{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Scale Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%config Completer.use_jedi = False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Values can be associated to scales in one object instance.\n",
    "They are internally represented as `pandas.Series`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from mcda.values import Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "values = Values([0, -2, 5, 10, 3])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Any type compatible with the `pandas.Series` constructor can be used to supply values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.166667\n",
       "1    0.000000\n",
       "2    0.583333\n",
       "3    1.000000\n",
       "4    0.416667\n",
       "dtype: float64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from mcda import normalize\n",
    "\n",
    "normalize(values).data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    10\n",
       "2     5\n",
       "4     3\n",
       "0     0\n",
       "1    -2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "values.sort().data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "values.is_within_scales"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can acess individual values and iterate over them as you would with an immutable mapping:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Value value=0 scale=QuantitativeScale(interval=[-2, 10])>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "values[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "values[0].value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 -> Value(value=0, scale=QuantitativeScale(interval=[-2, 10]))\n",
      "1 -> Value(value=-2, scale=QuantitativeScale(interval=[-2, 10]))\n",
      "2 -> Value(value=5, scale=QuantitativeScale(interval=[-2, 10]))\n",
      "3 -> Value(value=10, scale=QuantitativeScale(interval=[-2, 10]))\n",
      "4 -> Value(value=3, scale=QuantitativeScale(interval=[-2, 10]))\n"
     ]
    }
   ],
   "source": [
    "for k, v in values.items():\n",
    "    print(f\"{k} -> {v}\")"
   ]
  }
 ],
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