aule 0.0.2

Validation metrics and plots for machine learning models, with a focus on earth observation and climate science.

aule

aule provides validation metrics and plots for machine learning model outputs, with a focus on earth observation and climate science use cases (gridded fields, ensembles, multi-temporal data).

Named after Aulë, the Vala of craft in Tolkien's mythology — the one who forges and evaluates the work of his own hands.

Supported input shapes

Every function accepts numpy arrays in one of four shapes:

• (batch, H, W, C)
• (batch, H, W, C, T)
• (H, W, C)
• (H, W, C, T)

When an array is 4D, pass data_format="bhwc" (default) or data_format="hwct" to disambiguate, since the two shapes cannot be told apart from shape alone.

Installation

pip install aule

# with cartopy support for geographic basemaps
pip install aule[geo]

Quick example

import numpy as np
from aule.metrics import rmse, mae, pearson_r, ssim
from aule.plots import plot_field_comparison, plot_scatter

gt   = np.random.rand(64, 64, 1)
pred = gt + np.random.normal(0, 0.1, gt.shape)

print(rmse(gt, pred))
print(pearson_r(gt, pred))

fig, axes = plot_field_comparison(gt, pred)
fig, ax = plot_scatter(gt, pred, save_path="scatter.png")

Documentation

The documentation is produced using pdoc.

python build_doc.py