physics/course2/sem3/labs/lab3.02/scripts/Lab_3_02.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "JbaiJhV8sOOg"
      },
      "outputs": [
        {
          "ename": "ModuleNotFoundError",
          "evalue": "No module named 'matplotlib'",
          "output_type": "error",
          "traceback": [
            "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
            "\u001b[31mModuleNotFoundError\u001b[39m                       Traceback (most recent call last)",
            "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[3]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mmatplotlib\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m pyplot \u001b[38;5;28;01mas\u001b[39;00m plt\n\u001b[32m      2\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mnumpy\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mnp\u001b[39;00m\n",
            "\u001b[31mModuleNotFoundError\u001b[39m: No module named 'matplotlib'"
          ]
        }
      ],
      "source": [
        "from matplotlib import pyplot as plt\n",
        "import numpy as np"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Pd1nq7l6sOOh"
      },
      "source": [
        " Количество измерений $N=15$"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "syIO0ARGsOOj"
      },
      "outputs": [],
      "source": [
        "N = 16"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zpTujvCfsOOk"
      },
      "source": [
        "Переменное сопротивление $R=100,200,...,1500$"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y_ls_8qQsOOk",
        "outputId": "7ef17f94-cdc1-4334-a0e2-3629d2e7856c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[   0  100  200  300  400  500  600  700  800  900 1000 1100 1200 1300\n",
            " 1400 1500]\n"
          ]
        }
      ],
      "source": [
        "Rs = np.arange(0,1600,100)\n",
        "print(Rs)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3lao8aK-sOOm"
      },
      "source": [
        "Измеренная зависимость $U=U(I)$\n",
        "\n",
        "*СЮДА ВСТАВИТЬ ИЗМЕРЕНИЯ В 2 МАССИВА*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tKc9MNOWsOOn",
        "outputId": "83253800-09e2-45aa-9e70-66d6f88a3f01"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(16, 16)"
            ]
          },
          "execution_count": 36,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "Us = np.array([\n",
        "    0.1, 0.0, 1.7, 2.6, 3.4, 4.0, 4.6, 5.0, 5.4, 5.7, 6.0, 6.3, 6.5, 6.7, 6.9, 6.9\n",
        "])\n",
        "Is = np.array([\n",
        "    .015, .015, .012, .011, .010, .009, .008, .007, .007, .006, .006, .005, .005, .005, .005, .005\n",
        "])\n",
        "len(Us), len(Is)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OUWij17qsOOo"
      },
      "source": [
        "График зависимост $U = U(I)$ и $r, \\mathcal{E}$"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "x0XAR-zMsOOp",
        "outputId": "680cbe5f-7898-4e9d-d062-1e6b5acc02c8"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.grid()\n",
        "plt.scatter(Is, Us, marker=\"d\", color=\"brown\", s=6)\n",
        "plt.xlabel(\"$I$\")\n",
        "plt.ylabel(\"U(I)\")\n",
        "# plt.title(\"График зависимости U(I)\")\n",
        "plt.show()\n",
        "plt.savefig(\"1.png\", bbox_inches=\"tight\", dpi=300)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bMEcRDyasOOr",
        "outputId": "5a91f54f-c8db-4d96-f1e6-25b5717e4cfb"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "r = 663.5834189791025\tEpsilon = 9.9205892428914\n"
          ]
        }
      ],
      "source": [
        "r, Epsilon = np.polyfit(Is, Us, 1)\n",
        "r *= -1\n",
        "print(f\"r = {r}\\tEpsilon = {Epsilon}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4tKrKYXMsOOs"
      },
      "source": [
        "Расчёт $P_R = UI$;\n",
        "$P = \\mathcal{E}I$;\n",
        "$P_S = I^2r$ для каждого измерения"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5bhpLc1osOOt",
        "outputId": "d885ba85-b360-4f62-bced-983c9bf9fd64"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[[1.000e-01 1.500e-02 1.500e-03 1.493e-01 1.488e-01]\n",
            " [0.000e+00 1.500e-02 0.000e+00 1.493e-01 1.488e-01]\n",
            " [1.700e+00 1.200e-02 2.040e-02 9.556e-02 1.190e-01]\n",
            " [2.600e+00 1.100e-02 2.860e-02 8.029e-02 1.091e-01]\n",
            " [3.400e+00 1.000e-02 3.400e-02 6.636e-02 9.921e-02]\n",
            " [4.000e+00 9.000e-03 3.600e-02 5.375e-02 8.929e-02]\n",
            " [4.600e+00 8.000e-03 3.680e-02 4.247e-02 7.936e-02]\n",
            " [5.000e+00 7.000e-03 3.500e-02 3.252e-02 6.944e-02]\n",
            " [5.400e+00 7.000e-03 3.780e-02 3.252e-02 6.944e-02]\n",
            " [5.700e+00 6.000e-03 3.420e-02 2.389e-02 5.952e-02]\n",
            " [6.000e+00 6.000e-03 3.600e-02 2.389e-02 5.952e-02]\n",
            " [6.300e+00 5.000e-03 3.150e-02 1.659e-02 4.960e-02]\n",
            " [6.500e+00 5.000e-03 3.250e-02 1.659e-02 4.960e-02]\n",
            " [6.700e+00 5.000e-03 3.350e-02 1.659e-02 4.960e-02]\n",
            " [6.900e+00 5.000e-03 3.450e-02 1.659e-02 4.960e-02]\n",
            " [6.900e+00 5.000e-03 3.450e-02 1.659e-02 4.960e-02]]\n",
            "[ 0.100], [ 15.000], [ 1.500], [ 149.306], [ 148.809], \n",
            "[ 0.000], [ 15.000], [ 0.000], [ 149.306], [ 148.809], \n",
            "[ 1.700], [ 12.000], [ 20.400], [ 95.556], [ 119.047], \n",
            "[ 2.600], [ 11.000], [ 28.600], [ 80.294], [ 109.126], \n",
            "[ 3.400], [ 10.000], [ 34.000], [ 66.358], [ 99.206], \n",
            "[ 4.000], [ 9.000], [ 36.000], [ 53.750], [ 89.285], \n",
            "[ 4.600], [ 8.000], [ 36.800], [ 42.469], [ 79.365], \n",
            "[ 5.000], [ 7.000], [ 35.000], [ 32.516], [ 69.444], \n",
            "[ 5.400], [ 7.000], [ 37.800], [ 32.516], [ 69.444], \n",
            "[ 5.700], [ 6.000], [ 34.200], [ 23.889], [ 59.524], \n",
            "[ 6.000], [ 6.000], [ 36.000], [ 23.889], [ 59.524], \n",
            "[ 6.300], [ 5.000], [ 31.500], [ 16.590], [ 49.603], \n",
            "[ 6.500], [ 5.000], [ 32.500], [ 16.590], [ 49.603], \n",
            "[ 6.700], [ 5.000], [ 33.500], [ 16.590], [ 49.603], \n",
            "[ 6.900], [ 5.000], [ 34.500], [ 16.590], [ 49.603], \n",
            "[ 6.900], [ 5.000], [ 34.500], [ 16.590], [ 49.603], \n"
          ]
        }
      ],
      "source": [
        "P = Epsilon*Is\n",
        "Pr = Us*Is\n",
        "Ps = Is*Is*r\n",
        "\n",
        "arr = (np.vstack((Us, Is, Pr, Ps, P))).T\n",
        "np.set_printoptions(precision=3)\n",
        "print(\n",
        "    arr\n",
        ")\n",
        "\n",
        "arr[:, 1:] *= 1000\n",
        "for i in range(arr.shape[0]):\n",
        "    for j in range(arr.shape[1]):\n",
        "        print(f\"[{arr[i, j]: .3f}]\", end=', ')\n",
        "    print()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "69EADBCEsOOu"
      },
      "source": [
        "Графики для других можностей от Силы тока"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "DzlwFFWosOOu",
        "outputId": "be2efd11-69e7-4a34-8518-5806f769ef8c"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.scatter(Is, Pr, s=5, color=\"purple\", marker=\"d\", label=\"$P_R=P_R(I)$\")\n",
        "plt.scatter(Is, Ps, s=5, color=\"red\", marker=\"s\", label=\"$P_S=P_S(I)$\")\n",
        "plt.scatter(Is, P, s=5, color=\"blue\", marker=\"o\", label=\"$P=P(I)$\")\n",
        "plt.legend()\n",
        "# plt.title(\"Графики зависимости мощностей $P, P_R, P_S$ от силы тока\")\n",
        "plt.xlabel(\"$I$\")\n",
        "\n",
        "plt.grid()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HomT-OIhsOOv"
      },
      "source": [
        "Поиск $I^*, P_{Rmax}$"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 480
        },
        "id": "AM62TovvsOOv",
        "outputId": "0383a78e-7789-49c0-c67c-096af6d91a64"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "I* = 0.0075\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f9344b4b680>"
            ]
          },
          "execution_count": 41,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "I_star = Epsilon/(2*r)\n",
        "print(f\"I* = {I_star:.4f}\")\n",
        "\n",
        "i = np.polyfit(Is, Pr, 2)\n",
        "approx_Is = np.linspace(min(Is), max(Is))\n",
        "approx = np.polyval(i, approx_Is)\n",
        "\n",
        "\n",
        "plt.scatter(Is, Pr, s=5, color=\"purple\", marker=\"d\", label=\"$P_R=P_R(I)$\")\n",
        "plt.plot(approx_Is, approx, color=\"purple\", alpha=.2, linestyle='--', label=\"Аппроксимация $P_r(I)$\")\n",
        "plt.grid()\n",
        "\n",
        "\n",
        "plt.axvline([I_star], color=\"grey\", linestyle='--', linewidth=1, alpha=.5, label=\"$X=I^*=0.0075$\")\n",
        "plt.legend()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 480
        },
        "id": "wgUShvAAsOOv",
        "outputId": "f9c13997-3491-4fa0-ed72-80824cb01227"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "P_Rmax = 0.037\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f9344832780>"
            ]
          },
          "execution_count": 42,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "i = np.polyfit(Is, Pr, 2)\n",
        "P_Rmax = np.polyval(i, I_star)\n",
        "print(f\"P_Rmax = {P_Rmax:.3f}\")\n",
        "\n",
        "plt.scatter(Is, Pr)\n",
        "# plt.plot(np.linspace(Is[0], Is[-1]), )"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F9lwG3lCsOOw"
      },
      "source": [
        "Поиск сопротивления $R$"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_cXwUEx5sOOw",
        "outputId": "901c36f0-4aa8-42d3-c774-28e346357f10"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "R = 656.644\n"
          ]
        }
      ],
      "source": [
        "R =  P_Rmax / I_star**2\n",
        "print(f\"R = {R:.3f}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7HKpdQ-isOOw"
      },
      "source": [
        "Поиск значения КПД $\\eta=\\cfrac{P_R}{P}$ как функции $\\eta (I)$"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 915
        },
        "id": "kaDOu08gsOOx",
        "outputId": "9fad5133-b6d8-443a-9532-949d3b7e04ad"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[0.01  0.    0.171 0.262 0.343 0.403 0.464 0.504 0.544 0.575 0.605 0.635\n",
            " 0.655 0.675 0.696 0.696]\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "<>:7: SyntaxWarning: invalid escape sequence '\\e'\n",
            "<>:9: SyntaxWarning: invalid escape sequence '\\e'\n",
            "<>:12: SyntaxWarning: invalid escape sequence '\\e'\n",
            "<>:21: SyntaxWarning: invalid escape sequence '\\e'\n",
            "<>:7: SyntaxWarning: invalid escape sequence '\\e'\n",
            "<>:9: SyntaxWarning: invalid escape sequence '\\e'\n",
            "<>:12: SyntaxWarning: invalid escape sequence '\\e'\n",
            "<>:21: SyntaxWarning: invalid escape sequence '\\e'\n",
            "/tmp/ipython-input-2365880889.py:7: SyntaxWarning: invalid escape sequence '\\e'\n",
            "  plt.scatter(Is, eta, s=5, color=\"blue\", label=\"$\\eta=\\eta(I)$\")\n",
            "/tmp/ipython-input-2365880889.py:9: SyntaxWarning: invalid escape sequence '\\e'\n",
            "  plt.ylabel(\"$\\eta (I)$\")\n",
            "/tmp/ipython-input-2365880889.py:12: SyntaxWarning: invalid escape sequence '\\e'\n",
            "  plt.axhline([0.5], label=\"$\\eta=0.5$\", color=\"grey\", linestyle=\"--\", alpha=.5)\n",
            "/tmp/ipython-input-2365880889.py:21: SyntaxWarning: invalid escape sequence '\\e'\n",
            "  plt.plot(approx_x, approx, color=\"blue\", linestyle='--', alpha=.2, label=\"Аппроксимация $\\eta=\\eta (I)$\")\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "eta = Pr / P\n",
        "\n",
        "print(eta)\n",
        "\n",
        "plt.scatter(Is, eta, s=5, color=\"blue\", label=\"$\\eta=\\eta(I)$\")\n",
        "plt.xlabel(\"$I$\")\n",
        "plt.ylabel(\"$\\eta (I)$\")\n",
        "# plt.title(\"Значения КПД\")\n",
        "\n",
        "plt.axhline([0.5], label=\"$\\eta=0.5$\", color=\"grey\", linestyle=\"--\", alpha=.5)\n",
        "\n",
        "plt.xlim(0, 0.017)\n",
        "# plt.ylim(0, 0.7)\n",
        "\n",
        "approx_x = np.linspace(0, 0.0155)\n",
        "i = np.polyfit(Is, eta, 1)\n",
        "approx = np.polyval(i, approx_x)\n",
        "\n",
        "plt.plot(approx_x, approx, color=\"blue\", linestyle='--', alpha=.2, label=\"Аппроксимация $\\eta=\\eta (I)$\")\n",
        "\n",
        "\n",
        "plt.legend()\n",
        "plt.grid()\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ifpC9qF5sOOx"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Tb6_Zf81sOOx",
        "outputId": "fba91208-7e45-4564-ebb3-3a3f34eecbd8"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[0.01 ],\n",
              "       [0.   ],\n",
              "       [0.171],\n",
              "       [0.262],\n",
              "       [0.343],\n",
              "       [0.403],\n",
              "       [0.464],\n",
              "       [0.504],\n",
              "       [0.544],\n",
              "       [0.575],\n",
              "       [0.605],\n",
              "       [0.635],\n",
              "       [0.655],\n",
              "       [0.675],\n",
              "       [0.696],\n",
              "       [0.696]])"
            ]
          },
          "execution_count": 45,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "eta = np.reshape(eta, (16, 1))\n",
        "eta"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wGyhcs0tsOOy",
        "outputId": "89fe4f53-c788-47c3-cdf3-c4469126fb35"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[1.000e-01, 1.500e+01, 1.500e+00, 1.493e+02, 1.488e+02],\n",
              "       [0.000e+00, 1.500e+01, 0.000e+00, 1.493e+02, 1.488e+02],\n",
              "       [1.700e+00, 1.200e+01, 2.040e+01, 9.556e+01, 1.190e+02],\n",
              "       [2.600e+00, 1.100e+01, 2.860e+01, 8.029e+01, 1.091e+02],\n",
              "       [3.400e+00, 1.000e+01, 3.400e+01, 6.636e+01, 9.921e+01],\n",
              "       [4.000e+00, 9.000e+00, 3.600e+01, 5.375e+01, 8.929e+01],\n",
              "       [4.600e+00, 8.000e+00, 3.680e+01, 4.247e+01, 7.936e+01],\n",
              "       [5.000e+00, 7.000e+00, 3.500e+01, 3.252e+01, 6.944e+01],\n",
              "       [5.400e+00, 7.000e+00, 3.780e+01, 3.252e+01, 6.944e+01],\n",
              "       [5.700e+00, 6.000e+00, 3.420e+01, 2.389e+01, 5.952e+01],\n",
              "       [6.000e+00, 6.000e+00, 3.600e+01, 2.389e+01, 5.952e+01],\n",
              "       [6.300e+00, 5.000e+00, 3.150e+01, 1.659e+01, 4.960e+01],\n",
              "       [6.500e+00, 5.000e+00, 3.250e+01, 1.659e+01, 4.960e+01],\n",
              "       [6.700e+00, 5.000e+00, 3.350e+01, 1.659e+01, 4.960e+01],\n",
              "       [6.900e+00, 5.000e+00, 3.450e+01, 1.659e+01, 4.960e+01],\n",
              "       [6.900e+00, 5.000e+00, 3.450e+01, 1.659e+01, 4.960e+01]])"
            ]
          },
          "execution_count": 46,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "arr"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "aAvRAypusOOy"
      },
      "outputs": [],
      "source": [
        "arr = np.hstack((arr, eta))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WGyOPB4xsOOy",
        "outputId": "5cf2903d-73be-4b1c-c3c9-c2ad45e88567"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[ 0.100], [ 15.000], [ 1.500], [ 149.306], [ 148.809], [ 0.010], \n",
            "[ 0.000], [ 15.000], [ 0.000], [ 149.306], [ 148.809], [ 0.000], \n",
            "[ 1.700], [ 12.000], [ 20.400], [ 95.556], [ 119.047], [ 0.171], \n",
            "[ 2.600], [ 11.000], [ 28.600], [ 80.294], [ 109.126], [ 0.262], \n",
            "[ 3.400], [ 10.000], [ 34.000], [ 66.358], [ 99.206], [ 0.343], \n",
            "[ 4.000], [ 9.000], [ 36.000], [ 53.750], [ 89.285], [ 0.403], \n",
            "[ 4.600], [ 8.000], [ 36.800], [ 42.469], [ 79.365], [ 0.464], \n",
            "[ 5.000], [ 7.000], [ 35.000], [ 32.516], [ 69.444], [ 0.504], \n",
            "[ 5.400], [ 7.000], [ 37.800], [ 32.516], [ 69.444], [ 0.544], \n",
            "[ 5.700], [ 6.000], [ 34.200], [ 23.889], [ 59.524], [ 0.575], \n",
            "[ 6.000], [ 6.000], [ 36.000], [ 23.889], [ 59.524], [ 0.605], \n",
            "[ 6.300], [ 5.000], [ 31.500], [ 16.590], [ 49.603], [ 0.635], \n",
            "[ 6.500], [ 5.000], [ 32.500], [ 16.590], [ 49.603], [ 0.655], \n",
            "[ 6.700], [ 5.000], [ 33.500], [ 16.590], [ 49.603], [ 0.675], \n",
            "[ 6.900], [ 5.000], [ 34.500], [ 16.590], [ 49.603], [ 0.696], \n",
            "[ 6.900], [ 5.000], [ 34.500], [ 16.590], [ 49.603], [ 0.696], \n"
          ]
        }
      ],
      "source": [
        "for i in range(arr.shape[0]):\n",
        "    for j in range(arr.shape[1]):\n",
        "        print(f\"[{arr[i, j]: .3f}]\", end=', ')\n",
        "    print()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "oTZfbEZ_sOOy"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": ".venv",
      "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": "3.13.7"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}