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Accessing Data

How to query resources and download files from the data platform.

Querying Resources​

from vxp_client import PlatformClient, F

client = PlatformClient()

# Or query a specific resource type with filters
volumes = (
    client.query("Volume")
    .filter(F.volume_type == "T2", F.image_plane == "transversal")
    .limit(100)
    .collect()
)

# For convenience, you can pull all tables in full at once
dfs = client.get_all_dataframes()
patients = dfs["Patient"]
volumes = dfs["Volume"]

Explore available resource types and their fields in the frontend query tab or by looking at the payload schema docs.

For the full querying API (lineage filters, subqueries, ANY/ALL, joins), see the querying tutorial.

Downloading Files​

Table data is returned directly as Polars DataFrames. Blob data (MRIs, DICOMs, PDFs) is stored on S3.

Tables may contain URLs of blob files that are stored on S3 - for example, the Volume table holds a column path_nii that points to the location of the corresponding nifti file. We can a) download files individually by specifying their paths or b) pull all data contained in a single polars dataframe at once.

Download individual S3 URLs​

# Single file
local_path = client.download_files("s3://bucket/path/to/file.nii.gz", dest_dir=Path("./downloads"))

# Multiple files
local_paths = client.download_files(
    ["s3://bucket/file1.nii.gz", "s3://bucket/file2.nii.gz"],
    dest_dir=Path("./downloads")
)

Download all S3 URLs in a DataFrame​

from pathlib import Path

volumes = client.query("Volumes").filter(...).collect()  # build your dataframe of interest here

# Automatically detects S3 URL columns, downloads in parallel to disk, and replaces paths in dataframe with local paths
volumes_local = client.materialize_dataframe(volumes, dest_dir=Path("./downloads"))