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)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, TransactionLog and AuditLog
- Generate MDX Queries from existing cube views
- python (3.7 or higher)
- requests
- requests_negotiate_sspi
- TM1 11
- pandas
without pandas
pip install tm1py
with pandas
pip install "tm1py[pandas]"
on-premise
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)IBM cloud
with TM1Service(
base_url='https://mycompany.planning-analytics.ibmcloud.com/tm1/api/tm1/',
user="non_interactive_user",
namespace="LDAP",
password="U3lSn5QLwoQZY2",
ssl=True,
verify=True,
async_requests_mode=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
Detailed Installation instructions and Samples: https://github.com/cubewise-code/TM1py-samples
If you find issues, sign up in Github and open an Issue in this repository
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.