Testing

The repository includes a small test dataset and helper scripts that let you verify the MIMOSA installation.

Test files

All test files live in the test/ directory:

File	Description
`test/MIMOSA_test_results_alleles.tsv`	15 Staphylococcus aureus allele profiles in chewBBACA wide format, ready for drag-and-drop or CLI import.
`test/MIMOSA_test_Metadata.csv`	Supplementary metadata (hospital, postcode, collection date) for the same 15 samples. Used with `--supplementary_metadata` in CLI runs.
`test/MIMOSA_test_Metadata.xlsx`	Same metadata in Excel format. Can be dragged onto the Samples page to apply hospital, postcode and date after samples have been imported and clustered.
`test/test_data.json`	Synthetic features, clustering, similarity and distance data. Used by `test/test.py` to test the upload API layer directly without running the pipeline.

Option 1 — UI import (drag-and-drop)

This is the quickest way to verify that the frontend, backend and database are all working.

Open the Import page in the MIMOSA dashboard (admin login required).
Drag test/MIMOSA_test_results_alleles.tsv onto the upload area.
Select staphylococcus_aureus as the profile and click Import 15 samples.
The samples appear in the Pending for next run table.
Click Run pipeline to trigger clustering.
Navigate to the map or tree view to confirm the samples appear.

Adding metadata via the Samples page

After the pipeline has run, drag test/MIMOSA_test_Metadata.xlsx onto the Samples page to apply hospital, postcode and collection date to all 15 test samples. The map markers will update once the metadata is saved. See Samples for details on the Excel bulk-update format.

To remove the test samples when you are done, delete them from the Samples page or use the CLI method below.

Option 2 — Full pipeline via CLI

test/mimosa_chewbbaca_test.py wraps the pipeline command with the test files pre-configured. It requires a credentials file for the MIMOSA backend (Bonsai credentials are not needed since --bonsai false is used).

Run the pipeline

python test/mimosa_chewbbaca_test.py --credentials credentials.json

This is equivalent to:

python scripts/main.py \
  --credentials credentials.json \
  --chewbbaca test/MIMOSA_test_results_alleles.tsv \
  --profile staphylococcus_aureus \
  --bonsai false \
  --supplementary_metadata test/MIMOSA_test_Metadata.csv

Clean up

python test/mimosa_chewbbaca_test.py --credentials credentials.json --delete

This removes the 15 test samples from allele_profiles, features, and clustering in MongoDB.

Option 3 — Upload API test (synthetic data)

test/test.py uploads hand-crafted data from test/test_data.json directly to the API, bypassing the pipeline entirely. Use this to verify that the upload endpoints and frontend rendering work correctly.

Upload synthetic test data

python test/test.py --credentials credentials.json

Delete synthetic test data

python test/test.py --credentials credentials.json --delete

Note

Option 2 and Option 3 test different code paths and are complementary. Option 2 exercises the full pipeline (TSV parsing → ReporTree clustering → feature generation → upload). Option 3 exercises the upload API and frontend rendering.

Credentials file format

All test scripts accept a --credentials argument pointing to a JSON file with your MIMOSA admin credentials:

{
  "bonsai_username": "unused_for_chewbbaca_tests",
  "bonsai_password": "unused_for_chewbbaca_tests",
  "mimosa_username": "your_mimosa_admin",
  "mimosa_password": "your_mimosa_password"
}

bonsai_username and bonsai_password are not used by the chewBBACA test (--bonsai false) but the file format still requires them. Leave the values as dummy strings if you do not have a Bonsai instance.