TM1py is a Python package that wraps the TM1 REST API in a simple to use library.

Last update: Dec 15, 2022

Overview

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, TransactionLog and AuditLog
Generate MDX Queries from existing cube views

Requirements

python (3.7 or higher)
requests
requests_negotiate_sspi
TM1 11

Optional Requirements

pandas

Install

without pandas

pip install tm1py

with pandas

pip install "tm1py[pandas]"

Usage

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

Documentation

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.

Comments

TI vs TM1py which is a better ETL process?

Hello @MariusWirtz

Creation | Updation time is written in the cells. Other than time performance what other factors would you suggest to determine a better ETL process?
question

opened by pal-16 36
TM1 Git integration

I was going to start building out a GitService to incorporate the new features available in 2.07. Before I get started I wanted to make sure I wasn't duplicating work that was already underway.

Cheers.
question

opened by ghost 31

Issue with copying view

Describe what did you try to do with TM1py I am trying to copy a view and any named subsets from a Source server to a target server

Describe what's not working the way you expect Didn't get the expected result? Describe:

I expected the view to get migrated to the target server
The script is copying the subsets just fine and then bombing on the view create

Version

TM1py [1.6.2]
TM1 Server Version: [2.0.9.7]

Additional context Script:

from TM1py.Services import TM1Service
from configparser import ConfigParser

config = ConfigParser()
config.read(r'config.ini')

source_tm1 = TM1Service(**config['glcs-test'], password='somepassword')
target_tm1 = TM1Service(**config['glcs'], password='somepassword')
cube = 'plan_BudgetPlan'
view = 'Chad'


def copy_subs(sub_dict: dict) -> str:
    for _object in sub_dict:
        dimension = _object
        subset = sub_dict[_object]
        source_sub = source_tm1.dimensions.subsets.get(dimension_name=dimension, hierarchy_name=dimension,
                                                       subset_name=subset, private=False)
        if target_tm1.dimensions.subsets.exists(dimension_name=dimension, hierarchy_name=dimension,
                                                subset_name=subset, private=False):
            target_tm1.dimensions.subsets.update(subset=source_sub, private=False)
        else:
            target_tm1.dimensions.subsets.create(subset=source_sub, private=False)
    return "Some string"


source_view = source_tm1.cubes.views.get(cube_name=cube, view_name=view, private=False)
dimensions = source_view.titles
dimensions.extend(source_view.rows)
dimensions.extend(source_view.columns)
sub_list = dict()
for dim in dimensions:
    if dim.subset.name:
        sub_list[dim.dimension_name] = dim.subset.name
if sub_list:
    copy_subs(sub_list)
if target_tm1.cubes.views.exists(cube_name=cube, view_name=view, private=False):
    target_tm1.cubes.views.update(view=source_view, private=False)
else:
    target_tm1.cubes.views.create(view=source_view, private=False)

Stack Trace:

Traceback (most recent call last):
  File "C:\Users\charvey\PycharmProjects\TM1_Promote\ztemp.py", line 40, in <module>
    target_tm1.cubes.views.create(view=source_view, private=False)
  File "C:\Users\charvey\PycharmProjects\TM1_Promote\venv\lib\site-packages\TM1py\Services\ViewService.py", line 35, in create
    return self._rest.POST(url, view.body, **kwargs)
  File "C:\Users\charvey\PycharmProjects\TM1_Promote\venv\lib\site-packages\TM1py\Services\RestService.py", line 77, in wrapper
    self.verify_response(response=response)
  File "C:\Users\charvey\PycharmProjects\TM1_Promote\venv\lib\site-packages\TM1py\Services\RestService.py", line 436, in verify_response
    raise TM1pyRestException(response.text,
TM1py.Exceptions.Exceptions.TM1pyRestException: Text: '{"error":{"code":"278","message":"Selected element was not specified on Title axis subset."}}' - Status Code: 400 - Reason: 'Bad Request' - Headers: {'Content-Length': '103', 'Connection': 'keep-alive', 'Content-Encoding': 'gzip', 'Cache-Control': 'no-cache', 'Content-Type': 'application/json; charset=utf-8', 'OData-Version': '4.0'}

question

opened by harveyca307 25

Unable to install TM1py on server with no internet
Describe what did you try to do with TM1py I am trying to install TM1py but the server has no internet connection

Describe what's not working the way you expect I receive an error that i am including when running the setup.py after copying the source directory to the site library folder

Version

TM1py 1.3.1

TM1 Server Version: 11.4.00003.8

Thanks Additional context If you encounter an error, please add the error message and the stack trace
question
opened by jbeanz23 25
Connection to IBM cloud using tm1py

Hi all,

I am trying to connect our TM1 database/application called 'tm1' on our cloud environment with these code:

from TM1py import TM1Service with TM1Service( base_url=https://cwd.planning-analytics.ibmcloud.com/tm1/api/tm1/, namespace = LDAP, user=user, password=password, ssl=True, verify=True, async_requests_mode=True) as tm1: print(tm1.server.get_product_version())

Unfortunately i am getting this error message:

That problem is solved. I had to " " the base url, namespace, user and password.

But now, I am getting this error message:

I absolutely do not know what hat means. :-(

Every help is very much appreciated.

Thanks in advance.
question

opened by Andy-Hofelmeyer 24

write_values_through_cellset method

I am using write_values_through_cellset to move data from a source TM1 server to a target. I am noticing inconsistencies in the data being moved. I am testing with 2 MDX statements. "MDX1" seems to work but not "MDX2". The code runs without error using either MDX statement. Here is my code:


import configparser

from TM1py.Services import TM1Service

config = configparser.ConfigParser()
config.read(r'..\config.ini')

with TM1Service(**config['tm1server01']) as tm1source:
    with TM1Service(**config['tm1server02']) as tm1target:

        mdx1 = """
            SELECT non empty {Descendants([plan_version].[plan_version].members,100,LEAVES)} *
            {[plan_business_unit].[plan_business_unit].[10120]} *
            {[plan_department].[plan_department].[415] } *
            {[plan_chart_of_accounts].[plan_chart_of_accounts].[64055] } *
            {[plan_exchange_rates].[plan_exchange_rates].[local] } *
            {[plan_lines].[plan_lines].[line 1] }
            on 0, 
                   {[plan_time].[plan_time].[Dec-2004]} on 1 
                   FROM [plan_BudgetPlanLineItem]
            """

        mdx2 = """
                   SELECT non empty {[plan_version].[plan_version].[FY 2004 Budget]} *
                   {Descendants([plan_business_unit].[plan_business_unit].members,100,LEAVES)} *
                   {Descendants([plan_department].[plan_department].members,100,LEAVES) } *
                   {Descendants([plan_chart_of_accounts]. 
                   [plan_chart_of_accounts].members,100,LEAVES) } *
                   {[plan_exchange_rates].[plan_exchange_rates].[local] } *
                   {Descendants([plan_lines].[plan_lines].members,100,LEAVES) }
                   on 0, 
                   {[plan_time].[plan_time].[Dec-2004]} on 1 
                   FROM [plan_BudgetPlanLineItem]
                   """

        data = tm1source.data.execute_mdx(mdx1, cell_properties=['Value'])

        values = [cell['Value'] for cell in data.values()]

        tm1target.data.write_values_through_cellset(mdx1, values)

       # this for loop returns 1 row when MDX1 is used and that data does move to
       # the target server. Using MDX2 returns 13 rows but the values listed do not appear
       # in the target server
        for x in data.values():
            print(x['Value'])

Am I missing something? Thank you for your help!

question

opened by jimnaff 23

Cannot connect to TM1 with Intgreated mode=5 with SSO

I am trying to connect to TM1. It works when i have integrated mode=1.

But when I am trying with mode 5it says " line 15, in init self._tm1_rest = RestService(**kwargs) line 185, in init self._start_session( ", line 335, in _start_session token = self._build_authorization_token( , line 447, in _build_authorization_token return RestService._build_authorization_token_cam(user, password, namespace, gateway, verify) , line 468, in _build_authorization_token_cam raise RuntimeError( RuntimeError: Failed to authenticate through CAM. HTTP response does not contain 'cam_passport' cookie
bug

opened by aagrawal12 21
Subset used in MDX query
Describe what did you try to do with TM1py Does anyone know if you can assign a subset to the rows in an MDX query? I am trying to use a subset I created on a new MDX pull but am not sure how to do this. Also, does anyone know if you can use the roll-up of the subset in the MDX query (this would be preferred)?

Describe what's not working the way you expect Didn't get the expected result? Describe: I have a script creating a subset by subtracting elements that have data in 1 or both of 2 cubes from all elements in the dimension, leaving those with no data, code is below. I want to then create a new MDX query against just this subset, or even the rolled-up subset so I can drill down to all accounts. I cannot do this initially because I run out of memory.

df2 = tm1.cubes.cells.execute_mdx_dataframe(mdx2) for _, row in df2.iterrows(): material = row['Material EMEA'] used_assumption_materials.add(material) unused_materials = set(all_materials) - used_staging_materials - used_assumption_materials dimension_name = "Material EMEA" subset_name = "Unused Materials EMEA" elements = set(unused_materials) s = Subset(dimension_name=dimension_name, subset_name=subset_name, alias='', elements=elements) tm1.dimensions.subsets.create(subset=s, private=False)

Preferably in the next query (below) would like to use the roll-up of the subset created for the Materials and then just drill down on all N level accounts. I am using all the accounts that had data when I checked this because i don't know how to use a roll-up or the subset. I want to remove the elements from the first set that have data at N level accounts that gets reversed. and total to 0 at Total Account.

mdx = """ SELECT NON EMPTY {{TM1FILTERBYLEVEL( TM1SUBSETALL( [Material EMEA] ), 0)}} * {{[GL Account].[40100000],[GL Account].[40101500],[GL Account].[50156000], [GL Account].[40200000],[GL Account].[40120000],[GL Account].[40850000],[GL Account].[40880000], [GL Account].[40160000],[GL Account].[50100000],[GL Account].[50199300],[GL Account].[50200000], [GL Account].[50120000],[GL Account].[50810000],[GL Account].[50860000],[GL Account].[50880000]}} ON ROWS,

Version

TM1py - 1.4.1

TM1 Server Version: 2.0.9.1

Additional context Thanks
question
opened by jbeanz23 21
Unauthorized error while accessing tm1 cube data with tm1py

I am getting below error while accessing TM1 cube data using TM1py. Intigrated security mode:5 While accessing it is showing connection established and returning Application server name.

"TM1py.Exceptions.Exceptions.TM1pyException: Text: Status Code: 401 Reason: Unauthorized".
question

opened by srinivas1703 21
tm1py and powerbi getting started ?

Hi - Tm1py 1.5 looks like a fantastic release, very keen to explore the new features that support PowerBI integration. Do you have an example that I can use to get started with maybe ?. KR Nick
question

opened by watersnick 20
Python code not sending the parameters to the process it triggers when the code is triggered by another process

I have a Python code that triggers a TM1 process. When I run the Python code from PyCharm and Command Prompt, the code works as intended. However, when I trigger the Python code from an another process, the process that is triggered by the Python code does not receive the parameters that are sent by the code. I don't get any kind of error. I am not sure what is the problem.
question

opened by elcinsayin 18
Sandbox updates

Added load/unload sandbox functions. Also added Active, Loaded, Queued properties to sandbox object. I did not change the sandbox body, so all existing tests should still pass. I've taken a stab at adding tests for the new functions and properties.

opened by adscheevel 0
Run exe using TI process
Hello everyone, I Tried to create a public view using TI process containing tm1py code in exe file Here is the script i used: TI Code: cmd = '"\tm1-stg.hsbc.com\TI Files\AXP_IM\Dynamic\main.exe"'; port = '6001'; sCode = '%cmd% %port%'; ExecuteCommand(Expand(sCode),1);

Here is what's not working the way I expect Didn't get the expected result? Describe:

The TI is completed successfully but view is not created.

Same exe when ran at my local machine directly is working fine.

Version

TM1py [1.9.1]

Additional context Does it takes admin account then only it can create the private view ? Can someone please guide me..
question
opened by Rvee93 3
Federated authentication for PAoC

TM1py should allow federated authentication for PA in the IBM cloud.

https://www.ibm.com/docs/en/planning-analytics/2.0.0?topic=information-federated-authentication
enhancement

opened by MariusWirtz 0
Take full advantage of asynchronous request execution

There is currently no way to take full advantage of asynchronous request execution (asnyc_requests_mode).

It would be nice if TM1py could return the job-id to the calling function. This way python scripts could easily do stuff in parallel while polling the operations in TM1 to see if they are finished yet.

Reference: https://gist.github.com/rclapp/2d6d8d389b99838d6e0fec18642e658f
enhancement

opened by MariusWirtz 0
`execute_mdx_dataframe` to support multi axes MDX queries
The execute_mdx_dataframe function doesn't work with multi-axes MDX queries. Only the first two axes are considered in the resulting data frame. See below. This needs to be addressed.

from TM1py import TM1Service with TM1Service(base_url="https://localhost:12354", user="admin", password="apple") as tm1: mdx = """ SELECT Tm1FilterByLevel({TM1SubsetAll([d3])},0) on 0, Tm1FilterByLevel({TM1SubsetAll([d4])},0) on 1, Tm1FilterByLevel({TM1SubsetAll([d5])},0) on 2 FROM [c4] """ df = tm1.cells.execute_mdx_dataframe(mdx) print(df.head().to_markdown())

| | d4 | d3 | Value | |---:|:-----|:-----|--------:| | 0 | e01 | e01 | 8 | | 1 | e01 | e02 | 8 | | 2 | e01 | e03 | 8 | | 3 | e01 | e04 | 8 | | 4 | e01 | e05 | 8 |

Why not just stack selections on axis 1 or 0 ? When using tuples in the selections, multi-axes selections perform better than stacked selections
opened by MariusWirtz 0
Drop `MAX_STATEMENTS` in `write` `use_ti` logic in versions > 11.8.15

I was just informed that TM1 release 11.8.15 will not have a limitation on the number of statements that a TI process can have.

We can increase the MAX_STATEMENTS logic in the write_through_unbound_process function. This should lead to better performance as the CellPut statements won't need to be broken down into n sequentially executed processes.
enhancement

opened by MariusWirtz 2

Releases(1.10.1)

1.10.1(Oct 24, 2022)
Fixes issue #819 that was introduced with release 1.10

What's Changed

Add argument skip_sandbox_dimension to execute_mdx by @cubewise-gng in https://github.com/cubewise-code/tm1py/pull/812

Add option to pass element selection as iterator in get_value function by @tobiaskapser in https://github.com/cubewise-code/tm1py/pull/814

Fix verify_version function by @MariusWirtz in https://github.com/cubewise-code/tm1py/pull/820

New Contributors

@cubewise-gng made their first contribution in https://github.com/cubewise-code/tm1py/pull/812

Full Changelog: https://github.com/cubewise-code/tm1py/compare/1.10.0...1.10.1
Source code(tar.gz)
Source code(zip)
1.10.0(Oct 10, 2022)
Highlights

Performance improvements on all major TM1 I/O functions.

from TM1py import TM1Service with TM1Service(address="", port=12354, ssl=True, user="admin", password="") as tm1: df = tm1.cells.execute_view_dataframe(cube_name="Sales", view_name="Default", private=False)

| | Time | State | SalesMeasure | Value | |----:|-------:|:--------|:---------------|------------:| | 0 | 202001 | CA | Gross Margin | 13924.8 | | 1 | 202001 | CA | Revenue | 41330.4 | | 2 | 202001 | CA | COGS | 27405.6 |

from TM1py import TM1Service with TM1Service(base_url="https://localhost:12354", user="admin", password="") as tm1: cells = { ('Belgium', 'Actual', '2022', 'Apr', 'Revenue'): 10_000, ('Belgium', 'Actual', '2022', 'May', 'Revenue'): 15_000, ... ('Belgium', 'Actual', '2022', 'Jun', 'Revenue'): 20_000, ('Belgium', 'Actual', '2022', 'Apr', 'Revenue'): 45_000, } tm1.cells.write_async(cube_name="Sales", cells=cells, slice_size=32_000, max_workers=4, measure_dimension_elements={'Revenue': 'Numeric'})

Full support for TM1's git deployment functionality. Including the TM1Project and deployment definitions.

with TM1Service(address="", port=11247, ssl=True, user="admin", password="") as tm1: project = TM1Project(name="Project Definition") dev_deployment = TM1ProjectDeployment( deployment_name="Dev", settings={"ServerName": "dev"}) dev_deployment.add_task(TM1ProjectTask( task_name="Security Refresh", process="Bedrock.Security.Refresh")) dev_deployment.include_all_attribute_dimensions(tm1) dev_deployment.add_ignore(object_class="Cubes", object_name="*") project.add_deployment(deployment=dev_deployment) tm1.git.tm1project_put(project)

New and more efficient ways to query and control elements and hierarchies.

from TM1py import TM1Service with TM1Service(base_url="https://localhost:12354", user="admin", password="") as tm1: # break edge as one tiny atomic operation tm1.elements.remove_edge(dimension_name="Region", hierarchy_name="Region", parent="EU", component="UK") # add new edge as one tiny atomic operation tm1.elements.add_edges(dimension_name="Region", hierarchy_name="Region", edges={("Other", "UK"): 1})

from TM1py import TM1Service with TM1Service(base_url="https://localhost:12354", user="admin", password="") as tm1: is_parent = tm1.elements.element_is_parent(dimension_name="Region", hierarchy_name="Region", parent_name="Other", element_name="UK") is_ancestor = tm1.elements.element_is_ancestor(dimension_name="Region", hierarchy_name="Region", element_name="Other", ancestor_name="UK")

Heaps of miscellaneous features, optimizations and improvements that make your life easier when doing TM1 with Python.

from TM1py import TM1Service with TM1Service(base_url="https://localhost:12354", user="admin", password="") as tm1: process_names = tm1.processes.search_string_in_code(search_string="Sunrise", skip_control_processes=True)

from TM1py import TM1Service with TM1Service(base_url="https://localhost:12354", user="admin", password="apple") as tm1: tm1.cubes.cube_save_data("Sales")

New Features and Improvements

Fix in set_time function in ChoreStartTime class to allow 0 values by @samuelko123 in https://github.com/cubewise-code/tm1py/pull/686

Add substitute_title function to MDXView by @MariusWirtz in https://github.com/cubewise-code/tm1py/pull/687

Add include_headers argument to extract_cellset_csv function by to @MariusWirtz in https://github.com/cubewise-code/tm1py/pull/689

Add remove_edge function to ElementService to break individual parent-child relationship in one operation by @MariusWirtz in https://github.com/cubewise-code/tm1py/pull/693

Fix bug to allow dimension creation from JSON file by @MariusWirtz in https://github.com/cubewise-code/tm1py/pull/698

Improve performance of critical build_cellset_from_pandas_dataframe function by @Kevin-Dekker in https://github.com/cubewise-code/tm1py/pull/695

Add get_parents function to ElementService. Allows retrieval of all parents for an element by @MariusWirtz in https://github.com/cubewise-code/tm1py/pull/699

Fix doubled double quotes issue in element names by @MariusWirtz in https://github.com/cubewise-code/tm1py/pull/704

Explicitly add elements to leaves hierarchy so solve #702 by @MariusWirtz in https://github.com/cubewise-code/tm1py/pull/703

Handle duplicates in write_dataframe appropriately by @MariusWirtz in https://github.com/cubewise-code/tm1py/pull/708 & https://github.com/cubewise-code/tm1py/pull/712

Accept str for rules update on Cube object by @MariusWirtz in https://github.com/cubewise-code/tm1py/pull/710

New search function to find substrings in Rules or Processes (e.g., search_string_in_code) by @adscheevel in https://github.com/cubewise-code/tm1py/pull/723

Fix write failure for element names with : by @MariusWirtz in https://github.com/cubewise-code/tm1py/pull/724

Add get_element_principal_name function by @MaaYuu in https://github.com/cubewise-code/tm1py/pull/731

Add get_ancestors, get_descendants functions on Hierarchy by @MariusWirtz in https://github.com/cubewise-code/tm1py/pull/732

Read 'NA' element name as a string instead of pandas NaN by @MariusWirtz in https://github.com/cubewise-code/tm1py/pull/739

Align float numbers intelligently when writing through unbound processes, to avoid "Number too big" TI errors during writeback, by @pbuncik in https://github.com/cubewise-code/tm1py/pull/749

Add functions to find views that contain a specified subset by @adscheevel in https://github.com/cubewise-code/tm1py/pull/751

Improve write_async performance by exposing measure_dimension_elements to write_async function by @MariusWirtz in https://github.com/cubewise-code/tm1py/pull/753

Introduce get_descendant_edges function in Hierarchy class by @skriptmeister42 in https://github.com/cubewise-code/tm1py/pull/760

Fix the update function in ApplicationService to use the update operation instead of the delete and recreate approach, to avoid breaking existing references of the application by @jrobinsonLOR in https://github.com/cubewise-code/tm1py/pull/762

Implement element_is_parent and element_is_ancestor by @rclapp in https://github.com/cubewise-code/tm1py/pull/767 and https://github.com/cubewise-code/tm1py/pull/771

Allow queries with selection on more than 3 axes in the execute_mdx function by @MariusWirtz in https://github.com/cubewise-code/tm1py/pull/777

Add support for trace_cell_calculation, trace_cell_feeders, and check_cell_feeders by @rclapp in https://github.com/cubewise-code/tm1py/pull/780

Add function create_many in AnnotationService to create many annotations in one operation by @MariusWirtz in https://github.com/cubewise-code/tm1py/pull/785

Add new element functions like get_consolidated_elements, get_parents_of_all_elements by @adscheevel in https://github.com/cubewise-code/tm1py/pull/792

Adjust TM1py to changes in mdxpy 0.4 by @MariusWirtz in https://github.com/cubewise-code/tm1py/pull/793

Handle TM1Project in the GitService by @nicolasbisurgi in https://github.com/cubewise-code/tm1py/pull/775 and https://github.com/cubewise-code/tm1py/pull/796

Refactor Breakpoints and introduce new debugging functionality by @adscheevel in https://github.com/cubewise-code/tm1py/pull/791

Allow alternative separators for elements in get_value and other functions by @tobiaskapser in https://github.com/cubewise-code/tm1py/pull/801 and https://github.com/cubewise-code/tm1py/pull/805

Use 100k max statements in write function with use_ti=True by @MariusWirtz in https://github.com/cubewise-code/tm1py/pull/808

Enable TCP keepalive option for long run requests by @macsir in https://github.com/cubewise-code/tm1py/pull/807

Additional features in ServerService : CubeSaveData and DeleteAllPersistentFeeders by @Mr-SabyasachiBose in https://github.com/cubewise-code/tm1py/pull/810

New Contributors

@samuelko123 made their first contribution in https://github.com/cubewise-code/tm1py/pull/686

@Kevin-Dekker made their first contribution in https://github.com/cubewise-code/tm1py/pull/695

@MaaYuu made their first contribution in https://github.com/cubewise-code/tm1py/pull/731

@pbuncik made their first contribution in https://github.com/cubewise-code/tm1py/pull/749

@skriptmeister42 made their first contribution in https://github.com/cubewise-code/tm1py/pull/760

@jrobinsonLOR made their first contribution in https://github.com/cubewise-code/tm1py/pull/762

@nicolasbisurgi made their first contribution in https://github.com/cubewise-code/tm1py/pull/775

@tobiaskapser made their first contribution in https://github.com/cubewise-code/tm1py/pull/801

@Mr-SabyasachiBose made their first contribution in https://github.com/cubewise-code/tm1py/pull/810

How to upgrade TM1py

To upgrade TM1py, just use the following command:

pip install TM1py --upgrade

Full Changelog: https://github.com/cubewise-code/tm1py/compare/1.9.0...1.10.0
Source code(tar.gz)
Source code(zip)
1.9.0(Feb 7, 2022)
Highlights

New optional use_iterative_json parameter in execute_mdx_dataframe/execute_view_dataframe functions https://github.com/cubewise-code/tm1py/pull/646, https://github.com/cubewise-code/tm1py/pull/612

This new feature allows TM1py to use iterative JSON parsing.

When use_iterative_json is True TM1py requires a significantly smaller memory footprint (down to ~ 20% of the original value) at an almost negligible cost of performance (single percentage digit):

from TM1py import TM1Service with TM1Service( base_url="https://localhost:12354", user="admin", password="apple") as tm1: df = tm1.cells.execute_view_dataframe(cube_name="Sales", view_name="Very Large View", private=False, use_iterative_json=True)

This is a handy feature when dealing with large or very large data volumes (1M to 10M cells) in an environment with limited RAM.

New skip_non_updateable argument in write/write_dataframe functions https://github.com/cubewise-code/tm1py/pull/657

This optional argument to the write/write_dataframe functions asks TM1py to filter out cells that can not be updated before attempting to write. If not used, TM1py will fail with an error when attempting to write to rule-derived or consolidated cells.

from TM1py import TM1Service with TM1Service( base_url="https://localhost:12354", user="admin", password="apple") as tm1: cells = { ('Belgium', 'Revenue', '2022', 'Apr', 'Revenue'): 10_000, ('Belgium', 'Revenue', '2022', 'May', 'Revenue'): 15_000, ('Belgium', 'Revenue', '2022', 'Jun', 'Revenue'): 20_000, ('Belgium', 'Revenue', '2022', 'Q1', 'Revenue'): 45_000, } tm1.cells.write("Sales", cells, skip_non_updateable=True)

This is a useful feature, as it saves the TM1py user from verifying the validity of the cell updates yourself when not working with flawless data sources. Only errors w.r.t. updatability are suppressed! Attempts, for instance, to write to not existing elements will still raise errors.

New search functions to search through cubes, processes, and chores #660, #663, #665

from TM1py import TM1Service with TM1Service( base_url="https://localhost:12354", user="admin", password="apple") as tm1: processes = tm1.processes.search_string_in_code(search_string="Sales")

from TM1py import TM1Service with TM1Service( base_url="https://localhost:12354", user="admin", password="apple") as tm1: processes = tm1.processes.search_string_in_name(name_contains="Sales")

from TM1py import TM1Service with TM1Service( base_url="https://localhost:12354", user="admin", password="apple") as tm1: cubes = tm1.cubes.search_for_dimension(dimension_name="Product", skip_control_cubes=True)

New Features

add write to_message_log_function #621

allow skip_zeros in execute_mdx_values function #659

use python built-in csv module to create csv strings in execute_mdx_csv function #678

new rename function in ApplicationService #682

support compact_json in some execute_mdx_ functions #650

Improvements and Bugfixes

get_last_message_from_processerrorlog to return text ac0d72f2318077b26134c7e9dc02f3833d7d6391

raise exception when session creation fails a42753efd089f8179213596f0ce38001d53822c0

don't raise error if cellset is already deleted 3246391470a81215a5d47ec24a3a2d4e85529f64

improve execute_set_mdx function 61fe2ea8f06caf55ecb9f34025425e86f7ebacde

add update_or_create function for document from file 7f94f712c0377391c069f817081483dca7351ecc

drop obsolete arguments in TM1Service constructor #652

support attribute type change #664

fix get_values function issue to work with alternate hierarchies seamlessly #680

improve handling of alternate hierarchies in write function #679

optional measure_dimension_elementsdictionary argument to write and write_dataframe function to improve performance b21ac47ce8ee9c990aae269e5594c1a342cd0021

Acknowledgments

Big thanks to @rkvinoth, @jrobinsonAG, @gbryant-dev, @raeldor, @adscheevel, @jordanjeremy for contributing code to this release, and many others for reporting bugs and requesting new features.

How to upgrade TM1py

To upgrade TM1py, just use the following command:

pip install TM1py --upgrade
Source code(tar.gz)
Source code(zip)
1.8.0(Sep 29, 2021)
Highlights

Support MDX properties in execute_mdx_dataframe. This allows to query data and attribute data with a single query into one shared data frame.

from TM1py import TM1Service with TM1Service(address="", port=12354, user="admin", password="apple", ssl=True) as tm1: mdx = """ SELECT {Tm1SubsetAll([Region])} PROPERTIES [Region].[Currency] ON COLUMNS, {Tm1SubsetAll([Year])} ON ROWS FROM [Sales] """ data = tm1.cells.execute_mdx_dataframe(mdx, include_attributes=True)

| | Year | Region | Currency | Value | |---:|:----------|:------|:----------------------|--------:| | 0 | 2021 | US | USD | 18 | | 1 | 2021 | UK | GBP | 4 |

write_async and write_async_dataframe functions with max_workers argument. This is essentially an easy way to speed up TM1 writeback through parallelization.

from TM1py import TM1Service with TM1Service(address="", port=12354, ssl=True, user="admin", password="apple") as tm1: cells = { ('e1', 'e1'): 1, ('e1', 'e2'): 2, ('e2', 'e1'): 3, ('e2', 'e2'): 4, ('e3', 'e1'): 5, ('e3', 'e2'): 6, } tm1.cells.write_async(cube_name="c1", cells=cells, slice_size=1, max_workers=6)

from pandas import DataFrame from TM1py import TM1Service with TM1Service(address="", port=12354, ssl=True, user="admin", password="apple") as tm1: data = DataFrame({ 'd1': ['e1', 'e1', 'e2', 'e2', 'e3', 'e3'], 'd2': ['e1', 'e2', 'e1', 'e2', 'e1', 'e2'], 'value': [1, 2, 3, 4, 5, 6], }) tm1.cells.write_dataframe_async(cube_name="c1", data=data, slice_size_of_dataframe=1, max_workers=6)

TM1 git support #447 TM1py now allows you to manage the TM1 git repository. You can manage the TM1 database with git and TM1py.

New authentication mode based on cam_passport. Can be used from Jupyter within Cognos Analytics.

from ca_data_connector.ca_lib import OnPremSession session = OnPremSession() session.initialize() passport = session.get_cookies()['cam_passport'] with TM1Service(address=address, port=port, ssl=ssl, cam_passport=passport) as tm1: print(tm1.server.get_server_name())

New Features

AuditLog queries 3771dad

Changes to tm1.power_bi.execute_mdx function to provide data to PBI in the most friendly way #476

Improvement on PowerBI dimension retrieval function to fetch attribute for a parent instead of parent element name #478

Support MDX PROPERTIES in execute_mdx_dataframe #470

Introduce a new cancel_at_timeout argument to abort TM1 operation once the timeout is reached

Support process debugging a83da11

New arguments on execute_mdx, execute_view: skip_cell_properties, element_unique_names to control shape of response dictionary f751edf, 6d9e520

TM1 git support #447

New write_dataframe_async function 94081eb

New write_async function 3969c1d

Create, read, update, delete servers on admin host #296

New re_authenticate function in TM1Service 2028d3d

Better delta log requests aeed032

Allow login with cam_passport f070c06

Fixes

Never attempt to delete leaves hierarchy a0aa152

Make install with pandas work with zsh c458e40

Fix encoding of ui-data property 6438309

Fix deactivate / activate chore #515

Accept float as timeout 2d0c555

Fix is_admin property in User class d9ed1b2

Fix documentation on readthedocs

Remove linebreaks when writing with ti #568

Include empty strings suppression in extract_cellset_raw 01eb4a7

Allow parameters on unbound processes 144db38

Improve kwargs delegation through call stack ee70f54

Allow WITH MEMBERS combined with include_attributes=True in execute_mdx_dataframe function #593

Allow WITH MEMBERS combined with display_attribute=True in execute_mdx_dataframe_shaped function #593

Skip sandbox dimension in cubes.get #595

Acknowledgments

Big thanks to @lapstue, @kaleming, @beckyconning, @macsir, @jrobinsonAG, @tomasfelcman, @cwffonseca for contributing code to this release, and many others for reporting bugs and requesting features.

How to upgrade TM1py

To upgrade TM1py, just use the following command:

pip install TM1py --upgrade
Source code(tar.gz)
Source code(zip)
1.6.0(Jan 22, 2021)
Highlights

support for IntegratedSecurityMode3

tm1 = TM1Service(address=servername, port=instanceport, ssl=True, integrated_login=True)

faster write function

with TM1Service(address="", port=12354, ssl=True, user="admin", password="apple") as tm1: cells = dict() cells["e1", "e1"] = 1 cells["e2", "e2"] = 2 tm1.cells.write("c1", cells, use_ti=True)

new write_dataframe function https://github.com/cubewise-code/tm1py/commit/82a39bd1cbe1861706430caa469d7c2b97c14dda

with TM1Service(address="", port=12354, ssl=True, user="admin", password="apple") as tm1: df = pd.DataFrame({ 'd1': ['e1', 'e2'], 'd2': ['e1', 'e2'], 'Value': [1, 2]}) tm1.cells.write_dataframe('c1', df)

support for sandboxes

with TM1Service(address="", port=12354, ssl=True, user="admin", password="apple") as tm1: sandbox = Sandbox(name="My Sandbox") tm1.sandboxes.create(sandbox)

with TM1Service(address="", port=12354, ssl=True, user="admin", password="apple") as tm1: cube_name = 'c1' cells = { ("e1", "e1"): 1, ("e2", "e2"): 2 } tm1.cells.write(cube_name, cells, sandbox_name="My Sandbox")

with TM1Service(address="", port=12354, ssl=True, user="admin", password="apple") as tm1: mdx = """ SELECT {[d1].[e1], [d1].[e2]} ON ROWS, {[d2].[e1], [d2].[e2]} ON COLUMNS FROM [c1] """ df = tm1.cells.execute_mdx_dataframe(mdx, sandbox_name="My Sandbox")

impersonation #353

with TM1Service(address="", port=12354, ssl=True, user="admin", password="apple", impersonate="Marius") as tm1: print(tm1.whoami)

New Features

new query options on filter_message_log function: level, msg_contains https://github.com/cubewise-code/tm1py/commit/e9c27150e7fd82f8f178d1709e27909b0b43c2f3

new direct functions to add elements, edges or element attributes quickly: add_elements, add_edges, add_element_attributes

new security functions: get_read_only_users, get_custom_security_groups, update_user_password #393

functions to start and stop performance monitor: start_performance_monitor, stop_performance_monitor

new cube_lock, cube_unlock functions https://github.com/cubewise-code/tm1py/commit/8ea00963227056b7996d4664ce0ed6f46d1b45f4

new remove_edges_under_consolidation function https://github.com/cubewise-code/tm1py/commit/e5d287652954f70cc3c7409f53dcd32b26de3f86

new get_elements_by_wildcard function https://github.com/cubewise-code/tm1py/commit/e4837dca03e38095ec2fef8ce8e347c9465838b6

add remove_all_elements, remove_all_edges function to hierarchy class

optional increment argument in write function

Add add_component function to Hierarchy class

support for changesets in write operations #454

Fixes

improved test suite #301

fix get_number_of_elements function

fix get_members_under_consolidation #381

make get_attribute_of_elements work with numeric attributes https://github.com/cubewise-code/tm1py/commit/39d61fed29c84df5edd14e7b8f893da03bb6871e

avoid execute_dataframe method to fail over 0 cell https://github.com/cubewise-code/tm1py/commit/74509111648f141cea245f5c5cd3af58aa02f472

implement __sub__ function in CaseAndSpaceInsensitiveSet https://github.com/cubewise-code/tm1py/commit/8c807e7ea05dc4b3f965d3d0125c79495b96b994

issue in get_last_data_update function https://github.com/cubewise-code/tm1py/commit/9bc898e6c0c6a8510c8da1729dda978e47fa1279

Fix single-axis selection issue in execute_mdx #416

work around TM1 REST issue with RuleDerived cell property https://github.com/cubewise-code/tm1py/commit/b5d4bd87cd37aabcc6f205c736bc1ab4267d82e7

Fix chore reschedule issue cubewise-code/tm1py-samples#82

Compatibility Notes

requires Python 3.7 or higher

execute_mdx_values now returns a list object instead of a generator

the MDXUtils module has been removed. Use mdxpy instead

write_values now returns the changeset_id instead of a response object

Acknowledgments

Big thanks to @rkvinoth, @scrambldchannel, @andreyea, @adscheevel, @rclapp, @wimgielis for contributing code to this release, and many others for reporting bugs and requesting features.

How to upgrade TM1py

To upgrade TM1py, just use the following command:

pip install TM1py --upgrade
Source code(tar.gz)
Source code(zip)
1.5.0(Aug 5, 2020)
Highlights

Functions to pull TM1 data into PowerBI in a convenient shape. Allows building dashboards in PowerBI Desktop with refreshable TM1 data.

The execute_mdx and execute_view functions retrieve data from a cube.

with TM1Service(address="localhost", port=12354, user="admin", password="apple", ssl=True) as tm1: data = tm1.power_bi.execute_view(cube_name="Sales", view_name="PowerBI", private=False)

| | Time | Industry | Executive | Customer | Business Unit | Product | State | Gross Margin | Revenue | COGS | |----:|-------:|:-----------------|:---------------|-----------:|----------------:|----------:|:--------|---------------:|-----------:|----------:| | 0 | 201308 | CPG | Andrew Ma | 10008 | 49 | 30 | TX | -5048.78 | nan | 5048.78 | | 1 | 201308 | CPG | Andrew Ma | 10017 | 49 | 30 | NY | -5821.12 | nan | 5821.12 | | 2 | 201308 | Energy | Valery Ushakov | 10035 | 31 | 50 | OH | -60384.4 | nan | 60384.4 | | 3 | 201308 | Energy | Carlos Grilo | 10026 | 48 | 20 | FL | -24880 | nan | 24880 |

The get_member_properties function retrieves a slice of a dimension.

with TM1Service(address="localhost", port=12354, user="admin", password="apple", ssl=True) as tm1: customers = tm1.power_bi.get_member_properties( dimension_name="Customer", hierarchy_name="Customer", member_selection="{Tm1SubsetAll([Customer])}", skip_consolidations=True, attributes=["State", "City"], skip_parents=False)

| | Customer | Type | State | City | level000 | |----:|:-----------|:--------|:--------|:-------------------------|:---------------| | 0 | 1023 | Numeric | TX | Irving | Total Customer | | 1 | 10000 | Numeric | IL | Chicago | Total Customer | | 2 | 10001 | Numeric | IL | Westchester | Total Customer | | 3 | 10002 | Numeric | TX | Plano | Total Customer | | 4 | 10003 | Numeric | TX | Fort Worth | Total Customer |

New clear method to efficiently zero out a slice of a cube, without need to write an MDX query or create a cube view.

with TM1Service(address="localhost", port=12354, user="admin", password="apple", ssl=True) as tm1: tm1.cells.clear(cube="Sales", state="{[State].[CA]}", time="{[Time].[2020].Children}")

Python Type Hints are now available on all functions and arguments. This enables autocompletion on all TM1py objects and makes working with TM1py way more friendly.

New async_requests_mode to avoid 60s timeout when using TM1py with the IBM cloud #241

with TM1Service( base_url='https://hostname.planning-analytics.ibmcloud.com/tm1/api/tm1_database_name/', user="user", namespace="LDAP", password="xxxx", ssl=True, verify=True, async_requests_mode=True) as tm1: print(tm1.server.get_product_version())

Added functions to MonitoringService to clear a TM1 instance from all user traffic

cancel_all_threads

close_all_sessions

disconnect_all_users

New Features

New function execute_set_mdx in ElementService allows to execute MDX set expressions. #205

Add add_edges method to HierarchieService

Support execution of unbound TI processes #220

Added update_or_create methods to all services

Added plain clear_with_mdx method to CellService

Cube dimension re-order function update_storage_dimension_order now returns the percent change in memory footprint

Added get_element_names method to SubsetService to retrieve element names from static or dynamic subsets

Added user_exists, group_exists function to SecurityService

Improved performance in build_cellset_from_pandas_dataframe function

Add skip argument to all execute_view_, execute_mdx_ functions

New make_static function in SubsetService #262

Allow filtering of messagelog and transactionlog with since and until timestamps #268

Add skip_zeros, skip_consolidated, skip_rule_derived arguments to all execute_mdx and execute_view functions #273

New simplified function in ElementService to retrieve elements with one attribute as dictionary: get_attribute_of_elements

New function execute_mdx_elements_value_dict allows to retrieve data in dictionary with comma seperated element names as the key

New function clear_spread in CellService allows to execute a clear spread based on list unique element names. #207

Support individual timeout on function call #117

Bug Fixes

Fix timeout type issue #217

Fix redundant calls in dimension update and create methods #229

New improved format_url method to properly encode special characters in TM1 object names #212

Add type and enabled as properties to User class

Fix issue in dst_chore_time

Fix issue in extract_cellset_dataframe

Replace unofficial /Content REST API calls with official API calls #273

Fixcheck_rules function to behave in line with compile_process function #277

Encode % properly in object names #285

Compatibility Notes

By default, pandas will no longer be installed when installing TM1py with pip. #214

If you want to install tm1py with pandas, run:

pip install tm1py[pandas]

If a function requires pandas, but it isn't installed, TM1py will throw an ImportError:

ImportError: Function 'get_member_properties' requires pandas

The MDXUtils module has been deprecated. It still works but it throws a warning. It will be removed in the next major release of TM1py. Please use MDXpy instead.

Acknowledgments

Big thanks to @rkvinoth, @scrambldchannel, @rclapp, @ykud, @zPat for contributing code to this release, and many others for reporting bugs and requesting features.

How to upgrade TM1py

To upgrade TM1py, just use the following command:

pip install TM1py --upgrade
Source code(tar.gz)
Source code(zip)
1.4.1(Jan 2, 2020)
New Features

new methods in ElementService: get_number_of_elements, get_number_of_consolidated_elements, get_number_of_leaf_elements

Bugfixes

restrict escaping of single quotes in url to object names only

Fix for #124

Source code(tar.gz)
Source code(zip)
1.4.0(Dec 8, 2019)
Highlights

New create, get and delete methods in ApplicationService

with TM1Service(address=ADDRESS, port=PORT, user=USER, password=PASSWORD, ssl=SSL) as tm1: app = CubeApplication(path="Planning Sample", name="Sample Application", cube_name="Bike Shares") tm1.applications.create(application=app, private=False)

with TM1Service(address=ADDRESS, port=PORT, user=USER, password=PASSWORD, ssl=SSL) as tm1: with open(path_to_file, 'rb') as file: app = DocumentApplication(path="Planning Sample", name="Sample Application", content=file.read()) tm1.applications.create(application=app, private=False)

New Features

New create, get, delete methods for applications

execute_view_dataframe, execute_mdx_dataframe methods now expose all arguments from the pd.read_csv methods (e.g. dtypes, parse_dates) #143 More details on the arguments here: https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_csv.html

Add new method remove_all_edges to HierarchyService to unwind hierarchy efficiently

New get_element_identifiers methods that returns all element names and alias values in a set

Add get_last_data_update method to CubeService #177

Add load and unload methods to CubeService #163

Add check_rules method to CubeService

Bug Fixes

Fix issue in chore update method #133 and #134

Fix handling of Sandboxes dimension when writing to cube #136

Fix return of extract_cellset_rows_and_values method when MDX creates empty cellset #135

Escape single quotes in all object names in odata references #145

Undo silent type conversion in values column. Type of Value column in resulting dataframe from execute_mdx_dataframe, execute_view_dataframe is derived from data, unless specified otherwise through dtype argument.

Compatibility Notes

ApplicationService has been redesigned and is not backwards compatible.

Acknowledgments

Big thanks to @tombrzy , @ducklingasa , @rclapp , @pbuncik for contributing code to this release and many others for reporting bugs and requesting features.

How to upgrade TM1py

To upgrade TM1py, just use the following command:

pip install TM1py --upgrade
Source code(tar.gz)
Source code(zip)
1.3.1(May 28, 2019)

Fixes

Dynamic dependencies in setup.py script depending on OS. Fixes #128
Source code(tar.gz)
Source code(zip)
1.3.0(May 19, 2019)
TM1py version 1.3.0 is now available. Getting started with TM1py

Enhancements

TM1py now supports SSO authentication to TM1 using CAM gateway. Previously to connect to a TM1 instance with CAM security, you had to put your user name and passord in the configuration file as below:

[tm1srv01] address=localhost port=8884 user=vviau password=YourPassword namespace=CUBEWISE ssl=True

Now you can replace your user and password with the new gateway parameter such as below

[tm1srv02] address=localhost port=8884 namespace=CUBEWISE gateway=http://localhost:80/ibmcognos/cgi-bin/cognos.cgi ssl=True

TM1py will use your Windows login to authenticate with TM1. Thanks to adscheevel for adding this new feature to TM1py.

The whoami method has been added to get the user and group assignments, associated with your active session. #106

Get default element of a hierarchy without need to allocate full hierarchy in memory using the two new HierarchyService methods. #95: get_default_member(dimension_name, hierarchy_name) update_default_member(dimension_name, hierarchy_name, member_name)

New boolean parameter to enable base64 encoded passwords in the TM1Service constructor: decode64. #87

A new remove_element(element_name) method has been added to the Hierarchy class. It implicitly removes all edges from the hierarchy that are related to the element. #83

Introducing a new skip_contexts (boolean) argument in all execute_mdx functions to reduce the response size by omitting the title elements and thus speeds up the execution. #82

Two new methods have been added to the CubeService to query and update technical dimension order of a cube. #81

Updating a subset is now done entirely through the TM1 REST API instead of calling the SubsetDeleteAllElements TI function behind the scenes. #93

New argument in TM1Service constructor: connection_pool_size allows for a custom sized http connection pool. Required when using TM1py in a multithreaded environment.

New method in CellService: relative_proportional_spread

How to upgrade TM1py

To upgrade TM1py, just use the following command:

pip install TM1py --upgrade
Source code(tar.gz)
Source code(zip)
1.2.0(Dec 7, 2018)
TM1py version 1.2.0 is now available. Getting started with TM1py

Enhancements

RestService - TM1py supports now base64 encoded password, TM1py is going to try first to connect using plain password, if authentication fails, it is going to retry authentication with b64decoded password. #66

ServerService - New functions to get and update tm1s.cfg parameters live#76:

get_static_configuration: get the configuration, as specified in the tm1s.cfg file

get_active_configuration: get the configuration, as it currently applies in the TM1 Server

update_static_configuration: update the configuration in the tm1s.cfg and trigger TM1 to re-read the file

CellService - New functions to extract (pandas) pivot tables (in the shape of the view) from a cube view or MDX #74:

execute_view_dataframe_pivot: get data from a cube view

execute_mdx_dataframe_pivot get data from MDX

ViewService - new (dynamic) get method that returns MDX view or native view. #78

ProcessService - New process execution functions#68:

execute_with_return: execute a TM1 process and if process failed show the error.

get_error_log_file_content: get the content of error-log-files (e.g. TM1ProcessError_20180926213819_65708356_979b248b-232e622c6.log)

ElementService - New functions to help managing elements #64:

get_elements: get all elements

get_leaf_elements: get all leaf elements

get_leaf_element_names: get all leaf elements name

TM1Service - New optional parameter for session_context, that controls what is displayed in the session column in Arc / Pulse / TM1top. Default value is "TM1py" #61

New MDX parser to extract dimension composition from MDX query

Improved test suite. Now all tests are deterministic and stand-alone. Success of tests no longer depends on execution order #72

Bugfixes

Bugfix in default view format. Default view format is now: 0.#########

Operations on users and groups (e.g. add_user_to_groups) are now case-and-space-insensitive

Update of dimension name on python object triggers name update of same-named-hierarchy in dimension #70

Fix duplication of tasks when updating chores #69

How to upgrade TM1py

To upgrade TM1py, just use the following Python command:

pip install TM1py --upgrade

Source code(tar.gz)
Source code(zip)
1.1.0(Aug 8, 2018)
Bugfixes:

Remove variable_ui_data as well when removing variable from process

Add new variables to process as type 'Other' instead of 'Ignore'

Fix issue that cellsets would not be destroyed after usage

New Features

New functions to retrieve TM1 data in a UI friendly format: execute_mdx_ui_dygraph, execute_mdx_ui_array

Make tests compatible with TM1 11.3 (adapt to change in zero suppression for calculated Members in MDX)

Prettify execute function. Execute function in ProcessService now takes TI Parameters as keyword arguments. Rename of name_process argument to process_name.

Construct_mdx function in MDXUtils now more simple and robust

More robust test cases for construct_mdx and underlying functions

Source code(tar.gz)
Source code(zip)
1.0.3(Jun 26, 2018)
New Stuff

Get_cellset_cells_count function in CellService

New optimized execute_mdx functions in CellService: execute_mdx_get_csv, execute_mdx_get_dataframe

New get_members_under_consolidation function in ElementService

Improvements

Improved Cellset handling when executing Views and MDX. Now always delete Cellsets after using them.

Bugfixes

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

Source code(tar.gz)
Source code(zip)
1.0.2(Jun 4, 2018)
New stuff:

TM1Object (Parent class of Subset, Process, Dimension, etc.) now has eq, ne magic methods. Allows to compare instances of Subset, Dimension, etc for equality with ==, =!

RESTService can now be instantiated with 'session_id' parameter. Useful to avoid (unreasonably expensive) CAM Authentication when executing Python from another (REST) TM1 client

Refactoring of RESTService

get_cellset_cells_count function in CellService

build_cellset_from_pandas_dataframe function in Utils class

compile_process function added to ProcessService

Tests now use config.ini file

Execute_mdx_cell_values_only function to query cell-values only

Source code(tar.gz)
Source code(zip)
1.0.1(Apr 27, 2018)
Construcor in RESTService / TM1Service now accepts string value for ssl parameter

Update docstrings in Subsetservice

Add pandas module to project dependencies

Adjustments in subset_exists function. Now behaves like other exists functions

Bugfix in hierarchycreation fallback (TI), when elementnames start with #

Source code(tar.gz)
Source code(zip)
1.0.0(Feb 14, 2018)
Workaround: Dimensionupdates now work. Related to: https://www.ibm.com/developerworks/community/forums/html/topic?id=75f2b99e-6961-4c71-9364-1d5e1e083eff&ps=25

Delta request Support for TransactionLog and MessageLog. Sample: https://github.com/cubewise-code/TM1py-samples/blob/master/Other/transactionlog%20delta%20requests.py

Read TM1 Application https://github.com/cubewise-code/TM1py-samples/blob/master/Other/get%20TM1%20application.py

New Functions in ElementService -> get_all_leaves_under_consolidation and more

90+ automated Tests

Source code(tar.gz)
Source code(zip)
0.1.8(Dec 3, 2017)
get_all_names function in ViewService

disconnect_user function in MonitorService

Test Improvements

Source code(tar.gz)
Source code(zip)
0.1.7(Nov 27, 2017)
Attribute Creation Workaround to Create Dimensions (with Attributes) in PA 2.0.x

Bugfix in RESTService. Constructor can now handle namespace=None as a keyword argument (required for check.py sample to work with CAM)

Minor Test Improvements

Source code(tar.gz)
Source code(zip)
0.1.6(Nov 7, 2017)
TM1Service instance can be saved to file, so the session can be reused. https://github.com/cubewise-code/TM1py/issues/31

MDX against Dimensions now supported

https://github.com/cubewise-code/TM1py/issues/30

Source code(tar.gz)
Source code(zip)
0.1.5(Oct 29, 2017)

Bugfix in Process Implementation: #28
Source code(tar.gz)
Source code(zip)
0.1.4(Oct 22, 2017)
Bugfix in Process Implementation: https://github.com/cubewise-code/TM1py/issues/28

Minor improvements in Tests

Source code(tar.gz)
Source code(zip)
0.1.3(Oct 8, 2017)
Introduction of 'base_url' parameter in RESTService constructor. Allows connecting to TM1 in the IBM cloud

Query transaction log through ServerService

New Module: TI Obufscator. Allows obfuscating TI processes

Beautification of unit tests

Hierarchy now has boolean attribute: 'balanced'

Version now property of TM1 Service

Bugfixes:

Groupnames no longer space sensitive

Source code(tar.gz)
Source code(zip)
0.1.2(Sep 4, 2017)
New Dimension create function without PATCH. So creating dimension on TM1 11 doesn't run into this bug anymore: https://www.ibm.com/developerworks/community/forums/html/topic?id=75f2b99e-6961-4c71-9364-1d5e1e083eff&ps=25

Source code(tar.gz)
Source code(zip)
0.1.1(Sep 1, 2017)

Source code(tar.gz)
Source code(zip)
0.0.1(Aug 8, 2017)

First Release
Source code(tar.gz)
Source code(zip)

TM1py is a Python package that wraps the TM1 REST API in a simple to use library.

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Overview

Features

Requirements

Optional Requirements

Install

Usage

Documentation

Issues

Contribution

Comments

Releases(1.10.1)

1.10.1(Oct 24, 2022)

What's Changed

New Contributors

1.10.0(Oct 10, 2022)

Highlights

New Features and Improvements

New Contributors

How to upgrade TM1py

1.9.0(Feb 7, 2022)

Highlights

New optional use_iterative_json parameter in execute_mdx_dataframe/execute_view_dataframe functions https://github.com/cubewise-code/tm1py/pull/646, https://github.com/cubewise-code/tm1py/pull/612

New skip_non_updateable argument in write/write_dataframe functions https://github.com/cubewise-code/tm1py/pull/657

New search functions to search through cubes, processes, and chores #660, #663, #665

New Features

Improvements and Bugfixes

Acknowledgments

How to upgrade TM1py

1.8.0(Sep 29, 2021)

Highlights

New Features

Fixes

Acknowledgments

How to upgrade TM1py

1.6.0(Jan 22, 2021)

Highlights

New Features

Fixes

Compatibility Notes

Acknowledgments

How to upgrade TM1py

1.5.0(Aug 5, 2020)

Highlights

New Features

Bug Fixes

Compatibility Notes

Acknowledgments

How to upgrade TM1py

1.4.1(Jan 2, 2020)

New Features

Bugfixes

1.4.0(Dec 8, 2019)

Highlights

New Features

Bug Fixes

Compatibility Notes

Acknowledgments

How to upgrade TM1py

1.3.1(May 28, 2019)

Fixes

1.3.0(May 19, 2019)

Enhancements

How to upgrade TM1py

1.2.0(Dec 7, 2018)

Enhancements

Bugfixes

How to upgrade TM1py

1.1.0(Aug 8, 2018)

1.0.3(Jun 26, 2018)

1.0.2(Jun 4, 2018)

1.0.1(Apr 27, 2018)

1.0.0(Feb 14, 2018)

0.1.8(Dec 3, 2017)

0.1.7(Nov 27, 2017)

0.1.6(Nov 7, 2017)

0.1.5(Oct 29, 2017)

0.1.4(Oct 22, 2017)

0.1.3(Oct 8, 2017)

New optional `use_iterative_json` parameter in `execute_mdx_dataframe`/`execute_view_dataframe` functions https://github.com/cubewise-code/tm1py/pull/646, https://github.com/cubewise-code/tm1py/pull/612

New `skip_non_updateable` argument in `write`/`write_dataframe` functions https://github.com/cubewise-code/tm1py/pull/657

New `search` functions to search through cubes, processes, and chores #660, #663, #665