Analyze Results

Inputs

• Directory containing bin_*.h5 files (completed simulations)
• Observable names (see table below)

Outputs

• Dictionary with parameters and (mean, stderr) tuples for each observable

Procedure

Basic analysis:

python
1from dqmc_util import analyze_hub
2
3data = analyze_hub.get("data/run/", "sign", "den", "zzr")
4
5print(f"sign = {data['sign'][0]:.4f} +/- {data['sign'][1]:.4f}")
6print(f"density = {data['den'][0]:.4f} +/- {data['den'][1]:.4f}")

Available observables:

Collect from multiple directories:

python
1import os
2
3def collect_results(base_dir, observables):
4    results = []
5    for subdir in sorted(os.listdir(base_dir)):
6        path = os.path.join(base_dir, subdir)
7        if os.path.isdir(path):
8            try:
9                results.append(analyze_hub.get(path + "/", *observables))
10            except Exception as e:
11                print(f"Skipping {path}: {e}")
12    return results

Compute derived quantities:

python
1# Magnetic moment squared from spin correlator
2path = "data/run/"
3data = analyze_hub.get(path, "zzr")
4mz2 = 4 * data["zzr"][0][0, 0]       # [0] = mean, shape (Ny, Nx)
5mz2_err = 4 * data["zzr"][1][0, 0]   # [1] = stderr

Validation

• Errorbar on sign is significantly less than mean. Otherwise, sign problem is too severe.
• Errorbars on observable are reasonable (not >> mean)

Failure Modes