Library use

BiaPy can be used not only via its command-line interface or GUI, but also directly from Python. This is especially useful when integrating BiaPy into other pipelines or using it in custom scripts.

Minimal example

Here is a minimal example of how to run BiaPy programmatically from Python:

from biapy import BiaPy

# Set up your parameters
config_path = "/path/to/config.yaml"            # Path to your YAML configuration file
result_dir = "/path/to/results"                 # Directory to store the results
job_name = "my_biapy_job"                       # Name of the job
run_id = 1                                      # Run ID for logging/versioning
gpu = "0"                                       # GPU to use (as string, e.g., "0")

# Create and run the BiaPy job
biapy = BiaPy(config_path, result_dir=result_dir, name=job_name, run_id=run_id, gpu=gpu)
biapy.run_job()

This will execute the workflow specified in the YAML configuration file (defined by config_path) and store the output in the given result directory (defined by result_dir).

Note

When using BiaPy programmatically, make sure that any custom code dependencies or paths are correctly configured in your environment.

Data loading example

Besides running a full workflow, you can use many useful methods available in BiaPy’s API Overview. For example, here is a short Python script that loads 3D raw and label images into memory:

from biapy.data.data_manipulation import load_data_from_dir

# Set the paths to the image directories
raw_dir = '/content/data/train/raw'        # Directory containing raw images
label_dir = '/content/data/train/label'    # Directory containing label images

# Load 3D images into memory
raw_images = load_data_from_dir(raw_dir, is_3d=True)
label_images = load_data_from_dir(label_dir, is_3d=True)