v0.3.0

What's Changed

• New Prediction object output by model.predict, containing xarray or pandas data (#53)
• New TaskLoader functionality for generating satellite gap-filling training tasks (#23, example)
• DataProcessor scales each spatial dimension equally to fix stripe artefacts, and raises a warning if user-provided coord mappings don't do this (#77)
• Task operations now work like task.operation_method() and are tracked in the task["ops"] list
• TaskLoader does not sample target data for the Task if target_sampling isn't provided (supports context-only Tasks for inference with model.predict)
• Support spatiotemporal aux_at_targets xarray data (with a time dim), not spatial-only
• Fix model.predict not permitting numerical noise in X_t coordinates (#78)
• Key classes are now exposed from higher import levels (de876d4)
• Add CONTRIBUTING.md and CODE_OF_CONDUCT.md by @kallewesterling
• Issue templates by @kallewesterling
• Google docstrings added to the majority of the package by @kallewesterling
• DeepSensor Slack channel request form
• Preliminary sphinx readthedocs page by @kallewesterling

Breaking changes

• Replace any mean_ds, pred_ds, samples_ds = model.predict(...) with pred = model.predict(...). Prediction pandas/xarray objects can now be accessed with pred["<var_ID>"].
• Replace any remove_nans_from_task_Y_t_if_present(task) with task.remove_target_nans()
• DataProcessor now auto-normalises coordinates differently to preserve aspect ratio - see #77. If you aren't specifying spatial coordinate normalisation mapping explicitly (through x1_map/x2_map or through config), your trained models may start receiving different data.

Full Changelog: v0.2.5...v0.3.0