diff --git a/.gitignore b/.gitignore
index 1c515b1..a039eb4 100644
--- a/.gitignore
+++ b/.gitignore
@@ -6,6 +6,7 @@ src/pyEDITH/__pycache__
 build
 docs/source/imaging_tutorial.ipynb
 docs/source/spectroscopy_tutorial.ipynb
+docs/source/exoearth_spectroscopy_tutorial.ipynb
 src/pyEDITH/components/__pycache__
 src/pyEDITH/components/__pycache__/coronagraphs.cpython-312.pyc
 src/pyEDITH/components/__pycache__/telescopes.cpython-312.pyc
diff --git a/docs/source/conf.py b/docs/source/conf.py
index e817dfd..91540fc 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -95,7 +95,9 @@ def setup(app):
     tutorials_src = source_dir / "../../tutorials"
 
     # Copy specific notebooks directly to source folder
-    notebooks_to_copy = ["imaging_tutorial.ipynb", "spectroscopy_tutorial.ipynb"]
+    notebooks_to_copy = ["imaging_tutorial.ipynb",
+                         "spectroscopy_tutorial.ipynb",
+                         "exoearth_spectroscopy_tutorial.ipynb"]
 
     for notebook in notebooks_to_copy:
         src = tutorials_src / notebook
diff --git a/docs/source/index.md b/docs/source/index.md
index 678bd7c..116698c 100644
--- a/docs/source/index.md
+++ b/docs/source/index.md
@@ -45,6 +45,7 @@ installation
 run_pyedith
 imaging_tutorial
 spectroscopy_tutorial
+exoearth_spectroscopy_tutorial
 yippy_guide
 glossary
 validation
diff --git a/tutorials/exoearth_spectroscopy_tutorial.ipynb b/tutorials/exoearth_spectroscopy_tutorial.ipynb
new file mode 100644
index 0000000..a306457
--- /dev/null
+++ b/tutorials/exoearth_spectroscopy_tutorial.ipynb
@@ -0,0 +1,1580 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "c3142ca8c91dd016",
+   "metadata": {},
+   "source": [
+    "# pyEDITH Tutorial: ExoEarth Spectroscopy Case Study\n",
+    "\n",
+    "\n",
+    "Curator: [Aarynn Carter](https://www.stsci.edu/stsci-research/research-directory/aarynn-carter) (STScI)\n",
+    "\n",
+    "This notebook walks through assessing the feasibility of distinguishing between different Earth-like spectra for a given Habitable Worlds Observatory (HWO) Early Achitecture Design (EAD) concept."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "463c891fcfec9c0a",
+   "metadata": {},
+   "source": [
+    "Start by importing the necessary packages, set the verbosity to pyEDITH to \"info\" for full information, and set the default style for HWO plots."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "d88f2ac1cd5aa658",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-09-08T22:04:51.332188Z",
+     "iopub.status.busy": "2026-09-08T22:04:51.331917Z",
+     "iopub.status.idle": "2026-09-08T22:04:54.065442Z",
+     "shell.execute_reply": "2026-09-08T22:04:54.065117Z"
+    },
+    "ExecuteTime": {
+     "end_time": "2026-09-09T19:36:15.797626Z",
+     "start_time": "2026-09-09T19:36:14.304583Z"
+    }
+   },
+   "source": [
+    "import os\n",
+    "import glob\n",
+    "import copy\n",
+    "import itertools\n",
+    "import requests\n",
+    "import io\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from scipy.interpolate import interp1d\n",
+    "from scipy.optimize import minimize, NonlinearConstraint\n",
+    "import matplotlib.pyplot as plt\n",
+    "from matplotlib.colors import LogNorm\n",
+    "from astropy import units as u\n",
+    "from synphot import SourceSpectrum, BlackBodyNorm1D\n",
+    "from pyEDITH import Filter, set_verbosity, parse_input\n",
+    "from pyEDITH import AstrophysicalScene, Observatory, Observation\n",
+    "from pyEDITH import calculate_exposure_time_or_snr\n",
+    "import hwostyle\n",
+    "\n",
+    "# pyEDITH verbosity\n",
+    "set_verbosity(level=\"warning\")\n",
+    "\n",
+    "# Plot styling\n",
+    "hwostyle.use(\"light\")\n",
+    "colors = hwostyle.palette"
+   ],
+   "outputs": [],
+   "execution_count": 1
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5c7e1e2e0b64c8b9",
+   "metadata": {},
+   "source": [
+    "## 1: Initial Setup"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6b3581c6d592d309",
+   "metadata": {},
+   "source": [
+    "We need to provide a quantitative metric for how to distinguish between any two spectra. Let us assume a 5$\\sigma$ detection threshold, which corresponds to a $\\chi^2$ value of 25 for Gaussian uncertainties.\n",
+    "\n",
+    "We will also define the spectral channels of interest for the calculation, covering 0.4-1.8 $\\mu$m. In pyEDITH these are defined as `Filter` objects, each with its own wavelength bounds and spectral resolution, which are later passed to the calculation via the `filter_list` parameter.\n",
+    "\n",
+    "Note that if you need to conduct a more complex test such as comparing multiple models simultaneously, you may need to adopt a different metric."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "5b72139ff573a075",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-09-08T22:04:54.066956Z",
+     "iopub.status.busy": "2026-09-08T22:04:54.066803Z",
+     "iopub.status.idle": "2026-09-08T22:04:54.069170Z",
+     "shell.execute_reply": "2026-09-08T22:04:54.068921Z"
+    },
+    "ExecuteTime": {
+     "end_time": "2026-09-09T19:36:15.812547Z",
+     "start_time": "2026-09-09T19:36:15.799417Z"
+    }
+   },
+   "source": [
+    "SIGMA_TARGET = 5.0\n",
+    "CHI2_TARGET = SIGMA_TARGET ** 2\n",
+    "\n",
+    "# Define the spectral channels as pyEDITH Filter objects\n",
+    "FILTERS = [\n",
+    "    Filter(\"VIS\", low=0.4, high=0.85, resolution=140, type=\"IFS\"),\n",
+    "    Filter(\"Bridge\", low=0.85, high=1.1, resolution=140, type=\"IFS\"),\n",
+    "    Filter(\"NIR\", low=1.1, high=1.8, resolution=70, type=\"IFS\"),\n",
+    "]\n",
+    "\n",
+    "channel_names = [f.name for f in FILTERS]\n",
+    "n_channels = len(FILTERS)"
+   ],
+   "outputs": [],
+   "execution_count": 2
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8fe6f00d6f6607e",
+   "metadata": {},
+   "source": [
+    "Next, we need to provide all of the spectra we would like to compare, and extract the relevant information from them to provide to future calculations. Let's define a dictionary to do this, using the keys as the more readable names, and the values as the paths to the files."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "7aa7e5e9faf99d6c",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-09-08T22:04:54.070488Z",
+     "iopub.status.busy": "2026-09-08T22:04:54.070394Z",
+     "iopub.status.idle": "2026-09-08T22:04:57.315422Z",
+     "shell.execute_reply": "2026-09-08T22:04:57.315101Z"
+    },
+    "ExecuteTime": {
+     "end_time": "2026-09-09T19:36:16.223845Z",
+     "start_time": "2026-09-09T19:36:15.816388Z"
+    }
+   },
+   "source": [
+    "# Assemble spectra\n",
+    "def load_spectrum(url):\n",
+    "    response = requests.get(url)\n",
+    "    print(response.status_code, url)   # debug line — remove later\n",
+    "    response.raise_for_status()        # raises a clear HTTPError if something's wrong\n",
+    "    return np.loadtxt(io.StringIO(response.text))\n",
+    "\n",
+    "BASE_URL = \"https://raw.githubusercontent.com/spacetelescope/hwo-tools/main/coron_model/planets/\"\n",
+    "\n",
+    "EARTH_SPECTRA_FILES = {\n",
+    "    \"Archean Earth\": BASE_URL + \"ArcheanEarth_geo_albedo.txt\",\n",
+    "    \"Hazy Archean Earth\": BASE_URL + \"Hazy_ArcheanEarth_geo_albedo.txt\",\n",
+    "    \"Modern Earth\": BASE_URL + \"Earth_geo_albedo.txt\",\n",
+    "    \"Modern Earth 2\": BASE_URL + \"Earth2_geo_albedo.txt\",\n",
+    "    \"Proterozoic Earth (Low O2)\": BASE_URL + \"proterozoic_low_o2_geo_albedo.txt\",\n",
+    "    \"Proterozoic Earth (High O2)\": BASE_URL + \"proterozoic_hi_o2_geo_albedo.txt\",\n",
+    "}\n",
+    "\n",
+    "# Create a dictionary to store the extracted model spectra\n",
+    "models = {}\n",
+    "for label, path in EARTH_SPECTRA_FILES.items():\n",
+    "    arr = load_spectrum(path)\n",
+    "    wl = np.asarray(arr[:, 0], dtype=float)\n",
+    "    albedo = np.asarray(arr[:, 1], dtype=float)\n",
+    "    order = np.argsort(wl)\n",
+    "    models[label] = {\n",
+    "        \"path\": path,\n",
+    "        \"wavelength_um\": wl[order],\n",
+    "        \"albedo\": albedo[order],\n",
+    "    }\n",
+    "\n",
+    "# And also extra the names from the dictionary keys\n",
+    "model_names = sorted(models.keys())\n",
+    "if len(model_names) < 2:\n",
+    "    raise ValueError(\"Need at least two Earth models for pairwise comparison.\")"
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "200 https://raw.githubusercontent.com/spacetelescope/hwo-tools/main/coron_model/planets/ArcheanEarth_geo_albedo.txt\n",
+      "200 https://raw.githubusercontent.com/spacetelescope/hwo-tools/main/coron_model/planets/Hazy_ArcheanEarth_geo_albedo.txt\n",
+      "200 https://raw.githubusercontent.com/spacetelescope/hwo-tools/main/coron_model/planets/Earth_geo_albedo.txt\n",
+      "200 https://raw.githubusercontent.com/spacetelescope/hwo-tools/main/coron_model/planets/Earth2_geo_albedo.txt\n",
+      "200 https://raw.githubusercontent.com/spacetelescope/hwo-tools/main/coron_model/planets/proterozoic_low_o2_geo_albedo.txt\n",
+      "200 https://raw.githubusercontent.com/spacetelescope/hwo-tools/main/coron_model/planets/proterozoic_hi_o2_geo_albedo.txt\n"
+     ]
+    }
+   ],
+   "execution_count": 3
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7d8a836405b3806",
+   "metadata": {},
+   "source": [
+    "Let's take a pause here and plot all of our spectra to see that things are working correctly."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "e31bee91dfae8a5d",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-09-08T22:04:57.317111Z",
+     "iopub.status.busy": "2026-09-08T22:04:57.316983Z",
+     "iopub.status.idle": "2026-09-08T22:04:57.520332Z",
+     "shell.execute_reply": "2026-09-08T22:04:57.520064Z"
+    },
+    "ExecuteTime": {
+     "end_time": "2026-09-09T19:36:16.422537Z",
+     "start_time": "2026-09-09T19:36:16.225669Z"
+    }
+   },
+   "source": [
+    "for name, spectrum in models.items():\n",
+    "    plt.plot(spectrum[\"wavelength_um\"], spectrum[\"albedo\"], label=name)\n",
+    "plt.xlabel(\"Wavelength (um)\")\n",
+    "plt.ylabel(\"Geometric Albedo\")\n",
+    "plt.xlim([0, 2.5])\n",
+    "plt.ylim([0, 0.5])\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 4
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fcec20a8ee30d0fc",
+   "metadata": {},
+   "source": [
+    "Looks good! Now lets prepare our calculations. Specifically, we need to define the parameters for the simulations, including system specific parameters, instrument setup, and the input wavelength grid.\n",
+    "\n",
+    ".. important::\n",
+    "Note that the input wavelength grid is simply the grid on which we provide the input spectra (stellar flux, planet contrast, target SNR). The actual resolved wavelength grid of each observation is determined by the `Filter` objects we defined above. pyEDITH will automatically rebin things during the actual calculations for a given filter, we just need to make sure the input grid covers the wavelength range of every filter.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "4ee0778a4ce6754b",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-09-08T22:04:57.521694Z",
+     "iopub.status.busy": "2026-09-08T22:04:57.521564Z",
+     "iopub.status.idle": "2026-09-08T22:04:57.535904Z",
+     "shell.execute_reply": "2026-09-08T22:04:57.535692Z"
+    },
+    "ExecuteTime": {
+     "end_time": "2026-09-09T19:36:16.447139Z",
+     "start_time": "2026-09-09T19:36:16.424548Z"
+    }
+   },
+   "source": [
+    "# Define the system parameters, let's use Earth at 10 parsecs.\n",
+    "ap = 1 * u.au  # Semi-major axis of planet\n",
+    "rp = 1 * u.earthRad  # Radius of planet\n",
+    "phase_angle = np.pi/2\n",
+    "\n",
+    "# And parameters for star\n",
+    "ra = 236.00757736823\n",
+    "dec = 2.51516683165\n",
+    "dist = 10 * u.pc  # Distance to the system\n",
+    "Tstar = 5800 * u.K\n",
+    "Rsol = 1.0 * u.Rsun\n",
+    "\n",
+    "# And noise parameters\n",
+    "CRb_multiplier = 2.0 # Background multiplier\n",
+    "nzodis = 1.0 # Number of zodis\n",
+    "\n",
+    "# Define the input wavelength grid on which all input spectra are provided.\n",
+    "# It must fully cover the wavelength range of every filter.\n",
+    "wl_input = np.linspace(0.2, 1.9, 1000)\n",
+    "\n",
+    "# Define helper functions for stellar spectrum\n",
+    "def compute_blackbody_photon_flux(temp, wavelengths):\n",
+    "    \"\"\"Generate photon flux density (photon/s/cm^2/um) for a blackbody.\"\"\"\n",
+    "    bb = SourceSpectrum(BlackBodyNorm1D, temperature=temp)\n",
+    "    flux_photlam = bb(wavelengths)\n",
+    "    return flux_photlam\n",
+    "\n",
+    "# Calculate the observed Fstar at 10 pc\n",
+    "wl_input_u = wl_input * u.um\n",
+    "Fstar = compute_blackbody_photon_flux(Tstar, wl_input_u)\n",
+    "Fstar = Fstar.to(u.photon / (u.s * u.cm**2 * u.nm))  # Convert to pyEDITH units\n",
+    "Fstar_obs_10pc = Fstar * (1000*u.pc / dist)**2  # Scale from 1 kpc (synphot default) to 10 pc\n",
+    "\n",
+    "# Define the base parameters for the simulations. The spectral channels are\n",
+    "# specified through the filter_list parameter, and pyEDITH will rebin all\n",
+    "# input spectra onto each filter's resolved wavelength grid.\n",
+    "base_params = {\n",
+    "    # --- Observation setup ---\n",
+    "    \"observing_mode\": \"IFS\",\n",
+    "    \"wavelength\": wl_input,                     # input grid; rebinned per filter\n",
+    "    \"filter_list\": FILTERS,                     # spectral channels\n",
+    "    \"snr\": np.ones_like(wl_input),              # placeholder\n",
+    "    \"CRb_multiplier\": CRb_multiplier,\n",
+    "\n",
+    "    # --- Astrophysical scene: star ---\n",
+    "    \"distance\": dist.value,                     # pc\n",
+    "    \"stellar_radius\": Rsol.value,               # solar radii\n",
+    "    \"Fstar_10pc\": Fstar_obs_10pc.value,         # photon/s/cm^2/nm\n",
+    "    \"ra\": ra,                                   # deg\n",
+    "    \"dec\": dec,                                 # deg\n",
+    "\n",
+    "    # --- Astrophysical scene: planet ---\n",
+    "    \"Fp/Fs\": 1e-10 * np.ones_like(wl_input),    # placeholder\n",
+    "    \"separation\": ap.value / dist.value,        # arcsec\n",
+    "\n",
+    "    # --- Astrophysical scene: zodi ---\n",
+    "    \"nzodis\": nzodis,\n",
+    "\n",
+    "    # --- Observatory / instrument ---\n",
+    "    \"observatory_preset\": \"EAC1\",\n",
+    "    \"psf_trunc_ratio\": 0.3,\n",
+    "    \"IFS_eff\": [1.0]*len(wl_input),\n",
+    "    \"noisefloor_PPF\": 30,\n",
+    "    \"ez_PPF\": [np.inf]*len(wl_input),\n",
+    "}"
+   ],
+   "outputs": [],
+   "execution_count": 5
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c95e32bb261fc415",
+   "metadata": {},
+   "source": [
+    "Now we have defined some base settings for the calculation, we can parse the filter list to confirm which filters are active for our input wavelength range, and extract the resolved wavelength grid of each channel."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "fa161b066900f6bf",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-09-08T22:04:57.537225Z",
+     "iopub.status.busy": "2026-09-08T22:04:57.537120Z",
+     "iopub.status.idle": "2026-09-08T22:04:57.540223Z",
+     "shell.execute_reply": "2026-09-08T22:04:57.540005Z"
+    },
+    "ExecuteTime": {
+     "end_time": "2026-09-09T19:36:16.472543Z",
+     "start_time": "2026-09-09T19:36:16.448772Z"
+    }
+   },
+   "source": [
+    "# Parse the filter list to validate it against the input wavelength range.\n",
+    "parsed_filters = parse_input.parse_filters(base_params)\n",
+    "for f in parsed_filters:\n",
+    "    print(f)\n",
+    "\n",
+    "# Each Filter carries its own resolved wavelength grid.\n",
+    "wl_ch = {f.name: np.asarray(f.wavelength.to_value(u.um), dtype=float) for f in parsed_filters}"
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Filter(name='VIS', type='IFS', range=0.400 um-0.850 um, center=0.625 um, R=140)\n",
+      "Filter(name='Bridge', type='IFS', range=0.850 um-1.100 um, center=0.975 um, R=140)\n",
+      "Filter(name='NIR', type='IFS', range=1.100 um-1.800 um, center=1.450 um, R=70)\n"
+     ]
+    }
+   ],
+   "execution_count": 6
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fdc8e69412ec49ce",
+   "metadata": {},
+   "source": [
+    "The calculations we perform will be based on the contrast ratio of the planet to its host star, which is a function of the geometric albedo, the phase angle, the radius of the planet, and the semi-major axis of the orbit."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "6fa25698d89dce4a",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-09-08T22:04:57.541399Z",
+     "iopub.status.busy": "2026-09-08T22:04:57.541314Z",
+     "iopub.status.idle": "2026-09-08T22:04:57.545662Z",
+     "shell.execute_reply": "2026-09-08T22:04:57.545461Z"
+    },
+    "ExecuteTime": {
+     "end_time": "2026-09-09T19:36:16.490761Z",
+     "start_time": "2026-09-09T19:36:16.475972Z"
+    }
+   },
+   "source": [
+    "# Helper function for computing contrast_ratio\n",
+    "def contrast_ratio(geometric_albedo, phase_angle, planet_radius, separation):\n",
+    "    phase_function = (np.sin(phase_angle) + (np.pi - phase_angle) * np.cos(phase_angle)) / np.pi\n",
+    "    return geometric_albedo * phase_function * (planet_radius / separation).decompose().value ** 2\n",
+    "\n",
+    "fpfs_input_all = {}  # Contrast ratio on the input grid\n",
+    "# Loop over each model, compute the contrast ratio and interpolate onto the input grid\n",
+    "for name, model in models.items():\n",
+    "    fpfs_native = contrast_ratio(model[\"albedo\"], phase_angle, rp, ap)\n",
+    "    fpfs_input_all[name] = np.interp(wl_input, model[\"wavelength_um\"], fpfs_native)"
+   ],
+   "outputs": [],
+   "execution_count": 7
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f2ea572264cd13ca",
+   "metadata": {},
+   "source": [
+    "## 2: Running The SNR Calculations"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a6a4bdba6938d261",
+   "metadata": {},
+   "source": [
+    "We're now in a position to start conducting calculations. We'll start by defining a function that uses the base parameters and the contrast ratio to produce all the relevant setups for a pyEDITH calculation, and then returns a second function that can be used to calculate the SNR for a given input exposure time."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "c64d20fa7253abff",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-09-08T22:04:57.546919Z",
+     "iopub.status.busy": "2026-09-08T22:04:57.546842Z",
+     "iopub.status.idle": "2026-09-08T22:04:57.549681Z",
+     "shell.execute_reply": "2026-09-08T22:04:57.549474Z"
+    },
+    "ExecuteTime": {
+     "end_time": "2026-09-09T19:36:16.499426Z",
+     "start_time": "2026-09-09T19:36:16.492479Z"
+    }
+   },
+   "source": [
+    "def make_snr_runner(base_params, fpfs_input):\n",
+    "    \"\"\"\n",
+    "    Build pyEDITH's observation, scene, and observatory setups\n",
+    "    and return a function that can call the SNR calculation\n",
+    "    for a given exposure time.\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    base_params : dict\n",
+    "        Base parameters for the pyEDITH calculation, including system and instrument setup.\n",
+    "    fpfs_input : dict\n",
+    "        Contrast ratio for the planet to star, keyed by channel name.\n",
+    "\n",
+    "    Returns\n",
+    "    -------\n",
+    "    run : function\n",
+    "        A function that takes a reference exposure time (in hours) and returns the SNR results for each channel.\n",
+    "    fpfs_resolved : dict\n",
+    "        The contrast ratio rebinned onto the resolved wavelength grid of each channel, keyed by channel name.\n",
+    "    \"\"\"\n",
+    "    params = copy.deepcopy(base_params)\n",
+    "    params[\"Fp/Fs\"] = np.asarray(fpfs_input, dtype=float)\n",
+    "\n",
+    "    setups = []\n",
+    "    fpfs_resolved = {} #pyEDITH rebinned Fp/FS\n",
+    "    for f in parse_input.parse_filters(params):\n",
+    "        observation = Observation()\n",
+    "        observation.load_configuration(params, filter=f)\n",
+    "        observation.set_output_arrays()\n",
+    "        observation.validate_configuration()\n",
+    "\n",
+    "        scene = AstrophysicalScene()\n",
+    "        scene.load_configuration(params)\n",
+    "        scene.calculate_zodi_exozodi(params)\n",
+    "        scene.regrid_spectra(observation)\n",
+    "\n",
+    "        fpfs = scene.Fp_over_Fs\n",
+    "        fpfs_resolved[f.name] = np.asarray(getattr(fpfs, \"value\", fpfs), dtype=float)\n",
+    "\n",
+    "        observatory = Observatory()\n",
+    "        observatory.create_observatory(parse_input.get_observatory_config(params))\n",
+    "        observatory.load_configuration(params, observation, scene)\n",
+    "        observatory.validate_configuration()\n",
+    "\n",
+    "        setups.append((f, observation, scene, observatory))\n",
+    "\n",
+    "    def run(t_ref_hr):\n",
+    "        results = {}\n",
+    "        for f, observation, scene, observatory in setups:\n",
+    "            observation.set_output_arrays()  # reset outputs between runs\n",
+    "            observation.obstime = t_ref_hr * u.hr\n",
+    "            calculate_exposure_time_or_snr(observation, scene, observatory,\n",
+    "                                           mode=\"signal_to_noise\")\n",
+    "            results[f.name] = {\"wavelength\": observation.wavelength,\n",
+    "                               \"snr\": observation.fullsnr}\n",
+    "        return results\n",
+    "\n",
+    "    return run, fpfs_resolved"
+   ],
+   "outputs": [],
+   "execution_count": 8
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7dc673d7146f4897",
+   "metadata": {},
+   "source": [
+    "Now we can produce a measure of the SNR for any model and filter, however, we cannot simply scale a single reference exposure time to get the SNR for an arbitrary exposure. As pyEDITH accounts for multiple noise sources, the scaling is not linear. For example, if the reference exposure time is high we will be dominated by photon noise, but if it is low then we will be dominated by read noise. Therefore, instead of scaling a single reference exposure, we will build a grid calculations for a range of exposure times and then interpolate between them to get an appropriate noise estimate for any trial exposure time.\n",
+    "\n",
+    "Note this cell can take ~20 seconds to run with the default grid spacing."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "3d30eed6b6dc6aa2",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-09-08T22:04:57.550989Z",
+     "iopub.status.busy": "2026-09-08T22:04:57.550901Z",
+     "iopub.status.idle": "2026-09-08T22:05:18.658532Z",
+     "shell.execute_reply": "2026-09-08T22:05:18.658241Z"
+    },
+    "ExecuteTime": {
+     "end_time": "2026-09-09T19:36:41.435633Z",
+     "start_time": "2026-09-09T19:36:16.500972Z"
+    }
+   },
+   "source": [
+    "# Create a grid of reference exposure times in log-log space, from 1 to 100 hr\n",
+    "t_noise_grid = np.logspace(np.log10(1.0), np.log10(100), 30)\n",
+    "\n",
+    "# Determine the per-channel noise templates for each model at every grid time.\n",
+    "sigma_grid_ch = {}\n",
+    "fpfs_ch = {}\n",
+    "for name in model_names:\n",
+    "    runner, fpfs_ch[name] = make_snr_runner(base_params, fpfs_input_all[name])\n",
+    "    rows = {cn: [] for cn in channel_names}\n",
+    "    for t in t_noise_grid:\n",
+    "        results = runner(t)\n",
+    "        for cn in channel_names:\n",
+    "            snr = np.asarray(getattr(results[cn][\"snr\"], \"value\", results[cn][\"snr\"]), dtype=float)\n",
+    "            rows[cn].append(fpfs_ch[name][cn] / np.clip(snr, 1e-30, np.inf))\n",
+    "    sigma_grid_ch[name] = {cn: np.vstack(rows[cn]) for cn in channel_names}\n",
+    "\n",
+    "# log times for interpolation routine\n",
+    "_log_t_grid = np.log(t_noise_grid)\n",
+    "# Set a noise floor to avoid logging zero or negative values\n",
+    "_SIGMA_FLOOR = 1e-30\n",
+    "\n",
+    "# Define function to return an interpolation function/interpolator for a given noise grid\n",
+    "def _make_sigma_interp(sigma_grid_2d, kind=\"slinear\"):\n",
+    "    \"\"\"\n",
+    "    Returns a interpolation function that can determine a noise template for any given exposure time\n",
+    "    \"\"\"\n",
+    "    # Operate in log space\n",
+    "    log_sigma = np.log(np.clip(sigma_grid_2d, _SIGMA_FLOOR, None))\n",
+    "    #Build interpolation function\n",
+    "    f = interp1d(_log_t_grid, log_sigma, kind=kind, axis=0,\n",
+    "                 bounds_error=False, fill_value=\"extrapolate\")\n",
+    "    lo, hi = t_noise_grid[0], t_noise_grid[-1]\n",
+    "    return lambda t: np.exp(f(np.log(np.clip(t, lo, hi))))\n",
+    "\n",
+    "# Create interpolator for each individual channel of every model\n",
+    "sigma_interp_ch = {}\n",
+    "for name in model_names:\n",
+    "    # Dict comprehension over channels\n",
+    "    sigma_interp_ch[name] = {\n",
+    "        cn: _make_sigma_interp(sigma_grid_ch[name][cn])\n",
+    "        for cn in channel_names\n",
+    "    }"
+   ],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-09 15:36:16,510]\u001B[0m `FstarV_10pc` not specified in parameters. Calculating internally...\n",
+      "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-09 15:36:16,552]\u001B[0m Coronagraph 'eac1_aavc_2d' not found locally. Attempting to fetch from remote database...\n",
+      "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-09 15:36:16,878] \u001B[0mUnhandled header fields: {'TMULCHAR', 'TMULDET'}\n",
+      "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-09 15:36:16,878] \u001B[0mUsing default unit for D: m. Could not extract unit from comment: \"circumscribed diameter of the telescope in mete\"\n",
+      "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-09 15:36:16,879] \u001B[0mUsing default unit for D_INSC: m. Could not extract unit from comment: \"inscribed diameter of the telescope in meters\"\n",
+      "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-09 15:36:17,516]\u001B[0m No nrolls in YIPs, setting nrolls = 1.\n",
+      "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-09 15:36:17,602]\u001B[0m `FstarV_10pc` not specified in parameters. Calculating internally...\n",
+      "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-09 15:36:17,633]\u001B[0m Coronagraph 'eac1_aavc_2d' not found locally. Attempting to fetch from remote database...\n",
+      "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-09 15:36:18,044] \u001B[0mUnhandled header fields: {'TMULCHAR', 'TMULDET'}\n",
+      "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-09 15:36:18,045] \u001B[0mUsing default unit for D: m. Could not extract unit from comment: \"circumscribed diameter of the telescope in mete\"\n",
+      "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-09 15:36:18,045] \u001B[0mUsing default unit for D_INSC: m. Could not extract unit from comment: \"inscribed diameter of the telescope in meters\"\n",
+      "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-09 15:36:18,182]\u001B[0m No nrolls in YIPs, setting nrolls = 1.\n",
+      "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-09 15:36:18,263]\u001B[0m `FstarV_10pc` not specified in parameters. Calculating internally...\n",
+      "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-09 15:36:18,295]\u001B[0m Coronagraph 'eac1_aavc_2d' not found locally. Attempting to fetch from remote database...\n",
+      "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-09 15:36:18,633] \u001B[0mUnhandled header fields: {'TMULCHAR', 'TMULDET'}\n",
+      "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-09 15:36:18,634] \u001B[0mUsing default unit for D: m. Could not extract unit from comment: \"circumscribed diameter of the telescope in mete\"\n",
+      "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-09 15:36:18,635] \u001B[0mUsing default unit for D_INSC: m. Could not extract unit from comment: \"inscribed diameter of the telescope in meters\"\n",
+      "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-09 15:36:18,787]\u001B[0m No nrolls in YIPs, setting nrolls = 1.\n",
+      "/Users/aacarter/Documents/SOFTWARE/HWO/pyEDITH/src/pyEDITH/exposure_time_calculator.py:1066: RuntimeWarning: invalid value encountered in sqrt\n",
+      "  np.sqrt(CRp_arr.value**2 / (1 / time_factors.value + CRnf_arr.value**2))\n",
+      "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-09 15:36:21,215]\u001B[0m `FstarV_10pc` not specified in parameters. Calculating internally...\n",
+      "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-09 15:36:21,258]\u001B[0m Coronagraph 'eac1_aavc_2d' not found locally. Attempting to fetch from remote database...\n",
+      "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-09 15:36:21,591] \u001B[0mUnhandled header fields: {'TMULCHAR', 'TMULDET'}\n",
+      "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-09 15:36:21,592] \u001B[0mUsing default unit for D: m. Could not extract unit from comment: \"circumscribed diameter of the telescope in mete\"\n",
+      "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-09 15:36:21,592] \u001B[0mUsing default unit for D_INSC: m. Could not extract unit from comment: \"inscribed diameter of the telescope in meters\"\n",
+      "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-09 15:36:21,752]\u001B[0m No nrolls in YIPs, setting nrolls = 1.\n",
+      "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-09 15:36:21,834]\u001B[0m `FstarV_10pc` not specified in parameters. Calculating internally...\n",
+      "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-09 15:36:21,865]\u001B[0m Coronagraph 'eac1_aavc_2d' not found locally. Attempting to fetch from remote database...\n",
+      "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-09 15:36:22,197] \u001B[0mUnhandled header fields: {'TMULCHAR', 'TMULDET'}\n",
+      "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-09 15:36:22,198] \u001B[0mUsing default unit for D: m. Could not extract unit from comment: \"circumscribed diameter of the telescope in mete\"\n",
+      "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-09 15:36:22,198] \u001B[0mUsing default unit for D_INSC: m. Could not extract unit from comment: \"inscribed diameter of the telescope in meters\"\n",
+      "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-09 15:36:22,350]\u001B[0m No nrolls in YIPs, setting nrolls = 1.\n",
+      "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-09 15:36:22,432]\u001B[0m `FstarV_10pc` not specified in parameters. Calculating internally...\n",
+      "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-09 15:36:22,467]\u001B[0m Coronagraph 'eac1_aavc_2d' not found locally. Attempting to fetch from remote database...\n",
+      "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-09 15:36:22,795] \u001B[0mUnhandled header fields: {'TMULCHAR', 'TMULDET'}\n",
+      "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-09 15:36:22,796] \u001B[0mUsing default unit for D: m. Could not extract unit from comment: \"circumscribed diameter of the telescope in mete\"\n",
+      "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-09 15:36:22,797] \u001B[0mUsing default unit for D_INSC: m. Could not extract unit from comment: \"inscribed diameter of the telescope in meters\"\n",
+      "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-09 15:36:22,949]\u001B[0m No nrolls in YIPs, setting nrolls = 1.\n",
+      "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-09 15:36:25,278]\u001B[0m `FstarV_10pc` not specified in parameters. Calculating internally...\n",
+      "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-09 15:36:25,325]\u001B[0m Coronagraph 'eac1_aavc_2d' not found locally. Attempting to fetch from remote database...\n",
+      "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-09 15:36:25,680] \u001B[0mUnhandled header fields: {'TMULCHAR', 'TMULDET'}\n",
+      "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-09 15:36:25,681] \u001B[0mUsing default unit for D: m. Could not extract unit from comment: \"circumscribed diameter of the telescope in mete\"\n",
+      "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-09 15:36:25,681] \u001B[0mUsing default unit for D_INSC: m. Could not extract unit from comment: \"inscribed diameter of the telescope in meters\"\n",
+      "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-09 15:36:25,854]\u001B[0m No nrolls in YIPs, setting nrolls = 1.\n",
+      "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-09 15:36:25,944]\u001B[0m `FstarV_10pc` not specified in parameters. Calculating internally...\n",
+      "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-09 15:36:25,978]\u001B[0m Coronagraph 'eac1_aavc_2d' not found locally. Attempting to fetch from remote database...\n",
+      "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-09 15:36:26,314] \u001B[0mUnhandled header fields: {'TMULCHAR', 'TMULDET'}\n",
+      "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-09 15:36:26,315] \u001B[0mUsing default unit for D: m. Could not extract unit from comment: \"circumscribed diameter of the telescope in mete\"\n",
+      "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-09 15:36:26,316] \u001B[0mUsing default unit for D_INSC: m. Could not extract unit from comment: \"inscribed diameter of the telescope in meters\"\n",
+      "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-09 15:36:26,474]\u001B[0m No nrolls in YIPs, setting nrolls = 1.\n",
+      "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-09 15:36:26,558]\u001B[0m `FstarV_10pc` not specified in parameters. Calculating internally...\n",
+      "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-09 15:36:26,594]\u001B[0m Coronagraph 'eac1_aavc_2d' not found locally. Attempting to fetch from remote database...\n",
+      "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-09 15:36:26,939] \u001B[0mUnhandled header fields: {'TMULCHAR', 'TMULDET'}\n",
+      "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-09 15:36:26,940] \u001B[0mUsing default unit for D: m. Could not extract unit from comment: \"circumscribed diameter of the telescope in mete\"\n",
+      "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-09 15:36:26,940] \u001B[0mUsing default unit for D_INSC: m. Could not extract unit from comment: \"inscribed diameter of the telescope in meters\"\n",
+      "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-09 15:36:27,100]\u001B[0m No nrolls in YIPs, setting nrolls = 1.\n",
+      "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-09 15:36:29,291]\u001B[0m `FstarV_10pc` not specified in parameters. Calculating internally...\n",
+      "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-09 15:36:29,338]\u001B[0m Coronagraph 'eac1_aavc_2d' not found locally. Attempting to fetch from remote database...\n",
+      "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-09 15:36:29,663] \u001B[0mUnhandled header fields: {'TMULCHAR', 'TMULDET'}\n",
+      "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-09 15:36:29,664] \u001B[0mUsing default unit for D: m. Could not extract unit from comment: \"circumscribed diameter of the telescope in mete\"\n",
+      "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-09 15:36:29,664] \u001B[0mUsing default unit for D_INSC: m. Could not extract unit from comment: \"inscribed diameter of the telescope in meters\"\n",
+      "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-09 15:36:29,822]\u001B[0m No nrolls in YIPs, setting nrolls = 1.\n",
+      "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-09 15:36:29,906]\u001B[0m `FstarV_10pc` not specified in parameters. Calculating internally...\n",
+      "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-09 15:36:29,941]\u001B[0m Coronagraph 'eac1_aavc_2d' not found locally. Attempting to fetch from remote database...\n",
+      "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-09 15:36:30,263] \u001B[0mUnhandled header fields: {'TMULCHAR', 'TMULDET'}\n",
+      "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-09 15:36:30,264] \u001B[0mUsing default unit for D: m. Could not extract unit from comment: \"circumscribed diameter of the telescope in mete\"\n",
+      "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-09 15:36:30,265] \u001B[0mUsing default unit for D_INSC: m. Could not extract unit from comment: \"inscribed diameter of the telescope in meters\"\n",
+      "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-09 15:36:30,428]\u001B[0m No nrolls in YIPs, setting nrolls = 1.\n",
+      "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-09 15:36:30,593]\u001B[0m `FstarV_10pc` not specified in parameters. Calculating internally...\n",
+      "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-09 15:36:30,631]\u001B[0m Coronagraph 'eac1_aavc_2d' not found locally. Attempting to fetch from remote database...\n",
+      "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-09 15:36:30,958] \u001B[0mUnhandled header fields: {'TMULCHAR', 'TMULDET'}\n",
+      "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-09 15:36:30,959] \u001B[0mUsing default unit for D: m. Could not extract unit from comment: \"circumscribed diameter of the telescope in mete\"\n",
+      "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-09 15:36:30,959] \u001B[0mUsing default unit for D_INSC: m. Could not extract unit from comment: \"inscribed diameter of the telescope in meters\"\n",
+      "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-09 15:36:31,131]\u001B[0m No nrolls in YIPs, setting nrolls = 1.\n",
+      "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-09 15:36:33,394]\u001B[0m `FstarV_10pc` not specified in parameters. Calculating internally...\n",
+      "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-09 15:36:33,437]\u001B[0m Coronagraph 'eac1_aavc_2d' not found locally. Attempting to fetch from remote database...\n",
+      "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-09 15:36:33,778] \u001B[0mUnhandled header fields: {'TMULCHAR', 'TMULDET'}\n",
+      "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-09 15:36:33,779] \u001B[0mUsing default unit for D: m. Could not extract unit from comment: \"circumscribed diameter of the telescope in mete\"\n",
+      "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-09 15:36:33,779] \u001B[0mUsing default unit for D_INSC: m. Could not extract unit from comment: \"inscribed diameter of the telescope in meters\"\n",
+      "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-09 15:36:33,936]\u001B[0m No nrolls in YIPs, setting nrolls = 1.\n",
+      "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-09 15:36:34,019]\u001B[0m `FstarV_10pc` not specified in parameters. Calculating internally...\n",
+      "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-09 15:36:34,048]\u001B[0m Coronagraph 'eac1_aavc_2d' not found locally. Attempting to fetch from remote database...\n",
+      "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-09 15:36:34,373] \u001B[0mUnhandled header fields: {'TMULCHAR', 'TMULDET'}\n",
+      "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-09 15:36:34,373] \u001B[0mUsing default unit for D: m. Could not extract unit from comment: \"circumscribed diameter of the telescope in mete\"\n",
+      "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-09 15:36:34,374] \u001B[0mUsing default unit for D_INSC: m. Could not extract unit from comment: \"inscribed diameter of the telescope in meters\"\n",
+      "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-09 15:36:34,523]\u001B[0m No nrolls in YIPs, setting nrolls = 1.\n",
+      "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-09 15:36:34,603]\u001B[0m `FstarV_10pc` not specified in parameters. Calculating internally...\n",
+      "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-09 15:36:34,635]\u001B[0m Coronagraph 'eac1_aavc_2d' not found locally. Attempting to fetch from remote database...\n",
+      "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-09 15:36:34,960] \u001B[0mUnhandled header fields: {'TMULCHAR', 'TMULDET'}\n",
+      "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-09 15:36:34,961] \u001B[0mUsing default unit for D: m. Could not extract unit from comment: \"circumscribed diameter of the telescope in mete\"\n",
+      "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-09 15:36:34,962] \u001B[0mUsing default unit for D_INSC: m. Could not extract unit from comment: \"inscribed diameter of the telescope in meters\"\n",
+      "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-09 15:36:35,108]\u001B[0m No nrolls in YIPs, setting nrolls = 1.\n",
+      "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-09 15:36:37,547]\u001B[0m `FstarV_10pc` not specified in parameters. Calculating internally...\n",
+      "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-09 15:36:37,596]\u001B[0m Coronagraph 'eac1_aavc_2d' not found locally. Attempting to fetch from remote database...\n",
+      "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-09 15:36:37,935] \u001B[0mUnhandled header fields: {'TMULCHAR', 'TMULDET'}\n",
+      "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-09 15:36:37,936] \u001B[0mUsing default unit for D: m. Could not extract unit from comment: \"circumscribed diameter of the telescope in mete\"\n",
+      "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-09 15:36:37,937] \u001B[0mUsing default unit for D_INSC: m. Could not extract unit from comment: \"inscribed diameter of the telescope in meters\"\n",
+      "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-09 15:36:38,097]\u001B[0m No nrolls in YIPs, setting nrolls = 1.\n",
+      "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-09 15:36:38,183]\u001B[0m `FstarV_10pc` not specified in parameters. Calculating internally...\n",
+      "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-09 15:36:38,218]\u001B[0m Coronagraph 'eac1_aavc_2d' not found locally. Attempting to fetch from remote database...\n",
+      "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-09 15:36:38,553] \u001B[0mUnhandled header fields: {'TMULCHAR', 'TMULDET'}\n",
+      "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-09 15:36:38,554] \u001B[0mUsing default unit for D: m. Could not extract unit from comment: \"circumscribed diameter of the telescope in mete\"\n",
+      "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-09 15:36:38,555] \u001B[0mUsing default unit for D_INSC: m. Could not extract unit from comment: \"inscribed diameter of the telescope in meters\"\n",
+      "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-09 15:36:38,712]\u001B[0m No nrolls in YIPs, setting nrolls = 1.\n",
+      "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-09 15:36:38,798]\u001B[0m `FstarV_10pc` not specified in parameters. Calculating internally...\n",
+      "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-09 15:36:38,833]\u001B[0m Coronagraph 'eac1_aavc_2d' not found locally. Attempting to fetch from remote database...\n",
+      "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-09 15:36:39,163] \u001B[0mUnhandled header fields: {'TMULCHAR', 'TMULDET'}\n",
+      "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-09 15:36:39,164] \u001B[0mUsing default unit for D: m. Could not extract unit from comment: \"circumscribed diameter of the telescope in mete\"\n",
+      "\u001B[38;5;229m\u001B[48;5;16m[yippy]\u001B[0m \u001B[33mWARNING [2026-09-09 15:36:39,165] \u001B[0mUsing default unit for D_INSC: m. Could not extract unit from comment: \"inscribed diameter of the telescope in meters\"\n",
+      "\u001B[38;2;226;147;0m[pyEDITH] WARNING [2026-09-09 15:36:39,319]\u001B[0m No nrolls in YIPs, setting nrolls = 1.\n"
+     ]
+    }
+   ],
+   "execution_count": 9
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cbcfc4f752cea686",
+   "metadata": {},
+   "source": [
+    "## 3: Optimizing The Exposure Time"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c09d27150463b4d7",
+   "metadata": {},
+   "source": [
+    "Now we need to take our individual model estimates and compare them in a pairwise fashion to identify how well we can distinguish between any two models. As observations in different channels can have different exposure times, we need to optimize the exposure time in each channel simultaneously to find the minimum required exposure time to reach the desired $\\chi^2$ threshold.\n",
+    "\n",
+    "There are two pieces to this that we need to produce. First, a function that determines the $\\chi^2$ value for a given model pair and exposure time, and second, a function that optimizes the exposure times across the different channels together to reach the desired $\\chi^2$ threshold in the minimum amount of time.\n",
+    "\n",
+    "For the first, it's simply a case of calculating the sum of the squared differences between the two models, divided by the noise for the model we're assuming to be true."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "80656de75913ecf",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-09-08T22:05:18.660215Z",
+     "iopub.status.busy": "2026-09-08T22:05:18.660132Z",
+     "iopub.status.idle": "2026-09-08T22:05:18.662635Z",
+     "shell.execute_reply": "2026-09-08T22:05:18.662414Z"
+    },
+    "ExecuteTime": {
+     "end_time": "2026-09-09T19:36:41.493358Z",
+     "start_time": "2026-09-09T19:36:41.478497Z"
+    }
+   },
+   "source": [
+    "def chi2_multichannel(times_vector, fpfs_ch_a, fpfs_ch_b, sigma_interp_a, channel_names, t_min=0.01):\n",
+    "    \"\"\"\n",
+    "    Compute chi2 for a comparison between two models on a channel by channel basis\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "\n",
+    "    times_vector : 1D array\n",
+    "        Exposure times for each channel (hours), ordered as channel_names\n",
+    "    fpfs_ch_a : dict\n",
+    "        Contrast ratios for model A in each channel, keyed by channel name\n",
+    "    fpfs_ch_b : dict\n",
+    "        Contrast ratios for model B in each channel, keyed by channel name\n",
+    "    sigma_interp_a : dict\n",
+    "        Noise interpolators for model A in each channel, keyed by channel name\n",
+    "    channel_names : list of str\n",
+    "        Names of the channels (filter names)\n",
+    "    t_min : float, optional\n",
+    "        Minimum exposure time to avoid division by zero (default is 0.01 hours)\n",
+    "\n",
+    "    Returns\n",
+    "    -------\n",
+    "    chi2 : float\n",
+    "        Chi-squared value for the comparison between the two models\n",
+    "    \"\"\"\n",
+    "\n",
+    "    delta_all = []\n",
+    "    sigma_all = []\n",
+    "    for i, cn in enumerate(channel_names):\n",
+    "        # Calculate the delta contrast ratio\n",
+    "        delta_all.append(fpfs_ch_a[cn] - fpfs_ch_b[cn])\n",
+    "        # Determine noise for this exposure time using interpolator\n",
+    "        sigma_all.append(sigma_interp_a[cn](times_vector[i]))\n",
+    "\n",
+    "    delta_all = np.concatenate(delta_all)\n",
+    "    sigma_all = np.concatenate(sigma_all)\n",
+    "\n",
+    "    valid = np.isfinite(delta_all) & np.isfinite(sigma_all) & (sigma_all > 0)\n",
+    "    if not np.any(valid):\n",
+    "        return np.inf\n",
+    "\n",
+    "    # Calculate chi2\n",
+    "    chi2 = np.sum((delta_all[valid] / sigma_all[valid]) ** 2)\n",
+    "    return float(chi2)"
+   ],
+   "outputs": [],
+   "execution_count": 10
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1272f9202701e4b9",
+   "metadata": {},
+   "source": [
+    "And for the second, we can use `scipy.optimize.minimize` to find the optimal exposure times for each channel that minimize the total exposure time while still reaching the desired $\\chi^2$ threshold. To ensure we don't get caught in a local minimum, we'll repeat the optimization with multiple random starting points and keep the best result."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "7e8175a12e7bf313",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-09-08T22:05:18.663939Z",
+     "iopub.status.busy": "2026-09-08T22:05:18.663845Z",
+     "iopub.status.idle": "2026-09-08T22:05:18.667102Z",
+     "shell.execute_reply": "2026-09-08T22:05:18.666820Z"
+    },
+    "ExecuteTime": {
+     "end_time": "2026-09-09T19:36:41.527407Z",
+     "start_time": "2026-09-09T19:36:41.495249Z"
+    }
+   },
+   "source": [
+    "def required_times_for_pair_multichannel(fpfs_ch_a, fpfs_ch_b, sigma_interp_a,\n",
+    "                                         channel_names, chi2_target=CHI2_TARGET,\n",
+    "                                         t_min=0.01, t_max=100.0, n_starts=10):\n",
+    "    \"\"\"\n",
+    "    Find optimal per channel exposure times to distinguish two models that also\n",
+    "    minimizes the total exposure time across all channels.\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    fpfs_ch_a : dict\n",
+    "        Contrast ratios for model A in each channel, keyed by channel name\n",
+    "    fpfs_ch_b : dict\n",
+    "        Contrast ratios for model B in each channel, keyed by channel name\n",
+    "    sigma_interp_a : dict\n",
+    "        Noise interpolators for model A in each channel, keyed by channel name\n",
+    "    channel_names : list of str\n",
+    "        Names of the channels (filter names)\n",
+    "    chi2_target : float, optional\n",
+    "        Required chi2 value to distinguish the models\n",
+    "    t_min : float, optional\n",
+    "        Minimum exposure time to avoid division by zero (default is 0.01 hours)\n",
+    "    t_max : float, optional\n",
+    "        Maximum exposure time to avoid unbounded optimization (default is 100 hours)\n",
+    "    n_starts : int, optional\n",
+    "        Number of random starting points to try for optimization (default is 10)\n",
+    "\n",
+    "    Returns\n",
+    "    -------\n",
+    "    times_opt : 1D array\n",
+    "        Optimal exposure times for each channel (hours), ordered as channel_names\n",
+    "    chi2_final : float\n",
+    "        Final chi2 value for the optimized exposure times\n",
+    "    \"\"\"\n",
+    "\n",
+    "    n_channels = len(channel_names)\n",
+    "\n",
+    "    # Define the objective function we are trying to minimize (sum of times)\n",
+    "    def objective(times):\n",
+    "        return np.sum(times)\n",
+    "\n",
+    "    # Define our constraint function, i.e. the chi2 for a given set of exposure times.\n",
+    "    def constraint_chi2(times):\n",
+    "        return chi2_multichannel(times, fpfs_ch_a, fpfs_ch_b, sigma_interp_a, channel_names, t_min)\n",
+    "\n",
+    "    # Set the bounds on the optimization\n",
+    "    bounds = [(t_min, t_max) for _ in range(n_channels)]\n",
+    "\n",
+    "    # And set the constraint for our chi2 to be above the target\n",
+    "    constraint = NonlinearConstraint(constraint_chi2, chi2_target, np.inf)\n",
+    "\n",
+    "    best_result = None\n",
+    "    best_total_time = np.inf\n",
+    "    # Conduct multiple optimizations to avoid local minima\n",
+    "    for attempt in range(n_starts):\n",
+    "        if attempt == 0:\n",
+    "            # First attempt is at geometric mean as time is in log space\n",
+    "            x0 = np.full(n_channels, np.sqrt(t_min * t_max))\n",
+    "        else:\n",
+    "            # And the others are random in log space\n",
+    "            x0 = np.exp(np.random.uniform(np.log(t_min), np.log(t_max), n_channels))\n",
+    "\n",
+    "        # Run minimizer, choice of SLSQP is intentional here to enable constraint.\n",
+    "        result = minimize(objective, x0, method='SLSQP', bounds=bounds, constraints=constraint,\n",
+    "                          options={'ftol': 1e-6, 'maxiter': 1000})\n",
+    "\n",
+    "        # Check if the total time is better than the current one\n",
+    "        if result.success:\n",
+    "            total_time = np.sum(result.x)\n",
+    "            if total_time < best_total_time:\n",
+    "                best_total_time = total_time\n",
+    "                best_result = result\n",
+    "\n",
+    "    #Return best result\n",
+    "    if best_result is not None and best_result.success:\n",
+    "        times_opt = np.maximum(best_result.x, t_min)\n",
+    "        chi2_final = constraint_chi2(times_opt)\n",
+    "        return times_opt, chi2_final\n",
+    "    else:\n",
+    "        return np.full(n_channels, np.inf), 0.0"
+   ],
+   "outputs": [],
+   "execution_count": 11
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a228b068596aeb06",
+   "metadata": {},
+   "source": [
+    "Great! We're ready to run the optimization. We'll set up a loop over all possible pairs of models that we're investigating and run the optimization for each pair.\n",
+    "\n",
+    "Notice that we account for the bi-directionality of the comparison. The ability to distinguish between two scenarios depends on which one is assumed to be true, so we need to run the optimization twice for each direction and store the results.\n",
+    "\n",
+    "\n",
+    "Notice also that we define a pruning function to remove any contributions from channels at the lower edge of the time grid that contribute very little to the overall $\\chi^2$ value. These are likely unphysical, and an artifact of pyEDITH SNR constraints at very low exposure times."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "e1720248467fd099",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-09-08T22:05:18.668216Z",
+     "iopub.status.busy": "2026-09-08T22:05:18.668142Z",
+     "iopub.status.idle": "2026-09-08T22:05:18.670492Z",
+     "shell.execute_reply": "2026-09-08T22:05:18.670243Z"
+    },
+    "ExecuteTime": {
+     "end_time": "2026-09-09T19:36:41.561032Z",
+     "start_time": "2026-09-09T19:36:41.551180Z"
+    }
+   },
+   "source": [
+    "# Helper function to prune channels that are stuck at the lower bound and are not needed\n",
+    "def prune_unused_channels(times_opt, fpfs_ch_a, fpfs_ch_b, sigma_interp_a,\n",
+    "                          channel_names, chi2_target, t_min):\n",
+    "    \"\"\"Because the initial time grid starts at a non-zero value, need to make sure\n",
+    "    we set results at the lower end of the grid (which are typically set to NaN) to zero\n",
+    "    if they have very little impact on the chi2.\n",
+    "    \"\"\"\n",
+    "    times = np.asarray(times_opt, dtype=float).copy()\n",
+    "\n",
+    "    # Gather per channel chi2 contributions\n",
+    "    contrib = np.zeros(len(channel_names))\n",
+    "    for i, cn in enumerate(channel_names):\n",
+    "        delta = fpfs_ch_a[cn] - fpfs_ch_b[cn]\n",
+    "        sigma = sigma_interp_a[cn](times[i])\n",
+    "        valid = np.isfinite(delta) & np.isfinite(sigma) & (sigma > 0)\n",
+    "        contrib[i] = np.sum((delta[valid] / sigma[valid]) ** 2)\n",
+    "\n",
+    "    total = contrib.sum()\n",
+    "    # Find channels close to the lower time bound of the grid\n",
+    "    pinned = [i for i in np.argsort(contrib) if times[i] <= t_min * 1.01]\n",
+    "    for i in pinned:\n",
+    "        # Check if removal of channel affects chi2\n",
+    "        # Scale by slightly <1 to account for optimizer tolerance.\n",
+    "        if total - contrib[i] >= chi2_target * 0.999:\n",
+    "            total -= contrib[i]\n",
+    "            times[i] = 0.0 # Force time to zero\n",
+    "    return times"
+   ],
+   "outputs": [],
+   "execution_count": 12
+  },
+  {
+   "cell_type": "code",
+   "id": "42fd79c2d40d94f3",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-09-08T22:05:18.671743Z",
+     "iopub.status.busy": "2026-09-08T22:05:18.671675Z",
+     "iopub.status.idle": "2026-09-08T22:05:19.666118Z",
+     "shell.execute_reply": "2026-09-08T22:05:19.665767Z"
+    },
+    "ExecuteTime": {
+     "end_time": "2026-09-09T19:36:42.618559Z",
+     "start_time": "2026-09-09T19:36:41.563917Z"
+    }
+   },
+   "source": [
+    "# Need to define a minimum and maximum exposure time, let's just use the limits of the interpolation grid\n",
+    "t_min_per_channel = t_noise_grid[0]    # 1 hr\n",
+    "t_max_per_channel = t_noise_grid[-1]   # 100 hr\n",
+    "nstarts = 10   #Number of starting positions for the optimizer\n",
+    "\n",
+    "# Create an empty matrix to store the required exposure times\n",
+    "n = len(model_names)\n",
+    "t_required_matrix = np.full((n, n), np.nan, dtype=float)\n",
+    "\n",
+    "# Fill diagonals with zeros as you cannot distinguish a model from itself.\n",
+    "np.fill_diagonal(t_required_matrix, 0.0)\n",
+    "\n",
+    "pair_rows = []\n",
+    "# Loop over all possible pairs of models\n",
+    "for a, b in itertools.combinations(model_names, 2):\n",
+    "    # Case 1: Model A is true, distinguish from B (noise from A's interpolators)\n",
+    "    times_a_truth, chi2_a = required_times_for_pair_multichannel(\n",
+    "        fpfs_ch_a=fpfs_ch[a],\n",
+    "        fpfs_ch_b=fpfs_ch[b],\n",
+    "        sigma_interp_a=sigma_interp_ch[a],\n",
+    "        channel_names=channel_names,\n",
+    "        chi2_target=CHI2_TARGET,\n",
+    "        t_min=t_min_per_channel,\n",
+    "        t_max=t_max_per_channel,\n",
+    "        n_starts=nstarts\n",
+    "    )\n",
+    "    # Conduct pruning of low chi2 contributors\n",
+    "    times_a_truth = prune_unused_channels(times_a_truth, fpfs_ch[a], fpfs_ch[b],\n",
+    "                                      sigma_interp_ch[a], channel_names,\n",
+    "                                      CHI2_TARGET, t_min_per_channel)\n",
+    "\n",
+    "\n",
+    "    # Case 2: Model B is true, distinguish from A (noise from B's interpolators)\n",
+    "    times_b_truth, chi2_b = required_times_for_pair_multichannel(\n",
+    "        fpfs_ch_a=fpfs_ch[b],\n",
+    "        fpfs_ch_b=fpfs_ch[a],\n",
+    "        sigma_interp_a=sigma_interp_ch[b],\n",
+    "        channel_names=channel_names,\n",
+    "        chi2_target=CHI2_TARGET,\n",
+    "        t_min=t_min_per_channel,\n",
+    "        t_max=t_max_per_channel,\n",
+    "        n_starts=nstarts\n",
+    "    )\n",
+    "    # Conduct pruning of low chi2 contributors\n",
+    "    times_b_truth = prune_unused_channels(times_b_truth, fpfs_ch[b], fpfs_ch[a],\n",
+    "                                      sigma_interp_ch[b], channel_names,\n",
+    "                                      CHI2_TARGET, t_min_per_channel)\n",
+    "\n",
+    "    # Store total times in matrix\n",
+    "    ia = model_names.index(a)\n",
+    "    ib = model_names.index(b)\n",
+    "    t_required_matrix[ia, ib] = np.sum(times_a_truth)\n",
+    "    t_required_matrix[ib, ia] = np.sum(times_b_truth)\n",
+    "\n",
+    "    # Store per-channel breakdown\n",
+    "    pair_rows.append({\n",
+    "        \"model_a\": a,\n",
+    "        \"model_b\": b,\n",
+    "        \"t_required_a_truth\": np.sum(times_a_truth),\n",
+    "        \"t_required_b_truth\": np.sum(times_b_truth),\n",
+    "        \"max_required_hours\": max(np.sum(times_a_truth), np.sum(times_b_truth)),\n",
+    "        \"chi2_a_truth\": chi2_a,\n",
+    "        \"chi2_b_truth\": chi2_b,\n",
+    "        # Per-channel times for A truth\n",
+    "        **{f\"t_{channel_names[ch]}_a_truth\": times_a_truth[ch] for ch in range(n_channels)},\n",
+    "        # Per-channel times for B truth\n",
+    "        **{f\"t_{channel_names[ch]}_b_truth\": times_b_truth[ch] for ch in range(n_channels)},\n",
+    "    })\n",
+    "\n",
+    "# Convert to a dataframe to make the visualisation a little easier.\n",
+    "t_required_matrix_df = pd.DataFrame(t_required_matrix, index=model_names, columns=model_names)\n",
+    "ranked_pairs = pd.DataFrame(pair_rows).sort_values(\"max_required_hours\", ascending=True).reset_index(drop=True)"
+   ],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/aacarter/miniconda3/envs/hwo/lib/python3.14/site-packages/scipy/optimize/_numdiff.py:710: RuntimeWarning: invalid value encountered in subtract\n",
+      "  df = [f_eval - f0 for f_eval in f_evals]\n"
+     ]
+    }
+   ],
+   "execution_count": 13
+  },
+  {
+   "cell_type": "markdown",
+   "id": "99713e30c8713d23",
+   "metadata": {},
+   "source": [
+    "As you can see, the optimization itself is quite fast compared to the generation of the noise templates. The results are now stored in a dataframe, and we can display the required total exposure times for each model pair as follows:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "37ea1be13ea79ce6",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-09-08T22:05:19.667828Z",
+     "iopub.status.busy": "2026-09-08T22:05:19.667711Z",
+     "iopub.status.idle": "2026-09-08T22:05:19.678017Z",
+     "shell.execute_reply": "2026-09-08T22:05:19.677780Z"
+    },
+    "ExecuteTime": {
+     "end_time": "2026-09-09T19:36:42.703094Z",
+     "start_time": "2026-09-09T19:36:42.643578Z"
+    }
+   },
+   "source": [
+    "print(\"Directional matrix: rows are the assumed true model; columns are the competing model.\")\n",
+    "display(t_required_matrix_df.round(2))"
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Directional matrix: rows are the assumed true model; columns are the competing model.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "                             Archean Earth  Hazy Archean Earth  Modern Earth  \\\n",
+       "Archean Earth                         0.00                8.80         14.70   \n",
+       "Hazy Archean Earth                    8.25                0.00          9.37   \n",
+       "Modern Earth                         14.31                9.51          0.00   \n",
+       "Modern Earth 2                       23.67                5.68          8.39   \n",
+       "Proterozoic Earth (High O2)          75.76                6.73         12.61   \n",
+       "Proterozoic Earth (Low O2)           79.12                6.74         11.63   \n",
+       "\n",
+       "                             Modern Earth 2  Proterozoic Earth (High O2)  \\\n",
+       "Archean Earth                         22.93                        73.44   \n",
+       "Hazy Archean Earth                     5.24                         6.21   \n",
+       "Modern Earth                           8.05                        12.17   \n",
+       "Modern Earth 2                         0.00                        59.19   \n",
+       "Proterozoic Earth (High O2)           58.41                         0.00   \n",
+       "Proterozoic Earth (Low O2)            63.07                          inf   \n",
+       "\n",
+       "                             Proterozoic Earth (Low O2)  \n",
+       "Archean Earth                                     76.57  \n",
+       "Hazy Archean Earth                                 6.22  \n",
+       "Modern Earth                                      11.22  \n",
+       "Modern Earth 2                                    63.82  \n",
+       "Proterozoic Earth (High O2)                         inf  \n",
+       "Proterozoic Earth (Low O2)                         0.00  "
+      ],
+      "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>Archean Earth</th>\n",
+       "      <th>Hazy Archean Earth</th>\n",
+       "      <th>Modern Earth</th>\n",
+       "      <th>Modern Earth 2</th>\n",
+       "      <th>Proterozoic Earth (High O2)</th>\n",
+       "      <th>Proterozoic Earth (Low O2)</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Archean Earth</th>\n",
+       "      <td>0.00</td>\n",
+       "      <td>8.80</td>\n",
+       "      <td>14.70</td>\n",
+       "      <td>22.93</td>\n",
+       "      <td>73.44</td>\n",
+       "      <td>76.57</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Hazy Archean Earth</th>\n",
+       "      <td>8.25</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>9.37</td>\n",
+       "      <td>5.24</td>\n",
+       "      <td>6.21</td>\n",
+       "      <td>6.22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Modern Earth</th>\n",
+       "      <td>14.31</td>\n",
+       "      <td>9.51</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>8.05</td>\n",
+       "      <td>12.17</td>\n",
+       "      <td>11.22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Modern Earth 2</th>\n",
+       "      <td>23.67</td>\n",
+       "      <td>5.68</td>\n",
+       "      <td>8.39</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>59.19</td>\n",
+       "      <td>63.82</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Proterozoic Earth (High O2)</th>\n",
+       "      <td>75.76</td>\n",
+       "      <td>6.73</td>\n",
+       "      <td>12.61</td>\n",
+       "      <td>58.41</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>inf</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Proterozoic Earth (Low O2)</th>\n",
+       "      <td>79.12</td>\n",
+       "      <td>6.74</td>\n",
+       "      <td>11.63</td>\n",
+       "      <td>63.07</td>\n",
+       "      <td>inf</td>\n",
+       "      <td>0.00</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 14
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d0878aabb0907baa",
+   "metadata": {},
+   "source": [
+    "## 4: Visualizing the Results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "89ac78af3bc94bfa",
+   "metadata": {},
+   "source": [
+    "The table output is useful, but it would be nice to visualize things a little more clearly, and also to see the per-channel breakdown of the required exposure times so we know which mode is the most important.\n",
+    "\n",
+    "Let's make a heatmap figure instead:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "eb20bb5ffc297e58",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-09-08T22:05:19.679461Z",
+     "iopub.status.busy": "2026-09-08T22:05:19.679351Z",
+     "iopub.status.idle": "2026-09-08T22:05:19.886180Z",
+     "shell.execute_reply": "2026-09-08T22:05:19.885909Z"
+    },
+    "ExecuteTime": {
+     "end_time": "2026-09-09T19:36:43.146447Z",
+     "start_time": "2026-09-09T19:36:42.752702Z"
+    }
+   },
+   "source": [
+    "fig, ax = plt.subplots(figsize=(10, 8))\n",
+    "\n",
+    "# Heatmap data\n",
+    "data = t_required_matrix_df.values.astype(float)\n",
+    "\n",
+    "# Determine max finite value of required times for scaling of the colorbar\n",
+    "finite_mask = np.isfinite(data)\n",
+    "theoretical_max = n_channels * t_max_per_channel # i.e. 3 channels x 100 hrs = 300 hr max\n",
+    "inf_display_value = theoretical_max  # Place inf cells at theoretical max for coloring\n",
+    "data_display = np.copy(data)\n",
+    "data_display[~finite_mask] = inf_display_value\n",
+    "\n",
+    "# Create colormap\n",
+    "cmap = plt.cm.inferno_r.copy()\n",
+    "cmap.set_bad(\"grey\")\n",
+    "im = ax.imshow(data_display, origin=\"upper\", norm=LogNorm(), cmap=cmap)\n",
+    "\n",
+    "# Formatting\n",
+    "ax.set_xticks(np.arange(len(t_required_matrix_df.columns)))\n",
+    "ax.set_yticks(np.arange(len(t_required_matrix_df.index)))\n",
+    "ax.set_xticklabels(t_required_matrix_df.columns, rotation=45, ha=\"left\")\n",
+    "ax.set_yticklabels(t_required_matrix_df.index)\n",
+    "ax.xaxis.tick_top()\n",
+    "ax.xaxis.set_label_position(\"top\")\n",
+    "ax.tick_params(top=True, labeltop=True, bottom=False, labelbottom=False)\n",
+    "ax.set_xlabel(\"Competing model\")\n",
+    "ax.set_ylabel(\"Assumed true model\")\n",
+    "ax.grid(False)\n",
+    "\n",
+    "# Colorbar\n",
+    "cbar = plt.colorbar(im, ax=ax)\n",
+    "cbar.set_label(f\"Required Hours To Distinguish at {int(SIGMA_TARGET)}$\\\\sigma$ ($\\\\alpha=\\\\pi / 2$)\")\n",
+    "\n",
+    "# Build per-channel times lookup from ranked_pairs\n",
+    "model_names_list = list(t_required_matrix_df.index)\n",
+    "\n",
+    "# For each cell, extract per-channel times\n",
+    "per_channel_times = {}\n",
+    "for idx, row in ranked_pairs.iterrows():\n",
+    "    a, b = row[\"model_a\"], row[\"model_b\"]\n",
+    "    ia, ib = model_names_list.index(a), model_names_list.index(b)\n",
+    "    # A as model truth\n",
+    "    per_channel_times[(ia, ib)] = [row[f\"t_{channel_names[ch]}_a_truth\"] for ch in range(n_channels)]\n",
+    "    # B as model truth\n",
+    "    per_channel_times[(ib, ia)] = [row[f\"t_{channel_names[ch]}_b_truth\"] for ch in range(n_channels)]\n",
+    "\n",
+    "# Small function to format per channel times\n",
+    "def fmt_time(t, t_max=t_noise_grid[-1]):\n",
+    "    return f\">{t_max:.0f}\" if (not np.isfinite(t) or t >= t_max) else f\"{t:.1f}\"\n",
+    "\n",
+    "# Annotate cells with required times.\n",
+    "for i in range(t_required_matrix_df.shape[0]):\n",
+    "    for j in range(t_required_matrix_df.shape[1]):\n",
+    "        val = t_required_matrix_df.values[i, j]\n",
+    "        \n",
+    "        if i == j:\n",
+    "            # Diagonals are self comparisons - skip\n",
+    "            txt_total = \"\"\n",
+    "            txt_channel = \"\"\n",
+    "        elif not np.isfinite(val):\n",
+    "            # If no valid exposure time, assume it's above the theoretical maximum\n",
+    "            txt_total = f\">{theoretical_max:.0f}\"\n",
+    "            txt_channel = \"\\n\".join(f\"{name}: {fmt_time(t)}\" for name, t in zip(channel_names, per_channel_times[(i, j)]))\n",
+    "        else:\n",
+    "            # Time is just the total required time\n",
+    "            txt_total = f\"{val:.1f}\"\n",
+    "            txt_channel = \"\\n\".join([f\"{name}: {t:.1f}\" for name, t in zip(channel_names, per_channel_times[(i, j)])])\n",
+    "\n",
+    "        # Determine text color based on cell brightness\n",
+    "        if np.isfinite(val) and val > 0:\n",
+    "            rgba = im.cmap(im.norm(val))\n",
+    "            brightness = 0.2126*rgba[0] + 0.7152*rgba[1] + 0.0722*rgba[2]\n",
+    "            text_color = \"black\" if brightness > 0.5 else \"white\"\n",
+    "        else:\n",
+    "            # For inf cells, use the color at inf_display_value\n",
+    "            rgba = im.cmap(im.norm(inf_display_value))\n",
+    "            brightness = 0.2126*rgba[0] + 0.7152*rgba[1] + 0.0722*rgba[2]\n",
+    "            text_color = \"black\" if brightness > 0.5 else \"white\"\n",
+    "        \n",
+    "        # Add text for total and per channel times\n",
+    "        ax.text(j, i - 0.25, txt_total, ha=\"center\", va=\"center\", \n",
+    "                fontsize=12, fontweight=\"bold\", color=text_color)\n",
+    "        if txt_channel:\n",
+    "            ax.text(j, i + 0.15, txt_channel, ha=\"center\", va=\"center\", \n",
+    "                    fontsize=7, color=text_color, linespacing=1.2)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1000x800 with 2 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 15
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1be71cccc4fd7ceb",
+   "metadata": {},
+   "source": [
+    "Nice! It looks like we can constrain between most of the models in ~5-15 hours of exposure time. However, there are certainly some comparisons that are more challenging. We can also see that every comparison strongly prefers a single observation in the VIS channel, with the Bridge and NIR channels being disfavoured for this simple model comparison.\n",
+    "\n",
+    "Let's finish by plotting the spectra and noise templates some of the comparisons to see what's going on.\n",
+    "\n",
+    "The easiest model pair to distinguish is Hazy Archean Earth vs Modern Earth 2, which takes ~5 hours in the VIS channel to distinguish between. We can make another figure from the simulation results to see what's happening:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "647f1e333d18a850",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-09-08T22:05:19.887937Z",
+     "iopub.status.busy": "2026-09-08T22:05:19.887824Z",
+     "iopub.status.idle": "2026-09-08T22:05:19.893105Z",
+     "shell.execute_reply": "2026-09-08T22:05:19.892879Z"
+    },
+    "ExecuteTime": {
+     "end_time": "2026-09-09T19:36:43.161254Z",
+     "start_time": "2026-09-09T19:36:43.148512Z"
+    }
+   },
+   "source": [
+    "def plot_spectra_comparison(model_a, model_b):\n",
+    "    '''\n",
+    "    Plot the spectra comparison between two models with their respective noise templates.\n",
+    "    '''\n",
+    "\n",
+    "    # Find the pair regardless of stored order\n",
+    "    pair_row = ranked_pairs[ranked_pairs[['model_a', 'model_b']].apply(frozenset, axis=1) == frozenset({model_a, model_b})]\n",
+    "\n",
+    "    if pair_row.empty:\n",
+    "        print(f\"Pair ({model_a}, {model_b}) not found in results\")\n",
+    "    else:\n",
+    "        r = pair_row.iloc[0]\n",
+    "        # Because of the way the data is stored, we need to check if the provided order of model_a and model_b\n",
+    "        # is consistent with the stored order. If not, we can swap things round.\n",
+    "        a_is_stored_a = (r[\"model_a\"] == model_a)\n",
+    "        suf_a = \"a_truth\" if a_is_stored_a else \"b_truth\"\n",
+    "        suf_b = \"b_truth\" if a_is_stored_a else \"a_truth\"\n",
+    "\n",
+    "        # Use the per-channel noise interpolators to get the errors for each channel\n",
+    "        sigma_scaled_a = {cn: sigma_interp_ch[model_a][cn](r[f\"t_{cn}_{suf_a}\"]) for cn in channel_names}\n",
+    "        sigma_scaled_b = {cn: sigma_interp_ch[model_b][cn](r[f\"t_{cn}_{suf_b}\"]) for cn in channel_names}\n",
+    "\n",
+    "        # Build the labels for the legend\n",
+    "        def label_fmt(t):\n",
+    "            return f\">{t_noise_grid[-1]:.0f}\" if (not np.isfinite(t) or t >= t_noise_grid[-1]) else f\"{t:.1f}\"\n",
+    "        label_a = ', '.join(f\"{cn}={label_fmt(r[f't_{cn}_{suf_a}'])}\" for cn in channel_names) + ' hr'\n",
+    "        label_b = ', '.join(f\"{cn}={label_fmt(r[f't_{cn}_{suf_b}'])}\" for cn in channel_names) + ' hr'\n",
+    "\n",
+    "        # Create figure\n",
+    "        fig, ax = plt.subplots(figsize=(10, 6))\n",
+    "\n",
+    "        # Track whether we've already added the \"unused\" legend entry\n",
+    "        unused_labeled = False\n",
+    "\n",
+    "        # Plot each channel separately, only labelling the first to avoid duplicate legend entries\n",
+    "        for i, cn in enumerate(channel_names):\n",
+    "            wl = wl_ch[cn]\n",
+    "\n",
+    "            # Plot model A with errors\n",
+    "            ax.plot(wl, fpfs_ch[model_a][cn], 'o-', linewidth=2.5, markersize=5,\n",
+    "                    label=f'{model_a} ({label_a})' if i == 0 else None, color='C0', zorder=3)\n",
+    "            ax.errorbar(wl, fpfs_ch[model_a][cn], yerr=sigma_scaled_a[cn],\n",
+    "                        fmt='none', ecolor='C0', alpha=0.5, capsize=3, linewidth=1.5, zorder=2)\n",
+    "\n",
+    "            # Plot model B with errors\n",
+    "            ax.plot(wl, fpfs_ch[model_b][cn], 'o-', linewidth=2.5, markersize=5,\n",
+    "                    label=f'{model_b} ({label_b})' if i == 0 else None, color='C1', zorder=3)\n",
+    "            ax.errorbar(wl, fpfs_ch[model_b][cn], yerr=sigma_scaled_b[cn],\n",
+    "                        fmt='none', ecolor='C1', alpha=0.5, capsize=3, linewidth=1.5, zorder=2)\n",
+    "\n",
+    "            # Shade this channel's wavelength range in gray if time is 0 hours\n",
+    "            t_a = r[f\"t_{cn}_{suf_a}\"]\n",
+    "            t_b = r[f\"t_{cn}_{suf_b}\"]\n",
+    "            if (np.isfinite(t_a) and t_a <= 0) or (np.isfinite(t_b) and t_b <= 0):\n",
+    "                ax.axvspan(\n",
+    "                    wl.min(), wl.max(),\n",
+    "                    color='grey', alpha=0.3, zorder=0,\n",
+    "                    label='Unused Spectral Channel (0.0 hr)' if not unused_labeled else None\n",
+    "                )\n",
+    "                unused_labeled = True\n",
+    "\n",
+    "        # Formatting\n",
+    "        ax.set_xlabel(\"Wavelength (µm)\", fontsize=14)\n",
+    "        ax.set_ylabel(f\"Contrast Ratio ($\\\\alpha={phase_angle/np.pi:.2f}\\\\pi$)\", fontsize=14)\n",
+    "        ax.set_xlim([0.35, 1.85])\n",
+    "        ax.set_ylim([0, 5e-10])\n",
+    "        ax.set_title(f\"Spectral Comparison: {model_a} vs {model_b}\", fontsize=12)\n",
+    "        ax.tick_params(which='both', direction='in', top=True, right=True)\n",
+    "        ax.grid(True, alpha=0.3)\n",
+    "        ax.legend(fontsize=10, loc='best')\n",
+    "\n",
+    "        plt.tight_layout()\n",
+    "        plt.show()"
+   ],
+   "outputs": [],
+   "execution_count": 16
+  },
+  {
+   "cell_type": "code",
+   "id": "b489282218f9ffbe",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-09-08T22:05:19.894208Z",
+     "iopub.status.busy": "2026-09-08T22:05:19.894128Z",
+     "iopub.status.idle": "2026-09-08T22:05:19.971932Z",
+     "shell.execute_reply": "2026-09-08T22:05:19.971676Z"
+    },
+    "ExecuteTime": {
+     "end_time": "2026-09-09T19:36:43.386581Z",
+     "start_time": "2026-09-09T19:36:43.164331Z"
+    }
+   },
+   "source": [
+    "# Choose the models we'd like to compare and run the plotting function\n",
+    "model_a = \"Hazy Archean Earth\"\n",
+    "model_b = \"Modern Earth 2\"\n",
+    "plot_spectra_comparison(model_a, model_b)"
+   ],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 17
+  },
+  {
+   "cell_type": "markdown",
+   "id": "79a609a5edc299c1",
+   "metadata": {},
+   "source": [
+    "For this comparison we can see that the VIS channel was deemed the most important likely due to the large divergence in the models between 0.4 and 0.6 microns, in combination with the resolution available at the VIS wavelengths. While there are divergences in the Bridge and NIR channels, the resolution and SNR were likely not sufficient to make them as important.\n",
+    "\n",
+    "We can also see that the required observing time is a little longer if we assume the Modern Earth 2 model as the truth. This may seem counterintuitive, but likely results from the brighter model having a greater photon noise contribution, which makes it harder to distinguish - the noise from a brighter model can more easily mimic a fainter model than the noise from a fainter model can mimic a brighter one."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9618707ba049daa4",
+   "metadata": {},
+   "source": [
+    "Let's look at one more example between the two Proterozoic Earth models, which indicate that an exposure time greater than 100 hours for any channel would be needed to distinguish between them."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "7d4a282367c2f7f0",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-09-08T22:05:19.973424Z",
+     "iopub.status.busy": "2026-09-08T22:05:19.973322Z",
+     "iopub.status.idle": "2026-09-08T22:05:20.046733Z",
+     "shell.execute_reply": "2026-09-08T22:05:20.046460Z"
+    },
+    "ExecuteTime": {
+     "end_time": "2026-09-09T19:36:43.502744Z",
+     "start_time": "2026-09-09T19:36:43.388477Z"
+    }
+   },
+   "source": [
+    "model_a = \"Proterozoic Earth (Low O2)\"\n",
+    "model_b = \"Proterozoic Earth (High O2)\"\n",
+    "plot_spectra_comparison(model_a, model_b)"
+   ],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 18
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9a451f44f945a02b",
+   "metadata": {},
+   "source": [
+    "Here we can see that the two models are extremely similar, with only small divergences in the VIS channel. Even with the long observation, we cannot reach sufficient SNR across the three channels to distinguish between them. Nevertheless, if needed, we could calculate the require time to distinguish between the two by extending the noise template grid to longer exposure times."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c2b07a8ba1a8e727",
+   "metadata": {},
+   "source": [
+    "This marks the end of the notebook, congratulations on making it to the end! Hopefully you can see how the tools we've built can be used to distinguish between different atmospheric scenarios. More complex investigations could be conducted that build on this framework and explore more complex features such as atmospheric abundances and processes, across a wider range of planetary and stellar parameters, for a wider range of models."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "pyedith-dist-venv (3.12.0)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}