ecubevis 1.0.2

Earth CUBE VISualization with Python

Welcome to the repository of ecubevis

ecubevis: Earth CUBE VISualization with Python. Intended for the interactive exploration of n-dimensional (2D, 3D, 4D or 5D spatio-temporal) arrays on Jupyterlab. Supports both xarray.Dataset/DataArray (with metadata) or numpy.ndarray objects.

How to install

Install ecubevis from pypi:

pip install ecubevis

If installing cartopy with pip is not working, use conda:

conda install cartopy

If cartopy kills your Jupyter notebook kernel try this:

pip install shapely --upgrade --force-reinstall --no-binary shapely

How to use

Import the library:

import ecubevis as ecv

The main function in ecubevis is ecv.plot(). In interactive mode, the plot comes with sliders (thanks to hvplot/holoviews) allowing easy exploration of multi-dimensional data as 2D arrays across the time and additional dimensions. Under the hood, ecv.plot() calls one of the following functions depending on the data type:

• ecv.plot_ndarray(): For plotting an in-memory numpy.ndarray object with 2, 3, 4 or 5 dimensions (ndarrays do not carry metadata so the dimensions are given with the dimensions argument). The function can take a tuple of 2D ndarrays, even with different grid/image size.

• ecv.plot_dataset(): For plotting an in-memory xr.Dataset or xr.DataArray objects with 2, 3, or 4 dimensions. The dimensions expected are [lat, lon] for 2D arrays, [time, lat, lon] for 3D arrays or [time, level, lat, lon] for 4D arrays.

Examples

ecubevis will allow you to create:

Interactive	Static
plots of in-memory 2D, 3D and 4D xr.Dataset or xr.DataArray objects:	mosaics of in-memory 3D and 4D xr.Dataset or xr.DataArray objects:
plots of in-memory 2D, 3D and 4D numpy.ndarray objects (composition thanks to holoviews):	plots of in-memory 2D, 3D and 4D numpy.ndarray objects:
plots of in-memory xr.Dataset or xr.DataArray while sub-setting across dimensions:	plots of a tuple of in-memory 2D numpy.ndarray objects: