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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/MachineSaver/animated-system/blob/main/waterfall_spectra.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import plotly\n",
        "import plotly.graph_objects as go\n",
        "import plotly.io as pio\n",
        "import plotly.express as px\n",
        "import numpy as np\n",
        "\n",
        "def set_plotly_theme(name):\n",
        "  pio.templates[\"draft\"] = go.layout.Template(\n",
        "    layout_annotations=[\n",
        "        dict(\n",
        "            name=\"draft watermark\",\n",
        "            text=name,\n",
        "            textangle=-30,\n",
        "            opacity=0.1,\n",
        "            font=dict(color=\"white\", size=50),\n",
        "            xref=\"paper\",\n",
        "            yref=\"paper\",\n",
        "            x=0.5,\n",
        "            y=0.5,\n",
        "            showarrow=False,\n",
        "          )\n",
        "      ]\n",
        "  )\n",
        "  pio.templates.default = \"plotly_dark+draft\"\n",
        "\n",
        "set_plotly_theme('Machine Saver Inc.')\n",
        "\n",
        "# Helix equation\n",
        "t = np.linspace(0, 10, 50)\n",
        "x, y, z = np.cos(t), np.sin(t), t\n",
        "\n",
        "fig = px.scatter_3d(\n",
        "    title=\"Waterfall Plot\"\n",
        "    )\n",
        "fig.add_scatter3d(\n",
        "  x=x,\n",
        "  y=y,\n",
        "  z=z,\n",
        "  mode=\"lines\",\n",
        "  name=\"scatter_1\")\n",
        "fig.add_scatter3d(\n",
        "  x=-x,\n",
        "  y=-y,\n",
        "  z=np.linspace(0, 1, 50),\n",
        "  mode=\"lines\",\n",
        "  name=\"scatter_1\")\n",
        "fig.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "cellView": "code",
        "id": "oZ1U-IIphMri",
        "outputId": "50453ef2-84e5-438c-da72-a7467d6da914"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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    {
      "cell_type": "code",
      "source": [
        "from ipywidgets import interact\n",
        "import json\n",
        "import os\n",
        "import plotly\n",
        "import plotly.graph_objects as go\n",
        "import plotly.io as pio\n",
        "import plotly.express as px\n",
        "from google.colab import drive\n",
        "\n",
        "def setup_google_drive():\n",
        "  drive.mount(\"/content/gdrive\")\n",
        "\n",
        "def clone_data_msgithub():\n",
        "  ! git clone https://github.com/MachineSaver/shiny-chainsaw\n",
        "\n",
        "def set_directory():\n",
        "  %cd shiny-chainsaw/\n",
        "\n",
        "def set_plotly_theme():\n",
        "  pio.templates[\"draft\"] = go.layout.Template(\n",
        "    layout_annotations=[\n",
        "        dict(\n",
        "            name=\"draft watermark\",\n",
        "            text=\"Machine Saver Inc.\",\n",
        "            textangle=-30,\n",
        "            opacity=0.1,\n",
        "            font=dict(color=\"white\", size=50),\n",
        "            xref=\"paper\",\n",
        "            yref=\"paper\",\n",
        "            x=0.5,\n",
        "            y=0.5,\n",
        "            showarrow=False,\n",
        "          )\n",
        "      ]\n",
        "  )\n",
        "  pio.templates.default = \"plotly_dark+draft\"\n",
        "\n",
        "def filter_files(file_list):\n",
        "  def check_starts_with_num(file):\n",
        "    if file[0].isnumeric():\n",
        "      return True  \n",
        "    else:\n",
        "      return False\n",
        "\n",
        "  files_iterator = filter(check_starts_with_num, file_list)\n",
        "  data_sets = list(files_iterator)\n",
        "  return data_sets\n",
        "\n",
        "def axis_1_spec(file_1, file_2):\n",
        "  axis = \"Axis_1\"\n",
        "  with open(file_1) as a:\n",
        "    data_a = json.load(a)\n",
        "\n",
        "  with open(file_2) as b:\n",
        "    data_b = json.load(b)\n",
        "\n",
        "  fig = px.scatter_3d(\n",
        "      title=f\"Waterfall Plot {axis}\"\n",
        "      )\n",
        "  \n",
        "  fig.add_scatter3d(\n",
        "    x=[0.1] * len(data_a[\"spectrum_x\"]),\n",
        "    y=data_a[\"spectrum_x\"],\n",
        "    z=data_a[\"axis_1_SPEC\"],\n",
        "    mode=\"lines\",\n",
        "    name=data_a[\"readable_timestamp\"])\n",
        "  fig.add_scatter3d(\n",
        "    x=[0.2] * len(data_b[\"spectrum_x\"]),\n",
        "    y=data_b[\"spectrum_x\"],\n",
        "    z=data_b[\"axis_1_SPEC\"],\n",
        "    mode=\"lines\",\n",
        "    name=data_b[\"readable_timestamp\"])\n",
        "  return fig.show()\n",
        "\n",
        "# def axis_2_spec(file_1, file_2):\n",
        "#   axis = \"Axis_2\"\n",
        "#   with open(file_1) as a:\n",
        "#     data_a = json.load(a)\n",
        "\n",
        "#   with open(file_2) as b:\n",
        "#     data_b = json.load(b)\n",
        "  \n",
        "#   fig = px.scatter(\n",
        "#     title=\"%s Acceleration Spectrum\" % axis\n",
        "#     )\n",
        "#   fig.add_scatter(\n",
        "#     x=data_a[\"spectrum_x\"],\n",
        "#     y=data_a[\"axis_2_SPEC\"],\n",
        "#     mode=\"lines\",\n",
        "#     name=data_a[\"readable_timestamp\"])\n",
        "#   fig.add_scatter(\n",
        "#     x=data_b[\"spectrum_x\"],\n",
        "#     y=data_b[\"axis_2_SPEC\"],\n",
        "#     mode=\"lines\",\n",
        "#     name=data_b[\"readable_timestamp\"])\n",
        "#   return fig.show()\n",
        "\n",
        "# def axis_3_spec(file_1, file_2):\n",
        "#   axis = \"Axis_3\"\n",
        "#   with open(file_1) as a:\n",
        "#     data_a = json.load(a)\n",
        "\n",
        "#   with open(file_2) as b:\n",
        "#     data_b = json.load(b)\n",
        "  \n",
        "#   fig = px.scatter(\n",
        "#     title=\"%s Acceleration Spectrum\" % axis\n",
        "#     )\n",
        "#   fig.add_scatter(\n",
        "#     x=data_a[\"spectrum_x\"],\n",
        "#     y=data_a[\"axis_3_SPEC\"],\n",
        "#     mode=\"lines\",\n",
        "#     name=data_a[\"readable_timestamp\"])\n",
        "#   fig.add_scatter(\n",
        "#     x=data_b[\"spectrum_x\"],\n",
        "#     y=data_b[\"axis_3_SPEC\"],\n",
        "#     mode=\"lines\",\n",
        "#     name=data_b[\"readable_timestamp\"])\n",
        "#   return fig.show()\n",
        "\n",
        "def main():\n",
        "  setup_google_drive()\n",
        "  clone_data_msgithub()\n",
        "  set_directory()\n",
        "  set_plotly_theme()\n",
        "  # interact(axis_1_twf, file_1=filter_files(os.listdir()), file_2=filter_files(os.listdir()))\n",
        "  # interact(axis_2_twf, file_1=filter_files(os.listdir()), file_2=filter_files(os.listdir()))\n",
        "  # interact(axis_3_twf, file_1=filter_files(os.listdir()), file_2=filter_files(os.listdir()))\n",
        "  interact(axis_1_spec, file_1=filter_files(os.listdir()), file_2=filter_files(os.listdir()))\n",
        "  # interact(axis_2_spec, file_1=filter_files(os.listdir()), file_2=filter_files(os.listdir()))\n",
        "  # interact(axis_3_spec, file_1=filter_files(os.listdir()), file_2=filter_files(os.listdir()))\n",
        "  \n",
        "if(__name__==\"__main__\"):\n",
        "  main()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 745,
          "referenced_widgets": [
            "ec7af46e4a1a4d46980fe70e00222eaa",
            "0f8fca8ddafb45d7971f74f21777332b",
            "cd301e68cc334430893e162067412e94",
            "34c6b8c77dd8486faa1860a65f43b620",
            "b87d04eef6504955a7c34a26da343abf",
            "624973ecc7b04cef89c51a1ba3348c73",
            "b08be6cc35ed41ff82a06e2eaf5274f9",
            "e0bd9085ace14e2c95bb14cc49b82040",
            "719a3dd468624e29adb58f4a6515e96e",
            "88fe2da5475b4d4f990b68bc33d9f40d"
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        "id": "YsKGsJR7mI2U",
        "outputId": "4736a951-40bf-4226-f215-6d8e02aca24a"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/gdrive; to attempt to forcibly remount, call drive.mount(\"/content/gdrive\", force_remount=True).\n",
            "Cloning into 'shiny-chainsaw'...\n",
            "remote: Enumerating objects: 49, done.\u001b[K\n",
            "remote: Counting objects: 100% (49/49), done.\u001b[K\n",
            "remote: Compressing objects: 100% (48/48), done.\u001b[K\n",
            "remote: Total 49 (delta 11), reused 0 (delta 0), pack-reused 0\u001b[K\n",
            "Unpacking objects: 100% (49/49), done.\n",
            "/content/shiny-chainsaw/shiny-chainsaw/shiny-chainsaw/shiny-chainsaw\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "interactive(children=(Dropdown(description='file_1', options=('21030637_2022-06-02_11-21-36_4096_Engine30%_Flo…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ec7af46e4a1a4d46980fe70e00222eaa"
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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "3LzDtBZmmcB9"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}