GDL_plot.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xJwaWoSrYVk3",
        "outputId": "68d53fbb-8b7e-4258-fd04-db6a741b0f0a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'tsl'...\n",
            "remote: Enumerating objects: 578, done.\u001b[K\n",
            "remote: Counting objects: 100% (62/62), done.\u001b[K\n",
            "remote: Compressing objects: 100% (13/13), done.\u001b[K\n",
            "remote: Total 578 (delta 57), reused 49 (delta 49), pack-reused 516\u001b[K\n",
            "Receiving objects: 100% (578/578), 205.53 KiB | 13.70 MiB/s, done.\n",
            "Resolving deltas: 100% (314/314), done.\n",
            "Looking in links: https://download.pytorch.org/whl/torch_stable.html\n",
            "Collecting torch==1.10.1+cu111\n",
            "  Downloading https://download.pytorch.org/whl/cu111/torch-1.10.1%2Bcu111-cp37-cp37m-linux_x86_64.whl (2137.7 MB)\n",
            "\u001b[K     |████████████▌                   | 834.1 MB 1.4 MB/s eta 0:15:28tcmalloc: large alloc 1147494400 bytes == 0x39b7a000 @  0x7fe0b9762615 0x592b76 0x4df71e 0x59afff 0x515655 0x549576 0x593fce 0x548ae9 0x51566f 0x549576 0x593fce 0x548ae9 0x5127f1 0x598e3b 0x511f68 0x598e3b 0x511f68 0x598e3b 0x511f68 0x4bc98a 0x532e76 0x594b72 0x515600 0x549576 0x593fce 0x548ae9 0x5127f1 0x549576 0x593fce 0x5118f8 0x593dd7\n",
            "\u001b[K     |███████████████▉                | 1055.7 MB 1.3 MB/s eta 0:14:25tcmalloc: large alloc 1434370048 bytes == 0x7e1d0000 @  0x7fe0b9762615 0x592b76 0x4df71e 0x59afff 0x515655 0x549576 0x593fce 0x548ae9 0x51566f 0x549576 0x593fce 0x548ae9 0x5127f1 0x598e3b 0x511f68 0x598e3b 0x511f68 0x598e3b 0x511f68 0x4bc98a 0x532e76 0x594b72 0x515600 0x549576 0x593fce 0x548ae9 0x5127f1 0x549576 0x593fce 0x5118f8 0x593dd7\n",
            "\u001b[K     |████████████████████            | 1336.2 MB 1.2 MB/s eta 0:11:21tcmalloc: large alloc 1792966656 bytes == 0x3002000 @  0x7fe0b9762615 0x592b76 0x4df71e 0x59afff 0x515655 0x549576 0x593fce 0x548ae9 0x51566f 0x549576 0x593fce 0x548ae9 0x5127f1 0x598e3b 0x511f68 0x598e3b 0x511f68 0x598e3b 0x511f68 0x4bc98a 0x532e76 0x594b72 0x515600 0x549576 0x593fce 0x548ae9 0x5127f1 0x549576 0x593fce 0x5118f8 0x593dd7\n",
            "\u001b[K     |█████████████████████████▎      | 1691.1 MB 1.5 MB/s eta 0:04:54tcmalloc: large alloc 2241208320 bytes == 0x6ddea000 @  0x7fe0b9762615 0x592b76 0x4df71e 0x59afff 0x515655 0x549576 0x593fce 0x548ae9 0x51566f 0x549576 0x593fce 0x548ae9 0x5127f1 0x598e3b 0x511f68 0x598e3b 0x511f68 0x598e3b 0x511f68 0x4bc98a 0x532e76 0x594b72 0x515600 0x549576 0x593fce 0x548ae9 0x5127f1 0x549576 0x593fce 0x5118f8 0x593dd7\n",
            "\u001b[K     |████████████████████████████████| 2137.7 MB 1.2 MB/s eta 0:00:01tcmalloc: large alloc 2137653248 bytes == 0xf374c000 @  0x7fe0b97611e7 0x4a3940 0x4a39cc 0x592b76 0x4df71e 0x59afff 0x515655 0x549576 0x593fce 0x511e2c 0x549576 0x593fce 0x511e2c 0x549576 0x593fce 0x511e2c 0x549576 0x593fce 0x511e2c 0x549576 0x593fce 0x511e2c 0x593dd7 0x511e2c 0x549576 0x593fce 0x548ae9 0x5127f1 0x549576 0x593fce 0x548ae9\n",
            "tcmalloc: large alloc 2672066560 bytes == 0x1e7270000 @  0x7fe0b9762615 0x592b76 0x4df71e 0x59afff 0x515655 0x549576 0x593fce 0x511e2c 0x549576 0x593fce 0x511e2c 0x549576 0x593fce 0x511e2c 0x549576 0x593fce 0x511e2c 0x549576 0x593fce 0x511e2c 0x593dd7 0x511e2c 0x549576 0x593fce 0x548ae9 0x5127f1 0x549576 0x593fce 0x548ae9 0x5127f1 0x549576\n",
            "\u001b[K     |████████████████████████████████| 2137.7 MB 401 bytes/s \n",
            "\u001b[?25hCollecting torchvision==0.11.2+cu111\n",
            "  Downloading https://download.pytorch.org/whl/cu111/torchvision-0.11.2%2Bcu111-cp37-cp37m-linux_x86_64.whl (24.5 MB)\n",
            "\u001b[K     |████████████████████████████████| 24.5 MB 1.3 MB/s \n",
            "\u001b[?25hCollecting torchaudio==0.10.1\n",
            "  Downloading https://download.pytorch.org/whl/rocm4.1/torchaudio-0.10.1%2Brocm4.1-cp37-cp37m-linux_x86_64.whl (2.7 MB)\n",
            "\u001b[K     |████████████████████████████████| 2.7 MB 7.3 MB/s \n",
            "\u001b[?25hRequirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from torch==1.10.1+cu111) (4.2.0)\n",
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            "Requirement already satisfied: pillow!=8.3.0,>=5.3.0 in /usr/local/lib/python3.7/dist-packages (from torchvision==0.11.2+cu111) (7.1.2)\n",
            "Installing collected packages: torch, torchvision, torchaudio\n",
            "  Attempting uninstall: torch\n",
            "    Found existing installation: torch 1.11.0+cu113\n",
            "    Uninstalling torch-1.11.0+cu113:\n",
            "      Successfully uninstalled torch-1.11.0+cu113\n",
            "  Attempting uninstall: torchvision\n",
            "    Found existing installation: torchvision 0.12.0+cu113\n",
            "    Uninstalling torchvision-0.12.0+cu113:\n",
            "      Successfully uninstalled torchvision-0.12.0+cu113\n",
            "  Attempting uninstall: torchaudio\n",
            "    Found existing installation: torchaudio 0.11.0+cu113\n",
            "    Uninstalling torchaudio-0.11.0+cu113:\n",
            "      Successfully uninstalled torchaudio-0.11.0+cu113\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "torchtext 0.12.0 requires torch==1.11.0, but you have torch 1.10.1+cu111 which is incompatible.\u001b[0m\n",
            "Successfully installed torch-1.10.1+cu111 torchaudio-0.10.1+rocm4.1 torchvision-0.11.2+cu111\n",
            "Looking in links: https://data.pyg.org/whl/torch-1.10.1+cu113.html\n",
            "Collecting torch-scatter\n",
            "  Downloading https://data.pyg.org/whl/torch-1.10.0%2Bcu113/torch_scatter-2.0.9-cp37-cp37m-linux_x86_64.whl (7.9 MB)\n",
            "\u001b[K     |████████████████████████████████| 7.9 MB 3.4 MB/s \n",
            "\u001b[?25hCollecting torch-sparse\n",
            "  Downloading https://data.pyg.org/whl/torch-1.10.0%2Bcu113/torch_sparse-0.6.13-cp37-cp37m-linux_x86_64.whl (3.5 MB)\n",
            "\u001b[K     |████████████████████████████████| 3.5 MB 48.7 MB/s \n",
            "\u001b[?25hCollecting torch-geometric\n",
            "  Downloading torch_geometric-2.0.4.tar.gz (407 kB)\n",
            "\u001b[K     |████████████████████████████████| 407 kB 31.5 MB/s \n",
            "\u001b[?25hRequirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from torch-sparse) (1.4.1)\n",
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            "Building wheels for collected packages: torch-geometric\n",
            "  Building wheel for torch-geometric (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for torch-geometric: filename=torch_geometric-2.0.4-py3-none-any.whl size=616603 sha256=13bd400957d9afd8ff9ff18fa418c4539a1e8b7efc569062fdc4a1ac44856212\n",
            "  Stored in directory: /root/.cache/pip/wheels/18/a6/a4/ca18c3051fcead866fe7b85700ee2240d883562a1bc70ce421\n",
            "Successfully built torch-geometric\n",
            "Installing collected packages: torch-sparse, torch-scatter, torch-geometric\n",
            "Successfully installed torch-geometric-2.0.4 torch-scatter-2.0.9 torch-sparse-0.6.13\n",
            "Processing ./tsl\n",
            "\u001b[33m  DEPRECATION: A future pip version will change local packages to be built in-place without first copying to a temporary directory. We recommend you use --use-feature=in-tree-build to test your packages with this new behavior before it becomes the default.\n",
            "   pip 21.3 will remove support for this functionality. You can find discussion regarding this at https://github.com/pypa/pip/issues/7555.\u001b[0m\n",
            "Collecting einops\n",
            "  Downloading einops-0.4.1-py3-none-any.whl (28 kB)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from torch-spatiotemporal==0.1.0) (1.21.6)\n",
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            "Collecting pytorch_lightning>=1.5\n",
            "  Downloading pytorch_lightning-1.6.3-py3-none-any.whl (584 kB)\n",
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            "\u001b[?25hRequirement already satisfied: PyYAML in /usr/local/lib/python3.7/dist-packages (from torch-spatiotemporal==0.1.0) (3.13)\n",
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            "Collecting test_tube\n",
            "  Downloading test_tube-0.7.5.tar.gz (21 kB)\n",
            "Requirement already satisfied: torch>=1.9 in /usr/local/lib/python3.7/dist-packages (from torch-spatiotemporal==0.1.0) (1.10.1+cu111)\n",
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            "Collecting torchmetrics>=0.7\n",
            "  Downloading torchmetrics-0.8.2-py3-none-any.whl (409 kB)\n",
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            "Collecting fsspec[http]!=2021.06.0,>=2021.05.0\n",
            "  Downloading fsspec-2022.3.0-py3-none-any.whl (136 kB)\n",
            "\u001b[K     |████████████████████████████████| 136 kB 65.1 MB/s \n",
            "\u001b[?25hCollecting PyYAML\n",
            "  Downloading PyYAML-6.0-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (596 kB)\n",
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            "Building wheels for collected packages: torch-spatiotemporal, test-tube\n",
            "  Building wheel for torch-spatiotemporal (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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            "  Created wheel for test-tube: filename=test_tube-0.7.5-py3-none-any.whl size=25356 sha256=5bf39ef7585f689416d7cf5704b3db9b2b146f1818c2e39ed1b0ec247dd2b6f6\n",
            "  Stored in directory: /root/.cache/pip/wheels/1c/50/0d/15b3236957cc18a5c39ec4d4d4d21624f4d4a876756ec17064\n",
            "Successfully built torch-spatiotemporal test-tube\n",
            "Installing collected packages: multidict, frozenlist, yarl, asynctest, async-timeout, aiosignal, pyDeprecate, fsspec, aiohttp, torchmetrics, PyYAML, test-tube, pytorch-lightning, einops, torch-spatiotemporal\n",
            "  Attempting uninstall: PyYAML\n",
            "    Found existing installation: PyYAML 3.13\n",
            "    Uninstalling PyYAML-3.13:\n",
            "      Successfully uninstalled PyYAML-3.13\n",
            "Successfully installed PyYAML-6.0 aiohttp-3.8.1 aiosignal-1.2.0 async-timeout-4.0.2 asynctest-0.13.0 einops-0.4.1 frozenlist-1.3.0 fsspec-2022.3.0 multidict-6.0.2 pyDeprecate-0.3.2 pytorch-lightning-1.6.3 test-tube-0.7.5 torch-spatiotemporal-0.1.0 torchmetrics-0.8.2 yarl-1.7.2\n"
          ]
        }
      ],
      "source": [
        "!git clone https://github.com/TorchSpatiotemporal/tsl.git\n",
        "!pip install torch==1.10.1+cu111 torchvision==0.11.2+cu111 torchaudio==0.10.1 -f https://download.pytorch.org/whl/torch_stable.html\n",
        "!pip install torch-scatter torch-sparse torch-geometric -f https://data.pyg.org/whl/torch-1.10.1+cu113.html\n",
        "!pip install ./tsl"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hLAxpbejYVk4",
        "outputId": "6a5733b5-ab35-4cb7-bb07-4a5795f99efe"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tsl version  : 0.1.0\n",
            "torch version: 1.10.1+cu111\n"
          ]
        }
      ],
      "source": [
        "import tsl\n",
        "import torch\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "np.set_printoptions(suppress=True)\n",
        "tsl.logger.disabled = True\n",
        "\n",
        "print(f\"tsl version  : {tsl.__version__}\")\n",
        "print(f\"torch version: {torch.__version__}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BsPkG4uMYVk5",
        "outputId": "2974f713-a274-4974-f914-cc1dfbd008c5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MetrLA(length=34272, n_nodes=207, n_channels=1)\n"
          ]
        }
      ],
      "source": [
        "from tsl.datasets import MetrLA\n",
        "dataset = MetrLA()\n",
        "print(dataset)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 504
        },
        "id": "u72R4iL2AJGz",
        "outputId": "c5307a3e-dd18-453f-b28d-1ab76ebe06ce"
      },
      "outputs": [
        {
          "output_type": "execute_result",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-ff1a85fd-7b1b-4f87-bbeb-271607806257 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-ff1a85fd-7b1b-4f87-bbeb-271607806257');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "df = dataset.dataframe()\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "id": "CNQvmWtyISVr",
        "outputId": "0012f74f-c2eb-4698-e63c-24f4b25fd292"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "ax = sns.distplot(df, kde=True, hist=False)\n",
        "plt.xlabel(\"Speed\")\n",
        "plt.ylabel(\"Density\")\n",
        "plt.title(\"Whole Data\")\n",
        "plt.show(ax)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "p1, p2, p3 = df['773869'], df['767541'], df['767542']"
      ],
      "metadata": {
        "id": "b9nQgLnzmN7t"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "p1.quantile(0.025), p1.quantile(0.975)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ExsrXPHWmWes",
        "outputId": "5e55f45f-16ec-4e49-a327-aa7686fcec31"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(channels\n",
              " 0    17.25\n",
              " Name: 0.025, dtype: float64, channels\n",
              " 0    69.0\n",
              " Name: 0.975, dtype: float64)"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "p2.quantile(0.025), p2.quantile(0.975)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VCTE4mwrmngH",
        "outputId": "9bab7401-d08f-4ba8-a12f-eba693c7896b"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(channels\n",
              " 0    56.375\n",
              " Name: 0.025, dtype: float64, channels\n",
              " 0    68.111115\n",
              " Name: 0.975, dtype: float64)"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "p3.quantile(0.025), p3.quantile(0.975)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H_wvJhjLmnia",
        "outputId": "916cef1e-6eb6-435b-fb85-63b4f3e2c6d2"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(channels\n",
              " 0    24.777779\n",
              " Name: 0.025, dtype: float64, channels\n",
              " 0    69.625\n",
              " Name: 0.975, dtype: float64)"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "QTbRYGifD98i",
        "outputId": "08a12f9d-058e-4575-add6-04f93fc2213b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n",
            "  warnings.warn(msg, FutureWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n",
            "  warnings.warn(msg, FutureWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "p1, p2, p3 = df['773869'], df['767541'], df['767542']\n",
        "ax1 = sns.distplot(p1, kde=True, hist=False, label='773869', color = 'r')\n",
        "ax1.axvline(17.25, 0, 0.6, color = 'r')\n",
        "ax1.axvline(69, 0, 0.6, color = 'r')\n",
        "ax2 = sns.distplot(p2, kde=True, hist=False, label='767541', color = 'g')\n",
        "ax2.axvline(56.375, 0, 0.95, color = 'g')\n",
        "ax2.axvline(68.11, 0, 0.95, color = 'g')\n",
        "ax3 = sns.distplot(p3, kde=True, hist=False, label='767542', color = 'b')\n",
        "ax3.axvline(24.78, 0, 0.85, color = 'b')\n",
        "ax3.axvline(69.63, 0, 0.85, color = 'b')\n",
        "plt.xlabel(\"Speed\")\n",
        "plt.ylabel(\"Density\")\n",
        "plt.title(\"First 3 detectors\")\n",
        "plt.legend()\n",
        "plt.show(ax1)\n",
        "plt.show(ax2)\n",
        "plt.show(ax3)"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "collapsed_sections": [],
      "name": "GDL_plot.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.12"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}