aule 0.0.5

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

aule

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

Named after Aulë, the Vala of craft in Tolkien's mythology.

📓 Extensive usage notebooks are available on GitHub.

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Install

pip install aule

# optional: geographic basemaps (cartopy)
pip install aule[geo]

# optional: progress bars on heavy loops (tqdm)
pip install aule[progress]

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Supported input shapes

aule accepts two families of input, both normalised internally to a single canonical (batch, H, W, C, T) representation.

Spatial family

Shape	Meaning
(H, W, C)	single spatial field, one channel
(H, W, C, T)	spatial field with time axis — pass data_format="hwct"
(batch, H, W, C)	batch of spatial fields — pass data_format="bhwc" (default)
(batch, H, W, C, T)	full spatio-temporal batch

Series family (pure time series, no spatial extent)

Pass the axes keyword to disambiguate:

Shape	axes
(T,)	"t"
(C,)	"c"
(B, T)	"bt"
(C, T)	"ct"
(B, C, T)	"bct"

Series inputs are promoted to (B, 1, 1, C, T) internally, so every generic metric (RMSE, MAE, Pearson r, …) works directly on them. Functions that genuinely need spatial or temporal extent (SSIM, gradient error, field comparison plots, …) raise a clear error on degenerate inputs; pass force=True to bypass with a warning.

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Quick start

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

# ── spatial data ────────────────────────────────────────────────────────────
gt   = np.random.rand(8, 64, 64, 1)
pred = gt + np.random.normal(0, 0.1, gt.shape)

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

fig, axes = plot_field_comparison(gt[0], pred[0])
fig, ax   = plot_scatter(gt, pred)

# ── pure time series ─────────────────────────────────────────────────────────
ts_gt   = np.random.randn(4, 3, 200)   # (B=4, C=3, T=200)
ts_pred = np.roll(ts_gt, 5, axis=-1) + 0.05 * np.random.randn(*ts_gt.shape)

print(rmse(ts_gt, ts_pred, axes="bct"))
print(pearson_r(ts_gt, ts_pred, axes="bct"))

from aule.metrics import lag_correlation, dtw_distance
from aule.plots  import plot_lag_correlation, plot_multi_channel_series

corr = lag_correlation(ts_gt, ts_pred, max_lag=30, axes="bct")
dtw  = dtw_distance(ts_gt, ts_pred, axes="bct")

fig, ax  = plot_lag_correlation(ts_gt, ts_pred, max_lag=30, axes="bct")
fig, axs = plot_multi_channel_series(ts_gt, axes="bct",
                                      channel_names=["Temp", "Precip", "Wind"])

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Logging

aule is silent by default. Enable structured, coloured log output with a single call:

import aule
aule.set_log_level("DEBUG")   # DEBUG | INFO | WARNING | ERROR
# or via environment variable before importing:
# AULE_LOG_LEVEL=INFO python my_script.py

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Metrics

All importable directly from aule.metrics.

Core — rmse, mse, mae, bias, pearson_r, ssim, psnr, r2_score, mape, smape, nse, kge, max_error, explained_variance, wasserstein_distance, quantile_mapping_bias

Spectral / gradient — spectral_error, gradient_error, psd_radial_error, spectral_angle_mapper

Climate — seasonal_error, percentile_error, pixelwise_temporal_correlation, trend_error, extreme_event_duration_error, autocorrelation_error, wet_day_frequency_error, dry_spell_error, anomaly_correlation_coefficient

Ensemble — ensemble_spread, crps, rank_histogram, brier_score, spread_skill_ratio, crps_skill_score

Earth observation — normalized_difference_index, index_error, change_detection_error

Classification / segmentation — iou, dice, precision_recall_f1, confusion_matrix_metrics, cohen_kappa

Uncertainty — picp, pit_histogram

Spatial verification — fractions_skill_score (FSS), energy_score

Time series — lag_correlation, cross_channel_correlation, peak_timing_error, dtw_distance

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Plots

All importable directly from aule.plots.

Core — plot_scatter, plot_qq, plot_histogram_comparison, plot_error_histogram

Spatial — plot_field_comparison, plot_bias_map, plot_correlation_map

Climate — plot_temporal_trend, plot_temporal_scatter

Ensemble — plot_ensemble_spread_map, plot_rank_histogram

Diagnostics — plot_taylor_diagram, plot_boxplot_comparison, plot_violin_comparison, plot_time_series, plot_error_map

Classification — plot_confusion_matrix, plot_reliability_diagram

Advanced — plot_hovmoller, plot_cdf_comparison, plot_spectral_density, plot_time_evolution

Time series — plot_lag_correlation, plot_multi_channel_series, plot_dtw_alignment, plot_channel_correlation_matrix

Spatial plots accept optional lat/lon arrays for a cartopy basemap (requires aule[geo]). Every plot returns (fig, ax) and accepts an optional save_path.

Divergent colormap normalizations

plot_bias_map, plot_error_map, and plot_field_comparison accept norm_type to control how extreme values stand out:

fig, ax = plot_bias_map(gt, pred, norm_type="symlog", norm_kwargs={"linthresh": 0.02})
# norm_type options: "linear" (default) | "power" | "symlog" | "twoslope"

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Shape guardrails

Functions that require genuine spatial or temporal extent declare this explicitly. Passing a degenerate input raises a descriptive error:

series_gt = np.random.randn(1, 1, 4)   # H=W=1 — no real spatial extent
ssim(series_gt, series_gt)              # raises ValueError with a clear message

ssim(series_gt, series_gt, force=True)  # proceeds anyway with a warning

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Object-oriented API

Bind arrays once, call everything as a method — including all new functions:

from aule import aule

v = aule(gt, pred)
print(v.rmse())
print(v.pearson_r())
fig, ax = v.plot_scatter()
fig, ax = v.plot_bias_map(norm_type="power")

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Automatic validation report

from aule.report import generate_report

generate_report(gt, pred, save_path="report.html")

Produces a self-contained HTML file (figures embedded as base64 PNGs) with a metrics table and all key plots.

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Documentation

python build_doc.py

Documentation is built with pdoc.