tutorials/Type hinting and annotations.ipynb

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   "cell_type": "markdown",
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   "source": [
    "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
    "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#Introduction\" data-toc-modified-id=\"Introduction-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>Introduction</a></span></li><li><span><a href=\"#What-are-function-annotations?\" data-toc-modified-id=\"What-are-function-annotations?-2\"><span class=\"toc-item-num\">2&nbsp;&nbsp;</span>What are function annotations?</a></span></li><li><span><a href=\"#Difference-btw-type-hinting-and-annotation?\" data-toc-modified-id=\"Difference-btw-type-hinting-and-annotation?-3\"><span class=\"toc-item-num\">3&nbsp;&nbsp;</span>Difference btw type hinting and annotation?</a></span></li><li><span><a href=\"#Example-#1\" data-toc-modified-id=\"Example-#1-4\"><span class=\"toc-item-num\">4&nbsp;&nbsp;</span>Example #1</a></span></li><li><span><a href=\"#Example-#2\" data-toc-modified-id=\"Example-#2-5\"><span class=\"toc-item-num\">5&nbsp;&nbsp;</span>Example #2</a></span></li><li><span><a href=\"#Example-#3\" data-toc-modified-id=\"Example-#3-6\"><span class=\"toc-item-num\">6&nbsp;&nbsp;</span>Example #3</a></span></li><li><span><a href=\"#Example-#4\" data-toc-modified-id=\"Example-#4-7\"><span class=\"toc-item-num\">7&nbsp;&nbsp;</span>Example #4</a></span></li><li><span><a href=\"#Example-#5\" data-toc-modified-id=\"Example-#5-8\"><span class=\"toc-item-num\">8&nbsp;&nbsp;</span>Example #5</a></span></li><li><span><a href=\"#Example-of-various-annotations\" data-toc-modified-id=\"Example-of-various-annotations-9\"><span class=\"toc-item-num\">9&nbsp;&nbsp;</span>Example of various annotations</a></span><ul class=\"toc-item\"><li><span><a href=\"#Union-and-|\" data-toc-modified-id=\"Union-and-|-9.1\"><span class=\"toc-item-num\">9.1&nbsp;&nbsp;</span><code>Union</code> and <code>|</code></a></span></li><li><span><a href=\"#Optional\" data-toc-modified-id=\"Optional-9.2\"><span class=\"toc-item-num\">9.2&nbsp;&nbsp;</span><code>Optional</code></a></span></li></ul></li><li><span><a href=\"#List-vs.-list-in-type-hinting\" data-toc-modified-id=\"List-vs.-list-in-type-hinting-10\"><span class=\"toc-item-num\">10&nbsp;&nbsp;</span>List vs. <code>list</code> in type hinting</a></span></li><li><span><a href=\"#References\" data-toc-modified-id=\"References-11\"><span class=\"toc-item-num\">11&nbsp;&nbsp;</span>References</a></span></li><li><span><a href=\"#Requirements\" data-toc-modified-id=\"Requirements-12\"><span class=\"toc-item-num\">12&nbsp;&nbsp;</span>Requirements</a></span></li></ul></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction\n",
    "<hr style = \"border:2px solid black\" ></hr>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-warning\">\n",
    "<font color=black>\n",
    "\n",
    "**What?** Function annotation\n",
    "\n",
    "</font>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# What are function annotations?\n",
    "<hr style = \"border:2px solid black\" ></hr>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-info\">\n",
    "<font color=black>\n",
    "\n",
    "- Before we start keep in mind that:\n",
    "    - **Static typed** — performs type checking at compile-time and requires datatype declarations.\n",
    "    - **Dynamic typed** — performs type checking at runtime and does not require datatype declarations.\n",
    "- Function annotations are covered in PEP 3107 and they allow you to attach a metadata string to various types of object.\n",
    "- They are generally used to attach metadata to functions describing their parameters and return values.\n",
    "- The symbol `->` marks the return function annotation.\n",
    "- The main way to add type hints is using annotations.\n",
    "    \n",
    "</font>\n",
    "</div>"
   ]
  },
  {
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     "end_time": "2023-01-18T13:02:04.709856Z",
     "start_time": "2023-01-18T13:02:04.706440Z"
    }
   },
   "source": [
    "# Difference btw type hinting and annotation?\n",
    "<hr style = \"border:2px solid black\" ></hr>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-info\">\n",
    "<font color=black>\n",
    "\n",
    "- In Python, type hinting and annotations are similar concepts that are used to indicate the types of variables or the return types of functions. However, there is a **subtle difference** between the two.\n",
    "    - **Type hinting** refers to the practice of indicating the type of a variable or the return type of a function using the `->` syntax\n",
    "    - **Annotations**, on the other hand, refer to the use of the `:` symbol. \n",
    "\n",
    "</font>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Example #1\n",
    "<hr style = \"border:2px solid black\" ></hr>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-04T19:16:43.237722Z",
     "start_time": "2022-04-04T19:16:43.234948Z"
    },
    "code_folding": []
   },
   "outputs": [],
   "source": [
    "def kinetic_energy(m: 'in KG', v: 'in M/S') -> 'Joules':\n",
    "    return 1/2*m*v**2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-04T19:16:43.597917Z",
     "start_time": "2022-04-04T19:16:43.587761Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function __main__.kinetic_energy(m: 'in KG', v: 'in M/S') -> 'Joules'>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kinetic_energy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
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    "ExecuteTime": {
     "end_time": "2022-04-04T19:16:56.699196Z",
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    },
    "scrolled": true
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "['__annotations__',\n",
       " '__call__',\n",
       " '__class__',\n",
       " '__closure__',\n",
       " '__code__',\n",
       " '__defaults__',\n",
       " '__delattr__',\n",
       " '__dict__',\n",
       " '__dir__',\n",
       " '__doc__',\n",
       " '__eq__',\n",
       " '__format__',\n",
       " '__ge__',\n",
       " '__get__',\n",
       " '__getattribute__',\n",
       " '__globals__',\n",
       " '__gt__',\n",
       " '__hash__',\n",
       " '__init__',\n",
       " '__init_subclass__',\n",
       " '__kwdefaults__',\n",
       " '__le__',\n",
       " '__lt__',\n",
       " '__module__',\n",
       " '__name__',\n",
       " '__ne__',\n",
       " '__new__',\n",
       " '__qualname__',\n",
       " '__reduce__',\n",
       " '__reduce_ex__',\n",
       " '__repr__',\n",
       " '__setattr__',\n",
       " '__sizeof__',\n",
       " '__str__',\n",
       " '__subclasshook__']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dir(kinetic_energy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-04T19:16:44.620727Z",
     "start_time": "2022-04-04T19:16:44.616743Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'m': 'in KG', 'v': 'in M/S', 'return': 'Joules'}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kinetic_energy.__annotations__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-info\">\n",
    "<font color=black>\n",
    "\n",
    "- Annotations are dictionaries, so you can do this \n",
    "\n",
    "</font>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-22T08:54:40.686047Z",
     "start_time": "2022-01-22T08:54:40.682320Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'5,400.0 Joules'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'{:,} {}'.format(kinetic_energy(12, 30),\n",
    "                 kinetic_energy.__annotations__['return'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Example #2\n",
    "<hr style = \"border:2px solid black\" ></hr>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-info\">\n",
    "<font color=black>\n",
    "\n",
    "- You can also have a python data structure rather than just a string:\n",
    "\n",
    "</font>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-22T08:55:46.354610Z",
     "start_time": "2022-01-22T08:55:46.352282Z"
    }
   },
   "outputs": [],
   "source": [
    "rd = {'type': float, 'units': 'Joules',\n",
    "      'docstring': 'Given mass and velocity returns kinetic energy in Joules'}\n",
    "\n",
    "\n",
    "def f() -> rd:\n",
    "    pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-22T08:55:55.595516Z",
     "start_time": "2022-01-22T08:55:55.592258Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "float"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f.__annotations__['return']['type']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-22T08:56:03.211187Z",
     "start_time": "2022-01-22T08:56:03.208201Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Joules'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f.__annotations__['return']['units']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-22T08:56:09.651194Z",
     "start_time": "2022-01-22T08:56:09.648358Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Given mass and velocity returns kinetic energy in Joules'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f.__annotations__['return']['docstring']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Example #3\n",
    "<hr style = \"border:2px solid black\" ></hr>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-info\">\n",
    "<font color=black>\n",
    "\n",
    "- You can use function attributes to validate called values:\n",
    "\n",
    "</font>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-30T20:44:54.689425Z",
     "start_time": "2022-11-30T20:44:54.668202Z"
    }
   },
   "outputs": [],
   "source": [
    "def validate(func, locals):\n",
    "    for var, test in func.__annotations__.items():\n",
    "        value = locals[var]\n",
    "        try:\n",
    "            pr = test.__name__+': '+test.__docstring__\n",
    "        except AttributeError:\n",
    "            pr = test.__name__\n",
    "        msg = '{}=={}; Test: {}'.format(var, value, pr)\n",
    "        assert test(value), msg\n",
    "\n",
    "\n",
    "def between(lo, hi):\n",
    "    def _between(x):\n",
    "        return lo <= x <= hi\n",
    "    _between.__docstring__ = 'must be between {} and {}'.format(lo, hi)\n",
    "    return _between\n",
    "\n",
    "\n",
    "def f(x: between(3, 10), y: lambda _y: isinstance(_y, int)):\n",
    "    validate(f, locals())\n",
    "    print(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-30T20:44:55.392512Z",
     "start_time": "2022-11-30T20:44:55.187794Z"
    }
   },
   "outputs": [
    {
     "ename": "AssertionError",
     "evalue": "x==2; Test: _between: must be between 3 and 10",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/5x/lyqr8wv507n43bnwpsz4dq_c0000gn/T/ipykernel_63630/1401902152.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/var/folders/5x/lyqr8wv507n43bnwpsz4dq_c0000gn/T/ipykernel_63630/2946777871.py\u001b[0m in \u001b[0;36mf\u001b[0;34m(x, y)\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;32mdef\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbetween\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0m_y\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_y\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 20\u001b[0;31m     \u001b[0mvalidate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlocals\u001b[0m\u001b[0;34m(\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[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/var/folders/5x/lyqr8wv507n43bnwpsz4dq_c0000gn/T/ipykernel_63630/2946777871.py\u001b[0m in \u001b[0;36mvalidate\u001b[0;34m(func, locals)\u001b[0m\n\u001b[1;32m      7\u001b[0m             \u001b[0mpr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtest\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m         \u001b[0mmsg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'{}=={}; Test: {}'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvar\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m         \u001b[0;32massert\u001b[0m \u001b[0mtest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmsg\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAssertionError\u001b[0m: x==2; Test: _between: must be between 3 and 10"
     ]
    }
   ],
   "source": [
    "f(2,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-30T20:45:02.156948Z",
     "start_time": "2022-11-30T20:45:02.145733Z"
    }
   },
   "outputs": [
    {
     "ename": "AssertionError",
     "evalue": "y==2.1; Test: <lambda>",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/5x/lyqr8wv507n43bnwpsz4dq_c0000gn/T/ipykernel_63630/4002381112.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2.1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/var/folders/5x/lyqr8wv507n43bnwpsz4dq_c0000gn/T/ipykernel_63630/2946777871.py\u001b[0m in \u001b[0;36mf\u001b[0;34m(x, y)\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;32mdef\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbetween\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0m_y\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_y\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 20\u001b[0;31m     \u001b[0mvalidate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlocals\u001b[0m\u001b[0;34m(\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[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/var/folders/5x/lyqr8wv507n43bnwpsz4dq_c0000gn/T/ipykernel_63630/2946777871.py\u001b[0m in \u001b[0;36mvalidate\u001b[0;34m(func, locals)\u001b[0m\n\u001b[1;32m      7\u001b[0m             \u001b[0mpr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtest\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m         \u001b[0mmsg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'{}=={}; Test: {}'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvar\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m         \u001b[0;32massert\u001b[0m \u001b[0mtest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmsg\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAssertionError\u001b[0m: y==2.1; Test: <lambda>"
     ]
    }
   ],
   "source": [
    "f(3,2.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Example #4\n",
    "<hr style = \"border:2px solid black\" ></hr>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-30T20:47:57.392908Z",
     "start_time": "2022-11-30T20:47:57.389571Z"
    }
   },
   "outputs": [],
   "source": [
    "import math\n",
    "\n",
    "def circumference(radius: float) -> float:\n",
    "    return 2 * math.pi * radius"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-30T20:48:07.328005Z",
     "start_time": "2022-11-30T20:48:07.317862Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'radius': float, 'return': float}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "circumference.__annotations__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Example #5\n",
    "<hr style = \"border:2px solid black\" ></hr>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-info\">\n",
    "<font color=black>\n",
    "\n",
    "- Now, if we were to enter an incorrect arguments, what would happen?\n",
    "- In reality, nothing of significance — remember, Python does not enforce our type annotations.\n",
    "- Instead, they are used by third-party IDEs and linters. So, an erroneous annotation will be highlighted by our third-party tool — acting as an early warning system that our logic isn’t quite right.\n",
    "\n",
    "</font>\n",
    "</div>"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-30T21:06:20.409046Z",
     "start_time": "2022-11-30T21:06:20.406306Z"
    }
   },
   "outputs": [],
   "source": [
    "def add(x: int, y: int) -> int:\n",
    "    return x + y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-30T21:06:22.077956Z",
     "start_time": "2022-11-30T21:06:22.074370Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "add(7, 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-30T21:06:45.132038Z",
     "start_time": "2022-11-30T21:06:45.128253Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'74'"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "add(\"7\", \"4\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Example of various annotations\n",
    "<hr style = \"border:2px solid black\" ></hr>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## `Union` and `|`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Allow a set of different types for a single assignment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-30T21:16:20.938700Z",
     "start_time": "2022-11-30T21:16:20.935399Z"
    }
   },
   "outputs": [],
   "source": [
    "from typing import Union\n",
    "def add(x: Union[int, float], y: Union[int, float]) -> Union[int, float]:\n",
    "    return x + y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-30T21:16:55.989352Z",
     "start_time": "2022-11-30T21:16:55.982074Z"
    }
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "unsupported operand type(s) for |: 'type' and 'type'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/5x/lyqr8wv507n43bnwpsz4dq_c0000gn/T/ipykernel_63630/2077418342.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mdef\u001b[0m \u001b[0madd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mint\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mint\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mint\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0mfloat\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      2\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: unsupported operand type(s) for |: 'type' and 'type'"
     ]
    }
   ],
   "source": [
    "# Available from python 3.10\n",
    "def add(x: int | float, y: int | float) -> int | float:\n",
    "    return x + y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## `Optional`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- When we define a function with optional parameters, we can specify this using the Optional type. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-18T13:11:51.105734Z",
     "start_time": "2023-01-18T13:11:51.102843Z"
    }
   },
   "source": [
    "# List vs. `list` in type hinting\n",
    "<hr style = \"border:2px solid black\" ></hr>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-info\">\n",
    "<font color=black>\n",
    "\n",
    "- In Python, List and list both refer to the built-in list class, but there is a difference in how they are used.\n",
    "- `List` is used as a type hint to indicate that the function returns a list. Type hints were introduced in Python 3.5 and are used to indicate the type of a variable or the return type of a function. They are optional and do not affect the execution of the code. It is used to make the code more readable and also for the tools like IDEs, linters, type checkers etc to provide better suggestions, error messages etc.\n",
    "- `list` is used as a return type of the function, it means that the function should return an object of the built-in list class.\n",
    "    \n",
    "</font>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-18T13:15:23.148066Z",
     "start_time": "2023-01-18T13:15:23.144304Z"
    },
    "cell_style": "split"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function __main__.a() -> List>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from typing import List\n",
    "def a() -> List:\n",
    "    return [1, 2, 3]\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-18T13:15:23.486775Z",
     "start_time": "2023-01-18T13:15:23.483213Z"
    },
    "cell_style": "split"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function __main__.a() -> list>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def a() -> list:\n",
    "    return [1, 2, 3]\n",
    "a"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# References\n",
    "<hr style = \"border:2px solid black\" ></hr>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-warning\">\n",
    "<font color=black>\n",
    "\n",
    "- https://stackoverflow.com/questions/14379753/what-does-mean-in-python-function-definitions\n",
    "- https://www.python.org/dev/peps/pep-3107/\n",
    "- https://realpython.com/lessons/annotations/\n",
    "- https://towardsdatascience.com/type-annotations-in-python-d90990b172dc\n",
    "- [Type hints cheatsheet 1](https://mypy.readthedocs.io/en/stable/cheat_sheet_py3.html)\n",
    "- [type hints cheatsheet 2](https://mypy.readthedocs.io/en/stable/cheat_sheet_py3.html)\n",
    "    \n",
    "</font>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-27T22:06:38.853419Z",
     "start_time": "2023-04-27T22:06:38.849286Z"
    }
   },
   "source": [
    "# Requirements\n",
    "<hr style = \"border:2px solid black\" ></hr>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-27T22:08:36.378140Z",
     "start_time": "2023-04-27T22:08:36.334967Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python implementation: CPython\n",
      "Python version       : 3.9.7\n",
      "IPython version      : 7.29.0\n",
      "\n",
      "Compiler    : Clang 10.0.0 \n",
      "OS          : Darwin\n",
      "Release     : 22.4.0\n",
      "Machine     : x86_64\n",
      "Processor   : i386\n",
      "CPU cores   : 12\n",
      "Architecture: 64bit\n",
      "\n",
      "json    : 2.0.9\n",
      "numpy   : 1.21.6\n",
      "autopep8: 1.6.0\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%load_ext watermark\n",
    "%watermark -v -iv -m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "trainingAI",
   "language": "python",
   "name": "trainingai"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": true,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": true
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}