analysis-scripts
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.
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- NVIDIA/Quantum-Calibration-Agent-Blueprint
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- 20 de abril de 2026 a las 05:20
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- analysis-scripts
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- 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.
# 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
```python
#!/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
```bash
python /path/to/script.py <experiment_id>
```
Example:
```bash
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
```bash
# 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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