python-eigen-ingenuity 0.5.30b5

A python library used to query data from the Eigen Ingenuity system

Python Eigen Ingenuity SDK

Overview

Python Eigen Ingenuity is a comprehensive Python library for accessing industrial data from Eigen Ingenuity instances. It enables seamless integration with time-series data, asset models, event logs, and SQL databases for analytics, automation, and ML workflows.

Supports Python 3.12+

---

Table of Contents

• Python Eigen Ingenuity SDK
  • Overview
  • Table of Contents
  • Core Modules
    • 1. Historian Multi
    • 2. Asset Model
    • 3. Eventlog
    • 4. SQL
    • 5. Common Menu
    • 6. Elastic (Deprecated)
    • 7. Historian (Deprecated)
  • Authentication
  • Key Features
  • Further Documentation
  • Support

---

Core Modules

Each module is queried by first instantiating a client object that manages onnection to a certain api. e.g. get_histoiran_multi() then all queries are performed as methods on the returned class

e.g.

hm = get_historian_multi("demo.eigen.co", "Demo-influxdb")
current = hm.getCurrentDataPoints("DEMO_02TI301.PV")

1. Historian Multi

Query time-series data from multiple historians simultaneously. Handles reading, writing, and managing data point metadata.

Connection Function:

• get_historian_multi — Initialize Historian Multi client

Reading Data Functions:

• getCurrentDataPoints — Fetch the latest tag values instantly
• getInterpolatedPoints — Retrieve interpolated data at specific timestamps
• getInterpolatedRange — Get interpolated data across a time range
• getRawDatapoints — Fetch raw historian data without interpolation
• getClosestRawPoint — Find the nearest raw data point to a specific time

Aggregation Functions:

• getAggregates — Compute statistics (min, max, avg, stdDev, median, count, etc.)
• getAggregateIntervals — Calculate aggregates over regular time intervals

Metadata Functions:

• listHistorians — List all available historian connections
• listDataTags — Retrieve all data tags from a historian
• getMetaData — Fetch metadata for specific tags
• createTag — Create a new data tag
• updateTag — Modify existing tag configuration

Writing Data Functions:

• writePoints — Write individual data points to tags
• writePointsBatch — Batch write multiple data points efficiently
• createPoint — Create a formatted data point object
• createAndPopulateTag — Create a new tag and immediately populate it with data

---

2. Asset Model

Navigate the Neo4J asset model to browse assets, their properties, related assets, and associated measurements.

Connection Function:

• get_assetmodel — Initialize Asset Model Client

Core Functions:

• getRelatedAssets — Retrieve all instruments or components related to an asset
• getProperties — Get metadata fields and custom properties from assets
• getMeasurements — Fetch all timeseries measurements configured for an asset
• executeRawQuery — Run custom Neo4J Cypher queries against the asset model
• getDocuments — Retrieve reference documents attached to assets
• getLabels — Get asset classification labels
• getMatchingNodes — Find assets matching specific criteria

---

3. Eventlog

Query, filter, and manage event records in the Ingenuity event log system.

Connection Function:

• get_eventlog — Initialize Eventlog client

Query Functions:

• getEvents — Execute raw queries with filters (severity, type, source, etc.)
• getEventsBySource — Filter events by source identifier (partial or exact match)
• getEventsByType — Filter events by event type
• getEventsById — Retrieve events by event ID, external ID, or episode ID

Management Functions:

• pushToEventlog — Create and push new events to the event log
• deleteEventsById — Remove unwanted events in bulk

---

4. SQL

Execute read-only queries against SQL databases connected to Ingenuity instances.

Connection Function:

• get_sql — Initialize SQL client

Query Functions:

• executeRawQuery — Execute raw SQL queries with multiple output formats (JSON, DataFrame, CSV file)
• listDatabases — Retrieve list of available SQL databases
• listTables — List tables within a specific database

---

5. Common Menu

Query asset relationships, measurements, properties, and documents through a unified interface.

Connection Function:

• get_common_menu — Initialize Common Menu client

Functions:

• getRelatedAssets — Query related assets
• getProperties — Fetch asset properties
• getMeasurements — Get measurements for assets
• getDocuments — Retrieve associated documents
• getEvents — Query events from the Common Menu perspective
• getDrivers — Get driver information

---

6. Elastic (Deprecated)

Legacy module for querying Elasticsearch-backed data sources. Flagged for removal in a future release. Use Eventlog instead.

Connection Function:

• get_elastic — Initialize legacy Elastic client

---

7. Historian (Deprecated)

Legacy single-historian client. Flagged for removal in a future release. Use Historian Multi instead.

Connection Function:

• get_historian — Initialize legacy Historian client for a single data source

---

Authentication

The library supports Azure authentication with:

• User Credentials Flow — Interactive authentication
• CLIENT_CREDENTIALS Flow — Service principal authentication
• API Token — Direct token-based access

Configuration Functions:

• set_azure_tenant_id — Specify Azure tenant
• set_azure_client_id — Specify client/application ID
• set_azure_client_secret — Specify client secret (for CLIENT_CREDENTIALS)
• set_api_token — Set API token alternative to Azure auth
• set_auth_scope — Configure OAuth scope
• disable_azure_auth — Skip Azure authentication
• disable_auth_token_cache — Disable token caching for security
• clear_auth_token_cache — Remove cached tokens

---

Key Features

✅ Multi-Source Data Access — Query historians, asset models, SQL, and events in one library
✅ Aggregations & Statistics — Built-in support for min, max, average, standard deviation, median, and more
✅ Batch Operations — Efficient bulk writes and queries
✅ Azure Authentication — Enterprise authentication support
✅ Flexible Output — JSON, DataFrames, files, or raw responses
✅ Time-Series Focused — Purpose-built for industrial data workflows
✅ Neo4J Integration — Direct access to asset model graph database

---

Further Documentation

For complete function signatures, detailed parameters, and code examples, visit:

📖 https://docs.eigeningenuity.co/developing-with-eigen/python-library/

---

Support

• Documentation Portal: https://docs.eigeningenuity.co/
• Support Tickets: https://eigen.freshdesk.com