PAWpy 0.2.0

A TM1py-inspired Python wrapper for the IBM Planning Analytics Workspace (PAW) REST API.

PAWpy — Planning Analytics Workspace REST API Wrapper

A TM1py-inspired Python wrapper for the PAW REST API.

Install

uv sync --extra dev         # installs PAWpy + pytest
uv run python -m pytest     # 21 offline tests (no live server needed)

Requires Python ≥3.11 and requests. The URL-builder calls (paw.ui.*, *.get_embed_url) make no network request and work without a live PAW server.

Releasing

Publishing is automated by .github/workflows/publish.yml via PyPI Trusted Publishing (OIDC — no API token stored). To cut a release:

1. Bump version in pyproject.toml and add a CHANGELOG.md entry.
2. One-time on PyPI: add a Trusted Publisher for project PAWpy → bluestarleo/PAWpy, workflow publish.yml, environment pypi.
3. Tag and push:

   git tag v0.1.0 && git push origin v0.1.0
   

The workflow runs the tests (3.11–3.13), builds, checks the tag matches the package version, and publishes the sdist + wheel to PyPI.

Architecture

PAWService                  ← top-level entry point (mirrors TM1py's TM1Service)
├── RestService             ← session + auth + GET/POST/PATCH/PUT/DELETE core
├── ContentService          ← /pacontent/v1/Assets  (OData folders / books / assets)
├── BookService             ← books (type=book/dashboard) over ContentService
├── ViewService             ← views over ContentService
├── AdminService            ← /api/v1/admin  (servers, users, groups)
├── UIService               ← URL builder for /ui?type=… embed endpoints
└── TM1ProxyService         ← /api/v0/tm1/<db>/api/v1/…  (TM1 REST via PAW auth)

All base paths (content_base, admin_base) are constructor-overridable, since they vary across PAW builds (/pacontent/v1 vs /api/v1/content).

Auth Modes

Mode	How it works
oauth	Client-credentials grant against token_url → Authorization: Bearer
cam	CAM namespace login via POST /login → x-csrf-token
native	TM1 native username/password login via POST /login → x-csrf-token
passport	Cognos CAM passport (camid) via POST /login
session	Inject an existing csrf_token / session_cookie (dev/test)

Usage

OAuth (Recommended)

from PAWpy import PAWService

with PAWService(
    host="paw.mycompany.com",
    auth_mode="oauth",
    client_id="my-client-id",
    client_secret="my-client-secret",
    token_url="https://idp.mycompany.com/oauth2/token",  # required for oauth
    scope="paw",                    # optional
    database="Global FPA",          # optional default TM1 database
) as paw:

    # List books in a folder (returns Asset objects)
    books = paw.books.get_all("/shared/FP&A")

    # Get embed URL for an iframe (no HTTP call)
    url = paw.books.get_embed_url("/shared/FP&A/Monthly Report")

    # List registered TM1 servers
    servers = paw.admin.get_tm1_servers()

    # TM1 proxy call (MDX via PAW auth) — returns the raw cellset JSON
    tm1 = paw.tm1("Global FPA")
    data = tm1.execute_mdx("SELECT {[Account].[Revenue]} ON 0 FROM [Revenue Cube]")

    # Embed URL generation (no HTTP call)
    embed = paw.ui.cube_viewer_url("Global FPA", "Revenue Cube", view="Monthly View")

Legacy CAM

with PAWService(
    host="paw.mycompany.com",
    auth_mode="cam",
    namespace="LDAP",
    username="my-username",
    password="secret",
) as paw:
    ...

Multi-tenant (PAW Cloud)

with PAWService(
    host="planning-analytics.cloud.ibm.com",
    tenant_id="my-tenant-id",
    auth_mode="oauth",
    client_id="...",
    client_secret="...",
) as paw:
    ...

Mapping to TM1py

TM1py	PAWpy
TM1Service	PAWService
CubeService	TM1ProxyService (via PAW)
DimensionService	TM1ProxyService (via PAW)
ProcessService	TM1ProxyService (via PAW)
(no equivalent)	BookService
(no equivalent)	ContentService
(no equivalent)	AdminService
(no equivalent)	UIService

Versioning against PAW builds

The PAW REST API is still incomplete and grows with each IBM release, so PAWpy is versioned against two axes: its own semver (PAWpy.__version__) and the minimum PAW build each API group requires. Each service declares its API_GROUP; the per-group minimums live in PAWpy/version_requirements.py (MIN_PAW_VERSION) and are mirrored in coverage/COVERAGE.md.

paw = PAWService(host="paw.acme.com", auth_mode="oauth", ..., paw_version="2.1.21")

paw.requires("content")          # -> "2.1.21"  (min PAW build for Content Services)
paw.supports("content")          # -> True / False against the known paw_version
paw.assert_supported("content")  # raises PAWVersionError if the build is too old
paw.detect_paw_version()         # best-effort probe (overridable path/field)

When paw_version is unknown, gating is a no-op — PAWpy never blocks a call solely because it couldn't determine the version; the server still rejects genuinely-unsupported requests. The coverage matrix and its per-group version table are reconciled on each PAW release as part of a local maintenance workflow.

Coverage matrix & release tracking

coverage/COVERAGE.md is the source of truth for which PAW endpoints PAWpy wraps and which PAW build each needs. Because the PAW REST API is still growing, the matrix is reconciled on each PAW release: IBM's endpoint inventory (a Postman collection export or the published API references) is re-pulled and diffed against the matrix to flag endpoints PAW now exposes that PAWpy doesn't yet wrap, so the wrapper tracks IBM's cadence instead of drifting.

Roadmap (aligned with IBM's "future releases" promise)

• ViewService — PAW view CRUD
• UserGroupService — full user/group management
• EmbedTokenService — generate scoped embed tokens
• MCPService — PAW MCP endpoint integration
• Token refresh / OAuth expiry handling
• Async support (aiohttp)
• Pydantic models for Books, Assets, Servers