diff --git a/.gitignore b/.gitignore
index 7439ee5..4ff75e4 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,11 +1,14 @@
 # Local dev directory
+local/
 .idea/
 *.egg*/
 *__pycache__/
 
 # ISOFIT output directories
-isotuts/NEON/*
-!isotuts/NEON/*.ipynb
+NEON/output
+
+# Data directories
+NEON/data
 
 # Jupyter server files
 .ipynb*/
diff --git a/isotuts/NEON/data_prep.ipynb b/NEON/data_prep.ipynb
similarity index 98%
rename from isotuts/NEON/data_prep.ipynb
rename to NEON/data_prep.ipynb
index 36fae36..391d306 100644
--- a/isotuts/NEON/data_prep.ipynb
+++ b/NEON/data_prep.ipynb
@@ -86,26 +86,7 @@
    "outputs": [],
    "source": [
     "\n",
-    "\n",
-    "# Extract the image locations of each point of interest (POI)\n",
-    "# These are defined in the NEON report as pixel locations, so we round here to convert to indices\n",
-    "report = {}\n",
-    "report['173647'] = {           # Upp L Y  | Low R Y  | Upp L X | Low R X\n",
-    "    'WhiteTarp': np.round([2224.9626, 2230.9771, 316.0078, 324.9385,]).astype(int),\n",
-    "    'BlackTarp': np.round([2224.9626, 2231.0032, 328.0086, 333.9731,]).astype(int),\n",
-    "    'Veg'      : np.round([2245.0381, 2258.8103, 343.9006, 346.9423,]).astype(int),\n",
-    "    'RoadEW'   : np.round([2214.9905, 2216.9978, 348.9902, 373.0080,]).astype(int),\n",
-    "    'RoadNS'   : np.round([2205.9580, 2225.9612, 357.9536, 359.9608,]).astype(int)\n",
-    "}\n",
-    "report['174150'] = {           # Upp L Y | Low R Y | Upp L X  | Low R X\n",
-    "    'WhiteTarp': np.round([653.9626, 659.9771, 3143.0078, 3151.9385]).astype(int),\n",
-    "    'BlackTarp': np.round([653.9626, 660.0032, 3155.0086, 3160.9731]).astype(int),\n",
-    "    'Veg'      : np.round([674.0381, 687.8103, 3170.9006, 3173.9423]).astype(int),\n",
-    "    'RoadEW'   : np.round([643.9905, 645.9978, 3175.9902, 3200.0080]).astype(int),\n",
-    "    'RoadNS'   : np.round([634.9580, 654.9612, 3184.9536, 3186.9608]).astype(int)\n",
-    "}\n",
-    "# Converts numpy array to comma-separated string for ISOFIT\n",
-    "toString = lambda array: ', '.join(str(v) for v in array)"
+    "\n"
    ]
   },
   {
@@ -176,7 +157,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "isofit_env",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -190,9 +171,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.13"
+   "version": "3.10.8"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }
diff --git a/NEON/neon.ipynb b/NEON/neon.ipynb
new file mode 100644
index 0000000..adc2d40
--- /dev/null
+++ b/NEON/neon.ipynb
@@ -0,0 +1,1011 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "e30eef79",
+   "metadata": {},
+   "source": [
+    "# NEON\n",
+    "\n",
+    "This notebook is an excercise in executing ISOFIT on two dates from the NEON dataset and interpreting the outputs of ISOFIT.\n",
+    "\n",
+    "Prerequisites:\n",
+    "- Have ISOFIT installed and sRTMnet configured.\n",
+    "- Download the [sample data](https://avng.jpl.nasa.gov/pub/PBrodrick/isofit/tutorials/subset_data.zip) and place the unzipped `data` directory into the same directory of this notebook.\n",
+    "\n",
+    "Note: If you downloaded the [ISOFIT extra data](https://isofit.readthedocs.io/en/latest/custom/data.html) via `isofit download`, both sRTMnet and the NEON data files will be installed correctly and available with default settings for this notebook."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "44e2871f",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# Jupyter magics\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "e9b8d164-38f5-456b-b6d2-94bed6d8e75c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Builtin\n",
+    "import os\n",
+    "import shutil\n",
+    "from types import SimpleNamespace\n",
+    "\n",
+    "# External\n",
+    "import matplotlib.pyplot  as plt\n",
+    "import matplotlib.patches as patches\n",
+    "import numpy as np\n",
+    "from spectral.io import envi\n",
+    "\n",
+    "# Internal\n",
+    "import isofit\n",
+    "from isofit.data import env\n",
+    "from isofit.utils.apply_oe import apply_oe \n",
+    "from isofit.utils.surface_model import surface_model\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "14ccc3b5-019a-4c4c-b590-afa1b4760607",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Using environment paths:\n",
+      "- data = /home/mambauser/data\n",
+      "- examples = /home/mambauser/examples\n",
+      "- imagecube = /home/mambauser/imagecube\n",
+      "- srtmnet = /home/mambauser/srtmnet\n",
+      "- sixs = /home/mambauser/sixs\n",
+      "- modtran = /home/mambauser/modtran\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Below are the default values for the ISOFIT environment. Change these if your environment differs\n",
+    "\n",
+    "env.load('~/.isofit/isofit.ini') # Ini file to load\n",
+    "env.changeSection('DEFAULT')     # Section of the ini to use\n",
+    "# env.changeBase('~./isofit')      # Base path for ISOFIT extras (data, examples, etc)\n",
+    "# env.changePath('srtmnet', '/path/to/sRTMnet_v120.h5') # Overwrite the path to sRTMnet - copy this line for other products such as sixs if in non-default locations\n",
+    "\n",
+    "print('Using environment paths:')\n",
+    "for key, path in env.items():\n",
+    "    print(f\"- {key} = {path}\") \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "893fd5ac",
+   "metadata": {},
+   "source": [
+    "## Setup\n",
+    "\n",
+    "ISOFIT needs at minimum three pieces as input:\n",
+    "\n",
+    "    1. Radiance measurements (rdn)\n",
+    "    2. Observation values    (obs)\n",
+    "    3. Location information  (loc)\n",
+    "\n",
+    "This sample dataset from NEON has radiance and observation data, but no location values (more recent NEON datasets include the location file).  However, we can 'fake' the location file with sufficient accuracy for ISOFIT to run successfully.  Note that there are data available for two dates:\n",
+    "\n",
+    "```\n",
+    "Radiance\n",
+    "├── 173647\n",
+    "│   ├── NIS01_20210403_173647_obs_ort\n",
+    "│   ├── NIS01_20210403_173647_obs_ort.hdr\n",
+    "│   ├── NIS01_20210403_173647_rdn_ort\n",
+    "│   └── NIS01_20210403_173647_rdn_ort.hdr\n",
+    "└── 174150\n",
+    "    ├── NIS01_20210403_174150_obs_ort\n",
+    "    ├── NIS01_20210403_174150_obs_ort.hdr\n",
+    "    ├── NIS01_20210403_174150_rdn_ort\n",
+    "    └── NIS01_20210403_174150_rdn_ort.hdr\n",
+    "```\n",
+    "\n",
+    "These files have corresponding in situ data as well, and below we've encoded the locations of each, which we can use to help subset data files.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "e3f01d1a",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from utils.neon import report\n",
+    "\n",
+    "# Which NEON date to process - change this to process a different date\n",
+    "neon_id  = list(report.keys())[0]\n",
+    "neon_str = f\"NIS01_20210403_{neon_id}\"\n",
+    "\n",
+    "# Select the locations from the neon id -- roi == Regions of Interest\n",
+    "roi = report[neon_id]\n",
+    "\n",
+    "from types import SimpleNamespace\n",
+    "from pathlib import Path\n",
+    "\n",
+    "# Set the paths for this tutorial\n",
+    "base = Path(env.path('examples', 'NEON'))\n",
+    "data = base / 'data'\n",
+    "\n",
+    "paths = SimpleNamespace(\n",
+    "    rdn = str(data / f'{neon_str}_rdn_ort'),\n",
+    "    loc = str(data / f'{neon_str}_loc_ort'),\n",
+    "    obs = str(data / f'{neon_str}_obs_ort'),\n",
+    "    insitu = data / 'FieldSpectrometer',\n",
+    "    output = base / 'output',\n",
+    "    working = base / f'output/NIS01_20210403_{neon_id}',\n",
+    "    surface = str(base / 'output/surface.mat'),\n",
+    "    surface_config = env.path('examples', '20171108_Pasadena', 'configs', 'ang20171108t184227_surface.json')\n",
+    ")\n",
+    "\n",
+    "paths.output.mkdir(exist_ok=True, parents=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "98252646",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:2024-11-18,10:32:18 || faker.py:getMetadata() | Key 'band names' not found in the metadata, skipping\n",
+      "WARNING:2024-11-18,10:32:18 || faker.py:getMetadata() | Key 'band names' not found in the metadata, skipping\n"
+     ]
+    }
+   ],
+   "source": [
+    "# If you are missing either an OBS file or a LOC file, use these to create faked versions based off the radiance file\n",
+    "# This should not be needed if using the provided data\n",
+    "# Using this may cause the below plots to not generate the same results\n",
+    "\n",
+    "# from utils import faker\n",
+    "\n",
+    "# paths.obs = faker.fakeOBS(\n",
+    "    # f\"{paths.rdn}.hdr\",\n",
+    "    # sea = 153.4481201171875,\n",
+    "    # sez = 178.3806858062744,\n",
+    "    # soa = 39.8218994140625,\n",
+    "    # soz = 39.8218994140625,\n",
+    "    # slope = 31.813383102416992\n",
+    "# )[:-4] # Remove the .hdr extension from the return\n",
+    "# paths.loc = faker.fakeLOC(\n",
+    "#     rdn = f\"{paths.rdn}.hdr\",\n",
+    "#     lon = -105.237000,\n",
+    "#     lat = 40.125000,\n",
+    "#     elv = 1689.0\n",
+    "# )[:-4]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8a2cde13",
+   "metadata": {},
+   "source": [
+    "# Apply OE\n",
+    "\n",
+    "The next part walks through running the ISOFIT utility script `isofit/utils/apply_oe.py`. This is the first step of executing ISOFIT and will generate a default configuration."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "7357a326",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0 ['/home/mambauser/data/reflectance/surface_model_ucsb']\n",
+      "1 ['/home/mambauser/data/reflectance/surface_model_ucsb']\n",
+      "2 ['/home/mambauser/data/reflectance/surface_model_ucsb']\n",
+      "3 ['/home/mambauser/data/reflectance/surface_model_ucsb']\n",
+      "4 ['/home/mambauser/data/reflectance/surface_model_ucsb']\n",
+      "5 ['/home/mambauser/data/reflectance/surface_model_ucsb']\n",
+      "6 ['/home/mambauser/data/reflectance/surface_model_ucsb']\n",
+      "7 ['/home/mambauser/data/reflectance/surface_model_ucsb']\n"
+     ]
+    }
+   ],
+   "source": [
+    "# First build a surface model\n",
+    "surface_model(\n",
+    "    config_path = paths.surface_config,\n",
+    "    output_path = paths.surface,\n",
+    "    wavelength_path = f\"{paths.rdn}.hdr\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "30a35242-c068-49a0-9aec-b860d54870f0",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Help on function apply_oe in module isofit.utils.apply_oe:\n",
+      "\n",
+      "apply_oe(input_radiance, input_loc, input_obs, working_directory, sensor, surface_path, copy_input_files=False, modtran_path=None, wavelength_path=None, surface_category='multicomponent_surface', aerosol_climatology_path=None, rdn_factors_path=None, atmosphere_type='ATM_MIDLAT_SUMMER', channelized_uncertainty_path=None, model_discrepancy_path=None, lut_config_file=None, multiple_restarts=False, logging_level='INFO', log_file=None, n_cores=1, presolve=False, empirical_line=False, analytical_line=False, ray_temp_dir='/tmp/ray', emulator_base=None, segmentation_size=40, num_neighbors=[], atm_sigma=[2], pressure_elevation=False, prebuilt_lut=None, no_min_lut_spacing=False, inversion_windows=None)\n",
+      "    Applies OE over a flightline using a radiative transfer engine. This executes\n",
+      "    ISOFIT in a generalized way, accounting for the types of variation that might be\n",
+      "    considered typical.\n",
+      "    \n",
+      "    Observation (obs) and location (loc) files are used to determine appropriate\n",
+      "    geometry lookup tables and provide a heuristic means of determining atmospheric\n",
+      "    water ranges.\n",
+      "    \n",
+      "   \n",
+      "    Parameters\n",
+      "    ----------\n",
+      "    input_radiance : str\n",
+      "        Radiance data cube. Expected to be ENVI format\n",
+      "    input_loc : str\n",
+      "        Location data cube of shape (Lon, Lat, Elevation). Expected to be ENVI format\n",
+      "    input_obs : str\n",
+      "        Observation data cube of shape:\n",
+      "            (path length, to-sensor azimuth, to-sensor zenith,\n",
+      "            to-sun azimuth, to-sun zenith, phase,\n",
+      "            slope, aspect, cosine i, UTC time)\n",
+      "        Expected to be ENVI format\n",
+      "    working_directory : str\n",
+      "        Directory to stage multiple outputs, will contain subdirectories\n",
+      "    sensor : str\n",
+      "        The sensor used for acquisition, will be used to set noise and datetime\n",
+      "        settings\n",
+      "    surface_path : str\n",
+      "        Path to surface model or json dict of surface model configuration\n",
+      "    copy_input_files : bool, default=False\n",
+      "        Flag to choose to copy input_radiance, input_loc, and input_obs locally into\n",
+      "        the working_directory\n",
+      "    modtran_path : str, default=None\n",
+      "        Location of MODTRAN utility. Alternately set with `MODTRAN_DIR` environment\n",
+      "        variable\n",
+      "    wavelength_path : str, default=None\n",
+      "        Location to get wavelength information from, if not specified the radiance\n",
+      "        header will be used\n",
+      "    surface_category : str, default=\"multicomponent_surface\"\n",
+      "        The type of ISOFIT surface priors to use.  Default is multicomponent_surface\n",
+      "    aerosol_climatology_path : str, default=None\n",
+      "        Specific aerosol climatology information to use in MODTRAN\n",
+      "    rdn_factors_path : str, default=None\n",
+      "        Specify a radiometric correction factor, if desired\n",
+      "    atmosphere_type : str, default=\"ATM_MIDLAT_SUMMER\"\n",
+      "        TODO\n",
+      "    channelized_uncertainty_path : str, default=None\n",
+      "        Path to a channelized uncertainty file\n",
+      "    model_discrepancy_path : str, default=None\n",
+      "        TODO\n",
+      "    lut_config_file : str, default=None\n",
+      "        Path to a look up table configuration file, which will override defaults\n",
+      "        choices\n",
+      "    multiple_restarts : bool, default=False\n",
+      "        TODO\n",
+      "    logging_level : str, default=\"INFO\"\n",
+      "        Logging level with which to run ISOFIT\n",
+      "    log_file : str, default=None\n",
+      "        File path to write ISOFIT logs to\n",
+      "    n_cores : int, default=1\n",
+      "        Number of cores to run ISOFIT with. Substantial parallelism is available, and\n",
+      "        full runs will be very slow in serial. Suggested to max this out on the\n",
+      "        available system\n",
+      "    presolve : int, default=False\n",
+      "        Flag to use a presolve mode to estimate the available atmospheric water range.\n",
+      "        Runs a preliminary inversion over the image with a 1-D LUT of water vapor, and\n",
+      "        uses the resulting range (slightly expanded) to bound determine the full LUT.\n",
+      "        Advisable to only use with small cubes or in concert with the empirical_line\n",
+      "        setting, or a significant speed penalty will be incurred\n",
+      "    empirical_line : bool, default=False\n",
+      "        Use an empirical line interpolation to run full inversions over only a subset\n",
+      "        of pixels, determined using a SLIC superpixel segmentation, and use a KDTREE of\n",
+      "        local solutions to interpolate radiance->reflectance. Generally a good option\n",
+      "        if not trying to analyze the atmospheric state at fine scale resolution.\n",
+      "        Mutually exclusive with analytical_line\n",
+      "    analytical_line : bool, default=False\n",
+      "        TODO\n",
+      "        Mutually exclusive with empirical_line\n",
+      "    ray_temp_dir : str, default=\"/tmp/ray\"\n",
+      "        Location of temporary directory for ray parallelization engine\n",
+      "    emulator_base : str, default=None\n",
+      "        Location of emulator base path. Point this at the model folder (or h5 file) of\n",
+      "        sRTMnet to use the emulator instead of MODTRAN. An additional file with the\n",
+      "        same basename and the extention _aux.npz must accompany\n",
+      "        e.g. /path/to/emulator.h5 /path/to/emulator_aux.npz\n",
+      "    segmentation_size : int, default=40\n",
+      "        If empirical_line is enabled, sets the size of segments to construct\n",
+      "    num_neighbors : list[int], default=[]\n",
+      "        Forced number of neighbors for empirical line extrapolation - overides default\n",
+      "        set from segmentation_size parameter\n",
+      "    atm_sigma : list[int], default=[2]\n",
+      "        TODO\n",
+      "    pressure_elevation : bool, default=False\n",
+      "        Flag to retrieve elevation\n",
+      "    prebuilt_lut : str, default=None\n",
+      "        Use this pre-constructed look up table for all retrievals. Must be an\n",
+      "        ISOFIT-compatible RTE NetCDF\n",
+      "    no_min_lut_spacing : bool, default=False\n",
+      "        TODO\n",
+      "    inversion_windows : list[float], default=None\n",
+      "        TODO\n",
+      "        Must be in 2-item tuples\n",
+      "    \n",
+      "   \n",
+      "    References\n",
+      "    ----------\n",
+      "    D.R. Thompson, A. Braverman,P.G. Brodrick, A. Candela, N. Carbon, R.N. Clark,D. Connelly, R.O. Green, R.F.\n",
+      "    Kokaly, L. Li, N. Mahowald, R.L. Miller, G.S. Okin, T.H.Painter, G.A. Swayze, M. Turmon, J. Susilouto, and\n",
+      "    D.S. Wettergreen. Quantifying Uncertainty for Remote Spectroscopy of Surface Composition. Remote Sensing of\n",
+      "    Environment, 2020. doi: https://doi.org/10.1016/j.rse.2020.111898.\n",
+      "    \n",
+      "   \n",
+      "    sRTMnet emulator:\n",
+      "    P.G. Brodrick, D.R. Thompson, J.E. Fahlen, M.L. Eastwood, C.M. Sarture, S.R. Lundeen, W. Olson-Duvall,\n",
+      "    N. Carmon, and R.O. Green. Generalized radiative transfer emulation for imaging spectroscopy reflectance\n",
+      "    retrievals. Remote Sensing of Environment, 261:112476, 2021.doi: 10.1016/j.rse.2021.112476.\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# For reference, all of the available parameters to the apply_oe script\n",
+    "help(apply_oe)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "81330d85-2453-4065-bfa7-f6a09374709a",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-11-20 23:30:40,305\tINFO worker.py:1819 -- Started a local Ray instance.\n",
+      "INFO:2024-11-20,23:30:40 || apply_oe.py:apply_oe() | Checking input data files...\n",
+      "INFO:2024-11-20,23:30:40 || apply_oe.py:apply_oe() | ...Data file checks complete\n",
+      "INFO:2024-11-20,23:30:40 || apply_oe.py:apply_oe() | Setting up files and directories....\n",
+      "INFO:2024-11-20,23:30:40 || template_construction.py:__init__() | Flightline ID: NIS01_20210403_173647\n",
+      "INFO:2024-11-20,23:30:40 || template_construction.py:__init__() | no noise path found, proceeding without\n",
+      "INFO:2024-11-20,23:30:40 || template_construction.py:stage_files() | Staging /home/mambauser/examples/NEON/output/surface.mat to /home/mambauser/examples/NEON/output/NIS01_20210403_173647/data/surface.mat\n",
+      "INFO:2024-11-20,23:30:40 || apply_oe.py:apply_oe() | ...file/directory setup complete\n",
+      "INFO:2024-11-20,23:30:40 || apply_oe.py:apply_oe() | Using inversion windows: [[350.0, 1360.0], [1410, 1800.0], [1970.0, 2500.0]]\n",
+      "INFO:2024-11-20,23:30:40 || apply_oe.py:apply_oe() | No wavelength file provided. Obtaining wavelength grid from ENVI header of radiance cube.\n",
+      "INFO:2024-11-20,23:30:40 || apply_oe.py:apply_oe() | Wavelength units of nm inferred...converting to microns\n",
+      "WARNING:2024-11-20,23:30:40 || template_construction.py:check_surface_model() | Center wavelengths provided in surface model file do not match wavelengths in radiance cube. Please consider rebuilding your surface model for optimal performance.\n",
+      "INFO:2024-11-20,23:30:40 || apply_oe.py:apply_oe() | Observation means:\n",
+      "INFO:2024-11-20,23:30:40 || apply_oe.py:apply_oe() | Path (km): 1.0036078691482544\n",
+      "INFO:2024-11-20,23:30:40 || apply_oe.py:apply_oe() | To-sensor azimuth (deg): 153.4481201171875\n",
+      "INFO:2024-11-20,23:30:40 || apply_oe.py:apply_oe() | To-sensor zenith (deg): 1.619314193725586\n",
+      "INFO:2024-11-20,23:30:40 || apply_oe.py:apply_oe() | To-sun azimuth (deg): 145.23248291015625\n",
+      "INFO:2024-11-20,23:30:40 || apply_oe.py:apply_oe() | To-sun zenith (deg): 39.8218994140625\n",
+      "INFO:2024-11-20,23:30:40 || apply_oe.py:apply_oe() | Relative to-sun azimuth (deg): 31.813383102416992\n",
+      "INFO:2024-11-20,23:30:40 || apply_oe.py:apply_oe() | Altitude (km): 2.6922070875167847\n",
+      "INFO:2024-11-20,23:30:40 || apply_oe.py:apply_oe() | Segmenting...\n",
+      "2024-11-20 23:30:40,906\tINFO worker.py:1652 -- Calling ray.init() again after it has already been called.\n",
+      "\u001b[36m(segment_chunk pid=299)\u001b[0m INFO:2024-11-20,23:30:41 ||| 0: starting\n",
+      "INFO:2024-11-20,23:30:41 || apply_oe.py:apply_oe() | Extracting /home/mambauser/examples/NEON/output/NIS01_20210403_173647/input/NIS01_20210403_173647_subs_rdn\n",
+      "2024-11-20 23:30:41,625\tINFO worker.py:1652 -- Calling ray.init() again after it has already been called.\n",
+      "\u001b[36m(segment_chunk pid=299)\u001b[0m INFO:2024-11-20,23:30:41 ||| 0: completing\n",
+      "\u001b[36m(extract_chunk pid=299)\u001b[0m INFO:2024-11-20,23:30:41 ||| 0: starting\n",
+      "INFO:2024-11-20,23:30:41 || apply_oe.py:apply_oe() | Extracting /home/mambauser/examples/NEON/output/NIS01_20210403_173647/input/NIS01_20210403_173647_subs_obs\n",
+      "2024-11-20 23:30:41,678\tINFO worker.py:1652 -- Calling ray.init() again after it has already been called.\n",
+      "INFO:2024-11-20,23:30:41 || apply_oe.py:apply_oe() | Extracting /home/mambauser/examples/NEON/output/NIS01_20210403_173647/input/NIS01_20210403_173647_subs_loc\n",
+      "2024-11-20 23:30:41,715\tINFO worker.py:1652 -- Calling ray.init() again after it has already been called.\n",
+      "INFO:2024-11-20,23:30:41 || apply_oe.py:apply_oe() | Pre-solve H2O grid: [0.01 0.67 1.34 2.   2.67 3.33 4.   4.66 5.33 5.99]\n",
+      "INFO:2024-11-20,23:30:41 || apply_oe.py:apply_oe() | Writing H2O pre-solve configuration file.\n",
+      "\u001b[36m(extract_chunk pid=299)\u001b[0m INFO:2024-11-20,23:30:41 ||| 0: starting\n",
+      "INFO:2024-11-20,23:30:41 || apply_oe.py:apply_oe() | Run ISOFIT initial guess\n",
+      "\u001b[36m(extract_chunk pid=299)\u001b[0m INFO:2024-11-20,23:30:41 ||| 0: starting\n",
+      "WARNING:2024-11-20,23:30:41 || __init__.py:checkNumThreads() | \n",
+      "******************************************************************************************\n",
+      "! Number of threads is greater than 1, this may greatly impact performance\n",
+      "! Please set this the environment variables 'MKL_NUM_THREADS' and 'OMP_NUM_THREADS' to '1'\n",
+      "******************************************************************************************\n",
+      "INFO:2024-11-20,23:30:41 || configs.py:create_new_config() | Loading config file: /home/mambauser/examples/NEON/output/NIS01_20210403_173647/config/NIS01_20210403_173647_h2o.json\n",
+      "INFO:2024-11-20,23:30:41 || configs.py:get_config_errors() | Checking config sections for configuration issues\n",
+      "INFO:2024-11-20,23:30:41 || configs.py:get_config_errors() | Configuration file checks complete, no errors found.\n",
+      "2024-11-20 23:30:41,772\tINFO worker.py:1652 -- Calling ray.init() again after it has already been called.\n",
+      "INFO:2024-11-20,23:30:41 || isofit.py:run() | Building first forward model, will generate any necessary LUTs\n",
+      "INFO:2024-11-20,23:30:41 || radiative_transfer_engine.py:__init__() | Loading from wavelength_file: /home/mambauser/examples/NEON/output/NIS01_20210403_173647/data/wavelengths.txt\n",
+      "INFO:2024-11-20,23:30:41 || radiative_transfer_engine.py:__init__() | No LUT store found, beginning initialization and simulations\n",
+      "INFO:2024-11-20,23:30:41 || radiative_transfer_engine.py:__init__() | Initializing LUT file\n",
+      "INFO:2024-11-20,23:30:41 || radiative_transfer_engine.py:runSimulations() | Running any pre-sim functions\n",
+      "INFO:2024-11-20,23:30:41 || sRTMnet.py:preSim() | Creating a simulator configuration\n",
+      "INFO:2024-11-20,23:30:41 || sRTMnet.py:preSim() | Building simulator and executing (6S)\n",
+      "INFO:2024-11-20,23:30:41 || radiative_transfer_engine.py:__init__() | No LUT store found, beginning initialization and simulations\n",
+      "INFO:2024-11-20,23:30:41 || radiative_transfer_engine.py:__init__() | Initializing LUT file\n",
+      "INFO:2024-11-20,23:30:41 || radiative_transfer_engine.py:runSimulations() | Running any pre-sim functions\n",
+      "INFO:2024-11-20,23:30:41 || radiative_transfer_engine.py:runSimulations() | Executing parallel simulations\n",
+      "\u001b[36m(streamSimulation pid=299)\u001b[0m INFO:2024-11-20,23:30:42 ||| Loaded ini from: /home/mambauser/.isofit/isofit.ini\n",
+      "INFO:2024-11-20,23:30:45 || common.py:__call__() |  20.00% simulations complete (elapsed: 0:00:03.106160, rate: 0:00:00.310616, eta: 0:00:27.955440)\n",
+      "INFO:2024-11-20,23:30:45 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
+      "INFO:2024-11-20,23:30:45 || common.py:__call__() |  30.00% simulations complete (elapsed: 0:00:03.196983, rate: 0:00:00.319698, eta: 0:00:12.787932)\n",
+      "INFO:2024-11-20,23:30:45 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
+      "INFO:2024-11-20,23:30:45 || common.py:__call__() |  40.00% simulations complete (elapsed: 0:00:03.265636, rate: 0:00:00.326564, eta: 0:00:07.619817)\n",
+      "INFO:2024-11-20,23:30:45 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
+      "INFO:2024-11-20,23:30:45 || common.py:__call__() |  50.00% simulations complete (elapsed: 0:00:03.339618, rate: 0:00:00.333962, eta: 0:00:05.009427)\n",
+      "INFO:2024-11-20,23:30:45 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
+      "INFO:2024-11-20,23:30:45 || common.py:__call__() |  60.00% simulations complete (elapsed: 0:00:03.406902, rate: 0:00:00.340690, eta: 0:00:03.406902)\n",
+      "INFO:2024-11-20,23:30:45 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
+      "INFO:2024-11-20,23:30:45 || common.py:__call__() |  70.00% simulations complete (elapsed: 0:00:03.475695, rate: 0:00:00.347570, eta: 0:00:02.317130)\n",
+      "INFO:2024-11-20,23:30:45 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
+      "INFO:2024-11-20,23:30:45 || common.py:__call__() |  80.00% simulations complete (elapsed: 0:00:03.543349, rate: 0:00:00.354335, eta: 0:00:01.518578)\n",
+      "INFO:2024-11-20,23:30:45 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
+      "INFO:2024-11-20,23:30:45 || common.py:__call__() |  90.00% simulations complete (elapsed: 0:00:03.609925, rate: 0:00:00.360992, eta: 0:00:00.902481)\n",
+      "INFO:2024-11-20,23:30:45 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
+      "INFO:2024-11-20,23:30:45 || common.py:__call__() | 100.00% simulations complete (elapsed: 0:00:03.678686, rate: 0:00:00.367869, eta: 0:00:00.408743)\n",
+      "INFO:2024-11-20,23:30:45 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
+      "INFO:2024-11-20,23:30:45 || radiative_transfer_engine.py:runSimulations() | Running any post-sim functions\n",
+      "INFO:2024-11-20,23:30:46 || radiative_transfer_engine.py:runSimulations() | Saving post-sim data to index zero of all dimensions except wl\n",
+      "INFO:2024-11-20,23:30:46 || luts.py:load() | Loading LUT into memory\n",
+      "WARNING:2024-11-20,23:30:46 || luts.py:load() | Detected NaNs in the following LUT variable and may cause issues: thermal_upwelling\n",
+      "WARNING:2024-11-20,23:30:46 || luts.py:load() | Detected NaNs in the following LUT variable and may cause issues: thermal_downwelling\n",
+      "INFO:2024-11-20,23:30:46 || sRTMnet.py:preSim() | Interpolating simulator quantities to emulator size\n",
+      "INFO:2024-11-20,23:30:46 || sRTMnet.py:preSim() | Loading and predicting with emulator\n",
+      "INFO:2024-11-20,23:30:53 || sRTMnet.py:preSim() | Saving intermediary prediction results to: /home/mambauser/examples/NEON/output/NIS01_20210403_173647/lut_h2o/sRTMnet.predicts.nc\n",
+      "INFO:2024-11-20,23:30:53 || radiative_transfer_engine.py:runSimulations() | Saving pre-sim data to index zero of all dimensions except wl\n",
+      "INFO:2024-11-20,23:30:53 || radiative_transfer_engine.py:runSimulations() | Executing parallel simulations\n",
+      "\u001b[36m(streamSimulation pid=299)\u001b[0m INFO:2024-11-20,23:30:53 ||| Loading LUT into memory\n",
+      "INFO:2024-11-20,23:30:53 || common.py:__call__() |  20.00% simulations complete (elapsed: 0:00:00.183867, rate: 0:00:00.018387, eta: 0:00:01.654803)\n",
+      "INFO:2024-11-20,23:30:53 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
+      "INFO:2024-11-20,23:30:53 || common.py:__call__() |  30.00% simulations complete (elapsed: 0:00:00.260936, rate: 0:00:00.026094, eta: 0:00:01.043744)\n",
+      "INFO:2024-11-20,23:30:53 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
+      "INFO:2024-11-20,23:30:53 || common.py:__call__() |  40.00% simulations complete (elapsed: 0:00:00.333105, rate: 0:00:00.033310, eta: 0:00:00.777245)\n",
+      "INFO:2024-11-20,23:30:53 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
+      "INFO:2024-11-20,23:30:53 || common.py:__call__() |  50.00% simulations complete (elapsed: 0:00:00.405420, rate: 0:00:00.040542, eta: 0:00:00.608130)\n",
+      "INFO:2024-11-20,23:30:53 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
+      "INFO:2024-11-20,23:30:53 || common.py:__call__() |  60.00% simulations complete (elapsed: 0:00:00.471780, rate: 0:00:00.047178, eta: 0:00:00.471780)\n",
+      "INFO:2024-11-20,23:30:53 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
+      "INFO:2024-11-20,23:30:53 || common.py:__call__() |  70.00% simulations complete (elapsed: 0:00:00.548214, rate: 0:00:00.054821, eta: 0:00:00.365476)\n",
+      "INFO:2024-11-20,23:30:53 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
+      "INFO:2024-11-20,23:30:54 || common.py:__call__() |  80.00% simulations complete (elapsed: 0:00:00.616973, rate: 0:00:00.061697, eta: 0:00:00.264417)\n",
+      "INFO:2024-11-20,23:30:54 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
+      "INFO:2024-11-20,23:30:54 || common.py:__call__() |  90.00% simulations complete (elapsed: 0:00:00.684282, rate: 0:00:00.068428, eta: 0:00:00.171070)\n",
+      "INFO:2024-11-20,23:30:54 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
+      "INFO:2024-11-20,23:30:54 || common.py:__call__() | 100.00% simulations complete (elapsed: 0:00:00.754454, rate: 0:00:00.075445, eta: 0:00:00.083828)\n",
+      "INFO:2024-11-20,23:30:54 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
+      "INFO:2024-11-20,23:30:54 || radiative_transfer_engine.py:runSimulations() | Running any post-sim functions\n",
+      "INFO:2024-11-20,23:30:54 || luts.py:load() | Loading LUT into memory\n",
+      "WARNING:2024-11-20,23:30:54 || luts.py:load() | Detected NaNs in the following LUT variable and may cause issues: thermal_upwelling\n",
+      "WARNING:2024-11-20,23:30:54 || luts.py:load() | Detected NaNs in the following LUT variable and may cause issues: thermal_downwelling\n",
+      "INFO:2024-11-20,23:30:55 || isofit.py:run() | Beginning 420 inversions in 100 chunks using 10 cores\n",
+      "\u001b[36m(Worker pid=890)\u001b[0m INFO:2024-11-20,23:31:00 ||| Worker 1 completed 1/~42.0:: 2.38% complete\n",
+      "\u001b[36m(Worker pid=889)\u001b[0m INFO:2024-11-20,23:31:07 ||| Worker 9 completed 1/~42.0:: 2.38% complete\u001b[32m [repeated 9x across cluster] (Ray deduplicates logs by default. Set RAY_DEDUP_LOGS=0 to disable log deduplication, or see https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#log-deduplication for more options.)\u001b[0m\n",
+      "\u001b[36m(Worker pid=883)\u001b[0m INFO:2024-11-20,23:31:10 ||| Worker at start location (7,0) completed 3/4\n",
+      "\u001b[36m(Worker pid=883)\u001b[0m INFO:2024-11-20,23:31:13 ||| Worker 8 completed 5/~42.0:: 11.9% complete\n",
+      "\u001b[36m(Worker pid=887)\u001b[0m INFO:2024-11-20,23:31:14 ||| Worker 6 completed 5/~42.0:: 11.9% complete\n",
+      "\u001b[36m(Worker pid=889)\u001b[0m INFO:2024-11-20,23:31:17 ||| Worker at start location (3,0) completed 3/4\u001b[32m [repeated 9x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=889)\u001b[0m INFO:2024-11-20,23:31:20 ||| Worker 9 completed 5/~42.0:: 11.9% complete\u001b[32m [repeated 8x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=883)\u001b[0m INFO:2024-11-20,23:31:24 ||| Worker at start location (45,0) completed 3/4\n",
+      "\u001b[36m(Worker pid=885)\u001b[0m INFO:2024-11-20,23:31:24 ||| Worker at start location (58,0) completed 3/4\n",
+      "\u001b[36m(Worker pid=885)\u001b[0m INFO:2024-11-20,23:31:27 ||| Worker 4 completed 9/~42.0:: 21.43% complete\n",
+      "\u001b[36m(Worker pid=889)\u001b[0m INFO:2024-11-20,23:31:31 ||| Worker at start location (83,0) completed 3/4\u001b[32m [repeated 8x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=889)\u001b[0m INFO:2024-11-20,23:31:34 ||| Worker 9 completed 9/~42.0:: 21.43% complete\u001b[32m [repeated 9x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=891)\u001b[0m INFO:2024-11-20,23:31:37 ||| Worker at start location (96,0) completed 3/4\n",
+      "\u001b[36m(Worker pid=885)\u001b[0m INFO:2024-11-20,23:31:37 ||| Worker at start location (92,0) completed 3/4\n",
+      "\u001b[36m(Worker pid=885)\u001b[0m INFO:2024-11-20,23:31:41 ||| Worker 4 completed 13/~42.0:: 30.95% complete\n",
+      "\u001b[36m(Worker pid=886)\u001b[0m INFO:2024-11-20,23:31:42 ||| Worker at start location (105,0) completed 4/5\u001b[32m [repeated 6x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=884)\u001b[0m INFO:2024-11-20,23:31:47 ||| Worker 0 completed 15/~42.0:: 35.71% complete\u001b[32m [repeated 8x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=891)\u001b[0m INFO:2024-11-20,23:31:51 ||| Worker at start location (130,0) completed 3/4\u001b[32m [repeated 3x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=889)\u001b[0m INFO:2024-11-20,23:31:48 ||| Worker 9 completed 13/~42.0:: 30.95% complete\n",
+      "\u001b[36m(Worker pid=885)\u001b[0m INFO:2024-11-20,23:31:55 ||| Worker 4 completed 17/~42.0:: 40.48% complete\n",
+      "\u001b[36m(Worker pid=892)\u001b[0m INFO:2024-11-20,23:31:56 ||| Worker at start location (139,0) completed 4/5\u001b[32m [repeated 6x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=892)\u001b[0m INFO:2024-11-20,23:32:00 ||| Worker 3 completed 18/~42.0:: 42.86% complete\u001b[32m [repeated 6x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=889)\u001b[0m INFO:2024-11-20,23:32:01 ||| Worker at start location (164,0) completed 3/4\u001b[32m [repeated 3x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=889)\u001b[0m INFO:2024-11-20,23:32:04 ||| Worker 9 completed 17/~42.0:: 40.48% complete\u001b[32m [repeated 3x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=892)\u001b[0m INFO:2024-11-20,23:32:09 ||| Worker at start location (198,0) completed 3/4\u001b[32m [repeated 5x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=887)\u001b[0m INFO:2024-11-20,23:32:10 ||| Worker 6 completed 22/~42.0:: 52.38% complete\u001b[32m [repeated 3x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=890)\u001b[0m INFO:2024-11-20,23:32:15 ||| Worker at start location (194,0) completed 4/5\u001b[32m [repeated 4x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=884)\u001b[0m INFO:2024-11-20,23:32:15 ||| Worker 0 completed 23/~42.0:: 54.76% complete\u001b[32m [repeated 5x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=885)\u001b[0m INFO:2024-11-20,23:32:19 ||| Worker at start location (215,0) completed 3/4\u001b[32m [repeated 2x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=885)\u001b[0m INFO:2024-11-20,23:32:22 ||| Worker 4 completed 25/~42.0:: 59.52% complete\u001b[32m [repeated 3x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=886)\u001b[0m INFO:2024-11-20,23:32:25 ||| Worker at start location (236,0) completed 3/4\u001b[32m [repeated 4x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=892)\u001b[0m INFO:2024-11-20,23:32:25 ||| Worker 3 completed 26/~42.0:: 61.9% complete\u001b[32m [repeated 3x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=889)\u001b[0m INFO:2024-11-20,23:32:30 ||| Worker at start location (253,0) completed 3/4\u001b[32m [repeated 5x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=891)\u001b[0m INFO:2024-11-20,23:32:31 ||| Worker 7 completed 27/~42.0:: 64.29% complete\u001b[32m [repeated 5x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=892)\u001b[0m INFO:2024-11-20,23:32:35 ||| Worker at start location (270,0) completed 3/4\u001b[32m [repeated 3x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=887)\u001b[0m INFO:2024-11-20,23:32:36 ||| Worker 6 completed 30/~42.0:: 71.43% complete\u001b[32m [repeated 3x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=890)\u001b[0m INFO:2024-11-20,23:32:40 ||| Worker at start location (287,0) completed 3/4\u001b[32m [repeated 4x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=890)\u001b[0m INFO:2024-11-20,23:32:44 ||| Worker 1 completed 31/~42.0:: 73.81% complete\u001b[32m [repeated 5x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=889)\u001b[0m INFO:2024-11-20,23:32:43 ||| Worker at start location (295,0) completed 3/4\u001b[32m [repeated 3x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=887)\u001b[0m INFO:2024-11-20,23:32:49 ||| Worker 6 completed 34/~42.0:: 80.95% complete\u001b[32m [repeated 4x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=888)\u001b[0m INFO:2024-11-20,23:32:49 ||| Worker at start location (312,0) completed 3/4\u001b[32m [repeated 4x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=888)\u001b[0m INFO:2024-11-20,23:32:52 ||| Worker 5 completed 35/~42.0:: 83.33% complete\u001b[32m [repeated 3x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=886)\u001b[0m INFO:2024-11-20,23:32:54 ||| Worker at start location (317,0) completed 4/5\u001b[32m [repeated 4x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=889)\u001b[0m INFO:2024-11-20,23:33:00 ||| Worker 9 completed 34/~42.0:: 80.95% complete\u001b[32m [repeated 5x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=885)\u001b[0m INFO:2024-11-20,23:33:01 ||| Worker at start location (346,0) completed 3/4\u001b[32m [repeated 4x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=888)\u001b[0m INFO:2024-11-20,23:33:05 ||| Worker 5 completed 39/~42.0:: 92.86% complete\u001b[32m [repeated 4x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=890)\u001b[0m INFO:2024-11-20,23:33:06 ||| Worker at start location (363,0) completed 3/4\u001b[32m [repeated 4x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=886)\u001b[0m INFO:2024-11-20,23:33:11 ||| Worker 2 completed 40/~42.0:: 95.24% complete\u001b[32m [repeated 4x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=887)\u001b[0m INFO:2024-11-20,23:33:12 ||| Worker at start location (380,0) completed 3/4\u001b[32m [repeated 4x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=891)\u001b[0m INFO:2024-11-20,23:33:13 ||| Worker 7 completed 40/~42.0:: 95.24% complete\u001b[32m [repeated 2x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=890)\u001b[0m INFO:2024-11-20,23:33:20 ||| Worker at start location (406,0) completed 3/4\u001b[32m [repeated 5x across cluster]\u001b[0m\n",
+      "INFO:2024-11-20,23:33:28 || isofit.py:run() | Inversions complete.  152.69s total, 2.7506 spectra/s, 0.2751 spectra/s/core\n",
+      "INFO:2024-11-20,23:33:28 || apply_oe.py:apply_oe() | Full (non-aerosol) LUTs:\n",
+      "INFO:2024-11-20,23:33:28 || apply_oe.py:apply_oe() | Elevation: None\n",
+      "INFO:2024-11-20,23:33:28 || apply_oe.py:apply_oe() | To-sensor zenith: [0.9608 2.9675]\n",
+      "INFO:2024-11-20,23:33:28 || apply_oe.py:apply_oe() | To-sun zenith: None\n",
+      "INFO:2024-11-20,23:33:28 || apply_oe.py:apply_oe() | Relative to-sun azimuth: [3.80000e-03 4.12002e+01 8.23965e+01]\n",
+      "\u001b[36m(Worker pid=891)\u001b[0m INFO:2024-11-20,23:33:28 ||| Worker at start location (419,0) completed 4/5\u001b[32m [repeated 4x across cluster]\u001b[0mINFO:2024-11-20,23:33:28 || apply_oe.py:apply_oe() | H2O Vapor: [0.6083 0.6485]\n",
+      "\n",
+      "INFO:2024-11-20,23:33:28 || apply_oe.py:apply_oe() | /home/mambauser/examples/NEON/output/NIS01_20210403_173647/output/NIS01_20210403_173647_subs_state\n",
+      "INFO:2024-11-20,23:33:28 || apply_oe.py:apply_oe() | Writing main configuration file.\n",
+      "INFO:2024-11-20,23:33:28 || template_construction.py:load_climatology() | Loading Climatology\n",
+      "INFO:2024-11-20,23:33:28 || template_construction.py:load_climatology() | Climatology Loaded.  Aerosol State Vector:\n",
+      "{'AOT550': {'bounds': [0.001, 1.0], 'scale': 1, 'init': 0.1009, 'prior_sigma': 10.0, 'prior_mean': 0.1009}}\n",
+      "Aerosol LUT Grid:\n",
+      "{'AOT550': [0.001, 0.1009, 0.2008, 0.3007, 0.4006, 0.5005, 0.6004, 0.7003, 0.8002, 0.9001, 1.0]}\n",
+      "Aerosol model path:/home/mambauser/data/aerosol_model.txt\n",
+      "INFO:2024-11-20,23:33:28 || apply_oe.py:apply_oe() | Running ISOFIT with full LUT\n",
+      "WARNING:2024-11-20,23:33:28 || __init__.py:checkNumThreads() | \n",
+      "******************************************************************************************\n",
+      "! Number of threads is greater than 1, this may greatly impact performance\n",
+      "! Please set this the environment variables 'MKL_NUM_THREADS' and 'OMP_NUM_THREADS' to '1'\n",
+      "******************************************************************************************\n",
+      "INFO:2024-11-20,23:33:28 || configs.py:create_new_config() | Loading config file: /home/mambauser/examples/NEON/output/NIS01_20210403_173647/config/NIS01_20210403_173647_isofit.json\n",
+      "INFO:2024-11-20,23:33:28 || configs.py:get_config_errors() | Checking config sections for configuration issues\n",
+      "INFO:2024-11-20,23:33:28 || configs.py:get_config_errors() | Configuration file checks complete, no errors found.\n",
+      "2024-11-20 23:33:28,781\tINFO worker.py:1652 -- Calling ray.init() again after it has already been called.\n",
+      "INFO:2024-11-20,23:33:28 || isofit.py:run() | Building first forward model, will generate any necessary LUTs\n",
+      "INFO:2024-11-20,23:33:28 || radiative_transfer_engine.py:__init__() | Loading from wavelength_file: /home/mambauser/examples/NEON/output/NIS01_20210403_173647/data/wavelengths.txt\n",
+      "INFO:2024-11-20,23:33:28 || radiative_transfer_engine.py:__init__() | No LUT store found, beginning initialization and simulations\n",
+      "INFO:2024-11-20,23:33:28 || radiative_transfer_engine.py:__init__() | Initializing LUT file\n",
+      "INFO:2024-11-20,23:33:29 || radiative_transfer_engine.py:runSimulations() | Running any pre-sim functions\n",
+      "INFO:2024-11-20,23:33:29 || sRTMnet.py:preSim() | Creating a simulator configuration\n",
+      "INFO:2024-11-20,23:33:29 || sRTMnet.py:preSim() | Building simulator and executing (6S)\n",
+      "INFO:2024-11-20,23:33:29 || radiative_transfer_engine.py:__init__() | No LUT store found, beginning initialization and simulations\n",
+      "INFO:2024-11-20,23:33:29 || radiative_transfer_engine.py:__init__() | Initializing LUT file\n",
+      "INFO:2024-11-20,23:33:29 || radiative_transfer_engine.py:runSimulations() | Running any pre-sim functions\n",
+      "INFO:2024-11-20,23:33:29 || radiative_transfer_engine.py:runSimulations() | Executing parallel simulations\n",
+      "INFO:2024-11-20,23:33:35 || common.py:__call__() |  10.61% simulations complete (elapsed: 0:00:05.884491, rate: 0:00:00.044579, eta: 0:00:52.960419)\n",
+      "INFO:2024-11-20,23:33:35 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
+      "INFO:2024-11-20,23:33:38 || common.py:__call__() |  20.45% simulations complete (elapsed: 0:00:08.586929, rate: 0:00:00.065052, eta: 0:00:34.347716)\n",
+      "INFO:2024-11-20,23:33:38 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
+      "INFO:2024-11-20,23:33:41 || common.py:__call__() |  30.30% simulations complete (elapsed: 0:00:11.628591, rate: 0:00:00.088095, eta: 0:00:27.133379)\n",
+      "INFO:2024-11-20,23:33:41 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
+      "INFO:2024-11-20,23:33:45 || common.py:__call__() |  40.15% simulations complete (elapsed: 0:00:15.907742, rate: 0:00:00.120513, eta: 0:00:23.861613)\n",
+      "INFO:2024-11-20,23:33:45 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
+      "INFO:2024-11-20,23:33:48 || common.py:__call__() |  50.00% simulations complete (elapsed: 0:00:18.644423, rate: 0:00:00.141246, eta: 0:00:18.644423)\n",
+      "INFO:2024-11-20,23:33:48 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
+      "INFO:2024-11-20,23:33:50 || common.py:__call__() |  60.61% simulations complete (elapsed: 0:00:21.166233, rate: 0:00:00.160350, eta: 0:00:14.110822)\n",
+      "INFO:2024-11-20,23:33:50 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
+      "INFO:2024-11-20,23:33:54 || common.py:__call__() |  70.45% simulations complete (elapsed: 0:00:25.395109, rate: 0:00:00.192387, eta: 0:00:10.883618)\n",
+      "INFO:2024-11-20,23:33:54 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
+      "INFO:2024-11-20,23:33:57 || common.py:__call__() |  80.30% simulations complete (elapsed: 0:00:28.367045, rate: 0:00:00.214902, eta: 0:00:07.091761)\n",
+      "INFO:2024-11-20,23:33:57 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
+      "INFO:2024-11-20,23:34:00 || common.py:__call__() |  90.15% simulations complete (elapsed: 0:00:30.988081, rate: 0:00:00.234758, eta: 0:00:03.443120)\n",
+      "INFO:2024-11-20,23:34:00 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
+      "INFO:2024-11-20,23:34:03 || common.py:__call__() | 100.00% simulations complete (elapsed: 0:00:34.498852, rate: 0:00:00.261355, eta: 0:00:00)\n",
+      "INFO:2024-11-20,23:34:03 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
+      "INFO:2024-11-20,23:34:04 || radiative_transfer_engine.py:runSimulations() | Running any post-sim functions\n",
+      "INFO:2024-11-20,23:34:04 || radiative_transfer_engine.py:runSimulations() | Saving post-sim data to index zero of all dimensions except wl\n",
+      "INFO:2024-11-20,23:34:04 || luts.py:load() | Loading LUT into memory\n",
+      "WARNING:2024-11-20,23:34:04 || luts.py:load() | Detected NaNs in the following LUT variable and may cause issues: thermal_upwelling\n",
+      "WARNING:2024-11-20,23:34:04 || luts.py:load() | Detected NaNs in the following LUT variable and may cause issues: thermal_downwelling\n",
+      "INFO:2024-11-20,23:34:04 || sRTMnet.py:preSim() | Interpolating simulator quantities to emulator size\n",
+      "INFO:2024-11-20,23:34:04 || sRTMnet.py:preSim() | Loading and predicting with emulator\n",
+      "INFO:2024-11-20,23:34:09 || sRTMnet.py:preSim() | Saving intermediary prediction results to: /home/mambauser/examples/NEON/output/NIS01_20210403_173647/lut_full/sRTMnet.predicts.nc\n",
+      "INFO:2024-11-20,23:34:09 || radiative_transfer_engine.py:runSimulations() | Saving pre-sim data to index zero of all dimensions except wl\n",
+      "INFO:2024-11-20,23:34:09 || radiative_transfer_engine.py:runSimulations() | Executing parallel simulations\n",
+      "INFO:2024-11-20,23:34:09 || common.py:__call__() |  10.61% simulations complete (elapsed: 0:00:00.340668, rate: 0:00:00.002581, eta: 0:00:03.066012)\n",
+      "INFO:2024-11-20,23:34:09 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
+      "INFO:2024-11-20,23:34:10 || common.py:__call__() |  20.45% simulations complete (elapsed: 0:00:00.548051, rate: 0:00:00.004152, eta: 0:00:02.192204)\n",
+      "INFO:2024-11-20,23:34:10 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
+      "INFO:2024-11-20,23:34:10 || common.py:__call__() |  30.30% simulations complete (elapsed: 0:00:00.857750, rate: 0:00:00.006498, eta: 0:00:02.001417)\n",
+      "INFO:2024-11-20,23:34:10 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
+      "INFO:2024-11-20,23:34:10 || common.py:__call__() |  40.15% simulations complete (elapsed: 0:00:01.085104, rate: 0:00:00.008220, eta: 0:00:01.627656)\n",
+      "INFO:2024-11-20,23:34:10 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
+      "INFO:2024-11-20,23:34:10 || common.py:__call__() |  50.00% simulations complete (elapsed: 0:00:01.297816, rate: 0:00:00.009832, eta: 0:00:01.297816)\n",
+      "INFO:2024-11-20,23:34:10 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
+      "INFO:2024-11-20,23:34:11 || common.py:__call__() |  60.61% simulations complete (elapsed: 0:00:01.656411, rate: 0:00:00.012549, eta: 0:00:01.104274)\n",
+      "INFO:2024-11-20,23:34:11 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
+      "INFO:2024-11-20,23:34:11 || common.py:__call__() |  70.45% simulations complete (elapsed: 0:00:01.842987, rate: 0:00:00.013962, eta: 0:00:00.789852)\n",
+      "INFO:2024-11-20,23:34:11 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
+      "INFO:2024-11-20,23:34:11 || common.py:__call__() |  80.30% simulations complete (elapsed: 0:00:02.095662, rate: 0:00:00.015876, eta: 0:00:00.523916)\n",
+      "INFO:2024-11-20,23:34:11 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
+      "INFO:2024-11-20,23:34:11 || common.py:__call__() |  90.15% simulations complete (elapsed: 0:00:02.374645, rate: 0:00:00.017990, eta: 0:00:00.263849)\n",
+      "INFO:2024-11-20,23:34:11 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
+      "INFO:2024-11-20,23:34:12 || common.py:__call__() | 100.00% simulations complete (elapsed: 0:00:02.542582, rate: 0:00:00.019262, eta: 0:00:00)\n",
+      "INFO:2024-11-20,23:34:12 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
+      "INFO:2024-11-20,23:34:12 || radiative_transfer_engine.py:runSimulations() | Running any post-sim functions\n",
+      "INFO:2024-11-20,23:34:12 || luts.py:load() | Loading LUT into memory\n",
+      "WARNING:2024-11-20,23:34:12 || luts.py:load() | Detected NaNs in the following LUT variable and may cause issues: thermal_upwelling\n",
+      "WARNING:2024-11-20,23:34:12 || luts.py:load() | Detected NaNs in the following LUT variable and may cause issues: thermal_downwelling\n",
+      "INFO:2024-11-20,23:34:12 || isofit.py:run() | Beginning 420 inversions in 100 chunks using 10 cores\n",
+      "\u001b[36m(Worker pid=2232)\u001b[0m INFO:2024-11-20,23:34:20 ||| Worker 4 completed 1/~42.0:: 2.38% complete\n",
+      "\u001b[36m(Worker pid=2231)\u001b[0m INFO:2024-11-20,23:34:40 ||| Worker at start location (15,0) completed 3/4\n",
+      "\u001b[36m(Worker pid=2236)\u001b[0m INFO:2024-11-20,23:34:22 ||| Worker 0 completed 1/~42.0:: 2.38% complete\u001b[32m [repeated 9x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=2229)\u001b[0m INFO:2024-11-20,23:34:45 ||| Worker at start location (3,0) completed 3/4\u001b[32m [repeated 7x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=2231)\u001b[0m INFO:2024-11-20,23:34:48 ||| Worker 6 completed 5/~42.0:: 11.9% complete\n",
+      "\u001b[36m(Worker pid=2238)\u001b[0m INFO:2024-11-20,23:34:49 ||| Worker at start location (20,0) completed 4/5\u001b[32m [repeated 2x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=2230)\u001b[0m INFO:2024-11-20,23:34:56 ||| Worker 1 completed 6/~42.0:: 14.29% complete\u001b[32m [repeated 8x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=2232)\u001b[0m INFO:2024-11-20,23:35:09 ||| Worker at start location (58,0) completed 3/4\n",
+      "\u001b[36m(Worker pid=2238)\u001b[0m INFO:2024-11-20,23:34:56 ||| Worker 5 completed 6/~42.0:: 14.29% complete\n",
+      "\u001b[36m(Worker pid=2230)\u001b[0m INFO:2024-11-20,23:35:15 ||| Worker at start location (79,0) completed 3/4\u001b[32m [repeated 6x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=2232)\u001b[0m INFO:2024-11-20,23:35:16 ||| Worker 4 completed 9/~42.0:: 21.43% complete\n",
+      "\u001b[36m(Worker pid=2234)\u001b[0m INFO:2024-11-20,23:35:19 ||| Worker at start location (54,0) completed 4/5\u001b[32m [repeated 3x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=2230)\u001b[0m INFO:2024-11-20,23:35:22 ||| Worker 1 completed 10/~42.0:: 23.81% complete\u001b[32m [repeated 6x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=2236)\u001b[0m INFO:2024-11-20,23:35:39 ||| Worker at start location (96,0) completed 3/4\n",
+      "\u001b[36m(Worker pid=2234)\u001b[0m INFO:2024-11-20,23:35:27 ||| Worker 2 completed 10/~42.0:: 23.81% complete\u001b[32m [repeated 3x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=2238)\u001b[0m INFO:2024-11-20,23:35:44 ||| Worker at start location (117,0) completed 3/4\u001b[32m [repeated 5x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=2236)\u001b[0m INFO:2024-11-20,23:35:45 ||| Worker 0 completed 13/~42.0:: 30.95% complete\n",
+      "\u001b[36m(Worker pid=2229)\u001b[0m INFO:2024-11-20,23:35:47 ||| Worker at start location (105,0) completed 4/5\u001b[32m [repeated 3x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=2238)\u001b[0m INFO:2024-11-20,23:35:51 ||| Worker 5 completed 14/~42.0:: 33.33% complete\u001b[32m [repeated 4x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=2233)\u001b[0m INFO:2024-11-20,23:35:52 ||| Worker at start location (122,0) completed 4/5\n",
+      "\u001b[36m(Worker pid=2233)\u001b[0m INFO:2024-11-20,23:35:56 ||| Worker 7 completed 15/~42.0:: 35.71% complete\u001b[32m [repeated 5x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=2236)\u001b[0m INFO:2024-11-20,23:36:04 ||| Worker at start location (130,0) completed 3/4\n",
+      "\u001b[36m(Worker pid=2231)\u001b[0m INFO:2024-11-20,23:36:09 ||| Worker 6 completed 17/~42.0:: 40.48% complete\n",
+      "\u001b[36m(Worker pid=2229)\u001b[0m INFO:2024-11-20,23:36:10 ||| Worker at start location (164,0) completed 3/4\u001b[32m [repeated 3x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=2235)\u001b[0m INFO:2024-11-20,23:36:13 ||| Worker 3 completed 17/~42.0:: 40.48% complete\u001b[32m [repeated 2x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=2237)\u001b[0m INFO:2024-11-20,23:36:15 ||| Worker at start location (139,0) completed 4/5\u001b[32m [repeated 4x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=2233)\u001b[0m INFO:2024-11-20,23:36:21 ||| Worker 7 completed 19/~42.0:: 45.24% complete\u001b[32m [repeated 5x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=2233)\u001b[0m INFO:2024-11-20,23:36:17 ||| Worker at start location (168,0) completed 3/4\n",
+      "\u001b[36m(Worker pid=2234)\u001b[0m INFO:2024-11-20,23:36:24 ||| Worker at start location (160,0) completed 4/5\n",
+      "\u001b[36m(Worker pid=2237)\u001b[0m INFO:2024-11-20,23:36:22 ||| Worker 8 completed 18/~42.0:: 42.86% complete\n",
+      "\u001b[36m(Worker pid=2234)\u001b[0m INFO:2024-11-20,23:36:30 ||| Worker 2 completed 19/~42.0:: 45.24% complete\n",
+      "\u001b[36m(Worker pid=2236)\u001b[0m INFO:2024-11-20,23:36:27 ||| Worker at start location (172,0) completed 3/4\n",
+      "\u001b[36m(Worker pid=2235)\u001b[0m INFO:2024-11-20,23:36:31 ||| Worker at start location (181,0) completed 3/4\n",
+      "\u001b[36m(Worker pid=2236)\u001b[0m INFO:2024-11-20,23:36:34 ||| Worker 0 completed 21/~42.0:: 50.0% complete\n",
+      "\u001b[36m(Worker pid=2235)\u001b[0m INFO:2024-11-20,23:36:37 ||| Worker 3 completed 21/~42.0:: 50.0% complete\n",
+      "\u001b[36m(Worker pid=2232)\u001b[0m INFO:2024-11-20,23:36:35 ||| Worker at start location (198,0) completed 3/4\u001b[32m [repeated 3x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=2232)\u001b[0m INFO:2024-11-20,23:36:42 ||| Worker 4 completed 22/~42.0:: 52.38% complete\u001b[32m [repeated 3x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=2233)\u001b[0m INFO:2024-11-20,23:36:41 ||| Worker at start location (206,0) completed 3/4\u001b[32m [repeated 2x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=2237)\u001b[0m INFO:2024-11-20,23:36:49 ||| Worker 8 completed 22/~42.0:: 52.38% complete\u001b[32m [repeated 2x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=2238)\u001b[0m INFO:2024-11-20,23:36:43 ||| Worker at start location (194,0) completed 4/5\u001b[32m [repeated 2x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=2238)\u001b[0m INFO:2024-11-20,23:36:50 ||| Worker 5 completed 23/~42.0:: 54.76% complete\u001b[32m [repeated 2x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=2236)\u001b[0m INFO:2024-11-20,23:36:55 ||| Worker at start location (215,0) completed 3/4\u001b[32m [repeated 2x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=2234)\u001b[0m INFO:2024-11-20,23:37:00 ||| Worker 2 completed 24/~42.0:: 57.14% complete\n",
+      "\u001b[36m(Worker pid=2230)\u001b[0m INFO:2024-11-20,23:37:00 ||| Worker at start location (223,0) completed 3/4\u001b[32m [repeated 2x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=2236)\u001b[0m INFO:2024-11-20,23:37:02 ||| Worker 0 completed 25/~42.0:: 59.52% complete\n",
+      "\u001b[36m(Worker pid=2232)\u001b[0m INFO:2024-11-20,23:37:04 ||| Worker at start location (232,0) completed 3/4\u001b[32m [repeated 2x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=2235)\u001b[0m INFO:2024-11-20,23:37:07 ||| Worker 3 completed 25/~42.0:: 59.52% complete\u001b[32m [repeated 2x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=2238)\u001b[0m INFO:2024-11-20,23:37:12 ||| Worker at start location (249,0) completed 3/4\u001b[32m [repeated 3x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=2232)\u001b[0m INFO:2024-11-20,23:37:11 ||| Worker 4 completed 26/~42.0:: 61.9% complete\u001b[32m [repeated 2x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=2229)\u001b[0m INFO:2024-11-20,23:37:18 ||| Worker 9 completed 27/~42.0:: 64.29% complete\n",
+      "\u001b[36m(Worker pid=2233)\u001b[0m INFO:2024-11-20,23:37:19 ||| Worker 7 completed 27/~42.0:: 64.29% complete\n",
+      "\u001b[36m(Worker pid=2237)\u001b[0m INFO:2024-11-20,23:37:20 ||| Worker at start location (245,0) completed 4/5\n",
+      "\u001b[36m(Worker pid=2237)\u001b[0m INFO:2024-11-20,23:37:28 ||| Worker 8 completed 27/~42.0:: 64.29% complete\u001b[32m [repeated 2x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=2236)\u001b[0m INFO:2024-11-20,23:37:23 ||| Worker at start location (257,0) completed 3/4\u001b[32m [repeated 2x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=2234)\u001b[0m INFO:2024-11-20,23:37:31 ||| Worker 2 completed 28/~42.0:: 66.67% complete\u001b[32m [repeated 2x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=2232)\u001b[0m INFO:2024-11-20,23:37:33 ||| Worker at start location (274,0) completed 3/4\u001b[32m [repeated 2x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=2230)\u001b[0m INFO:2024-11-20,23:37:39 ||| Worker 1 completed 30/~42.0:: 71.43% complete\n",
+      "\u001b[36m(Worker pid=2235)\u001b[0m INFO:2024-11-20,23:37:38 ||| Worker at start location (262,0) completed 4/5\u001b[32m [repeated 2x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=2231)\u001b[0m INFO:2024-11-20,23:37:41 ||| Worker 6 completed 30/~42.0:: 71.43% complete\n",
+      "\u001b[36m(Worker pid=2238)\u001b[0m INFO:2024-11-20,23:37:44 ||| Worker at start location (287,0) completed 3/4\u001b[32m [repeated 2x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=2235)\u001b[0m INFO:2024-11-20,23:37:46 ||| Worker 3 completed 30/~42.0:: 71.43% complete\u001b[32m [repeated 2x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=2237)\u001b[0m INFO:2024-11-20,23:37:50 ||| Worker at start location (291,0) completed 3/4\u001b[32m [repeated 2x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=2238)\u001b[0m INFO:2024-11-20,23:37:52 ||| Worker 5 completed 31/~42.0:: 73.81% complete\u001b[32m [repeated 2x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=2234)\u001b[0m INFO:2024-11-20,23:37:52 ||| Worker at start location (295,0) completed 3/4\n",
+      "\u001b[36m(Worker pid=2234)\u001b[0m INFO:2024-11-20,23:38:00 ||| Worker 2 completed 32/~42.0:: 76.19% complete\u001b[32m [repeated 3x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=2236)\u001b[0m INFO:2024-11-20,23:38:00 ||| Worker at start location (300,0) completed 4/5\n",
+      "\u001b[36m(Worker pid=2230)\u001b[0m INFO:2024-11-20,23:38:07 ||| Worker 1 completed 34/~42.0:: 80.95% complete\n",
+      "\u001b[36m(Worker pid=2231)\u001b[0m INFO:2024-11-20,23:38:03 ||| Worker at start location (312,0) completed 3/4\u001b[32m [repeated 3x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=2236)\u001b[0m INFO:2024-11-20,23:38:10 ||| Worker 0 completed 34/~42.0:: 80.95% complete\n",
+      "\u001b[36m(Worker pid=2235)\u001b[0m INFO:2024-11-20,23:38:16 ||| Worker at start location (317,0) completed 4/5\u001b[32m [repeated 3x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=2231)\u001b[0m INFO:2024-11-20,23:38:11 ||| Worker 6 completed 34/~42.0:: 80.95% complete\u001b[32m [repeated 2x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=2234)\u001b[0m INFO:2024-11-20,23:38:21 ||| Worker at start location (338,0) completed 3/4\u001b[32m [repeated 2x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=2235)\u001b[0m INFO:2024-11-20,23:38:23 ||| Worker 3 completed 35/~42.0:: 83.33% complete\u001b[32m [repeated 3x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=2230)\u001b[0m INFO:2024-11-20,23:38:28 ||| Worker at start location (346,0) completed 3/4\u001b[32m [repeated 2x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=2234)\u001b[0m INFO:2024-11-20,23:38:29 ||| Worker 2 completed 36/~42.0:: 85.71% complete\u001b[32m [repeated 2x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=2236)\u001b[0m INFO:2024-11-20,23:38:31 ||| Worker at start location (342,0) completed 3/4\n",
+      "\u001b[36m(Worker pid=2237)\u001b[0m INFO:2024-11-20,23:38:35 ||| Worker 8 completed 36/~42.0:: 85.71% complete\n",
+      "\u001b[36m(Worker pid=2231)\u001b[0m INFO:2024-11-20,23:38:35 ||| Worker at start location (355,0) completed 3/4\n",
+      "\u001b[36m(Worker pid=2230)\u001b[0m INFO:2024-11-20,23:38:37 ||| Worker 1 completed 38/~42.0:: 90.48% complete\n",
+      "\u001b[36m(Worker pid=2238)\u001b[0m INFO:2024-11-20,23:38:42 ||| Worker at start location (363,0) completed 3/4\u001b[32m [repeated 2x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=2236)\u001b[0m INFO:2024-11-20,23:38:40 ||| Worker 0 completed 38/~42.0:: 90.48% complete\n",
+      "\u001b[36m(Worker pid=2231)\u001b[0m INFO:2024-11-20,23:38:44 ||| Worker 6 completed 38/~42.0:: 90.48% complete\n",
+      "\u001b[36m(Worker pid=2229)\u001b[0m INFO:2024-11-20,23:38:48 ||| Worker at start location (372,0) completed 3/4\u001b[32m [repeated 2x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=2232)\u001b[0m INFO:2024-11-20,23:38:50 ||| Worker 4 completed 39/~42.0:: 92.86% complete\n",
+      "\u001b[36m(Worker pid=2238)\u001b[0m INFO:2024-11-20,23:38:53 ||| Worker 5 completed 39/~42.0:: 92.86% complete\n",
+      "\u001b[36m(Worker pid=2234)\u001b[0m INFO:2024-11-20,23:38:56 ||| Worker at start location (376,0) completed 3/4\n",
+      "\u001b[36m(Worker pid=2235)\u001b[0m INFO:2024-11-20,23:38:57 ||| Worker at start location (368,0) completed 4/5\n",
+      "\u001b[36m(Worker pid=2229)\u001b[0m INFO:2024-11-20,23:38:57 ||| Worker 9 completed 40/~42.0:: 95.24% complete\u001b[32m [repeated 2x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=2237)\u001b[0m INFO:2024-11-20,23:39:01 ||| Worker at start location (380,0) completed 3/4\n",
+      "\u001b[36m(Worker pid=2235)\u001b[0m INFO:2024-11-20,23:39:06 ||| Worker 3 completed 40/~42.0:: 95.24% complete\u001b[32m [repeated 2x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=2236)\u001b[0m INFO:2024-11-20,23:39:07 ||| Worker at start location (389,0) completed 3/4\n",
+      "\u001b[36m(Worker pid=2232)\u001b[0m INFO:2024-11-20,23:39:16 ||| Worker at start location (397,0) completed 3/4\u001b[32m [repeated 3x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=2229)\u001b[0m INFO:2024-11-20,23:39:25 ||| Worker at start location (410,0) completed 3/4\u001b[32m [repeated 2x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=2234)\u001b[0m INFO:2024-11-20,23:39:31 ||| Worker at start location (414,0) completed 3/4\u001b[32m [repeated 2x across cluster]\u001b[0m\n",
+      "INFO:2024-11-20,23:39:40 || isofit.py:run() | Inversions complete.  327.73s total, 1.2815 spectra/s, 0.1282 spectra/s/core\n",
+      "INFO:2024-11-20,23:39:40 || apply_oe.py:apply_oe() | Analytical line inference\n",
+      "INFO:2024-11-20,23:39:40 || configs.py:create_new_config() | Loading config file: /home/mambauser/examples/NEON/output/NIS01_20210403_173647/config/NIS01_20210403_173647_isofit.json\n",
+      "\u001b[36m(Worker pid=2235)\u001b[0m INFO:2024-11-20,23:39:40 ||| Worker at start location (419,0) completed 4/5\n",
+      "INFO:2024-11-20,23:39:41 || radiative_transfer_engine.py:__init__() | Loading from wavelength_file: /home/mambauser/examples/NEON/output/NIS01_20210403_173647/data/wavelengths.txt\n",
+      "INFO:2024-11-20,23:39:41 || radiative_transfer_engine.py:__init__() | Prebuilt LUT provided\n",
+      "INFO:2024-11-20,23:39:41 || luts.py:load() | Loading LUT into memory\n",
+      "WARNING:2024-11-20,23:39:41 || luts.py:load() | Detected NaNs in the following LUT variable and may cause issues: thermal_upwelling\n",
+      "WARNING:2024-11-20,23:39:41 || luts.py:load() | Detected NaNs in the following LUT variable and may cause issues: thermal_downwelling\n",
+      "INFO:2024-11-20,23:39:41 || radiative_transfer_engine.py:__init__() | LUT grid loaded from file: OrderedDict([('AOT550', [0.001, 0.1009, 0.2008, 0.3007, 0.4006, 0.5005, 0.6004, 0.7003, 0.8002, 0.9001, 1.0]), ('H2OSTR', [0.6083, 0.6485]), ('observer_zenith', [0.9608, 2.9675]), ('relative_azimuth', [0.0038, 41.2002, 82.3965])])\n",
+      "2024-11-20 23:39:42,027\tINFO worker.py:1652 -- Calling ray.init() again after it has already been called.\n",
+      "INFO:2024-11-20,23:39:42 || atm_interpolation.py:atm_interpolation() | Beginning atmospheric interpolation 10 cores\n",
+      "INFO:2024-11-20,23:39:43 || atm_interpolation.py:atm_interpolation() | Parallel atmospheric interpolations complete.  1.1534008979797363 s total, 3659.612201961505 spectra/s, 365.9612201961505 spectra/s/core\n",
+      "2024-11-20 23:39:43,191\tINFO worker.py:1652 -- Calling ray.init() again after it has already been called.\n",
+      "\u001b[36m(Worker pid=3155)\u001b[0m INFO:2024-11-20,23:40:03 ||| Analytical line writing line 4\n",
+      "\u001b[36m(Worker pid=3155)\u001b[0m INFO:2024-11-20,23:40:22 ||| Analytical line writing line 10\u001b[32m [repeated 10x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=3155)\u001b[0m INFO:2024-11-20,23:40:42 ||| Analytical line writing line 20\u001b[32m [repeated 10x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=3155)\u001b[0m INFO:2024-11-20,23:41:02 ||| Analytical line writing line 30\u001b[32m [repeated 10x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=3155)\u001b[0m INFO:2024-11-20,23:41:25 ||| Analytical line writing line 40\u001b[32m [repeated 10x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=3155)\u001b[0m INFO:2024-11-20,23:41:46 ||| Analytical line writing line 50\u001b[32m [repeated 10x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=3155)\u001b[0m INFO:2024-11-20,23:42:08 ||| Analytical line writing line 60\u001b[32m [repeated 10x across cluster]\u001b[0m\n",
+      "INFO:2024-11-20,23:42:08 || analytical_line.py:analytical_line() | Analytical line inversions complete.  145.51s total, 29.0084 spectra/s, 2.9008 spectra/s/core\n",
+      "INFO:2024-11-20,23:42:08 || apply_oe.py:apply_oe() | Done.\n",
+      "\u001b[36m(Worker pid=3160)\u001b[0m INFO:2024-11-20,23:42:08 ||| Analytical line writing line 61\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Add a ray shutdown, just in case this is being re-called\n",
+    "import ray\n",
+    "ray.shutdown()\n",
+    "\n",
+    "# Cleanup any previous runs; comment this out if you want to preserve a previous run's output\n",
+    "if Path(paths.working).exists():\n",
+    "    shutil.rmtree(paths.working)\n",
+    "\n",
+    "apply_oe(\n",
+    "    input_radiance    = paths.rdn, # Radiance\n",
+    "    input_loc         = paths.loc, # Location\n",
+    "    input_obs         = paths.obs, # Observations\n",
+    "    working_directory = str(paths.working), # Output directory\n",
+    "    sensor            = \"neon\", \n",
+    "    surface_path      = paths.surface, # Surface priors - often changes\n",
+    "    emulator_base     = f\"{env.srtmnet}/sRTMnet_v120.h5\",\n",
+    "    surface_category  = \"multicomponent_surface\",\n",
+    "\n",
+    "    modtran_path    = None,\n",
+    "    atmosphere_type = \"ATM_MIDLAT_SUMMER\", # MODTRAN\n",
+    "    aerosol_climatology_path = None,       # MODTRAN\n",
+    "    \n",
+    "    rdn_factors_path       = None, # RCC update used 'on the fly'\n",
+    "    model_discrepancy_path = None, # Model discrepancy term - handle things like unknown radiative transfer model effects\n",
+    "    channelized_uncertainty_path = None, # Channelized uncertainty - if you have an instrument model\n",
+    "\n",
+    "    multiple_restarts = False, # Useful if the AOD conditions are really challenging\n",
+    "    \n",
+    "    presolve        = True,  # Attempts to solve for the right wv range\n",
+    "    empirical_line  = False, # wavelength-specific local linear interpolation between radiance and reflectance\n",
+    "    analytical_line = True,  # mathematical representation of OE given that the atmsophere is known\n",
+    "    \n",
+    "    segmentation_size  = 10,\n",
+    "    num_neighbors      = [5],\n",
+    "    atm_sigma          = [0.5, 0.5],\n",
+    "    pressure_elevation = False,\n",
+    "\n",
+    "    n_cores = os.cpu_count(),\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8ea06539-8f6a-4806-b24e-c85fbcc18b28",
+   "metadata": {},
+   "source": [
+    "# Plotting\n",
+    "\n",
+    "Below plots the regions of interest defined by a NEON report. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "d07cc4e0-2b61-4c8f-998a-2b3f1b064b48",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load in the ISOFIT reflectance output\n",
+    "ds = envi.open(paths.working / f\"output/{neon_str}_rfl.hdr\")\n",
+    "rfl = ds.open_memmap(interleave='bip')\n",
+    "rgb = rfl[:, :, [60, 40, 30]].copy()\n",
+    "wl = np.array(ds.metadata['wavelength'], dtype=float)\n",
+    "\n",
+    "# Find the bounding box for all regions of interest (RoI)\n",
+    "regions = report[neon_id]\n",
+    "bounds = np.vstack(list(regions.values()))\n",
+    "y = bounds[:, 0].min() - 5 # , bounds[:, 1].max() + 5\n",
+    "x = bounds[:, 2].min() - 5 # , bounds[:, 3].max() + 5\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "6dcf2273-ebd0-480b-b3de-260a67814809",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0xffff84626a40>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 700x700 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot the RoIs\n",
+    "fig, ax = plt.subplots(figsize=(7, 7))\n",
+    "\n",
+    "ax.imshow(rgb / np.max(rgb, axis=(0, 1))) # Dividing brightens the image\n",
+    "ax.set_title(neon_str)\n",
+    "\n",
+    "for i, (roi, region) in enumerate(regions.items()):\n",
+    "    rect = patches.Rectangle(\n",
+    "        (region[2] - x, region[0] - y), \n",
+    "        region[3] - region[2], \n",
+    "        region[1] - region[0], \n",
+    "        linewidth = 1, \n",
+    "        edgecolor = f'C{i}', \n",
+    "        facecolor = 'none',\n",
+    "        label = roi\n",
+    "    )\n",
+    "    ax.add_patch(rect)\n",
+    "ax.legend(loc='lower right')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "f358f265-0ddd-47bf-9407-4c30623dd454",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x1500 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axes = plt.subplots(len(regions), sharex=True, figsize=(10, 3*len(regions)))\n",
+    "\n",
+    "for i, (roi, region) in enumerate(regions.items()):\n",
+    "    ax = axes[i]\n",
+    "    \n",
+    "    in_situ = np.genfromtxt(paths.insitu / f'{roi}01/Data/{roi}01_Refl.dat', skip_header=3)\n",
+    "    ax.plot(in_situ[:, 0], in_situ[:, 1], label='In Situ', c='red', ls='-')\n",
+    "\n",
+    "    mean_rfl = np.mean(\n",
+    "        rfl[\n",
+    "            region[0] - y : region[1] - y,\n",
+    "            region[2] - x : region[3] - x,\n",
+    "        ],\n",
+    "        axis = (0, 1)\n",
+    "    )\n",
+    "    \n",
+    "    ax.plot(wl, mean_rfl, label='Isofit', c='black')\n",
+    "    \n",
+    "    ax.set_ylabel('Reflectance')\n",
+    "    ax.set_title(roi)\n",
+    "    ax.legend()\n",
+    "\n",
+    "ax.set_xlabel('Wavelength')\n",
+    "plt.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "411124d3",
+   "metadata": {},
+   "source": [
+    "We can plot out the mapped reflectance (as above), but also the interpolated atmospheric conditions.  The windows size is small enough here (and the atmospheric parameters are chosen in such a way) that the map is going to be pretty static...but we can still see it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "82b2055e-5309-4974-b621-fb7685ef734d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.colorbar.Colorbar at 0xffff8422aaa0>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 700x700 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 700x700 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "dat = envi.open(paths.working / f\"output/{neon_str}_atm_interp.hdr\")\n",
+    "atm = dat.open_memmap(interleave='bip').copy()\n",
+    "\n",
+    "plt.figure(figsize=(7, 7))\n",
+    "plt.title('AOD')\n",
+    "plt.imshow(atm[..., 0])\n",
+    "plt.colorbar()\n",
+    "\n",
+    "plt.figure(figsize=(7, 7))\n",
+    "plt.title('Water Vapor')\n",
+    "plt.imshow(atm[..., 1])\n",
+    "plt.colorbar()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "isofit",
+   "language": "python",
+   "name": "isofit"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.15"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/isotuts/NEON/neon_single_pixel.ipynb b/NEON/neon_single_pixel.ipynb
similarity index 98%
rename from isotuts/NEON/neon_single_pixel.ipynb
rename to NEON/neon_single_pixel.ipynb
index 9e11b7a..07ded6f 100644
--- a/isotuts/NEON/neon_single_pixel.ipynb
+++ b/NEON/neon_single_pixel.ipynb
@@ -59,27 +59,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import numpy as np\n",
-    "\n",
-    "# Extract the image locations of each point of interest (POI)\n",
-    "# These are defined in the NEON report as pixel locations, so we round here to convert to indices\n",
-    "report = {}\n",
-    "report['173647'] = {           # Upp L Y  | Low R Y  | Upp L X | Low R X\n",
-    "    'WhiteTarp': np.round([2224.9626, 2230.9771, 316.0078, 324.9385,]).astype(int),\n",
-    "    'BlackTarp': np.round([2224.9626, 2231.0032, 328.0086, 333.9731,]).astype(int),\n",
-    "    'Veg'      : np.round([2245.0381, 2258.8103, 343.9006, 346.9423,]).astype(int),\n",
-    "    'RoadEW'   : np.round([2214.9905, 2216.9978, 348.9902, 373.0080,]).astype(int),\n",
-    "    'RoadNS'   : np.round([2205.9580, 2225.9612, 357.9536, 359.9608,]).astype(int)\n",
-    "}\n",
-    "report['174150'] = {           # Upp L Y | Low R Y | Upp L X  | Low R X\n",
-    "    'WhiteTarp': np.round([653.9626, 659.9771, 3143.0078, 3151.9385]).astype(int),\n",
-    "    'BlackTarp': np.round([653.9626, 660.0032, 3155.0086, 3160.9731]).astype(int),\n",
-    "    'Veg'      : np.round([674.0381, 687.8103, 3170.9006, 3173.9423]).astype(int),\n",
-    "    'RoadEW'   : np.round([643.9905, 645.9978, 3175.9902, 3200.0080]).astype(int),\n",
-    "    'RoadNS'   : np.round([634.9580, 654.9612, 3184.9536, 3186.9608]).astype(int)\n",
-    "}\n",
-    "# Converts numpy array to comma-separated string for ISOFIT\n",
-    "toString = lambda array: ', '.join(str(v) for v in array)"
+    "\n"
    ]
   },
   {
@@ -211,7 +191,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "isofit_env",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -225,9 +205,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.13"
+   "version": "3.10.8"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }
diff --git a/isotuts/utils.py b/NEON/utils/faker.py
similarity index 97%
rename from isotuts/utils.py
rename to NEON/utils/faker.py
index b81b6ae..3bcd59e 100644
--- a/isotuts/utils.py
+++ b/NEON/utils/faker.py
@@ -27,6 +27,7 @@ def getMetadata(file, remove=['fwhm', 'band names', 'wavelength', 'wavelength un
 
     return metadata
 
+
 def fakeLOC(rdn, lon, lat, elv, output=None, **kwargs):
     """
     Creates a fake LOC file
@@ -66,6 +67,9 @@ def fakeLOC(rdn, lon, lat, elv, output=None, **kwargs):
 
     del ds, loc
 
+    return output
+
+
 def fakeOBS(rdn, param0=0, sea=0, sez=0, soa=0, soz=0, phase=0, slope=0, aspect=0, cosi=0, param9=0, param10=0, output=None, **kwargs):
     """
     Creates a fake OBS file
@@ -104,7 +108,7 @@ def fakeOBS(rdn, param0=0, sea=0, sez=0, soa=0, soz=0, phase=0, slope=0, aspect=
     """
     if not output:
         if 'rdn' in rdn:
-            output = rdn.replace('rdn', 'loc')
+            output = rdn.replace('rdn', 'obs')
         else:
             Logger.error('No ouput file specified and cannot generate a unique name')
             return False
@@ -129,3 +133,5 @@ def fakeOBS(rdn, param0=0, sea=0, sez=0, soa=0, soz=0, phase=0, slope=0, aspect=
     obs[..., 10] = param10
 
     del ds, obs
+
+    return output
diff --git a/NEON/utils/neon.py b/NEON/utils/neon.py
new file mode 100644
index 0000000..cae44f3
--- /dev/null
+++ b/NEON/utils/neon.py
@@ -0,0 +1,21 @@
+import numpy as np
+
+# Extract the image locations of each point of interest (POI)
+# These are defined in the NEON report as pixel locations, so we round here to convert to indices
+report = {}
+report['173647'] = {           # Upp L Y  | Low R Y  | Upp L X | Low R X
+    'WhiteTarp': np.round([2224.9626, 2230.9771, 316.0078, 324.9385,]).astype(int),
+    'BlackTarp': np.round([2224.9626, 2231.0032, 328.0086, 333.9731,]).astype(int),
+    'Veg'      : np.round([2245.0381, 2258.8103, 343.9006, 346.9423,]).astype(int),
+    'RoadEW'   : np.round([2214.9905, 2216.9978, 348.9902, 373.0080,]).astype(int),
+    'RoadNS'   : np.round([2205.9580, 2225.9612, 357.9536, 359.9608,]).astype(int)
+}
+report['174150'] = {           # Upp L Y | Low R Y | Upp L X  | Low R X
+    'WhiteTarp': np.round([653.9626, 659.9771, 3143.0078, 3151.9385]).astype(int),
+    'BlackTarp': np.round([653.9626, 660.0032, 3155.0086, 3160.9731]).astype(int),
+    'Veg'      : np.round([674.0381, 687.8103, 3170.9006, 3173.9423]).astype(int),
+    'RoadEW'   : np.round([643.9905, 645.9978, 3175.9902, 3200.0080]).astype(int),
+    'RoadNS'   : np.round([634.9580, 654.9612, 3184.9536, 3186.9608]).astype(int)
+}
+# Converts numpy array to comma-separated string for ISOFIT
+toString = lambda array: ', '.join(str(v) for v in array)
diff --git a/isotuts/NEON/neon.ipynb b/isotuts/NEON/neon.ipynb
deleted file mode 100644
index 1593bb1..0000000
--- a/isotuts/NEON/neon.ipynb
+++ /dev/null
@@ -1,573 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "e30eef79",
-   "metadata": {},
-   "source": [
-    "# NEON\n",
-    "\n",
-    "This notebook is an excercise in executing ISOFIT on two dates from the NEON dataset and interpreting the outputs of ISOFIT. \n",
-    "\n",
-    "Prerequisites:\n",
-    "- Download sample data from https://avng.jpl.nasa.gov/pub/PBrodrick/isofit/tutorials/subset_data.zip.  This dataset was prepped already from the data_prep notebook.  Place the dataset into the NEON folder in this repo and unzip it, which will create the 'data' folder which includes the 'subsets' directory.\n",
-    "- Have a working installation of ISOFIT, with sRTMnet installed and configured (see environment variable specification on the next line)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "id": "44e2871f",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Data subset directory at: /Users/brodrick/repos/isofit-tutorials/isotuts/NEON/data/subsets\n",
-      "Surface model at: /Users/brodrick/repos/isofit/examples/20171108_Pasadena/configs/ang20171108t184227_surface.json\n",
-      "sRTMnet emulator path (required): /Users/brodrick/isofit_support/sRTMnet_v120.h5\n",
-      "6s path (required): /Users/brodrick/6s/\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Jupyter magics\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "# Setup logging\n",
-    "import logging\n",
-    "import os\n",
-    "\n",
-    "from types import SimpleNamespace\n",
-    "import scipy\n",
-    "\n",
-    "import isofit\n",
-    "from isofit.utils.apply_oe import apply_oe \n",
-    "from isofit.utils.surface_model import surface_model\n",
-    "\n",
-    "# Enable the ISOFIT logger\n",
-    "logging.getLogger().setLevel(logging.INFO)\n",
-    "\n",
-    "# Find where we're running the tutorial from\n",
-    "home = os.path.abspath(os.getcwd())\n",
-    "\n",
-    "# Path to the input NEON data\n",
-    "indata = os.path.join(home, 'data') \n",
-    "subset_dir = os.path.join(indata, 'subsets')\n",
-    "print(f'Data subset directory at: {subset_dir}')\n",
-    "\n",
-    "# Path to write isofit output\n",
-    "output = os.path.join(home,'outputs')\n",
-    "if os.path.isdir(output) is False:\n",
-    "    os.mkdir(output)\n",
-    "\n",
-    "if os.path.isdir(subset_dir) is False:\n",
-    "    os.mkdir(subset_dir)\n",
-    "\n",
-    "surface_model_path = os.path.join(isofit.root, 'examples/20171108_Pasadena/configs/ang20171108t184227_surface.json')\n",
-    "print(f'Surface model at: {surface_model_path}')\n",
-    "neon_id = '173647'\n",
-    "\n",
-    "\n",
-    "# Optionally set some environment variables as needed\n",
-    "#os.environ['EMULATOR_PATH'] = '/Users/brodrick/isofit_support/sRTMnet_v120.h5'\n",
-    "#os.environ['SIXS_DIR'] = '/Users/brodrick/6s/'\n",
-    "\n",
-    "print(f'sRTMnet emulator path (required): {os.environ[\"EMULATOR_PATH\"]}')\n",
-    "print(f'6s path (required): {os.environ[\"SIXS_DIR\"]}')\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "%matplotlib inline"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "893fd5ac",
-   "metadata": {},
-   "source": [
-    "## Setup\n",
-    "\n",
-    "ISOFIT needs at minimum three pieces as input:\n",
-    "\n",
-    "    1. Radiance measurements (rdn)\n",
-    "    2. Observation values    (obs)\n",
-    "    3. Location information  (loc)\n",
-    "\n",
-    "This sample dataset from NEON has radiance and observation data, but no location values (more recent NEON datasets include the location file).  However, we can 'fake' the location file with sufficient accuracy for ISOFIT to run successfully.  Note that there are data available for two dates:\n",
-    "\n",
-    "```\n",
-    "Radiance\n",
-    "├── 173647\n",
-    "│   ├── NIS01_20210403_173647_obs_ort\n",
-    "│   ├── NIS01_20210403_173647_obs_ort.hdr\n",
-    "│   ├── NIS01_20210403_173647_rdn_ort\n",
-    "│   └── NIS01_20210403_173647_rdn_ort.hdr\n",
-    "└── 174150\n",
-    "    ├── NIS01_20210403_174150_obs_ort\n",
-    "    ├── NIS01_20210403_174150_obs_ort.hdr\n",
-    "    ├── NIS01_20210403_174150_rdn_ort\n",
-    "    └── NIS01_20210403_174150_rdn_ort.hdr\n",
-    "```\n",
-    "\n",
-    "These files have corresponding in situ data as well, and below we've encoded the locations of each, which we can use to help subset data files.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "be6e2d53",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "\n",
-    "# Extract the image locations of each point of interest (POI)\n",
-    "# These are defined in the NEON report as pixel locations, so we round here to convert to indices\n",
-    "report = {}\n",
-    "report['173647'] = {           # Upp L Y  | Low R Y  | Upp L X | Low R X\n",
-    "    'WhiteTarp': np.round([2224.9626, 2230.9771, 316.0078, 324.9385,]).astype(int),\n",
-    "    'BlackTarp': np.round([2224.9626, 2231.0032, 328.0086, 333.9731,]).astype(int),\n",
-    "    'Veg'      : np.round([2245.0381, 2258.8103, 343.9006, 346.9423,]).astype(int),\n",
-    "    'RoadEW'   : np.round([2214.9905, 2216.9978, 348.9902, 373.0080,]).astype(int),\n",
-    "    'RoadNS'   : np.round([2205.9580, 2225.9612, 357.9536, 359.9608,]).astype(int)\n",
-    "}\n",
-    "report['174150'] = {           # Upp L Y | Low R Y | Upp L X  | Low R X\n",
-    "    'WhiteTarp': np.round([653.9626, 659.9771, 3143.0078, 3151.9385]).astype(int),\n",
-    "    'BlackTarp': np.round([653.9626, 660.0032, 3155.0086, 3160.9731]).astype(int),\n",
-    "    'Veg'      : np.round([674.0381, 687.8103, 3170.9006, 3173.9423]).astype(int),\n",
-    "    'RoadEW'   : np.round([643.9905, 645.9978, 3175.9902, 3200.0080]).astype(int),\n",
-    "    'RoadNS'   : np.round([634.9580, 654.9612, 3184.9536, 3186.9608]).astype(int)\n",
-    "}\n",
-    "# Converts numpy array to comma-separated string for ISOFIT\n",
-    "toString = lambda array: ', '.join(str(v) for v in array)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "e3f01d1a",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# Which NEON date to process - change this to process a different date\n",
-    "neon_id = list(report.keys())[0]\n",
-    "\n",
-    "# Select the locations from the neon id -- roi == Regions of Interest\n",
-    "roi = report[neon_id]\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1b0faef2",
-   "metadata": {},
-   "source": [
-    "## Loc file generation\n",
-    "\n",
-    "NEON doesn't distribute (?) a loc file, so let's fake one for now.  We'll do this for the full file and for the subset."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "98252646",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Key 'band names' not found in the metadata, skipping\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Key 'band names' not found in the metadata, skipping\n"
-     ]
-    }
-   ],
-   "source": [
-    "from isotuts.utils import fakeLOC\n",
-    "\n",
-    "fakeLOC(\n",
-    "    rdn = os.path.join(indata,f'NIS01_20210403_{neon_id}_rdn_ort.hdr'),\n",
-    "    lon = -105.237000,\n",
-    "    lat = 40.125000,\n",
-    "    elv = 1689.0\n",
-    ")\n",
-    "\n",
-    "\n",
-    "fakeLOC(\n",
-    "    rdn = os.path.join(subset_dir,f'NIS01_20210403_{neon_id}_rdn_ort.hdr'),\n",
-    "    lon = -105.237000,\n",
-    "    lat = 40.125000,\n",
-    "    elv = 1689.0\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8a2cde13",
-   "metadata": {},
-   "source": [
-    "# Apply OE\n",
-    "\n",
-    "The next part walks through running the ISOFIT utility script `isofit/utils/apply_oe.py`. This is the first step of executing ISOFIT and will generate a default configuration."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "id": "7357a326",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0 ['../../../data/reflectance/surface_model_ucsb']\n",
-      "1 ['../../../data/reflectance/surface_model_ucsb']\n",
-      "2 ['../../../data/reflectance/surface_model_ucsb']\n",
-      "3 ['../../../data/reflectance/surface_model_ucsb']\n",
-      "4 ['../../../data/reflectance/surface_model_ucsb']\n",
-      "5 ['../../../data/reflectance/surface_model_ucsb']\n",
-      "6 ['../../../data/reflectance/surface_model_ucsb']\n",
-      "7 ['../../../data/reflectance/surface_model_ucsb']\n"
-     ]
-    }
-   ],
-   "source": [
-    "output_surface_file = os.path.join(output, 'surface.mat')\n",
-    "surface_model(**{\n",
-    "    'config_path': surface_model_path,\n",
-    "    'output_path': output_surface_file,\n",
-    "    'wavelength_path': os.path.join(subset_dir,f'NIS01_20210403_{neon_id}_rdn_ort.hdr')\n",
-    "}\n",
-    "\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "id": "81330d85-2453-4065-bfa7-f6a09374709a",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2024-07-22 09:56:47,340\tINFO worker.py:1724 -- Started a local Ray instance.\n",
-      "INFO:2024-07-22,09:56:47 || apply_oe.py:apply_oe() | namespace(input_radiance='/Users/brodrick/repos/isofit-tutorials/isotuts/NEON/data/subsets/NIS01_20210403_173647_rdn_ort', input_loc='/Users/brodrick/repos/isofit-tutorials/isotuts/NEON/data/subsets/NIS01_20210403_173647_loc_ort', input_obs='/Users/brodrick/repos/isofit-tutorials/isotuts/NEON/data/subsets/NIS01_20210403_173647_rdn_obs_ort', working_directory='/Users/brodrick/repos/isofit-tutorials/isotuts/NEON/outputs/NIS01_20210403_173647', sensor='neon', surface_path='/Users/brodrick/repos/isofit-tutorials/isotuts/NEON/outputs/surface.mat', emulator_base='/Users/brodrick/isofit_support/sRTMnet_v120.h5', modtran_path=None, n_cores=4, copy_input_files=False, wavelength_path=None, surface_category='multicomponent_surface', aerosol_climatology_path=None, atmosphere_type='ATM_MIDLAT_SUMMER', rdn_factors_path=None, channelized_uncertainty_path=None, model_discrepancy_path=None, lut_config_file=None, multiple_restarts=False, logging_level='INFO', log_file=None, num_cpus=1, memory_gb=-1, presolve=True, empirical_line=False, analytical_line=True, ray_temp_dir='/tmp/ray', segmentation_size=10, num_neighbors=[5], atm_sigma=[0.5, 0.5], pressure_elevation=False, prebuilt_lut=None)\n",
-      "INFO:2024-07-22,09:56:47 || apply_oe.py:apply_oe() | Checking input data files...\n",
-      "INFO:2024-07-22,09:56:47 || apply_oe.py:apply_oe() | ...Data file checks complete\n",
-      "INFO:2024-07-22,09:56:47 || apply_oe.py:apply_oe() | Setting up files and directories....\n",
-      "INFO:2024-07-22,09:56:47 || template_construction.py:__init__() | Flightline ID: NIS01_20210403_173647\n",
-      "INFO:2024-07-22,09:56:47 || template_construction.py:__init__() | no noise path found, proceeding without\n",
-      "INFO:2024-07-22,09:56:47 || apply_oe.py:apply_oe() | ...file/directory setup complete\n",
-      "INFO:2024-07-22,09:56:47 || apply_oe.py:apply_oe() | No wavelength file provided. Obtaining wavelength grid from ENVI header of radiance cube.\n",
-      "INFO:2024-07-22,09:56:47 || apply_oe.py:apply_oe() | Wavelength units of nm inferred...converting to microns\n",
-      "WARNING:2024-07-22,09:56:47 || template_construction.py:check_surface_model() | Center wavelengths provided in surface model file do not match wavelengths in radiance cube. Please consider rebuilding your surface model for optimal performance.\n",
-      "INFO:2024-07-22,09:56:47 || apply_oe.py:apply_oe() | Observation means:\n",
-      "INFO:2024-07-22,09:56:47 || apply_oe.py:apply_oe() | Path (km): 1.0036078691482544\n",
-      "INFO:2024-07-22,09:56:47 || apply_oe.py:apply_oe() | To-sensor azimuth (deg): 153.4481201171875\n",
-      "INFO:2024-07-22,09:56:47 || apply_oe.py:apply_oe() | To-sensor zenith (deg): 178.3806858062744\n",
-      "INFO:2024-07-22,09:56:47 || apply_oe.py:apply_oe() | To-sun azimuth (deg): 39.8218994140625\n",
-      "INFO:2024-07-22,09:56:47 || apply_oe.py:apply_oe() | To-sun zenith (deg): 39.8218994140625\n",
-      "INFO:2024-07-22,09:56:47 || apply_oe.py:apply_oe() | Relative to-sun azimuth (deg): 31.813383102416992\n",
-      "INFO:2024-07-22,09:56:47 || apply_oe.py:apply_oe() | Altitude (km): 2.692207074296544\n",
-      "INFO:2024-07-22,09:56:47 || apply_oe.py:apply_oe() | Existing h2o-presolve solutions found, using those.\n",
-      "INFO:2024-07-22,09:56:47 || apply_oe.py:apply_oe() | Full (non-aerosol) LUTs:\n",
-      "INFO:2024-07-22,09:56:47 || apply_oe.py:apply_oe() | Elevation: None\n",
-      "INFO:2024-07-22,09:56:47 || apply_oe.py:apply_oe() | To-sensor zenith: [177.0325 179.0392]\n",
-      "INFO:2024-07-22,09:56:47 || apply_oe.py:apply_oe() | To-sun zenith: None\n",
-      "INFO:2024-07-22,09:56:47 || apply_oe.py:apply_oe() | Relative to-sun azimuth: [3.80000e-03 4.12002e+01 8.23965e+01]\n",
-      "INFO:2024-07-22,09:56:47 || apply_oe.py:apply_oe() | H2O Vapor: [0.6083 0.6485]\n",
-      "INFO:2024-07-22,09:56:47 || apply_oe.py:apply_oe() | /Users/brodrick/repos/isofit-tutorials/isotuts/NEON/outputs/NIS01_20210403_173647/output/NIS01_20210403_173647_subs_state\n",
-      "INFO:2024-07-22,09:56:47 || apply_oe.py:apply_oe() | Analytical line inference\n",
-      "INFO:2024-07-22,09:56:47 || configs.py:create_new_config() | Loading config file: /Users/brodrick/repos/isofit-tutorials/isotuts/NEON/outputs/NIS01_20210403_173647/config/NIS01_20210403_173647_isofit.json\n",
-      "INFO:2024-07-22,09:56:47 || radiative_transfer_engine.py:__init__() | Loading from wavelength_file: /Users/brodrick/repos/isofit-tutorials/isotuts/NEON/outputs/NIS01_20210403_173647/data/wavelengths.txt\n",
-      "INFO:2024-07-22,09:56:47 || radiative_transfer_engine.py:__init__() | Prebuilt LUT provided\n",
-      "WARNING:2024-07-22,09:56:47 || luts.py:load() | Detected NaNs in the following LUT variable and may cause issues: thermal_upwelling\n",
-      "WARNING:2024-07-22,09:56:47 || luts.py:load() | Detected NaNs in the following LUT variable and may cause issues: thermal_downwelling\n",
-      "INFO:2024-07-22,09:56:47 || radiative_transfer_engine.py:__init__() | Resampling LUT to instrument spectral response.\n",
-      "2024-07-22 09:56:52,015\tINFO worker.py:1558 -- Calling ray.init() again after it has already been called.\n",
-      "INFO:2024-07-22,09:56:52 || atm_interpolation.py:atm_interpolation() | Beginning atmospheric interpolation 4 cores\n",
-      "INFO:2024-07-22,09:56:53 || atm_interpolation.py:atm_interpolation() | Parallel atmospheric interpolations complete.  1.8515851497650146 s total, 2279.6683158404508 spectra/s, 569.9170789601127 spectra/s/core\n",
-      "2024-07-22 09:56:53,938\tINFO worker.py:1558 -- Calling ray.init() again after it has already been called.\n",
-      "\u001b[36m(Worker pid=25201)\u001b[0m INFO:2024-07-22,09:56:58 ||| Analytical line writing line 1\n",
-      "\u001b[36m(Worker pid=25201)\u001b[0m INFO:2024-07-22,09:57:05 ||| Analytical line writing line 9\u001b[32m [repeated 8x across cluster]\u001b[0m\n",
-      "\u001b[36m(Worker pid=25201)\u001b[0m INFO:2024-07-22,09:57:11 ||| Analytical line writing line 17\u001b[32m [repeated 8x across cluster]\u001b[0m\n",
-      "\u001b[36m(Worker pid=25201)\u001b[0m INFO:2024-07-22,09:57:18 ||| Analytical line writing line 27\u001b[32m [repeated 8x across cluster]\u001b[0m\n",
-      "\u001b[36m(Worker pid=25201)\u001b[0m INFO:2024-07-22,09:57:25 ||| Analytical line writing line 33\u001b[32m [repeated 8x across cluster]\u001b[0m\n",
-      "\u001b[36m(Worker pid=25201)\u001b[0m INFO:2024-07-22,09:57:31 ||| Analytical line writing line 43\u001b[32m [repeated 8x across cluster]\u001b[0m\n",
-      "\u001b[36m(Worker pid=25201)\u001b[0m INFO:2024-07-22,09:57:38 ||| Analytical line writing line 50\u001b[32m [repeated 8x across cluster]\u001b[0m\n",
-      "\u001b[36m(Worker pid=25201)\u001b[0m INFO:2024-07-22,09:57:45 ||| Analytical line writing line 59\u001b[32m [repeated 8x across cluster]\u001b[0m\n",
-      "INFO:2024-07-22,09:57:51 || analytical_line.py:analytical_line() | Analytical line inversions complete.  57.43s total, 73.4973 spectra/s, 18.3743 spectra/s/core\n",
-      "INFO:2024-07-22,09:57:51 || apply_oe.py:apply_oe() | Done.\n",
-      "\u001b[36m(Worker pid=25202)\u001b[0m INFO:2024-07-22,09:57:48 ||| Analytical line writing line 61\u001b[32m [repeated 5x across cluster]\u001b[0m\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Add a ray shutdown, just in case this is being re-called\n",
-    "import ray\n",
-    "ray.shutdown()\n",
-    "\n",
-    "args = SimpleNamespace(**{\n",
-    "        'input_radiance': os.path.join(subset_dir,f'NIS01_20210403_{neon_id}_rdn_ort'), # Radiance\n",
-    "        'input_loc': os.path.join(subset_dir,f'NIS01_20210403_{neon_id}_loc_ort'), # Location\n",
-    "        'input_obs': os.path.join(subset_dir,f'NIS01_20210403_{neon_id}_rdn_obs_ort'), # Observations\n",
-    "        'working_directory': os.path.join(output, f'NIS01_20210403_{neon_id}'), # Output directory\n",
-    "        'sensor': 'neon', \n",
-    "\n",
-    "        \"surface_path\": output_surface_file, # Surface priors - often changes\n",
-    "\n",
-    "        'emulator_base': os.environ['EMULATOR_PATH'],\n",
-    "        \"modtran_path\": None,\n",
-    "        'n_cores': 4,\n",
-    "        \"copy_input_files\": False,\n",
-    "        \"wavelength_path\": None,\n",
-    "        \"surface_category\": \"multicomponent_surface\",\n",
-    "        \"aerosol_climatology_path\": None, # MODTRAN\n",
-    "        \"atmosphere_type\": \"ATM_MIDLAT_SUMMER\", # MODTRAN\n",
-    "        \"rdn_factors_path\": None, # RCC update used 'on the fly'\n",
-    "        \"channelized_uncertainty_path\": None, # Channelized uncertainty - if you have an instrument model\n",
-    "        \"model_discrepancy_path\": None, # Model discrepancy term - handle things like unknown radiative transfer model effects\n",
-    "\n",
-    "        \"lut_config_file\": None,\n",
-    "        \"multiple_restarts\": False, # Useful if the AOD conditions are really challenging\n",
-    "        \"logging_level\": \"INFO\",\n",
-    "        \"log_file\": None,\n",
-    "        \"num_cpus\": 1,\n",
-    "        \"memory_gb\": -1,\n",
-    "        \"presolve\": True, # Attempts to solve for the right wv range\n",
-    "\n",
-    "        \"empirical_line\": False, # wavelength-specific local linear interpolation between radiance and reflectance\n",
-    "        \"analytical_line\": True, # mathematical representation of OE given that the atmsophere is known\n",
-    "\n",
-    "        \"ray_temp_dir\": \"/tmp/ray\",\n",
-    "        \"segmentation_size\": 10,\n",
-    "        \"num_neighbors\": [5],\n",
-    "        \"atm_sigma\": [0.5, 0.5],\n",
-    "        \"pressure_elevation\": False,\n",
-    "        \"prebuilt_lut\": None\n",
-    "    })\n",
-    "\n",
-    "apply_oe(args)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "id": "61cea885",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from spectral.io import envi\n",
-    "import matplotlib.pyplot as plt\n",
-    "for _key, key in enumerate(report.keys()):\n",
-    "  if _key < 1:\n",
-    "\n",
-    "    rfl_ds = envi.open(os.path.join(output, f'NIS01_20210403_{key}','output',f'NIS01_20210403_{key}_rfl.hdr'))\n",
-    "    rfl_rgb = rfl_ds.open_memmap(interleave='bip')[:,:,np.array([60,40,30])].copy()\n",
-    "    wl = np.array([float(x) for x in rfl_ds.metadata['wavelength']])\n",
-    "\n",
-    "    miny = np.min([np.min([i[0],i[1]]) for k,i in report[key].items()])-5\n",
-    "    maxy = np.max([np.max([i[0],i[1]]) for k,i in report[key].items()])+5\n",
-    "    minx = np.min([np.min([i[2],i[3]]) for k,i in report[key].items()])-5\n",
-    "    maxx = np.max([np.max([i[2],i[3]]) for k,i in report[key].items()])+5\n",
-    "\n",
-    "    plt.figure()\n",
-    "    plt.imshow(rfl_rgb / np.max(rfl_rgb,axis=(0,1)))\n",
-    "    plt.title(f'NIS01_20210403_{key}')\n",
-    "    for k,i in report[key].items():\n",
-    "        plt.plot([i[2]-minx,i[3]-minx,i[3]-minx,i[2]-minx,i[2]-minx],[i[0]-miny,i[0]-miny,i[1]-miny,i[1]-miny,i[0]-miny],label=k)\n",
-    "\n",
-    "    for k,i in report[key].items():\n",
-    "        plt.figure()\n",
-    "        in_situ = np.genfromtxt(f'data/FieldSpectrometer/{k}01/Data/{k}01_Refl.dat', skip_header=3)\n",
-    "        plt.plot(in_situ[:,0], in_situ[:,1], label=f'In Situ {k}',c='red',ls='-')\n",
-    "        mean_rfl = np.mean(rfl_ds.open_memmap(interleave='bip')[i[0]-miny:i[1]-miny,i[2]-minx:i[3]-minx,:],axis=(0,1))\n",
-    "        plt.plot(wl, mean_rfl, label=f'Ret. {k}', c='black')\n",
-    "        plt.legend()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "411124d3",
-   "metadata": {},
-   "source": [
-    "We can also plot out the mapped reflectance (as above), but also the interpolated atmospheric conditions.  The windows size is small enough here (and the atmospheric parameters are chosen in such a way) that the map is going to be pretty static...but we can still see it."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "id": "7b27704a",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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7776r8PBwDR061FK/dOlSjRw5UhdffLEuvvhi3Xzzzfrggw/87hcBAwAANgI92rq1+GPNmjXKzs7WnDlzVFlZqZEjR2rcuHGqqqo663P19fV64IEHNGbMGK9rmzZt0oQJE/TOO+/ovffeU2JiojIyMnTgwAG/+kbAAACAjdYpiUCLPwoLCzV58mRNmTJFgwYNUlFRkRISErR48eKzPpeZmamJEycqLS3N69rLL7+sqVOnaujQoRo4cKCWLl0qt9utt956y6++ETAAANDOGhoaLKWpqcnrnubmZlVUVCgjI8NSn5GRofLy8jO2vWLFCn322WeaN29em/py/Phx/f3vf1f37t39GgMBAwAANtzqFJQiSQkJCYqNjfWUgoICr887dOiQWlpa5HK5LPUul0u1tbW2fdy7d69ycnL08ssvKzy8be8x5OTk6NJLL9XNN9/s198Hb0kAAGCjxTjUEuBbDq3PV1dXKyYmxlPvdDrP+IzDYf1MY4xXnSS1tLRo4sSJevzxx3XFFVe0qT8LFy7Uq6++qk2bNikyMrJNz7QiYAAAoJ3FxMRYAgY7PXv2VFhYmFc2oa6uzivrIEmNjY3asWOHKisrNX36dEmS2+2WMUbh4eEqKSnRTTfd5Ln/mWeeUX5+vv74xz9qyJAhfo+BgAEAABvnex+GiIgIpaSkqLS0VHfddZenvrS0VHfccYfX/TExMdq1a5elrri4WG+//bbWrl2rpKQkT/3TTz+tJ554Qhs3blRqauo5jISAAQAAWyYIp1UaP5+fNWuWJk2apNTUVKWlpWnJkiWqqqpSVlaWJCk3N1cHDhzQqlWr1KlTJyUnJ1ue79WrlyIjIy31Cxcu1GOPPaZXXnlFl112mSeD0a1bN3Xr1q3NfSNgAADARoscagnw8Ch/nx8/frwOHz6s+fPnq6amRsnJyVq/fr369u0rSaqpqfG5J8PpiouL1dzcrB/+8IeW+nnz5ikvL6/N7TiMMcavT25nDQ0Nio2NVeKCJ9TJzwUZAIDQ5z55UlU5c1VfX+9zXcC5aP0emrz5R4ro1jmgtpqP/l3LRv1nu/X1fCLDAACADbcJ/CwId4f6lTwwBAwAANhwB2ENQ6DPdyShMxIAANBuyDAAAGDDLYfcAS56DPT5joSAAQAAG8Hc6TEUMCUBAAB8IsMAAIANFj1aETAAAGDDrSBsDR1CaxhCJ/QBAADthgwDAAA2TBDekjAhlGEgYAAAwMb5Pq2yoyNgAADABoserUJnJAAAoN2QYQAAwAZTElYEDAAA2GBraCumJAAAgE9kGAAAsMGUhBUBAwAANggYrJiSAAAAPpFhAADABhkGKwIGAABsEDBYMSUBAAB8IsMAAIANo8D3UTDB6UqHQMAAAIANpiSsCBgAALBBwGDFGgYAAOATGQYAAGyQYbAiYAAAwAYBgxVTEgAAwCcyDAAA2DDGIRNghiDQ5zsSAgYAAGy45Qh4H4ZAn+9I/JqSKCgo0HXXXafo6Gj16tVLd955pz7++GPLPcYY5eXlKT4+Xl26dNHo0aO1e/fuoHYaAACcX34FDJs3b9a0adO0bds2lZaW6uuvv1ZGRoaOHTvmuWfhwoUqLCzUokWLtH37dsXFxWns2LFqbGwMeucBAGgvrYseAy2hwq8piTfffNPy84oVK9SrVy9VVFToe9/7nowxKioq0pw5c3T33XdLklauXCmXy6VXXnlFmZmZwes5AADtiDUMVgG9JVFfXy9J6t69uyRp//79qq2tVUZGhucep9OpUaNGqby83LaNpqYmNTQ0WAoAAOhYzjlgMMZo1qxZuvHGG5WcnCxJqq2tlSS5XC7LvS6Xy3PtdAUFBYqNjfWUhISEc+0SAABBc6GmJIqLi5WUlKTIyEilpKSorKysTc+9++67Cg8P19ChQy31u3fv1j//8z/rsssuk8PhUFFRkd99kgIIGKZPn64PP/xQr776qtc1h8P6F2SM8aprlZubq/r6ek+prq4+1y4BABA0rVMSgRZ/rFmzRtnZ2ZozZ44qKys1cuRIjRs3TlVVVWd9rr6+Xg888IDGjBnjde348eO6/PLLtWDBAsXFxfnVn287p4BhxowZeuONN/TOO++oT58+nvrWjpyeTairq/PKOrRyOp2KiYmxFAAALjQThOyCvwFDYWGhJk+erClTpmjQoEEqKipSQkKCFi9efNbnMjMzNXHiRKWlpXldu+666/T000/r3nvvldPp9Ks/3+ZXwGCM0fTp0/Xaa6/p7bffVlJSkuV6UlKS4uLiVFpa6qlrbm7W5s2blZ6efs6dBADgH9npa/Wampq87mlublZFRYVlHaAkZWRknHEdoHTqBYTPPvtM8+bNC3q/v82vgGHatGl66aWX9Morryg6Olq1tbWqra3ViRMnJJ2aisjOzlZ+fr7WrVunP//5z3rooYfUtWtXTZw4sV0GAABAezCSjAmwfNNWQkKCZb1eQUGB1+cdOnRILS0tfq0D3Lt3r3JycvTyyy8rPLx992L0q/XWlMjo0aMt9StWrNBDDz0kSZo9e7ZOnDihqVOn6siRIxo+fLhKSkoUHR0dlA4DAHA+uOWQI0g7PVZXV1um3M82NdDWdYAtLS2aOHGiHn/8cV1xxRUB9bMt/AoYjDE+73E4HMrLy1NeXt659gkAgJDSljV6PXv2VFhYWJvXATY2NmrHjh2qrKzU9OnTJUlut1vGGIWHh6ukpEQ33XRT0MbAWRIAANg43xs3RUREKCUlRaWlpbrrrrs89aWlpbrjjju87o+JidGuXbssdcXFxXr77be1du1ar3WGgSJgAADAhts45AgwYPB3H4ZZs2Zp0qRJSk1NVVpampYsWaKqqiplZWVJOrUVwYEDB7Rq1Sp16tTJsw9Sq169eikyMtJS39zcrD179nj+fODAAe3cuVPdunVT//7929w3AgYAADqI8ePH6/Dhw5o/f75qamqUnJys9evXq2/fvpKkmpoan3synO7LL7/UsGHDPD8/88wzeuaZZzRq1Cht2rSpze04TFsWJpxHDQ0Nio2NVeKCJ9QpMvJCdwcA0MG4T55UVc5c1dfXt8vePa3fQ1ev+VeFdT33fQskqeV4k3aPf7rd+no+kWEAAMAGh09ZBXT4FAAA+G4gwwAAgA0yDFYEDAAA2LgQb0l0ZAQMAADYaN3eOdA2QgVrGAAAgE9kGAAAsHEqwxDoGoYgdaYDIGAAAMAGix6tmJIAAAA+kWEAAMCG+aYE2kaoIGAAAMAGUxJWTEkAAACfyDAAAGCHOQkLAgYAAOwEYUpCITQlQcAAAIANdnq0Yg0DAADwiQwDAAA2eEvCioABAAA7xhH4GoQQChiYkgAAAD6RYQAAwAaLHq0IGAAAsMM+DBZMSQAAAJ/IMAAAYIO3JKwIGAAAOJMQmlIIFFMSAADAJzIMAADYYErCioABAAA7vCVhQcAAAIAtxzcl0DZCA2sYAACAT2QYAACww5SEBQEDAAB2CBgsmJIAAAA+kWEAAMAOx1tbEDAAAGCD0yqtmJIAAAA+kWEAAMAOix4tyDAAAGCndQ1DoMVPxcXFSkpKUmRkpFJSUlRWVtam5959912Fh4dr6NChXtf+8Ic/6KqrrpLT6dRVV12ldevW+d0vAgYAADqINWvWKDs7W3PmzFFlZaVGjhypcePGqaqq6qzP1dfX64EHHtCYMWO8rr333nsaP368Jk2apP/93//VpEmT9KMf/Ujvv/++X30jYAAAwIbDBKf4o7CwUJMnT9aUKVM0aNAgFRUVKSEhQYsXLz7rc5mZmZo4caLS0tK8rhUVFWns2LHKzc3VwIEDlZubqzFjxqioqMivvhEwAABgxwSpSGpoaLCUpqYmr49rbm5WRUWFMjIyLPUZGRkqLy8/YzdXrFihzz77TPPmzbO9/t5773m1ecstt5y1TTsEDAAA2AniGoaEhATFxsZ6SkFBgdfHHTp0SC0tLXK5XJZ6l8ul2tpa2y7u3btXOTk5evnllxUebv8eQ21trV9tnglvSQAA0M6qq6sVExPj+dnpdJ7xXofDulDSGONVJ0ktLS2aOHGiHn/8cV1xxRVn/fy2tnk2BAwAANgJ4muVMTExloDBTs+ePRUWFub1m39dXZ1XhkCSGhsbtWPHDlVWVmr69OmSJLfbLWOMwsPDVVJSoptuuklxcXFtbvNsmJIAAMBOENcwtEVERIRSUlJUWlpqqS8tLVV6errX/TExMdq1a5d27tzpKVlZWbryyiu1c+dODR8+XJKUlpbm1WZJSYltm2fjV8CwePFiDRkyxBMppaWlacOGDZ7rxhjl5eUpPj5eXbp00ejRo7V7926/OgQAwHfVrFmz9Nvf/lbLly/XRx99pEcffVRVVVXKysqSJOXm5uqBBx6QJHXq1EnJycmW0qtXL0VGRio5OVlRUVGSpJkzZ6qkpERPPfWU/u///k9PPfWU/vjHPyo7O9uvvvkVMPTp00cLFizQjh07tGPHDt1000264447PEHBwoULVVhYqEWLFmn79u2Ki4vT2LFj1djY6FenAAC44M5zhkGSxo8fr6KiIs2fP19Dhw7Vli1btH79evXt21eSVFNT43NPhtOlp6dr9erVWrFihYYMGaLf/e53WrNmjScD0VYOYwI7GqN79+56+umn9ZOf/ETx8fHKzs7WL37xC0lSU1OTXC6XnnrqKWVmZrapvYaGBsXGxipxwRPqFBkZSNcAACHIffKkqnLmqr6+3ue6gHPR+j2U8PQT6tQlsO8h94mTqv7X9uvr+XTOaxhaWlq0evVqHTt2TGlpadq/f79qa2st73o6nU6NGjXqrO96NjU1eb2fCgAAOha/A4Zdu3apW7ducjqdysrK0rp163TVVVd5VmD6+65nQUGB5d3UhIQEf7sEAEDQXYidHjsyvwOG1tWX27Zt0yOPPKIHH3xQe/bs8Vz3913P3Nxc1dfXe0p1dbW/XQIAIPguwBqGjszvfRgiIiLUv39/SVJqaqq2b9+uX/3qV551C7W1terdu7fnfl/vejqdzrNuYAEAAC68gPdhMMaoqalJSUlJiouLs7zr2dzcrM2bN/v9ricAAOhY/Mow/PKXv9S4ceOUkJCgxsZGrV69Wps2bdKbb74ph8Oh7Oxs5efna8CAARowYIDy8/PVtWtXTZw4sb36DwBAu3Ao8DUI/m2+3LH5FTAcPHhQkyZNUk1NjWJjYzVkyBC9+eabGjt2rCRp9uzZOnHihKZOnaojR45o+PDhKikpUXR0dLt0HgCAdvOtw6MCaiNE+BUwLFu27KzXHQ6H8vLylJeXF0ifAABAB8PhUwAA2Ani4VOhgIABAAA7BAwWnFYJAAB8IsMAAICNYOzUGEo7PRIwAABghykJC6YkAACAT2QYAACwQ4bBgoABAAAbrGGwYkoCAAD4RIYBAAA7bA1tQcAAAIAd1jBYEDAAAGCDNQxWrGEAAAA+kWEAAMAOUxIWBAwAANgJwpREKAUMTEkAAACfyDAAAGCHKQkLAgYAAOwQMFgwJQEAAHwiwwAAgA32YbAiwwAAAHwiYAAAAD4xJQEAgB0WPVoQMAAAYIM1DFYEDAAAnEkIfeEHijUMAADAJzIMAADYYQ2DBRkGAABstK5hCLT4q7i4WElJSYqMjFRKSorKysrOeO/WrVs1YsQI9ejRQ126dNHAgQP13HPPWe75+9//rvnz56tfv36KjIzUNddcozfffNPvfpFhAACgg1izZo2ys7NVXFysESNG6IUXXtC4ceO0Z88eJSYmet0fFRWl6dOna8iQIYqKitLWrVuVmZmpqKgoPfzww5KkuXPn6qWXXtLSpUs1cOBAbdy4UXfddZfKy8s1bNiwNvfNYYzpUAmThoYGxcbGKnHBE+oUGXmhuwMA6GDcJ0+qKmeu6uvrFRMTE/T2W7+HBvxrvsKcgX0PtTSd1N6nf9nmvg4fPlzXXnutFi9e7KkbNGiQ7rzzThUUFLTpM++++25FRUXpxRdflCTFx8drzpw5mjZtmueeO++8U926ddNLL73U5rEwJQEAgI1gTkk0NDRYSlNTk9fnNTc3q6KiQhkZGZb6jIwMlZeXt6nPlZWVKi8v16hRozx1TU1NijztF/AuXbpo69atfv19EDAAANDOEhISFBsb6yl22YJDhw6ppaVFLpfLUu9yuVRbW3vW9vv06SOn06nU1FRNmzZNU6ZM8Vy75ZZbVFhYqL1798rtdqu0tFT/9V//pZqaGr/GwBoGAADsBPEtierqasuUhNPpPOMjDofD2oQxXnWnKysr09GjR7Vt2zbl5OSof//+mjBhgiTpV7/6lX76059q4MCBcjgc6tevn3784x9rxYoVfg2FgAEAADtBDBhiYmJ8rmHo2bOnwsLCvLIJdXV1XlmH0yUlJUmSBg8erIMHDyovL88TMFxyySV6/fXXdfLkSR0+fFjx8fHKycnxPNNWTEkAANABREREKCUlRaWlpZb60tJSpaent7kdY4ztGonIyEhdeuml+vrrr/WHP/xBd9xxh1/9I8MAAICNC3GWxKxZszRp0iSlpqYqLS1NS5YsUVVVlbKysiRJubm5OnDggFatWiVJev7555WYmKiBAwdKOrUvwzPPPKMZM2Z42nz//fd14MABDR06VAcOHFBeXp7cbrdmz57tV98IGAAAsHMBdnocP368Dh8+rPnz56umpkbJyclav369+vbtK0mqqalRVVWV5363263c3Fzt379f4eHh6tevnxYsWKDMzEzPPSdPntTcuXO1b98+devWTbfeeqtefPFFXXTRRX71jX0YAAD/UM7XPgxXzgzOPgwf/6rt+zB0ZKxhAAAAPjElAQCAjQuxhqEjI2AAAMAOp1VaMCUBAAB8IsMAAIANpiSsCBgAALDDlIQFUxIAAMAnMgwAANghw2BBwAAAgA3HNyXQNkJFQFMSBQUFcjgcys7O9tQZY5SXl6f4+Hh16dJFo0eP1u7duwPtJwAAuIDOOWDYvn27lixZoiFDhljqFy5cqMLCQi1atEjbt29XXFycxo4dq8bGxoA7CwDAeWOCVELEOQUMR48e1X333aelS5fq4osv9tQbY1RUVKQ5c+bo7rvvVnJyslauXKnjx4/rlVdeCVqnAQBob62vVQZaQsU5BQzTpk3TbbfdpptvvtlSv3//ftXW1iojI8NT53Q6NWrUKJWXl9u21dTUpIaGBksBAOCCI8Ng4feix9WrV+tPf/qTtm/f7nWttrZWkuRyuSz1LpdLn3/+uW17BQUFevzxx/3tBgAAOI/8yjBUV1dr5syZeumllxR5lqOnHQ7rulBjjFddq9zcXNXX13tKdXW1P10CAKD9kF3w8CvDUFFRobq6OqWkpHjqWlpatGXLFi1atEgff/yxpFOZht69e3vuqaur88o6tHI6nXI6nefSdwAA2g1bQ1v5lWEYM2aMdu3apZ07d3pKamqq7rvvPu3cuVOXX3654uLiVFpa6nmmublZmzdvVnp6etA7DwAAzg+/MgzR0dFKTk621EVFRalHjx6e+uzsbOXn52vAgAEaMGCA8vPz1bVrV02cODF4vQYAoL2x06NF0Hd6nD17tk6cOKGpU6fqyJEjGj58uEpKShQdHR3sjwIAoN0wJWEVcMCwadMmy88Oh0N5eXnKy8sLtGkAANBBcJYEAAB2mJKwIGAAAMAGUxJWAR0+BQAAvhvIMAAAYIcpCQsCBgAA7BAwWBAwAABggzUMVqxhAAAAPpFhAADADlMSFgQMAADYcBgjhwnsGz/Q5zsSpiQAAIBPZBgAALDDlIQFAQMAADZ4S8KKKQkAAOATGQYAAOwwJWFBwAAAgA2mJKyYkgAAAD4RMAAAYMcEqfipuLhYSUlJioyMVEpKisrKys5479atWzVixAj16NFDXbp00cCBA/Xcc8953VdUVKQrr7xSXbp0UUJCgh599FGdPHnSr34xJQEAgI0LMSWxZs0aZWdnq7i4WCNGjNALL7ygcePGac+ePUpMTPS6PyoqStOnT9eQIUMUFRWlrVu3KjMzU1FRUXr44YclSS+//LJycnK0fPlypaen65NPPtFDDz0kSbbBxZkQMAAAYOcCLHosLCzU5MmTNWXKFEmnMgMbN27U4sWLVVBQ4HX/sGHDNGzYMM/Pl112mV577TWVlZV5Aob33ntPI0aM0MSJEz33TJgwQR988IFffWNKAgCAdtbQ0GApTU1NXvc0NzeroqJCGRkZlvqMjAyVl5e36XMqKytVXl6uUaNGeepuvPFGVVRUeAKEffv2af369brtttv8GgMZBgAAziBYbzkkJCRYfp43b57y8vIsdYcOHVJLS4tcLpel3uVyqba29qzt9+nTR3/961/19ddfKy8vz5OhkKR7771Xf/3rX3XjjTfKGKOvv/5ajzzyiHJycvwaAwEDAAB2jDlVAm1DUnV1tWJiYjzVTqfzjI84HI7TmjBedacrKyvT0aNHtW3bNuXk5Kh///6aMGGCJGnTpk168sknVVxcrOHDh+vTTz/VzJkz1bt3bz322GNtHgoBAwAA7SwmJsYSMNjp2bOnwsLCvLIJdXV1XlmH0yUlJUmSBg8erIMHDyovL88TMDz22GOaNGmSJ+swePBgHTt2TA8//LDmzJmjTp3atjqBNQwAANhofUsi0NJWERERSklJUWlpqaW+tLRU6enpbW7HGGNZI3H8+HGvoCAsLEzGGBk/MihkGAAAsHMB3pKYNWuWJk2apNTUVKWlpWnJkiWqqqpSVlaWJCk3N1cHDhzQqlWrJEnPP/+8EhMTNXDgQEmn9mV45plnNGPGDE+bt99+uwoLCzVs2DDPlMRjjz2mf/qnf1JYWFib+0bAAABABzF+/HgdPnxY8+fPV01NjZKTk7V+/Xr17dtXklRTU6OqqirP/W63W7m5udq/f7/Cw8PVr18/LViwQJmZmZ575s6dK4fDoblz5+rAgQO65JJLdPvtt+vJJ5/0q28O408+4jxoaGhQbGysEhc8oU6RkRe6OwCADsZ98qSqcuaqvr7e57qAc9H6PXTdXU8ovHNg30Nf//2ktq9rv76eT2QYAACww2mVFix6BAAAPpFhAADABsdbWxEwAABgJ4gbN4UCAgYAAGyQYbBiDQMAAPCJDAMAAHZ4S8KCgAEAABtMSVgxJQEAAHwiwwAAgB3ekrAgYAAAwAZTElZMSQAAAJ/IMAAAYIe3JCwIGAAAsMGUhBVTEgAAwCcyDAAA2HGbUyXQNkIEAQMAAHZYw2BBwAAAgA2HgrCGISg96RhYwwAAAHwiwwAAgB12erQgYAAAwAavVVr5NSWRl5cnh8NhKXFxcZ7rxhjl5eUpPj5eXbp00ejRo7V79+6gdxoAAJxffq9huPrqq1VTU+Mpu3bt8lxbuHChCgsLtWjRIm3fvl1xcXEaO3asGhsbg9ppAADanQlSCRF+T0mEh4dbsgqtjDEqKirSnDlzdPfdd0uSVq5cKZfLpVdeeUWZmZmB9xYAgPPEYYwcAa5BCPT5jsTvDMPevXsVHx+vpKQk3Xvvvdq3b58kaf/+/aqtrVVGRobnXqfTqVGjRqm8vPyM7TU1NamhocFSAABAx+JXwDB8+HCtWrVKGzdu1NKlS1VbW6v09HQdPnxYtbW1kiSXy2V5xuVyea7ZKSgoUGxsrKckJCScwzAAAAgyd5BKiPBrSmLcuHGePw8ePFhpaWnq16+fVq5cqRtuuEGS5HBYt6kwxnjVfVtubq5mzZrl+bmhoYGgAQBwwTElYRXQxk1RUVEaPHiw9u7d61nXcHo2oa6uzivr8G1Op1MxMTGWAgAAOpaAAoampiZ99NFH6t27t5KSkhQXF6fS0lLP9ebmZm3evFnp6ekBdxQAgPOKtyQs/JqS+PnPf67bb79diYmJqqur0xNPPKGGhgY9+OCDcjgcys7OVn5+vgYMGKABAwYoPz9fXbt21cSJE9ur/wAAtA92erTwK2D44osvNGHCBB06dEiXXHKJbrjhBm3btk19+/aVJM2ePVsnTpzQ1KlTdeTIEQ0fPlwlJSWKjo5ul84DANBe2OnRyq+AYfXq1We97nA4lJeXp7y8vED6BAAAOhjOkgAAwA5TEhYEDAAA2HC4T5VA2wgVAb0lAQAAvhsIGAAAsNM6JRFo8VNxcbGSkpIUGRmplJQUlZWVnfHerVu3asSIEerRo4e6dOmigQMH6rnnnrPcM3r0aK+Tph0Oh2677Ta/+sWUBAAAdoKxj4Kfz69Zs0bZ2dkqLi7WiBEj9MILL2jcuHHas2ePEhMTve6PiorS9OnTNWTIEEVFRWnr1q3KzMxUVFSUHn74YUnSa6+9pubmZs8zhw8f1jXXXKN77rnHr76RYQAAoIMoLCzU5MmTNWXKFA0aNEhFRUVKSEjQ4sWLbe8fNmyYJkyYoKuvvlqXXXaZ7r//ft1yyy2WrET37t0VFxfnKaWlperatSsBAwAAwdB6lkSgRZLXqcxNTU1en9fc3KyKigrLqc+SlJGRcdZTn7+tsrJS5eXlGjVq1BnvWbZsme69915FRUX58bdBwAAAgL0grmFISEiwnMxcUFDg9XGHDh1SS0uL36c+S1KfPn3kdDqVmpqqadOmacqUKbb3ffDBB/rzn/98xutnwxoGAADaWXV1teVwRafTecZ7/T31WZLKysp09OhRbdu2TTk5Oerfv78mTJjgdd+yZcuUnJys66+/3s8REDAAAGDPSAp0H4VvFj225TTmnj17KiwszO9TnyUpKSlJkjR48GAdPHhQeXl5XgHD8ePHtXr1as2fP9/PQZzClAQAADaCuYahLSIiIpSSkmI59VmSSktL/Tr12Rhju0biP//zP9XU1KT777+/zW19GxkGAADsGAVha2j/bp81a5YmTZqk1NRUpaWlacmSJaqqqlJWVpYkKTc3VwcOHNCqVaskSc8//7wSExM1cOBASaf2ZXjmmWc0Y8YMr7aXLVumO++8Uz169DinoRAwAADQQYwfP16HDx/W/PnzVVNTo+TkZK1fv95zKnRNTY2qqqo897vdbuXm5mr//v0KDw9Xv379tGDBAmVmZlra/eSTT7R161aVlJScc98cxnSskzEaGhoUGxurxAVPqFNk5IXuDgCgg3GfPKmqnLmqr6/3uS7gXLR+D910zS8UHnbmxYlt8XVLk97+36fara/nExkGAADsuCWd/eWEtrURIlj0CAAAfCLDAACADX/fcjhTG6GCgAEAADvneNqkVxshgikJAADgExkGAADskGGwIGAAAMAOAYMFUxIAAMAnMgwAANhhHwYLAgYAAGzwWqUVAQMAAHZYw2DBGgYAAOATGQYAAOy4jeQIMEPgDp0MAwEDAAB2mJKwYEoCAAD4RIYBAABbQcgwKHQyDAQMAADYYUrCgikJAADgExkGAADsuI0CnlLgLQkAAEKccZ8qgbYRIpiSAAAAPpFhAADADoseLQgYAACwwxoGCwIGAADskGGwYA0DAADwiQwDAAB2jIKQYQhKTzoEAgYAAOwwJWHBlAQAAPCJDAMAAHbcbkkBbrzkDp2NmwgYAACww5SEBVMSAADAJzIMAADYIcNgQcAAAIAddnq0YEoCAAD45HfAcODAAd1///3q0aOHunbtqqFDh6qiosJz3RijvLw8xcfHq0uXLho9erR2794d1E4DANDejHEHpYQKvwKGI0eOaMSIEercubM2bNigPXv26Nlnn9VFF13kuWfhwoUqLCzUokWLtH37dsXFxWns2LFqbGwMdt8BAGg/xpyaUgikhNAaBr8ChqeeekoJCQlasWKFrr/+el122WUaM2aM+vXrJ+lUdqGoqEhz5szR3XffreTkZK1cuVLHjx/XK6+80i4DAACgXbQuegy0+Km4uFhJSUmKjIxUSkqKysrKznjv1q1bNWLECPXo0UNdunTRwIED9dxzz3nd99VXX2natGnq3bu3IiMjNWjQIK1fv96vfvkVMLzxxhtKTU3VPffco169emnYsGFaunSp5/r+/ftVW1urjIwMT53T6dSoUaNUXl5u22ZTU5MaGhosBQCA76I1a9YoOztbc+bMUWVlpUaOHKlx48apqqrK9v6oqChNnz5dW7Zs0UcffaS5c+dq7ty5WrJkieee5uZmjR07Vn/5y1+0du1affzxx1q6dKkuvfRSv/rmV8Cwb98+LV68WAMGDNDGjRuVlZWln/3sZ1q1apUkqba2VpLkcrksz7lcLs+10xUUFCg2NtZTEhIS/BoAAADtwu0OTvFDYWGhJk+erClTpmjQoEEqKipSQkKCFi9ebHv/sGHDNGHCBF199dW67LLLdP/99+uWW26xZCWWL1+uv/3tb3r99dc1YsQI9e3bVzfeeKOuueYav/rmV8Dgdrt17bXXKj8/X8OGDVNmZqZ++tOfeg3E4XBYfjbGeNW1ys3NVX19vadUV1f7NQAAANpFEKckTs+kNzU1eX1cc3OzKioqLFl6ScrIyDhjlv50lZWVKi8v16hRozx1b7zxhtLS0jRt2jS5XC4lJycrPz9fLS0tfv11+BUw9O7dW1dddZWlbtCgQZ5USVxcnCR5ZRPq6uq8sg6tnE6nYmJiLAUAgFCSkJBgyaYXFBR43XPo0CG1tLT4laVv1adPHzmdTqWmpmratGmaMmWK59q+ffu0du1atbS0aP369Zo7d66effZZPfnkk36Nwa+Nm0aMGKGPP/7YUvfJJ5+ob9++kqSkpCTFxcWptLRUw4YNk3QqYtq8ebOeeuopvzoGAMCFZNxuGUdgr0W2vlZZXV1t+YXY6XSe8Rl/svStysrKdPToUW3btk05OTnq37+/JkyYIOnU7ECvXr20ZMkShYWFKSUlRV9++aWefvpp/du//Vubx+JXwPDoo48qPT1d+fn5+tGPfqQPPvhAS5Ys8SyucDgcys7OVn5+vgYMGKABAwYoPz9fXbt21cSJE/35KAAALiwThJ0ev5mSaEsGvWfPngoLC/MrS98qKSlJkjR48GAdPHhQeXl5noChd+/e6ty5s8LCwjz3Dxo0SLW1tWpublZERESbhuLXlMR1112ndevW6dVXX1VycrL+/d//XUVFRbrvvvs898yePVvZ2dmaOnWqUlNTdeDAAZWUlCg6OtqfjwIA4DslIiJCKSkpKi0ttdSXlpYqPT29ze0YYyxrJEaMGKFPP/1U7m8twPzkk0/Uu3fvNgcL0jmcJfGDH/xAP/jBD8543eFwKC8vT3l5ef42DQBAx+E2kuP8Hj41a9YsTZo0SampqUpLS9OSJUtUVVWlrKwsSadeFDhw4IDn7cTnn39eiYmJGjhwoKRT+zI888wzmjFjhqfNRx55RL/5zW80c+ZMzZgxQ3v37lV+fr5+9rOf+dU3Dp8CAMCOMZIC3NrZz4Bh/PjxOnz4sObPn6+amholJydr/fr1nrWCNTU1lj0Z3G63cnNztX//foWHh6tfv35asGCBMjMzPfckJCSopKREjz76qIYMGaJLL71UM2fO1C9+8Qu/+uYwpmPtW9nQ0KDY2FglLnhCnSIjL3R3AAAdjPvkSVXlzFV9fX27vFnX+j10U8Q9Cnd0Dqitr83f9Xbz79utr+cTGQYAAGwYt5EJcEqig/1OHhACBgAA7Bi3Ap+SCJ3TKgkYAACwQYbByq/XKgEAwHdTh8swtEZj7pMnL3BPAAAdUev3Q3v/9v61aQp4SuFr/T1IvbnwOtxbEl988QUnVgIAfKqurlafPn2C3u7JkyeVlJTk8/yGtoqLi9P+/fsV+Q/+5l+HCxjcbre+/PJLRUdHq7GxUQkJCV57cIeihoYGxhpivivjlBhrKOrI4zTGqLGxUfHx8erUqX1m1k+ePKnm5uagtBUREfEPHyxIHXBKolOnTp6IsfWwje/SKZaMNfR8V8YpMdZQ1FHHGRsb267tR0ZGhsSXfDCx6BEAAPhEwAAAAHzq0AGD0+nUvHnzznpueKhgrKHnuzJOibGGou/KONF2HW7RIwAA6Hg6dIYBAAB0DAQMAADAJwIGAADgEwEDAADwiYABAAD41KEDhuLiYiUlJSkyMlIpKSkqKyu70F0K2JYtW3T77bcrPj5eDodDr7/+uuW6MUZ5eXmKj49Xly5dNHr0aO3evfvCdDYABQUFuu666xQdHa1evXrpzjvv1Mcff2y5J1TGunjxYg0ZMsSzI15aWpo2bNjguR4q4zxdQUGBHA6HsrOzPXWhMta8vDw5HA5LiYuL81wPlXG2OnDggO6//3716NFDXbt21dChQ1VRUeG5HmrjxbnpsAHDmjVrlJ2drTlz5qiyslIjR47UuHHjVFVVdaG7FpBjx47pmmuu0aJFi2yvL1y4UIWFhVq0aJG2b9+uuLg4jR07Vo2Njee5p4HZvHmzpk2bpm3btqm0tFRff/21MjIydOzYMc89oTLWPn36aMGCBdqxY4d27Nihm266SXfccYfnP6ihMs5v2759u5YsWaIhQ4ZY6kNprFdffbVqamo8ZdeuXZ5roTTOI0eOaMSIEercubM2bNigPXv26Nlnn9VFF13kuSeUxosAmA7q+uuvN1lZWZa6gQMHmpycnAvUo+CTZNatW+f52e12m7i4OLNgwQJP3cmTJ01sbKz5j//4jwvQw+Cpq6szkszmzZuNMaE9VmOMufjii81vf/vbkBxnY2OjGTBggCktLTWjRo0yM2fONMaE1r/pvHnzzDXXXGN7LZTGaYwxv/jFL8yNN954xuuhNl6cuw6ZYWhublZFRYUyMjIs9RkZGSovL79AvWp/+/fvV21trWXcTqdTo0aN+ocfd319vSSpe/fukkJ3rC0tLVq9erWOHTumtLS0kBzntGnTdNttt+nmm2+21IfaWPfu3av4+HglJSXp3nvv1b59+ySF3jjfeOMNpaam6p577lGvXr00bNgwLV261HM91MaLc9chA4ZDhw6ppaVFLpfLUu9yuYJ2PnlH1Dq2UBu3MUazZs3SjTfeqOTkZEmhN9Zdu3apW7ducjqdysrK0rp163TVVVeF3DhXr16tP/3pTyooKPC6FkpjHT58uFatWqWNGzdq6dKlqq2tVXp6ug4fPhxS45Skffv2afHixRowYIA2btyorKws/exnP9OqVaskhda/KwLT4Y63/rbW461bGWO86kJRqI17+vTp+vDDD7V161ava6Ey1iuvvFI7d+7UV199pT/84Q968MEHtXnzZs/1UBhndXW1Zs6cqZKSkrMe+xsKYx03bpznz4MHD1ZaWpr69eunlStX6oYbbpAUGuOUJLfbrdTUVOXn50uShg0bpt27d2vx4sV64IEHPPeFynhx7jpkhqFnz54KCwvzil7r6uq8otxQ0roKO5TGPWPGDL3xxht655131KdPH099qI01IiJC/fv3V2pqqgoKCnTNNdfoV7/6VUiNs6KiQnV1dUpJSVF4eLjCw8O1efNm/frXv1Z4eLhnPKEw1tNFRUVp8ODB2rt3b0j9m0pS7969ddVVV1nqBg0a5FlgHmrjxbnrkAFDRESEUlJSVFpaaqkvLS1Venr6BepV+0tKSlJcXJxl3M3Nzdq8efM/3LiNMZo+fbpee+01vf3220pKSrJcD6Wx2jHGqKmpKaTGOWbMGO3atUs7d+70lNTUVN13333auXOnLr/88pAZ6+mampr00UcfqXfv3iH1bypJI0aM8Hrl+ZNPPlHfvn0lhf7/V+GHC7Xa0pfVq1ebzp07m2XLlpk9e/aY7OxsExUVZf7yl79c6K4FpLGx0VRWVprKykojyRQWFprKykrz+eefG2OMWbBggYmNjTWvvfaa2bVrl5kwYYLp3bu3aWhouMA9988jjzxiYmNjzaZNm0xNTY2nHD9+3HNPqIw1NzfXbNmyxezfv998+OGH5pe//KXp1KmTKSkpMcaEzjjtfPstCWNCZ6z/8i//YjZt2mT27dtntm3bZn7wgx+Y6Ohoz39/QmWcxhjzwQcfmPDwcPPkk0+avXv3mpdfftl07drVvPTSS557Qmm8OHcdNmAwxpjnn3/e9O3b10RERJhrr73W80reP7J33nnHSPIqDz74oDHm1CtM8+bNM3FxccbpdJrvfe97ZteuXRe20+fAboySzIoVKzz3hMpYf/KTn3j+d3rJJZeYMWPGeIIFY0JnnHZODxhCZazjx483vXv3Np07dzbx8fHm7rvvNrt37/ZcD5Vxtvrv//5vk5ycbJxOpxk4cKBZsmSJ5XqojRfnxmGMMRcmtwEAAP5RdMg1DAAAoGMhYAAAAD4RMAAAAJ8IGAAAgE8EDAAAwCcCBgAA4BMBAwAA8ImAAQAA+ETAAAAAfCJgAAAAPhEwAAAAn/4fI2UtaC+w7RYAAAAASUVORK5CYII=",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "for _key, key in enumerate(report.keys()):\n",
-    "  if _key < 1:\n",
-    "\n",
-    "    rfl_ds = envi.open(os.path.join(output, f'NIS01_20210403_{key}','output',f'NIS01_20210403_{key}_rfl.hdr'))\n",
-    "    rfl_rgb = rfl_ds.open_memmap(interleave='bip')[:,:,np.array([60,40,30])].copy()\n",
-    "    wl = np.array([float(x) for x in rfl_ds.metadata['wavelength']])\n",
-    "\n",
-    "    miny = np.min([np.min([i[0],i[1]]) for k,i in report[key].items()])-5\n",
-    "    maxy = np.max([np.max([i[0],i[1]]) for k,i in report[key].items()])+5\n",
-    "    minx = np.min([np.min([i[2],i[3]]) for k,i in report[key].items()])-5\n",
-    "    maxx = np.max([np.max([i[2],i[3]]) for k,i in report[key].items()])+5\n",
-    "\n",
-    "    plt.figure()\n",
-    "    plt.imshow(rfl_rgb / np.max(rfl_rgb,axis=(0,1)))\n",
-    "    plt.title(f'NIS01_20210403_{key}')\n",
-    "    for k,i in report[key].items():\n",
-    "        plt.plot([i[2]-minx,i[3]-minx,i[3]-minx,i[2]-minx,i[2]-minx],[i[0]-miny,i[0]-miny,i[1]-miny,i[1]-miny,i[0]-miny],label=k)\n",
-    "\n",
-    "    plt.figure()\n",
-    "    atm_ds = envi.open(os.path.join(output, f'NIS01_20210403_{key}','output',f'NIS01_20210403_{key}_atm_interp.hdr'))\n",
-    "    atm = atm_ds.open_memmap(interleave='bip').copy()\n",
-    "    plt.imshow(atm[...,0])\n",
-    "    plt.title('AOD')\n",
-    "    plt.colorbar()\n",
-    "\n",
-    "    plt.figure()\n",
-    "    plt.imshow(atm[...,1])\n",
-    "    plt.title('Water Vapor')\n",
-    "    plt.colorbar()\n",
-    "\n",
-    "    plt.imshow"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "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": "3.10.13"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/isotuts/__init__.py b/isotuts/__init__.py
deleted file mode 100644
index e69de29..0000000