control-metrics-LBFGSB-Testes-Extras.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div align='center'>\n",
    "    <h2>Controle Quântico Ótimo</h2>\n",
    "    <h4>Utilização do Algoritmo Genético para obtenção dos valores de controle</h4>\n",
    "</div>\n",
    "\n",
    "<div align='justify'>\n",
    "    <p>Para que haja um controle ótimo de uma partícula, supondo que esta esteja em um estado inicial, é necessário formas de controle suficientemente eficientes já que a particula é a função de onda da partícula é definida em função do tempo. Para que isso seja possível utiliza-se a eficiência do algoritmo genético que pode usar ou não utilizar-se do elitismo juntamente com o controle preditivo, MPC. </p>\n",
    "    <p>Abaixo são revelados os parâmetros para esse experimento, do qual podem ser ajustados:</p>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Bilbiotecas para auxílio na programação matemática\n",
    "import math, sys \n",
    "import numpy as np\n",
    "import sympy as sp\n",
    "import cmath\n",
    "import time\n",
    "\n",
    "from scipy import sparse # Produção das diagonais das matrizes\n",
    "from scipy.sparse import diags \n",
    "\n",
    "# Plotagem 2D e 3D\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import cm\n",
    "\n",
    "\n",
    "from os import path # Suficiente para manipulação de arquivos\n",
    "    \n",
    "# Para solução exata\n",
    "from scipy.special import hermite\n",
    "from math import factorial\n",
    "\n",
    "%matplotlib inline\n",
    "count = 0\n",
    "\n",
    "# Para otimização dos sistemas\n",
    "from scipy import optimize\n",
    "from random import randint, uniform, random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "## CONSTANTES PARA O CONTROLADOR\n",
    "\n",
    "# Considerações: constante de Planck verdadeira: 1, massa: 1\n",
    "\n",
    "TEMPO_ANALISE = 10 \n",
    "QUANTIDADE_PONTOS_AMOSTRAGEM = 100\n",
    "PASSO = TEMPO_ANALISE/QUANTIDADE_PONTOS_AMOSTRAGEM\n",
    "\n",
    "MIN_HORIZONTE = 3 # Horizonte mínimo da análise\n",
    "MAX_HORIZONTE = 10 # Horizonte máximo da análise\n",
    "\n",
    "ONDA_DESEJADA = [[complex(1.0,0.0)],[complex(0.0,0.0)]] # [[c1];[c2]] = [[0.707],[0.707]]\n",
    "\n",
    "# Determinação dos valores de início e fim da análise\n",
    "num = PASSO\n",
    "contador = 0\n",
    "while num < 1:\n",
    "    num *= 10\n",
    "    contador += 1\n",
    "\n",
    "EXPOENTE = contador\n",
    "INICIO_ANALISE = 0*10**(-EXPOENTE) # Tempo inicial da analise em um horizonte\n",
    "FINAL_ANALISE = (1*10**(-EXPOENTE))+PASSO # Tempo final da analise em um horizonte"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Manipulação das matrizes, soma e subtração\n",
    "\n",
    "def somar(A, B):\n",
    "    C = []\n",
    "    num_linhas_a = len(A)\n",
    "    num_colunas_a = len(A[0])\n",
    "    \n",
    "    for i in range (num_linhas_a):\n",
    "        linha = [0]*num_colunas_a\n",
    "        C.append(linha)\n",
    "        for j in range(num_colunas_a):\n",
    "            C[i][j] = A[i][j] + B[i][j]\n",
    "\n",
    "    return C\n",
    "\n",
    "def sub(A, B):\n",
    "    C = []\n",
    "    num_linhas_a = len(A)\n",
    "    num_colunas_a = len(A[0])\n",
    "    \n",
    "    for i in range (num_linhas_a):\n",
    "        linha = [0]*num_colunas_a\n",
    "        C.append(linha)\n",
    "        for j in range(num_linhas_a):\n",
    "            C[i][j] = A[i][j] - B[i][j]\n",
    "\n",
    "    return C"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div align='justify'>\n",
    "    <h4>Runge-Kutta de quarta ordem</h4>\n",
    "    <p>Nos procedimentos com estados quânticos, é necessário utilizar-se da equação de Schrödinger com o o objetivo de avaliar o valor da função de onda com o tempo. Sabe-se que sua equação se dá pela função de onda relacionada com a posição e tempo, no entanto, utilizando a notação de Dirac, a posição se torna um atributo irrelevante para a análise já que analisaremos níveis. Portanto: </p>\n",
    "</div>\n",
    "\n",
    "$$\\frac{\\partial{\\ket{\\psi(t)}}}{\\partial{t}} = -iH\\ket{\\psi(t)}$$\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "## psi' = -i * H * ket{psi}\n",
    "def dpsi_dt(t, psi, H): # A derivada da onda em relação ao tempo não tem dependência temporal\n",
    "    A = np.zeros((2,2), dtype=np.complex128)\n",
    "    A = np.dot(complex(0,1),H) # i * H\n",
    "    return -1*np.matmul(A,psi) # - i * H * ket{psi}\n",
    "\n",
    "# Runge-Kutta de quarta ordem\n",
    "\n",
    "def runge_kutta(onda, fator_runge_kutta, hamiltoniano, tempo_inicial = 0):\n",
    "    \n",
    "    # onda = [[c0],[c1]]\n",
    "    \n",
    "    k1 = dpsi_dt(tempo_inicial, onda, hamiltoniano)\n",
    "    k2 = dpsi_dt(tempo_inicial + 0.5 * fator_runge_kutta, somar(onda, np.dot((0.5*fator_runge_kutta), k1)), hamiltoniano)\n",
    "    k3 = dpsi_dt(tempo_inicial + 0.5 * fator_runge_kutta, somar(onda, np.dot((0.5*fator_runge_kutta), k2)), hamiltoniano)\n",
    "    k4 = dpsi_dt(tempo_inicial + fator_runge_kutta, somar(onda, np.dot(fator_runge_kutta, k3)), hamiltoniano)\n",
    "    \n",
    "    ## y(i+1) = y(i) + h/6*(k1+2*k2+2*k3+k4)\n",
    "    \n",
    "    A = somar(np.dot(2,k3), k4)\n",
    "    B = somar(np.dot(2,k2), k1)\n",
    "    C = somar(A, B)\n",
    "\n",
    "    runge = somar(onda,np.dot((fator_runge_kutta / 6.0),(C)))\n",
    "        \n",
    "    return runge"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div align='justify'>\n",
    "    <h4>Estabelecimento da função objetivo</h4>\n",
    "    <p>O estudo se baseia em uma função de erro, da qual exije a comparação de um sistema de dois níveis em um certo tempo em relação ao seu desejado. Para que isso seja possível estabelece-se a noção de controle preditivo.</p>\n",
    "    <p>O controle preditivo é um modelo que se utiliza da avaliação futura para reajustar uma curva em relação a sua referência, ou seja, esta família de controles tem a capacidade de visualizar instantes a frente para definir qual é a melhor rota. Com esse tipo de controle, a função objetivo estabelece-se da seguinte forma:</p>\n",
    "</div>\n",
    "\n",
    "$$\\min ||\\sum^{i+h}_{j = i} \\ket{\\psi(j)} - \\ket{\\psi_d(j)}||$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def func_objetivo(x, it, onda_desejada, hamiltoniano, \n",
    "                  onda_inicial, horizonte, fator_runge_kutta):\n",
    "    \n",
    "    fo = 0 # Variável para armazenar o valor do somatório da função objetivo\n",
    "    runge = np.zeros((2,1),dtype=np.complex128) # Armazena o próximo valor da iteração (runge-kutta)\n",
    "        \n",
    "    tempo = it   \n",
    "    \n",
    "    tempo_inicial = INICIO_ANALISE\n",
    "    tempo_final = FINAL_ANALISE\n",
    "    \n",
    "    controles = np.zeros((2,2), dtype=np.complex128)\n",
    "    controles = [[0, x[0]], [x[0], 0]]\n",
    "    \n",
    "    matriz_inicial = np.zeros((2,1),dtype=np.complex128)\n",
    "    matriz_inicial = [[onda_inicial[0][0]], [onda_inicial[1][0]]] \n",
    "\n",
    "    matriz_desejada = np.zeros((2,1),dtype=np.complex128)\n",
    "    \n",
    "    ## Função-Objetivo (Return) = somatorio ||(Matriz_Origem - Matriz_Destino)||^2\n",
    "    ## Avanço temporal\n",
    "    matriz_desejada[0][0] = onda_desejada[0][0]*np.exp(-1*complex(0,1)*(1/2*np.pi)*tempo)\n",
    "    matriz_desejada[1][0] = onda_desejada[1][0]*np.exp(-1*complex(0,1)*(3/2*np.pi)*tempo)\n",
    "\n",
    "    fo += (np.linalg.norm(matriz_inicial-matriz_desejada))\n",
    "    \n",
    "    runge = runge_kutta(matriz_inicial, fator_runge_kutta, somar(hamiltoniano, controles), tempo_inicial = tempo_inicial)\n",
    "    \n",
    "    matriz_inicial = runge\n",
    "\n",
    "    tempo_inicial = round(tempo_inicial+PASSO, EXPOENTE)\n",
    "    tempo_final = round(tempo_final+PASSO, EXPOENTE)\n",
    "    tempo = round(tempo+PASSO, EXPOENTE)\n",
    "    \n",
    "    while horizonte > 1: # Repetição para o tamanho do horizonte\n",
    "        \n",
    "        matriz_desejada[0][0] = onda_desejada[0][0]*np.exp(-1*complex(0,1)*(1/2*np.pi)*tempo)\n",
    "        matriz_desejada[1][0] = onda_desejada[1][0]*np.exp(-1*complex(0,1)*(3/2*np.pi)*tempo)\n",
    "        \n",
    "        fo += (np.linalg.norm(matriz_inicial-matriz_desejada))\n",
    "        \n",
    "        controles = [[0, x[len(x)-horizonte+1]],[x[len(x)-horizonte+1], 0]]\n",
    "\n",
    "        runge = runge_kutta(matriz_inicial, fator_runge_kutta, somar(hamiltoniano, controles),tempo_inicial = tempo_inicial)\n",
    "\n",
    "        matriz_inicial = runge\n",
    "\n",
    "        tempo_inicial = round(tempo_inicial+PASSO, EXPOENTE)\n",
    "        tempo_final = round(tempo_final+PASSO, EXPOENTE)\n",
    "        tempo = round(tempo+PASSO, EXPOENTE)\n",
    "        horizonte -= 1\n",
    "    \n",
    "    return fo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div align='justify'>\n",
    "<p>Para o funcionamento do MPC, cria-se um looping de forma a obter o melhor resultado para o ajuste da curva. Com esse valor em mãos, utiliza-se para os pontos da próxima iteração do método de controle além de efetivar o ajuste realizando o Runge-Kutta com o valor otimizado.</p>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div align='jusitfy'>\n",
    "    <h4>Avaliação dos controladores</h4>\n",
    "    <p>Como a função objetivo trata-se de uma minimização, é importante que o valor dos controladores tendam a zero de forma que a curva inicial alcance a desejada. Para isso, plota-se os valores da função objetivo com os controladores.</p>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def vfo(valor):\n",
    "    ts = np.linspace(0, TEMPO_ANALISE, QUANTIDADE_PONTOS_AMOSTRAGEM)\n",
    "\n",
    "    plt.ylabel('Função Objetivo') \n",
    "    plt.xlabel('Tempo') \n",
    "    plt.plot(ts, abs(valor[0])/abs(valor[0][0]), color='g', marker='o', linewidth=1, markersize=1, label=\"Valor da FO\")\n",
    "    plt.legend(loc=\"upper right\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div align='jusitfy'>\n",
    "    <h4>Avaliação das componentes</h4>\n",
    "    <p>Um grande problemas dos métodos de minimização está concentrado no cálculo numérico. O cálculo númerico se trata de um processo de aproximação de forma que quanto pior é a manipulação das variáveis, pior ficará a aproximação. Nesse sistema se torna perceptível a melhoria do ajuste através da desafagem de uma curva em relação a outra.</p>\n",
    "    <p>Conforme o livro, introdução à computação clássica e quântica de Thomas Wong (2022), a defasagem apresentada por um sistema quântico não tem significado físico se for a mesma para todo o sistema, ou seja, todas as componentes devem ter a mesma diferença de fases. Para realizar essa avaliação, se faz necessário propor uma diferença entre a curva original e o destino, tanto na primeira componente quanto na segunda, além de realizar a diferença entre essas partes.</p>\n",
    "    \n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def evolucao_desejada():\n",
    "    evolucao_d = np.zeros((2,QUANTIDADE_PONTOS_AMOSTRAGEM), dtype=np.complex128)\n",
    "    \n",
    "    tempo = 0\n",
    "    pos = 0\n",
    "    while tempo < TEMPO_ANALISE:\n",
    "        \n",
    "        evolucao_d[0][pos] = (ONDA_DESEJADA[0][0]*np.exp(-1*complex(0,1)*(1/2*np.pi)*tempo))\n",
    "        evolucao_d[1][pos] = (ONDA_DESEJADA[1][0]*np.exp(-1*complex(0,1)*(3/2*np.pi)*tempo))\n",
    "    \n",
    "        tempo = round(tempo+PASSO, EXPOENTE)\n",
    "        pos += 1\n",
    "        \n",
    "    return evolucao_d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_componentes(valores_onda, evolucao_d, componente):\n",
    "    \n",
    "    ts = np.linspace(0, TEMPO_ANALISE, QUANTIDADE_PONTOS_AMOSTRAGEM)\n",
    "    \n",
    "    real_inicial = [elemento.real for elemento in valores_onda[componente-1]]\n",
    "    imaginario_inicial = [elemento.imag for elemento in valores_onda[componente-1]]\n",
    "    \n",
    "    real_desejado = [elemento.real for elemento in evolucao_d[componente-1]]\n",
    "    imaginario_desejado = [elemento.imag for elemento in evolucao_d[componente-1]]\n",
    "    \n",
    "    # Plotagem\n",
    "\n",
    "    if componente == 1:\n",
    "        plt.subplot(2,1,1)\n",
    "    \n",
    "        plt.ylabel('Real') \n",
    "        plt.xlabel('Tempo (em segundos)') \n",
    "        plt.title(r'Componente $c_0$ $\\rightarrow$ real')\n",
    "        plt.plot(ts, real_inicial, color='r', marker='*', linewidth=1, markersize=5, label=\"Comportamento controlado\")\n",
    "        plt.plot(ts, real_desejado, color='b', marker='v',  linewidth=1, markersize=5, label=\"Comportamento desejado\")\n",
    "        plt.legend(loc='upper right')\n",
    "    \n",
    "        plt.grid()\n",
    "        \n",
    "        plt.subplot(2,1,2)\n",
    "    \n",
    "        plt.ylabel('Imaginário') \n",
    "        plt.xlabel('Tempo (em segundos)') \n",
    "        plt.title(r'Componente $c_0$ $\\rightarrow$ imaginária')\n",
    "        plt.plot(ts, imaginario_inicial, color='r', marker='*', linewidth=1, markersize=5, label=\"Comportamento controlado\")\n",
    "        plt.plot(ts, imaginario_desejado, color='b', marker='v',  linewidth=1, markersize=5, label=\"Comportamento desejado\")\n",
    "        plt.legend(loc='lower right')\n",
    "    \n",
    "        plt.grid()\n",
    "    \n",
    "        plt.tight_layout()\n",
    "        plt.show()\n",
    "\n",
    "    elif componente == 2:\n",
    "        plt.subplot(2,1,1)\n",
    "    \n",
    "        plt.ylabel('Real') \n",
    "        plt.xlabel('Tempo (em segundos)') \n",
    "        plt.title(r'Componente $c_1$ $\\rightarrow$ real')\n",
    "        plt.plot(ts, real_inicial, color='r', marker='*', linewidth=1, markersize=5, label=\"Comportamento controlado\")\n",
    "        plt.plot(ts, real_desejado, color='b', marker='v',  linewidth=1, markersize=5, label=\"Comportamento desejado\")\n",
    "        plt.legend(loc='upper right')\n",
    "    \n",
    "        plt.grid()\n",
    "        \n",
    "        plt.subplot(2,1,2)\n",
    "    \n",
    "        plt.ylabel('Imaginário') \n",
    "        plt.xlabel('Tempo (em segundos)') \n",
    "        plt.title(r'Componente $c_1$ $\\rightarrow$ imaginária')\n",
    "        plt.plot(ts, imaginario_inicial, color='r', marker='*', linewidth=1, markersize=5, label=\"Comportamento controlado\")\n",
    "        plt.plot(ts, imaginario_desejado, color='b', marker='v',  linewidth=1, markersize=5, label=\"Comportamento desejado\")\n",
    "        plt.legend(loc='lower right')\n",
    "    \n",
    "        plt.grid()\n",
    "    \n",
    "        plt.tight_layout()\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div align='justify'>\n",
    "    <h4>Gráficos - Avaliação da densidade de probabilidade para encontrar uma partícula no ponto x</h4>\n",
    "    <p> Conforme descrito durante os capítulos 1, 2 e 3 do livro Mecânica Quântica por David Griffths, a estatística é um dos principais pontos para o entendimento da mecânica quântica, dessa forma é interessante entender qual é a probabilidade de uma partícula estar em um determinado lugar em um certo tempo e para tal se define:</p>\n",
    "</div>\n",
    "\n",
    "$$\\int |\\Psi(x,t)|^{2}dx = 1$$ \n",
    "\n",
    "<div align='justify'>\n",
    "    <p>Ainda nessa perspectiva, foi definido que o resultado da equação de Schrödinger pode ser definido a partir da escolha do potêncial V(x,t) e ainda que os enumeros resultados podem formar outros por meio de uma combinação linear de forma que o conjunto do quadrado das constantes que o formam, em sua soma resultam em 1. Isso define a probabilidade de um estado. Para este estudo considerou-se dois estados:</p>\n",
    "</div>\n",
    "\n",
    "$$0,80\\psi_0(x,t)+0,60\\psi_1(x,t)$$\n",
    "\n",
    "<div align='justify'>\n",
    "    <p>de forma que para o primeiro estado a probabilidade é de 64% enquanto para o segundo é de 36%.</p>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def probabilidade(constantes):\n",
    "    \n",
    "    ts = np.linspace(0, TEMPO_ANALISE, QUANTIDADE_PONTOS_AMOSTRAGEM)\n",
    "    \n",
    "    dp = []\n",
    "    for i in range(0, QUANTIDADE_PONTOS_AMOSTRAGEM):\n",
    "        dp.append((abs(constantes[0][i])**2+abs(constantes[1][i])**2))\n",
    "\n",
    "    plt.ylabel('Densidade probabilidade') \n",
    "    plt.xlabel('Tempo') \n",
    "    plt.plot(ts, dp, color='r', marker='*', linewidth=1, markersize=1, label=\"Valor do somatório das constantes\")\n",
    "    plt.legend(loc=\"upper right\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div align='justify'>\n",
    "    <h4>Programa principal</h4>\n",
    "    <p>O programa principal utiliza a onda inicial e o horizonte desejado para encontrar o melhor valor para controladores por meio de um laço que chama, a todo momento, as funções observadas acima.</p>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def main(horizonte, onda_inicial):\n",
    "    \n",
    "    u = [] # Controladores iniciais\n",
    "    u.append(uniform(-0.75,0.75))\n",
    "    while len(u) < horizonte:\n",
    "        u.append(u[0])\n",
    "        \n",
    "    # O que será retornado por essa função\n",
    "    \n",
    "    vetor_controles = np.zeros((1, QUANTIDADE_PONTOS_AMOSTRAGEM), dtype=np.complex128) # Valores de controle\n",
    "    valores_funcao_objetivo = np.zeros((1, QUANTIDADE_PONTOS_AMOSTRAGEM), dtype=np.complex128) # Utilização do controle para comparação com o valor esperado\n",
    "    constantes = np.zeros((2, QUANTIDADE_PONTOS_AMOSTRAGEM), dtype=np.complex128) # Para verificar se as integrais estão resultando em 1\n",
    "    valores_onda = np.zeros((2, QUANTIDADE_PONTOS_AMOSTRAGEM), dtype=np.complex128)\n",
    "    \n",
    "    hamiltoniano = np.zeros((2,2), dtype=np.complex128)\n",
    "    hamiltoniano = [[(1/2*np.pi), 0], [0, (3/2*np.pi)]]\n",
    "    \n",
    "    tempo = 0\n",
    "    pos = 0\n",
    "    while tempo < TEMPO_ANALISE:\n",
    "        \n",
    "        # Variação do Psi\n",
    "        valores_onda[0][pos] = onda_inicial[0][0]\n",
    "        valores_onda[1][pos] = onda_inicial[1][0]\n",
    "        \n",
    "        # Obtenção das constantes que multiplicam Psi\n",
    "        # C_0 = Psi_0/[e^(1/2*pi*tempo)] equação 2.18 Griffiths\n",
    "        constantes[0][pos] = onda_inicial[0][0]/np.exp(-1*complex(0,1)*(1/2*np.pi)*tempo)\n",
    "        constantes[1][pos] = onda_inicial[1][0]/np.exp(-1*complex(0,1)*(3/2*np.pi)*tempo)\n",
    "        \n",
    "        ## Minimização\n",
    "\n",
    "        bounds = [(-0.75,0.75)]\n",
    "        resultado = optimize.minimize(func_objetivo, u, args=(tempo, ONDA_DESEJADA, hamiltoniano, onda_inicial, horizonte, PASSO), bounds=bounds, method='L-BFGS-B')\n",
    "        \n",
    "        vetor_controles[0][pos] = resultado.x[0]\n",
    "\n",
    "        valores_funcao_objetivo[0][pos] = func_objetivo(resultado.x, tempo, ONDA_DESEJADA, hamiltoniano, onda_inicial, horizonte, PASSO)\n",
    "        \n",
    "        controle = np.zeros((2,1), dtype=np.complex128)\n",
    "        controle = [[0, resultado.x[0]],[resultado.x[0], 0]]\n",
    "        hamiltoniano = somar(hamiltoniano, controle)\n",
    "\n",
    "        ## Adaptação da onda ao controle\n",
    "\n",
    "        onda_avancada = np.zeros((2,2), dtype=np.complex128)\n",
    "        onda_avancada = runge_kutta(onda_inicial, PASSO, hamiltoniano, tempo_inicial = INICIO_ANALISE)\n",
    "\n",
    "        onda_inicial[0][0] = onda_avancada[0][0]\n",
    "        onda_inicial[1][0] = onda_avancada[1][0]\n",
    "\n",
    "        # Reset\n",
    "        hamiltoniano = [[(1/2*np.pi), 0], [0, (3/2*np.pi)]]\n",
    "\n",
    "        u = [] # Controladores iniciais\n",
    "        while len(u) < horizonte:\n",
    "            u.append(resultado.x[0])\n",
    "        \n",
    "        pos += 1\n",
    "        tempo = round(tempo+PASSO, EXPOENTE)\n",
    "    \n",
    "    return vetor_controles, valores_funcao_objetivo, constantes, valores_onda\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div align='justify'>\n",
    "    <h4>Variação dos horizontes</h4>\n",
    "    <p>Como será observado a frente, a variação dos horizontes auxiliam na melhoria da defasagem apresentada pelo sistema, no entanto isso gera complicações no custo computacional.</p>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "desejo = evolucao_desejada()\n",
    "desejo_estr = np.zeros((2, QUANTIDADE_PONTOS_AMOSTRAGEM), dtype=np.complex128) \n",
    "tempo = 0\n",
    "pos = 0\n",
    "\n",
    "while tempo < TEMPO_ANALISE:\n",
    "    desejo_estr[0][pos] = ONDA_DESEJADA[0][0]\n",
    "    desejo_estr[1][pos] = ONDA_DESEJADA[1][0]\n",
    "\n",
    "    pos += 1\n",
    "    tempo = round(tempo+PASSO, EXPOENTE)\n",
    "\n",
    "# Primeiro experimento\n",
    "onda_inicial = [[complex(1/np.sqrt(2),0.0)],[complex(1/np.sqrt(2),0.0)]]\n",
    "ONDA_DESEJADA = [[complex(1.0,0.0)],[complex(0.0,0.0)]]\n",
    "vetor_controles_1, valores_funcao_objetivo, constantes, valores_onda = main(10, onda_inicial)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_componentes(valores_onda, desejo, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_componentes(valores_onda, desejo, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "probabilidade(constantes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "vfo(valores_funcao_objetivo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def acao_controle(valor):\n",
    "    ts = np.linspace(0, TEMPO_ANALISE, QUANTIDADE_PONTOS_AMOSTRAGEM)\n",
    "\n",
    "    plt.ylabel('Valor da lei de controle')\n",
    "    plt.xlabel('Tempo (em segundos)')\n",
    "    plt.plot(ts, valor[0][0], color='k', marker='o', linewidth=1, markersize=5, label=r\"$u(t) \\rightarrow$ Primeiro experimento\")\n",
    "    plt.plot(ts, valor[1][0], color='b', marker='^', linewidth=1, markersize=5, label=r\"$u(t) \\rightarrow$ Segundo experimento\")\n",
    "    plt.plot(ts, valor[2][0], color='g', marker='*', linewidth=1, markersize=5, label=r\"$u(t) \\rightarrow$ Terceiro experimento\")\n",
    "    plt.legend(loc=\"upper right\")\n",
    "    plt.title(f\"Evolução da lei de controle\")\n",
    "    plt.grid()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Segundo experimento\n",
    "onda_inicial = [[complex(0.0,0.0)],[complex(1.0,0.0)]]\n",
    "ONDA_DESEJADA = [[complex(1.0,0.0)],[complex(0.0,0.0)]]\n",
    "vetor_controles_2, valores_funcao_objetivo, constantes, valores_onda = main(10, onda_inicial)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Terceiro experimento\n",
    "onda_inicial = [[complex(1/np.sqrt(2),0.0)],[complex(-1/np.sqrt(2),0.0)]]\n",
    "ONDA_DESEJADA = [[complex(1.0,0.0)],[complex(0.0,0.0)]]\n",
    "vetor_controles_3, valores_funcao_objetivo, constantes, valores_onda = main(10, onda_inicial)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "valores_controle = [vetor_controles_1, vetor_controles_2, vetor_controles_3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/gabriel/.pyenv/versions/miniconda3-latest/lib/python3.12/site-packages/matplotlib/cbook.py:1762: ComplexWarning: Casting complex values to real discards the imaginary part\n",
      "  return math.isfinite(val)\n",
      "/home/gabriel/.pyenv/versions/miniconda3-latest/lib/python3.12/site-packages/matplotlib/cbook.py:1398: ComplexWarning: Casting complex values to real discards the imaginary part\n",
      "  return np.asarray(x, float)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "acao_controle(valores_controle)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from mpl_toolkits.axes_grid1.inset_locator import inset_axes\n",
    "\n",
    "def const(valor, desejo, valor1, desejo1, horizonte):\n",
    "    ts = np.linspace(0, TEMPO_ANALISE, QUANTIDADE_PONTOS_AMOSTRAGEM)\n",
    "\n",
    "    fig, ax = plt.subplots()\n",
    "\n",
    "    ax.set_ylabel('$|c_n|^2$')\n",
    "    ax.set_xlabel('Tempo (em segundos)')\n",
    "    ax.set_title(f\"Horizonte {horizonte}\")\n",
    "\n",
    "    ax.plot(ts, abs(valor)**2, color='k', marker='o', linewidth=1, markersize=1, label=\"$|c_0|^2$ obtido\")\n",
    "    ax.plot(ts, abs(desejo)**2, color='g', marker='o', linewidth=1, markersize=1, label=\"$|c_0|^2$ desejado\")\n",
    "    ax.plot(ts, abs(valor1)**2, color='b', marker='o', linewidth=1, markersize=1, label=\"$|c_1|^2$ obtido\")\n",
    "    ax.plot(ts, abs(desejo1)**2, color='r', marker='o', linewidth=1, markersize=1, label=\"$|c_1|^2$ desejado\")\n",
    "\n",
    "    # Cria a área de zoom\n",
    "    x1, x2, y1, y2 = 2.5, 4, 0, max(max(abs(valor)**2), max(abs(desejo)**2), max(abs(valor1)**2), max(abs(desejo1)**2))\n",
    "    axins = inset_axes(ax, width=\"30%\", height=\"30%\", loc='upper right', borderpad=3)\n",
    "    axins.set_xlim(3, 3.6)\n",
    "    axins.set_ylim(0.9, 1)\n",
    "\n",
    "    # Plota o zoom dentro da área de zoom\n",
    "    axins.plot(ts, abs(valor)**2, color='k', marker='o', linewidth=1, markersize=1, label=\"$|c_0|^2$ obtido\")\n",
    "    axins.plot(ts, abs(desejo)**2, color='g', marker='o', linewidth=1, markersize=1, label=\"$|c_0|^2$ desejado\")\n",
    "    axins.plot(ts, abs(valor1)**2, color='b', marker='o', linewidth=1, markersize=1, label=\"$|c_1|^2$ obtido\")\n",
    "    axins.plot(ts, abs(desejo1)**2, color='r', marker='o', linewidth=1, markersize=1, label=\"$|c_1|^2$ desejado\")\n",
    "\n",
    "    # Desenha a caixa delimitadora em torno da área de zoom no gráfico principal\n",
    "    ax.indicate_inset_zoom(axins)\n",
    "\n",
    "    ax.legend(loc=\"lower right\")\n",
    "\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "from mpl_toolkits.axes_grid1.inset_locator import inset_axes\n",
    "\n",
    "def const(valor, desejo, valor1, desejo1, horizonte):\n",
    "    ts = np.linspace(0, TEMPO_ANALISE, QUANTIDADE_PONTOS_AMOSTRAGEM)\n",
    "\n",
    "    fig, ax = plt.subplots()\n",
    "\n",
    "    ax.set_ylabel('$|c_n|^2$')\n",
    "    ax.set_xlabel('Tempo (em segundos)')\n",
    "    ax.set_title(f\"Horizonte {horizonte}\")\n",
    "\n",
    "    ax.plot(ts, abs(valor)**2, color='k', marker='o', linewidth=1, markersize=5, label=\"$|c_0|^2$ obtido\")\n",
    "    ax.plot(ts, abs(desejo)**2, color='g', marker='v', linewidth=1, markersize=5, label=\"$|c_0|^2$ desejado\")\n",
    "    ax.plot(ts, abs(valor1)**2, color='b', marker='+', linewidth=1, markersize=5, label=\"$|c_1|^2$ obtido\")\n",
    "    ax.plot(ts, abs(desejo1)**2, color='r', marker='*', linewidth=1, markersize=5, label=\"$|c_1|^2$ desejado\")\n",
    "\n",
    "    plt.grid()\n",
    "\n",
    "    # Cria a área de zoom\n",
    "    x1, x2, y1, y2 = 2.5, 4, 0, max(max(abs(valor)**2), max(abs(desejo)**2), max(abs(valor1)**2), max(abs(desejo1)**2))\n",
    "    axins = inset_axes(ax, width=\"30%\", height=\"30%\", loc='upper right', borderpad=3)\n",
    "    axins.set_xlim(3, 3.5)\n",
    "    axins.set_ylim(0.9, 1)\n",
    "\n",
    "    # Plota o zoom dentro da área de zoom\n",
    "    axins.plot(ts, abs(valor)**2, color='k', marker='o', linewidth=1, markersize=5, label=\"$|c_0|^2$ obtido\")\n",
    "    axins.plot(ts, abs(desejo)**2, color='g', marker='v', linewidth=1, markersize=5, label=\"$|c_0|^2$ desejado\")\n",
    "    axins.plot(ts, abs(valor1)**2, color='b', marker='+', linewidth=1, markersize=5, label=\"$|c_1|^2$ obtido\")\n",
    "    axins.plot(ts, abs(desejo1)**2, color='r', marker='*', linewidth=1, markersize=5, label=\"$|c_1|^2$ desejado\")\n",
    "\n",
    "    # Desenha a caixa delimitadora em torno da área de zoom no gráfico principal\n",
    "    ax.indicate_inset_zoom(axins)\n",
    "\n",
    "    ax.legend(loc=\"lower right\")\n",
    "\n",
    "    plt.grid()\n",
    "\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "const(constantes[0], desejo_estr[0], constantes[1], desejo_estr[1], 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "def zoom(valor, desejo, valor1, desejo1, horizonte):\n",
    "    ts = np.linspace(0, TEMPO_ANALISE, QUANTIDADE_PONTOS_AMOSTRAGEM)\n",
    "\n",
    "    plt.ylabel('$|c_n|^2$')\n",
    "    plt.xlabel('Tempo (em segundos)')\n",
    "    plt.title(f\"Horizonte {horizonte}\")\n",
    "    plt.plot(ts, abs(valor)**2, color='k', marker='o', linewidth=1, markersize=1, label=\"$|c_0|^2$ obtido\")\n",
    "    plt.plot(ts, abs(desejo)**2, color='g', marker='o', linewidth=1, markersize=1, label=\"$|c_0|^2$ desejado\")\n",
    "    plt.plot(ts, abs(valor1)**2, color='b', marker='o', linewidth=1, markersize=1, label=\"$|c_1|^2$ obtido\")\n",
    "    plt.plot(ts, abs(desejo1)**2, color='r', marker='o', linewidth=1, markersize=1, label=\"$|c_1|^2$ desejado\")\n",
    "    plt.legend(loc=\"center right\")\n",
    "\n",
    "    plt.xlim(2.5, 4)\n",
    "    \n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "zoom(constantes[0], desejo_estr[0], constantes[1], desejo_estr[1], 10)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}