LLNL/kosh: Release 3.2.0

3.2.0 Release

Description

This release includes several new defining features that improve Kosh data management and functionality.

New in this release

• Pandas read functions were added as loader types and added examples to notebooks.
• examples/Example_Simulation_Workflow.ipynb notebook shows the user how to add, update, and access data for Kosh in a generic simulation workflow.
• examples/MPI_Kosh_data_management.ipynb notebook shows the user how to load and operate on large datasets in a distributed way.
• Convert .find() methods to Pandas DataFrames
• L1, L2 and L* operators.
• Organize datasets within ensembles with ensemble_tags which also work with ensemble.find_datasets(data=target_data, ensemble_tags=target_ensemble_tags)).
• Lock Strategies for parallel calls see: examples/Example_ThreadSafe.ipynb
• Added connection_type='append' which allows the user to only add data but not modify or delete data.

Improvements

• koshClustering operator can return a non-dimensional loss, distribute data from one rank, and added example updates.
• dataset.list_features(use_cache=True) allows for faster listing of features since they are now cached.
• original import sina is much faster as most imports are delayed until actually needed (lazy imports).
• When printing extremely long strings stored as attributes, the terminal output is overwhelming for users. We now utilize the reprlib library.

Bug fixes

• Fixes to internal tests
• tar -v command is now accepted
• Some special sina files couldn't be imported by Kosh (e.g no data section)