ReddRadar
Agent skill
dqmc-analyze
Extract physical observables with error estimates from completed DQMC simulations. Use when computing density, double occupancy, spin correlations, structure factors, or any measured quantity from simulation data.
Stars
163
Forks
31
Install this agent skill to your Project
npx add-skill https://github.com/majiayu000/claude-skill-registry/tree/main/skills/data/dqmc-analyze
SKILL.md
Analyze Results
Inputs
- Directory containing
bin_*.h5files (completed simulations) - Observable names (see table below)
Outputs
- Dictionary with parameters and
(mean, stderr)tuples for each observable
Procedure
Basic analysis:
python
from dqmc_util import analyze_hub
data = analyze_hub.get("data/run/", "sign", "den", "zzr")
print(f"sign = {data['sign'][0]:.4f} +/- {data['sign'][1]:.4f}")
print(f"density = {data['den'][0]:.4f} +/- {data['den'][1]:.4f}")
Available observables:
| Name | Description | Requires |
|---|---|---|
sign |
Fermion sign | - |
den |
Density | - |
docc |
Double occupancy <n_up n_down> | - |
gr, gk |
Green's function (real/k-space) | - |
nnr, nnq |
Density correlator / structure factor | - |
zzr, zzq |
Spin-z correlator / structure factor | - |
xxr |
Spin-x correlator | - |
swq0 |
S-wave pair structure factor | - |
nnrw0, zzrw0 |
Zero-freq susceptibilities | period_uneqlt > 0 |
dwq0t |
D-wave pair susceptibility | period_uneqlt > 0 |
Collect from multiple directories:
python
import os
def collect_results(base_dir, observables):
results = []
for subdir in sorted(os.listdir(base_dir)):
path = os.path.join(base_dir, subdir)
if os.path.isdir(path):
try:
results.append(analyze_hub.get(path + "/", *observables))
except Exception as e:
print(f"Skipping {path}: {e}")
return results
Compute derived quantities:
python
# Magnetic moment squared from spin correlator
path = "data/run/"
data = analyze_hub.get(path, "zzr")
mz2 = 4 * data["zzr"][0][0, 0] # [0] = mean, shape (Ny, Nx)
mz2_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
| Symptom | Cause | Recovery |
|---|---|---|
| KeyError for observable | Observable not computed | Check period_uneqlt setting |
| "No files found" | Wrong path or no bin_*.h5 |
Verify directory structure |
| Large error bars | Insufficient statistics | Run more sweeps or bins |
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