Fixes

• Updated documentation on Installation and data download (of CAMELS US).

Added

• Dataset class for the Caravan dataset

Updates/Fixes

• Adapted CAMELS-CL data loader to new data layout of "Enero 2022" dataset version.
• Resolved np.int deprecation warnings
• In LamaH discharge loader, replace -999 values (invalid observation marker) with np.nan, when loading data.
• Run scheduler now moves processed configs to newly created sub-directory, which makes it easier to continue the scheduler in case it fails at any point.

Additions

• Added new metric that checks if a model misses or hits a flood peak.

Internal, but worth to mention

• Dataset objects now contain the date that corresponds to the target(s).

First time contributors

• @SebaDro with #95
• @shamshaw with #97

New Features:

• Added new dataset classes for the CAMELS-BR, CAMELS-AUS, and LamaH-CE dataset.
• Added the AR-LSTM (as proposed in this paper). This model can be used by setting the config argument model: arlstm. Please refer to the documentation about specific requirements of this model.
  • Added an option random_holdout_from_dynamic_features to the config that applies dropout on the time series features by sampling two Bernoulli processes with different rate parameters. This is used for training the arlstm to simulate missing (autoregressive) inputs already during training, which are then replaced by the model outputs of previous timesteps.

Fixes:

• A bug when writing the metrics.csv file during validation if the first basin (in the basin list) has no validation data.

Bugfixes

• fixed InputLayer that was misbehaving when using single frequency data that was resampled from a different temporal resolution (from raw data).
• fixed severe bug when using use_basin_id_encoding: True. Essentially during training, all samples in a batch had the one-hot vector of the last sample in the batch.

Updates

• Removed prediction_interval_plots as it was a duplicate of percentile_plot. Also updated docstring of percentile_plot

Fixes

• Beta-KGE was not in the list of available metrics returned by neuralhydrology.evaluation.metrics.get_available_metrics(). Equally, it was not computed in neuralhydrology.evaluation.metrics.calculate_all_metrics() and neuralhydrology.evaluation.metrics.calculate_metrics() raised an ValueError because of an unknown metric

Other

• Added CITATION.cff for the JOSS paper

Updates

• removed broken cuda9.2 environment and added a new cuda11.3 environment
• updated docs of GenericDataset and description in Tutorial 3

New

• NeuralHydrology can now be installed from PyPI (we added automatic upload to PyPI for every tagged released). #66

Fixes

• Some typo and type annotation fixes in the Transformer model

Updates

• Updated installation guide to include info about PyPI installation options
• Added missing config argument allow_subsequent_nan_losses to config docs

Updates and Additions

• Added tutorial that explains the download process for data that is required to run our tutorials locally. #68
• Updated tutorials better highlight the data requirements. #68
• Update tutorials to be easier to run on CPU environments without changes of the config/code #68
• Added templates for opening an issue
• Fixed typos

Fixes

• Fixed problem of loading hourly streamflow data from csv (CAMELS US dataset) #67
• Fixed problems with logging the git hash that made the commit hash appear badly formatted

New Feature

• If you use NeuralHydrology as a git repository and have uncommited changes in your local copy, we added an option to include the git diff in the run directory. Set save_git_diff to True to make use of this feature.

Fixes

• fixed a bug from v.1.1.0 that caused problems when evaluating basins with all-NaN targets

Other

• Added guide for contributing to NeuralHydrology
• Update the tutorials to include to the underlying jupyter notebooks and removed full paths from some notebooks.
• Updated the quickstart guide to include a better guide for setting up NeuralHydrology

New Features

• Besides logging validation metrics, now also the validation loss is logged to tensorboard (always), computed as the weighted (by number of batches) average loss across all basins.

Fixes

• Spelling mistakes