RideScan Python SDK

The official Python client for the RideScan Safety Layer API. This SDK allows developers to programmatically manage robots, missions, and file uploads, and retrieve risk scores directly from their Python applications.

Installation

pip install ridescanapi

Getting Started

1. Obtain your API Key

To use this SDK, you must have a valid API Key.

1. Go to the RideScan Developer Console.
2. Create an account or Log in.
3. Navigate to the API Keys section in your dashboard.
4. Click Generate New Key.
5. Copy the key (it starts with rsk_...).

2. Initialize the Client

You can use the client as a context manager (recommended) to automatically handle session closing, or as a standard object.

Using Context Manager (Recommended):

from ridescanapi import RideScanClient

API_KEY = "rsk_your_api_key_here"

with RideScanClient(api_key=API_KEY) as client:
    # Your code here
    robots = client.get_robots()
    print(robots)

Using Standard Initialization:

client = RideScanClient(api_key=API_KEY)
try:
    robots = client.get_robots()
finally:
    client.session.close() # Always close the session manually

API Reference

Robot Resources

create_robot(name, robot_type)

Registers a new robot in the system.

• Arguments:

• name (str): A friendly name for the robot (e.g., "Warehouse-Spot-01").

• robot_type (str or int): The type identifier (e.g., "SPOT", "UR6").

• Sample Usage:

response = client.create_robot(
    name="Warehouse-Spot-01",
    robot_type="SPOT"
)
print(response)

• Returns: dict

{
  "created_time": "2026-01-07Txx:xx:xx",
  "message": "Robot created",
  "modified_time": "2026-01-07Txx:xx:xx",
  "robot_id": "e7201eff-xxxxxx",
  "robot_name": "SDK-Test-Bot-xxxxxx",
  "robot_type": "spot"
}

get_robots(robot_id=None, name=None, robot_type=None)

Search for robots matching specific criteria. If no arguments are provided, returns all robots.

• Arguments:

• robot_id (str, optional): Search by specific UUID.

• name (str, optional): Filter by name.

• robot_type (str, optional): Filter by type.

• Sample Usage:

# Get all robots
all_robots = client.get_robots()

# Filter by type
spot_robots = client.get_robots(robot_type="SPOT")

# Get specific robot by ID
specific_robot = client.get_robots(robot_id="123e4567-e89b-12d3-a456-426614174000")

• Returns: List[dict]

[
  {
    "created_time": "2026-01-07Txx:xx:xx",
    "modified_time": "2026-01-07Txx:xx:xx",
    "robot_id": "e7201eff-xxxxxx",
    "robot_name": "SDK-Test-Bot-xxxxxx",
    "robot_type": "spot"
  }
]

edit_robot(robot_id, new_name=None, new_type=None)

Updates a robot's details.

• Arguments:

• robot_id (str): The UUID of the robot to update.

• new_name (str, optional): New name.

• new_type (str or int, optional): New type.

• Sample Usage:

updated_robot = client.edit_robot(
    robot_id="123e4567-e89b-12d3-a456-426614174000",
    new_name="Warehouse-Spot-02-Renamed"
)

• Returns: dict (Updated robot object).

{
  "created_time": "2026-01-07Txx:xx:xx",
  "message": "Robot edited",
  "modified_time": "2026-01-07Txx:xx:xx",
  "robot_id": "e7201eff-xxxxxx",
  "robot_name": "SDK-Test-Bot-xxxxxx-Edited",
  "robot_type": "spot"
}

delete_robot(robot_id)

Permanently deletes a robot and all associated missions and files.

• Arguments: robot_id (str).
• Sample Usage:

client.delete_robot(robot_id="123e4567-e89b-12d3-a456-426614174000")

• Returns: dict

{
  "message": "Robot deleted",
  "robot_id": "e7201eff-xxxxxx",
  "robot_name": "SDK-Test-Bot-xxxxxx-Edited",
  "robot_type": "spot"
}

Mission Resources

create_mission(robot_id, mission_name)

Creates a new mission scope under a specific robot.

• Arguments:

• robot_id (str): The UUID of the parent robot.

• mission_name (str): Descriptive name (e.g., "Calibration-Run-Jan").

• Sample Usage:

mission = client.create_mission(
    robot_id="123e4567-e89b-12d3-a456-426614174000",
    mission_name="Site-Inspection-Alpha"
)
print(f"New Mission ID: {mission['mission_id']}")

• Returns: dict

{
  "calibration_possible": false,
  "created_time": "2026-01-07Txx:xx:xx",
  "inference_possible": false,
  "message": "Mission created",
  "mission_id": "9384ac10-xxxxxx",
  "mission_name": "SDK-Readme-Mission-xxxxxx",
  "modified_time": "2026-01-07Txx:xx:xx",
  "robot_id": "e7201eff-xxxxxx"
}

get_missions(robot_id=None, mission_id=None, mission_name=None, ...)

Search for missions.

• Arguments:

• robot_id (str, optional): Filter by robot.

• mission_id (str, optional): Filter by mission UUID.

• mission_name (str, optional): Filter by name.

• start_time / end_time (str, optional): Filter by date range (ISO format).

• Sample Usage:

# Get all missions for a specific robot
robot_missions = client.get_missions(
    robot_id="123e4567-e89b-12d3-a456-426614174000"
)

# Search by mission name
specific_mission = client.get_missions(
    mission_name="Calibration-Run-Jan"
)

• Returns: List[dict]

[
  {
    "calibration_possible": false,
    "created_time": "2026-01-07Txx:xx:xx",
    "inference_possible": false,
    "mission_id": "9384ac10-xxxxxx",
    "mission_name": "SDK-Readme-Mission-xxxxxx",
    "modified_time": "2026-01-07Txx:xx:xx",
    "robot_id": "e7201eff-xxxxxx",
    "robot_name": "SDK-Test-Bot-xxxxxx-Edited",
    "robot_type": "spot"
  }
]

edit_mission(robot_id, mission_id, new_name)

Renames an existing mission.

• Arguments: robot_id, mission_id, new_name.
• Sample Usage:

client.edit_mission(
    robot_id="123e4567-e89b-12d3-a456-426614174000",
    mission_id="987fcdeb-51a2-12d3-a456-426614174000",
    new_name="Site-Inspection-Beta"
)

• Returns: dict (Updated mission object)

{
  "calibration_possible": false,
  "created_time": "2026-01-07Txx:xx:xx",
  "inference_possible": false,
  "message": "Mission edited",
  "mission_id": "9384ac10-xxxxxx",
  "mission_name": "SDK-Readme-Mission-xxxxxx-v2",
  "modified_time": "2026-01-07Txx:xx:xx",
  "robot_id": "e7201eff-xxxxxx"
}

delete_mission(robot_id, mission_id)

Permanently deletes a mission.

• Arguments: robot_id, mission_id.
• Sample Usage:

client.delete_mission(
    robot_id="123e4567-e89b-12d3-a456-426614174000",
    mission_id="987fcdeb-51a2-12d3-a456-426614174000"
)

• Returns: dict

{
  "message": "Mission deleted",
  "mission_id": "9384ac10-xxxxxx",
  "mission_name": "SDK-Readme-Mission-xxxxxx-v2",
  "robot_id": "e7201eff-xxxxxx"
}

File Resources

upload_files(robot_id, mission_id, file_paths, file_type='calib_file')

Bulk uploads files (.bag, .csv, .zip) to the server. This handles large file streaming automatically.

• Arguments:

• robot_id (str): Robot UUID.

• mission_id (str): Mission UUID.

• file_data (Dict[str, str]): A dictionary mapping absolute local file paths to their corresponding event times in ISO 8601 format.

• file_type (str):

• 'calib_file' (Default) - Use for model training/calibration.

• 'process_file' - Use for inference/risk analysis.

• Sample Usage:

# Upload calibration data
result = client.upload_files(
    robot_id="123e4567-e89b-12d3-a456-426614174000",
    mission_id="987fcdeb-51a2-12d3-a456-426614174000",
    file_data={
        "/path/to/data/run1.bag": "2026-03-04T10:15:30",
        "/path/to/data/run2.csv": "2026-03-04T10:20:00"
    },
    file_type="calib_file"
)

• Returns: dict

{
  "data": {
    "details": {
      "failed_files": [],
      "file type": "calib_file",
      "success": true,
      "uploaded_files": [
        {
          "event_time": "2026-03-04T10:30:00",
          "original_filename": "Day1.bag",
          "unique_filename": "7fec2dd2-xxxxxx_Day1.bag"
        },
        {
          "event_time": "2026-03-03T10:30:00",
          "original_filename": "Day2.bag",
          "unique_filename": "42b020b1-xxxxxx_Day2.bag"
        }
      ]
    },
    "mission_id": "9384ac10-xxxxxx",
    "org_id": "b8c16c60-xxxxxx",
    "robot_id": "e7201eff-xxxxxx"
  },
  "errors": null,
  "message": "Files uploaded successfully",
  "success": true
}

list_files(robot_id, mission_id)

Lists all files uploaded for a specific mission.

• Arguments: robot_id, mission_id.
• Sample Usage:

files = client.list_files(
    robot_id="123e4567-e89b-12d3-a456-426614174000",
    mission_id="987fcdeb-51a2-12d3-a456-426614174000"
    file_type="process_file"
)
for file in files:
    print(f"File: {file['original_filename']} (ID: {file['unique_filename']})")

• Returns: List[dict]

[
  {
    "file_size": 0,
    "original_filename": "Day1.bag",
    "risk_score": null,
    "unique_filename": "7fec2dd2-xxxxxx_Day1.bag"
  },
  {
    "file_size": 0,
    "original_filename": "Day10.bag",
    "risk_score": null,
    "unique_filename": "42b020b1-xxxxxx_Day10.bag"
  }
]

delete_file(robot_id, mission_id, unique_filename)

Deletes a specific file from storage and database.

• Arguments:

• robot_id (str): Robot UUID.

• mission_id (str): Mission UUID.

• unique_filename (str): The unique ID returned by list_files (e.g., abc123_data.csv).

• Sample Usage:

client.delete_file(
    robot_id="123e4567-e89b-12d3-a456-426614174000",
    mission_id="987fcdeb-51a2-12d3-a456-426614174000",
    unique_filename="550e8400-e29b-41d4-a716-446655440000_data.bag"
)

• Returns: dict

{
  "data": null,
  "errors": null,
  "message": "Blob and DB record deleted successfully",
  "success": true
}

Model & Inference Resources

calibrate_model(robot_id, mission_id, epochs=100, robot_type="SPOT", retrain=False, ...)

Triggers an asynchronous Kubernetes job to train a model using uploaded calibration files.

• Arguments:

• robot_id (str): Robot UUID.

• mission_id (str): Mission UUID.

• epochs (int): Training duration (Default: 100).

• robot_type (str or int): Robot type identifier (Default: "SPOT").

• retrain (bool): Force re-training if a model already exists (Default: False).

• file_names (List[str], optional): specific subset of unique filenames to use. If None, uses all uploaded calibration files.

• Sample Usage:

training_response = client.calibrate_model(
    robot_id="123e4567-e89b-12d3-a456-426614174000",
    mission_id="987fcdeb-51a2-12d3-a456-426614174000",
    epochs=200,
    robot_type="SPOT",
    retrain=True
)

• Returns: dict indicating the task was queued.

{
  "details": {
    "task_id": "a493b453-xxxxxx"
  },
  "message": "Training task queued"
}

run_inference(robot_id, mission_id, file_names=None, device='cpu')

Runs risk analysis on uploaded inference files using the trained model.

• Arguments:

• robot_id (str): Robot UUID.

• mission_id (str): Mission UUID.

• device (str): Compute device ('cpu' or 'cuda').

• file_names (List[str], optional): specific subset of unique filenames to analyze. If None, uses all available files.

• Sample Usage:

# Run inference on GPU
inference_results = client.run_inference(
    robot_id="123e4567-e89b-12d3-a456-426614174000",
    mission_id="987fcdeb-51a2-12d3-a456-426614174000",
    device="cpu"
)

• Returns: dict

{
  "file sent": [
    "1a3a81da-xxxxxx_Day16.bag",
    "ac8d4704-xxxxxx_Day17.bag",
    "5e4f36de-xxxxxx_Day18.bag",
    "1e91a258-xxxxxx_Day19.bag"
  ],
  "message": "Processing queued"
}

get_model_status(mission_id)

Checks the status of calibration or inference tasks.

• Arguments: mission_id.
• Sample Usage:

status = client.get_model_status(mission_id="987fcdeb-51a2-12d3-a456-426614174000")

• Returns: dict

{
  "calibration_status": "calibration_completed",
  "epochs": 3,
  "files": [
    {
      "filename": "Day10.bag",
      "risk_score": null
    },
    {
      "filename": "Day11.bag",
      "risk_score": null
    }
  ],
  "inference_possible": true,
  "inference_status": "processing",
  "mission_avg_risk_score": null,
  "mission_length": 2073,
  "robot_avg_risk_score": null,
  "robot_type": "0",
  "training_time": "Wed, 07 Jan 2026 xx:xx:xx GMT",
  "upload_time": "Wed, 07 Jan 2026 xx:xx:xx GMT"
}

Error Handling

The SDK raises specific exceptions from ridescanapi.exceptions to help you handle errors gracefully.

Example Usage:

from ridescanapi.exceptions import ResourceNotFoundError, ValidationError

try:
    client.delete_robot("invalid-id")
except ResourceNotFoundError:
    print("Robot not found!")
except ValidationError as e:
    print(f"Invalid input: {e}")

Enums & Values

robot_type

Used in create_robot and calibrate_model.

• "SPOT" (Boston Dynamics Spot)
• "UR6"

file_type

Used in upload_files.

• "calib_file": Files used to train/calibrate the model.
• "process_file": Files used for inference/risk assessment.

device

Used in run_inference.

• "cpu" (Default)
• "cuda" (GPU - Requires backend support)