sharp-sparc

Hosted statistical capability for identifying a compact predictive feature core in high-dimensional biological data.

sharp-sparc is the thin, official Python client for SPARC. It provides reproducible statistical discovery and decision boundaries for high-dimensional biological datasets ($P \gg N$).

Installation

pip install sharp-sparc

5-Line Quickstart

from sharp_sparc import SPARC

# 1. Initialize client with your API key
sparc = SPARC(api_key="sparc_live_...")

# 2. Discover predictive core signals from tabular data
result = sparc.discover(X, y, top_k=20, approve_upload=True)

# 3. Inspect discovered core signals
for rank, signal in enumerate(result.core_signals, start=1):
    print(f"#{rank}: {signal.name} (relevance={signal.predictive_relevance:.4f})")

Privacy Boundary and Local Preflight

SPARC enforces a strict privacy boundary:

1. Local Metadata Inspection: Your dataset's dimensions, non-finite cell counts, and SHA256 digest are computed locally on your machine. Raw cell values and feature names never leave your environment during preflight.
2. Explicit Caller Approval: Raw data uploads only after explicit caller approval using a server-issued signed URL (approve_upload=True). All remote uploads enforce TLS (https://) transport.

Discovering from a Local CSV/TSV File

result = sparc.discover_file(
    path="patient_cohort_rnaseq.csv",
    target_column="treatment_response",
    top_k=15,
    approve_upload=True,
)

Granular REST Lifecycle

For production pipelines and workflow orchestrators, sharp-sparc exposes the full granular lifecycle:

from sharp_sparc import SPARC, inspect_dataset

client = SPARC(api_key="sparc_live_...")

# 1. Check account quota
account = client.get_account_status()
print(f"Tier: {account.tier}, Runs Remaining: {account.runs_remaining}")

# 2. Inspect aggregate metadata locally
metadata = inspect_dataset("data.csv", target_index=10)

# 3. Validate against tier limits (zero raw data sent)
preflight = client.preflight(metadata=metadata)

# 4. Stream upload with signed authorization
upload = client.upload_dataset(
    file_path_or_data="data.csv",
    preflight_token=preflight.upload_token,
    metadata=metadata,
)

# 5. Submit analysis with idempotency protection
receipt = client.submit_analysis(
    dataset_ref=upload.dataset_ref,
    target_column="target",
    top_k=10,
    idempotency_key="pipeline-run-2026-08-batch-1",
)

# 6. Poll status
status = client.poll_analysis(receipt.job_id)

# 7. Export standalone Python and C++ decision trees (Pro tier)
coretree = client.export_coretree(receipt.job_id)
print(coretree.python_source)

Async Client (AsyncSPARC)

import asyncio
from sharp_sparc import AsyncSPARC

async def main():
    async with AsyncSPARC(api_key="sparc_live_...") as client:
        account = await client.get_account_status()
        print(f"Account Tier: {account.tier}")

asyncio.run(main())

Canonical API Route Map