Release Release 3.2.0 · llnl/kosh

3.2.0 Release

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

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.

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.