GLIMPSE is a graph-based desktop application to visualize and update GridLAB-D power grid models. With GLIMPSE, you can:

• Search and highlight power grid model objects
• Update model attributes
• Export modified models for future simulations
• Leverage GPU acceleration for rendering large power grids

The application is built with React.js, Electron.js, Node.js, Sigma.js, and Python.

Note

If you're looking for the EPA-developed energy planning tool called glimpse, visit epa.gov/glimpse.

Installation

Option 1: Pre-Built Installers (Easiest)

comming soon

Option 2: Build from Source

Quick Overview

This section will walk you through installing dependencies and building GLIMPSE. Here's what you'll do:

1. ✅ Install Node.js (and optionally Nim)
2. ✅ Clone the repository and install Node dependencies
3. ✅ Create and activate a Python environment
4. ✅ Install Python dependencies and plugins
5. ✅ Start the development server

Prerequisites

1. Node.js — Required for all users
2. Nim — Only needed if:
   • You're on Apple silicon (M chips), OR
   • You plan to export modified GLM files

Step 1: Clone the Repository

In a directory of your choice, clone the repository:

git clone http://github.com/pnnl/GLIMPSE

cd GLIMPSE

Step 2: Install Node Dependencies

npm install

Step 3: Set Up Python Environment

Navigate to the local server directory:

cd GLIMPSE/local-server/

Choose your package manager and create the environment:

Option A: UV (Recommended)

uv add -r requirements.txt --prerelease=allow

Option B: VENV

python -m venv .venv

Option C: Conda

conda create -n glimpse_env
conda activate glimpse_env

Activate your environment:

Platform	Shell	Command
POSIX	bash/zsh	source .venv/bin/activate
-	fish	source .venv/bin/activate.fish
-	csh/tcsh	source .venv/bin/activate.csh
-	PowerShell	.\.venv\Scripts\activate.ps1
Windows	cmd.exe	.venv\Scripts\activate.bat
-	PowerShell	.\.venv\Scripts\activate.ps1
macOS	bash/zsh	source .venv/bin/activate

Note

You'll know the environment is active when you see (.venv) at the start of your command line. For conda, use conda activate glimpse_env instead.

Install dependencies (skip if using UV):

If you used VENV or Conda, install requirements:

pip install -r requirements.txt

Step 4: Install CIM-Builder (Required)

CIM-Builder is needed to export modified CIM/XML files.

In GLIMPSE/local-server/, clone CIM-Builder:

git clone -b develop https://github.com/PNNL-CIM-Tools/CIM-Builder.git

Navigate to the CIM-Builder directory and install it (without dependencies, as it requires an older version of cim-graph):

cd CIM-Builder

With PIP:

python -m pip install . --no-deps

With UV:

uv pip install . --no-deps

Step 5: Install GLM Parser

Standard Installation (Windows, Linux, Intel/AMD Mac)

With PIP:

pip install glm

With UV:

uv pip install glm

Special Instructions for Apple Silicon (M Chips)

You'll need to build the GLM parser from source using Nim.

Clone the GLM parser:

cd GLIMPSE/local-server/

git clone https://github.com/NREL/glm.git

cd glm

Build the parser (ensure Nim is installed and in your PATH):

nim c -d:release --opt:size --passC:"-flto" --passL:"-flto" --app:lib --out:lib/_glm.so src/glm.nim

Create a wheel:

python setup.py bdist_wheel
# or
python3 setup.py bdist_wheel

Install the wheel from dist/ folder:

With PIP:

pip install dist/<whl-filename>.whl

With UV:

uv pip install dist/<whl-filename>.whl

Start GLIMPSE

From the GLIMPSE/ root directory, run:

npm run dev

The application will start in development mode. Open your browser and navigate to the provided local address (typically http://localhost:5173/) to access GLIMPSE.

Run with Docker

The repository ships with a Docker Compose setup that builds and runs GLIMPSE as two containers — the React frontend (served by nginx) and the Flask + SocketIO backend — so you don't need to install Node, Python, or any of the plugins yourself.

Note

This section assumes you already have a working Docker Engine with the Docker Compose plugin (docker compose version should print a version). If not, see Docker's install guide.

Step 1: Clone the Repository

git clone http://github.com/pnnl/GLIMPSE
cd GLIMPSE

Step 2: Build and Start the Containers

From the GLIMPSE/ root directory (where docker-compose.yml lives), run:

docker compose up --build

The first build takes a few minutes while images are created; subsequent runs are cached and start quickly. Add -d to run detached (in the background):

docker compose up --build -d

Step 3: Open GLIMPSE

Once the containers are running, open your browser and navigate to:

http://localhost:5173

The frontend serves the UI on port 5173 and the backend listens on port 5052.

Stopping the Containers

docker compose down

This stops and removes the containers and network. Built images remain cached for the next start. (If you ran in the foreground, you can also press Ctrl+C first, then run docker compose down to clean up.)

Supported Input Files

JSON Formats

GLIMPSE supports two JSON file formats for custom graph visualizations:

1. GLIMPSE JSON Format — Based on glm2json parser output

   • Example 1
   • Example 2
   • Example 3
   • Example 4

2. NetworkX Node-Link Format — From NetworkX's node_link_data function

   • Fishing example

GridLAB-D (.glm) Files

To get started with GridLAB-D models:

1. Start with example models in GLIMPSE/testing/123/ — upload all .glm files from this folder
2. Try larger models: 3000/, 8500/, and 9500/ to experience GPU-accelerated rendering with Sigma.js
3. To re-upload files after visualization, click the LOAD button at the top right

CIM/XML Files

GLIMPSE can import and export CIM (Common Information Model) files.

• Example CIM files are available here
• Modified models can be exported as CIM/XML files through the GLIMPSE interface

Cite as

@inproceedings{sanchez2024glimpse,
  title={GLIMPSE of Future Power Grid Models},
  author={Sanchez, Armando Mendoza and Purohit, Sumit},
  booktitle={2024 IEEE 18th International Conference on Semantic Computing (ICSC)},
  pages={224--225},
  year={2024},
  organization={IEEE}
}