By wrapping the IBM Planning Analytics (TM1) REST API in a concise Python framework, TM1py facilitates Python developments for TM1.

Interacting with TM1 programmatically has never been easier.

with TM1Service(address='localhost', port=8001, user='admin', password='apple', ssl=True) as tm1:
    subset = Subset(dimension_name='Month', subset_name='Q1', elements=['Jan', 'Feb', 'Mar'])
    tm1.subsets.create(subset, private=True)

Features

TM1py offers handy features to interact with TM1 from Python, such as

• Read data from cubes through cube views and MDX Queries
• Write data into cubes
• Execute processes and chores
• Execute loose statements of TI
• CRUD features for TM1 objects (cubes, dimensions, subsets, etc.)
• Query and kill threads
• Query MessageLog and TransactionLog
• Generate MDX Queries from existing cube views

Requirements

• Python (3.5 or higher)
• TM1 (10.2.2 FP 5 or higher)

Usage

pip install TM1py

from TM1py.Services import TM1Service

with TM1Service(address='localhost', port=8001, user='admin', password='apple', ssl=True) as tm1:
    for chore in tm1.chores.get_all():
        chore.reschedule(hours=-1)
        tm1.chores.update(chore)

Samples: https://github.com/cubewise-code/TM1py-samples

Documentation

Code Documentation: http://tm1py.readthedocs.io/en/latest/

Detailed Installation instructions and Samples: https://github.com/cubewise-code/TM1py-samples

Issues

If you find issues, sign up in Github and open an Issue in this repository

Contribution

TM1py is an open source project. It thrives on contribution from the TM1 community. If you find a bug or feel like you can contribute please fork the repository, update the code and then create a pull request so we can merge in the changes.