diff --git a/M31_Example/.ipynb_checkpoints/M31_BEAST_workflow_example-checkpoint.ipynb b/M31_Example/.ipynb_checkpoints/M31_BEAST_workflow_example-checkpoint.ipynb
new file mode 100644
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+++ b/M31_Example/.ipynb_checkpoints/M31_BEAST_workflow_example-checkpoint.ipynb
@@ -0,0 +1,2703 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# BEAST Workflow Example\n",
+    "\n",
+    "In this notebook we will be walking through a standard BEAST workflow example using some data from M31.\n",
+    "\n",
+    "You'll need a couple of datafiles to get started though. Please visit https://www.dropbox.com/sh/91aefrp9gzdc9z0/AAC9Gc4KIRIB520g6a0uLLama?dl=0 and download all the files (can omit wrangling_data.ipynb) into the same folder this Jupyter Notebook is in. \n",
+    "\n",
+    "Before we do anything, we have to import the following packages. This seems like a lot but they are all here to make our lives easier down the line. And running them all as the first cell means that if our kernel ever crashes halfway through, we can just reimport everything at once rather than stepping through the cells individually."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Auto-detected type: fits\n",
+      "Auto-detected type: fits\n"
+     ]
+    }
+   ],
+   "source": [
+    "import h5py\n",
+    "\n",
+    "import numpy as np\n",
+    "from astropy import wcs\n",
+    "from astropy.io import fits\n",
+    "from astropy.table import Table\n",
+    "#import tables\n",
+    "\n",
+    "import glob\n",
+    "import os\n",
+    "import types\n",
+    "import argparse\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "from beast.plotting import (plot_mag_hist, plot_ast_histogram, plot_noisemodel)\n",
+    "\n",
+    "from beast.tools.run import (\n",
+    "    create_physicsmodel,\n",
+    "    make_ast_inputs,\n",
+    "    create_obsmodel,\n",
+    "    run_fitting,\n",
+    "    merge_files,\n",
+    "    create_filenames,\n",
+    ")\n",
+    "\n",
+    "from beast.physicsmodel.grid import FileSEDGrid\n",
+    "from beast.fitting import trim_grid\n",
+    "import beast.observationmodel.noisemodel.generic_noisemodel as noisemodel\n",
+    "\n",
+    "\n",
+    "from beast.tools.run import (\n",
+    "    run_fitting,\n",
+    "    merge_files,\n",
+    ")    \n",
+    "    \n",
+    "from beast.tools import (\n",
+    "     create_background_density_map,\n",
+    "     split_ast_input_file,\n",
+    "     split_catalog_using_map,\n",
+    "#     subdivide_obscat_by_source_density,\n",
+    "     cut_catalogs,\n",
+    "#     split_asts_by_source_density,\n",
+    "     setup_batch_beast_trim,\n",
+    "#     setup_batch_beast_fit,\n",
+    " )\n",
+    "\n",
+    "import importlib"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Step -1. Obtain data file and convert to fits file\n",
+    "\n",
+    "Sometimes photometric catalogs are delivered as HDF5 files. While these are great for storing data in heirarchies, it's a little hard to work with directly, so we have to convert our HDF5 file to a FITS file.\n",
+    "\n",
+    "Thankfully, our photometric catalog for this example is already in a FITS format so we don't need to worry about this and can move straight on to Step 1."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Step 1a. Make magnitude histograms\n",
+    "\n",
+    "The first thing we need to do is understand the range of stellar magnitudes we are working with in this data set.\n",
+    "\n",
+    "To do this we can make histograms of all the magnitudes of all the stars in all the different filters from the photometric catalog. This is done so that we know where the peaks of the histograms are in each filter. These peaks will then be used later when we make source density maps. \n",
+    "\n",
+    "Essentially what happens is that, for the density maps, we only count objects within a certain range, currently set to mag_cut = 15 - (peak_for_filter-0.5). So if the peak was 17.5, then the objects that would be counted would have to be in the range between 15 and 18. \n",
+    "\n",
+    "The reason we only count brighter sources is because dimmer sources tend to not be properly observed, especially as the magnitudes near the telescope limit. There will always be far more dim sources than bright sources, but if we know how many bright sources there are, then we can extrapolate as to how many dim sources there should be, and probably get a better understand from that than if we were to try and actually count all the dim sources we detect.  \n",
+    "\n",
+    "**Variable Information**\n",
+    "\n",
+    "* **field_name** : the string name of the main photometric catalog we are working with. This variable will be used to rename a lot of different files in the future which is why we have it as a separate variable.\n",
+    "* **gst_file** : stands for good-stars, this is the full name for the original photometric catalog we are working with."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "field_name = \"M31-B09-EAST_chunk\"\n",
+    "gst_file = \"./%s.st.fits\" %field_name"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can see what type of data this fits file holds by making a table. There should be around 50,000 sources in this calalog, which is quite small compared to the original file.\n",
+    "\n",
+    "*Note: **st** stands for stars. We also sometimes name things **gst** for good stars to signify when cuts have been made.*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<i>Table length=50625</i>\n",
+       "<table id=\"table140635094349304\" class=\"table-striped table-bordered table-condensed\">\n",
+       "<thead><tr><th>F814W_ST</th><th>F814W_GST</th><th>F475W_ST</th><th>F475W_GST</th><th>F275W_ST</th><th>F275W_GST</th><th>F160W_ST</th><th>F160W_GST</th><th>F336W_ST</th><th>F336W_GST</th><th>F110W_ST</th><th>F110W_GST</th><th>X</th><th>Y</th><th>RA</th><th>DEC</th><th>CHI_GL</th><th>SNR_GL</th><th>SHARP_GL</th><th>ROUND_GL</th><th>CROWD_GL</th><th>F475W_COUNT</th><th>F475W_SKY</th><th>F475W_RATE</th><th>F475W_RATERR</th><th>F475W_VEGA</th><th>F475W_TRANS</th><th>F475W_ERR</th><th>F475W_CHI</th><th>F475W_SNR</th><th>F475W_SHARP</th><th>F475W_ROUND</th><th>F475W_CROWD</th><th>F814W_COUNT</th><th>F814W_SKY</th><th>F814W_RATE</th><th>F814W_RATERR</th><th>F814W_VEGA</th><th>F814W_TRANS</th><th>F814W_ERR</th><th>F814W_CHI</th><th>F814W_SNR</th><th>F814W_SHARP</th><th>F814W_ROUND</th><th>F814W_CROWD</th><th>F275W_COUNT</th><th>F275W_SKY</th><th>F275W_RATE</th><th>F275W_RATERR</th><th>F275W_VEGA</th><th>F275W_TRANS</th><th>F275W_ERR</th><th>F275W_CHI</th><th>F275W_SNR</th><th>F275W_SHARP</th><th>F275W_ROUND</th><th>F275W_CROWD</th><th>F336W_COUNT</th><th>F336W_SKY</th><th>F336W_RATE</th><th>F336W_RATERR</th><th>F336W_VEGA</th><th>F336W_TRANS</th><th>F336W_ERR</th><th>F336W_CHI</th><th>F336W_SNR</th><th>F336W_SHARP</th><th>F336W_ROUND</th><th>F336W_CROWD</th><th>F110W_COUNT</th><th>F110W_SKY</th><th>F110W_RATE</th><th>F110W_RATERR</th><th>F110W_VEGA</th><th>F110W_TRANS</th><th>F110W_ERR</th><th>F110W_CHI</th><th>F110W_SNR</th><th>F110W_SHARP</th><th>F110W_ROUND</th><th>F110W_CROWD</th><th>F160W_COUNT</th><th>F160W_SKY</th><th>F160W_RATE</th><th>F160W_RATERR</th><th>F160W_VEGA</th><th>F160W_TRANS</th><th>F160W_ERR</th><th>F160W_CHI</th><th>F160W_SNR</th><th>F160W_SHARP</th><th>F160W_ROUND</th><th>F160W_CROWD</th><th>EXT</th><th>CHIP</th><th>MAJAX_GL</th><th>OBJTYPE_GL</th><th>F475W_FLAG</th><th>F814W_FLAG</th><th>F275W_FLAG</th><th>F336W_FLAG</th><th>F110W_FLAG</th><th>F160W_FLAG</th></tr></thead>\n",
+       "<thead><tr><th>bool</th><th>bool</th><th>bool</th><th>bool</th><th>bool</th><th>bool</th><th>bool</th><th>bool</th><th>bool</th><th>bool</th><th>bool</th><th>bool</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>int64</th><th>int64</th><th>int64</th><th>int64</th><th>int64</th><th>int64</th><th>int64</th><th>int64</th><th>int64</th><th>int64</th></tr></thead>\n",
+       "<tr><td>True</td><td>True</td><td>True</td><td>True</td><td>True</td><td>True</td><td>True</td><td>True</td><td>True</td><td>True</td><td>True</td><td>True</td><td>869.8900000046692</td><td>44.40999999483233</td><td>11.131832570656686</td><td>41.603419438518735</td><td>4.03</td><td>2917.0</td><td>-0.03</td><td>0.02</td><td>0.251</td><td>233000000.0</td><td>87358.19</td><td>9.039999999999999e-08</td><td>1.3800000000000002e-09</td><td>17.61</td><td>17.721</td><td>0.017</td><td>2.48</td><td>65.4</td><td>0.005</td><td>0.038</td><td>0.175</td><td>198000000.0</td><td>102360.6</td><td>1.59e-07</td><td>1.27e-09</td><td>16.996</td><td>16.986</td><td>0.009000000000000001</td><td>1.77</td><td>124.8</td><td>0.01</td><td>0.049</td><td>0.22699999999999998</td><td>5970000.0</td><td>2256.98</td><td>1.25e-07</td><td>1.16e-09</td><td>17.255</td><td>nan</td><td>0.01</td><td>2.92</td><td>107.7</td><td>-0.038</td><td>0.018000000000000002</td><td>0.086</td><td>23800000.0</td><td>7534.45</td><td>1.69e-07</td><td>6.92e-10</td><td>16.932000000000002</td><td>nan</td><td>0.004</td><td>2.63</td><td>243.9</td><td>-0.031</td><td>0.059000000000000004</td><td>0.084</td><td>79700000.0</td><td>88039.23</td><td>1.6e-07</td><td>2.58e-10</td><td>16.992</td><td>nan</td><td>0.002</td><td>2.23</td><td>619.1</td><td>-0.004</td><td>0.1</td><td>0.35</td><td>56000000.0</td><td>93657.59</td><td>1.8e-07</td><td>2.53e-10</td><td>16.864</td><td>nan</td><td>0.002</td><td>1.51</td><td>708.8</td><td>-0.018000000000000002</td><td>0.027000000000000003</td><td>0.396</td><td>0</td><td>1</td><td>165</td><td>1</td><td>2</td><td>2</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
+       "<tr><td>True</td><td>True</td><td>True</td><td>True</td><td>True</td><td>True</td><td>True</td><td>True</td><td>True</td><td>True</td><td>True</td><td>True</td><td>868.3900000040028</td><td>42.189999993549804</td><td>11.13180414033402</td><td>41.60338891056161</td><td>4.13</td><td>2566.5</td><td>-0.05</td><td>0.028999999999999998</td><td>0.27699999999999997</td><td>125000000.0</td><td>46004.84</td><td>4.85e-08</td><td>2.51e-10</td><td>18.285999999999998</td><td>18.412</td><td>0.006</td><td>3.02</td><td>193.3</td><td>-0.053</td><td>-0.006</td><td>0.231</td><td>118000000.0</td><td>77080.48</td><td>9.23e-08</td><td>1.24e-09</td><td>17.587</td><td>17.577</td><td>0.015</td><td>5.87</td><td>74.3</td><td>-0.096</td><td>0.044000000000000004</td><td>0.324</td><td>4360000.0</td><td>2332.74</td><td>9.15e-08</td><td>7.099999999999999e-10</td><td>17.597</td><td>nan</td><td>0.008</td><td>2.08</td><td>128.8</td><td>-0.057</td><td>0.008</td><td>0.092</td><td>16500000.0</td><td>8339.99</td><td>1.1699999999999999e-07</td><td>9.25e-10</td><td>17.329</td><td>nan</td><td>0.009000000000000001</td><td>3.74</td><td>126.5</td><td>-0.085</td><td>-0.006999999999999999</td><td>0.106</td><td>49500000.0</td><td>82165.18</td><td>9.92e-08</td><td>2.82e-10</td><td>17.508</td><td>nan</td><td>0.003</td><td>2.79</td><td>352.5</td><td>0.013000000000000001</td><td>-0.079</td><td>0.516</td><td>35100000.0</td><td>87515.55</td><td>1.13e-07</td><td>3.31e-10</td><td>17.371</td><td>nan</td><td>0.003</td><td>2.18</td><td>340.0</td><td>-0.026000000000000002</td><td>-0.004</td><td>0.635</td><td>0</td><td>1</td><td>75</td><td>1</td><td>2</td><td>2</td><td>0</td><td>2</td><td>0</td><td>0</td></tr>\n",
+       "<tr><td>True</td><td>True</td><td>False</td><td>False</td><td>False</td><td>False</td><td>True</td><td>True</td><td>False</td><td>False</td><td>True</td><td>True</td><td>297.1500000049341</td><td>22.169999994536283</td><td>11.135275566985227</td><td>41.595888643103535</td><td>1.17</td><td>1663.1</td><td>-0.08800000000000001</td><td>0.061</td><td>0.008</td><td>29910.7</td><td>10270.95</td><td>1.1499999999999999e-11</td><td>1.34e-12</td><td>27.348000000000003</td><td>27.805999999999997</td><td>0.126</td><td>1.34</td><td>8.6</td><td>-0.634</td><td>0.177</td><td>0.025</td><td>4160000.0</td><td>21190.25</td><td>3.17e-09</td><td>1.0000000000000001e-11</td><td>21.248</td><td>21.423000000000002</td><td>0.003</td><td>0.9</td><td>316.3</td><td>-0.063</td><td>0.049</td><td>0.001</td><td>-1.7</td><td>44.5</td><td>-6.900000000000001e-14</td><td>1.49e-11</td><td>nan</td><td>nan</td><td>9.999</td><td>0.87</td><td>-0.0</td><td>1.058</td><td>2.951</td><td>0.002</td><td>-565.3</td><td>268.32</td><td>-4.1e-12</td><td>6.389999999999999e-12</td><td>nan</td><td>nan</td><td>9.999</td><td>1.05</td><td>-0.6</td><td>0.0</td><td>0.0</td><td>0.0</td><td>39700000.0</td><td>73069.77</td><td>7.96e-08</td><td>1.1e-10</td><td>17.747</td><td>nan</td><td>0.001</td><td>1.35</td><td>723.9</td><td>0.012</td><td>-0.027000000000000003</td><td>0.011000000000000001</td><td>67100000.0</td><td>85406.73</td><td>2.15e-07</td><td>1.8399999999999998e-10</td><td>16.668</td><td>nan</td><td>0.001</td><td>1.11</td><td>1172.0</td><td>-0.004</td><td>-0.005</td><td>0.012</td><td>0</td><td>1</td><td>10</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>2</td><td>2</td></tr>\n",
+       "<tr><td>True</td><td>True</td><td>True</td><td>True</td><td>True</td><td>True</td><td>True</td><td>True</td><td>True</td><td>True</td><td>True</td><td>True</td><td>884.8300000051681</td><td>95.5499999940555</td><td>11.132618614902443</td><td>41.60386858784989</td><td>1.9</td><td>2292.6</td><td>-0.025</td><td>0.013000000000000001</td><td>0.01</td><td>66100000.0</td><td>22123.93</td><td>2.5299999999999998e-08</td><td>2.46e-11</td><td>18.99</td><td>18.977</td><td>0.001</td><td>1.67</td><td>1031.4</td><td>-0.023</td><td>0.013999999999999999</td><td>0.0</td><td>34800000.0</td><td>30329.55</td><td>2.64e-08</td><td>2.7e-11</td><td>18.944000000000003</td><td>18.934</td><td>0.001</td><td>1.23</td><td>980.7</td><td>0.004</td><td>0.018000000000000002</td><td>0.002</td><td>5440000.0</td><td>1002.34</td><td>1.14e-07</td><td>1.13e-09</td><td>17.355999999999998</td><td>nan</td><td>0.011000000000000001</td><td>2.74</td><td>101.0</td><td>-0.059000000000000004</td><td>0.046</td><td>0.0</td><td>12000000.0</td><td>2448.62</td><td>8.51e-08</td><td>5.41e-10</td><td>17.676</td><td>nan</td><td>0.006999999999999999</td><td>2.98</td><td>157.3</td><td>-0.085</td><td>0.037000000000000005</td><td>0.0</td><td>11400000.0</td><td>75079.13</td><td>2.28e-08</td><td>5.56e-11</td><td>19.104</td><td>nan</td><td>0.003</td><td>1.38</td><td>411.0</td><td>-0.045</td><td>-0.009000000000000001</td><td>0.03</td><td>7260000.0</td><td>80299.63</td><td>2.33e-08</td><td>5.929999999999999e-11</td><td>19.082</td><td>nan</td><td>0.003</td><td>0.93</td><td>392.4</td><td>-0.017</td><td>-0.003</td><td>0.079</td><td>0</td><td>1</td><td>130</td><td>1</td><td>0</td><td>0</td><td>2</td><td>0</td><td>0</td><td>0</td></tr>\n",
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+       "<tr><td>True</td><td>True</td><td>True</td><td>True</td><td>False</td><td>False</td><td>True</td><td>True</td><td>False</td><td>False</td><td>True</td><td>True</td><td>238.33000000445975</td><td>474.95999999429125</td><td>11.14351186487519</td><td>41.597389281034474</td><td>2.03</td><td>1436.1</td><td>-0.083</td><td>0.183</td><td>0.10800000000000001</td><td>33068.2</td><td>9542.11</td><td>1.28e-11</td><td>2.0299999999999996e-12</td><td>27.233</td><td>27.881</td><td>0.172</td><td>2.4</td><td>6.3</td><td>-0.405</td><td>0.633</td><td>0.354</td><td>1180000.0</td><td>18492.85</td><td>9.01e-10</td><td>1.1099999999999999e-11</td><td>22.613000000000003</td><td>22.715</td><td>0.013000000000000001</td><td>2.24</td><td>81.5</td><td>-0.065</td><td>0.201</td><td>0.10099999999999999</td><td>-962.1</td><td>3.96</td><td>-2.0000000000000002e-11</td><td>1.56e-11</td><td>nan</td><td>nan</td><td>9.999</td><td>0.95</td><td>-1.3</td><td>0.0</td><td>0.0</td><td>0.0</td><td>-724.2</td><td>249.83</td><td>-5.3e-12</td><td>5.679999999999999e-12</td><td>nan</td><td>nan</td><td>9.999</td><td>1.03</td><td>-0.9</td><td>0.0</td><td>0.0</td><td>0.0</td><td>28500000.0</td><td>67956.21</td><td>5.71e-08</td><td>6.23e-11</td><td>18.108</td><td>nan</td><td>0.001</td><td>1.03</td><td>916.4</td><td>-0.003</td><td>-0.008</td><td>0.03</td><td>41000000.0</td><td>77900.65</td><td>1.31e-07</td><td>1.42e-10</td><td>17.204</td><td>nan</td><td>0.001</td><td>1.15</td><td>924.9</td><td>0.005</td><td>0.009000000000000001</td><td>0.047</td><td>0</td><td>1</td><td>125</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
+       "<tr><td>True</td><td>True</td><td>True</td><td>True</td><td>True</td><td>True</td><td>True</td><td>True</td><td>True</td><td>True</td><td>True</td><td>True</td><td>824.9700000049659</td><td>176.12999999423107</td><td>11.134414672143825</td><td>41.603495862988545</td><td>1.71</td><td>2286.3</td><td>-0.013000000000000001</td><td>0.01</td><td>0.015</td><td>48100000.0</td><td>18255.8</td><td>1.85e-08</td><td>1.7800000000000003e-11</td><td>19.333</td><td>19.359</td><td>0.001</td><td>1.61</td><td>1040.5</td><td>-0.004</td><td>0.013999999999999999</td><td>0.006999999999999999</td><td>29200000.0</td><td>28051.12</td><td>2.2200000000000004e-08</td><td>1.81e-11</td><td>19.133</td><td>19.123</td><td>0.001</td><td>1.11</td><td>1230.7</td><td>0.005</td><td>0.011000000000000001</td><td>0.006999999999999999</td><td>1690000.0</td><td>448.87</td><td>3.54e-08</td><td>5.58e-10</td><td>18.628</td><td>nan</td><td>0.017</td><td>2.61</td><td>63.4</td><td>-0.07400000000000001</td><td>-0.017</td><td>0.0</td><td>5410000.0</td><td>1424.62</td><td>3.84e-08</td><td>3.85e-10</td><td>18.541</td><td>nan</td><td>0.011000000000000001</td><td>2.95</td><td>99.7</td><td>-0.085</td><td>0.02</td><td>0.0</td><td>10200000.0</td><td>70618.53</td><td>2.04e-08</td><td>4.5299999999999995e-11</td><td>19.224</td><td>nan</td><td>0.002</td><td>1.03</td><td>450.7</td><td>-0.012</td><td>-0.019</td><td>0.09</td><td>6700000.0</td><td>76931.27</td><td>2.15e-08</td><td>6.58e-11</td><td>19.17</td><td>nan</td><td>0.003</td><td>1.08</td><td>326.6</td><td>-0.001</td><td>0.012</td><td>0.09</td><td>0</td><td>1</td><td>135</td><td>1</td><td>2</td><td>0</td><td>2</td><td>2</td><td>0</td><td>0</td></tr>\n",
+       "<tr><td>True</td><td>True</td><td>True</td><td>True</td><td>False</td><td>False</td><td>True</td><td>True</td><td>True</td><td>True</td><td>True</td><td>True</td><td>590.0900000061857</td><td>332.8599999953118</td><td>11.138699638637968</td><td>41.60123630320331</td><td>1.37</td><td>2254.4</td><td>-0.019</td><td>0.011000000000000001</td><td>0.006</td><td>4650000.0</td><td>11464.72</td><td>1.79e-09</td><td>4.8899999999999996e-12</td><td>21.869</td><td>22.534000000000002</td><td>0.003</td><td>1.55</td><td>365.6</td><td>-0.02</td><td>0.006999999999999999</td><td>0.003</td><td>37700000.0</td><td>30650.81</td><td>2.8700000000000002e-08</td><td>2.05e-11</td><td>18.855</td><td>18.892</td><td>0.001</td><td>1.05</td><td>1401.2</td><td>-0.015</td><td>0.008</td><td>0.002</td><td>-392.4</td><td>18.53</td><td>-8.4e-12</td><td>1.84e-11</td><td>nan</td><td>nan</td><td>9.999</td><td>1.11</td><td>-0.5</td><td>-0.004</td><td>0.9079999999999999</td><td>0.006999999999999999</td><td>8649.2</td><td>252.79</td><td>6.34e-11</td><td>9.12e-12</td><td>25.495</td><td>nan</td><td>0.156</td><td>1.11</td><td>6.9</td><td>-0.09</td><td>0.254</td><td>0.006</td><td>34700000.0</td><td>72915.04</td><td>6.950000000000001e-08</td><td>8.9e-11</td><td>17.895</td><td>nan</td><td>0.001</td><td>1.31</td><td>781.0</td><td>-0.006999999999999999</td><td>-0.034</td><td>0.024</td><td>51000000.0</td><td>83288.02</td><td>1.6300000000000002e-07</td><td>1.36e-10</td><td>16.967</td><td>nan</td><td>0.001</td><td>0.78</td><td>1203.3</td><td>-0.008</td><td>-0.0</td><td>0.027999999999999997</td><td>0</td><td>1</td><td>105</td><td>1</td><td>2</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
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+       "<tr><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>139.70000000664345</td><td>235.7399999952504</td><td>11.140027137243239</td><td>41.594916190423845</td><td>1.13</td><td>4.2</td><td>0.001</td><td>0.47</td><td>2.877</td><td>1123.5</td><td>8802.05</td><td>4.2299999999999996e-13</td><td>8.909999999999998e-13</td><td>30.934</td><td>31.000999999999998</td><td>2.286</td><td>1.11</td><td>0.5</td><td>0.18</td><td>1.0170000000000001</td><td>1.193</td><td>819.6</td><td>16521.3</td><td>6.02e-13</td><td>2.7400000000000004e-12</td><td>30.551</td><td>30.541999999999998</td><td>4.939</td><td>1.3</td><td>0.2</td><td>-0.39399999999999996</td><td>-0.866</td><td>1.8430000000000002</td><td>1414.3</td><td>6.4</td><td>2.99e-11</td><td>1.61e-11</td><td>26.31</td><td>nan</td><td>0.583</td><td>0.92</td><td>1.9</td><td>0.042</td><td>-1.079</td><td>0.02</td><td>524.0</td><td>199.47</td><td>3.96e-12</td><td>6.179999999999999e-12</td><td>28.505</td><td>nan</td><td>1.692</td><td>0.84</td><td>0.6</td><td>-0.015</td><td>2.428</td><td>0.025</td><td>5249.1</td><td>63158.89</td><td>1.05e-11</td><td>1.26e-11</td><td>27.445</td><td>nan</td><td>1.3</td><td>1.33</td><td>0.8</td><td>-0.541</td><td>-0.055</td><td>4.156000000000001</td><td>11899.2</td><td>70717.09</td><td>3.820000000000001e-11</td><td>2.1899999999999997e-11</td><td>26.046</td><td>nan</td><td>0.623</td><td>1.07</td><td>1.7</td><td>0.121</td><td>0.021</td><td>2.753</td><td>0</td><td>1</td><td>165</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
+       "<tr><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>45.02000000496014</td><td>833.5599999946328</td><td>11.151015306251551</td><td>41.59667641295129</td><td>1.11</td><td>4.8</td><td>-0.182</td><td>0.67</td><td>0.415</td><td>8471.2</td><td>9065.82</td><td>3.28e-12</td><td>1.09e-12</td><td>28.711</td><td>28.980999999999998</td><td>0.36</td><td>1.11</td><td>3.0</td><td>0.057999999999999996</td><td>0.154</td><td>0.632</td><td>13201.0</td><td>16691.07</td><td>1.0100000000000001e-11</td><td>3.18e-12</td><td>27.491999999999997</td><td>27.485</td><td>0.34299999999999997</td><td>1.26</td><td>3.2</td><td>0.159</td><td>-0.263</td><td>0.92</td><td>561.8</td><td>3.98</td><td>1.19e-11</td><td>1.35e-11</td><td>27.315</td><td>nan</td><td>1.235</td><td>0.85</td><td>0.9</td><td>-0.47</td><td>1.2</td><td>0.062</td><td>-430.5</td><td>230.36</td><td>-3.2e-12</td><td>4.150000000000001e-12</td><td>nan</td><td>nan</td><td>9.999</td><td>0.88</td><td>-0.8</td><td>-0.371</td><td>3.103</td><td>0.08800000000000001</td><td>7197.4</td><td>61992.18</td><td>1.4400000000000002e-11</td><td>8.859999999999999e-12</td><td>27.101999999999997</td><td>nan</td><td>0.667</td><td>0.91</td><td>1.6</td><td>0.47200000000000003</td><td>-0.899</td><td>3.343</td><td>351.2</td><td>68266.34</td><td>1.1100000000000001e-12</td><td>2.0699999999999997e-11</td><td>29.884</td><td>nan</td><td>9.999</td><td>1.07</td><td>0.1</td><td>-0.28300000000000003</td><td>-0.382</td><td>3.883</td><td>0</td><td>1</td><td>10</td><td>1</td><td>0</td><td>0</td><td>2</td><td>0</td><td>0</td><td>0</td></tr>\n",
+       "<tr><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>124.22000000354114</td><td>891.7099999947256</td><td>11.15149356523698</td><td>41.597992980970396</td><td>1.07</td><td>4.4</td><td>0.27899999999999997</td><td>1.075</td><td>0.871</td><td>3799.5</td><td>8730.35</td><td>1.47e-12</td><td>1.03e-12</td><td>29.581</td><td>30.06</td><td>0.762</td><td>0.99</td><td>1.4</td><td>0.46</td><td>0.057999999999999996</td><td>1.1540000000000001</td><td>10487.5</td><td>16054.26</td><td>8e-12</td><td>3.0600000000000003e-12</td><td>27.741999999999997</td><td>27.745</td><td>0.415</td><td>1.23</td><td>2.6</td><td>0.10099999999999999</td><td>1.024</td><td>1.421</td><td>-946.2</td><td>1.81</td><td>-2.0000000000000002e-11</td><td>1.26e-11</td><td>nan</td><td>nan</td><td>9.999</td><td>1.11</td><td>-1.6</td><td>3.847</td><td>9.999</td><td>0.035</td><td>-859.3</td><td>208.6</td><td>-6.3e-12</td><td>4.03e-12</td><td>nan</td><td>nan</td><td>9.999</td><td>1.0</td><td>-1.6</td><td>0.015</td><td>-0.023</td><td>0.022000000000000002</td><td>10494.9</td><td>61350.07</td><td>2.1e-11</td><td>1.2900000000000002e-11</td><td>26.693</td><td>nan</td><td>0.667</td><td>1.42</td><td>1.6</td><td>0.102</td><td>-0.062</td><td>3.245</td><td>16822.6</td><td>66808.6</td><td>5.3899999999999996e-11</td><td>2.0899999999999997e-11</td><td>25.671</td><td>nan</td><td>0.42200000000000004</td><td>0.68</td><td>2.6</td><td>-0.124</td><td>0.821</td><td>2.8139999999999996</td><td>0</td><td>1</td><td>135</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
+       "<tr><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>491.7300000047286</td><td>28.15999999404994</td><td>11.13407882831087</td><td>41.59843930637129</td><td>1.3</td><td>3.6</td><td>1.249</td><td>1.4040000000000001</td><td>0.9420000000000001</td><td>-3482.7</td><td>9812.03</td><td>-1.3000000000000001e-12</td><td>9.75e-13</td><td>nan</td><td>nan</td><td>9.999</td><td>1.25</td><td>-1.4</td><td>0.9009999999999999</td><td>-1.339</td><td>0.451</td><td>17882.3</td><td>18260.27</td><td>1.3599999999999999e-11</td><td>3.22e-12</td><td>27.164</td><td>nan</td><td>0.257</td><td>1.45</td><td>4.2</td><td>1.393</td><td>1.757</td><td>1.099</td><td>-1014.1</td><td>51.71</td><td>-2.1e-11</td><td>1.4699999999999998e-11</td><td>nan</td><td>nan</td><td>9.999</td><td>0.9</td><td>-1.4</td><td>0.0</td><td>0.0</td><td>0.0</td><td>-2806.0</td><td>259.79</td><td>-2.0000000000000002e-11</td><td>1.0800000000000001e-11</td><td>nan</td><td>nan</td><td>9.999</td><td>0.87</td><td>-1.8</td><td>0.0</td><td>0.0</td><td>0.0</td><td>-843.3</td><td>67460.74</td><td>-1.6999999999999998e-12</td><td>1.6e-11</td><td>nan</td><td>nan</td><td>9.999</td><td>1.71</td><td>-0.1</td><td>0.0</td><td>0.0</td><td>0.0</td><td>14713.1</td><td>74903.59</td><td>4.72e-11</td><td>2.64e-11</td><td>25.816</td><td>nan</td><td>0.607</td><td>1.26</td><td>1.8</td><td>0.32799999999999996</td><td>1.579</td><td>2.6</td><td>0</td><td>1</td><td>30</td><td>1</td><td>2</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
+       "<tr><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>45.10000000349737</td><td>784.4199999943728</td><td>11.150163527414094</td><td>41.596431973103726</td><td>1.13</td><td>4.5</td><td>0.297</td><td>0.653</td><td>1.6269999999999998</td><td>10956.9</td><td>8636.31</td><td>4.24e-12</td><td>1.08e-12</td><td>28.430999999999997</td><td>nan</td><td>0.276</td><td>1.03</td><td>3.9</td><td>0.131</td><td>-0.102</td><td>1.251</td><td>-3891.8</td><td>15874.79</td><td>-2.9999999999999997e-12</td><td>3.39e-12</td><td>nan</td><td>nan</td><td>9.999</td><td>1.31</td><td>-0.9</td><td>2.092</td><td>8.902000000000001</td><td>3.199</td><td>-1522.4</td><td>26.91</td><td>-3.2e-11</td><td>1.64e-11</td><td>nan</td><td>nan</td><td>9.999</td><td>1.2</td><td>-2.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>1473.8</td><td>225.73</td><td>1.07e-11</td><td>5.480000000000001e-12</td><td>27.421999999999997</td><td>nan</td><td>0.5529999999999999</td><td>0.9</td><td>2.0</td><td>0.431</td><td>0.9179999999999999</td><td>0.053</td><td>13270.7</td><td>60622.41</td><td>2.66e-11</td><td>1.16e-11</td><td>26.438000000000002</td><td>nan</td><td>0.475</td><td>1.47</td><td>2.3</td><td>0.249</td><td>0.769</td><td>2.782</td><td>1628.0</td><td>66785.31</td><td>5.26e-12</td><td>2.0200000000000002e-11</td><td>28.198</td><td>nan</td><td>4.17</td><td>0.87</td><td>0.3</td><td>0.149</td><td>0.026000000000000002</td><td>3.0269999999999997</td><td>0</td><td>1</td><td>150</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
+       "<tr><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>300.67000000529197</td><td>124.46999999461252</td><td>11.13702404373161</td><td>41.59644549997782</td><td>1.24</td><td>3.8</td><td>-0.239</td><td>2.224</td><td>0.988</td><td>8530.9</td><td>8828.88</td><td>3.28e-12</td><td>9.2e-13</td><td>28.712</td><td>29.011999999999997</td><td>0.305</td><td>1.18</td><td>3.6</td><td>-0.11900000000000001</td><td>3.517</td><td>1.002</td><td>14136.9</td><td>17768.3</td><td>1.0800000000000001e-11</td><td>2.9e-12</td><td>27.42</td><td>27.414</td><td>0.292</td><td>1.31</td><td>3.7</td><td>-0.193</td><td>1.558</td><td>1.268</td><td>-1678.8</td><td>-5.81</td><td>-3.6e-11</td><td>1.68e-11</td><td>nan</td><td>nan</td><td>9.999</td><td>1.18</td><td>-2.1</td><td>-0.41200000000000003</td><td>-1.262</td><td>0.037000000000000005</td><td>20.4</td><td>204.34</td><td>1.2e-13</td><td>6.4599999999999995e-12</td><td>32.298</td><td>nan</td><td>9.999</td><td>1.13</td><td>0.0</td><td>-1.379</td><td>0.364</td><td>0.099</td><td>6309.5</td><td>64820.31</td><td>1.26e-11</td><td>1.39e-11</td><td>27.245</td><td>nan</td><td>1.195</td><td>1.62</td><td>0.9</td><td>-0.172</td><td>0.163</td><td>3.687</td><td>1575.6</td><td>71781.3</td><td>5.0500000000000005e-12</td><td>1.97e-11</td><td>28.241999999999997</td><td>nan</td><td>4.245</td><td>0.88</td><td>0.3</td><td>0.062</td><td>-0.011000000000000001</td><td>3.931</td><td>0</td><td>1</td><td>100</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
+       "<tr><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>328.90000000475607</td><td>371.37999999437136</td><td>11.141112398449446</td><td>41.59804507142715</td><td>1.16</td><td>3.8</td><td>-0.11800000000000001</td><td>0.616</td><td>3.438</td><td>-6049.9</td><td>8542.92</td><td>-2.3e-12</td><td>1e-12</td><td>nan</td><td>nan</td><td>9.999</td><td>1.02</td><td>-2.3</td><td>-0.151</td><td>9.596</td><td>2.115</td><td>-5593.7</td><td>16425.26</td><td>-4.3e-12</td><td>2.5000000000000003e-12</td><td>nan</td><td>nan</td><td>9.999</td><td>1.1</td><td>-1.7</td><td>0.376</td><td>-2.5469999999999997</td><td>2.073</td><td>4632.4</td><td>40.89</td><td>9.83e-11</td><td>3.2100000000000004e-11</td><td>25.018</td><td>nan</td><td>0.355</td><td>1.3</td><td>3.1</td><td>-0.1</td><td>0.402</td><td>0.053</td><td>-1094.0</td><td>197.85</td><td>-8e-12</td><td>5.59e-12</td><td>nan</td><td>nan</td><td>9.999</td><td>0.83</td><td>-1.4</td><td>0.0</td><td>0.0</td><td>0.0</td><td>-1385.9</td><td>63262.95</td><td>-2.7999999999999998e-12</td><td>1.64e-11</td><td>nan</td><td>nan</td><td>9.999</td><td>1.55</td><td>-0.2</td><td>0.0</td><td>0.0</td><td>0.0</td><td>32938.1</td><td>70232.93</td><td>1.06e-10</td><td>2.7300000000000002e-11</td><td>24.94</td><td>nan</td><td>0.28</td><td>1.33</td><td>3.9</td><td>0.132</td><td>0.342</td><td>2.597</td><td>0</td><td>1</td><td>160</td><td>1</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
+       "<tr><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>546.9500000037107</td><td>61.31999999343384</td><td>11.134284148248089</td><td>41.5993203942432</td><td>1.08</td><td>3.6</td><td>0.297</td><td>0.861</td><td>2.937</td><td>-1815.0</td><td>9237.35</td><td>-6.9e-13</td><td>8.4e-13</td><td>nan</td><td>nan</td><td>9.999</td><td>1.0</td><td>-0.8</td><td>0.084</td><td>0.23600000000000002</td><td>1.24</td><td>119.8</td><td>17602.08</td><td>1.0699999999999999e-13</td><td>2.54e-12</td><td>32.423</td><td>nan</td><td>9.999</td><td>1.09</td><td>0.0</td><td>0.027000000000000003</td><td>0.782</td><td>2.589</td><td>2267.6</td><td>4.87</td><td>4.75e-11</td><td>1.81e-11</td><td>25.807</td><td>nan</td><td>0.41200000000000003</td><td>0.91</td><td>2.6</td><td>0.43</td><td>1.1159999999999999</td><td>0.013999999999999999</td><td>-1588.6</td><td>199.09</td><td>-1.1e-11</td><td>7.639999999999999e-12</td><td>nan</td><td>nan</td><td>9.999</td><td>1.25</td><td>-1.5</td><td>0.0</td><td>0.0</td><td>0.0</td><td>5653.1</td><td>67000.59</td><td>1.13e-11</td><td>1.56e-11</td><td>27.365</td><td>nan</td><td>1.499</td><td>1.52</td><td>0.7</td><td>1.1640000000000001</td><td>6.152</td><td>4.522</td><td>23006.9</td><td>73242.07</td><td>7.379999999999999e-11</td><td>2.6899999999999996e-11</td><td>25.33</td><td>nan</td><td>0.396</td><td>1.04</td><td>2.7</td><td>0.524</td><td>0.693</td><td>3.137</td><td>0</td><td>1</td><td>35</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
+       "<tr><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>729.7300000053044</td><td>216.06999999517893</td><td>11.135743163679448</td><td>41.602461665939074</td><td>1.12</td><td>3.6</td><td>0.079</td><td>2.228</td><td>1.945</td><td>4766.7</td><td>9215.83</td><td>1.82e-12</td><td>7.08e-13</td><td>29.348000000000003</td><td>29.593000000000004</td><td>0.42100000000000004</td><td>1.07</td><td>2.6</td><td>0.105</td><td>2.197</td><td>1.162</td><td>6956.3</td><td>16934.97</td><td>5.3e-12</td><td>1.96e-12</td><td>28.189</td><td>28.18</td><td>0.401</td><td>1.16</td><td>2.7</td><td>0.159</td><td>1.1420000000000001</td><td>1.744</td><td>-2312.7</td><td>89.92</td><td>-4.7999999999999996e-11</td><td>1.72e-11</td><td>nan</td><td>nan</td><td>9.999</td><td>1.1</td><td>-2.8</td><td>0.193</td><td>0.363</td><td>0.371</td><td>-856.9</td><td>206.22</td><td>-6.0999999999999995e-12</td><td>5.679999999999999e-12</td><td>nan</td><td>nan</td><td>9.999</td><td>0.96</td><td>-1.1</td><td>-0.322</td><td>8.344</td><td>0.038</td><td>3614.7</td><td>64680.2</td><td>7.240000000000001e-12</td><td>1.4499999999999998e-11</td><td>27.85</td><td>nan</td><td>2.175</td><td>1.49</td><td>0.5</td><td>-1.128</td><td>0.457</td><td>4.6610000000000005</td><td>-2560.7</td><td>72170.48</td><td>-8.2e-12</td><td>2.43e-11</td><td>nan</td><td>nan</td><td>9.999</td><td>1.24</td><td>-0.3</td><td>2.322</td><td>0.456</td><td>4.119</td><td>0</td><td>1</td><td>90</td><td>1</td><td>2</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
+       "<tr><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>735.9100000053115</td><td>553.1799999945795</td><td>11.141541865647177</td><td>41.604226347405195</td><td>1.06</td><td>3.6</td><td>0.521</td><td>3.795</td><td>1.446</td><td>6386.6</td><td>9697.99</td><td>2.46e-12</td><td>6.929999999999999e-13</td><td>29.022</td><td>29.070999999999998</td><td>0.305</td><td>1.07</td><td>3.6</td><td>0.262</td><td>3.745</td><td>0.851</td><td>4251.1</td><td>17863.19</td><td>3.2400000000000003e-12</td><td>1.7799999999999999e-12</td><td>28.725</td><td>28.715999999999998</td><td>0.598</td><td>1.02</td><td>1.8</td><td>1.04</td><td>2.144</td><td>1.3359999999999999</td><td>-1733.4</td><td>33.06</td><td>-3.7e-11</td><td>1.52e-11</td><td>nan</td><td>nan</td><td>9.999</td><td>0.97</td><td>-2.4</td><td>5.979</td><td>9.999</td><td>0.113</td><td>-1083.8</td><td>247.92</td><td>-8e-12</td><td>6.29e-12</td><td>nan</td><td>nan</td><td>9.999</td><td>0.95</td><td>-1.3</td><td>0.68</td><td>0.337</td><td>0.152</td><td>-3049.3</td><td>65793.02</td><td>-6.0999999999999995e-12</td><td>1.05e-11</td><td>nan</td><td>nan</td><td>9.999</td><td>1.27</td><td>-0.6</td><td>0.0</td><td>0.0</td><td>0.0</td><td>2055.5</td><td>71931.6</td><td>6.56e-12</td><td>1.96e-11</td><td>27.958000000000002</td><td>nan</td><td>3.238</td><td>0.98</td><td>0.3</td><td>-0.23</td><td>0.266</td><td>2.713</td><td>0</td><td>1</td><td>90</td><td>1</td><td>0</td><td>2</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "<Table length=50625>\n",
+       "F814W_ST F814W_GST F475W_ST F475W_GST ... F336W_FLAG F110W_FLAG F160W_FLAG\n",
+       "  bool      bool     bool      bool   ...   int64      int64      int64   \n",
+       "-------- --------- -------- --------- ... ---------- ---------- ----------\n",
+       "    True      True     True      True ...          0          0          0\n",
+       "    True      True     True      True ...          2          0          0\n",
+       "    True      True    False     False ...          0          2          2\n",
+       "    True      True     True      True ...          0          0          0\n",
+       "    True      True    False     False ...          0          2          2\n",
+       "    True      True     True      True ...          0          0          0\n",
+       "    True      True     True      True ...          0          0          0\n",
+       "    True      True     True      True ...          2          0          0\n",
+       "    True      True     True      True ...          0          0          0\n",
+       "    True      True     True      True ...          0          0          0\n",
+       "     ...       ...      ...       ... ...        ...        ...        ...\n",
+       "   False     False    False     False ...          0          0          0\n",
+       "   False     False    False     False ...          0          0          0\n",
+       "   False     False    False     False ...          0          0          0\n",
+       "   False     False    False     False ...          0          0          0\n",
+       "   False     False    False     False ...          0          0          0\n",
+       "   False     False    False     False ...          0          0          0\n",
+       "   False     False    False     False ...          0          0          0\n",
+       "   False     False    False     False ...          0          0          0\n",
+       "   False     False    False     False ...          0          0          0\n",
+       "   False     False    False     False ...          0          0          0"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "hdul = fits.open(gst_file)\n",
+    "Table(hdul[1].data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As we can see, there's a lot of columns and even more rows. For plotting the magnitude histograms, we're going to be interested in any column that contains the name VEGA. These are the columns with the magnitudes for each filter.\n",
+    "\n",
+    "We could also use the X and Y columns to plot where are the sources are located, or the RA and DEC to map their actual position in the sky.\n",
+    "\n",
+    "In larger projects we might have multiple fields to analyze during each run, so there would be multiple **field_names**. Since this is just a small example, we just have one field so our index will always be equal to **0**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# the list of fields (we only have 1 for this example.)\n",
+    "field_names = [field_name]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can create some histogram plots to visualize the magnitude distribution of our sources."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# this 'if' statement just checks if there's already a histogram file\n",
+    "if not os.path.isfile('./'+field_names[0]+'.st_maghist.pdf'):\n",
+    "    peak_mags = plot_mag_hist.plot_mag_hist(gst_file, stars_per_bin=70, max_bins=75)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can check out the results for the histograms in the file ending with **_maghist.pdf**\n",
+    "\n",
+    "From this plot, we can also see what filters exist for the data. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Step 1b. Make source density maps"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next we'll be creating source density maps. These are maps of our data field colored such that they show how many stars/sources there are in each degree field. The standard size is 5 arc seconds squared. The size can easily be changed by modifying the **pixsize** variable below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Pick the filter with the dimmest peak from the histogram\n",
+    "ref_filter =[\"F475W\"]\n",
+    "\n",
+    "# choose a filter to use for removing artifacts\n",
+    "# (remove catalog sources with filter_FLAG > 99)\n",
+    "flag_filter = [\"F275W\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# check to see if the sourde density file already exists\n",
+    "if not os.path.isfile(gst_file.replace(\".fits\", \"_source_den_image.fits\")):\n",
+    "    # if not, run all this other code\n",
+    "    \n",
+    "    # - pixel size of 5 arcsec\n",
+    "    # - use ref_filter[b] between vega mags of 15 and peak_mags[ref_filter[b]]-0.5\n",
+    "        # since we're only working with one field, our index b is set to 0\n",
+    "    sourceden_args = types.SimpleNamespace(\n",
+    "        subcommand=\"sourceden\",\n",
+    "        catfile=gst_file,\n",
+    "        pixsize=5,\n",
+    "        npix=None,\n",
+    "        mag_name=ref_filter[0]+ \"_VEGA\",\n",
+    "        mag_cut=[17, peak_mags[ref_filter[0]] - 0.5],\n",
+    "        flag_name=flag_filter[0]+'_FLAG',\n",
+    "    )\n",
+    "    create_background_density_map.main_make_map(sourceden_args)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# new file name with the source density column\n",
+    "gst_file_sd = gst_file.replace(\".fits\", \"_with_sourceden.fits\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This function should create 3 files: \n",
+    "* *M31-B09-EAST_subset.st_source_den_image.fits* : a file for viewing the source density information in ds9 or with matplotlib\n",
+    "\n",
+    "* *M31-B09-EAST_subset.st_sourceden_map.hd5* : the same file as source_den_image but now with even more data (the split_catalog_using_map function will end up using this file later on) \n",
+    "\n",
+    "* *M31-B09-EAST_subset.st_with_sourceden.fits* : the same as the original photometric file (gst_file) but now with an additional column for what density bin the source is located in"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### View the fits images of the source density maps\n",
+    "\n",
+    "Now that we have the source density maps outputted, we can plot the image and see that the density looks like."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Filename: ./M31-B09-EAST_chunk.st_source_den_image.fits\n",
+      "No.    Name      Ver    Type      Cards   Dimensions   Format\n",
+      "  0  PRIMARY       1 PrimaryHDU      19   (12, 12)   float64   \n",
+      "<class 'numpy.ndarray'>\n",
+      "(12, 12)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# open the fits file\n",
+    "hdu_list = fits.open(\"./%s.st_source_den_image.fits\"%field_name)\n",
+    "hdu_list.info()\n",
+    "\n",
+    "# extract the image data\n",
+    "image_data = hdu_list[0].data\n",
+    "\n",
+    "# take a look at what the image should look like\n",
+    "print(type(image_data))\n",
+    "print(image_data.shape)\n",
+    "\n",
+    "# close the fits file\n",
+    "hdu_list.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Density of Sources per 5 arcsec^2')"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x720 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# plot the extracted image data\n",
+    "fig = plt.figure(0, [10,10])\n",
+    "im = plt.imshow(image_data, origin=\"lower\")\n",
+    "plt.colorbar(im)\n",
+    "plt.xlabel(\"Pixel (originally RA)\")\n",
+    "plt.ylabel(\"Pixel (originally DEC)\")\n",
+    "plt.title(\"Density of Sources per 5 arcsec^2\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Step 1c. Set up datamodel file\n",
+    "\n",
+    "At this point, we have a basic understanding of the information we are working with, so it's about time we set up our datamodel file. \n",
+    "\n",
+    "The datamodel file is a sort of catch-all file used to store any sort of infomation we might need to run the BEAST code on our data. We'll go through and talk about what all the different variables mean, and which ones would need to be changed for any future projects.\n",
+    "\n",
+    "Go ahead and open the datamodel.py file in a text editor now and ensure that the following variables match:\n",
+    "\n",
+    "*  **project** : the same as the field_name variable we noted earlier\n",
+    "    * *project = \"M31-B09-EAST_chunk\" *\n",
+    "* **surveyname** : the overall name for the survey (this variable isn't actually important for the code)\n",
+    "    * *surveyname = \"PHAT-M31\"*\n",
+    "* **filters** : the full filter names from the photometric catalog, also the names that show up in our magnitude histograms so you can add them from there\n",
+    "    * *filters = [\"HST_WFC3_F475W\", \"HST_WFC3_F275W\", \"HST_WFC3_F336W\", \"HST_WFC3_F814W\", \"HST_WFC3_F110W\", \"HST_WFC3_F160W\",]*\n",
+    "    \n",
+    "* **base filters** : shortened versions of the filter names\n",
+    "    * *basefilters = [\"F475W\", \"F275W\", \"F336W\", \"F814W\", \"F110W\", \"F160W\"]*\n",
+    "* **obsfile** : the name of the photometric catalog (now including the source density information\n",
+    "    * *obsfile = \"./M31-B09-EAST_chunk.st_with_sourceden_cut.fits\"*\n",
+    "    \n",
+    "* **ast_with_positions** : make sure is set to *True* if you have the locations included in your obsfile\n",
+    "\n",
+    "* **ast_density_table** : the source density map created in step 1b  \n",
+    "    * *ast_density_table = './M31-B09-EAST_chunk.st_sourceden_map.hd5'*\n",
+    "    \n",
+    "* **ast_reference_image** : the original photometric FITS catalog which is required if you use the ast_with_positions as true  \n",
+    "    * *ast_reference_image = \"./M31-B09-EAST_chunk_F475W_drz.chip1.fits\"*\n",
+    "    \n",
+    "* **astfile** : the file of ASTs we will be creating in step 3, but since ASTs normally have to be processed by a specialist, we have already included a finished AST file for us to use in this example\n",
+    "    * *astfile = \"M31-B09_EAST_chunk.gst.fake.fits\"*\n",
+    "    \n",
+    "* **n_subgrid** : the number of subgrids to use for generating the physics model later on (with 1 meaning no subgrids)\n",
+    "    * *n_subgrid = 1*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This file is also where you specify the parameters and resolution of your physics model which will become relevant in step 2. The resolution of these parameters for your own runs will differ depending on what sorts of ASTs you want to model. There are 8 parameters that can be set.\n",
+    "\n",
+    "1. **Distance** : either a fixed value or a range with stepsizes\n",
+    "2. **Velocity** : what is the heliocentric velocity of your location or galaxy in km/s\n",
+    "3. **Age** : the log10 age range of the ASTs being modeled\n",
+    "4. **Mass** : the mass of the ASTs\n",
+    "5. **Metallicity** : the metallicity range of the ASTs\n",
+    "\n",
+    "6. **A(v)** : the range of dust extinction in magnitudes that could be dimming the intrinsic brightness of the ASTs\n",
+    "7. **R(v)** : the range of dust grain sizes \n",
+    "8. **f(A)** the mixture factor between the Milky Way and Small Magellanic Cloud extinction curves\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Auto-detected type: fits\n",
+      "Auto-detected type: fits\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<module 'datamodel' from '/home/christina/beast_testing/BEAST_example/datamodel.py'>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import datamodel\n",
+    "\n",
+    "importlib.reload(datamodel)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Our goal after this would normally be to eventually run a bunch of **ASTs** (Artificial Star Tests), but before we can do that, we need to generate the fake stars to use.\n",
+    "\n",
+    "Since the ASTs would normally need to be analyzed by a specialist after being created and that's a little overkill for a small example, these next couple of steps are just to illustrate how the ASTs are actually generated. A finished file of the analyzed ASTs already exists so we will end up using that in step 4 and beyond.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Step 2. Create physics model\n",
+    "\n",
+    "In order to generate a diverse and representative sample of fake stars to use for our ASTs, we need to set up a N-dimensional model of possible stellar parameters, so that we can easily and randomly select stars from the model.\n",
+    "\n",
+    "This model is called a **physics model**, and we will be using the parameters set in the datamodel.py file to create this N-dimensional grid.\n",
+    "\n",
+    "*As a quick note, the resolution on the stellar parameters (the step size, often specified as the third input e.g. logt = [6.0, 10.13, 1.0], where 1.0 is the step size) is the main factor driving how long this physics grid will take to set up. If things take a very long time to run, consider making the step size larger for testing's sake.*\n",
+    "\n",
+    "Sometimes we are able to have access to high-performance computing resources, meaning we can split the physics model into subgrids and run them in parallel, cutting a lot of the computation time. While we're like not running this notebook in parallel here, we've still specified a number of subgrids in the datamodel.py file. \n",
+    "\n",
+    "We can check how many subgrids are set up."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "datamodel.n_subgrid"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "So we can now see that we've asked for 1 grid in the datamodel.py file.\n",
+    "\n",
+    "If we've already generated a physics model, we certainly don't want to run it again, so the following code checks to make sure all the subgrids for the physics model are present."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# set up the naming conventions for the physics model\n",
+    "gs_str = \"\"\n",
+    "\n",
+    "# this is only relevant if we run with multiple subgrids\n",
+    "if datamodel.n_subgrid > 1:\n",
+    "    gs_str = \"sub*\"\n",
+    "\n",
+    "# collects any physics models that have already been created\n",
+    "# if none have, sed_files will be empty\n",
+    "sed_files = glob.glob(\n",
+    "    \"./{0}/{0}_seds.grid{1}.hd5\".format(field_names[0], gs_str)\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# only make the physics model they don't already exist\n",
+    "if len(sed_files) < datamodel.n_subgrid:\n",
+    "    # directly create physics model grids\n",
+    "    create_physicsmodel.create_physicsmodel(nprocs=1, nsubs=datamodel.n_subgrid)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# list of SED files (physics models)\n",
+    "model_grid_files = sorted(\n",
+    "    glob.glob(\n",
+    "        \"./{0}/{0}_seds.grid{1}.hd5\".format(field_names[0], gs_str)\n",
+    "    )\n",
+    ")\n",
+    "sed_files = model_grid_files"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Hopefully a spectral grid and an SED grid should have started generating. In the end you should have a new folder with the same name as your project, with a one SED and spectral grid if you have only 1 subgrid."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Step 3. Create Input ASTs!\n",
+    "\n",
+    "Now that we have our physics model generated, we can start to generate some input ASTs. ASTs are artificial sources inserted into the observations we have, which are then extracted with the same software that was used for the original photometry catalog. So the step that we're running now is just generating the artifical sources that will then later be inserted. \n",
+    "\n",
+    "We need to make sure that the ASTs cover the same range of magnitudes as our original photometric catalog does, so to do that\n",
+    "\n",
+    "\n",
+    "First thing's first, we're gonna check that there isn't already a file of AST inputs present in the folder we're working in."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'./M31-B09-EAST_chunk/M31-B09-EAST_chunk_inputAST.txt'"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# only create an AST input list if the ASTs don't already exist\n",
+    "ast_input_file = (\"./{0}/{0}_inputAST.txt\".format(field_names[0]))\n",
+    "\n",
+    "ast_input_file"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can create the ASTs if they don't already exist.\n",
+    "\n",
+    "The way that "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "if not os.path.isfile(ast_input_file):\n",
+    "    make_ast_inputs.make_ast_inputs(flux_bin_method=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<i>Table length=33418</i>\n",
+       "<table id=\"table140635053575976\" class=\"table-striped table-bordered table-condensed\">\n",
+       "<thead><tr><th>zeros</th><th>ones</th><th>X</th><th>Y</th><th>HST_WFC3_F475W</th><th>HST_WFC3_F275W</th><th>HST_WFC3_F336W</th><th>HST_WFC3_F814W</th><th>HST_WFC3_F110W</th><th>HST_WFC3_F160W</th></tr></thead>\n",
+       "<thead><tr><th>int64</th><th>int64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th></tr></thead>\n",
+       "<tr><td>0</td><td>1</td><td>408.03456</td><td>897.52366</td><td>33.95055</td><td>38.18357</td><td>36.99705</td><td>28.99129</td><td>27.22613</td><td>26.11148</td></tr>\n",
+       "<tr><td>0</td><td>1</td><td>419.17151</td><td>897.33494</td><td>33.95055</td><td>38.18357</td><td>36.99705</td><td>28.99129</td><td>27.22613</td><td>26.11148</td></tr>\n",
+       "<tr><td>0</td><td>1</td><td>427.91019</td><td>897.03469</td><td>33.95055</td><td>38.18357</td><td>36.99705</td><td>28.99129</td><td>27.22613</td><td>26.11148</td></tr>\n",
+       "<tr><td>0</td><td>1</td><td>414.32003</td><td>893.16622</td><td>33.95055</td><td>38.18357</td><td>36.99705</td><td>28.99129</td><td>27.22613</td><td>26.11148</td></tr>\n",
+       "<tr><td>0</td><td>1</td><td>425.36739</td><td>897.72325</td><td>33.95055</td><td>38.18357</td><td>36.99705</td><td>28.99129</td><td>27.22613</td><td>26.11148</td></tr>\n",
+       "<tr><td>0</td><td>1</td><td>419.51676</td><td>899.53858</td><td>33.95055</td><td>38.18357</td><td>36.99705</td><td>28.99129</td><td>27.22613</td><td>26.11148</td></tr>\n",
+       "<tr><td>0</td><td>1</td><td>423.49592</td><td>895.02481</td><td>33.95055</td><td>38.18357</td><td>36.99705</td><td>28.99129</td><td>27.22613</td><td>26.11148</td></tr>\n",
+       "<tr><td>0</td><td>1</td><td>419.74966</td><td>899.3343</td><td>33.95055</td><td>38.18357</td><td>36.99705</td><td>28.99129</td><td>27.22613</td><td>26.11148</td></tr>\n",
+       "<tr><td>0</td><td>1</td><td>416.9429</td><td>896.49331</td><td>33.95055</td><td>38.18357</td><td>36.99705</td><td>28.99129</td><td>27.22613</td><td>26.11148</td></tr>\n",
+       "<tr><td>0</td><td>1</td><td>417.38656</td><td>895.29865</td><td>33.95055</td><td>38.18357</td><td>36.99705</td><td>28.99129</td><td>27.22613</td><td>26.11148</td></tr>\n",
+       "<tr><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td></tr>\n",
+       "<tr><td>0</td><td>1</td><td>855.5205</td><td>31.25829</td><td>49.58666</td><td>50.47117</td><td>52.34013</td><td>41.58012</td><td>38.18065</td><td>36.0759</td></tr>\n",
+       "<tr><td>0</td><td>1</td><td>887.73473</td><td>86.32071</td><td>49.58666</td><td>50.47117</td><td>52.34013</td><td>41.58012</td><td>38.18065</td><td>36.0759</td></tr>\n",
+       "<tr><td>0</td><td>1</td><td>877.40643</td><td>94.66438</td><td>49.58666</td><td>50.47117</td><td>52.34013</td><td>41.58012</td><td>38.18065</td><td>36.0759</td></tr>\n",
+       "<tr><td>0</td><td>1</td><td>858.77566</td><td>38.27633</td><td>49.58666</td><td>50.47117</td><td>52.34013</td><td>41.58012</td><td>38.18065</td><td>36.0759</td></tr>\n",
+       "<tr><td>0</td><td>1</td><td>868.73697</td><td>41.42387</td><td>49.58666</td><td>50.47117</td><td>52.34013</td><td>41.58012</td><td>38.18065</td><td>36.0759</td></tr>\n",
+       "<tr><td>0</td><td>1</td><td>862.34635</td><td>41.58933</td><td>49.58666</td><td>50.47117</td><td>52.34013</td><td>41.58012</td><td>38.18065</td><td>36.0759</td></tr>\n",
+       "<tr><td>0</td><td>1</td><td>878.7805</td><td>47.20022</td><td>49.58666</td><td>50.47117</td><td>52.34013</td><td>41.58012</td><td>38.18065</td><td>36.0759</td></tr>\n",
+       "<tr><td>0</td><td>1</td><td>858.79055</td><td>82.36003</td><td>49.58666</td><td>50.47117</td><td>52.34013</td><td>41.58012</td><td>38.18065</td><td>36.0759</td></tr>\n",
+       "<tr><td>0</td><td>1</td><td>857.74405</td><td>41.31581</td><td>49.58666</td><td>50.47117</td><td>52.34013</td><td>41.58012</td><td>38.18065</td><td>36.0759</td></tr>\n",
+       "<tr><td>0</td><td>1</td><td>852.41009</td><td>60.99223</td><td>49.58666</td><td>50.47117</td><td>52.34013</td><td>41.58012</td><td>38.18065</td><td>36.0759</td></tr>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "<Table length=33418>\n",
+       "zeros  ones     X         Y     ... HST_WFC3_F814W HST_WFC3_F110W HST_WFC3_F160W\n",
+       "int64 int64  float64   float64  ...    float64        float64        float64    \n",
+       "----- ----- --------- --------- ... -------------- -------------- --------------\n",
+       "    0     1 408.03456 897.52366 ...       28.99129       27.22613       26.11148\n",
+       "    0     1 419.17151 897.33494 ...       28.99129       27.22613       26.11148\n",
+       "    0     1 427.91019 897.03469 ...       28.99129       27.22613       26.11148\n",
+       "    0     1 414.32003 893.16622 ...       28.99129       27.22613       26.11148\n",
+       "    0     1 425.36739 897.72325 ...       28.99129       27.22613       26.11148\n",
+       "    0     1 419.51676 899.53858 ...       28.99129       27.22613       26.11148\n",
+       "    0     1 423.49592 895.02481 ...       28.99129       27.22613       26.11148\n",
+       "    0     1 419.74966  899.3343 ...       28.99129       27.22613       26.11148\n",
+       "    0     1  416.9429 896.49331 ...       28.99129       27.22613       26.11148\n",
+       "    0     1 417.38656 895.29865 ...       28.99129       27.22613       26.11148\n",
+       "  ...   ...       ...       ... ...            ...            ...            ...\n",
+       "    0     1  855.5205  31.25829 ...       41.58012       38.18065        36.0759\n",
+       "    0     1 887.73473  86.32071 ...       41.58012       38.18065        36.0759\n",
+       "    0     1 877.40643  94.66438 ...       41.58012       38.18065        36.0759\n",
+       "    0     1 858.77566  38.27633 ...       41.58012       38.18065        36.0759\n",
+       "    0     1 868.73697  41.42387 ...       41.58012       38.18065        36.0759\n",
+       "    0     1 862.34635  41.58933 ...       41.58012       38.18065        36.0759\n",
+       "    0     1  878.7805  47.20022 ...       41.58012       38.18065        36.0759\n",
+       "    0     1 858.79055  82.36003 ...       41.58012       38.18065        36.0759\n",
+       "    0     1 857.74405  41.31581 ...       41.58012       38.18065        36.0759\n",
+       "    0     1 852.41009  60.99223 ...       41.58012       38.18065        36.0759"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ast = Table.read(ast_input_file, format=\"ascii\")\n",
+    "ast"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "5569.666666666667"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "33418/6"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Check to see how the SEDs and the ASTs compare\n",
+    "\n",
+    "The histogram that is produced should have both the SED distribution and the AST distribution plotted on it. The thing we want to test for is whether the AST distribution fully samples the SED range."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Auto-detected type: hd5\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/christina/anaconda3/envs/astroconda/lib/python3.6/site-packages/beast/plotting/plot_ast_histogram.py:73: MatplotlibDeprecationWarning: \n",
+      "The 'normed' kwarg was deprecated in Matplotlib 2.1 and will be removed in 3.1. Use 'density' instead.\n",
+      "  label='ASTs'\n",
+      "/home/christina/anaconda3/envs/astroconda/lib/python3.6/site-packages/beast/plotting/plot_ast_histogram.py:84: MatplotlibDeprecationWarning: \n",
+      "The 'normed' kwarg was deprecated in Matplotlib 2.1 and will be removed in 3.1. Use 'density' instead.\n",
+      "  label='Model grid'\n",
+      "/home/christina/anaconda3/envs/astroconda/lib/python3.6/site-packages/beast/plotting/plot_ast_histogram.py:73: MatplotlibDeprecationWarning: \n",
+      "The 'normed' kwarg was deprecated in Matplotlib 2.1 and will be removed in 3.1. Use 'density' instead.\n",
+      "  label='ASTs'\n",
+      "/home/christina/anaconda3/envs/astroconda/lib/python3.6/site-packages/beast/plotting/plot_ast_histogram.py:84: MatplotlibDeprecationWarning: \n",
+      "The 'normed' kwarg was deprecated in Matplotlib 2.1 and will be removed in 3.1. Use 'density' instead.\n",
+      "  label='Model grid'\n",
+      "/home/christina/anaconda3/envs/astroconda/lib/python3.6/site-packages/beast/plotting/plot_ast_histogram.py:73: MatplotlibDeprecationWarning: \n",
+      "The 'normed' kwarg was deprecated in Matplotlib 2.1 and will be removed in 3.1. Use 'density' instead.\n",
+      "  label='ASTs'\n",
+      "/home/christina/anaconda3/envs/astroconda/lib/python3.6/site-packages/beast/plotting/plot_ast_histogram.py:84: MatplotlibDeprecationWarning: \n",
+      "The 'normed' kwarg was deprecated in Matplotlib 2.1 and will be removed in 3.1. Use 'density' instead.\n",
+      "  label='Model grid'\n",
+      "/home/christina/anaconda3/envs/astroconda/lib/python3.6/site-packages/beast/plotting/plot_ast_histogram.py:73: MatplotlibDeprecationWarning: \n",
+      "The 'normed' kwarg was deprecated in Matplotlib 2.1 and will be removed in 3.1. Use 'density' instead.\n",
+      "  label='ASTs'\n",
+      "/home/christina/anaconda3/envs/astroconda/lib/python3.6/site-packages/beast/plotting/plot_ast_histogram.py:84: MatplotlibDeprecationWarning: \n",
+      "The 'normed' kwarg was deprecated in Matplotlib 2.1 and will be removed in 3.1. Use 'density' instead.\n",
+      "  label='Model grid'\n",
+      "/home/christina/anaconda3/envs/astroconda/lib/python3.6/site-packages/beast/plotting/plot_ast_histogram.py:73: MatplotlibDeprecationWarning: \n",
+      "The 'normed' kwarg was deprecated in Matplotlib 2.1 and will be removed in 3.1. Use 'density' instead.\n",
+      "  label='ASTs'\n",
+      "/home/christina/anaconda3/envs/astroconda/lib/python3.6/site-packages/beast/plotting/plot_ast_histogram.py:84: MatplotlibDeprecationWarning: \n",
+      "The 'normed' kwarg was deprecated in Matplotlib 2.1 and will be removed in 3.1. Use 'density' instead.\n",
+      "  label='Model grid'\n",
+      "/home/christina/anaconda3/envs/astroconda/lib/python3.6/site-packages/beast/plotting/plot_ast_histogram.py:73: MatplotlibDeprecationWarning: \n",
+      "The 'normed' kwarg was deprecated in Matplotlib 2.1 and will be removed in 3.1. Use 'density' instead.\n",
+      "  label='ASTs'\n",
+      "/home/christina/anaconda3/envs/astroconda/lib/python3.6/site-packages/beast/plotting/plot_ast_histogram.py:84: MatplotlibDeprecationWarning: \n",
+      "The 'normed' kwarg was deprecated in Matplotlib 2.1 and will be removed in 3.1. Use 'density' instead.\n",
+      "  label='Model grid'\n"
+     ]
+    }
+   ],
+   "source": [
+    "plot_ast_histogram.plot_ast(ast_file = ast_input_file, sed_grid_file = model_grid_files[0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Step 4. Edit/Split the Catalog"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We have to remove sources from the input photometry catalog that are in regions without full imaging coverage or flagged as bad in flag_filter. This step should mostly just be removing any sources where one of the filters might not have a value."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gst_file_cut = gst_file.replace(\".fits\", \"_with_sourceden_cut.fits\")\n",
+    "\n",
+    "# check to see if the trimmed catalog already exists\n",
+    "if not os.path.isfile(gst_file_cut):\n",
+    "    # and if not\n",
+    "    cut_catalogs.cut_catalogs(\n",
+    "        gst_file_sd,\n",
+    "        gst_file_cut,\n",
+    "        partial_overlap=True,\n",
+    "        flagged=True,\n",
+    "        flag_filter=flag_filter[0],\n",
+    "        region_file=True,\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Step 4.5 Update Datamodel\n",
+    "**After making these cuts, we should now update the obs_file name in datamodel.py (~line 62)  with this new trimmed filename: './M31-B09-EAST_chunk.st_with_sourceden_cut.fits'**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Auto-detected type: fits\n",
+      "Auto-detected type: fits\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<module 'datamodel' from '/home/christina/beast_testing/BEAST_example/datamodel.py'>"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "importlib.reload(datamodel)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Step 5. Edit/Split the ASTs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now for this step, we're doing things a little unconventionally since actually placing all the input ASTs we generated in Step 3 back into our image and rerunning the analysis would take several days of computational time. \n",
+    "\n",
+    "Instead, we've already procurred a polished AST results file (kindly provided by Ben Williams from the University of Washington) which we can use to complete our analysis. The AST file should be named *'./M31-B09-EAST_chunk.gst.fake.fits'* while the input ASTs we generated were named *'./M31-B09-EAST_chunk/M31-B09-EAST_chunk_beast_inputAST.txt'*.\n",
+    "\n",
+    "We will now use the same cutting procedure as for the catalog to trim down the AST file with the same criteria as in Step 4."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'./M31-B09-EAST_chunk.gst.fake.fits'"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ast_file = \"./\" + field_names[0] + \".gst.fake.fits\"\n",
+    "ast_file "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<i>Table length=51549</i>\n",
+       "<table id=\"table140635143777192\" class=\"table-striped table-bordered table-condensed\">\n",
+       "<thead><tr><th>F110W_IN</th><th>F110W_RATE</th><th>F110W_RATERR</th><th>F110W_VEGA</th><th>F110W_STD</th><th>F110W_ERR</th><th>F110W_CHI</th><th>F110W_SNR</th><th>F110W_SHARP</th><th>F110W_ROUND</th><th>F110W_CROWD</th><th>F110W_FLAG</th><th>F160W_IN</th><th>F160W_RATE</th><th>F160W_RATERR</th><th>F160W_VEGA</th><th>F160W_STD</th><th>F160W_ERR</th><th>F160W_CHI</th><th>F160W_SNR</th><th>F160W_SHARP</th><th>F160W_ROUND</th><th>F160W_CROWD</th><th>F160W_FLAG</th><th>F275W_IN</th><th>F275W_RATE</th><th>F275W_RATERR</th><th>F275W_VEGA</th><th>F275W_STD</th><th>F275W_ERR</th><th>F275W_CHI</th><th>F275W_SNR</th><th>F275W_SHARP</th><th>F275W_ROUND</th><th>F275W_CROWD</th><th>F275W_FLAG</th><th>F336W_IN</th><th>F336W_RATE</th><th>F336W_RATERR</th><th>F336W_VEGA</th><th>F336W_STD</th><th>F336W_ERR</th><th>F336W_CHI</th><th>F336W_SNR</th><th>F336W_SHARP</th><th>F336W_ROUND</th><th>F336W_CROWD</th><th>F336W_FLAG</th><th>F475W_IN</th><th>F475W_RATE</th><th>F475W_RATERR</th><th>F475W_VEGA</th><th>F475W_STD</th><th>F475W_ERR</th><th>F475W_CHI</th><th>F475W_SNR</th><th>F475W_SHARP</th><th>F475W_ROUND</th><th>F475W_CROWD</th><th>F475W_FLAG</th><th>F814W_IN</th><th>F814W_RATE</th><th>F814W_RATERR</th><th>F814W_VEGA</th><th>F814W_STD</th><th>F814W_ERR</th><th>F814W_CHI</th><th>F814W_SNR</th><th>F814W_SHARP</th><th>F814W_ROUND</th><th>F814W_CROWD</th><th>F814W_FLAG</th><th>YIN</th><th>XIN</th><th>Y</th><th>X</th><th>RA_J2000</th><th>DEC_J2000</th></tr></thead>\n",
+       "<thead><tr><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>int16</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>int16</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>int16</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>int16</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>int16</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>int16</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th></tr></thead>\n",
+       "<tr><td>33.267</td><td>0.0</td><td>9999.0</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99</td><td>31.935</td><td>0.0</td><td>9999.0</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99</td><td>44.611</td><td>0.0</td><td>9999.0</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99</td><td>45.472</td><td>0.0</td><td>9999.0</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99</td><td>41.082</td><td>0.0</td><td>9999.0</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99</td><td>35.465</td><td>0.0</td><td>9999.0</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99</td><td>7.389484365972712</td><td>3.6699673430471402</td><td>7.389484365972712</td><td>3.6699673430471402</td><td>11.145614732485846</td><td>41.59773092319124</td></tr>\n",
+       "<tr><td>33.267</td><td>0.0</td><td>9999.0</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99</td><td>31.935</td><td>0.0</td><td>9999.0</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99</td><td>44.611</td><td>0.0</td><td>9999.0</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99</td><td>45.472</td><td>0.0</td><td>9999.0</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99</td><td>41.082</td><td>0.0</td><td>9999.0</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99</td><td>35.465</td><td>0.0</td><td>9999.0</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99</td><td>9.188675903235886</td><td>4.602542965133008</td><td>9.188675903235886</td><td>4.602542965133008</td><td>11.148956385132271</td><td>41.599026017115044</td></tr>\n",
+       "<tr><td>33.267</td><td>0.0</td><td>9999.0</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99</td><td>31.935</td><td>0.0</td><td>9999.0</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99</td><td>44.611</td><td>0.0</td><td>9999.0</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99</td><td>45.472</td><td>0.0</td><td>9999.0</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99</td><td>41.082</td><td>0.0</td><td>9999.0</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99</td><td>35.465</td><td>0.0</td><td>9999.0</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99</td><td>7.48594129972647</td><td>5.80299643256212</td><td>7.48594129972647</td><td>5.80299643256212</td><td>11.14579404466301</td><td>41.60069345799913</td></tr>\n",
+       "<tr><td>33.267</td><td>0.0</td><td>9999.0</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99</td><td>31.935</td><td>0.0</td><td>9999.0</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99</td><td>44.611</td><td>0.0</td><td>9999.0</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99</td><td>45.472</td><td>0.0</td><td>9999.0</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99</td><td>41.082</td><td>0.0</td><td>9999.0</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99</td><td>35.465</td><td>0.0</td><td>9999.0</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99</td><td>2.114894903876398</td><td>5.902286117142443</td><td>2.114894903876398</td><td>5.902286117142443</td><td>11.135818296825672</td><td>41.60083124801925</td></tr>\n",
+       "<tr><td>33.267</td><td>0.0</td><td>9999.0</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99</td><td>31.935</td><td>0.0</td><td>9999.0</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99</td><td>44.611</td><td>0.0</td><td>9999.0</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99</td><td>45.472</td><td>0.0</td><td>9999.0</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99</td><td>41.082</td><td>0.0</td><td>9999.0</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99</td><td>35.465</td><td>0.0</td><td>9999.0</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99</td><td>7.830597191970796</td><td>4.983260476277582</td><td>7.830597191970796</td><td>4.983260476277582</td><td>11.146434103454093</td><td>41.59955491360546</td></tr>\n",
+       "<tr><td>33.267</td><td>0.0</td><td>9999.0</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99</td><td>31.935</td><td>0.0</td><td>9999.0</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99</td><td>44.611</td><td>0.0</td><td>9999.0</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99</td><td>45.472</td><td>0.0</td><td>9999.0</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99</td><td>41.082</td><td>0.0</td><td>9999.0</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99</td><td>35.465</td><td>0.0</td><td>9999.0</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99</td><td>1.8512388047197121</td><td>2.4162258092939237</td><td>1.8512388047197121</td><td>2.4162258092939237</td><td>11.135329111316906</td><td>41.595989469915736</td></tr>\n",
+       "<tr><td>17.854</td><td>7.27e-08</td><td>4.94e-11</td><td>17.845</td><td>99.999</td><td>0.001</td><td>0.07</td><td>1471.8</td><td>-0.002</td><td>0.001</td><td>0.015</td><td>0</td><td>17.001</td><td>1.59e-07</td><td>9.64e-11</td><td>16.993</td><td>99.999</td><td>0.001</td><td>0.09</td><td>1655.0</td><td>-0.004</td><td>-0.001</td><td>0.02</td><td>0</td><td>23.028</td><td>5.85e-10</td><td>2.5e-11</td><td>23.082</td><td>99.999</td><td>0.046</td><td>0.91</td><td>23.4</td><td>-0.007</td><td>0.055</td><td>0.004</td><td>0</td><td>22.208</td><td>1.29e-09</td><td>1.85e-11</td><td>22.224</td><td>99.999</td><td>0.016</td><td>0.73</td><td>69.6</td><td>0.008</td><td>-0.002</td><td>0.006</td><td>0</td><td>21.749</td><td>1.99e-09</td><td>4.18e-12</td><td>21.751</td><td>22.383</td><td>0.002</td><td>0.43</td><td>477.0</td><td>-0.003</td><td>-0.007</td><td>0.004</td><td>2</td><td>19.085</td><td>2.33e-08</td><td>1.78e-11</td><td>19.08</td><td>19.106</td><td>0.001</td><td>0.15</td><td>1313.8</td><td>-0.004</td><td>-0.001</td><td>0.008</td><td>2</td><td>4.00723590880484</td><td>6.195528733772067</td><td>4.00723590880484</td><td>6.195528733772067</td><td>11.139332963458779</td><td>41.6012386673917</td></tr>\n",
+       "<tr><td>17.854</td><td>8.22e-08</td><td>5.21e-11</td><td>17.713</td><td>99.999</td><td>0.001</td><td>0.93</td><td>1577.6</td><td>-0.028</td><td>0.002</td><td>0.003</td><td>0</td><td>17.001</td><td>1.86e-07</td><td>1.05e-10</td><td>16.825</td><td>99.999</td><td>0.001</td><td>0.96</td><td>1773.9</td><td>-0.026</td><td>0.006</td><td>0.003</td><td>0</td><td>23.028</td><td>5.88e-10</td><td>2.81e-11</td><td>23.076</td><td>99.999</td><td>0.052</td><td>1.15</td><td>20.9</td><td>0.054</td><td>-0.041</td><td>0.0</td><td>0</td><td>22.208</td><td>1.26e-09</td><td>1.77e-11</td><td>22.246</td><td>99.999</td><td>0.015</td><td>0.78</td><td>71.4</td><td>0.005</td><td>-0.012</td><td>0.0</td><td>0</td><td>21.749</td><td>2.02e-09</td><td>3.23e-12</td><td>21.738</td><td>22.375</td><td>0.002</td><td>0.5</td><td>623.8</td><td>-0.011</td><td>-0.004</td><td>0.0</td><td>0</td><td>19.085</td><td>2.46e-08</td><td>1.59e-11</td><td>19.024</td><td>19.051</td><td>0.001</td><td>0.65</td><td>1540.6</td><td>-0.027</td><td>0.006</td><td>0.0</td><td>0</td><td>9.222428132815132</td><td>7.808316641626685</td><td>9.222428132815132</td><td>7.808316641626685</td><td>11.14901954911686</td><td>41.60347847678029</td></tr>\n",
+       "<tr><td>17.854</td><td>7.24e-08</td><td>4.94e-11</td><td>17.851</td><td>99.999</td><td>0.001</td><td>0.07</td><td>1467.0</td><td>-0.003</td><td>0.003</td><td>0.012</td><td>0</td><td>17.001</td><td>1.59e-07</td><td>9.7e-11</td><td>16.996</td><td>99.999</td><td>0.001</td><td>0.08</td><td>1640.3</td><td>-0.003</td><td>0.001</td><td>0.01</td><td>0</td><td>23.028</td><td>5.72e-10</td><td>2.56e-11</td><td>23.106</td><td>99.999</td><td>0.049</td><td>0.99</td><td>22.3</td><td>0.001</td><td>0.121</td><td>0.019</td><td>0</td><td>22.208</td><td>1.27e-09</td><td>1.74e-11</td><td>22.24</td><td>99.999</td><td>0.015</td><td>0.86</td><td>72.8</td><td>0.014</td><td>-0.001</td><td>0.001</td><td>0</td><td>21.749</td><td>1.99e-09</td><td>4.14e-12</td><td>21.755</td><td>22.387</td><td>0.002</td><td>0.38</td><td>479.9</td><td>0.002</td><td>0.001</td><td>0.009</td><td>0</td><td>19.085</td><td>2.33e-08</td><td>2.06e-11</td><td>19.082</td><td>19.108</td><td>0.001</td><td>0.15</td><td>1128.4</td><td>-0.001</td><td>-0.001</td><td>0.006</td><td>2</td><td>6.319518622623847</td><td>0.8399749720419734</td><td>6.319518622623847</td><td>0.8399749720419734</td><td>11.14362746572599</td><td>41.59380042157581</td></tr>\n",
+       "<tr><td>17.854</td><td>7.28e-08</td><td>4.92e-11</td><td>17.845</td><td>99.999</td><td>0.001</td><td>0.11</td><td>1478.1</td><td>-0.006</td><td>0.002</td><td>0.007</td><td>0</td><td>17.001</td><td>1.6e-07</td><td>9.53e-11</td><td>16.992</td><td>99.999</td><td>0.001</td><td>0.1</td><td>1674.3</td><td>-0.006</td><td>0.002</td><td>0.007</td><td>0</td><td>23.028</td><td>5.58e-10</td><td>2.48e-11</td><td>23.133</td><td>99.999</td><td>0.048</td><td>0.94</td><td>22.5</td><td>0.01</td><td>0.058</td><td>0.0</td><td>0</td><td>22.208</td><td>1.29e-09</td><td>1.78e-11</td><td>22.221</td><td>99.999</td><td>0.015</td><td>0.82</td><td>72.6</td><td>0.015</td><td>-0.023</td><td>0.0</td><td>0</td><td>21.749</td><td>2.02e-09</td><td>3.61e-12</td><td>21.738</td><td>22.369</td><td>0.002</td><td>0.41</td><td>559.4</td><td>-0.005</td><td>0.005</td><td>0.002</td><td>0</td><td>19.085</td><td>2.34e-08</td><td>1.71e-11</td><td>19.078</td><td>19.103</td><td>0.001</td><td>0.19</td><td>1365.0</td><td>-0.004</td><td>0.004</td><td>0.002</td><td>0</td><td>1.270292136158215</td><td>4.915256592030158</td><td>1.270292136158215</td><td>4.915256592030158</td><td>11.13424976551128</td><td>41.59946027756628</td></tr>\n",
+       "<tr><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td></tr>\n",
+       "<tr><td>33.267</td><td>0.0</td><td>9999.0</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99</td><td>31.935</td><td>0.0</td><td>9999.0</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99</td><td>44.611</td><td>0.0</td><td>9999.0</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99</td><td>45.472</td><td>0.0</td><td>9999.0</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99</td><td>41.082</td><td>0.0</td><td>9999.0</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99</td><td>35.465</td><td>0.0</td><td>9999.0</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99</td><td>1.438812740269939</td><td>6.681951311451548</td><td>1.438812740269939</td><td>6.681951311451548</td><td>11.134562468860524</td><td>41.60191404109182</td></tr>\n",
+       "<tr><td>33.267</td><td>0.0</td><td>9999.0</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99</td><td>31.935</td><td>0.0</td><td>9999.0</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99</td><td>44.611</td><td>0.0</td><td>9999.0</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99</td><td>45.472</td><td>0.0</td><td>9999.0</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99</td><td>41.082</td><td>0.0</td><td>9999.0</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99</td><td>35.465</td><td>0.0</td><td>9999.0</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99</td><td>4.4022893584998695</td><td>2.543106238531561</td><td>4.4022893584998695</td><td>2.543106238531561</td><td>11.140066869301677</td><td>41.59616587367655</td></tr>\n",
+       "<tr><td>33.267</td><td>0.0</td><td>9999.0</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99</td><td>31.935</td><td>0.0</td><td>9999.0</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99</td><td>44.611</td><td>0.0</td><td>9999.0</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99</td><td>45.472</td><td>0.0</td><td>9999.0</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99</td><td>41.082</td><td>0.0</td><td>9999.0</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99</td><td>35.465</td><td>0.0</td><td>9999.0</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99</td><td>5.849705476236796</td><td>3.599122498844558</td><td>5.849705476236796</td><td>3.599122498844558</td><td>11.142755005660842</td><td>41.597632579100775</td></tr>\n",
+       "<tr><td>33.267</td><td>0.0</td><td>9999.0</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99</td><td>31.935</td><td>0.0</td><td>9999.0</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99</td><td>44.611</td><td>0.0</td><td>9999.0</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99</td><td>45.472</td><td>0.0</td><td>9999.0</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99</td><td>41.082</td><td>0.0</td><td>9999.0</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99</td><td>35.465</td><td>0.0</td><td>9999.0</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99</td><td>0.7500970014044301</td><td>5.5270923510957415</td><td>0.7500970014044301</td><td>5.5270923510957415</td><td>11.133283500687682</td><td>41.600309979631795</td></tr>\n",
+       "<tr><td>33.267</td><td>0.0</td><td>9999.0</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99</td><td>31.935</td><td>0.0</td><td>9999.0</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99</td><td>44.611</td><td>0.0</td><td>9999.0</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99</td><td>45.472</td><td>0.0</td><td>9999.0</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99</td><td>41.082</td><td>0.0</td><td>9999.0</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99</td><td>35.465</td><td>0.0</td><td>9999.0</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99</td><td>9.705313961886509</td><td>5.288131846190785</td><td>9.705313961886509</td><td>5.288131846190785</td><td>11.149916036089541</td><td>41.599978162934065</td></tr>\n",
+       "<tr><td>33.267</td><td>0.0</td><td>9999.0</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99</td><td>31.935</td><td>0.0</td><td>9999.0</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99</td><td>44.611</td><td>0.0</td><td>9999.0</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99</td><td>45.472</td><td>0.0</td><td>9999.0</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99</td><td>41.082</td><td>0.0</td><td>9999.0</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99</td><td>35.465</td><td>0.0</td><td>9999.0</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99</td><td>10.36644377198906</td><td>5.516683266373184</td><td>10.36644377198906</td><td>5.516683266373184</td><td>11.151143994385318</td><td>41.60029550582017</td></tr>\n",
+       "<tr><td>33.267</td><td>0.0</td><td>9999.0</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99</td><td>31.935</td><td>0.0</td><td>9999.0</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99</td><td>44.611</td><td>0.0</td><td>9999.0</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99</td><td>45.472</td><td>0.0</td><td>9999.0</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99</td><td>41.082</td><td>0.0</td><td>9999.0</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99</td><td>35.465</td><td>0.0</td><td>9999.0</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99</td><td>5.181023739171517</td><td>3.2316854307004776</td><td>5.181023739171517</td><td>3.2316854307004776</td><td>11.141513115461178</td><td>41.59712225014788</td></tr>\n",
+       "<tr><td>33.267</td><td>0.0</td><td>9999.0</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99</td><td>31.935</td><td>0.0</td><td>9999.0</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99</td><td>44.611</td><td>0.0</td><td>9999.0</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99</td><td>45.472</td><td>0.0</td><td>9999.0</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99</td><td>41.082</td><td>0.0</td><td>9999.0</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99</td><td>35.465</td><td>0.0</td><td>9999.0</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99</td><td>1.5843145598338926</td><td>7.329627470830625</td><td>1.5843145598338926</td><td>7.329627470830625</td><td>11.134832615552707</td><td>41.60281360865252</td></tr>\n",
+       "<tr><td>33.267</td><td>0.0</td><td>9999.0</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99</td><td>31.935</td><td>0.0</td><td>9999.0</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99</td><td>44.611</td><td>0.0</td><td>9999.0</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99</td><td>45.472</td><td>0.0</td><td>9999.0</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99</td><td>41.082</td><td>0.0</td><td>9999.0</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99</td><td>35.465</td><td>0.0</td><td>9999.0</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99</td><td>10.49952161053865</td><td>3.1439986376199274</td><td>10.49952161053865</td><td>3.1439986376199274</td><td>11.151390686406096</td><td>41.59700009092635</td></tr>\n",
+       "<tr><td>33.267</td><td>0.0</td><td>9999.0</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99</td><td>31.935</td><td>0.0</td><td>9999.0</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99</td><td>44.611</td><td>0.0</td><td>9999.0</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99</td><td>45.472</td><td>0.0</td><td>9999.0</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99</td><td>41.082</td><td>0.0</td><td>9999.0</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99</td><td>35.465</td><td>0.0</td><td>9999.0</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99.999</td><td>99</td><td>0.2881930149088294</td><td>8.04900852358021</td><td>0.2881930149088294</td><td>8.04900852358021</td><td>11.132425078368692</td><td>41.60381257215712</td></tr>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "<Table length=51549>\n",
+       "F110W_IN F110W_RATE F110W_RATERR ...      RA_J2000          DEC_J2000     \n",
+       "float32   float32     float32    ...      float64            float64      \n",
+       "-------- ---------- ------------ ... ------------------ ------------------\n",
+       "  33.267        0.0       9999.0 ... 11.145614732485846  41.59773092319124\n",
+       "  33.267        0.0       9999.0 ... 11.148956385132271 41.599026017115044\n",
+       "  33.267        0.0       9999.0 ...  11.14579404466301  41.60069345799913\n",
+       "  33.267        0.0       9999.0 ... 11.135818296825672  41.60083124801925\n",
+       "  33.267        0.0       9999.0 ... 11.146434103454093  41.59955491360546\n",
+       "  33.267        0.0       9999.0 ... 11.135329111316906 41.595989469915736\n",
+       "  17.854   7.27e-08     4.94e-11 ... 11.139332963458779   41.6012386673917\n",
+       "  17.854   8.22e-08     5.21e-11 ...  11.14901954911686  41.60347847678029\n",
+       "  17.854   7.24e-08     4.94e-11 ...  11.14362746572599  41.59380042157581\n",
+       "  17.854   7.28e-08     4.92e-11 ...  11.13424976551128  41.59946027756628\n",
+       "     ...        ...          ... ...                ...                ...\n",
+       "  33.267        0.0       9999.0 ... 11.134562468860524  41.60191404109182\n",
+       "  33.267        0.0       9999.0 ... 11.140066869301677  41.59616587367655\n",
+       "  33.267        0.0       9999.0 ... 11.142755005660842 41.597632579100775\n",
+       "  33.267        0.0       9999.0 ... 11.133283500687682 41.600309979631795\n",
+       "  33.267        0.0       9999.0 ... 11.149916036089541 41.599978162934065\n",
+       "  33.267        0.0       9999.0 ... 11.151143994385318  41.60029550582017\n",
+       "  33.267        0.0       9999.0 ... 11.141513115461178  41.59712225014788\n",
+       "  33.267        0.0       9999.0 ... 11.134832615552707  41.60281360865252\n",
+       "  33.267        0.0       9999.0 ... 11.151390686406096  41.59700009092635\n",
+       "  33.267        0.0       9999.0 ... 11.132425078368692  41.60381257215712"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Table.read(ast_file)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "# - ASTs\n",
+    "ast_file_cut = ast_file.replace(\".fits\", \"_cut.fits\")\n",
+    "\n",
+    "# check to see if the trimmed AST file already exists\n",
+    "if not os.path.isfile(ast_file_cut):\n",
+    "    cut_catalogs.cut_catalogs(\n",
+    "        ast_file,\n",
+    "        ast_file_cut,\n",
+    "        partial_overlap=True,\n",
+    "        flagged=True,\n",
+    "        flag_filter=flag_filter[0],\n",
+    "        region_file=True,\n",
+    "    )\n",
+    "\n",
+    "# so now we've generated the cut ast file"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can plot the AST magnitudes against our original source magnitudes again, just to check that we are within a reasonable range."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# check to see if the plotted AST file already exists\n",
+    "if not os.path.isfile(ast_file_cut.replace(\".fits\", \"_maghist.pdf\")):\n",
+    "    \n",
+    "    test = plot_mag_hist.plot_mag_hist(ast_file_cut, stars_per_bin=200, max_bins=30)\n",
+    "\n",
+    "    # and so this should plot a histogram of the different asts that remain after cutting"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Step 5.5 Update Datamodel Again\n",
+    "\n",
+    "**Same with these cuts, we now have to update the astfile variable in datamodel.py (~line 144)  with this new trimmed filename: './M31-B09-EAST_chunk.gst.fake_cut.fits'**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Auto-detected type: fits\n",
+      "Auto-detected type: fits\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<module 'datamodel' from '/home/christina/beast_testing/BEAST_example/datamodel.py'>"
+      ]
+     },
+     "execution_count": 71,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "importlib.reload(datamodel)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Step 6. Split catalog by source density"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For the next fitting step, we're going to have to break our catalog and AST file into bins based on the source density, and then further into sub-bins if there are more than ~6250 sources in the bins. \n",
+    "\n",
+    "We split things into source density bins so that we can later study how the actual source density of region effects the noise or bias. We further split things into sub-bins, just to make things a little more computationally accessible.\n",
+    "\n",
+    "One thing to note is that the source density bins are first sorted by magnitude (typically F475W if it's there) before being split into sub-bins. This means that the first sub-bin file (for a source density bin that has more than 6250 sources) will end up having all the dimmest sources or any sources with NAN values, and the last sub file will have all the brightest sources. This will become handy in Step 8 when we create physics (SED) models and noisemoels tailored specifically to each sub-bin file."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# check to see if any sub files exist yet\n",
+    "if len(glob.glob(gst_file_cut.replace('.fits','*sub*fits') )) == 0:\n",
+    "    # if no sub files exist, they can now be created\n",
+    "    # a smaller value for n_per_file will mean more individual files/runs,\n",
+    "    # but each run will take a shorter amount of time\n",
+    "    \n",
+    "    #split the gst file and ast file\n",
+    "    split_catalog_using_map.split_main(\n",
+    "        gst_file_cut,\n",
+    "        ast_file_cut,\n",
+    "        gst_file.replace('.fits','_sourceden_map.hd5'), #get full sourceden_mad.hd5 file from dust folder\n",
+    "        bin_width=1,\n",
+    "        n_per_file=6250, #this is the max number of sources per bin before it splits \n",
+    "\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "So these are all the different source density bins, with some of them being split into sub bins to limit the number of entries to ~6250. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Rather than reading in all the files we just created, the developers of this code instead wrote this handy little function that generates a dictionary of all the files that have just been created (assuming the function ran correctly) and all the files that we hope to generate in the future.\n",
+    "\n",
+    "Because of this, I recommend not changing any of the naming for Step 6 or beyond, just because that then makes this dictionary point to incorrect files."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Auto-detected type: fits\n",
+      "Auto-detected type: fits\n"
+     ]
+    }
+   ],
+   "source": [
+    "# generate file name lists\n",
+    "file_dict = create_filenames.create_filenames(\n",
+    "    use_sd=True, nsubs=datamodel.n_subgrid\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If we take a look in our folder, we should be able to see some bins with sub-bins notation. We can do a quick check to see if the sub-binning generated from the dictionary matchs up with the files split in our data folder."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[['2', '0'],\n",
+       " ['3', '0'],\n",
+       " ['3', '1'],\n",
+       " ['3', '2'],\n",
+       " ['4', '0'],\n",
+       " ['4', '1'],\n",
+       " ['4', '2'],\n",
+       " ['4', '3'],\n",
+       " ['4', '4'],\n",
+       " ['4', '5'],\n",
+       " ['5', '0'],\n",
+       " ['6', '0'],\n",
+       " ['9', '0']]"
+      ]
+     },
+     "execution_count": 74,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sd_sub_info = file_dict[\"sd_sub_info\"]\n",
+    "sd_sub_info"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Hint: If sd_sub_info is empty, make sure you've updated the obsfile and astfile variables in datamodel (Step 4.5 and 5.5), reloaded the datamodel, and try to run create_filenames again.**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "** total SD bins: 6\n",
+      "** total SD subfiles: 13\n"
+     ]
+    }
+   ],
+   "source": [
+    "# - number of SD bins\n",
+    "temp = set([i[0] for i in sd_sub_info])\n",
+    "print(\"** total SD bins: \" + str(len(temp)))\n",
+    "\n",
+    "# - the unique sets of SD+sub\n",
+    "unique_sd_sub = [\n",
+    "    x for i, x in enumerate(sd_sub_info) if i == sd_sub_info.index(x)\n",
+    "]\n",
+    "print(\"** total SD subfiles: \" + str(len(unique_sd_sub)))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Just another quick was to ensure that all the binning and sub-binning matches up. If it doesn't, none of the next steps will run properly."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Step 7. Make Noise Models"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We're now on to creating our observational noise models! These models will be used to adjust the bias and uncertainty in Steps 8 and 9. \n",
+    "\n",
+    "The **uncertainty** (also known as sigma) is the standard deviation calculated for all the detected sources.\n",
+    "\n",
+    "The **bias** is the average offset between the input flux we have for the ASTs and the measured flux. Bias tends to become more prominent in regions of high source density, where it's harder to detect all the faint stars if they get blended together. If this happens, then some of the stars are assumed to be part of the background (raising the average), which gets subtracted from the detected sources. If the background is raised, then the detected sources are measured to be systematically fainter than they should be."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# these are what the noise files should be named once generated\n",
+    "noise_files = file_dict[\"noise_files\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['M31-B09-EAST_chunk.gst.fake_cut_bin2.fits',\n",
+       " 'M31-B09-EAST_chunk.gst.fake_cut_bin3.fits',\n",
+       " 'M31-B09-EAST_chunk.gst.fake_cut_bin4.fits',\n",
+       " 'M31-B09-EAST_chunk.gst.fake_cut_bin5.fits',\n",
+       " 'M31-B09-EAST_chunk.gst.fake_cut_bin6.fits',\n",
+       " 'M31-B09-EAST_chunk.gst.fake_cut_bin9.fits']"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# gather up the split AST files\n",
+    "ast_file_list = sorted(glob.glob(datamodel.astfile.replace(\".fits\", \"*_bin*\")))\n",
+    "ast_file_list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Auto-detected type: fits\n",
+      "Auto-detected type: fits\n",
+      "sd list:  ['2', '3', '4', '5', '6', '9']\n",
+      "\n",
+      "M31-B09-EAST_chunk/M31-B09-EAST_chunk_noisemodel_bin2.grid.hd5 already exists\n",
+      "M31-B09-EAST_chunk/M31-B09-EAST_chunk_noisemodel_bin2.grid.hd5\n",
+      "\n",
+      "creating M31-B09-EAST_chunk/M31-B09-EAST_chunk_noisemodel_bin3.grid.hd5\n",
+      "Auto-detected type: hd5\n",
+      "Auto-detected type: fits\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Fitting model: 100%|██████████| 6/6 [00:00<00:00, 122.37it/s]\n",
+      "Evaluating model: 100%|██████████| 6/6 [00:00<00:00, 30.40it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Writing to disk into M31-B09-EAST_chunk/M31-B09-EAST_chunk_noisemodel_bin3.grid.hd5\n",
+      "M31-B09-EAST_chunk/M31-B09-EAST_chunk_noisemodel_bin3.grid.hd5\n",
+      "\n",
+      "creating M31-B09-EAST_chunk/M31-B09-EAST_chunk_noisemodel_bin4.grid.hd5\n",
+      "Auto-detected type: hd5\n",
+      "Auto-detected type: fits\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Fitting model: 100%|██████████| 6/6 [00:00<00:00, 63.10it/s]\n",
+      "Evaluating model: 100%|██████████| 6/6 [00:00<00:00, 29.23it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Writing to disk into M31-B09-EAST_chunk/M31-B09-EAST_chunk_noisemodel_bin4.grid.hd5\n",
+      "M31-B09-EAST_chunk/M31-B09-EAST_chunk_noisemodel_bin4.grid.hd5\n",
+      "\n",
+      "creating M31-B09-EAST_chunk/M31-B09-EAST_chunk_noisemodel_bin5.grid.hd5\n",
+      "Auto-detected type: hd5\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Fitting model: 100%|██████████| 6/6 [00:00<00:00, 249.56it/s]\n",
+      "Evaluating model:   0%|          | 0/6 [00:00<?, ?it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Auto-detected type: fits\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Evaluating model: 100%|██████████| 6/6 [00:00<00:00, 31.95it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Writing to disk into M31-B09-EAST_chunk/M31-B09-EAST_chunk_noisemodel_bin5.grid.hd5\n",
+      "M31-B09-EAST_chunk/M31-B09-EAST_chunk_noisemodel_bin5.grid.hd5\n",
+      "\n",
+      "creating M31-B09-EAST_chunk/M31-B09-EAST_chunk_noisemodel_bin6.grid.hd5\n",
+      "Auto-detected type: hd5\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Fitting model: 100%|██████████| 6/6 [00:00<00:00, 728.66it/s]\n",
+      "Evaluating model:  67%|██████▋   | 4/6 [00:00<00:00, 39.19it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Auto-detected type: fits\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Evaluating model: 100%|██████████| 6/6 [00:00<00:00, 39.15it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Writing to disk into M31-B09-EAST_chunk/M31-B09-EAST_chunk_noisemodel_bin6.grid.hd5\n",
+      "M31-B09-EAST_chunk/M31-B09-EAST_chunk_noisemodel_bin6.grid.hd5\n",
+      "\n",
+      "creating M31-B09-EAST_chunk/M31-B09-EAST_chunk_noisemodel_bin9.grid.hd5\n",
+      "Auto-detected type: hd5\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Fitting model: 100%|██████████| 6/6 [00:00<00:00, 388.61it/s]\n",
+      "Evaluating model:   0%|          | 0/6 [00:00<?, ?it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Auto-detected type: fits\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Evaluating model: 100%|██████████| 6/6 [00:00<00:00, 33.88it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Writing to disk into M31-B09-EAST_chunk/M31-B09-EAST_chunk_noisemodel_bin9.grid.hd5\n",
+      "M31-B09-EAST_chunk/M31-B09-EAST_chunk_noisemodel_bin9.grid.hd5\n"
+     ]
+    }
+   ],
+   "source": [
+    "# create the noise model with our ASTs  \n",
+    "create_obsmodel.create_obsmodel(\n",
+    "        use_sd=True, nsubs=datamodel.n_subgrid, nprocs=1\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Step 7.5 Visualize Noise Models (Optional)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This next cell is some older plotting code for visualizing the noise models. It should (hopefully) work if you uncomment and run it, but the lack of a log scale for the y-axis makes the results a little harder to fully interpret.\n",
+    "\n",
+    "As an alternative, the same plot is recreated down below but the steps have been broken down to hopefully help you gain a better sense of what's going on (and plot the y-axis with a log scale). \n",
+    "\n",
+    "If you're not interested in visualizing the noise models, feel free to skip this step."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# plot_noisemodel.plot_noisemodel(sed_file=\"M31-B09-EAST_chunk/M31-B09-EAST_chunk_seds.grid.hd5\", \n",
+    "#                                 noise_file_list=noise_files, \n",
+    "#                                 plot_file=\"noise_model_plot.png\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Alternative plot\n",
+    "I'm going to try to recreate this noise model plot using some of the filters used in Dreiss' paper.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Auto-detected type: hd5\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(510048, 6)"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# set some basic plotting stuff\n",
+    "samp=100 # makes it so we plot every 100th point from the SED files\n",
+    "color=[\"black\", \"red\", \"gold\", \"lime\", \"xkcd:azure\"]\n",
+    "label=None\n",
+    "\n",
+    "# load in the physics model as an object\n",
+    "sed_object = FileSEDGrid(sed_files[0])\n",
+    "\n",
+    "# read the flux values for all the sources\n",
+    "if hasattr(sed_object.seds, \"read\"):\n",
+    "    sed_grid = sed_object.seds.read()\n",
+    "else:\n",
+    "    sed_grid = sed_object.seds\n",
+    "    \n",
+    "sed_object.seds.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "So this sed_grid comes from back in Step 2, where the physics model created ~500,000 points based off of the original parameters we specified in the datamodel, and for each point, the expected flux for each filter is calculated. We can now use the noise models we created with the ASTs to see how the bias and uncertainty is expected to scale with the flux from a specific filter. We'll plot the log10 of the flux on the x-axis and then the flux-normalized uncertainty and bias on the y-axis. We can also color our results based on what source density bin the ASTs came from, as well as compare how different filters compare to one another\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# pull out the list of filters\n",
+    "filter_list = sed_object.filters\n",
+    "\n",
+    "# for this plot, I just want to plot the first two filter\n",
+    "# feel free to change this and see what the other filters look like\n",
+    "filter_list_plot = filter_list[0:2]\n",
+    "n_filter = len(filter_list_plot)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "* reading M31-B09-EAST_chunk/M31-B09-EAST_chunk_noisemodel_bin2.grid.hd5\n",
+      "* reading M31-B09-EAST_chunk/M31-B09-EAST_chunk_noisemodel_bin3.grid.hd5\n",
+      "* reading M31-B09-EAST_chunk/M31-B09-EAST_chunk_noisemodel_bin3.grid.hd5\n",
+      "* reading M31-B09-EAST_chunk/M31-B09-EAST_chunk_noisemodel_bin3.grid.hd5\n",
+      "* reading M31-B09-EAST_chunk/M31-B09-EAST_chunk_noisemodel_bin4.grid.hd5\n",
+      "* reading M31-B09-EAST_chunk/M31-B09-EAST_chunk_noisemodel_bin4.grid.hd5\n",
+      "* reading M31-B09-EAST_chunk/M31-B09-EAST_chunk_noisemodel_bin4.grid.hd5\n",
+      "* reading M31-B09-EAST_chunk/M31-B09-EAST_chunk_noisemodel_bin4.grid.hd5\n",
+      "* reading M31-B09-EAST_chunk/M31-B09-EAST_chunk_noisemodel_bin4.grid.hd5\n",
+      "* reading M31-B09-EAST_chunk/M31-B09-EAST_chunk_noisemodel_bin4.grid.hd5\n",
+      "* reading M31-B09-EAST_chunk/M31-B09-EAST_chunk_noisemodel_bin5.grid.hd5\n",
+      "* reading M31-B09-EAST_chunk/M31-B09-EAST_chunk_noisemodel_bin6.grid.hd5\n",
+      "* reading M31-B09-EAST_chunk/M31-B09-EAST_chunk_noisemodel_bin9.grid.hd5\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x576 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# set up the figure frame work\n",
+    "# have it scale with the number of filters we're plotting\n",
+    "fig, axes = plt.subplots(2, len(filter_list_plot), sharex=True, figsize=(5*len(filter_list_plot),8))\n",
+    "\n",
+    "# go through noise files\n",
+    "for n, nfile in enumerate(noise_files):\n",
+    "    \n",
+    "    print(\"* reading \" + nfile)\n",
+    "\n",
+    "    # read in the values\n",
+    "    noisemodel_vals = noisemodel.get_noisemodelcat(nfile)\n",
+    "\n",
+    "    # extract error and bias\n",
+    "    noise_err = noisemodel_vals.root.error[:]\n",
+    "    noise_bias = noisemodel_vals.root.bias[:]\n",
+    "    \n",
+    "    cmaps = plt.get_cmap('viridis')\n",
+    "\n",
+    "    gradient = np.linspace(0, 1, len(noise_files))    \n",
+    "\n",
+    "    # now we can start plotting things\n",
+    "    for f, filt in enumerate(filter_list_plot):\n",
+    "        \n",
+    "        # error is negative where it's been extrapolated -> trim those\n",
+    "        good_err = np.where(noise_err[:, f] > 0)[0]\n",
+    "        plot_sed = sed_grid[good_err, f][::samp] # only pulls every 100th point\n",
+    "        plot_err = noise_err[good_err, f][::samp]\n",
+    "        plot_bias = noise_bias[good_err, f][::samp]\n",
+    "\n",
+    "        # plot bias\n",
+    "        axes[0, f].set_yscale('log')\n",
+    "\n",
+    "        axes[0, f].plot(\n",
+    "            np.log10(plot_sed),\n",
+    "            np.abs(plot_bias) / plot_sed,\n",
+    "            marker=\"o\",\n",
+    "            linestyle=\"none\",\n",
+    "            mew=0,\n",
+    "            ms=2,\n",
+    "            alpha=1,\n",
+    "            c=cmaps(int(nfile[-10])/9.),\n",
+    "            label=noise_files[n][-10]\n",
+    "            )\n",
+    "        \n",
+    "        axes[0, f].set_ylabel(r\"Abs Bias ($\\mu$/F)\", fontsize=10)\n",
+    "        # xlabel is still in flux, not mag\n",
+    "        axes[0, f].legend()\n",
+    "\n",
+    "        # plot error (uncertainty)\n",
+    "        axes[1, f].set_yscale('log')\n",
+    "\n",
+    "        axes[1, f].plot(\n",
+    "            np.log10(plot_sed),\n",
+    "            plot_err / plot_sed,\n",
+    "            marker=\"o\",\n",
+    "            linestyle=\"none\",\n",
+    "            mew=0,\n",
+    "            ms=2,\n",
+    "            color=color[0 % len(color)],\n",
+    "            alpha=0.1,)\n",
+    "        axes[1, f].set_ylabel(r\"Error ($\\sigma$/F)\", fontsize=10)\n",
+    "        axes[1, f].set_xlabel(\"log \" + filt[-5:], fontsize=10)\n",
+    "\n",
+    "        plt.tight_layout()\n",
+    "        \n",
+    "    #fig.colorbar(plt.cm.ScalarMappable(norm=np.arange(0,12), cmap=cmaps), ax=axes)\n",
+    "    \n",
+    "\n",
+    "    # Need to figure out if it's worth comparing the bias and the uncertainty to one another."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As you can probably tell, this plot isn't the most beautiful plot in the world (especially that coloring and legend) but I'm proud of her. It does, however, let you see the scale of the bias and uncertainty (error) for different filters and how the source density and magnitudes are correlated.\n",
+    "\n",
+    "The most notable thing to note is that the uncertainty and bias tend to be larger at lower fluxes. This probably doesn't come as a shock to anyone, but it's important to accurately take this into consideration when we make our fittings in Step 9."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Step 8. Trim Models\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now that we have our SED and or noise models created, we can go ahead and trim them of any sources that are so bright or so faint (compared to min/max flux in the observation file) that they will by definition produce effectively zero likelihood fits. \n",
+    "\n",
+    "One thing to note is that, since our noise models are correlated with source density, we are in a sense 'convolving' each of our noise models with the original physics grid, meaning we will end up with a lot of physics grids trimmed for each source density scenario thanks to our noise models (and these physics grids are still essentially as large as the original physics grid, making this a very storage-intensive step). However, this trimming of the 'parameter space', as you could call it, will help speed up fittings in Step 9.\n",
+    "\n",
+    "**This step is very storage intensive so I'd make sure to have at least ~5GB of storage available.**\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Auto-detected type: hd5\n",
+      "Auto-detected type: fits\n",
+      "working on filter # =  0\n",
+      "working on filter # =  1\n",
+      "working on filter # =  2\n",
+      "working on filter # =  3\n",
+      "working on filter # =  4\n",
+      "working on filter # =  5\n",
+      "number of original models =  510048\n",
+      "number of ast trimmed models =  509563\n",
+      "number of trimmed models =  479060\n",
+      "Writing trimmed sedgrid to disk into M31-B09-EAST_chunk/M31-B09-EAST_chunk_bin2_sub0_seds_trim.grid.hd5\n",
+      "Auto-detected type: hd5\n",
+      "Warning: Table does not exists.  New table will be created\n",
+      "M31-B09-EAST_chunk/M31-B09-EAST_chunk_bin2_sub0_seds_trim.grid.hd5 (File) ''\n",
+      "Last modif.: 'Tue May  5 16:08:41 2020'\n",
+      "Object Tree: \n",
+      "/ (RootGroup) ''\n",
+      "/grid (Table(479060,)) 'grid'\n",
+      "/lamb (EArray(6,)) 'lamb'\n",
+      "/seds (EArray(479060, 6)) 'seds'\n",
+      "\n",
+      "Writing trimmed noisemodel to disk into M31-B09-EAST_chunk/M31-B09-EAST_chunk_bin2_sub0_noisemodel_trim.grid.hd5\n",
+      "Auto-detected type: hd5\n",
+      "Auto-detected type: fits\n",
+      "working on filter # =  0\n",
+      "working on filter # =  1\n",
+      "working on filter # =  2\n",
+      "working on filter # =  3\n",
+      "working on filter # =  4\n",
+      "working on filter # =  5\n",
+      "number of original models =  510048\n",
+      "number of ast trimmed models =  510048\n",
+      "number of trimmed models =  479552\n",
+      "Writing trimmed sedgrid to disk into M31-B09-EAST_chunk/M31-B09-EAST_chunk_bin3_sub0_seds_trim.grid.hd5\n",
+      "Auto-detected type: hd5\n",
+      "Warning: Table does not exists.  New table will be created\n",
+      "M31-B09-EAST_chunk/M31-B09-EAST_chunk_bin3_sub0_seds_trim.grid.hd5 (File) ''\n",
+      "Last modif.: 'Tue May  5 16:09:08 2020'\n",
+      "Object Tree: \n",
+      "/ (RootGroup) ''\n",
+      "/grid (Table(479552,)) 'grid'\n",
+      "/lamb (EArray(6,)) 'lamb'\n",
+      "/seds (EArray(479552, 6)) 'seds'\n",
+      "\n",
+      "Writing trimmed noisemodel to disk into M31-B09-EAST_chunk/M31-B09-EAST_chunk_bin3_sub0_noisemodel_trim.grid.hd5\n",
+      "Auto-detected type: hd5\n",
+      "Auto-detected type: fits\n",
+      "working on filter # =  0\n",
+      "working on filter # =  1\n",
+      "working on filter # =  2\n",
+      "working on filter # =  3\n",
+      "working on filter # =  4\n",
+      "working on filter # =  5\n",
+      "number of original models =  510048\n",
+      "number of ast trimmed models =  510048\n",
+      "number of trimmed models =  479552\n",
+      "Writing trimmed sedgrid to disk into M31-B09-EAST_chunk/M31-B09-EAST_chunk_bin3_sub1_seds_trim.grid.hd5\n",
+      "Auto-detected type: hd5\n",
+      "Warning: Table does not exists.  New table will be created\n",
+      "M31-B09-EAST_chunk/M31-B09-EAST_chunk_bin3_sub1_seds_trim.grid.hd5 (File) ''\n",
+      "Last modif.: 'Tue May  5 16:09:33 2020'\n",
+      "Object Tree: \n",
+      "/ (RootGroup) ''\n",
+      "/grid (Table(479552,)) 'grid'\n",
+      "/lamb (EArray(6,)) 'lamb'\n",
+      "/seds (EArray(479552, 6)) 'seds'\n",
+      "\n",
+      "Writing trimmed noisemodel to disk into M31-B09-EAST_chunk/M31-B09-EAST_chunk_bin3_sub1_noisemodel_trim.grid.hd5\n",
+      "Auto-detected type: hd5\n",
+      "Auto-detected type: fits\n",
+      "working on filter # =  0\n",
+      "working on filter # =  1\n",
+      "working on filter # =  2\n",
+      "working on filter # =  3\n",
+      "working on filter # =  4\n",
+      "working on filter # =  5\n",
+      "number of original models =  510048\n",
+      "number of ast trimmed models =  510048\n",
+      "number of trimmed models =  479552\n",
+      "Writing trimmed sedgrid to disk into M31-B09-EAST_chunk/M31-B09-EAST_chunk_bin3_sub2_seds_trim.grid.hd5\n",
+      "Auto-detected type: hd5\n",
+      "Warning: Table does not exists.  New table will be created\n",
+      "M31-B09-EAST_chunk/M31-B09-EAST_chunk_bin3_sub2_seds_trim.grid.hd5 (File) ''\n",
+      "Last modif.: 'Tue May  5 16:09:59 2020'\n",
+      "Object Tree: \n",
+      "/ (RootGroup) ''\n",
+      "/grid (Table(479552,)) 'grid'\n",
+      "/lamb (EArray(6,)) 'lamb'\n",
+      "/seds (EArray(479552, 6)) 'seds'\n",
+      "\n",
+      "Writing trimmed noisemodel to disk into M31-B09-EAST_chunk/M31-B09-EAST_chunk_bin3_sub2_noisemodel_trim.grid.hd5\n",
+      "Auto-detected type: hd5\n",
+      "Auto-detected type: fits\n",
+      "working on filter # =  0\n",
+      "working on filter # =  1\n",
+      "working on filter # =  2\n",
+      "working on filter # =  3\n",
+      "working on filter # =  4\n",
+      "working on filter # =  5\n",
+      "number of original models =  510048\n",
+      "number of ast trimmed models =  510040\n",
+      "number of trimmed models =  479552\n",
+      "Writing trimmed sedgrid to disk into M31-B09-EAST_chunk/M31-B09-EAST_chunk_bin4_sub0_seds_trim.grid.hd5\n",
+      "Auto-detected type: hd5\n",
+      "Warning: Table does not exists.  New table will be created\n",
+      "M31-B09-EAST_chunk/M31-B09-EAST_chunk_bin4_sub0_seds_trim.grid.hd5 (File) ''\n",
+      "Last modif.: 'Tue May  5 16:10:23 2020'\n",
+      "Object Tree: \n",
+      "/ (RootGroup) ''\n",
+      "/grid (Table(479552,)) 'grid'\n",
+      "/lamb (EArray(6,)) 'lamb'\n",
+      "/seds (EArray(479552, 6)) 'seds'\n",
+      "\n",
+      "Writing trimmed noisemodel to disk into M31-B09-EAST_chunk/M31-B09-EAST_chunk_bin4_sub0_noisemodel_trim.grid.hd5\n",
+      "Auto-detected type: hd5\n",
+      "Auto-detected type: fits\n",
+      "working on filter # =  0\n",
+      "working on filter # =  1\n",
+      "working on filter # =  2\n",
+      "working on filter # =  3\n",
+      "working on filter # =  4\n",
+      "working on filter # =  5\n",
+      "number of original models =  510048\n",
+      "number of ast trimmed models =  510040\n",
+      "number of trimmed models =  479552\n",
+      "Writing trimmed sedgrid to disk into M31-B09-EAST_chunk/M31-B09-EAST_chunk_bin4_sub1_seds_trim.grid.hd5\n",
+      "Auto-detected type: hd5\n",
+      "Warning: Table does not exists.  New table will be created\n",
+      "M31-B09-EAST_chunk/M31-B09-EAST_chunk_bin4_sub1_seds_trim.grid.hd5 (File) ''\n",
+      "Last modif.: 'Tue May  5 16:10:46 2020'\n",
+      "Object Tree: \n",
+      "/ (RootGroup) ''\n",
+      "/grid (Table(479552,)) 'grid'\n",
+      "/lamb (EArray(6,)) 'lamb'\n",
+      "/seds (EArray(479552, 6)) 'seds'\n",
+      "\n",
+      "Writing trimmed noisemodel to disk into M31-B09-EAST_chunk/M31-B09-EAST_chunk_bin4_sub1_noisemodel_trim.grid.hd5\n",
+      "Auto-detected type: hd5\n",
+      "Auto-detected type: fits\n",
+      "working on filter # =  0\n",
+      "working on filter # =  1\n",
+      "working on filter # =  2\n",
+      "working on filter # =  3\n",
+      "working on filter # =  4\n",
+      "working on filter # =  5\n",
+      "number of original models =  510048\n",
+      "number of ast trimmed models =  510040\n",
+      "number of trimmed models =  479552\n",
+      "Writing trimmed sedgrid to disk into M31-B09-EAST_chunk/M31-B09-EAST_chunk_bin4_sub2_seds_trim.grid.hd5\n",
+      "Auto-detected type: hd5\n",
+      "Warning: Table does not exists.  New table will be created\n",
+      "M31-B09-EAST_chunk/M31-B09-EAST_chunk_bin4_sub2_seds_trim.grid.hd5 (File) ''\n",
+      "Last modif.: 'Tue May  5 16:11:10 2020'\n",
+      "Object Tree: \n",
+      "/ (RootGroup) ''\n",
+      "/grid (Table(479552,)) 'grid'\n",
+      "/lamb (EArray(6,)) 'lamb'\n",
+      "/seds (EArray(479552, 6)) 'seds'\n",
+      "\n",
+      "Writing trimmed noisemodel to disk into M31-B09-EAST_chunk/M31-B09-EAST_chunk_bin4_sub2_noisemodel_trim.grid.hd5\n",
+      "Auto-detected type: hd5\n",
+      "Auto-detected type: fits\n",
+      "working on filter # =  0\n",
+      "working on filter # =  1\n",
+      "working on filter # =  2\n",
+      "working on filter # =  3\n",
+      "working on filter # =  4\n",
+      "working on filter # =  5\n",
+      "number of original models =  510048\n",
+      "number of ast trimmed models =  510040\n",
+      "number of trimmed models =  479552\n",
+      "Writing trimmed sedgrid to disk into M31-B09-EAST_chunk/M31-B09-EAST_chunk_bin4_sub3_seds_trim.grid.hd5\n",
+      "Auto-detected type: hd5\n",
+      "Warning: Table does not exists.  New table will be created\n",
+      "M31-B09-EAST_chunk/M31-B09-EAST_chunk_bin4_sub3_seds_trim.grid.hd5 (File) ''\n",
+      "Last modif.: 'Tue May  5 16:11:34 2020'\n",
+      "Object Tree: \n",
+      "/ (RootGroup) ''\n",
+      "/grid (Table(479552,)) 'grid'\n",
+      "/lamb (EArray(6,)) 'lamb'\n",
+      "/seds (EArray(479552, 6)) 'seds'\n",
+      "\n",
+      "Writing trimmed noisemodel to disk into M31-B09-EAST_chunk/M31-B09-EAST_chunk_bin4_sub3_noisemodel_trim.grid.hd5\n",
+      "Auto-detected type: hd5\n",
+      "Auto-detected type: fits\n",
+      "working on filter # =  0\n",
+      "working on filter # =  1\n",
+      "working on filter # =  2\n",
+      "working on filter # =  3\n",
+      "working on filter # =  4\n",
+      "working on filter # =  5\n",
+      "number of original models =  510048\n",
+      "number of ast trimmed models =  510040\n",
+      "number of trimmed models =  479552\n",
+      "Writing trimmed sedgrid to disk into M31-B09-EAST_chunk/M31-B09-EAST_chunk_bin4_sub4_seds_trim.grid.hd5\n",
+      "Auto-detected type: hd5\n",
+      "Warning: Table does not exists.  New table will be created\n",
+      "M31-B09-EAST_chunk/M31-B09-EAST_chunk_bin4_sub4_seds_trim.grid.hd5 (File) ''\n",
+      "Last modif.: 'Tue May  5 16:11:58 2020'\n",
+      "Object Tree: \n",
+      "/ (RootGroup) ''\n",
+      "/grid (Table(479552,)) 'grid'\n",
+      "/lamb (EArray(6,)) 'lamb'\n",
+      "/seds (EArray(479552, 6)) 'seds'\n",
+      "\n",
+      "Writing trimmed noisemodel to disk into M31-B09-EAST_chunk/M31-B09-EAST_chunk_bin4_sub4_noisemodel_trim.grid.hd5\n",
+      "Auto-detected type: hd5\n",
+      "Auto-detected type: fits\n",
+      "working on filter # =  0\n",
+      "working on filter # =  1\n",
+      "working on filter # =  2\n",
+      "working on filter # =  3\n",
+      "working on filter # =  4\n",
+      "working on filter # =  5\n",
+      "number of original models =  510048\n",
+      "number of ast trimmed models =  510040\n",
+      "number of trimmed models =  479552\n",
+      "Writing trimmed sedgrid to disk into M31-B09-EAST_chunk/M31-B09-EAST_chunk_bin4_sub5_seds_trim.grid.hd5\n",
+      "Auto-detected type: hd5\n",
+      "Warning: Table does not exists.  New table will be created\n",
+      "M31-B09-EAST_chunk/M31-B09-EAST_chunk_bin4_sub5_seds_trim.grid.hd5 (File) ''\n",
+      "Last modif.: 'Tue May  5 16:12:23 2020'\n",
+      "Object Tree: \n",
+      "/ (RootGroup) ''\n",
+      "/grid (Table(479552,)) 'grid'\n",
+      "/lamb (EArray(6,)) 'lamb'\n",
+      "/seds (EArray(479552, 6)) 'seds'\n",
+      "\n",
+      "Writing trimmed noisemodel to disk into M31-B09-EAST_chunk/M31-B09-EAST_chunk_bin4_sub5_noisemodel_trim.grid.hd5\n",
+      "Auto-detected type: hd5\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Auto-detected type: fits\n",
+      "working on filter # =  0\n",
+      "working on filter # =  1\n",
+      "working on filter # =  2\n",
+      "working on filter # =  3\n",
+      "working on filter # =  4\n",
+      "working on filter # =  5\n",
+      "number of original models =  510048\n",
+      "number of ast trimmed models =  510025\n",
+      "number of trimmed models =  479530\n",
+      "Writing trimmed sedgrid to disk into M31-B09-EAST_chunk/M31-B09-EAST_chunk_bin5_sub0_seds_trim.grid.hd5\n",
+      "Auto-detected type: hd5\n",
+      "Warning: Table does not exists.  New table will be created\n",
+      "M31-B09-EAST_chunk/M31-B09-EAST_chunk_bin5_sub0_seds_trim.grid.hd5 (File) ''\n",
+      "Last modif.: 'Tue May  5 16:12:48 2020'\n",
+      "Object Tree: \n",
+      "/ (RootGroup) ''\n",
+      "/grid (Table(479530,)) 'grid'\n",
+      "/lamb (EArray(6,)) 'lamb'\n",
+      "/seds (EArray(479530, 6)) 'seds'\n",
+      "\n",
+      "Writing trimmed noisemodel to disk into M31-B09-EAST_chunk/M31-B09-EAST_chunk_bin5_sub0_noisemodel_trim.grid.hd5\n",
+      "Auto-detected type: hd5\n",
+      "Auto-detected type: fits\n",
+      "working on filter # =  0\n",
+      "working on filter # =  1\n",
+      "working on filter # =  2\n",
+      "working on filter # =  3\n",
+      "working on filter # =  4\n",
+      "working on filter # =  5\n",
+      "number of original models =  510048\n",
+      "number of ast trimmed models =  323563\n",
+      "number of trimmed models =  293022\n",
+      "Writing trimmed sedgrid to disk into M31-B09-EAST_chunk/M31-B09-EAST_chunk_bin6_sub0_seds_trim.grid.hd5\n",
+      "Auto-detected type: hd5\n",
+      "Warning: Table does not exists.  New table will be created\n",
+      "M31-B09-EAST_chunk/M31-B09-EAST_chunk_bin6_sub0_seds_trim.grid.hd5 (File) ''\n",
+      "Last modif.: 'Tue May  5 16:13:10 2020'\n",
+      "Object Tree: \n",
+      "/ (RootGroup) ''\n",
+      "/grid (Table(293022,)) 'grid'\n",
+      "/lamb (EArray(6,)) 'lamb'\n",
+      "/seds (EArray(293022, 6)) 'seds'\n",
+      "\n",
+      "Writing trimmed noisemodel to disk into M31-B09-EAST_chunk/M31-B09-EAST_chunk_bin6_sub0_noisemodel_trim.grid.hd5\n",
+      "Auto-detected type: hd5\n",
+      "Auto-detected type: fits\n",
+      "working on filter # =  0\n",
+      "working on filter # =  1\n",
+      "working on filter # =  2\n",
+      "working on filter # =  3\n",
+      "working on filter # =  4\n",
+      "working on filter # =  5\n",
+      "number of original models =  510048\n",
+      "number of ast trimmed models =  381265\n",
+      "number of trimmed models =  350814\n",
+      "Writing trimmed sedgrid to disk into M31-B09-EAST_chunk/M31-B09-EAST_chunk_bin9_sub0_seds_trim.grid.hd5\n",
+      "Auto-detected type: hd5\n",
+      "Warning: Table does not exists.  New table will be created\n",
+      "M31-B09-EAST_chunk/M31-B09-EAST_chunk_bin9_sub0_seds_trim.grid.hd5 (File) ''\n",
+      "Last modif.: 'Tue May  5 16:13:30 2020'\n",
+      "Object Tree: \n",
+      "/ (RootGroup) ''\n",
+      "/grid (Table(350814,)) 'grid'\n",
+      "/lamb (EArray(6,)) 'lamb'\n",
+      "/seds (EArray(350814, 6)) 'seds'\n",
+      "\n",
+      "Writing trimmed noisemodel to disk into M31-B09-EAST_chunk/M31-B09-EAST_chunk_bin9_sub0_noisemodel_trim.grid.hd5\n"
+     ]
+    }
+   ],
+   "source": [
+    "# check to see if any sub files exist yet\n",
+    "if len(glob.glob(file_dict[\"noise_trim_files\"][0].replace('bin2_sub0','bin*_sub*'))) == 0:\n",
+    "    \n",
+    "    for i, sub_files in enumerate(file_dict[\"noise_trim_files\"]):\n",
+    "        # pull out physics grid\n",
+    "        modelsedgrid = FileSEDGrid(model_grid_files[0])\n",
+    "        # trim for each noise file separately \n",
+    "        noisemodel_vals = noisemodel.get_noisemodelcat(noise_files[i])\n",
+    "        obsdata = datamodel.get_obscat(gst_file_cut, modelsedgrid.filters)\n",
+    "\n",
+    "        # need to iterate over all the sub-bins\n",
+    "        trim_grid.trim_models(modelsedgrid, noisemodel_vals, obsdata, file_dict[\"modelsedgrid_trim_files\"][i], file_dict[\"noise_trim_files\"][i])\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#  Step 9. Fit Models (WARNING! This step takes a while)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we're going to fit all our sources from our observational photometric catalog to our new trimmed physics and noise models. This will take quite some time just because every source has to be evaluated at each step in its physics model. \n",
+    "\n",
+    "So for every sub-bin of sources (max 6250 sources), every source in that photometry file is evaluated at every potential step in the physics grid that has been trimmed to specifically fit that sub-bin (hence the data-intensive code we ran back in Step 8). From this, we essentially get a report of how well every point in the physics model (AKA combo of parameters) matched with a source, what is often referred to as a likelihood. If we then take these likelihoods and figure out what parameter values they point back to, we can create a distribution of parameter values (metallicity, distance, Av, Rv, etc.) that best model each source. I hope that made sense (and is the correct interpretation).\n",
+    "\n",
+    "This function uses the trimmed photometric files we have, the trimmed physics models, and the trimmed noise models to create statistic files for each sub-binned source density bin.\n",
+    "\n",
+    "It'll take a long time though (~5 hours for me at least, but maybe you have a better computer (8GB RAM, for reference))."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Auto-detected type: fits\n",
+      "Auto-detected type: fits\n",
+      "Auto-detected type: fits\n",
+      "Auto-detected type: fits\n",
+      "Auto-detected type: fits\n",
+      "Auto-detected type: hd5\n",
+      "not using full covariance matrix\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Calculating Lnp/Stats: 100%|██████████| 25/25 [00:05<00:00,  4.35it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Done fitting on grid M31-B09-EAST_chunk/M31-B09-EAST_chunk_bin2_sub0_seds_trim.grid.hd5\n",
+      "None\n",
+      "Auto-detected type: fits\n",
+      "Auto-detected type: hd5\n",
+      "not using full covariance matrix\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Calculating Lnp/Stats: 100%|██████████| 6250/6250 [34:16<00:00,  3.04it/s]  \n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Done fitting on grid M31-B09-EAST_chunk/M31-B09-EAST_chunk_bin3_sub0_seds_trim.grid.hd5\n",
+      "None\n",
+      "Auto-detected type: fits\n",
+      "Auto-detected type: hd5\n",
+      "not using full covariance matrix\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Calculating Lnp/Stats: 100%|██████████| 6250/6250 [27:55<00:00,  3.73it/s]  \n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Done fitting on grid M31-B09-EAST_chunk/M31-B09-EAST_chunk_bin3_sub1_seds_trim.grid.hd5\n",
+      "None\n",
+      "Auto-detected type: fits\n",
+      "Auto-detected type: hd5\n",
+      "not using full covariance matrix\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Calculating Lnp/Stats: 100%|██████████| 985/985 [03:05<00:00,  5.32it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Done fitting on grid M31-B09-EAST_chunk/M31-B09-EAST_chunk_bin3_sub2_seds_trim.grid.hd5\n",
+      "None\n",
+      "Auto-detected type: fits\n",
+      "Auto-detected type: hd5\n",
+      "not using full covariance matrix\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Calculating Lnp/Stats: 100%|██████████| 6250/6250 [26:55<00:00,  3.87it/s] \n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Done fitting on grid M31-B09-EAST_chunk/M31-B09-EAST_chunk_bin4_sub0_seds_trim.grid.hd5\n",
+      "None\n",
+      "Auto-detected type: fits\n",
+      "Auto-detected type: hd5\n",
+      "not using full covariance matrix\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Calculating Lnp/Stats: 100%|██████████| 6250/6250 [27:01<00:00,  3.85it/s]  \n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Done fitting on grid M31-B09-EAST_chunk/M31-B09-EAST_chunk_bin4_sub1_seds_trim.grid.hd5\n",
+      "None\n",
+      "Auto-detected type: fits\n",
+      "Auto-detected type: hd5\n",
+      "not using full covariance matrix\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Calculating Lnp/Stats: 100%|██████████| 6250/6250 [23:10<00:00,  4.49it/s] \n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Done fitting on grid M31-B09-EAST_chunk/M31-B09-EAST_chunk_bin4_sub2_seds_trim.grid.hd5\n",
+      "None\n",
+      "Auto-detected type: fits\n",
+      "Auto-detected type: hd5\n",
+      "not using full covariance matrix\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Calculating Lnp/Stats: 100%|██████████| 6250/6250 [21:23<00:00,  4.87it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Done fitting on grid M31-B09-EAST_chunk/M31-B09-EAST_chunk_bin4_sub3_seds_trim.grid.hd5\n",
+      "None\n",
+      "Auto-detected type: fits\n",
+      "Auto-detected type: hd5\n",
+      "not using full covariance matrix\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Calculating Lnp/Stats: 100%|██████████| 6250/6250 [20:40<00:00,  5.04it/s] \n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Done fitting on grid M31-B09-EAST_chunk/M31-B09-EAST_chunk_bin4_sub4_seds_trim.grid.hd5\n",
+      "None\n",
+      "Auto-detected type: fits\n",
+      "Auto-detected type: hd5\n",
+      "not using full covariance matrix\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Calculating Lnp/Stats: 100%|██████████| 2587/2587 [10:27<00:00,  4.12it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Done fitting on grid M31-B09-EAST_chunk/M31-B09-EAST_chunk_bin4_sub5_seds_trim.grid.hd5\n",
+      "None\n",
+      "Auto-detected type: fits\n",
+      "Auto-detected type: hd5\n",
+      "not using full covariance matrix\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Calculating Lnp/Stats: 100%|██████████| 2769/2769 [13:05<00:00,  3.52it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Done fitting on grid M31-B09-EAST_chunk/M31-B09-EAST_chunk_bin5_sub0_seds_trim.grid.hd5\n",
+      "None\n",
+      "Auto-detected type: fits\n",
+      "Auto-detected type: hd5\n",
+      "not using full covariance matrix\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Calculating Lnp/Stats: 100%|██████████| 324/324 [00:46<00:00,  6.92it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Done fitting on grid M31-B09-EAST_chunk/M31-B09-EAST_chunk_bin6_sub0_seds_trim.grid.hd5\n",
+      "None\n",
+      "Auto-detected type: fits\n",
+      "Auto-detected type: hd5\n",
+      "not using full covariance matrix\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Calculating Lnp/Stats: 100%|██████████| 8/8 [00:01<00:00,  6.56it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Done fitting on grid M31-B09-EAST_chunk/M31-B09-EAST_chunk_bin9_sub0_seds_trim.grid.hd5\n",
+      "None\n",
+      "time to fit:  212.80141111666666  min\n"
+     ]
+    }
+   ],
+   "source": [
+    "#if len(glob.glob(file_dict[\"modelsedgrid_trim_files\"][0].replace('bin2_sub0','bin*_sub*'))) == 0:\n",
+    "run_fitting.run_fitting(\n",
+    "    use_sd = True,\n",
+    "    nsubs = 1,\n",
+    "    nprocs = 1,\n",
+    "    choose_sd_sub=None,\n",
+    "    choose_subgrid=None,\n",
+    "    pdf2d_param_list=['Av', 'Rv', 'f_A', 'M_ini', 'logA', 'Z', 'distance'],\n",
+    "    resume=False,\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Step 10. Merge fits"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Whoo-hoo! You finished running the big Step 9!\n",
+    "\n",
+    "We are now onto the final step where we just have to merge all the trimmed SED model results together. This should produce one final **stats.fits** file which is very similar to our original photometric file, except now all the sources have estimates for what their metallicity, distance, age, mass, dust, etc. might be.\n",
+    "\n",
+    "Using these new columns of data, we can create lots of cool visuals which will be shown in the epilogue."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Auto-detected type: fits\n",
+      "Auto-detected type: fits\n",
+      "Auto-detected type: fits\n",
+      "Auto-detected type: fits\n"
+     ]
+    }
+   ],
+   "source": [
+    "merge_files.merge_files(use_sd=True, nsubs=datamodel.n_subgrid)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Hopefully there is now a stats.fits file in your folder. We can read it in to better understand what really happened."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<i>Table length=50448</i>\n",
+       "<table id=\"table139679946729568\" class=\"table-striped table-bordered table-condensed\">\n",
+       "<thead><tr><th>Name</th><th>RA</th><th>DEC</th><th>HST_WFC3_F475W</th><th>HST_WFC3_F275W</th><th>HST_WFC3_F336W</th><th>HST_WFC3_F814W</th><th>HST_WFC3_F110W</th><th>HST_WFC3_F160W</th><th>Av_Best</th><th>Av_Exp</th><th>Av_p16</th><th>Av_p50</th><th>Av_p84</th><th>M_act_Best</th><th>M_act_Exp</th><th>M_act_p16</th><th>M_act_p50</th><th>M_act_p84</th><th>M_ini_Best</th><th>M_ini_Exp</th><th>M_ini_p16</th><th>M_ini_p50</th><th>M_ini_p84</th><th>Rv_Best</th><th>Rv_Exp</th><th>Rv_p16</th><th>Rv_p50</th><th>Rv_p84</th><th>Rv_A_Best</th><th>Rv_A_Exp</th><th>Rv_A_p16</th><th>Rv_A_p50</th><th>Rv_A_p84</th><th>Z_Best</th><th>Z_Exp</th><th>Z_p16</th><th>Z_p50</th><th>Z_p84</th><th>distance_Best</th><th>distance_Exp</th><th>distance_p16</th><th>distance_p50</th><th>distance_p84</th><th>f_A_Best</th><th>f_A_Exp</th><th>f_A_p16</th><th>f_A_p50</th><th>f_A_p84</th><th>logA_Best</th><th>logA_Exp</th><th>logA_p16</th><th>logA_p50</th><th>logA_p84</th><th>logHST_WFC3_F110W_nd_Best</th><th>logHST_WFC3_F110W_nd_Exp</th><th>logHST_WFC3_F110W_nd_p16</th><th>logHST_WFC3_F110W_nd_p50</th><th>logHST_WFC3_F110W_nd_p84</th><th>logHST_WFC3_F110W_wd_Best</th><th>logHST_WFC3_F110W_wd_Exp</th><th>logHST_WFC3_F110W_wd_p16</th><th>logHST_WFC3_F110W_wd_p50</th><th>logHST_WFC3_F110W_wd_p84</th><th>logHST_WFC3_F160W_nd_Best</th><th>logHST_WFC3_F160W_nd_Exp</th><th>logHST_WFC3_F160W_nd_p16</th><th>logHST_WFC3_F160W_nd_p50</th><th>logHST_WFC3_F160W_nd_p84</th><th>logHST_WFC3_F160W_wd_Best</th><th>logHST_WFC3_F160W_wd_Exp</th><th>logHST_WFC3_F160W_wd_p16</th><th>logHST_WFC3_F160W_wd_p50</th><th>logHST_WFC3_F160W_wd_p84</th><th>logHST_WFC3_F275W_nd_Best</th><th>logHST_WFC3_F275W_nd_Exp</th><th>logHST_WFC3_F275W_nd_p16</th><th>logHST_WFC3_F275W_nd_p50</th><th>logHST_WFC3_F275W_nd_p84</th><th>logHST_WFC3_F275W_wd_Best</th><th>logHST_WFC3_F275W_wd_Exp</th><th>logHST_WFC3_F275W_wd_p16</th><th>logHST_WFC3_F275W_wd_p50</th><th>logHST_WFC3_F275W_wd_p84</th><th>logHST_WFC3_F336W_nd_Best</th><th>logHST_WFC3_F336W_nd_Exp</th><th>logHST_WFC3_F336W_nd_p16</th><th>logHST_WFC3_F336W_nd_p50</th><th>logHST_WFC3_F336W_nd_p84</th><th>logHST_WFC3_F336W_wd_Best</th><th>logHST_WFC3_F336W_wd_Exp</th><th>logHST_WFC3_F336W_wd_p16</th><th>logHST_WFC3_F336W_wd_p50</th><th>logHST_WFC3_F336W_wd_p84</th><th>logHST_WFC3_F475W_nd_Best</th><th>logHST_WFC3_F475W_nd_Exp</th><th>logHST_WFC3_F475W_nd_p16</th><th>logHST_WFC3_F475W_nd_p50</th><th>logHST_WFC3_F475W_nd_p84</th><th>logHST_WFC3_F475W_wd_Best</th><th>logHST_WFC3_F475W_wd_Exp</th><th>logHST_WFC3_F475W_wd_p16</th><th>logHST_WFC3_F475W_wd_p50</th><th>logHST_WFC3_F475W_wd_p84</th><th>logHST_WFC3_F814W_nd_Best</th><th>logHST_WFC3_F814W_nd_Exp</th><th>logHST_WFC3_F814W_nd_p16</th><th>logHST_WFC3_F814W_nd_p50</th><th>logHST_WFC3_F814W_nd_p84</th><th>logHST_WFC3_F814W_wd_Best</th><th>logHST_WFC3_F814W_wd_Exp</th><th>logHST_WFC3_F814W_wd_p16</th><th>logHST_WFC3_F814W_wd_p50</th><th>logHST_WFC3_F814W_wd_p84</th><th>logL_Best</th><th>logL_Exp</th><th>logL_p16</th><th>logL_p50</th><th>logL_p84</th><th>logT_Best</th><th>logT_Exp</th><th>logT_p16</th><th>logT_p50</th><th>logT_p84</th><th>logg_Best</th><th>logg_Exp</th><th>logg_p16</th><th>logg_p50</th><th>logg_p84</th><th>mbolmag_Best</th><th>mbolmag_Exp</th><th>mbolmag_p16</th><th>mbolmag_p50</th><th>mbolmag_p84</th><th>radius_Best</th><th>radius_Exp</th><th>radius_p16</th><th>radius_p50</th><th>radius_p84</th><th>symlogHST_WFC3_F475W_wd_bias_Best</th><th>symlogHST_WFC3_F475W_wd_bias_Exp</th><th>symlogHST_WFC3_F475W_wd_bias_p16</th><th>symlogHST_WFC3_F475W_wd_bias_p50</th><th>symlogHST_WFC3_F475W_wd_bias_p84</th><th>symlogHST_WFC3_F275W_wd_bias_Best</th><th>symlogHST_WFC3_F275W_wd_bias_Exp</th><th>symlogHST_WFC3_F275W_wd_bias_p16</th><th>symlogHST_WFC3_F275W_wd_bias_p50</th><th>symlogHST_WFC3_F275W_wd_bias_p84</th><th>symlogHST_WFC3_F336W_wd_bias_Best</th><th>symlogHST_WFC3_F336W_wd_bias_Exp</th><th>symlogHST_WFC3_F336W_wd_bias_p16</th><th>symlogHST_WFC3_F336W_wd_bias_p50</th><th>symlogHST_WFC3_F336W_wd_bias_p84</th><th>symlogHST_WFC3_F814W_wd_bias_Best</th><th>symlogHST_WFC3_F814W_wd_bias_Exp</th><th>symlogHST_WFC3_F814W_wd_bias_p16</th><th>symlogHST_WFC3_F814W_wd_bias_p50</th><th>symlogHST_WFC3_F814W_wd_bias_p84</th><th>symlogHST_WFC3_F110W_wd_bias_Best</th><th>symlogHST_WFC3_F110W_wd_bias_Exp</th><th>symlogHST_WFC3_F110W_wd_bias_p16</th><th>symlogHST_WFC3_F110W_wd_bias_p50</th><th>symlogHST_WFC3_F110W_wd_bias_p84</th><th>symlogHST_WFC3_F160W_wd_bias_Best</th><th>symlogHST_WFC3_F160W_wd_bias_Exp</th><th>symlogHST_WFC3_F160W_wd_bias_p16</th><th>symlogHST_WFC3_F160W_wd_bias_p50</th><th>symlogHST_WFC3_F160W_wd_bias_p84</th><th>chi2min</th><th>chi2min_indx</th><th>Pmax</th><th>Pmax_indx</th><th>specgrid_indx</th><th>total_log_norm</th><th>reorder_tag</th></tr></thead>\n",
+       "<thead><tr><th>str29</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>int64</th><th>float64</th><th>int64</th><th>int64</th><th>float64</th><th>str9</th></tr></thead>\n",
+       "<tr><td>PHAT-M31 J004435.01+413540.96</td><td>11.145879914566734</td><td>41.59471065971137</td><td>2.6938229780643986e-21</td><td>9.227016583504053e-21</td><td>2.7366301117024876e-20</td><td>1.1307473476472241e-20</td><td>2.834051501297643e-20</td><td>9.345915577912443e-21</td><td>0.01</td><td>0.5345616614249643</td><td>0.01</td><td>0.4382608664417967</td><td>1.4804614145049466</td><td>0.857</td><td>1.0283289825110937</td><td>0.7906315444548273</td><td>0.9506702769297549</td><td>1.2784489155396122</td><td>0.8580500484</td><td>1.0291353890422434</td><td>0.7819795557088898</td><td>0.944031562505562</td><td>1.2781117863902764</td><td>6.0</td><td>3.7458525650022922</td><td>2.4215937264885907</td><td>3.5712217822214045</td><td>5.206528567067864</td><td>6.0</td><td>4.000289432595785</td><td>2.789491142898364</td><td>3.9073257884448243</td><td>5.2109644469657</td><td>0.004</td><td>0.014428267510348527</td><td>0.004218770912196449</td><td>0.012854141338511102</td><td>0.026976716233826286</td><td>907820.5301781861</td><td>907820.530178186</td><td>907820.5301781861</td><td>907820.5301781861</td><td>907820.5301781861</td><td>1.0</td><td>0.8107759668724546</td><td>0.49823796581504753</td><td>0.861450169097743</td><td>1.0</td><td>10.0</td><td>9.730551012329594</td><td>9.0518586592916</td><td>9.743355984214187</td><td>10.0</td><td>-20.67636129725098</td><td>-20.6236537008216</td><td>-21.0509735696428</td><td>-20.64286101773007</td><td>-20.256706515414454</td><td>-20.678330849908075</td><td>-20.711449908394854</td><td>-21.113535322392185</td><td>-20.709933659111115</td><td>-20.34368071109016</td><td>-20.980377715492033</td><td>-20.91301124176117</td><td>-21.316712892428228</td><td>-20.937429738193238</td><td>-20.562456775522598</td><td>-20.98152257240765</td><td>-20.96456690920686</td><td>-21.34084568168185</td><td>-20.97544015553115</td><td>-20.6265449026026</td><td>-20.493497753001503</td><td>-20.78341194302714</td><td>-21.54356800434706</td><td>-20.809517111341446</td><td>-20.12574372918582</td><td>-20.499578985655024</td><td>-21.205510187506228</td><td>-21.969076273909813</td><td>-21.26948971788478</td><td>-20.420943309950818</td><td>-20.209204563145295</td><td>-20.356128902590655</td><td>-20.97047753956093</td><td>-20.353387689550278</td><td>-19.846291582669707</td><td>-20.214935104383855</td><td>-20.719670141133243</td><td>-21.346614909313963</td><td>-20.740925996643867</td><td>-20.102449153616963</td><td>-20.0325403458445</td><td>-20.046368231410447</td><td>-20.578511367373867</td><td>-20.064563927672037</td><td>-19.586323818826365</td><td>-20.037341419776936</td><td>-20.316226964559053</td><td>-20.836999121500778</td><td>-20.332117355514576</td><td>-19.800693067142216</td><td>-20.36622352609169</td><td>-20.32894352025428</td><td>-20.77521370573467</td><td>-20.33923764287305</td><td>-19.942563121414988</td><td>-20.369186946063625</td><td>-20.47051295376766</td><td>-20.89623779326875</td><td>-20.45771989176555</td><td>-20.07775163049944</td><td>0.208</td><td>0.2293109126043186</td><td>-0.26899134398819724</td><td>0.2081798831846714</td><td>0.6585493195286296</td><td>3.7881</td><td>3.7701193479246458</td><td>3.714764683176444</td><td>3.7635601134364034</td><td>3.817939004188109</td><td>4.267</td><td>4.237982522506652</td><td>4.0063021598212085</td><td>4.2875971415755085</td><td>4.509207381457927</td><td>4.249</td><td>4.196747632013251</td><td>3.122805590535022</td><td>4.248706579295332</td><td>5.441610700061632</td><td>1.1231898258888846</td><td>1.3888274396678273</td><td>0.839950141489487</td><td>1.198924667985902</td><td>1.7648568606536434</td><td>-1.080334102553474e-21</td><td>2.8021647353763844e-22</td><td>-1.1709965863863626e-20</td><td>-1.1709965863863626e-20</td><td>6.780091509980849e-18</td><td>-4.022376224076914e-20</td><td>-3.9921181535628153e-20</td><td>-4.8305214307145006e-20</td><td>-4.8305214307145006e-20</td><td>6.6734851687964656e-18</td><td>-3.0124820022351794e-21</td><td>-4.477328062729628e-21</td><td>-1.750657523296632e-20</td><td>-1.750657523296632e-20</td><td>6.6508393825675464e-18</td><td>8.253190878307196e-21</td><td>8.802754736035475e-21</td><td>3.979466120141786e-21</td><td>3.979466120141786e-21</td><td>1.917672252650435e-18</td><td>-6.434665075977322e-21</td><td>-5.741406475711803e-21</td><td>-8.53161820341285e-21</td><td>-8.53161820341285e-21</td><td>9.239171396671834e-19</td><td>-7.337125257302916e-21</td><td>-6.7303603403994876e-21</td><td>-8.379867813948075e-21</td><td>-8.379867813948075e-21</td><td>4.344661899846129e-19</td><td>3.5809198565301386</td><td>205808</td><td>250.1830088275045</td><td>38388</td><td>4967</td><td>256.90977146717074</td><td>bin2_sub0</td></tr>\n",
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+       "<tr><td>PHAT-M31 J004435.03+413540.80</td><td>11.145942804724289</td><td>41.59466703717687</td><td>2.017726230628628e-20</td><td>1.4382191840684453e-21</td><td>1.3308938355366436e-20</td><td>8.43872557595984e-21</td><td>1.6171244877721415e-20</td><td>-2.7403147527829693e-21</td><td>0.01</td><td>0.538599153076297</td><td>0.01</td><td>0.438958343060407</td><td>1.4494690936359884</td><td>0.873</td><td>0.9823547379504772</td><td>0.7513674688251374</td><td>0.9176887300111515</td><td>1.2143105476311264</td><td>0.8730948567</td><td>0.9829976094823321</td><td>0.7482746004838547</td><td>0.9156817119248021</td><td>1.2001815147894779</td><td>6.0</td><td>3.7729769478464403</td><td>2.438492647047819</td><td>3.597758450921028</td><td>5.24733023876773</td><td>6.0</td><td>4.024759581302903</td><td>2.8119858857562954</td><td>3.934182665332286</td><td>5.253213496300524</td><td>0.03</td><td>0.014518396019758784</td><td>0.004236636076610577</td><td>0.012910602728475003</td><td>0.027272630808727057</td><td>907820.5301781861</td><td>907820.5301781861</td><td>907820.5301781861</td><td>907820.5301781861</td><td>907820.5301781861</td><td>1.0</td><td>0.8136739455128195</td><td>0.5041978337875592</td><td>0.8637000695776341</td><td>1.0</td><td>10.0</td><td>9.760696013790197</td><td>9.082157420338172</td><td>9.771953485349318</td><td>10.0</td><td>-21.06739988403348</td><td>-20.733102199512107</td><td>-21.139267308235308</td><td>-20.745535175441695</td><td>-20.342571197463965</td><td>-21.06934353764487</td><td>-20.82151251056995</td><td>-21.182876302644786</td><td>-20.851202958644553</td><td>-20.413429006614884</td><td>-21.30298325840933</td><td>-21.016880690904607</td><td>-21.394996250522908</td><td>-21.03680019466336</td><td>-20.649249899778624</td><td>-21.304123820145684</td><td>-21.06899046168689</td><td>-21.41653967766587</td><td>-21.090558214907574</td><td>-20.70930614854087</td><td>-21.908785297932866</td><td>-20.959146818620418</td><td>-21.718174193279353</td><td>-20.9594574455786</td><td>-20.240459881871722</td><td>-21.91481390085925</td><td>-21.36114215916725</td><td>-22.02938247280449</td><td>-21.448519702937613</td><td>-20.540643219215358</td><td>-21.189751135030548</td><td>-20.5052141665963</td><td>-21.119332550587714</td><td>-20.474038374683513</td><td>-19.947482161444537</td><td>-21.19547382296284</td><td>-20.86528331275672</td><td>-21.3989308391202</td><td>-20.926617454425173</td><td>-20.177006942509173</td><td>-20.68257532340904</td><td>-20.180096415872608</td><td>-20.702377562229582</td><td>-20.17988507742792</td><td>-19.700435215014288</td><td>-20.687336700640163</td><td>-20.450665993730293</td><td>-20.900679092237755</td><td>-20.49927480366198</td><td>-19.87928859591284</td><td>-20.818035727405633</td><td>-20.443895276392755</td><td>-20.87502281844871</td><td>-20.447599979117644</td><td>-20.02968925485091</td><td>-20.82098770761431</td><td>-20.58641342075565</td><td>-20.962169775942286</td><td>-20.626929007263257</td><td>-20.135530105057303</td><td>-0.314</td><td>0.1065890440690902</td><td>-0.3758895966314046</td><td>0.09129080665724185</td><td>0.5438744756675096</td><td>3.7114</td><td>3.762452805031711</td><td>3.711358271539138</td><td>3.7565528306418896</td><td>3.8086850018866123</td><td>4.49</td><td>4.309901649409128</td><td>4.087751047867111</td><td>4.34666571024495</td><td>4.555089066573893</td><td>5.554</td><td>4.503552932761113</td><td>3.409486945151996</td><td>4.540923404455293</td><td>5.708850966907397</td><td>0.8767043488657612</td><td>1.2258758958449798</td><td>0.7742225185540478</td><td>1.090365063641166</td><td>1.6281819403590587</td><td>-3.8296291743766466e-21</td><td>3.4278283808379603e-23</td><td>-1.1709965863863626e-20</td><td>-1.1709965863863626e-20</td><td>6.780091509980849e-18</td><td>-4.446228833693227e-20</td><td>-3.9020895003348785e-20</td><td>-4.8305214307145006e-20</td><td>-4.8305214307145006e-20</td><td>6.6734851687964656e-18</td><td>-6.442405652434222e-21</td><td>-5.023969623494162e-21</td><td>-1.750657523296632e-20</td><td>-1.750657523296632e-20</td><td>6.6508393825675464e-18</td><td>5.4895250010597344e-21</td><td>8.004684230665902e-21</td><td>3.979466120141786e-21</td><td>3.979466120141786e-21</td><td>1.917672252650435e-18</td><td>-7.679581017358345e-21</td><td>-6.283639451187888e-21</td><td>-8.53161820341285e-21</td><td>-8.53161820341285e-21</td><td>9.239171396671834e-19</td><td>-7.884138394729947e-21</td><td>-7.118788717000403e-21</td><td>-8.379867813948075e-21</td><td>-8.379867813948075e-21</td><td>4.344661899846129e-19</td><td>1.0819167650310035</td><td>162090</td><td>253.0082879753725</td><td>35576</td><td>1655</td><td>259.8672967594927</td><td>bin2_sub0</td></tr>\n",
+       "<tr><td>PHAT-M31 J004435.01+413540.61</td><td>11.145892087843466</td><td>41.59461297070005</td><td>2.471978732812037e-20</td><td>-8.965522186400696e-20</td><td>1.0608102454399653e-21</td><td>3.1409648545756225e-20</td><td>2.1561659836961886e-20</td><td>1.773993234696343e-20</td><td>0.01</td><td>1.8770727733996275</td><td>0.01</td><td>1.0545884147243227</td><td>3.8644476107955392</td><td>1.048</td><td>1.0102488195819606</td><td>0.4033337111156204</td><td>0.9572455542521109</td><td>1.5927487364043333</td><td>1.0499697924</td><td>1.0128778311173678</td><td>0.40787534268905706</td><td>0.9676165833096707</td><td>1.5989073304783892</td><td>2.0</td><td>3.7101466722113123</td><td>2.4039025981829436</td><td>3.5398607610250816</td><td>5.135404103164531</td><td>2.0</td><td>3.9674591632620855</td><td>2.764178411862605</td><td>3.8746367864742783</td><td>5.136501193548711</td><td>0.019</td><td>0.01577542310151187</td><td>0.0051771714228217875</td><td>0.01432898532403444</td><td>0.02848567548759949</td><td>907820.5301781861</td><td>907820.5301781863</td><td>907820.5301781861</td><td>907820.5301781861</td><td>907820.5301781861</td><td>1.0</td><td>0.807943311728434</td><td>0.49162293092520776</td><td>0.8590009626861705</td><td>1.0</td><td>10.0</td><td>9.716648755939588</td><td>9.03815055423121</td><td>9.731274441970983</td><td>10.0</td><td>-19.69186133545107</td><td>-20.65149666600831</td><td>-22.230588911621233</td><td>-20.409817295500865</td><td>-19.58119333040519</td><td>-19.692907957053865</td><td>-20.93438836933909</td><td>-22.808177991334453</td><td>-20.47022861932239</td><td>-19.775850352743614</td><td>-19.87966316593967</td><td>-20.91218931036272</td><td>-22.421188752526017</td><td>-20.71145310328239</td><td>-19.84464067597841</td><td>-19.88026936038928</td><td>-21.0852826504506</td><td>-22.822137943344305</td><td>-20.756346250998046</td><td>-19.953367874662973</td><td>-20.82862875462559</td><td>-21.555327411129728</td><td>-25.042996656486142</td><td>-20.895511654603876</td><td>-19.688717991249202</td><td>-20.839841515270532</td><td>-22.473675485602403</td><td>-26.20195136382335</td><td>-21.54906776425066</td><td>-20.540238685735748</td><td>-20.04430388063852</td><td>-20.84101628255837</td><td>-23.567247084262423</td><td>-20.24766152920076</td><td>-19.381924234138413</td><td>-20.053102565307096</td><td>-22.01422552273299</td><td>-25.740254217307598</td><td>-20.79556575835828</td><td>-20.079963263797058</td><td>-19.43136336181124</td><td>-20.316462099027845</td><td>-22.602139515719497</td><td>-19.850663326950723</td><td>-18.9825978953347</td><td>-19.43687443099269</td><td>-21.239362378845954</td><td>-24.222581729697247</td><td>-20.1225924410769</td><td>-19.59088772406159</td><td>-19.481815454059248</td><td>-20.411065467035208</td><td>-22.140813876670173</td><td>-20.10972396633266</td><td>-19.295384095288973</td><td>-19.483877692076387</td><td>-20.880917166443126</td><td>-23.02899961070236</td><td>-20.21303091862863</td><td>-19.626085691992206</td><td>1.029</td><td>0.10318657991126835</td><td>-1.7203796780375855</td><td>0.44644659955193994</td><td>1.3029718953271052</td><td>3.6745</td><td>3.70559297047711</td><td>3.537559690326779</td><td>3.711093815619454</td><td>3.835775278644234</td><td>3.079</td><td>4.030125156314644</td><td>3.1398186719889276</td><td>4.171202105243887</td><td>4.834237747985195</td><td>2.197</td><td>4.512086886501234</td><td>1.5117814918285433</td><td>3.6530517459560192</td><td>9.07000869626439</td><td>4.877004551098602</td><td>2.468969367682068</td><td>0.39932990997689793</td><td>1.4999716783632162</td><td>4.673765162698329</td><td>3.2227413500106193e-20</td><td>7.769004393919891e-21</td><td>-1.1709965863863626e-20</td><td>-1.1709965863863626e-20</td><td>6.780091509980849e-18</td><td>-4.811894990802553e-20</td><td>-3.9702361915183306e-20</td><td>-4.8305214307145006e-20</td><td>-4.8305214307145006e-20</td><td>6.6734851687964656e-18</td><td>-1.6864111718050575e-21</td><td>-7.147382352380974e-21</td><td>-1.750657523296632e-20</td><td>-1.750657523296632e-20</td><td>6.6508393825675464e-18</td><td>4.1880530427349703e-20</td><td>1.5762563405233377e-20</td><td>3.979466120141786e-21</td><td>3.979466120141786e-21</td><td>1.917672252650435e-18</td><td>1.1749118479815233e-20</td><td>-1.0130103318978867e-22</td><td>-8.53161820341285e-21</td><td>-8.53161820341285e-21</td><td>9.239171396671834e-19</td><td>4.957030890163424e-21</td><td>-1.7879113503345323e-21</td><td>-8.379867813948075e-21</td><td>-8.379867813948075e-21</td><td>4.344661899846129e-19</td><td>0.8559225136573132</td><td>75468</td><td>248.69743699327438</td><td>2296</td><td>2832</td><td>256.70337816560055</td><td>bin2_sub0</td></tr>\n",
+       "<tr><td>PHAT-M31 J004435.02+413540.82</td><td>11.145907381542465</td><td>41.59467244661696</td><td>3.005461322585575e-20</td><td>1.3336214252271036e-20</td><td>-1.399228237850261e-20</td><td>2.1149163354142527e-20</td><td>1.621208135468536e-20</td><td>1.572075305543914e-20</td><td>0.01</td><td>4.707092254744434</td><td>1.0465704728322278</td><td>4.6343313793368965</td><td>8.385319046510086</td><td>0.2</td><td>0.4126209954183326</td><td>0.177742181135763</td><td>0.3176577805726568</td><td>0.6610347294775856</td><td>0.200000003</td><td>0.41293778490101735</td><td>0.17865371542407435</td><td>0.32049450239943317</td><td>0.6686947246388931</td><td>2.0</td><td>3.6404209326893726</td><td>2.3615432214798844</td><td>3.47541375029198</td><td>5.009241768122064</td><td>2.0</td><td>3.901409938745835</td><td>2.6988175943436925</td><td>3.8081855829753084</td><td>5.0092053394413325</td><td>0.03</td><td>0.01579162691128942</td><td>0.005272890406924188</td><td>0.01433839850664098</td><td>0.028567239014896065</td><td>907820.5301781861</td><td>907820.5301781864</td><td>907820.5301781861</td><td>907820.5301781861</td><td>907820.5301781861</td><td>1.0</td><td>0.8031593159004108</td><td>0.4786223416266069</td><td>0.855285651388512</td><td>1.0</td><td>10.0</td><td>9.908882964498181</td><td>9.229879937306805</td><td>9.912768530550647</td><td>10.0</td><td>-22.695360677390212</td><td>-22.199603697612975</td><td>-22.811718576100517</td><td>-22.39920091532786</td><td>-21.50135509962169</td><td>-22.696397784629713</td><td>-22.883161436966297</td><td>-23.71311468863416</td><td>-23.02848591236596</td><td>-22.11829232011442</td><td>-22.911719242848285</td><td>-22.411381347555764</td><td>-23.033156155876565</td><td>-22.597907042983355</td><td>-21.674856799204417</td><td>-22.91232352827978</td><td>-22.838075580736167</td><td>-23.548883356952583</td><td>-23.015314233251143</td><td>-22.118705130512573</td><td>-25.545443154181353</td><td>-24.622196379883814</td><td>-25.665127670763294</td><td>-25.22387151822748</td><td>-23.20659145292515</td><td>-25.552653282544842</td><td>-26.205539142549316</td><td>-27.454348330188434</td><td>-26.521873180237368</td><td>-25.267845573612043</td><td>-24.06236610673095</td><td>-23.312123527471442</td><td>-24.226595565373756</td><td>-23.748539151788645</td><td>-22.087931320534725</td><td>-24.070991690906162</td><td>-26.107658298647795</td><td>-28.189925672021356</td><td>-26.50070061290776</td><td>-24.051602005008895</td><td>-23.13774595506965</td><td>-22.434421864867762</td><td>-23.229353855894455</td><td>-22.77327582663816</td><td>-21.41873249313129</td><td>-23.143200745490617</td><td>-24.718021530028373</td><td>-26.715822830326104</td><td>-24.81523166871432</td><td>-22.948256007765263</td><td>-22.620593928818764</td><td>-22.07766837781717</td><td>-22.745928187102134</td><td>-22.32231075682519</td><td>-21.31636298588381</td><td>-22.62257373712634</td><td>-23.226738438156637</td><td>-24.361918139538307</td><td>-23.336369680330474</td><td>-22.239648999921112</td><td>-2.5</td><td>-1.7123255705960905</td><td>-2.5302212624290665</td><td>-1.9387097107417088</td><td>-0.8265428034075548</td><td>3.4511</td><td>3.547286642147491</td><td>3.4678544432877527</td><td>3.5186538123323654</td><td>3.6496567832233673</td><td>4.996</td><td>4.823261578678676</td><td>4.613714569323537</td><td>4.900752407513742</td><td>5.072147770111595</td><td>11.021</td><td>9.050964728839462</td><td>6.835461367506971</td><td>9.615822820982006</td><td>11.094572014786786</td><td>0.23466761574442707</td><td>0.5333876249753338</td><td>0.20551033763914012</td><td>0.3359814716324623</td><td>0.6631619767724902</td><td>4.503384990234952e-21</td><td>4.464722707323373e-21</td><td>-1.1709965863863626e-20</td><td>-1.1709965863863626e-20</td><td>6.780091509980849e-18</td><td>-4.019927688975977e-20</td><td>-4.01361206903956e-20</td><td>-4.8305214307145006e-20</td><td>-4.8305214307145006e-20</td><td>6.6734851687964656e-18</td><td>-1.7505726047035988e-20</td><td>-1.6936203817357627e-20</td><td>-1.750657523296632e-20</td><td>-1.750657523296632e-20</td><td>6.6508393825675464e-18</td><td>4.0032486933770836e-21</td><td>4.4062796238849116e-21</td><td>3.979466120141786e-21</td><td>3.979466120141786e-21</td><td>1.917672252650435e-18</td><td>-8.511887768400036e-21</td><td>-8.1775848553636e-21</td><td>-8.53161820341285e-21</td><td>-8.53161820341285e-21</td><td>9.239171396671834e-19</td><td>-8.368352112107116e-21</td><td>-8.045339810521697e-21</td><td>-8.379867813948075e-21</td><td>-8.379867813948075e-21</td><td>4.344661899846129e-19</td><td>2.175457188358515</td><td>117877</td><td>252.76013625202543</td><td>1267</td><td>1632</td><td>260.46615762377166</td><td>bin2_sub0</td></tr>\n",
+       "<tr><td>PHAT-M31 J004435.90+413607.11</td><td>11.149592361880686</td><td>41.60197589449097</td><td>3.464995830608325e-20</td><td>-3.1005764227969083e-20</td><td>1.5847073298443655e-20</td><td>4.0413747795539675e-20</td><td>2.6788728888346585e-20</td><td>4.802762171982783e-21</td><td>0.01</td><td>0.6211592469305361</td><td>0.01</td><td>0.5446832661014216</td><td>1.6011593508448438</td><td>1.55</td><td>1.471241403129115</td><td>0.9444218325070708</td><td>1.4511565531101025</td><td>1.9917873580522278</td><td>1.5499999523</td><td>1.4728011024543186</td><td>0.9593369541341771</td><td>1.4564934927113866</td><td>1.9832387766222084</td><td>2.0</td><td>3.7062284750801173</td><td>2.4022160478709385</td><td>3.536553353986476</td><td>5.126720732499381</td><td>2.0</td><td>3.9633302174156277</td><td>2.7613990973701696</td><td>3.870254834270204</td><td>5.127357185430398</td><td>0.03</td><td>0.017151904706962127</td><td>0.005702211072204382</td><td>0.015751320965538322</td><td>0.029920131244354695</td><td>907820.5301781861</td><td>907820.5301781861</td><td>907820.5301781861</td><td>907820.5301781861</td><td>907820.5301781861</td><td>1.0</td><td>0.8079081822616827</td><td>0.4917283087548201</td><td>0.8589174573761534</td><td>1.0</td><td>9.0</td><td>9.342920130197303</td><td>8.44614279606234</td><td>9.362543371764858</td><td>10.0</td><td>-20.19510484622564</td><td>-19.96059913193318</td><td>-20.30612719127283</td><td>-19.92227595422891</td><td>-19.58394559834193</td><td>-20.196195834837035</td><td>-20.06143924611632</td><td>-20.340723428413664</td><td>-20.02654316850486</td><td>-19.73312632623973</td><td>-20.549282622139735</td><td>-20.28319375238829</td><td>-20.676661433172686</td><td>-20.259902517752703</td><td>-19.88440357659361</td><td>-20.549891370715184</td><td>-20.34189939581413</td><td>-20.679241942853633</td><td>-20.33349707161132</td><td>-19.961822588804534</td><td>-19.918223800439392</td><td>-19.978977277928806</td><td>-20.94146338276442</td><td>-19.81541307340982</td><td>-19.144613368124144</td><td>-19.930053013958617</td><td>-20.48382307493895</td><td>-21.26606113524431</td><td>-20.3191186289773</td><td>-19.787171815611124</td><td>-19.640476628223155</td><td>-19.617509060694342</td><td>-20.205920611427903</td><td>-19.577259990846933</td><td>-19.041146821542466</td><td>-19.649362019861933</td><td>-20.047873216876017</td><td>-20.503217312633186</td><td>-19.948111613181048</td><td>-19.569079167161647</td><td>-19.417678215819908</td><td>-19.279385374492307</td><td>-19.68593591460069</td><td>-19.27803636157667</td><td>-18.784148266557178</td><td>-19.423390323912272</td><td>-19.59671245334538</td><td>-19.89395310021796</td><td>-19.503237428759924</td><td>-19.221685192010714</td><td>-19.849550735247973</td><td>-19.644225634097314</td><td>-19.98582838147841</td><td>-19.60686378676127</td><td>-19.268392245358214</td><td>-19.851650950682117</td><td>-19.80710859774914</td><td>-20.031180851316755</td><td>-19.757100323492725</td><td>-19.51086860783799</td><td>0.755</td><td>0.9758625316370323</td><td>0.598960100171722</td><td>0.9980568252015095</td><td>1.4055059155723066</td><td>3.8344</td><td>3.810945202535007</td><td>3.688680111774429</td><td>3.8332539714785923</td><td>3.8997535265790386</td><td>4.163</td><td>3.799526112070216</td><td>3.2035407893368046</td><td>3.9614131412929336</td><td>4.254721481751237</td><td>2.882</td><td>2.330361707577575</td><td>1.2554826672178365</td><td>2.27405386477025</td><td>3.271775648401377</td><td>1.703543237601374</td><td>2.8499157012239325</td><td>1.492812499328568</td><td>2.3307790079010244</td><td>4.291051006027102</td><td>3.3538108105199917e-20</td><td>2.790170551454629e-20</td><td>-1.1709965863863626e-20</td><td>-1.1709965863863626e-20</td><td>6.780091509980849e-18</td><td>-3.3208138787456643e-20</td><td>-3.603450135284845e-20</td><td>-4.8305214307145006e-20</td><td>-4.8305214307145006e-20</td><td>6.6734851687964656e-18</td><td>6.552446940602217e-21</td><td>1.713440093120825e-21</td><td>-1.750657523296632e-20</td><td>-1.750657523296632e-20</td><td>6.6508393825675464e-18</td><td>1.8502395937227747e-20</td><td>2.512096699275581e-20</td><td>3.979466120141786e-21</td><td>3.979466120141786e-21</td><td>1.917672252650435e-18</td><td>-2.1669221975520064e-21</td><td>3.3285238745933494e-21</td><td>-8.53161820341285e-21</td><td>-8.53161820341285e-21</td><td>9.239171396671834e-19</td><td>-5.561501178294643e-21</td><td>-1.0862427147200517e-21</td><td>-8.379867813948075e-21</td><td>-8.379867813948075e-21</td><td>4.344661899846129e-19</td><td>0.2501448669933298</td><td>200923</td><td>250.4428991584327</td><td>1066</td><td>1430</td><td>256.79127421677686</td><td>bin2_sub0</td></tr>\n",
+       "<tr><td>PHAT-M31 J004435.01+413540.60</td><td>11.145860779753994</td><td>41.59461078642914</td><td>3.850582256880288e-20</td><td>-1.0459775884134147e-19</td><td>-2.1801928357201742e-21</td><td>1.9788078583826416e-20</td><td>8.657333116355908e-21</td><td>2.5528195328557133e-21</td><td>0.01</td><td>3.013609090653222</td><td>0.06350450487477617</td><td>1.789626857477154</td><td>7.118746294861238</td><td>0.673</td><td>0.6724433722692423</td><td>0.22018570015408076</td><td>0.6368977573267285</td><td>1.0586516510885438</td><td>0.6731725335</td><td>0.672801903700452</td><td>0.22167485105388887</td><td>0.6404511796375906</td><td>1.0586727277837629</td><td>2.0</td><td>3.7018940408676406</td><td>2.396249655018953</td><td>3.53180610032362</td><td>5.126916414377285</td><td>2.0</td><td>3.959121193324531</td><td>2.7513387833775678</td><td>3.8667327685072572</td><td>5.127982586638896</td><td>0.004</td><td>0.015170938289811673</td><td>0.00489290723400701</td><td>0.013725671969851897</td><td>0.027793604531471727</td><td>907820.5301781861</td><td>907820.530178186</td><td>907820.5301781861</td><td>907820.5301781861</td><td>907820.5301781861</td><td>1.0</td><td>0.8077864124790533</td><td>0.4905674183036206</td><td>0.8590065752696497</td><td>1.0</td><td>10.0</td><td>9.823203600233695</td><td>9.144895829304662</td><td>9.831866168656436</td><td>10.0</td><td>-21.32532823678483</td><td>-21.561201156293187</td><td>-22.675314728468596</td><td>-21.506674319843583</td><td>-20.456380978037405</td><td>-21.326388318461664</td><td>-22.004221255587847</td><td>-23.467998961850796</td><td>-22.10515259867406</td><td>-20.524114837909472</td><td>-21.56018219077681</td><td>-21.805610555402996</td><td>-22.908283458214903</td><td>-21.691498505975265</td><td>-20.754666079587345</td><td>-21.560789157550055</td><td>-22.08060200379629</td><td>-23.36398035805215</td><td>-22.10516802680434</td><td>-20.807431403302992</td><td>-21.866310605783195</td><td>-23.06227737503184</td><td>-25.51840747567177</td><td>-23.144387898613424</td><td>-20.584662489807283</td><td>-21.877896387219796</td><td>-24.180270700464554</td><td>-27.135552256640604</td><td>-25.24560943742982</td><td>-20.897755294096076</td><td>-21.323177883931834</td><td>-22.112762198843278</td><td>-24.064200667690276</td><td>-22.046930143244964</td><td>-20.168518149097817</td><td>-21.331998345442113</td><td>-23.920195531588956</td><td>-27.749296330308812</td><td>-24.00991882791097</td><td>-20.422410906483904</td><td>-20.92025161445917</td><td>-21.462812932506772</td><td>-23.07550897353269</td><td>-21.388545933371446</td><td>-19.87314200409724</td><td>-20.925824864182193</td><td>-22.929247377529368</td><td>-26.07490389855313</td><td>-22.92086501678753</td><td>-20.036406349787327</td><td>-21.07478492779242</td><td>-21.36845633774829</td><td>-22.617969724175556</td><td>-21.312433686823407</td><td>-20.149027389813938</td><td>-21.07685681288068</td><td>-22.109820758984018</td><td>-24.007066888139896</td><td>-22.224633854155364</td><td>-20.2528644501472</td><td>-0.561</td><td>-0.9227047314213337</td><td>-2.352160173735842</td><td>-0.8243271440734157</td><td>0.4098389720360759</td><td>3.7122</td><td>3.6390778543192672</td><td>3.4851488799504664</td><td>3.6511499532390648</td><td>3.779896578333837</td><td>4.628</td><td>4.592092129430637</td><td>4.178339963241507</td><td>4.64601557618878</td><td>5.016815767098783</td><td>6.173</td><td>7.076862218234221</td><td>3.744568977566741</td><td>6.829922330366011</td><td>10.649428229171797</td><td>0.6572841518164255</td><td>0.8589209588666448</td><td>0.2432105242586193</td><td>0.6276691991034047</td><td>1.446771803762472</td><td>5.327709550452713e-22</td><td>4.442259047864839e-21</td><td>-1.1709965863863626e-20</td><td>-1.1709965863863626e-20</td><td>6.780091509980849e-18</td><td>-4.381249859209011e-20</td><td>-4.0323202112963177e-20</td><td>-4.8305214307145006e-20</td><td>-4.8305214307145006e-20</td><td>6.6734851687964656e-18</td><td>-6.947173173377488e-21</td><td>-1.1902129254548252e-20</td><td>-1.750657523296632e-20</td><td>-1.750657523296632e-20</td><td>6.6508393825675464e-18</td><td>4.8172075660811435e-21</td><td>6.617473466628315e-21</td><td>3.979466120141786e-21</td><td>3.979466120141786e-21</td><td>1.917672252650435e-18</td><td>-8.060365433770735e-21</td><td>-7.045874554657362e-21</td><td>-8.53161820341285e-21</td><td>-8.53161820341285e-21</td><td>9.239171396671834e-19</td><td>-8.105666301500592e-21</td><td>-7.517455556648203e-21</td><td>-8.379867813948075e-21</td><td>-8.379867813948075e-21</td><td>4.344661899846129e-19</td><td>1.6290317789780586</td><td>3228</td><td>251.76704915499877</td><td>4082</td><td>4946</td><td>259.28608520147225</td><td>bin2_sub0</td></tr>\n",
+       "<tr><td>PHAT-M31 J004435.08+413540.66</td><td>11.146162551904405</td><td>41.594626390520745</td><td>3.9034023152737074e-20</td><td>-8.218395337533972e-20</td><td>-4.555626820907827e-20</td><td>8.334026747473985e-20</td><td>4.0754804010015076e-20</td><td>1.6730342701201285e-20</td><td>0.01</td><td>0.8696790937881795</td><td>0.01</td><td>0.7803502071237477</td><td>1.8517001848699115</td><td>1.049</td><td>1.3172063687091058</td><td>0.8816351485155087</td><td>1.0559532846147692</td><td>2.048339032813408</td><td>1.0504361391</td><td>1.3354772720054187</td><td>0.9494438183530762</td><td>1.1133543995095627</td><td>2.0417684396553004</td><td>2.0</td><td>3.695713738863426</td><td>2.3883644958092782</td><td>3.525566201892465</td><td>5.125132023419723</td><td>2.0</td><td>3.952305329569895</td><td>2.7371514682245244</td><td>3.8609330180917167</td><td>5.126241005009526</td><td>0.019</td><td>0.016953799992416727</td><td>0.006117344924069415</td><td>0.015544326794940722</td><td>0.029247888709606238</td><td>907820.5301781861</td><td>907820.530178186</td><td>907820.5301781861</td><td>907820.5301781861</td><td>907820.5301781861</td><td>1.0</td><td>0.8080756728070287</td><td>0.4903570560732897</td><td>0.8594220671130723</td><td>1.0</td><td>10.0</td><td>9.721780019156721</td><td>9.043254063303326</td><td>9.734748165072753</td><td>10.0</td><td>-19.636761027317537</td><td>-19.330287969064397</td><td>-19.693043786168587</td><td>-19.29491604799616</td><td>-18.985259426562795</td><td>-19.637807004897336</td><td>-19.46527713343218</td><td>-19.743925376539547</td><td>-19.44105587693523</td><td>-19.19404044542893</td><td>-19.822163256303618</td><td>-19.54806941230371</td><td>-19.923907924316005</td><td>-19.491791758937683</td><td>-19.177259951185135</td><td>-19.822769409155896</td><td>-19.628317251802397</td><td>-19.953159762101276</td><td>-19.593043016826034</td><td>-19.32056892691437</td><td>-20.79561517572881</td><td>-20.35762070206784</td><td>-21.013171677236866</td><td>-20.45885596567463</td><td>-19.702310080493465</td><td>-20.806814695824713</td><td>-21.039737100383526</td><td>-21.497931925551217</td><td>-20.980071434903074</td><td>-20.598749268536555</td><td>-20.00349488662563</td><td>-19.592294064160246</td><td>-20.04502377439001</td><td>-19.61210172925069</td><td>-19.140466244157988</td><td>-20.01229220209362</td><td>-20.200975372727378</td><td>-20.50066658297991</td><td>-20.13169720510673</td><td>-19.843082255158915</td><td>-19.38293833565753</td><td>-18.993511238626912</td><td>-19.39442786940602</td><td>-18.958335000159245</td><td>-18.650236710661645</td><td>-19.38844707038205</td><td>-19.431703664533575</td><td>-19.678388908030115</td><td>-19.404185953182424</td><td>-19.16149326321764</td><td>-19.428712602992977</td><td>-19.099342881635334</td><td>-19.459325378369844</td><td>-19.064293240105854</td><td>-18.788001320611507</td><td>-19.430774338487</td><td>-19.322750956821828</td><td>-19.56765171755133</td><td>-19.317899410715693</td><td>-19.09040276419552</td><td>1.082</td><td>1.4335202203005344</td><td>1.053304641487035</td><td>1.4492969817098302</td><td>1.7416124491896063</td><td>3.6725</td><td>3.7025310303194106</td><td>3.6538885598161723</td><td>3.684085843729845</td><td>3.721805533352531</td><td>3.019</td><td>2.8517663277631824</td><td>2.4033666795000976</td><td>2.8302379506600945</td><td>3.2080259988874267</td><td>2.066</td><td>1.1862658291278152</td><td>0.4180596469901307</td><td>1.1473140730817626</td><td>2.135937096706054</td><td>5.231824422701511</td><td>7.405524956706641</td><td>4.3223296392308495</td><td>7.514740540386096</td><td>10.647177927261449</td><td>3.7104689885949184e-20</td><td>3.694274883236897e-20</td><td>-1.1709965863863626e-20</td><td>-1.1709965863863626e-20</td><td>6.780091509980849e-18</td><td>-4.759432340308314e-20</td><td>-3.9474200863746714e-20</td><td>-4.8305214307145006e-20</td><td>-4.8305214307145006e-20</td><td>6.6734851687964656e-18</td><td>-1.2664174592635125e-21</td><td>-2.0292245585900058e-21</td><td>-1.750657523296632e-20</td><td>-1.750657523296632e-20</td><td>6.6508393825675464e-18</td><td>4.516522322655732e-20</td><td>5.608161879961565e-20</td><td>3.979466120141786e-21</td><td>3.979466120141786e-21</td><td>1.917672252650435e-18</td><td>1.4492641195016916e-20</td><td>3.962689271485453e-20</td><td>-8.53161820341285e-21</td><td>-8.53161820341285e-21</td><td>9.239171396671834e-19</td><td>8.704619108543779e-21</td><td>2.6552869530011744e-20</td><td>-8.379867813948075e-21</td><td>-8.379867813948075e-21</td><td>4.344661899846129e-19</td><td>8.601933066333634</td><td>123729</td><td>244.64471369130555</td><td>2297</td><td>2833</td><td>250.7234999018597</td><td>bin2_sub0</td></tr>\n",
+       "<tr><td>PHAT-M31 J004435.02+413540.74</td><td>11.145911820551795</td><td>41.594651105749705</td><td>4.0777085079719925e-20</td><td>-1.6063227250634582e-19</td><td>-1.0087459389153045e-20</td><td>2.743109306329377e-20</td><td>1.9887364281440227e-20</td><td>4.211431093750669e-21</td><td>0.01</td><td>0.5583548153998981</td><td>0.01</td><td>0.4521901041370054</td><td>1.5646766499886327</td><td>1.018</td><td>1.1847813171347712</td><td>0.8405318327882585</td><td>1.0377326117411132</td><td>1.6890674600433728</td><td>1.0191354752</td><td>1.1857456277067022</td><td>0.8427166368069484</td><td>1.0383248093909512</td><td>1.6918789593822978</td><td>2.0</td><td>3.711549687842278</td><td>2.4038067391938203</td><td>3.544129049665553</td><td>5.1321893865276795</td><td>2.0</td><td>3.968638233470674</td><td>2.7604710416039273</td><td>3.881285751297616</td><td>5.1330980705631095</td><td>0.019</td><td>0.01437384938992282</td><td>0.005164163912439946</td><td>0.01327005943161334</td><td>0.02520569854846135</td><td>907820.5301781861</td><td>907820.5301781863</td><td>907820.5301781861</td><td>907820.5301781861</td><td>907820.5301781861</td><td>1.0</td><td>0.8092094392387956</td><td>0.49569771429316956</td><td>0.8599022742542259</td><td>1.0</td><td>10.0</td><td>9.625613461841763</td><td>8.846509639477645</td><td>9.641684771033038</td><td>10.0</td><td>-20.480533719055046</td><td>-20.32933468200931</td><td>-20.787715722811484</td><td>-20.371583122549154</td><td>-19.78497432363912</td><td>-20.481603752158396</td><td>-20.42097044802942</td><td>-20.89813073974005</td><td>-20.413088428010102</td><td>-19.945876919816406</td><td>-20.753211712084653</td><td>-20.630391561274436</td><td>-21.081903578263017</td><td>-20.677249945322338</td><td>-20.103917827418076</td><td>-20.753819270828597</td><td>-20.683903553728044</td><td>-21.138284791242423</td><td>-20.711718183331527</td><td>-20.199574654409755</td><td>-20.6792294197491</td><td>-20.442762235289717</td><td>-21.070714885314246</td><td>-20.62536695154307</td><td>-19.461824643778957</td><td>-20.690850297362044</td><td>-20.888010734721448</td><td>-21.70891452110661</td><td>-20.777655833109982</td><td>-20.362237741922577</td><td>-20.245347379184587</td><td>-20.03967889811257</td><td>-20.527580208329613</td><td>-20.144394368652957</td><td>-19.319462276576612</td><td>-20.254187859991273</td><td>-20.421387598133364</td><td>-21.081805082204713</td><td>-20.302498211243826</td><td>-19.943755756679458</td><td>-19.949471085429686</td><td>-19.716738223127283</td><td>-20.224848494317712</td><td>-19.778631303065605</td><td>-19.068402945626804</td><td>-19.955091518292736</td><td>-20.000598074984772</td><td>-20.61895239915505</td><td>-19.909491596823056</td><td>-19.520164209182987</td><td>-20.197352605926724</td><td>-20.02738616539259</td><td>-20.498867408269504</td><td>-20.07432995098596</td><td>-19.48055155417639</td><td>-20.19943389918109</td><td>-20.174857760361004</td><td>-20.692734485746943</td><td>-20.13514009577575</td><td>-19.719270384782508</td><td>0.336</td><td>0.5501259160830735</td><td>0.024604263803056747</td><td>0.4879794901700454</td><td>1.1505335496573037</td><td>3.7507</td><td>3.7834330501325373</td><td>3.708294611060103</td><td>3.7690092773337662</td><td>3.865133825931694</td><td>4.065</td><td>4.023644175299346</td><td>3.5889505020454013</td><td>4.119895127417233</td><td>4.390679590120203</td><td>3.93</td><td>3.3947087040595445</td><td>1.8928697057799397</td><td>3.549221603767407</td><td>4.707636414880786</td><td>1.5461554686964338</td><td>1.979841848679362</td><td>1.0332838763353422</td><td>1.5880019859908967</td><td>3.0674518870614014</td><td>2.5816449654128326e-21</td><td>9.205148270655048e-21</td><td>-1.1709965863863626e-20</td><td>-1.1709965863863626e-20</td><td>6.780091509980849e-18</td><td>-4.5401576307847165e-20</td><td>-4.208952680926733e-20</td><td>-4.8305214307145006e-20</td><td>-4.8305214307145006e-20</td><td>6.6734851687964656e-18</td><td>-3.2662672759648592e-21</td><td>-2.969355550690471e-21</td><td>-1.750657523296632e-20</td><td>-1.750657523296632e-20</td><td>6.6508393825675464e-18</td><td>1.0297205411386088e-20</td><td>1.49699452756898e-20</td><td>3.979466120141786e-21</td><td>3.979466120141786e-21</td><td>1.917672252650435e-18</td><td>-5.232900740379237e-21</td><td>-2.381048440062279e-21</td><td>-8.53161820341285e-21</td><td>-8.53161820341285e-21</td><td>9.239171396671834e-19</td><td>-6.617879719421357e-21</td><td>-4.7321162753703474e-21</td><td>-8.379867813948075e-21</td><td>-8.379867813948075e-21</td><td>4.344661899846129e-19</td><td>5.949308333447907</td><td>3228</td><td>247.95367832161068</td><td>2274</td><td>2810</td><td>254.37167819782496</td><td>bin2_sub0</td></tr>\n",
+       "<tr><td>PHAT-M31 J004435.92+413606.36</td><td>11.149685489296683</td><td>41.60176790872187</td><td>5.387645956128798e-20</td><td>-4.109197668766986e-20</td><td>3.250765138633514e-20</td><td>2.4394827037203998e-20</td><td>2.7115420704058136e-20</td><td>8.422862187501338e-21</td><td>0.01</td><td>0.3197463051146704</td><td>0.01</td><td>0.2866889067041637</td><td>0.9926651776383152</td><td>1.588</td><td>1.5120483746181594</td><td>1.019018055160869</td><td>1.5251751781052776</td><td>1.9021013853858604</td><td>1.5878793001</td><td>1.512423188204155</td><td>1.0259935100876283</td><td>1.5235631174999245</td><td>1.868393532953977</td><td>6.0</td><td>3.7709490392737286</td><td>2.434085281060679</td><td>3.596141672670199</td><td>5.248841830470446</td><td>6.0</td><td>4.021387490827897</td><td>2.802841219236699</td><td>3.931995497373137</td><td>5.254856695362091</td><td>0.019</td><td>0.0158036557019334</td><td>0.004966995421904957</td><td>0.014349428692211022</td><td>0.028505310165605008</td><td>907820.5301781861</td><td>907820.530178186</td><td>907820.5301781861</td><td>907820.5301781861</td><td>907820.5301781861</td><td>1.0</td><td>0.8146431958686086</td><td>0.5056239225101125</td><td>0.8645488210393134</td><td>1.0</td><td>9.0</td><td>9.127647367884661</td><td>8.31200686620983</td><td>9.140123528610152</td><td>9.869648723067895</td><td>-20.131881864808044</td><td>-20.15205315530207</td><td>-20.43280678917141</td><td>-20.139207558906943</td><td>-19.778562603287966</td><td>-20.13387408685939</td><td>-20.206278605071436</td><td>-20.45636884477554</td><td>-20.154211207389526</td><td>-19.893015200803365</td><td>-20.504340424173797</td><td>-20.51440457176326</td><td>-20.787961305938474</td><td>-20.520926534087636</td><td>-20.16263422473638</td><td>-20.505488741618382</td><td>-20.545827921677187</td><td>-20.812548094102205</td><td>-20.52677243629086</td><td>-20.24786629076301</td><td>-19.618094840617818</td><td>-19.809780493337293</td><td>-20.513842162374026</td><td>-19.64139860949332</td><td>-19.224386068701985</td><td>-19.62419277111549</td><td>-20.06039953076785</td><td>-20.679581161001114</td><td>-19.78412075998505</td><td>-19.45497981397492</td><td>-19.480705333340275</td><td>-19.593672076603596</td><td>-20.05833070222295</td><td>-19.495414281903468</td><td>-19.14347234388858</td><td>-19.486441870780407</td><td>-19.808553401814724</td><td>-20.211042194502692</td><td>-19.63605139254731</td><td>-19.341397389148735</td><td>-19.283002633295126</td><td>-19.34079458205498</td><td>-19.728521143399686</td><td>-19.266023561539495</td><td>-18.902541839117646</td><td>-19.28782540420385</td><td>-19.50299245394515</td><td>-19.82762025073626</td><td>-19.36483037597527</td><td>-19.109877809902073</td><td>-19.773470905945572</td><td>-19.803819315094913</td><td>-20.073817541756526</td><td>-19.77241494248958</td><td>-19.425836917612173</td><td>-19.776445476910673</td><td>-19.889836999529447</td><td>-20.13442195389409</td><td>-19.806152594701686</td><td>-19.560331398040887</td><td>0.88</td><td>0.8538980448146264</td><td>0.5113954169623292</td><td>0.9033520007605067</td><td>1.2658583717833518</td><td>3.8608</td><td>3.853059142193125</td><td>3.763348876998764</td><td>3.858723087735289</td><td>3.9213716617681316</td><td>4.154</td><td>4.109161728629481</td><td>3.9000884062818875</td><td>4.172401161285049</td><td>4.36029286033738</td><td>2.571</td><td>2.6352678540693257</td><td>1.60456343812025</td><td>2.510811173046076</td><td>3.4906829619310704</td><td>1.7420200965166706</td><td>1.9305167564140704</td><td>1.2400459527936167</td><td>1.6877200475080996</td><td>2.5170122579526866</td><td>4.9012085769120567e-20</td><td>4.2285460639113553e-20</td><td>-1.1709965863863626e-20</td><td>-1.1709965863863626e-20</td><td>6.780091509980849e-18</td><td>-3.2211382264691606e-20</td><td>-3.3819655145915936e-20</td><td>-4.8305214307145006e-20</td><td>-4.8305214307145006e-20</td><td>6.6734851687964656e-18</td><td>1.6206365579906808e-20</td><td>1.0020312579246566e-20</td><td>-1.750657523296632e-20</td><td>-1.750657523296632e-20</td><td>6.6508393825675464e-18</td><td>2.1882655042858444e-20</td><td>2.2318723584545698e-20</td><td>3.979466120141786e-21</td><td>3.979466120141786e-21</td><td>1.917672252650435e-18</td><td>-1.1847380400480102e-21</td><td>-1.3740205958177756e-22</td><td>-8.53161820341285e-21</td><td>-8.53161820341285e-21</td><td>9.239171396671834e-19</td><td>-5.258025802125815e-21</td><td>-4.2888463053513415e-21</td><td>-8.379867813948075e-21</td><td>-8.379867813948075e-21</td><td>4.344661899846129e-19</td><td>0.5077199666840535</td><td>156702</td><td>249.85188165479644</td><td>36326</td><td>2577</td><td>255.87952918447064</td><td>bin2_sub0</td></tr>\n",
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+       "<tr><td>PHAT-M31 J004434.52+413626.25</td><td>11.143851071014724</td><td>41.60729097374946</td><td>-1.3733215182289095e-20</td><td>-1.7931044372801392e-20</td><td>-2.3754339851876524e-20</td><td>-1.361084770316103e-20</td><td>3.02189929533178e-20</td><td>3.490295632491992e-21</td><td>0.01</td><td>1.8468934882582706</td><td>0.19028170452290782</td><td>1.6542876573982799</td><td>3.530495070376391</td><td>0.55</td><td>0.8454292358180338</td><td>0.6356197121101788</td><td>0.7982624949765745</td><td>1.025668141955464</td><td>0.5500000119</td><td>0.8464490335416099</td><td>0.6426666106833268</td><td>0.8011562656414661</td><td>1.0267593187226058</td><td>6.0</td><td>3.640164330393321</td><td>2.368949015350546</td><td>3.4747607036760226</td><td>4.996014898624905</td><td>6.0</td><td>3.903913526327537</td><td>2.7173094262364286</td><td>3.8065733640797372</td><td>4.996997589451423</td><td>0.004</td><td>0.016295787758696917</td><td>0.005366283578030384</td><td>0.01482943378226398</td><td>0.029173675045171956</td><td>907820.5301781861</td><td>907820.5301781863</td><td>907820.5301781861</td><td>907820.5301781861</td><td>907820.5301781861</td><td>1.0</td><td>0.8005697352409299</td><td>0.47367699315565237</td><td>0.8528472077094287</td><td>1.0</td><td>10.0</td><td>9.870662227882008</td><td>9.19218188754681</td><td>9.876885777550758</td><td>10.0</td><td>-21.723086913713882</td><td>-21.032525090472745</td><td>-21.53824609902833</td><td>-21.146864054508477</td><td>-20.576975388612425</td><td>-21.725006202866158</td><td>-21.31770717459445</td><td>-21.653725348144697</td><td>-21.40455813040668</td><td>-20.997284688644015</td><td>-21.88412652262523</td><td>-21.264126694900977</td><td>-21.706113835690168</td><td>-21.37935205719452</td><td>-20.860555359041797</td><td>-21.88526191973585</td><td>-21.435259717618237</td><td>-21.766751503367026</td><td>-21.535100344106016</td><td>-21.12765640719761</td><td>-23.489771091192253</td><td>-21.9169339418805</td><td>-23.279375714983665</td><td>-21.762827423422067</td><td>-20.781452447114116</td><td>-23.49568525932897</td><td>-23.33301847001913</td><td>-24.13124427716369</td><td>-23.39395722695622</td><td>-22.59027772460191</td><td>-22.406911565682726</td><td>-21.178862675627087</td><td>-22.178136397536335</td><td>-21.157859452188937</td><td>-20.313201317990696</td><td>-22.412614837302737</td><td>-22.464642797469967</td><td>-23.109453919366565</td><td>-22.48425257694475</td><td>-21.845001522870778</td><td>-21.650066021424532</td><td>-20.671874472137326</td><td>-21.403105182029254</td><td>-20.728263651543603</td><td>-20.00047110714066</td><td>-21.654806364584523</td><td>-21.59824289061159</td><td>-22.059589090069398</td><td>-21.63182996550562</td><td>-21.15754784893315</td><td>-21.54688861098329</td><td>-20.78957350213449</td><td>-21.354053345474743</td><td>-20.87420474546141</td><td>-20.273930999758235</td><td>-21.549821576935987</td><td>-21.26229680005745</td><td>-21.59556584178674</td><td>-21.329314640692466</td><td>-20.93655386967302</td><td>-1.04</td><td>-0.2647747280462432</td><td>-0.8444252973677335</td><td>-0.36360416040452703</td><td>0.27691963621371973</td><td>3.6338</td><td>3.7098329717486163</td><td>3.6473381552305053</td><td>3.7090638441473893</td><td>3.7698573545350658</td><td>4.706</td><td>4.4068604764115085</td><td>4.214641290717444</td><td>4.5241358787441195</td><td>4.6543241747091155</td><td>7.371</td><td>5.431943815870112</td><td>4.0779907603934475</td><td>5.679374870275112</td><td>6.881483726329092</td><td>0.5433168334308155</td><td>1.1774188734024482</td><td>0.6308544622996619</td><td>0.797854369607052</td><td>1.3080229100196505</td><td>-1.3595986242125455e-20</td><td>-1.3087312813401e-20</td><td>-1.3814457507944085e-20</td><td>-1.3814457507944085e-20</td><td>6.782959312883066e-18</td><td>-3.25321559565299e-20</td><td>-3.353322374483019e-20</td><td>-4.8707856937125215e-20</td><td>-4.8707856937125215e-20</td><td>6.676817374745334e-18</td><td>-1.3422079795006679e-20</td><td>-1.1798436182506173e-20</td><td>-1.7721160464187262e-20</td><td>-1.7721160464187262e-20</td><td>6.643767541092869e-18</td><td>-1.039399874189321e-20</td><td>-9.832379042153697e-21</td><td>-1.0533539616411952e-20</td><td>-1.0533539616411952e-20</td><td>1.9061512411135495e-18</td><td>3.1967497999623258e-21</td><td>3.912934533432138e-21</td><td>3.1715540226750354e-21</td><td>3.1715540226750354e-21</td><td>9.381164283310888e-19</td><td>-2.20592449371011e-21</td><td>-1.493547389803298e-21</td><td>-2.2281849087323182e-21</td><td>-2.2281849087323182e-21</td><td>4.411190713751039e-19</td><td>3.8870709962286973</td><td>307925</td><td>259.2053028361969</td><td>36499</td><td>4941</td><td>265.35543663894674</td><td>bin9_sub0</td></tr>\n",
+       "<tr><td>PHAT-M31 J004434.54+413626.48</td><td>11.143937164336762</td><td>41.60735672926471</td><td>1.83285602625166e-20</td><td>-1.7557480948368033e-20</td><td>-3.0913181999017394e-21</td><td>5.1930618928983626e-20</td><td>4.246993604250068e-20</td><td>2.0768701284249874e-20</td><td>1.01</td><td>1.5045640301470233</td><td>0.49152675406829965</td><td>1.5352820234026883</td><td>2.5048582881705665</td><td>1.08</td><td>1.2996326685396096</td><td>0.8581391477567238</td><td>1.0496387321650142</td><td>2.0140078951333735</td><td>1.0826489925</td><td>1.3242112714144154</td><td>0.93149355233446</td><td>1.0988592718864771</td><td>2.0336082262202573</td><td>2.0</td><td>3.460185847231987</td><td>2.306778631048556</td><td>3.329757916117318</td><td>4.665024223671692</td><td>2.0</td><td>3.743520597307661</td><td>2.6384311785906918</td><td>3.6541589123306637</td><td>4.7398876774709375</td><td>0.03</td><td>0.015458054003605736</td><td>0.004974938803778259</td><td>0.013970415659394745</td><td>0.028339128788758296</td><td>907820.5301781861</td><td>907820.5301781861</td><td>907820.5301781861</td><td>907820.5301781861</td><td>907820.5301781861</td><td>1.0</td><td>0.7809194910163747</td><td>0.4366059920918486</td><td>0.834782051500548</td><td>1.0</td><td>10.0</td><td>9.739182912739851</td><td>9.061002343820542</td><td>9.75161371041378</td><td>10.0</td><td>-19.28246725057895</td><td>-19.15261130374897</td><td>-19.350677828217272</td><td>-19.12514556316142</td><td>-18.974892031713825</td><td>-19.386030779184075</td><td>-19.378070852256215</td><td>-19.474724760201237</td><td>-19.369680177768945</td><td>-19.27662804482838</td><td>-19.444169886388828</td><td>-19.35609947748035</td><td>-19.540950548750487</td><td>-19.32621904052648</td><td>-19.17015312454773</td><td>-19.505329987315896</td><td>-19.49097550395703</td><td>-19.609193285652537</td><td>-19.473617464447738</td><td>-19.362123344080377</td><td>-21.253806156273704</td><td>-20.328042347958267</td><td>-21.02394060693458</td><td>-20.325514873951445</td><td>-19.695313148748312</td><td>-22.188337807643734</td><td>-21.523069048766462</td><td>-22.018120836955603</td><td>-21.514205451793</td><td>-21.0161011251915</td><td>-20.01529019042115</td><td>-19.49949054762853</td><td>-19.894203054920283</td><td>-19.506997623286072</td><td>-19.12983595308304</td><td>-20.89657144877886</td><td>-20.573792834180928</td><td>-20.939362109335004</td><td>-20.581828522573606</td><td>-20.170730701971515</td><td>-19.155643651663393</td><td>-18.86349942840975</td><td>-19.156260780079837</td><td>-18.856649496005485</td><td>-18.614080736838098</td><td>-19.699756883979322</td><td>-19.622415298525553</td><td>-19.814939740967947</td><td>-19.62673307245874</td><td>-19.40184817827353</td><td>-19.10414010494045</td><td>-18.933403518998425</td><td>-19.158266051043352</td><td>-18.913117902370313</td><td>-18.744292810326268</td><td>-19.30976109956992</td><td>-19.312220799762112</td><td>-19.397352551711194</td><td>-19.311642235047334</td><td>-19.207679393883023</td><td>1.409</td><td>1.5894169718025615</td><td>1.355961384843823</td><td>1.5827188680884168</td><td>1.7886384310385905</td><td>3.6484</td><td>3.6882634339419367</td><td>3.6471978173999906</td><td>3.678403409469621</td><td>3.7132151186469033</td><td>2.608</td><td>2.6300792560504282</td><td>2.372853694876543</td><td>2.599142187913906</td><td>2.8596008721559274</td><td>1.248</td><td>0.7964714217541363</td><td>0.298517664431594</td><td>0.8133405605725164</td><td>1.3802606849798127</td><td>8.518304506271791</td><td>8.888888990775484</td><td>7.1555782032660895</td><td>9.033800245587742</td><td>10.725146535497046</td><td>2.539668143122612e-20</td><td>2.96370969079529e-20</td><td>-1.3814457507944085e-20</td><td>-1.3814457507944085e-20</td><td>6.782959312883066e-18</td><td>-4.068791333324955e-20</td><td>-4.3789862989267145e-20</td><td>-4.8707856937125215e-20</td><td>-4.8707856937125215e-20</td><td>6.676817374745334e-18</td><td>3.0136238784355522e-21</td><td>1.2270364013744571e-21</td><td>-1.7721160464187262e-20</td><td>-1.7721160464187262e-20</td><td>6.643767541092869e-18</td><td>4.591506887419393e-20</td><td>4.5942835936122e-20</td><td>-1.0533539616411952e-20</td><td>-1.0533539616411952e-20</td><td>1.9061512411135495e-18</td><td>4.4120445907470315e-20</td><td>4.541056823182231e-20</td><td>3.1715540226750354e-21</td><td>3.1715540226750354e-21</td><td>9.381164283310888e-19</td><td>2.890088739328018e-20</td><td>3.079592075749793e-20</td><td>-2.2281849087323182e-21</td><td>-2.2281849087323182e-21</td><td>4.411190713751039e-19</td><td>0.7467748575007896</td><td>79724</td><td>254.41401042317747</td><td>38266</td><td>1708</td><td>259.94374329900137</td><td>bin9_sub0</td></tr>\n",
+       "<tr><td>PHAT-M31 J004434.48+413626.02</td><td>11.143678702958839</td><td>41.607227720825406</td><td>5.968666598456414e-20</td><td>-2.3534495739301832e-20</td><td>9.794597664951826e-22</td><td>3.423651691487428e-20</td><td>-4.0836476963942965e-22</td><td>2.148983674550855e-21</td><td>1.01</td><td>0.7466167217335576</td><td>0.022246234757267034</td><td>0.7295403471521945</td><td>1.6083092023467676</td><td>2.033</td><td>1.7216883832140122</td><td>0.9892602290368961</td><td>1.894602912880007</td><td>2.3071154577454833</td><td>2.0326611996</td><td>1.7225838091189178</td><td>1.0072500519107082</td><td>1.8804876110884006</td><td>2.2694355925291663</td><td>2.0</td><td>3.388154724299593</td><td>2.242140981387256</td><td>3.2516029272712936</td><td>4.597754779598046</td><td>2.0</td><td>3.6670112788723865</td><td>2.5052586471813787</td><td>3.5836460455382215</td><td>4.690589027814957</td><td>0.019</td><td>0.016596982127320598</td><td>0.005354920098469737</td><td>0.015153547875449598</td><td>0.029460849452124103</td><td>907820.5301781861</td><td>907820.5301781861</td><td>907820.5301781861</td><td>907820.5301781861</td><td>907820.5301781861</td><td>1.0</td><td>0.781970250092053</td><td>0.431363270259655</td><td>0.8376056537869422</td><td>1.0</td><td>9.0</td><td>9.275259215703583</td><td>8.416309789144412</td><td>9.285159176306625</td><td>9.986929091944432</td><td>-19.581842325947736</td><td>-19.616777500420458</td><td>-19.757822074717367</td><td>-19.609849456558344</td><td>-19.469458011173636</td><td>-19.691647892978985</td><td>-19.730184832730007</td><td>-19.838848387856387</td><td>-19.718961043254247</td><td>-19.613211130681336</td><td>-19.962735260430463</td><td>-19.951705360338742</td><td>-20.09267456147304</td><td>-19.940274814988836</td><td>-19.795968483517427</td><td>-20.024222228151757</td><td>-20.01709478894593</td><td>-20.166798954477056</td><td>-20.00974270164858</td><td>-19.863608712618944</td><td>-19.10151603148278</td><td>-19.55931995341687</td><td>-20.535088163507066</td><td>-19.340339494415556</td><td>-18.977289019778457</td><td>-20.299261734114605</td><td>-20.231098957725287</td><td>-20.624779435723468</td><td>-20.15869427843202</td><td>-19.85870527043984</td><td>-18.964323843913192</td><td>-19.23752815566815</td><td>-19.844491952910428</td><td>-19.055598887120553</td><td>-18.826029854107045</td><td>-19.859789899497176</td><td>-19.787336651558313</td><td>-19.999309715912563</td><td>-19.766729505185303</td><td>-19.563489173343438</td><td>-18.713055742779154</td><td>-18.896804582531843</td><td>-19.347371856683484</td><td>-18.797933287042795</td><td>-18.605791730886224</td><td>-19.28585542235525</td><td>-19.288863267662144</td><td>-19.451730194753132</td><td>-19.27451379539409</td><td>-19.11466270588921</td><td>-19.221882851935934</td><td>-19.292561941451712</td><td>-19.47910177087278</td><td>-19.28303930114179</td><td>-19.11866148840054</td><td>-19.432355208955084</td><td>-19.480356365447047</td><td>-19.60078500015695</td><td>-19.471584878271727</td><td>-19.353636614779838</td><td>1.433</td><td>1.336517483460224</td><td>1.0623630380133458</td><td>1.358416736544259</td><td>1.5595498106092167</td><td>3.8636</td><td>3.821529048060675</td><td>3.6945771651586194</td><td>3.8459366914294772</td><td>3.9028511923135785</td><td>3.72</td><td>3.5488649459201183</td><td>3.1022223673806306</td><td>3.6471786667482493</td><td>3.8551491660217505</td><td>1.189</td><td>1.4288620246186783</td><td>0.8712659033685459</td><td>1.3741220196905473</td><td>2.114290755032104</td><td>3.2505223825500065</td><td>3.645909731517939</td><td>2.7235829145546777</td><td>3.554190012410885</td><td>4.734496904674654</td><td>4.9445947506078046e-20</td><td>5.139463506139579e-20</td><td>-1.3814457507944085e-20</td><td>-1.3814457507944085e-20</td><td>6.782959312883066e-18</td><td>-3.5137570208305006e-20</td><td>-3.522342394922159e-20</td><td>-4.8707856937125215e-20</td><td>-4.8707856937125215e-20</td><td>6.676817374745334e-18</td><td>7.641700325131997e-21</td><td>1.2111613779631108e-20</td><td>-1.7721160464187262e-20</td><td>-1.7721160464187262e-20</td><td>6.643767541092869e-18</td><td>2.920822362404232e-20</td><td>2.509992609436065e-20</td><td>-1.0533539616411952e-20</td><td>-1.0533539616411952e-20</td><td>1.9061512411135495e-18</td><td>2.3348441836196606e-20</td><td>2.2103799828806414e-20</td><td>3.1715540226750354e-21</td><td>3.1715540226750354e-21</td><td>9.381164283310888e-19</td><td>7.121368361736452e-21</td><td>7.773483768941656e-21</td><td>-2.2281849087323182e-21</td><td>-2.2281849087323182e-21</td><td>4.411190713751039e-19</td><td>5.178899603671387</td><td>39872</td><td>253.58432046379187</td><td>39004</td><td>2608</td><td>258.7604390619035</td><td>bin9_sub0</td></tr>\n",
+       "<tr><td>PHAT-M31 J004434.53+413626.22</td><td>11.143883917385438</td><td>41.607283629159234</td><td>1.6743958510714009e-19</td><td>-1.3448283279601044e-19</td><td>-1.7897105367852176e-20</td><td>1.8531692641996174e-19</td><td>8.984024932067452e-20</td><td>8.30748051369995e-20</td><td>0.01</td><td>0.010511898758699413</td><td>0.01</td><td>0.010511898758699392</td><td>0.6905118987586993</td><td>1.081</td><td>1.0572198965538917</td><td>0.9461580190522767</td><td>1.06248519879039</td><td>1.2224864748122268</td><td>1.0847992897</td><td>1.0733997750436561</td><td>0.9928440247122967</td><td>1.1122022119941861</td><td>1.2639890938878184</td><td>6.0</td><td>3.671171793416996</td><td>2.379104008273275</td><td>3.5041472524226447</td><td>5.067534946538966</td><td>6.0</td><td>3.930202369284569</td><td>2.7253134057121056</td><td>3.8378739665256494</td><td>5.067583046715271</td><td>0.03</td><td>0.027265360660671163</td><td>0.023723623492209495</td><td>0.02889501398345803</td><td>0.03</td><td>907820.5301781861</td><td>907820.530178186</td><td>907820.5301781861</td><td>907820.5301781861</td><td>907820.5301781861</td><td>1.0</td><td>0.8056311720946588</td><td>0.4852704662776439</td><td>0.8571932138385903</td><td>1.0</td><td>10.0</td><td>9.999550047760838</td><td>9.319550047760838</td><td>9.999550047760838</td><td>10.0</td><td>-18.831774137085002</td><td>-18.87800427022069</td><td>-18.985943471013574</td><td>-18.86913358863811</td><td>-18.76585162796398</td><td>-18.833676141298426</td><td>-18.879625677109647</td><td>-18.990973312765842</td><td>-18.88491784244975</td><td>-18.78855103469769</td><td>-18.960472972488578</td><td>-19.01834875715259</td><td>-19.137152815361013</td><td>-19.011038682238578</td><td>-18.901900854308224</td><td>-18.961606146490023</td><td>-19.01931545777823</td><td>-19.1362495679498</td><td>-19.029814043680684</td><td>-18.933158647417134</td><td>-21.43494573230589</td><td>-21.256697504386434</td><td>-21.59649660061805</td><td>-21.361160846143264</td><td>-20.908379746655974</td><td>-21.440097739519164</td><td>-21.263994347316824</td><td>-21.4647383709646</td><td>-21.271535055868</td><td>-20.882868867266268</td><td>-19.894423069961142</td><td>-19.828365807325863</td><td>-19.998278309684576</td><td>-19.826704699721525</td><td>-19.64012098793635</td><td>-19.90011649839133</td><td>-19.83568821937638</td><td>-19.940711279437558</td><td>-19.782883250217544</td><td>-19.65476216163529</td><td>-18.833834284045675</td><td>-18.83418363159709</td><td>-18.958945688289482</td><td>-18.828655114672408</td><td>-18.685173538355997</td><td>-18.838550246645486</td><td>-18.83944090243339</td><td>-18.952197659223383</td><td>-18.83331763890152</td><td>-18.71504568501638</td><td>-18.686988027295918</td><td>-18.72162950073329</td><td>-18.81046876954554</td><td>-18.715426838101756</td><td>-18.601721443017563</td><td>-18.689918264186115</td><td>-18.724330908279487</td><td>-18.844185971156858</td><td>-18.73631375734867</td><td>-18.630022144879167</td><td>1.842</td><td>1.8022603847968028</td><td>1.6713991609769991</td><td>1.7926137852221822</td><td>1.9198331565793907</td><td>3.623</td><td>3.631917516808248</td><td>3.603162652501873</td><td>3.627311583256459</td><td>3.648834067505488</td><td>2.073</td><td>2.1390263175622493</td><td>2.0305637470868505</td><td>2.149784678807889</td><td>2.258332763054811</td><td>0.164</td><td>0.2640060451280654</td><td>-0.029484433055721126</td><td>0.2885788158605248</td><td>0.5916294974688509</td><td>15.763518503083702</td><td>14.529865211515975</td><td>12.406758473603206</td><td>14.43127211101245</td><td>16.839284143682054</td><td>1.3287371913370048e-19</td><td>1.3380357541223436e-19</td><td>-1.3814457507944085e-20</td><td>-1.3814457507944085e-20</td><td>6.782959312883066e-18</td><td>-4.127926023231756e-20</td><td>-4.288268509190453e-20</td><td>-4.8707856937125215e-20</td><td>-4.8707856937125215e-20</td><td>6.676817374745334e-18</td><td>6.1399700301966e-21</td><td>9.019463352110728e-21</td><td>-1.7721160464187262e-20</td><td>-1.7721160464187262e-20</td><td>6.643767541092869e-18</td><td>2.121417518247452e-19</td><td>1.978393084949051e-19</td><td>-1.0533539616411952e-20</td><td>-1.0533539616411952e-20</td><td>1.9061512411135495e-18</td><td>1.494013288689578e-19</td><td>1.3605133823428362e-19</td><td>3.1715540226750354e-21</td><td>3.1715540226750354e-21</td><td>9.381164283310888e-19</td><td>1.1332658702939834e-19</td><td>9.935861843649883e-20</td><td>-2.2281849087323182e-21</td><td>-2.2281849087323182e-21</td><td>4.411190713751039e-19</td><td>21.03754612547579</td><td>33945</td><td>240.94273592054884</td><td>33945</td><td>1721</td><td>244.64134368096688</td><td>bin9_sub0</td></tr>\n",
+       "<tr><td>PHAT-M31 J004434.47+413626.04</td><td>11.143623529333263</td><td>41.60723204926833</td><td>1.679677856910743e-19</td><td>-2.988507395466899e-20</td><td>-1.5293890041619133e-20</td><td>3.790097591187918e-19</td><td>3.438431360363998e-19</td><td>2.596087660531234e-19</td><td>1.01</td><td>0.9749006232708429</td><td>0.2949006232708429</td><td>0.9749006232708428</td><td>1.6783596297043972</td><td>0.911</td><td>1.3310522110249905</td><td>0.8471648084285377</td><td>1.0078728512539212</td><td>2.4003210113050435</td><td>0.9222961068</td><td>1.3535397007644057</td><td>0.8773755634067267</td><td>1.0369111500201584</td><td>2.1788590985791676</td><td>3.0</td><td>3.394099972183035</td><td>2.3022838423923035</td><td>3.2588273762367224</td><td>4.497370915017271</td><td>4.193877551020409</td><td>3.7136284632653767</td><td>2.6841364451879604</td><td>3.6096950427075587</td><td>4.619820650635235</td><td>0.004</td><td>0.012274794346526448</td><td>0.004</td><td>0.008624310380793341</td><td>0.028436042596331</td><td>907820.5301781861</td><td>907820.530178186</td><td>907820.5301781861</td><td>907820.5301781861</td><td>907820.5301781861</td><td>0.25</td><td>0.7453693623210234</td><td>0.37592587311920467</td><td>0.8023054568166809</td><td>1.0</td><td>10.0</td><td>9.705218846045193</td><td>9.025218846045192</td><td>9.705218846045192</td><td>10.0</td><td>-18.34454778305779</td><td>-18.34514596886508</td><td>-18.47032074650687</td><td>-18.356710826497146</td><td>-18.26477096806103</td><td>-18.486806646231962</td><td>-18.489358941599694</td><td>-18.55313821915494</td><td>-18.47522757410825</td><td>-18.390647319124174</td><td>-18.480617431505813</td><td>-18.48518801755628</td><td>-18.607108399649633</td><td>-18.506145934048114</td><td>-18.411930409159574</td><td>-18.567428489690474</td><td>-18.571977062224263</td><td>-18.677393948583408</td><td>-18.582340069008488</td><td>-18.506132578777606</td><td>-20.20215333719786</td><td>-20.43799219177489</td><td>-20.831602281508236</td><td>-20.273868994374954</td><td>-19.99630502722957</td><td>-21.007731368666512</td><td>-21.150853691580856</td><td>-21.430287554050125</td><td>-21.09905947060173</td><td>-20.872797258617627</td><td>-19.11779568785308</td><td>-19.211404565671934</td><td>-19.389035588466196</td><td>-19.192796112690193</td><td>-19.002278618506736</td><td>-19.867997409264152</td><td>-19.907537882717726</td><td>-20.05712131837717</td><td>-19.899444800284677</td><td>-19.732477366787837</td><td>-18.251516461991955</td><td>-18.272926217068974</td><td>-18.39250864596669</td><td>-18.275479603872366</td><td>-18.157058868139597</td><td>-18.761342341038404</td><td>-18.76195208729984</td><td>-18.92807103139964</td><td>-18.78825300471971</td><td>-18.67413263223474</td><td>-18.18089915325129</td><td>-18.184470128238708</td><td>-18.25002390737981</td><td>-18.152880889946285</td><td>-18.046347942085415</td><td>-18.42788019540662</td><td>-18.428997222030844</td><td>-18.50967033216678</td><td>-18.425991992065608</td><td>-18.34238203248856</td><td>2.344</td><td>2.339613567800865</td><td>2.2017916865122777</td><td>2.3210133148618954</td><td>2.4442619436296806</td><td>3.6351</td><td>3.634253400321414</td><td>3.6201813617710195</td><td>3.639648124337998</td><td>3.662344234118167</td><td>1.546</td><td>1.667117171981709</td><td>1.4896263732051087</td><td>1.6001825455674994</td><td>1.9562066941498235</td><td>-1.091</td><td>-1.0793292842646105</td><td>-1.3406174944357623</td><td>-1.032481564567934</td><td>-0.7344136048741836</td><td>26.57376933086111</td><td>26.581317877621697</td><td>24.424929086474624</td><td>27.182265105957175</td><td>30.887279487990806</td><td>1.5987150456699575e-19</td><td>1.6021149084756076e-19</td><td>-1.3814457507944085e-20</td><td>-1.3814457507944085e-20</td><td>6.782959312883066e-18</td><td>-4.6582440434433046e-20</td><td>-4.4800256978089625e-20</td><td>-4.8707856937125215e-20</td><td>-4.8707856937125215e-20</td><td>6.676817374745334e-18</td><td>7.324654710313677e-21</td><td>6.214956356216739e-21</td><td>-1.7721160464187262e-20</td><td>-1.7721160464187262e-20</td><td>6.643767541092869e-18</td><td>3.7845516862609264e-19</td><td>3.779605579395363e-19</td><td>-1.0533539616411952e-20</td><td>-1.0533539616411952e-20</td><td>1.9061512411135495e-18</td><td>3.3464574653628153e-19</td><td>3.3298664209840716e-19</td><td>3.1715540226750354e-21</td><td>3.1715540226750354e-21</td><td>9.381164283310888e-19</td><td>2.6810812132671347e-19</td><td>2.660989218064506e-19</td><td>-2.2281849087323182e-21</td><td>-2.1906618178017277e-21</td><td>4.411565944660345e-19</td><td>3.109006718708085</td><td>59885</td><td>249.33713801569425</td><td>45346</td><td>5026</td><td>251.7238312227992</td><td>bin9_sub0</td></tr>\n",
+       "<tr><td>PHAT-M31 J004434.54+413626.05</td><td>11.14391285625871</td><td>41.60723601668284</td><td>2.677976960546373e-19</td><td>-8.965522186400696e-20</td><td>3.0490159508504524e-20</td><td>3.0257961432411827e-19</td><td>3.8917162546637646e-19</td><td>1.903797617722905e-19</td><td>0.01</td><td>0.011548740410356867</td><td>0.01</td><td>0.011548740410356797</td><td>0.6915487404103569</td><td>0.979</td><td>1.0037582204900872</td><td>0.8620210986005108</td><td>0.9937539251429865</td><td>1.1134185867450417</td><td>1.0591981411</td><td>1.0482643104039497</td><td>0.983329226635652</td><td>1.1029127928239404</td><td>1.2599562465773706</td><td>2.0</td><td>3.6715744199044</td><td>2.378672564385472</td><td>3.5042579095540147</td><td>5.070185605019213</td><td>2.0</td><td>3.930314089753667</td><td>2.724244692594054</td><td>3.8378332348638238</td><td>5.070330231110742</td><td>0.019</td><td>0.017636543120843933</td><td>0.011579316728549133</td><td>0.016165717300130077</td><td>0.021491060263940626</td><td>907820.5301781861</td><td>907820.5301781861</td><td>907820.5301781861</td><td>907820.5301781861</td><td>907820.5301781861</td><td>1.0</td><td>0.8058223816952909</td><td>0.4856422830633405</td><td>0.8573685914171878</td><td>1.0</td><td>10.0</td><td>9.997666197798539</td><td>9.317666197798541</td><td>9.99766619779854</td><td>10.0</td><td>-18.587778822732048</td><td>-18.580140586129584</td><td>-18.636553805702306</td><td>-18.54772298941494</td><td>-18.44406017019738</td><td>-18.588807730849904</td><td>-18.581957139010814</td><td>-18.663391410662086</td><td>-18.582566841361384</td><td>-18.49960517323177</td><td>-18.713488269004678</td><td>-18.70375586642771</td><td>-18.763716980796225</td><td>-18.673353604678955</td><td>-18.56798818687838</td><td>-18.71409339784631</td><td>-18.7048398761363</td><td>-18.796438522001697</td><td>-18.694877502056258</td><td>-18.60770712010707</td><td>-20.804071668041523</td><td>-20.872904150732715</td><td>-21.06351422366998</td><td>-20.871035060515855</td><td>-20.668641379625427</td><td>-20.813941451292067</td><td>-20.88114854550803</td><td>-21.01426143232847</td><td>-20.84758776444845</td><td>-20.67691054863898</td><td>-19.4718135286374</td><td>-19.517722872409745</td><td>-19.645391709070704</td><td>-19.513166746234113</td><td>-19.376468487700613</td><td>-19.48054192139061</td><td>-19.525638646384188</td><td>-19.68645744665447</td><td>-19.529973784757335</td><td>-19.36576524205168</td><td>-18.539411913349575</td><td>-18.550650999065663</td><td>-18.709319396004737</td><td>-18.592086101487613</td><td>-18.479196458314185</td><td>-18.544842477384368</td><td>-18.55639910162421</td><td>-18.623552976188066</td><td>-18.509460125325823</td><td>-18.388224560783023</td><td>-18.435459770936887</td><td>-18.43177028778741</td><td>-18.502671477399083</td><td>-18.40879272551435</td><td>-18.314637628697724</td><td>-18.437506787381007</td><td>-18.434774268662768</td><td>-18.509494725031587</td><td>-18.426420615506334</td><td>-18.34312326462966</td><td>2.097</td><td>2.100824420035329</td><td>2.021936149735109</td><td>2.1412458274134596</td><td>2.2675647440097566</td><td>3.6278</td><td>3.6245552481309122</td><td>3.6001426215669428</td><td>3.6219961873292768</td><td>3.6410726240270677</td><td>1.795</td><td>1.7884837703919765</td><td>1.6974247824070492</td><td>1.7786783072887726</td><td>1.8517595452042426</td><td>-0.472</td><td>-0.48193980492929467</td><td>-0.8988539109032374</td><td>-0.5830419037884739</td><td>-0.28475381051551424</td><td>20.680125064902555</td><td>21.12117607498618</td><td>18.869062335916784</td><td>21.65568872177345</td><td>24.165216609955184</td><td>2.7436827091574524e-19</td><td>2.671198252217394e-19</td><td>-1.3814457507944085e-20</td><td>-1.3814457507944085e-20</td><td>6.782959312883066e-18</td><td>-4.7413039960478154e-20</td><td>-4.7115834955912954e-20</td><td>-4.8707856937125215e-20</td><td>-4.8707856937125215e-20</td><td>6.676817374745334e-18</td><td>2.7598613360107134e-20</td><td>2.466098831698802e-20</td><td>-1.7721160464187262e-20</td><td>-1.7721160464187262e-20</td><td>6.643767541092869e-18</td><td>3.7040727422292555e-19</td><td>3.729779298005612e-19</td><td>-1.0533539616411952e-20</td><td>-1.0533539616411952e-20</td><td>1.9061512411135495e-18</td><td>2.641548518285481e-19</td><td>2.688352915261054e-19</td><td>3.1715540226750354e-21</td><td>3.1715540226750354e-21</td><td>9.381164283310888e-19</td><td>1.9590306126647444e-19</td><td>2.0042246156737256e-19</td><td>-2.2281849087323182e-21</td><td>-2.2281849087323182e-21</td><td>4.411190713751039e-19</td><td>19.64264898329602</td><td>2322</td><td>241.30946957099817</td><td>2328</td><td>2944</td><td>244.80122395959603</td><td>bin9_sub0</td></tr>\n",
+       "<tr><td>PHAT-M31 J004434.52+413626.08</td><td>11.143832038690674</td><td>41.607245302189014</td><td>4.801343307961841e-19</td><td>-3.3994271623435975e-20</td><td>2.4828166173947658e-20</td><td>7.402207173949884e-19</td><td>5.145396097456814e-19</td><td>4.2402765122010153e-19</td><td>0.01</td><td>0.010143262095293329</td><td>0.01</td><td>0.010143262095293362</td><td>0.6901432620952933</td><td>0.964</td><td>0.9725637516200862</td><td>0.8401338142022106</td><td>0.9394151732673978</td><td>1.056559341333305</td><td>0.9758481383</td><td>0.985128980228211</td><td>0.8543945500593537</td><td>0.9562254135685425</td><td>1.0764218765316225</td><td>6.0</td><td>3.677839827195582</td><td>2.38311495044248</td><td>3.5102379798039864</td><td>5.079876208935227</td><td>6.0</td><td>3.9363596872969877</td><td>2.7314235670885028</td><td>3.843870588915117</td><td>5.080031321533716</td><td>0.008</td><td>0.008167147474569916</td><td>0.006095771161239759</td><td>0.010551251244839572</td><td>0.015004297419543821</td><td>907820.5301781861</td><td>907820.530178186</td><td>907820.5301781861</td><td>907820.5301781861</td><td>907820.5301781861</td><td>1.0</td><td>0.8061657202879973</td><td>0.4867021864181129</td><td>0.8576110665887378</td><td>1.0</td><td>10.0</td><td>9.992578973267337</td><td>9.31257897326734</td><td>9.99257897326734</td><td>10.0</td><td>-18.24766015288705</td><td>-18.253631572796408</td><td>-18.351280555133243</td><td>-18.262574606520687</td><td>-18.17386865790813</td><td>-18.249556533911672</td><td>-18.25519553285275</td><td>-18.314491088038316</td><td>-18.236585752500915</td><td>-18.158149457093273</td><td>-18.365636791580226</td><td>-18.372389227778694</td><td>-18.473497009724003</td><td>-18.383264361764866</td><td>-18.293044638000893</td><td>-18.366769173285665</td><td>-18.373325607542213</td><td>-18.470547598033818</td><td>-18.39458261212486</td><td>-18.318677719740908</td><td>-21.010829245948003</td><td>-21.007756167891717</td><td>-21.174328581951286</td><td>-21.017188709062236</td><td>-20.859940125135267</td><td>-21.015642225606793</td><td>-21.013746582418</td><td>-21.103548544303752</td><td>-20.97093214914452</td><td>-20.83822400855049</td><td>-19.491235783634767</td><td>-19.488116787865312</td><td>-19.637497394529266</td><td>-19.502950727084436</td><td>-19.370983474557946</td><td>-19.49692273743676</td><td>-19.49512884972419</td><td>-19.588502575915278</td><td>-19.460509280176527</td><td>-19.332434262959296</td><td>-18.304868768951078</td><td>-18.30731129152037</td><td>-18.380169205452</td><td>-18.26743824895783</td><td>-18.154629303987154</td><td>-18.30957516878668</td><td>-18.312353113104418</td><td>-18.44491186786532</td><td>-18.330819017003073</td><td>-18.216726166140827</td><td>-18.117856783264372</td><td>-18.122967455547503</td><td>-18.22664183641542</td><td>-18.132895047986064</td><td>-18.03914825955671</td><td>-18.120783931404485</td><td>-18.125575561048137</td><td>-18.17221767176472</td><td>-18.089151841306723</td><td>-17.989019656711204</td><td>2.419</td><td>2.413388778413871</td><td>2.357778387605774</td><td>2.476920354092713</td><td>2.6119049448606964</td><td>3.6126</td><td>3.6132084814394103</td><td>3.5983961900111145</td><td>3.6174458578819584</td><td>3.6365059738449994</td><td>1.405</td><td>1.4153376876171422</td><td>1.3861269093894517</td><td>1.4568588575120243</td><td>1.5276429669611957</td><td>-1.278</td><td>-1.263971040270585</td><td>-1.7597445272198744</td><td>-1.4222673251611988</td><td>-1.1243985294044168</td><td>32.13313960521454</td><td>31.844904167321076</td><td>29.005937653881684</td><td>32.20275505862692</td><td>36.315852621349464</td><td>4.840644668117536e-19</td><td>4.809849522243468e-19</td><td>-1.3814457507944085e-20</td><td>-1.3814457507944085e-20</td><td>6.782959312883066e-18</td><td>-4.643061227283805e-20</td><td>-4.646726358286693e-20</td><td>-4.8707856937125215e-20</td><td>-4.8707856937125215e-20</td><td>6.676817374745334e-18</td><td>2.638317567654483e-20</td><td>2.653774583942033e-20</td><td>-1.7721160464187262e-20</td><td>-1.7721160464187262e-20</td><td>6.643767541092869e-18</td><td>7.602131839931891e-19</td><td>7.522202680021029e-19</td><td>-1.0533539616411952e-20</td><td>-1.0533539616411952e-20</td><td>1.9061512411135495e-18</td><td>5.634671509584809e-19</td><td>5.567768738607468e-19</td><td>3.1715540226750354e-21</td><td>3.1715540226750354e-21</td><td>9.381164283310888e-19</td><td>4.315518985265441e-19</td><td>4.2518209772367427e-19</td><td>2.064058180483671e-19</td><td>6.497530743322034e-19</td><td>1.0931003306160396e-18</td><td>2.1166641300370985</td><td>35753</td><td>249.13977763829718</td><td>35753</td><td>3961</td><td>251.4799237559774</td><td>bin9_sub0</td></tr>\n",
+       "<tr><td>PHAT-M31 J004434.51+413626.15</td><td>11.143794296497187</td><td>41.607265224400315</td><td>4.980931506499466e-19</td><td>4.557473778087021e-20</td><td>2.3949581001344e-20</td><td>4.8999051731379715e-19</td><td>4.2469936042500693e-19</td><td>2.307633476027764e-19</td><td>0.01</td><td>0.01090425766335658</td><td>0.01</td><td>0.010301510102058938</td><td>0.6905065369714589</td><td>2.393</td><td>2.280714407180662</td><td>2.2377017179181764</td><td>2.5282067918590085</td><td>2.8668328590525367</td><td>2.3933498859</td><td>2.285437670917251</td><td>1.9380120049264338</td><td>2.1914036474674043</td><td>2.4862295433048267</td><td>6.0</td><td>3.6856477140719504</td><td>2.3885905409315455</td><td>3.5174048271709144</td><td>5.092726370007371</td><td>6.0</td><td>3.9444645408087613</td><td>2.7409739237195594</td><td>3.8519295481790983</td><td>5.092940654874456</td><td>0.03</td><td>0.028203346658678224</td><td>0.024649356053522822</td><td>0.02944604526995406</td><td>0.03</td><td>907820.5301781861</td><td>907820.5301781861</td><td>907820.5301781861</td><td>907820.5301781861</td><td>907820.5301781861</td><td>1.0</td><td>0.8061706797529756</td><td>0.4867884034391947</td><td>0.8576108903512053</td><td>1.0</td><td>9.0</td><td>9.074886567895142</td><td>8.34569034173983</td><td>9.074909146951871</td><td>9.755114173821271</td><td>-18.436508594647943</td><td>-18.435508839370893</td><td>-18.482953688816497</td><td>-18.394233706496035</td><td>-18.30466796558686</td><td>-18.438420944333842</td><td>-18.437232541867914</td><td>-18.542824881823293</td><td>-18.464885176072407</td><td>-18.386979840535</td><td>-18.597193240451</td><td>-18.595286891227442</td><td>-18.735632447075684</td><td>-18.63927205093343</td><td>-18.549025125011337</td><td>-18.59832847069289</td><td>-18.596309982581225</td><td>-18.693805793665536</td><td>-18.617858860281753</td><td>-18.5419539678978</td><td>-20.53555991393377</td><td>-20.531617830274374</td><td>-20.721610974715418</td><td>-20.55985965547256</td><td>-20.38972582477465</td><td>-20.54128868033189</td><td>-20.53970211499126</td><td>-20.721482707079083</td><td>-20.584974534609177</td><td>-20.44139203764663</td><td>-19.25276888034134</td><td>-19.25381054867478</td><td>-19.442883641609125</td><td>-19.30720594149043</td><td>-19.175217811109594</td><td>-19.258474149220994</td><td>-19.261315013718903</td><td>-19.39845934751976</td><td>-19.26686598183644</td><td>-19.13885598347095</td><td>-18.328487704086555</td><td>-18.328759347690266</td><td>-18.380538244880587</td><td>-18.267789453806664</td><td>-18.154706241506602</td><td>-18.333211342993504</td><td>-18.334204707397372</td><td>-18.44491186786532</td><td>-18.330819017003073</td><td>-18.216726166140827</td><td>-18.263474974302596</td><td>-18.263025679647516</td><td>-18.364478030659907</td><td>-18.270716411037533</td><td>-18.176954791415156</td><td>-18.266412477637896</td><td>-18.26586486939244</td><td>-18.38698072594279</td><td>-18.30386944341261</td><td>-18.220803612954615</td><td>2.25</td><td>2.251005374091322</td><td>2.2062559436071503</td><td>2.325416758926475</td><td>2.4457135222065474</td><td>3.6446</td><td>3.644155298316326</td><td>3.6264271923109552</td><td>3.6454798739309195</td><td>3.6645325555508843</td><td>2.097</td><td>2.062326687123907</td><td>1.9912074356627565</td><td>2.067195181732675</td><td>2.1432281981610957</td><td>-0.855</td><td>-0.8574738330496734</td><td>-1.3442466099807455</td><td>-1.0434906877121488</td><td>-0.7455747676785938</td><td>22.827227172575075</td><td>22.902601229874122</td><td>21.775845380133813</td><td>24.12390796184676</td><td>26.88126515709005</td><td>4.575075387587903e-19</td><td>4.564700126947421e-19</td><td>-1.3814457507944085e-20</td><td>-1.3814457507944085e-20</td><td>6.782959312883066e-18</td><td>-4.170730720878932e-20</td><td>-4.172054306826738e-20</td><td>-4.8707856937125215e-20</td><td>-4.8707856937125215e-20</td><td>6.676817374745334e-18</td><td>4.9516679335322976e-20</td><td>4.931429616628205e-20</td><td>-1.7721160464187262e-20</td><td>-1.7721160464187262e-20</td><td>6.643767541092869e-18</td><td>5.44326850481521e-19</td><td>5.450331588668004e-19</td><td>-1.0533539616411952e-20</td><td>-1.0533539616411952e-20</td><td>1.9061512411135495e-18</td><td>3.7248556321291615e-19</td><td>3.7347253065607364e-19</td><td>3.1715540226750354e-21</td><td>3.1715540226750354e-21</td><td>9.381164283310888e-19</td><td>2.5080549991810754e-19</td><td>2.5193854876322776e-19</td><td>-2.2281849087323182e-21</td><td>-2.2281849087323182e-21</td><td>4.411190713751039e-19</td><td>24.58100797594983</td><td>148245</td><td>235.976712604482</td><td>33814</td><td>1577</td><td>238.2342555608859</td><td>bin9_sub0</td></tr>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "<Table length=50448>\n",
+       "             Name                     RA         ... reorder_tag\n",
+       "            str29                  float64       ...     str9   \n",
+       "----------------------------- ------------------ ... -----------\n",
+       "PHAT-M31 J004435.01+413540.96 11.145879914566734 ...   bin2_sub0\n",
+       "PHAT-M31 J004435.89+413607.17 11.149551498429465 ...   bin2_sub0\n",
+       "PHAT-M31 J004435.03+413540.80 11.145942804724289 ...   bin2_sub0\n",
+       "PHAT-M31 J004435.01+413540.61 11.145892087843466 ...   bin2_sub0\n",
+       "PHAT-M31 J004435.02+413540.82 11.145907381542465 ...   bin2_sub0\n",
+       "PHAT-M31 J004435.90+413607.11 11.149592361880686 ...   bin2_sub0\n",
+       "PHAT-M31 J004435.01+413540.60 11.145860779753994 ...   bin2_sub0\n",
+       "PHAT-M31 J004435.08+413540.66 11.146162551904405 ...   bin2_sub0\n",
+       "PHAT-M31 J004435.02+413540.74 11.145911820551795 ...   bin2_sub0\n",
+       "PHAT-M31 J004435.92+413606.36 11.149685489296683 ...   bin2_sub0\n",
+       "                          ...                ... ...         ...\n",
+       "PHAT-M31 J004431.63+413612.20  11.13180414033402 ...   bin6_sub0\n",
+       "PHAT-M31 J004431.64+413612.31 11.131832570656686 ...   bin6_sub0\n",
+       "PHAT-M31 J004434.52+413626.25 11.143851071014724 ...   bin9_sub0\n",
+       "PHAT-M31 J004434.54+413626.48 11.143937164336762 ...   bin9_sub0\n",
+       "PHAT-M31 J004434.48+413626.02 11.143678702958839 ...   bin9_sub0\n",
+       "PHAT-M31 J004434.53+413626.22 11.143883917385438 ...   bin9_sub0\n",
+       "PHAT-M31 J004434.47+413626.04 11.143623529333263 ...   bin9_sub0\n",
+       "PHAT-M31 J004434.54+413626.05  11.14391285625871 ...   bin9_sub0\n",
+       "PHAT-M31 J004434.52+413626.08 11.143832038690674 ...   bin9_sub0\n",
+       "PHAT-M31 J004434.51+413626.15 11.143794296497187 ...   bin9_sub0"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "hdul = fits.open(sed_files[0].replace('seds.grid.hd5', 'stats.fits'))\n",
+    "Table(hdul[1].data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As you can hopefully see, for every source, there are now several parameters assigned to each one. These are all the parameters we originally had set up in our datamodel and specified in Step 9."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Epilogue: Visualizating!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from beast.plotting import (\n",
+    "    plot_triangle, \n",
+    "    plot_indiv_fit, \n",
+    "    plot_cmd_with_fits, \n",
+    "    plot_completeness, \n",
+    "    plot_chi2_hist,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Triangle Plot\n",
+    "\n",
+    "This first plot displays a posterior distributions of the parameters of all the fitted stars. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "plot_triangle.plot_triangle(\"M31-B09-EAST_chunk/M31-B09-EAST_chunk_stats.fits\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### CMD Plot\n",
+    "\n",
+    "You can also make a color-magnitude diagram of the observations and color-code the data points using one of the parameters from the BEAST fitting (feel free to change this from the example, just remember that the param must match a column name from the stat.fits file). \n",
+    "\n",
+    "Inputs are the photometry file, three filters, the BEAST stats file from Step 10, and the parameter to use and apply color to after taking the log10."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/christina/anaconda3/envs/astroconda/lib/python3.6/site-packages/beast/plotting/plot_cmd_with_fits.py:97: RuntimeWarning: invalid value encountered in greater\n",
+      "  col[col > 99] = np.nan\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 504x432 with 2 Axes>"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 504x432 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_cmd_with_fits.plot(data_fits_file=\"M31-B09-EAST_chunk.st_with_sourceden_cut.fits\", \n",
+    "                        beast_fits_file=\"M31-B09-EAST_chunk/M31-B09-EAST_chunk_stats.fits\", \n",
+    "                        mag1_filter=\"F475W\",\n",
+    "                        mag2_filter=\"F814W\",\n",
+    "                        mag3_filter=\"F475W\",\n",
+    "                        param=\"Z_Best\", #metallicity\n",
+    "                       )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Completeness Plot\n",
+    "This next plot shows the completeness (how many AST sources were detected out of the total number of AST that exist for that parameter bin) for each parameter, although it should be noted that the *distance* parameter was purposefully left out because all the sources have the same distance value, and thus the plotting code isn't sure how to handle it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Auto-detected type: hd5\n",
+      "Completeness from HST_WFC3_F475W\n",
+      "Auto-detected type: hd5\n",
+      "Completeness from HST_WFC3_F475W\n",
+      "Auto-detected type: hd5\n",
+      "Completeness from HST_WFC3_F475W\n",
+      "Auto-detected type: hd5\n",
+      "Completeness from HST_WFC3_F475W\n",
+      "Auto-detected type: hd5\n",
+      "Completeness from HST_WFC3_F475W\n",
+      "Auto-detected type: hd5\n",
+      "Completeness from HST_WFC3_F475W\n",
+      "Auto-detected type: hd5\n",
+      "Completeness from HST_WFC3_F475W\n",
+      "Auto-detected type: hd5\n",
+      "Completeness from HST_WFC3_F475W\n",
+      "Auto-detected type: hd5\n",
+      "Completeness from HST_WFC3_F475W\n",
+      "Auto-detected type: hd5\n",
+      "Completeness from HST_WFC3_F475W\n",
+      "Auto-detected type: hd5\n",
+      "Completeness from HST_WFC3_F475W\n",
+      "Auto-detected type: hd5\n",
+      "Completeness from HST_WFC3_F475W\n",
+      "Auto-detected type: hd5\n",
+      "Completeness from HST_WFC3_F475W\n",
+      "plotting Av and Av\n",
+      "plotting Av and Rv\n",
+      "plotting Av and logA\n",
+      "plotting Av and f_A\n",
+      "plotting Av and M_ini\n",
+      "plotting Av and Z\n",
+      "plotting Rv and Rv\n",
+      "plotting Rv and logA\n",
+      "plotting Rv and f_A\n",
+      "plotting Rv and M_ini\n",
+      "plotting Rv and Z\n",
+      "plotting logA and logA\n",
+      "plotting logA and f_A\n",
+      "plotting logA and M_ini\n",
+      "plotting logA and Z\n",
+      "plotting f_A and f_A\n",
+      "plotting f_A and M_ini\n",
+      "plotting f_A and Z\n",
+      "plotting M_ini and M_ini\n",
+      "plotting M_ini and Z\n",
+      "plotting Z and Z\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/christina/anaconda3/envs/astroconda/lib/python3.6/site-packages/beast/plotting/plot_completeness.py:196: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n",
+      "  gs.tight_layout(fig)\n"
+     ]
+    }
+   ],
+   "source": [
+    "plot_completeness.plot_completeness(physgrid_list=file_dict[\"modelsedgrid_trim_files\"],\n",
+    "                                    noise_model_list=file_dict[\"noise_trim_files\"],\n",
+    "                                    output_plot_filename=\"completeness_plot.pdf\",\n",
+    "                                    param_list=['Av', 'Rv', 'logA', 'f_A', 'M_ini', 'Z'],\n",
+    "                                    #, 'distance'],\n",
+    "                                    compl_filter='F475W',)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Chi Squared Plot\n",
+    "Make a histogram of the best chi2 values (chi2=1 and the median chi2 are marked). Note that there is no plot of reduced chi2, because it is mathematically difficult to define the number of degrees of freedom. Inputs are the BEAST stats file and optionally the number of bins to use for the histogram."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plot_chi2_hist.plot(beast_stats_file=\"M31-B09-EAST_chunk/M31-B09-EAST_chunk_stats.fits\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There's another cool plot for plotting the individual fits of stars, but unfortunately, this code works with a file that only gets generated when using multiple subgrids (remember how we checked that we had a subgrid = 1 back in Step 2?). If it had worked with the code below, it would have made a multi-panel plot that shows the PDFs and best fits of each parameter for any given star, as well as the SED (similar to Figure 14 in Gordon+16)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#plot_indiv_fit.plot_beast_ifit(filter=datamodel.filters, waves, stats, pdf1d_hdu, starnum=0):"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Sorry I wasn't able to show you all that last plot. But thanks for reading through this notebook til the end. Hopefully you found it to be somewhat helpful and if you have any suggestions for how to make it better, you can find me at cwlind@jhu.edu.\n",
+    "\n",
+    "Thanks,\\\n",
+    "Christina Lindberg\\\n",
+    "(she/her)"
+   ]
+  }
+ ],
+ "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": "3.6.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/M31_Example/M31_workflow.ipynb b/M31_Example/M31_workflow.ipynb
new file mode 100644
index 0000000..02219b9
--- /dev/null
+++ b/M31_Example/M31_workflow.ipynb
@@ -0,0 +1,2545 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# BEAST Workflow Example\n",
+    "\n",
+    "In this notebook we will be walking through a standard BEAST workflow example using some data from M31.\n",
+    "\n",
+    "Please make sure you are running *at least* **BEAST v2.0**.\n",
+    "\n",
+    "You'll need a couple of datafiles to get started though. These file can be found at https://www.dropbox.com/sh/91aefrp9gzdc9z0/AAC9Gc4KIRIB520g6a0uLLama?dl=0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--2020-07-22 14:25:15--  https://www.dropbox.com/sh/91aefrp9gzdc9z0/AAC9Gc4KIRIB520g6a0uLLama?dl=1\n",
+      "Resolving www.dropbox.com (www.dropbox.com)... 162.125.6.1, 2620:100:601c:1::a27d:601\n",
+      "Connecting to www.dropbox.com (www.dropbox.com)|162.125.6.1|:443... connected.\n",
+      "HTTP request sent, awaiting response... 301 Moved Permanently\n",
+      "Location: /sh/dl/91aefrp9gzdc9z0/AAC9Gc4KIRIB520g6a0uLLama [following]\n",
+      "--2020-07-22 14:25:16--  https://www.dropbox.com/sh/dl/91aefrp9gzdc9z0/AAC9Gc4KIRIB520g6a0uLLama\n",
+      "Reusing existing connection to www.dropbox.com:443.\n",
+      "HTTP request sent, awaiting response... 302 Found\n",
+      "Location: https://uc20972010ed0ad7747238852bd6.dl.dropboxusercontent.com/zip_download_get/AfYgH9KXJFEWG0Q8aXDzK9uIGQj1iE7RMLI5gedC795gMN0yZyokRmXfMnab1uTmHJlHLoRGrdkqLQD-sjJUnBUbg1IlqFS59C4M9r6MU-7FWA?dl=1 [following]\n",
+      "--2020-07-22 14:25:16--  https://uc20972010ed0ad7747238852bd6.dl.dropboxusercontent.com/zip_download_get/AfYgH9KXJFEWG0Q8aXDzK9uIGQj1iE7RMLI5gedC795gMN0yZyokRmXfMnab1uTmHJlHLoRGrdkqLQD-sjJUnBUbg1IlqFS59C4M9r6MU-7FWA?dl=1\n",
+      "Resolving uc20972010ed0ad7747238852bd6.dl.dropboxusercontent.com (uc20972010ed0ad7747238852bd6.dl.dropboxusercontent.com)... 162.125.6.15, 2620:100:601c:15::a27d:60f\n",
+      "Connecting to uc20972010ed0ad7747238852bd6.dl.dropboxusercontent.com (uc20972010ed0ad7747238852bd6.dl.dropboxusercontent.com)|162.125.6.15|:443... connected.\n",
+      "HTTP request sent, awaiting response... 200 OK\n",
+      "Length: 57364410 (55M) [application/zip]\n",
+      "Saving to: ‘data.zip’\n",
+      "\n",
+      "data.zip            100%[===================>]  54.71M  9.57MB/s    in 5.5s    \n",
+      "\n",
+      "2020-07-22 14:25:23 (9.92 MB/s) - ‘data.zip’ saved [57364410/57364410]\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "!wget -O data.zip https://www.dropbox.com/sh/91aefrp9gzdc9z0/AAC9Gc4KIRIB520g6a0uLLama?dl=1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And now we can extract our files from the zip file."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import zipfile\n",
+    "import os\n",
+    "\n",
+    "zip_file = 'data.zip'\n",
+    "\n",
+    "with zipfile.ZipFile(zip_file, 'r') as zip_ref:\n",
+    "        zip_ref.extractall(\"./\")\n",
+    "\n",
+    "# go ahead and delete the zip file\n",
+    "if os.path.isfile(zip_file):\n",
+    "    os.remove(zip_file)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Before we do anything, we have to import the following packages. This seems like a lot but they are all here to make our lives easier down the line. And running them all as the first cell means that if our kernel ever crashes halfway through, we can just reimport everything at once rather than stepping through the cells individually."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "import h5py\n",
+    "\n",
+    "import numpy as np\n",
+    "from astropy import wcs\n",
+    "from astropy.io import fits\n",
+    "from astropy.table import Table\n",
+    "\n",
+    "import glob\n",
+    "import os\n",
+    "import types\n",
+    "import argparse\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "from beast.plotting import (plot_mag_hist, plot_ast_histogram, plot_noisemodel)\n",
+    "\n",
+    "from beast.tools.run import (\n",
+    "    create_physicsmodel,\n",
+    "    make_ast_inputs,\n",
+    "    create_obsmodel,\n",
+    "    run_fitting,\n",
+    "    merge_files,\n",
+    "    create_filenames,\n",
+    ")\n",
+    "\n",
+    "from beast.physicsmodel.grid import SEDGrid\n",
+    "from beast.fitting import trim_grid\n",
+    "import beast.observationmodel.noisemodel.generic_noisemodel as noisemodel\n",
+    "from beast.observationmodel.observations import Observations\n",
+    "\n",
+    "    \n",
+    "from beast.tools import (\n",
+    "     beast_settings,\n",
+    "     create_background_density_map,\n",
+    "     split_ast_input_file,\n",
+    "     split_catalog_using_map,\n",
+    "#     subdivide_obscat_by_source_density,\n",
+    "     cut_catalogs,\n",
+    "#     split_asts_by_source_density,\n",
+    "     setup_batch_beast_trim,\n",
+    "#     setup_batch_beast_fit,\n",
+    " )\n",
+    "\n",
+    "import importlib"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Step -1. Obtain data file and convert to fits file\n",
+    "\n",
+    "Sometimes photometric catalogs are delivered as HDF5 files. While these are great for storing data in heirarchies, it's a little hard to work with directly, so we have to convert our HDF5 file to a FITS file.\n",
+    "\n",
+    "Thankfully, our photometric catalog for this example is already in a FITS format so we don't need to worry about this and can move straight on to Step 1."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Step 1a. Make magnitude histograms\n",
+    "\n",
+    "The first thing we need to do is understand the range of stellar magnitudes we are working with in this data set.\n",
+    "\n",
+    "To do this we can make histograms of all the magnitudes of all the stars in all the different filters from the photometric catalog. This is done so that we know where the peaks of the histograms are in each filter. These peaks will then be used later when we make source density maps. \n",
+    "\n",
+    "Essentially what happens is that, for the density maps, we only count objects within a certain range, currently set to mag_cut = 15 - (peak_for_filter-0.5). So if the peak was 17.5, then the objects that would be counted would have to be in the range between 15 and 18. \n",
+    "\n",
+    "The reason we only count brighter sources is because dimmer sources tend to not be properly observed, especially as the magnitudes near the telescope limit. There will always be far more dim sources than bright sources, but if we know how many bright sources there are, then we can extrapolate as to how many dim sources there should be, and probably get a better understand from that than if we were to try and actually count all the dim sources we detect.  \n",
+    "\n",
+    "**Variable Information**\n",
+    "\n",
+    "* **field_name** : the string name of the main photometric catalog we are working with. This variable will be used to rename a lot of different files in the future which is why we have it as a separate variable.\n",
+    "* **gst_file** : stands for good-stars, this is the full name for the original photometric catalog we are working with."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "field_name = \"M31-B09-EAST_chunk\"\n",
+    "gst_file = \"./%s.st.fits\" %field_name"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can see what type of data this fits file holds by making a table. There should be around 50,000 sources in this calalog, which is quite small compared to the original file.\n",
+    "\n",
+    "*Note: **st** stands for stars. We also sometimes name things **gst** for good stars to signify when cuts have been made.*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<i>Table length=50507</i>\n",
+       "<table id=\"table140396744002472\" class=\"table-striped table-bordered table-condensed\">\n",
+       "<thead><tr><th>F814W_ST</th><th>F814W_GST</th><th>F475W_ST</th><th>F475W_GST</th><th>F275W_ST</th><th>F275W_GST</th><th>F160W_ST</th><th>F160W_GST</th><th>F336W_ST</th><th>F336W_GST</th><th>F110W_ST</th><th>F110W_GST</th><th>X</th><th>Y</th><th>RA</th><th>DEC</th><th>CHI_GL</th><th>SNR_GL</th><th>SHARP_GL</th><th>ROUND_GL</th><th>CROWD_GL</th><th>F475W_COUNT</th><th>F475W_SKY</th><th>F475W_RATE</th><th>F475W_RATERR</th><th>F475W_VEGA</th><th>F475W_TRANS</th><th>F475W_ERR</th><th>F475W_CHI</th><th>F475W_SNR</th><th>F475W_SHARP</th><th>F475W_ROUND</th><th>F475W_CROWD</th><th>F814W_COUNT</th><th>F814W_SKY</th><th>F814W_RATE</th><th>F814W_RATERR</th><th>F814W_VEGA</th><th>F814W_TRANS</th><th>F814W_ERR</th><th>F814W_CHI</th><th>F814W_SNR</th><th>F814W_SHARP</th><th>F814W_ROUND</th><th>F814W_CROWD</th><th>F275W_COUNT</th><th>F275W_SKY</th><th>F275W_RATE</th><th>F275W_RATERR</th><th>F275W_VEGA</th><th>F275W_TRANS</th><th>F275W_ERR</th><th>F275W_CHI</th><th>F275W_SNR</th><th>F275W_SHARP</th><th>F275W_ROUND</th><th>F275W_CROWD</th><th>F336W_COUNT</th><th>F336W_SKY</th><th>F336W_RATE</th><th>F336W_RATERR</th><th>F336W_VEGA</th><th>F336W_TRANS</th><th>F336W_ERR</th><th>F336W_CHI</th><th>F336W_SNR</th><th>F336W_SHARP</th><th>F336W_ROUND</th><th>F336W_CROWD</th><th>F110W_COUNT</th><th>F110W_SKY</th><th>F110W_RATE</th><th>F110W_RATERR</th><th>F110W_VEGA</th><th>F110W_TRANS</th><th>F110W_ERR</th><th>F110W_CHI</th><th>F110W_SNR</th><th>F110W_SHARP</th><th>F110W_ROUND</th><th>F110W_CROWD</th><th>F160W_COUNT</th><th>F160W_SKY</th><th>F160W_RATE</th><th>F160W_RATERR</th><th>F160W_VEGA</th><th>F160W_TRANS</th><th>F160W_ERR</th><th>F160W_CHI</th><th>F160W_SNR</th><th>F160W_SHARP</th><th>F160W_ROUND</th><th>F160W_CROWD</th><th>EXT</th><th>CHIP</th><th>MAJAX_GL</th><th>OBJTYPE_GL</th><th>F475W_FLAG</th><th>F814W_FLAG</th><th>F275W_FLAG</th><th>F336W_FLAG</th><th>F110W_FLAG</th><th>F160W_FLAG</th></tr></thead>\n",
+       "<thead><tr><th>bool</th><th>bool</th><th>bool</th><th>bool</th><th>bool</th><th>bool</th><th>bool</th><th>bool</th><th>bool</th><th>bool</th><th>bool</th><th>bool</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>int64</th><th>int64</th><th>int64</th><th>int64</th><th>int64</th><th>int64</th><th>int64</th><th>int64</th><th>int64</th><th>int64</th></tr></thead>\n",
+       "<tr><td>True</td><td>True</td><td>True</td><td>True</td><td>True</td><td>True</td><td>True</td><td>True</td><td>True</td><td>True</td><td>True</td><td>True</td><td>869.8900000046692</td><td>44.40999999483233</td><td>11.131832570656686</td><td>41.603419438518735</td><td>4.03</td><td>2917.0</td><td>-0.03</td><td>0.02</td><td>0.251</td><td>233000000.0</td><td>87358.19</td><td>9.039999999999999e-08</td><td>1.3800000000000002e-09</td><td>17.61</td><td>17.721</td><td>0.017</td><td>2.48</td><td>65.4</td><td>0.005</td><td>0.038</td><td>0.175</td><td>198000000.0</td><td>102360.6</td><td>1.59e-07</td><td>1.27e-09</td><td>16.996</td><td>16.986</td><td>0.009000000000000001</td><td>1.77</td><td>124.8</td><td>0.01</td><td>0.049</td><td>0.22699999999999998</td><td>5970000.0</td><td>2256.98</td><td>1.25e-07</td><td>1.16e-09</td><td>17.255</td><td>nan</td><td>0.01</td><td>2.92</td><td>107.7</td><td>-0.038</td><td>0.018000000000000002</td><td>0.086</td><td>23800000.0</td><td>7534.45</td><td>1.69e-07</td><td>6.92e-10</td><td>16.932000000000002</td><td>nan</td><td>0.004</td><td>2.63</td><td>243.9</td><td>-0.031</td><td>0.059000000000000004</td><td>0.084</td><td>79700000.0</td><td>88039.23</td><td>1.6e-07</td><td>2.58e-10</td><td>16.992</td><td>nan</td><td>0.002</td><td>2.23</td><td>619.1</td><td>-0.004</td><td>0.1</td><td>0.35</td><td>56000000.0</td><td>93657.59</td><td>1.8e-07</td><td>2.53e-10</td><td>16.864</td><td>nan</td><td>0.002</td><td>1.51</td><td>708.8</td><td>-0.018000000000000002</td><td>0.027000000000000003</td><td>0.396</td><td>0</td><td>1</td><td>165</td><td>1</td><td>2</td><td>2</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
+       "<tr><td>True</td><td>True</td><td>True</td><td>True</td><td>True</td><td>True</td><td>True</td><td>True</td><td>True</td><td>True</td><td>True</td><td>True</td><td>868.3900000040028</td><td>42.189999993549804</td><td>11.13180414033402</td><td>41.60338891056161</td><td>4.13</td><td>2566.5</td><td>-0.05</td><td>0.028999999999999998</td><td>0.27699999999999997</td><td>125000000.0</td><td>46004.84</td><td>4.85e-08</td><td>2.51e-10</td><td>18.285999999999998</td><td>18.412</td><td>0.006</td><td>3.02</td><td>193.3</td><td>-0.053</td><td>-0.006</td><td>0.231</td><td>118000000.0</td><td>77080.48</td><td>9.23e-08</td><td>1.24e-09</td><td>17.587</td><td>17.577</td><td>0.015</td><td>5.87</td><td>74.3</td><td>-0.096</td><td>0.044000000000000004</td><td>0.324</td><td>4360000.0</td><td>2332.74</td><td>9.15e-08</td><td>7.099999999999999e-10</td><td>17.597</td><td>nan</td><td>0.008</td><td>2.08</td><td>128.8</td><td>-0.057</td><td>0.008</td><td>0.092</td><td>16500000.0</td><td>8339.99</td><td>1.1699999999999999e-07</td><td>9.25e-10</td><td>17.329</td><td>nan</td><td>0.009000000000000001</td><td>3.74</td><td>126.5</td><td>-0.085</td><td>-0.006999999999999999</td><td>0.106</td><td>49500000.0</td><td>82165.18</td><td>9.92e-08</td><td>2.82e-10</td><td>17.508</td><td>nan</td><td>0.003</td><td>2.79</td><td>352.5</td><td>0.013000000000000001</td><td>-0.079</td><td>0.516</td><td>35100000.0</td><td>87515.55</td><td>1.13e-07</td><td>3.31e-10</td><td>17.371</td><td>nan</td><td>0.003</td><td>2.18</td><td>340.0</td><td>-0.026000000000000002</td><td>-0.004</td><td>0.635</td><td>0</td><td>1</td><td>75</td><td>1</td><td>2</td><td>2</td><td>0</td><td>2</td><td>0</td><td>0</td></tr>\n",
+       "<tr><td>True</td><td>True</td><td>False</td><td>False</td><td>False</td><td>False</td><td>True</td><td>True</td><td>False</td><td>False</td><td>True</td><td>True</td><td>297.1500000049341</td><td>22.169999994536283</td><td>11.135275566985227</td><td>41.595888643103535</td><td>1.17</td><td>1663.1</td><td>-0.08800000000000001</td><td>0.061</td><td>0.008</td><td>29910.7</td><td>10270.95</td><td>1.1499999999999999e-11</td><td>1.34e-12</td><td>27.348000000000003</td><td>27.805999999999997</td><td>0.126</td><td>1.34</td><td>8.6</td><td>-0.634</td><td>0.177</td><td>0.025</td><td>4160000.0</td><td>21190.25</td><td>3.17e-09</td><td>1.0000000000000001e-11</td><td>21.248</td><td>21.423000000000002</td><td>0.003</td><td>0.9</td><td>316.3</td><td>-0.063</td><td>0.049</td><td>0.001</td><td>-1.7</td><td>44.5</td><td>-6.900000000000001e-14</td><td>1.49e-11</td><td>nan</td><td>nan</td><td>9.999</td><td>0.87</td><td>-0.0</td><td>1.058</td><td>2.951</td><td>0.002</td><td>-565.3</td><td>268.32</td><td>-4.1e-12</td><td>6.389999999999999e-12</td><td>nan</td><td>nan</td><td>9.999</td><td>1.05</td><td>-0.6</td><td>0.0</td><td>0.0</td><td>0.0</td><td>39700000.0</td><td>73069.77</td><td>7.96e-08</td><td>1.1e-10</td><td>17.747</td><td>nan</td><td>0.001</td><td>1.35</td><td>723.9</td><td>0.012</td><td>-0.027000000000000003</td><td>0.011000000000000001</td><td>67100000.0</td><td>85406.73</td><td>2.15e-07</td><td>1.8399999999999998e-10</td><td>16.668</td><td>nan</td><td>0.001</td><td>1.11</td><td>1172.0</td><td>-0.004</td><td>-0.005</td><td>0.012</td><td>0</td><td>1</td><td>10</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>2</td><td>2</td></tr>\n",
+       "<tr><td>True</td><td>True</td><td>True</td><td>True</td><td>True</td><td>True</td><td>True</td><td>True</td><td>True</td><td>True</td><td>True</td><td>True</td><td>884.8300000051681</td><td>95.5499999940555</td><td>11.132618614902443</td><td>41.60386858784989</td><td>1.9</td><td>2292.6</td><td>-0.025</td><td>0.013000000000000001</td><td>0.01</td><td>66100000.0</td><td>22123.93</td><td>2.5299999999999998e-08</td><td>2.46e-11</td><td>18.99</td><td>18.977</td><td>0.001</td><td>1.67</td><td>1031.4</td><td>-0.023</td><td>0.013999999999999999</td><td>0.0</td><td>34800000.0</td><td>30329.55</td><td>2.64e-08</td><td>2.7e-11</td><td>18.944000000000003</td><td>18.934</td><td>0.001</td><td>1.23</td><td>980.7</td><td>0.004</td><td>0.018000000000000002</td><td>0.002</td><td>5440000.0</td><td>1002.34</td><td>1.14e-07</td><td>1.13e-09</td><td>17.355999999999998</td><td>nan</td><td>0.011000000000000001</td><td>2.74</td><td>101.0</td><td>-0.059000000000000004</td><td>0.046</td><td>0.0</td><td>12000000.0</td><td>2448.62</td><td>8.51e-08</td><td>5.41e-10</td><td>17.676</td><td>nan</td><td>0.006999999999999999</td><td>2.98</td><td>157.3</td><td>-0.085</td><td>0.037000000000000005</td><td>0.0</td><td>11400000.0</td><td>75079.13</td><td>2.28e-08</td><td>5.56e-11</td><td>19.104</td><td>nan</td><td>0.003</td><td>1.38</td><td>411.0</td><td>-0.045</td><td>-0.009000000000000001</td><td>0.03</td><td>7260000.0</td><td>80299.63</td><td>2.33e-08</td><td>5.929999999999999e-11</td><td>19.082</td><td>nan</td><td>0.003</td><td>0.93</td><td>392.4</td><td>-0.017</td><td>-0.003</td><td>0.079</td><td>0</td><td>1</td><td>130</td><td>1</td><td>0</td><td>0</td><td>2</td><td>0</td><td>0</td><td>0</td></tr>\n",
+       "<tr><td>True</td><td>True</td><td>False</td><td>False</td><td>False</td><td>False</td><td>True</td><td>True</td><td>False</td><td>False</td><td>True</td><td>True</td><td>805.8000000041931</td><td>624.1299999942455</td><td>11.142303960527883</td><td>41.605486300332416</td><td>1.23</td><td>1555.9</td><td>-0.122</td><td>0.165</td><td>0.016</td><td>-8222.3</td><td>10309.21</td><td>-3.2e-12</td><td>8.149999999999999e-13</td><td>nan</td><td>nan</td><td>9.999</td><td>1.18</td><td>-3.9</td><td>-4.412</td><td>0.713</td><td>0.478</td><td>854724.4</td><td>18592.63</td><td>6.51e-10</td><td>4.58e-12</td><td>22.965999999999998</td><td>nan</td><td>0.008</td><td>1.27</td><td>142.1</td><td>-0.157</td><td>0.221</td><td>0.006</td><td>-836.7</td><td>44.69</td><td>-1.8e-11</td><td>1.98e-11</td><td>nan</td><td>nan</td><td>9.999</td><td>1.2</td><td>-0.9</td><td>0.84</td><td>-0.28</td><td>0.037000000000000005</td><td>-490.7</td><td>257.91</td><td>-3.6000000000000004e-12</td><td>6.37e-12</td><td>nan</td><td>nan</td><td>9.999</td><td>0.93</td><td>-0.6</td><td>0.0</td><td>0.0</td><td>0.0</td><td>36400000.0</td><td>71988.72</td><td>7.299999999999999e-08</td><td>1.68e-10</td><td>17.842</td><td>nan</td><td>0.003</td><td>2.24</td><td>433.8</td><td>-0.059000000000000004</td><td>0.075</td><td>0.006999999999999999</td><td>63800000.0</td><td>85344.55</td><td>2.05e-07</td><td>1.8399999999999998e-10</td><td>16.723</td><td>nan</td><td>0.001</td><td>0.98</td><td>1114.1</td><td>0.009000000000000001</td><td>0.002</td><td>0.01</td><td>0</td><td>1</td><td>15</td><td>1</td><td>3</td><td>3</td><td>0</td><td>0</td><td>2</td><td>2</td></tr>\n",
+       "<tr><td>True</td><td>True</td><td>True</td><td>True</td><td>False</td><td>False</td><td>True</td><td>True</td><td>False</td><td>False</td><td>True</td><td>True</td><td>158.71000000585047</td><td>879.0699999946071</td><td>11.15104419200253</td><td>41.598376680664366</td><td>1.21</td><td>1504.4</td><td>-0.057999999999999996</td><td>0.11800000000000001</td><td>0.008</td><td>174859.8</td><td>10226.12</td><td>6.75e-11</td><td>1.7799999999999999e-12</td><td>25.427</td><td>26.089000000000002</td><td>0.028999999999999998</td><td>1.24</td><td>37.8</td><td>-0.16</td><td>0.318</td><td>0.01</td><td>5210000.0</td><td>21725.85</td><td>3.97e-09</td><td>1.1e-11</td><td>21.003</td><td>21.096</td><td>0.003</td><td>0.86</td><td>362.1</td><td>-0.034</td><td>0.033</td><td>0.003</td><td>-124.2</td><td>31.75</td><td>-2.6000000000000002e-12</td><td>1.3599999999999999e-11</td><td>nan</td><td>nan</td><td>9.999</td><td>0.85</td><td>-0.2</td><td>0.06</td><td>6.64</td><td>0.0</td><td>746.3</td><td>233.95</td><td>5.4500000000000004e-12</td><td>6.2599999999999996e-12</td><td>28.159000000000002</td><td>nan</td><td>1.246</td><td>1.17</td><td>0.9</td><td>-0.039</td><td>-0.7020000000000001</td><td>0.006</td><td>25900000.0</td><td>68934.5</td><td>5.2e-08</td><td>1.01e-10</td><td>18.21</td><td>nan</td><td>0.002</td><td>1.76</td><td>512.9</td><td>-0.054000000000000006</td><td>0.065</td><td>0.011000000000000001</td><td>45900000.0</td><td>80340.59</td><td>1.47e-07</td><td>1.35e-10</td><td>17.08</td><td>nan</td><td>0.001</td><td>1.04</td><td>1088.4</td><td>-0.024</td><td>0.006999999999999999</td><td>0.013000000000000001</td><td>0</td><td>1</td><td>150</td><td>1</td><td>2</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
+       "<tr><td>True</td><td>True</td><td>True</td><td>True</td><td>False</td><td>False</td><td>True</td><td>True</td><td>False</td><td>False</td><td>True</td><td>True</td><td>238.33000000445975</td><td>474.95999999429125</td><td>11.14351186487519</td><td>41.597389281034474</td><td>2.03</td><td>1436.1</td><td>-0.083</td><td>0.183</td><td>0.10800000000000001</td><td>33068.2</td><td>9542.11</td><td>1.28e-11</td><td>2.0299999999999996e-12</td><td>27.233</td><td>27.881</td><td>0.172</td><td>2.4</td><td>6.3</td><td>-0.405</td><td>0.633</td><td>0.354</td><td>1180000.0</td><td>18492.85</td><td>9.01e-10</td><td>1.1099999999999999e-11</td><td>22.613000000000003</td><td>22.715</td><td>0.013000000000000001</td><td>2.24</td><td>81.5</td><td>-0.065</td><td>0.201</td><td>0.10099999999999999</td><td>-962.1</td><td>3.96</td><td>-2.0000000000000002e-11</td><td>1.56e-11</td><td>nan</td><td>nan</td><td>9.999</td><td>0.95</td><td>-1.3</td><td>0.0</td><td>0.0</td><td>0.0</td><td>-724.2</td><td>249.83</td><td>-5.3e-12</td><td>5.679999999999999e-12</td><td>nan</td><td>nan</td><td>9.999</td><td>1.03</td><td>-0.9</td><td>0.0</td><td>0.0</td><td>0.0</td><td>28500000.0</td><td>67956.21</td><td>5.71e-08</td><td>6.23e-11</td><td>18.108</td><td>nan</td><td>0.001</td><td>1.03</td><td>916.4</td><td>-0.003</td><td>-0.008</td><td>0.03</td><td>41000000.0</td><td>77900.65</td><td>1.31e-07</td><td>1.42e-10</td><td>17.204</td><td>nan</td><td>0.001</td><td>1.15</td><td>924.9</td><td>0.005</td><td>0.009000000000000001</td><td>0.047</td><td>0</td><td>1</td><td>125</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
+       "<tr><td>True</td><td>True</td><td>True</td><td>True</td><td>True</td><td>True</td><td>True</td><td>True</td><td>True</td><td>True</td><td>True</td><td>True</td><td>824.9700000049659</td><td>176.12999999423107</td><td>11.134414672143825</td><td>41.603495862988545</td><td>1.71</td><td>2286.3</td><td>-0.013000000000000001</td><td>0.01</td><td>0.015</td><td>48100000.0</td><td>18255.8</td><td>1.85e-08</td><td>1.7800000000000003e-11</td><td>19.333</td><td>19.359</td><td>0.001</td><td>1.61</td><td>1040.5</td><td>-0.004</td><td>0.013999999999999999</td><td>0.006999999999999999</td><td>29200000.0</td><td>28051.12</td><td>2.2200000000000004e-08</td><td>1.81e-11</td><td>19.133</td><td>19.123</td><td>0.001</td><td>1.11</td><td>1230.7</td><td>0.005</td><td>0.011000000000000001</td><td>0.006999999999999999</td><td>1690000.0</td><td>448.87</td><td>3.54e-08</td><td>5.58e-10</td><td>18.628</td><td>nan</td><td>0.017</td><td>2.61</td><td>63.4</td><td>-0.07400000000000001</td><td>-0.017</td><td>0.0</td><td>5410000.0</td><td>1424.62</td><td>3.84e-08</td><td>3.85e-10</td><td>18.541</td><td>nan</td><td>0.011000000000000001</td><td>2.95</td><td>99.7</td><td>-0.085</td><td>0.02</td><td>0.0</td><td>10200000.0</td><td>70618.53</td><td>2.04e-08</td><td>4.5299999999999995e-11</td><td>19.224</td><td>nan</td><td>0.002</td><td>1.03</td><td>450.7</td><td>-0.012</td><td>-0.019</td><td>0.09</td><td>6700000.0</td><td>76931.27</td><td>2.15e-08</td><td>6.58e-11</td><td>19.17</td><td>nan</td><td>0.003</td><td>1.08</td><td>326.6</td><td>-0.001</td><td>0.012</td><td>0.09</td><td>0</td><td>1</td><td>135</td><td>1</td><td>2</td><td>0</td><td>2</td><td>2</td><td>0</td><td>0</td></tr>\n",
+       "<tr><td>True</td><td>True</td><td>True</td><td>True</td><td>False</td><td>False</td><td>True</td><td>True</td><td>True</td><td>True</td><td>True</td><td>True</td><td>590.0900000061857</td><td>332.8599999953118</td><td>11.138699638637968</td><td>41.60123630320331</td><td>1.37</td><td>2254.4</td><td>-0.019</td><td>0.011000000000000001</td><td>0.006</td><td>4650000.0</td><td>11464.72</td><td>1.79e-09</td><td>4.8899999999999996e-12</td><td>21.869</td><td>22.534000000000002</td><td>0.003</td><td>1.55</td><td>365.6</td><td>-0.02</td><td>0.006999999999999999</td><td>0.003</td><td>37700000.0</td><td>30650.81</td><td>2.8700000000000002e-08</td><td>2.05e-11</td><td>18.855</td><td>18.892</td><td>0.001</td><td>1.05</td><td>1401.2</td><td>-0.015</td><td>0.008</td><td>0.002</td><td>-392.4</td><td>18.53</td><td>-8.4e-12</td><td>1.84e-11</td><td>nan</td><td>nan</td><td>9.999</td><td>1.11</td><td>-0.5</td><td>-0.004</td><td>0.9079999999999999</td><td>0.006999999999999999</td><td>8649.2</td><td>252.79</td><td>6.34e-11</td><td>9.12e-12</td><td>25.495</td><td>nan</td><td>0.156</td><td>1.11</td><td>6.9</td><td>-0.09</td><td>0.254</td><td>0.006</td><td>34700000.0</td><td>72915.04</td><td>6.950000000000001e-08</td><td>8.9e-11</td><td>17.895</td><td>nan</td><td>0.001</td><td>1.31</td><td>781.0</td><td>-0.006999999999999999</td><td>-0.034</td><td>0.024</td><td>51000000.0</td><td>83288.02</td><td>1.6300000000000002e-07</td><td>1.36e-10</td><td>16.967</td><td>nan</td><td>0.001</td><td>0.78</td><td>1203.3</td><td>-0.008</td><td>-0.0</td><td>0.027999999999999997</td><td>0</td><td>1</td><td>105</td><td>1</td><td>2</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
+       "<tr><td>True</td><td>True</td><td>True</td><td>True</td><td>False</td><td>False</td><td>True</td><td>True</td><td>False</td><td>False</td><td>True</td><td>True</td><td>192.55000000367136</td><td>766.0499999944673</td><td>11.148860243216241</td><td>41.5982505014612</td><td>1.23</td><td>1310.9</td><td>-0.023</td><td>0.161</td><td>0.008</td><td>80824.0</td><td>10965.4</td><td>3.12e-11</td><td>1.7200000000000001e-12</td><td>26.264</td><td>26.785</td><td>0.06</td><td>1.39</td><td>18.1</td><td>-0.105</td><td>0.457</td><td>0.018000000000000002</td><td>7870000.0</td><td>23112.07</td><td>6e-09</td><td>1.32e-11</td><td>20.555</td><td>20.71</td><td>0.002</td><td>0.84</td><td>455.1</td><td>-0.028999999999999998</td><td>0.08</td><td>0.002</td><td>855.8</td><td>3.0</td><td>1.81e-11</td><td>1.63e-11</td><td>26.855999999999998</td><td>nan</td><td>0.981</td><td>0.93</td><td>1.1</td><td>0.107</td><td>0.752</td><td>0.001</td><td>876.4</td><td>285.08</td><td>6.4e-12</td><td>8.890000000000001e-12</td><td>27.984</td><td>nan</td><td>1.507</td><td>1.36</td><td>0.7</td><td>1.551</td><td>1.632</td><td>0.006</td><td>21000000.0</td><td>66448.02</td><td>4.21e-08</td><td>1.31e-10</td><td>18.439</td><td>nan</td><td>0.003</td><td>2.44</td><td>321.1</td><td>-0.064</td><td>0.017</td><td>0.009000000000000001</td><td>34900000.0</td><td>75358.92</td><td>1.12e-07</td><td>1.29e-10</td><td>17.379</td><td>nan</td><td>0.001</td><td>0.98</td><td>869.2</td><td>-0.044000000000000004</td><td>0.026000000000000002</td><td>0.011000000000000001</td><td>0</td><td>1</td><td>70</td><td>1</td><td>2</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
+       "<tr><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td></tr>\n",
+       "<tr><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>139.70000000664345</td><td>235.7399999952504</td><td>11.140027137243239</td><td>41.594916190423845</td><td>1.13</td><td>4.2</td><td>0.001</td><td>0.47</td><td>2.877</td><td>1123.5</td><td>8802.05</td><td>4.2299999999999996e-13</td><td>8.909999999999998e-13</td><td>30.934</td><td>31.000999999999998</td><td>2.286</td><td>1.11</td><td>0.5</td><td>0.18</td><td>1.0170000000000001</td><td>1.193</td><td>819.6</td><td>16521.3</td><td>6.02e-13</td><td>2.7400000000000004e-12</td><td>30.551</td><td>30.541999999999998</td><td>4.939</td><td>1.3</td><td>0.2</td><td>-0.39399999999999996</td><td>-0.866</td><td>1.8430000000000002</td><td>1414.3</td><td>6.4</td><td>2.99e-11</td><td>1.61e-11</td><td>26.31</td><td>nan</td><td>0.583</td><td>0.92</td><td>1.9</td><td>0.042</td><td>-1.079</td><td>0.02</td><td>524.0</td><td>199.47</td><td>3.96e-12</td><td>6.179999999999999e-12</td><td>28.505</td><td>nan</td><td>1.692</td><td>0.84</td><td>0.6</td><td>-0.015</td><td>2.428</td><td>0.025</td><td>5249.1</td><td>63158.89</td><td>1.05e-11</td><td>1.26e-11</td><td>27.445</td><td>nan</td><td>1.3</td><td>1.33</td><td>0.8</td><td>-0.541</td><td>-0.055</td><td>4.156000000000001</td><td>11899.2</td><td>70717.09</td><td>3.820000000000001e-11</td><td>2.1899999999999997e-11</td><td>26.046</td><td>nan</td><td>0.623</td><td>1.07</td><td>1.7</td><td>0.121</td><td>0.021</td><td>2.753</td><td>0</td><td>1</td><td>165</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
+       "<tr><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>45.02000000496014</td><td>833.5599999946328</td><td>11.151015306251551</td><td>41.59667641295129</td><td>1.11</td><td>4.8</td><td>-0.182</td><td>0.67</td><td>0.415</td><td>8471.2</td><td>9065.82</td><td>3.28e-12</td><td>1.09e-12</td><td>28.711</td><td>28.980999999999998</td><td>0.36</td><td>1.11</td><td>3.0</td><td>0.057999999999999996</td><td>0.154</td><td>0.632</td><td>13201.0</td><td>16691.07</td><td>1.0100000000000001e-11</td><td>3.18e-12</td><td>27.491999999999997</td><td>27.485</td><td>0.34299999999999997</td><td>1.26</td><td>3.2</td><td>0.159</td><td>-0.263</td><td>0.92</td><td>561.8</td><td>3.98</td><td>1.19e-11</td><td>1.35e-11</td><td>27.315</td><td>nan</td><td>1.235</td><td>0.85</td><td>0.9</td><td>-0.47</td><td>1.2</td><td>0.062</td><td>-430.5</td><td>230.36</td><td>-3.2e-12</td><td>4.150000000000001e-12</td><td>nan</td><td>nan</td><td>9.999</td><td>0.88</td><td>-0.8</td><td>-0.371</td><td>3.103</td><td>0.08800000000000001</td><td>7197.4</td><td>61992.18</td><td>1.4400000000000002e-11</td><td>8.859999999999999e-12</td><td>27.101999999999997</td><td>nan</td><td>0.667</td><td>0.91</td><td>1.6</td><td>0.47200000000000003</td><td>-0.899</td><td>3.343</td><td>351.2</td><td>68266.34</td><td>1.1100000000000001e-12</td><td>2.0699999999999997e-11</td><td>29.884</td><td>nan</td><td>9.999</td><td>1.07</td><td>0.1</td><td>-0.28300000000000003</td><td>-0.382</td><td>3.883</td><td>0</td><td>1</td><td>10</td><td>1</td><td>0</td><td>0</td><td>2</td><td>0</td><td>0</td><td>0</td></tr>\n",
+       "<tr><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>124.22000000354114</td><td>891.7099999947256</td><td>11.15149356523698</td><td>41.597992980970396</td><td>1.07</td><td>4.4</td><td>0.27899999999999997</td><td>1.075</td><td>0.871</td><td>3799.5</td><td>8730.35</td><td>1.47e-12</td><td>1.03e-12</td><td>29.581</td><td>30.06</td><td>0.762</td><td>0.99</td><td>1.4</td><td>0.46</td><td>0.057999999999999996</td><td>1.1540000000000001</td><td>10487.5</td><td>16054.26</td><td>8e-12</td><td>3.0600000000000003e-12</td><td>27.741999999999997</td><td>27.745</td><td>0.415</td><td>1.23</td><td>2.6</td><td>0.10099999999999999</td><td>1.024</td><td>1.421</td><td>-946.2</td><td>1.81</td><td>-2.0000000000000002e-11</td><td>1.26e-11</td><td>nan</td><td>nan</td><td>9.999</td><td>1.11</td><td>-1.6</td><td>3.847</td><td>9.999</td><td>0.035</td><td>-859.3</td><td>208.6</td><td>-6.3e-12</td><td>4.03e-12</td><td>nan</td><td>nan</td><td>9.999</td><td>1.0</td><td>-1.6</td><td>0.015</td><td>-0.023</td><td>0.022000000000000002</td><td>10494.9</td><td>61350.07</td><td>2.1e-11</td><td>1.2900000000000002e-11</td><td>26.693</td><td>nan</td><td>0.667</td><td>1.42</td><td>1.6</td><td>0.102</td><td>-0.062</td><td>3.245</td><td>16822.6</td><td>66808.6</td><td>5.3899999999999996e-11</td><td>2.0899999999999997e-11</td><td>25.671</td><td>nan</td><td>0.42200000000000004</td><td>0.68</td><td>2.6</td><td>-0.124</td><td>0.821</td><td>2.8139999999999996</td><td>0</td><td>1</td><td>135</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
+       "<tr><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>491.7300000047286</td><td>28.15999999404994</td><td>11.13407882831087</td><td>41.59843930637129</td><td>1.3</td><td>3.6</td><td>1.249</td><td>1.4040000000000001</td><td>0.9420000000000001</td><td>-3482.7</td><td>9812.03</td><td>-1.3000000000000001e-12</td><td>9.75e-13</td><td>nan</td><td>nan</td><td>9.999</td><td>1.25</td><td>-1.4</td><td>0.9009999999999999</td><td>-1.339</td><td>0.451</td><td>17882.3</td><td>18260.27</td><td>1.3599999999999999e-11</td><td>3.22e-12</td><td>27.164</td><td>nan</td><td>0.257</td><td>1.45</td><td>4.2</td><td>1.393</td><td>1.757</td><td>1.099</td><td>-1014.1</td><td>51.71</td><td>-2.1e-11</td><td>1.4699999999999998e-11</td><td>nan</td><td>nan</td><td>9.999</td><td>0.9</td><td>-1.4</td><td>0.0</td><td>0.0</td><td>0.0</td><td>-2806.0</td><td>259.79</td><td>-2.0000000000000002e-11</td><td>1.0800000000000001e-11</td><td>nan</td><td>nan</td><td>9.999</td><td>0.87</td><td>-1.8</td><td>0.0</td><td>0.0</td><td>0.0</td><td>-843.3</td><td>67460.74</td><td>-1.6999999999999998e-12</td><td>1.6e-11</td><td>nan</td><td>nan</td><td>9.999</td><td>1.71</td><td>-0.1</td><td>0.0</td><td>0.0</td><td>0.0</td><td>14713.1</td><td>74903.59</td><td>4.72e-11</td><td>2.64e-11</td><td>25.816</td><td>nan</td><td>0.607</td><td>1.26</td><td>1.8</td><td>0.32799999999999996</td><td>1.579</td><td>2.6</td><td>0</td><td>1</td><td>30</td><td>1</td><td>2</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
+       "<tr><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>45.10000000349737</td><td>784.4199999943728</td><td>11.150163527414094</td><td>41.596431973103726</td><td>1.13</td><td>4.5</td><td>0.297</td><td>0.653</td><td>1.6269999999999998</td><td>10956.9</td><td>8636.31</td><td>4.24e-12</td><td>1.08e-12</td><td>28.430999999999997</td><td>nan</td><td>0.276</td><td>1.03</td><td>3.9</td><td>0.131</td><td>-0.102</td><td>1.251</td><td>-3891.8</td><td>15874.79</td><td>-2.9999999999999997e-12</td><td>3.39e-12</td><td>nan</td><td>nan</td><td>9.999</td><td>1.31</td><td>-0.9</td><td>2.092</td><td>8.902000000000001</td><td>3.199</td><td>-1522.4</td><td>26.91</td><td>-3.2e-11</td><td>1.64e-11</td><td>nan</td><td>nan</td><td>9.999</td><td>1.2</td><td>-2.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>1473.8</td><td>225.73</td><td>1.07e-11</td><td>5.480000000000001e-12</td><td>27.421999999999997</td><td>nan</td><td>0.5529999999999999</td><td>0.9</td><td>2.0</td><td>0.431</td><td>0.9179999999999999</td><td>0.053</td><td>13270.7</td><td>60622.41</td><td>2.66e-11</td><td>1.16e-11</td><td>26.438000000000002</td><td>nan</td><td>0.475</td><td>1.47</td><td>2.3</td><td>0.249</td><td>0.769</td><td>2.782</td><td>1628.0</td><td>66785.31</td><td>5.26e-12</td><td>2.0200000000000002e-11</td><td>28.198</td><td>nan</td><td>4.17</td><td>0.87</td><td>0.3</td><td>0.149</td><td>0.026000000000000002</td><td>3.0269999999999997</td><td>0</td><td>1</td><td>150</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
+       "<tr><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>300.67000000529197</td><td>124.46999999461252</td><td>11.13702404373161</td><td>41.59644549997782</td><td>1.24</td><td>3.8</td><td>-0.239</td><td>2.224</td><td>0.988</td><td>8530.9</td><td>8828.88</td><td>3.28e-12</td><td>9.2e-13</td><td>28.712</td><td>29.011999999999997</td><td>0.305</td><td>1.18</td><td>3.6</td><td>-0.11900000000000001</td><td>3.517</td><td>1.002</td><td>14136.9</td><td>17768.3</td><td>1.0800000000000001e-11</td><td>2.9e-12</td><td>27.42</td><td>27.414</td><td>0.292</td><td>1.31</td><td>3.7</td><td>-0.193</td><td>1.558</td><td>1.268</td><td>-1678.8</td><td>-5.81</td><td>-3.6e-11</td><td>1.68e-11</td><td>nan</td><td>nan</td><td>9.999</td><td>1.18</td><td>-2.1</td><td>-0.41200000000000003</td><td>-1.262</td><td>0.037000000000000005</td><td>20.4</td><td>204.34</td><td>1.2e-13</td><td>6.4599999999999995e-12</td><td>32.298</td><td>nan</td><td>9.999</td><td>1.13</td><td>0.0</td><td>-1.379</td><td>0.364</td><td>0.099</td><td>6309.5</td><td>64820.31</td><td>1.26e-11</td><td>1.39e-11</td><td>27.245</td><td>nan</td><td>1.195</td><td>1.62</td><td>0.9</td><td>-0.172</td><td>0.163</td><td>3.687</td><td>1575.6</td><td>71781.3</td><td>5.0500000000000005e-12</td><td>1.97e-11</td><td>28.241999999999997</td><td>nan</td><td>4.245</td><td>0.88</td><td>0.3</td><td>0.062</td><td>-0.011000000000000001</td><td>3.931</td><td>0</td><td>1</td><td>100</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
+       "<tr><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>328.90000000475607</td><td>371.37999999437136</td><td>11.141112398449446</td><td>41.59804507142715</td><td>1.16</td><td>3.8</td><td>-0.11800000000000001</td><td>0.616</td><td>3.438</td><td>-6049.9</td><td>8542.92</td><td>-2.3e-12</td><td>1e-12</td><td>nan</td><td>nan</td><td>9.999</td><td>1.02</td><td>-2.3</td><td>-0.151</td><td>9.596</td><td>2.115</td><td>-5593.7</td><td>16425.26</td><td>-4.3e-12</td><td>2.5000000000000003e-12</td><td>nan</td><td>nan</td><td>9.999</td><td>1.1</td><td>-1.7</td><td>0.376</td><td>-2.5469999999999997</td><td>2.073</td><td>4632.4</td><td>40.89</td><td>9.83e-11</td><td>3.2100000000000004e-11</td><td>25.018</td><td>nan</td><td>0.355</td><td>1.3</td><td>3.1</td><td>-0.1</td><td>0.402</td><td>0.053</td><td>-1094.0</td><td>197.85</td><td>-8e-12</td><td>5.59e-12</td><td>nan</td><td>nan</td><td>9.999</td><td>0.83</td><td>-1.4</td><td>0.0</td><td>0.0</td><td>0.0</td><td>-1385.9</td><td>63262.95</td><td>-2.7999999999999998e-12</td><td>1.64e-11</td><td>nan</td><td>nan</td><td>9.999</td><td>1.55</td><td>-0.2</td><td>0.0</td><td>0.0</td><td>0.0</td><td>32938.1</td><td>70232.93</td><td>1.06e-10</td><td>2.7300000000000002e-11</td><td>24.94</td><td>nan</td><td>0.28</td><td>1.33</td><td>3.9</td><td>0.132</td><td>0.342</td><td>2.597</td><td>0</td><td>1</td><td>160</td><td>1</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
+       "<tr><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>546.9500000037107</td><td>61.31999999343384</td><td>11.134284148248089</td><td>41.5993203942432</td><td>1.08</td><td>3.6</td><td>0.297</td><td>0.861</td><td>2.937</td><td>-1815.0</td><td>9237.35</td><td>-6.9e-13</td><td>8.4e-13</td><td>nan</td><td>nan</td><td>9.999</td><td>1.0</td><td>-0.8</td><td>0.084</td><td>0.23600000000000002</td><td>1.24</td><td>119.8</td><td>17602.08</td><td>1.0699999999999999e-13</td><td>2.54e-12</td><td>32.423</td><td>nan</td><td>9.999</td><td>1.09</td><td>0.0</td><td>0.027000000000000003</td><td>0.782</td><td>2.589</td><td>2267.6</td><td>4.87</td><td>4.75e-11</td><td>1.81e-11</td><td>25.807</td><td>nan</td><td>0.41200000000000003</td><td>0.91</td><td>2.6</td><td>0.43</td><td>1.1159999999999999</td><td>0.013999999999999999</td><td>-1588.6</td><td>199.09</td><td>-1.1e-11</td><td>7.639999999999999e-12</td><td>nan</td><td>nan</td><td>9.999</td><td>1.25</td><td>-1.5</td><td>0.0</td><td>0.0</td><td>0.0</td><td>5653.1</td><td>67000.59</td><td>1.13e-11</td><td>1.56e-11</td><td>27.365</td><td>nan</td><td>1.499</td><td>1.52</td><td>0.7</td><td>1.1640000000000001</td><td>6.152</td><td>4.522</td><td>23006.9</td><td>73242.07</td><td>7.379999999999999e-11</td><td>2.6899999999999996e-11</td><td>25.33</td><td>nan</td><td>0.396</td><td>1.04</td><td>2.7</td><td>0.524</td><td>0.693</td><td>3.137</td><td>0</td><td>1</td><td>35</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
+       "<tr><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>729.7300000053044</td><td>216.06999999517893</td><td>11.135743163679448</td><td>41.602461665939074</td><td>1.12</td><td>3.6</td><td>0.079</td><td>2.228</td><td>1.945</td><td>4766.7</td><td>9215.83</td><td>1.82e-12</td><td>7.08e-13</td><td>29.348000000000003</td><td>29.593000000000004</td><td>0.42100000000000004</td><td>1.07</td><td>2.6</td><td>0.105</td><td>2.197</td><td>1.162</td><td>6956.3</td><td>16934.97</td><td>5.3e-12</td><td>1.96e-12</td><td>28.189</td><td>28.18</td><td>0.401</td><td>1.16</td><td>2.7</td><td>0.159</td><td>1.1420000000000001</td><td>1.744</td><td>-2312.7</td><td>89.92</td><td>-4.7999999999999996e-11</td><td>1.72e-11</td><td>nan</td><td>nan</td><td>9.999</td><td>1.1</td><td>-2.8</td><td>0.193</td><td>0.363</td><td>0.371</td><td>-856.9</td><td>206.22</td><td>-6.0999999999999995e-12</td><td>5.679999999999999e-12</td><td>nan</td><td>nan</td><td>9.999</td><td>0.96</td><td>-1.1</td><td>-0.322</td><td>8.344</td><td>0.038</td><td>3614.7</td><td>64680.2</td><td>7.240000000000001e-12</td><td>1.4499999999999998e-11</td><td>27.85</td><td>nan</td><td>2.175</td><td>1.49</td><td>0.5</td><td>-1.128</td><td>0.457</td><td>4.6610000000000005</td><td>-2560.7</td><td>72170.48</td><td>-8.2e-12</td><td>2.43e-11</td><td>nan</td><td>nan</td><td>9.999</td><td>1.24</td><td>-0.3</td><td>2.322</td><td>0.456</td><td>4.119</td><td>0</td><td>1</td><td>90</td><td>1</td><td>2</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
+       "<tr><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>735.9100000053115</td><td>553.1799999945795</td><td>11.141541865647177</td><td>41.604226347405195</td><td>1.06</td><td>3.6</td><td>0.521</td><td>3.795</td><td>1.446</td><td>6386.6</td><td>9697.99</td><td>2.46e-12</td><td>6.929999999999999e-13</td><td>29.022</td><td>29.070999999999998</td><td>0.305</td><td>1.07</td><td>3.6</td><td>0.262</td><td>3.745</td><td>0.851</td><td>4251.1</td><td>17863.19</td><td>3.2400000000000003e-12</td><td>1.7799999999999999e-12</td><td>28.725</td><td>28.715999999999998</td><td>0.598</td><td>1.02</td><td>1.8</td><td>1.04</td><td>2.144</td><td>1.3359999999999999</td><td>-1733.4</td><td>33.06</td><td>-3.7e-11</td><td>1.52e-11</td><td>nan</td><td>nan</td><td>9.999</td><td>0.97</td><td>-2.4</td><td>5.979</td><td>9.999</td><td>0.113</td><td>-1083.8</td><td>247.92</td><td>-8e-12</td><td>6.29e-12</td><td>nan</td><td>nan</td><td>9.999</td><td>0.95</td><td>-1.3</td><td>0.68</td><td>0.337</td><td>0.152</td><td>-3049.3</td><td>65793.02</td><td>-6.0999999999999995e-12</td><td>1.05e-11</td><td>nan</td><td>nan</td><td>9.999</td><td>1.27</td><td>-0.6</td><td>0.0</td><td>0.0</td><td>0.0</td><td>2055.5</td><td>71931.6</td><td>6.56e-12</td><td>1.96e-11</td><td>27.958000000000002</td><td>nan</td><td>3.238</td><td>0.98</td><td>0.3</td><td>-0.23</td><td>0.266</td><td>2.713</td><td>0</td><td>1</td><td>90</td><td>1</td><td>0</td><td>2</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "<Table length=50507>\n",
+       "F814W_ST F814W_GST F475W_ST F475W_GST ... F336W_FLAG F110W_FLAG F160W_FLAG\n",
+       "  bool      bool     bool      bool   ...   int64      int64      int64   \n",
+       "-------- --------- -------- --------- ... ---------- ---------- ----------\n",
+       "    True      True     True      True ...          0          0          0\n",
+       "    True      True     True      True ...          2          0          0\n",
+       "    True      True    False     False ...          0          2          2\n",
+       "    True      True     True      True ...          0          0          0\n",
+       "    True      True    False     False ...          0          2          2\n",
+       "    True      True     True      True ...          0          0          0\n",
+       "    True      True     True      True ...          0          0          0\n",
+       "    True      True     True      True ...          2          0          0\n",
+       "    True      True     True      True ...          0          0          0\n",
+       "    True      True     True      True ...          0          0          0\n",
+       "     ...       ...      ...       ... ...        ...        ...        ...\n",
+       "   False     False    False     False ...          0          0          0\n",
+       "   False     False    False     False ...          0          0          0\n",
+       "   False     False    False     False ...          0          0          0\n",
+       "   False     False    False     False ...          0          0          0\n",
+       "   False     False    False     False ...          0          0          0\n",
+       "   False     False    False     False ...          0          0          0\n",
+       "   False     False    False     False ...          0          0          0\n",
+       "   False     False    False     False ...          0          0          0\n",
+       "   False     False    False     False ...          0          0          0\n",
+       "   False     False    False     False ...          0          0          0"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "hdul = fits.open(gst_file)\n",
+    "Table(hdul[1].data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As we can see, there's a lot of columns and even more rows. For plotting the magnitude histograms, we're going to be interested in any column that contains the name VEGA. These are the columns with the magnitudes for each filter.\n",
+    "\n",
+    "We could also use the X and Y columns to plot where are the sources are located, or the RA and DEC to map their actual position in the sky.\n",
+    "\n",
+    "In larger projects we might have multiple fields to analyze during each run, so there would be multiple **field_names**. Since this is just a small example, we just have one field so our index will always be equal to **0**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# the list of fields (we only have 1 for this example.)\n",
+    "field_names = [field_name]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can create some histogram plots to visualize the magnitude distribution of our sources."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/christina/anaconda3/envs/astroconda/lib/python3.6/site-packages/beast/plotting/plot_mag_hist.py:54: RuntimeWarning: invalid value encountered in less\n",
+      "  np.where(data_table[filt + \"_VEGA\"] < 90)\n",
+      "/home/christina/anaconda3/envs/astroconda/lib/python3.6/site-packages/beast/plotting/plot_mag_hist.py:54: RuntimeWarning: invalid value encountered in less\n",
+      "  np.where(data_table[filt + \"_VEGA\"] < 90)\n",
+      "/home/christina/anaconda3/envs/astroconda/lib/python3.6/site-packages/beast/plotting/plot_mag_hist.py:54: RuntimeWarning: invalid value encountered in less\n",
+      "  np.where(data_table[filt + \"_VEGA\"] < 90)\n",
+      "/home/christina/anaconda3/envs/astroconda/lib/python3.6/site-packages/beast/plotting/plot_mag_hist.py:54: RuntimeWarning: invalid value encountered in less\n",
+      "  np.where(data_table[filt + \"_VEGA\"] < 90)\n",
+      "/home/christina/anaconda3/envs/astroconda/lib/python3.6/site-packages/beast/plotting/plot_mag_hist.py:54: RuntimeWarning: invalid value encountered in less\n",
+      "  np.where(data_table[filt + \"_VEGA\"] < 90)\n",
+      "/home/christina/anaconda3/envs/astroconda/lib/python3.6/site-packages/beast/plotting/plot_mag_hist.py:54: RuntimeWarning: invalid value encountered in less\n",
+      "  np.where(data_table[filt + \"_VEGA\"] < 90)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# this 'if' statement just checks if there's already a histogram file\n",
+    "if not os.path.isfile('./'+field_names[0]+'.st_maghist.pdf'):\n",
+    "    peak_mags = plot_mag_hist.plot_mag_hist(gst_file, stars_per_bin=70, max_bins=75)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can check out the results for the histograms in the file ending with **_maghist.pdf**\n",
+    "\n",
+    "From this plot, we can also see what filters exist for the data. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Step 1b. Make source density maps"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next we'll be creating source density maps. These are maps of our data field colored such that they show how many stars/sources there are in each degree field. The standard size is 5 arc seconds squared but because we are working with a smaller subset of data, we run the risk of not having enough stars in each source density bin later on when we calculate the noisemodel, so in order to avoid this, we have set our pixel size for this example to 15 arc seconds, thereby giving us fewer source density bins but more stars in each source density bin. The size can easily be changed by modifying the **pixsize** variable below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Pick the filter with the dimmest peak from the histogram\n",
+    "ref_filter =[\"F475W\"]\n",
+    "\n",
+    "# choose a filter to use for removing artifacts\n",
+    "# (remove catalog sources with filter_FLAG > 99)\n",
+    "flag_filter = [\"F275W\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "# of x & y pixels =  4 4\n",
+      "working on converting ra, dec to pix x,y\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/christina/anaconda3/envs/astroconda/lib/python3.6/site-packages/astropy/table/column.py:1020: RuntimeWarning: invalid value encountered in greater_equal\n",
+      "  result = getattr(super(), op)(other)\n",
+      "/home/christina/anaconda3/envs/astroconda/lib/python3.6/site-packages/astropy/table/column.py:1020: RuntimeWarning: invalid value encountered in less_equal\n",
+      "  result = getattr(super(), op)(other)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# check to see if the sourde density file already exists\n",
+    "if not os.path.isfile(gst_file.replace(\".fits\", \"_source_den_image.fits\")):\n",
+    "    # if not, run all this other code\n",
+    "    \n",
+    "    # - pixel size of 15 arcsec\n",
+    "    # - use ref_filter[b] between vega mags of 15 and peak_mags[ref_filter[b]]-0.5\n",
+    "        # since we're only working with one field, our index b is set to 0\n",
+    "    sourceden_args = types.SimpleNamespace(\n",
+    "        subcommand=\"sourceden\",\n",
+    "        catfile=gst_file,\n",
+    "        pixsize=15,\n",
+    "        npix=None,\n",
+    "        mag_name=ref_filter[0]+ \"_VEGA\",\n",
+    "        mag_cut=[17, peak_mags[ref_filter[0]] - 0.5],\n",
+    "        flag_name=flag_filter[0]+'_FLAG',\n",
+    "    )\n",
+    "    create_background_density_map.main_make_map(sourceden_args)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# new file name with the source density column\n",
+    "gst_file_sd = gst_file.replace(\".fits\", \"_with_sourceden.fits\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This function should create 3 files: \n",
+    "* *M31-B09-EAST_subset.st_source_den_image.fits* : a file for viewing the source density information in ds9 or with matplotlib\n",
+    "\n",
+    "* *M31-B09-EAST_subset.st_sourceden_map.hd5* : the same file as source_den_image but now with even more data (the split_catalog_using_map function will end up using this file later on) \n",
+    "\n",
+    "* *M31-B09-EAST_subset.st_with_sourceden.fits* : the same as the original photometric file (gst_file) but now with an additional column for what density bin the source is located in"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### View the fits images of the source density maps\n",
+    "\n",
+    "Now that we have the source density maps outputted, we can plot the image and see that the density looks like."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Filename: ./M31-B09-EAST_chunk.st_source_den_image.fits\n",
+      "No.    Name      Ver    Type      Cards   Dimensions   Format\n",
+      "  0  PRIMARY       1 PrimaryHDU      19   (4, 4)   float64   \n",
+      "<class 'numpy.ndarray'>\n",
+      "(4, 4)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# open the fits file\n",
+    "hdu_list = fits.open(\"./%s.st_source_den_image.fits\"%field_name)\n",
+    "hdu_list.info()\n",
+    "\n",
+    "# extract the image data\n",
+    "image_data = hdu_list[0].data\n",
+    "\n",
+    "# take a look at what the image should look like\n",
+    "print(type(image_data))\n",
+    "print(image_data.shape)\n",
+    "\n",
+    "# close the fits file\n",
+    "hdu_list.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Density of Sources per 15 arcsec^2')"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x720 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# plot the extracted image data\n",
+    "fig = plt.figure(0, [10,10])\n",
+    "im = plt.imshow(image_data, origin=\"lower\")\n",
+    "plt.colorbar(im)\n",
+    "plt.xlabel(\"Pixel (originally RA)\")\n",
+    "plt.ylabel(\"Pixel (originally DEC)\")\n",
+    "plt.title(\"Density of Sources per 15 arcsec^2\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Step 1c. Set up BEAST settings\n",
+    "\n",
+    "At this point, we have a basic understanding of the information we are working with, so it's about time we set up our settings class. \n",
+    "\n",
+    "The settings class is a sort of catch-all file used to store any sort of infomation we might need to run the BEAST code on our data. We'll go through and talk about what all the different variables mean.\n",
+    "\n",
+    "Go ahead and open the beast_settings.txt file in a text editor and ensure that the following variables match:\n",
+    "\n",
+    "*  **project** : the same as the field_name variable we noted earlier\n",
+    "    * *project = \"M31-B09-EAST_chunk\"*\n",
+    "    \n",
+    "    \n",
+    "* **surveyname** : the overall name for the survey (this variable isn't actually important for the code)\n",
+    "    * *surveyname = \"PHAT-M31\"*\n",
+    "    \n",
+    "    \n",
+    "* **filters** : the full filter names from the photometric catalog, also the names that show up in our magnitude histograms so you can add them from there\n",
+    "    * *filters = [\"HST_WFC3_F475W\", \"HST_WFC3_F275W\", \"HST_WFC3_F336W\", \"HST_WFC3_F814W\", \"HST_WFC3_F110W\", \"HST_WFC3_F160W\",]*\n",
+    "    \n",
+    "    \n",
+    "* **base filters** : shortened versions of the filter names\n",
+    "    * *basefilters = [\"F475W\", \"F275W\", \"F336W\", \"F814W\", \"F110W\", \"F160W\"]*\n",
+    "    \n",
+    "    \n",
+    "* **obsfile** : the name of the photometric catalog (now including the source density information)\n",
+    "    * *obsfile = \"./M31-B09-EAST_chunk.st_with_sourceden.fits\"*\n",
+    "    \n",
+    "    \n",
+    "* **ast_with_positions** : make sure is set to *True* if you have the locations (RA/Dec) included in your obsfile (which we do in this case)\n",
+    "    * *ast_with_positions = True*\n",
+    "\n",
+    "\n",
+    "* **ast_density_table** : the source density map created in step 1b  \n",
+    "    * *ast_density_table = './M31-B09-EAST_chunk.st_sourceden_map.hd5'*\n",
+    "    \n",
+    "    \n",
+    "* **ast_reference_image** : the original photometric FITS catalog which is required if you use the ast_with_positions as true  \n",
+    "    * *ast_reference_image = \"./M31-B09-EAST_chunk_F475W_drz.chip1.fits\"*\n",
+    "    \n",
+    "    \n",
+    "* **astfile** : since ASTs normally have to be processed by a specialist, we have already included a finished AST file for us to use in this example\n",
+    "    * *astfile = \"M31-B09_EAST_chunk.gst.fake.fits\"*\n",
+    "    \n",
+    "    \n",
+    "* **n_subgrid** : the number of subgrids to use for generating the physics model later on (with 1 meaning no subgrids)\n",
+    "    * *n_subgrid = 1*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This file is also where you specify the parameters and resolution of your physics model which will become relevant in step 2. The resolution of these parameters for your own runs will differ depending on what sorts of ASTs you want to model. There are 8 parameters that can be set.\n",
+    "\n",
+    "1. **Distance** : either a fixed value or a range with stepsizes\n",
+    "2. **Velocity** : what is the heliocentric velocity of your location or galaxy in km/s\n",
+    "3. **Age** : the log10 age range of the ASTs being modeled\n",
+    "4. **Mass** : the mass of the ASTs\n",
+    "5. **Metallicity** : the metallicity range of the ASTs\n",
+    "\n",
+    "6. **A(v)** : the range of dust extinction in magnitudes that could be dimming the intrinsic brightness of the ASTs\n",
+    "7. **R(v)** : the range of dust grain sizes \n",
+    "8. **f(A)** the mixture factor between the Milky Way and Small Magellanic Cloud extinction curves\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "settings = beast_settings.beast_settings(\"beast_settings.txt\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Step 2. Create physics model\n",
+    "\n",
+    "We need to set up a model of possible stellar parameter combinations, and from that, calculate what the expected SED is for each point. These simulated SEDs will later be compared to our actual source SED to figure out which parameters best fit each source.\n",
+    "\n",
+    "This model is called a **physics model**, and we will be using the parameters set in the settings to create this N-dimensional grid.\n",
+    "\n",
+    "*As a quick note, the resolution on the stellar parameters (the step size, often specified as the third input e.g. logt = [6.0, 10.13, 1.0], where 1.0 is the step size) is the main factor driving how long this physics grid will take to set up. If things take a very long time to run, consider making the step size larger for testing's sake.*\n",
+    "\n",
+    "Sometimes we are able to have access to high-performance computing resources, meaning we can split the physics model into subgrids and run them in parallel, cutting a lot of the computation time. While we're like not running this notebook in parallel here, we've still specified a number of subgrids in the settings. \n",
+    "\n",
+    "We can check how many subgrids are set up."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "settings.n_subgrid"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "So we can now see that we've asked for 1 grid in the settings file.\n",
+    "\n",
+    "If we've already generated a physics model, we certainly don't want to run it again, so the following code checks to make sure all the subgrids for the physics model are present."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# set up the naming conventions for the physics model\n",
+    "gs_str = \"\"\n",
+    "\n",
+    "# this is only relevant if we run with multiple subgrids\n",
+    "if settings.n_subgrid > 1:\n",
+    "    gs_str = \"sub*\"\n",
+    "\n",
+    "# collects any physics models that have already been created\n",
+    "# if none have, sed_files will be empty\n",
+    "sed_files = glob.glob(\n",
+    "    \"./{0}/{0}_seds.grid{1}.hd5\".format(field_names[0], gs_str)\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Interrogating http://stev.oapd.inaf.it...\n",
+      "Downloading data...http://stev.oapd.inaf.it/tmp/output231553786740.dat\n",
+      "Interrogating http://stev.oapd.inaf.it...\n",
+      "Downloading data...http://stev.oapd.inaf.it/tmp/output717945759025.dat\n",
+      "Interrogating http://stev.oapd.inaf.it...\n",
+      "Downloading data...http://stev.oapd.inaf.it/tmp/output189489548401.dat\n",
+      "Interrogating http://stev.oapd.inaf.it...\n",
+      "Downloading data...http://stev.oapd.inaf.it/tmp/output204956899005.dat\n",
+      "M31-B09-EAST_chunk/M31-B09-EAST_chunk Isochrones\n",
+      "Make spectra\n",
+      "applying 1 distances\n",
+      "Adding spectral properties: True\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Spectral grid: 100%|██████████| 809/809 [00:00<00:00, 1009.19it/s]\n",
+      "Spectral grid: 100%|██████████| 4343/4343 [00:04<00:00, 1063.82it/s]\n",
+      "Distance grid: 100%|██████████| 1/1 [00:00<00:00,  9.63it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Make Prior Weights\n",
+      "computing the distance plus weights for dist =  907820.5301781861\n",
+      "computing the age-mass-metallicity grid weight for Z =  0.004\n",
+      "computing the age-mass-metallicity grid weight for Z =  0.008\n",
+      "computing the age-mass-metallicity grid weight for Z =  0.019\n",
+      "computing the age-mass-metallicity grid weight for Z =  0.03\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\r",
+      "SED grid:   0%|          | 0/99 [00:00<?, ?it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Make SEDS\n",
+      "number of initially requested points = 275\n",
+      "              number of valid points = 99 (based on restrictions in R(V)\n",
+      "                 versus f_A plane)\n",
+      "              \n",
+      "Generating a final grid of 510048 points\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING: AstropyDeprecationWarning: Class F20 defines class attributes ``inputs``.\n",
+      "            This has been deprecated in v4.0 and support will be removed in v4.1.\n",
+      "            Starting with v4.0 classes must define a class attribute ``n_inputs``.\n",
+      "            Please consult the documentation for details.\n",
+      "             [astropy.modeling.core]\n",
+      "WARNING: AstropyDeprecationWarning: Class G03_SMCBar defines class attributes ``inputs``.\n",
+      "            This has been deprecated in v4.0 and support will be removed in v4.1.\n",
+      "            Starting with v4.0 classes must define a class attribute ``n_inputs``.\n",
+      "            Please consult the documentation for details.\n",
+      "             [astropy.modeling.core]\n",
+      "WARNING: AstropyDeprecationWarning: Class FM90 defines class attributes ``inputs``.\n",
+      "            This has been deprecated in v4.0 and support will be removed in v4.1.\n",
+      "            Starting with v4.0 classes must define a class attribute ``n_inputs``.\n",
+      "            Please consult the documentation for details.\n",
+      "             [astropy.modeling.core]\n",
+      "SED grid: 100%|██████████| 99/99 [01:36<00:00,  1.02it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# only make the physics model they don't already exist\n",
+    "if len(sed_files) < settings.n_subgrid:\n",
+    "    # directly create physics model grids\n",
+    "    create_physicsmodel.create_physicsmodel(settings, nprocs=1, nsubs=settings.n_subgrid)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# list of SED files (physics models)\n",
+    "model_grid_files = sorted(\n",
+    "    glob.glob(\n",
+    "        \"./{0}/{0}_seds.grid{1}.hd5\".format(field_names[0], gs_str)\n",
+    "    )\n",
+    ")\n",
+    "sed_files = model_grid_files"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Hopefully a spectral grid and an SED grid should have started generating. In the end you should have a new folder with the same name as your project, with a one SED and spectral grid if you have only 1 subgrid.\n",
+    "\n",
+    "Our goal after this would normally be to eventually run a bunch of **ASTs** (Artificial Star Tests), but before we can do that, we need to generate the fake stars to use.\n",
+    "\n",
+    "Since the ASTs would normally need to be analyzed by a specialist after being created and that's a little overkill for a small example, these next couple of steps are just to illustrate how AST inputs are actually generated. A finished file of the analyzed ASTs already exists so we will end up using that in step 4 and beyond."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Step 3. Create Input ASTs!\n",
+    "\n",
+    "Now that we have our physics model generated, we can start to generate some input ASTs. ASTs are artificial sources inserted into the observations we have, which are then extracted with the same software that was used for the original photometry catalog. So the step that we're running now is just generating the artifical sources that will then later be inserted. \n",
+    "\n",
+    "We need to make sure that the ASTs cover the same range of magnitudes as our original photometric catalog does, so to do that\n",
+    "\n",
+    "\n",
+    "First thing's first, we're gonna check that there isn't already a file of AST inputs present in the folder we're working in."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'./M31-B09-EAST_chunk/M31-B09-EAST_chunk_inputAST.txt'"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# only create an AST input list if the ASTs don't already exist\n",
+    "ast_input_file = (\"./{0}/{0}_inputAST.txt\".format(field_names[0]))\n",
+    "\n",
+    "ast_input_file"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can create the ASTs if they don't already exist."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Selecting SEDs for ASTs\n",
+      "Assigning positions to artifical stars\n",
+      "removing 177 stars from ./M31-B09-EAST_chunk.st_with_sourceden.fits\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING: FITSFixedWarning: 'datfix' made the change 'Invalid parameter values: MJD-OBS and DATE-OBS are inconsistent'. [astropy.wcs.wcs]\n",
+      "3077.00 models per map bin:   0%|          | 0/11 [00:00<?, ?it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1  repeats of each model in each map bin\n",
+      "11 non-empty map bins (out of 26) found between 2.628383223014101 and 3.6503170374646996\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "3077.00 models per map bin: 100%|██████████| 11/11 [00:15<00:00,  1.41s/it]\n"
+     ]
+    }
+   ],
+   "source": [
+    "if not os.path.isfile(ast_input_file):\n",
+    "    \n",
+    "    # check that the right obsfile is being used\n",
+    "    # this file changes in step 4\n",
+    "    if settings.obsfile == './M31-B09-EAST_chunk.st_with_sourceden.fits':\n",
+    "        make_ast_inputs.make_ast_inputs(settings, flux_bin_method=True)\n",
+    "    \n",
+    "    # if not, reset the obfile and generate AST inputs\n",
+    "    else:\n",
+    "        settings.obsfile == './M31-B09-EAST_chunk.st_with_sourceden.fits'\n",
+    "        make_ast_inputs.make_ast_inputs(settings, flux_bin_method=True)\n",
+    "        "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<i>Table length=33847</i>\n",
+       "<table id=\"table140396699431992\" class=\"table-striped table-bordered table-condensed\">\n",
+       "<thead><tr><th>zeros</th><th>ones</th><th>X</th><th>Y</th><th>HST_WFC3_F475W</th><th>HST_WFC3_F275W</th><th>HST_WFC3_F336W</th><th>HST_WFC3_F814W</th><th>HST_WFC3_F110W</th><th>HST_WFC3_F160W</th></tr></thead>\n",
+       "<thead><tr><th>int64</th><th>int64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th></tr></thead>\n",
+       "<tr><td>0</td><td>1</td><td>686.75304</td><td>890.50384</td><td>36.89248</td><td>39.7683</td><td>38.81036</td><td>32.0627</td><td>29.66778</td><td>28.00124</td></tr>\n",
+       "<tr><td>0</td><td>1</td><td>700.64395</td><td>897.39349</td><td>36.89248</td><td>39.7683</td><td>38.81036</td><td>32.0627</td><td>29.66778</td><td>28.00124</td></tr>\n",
+       "<tr><td>0</td><td>1</td><td>676.10245</td><td>888.17202</td><td>36.89248</td><td>39.7683</td><td>38.81036</td><td>32.0627</td><td>29.66778</td><td>28.00124</td></tr>\n",
+       "<tr><td>0</td><td>1</td><td>654.08922</td><td>880.75184</td><td>36.89248</td><td>39.7683</td><td>38.81036</td><td>32.0627</td><td>29.66778</td><td>28.00124</td></tr>\n",
+       "<tr><td>0</td><td>1</td><td>650.63909</td><td>877.01596</td><td>36.89248</td><td>39.7683</td><td>38.81036</td><td>32.0627</td><td>29.66778</td><td>28.00124</td></tr>\n",
+       "<tr><td>0</td><td>1</td><td>636.60122</td><td>880.43267</td><td>36.89248</td><td>39.7683</td><td>38.81036</td><td>32.0627</td><td>29.66778</td><td>28.00124</td></tr>\n",
+       "<tr><td>0</td><td>1</td><td>692.06356</td><td>895.37596</td><td>36.89248</td><td>39.7683</td><td>38.81036</td><td>32.0627</td><td>29.66778</td><td>28.00124</td></tr>\n",
+       "<tr><td>0</td><td>1</td><td>691.03737</td><td>896.21658</td><td>36.89248</td><td>39.7683</td><td>38.81036</td><td>32.0627</td><td>29.66778</td><td>28.00124</td></tr>\n",
+       "<tr><td>0</td><td>1</td><td>644.01532</td><td>875.48925</td><td>36.89248</td><td>39.7683</td><td>38.81036</td><td>32.0627</td><td>29.66778</td><td>28.00124</td></tr>\n",
+       "<tr><td>0</td><td>1</td><td>658.72265</td><td>882.2672</td><td>36.89248</td><td>39.7683</td><td>38.81036</td><td>32.0627</td><td>29.66778</td><td>28.00124</td></tr>\n",
+       "<tr><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td></tr>\n",
+       "<tr><td>0</td><td>1</td><td>823.07723</td><td>563.01986</td><td>49.06702</td><td>50.42726</td><td>52.12937</td><td>41.51828</td><td>38.10228</td><td>35.92042</td></tr>\n",
+       "<tr><td>0</td><td>1</td><td>866.76615</td><td>485.88239</td><td>49.06702</td><td>50.42726</td><td>52.12937</td><td>41.51828</td><td>38.10228</td><td>35.92042</td></tr>\n",
+       "<tr><td>0</td><td>1</td><td>790.09471</td><td>496.60087</td><td>49.06702</td><td>50.42726</td><td>52.12937</td><td>41.51828</td><td>38.10228</td><td>35.92042</td></tr>\n",
+       "<tr><td>0</td><td>1</td><td>861.6476</td><td>541.35707</td><td>49.06702</td><td>50.42726</td><td>52.12937</td><td>41.51828</td><td>38.10228</td><td>35.92042</td></tr>\n",
+       "<tr><td>0</td><td>1</td><td>828.85124</td><td>454.53435</td><td>49.06702</td><td>50.42726</td><td>52.12937</td><td>41.51828</td><td>38.10228</td><td>35.92042</td></tr>\n",
+       "<tr><td>0</td><td>1</td><td>850.04582</td><td>481.80326</td><td>49.06702</td><td>50.42726</td><td>52.12937</td><td>41.51828</td><td>38.10228</td><td>35.92042</td></tr>\n",
+       "<tr><td>0</td><td>1</td><td>882.59711</td><td>524.58952</td><td>49.06702</td><td>50.42726</td><td>52.12937</td><td>41.51828</td><td>38.10228</td><td>35.92042</td></tr>\n",
+       "<tr><td>0</td><td>1</td><td>875.17219</td><td>614.73779</td><td>49.06702</td><td>50.42726</td><td>52.12937</td><td>41.51828</td><td>38.10228</td><td>35.92042</td></tr>\n",
+       "<tr><td>0</td><td>1</td><td>865.94871</td><td>602.10219</td><td>49.06702</td><td>50.42726</td><td>52.12937</td><td>41.51828</td><td>38.10228</td><td>35.92042</td></tr>\n",
+       "<tr><td>0</td><td>1</td><td>880.61692</td><td>631.64943</td><td>49.06702</td><td>50.42726</td><td>52.12937</td><td>41.51828</td><td>38.10228</td><td>35.92042</td></tr>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "<Table length=33847>\n",
+       "zeros  ones     X         Y     ... HST_WFC3_F814W HST_WFC3_F110W HST_WFC3_F160W\n",
+       "int64 int64  float64   float64  ...    float64        float64        float64    \n",
+       "----- ----- --------- --------- ... -------------- -------------- --------------\n",
+       "    0     1 686.75304 890.50384 ...        32.0627       29.66778       28.00124\n",
+       "    0     1 700.64395 897.39349 ...        32.0627       29.66778       28.00124\n",
+       "    0     1 676.10245 888.17202 ...        32.0627       29.66778       28.00124\n",
+       "    0     1 654.08922 880.75184 ...        32.0627       29.66778       28.00124\n",
+       "    0     1 650.63909 877.01596 ...        32.0627       29.66778       28.00124\n",
+       "    0     1 636.60122 880.43267 ...        32.0627       29.66778       28.00124\n",
+       "    0     1 692.06356 895.37596 ...        32.0627       29.66778       28.00124\n",
+       "    0     1 691.03737 896.21658 ...        32.0627       29.66778       28.00124\n",
+       "    0     1 644.01532 875.48925 ...        32.0627       29.66778       28.00124\n",
+       "    0     1 658.72265  882.2672 ...        32.0627       29.66778       28.00124\n",
+       "  ...   ...       ...       ... ...            ...            ...            ...\n",
+       "    0     1 823.07723 563.01986 ...       41.51828       38.10228       35.92042\n",
+       "    0     1 866.76615 485.88239 ...       41.51828       38.10228       35.92042\n",
+       "    0     1 790.09471 496.60087 ...       41.51828       38.10228       35.92042\n",
+       "    0     1  861.6476 541.35707 ...       41.51828       38.10228       35.92042\n",
+       "    0     1 828.85124 454.53435 ...       41.51828       38.10228       35.92042\n",
+       "    0     1 850.04582 481.80326 ...       41.51828       38.10228       35.92042\n",
+       "    0     1 882.59711 524.58952 ...       41.51828       38.10228       35.92042\n",
+       "    0     1 875.17219 614.73779 ...       41.51828       38.10228       35.92042\n",
+       "    0     1 865.94871 602.10219 ...       41.51828       38.10228       35.92042\n",
+       "    0     1 880.61692 631.64943 ...       41.51828       38.10228       35.92042"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ast = Table.read(ast_input_file, format=\"ascii\")\n",
+    "ast"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Check to see how the SEDs and the ASTs compare\n",
+    "\n",
+    "The histogram that is produced should have both the SED distribution and the AST distribution plotted on it. The thing we want to test for is whether the AST distribution fully samples the SED range."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plot_ast_histogram.plot_ast(ast_file = ast_input_file, sed_grid_file = model_grid_files[0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Step 4. Edit/Split the Catalog"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We have to remove sources from the input photometry catalog that are in regions without full imaging coverage or flagged as bad in flag_filter. This step should mostly just be removing any sources where one of the filters might not have a value."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "removing 177 stars from ./M31-B09-EAST_chunk.st_with_sourceden.fits\n"
+     ]
+    }
+   ],
+   "source": [
+    "gst_file_cut = gst_file.replace(\".fits\", \"_with_sourceden_cut.fits\")\n",
+    "\n",
+    "# check to see if the trimmed catalog already exists\n",
+    "if not os.path.isfile(gst_file_cut):\n",
+    "    # and if not\n",
+    "    cut_catalogs.cut_catalogs(\n",
+    "        gst_file_sd,\n",
+    "        gst_file_cut,\n",
+    "        partial_overlap=True,\n",
+    "        flagged=True,\n",
+    "        flag_filter=flag_filter[0],\n",
+    "        region_file=True,\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# update the obsfile\n",
+    "settings.obsfile = gst_file_cut"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Step 5. Edit/Split the ASTs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now for this step, we're doing things a little unconventionally since actually placing all the input ASTs we generated in Step 3 back into our image and rerunning the analysis would take several days of computational time. \n",
+    "\n",
+    "Instead, we've already procurred a polished AST results file (kindly provided by Ben Williams from the University of Washington) which we can use to complete our analysis. The AST file should be named *'./M31-B09-EAST_chunk.gst.fake.fits'* while the input ASTs we generated were named *'./M31-B09-EAST_chunk/M31-B09-EAST_chunk_beast_inputAST.txt'*.\n",
+    "\n",
+    "We will now use the same cutting procedure as for the catalog to trim down the AST file with the same criteria as in Step 4."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ast_file = \"./\" + field_names[0] + \".gst.fake.fits\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "removing 12631 stars from ./M31-B09-EAST_chunk.gst.fake.fits\n"
+     ]
+    }
+   ],
+   "source": [
+    "# - ASTs\n",
+    "ast_file_cut = ast_file.replace(\".fits\", \"_cut.fits\")\n",
+    "\n",
+    "# check to see if the trimmed AST file already exists\n",
+    "if not os.path.isfile(ast_file_cut):\n",
+    "    cut_catalogs.cut_catalogs(\n",
+    "        ast_file,\n",
+    "        ast_file_cut,\n",
+    "        partial_overlap=True,\n",
+    "        flagged=True,\n",
+    "        flag_filter=flag_filter[0],\n",
+    "        region_file=True,\n",
+    "    )\n",
+    "\n",
+    "# so now we've generated the cut ast file"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# update the AST file\n",
+    "settings.astfile = ast_file_cut"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can plot the AST magnitudes against our original source magnitudes again, just to check that we are within a reasonable range."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# check to see if the plotted AST file already exists\n",
+    "if not os.path.isfile(ast_file_cut.replace(\".fits\", \"_maghist.pdf\")):\n",
+    "    \n",
+    "    test = plot_mag_hist.plot_mag_hist(ast_file_cut, stars_per_bin=200, max_bins=30)\n",
+    "\n",
+    "    # and so this should plot a histogram of the different asts that remain after cutting"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Step 6. Split catalog by source density"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For the next fitting step, we're going to have to break our catalog and AST file into bins based on the source density, and then further into sub-bins if there are more than ~6250 sources in the bins. \n",
+    "\n",
+    "We split things into source density bins so that we can later study how the actual source density of region effects the noise or bias. We further split things into sub-bins, just to make things a little more computationally accessible.\n",
+    "\n",
+    "One thing to note is that the source density bins are first sorted by magnitude (typically F475W if it's there) before being split into sub-bins. This means that the first sub-bin file (for a source density bin that has more than 6250 sources) will end up having all the dimmest sources or any sources with NAN values, and the last sub file will have all the brightest sources. This will become handy in Step 8 when we create physics (SED) models and noisemoels tailored specifically to each sub-bin file."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "bin edges:  [2.62838322 3.62838322 4.62838322]\n",
+      "Splitting catalog\n",
+      "bin 1: 48145 sources\n",
+      "dividing into 8 subfiles for later fitting speed\n",
+      "bin 2: 2185 sources\n",
+      "dividing into 1 subfiles for later fitting speed\n",
+      "\n",
+      "Splitting ASTs\n",
+      "bin 1: 37364 sources\n",
+      "bin 2: 1554 sources\n"
+     ]
+    }
+   ],
+   "source": [
+    "# check to see if any sub files exist yet\n",
+    "if len(glob.glob(gst_file_cut.replace('.fits','*sub*fits') )) == 0:\n",
+    "    # if no sub files exist, they can now be created\n",
+    "    # a smaller value for n_per_file will mean more individual files/runs,\n",
+    "    # but each run will take a shorter amount of time\n",
+    "    \n",
+    "    #split the gst file and ast file\n",
+    "    split_catalog_using_map.split_main(\n",
+    "        gst_file_cut,\n",
+    "        ast_file_cut,\n",
+    "        gst_file.replace('.fits','_sourceden_map.hd5'), #get full sourceden_mad.hd5 file from dust folder\n",
+    "        bin_width=1,\n",
+    "        n_per_file=6250, #this is the max number of sources per bin before it splits \n",
+    "\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "So these are all the different source density bins, with some of them being split into sub bins to limit the number of entries to ~6250. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Rather than reading in all the files we just created, the developers of this code instead wrote this handy little function that generates a dictionary of all the files that have just been created (assuming the function ran correctly) and all the files that we hope to generate in the future.\n",
+    "\n",
+    "Because of this, I recommend not changing any of the naming for Step 6 or beyond, just because that then makes this dictionary point to incorrect files."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# generate file name lists\n",
+    "file_dict = create_filenames.create_filenames(\n",
+    "    settings, use_sd=True, nsubs=settings.n_subgrid\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If we take a look in our folder, we should be able to see some bins with sub-bins notation. We can do a quick check to see if the sub-binning generated from the dictionary matchs up with the files split in our data folder."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[['1', '0'],\n",
+       " ['1', '1'],\n",
+       " ['1', '2'],\n",
+       " ['1', '3'],\n",
+       " ['1', '4'],\n",
+       " ['1', '5'],\n",
+       " ['1', '6'],\n",
+       " ['1', '7'],\n",
+       " ['2', '0']]"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sd_sub_info = file_dict[\"sd_sub_info\"]\n",
+    "sd_sub_info"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "** total SD bins: 2\n",
+      "** total SD subfiles: 9\n"
+     ]
+    }
+   ],
+   "source": [
+    "# - number of SD bins\n",
+    "temp = set([i[0] for i in sd_sub_info])\n",
+    "print(\"** total SD bins: \" + str(len(temp)))\n",
+    "\n",
+    "# - the unique sets of SD+sub\n",
+    "unique_sd_sub = [\n",
+    "    x for i, x in enumerate(sd_sub_info) if i == sd_sub_info.index(x)\n",
+    "]\n",
+    "print(\"** total SD subfiles: \" + str(len(unique_sd_sub)))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Just another quick was to ensure that all the binning and sub-binning matches up. If it doesn't, none of the next steps will run properly."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Step 7. Make Noise Models"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We're now on to creating our observational noise models! These models will be used to adjust the bias and uncertainty in Steps 8 and 9. \n",
+    "\n",
+    "The **uncertainty** (also known as sigma) is the standard deviation calculated for all the detected sources.\n",
+    "\n",
+    "The **bias** is the average offset between the input flux we have for the ASTs and the measured flux. Bias tends to become more prominent in regions of high source density, where it's harder to detect all the faint stars if they get blended together. If this happens, then some of the stars are assumed to be part of the background (raising the average), which gets subtracted from the detected sources. If the background is raised, then the detected sources are measured to be systematically fainter than they should be."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# these are what the noise files should be named once generated\n",
+    "noise_files = file_dict[\"noise_files\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['./M31-B09-EAST_chunk.gst.fake_cut_bin1.fits',\n",
+       " './M31-B09-EAST_chunk.gst.fake_cut_bin2.fits']"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# gather up the split AST files\n",
+    "ast_file_list = sorted(glob.glob(settings.astfile.replace(\".fits\", \"*_bin*\")))\n",
+    "ast_file_list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "sd list:  ['1', '2']\n",
+      "\n",
+      "creating M31-B09-EAST_chunk/M31-B09-EAST_chunk_noisemodel_bin1.grid.hd5\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Fitting model: 100%|██████████| 6/6 [00:00<00:00, 28.12it/s]\n",
+      "Evaluating model: 100%|██████████| 6/6 [00:00<00:00, 32.95it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Writing to disk into M31-B09-EAST_chunk/M31-B09-EAST_chunk_noisemodel_bin1.grid.hd5\n",
+      "M31-B09-EAST_chunk/M31-B09-EAST_chunk_noisemodel_bin1.grid.hd5\n",
+      "\n",
+      "creating M31-B09-EAST_chunk/M31-B09-EAST_chunk_noisemodel_bin2.grid.hd5\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Fitting model: 100%|██████████| 6/6 [00:00<00:00, 125.87it/s]\n",
+      "Evaluating model: 100%|██████████| 6/6 [00:00<00:00, 37.40it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Writing to disk into M31-B09-EAST_chunk/M31-B09-EAST_chunk_noisemodel_bin2.grid.hd5\n",
+      "M31-B09-EAST_chunk/M31-B09-EAST_chunk_noisemodel_bin2.grid.hd5\n"
+     ]
+    }
+   ],
+   "source": [
+    "# create the noise model with our ASTs  \n",
+    "create_obsmodel.create_obsmodel(\n",
+    "        settings, use_sd=True, nsubs=settings.n_subgrid, nprocs=1\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Step 7.5 Visualize Noise Models (Optional)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This next cell is some older plotting code for visualizing the noise models. It should (hopefully) work if you uncomment and run it, but the lack of a log scale for the y-axis makes the results a little harder to fully interpret.\n",
+    "\n",
+    "As an alternative, the same plot is recreated down below but the steps have been broken down to hopefully help you gain a better sense of what's going on (and plot the y-axis with a log scale). \n",
+    "\n",
+    "If you're not interested in visualizing the noise models, feel free to skip this step."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# plot_noisemodel.plot_noisemodel(sed_file=\"M31-B09-EAST_chunk/M31-B09-EAST_chunk_seds.grid.hd5\", \n",
+    "#                                 noise_file_list=noise_files, \n",
+    "#                                 plot_file=\"noise_model_plot.png\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Alternative plot\n",
+    "I'm going to try to recreate this noise model plot using some of the filters used in Dreiss' paper.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(510048, 6)"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# set some basic plotting stuff\n",
+    "samp=100 # makes it so we plot every 100th point from the SED files\n",
+    "color=[\"black\", \"red\", \"gold\", \"lime\", \"xkcd:azure\"]\n",
+    "label=None\n",
+    "\n",
+    "# load in the physics model as an object\n",
+    "sed_object = SEDGrid(sed_files[0])\n",
+    "\n",
+    "# read the flux values for all the sources\n",
+    "if hasattr(sed_object.seds, \"read\"):\n",
+    "    sed_grid = sed_object.seds.read()\n",
+    "else:\n",
+    "    sed_grid = sed_object.seds\n",
+    "    \n",
+    "sed_object.seds.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "So this sed_grid comes from back in Step 2, where the physics model created ~500,000 points based off of the original parameters we specified in the settings, and for each point, the expected flux for each filter is calculated. We can now use the noise models we created with the ASTs to see how the bias and uncertainty is expected to scale with the flux from a specific filter. We'll plot the log10 of the flux on the x-axis and then the flux-normalized uncertainty and bias on the y-axis. We can also color our results based on what source density bin the ASTs came from, as well as compare how different filters compare to one another\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# pull out the list of filters\n",
+    "filter_list = sed_object.filters\n",
+    "\n",
+    "# for this plot, I just want to plot the first two filter\n",
+    "# feel free to change this and see what the other filters look like\n",
+    "filter_list_plot = filter_list[0:2]\n",
+    "n_filter = len(filter_list_plot)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "* reading M31-B09-EAST_chunk/M31-B09-EAST_chunk_noisemodel_bin1.grid.hd5\n",
+      "* reading M31-B09-EAST_chunk/M31-B09-EAST_chunk_noisemodel_bin1.grid.hd5\n",
+      "* reading M31-B09-EAST_chunk/M31-B09-EAST_chunk_noisemodel_bin1.grid.hd5\n",
+      "* reading M31-B09-EAST_chunk/M31-B09-EAST_chunk_noisemodel_bin1.grid.hd5\n",
+      "* reading M31-B09-EAST_chunk/M31-B09-EAST_chunk_noisemodel_bin1.grid.hd5\n",
+      "* reading M31-B09-EAST_chunk/M31-B09-EAST_chunk_noisemodel_bin1.grid.hd5\n",
+      "* reading M31-B09-EAST_chunk/M31-B09-EAST_chunk_noisemodel_bin1.grid.hd5\n",
+      "* reading M31-B09-EAST_chunk/M31-B09-EAST_chunk_noisemodel_bin1.grid.hd5\n",
+      "* reading M31-B09-EAST_chunk/M31-B09-EAST_chunk_noisemodel_bin2.grid.hd5\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x576 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# set up the figure frame work\n",
+    "# have it scale with the number of filters we're plotting\n",
+    "fig, axes = plt.subplots(2, len(filter_list_plot), sharex=True, figsize=(5*len(filter_list_plot),8))\n",
+    "\n",
+    "# go through noise files\n",
+    "for n, nfile in enumerate(noise_files):\n",
+    "    \n",
+    "    print(\"* reading \" + nfile)\n",
+    "\n",
+    "    # read in the values\n",
+    "    noisemodel_vals = noisemodel.get_noisemodelcat(nfile)\n",
+    "\n",
+    "    # extract error and bias\n",
+    "    noise_err = noisemodel_vals['error']\n",
+    "    noise_bias = noisemodel_vals['bias']\n",
+    "    \n",
+    "    cmaps = plt.get_cmap('viridis')\n",
+    "\n",
+    "    gradient = np.linspace(0, 1, len(noise_files))    \n",
+    "\n",
+    "    # now we can start plotting things\n",
+    "    for f, filt in enumerate(filter_list_plot):\n",
+    "        \n",
+    "        # error is negative where it's been extrapolated -> trim those\n",
+    "        good_err = np.where(noise_err[:, f] > 0)[0]\n",
+    "        plot_sed = sed_grid[good_err, f][::samp] # only pulls every 100th point\n",
+    "        plot_err = noise_err[good_err, f][::samp]\n",
+    "        plot_bias = noise_bias[good_err, f][::samp]\n",
+    "\n",
+    "        # plot bias\n",
+    "        axes[0, f].set_yscale('log')\n",
+    "\n",
+    "        axes[0, f].plot(\n",
+    "            np.log10(plot_sed),\n",
+    "            np.abs(plot_bias) / plot_sed,\n",
+    "            marker=\"o\",\n",
+    "            linestyle=\"none\",\n",
+    "            mew=0,\n",
+    "            ms=2,\n",
+    "            alpha=1,\n",
+    "            c=cmaps(int(nfile[-10])/9.),\n",
+    "            label=noise_files[n][-10]\n",
+    "            )\n",
+    "        \n",
+    "        axes[0, f].set_ylabel(r\"Abs Bias ($\\mu$/F)\", fontsize=10)\n",
+    "        # xlabel is still in flux, not mag\n",
+    "        axes[0, f].legend()\n",
+    "\n",
+    "        # plot error (uncertainty)\n",
+    "        axes[1, f].set_yscale('log')\n",
+    "\n",
+    "        axes[1, f].plot(\n",
+    "            np.log10(plot_sed),\n",
+    "            plot_err / plot_sed,\n",
+    "            marker=\"o\",\n",
+    "            linestyle=\"none\",\n",
+    "            mew=0,\n",
+    "            ms=2,\n",
+    "            color=color[0 % len(color)],\n",
+    "            alpha=0.1,)\n",
+    "        axes[1, f].set_ylabel(r\"Error ($\\sigma$/F)\", fontsize=10)\n",
+    "        axes[1, f].set_xlabel(\"log \" + filt[-5:], fontsize=10)\n",
+    "\n",
+    "        plt.tight_layout()\n",
+    "        \n",
+    "    #fig.colorbar(plt.cm.ScalarMappable(norm=np.arange(0,12), cmap=cmaps), ax=axes)\n",
+    "    \n",
+    "\n",
+    "    # Need to figure out if it's worth comparing the bias and the uncertainty to one another."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As you can probably tell, this plot isn't the most beautiful plot in the world (especially that coloring and legend) but I'm proud of her. It does, however, let you see the scale of the bias and uncertainty (error) for different filters and how the source density and magnitudes are correlated.\n",
+    "\n",
+    "The most notable thing to note is that the uncertainty and bias tend to be larger at lower fluxes. This probably doesn't come as a shock to anyone, but it's important to accurately take this into consideration when we make our fittings in Step 9."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Step 8. Trim Models\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now that we have our SED and or noise models created, we can go ahead and trim them of any sources that are so bright or so faint (compared to min/max flux in the observation file) that they will by definition produce effectively zero likelihood fits. \n",
+    "\n",
+    "One thing to note is that, since our noise models are correlated with source density, we are in a sense 'convolving' each of our noise models with the original physics grid, meaning we will end up with a lot of physics grids trimmed for each source density scenario thanks to our noise models (and these physics grids are still essentially as large as the original physics grid, making this a very storage-intensive step). However, this trimming of the 'parameter space', as you could call it, will help speed up fittings in Step 9.\n",
+    "\n",
+    "**This step is very storage intensive so I'd make sure to have at least ~5GB of storage available.**\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "working on filter # =  0\n",
+      "working on filter # =  1\n",
+      "working on filter # =  2\n",
+      "working on filter # =  3\n",
+      "working on filter # =  4\n",
+      "working on filter # =  5\n",
+      "number of original models =  510048\n",
+      "number of ast trimmed models =  510048\n",
+      "number of trimmed models =  479563\n",
+      "Writing trimmed sedgrid to disk into M31-B09-EAST_chunk/M31-B09-EAST_chunk_bin1_sub0_seds_trim.grid.hd5\n",
+      "Writing trimmed noisemodel to disk into M31-B09-EAST_chunk/M31-B09-EAST_chunk_bin1_sub0_noisemodel_trim.grid.hd5\n",
+      "working on filter # =  0\n",
+      "working on filter # =  1\n",
+      "working on filter # =  2\n",
+      "working on filter # =  3\n",
+      "working on filter # =  4\n",
+      "working on filter # =  5\n",
+      "number of original models =  510048\n",
+      "number of ast trimmed models =  510048\n",
+      "number of trimmed models =  479563\n",
+      "Writing trimmed sedgrid to disk into M31-B09-EAST_chunk/M31-B09-EAST_chunk_bin1_sub1_seds_trim.grid.hd5\n",
+      "Writing trimmed noisemodel to disk into M31-B09-EAST_chunk/M31-B09-EAST_chunk_bin1_sub1_noisemodel_trim.grid.hd5\n",
+      "working on filter # =  0\n",
+      "working on filter # =  1\n",
+      "working on filter # =  2\n",
+      "working on filter # =  3\n",
+      "working on filter # =  4\n",
+      "working on filter # =  5\n",
+      "number of original models =  510048\n",
+      "number of ast trimmed models =  510048\n",
+      "number of trimmed models =  479563\n",
+      "Writing trimmed sedgrid to disk into M31-B09-EAST_chunk/M31-B09-EAST_chunk_bin1_sub2_seds_trim.grid.hd5\n",
+      "Writing trimmed noisemodel to disk into M31-B09-EAST_chunk/M31-B09-EAST_chunk_bin1_sub2_noisemodel_trim.grid.hd5\n",
+      "working on filter # =  0\n",
+      "working on filter # =  1\n",
+      "working on filter # =  2\n",
+      "working on filter # =  3\n",
+      "working on filter # =  4\n",
+      "working on filter # =  5\n",
+      "number of original models =  510048\n",
+      "number of ast trimmed models =  510048\n",
+      "number of trimmed models =  479563\n",
+      "Writing trimmed sedgrid to disk into M31-B09-EAST_chunk/M31-B09-EAST_chunk_bin1_sub3_seds_trim.grid.hd5\n",
+      "Writing trimmed noisemodel to disk into M31-B09-EAST_chunk/M31-B09-EAST_chunk_bin1_sub3_noisemodel_trim.grid.hd5\n",
+      "working on filter # =  0\n",
+      "working on filter # =  1\n",
+      "working on filter # =  2\n",
+      "working on filter # =  3\n",
+      "working on filter # =  4\n",
+      "working on filter # =  5\n",
+      "number of original models =  510048\n",
+      "number of ast trimmed models =  510048\n",
+      "number of trimmed models =  479563\n",
+      "Writing trimmed sedgrid to disk into M31-B09-EAST_chunk/M31-B09-EAST_chunk_bin1_sub4_seds_trim.grid.hd5\n",
+      "Writing trimmed noisemodel to disk into M31-B09-EAST_chunk/M31-B09-EAST_chunk_bin1_sub4_noisemodel_trim.grid.hd5\n",
+      "working on filter # =  0\n",
+      "working on filter # =  1\n",
+      "working on filter # =  2\n",
+      "working on filter # =  3\n",
+      "working on filter # =  4\n",
+      "working on filter # =  5\n",
+      "number of original models =  510048\n",
+      "number of ast trimmed models =  510048\n",
+      "number of trimmed models =  479563\n",
+      "Writing trimmed sedgrid to disk into M31-B09-EAST_chunk/M31-B09-EAST_chunk_bin1_sub5_seds_trim.grid.hd5\n",
+      "Writing trimmed noisemodel to disk into M31-B09-EAST_chunk/M31-B09-EAST_chunk_bin1_sub5_noisemodel_trim.grid.hd5\n",
+      "working on filter # =  0\n",
+      "working on filter # =  1\n",
+      "working on filter # =  2\n",
+      "working on filter # =  3\n",
+      "working on filter # =  4\n",
+      "working on filter # =  5\n",
+      "number of original models =  510048\n",
+      "number of ast trimmed models =  510048\n",
+      "number of trimmed models =  479563\n",
+      "Writing trimmed sedgrid to disk into M31-B09-EAST_chunk/M31-B09-EAST_chunk_bin1_sub6_seds_trim.grid.hd5\n",
+      "Writing trimmed noisemodel to disk into M31-B09-EAST_chunk/M31-B09-EAST_chunk_bin1_sub6_noisemodel_trim.grid.hd5\n",
+      "working on filter # =  0\n",
+      "working on filter # =  1\n",
+      "working on filter # =  2\n",
+      "working on filter # =  3\n",
+      "working on filter # =  4\n",
+      "working on filter # =  5\n",
+      "number of original models =  510048\n",
+      "number of ast trimmed models =  510048\n",
+      "number of trimmed models =  479563\n",
+      "Writing trimmed sedgrid to disk into M31-B09-EAST_chunk/M31-B09-EAST_chunk_bin1_sub7_seds_trim.grid.hd5\n",
+      "Writing trimmed noisemodel to disk into M31-B09-EAST_chunk/M31-B09-EAST_chunk_bin1_sub7_noisemodel_trim.grid.hd5\n",
+      "working on filter # =  0\n",
+      "working on filter # =  1\n",
+      "working on filter # =  2\n",
+      "working on filter # =  3\n",
+      "working on filter # =  4\n",
+      "working on filter # =  5\n",
+      "number of original models =  510048\n",
+      "number of ast trimmed models =  510027\n",
+      "number of trimmed models =  479522\n",
+      "Writing trimmed sedgrid to disk into M31-B09-EAST_chunk/M31-B09-EAST_chunk_bin2_sub0_seds_trim.grid.hd5\n",
+      "Writing trimmed noisemodel to disk into M31-B09-EAST_chunk/M31-B09-EAST_chunk_bin2_sub0_noisemodel_trim.grid.hd5\n"
+     ]
+    }
+   ],
+   "source": [
+    "# check to see if any sub files exist yet\n",
+    "if len(glob.glob(file_dict[\"noise_trim_files\"][0].replace('bin2_sub0','bin*_sub*'))) == 0:\n",
+    "    \n",
+    "    for i, sub_files in enumerate(file_dict[\"noise_trim_files\"]):\n",
+    "        # pull out physics grid\n",
+    "        modelsedgrid = SEDGrid(model_grid_files[0])\n",
+    "        # trim for each noise file separately \n",
+    "        noisemodel_vals = noisemodel.get_noisemodelcat(noise_files[i])\n",
+    "        # read in the photometry catalog\n",
+    "        obsdata = Observations(\n",
+    "            settings.obsfile, settings.filters, obs_colnames=settings.obs_colnames\n",
+    "        )\n",
+    "                \n",
+    "        # need to iterate over all the sub-bins\n",
+    "        trim_grid.trim_models(modelsedgrid, noisemodel_vals, obsdata, file_dict[\"modelsedgrid_trim_files\"][i], file_dict[\"noise_trim_files\"][i])\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#  Step 9. Fit Models (WARNING! This step takes a while)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we're going to fit all our sources from our observational photometric catalog to our new trimmed physics and noise models. This will take quite some time just because every source has to be evaluated at each step in its physics model. \n",
+    "\n",
+    "So for every sub-bin of sources (max 6250 sources), every source in that photometry file is evaluated at every potential step in the physics grid that has been trimmed to specifically fit that sub-bin (hence the data-intensive code we ran back in Step 8). From this, we essentially get a report of how well every point in the physics model (AKA combo of parameters) matched with a source, what is often referred to as a likelihood. If we then take these likelihoods and figure out what parameter values they point back to, we can create a distribution of parameter values (metallicity, distance, Av, Rv, etc.) that best model each source. I hope that made sense (and is the correct interpretation).\n",
+    "\n",
+    "This function uses the trimmed photometric files we have, the trimmed physics models, and the trimmed noise models to create statistic files for each sub-binned source density bin.\n",
+    "\n",
+    "It'll take a long time though (~5 hours for me at least, but maybe you have a better computer (8GB RAM, for reference))."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "not using full covariance matrix\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Calculating Lnp/Stats: 100%|██████████| 6250/6250 [30:59<00:00,  3.36it/s]  \n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Done fitting on grid M31-B09-EAST_chunk/M31-B09-EAST_chunk_bin1_sub0_seds_trim.grid.hd5\n",
+      "None\n",
+      "not using full covariance matrix\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Calculating Lnp/Stats: 100%|██████████| 6250/6250 [31:30<00:00,  3.31it/s]  \n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Done fitting on grid M31-B09-EAST_chunk/M31-B09-EAST_chunk_bin1_sub1_seds_trim.grid.hd5\n",
+      "None\n",
+      "not using full covariance matrix\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Calculating Lnp/Stats: 100%|██████████| 6250/6250 [30:36<00:00,  3.40it/s]  \n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Done fitting on grid M31-B09-EAST_chunk/M31-B09-EAST_chunk_bin1_sub2_seds_trim.grid.hd5\n",
+      "None\n",
+      "not using full covariance matrix\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Calculating Lnp/Stats: 100%|██████████| 6250/6250 [37:50<00:00,  2.75it/s]  \n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Done fitting on grid M31-B09-EAST_chunk/M31-B09-EAST_chunk_bin1_sub3_seds_trim.grid.hd5\n",
+      "None\n",
+      "not using full covariance matrix\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Calculating Lnp/Stats: 100%|██████████| 6250/6250 [32:46<00:00,  3.18it/s]  \n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Done fitting on grid M31-B09-EAST_chunk/M31-B09-EAST_chunk_bin1_sub4_seds_trim.grid.hd5\n",
+      "None\n",
+      "not using full covariance matrix\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Calculating Lnp/Stats: 100%|██████████| 6250/6250 [33:11<00:00,  3.14it/s]  \n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Done fitting on grid M31-B09-EAST_chunk/M31-B09-EAST_chunk_bin1_sub5_seds_trim.grid.hd5\n",
+      "None\n",
+      "not using full covariance matrix\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Calculating Lnp/Stats: 100%|██████████| 6250/6250 [37:01<00:00,  2.81it/s]  \n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Done fitting on grid M31-B09-EAST_chunk/M31-B09-EAST_chunk_bin1_sub6_seds_trim.grid.hd5\n",
+      "None\n",
+      "not using full covariance matrix\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Calculating Lnp/Stats: 100%|██████████| 4395/4395 [16:24<00:00,  4.46it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Done fitting on grid M31-B09-EAST_chunk/M31-B09-EAST_chunk_bin1_sub7_seds_trim.grid.hd5\n",
+      "None\n",
+      "not using full covariance matrix\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Calculating Lnp/Stats: 100%|██████████| 2185/2185 [08:52<00:00,  4.10it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Done fitting on grid M31-B09-EAST_chunk/M31-B09-EAST_chunk_bin2_sub0_seds_trim.grid.hd5\n",
+      "None\n",
+      "time to fit:  256.77380966666664  min\n"
+     ]
+    }
+   ],
+   "source": [
+    "#if len(glob.glob(file_dict[\"modelsedgrid_trim_files\"][0].replace('bin2_sub0','bin*_sub*'))) == 0:\n",
+    "run_fitting.run_fitting(\n",
+    "    settings,\n",
+    "    use_sd = True,\n",
+    "    nsubs = 1,\n",
+    "    nprocs = 1,\n",
+    "    choose_sd_sub=None,\n",
+    "    choose_subgrid=None,\n",
+    "    pdf2d_param_list=['Av', 'Rv', 'f_A', 'M_ini', 'logA', 'Z', 'distance'],\n",
+    "    resume=False,\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Step 10. Merge fits"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Whoo-hoo! You finished running the big Step 9!\n",
+    "\n",
+    "We are now onto the final step where we just have to merge all the trimmed SED model results together. This should produce one final **stats.fits** file which is very similar to our original photometric file, except now all the sources have estimates for what their metallicity, distance, age, mass, dust, etc. might be.\n",
+    "\n",
+    "Using these new columns of data, we can create lots of cool visuals which will be shown in the epilogue."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "merge_files.merge_files(settings, use_sd=True, nsubs=settings.n_subgrid)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Hopefully there is now a stats.fits file in your folder. We can read it in to better understand what really happened."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<i>Table length=50330</i>\n",
+       "<table id=\"table140195742402992\" class=\"table-striped table-bordered table-condensed\">\n",
+       "<thead><tr><th>Name</th><th>RA</th><th>DEC</th><th>HST_WFC3_F475W</th><th>HST_WFC3_F275W</th><th>HST_WFC3_F336W</th><th>HST_WFC3_F814W</th><th>HST_WFC3_F110W</th><th>HST_WFC3_F160W</th><th>Av_Best</th><th>Av_Exp</th><th>Av_p16</th><th>Av_p50</th><th>Av_p84</th><th>Rv_Best</th><th>Rv_Exp</th><th>Rv_p16</th><th>Rv_p50</th><th>Rv_p84</th><th>Rv_A_Best</th><th>Rv_A_Exp</th><th>Rv_A_p16</th><th>Rv_A_p50</th><th>Rv_A_p84</th><th>f_A_Best</th><th>f_A_Exp</th><th>f_A_p16</th><th>f_A_p50</th><th>f_A_p84</th><th>distance_Best</th><th>distance_Exp</th><th>distance_p16</th><th>distance_p50</th><th>distance_p84</th><th>radius_Best</th><th>radius_Exp</th><th>radius_p16</th><th>radius_p50</th><th>radius_p84</th><th>logL_Best</th><th>logL_Exp</th><th>logL_p16</th><th>logL_p50</th><th>logL_p84</th><th>logg_Best</th><th>logg_Exp</th><th>logg_p16</th><th>logg_p50</th><th>logg_p84</th><th>mbolmag_Best</th><th>mbolmag_Exp</th><th>mbolmag_p16</th><th>mbolmag_p50</th><th>mbolmag_p84</th><th>logA_Best</th><th>logA_Exp</th><th>logA_p16</th><th>logA_p50</th><th>logA_p84</th><th>logT_Best</th><th>logT_Exp</th><th>logT_p16</th><th>logT_p50</th><th>logT_p84</th><th>M_ini_Best</th><th>M_ini_Exp</th><th>M_ini_p16</th><th>M_ini_p50</th><th>M_ini_p84</th><th>M_act_Best</th><th>M_act_Exp</th><th>M_act_p16</th><th>M_act_p50</th><th>M_act_p84</th><th>Z_Best</th><th>Z_Exp</th><th>Z_p16</th><th>Z_p50</th><th>Z_p84</th><th>logHST_WFC3_F475W_nd_Best</th><th>logHST_WFC3_F475W_nd_Exp</th><th>logHST_WFC3_F475W_nd_p16</th><th>logHST_WFC3_F475W_nd_p50</th><th>logHST_WFC3_F475W_nd_p84</th><th>logHST_WFC3_F275W_nd_Best</th><th>logHST_WFC3_F275W_nd_Exp</th><th>logHST_WFC3_F275W_nd_p16</th><th>logHST_WFC3_F275W_nd_p50</th><th>logHST_WFC3_F275W_nd_p84</th><th>logHST_WFC3_F336W_nd_Best</th><th>logHST_WFC3_F336W_nd_Exp</th><th>logHST_WFC3_F336W_nd_p16</th><th>logHST_WFC3_F336W_nd_p50</th><th>logHST_WFC3_F336W_nd_p84</th><th>logHST_WFC3_F814W_nd_Best</th><th>logHST_WFC3_F814W_nd_Exp</th><th>logHST_WFC3_F814W_nd_p16</th><th>logHST_WFC3_F814W_nd_p50</th><th>logHST_WFC3_F814W_nd_p84</th><th>logHST_WFC3_F110W_nd_Best</th><th>logHST_WFC3_F110W_nd_Exp</th><th>logHST_WFC3_F110W_nd_p16</th><th>logHST_WFC3_F110W_nd_p50</th><th>logHST_WFC3_F110W_nd_p84</th><th>logHST_WFC3_F160W_nd_Best</th><th>logHST_WFC3_F160W_nd_Exp</th><th>logHST_WFC3_F160W_nd_p16</th><th>logHST_WFC3_F160W_nd_p50</th><th>logHST_WFC3_F160W_nd_p84</th><th>logHST_WFC3_F475W_wd_Best</th><th>logHST_WFC3_F475W_wd_Exp</th><th>logHST_WFC3_F475W_wd_p16</th><th>logHST_WFC3_F475W_wd_p50</th><th>logHST_WFC3_F475W_wd_p84</th><th>logHST_WFC3_F275W_wd_Best</th><th>logHST_WFC3_F275W_wd_Exp</th><th>logHST_WFC3_F275W_wd_p16</th><th>logHST_WFC3_F275W_wd_p50</th><th>logHST_WFC3_F275W_wd_p84</th><th>logHST_WFC3_F336W_wd_Best</th><th>logHST_WFC3_F336W_wd_Exp</th><th>logHST_WFC3_F336W_wd_p16</th><th>logHST_WFC3_F336W_wd_p50</th><th>logHST_WFC3_F336W_wd_p84</th><th>logHST_WFC3_F814W_wd_Best</th><th>logHST_WFC3_F814W_wd_Exp</th><th>logHST_WFC3_F814W_wd_p16</th><th>logHST_WFC3_F814W_wd_p50</th><th>logHST_WFC3_F814W_wd_p84</th><th>logHST_WFC3_F110W_wd_Best</th><th>logHST_WFC3_F110W_wd_Exp</th><th>logHST_WFC3_F110W_wd_p16</th><th>logHST_WFC3_F110W_wd_p50</th><th>logHST_WFC3_F110W_wd_p84</th><th>logHST_WFC3_F160W_wd_Best</th><th>logHST_WFC3_F160W_wd_Exp</th><th>logHST_WFC3_F160W_wd_p16</th><th>logHST_WFC3_F160W_wd_p50</th><th>logHST_WFC3_F160W_wd_p84</th><th>symlogHST_WFC3_F475W_wd_bias_Best</th><th>symlogHST_WFC3_F475W_wd_bias_Exp</th><th>symlogHST_WFC3_F475W_wd_bias_p16</th><th>symlogHST_WFC3_F475W_wd_bias_p50</th><th>symlogHST_WFC3_F475W_wd_bias_p84</th><th>symlogHST_WFC3_F275W_wd_bias_Best</th><th>symlogHST_WFC3_F275W_wd_bias_Exp</th><th>symlogHST_WFC3_F275W_wd_bias_p16</th><th>symlogHST_WFC3_F275W_wd_bias_p50</th><th>symlogHST_WFC3_F275W_wd_bias_p84</th><th>symlogHST_WFC3_F336W_wd_bias_Best</th><th>symlogHST_WFC3_F336W_wd_bias_Exp</th><th>symlogHST_WFC3_F336W_wd_bias_p16</th><th>symlogHST_WFC3_F336W_wd_bias_p50</th><th>symlogHST_WFC3_F336W_wd_bias_p84</th><th>symlogHST_WFC3_F814W_wd_bias_Best</th><th>symlogHST_WFC3_F814W_wd_bias_Exp</th><th>symlogHST_WFC3_F814W_wd_bias_p16</th><th>symlogHST_WFC3_F814W_wd_bias_p50</th><th>symlogHST_WFC3_F814W_wd_bias_p84</th><th>symlogHST_WFC3_F110W_wd_bias_Best</th><th>symlogHST_WFC3_F110W_wd_bias_Exp</th><th>symlogHST_WFC3_F110W_wd_bias_p16</th><th>symlogHST_WFC3_F110W_wd_bias_p50</th><th>symlogHST_WFC3_F110W_wd_bias_p84</th><th>symlogHST_WFC3_F160W_wd_bias_Best</th><th>symlogHST_WFC3_F160W_wd_bias_Exp</th><th>symlogHST_WFC3_F160W_wd_bias_p16</th><th>symlogHST_WFC3_F160W_wd_bias_p50</th><th>symlogHST_WFC3_F160W_wd_bias_p84</th><th>chi2min</th><th>chi2min_indx</th><th>Pmax</th><th>Pmax_indx</th><th>specgrid_indx</th><th>total_log_norm</th><th>reorder_tag</th></tr></thead>\n",
+       "<thead><tr><th>str29</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>int64</th><th>float64</th><th>int64</th><th>int64</th><th>float64</th><th>str9</th></tr></thead>\n",
+       "<tr><td>PHAT-M31 J004434.89+413625.12</td><td>11.145373458361576</td><td>41.606976807232215</td><td>-4.965085488981442e-20</td><td>-1.1580466157434235e-20</td><td>3.0067137017991655e-22</td><td>-2.1986753982029354e-20</td><td>1.2985999674533866e-19</td><td>1.1350672160211563e-19</td><td>10.01</td><td>9.6147028365746</td><td>8.803971876957085</td><td>9.633345875059875</td><td>10.01</td><td>5.0</td><td>4.15610815900023</td><td>2.9470563287300626</td><td>3.99631933634331</td><td>5.518911563200504</td><td>5.0</td><td>4.400131427022444</td><td>3.2729558074951255</td><td>4.292591613135361</td><td>5.492596254492806</td><td>1.0</td><td>0.8386394657004366</td><td>0.5624771585516791</td><td>0.8786002002062236</td><td>1.0</td><td>907820.5301781861</td><td>907820.5301781861</td><td>907820.5301781861</td><td>907820.5301781861</td><td>907820.5301781861</td><td>0.23466761574442707</td><td>0.20088624102576708</td><td>0.164978874224777</td><td>0.196868547782103</td><td>0.23991052484145398</td><td>-2.5</td><td>-2.5324073131920644</td><td>-2.7395886068757656</td><td>-2.5505911854452923</td><td>-2.3511453557673563</td><td>4.996</td><td>5.075182601388402</td><td>5.006027025857834</td><td>5.089234226836528</td><td>5.149242798408192</td><td>11.021</td><td>11.101304284929181</td><td>10.646891235179918</td><td>11.145495800048769</td><td>11.617979868659509</td><td>10.0</td><td>9.92979513139387</td><td>9.250047883341699</td><td>9.930735932478457</td><td>10.0</td><td>3.4511</td><td>3.479714727591035</td><td>3.4478479014572123</td><td>3.4717989270436296</td><td>3.5103708547082766</td><td>0.200000003</td><td>0.17464217887475672</td><td>0.13706192664293837</td><td>0.1701683625291959</td><td>0.2151138089316248</td><td>0.2</td><td>0.17465186950587952</td><td>0.13663289796308958</td><td>0.1693616035872862</td><td>0.2137203269558849</td><td>0.03</td><td>0.012000218954929899</td><td>0.0042185955396098745</td><td>0.011218571887272155</td><td>0.02086285721502063</td><td>-23.13774595506965</td><td>-23.25669397757423</td><td>-23.426177728552616</td><td>-23.24239306043068</td><td>-23.10187340671592</td><td>-25.545443154181353</td><td>-25.68398611494468</td><td>-25.834568245806093</td><td>-25.669692850826</td><td>-25.523598937950755</td><td>-24.06236610673095</td><td>-24.241413851766414</td><td>-24.39528403252697</td><td>-24.221681313227254</td><td>-24.07927341863134</td><td>-22.620593928818764</td><td>-22.7653418305239</td><td>-22.920470113405358</td><td>-22.765048238395092</td><td>-22.616815708033755</td><td>-22.695360677390212</td><td>-22.856903085594222</td><td>-23.016451418597224</td><td>-22.867399380104544</td><td>-22.695202608140765</td><td>-22.911719242848285</td><td>-23.068969791564648</td><td>-23.22000837264263</td><td>-23.08086372987864</td><td>-22.905460918919303</td><td>-27.834943161572397</td><td>-27.810388364185215</td><td>-28.085749095641162</td><td>-27.822433632174732</td><td>-27.526224199860913</td><td>-28.32346989371389</td><td>-28.228437021192022</td><td>-28.38771171845263</td><td>-28.229518437675306</td><td>-28.077717947737643</td><td>-29.130206062775017</td><td>-29.053370805913936</td><td>-29.18136050547936</td><td>-29.055307829066408</td><td>-28.91678747626044</td><td>-25.321932791794268</td><td>-25.20936553651759</td><td>-25.369792920823315</td><td>-25.213605755724537</td><td>-25.066315042744566</td><td>-24.367775608152236</td><td>-24.33182668648592</td><td>-24.44660096325723</td><td>-24.329558863372238</td><td>-24.218964445595475</td><td>-23.97796360199281</td><td>-24.009821970659175</td><td>-24.09967766795035</td><td>-24.00684394451492</td><td>-23.921868906896638</td><td>2.620652624711636e-20</td><td>2.6603358843773592e-20</td><td>8.046763201577162e-22</td><td>8.046763201577162e-22</td><td>3.3627885294562747e-18</td><td>-5.57879831736645e-20</td><td>-5.2867285157920657e-20</td><td>-6.163796558180469e-20</td><td>-6.163796558180469e-20</td><td>3.2660043340192805e-18</td><td>-2.933874368211007e-21</td><td>-2.5818859204511595e-21</td><td>-9.272995742758861e-21</td><td>-9.272995742758861e-21</td><td>3.290303327229136e-18</td><td>2.1065405761576962e-20</td><td>1.9770581396192653e-20</td><td>6.554719376325041e-21</td><td>6.554719376325041e-21</td><td>9.537230712447546e-19</td><td>1.8528095409103405e-20</td><td>1.7683149909195976e-20</td><td>4.003441960824768e-21</td><td>4.003441960824768e-21</td><td>4.640117069537074e-19</td><td>7.226853195230868e-21</td><td>7.15148035963646e-21</td><td>2.3426634336850777e-21</td><td>2.3426634336850777e-21</td><td>2.217820155445873e-19</td><td>8.761253454972268</td><td>472638</td><td>248.9825386966303</td><td>470891</td><td>1632</td><td>252.55330449639095</td><td>bin1_sub0</td></tr>\n",
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+       "<tr><td>PHAT-M31 J004434.12+413556.83</td><td>11.142166807061688</td><td>41.59911895022069</td><td>-4.0671444962933085e-20</td><td>-8.591958761967334e-20</td><td>-3.579421073570435e-20</td><td>-1.5704824272878112e-20</td><td>2.2950100053735948e-20</td><td>1.3268892487159643e-19</td><td>10.01</td><td>9.51210207510154</td><td>8.60704449871585</td><td>9.54904811751551</td><td>10.01</td><td>5.0</td><td>4.034288329788117</td><td>2.7331097704867124</td><td>3.858175484365248</td><td>5.459231312760148</td><td>5.0</td><td>4.283448009966403</td><td>3.1330184845534435</td><td>4.180924083461053</td><td>5.453103046720438</td><td>1.0</td><td>0.8258738334439514</td><td>0.5333699178166812</td><td>0.8710890241477852</td><td>1.0</td><td>907820.5301781861</td><td>907820.530178186</td><td>907820.5301781861</td><td>907820.5301781861</td><td>907820.5301781861</td><td>0.23466761574442707</td><td>0.2919990314167594</td><td>0.16399432323857444</td><td>0.19587163588696868</td><td>0.23757732739569098</td><td>-2.5</td><td>-2.5298011562555662</td><td>-2.7479657955963384</td><td>-2.556287979808336</td><td>-2.3594230411683608</td><td>4.996</td><td>5.069670102976122</td><td>5.011860100300384</td><td>5.0910818010225</td><td>5.15005535399962</td><td>11.021</td><td>11.094779903902399</td><td>10.667585033261103</td><td>11.15973750005884</td><td>11.63892242004597</td><td>10.0</td><td>9.930267777861063</td><td>9.250511680230355</td><td>9.93117438484433</td><td>10.0</td><td>3.4511</td><td>3.4795715660030107</td><td>3.447671848906505</td><td>3.4713571031377257</td><td>3.5104743630378064</td><td>0.200000003</td><td>0.17467332600382904</td><td>0.13601591187686207</td><td>0.16928878215872428</td><td>0.2132043056705011</td><td>0.2</td><td>0.17462770551991752</td><td>0.13559784980846293</td><td>0.16849264480153867</td><td>0.21183739986490682</td><td>0.03</td><td>0.01181214955333284</td><td>0.004228911237587416</td><td>0.011142282311204767</td><td>0.020372253832376078</td><td>-23.13774595506965</td><td>-23.25278816583737</td><td>-23.430618362177967</td><td>-23.24731186580484</td><td>-23.108801315623754</td><td>-25.545443154181353</td><td>-25.680744436351162</td><td>-25.840406310772167</td><td>-25.673097302247037</td><td>-25.52983640281954</td><td>-24.06236610673095</td><td>-24.238512943209653</td><td>-24.397335614239207</td><td>-24.225525341657367</td><td>-24.08852029080427</td><td>-22.620593928818764</td><td>-22.761941951583893</td><td>-22.9258215251968</td><td>-22.76914661754738</td><td>-22.626520802233458</td><td>-22.695360677390212</td><td>-22.85359866476925</td><td>-23.01882548981312</td><td>-22.872280332305788</td><td>-22.702840722719245</td><td>-22.911719242848285</td><td>-23.06541420651609</td><td>-23.222387472163998</td><td>-23.08484735602558</td><td>-22.916747799744254</td><td>-27.834943161572397</td><td>-27.766974188375723</td><td>-28.06329336523064</td><td>-27.783955991215002</td><td>-27.478589740267076</td><td>-28.32346989371389</td><td>-28.174281507515985</td><td>-28.36660157415402</td><td>-28.19457005612314</td><td>-27.999276480085925</td><td>-29.130206062775017</td><td>-29.001975605786683</td><td>-29.167199423191914</td><td>-29.01735029851868</td><td>-28.849347870571762</td><td>-25.321932791794268</td><td>-25.151310121748047</td><td>-25.349160656078954</td><td>-25.17692334191256</td><td>-24.97088545382576</td><td>-24.367775608152236</td><td>-24.288668159547203</td><td>-24.431480298091255</td><td>-24.301923856606145</td><td>-24.169718094403613</td><td>-23.97796360199281</td><td>-23.980602175092937</td><td>-24.070084007231408</td><td>-23.988110992388876</td><td>-23.905037985091766</td><td>2.620652624711636e-20</td><td>2.6763065733453112e-20</td><td>8.046763201577162e-22</td><td>8.046763201577162e-22</td><td>3.3627885294562747e-18</td><td>-5.57879831736645e-20</td><td>-5.181120458992231e-20</td><td>-6.163796558180469e-20</td><td>-6.163796558180469e-20</td><td>3.2660043340192805e-18</td><td>-2.933874368211007e-21</td><td>-2.4650747335533e-21</td><td>-9.272995742758861e-21</td><td>-9.272995742758861e-21</td><td>3.290303327229136e-18</td><td>2.1065405761576962e-20</td><td>1.9341085304807968e-20</td><td>6.554719376325041e-21</td><td>6.554719376325041e-21</td><td>9.537230712447546e-19</td><td>1.8528095409103405e-20</td><td>1.6897446818726072e-20</td><td>4.003441960824768e-21</td><td>4.003441960824768e-21</td><td>4.640117069537074e-19</td><td>7.226853195230868e-21</td><td>7.393943518367311e-21</td><td>2.3426634336850777e-21</td><td>2.343221060407266e-21</td><td>2.2178257317130958e-19</td><td>10.183078530792898</td><td>472638</td><td>248.25260580910094</td><td>470891</td><td>1632</td><td>251.93226671341878</td><td>bin1_sub0</td></tr>\n",
+       "<tr><td>PHAT-M31 J004435.17+413547.12</td><td>11.146526354983829</td><td>41.596420911040575</td><td>-3.486123853965693e-20</td><td>3.810346929220296e-20</td><td>-8.135047894478264e-21</td><td>-2.30337422668879e-20</td><td>2.2092534037493143e-19</td><td>8.321903222925122e-20</td><td>10.01</td><td>9.682059080842725</td><td>9.005906701584822</td><td>9.699478177185183</td><td>10.01</td><td>6.0</td><td>4.725676455374129</td><td>3.4308887586739214</td><td>4.7523700958855795</td><td>5.953842038630127</td><td>6.0</td><td>4.868782732904655</td><td>3.806749499182859</td><td>4.8273086259347915</td><td>5.955732135674026</td><td>1.0</td><td>0.9237039855567227</td><td>0.7600816216527811</td><td>0.9394520542519517</td><td>1.0</td><td>907820.5301781861</td><td>907820.5301781863</td><td>907820.5301781861</td><td>907820.5301781861</td><td>907820.5301781861</td><td>0.23466761574442707</td><td>0.20582564065039463</td><td>0.1663463858001396</td><td>0.2042723578546502</td><td>0.24633652996726527</td><td>-2.5</td><td>-2.5077096352360897</td><td>-2.7301985362889747</td><td>-2.527672823672902</td><td>-2.3076761350775796</td><td>4.996</td><td>5.067265533156791</td><td>4.994874634985044</td><td>5.077377109156543</td><td>5.142784241196336</td><td>11.021</td><td>11.039521253058213</td><td>10.538220364988183</td><td>11.088201045791525</td><td>11.594505163439676</td><td>10.0</td><td>9.928434875314135</td><td>9.248716542656494</td><td>9.929487504432942</td><td>10.0</td><td>3.4511</td><td>3.4807807921471214</td><td>3.4480277308880156</td><td>3.4724452036568088</td><td>3.515187294757263</td><td>0.200000003</td><td>0.1801372889582057</td><td>0.13855000340859883</td><td>0.1819071027703024</td><td>0.22066810144777113</td><td>0.2</td><td>0.18014586803178906</td><td>0.13810483540480928</td><td>0.18095466630932083</td><td>0.21919731569049128</td><td>0.03</td><td>0.012490206646846093</td><td>0.0042400398795919515</td><td>0.01154492287787029</td><td>0.021837568965023132</td><td>-23.13774595506965</td><td>-23.236208502079496</td><td>-23.4202578387161</td><td>-23.21476082937647</td><td>-23.082205975919074</td><td>-25.545443154181353</td><td>-25.663551296726972</td><td>-25.829453334419647</td><td>-25.649925346174385</td><td>-25.503239231660714</td><td>-24.06236610673095</td><td>-24.22101202067921</td><td>-24.38590703626952</td><td>-24.200107904405318</td><td>-24.056332221545947</td><td>-22.620593928818764</td><td>-22.744910367828986</td><td>-22.912703420379295</td><td>-22.739831262693258</td><td>-22.59438199396802</td><td>-22.695360677390212</td><td>-22.836487872625046</td><td>-23.011397203267123</td><td>-22.828080020789603</td><td>-22.672083599695043</td><td>-22.911719242848285</td><td>-23.048527307659665</td><td>-23.21478567251096</td><td>-23.056565524210736</td><td>-22.882259646250535</td><td>-27.794816889953587</td><td>-27.791446014666086</td><td>-28.047666213790947</td><td>-27.789456657928117</td><td>-27.527539431310746</td><td>-28.417147821037332</td><td>-28.3190382212256</td><td>-28.441257202929556</td><td>-28.319609112617478</td><td>-28.200217246264963</td><td>-29.151003196156786</td><td>-29.112435027098794</td><td>-29.25582538256838</td><td>-29.113796879904783</td><td>-28.96726696086683</td><td>-25.417495918451078</td><td>-25.299947499272157</td><td>-25.429878301019787</td><td>-25.31221851816217</td><td>-25.188227948301293</td><td>-24.446414207995932</td><td>-24.400673853761926</td><td>-24.501642695764268</td><td>-24.40759870855031</td><td>-24.309552155073654</td><td>-24.02854155963452</td><td>-24.04530240752171</td><td>-24.140120718933915</td><td>-24.0425622208245</td><td>-23.945217945851297</td><td>2.6456471513760418e-20</td><td>2.6694095911515427e-20</td><td>8.046763201577162e-22</td><td>8.046763201577162e-22</td><td>3.3627885294562747e-18</td><td>-5.791772597931472e-20</td><td>-5.549111451853164e-20</td><td>-6.163796558180469e-20</td><td>-6.163796558180469e-20</td><td>3.2660043340192805e-18</td><td>-3.0067868742210288e-21</td><td>-2.845661507787597e-21</td><td>-9.272995742758861e-21</td><td>-9.272995742758861e-21</td><td>3.290303327229136e-18</td><td>2.1810058339302108e-20</td><td>2.0741380564588302e-20</td><td>6.554719376325041e-21</td><td>6.554719376325041e-21</td><td>9.537230712447546e-19</td><td>2.02364026164042e-20</td><td>1.9268555160712393e-20</td><td>4.003441960824768e-21</td><td>4.003441960824768e-21</td><td>4.640117069537074e-19</td><td>7.14750279036335e-21</td><td>7.09057915386428e-21</td><td>2.3426634336850777e-21</td><td>2.3426634336850777e-21</td><td>2.217820155445873e-19</td><td>15.437377190233313</td><td>472638</td><td>245.6220564999016</td><td>476043</td><td>1632</td><td>248.65270403855982</td><td>bin1_sub0</td></tr>\n",
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+       "<tr><td>PHAT-M31 J004434.59+413626.14</td><td>11.144140816897673</td><td>41.60725978983544</td><td>-3.3276636787854345e-20</td><td>-6.724141639800522e-20</td><td>-1.496848812584e-20</td><td>4.8894352902893846e-21</td><td>2.1234968021250342e-19</td><td>3.504718341717166e-20</td><td>9.01</td><td>9.156662923220065</td><td>8.295131683079791</td><td>9.407965799087021</td><td>10.01</td><td>5.0</td><td>4.155186912117212</td><td>2.920722571845155</td><td>4.029027648706853</td><td>5.529976356212372</td><td>5.0</td><td>4.388180362640304</td><td>3.247279300621499</td><td>4.310068631691301</td><td>5.496666262719759</td><td>1.0</td><td>0.8498631252209264</td><td>0.5840057090829444</td><td>0.8868903096791192</td><td>1.0</td><td>907820.5301781861</td><td>907820.530178186</td><td>907820.5301781861</td><td>907820.5301781861</td><td>907820.5301781861</td><td>0.19442547151397477</td><td>0.20353550168585804</td><td>0.14958971075193236</td><td>0.19178551518160564</td><td>0.25283575366371713</td><td>-2.675</td><td>-2.533645666880823</td><td>-2.8039491912527534</td><td>-2.5711834498100026</td><td>-2.2795806941577244</td><td>5.062</td><td>5.077008173504564</td><td>4.987202018133861</td><td>5.096047587768213</td><td>5.165697998649285</td><td>11.459</td><td>11.104275535452391</td><td>10.467983172677549</td><td>11.196975427523611</td><td>11.778878099621835</td><td>10.0</td><td>9.92970869099923</td><td>9.249955980982012</td><td>9.930629394420764</td><td>10.0</td><td>3.4482</td><td>3.479972759809645</td><td>3.4480121224116407</td><td>3.4723293089085283</td><td>3.5104311003654605</td><td>0.1599999964</td><td>0.17879167693640274</td><td>0.12218447815535725</td><td>0.1654851924792045</td><td>0.22511464960379418</td><td>0.16</td><td>0.17879490482053836</td><td>0.12190686299536373</td><td>0.1647349862857795</td><td>0.2235819774791217</td><td>0.019</td><td>0.011533677926456175</td><td>0.004001980935946527</td><td>0.010600939444130687</td><td>0.020475669576514588</td><td>-23.25861830973603</td><td>-23.257409062544323</td><td>-23.48706790263624</td><td>-23.26811580132508</td><td>-23.066722830090868</td><td>-25.695046113429015</td><td>-25.68383910353229</td><td>-25.915266707404044</td><td>-25.690257472724838</td><td>-25.492126699436255</td><td>-24.266891015939997</td><td>-24.240905041646574</td><td>-24.454934596816543</td><td>-24.2443549472002</td><td>-24.039528775824234</td><td>-22.77997149289437</td><td>-22.766838993813515</td><td>-23.008426566902216</td><td>-22.785012296612333</td><td>-22.57688881373224</td><td>-22.879078567083837</td><td>-22.85831886449142</td><td>-23.078486837273207</td><td>-22.893074708494453</td><td>-22.64692653286278</td><td>-23.091814461069564</td><td>-23.069973007534994</td><td>-23.28250970875648</td><td>-23.100124889193168</td><td>-22.8596725204465</td><td>-27.483498288464</td><td>-27.598814871946637</td><td>-28.048367152140244</td><td>-27.72588357870675</td><td>-27.38548411276559</td><td>-28.27964833231166</td><td>-28.132150466333577</td><td>-28.378513328450865</td><td>-28.226039066794108</td><td>-28.05949091170089</td><td>-29.036882080938458</td><td>-28.909719506973826</td><td>-29.176082682977775</td><td>-29.035690713885312</td><td>-28.860311903753807</td><td>-25.228890105632598</td><td>-25.09578709586289</td><td>-25.348032360450997</td><td>-25.201640397201455</td><td>-25.027464014971446</td><td>-24.397879832151634</td><td>-24.26536247810059</td><td>-24.452528901704117</td><td>-24.336714753734675</td><td>-24.207557600111933</td><td>-24.052957820478635</td><td>-23.96530749590479</td><td>-24.134000932874706</td><td>-24.020766484752706</td><td>-23.88419741678865</td><td>2.864350222104931e-20</td><td>2.6789649046560326e-20</td><td>8.046763201577162e-22</td><td>8.046763201577162e-22</td><td>3.3627885294562747e-18</td><td>-5.462259638078805e-20</td><td>-5.2284131592382313e-20</td><td>-6.163796558180469e-20</td><td>-6.163796558180469e-20</td><td>3.2660043340192805e-18</td><td>-2.560068482862491e-21</td><td>-2.7643247988416314e-21</td><td>-9.272995742758861e-21</td><td>-9.272995742758861e-21</td><td>3.290303327229136e-18</td><td>2.0164658560331052e-20</td><td>1.9707609282519504e-20</td><td>6.554719376325041e-21</td><td>6.554719376325041e-21</td><td>9.537230712447546e-19</td><td>1.911430327850164e-20</td><td>1.832085907235938e-20</td><td>4.003441960824768e-21</td><td>4.003441960824768e-21</td><td>4.640117069537074e-19</td><td>7.092345726758225e-21</td><td>7.165302142896062e-21</td><td>2.3426634336850777e-21</td><td>2.3426634336850777e-21</td><td>2.217820155445873e-19</td><td>4.075939005194076</td><td>463434</td><td>251.5667337795519</td><td>426238</td><td>2770</td><td>255.54394447136863</td><td>bin1_sub0</td></tr>\n",
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+       "<tr><td>PHAT-M31 J004433.85+413617.50</td><td>11.141049820932293</td><td>41.604859963217116</td><td>2.2501344875596745e-18</td><td>2.8353463914492203e-18</td><td>2.3851960426610268e-18</td><td>4.460170093497384e-19</td><td>1.3026836151497805e-19</td><td>5.4085159594400715e-20</td><td>1.01</td><td>0.3317667311344631</td><td>0.01</td><td>0.331766731134463</td><td>1.0154907203371644</td><td>3.0</td><td>3.506349684116872</td><td>2.374221726435154</td><td>3.332983077249541</td><td>4.707732361004791</td><td>4.193877551020409</td><td>3.920029072353699</td><td>2.9702584072219453</td><td>3.8084263918816297</td><td>4.781781233172034</td><td>0.25</td><td>0.6846540857137756</td><td>0.2945706074612343</td><td>0.7395218144181157</td><td>1.0</td><td>907820.5301781861</td><td>907820.5301781861</td><td>907820.5301781861</td><td>907820.5301781861</td><td>907820.5301781861</td><td>4.736452222639006</td><td>4.756827478860122</td><td>4.375551145959134</td><td>4.6465184348341415</td><td>4.941606016207893</td><td>3.746</td><td>3.0182388473938664</td><td>2.6029829795649544</td><td>2.713343405677133</td><td>3.7379676622883915</td><td>4.049</td><td>3.827933757240959</td><td>3.684788339136737</td><td>3.7458352498659697</td><td>4.033867710410044</td><td>-4.595</td><td>-2.775930092573786</td><td>-4.575585723639159</td><td>-2.014076503583291</td><td>-1.738180976816652</td><td>7.0</td><td>6.323320317698655</td><td>6.0</td><td>6.322267401439416</td><td>7.010319021133562</td><td>4.3601</td><td>4.177301054808669</td><td>4.082469635716945</td><td>4.097801167469977</td><td>4.364065463023494</td><td>9.2224760056</td><td>5.951766490461266</td><td>4.362454375293116</td><td>4.74293367031203</td><td>8.898807554469194</td><td>9.214</td><td>5.94815361941521</td><td>4.238081996401148</td><td>4.604843901021743</td><td>9.326790379999082</td><td>0.03</td><td>0.029899073433966687</td><td>0.025496357867906102</td><td>0.02995021301661077</td><td>0.03</td><td>-17.2036019545691</td><td>-17.523242671565864</td><td>-17.686283787687046</td><td>-17.59109815583018</td><td>-17.18453438783237</td><td>-16.561108486869415</td><td>-17.204238719759704</td><td>-17.522919152335707</td><td>-17.39533089882836</td><td>-16.566379176762446</td><td>-16.83781176769705</td><td>-17.374713984080604</td><td>-17.696454694778822</td><td>-17.586365873398858</td><td>-16.869765743604983</td><td>-18.070120924744373</td><td>-18.323825726119207</td><td>-18.516988549126026</td><td>-18.435080621821182</td><td>-18.064781491513138</td><td>-18.573292267636333</td><td>-18.790891846787243</td><td>-18.960853813915236</td><td>-18.882251439019406</td><td>-18.60522423815049</td><td>-19.06829357691101</td><td>-19.271681732663154</td><td>-19.412669943616375</td><td>-19.332744851549013</td><td>-19.05090250414086</td><td>-17.74570185718472</td><td>-17.701039596375658</td><td>-17.7841469611142</td><td>-17.694126291166373</td><td>-17.604093010965588</td><td>-17.502870041737967</td><td>-17.51216365795825</td><td>-17.567394534848166</td><td>-17.47563552334717</td><td>-17.38342621594936</td><td>-17.60227737640115</td><td>-17.62365285762787</td><td>-17.705202019361863</td><td>-17.60796518650875</td><td>-17.51072835365563</td><td>-18.325687580182688</td><td>-18.408144677752286</td><td>-18.4900622056549</td><td>-18.405772812545084</td><td>-18.337579236943057</td><td>-18.72720207783541</td><td>-18.842007400052022</td><td>-18.9341195231081</td><td>-18.84831291103509</td><td>-18.76954070152175</td><td>-19.156285092383673</td><td>-19.300744442695578</td><td>-19.40020488747964</td><td>-19.320548565651354</td><td>-19.138921712048422</td><td>1.8159276483868584e-18</td><td>2.0150601993139923e-18</td><td>5.568236162948299e-21</td><td>5.568236162948299e-21</td><td>3.3652922223000045e-18</td><td>3.015421856880924e-18</td><td>2.951140432406718e-18</td><td>1.5195600968645958e-18</td><td>4.848085212963767e-18</td><td>8.176610329062937e-18</td><td>2.456857547385053e-18</td><td>2.3394218128802116e-18</td><td>-1.5459207662868333e-20</td><td>1.4195389308813359e-18</td><td>4.72085538086417e-18</td><td>4.741721903475319e-19</td><td>3.9729239985341e-19</td><td>-9.013322236632383e-22</td><td>-9.013322236632383e-22</td><td>9.461362678470826e-19</td><td>1.9727673744044356e-19</td><td>1.5547850682206438e-19</td><td>1.063361511649631e-20</td><td>1.063361511649631e-20</td><td>4.716327151946377e-19</td><td>6.736135616209425e-20</td><td>4.9451515573819067e-20</td><td>5.5365268543121086e-21</td><td>5.5365268543121086e-21</td><td>2.246326101004151e-19</td><td>24.11219965189775</td><td>34939</td><td>220.4078669075165</td><td>43366</td><td>140</td><td>222.34498720466135</td><td>bin2_sub0</td></tr>\n",
+       "<tr><td>PHAT-M31 J004433.71+413621.25</td><td>11.140455759788615</td><td>41.60590349305397</td><td>2.461414721133353e-18</td><td>4.706899147860366e-18</td><td>3.254019157791305e-18</td><td>5.329170369929972e-19</td><td>1.9928200758404172e-19</td><td>9.648792471641086e-20</td><td>1.01</td><td>0.8591437641189078</td><td>0.17914376411890784</td><td>0.8591437641189077</td><td>1.6331497968638562</td><td>3.0</td><td>3.0557571445713108</td><td>2.30784678020247</td><td>3.0239076547759964</td><td>3.7397692572961585</td><td>4.193877551020409</td><td>3.9753253874031325</td><td>3.5034162568653384</td><td>3.890621727617047</td><td>4.325732075514855</td><td>0.25</td><td>0.375141326742015</td><td>0.25</td><td>0.33070177578223187</td><td>0.6226181903532262</td><td>907820.5301781861</td><td>907820.5301781859</td><td>907820.5301781861</td><td>907820.5301781861</td><td>907820.5301781861</td><td>4.666090533030073</td><td>4.802161513787831</td><td>4.298223316005483</td><td>4.770425585615158</td><td>5.2290550966541325</td><td>4.019</td><td>3.812192176986849</td><td>3.8423830942804775</td><td>3.9820758882240654</td><td>4.07765679033613</td><td>4.134</td><td>4.046623782119235</td><td>3.9249177301212397</td><td>4.10084620107723</td><td>4.192502940983499</td><td>-5.279</td><td>-4.761240375848287</td><td>-5.424791496226234</td><td>-5.185844037739314</td><td>-4.836619063459182</td><td>7.0</td><td>7.148987222239397</td><td>6.3746491463532164</td><td>7.1492662028086915</td><td>7.830539511917137</td><td>4.4316</td><td>4.374208569845005</td><td>4.364607597829985</td><td>4.42223515105744</td><td>4.445332989245076</td><td>10.8878173828</td><td>9.722845619301047</td><td>9.677377563672453</td><td>10.425410353753021</td><td>11.160492972446804</td><td>10.892</td><td>9.723919116624614</td><td>9.37262316750705</td><td>10.523065030156612</td><td>11.386931584519889</td><td>0.008</td><td>0.01730590347472201</td><td>0.0048490958904622355</td><td>0.015567950054977273</td><td>0.03</td><td>-17.10118178266259</td><td>-17.17218885271334</td><td>-17.249680125730944</td><td>-17.11232467461281</td><td>-17.02304364136878</td><td>-16.384064058334438</td><td>-16.534035686492377</td><td>-16.563729026434462</td><td>-16.462043763926403</td><td>-16.360364699869308</td><td>-16.684019733913807</td><td>-16.807470758546156</td><td>-16.75843487956655</td><td>-16.670504066970217</td><td>-16.58209942989559</td><td>-17.984063466922585</td><td>-18.038116570340105</td><td>-18.063831670170178</td><td>-17.99843803839355</td><td>-17.933044406616926</td><td>-18.496920565293824</td><td>-18.541637049129505</td><td>-18.603356795897692</td><td>-18.51257878435731</td><td>-18.44982423873713</td><td>-18.998033346216502</td><td>-19.038463163564955</td><td>-19.125650354267343</td><td>-19.009893297851608</td><td>-18.93505238684011</td><td>-17.64377819743691</td><td>-17.63357277733874</td><td>-17.74726891499503</td><td>-17.632514080551108</td><td>-17.520391202943415</td><td>-17.32704936584866</td><td>-17.335649875027606</td><td>-17.431791068216974</td><td>-17.340032056715977</td><td>-17.248273045214983</td><td>-17.44876375365095</td><td>-17.45667599912918</td><td>-17.562206676930813</td><td>-17.464969844077693</td><td>-17.36773301122458</td><td>-18.23992399639022</td><td>-18.255906759538473</td><td>-18.321837936714655</td><td>-18.254442366263135</td><td>-18.177729751395837</td><td>-18.651009391082123</td><td>-18.673008844105638</td><td>-18.767478908200527</td><td>-18.667254702189805</td><td>-18.59797003290017</td><td>-19.086049735236212</td><td>-19.113387658433663</td><td>-19.19662500308857</td><td>-19.10458988173211</td><td>-19.04096630016408</td><td>2.2887565948169025e-18</td><td>2.3475817217186253e-18</td><td>5.568236162948299e-21</td><td>7.509142863806765e-19</td><td>4.110638272517733e-18</td><td>4.564240216812152e-18</td><td>4.475751413183636e-18</td><td>1.5195600968645958e-18</td><td>4.848085212963767e-18</td><td>8.176610329062937e-18</td><td>3.513459795930873e-18</td><td>3.44973011646617e-18</td><td>1.5381014746819951e-18</td><td>4.83941792466483e-18</td><td>8.140734374647664e-18</td><td>5.806779567670004e-19</td><td>5.631750884205926e-19</td><td>-9.013322236632383e-22</td><td>-9.013322236632383e-22</td><td>9.461362678470826e-19</td><td>2.334579963878e-19</td><td>2.247774504051371e-19</td><td>1.063361511649631e-20</td><td>1.063361511649631e-20</td><td>4.716327151946377e-19</td><td>7.980053884229094e-20</td><td>7.609565908909859e-20</td><td>5.5365268543121086e-21</td><td>5.5365268543121086e-21</td><td>2.246326101004151e-19</td><td>20.25365346719358</td><td>43674</td><td>226.73038403538976</td><td>43674</td><td>488</td><td>228.09972668939702</td><td>bin2_sub0</td></tr>\n",
+       "<tr><td>PHAT-M31 J004433.07+413616.96</td><td>11.137773091752802</td><td>41.60471072979164</td><td>2.5987468729562435e-18</td><td>2.1479896904918335e-18</td><td>1.9231253222546613e-18</td><td>1.5809523101363965e-18</td><td>9.759917994382369e-19</td><td>7.514231506315405e-19</td><td>3.01</td><td>3.0100000000000007</td><td>2.3299999999999996</td><td>3.01</td><td>3.6899999999999995</td><td>4.0</td><td>4.0</td><td>3.32</td><td>4.0</td><td>4.68</td><td>4.724137931034483</td><td>4.700962263000414</td><td>4.143997753024095</td><td>4.483997753024095</td><td>4.834709976440702</td><td>0.75</td><td>0.7580011234879523</td><td>0.582645011779649</td><td>0.7580011234879522</td><td>0.9280011234879523</td><td>907820.5301781861</td><td>907820.530178186</td><td>907820.5301781861</td><td>907820.5301781861</td><td>907820.5301781861</td><td>17.48047223263124</td><td>17.25178602042794</td><td>16.38683703329281</td><td>17.4139503396005</td><td>18.699157759441402</td><td>4.881</td><td>4.887580449923624</td><td>4.782402690403878</td><td>4.862693970335891</td><td>4.93112669760862</td><td>3.185</td><td>3.198760055739895</td><td>3.160631452215303</td><td>3.2073624010759443</td><td>3.2639545979628246</td><td>-7.431</td><td>-7.447921292969931</td><td>-7.55841033205883</td><td>-7.3872602840861</td><td>-7.186489902771764</td><td>7.0</td><td>7.0</td><td>6.32</td><td>7.0</td><td>7.68</td><td>4.3603</td><td>4.364960362676834</td><td>4.353058140097692</td><td>4.365985057830544</td><td>4.382008605289597</td><td>17.5100517273</td><td>17.577626856595526</td><td>16.340615010936897</td><td>17.46899422811582</td><td>18.58115302547348</td><td>17.162</td><td>17.2372252718792</td><td>15.841805208598398</td><td>16.897007582230387</td><td>17.876217108905887</td><td>0.019</td><td>0.01807148867790654</td><td>0.011593239450740029</td><td>0.016402957527969858</td><td>0.022137560887126286</td><td>-16.067687930891616</td><td>-16.070739567641642</td><td>-16.10449842969928</td><td>-16.03994036745597</td><td>-15.96461679796438</td><td>-15.408337588414545</td><td>-15.406385869805456</td><td>-15.510084994684604</td><td>-15.423746351211118</td><td>-15.337407707737633</td><td>-15.686998833800098</td><td>-15.686609122600366</td><td>-15.737277154869995</td><td>-15.662611253679907</td><td>-15.587536258814442</td><td>-16.927002216332806</td><td>-16.931618675982694</td><td>-16.977892122260936</td><td>-16.922363527624725</td><td>-16.86682883321558</td><td>-17.428714943414594</td><td>-17.434087032617388</td><td>-17.483870592315228</td><td>-17.430582961231735</td><td>-17.377289476543407</td><td>-17.923056907042607</td><td>-17.928918875607348</td><td>-17.98986708209274</td><td>-17.935659225635515</td><td>-17.88144541448741</td><td>-17.594723500533625</td><td>-17.59782851517496</td><td>-17.65174507724318</td><td>-17.561724407295358</td><td>-17.471703737347532</td><td>-17.72406334804432</td><td>-17.72202638807866</td><td>-17.83661023660372</td><td>-17.74485122510272</td><td>-17.653092213601727</td><td>-17.67839871690966</td><td>-17.677365547110252</td><td>-17.715169365102746</td><td>-17.61793253224963</td><td>-17.514156886723097</td><td>-17.75201400825945</td><td>-17.756804134487403</td><td>-17.811341627012926</td><td>-17.743946056561406</td><td>-17.676543082758883</td><td>-17.95631393859149</td><td>-17.961896874831805</td><td>-18.04858574142427</td><td>-17.98589441237698</td><td>-17.927061767500646</td><td>-18.227118560614894</td><td>-18.233017367331787</td><td>-18.299984050412327</td><td>-18.245958482872922</td><td>-18.19192698066694</td><td>2.5590487530963874e-18</td><td>2.5414086934135688e-18</td><td>7.353248883586612e-19</td><td>4.095048874495718e-18</td><td>7.976942994228709e-18</td><td>1.781060739328145e-18</td><td>1.790280113642714e-18</td><td>-4.680466365266143e-20</td><td>-4.680466365266143e-20</td><td>3.2817204524465094e-18</td><td>2.05678032475464e-18</td><td>2.0619725546939154e-18</td><td>-1.5459207662868333e-20</td><td>-1.5459207662868333e-20</td><td>3.285857242319966e-18</td><td>1.7923079634158468e-18</td><td>1.7737063036390765e-18</td><td>4.447634207508054e-19</td><td>1.3918010208215513e-18</td><td>2.3388386208922973e-18</td><td>1.1317480553557585e-18</td><td>1.117795036314968e-18</td><td>9.054397461343495e-19</td><td>1.3664388462124911e-18</td><td>1.8274885866832887e-18</td><td>5.951470273108634e-19</td><td>5.876713578226212e-19</td><td>4.308406884496885e-19</td><td>6.499367716957915e-19</td><td>8.690328549418942e-19</td><td>39.58506574852113</td><td>147239</td><td>214.83215957076965</td><td>147239</td><td>338</td><td>215.18376375015436</td><td>bin2_sub0</td></tr>\n",
+       "<tr><td>PHAT-M31 J004433.44+413620.92</td><td>11.139340897514375</td><td>41.60581037461429</td><td>2.6198748963136115e-18</td><td>1.905173464610148e-18</td><td>1.9524114946747828e-18</td><td>6.334279123394172e-19</td><td>2.8299678536012477e-19</td><td>1.191315781999333e-19</td><td>1.01</td><td>1.0100000000000002</td><td>0.33</td><td>1.01</td><td>1.69</td><td>5.0</td><td>5.058514232308462</td><td>4.198826325122737</td><td>5.0701413214310636</td><td>5.8852609374877165</td><td>5.0</td><td>5.092017525713106</td><td>4.5264646645962365</td><td>5.047966216930616</td><td>5.786608709578503</td><td>1.0</td><td>0.9884333868007776</td><td>0.8184333868007777</td><td>0.9884333868007776</td><td>1.0</td><td>907820.5301781861</td><td>907820.5301781863</td><td>907820.5301781861</td><td>907820.5301781861</td><td>907820.5301781861</td><td>9.900409560170122</td><td>9.800760513295723</td><td>8.96612016758143</td><td>9.589194912353262</td><td>10.190926448330696</td><td>3.188</td><td>3.20303363802606</td><td>3.1643817752295114</td><td>3.2328145025022383</td><td>3.30124973166676</td><td>3.151</td><td>3.1580411972110096</td><td>3.1122566184212097</td><td>3.159476415977925</td><td>3.1979252285982973</td><td>-3.2</td><td>-3.2377006221904825</td><td>-3.4838128140744744</td><td>-3.312728175632163</td><td>-3.141649791793779</td><td>8.0</td><td>8.0</td><td>7.32</td><td>8.0</td><td>8.68</td><td>4.0605</td><td>4.066548408876049</td><td>4.051400379643603</td><td>4.064182378256669</td><td>4.078177560695054</td><td>5.0909461975</td><td>5.068130601825461</td><td>4.6821121444620735</td><td>4.95183992370866</td><td>5.245055201267074</td><td>5.092</td><td>5.068833968135791</td><td>4.869935188087867</td><td>5.152314401069702</td><td>5.499376121285656</td><td>0.03</td><td>0.02360675583563545</td><td>0.012629718617653454</td><td>0.025609122523297816</td><td>0.03</td><td>-17.09879211850121</td><td>-17.100712451982865</td><td>-17.15224388311281</td><td>-17.0876858208695</td><td>-17.010672944530434</td><td>-17.015626847373753</td><td>-16.99680673436558</td><td>-17.121274088090107</td><td>-17.0118273237686</td><td>-16.902926336938094</td><td>-17.108073675438938</td><td>-17.098762764554433</td><td>-17.164110324941067</td><td>-17.08944442375098</td><td>-17.014778522560892</td><td>-17.852740170238526</td><td>-17.855699616266847</td><td>-17.89962246917315</td><td>-17.84409387453694</td><td>-17.77563022207051</td><td>-18.295504476086272</td><td>-18.29954938202229</td><td>-18.352963039116048</td><td>-18.299642964760874</td><td>-18.241740838524265</td><td>-18.766148876021933</td><td>-18.77041180180437</td><td>-18.80995679350731</td><td>-18.755748937050083</td><td>-18.6889136809131</td><td>-17.598617576937663</td><td>-17.60078268345407</td><td>-17.652425966490316</td><td>-17.562405296542494</td><td>-17.47192274834925</td><td>-17.69581582494556</td><td>-17.678717486406846</td><td>-17.81406713871463</td><td>-17.698567246467125</td><td>-17.600864007966745</td><td>-17.720356726320563</td><td>-17.7121984133967</td><td>-17.848167885147546</td><td>-17.750868473723006</td><td>-17.653631640869893</td><td>-18.14459095518414</td><td>-18.14732329588115</td><td>-18.207859769006532</td><td>-18.140422082658283</td><td>-18.072955564421093</td><td>-18.490008098571074</td><td>-18.49375869960751</td><td>-18.56862143832802</td><td>-18.507589022854734</td><td>-18.448754226948743</td><td>-18.876782421683394</td><td>-18.88089986704374</td><td>-18.935381061707982</td><td>-18.88069666512584</td><td>-18.82658689081687</td><td>2.53645916882391e-18</td><td>2.525048882930829e-18</td><td>2.2291181243039963e-19</td><td>3.582635798567457e-18</td><td>7.703312649813593e-18</td><td>1.9051020350928955e-18</td><td>1.988778474408519e-18</td><td>-4.680466365266143e-20</td><td>-4.680466365266143e-20</td><td>3.2817204524465094e-18</td><td>1.8644597693517934e-18</td><td>1.9013003324201037e-18</td><td>-1.5459207662868333e-20</td><td>-1.5459207662868333e-20</td><td>3.285857242319966e-18</td><td>7.267454336232417e-19</td><td>7.2264724289812385e-19</td><td>3.024966995996893e-19</td><td>1.249534299670435e-18</td><td>2.2881336813327457e-18</td><td>3.34422254542141e-19</td><td>3.318820289338621e-19</td><td>1.063361511649631e-20</td><td>6.891498382927963e-20</td><td>5.299140839074211e-19</td><td>1.3232296031751557e-19</td><td>1.3106074195310487e-19</td><td>5.5365268543121086e-21</td><td>5.5365268543121086e-21</td><td>2.246326101004151e-19</td><td>9.428364558182038</td><td>72103</td><td>230.28635250024163</td><td>72103</td><td>1267</td><td>231.3879451214555</td><td>bin2_sub0</td></tr>\n",
+       "<tr><td>PHAT-M31 J004432.96+413616.92</td><td>11.137321992746005</td><td>41.60470130322815</td><td>3.0001793167462328e-18</td><td>1.4643686237787805e-18</td><td>1.8905851306767478e-18</td><td>6.103941700725293e-19</td><td>2.5849489918175895e-19</td><td>8.769007208905502e-20</td><td>1.01</td><td>1.01</td><td>0.33</td><td>1.01</td><td>1.69</td><td>5.0</td><td>4.753087442157451</td><td>4.067989724587414</td><td>4.751729503402897</td><td>5.5792898645684605</td><td>5.0</td><td>4.830441189160246</td><td>4.345586422056908</td><td>4.854581121836828</td><td>5.291779600255351</td><td>1.0</td><td>0.9732945397252257</td><td>0.8032945397252257</td><td>0.9732945397252256</td><td>1.0</td><td>907820.5301781861</td><td>907820.530178186</td><td>907820.5301781861</td><td>907820.5301781861</td><td>907820.5301781861</td><td>11.098215688482993</td><td>11.02885087373735</td><td>10.551302600088745</td><td>11.209550302851383</td><td>12.002116785178327</td><td>3.192</td><td>3.206709105429274</td><td>3.1643854545454553</td><td>3.2328181818181823</td><td>3.3012509090909092</td><td>3.051</td><td>3.054230278120342</td><td>2.9943284145514384</td><td>3.0372420310603383</td><td>3.0772981500486476</td><td>-3.211</td><td>-3.2473529273718755</td><td>-3.4838157575757576</td><td>-3.3127373737373738</td><td>-3.14165898989899</td><td>8.0</td><td>8.0</td><td>7.32</td><td>8.0</td><td>8.68</td><td>4.0367</td><td>4.0418144855159674</td><td>4.023352134015041</td><td>4.036642968241962</td><td>4.057056460003442</td><td>5.0913715363</td><td>5.0583104730467845</td><td>4.6821121444620735</td><td>4.95183992370866</td><td>5.245055201267074</td><td>5.092</td><td>5.0587750912727305</td><td>4.834234242834484</td><td>5.11661345581632</td><td>5.479460747313166</td><td>0.03</td><td>0.02395331769221683</td><td>0.011769805414446064</td><td>0.027311557915068833</td><td>0.03</td><td>-17.049629601768743</td><td>-17.04621068307529</td><td>-17.130752566939623</td><td>-17.065536811041625</td><td>-17.000978748798314</td><td>-17.04300418517167</td><td>-17.02166067424207</td><td>-17.146041307249327</td><td>-17.040714171235145</td><td>-16.954375527761663</td><td>-17.116651244643094</td><td>-17.10095540403583</td><td>-17.164110324941067</td><td>-17.08944442375098</td><td>-17.014778522560892</td><td>-17.793047435678194</td><td>-17.791274704900697</td><td>-17.8734822667978</td><td>-17.81307299858818</td><td>-17.757544403951968</td><td>-18.22859182524915</td><td>-18.228218564713202</td><td>-18.27646363141839</td><td>-18.2231760003349</td><td>-18.164165713157317</td><td>-18.696468918912164</td><td>-18.696532493582374</td><td>-18.777438148130482</td><td>-18.711036732334087</td><td>-18.656828875876858</td><td>-17.549324614421295</td><td>-17.54956672161866</td><td>-17.65174507724318</td><td>-17.561724407295358</td><td>-17.471703737347532</td><td>-17.722928176835158</td><td>-17.724967188868142</td><td>-17.836443820498936</td><td>-17.744333171326254</td><td>-17.65257415982526</td><td>-17.72878466688021</td><td>-17.725877651032253</td><td>-17.848197361792916</td><td>-17.7509605289398</td><td>-17.653723696086686</td><td>-18.084624642696497</td><td>-18.07949543162718</td><td>-18.11667753091509</td><td>-18.04928196046357</td><td>-17.97666284980376</td><td>-18.423005166688856</td><td>-18.419076477250897</td><td>-18.48292103397815</td><td>-18.42344527584211</td><td>-18.364590930575048</td><td>-18.807077824012726</td><td>-18.805080097037052</td><td>-18.855934969297262</td><td>-18.801407643922833</td><td>-18.74734631780162</td><td>2.837912280123902e-18</td><td>2.8370658861518982e-18</td><td>1.5866148178745042e-18</td><td>4.946338804011561e-18</td><td>8.306062790148617e-18</td><td>1.785891490707071e-18</td><td>1.7778560656646164e-18</td><td>-4.680466365266143e-20</td><td>-4.680466365266143e-20</td><td>3.2817204524465094e-18</td><td>1.8280198637272026e-18</td><td>1.8406994875514063e-18</td><td>-1.5459207662868333e-20</td><td>-1.5459207662868333e-20</td><td>3.285857242319966e-18</td><td>8.3647924045101425e-19</td><td>8.471167882402165e-19</td><td>4.447634207508054e-19</td><td>1.3918010208215513e-18</td><td>2.3388386208922973e-18</td><td>3.890706325597401e-19</td><td>3.927866006561867e-19</td><td>2.2743571483339996e-19</td><td>6.884348149115415e-19</td><td>1.149528190130915e-18</td><td>1.5787315764958882e-19</td><td>1.5876735203019828e-19</td><td>5.5365268543121086e-21</td><td>2.657076621643165e-20</td><td>2.4566684946253464e-19</td><td>41.91891954839599</td><td>72105</td><td>213.92061844941742</td><td>72105</td><td>1269</td><td>214.45507960651636</td><td>bin2_sub0</td></tr>\n",
+       "<tr><td>PHAT-M31 J004433.44+413617.32</td><td>11.139347207625237</td><td>41.604811325845326</td><td>3.3012536495887243e-18</td><td>3.6758640964242854e-18</td><td>3.2019548512666434e-18</td><td>6.54367678036588e-19</td><td>2.478774151711338e-19</td><td>8.220944258348908e-20</td><td>1.01</td><td>1.01</td><td>0.33</td><td>1.01</td><td>1.69</td><td>2.0</td><td>2.0</td><td>2.0</td><td>2.0</td><td>2.6799999999999997</td><td>2.0</td><td>2.0</td><td>2.0</td><td>2.0</td><td>2.34</td><td>1.0</td><td>1.0</td><td>0.83</td><td>1.0</td><td>1.0</td><td>907820.5301781861</td><td>907820.5301781863</td><td>907820.5301781861</td><td>907820.5301781861</td><td>907820.5301781861</td><td>5.9381773054647695</td><td>5.81361772327781</td><td>5.432826978982182</td><td>5.85121684320591</td><td>6.323953444620372</td><td>4.084</td><td>4.09611185727926</td><td>3.9926436610141964</td><td>4.0800155248419765</td><td>4.162213607939417</td><td>3.937</td><td>3.96094034121752</td><td>3.896708708547145</td><td>3.9450718811432113</td><td>3.991336646966741</td><td>-5.441</td><td>-5.470689835847333</td><td>-5.636179296692464</td><td>-5.430688214116139</td><td>-5.212262939363703</td><td>7.0</td><td>7.0</td><td>6.32</td><td>7.0</td><td>7.68</td><td>4.3955</td><td>4.403185247177744</td><td>4.385319705790059</td><td>4.40155021695757</td><td>4.422854793137939</td><td>11.2044124603</td><td>11.340158805729457</td><td>10.792572028103173</td><td>11.418620618025072</td><td>12.09440113893448</td><td>11.205</td><td>11.342193310668165</td><td>10.614137735483533</td><td>11.405862853764212</td><td>12.202041277423998</td><td>0.03</td><td>0.025328124743043</td><td>0.015373205876868305</td><td>0.02532389972189756</td><td>0.03</td><td>-16.93434022986515</td><td>-16.94557598620867</td><td>-17.036128473824032</td><td>-16.97157041158072</td><td>-16.907012349337407</td><td>-16.245353364951868</td><td>-16.251057395255994</td><td>-16.36766024146231</td><td>-16.257371621693306</td><td>-16.152120974770476</td><td>-16.534519194551397</td><td>-16.542600490728105</td><td>-16.615096345602193</td><td>-16.540430444412106</td><td>-16.46576454322202</td><td>-17.80976334750463</td><td>-17.822305982240522</td><td>-17.876139829481474</td><td>-17.820611234845266</td><td>-17.76508264020906</td><td>-18.318963307295345</td><td>-18.33207841507351</td><td>-18.405461969433503</td><td>-18.337397892481835</td><td>-18.27279456024918</td><td>-18.817964976765886</td><td>-18.831383804964354</td><td>-18.8672552841407</td><td>-18.813047427683472</td><td>-18.758497090777073</td><td>-17.522376979540823</td><td>-17.53368054610125</td><td>-17.65174507724318</td><td>-17.561724407295358</td><td>-17.471703737347532</td><td>-17.49045761138526</td><td>-17.49635466763634</td><td>-17.566730791012557</td><td>-17.47497177951156</td><td>-17.383212768010562</td><td>-17.438631489174536</td><td>-17.446744631970375</td><td>-17.562206676930813</td><td>-17.464969844077693</td><td>-17.36773301122458</td><td>-18.024697332094146</td><td>-18.037262846631286</td><td>-18.108664138219932</td><td>-18.041268567768412</td><td>-17.973872997316892</td><td>-18.432873646836043</td><td>-18.445995853483844</td><td>-18.483744317350975</td><td>-18.424911672474643</td><td>-18.36570732814439</td><td>-18.879606261835683</td><td>-18.893025835628805</td><td>-18.936071889934</td><td>-18.882046322394594</td><td>-18.82768064937135</td><td>3.017763082262247e-18</td><td>2.9417361952261205e-18</td><td>1.5866148178745042e-18</td><td>4.946338804011561e-18</td><td>8.306062790148617e-18</td><td>3.1053998390522702e-18</td><td>3.062660714488372e-18</td><td>1.5195600968645958e-18</td><td>4.848085212963767e-18</td><td>8.176610329062937e-18</td><td>3.597347290904912e-18</td><td>3.530509848883841e-18</td><td>1.5381014746819951e-18</td><td>4.83941792466483e-18</td><td>8.140734374647664e-18</td><td>9.623730145728689e-19</td><td>9.348456504326937e-19</td><td>4.447634207508054e-19</td><td>1.3918010208215513e-18</td><td>2.3388386208922973e-18</td><td>3.804828769053107e-19</td><td>3.6950226938340704e-19</td><td>2.232937542257028e-19</td><td>6.842928543038443e-19</td><td>1.1481940677539293e-18</td><td>1.313718225267868e-19</td><td>1.2699706758572679e-19</td><td>5.5365268543121086e-21</td><td>5.5365268543121086e-21</td><td>2.246326101004151e-19</td><td>86.53073568513557</td><td>38716</td><td>192.56555471846661</td><td>38716</td><td>147</td><td>193.41399127926908</td><td>bin2_sub0</td></tr>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "<Table length=50330>\n",
+       "             Name                     RA         ... reorder_tag\n",
+       "            str29                  float64       ...     str9   \n",
+       "----------------------------- ------------------ ... -----------\n",
+       "PHAT-M31 J004434.89+413625.12 11.145373458361576 ...   bin1_sub0\n",
+       "PHAT-M31 J004433.96+413539.24 11.141481294584898 ...   bin1_sub0\n",
+       "PHAT-M31 J004435.09+413600.66 11.146199581807522 ...   bin1_sub0\n",
+       "PHAT-M31 J004436.17+413547.25 11.150723096532829 ...   bin1_sub0\n",
+       "PHAT-M31 J004434.01+413539.96 11.141726732037966 ...   bin1_sub0\n",
+       "PHAT-M31 J004432.23+413612.29 11.134295101001706 ...   bin1_sub0\n",
+       "PHAT-M31 J004434.12+413556.83 11.142166807061688 ...   bin1_sub0\n",
+       "PHAT-M31 J004435.17+413547.12 11.146526354983829 ...   bin1_sub0\n",
+       "PHAT-M31 J004435.53+413610.30 11.148027177661621 ...   bin1_sub0\n",
+       "PHAT-M31 J004434.59+413626.14 11.144140816897673 ...   bin1_sub0\n",
+       "                          ...                ... ...         ...\n",
+       "PHAT-M31 J004433.11+413617.67 11.137949885163916 ...   bin2_sub0\n",
+       "PHAT-M31 J004433.87+413616.65 11.141113594228456 ...   bin2_sub0\n",
+       "PHAT-M31 J004433.88+413617.66 11.141164504888842 ...   bin2_sub0\n",
+       "PHAT-M31 J004433.29+413620.78  11.13869716051958 ...   bin2_sub0\n",
+       "PHAT-M31 J004433.85+413617.50 11.141049820932293 ...   bin2_sub0\n",
+       "PHAT-M31 J004433.71+413621.25 11.140455759788615 ...   bin2_sub0\n",
+       "PHAT-M31 J004433.07+413616.96 11.137773091752802 ...   bin2_sub0\n",
+       "PHAT-M31 J004433.44+413620.92 11.139340897514375 ...   bin2_sub0\n",
+       "PHAT-M31 J004432.96+413616.92 11.137321992746005 ...   bin2_sub0\n",
+       "PHAT-M31 J004433.44+413617.32 11.139347207625237 ...   bin2_sub0"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "hdul = fits.open(sed_files[0].replace('seds.grid.hd5', 'stats.fits'))\n",
+    "Table(hdul[1].data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As you can hopefully see, for every source, there are now several parameters assigned to each one. These are all the parameters we originally had set up in our settings and specified in Step 9."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Epilogue: Visualizating!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from beast.plotting import (\n",
+    "    plot_triangle, \n",
+    "    plot_indiv_fit, \n",
+    "    plot_cmd_with_fits, \n",
+    "    plot_completeness, \n",
+    "    plot_chi2_hist,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Triangle Plot\n",
+    "\n",
+    "This first plot displays a posterior distributions of the parameters of all the fitted stars. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "plot_triangle.plot_triangle(\"M31-B09-EAST_chunk/M31-B09-EAST_chunk_stats.fits\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### CMD Plot\n",
+    "\n",
+    "You can also make a color-magnitude diagram of the observations and color-code the data points using one of the parameters from the BEAST fitting (feel free to change this from the example, just remember that the param must match a column name from the stat.fits file). \n",
+    "\n",
+    "Inputs are the photometry file, three filters, the BEAST stats file from Step 10, and the parameter to use and apply color to after taking the log10."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/christina/anaconda3/envs/astroconda/lib/python3.6/site-packages/beast/plotting/plot_cmd_with_fits.py:97: RuntimeWarning: invalid value encountered in greater\n",
+      "  col[col > 99] = np.nan\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 504x432 with 2 Axes>"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 504x432 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_cmd_with_fits.plot(data_fits_file=\"M31-B09-EAST_chunk.st_with_sourceden_cut.fits\", \n",
+    "                        beast_fits_file=\"M31-B09-EAST_chunk/M31-B09-EAST_chunk_stats.fits\", \n",
+    "                        mag1_filter=\"F475W\",\n",
+    "                        mag2_filter=\"F814W\",\n",
+    "                        mag3_filter=\"F475W\",\n",
+    "                        param=\"Z_Best\", #metallicity\n",
+    "                       )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Completeness Plot\n",
+    "This next plot shows the completeness (how many AST sources were detected out of the total number of AST that exist for that parameter bin) for each parameter, although it should be noted that the *distance* parameter was purposefully left out because all the sources have the same distance value, and thus the plotting code isn't sure how to handle it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib\n",
+    "import matplotlib.pyplot as plt\n",
+    "from matplotlib.gridspec import GridSpec\n",
+    "import copy\n",
+    "from scipy.stats import binned_statistic, binned_statistic_2d\n",
+    "from astropy.table import Table, vstack\n",
+    "\n",
+    "from beast.physicsmodel.grid import SEDGrid\n",
+    "import beast.observationmodel.noisemodel.generic_noisemodel as noisemodel\n",
+    "\n",
+    "\n",
+    "def plot_completeness(\n",
+    "    physgrid_list,\n",
+    "    noise_model_list,\n",
+    "    output_plot_filename,\n",
+    "    param_list=[\"Av\", \"Rv\", \"logA\", \"f_A\", \"M_ini\", \"Z\", \"distance\"],\n",
+    "    compl_filter=\"F475W\",\n",
+    "):\n",
+    "    \"\"\"\n",
+    "    Make visualization of the completeness\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    physgrid_list : string or list of strings\n",
+    "        Name of the physics model file.  If there are multiple physics model\n",
+    "        grids (i.e., if there are subgrids), list them all here.\n",
+    "\n",
+    "    noise_model_list : string or list of strings\n",
+    "        Name of the noise model file.  If there are multiple files for\n",
+    "        physgrid_list (because of subgrids), list the noise model file\n",
+    "        associated with each physics model file.\n",
+    "\n",
+    "    param_list : list of strings\n",
+    "        names of the parameters to plot\n",
+    "\n",
+    "    compl_filter : str\n",
+    "        filter to use for completeness (required for toothpick model)\n",
+    "\n",
+    "    output_plot_filename : string\n",
+    "        name of the file in which to save the output plot\n",
+    "\n",
+    "    \"\"\"\n",
+    "\n",
+    "    n_params = len(param_list)\n",
+    "\n",
+    "    # If there are subgrids, we can't read them all into memory.  Therefore,\n",
+    "    # we'll go through each one and just grab the relevant parts.\n",
+    "    compl_table_list = []\n",
+    "\n",
+    "    # make a table for each physics model + noise model\n",
+    "    for physgrid, noise_model in zip(\n",
+    "        np.atleast_1d(physgrid_list), np.atleast_1d(noise_model_list)\n",
+    "    ):\n",
+    "\n",
+    "        # get the physics model grid - includes priors\n",
+    "        modelsedgrid = SEDGrid(str(physgrid))\n",
+    "        # get list of filters\n",
+    "        short_filters = [\n",
+    "            filter.split(sep=\"_\")[-1].upper() for filter in modelsedgrid.filters\n",
+    "        ]\n",
+    "        if compl_filter.upper() not in short_filters:\n",
+    "            raise ValueError(\"requested completeness filter not present\")\n",
+    "        filter_k = short_filters.index(compl_filter.upper())\n",
+    "        print(\"Completeness from {0}\".format(modelsedgrid.filters[filter_k]))\n",
+    "\n",
+    "        # read in the noise model\n",
+    "        noisegrid = noisemodel.get_noisemodelcat(str(noise_model))\n",
+    "        # get the completeness\n",
+    "        model_compl = noisegrid[\"completeness\"]\n",
+    "        # close the file to save memory\n",
+    "        #noisegrid.close()\n",
+    "\n",
+    "        # put it all into a table\n",
+    "        table_dict = {x: modelsedgrid[x] for x in param_list}\n",
+    "        table_dict[\"compl\"] = model_compl[:, filter_k]\n",
+    "\n",
+    "        # append to the list\n",
+    "        compl_table_list.append(Table(table_dict))\n",
+    "\n",
+    "    # stack all the tables into one\n",
+    "    compl_table = vstack(compl_table_list)\n",
+    "\n",
+    "    # import pdb; pdb.set_trace()\n",
+    "\n",
+    "    # figure\n",
+    "    fig = plt.figure(figsize=(4 * n_params, 4 * n_params))\n",
+    "\n",
+    "    # label font sizes\n",
+    "    label_font = 25\n",
+    "    tick_font = 22\n",
+    "\n",
+    "    # load in color map\n",
+    "    cmap = matplotlib.cm.get_cmap(\"magma\")\n",
+    "\n",
+    "    # iterate through the panels\n",
+    "    for i, pi in enumerate(param_list):\n",
+    "        for j, pj in enumerate(param_list[i:], i):\n",
+    "\n",
+    "            print(\"plotting {0} and {1}\".format(pi, pj))\n",
+    "\n",
+    "            # not along diagonal\n",
+    "            if i != j:\n",
+    "\n",
+    "                # set up subplot\n",
+    "                plt.subplot(n_params, n_params, i + j * (n_params) + 1)\n",
+    "                ax = plt.gca()\n",
+    "\n",
+    "                # create image and labels\n",
+    "                x_col, x_bins, x_label = setup_axis(compl_table, pi)\n",
+    "                y_col, y_bins, y_label = setup_axis(compl_table, pj)\n",
+    "                compl_image, _, _, _ = binned_statistic_2d(\n",
+    "                    x_col,\n",
+    "                    y_col,\n",
+    "                    compl_table[\"compl\"],\n",
+    "                    statistic=\"mean\",\n",
+    "                    bins=(x_bins, y_bins),\n",
+    "                )\n",
+    "\n",
+    "                # plot points\n",
+    "                im = plt.imshow(\n",
+    "                    compl_image.T,\n",
+    "                    # np.random.random((4,4)),\n",
+    "                    extent=(\n",
+    "                        np.min(x_bins),\n",
+    "                        np.max(x_bins),\n",
+    "                        np.min(y_bins),\n",
+    "                        np.max(y_bins),\n",
+    "                    ),\n",
+    "                    cmap=\"magma\",\n",
+    "                    vmin=0,\n",
+    "                    vmax=1,\n",
+    "                    aspect=\"auto\",\n",
+    "                    origin=\"lower\",\n",
+    "                )\n",
+    "\n",
+    "                ax.tick_params(\n",
+    "                    axis=\"both\",\n",
+    "                    which=\"both\",\n",
+    "                    direction=\"in\",\n",
+    "                    labelsize=tick_font,\n",
+    "                    bottom=True,\n",
+    "                    top=True,\n",
+    "                    left=True,\n",
+    "                    right=True,\n",
+    "                )\n",
+    "\n",
+    "                # axis labels and ticks\n",
+    "                if i == 0:\n",
+    "                    ax.set_ylabel(y_label, fontsize=label_font)\n",
+    "                    # ax.get_yaxis().set_label_coords(-0.35,0.5)\n",
+    "                else:\n",
+    "                    ax.set_yticklabels([])\n",
+    "                if j == n_params - 1:\n",
+    "                    ax.set_xlabel(x_label, fontsize=label_font)\n",
+    "                    plt.xticks(rotation=-45)\n",
+    "                else:\n",
+    "                    ax.set_xticklabels([])\n",
+    "\n",
+    "            # along diagonal\n",
+    "            if i == j:\n",
+    "\n",
+    "                # set up subplot\n",
+    "                plt.subplot(n_params, n_params, i + j * (n_params) + 1)\n",
+    "                ax = plt.gca()\n",
+    "\n",
+    "                # create histogram and labels\n",
+    "                x_col, x_bins, x_label = setup_axis(compl_table, pi)\n",
+    "                compl_hist, _, _ = binned_statistic(\n",
+    "                    x_col, compl_table[\"compl\"], statistic=\"mean\", bins=x_bins,\n",
+    "                )\n",
+    "                # make histogram\n",
+    "                _, _, patches = plt.hist(x_bins[:-1], x_bins, weights=compl_hist)\n",
+    "                # color each bar by its completeness\n",
+    "                for c, comp in enumerate(compl_hist):\n",
+    "                    patches[c].set_color(cmap(comp))\n",
+    "                    patches[c].set_linewidth = 0.1\n",
+    "                # make a black outline so it stands out as a histogram\n",
+    "                plt.hist(\n",
+    "                    x_bins[:-1], x_bins, weights=compl_hist, histtype=\"step\", color=\"k\"\n",
+    "                )\n",
+    "                # axis ranges\n",
+    "                plt.xlim(np.min(x_bins), np.max(x_bins))\n",
+    "                plt.ylim(0, 1.05)\n",
+    "\n",
+    "                ax.tick_params(axis=\"y\", which=\"both\", length=0, labelsize=tick_font)\n",
+    "                ax.tick_params(\n",
+    "                    axis=\"x\", which=\"both\", direction=\"in\", labelsize=tick_font\n",
+    "                )\n",
+    "\n",
+    "                # axis labels and ticks\n",
+    "                ax.set_yticklabels([])\n",
+    "                if i < n_params - 1:\n",
+    "                    ax.set_xticklabels([])\n",
+    "                if i == n_params - 1:\n",
+    "                    ax.set_xlabel(x_label, fontsize=label_font)\n",
+    "                    plt.xticks(rotation=-45)\n",
+    "\n",
+    "    # plt.subplots_adjust(wspace=0.05, hspace=0.05)\n",
+    "    plt.tight_layout()\n",
+    "\n",
+    "    # add a colorbar\n",
+    "    gs = GridSpec(nrows=20, ncols=n_params)\n",
+    "    cax = fig.add_subplot(gs[0, 2:])\n",
+    "    cbar = plt.colorbar(im, cax=cax, orientation=\"horizontal\")\n",
+    "    cbar.set_label(\"Completeness\", fontsize=label_font)\n",
+    "    cbar.ax.tick_params(labelsize=tick_font)\n",
+    "    gs.tight_layout(fig)\n",
+    "\n",
+    "    fig.savefig(output_plot_filename)\n",
+    "    plt.close(fig)\n",
+    "\n",
+    "\n",
+    "def setup_axis(compl_table, param):\n",
+    "    \"\"\"\n",
+    "    Set up the bins and labels for a parameter\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    compl_table : astropy table\n",
+    "        table with each set of physical parameters and their completeness\n",
+    "\n",
+    "    param : string\n",
+    "        name of the parameter we're binning/labeling\n",
+    "\n",
+    "    Returns\n",
+    "    -------\n",
+    "    col : numpy array\n",
+    "        column to plot\n",
+    "\n",
+    "    bins : numpy array\n",
+    "        bin edges\n",
+    "\n",
+    "    label : string\n",
+    "        the axis label to use\n",
+    "\n",
+    "    \"\"\"\n",
+    "\n",
+    "    # mass isn't reguarly spaced, so take log and manually define bins\n",
+    "    if \"M_\" in param:\n",
+    "        col = np.log10(compl_table[param])\n",
+    "        bins = np.linspace(np.min(col), np.max(col), 20)\n",
+    "        label = \"log \" + param\n",
+    "    # metallicity just needs to be log\n",
+    "    elif param == \"Z\":\n",
+    "        col = np.log10(compl_table[param])\n",
+    "        bins = np.linspace(np.min(col), np.max(col), len(np.unique(col)) + 1)\n",
+    "        label = \"log \" + param\n",
+    "    # for all others, standard linear spacing is ok\n",
+    "    else:\n",
+    "        col = copy.copy(compl_table[param])\n",
+    "        bins = np.linspace(np.min(col), np.max(col), len(np.unique(col)) + 1)\n",
+    "        label = copy.copy(param)\n",
+    "\n",
+    "    return col, bins, label"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Completeness from HST_WFC3_F475W\n",
+      "Completeness from HST_WFC3_F475W\n",
+      "Completeness from HST_WFC3_F475W\n",
+      "Completeness from HST_WFC3_F475W\n",
+      "Completeness from HST_WFC3_F475W\n",
+      "Completeness from HST_WFC3_F475W\n",
+      "Completeness from HST_WFC3_F475W\n",
+      "Completeness from HST_WFC3_F475W\n",
+      "Completeness from HST_WFC3_F475W\n",
+      "plotting Av and Av\n",
+      "plotting Av and Rv\n",
+      "plotting Av and logA\n",
+      "plotting Av and f_A\n",
+      "plotting Av and M_ini\n",
+      "plotting Av and Z\n",
+      "plotting Rv and Rv\n",
+      "plotting Rv and logA\n",
+      "plotting Rv and f_A\n",
+      "plotting Rv and M_ini\n",
+      "plotting Rv and Z\n",
+      "plotting logA and logA\n",
+      "plotting logA and f_A\n",
+      "plotting logA and M_ini\n",
+      "plotting logA and Z\n",
+      "plotting f_A and f_A\n",
+      "plotting f_A and M_ini\n",
+      "plotting f_A and Z\n",
+      "plotting M_ini and M_ini\n",
+      "plotting M_ini and Z\n",
+      "plotting Z and Z\n"
+     ]
+    },
+    {
+     "ename": "UserWarning",
+     "evalue": "This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mUserWarning\u001b[0m                               Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-55-f57891a9a25a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      4\u001b[0m                                     \u001b[0mparam_list\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Av'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Rv'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'logA'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'f_A'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'M_ini'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Z'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m                                     \u001b[0;31m#, 'distance'],\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m                                     compl_filter='F475W',)\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-54-c11825855fd0>\u001b[0m in \u001b[0;36mplot_completeness\u001b[0;34m(physgrid_list, noise_model_list, output_plot_filename, param_list, compl_filter)\u001b[0m\n\u001b[1;32m    205\u001b[0m     \u001b[0mcbar\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_label\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Completeness\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfontsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlabel_font\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    206\u001b[0m     \u001b[0mcbar\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtick_params\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabelsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtick_font\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 207\u001b[0;31m     \u001b[0mgs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtight_layout\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    208\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    209\u001b[0m     \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msavefig\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput_plot_filename\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/envs/astroconda/lib/python3.6/site-packages/matplotlib/gridspec.py\u001b[0m in \u001b[0;36mtight_layout\u001b[0;34m(self, figure, renderer, pad, h_pad, w_pad, rect)\u001b[0m\n\u001b[1;32m    334\u001b[0m             figure.axes, grid_spec=self)\n\u001b[1;32m    335\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32min\u001b[0m \u001b[0msubplotspec_list\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 336\u001b[0;31m             cbook._warn_external(\"This figure includes Axes that are not \"\n\u001b[0m\u001b[1;32m    337\u001b[0m                                  \u001b[0;34m\"compatible with tight_layout, so results \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    338\u001b[0m                                  \"might be incorrect.\")\n",
+      "\u001b[0;32m~/anaconda3/envs/astroconda/lib/python3.6/site-packages/matplotlib/cbook/__init__.py\u001b[0m in \u001b[0;36m_warn_external\u001b[0;34m(message, category)\u001b[0m\n\u001b[1;32m   2076\u001b[0m             \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2077\u001b[0m         \u001b[0mframe\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mframe\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf_back\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2078\u001b[0;31m     \u001b[0mwarnings\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwarn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcategory\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstacklevel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2079\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2080\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mUserWarning\u001b[0m: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect."
+     ]
+    },
+    {
+     "data": {
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\n",
+      "text/plain": [
+       "<Figure size 1728x1728 with 22 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_completeness(physgrid_list=file_dict[\"modelsedgrid_trim_files\"],\n",
+    "                                    noise_model_list=file_dict[\"noise_trim_files\"],\n",
+    "                                    output_plot_filename=\"completeness_plot.pdf\",\n",
+    "                                    param_list=['Av', 'Rv', 'logA', 'f_A', 'M_ini', 'Z'],\n",
+    "                                    #, 'distance'],\n",
+    "                                    compl_filter='F475W',)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Chi Squared Plot\n",
+    "Make a histogram of the best chi2 values (chi2=1 and the median chi2 are marked). Note that there is no plot of reduced chi2, because it is mathematically difficult to define the number of degrees of freedom. Inputs are the BEAST stats file and optionally the number of bins to use for the histogram."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plot_chi2_hist.plot(beast_stats_file=\"M31-B09-EAST_chunk/M31-B09-EAST_chunk_stats.fits\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There's another cool plot for plotting the individual fits of stars, but unfortunately, this code works with a file that only gets generated when using multiple subgrids (remember how we checked that we had a subgrid = 1 back in Step 2?). If it had worked with the code below, it would have made a multi-panel plot that shows the PDFs and best fits of each parameter for any given star, as well as the SED (similar to Figure 14 in Gordon+16)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#plot_indiv_fit.plot_beast_ifit(filter=settings.filters, waves, stats, pdf1d_hdu, starnum=0):"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Sorry I wasn't able to show you all that last plot. But thanks for reading through this notebook til the end. Hopefully you found it to be somewhat helpful and if you have any suggestions for how to make it better, you can find me at cwlind@jhu.edu.\n",
+    "\n",
+    "Thanks,\\\n",
+    "Christina Lindberg\\\n",
+    "(she/her)"
+   ]
+  }
+ ],
+ "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": "3.6.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/M31_Example/beast_settings.txt b/M31_Example/beast_settings.txt
new file mode 100644
index 0000000..2f89fae
--- /dev/null
+++ b/M31_Example/beast_settings.txt
@@ -0,0 +1,258 @@
+# """ Data Model interface v2.0
+# BEAST datamodel for M31 Example
+# """
+import numpy as np
+
+from astropy import units
+
+# BEAST imports
+from beast.physicsmodel.stars import isochrone
+from beast.physicsmodel.stars import stellib
+from beast.physicsmodel.dust import extinction
+#from beast.observationmodel.observations import Observations
+#from beast.observationmodel.vega import Vega
+from beast.observationmodel.noisemodel import absflux_covmat
+
+# from extra_filters import make_integration_filter, make_top_hat_filter
+
+# -----------------------------------------------------------------
+# User inputs                                   [sec:conf]
+# -----------------------------------------------------------------
+# Parameters that are required to make models
+# and to fit the data
+# -----------------------------------------------------------------
+# AC == authomatically created
+# indicates where user's input change is NOT necessary/recommended
+# -----------------------------------------------------------------
+
+# project : string
+#   the name of the output results directory
+project = "M31-B09-EAST_chunk"
+
+# name of the survey
+#  used for the creation of the unique name for each source
+surveyname = "PHAT-M31"
+
+# filters : list of strings
+#   full filter names in BEAST filter database
+filters = [
+    "HST_WFC3_F475W",
+    "HST_WFC3_F275W",
+    "HST_WFC3_F336W",
+    "HST_WFC3_F814W",
+    "HST_WFC3_F110W",
+    "HST_WFC3_F160W",
+]
+
+
+# basefilters : list of strings
+#   short names for filters
+basefilters = ["F475W", "F275W", "F336W", "F814W", "F110W", "F160W"]
+
+# obs_colnames : list of strings
+#   names of columns for filters in the observed catalog
+#   need to match column names in the observed catalog,
+#   input data MUST be in fluxes, NOT in magnitudes
+#   fluxes MUST be in normalized Vega units
+obs_colnames = [f.upper() + "_RATE" for f in basefilters]
+
+# obsfile : string
+#   pathname of the observed catalog
+obsfile = "./M31-B09-EAST_chunk.st_with_sourceden.fits"
+
+# ------------------------------------------------------
+# Artificial Star Test Input File Generation Parameters
+# ------------------------------------------------------
+
+# ast_models_selected_per_age : integer
+# Number of models to pick per age (Default = 70).
+ast_models_selected_per_age = 70  # NOT USED in flux bin method
+
+# ast_bands_above_maglimit : integer
+# Number of filters that must be above the magnitude limit
+# for an AST to be included in the list (Default = 3)
+ast_bands_above_maglimit = 3  # NOT USED in flux bin method
+
+# ast_n_flux_bins : integer
+# The number of flux bins into which the dynamic range of the
+# model grid in each filter is divided
+ast_n_flux_bins = 40
+
+# ast_n_per_flux_bin : integer
+# Minimum number of model seds that need to fall into each bin
+ast_n_per_flux_bin = 50
+
+# ast_realization_per_model : integer
+# Number of Realizations of each included AST model
+# to be put into the list. (Default = 20)
+ast_realization_per_model = 1  # for the toothpick model (NOT truncheon)
+
+
+# ast_maglimit : float (single value or array with one value per filter)
+# (1) option 1: [number] to change the number of mags fainter than
+#                  the 90th percentile
+#               faintest star in the photometry catalog to be used for
+#                  the mag cut.
+#               (Default = 1)
+# (2) option 2: [space-separated list of numbers] to set custom faint end limits
+#               (one value for each band).
+ast_maglimit = [1.0]  # NOT USED for this production run
+
+# ast_with_positions :  (bool,optional)
+# If True, the ast list is produced with X,Y positions.
+# If False, the ast list is produced with only magnitudes.
+ast_with_positions = True
+
+# ast_density_table :  (string,optional)
+# Name of density table created by
+# tools/create_background_density_map.py, containing either the source
+# density map or the background density map. If supplied, the ASTs will
+# be repeated for each density bin in the table
+ast_density_table = './M31-B09-EAST_chunk.st_sourceden_map.hd5'
+
+# ast_N_bins : (int, optional)
+# Number of source or background bins that you want ASTs repeated over
+ast_N_bins = 26
+
+
+# ast_pixel_distribution : float (optional)
+# (Used if ast_with_positions is True), minimum pixel separation between AST
+# position and catalog star used to determine the AST spatial distribution
+ast_pixel_distribution = 10.0
+
+# ast_reference_image : string (optional, but required if ast_with_positions
+# is True and no X and Y information  is present in the photometry catalog)
+# Name of the reference image used by DOLPHOT when running the measured
+# photometry.
+ast_reference_image = "M31-B09-EAST_chunk_F475W_drz.chip1.fits"
+
+# ast_coord_boundary : None, or list of two arrays (optional)
+# If supplied, these RA/Dec coordinates will be used to limit the region
+# over which ASTs are generated.  Input should be list of two arrays, the
+# first RA and the second Dec, ordered sequentially around the region
+# (either CW or CCW).
+ast_coord_boundary = None
+
+
+# -------------------------------------------
+# Noise Model Artificial Star Test Parameters
+# -------------------------------------------
+
+# astfile : string
+#   pathname of the AST files (single camera ASTs)
+astfile = "./M31-B09-EAST_chunk.gst.fake.fits"
+
+# ast_colnames : list of strings
+#   names of columns for filters in the AST catalog (AC)
+ast_colnames = np.array(basefilters)
+
+# noisefile : string
+#   create a name for the noise model
+noisefile = project + "/" + project + "_noisemodel.hd5"
+
+# absflux calibration covariance matrix for HST specific filters (AC)
+absflux_a_matrix = absflux_covmat.hst_frac_matrix(filters)
+
+# -------------------------------------------
+# Grid
+# -------------------------------------------
+
+# n_subgrid : integer
+#     Number of sub-grids to use (1 means no subgrids).  These are
+#     useful when the physics model grid is too large to read into
+#     memory.
+n_subgrid = 1
+
+################
+
+# Distance/Velocity
+
+# Distances: distance to the galaxy [min, max, step] or [fixed number]
+distances = [24.79]#[24.29, 25.29, 0.25] #number was originally 24.79
+#used 2013AJ....146...86T as a reference
+distance_prior_model = {"name": "flat"}
+
+# Distance unit (any length or units.mag)
+distance_unit = units.mag
+
+# velocity of galaxy
+# velocity should be heliocentric
+velocity = -179 * units.km / units.s
+
+################
+
+# Stellar grid definition
+
+# log10(Age) -- [min,max,step] to generate the isochrones in years
+#   example [6.0, 10.13, 1.0]
+logt = [6.0, 10.13, 1.0]
+age_prior_model = {'name': 'flat'}
+
+# note: Mass is not sampled, instead the isochrone supplied
+#       mass spacing is used instead
+mass_prior_model = {'name': "kroupa"}
+
+# Metallicity : list of floats
+#   Here: Z == Z_initial, NOT Z(t) surface abundance
+#   PARSECv1.2S accepts values 1.e-4 < Z < 0.06
+#   example z = [0.03, 0.019, 0.008, 0.004]
+#   can they be set as [min, max, step]?
+z = ([0.03, 0.019, 0.008, 0.004])
+met_prior_model = {'name': 'flat'}
+#    10 ** np.array([-2.1, -1.8, -1.5, -1.2, -0.9, -0.6, -0.3, 0.0, 0.3]) * 0.0152
+#).tolist()
+
+# Isochrone Model Grid
+#   Current Choices: Padova or MIST
+#   PadovaWeb() -- `modeltype` param for iso sets from ezpadova
+#      (choices: parsec12s_r14, parsec12s, 2010, 2008, 2002)
+#   MISTWeb() -- `rotation` param (choices: vvcrit0.0=default, vvcrit0.4)
+#
+# Default: PARSEC+CALIBRI
+oiso = isochrone.PadovaWeb()
+# Alternative: PARSEC1.2S -- old grid parameters
+# oiso = isochrone.PadovaWeb(modeltype='parsec12s', filterPMS=True)
+# Alternative: MIST -- v1, no rotation
+# oiso = isochrone.MISTWeb()
+
+# Stellar Atmospheres library definition
+osl = stellib.Tlusty() + stellib.Kurucz()
+
+################
+
+# Dust extinction grid definition
+extLaw = extinction.Generalized_RvFALaw(ALaw=extinction.Generalized_DustExt(curve="F19"),    BLaw=extinction.Generalized_DustExt(curve="G03_SMCBar"))
+
+# A(V): dust column in magnitudes
+#   acceptable avs > 0.0
+#   example [min, max, step] = [0.0, 10.055, 1.0]
+avs = [0.01, 10.0, 1.0]
+av_prior_model = {"name": "flat"}
+# av_prior_model = {'name': 'lognormal',
+#                  'max_pos': 2.0,
+#                  'sigma': 1.0,
+#                  'N': 10.}
+
+# R(V): dust average grain size
+#   example [min, max, step] = [2.0,6.0,1.0]
+rvs = [2.0, 6.0, 1.0]
+rv_prior_model = {"name": "flat"}
+# rv_prior_model = {'name': 'lognormal',
+#                  'max_pos': 2.0,
+#                  'sigma': 1.0,
+#                  'N': 10.}
+
+# fA: mixture factor between "MW" and "SMCBar" extinction curves
+#   example [min, max, step] = [0.0,1.0, 0.25]
+fAs = [0.0, 1.0, 0.25]
+fA_prior_model = {"name": "flat"}
+# fA_prior_model = {'name': 'lognormal',
+#                  'max_pos': 0.5,
+#                  'sigma': 0.2,
+#                  'N': 10.}
+
+################
+
+# add in the standard filters to enable output of stats and pdf1d values
+# for the observed fitlers (AC)
+add_spectral_properties_kwargs = dict(filternames=filters)
diff --git a/phat_small/beast_settings.txt b/phat_small/beast_settings.txt
index 4d172a9..3b310d6 100755
--- a/phat_small/beast_settings.txt
+++ b/phat_small/beast_settings.txt
@@ -71,6 +71,14 @@ ast_models_selected_per_age = 70
 # for an AST to be included in the list (Default = 3)
 ast_bands_above_maglimit = 3
 
+# ast_n_flux_bins : integer
+# The number of flux bins into which the dynamic range of the
+# model grid in each filter is divided
+ast_n_flux_bins = 40
+
+# ast_n_per_flux_bin : integer
+# Minimum number of model seds that need to fall into each bin
+ast_n_per_flux_bin = 50
 
 # ast_realization_per_model : integer
 # Number of Realizations of each included AST model
diff --git a/phat_small_multidistance/beast_settings.txt b/phat_small_multidistance/beast_settings.txt
index 2e37a76..0f6b7b9 100755
--- a/phat_small_multidistance/beast_settings.txt
+++ b/phat_small_multidistance/beast_settings.txt
@@ -70,6 +70,14 @@ ast_models_selected_per_age = 70
 # for an AST to be included in the list (Default = 3)
 ast_bands_above_maglimit = 3
 
+# ast_n_flux_bins : integer
+# The number of flux bins into which the dynamic range of the
+# model grid in each filter is divided
+ast_n_flux_bins = 40
+
+# ast_n_per_flux_bin : integer
+# Minimum number of model seds that need to fall into each bin
+ast_n_per_flux_bin = 50
 
 # ast_realization_per_model : integer
 # Number of Realizations of each included AST model