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NVIDIAKnown publisherNVIDIA/Quantum-Calibration-Agent-Blueprint/data/knowledge/skills/analysis-scripts/SKILL.md

analysis-scripts

Skill69 starsChanged 7 months agoApache-2.0View on GitHub

Write and run Python scripts to analyze quantum experiment data stored in HDF5 files. Use when the user asks to analyze experiment results, fit peaks or curves, extract features from measurement arrays, or when reusable analysis logic should be saved alongside an experiment for future reuse.

The file

We split this skill into the rules it gives an agent

4 rules. About 0 are common, 3 are distinctive, and 1 is very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈3

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 1

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Write Python scripts to analyze experiment data.

    line 8
  2. Very specific:

    Check for existing scripts: ls {file_path.replace('.h5', '_scripts/')}

    line 90
  3. Distinctive:

    If script exists: run it

    line 91
  4. Distinctive:

    If not: write new script using template, then run it

    line 92

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Analysis Scripts

Write Python scripts to analyze experiment data. Scripts are saved alongside the experiment for reuse.

Storage Location

Scripts go in a subfolder next to the experiment's HDF5 file:

data/experiments/
├── YYYYMMDD_HHMMSS_type.h5           # Experiment data
└── YYYYMMDD_HHMMSS_type_scripts/     # Scripts for this experiment
    └── analysis_name.py

To find the scripts folder:

  1. Get experiment: lab(action="history_show", experiment_id="...")
  2. Response includes file_path (e.g., .../20240315_143022_qubit_spectroscopy.h5)
  3. Scripts folder: replace .h5 with _scripts/

Script Template

#!/usr/bin/env python
"""Description of what this script does."""
import sys
import json
from pathlib import Path

# Import core storage
from core import storage

DATA_DIR = Path(__file__).parent.parent.parent  # data/experiments/

# Get experiment ID from command line
experiment_id = sys.argv[1]

# Load experiment data
exp = storage.load_experiment(experiment_id, DATA_DIR)
if exp is None:
    print(json.dumps({"error": f"Experiment {experiment_id} not found"}))
    sys.exit(1)

data = exp.to_dict()

# =============================================================================
# Your analysis code here
# =============================================================================

import numpy as np

# Available data:
#   data['params']   - input parameters dict
#   data['results']  - scalar results dict
#   data['arrays']   - numerical arrays dict (e.g., frequencies, amplitudes)
#   data['plots']    - plot data list

# Example:
# frequencies = np.array(data['arrays']['frequencies'])
# amplitudes = np.array(data['arrays']['amplitudes'])

result = {
    "status": "success",
    # your results here
}

print(json.dumps(result))

Running a Script

python /path/to/script.py <experiment_id>

Example:

python data/experiments/20240315_143022_qubit_spectroscopy_scripts/fit_peak.py 20240315_143022_qubit_spectroscopy

Workflow

  1. User asks to analyze an experiment
  2. Get experiment info: lab(action="history_show", experiment_id="...")
  3. Check for existing scripts: ls {file_path.replace('.h5', '_scripts/')}
  4. If script exists: run it
  5. If not: write new script using template, then run it

Finding Scripts from Similar Experiments

# Find experiments of same type
lab(action="history_list", filter_type="qubit_spectroscopy")

# Check each for scripts
ls data/experiments/20240315_143022_qubit_spectroscopy_scripts/

# Read a script to copy/adapt
cat data/experiments/.../fit_peak.py

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