{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Rabi calibration with QCal Copilot\n",
    "\n",
    "Load a raw `.npy` Rabi trace, auto-fit a damped sine, hand the plot + fit numbers to\n",
    "the Ising Calibration VLM, and emit a CUDA-Q script seeded with the recommended\n",
    "pulse amplitude.\n",
    "\n",
    "Inputs expected: a 1-D numpy array of readout populations vs drive amplitude (or time)\n",
    "and an optional matching `x` array. Works offline — the local VLM is optional."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "# Synthesize a Rabi sweep (replace with `np.load('your_trace.npy')`).\n",
    "t = np.linspace(0, 500e-9, 201)              # seconds\n",
    "y = 0.5 * np.exp(-t/300e-9) * np.sin(2*np.pi*10e6*t) + 0.5\n",
    "np.save('rabi_trace.npy', y)\n",
    "np.save('rabi_time.npy', t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from qcal.data import from_npy\n",
    "\n",
    "payload = from_npy(\n",
    "    'rabi_trace.npy',\n",
    "    experiment_type='rabi',\n",
    "    x_path='rabi_time.npy',\n",
    "    x_unit='s',\n",
    ")\n",
    "payload.fit  # FitResult — rich-displays in Jupyter as a table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "payload.image  # the rendered plot the VLM will see"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from qcal.analyzer import analyze_payload\n",
    "\n",
    "# backend='auto' uses NIM when NVIDIA_API_KEY is set, else the local HF weights.\n",
    "result = analyze_payload(payload, backend='auto')\n",
    "result  # _repr_markdown_ — shows experiment, issues, recommended params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from qcal.codegen import generate_script\n",
    "\n",
    "print(generate_script(result.parsed))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {"display_name": "Python 3", "language": "python", "name": "python3"},
  "language_info": {"name": "python", "version": "3.11"}
 },
 "nbformat": 4,
 "nbformat_minor": 5
}