diff --git a/Workspace_tutorial.ipynb b/Workspace_tutorial.ipynb
index 57cd3a9..839dcee 100644
--- a/Workspace_tutorial.ipynb
+++ b/Workspace_tutorial.ipynb
@@ -1,623 +1,608 @@
 {
-  "nbformat": 4,
-  "nbformat_minor": 0,
-  "metadata": {
-    "colab": {
-      "provenance": [],
-      "collapsed_sections": [
-        "o3NsLMqUQnOF"
-      ],
-      "authorship_tag": "ABX9TyOTajJcyYgAnYRXXXMq+sml",
-      "include_colab_link": true
-    },
-    "kernelspec": {
-      "name": "python3",
-      "display_name": "Python 3"
-    },
-    "language_info": {
-      "name": "python"
-    }
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a href=\"https://colab.research.google.com/github/wandb/wandb-workspaces/blob/Update-wandb-workspaces-tuturial/Workspace_tutorial.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+   ]
   },
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "view-in-github",
-        "colab_type": "text"
-      },
-      "source": [
-        "<a href=\"https://colab.research.google.com/github/wandb/wandb-workspaces/blob/Update-wandb-workspaces-tuturial/Workspace_tutorial.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "<img src=\"http://wandb.me/logo-im-png\" width=\"400\" alt=\"Weights & Biases\" />\n",
-        "<!--- @wandbcode{intro-colab} -->\n",
-        "\n",
-        "# Tutorial for [Programmatic Workspace](https://docs.wandb.ai/guides/app/pages/workspaces#saved-workspace-views)\n",
-        "\n",
-        "\n",
-        "### Overview\n",
-        "* [wandb-workspaces](https://github.com/wandb/wandb-workspaces/tree/main) is a Python library for programmatically working with Weights & Biases workspaces and Reports.\n",
-        "\n",
-        "* Programmatically create and customize workspaces by defining configurations, setting panel layouts, and organizing sections. Load and modify workspaces via URL, filter and group runs using expressions, and customize run appearances.\n",
-        "\n",
-        "### Known limitations\n",
-        "* Only compatible with [Saved Views](https://docs.wandb.ai/guides/app/pages/workspaces#workspace-types) (URLs ending with ?nw=...). Clone a `Personal Workspace` as a `Saved View` for programmatic interaction.\n",
-        "\n",
-        "### Notebook Instructions\n",
-        "* This notebook will create the project, log sample a to it, and then demonstrate the workspace examples using this data.\n",
-        "* Run each cell step by step. The notebook will log sample data to a project named `workspace-api-example`.\n"
-      ],
-      "metadata": {
-        "id": "MmxL0wjvrNtQ"
-      }
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "# 1. Install and Import Dependencies"
-      ],
-      "metadata": {
-        "id": "X6AencUCi19a"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "# Install dependencies\n",
-        "!pip install wandb wandb-workspaces"
-      ],
-      "metadata": {
-        "id": "99KU0ZiY4t36"
-      },
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "# Import dependencies\n",
-        "\n",
-        "import os\n",
-        "import wandb\n",
-        "import wandb_workspaces.workspaces as ws\n",
-        "import wandb_workspaces.reports.v2 as wr # We use the Reports API for adding panels\n",
-        "\n",
-        "# Improve output formatting\n",
-        "%load_ext rich"
-      ],
-      "metadata": {
-        "id": "IQigRhck4zbF"
-      },
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "# 2. Create a Project to interact with programmatically for notebook examples\n",
-        "\n",
-        "Create a new project or load an existing workspace with its unique `Saved view` URL. For this notebook, we will create a new workspace to use."
-      ],
-      "metadata": {
-        "id": "1CAls6JljDFo"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "# Alternatively, you can load an existing view from a W&B via URL with ws.Workspace.from_url\n",
-        "\n",
-        "# wandb.login()\n",
-        "\n",
-        "# workspace = ws.Workspace.from_url(\"https://wandb.ai/[SOURCE-ENTITY]/[SOURCE-USER]?nw=abc\").\n",
-        "\n",
-        "#  workspace = ws.Workspace(\n",
-        "#  entity=\"NEW-ENTITY\",\n",
-        "#  project=NEW-PROJECT,\n",
-        "#  name=\"NEW-SAVED-VIEW-NAME\",)"
-      ],
-      "metadata": {
-        "id": "uZF7Zgx3uOfu"
-      },
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "# Initialize Weights & Biases and Login\n",
-        "wandb.login()\n",
-        "\n",
-        "# Function to create a new project and log sample data\n",
-        "def create_project_and_log_data():\n",
-        "    project = \"workspace-api-example\"  # Default project name\n",
-        "\n",
-        "    # Initialize a run to log some sample data\n",
-        "    with wandb.init(project=project, name=\"sample_run\") as run:\n",
-        "        for step in range(100):\n",
-        "            wandb.log({\n",
-        "                \"Step\": step,\n",
-        "                \"val_loss\": 1.0 / (step + 1),\n",
-        "                \"val_accuracy\": step / 100.0,\n",
-        "                \"train_loss\": 1.0 / (step + 2),\n",
-        "                \"train_accuracy\": step / 110.0,\n",
-        "                \"f1_score\": step / 100.0,\n",
-        "                \"recall\": step / 120.0,\n",
-        "            })\n",
-        "    return project\n",
-        "\n",
-        "# Create a new project and log data\n",
-        "project = create_project_and_log_data()\n",
-        "entity = wandb.Api().default_entity"
-      ],
-      "metadata": {
-        "id": "rApkZmZB9Ueg",
-        "collapsed": true
-      },
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "# 3. Programmatic Workspace Examples\n",
-        "The API allows you to create, manage, and customize workspaces programmatically. These capabilities are designed to help you organize and visualize your machine learning experiments more effectively.\n",
-        "\n",
-        "Below are examples for using programmatic workspace features:"
-      ],
-      "metadata": {
-        "id": "E7WAoOlsiQTL"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "# See all available settings for workspaces, sections, and panels.\n",
-        "all_settings_objects = [x for x in dir(ws) if isinstance(getattr(ws, x), type)]\n",
-        "all_settings_objects"
-      ],
-      "metadata": {
-        "id": "irVaxJ289yMa"
-      },
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "## Creating a workspace `saved view`\n",
-        "\n",
-        "This example demonstrates how to create a new workspace and populate it with sections and panels. Workspaces can be edited like regular Python objects, providing flexibility and ease of use.\n",
-        "\n"
-      ],
-      "metadata": {
-        "id": "kHD8tEjEPqjL"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "def sample_workspace_saved_example(entity: str, project: str) -> str:\n",
-        "    workspace: ws.Workspace = ws.Workspace(\n",
-        "        name=\"Example W&B Workspace\",\n",
-        "        entity=entity,\n",
-        "        project=project,\n",
-        "        sections=[\n",
-        "            ws.Section(\n",
-        "                name=\"Validation Metrics\",\n",
-        "                panels=[\n",
-        "                    wr.LinePlot(x=\"Step\", y=[\"val_loss\"]),\n",
-        "                    wr.BarPlot(metrics=[\"val_accuracy\"]),\n",
-        "                    wr.ScalarChart(metric=\"f1_score\", groupby_aggfunc=\"mean\"),\n",
-        "                ],\n",
-        "                is_open=True,\n",
-        "            ),\n",
-        "        ],\n",
-        "    )\n",
-        "    workspace.save()\n",
-        "    print(\"Sample Workspace saved.\")\n",
-        "    return workspace.url\n",
-        "\n",
-        "workspace_url: str = sample_workspace_saved_example(entity, project)"
-      ],
-      "metadata": {
-        "id": "sWmN5lNJB-pH"
-      },
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "## Loading a workspace from a URL\n",
-        "\n",
-        "This example demonstrates how to load an existing workspace and save it as a new view. This allows you to easily duplicate and customize workspaces without affecting the original setup."
-      ],
-      "metadata": {
-        "id": "SIIcxNVSQXmu"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "def save_new_workspace_view_example(url: str) -> None:\n",
-        "    workspace: ws.Workspace = ws.Workspace.from_url(url)\n",
-        "\n",
-        "    workspace.name = \"Updated Workspace Name\"\n",
-        "    workspace.save()\n",
-        "\n",
-        "    print(f\"Workspace saved as new view.\")\n",
-        "\n",
-        "save_new_workspace_view_example(workspace_url)"
-      ],
-      "metadata": {
-        "id": "_gWiaq_r9xIM"
-      },
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "## Basic Settings\n",
-        "\n",
-        "This example demonstrates how to create a workspace, add sections with panels, and configure settings for the workspace, individual sections, and panels.\n"
-      ],
-      "metadata": {
-        "id": "o3NsLMqUQnOF"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "\n",
-        "# Function to create and configure a workspace with custom settings\n",
-        "def custom_settings_example(entity: str, project: str) -> None:\n",
-        "    workspace: ws.Workspace = ws.Workspace(name=\"An example workspace\", entity=entity, project=project)\n",
-        "    workspace.sections = [\n",
-        "        ws.Section(\n",
-        "            name=\"Validation\",\n",
-        "            panels=[\n",
-        "                wr.LinePlot(x=\"Step\", y=[\"val_loss\"]),\n",
-        "                wr.LinePlot(x=\"Step\", y=[\"val_accuracy\"]),\n",
-        "                wr.ScalarChart(metric=\"f1_score\", groupby_aggfunc=\"mean\"),\n",
-        "                wr.ScalarChart(metric=\"recall\", groupby_aggfunc=\"mean\"),\n",
-        "            ],\n",
-        "            is_open=True,\n",
-        "        ),\n",
-        "        ws.Section(\n",
-        "            name=\"Training\",\n",
-        "            panels=[\n",
-        "                wr.LinePlot(x=\"Step\", y=[\"train_loss\"]),\n",
-        "                wr.LinePlot(x=\"Step\", y=[\"train_accuracy\"]),\n",
-        "            ],\n",
-        "            is_open=False,\n",
-        "        ),\n",
-        "    ]\n",
-        "\n",
-        "    workspace.settings = ws.WorkspaceSettings(\n",
-        "        x_axis=\"Step\",\n",
-        "        x_min=0,\n",
-        "        x_max=75,\n",
-        "        smoothing_type=\"gaussian\",\n",
-        "        smoothing_weight=20.0,\n",
-        "        ignore_outliers=False,\n",
-        "        remove_legends_from_panels=False,\n",
-        "        tooltip_number_of_runs=\"default\",\n",
-        "        tooltip_color_run_names=True,\n",
-        "        max_runs=20,\n",
-        "        point_visualization_method=\"bucketing\",\n",
-        "        auto_expand_panel_search_results=False,\n",
-        "    )\n",
-        "\n",
-        "    section = workspace.sections[0]\n",
-        "    section.panel_settings = ws.SectionPanelSettings(\n",
-        "        x_min=25,\n",
-        "        x_max=50,\n",
-        "        smoothing_type=\"none\",\n",
-        "    )\n",
-        "\n",
-        "    panel = section.panels[0]\n",
-        "    panel.title = \"Validation Loss Custom Title\"\n",
-        "    panel.title_x = \"Custom x-axis title\"\n",
-        "\n",
-        "    workspace.save()\n",
-        "    print(\"Workspace with custom settings saved.\")\n",
-        "\n",
-        "# Run the function to create and configure the workspace\n",
-        "custom_settings_example(entity, project)"
-      ],
-      "metadata": {
-        "id": "Jeo-2lqMSKR5"
-      },
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "## Filtering Runs\n",
-        "\n",
-        "This example demonstrates how to use run filters to templatize powerful filtering capabilities aligned with specific data patterns.\n",
-        "\n",
-        "\n"
-      ],
-      "metadata": {
-        "id": "oT54K4DLQos-"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "def advanced_filter_example(entity: str, project: str) -> None:\n",
-        "    # Get all runs in the project\n",
-        "    runs: list = wandb.Api().runs(f\"{entity}/{project}\")\n",
-        "\n",
-        "    # Apply multiple filters: val_loss < 0.1, val_accuracy > 0.8, and run name matches regex pattern\n",
-        "    workspace: ws.Workspace = ws.Workspace(\n",
-        "        name=\"Advanced Filtered Workspace with Regex\",\n",
-        "        entity=entity,\n",
-        "        project=project,\n",
-        "        sections=[\n",
-        "            ws.Section(\n",
-        "                name=\"Advanced Filtered Section\",\n",
-        "                panels=[\n",
-        "                    wr.LinePlot(x=\"Step\", y=[\"val_loss\"]),\n",
-        "                    wr.LinePlot(x=\"Step\", y=[\"val_accuracy\"]),\n",
-        "                ],\n",
-        "                is_open=True,\n",
-        "            ),\n",
-        "        ],\n",
-        "        runset_settings=ws.RunsetSettings(\n",
-        "            filters=[\n",
-        "                (ws.Summary(\"val_loss\") < 0.1),  # Filter runs by the 'val_loss' summary\n",
-        "                (ws.Summary(\"val_accuracy\") > 0.8),  # Filter runs by the 'val_accuracy' summary\n",
-        "                (ws.Metric(\"ID\").isin([run.id for run in wandb.Api().runs(f\"{entity}/{project}\")])),\n",
-        "            ],\n",
-        "            regex_query=True,\n",
-        "        )\n",
-        "    )\n",
-        "\n",
-        "    # Add regex search to match run names starting with 's'\n",
-        "    workspace.runset_settings.query = \"^s\"\n",
-        "    workspace.runset_settings.regex_query = True\n",
-        "\n",
-        "    workspace.save()\n",
-        "    print(\"Workspace with advanced filters and regex search saved.\")\n",
-        "\n",
-        "advanced_filter_example(entity, project)"
-      ],
-      "metadata": {
-        "id": "IaBvnHSISobu"
-      },
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "## Changing Runs Colors\n",
-        "This example demonstrates how to change the colors of the runs in a workspace and allows users to programmatically distinguish between different runs by assigning unique colors."
-      ],
-      "metadata": {
-        "id": "-boRSciuQo1a"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "def run_color_example(entity: str, project: str) -> None:\n",
-        "    # Get all runs in the project\n",
-        "    runs: list = wandb.Api().runs(f\"{entity}/{project}\")\n",
-        "\n",
-        "    # Dynamically assign colors to the runs\n",
-        "    run_colors: list = ['purple', 'orange', 'teal', 'magenta']\n",
-        "    run_settings: dict = {}\n",
-        "    for i, run in enumerate(runs):\n",
-        "        run_settings[run.id] = ws.RunSettings(color=run_colors[i % len(run_colors)])\n",
-        "\n",
-        "    workspace: ws.Workspace = ws.Workspace(\n",
-        "        name=\"Run Colors Workspace\",\n",
-        "        entity=entity,\n",
-        "        project=project,\n",
-        "        sections=[\n",
-        "            ws.Section(\n",
-        "                name=\"Run Colors Section\",\n",
-        "                panels=[\n",
-        "                    wr.LinePlot(x=\"Step\", y=[\"val_loss\"]),\n",
-        "                    wr.LinePlot(x=\"Step\", y=[\"val_accuracy\"]),\n",
-        "                ],\n",
-        "                is_open=True,\n",
-        "            ),\n",
-        "        ],\n",
-        "        runset_settings=ws.RunsetSettings(\n",
-        "            run_settings=run_settings\n",
-        "        )\n",
-        "    )\n",
-        "\n",
-        "    workspace.save()\n",
-        "    print(\"Workspace with run colors saved.\")\n",
-        "\n",
-        "run_color_example(entity, project)"
-      ],
-      "metadata": {
-        "id": "6JnC9tFKbYgm"
-      },
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "## Grouping Runs\n",
-        "\n",
-        "This example demonstrates how to group runs by specific metrics.\n"
-      ],
-      "metadata": {
-        "id": "09GiaVZCQo9Y"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "def grouping_example(entity: str, project: str) -> None:\n",
-        "    workspace: ws.Workspace = ws.Workspace(\n",
-        "        name=\"Grouped Runs Workspace\",\n",
-        "        entity=entity,\n",
-        "        project=project,\n",
-        "        sections=[\n",
-        "            ws.Section(\n",
-        "                name=\"Grouped Runs\",\n",
-        "                panels=[\n",
-        "                    wr.LinePlot(x=\"Step\", y=[\"val_loss\"]),\n",
-        "                    wr.LinePlot(x=\"Step\", y=[\"val_accuracy\"]),\n",
-        "                ],\n",
-        "                is_open=True,\n",
-        "            ),\n",
-        "        ],\n",
-        "        runset_settings=ws.RunsetSettings(\n",
-        "            groupby=[ws.Metric(\"Name\")]\n",
-        "        )\n",
-        "    )\n",
-        "    workspace.save()\n",
-        "    print(\"Workspace with grouped runs saved.\")\n",
-        "\n",
-        "grouping_example(entity, project)"
-      ],
-      "metadata": {
-        "id": "oW4ZSYw5c1L_"
-      },
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "## Sorting Runs\n",
-        "This example demonstrates how to sort runs by specific metrics."
-      ],
-      "metadata": {
-        "id": "3-soac7eOXRY"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "def sorting_example(entity: str, project: str) -> None:\n",
-        "    workspace: ws.Workspace = ws.Workspace(\n",
-        "        name=\"Sorted Runs Workspace\",\n",
-        "        entity=entity,\n",
-        "        project=project,\n",
-        "        sections=[\n",
-        "            ws.Section(\n",
-        "                name=\"Sorted Runs\",\n",
-        "                panels=[\n",
-        "                    wr.LinePlot(x=\"Step\", y=[\"val_loss\"]),\n",
-        "                    wr.LinePlot(x=\"Step\", y=[\"val_accuracy\"]),\n",
-        "                ],\n",
-        "                is_open=True,\n",
-        "            ),\n",
-        "        ],\n",
-        "        runset_settings=ws.RunsetSettings(\n",
-        "            order=[ws.Ordering(ws.Summary(\"val_loss\"))] #Order using val_loss summary\n",
-        "        )\n",
-        "    )\n",
-        "    workspace.save()\n",
-        "    print(\"Workspace with sorted runs saved.\")\n",
-        "\n",
-        "sorting_example(entity, project)"
-      ],
-      "metadata": {
-        "id": "UbIqEPtJdFR5"
-      },
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "# 4. Putting it all together: comprehenive example\n",
-        "\n",
-        "This example demonstrates how to create a comprehensive workspace, configure its settings, and add panels to sections."
-      ],
-      "metadata": {
-        "id": "3BwWS8b_QpMI"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "def full_end_to_end_example(entity: str, project: str) -> None:\n",
-        "    # Get all runs in the project\n",
-        "    runs: list = wandb.Api().runs(f\"{entity}/{project}\")\n",
-        "\n",
-        "    # Dynamically assign colors to the runs and create run settings\n",
-        "    run_colors: list = ['red', 'blue', 'green', 'orange', 'purple', 'teal', 'magenta', '#FAC13C']\n",
-        "    run_settings: dict = {}\n",
-        "    for i, run in enumerate(runs):\n",
-        "        run_settings[run.id] = ws.RunSettings(color=run_colors[i % len(run_colors)], disabled=False)\n",
-        "\n",
-        "    workspace: ws.Workspace = ws.Workspace(\n",
-        "        name=\"My Workspace Template\",\n",
-        "        entity=entity,\n",
-        "        project=project,\n",
-        "        sections=[\n",
-        "            ws.Section(\n",
-        "                name=\"Main Metrics\",\n",
-        "                panels=[\n",
-        "                    wr.LinePlot(x=\"Step\", y=[\"val_loss\"]),\n",
-        "                    wr.LinePlot(x=\"Step\", y=[\"val_accuracy\"]),\n",
-        "                    wr.ScalarChart(metric=\"f1_score\", groupby_aggfunc=\"mean\"),\n",
-        "                ],\n",
-        "                is_open=True,\n",
-        "            ),\n",
-        "            ws.Section(\n",
-        "                name=\"Additional Metrics\",\n",
-        "                panels=[\n",
-        "                    wr.ScalarChart(metric=\"precision\", groupby_aggfunc=\"mean\"),\n",
-        "                    wr.ScalarChart(metric=\"recall\", groupby_aggfunc=\"mean\"),\n",
-        "                ],\n",
-        "            ),\n",
-        "        ],\n",
-        "        settings=ws.WorkspaceSettings(\n",
-        "            x_axis=\"Step\",\n",
-        "            x_min=0,\n",
-        "            x_max=100,\n",
-        "            smoothing_type=\"none\",\n",
-        "            smoothing_weight=0,\n",
-        "            ignore_outliers=False,\n",
-        "            remove_legends_from_panels=False,\n",
-        "            tooltip_number_of_runs=\"default\",\n",
-        "            tooltip_color_run_names=True,\n",
-        "            max_runs=20,\n",
-        "            point_visualization_method=\"bucketing\",\n",
-        "            auto_expand_panel_search_results=False,\n",
-        "        ),\n",
-        "        runset_settings=ws.RunsetSettings(\n",
-        "            query=\"\",\n",
-        "            regex_query=False,\n",
-        "            filters=[\n",
-        "                ws.Summary(\"val_loss\") < 1,\n",
-        "                ws.Metric(\"Name\") == \"sample_run\",\n",
-        "            ],\n",
-        "            groupby=[ws.Metric(\"Name\")],\n",
-        "            order=[ws.Ordering(ws.Summary(\"Step\"), ascending=True)],\n",
-        "            run_settings=run_settings\n",
-        "        )\n",
-        "    )\n",
-        "    workspace.save()\n",
-        "    print(\"Workspace created and saved.\")\n",
-        "\n",
-        "full_end_to_end_example(entity, project)"
-      ],
-      "metadata": {
-        "id": "aztgJmIPdVBl"
-      },
-      "execution_count": null,
-      "outputs": []
-    }
-  ]
-}
\ No newline at end of file
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<img src=\"http://wandb.me/logo-im-png\" width=\"400\" alt=\"Weights & Biases\" />\n",
+    "<!--- @wandbcode{intro-colab} -->\n",
+    "\n",
+    "# Programmatic Workspaces\n",
+    "\n",
+    "Organize and visualize your machine learning experiments more effectively by programmatically create, manage, and customize workspaces by defining configurations, setting panel layouts, and organizing sections with the [`wandb-workspaces`](https://github.com/wandb/wandb-workspaces/tree/main) W&B library. Load and modify workspaces with URLs, filter and group runs using expressions, and customize run appearances.\n",
+    "\n",
+    "`wandb-workspaces` is a Python library for programmatically creating and customizing W&B [Workspaces](https://docs.wandb.ai/guides/app/pages/workspaces) and [Reports](https://docs.wandb.ai/guides/reports).\n",
+    "\n",
+    "In this tutorial you will see how to use `wandb-workspaces` to create and customize workspaces by defining configurations, set panel layouts, and organize sections.\n",
+    "\n",
+    "\n",
+    "### How to use this notebook\n",
+    "* Run each cell one at a time. \n",
+    "* Copy and paste the URL that is printed after you run a cell to view the changes made to the workspace.\n",
+    "\n",
+    "\n",
+    "### Note\n",
+    "Programmatic interaction with workspaces is currently supported for [**Saved workspaces views**](https://docs.wandb.ai/guides/app/pages/workspaces#saved-workspace-views). Saved workspaces views are collaborative snapshots of a workspace. Anyone on your team can view, edit, and save changes to saved workspace views. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. Install and import dependencies"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Install dependencies\n",
+    "!pip install wandb wandb-workspaces rich"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Import dependencies\n",
+    "import os\n",
+    "import wandb\n",
+    "import wandb_workspaces.workspaces as ws\n",
+    "import wandb_workspaces.reports.v2 as wr # We use the Reports API for adding panels\n",
+    "\n",
+    "# Improve output formatting\n",
+    "%load_ext rich"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. Create a new project and workspace\n",
+    "\n",
+    "For this tutorial we will create a new project so that we can experiment with the `wandb_workspaces` API:\n",
+    "\n",
+    "Note: You can load an existing workspace using its unique `Saved view` URL. See the next code block to see how to do this."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Initialize Weights & Biases and Login\n",
+    "wandb.login()\n",
+    "\n",
+    "# Function to create a new project and log sample data\n",
+    "def create_project_and_log_data():\n",
+    "    project = \"workspace-api-example\"  # Default project name\n",
+    "\n",
+    "    # Initialize a run to log some sample data\n",
+    "    with wandb.init(project=project, name=\"sample_run\") as run:\n",
+    "        for step in range(100):\n",
+    "            wandb.log({\n",
+    "                \"Step\": step,\n",
+    "                \"val_loss\": 1.0 / (step + 1),\n",
+    "                \"val_accuracy\": step / 100.0,\n",
+    "                \"train_loss\": 1.0 / (step + 2),\n",
+    "                \"train_accuracy\": step / 110.0,\n",
+    "                \"f1_score\": step / 100.0,\n",
+    "                \"recall\": step / 120.0,\n",
+    "            })\n",
+    "    return project\n",
+    "\n",
+    "# Create a new project and log data\n",
+    "project = create_project_and_log_data()\n",
+    "entity = wandb.Api().default_entity"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### (Optional) Load an existing project and workspace\n",
+    "Instead of creating a new project, you can load one of your own existing project and workspace. To do this, find the unique workspace URL and pass it to `ws.Workspace.from_url` as a string. The URL has the form `https://wandb.ai/[SOURCE-ENTITY]/[SOURCE-USER]?nw=abc`. \n",
+    "\n",
+    "For example:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# wandb.login()\n",
+    "\n",
+    "# workspace = ws.Workspace.from_url(\"https://wandb.ai/[SOURCE-ENTITY]/[SOURCE-USER]?nw=abc\").\n",
+    "\n",
+    "# workspace = ws.Workspace(\n",
+    "#     entity=\"NEW-ENTITY\",\n",
+    "#     project=NEW-PROJECT,\n",
+    "#     name=\"NEW-SAVED-VIEW-NAME\"\n",
+    "# )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3. Programmatic workspace examples\n",
+    "Below are examples for using programmatic workspace features:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# See all available settings for workspaces, sections, and panels.\n",
+    "all_settings_objects = [x for x in dir(ws) if isinstance(getattr(ws, x), type)]\n",
+    "all_settings_objects"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Create a workspace with `saved view`\n",
+    "This example demonstrates how to create a new workspace and populate it with sections and panels. Workspaces can be edited like regular Python objects, providing flexibility and ease of use."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def sample_workspace_saved_example(entity: str, project: str) -> str:\n",
+    "    workspace: ws.Workspace = ws.Workspace(\n",
+    "        name=\"Example W&B Workspace\",\n",
+    "        entity=entity,\n",
+    "        project=project,\n",
+    "        sections=[\n",
+    "            ws.Section(\n",
+    "                name=\"Validation Metrics\",\n",
+    "                panels=[\n",
+    "                    wr.LinePlot(x=\"Step\", y=[\"val_loss\"]),\n",
+    "                    wr.BarPlot(metrics=[\"val_accuracy\"]),\n",
+    "                    wr.ScalarChart(metric=\"f1_score\", groupby_aggfunc=\"mean\"),\n",
+    "                ],\n",
+    "                is_open=True,\n",
+    "            ),\n",
+    "        ],\n",
+    "    )\n",
+    "    workspace.save()\n",
+    "    print(\"Sample Workspace saved.\")\n",
+    "    return workspace.url\n",
+    "\n",
+    "workspace_url: str = sample_workspace_saved_example(entity, project)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Load a workspace from a URL\n",
+    "Duplicate and customize workspaces without affecting the original setup. To do this, load an existing workspace and save it as a new view:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def save_new_workspace_view_example(url: str) -> None:\n",
+    "    workspace: ws.Workspace = ws.Workspace.from_url(url)\n",
+    "\n",
+    "    workspace.name = \"Updated Workspace Name\"\n",
+    "    workspace.save()\n",
+    "\n",
+    "    print(f\"Workspace saved as new view.\")\n",
+    "\n",
+    "save_new_workspace_view_example(workspace_url)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note that your workspace is now named \"Updated Workspace Name\"."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Basic settings\n",
+    "The following code shows how to create a workspace, add sections with panels, and configure settings for the workspace, individual sections, and panels:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Function to create and configure a workspace with custom settings\n",
+    "def custom_settings_example(entity: str, project: str) -> None:\n",
+    "    workspace: ws.Workspace = ws.Workspace(name=\"An example workspace\", entity=entity, project=project)\n",
+    "    workspace.sections = [\n",
+    "        ws.Section(\n",
+    "            name=\"Validation\",\n",
+    "            panels=[\n",
+    "                wr.LinePlot(x=\"Step\", y=[\"val_loss\"]),\n",
+    "                wr.LinePlot(x=\"Step\", y=[\"val_accuracy\"]),\n",
+    "                wr.ScalarChart(metric=\"f1_score\", groupby_aggfunc=\"mean\"),\n",
+    "                wr.ScalarChart(metric=\"recall\", groupby_aggfunc=\"mean\"),\n",
+    "            ],\n",
+    "            is_open=True,\n",
+    "        ),\n",
+    "        ws.Section(\n",
+    "            name=\"Training\",\n",
+    "            panels=[\n",
+    "                wr.LinePlot(x=\"Step\", y=[\"train_loss\"]),\n",
+    "                wr.LinePlot(x=\"Step\", y=[\"train_accuracy\"]),\n",
+    "            ],\n",
+    "            is_open=False,\n",
+    "        ),\n",
+    "    ]\n",
+    "\n",
+    "    workspace.settings = ws.WorkspaceSettings(\n",
+    "        x_axis=\"Step\",\n",
+    "        x_min=0,\n",
+    "        x_max=75,\n",
+    "        smoothing_type=\"gaussian\",\n",
+    "        smoothing_weight=20.0,\n",
+    "        ignore_outliers=False,\n",
+    "        remove_legends_from_panels=False,\n",
+    "        tooltip_number_of_runs=\"default\",\n",
+    "        tooltip_color_run_names=True,\n",
+    "        max_runs=20,\n",
+    "        point_visualization_method=\"bucketing\",\n",
+    "        auto_expand_panel_search_results=False,\n",
+    "    )\n",
+    "\n",
+    "    section = workspace.sections[0]\n",
+    "    section.panel_settings = ws.SectionPanelSettings(\n",
+    "        x_min=25,\n",
+    "        x_max=50,\n",
+    "        smoothing_type=\"none\",\n",
+    "    )\n",
+    "\n",
+    "    panel = section.panels[0]\n",
+    "    panel.title = \"Validation Loss Custom Title\"\n",
+    "    panel.title_x = \"Custom x-axis title\"\n",
+    "\n",
+    "    workspace.save()\n",
+    "    print(\"Workspace with custom settings saved.\")\n",
+    "\n",
+    "# Run the function to create and configure the workspace\n",
+    "custom_settings_example(entity, project)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note that you are now viewing a different saved view called \"An example workspace\"."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Customize runs\n",
+    "The following code cells show you how to filter, change the color, group, and sort runs programmatically. \n",
+    "\n",
+    "In each example, the general workflow is to specify the desired customization as an argument to the appropiate parameter in `ws.RunsetSettings`.\n",
+    "\n",
+    "### Filter runs\n",
+    "You can create filters with python expressions and metrics you log with `wandb.log` or that are logged automatically as part of the run such as **Created Timestamp**.  You can also reference filters by how they appear in the W&B App UI such as the **Name**, **Tags**, or **ID**.\n",
+    "\n",
+    "The following example shows how to filter runs based on the validation loss summary, validation accuracy summary, and the regex specified:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def advanced_filter_example(entity: str, project: str) -> None:\n",
+    "    # Get all runs in the project\n",
+    "    runs: list = wandb.Api().runs(f\"{entity}/{project}\")\n",
+    "\n",
+    "    # Apply multiple filters: val_loss < 0.1, val_accuracy > 0.8, and run name matches regex pattern\n",
+    "    workspace: ws.Workspace = ws.Workspace(\n",
+    "        name=\"Advanced Filtered Workspace with Regex\",\n",
+    "        entity=entity,\n",
+    "        project=project,\n",
+    "        sections=[\n",
+    "            ws.Section(\n",
+    "                name=\"Advanced Filtered Section\",\n",
+    "                panels=[\n",
+    "                    wr.LinePlot(x=\"Step\", y=[\"val_loss\"]),\n",
+    "                    wr.LinePlot(x=\"Step\", y=[\"val_accuracy\"]),\n",
+    "                ],\n",
+    "                is_open=True,\n",
+    "            ),\n",
+    "        ],\n",
+    "        runset_settings=ws.RunsetSettings(\n",
+    "            filters=[\n",
+    "                (ws.Summary(\"val_loss\") < 0.1),  # Filter runs by the 'val_loss' summary\n",
+    "                (ws.Summary(\"val_accuracy\") > 0.8),  # Filter runs by the 'val_accuracy' summary\n",
+    "                (ws.Metric(\"ID\").isin([run.id for run in wandb.Api().runs(f\"{entity}/{project}\")])),\n",
+    "            ],\n",
+    "            regex_query=True,\n",
+    "        )\n",
+    "    )\n",
+    "\n",
+    "    # Add regex search to match run names starting with 's'\n",
+    "    workspace.runset_settings.query = \"^s\"\n",
+    "    workspace.runset_settings.regex_query = True\n",
+    "\n",
+    "    workspace.save()\n",
+    "    print(\"Workspace with advanced filters and regex search saved.\")\n",
+    "\n",
+    "advanced_filter_example(entity, project)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note that passing in a list of filter expressions applies the boolean \"AND\" logic."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Change the colors of runs\n",
+    "This example demonstrates how to change the colors of the runs in a workspace:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def run_color_example(entity: str, project: str) -> None:\n",
+    "    # Get all runs in the project\n",
+    "    runs: list = wandb.Api().runs(f\"{entity}/{project}\")\n",
+    "\n",
+    "    # Dynamically assign colors to the runs\n",
+    "    run_colors: list = ['purple', 'orange', 'teal', 'magenta']\n",
+    "    run_settings: dict = {}\n",
+    "    for i, run in enumerate(runs):\n",
+    "        run_settings[run.id] = ws.RunSettings(color=run_colors[i % len(run_colors)])\n",
+    "\n",
+    "    workspace: ws.Workspace = ws.Workspace(\n",
+    "        name=\"Run Colors Workspace\",\n",
+    "        entity=entity,\n",
+    "        project=project,\n",
+    "        sections=[\n",
+    "            ws.Section(\n",
+    "                name=\"Run Colors Section\",\n",
+    "                panels=[\n",
+    "                    wr.LinePlot(x=\"Step\", y=[\"val_loss\"]),\n",
+    "                    wr.LinePlot(x=\"Step\", y=[\"val_accuracy\"]),\n",
+    "                ],\n",
+    "                is_open=True,\n",
+    "            ),\n",
+    "        ],\n",
+    "        runset_settings=ws.RunsetSettings(\n",
+    "            run_settings=run_settings\n",
+    "        )\n",
+    "    )\n",
+    "\n",
+    "    workspace.save()\n",
+    "    print(\"Workspace with run colors saved.\")\n",
+    "\n",
+    "run_color_example(entity, project)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Group runs\n",
+    "\n",
+    "This example demonstrates how to group runs by specific metrics.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def grouping_example(entity: str, project: str) -> None:\n",
+    "    workspace: ws.Workspace = ws.Workspace(\n",
+    "        name=\"Grouped Runs Workspace\",\n",
+    "        entity=entity,\n",
+    "        project=project,\n",
+    "        sections=[\n",
+    "            ws.Section(\n",
+    "                name=\"Grouped Runs\",\n",
+    "                panels=[\n",
+    "                    wr.LinePlot(x=\"Step\", y=[\"val_loss\"]),\n",
+    "                    wr.LinePlot(x=\"Step\", y=[\"val_accuracy\"]),\n",
+    "                ],\n",
+    "                is_open=True,\n",
+    "            ),\n",
+    "        ],\n",
+    "        runset_settings=ws.RunsetSettings(\n",
+    "            groupby=[ws.Metric(\"Name\")]\n",
+    "        )\n",
+    "    )\n",
+    "    workspace.save()\n",
+    "    print(\"Workspace with grouped runs saved.\")\n",
+    "\n",
+    "grouping_example(entity, project)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Sort runs\n",
+    "This example demonstrates how to sort runs based on the validation loss summary:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def sorting_example(entity: str, project: str) -> None:\n",
+    "    workspace: ws.Workspace = ws.Workspace(\n",
+    "        name=\"Sorted Runs Workspace\",\n",
+    "        entity=entity,\n",
+    "        project=project,\n",
+    "        sections=[\n",
+    "            ws.Section(\n",
+    "                name=\"Sorted Runs\",\n",
+    "                panels=[\n",
+    "                    wr.LinePlot(x=\"Step\", y=[\"val_loss\"]),\n",
+    "                    wr.LinePlot(x=\"Step\", y=[\"val_accuracy\"]),\n",
+    "                ],\n",
+    "                is_open=True,\n",
+    "            ),\n",
+    "        ],\n",
+    "        runset_settings=ws.RunsetSettings(\n",
+    "            order=[ws.Ordering(ws.Summary(\"val_loss\"))] #Order using val_loss summary\n",
+    "        )\n",
+    "    )\n",
+    "    workspace.save()\n",
+    "    print(\"Workspace with sorted runs saved.\")\n",
+    "\n",
+    "sorting_example(entity, project)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4. Putting it all together: comprehenive example\n",
+    "\n",
+    "This example demonstrates how to create a comprehensive workspace, configure its settings, and add panels to sections:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def full_end_to_end_example(entity: str, project: str) -> None:\n",
+    "    # Get all runs in the project\n",
+    "    runs: list = wandb.Api().runs(f\"{entity}/{project}\")\n",
+    "\n",
+    "    # Dynamically assign colors to the runs and create run settings\n",
+    "    run_colors: list = ['red', 'blue', 'green', 'orange', 'purple', 'teal', 'magenta', '#FAC13C']\n",
+    "    run_settings: dict = {}\n",
+    "    for i, run in enumerate(runs):\n",
+    "        run_settings[run.id] = ws.RunSettings(color=run_colors[i % len(run_colors)], disabled=False)\n",
+    "\n",
+    "    workspace: ws.Workspace = ws.Workspace(\n",
+    "        name=\"My Workspace Template\",\n",
+    "        entity=entity,\n",
+    "        project=project,\n",
+    "        sections=[\n",
+    "            ws.Section(\n",
+    "                name=\"Main Metrics\",\n",
+    "                panels=[\n",
+    "                    wr.LinePlot(x=\"Step\", y=[\"val_loss\"]),\n",
+    "                    wr.LinePlot(x=\"Step\", y=[\"val_accuracy\"]),\n",
+    "                    wr.ScalarChart(metric=\"f1_score\", groupby_aggfunc=\"mean\"),\n",
+    "                ],\n",
+    "                is_open=True,\n",
+    "            ),\n",
+    "            ws.Section(\n",
+    "                name=\"Additional Metrics\",\n",
+    "                panels=[\n",
+    "                    wr.ScalarChart(metric=\"precision\", groupby_aggfunc=\"mean\"),\n",
+    "                    wr.ScalarChart(metric=\"recall\", groupby_aggfunc=\"mean\"),\n",
+    "                ],\n",
+    "            ),\n",
+    "        ],\n",
+    "        settings=ws.WorkspaceSettings(\n",
+    "            x_axis=\"Step\",\n",
+    "            x_min=0,\n",
+    "            x_max=100,\n",
+    "            smoothing_type=\"none\",\n",
+    "            smoothing_weight=0,\n",
+    "            ignore_outliers=False,\n",
+    "            remove_legends_from_panels=False,\n",
+    "            tooltip_number_of_runs=\"default\",\n",
+    "            tooltip_color_run_names=True,\n",
+    "            max_runs=20,\n",
+    "            point_visualization_method=\"bucketing\",\n",
+    "            auto_expand_panel_search_results=False,\n",
+    "        ),\n",
+    "        runset_settings=ws.RunsetSettings(\n",
+    "            query=\"\",\n",
+    "            regex_query=False,\n",
+    "            filters=[\n",
+    "                ws.Summary(\"val_loss\") < 1,\n",
+    "                ws.Metric(\"Name\") == \"sample_run\",\n",
+    "            ],\n",
+    "            groupby=[ws.Metric(\"Name\")],\n",
+    "            order=[ws.Ordering(ws.Summary(\"Step\"), ascending=True)],\n",
+    "            run_settings=run_settings\n",
+    "        )\n",
+    "    )\n",
+    "    workspace.save()\n",
+    "    print(\"Workspace created and saved.\")\n",
+    "\n",
+    "full_end_to_end_example(entity, project)"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "authorship_tag": "ABX9TyOTajJcyYgAnYRXXXMq+sml",
+   "collapsed_sections": [
+    "o3NsLMqUQnOF"
+   ],
+   "include_colab_link": true,
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}