Notebooks/Metodo_de_Numerov.ipynb

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  {
   "cell_type": "markdown",
   "id": "6a8578e5-58d6-410e-a5a1-0b26b2e87d13",
   "metadata": {},
   "source": [
    "# Método Numérico de Numerov"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c307e10f-b3e0-4e15-b587-c66d0ce8107a",
   "metadata": {},
   "source": [
    "* PET - Física UFRN\n",
    "* Petiano: José Arthur de Luna Oliveira\n",
    "* Data: 06/05/2024"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f59019b-fe34-492b-ad62-e29cba98e7e0",
   "metadata": {},
   "source": [
    "$\\quad$ Nesse `Notebook` exploraremos o _método numérico de Numerov_, tal método é utilizado para resolver equações diferenciais de segunda ordem. Aqui neste `Notebook` desenvolveremos todo o método e o usaremos para solucionar o problema do átomo de hidrogênio. Vale salientar que este trabalho é baseado nas referências _Applications of the Numerov method to simple quantum systems using Python_ **[1]** e _Investigação da energia de ligação entre átomos utilizando o potencial de Lennard-Jones_ **[2]**."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55b1ba39-973a-4e53-bf6a-126a65c0881b",
   "metadata": {},
   "source": [
    "Neste `Notebook` haverá as seguintes seções:\n",
    "* Introdução;\n",
    "* Problema do Átomo de Hidrogênio;\n",
    "* Solucionando o problema;\n",
    "* Conclusão."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23121e91-ebae-4636-b267-5db90b68d076",
   "metadata": {},
   "source": [
    "## Importando Bibliotecas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "95c41062-45a1-4c53-bb5d-d6e93dfd8aaa",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "def plot_ticks(): #essa é uma função que mexe na aparência dos gráficos\n",
    "    plt.figure(figsize = (8,5))\n",
    "    plt.minorticks_on()\n",
    "    plt.tick_params(axis='x', which='both', top = True, right = True, direction = 'in', length=5, width=1)\n",
    "    plt.tick_params(axis='y', which='both', top = True, right = True, direction = 'in', length=5, width=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e3c113d-d460-4adf-93d4-1843bd859212",
   "metadata": {},
   "source": [
    "## Detalhes da Biblioteca"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8f0a7a26-6abb-40cf-b08d-d636406e48cd",
   "metadata": {},
   "outputs": [
    {
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       "Software versions": [
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         "module": "Python",
         "version": "3.11.9 64bit [MSC v.1916 64 bit (AMD64)]"
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         "version": "8.20.0"
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         "version": "Windows 10 10.0.22631 SP0"
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         "module": "Matplotlib",
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         "module": "Numpy",
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       "<table><tr><th>Software</th><th>Version</th></tr><tr><td>Python</td><td>3.11.9 64bit [MSC v.1916 64 bit (AMD64)]</td></tr><tr><td>IPython</td><td>8.20.0</td></tr><tr><td>OS</td><td>Windows 10 10.0.22631 SP0</td></tr><tr><td>Matplotlib</td><td>3.8.4</td></tr><tr><td>Numpy</td><td>1.26.4</td></tr><tr><td colspan='2'>Sat May 11 21:19:32 2024 Hora Padrão de Buenos Aires</td></tr></table>"
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       "\\begin{tabular}{|l|l|}\\hline\n",
       "{\\bf Software} & {\\bf Version} \\\\ \\hline\\hline\n",
       "Python & 3.11.9 64bit [MSC v.1916 64 bit (AMD64)] \\\\ \\hline\n",
       "IPython & 8.20.0 \\\\ \\hline\n",
       "OS & Windows 10 10.0.22631 SP0 \\\\ \\hline\n",
       "Matplotlib & 3.8.4 \\\\ \\hline\n",
       "Numpy & 1.26.4 \\\\ \\hline\n",
       "\\hline \\multicolumn{2}{|l|}{Sat May 11 21:19:32 2024 Hora Padrão de Buenos Aires} \\\\ \\hline\n",
       "\\end{tabular}\n"
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      "text/plain": [
       "Software versions\n",
       "Python 3.11.9 64bit [MSC v.1916 64 bit (AMD64)]\n",
       "IPython 8.20.0\n",
       "OS Windows 10 10.0.22631 SP0\n",
       "Matplotlib 3.8.4\n",
       "Numpy 1.26.4\n",
       "Sat May 11 21:19:32 2024 Hora Padrão de Buenos Aires"
      ]
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   ],
   "source": [
    "%load_ext version_information\n",
    "%version_information Matplotlib, Numpy"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14e40dab-04c6-48b8-be6c-41b7b6c3090c",
   "metadata": {},
   "source": [
    "## 1. Introdução"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bed3adee-0b9c-412b-aa9c-26284eb41f08",
   "metadata": {},
   "source": [
    "$\\quad$ Antes de iniciarmos, vale ressaltar que essa seção é toda baseada na construção que método do artigo *Applications of the Numerov method to simple quantum systems using Python* **[1]**."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33819bad-492e-477d-96fe-9cf42bf0ae41",
   "metadata": {},
   "source": [
    "$\\quad$ O principal motivo para Boris Numerov desenvolver esse método foi que ele queria determinar as correções na trajetória do cometa Halley. Esse método, por sua vez, surgiu inicialmente para solucionar problemas de autovalores relacionados a equações diferenciais ordinárias de segunda ordem para corpos celestes. Tais equações não possuem informação da primeira derivada de uma função desconhecida, $y(x)$, em outras palavras, equações com a seguinte forma"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0cb00278-cda3-4534-9a5d-3ef2ebc6348f",
   "metadata": {},
   "source": [
    "$$ \\frac{d^2y}{dx^2} = f(x, \\ y) \\ . \\tag{1.1}$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "031f2f83-d951-4318-8ee4-09cb2f3f9ba1",
   "metadata": {},
   "source": [
    "$\\quad$ Nos métodos mais tradicionais para equações diferenciais, por exemplo, método de Euler ou o Runge-Kutta, cria-se um sistema de equações para resolvê-la como,"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5f23640-8d68-48b5-a178-ec4cfaa5ac66",
   "metadata": {},
   "source": [
    "$$  {\\begin{cases}\n",
    "        z = \\frac{dy}{dx}\\\\\n",
    "        \\\\\n",
    "        \\frac{dz}{dx} = f(x, \\ y)\n",
    "        \\end{cases}\n",
    "        } \\ .$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3cf822e2-e422-4515-9fb1-15d0b9fedd0e",
   "metadata": {},
   "source": [
    "$\\quad$ No entanto, em problemas que $\\frac{dy}{dx}$ são desconhecidos, não podem utilizar esses métodos tradicionais para solucionar esses problemas, então deve-se usar outros métodos.\n",
    "\n",
    "$\\quad$ Para desenvolver o método que usaremos neste `Notebook`, usaremos o problema de estados ligados da Mecânica Quântica. Suponha que uma partícula de massa $m$, confinada em um potencial $V(x)$, em um dado intervalo $a<x<b$, onde as energias permitidas ($E$) e as funções de ondas correspondentes $\\psi(x)$ que descrevem esses estados estacionários satisfazem a equação de autovalor de Schrödinger"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3c5e645-b1c0-4cee-a37a-47ae7aaf9deb",
   "metadata": {},
   "source": [
    "$$ \\frac{d^2\\psi(x)}{dx^2} + k^2(x)\\psi(x) = 0 \\ ,\\tag{1.2}$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e61e875-8fdd-46b7-a5f2-7b75865f88fa",
   "metadata": {},
   "source": [
    "na qual $k=\\sqrt{2m[E - V(x)]}/\\hbar$ e $\\hbar = 1.055\\times 10^{-34}\\ J.s $ é a constante de Planck reduzida. Nesse caso, podemos estabelecer condições de continuidade para os valores $\\psi$ e $d\\psi/dx$ em dois ou mais pontos do domínio da função de onda. Assim, tornando desnecessária a transformação de uma equação diferencial de segunda ordem em um sistema de primeira ordem. Vale ressaltar que o método de Numerov nos permite a determinação simultânea do espectro de energia da partícula e das autofunções associadas a cada valor de energia."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb932e93-9ecc-4dc0-9057-2aef69a219d9",
   "metadata": {},
   "source": [
    "### 1.2 Encontrando a Fórmula de Numerov"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e560a83c-42a3-423a-8838-59ddc5b84cd9",
   "metadata": {},
   "source": [
    "$\\quad$ O método numérico de Numerov é um método interativo, assim a solução deve ser conhecida em dois pontos subsequentes no intervalo $[a, \\ b]$, ou seja, $\\psi(x - \\delta)$ e $\\psi(x)$ devem ser conhecidos, de forma que $\\delta$ é um valor muito pequeno, e será o passo de integração do nosso algoritmo.\n",
    "\n",
    "$\\quad$ Para construir o nosso algoritmo, começaremos expandindo em série de Taylor o $\\psi(x \\pm \\delta)$ até a quarta ordem,"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d28cb9d4-c036-47fd-ba4e-523556e0f7c7",
   "metadata": {},
   "source": [
    "$$ \\psi(x \\pm \\delta) = \\psi(x) \\pm \\delta \\psi^{(1)}(x) + \\frac{\\delta^2}{2}\\psi^{(2)}(x) \\pm \\frac{\\delta^3}{6}\\psi^{(3)}(x) + \\frac{\\delta^4}{24}\\psi^{(4)}(x) \\ , \\tag{1.3}$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "376266bc-daec-44c0-a399-b59d25c1b3b9",
   "metadata": {},
   "source": [
    "somando os termos $\\psi(x + \\delta)$ e $\\psi(x-\\delta)$, temos"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5cf1fa5-3d5c-47a5-8f13-ff3688c5a0bb",
   "metadata": {},
   "source": [
    "$$ \\psi(x+\\delta) + \\psi(x-\\delta) = \\psi(x) + \\delta \\psi^{(1)}(x) + \\frac{\\delta^2}{2}\\psi^{(2)}(x) + \\frac{\\delta^3}{6}\\psi^{(3)}(x) + \\frac{\\delta^4}{24}\\psi^{(4)}(x) + \\psi(x) - \\delta \\psi^{(1)}(x) + \\frac{\\delta^2}{2}\\psi^{(2)}(x) - \\frac{\\delta^3}{6}\\psi^{(3)}(x) + \\frac{\\delta^4}{24}\\psi^{(4)}(x)$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3fafe0f1-16be-4afc-9219-6bd4d0e256f5",
   "metadata": {},
   "source": [
    "$$ \\implies \\psi(x+\\delta) + \\psi(x-\\delta) = 2\\psi(x) + \\delta^2\\psi^{(2)}(x)+ \\frac{\\delta^4}{12}\\psi^{(4)}(x)  \\implies \\psi(x+\\delta) + \\psi(x-\\delta) - 2\\psi(x) = \\delta^2\\psi^{(2)}(x)+ \\frac{\\delta^4}{12}\\psi^{(4)}(x)$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4330ad4c-dd47-4efa-96d3-3f76563e52f3",
   "metadata": {},
   "source": [
    "$$ \\therefore \\boxed{ \\frac{\\psi(x+\\delta) + \\psi(x-\\delta) - 2\\psi(x)}{\\delta^2} = \\left(1 + \\frac{\\delta^2}{12}\\frac{d^2}{dx^2} \\right)\\psi^{(2)}(x)} \\ .\\tag{1.4}$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28a12016-b373-4c8a-b115-c34f0dcf7624",
   "metadata": {},
   "source": [
    "$\\quad$ Escrevendo a Equação **(1.2)** da seguinte forma, $\\frac{d^2\\psi(x)}{dx^2} = - k^2(x)\\psi(x)$, podemos substituir isso na Equação **(1.4)**, assim"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6a16343-1029-4c33-9c09-f235a5916201",
   "metadata": {},
   "source": [
    "$$ \\frac{\\psi(x+\\delta) + \\psi(x-\\delta) - 2\\psi(x)}{\\delta^2} = \\left(1 + \\frac{\\delta^2}{12}\\frac{d^2}{dx^2} \\right)\\left[-k^2(x)\\psi(x)\\right]$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b79412a-d551-4f32-bcc8-10ec3379fb68",
   "metadata": {},
   "source": [
    "$$ \\implies \\frac{\\psi(x+\\delta) + \\psi(x-\\delta) - 2\\psi(x)}{\\delta^2} = -k^2(x)\\psi(x) - \\frac{\\delta^2}{12}\\frac{d^2}{dx^2}\\left[k^2(x)\\psi(x)\\right]\\ , \\tag{1.5}$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "282c377c-a276-4c7a-9b17-b1ad80abcc4b",
   "metadata": {},
   "source": [
    "agora faremos a seguinte manipulação na Equação **(1.4)**,"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b93f1e08-c0d9-489c-8de6-e82cfdf008a7",
   "metadata": {},
   "source": [
    "$$ \\frac{\\psi(x+\\delta) + \\psi(x-\\delta) - 2\\psi(x)}{\\delta^2} = \\psi^{(2)}(x)+ \\frac{\\delta^2}{12}\\psi^{(4)}(x)$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90c3a307-a45b-4b2a-8da2-d43a32e5a81b",
   "metadata": {},
   "source": [
    "$$\\implies \\psi^{(2)}(x) = \\frac{\\psi(x+\\delta) + \\psi(x-\\delta) - 2\\psi(x)}{\\delta^2} - \\frac{\\delta^2}{12}\\psi^{(4)}(x) , $$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "293b9517-c6fd-4d17-bc92-bfaaf1238a46",
   "metadata": {},
   "source": [
    "vamos fazer a seguinte troca $\\psi(x) \\rightarrow k^2(x)\\psi(x)$, então"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da9546fc-8f4e-4f6f-8766-b8d43e9880d1",
   "metadata": {},
   "source": [
    "$$ \\frac{d^2}{dx^2}[k^2(x)\\psi(x)] = \\frac{k^2(x+\\delta)\\psi(x+\\delta) + k^2(x-\\delta)\\psi(x-\\delta) - 2k^2(x)\\psi(x)}{\\delta^2} - \\frac{\\delta^2}{12}[k^2(x)\\psi(x)]^{(4)} \\ ,$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6ba56be-387d-4171-99c6-d29aa4551c06",
   "metadata": {},
   "source": [
    "substituindo esse resultado na Equação **(1.5)**, temos"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55dc7714-bc1a-4a8a-8f79-7a01095023fc",
   "metadata": {},
   "source": [
    "$$\\frac{\\psi(x+\\delta) + \\psi(x-\\delta) - 2\\psi(x)}{\\delta^2} =-k^2(x)\\psi(x) - \\frac{\\delta^2}{12}\\left[\\frac{k^2(x+\\delta)\\psi(x+\\delta) + k^2(x-\\delta)\\psi(x-\\delta) - 2k^2(x)\\psi(x)}{\\delta^2}\\right] - \\frac{\\delta^4}{144}[k^2(x)\\psi(x)]^{(4)} \\ ,$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc4fd7a5-68ea-4c65-a356-ed91fa84020d",
   "metadata": {},
   "source": [
    "considerando que $\\mathcal{O(\\delta^4)} = - \\frac{\\delta^4}{144}[k^2(x)\\psi(x)]^{(4)}$, então"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ea418dc-06c5-4fee-b995-a99456d27ad7",
   "metadata": {},
   "source": [
    "$$\\frac{\\psi(x+\\delta) + \\psi(x-\\delta) - 2\\psi(x)}{\\delta^2} =-k^2(x)\\psi(x) - \\frac{\\delta^2}{12}\\left[\\frac{k^2(x+\\delta)\\psi(x+\\delta) + k^2(x-\\delta)\\psi(x-\\delta) - 2k^2(x)\\psi(x)}{\\delta^2}\\right] + \\mathcal{O(\\delta^4)}$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7a6cfaa-d0dd-483b-a9b7-f90b85640d1a",
   "metadata": {},
   "source": [
    "$$\\implies \\psi(x+\\delta) + \\psi(x-\\delta) - 2\\psi(x) =-\\delta^2 k^2(x)\\psi(x) - \\frac{\\delta^2}{12}\\left[k^2(x+\\delta)\\psi(x+\\delta) + k^2(x-\\delta)\\psi(x-\\delta) - 2k^2(x)\\psi(x)\\right] + \\mathcal{O(\\delta^4)}$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03d16089-e35a-4bc3-9f38-1ed234bd66f9",
   "metadata": {},
   "source": [
    "$$\\implies \\psi(x+\\delta) + \\frac{\\delta^2}{12}k^2(x+\\delta)\\psi(x+\\delta) = 2\\psi(x) -\\delta^2 k^2(x)\\psi(x) + \\frac{\\delta^2}{12}2k^2(x)\\psi(x) -\\psi(x-\\delta) - \\frac{\\delta^2}{12} k^2(x-\\delta)\\psi(x-\\delta) + \\mathcal{O(\\delta^4)}$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5c434fc-58b7-4e91-8a6e-27e11486fd41",
   "metadata": {},
   "source": [
    "$$\\implies \\left[1 + \\frac{\\delta^2}{12}k^2(x+\\delta)\\right]\\psi(x+\\delta) = \\left[2  + \\frac{\\delta^2k^2(x) -6\\delta^2 k^2(x)}{6}\\right]\\psi(x) -\\left[1 + \\frac{\\delta^2}{12} k^2(x-\\delta)\\right]\\psi(x-\\delta) + \\mathcal{O(\\delta^4)}$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d74b649-1ade-47bf-8f6d-91a17ac86a39",
   "metadata": {},
   "source": [
    "$$\\implies \\left[1 + \\frac{\\delta^2}{12}k^2(x+\\delta)\\right]\\psi(x+\\delta) = 2\\left[1  - \\frac{5\\delta^2}{12} k^2(x)\\right]\\psi(x) -\\left[1 + \\frac{\\delta^2}{12} k^2(x-\\delta)\\right]\\psi(x-\\delta) + \\mathcal{O(\\delta^4)} \\ ,$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6575ab14-986b-43d3-8bcd-3673f8e5dbb4",
   "metadata": {},
   "source": [
    "consideraremos $\\mathcal{O(\\delta^4)} \\approx 0$, assim"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c490626-28e5-4a10-8fc1-0b9ef54e0ed6",
   "metadata": {},
   "source": [
    "$$\\left[1 + \\frac{\\delta^2}{12}k^2(x+\\delta)\\right]\\psi(x+\\delta) = 2\\left[1  - \\frac{5\\delta^2}{12} k^2(x)\\right]\\psi(x) -\\left[1 + \\frac{\\delta^2}{12} k^2(x-\\delta)\\right]\\psi(x-\\delta)$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "abefc3de-6d23-4e0f-8ef0-23bf6b5bceba",
   "metadata": {},
   "source": [
    "$$\\therefore \\ \\boxed{\\psi(x+\\delta) = \\frac{2\\left[1  - \\frac{5\\delta^2}{12} k^2(x)\\right]\\psi(x) -\\left[1 + \\frac{\\delta^2}{12} k^2(x-\\delta)\\right]\\psi(x-\\delta)}{\\left[1 + \\frac{\\delta^2}{12}k^2(x+\\delta)\\right]}} \\ . \\tag{1.6}$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb15b08b-b861-4add-ad8c-9f897b607b3d",
   "metadata": {},
   "source": [
    "$\\quad$ Nosso objetivo é resolver uma equação de autovalor, a técnica de integração numérica da equação de Schrödinger unidimensional para uma partícula em um poço, precisará de um chute inicial para os autovalores (a energia $E$) e os dois pontos respectivos das autofunções. Para a escolha do valor arbitrário da energia $E$, devemos lembrar da relação de incerteza de Heisenberg, que diz que a energia $E$ de uma partícula em um poço de potencial $V(x)$ deve ser maior que o valor mínimo do poço, ou seja, $E_{initial} = V_{min} + \\Delta E$, em que $\\Delta E > 0$.\n",
    "\n",
    "$\\quad$ A escolha desse valor determinará os pontos do retorno da partícula, que para encontrá-los basta igualar o valor arbitrário $E$ e o potencial $V(x)$. Fazendo isso, encontrará dois pontos de retorno da partícula $x_r$ e $x_l$. Na Mecânica Clássica, a região $[x_r, \\ x_l]$ será a única região onde a partícula poderá ficar. Em outras palavras, a partícula é restrita a se manter nessa região, enquanto as regiões além desses pontos ($x<x_r$ e $x>x_l$ ) são chamadas na mecânica clássica de regiões proibidas.\n",
    "\n",
    "$\\quad$  No entanto, na Mecânica Quântica é possível da partícula se encontrar além dos pontos de retorno, ou seja, a equação Schrödinger possui solução nessas regiões. Assim, cada valor de energia possui uma autofunção associada, as quais terão solução na região proibida, no entanto, ela tenderá a zero.\n",
    "Dessa forma, esses pontos, onde a autofunção se anula, são os pontos de contorno $a$ e $b$, onde $b>a$, do domínio de integração.\n",
    "\n",
    "$\\quad$ O método de Numerov requer um esquema de interação usando a Equação **(1.6)**, que a partir do ponto $a$ para a direita, criará uma função que até um ponto ($x_{match}$), e do ponto $b$ para a esquerda, criará outra função até esse mesmo ponto. Sendo assim, teremos duas funções, $\\psi^{\\ l}(x)$ e $\\psi^{\\ r}(x)$, a primeira virá da esquerda e a outra da direita, a partir dos pontos $a$ e $b$, respectivamente."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9af4679-c39b-446e-8ed8-8698e9e73178",
   "metadata": {},
   "source": [
    "### 1.3 Construindo o Algoritmo"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01ca64ef-0d15-4349-beb6-96342ec2d510",
   "metadata": {},
   "source": [
    "$\\quad$ Diante do que foi dito, devemos escolher um valor inicial para $E$, obter duas soluções, uma pela esquerda, $\\psi^{\\ l}(x)$ e uma pela direita, $\\psi^{\\ l}(x)$, nas quais devemos começar com os dois pontos consecutivos iniciais e aplicar na Equação **(1.6)** para obter o resto da solução. Em seguida, explicitaremos o que deve ser feito."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38104b79-6bee-45e9-9ff5-c2bb9cc22084",
   "metadata": {},
   "source": [
    "#### Solução pela esquerda ($x < x_{match}$)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb5f6be7-c461-4441-8821-9475f80bde01",
   "metadata": {},
   "source": [
    "$\\quad$ Dado o chute inicial para energia, assim podemos escolher o $x_{match}$, que será o ponto de encontro das duas soluções, o qual deve está no intervalo de integração $[a, \\ b]$. Em seguida, devemos definir os dois pontos iniciais consecutivos da solução $\\psi^{\\ l}(x)$ como:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "342609f3-b14f-48a9-b147-7faeb318329a",
   "metadata": {},
   "source": [
    "$$  {\\begin{cases}\n",
    "        \\psi^{\\ l}(a) = 0\\\\\n",
    "        \\\\\n",
    "        \\psi^{\\ l}(a+\\delta) = \\delta^{\\ l}, \\ (\\delta^{\\ l} \\ll 1)\n",
    "        \\end{cases}\n",
    "        } \\ ,$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "026d3ba1-aa1c-4866-a8f5-55940101469e",
   "metadata": {},
   "source": [
    "as quais, o $\\delta$ é o passo de integração e $\\delta^l$ é um valor muito pequeno. Note que igualamos o $\\psi^l(a+\\delta)$ a um valor muito pequeno, pois como estamos dividindo a função em intervalos muito pequenos ($\\delta$), o próximo ponto consecutivo de zero ($\\psi^l=0$) será um número muito próximo de zero. \n",
    "\n",
    "$\\quad$ Definidos esses pontos iniciais, basta aplicar a Equação **(1.6)**, que obtém cada ponto da solução sequencialmente até $x_{match}$."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b43fff1a-ce2d-4d52-a64b-003e1c8932b8",
   "metadata": {},
   "source": [
    "#### Solução pela Direita ($x>x_{match}$)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab456598-2519-47e9-8d0b-84fc337cacd4",
   "metadata": {},
   "source": [
    "$\\quad$ Análogo ao processo explicado anteriormente, com o mesmo valor de energia e o mesmo $x_{match}$, definimos os dois pontos iniciais consecutivos da solução $\\psi^{\\ r}(x)$ como"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a7f41d2-30b0-4ef6-82aa-6ad6d3be37ba",
   "metadata": {},
   "source": [
    "$$  {\\begin{cases}\n",
    "        \\psi^{\\ r}(b) = 0\\\\\n",
    "        \\\\\n",
    "        \\psi^{\\ r}(b-\\delta) = \\delta^{\\ r}, \\ (\\delta^{\\ r} \\ll 1)\n",
    "        \\end{cases}\n",
    "        } \\ .$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5760a9d-cc38-44d8-a801-6f4057647d19",
   "metadata": {},
   "source": [
    "$\\quad$ Dessa forma, aplicando a Equação **(1.6)** obtêm a solução $\\psi^{\\ r}(x)$ até $x_{match}$.\n",
    "\n",
    "$\\quad$ Note que a construção das duas soluções termina em $x_{match}$ e nesse ponto estas devem ser iguais nesse ponto, garantindo a continuidade da solução,"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6276b3c4-81f2-4475-922e-ed5fd6934459",
   "metadata": {},
   "source": [
    "$$\\psi^{\\ l}(x_{match}) = \\psi^{\\ r}(x_{match}) \\ ,\\tag{1.7}$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2375d5f-cded-4405-a780-0b7a646e20b9",
   "metadata": {},
   "source": [
    "no entanto nem sempre as soluções possíveis atendem a essa condição, então, para garantir a continuidade da solução, redefiniremos o $\\psi^{\\ l}$ da seguinte forma,"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ac8dc3a-d8c9-4f29-9d32-74af0c349517",
   "metadata": {},
   "source": [
    "$$ \\psi^{\\ l}(x) \\rightarrow \\psi^{\\ l}(x)\\frac{\\psi^{\\ r}(x_{match})}{\\psi^{\\ l}(x_{match})} \\ ,\\tag{1.8}$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe88491c-6df9-48db-8108-ea6de06ee4b3",
   "metadata": {},
   "source": [
    "na qual $a \\leq x \\leq x_{match}$.\n",
    "\n",
    "$\\quad$ Feito isso, deve verificar se as derivadas primeiras das soluções são tão próximas quanto queremos, no ponto $x_{match}$, ou seja, a diferença entre essas duas derivadas deve ser menor que $\\epsilon$, esse valor nós escolhemos. Dessa forma, deve determinar a derivada das soluções no ponto $x_{match}$ numericamente e verificar se o módulo da diferença é menor que o erro que escolhemos. Para calcular as derivadas numericamente, basta aplicar as seguintes equações:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "623b5cc5-ec98-4c11-a373-d2aa16839425",
   "metadata": {},
   "source": [
    "$$  {\\begin{cases}\n",
    "        \\frac{d\\psi^{\\ l}}{dx}\\vert_{x_{match}} = \\frac{\\psi^{\\ l}(x_{match} + \\delta) - \\psi^{\\ l}(x_{match} - \\delta)}{2\\delta}\\\\\n",
    "        \\\\\n",
    "        \\frac{d\\psi^{\\ r}}{dx}\\vert_{x_{match}} = \\frac{\\psi^{\\ r}(x_{match} + \\delta) - \\psi^{\\ r}(x_{match} - \\delta)}{2\\delta}\n",
    "        \\end{cases}\n",
    "        } \\ . \\tag{1.9}$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "372b81b6-f2d2-434f-b8cf-f7e269f3b54f",
   "metadata": {},
   "source": [
    "$\\quad$ Caso a diferença entre as derivadas for menor que o erro escolhido, então encontramos o nosso autovalor e a respectiva autofunção. Porém, se a condição de continuidade não for atendida, então devemos aumentar o valor da energia e recomeçar todo o processo novamente. Isso deve ser feito até encontrar os autovalores e as autofunções. Vale ressaltar que esse procedimento pode ser repetido até o número desejado de autovalores e autofunções do problema. Devido ao método ser baseado na expansão de série Taylor até a quarta ordem, e por isso seu erro é muito pequeno comparado a outros métodos baseados em expansão de ordem inferior."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a43dfde4-8e6f-4229-9430-7103b0b614a3",
   "metadata": {},
   "source": [
    "### 1.4 Resumindo o Método de Numerov"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b48e8618-e8c0-4b73-ad58-dc3d30f861bf",
   "metadata": {},
   "source": [
    "$\\quad$ Aqui nessa seção resumiremos tudo o que foi dito sobre método numérico de Numerov em passos para poder aplicá-lo:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8724ff79-a517-4cd3-8940-1afd29d9dc3b",
   "metadata": {},
   "source": [
    "* 1ª Passo: Se deve dar um chute inicial para o valor da energia $E$ e os pontos consecutivos de cada solução, como foi definido anteriormente.\n",
    "* 2ª Passo: Com o valor inicial da energia, devemos o escolher o $x_{match}$.\n",
    "* 3ª Passo: Com a Equação **(1.6)** deve-se determinar as soluções $\\psi^{\\ l}(x)$ e $\\psi^{\\ r}(x)$, a partir dos pontos consecutivos até $x_{match}$.\n",
    "* 4ª Passo: Verificar a condição de continuidade ($\\frac{d\\psi^{\\ l}}{dx}\\vert_{x_{match}} = \\frac{d\\psi^{\\ r}}{dx}\\vert_{x_{match}}$), ou seja, verificar se a diferença das derivadas de primeira ordem de $\\psi^{\\ l}(x)$ e $\\psi^{\\ r}(x)$ atingir a precisão que queremos. Caso não seja satisfeita, então devemos aumentar o valor da energia e voltarmos ao primeiro passo.\n",
    "* 5ª Passo: Se o quarto passo for satisfeito, então encontramos um autovalor e sua respectiva autofunção. Assim, caso queiramos, podemos ir atrás dos outros autoestados do problema. Além disso, a quantidade de vezes que a autofunção passa pelo zero indica em qual nível está pertence o autoestado encontrado."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "422db25e-1793-4a5d-b446-edf31bfaf778",
   "metadata": {},
   "source": [
    "## 2. Problema do Átomo de Hidrogênio"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7da3a798-a929-46bb-8ef1-fe615d3cfeaa",
   "metadata": {},
   "source": [
    "$\\quad$ Agora, após descrito todo o método numérico de Numerov, agora podemos aplicá-lo em algum problema. O primeiro problema que solucionaremos com este método é o problema do Átomo de Hidrogênio. Para este problema, consideraremos uma partícula de massa $m$ sobre a ação de um campo elétrico coulombiano, como o elétron no átomo de hidrogênio. Então, a equação radial de Schrödinger para este caso é"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3bc31245-70aa-4647-bcd3-45ca6ddb04f2",
   "metadata": {},
   "source": [
    "$$ \\frac{d^2 R(r)}{dr^2} + \\frac{2}{r} \\frac{dR(r)}{dr} + \\frac{2m}{\\hbar^2} \\left[E + \\frac{e^2}{r} - \\frac{\\hbar^2}{2m} \\frac{l(l+1)}{r^2} \\right] R(r) = 0 \\ .\\tag{2.1} $$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67bb5227-5f42-4387-be8f-cb7f57b16b6b",
   "metadata": {},
   "source": [
    "$\\quad$ A seguir faremos as seguintes substituições, $r=x a_B$, em que $a_B = \\frac{\\hbar^2}{me^2}$ é o raio de Bohr, e também $\\psi(x) = R(r)$. Conseguinte a Equação **(2.1)** pode ser reescrita como"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e825bd6d-e6b8-4aca-8c77-48e959a760f0",
   "metadata": {},
   "source": [
    "$$\\frac{1}{a_B^2}\\frac{d^2 \\psi(x)}{dx^2} + \\frac{2}{xa_B} \\frac{1}{a_B}\\frac{d\\psi(x)}{dx} + \\frac{2}{e^2a_B} \\left[E + \\frac{e^2}{xa_B} - \\frac{e^2a_B}{2} \\frac{l(l+1)}{x^2a_B^2} \\right] \\psi(x) = 0$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6b72c5b-030c-44a7-9310-7329f4e90b8b",
   "metadata": {},
   "source": [
    "$$ \\implies \\frac{1}{a_B^2}\\left\\{ \\frac{d^2 \\psi(x)}{dx^2} + \\frac{2}{x}\\frac{d\\psi(x)}{dx} +  \\frac{2}{e^2a_B}\\left[Ea_B^2 + \\frac{e^2a_B}{x} - \\frac{e^2a_B}{2} \\frac{l(l+1)}{x^2} \\right] \\psi(x) \\right\\}= 0 $$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0057c56-4142-46a0-8735-470959527f6a",
   "metadata": {},
   "source": [
    "$$ \\implies \\frac{d^2 \\psi(x)}{dx^2} + \\frac{2}{x}\\frac{d\\psi(x)}{dx} +  \\frac{2}{e^2a_B}\\left[Ea_B^2 + \\frac{e^2a_B}{x} - \\frac{e^2a_B}{2}\\frac{l(l+1)}{x^2} \\right] \\psi(x)= 0$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6042f680-c9c9-44eb-bd72-fd5c475f1e6d",
   "metadata": {},
   "source": [
    "$$ \\implies \\frac{d^2\\psi(x)}{dx^2} + \\frac{2}{x}\\frac{d\\psi(x)}{dx} +  \\left[\\frac{2a_B}{e^2}E + \\frac{2}{x} - \\frac{l(l+1)}{x^2} \\right]\\psi(x)= 0 \\ ,$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99107f97-8597-4e96-84f3-371ee72664e6",
   "metadata": {},
   "source": [
    "faremos as seguintes substituições $\\epsilon = \\frac{2a_B}{e^2}E$ e $V(x) = \\frac{l(l+1)}{x^2} - \\frac{2}{x}$, são, respectivamente, a energia e o potencial efetivo em unidade atômicas, então"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77d83bd3-7c58-414f-8cd4-d69919f324e5",
   "metadata": {},
   "source": [
    "$$ \\frac{d^2\\psi(x)}{dx^2} = - \\frac{2}{x}\\frac{d\\psi(x)}{dx} - \\left[\\epsilon - V(x)\\right] \\psi(x) \\tag{2.2}$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d67557d-0dd1-461f-ad2f-62920c884e9d",
   "metadata": {},
   "source": [
    "### 2.2 Encontrando a Equação de Recorrência"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9977ec7c-0b29-4219-8c3a-e434da48be2c",
   "metadata": {},
   "source": [
    "$\\quad$ Note que essa equação diferencial possui um termo de derivada de primeira ordem, mesmo que o método de Numerov tenha sido feito para solucionar equações diferenciais sem a informação dessa deriva, podemos solucionar esse tio de equação diferencial. No entanto, primeiramente teremos que encontrar a relação de recorrência para esse problema, e depois aplicarmos o método. Para isso, expressaremos a Equação **(2.2)** da seguinte forma"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd6732eb-7148-41e9-9440-b26e76ad3253",
   "metadata": {},
   "source": [
    "$$ \\frac{d^2\\psi(x)}{dx^2} = -p(x)\\frac{d\\psi(x)}{dx} - s(x) \\psi(x) \\ ,\\tag{2.3}$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ade0575-0f4b-492a-abf3-805ef4ea2fc5",
   "metadata": {},
   "source": [
    "em que $p(x) = \\frac{2}{x}$ e $s(x) = \\epsilon - V(x)$. Agora multiplicaremos as Equação **(2.3)** pelo termo $\\left(1 + \\frac{\\delta^2}{12}\\frac{d^2}{dx^2} \\right)$, assim"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5fb88352-9eca-407f-8f8b-4104e9d83fdf",
   "metadata": {},
   "source": [
    "$$\\left(1 + \\frac{\\delta^2}{12}\\frac{d^2}{dx^2} \\right)\\frac{d^2\\psi(x)}{dx^2} =\\left(1 + \\frac{\\delta^2}{12}\\frac{d^2}{dx^2} \\right)\\left[ -p(x)\\frac{d\\psi(x)}{dx} - s(x) \\psi(x) \\right]$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea373c2f-9ca2-4d19-898e-282d1efc3d7c",
   "metadata": {},
   "source": [
    "$$\\implies \\left(1 + \\frac{\\delta^2}{12}\\frac{d^2}{dx^2} \\right)\\frac{d^2\\psi(x)}{dx^2} = -p(x)\\frac{d\\psi(x)}{dx} - s(x) \\psi(x) - \\frac{\\delta^2}\n",
    "{12}\\frac{d^2}{dx^2}\\left[p(x)\\frac{d\\psi(x)}{dx} + s(x) \\psi(x) \\right] \\ . \\tag{2.4}$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ddb6c976-a9d2-4562-a39f-dd3249393397",
   "metadata": {},
   "source": [
    "$\\quad$ Na Equação **(1.4)** podemos fazer a seguinte troca $\\psi(x) \\rightarrow p(x)\\frac{d\\psi(x)}{dx} + s(x) \\psi(x)$, assim podemos reescreve-la da seguinte forma,"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b570ab3-574f-45e7-b25d-ad5b7cc64056",
   "metadata": {},
   "source": [
    "$$\\frac{d^2}{dx^2}\\left[p(x)\\frac{d\\psi(x)}{dx} + s(x) \\psi(x) \\right]  = \\frac{1}{\\delta^2}\\left[p(x+\\delta)\\frac{d\\psi(x+\\delta)}{dx} + s(x+\\delta) \\psi(x+\\delta) + p(x-\\delta)\\frac{d\\psi(x-\\delta)}{dx} + s(x-\\delta) \\psi(x-\\delta) - 2p(x)\\frac{d\\psi(x)}{dx} - 2s(x) \\psi(x)\\right] - \\frac{\\delta^2}{12}\\psi^{(4)}(x) \\ ,$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d31470e-94da-4411-94ed-0bdcd6073f73",
   "metadata": {},
   "source": [
    "substituindo as derivadas de primeira ordem com suas respectivas aproximações **[3,4]**,"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "684c95c0-04e7-47df-9d96-df36d617414e",
   "metadata": {},
   "source": [
    "$$  {\\begin{cases}\n",
    "        \\frac{d\\psi(x)}{dx} = \\frac{\\psi(x+\\delta) - \\psi(x-\\delta)}{2\\delta}\\\\\n",
    "        \\\\\n",
    "        \\frac{d\\psi(x+\\delta)}{dx} = \\frac{\\psi(x+\\delta) - \\psi(x)}{\\delta}\\\\\n",
    "        \\\\\n",
    "        \\frac{d\\psi(x-\\delta)}{dx} = \\frac{\\psi(x) - \\psi(x-\\delta)}{\\delta}\\\\\n",
    "        \\end{cases}\n",
    "        } \\ .$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee081b9d-bb0f-41f1-83c3-5f57fa0f0f64",
   "metadata": {},
   "source": [
    "Assim teremos que,"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d4b0985-81b7-438d-803d-3a17052bdae7",
   "metadata": {},
   "source": [
    "$$\\frac{d^2}{dx^2}\\left[p(x)\\frac{d\\psi(x)}{dx} + s(x) \\psi(x) \\right]  = \\frac{1}{\\delta^2}\\left\\{p(x+\\delta)\\left[\\frac{\\psi(x+\\delta) - \\psi(x)}{\\delta}\\right] + s(x+\\delta) \\psi(x+\\delta)\\right\\} +$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5fcc1c0-fd1c-451d-a277-0708e37ad0b3",
   "metadata": {},
   "source": [
    "$$ + \\frac{1}{\\delta^2}\\left\\{p(x-\\delta)\\left[\\frac{\\psi(x) - \\psi(x-\\delta)}{\\delta}\\right] + s(x-\\delta) \\psi(x-\\delta)\\right\\} - \\frac{1}{\\delta^2}\\left\\{2p(x)\\left[\\frac{\\psi(x+\\delta) - \\psi(x-\\delta)}{2\\delta} \\right]+ 2s(x) \\psi(x)\\right\\} - \\frac{\\delta^2}{12}\\psi^{(4)}(x)$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e848ac8a-7f65-4a36-a9a7-4ad44419769b",
   "metadata": {},
   "source": [
    "$$\\implies \\frac{d^2}{dx^2}\\left[p(x)\\frac{d\\psi(x)}{dx} + s(x) \\psi(x) \\right]  = \\frac{1}{\\delta^2}\\left[p(x+\\delta)\\frac{\\psi(x+\\delta)}{\\delta} - p(x+\\delta)\\frac{\\psi(x)}{\\delta} + s(x+\\delta) \\psi(x+\\delta)\\right] +$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cdae02c9-f2d8-41a6-acad-bfaa75f54994",
   "metadata": {},
   "source": [
    "$$ + \\frac{1}{\\delta^2}\\left[p(x-\\delta)\\frac{\\psi(x)}{\\delta} - p(x-\\delta)\\frac{\\psi(x-\\delta)}{\\delta} + s(x-\\delta) \\psi(x-\\delta)\\right] - \\frac{1}{\\delta^2}\\left[p(x)\\frac{\\psi(x+\\delta)}{\\delta} - p(x)\\frac{\\psi(x-\\delta)}{\\delta}+ 2s(x) \\psi(x)\\right] - \\frac{\\delta^2}{12}\\psi^{(4)}(x) \\, $$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68d1fb57-a857-4215-b22b-5dc193817649",
   "metadata": {},
   "source": [
    "aplicando a regra do produto notável, temos"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f22b864-54dd-4b4b-9b20-f2ac20ceabc6",
   "metadata": {},
   "source": [
    "$$\\frac{d^2}{dx^2}\\left[p(x)\\frac{d\\psi(x)}{dx} + s(x) \\psi(x) \\right]  = \\frac{1}{\\delta^2}\\left\\{\\left[\\frac{p(x+\\delta) - p(x)}{\\delta} + s(x+\\delta)\\right]\\psi(x+\\delta) + \\left[ \\frac{p(x) - p(x-\\delta)}{\\delta} + s(x-\\delta)\\right]\\psi(x-\\delta)\\right\\} + $$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "401df410-c534-4dd1-9c53-90406a33c46a",
   "metadata": {},
   "source": [
    "$$- \\frac{2}{\\delta^2}\\left[\\frac{p(x+\\delta) - p(x-\\delta)}{2\\delta}+ s(x)\\right]\\psi(x) - \\frac{\\delta^2}{12}\\psi^{(4)}(x) \\ .$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83a1eb08-d879-48a3-9df3-d2f31a5b8b9f",
   "metadata": {},
   "source": [
    "$\\quad$ Note que, podemos fazer as seguintes substituições **[3,4]**,"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "372b4818-1f39-47b8-b160-9c4fe318c75e",
   "metadata": {},
   "source": [
    "$$  {\\begin{cases}\n",
    "        \\frac{p(x+\\delta) - p(x-\\delta)}{2\\delta} = \\frac{dp(x)}{dx}\\\\\n",
    "        \\\\\n",
    "        \\frac{p(x+\\delta) - p(x)}{\\delta} = \\frac{dp(x)}{dx}\\\\\n",
    "        \\\\\n",
    "        \\frac{p(x) - p(x-\\delta)}{\\delta} = \\frac{dp(x)}{dx}\\\\\n",
    "        \\end{cases}\n",
    "        } \\ .$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46db3188-228f-461f-8a23-cc2c912703bd",
   "metadata": {},
   "source": [
    "$\\quad$ Assim,"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ae7b698-0e99-4eda-9da6-838682334ca1",
   "metadata": {},
   "source": [
    "$$\\frac{d^2}{dx^2}\\left[p(x)\\frac{d\\psi(x)}{dx} + s(x) \\psi(x) \\right]  = \\frac{1}{\\delta^2}\\left\\{\\left[\\frac{dp(x)}{dx} + s(x+\\delta)\\right]\\psi(x+\\delta) + \\left[\\frac{dp(x-\\delta)}{dx} + s(x-\\delta)\\right]\\psi(x-\\delta) - 2\\left[\\frac{dp(x)}{dx}+ s(x)\\right]\\psi(x)\\right\\} - \\frac{\\delta^2}{12}\\psi^{(4)}(x) \\ . \\tag{2.5}$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b7b9ee6-1263-4636-b561-4afa060da3bd",
   "metadata": {},
   "source": [
    "$\\quad$ Agora, substituiremos as Equações **(1.4)** e **(2.5)** na Equação **(2.4)**, além disso, o termo que é derivada primeira de $\\psi(x)$ vamos por na forma numérica. Logo,"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4c8e564-570f-4b5e-8229-58c4f001ac4d",
   "metadata": {},
   "source": [
    "$$\\frac{\\psi(x+\\delta) + \\psi(x-\\delta) - 2\\psi(x)}{\\delta^2}= -p(x)\\left[\\frac{\\psi(x+\\delta) - \\psi(x-\\delta)}{2\\delta}\\right] - s(x) \\psi(x) -\\frac{\\delta^2}{12}\\frac{1}{\\delta^2}\\left[\\frac{dp(x)}{dx} + s(x+\\delta)\\right]\\psi(x+\\delta) \\ + $$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9f5ade0-f64c-4e12-927b-c08a13b0260f",
   "metadata": {},
   "source": [
    "$$- \\ \\frac{\\delta^2}{12}\\frac{1}{\\delta^2}\\left\\{\\left[\\frac{dp(x)}{dx} + s(x-\\delta)\\right]\\psi(x-\\delta) - 2\\left[\\frac{dp(x)}{dx}+ s(x)\\right]\\psi(x)\\right\\} - \\frac{\\delta^2}{12}\\frac{\\delta^2}{12}\\psi^{(4)}(x)$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c726df9-519e-4a32-98d0-5372435ea70c",
   "metadata": {},
   "source": [
    "$$\\implies \\frac{\\psi(x+\\delta) + \\psi(x-\\delta) - 2\\psi(x)}{\\delta^2}= -p(x)\\left[\\frac{\\psi(x+\\delta) - \\psi(x-\\delta)}{2\\delta}\\right] - s(x) \\psi(x) - \\frac{1}{12}\\left[\\frac{dp(x)}{dx} + s(x+\\delta)\\right]\\psi(x+\\delta) \\ +$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "355a8a69-f74e-4b08-95ca-bc6aa2b9f57f",
   "metadata": {},
   "source": [
    "$$- \\ \\frac{1}{12}\\left[\\frac{dp(x)}{dx} + s(x-\\delta)\\right]\\psi(x-\\delta) + \\frac{1}{6}\\left[\\frac{dp(x)}{dx}+ s(x)\\right]\\psi(x)- \\frac{\\delta^4}{144}\\psi^{(4)}(x) \\ .$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd07e427-8d15-44d4-980e-c910f876646f",
   "metadata": {},
   "source": [
    "$\\quad$ Observe que $\\frac{\\delta^4}{144}\\psi^{(4)}(x) = \\mathcal{O(\\delta^4)} $, como foi feito na seção anterior, consideraremos este termo nulo. Então, agora isolaremos o termo $\\psi(x+\\delta)$, feito finalmente teremos a relação de recorrência,"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff3c3c73-0fbc-4cf1-b085-41730155e1b8",
   "metadata": {},
   "source": [
    "$$\\frac{\\psi(x+\\delta)}{\\delta^2} + p(x)\\frac{\\psi(x+\\delta)}{2\\delta} + \\frac{1}{12}\\left[\\frac{dp(x)}{dx} + s(x+\\delta)\\right]\\psi(x+\\delta)= \\frac{2\\psi(x)}{\\delta^2} - s(x) \\psi(x)  + \\frac{1}{6}\\left[\\frac{dp(x)}{dx}+ s(x)\\right]\\psi(x) $$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e49bdf17-6551-49f2-8708-c0b84dbf5498",
   "metadata": {},
   "source": [
    "$$ + p(x)\\frac{\\psi(x-\\delta)}{2\\delta} -\\frac{\\psi(x-\\delta)}{\\delta^2}- \\frac{1}{12}\\left[\\frac{dp(x)}{dx} + s(x-\\delta)\\right]\\psi(x-\\delta) $$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a46e14d0-c61b-425d-a15a-c72fc359c901",
   "metadata": {},
   "source": [
    "$$\\implies \\psi(x+\\delta) \\left\\{1 +p(x)\\frac{\\delta}{2} + \\frac{\\delta^2}{12}\\left[\\frac{dp(x)}{dx} + s(x+\\delta)\\right]\\right\\}= \\left\\{2 - \\delta^2 s(x) + \\frac{\\delta^2}{6}\\left[\\frac{dp(x)}{dx}+ s(x)\\right]\\right\\}\\psi(x) + \\left\\{p(x)\\frac{\\delta}{2} -1-\\frac{\\delta^2}{12}\\left[\\frac{dp(x)}{dx} + s(x-\\delta)\\right]\\right\\}\\psi(x-\\delta)$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33a71380-62e5-4c89-b263-7f520d9e29a3",
   "metadata": {},
   "source": [
    "$$\\implies \\psi(x+\\delta) \\left\\{1 +p(x)\\frac{\\delta}{2} + \\frac{\\delta^2}{12}\\left[\\frac{dp(x)}{dx} + s(x+\\delta)\\right]\\right\\}= \\left[2 + \\frac{\\delta^2}{6}\\frac{dp(x)}{dx}- \\frac{5\\delta^2}{6}s(x)\\right]\\psi(x) - \\left\\{ 1 -p(x)\\frac{\\delta}{2} +\\frac{\\delta^2}{12}\\left[\\frac{dp(x)}{dx} + s(x-\\delta)\\right]\\right\\}\\psi(x-\\delta)$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea83fa68-e541-49c1-94ac-d9f9534244c7",
   "metadata": {},
   "source": [
    "$$\\therefore \\boxed{\\psi(x+\\delta) = \\frac{2\\left\\{1 + \\frac{5\\delta^2}{12}\\left[\\frac{1}{5}\\frac{dp(x)}{dx}- s(x)\\right]\\right\\}\\psi(x) - \\left\\{ 1 -p(x)\\frac{\\delta}{2} +\\frac{\\delta^2}{12}\\left[\\frac{dp(x)}{dx} + s(x-\\delta)\\right]\\right\\}\\psi(x-\\delta)}{\\left\\{1 +p(x)\\frac{\\delta}{2} + \\frac{\\delta^2}{12}\\left[\\frac{dp(x)}{dx} + s(x+\\delta)\\right]\\right\\}}} \\ .\\tag{2.6}$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b721340c-6fa1-433a-b9a8-b3b1c505e7a4",
   "metadata": {},
   "source": [
    "$\\quad$ Finalmente, obtemos a equação de recorrência para o nosso problema. Assim, no quarto passo da aplicação do método, em vez de usarmos a Equação **(1.6)** para determinar as soluções, utilizaremos a Equação **(2.6)**. Todo esse desenvolvimento se encontra na referência **[1]**."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "020228c1-023f-4623-8c80-ef8cc422624a",
   "metadata": {},
   "source": [
    "## 3. Solucionando o problema"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6a0f817-bc9a-4a12-9b9b-870a00fd13de",
   "metadata": {},
   "source": [
    "$\\quad$ Antes de aplicarmos o _método numérico de Numerov_, definiremos algumas funções em Python para facilitar a explicação do código que faremos."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa8fac7a-e949-4d65-a20c-5b20f10d9fb3",
   "metadata": {},
   "source": [
    "### 3.1 Definindo Funções"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2d8c4fd-8596-4330-990c-3a16936f1ccd",
   "metadata": {},
   "source": [
    "$\\quad$ Nessa seção definiremos as funções para encontrar os pontos de retorno no potencial, para $s(x)$, $p(x)$ e $\\frac{dp(x)}{dx}$, uma função para a Equação **(2.6)**, para calcular a derivada de primeira ordem, para determinar as soluções $\\psi^{\\ l}(x)$ e $\\psi^{\\ r}(x)$  e finalmente uma função para normalizar as autofunções.\n",
    "\n",
    "$\\quad$ Então, agora definiremos a função para $s(x) = \\epsilon - V(x)$, $p(x) = \\frac{2}{x}$ e $\\frac{dp(x)}{dx} = -\\frac{2}{x^2}$, lembre-se que $V(x) = \\frac{l(l+1)}{x^2} - \\frac{2}{x}$. Note que nesse problema para o termo $\\epsilon$ na Equação **(2.2)**, se tornou a energia, o autovalor que queremos encontrar. Então, nosso chute inicial não será mais $E$ e sim $\\epsilon$. Definido o valor inicial de $\\epsilon$. Assim,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5fb3c261-8bab-4df2-8962-7363f1311823",
   "metadata": {},
   "outputs": [],
   "source": [
    "def s(eps, l, x): #função para s(x)\n",
    "    V = l*(l+1)/(x**2) - 2/x #potencial\n",
    "    return eps - V\n",
    "\n",
    "def p(x): #função para p(x)\n",
    "    return 2/x\n",
    "\n",
    "def dp(x): #função para a derivada de p(x)\n",
    "    return -2/(x**2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6bb984d4-fb47-469b-a46e-54a6488288a7",
   "metadata": {},
   "source": [
    "$\\quad$ Agora criaremos uma função para Equação **(2.6)**, a qual é a equação de recorrência para esse problema, que retornará o valor do próximo valor das soluções. Essa função deverá receber o valor da energia (a variável `eps` representará esse valor), o valor de $l$, o $\\delta$ (a variável `delta` será esse valor). Além disso, receberá todo o eixo x (o array x contará essa informação), também deverá receber todos os pontos de $\\psi$ que determinamos até então (o array `psi` terá todas essas quantidades) e por fim deverá receber a variável `i`, essa variável terá a informação em qual ponto estamos, e assim calcular o $\\psi(x+\\delta)$. Assim, a função ficará,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "47fe9e15-3b3a-4a5c-b9ea-2828e48ca4a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Dividimos as Equação (2.6) em partes, para facilitar a visualização\n",
    "def eq_rec(eps, l, delta, x, psi, i):\n",
    "    temp1 = 2*(1+(5*(delta**2)/12)*(dp(x[i-1])/5 - s(eps, l, x[i-1])))*psi[i-1]\n",
    "    temp2 = (1 - p(x[i-1])*delta/2 + (delta**2/12)*(dp(x[i-1]) + s(eps, l, x[i-2])))*psi[i-2]\n",
    "    temp3 = 1 + p(x[i-1])*delta/2 + (delta**2/12)*(dp(x[i-1]) + s(eps, l, x[i-1]+delta))\n",
    "    return (temp1 - temp2)/temp3"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5743c457-817d-4f0a-ba80-2f842ec3f472",
   "metadata": {},
   "source": [
    "$\\quad$ Em seguida, criaremos uma função que determinará a derivada de primeira ordem, esta será importante para verificar se as soluções encontradas. Lembre-se que para determinar a derivada numérica podemos usar a seguinte equação $\\frac{d\\psi(x)}{dx} = \\frac{\\psi(x) - \\psi(x-delta)}{\\delta}$ **[3, 4]**, perceba que esse é a fórmula regressiva. Logo,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d0be10f3-a68a-40e5-828c-a6670d729ebd",
   "metadata": {},
   "outputs": [],
   "source": [
    "def df(psi, x):\n",
    "    delta = abs(x[0]-x[1]) #determinando o delta\n",
    "    d_psi = abs(psi[len(psi) - 1] - psi[len(psi)-2])/delta #determinando a derivada\n",
    "    return d_psi"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33d23230-4239-45db-b72d-d01e40defa11",
   "metadata": {},
   "source": [
    "$\\quad$ Finalmente, faremos as funções para encontrar as soluções $\\psi^{\\ l}$ e $\\psi^{\\ r}$, a primeira deverá receber as variáveis $a$, `eps` e $l$, a última deverá receber as mesmas variáveis, no entanto, em vez da variável $a$ deverá receber $b$. Observe que os números $a$ e $b$ são os limites do intervalo de integração $[a,\\ b]$. O ponto de encontro entre as duas soluções será $x_{match} = 1$, esse valor foi escolhido após vários teste até achar um bom."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "cf049e3e-0970-4c4a-bc3a-b22eb766300a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def fpsi_l(a, eps, l):\n",
    "    x_0 = a #posição inicial\n",
    "    x_match = 1 #o ponto de encontro das duas soluções\n",
    "    x = np.linspace(x_0, x_match, 10000) #nosso intervalo de integração para essa solução\n",
    "    delta = abs(x[1] - x[0]) #determinando o delta\n",
    "    \n",
    "    psi_l = np.zeros(len(x)) #criando um array para armazenar a solução\n",
    "    psi_l[0] = 0 #primeiro ponto da solução\n",
    "    psi_l[1] = delta #segundo ponto da solução\n",
    "\n",
    "    #determinando o resto dos pontos com a equação de recorrência\n",
    "    for i in range(2, len(x)):\n",
    "        psi_l[i] = eq_rec(eps, l, delta, x, psi_l, i) \n",
    "        \n",
    "    return psi_l, x\n",
    "\n",
    "def fpsi_r(b, eps, l):\n",
    "    x_0 = b #posição inicial\n",
    "    x_match = 1\n",
    "    x = np.linspace(x_0, x_match, 10000)\n",
    "    delta = abs(x[1] - x[0])\n",
    "    psi_r = np.zeros(len(x))\n",
    "    psi_r[0] = 0\n",
    "    psi_r[1] = delta\n",
    "\n",
    "    for i in range(2, len(x)):\n",
    "        psi_r[i] = eq_rec(eps, l, delta, x, psi_r, i)\n",
    "\n",
    "    return psi_r, x"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd62fd5a-d936-4357-abb4-0a8d33306330",
   "metadata": {},
   "source": [
    "$\\quad$ Agora definiremos uma função para normalizar as funções de ondas obtidas. Assim, saiba que uma função de onda seja normalizada a mesma atender a propriedade $ \\int^{\\infty}_{-\\infty}\\left|\\Psi(x, \\ t)\\right|^2dx = 1$. Então,"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf7a4175-a2b5-478e-89bb-4f89f573042f",
   "metadata": {},
   "source": [
    "$$ \\int^{\\infty}_{-\\infty}\\left|\\Psi(x, \\ t)\\right|^2dx = 1 \\implies \\int^{\\infty}_{-\\infty}A^2\\left|f(x, \\ t)\\right|^2dx = 1$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8def7118-c937-45e7-a8af-bb4a6f909c76",
   "metadata": {},
   "source": [
    "$$\\therefore \\boxed{A = \\frac{1}{\\sqrt{\\int^{\\infty}_{-\\infty}\\left|f(x, \\ t)\\right|^2dx}}} \\ ,$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc817d8d-5396-4004-ae92-b38b27830aab",
   "metadata": {},
   "source": [
    "em que $A$ é a amplitude da função de onda, $\\Psi(x, \\ t) = Af(x, \\ t)$. Dessa forma, para normalizar a autofunção devemos determinar a amplitude $A$ e depois multiplicarmos a autofunção não normalizada por essa amplitude. Vale ressaltar, que a fim de calcular a integral, utilizaremos o método de Simpson **[3, 4]** e a função receberá o intervalo de integração,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "24082d92-bc62-4db4-a7b5-8d8f6c789f7b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def psi_norm(psi, a, b):\n",
    "    #calculando a integral\n",
    "    N = len(psi) #determinando N\n",
    "    h = abs(b-a)/N #determinando o passo de integração\n",
    "    x = np.linspace(a, b, N) #nosso intervalo d eintegração\n",
    "    s_p, s_i = 0, 0 #essas variáveis receberam os valores dos somatórios de números pares e ímpares, respectivamente\n",
    "    s_0 = abs(psi[0])**2 + abs(psi[N-1])**2 \n",
    "\n",
    "    #calculando a s_i e s_p\n",
    "    for i in range(1, N-1):\n",
    "        if i%2 == 0:\n",
    "            s_p += abs(psi[i])**2\n",
    "        else:\n",
    "            s_i += abs(psi[i])**2 \n",
    "    #resultado da integral\n",
    "    S_f = h*(s_0 + 2*s_p + 4*s_i)/3\n",
    "\n",
    "    #amplitude\n",
    "    A = np.sqrt(1/S_f)\n",
    "    psi_n = A*psi\n",
    "    return psi_n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2bf204fb-f50d-4690-bce0-e350c5ea64e7",
   "metadata": {},
   "source": [
    "### 3.2 Aplicando o _método numérico de Numerov_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92a957d8-bb5f-4b0f-a820-2b2385c5a398",
   "metadata": {},
   "source": [
    "$\\quad$ Definido essas funções, agora aplicaremos o _método numérico de Numerov_. O código que montaremos é baseado no que foi feito em **[2]**. Diante disso, a fim de solucionar o nosso problema consideraremos o chute inicial para energia $\\epsilon = -1$ e o $l=0$. Para $l=0$, os pontos de retorno são $x_{in} = 0$ e $x_{out} = -\\frac{2}{\\epsilon}$, para visualizarmos melhor plotaremos em seguida o gráfico desse potencial:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "bf9e77a8-dca9-4f46-b47f-a7b4a6efc5cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'x')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(0.1, 10, 1000) #nosso eixo x\n",
    "V = -2/x #potencial\n",
    "eps = -1\n",
    "\n",
    "plot_ticks()\n",
    "plt.plot(x, V)\n",
    "\n",
    "plt.xlim(0, 10)\n",
    "plt.ylim(-10, 0) \n",
    "\n",
    "plt.plot(0, eps, 'o', color = 'black')\n",
    "plt.text(0.1, eps+0.2, r\"$x_{in}$\", fontsize = 15)\n",
    "plt.axhline(y = eps, xmin = 0, xmax = -2/eps, color = 'black', linestyle = '--')\n",
    "\n",
    "plt.plot(-2/eps, eps, 'o', color = 'black')\n",
    "plt.text(-2/eps, eps + 0.2, r\"$x_{out}$\", fontsize = 15)\n",
    "\n",
    "plt.title(\"Gráfico do potencial com l = 0\")\n",
    "plt.ylabel(\"V(x)\")\n",
    "plt.xlabel(\"x\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f018a889-02ee-4be7-8c4b-891d46de06ca",
   "metadata": {},
   "source": [
    "$\\quad$ Observe que o potencial diverge em posição próximas de zero. Assim, devemos ter cautela em definir o intervalo de integração, pois o $a$ não pode ser muito pequeno, se não o código poderá ter erros de overflows ou todas as autofunções encontradas não serão bem definidas, dificultando a aplicação do método. Vale ressaltar, que como o potencial tende a infinito quando $x = 0$, então podemos dizer que a partícula não consegue ir para posições de valores negativos. Em outras palavras, a partícula está confinada em um intervalo $[0, \\infty]$. Outra coisa que deve ser salientada, é que quanto maior o valor da energia maior será o $x_{out}$, em $\\epsilon = 0$ a partícula não está mais sobre a ação do potencial, ou seja, é uma partícula livre.\n",
    "\n",
    "$\\quad$ Contudo, após vários teste, vimos que o melhor intervalo a ser usado para o este problema é $[0.5, \\ 60]$. Assim, devemos por $a = 0.5$ e $b=60$."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fba4ee95-a71a-4fb7-b9f3-800d51061f1e",
   "metadata": {},
   "source": [
    "$\\quad$ Dito isso, criaremos a seguir o código para obter as soluções do nosso problema."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c608c90b-fb54-481e-a7d9-d6820c2eebe1",
   "metadata": {},
   "outputs": [],
   "source": [
    "eps = -1 #valor incial para a energia\n",
    "l = 0 #valor do l\n",
    "a = 0.5\n",
    "b = 60\n",
    "autovalor = [] #essa será uma lista para armazenar os autovalores encontrados\n",
    "while eps<-0.01: #condição para que o código não encontre energias pequenas demais\n",
    "    dpsi_l = 0 #valor inicial da derivada da solução pela esquerda\n",
    "    dpsi_r = 1#valor inicial da derivada da solução pela direita\n",
    "    while abs(dpsi_l-dpsi_r)>1e-4*(dpsi_r+dpsi_l)/2 and eps<0: #condição para verificar se precisão que queremos foi atingida\n",
    "        psi_r, x_r= fpsi_r(b, eps, l) #determinando a solução pela direita\n",
    "        psi_l, x_l = fpsi_l(a, eps, l) #determinando a solução pela esquerda\n",
    "        psi_l = psi_l*psi_r[len(psi_r)-1]/psi_l[len(psi_l)-1] #reidefinindo a solução pela a esquerda\n",
    "\n",
    "        #calculando as derivadas das soluções\n",
    "        dpsi_r = df(psi_r, x_r)\n",
    "        dpsi_l = df(psi_l, x_l)\n",
    "\n",
    "        eps_old = eps #guardando o valor da energia\n",
    "        eps += 0.005*abs((dpsi_l-dpsi_r)/(dpsi_r+dpsi_l)/2) #aumentando o valor da energia\n",
    "        \n",
    "        if abs(eps_old-eps)<1e-9: #verificando se o código está preciso em um autovalor\n",
    "            eps+=0.01 #assim ele dará um passo maior\n",
    "            \n",
    "    if abs(len(autovalor)) == 0: \n",
    "            autovalor.append(eps) #guardando o autovalor determinado na lista\n",
    "        \n",
    "    if abs(autovalor[len(autovalor)-1]-eps) >1e-2: #verificando se os autovalores encontrados são muito próximos\n",
    "        autovalor.append(eps) #guardando o autovalor determinado na lista"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c00d5c0-91ca-4c04-a36e-3bb281d0bb72",
   "metadata": {},
   "source": [
    "* **Observações:**\n",
    "\n",
    "$\\quad$ Note que o passo que se definirmos um passo tiver um tamanho constante, pode acontecer do código não encontrar o autovalor, pois, ele pode passar direto do valor que queremos, assim utilizamos um passo interativo. No qual é diretamente proporcional ao erro relativo das derivadas, "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c484add1-0aba-4e1e-8f30-cf095dee3f6c",
   "metadata": {},
   "source": [
    "$$ passo = 0.005 \\left|\\frac{\\frac{d\\psi^{\\ l}(x_{match})}{dx} - \\frac{d\\psi^{\\ r}(x_{match})}{dx}}{med} \\right| \\ ,$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8405171-8dbc-479a-a6bb-10500f6274f5",
   "metadata": {},
   "source": [
    "tal qual, o termo $med$ é a média entre $\\frac{d\\psi^{\\ l}(x_{match})}{dx}$ e $\\frac{d\\psi^{\\ r}(x_{match})}{dx}$, ou seja, $med = \\frac{\\frac{d\\psi^{\\ l}(x_{match})}{dx}+\\frac{d\\psi^{\\ r}(x_{match})}{dx}}{2}$. Dessa forma, quanto mais próximas às derivadas forem, menor será o tamanho do passo, assim o valor da energia irá convergir até atingir a precisão que queremos. No entanto, surge um problema, que a energia não para de convergir, por conseguinte, ficava “presa” em um autovalor. A fim de impedir isso, colocamos um `if` para verificar se isso está acontecendo, caso esteja, o programa dá um passo maior de $0.01$.\n",
    "\n",
    "$\\quad$ Observe também que o erro que queremos atingir é diretamente proporcional à média das derivadas. Pomos assim, devido aos vários testes que foram feitos, vimos que as nossas autofunções não normalizadas tinham uma ordem muito alta, por exemplo, ordem de $10^{10}$. Dessa forma, as suas derivadas também tinham. Assim, a diferença das derivadas nunca chegava ao erro desejado, mesmo que esta fosse muito menor que as derivadas, sua ordem era muito alta. Dessa forma, multiplicando o erro que queríamos pelas médias dessas derivadas, conseguimos pôr a precisão comparável à diferença daquelas."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dfb6f53d-85ac-4669-ad72-9e1ce001eaeb",
   "metadata": {},
   "source": [
    "$\\quad$ Daremos uma olhada nas autofunções encontratadas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "72df8ca7-7b09-49fe-9d28-a072e7006cc3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-0.49559487821488746, -0.48555710884185505, -0.20034173188886717, -0.1903410835736894, -0.16055938822000576, -0.1505560010775531, -0.0940804471192889, -0.08408033470053607, -0.07127589439838396, -0.05483939934411981, -0.04483939883633807, -0.029830698019894512, -0.01983044712076063, -0.0072952202499986015] 14\n"
     ]
    }
   ],
   "source": [
    "print(autovalor, len(autovalor))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8377b7d-86bc-4f09-afce-6981ec77d391",
   "metadata": {},
   "source": [
    "$\\quad$ Vamos plotar o gráfico de cada autofunção encontrada e deletar os que tiverem as curvas menos suaves."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b6739f16-4faa-401e-892f-9eef166f57ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1500 with 14 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "psi = np.zeros(2*len(psi_r)) #criando um array para a solução geral\n",
    "x = np.zeros(2*len(psi_r)) #criando um array para x\n",
    "N = len(autovalor) \n",
    "fig, ax = plt.subplots(int(N/2), int(N/7), figsize = (10, 15)) #criando uma figura com subplots\n",
    "\n",
    "for i in range(N):\n",
    "    psi_r, x_r = fpsi_r(b, autovalor[i], l)\n",
    "    psi_l, x_l = fpsi_l(a, autovalor[i], l)\n",
    "    psi_l = psi_l*psi_r[len(psi_r)-1]/psi_l[len(psi_l)-1]\n",
    "\n",
    "    #armazenando a solução pela esquerda\n",
    "    for j in range(len(psi_l)):\n",
    "        psi[j] = psi_l[j]\n",
    "        x[j] = x_l[j]\n",
    "\n",
    "    #armazenando a solução pela direita\n",
    "    for j in range(len(psi_r)):\n",
    "        psi[len(psi_r)+j] = psi_r[len(psi_r) - 1 - j]\n",
    "        x[len(psi_r)+j] = x_r[len(psi_r) - 1 - j]\n",
    "\n",
    "    #normalizando a autofunção\n",
    "    psi_n = psi_norm(psi, a, b)\n",
    "\n",
    "    #plotando as autofunções\n",
    "    if i < N/2:\n",
    "        ax[i, 0].plot(x, psi_n)\n",
    "        ax[i, 0].set_title(\"n = %.f\"%i)\n",
    "        fig.tight_layout(pad=0.1)\n",
    "    else:\n",
    "        ax[i-7, 1].plot(x, psi_n)\n",
    "        ax[i-7, 1].set_title(\"n = %.f\"%i)\n",
    "        fig.tight_layout(pad=0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2a164ae-9894-4a7f-bebd-369f59a52370",
   "metadata": {},
   "source": [
    "$\\quad$ Perceba que alguns autoestados foram encontrados vários autovalores, os quais são resultados _outliers_, para isso usaremos o comando `delete` da biblioteca `Numpy` para retirá-los."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "cd8f236d-3f59-44bd-81d6-01bf820c9fef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.49559488, -0.150556  , -0.0548394 , -0.0298307 ])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "autovalor = np.delete(autovalor, [1, 2, 3, 4, 6, 7, 8, 10, 12, 13])\n",
    "autovalor"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30dfc16a-a6ad-4e00-8792-a6b9c76ff29e",
   "metadata": {},
   "source": [
    "$\\quad$ A seguir, plotaremos as autofunções dos autovalores após os outliers excluidos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2dc125d4-b46a-48f9-8c8d-c74e12c60122",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "psi = np.zeros(2*len(psi_r))\n",
    "x = np.zeros(2*len(psi_r))\n",
    "N = len(autovalor) \n",
    "fig, ax = plt.subplots(int(N/2), int(N/2), figsize = (10, 5)) \n",
    "\n",
    "for i in range(N):\n",
    "    psi_r, x_r = fpsi_r(b, autovalor[i], l)\n",
    "    psi_l, x_l = fpsi_l(a, autovalor[i], l)\n",
    "    psi_l = psi_l*psi_r[len(psi_r)-1]/psi_l[len(psi_l)-1]\n",
    "\n",
    "    for j in range(len(psi_l)):\n",
    "        psi[j] = psi_l[j]\n",
    "        x[j] = x_l[j]\n",
    "\n",
    "    for j in range(len(psi_r)):\n",
    "        psi[len(psi_r)+j] = psi_r[len(psi_r) - 1 - j]\n",
    "        x[len(psi_r)+j] = x_r[len(psi_r) - 1 - j]\n",
    "\n",
    "    psi_n = psi_norm(psi, a, b)\n",
    "\n",
    "    if i < N/2:\n",
    "        ax[i, 0].plot(x, psi_n)\n",
    "        ax[i, 0].set_title(\"n = %.f\"%i)\n",
    "        fig.tight_layout(pad=0.1)\n",
    "    else:\n",
    "        ax[i-2, 1].plot(x, psi_n)\n",
    "        ax[i-2, 1].set_title(\"n = %.f\"%i)\n",
    "        fig.tight_layout(pad=0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25350745-3673-49ec-8bcb-1f8796b4903a",
   "metadata": {},
   "source": [
    "$\\quad$ Plotaremos todas juntas agora."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "dd56c7a7-b0f9-4a36-bd85-7b7fa042e1b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x276f6bbcf10>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "psi = np.zeros(2*len(psi_r)) #solução geral\n",
    "x = np.zeros(2*len(psi_r))\n",
    "N = len(autovalor)\n",
    "plot_ticks()\n",
    "for i in range(N):\n",
    "    psi_r, x_r = fpsi_r(b, autovalor[i], l)\n",
    "    psi_l, x_l = fpsi_l(a, autovalor[i], l)\n",
    "    psi_l = psi_l*psi_r[len(psi_r)-1]/psi_l[len(psi_l)-1]\n",
    "    \n",
    "    for j in range(len(psi_l)):\n",
    "        psi[j] = psi_l[j]\n",
    "        x[j] = x_l[j]\n",
    "    for j in range(len(psi_r)):\n",
    "        psi[len(psi_r)+j] = psi_r[len(psi_r) - 1 - j]\n",
    "        x[len(psi_r)+j] = x_r[len(psi_r) - 1 - j]\n",
    "        \n",
    "    psi_n = psi_norm(psi, a, b)\n",
    "\n",
    "    plt.plot(x, psi_n, label = \"E = %.3f\" %autovalor[i])\n",
    "plt.title(\"Gráfico das funções de ondas encontradas\")\n",
    "plt.xlabel(\"x\")\n",
    "plt.ylabel(r\"$\\psi(x)$\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4838d006-08fa-4df8-b06e-ed696c210cda",
   "metadata": {},
   "source": [
    "### 3.3 Determinando o erro do método"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e7b784c-cb79-4fd1-930d-0bf5513129b7",
   "metadata": {},
   "source": [
    "$\\quad$ Note que os autovalores do problema do átomo de Hidrogênio estão em unidade de Hartree,  $E_n = \\frac{1}{2n^2}\\ Ha$. Com isso, sabemos quais são os autovalores exatos para o estado fundamental, primeiro, segundo e terceiro estado excitado, os quais são $-0.500$, $-0.125$, $-0.0556$ e $-0.0312$, respectivamente.\n",
    "\n",
    "$\\quad$ Assim, conseguimos determinar o erro do método, utilizando o erro relativo $err = \\frac{|v_e - v_a|}{v_e}$, em que $v_e$ é o valor exato e $v_a$ é o valor aproximado. Logo, determinamos o erro comparando os autovalores que encontramos com os autovalores teóricos dos respectivos autoestados. Como pode ser visualizado na tabela abaixo. Obtivemos um erro médio de $6.425 \\ \\%$."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d03e1c2-7375-46cc-ad9b-0486acf8dcdf",
   "metadata": {},
   "source": [
    "|       | $E_a \\ (Ha)$ | $E_e \\ (Ha)$ | $err \\ (\\%)$ |\n",
    "|-------|-------|-------|-------|\n",
    "| $n=1$ | $-0.496$ | $-0.500$ | $0.800$ |\n",
    "| $n=2$ | $-0.151$ | $-0.125$ | $20.8$ |\n",
    "| $n=3$ | $-0.055$ | $-0.0556$ | $1.1$ |\n",
    "| $n=4$ | $-0.030$ | $-0.0312$ | $3.00$ |"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a95d231b-b74b-4d65-8266-13e5db2b7a22",
   "metadata": {},
   "source": [
    "## 4. Conclusão"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3800445-6008-4e47-ae40-2e3fa14a26e5",
   "metadata": {},
   "source": [
    "$\\quad$ Neste `Notebook` foi feito todo o desenvolvimento do _método numérico de Numerov_, em seguida fizemos a aplicação do mesmo para solucionar o problema do Átomo de Hidrogênio. No entanto, tivemos que adaptar o método para o problema, porque o método de Numerov foi feito inicialmente para solucionar uma EDO de segunda ordem sem a presença do termo de derivada de primeira ordem e o problema que queríamos solucionar possuía esse termo. Feito essa adaptação, o método foi aplicado e obtivemos resultados com uma média de erro de $6.425 \\ \\%$. Esse é um erro considerável, possivelmente um dos motivos para ele ser um pouco grande é porque tivemos que adaptar o método para solucionar o problema proposto."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b251fda7-89d6-408f-8e38-211b90c2b8fd",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b8a264a-4095-4761-8a89-714ffb5e54e5",
   "metadata": {},
   "source": [
    "# Referências"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8359ab0-a9b0-42b8-9172-cb1987e35326",
   "metadata": {},
   "source": [
    "**[1]** Caruso, Francisco, et al. “Applications of the Numerov Method to Simple Quantum Systems Using Python.” Revista Brasileira de Ensino de Física, vol. 44, no. e20220098, 2022, https://doi.org/10.1590/1806-9126-rbef-2022-0098. Accessed 29 Mar. 2024.\n",
    "\n",
    "**[2]** Investigação da energia de ligação entre átomos utilizando o potencial de Lennard-Jones: https://github.com/PETfisicaUFRN/PET.py/blob/main/Notebooks/Investiga%C3%A7%C3%A3o%20da%20energia%20de%20liga%C3%A7%C3%A3o%20entre%20%C3%A1tomos%20utilizando%20o%20potencial%20de%20Lennard-Jones.ipynb\n",
    "\n",
    "**[3]** Newman, Mark. Computacional Physics. 2012. University of Michigan, 2013.\n",
    "\n",
    "**[4]** Giordano, N. J., & Nakanishi, H. (2006). Computational physics."
   ]
  }
 ],
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