utils.py

"""
Shared utilities for the cudaq-performance-benchmarking suite.

Provides common helpers used by both the Streamlit dashboard and the static
plot generation script to avoid logic drift between the two.
"""

from typing import Any

# ─── Visual Constants ───────────────────────────────────────────────
COLORS = ['#E63946', '#2A9D8F', '#264653', '#E9C46A', '#F4A261', '#6A0572', '#1B998B']
MARKER_STYLES = ['o', 's', '^', 'd', 'v', 'p', '*']


# ─── Data Extraction ───────────────────────────────────────────────

def extract_value(val: Any) -> float:
    """Extract the primary latency value from either new multi-trial or legacy format."""
    if isinstance(val, dict):
        return val["mean"]
    return val


def extract_std(val: Any) -> float:
    """Extract standard deviation from multi-trial format, or 0.0 for legacy."""
    if isinstance(val, dict):
        return val.get("std", 0.0)
    return 0.0


def extract_values(data_dict: dict, qubits: list[int]) -> tuple[list[float], list[float]]:
    """Extract mean/std lists for a sequence of qubit counts.

    Works with both the new multi-trial format ({mean, std, ...}) and the
    legacy flat format (single float per qubit count).
    """
    means = [extract_value(data_dict[str(q)]) for q in qubits]
    stds = [extract_std(data_dict[str(q)]) for q in qubits]
    return means, stds


# ─── Key Parsing ───────────────────────────────────────────────────

def parse_result_keys(results: dict) -> tuple[set[str], set[str]]:
    """Parse result keys into (circuits, targets) sets.

    Keys are expected to follow the ``target_circuit`` pattern
    (e.g. ``nvidia_ghz``, ``qpp-cpu_hea``).
    """
    circuits: set[str] = set()
    targets: set[str] = set()
    for key in results:
        if "_" in key:
            t, c = key.rsplit('_', 1)
            circuits.add(c)
            targets.add(t)
    return circuits, targets