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.

Metadata

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