analysis-scripts

# 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 ```