EstimatorAlgorithm.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "## Importando Dependências"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2023-07-05T16:43:47.839540Z",
     "start_time": "2023-07-05T16:43:47.829537400Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import math\n",
    "from IPython.display import Latex\n"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Declarando a Classe de Teste"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [],
   "source": [
    "class Estimator:\n",
    "\n",
    "    _w = None\n",
    "    _k = None\n",
    "    _a = 150\n",
    "    _gainSpace = None\n",
    "    _limitRatio = 0.25\n",
    "    # Lista de valores para b de 0 -> 1500mm\n",
    "    # 150 Samples gerados incluindo o ultimo valor \"1500\"mm\n",
    "    # a -> 150 mm\n",
    "\n",
    "    b_values = None\n",
    "    c_values = None\n",
    "\n",
    "    def __init__(self,width:float,kFactor:float,steps:int) -> None:\n",
    "\n",
    "        self._w = width\n",
    "        self._k = kFactor\n",
    "        self.b_values = np.linspace(0,width,steps,True)\n",
    "        self._gainSpace = np.linspace(0,1,steps,True)\n",
    "        self.c_values = [np.sqrt(np.power(b_val,2) + np.power(150,2)) for b_val in self.b_values]\n",
    "\n",
    "\n",
    "    @property\n",
    "    def directDistanceSamples(self):\n",
    "        return self.c_values\n",
    "\n",
    "    @property\n",
    "    def gainSpace(self):\n",
    "        return self._gainSpace\n",
    "\n",
    "    @property\n",
    "    def verticalSamples(self):\n",
    "        return self.b_values\n",
    "\n",
    "    @property\n",
    "    def boxWidthValue(self):\n",
    "        return self._w\n",
    "\n",
    "    @property\n",
    "    def horizontalSample(self):\n",
    "        return self._a\n",
    "\n",
    "\n",
    "    def Expression(self,directDistance,vDistance,boxWidth,hDistance):\n",
    "\n",
    "        # Apenas retorna a distância direta para n retornar erros\n",
    "        if vDistance == 0 and hDistance==0: return self.RateSingleAxisDistance(directDistance)\n",
    "\n",
    "        if vDistance == 0 and hDistance != 0:\n",
    "            return self.RateSingleAxisDistance(hDistance)\n",
    "\n",
    "        if hDistance == 0 and vDistance != 0:\n",
    "            return self.RateSingleAxisDistance(directDistance)\n",
    "\n",
    "        vThreshold = vDistance/hDistance\n",
    "        hThreshold = hDistance < (0.1 * vDistance)\n",
    "\n",
    "        # Se o valor da distância horizontal for muito menor ao valor vertical vDist >> hDist\n",
    "        # O ponto de conexão está próx a lateral da caixa\n",
    "        # Considera-se somente o valor da distância direta = distancia vertical adicionada de uma fração do seu valor moderada pela variavel \"limiting ratio\"\n",
    "        if hThreshold : return self.RateSingleAxisDistance(vDistance)\n",
    "\n",
    "        # Se o valor da distância vertical for muito menor ao valor horizontal vDist << hDistance\n",
    "        # O ponto de conexão está próx a altura \"y\" do chicote\n",
    "        # Considera-se somente o valor da distância direta adicionada de uma fração do seu valor moderada pela variavel \"limiting ratio\"\n",
    "        if vThreshold < .1: return directDistance + (self._limitRatio * directDistance)\n",
    "\n",
    "        denominator = (boxWidth - ( 2 * hDistance ))\n",
    "        raw_decreasing_factor = (vDistance / denominator)\n",
    "        flattened_factor = np.exp(-raw_decreasing_factor)\n",
    "        result = (directDistance * (1 + self.CalibrateWeigths(flattened_factor,self._limitRatio)))\n",
    "        return result\n",
    "\n",
    "    def ExpressionTest(self,directDistance,vDistance,boxWidth,hDistance,gain):\n",
    "        return self.Expression(directDistance,vDistance,boxWidth,hDistance)*gain\n",
    "\n",
    "    def RateSingleAxisDistance(self,distance):\n",
    "        return distance + (self._limitRatio * distance)\n",
    "\n",
    "    def CalibrateWeigths(self,currentFactor,limit):\n",
    "        #Calcula a diff abosulta entre o valor atual e o limite especificado\n",
    "        diff = np.absolute(limit - currentFactor)\n",
    "\n",
    "        if currentFactor < limit:\n",
    "            return currentFactor + diff\n",
    "        else:\n",
    "            return currentFactor - diff\n",
    "\n",
    "\n",
    "\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-07-05T16:43:48.038542Z",
     "start_time": "2023-07-05T16:43:47.833538Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Testes"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [],
   "source": [
    "\n",
    "step = 150\n",
    "expTest = Estimator(1300, 1, step)\n",
    "rng = range(0,len(expTest.directDistanceSamples))\n",
    "markers = [25,50,75,100,149]\n",
    "\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-07-05T16:43:48.224545Z",
     "start_time": "2023-07-05T16:43:48.039554700Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Bases de Cálculo e Dados Gráficos"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Distância Estimada - K = 1"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [],
   "source": [
    "estimated_dist_no_gain = [expTest.Expression(expTest.directDistanceSamples[idx],expTest.verticalSamples[idx],expTest.boxWidthValue,expTest.horizontalSample) for idx in rng]"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-07-05T16:43:48.429557Z",
     "start_time": "2023-07-05T16:43:48.226551100Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "###  Sobra Absoluta - D/C"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [],
   "source": [
    "estimated_dist_ratio = [estimated_dist_no_gain[idx]/expTest.directDistanceSamples[idx] for idx in rng]\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-07-05T16:43:48.618553900Z",
     "start_time": "2023-07-05T16:43:48.431551Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Soma das Alturas Verticais e Horizontais - A + B"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [],
   "source": [
    "ab_ratio = [(bVal + expTest.horizontalSample) for bVal in expTest.verticalSamples]"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-07-05T16:43:48.826574200Z",
     "start_time": "2023-07-05T16:43:48.620580300Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Comportamento da Distância Estimada em Função de K"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [],
   "source": [
    "last_y_k_Values =[expTest.ExpressionTest(expTest.directDistanceSamples[step-1],expTest.verticalSamples[step-1],expTest.boxWidthValue,expTest.horizontalSample,expTest.gainSpace[idx]) for idx in rng]"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-07-05T16:43:49.048566400Z",
     "start_time": "2023-07-05T16:43:48.830561200Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Gráficos"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Sobra de Comprimento - Estimativa"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [
    {
     "data": {
      "text/plain": "<AxesSubplot:xlabel='Estimado', ylabel='D/C'>"
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "estimated_cols = ['Estimado','D/C']\n",
    "estimated_frame = pd.DataFrame(np.array([estimated_dist_no_gain,estimated_dist_ratio]))\n",
    "transposed_estimate = estimated_frame.T\n",
    "transposed_estimate.columns = estimated_cols\n",
    "sns.lineplot(\n",
    "    x=r\"Estimado\",\n",
    "    y=r\"D/C\",\n",
    "    data=transposed_estimate\n",
    ")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-07-05T16:43:49.286573400Z",
     "start_time": "2023-07-05T16:43:49.051563700Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Relação Entre 'A+B'"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [
    {
     "data": {
      "text/plain": "<AxesSubplot:xlabel='$D_{dir}$', ylabel='$D_{dir}(D_{vert},D_{hor})$'>"
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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FUepmW2g0GioqKtDr9Wi12rptfn7897atj1qtQqv1qHe75qJWO1l1PGtwtJ4crR9oXE+nL1Ww4PMMjpwvoU8XL5bH3kZkkE8LVdhwjvY+OVI/jZoltWvXLtzc3Bg2bBg6na5RA//6HITBYMDb2xtPT08MBsN1j3t5ef3utvUxmRSr3kjeEW9c72g9OVo/0LCeKqpqWX0om4+/z8fT1ZnZo0P4Q8RPV2m3pv8ujvY+2Vs/fn5eN3yuUQcqExMTCQoKYvfu3bz66quNKio8PJzU1FQAkpOTiYyMpH///qSkpGA2m8nPz8dsNuPr6/u72wohLGNWFD49XsC49d+w/ft8xkZ05eMpA3nkNn9Z0kM0SKP2MDZt2kRWVhbu7u5MmTKlUQPPnj2befPmkZCQgE6nIyYmBrVaTWRkJLGxsZjNZuLj42+4rRCifj/kl7Nkz1kyLum5rZs3M0f1o1dnuUpbNI5KURSloS9avHgxs2fPpqamhjfffJPXX3+9JWprEqPRJIekmsjRenK0fuDGPRUbakg8cJ7PT17Cz9OFadE6Ynrbx1XajvY+2Vs/Nzsk1ag9jOrqak6cOEHfvn1pRN4IIVpIrcnM1u/yWXs4h+paM5MHBjBlSAAaF1nUQTRdo/6KZs2axaZNm9i0aRP33HNPc9ckhGiE1JxSlu3J4nzJNYYF+zDjjp4E+TrG7BzROjQqMGbOnIm7uztqtZrU1FRGjhzZ3HUJISyUX1bF8v3n2HummG7t3Vg29hZG6Hzt4vCTsC+NCoyQkBCmT58OQGlpaXPWI4SwUJXRxHt7zrDmwHlUwN+iejBxQHdc5Spt0UIaHBivvvoqOTk5bNq0iSFDhtCzZ8+WqEsIcQOKorD37BWW78uioLyaMb38mCb30hZW0KhZUlVVVZw8eZIffviBc+fO8cYbb7REbU0is6SaztF6coR+zl0xsGxPFmkXrhLSUcNrD4XTy8FWk3WE9+nX7K2fZpklVVhYSFlZGQEBAXh4eDBgwAAGDBjQLAUKIW5OX13L2sM5bP0uH492al66syfjbvWno6/Grj6MhH2rNzByc3OJi4ujuLgYV1dXioqKGDp0KLNnzyY4ONgaNQrRZpkVhf938hLvHzhP6TUjYyO6MHV4D3w8XGxdmmiD6g2MpUuXEhsby/jx4wGora0lKSmJv/zlL6xbt44ePXq0dI1CtEknCytYuucsJwoq6NfVm+Xj+tKn840PFwjR0uoNjOzsbJYvX/7LC5ydiY2NpVu3brz//vssWbKkJesTos0puVbDygPZ7DxRiI9HO167pxf3hnfCSabJChurNzBuNJc7KiqKhISEZi9IiLaq1qyw/ft81hzKptJo5vHI7vzXkEA8XeUqbdE61PuXWFRUxPbt2wkLCyMkJASNRlP3nFwYJETz+PbCVZbsOcu5K9cYEuTDi6N60qODXKUtWpd6AyMuLo5Tp06xY8cOzpw5g0ajITQ0lNDQUIqLi61RoxAOq7C8inf3n2PX6WL8vV1Z8lA4I0M6yJcx0SrVGxixsbHX/VxYWEhmZiaZmZkMHDiwxQoTwpFV15rZ+O1FPky9CMAzw4L4U2R33NqpbVyZEDfW4IOjXbp0oUuXLrJ+lBCNoCgKyVlXSNh3jvyyKkaHdeT5kTq6ylXawg7I2TQhrCS75BrL9mZxJLuU4A4evP9IPwa1ontpC1GfRgfG5cuX0Wq1uLjIBURC3Iyhppb1Ry7wr/Q83No5MWNUTx69tSvOalkkUNiXRgfGrFmzuHDhAjExMcyePbs5axLCISiKwlening3+RxF+hoe6tuZv0UF00EjX7KEfWp0YHz00UcoisLZs2ebsx4hHMLZIgNL9pzlaG4ZfTp7svjBcPr5e9u6LCGapFGBsX79eqZMmYJKpUKtllkdQvysoqqWNYdz2P5dHp6uzrw8JpSH+3ZB7STTZIX9a1BglJeXs2jRIs6fP4+rqyu9evUiKSmJRYsWtVR9QtgFs6LwxY+XWJH80yKB427tyl+H90Dr3s7WpQnRbBoUGN7e3ixatIjPP/+coKAgMjMzGTNmTEvVJoRdOHWpgrd3Z/FDQTn9unrx7ri+9JZFAoUDatQhqYKCAu6//3769u3bpMGNRiNz5swhLy8PJycnFixYgLOzM3PmzEGlUhEaGsr8+fNxcnIiMTGRffv24ezszNy5c4mIiGjS2EI0VVmlkQ8OZvPJsQJ8PNoRHxPG/bd0lkUChcNqVGDs2rULNzc3hg0bhk6na/Tg+/fvp7a2li1btnDw4EGWL1+O0Whk+vTpDB48mPj4eHbv3o2/vz9paWls376dgoIC4uLiSEpKavS4QjSFyayw40QhKw+cR19dS2z/bjw9NAgvN7msSTi2Rv2FJyYmkpGRwa5du7hw4QILFy5s1ODBwcGYTCbMZjN6vR5nZ2e+//57Bg0aBEB0dDQHDx4kODiYqKgoVCoV/v7+mEwmSkpK8PX1bdS4QjTWD/nlLNlzloxLem7v3p6X7uxJqJ+nrcsSwioaFRirVq3CYDCwaNEiDhw40OjBPTw8yMvL495776W0tJRVq1bxzTff1C28ptFoqKioQK/Xo9Vq61738+M3Cwy1WoVWa73VPtVqJ6uOZw2O1lNT+rmir2bp16f5+Ggenb1cSXg0ggf6dbX5IoGO9h6B4/XkSP00KjCcnJzo1q0bAKmpqYwYMaJRg3/00UdERUXx4osvUlBQwBNPPIHRaKx73mAw4O3tjaenJwaD4brHvbxuflLRZFKseq9je7vRuyUcrafG9FNrVkj6Pp9V/3uPikmR3fmvoYFoXJwpK6tsoUot52jvETheT/bWj5/fjT9bG7U2gbu7O3q9HqPRSEFBQaML8/b2rvvgb9++PbW1tYSHh5OamgpAcnIykZGR9O/fn5SUFMxmM/n5+ZjNZjkcJVrcd7llTN54lKV7swjv7MWWyQOYNlKHxkXOVYi2SaUoitLQF9XW1rJ161ZOnTrFnXfeyahRoxo1uMFgYO7cuRQVFWE0Gpk8eTJ9+/Zl3rx5GI1GdDodCxcuRK1Ws2LFCpKTkzGbzbz88stERkbe9HcbjSbZw2giR+vJ0n6K9NW8l3yef2dcprOXKzPu0DEqtKPNDz/9Hkd7j8DxerK3fm62h9GowKisrMTd3b1JRbU0CYymc7Se6uun1mRm89E81h2+gNFsZtLAAP48KKBV36PC0d4jcLye7K2fmwVGo/atFyxYQEREBI899hhOTrLiprB/qTmlLNuTxfmSawwP9uXFUT0J8GndX4qEsLZGBcbf//53vv32W1555RVGjx7NXXfd1dx1CWEVheVVLN9/jt2ni+nW3o2EsbcwomcHW5clRKvUqMBIS0vjzJkzuLu7895770lgCLtTU2tmU3ou649cQOGnW6ROGhiAq7PsMQtxIxYHRmFhIWVlZQQEBHDu3Dl69+7NQw89VO/0ViFam4PnSli29ywXr1ZxR0gHXrijJ/7t5RapQtSn3sDIzc0lLi6O4uJiXF1dKS4uZsiQIQwaNEjCQtiVCyXXeG3HCQ6cKyHQx533/tiXoT1kerYQlqo3MJYuXUpsbCzjx48HfppSm5SUxNNPP826devo0aNHS9coRJNUGU38d9pF/vltLmoVxI0IZsKAbrSTW6QK0SD1BkZ2djbLly//5QXOzsTGxtKtWzfef/99lixZ0pL1CdFoiqKw/+wVEvZlUVBezf39ujB1aBCdvVxtXZoQdqner1g3ulgpKiqKrKysZi9IiOZwobSS5z85wUs7f8S9nZpVj0Ww/LHbJCyEaIJ69zCKiorYvn07YWFhhISEoNFo6p5rjVe+iratymjiw7SLbPjmIi5qJ164Q8djt/njLIefhGiyegMjLi6OU6dOsWPHDs6cOYNGoyE0NJTQ0FCKi4utUaMQFtl/9goJe8+SX17NPX068Xx0MB09ZY9CiOZSb2DExsZe93NhYSGZmZlkZmYycODAFitMCEvlXq1k2d4sUs6VENzBg1WPRTAgQGvrsoRwOPUGhqIo1x166tKlC126dGHkyJE33EYIa6iuNfPPtIt8lHYBtZOKadHBTOjfTQ4/CdFC6g2MyZMnc/fddzN69Gj8/f3rHq+pqSE9PZ1PP/2UwYMHM27cuBYtVIhfO3iuhCV7zpJXVsWYXn48P1InJ7SFaGH1Bsa6dev4+OOPmTFjBrm5uXh7e1NVVYWiKAwfPpwnnniC8PBwa9QqBPllVbyzL4t9Z68Q5ONO4iP9GBzkY+uyhGgT6g0MV1dXHn/8cR5//HGMRiMlJSU4OTnh5+dnjfqEAH5a+2njt7msT72ACnhuRDAT5eI7IayqQf/a2rVrR+fOndmxY0dL1SPE/3Eku4QJ/0zng4PZDA/2ZfufI3liUICEhRBW1qjVanft2oWbmxvDhg1Dp9M1d01CANcvPR6gdePdcX0ZFixrPwlhK40KjMTERDIyMti1axcXLlxg4cKFzV2XaMOMJjP/Ss9j3eEcFOCvw4OYFBmAiyw9LoRNNTgw3n33XUwmE71792bMmDEEBwe3RF2ijfrmQilv7z5Ldkkl0T078OIoWXpciNaiwYHx/PPPU1xcTEZGBl9//bXsYYhmcbmimnf3n+OrzCL85c53QrRKDQ6MnTt30q9fP0aMGMGIESNaoibRhtSazGz9Lp81h3KoNZv5y9BAJg8MwK2d2talCSF+o8GB4evry+uvv47RaMTHx4cePXowc+bMlqhNOLijuVd5e/dZsoqvMTzYl5l39qS71t3WZQkhbqDBgREVFcU333zDCy+8gF6vJyEhoSXqEg6s2FDDe/vP8WXGZbp6u7L04XCie3aQ5WWEaOUaNUtKr9dz4sQJwsLCqKysbFIBq1evZs+ePRiNRiZMmMCgQYOYM2cOKpWK0NBQ5s+fj5OTE4mJiezbtw9nZ2fmzp1LREREk8YV1mcyKyQdy2dlSjY1JjNTBgfw58GBcvhJCDthcWAUFhZSVlZGQEAAc+bMYfPmzWzatKlJ5zFSU1P57rvv2Lx5M5WVlaxfv55FixYxffp0Bg8eTHx8PLt378bf35+0tDS2b99OQUEBcXFxJCUlNXpcYX0/Flbw1q4zZFzSMzhIy0t3hhDk62HrsoQQDVBvYOTm5hIXF0dxcTGurq4UFxczZMgQZs2axeTJk5s0eEpKCmFhYTz77LPo9XpmzZrFtm3bGDRoEADR0dEcPHiQ4OBgoqKiUKlU+Pv7YzKZKCkpwddXLuJq7SqqavngYDYff59PB40Lb97fmzG9/OTwkxB2qN7AWLp0KbGxsYwfPx6A2tpakpKSePrpp/nHP/5BUFBQowcvLS0lPz+fVatWkZuby9SpU69bKl2j0VBRUYFer0er1da97ufHbxYYarUKrdZ632DVaierjmcNTelJURQ+O17Aon+fosRQw5+GBPLC6FC83No1c5WWk/fIPjhaT47UT72BkZ2dzfLly395gbMzsbGxdOvWjcTERJYsWdLowbVaLTqdDhcXF3Q6Ha6urhQWFtY9bzAY8Pb2xtPTE4PBcN3jXl5eN/3dJpPC1avXGl1bQ2m1HlYdzxoa21N2yTXe3n2Wby5cJbyLFwljb6FPZy9MVUauVhlboFLLyHtkHxytJ3vrx8/vxp+t9a61cKNDB1FRUWRlZTW+KmDAgAEcOHAARVG4dOkSlZWVDB06lNTUVACSk5OJjIykf//+pKSkYDabyc/Px2w2y+GoVqjKaGLVwWwm/jOdjEsVzBodwvoJt9Gn883DXQhhH+rdwygqKmL79u2EhYUREhKCRqOpe66px6FHjRrFN998wyOPPIKiKMTHx9O9e3fmzZtHQkICOp2OmJgY1Go1kZGRxMbGYjabiY+Pb9K4ovkdOl/C27t/uqHRPX068fxIHR01LrYuSwjRjFSKoig322Dr1q2cPn2azMxMzpw5g0ajITQ0lNDQUD777DP2799vrVobxGg0ySGpJrKkp8sV1STsy2L36WKCfNyZfVcIAwNb5w2N2up7ZG8crSd76+dmh6Tq3cOIjY297ufCwkIyMzPJzMxk4MCBTa9O2KVas8K27/JYfTAHk6IwdXgP/hTZXVaUFcKBNfjCvS5dutClSxdGjhzZEvUIO/BDfjlv7TrD6SIDw4J9eOnOEFnSQ4g2oFFXeou2qazSyPsp5/n0eCF+ni4sfrAPo0I7yjUVQrQREhiiXoqi8MWPl3l3/znKq4xMGNCNp4cFoXGRPx8h2hL5Fy9uKqfkGot2nSH9Yhn9unox565+hHXytHVZQggbkMAQv6um1syKvWf5YH8Wrs5OvDwmlLH9uuAkh5+EaLMkMMT/8V1uGYu+PsP5kmuM6eXHjFE95ZoKIYQEhvhFeZWRFcnn+fSHQrp6u7Ju0gBu7aSp/4VCiDZBAkOgKApfZxaxbG8WZZVG/hTZnaeHBdHVz8uuLjgSQrQsCYw2Lq+sksW7znI4u5Q+nT1574/96CUntYUQv0MCo42qNZnZfDSP1YdyUKtUvDiqJ4/e5o/aSU5qCyF+nwRGG3SyoJw3vz7DmSIDI3t2YOadPeni7WbrsoQQrZwERhtiqKnlg5Rstn2XT0dPF95+KJxRoR1tXZYQwk5IYLQRyVlXWLzrDEX6Gh69zZ+pUT3wdJW3XwhhOfnEcHCl12pYuieLrzKLCOmoYfFD4fTt6m3rsoQQdkgCw0EpisK/T11m2Z4srhlN/HV4EJMHBtBOLcuPCyEaRwLDARWWV7F491lSzpXQr6sXr8aEoesgF+AJIZpGAsOBmBWF/zlewIrk85jMCjNG9eQxmSorhGgmEhgO4kJpJQu/Os13uWUMCtQy9+5QurWXmxoJIZqPBIadqzUr/OvbXNYczsFF7cS8mDAevKWz3NRICNHsJDDs2OnLehb85zSnLuu5I6QDs0eH0NHT1dZlCSEclASGHTKazPzjyAU+SrtIezdn3nqwD3fKrVKFEC2sVQTGlStXGDduHOvXr8fZ2Zk5c+agUqkIDQ1l/vz5ODk5kZiYyL59+3B2dmbu3LlERETYumybOFOk57UvMzldZOC+8E7MuKMn7d3b2bosIUQbYPPAMBqNxMfH4+b201pGixYtYvr06QwePJj4+Hh2796Nv78/aWlpbN++nYKCAuLi4khKSrJx5dZVa1b4Z9pF1h7OwdvNmaUP38LIkA62LksI0YbY/CquxYsXM378eDp16gTAyZMnGTRoEADR0dEcOnSI9PR0oqKiUKlU+Pv7YzKZKCkpsWXZVnX+yjX+a/P3fHAwm1GhHdn6RKSEhRDC6my6h/HJJ5/g6+vLiBEjWLNmDfDTFco/H4vXaDRUVFSg1+vRarV1r/v5cV9f3xv+brVahVbr0aL1Xz+eU7OPZzIrfHQ4m4RdZ9C4qHn3sVu5r1/XZh3jZlqiJ1tytH5AerIHjtSPTQMjKSkJlUrF4cOHycjIYPbs2dftORgMBry9vfH09MRgMFz3uJeX101/t8mkWPVucVqtR7OOd7G0ktf/ncmx/HJG9uzAy2NC6aBxseuebM3R+gHpyR7YWz9+fjf+bLXpIalNmzaxceNGNmzYQJ8+fVi8eDHR0dGkpqYCkJycTGRkJP379yclJQWz2Ux+fj5ms/mmexf2zKwobPsuj4n/TCfrioHX7+3FkofD6aBxsXVpQog2zuYnvX9r9uzZzJs3j4SEBHQ6HTExMajVaiIjI4mNjcVsNhMfH2/rMltEsb6a1/99miM5pQzp4cOrd4fR2UuuqxBCtA4qRVEUWxfREoxGk10dvtl/9goL/pNJVa2ZF+7QMS6iq82vq7C3Xen6OFo/ID3ZA3vr52aHpFrdHkZbU2U0sXz/OZKOFRDmp2Hh/X0I7uAYJ8iEEI5FAsOGMi/pefWLDLJLKpkU2Z2/Du+Bi7PNZzoLIcTvksCwAbOisOnbXFamZOPj0Y73H+nHoCAfW5clhBA3JYFhZVcMNcR/cYq0C1e5I6QDr9wdhlaW9hBC2AEJDCtKv3iVVz4/hb66lrljQhnbr4vNT2wLIYSlJDCswKwo/HfaRVYdzKa71p0Vf+xLqJ+nrcsSQogGkcBoYVevGYn/8hSHs0u5u5cfc+8OReMi/9mFEPZHPrla0MnCCmbtOElppZE5d4W0imsrhBCisSQwWsgXP17iza9O00HjwvoJt9G7883XvhJCiNZOAqOZ1ZoVEpPPsyk9lwEB7XnrgXC0HjILSghh/yQwmlFZpZFXPs8gNecqsbf7M32kDme1XIgnhHAMEhjNJPuKgSn/+o6C8mpevTuUh6143wohhLAGCYxm8EN+OS/uOImiwKrHIri1W3tblySEEM1OAqOJ9p0p5tUvTtHF2413xt5CgI+7rUsSQogWIYHRBJ+dKGThV6cJ7+LFuicGojbW2rokIYRoMXJGtpE+/j6fN/5zmoGBWlY+GiF3xBNCODzZw2iEf6Xn8s6+c4zQ+bLowXBcZUlyIUQbIIHRQP9zvIB39p1jdFhHFtzXm3YybVYI0UZIYDTA15lFLPr6DMODfVl4X2+5xkII0abIJ56F0i9eJf6LU9zazZu3HuwjYSGEaHPkU88CBeVVzPksgwCtOwlj++LWTm3rkoQQwuokMOpRZTQxa8eP1JrNLHk4HC83OYonhGib5NOvHiuSz5N5WU/CH24hyNfD1uUIIYTN2DQwjEYjc+fOJS8vj5qaGqZOnUpISAhz5sxBpVIRGhrK/PnzcXJyIjExkX379uHs7MzcuXOJiIho8fq+zy1j+/f5PHa7P1G6Di0+nhBCtGY2DYydO3ei1WpZsmQJV69eZezYsfTu3Zvp06czePBg4uPj2b17N/7+/qSlpbF9+3YKCgqIi4sjKSmpRWurqTWz8KvTdPF25W9RwS06lhBC2AObBsY999xDTEwMAIqioFarOXnyJIMGDQIgOjqagwcPEhwcTFRUFCqVCn9/f0wmEyUlJfj6+rZYbf/vZCE5pZUs/0NfPFzkJLcQQtg0MDQaDQB6vZ5p06Yxffp0Fi9eXHcbU41GQ0VFBXq9Hq1We93rKioqbhoYarUKrbZx5xyMJjMbvs3j1u7tue/2bhbdVlWtdmr0eK2Vo/XkaP2A9GQPHKkfm5/0Ligo4Nlnn2XixIk8+OCDLFmypO45g8GAt7c3np6eGAyG6x738rr5LU9NJoWrV681qqb/ZFwm92olL9yho6ys0qLXaLUejR6vtXK0nhytH5Ce7IG99ePnd+PPVptOqy0uLmbKlCm89NJLPPLIIwCEh4eTmpoKQHJyMpGRkfTv35+UlBTMZjP5+fmYzeYWPRz1RcYl/L1dGaFruTGEEMLe2HQPY9WqVZSXl7Ny5UpWrlwJwCuvvMLChQtJSEhAp9MRExODWq0mMjKS2NhYzGYz8fHxLVZTda2Zby9c5ZHb/C06FCWEEG2FSlEUxdZFtASj0dSo3cBjeWU8teUYSx++hZEhlk+ltbfdTks4Wk+O1g9IT/bA3vpptYekWqOc0p/OWeg6OMZJKiGEaC4SGL9RVmkEwFfTzsaVCCFE6yKB8RvhXbwY08sPD1lgUAghrmPzabWtzYAALQMCtLYuQwghWh3ZwxBCCGERCQwhhBAWkcAQQghhEQkMIYQQFpHAEEIIYREJDCGEEBaRwBBCCGERCQwhhBAWcdjFB4UQQjQv2cMQQghhEQkMIYQQFpHAEEIIYREJDCGEEBaRwBBCCGERCQwhhBAWkcAQQghhEbmBkoWMRiNz584lLy+Pmpoapk6dSkhICHPmzEGlUhEaGsr8+fNxcnIiMTGRffv24ezszNy5c4mIiLB1+Td15coVxo0bx/r163F2drb7nlavXs2ePXswGo1MmDCBQYMG2W1PRqOROXPmkJeXh5OTEwsWLLDr9+jYsWMsXbqUDRs2kJOTY3EfN9rW1n7dT0ZGBgsWLECtVuPi4sLixYvp2LEj27ZtY8uWLTg7OzN16lRGjRpFSUkJM2fOpKqqik6dOrFo0SLc3d1t3U79FGGRjz/+WFm4cKGiKIpSWlqqjBw5UnnmmWeUI0eOKIqiKPPmzVO++uor5cSJE8qkSZMUs9ms5OXlKePGjbNl2fWqqalR/va3vyl33323cvbsWbvv6ciRI8ozzzyjmEwmRa/XK++9955d9/T1118r06ZNUxRFUVJSUpTnnnvObvtZs2aN8sADDyiPPvqooihKg/r4vW1t7bf9PP7448qPP/6oKIqibN68Wfn73/+uXL58WXnggQeU6upqpby8vO7/L1iwQElKSlIURVFWr16tfPjhh7Zqo0FsH9F24p577uH5558HQFEU1Go1J0+eZNCgQQBER0dz6NAh0tPTiYqKQqVS4e/vj8lkoqSkxJal39TixYsZP348nTp1ArD7nlJSUggLC+PZZ5/lr3/9K3fccYdd9xQcHIzJZMJsNqPX63F2drbbfgIDA1mxYkXdzw3p4/e2tbXf9pOQkECfPn0AMJlMuLq6cvz4cW6//XZcXFzw8vIiMDCQU6dOkZ6ezogRI4DW048lJDAspNFo8PT0RK/XM23aNKZPn46iKKhUqrrnKyoq0Ov1eHp6Xve6iooKW5V9U5988gm+vr51f7iA3fdUWlrKiRMnePfdd3n99deZOXOmXffk4eFBXl4e9957L/PmzWPSpEl2209MTAzOzr8cBW9IH7+3ra39tp+fv3QdPXqUjRs38uSTT6LX6/Hy8qrbRqPRoNfrr3u8tfRjCTmH0QAFBQU8++yzTJw4kQcffJAlS5bUPWcwGPD29sbT0xODwXDd47/+g2lNkpKSUKlUHD58mIyMDGbPnn3dt1J77Emr1aLT6XBxcUGn0+Hq6kphYWHd8/bW00cffURUVBQvvvgiBQUFPPHEExiNxrrn7a2fX/v1OYj6+vi9bVujL774gg8++IA1a9bg6+t7w35+ftzNza1V9/NbsodhoeLiYqZMmcJLL73EI488AkB4eDipqakAJCcnExkZSf/+/UlJScFsNpOfn4/ZbMbX19eWpd/Qpk2b2LhxIxs2bKBPnz4sXryY6Ohou+5pwIABHDhwAEVRuHTpEpWVlQwdOtRue/L29q774G/fvj21tbV2/3f3s4b08XvbtjY7duyo+/cUEBAAQEREBOnp6VRXV1NRUUFWVhZhYWH079+f/fv3Az/1M2DAAFuWbjFZrdZCCxcu5Msvv0Sn09U99sorr7Bw4UKMRiM6nY6FCxeiVqtZsWIFycnJmM1mXn755Vb5x/1bkyZN4rXXXsPJyYl58+bZdU9vv/02qampKIrCCy+8QPfu3e22J4PBwNy5cykqKsJoNDJ58mT69u1rt/3k5uYyY8YMtm3bxvnz5y3u40bb2trP/WzevJmhQ4fStWvXur2FgQMHMm3aNLZt28bWrVtRFIVnnnmGmJgYiouLmT17NgaDAR8fH5YtW4aHh4eNu6mfBIYQQgiLyCEpIYQQFpHAEEIIYREJDCGEEBaRwBBCCGERCQwhhBAWkcAQQghhEbnSW4gWsGXLFhITE+nQoQOKotCjRw9eeumlugu6hLBHsochRAs4ffo006ZNY8eOHezcuZOhQ4cSFxeHXPYk7JkEhhAtIDMzk9DQ0LqfJ0yYQHFxMQUFBTasSoimkcAQogWcOXPmusAAcHV1pby83EYVCdF0EhhCNLOCggI8PDyuW6bbaDRSVFRE9+7d6x47cOAAn376KQDLly+/7nfIoSvRGslJbyGa2enTp//P3sUnn3zCkCFDcHFx4Y033sDT05Njx44RHx9PUVERtbW1FBUV8dxzzzF69GjGjh1bd38FIVoLCQwhmtlvz1+kpKSwevVq1q5dy+bNmxk7diwRERFMmDABnU7HgQMH6NOnDxkZGdx///1MnjzZhtULcWMSGEI0s8zMTNLS0jhy5AiKoqDT6Vi3bh06nY61a9cyfvx49Ho9Pj4+qFQqTp06xejRo9m9ezejR4+2dflC3JAEhhDNbNmyZTd8Lioqivnz5+Pm5kaPHj0AyM7OJjg4uO5/hWit5H4YQgghLCKzpIQQQlhEAkMIIYRFJDCEEEJYRAJDCCGERSQwhBBCWEQCQwghhEUkMIQQQlhEAkMIIYRFJDCEEEJY5P8D3CX1Gayw2EEAAAAASUVORK5CYII=\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Relação entre C e os valores de A e B\n",
    "\n",
    "ab_cols = [r'$D_{dir}$',r'$D_{dir}(D_{vert},D_{hor})$']\n",
    "\n",
    "ab_frame = pd.DataFrame(np.array([\n",
    "    expTest.directDistanceSamples,\n",
    "    ab_ratio\n",
    "]))\n",
    "\n",
    "ab_transposed = ab_frame.T\n",
    "ab_transposed.columns = ab_cols\n",
    "\n",
    "sns.lineplot(\n",
    "    x=ab_transposed.columns[0],\n",
    "    y=ab_transposed.columns[1],\n",
    "    data = ab_transposed\n",
    ")\n",
    "\n",
    "\n",
    "#plt.plot(expTest.directDistanceSamples,ab_ratio,'-gD',markevery=markers)\n",
    "#plt.ylabel(\"C [mm]\")\n",
    "#plt.title(\"C(A,B) [mm]\")\n",
    "#plt.xlabel(r'$A + B$')"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-07-05T16:43:49.775595600Z",
     "start_time": "2023-07-05T16:43:49.302568300Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Preview"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [
    {
     "data": {
      "text/plain": "       $D_{dir}$  $D_{dir}(D_{vert},D_{hor})$\n0     150.000000                   150.000000\n1     150.253528                   158.724832\n2     151.011558                   167.449664\n3     152.266557                   176.174497\n4     154.006374                   184.899329\n..           ...                          ...\n145  1273.962208                  1415.100671\n146  1282.626763                  1423.825503\n147  1291.292129                  1432.550336\n148  1299.958291                  1441.275168\n149  1308.625233                  1450.000000\n\n[150 rows x 2 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>$D_{dir}$</th>\n      <th>$D_{dir}(D_{vert},D_{hor})$</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>150.000000</td>\n      <td>150.000000</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>150.253528</td>\n      <td>158.724832</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>151.011558</td>\n      <td>167.449664</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>152.266557</td>\n      <td>176.174497</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>154.006374</td>\n      <td>184.899329</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>145</th>\n      <td>1273.962208</td>\n      <td>1415.100671</td>\n    </tr>\n    <tr>\n      <th>146</th>\n      <td>1282.626763</td>\n      <td>1423.825503</td>\n    </tr>\n    <tr>\n      <th>147</th>\n      <td>1291.292129</td>\n      <td>1432.550336</td>\n    </tr>\n    <tr>\n      <th>148</th>\n      <td>1299.958291</td>\n      <td>1441.275168</td>\n    </tr>\n    <tr>\n      <th>149</th>\n      <td>1308.625233</td>\n      <td>1450.000000</td>\n    </tr>\n  </tbody>\n</table>\n<p>150 rows × 2 columns</p>\n</div>"
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ab_transposed"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-07-05T16:43:49.835581700Z",
     "start_time": "2023-07-05T16:43:49.780615Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Distância Estimada - D [mm]"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Fórmula"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [
    {
     "data": {
      "text/plain": "<IPython.core.display.Latex object>",
      "text/latex": "\\begin{aligned}\n    \\begin{equation}\n        \\huge \\mathbf{D} = D_{dir} \\cdot \\Bigl[ 1 + e^{- \\bigl( \\frac{D_{vert}}{B_{width} - 2\\cdot D_{hor}} \\bigr) } \\Bigr] \\cdot K\n    \\end{equation}\n\\end{aligned}\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%latex\n",
    "\\begin{aligned}\n",
    "    \\begin{equation}\n",
    "        \\huge \\mathbf{D} = D_{dir} \\cdot \\Bigl[ 1 + e^{- \\bigl( \\frac{D_{vert}}{B_{width} - 2\\cdot D_{hor}} \\bigr) } \\Bigr] \\cdot K\n",
    "    \\end{equation}\n",
    "\\end{aligned}"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-07-05T16:43:49.836582200Z",
     "start_time": "2023-07-05T16:43:49.811578200Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "##### Onde\n",
    "\n",
    "- $ \\large D$ = Distância Estimada [mm]\n",
    "- $ \\large D_{dir}$ = Distância direta entre $P_{1}$ e $P_{2}$\n",
    "- $ \\large D_{vert}$ = Distância vertical entre $P_{1}$ e $P_{2}$\n",
    "- $ \\large D_{hor}$ = Distância horizontal entre $P_{1}$ e $P_{2}$\n",
    "- $ \\large K$ = Ganho Limite\n",
    "- $ \\large B_{width}$ = Largura atual da caixa"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "outputs": [
    {
     "data": {
      "text/plain": "<AxesSubplot:xlabel='$D_{dir} [mm]$', ylabel='D[mm]'>"
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "d_estimated_cols = [r\"$D_{dir} [mm]$\",r'D[mm]']\n",
    "\n",
    "d_estimated_frame = pd.DataFrame(np.array([\n",
    "    expTest.directDistanceSamples,\n",
    "    estimated_dist_no_gain\n",
    "])).T\n",
    "\n",
    "d_estimated_frame.columns = d_estimated_cols\n",
    "\n",
    "sns.lineplot(\n",
    "    x=d_estimated_frame.columns[0],\n",
    "    y=d_estimated_frame.columns[1],\n",
    "    data=d_estimated_frame\n",
    ")\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-07-05T16:43:50.134585800Z",
     "start_time": "2023-07-05T16:43:49.854583400Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Preview"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "outputs": [
    {
     "data": {
      "text/plain": "     $D_{dir} [mm]$        D[mm]\n0        150.000000   187.500000\n1        150.253528   187.816910\n2        151.011558   188.764448\n3        152.266557   190.333196\n4        154.006374   192.507967\n..              ...          ...\n145     1273.962208  1592.452760\n146     1282.626763  1603.283454\n147     1291.292129  1614.115162\n148     1299.958291  1624.947864\n149     1308.625233  1635.781541\n\n[150 rows x 2 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>$D_{dir} [mm]$</th>\n      <th>D[mm]</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>150.000000</td>\n      <td>187.500000</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>150.253528</td>\n      <td>187.816910</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>151.011558</td>\n      <td>188.764448</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>152.266557</td>\n      <td>190.333196</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>154.006374</td>\n      <td>192.507967</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>145</th>\n      <td>1273.962208</td>\n      <td>1592.452760</td>\n    </tr>\n    <tr>\n      <th>146</th>\n      <td>1282.626763</td>\n      <td>1603.283454</td>\n    </tr>\n    <tr>\n      <th>147</th>\n      <td>1291.292129</td>\n      <td>1614.115162</td>\n    </tr>\n    <tr>\n      <th>148</th>\n      <td>1299.958291</td>\n      <td>1624.947864</td>\n    </tr>\n    <tr>\n      <th>149</th>\n      <td>1308.625233</td>\n      <td>1635.781541</td>\n    </tr>\n  </tbody>\n</table>\n<p>150 rows × 2 columns</p>\n</div>"
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d_estimated_frame"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-07-05T16:43:50.135587900Z",
     "start_time": "2023-07-05T16:43:50.053589300Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Comprimento Estimado em Função do Ganho limite (K)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "outputs": [
    {
     "data": {
      "text/plain": "<AxesSubplot:xlabel='K', ylabel='D [mm]'>"
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plt.plot(expTest.gainSpace,last_y_k_Values,'-gD',markevery=markers)\n",
    "#plt.ylabel(\"D [mm]\")\n",
    "#plt.title(rf\"$D(150)(K)$\")\n",
    "#plt.xlabel('K')\n",
    "\n",
    "estimated_k_related_framecols = [rf\"K\",'D [mm]']\n",
    "estimated_k_related_frame = pd.DataFrame(np.array([\n",
    "    expTest.gainSpace,\n",
    "    last_y_k_Values\n",
    "])).T\n",
    "\n",
    "estimated_k_related_frame.columns = estimated_k_related_framecols\n",
    "\n",
    "sns.lineplot(\n",
    "    x=estimated_k_related_frame.columns[0],\n",
    "    y=estimated_k_related_frame.columns[1],\n",
    "    data=estimated_k_related_frame\n",
    ")\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-07-05T16:43:50.389592400Z",
     "start_time": "2023-07-05T16:43:50.062587700Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Preview"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "outputs": [
    {
     "data": {
      "text/plain": "            K       D [mm]\n0    0.000000     0.000000\n1    0.006711    10.978400\n2    0.013423    21.956799\n3    0.020134    32.935199\n4    0.026846    43.913598\n..        ...          ...\n145  0.973154  1591.867943\n146  0.979866  1602.846342\n147  0.986577  1613.824742\n148  0.993289  1624.803141\n149  1.000000  1635.781541\n\n[150 rows x 2 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>K</th>\n      <th>D [mm]</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0.006711</td>\n      <td>10.978400</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0.013423</td>\n      <td>21.956799</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0.020134</td>\n      <td>32.935199</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0.026846</td>\n      <td>43.913598</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>145</th>\n      <td>0.973154</td>\n      <td>1591.867943</td>\n    </tr>\n    <tr>\n      <th>146</th>\n      <td>0.979866</td>\n      <td>1602.846342</td>\n    </tr>\n    <tr>\n      <th>147</th>\n      <td>0.986577</td>\n      <td>1613.824742</td>\n    </tr>\n    <tr>\n      <th>148</th>\n      <td>0.993289</td>\n      <td>1624.803141</td>\n    </tr>\n    <tr>\n      <th>149</th>\n      <td>1.000000</td>\n      <td>1635.781541</td>\n    </tr>\n  </tbody>\n</table>\n<p>150 rows × 2 columns</p>\n</div>"
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "estimated_k_related_frame"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-07-05T16:43:50.391595400Z",
     "start_time": "2023-07-05T16:43:50.273592200Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Dataframe dos Resultados"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "outputs": [],
   "source": [
    "arr = [\n",
    "    expTest.verticalSamples,\n",
    "    expTest.directDistanceSamples,\n",
    "    estimated_dist_no_gain,\n",
    "    ab_ratio,\n",
    "    last_y_k_Values,\n",
    "    estimated_dist_ratio\n",
    "]\n",
    "\n",
    "columns = [\"Vertical Distance [B]\",\"Direct Distance [C]\",\"Estimated Distance [D]\",\"A + B\",\"D(150)(K)\",\"D/C\"]\n",
    "\n",
    "ar = np.array(arr)\n",
    "\n",
    "df = pd.DataFrame(ar).T\n",
    "df.columns = columns"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-07-05T16:43:50.391595400Z",
     "start_time": "2023-07-05T16:43:50.310592700Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Resultado Completo"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "outputs": [
    {
     "data": {
      "text/plain": "     Vertical Distance [B]  Direct Distance [C]  Estimated Distance [D]  \\\n0                 0.000000           150.000000              187.500000   \n1                 8.724832           150.253528              187.816910   \n2                17.449664           151.011558              188.764448   \n3                26.174497           152.266557              190.333196   \n4                34.899329           154.006374              192.507967   \n..                     ...                  ...                     ...   \n145            1265.100671          1273.962208             1592.452760   \n146            1273.825503          1282.626763             1603.283454   \n147            1282.550336          1291.292129             1614.115162   \n148            1291.275168          1299.958291             1624.947864   \n149            1300.000000          1308.625233             1635.781541   \n\n           A + B    D(150)(K)   D/C  \n0     150.000000     0.000000  1.25  \n1     158.724832    10.978400  1.25  \n2     167.449664    21.956799  1.25  \n3     176.174497    32.935199  1.25  \n4     184.899329    43.913598  1.25  \n..           ...          ...   ...  \n145  1415.100671  1591.867943  1.25  \n146  1423.825503  1602.846342  1.25  \n147  1432.550336  1613.824742  1.25  \n148  1441.275168  1624.803141  1.25  \n149  1450.000000  1635.781541  1.25  \n\n[150 rows x 6 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Vertical Distance [B]</th>\n      <th>Direct Distance [C]</th>\n      <th>Estimated Distance [D]</th>\n      <th>A + B</th>\n      <th>D(150)(K)</th>\n      <th>D/C</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0.000000</td>\n      <td>150.000000</td>\n      <td>187.500000</td>\n      <td>150.000000</td>\n      <td>0.000000</td>\n      <td>1.25</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>8.724832</td>\n      <td>150.253528</td>\n      <td>187.816910</td>\n      <td>158.724832</td>\n      <td>10.978400</td>\n      <td>1.25</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>17.449664</td>\n      <td>151.011558</td>\n      <td>188.764448</td>\n      <td>167.449664</td>\n      <td>21.956799</td>\n      <td>1.25</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>26.174497</td>\n      <td>152.266557</td>\n      <td>190.333196</td>\n      <td>176.174497</td>\n      <td>32.935199</td>\n      <td>1.25</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>34.899329</td>\n      <td>154.006374</td>\n      <td>192.507967</td>\n      <td>184.899329</td>\n      <td>43.913598</td>\n      <td>1.25</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>145</th>\n      <td>1265.100671</td>\n      <td>1273.962208</td>\n      <td>1592.452760</td>\n      <td>1415.100671</td>\n      <td>1591.867943</td>\n      <td>1.25</td>\n    </tr>\n    <tr>\n      <th>146</th>\n      <td>1273.825503</td>\n      <td>1282.626763</td>\n      <td>1603.283454</td>\n      <td>1423.825503</td>\n      <td>1602.846342</td>\n      <td>1.25</td>\n    </tr>\n    <tr>\n      <th>147</th>\n      <td>1282.550336</td>\n      <td>1291.292129</td>\n      <td>1614.115162</td>\n      <td>1432.550336</td>\n      <td>1613.824742</td>\n      <td>1.25</td>\n    </tr>\n    <tr>\n      <th>148</th>\n      <td>1291.275168</td>\n      <td>1299.958291</td>\n      <td>1624.947864</td>\n      <td>1441.275168</td>\n      <td>1624.803141</td>\n      <td>1.25</td>\n    </tr>\n    <tr>\n      <th>149</th>\n      <td>1300.000000</td>\n      <td>1308.625233</td>\n      <td>1635.781541</td>\n      <td>1450.000000</td>\n      <td>1635.781541</td>\n      <td>1.25</td>\n    </tr>\n  </tbody>\n</table>\n<p>150 rows × 6 columns</p>\n</div>"
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-07-05T16:43:50.392592600Z",
     "start_time": "2023-07-05T16:43:50.311593400Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Média de Sobras Absoluta"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "outputs": [
    {
     "data": {
      "text/plain": "'25.0%'"
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f'{abs(1 - df[\"D/C\"].mean())*100}%'"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-07-05T16:43:50.393593300Z",
     "start_time": "2023-07-05T16:43:50.327593900Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-07-05T16:43:50.393593300Z",
     "start_time": "2023-07-05T16:43:50.354591900Z"
    }
   }
  }
 ],
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