Rindle for Python
Rindle turns collections of per-ticker CSV files into contiguous sliding-window tensors that are ready for deep learning workflows. The Python extension wraps the C++20 data preparation engine behind a small, NumPy-friendly API so you can configure builds, materialize datasets, and recover fitted scalers directly from notebooks or training scripts.
Highlights
- Deterministic dataset builds – declare the window geometry, scaler, and
input schema with
rindle.create_configand let the engine emit consistent results across runs. - Multi-threaded – both
build_datasetandget_datasetparallelize work across tickers and windows using a C++ thread pool. An optionalthread_countparameter (default0= auto) gives explicit control. The Python GIL is released during these calls so other threads stay responsive. - Manifest-driven reloads – rehydrate tensors on demand with
rindle.get_datasetusing the in-memory manifest returned by a build or a savedmanifest.jsonfile. - NumPy integration – feature (
Dataset.X) and target (Dataset.Y) tensors are exposed as NumPy arrays with shape(windows, sequence_length, features)andfloat32precision for direct use with frameworks such as PyTorch or TensorFlow. - Scaler introspection – fetch the fitted scaler for any ticker/feature pair to invert predictions or understand the normalization that was applied.
Installation
The package ships with pre-built wheels when possible and can also be compiled locally with a C++20 toolchain.
pip install rindle
Building from source requires a compiler with C++20 support, CMake 3.18+, and Python 3.9 or newer. When working from a clone of the repository:
python -m pip install --upgrade pip
python -m pip install build
python -m build
python -m pip install dist/rindle-*.whl
Quickstart
from pathlib import Path
import rindle
config = rindle.create_config(
input_dir=Path("data/raw_prices"),
output_dir=Path("data/processed"),
feature_columns=["Open", "High", "Low", "Close", "Volume"],
seq_length=64,
future_horizon=8,
target_column="Close",
time_mode=rindle.TimeMode.UTC_NS,
row_major=False,
scaler_kind=rindle.ScalerKind.Standard,
)
manifest = rindle.build_dataset(config) # parallelized across tickers
# Load full dataset (default)
dataset = rindle.get_dataset(manifest) # parallelized tensor fill
# Load a random 10% sample (maintains ticker distribution)
dataset_small = rindle.get_dataset(manifest, percentage=0.1)
# Explicit thread count (0 = auto-detect)
dataset = rindle.get_dataset(manifest, thread_count=4)
X = dataset.X # NumPy array: (windows, seq_length, n_features), dtype=float32
Y = dataset.Y # NumPy array aligned with X when targets are enabled
meta = dataset.meta # List of WindowMeta objects with ticker provenance
print("total windows:", dataset.n_windows())
The manifest stores the configuration, aggregate statistics, and ticker-level
metadata. A copy is written to <output_dir>/manifest.json during the build so
you can reload tensors later without repeating the pipeline:
from pathlib import Path
manifest_path = Path(config.output_dir) / "manifest.json"
reloaded = rindle.get_dataset(manifest_path)
Inspecting manifests and scalers
Each ManifestContent instance exposes the fields captured during the build,
including feature_columns, total_windows, and ticker_stats. The helper
method find_stats("AAPL") returns the TickerStats record for a ticker, and
build_ticker_index() can be called if you mutate ticker_stats manually.
To invert normalized values or apply identical scaling elsewhere:
scaler = rindle.get_feature_scaler(manifest, ticker="AAPL", feature="Close")
original_value = rindle.inverse_transform_value(scaler, value=0.42)
The returned FittedScaler exposes transform and inverse_transform methods
as well as a params property that includes summary statistics (mean, standard
deviation, quartiles, and min/max bounds).
Data layout
Dataset.XandDataset.Yare three-dimensional NumPy arrays backed by the underlying C++ tensors (float32). Whenrow_major=False(the default), the layout is[window][time][feature]with contiguous storage, making it ideal for training recurrent and convolutional models.Dataset.metais a list ofWindowMetaobjects describing where each window originated. Fields includeticker,start_row,end_row, and optionaltarget_start/target_endindices.
API reference snapshot
| Function | Description |
|---|---|
rindle.create_config(...) |
Validate paths, choose feature columns, configure window geometry and scaling. Returns a DatasetConfig. |
rindle.build_dataset(config, thread_count=0) |
Run discovery → scaling → windowing in parallel and return a ManifestContent. |
rindle.get_dataset(manifest_or_path, percentage=1.0, thread_count=0) |
Load feature/target tensors in parallel. Optional percentage (0.0 < p <= 1.0) loads a random subset of windows per ticker. |
rindle.get_feature_scaler(manifest_or_path, ticker, feature) |
Retrieve the fitted scaler for a ticker/feature pair to apply or invert scaling. |
rindle.inverse_transform_value(scaler, value) |
Convenience helper to undo scaling with a FittedScaler. |
Additional classes such as DatasetConfig, ManifestContent, Dataset, and
TickerStats expose their fields as Python attributes for straightforward
inspection or serialization.
Project resources
- Source repository: https://github.com/EricGilerson/rindle
- Issue tracker: https://github.com/EricGilerson/rindle/issues
Although the core engine is implemented in C++, the Python package provides a self-contained workflow for assembling time-series datasets without leaving the Python ecosystem.
1 maintainer
Metadata
1 maintainer
Release files for rindle 1.0.1
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Transparency logSecurity for rindle 1.0.1
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
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Attestation for rindle-1.0.1.tar.gz
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on May 4, 2026.
Transparency logA note on package security
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