🌐 MPAS-Viewer

A Python package for efficient visualization of the MPAS-Atmosphere unstructured mesh

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🎯 Key Features

• ⚡ Fast rendering on native unstructured MPAS mesh
• 🌍 Supports both global and regional domains
• 📦 Lightweight and easy to install (minimal dependencies)
• 📈 Accurate representation of MPAS-A data without resampling
• 🧩 Compatible with NetCDF outputs from MPAS-A
• 💻 Portable across platforms (Linux, macOS, Windows)

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📦 Installation

You can install MPAS-Viewer directly from GitHub:

uv add "mpasviewer @ https://github.com/jhbravo/mpasviewer.git"

pip install git+https://github.com/jhbravo/mpasviewer.git

🛠️ Prerequisites

To use MPAS-Viewer, you’ll need the following Python packages:

• Python 3.10+
• xarray
• dask
• numpy
• matplotlib
• cartopy

Optional (for remote access or cloud workflows):

• fsspec — abstract file system layer (e.g. S3, GCS, HTTPS)
• zarr — for chunked, compressed, cloud-optimized data

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🧪 Examples of Use

Below are a few basic examples showing how to use MPAS-Viewer to load and visualize MPAS-Atmosphere data.

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📈 Example 1: Load and Plot a Variable

# Initialize the main object by providing the mesh file (grid or static)
# and the diagnostic output data (single file or directory)
from mpasviewer import scvtmesh

mpasd = scvtmesh(
    grid_file='/path/to/some/file.grid.nc', 
    diag_list='/path/to/some/list/of/files/diag'
)

# Load dataset metadata and variable structure
mpasd.dataset()

# (Optional) Compute rain rate from output variables, if applicable
mpasd.rain_rate()


# Load the full dataset (with Dask support if enabled)
dta = mpasd.load()

# Plot a specific variable as a spatial map at a given time index
mpasd.show(dta, var_name='refl10cm_max', time_index='2021-09-02T00')