tm1gitpy 1.2.9

Utilities for exporting and comparing TM1 models for Git workflows.

tm1_git_py

tm1_git_py is a Python-based drop-in replacement for TM1 Git. It keeps TM1 Git’s on-disk file layout so you can move between tools with minimal friction.

• It understands tm1project.json and the same filtering rules used by TM1 Git workflows.
• It is not embedded in TM1, which keeps deployment flexible—ideal for CI/CD, agents, and pipelines that run outside the TM1 server. It talks to TM1 over the REST API via TM1py.
• You can run it as a stand-alone command-line tool or import it as a library and embed it in a larger ecosystem (automation, CI/CD, custom apps).

Features

tm1_git_py allows you to:

• Export TM1 models (cubes, dimensions, processes, chores) to a structured folder format compatible with TM1 Git
• Apply filter rules during export (narrowed objects and SQLite-backed export cache), during compare (fine-grained changeset without mutating on-disk exports), or on a changeset (toggle apply flags only)
• Compare models (either file-based schema or TM1 servers) and collect differences to changesets.
• Apply changsets to target server

TM1 Git vs tm1gitpy: Technical Comparison

A detailed technical comparison between TM1git and TM1gitpy, categorized by capability areas.

Feature Category	Feature	TM1git	tm1gitpy
Core Architecture	Embeddable	✅ (via REST API)	✅ (as a Python package)
	CLI Support	❌ (only REST API over CLI or Postman)	✅
Schema & Objects	Model Schema Export	✅	✅
	Object Deletes	❌	✅
	Settings (Server Config)	✅	🟠 Upcoming release
	Files	✅	❌ (only via Python hooks)
	Large schema files (>100 MB) on GitHub	❌	✅ (since git push is externally managed, GitHub large files can be used here)
Filtering Capabilities	Basic Filtering	✅ (tm1project.json)	✅ (tm1project.json or separate rules)
	Advanced Filtering	❌ (no wildcard support for technical object unignores, no trailing wildcards)	✅ (wildcard support for technical object unignores; leading/trailing wildcards on any level)
	Element-level Filtering	❌	✅
	Rule Markups	❌	🟠 Upcoming release
Changeset Management	Changeset as a file	❌ (requires Git PR to review changes)	✅
	Changeset Post-filtering	❌	✅
	Transactional Changeset apply	✅	🟠 Upcoming release
	Progress Tracking	❌	✅
DevOps & Extensibility	Pre/Post Pull/Push	✅ (via TI processes)	🟠 Upcoming release (via TI processes or Python hooks)
	No-Git Preview Mode	❌	✅

Benchmarks

Benchmark Section 1: Small and Moderate Models

Benchmark Section 2: Large and Extra Large Models

In these tests, GitHub push did not work due to file size limits (100Mb) so only the first part of the export is compared. Actual GitHub transfer is not calculated to the numbers.

Installation

Install from PyPI (recommended)

Isolated CLI install with pipx:

pipx install tm1gitpy
tm1gitpy --version

Upgrade:

pipx upgrade tm1gitpy

Or install into the active environment:

pip install tm1gitpy
tm1gitpy --version

Pre-release builds (TestPyPI):

pipx install --index-url https://test.pypi.org/simple/ --extra-index-url https://pypi.org/simple/ tm1gitpy

Standalone binary (no Python required)

Download the platform asset from GitHub Releases:

Platform	Asset
Linux x86_64	tm1gitpy-linux-x86_64
macOS (Apple Silicon)	tm1gitpy-macos-arm64
Windows x86_64	tm1gitpy-windows-x86_64.exe

Verify checksums using SHA256SUMS.txt from the same release, then run:

chmod +x tm1gitpy-linux-x86_64   # Linux/macOS only
./tm1gitpy-linux-x86_64 --version

Upgrade: download the newer release asset and replace the binary.

From Source

To use the package (runtime dependencies only):

git clone <repository-url>
cd tm1_git_py
pip install -e .
tm1gitpy --version

Or install from a requirements file: pip install -r requirements.txt then pip install -e .

To run tests or develop (runtime + test dependencies):

pip install -r requirements-dev.txt
# or
pip install -e ".[dev]"

Requirements

• Python 3.10 or higher
• TM1py >= 2.1, < 3.0
• requests >= 2.25
• PyYAML >= 6.0

Configuration

Create a configuration file at .tm1gitpy/tm1servers.yaml (local directory) or ~/.tm1gitpy/tm1servers.yaml (user home):

servers:
  dev:
    base_url: http://localhost:12354/api/v1/
    user: admin
    password: your_password  # Optional - can use environment variables
  
  prod:
    base_url: https://prod-server.company.com:12354/api/v1/
    user: admin
    password: ${TM1_PROD_PASSWORD}  # Environment variable placeholder

Usage

Export TM1 Model

Export a full TM1 model from a server:

python tm1_git_py/main.py export --server dev --model-output-folder model_dir --overwrite

Filtering

Use the same rule language in three places:

• Export (-f / --filter): rules are applied while pulling from TM1 and affect the export folder and internal SQLite-backed cache for that export. To change what is on disk after an export, re-run export with updated rules (there is no separate “filter folder only” command).

python tm1_git_py/main.py export --server dev --model-output-folder model_dir --filter file://examples/filter.txt --overwrite

• Compare (--filter-rules): rules narrow what appears in the emitted changeset; they do not rewrite serialized model folders.

• Changeset filter (changset-filter / changeset-filter, --filter-rules): toggles apply flags on matching changes in place; changeset length is unchanged.

python tm1_git_py/main.py changset-filter --changeset-path changeset.yml --filter-rules file://examples/filter.txt

Filter file format (one pattern per line, # for comments):

# Exclude technical dimensions
Dimensions('}*')

# Force-include all BW dimensions
!Dimensions('BW*')

# Exclude BW Comp dimensions
Dimensions('BW Comp*')

# Exclude technical hierarchies for all dimensions
Dimensions('*')/Hierarchies('}*')

# Chore task rules target the underlying process_name
Chores('Daily*')/Tasks('LoadData')

Filter Rule Logic

• Each rule line is a TM1 URL-style selector, optionally prefixed with !.
• No prefix means exclude.
• ! prefix means force include.
• Wildcards in quoted identifiers are supported:
  • a* -> starts with a
  • *a -> ends with a
  • a -> exact match
• Rules are evaluated per entity level (dimensions, hierarchies, elements, subsets, cubes, views, processes, chores, tasks).
• Hierarchy traversal is parent-first, with force-include branch retention:
  • normally, excluded parent excludes descendants
  • if a descendant is force-included (!), its required parent chain is retained (e.g. force-include element keeps matching hierarchy and dimension references)
• At each level, filter expression is composed as:
  • base excludes: not (<exclude_1>) and not (<exclude_2>) and ...
  • plus force includes: or (<include_group>)
  • effective shape: (not (<exclude_1>) and not (<exclude_2>) and ...) or (<include_group>)
• TM1 export filters inherit force-includes from descendants:
  • a force-included hierarchy contributes include criteria to the dimension-level TM1 filter
  • a force-included element/subset/edge contributes include criteria to the hierarchy-level TM1 filter

Supported Rule Patterns

Level	Pattern
Dimension	Dimensions('<pattern>')
Hierarchy	Dimensions('<dim_pattern>')/Hierarchies('<hier_pattern>')
Element	Dimensions('<dim_pattern>')/Hierarchies('<hier_pattern>')/Elements('<elem_pattern>')
Subset	Dimensions('<dim_pattern>')/Hierarchies('<hier_pattern>')/Subsets('<subset_pattern>')
Edge	Dimensions('<dim_pattern>')/Hierarchies('<hier_pattern>')/Edges(...)
Cube	Cubes('<pattern>')
View	Cubes('<cube_pattern>')/Views('<view_pattern>')
Rule	Cubes('<cube_pattern>')/Rules(...)
Process	Processes('<pattern>')
Chore	Chores('<pattern>')
Task	Chores('<chore_pattern>')/Tasks('<process_name_pattern>')

Use ! prefix on any supported pattern to force-include matching objects.

Rule Pattern Shortcuts

Collection segments without ('<pattern>') are treated as ('*').

• Cubes/Views means Cubes('*')/Views('*')
• Dimensions/Hierarchies/Elements means Dimensions('*')/Hierarchies('*')/Elements('*')
• Processes means Processes('*')

Filter Rule Input Formats (CLI)

• export: -f / --filter — file path, file:// URI, or comma-separated rules (same loaders as below).
• compare: -f / --filter-rules — same three input forms.
• changset-filter and changeset-filter (alias): --filter-rules — same three input forms.

For those flags:

• File path: examples/filter.txt
• File URI: file://examples/filter.txt
• Inline comma-separated rules: Dimensions('}*'),!Dimensions('BW*')

Command-Line Arguments

python tm1_git_py/main.py <command> [options]

Commands:
  export           Export model from TM1 to a folder
  compare          Compare two model folders and write a changeset
  apply            Apply a changeset file to a TM1 server
  changset-filter  Toggle apply flags in a changeset using filter rules
                   (alias: changeset-filter)

Shared options (all commands):
  --log-file PATH  Optional log file path (or directory for timestamped logs)
  --console-logs   Enable console log output in addition to progress UI
  --debug          Enable detailed worker/thread progress bars

export:
  -s, --server SERVER
  -mo, --model-output-folder PATH   (default: export)
  -o, --overwrite
  -f, --filter RULES_OR_FILE
  --max-workers N

compare:
  --source PATH
  --target PATH
  -o, --output PATH                 (default by format: changeset.yaml/json)
  --mode {full,add_only}            (default: full)
  -f, --filter-rules RULES_OR_FILE
  --format {yaml,json}              (default: yaml)
  --max-workers N

apply:
  -s, --server SERVER
  -c, --changeset PATH
  --status-dir PATH
  --execution-id ID
  --no-fail-fast

changset-filter / changeset-filter:
  --changeset-path PATH
  --filter-rules RULES_OR_FILE

Logging defaults to INFO. You can also set TM1GITPY_LOG_LEVEL in the environment. Pass --debug to set the log level to DEBUG for that run.

Progress output shows a total progress bar by default. Pass --debug to also render detailed per-worker/thread progress bars.

Worker counts are split into two worker types:

• cpu-worker: process-based workers used for CPU-bound work such as content hashing.
• io-worker: thread-based workers used for IO-bound work such as TM1 page fetching.

--max-workers means the total CPU + IO worker budget. When it is provided:

• cpu-worker ~= --max-workers / 4
• io-worker = --max-workers - cpu-worker
• the split is rounded to stay near a 1:3 CPU/IO ratio

When --max-workers is omitted:

• cpu-worker = cpu_count // 2 + 1
• io-worker = cpu-worker * 3

When the resolved CPU worker count is 1, content hashing and model serialization run serially.

For export, content hash calculation uses CPU workers and TM1 fetch/page work uses IO workers. For compare, the resolved CPU worker count is split between source and target model deserialization:

• source workers = max(1, cpu_workers // 2)
• target workers = max(1, cpu_workers - source_workers)
• odd values give one extra worker to target

Examples

See the examples directory for usage examples:

• config_usage.py - Server configuration examples
• filter.txt - Filter pattern examples

For model comparison and changeset workflows, use the Python API (tm1_git_py.comparator, tm1_git_py.changeset, tm1_git_py.apply).

For paginated element/subset fetching (e.g., large hierarchies), use tm1_git_py.get_elements, tm1_git_py.get_subsets, and related functions.

Building Binary

Build a local standalone executable with PyInstaller (authoritative spec: tm1gitpy.spec):

make build-binary

The binary is produced at dist/tm1gitpy (or dist/tm1gitpy.exe on Windows).

Other useful targets:

make clean-binary
make rebuild-binary

Official cross-platform binaries are built automatically on GitHub Release publish (workflow: .github/workflows/release-binaries.yml).

Development

Running Tests

pytest tests/

Integration tests (TM1 container/local TM1 required):

PYTHONPATH=. pytest test_integration/

License

See LICENSE file for details.