{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "5e7b7765",
   "metadata": {},
   "source": [
    "# NV photoluminescence collection efficiency by microscope\n",
    "\n",
    "**Author: Haneul Lee, Korea Advanced Institute of Science and Technology**\n",
    "\n",
    "This notebook evaluates the fluorescence collection efficiency of diamond NV centers through a microscope objective for the following geometries:\n",
    "\n",
    "1. A spherical nanodiamond on a silica substrate\n",
    "2. A bulk-diamond NV center located beneath a planar air–diamond interface\n",
    "3. An NV center embedded in a finite-thickness diamond film on a silica substrate\n",
    "\n",
    "The primary parameters investigated through systematic sweeps are:\n",
    "\n",
    "- Nanodiamond diameter\n",
    "- Polar and azimuthal orientations of the NV symmetry axis\n",
    "- NV depth below the surface or position within the nanodiamond\n",
    "- Emission wavelength\n",
    "- Mesh spacing and near-to-far-field projection area\n",
    "\n",
    "This notebook is developed using the official Tidy3D API from the 2.12 release series. Throughout the simulations, Tidy3D uses µm for length, Hz for frequency, and s for time.\n",
    "\n",
    "> **Cloud execution and cost:** Cells that call `run_batch_with_cost_estimate(...)` first print the maximum estimated cost and then submit the batch to the Tidy3D cloud. Run the baseline and convergence stages selectively rather than using Run All, and review the task count printed by each stage before executing its batch cell.\n",
    "\n",
    "<img src=\"img/nv_photoluminescence_nanodiamond.png\" alt=\"NV_schematic\" width=500>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b8ab5db",
   "metadata": {},
   "source": [
    "## 0. Modeling Preliminaries\n",
    "\n",
    "### Distinction Between Established Facts and Modeling Assumptions\n",
    "#### Facts Supported by the Literature and Tidy3D API\n",
    "\n",
    "- The optical emission of an NV center is not modeled as a single dipole oriented along the NV symmetry axis. Instead, it is represented by two mutually orthogonal optical transition dipoles lying in the plane perpendicular to the NV symmetry axis.\n",
    "- A three-dimensional Tidy3D `FluxMonitor` enclosing the source integrates the net outward electromagnetic power flux through the closed monitoring surface.\n",
    "- A `FieldProjectionAngleMonitor` projects the recorded near-field distribution into specified far-field angular directions parameterized by $(\\theta,\\phi)$.\n",
    "- When an open projection surface is used for near-to-far-field transformation, convergence with respect to the projection-surface size must be verified. In particular, the projection boundary should be placed sufficiently far from the region of significant near-field amplitude so that truncation of the recorded field does not appreciably affect the projected far field.\n",
    "\n",
    "#### Initial Modeling Assumptions Used in This Notebook\n",
    "\n",
    "- The nanodiamond is assumed to be a perfect sphere.\n",
    "- Diamond and silica are treated as isotropic, linear, nonmagnetic, and lossless dielectric materials.\n",
    "- The NV center is modeled as a point electric dipole emitter.\n",
    "- Unless otherwise specified, the NV center is assumed to be located at the center of the nanodiamond.\n",
    "- The optical axis of the microscope objective is assumed to coincide with the surface normal, defined as the $+z$ direction.\n",
    "- The silica substrate surface is assumed to be perfectly planar and laterally infinite.\n",
    "\n",
    "If experimentally relevant effects such as the actual AFM/SEM-derived nanodiamond morphology, crystallographic faceting, the true spatial offset of the NV center within the nanodiamond, surface layers, material absorption, or surface roughness become important, the corresponding geometry and/or material models should be replaced accordingly while retaining the overall simulation framework.\n",
    "\n",
    "\n",
    "### Physical Quantity of Interest\n",
    "\n",
    "Consider a microscope objective located on the air side, collecting radiation in the $+z$ direction. Let the refractive index of the medium on the objective side be $n_{\\mathrm{obj}}$. The maximum polar angle accepted by an objective with numerical aperture $\\mathrm{NA}$ is\n",
    "\n",
    "$$\n",
    "\\theta_{\\max}\n",
    "=\n",
    "\\sin^{-1}\\!\\left(\n",
    "\\frac{\\mathrm{NA}}{n_{\\mathrm{obj}}}\n",
    "\\right).\n",
    "$$\n",
    "\n",
    "If the far-field angular radiation intensity is expressed as\n",
    "\n",
    "$$\n",
    "\\frac{dP}{d\\Omega}(\\theta,\\phi,\\lambda),\n",
    "$$\n",
    "\n",
    "the optical power collected within the numerical aperture of the objective is\n",
    "\n",
    "$$\n",
    "P_{\\mathrm{NA}}(\\lambda)\n",
    "=\n",
    "\\int_{0}^{2\\pi}\n",
    "\\int_{0}^{\\theta_{\\max}}\n",
    "\\frac{dP}{d\\Omega}(\\theta,\\phi,\\lambda)\n",
    "\\sin\\theta \\, d\\theta \\, d\\phi.\n",
    "$$\n",
    "\n",
    "Let $P_{\\mathrm{tot}}(\\lambda)$ denote the total radiated power obtained by integrating the electromagnetic power flux through a closed flux-monitor box surrounding the emitter. The fluorescence collection efficiency is then defined as\n",
    "\n",
    "$$\n",
    "\\boxed{\n",
    "\\eta_{\\mathrm{coll}}(\\lambda)\n",
    "=\n",
    "\\frac{\n",
    "P_{\\mathrm{NA}}(\\lambda)\n",
    "}{\n",
    "P_{\\mathrm{tot}}(\\lambda)\n",
    "}\n",
    "}.\n",
    "$$\n",
    "\n",
    "The quantity $\\eta_{\\mathrm{coll}}$ represents the fraction of the total electromagnetic power radiated by the emitter that falls within the angular acceptance cone of the objective and can therefore reach its entrance pupil.\n",
    "\n",
    "This definition does **not** include the following experimental losses or efficiencies:\n",
    "\n",
    "- Objective transmission\n",
    "- Dichroic-mirror and spectral-filter transmission\n",
    "- Detector quantum efficiency\n",
    "- Fiber-mode overlap and fiber-coupling efficiency\n",
    "- Internal quantum efficiency of the NV center\n",
    "- Nonradiative quenching associated with surfaces or interfaces\n",
    "- Aberrations of the actual microscope objective\n",
    "- Finite-pupil apodization and other pupil-dependent transmission effects\n",
    "\n",
    "Therefore, $\\eta_{\\mathrm{coll}}$ should not be interpreted as the overall photon-detection efficiency of the experimental setup. To predict the experimentally measured photon count rate, the relevant optical transmission factors, detector response, and emitter quantum efficiency must be incorporated separately."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "300ff1c4",
   "metadata": {},
   "source": [
    "## 1. Prerequisites and Setup\n",
    "\n",
    "Using the Tidy3D cloud solver requires a Flexcompute account and a valid API key; see the [Get Started](https://docs.flexcompute.com/projects/tidy3d/en/latest/index.html) page for installation and authentication instructions. \n",
    "\n",
    "The cell below imports the required packages and sets up the results directory for simulation outputs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a03c1d6d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tidy3D version: 2.12.0\n",
      "Result directory: tidy3d_nv_results\n"
     ]
    }
   ],
   "source": [
    "import json\n",
    "from collections.abc import Mapping\n",
    "from dataclasses import asdict, dataclass, replace\n",
    "from pathlib import Path\n",
    "from typing import Literal\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import tidy3d as td\n",
    "from IPython.display import display\n",
    "from tidy3d import web\n",
    "\n",
    "td.config.local_cache.enabled = False\n",
    "\n",
    "RESULTS_DIR = Path(\"tidy3d_nv_results\")\n",
    "RESULTS_DIR.mkdir(parents=True, exist_ok=True)\n",
    "RESULT_SET_TAG = \"domain8_air1p4_film_absorber40\"\n",
    "\n",
    "\n",
    "def result_dir(stage_name: str) -> Path:\n",
    "    \"\"\"Return a settings-tagged directory so incompatible checkpoints cannot mix.\"\"\"\n",
    "    return RESULTS_DIR / f\"{stage_name}_{RESULT_SET_TAG}\"\n",
    "\n",
    "\n",
    "print(\"Tidy3D version:\", td.__version__)\n",
    "print(\"Result directory:\", RESULTS_DIR)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b10afddb",
   "metadata": {},
   "source": [
    "## 2. Objective Acceptance Cone and Optical Constants\n",
    "\n",
    "### 2.1 Acceptance Angle for an Objective with NA = 0.6\n",
    "\n",
    "For a dry objective operating in air, $n_{\\mathrm{obj}} = 1$. Therefore, the maximum collection angle is\n",
    "\n",
    "$$\n",
    "\\theta_{\\max}\n",
    "=\n",
    "\\sin^{-1}\\!\\left(\\frac{\\mathrm{NA}}{n_{\\mathrm{obj}}}\\right)\n",
    "=\n",
    "\\sin^{-1}(0.6)\n",
    "=\n",
    "36.87^\\circ.\n",
    "$$\n",
    "\n",
    "Throughout this notebook, the $+z$ direction is defined as the direction toward the microscope objective."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "63f3f516",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Objective half-angle = 36.869898 deg\n"
     ]
    }
   ],
   "source": [
    "OBJECTIVE_NA = 0.60\n",
    "COLLECTION_INDEX = 1.0\n",
    "\n",
    "if OBJECTIVE_NA > COLLECTION_INDEX:\n",
    "    raise ValueError(\"NA must not exceed the collection-medium refractive index.\")\n",
    "\n",
    "theta_objective_rad = np.arcsin(OBJECTIVE_NA / COLLECTION_INDEX)\n",
    "theta_objective_deg = np.degrees(theta_objective_rad)\n",
    "print(f\"Objective half-angle = {theta_objective_deg:.6f} deg\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5723a616",
   "metadata": {},
   "source": [
    "### 2.2 Refractive Indices of Diamond and Silica\n",
    "\n",
    "For silica, the refractive index is calculated using the Malitson Sellmeier equation for fused silica [1]:\n",
    "\n",
    "$$\n",
    "n^2 - 1\n",
    "=\n",
    "\\frac{0.6961663\\lambda^2}{\\lambda^2 - 0.0684043^2}\n",
    "+\n",
    "\\frac{0.4079426\\lambda^2}{\\lambda^2 - 0.1162414^2}\n",
    "+\n",
    "\\frac{0.8974794\\lambda^2}{\\lambda^2 - 9.896161^2},\n",
    "$$\n",
    "\n",
    "where the wavelength $\\lambda$ is expressed in $\\mathrm{\\mu m}$.\n",
    "\n",
    "For diamond, the refractive index is calculated using the Peter diamond Sellmeier equation [2]:\n",
    "\n",
    "$$\n",
    "n^2 - 1\n",
    "=\n",
    "\\frac{0.3306\\lambda^2}{\\lambda^2 - 0.1750^2}\n",
    "+\n",
    "\\frac{4.3356\\lambda^2}{\\lambda^2 - 0.1060^2},\n",
    "$$\n",
    "\n",
    "where $\\lambda$ is also expressed in $\\mathrm{\\mu m}$.\n",
    "\n",
    "The reported validity range of the Peter refractive-index data extends to approximately 760 nm. Therefore, refractive-index values evaluated at wavelengths longer than 760 nm are treated as extrapolations of the Sellmeier model and should be interpreted accordingly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "73600b0d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 700x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "SILICA_MALITSON_COEFFS = (\n",
    "    (0.6961663, 0.0684043**2),\n",
    "    (0.4079426, 0.1162414**2),\n",
    "    (0.8974794, 9.896161**2),\n",
    ")\n",
    "\n",
    "DIAMOND_PETER_COEFFS = (\n",
    "    (0.3306, 0.1750**2),\n",
    "    (4.3356, 0.1060**2),\n",
    ")\n",
    "\n",
    "AIR = td.Medium(permittivity=1.0, name=\"air\")\n",
    "SILICA = td.Sellmeier(coeffs=SILICA_MALITSON_COEFFS, name=\"silica_malitson\")\n",
    "DIAMOND_PETER = td.Sellmeier(coeffs=DIAMOND_PETER_COEFFS, name=\"diamond_peter\")\n",
    "\n",
    "\n",
    "def sellmeier_n(\n",
    "    wavelength_um: np.ndarray, coeffs: tuple[tuple[float, float], ...]\n",
    ") -> np.ndarray:\n",
    "    \"\"\"Evaluate n(lambda) for Tidy3D-style Sellmeier coefficients (B_i, C_i).\"\"\"\n",
    "    wavelength_um = np.asarray(wavelength_um, dtype=float)\n",
    "    lam2 = wavelength_um**2\n",
    "    n2 = np.ones_like(wavelength_um)\n",
    "    for b_i, c_i in coeffs:\n",
    "        n2 += b_i * lam2 / (lam2 - c_i)\n",
    "    return np.sqrt(n2)\n",
    "\n",
    "\n",
    "wvl_plot_nm = np.linspace(637.0, 800.0, 300)\n",
    "wvl_plot_um = 1e-3 * wvl_plot_nm\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(7, 4))\n",
    "ax.plot(\n",
    "    wvl_plot_nm,\n",
    "    sellmeier_n(wvl_plot_um, SILICA_MALITSON_COEFFS),\n",
    "    label=\"Silica: Malitson\",\n",
    ")\n",
    "ax.plot(\n",
    "    wvl_plot_nm,\n",
    "    sellmeier_n(wvl_plot_um, DIAMOND_PETER_COEFFS),\n",
    "    label=\"Diamond: Peter fit\",\n",
    ")\n",
    "ax.axvline(760.0, linestyle=\":\", label=\"Peter data upper bound\")\n",
    "ax.set_xlabel(\"Vacuum wavelength (nm)\")\n",
    "ax.set_ylabel(\"Refractive index\")\n",
    "ax.grid(True)\n",
    "ax.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "935acf30",
   "metadata": {},
   "source": [
    "## 3. NV Symmetry Axis and Two Optical Transition Dipoles\n",
    "\n",
    "Let the polar and azimuthal angles of the NV symmetry axis be denoted by $\\beta$ and $\\alpha$, respectively. The corresponding unit vector is\n",
    "\n",
    "$$\n",
    "\\hat{\\mathbf{u}}_{\\mathrm{NV}}\n",
    "=\n",
    "\\left(\n",
    "\\sin\\beta\\cos\\alpha,\\;\n",
    "\\sin\\beta\\sin\\alpha,\\;\n",
    "\\cos\\beta\n",
    "\\right).\n",
    "$$\n",
    "\n",
    "The two optical transition dipoles, $\\hat{\\mathbf{e}}_1$ and $\\hat{\\mathbf{e}}_2$, are constructed such that both are perpendicular to the NV symmetry axis and mutually orthogonal:\n",
    "\n",
    "$$\n",
    "\\hat{\\mathbf{e}}_1 \\cdot \\hat{\\mathbf{u}}_{\\mathrm{NV}} = 0,\n",
    "\\qquad\n",
    "\\hat{\\mathbf{e}}_2 \\cdot \\hat{\\mathbf{u}}_{\\mathrm{NV}} = 0,\n",
    "\\qquad\n",
    "\\hat{\\mathbf{e}}_1 \\cdot \\hat{\\mathbf{e}}_2 = 0.\n",
    "$$\n",
    "\n",
    "Because the two optical transitions are treated as mutually incoherent, they are simulated independently and their radiated powers are summed at the power level.\n",
    "\n",
    "The resulting NV-center collection efficiency is therefore defined as\n",
    "\n",
    "$$\n",
    "\\eta_{\\mathrm{NV}}\n",
    "=\n",
    "\\frac{\n",
    "P_{\\mathrm{NA},1} + P_{\\mathrm{NA},2}\n",
    "}{\n",
    "P_{\\mathrm{tot},1} + P_{\\mathrm{tot},2}\n",
    "}.\n",
    "$$\n",
    "\n",
    "In general, the arithmetic mean of the individual collection efficiencies,\n",
    "\n",
    "$$\n",
    "\\frac{\\eta_1+\\eta_2}{2},\n",
    "$$\n",
    "\n",
    "is not equivalent to the expression above when the total radiated powers of the two transitions differ. The physically relevant quantity is the ratio of the sum of the collected powers to the sum of the total radiated powers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2951acc4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def unit_vector_from_angles(beta_deg: float, alpha_deg: float) -> np.ndarray:\n",
    "    \"\"\"Polar beta is measured from +z; azimuth alpha is measured from +x toward +y.\"\"\"\n",
    "    beta = np.deg2rad(beta_deg)\n",
    "    alpha = np.deg2rad(alpha_deg)\n",
    "    return np.array(\n",
    "        [np.sin(beta) * np.cos(alpha), np.sin(beta) * np.sin(alpha), np.cos(beta)],\n",
    "        dtype=float,\n",
    "    )\n",
    "\n",
    "\n",
    "def nv_transition_basis(\n",
    "    beta_deg: float, alpha_deg: float\n",
    ") -> tuple[np.ndarray, np.ndarray, np.ndarray]:\n",
    "    \"\"\"Return NV axis u and a robust orthonormal transition-dipole basis e1, e2.\"\"\"\n",
    "    u_nv = unit_vector_from_angles(beta_deg, alpha_deg)\n",
    "    u_nv /= np.linalg.norm(u_nv)\n",
    "\n",
    "    z_hat = np.array([0.0, 0.0, 1.0])\n",
    "    x_hat = np.array([1.0, 0.0, 0.0])\n",
    "    reference = z_hat if abs(np.dot(u_nv, z_hat)) < 0.90 else x_hat\n",
    "\n",
    "    e1 = np.cross(u_nv, reference)\n",
    "    e1 /= np.linalg.norm(e1)\n",
    "    e2 = np.cross(u_nv, e1)\n",
    "    e2 /= np.linalg.norm(e2)\n",
    "    return u_nv, e1, e2\n",
    "\n",
    "\n",
    "def vector_to_spherical_angles(vector: np.ndarray) -> tuple[float, float]:\n",
    "    \"\"\"Return Tidy3D polar and azimuth angles in radians for a Cartesian direction.\"\"\"\n",
    "    vector = np.asarray(vector, dtype=float)\n",
    "    vector /= np.linalg.norm(vector)\n",
    "    theta = np.arccos(np.clip(vector[2], -1.0, 1.0))\n",
    "    phi = np.mod(np.arctan2(vector[1], vector[0]), 2 * np.pi)\n",
    "    return float(theta), float(phi)\n",
    "\n",
    "\n",
    "u_test, e1_test, e2_test = nv_transition_basis(beta_deg=54.7356, alpha_deg=0.0)\n",
    "G = np.array(\n",
    "    [\n",
    "        [np.dot(u_test, u_test), np.dot(u_test, e1_test), np.dot(u_test, e2_test)],\n",
    "        [np.dot(e1_test, u_test), np.dot(e1_test, e1_test), np.dot(e1_test, e2_test)],\n",
    "        [np.dot(e2_test, u_test), np.dot(e2_test, e1_test), np.dot(e2_test, e2_test)],\n",
    "    ]\n",
    ")\n",
    "\n",
    "fig = plt.figure(figsize=(6, 5))\n",
    "ax = fig.add_subplot(111, projection=\"3d\")\n",
    "origin = np.zeros(3)\n",
    "for vector, label in [\n",
    "    (u_test, \"NV axis\"),\n",
    "    (e1_test, \"transition 1\"),\n",
    "    (e2_test, \"transition 2\"),\n",
    "]:\n",
    "    ax.quiver(*origin, *vector, length=1.0, normalize=True)\n",
    "    ax.text(*(1.08 * vector), label)\n",
    "ax.set_xlim(-1.2, 1.2)\n",
    "ax.set_ylim(-1.2, 1.2)\n",
    "ax.set_zlim(-1.2, 1.2)\n",
    "ax.set_xlabel(\"x\")\n",
    "ax.set_ylabel(\"y\")\n",
    "ax.set_zlabel(\"z\")\n",
    "ax.set_title(\"NV axis and two orthogonal optical transition dipoles\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66122431",
   "metadata": {},
   "source": [
    "## 4. Simulation Conditions and Geometry\n",
    "\n",
    "The coordinate convention used throughout this notebook is defined as follows:\n",
    "\n",
    "- Planar sample surface: `z = 0`\n",
    "- Microscope objective: located in the $+z$ direction\n",
    "- Nanodiamond geometry: a spherical nanodiamond in contact with the silica substrate\n",
    "- Bulk-diamond geometry: the entire half-space $z < 0$ is occupied by diamond\n",
    "- Diamond-film geometry: the region $-t_{\\mathrm{film}} < z < 0$ is occupied by diamond, with silica underneath\n",
    "\n",
    "The parameter `nv_depth_nm` denotes the depth of the NV center measured downward from the top surface in the bulk-diamond and diamond-film geometries.\n",
    "\n",
    "The parameter `nv_offset_nm` denotes the displacement of the NV center relative to the center of the nanodiamond in the nanodiamond geometry."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d636dd5a",
   "metadata": {},
   "outputs": [],
   "source": [
    "GeometryKind = Literal[\"nanodiamond\", \"bulk_halfspace\", \"diamond_film\"]\n",
    "DiamondModel = Literal[\"peter_sellmeier\"]\n",
    "\n",
    "# Large lateral reference domain used by the physical sweeps. The PML is\n",
    "# added outside this non-PML domain by Tidy3D.\n",
    "LARGE_DOMAIN_XY_SPAN_UM = 8.0\n",
    "LARGE_PROJECTION_SPAN_UM = 4.8\n",
    "# Half a wavelength is a hard construction guard, not a convergence claim.\n",
    "# The baseline below targets about two wavelengths of lateral clearance at 700 nm.\n",
    "MIN_PML_CLEARANCE_WAVELENGTHS = 0.5\n",
    "\n",
    "@dataclass(frozen=True)\n",
    "class ModelConfig:\n",
    "    geometry: GeometryKind = \"nanodiamond\"\n",
    "    wavelengths_nm: tuple[float, ...] = (700.0,)\n",
    "\n",
    "    objective_na: float = 0.60\n",
    "    collection_index: float = 1.0\n",
    "\n",
    "    nd_diameter_nm: float = 40.0\n",
    "    nd_embed_nm: float = 0.0\n",
    "    nv_offset_nm: tuple[float, float, float] = (0.0, 0.0, 0.0)\n",
    "\n",
    "    nv_depth_nm: float = 20.0\n",
    "    film_thickness_nm: float = 500.0\n",
    "\n",
    "    nv_beta_deg: float = 54.7356\n",
    "    nv_alpha_deg: float = 0.0\n",
    "\n",
    "    diamond_model: DiamondModel = \"peter_sellmeier\"\n",
    "\n",
    "    mesh_dl_nm: float = 5.0\n",
    "    min_steps_per_wvl: int = 20\n",
    "\n",
    "    xy_span_um: float = LARGE_DOMAIN_XY_SPAN_UM\n",
    "    air_buffer_um: float = 1.40\n",
    "    substrate_buffer_um: float = 0.80\n",
    "\n",
    "    projection_gap_um: float = 0.15\n",
    "    projection_span_um: float = LARGE_PROJECTION_SPAN_UM\n",
    "    projection_distance_um: float = 1000.0\n",
    "    far_theta_max_deg: float = 80.0\n",
    "    n_theta: int = 81\n",
    "    n_phi: int = 181\n",
    "\n",
    "    run_time_s: float = 2e-13\n",
    "    shutoff: float = 1e-6\n",
    "    film_absorber_num_layers: int = 40\n",
    "\n",
    "\n",
    "def nm_to_um(value_nm: float | np.ndarray) -> float | np.ndarray:\n",
    "    return 1e-3 * np.asarray(value_nm)\n",
    "\n",
    "\n",
    "def get_diamond_medium(cfg: ModelConfig):\n",
    "    if cfg.diamond_model == \"peter_sellmeier\":\n",
    "        if max(cfg.wavelengths_nm) > 760.0:\n",
    "            print(\n",
    "                \"WARNING: Peter diamond dispersion is being extrapolated above 760 nm.\"\n",
    "            )\n",
    "        return DIAMOND_PETER\n",
    "    raise ValueError(f\"Unknown diamond model: {cfg.diamond_model}\")\n",
    "\n",
    "\n",
    "def validate_config(cfg: ModelConfig) -> None:\n",
    "    if not cfg.wavelengths_nm or min(cfg.wavelengths_nm) <= 0:\n",
    "        raise ValueError(\"wavelengths_nm must contain positive values.\")\n",
    "    if not (0 < cfg.objective_na <= cfg.collection_index):\n",
    "        raise ValueError(\"Require 0 < objective_na <= collection_index.\")\n",
    "    if cfg.far_theta_max_deg <= np.degrees(\n",
    "        np.arcsin(cfg.objective_na / cfg.collection_index)\n",
    "    ):\n",
    "        raise ValueError(\n",
    "            \"far_theta_max_deg must exceed the objective acceptance angle.\"\n",
    "        )\n",
    "    if cfg.projection_span_um >= cfg.xy_span_um:\n",
    "        raise ValueError(\"projection_span_um must be smaller than xy_span_um.\")\n",
    "    longest_wavelength_um = 1e-3 * max(cfg.wavelengths_nm)\n",
    "    minimum_clearance_um = MIN_PML_CLEARANCE_WAVELENGTHS * longest_wavelength_um\n",
    "    lateral_monitor_buffer = 0.5 * (cfg.xy_span_um - cfg.projection_span_um)\n",
    "    if lateral_monitor_buffer < minimum_clearance_um - 1e-12:\n",
    "        raise ValueError(\n",
    "            f\"Require at least {MIN_PML_CLEARANCE_WAVELENGTHS:g} times the longest \"\n",
    "            \"vacuum wavelength between each projection-plane edge and the lateral PML start.\"\n",
    "        )\n",
    "    vertical_monitor_buffer = cfg.air_buffer_um - cfg.projection_gap_um\n",
    "    if vertical_monitor_buffer < minimum_clearance_um - 1e-12:\n",
    "        raise ValueError(\n",
    "            f\"Require at least {MIN_PML_CLEARANCE_WAVELENGTHS:g} times the longest \"\n",
    "            \"vacuum wavelength between the projection plane and the +z PML start.\"\n",
    "        )\n",
    "    if cfg.mesh_dl_nm <= 0:\n",
    "        raise ValueError(\"mesh_dl_nm must be positive.\")\n",
    "    if cfg.film_absorber_num_layers <= 0:\n",
    "        raise ValueError(\"film_absorber_num_layers must be positive.\")\n",
    "\n",
    "    if cfg.geometry == \"nanodiamond\":\n",
    "        radius_nm = 0.5 * cfg.nd_diameter_nm\n",
    "        if radius_nm <= 0:\n",
    "            raise ValueError(\"nd_diameter_nm must be positive.\")\n",
    "        if not (0 <= cfg.nd_embed_nm < cfg.nd_diameter_nm):\n",
    "            raise ValueError(\"Require 0 <= nd_embed_nm < nd_diameter_nm.\")\n",
    "        if np.linalg.norm(np.asarray(cfg.nv_offset_nm, dtype=float)) >= radius_nm:\n",
    "            raise ValueError(\n",
    "                \"The NV offset must remain inside the spherical nanodiamond.\"\n",
    "            )\n",
    "    elif cfg.geometry == \"bulk_halfspace\":\n",
    "        if cfg.nv_depth_nm <= 0:\n",
    "            raise ValueError(\"nv_depth_nm must be positive for bulk_halfspace.\")\n",
    "    elif cfg.geometry == \"diamond_film\":\n",
    "        if cfg.film_thickness_nm <= 0:\n",
    "            raise ValueError(\"film_thickness_nm must be positive.\")\n",
    "        if not (0 < cfg.nv_depth_nm < cfg.film_thickness_nm):\n",
    "            raise ValueError(\n",
    "                \"For diamond_film, require 0 < nv_depth_nm < film_thickness_nm.\"\n",
    "            )\n",
    "    else:\n",
    "        raise ValueError(f\"Unknown geometry: {cfg.geometry}\")\n",
    "\n",
    "\n",
    "def wavelength_frequency_arrays(cfg: ModelConfig) -> tuple[np.ndarray, np.ndarray]:\n",
    "    wavelengths_um = 1e-3 * np.asarray(cfg.wavelengths_nm, dtype=float)\n",
    "    freqs = np.sort(td.C_0 / wavelengths_um)\n",
    "    return wavelengths_um, freqs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "134180e0",
   "metadata": {},
   "source": [
    "Extend planar substrate geometries well beyond the nominal simulation domain so that they pass completely through the -z PML.\n",
    "This does not enlarge the FDTD computational domain."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "75c407dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "PML_STRUCTURE_EXTENSION_UM = 2.0\n",
    "\n",
    "\n",
    "def build_geometry(cfg: ModelConfig) -> dict:\n",
    "    \"\"\"Construct structures and return useful geometric coordinates.\"\"\"\n",
    "    validate_config(cfg)\n",
    "    diamond = get_diamond_medium(cfg)\n",
    "    xy_span = max(cfg.xy_span_um, cfg.projection_span_um + 0.35)\n",
    "\n",
    "    structures = []\n",
    "\n",
    "    if cfg.geometry == \"nanodiamond\":\n",
    "        radius = 0.5 * float(nm_to_um(cfg.nd_diameter_nm))\n",
    "        embed = float(nm_to_um(cfg.nd_embed_nm))\n",
    "        sphere_center_z = radius - embed\n",
    "        sphere_bottom_z = sphere_center_z - radius\n",
    "        sphere_top_z = sphere_center_z + radius\n",
    "\n",
    "        z_min = -max(cfg.substrate_buffer_um, abs(sphere_bottom_z) + 0.50)\n",
    "        z_max = sphere_top_z + cfg.air_buffer_um\n",
    "        z_structure_min = z_min - PML_STRUCTURE_EXTENSION_UM\n",
    "\n",
    "        substrate = td.Structure(\n",
    "            geometry=td.Box(\n",
    "                center=(0.0, 0.0, 0.5 * z_structure_min),\n",
    "                size=(td.inf, td.inf, -z_structure_min),\n",
    "            ),\n",
    "            medium=SILICA,\n",
    "            name=\"silica_substrate\",\n",
    "        )\n",
    "        sphere = td.Structure(\n",
    "            geometry=td.Sphere(center=(0.0, 0.0, sphere_center_z), radius=radius),\n",
    "            medium=diamond,\n",
    "            name=\"nanodiamond\",\n",
    "        )\n",
    "        # Later structures override earlier structures in overlap regions.\n",
    "        structures.extend([substrate, sphere])\n",
    "\n",
    "        offset_um = np.asarray(cfg.nv_offset_nm, dtype=float) * 1e-3\n",
    "        nv_position = np.array([0.0, 0.0, sphere_center_z]) + offset_um\n",
    "        object_top_z = sphere_top_z\n",
    "        object_bottom_z = min(0.0, sphere_bottom_z)\n",
    "\n",
    "    elif cfg.geometry == \"bulk_halfspace\":\n",
    "        depth = float(nm_to_um(cfg.nv_depth_nm))\n",
    "        z_min = -max(cfg.substrate_buffer_um, depth + 0.60)\n",
    "        z_max = cfg.air_buffer_um\n",
    "        z_structure_min = z_min - PML_STRUCTURE_EXTENSION_UM\n",
    "        bulk = td.Structure(\n",
    "            geometry=td.Box(\n",
    "                center=(0.0, 0.0, 0.5 * z_structure_min),\n",
    "                size=(td.inf, td.inf, -z_structure_min),\n",
    "            ),\n",
    "            medium=diamond,\n",
    "            name=\"bulk_diamond\",\n",
    "        )\n",
    "        structures.append(bulk)\n",
    "        nv_position = np.array([0.0, 0.0, -depth])\n",
    "        object_top_z = 0.0\n",
    "        object_bottom_z = z_min\n",
    "\n",
    "    elif cfg.geometry == \"diamond_film\":\n",
    "        film_thickness = float(nm_to_um(cfg.film_thickness_nm))\n",
    "        depth = float(nm_to_um(cfg.nv_depth_nm))\n",
    "        z_min = -(film_thickness + cfg.substrate_buffer_um)\n",
    "        z_max = cfg.air_buffer_um\n",
    "        z_structure_min = z_min - PML_STRUCTURE_EXTENSION_UM\n",
    "        silica_thickness = -film_thickness - z_structure_min\n",
    "        substrate = td.Structure(\n",
    "            geometry=td.Box(\n",
    "                center=(0.0, 0.0, 0.5 * (z_structure_min - film_thickness)),\n",
    "                size=(td.inf, td.inf, silica_thickness),\n",
    "            ),\n",
    "            medium=SILICA,\n",
    "            name=\"silica_substrate\",\n",
    "        )\n",
    "        film = td.Structure(\n",
    "            geometry=td.Box(\n",
    "                center=(0.0, 0.0, -0.5 * film_thickness),\n",
    "                size=(td.inf, td.inf, film_thickness),\n",
    "            ),\n",
    "            medium=diamond,\n",
    "            name=\"diamond_film\",\n",
    "        )\n",
    "        structures.extend([substrate, film])\n",
    "        nv_position = np.array([0.0, 0.0, -depth])\n",
    "        object_top_z = 0.0\n",
    "        object_bottom_z = -film_thickness\n",
    "\n",
    "    else:\n",
    "        raise ValueError(f\"Unknown geometry: {cfg.geometry}\")\n",
    "\n",
    "    sim_center = (0.0, 0.0, 0.5 * (z_min + z_max))\n",
    "    sim_size = (xy_span, xy_span, z_max - z_min)\n",
    "\n",
    "    return {\n",
    "        \"structures\": tuple(structures),\n",
    "        \"nv_position\": tuple(float(v) for v in nv_position),\n",
    "        \"sim_center\": sim_center,\n",
    "        \"sim_size\": sim_size,\n",
    "        \"xy_span\": xy_span,\n",
    "        \"z_min\": z_min,\n",
    "        \"z_max\": z_max,\n",
    "        \"object_top_z\": object_top_z,\n",
    "        \"object_bottom_z\": object_bottom_z,\n",
    "    }"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d111b076",
   "metadata": {},
   "source": [
    "## 5. Tidy3D Sources, Monitors, Mesh, and Simulation Setup\n",
    "\n",
    "Each transition-dipole simulation contains the following three monitors:\n",
    "\n",
    "1. `total_flux`: a three-dimensional closed surface enclosing the NV center and the adjacent structure. Used to calculate $P_{\\mathrm{tot}}$.\n",
    "2. `air_flux`: a planar monitor that measures the $+z$ flux through the air region. Used as an auxiliary diagnostic quantity.\n",
    "3. `far_field`: projects the near field recorded in the same air region into the angular far field. Used to calculate $P_{\\mathrm{NA}}$.\n",
    "\n",
    "Because `FieldProjectionAngleMonitor` assumes a homogeneous propagation medium, the entire projection plane is placed within the air region. Since the open projection plane has a finite size, convergence with respect to `projection_span_um` is examined in Stage 8.\n",
    "\n",
    "A single transition dipole with an arbitrary orientation is represented as a coherent combination of Cartesian component sources. A negative Cartesian component is represented by adding a phase shift of $\\pi$ rather than by using a negative amplitude.\n",
    "\n",
    "In contrast, the two NV optical transitions are simulated separately, and their powers are summed only after the simulations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f1d8e892",
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_angular_grid(cfg: ModelConfig) -> tuple[np.ndarray, np.ndarray]:\n",
    "    theta_obj = np.arcsin(cfg.objective_na / cfg.collection_index)\n",
    "    theta = np.linspace(0.0, np.deg2rad(cfg.far_theta_max_deg), cfg.n_theta)\n",
    "    theta = np.unique(np.sort(np.append(theta, theta_obj)))\n",
    "    phi = np.linspace(0.0, 2.0 * np.pi, cfg.n_phi, endpoint=True)\n",
    "    return theta, phi\n",
    "\n",
    "\n",
    "def make_source_time(freqs: np.ndarray) -> td.GaussianPulse:\n",
    "    freqs = np.asarray(freqs, dtype=float)\n",
    "    f_min, f_max = float(freqs.min()), float(freqs.max())\n",
    "    freq0 = 0.5 * (f_min + f_max)\n",
    "    if np.isclose(f_min, f_max):\n",
    "        fwidth = freq0 / 10.0\n",
    "    else:\n",
    "        fwidth = max(0.60 * (f_max - f_min), freq0 / 30.0)\n",
    "    return td.GaussianPulse(freq0=freq0, fwidth=fwidth)\n",
    "\n",
    "\n",
    "def make_directional_dipole_sources(\n",
    "    direction: np.ndarray,\n",
    "    source_time: td.GaussianPulse,\n",
    "    center: tuple[float, float, float],\n",
    ") -> tuple[td.PointDipole, ...]:\n",
    "    direction = np.asarray(direction, dtype=float)\n",
    "    direction /= np.linalg.norm(direction)\n",
    "\n",
    "    sources = []\n",
    "    for polarization, component in zip((\"Ex\", \"Ey\", \"Ez\"), direction):\n",
    "        if np.isclose(component, 0.0, rtol=0.0, atol=1e-12):\n",
    "            continue\n",
    "\n",
    "        # Tidy3D Pulse.amplitude is non-negative. Encode a negative Cartesian\n",
    "        # component as a pi phase shift so that all component sources remain coherent.\n",
    "        component_phase = np.pi if component < 0.0 else 0.0\n",
    "        component_time = source_time.updated_copy(\n",
    "            amplitude=source_time.amplitude * abs(float(component)),\n",
    "            phase=source_time.phase + component_phase,\n",
    "        )\n",
    "        sources.append(\n",
    "            td.PointDipole(\n",
    "                source_time=component_time,\n",
    "                polarization=polarization,\n",
    "                center=center,\n",
    "                interpolate=True,\n",
    "            )\n",
    "        )\n",
    "\n",
    "    return tuple(sources)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9f5b2be0",
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_mesh_override(cfg: ModelConfig, geom: Mapping) -> td.MeshOverrideStructure:\n",
    "    nv_z = geom[\"nv_position\"][2]\n",
    "    dl_um = float(nm_to_um(cfg.mesh_dl_nm))\n",
    "\n",
    "    if cfg.geometry == \"nanodiamond\":\n",
    "        radius = 0.5 * float(nm_to_um(cfg.nd_diameter_nm))\n",
    "        center_z = radius - float(nm_to_um(cfg.nd_embed_nm))\n",
    "        z_lo = min(-0.08, center_z - radius - 0.08, nv_z - 0.15)\n",
    "        z_hi = max(center_z + radius + 0.08, nv_z + 0.15)\n",
    "        span_xy = max(2.0 * radius + 0.16, 0.36)\n",
    "    elif cfg.geometry == \"bulk_halfspace\":\n",
    "        z_lo = min(nv_z - 0.22, -0.12)\n",
    "        z_hi = 0.12\n",
    "        span_xy = 0.50\n",
    "    else:  # diamond_film\n",
    "        film_bottom = -float(nm_to_um(cfg.film_thickness_nm))\n",
    "        z_lo = min(nv_z - 0.22, film_bottom - 0.05)\n",
    "        z_hi = 0.12\n",
    "        span_xy = 0.50\n",
    "\n",
    "    z_lo = max(z_lo, geom[\"z_min\"] + 0.08)\n",
    "    z_hi = min(z_hi, geom[\"z_max\"] - 0.08)\n",
    "\n",
    "    return td.MeshOverrideStructure(\n",
    "        geometry=td.Box(\n",
    "            center=(0.0, 0.0, 0.5 * (z_lo + z_hi)),\n",
    "            size=(span_xy, span_xy, z_hi - z_lo),\n",
    "        ),\n",
    "        dl=(dl_um, dl_um, dl_um),\n",
    "        enforce=True,\n",
    "        name=\"local_refinement\",\n",
    "    )\n",
    "\n",
    "\n",
    "def make_monitors(cfg: ModelConfig, geom: Mapping, freqs: np.ndarray):\n",
    "    theta, phi = make_angular_grid(cfg)\n",
    "    nv_z = geom[\"nv_position\"][2]\n",
    "    projection_z = geom[\"object_top_z\"] + cfg.projection_gap_um\n",
    "\n",
    "    if projection_z >= geom[\"z_max\"] - 0.12:\n",
    "        raise ValueError(\n",
    "            \"Increase air_buffer_um: projection plane is too close to the +z boundary/PML.\"\n",
    "        )\n",
    "\n",
    "    if cfg.geometry == \"nanodiamond\":\n",
    "        radius = 0.5 * float(nm_to_um(cfg.nd_diameter_nm))\n",
    "        half_xy = max(radius + 0.10, 0.18)\n",
    "        z_lo_target = min(nv_z - 0.18, geom[\"object_bottom_z\"] - 0.08)\n",
    "        z_hi_target = max(nv_z + 0.18, geom[\"object_top_z\"] + 0.08)\n",
    "    else:\n",
    "        half_xy = 0.25\n",
    "        z_lo_target = nv_z - 0.20\n",
    "        z_hi_target = nv_z + 0.20\n",
    "\n",
    "    z_lo = max(geom[\"z_min\"] + 0.10, z_lo_target)\n",
    "    z_hi = min(projection_z - 0.04, geom[\"z_max\"] - 0.10, z_hi_target)\n",
    "    if not (z_lo < nv_z < z_hi):\n",
    "        raise ValueError(\n",
    "            \"The total-flux box does not enclose the NV. Adjust buffers or monitor construction.\"\n",
    "        )\n",
    "\n",
    "    total_flux = td.FluxMonitor(\n",
    "        center=(0.0, 0.0, 0.5 * (z_lo + z_hi)),\n",
    "        size=(2.0 * half_xy, 2.0 * half_xy, z_hi - z_lo),\n",
    "        freqs=freqs,\n",
    "        name=\"total_flux\",\n",
    "    )\n",
    "\n",
    "    air_flux = td.FluxMonitor(\n",
    "        center=(0.0, 0.0, projection_z),\n",
    "        size=(cfg.projection_span_um, cfg.projection_span_um, 0.0),\n",
    "        freqs=freqs,\n",
    "        normal_dir=\"+\",\n",
    "        name=\"air_flux\",\n",
    "    )\n",
    "\n",
    "    far_field = td.FieldProjectionAngleMonitor(\n",
    "        center=(0.0, 0.0, projection_z),\n",
    "        size=(cfg.projection_span_um, cfg.projection_span_um, 0.0),\n",
    "        freqs=freqs,\n",
    "        theta=theta,\n",
    "        phi=phi,\n",
    "        proj_distance=cfg.projection_distance_um,\n",
    "        custom_origin=geom[\"nv_position\"],\n",
    "        far_field_approx=True,\n",
    "        normal_dir=\"+\",\n",
    "        medium=AIR,\n",
    "        interval_space=(1, 1, 1),\n",
    "        name=\"far_field\",\n",
    "    )\n",
    "\n",
    "    return total_flux, air_flux, far_field\n",
    "\n",
    "\n",
    "def make_boundary_spec(cfg: ModelConfig) -> td.BoundarySpec:\n",
    "    \"\"\"Use stable absorbers where the dispersive thin-film stack reaches a boundary.\"\"\"\n",
    "    if cfg.geometry != \"diamond_film\":\n",
    "        return td.BoundarySpec.all_sides(boundary=td.PML())\n",
    "\n",
    "    # Absorbing layers are added outside Simulation.size; increasing num_layers\n",
    "    # does not reduce the monitor-to-absorber-start clearance inside the domain.\n",
    "    # The infinite diamond/silica slab intersects both lateral boundaries and\n",
    "    # supports guided modes. Standard PML can become unstable when dispersive\n",
    "    # media enter it, so use adiabatic absorbers on x/y. Silica also reaches\n",
    "    # the -z boundary; +z is homogeneous air and can retain the lower-reflection PML.\n",
    "    absorber_layers = cfg.film_absorber_num_layers\n",
    "    return td.BoundarySpec(\n",
    "        x=td.Boundary.absorber(num_layers=absorber_layers),\n",
    "        y=td.Boundary.absorber(num_layers=absorber_layers),\n",
    "        z=td.Boundary(\n",
    "            plus=td.PML(),\n",
    "            minus=td.Absorber(num_layers=absorber_layers),\n",
    "        ),\n",
    "    )\n",
    "\n",
    "\n",
    "def build_transition_simulation(\n",
    "    cfg: ModelConfig,\n",
    "    geom: Mapping,\n",
    "    direction: np.ndarray,\n",
    "    source_time: td.GaussianPulse,\n",
    "    freqs: np.ndarray,\n",
    ") -> td.Simulation:\n",
    "    sources = make_directional_dipole_sources(\n",
    "        direction, source_time, geom[\"nv_position\"]\n",
    "    )\n",
    "    monitors = make_monitors(cfg, geom, freqs)\n",
    "    mesh_override = make_mesh_override(cfg, geom)\n",
    "\n",
    "    grid_spec = td.GridSpec.auto(\n",
    "        wavelength=min(cfg.wavelengths_nm) * 1e-3,\n",
    "        min_steps_per_wvl=cfg.min_steps_per_wvl,\n",
    "        override_structures=[mesh_override],\n",
    "    )\n",
    "\n",
    "    return td.Simulation(\n",
    "        center=geom[\"sim_center\"],\n",
    "        size=geom[\"sim_size\"],\n",
    "        medium=AIR,\n",
    "        structures=geom[\"structures\"],\n",
    "        sources=sources,\n",
    "        monitors=monitors,\n",
    "        grid_spec=grid_spec,\n",
    "        boundary_spec=make_boundary_spec(cfg),\n",
    "        run_time=cfg.run_time_s,\n",
    "        shutoff=cfg.shutoff,\n",
    "        normalize_index=None,\n",
    "    )\n",
    "\n",
    "\n",
    "def build_nv_transition_pair(cfg: ModelConfig) -> tuple[dict[str, td.Simulation], dict]:\n",
    "    validate_config(cfg)\n",
    "    geom = build_geometry(cfg)\n",
    "    _, freqs = wavelength_frequency_arrays(cfg)\n",
    "    pulse = make_source_time(freqs)\n",
    "    u_nv, e1, e2 = nv_transition_basis(cfg.nv_beta_deg, cfg.nv_alpha_deg)\n",
    "\n",
    "    simulations = {\n",
    "        \"t1\": build_transition_simulation(cfg, geom, e1, pulse, freqs),\n",
    "        \"t2\": build_transition_simulation(cfg, geom, e2, pulse, freqs),\n",
    "    }\n",
    "    info = {\"geometry\": geom, \"u_nv\": u_nv, \"e1\": e1, \"e2\": e2, \"freqs\": freqs}\n",
    "    return simulations, info"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5bf1b15f",
   "metadata": {},
   "source": [
    "## 6. Local Validation, Geometry/Mesh Inspection, and Cost Preview\n",
    "\n",
    "Before submitting a simulation to the cloud, the following items must be verified:\n",
    "\n",
    "1. Does `sim.validate_pre_upload()` complete successfully?\n",
    "2. Is the NV center located within the intended material region?\n",
    "3. Is the projection plane entirely located within the air region?\n",
    "4. Does the `total_flux` box enclose the NV center and all required surrounding structures?\n",
    "5. Is there sufficient separation between the PML boundaries and the structures/monitors?\n",
    "6. Does the local mesh adequately resolve the nanodiamond geometry and the region surrounding the source?\n",
    "\n",
    "The baseline case used below corresponds to a wavelength of 700 nm, a nanodiamond diameter of 40 nm, and an NV-axis polar angle of $54.7356^\\circ$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1deda71b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation passed.\n",
      "NV position (um): (0.0, 0.0, 0.02)\n",
      "Simulation size (um): (8.0, 8.0, 2.24)\n",
      "Projection edge to lateral PML start (um): 1.6\n",
      "Projection plane to +z PML start (um): 1.25\n",
      "Lateral clearance / longest wavelength: 2.2857142857142856\n",
      "Vertical clearance / longest wavelength: 1.7857142857142856\n",
      "Yee cells: 27342000\n",
      "Computational grid points: 27959188\n",
      "Time steps: 20982\n",
      "Estimated monitor storage (bytes):\n",
      "{\n",
      "  \"total_flux\": 4.0,\n",
      "  \"air_flux\": 4.0,\n",
      "  \"far_field\": 712416.0\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "base_cfg = ModelConfig(\n",
    "    geometry=\"nanodiamond\",\n",
    "    wavelengths_nm=(700.0,),\n",
    "    nd_diameter_nm=40.0,\n",
    "    nv_beta_deg=54.7356,\n",
    "    nv_alpha_deg=0.0,\n",
    "    mesh_dl_nm=5.0,\n",
    "    diamond_model=\"peter_sellmeier\",\n",
    "    xy_span_um=LARGE_DOMAIN_XY_SPAN_UM,\n",
    "    air_buffer_um=1.40,\n",
    "    projection_span_um=LARGE_PROJECTION_SPAN_UM,\n",
    ")\n",
    "\n",
    "base_pair, base_info = build_nv_transition_pair(base_cfg)\n",
    "base_sim = base_pair[\"t1\"]\n",
    "\n",
    "base_sim.validate_pre_upload()\n",
    "print(\"Validation passed.\")\n",
    "print(\"NV position (um):\", base_info[\"geometry\"][\"nv_position\"])\n",
    "print(\"Simulation size (um):\", base_sim.size)\n",
    "base_wavelength_um = 1e-3 * max(base_cfg.wavelengths_nm)\n",
    "base_lateral_clearance_um = 0.5 * (base_cfg.xy_span_um - base_cfg.projection_span_um)\n",
    "base_vertical_clearance_um = base_cfg.air_buffer_um - base_cfg.projection_gap_um\n",
    "print(\"Projection edge to lateral PML start (um):\", base_lateral_clearance_um)\n",
    "print(\"Projection plane to +z PML start (um):\", base_vertical_clearance_um)\n",
    "print(\n",
    "    \"Lateral clearance / longest wavelength:\",\n",
    "    base_lateral_clearance_um / base_wavelength_um,\n",
    ")\n",
    "print(\n",
    "    \"Vertical clearance / longest wavelength:\",\n",
    "    base_vertical_clearance_um / base_wavelength_um,\n",
    ")\n",
    "print(\"Yee cells:\", base_sim.num_cells)\n",
    "print(\"Computational grid points:\", base_sim.num_computational_grid_points)\n",
    "print(\"Time steps:\", base_sim.num_time_steps)\n",
    "print(\"Estimated monitor storage (bytes):\")\n",
    "print(json.dumps(base_sim.monitors_data_size, indent=2, default=str))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "geometry-gallery-heading",
   "metadata": {},
   "source": [
    "### Inspection of the Simulation Domain for All Geometries\n",
    "\n",
    "The cell below constructs the actual simulation objects for `nanodiamond`, `bulk_halfspace`, and `diamond_film`, and compares the structures, source, monitors, and computational grid in the $y=0$ cross-sectional plane.\n",
    "\n",
    "Only local simulation objects are generated in this step; **no cloud tasks are submitted**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "geometry-gallery-code",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Nanodiamond on silica (nanodiamond; diameter=40 nm): center=(0.0, 0.0, 0.31999999999999995) um, size=(8.0, 8.0, 2.24) um, NV=(0.0, 0.0, 0.02) um, z=[-0.800, 1.440] um\n",
      "Bulk diamond half-space (bulk_halfspace; NV depth=20 nm): center=(0.0, 0.0, 0.29999999999999993) um, size=(8.0, 8.0, 2.2) um, NV=(0.0, 0.0, -0.02) um, z=[-0.800, 1.400] um\n",
      "Thin diamond film on silica (diamond_film; film thickness=500 nm, NV depth=20 nm): center=(0.0, 0.0, 0.04999999999999993) um, size=(8.0, 8.0, 2.7) um, NV=(0.0, 0.0, -0.02) um, z=[-1.300, 1.400] um\n"
     ]
    },
    {
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      "text/plain": [
       "<Figure size 1400x1440 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_geometry_gallery(\n",
    "    configs: Mapping[str, ModelConfig],\n",
    "    *,\n",
    "    slice_axis: Literal[\"x\", \"y\"] = \"y\",\n",
    "    slice_position_um: float = 0.0,\n",
    ") -> dict[str, td.Simulation]:\n",
    "    \"\"\"Plot structure/monitor placement and grid for each geometry configuration.\"\"\"\n",
    "    if slice_axis not in {\"x\", \"y\"}:\n",
    "        raise ValueError(\"slice_axis must be 'x' or 'y'.\")\n",
    "\n",
    "    simulations = {}\n",
    "    fig, axes = plt.subplots(\n",
    "        len(configs), 2, figsize=(14, 4.8 * len(configs)), squeeze=False\n",
    "    )\n",
    "    slice_kwargs = {slice_axis: slice_position_um}\n",
    "\n",
    "    for row, (label, cfg) in enumerate(configs.items()):\n",
    "        pair, info = build_nv_transition_pair(cfg)\n",
    "        sim = pair[\"t1\"]\n",
    "        sim.validate_pre_upload()\n",
    "        simulations[label] = sim\n",
    "\n",
    "        sim.plot(ax=axes[row, 0], **slice_kwargs)\n",
    "        axes[row, 0].set_title(f\"{label}: geometry, source, monitors\")\n",
    "        sim.plot_grid(ax=axes[row, 1], **slice_kwargs)\n",
    "        axes[row, 1].set_title(f\"{label}: Yee grid\")\n",
    "\n",
    "        geom = info[\"geometry\"]\n",
    "        if cfg.geometry == \"nanodiamond\":\n",
    "            geometry_detail = f\"diameter={cfg.nd_diameter_nm:g} nm\"\n",
    "        elif cfg.geometry == \"bulk_halfspace\":\n",
    "            geometry_detail = f\"NV depth={cfg.nv_depth_nm:g} nm\"\n",
    "        else:\n",
    "            geometry_detail = (\n",
    "                f\"film thickness={cfg.film_thickness_nm:g} nm, \"\n",
    "                f\"NV depth={cfg.nv_depth_nm:g} nm\"\n",
    "            )\n",
    "        print(\n",
    "            f\"{label} ({cfg.geometry}; {geometry_detail}): \"\n",
    "            f\"center={sim.center} um, size={sim.size} um, \"\n",
    "            f\"NV={geom['nv_position']} um, z=[{geom['z_min']:.3f}, {geom['z_max']:.3f}] um\"\n",
    "        )\n",
    "\n",
    "    fig.suptitle(\n",
    "        f\"Simulation geometries at {slice_axis}={slice_position_um:g} um\", y=1.002\n",
    "    )\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "    return simulations\n",
    "\n",
    "\n",
    "geometry_preview_configs = {\n",
    "    \"Nanodiamond on silica\": replace(base_cfg, geometry=\"nanodiamond\"),\n",
    "    \"Bulk diamond half-space\": replace(\n",
    "        base_cfg, geometry=\"bulk_halfspace\", nv_depth_nm=20.0\n",
    "    ),\n",
    "    \"Thin diamond film on silica\": replace(\n",
    "        base_cfg, geometry=\"diamond_film\", film_thickness_nm=500.0, nv_depth_nm=20.0\n",
    "    ),\n",
    "}\n",
    "geometry_preview_simulations = plot_geometry_gallery(geometry_preview_configs)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c6887dd",
   "metadata": {},
   "source": [
    "### Batch Construction and Cost Estimate\n",
    "\n",
    "A single NV orientation requires at least two separate simulations because the two optical transition dipoles must be evaluated independently.\n",
    "\n",
    "Since the number of simulation tasks can increase rapidly during parameter sweeps, the simulations are grouped into a `Batch`. The helper below prints the maximum estimated computational cost and then submits the batch; it does not pause for confirmation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "300d0f4f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def prepare_batch(\n",
    "    cases: Mapping[str, ModelConfig],\n",
    "    folder_name: str,\n",
    ") -> tuple[web.Batch, dict[str, td.Simulation]]:\n",
    "    simulations: dict[str, td.Simulation] = {}\n",
    "    for case_name, cfg in cases.items():\n",
    "        pair, _ = build_nv_transition_pair(cfg)\n",
    "        simulations[f\"{case_name}__t1\"] = pair[\"t1\"]\n",
    "        simulations[f\"{case_name}__t2\"] = pair[\"t2\"]\n",
    "    batch = web.Batch(simulations=simulations, folder_name=folder_name, verbose=True)\n",
    "    return batch, simulations\n",
    "\n",
    "\n",
    "def run_batch_with_cost_estimate(\n",
    "    batch: web.Batch,\n",
    "    path_dir: Path,\n",
    "    cases: Mapping[str, ModelConfig] | None = None,\n",
    "):\n",
    "    path_dir.mkdir(parents=True, exist_ok=True)\n",
    "    if cases is not None:\n",
    "        case_definitions = json.loads(\n",
    "            json.dumps({name: asdict(cfg) for name, cfg in cases.items()})\n",
    "        )\n",
    "        manifest_path = path_dir / \"case_definitions.json\"\n",
    "        if manifest_path.exists():\n",
    "            previous_definitions = json.loads(manifest_path.read_text(encoding=\"utf-8\"))\n",
    "            if previous_definitions != case_definitions:\n",
    "                raise RuntimeError(\n",
    "                    f\"{manifest_path} describes different simulations. \"\n",
    "                    \"Change RESULT_SET_TAG or choose a new result directory before submitting.\"\n",
    "                )\n",
    "        manifest_path.write_text(\n",
    "            json.dumps(case_definitions, indent=2, sort_keys=True),\n",
    "            encoding=\"utf-8\",\n",
    "        )\n",
    "    estimated_cost = batch.estimate_cost(verbose=True)\n",
    "    print(\"Estimated maximum batch cost:\", estimated_cost)\n",
    "    return batch.run(path_dir=path_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29779f8e",
   "metadata": {},
   "source": [
    "## 7. Baseline Simulation and Post-Processing\n",
    "\n",
    "Before performing any parameter sweep, run a single baseline case first.\n",
    "\n",
    "Proceed to the convergence tests in Stage 8 only after confirming that the baseline simulation is physically and numerically well behaved."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e275eed6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of simulations: 2\n",
      "nd40_beta54p7_700nm__t1 cells = 27342000\n",
      "nd40_beta54p7_700nm__t2 cells = 27342000\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">21:23:24 KST </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.680</span> for the whole batch.                \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m21:23:24 KST\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.680\u001b[0m for the whole batch.                \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "57130402f1364c28b1a6901ea6e10e37",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Estimated maximum batch cost: 0.679631294311451\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">21:23:25 KST </span>Started working on Batch containing <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2</span> tasks.                       \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m21:23:25 KST\u001b[0m\u001b[2;36m \u001b[0mStarted working on Batch containing \u001b[1;36m2\u001b[0m tasks.                       \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">21:23:28 KST </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.680</span> for the whole batch.                \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m21:23:28 KST\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.680\u001b[0m for the whole batch.                \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Use <span style=\"color: #008000; text-decoration-color: #008000\">'Batch.real_cost()'</span> to get the billed FlexCredit cost after    \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>completion.                                                        \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mUse \u001b[32m'Batch.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get the billed FlexCredit cost after    \n",
       "\u001b[2;36m             \u001b[0mcompletion.                                                        \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "008f2ae100084bb79e55a2ce90420039",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">21:23:29 KST </span>Batch complete.                                                    \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m21:23:29 KST\u001b[0m\u001b[2;36m \u001b[0mBatch complete.                                                    \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "single_cases = {\"nd40_beta54p7_700nm\": base_cfg}\n",
    "single_batch, single_simulations = prepare_batch(\n",
    "    single_cases,\n",
    "    folder_name=\"NV_collection_single_case\",\n",
    ")\n",
    "\n",
    "print(\"Number of simulations:\", len(single_simulations))\n",
    "for task_name, sim in single_simulations.items():\n",
    "    print(task_name, \"cells =\", sim.num_cells)\n",
    "\n",
    "single_data = run_batch_with_cost_estimate(\n",
    "    single_batch,\n",
    "    result_dir(\"stage7_single_case\"),\n",
    "    single_cases,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2eada2dc",
   "metadata": {},
   "source": [
    "### 7.1 Far-Field Cone Integration\n",
    "\n",
    "The radial Poynting flux of the Tidy3D projected field is given by\n",
    "\n",
    "$$\n",
    "S_r\n",
    "=\n",
    "\\frac{1}{2}\n",
    "\\operatorname{Re}\n",
    "\\left(\n",
    "E_\\theta H_\\phi^*\n",
    "-\n",
    "E_\\phi H_\\theta^*\n",
    "\\right).\n",
    "$$\n",
    "\n",
    "For a projection radius $r$, the optical power contained within the collection cone is\n",
    "\n",
    "$$\n",
    "P_{\\mathrm{cone}}\n",
    "=\n",
    "r^2\n",
    "\\int\\!\\!\\int\n",
    "S_r\n",
    "\\sin\\theta\n",
    "\\, d\\theta \\, d\\phi.\n",
    "$$\n",
    "\n",
    "Tidy3D's built-in integrator requires full-sphere integration over the entire spherical domain, $0 \\leq \\theta \\leq \\pi$. Since the present calculation uses only the air-side open projection plane and the objective acceptance cone, the angular integration is implemented numerically."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "680e8f56",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Angular-cone integrator unit test passed.\n"
     ]
    }
   ],
   "source": [
    "# NumPy >= 2.0 provides trapezoid; older releases use trapz.\n",
    "# Use an explicit branch because getattr(..., np.trapz) evaluates np.trapz eagerly.\n",
    "if hasattr(np, \"trapezoid\"):\n",
    "    _TRAPEZOID = np.trapezoid\n",
    "else:\n",
    "    _TRAPEZOID = np.trapz\n",
    "\n",
    "\n",
    "def integrate_angular_power_array(\n",
    "    power_values: np.ndarray,\n",
    "    theta: np.ndarray,\n",
    "    phi: np.ndarray,\n",
    "    theta_limit_rad: float,\n",
    "    *,\n",
    "    projection_radius_um: float = 1.0,\n",
    ") -> np.ndarray:\n",
    "    \"\"\"Integrate S_r over a cone for arrays shaped (theta, phi, ...).\"\"\"\n",
    "    theta = np.asarray(theta, dtype=float)\n",
    "    phi = np.asarray(phi, dtype=float)\n",
    "    values = np.real(np.asarray(power_values))\n",
    "\n",
    "    if values.shape[0] != theta.size or values.shape[1] != phi.size:\n",
    "        raise ValueError(\"power_values must have leading dimensions (theta, phi).\")\n",
    "\n",
    "    mask = theta <= theta_limit_rad + 1e-12\n",
    "    if mask.sum() < 2:\n",
    "        raise ValueError(\"Not enough theta samples inside the requested cone.\")\n",
    "    if theta[mask][-1] < theta_limit_rad - 1e-9:\n",
    "        raise ValueError(\"theta_limit is not included in the angular grid.\")\n",
    "    if not np.isclose(phi[0], 0.0) or not np.isclose(phi[-1], 2.0 * np.pi):\n",
    "        raise ValueError(\"phi must cover 0 to 2pi, including both endpoints.\")\n",
    "\n",
    "    theta_sel = theta[mask]\n",
    "    values_sel = values[mask, ...]\n",
    "    sin_shape = (theta_sel.size,) + (1,) * (values_sel.ndim - 1)\n",
    "    integrand = values_sel * np.sin(theta_sel).reshape(sin_shape)\n",
    "\n",
    "    integral_phi = _TRAPEZOID(integrand, x=phi, axis=1)\n",
    "    integral_theta_phi = _TRAPEZOID(integral_phi, x=theta_sel, axis=0)\n",
    "    return projection_radius_um**2 * integral_theta_phi\n",
    "\n",
    "\n",
    "def integrate_projected_power_cone(far_data, theta_limit_rad: float) -> np.ndarray:\n",
    "    \"\"\"Integrate r^2 S_r over 0 <= theta <= theta_limit and 0 <= phi <= 2pi.\"\"\"\n",
    "    power = far_data.power\n",
    "    if \"r\" in power.dims:\n",
    "        power = power.isel(r=0)\n",
    "    power = power.transpose(\"theta\", \"phi\", \"f\")\n",
    "\n",
    "    theta = np.asarray(power.coords[\"theta\"].values, dtype=float)\n",
    "    phi = np.asarray(power.coords[\"phi\"].values, dtype=float)\n",
    "    r_um = float(far_data.monitor.proj_distance)\n",
    "    return integrate_angular_power_array(\n",
    "        power.values,\n",
    "        theta,\n",
    "        phi,\n",
    "        theta_limit_rad,\n",
    "        projection_radius_um=r_um,\n",
    "    )\n",
    "\n",
    "\n",
    "# Local unit test: homogeneous-medium z-dipole pattern dP/dOmega proportional to sin^2(theta).\n",
    "theta_limit_test = theta_objective_rad\n",
    "theta_test = np.unique(\n",
    "    np.sort(np.append(np.linspace(0.0, np.pi, 1001), theta_limit_test))\n",
    ")\n",
    "phi_test = np.linspace(0.0, 2.0 * np.pi, 721)\n",
    "dipole_z_pattern = np.sin(theta_test)[:, None] ** 2 * np.ones((1, phi_test.size))\n",
    "cone_numeric = float(\n",
    "    integrate_angular_power_array(\n",
    "        dipole_z_pattern,\n",
    "        theta_test,\n",
    "        phi_test,\n",
    "        theta_limit_test,\n",
    "    )\n",
    ")\n",
    "cos_limit = np.cos(theta_limit_test)\n",
    "cone_exact = 2.0 * np.pi * (2.0 / 3.0 - cos_limit + cos_limit**3 / 3.0)\n",
    "assert np.isclose(cone_numeric, cone_exact, rtol=5e-5)\n",
    "print(\"Angular-cone integrator unit test passed.\")\n",
    "\n",
    "\n",
    "def _real_1d(data_array) -> np.ndarray:\n",
    "    return np.real(np.asarray(data_array.values)).reshape(-1)\n",
    "\n",
    "\n",
    "def extract_transition_metrics(sim_data, cfg: ModelConfig) -> pd.DataFrame:\n",
    "    total_flux_da = sim_data[\"total_flux\"].flux\n",
    "    air_flux_da = sim_data[\"air_flux\"].flux\n",
    "    far_data = sim_data[\"far_field\"]\n",
    "\n",
    "    freqs = np.asarray(total_flux_da.coords[\"f\"].values, dtype=float)\n",
    "    p_total = _real_1d(total_flux_da)\n",
    "    p_air_plane = _real_1d(air_flux_da)\n",
    "\n",
    "    theta_obj = np.arcsin(cfg.objective_na / cfg.collection_index)\n",
    "    theta_ff_max = np.deg2rad(cfg.far_theta_max_deg)\n",
    "    p_na = integrate_projected_power_cone(far_data, theta_obj)\n",
    "    p_far_cap = integrate_projected_power_cone(far_data, theta_ff_max)\n",
    "\n",
    "    df = pd.DataFrame(\n",
    "        {\n",
    "            \"frequency_hz\": freqs,\n",
    "            \"wavelength_nm\": 1e3 * td.C_0 / freqs,\n",
    "            \"p_total\": p_total,\n",
    "            \"p_na\": p_na,\n",
    "            \"p_air_plane\": p_air_plane,\n",
    "            \"p_far_cap\": p_far_cap,\n",
    "        }\n",
    "    )\n",
    "    df[\"eta_na\"] = df[\"p_na\"] / df[\"p_total\"]\n",
    "    df[\"eta_air_plane\"] = df[\"p_air_plane\"] / df[\"p_total\"]\n",
    "    df[\"na_fraction_of_air_plane\"] = df[\"p_na\"] / df[\"p_air_plane\"]\n",
    "    return df.sort_values(\"wavelength_nm\").reset_index(drop=True)\n",
    "\n",
    "\n",
    "def combine_nv_transitions(t1: pd.DataFrame, t2: pd.DataFrame) -> pd.DataFrame:\n",
    "    merged = t1.merge(t2, on=[\"frequency_hz\", \"wavelength_nm\"], suffixes=(\"_t1\", \"_t2\"))\n",
    "\n",
    "    out = merged[[\"frequency_hz\", \"wavelength_nm\"]].copy()\n",
    "    for quantity in [\"p_total\", \"p_na\", \"p_air_plane\", \"p_far_cap\"]:\n",
    "        out[quantity] = merged[f\"{quantity}_t1\"] + merged[f\"{quantity}_t2\"]\n",
    "\n",
    "    out[\"eta_collection\"] = out[\"p_na\"] / out[\"p_total\"]\n",
    "    out[\"eta_air_plane\"] = out[\"p_air_plane\"] / out[\"p_total\"]\n",
    "    out[\"na_fraction_of_air_plane\"] = out[\"p_na\"] / out[\"p_air_plane\"]\n",
    "    out[\"eta_t1\"] = merged[\"eta_na_t1\"]\n",
    "    out[\"eta_t2\"] = merged[\"eta_na_t2\"]\n",
    "    return out.sort_values(\"wavelength_nm\").reset_index(drop=True)\n",
    "\n",
    "\n",
    "def process_case(batch_data, case_name: str, cfg: ModelConfig) -> pd.DataFrame:\n",
    "    t1_task_name = f\"{case_name}__t1\"\n",
    "    t2_task_name = f\"{case_name}__t2\"\n",
    "    t1_data = batch_data.load_sim_data(t1_task_name)\n",
    "    t2_data = batch_data.load_sim_data(t2_task_name)\n",
    "    for task_name, sim_data in ((t1_task_name, t1_data), (t2_task_name, t2_data)):\n",
    "        if bool(getattr(sim_data, \"diverged\", False)):\n",
    "            raise RuntimeError(\n",
    "                f\"{task_name} diverged; its monitor data cannot be used for efficiency calculations.\"\n",
    "            )\n",
    "    t1 = extract_transition_metrics(t1_data, cfg)\n",
    "    t2 = extract_transition_metrics(t2_data, cfg)\n",
    "    result = combine_nv_transitions(t1, t2)\n",
    "\n",
    "    result.insert(0, \"case\", case_name)\n",
    "    result[\"geometry\"] = cfg.geometry\n",
    "    result[\"nd_diameter_nm\"] = cfg.nd_diameter_nm\n",
    "    result[\"nv_depth_nm\"] = cfg.nv_depth_nm\n",
    "    result[\"film_thickness_nm\"] = cfg.film_thickness_nm\n",
    "    result[\"nv_beta_deg\"] = cfg.nv_beta_deg\n",
    "    result[\"nv_alpha_deg\"] = cfg.nv_alpha_deg\n",
    "    result[\"mesh_dl_nm\"] = cfg.mesh_dl_nm\n",
    "    result[\"xy_span_um\"] = cfg.xy_span_um\n",
    "    result[\"projection_span_um\"] = cfg.projection_span_um\n",
    "    result[\"air_buffer_um\"] = cfg.air_buffer_um\n",
    "    result[\"substrate_buffer_um\"] = cfg.substrate_buffer_um\n",
    "    result[\"boundary_scheme\"] = (\n",
    "        f\"xy_absorber{cfg.film_absorber_num_layers}_zminus_absorber_zplus_pml\"\n",
    "        if cfg.geometry == \"diamond_film\"\n",
    "        else \"all_pml\"\n",
    "    )\n",
    "    result[\"lateral_monitor_to_pml_um\"] = 0.5 * (\n",
    "        cfg.xy_span_um - cfg.projection_span_um\n",
    "    )\n",
    "    result[\"vertical_monitor_to_pml_um\"] = cfg.air_buffer_um - cfg.projection_gap_um\n",
    "    longest_wavelength_um = 1e-3 * max(cfg.wavelengths_nm)\n",
    "    result[\"lateral_clearance_wavelengths\"] = (\n",
    "        result[\"lateral_monitor_to_pml_um\"] / longest_wavelength_um\n",
    "    )\n",
    "    result[\"vertical_clearance_wavelengths\"] = (\n",
    "        result[\"vertical_monitor_to_pml_um\"] / longest_wavelength_um\n",
    "    )\n",
    "    result[\"diamond_model\"] = cfg.diamond_model\n",
    "    result[\"nv_offset_x_nm\"] = cfg.nv_offset_nm[0]\n",
    "    result[\"nv_offset_y_nm\"] = cfg.nv_offset_nm[1]\n",
    "    result[\"nv_offset_z_nm\"] = cfg.nv_offset_nm[2]\n",
    "    if cfg.geometry == \"nanodiamond\":\n",
    "        result[\"nd_top_surface_depth_nm\"] = (\n",
    "            0.5 * cfg.nd_diameter_nm - cfg.nv_offset_nm[2]\n",
    "        )\n",
    "        result[\"nv_height_above_substrate_nm\"] = (\n",
    "            0.5 * cfg.nd_diameter_nm - cfg.nd_embed_nm + cfg.nv_offset_nm[2]\n",
    "        )\n",
    "    else:\n",
    "        result[\"nd_top_surface_depth_nm\"] = np.nan\n",
    "        result[\"nv_height_above_substrate_nm\"] = np.nan\n",
    "    return result\n",
    "\n",
    "\n",
    "def collect_batch_results(batch_data, cases: Mapping[str, ModelConfig]) -> pd.DataFrame:\n",
    "    frames = [\n",
    "        process_case(batch_data, case_name, cfg) for case_name, cfg in cases.items()\n",
    "    ]\n",
    "    return pd.concat(frames, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e362ec67",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>case</th>\n",
       "      <th>frequency_hz</th>\n",
       "      <th>wavelength_nm</th>\n",
       "      <th>p_total</th>\n",
       "      <th>p_na</th>\n",
       "      <th>p_air_plane</th>\n",
       "      <th>p_far_cap</th>\n",
       "      <th>eta_collection</th>\n",
       "      <th>eta_air_plane</th>\n",
       "      <th>na_fraction_of_air_plane</th>\n",
       "      <th>...</th>\n",
       "      <th>lateral_monitor_to_pml_um</th>\n",
       "      <th>vertical_monitor_to_pml_um</th>\n",
       "      <th>lateral_clearance_wavelengths</th>\n",
       "      <th>vertical_clearance_wavelengths</th>\n",
       "      <th>diamond_model</th>\n",
       "      <th>nv_offset_x_nm</th>\n",
       "      <th>nv_offset_y_nm</th>\n",
       "      <th>nv_offset_z_nm</th>\n",
       "      <th>nd_top_surface_depth_nm</th>\n",
       "      <th>nv_height_above_substrate_nm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>nd40_beta54p7_700nm</td>\n",
       "      <td>4.282749e+14</td>\n",
       "      <td>700.0</td>\n",
       "      <td>1.516450e-27</td>\n",
       "      <td>7.595780e-29</td>\n",
       "      <td>2.843069e-28</td>\n",
       "      <td>2.675983e-28</td>\n",
       "      <td>0.050089</td>\n",
       "      <td>0.187482</td>\n",
       "      <td>0.267168</td>\n",
       "      <td>...</td>\n",
       "      <td>1.6</td>\n",
       "      <td>1.25</td>\n",
       "      <td>2.285714</td>\n",
       "      <td>1.785714</td>\n",
       "      <td>peter_sellmeier</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  case  frequency_hz  wavelength_nm       p_total  \\\n",
       "0  nd40_beta54p7_700nm  4.282749e+14          700.0  1.516450e-27   \n",
       "\n",
       "           p_na   p_air_plane     p_far_cap  eta_collection  eta_air_plane  \\\n",
       "0  7.595780e-29  2.843069e-28  2.675983e-28        0.050089       0.187482   \n",
       "\n",
       "   na_fraction_of_air_plane  ...  lateral_monitor_to_pml_um  \\\n",
       "0                  0.267168  ...                        1.6   \n",
       "\n",
       "   vertical_monitor_to_pml_um lateral_clearance_wavelengths  \\\n",
       "0                        1.25                      2.285714   \n",
       "\n",
       "   vertical_clearance_wavelengths    diamond_model  nv_offset_x_nm  \\\n",
       "0                        1.785714  peter_sellmeier             0.0   \n",
       "\n",
       "   nv_offset_y_nm  nv_offset_z_nm  nd_top_surface_depth_nm  \\\n",
       "0             0.0             0.0                     20.0   \n",
       "\n",
       "   nv_height_above_substrate_nm  \n",
       "0                          20.0  \n",
       "\n",
       "[1 rows x 34 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "if single_data is not None:\n",
    "    single_df = collect_batch_results(single_data, single_cases)\n",
    "    display(single_df)\n",
    "\n",
    "    ax = single_df.plot(\n",
    "        x=\"wavelength_nm\",\n",
    "        y=[\"eta_collection\", \"eta_air_plane\"],\n",
    "        marker=\"o\",\n",
    "        figsize=(7, 4),\n",
    "    )\n",
    "    ax.set_ylabel(\"Efficiency\")\n",
    "    ax.grid(True)\n",
    "    plt.show()\n",
    "else:\n",
    "    print(\n",
    "        \"No baseline BatchData is available. Run the Stage 7 batch cell before post-processing.\"\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b00fb153",
   "metadata": {},
   "source": [
    "## 8. Numerical Convergence Tests\n",
    "\n",
    "Before performing the final physical parameter sweeps, at least the following two convergence tests should be carried out.\n",
    "\n",
    "### 8.1 Local Mesh Convergence\n",
    "\n",
    "The sweep below samples ten local mesh spacings from 20 nm down to 3 nm. The coarsest values intentionally under-resolve a 40 nm nanodiamond and serve only to reveal the convergence trend; final results should be taken from the converged fine-mesh region.\n",
    "\n",
    "An example definition of the relative convergence error is\n",
    "\n",
    "$$\n",
    "\\epsilon_i\n",
    "=\n",
    "\\frac{\n",
    "\\left|\\eta_i-\\eta_{\\mathrm{finest}}\\right|\n",
    "}{\n",
    "\\eta_{\\mathrm{finest}}\n",
    "}.\n",
    "$$\n",
    "\n",
    "### 8.2 Projection-Plane Span Convergence\n",
    "\n",
    "For an open near-to-far-field projection surface, the field amplitude should be sufficiently small near the edges of the projection plane.\n",
    "\n",
    "The parameter `projection_span_um` is therefore increased systematically to verify that $\\eta_{\\mathrm{coll}}$ converges to a stable value. To isolate aperture truncation from domain/PML effects, `xy_span_um` is held fixed at 8.0 µm for every case in this sweep. The largest 4.8 µm projection plane retains 1.6 µm of non-PML clearance on each lateral side, corresponding to about 2.3 vacuum wavelengths at 700 nm.\n",
    "\n",
    "> **Caution:** Convergence with respect to the projection-plane span should not be verified only once for the baseline nanodiamond case. The test should also be repeated for representative conditions in which the angular spectrum may differ significantly, such as the bulk-diamond and diamond-film geometries, the shortest wavelength, and the largest nanodiamond.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "530790ca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mesh convergence cases: ['mesh_20p0nm', 'mesh_16p0nm', 'mesh_14p0nm', 'mesh_12p0nm', 'mesh_10p0nm', 'mesh_8p0nm', 'mesh_6p0nm', 'mesh_5p0nm', 'mesh_4p0nm', 'mesh_3p0nm']\n",
      "Projection-span convergence cases: ['span_0p48um', 'span_0p96um', 'span_1p44um', 'span_1p92um', 'span_2p4um', 'span_2p88um', 'span_3p36um', 'span_3p84um', 'span_4p32um', 'span_4p8um']\n"
     ]
    }
   ],
   "source": [
    "CONVERGENCE_RTOL = 0.01\n",
    "mesh_values_nm = [20.0, 16.0, 14.0, 12.0, 10.0, 8.0, 6.0, 5.0, 4.0, 3.0]\n",
    "mesh_cases = {\n",
    "    f\"mesh_{str(dl).replace('.', 'p')}nm\": replace(base_cfg, mesh_dl_nm=dl)\n",
    "    for dl in mesh_values_nm\n",
    "}\n",
    "\n",
    "projection_spans_um = [0.48, 0.96, 1.44, 1.92, 2.40, 2.88, 3.36, 3.84, 4.32, 4.8]\n",
    "SPAN_SWEEP_FIXED_DOMAIN_UM = LARGE_DOMAIN_XY_SPAN_UM\n",
    "span_cases = {\n",
    "    f\"span_{str(span).replace('.', 'p')}um\": replace(\n",
    "        base_cfg,\n",
    "        projection_span_um=span,\n",
    "        xy_span_um=SPAN_SWEEP_FIXED_DOMAIN_UM,\n",
    "    )\n",
    "    for span in projection_spans_um\n",
    "}\n",
    "\n",
    "print(\"Mesh convergence cases:\", list(mesh_cases))\n",
    "print(\"Projection-span convergence cases:\", list(span_cases))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "stage8-run-mesh",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">21:24:46 KST </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">5.141</span> for the whole batch.                \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m21:24:46 KST\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m5.141\u001b[0m for the whole batch.                \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a26250ae00064045bf05183ac64a6cae",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Estimated maximum batch cost: 5.1412896046428695\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">21:24:48 KST </span>Started working on Batch containing <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">20</span> tasks.                      \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m21:24:48 KST\u001b[0m\u001b[2;36m \u001b[0mStarted working on Batch containing \u001b[1;36m20\u001b[0m tasks.                      \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">21:25:11 KST </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">5.141</span> for the whole batch.                \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m21:25:11 KST\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m5.141\u001b[0m for the whole batch.                \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Use <span style=\"color: #008000; text-decoration-color: #008000\">'Batch.real_cost()'</span> to get the billed FlexCredit cost after    \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>completion.                                                        \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mUse \u001b[32m'Batch.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get the billed FlexCredit cost after    \n",
       "\u001b[2;36m             \u001b[0mcompletion.                                                        \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e65ec11cfcba45828b980a0f0426af6b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">21:25:26 KST </span>Batch complete.                                                    \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m21:25:26 KST\u001b[0m\u001b[2;36m \u001b[0mBatch complete.                                                    \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mesh_batch, _ = prepare_batch(mesh_cases, folder_name=\"NV_collection_mesh_convergence\")\n",
    "mesh_data = run_batch_with_cost_estimate(\n",
    "    mesh_batch, result_dir(\"stage8_mesh_convergence\"), mesh_cases\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "stage8-run-span",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">21:26:39 KST </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">6.791</span> for the whole batch.                \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m21:26:39 KST\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m6.791\u001b[0m for the whole batch.                \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "84258ee71ec34e0fb27a5ee4ae5c0bc0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Estimated maximum batch cost: 6.791108951113984\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">21:26:40 KST </span>Started working on Batch containing <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">20</span> tasks.                      \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m21:26:40 KST\u001b[0m\u001b[2;36m \u001b[0mStarted working on Batch containing \u001b[1;36m20\u001b[0m tasks.                      \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">21:27:04 KST </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">6.791</span> for the whole batch.                \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m21:27:04 KST\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m6.791\u001b[0m for the whole batch.                \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Use <span style=\"color: #008000; text-decoration-color: #008000\">'Batch.real_cost()'</span> to get the billed FlexCredit cost after    \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>completion.                                                        \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mUse \u001b[32m'Batch.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get the billed FlexCredit cost after    \n",
       "\u001b[2;36m             \u001b[0mcompletion.                                                        \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c4aa9972f1444836b6a68e4878aa27f1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">21:27:19 KST </span>Batch complete.                                                    \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m21:27:19 KST\u001b[0m\u001b[2;36m \u001b[0mBatch complete.                                                    \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "span_batch, _ = prepare_batch(span_cases, folder_name=\"NV_collection_span_convergence\")\n",
    "span_data = run_batch_with_cost_estimate(\n",
    "    span_batch, result_dir(\"stage8_span_convergence\"), span_cases\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ce076e30",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mesh_dl_nm</th>\n",
       "      <th>eta_collection</th>\n",
       "      <th>relative_error_to_finest</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20.0</td>\n",
       "      <td>0.047714</td>\n",
       "      <td>0.045617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>16.0</td>\n",
       "      <td>0.049382</td>\n",
       "      <td>0.012258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14.0</td>\n",
       "      <td>0.047336</td>\n",
       "      <td>0.053170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12.0</td>\n",
       "      <td>0.049162</td>\n",
       "      <td>0.016647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10.0</td>\n",
       "      <td>0.049645</td>\n",
       "      <td>0.006999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>8.0</td>\n",
       "      <td>0.050430</td>\n",
       "      <td>0.008715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6.0</td>\n",
       "      <td>0.049406</td>\n",
       "      <td>0.011773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5.0</td>\n",
       "      <td>0.050089</td>\n",
       "      <td>0.001895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4.0</td>\n",
       "      <td>0.050065</td>\n",
       "      <td>0.001405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0.049995</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   mesh_dl_nm  eta_collection  relative_error_to_finest\n",
       "0        20.0        0.047714                  0.045617\n",
       "1        16.0        0.049382                  0.012258\n",
       "2        14.0        0.047336                  0.053170\n",
       "3        12.0        0.049162                  0.016647\n",
       "4        10.0        0.049645                  0.006999\n",
       "5         8.0        0.050430                  0.008715\n",
       "6         6.0        0.049406                  0.011773\n",
       "7         5.0        0.050089                  0.001895\n",
       "8         4.0        0.050065                  0.001405\n",
       "9         3.0        0.049995                  0.000000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>projection_span_um</th>\n",
       "      <th>xy_span_um</th>\n",
       "      <th>eta_collection</th>\n",
       "      <th>lateral_monitor_to_pml_um</th>\n",
       "      <th>lateral_clearance_wavelengths</th>\n",
       "      <th>p_total</th>\n",
       "      <th>p_na</th>\n",
       "      <th>p_air_plane</th>\n",
       "      <th>eta_air_plane</th>\n",
       "      <th>na_fraction_of_air_plane</th>\n",
       "      <th>relative_change_from_previous</th>\n",
       "      <th>relative_error_to_largest_span</th>\n",
       "      <th>converged_to_largest_span</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.48</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.036422</td>\n",
       "      <td>3.76</td>\n",
       "      <td>5.371429</td>\n",
       "      <td>1.516450e-27</td>\n",
       "      <td>5.523265e-29</td>\n",
       "      <td>2.090975e-28</td>\n",
       "      <td>0.137886</td>\n",
       "      <td>0.264148</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.272851</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.96</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.067806</td>\n",
       "      <td>3.52</td>\n",
       "      <td>5.028571</td>\n",
       "      <td>1.516450e-27</td>\n",
       "      <td>1.028241e-28</td>\n",
       "      <td>2.922718e-28</td>\n",
       "      <td>0.192734</td>\n",
       "      <td>0.351810</td>\n",
       "      <td>0.861654</td>\n",
       "      <td>0.353700</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.44</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.056733</td>\n",
       "      <td>3.28</td>\n",
       "      <td>4.685714</td>\n",
       "      <td>1.516450e-27</td>\n",
       "      <td>8.603213e-29</td>\n",
       "      <td>3.006119e-28</td>\n",
       "      <td>0.198234</td>\n",
       "      <td>0.286190</td>\n",
       "      <td>0.163308</td>\n",
       "      <td>0.132631</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.92</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.051002</td>\n",
       "      <td>3.04</td>\n",
       "      <td>4.342857</td>\n",
       "      <td>1.516450e-27</td>\n",
       "      <td>7.734229e-29</td>\n",
       "      <td>2.962144e-28</td>\n",
       "      <td>0.195334</td>\n",
       "      <td>0.261102</td>\n",
       "      <td>0.101007</td>\n",
       "      <td>0.018227</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.40</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.048615</td>\n",
       "      <td>2.80</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.516450e-27</td>\n",
       "      <td>7.372293e-29</td>\n",
       "      <td>2.922630e-28</td>\n",
       "      <td>0.192728</td>\n",
       "      <td>0.252249</td>\n",
       "      <td>0.046797</td>\n",
       "      <td>0.029423</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2.88</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.048894</td>\n",
       "      <td>2.56</td>\n",
       "      <td>3.657143</td>\n",
       "      <td>1.516450e-27</td>\n",
       "      <td>7.414571e-29</td>\n",
       "      <td>2.899769e-28</td>\n",
       "      <td>0.191221</td>\n",
       "      <td>0.255695</td>\n",
       "      <td>0.005735</td>\n",
       "      <td>0.023857</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3.36</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.049766</td>\n",
       "      <td>2.32</td>\n",
       "      <td>3.314286</td>\n",
       "      <td>1.516450e-27</td>\n",
       "      <td>7.546727e-29</td>\n",
       "      <td>2.884043e-28</td>\n",
       "      <td>0.190184</td>\n",
       "      <td>0.261672</td>\n",
       "      <td>0.017824</td>\n",
       "      <td>0.006458</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3.84</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.050361</td>\n",
       "      <td>2.08</td>\n",
       "      <td>2.971429</td>\n",
       "      <td>1.516450e-27</td>\n",
       "      <td>7.636956e-29</td>\n",
       "      <td>2.869337e-28</td>\n",
       "      <td>0.189214</td>\n",
       "      <td>0.266158</td>\n",
       "      <td>0.011956</td>\n",
       "      <td>0.005421</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4.32</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.050365</td>\n",
       "      <td>1.84</td>\n",
       "      <td>2.628571</td>\n",
       "      <td>1.516450e-27</td>\n",
       "      <td>7.637595e-29</td>\n",
       "      <td>2.855175e-28</td>\n",
       "      <td>0.188280</td>\n",
       "      <td>0.267500</td>\n",
       "      <td>0.000084</td>\n",
       "      <td>0.005505</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4.80</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.050089</td>\n",
       "      <td>1.60</td>\n",
       "      <td>2.285714</td>\n",
       "      <td>1.516450e-27</td>\n",
       "      <td>7.595780e-29</td>\n",
       "      <td>2.843069e-28</td>\n",
       "      <td>0.187482</td>\n",
       "      <td>0.267168</td>\n",
       "      <td>0.005475</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   projection_span_um  xy_span_um  eta_collection  lateral_monitor_to_pml_um  \\\n",
       "0                0.48         8.0        0.036422                       3.76   \n",
       "1                0.96         8.0        0.067806                       3.52   \n",
       "2                1.44         8.0        0.056733                       3.28   \n",
       "3                1.92         8.0        0.051002                       3.04   \n",
       "4                2.40         8.0        0.048615                       2.80   \n",
       "5                2.88         8.0        0.048894                       2.56   \n",
       "6                3.36         8.0        0.049766                       2.32   \n",
       "7                3.84         8.0        0.050361                       2.08   \n",
       "8                4.32         8.0        0.050365                       1.84   \n",
       "9                4.80         8.0        0.050089                       1.60   \n",
       "\n",
       "   lateral_clearance_wavelengths       p_total          p_na   p_air_plane  \\\n",
       "0                       5.371429  1.516450e-27  5.523265e-29  2.090975e-28   \n",
       "1                       5.028571  1.516450e-27  1.028241e-28  2.922718e-28   \n",
       "2                       4.685714  1.516450e-27  8.603213e-29  3.006119e-28   \n",
       "3                       4.342857  1.516450e-27  7.734229e-29  2.962144e-28   \n",
       "4                       4.000000  1.516450e-27  7.372293e-29  2.922630e-28   \n",
       "5                       3.657143  1.516450e-27  7.414571e-29  2.899769e-28   \n",
       "6                       3.314286  1.516450e-27  7.546727e-29  2.884043e-28   \n",
       "7                       2.971429  1.516450e-27  7.636956e-29  2.869337e-28   \n",
       "8                       2.628571  1.516450e-27  7.637595e-29  2.855175e-28   \n",
       "9                       2.285714  1.516450e-27  7.595780e-29  2.843069e-28   \n",
       "\n",
       "   eta_air_plane  na_fraction_of_air_plane  relative_change_from_previous  \\\n",
       "0       0.137886                  0.264148                            NaN   \n",
       "1       0.192734                  0.351810                       0.861654   \n",
       "2       0.198234                  0.286190                       0.163308   \n",
       "3       0.195334                  0.261102                       0.101007   \n",
       "4       0.192728                  0.252249                       0.046797   \n",
       "5       0.191221                  0.255695                       0.005735   \n",
       "6       0.190184                  0.261672                       0.017824   \n",
       "7       0.189214                  0.266158                       0.011956   \n",
       "8       0.188280                  0.267500                       0.000084   \n",
       "9       0.187482                  0.267168                       0.005475   \n",
       "\n",
       "   relative_error_to_largest_span  converged_to_largest_span  \n",
       "0                        0.272851                      False  \n",
       "1                        0.353700                      False  \n",
       "2                        0.132631                      False  \n",
       "3                        0.018227                      False  \n",
       "4                        0.029423                      False  \n",
       "5                        0.023857                      False  \n",
       "6                        0.006458                       True  \n",
       "7                        0.005421                       True  \n",
       "8                        0.005505                       True  \n",
       "9                        0.000000                       True  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Largest-span consecutive change: 0.55%. Confirm this plateau with the independent domain/PML sweeps in Stage 8B.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mesh_data = globals().get(\"mesh_data\")\n",
    "span_data = globals().get(\"span_data\")\n",
    "\n",
    "if mesh_data is not None:\n",
    "    mesh_df = collect_batch_results(mesh_data, mesh_cases)\n",
    "    mesh_one = mesh_df.loc[mesh_df.groupby(\"case\")[\"wavelength_nm\"].idxmin()].copy()\n",
    "    mesh_one = mesh_one.sort_values(\"mesh_dl_nm\", ascending=False)\n",
    "    eta_ref = mesh_one.sort_values(\"mesh_dl_nm\").iloc[0][\"eta_collection\"]\n",
    "    mesh_one[\"relative_error_to_finest\"] = np.abs(\n",
    "        mesh_one[\"eta_collection\"] - eta_ref\n",
    "    ) / abs(eta_ref)\n",
    "    display(mesh_one[[\"mesh_dl_nm\", \"eta_collection\", \"relative_error_to_finest\"]])\n",
    "\n",
    "    ax = mesh_one.plot(x=\"mesh_dl_nm\", y=\"eta_collection\", marker=\"o\", figsize=(7, 4))\n",
    "    ax.invert_xaxis()\n",
    "    ax.set_ylabel(\"Collection efficiency\")\n",
    "    ax.grid(True)\n",
    "    plt.show()\n",
    "\n",
    "if span_data is not None:\n",
    "    span_df = collect_batch_results(span_data, span_cases)\n",
    "    span_one = span_df.loc[span_df.groupby(\"case\")[\"wavelength_nm\"].idxmin()].copy()\n",
    "    span_one = span_one.sort_values(\"projection_span_um\")\n",
    "    eta_ref = span_one.iloc[-1][\"eta_collection\"]\n",
    "    span_one[\"relative_error_to_largest_span\"] = np.abs(\n",
    "        span_one[\"eta_collection\"] - eta_ref\n",
    "    ) / abs(eta_ref)\n",
    "    span_one[\"relative_change_from_previous\"] = (\n",
    "        span_one[\"eta_collection\"].pct_change().abs()\n",
    "    )\n",
    "    span_one[\"converged_to_largest_span\"] = (\n",
    "        span_one[\"relative_error_to_largest_span\"] <= CONVERGENCE_RTOL\n",
    "    )\n",
    "    span_columns = [\n",
    "        \"projection_span_um\",\n",
    "        \"xy_span_um\",\n",
    "        \"eta_collection\",\n",
    "        \"lateral_monitor_to_pml_um\",\n",
    "        \"lateral_clearance_wavelengths\",\n",
    "        \"p_total\",\n",
    "        \"p_na\",\n",
    "        \"p_air_plane\",\n",
    "        \"eta_air_plane\",\n",
    "        \"na_fraction_of_air_plane\",\n",
    "        \"relative_change_from_previous\",\n",
    "        \"relative_error_to_largest_span\",\n",
    "        \"converged_to_largest_span\",\n",
    "    ]\n",
    "    display(span_one[span_columns])\n",
    "    final_step_change = float(span_one.iloc[-1][\"relative_change_from_previous\"])\n",
    "    if final_step_change > CONVERGENCE_RTOL:\n",
    "        print(\n",
    "            f\"WARNING: the two largest projection spans differ by {final_step_change:.2%}; \"\n",
    "            \"the aperture sweep has not met the 1% consecutive-step criterion.\"\n",
    "        )\n",
    "    else:\n",
    "        print(\n",
    "            f\"Largest-span consecutive change: {final_step_change:.2%}. \"\n",
    "            \"Confirm this plateau with the independent domain/PML sweeps in Stage 8B.\"\n",
    "        )\n",
    "\n",
    "    fig, axes = plt.subplots(2, 1, figsize=(7, 7), sharex=True)\n",
    "    span_one.plot(\n",
    "        x=\"projection_span_um\",\n",
    "        y=[\"eta_collection\", \"eta_air_plane\"],\n",
    "        marker=\"o\",\n",
    "        ax=axes[0],\n",
    "    )\n",
    "    axes[0].set_ylabel(\"Efficiency\")\n",
    "    axes[0].grid(True)\n",
    "    span_one.plot(\n",
    "        x=\"projection_span_um\",\n",
    "        y=\"relative_error_to_largest_span\",\n",
    "        marker=\"o\",\n",
    "        ax=axes[1],\n",
    "        legend=False,\n",
    "    )\n",
    "    axes[1].axhline(\n",
    "        CONVERGENCE_RTOL, color=\"tab:red\", linestyle=\"--\", label=\"1% criterion\"\n",
    "    )\n",
    "    axes[1].set_ylabel(\"Relative error\")\n",
    "    axes[1].grid(True)\n",
    "    axes[1].legend()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "stage8b-domain-aperture",
   "metadata": {},
   "source": [
    "## 8B. Independent Lateral- and Vertical-PML Convergence Tests\n",
    "\n",
    "The projection-aperture convergence test is already performed in Stage 8.2 with a fixed 8.0 µm lateral domain. This stage independently tests the lateral and vertical distances to the PML.\n",
    "\n",
    "For the **lateral domain/PML test**, `projection_span_um` is held fixed at 4.8 µm while `xy_span_um` is varied from 6.0 to 9.0 µm. This includes direct comparisons at 6, 7, 8, and 9 µm.\n",
    "\n",
    "For the **vertical PML test**, `xy_span_um=8.0` µm and `projection_span_um=4.8` µm are fixed while `air_buffer_um` is varied from 0.7 to 2.8 µm. Since the projection plane is `projection_gap_um=0.15` µm above the object, its distance to the +z PML start is `air_buffer_um - projection_gap_um`.\n",
    "\n",
    "The 0.7 µm air-buffer case is retained only as an intentionally close diagnostic point. The baseline uses 1.4 µm. A half-wavelength clearance is treated only as a construction guard; numerical convergence, rather than that guard, determines the final accepted distance.\n",
    "\n",
    "For each sweep, the `eta_collection` value obtained from the largest tested case is used as the reference. The relative error and the corresponding convergence pass/fail criterion are then evaluated with respect to this reference value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "stage8b-build-cases",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Domain/PML cases: ['domain_6p0um', 'domain_6p5um', 'domain_7p0um', 'domain_7p5um', 'domain_8p0um', 'domain_8p5um', 'domain_9p0um']\n",
      "Air-buffer/PML cases: ['air_buffer_0p7um', 'air_buffer_1p0um', 'air_buffer_1p4um', 'air_buffer_1p8um', 'air_buffer_2p2um', 'air_buffer_2p8um']\n",
      "Total FDTD simulations: 26\n"
     ]
    }
   ],
   "source": [
    "DOMAIN_SWEEP_FIXED_PROJECTION_UM = LARGE_PROJECTION_SPAN_UM\n",
    "# With a fixed 3.2 um projection plane, the monitor-to-PML-start clearance\n",
    "# increases from 0.90 um to 2.65 um across this sweep.\n",
    "domain_xy_spans_um = [6.0, 6.5, 7.0, 7.5, 8.0, 8.5, 9.0]\n",
    "domain_cases = {\n",
    "    f\"domain_{str(xy).replace('.', 'p')}um\": replace(\n",
    "        base_cfg,\n",
    "        xy_span_um=xy,\n",
    "        projection_span_um=DOMAIN_SWEEP_FIXED_PROJECTION_UM,\n",
    "    )\n",
    "    for xy in domain_xy_spans_um\n",
    "}\n",
    "\n",
    "air_buffer_values_um = [0.70, 1.00, 1.40, 1.80, 2.20, 2.80]\n",
    "air_buffer_cases = {\n",
    "    f\"air_buffer_{str(buffer).replace('.', 'p')}um\": replace(\n",
    "        base_cfg,\n",
    "        air_buffer_um=buffer,\n",
    "        xy_span_um=LARGE_DOMAIN_XY_SPAN_UM,\n",
    "        projection_span_um=LARGE_PROJECTION_SPAN_UM,\n",
    "    )\n",
    "    for buffer in air_buffer_values_um\n",
    "}\n",
    "print(\"Domain/PML cases:\", list(domain_cases))\n",
    "print(\"Air-buffer/PML cases:\", list(air_buffer_cases))\n",
    "print(\"Total FDTD simulations:\", 2 * (len(domain_cases) + len(air_buffer_cases)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "stage8b-run-domain",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">21:28:12 KST </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">4.346</span> for the whole batch.                \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m21:28:12 KST\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m4.346\u001b[0m for the whole batch.                \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7ae2e4c1b19c4e24ba84b9c4efce3f32",
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       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Estimated maximum batch cost: 4.34630537629622\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">21:28:13 KST </span>Started working on Batch containing <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">14</span> tasks.                      \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m21:28:13 KST\u001b[0m\u001b[2;36m \u001b[0mStarted working on Batch containing \u001b[1;36m14\u001b[0m tasks.                      \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">WARNING: No connection: Retrying for </span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">180</span><span style=\"color: #800000; text-decoration-color: #800000\"> seconds.                  </span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING: No connection: Retrying for \u001b[0m\u001b[1;36m180\u001b[0m\u001b[31m seconds.                  \u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">21:28:32 KST </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">4.346</span> for the whole batch.                \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m21:28:32 KST\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m4.346\u001b[0m for the whole batch.                \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Use <span style=\"color: #008000; text-decoration-color: #008000\">'Batch.real_cost()'</span> to get the billed FlexCredit cost after    \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>completion.                                                        \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mUse \u001b[32m'Batch.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get the billed FlexCredit cost after    \n",
       "\u001b[2;36m             \u001b[0mcompletion.                                                        \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b2296da394b94f659ccb1cfd4699953e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">21:28:43 KST </span>Batch complete.                                                    \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m21:28:43 KST\u001b[0m\u001b[2;36m \u001b[0mBatch complete.                                                    \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "domain_batch, _ = prepare_batch(\n",
    "    domain_cases, folder_name=\"NV_collection_domain_pml_convergence\"\n",
    ")\n",
    "domain_data = run_batch_with_cost_estimate(\n",
    "    domain_batch, result_dir(\"stage8b_domain_pml_convergence\"), domain_cases\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "stage8b-run-air-buffer",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">21:29:27 KST </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">4.186</span> for the whole batch.                \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m21:29:27 KST\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m4.186\u001b[0m for the whole batch.                \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fdab0ad3ae3f40d4ac3c71cbe1c8b627",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Estimated maximum batch cost: 4.186049905271815\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">21:29:28 KST </span>Started working on Batch containing <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">12</span> tasks.                      \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m21:29:28 KST\u001b[0m\u001b[2;36m \u001b[0mStarted working on Batch containing \u001b[1;36m12\u001b[0m tasks.                      \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">21:29:43 KST </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">4.186</span> for the whole batch.                \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m21:29:43 KST\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m4.186\u001b[0m for the whole batch.                \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Use <span style=\"color: #008000; text-decoration-color: #008000\">'Batch.real_cost()'</span> to get the billed FlexCredit cost after    \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>completion.                                                        \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mUse \u001b[32m'Batch.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get the billed FlexCredit cost after    \n",
       "\u001b[2;36m             \u001b[0mcompletion.                                                        \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8b6331b948414a8c97d64ea63ba3a76b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">21:29:53 KST </span>Batch complete.                                                    \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m21:29:53 KST\u001b[0m\u001b[2;36m \u001b[0mBatch complete.                                                    \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "air_buffer_batch, _ = prepare_batch(\n",
    "    air_buffer_cases, folder_name=\"NV_collection_air_buffer_pml_convergence\"\n",
    ")\n",
    "air_buffer_data = run_batch_with_cost_estimate(\n",
    "    air_buffer_batch, result_dir(\"stage8b_air_buffer_pml_convergence\"), air_buffer_cases\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "stage8b-analyze-cases",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>wavelength_nm</th>\n",
       "      <th>xy_span_um</th>\n",
       "      <th>projection_span_um</th>\n",
       "      <th>eta_collection</th>\n",
       "      <th>lateral_monitor_to_pml_um</th>\n",
       "      <th>vertical_monitor_to_pml_um</th>\n",
       "      <th>lateral_clearance_wavelengths</th>\n",
       "      <th>vertical_clearance_wavelengths</th>\n",
       "      <th>eta_air_plane</th>\n",
       "      <th>relative_change_from_previous</th>\n",
       "      <th>eta_reference</th>\n",
       "      <th>relative_error_to_largest</th>\n",
       "      <th>converged</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>700.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.8</td>\n",
       "      <td>0.050102</td>\n",
       "      <td>0.60</td>\n",
       "      <td>1.25</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>1.785714</td>\n",
       "      <td>0.187543</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.050093</td>\n",
       "      <td>0.000195</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>700.0</td>\n",
       "      <td>6.5</td>\n",
       "      <td>4.8</td>\n",
       "      <td>0.050092</td>\n",
       "      <td>0.85</td>\n",
       "      <td>1.25</td>\n",
       "      <td>1.214286</td>\n",
       "      <td>1.785714</td>\n",
       "      <td>0.187494</td>\n",
       "      <td>0.000207</td>\n",
       "      <td>0.050093</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>700.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.8</td>\n",
       "      <td>0.050092</td>\n",
       "      <td>1.10</td>\n",
       "      <td>1.25</td>\n",
       "      <td>1.571429</td>\n",
       "      <td>1.785714</td>\n",
       "      <td>0.187496</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.050093</td>\n",
       "      <td>0.000018</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>700.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>4.8</td>\n",
       "      <td>0.050097</td>\n",
       "      <td>1.35</td>\n",
       "      <td>1.25</td>\n",
       "      <td>1.928571</td>\n",
       "      <td>1.785714</td>\n",
       "      <td>0.187516</td>\n",
       "      <td>0.000100</td>\n",
       "      <td>0.050093</td>\n",
       "      <td>0.000083</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>700.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.8</td>\n",
       "      <td>0.050089</td>\n",
       "      <td>1.60</td>\n",
       "      <td>1.25</td>\n",
       "      <td>2.285714</td>\n",
       "      <td>1.785714</td>\n",
       "      <td>0.187482</td>\n",
       "      <td>0.000151</td>\n",
       "      <td>0.050093</td>\n",
       "      <td>0.000068</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>700.0</td>\n",
       "      <td>8.5</td>\n",
       "      <td>4.8</td>\n",
       "      <td>0.050101</td>\n",
       "      <td>1.85</td>\n",
       "      <td>1.25</td>\n",
       "      <td>2.642857</td>\n",
       "      <td>1.785714</td>\n",
       "      <td>0.187535</td>\n",
       "      <td>0.000228</td>\n",
       "      <td>0.050093</td>\n",
       "      <td>0.000160</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>700.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>4.8</td>\n",
       "      <td>0.050093</td>\n",
       "      <td>2.10</td>\n",
       "      <td>1.25</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.785714</td>\n",
       "      <td>0.187497</td>\n",
       "      <td>0.000160</td>\n",
       "      <td>0.050093</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   wavelength_nm  xy_span_um  projection_span_um  eta_collection  \\\n",
       "0          700.0         6.0                 4.8        0.050102   \n",
       "1          700.0         6.5                 4.8        0.050092   \n",
       "2          700.0         7.0                 4.8        0.050092   \n",
       "3          700.0         7.5                 4.8        0.050097   \n",
       "4          700.0         8.0                 4.8        0.050089   \n",
       "5          700.0         8.5                 4.8        0.050101   \n",
       "6          700.0         9.0                 4.8        0.050093   \n",
       "\n",
       "   lateral_monitor_to_pml_um  vertical_monitor_to_pml_um  \\\n",
       "0                       0.60                        1.25   \n",
       "1                       0.85                        1.25   \n",
       "2                       1.10                        1.25   \n",
       "3                       1.35                        1.25   \n",
       "4                       1.60                        1.25   \n",
       "5                       1.85                        1.25   \n",
       "6                       2.10                        1.25   \n",
       "\n",
       "   lateral_clearance_wavelengths  vertical_clearance_wavelengths  \\\n",
       "0                       0.857143                        1.785714   \n",
       "1                       1.214286                        1.785714   \n",
       "2                       1.571429                        1.785714   \n",
       "3                       1.928571                        1.785714   \n",
       "4                       2.285714                        1.785714   \n",
       "5                       2.642857                        1.785714   \n",
       "6                       3.000000                        1.785714   \n",
       "\n",
       "   eta_air_plane  relative_change_from_previous  eta_reference  \\\n",
       "0       0.187543                            NaN       0.050093   \n",
       "1       0.187494                       0.000207       0.050093   \n",
       "2       0.187496                       0.000005       0.050093   \n",
       "3       0.187516                       0.000100       0.050093   \n",
       "4       0.187482                       0.000151       0.050093   \n",
       "5       0.187535                       0.000228       0.050093   \n",
       "6       0.187497                       0.000160       0.050093   \n",
       "\n",
       "   relative_error_to_largest  converged  \n",
       "0                   0.000195       True  \n",
       "1                   0.000013       True  \n",
       "2                   0.000018       True  \n",
       "3                   0.000083       True  \n",
       "4                   0.000068       True  \n",
       "5                   0.000160       True  \n",
       "6                   0.000000       True  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>wavelength_nm</th>\n",
       "      <th>air_buffer_um</th>\n",
       "      <th>projection_span_um</th>\n",
       "      <th>eta_collection</th>\n",
       "      <th>lateral_monitor_to_pml_um</th>\n",
       "      <th>vertical_monitor_to_pml_um</th>\n",
       "      <th>lateral_clearance_wavelengths</th>\n",
       "      <th>vertical_clearance_wavelengths</th>\n",
       "      <th>eta_air_plane</th>\n",
       "      <th>relative_change_from_previous</th>\n",
       "      <th>eta_reference</th>\n",
       "      <th>relative_error_to_largest</th>\n",
       "      <th>converged</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>700.0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>4.8</td>\n",
       "      <td>0.050091</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.55</td>\n",
       "      <td>2.285714</td>\n",
       "      <td>0.785714</td>\n",
       "      <td>0.187484</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.050089</td>\n",
       "      <td>0.000042</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>700.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.8</td>\n",
       "      <td>0.050077</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.85</td>\n",
       "      <td>2.285714</td>\n",
       "      <td>1.214286</td>\n",
       "      <td>0.187468</td>\n",
       "      <td>0.000291</td>\n",
       "      <td>0.050089</td>\n",
       "      <td>0.000249</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>700.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>4.8</td>\n",
       "      <td>0.050089</td>\n",
       "      <td>1.6</td>\n",
       "      <td>1.25</td>\n",
       "      <td>2.285714</td>\n",
       "      <td>1.785714</td>\n",
       "      <td>0.187482</td>\n",
       "      <td>0.000246</td>\n",
       "      <td>0.050089</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>700.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>4.8</td>\n",
       "      <td>0.050082</td>\n",
       "      <td>1.6</td>\n",
       "      <td>1.65</td>\n",
       "      <td>2.285714</td>\n",
       "      <td>2.357143</td>\n",
       "      <td>0.187477</td>\n",
       "      <td>0.000142</td>\n",
       "      <td>0.050089</td>\n",
       "      <td>0.000145</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>700.0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>4.8</td>\n",
       "      <td>0.050079</td>\n",
       "      <td>1.6</td>\n",
       "      <td>2.05</td>\n",
       "      <td>2.285714</td>\n",
       "      <td>2.928571</td>\n",
       "      <td>0.187468</td>\n",
       "      <td>0.000062</td>\n",
       "      <td>0.050089</td>\n",
       "      <td>0.000207</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>700.0</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.8</td>\n",
       "      <td>0.050089</td>\n",
       "      <td>1.6</td>\n",
       "      <td>2.65</td>\n",
       "      <td>2.285714</td>\n",
       "      <td>3.785714</td>\n",
       "      <td>0.187481</td>\n",
       "      <td>0.000207</td>\n",
       "      <td>0.050089</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   wavelength_nm  air_buffer_um  projection_span_um  eta_collection  \\\n",
       "0          700.0            0.7                 4.8        0.050091   \n",
       "1          700.0            1.0                 4.8        0.050077   \n",
       "2          700.0            1.4                 4.8        0.050089   \n",
       "3          700.0            1.8                 4.8        0.050082   \n",
       "4          700.0            2.2                 4.8        0.050079   \n",
       "5          700.0            2.8                 4.8        0.050089   \n",
       "\n",
       "   lateral_monitor_to_pml_um  vertical_monitor_to_pml_um  \\\n",
       "0                        1.6                        0.55   \n",
       "1                        1.6                        0.85   \n",
       "2                        1.6                        1.25   \n",
       "3                        1.6                        1.65   \n",
       "4                        1.6                        2.05   \n",
       "5                        1.6                        2.65   \n",
       "\n",
       "   lateral_clearance_wavelengths  vertical_clearance_wavelengths  \\\n",
       "0                       2.285714                        0.785714   \n",
       "1                       2.285714                        1.214286   \n",
       "2                       2.285714                        1.785714   \n",
       "3                       2.285714                        2.357143   \n",
       "4                       2.285714                        2.928571   \n",
       "5                       2.285714                        3.785714   \n",
       "\n",
       "   eta_air_plane  relative_change_from_previous  eta_reference  \\\n",
       "0       0.187484                            NaN       0.050089   \n",
       "1       0.187468                       0.000291       0.050089   \n",
       "2       0.187482                       0.000246       0.050089   \n",
       "3       0.187477                       0.000142       0.050089   \n",
       "4       0.187468                       0.000062       0.050089   \n",
       "5       0.187481                       0.000207       0.050089   \n",
       "\n",
       "   relative_error_to_largest  converged  \n",
       "0                   0.000042       True  \n",
       "1                   0.000249       True  \n",
       "2                   0.000003       True  \n",
       "3                   0.000145       True  \n",
       "4                   0.000207       True  \n",
       "5                   0.000000       True  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def convergence_against_largest_case(\n",
    "    result_df: pd.DataFrame, x_col: str, rtol: float = CONVERGENCE_RTOL\n",
    ") -> pd.DataFrame:\n",
    "    \"\"\"Compare eta_collection at each wavelength with the largest-x case.\"\"\"\n",
    "    largest_x = float(result_df[x_col].max())\n",
    "    reference = result_df.loc[\n",
    "        np.isclose(result_df[x_col], largest_x), [\"wavelength_nm\", \"eta_collection\"]\n",
    "    ].rename(columns={\"eta_collection\": \"eta_reference\"})\n",
    "    out = result_df.merge(\n",
    "        reference, on=\"wavelength_nm\", how=\"left\", validate=\"many_to_one\"\n",
    "    )\n",
    "    denominator = np.maximum(np.abs(out[\"eta_reference\"]), np.finfo(float).eps)\n",
    "    out[\"relative_error_to_largest\"] = (\n",
    "        np.abs(out[\"eta_collection\"] - out[\"eta_reference\"]) / denominator\n",
    "    )\n",
    "    numerical_slack = 10.0 * np.finfo(float).eps\n",
    "    out[\"converged\"] = out[\"relative_error_to_largest\"] <= rtol + numerical_slack\n",
    "    out = out.sort_values([\"wavelength_nm\", x_col]).reset_index(drop=True)\n",
    "    out[\"relative_change_from_previous\"] = (\n",
    "        out.groupby(\"wavelength_nm\")[\"eta_collection\"].pct_change().abs()\n",
    "    )\n",
    "    return out\n",
    "\n",
    "\n",
    "def report_size_convergence(batch_data, cases, x_col: str, output_csv: Path):\n",
    "    if batch_data is None:\n",
    "        print(f\"{x_col}: no cloud result loaded.\")\n",
    "        return None\n",
    "    collected = collect_batch_results(batch_data, cases)\n",
    "    if x_col not in collected.columns:\n",
    "        collected[x_col] = collected[\"case\"].map(\n",
    "            {name: getattr(cfg, x_col) for name, cfg in cases.items()}\n",
    "        )\n",
    "    summary = convergence_against_largest_case(collected, x_col)\n",
    "    output_csv.parent.mkdir(parents=True, exist_ok=True)\n",
    "    summary.to_csv(output_csv, index=False)\n",
    "    cols = [\n",
    "        \"wavelength_nm\",\n",
    "        x_col,\n",
    "        \"projection_span_um\",\n",
    "        \"eta_collection\",\n",
    "        \"lateral_monitor_to_pml_um\",\n",
    "        \"vertical_monitor_to_pml_um\",\n",
    "        \"lateral_clearance_wavelengths\",\n",
    "        \"vertical_clearance_wavelengths\",\n",
    "        \"eta_air_plane\",\n",
    "        \"relative_change_from_previous\",\n",
    "        \"eta_reference\",\n",
    "        \"relative_error_to_largest\",\n",
    "        \"converged\",\n",
    "    ]\n",
    "    display(summary[cols])\n",
    "    _fig, ax = plt.subplots(figsize=(7, 4))\n",
    "    for wavelength_nm, group in summary.groupby(\"wavelength_nm\"):\n",
    "        group = group.sort_values(x_col)\n",
    "        ax.plot(\n",
    "            group[x_col],\n",
    "            group[\"eta_collection\"],\n",
    "            marker=\"o\",\n",
    "            label=f\"{wavelength_nm:g} nm\",\n",
    "        )\n",
    "    ax.set(xlabel=x_col, ylabel=\"Collection efficiency\")\n",
    "    ax.grid(True)\n",
    "    ax.legend()\n",
    "    plt.show()\n",
    "    return summary\n",
    "\n",
    "\n",
    "domain_convergence_df = report_size_convergence(\n",
    "    globals().get(\"domain_data\"),\n",
    "    domain_cases,\n",
    "    \"xy_span_um\",\n",
    "    result_dir(\"stage8b_domain_pml_convergence\") / \"domain_convergence.csv\",\n",
    ")\n",
    "air_buffer_convergence_df = report_size_convergence(\n",
    "    globals().get(\"air_buffer_data\"),\n",
    "    air_buffer_cases,\n",
    "    \"air_buffer_um\",\n",
    "    result_dir(\"stage8b_air_buffer_pml_convergence\") / \"air_buffer_convergence.csv\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8cba843",
   "metadata": {},
   "source": [
    "## 9. Nanodiamond Diameter Sweep\n",
    "\n",
    "Changing the nanodiamond diameter can simultaneously affect:\n",
    "\n",
    "- The local density of optical states (LDOS) around the NV center\n",
    "- Interference associated with the diamond–air and diamond–silica interfaces\n",
    "- The angular radiation pattern of the dielectric particle\n",
    "- Resonant effects when the particle size becomes comparable to the wavelength inside the diamond\n",
    "\n",
    "For a diameter sweep, the definition of the NV position must be specified explicitly.\n",
    "\n",
    "1. **`center`**: The NV center is placed at the center of the nanodiamond for every diameter. In this case, the distance from the NV center to the top surface is $d/2$, so the nanodiamond diameter and the NV surface depth vary simultaneously.\n",
    "\n",
    "2. **`fixed_top_depth`**: The NV center is maintained at a fixed depth below the top surface for every nanodiamond diameter. This partially separates the effect of particle geometry from variations in the top-surface depth. However, the distance to the substrate and the distances to the lateral surfaces may still vary with nanodiamond diameter.\n",
    "\n",
    "The default setting below is `center`. By changing `DIAMETER_POSITION_MODE`, the two cases can be calculated and stored in separate directories for direct comparison.\n",
    "\n",
    "Representative NV-axis polar angles are $0^\\circ$, $54.7356^\\circ$, and $90^\\circ$.\n",
    "\n",
    "For small nanodiamonds, the local mesh spacing is automatically constrained to a smaller value to ensure a sufficient number of grid cells across the particle diameter.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "bcd8cf11",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NV position mode: center\n",
      "Cases: 15\n",
      "FDTD simulations including two transitions: 30\n"
     ]
    }
   ],
   "source": [
    "diameters_nm = [20.0, 40.0, 60.0, 80.0, 100.0]\n",
    "representative_betas_deg = [0.0, 54.7356, 90.0]\n",
    "\n",
    "DIAMETER_POSITION_MODE = \"center\"\n",
    "FIXED_TOP_DEPTH_NM = 10.0\n",
    "CONVERGED_MESH_NM = 5.0\n",
    "\n",
    "\n",
    "def nd_offset_for_top_surface_depth(\n",
    "    diameter_nm: float,\n",
    "    top_depth_nm: float,\n",
    ") -> tuple[float, float, float]:\n",
    "    \"\"\"Return sphere-center-referenced NV offset for a specified top-surface depth.\"\"\"\n",
    "    if not (0.0 < top_depth_nm < diameter_nm):\n",
    "        raise ValueError(\"Require 0 < top_depth_nm < diameter_nm.\")\n",
    "    radius_nm = 0.5 * diameter_nm\n",
    "    return (0.0, 0.0, radius_nm - top_depth_nm)\n",
    "\n",
    "\n",
    "if DIAMETER_POSITION_MODE not in {\"center\", \"fixed_top_depth\"}:\n",
    "    raise ValueError(\"DIAMETER_POSITION_MODE must be 'center' or 'fixed_top_depth'.\")\n",
    "\n",
    "\n",
    "diameter_cases: dict[str, ModelConfig] = {}\n",
    "for diameter_nm in diameters_nm:\n",
    "    if DIAMETER_POSITION_MODE == \"center\":\n",
    "        nv_offset_nm = (0.0, 0.0, 0.0)\n",
    "    else:\n",
    "        nv_offset_nm = nd_offset_for_top_surface_depth(\n",
    "            diameter_nm,\n",
    "            FIXED_TOP_DEPTH_NM,\n",
    "        )\n",
    "\n",
    "    for beta_deg in representative_betas_deg:\n",
    "        local_dl_nm = min(CONVERGED_MESH_NM, diameter_nm / 8.0)\n",
    "        name = f\"{DIAMETER_POSITION_MODE}_d{int(diameter_nm)}_b{beta_deg:.1f}\".replace(\n",
    "            \".\", \"p\"\n",
    "        )\n",
    "        diameter_cases[name] = replace(\n",
    "            base_cfg,\n",
    "            nd_diameter_nm=diameter_nm,\n",
    "            nv_offset_nm=nv_offset_nm,\n",
    "            nv_beta_deg=beta_deg,\n",
    "            mesh_dl_nm=local_dl_nm,\n",
    "        )\n",
    "\n",
    "\n",
    "diameter_result_dir = result_dir(f\"stage9_diameter_sweep_{DIAMETER_POSITION_MODE}\")\n",
    "\n",
    "print(\"NV position mode:\", DIAMETER_POSITION_MODE)\n",
    "if DIAMETER_POSITION_MODE == \"fixed_top_depth\":\n",
    "    print(\"Fixed top-surface depth (nm):\", FIXED_TOP_DEPTH_NM)\n",
    "print(\"Cases:\", len(diameter_cases))\n",
    "print(\"FDTD simulations including two transitions:\", 2 * len(diameter_cases))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "197eef81",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">21:31:49 KST </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">16.096</span> for the whole batch.               \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m21:31:49 KST\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m16.096\u001b[0m for the whole batch.               \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a425133794d149f487ef8c9cc1745b0e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Estimated maximum batch cost: 16.09558698130048\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">21:31:51 KST </span>Started working on Batch containing <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">30</span> tasks.                      \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m21:31:51 KST\u001b[0m\u001b[2;36m \u001b[0mStarted working on Batch containing \u001b[1;36m30\u001b[0m tasks.                      \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">21:32:47 KST </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">16.096</span> for the whole batch.               \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m21:32:47 KST\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m16.096\u001b[0m for the whole batch.               \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Use <span style=\"color: #008000; text-decoration-color: #008000\">'Batch.real_cost()'</span> to get the billed FlexCredit cost after    \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>completion.                                                        \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mUse \u001b[32m'Batch.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get the billed FlexCredit cost after    \n",
       "\u001b[2;36m             \u001b[0mcompletion.                                                        \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8e73ed7628cb419faca829ca7acab024",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">21:33:08 KST </span>Batch complete.                                                    \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m21:33:08 KST\u001b[0m\u001b[2;36m \u001b[0mBatch complete.                                                    \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "diameter_batch, _ = prepare_batch(\n",
    "    diameter_cases,\n",
    "    folder_name=f\"NV_collection_diameter_sweep_{DIAMETER_POSITION_MODE}\",\n",
    ")\n",
    "diameter_data = run_batch_with_cost_estimate(\n",
    "    diameter_batch,\n",
    "    diameter_result_dir,\n",
    "    diameter_cases,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "a6531f73",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "if diameter_data is not None:\n",
    "    diameter_df = collect_batch_results(diameter_data, diameter_cases)\n",
    "    diameter_df.to_csv(diameter_result_dir / \"diameter_sweep.csv\", index=False)\n",
    "\n",
    "    fig, ax = plt.subplots(figsize=(8, 5))\n",
    "    for beta_deg, group in diameter_df.groupby(\"nv_beta_deg\"):\n",
    "        group = group.sort_values(\"nd_diameter_nm\")\n",
    "        ax.plot(\n",
    "            group[\"nd_diameter_nm\"],\n",
    "            group[\"eta_collection\"],\n",
    "            marker=\"o\",\n",
    "            label=f\"beta={beta_deg:g} deg\",\n",
    "        )\n",
    "    ax.set_xlabel(\"Nanodiamond diameter (nm)\")\n",
    "    ax.set_ylabel(\"NA 0.6 collection efficiency\")\n",
    "    ax.grid(True)\n",
    "    ax.legend()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b041d79",
   "metadata": {},
   "source": [
    "## 10. NV-Axis Orientation Sweep\n",
    "\n",
    "If a spherical nanodiamond is centered on the substrate and the NV center is located exactly at the particle center, the geometry is rotationally symmetric about the $z$ axis. Under this idealized condition, the collection efficiency should, in principle, depend on the polar angle $\\beta$ rather than on the azimuthal angle $\\alpha$.\n",
    "\n",
    "Dependence on $\\alpha$ should also be evaluated in cases where:\n",
    "\n",
    "- The NV center is displaced from the center of the nanodiamond\n",
    "- The nanodiamond is faceted or nonspherical\n",
    "- The particle is tilted\n",
    "- Asymmetric structures are present in the surrounding environment\n",
    "\n",
    "The example below evaluates $\\beta$ from $0^\\circ$ to $90^\\circ$ in increments of $10^\\circ$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "0ec2c853",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cases: 36\n",
      "FDTD simulations including two transitions: 72\n"
     ]
    }
   ],
   "source": [
    "orientation_diameters_nm = [20.0, 40.0, 100.0]\n",
    "beta_values_deg = np.arange(0.0, 90.0 + 1e-9, 8.0)\n",
    "\n",
    "orientation_cases: dict[str, ModelConfig] = {}\n",
    "for diameter_nm in orientation_diameters_nm:\n",
    "    for beta_deg in beta_values_deg:\n",
    "        name = f\"d{int(diameter_nm)}_beta{int(beta_deg)}\"\n",
    "        orientation_cases[name] = replace(\n",
    "            base_cfg,\n",
    "            nd_diameter_nm=diameter_nm,\n",
    "            nv_beta_deg=float(beta_deg),\n",
    "            mesh_dl_nm=min(CONVERGED_MESH_NM, diameter_nm / 8.0),\n",
    "        )\n",
    "\n",
    "print(\"Cases:\", len(orientation_cases))\n",
    "print(\"FDTD simulations including two transitions:\", 2 * len(orientation_cases))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "1f71e4fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">21:37:41 KST </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">48.234</span> for the whole batch.               \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m21:37:41 KST\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m48.234\u001b[0m for the whole batch.               \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fc645816f7534df38290845bf6f59648",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Estimated maximum batch cost: 48.23370786531777\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">21:37:46 KST </span>Started working on Batch containing <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">72</span> tasks.                      \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m21:37:46 KST\u001b[0m\u001b[2;36m \u001b[0mStarted working on Batch containing \u001b[1;36m72\u001b[0m tasks.                      \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">21:39:26 KST </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">48.234</span> for the whole batch.               \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m21:39:26 KST\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m48.234\u001b[0m for the whole batch.               \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Use <span style=\"color: #008000; text-decoration-color: #008000\">'Batch.real_cost()'</span> to get the billed FlexCredit cost after    \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>completion.                                                        \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mUse \u001b[32m'Batch.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get the billed FlexCredit cost after    \n",
       "\u001b[2;36m             \u001b[0mcompletion.                                                        \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6f55de795dab4e34914908c76c38d99e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">21:40:22 KST </span>Batch complete.                                                    \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m21:40:22 KST\u001b[0m\u001b[2;36m \u001b[0mBatch complete.                                                    \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "orientation_batch, _ = prepare_batch(\n",
    "    orientation_cases,\n",
    "    folder_name=\"NV_collection_orientation_sweep\",\n",
    ")\n",
    "orientation_data = run_batch_with_cost_estimate(\n",
    "    orientation_batch,\n",
    "    result_dir(\"stage10_orientation_sweep\"),\n",
    "    orientation_cases,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "f1e81fd4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "if orientation_data is not None:\n",
    "    orientation_df = collect_batch_results(orientation_data, orientation_cases)\n",
    "    orientation_df.to_csv(\n",
    "        result_dir(\"stage10_orientation_sweep\") / \"orientation_sweep.csv\", index=False\n",
    "    )\n",
    "\n",
    "    fig, ax = plt.subplots(figsize=(8, 5))\n",
    "    for diameter_nm, group in orientation_df.groupby(\"nd_diameter_nm\"):\n",
    "        group = group.sort_values(\"nv_beta_deg\")\n",
    "        ax.plot(\n",
    "            group[\"nv_beta_deg\"],\n",
    "            group[\"eta_collection\"],\n",
    "            marker=\"o\",\n",
    "            label=f\"d={diameter_nm:g} nm\",\n",
    "        )\n",
    "    ax.set_xlabel(\"NV-axis polar angle beta (deg)\")\n",
    "    ax.set_ylabel(\"NA 0.6 collection efficiency\")\n",
    "    ax.grid(True)\n",
    "    ax.legend()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79c21f2b",
   "metadata": {},
   "source": [
    "## 11. Comparison of Nanodiamond, Bulk Half-Space, and Finite Diamond Film\n",
    "\n",
    "For a fair comparison, two different comparison schemes should be distinguished:\n",
    "\n",
    "1. **Comparison under actual experimental conditions:**  \n",
    "   Use the actual NV position for each sample geometry.\n",
    "\n",
    "2. **Comparison at the same distance from the top surface:**  \n",
    "   For a 40 nm-diameter nanodiamond with the NV center located at its center, the NV is 20 nm below the top surface. Accordingly, the NV depth in the bulk-diamond and diamond-film geometries is also set to 20 nm.\n",
    "\n",
    "The code below implements the second comparison scheme.\n",
    "\n",
    "The diamond-film thickness is set to 500 nm as an example.\n",
    "\n",
    "> **Thin-film boundary-stability note**\n",
    ">\n",
    "> The laterally infinite diamond/silica film stack supports guided modes that propagate into the x/y absorbing boundaries. A standard PML can become numerically unstable when dispersive Sellmeier media extend into it; in test runs, both thin-film transition-dipole simulations diverged, whereas the nanodiamond and bulk cases decayed normally. Therefore, `diamond_film` uses 40-layer adiabatic absorbers on x/y and -z, while the homogeneous-air +z boundary retains a PML. Nanodiamond and bulk cases retain the original all-PML boundary scheme."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "32bb6502",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "nanodiamond_d40 nanodiamond NV depth/diameter = 20.0 40.0\n",
      "bulk_depth20 bulk_halfspace NV depth/diameter = 20.0 40.0\n",
      "film500_depth20 diamond_film NV depth/diameter = 20.0 40.0\n"
     ]
    }
   ],
   "source": [
    "comparison_wavelength_nm = 700.0\n",
    "comparison_beta_deg = 54.7356\n",
    "matched_surface_distance_nm = 20.0\n",
    "\n",
    "comparison_cases = {\n",
    "    \"nanodiamond_d40\": replace(\n",
    "        base_cfg,\n",
    "        geometry=\"nanodiamond\",\n",
    "        wavelengths_nm=(comparison_wavelength_nm,),\n",
    "        nd_diameter_nm=40.0,\n",
    "        nv_offset_nm=(0.0, 0.0, 0.0),\n",
    "        nv_beta_deg=comparison_beta_deg,\n",
    "    ),\n",
    "    \"bulk_depth20\": replace(\n",
    "        base_cfg,\n",
    "        geometry=\"bulk_halfspace\",\n",
    "        wavelengths_nm=(comparison_wavelength_nm,),\n",
    "        nv_depth_nm=matched_surface_distance_nm,\n",
    "        nv_beta_deg=comparison_beta_deg,\n",
    "    ),\n",
    "    \"film500_depth20\": replace(\n",
    "        base_cfg,\n",
    "        geometry=\"diamond_film\",\n",
    "        wavelengths_nm=(comparison_wavelength_nm,),\n",
    "        film_thickness_nm=500.0,\n",
    "        nv_depth_nm=matched_surface_distance_nm,\n",
    "        nv_beta_deg=comparison_beta_deg,\n",
    "    ),\n",
    "}\n",
    "\n",
    "for name, cfg in comparison_cases.items():\n",
    "    print(\n",
    "        name, cfg.geometry, \"NV depth/diameter =\", cfg.nv_depth_nm, cfg.nd_diameter_nm\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "473e850e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">21:40:46 KST </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">6.062</span> for the whole batch.                \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m21:40:46 KST\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m6.062\u001b[0m for the whole batch.                \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e8b5c56e160e4f56a11b16ff56f2c901",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Estimated maximum batch cost: 6.061913820618922\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">21:40:47 KST </span>Started working on Batch containing <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">6</span> tasks.                       \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m21:40:47 KST\u001b[0m\u001b[2;36m \u001b[0mStarted working on Batch containing \u001b[1;36m6\u001b[0m tasks.                       \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">21:40:55 KST </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">6.062</span> for the whole batch.                \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m21:40:55 KST\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m6.062\u001b[0m for the whole batch.                \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Use <span style=\"color: #008000; text-decoration-color: #008000\">'Batch.real_cost()'</span> to get the billed FlexCredit cost after    \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>completion.                                                        \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mUse \u001b[32m'Batch.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get the billed FlexCredit cost after    \n",
       "\u001b[2;36m             \u001b[0mcompletion.                                                        \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "400cc54f95084563a3243c689304099c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">21:41:00 KST </span>Batch complete.                                                    \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m21:41:00 KST\u001b[0m\u001b[2;36m \u001b[0mBatch complete.                                                    \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "comparison_batch, _ = prepare_batch(\n",
    "    comparison_cases,\n",
    "    folder_name=\"NV_collection_geometry_comparison\",\n",
    ")\n",
    "comparison_data = run_batch_with_cost_estimate(\n",
    "    comparison_batch,\n",
    "    result_dir(\"stage11_geometry_comparison\"),\n",
    "    comparison_cases,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "af961f98",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>case</th>\n",
       "      <th>geometry</th>\n",
       "      <th>wavelength_nm</th>\n",
       "      <th>eta_collection</th>\n",
       "      <th>eta_air_plane</th>\n",
       "      <th>eta_t1</th>\n",
       "      <th>eta_t2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>nanodiamond_d40</td>\n",
       "      <td>nanodiamond</td>\n",
       "      <td>700.0</td>\n",
       "      <td>0.050089</td>\n",
       "      <td>0.187482</td>\n",
       "      <td>0.077181</td>\n",
       "      <td>0.032007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>bulk_depth20</td>\n",
       "      <td>bulk_halfspace</td>\n",
       "      <td>700.0</td>\n",
       "      <td>0.016966</td>\n",
       "      <td>0.046701</td>\n",
       "      <td>0.018262</td>\n",
       "      <td>0.014147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>film500_depth20</td>\n",
       "      <td>diamond_film</td>\n",
       "      <td>700.0</td>\n",
       "      <td>0.015354</td>\n",
       "      <td>0.055026</td>\n",
       "      <td>0.016492</td>\n",
       "      <td>0.012862</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              case        geometry  wavelength_nm  eta_collection  \\\n",
       "0  nanodiamond_d40     nanodiamond          700.0        0.050089   \n",
       "1     bulk_depth20  bulk_halfspace          700.0        0.016966   \n",
       "2  film500_depth20    diamond_film          700.0        0.015354   \n",
       "\n",
       "   eta_air_plane    eta_t1    eta_t2  \n",
       "0       0.187482  0.077181  0.032007  \n",
       "1       0.046701  0.018262  0.014147  \n",
       "2       0.055026  0.016492  0.012862  "
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
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wYjFx4kS1b99eR44cUffu3fXmm29mZ1wAAAAAXEiGEwtJatSokRo1aqRt27apZ8+e2RUTAAAAABeT4TUWf/XFF19kdRwAAAAAXJhdiYUxRlOnTlWtWrVUpEgRa/nQoUMVExOTZcEBAAAAcA12JRYTJkzQ6NGjFRkZqdjYWGt5aGioRo0alWXBAQAAAHANdiUWU6ZM0YIFC9SvXz+b8mbNmmnhwoVZEhgAAAAA12FXYnHixAlVrVpVUtop3Hf4+voqLi4uayIDAAAA4DLsSixKly6t3bt3S7JNLBYtWqSwsLCsiQwAAACAy8jUdrN3vP766+rSpYvGjBkjSdq8ebOWL1+ucePGafLkyVkaIAAAAIAHn12JRe/evZWYmKhevXopNTVVDRo0UKFChTR69Gh17do1i0MEAAAA8KCzK7GQpAEDBqh///46ceKEUlNTVaZMGbm7u2dlbAAAAABchN2JhSS5ubkpJCQkq2IBAAAA4KLsWrwNAAAAAH/l0IhFVjh69KimT5+u8+fP6+GHH9Zrr70mHx8fh+v8/PPPWrFihRISElS5cmVFRkbKz88vO7sCAAAA5FpOHbH4448/VL16dR07dkwPP/ywpk+frvDwcN2+fduhOq+99po+/vhj+fv7KywsTDNmzFC1atV0+fLl+9EtAAAAINexGGOMs755y5YtlZKSohUrVkiSLly4oODgYP3nP/9R9+7d7a4TExOjwMBAa534+HgVLlxYM2bMUGRkZIZii4+PV4ECBRQXF6f8+fM70k2X4N+zrrNDQDY4N3Wbs0MAAAAuLDOfie2aCnXr1i1NmjRJmzdvVmxsbLr769at+8c2bt++rdWrV2vKlCnWsuLFi6tJkyZasmTJXROLjNb5a1IhSWfPnlVKSoqCgoIy2kUAAAAAmWBXYtGnTx8tXbpU7du310MPPWTXNz558qSSkpIUHBxsUx4cHKyNGzc6XOePP/7Q2LFjFR8fr99++03Tpk1To0aN7hlPYmKiEhMTrdfx8fGSpKSkJCUlJWWmay7J2yOPs0NANsgNP7sAACD7ZOazhF2JxaJFi7R+/Xo9/PDD9lSXJN28eVOS5Ovra1Pu5+dnvedInWLFiumpp57SpUuXFBMTo+nTp6tDhw4qXLjwXduOiorSyJEj05WvXLlSefPmzVinXNiEZm84OwRkg2XLljk7BAAA4MJu3LiR4WftSiy8vb1VpkwZe6paFShQQJJ05coVm/LY2FjrPUfqBAQEKCIiQlLaYu4KFSpo/Pjxev/99+/a9tChQzVw4EDrdXx8vIKCgtS8efNcscYidEBTZ4eAbHB4/BpnhwAAAFzYnVk8GWFXYtGyZUvNmjVLvXr1sqe6JCkoKEgFCxbU3r171aJFC2v5nj177jkSYk8dScqbN6/Kli2r6Ojoez7j5eUlLy+vdOWenp7y9PTMQI9c263ke+/EBdeVG352AQBA9snMZwm7tpu9fv26evfuraZNm6pHjx7q2bOnzb+MsFgs6tSpk7788ktdu3ZNkrRlyxbt2LFDnTt3tj735ZdfaujQoRmuk5iYqB9//NHme/3xxx/69ddfVb9+fXu6CwAAAOAf2JVYJCUlqX379ipUqJAuX76sS5cu2fzLqNGjR8vPz09VqlRRy5Yt9eSTT2rAgAFq3ry59ZmtW7fqv//9b4bruLu7a86cOapUqZKeffZZNWnSRHXr1lXXrl3vuYUtAAAAAMc49RwLSUpJSdHmzZutp2iHhYXZ3N+2bZsuXbqkVq1aZbiOJJ04cUK//fab8uXLpypVqsjf3z9TcXGOBXICzrEAAACOyMxnYqcnFg8qEgvkBCQWAADAEZn5TGzXVCgpbcF0165dVadOHdWuXVtdu3bV3r177W0OAAAAgAuzK7FYsmSJqlevrujoaDVu3FhNmzZVdHS0qlWrpiVLlmR1jAAAAAAecHZtN/vOO+9o7NixNuc+SNLHH3+sd955x2Y9BAAAAICcz64RiwMHDqhbt27pyrt166aDBw86HBQAAAAA12JXYlGsWDH9/vvv6cp/++03FStWzOGgAAAAALgWu6ZCdevWTc8//7zeeecd1a5dW5K0fft2jRo1Sj169MjSAAEAAAA8+OxKLEaMGCFvb28NHz5cV69elSQVLFhQgwYN0uDBg7MyPgAAAAAuwKFzLIwxOnPmjCwWiwICAmSxWLIyNqfiHAvkBJxjAQAAHJGZz8R2jVjcYbFYFBgY6EgTAAAAAHKADCcWERERkqRZs2ZZv76XWbNmORYVAAAAAJeS4cTCw8Pjrl8DAAAAQIYzhJkzZ1q/7tmzp+rWvfuc/G3bmNMNAAAA5DZ2nWNRr149u+4BAAAAyJnsSizuJS4uTr6+vlnZJAAAAAAXkKnFEl27dr3r15KUmpqqPXv2qE6dOlkRFwAAAAAXkqnEIjk5+a5fS5Knp6fatm3LydsAAABALpSpxOLONrJFixbVJ598kh3xAAAAAHBBdq2xIKkAAAAA8Fd2JRYrV65Ut27d0pV369ZNq1atcjgoAAAAAK7FrsTirbfeUr9+/dKV9+vXT0OGDHE4KAAAAACuxa7E4uDBgwoJCUlXHhISov379zscFAAAAADXYldiERISohUrVqQr/+mnn1S6dGmHgwIAAADgWjK1K9Qd/fr1U/fu3XX8+HE9/vjjMsZow4YNGj16tKKiorI6RgAAAAAPOLsSi169eikhIUFRUVEaPHiwJKlQoUIaNmyYevfunaUBAgAAAHjw2ZVYSNKgQYM0cOBARUdHy2KxKDg4WO7u7lkZGwAAAAAXYXdiIUnu7u4qV65cVsUCAAAAwEXZtXjbGKOpU6eqVq1aKlKkiLV86NChiomJybLgAAAAALgGuxKLCRMmaPTo0YqMjFRsbKy1PDQ0VKNGjcqy4AAAAAC4BrsSiylTpmjBggXpDslr1qyZFi5cmCWBAQAAAHAddiUWJ06cUNWqVSVJFovFWu7r66u4uLisiQwAAACAy7ArsShdurR2794tyTaxWLRokcLCwrImMgAAAAAuw65doV5//XV16dJFY8aMkSRt3rxZy5cv17hx4zR58uQsDRAAAADAg8+uxKJ3795KTExUr169lJqaqgYNGqhQoUIaPXq0unbtmsUhAgAAAHjQ2X2OxYABA9S/f3+dOHFCqampKlOmDAfkAQAAALmUQwfkubm5KSQkJKtiAQAAAOCiMpxYREREZLjRWbNm2RUMAAAAANeU4cTCw8OhwQ0AAAAAOViGs4WZM2dmYxgAAAAAXJld51gAAAAAwF+xxgIAAACAw1hjAQAAAMBhD8wai6SkJHl6emZpndTUVLm5MdsLAAAAyG5O/9T9/vvvq0iRIvL29lalSpW0evVqh+ssXbpUjRs3Vv78+eXr66snn3xSe/fuza4uAAAAALme3YnFnj171LVrV9WpU0e1a9dW165dM/3hfdKkSfrwww+1cOFCJSQkqHPnzmrdurWOHj1qd52UlBRNmTJF7777ri5evKhTp06pcOHCat68ueLi4uztLgAAAIC/YVdisWTJElWvXl3R0dFq3LixmjZtqujoaFWrVk1LlizJcDuffPKJunXrpkaNGsnHx0fDhw9X8eLFNXXqVLvruLu7a8mSJQoPD5ePj48KFSqkDz/8UGfPntX27dvt6S4AAACAf2DXiux33nlHY8eO1cCBA23KP/74Y73zzjtq1arVP7Zx+fJlHTlyRI8//rhNeXh4uLZt25ZldSQpJiZGklSkSJF/jAsAAABA5tmVWBw4cEDdunVLV96tWzcNHTo0Q22cP39eklSsWDGb8mLFit1zZMGeOrdv31b//v1Vt25d1ahR457xJCYmKjEx0XodHx8vKW2BeFJS0j/0xvV5e+RxdgjIBrnhZxcAAGSfzHyWsCuxKFasmH7//fd0Iwe//fZbug/9/yQ1NTXdtcViyZI6KSkpioiI0JkzZ7Rp06a/bTcqKkojR45MV75y5UrlzZv3b+PJCSY0e8PZISAbLFu2zNkhAAAAF3bjxo0MP2tXYtGtWzc9//zzeuedd1S7dm1J0vbt2zVq1Cj16NEjQ22ULFlSknThwgWb8gsXLiggIMDhOikpKerSpYu2bNmi9evXq3Tp0n8bz9ChQ22mdsXHxysoKEjNmzdX/vz5M9QnVxY6oKmzQ0A2ODx+jbNDAAAALuzOLJ6MsCuxGDFihLy9vTV8+HBdvXpVklSwYEENGjRIgwcPzlAbBQsWVOXKlbVmzRp16NBBUtrIw88//6xXX33V+lxiYqJSU1Pl4+OT4Tqpqal66aWXtH79eq1bt07lypX7x3i8vLzk5eWVrtzT0zPT52u4olvJt50dArJBbvjZBQAA2ScznyXs2hXK3d1db7/9tmJjY3X69GnFxMQoNjZWb7/9ttzd3TPczpAhQzRjxgzNmzdPJ0+eVL9+/XTz5k316tXL+kyfPn1Uq1atDNcxxuiVV17RqlWrtGTJEvn7+yshIUEJCQlKTk62p7sAAAAA/oFdIxZ3WCwWBQYG2l0/IiJCN27c0MiRI3X+/Hk9/PDDWr16tXXKkyR5e3vbrHH4pzqxsbH67rvvJEkNGjSw+X6TJk3SSy+9ZHe8AAAAAO7OYowxma20cuVKzZs3T19++aVNebdu3dSpUyc1a9YsywJ0lvj4eBUoUEBxcXG5Yo2Ff8+6zg4B2eDc1HtvwwwAAPBPMvOZ2K6pUG+99Zb69euXrrxfv34aMmSIPU0CAAAAcGF2JRYHDx5USEhIuvKQkBDt37/f4aAAAAAAuBa7EouQkBCtWLEiXflPP/30j9u6AgAAAMh57Fq83a9fP3Xv3l3Hjx/X448/LmOMNmzYoNGjRysqKiqrYwQAAADwgLMrsejVq5cSEhIUFRVlPbeiUKFCGjZsmHr37p2lAQIAAAB48Nm93eygQYM0cOBARUdHy2KxKDg4OFNnWAAAAADIORw6x8Ld3T1Dp1oDAAAAyNnsWrwNAAAAAH9FYgEAAADAYSQWAAAAABzm0BqLO27fvi2LxSJPT8+saA4A8Df8e9Z1dgjIBuembnN2CADgkEyNWMTFxenbb7+1Xl+5ckXt27eXr6+vfH19FRkZqevXr2d5kAAAAAAebJlKLAYNGqTk5GTr9ZAhQ7R3715NmTJFkydP1vbt2zVixIgsDxIAAADAgy1TU6EWLFigDz/80Hq9ZMkS/fTTT6pataokqWbNmmrbtq0++uijrI0SAAAAwAMtUyMWnp6eunjxovU6NjbW5hyL8uXL6/z581kXHQAAAACXkKnEok2bNurTp4/i4uIkSc2bN9f8+fOt9+fOnasqVapkbYQAAAAAHniZmgoVFRWl8PBwlSlTRi1atFDZsmXVvXt3zZgxQ8YY7dy5U8uWLcuuWAEAAAA8oDI1YlGsWDH98ssvGjRokI4cOaJvvvlGBQoUUGxsrB566CHt2rVLjRo1yqZQAQAAADyoMn2ORb58+fT222/r7bffzo54AAAAALigLDkgDwAAAHfHoZY5E4dappepqVD/xGKxyGKxZGWTAAAAAFxAlo5YjB8/PiubAwAAAOAisjSxeP3117OyOQAAAAAuwqGpUElJSUpKSsqqWAAAAAC4qEwnFmvXrlXr1q1VpEgR5cmTR3ny5FGRIkXUunVrrV27NjtiBAAAAPCAy9RUqLlz5yoiIkJNmjTRoEGDVKxYMVksFl24cEFr1qxRs2bNNGvWLHXq1Cm74gUAAADwAMpUYjFixAhNmTJF3bt3T3dvyJAh+vzzz/Xuu++SWAAAAAC5TKamQh0/flwdOnS45/0OHTro+PHjDgcFAAAAwLVkKrEoV66c5s6de8/7c+bMUbly5RwOCgAAAIBrydRUqFGjRqlTp06aP3++GjdurGLFikmSLl68qHXr1mnjxo2aN29etgQKAAAA4MGVqcSiffv2Wr9+vT7++GNNnTpV58+flySVKFFC9evX1/r161W/fv1sCRQAAADAgyvTB+TVr1+f5AEAAACADYcOyAMAAAAAicQCAAAAQBYgsQAAAADgMBILAAAAAA7L0sTi9u3bWrBgQVY2CQAAAMAFZElisW/fPg0cOFCBgYF64YUXsqJJAAAAAC7E7sQiISFBX375perVq6cqVaro2LFjGjt2rPVsCwAAAAC5R6bPsdi6dau+/PJLzZs3TyVKlNCrr76qbdu26YcffsiG8AAAAAC4gkyNWFSuXFmNGjVSfHy8Fi1apMOHD2vIkCHZFRsAAAAAF5GpxOLAgQOqXLmy2rZtq4YNG8pisWRXXAAAAABcSKYSi8OHD+upp57SoEGDVLJkSQ0YMED79+93KIAbN25owYIF+vTTT7V+/fosq5OSkqIlS5boo48+0qlTpxyKEQAAAMDfy1RiUa5cOY0ePVonT57UzJkzdezYMT3yyCOSpGnTpuns2bOZ+uZnz57VI488og8++EA7d+5Ux44dFRER4XCd7777TuXLl9fYsWM1aNAgHT16NFNxAQAAAMicTC/eliR3d3e1bt1arVu31rlz5zRz5kyNGzdOvXr1Up06dbR169YMtTN48GD5+flp69at8vLy0t69e/XII4+oQ4cOatu2rd11ihUrpnXr1snd3V1BQUH2dBEAAABAJjh8joW/v7+GDBmiQ4cOae3atSpfvnyG6qWkpOj777/XSy+9JC8vL0lSlSpV1KBBA82fP9+hOuHh4QoODnawZwAAAAAyKlMjFgcPHvzb+yVKlNA777yTobZOnTql69evq2LFijblYWFh2rFjR5bVyajExEQlJiZar+Pj4yVJSUlJSkpKcqhtV+DtkcfZISAb5Iaf3dyI9zVn4n3NuXhnc6bc8s5mpp+ZSiwqVaqUoeeMMf/4zLVr1yRJBQsWtCkvWLCg9V5W1MmoqKgojRw5Ml35ypUrlTdvXofadgUTmr3h7BCQDZYtW+bsEJANeF9zJt7XnIt3NmfKLe/sjRs3MvxsphKLPXv23LXcGKNvv/1WEyZMkK+vb4bauvNh/c7IwB1xcXHKly9fltXJqKFDh2rgwIHW6/j4eAUFBal58+bKnz+/Q227gtABTZ0dArLB4fFrnB0CsgHva87E+5pz8c7mTLnlnf3fz91/J1OJRZUqVdKVrVy5UoMHD9bhw4f1xhtv6K233spQW8HBwfLy8kq3Y9PRo0cVGhqaZXUyysvLy7pu4688PT3l6enpUNuu4FbybWeHgGyQG352cyPe15yJ9zXn4p3NmXLLO5uZftq9eHvnzp164okn9PTTT6tOnTo6cuSIRo0aleG/7nt4eOjpp5/Wt99+q9TUVElSdHS01q9fr3bt2lmfW7lypWbOnJmpOgAAAADur0xvN3vs2DENGzZM8+bNU9u2bbVv3z5VqFDBrm8+duxY1a9fX82bN1ft2rU1d+5cNW7cWJ06dbI+M3/+fG3btk1du3bNcJ19+/bpp59+UlxcnCRp3rx5+vXXX1W/fn3Vr1/frlgBAAAA3FumRiz69++vSpUqKSYmRlu2bNHChQvtTiqktAP39u7dqzZt2shisWj06NFatmyZ3N3drc88+eSTevnllzNV5+bNmzp37pxu3rypN954Q/ny5dO5c+eUkJBgd6wAAAAA7s1iMrKF052HLRZ5e3tbT9u+l23btjkcmLPFx8erQIECiouLyxWLt/171nV2CMgG56a6/ruI9Hhfcybe15yLdzZnyi3vbGY+E2dqKlT//v0dCgwAAABAzpSpxOKTTz7JpjAAAAAAuDK7d4UCAAAAgDtILAAAAAA4jMQCAAAAgMNILAAAAAA4jMQCAAAAgMNILAAAAAA4jMQCAAAAgMNILAAAAAA4jMQCAAAAgMNILAAAAAA4jMQCAAAAgMNILAAAAAA4jMQCAAAAgMNILAAAAAA4jMQCAAAAgMNILAAAAAA4jMQCAAAAgMNILAAAAAA4jMQCAAAAgMNILAAAAAA4jMQCAAAAgMNILAAAAAA4jMQCAAAAgMNILAAAAAA4jMQCAAAAgMNILAAAAAA4jMQCAAAAgMNILAAAAAA4jMQCAAAAgMNILAAAAAA4jMQCAAAAgMNILAAAAAA4jMQCAAAAgMNILAAAAAA4jMQCAAAAgMNILAAAAAA4jMQCAAAAgMNILAAAAAA4jMQCAAAAgMNILAAAAAA4zOmJxdKlS/Xkk0+qWrVqioyM1LFjx7Kkjj3tAgAAALCPUxOLZcuWqW3btmrWrJkmTZqkmzdvqmHDhrpy5YpDdexpFwAAAID9nJpYvPvuu+rcubPefPNNPfbYY5o9e7Zu3rypqVOnOlTHnnYBAAAA2M9piUVCQoJ+/fVXPfXUU9ayPHny6IknntDatWvtrmNPuwAAAAAc4+Gsb3z69GkZYxQQEGBT7u/vr71799pdx552JSkxMVGJiYnW67i4OElSbGyskpKSMt4xF5Un1enLbZANLl++7OwQkA14X3Mm3teci3c2Z8ot7+y1a9ckScaYf3zWaYlFSkqKpLTRhL/y8vJScnKy3XXsaVeSoqKiNHLkyHTlISEhf9cN4IFW9POizg4BQAbxvgKuJbe9s9euXVOBAgX+9hmnJRZFihSRJF26dMmm/NKlS9Z79tSxp11JGjp0qAYOHGi9Tk1NVWxsrIoUKSKLxZKRLsEFxMfHKygoSKdOnVL+/PmdHQ6Av8H7CrgW3tmcyRija9euqWTJkv/4rNMSC39/f5UqVUrbtm1TmzZtrOVbt25Vs2bN7K5jT7tS2oiGl5eXTVnBggXt6RpcQP78+fmPHuAieF8B18I7m/P800jFHU6d9NejRw998cUXOnz4sCRp+vTpOnLkiLp37259Zvjw4TYJQkbqZOQZAAAAAFnHaSMWkjRkyBCdPHlSVapUUYECBZScnKyZM2eqatWq1mfOnj1rc7hdRupk5BkAAAAAWcdiMrLEO5vFx8fr8uXLKlWqlDw9PW3unTt3Tjdv3ky3iPrv6mTmGeQuiYmJioqK0tChQ9NNfQPwYOF9BVwL7yweiMQCAAAAgGtjY2UAAAAADiOxAAAAAOAwEgsAAAAADiOxAAAAAOAwEgvkKDdv3tTFixedHQaADDp//rxu377t7DAAZMD169d1+fJlZ4eBBxiJBXKEq1evqkuXLipWrJhKliypRx99VIcOHXJ2WADuYe7cuQoJCVFISIiKFCmicePGOTskAPdw/vx5tW/fXsWLF1eJEiX0+OOP6/Tp084OCw8gEgvkCJGRkTp37pxOnTql8+fPq0iRInriiScUGxvr7NAA/I9169apa9eu+uyzz3Tjxg1NnTpVQ4YM0ZQpU5wdGoC7aNu2rdzc3HThwgWdPn1aSUlJat68uW7cuOHs0PCA4RwLuJQDBw5o9+7d6ty5s7Xs8uXLKlq0qNatW6fw8HBJaSMYFSpU0Isvvqjx48c7K1wAd/HKK6/o4sWL+u9//2stGzp0qCZPnqzjx4+rcOHCTowOwF8dOnRIFStW1N69e1W5cmVJUkxMjCpWrKihQ4fqnXfecXKEeJAwYgGXER0drcqVK+uVV17R1atXreV3vvbw8LCWFSxYUP3799cXX3yhhISE+xwpkLvFxMSoZ8+eCg0NValSpdSjRw9du3bNev/KlSs276skvfXWW0pKStL06dPvd7hArvfVV18pPDxcxYoVU61atbRmzRrrvStXrkiy/R0bGBio7t27a+LEiUpJSbnv8eLBRWKBB8off/yhvn373vXe0qVLFRERIWOMFixYYC0PDAyUh4eHdu7cafN8586dlZCQoFWrVmVrzAD+z969e/XYY4+pVKlS+vHHHzVt2jQtWrRIb731lvWZMmXKaNeuXTb1ChUqpBYtWmjRokX3O2Qg1zLG6NVXX9X06dM1bNgwbdiwQRUrVlTbtm0VExMjKe19lXTX37Hnz5/Xli1b7nfYeICRWOCB4unpqU8//VS///57untLly7VCy+8oJYtW+qbb76xlnt7e+uxxx7T0qVLbZ6/szD0119/zfa4AaQZN26cZs+erWHDhumhhx7S008/rd69e+uHH36wPtO0aVOdPHlSe/bssanbtGlT7dq1S8zQBe6PXbt26datW/r555/VrFkzVapUSZ9//rmuX7+u1atXS5JKlCihypUrp/sdW7NmTRUsWJDfsbBBYoEHSqVKlVSzZk2bxEFK20Z269atCg8PV0REhDZt2qTo6Gjr/cjISK1evVrHjh2zqRcQEMBUKCCLDBs2TP/617/+9plp06apfv36NmU+Pj5yd3e3Xjdv3lwlSpTQZ599ZvNcQECAbt26peTk5KwLGsilDh06pLJly/7t9rA1atTQjBkzbN5PDw8PeXh42JRFRkZq0aJFNtu5u7m5qXjx4vyOhQ0SCzxwunTpotmzZ9vM21yzZo0effRR5c2bV61atVKBAgX09ddfa/369bp69aoiIiJUunRpvfHGG9Y6ycnJOn78uKpWreqMbgA5Trly5fTtt98qMTFRkpSUlKRdu3bZJPR58uRJV++nn35Sq1atbJ4ZNGiQPv/8c+3bt89afujQIYWFhcnT0zMbewHkDmXLltX169c1b948a1lMTIy2b99ufYctFku69+3O+oonn3zSWtajRw/ly5dPQ4cOtZYlJCTozJkz/I6FLQM8ALZv326aNm1qihcvbh5++GEjyaxYscJ6v2fPnubDDz80xhhz4MABU61aNSPJVK5c2fz222/GGGM2btxofHx8TMeOHc2sWbNM8+bNTbVq1cyNGzec0ifA1f3xxx8mLi7Oeh0fH298fHzMggULzPLly03JkiVNQECAsVgsJioq6q5t7N+/37i5uZm9e/falCclJZlmzZqZkiVLmsmTJ5vRo0eb/Pnzm++//z5b+wTkVNeuXbP+Pryjf//+pk6dOubatWumbdu2pkCBAsbX19c89NBD5vz583dtp23btiYyMjJd+X//+1/j6elpXn75ZfPNN9+Yxx57zISHh5ukpKRs6Q9cEyMWyHLGGB0+fDjDzx86dEiPP/642rRpoz179mjw4MHy9va2mQ61bNkyHT16VLVq1VKjRo0UFhYmSZo+fboeeeQRSVKDBg20Y8cO5c+fXwsWLNCTTz6pTZs2ycfHJ2s7COQCycnJaty4sebMmSMp7T29du2a2rZtq48//lj9+/fX0qVLdebMGc2ePVvDhw+/63s/btw4PfXUU9ZtKu/w8PDQkiVLNHDgQC1fvlwnTpzQ2rVr1a5du/vSPyCn+fTTT9WuXTsZY3Tz5k1t27ZNXbp00fbt29WxY0eFhYXp4sWL1oPtRo4cma6NQ4cOafHixXrzzTfT3WvVqpU2b96slJQULVq0SC+88IJWrFiRboc35HLOzmyQ88ydO9f4+flleKSgT58+pnHjxjZlH3/8scmXL5+5du2aMcaY8PBw88ILL5hly5aZ5ORkk5qaakJCQkyfPn2yPH4AaXr16mVCQ0NNnTp1TEBAgFmyZIlZtmyZkZRuhKJmzZrmjTfesCmLiYkxefLkMevWrTPGGJOammo2btxokpOT71sfgNwiOjraSDJt2rQxhQsXNs8++6wxxpiHHnrIFC1a1KSmplqfnTNnjsmXL59JSEiwaePVV181Tz31lPX6/Pnz6UYbgb/DiAWyXKtWrZSamqoff/wxQ89funRJxYsXtyl77bXXdPv2bX3//feS0k7qnT17tlq0aCF3d3dZLBZFRERo8eLF7CADZIMTJ07o22+/1eHDh/XKK6/o1KlTevrpp60Lr/937/o7a6NSU1OtZZ988omqV68uf39/DR8+XGXLllXv3r2tfzEFkHX+/e9/y8vLS+fPn9fevXu1cOFCSWkLr5OTk2WxWKzPtm3bVu7u7tbfsZJ09uxZffPNN+rfv7/mzZunVq1aKSwsLN1uUMDfIbFAlsuXL5+effbZdDs73UuFChX0yy+/pGujUqVKf9vGgAEDtHfvXpv/WAKwz6+//qqJEydat5gMDg7WmTNnVLFiRZ09e9a6Q4y7u7teeOEFLVmyxKZ+p06ddPHiRevCz5s3b+qzzz7TwYMH1aRJE928eVM//PCD/vjjDwUHB9/fzgE5zNWrV/X111/ryy+/1KVLlyRJkyZN0tSpU/Xnn3/anF7/4osvKj4+Xps3b7aWeXt7q0OHDpo1a5a1bOrUqUpKStKLL76omTNnqnPnzjp9+rTNGTTAP7EY/tyLLLRixQrt379fFy5c0EcffaSYmJh0oxH/688//1TlypX1/fffq02bNpKklJQUBQUF6fz58zp58qQCAwPvR/hArnJnTcS7776rzZs3q2LFilq/fr2ee+45TZ8+XR4eHvrggw80Y8YMHTlyxFpv165dqlmzpg4dOqTQ0FBreatWrVS4cGF9/fXXktKS/5YtW6pp06Zyc+PvWIAjbt68qV9++UVJSUnq1KmTqlWrpujoaMXHx2v58uWqXr26rl27phIlSujrr79Whw4drHWbNGmiChUqaOrUqdaydevW6YknntCpU6cUEBCgH3/8UceOHVPnzp1VokQJZ3QROYGTp2Ihh0hMTDRNmjQxYWFhZsCAAaZq1apGkhk/fnyG6vft29cULlzYLF682Bw+fNj06tXLNG/e3NSqVcv8/PPP2Rs8kEs1a9bMlCpVyjRs2NDcunXLGGPM7t27jZ+fn5kwYYIxJm3etsViMVu2bLGpW7lyZTN8+HCbsgULFpi2bdven+CBXGbFihXGw8PDBAQEWNctJScnmw4dOphy5cqZ27dvG2OMeeGFF0ybNm1s6s6YMcMUKlTI+p4bk7bm6bHHHjMbNmy4f51AjkdigSwxbtw4U6ZMGXPz5k1jjDG3bt0yDRs2NDVq1MhQ/ZSUFDN48GDj5+dn8ufPb3r06GGuXLmSjREDOUdKSorp1q2b2bZt213v375922zbts1s3LjR+uHDGGO++eYbI8lMnTrV5vnhw4cbf39/k5KSYowx5vHHHzc9evSweWbMmDEmJCTEZkEogIxZtmyZGThw4D3vnzhxwqxatcqcOnXKWpaSkmJKlixpypcvb/PsuXPnjI+Pj/n666+tbXt6etpsJ3tnq+jvvvsui3sC2GJsGhlmjNG3336riIgI9ejRQzExMdZ7GzduVHh4uLy9vSVJXl5eGjt2rHbt2qX9+/f/Y9tubm4aM2aM4uPjFRcXp6lTp6pgwYLZ1RUgR3Fzc9ORI0c0Y8aMdPfmz5+v4OBgde/eXZ07d1a1atWsB9q1a9dOvr6+un79uk2djh076ty5c9q7d6+ktMOxvvnmGy1evFgTJ07Um2++qRdffFHt2rXTzZs3s7+DQA5jsVj0n//8x+Yka0k6f/682rRpo5o1a2rIkCEKDQ3VJ598IintPe/cuXO697VEiRJq2LChVq1aJSntZPvAwEANHjxYS5Ys0SuvvKITJ07ovffeU9GiRe9L/5B7kVjgH23cuFGzZ8/Wq6++qs8//1w1atTQH3/8ocaNGyspKUmS5Ovrq/Pnz9vUq1u3rooVK5bhRdwA7NelSxfNnz/feqKuJK1fv17/+te/9P333+uPP/7QsWPHFBYWptdee03S/2208L+7vgQFBUmS4uPjJUkvvPCCXn31Vb333nv6/fff1aJFC5UqVUrjxo1T3rx571MPgZyjWbNmKlq0qPWcGCntj3dt27ZVSEiITp8+rV9//VU//fSTBg0apF27dklKe8/Pnj2r3bt327QXFBRkfV/d3d31zTff6NChQ/rPf/6jhx9+WCVLltSbb76p8PDw+9dJ5E7OHjLBg2/8+PEmb968pmnTptZpD1euXDEFCxY0X375pTEmbf5m3rx5zcWLF23qli9f3gQFBVmnVADIHnFxcemmOjz//PNm5MiRxhhjjh49akaMGGFCQkJMWFiYuXTpkjHGmFWrVhlJZunSpdZ6ixYtMvnz5zdXr169v50AcpGBAweaRx991Hr9yy+/mEKFCpnk5GRz69YtM3/+fNOqVSvj5+dnJk+ebH3ukUceMc2aNbOeeJ2UlGTCwsLM2LFj73sfgP/FiAWsYmJiNHPmTH311VeKjY21lr/wwgu6ffu2mjZtat3atWDBgurcubNmzpwpKW07uxIlSuj111+3niuxc+dOxcfHKyYmRuvWrbvr97xy5Yo+++wzNWzYMMPnXgBIc+HCBXXt2lUFCxZU2bJl5eXlZTNCeP78eW3dulUNGzZU/fr1deXKFc2fP18HDhxQkSJFJKXtFhMYGKgOHTqob9++6tu3r15++WVNmDBBBQoUcFbXgBxp9uzZCgsLk7e3txYuXKhff/1VBw8elJT2vqakpKhPnz4KDAzUtGnT1LFjR505c0a9evWythEZGalVq1YpPDxcI0aMUJ06dVS4cGH17dvXWd0CrEgsICntIKtq1arphx9+0OjRoxUaGqotW7ZISpu/2bx5c+3bt8+mTmRkpDZt2qTo6Gh5enpq3rx51i3v2rdvr6eeekqff/65lixZolq1alnrJSUl6ccff1SHDh0UEhKiNWvWaPDgwXr66afva58BV2aM0bPPPqurV6/qzz//1G+//abq1atr2bJlunz5siSpYsWK2rJliwYPHqzTp09rwoQJevTRRyVJJ0+elJQ2b/vFF19U8eLFVbZsWYWEhGjHjh3q2rWrs7oG5EiLFy9Wjx49NGHCBF29elXDhg2zTluS0t7X+Ph4paam6vfff9fq1avVpUsX+fr66tq1a7p69aqktD/kubu7q169evLw8NCwYcO0ceNG6xpHwKmcPWQC59uwYYPx8fExBw4cMMakbV/Xrl07ExgYaG7cuGGMMWbOnDnG19fXXL9+3aZuaGioGTVqlPX6zJkzZuLEiWbq1Knm3Llz6b7X559/booWLWrCw8PNF198YeLi4rKxZ0DO9euvvxpJ5sSJE9ayy5cvGz8/PzNp0iRjTNq7LcnMnTvXpu7atWtNvXr1rNd79uwxksy+ffvuT/BALvTEE0+YXr162ZQNHDjQlC5d2jrNuHbt2qZ+/fomMTHR+kxCQoJ58sknzerVq61lTz75pHn55ZfvT+BAJpBY5CJ79uwx48aNM/PmzbPZIrJfv36mUaNGNs+eOXPGeHl5mZkzZxpjjLlx44bJnz+/mTVrls1zI0eONBUrVsxwDAcOHLD5IATkZi+99JJ1nVJmrV692khKl5y/8sorpk6dOtbrHj16GE9PTzNw4EAzdepU06lTJ1O2bFlz6NAhm3rVqlUzQ4YMsSsWIDc5fPiwGTNmjBk+fLjZuXNnhus9+uijZujQoTZlhw4dMpLM2rVrjTHG7Nq1y/j5+Zm6deuaSZMmmXfffdcEBQXZ/AHPGGNmzZpl/Pz8rH/8Ax4UTIXKJT766CO1aNFCv/zyi1577TX16dPH5n5CQoLNdUBAgOrXr29dG+Hj46P27dun2+HplVde0bhx46zrKv5JWFiYSpcubX9HABe2e/duzZ8/33rt5+eXbovYo0ePKiIiwjqd6V7CwsIkSVu3brUpr169urZv3249VXvKlCn66quvFBMTozVr1qhevXrau3evzYnZUtpuM7Nnz7a7b0Bu8NVXX6l69eo6deqUjhw5otq1a+uLL77IUN1KlSqle19DQ0OVL18+6+/W6tWra/fu3apbt65WrFih2NhY/fe//9U777xjU69du3YyxmjJkiVZ0zEgqzg7s0H2WLp0qWnRooV59NFHzfvvv28qV65srl27ZoxJmwZhsVjM3r17jTHGzJ0713h4eJjTp0/btNGpUyfz3HPPWa9//vln4+HhYS5fvnz/OgLkIFOmTDHFihWz7uayY8cOY7FYzLFjx6zPtG3b1gwaNChD7T311FPmiSeesCkbNGiQkZTuVOx/cuXKFZvDuADYio+PN/ny5TNz5syxlv2///f/jIeHh9mzZ88/1l+3bp2RZHbs2GEtu3jxorFYLCZ//vyZHn3Yv38/Oy7igUNikYOcOnXKREREmC+//NI8/PDD5quvvjL9+vUzksxbb71l82ytWrXMgAEDjDFpp2SXLFnSvPDCC9YpUgkJCSYwMNA6V9sYY1JTU5nGBDggNjbW5MmTx/z3v/+1llWsWNG89957xpi07Sbz589v3Qr2n/z222/G19fXvPLKK+bXX381n332mfH39zevv/66GTx4cLb0AcjpNmzYYD788EOzYMEC6x8BjDFmy5YtRlK607CrVKli2rdvn6G2O3bsaAIDA83ixYvNpk2bTNOmTU3Pnj1NvXr10k1PBFwRicUDLLMLmxMSEoyvr6/Jly+fuXDhgrW8cePGNiMPxhgzYcIE4+/vb5KTk40xaXvZe3t7m0aNGpnBgweb0NBQ07p1a3P79m3HOwLAql27djbv46hRo0yFChWMMcY0a9bMjBgxIlPtbd++3dSpU8cULlzYPP3002b37t1ZGC2QeyQmJpoWLVqYcuXKmVdffdWUKFHC1K1b18THxxtjjNm5c6eRlG5dxdSpU427u3uG/iBw+/Zt079/f1OiRAlToUIFM3bsWOvvYSAnILF4QM2fP98EBATc9T840dHRZvLkyea9995L9x+4Ll26mLJly9qUzZ071/j6+pqEhARr2YULF4yHh4dZvny5tezQoUPm3//+txk8eLBZuXJlFvcIyJ1u375tVq1aZb1etGiR8fb2th4+Fx0dbSwWixk7dqwpWLAgO6UBTjJu3DgTFBRk3f0wJibGBAYGmoiICGNM2iYmXl5e6Q6iO336tJFkFixYcN9jBh40LN5+QFy8eFEdOnTQpUuXJEmNGzfWjRs3tHfvXuszdw7EqVWrljZt2qR9+/apXr16+v77763PREZGKjo6WhcuXLCWPfPMM3Jzc9OiRYusZcWKFdOTTz5psxg7NDRUI0eO1JgxY9SsWbPs7C6Qaxw4cEDNmjXT/v37JUktW7ZU3rx59d1330mSgoOD1bBhQ40ePVrXr19X+/bt9fPPPzszZCDHun79umbNmqXmzZtr5MiRNvc2btyoxx9/XHnz5pUklSxZUh999JFmzZqlvXv3ysfHR82bN9eCBQts6gUGBiowMFBHjhy5b/0AHlQkFg+IwoULa/PmzZo7d64kqWjRojpz5ozNbku+vr4aMGCAYmJi9O2332ru3LkaMWKE+vbtq6SkJElpp+gGBARY25Ekb29vdejQQV9//bXN9+zbt6/1sCwAf+/y5cvpdnT5XwkJCXrvvfdsTq6vWrWqqlatan3/8uTJo+eff94mqe/SpYvc3d114MABVa5cWW3atFGdOnW0aNGif9xx7Z/uA5A2b96sl156SUFBQZo/f766d++uwYMH2zzj7u5u8+5KUseOHVWqVCnrzk+9evXSL7/8Yt0x8Q4PDw9rQvJ3eF+R4zl3wAR/NWDAAFOrVi3r9ffff298fHys8zv/as2aNea5554zBQsWNB4eHmbRokXWe4MGDTI1a9a0ef7nn382bm5uJiYmJtviB3Ky8ePHm8DAwL/dhSUxMdEULlzYTJkyxaZ87NixplSpUta6W7ZsMRaLxURHRxtjjLl69arx9va2vseXL182I0eONCVKlDB9+/a1WUBqTNqGCwsXLjRt27Y1zzzzTNZ1Esih3n77bVOsWDFz8eLFez7z7rvvGj8/v3RrC1977TWb85qaN29uypcvb86cOWOMMWb58uXG09PTHD58+J5tHzx40AwbNsyUKVPG/P777w72BnhwkVg8QHbt2mUkmYMHDxpj0j48FCxY0MyYMcP6TEpKinnxxRdNcHCwmTx5srly5Ypp2rSpzY4Uf/zxh5Fk9u/fby1LTU01S5cuTfcBBUDGnDt3znh4eFjXS6xevdr06dPHvPHGGzYfKHr27Gnq169vU/f06dPGzc3NrFmzxlpWvnx5m0OvnnvuOfPss8/+bQybN282PXr0MEWKFDFNmjQxM2bMuOsfHgDYOnjwoHXh9cGDB83EiRPN2LFjrduuG/N/v4OXLl1qU/err74ybm5u1l0TL126ZBo2bGj8/PxMzZo1TZEiRczs2bPTfc+LFy+aiRMnmtq1a5uSJUuaN954w/z222/Z21HAyUgsnOzgwYOmZcuWxsfHx5QuXdp4eHiYd955x3r/tddeM02aNLFeL1iwwBQpUsS68NMYYyIjI42Xl5eJjY21llWrVi3dX00BOKZFixYmMjLSunPakCFDTHh4uClYsKDZtm2bMcaYTZs2GUnm6NGjNnUbNmxounbtar3+31Pr16xZc8+Tr48fP27Kli1rKleubKKiojhvArBDrVq1TJkyZYy/v79p3769qVmzpvH09DRffPGFzTN//Z1rjDHz5s0z3t7e1sTijp07d5oNGzZYF3v/1aBBg0zBggVNRESEWbFiBTs/IdcgsXCia9eumZIlS5oBAwaYa9eumT179pjQ0FATHBxs/Q/Yxo0bjZubmzl58qQxxphp06bZTMeIi4szpUqVMr6+vmbatGnWtm/evHn/OwTkcHPmzDE+Pj7G39/fOlKQmppqOnbsaCpXrmx9L8uWLWtGjhxpU7dLly7Gz8/PegjW0aNHjY+PT7oE5G5SUlLMrl27srg3QM60fPlyM3z4cJspwsYYM3HiRPPMM8/YjPK9+eabJl++fNZpwncOkJ08ebIxJu3da9++faanHB48eNBmJ0YgtyCxuE/udh7EzJkzTaFChWymJ93ZJ3v9+vXGmLQPLSEhISYqKsoYkzYdw8/Pz7Rv396MGjXKlC1b1gwdOtTMnz/fZkgXQNa7ceOGyZ8/v2nVqpVNeXR0tHFzczNLliwxxqSdTREQEGA97f7ChQsmNDTU5MuXz2bKRGZP2gVyowMHDmT4L/6dO3c21atXNy+//LLx8fExY8aMsd67fPmyzWi/MWnnP3l6eppPP/3UWvbpp58aT09P89hjj5nKlSub6tWrm9OnT2dNZ4Acjl2hstmKFStUo0YN5cmTR/7+/po0aZL13tWrV+Xr6ysPDw9rWY0aNVS7dm3rDjIWi0URERHWHWRKlCih9evXq0CBAjp79qxmzpyp0aNHq2PHjqpcufL97RyQy/j4+Kh9+/Y6c+aMTXlwcLCqVKmizZs3S0rbcc3T01OPPfaYevTooapVq+rf//635syZo/DwcJv2ANzb1atXVbVqVa1atSrdvdTUVH366aeqVauWHnnkEfXv31/Xrl3Tzp07NX36dE2ePFnvv/++daenwoULq0CBAjZt5MuXT4UKFVJcXJy1rE+fPjpy5Ij69eunadOmaefOnQoMDMzejgI5hbMzm5zsp59+MgULFjSLFy82ly5dMp988olxc3OzzudcsWKFkZRuvnSPHj1MgQIFrNOZDh06dNfTPgHcf3d2WPvfHWAaNGhgs0bizJkz5oMPPjBjxowxBw4cuN9hAi7nxo0bJjU11bRv394sW7bMWt6mTRvTuXNn6/Uvv/xiXnzxRfPWW2+ZVq1amcWLF5uXXnrJSLLZ7CQpKckUL17cTJo06Z7fc/ny5cbNzY2phkAWIbHIAqmpqSY6OjrduoZatWqlW4zZv39/U6JECXP79m1z+/ZtExISYvr372/zTIsWLYwkM2/ePGvZZ599Zt3aDoDzpKammtKlS5tmzZqZxMREY0zaVA0vLy+zadMmJ0cHuJ64uDgzYMAAU6hQIbNv3z7Ttm1b8/zzz1vvL1iwwOTNm9e6NuLIkSNGkqlQoYJ1KnFycrIpW7aszeYnxhjTr18/U7duXev1gQMHzIIFC8zChQtNv379jJ+fn800KACOYSqUA5KTk7Vo0SIFBwerfPnyKl68uM1w7ZkzZ1SiRAmbOn379tX58+e1ZcsWeXp6aty4cfr000/13nvvaffu3Ro1apT+/PNPde/e3WZotnv37goICLhvfQNwdxaLRS+++KJWrVqlSpUqqXXr1qpXr55GjBihxx57zNnhAS6nT58+Onz4sPbs2aOHHnpIY8eO1SeffKKUlBRJUuvWrZUnTx4tXLhQklSuXDnVr19f5cuXt04ldnd31yuvvKL58+fbtB0REaFt27ZZT8UuXry4du/erR9//FFFixbV7t271adPn/vYWyCHc3Zm46ratWtnOnbsaB566CHzyy+/mKSkJNOnTx8TGBhoHblo1KiReeGFF9LV9fPzsxmu/f77702lSpVM8eLFTWRkJFtJAg+4/fv3G0lm/vz5ZsmSJYwmAg6oWbOm+c9//mOMSRuNePfdd025cuXM559/bn3mf7denzJliilWrJjN5ifR0dHGYrGYrVu32rQfFhZmRowYkb2dAGCMMcZiDOfL2yMqKkpvv/225s6dq+eff16SlJCQoJIlS2rKlCl68cUXNWXKFL3++us6dOiQgoODJUmxsbEqVqyYVq9ercaNGzuzCwAcsG/fPjZMALJAr169tH79ehUqVEhHjx7V888/r1OnTuny5ctav369JGnTpk0KDw/XiRMnVKpUKV25ckX+/v76/vvv9fTTT1vbCg8PV+XKlTV58mRr2dy5c5U3b161adPmvvcNyG1ILO4hJSVFq1ev1rx58/T777+rdOnSioqKUlhYmCTp5MmTKlOmjBYtWqRnnnnGWu/ll1/W2bNntXz5ciUmJqpWrVoyxmjatGny8fHR0KFDdf36dW3YsEEWi8VZ3QMAwOnmzJmjHj166Nq1a5o2bZpeeeUVeXh4aOvWrXrsscd07NgxlSlTRsYYlStXTj169NDgwYMlSc8++6zy5MmjuXPnWtv74osvNHjwYJ09e1Z58uRxVreAXIs1FncRGxuroKAgDR8+XFWrVtUHH3yg/Pnzq0mTJrp69aokqXTp0goPD9eKFSts6kZGRmr16tU6d+6cvLy8tHLlSlWqVEktW7bUs88+q2rVqmn58uUkFQCAXO+pp57S6dOnVb58eV26dMm6ZqJevXoqV66cvv32W0npt16X0n7f/vjjjzbrEZ977jmtXLmSpAJwEkYsJBlj0n3Qj46OVpkyZazXu3btUoMGDTR8+HANHTpUkjR9+nQNGjTI5i8jqampCg4O1sCBAzVgwID71gcAAFzVyJEjNXfuXB04cMCmbM6cOTp48KAk6fDhw6pQoYJ27typGjVq6Pbt2ypXrpy++uorNWnSxFmhA/iLXD1iceDAAbVq1Up+fn6qUKGCtm/fbr13J6mYPXu2HnnkEXXo0EE1a9bUzJkzrc906NBBN2/e1NKlS61lbm5uioiI0P79++9XNwAAcGkRERE6ePCgfv31V5uyP//8Uzt27JAkhYaGqm7dutZRizx58ig6OpqkAniA5MoRi3//+9+6fv26li5dqjfeeEOPP/64PvjgA61du1aHDx+Wt7e3JGno0KGaP3++Jk6cqBYtWmjDhg1q1KiRduzYoVq1akmSOnXqpMTERC1atMja/t1GQAAAwL099thjevTRRzVhwgSbsho1amjixImSpN27d6tYsWIqVaqUs8IE8Ddy5YhF/vz59fHHH+ull15S9+7dVbFiRU2ePFkJCQmaM2eOpLQpTePHj9fEiRPVsmVLWSwW657af53j2b17d5UuXdqmfZIKAAAyJzIyUnPmzFFycrK1rE+fPipevLj1unr16iQVwAMsV45YnDlzRkFBQZo5c6YiIyOt5T169NDhw4f1888/S5JKlCiht956S2+88Ybi4+PVrl07Xb16VefPn9epU6dIIAAAyCKxsbEKCAhIt4UsANeRK0csSpYsqaZNm1r3x74jMjJS69ev1+nTpyVJ77//vgYPHqy6deuqfPnyqlixon7++Wf9/vvvJBUAAGShwoULa/ny5QoPD3d2KADslCtHLKS06Ux9+/bVuXPnrGsq7rZP9u+//67Dhw+rTp06CgoKcmbIAAAAwAMr1yYW169fV4kSJTR9+nQ999xz1vLhw4dr3bp12rhxoxOjAwAAAFxLrk0sJKlLly6KjY3VkiVLrGXXrl2Tl5cXh+sAAAAAmZAr11jc0aVLF8XHx1t3e5IkPz8/kgoAAAAgk3L1iAUAAACArJGrRywAAAAAZA0SCwAAAAAOI7EAAAAA4DASCwAAAAAOI7EAAAAA4DASCwAAAAAOI7EAAAAA4DASCwAAAAAOI7EAAAAA4DASCwAAAAAO83B2AACAnC8pKUk7duzQlStXVKtWLZUoUcJ6b/Xq1bp06ZIsFosCAgJUvXp1+fn5pWtj165dOn36tEJDQ1WpUiWbe7dv39a2bdsUFxenhx56SOXKlcv2PgEAbFmMMcbZQQAAcq7du3fr2WeflZubm8LCwrR//369//77ioiIkCQNHz5chw8fljFGx44d08mTJ7Vw4UI1aNBAkpSYmKgWLVroyJEjqlGjho4eParQ0FAtXLhQFotFv/32m9q2bavChQsrMDBQ27ZtU/v27TV16lRndhsAch1GLAAA2SYxMVHPPPOMmjZtqi+++ELu7u66deuW1q9fb33m/ffft6kzevRo9erVS3v27JGUNqLx22+/6dSpU8qXL58k6ccff5QxRsnJyWrXrp1ef/11vf7665Kkixcv6pFHHlGjRo3UqVOn+9NRAACJBQAg+/z88886deqUxowZI3d3d0mSt7e3nnzySZvnTp48qQMHDujq1avy9PTU3r17dePGDeXNm1c+Pj5KTEzUn3/+qRo1akiSnnnmGUlpSceJEydUvHhxfffddzLGyBijcuXKae3atSQWAHAfkVgAALLNyZMnlT9/fps1Ff+rb9++mjFjhmrVqqWiRYvq5s2bktJGHoKDg9WkSRP169dPTZo0UYkSJdS0aVO99tprqlatmqKjo5UnTx4tXrzYps3AwEBVqFAhW/sGALDFGgsAQLaZN2+eOnfurBs3bsjLyyvd/V27dunRRx/V4cOHrQuut23bpnr16un48eMqU6aM9dmkpCTt3LlTs2fP1tSpU7V7927t2bNHnTt3Vnx8vHx9fe9XtwAAd8F2swCAbBMeHi4PDw99++23NuUXL16UJJ07d04+Pj4KDg623vvuu+9snj137pxSUlLk6empunXr6j//+Y/8/Py0c+dONWrUSHny5NFnn31mUyclJUXnz5/Ppl4BAO6GqVAAgGzj7++vDz/80LoY+6GHHtKWLVtUtGhRffjhh6pXr558fX313HPPqVWrVtq+fbsWLFhg08bmzZv13nvvqUOHDipdurTWrl0ri8WiJk2ayN/fX5988on69u2rgwcPqk6dOjp9+rQWLlyo0aNHq1WrVk7qOQDkPkyFAgBkuy1btmjBggVKSEhQw4YNFRkZKYvFIkk6fvy4pkyZonPnzqlixYp65plnNGrUKE2cOFHFihWTJO3fv19z587V6dOnVbZsWXXt2lWlSpWytr97927NmzdPZ8+eVdmyZdWpUydVrFjRKX0FgNyKxAIAAACAw1hjAQAAAMBhJBYAAAAAHEZiAQAAAMBhJBYAAAAAHEZiAQAAAMBhJBYAAAAAHEZiAQAAAMBhJBYAAAAAHEZiAQAAAMBhJBYAAAAAHEZiAQAAAMBhJBYAAAAAHPb/AdTDQoEHNtbTAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "if comparison_data is not None:\n",
    "    comparison_df = collect_batch_results(comparison_data, comparison_cases)\n",
    "    comparison_df.to_csv(\n",
    "        result_dir(\"stage11_geometry_comparison\") / \"geometry_comparison.csv\",\n",
    "        index=False,\n",
    "    )\n",
    "    display(\n",
    "        comparison_df[\n",
    "            [\n",
    "                \"case\",\n",
    "                \"geometry\",\n",
    "                \"wavelength_nm\",\n",
    "                \"eta_collection\",\n",
    "                \"eta_air_plane\",\n",
    "                \"eta_t1\",\n",
    "                \"eta_t2\",\n",
    "            ]\n",
    "        ]\n",
    "    )\n",
    "\n",
    "    ax = comparison_df.plot.bar(\n",
    "        x=\"case\",\n",
    "        y=\"eta_collection\",\n",
    "        legend=False,\n",
    "        figsize=(8, 4),\n",
    "    )\n",
    "    ax.set_ylabel(\"NA 0.6 collection efficiency\")\n",
    "    ax.grid(True, axis=\"y\")\n",
    "    plt.xticks(rotation=20, ha=\"right\")\n",
    "    plt.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ffc4e6fb",
   "metadata": {},
   "source": [
    "## Reference & Related Tidy3D Official Document\n",
    "\n",
    "### Reference for NV Center Properties\n",
    "\n",
    "1. J. Hadden et al., “Micro-concave waveguide antenna for high photon extraction from nitrogen vacancy centers in nanodiamond,” *Scientific Reports* **5**, 12013 (2015).\n",
    "    <https://www.nature.com/articles/srep12013>\n",
    "2. I. H. Malitson, “Interspecimen Comparison of the Refractive Index of Fused Silica,” *JOSA* **55**, 1205–1209 (1965).  \n",
    "    <https://opg.optica.org/josa/abstract.cfm?uri=josa-55-10-1205>\n",
    "3. F. Peter diamond dispersion dataset as curated by RefractiveIndex.INFO; reported wavelength range 0.226–0.760 µm.  \n",
    "    <https://refractiveindex.info/?shelf=main&book=C&page=Peter>\n",
    "4. M. N. Polyanskiy, “Refractiveindex.info database of optical constants,” *Scientific Data* **11**, 94 (2024).  \n",
    "    <https://www.nature.com/articles/s41597-023-02898-2>\n",
    "\n",
    "### Tidy3D Official Document\n",
    "\n",
    "5. Point dipole and arbitrary-direction helper:  \n",
    "   <https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.PointDipole.html>\n",
    "6. Field projection angle monitor:  \n",
    "   <https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.FieldProjectionAngleMonitor.html>\n",
    "7. Flux monitor:  \n",
    "   <https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.FluxMonitor.html>\n",
    "8. Simulation and `normalize_index`:  \n",
    "   <https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.Simulation.html>\n",
    "9. Mesh override:  \n",
    "   <https://docs.flexcompute.com/projects/tidy3d/en/latest/api/discretization/_autosummary/tidy3d.MeshOverrideStructure.html>\n",
    "10. Sellmeier medium:  \n",
    "   <https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.Sellmeier.html>\n",
    "11. Batch execution:  \n",
    "   <https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.web.api.container.Batch.html>\n",
    "12. BatchData:  \n",
    "   <https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.web.BatchData.html>"
   ]
  }
 ],
 "metadata": {
  "applications": [
   "Quantum emitters"
  ],
  "description": "This notebook demonstrates calculation of the fluorescence collection efficiency of diamond NV centers imaged through a microscope objective in Tidy3D, using near-to-far-field projection of two orthogonal transition-dipole emitters to evaluate how nanodiamond size, NV depth and orientation, and emission wavelength affect the fraction of radiated power captured within the objective's numerical aperture, across nanodiamond, bulk-diamond, and thin-film geometries.",
  "feature_image": "./img/nv_photoluminescence_nanodiamond.png",
  "features": [
   "Far field projection",
   "Parameter Sweep"
  ],
  "kernelspec": {
   "display_name": "flex",
   "language": "python",
   "name": "python3"
  },
  "keywords": "NV center, nitrogen-vacancy, nanodiamond, photoluminescence, collection efficiency, near-to-far-field projection, transition dipole, numerical aperture, diamond, Tidy3D, FDTD",
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.14.7"
  },
  "title": "NV Center Photoluminescence Collection Efficiency by Microscope in Tidy3D | Flexcompute"
 },
 "nbformat": 4,
 "nbformat_minor": 5
}