{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f705780b",
   "metadata": {},
   "source": [
    "# 07 - Results & Visualization\n",
    "\n",
    "Deep dive into the Result object: counts, probabilities, histograms, DataFrames.\n",
    "\n",
    "**Concepts:** Result API, plotting, pandas, expectation values\n",
    "\n",
    "**Requires:** `pip install quantsdk[viz]`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d3a83311",
   "metadata": {},
   "outputs": [],
   "source": [
    "import quantsdk as qs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4346d9e",
   "metadata": {},
   "source": [
    "## Basic Result Properties"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "49bf1190",
   "metadata": {},
   "outputs": [],
   "source": [
    "circuit = qs.Circuit(3).h(0).cx(0, 1).cx(0, 2).measure_all()\n",
    "result = qs.run(circuit, shots=5000, seed=42)\n",
    "\n",
    "print(f\"Counts:        {result.counts}\")\n",
    "print(f\"Probabilities: {result.probabilities}\")\n",
    "print(f\"Most likely:   {result.most_likely}\")\n",
    "print(f\"Shots:         {result.shots}\")\n",
    "print(f\"Backend:       {result.backend}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cfb9ad2c",
   "metadata": {},
   "source": [
    "## Top-K Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ea7fe374",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get top-K most frequent outcomes\n",
    "print(\"Top 1:\", result.top_k(1))\n",
    "print(\"Top 2:\", result.top_k(2))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "788b49a6",
   "metadata": {},
   "source": [
    "## Summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a42aaefe",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(result.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6ba4d0c",
   "metadata": {},
   "source": [
    "## Histogram Visualization\n",
    "\n",
    "Requires `matplotlib`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b2ec4e61",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plot a histogram of measurement results\n",
    "result.plot_histogram()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5fbc16e",
   "metadata": {},
   "source": [
    "## Pandas DataFrame\n",
    "\n",
    "Requires `pandas`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3768f660",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = result.to_pandas()\n",
    "print(df)\n",
    "print(f\"\\nDataFrame columns: {list(df.columns)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "efbe7f98",
   "metadata": {},
   "source": [
    "## Expectation Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a53c5632",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Z-basis expectation value for each qubit\n",
    "circuit = qs.Circuit(2).h(0).measure_all()  # q0 in superposition, q1 in |0>\n",
    "result = qs.run(circuit, shots=5000, seed=42)\n",
    "\n",
    "# <Z0> should be ~0 (superposition), <Z1> should be ~1 (always |0>)\n",
    "exp_z0 = result.expectation_value(qubit=0)\n",
    "exp_z1 = result.expectation_value(qubit=1)\n",
    "print(f\"<Z0> = {exp_z0:.4f}  (expect ~0.0 for superposition)\")\n",
    "print(f\"<Z1> = {exp_z1:.4f}  (expect ~1.0 for |0>)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c04137e",
   "metadata": {},
   "source": [
    "## Serialization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6d9b469b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "\n",
    "result_dict = result.to_dict()\n",
    "print(json.dumps(result_dict, indent=2))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1bd20a79",
   "metadata": {},
   "source": [
    "## Comparing Runs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cf00973b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Run the same circuit with different shot counts\n",
    "circuit = qs.Circuit(2).h(0).cx(0, 1).measure_all()\n",
    "\n",
    "for shots in [10, 100, 1000, 10000]:\n",
    "    result = qs.run(circuit, shots=shots, seed=42)\n",
    "    probs = result.probabilities\n",
    "    p00 = probs.get('00', 0)\n",
    "    p11 = probs.get('11', 0)\n",
    "    print(f\"Shots={shots:5d}: P(00)={p00:.4f}, P(11)={p11:.4f}\")"
   ]
  }
 ],
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