ts-shape

ts-shape is a pandas library for manufacturing and IIoT time series. It works on long-format signal data (systime, uuid, value_double / value_integer / value_bool / value_string) and provides loaders, filters, feature extraction and event detectors for production (OEE, downtime, shift reports, traceability), quality (outliers, SPC rules, capability), process engineering, maintenance, energy, supply chain and process development. Every detector takes a DataFrame and returns a DataFrame, and any detector output can be turned into an OCEL 2.0 or XES event log for process mining.

Install

pip install ts-shape

Python 3.11 or later. The core depends only on pandas, numpy and scipy. Loader backends and a few detectors need extras:

Extra	Installs	Needed for
parquet	pyarrow	reading parquet files
s3	s3fs, pyarrow	S3 loaders
azure	azure-storage-blob, pyarrow	Azure Blob loaders
api	requests	REST metadata and energy API loaders
sql	sqlalchemy	TimescaleDB and SQL metadata loaders
postgres	sqlalchemy, psycopg2-binary	sql plus the PostgreSQL driver
yaml	pyyaml	signal specs and lambda rules from YAML
units	pint	UnitConverter
ml	scikit-learn	Isolation Forest outlier detection
all	all of the above

pip install "ts-shape[parquet,azure]"

Example

import pandas as pd
from ts_shape import (
    MachineStateEvents,
    OutlierDetectionEvents,
    concat,
    make_timeseries,
    profile,
    to_event_log,
)

# Synthetic data in the standard format; use a loader or pandas for your own
temp = make_timeseries(["oven:temp"], n_points=1440, freq="1min", n_outliers=4)
running = make_timeseries(["oven:running"], n_points=1440, freq="1min", value_column="value_bool")
df = pd.concat([temp, running], ignore_index=True)

print(profile(df).summary())   # signal kinds, gaps, counter resets, duplicates

outliers = OutlierDetectionEvents(temp, value_column="value_double").detect_outliers_zscore(n_sigma=4.0)
intervals = MachineStateEvents(df, run_state_uuid="oven:running").detect_run_idle(min_duration="5min")

log = concat(
    to_event_log(outliers, detector="OutlierDetectionEvents.detect_outliers_zscore"),
    to_event_log(intervals, detector="MachineStateEvents.detect_run_idle"),
)
print(log.events[["ocel:activity", "ocel:timestamp"]].head())

ts_shape.list_detectors() lists the public classes (name, category, module): the detectors, and also the transform, feature, loader, event-log and pipeline classes. The examples/ folder has one self-contained script per area of the library.

Links

• Documentation: https://ts-shape.github.io/ts-shape/ (quick start, guides, migration and deprecations)
• Source and issues: https://github.com/ts-shape/ts-shape
• Development setup and release process: development guide
• License: MIT, see LICENSE.txt

Parts of this library were developed with the assistance of Claude, Anthropic's AI assistant. All code is reviewed and maintained by the project authors.