ECT - MMF2 - Experimento 6 - Ondas Estacionárias

ect_mmf2_exp6_ondas_estacionarias.ipynb

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        "<a href=\"https://colab.research.google.com/gist/nelisjunior/644b01091cc0366cd8a399d3d1f09172/ect_mmf2_exp6_ondas_estacionarias.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    },
    {
      "cell_type": "markdown",
      "source": [
        "# ECT - MMF2 - Experimento 6 - Ondas Estacionárias"
      ],
      "metadata": {
        "id": "HUufeDFh54ml"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Versão 1"
      ],
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        "id": "edd4h-Xb8G0h"
      }
    },
    {
      "cell_type": "code",
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        "id": "lIYdn1woOS1n",
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      "source": [
        "# @title Bibiotecas\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import scipy.stats as stats\n",
        "from scipy.optimize import curve_fit\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# @title Dados experimentais\n",
        "modos = np.array([6, 5, 4, 3, 2, 1])\n",
        "frequencias_1_disco = np.array([33.6, 28, 22.4, 16.6, 11.2, 5.3])\n",
        "comprimentos_onda_1_disco = np.array([0.76, 0.92, 1.16, 1.58, 2.32, 4.64])\n",
        "lambda_f_1_disco = np.array([25.536, 25.76, 25.984, 26.228, 25.984, 24.592])\n",
        "L = 2.32  # Comprimento da linha em metros\n",
        "FT1 = 1.06  # Força de tensão em Newtons"
      ],
      "metadata": {
        "id": "fOT47Zls6Hr4",
        "cellView": "form"
      },
      "execution_count": null,
      "outputs": []
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    {
      "cell_type": "code",
      "source": [
        "# @title Regressão linear f = n * (v/2L)\n",
        "def regressao_linear(x, a):\n",
        "    return a * x\n",
        "\n",
        "# Regressão à média fλ = v\n",
        "def regressao_media(dados):\n",
        "    media = np.mean(dados)\n",
        "    desvio = stats.sem(dados)\n",
        "    return media, desvio\n",
        "\n",
        "# Ajuste para 1 disco\n",
        "param_1_disco, cov_1_disco = curve_fit(regressao_linear, modos, frequencias_1_disco)\n",
        "v_rl_1_disco = param_1_disco[0] * (2 * L)\n",
        "incerteza_rl_1_disco = np.sqrt(np.diag(cov_1_disco))[0] * (2 * L)\n",
        "\n",
        "# Cálculo da regressão à média\n",
        "v_rm_1_disco, erro_v_rm_1_disco = regressao_media(lambda_f_1_disco)\n",
        "\n",
        "delta_v_1_disco = v_rl_1_disco - v_rm_1_disco\n",
        "graus_liberdade = len(modos) - 1\n",
        "\n",
        "# Teste-t\n",
        "t_1_disco, p_1_disco = stats.ttest_rel(frequencias_1_disco, modos * (v_rl_1_disco / (2 * L)))\n",
        "\n",
        "# Estatísticas adicionais\n",
        "r2_1_disco = np.corrcoef(modos, frequencias_1_disco)[0, 1]**2\n",
        "se_1_disco = np.std(frequencias_1_disco - regressao_linear(modos, *param_1_disco))\n",
        "F_1_disco = (r2_1_disco / (1 - r2_1_disco)) * graus_liberdade\n",
        "SS_reg_1_disco = np.sum((regressao_linear(modos, *param_1_disco) - np.mean(frequencias_1_disco))**2)\n",
        "SS_res_1_disco = np.sum((frequencias_1_disco - regressao_linear(modos, *param_1_disco))**2)\n",
        "\n",
        "# Cálculo da densidade linear da corda\n",
        "mu_1_disco = FT1 / v_rl_1_disco**2\n",
        "\n",
        "# Exibir resultados\n",
        "print(f\"Velocidade estimada pela regressão linear (1 disco): {v_rl_1_disco:.3f} ± {incerteza_rl_1_disco:.3f} m/s\")\n",
        "print(f\"Velocidade estimada pela regressão à média (1 disco): {v_rm_1_disco:.3f} ± {erro_v_rm_1_disco:.3f} m/s\")\n",
        "print(f\"Teste-t para 1 disco: t = {t_1_disco:.3f}, p = {p_1_disco:.3f}\")\n",
        "print(f\"Coeficiente de determinação R²: {r2_1_disco:.4f}\")\n",
        "print(f\"Incerteza padrão da regressão: {se_1_disco:.4f}\")\n",
        "print(f\"Estatística-F: {F_1_disco:.4f}\")\n",
        "print(f\"Graus de liberdade dos resíduos: {graus_liberdade}\")\n",
        "print(f\"Soma dos quadrados da regressão: {SS_reg_1_disco:.4f}\")\n",
        "print(f\"Soma dos quadrados dos resíduos: {SS_res_1_disco:.4f}\")\n",
        "print(f\"Densidade linear estimada (1 disco): {mu_1_disco:.6f} kg/m\")\n"
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          "text": [
            "Velocidade estimada pela regressão linear (1 disco): 25.938 ± 0.076 m/s\n",
            "Velocidade estimada pela regressão à média (1 disco): 25.681 ± 0.238 m/s\n",
            "Teste-t para 1 disco: t = -0.816, p = 0.451\n",
            "Coeficiente de determinação R²: 0.9999\n",
            "Incerteza padrão da regressão: 0.1335\n",
            "Estatística-F: 59340.0152\n",
            "Graus de liberdade dos resíduos: 5\n",
            "Soma dos quadrados da regressão: 546.8775\n",
            "Soma dos quadrados dos resíduos: 0.1211\n",
            "Densidade linear estimada (1 disco): 0.001576 kg/m\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Versão 2"
      ],
      "metadata": {
        "id": "LfvgLw-U8MkE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# @title Análise de Ondas Estacionárias.ipynb\n",
        "\n",
        "# Importação de bibliotecas\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from scipy import stats\n",
        "import matplotlib.pyplot as plt\n"
      ],
      "metadata": {
        "cellView": "form",
        "id": "DC0GIgvj8Pzx"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# @title Entrada de dados (Cole suas medições abaixo)\n",
        "# TABELA 1 (1 disco)\n",
        "data_1_disco = {\n",
        "    'n': [6, 5, 4, 3, 2, 1],\n",
        "    'f (Hz)': [120.0, 100.0, 80.0, 60.0, 40.0, 20.0],  # Substituir pelos dados reais\n",
        "    'λ (m)': [0.5, 0.6, 0.75, 1.0, 1.5, 3.0]           # Substituir pelos dados reais\n",
        "}\n",
        "\n",
        "# TABELA 2 (2 discos)\n",
        "data_2_discos = {\n",
        "    'n': [6, 5, 4, 3, 2, 1],\n",
        "    'f (Hz)': [170.0, 141.7, 113.3, 85.0, 56.7, 28.3], # Substituir pelos dados reais\n",
        "    'λ (m)': [0.4, 0.48, 0.6, 0.8, 1.2, 2.4]           # Substituir pelos dados reais\n",
        "}\n"
      ],
      "metadata": {
        "cellView": "form",
        "id": "PizGxCzm8jov"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# @title Constantes do experimento (preencher com seus dados)\n",
        "L = 1.5  # Comprimento útil da linha (metros)\n",
        "m_suporte = 0.050  # Massa do suporte (kg)\n",
        "m_disco = 0.100    # Massa de cada disco (kg)\n",
        "g = 9.81           # Aceleração gravitacional (m/s²)\n",
        "\n",
        "# Cálculo das tensões FT1 e FT2\n",
        "FT1 = (m_suporte + 1 * m_disco) * g  # Tensão com 1 disco\n",
        "FT2 = (m_suporte + 2 * m_disco) * g  # Tensão com 2 discos\n",
        "\n",
        "# Função para processar cada conjunto de dados\n",
        "def analyze_data(data, FT, L):\n",
        "    df = pd.DataFrame(data)\n",
        "    df['fλ'] = df['f (Hz)'] * df['λ (m)']\n",
        "\n",
        "    # Regressão Linear (f vs n)\n",
        "    X = df['n'].values.reshape(-1, 1)\n",
        "    Y = df['f (Hz)'].values\n",
        "    slope, intercept, r_value, p_value, std_err = stats.linregress(X.flatten(), Y)\n",
        "\n",
        "    # Teste-t para intercept (b0)\n",
        "    t0 = intercept / std_err  # Supondo erro padrão do intercept (ajuste conforme PROJ.LIN)\n",
        "\n",
        "    # Regressão à Média (fλ)\n",
        "    mean_fλ = np.mean(df['fλ'])\n",
        "    std_fλ = np.std(df['fλ'], ddof=1)\n",
        "    n = len(df)\n",
        "    s0_hat = std_fλ / np.sqrt(n)\n",
        "    k = stats.t.ppf(0.975, n-1)  # 95% confiança\n",
        "\n",
        "    return {\n",
        "        'RL': {'b1': slope, 'b0': intercept, 'std_err': std_err, 't0': t0},\n",
        "        'RM': {'a0': mean_fλ, 's0_hat': s0_hat, 'k': k},\n",
        "        'data': df\n",
        "    }\n",
        "\n",
        "# Processamento dos dados\n",
        "result_1 = analyze_data(data_1_disco, FT1, L)\n",
        "result_2 = analyze_data(data_2_discos, FT2, L)\n",
        "\n",
        "# Cálculo da velocidade e densidade linear\n",
        "def calculate_velocity_and_density(result, FT):\n",
        "    v_RL = result['RL']['b1'] * 2 * L\n",
        "    v_RM = result['RM']['a0']\n",
        "\n",
        "    # Densidade linear μ = FT / v² (usando RM como referência)\n",
        "    mu = FT / (v_RM ** 2)\n",
        "    mu_err = (2 * FT / (v_RM ** 3)) * result['RM']['s0_hat']\n",
        "\n",
        "    return {'v_RL': v_RL, 'v_RM': v_RM, 'mu': mu, 'mu_err': mu_err}\n",
        "\n",
        "mu_1 = calculate_velocity_and_density(result_1, FT1)\n",
        "mu_2 = calculate_velocity_and_density(result_2, FT2)\n"
      ],
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          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-3-aac4e91e321b>:22: RuntimeWarning: invalid value encountered in scalar divide\n",
            "  t0 = intercept / std_err  # Supondo erro padrão do intercept (ajuste conforme PROJ.LIN)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# @title Saída dos resultados\n",
        "print(\"=\"*50)\n",
        "print(\"Regressão Linear (1 disco):\")\n",
        "print(f\"b1 = {result_1['RL']['b1']:.4f} Hz/n\")\n",
        "print(f\"t0 = {result_1['RL']['t0']:.4f}\")\n",
        "print(\"\\nRegressão à Média (1 disco):\")\n",
        "print(f\"a0 = {result_1['RM']['a0']:.3f} ± {result_1['RM']['k'] * result_1['RM']['s0_hat']:.3f} m/s\")\n",
        "\n",
        "print(\"\\n\" + \"=\"*50)\n",
        "print(\"Velocidades (1 disco):\")\n",
        "print(f\"v_RL = {mu_1['v_RL']:.3f} m/s\")\n",
        "print(f\"v_RM = {mu_1['v_RM']:.3f} m/s\")\n",
        "print(f\"Densidade linear μ = {mu_1['mu']:.5f} ± {mu_1['mu_err']:.5f} kg/m\")\n",
        "\n",
        "print(\"\\n\" + \"=\"*50)\n",
        "print(\"Tensão para nota Lá (A2):\")\n",
        "f_LA = 110.0\n",
        "mu = mu_1['mu']  # Usando μ do experimento 1\n",
        "F_required = (2 * L * f_LA)**2 * mu\n",
        "print(f\"F_T = {F_required:.2f} N\")\n"
      ],
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        "id": "5AtI_hFm8uxV",
        "outputId": "7899b6f0-6d31-4d34-9a73-6c95af98f51c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "==================================================\n",
            "Regressão Linear (1 disco):\n",
            "b1 = 20.0000 Hz/n\n",
            "t0 = nan\n",
            "\n",
            "Regressão à Média (1 disco):\n",
            "a0 = 60.000 ± 0.000 m/s\n",
            "\n",
            "==================================================\n",
            "Velocidades (1 disco):\n",
            "v_RL = 60.000 m/s\n",
            "v_RM = 60.000 m/s\n",
            "Densidade linear μ = 0.00041 ± 0.00000 kg/m\n",
            "\n",
            "==================================================\n",
            "Tensão para nota Lá (A2):\n",
            "F_T = 44.51 N\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# @title Gráficos (exemplo para 1 disco)\n",
        "plt.figure(figsize=(10, 4))\n",
        "plt.subplot(121)\n",
        "plt.plot(result_1['data']['n'], result_1['data']['f (Hz)'], 'bo', label='Dados')\n",
        "plt.plot(result_1['data']['n'], result_1['RL']['b1'] * result_1['data']['n'] + result_1['RL']['b0'], 'r--', label='Regressão')\n",
        "plt.xlabel('n'); plt.ylabel('f (Hz)'); plt.title('Regressão Linear (1 disco)')\n",
        "plt.legend()\n",
        "\n",
        "plt.subplot(122)\n",
        "plt.plot(result_1['data']['n'], result_1['data']['fλ'], 'go', label='Dados')\n",
        "plt.axhline(result_1['RM']['a0'], color='purple', linestyle='--', label='Média')\n",
        "plt.xlabel('n'); plt.ylabel('fλ (m/s)'); plt.title('Regressão à Média (1 disco)')\n",
        "plt.legend()\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
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          "height": 369
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      "execution_count": 5,
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\n"
          },
          "metadata": {}
        }
      ]
    }
  ],
  "metadata": {
    "colab": {
      "name": "ect_mmf2_exp6_ondas_estacionarias",
      "provenance": [],
      "toc_visible": true,
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}