tagreader 4.2.0

Tagreader is a Python package for reading trend data from the OSIsoft PI and Aspen Infoplus.21 IMS systems.

tagreader-python

Tagreader is a Python package for reading trend data from the OSIsoft PI and Aspen Infoplus.21 IMS systems. It is intended to be easy to use, and present as similar interfaces as possible to the backend historians.

While originally developed for Windows, Tagreader can since release 3.0.0 also be used on Linux and Windows platforms.

Queries can be performed using either REST API (preferred) or ODBC queries. The use of ODBC queries require installation of proprietary drivers from AspenTech and OSIsoft that are only available for Windows.

Trend data is output as Pandas Dataframes. The HDF5 file format is used to cache results.

Requirements

Python >=3.8 with the following packages:

• pandas >= 1.0.0
• requests
• requests-kerberos
• certifi >= 2023.5.7
• diskcache
• pyodbc (**)

**) If using ODBC connections, you must also install proprietary drivers for PI ODBC and/or Aspen IP.21 SQLPlus. These drivers are only available for Microsoft Windows. Pyodbc will therefore not be installed for non-Windows systems.

Installation

To install and/or upgrade:

pip install --upgrade tagreader

If you wish to use ODBC connections to the IMS servers, you will also need to install some proprietary drivers. There is more information in the manual. Please note that the web APIs should normally be preferred.

Usage example

import tagreader
c = tagreader.IMSClient("mysource", "aspenone")
print(c.search("tag*"))
df = c.read_tags(["tag1", "tag2"], "18.06.2020 08:00:00", "18.06.2020 09:00:00", 60)

Jupyter Notebook Quickstart

Jupyter Notebook examples can be found in /examples. In order to run these examples, you need to install the optional dependencies.

pip install tagreader[notebooks]

Documentation

There is a quickstart example file that should get you going. Also check out the manual for more information.

Contributing

All contributions are welcome, including code, bug reports, issues, feature requests, and documentation. The preferred way of submitting a contribution is to either create an issue on GitHub or to fork the project and make a pull request.