limen.data.utils

Public helpers that form bars and split raw frames into the model-ready data_dict schema.

Canonical docs

• Historical Data
• Data Bars

What this package owns

Owns the manifest-facing data helpers: the compute_data_bars() bar-formation entry point, the train/validation/test split functions, and random window slicing. Does not own raw data access (owned by limen.data.HistoricalData) or the low-level bar builders (owned by limen.data.bars).

Key entry points

Entry point	Use case	Notes
compute_data_bars	Public bar-formation entry point	Dispatches to limen.data.bars; used by set_bar_formation()
split_sequential	Ordered train/validation/test windows	The standard manifest split
split_random	Randomised split	Non-sequential alternative
split_by_dates	Explicit date-boundary split	For fixed calendar windows
split_data_to_prep_output	Build the data_dict (x_train, y_test, …)	Does not mutate the passed split list or column list
split_data_to_rule_based_prep_output	data_dict variant for rule-based SFDs	Preserves columns rule strategies read
random_slice	Random contiguous window	Sampling helper

Adjacent modules

• limen.data.HistoricalData produces the raw frames these helpers split.
• limen.data.bars provides the bar builders compute_data_bars() dispatches to.
• limen.experiment consumes the resulting data_dict through manifests and the Universal Experiment Loop.

Quick orientation

utils/
├── compute_data_bars.py   # Public bar-formation entry point
├── splits.py              # Train/validation/test split helpers
└── random_slice.py        # Random window slicing helper

Things to know

• split_data_to_prep_output() is non-mutating: it copies the split list and column list before building data_dict.
• The data_dict keys (x_train, y_train, x_val, x_test, y_test, …) are the schema the rest of Limen's model code expects.
• Rule-based SFDs need split_data_to_rule_based_prep_output() because they read raw columns the standard prep drops.

Read next

• Historical Data
• Data Bars
• Experiment Manifest