r2 testes.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "def rsquare(x,z,degree=1):\n",
    "    '''\n",
    "    Entrada:\n",
    "    x = posição em x\n",
    "    z = posição em z ou y\n",
    "    degree = ordem da função\n",
    "    Saída:\n",
    "    r2 = r quadrado\n",
    "    coeffs[0] = a inclinação\n",
    "    coeffs[1] = b\n",
    "    '''\n",
    "    coeffs = np.polyfit(x,z,degree)\n",
    "    p = np.poly1d(coeffs)\n",
    "    zhat = p(x)\n",
    "    zbar = np.sum(z)/len(z)\n",
    "    ssreg = np.sum((zhat-zbar)**2)\n",
    "    sstot = np.sum((z-zbar)**2)\n",
    "    r2 = ssreg/sstot\n",
    "    return r2, coeffs[degree-1:degree+1]\n",
    "def selectway(data,anchor,theta,error):\n",
    "    '''\n",
    "    Entrada:\n",
    "    data = matriz[m,n]\n",
    "    anchor = ponto de referência [i,j]\n",
    "    theta = ângulo de rotação\n",
    "    error = erro aceitável\n",
    "    Saída:\n",
    "    x = posição x\n",
    "    z = posição z\n",
    "    '''\n",
    "    #equação da reta y = tan(thetta)*x+b\n",
    "    b = anchor[1]-round(np.tan(np.radians(theta)),2)*anchor[0]\n",
    "    x, z = np.zeros(len(data[0,:])*3), np.zeros(len(data[1,:])*3)\n",
    "    count = 0\n",
    "    for i in range(0,len(data[0,:])):\n",
    "         for j in range(0,len(data[1,:])):\n",
    "                if b-error<=j-round(np.tan(np.radians(theta)),2)*i and j-round(np.tan(np.radians(theta)),2)*i<=b+error:\n",
    "                    x[count] = round(np.cos(np.radians(theta))*i + np.sin(np.radians(theta))*j,2)\n",
    "                    z[count] = data[i,j]\n",
    "                    count+=1\n",
    "    z = z[0:np.count_nonzero(z)]\n",
    "    x = x[0:len(z)]\n",
    "    return x, z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r2 0.12346120065061567\n",
      "coeffs [0.24751348 2.82212098]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = np.random.randint(10, size=(10,10)) #cria matriz aleatoria\n",
    "#plot 2d\n",
    "plt.figure(figsize=(10,10))\n",
    "plt.imshow(data)\n",
    "#calcula rsquare na direção theta\n",
    "x,z = selectway(data,[0,0],45,0.5)\n",
    "r2, coeffs = rsquare(x,z)\n",
    "print('r2',r2)\n",
    "print('coeffs',coeffs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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