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 2, 3] 1\n",
      "[9, 2, 3] 1\n",
      "[9, 2, 3] 9\n"
     ]
    }
   ],
   "source": [
    "# Pass by value\n",
    "a = [1, 2, 3]\n",
    "b = a[0]\n",
    "print(a, b)\n",
    "\n",
    "a[0] = 9\n",
    "print(a, b)\n",
    "\n",
    "b = 9\n",
    "print(a, b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "cc5ff3ae",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 2, 3] [1, 2, 3]\n",
      "[9, 2, 3] [9, 2, 3]\n",
      "[9, 2, 3] [1, 2, 3]\n"
     ]
    }
   ],
   "source": [
    "# Pass by reference. Python lists are mutable\n",
    "a = [1, 2, 3]\n",
    "b = a\n",
    "print(a, b)\n",
    "\n",
    "a[0] = 9\n",
    "print(a, b)\n",
    "\n",
    "a = [1, 2, 3]\n",
    "b = a.copy()\n",
    "a[0] = 9\n",
    "print(a, b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "d37083ad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "some value\n",
      "some other value\n"
     ]
    }
   ],
   "source": [
    "# Pass by reference matters with weird objects we don't know about, which are usually\n",
    "# mutable\n",
    "\n",
    "class SomeWeirdObject:\n",
    "    def __init__(self):\n",
    "        self.some_weird_property = \"some value\"\n",
    "\n",
    "a = SomeWeirdObject()\n",
    "print(a.some_weird_property)\n",
    "\n",
    "b = a\n",
    "b.some_weird_property = \"some other value\"\n",
    "print(a.some_weird_property)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67a8c4a6",
   "metadata": {},
   "source": [
    "# Abstract data types\n",
    "\n",
    "\n",
    "Abstract data types (how Python built-ins operate behind the scenes)\n",
    "\n",
    "+ Hash tables\n",
    "+ Linked Lists\n",
    "+ Stacks\n",
    "+ Queues\n",
    "+ Heaps / Priority Queues\n",
    "+ Trees\n",
    "\n",
    "Each structure represents a different abstract way that data is represented and stored. Each has different costs to read, write, search, etc for value. \n",
    "\n",
    "Data type of choice depends on\n",
    "1. Intrinsic structure of data. For example, spatial coordinates are ordered, making a lists a good choice\n",
    "2. Whether you are reading or writing to structure more often\n",
    "3. Whether memory or clock speed are your limiting variables\n",
    "\n",
    "Next week: Runtime and space complexity and Big-O notation; how does the runtime and space usage of different datatypes, and operations acting on them, change with the size of their input $N$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "id": "baf06551",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 277,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(\"../resources/datatypes.png\")\n",
    "# Source: https://www.tutorialandexample.com/python-data-structures"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96b1efcf",
   "metadata": {},
   "source": [
    "# Printing and strings\n",
    "\n",
    "(Skip this in class)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "1c00ff77",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello world\n",
      "Hello world\n",
      "Hello world\n",
      "Hello world\n",
      "Hello World o7\n"
     ]
    }
   ],
   "source": [
    "# print statement\n",
    "\n",
    "print(\"Hello world\")\n",
    "\n",
    "print(\"Hello\", \"world\")\n",
    "\n",
    "msg = \"Hello\"\n",
    "msg += \" world\"\n",
    "print(msg)\n",
    "\n",
    "msg = \" \".join([\"Hello\", \"world\"])\n",
    "print(msg)\n",
    "\n",
    "a = 7\n",
    "print(f\"Hello World o{a}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "76b74809",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Begin long computation without flushing\n",
      "40 iterations complete\n",
      "Begin long computation with flushing\n",
      "40 iterations complete\n"
     ]
    }
   ],
   "source": [
    "## During a long computation, you can force Python to print out the progress of the computation\n",
    "\n",
    "import time\n",
    "\n",
    "print(\"Begin long computation without flushing\")\n",
    "for i in range(100):\n",
    "    time.sleep(0.05)\n",
    "    if i == 0:\n",
    "        print(\"40 iterations complete\")\n",
    "\n",
    "print(\"Begin long computation with flushing\")\n",
    "for i in range(100):\n",
    "    time.sleep(0.05)\n",
    "    if i == 4:\n",
    "        print(\"40 iterations complete\", flush=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c3b57c8d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1ae72753",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "32516871",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.8.9 64-bit",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  },
  "vscode": {
   "interpreter": {
    "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}