# Collection Campaign Scripting

You can now use the Python client to grab the formatted results from either your (a) [unlabeled collection campaigns](https://legacy-docs.aquariumlearning.com/aquarium/python-sdk/code-snippets-and-examples/broken-reference) in the UI or (b) more traditional Python client [collection campaign](https://legacy-docs.aquariumlearning.com/aquarium/python-sdk/code-snippets-and-examples/broken-reference).

Full python client docs are [available here](https://aquarium-not-pypi.web.app/aquariumlearning/docs/#aquariumlearning.CollectionClient.get_unlabeled_results_for_issue), and a brief example follows.

```python
import aquariumlearning as al
import json

# Your issue must have been created under the Rare Scenario issue type
ISSUE_UUID = "..."

# Either the unlabeled dataset that you want to search through
# OR "" if you are querying the results of a Python-client-triggered
# collection campaign
UNLABELED_DATASET_NAME = "..."

OUTPUT_JSON = "..."

collection_client = al.CollectionClient()
collection_client.set_credentials(api_key="YOUR_API_KEY")

results = collection_client.get_unlabeled_results_for_issue(
    issue_uuid=ISSUE_UUID,
    unlabeled_dataset_name=UNLABELED_DATASET_NAME,
    # You can add the option retrigger_search=True to run the search
    # for the first time (or rerun)
)

print(f"{len(results)} results found")
with open(OUTPUT_JSON, "w") as f:
    json.dump(results, f)

```

A particular unlabeled result might look something like the following (note: this is for the bounding box objection detection task):

{% code lineNumbers="true" %}

```json
{
    "classifierScore": 0.8154874556308199,
    "collectionFrameId": 536914,
    "deviceId": "default_device",
    "images": [
      {
        "coordinateFrame": "img0",
        "dateCaptured": "2022-01-04T14:56:34.384042+00:00",
        "imageBoxes": [
          {
            "height": 39.599999999999994,
            "label": "car",
            "left": 741.18,
            "top": 168.83,
            "user__width": 51.07000000000005,
            "user__width_bucket": "medium",
            "uuid": "000008_8_1_gt",
            "width": 51.07000000000005,
            "windowId": 1641326258849
          },
          {
            "height": 16.580000000000013,
            "label": "dontcare",
            "left": 826.87,
            "top": 162.28,
            "user__width": 18.970000000000027,
            "user__width_bucket": "small",
            "uuid": "000008_8_2_gt",
            "width": 18.970000000000027,
            "windowId": 1641326258849
          },
        ],
        "image_url": "https://storage.googleapis.com/aquarium-public/kitti_2d_obj_train/image_2/000008.png",
        "preview_url": "https://storage.googleapis.com/aquarium-public/kitti_2d_obj_train/webp_images/000008.webp",
        "sensorId": "img0"
      }
    ],
    "issueVersion": 1,
    "labeled": false,
    "sampledElementId": "000008_8_2_gt",
    "similarityScore": 0.8673680939432816,
    "sortStatus": "UNSORTED",
    "sourceMetadata": {
      "source": "unlabeled_dataset",
      "source_dataset": "test_pca_diff.unlabeled_real_str"
    },
    "taskId": "000008_8"
  },

```

{% endcode %}