{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ramsey detuning drift over a shift\n",
    "\n",
    "Track how the detuning (qubit frequency - drive frequency) drifts across a series\n",
    "of Ramsey experiments run over several hours — the signature of a TLS hop, a thermal\n",
    "excursion, or flux-line crosstalk that the VLM is good at flagging.\n",
    "\n",
    "Typical workflow: your control stack dumps one `.npz` per Ramsey run into a\n",
    "watched directory; this notebook walks that directory, auto-fits each trace, and\n",
    "plots the extracted detuning vs wall-clock time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "rng = np.random.default_rng(1)\n",
    "tau = np.linspace(0, 10e-6, 201)                 # 10 µs delay sweep\n",
    "wall_times = np.arange(8) * 30 * 60               # every 30 min, 4 hours total\n",
    "\n",
    "# Simulate detuning drifting from 120 kHz → 180 kHz with a TLS-like jump halfway.\n",
    "detunings = np.array([120e3, 125e3, 132e3, 140e3, 170e3, 172e3, 175e3, 180e3])\n",
    "\n",
    "for i, det in enumerate(detunings):\n",
    "    phase = rng.uniform(0, 2*np.pi)\n",
    "    y = 0.5 * np.exp(-tau/4e-6) * np.cos(2*np.pi*det*tau + phase) + 0.5\n",
    "    y += rng.normal(0, 0.01, tau.shape)          # readout noise\n",
    "    np.savez(f'ramsey_{i:02d}.npz', x=tau, y=y, wall_time=wall_times[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "from qcal.data import from_npz\n",
    "\n",
    "records = []\n",
    "for path in sorted(Path('.').glob('ramsey_*.npz')):\n",
    "    p = from_npz(path, experiment_type='ramsey', x_unit='s')\n",
    "    if p.fit and p.fit.ok:\n",
    "        det = next(v for k, v in p.fit.params.items() if k.startswith('detuning_per_'))\n",
    "        t2s = next(v for k, v in p.fit.params.items() if k.startswith('t2star_'))\n",
    "        records.append({'file': path.name, 'detuning_hz': det, 't2star_s': t2s,\n",
    "                        'fit_quality': p.fit.fit_quality})\n",
    "\n",
    "import pandas as pd\n",
    "df = pd.DataFrame(records)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots(figsize=(6.5, 3.8))\n",
    "ax.plot(wall_times/3600, df['detuning_hz']/1e3, marker='o')\n",
    "ax.set_xlabel('Wall time (hours)')\n",
    "ax.set_ylabel('Fitted detuning (kHz)')\n",
    "ax.set_title('Qubit detuning drift')\n",
    "ax.grid(True, alpha=0.3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hand the **most recent** Ramsey trace to the VLM. It sees both the plot and the\n",
    "fitted numbers in the prompt, so `notes` / `drift_prediction` usually flag the\n",
    "step change in detuning."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from qcal.analyzer import analyze_payload\n",
    "\n",
    "latest = from_npz('ramsey_07.npz', experiment_type='ramsey', x_unit='s')\n",
    "analyze_payload(latest, backend='auto')"
   ]
  }
 ],
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