diff --git a/docs/src/tutorials.md b/docs/src/tutorials.md
index 17077a5b..9eab0292 100644
--- a/docs/src/tutorials.md
+++ b/docs/src/tutorials.md
@@ -1,5 +1,4 @@
 # [Tutorial notebooks](@id tutorials)
 Here are a few tutorials in the form of Jupyter Notebooks to help you get started using Korg.  Each of the following bullets link to a folder on GitHub containing the associated Julia and Python notebooks (the `.ipynb` files) and any supporting data.  You can download everything if you want to run the notebook yourself, or just view the notebook on GitHub.
 - [The Basics](https://github.com/ajwheeler/Korg.jl/tree/main/misc/Tutorial%20notebooks/basics) demonstrates how to read linelists, read or interpolate model atmospheres, and synthesize spectra. It also goes into some of the other data returned when you synthesize a spectrum.
-- [Fitting](https://github.com/ajwheeler/Korg.jl/tree/main/misc/Tutorial%20notebooks/fitting) demonstrates the use of [`Korg.Fit.fit_spectrum`](@ref) to fit observational data.
-- [EW fitting](https://github.com/ajwheeler/Korg.jl/tree/main/misc/Tutorial%20notebooks/EW%20fitting) demonstrates the use of [`Korg.Fit.ews_to_abundances`](@ref) to calculate abundances from equivalent widths.
\ No newline at end of file
+- [Fitting](https://github.com/ajwheeler/Korg.jl/tree/main/misc/Tutorial%20notebooks/fitting) demonstrates the use of [`Korg.Fit.fit_spectrum`](@ref) to fit observational data.
\ No newline at end of file
diff --git a/misc/Tutorial notebooks/EW fitting/EW fitting in python.ipynb b/misc/Tutorial notebooks/EW fitting/EW fitting in python.ipynb
deleted file mode 100644
index 4c043d12..00000000
--- a/misc/Tutorial notebooks/EW fitting/EW fitting in python.ipynb	
+++ /dev/null
@@ -1,648 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "b1c9787e-781e-439f-9599-ea5c0d2f644b",
-   "metadata": {},
-   "source": [
-    "# Equivalent width fitting\n",
-    "This notebook is an example of how to go from measured equivalent widths to abundances.  We'll use the data from [Melendez 2014](https://ui.adsabs.harvard.edu/abs/2014ApJ...791...14M/abstract) and redo their differential analysis of iron lines 18 Sco. \n",
-    "\n",
-    "A massive thanks to Emily Griffith for helping translate this to python."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "25fa263f-d156-4a4a-ab1e-02bea37f9d9b",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-26T19:09:22.850731Z",
-     "iopub.status.busy": "2024-06-26T19:09:22.850385Z",
-     "iopub.status.idle": "2024-06-26T19:09:26.233538Z",
-     "shell.execute_reply": "2024-06-26T19:09:26.233141Z",
-     "shell.execute_reply.started": "2024-06-26T19:09:22.850700Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "from matplotlib import pyplot as plt\n",
-    "from juliacall import Main as jl\n",
-    "import time\n",
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "\n",
-    "jl.seval(\"using Korg\")\n",
-    "Korg = jl.Korg"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "988c6101-cff0-4320-a444-954379815fdc",
-   "metadata": {},
-   "source": [
-    "## parsing the linelist"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "b9e28b1e-d8e8-4749-b5e0-cc8decba9284",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-26T19:09:26.234343Z",
-     "iopub.status.busy": "2024-06-26T19:09:26.234216Z",
-     "iopub.status.idle": "2024-06-26T19:09:26.245501Z",
-     "shell.execute_reply": "2024-06-26T19:09:26.245240Z",
-     "shell.execute_reply.started": "2024-06-26T19:09:26.234335Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>wl</th>\n",
-       "      <th>species</th>\n",
-       "      <th>ExPot</th>\n",
-       "      <th>log_gf</th>\n",
-       "      <th>C6</th>\n",
-       "      <th>EW_18Sco</th>\n",
-       "      <th>EW_Sun</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>5044.211</td>\n",
-       "      <td>26.0</td>\n",
-       "      <td>2.8512</td>\n",
-       "      <td>-2.058</td>\n",
-       "      <td>2.710000e-31</td>\n",
-       "      <td>74.8</td>\n",
-       "      <td>74.3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>5054.642</td>\n",
-       "      <td>26.0</td>\n",
-       "      <td>3.6400</td>\n",
-       "      <td>-1.921</td>\n",
-       "      <td>4.680000e-32</td>\n",
-       "      <td>40.9</td>\n",
-       "      <td>40.5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>5127.359</td>\n",
-       "      <td>26.0</td>\n",
-       "      <td>0.9150</td>\n",
-       "      <td>-3.307</td>\n",
-       "      <td>1.840000e-32</td>\n",
-       "      <td>97.5</td>\n",
-       "      <td>96.1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>5127.679</td>\n",
-       "      <td>26.0</td>\n",
-       "      <td>0.0520</td>\n",
-       "      <td>-6.125</td>\n",
-       "      <td>1.200000e-32</td>\n",
-       "      <td>18.9</td>\n",
-       "      <td>19.1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>5198.711</td>\n",
-       "      <td>26.0</td>\n",
-       "      <td>2.2230</td>\n",
-       "      <td>-2.135</td>\n",
-       "      <td>4.610000e-32</td>\n",
-       "      <td>99.3</td>\n",
-       "      <td>98.1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>318</th>\n",
-       "      <td>3694.810</td>\n",
-       "      <td>66.1</td>\n",
-       "      <td>0.1030</td>\n",
-       "      <td>-0.110</td>\n",
-       "      <td>2.800000e+00</td>\n",
-       "      <td>17.3</td>\n",
-       "      <td>13.9</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>319</th>\n",
-       "      <td>4077.970</td>\n",
-       "      <td>66.1</td>\n",
-       "      <td>0.1030</td>\n",
-       "      <td>-0.040</td>\n",
-       "      <td>2.800000e+00</td>\n",
-       "      <td>14.8</td>\n",
-       "      <td>10.7</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>320</th>\n",
-       "      <td>4103.310</td>\n",
-       "      <td>66.1</td>\n",
-       "      <td>0.1030</td>\n",
-       "      <td>-0.380</td>\n",
-       "      <td>2.800000e+00</td>\n",
-       "      <td>17.8</td>\n",
-       "      <td>15.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>321</th>\n",
-       "      <td>4449.700</td>\n",
-       "      <td>66.1</td>\n",
-       "      <td>0.0000</td>\n",
-       "      <td>-1.030</td>\n",
-       "      <td>2.800000e+00</td>\n",
-       "      <td>4.3</td>\n",
-       "      <td>3.3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>322</th>\n",
-       "      <td>3694.190</td>\n",
-       "      <td>70.1</td>\n",
-       "      <td>0.0000</td>\n",
-       "      <td>-0.300</td>\n",
-       "      <td>2.800000e+00</td>\n",
-       "      <td>81.2</td>\n",
-       "      <td>73.9</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>323 rows × 7 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "           wl  species   ExPot  log_gf            C6  EW_18Sco  EW_Sun\n",
-       "0    5044.211     26.0  2.8512  -2.058  2.710000e-31      74.8    74.3\n",
-       "1    5054.642     26.0  3.6400  -1.921  4.680000e-32      40.9    40.5\n",
-       "2    5127.359     26.0  0.9150  -3.307  1.840000e-32      97.5    96.1\n",
-       "3    5127.679     26.0  0.0520  -6.125  1.200000e-32      18.9    19.1\n",
-       "4    5198.711     26.0  2.2230  -2.135  4.610000e-32      99.3    98.1\n",
-       "..        ...      ...     ...     ...           ...       ...     ...\n",
-       "318  3694.810     66.1  0.1030  -0.110  2.800000e+00      17.3    13.9\n",
-       "319  4077.970     66.1  0.1030  -0.040  2.800000e+00      14.8    10.7\n",
-       "320  4103.310     66.1  0.1030  -0.380  2.800000e+00      17.8    15.0\n",
-       "321  4449.700     66.1  0.0000  -1.030  2.800000e+00       4.3     3.3\n",
-       "322  3694.190     70.1  0.0000  -0.300  2.800000e+00      81.2    73.9\n",
-       "\n",
-       "[323 rows x 7 columns]"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# use pandas to read the contents of Table 1, which were downloaded from the journal website\n",
-    "lines = pd.read_csv(\"Table 1.dat\", delim_whitespace=True, skiprows=24,\n",
-    "                    names=[\"wl\", \"species\", \"ExPot\", \"log_gf\", \"C6\", \"EW_18Sco\", \"EW_Sun\"])\n",
-    "lines"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "7e400290-7608-473c-af8e-9686e8fb9a61",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-26T19:09:26.246109Z",
-     "iopub.status.busy": "2024-06-26T19:09:26.246021Z",
-     "iopub.status.idle": "2024-06-26T19:09:27.536472Z",
-     "shell.execute_reply": "2024-06-26T19:09:27.536042Z",
-     "shell.execute_reply.started": "2024-06-26T19:09:26.246100Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "# convert the numbers in the species column to Korg.Species objects\n",
-    "# jl.broadcast is how you use Julia's broadcasting (\".\" syntax) from python\n",
-    "# this called Korg.Species on each element in the line.species column\n",
-    "lines.species = jl.broadcast(Korg.Species, lines.species)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "41af8329-f8cc-47bc-a55d-2ef9266bc52b",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-26T19:09:27.538462Z",
-     "iopub.status.busy": "2024-06-26T19:09:27.538349Z",
-     "iopub.status.idle": "2024-06-26T19:09:28.064649Z",
-     "shell.execute_reply": "2024-06-26T19:09:28.064350Z",
-     "shell.execute_reply.started": "2024-06-26T19:09:27.538453Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>wl</th>\n",
-       "      <th>species</th>\n",
-       "      <th>ExPot</th>\n",
-       "      <th>log_gf</th>\n",
-       "      <th>C6</th>\n",
-       "      <th>EW_18Sco</th>\n",
-       "      <th>EW_Sun</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>5044.211</td>\n",
-       "      <td>Fe I</td>\n",
-       "      <td>2.8512</td>\n",
-       "      <td>-2.058</td>\n",
-       "      <td>2.710000e-31</td>\n",
-       "      <td>74.8</td>\n",
-       "      <td>74.3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>5054.642</td>\n",
-       "      <td>Fe I</td>\n",
-       "      <td>3.6400</td>\n",
-       "      <td>-1.921</td>\n",
-       "      <td>4.680000e-32</td>\n",
-       "      <td>40.9</td>\n",
-       "      <td>40.5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>5127.359</td>\n",
-       "      <td>Fe I</td>\n",
-       "      <td>0.9150</td>\n",
-       "      <td>-3.307</td>\n",
-       "      <td>1.840000e-32</td>\n",
-       "      <td>97.5</td>\n",
-       "      <td>96.1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>5127.679</td>\n",
-       "      <td>Fe I</td>\n",
-       "      <td>0.0520</td>\n",
-       "      <td>-6.125</td>\n",
-       "      <td>1.200000e-32</td>\n",
-       "      <td>18.9</td>\n",
-       "      <td>19.1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>86</th>\n",
-       "      <td>5197.577</td>\n",
-       "      <td>Fe II</td>\n",
-       "      <td>3.2306</td>\n",
-       "      <td>-2.220</td>\n",
-       "      <td>8.690000e-33</td>\n",
-       "      <td>84.0</td>\n",
-       "      <td>80.7</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>81</th>\n",
-       "      <td>6810.263</td>\n",
-       "      <td>Fe I</td>\n",
-       "      <td>4.6070</td>\n",
-       "      <td>-0.986</td>\n",
-       "      <td>4.500000e-31</td>\n",
-       "      <td>53.1</td>\n",
-       "      <td>50.6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>82</th>\n",
-       "      <td>6837.006</td>\n",
-       "      <td>Fe I</td>\n",
-       "      <td>4.5930</td>\n",
-       "      <td>-1.687</td>\n",
-       "      <td>2.460000e-32</td>\n",
-       "      <td>20.0</td>\n",
-       "      <td>18.9</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>83</th>\n",
-       "      <td>6839.830</td>\n",
-       "      <td>Fe I</td>\n",
-       "      <td>2.5590</td>\n",
-       "      <td>-3.350</td>\n",
-       "      <td>3.950000e-32</td>\n",
-       "      <td>32.5</td>\n",
-       "      <td>31.6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>84</th>\n",
-       "      <td>6843.656</td>\n",
-       "      <td>Fe I</td>\n",
-       "      <td>4.5480</td>\n",
-       "      <td>-0.860</td>\n",
-       "      <td>2.940000e-31</td>\n",
-       "      <td>64.1</td>\n",
-       "      <td>63.3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>85</th>\n",
-       "      <td>6858.150</td>\n",
-       "      <td>Fe I</td>\n",
-       "      <td>4.6070</td>\n",
-       "      <td>-0.930</td>\n",
-       "      <td>3.240000e-31</td>\n",
-       "      <td>53.6</td>\n",
-       "      <td>52.4</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>98 rows × 7 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "          wl species   ExPot  log_gf            C6  EW_18Sco  EW_Sun\n",
-       "0   5044.211    Fe I  2.8512  -2.058  2.710000e-31      74.8    74.3\n",
-       "1   5054.642    Fe I  3.6400  -1.921  4.680000e-32      40.9    40.5\n",
-       "2   5127.359    Fe I  0.9150  -3.307  1.840000e-32      97.5    96.1\n",
-       "3   5127.679    Fe I  0.0520  -6.125  1.200000e-32      18.9    19.1\n",
-       "86  5197.577   Fe II  3.2306  -2.220  8.690000e-33      84.0    80.7\n",
-       "..       ...     ...     ...     ...           ...       ...     ...\n",
-       "81  6810.263    Fe I  4.6070  -0.986  4.500000e-31      53.1    50.6\n",
-       "82  6837.006    Fe I  4.5930  -1.687  2.460000e-32      20.0    18.9\n",
-       "83  6839.830    Fe I  2.5590  -3.350  3.950000e-32      32.5    31.6\n",
-       "84  6843.656    Fe I  4.5480  -0.860  2.940000e-31      64.1    63.3\n",
-       "85  6858.150    Fe I  4.6070  -0.930  3.240000e-31      53.6    52.4\n",
-       "\n",
-       "[98 rows x 7 columns]"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# look at Fe lines only, and sort by wavelength\n",
-    "is_iron = [Korg.get_atoms(s) == jl.Vector([26]) for s in lines.species]\n",
-    "lines = lines.loc[is_iron]\n",
-    "lines = lines.sort_values(\"wl\")\n",
-    "lines"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "5b75be64-3518-45de-8f0d-9fd08ef6d2be",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-26T19:09:28.065169Z",
-     "iopub.status.busy": "2024-06-26T19:09:28.065088Z",
-     "iopub.status.idle": "2024-06-26T19:09:28.180243Z",
-     "shell.execute_reply": "2024-06-26T19:09:28.179886Z",
-     "shell.execute_reply.started": "2024-06-26T19:09:28.065162Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "# to pass the lines to Korg, we need to turn each row of the \"lines\" dataframe into a Korg.Line object\n",
-    "linelist = jl.broadcast(Korg.Line, lines.wl, # can be in either cm or Å (like these), but NOT nm\n",
-    "                        lines.log_gf,lines.species, # needs to be a Korg.Species, which we handled in the cell above\n",
-    "                        lines.ExPot)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9e0db71d-1010-4a39-918c-bd1ce71c2069",
-   "metadata": {},
-   "source": [
-    "## Set up params for each star"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "69f5b674-9538-4fd8-b95e-ae6e17a0f9d8",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-26T19:09:28.180826Z",
-     "iopub.status.busy": "2024-06-26T19:09:28.180727Z",
-     "iopub.status.idle": "2024-06-26T19:09:36.323403Z",
-     "shell.execute_reply": "2024-06-26T19:09:36.323100Z",
-     "shell.execute_reply.started": "2024-06-26T19:09:28.180819Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "# solar params\n",
-    "sun_Teff, sun_logg, sun_Fe_H, sun_vmic = 5777, 4.44, 0.0, 1.0\n",
-    "\n",
-    "# vector of abundances for the sun\n",
-    "sun_A_X = Korg.format_A_X(sun_Fe_H)\n",
-    "\n",
-    "# interpolate a model atmosphere for the sun\n",
-    "sun_atm = Korg.interpolate_marcs(sun_Teff, sun_logg, sun_A_X)  \n",
-    "\n",
-    "# and likewise for 18 Sco\n",
-    "sco_teff, sco_logg, sco_fe_h, sco_vmic = (5823, 4.45, 0.054, sun_vmic + 0.02)\n",
-    "sco_A_X = Korg.format_A_X(sco_fe_h)\n",
-    "sco_atm = Korg.interpolate_marcs(sco_teff, sco_logg, sco_A_X)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e1e6a43b-2d28-4d03-b5d3-3a1e0ef6d135",
-   "metadata": {},
-   "source": [
-    "## Calculate the abundances for each star\n",
-    "Because Julia uses \"just in time\" compilation, the first call to `Korg.Fit.ews_to_abundances` will take several seconds.  The second is much faster, because the code is already compiled.  If you rerun the cell below, both calculations will be fast, even though no data is cached."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "fc38fcb2-2bae-4fe8-af6f-c3604cc05832",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-26T19:09:36.323891Z",
-     "iopub.status.busy": "2024-06-26T19:09:36.323822Z",
-     "iopub.status.idle": "2024-06-26T19:09:44.196202Z",
-     "shell.execute_reply": "2024-06-26T19:09:44.195932Z",
-     "shell.execute_reply.started": "2024-06-26T19:09:36.323884Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "7.6071089999999995\n",
-      "0.276288000000001\n"
-     ]
-    }
-   ],
-   "source": [
-    "# calculate abundances from the EWs for each star\n",
-    "\n",
-    "# in order to pass the EWs to Korg.Fit.ews_to_abundances, we convert them to numpy arrays, which \n",
-    "# are handled automatically, unlike pandas series.  We also wrap the returned julia vector in np.array.\n",
-    "t1 = time.process_time()\n",
-    "lines[\"A_sun\"] = np.array(Korg.Fit.ews_to_abundances(sun_atm, linelist, sun_A_X, np.array(lines.EW_Sun), vmic=sun_vmic))\n",
-    "t2 = time.process_time()\n",
-    "lines[\"A_18Sco\"] = np.array(Korg.Fit.ews_to_abundances(sco_atm, linelist, sco_A_X, np.array(lines.EW_18Sco), vmic=sco_vmic))\n",
-    "t3 = time.process_time()\n",
-    "print(t2-t1)\n",
-    "print(t3-t2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "31560282-f1b1-4538-a3c0-cc1961b52ecb",
-   "metadata": {},
-   "source": [
-    "# Plot the results\n",
-    "Plotting as a function of wavelength and equivalenth width is left as an excercise for the reader."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "836607ca-df58-4513-816a-33e09aa87739",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-26T19:09:44.196685Z",
-     "iopub.status.busy": "2024-06-26T19:09:44.196604Z",
-     "iopub.status.idle": "2024-06-26T19:09:44.198455Z",
-     "shell.execute_reply": "2024-06-26T19:09:44.198204Z",
-     "shell.execute_reply.started": "2024-06-26T19:09:44.196678Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "# get a bitmask for the lines of Fe I vs Fe II\n",
-    "neutrals = np.array([spec.charge == 0 for spec in lines.species])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "e6dd0392-6ed9-4640-b22c-b67383a19910",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-26T19:09:44.198824Z",
-     "iopub.status.busy": "2024-06-26T19:09:44.198758Z",
-     "iopub.status.idle": "2024-06-26T19:09:44.279104Z",
-     "shell.execute_reply": "2024-06-26T19:09:44.278737Z",
-     "shell.execute_reply.started": "2024-06-26T19:09:44.198818Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "''"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x200 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(8, 2))\n",
-    "plt.scatter(lines.ExPot[neutrals], (lines.A_18Sco - lines.A_sun)[neutrals], label=\"Fe I\")\n",
-    "plt.scatter(lines.ExPot[~neutrals], (lines.A_18Sco - lines.A_sun)[~neutrals], label=\"Fe II\")\n",
-    "plt.ylabel(\"A(Fe)_\\mathrm{18 Sco} - A(Fe)_\\mathrm{sun}\")\n",
-    "plt.xlabel(\"χ [eV]\")\n",
-    "plt.legend()\n",
-    ";"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "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": "3.11.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/misc/Tutorial notebooks/EW fitting/EW fitting.ipynb b/misc/Tutorial notebooks/EW fitting/EW fitting.ipynb
deleted file mode 100644
index 03b626a9..00000000
--- a/misc/Tutorial notebooks/EW fitting/EW fitting.ipynb	
+++ /dev/null
@@ -1,220 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "b1c9787e-781e-439f-9599-ea5c0d2f644b",
-   "metadata": {},
-   "source": [
-    "# Equivalent width fitting\n",
-    "This notebook is an example of how to go from measured equivalent widths to abundances.  We'll use the data from [Melendez 2014](https://ui.adsabs.harvard.edu/abs/2014ApJ...791...14M/abstract) and redo their differential analysis of iron lines 18 Sco."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "25fa263f-d156-4a4a-ab1e-02bea37f9d9b",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mPrecompiling Korg [acafc109-a718-429c-b0e5-afd7f8c7ae46]\n"
-     ]
-    }
-   ],
-   "source": [
-    "using Revise, Korg, DataFrames, CSV, PyPlot"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "988c6101-cff0-4320-a444-954379815fdc",
-   "metadata": {},
-   "source": [
-    "## parsing the linelist"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "eeac654b-0dcd-420c-82b1-bdee393e2f44",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# use the CSV library to read the contents of Table 1, which were downloaded from the journal website\n",
-    "# the \"lines\" objects is a DataFrame, which we'll use to organize our data\n",
-    "lines = CSV.read(\"Table 1.dat\", DataFrame; skipto=25, delim=' ', ignorerepeated=true,\n",
-    "    header=[\"wl\", \"species\", \"ExPot\", \"log_gf\", \"C6\", \"EW_18Sco\", \"EW_Sun\"])\n",
-    "\n",
-    "# convert the numbers in the species column to Korg.Species objects\n",
-    "lines.species = Korg.Species.(lines.species)\n",
-    "\n",
-    "# let's look at Fe lines only\n",
-    "filter!(lines) do row\n",
-    "    Korg.get_atoms(row.species) == [26]\n",
-    "end\n",
-    "\n",
-    "#sort by wavelength\n",
-    "sort!(lines, :wl)\n",
-    ";"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "87d2b027-79bc-4b86-9480-82da48dcae3a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# to pass the lines to Korg, we need to turn each row of the \"lines\" DataFrame into a Korg.Line object\n",
-    "linelist = Korg.Line.(lines.wl, # can be in either cm or Å (like these), but NOT nm\n",
-    "                      lines.log_gf,\n",
-    "                      lines.species, # needs to be a Korg.Species, which we handled in the cell above\n",
-    "                      lines.ExPot) # excitation potential, i.e. lower level energy (must be in eV)\n",
-    ";"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9e0db71d-1010-4a39-918c-bd1ce71c2069",
-   "metadata": {},
-   "source": [
-    "## Set up params for each star"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "69f5b674-9538-4fd8-b95e-ae6e17a0f9d8",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mloading the model atmosphere grid into memory. This will take a few seconds, but will only happen once per julia session.\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "Korg.PlanarAtmosphere{Float64, Float64, Float64, Float64, Float64} with 56 layers"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# solar params\n",
-    "sun_Teff, sun_logg, sun_Fe_H, sun_vmic = 5777, 4.44, 0.0, 1.0\n",
-    "\n",
-    "# vector of abundances for the sun\n",
-    "sun_A_X = Korg.format_A_X(sun_Fe_H)\n",
-    "\n",
-    "# interpolate a model atmosphere for the sun\n",
-    "sun_atm = Korg.interpolate_marcs(sun_Teff, sun_logg, sun_A_X)  \n",
-    "\n",
-    "# and likewise for 18 Sco\n",
-    "sco_teff, sco_logg, sco_fe_h, sco_vmic = (5823, 4.45, 0.054, sun_vmic + 0.02)\n",
-    "sco_A_X = Korg.format_A_X(sco_fe_h)\n",
-    "sco_atm = Korg.interpolate_marcs(sco_teff, sco_logg, sco_A_X)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e1e6a43b-2d28-4d03-b5d3-3a1e0ef6d135",
-   "metadata": {},
-   "source": [
-    "## Calculate the abundances for each star\n",
-    "Because Julia uses \"just in time\" compilation, the first call to `Korg.Fit.ews_to_abundances` will take several seconds.  The second is much faster, because the code is already compiled.  If you rerun the cell below, both calculations will be fast, even though no data is cached."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "fc38fcb2-2bae-4fe8-af6f-c3604cc05832",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      " 18.033960 seconds (103.51 M allocations: 7.324 GiB, 5.10% gc time, 84.61% compilation time)\n",
-      "  2.616027 seconds (15.51 M allocations: 2.969 GiB, 8.50% gc time)\n"
-     ]
-    }
-   ],
-   "source": [
-    "# calculate abundances from the EWs for each star\n",
-    "@time lines.A_sun = Korg.Fit.ews_to_abundances(sun_atm, linelist, sun_A_X, lines.EW_Sun, vmic=sun_vmic)\n",
-    "@time lines.A_18Sco = Korg.Fit.ews_to_abundances(sco_atm, linelist, sco_A_X, lines.EW_18Sco, vmic=sco_vmic)\n",
-    ";"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "31560282-f1b1-4538-a3c0-cc1961b52ecb",
-   "metadata": {},
-   "source": [
-    "# Plot the results\n",
-    "Plotting as a function of wavelength and equivalenth width is left as an excercise for the reader."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "e4acef29-6ad0-4ae3-8263-e72d5e2f4506",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "Figure(PyObject <Figure size 800x200 with 1 Axes>)"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# get a bitmask for the lines of Fe I vs Fe II\n",
-    "neutrals = [spec.charge == 0 for spec in lines.species]\n",
-    "\n",
-    "figure(figsize=(8, 2))\n",
-    "scatter(lines.ExPot[neutrals], (lines.A_18Sco - lines.A_sun)[neutrals], label=\"Fe I\")\n",
-    "scatter(lines.ExPot[.! neutrals], (lines.A_18Sco - lines.A_sun)[.! neutrals], label=\"Fe II\")\n",
-    "ylabel(L\"A(Fe)_\\mathrm{18 Sco} - A(Fe)_\\mathrm{sun}\")\n",
-    "xlabel(\"χ [eV]\")\n",
-    "legend()\n",
-    ";"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "a1f62ac9-37f0-4b91-809f-4de341a4b250",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Julia 1.8.5",
-   "language": "julia",
-   "name": "julia-1.8"
-  },
-  "language_info": {
-   "file_extension": ".jl",
-   "mimetype": "application/julia",
-   "name": "julia",
-   "version": "1.8.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/misc/Tutorial notebooks/EW fitting/Table 1.dat b/misc/Tutorial notebooks/EW fitting/Table 1.dat
deleted file mode 100644
index f2d00f01..00000000
--- a/misc/Tutorial notebooks/EW fitting/Table 1.dat	
+++ /dev/null
@@ -1,347 +0,0 @@
-Title: 18 Sco: a solar twin rich in refractory and neutron-capture elements. 
-       Implications for chemical tagging. 
-Authors: Melendez J., Ramirez I., Karakas A.I., Yong D., Monroe T.R., Bedell M.,
-         Bergemann M., Asplund M., Maia M.T., Bean J., do Nascimento J.-D., 
-         Bazot M., Alves-Brito A., Freitas F.C., Castro M. 
-Table: Adopted atomic data and equivalent widths
-================================================================================
-Byte-by-byte Description of file: apj497906t1_mrt.txt
---------------------------------------------------------------------------------
-   Bytes Format Units  Label    Explanations
---------------------------------------------------------------------------------
-   1-  9 F9.3   0.1nm  Wave     Wavelength in Angstroms
-  11- 14 F4.1   ---    Ion      Ion numeric identifier (1)
-  16- 21 F6.4   eV     ExPot    Excitation potential
-  23- 28 F6.3   [-]    log(gf)  Log of the oscillator strength times 
-                                 the statistical weight
-  31- 38 E8.2   ---    C6       Damping constant 
-  40- 44 F5.1   10-13m EW-18S   The 18 Sco differential equivalent width (2)
-  46- 51 F6.2   10-13m EW-Sun   Solar differential equivalent width (2)
---------------------------------------------------------------------------------
-Note (1): Where the number before the digit is the atomic number and the number
-          after the digit is the ionization level, e.g. 26.1 = Fe II.
-Note (2): In units of milli-Angstroms.
---------------------------------------------------------------------------------
- 5044.211 26.0 2.8512 -2.058  2.71e-31  74.8  74.3
- 5054.642 26.0 3.640  -1.921  4.68e-32  40.9  40.5
- 5127.359 26.0 0.915  -3.307  1.84e-32  97.5  96.1
- 5127.679 26.0 0.052  -6.125  1.20e-32  18.9  19.1
- 5198.711 26.0 2.223  -2.135  4.61e-32  99.3  98.1
- 5225.525 26.0 0.1101 -4.789  1.23e-32  72.5  72.1
- 5242.491 26.0 3.634  -0.967  4.95e-32  88.3  86.9
- 5247.050 26.0 0.0872 -4.946  1.22e-32  67.4  66.9
- 5250.208 26.0 0.1212 -4.938  1.23e-32  66.3  65.9
- 5295.312 26.0 4.415  -1.49   6.54e-31  31.0  30.3
- 5322.041 26.0 2.279  -2.80   4.29e-32  62.9  61.5
- 5373.709 26.0 4.473  -0.77   7.04e-31  65.2  63.9
- 5379.574 26.0 3.694  -1.514  5.02e-32  62.9  61.5
- 5386.334 26.0 4.154  -1.74   5.27e-31  34.8  33.6
- 5466.396 26.0 4.371  -0.565  4.40e-31  81.3  79.4
- 5466.987 26.0 3.573  -2.23   2.80e+00  35.8  35.2
- 5522.446 26.0 4.209  -1.31   3.02e-31  45.8  43.7
- 5546.506 26.0 4.371  -1.18   3.91e-31  52.6  51.4
- 5560.211 26.0 4.434  -1.16   4.79e-31  53.8  52.0
- 5577.02  26.0 5.0331 -1.455  2.80e+00  11.9  11.2
- 5618.633 26.0 4.209  -1.276  2.90e-31  51.8  50.2
- 5636.696 26.0 3.640  -2.56   5.19e-32  20.7  19.7
- 5638.262 26.0 4.220  -0.81   2.88e-31  78.8  77.6
- 5649.987 26.0 5.0995 -0.8    2.77e-31  37.8  35.9
- 5651.469 26.0 4.473  -1.75   4.83e-31  20.3  18.9
- 5661.348 26.0 4.2843 -1.756  3.24e-31  24.4  23.0
- 5679.023 26.0 4.652  -0.75   8.13e-31  61.7  59.6
- 5696.089 26.0 4.548  -1.720  5.78e-31  14.5  13.7
- 5701.544 26.0 2.559  -2.216  4.95e-32  86.4  84.7
- 5705.464 26.0 4.301  -1.355  3.02e-31  39.7  38.1
- 5855.076 26.0 4.6075 -1.478  5.74e-31  23.8  22.9
- 5905.672 26.0 4.652  -0.69   6.23e-31  61.7  60.1
- 5916.247 26.0 2.453  -2.936  4.29e-32  55.4  55.5
- 5927.789 26.0 4.652  -1.04   6.07e-31  46.0  43.6
- 5934.655 26.0 3.928  -1.07   5.69e-31  79.1  77.9
- 5956.694 26.0 0.8589 -4.605  1.55e-32  52.3  52.8
- 5987.065 26.0 4.795  -0.212  1.55e-32  70.3  68.2
- 6003.012 26.0 3.881  -1.060  4.83e-31  85.5  84.5
- 6005.541 26.0 2.588  -3.43   2.80e+00  23.9  23.0
- 6024.058 26.0 4.548  -0.02   3.88e-31 114.0 111.2
- 6027.050 26.0 4.0758 -1.09   2.80e+00  65.7  64.0
- 6056.005 26.0 4.733  -0.45   6.79e-31  74.2  72.3
- 6065.482 26.0 2.6085 -1.530  4.71e-32 118.8 117.3
- 6079.009 26.0 4.652  -1.10   5.13e-31  47.5  46.5
- 6082.711 26.0 2.223  -3.573  3.27e-32  35.3  34.0
- 6093.644 26.0 4.607  -1.30   4.41e-31  32.8  30.8
- 6096.665 26.0 3.9841 -1.81   5.75e-31  39.3  37.6
- 6151.618 26.0 2.1759 -3.299  2.55e-32  51.2  49.9
- 6157.728 26.0 4.076  -1.22   2.80e+00  64.7  62.7
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- 6173.335 26.0 2.223  -2.880  2.65e-32  69.0  68.7
- 6187.990 26.0 3.943  -1.67   4.90e-31  49.4  47.6
- 6200.313 26.0 2.6085 -2.437  4.58e-32  74.6  73.6
- 6213.430 26.0 2.2227 -2.52   2.62e-32  83.9  82.7
- 6219.281 26.0 2.198  -2.433  2.58e-32  91.2  90.3
- 6226.736 26.0 3.883  -2.1    4.15e-31  31.5  30.1
- 6240.646 26.0 2.2227 -3.233  3.14e-32  49.3  48.7
- 6252.555 26.0 2.4040 -1.687  3.84e-32 123.1 121.6
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- 6380.743 26.0 4.186  -1.376  2.80e+00  54.3  53.2
- 6392.539 26.0 2.279  -4.03   3.38e-32  17.3  16.7
- 6430.846 26.0 2.1759 -2.006  2.42e-32 113.4 114.0
- 6498.939 26.0 0.9581 -4.699  1.53e-32  47.2  46.5
- 6593.871 26.0 2.4326 -2.422  3.69e-32  86.6  86.3
- 6597.561 26.0 4.795  -0.98   4.76e-31  46.2  44.7
- 6625.022 26.0 1.011  -5.336  2.80e+00  15.8  14.9
- 6677.987 26.0 2.692  -1.418  3.46e-32 132.8 129.9
- 6703.567 26.0 2.7585 -3.023  3.66e-32  38.3  37.6
- 6705.102 26.0 4.607  -0.98   2.80e+00  47.9  46.3
- 6710.319 26.0 1.485  -4.88   2.01e-32  15.7  15.7
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- 6739.522 26.0 1.557  -4.79   2.10e-32  13.2  12.4
- 6750.152 26.0 2.4241 -2.621  4.11e-32  75.3  75.3
- 6752.707 26.0 4.638  -1.204  3.37e-31  39.0  36.8
- 6793.259 26.0 4.076  -2.326  2.80e+00  14.3  13.0
- 6806.845 26.0 2.727  -3.11   3.46e-32  35.8  35.4
- 6810.263 26.0 4.607  -0.986  4.50e-31  53.1  50.6
- 6837.006 26.0 4.593  -1.687  2.46e-32  20.0  18.9
- 6839.830 26.0 2.559  -3.35   3.95e-32  32.5  31.6
- 6843.656 26.0 4.548  -0.86   2.94e-31  64.1  63.3
- 6858.150 26.0 4.607  -0.930  3.24e-31  53.6  52.4
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- 5264.812 26.1 3.2304 -3.13   9.43e-33  48.6  45.8
- 5325.553 26.1 3.2215 -3.16   8.57e-33  42.7  41.3
- 5414.073 26.1 3.2215 -3.58   9.30e-33  28.5  26.7
- 5425.257 26.1 3.1996 -3.22   8.45e-33  43.0  41.3
- 6084.111 26.1 3.1996 -3.79   7.87e-33  22.6  20.9
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- 5052.167 06.0 7.685  -1.24   2.80e+00  34.0  34.5
- 5380.337 06.0 7.685  -1.57   2.80e+00  20.5  20.6
- 6587.61  06.0 8.537  -1.05   2.80e+00  15.6  15.2
- 7111.47  06.0 8.640  -1.07   2.91e-30  14.9  14.3
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- 7771.944 08.0 9.146   0.37   8.41e-32  73.0  71.1
- 7774.166 08.0 9.146   0.22   8.41e-32  65.6  63.7
- 7775.388 08.0 9.146   0.00   8.41e-32  51.7  51.1
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- 6160.747 11.0 2.1044 -1.246  2.80e+00  54.0  54.3
- 4571.095 12.0 0.000  -5.623  2.80e+00 105.6 106.0
- 4730.040 12.0 4.340  -2.389  2.80e+00  69.3  69.0
- 5711.088 12.0 4.345  -1.729  2.80e+00 106.4 105.6
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- 6696.018 13.0 3.143  -1.481  2.80e+00  37.0  36.3
- 6698.667 13.0 3.143  -1.782  2.80e+00  21.7  21.2
- 7835.309 13.0 4.021  -0.68   2.80e+00  43.6  43.6
- 7836.134 13.0 4.021  -0.45   2.80e+00  60.9  57.7
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- 5665.554 14.0 4.920  -1.94   2.80e+00  43.2  41.1
- 5684.484 14.0 4.953  -1.55   2.80e+00  64.3  62.0
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- 8694.62  16.0 7.870   0.1    1.51e-30  30.3  28.6
- 7698.974 19.0 0.000  -0.168  1.04e-31 157.3 157.0
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- 6499.650 20.0 2.523  -0.818  5.05e-32  88.6  86.5
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- 6320.843 21.1 1.500  -1.85   2.80e+00   9.7   9.0
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- 4875.486 23.0 0.040  -0.81   1.98e-32  46.1  46.6
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- 6362.35  30.0 5.79    0.14   2.80e+00  23.4  23.0
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- 5087.420 39.1 1.0841 -0.17   2.80e+00  51.1  47.7
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- 4208.980 40.1 0.713  -0.51   2.80e+00  45.5  42.1
- 4442.992 40.1 1.486  -0.42   2.80e+00  26.8  24.4
- 3158.16  42.0 0.000  -0.31   2.80e+00  13.3  11.6
- 3193.97  42.0 0.000   0.07   2.80e+00  16.2  15.0
- 3436.736 44.0 0.148   0.165  2.80e+00   8.8   7.0
- 3498.942 44.0 0.000   0.322  2.80e+00  27.4  25.4
- 3242.698 46.0 0.8138  0.07   2.80e+00  27.2  25.5
- 3404.576 46.0 0.8138  0.33   2.80e+00  32.9  30.2
- 3516.944 46.0 0.9615 -0.21   2.80e+00  16.2  14.1
- 3280.68  47.0 0.0000 -0.022  2.80e+00  40.6  38.5
- 3382.89  47.0 0.0000 -0.334  2.80e+00  29.3  26.9
- 5853.67  56.1 0.604  -0.91   5.30e-32  67.7  63.4
- 6141.71  56.1 0.704  -0.08   5.30e-32 121.2 114.9
- 6496.90  56.1 0.604  -0.38   5.30e-32 106.8  99.9
- 4662.50  57.1 0.0000 -1.24   2.80e+00   7.6   6.1
- 4748.73  57.1 0.9265 -0.54   2.80e+00   4.8   3.9
- 5303.53  57.1 0.3213 -1.35   2.80e+00   4.1   3.2
- 3942.151 58.1 0.000  -0.22   2.80e+00  13.4  11.9
- 3999.237 58.1 0.295   0.06   2.80e+00  20.4  17.2
- 4042.581 58.1 0.495   0.00   2.80e+00  12.1  10.0
- 4073.474 58.1 0.477   0.21   2.80e+00  16.4  14.4
- 4364.653 58.1 0.495  -0.17   2.80e+00  13.6  11.4
- 4523.075 58.1 0.516  -0.08   2.80e+00  15.0  12.9
- 4562.359 58.1 0.477   0.21   2.80e+00  27.4  24.7
- 5274.229 58.1 1.044   0.13   2.80e+00   9.8   8.5
- 5259.73  59.1 0.633   0.114  2.80e+00   2.9   2.3
- 4021.33  60.1 0.320  -0.10   2.80e+00  14.8  12.4
- 4059.95  60.1 0.204  -0.52   2.80e+00   7.5   5.4
- 4446.38  60.1 0.204  -0.35   2.80e+00  11.9   9.7
- 5234.19  60.1 0.550  -0.51   2.80e+00   6.7   5.2
- 5293.16  60.1 0.822   0.10   2.80e+00  12.5  10.0
- 5319.81  60.1 0.550  -0.14   2.80e+00  13.3  10.8
- 3760.70  62.1 0.18   -0.42   2.80e+00   9.9   7.1
- 4318.936 62.1 0.277  -0.25   2.80e+00  14.1  10.4
- 4467.341 62.1 0.659   0.15   2.80e+00  14.5  12.6
- 4519.630 62.1 0.543  -0.35   2.80e+00   6.7   5.3
- 4676.902 62.1 0.040  -0.87   2.80e+00   6.6   5.0
- 3819.67  63.1 0.000   0.51   2.80e+00  47.4  33.2
- 3907.11  63.1 0.207   0.17   2.80e+00  28.8  19.0
- 4129.72  63.1 0.000   0.22   2.80e+00  70.1  56.3
- 6645.10  63.1 1.379   0.12   2.80e+00   8.0   6.6
- 3331.387 64.1 0.000  -0.28   2.80e+00  10.2   7.8
- 3697.733 64.1 0.032  -0.34   2.80e+00   6.0   4.5
- 3712.704 64.1 0.382   0.04   2.80e+00  15.5  12.3
- 3768.396 64.1 0.078   0.21   2.80e+00  17.7  14.0
- 4251.731 64.1 0.382  -0.22   2.80e+00  13.3  10.4
- 3531.71  66.1 0.000   0.77   2.80e+00  40.3  36.1
- 3536.02  66.1 0.538   0.53   2.80e+00  24.4  19.8
- 3694.81  66.1 0.103  -0.11   2.80e+00  17.3  13.9
- 4077.97  66.1 0.103  -0.04   2.80e+00  14.8  10.7
- 4103.31  66.1 0.103  -0.38   2.80e+00  17.8  15.0
- 4449.70  66.1 0.000  -1.03   2.80e+00   4.3   3.3
- 3694.19  70.1 0.000  -0.30   2.80e+00  81.2  73.9
diff --git a/src/fit.jl b/src/fit.jl
index b9804fc8..f2ed84d7 100644
--- a/src/fit.jl
+++ b/src/fit.jl
@@ -436,6 +436,11 @@ end
 """
     ews_to_abundances(atm, linelist, A_X, measured_EWs; kwargs... )
 
+!!! danger
+
+    Functions using equivalent widths have been dissabled while we
+    [fix some bugs](https://github.com/ajwheeler/Korg.jl/pull/331).
+
 Compute per-line abundances on the linear part of the curve of growth given a model atmosphere and a
 list of lines with equivalent widths.
 
@@ -466,6 +471,7 @@ A vector of abundances (`A(X) = log10(n_X/n_H) + 12` format) for each line in `l
 """
 function ews_to_abundances(atm, linelist, A_X, measured_EWs; ew_window_size::Real=2.0, wl_step=0.01,
                            blend_warn_threshold=0.01, synthesize_kwargs...)
+    throw("This function is currently disabled while we fix some bugs. See https://github.com/ajwheeler/Korg.jl/pull/331 for details.")
     synthesize_kwargs = Dict(synthesize_kwargs)
     if length(linelist) != length(measured_EWs)
         throw(ArgumentError("length of linelist does not match length of ews ($(length(linelist)) != $(length(measured_EWs)))"))
@@ -537,6 +543,11 @@ end
 """
     ews_to_stellar_parameters(linelist, measured_EWs, [measured_EW_err]; kwargs...)
 
+!!! danger
+
+    Functions using equivalent widths have been dissabled while we
+    [fix some bugs](https://github.com/ajwheeler/Korg.jl/pull/331).
+
 Find stellar parameters from equivalent widths the "old fashioned" way.  This function finds the
 values of ``T_\\mathrm{eff}``, ``\\log g``, ``v_{mic}``, and [m/H] which satisfy the following conditions
 (using a Newton-Raphson solver):
@@ -607,6 +618,7 @@ function ews_to_stellar_parameters(linelist, measured_EWs,
                                                       Korg._sdss_marcs_atmospheres[1][3][end])],
                                    fix_params=[false, false, false, false],
                                    callback=Returns(nothing), max_iterations=30, passed_kwargs...)
+    throw("This function is currently disabled while we fix some bugs. See https://github.com/ajwheeler/Korg.jl/pull/331 for details.")
     if :vmic in keys(passed_kwargs)
         throw(ArgumentError("vmic must not be specified, because it is a parameter fit by ews_to_stellar_parameters.  Did you mean to specify vmic0, the starting value? See the documentation for ews_to_stellar_parameters if you would like to fix microturbulence to a given value."))
     end
diff --git a/test/fit.jl b/test/fit.jl
index d34a2fae..678f5267 100644
--- a/test/fit.jl
+++ b/test/fit.jl
@@ -123,174 +123,179 @@ using Random
                                                                                                1:end-1])
     end
 
-    @testset "ews_to_abundances" begin
-        @testset "require sorted linelists" begin
-            sun_Teff, sun_logg, sun_Fe_H, sun_vmic = (5777, 4.44, 0.0, 1.0)
-            sun_A_X = Korg.format_A_X(sun_Fe_H)
-            sun_atm = Korg.read_model_atmosphere("data/sun.mod")
-
-            linelist = [
-                Korg.Line(5054.642 * 1e-8, -1.92100, Korg.Species("26.0"), 3.64, 4.68e-32),
-                Korg.Line(5044.211 * 1e-8, -2.05800, Korg.Species("26.0"), 2.8512, 2.71e-31)
-            ]
-            sun_ews = [74.3, 40.5]
-            @test_throws ArgumentError Korg.Fit.ews_to_abundances(sun_atm, linelist, sun_A_X,
-                                                                  sun_ews, vmic=sun_vmic)
-        end
-
-        @testset "length of linelist and ews should match" begin
-            sun_Teff, sun_logg, sun_Fe_H, sun_vmic = (5777, 4.44, 0.0, 1.0)
-            sun_A_X = Korg.format_A_X(sun_Fe_H)
-            sun_atm = Korg.read_model_atmosphere("data/sun.mod")
-            linelist = [Korg.Line(5044.211 * 1e-8, -2.05800, Korg.Species("26.0"), 2.8512,
-                                  2.71e-31)]
-            @test_throws ArgumentError Korg.Fit.ews_to_abundances(sun_atm, linelist, sun_A_X, [],
-                                                                  vmic=sun_vmic)
-        end
+    if false # SKIP EWs tests until we fix them
+        @testset "ews_to_abundances" begin
+            @testset "require sorted linelists" begin
+                sun_Teff, sun_logg, sun_Fe_H, sun_vmic = (5777, 4.44, 0.0, 1.0)
+                sun_A_X = Korg.format_A_X(sun_Fe_H)
+                sun_atm = Korg.read_model_atmosphere("data/sun.mod")
+
+                linelist = [
+                    Korg.Line(5054.642 * 1e-8, -1.92100, Korg.Species("26.0"), 3.64, 4.68e-32),
+                    Korg.Line(5044.211 * 1e-8, -2.05800, Korg.Species("26.0"), 2.8512, 2.71e-31)
+                ]
+                sun_ews = [74.3, 40.5]
+                @test_throws ArgumentError Korg.Fit.ews_to_abundances(sun_atm, linelist, sun_A_X,
+                                                                      sun_ews, vmic=sun_vmic)
+            end
 
-        @testset "disallow molecules" begin
-            sun_Teff, sun_logg, sun_Fe_H, sun_vmic = (5777, 4.44, 0.0, 1.0)
-            sun_A_X = Korg.format_A_X(sun_Fe_H)
-            sun_atm = Korg.read_model_atmosphere("data/sun.mod")
-            linelist = [
-                Korg.Line(5044.211 * 1e-8, -2.05800, Korg.Species("26.0"), 2.8512, 2.71e-31),
-                Korg.Line(5044.211 * 1e-8, -2.05800, Korg.Species("106.0"), 2.8512, 2.71e-31)]
-            sun_ews = [74.3, 40.5]
-            @test_throws ArgumentError Korg.Fit.ews_to_abundances(sun_atm, linelist, sun_A_X,
-                                                                  sun_ews, vmic=sun_vmic)
-        end
+            @testset "length of linelist and ews should match" begin
+                sun_Teff, sun_logg, sun_Fe_H, sun_vmic = (5777, 4.44, 0.0, 1.0)
+                sun_A_X = Korg.format_A_X(sun_Fe_H)
+                sun_atm = Korg.read_model_atmosphere("data/sun.mod")
+                linelist = [Korg.Line(5044.211 * 1e-8, -2.05800, Korg.Species("26.0"), 2.8512,
+                                      2.71e-31)]
+                @test_throws ArgumentError Korg.Fit.ews_to_abundances(sun_atm, linelist, sun_A_X,
+                                                                      [],
+                                                                      vmic=sun_vmic)
+            end
 
-        @testset "Melendez et al. (2014) sanity check" begin
-            sun_ews = [74.3, 40.5, 96.1, 19.1, 80.7]
-            sco_ews = [74.8, 40.9, 97.5, 18.9, 84.0]
-            linelist = [
-                Korg.Line(5044.211 * 1e-8, -2.05800, Korg.Species("26.0"), 2.8512, 2.71e-31),
-                Korg.Line(5054.642 * 1e-8, -1.92100, Korg.Species("26.0"), 3.64, 4.68e-32),
-                Korg.Line(5127.359 * 1e-8, -3.30700, Korg.Species("26.0"), 0.915, 1.84e-32),
-                # don't convert these ones to cm.  It should still work.
-                Korg.Line(5127.679, -6.12500, Korg.Species("26.0"), 0.052, 1.2e-32),
-                Korg.Line(5197.577, -2.22000, Korg.Species("26.1"), 3.2306, 8.69e-33)
-            ]
-            sun_Teff, sun_logg, sun_Fe_H, sun_vmic = (5777, 4.44, 0.0, 1.0)
-            sun_A_X = Korg.format_A_X(sun_Fe_H)
-            sun_atm = Korg.read_model_atmosphere("data/sun.mod")
-
-            sco_teff, sco_logg, sco_fe_h, sco_vmic = (5823, 4.45, 0.054, sun_vmic + 0.02)
-            sco_A_X = Korg.format_A_X(sco_fe_h)
-            # Note: NOT true, but just for testing the whole performance
-            sco_atm = sun_atm
-
-            sun_abundances = Korg.Fit.ews_to_abundances(sun_atm, linelist, sun_A_X, sun_ews;
-                                                        vmic=sun_vmic)
-            sco_abundances = Korg.Fit.ews_to_abundances(sco_atm, linelist, sco_A_X, sco_ews;
-                                                        vmic=sco_vmic)
-            diff_abundances = sco_abundances .- sun_abundances
-
-            mean_diff_abundances = sum(diff_abundances) / length(diff_abundances)
-            @test abs(mean_diff_abundances) < 0.05
-            # TODO: test for stddev?        
-        end
-    end
+            @testset "disallow molecules" begin
+                sun_Teff, sun_logg, sun_Fe_H, sun_vmic = (5777, 4.44, 0.0, 1.0)
+                sun_A_X = Korg.format_A_X(sun_Fe_H)
+                sun_atm = Korg.read_model_atmosphere("data/sun.mod")
+                linelist = [
+                    Korg.Line(5044.211 * 1e-8, -2.05800, Korg.Species("26.0"), 2.8512, 2.71e-31),
+                    Korg.Line(5044.211 * 1e-8, -2.05800, Korg.Species("106.0"), 2.8512, 2.71e-31)]
+                sun_ews = [74.3, 40.5]
+                @test_throws ArgumentError Korg.Fit.ews_to_abundances(sun_atm, linelist, sun_A_X,
+                                                                      sun_ews, vmic=sun_vmic)
+            end
 
-    @testset "stellar parameters via EWs" begin
-        # used in both tests below
-        good_linelist = [Korg.Line(5e-5, -2.05800, Korg.species"Fe I", 4.1),
-            Korg.Line(6e-5, -1.92100, Korg.species"Fe I", 4.2),
-            Korg.Line(7e-5, -1.92100, Korg.species"Fe I", 4.3),
-            Korg.Line(8e-5, -1.92100, Korg.species"Fe II", 4.4)]
-
-        @testset "validate arguments" begin
-            # vmic can't be specified
-            @test_throws ArgumentError Korg.Fit.ews_to_stellar_parameters(good_linelist,
-                                                                          ones(length(good_linelist));
-                                                                          vmic=1.0)
-
-            # number of EWs, EW uncertainties, and lines should match
-            @test_throws ArgumentError Korg.Fit.ews_to_stellar_parameters(good_linelist, [1.0])
-            @test_throws ArgumentError Korg.Fit.ews_to_stellar_parameters(good_linelist,
-                                                                          ones(length(good_linelist)),
-                                                                          [1.0])
-
-            # different elements
-            linelist = [Korg.Line(5e-5, -2.05800, Korg.species"Fe I", 0, 0),
-                Korg.Line(6e-5, -1.92100, Korg.species"Fe I", 0, 0),
-                Korg.Line(7e-5, -1.92100, Korg.species"Mn I", 0, 0),
-                Korg.Line(8e-5, -1.92100, Korg.species"Fe II", 0, 0)]
-            @test_throws ArgumentError Korg.Fit.ews_to_stellar_parameters(linelist,
-                                                                          ones(length(linelist)))
-
-            # can't use a molecule
-            linelist = [Korg.Line(5e-5, -2.05800, Korg.species"CO I", 0, 0),
-                Korg.Line(6e-5, -1.92100, Korg.species"CO I", 0, 0),
-                Korg.Line(7e-5, -1.92100, Korg.species"CO I", 0, 0),
-                Korg.Line(8e-5, -1.92100, Korg.species"CO II", 0, 0)]
-            @test_throws ArgumentError Korg.Fit.ews_to_stellar_parameters(linelist,
-                                                                          ones(length(linelist)))
-
-            # no ions
-            linelist = [Korg.Line(5e-5, -2.05800, Korg.species"Fe I", 0, 0),
-                Korg.Line(6e-5, -1.92100, Korg.species"Fe I", 0, 0),
-                Korg.Line(6.5e-5, -1.92100, Korg.species"Fe I", 0, 0),
-                Korg.Line(7e-5, -1.92100, Korg.species"Fe I", 0, 0)]
-            @test_throws ArgumentError Korg.Fit.ews_to_stellar_parameters(linelist,
-                                                                          ones(length(linelist)))
-
-            # not enough lines
-            linelist = [Korg.Line(5e-5, -2.05800, Korg.species"Fe I", 0, 0),
-                Korg.Line(6e-5, -1.92100, Korg.species"Fe I", 0, 0),
-                Korg.Line(7e-5, -1.92100, Korg.species"Fe II", 0, 0)]
-            @test_throws ArgumentError Korg.Fit.ews_to_stellar_parameters(linelist,
-                                                                          ones(length(linelist)))
-
-            # parameter ranges must have positive measure
-            @test_throws ArgumentError Korg.Fit.ews_to_stellar_parameters(good_linelist,
-                                                                          ones(length(good_linelist));
-                                                                          parameter_ranges=[(5777,
-                                                                                             5777),
-                                                                              (3.0, 4.0),
-                                                                              (1.0, 2.0),
-                                                                              (-0.5, 0.0)])
-
-            # vmic0 can't be 0
-            @test_throws ArgumentError Korg.Fit.ews_to_stellar_parameters(good_linelist,
-                                                                          ones(length(good_linelist));
-                                                                          vmic0=0)
+            @testset "Melendez et al. (2014) sanity check" begin
+                sun_ews = [74.3, 40.5, 96.1, 19.1, 80.7]
+                sco_ews = [74.8, 40.9, 97.5, 18.9, 84.0]
+                linelist = [
+                    Korg.Line(5044.211 * 1e-8, -2.05800, Korg.Species("26.0"), 2.8512, 2.71e-31),
+                    Korg.Line(5054.642 * 1e-8, -1.92100, Korg.Species("26.0"), 3.64, 4.68e-32),
+                    Korg.Line(5127.359 * 1e-8, -3.30700, Korg.Species("26.0"), 0.915, 1.84e-32),
+                    # don't convert these ones to cm.  It should still work.
+                    Korg.Line(5127.679, -6.12500, Korg.Species("26.0"), 0.052, 1.2e-32),
+                    Korg.Line(5197.577, -2.22000, Korg.Species("26.1"), 3.2306, 8.69e-33)
+                ]
+                sun_Teff, sun_logg, sun_Fe_H, sun_vmic = (5777, 4.44, 0.0, 1.0)
+                sun_A_X = Korg.format_A_X(sun_Fe_H)
+                sun_atm = Korg.read_model_atmosphere("data/sun.mod")
+
+                sco_teff, sco_logg, sco_fe_h, sco_vmic = (5823, 4.45, 0.054, sun_vmic + 0.02)
+                sco_A_X = Korg.format_A_X(sco_fe_h)
+                # Note: NOT true, but just for testing the whole performance
+                sco_atm = sun_atm
+
+                sun_abundances = Korg.Fit.ews_to_abundances(sun_atm, linelist, sun_A_X, sun_ews;
+                                                            vmic=sun_vmic)
+                sco_abundances = Korg.Fit.ews_to_abundances(sco_atm, linelist, sco_A_X, sco_ews;
+                                                            vmic=sco_vmic)
+                diff_abundances = sco_abundances .- sun_abundances
+
+                mean_diff_abundances = sum(diff_abundances) / length(diff_abundances)
+                @test abs(mean_diff_abundances) < 0.05
+                # TODO: test for stddev?        
+            end
         end
 
-        @testset "basic fit" begin
-            # 2 Å wide window around each line
-            synth_wls = map(good_linelist) do line
-                wl = line.wl * 1e8
-                wl-1.0:0.01:wl+1.0
-            end
-            sol = synthesize(interpolate_marcs(5777.0, 4.44), good_linelist, format_A_X(),
-                             synth_wls; hydrogen_lines=false)
-
-            # EWs you get for the fake linelist with solar params
-            # the real implementation uses the trapezoid rule, but this is close enough
-            EWs = [sum((1 .- sol.flux./sol.cntm)[r]) for r in sol.subspectra] * 10 #0.01 Å -> mÅ
-            EW_err = ones(length(EWs)) * 0.5
-            best_fit_params, stat_err, sys_err = Korg.Fit.ews_to_stellar_parameters(good_linelist,
-                                                                                    EWs, EW_err)
-
-            @test sys_err == [0.0, 0.0, 0.0, 0.0]
-            for (i, p) in enumerate([5777.0, 4.44, 1.0, 0.0])
-                @test best_fit_params[i]≈p atol=stat_err[i]
+        @testset "stellar parameters via EWs" begin
+            # used in both tests below
+            good_linelist = [Korg.Line(5e-5, -2.05800, Korg.species"Fe I", 4.1),
+                Korg.Line(6e-5, -1.92100, Korg.species"Fe I", 4.2),
+                Korg.Line(7e-5, -1.92100, Korg.species"Fe I", 4.3),
+                Korg.Line(8e-5, -1.92100, Korg.species"Fe II", 4.4)]
+
+            @testset "validate arguments" begin
+                # vmic can't be specified
+                @test_throws ArgumentError Korg.Fit.ews_to_stellar_parameters(good_linelist,
+                                                                              ones(length(good_linelist));
+                                                                              vmic=1.0)
+
+                # number of EWs, EW uncertainties, and lines should match
+                @test_throws ArgumentError Korg.Fit.ews_to_stellar_parameters(good_linelist, [1.0])
+                @test_throws ArgumentError Korg.Fit.ews_to_stellar_parameters(good_linelist,
+                                                                              ones(length(good_linelist)),
+                                                                              [1.0])
+
+                # different elements
+                linelist = [Korg.Line(5e-5, -2.05800, Korg.species"Fe I", 0, 0),
+                    Korg.Line(6e-5, -1.92100, Korg.species"Fe I", 0, 0),
+                    Korg.Line(7e-5, -1.92100, Korg.species"Mn I", 0, 0),
+                    Korg.Line(8e-5, -1.92100, Korg.species"Fe II", 0, 0)]
+                @test_throws ArgumentError Korg.Fit.ews_to_stellar_parameters(linelist,
+                                                                              ones(length(linelist)))
+
+                # can't use a molecule
+                linelist = [Korg.Line(5e-5, -2.05800, Korg.species"CO I", 0, 0),
+                    Korg.Line(6e-5, -1.92100, Korg.species"CO I", 0, 0),
+                    Korg.Line(7e-5, -1.92100, Korg.species"CO I", 0, 0),
+                    Korg.Line(8e-5, -1.92100, Korg.species"CO II", 0, 0)]
+                @test_throws ArgumentError Korg.Fit.ews_to_stellar_parameters(linelist,
+                                                                              ones(length(linelist)))
+
+                # no ions
+                linelist = [Korg.Line(5e-5, -2.05800, Korg.species"Fe I", 0, 0),
+                    Korg.Line(6e-5, -1.92100, Korg.species"Fe I", 0, 0),
+                    Korg.Line(6.5e-5, -1.92100, Korg.species"Fe I", 0, 0),
+                    Korg.Line(7e-5, -1.92100, Korg.species"Fe I", 0, 0)]
+                @test_throws ArgumentError Korg.Fit.ews_to_stellar_parameters(linelist,
+                                                                              ones(length(linelist)))
+
+                # not enough lines
+                linelist = [Korg.Line(5e-5, -2.05800, Korg.species"Fe I", 0, 0),
+                    Korg.Line(6e-5, -1.92100, Korg.species"Fe I", 0, 0),
+                    Korg.Line(7e-5, -1.92100, Korg.species"Fe II", 0, 0)]
+                @test_throws ArgumentError Korg.Fit.ews_to_stellar_parameters(linelist,
+                                                                              ones(length(linelist)))
+
+                # parameter ranges must have positive measure
+                @test_throws ArgumentError Korg.Fit.ews_to_stellar_parameters(good_linelist,
+                                                                              ones(length(good_linelist));
+                                                                              parameter_ranges=[(5777,
+                                                                                                 5777),
+                                                                                  (3.0, 4.0),
+                                                                                  (1.0, 2.0),
+                                                                                  (-0.5, 0.0)])
+
+                # vmic0 can't be 0
+                @test_throws ArgumentError Korg.Fit.ews_to_stellar_parameters(good_linelist,
+                                                                              ones(length(good_linelist));
+                                                                              vmic0=0)
             end
 
-            # check that fixing parameters works
-            for (i, param) in enumerate([:Teff0, :logg0, :vmic0, :m_H0])
-                fixed_params = zeros(Bool, 4)
-                fixed_params[i] = true
-                kwargs = Dict(param => best_fit_params[i])
-                # start teff and logg close to right answer to make it faster.
-                bestfit_fixed, _, _ = Korg.Fit.ews_to_stellar_parameters(good_linelist, EWs, EW_err;
-                                                                         Teff0=5740.0, logg0=4.4,
-                                                                         fix_params=fixed_params,
-                                                                         kwargs...)
-
-                for i in 1:4
-                    @test bestfit_fixed[i]≈best_fit_params[i] atol=stat_err[i]/10
+            @testset "basic EW fit" begin
+                # 2 Å wide window around each line
+                synth_wls = map(good_linelist) do line
+                    wl = line.wl * 1e8
+                    wl-1.0:0.01:wl+1.0
+                end
+                sol = synthesize(interpolate_marcs(5777.0, 4.44), good_linelist, format_A_X(),
+                                 synth_wls; hydrogen_lines=false)
+
+                # EWs you get for the fake linelist with solar params
+                # the real implementation uses the trapezoid rule, but this is close enough
+                EWs = [sum((1 .- sol.flux./sol.cntm)[r]) for r in sol.subspectra] * 10 #0.01 Å -> mÅ
+                EW_err = ones(length(EWs)) * 0.5
+                best_fit_params, stat_err, sys_err = Korg.Fit.ews_to_stellar_parameters(good_linelist,
+                                                                                        EWs, EW_err)
+
+                @test sys_err == [0.0, 0.0, 0.0, 0.0]
+                for (i, p) in enumerate([5777.0, 4.44, 1.0, 0.0])
+                    @test best_fit_params[i]≈p atol=stat_err[i]
+                end
+
+                # check that fixing parameters works
+                for (i, param) in enumerate([:Teff0, :logg0, :vmic0, :m_H0])
+                    fixed_params = zeros(Bool, 4)
+                    fixed_params[i] = true
+                    kwargs = Dict(param => best_fit_params[i])
+                    # start teff and logg close to right answer to make it faster.
+                    bestfit_fixed, _, _ = Korg.Fit.ews_to_stellar_parameters(good_linelist, EWs,
+                                                                             EW_err;
+                                                                             Teff0=5740.0,
+                                                                             logg0=4.4,
+                                                                             fix_params=fixed_params,
+                                                                             kwargs...)
+
+                    for i in 1:4
+                        @test bestfit_fixed[i]≈best_fit_params[i] atol=stat_err[i]/10
+                    end
                 end
             end
         end