20180830_euroscipy_dataclasses/20180830_dataclasses.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Extending Python 3.7s Data Classes\n",
    "\n",
    "**Geir Arne Hjelle**, `@gahjelle`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "[![](real-python_data-classes.png)](https://realpython.com/python-data-classes/)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Let's dive right in, with an example of a data class:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "from dataclasses import dataclass\n",
    "\n",
    "@dataclass\n",
    "class Card:\n",
    "    suit: str\n",
    "    rank: str"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "The data class can be used as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Card(suit='♠', rank='A')"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c = Card(\"♠\", \"A\")\n",
    "c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'♠'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c.suit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c == Card(\"♢\", \"A\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Data classes:\n",
    "\n",
    "- Are just regular classes\n",
    "\n",
    "\n",
    "- Are new in the standard library in Python 3.7\n",
    "    - You can `pip install dataclasses` in Python 3.6\n",
    "\n",
    "\n",
    "- Are inspired by the `attrs` project (and `namedtuple`, `namedlist`, `attrdict`, `plumber`, ...)\n",
    "\n",
    "- Provide a way to simplify class definitions\n",
    "    - The `@dataclass` decorator writes a lot of code, so you don't have to"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Compare with the regular definition of a class:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "class OldCard:\n",
    "    def __init__(self, suit, rank):\n",
    "        self.suit = suit\n",
    "        self.rank = rank"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Not so bad... Let's use it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<__main__.OldCard at 0x7f7f7436c438>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "oc = OldCard(\"♡\", \"Q\")\n",
    "oc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'♡'"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "oc.suit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "oc == OldCard(\"♡\", \"Q\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Data classes are providing some nice features behind the scenes!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "By default, `@dataclass` provide:\n",
    "\n",
    "- `.__init__()`\n",
    "\n",
    "- `.__repr__()`\n",
    "\n",
    "- `.__eq__()`\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "They can easily be configured to provide:\n",
    "\n",
    "- ordering methods like `.__gt__()`, `.__ge__()` and so on\n",
    "\n",
    "- `.__hash__()`\n",
    "\n",
    "- immutability"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Another example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "@dataclass\n",
    "class Coordinate:\n",
    "    x: float\n",
    "    y: float\n",
    "    z: float\n",
    "    t: float"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Coordinate(x=1, y=0, z=0, t=0), Coordinate(x=0, y=1, z=2, t=3))"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p = Coordinate(1, 0, 0, 0)\n",
    "q = Coordinate(0, 1, 2, 3)\n",
    "p, q"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p == q"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'>' not supported between instances of 'Coordinate' and 'Coordinate'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-13-aea7de878829>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mp\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0mq\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m: '>' not supported between instances of 'Coordinate' and 'Coordinate'"
     ]
    }
   ],
   "source": [
    "p > q"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Let's add ordering:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "@dataclass(order=True)\n",
    "class Coordinate:\n",
    "    x: float\n",
    "    y: float\n",
    "    z: float\n",
    "    t: float"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Coordinate(x=1, y=0, z=0, t=0), Coordinate(x=0, y=1, z=2, t=3))"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p = Coordinate(1, 0, 0, 0)\n",
    "q = Coordinate(0, 1, 2, 3)\n",
    "p, q"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p > q"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Coordinate(x=0, y=1, z=2, t=3), Coordinate(x=1, y=0, z=0, t=0)]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted([p, q])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Data classes are not magic, though:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "unsupported operand type(s) for +: 'Coordinate' and 'Coordinate'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-18-60954a2f3125>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mp\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mq\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m: unsupported operand type(s) for +: 'Coordinate' and 'Coordinate'"
     ]
    }
   ],
   "source": [
    "p + q"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "You can add methods and default values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "@dataclass(order=True)\n",
    "class Coordinate:\n",
    "    x: float\n",
    "    y: float\n",
    "    z: float\n",
    "    t: float = 0\n",
    "        \n",
    "    def __add__(self, other):\n",
    "        if other.__class__ is not self.__class__:\n",
    "            return NotImplemented\n",
    "        return self.__class__(self.x + other.x,\n",
    "                              self.y + other.y,\n",
    "                              self.z + other.z,\n",
    "                              self.t + other.t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Coordinate(x=1, y=0, z=0, t=0), Coordinate(x=0, y=1, z=2, t=0))"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p = Coordinate(1, 0, 0)\n",
    "q = Coordinate(0, 1, 2)\n",
    "p, q"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Coordinate(x=1, y=1, z=2, t=0)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p + q"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "OK, what's with that `suit: str` notation?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "@dataclass\n",
    "class Card:\n",
    "    suit: str\n",
    "    rank: str"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Data classes use variable annotations (Python 3.6+) to indicate that an attribute belongs to the data class."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Typically, you'd let the annotation double as a type hint. But the annotation could be any valid Python expression ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hi there\n"
     ]
    }
   ],
   "source": [
    "@dataclass\n",
    "class Silly:\n",
    "    foo: print(\"Hi there\")\n",
    "    bar: 42 = \"EuroSciPy\"\n",
    "    nope = \"nothing\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Silly(foo='stuff', bar='EuroSciPy')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = Silly(\"stuff\")\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'nothing'"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.nope"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "The `field` function gives you more control of how each field is defined:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "from dataclasses import field"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "field?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Say you have a field dependent on other fields:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "@dataclass\n",
    "class Image:\n",
    "    height: int\n",
    "    width: int\n",
    "    data: bytearray = field(init=False, repr=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Image(height=1000, width=1500)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img = Image(1000, 1500)\n",
    "img"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'Image' object has no attribute 'data'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-30-45fe4ff6492a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mimg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m: 'Image' object has no attribute 'data'"
     ]
    }
   ],
   "source": [
    "img.data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "You can do extra initialization in `.__post_init__()`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "@dataclass\n",
    "class Image:\n",
    "    height: int\n",
    "    width: int\n",
    "    data: bytearray = field(init=False, repr=False)\n",
    "        \n",
    "    def __post_init__(self):\n",
    "        self.data = bytearray(self.height * self.width)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Image(height=10, width=20)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img = Image(10, 20)\n",
    "img"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "bytearray(b'\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img.data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Remember: Data classes are regular classes!\n",
    "    \n",
    "... so they play nicely with tools we already know:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "@dataclass\n",
    "class Coordinate:\n",
    "    position: np.ndarray\n",
    "    time: np.ndarray"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "pos = np.cumsum(np.random.randint(0, 100, (5, 3)), axis=0) / 10\n",
    "t = np.arange(0, 50, 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Coordinate(position=array([[ 4. ,  8.1,  0.6],\n",
       "       [ 9.9, 17.1,  4.8],\n",
       "       [17.1, 25.5, 14.5],\n",
       "       [19. , 29.8, 18.3],\n",
       "       [21.8, 30.8, 20.8]]), time=array([ 0, 10, 20, 30, 40]))"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c = Coordinate(pos, t)\n",
    "c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.plot(c.time, c.position)\n",
    "plt.legend((\"x\", \"y\", \"z\"));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Data classes do not have explicit validators.\n",
    "\n",
    "But you can use properties:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "@dataclass\n",
    "class Coordinate:\n",
    "    position: np.ndarray\n",
    "    time: np.ndarray\n",
    "        \n",
    "    def __len__(self):\n",
    "        if hasattr(self, \"_position\"):\n",
    "            return len(self._position)\n",
    "        if hasattr(self, \"_time\"):\n",
    "            return len(self._time)\n",
    "        return 0\n",
    "\n",
    "    @property\n",
    "    def position(self):\n",
    "        return self._position\n",
    "    \n",
    "    @position.setter\n",
    "    def position(self, value):\n",
    "        value = np.array(value)\n",
    "        if len(self) and len(self) != len(value):\n",
    "            raise ValueError(\"Position must have the same length as other fields\")\n",
    "        self._position = value\n",
    "        \n",
    "    @property\n",
    "    def time(self):\n",
    "        return self._time\n",
    "    \n",
    "    @time.setter\n",
    "    def time(self, value):\n",
    "        value = np.array(value)\n",
    "        if len(self) and len(self) != len(value):\n",
    "            raise ValueError(\"Time must have the same length as other fields\")\n",
    "        self._time = value"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Let's try it out:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Time must have the same length as other fields",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-39-37f8f75840bb>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mCoordinate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpos\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3\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<string>\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, position, time)\u001b[0m\n",
      "\u001b[0;32m<ipython-input-38-83620b9dfac3>\u001b[0m in \u001b[0;36mtime\u001b[0;34m(self, value)\u001b[0m\n\u001b[1;32m     30\u001b[0m         \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     31\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 32\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Time must have the same length as other fields\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     33\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_time\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Time must have the same length as other fields"
     ]
    }
   ],
   "source": [
    "c = Coordinate(pos, range(3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "c = Coordinate(pos, range(5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Coordinate(position=array([[ 4. ,  8.1,  0.6],\n",
       "       [ 9.9, 17.1,  4.8],\n",
       "       [17.1, 25.5, 14.5],\n",
       "       [19. , 29.8, 18.3],\n",
       "       [21.8, 30.8, 20.8]]), time=array([0, 1, 2, 3, 4]))"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Thank you\n",
    "\n",
    "[![](real-python_data-classes.png)](https://realpython.com/python-data-classes/)"
   ]
  }
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