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get_jupyter_settings async 

get_jupyter_settings(
    auth: DomoAuth,
    return_raw: bool = False,
    *,
    context: RouteContext | None = None,
    **context_kwargs
) -> ResponseGetData

Retrieve Jupyter settings for the Domo instance.

GET /api/datascience/v1/settings

Returns instance types, limits, kernel specs, feature flags, etc.

Example response shape::

{
    "instanceTypes": [{"cpu": 0.5, "memory": 4.0}, ...],
    "limits": {...},
    "isEnabled": true,
    "userAccess": {...},
    "currentUserInstanceTypes": [...],
    "jupyterKernels": [...],
    "featureFlags": {...},
    "locale": "en-US",
    "timeZone": "America/Denver",
    "maintenance": {...}
}

Parameters:

Name Type Description Default
auth DomoAuth

Authentication object (standard DomoAuth).

 required
return_raw bool

Return raw response without error checking.

 False
context RouteContext | None

Optional RouteContext.

 None

Returns:

Type Description
ResponseGetData

ResponseGetData with settings dict.

Raises:

Type Description
Jupyter_GET_Error

On failure.
Source code in src/crew_dcs/routes/jupyter/config.py 
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@gd.route_function
@log_call(**_LOG_DECORATORS)
async def get_jupyter_settings(
    auth: DomoAuth,
    return_raw: bool = False,
    *,
    context: RouteContext | None = None,
    **context_kwargs,
) -> rgd.ResponseGetData:
    """Retrieve Jupyter settings for the Domo instance.

    ``GET /api/datascience/v1/settings``

    Returns instance types, limits, kernel specs, feature flags, etc.

    Example response shape::

        {
            "instanceTypes": [{"cpu": 0.5, "memory": 4.0}, ...],
            "limits": {...},
            "isEnabled": true,
            "userAccess": {...},
            "currentUserInstanceTypes": [...],
            "jupyterKernels": [...],
            "featureFlags": {...},
            "locale": "en-US",
            "timeZone": "America/Denver",
            "maintenance": {...}
        }

    Args:
        auth: Authentication object (standard ``DomoAuth``).
        return_raw: Return raw response without error checking.
        context: Optional ``RouteContext``.

    Returns:
        ``ResponseGetData`` with settings dict.

    Raises:
        Jupyter_GET_Error: On failure.
    """
    context = RouteContext.build_context(context=context, **context_kwargs)

    url = f"https://{auth.domo_instance}.domo.com/api/datascience/v1/settings"

    res = await gd.get_data(
        url=url,
        method="GET",
        auth=auth,
        context=context,
    )

    if return_raw:
        return res

    if not res.is_success:
        raise Jupyter_GET_Error(message="Failed to retrieve Jupyter settings", res=res)

    return res

get_jupyter_user_namespace async 

get_jupyter_user_namespace(
    auth: DomoAuth,
    return_raw: bool = False,
    *,
    context: RouteContext | None = None,
    **context_kwargs
) -> ResponseGetData

Retrieve the Jupyter user namespace for the current user.

GET /api/datascience/v1/workspaces/user

The namespace string (e.g. "my-company-config-672596669") appears in the service_prefix path of all Jupyter server URLs: /user/{namespace}/{workspace_short_id}/api/contents/...

Example response::

{"name": "my-company-config-672596669"}

Parameters:

Name Type Description Default
auth DomoAuth

Authentication object (standard DomoAuth).

 required
return_raw bool

Return raw response without error checking.

 False
context RouteContext | None

Optional RouteContext.

 None

Returns:

Type Description
ResponseGetData

ResponseGetData whose .response is {"name": str}.

Raises:

Type Description
Jupyter_GET_Error

On failure.
Source code in src/crew_dcs/routes/jupyter/config.py 
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@gd.route_function
@log_call(**_LOG_DECORATORS)
async def get_jupyter_user_namespace(
    auth: DomoAuth,
    return_raw: bool = False,
    *,
    context: RouteContext | None = None,
    **context_kwargs,
) -> rgd.ResponseGetData:
    """Retrieve the Jupyter user namespace for the current user.

    ``GET /api/datascience/v1/workspaces/user``

    The namespace string (e.g. ``"my-company-config-672596669"``) appears in
    the ``service_prefix`` path of all Jupyter server URLs:
    ``/user/{namespace}/{workspace_short_id}/api/contents/...``

    Example response::

        {"name": "my-company-config-672596669"}

    Args:
        auth: Authentication object (standard ``DomoAuth``).
        return_raw: Return raw response without error checking.
        context: Optional ``RouteContext``.

    Returns:
        ``ResponseGetData`` whose ``.response`` is ``{"name": str}``.

    Raises:
        Jupyter_GET_Error: On failure.
    """
    context = RouteContext.build_context(context=context, **context_kwargs)

    url = f"https://{auth.domo_instance}.domo.com/api/datascience/v1/workspaces/user"

    res = await gd.get_data(
        url=url,
        method="GET",
        auth=auth,
        context=context,
    )

    if return_raw:
        return res

    if not res.is_success:
        raise Jupyter_GET_Error(
            message="Failed to retrieve Jupyter user namespace", res=res
        )

    return res

resolve_compute_tier 

resolve_compute_tier(
    cpu: float | int | None,
    memory: float | int | None,
    instance_types: list[dict] | None = None,
) -> str | None

Resolve a human-readable compute tier label for a workspace's cpu/memory.

Domo's instanceTypes (returned by both get_jupyter_settings and the per-workspace GET /api/datascience/v1/workspaces/{id} response) only carries {"cpu": ..., "memory": ...} pairs — there is no name/label field to surface as-is (confirmed against a live instance while implementing this — see issue #1497). The label is therefore synthesized as "{cpu} CPU / {memory} GB".

When instance_types is supplied, the cpu/memory pair must match one of the listed tiers or None is returned — this distinguishes a known/standard tier from a custom or unrecognized allocation. When instance_types is omitted, the label is synthesized unconditionally.

Parameters:

Name Type Description Default
cpu float | int | None

Workspace cpu allocation (e.g. 0.5).

 required
memory float | int | None

Workspace memory allocation in GB (e.g. 4.0).

 required
instance_types list[dict] | None

Optional list of {"cpu": ..., "memory": ...} dicts (from workspace instanceTypes or settings instanceTypes/currentUserInstanceTypes) to validate against.

 None

Returns:

Type Description
str | None

A label like "0.5 CPU / 4 GB", or None if cpu/memory is
str | None

missing, or if instance_types was provided and no entry matches.
Source code in src/crew_dcs/routes/jupyter/config.py 
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def resolve_compute_tier(
    cpu: float | int | None,
    memory: float | int | None,
    instance_types: list[dict] | None = None,
) -> str | None:
    """Resolve a human-readable compute tier label for a workspace's cpu/memory.

    Domo's ``instanceTypes`` (returned by both ``get_jupyter_settings`` and
    the per-workspace ``GET /api/datascience/v1/workspaces/{id}`` response)
    only carries ``{"cpu": ..., "memory": ...}`` pairs — there is no
    ``name``/``label`` field to surface as-is (confirmed against a live
    instance while implementing this — see issue #1497). The label is
    therefore synthesized as ``"{cpu} CPU / {memory} GB"``.

    When ``instance_types`` is supplied, the cpu/memory pair must match one
    of the listed tiers or ``None`` is returned — this distinguishes a
    known/standard tier from a custom or unrecognized allocation. When
    ``instance_types`` is omitted, the label is synthesized unconditionally.

    Args:
        cpu: Workspace cpu allocation (e.g. ``0.5``).
        memory: Workspace memory allocation in GB (e.g. ``4.0``).
        instance_types: Optional list of ``{"cpu": ..., "memory": ...}``
            dicts (from workspace ``instanceTypes`` or settings
            ``instanceTypes``/``currentUserInstanceTypes``) to validate against.

    Returns:
        A label like ``"0.5 CPU / 4 GB"``, or ``None`` if cpu/memory is
        missing, or if ``instance_types`` was provided and no entry matches.
    """
    if cpu is None or memory is None:
        return None

    if instance_types is not None:
        is_known = any(
            it.get("cpu") == cpu and it.get("memory") == memory for it in instance_types
        )
        if not is_known:
            return None

    return f"{_format_number(cpu)} CPU / {_format_number(memory)} GB"

resolve_python_version 

resolve_python_version(
    jupyter_kernel: str | None,
) -> str | None

Parse a Python version string out of a Domo jupyterKernel code.

The workspace API (GET /api/datascience/v1/workspaces/{id}) returns the active kernel as a code such as "PYTHON_3_12" or "R_4_1" on the workspace itself (confirmed present on live workspaces regardless of whether the workspace is currently running — see issue #1497). This parses that code directly rather than requiring a separate get_jupyter_settings call to look the code up in jupyterKernels.

Parameters:

Name Type Description Default
jupyter_kernel str | None

Raw kernel code, e.g. "PYTHON_3_12".

 required

Returns:

Type Description
str | None

A version string like "3.12" for Python kernels, or None for
str | None

non-Python kernels (e.g. R) or a missing value.
Source code in src/crew_dcs/routes/jupyter/config.py 
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def resolve_python_version(jupyter_kernel: str | None) -> str | None:
    """Parse a Python version string out of a Domo ``jupyterKernel`` code.

    The workspace API (``GET /api/datascience/v1/workspaces/{id}``) returns
    the active kernel as a code such as ``"PYTHON_3_12"`` or ``"R_4_1"`` on
    the workspace itself (confirmed present on live workspaces regardless of
    whether the workspace is currently running — see issue #1497). This
    parses that code directly rather than requiring a separate
    ``get_jupyter_settings`` call to look the code up in ``jupyterKernels``.

    Args:
        jupyter_kernel: Raw kernel code, e.g. ``"PYTHON_3_12"``.

    Returns:
        A version string like ``"3.12"`` for Python kernels, or ``None`` for
        non-Python kernels (e.g. R) or a missing value.
    """
    if not jupyter_kernel or not jupyter_kernel.upper().startswith("PYTHON_"):
        return None

    version_parts = jupyter_kernel.split("_")[1:]
    if not version_parts:
        return None

    return ".".join(version_parts)

update_jupyter_workspace_config async 

update_jupyter_workspace_config(
    auth: DomoAuth,
    workspace_id: str,
    config: dict,
    return_raw: bool = False,
    *,
    context: RouteContext | None = None,
    **context_kwargs
) -> ResponseGetData

Update the configuration of a Jupyter workspace.

PUT /api/datascience/v1/workspaces/{workspace_id}

Parameters:

Name Type Description Default
auth DomoAuth

Authentication object.

 required
workspace_id str

UUID of the workspace.

 required
config dict

Configuration dict (inputConfiguration, outputConfiguration, etc.).

 required
return_raw bool

Return raw response without error checking.

 False
context RouteContext | None

Optional RouteContext.

 None

Returns:

Type Description
ResponseGetData

ResponseGetData with updated workspace configuration.

Raises:

Type Description
Jupyter_CRUD_Error

On update failure.
SearchJupyterNotFoundError

If workspace does not exist (404).
Source code in src/crew_dcs/routes/jupyter/config.py 
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@gd.route_function
@log_call(**_LOG_DECORATORS)
async def update_jupyter_workspace_config(
    auth: DomoAuth,
    workspace_id: str,
    config: dict,
    return_raw: bool = False,
    *,
    context: RouteContext | None = None,
    **context_kwargs,
) -> rgd.ResponseGetData:
    """Update the configuration of a Jupyter workspace.

    ``PUT /api/datascience/v1/workspaces/{workspace_id}``

    Args:
        auth: Authentication object.
        workspace_id: UUID of the workspace.
        config: Configuration dict (inputConfiguration, outputConfiguration, etc.).
        return_raw: Return raw response without error checking.
        context: Optional ``RouteContext``.

    Returns:
        ``ResponseGetData`` with updated workspace configuration.

    Raises:
        Jupyter_CRUD_Error: On update failure.
        SearchJupyterNotFoundError: If workspace does not exist (404).
    """
    context = RouteContext.build_context(context=context, **context_kwargs)

    url = f"https://{auth.domo_instance}.domo.com/api/datascience/v1/workspaces/{workspace_id}"

    res = await gd.get_data(
        url=url,
        method="PUT",
        auth=auth,
        body=config,
        context=context,
    )

    if return_raw:
        return res

    if res.status == 404:
        raise SearchJupyterNotFoundError(
            search_criteria=f"workspace_id: {workspace_id}", res=res
        )

    if not res.is_success:
        raise Jupyter_CRUD_Error(
            operation="update_config",
            workspace_id=workspace_id,
            message=f"Error updating workspace configuration for {workspace_id}",
            res=res,
        )

    return res