This section guides you through creating a general ledger analysis, creating a TRA vendor analysis, retrieving an analysis by its identifier, and querying analyses using the Python SDK.

Creating a general ledger analysis

To set up an analysis, you first need to create or select an existing organization and engagement. For details on how to work with these resources, see their respective guides. If you omit library_id from the EngagementItem, the MindBridge SDK creates a library using the MindBridge for-profit (Mac v2) Library. That library includes the general ledger, accounts payable, and accounts receivable analysis types by default.

# get-or-create our organization
organization_name = "Chapter 3 - Creating an Analysis"
try:
    organization = next(server.organizations.get({"name": organization_name}))
    print(f"Organization '{organization_name}' already exists.")
except StopIteration:
    new_organization_item = mbapi.OrganizationItem(
        name=organization_name,
        external_client_code="My Client ID",  # Optional
        manager_user_ids=[user.id],  # Optional
    )
    organization_item = mbapi.OrganizationItem(name=organization_name)
    organization = server.organizations.create(organization_item)
    print(f"Organization '{organization_name}' created.")


engagement_name = "Engagement - MindBridge for-profit Library"
try:
    new_engagement_item = mbapi.EngagementItem(
        organization_id=organization.id,
        name=engagement_name,
        engagement_lead_id=user.id,
    )
    engagement = server.engagements.create(new_engagement_item)
    print(f"Created the new engagement {engagement.name}")
except mbapi.exceptions.ValidationError:
    engagement = next(
        server.engagements.get(
            {"organizationId": organization.id, "name": engagement_name}
        )
    )
    print(
        f"Fetched the engagement '{engagement.name}' from the organization with id "
        f"{organization.id}"
    )

After the engagement Engagement - MindBridge for-profit Library exists, create an analysis within it.

from datetime import date

new_gl_analysis_item = mbapi.AnalysisItem(
    name="GL Analysis",
    engagement_id=engagement.id,
    currency_code="CAD",
    analysis_periods=[
        mbapi.AnalysisPeriod(end_date=date(2024, 12, 31), start_date=date(2024, 1, 1))
    ],
    analysis_type_id=mbapi.AnalysisTypeItem.GENERAL_LEDGER,
)

analysis = server.analyses.create(new_gl_analysis_item)

print(
    f"https://{os.getenv('MINDBRIDGE_URL')}/app/organization/{organization.id}/"
    f"engagement/{engagement.id}/analysis-list"
)

At the link printed above, an analysis is ready for data upload.

Creating a custom analysis

To create a custom analysis, you first need a library with your desired analysis type. Then create an engagement that uses that library. In this example, you set up a TRA vendor analysis by creating an analysis type from the TRA Vendor Template that MindBridge provides. Create a TRA vendor analysis type in the Analysis Designer:

Open the Analysis Designer.
Find the analysis type named TRA Vendor Template.
Duplicate TRA Vendor Template and set the name to SDK TRA Vendor.
Click Save.
Return to the Analysis Designer page.
Select Publish on SDK TRA Vendor.

Then create a library that includes that analysis type:

Open the Libraries page.
Select Create Library.
Set the name to SDK TRA Vendor Library.
Set the base library to MindBridge for-profit (MAC v.2).
Add SDK TRA Vendor to the analysis types.
Set the account grouping to Mac v.2.
Select Create Library. You should now have a library named SDK TRA Vendor Library that contains the analysis type SDK TRA Vendor.

Create an engagement that uses this library by setting library_id on the EngagementItem.

all_libraries = server.libraries.get()
vendor_library = next(
    library for library in all_libraries if library.name == "SDK TRA Vendor Library"
)

organization_name = "Chapter 3 - Creating an Analysis"
try:
    organization = next(server.organizations.get({"name": organization_name}))
    print(f"Organization '{organization_name}' already exists.")
except StopIteration:
    new_organization_item = mbapi.OrganizationItem(
        name=organization_name,
        external_client_code="My Client ID",  # Optional
        manager_user_ids=[user.id],  # Optional
    )
    organization_item = mbapi.OrganizationItem(name=organization_name)
    organization = server.organizations.create(organization_item)
    print(f"Organization '{organization_name}' created.")


engagement_name = "Engagement - SDK TRA Vendor Library"
try:
    new_engagement_item = mbapi.EngagementItem(
        organization_id=organization.id,
        name=engagement_name,
        engagement_lead_id=user.id,
        library_id=vendor_library.id,
    )
    engagement = server.engagements.create(new_engagement_item)
    print(f"Created the new engagement '{engagement.name}'")
except mbapi.exceptions.ValidationError:
    engagement = next(
        server.engagements.get(
            {"organizationId": organization.id, "name": engagement_name}
        )
    )
    print(
        f"Fetched the engagement '{engagement.name}' from the organization with id "
        f"{organization.id}"
    )

After the engagement Engagement - SDK TRA Vendor Library exists, create an analysis within it. Fetch the analysis type from the engagement as shown below.

from datetime import date

all_libraries = server.libraries.get()
vendor_library = next(
    library for library in all_libraries if library.name == "SDK TRA Vendor Library"
)
available_analysis_types = list(vendor_library.analysis_types)

print(f"These are the Analysis Types available in the library '{vendor_library.name}'")
for at in available_analysis_types:
    print(at.name)

tra_vendor_analysis_type = next(
    analysis_type
    for analysis_type in available_analysis_types
    if analysis_type.name == "SDK TRA Vendor"
)

new_gl_analysis_item = mbapi.AnalysisItem(
    name="GL Analysis",
    engagement_id=engagement.id,
    currency_code="CAD",
    analysis_periods=[
        mbapi.AnalysisPeriod(end_date=date(2024, 12, 31), start_date=date(2024, 1, 1))
    ],
    analysis_type_id=tra_vendor_analysis_type.id,
)

analysis = server.analyses.create(new_gl_analysis_item)

print(
    f"https://{os.getenv('MINDBRIDGE_URL')}/app/organization/{organization.id}/"
    f"engagement/{engagement.id}/analysis-list"
)

​Creating a general ledger analysis

​Creating a custom analysis

Creating a general ledger analysis

Creating a custom analysis