diff --git a/02_coords.ipynb b/02_coords.ipynb
index 1d02ecf..b910f38 100644
--- a/02_coords.ipynb
+++ b/02_coords.ipynb
@@ -1,5 +1,45 @@
 {
  "cells": [
+  {
+   "cell_type": "raw",
+   "metadata": {
+    "tags": [
+     "remove-cell"
+    ]
+   },
+   "source": [
+    "---\n",
+    "title: \"Coordinate Transformations\"\n",
+    "teaching: 3000\n",
+    "exercises: 0\n",
+    "questions:\n",
+    "\n",
+    "- \"How do we transform celestial coordinates from one frame to another and save results in files?\"\n",
+    "\n",
+    "objectives:\n",
+    "\n",
+    "- \"Use Python string formatting to compose more complex ADQL queries.\"\n",
+    "\n",
+    "- \"Work with coordinates and other quantities that have units.\"\n",
+    "\n",
+    "- \"Download the results of a query and store them in a file.\"\n",
+    "\n",
+    "keypoints:\n",
+    "\n",
+    "- \"For measurements with units, use `Quantity` objects that represent units explicitly and check for errors.\"\n",
+    "\n",
+    "- \"Use the `format` function to compose queries; it is often faster and less error-prone.\"\n",
+    "\n",
+    "- \"Develop queries incrementally: start with something simple, test it, and add a little bit at a time.\"\n",
+    "\n",
+    "- \"Once you have a query working, save the data in a local file.  If you shut down the notebook and come back to it later, you can reload the file; you don't have to run the query again.\"\n",
+    "\n",
+    "---\n",
+    "FIXME\n",
+    "\n",
+    "{% include links.md %}\n"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -1955,6 +1995,7 @@
   }
  ],
  "metadata": {
+  "celltoolbar": "Tags",
   "kernelspec": {
    "display_name": "Python 3",
    "language": "python",
diff --git a/03_motion.ipynb b/03_motion.ipynb
index ac31939..6e85b0b 100644
--- a/03_motion.ipynb
+++ b/03_motion.ipynb
@@ -1,5 +1,49 @@
 {
  "cells": [
+  {
+   "cell_type": "raw",
+   "metadata": {
+    "tags": [
+     "remove-cell"
+    ]
+   },
+   "source": [
+    "---\n",
+    "title: \"Plotting and Pandas\"\n",
+    "teaching: 3000\n",
+    "exercises: 0\n",
+    "questions:\n",
+    "\n",
+    "- \"How do we make scatter plots in Matplotlib?\"\n",
+    "\n",
+    "- \"How do we store data in a Pandas `DataFrame`?\"\n",
+    "\n",
+    "objectives:\n",
+    "\n",
+    "- \"Select rows and columns from an Astropy `Table`.\"\n",
+    "\n",
+    "- \"Use Matplotlib to make a scatter plot.\"\n",
+    "\n",
+    "- \"Use Gala to transform coordinates.\"\n",
+    "\n",
+    "- \"Make a Pandas `DataFrame` and use a Boolean `Series` to select rows.\"\n",
+    "\n",
+    "- \"Save a `DataFrame` in an HDF5 file.\"\n",
+    "\n",
+    "keypoints:\n",
+    "\n",
+    "- \"When you make a scatter plot, adjust the size of the markers and their transparency so the figure is not overplotted; otherwise it can misrepresent the data badly.\n",
+    "\n",
+    "- \"For simple scatter plots in Matplotlib, `plot` is faster than `scatter`.\n",
+    "\n",
+    "- \"An Astropy `Table` and a Pandas `DataFrame` are similar in many ways and they provide many of the same functions.  They have pros and cons, but for many projects, either one would be a reasonable choice.\"\n",
+    "\n",
+    "---\n",
+    "FIXME\n",
+    "\n",
+    "{% include links.md %}\n"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -1075,7 +1119,7 @@
    "source": [
     "## Exploring data\n",
     "\n",
-    "One benefit of using Pandas is that it provides function for exploring the data and checking for problems.\n",
+    "One benefit of using Pandas is that it provides functions for exploring the data and checking for problems.\n",
     "\n",
     "One of the most useful of these functions is `describe`, which computes summary statistics for each column."
    ]
diff --git a/04_select.ipynb b/04_select.ipynb
index 331f0a5..a633d49 100644
--- a/04_select.ipynb
+++ b/04_select.ipynb
@@ -1,5 +1,45 @@
 {
  "cells": [
+  {
+   "cell_type": "raw",
+   "metadata": {
+    "tags": [
+     "remove-cell"
+    ]
+   },
+   "source": [
+    "---\n",
+    "title: \"Transform and Select\"\n",
+    "teaching: 3000\n",
+    "exercises: 0\n",
+    "questions:\n",
+    "\n",
+    "- \"Question?\"\n",
+    "\n",
+    "objectives:\n",
+    "\n",
+    "- \"Transform proper motions from one frame to another.\"\n",
+    "\n",
+    "- \"Compute the convex hull of a set of points.\"\n",
+    "\n",
+    "- \"Write an ADQL query that selects based on proper motion.\"\n",
+    "\n",
+    "- \"Save data in CSV format.\"\n",
+    "\n",
+    "keypoints:\n",
+    "\n",
+    "- \"When possible, 'move the computation to the data'; that is, do as much of the work as possible on the database server before downloading the data.\"\n",
+    "\n",
+    "- \"For most applications, saving data in FITS or HDF5 is better than CSV.  FITS and HDF5 are binary formats, so the files are usually smaller, and they store metadata, so you don't lose anything when you read the file back.\"\n",
+    "\n",
+    "- \"On the other hand, CSV is a 'least common denominator' format; that is, it can be read by practically any application that works with data.\"\n",
+    "\n",
+    "---\n",
+    "FIXME\n",
+    "\n",
+    "{% include links.md %}\n"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -35,9 +75,13 @@
     "\n",
     "After completing this lesson, you should be able to\n",
     "\n",
-    "* Convert proper motion between frames.\n",
+    "* Transform proper motions from one frame to another.\n",
     "\n",
-    "* Write an ADQL query that selects based on proper motion."
+    "* Compute the convex hull of a set of points.\n",
+    "\n",
+    "* Write an ADQL query that selects based on proper motion.\n",
+    "\n",
+    "* Save data in CSV format."
    ]
   },
   {
@@ -118,6 +162,16 @@
    "source": [
     "## Selection by proper motion\n",
     "\n",
+    "Let's review how we got to this point.\n",
+    "\n",
+    "1. We made an ADQL query to the Gaia server to get data for stars in the vicinity of GD-1.\n",
+    "\n",
+    "2. We transformed the coordinates to the `GD1Koposov10` frame so we could select stars along the centerline of GD-1.\n",
+    "\n",
+    "3. We plotted the proper motion of the centerline stars to identify the bounds of the overdense region.\n",
+    "\n",
+    "4. We made a mask that selects stars whose proper motion is in the overdense region.\n",
+    "\n",
     "At this point we have downloaded data for a relatively large number of stars (more than 100,000) and selected a relatively small number (around 1000).\n",
     "\n",
     "It would be more efficient to use ADQL to select only the stars we need.  That would also make it possible to download data covering a larger region of the sky.\n",
@@ -249,12 +303,18 @@
    "source": [
     "The proper motions of the selected stars are more spread out in this frame, which is why it was preferable to do the selection in the GD-1 frame.\n",
     "\n",
-    "But now we can define a polygon that encloses the proper motions of these stars in ICRS, \n",
-    "and use the polygon as a selection criterion in an ADQL query.\n",
+    "But now we can define a polygon that encloses the proper motions of these stars in ICRS, and use that polygon as a selection criterion in an ADQL query."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Convex Hull\n",
     "\n",
     "SciPy provides a function that computes the [convex hull](https://en.wikipedia.org/wiki/Convex_hull) of a set of points, which is the smallest convex polygon that contains all of the points.\n",
     "\n",
-    "To use it, I'll select columns `pmra` and `pmdec` and convert them to a NumPy array."
+    "To use it, we'll select columns `pmra` and `pmdec` and convert them to a NumPy array."
    ]
   },
   {
@@ -289,8 +349,13 @@
     "```\n",
     "points = selected[['pmra','pmdec']].values\n",
     "\n",
-    "```\n",
-    "\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
     "We'll pass the points to `ConvexHull`, which returns an object that contains the results. "
    ]
   },
@@ -302,7 +367,7 @@
     {
      "data": {
       "text/plain": [
-       "<scipy.spatial.qhull.ConvexHull at 0x7f8dec2b8c10>"
+       "<scipy.spatial.qhull.ConvexHull at 0x7f0d570cd700>"
       ]
      },
      "execution_count": 9,
@@ -408,6 +473,8 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
+    "This use of `transpose` is a bit of a NumPy trick.  Because `pm_vertices` has two columns, its transpose has two rows, which are assigned to the two variables `pmra_poly` and `pmdec_poly`.\n",
+    "\n",
     "The following figure shows proper motion in ICRS again, along with the convex hull we just computed."
    ]
   },
@@ -451,6 +518,45 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
+    "So `pm_vertices` represents the polygon we want to select.\n",
+    "The next step is to use it as part of an ADQL query."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Assembling the query\n",
+    "\n",
+    "Here's the base string we used for the query in the previous lesson."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query_base = \"\"\"SELECT \n",
+    "{columns}\n",
+    "FROM gaiadr2.gaia_source\n",
+    "WHERE parallax < 1\n",
+    "  AND bp_rp BETWEEN -0.75 AND 2 \n",
+    "  AND 1 = CONTAINS(POINT(ra, dec), \n",
+    "                   POLYGON({point_list}))\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And here are the changes we'll make in this lesson:\n",
+    "\n",
+    "1. We will add another clause to select stars whose proper motion is in the polygon we just computed, `pm_vertices`.\n",
+    "\n",
+    "2. We will select stars with coordinates in a larger region.\n",
+    "\n",
     "To use `pm_vertices` as part of an ADQL query, we have to convert it to a string.\n",
     "\n",
     "We'll use `flatten` to convert from a 2-D array to a 1-D array, and `str` to convert each element to a string."
@@ -458,7 +564,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -498,7 +604,7 @@
        " '-14.7464117578883']"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -517,7 +623,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -526,7 +632,7 @@
        "'-4.050371212154984, -14.75623260987968, -3.4198108491382455, -14.723655456335619, -3.035219883740934, -14.443571352854612, -2.268479190206636, -13.714023598831554, -2.611722027231764, -13.247974712069263, -2.7347140078529106, -13.090544709622938, -3.199231461993783, -12.594265302440828, -3.34082545787549, -12.476119260818695, -5.674894125178565, -11.160833381392624, -5.95159272432137, -11.105478836426514, -6.423940229776128, -11.05981294804957, -7.096310230579248, -11.951878058650085, -7.306415190921692, -12.245599765990594, -7.040166963232815, -12.885807024935527, -6.0034770546523735, -13.759120984106968, -4.42442296194263, -14.7464117578883'"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -540,40 +646,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Selecting the region\n",
+    "We'll add this string to the query soon, but first let's compute the other polygon, the one that specifies the region of the sky we want.\n",
     "\n",
-    "Let's review how we got to this point.\n",
-    "\n",
-    "1. We made an ADQL query to the Gaia server to get data for stars in the vicinity of GD-1.\n",
-    "\n",
-    "2. We transformed to `GD1` coordinates so we could select stars along the centerline of GD-1.\n",
-    "\n",
-    "3. We plotted the proper motion of the centerline stars to identify the bounds of the overdense region.\n",
-    "\n",
-    "4. We made a mask that selects stars whose proper motion is in the overdense region.\n",
-    "\n",
-    "The problem is that we downloaded data for more than 100,000 stars and selected only about 1000 of them.\n",
-    "\n",
-    "It will be more efficient if we select on proper motion as part of the query.  That will allow us to work with a larger region of the sky in a single query, and download less unneeded data.\n",
-    "\n",
-    "This query will select on the following conditions:\n",
-    "\n",
-    "* `parallax < 1`\n",
-    "\n",
-    "* `bp_rp BETWEEN -0.75 AND 2`\n",
-    "\n",
-    "* Coordinates within a rectangle in the GD-1 frame, transformed to ICRS.\n",
-    "\n",
-    "* Proper motion with the polygon we just computed.\n",
-    "\n",
-    "The first three conditions are the same as in the previous query.  Only the last one is new.\n",
-    "\n",
-    "Here's the rectangle in the GD-1 frame we'll select."
+    "Here are the coordinates of the rectangle we'll select, in the GD-1 frame."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -585,7 +665,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -602,9 +682,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 19,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING: AstropyDeprecationWarning: Transforming a frame instance to a frame class (as opposed to another frame instance) will not be supported in the future.  Either explicitly instantiate the target frame, or first convert the source frame instance to a `astropy.coordinates.SkyCoord` and use its `transform_to()` method. [astropy.coordinates.baseframe]\n"
+     ]
+    }
+   ],
    "source": [
     "import gala.coordinates as gc\n",
     "import astropy.coordinates as coord\n",
@@ -622,7 +710,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
@@ -631,7 +719,7 @@
        "'135.30559858565638, 8.398623940157561, 126.50951508623503, 13.44494195652069, 163.0173655836748, 54.24242734020255, 172.9328536286811, 46.47260492416258'"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -650,21 +738,13 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Now we have everything we need to assemble the query."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Assemble the query\n",
-    "\n",
-    "Here's the base string we used for the query in the previous lesson."
+    "Now we have everything we need to assemble the query.\n",
+    "Here's the base query from the previous lesson again:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -682,12 +762,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Exercise:** Modify `query_base` by adding a new clause to select stars whose coordinates of proper motion, `pmra` and `pmdec`, fall within the polygon defined by `pm_point_list`."
+    "### Exercise\n",
+    "\n",
+    "Modify `query_base` by adding a new clause to select stars whose coordinates of proper motion, `pmra` and `pmdec`, fall within the polygon defined by `pm_point_list`."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 22,
    "metadata": {
     "tags": [
      "hide-cell"
@@ -718,23 +800,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [],
    "source": [
-    "columns = 'source_id, ra, dec, pmra, pmdec, parallax, parallax_error, radial_velocity'"
+    "columns = 'source_id, ra, dec, pmra, pmdec, parallax, radial_velocity'"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Exercise:** Use `format` to format `query_base` and define `query`, filling in the values of `columns`, `point_list`, and `pm_point_list`."
+    "### Exercise\n",
+    "\n",
+    "Use `format` to format `query_base` and define `query`, filling in the values of `columns`, `point_list`, and `pm_point_list`."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 24,
    "metadata": {
     "tags": [
      "hide-cell"
@@ -746,7 +830,7 @@
      "output_type": "stream",
      "text": [
       "SELECT \n",
-      "source_id, ra, dec, pmra, pmdec, parallax, parallax_error, radial_velocity\n",
+      "source_id, ra, dec, pmra, pmdec, parallax, radial_velocity\n",
       "FROM gaiadr2.gaia_source\n",
       "WHERE parallax < 1\n",
       "  AND bp_rp BETWEEN -0.75 AND 2 \n",
@@ -771,12 +855,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Here's how we run it."
+    "Now we can run the query like this:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 25,
    "metadata": {
     "scrolled": true
    },
@@ -794,19 +878,23 @@
       "\tHost: geadata.esac.esa.int\n",
       "\tUse HTTPS: True\n",
       "\tPort: 443\n",
-      "\tSSL Port: 443\n"
-     ]
-    },
-    {
-     "ename": "HTTPError",
-     "evalue": "OK",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mHTTPError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-24-05c718e32682>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mastroquery\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgaia\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mGaia\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mjob\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mGaia\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlaunch_job_async\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mquery\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      4\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mjob\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/AstronomicalData/lib/python3.8/site-packages/astroquery/utils/tap/core.py\u001b[0m in \u001b[0;36mlaunch_job_async\u001b[0;34m(self, query, name, output_file, output_format, verbose, dump_to_file, background, upload_resource, upload_table_name, autorun)\u001b[0m\n\u001b[1;32m    422\u001b[0m                 self.__connHandler.dump_to_file(suitableOutputFile,\n\u001b[1;32m    423\u001b[0m                                                 response)\n\u001b[0;32m--> 424\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mrequests\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexceptions\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mHTTPError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresponse\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreason\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    425\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    426\u001b[0m             location = self.__connHandler.find_header(\n",
-      "\u001b[0;31mHTTPError\u001b[0m: OK"
+      "\tSSL Port: 443\n",
+      "INFO: Query finished. [astroquery.utils.tap.core]\n",
+      "<Table length=7346>\n",
+      "      name       dtype    unit                              description                             n_bad\n",
+      "--------------- ------- -------- ------------------------------------------------------------------ -----\n",
+      "      source_id   int64          Unique source identifier (unique within a particular Data Release)     0\n",
+      "             ra float64      deg                                                    Right ascension     0\n",
+      "            dec float64      deg                                                        Declination     0\n",
+      "           pmra float64 mas / yr                         Proper motion in right ascension direction     0\n",
+      "          pmdec float64 mas / yr                             Proper motion in declination direction     0\n",
+      "       parallax float64      mas                                                           Parallax     0\n",
+      "radial_velocity float64   km / s                                                    Radial velocity  7295\n",
+      "Jobid: 1607614394159O\n",
+      "Phase: COMPLETED\n",
+      "Owner: None\n",
+      "Output file: async_20201210103314.vot\n",
+      "Results: None\n"
      ]
     }
    ],
@@ -826,9 +914,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 26,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "7346"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "candidate_table = job.get_results()\n",
     "len(candidate_table)"
@@ -845,9 +944,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 27,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "x = candidate_table['ra']\n",
     "y = candidate_table['dec']\n",
@@ -868,7 +980,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -880,10 +992,10 @@
     "    returns: Pandas DataFrame\n",
     "    \"\"\"\n",
     "    skycoord = coord.SkyCoord(\n",
-    "               ra=results['ra'], \n",
-    "               dec=results['dec'],\n",
-    "               pm_ra_cosdec=results['pmra'],\n",
-    "               pm_dec=results['pmdec'], \n",
+    "               ra=table['ra'], \n",
+    "               dec=table['dec'],\n",
+    "               pm_ra_cosdec=table['pmra'],\n",
+    "               pm_dec=table['pmdec'], \n",
     "               distance=8*u.kpc, \n",
     "               radial_velocity=0*u.km/u.s)\n",
     "\n",
@@ -907,7 +1019,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -923,9 +1035,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 32,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "x = candidate_df['phi1']\n",
     "y = candidate_df['phi2']\n",
@@ -966,7 +1091,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -984,9 +1109,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 34,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-rw-rw-r-- 1 downey downey 698K Dec 10 19:18 gd1_candidates.hdf5\r\n"
+     ]
+    }
+   ],
    "source": [
     "!ls -lh gd1_candidates.hdf5"
    ]
@@ -1023,7 +1156,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1039,9 +1172,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 36,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-rw-rw-r-- 1 downey downey 1.4M Dec 10 19:19 gd1_candidates.csv\r\n"
+     ]
+    }
+   ],
    "source": [
     "!ls -lh gd1_candidates.csv"
    ]
@@ -1057,9 +1198,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 37,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      ",source_id,ra,dec,pmra,pmdec,parallax,radial_velocity,phi1,phi2,pm_phi1,pm_phi2\r\n",
+      "0,635559124339440000,137.58671691646745,19.1965441084838,-3.770521900009566,-12.490481778113859,0.7913934419894347,,-59.63048941944402,-1.2164852515042963,-7.361362712597496,-0.592632882064492\r\n",
+      "1,635860218726658176,138.5187065217173,19.09233926905897,-5.941679495793577,-11.346409129876392,0.30745551377348623,,-59.247329893833296,-2.016078400820631,-7.527126084640531,1.7487794924176672\r\n"
+     ]
+    }
+   ],
    "source": [
     "!head -3 gd1_candidates.csv"
    ]
@@ -1075,7 +1226,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1091,18 +1242,218 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 39,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>source_id</th>\n",
+       "      <th>ra</th>\n",
+       "      <th>dec</th>\n",
+       "      <th>pmra</th>\n",
+       "      <th>pmdec</th>\n",
+       "      <th>parallax</th>\n",
+       "      <th>radial_velocity</th>\n",
+       "      <th>phi1</th>\n",
+       "      <th>phi2</th>\n",
+       "      <th>pm_phi1</th>\n",
+       "      <th>pm_phi2</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>635559124339440000</td>\n",
+       "      <td>137.586717</td>\n",
+       "      <td>19.196544</td>\n",
+       "      <td>-3.770522</td>\n",
+       "      <td>-12.490482</td>\n",
+       "      <td>0.791393</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-59.630489</td>\n",
+       "      <td>-1.216485</td>\n",
+       "      <td>-7.361363</td>\n",
+       "      <td>-0.592633</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>635860218726658176</td>\n",
+       "      <td>138.518707</td>\n",
+       "      <td>19.092339</td>\n",
+       "      <td>-5.941679</td>\n",
+       "      <td>-11.346409</td>\n",
+       "      <td>0.307456</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-59.247330</td>\n",
+       "      <td>-2.016078</td>\n",
+       "      <td>-7.527126</td>\n",
+       "      <td>1.748779</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>635674126383965568</td>\n",
+       "      <td>138.842874</td>\n",
+       "      <td>19.031798</td>\n",
+       "      <td>-3.897001</td>\n",
+       "      <td>-12.702780</td>\n",
+       "      <td>0.779463</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-59.133391</td>\n",
+       "      <td>-2.306901</td>\n",
+       "      <td>-7.560608</td>\n",
+       "      <td>-0.741800</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            source_id          ra        dec      pmra      pmdec  parallax  \\\n",
+       "0  635559124339440000  137.586717  19.196544 -3.770522 -12.490482  0.791393   \n",
+       "1  635860218726658176  138.518707  19.092339 -5.941679 -11.346409  0.307456   \n",
+       "2  635674126383965568  138.842874  19.031798 -3.897001 -12.702780  0.779463   \n",
+       "\n",
+       "   radial_velocity       phi1      phi2   pm_phi1   pm_phi2  \n",
+       "0              NaN -59.630489 -1.216485 -7.361363 -0.592633  \n",
+       "1              NaN -59.247330 -2.016078 -7.527126  1.748779  \n",
+       "2              NaN -59.133391 -2.306901 -7.560608 -0.741800  "
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "candidate_df.head(3)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 40,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Unnamed: 0</th>\n",
+       "      <th>source_id</th>\n",
+       "      <th>ra</th>\n",
+       "      <th>dec</th>\n",
+       "      <th>pmra</th>\n",
+       "      <th>pmdec</th>\n",
+       "      <th>parallax</th>\n",
+       "      <th>radial_velocity</th>\n",
+       "      <th>phi1</th>\n",
+       "      <th>phi2</th>\n",
+       "      <th>pm_phi1</th>\n",
+       "      <th>pm_phi2</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>635559124339440000</td>\n",
+       "      <td>137.586717</td>\n",
+       "      <td>19.196544</td>\n",
+       "      <td>-3.770522</td>\n",
+       "      <td>-12.490482</td>\n",
+       "      <td>0.791393</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-59.630489</td>\n",
+       "      <td>-1.216485</td>\n",
+       "      <td>-7.361363</td>\n",
+       "      <td>-0.592633</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>635860218726658176</td>\n",
+       "      <td>138.518707</td>\n",
+       "      <td>19.092339</td>\n",
+       "      <td>-5.941679</td>\n",
+       "      <td>-11.346409</td>\n",
+       "      <td>0.307456</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-59.247330</td>\n",
+       "      <td>-2.016078</td>\n",
+       "      <td>-7.527126</td>\n",
+       "      <td>1.748779</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>635674126383965568</td>\n",
+       "      <td>138.842874</td>\n",
+       "      <td>19.031798</td>\n",
+       "      <td>-3.897001</td>\n",
+       "      <td>-12.702780</td>\n",
+       "      <td>0.779463</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-59.133391</td>\n",
+       "      <td>-2.306901</td>\n",
+       "      <td>-7.560608</td>\n",
+       "      <td>-0.741800</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Unnamed: 0           source_id          ra        dec      pmra      pmdec  \\\n",
+       "0           0  635559124339440000  137.586717  19.196544 -3.770522 -12.490482   \n",
+       "1           1  635860218726658176  138.518707  19.092339 -5.941679 -11.346409   \n",
+       "2           2  635674126383965568  138.842874  19.031798 -3.897001 -12.702780   \n",
+       "\n",
+       "   parallax  radial_velocity       phi1      phi2   pm_phi1   pm_phi2  \n",
+       "0  0.791393              NaN -59.630489 -1.216485 -7.361363 -0.592633  \n",
+       "1  0.307456              NaN -59.247330 -2.016078 -7.527126  1.748779  \n",
+       "2  0.779463              NaN -59.133391 -2.306901 -7.560608 -0.741800  "
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "read_back_csv.head(3)"
    ]
diff --git a/05_join.ipynb b/05_join.ipynb
index fab853e..ef07f88 100644
--- a/05_join.ipynb
+++ b/05_join.ipynb
@@ -1,5 +1,35 @@
 {
  "cells": [
+  {
+   "cell_type": "raw",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "title: \"Join\"\n",
+    "teaching: 3000\n",
+    "exercises: 0\n",
+    "questions:\n",
+    "\n",
+    "- \"How do we use `JOIN` to combine information from multiple tables?\"\n",
+    "\n",
+    "objectives:\n",
+    "\n",
+    "- \"Upload a table to the Gaia server.\"\n",
+    "\n",
+    "- \"Write ADQL queries involving `JOIN` operations.\"\n",
+    "\n",
+    "keypoints:\n",
+    "\n",
+    "- \"Use `JOIN` operations to combine data from multiple tables in a databased, using some kind of identifier to match up records from one table with records from another.\"\n",
+    "\n",
+    "* \"This is another example of a practice we saw in the previous notebook, moving the computation to the data.\"\n",
+    "\n",
+    "---\n",
+    "FIXME\n",
+    "\n",
+    "{% include links.md %}\n"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -10,11 +40,11 @@
     "\n",
     "As a continuing example, we will replicate part of the analysis in a recent paper, \"[Off the beaten path: Gaia reveals GD-1 stars outside of the main stream](https://arxiv.org/abs/1805.00425)\" by Adrian M. Price-Whelan and Ana Bonaca.\n",
     "\n",
-    "Picking up where we left off, the next step in the analysis is to select candidate stars based on photometry.  The following figure from the paper is a color-magnitude diagram for the stars selected based on proper motion:\n",
+    "Picking up where we left off, the next step in the analysis is to select candidate stars based on photometry data.  The following figure from the paper is a color-magnitude diagram for the stars selected based on proper motion:\n",
     "\n",
     "<img width=\"300\" src=\"https://github.com/datacarpentry/astronomy-python/raw/gh-pages/fig/gd1-3.png\">\n",
     "\n",
-    "In red is a theoretical isochrone, showing where we expect the stars in GD-1 to fall based on the metallicity and age of their original globular cluster. \n",
+    "In red is a [stellar isochrone](https://en.wikipedia.org/wiki/Stellar_isochrone), showing where we expect the stars in GD-1 to fall based on the metallicity and age of their original globular cluster. \n",
     "\n",
     "By selecting stars in the shaded area, we can further distinguish the main sequence of GD-1 from younger background stars."
    ]
@@ -311,7 +341,7 @@
      "output_type": "stream",
      "text": [
       "<?xml version=\"1.0\" encoding=\"utf-8\"?>\r\n",
-      "<!-- Produced with astropy.io.votable version 4.0.2\r\n",
+      "<!-- Produced with astropy.io.votable version 4.2\r\n",
       "     http://www.astropy.org/ -->\r\n",
       "<VOTABLE version=\"1.4\" xmlns=\"http://www.ivoa.net/xml/VOTable/v1.4\" xmlns:xsi=\"http://www.w3.org/2001/XMLSchema-instance\" xsi:noNamespaceSchemaLocation=\"http://www.ivoa.net/xml/VOTable/v1.4\">\r\n",
       " <RESOURCE type=\"results\">\r\n",
@@ -350,7 +380,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "-rw-rw-r-- 1 downey downey 396K Nov 18 19:21 candidate_df.xml\r\n"
+      "-rw-rw-r-- 1 downey downey 396K Dec 10 11:33 candidate_df.xml\r\n"
      ]
     }
    ],
@@ -373,7 +403,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Exercise:** There's a gotcha here we want to warn you about.  Why do you think we used double brackets to specify the column we wanted?  What happens if you use single brackets?\n",
+    "### Exercise\n",
+    "\n",
+    "There's a gotcha here we want to warn you about.  Why do you think we used double brackets to specify the column we wanted?  What happens if you use single brackets?\n",
     "\n",
     "Run these cells to find out."
    ]
@@ -426,6 +458,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "# This line is commented out because it would cause an error\n",
+    "\n",
     "# writeto(column, 'candidate_df.xml')"
    ]
   },
@@ -517,7 +551,7 @@
      "data": {
       "text/html": [
        "<i>Table length=7346</i>\n",
-       "<table id=\"table140178070186304\" class=\"table-striped table-bordered table-condensed\">\n",
+       "<table id=\"table140668290919488\" class=\"table-striped table-bordered table-condensed\">\n",
        "<thead><tr><th>source_id</th></tr></thead>\n",
        "<thead><tr><th>int64</th></tr></thead>\n",
        "<tr><td>635559124339440000</td></tr>\n",
@@ -655,7 +689,7 @@
     "query1 = \"\"\"SELECT *\n",
     "FROM gaiadr2.panstarrs1_best_neighbour as best\n",
     "JOIN tap_upload.candidate_df as candidate_df\n",
-    "ON best.source_id = candidate_df.source_id\n",
+    "  ON best.source_id = candidate_df.source_id\n",
     "\"\"\""
    ]
   },
@@ -713,7 +747,7 @@
      "data": {
       "text/html": [
        "<i>Table length=3724</i>\n",
-       "<table id=\"table140178046436112\" class=\"table-striped table-bordered table-condensed\">\n",
+       "<table id=\"table140668268803072\" class=\"table-striped table-bordered table-condensed\">\n",
        "<thead><tr><th>source_id</th><th>original_ext_source_id</th><th>angular_distance</th><th>number_of_neighbours</th><th>number_of_mates</th><th>best_neighbour_multiplicity</th><th>gaia_astrometric_params</th><th>source_id_2</th></tr></thead>\n",
        "<thead><tr><th></th><th></th><th>arcsec</th><th></th><th></th><th></th><th></th><th></th></tr></thead>\n",
        "<thead><tr><th>int64</th><th>int64</th><th>float64</th><th>int32</th><th>int16</th><th>int16</th><th>int16</th><th>int64</th></tr></thead>\n",
@@ -819,7 +853,7 @@
    "source": [
     "Because one of the column names appears in both tables, the second instance of `source_id` has been appended with the suffix `_2`.\n",
     "\n",
-    "The length of the results table is about 2000, which means we were not able to find matches for all stars in the list of candidate_df."
+    "The length of `results1` is about 3000, which means we were not able to find matches for all stars in the list of candidates."
    ]
   },
   {
@@ -846,9 +880,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "To get more information about the matching process, we can inspect `best_neighbour_multiplicity`, which indicates for each star in Gaia how many stars in Pan-STARRS are equally likely matches.\n",
-    "\n",
-    "For this kind of data exploration, we'll convert a column from the table to a Pandas `Series` so we can use `value_counts`, which counts the number of times each value appears in a `Series`, like a histogram."
+    "To get more information about the matching process, we can inspect `best_neighbour_multiplicity`, which indicates for each star in Gaia how many stars in Pan-STARRS are equally likely matches."
    ]
   },
   {
@@ -858,9 +890,63 @@
    "outputs": [
     {
      "data": {
+      "text/html": [
+       "&lt;MaskedColumn name=&apos;best_neighbour_multiplicity&apos; dtype=&apos;int16&apos; description=&apos;Number of neighbours with same probability as best neighbour&apos; length=3724&gt;\n",
+       "<table>\n",
+       "<tr><td>1</td></tr>\n",
+       "<tr><td>1</td></tr>\n",
+       "<tr><td>1</td></tr>\n",
+       "<tr><td>1</td></tr>\n",
+       "<tr><td>1</td></tr>\n",
+       "<tr><td>1</td></tr>\n",
+       "<tr><td>1</td></tr>\n",
+       "<tr><td>1</td></tr>\n",
+       "<tr><td>1</td></tr>\n",
+       "<tr><td>1</td></tr>\n",
+       "<tr><td>1</td></tr>\n",
+       "<tr><td>1</td></tr>\n",
+       "<tr><td>...</td></tr>\n",
+       "<tr><td>1</td></tr>\n",
+       "<tr><td>1</td></tr>\n",
+       "<tr><td>1</td></tr>\n",
+       "<tr><td>1</td></tr>\n",
+       "<tr><td>1</td></tr>\n",
+       "<tr><td>1</td></tr>\n",
+       "<tr><td>1</td></tr>\n",
+       "<tr><td>1</td></tr>\n",
+       "<tr><td>1</td></tr>\n",
+       "<tr><td>1</td></tr>\n",
+       "<tr><td>1</td></tr>\n",
+       "<tr><td>1</td></tr>\n",
+       "</table>"
+      ],
       "text/plain": [
-       "1    3724\n",
-       "dtype: int64"
+       "<MaskedColumn name='best_neighbour_multiplicity' dtype='int16' description='Number of neighbours with same probability as best neighbour' length=3724>\n",
+       "  1\n",
+       "  1\n",
+       "  1\n",
+       "  1\n",
+       "  1\n",
+       "  1\n",
+       "  1\n",
+       "  1\n",
+       "  1\n",
+       "  1\n",
+       "  1\n",
+       "  1\n",
+       "...\n",
+       "  1\n",
+       "  1\n",
+       "  1\n",
+       "  1\n",
+       "  1\n",
+       "  1\n",
+       "  1\n",
+       "  1\n",
+       "  1\n",
+       "  1\n",
+       "  1\n",
+       "  1"
       ]
      },
      "execution_count": 22,
@@ -869,21 +955,16 @@
     }
    ],
    "source": [
-    "import pandas as pd\n",
-    "\n",
-    "nn = pd.Series(results1['best_neighbour_multiplicity'])\n",
-    "nn.value_counts()"
+    "results1['best_neighbour_multiplicity']"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The result shows that `1` is the only value in the `Series`, appearing xxx times.\n",
+    "It looks like most of the values are `1`, which is good; that means that for each candidate star we have identified exactly one source in Pan-STARRS that is likely to be the same star.\n",
     "\n",
-    "That means that in every case where a match was found, the matching algorithm identified a single neighbor as the most likely match.\n",
-    "\n",
-    "Similarly, `number_of_mates` indicates the number of other stars in Gaia that match with the same star in Pan-STARRS."
+    "To check whether there are any values other than `1`, we can convert this column to a Pandas `Series` and use `describe`, which we saw in in Lesson 3."
    ]
   },
   {
@@ -894,8 +975,15 @@
     {
      "data": {
       "text/plain": [
-       "0    3724\n",
-       "dtype: int64"
+       "count    3724.0\n",
+       "mean        1.0\n",
+       "std         0.0\n",
+       "min         1.0\n",
+       "25%         1.0\n",
+       "50%         1.0\n",
+       "75%         1.0\n",
+       "max         1.0\n",
+       "dtype: float64"
       ]
      },
      "execution_count": 23,
@@ -904,17 +992,57 @@
     }
    ],
    "source": [
-    "nm = pd.Series(results1['number_of_mates'])\n",
-    "nm.value_counts()"
+    "import pandas as pd\n",
+    "\n",
+    "multiplicity = pd.Series(results1['best_neighbour_multiplicity'])\n",
+    "multiplicity.describe()"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "For this set of candidate_df, almost all of the stars we've selected from Pan-STARRS are only matched with a single star in the Gaia catalog.\n",
+    "In fact, `1` is the only value in the `Series`, so every candidate star has a single best match.\n",
     "\n",
-    "**Detail** The table also contains `number_of_neighbors` which is the number of stars in Pan-STARRS that match in terms of position, before using other critieria to choose the most likely match."
+    "Similarly, `number_of_mates` indicates the number of *other* stars in Gaia that match with the same star in Pan-STARRS."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "count    3724.0\n",
+       "mean        0.0\n",
+       "std         0.0\n",
+       "min         0.0\n",
+       "25%         0.0\n",
+       "50%         0.0\n",
+       "75%         0.0\n",
+       "max         0.0\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mates = pd.Series(results1['number_of_mates'])\n",
+    "mates.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "All values in this column are `0`, which means that for each match we found in Pan-STARRS, there are no other stars in Gaia that also match. \n",
+    "\n",
+    "**Detail:** The table also contains `number_of_neighbors` which is the number of stars in Pan-STARRS that match in terms of position, before using other criteria to choose the most likely match."
    ]
   },
   {
@@ -935,14 +1063,21 @@
     "\n",
     "4. Run the query using the uploaded table.\n",
     "\n",
-    "Since we've done everything here before, we'll do these steps as an exercise.\n",
+    "Since we've done everything here before, we'll do these steps as an exercise."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Exercise\n",
     "\n",
-    "**Exercise:** Select `source_id` and `original_ext_source_id` from `results1` and write the resulting table as a file named `external.xml`."
+    "Select `source_id` and `original_ext_source_id` from `results1` and write the resulting table as a file named `external.xml`."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 25,
    "metadata": {
     "tags": [
      "hide-cell"
@@ -965,7 +1100,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
@@ -973,7 +1108,7 @@
      "output_type": "stream",
      "text": [
       "<?xml version=\"1.0\" encoding=\"utf-8\"?>\r\n",
-      "<!-- Produced with astropy.io.votable version 4.0.2\r\n",
+      "<!-- Produced with astropy.io.votable version 4.2\r\n",
       "     http://www.astropy.org/ -->\r\n",
       "<VOTABLE version=\"1.4\" xmlns=\"http://www.ivoa.net/xml/VOTable/v1.4\" xmlns:xsi=\"http://www.w3.org/2001/XMLSchema-instance\" xsi:noNamespaceSchemaLocation=\"http://www.ivoa.net/xml/VOTable/v1.4\">\r\n",
       " <RESOURCE type=\"results\">\r\n",
@@ -993,7 +1128,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Exercise:** Read [the documentation of the Pan-STARRS table](https://gea.esac.esa.int/archive/documentation/GDR2/Gaia_archive/chap_datamodel/sec_dm_external_catalogues/ssec_dm_panstarrs1_original_valid.html) and make note of `obj_id`, which contains the object IDs we'll use to find the rows we want.\n",
+    "### Exercise\n",
+    "\n",
+    "Read [the documentation of the Pan-STARRS table](https://gea.esac.esa.int/archive/documentation/GDR2/Gaia_archive/chap_datamodel/sec_dm_external_catalogues/ssec_dm_panstarrs1_original_valid.html) and make note of `obj_id`, which contains the object IDs we'll use to find the rows we want.\n",
     "\n",
     "Write a query that uses each value of `original_ext_source_id` from the uploaded table to find a row in `gaiadr2.panstarrs1_original_valid` with the same value in `obj_id`, and select all columns from both tables.\n",
     "\n",
@@ -1017,23 +1154,6 @@
     "Hint: When you select a column from a join, you have to specify which table the column is in."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {
-    "tags": [
-     "hide-cell"
-    ]
-   },
-   "outputs": [],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "query2 = \"\"\"SELECT *\n",
-    "FROM tap_upload.external as external\n",
-    "\"\"\""
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 27,
@@ -1046,75 +1166,46 @@
    "source": [
     "# Solution\n",
     "\n",
+    "# First test\n",
+    "\n",
+    "query2 = \"\"\"SELECT *\n",
+    "FROM tap_upload.external as external\n",
+    "\"\"\"\n",
+    "\n",
+    "# Second test\n",
+    "\n",
     "query2 = \"\"\"SELECT TOP 10 *\n",
     "FROM gaiadr2.panstarrs1_original_valid\n",
-    "\"\"\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {
-    "tags": [
-     "hide-cell"
-    ]
-   },
-   "outputs": [],
-   "source": [
-    "# Solution\n",
+    "\"\"\"\n",
+    "\n",
+    "# Third test\n",
     "\n",
     "query2 = \"\"\"SELECT *\n",
     "FROM gaiadr2.panstarrs1_original_valid as ps\n",
     "JOIN tap_upload.external as external\n",
-    "ON ps.obj_id = external.original_ext_source_id\n",
-    "\"\"\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {
-    "tags": [
-     "hide-cell"
-    ]
-   },
-   "outputs": [],
-   "source": [
-    "# Solution\n",
+    "  ON ps.obj_id = external.original_ext_source_id\n",
+    "\"\"\"\n",
+    "\n",
+    "# Complete query\n",
     "\n",
     "query2 = \"\"\"SELECT\n",
     "external.source_id, ps.g_mean_psf_mag, ps.i_mean_psf_mag\n",
     "FROM gaiadr2.panstarrs1_original_valid as ps\n",
     "JOIN tap_upload.external as external\n",
-    "ON ps.obj_id = external.original_ext_source_id\n",
+    "  ON ps.obj_id = external.original_ext_source_id\n",
     "\"\"\""
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 30,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "SELECT\n",
-      "external.source_id, ps.g_mean_psf_mag, ps.i_mean_psf_mag\n",
-      "FROM gaiadr2.panstarrs1_original_valid as ps\n",
-      "JOIN tap_upload.external as external\n",
-      "ON ps.obj_id = external.original_ext_source_id\n",
-      "\n"
-     ]
-    }
-   ],
    "source": [
-    "print(query2)"
+    "Here's how we launch the job and get the results."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [
     {
@@ -1133,14 +1224,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
        "<i>Table length=3724</i>\n",
-       "<table id=\"table140178044101488\" class=\"table-striped table-bordered table-condensed\">\n",
+       "<table id=\"table140668299958448\" class=\"table-striped table-bordered table-condensed\">\n",
        "<thead><tr><th>source_id</th><th>g_mean_psf_mag</th><th>i_mean_psf_mag</th></tr></thead>\n",
        "<thead><tr><th></th><th></th><th>mag</th></tr></thead>\n",
        "<thead><tr><th>int64</th><th>float64</th><th>float64</th></tr></thead>\n",
@@ -1194,7 +1285,7 @@
        "612256418500423168 20.8715991973877 19.9612007141113"
       ]
      },
-     "execution_count": 32,
+     "execution_count": 29,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1208,13 +1299,108 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Challenge exercise**\n",
+    "### Exercise\n",
     "\n",
-    "Do both joins in one query.\n",
+    "Optional Challenge: Do both joins in one query.\n",
     "\n",
     "There's an [example here](https://github.com/smoh/Getting-started-with-Gaia/blob/master/gaia-adql-snippets.md) you could start with."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "INFO: Query finished. [astroquery.utils.tap.core]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<i>Table length=3724</i>\n",
+       "<table id=\"table140668275467456\" class=\"table-striped table-bordered table-condensed\">\n",
+       "<thead><tr><th>source_id</th><th>g_mean_psf_mag</th><th>i_mean_psf_mag</th></tr></thead>\n",
+       "<thead><tr><th></th><th></th><th>mag</th></tr></thead>\n",
+       "<thead><tr><th>int64</th><th>float64</th><th>float64</th></tr></thead>\n",
+       "<tr><td>635860218726658176</td><td>17.8978004455566</td><td>17.5174007415771</td></tr>\n",
+       "<tr><td>635674126383965568</td><td>19.2873001098633</td><td>17.6781005859375</td></tr>\n",
+       "<tr><td>635535454774983040</td><td>16.9237995147705</td><td>16.478099822998</td></tr>\n",
+       "<tr><td>635497276810313600</td><td>19.9242000579834</td><td>18.3339996337891</td></tr>\n",
+       "<tr><td>635614168640132864</td><td>16.1515998840332</td><td>14.6662998199463</td></tr>\n",
+       "<tr><td>635598607974369792</td><td>16.5223999023438</td><td>16.1375007629395</td></tr>\n",
+       "<tr><td>635737661835496576</td><td>14.5032997131348</td><td>13.9849004745483</td></tr>\n",
+       "<tr><td>635850945892748672</td><td>16.5174999237061</td><td>16.0450000762939</td></tr>\n",
+       "<tr><td>635600532119713664</td><td>20.4505996704102</td><td>19.5177001953125</td></tr>\n",
+       "<tr><td>...</td><td>...</td><td>...</td></tr>\n",
+       "<tr><td>612241781249124608</td><td>20.2343997955322</td><td>18.6518001556396</td></tr>\n",
+       "<tr><td>612332147361443072</td><td>21.3848991394043</td><td>20.3076000213623</td></tr>\n",
+       "<tr><td>612426744016802432</td><td>17.8281002044678</td><td>17.4281005859375</td></tr>\n",
+       "<tr><td>612331739340341760</td><td>21.8656997680664</td><td>19.5223007202148</td></tr>\n",
+       "<tr><td>612282738058264960</td><td>22.5151996612549</td><td>19.9743995666504</td></tr>\n",
+       "<tr><td>612386332668697600</td><td>19.3792991638184</td><td>17.9923000335693</td></tr>\n",
+       "<tr><td>612296172717818624</td><td>17.4944000244141</td><td>16.926700592041</td></tr>\n",
+       "<tr><td>612250375480101760</td><td>15.3330001831055</td><td>14.6280002593994</td></tr>\n",
+       "<tr><td>612394926899159168</td><td>16.4414005279541</td><td>15.8212003707886</td></tr>\n",
+       "<tr><td>612256418500423168</td><td>20.8715991973877</td><td>19.9612007141113</td></tr>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "<Table length=3724>\n",
+       "    source_id       g_mean_psf_mag   i_mean_psf_mag \n",
+       "                                          mag       \n",
+       "      int64            float64          float64     \n",
+       "------------------ ---------------- ----------------\n",
+       "635860218726658176 17.8978004455566 17.5174007415771\n",
+       "635674126383965568 19.2873001098633 17.6781005859375\n",
+       "635535454774983040 16.9237995147705  16.478099822998\n",
+       "635497276810313600 19.9242000579834 18.3339996337891\n",
+       "635614168640132864 16.1515998840332 14.6662998199463\n",
+       "635598607974369792 16.5223999023438 16.1375007629395\n",
+       "635737661835496576 14.5032997131348 13.9849004745483\n",
+       "635850945892748672 16.5174999237061 16.0450000762939\n",
+       "635600532119713664 20.4505996704102 19.5177001953125\n",
+       "               ...              ...              ...\n",
+       "612241781249124608 20.2343997955322 18.6518001556396\n",
+       "612332147361443072 21.3848991394043 20.3076000213623\n",
+       "612426744016802432 17.8281002044678 17.4281005859375\n",
+       "612331739340341760 21.8656997680664 19.5223007202148\n",
+       "612282738058264960 22.5151996612549 19.9743995666504\n",
+       "612386332668697600 19.3792991638184 17.9923000335693\n",
+       "612296172717818624 17.4944000244141  16.926700592041\n",
+       "612250375480101760 15.3330001831055 14.6280002593994\n",
+       "612394926899159168 16.4414005279541 15.8212003707886\n",
+       "612256418500423168 20.8715991973877 19.9612007141113"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "query3 = \"\"\"SELECT\n",
+    "candidate_df.source_id, ps.g_mean_psf_mag, ps.i_mean_psf_mag\n",
+    "FROM tap_upload.candidate_df as candidate_df\n",
+    "JOIN gaiadr2.panstarrs1_best_neighbour as best\n",
+    "  ON best.source_id = candidate_df.source_id\n",
+    "JOIN gaiadr2.panstarrs1_original_valid as ps\n",
+    "  ON ps.obj_id = best.original_ext_source_id\n",
+    "\"\"\"\n",
+    "\n",
+    "job3 = Gaia.launch_job_async(query=query3, \n",
+    "                       upload_resource='candidate_df.xml', \n",
+    "                       upload_table_name='candidate_df')\n",
+    "\n",
+    "results3 = job3.get_results()\n",
+    "results3"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -1226,7 +1412,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1243,14 +1429,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "-rw-rw-r-- 1 downey downey 96K Nov 18 19:22 gd1_photo.fits\r\n"
+      "-rw-rw-r-- 1 downey downey 96K Dec 10 11:34 gd1_photo.fits\r\n"
      ]
     }
    ],
diff --git a/06_photo.ipynb b/06_photo.ipynb
index 8b74810..2ff421b 100644
--- a/06_photo.ipynb
+++ b/06_photo.ipynb
@@ -1,5 +1,39 @@
 {
  "cells": [
+  {
+   "cell_type": "raw",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "title: \"Title\"\n",
+    "teaching: 3000\n",
+    "exercises: 0\n",
+    "questions:\n",
+    "\n",
+    "- \"How do we use Matplotlib to select a polygon and Pandas to merge data from multiple tables?\"\n",
+    "\n",
+    "objectives:\n",
+    "\n",
+    "- \"Use Matplotlib to specify a `Polygon` and determine which points fall inside it.\"\n",
+    "\n",
+    "- \"Use Pandas to merge data from multiple `DataFrames`, much like a database `JOIN` operation.\"\n",
+    "\n",
+    "keypoints:\n",
+    "\n",
+    "- \"If you want to perform something like a database `JOIN` operation with data that is in a Pandas `DataFrame`, you can use the `join` or `merge` function.  In many cases, `merge` is easier to use because the arguments are more like SQL.\"\n",
+    "\n",
+    "- \"Use Matplotlib options to control the size and aspect ratio of figures to make them easier to interpret.\"\n",
+    "\n",
+    "- \"Matplotlib also provides operations for working with points, polygons, and other geometric entities, so it's not just for making figures.\"\n",
+    "\n",
+    "- \"Be sure to record every element of the data analysis pipeline that would be needed to replicate the results.\"\n",
+    "\n",
+    "---\n",
+    "FIXME\n",
+    "\n",
+    "{% include links.md %}\n"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -12,13 +46,14 @@
     "\n",
     "In the previous lesson we downloaded photometry data from Pan-STARRS, which is available from the same server we've been using to get Gaia data. \n",
     "\n",
-    "The next step in the analysis is to select candidate stars based on the photometry data.  The following figure from the paper is a color-magnitude diagram for the stars selected based on proper motion:\n",
+    "The next step in the analysis is to select candidate stars based on the photometry data.  \n",
+    "The following figure from the paper is a color-magnitude diagram showing the stars we previously selected based on proper motion:\n",
     "\n",
     "<img width=\"300\" src=\"https://github.com/datacarpentry/astronomy-python/raw/gh-pages/fig/gd1-3.png\">\n",
     "\n",
     "In red is a theoretical isochrone, showing where we expect the stars in GD-1 to fall based on the metallicity and age of their original globular cluster. \n",
     "\n",
-    "By selecting stars in the shaded area, we can further distinguish the main sequence of GD-1 from younger background stars."
+    "By selecting stars in the shaded area, we can further distinguish the main sequence of GD-1 from mostly younger background stars."
    ]
   },
   {
@@ -31,7 +66,7 @@
     "\n",
     "1. We'll reload the data from the previous notebook and make a color-magnitude diagram.\n",
     "\n",
-    "2. Then we'll specify a polygon in the diagram that contains stars with the photometry we expect.\n",
+    "2. We'll use an isochrone computed by MIST to specify a polygonal region in the color-magnitude diagram and select the stars inside it.\n",
     "\n",
     "3. Then we'll merge the photometry data with the list of candidate stars, storing the result in a Pandas `DataFrame`.\n",
     "\n",
@@ -59,7 +94,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 55,
    "metadata": {
     "tags": [
      "remove-cell"
@@ -87,7 +122,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 56,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -110,7 +145,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 57,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -140,9 +175,18 @@
     "Since we expect the stars in GD-1 to be older than the background stars, the stars in the lower-left are more likely to be in GD-1."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The following function takes a table containing photometry data and draws a color-magnitude diagram.\n",
+    "The input can be an Astropy `Table` or Pandas `DataFrame`, as long as it has columns named `g_mean_psf_mag` and `i_mean_psf_mag`.\n",
+    "\n"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -198,7 +242,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -224,9 +268,7 @@
    "source": [
     "Our figure does not look exactly like the one in the paper because we are working with a smaller region of the sky, so we don't have as many stars.  But we can see an overdense region in the lower left that contains stars with the photometry we expect for GD-1.\n",
     "\n",
-    "The authors of the original paper derive a detailed polygon that defines a boundary between stars that are likely to be in GD-1 or not.\n",
-    "\n",
-    "As a simplification, we'll choose a boundary by eye that seems to contain the overdense region."
+    "In the next section we'll use an isochrone to specify a polygon that contains this overdense regioin."
    ]
   },
   {
@@ -235,71 +277,51 @@
    "source": [
     "## Isochrone\n",
     "\n",
-    "http://waps.cfa.harvard.edu/MIST/interp_isos.html\n",
+    "Based on our best estimates for the ages of the stars in GD-1 and their metallicity, we can compute a [stellar isochrone](https://en.wikipedia.org/wiki/Stellar_isochrone) that predicts the relationship between their magnitude and color.\n",
+    "\n",
+    "In fact, we can use [MESA Isochrones & Stellar Tracks](http://waps.cfa.harvard.edu/MIST/) (MIST) to compute it for us.\n",
+    "\n",
+    "Using the [MIST Version 1.2 web interface](http://waps.cfa.harvard.edu/MIST/interp_isos.html), we computed an isochrone with the following parameters:\n",
     "    \n",
-    "MIST Version 1.2\n",
+    "* Rotation initial v/v_crit = 0.4\n",
     "\n",
-    "Rotation initial v/v_crit = 0.4\n",
+    "* Single age, linear scale = 12e9\n",
     "\n",
-    "Single age, log10 scale = 10.079\n",
+    "* Composition [Fe/H] = -1.35\n",
     "\n",
-    "Composition [Fe/H] = -1.35\n",
+    "* Synthetic Photometry, PanStarrs\n",
     "\n",
-    "Synthetic Photometry, PanStarrs\n",
+    "* Extinction av = 0\n",
     "\n",
-    "Extinction av = 0\n",
-    "    "
+    "The following cell downloads the results:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 132,
+   "execution_count": 6,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "10.079181246047625"
-      ]
-     },
-     "execution_count": 132,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "import numpy as np\n",
+    "import os\n",
+    "from wget import download\n",
     "\n",
-    "log_age = np.log10(12e9)\n",
-    "log_age"
+    "filename = 'gd1_photo.fits'\n",
+    "filepath = 'https://github.com/AllenDowney/AstronomicalData/raw/main/data/'\n",
+    "\n",
+    "if not os.path.exists(filename):\n",
+    "    print(download(filepath+filename))"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 182,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "10.176091259055681"
-      ]
-     },
-     "execution_count": 182,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
-    "import numpy as np\n",
-    "\n",
-    "log_age = np.log10(15e9)\n",
-    "log_age"
+    "To read this file we'll download a Python module [from this repository](https://github.com/jieunchoi/MIST_codes)."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 147,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -313,521 +335,38 @@
     "    print(download(filepath+filename))"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 149,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Reading in: mist_iso_12.0_-1.35.cmd\n"
-     ]
-    }
-   ],
-   "source": [
-    "import read_mist_models\n",
-    "\n",
-    "filename = 'mist_iso_12.0_-1.35.cmd'\n",
-    "iso = read_mist_models.ISOCMD(filename)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 150,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "read_mist_models.ISOCMD"
-      ]
-     },
-     "execution_count": 150,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "type(iso)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 151,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "list"
-      ]
-     },
-     "execution_count": 151,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "type(iso.isocmds)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 152,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1"
-      ]
-     },
-     "execution_count": 152,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(iso.isocmds)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 153,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "numpy.ndarray"
-      ]
-     },
-     "execution_count": 153,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "type(iso.isocmds[0])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 154,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "dtype([('EEP', '<i4'), ('log10_isochrone_age_yr', '<f8'), ('initial_mass', '<f8'), ('star_mass', '<f8'), ('log_Teff', '<f8'), ('log_g', '<f8'), ('log_L', '<f8'), ('[Fe/H]_init', '<f8'), ('[Fe/H]', '<f8'), ('PS_g', '<f8'), ('PS_r', '<f8'), ('PS_i', '<f8'), ('PS_z', '<f8'), ('PS_y', '<f8'), ('PS_w', '<f8'), ('PS_open', '<f8'), ('phase', '<f8')])"
-      ]
-     },
-     "execution_count": 154,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "iso.isocmds[0].dtype"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 155,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<i>Table length=5</i>\n",
-       "<table id=\"table139627634205072\" class=\"table-striped table-bordered table-condensed\">\n",
-       "<thead><tr><th>EEP</th><th>log10_isochrone_age_yr</th><th>initial_mass</th><th>star_mass</th><th>log_Teff</th><th>log_g</th><th>log_L</th><th>[Fe/H]_init</th><th>[Fe/H]</th><th>PS_g</th><th>PS_r</th><th>PS_i</th><th>PS_z</th><th>PS_y</th><th>PS_w</th><th>PS_open</th><th>phase</th></tr></thead>\n",
-       "<thead><tr><th>int32</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><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>251</td><td>10.079</td><td>0.10587085010820896</td><td>0.10586970558936482</td><td>3.5403606829263956</td><td>5.321292252841703</td><td>-2.7463861921790302</td><td>-1.35</td><td>-1.309024</td><td>13.833278</td><td>12.395283</td><td>11.638535</td><td>11.286309</td><td>11.099877</td><td>12.304645</td><td>11.970072</td><td>0.0</td></tr>\n",
-       "<tr><td>252</td><td>10.079</td><td>0.10880997479817517</td><td>0.10880875821722344</td><td>3.5425829062520973</td><td>5.312318300317242</td><td>-2.7181724188486394</td><td>-1.35</td><td>-1.308887</td><td>13.728065</td><td>12.302358</td><td>11.562305</td><td>11.216564</td><td>11.030572</td><td>12.220695</td><td>11.891695</td><td>0.0</td></tr>\n",
-       "<tr><td>253</td><td>10.079</td><td>0.11265246844845123</td><td>0.11265115316268977</td><td>3.5455190149596207</td><td>5.300571015130943</td><td>-2.6811483213239216</td><td>-1.35</td><td>-1.3087</td><td>13.590113</td><td>12.180699</td><td>11.461113</td><td>11.124659</td><td>10.939051</td><td>12.110069</td><td>11.788712</td><td>0.0</td></tr>\n",
-       "<tr><td>254</td><td>10.079</td><td>0.11642732871189566</td><td>0.11642590928591964</td><td>3.5484038428414566</td><td>5.288998772212527</td><td>-2.644742589781073</td><td>-1.35</td><td>-1.308527</td><td>13.454544</td><td>12.061398</td><td>11.361128</td><td>11.033194</td><td>10.848769</td><td>12.000729</td><td>11.686994</td><td>0.0</td></tr>\n",
-       "<tr><td>255</td><td>10.079</td><td>0.12022239788961175</td><td>0.12022086648010438</td><td>3.551307877859248</td><td>5.277331450307816</td><td>-2.608093791390564</td><td>-1.35</td><td>-1.308285</td><td>13.31837</td><td>11.94182</td><td>11.260087</td><td>10.940106</td><td>10.757708</td><td>11.89031</td><td>11.584202</td><td>0.0</td></tr>\n",
-       "</table>"
-      ],
-      "text/plain": [
-       "<Table length=5>\n",
-       " EEP  log10_isochrone_age_yr     initial_mass    ...    PS_w    PS_open   phase \n",
-       "int32        float64               float64       ...  float64   float64  float64\n",
-       "----- ---------------------- ------------------- ... --------- --------- -------\n",
-       "  251                 10.079 0.10587085010820896 ... 12.304645 11.970072     0.0\n",
-       "  252                 10.079 0.10880997479817517 ... 12.220695 11.891695     0.0\n",
-       "  253                 10.079 0.11265246844845123 ... 12.110069 11.788712     0.0\n",
-       "  254                 10.079 0.11642732871189566 ... 12.000729 11.686994     0.0\n",
-       "  255                 10.079 0.12022239788961175 ...  11.89031 11.584202     0.0"
-      ]
-     },
-     "execution_count": 155,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from astropy.table import Table \n",
-    "\n",
-    "iso_table = Table(iso.isocmds[0])\n",
-    "iso_table[:5]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 156,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['EEP',\n",
-       " 'log10_isochrone_age_yr',\n",
-       " 'initial_mass',\n",
-       " 'star_mass',\n",
-       " 'log_Teff',\n",
-       " 'log_g',\n",
-       " 'log_L',\n",
-       " '[Fe/H]_init',\n",
-       " '[Fe/H]',\n",
-       " 'PS_g',\n",
-       " 'PS_r',\n",
-       " 'PS_i',\n",
-       " 'PS_z',\n",
-       " 'PS_y',\n",
-       " 'PS_w',\n",
-       " 'PS_open',\n",
-       " 'phase']"
-      ]
-     },
-     "execution_count": 156,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "iso_table.colnames"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 157,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "14.4604730134524"
-      ]
-     },
-     "execution_count": 157,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import astropy.coordinates as coord\n",
-    "import astropy.units as u\n",
-    "\n",
-    "distance = 7.8 * u.kpc\n",
-    "dm = coord.Distance(distance).distmod.value\n",
-    "dm"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 158,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "g = iso_table['PS_g'] + dm\n",
-    "gi = iso_table['PS_g'] - iso_table['PS_i']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 159,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7efd97389fd0>]"
-      ]
-     },
-     "execution_count": 159,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "plot_cmd(photo_table)\n",
-    "plt.plot(gi, g)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 169,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def read_and_clean_cmd(filename, distance):\n",
-    "    iso = read_mist_models.ISOCMD(filename)\n",
-    "    iso_table = Table(iso.isocmds[0])\n",
-    "\n",
-    "    phase_mask = (iso_table['phase'] >= 0) & (iso_table['phase'] < 3)\n",
-    "    table = iso_table[phase_mask]\n",
-    "    \n",
-    "    dm = coord.Distance(distance).distmod.value\n",
-    "    g = iso_table['PS_g'] + dm\n",
-    "    gi = iso_table['PS_g'] - iso_table['PS_i']\n",
-    "    \n",
-    "    return gi, g"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 170,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Reading in: mist_iso_12.0_-1.35.cmd\n"
-     ]
-    }
-   ],
-   "source": [
-    "filename = 'mist_iso_12.0_-1.35.cmd'\n",
-    "\n",
-    "gi1, g1 = read_and_clean_cmd(filename, distance)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 183,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Reading in: mist_iso_15.0_-1.35.cmd\n"
-     ]
-    }
-   ],
-   "source": [
-    "filename = 'mist_iso_15.0_-1.35.cmd'\n",
-    "\n",
-    "gi2, g2 = read_and_clean_cmd(filename, distance)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 184,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7efd9703c550>]"
-      ]
-     },
-     "execution_count": 184,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "plot_cmd(photo_table)\n",
-    "plt.plot(gi1, g1)\n",
-    "plt.plot(gi2, g2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 173,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "left_gi = gi - 0.5*(g/28)**5\n",
-    "right_gi = gi + 0.55*(g/28)**5"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 172,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7efd97a51b80>]"
-      ]
-     },
-     "execution_count": 172,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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MEWStmg+v/BK5bydlg+bwjU2zacjK18bz+4aU8M4D8MovaO4xjJu7Xss/1vXhlHH9uW3OFAb0MDH9BsHRYTMAIcRDwDnADinlpNZjvwK+CexsLfYLKeWL6abFIBj8hGDaNXy7QuE3nLPN+X3V0Zw621fBtveRW96DXWvIlREAqrP7Ud4ymtKmGVTIMazJGsPIgUOZPrIPp43ow7Uje+uZpWLakVVvIMJ1NGXl8V72FJ5rOp3XW46CnIH8reefmLz3TVpye3DVvit4JXI0xxf25dYLpzC8eQM8di6s+x+1fSbz4/C1LFo3lPOmDuHnZ01IfOFVeD88/0N4/yl2DT2Ni7Z9lW3Vedx83gQuO3ZkRtv6DTon2sME9AjwJ+BR2/G7pZR3tEP7BimGaopRmX8oFKK4uDiOmTuZgWI+goZ61iz9LxMKWsjZ9SFsX4XcvioWmQNQm9WbisgRvNd8PuWR0XwoxlBQMJxJQ3oxaWhPzhjai4lDehHKyYqGZ376JqzZGV0lG2kB2YI8sJeWqjfI3lIaDfPM6ctrzUfzUngqy+QkJo4cxMnHDeCxoWHGLr8O8ekSNvY7kUu3f5nduX245ewJXFLcB7HkVlj+Z1pyu/Nwrx/wu61HM25wb566dCJHH6HfM9fXLKjmU3jyK8jtH/D2qO/wpdXHM3ZgTxZ8cxrjBmVehI/BoYH22BR+iRBiVLrbMWhf6Jy04XC4TWqHmE0/Owuq18Kuj2jeVsneNUspaN5G7q6PmByJbn3VLHLZkD2civCRfNByGqvlCD7JGsmAwSOYNLQnk4b04uqhvRg7MP9ghA5EF0atfBrWvBhdHRtpu5WWAFZHRvF65Hxea5nG1qwjObFoIOeNG8AdY/vRu2tuNL/PM9cSibRwf88fcdummZx05ABuuWASQ7a+Bn/+OezdxHt9z+bKrecSDhdw03lHcukxIx1z8/vyg3y8EJ65Aongn2Pu4PpVgzlv6hBuu2iKyd9jkFZ0pBP4e0KIrwKlwE+klLu9LjDoeKgLtdow+2zB1JG9CW1aHmXKu9fBro/I3fUxsnotIhI1G+UAIqsfy+QIKprOYnVkBB/KEWzPHsTY/v2YMrw3k4b04gtDezGmf/e2aZAjEdi8Mmq3X/MybH8fgKY+Y1gz/FL+0zCFFzd3obEli655uUwc3pehfXsyuF8BEwq6Mbtvd8YOyD/oD9hXDU/9ED58nm29juLL1f/HjqbB/H5OEXOOaEL896tQtZDaHkdyddZvWLRlNF86egTXfHac50YqrmG9kQj87w5Y9DtaBkzkl6HrmLcqm29+5gh+ftaEjFnNa3Dool2cwK0zgBcUH8BAYBcggZuBwVLKrztceyVwJcCIESOmr1+/Pu30GriguZFw9TpCdRuJrLifrOxcaKiF2g3Iui2IVhs9QAtZbM0azEctg1nTMpi1cghrI0PYmDWEIQMGcGJBDb1GFDN2UA9GF3Rh40fvO6/8bWqILt76qJXp129Diiz29pvGyq6zeLxmAm/s6g3AEf26MbGP5IsnFHFs4QBkS7Oz9r2rCh4+CxpqeLLn//HzbSdz6oRB/PbzYxlYfh+8dTctWbk8HLqUW6o/w9SR/bjp3IlMGhowfYMdDbXw72/DRy9RO/YiLt58MZ/URrjhnCK+dtyo5Oo2MLAho8JApZTbrd9CiAeBF1zKPgA8ANEooPRTZxAOhwltL4tGyOzdCns3w55NyL2bEft2xrY4tPTyytyJrGsZw8dNM9kk+7NJ9meHGEBOn6EM69uLST3rmSA2cF79/xjyyV+jF+1u/Uz8Iw1HfJl31tWwaFdPnn52FT87cyyD+uRD/Q74KJqigbVvQHMDMrc7W/ofz6IeX+fBrYWs39iFnCzBMaMLuOHYgZw6fgBH9Ovuf1Fa415oPgCRZibtL+H03H78/qhsej32Tdj9KWW9TuPK7Rciewzi9xdH8+wIIZKLbtpeCU9+GWo3UDHll1z83iR6dslh3pXTmDFK70cwMEgHOmoGMFhKubX194+BY6SUl3jVY8JA45GOnEEWw5y55DJE/XZtmS2ygA8jI3knMp5lkYlUypEcNyDMOflVfHHLLew95id0a9xJzoalULPWsa23WiZyRdO1NMZEimSs2MyvR33AMVmVZG1eCUia8oewptcJPNdQzD+3DachkkNB9xCnjBvASWMLOHnCoGh+Hpc+uY7Tgb3w7j9oXvoncvZFnc+13UZxzf6vsDhcxNdPOILvn1oYy7OT1ErnygXw728h83rw2PBfc+N7PTh6VAF/+vJRJsTTIG3osGygQogngJOBfsB2YG7r/6lETUDrgG9ZAsENRgAcRDpzBoXDYUJbSuCDZ2jZX8O+bWvJz40Qrq/lQP1uerCfbOH+3OyR3egl9ruWiUhBI7kcIMQBQmQTYYCoBWBvwWRK847l8d0TeKN2ICCYMLgnp40fwKkTBlA8rLf/TdH9ojnMS0/+hf9VbuDplpM4duwg5n5+IoUD2ibJCyx8pYT/3Qlv3EzzkBn8WFzD82sjfOXYkdz4+SJy/W73mAwNBoctTDroQxBOO2alRSAodTbuWk/en6bE/tcMO52N/Y7nsUrJR3W5bJADqCWfcT2bOaHbRnqHJP26RhjSHQZ2hZAMEwnvY+vWrWTnZHFg/z7CBxpoaGykpGUsr7ccxXYKyMvJ4vjCfpw6fgCnjh/AkN5dU9ovHQ40tXDby6s5dnRfPls00DH2PtA4Nx2A538AFU+yd+z5zNl8KZ/uaeHX503iS0ePSIjOwz1poEEwGAFwGKA9mEKsjSP6EurRH7r0jJ17f2M1N//3I04a159LZg6nR0i40mNfTJaTk8uOukbWVe8j3Bxh5qgCuoYyLwwy0DjX74za+ze+TdWkH3H++7PokpvD/Zclb+83MwADvzAC4DBBR+wR7HZO3WTG6bqgKScyAb7o2b4K/nUJct9OXj3yJr717jCmDOvFX78yncG90j+bMTCw4CQAghseDTIa7cEk3Zh/WVlZTLO3FoaFw2HtObf61PP2azMBnuP80Svw988iI038aeS9fOvdYXy+eAhPfWuWYf4GGQMjAAxSBvuiJ/W/+tuLodvPd+QeCQkJnXcehCcuoaXPaH6Qfyd3rurO908t5N5LppqVvQYZBSMADDxhZ4JuTFHNAqr+V3+HQiGKiopct3q0M/yOYv6BZh5Swms3wYvX0DDqDM7ffz0vrRf8fs4UfvLZcSaZm0HGwQgAA1foTDdeTNGPhm+ZhpyQCfZ+L0EVh5YmeO4qeOsuqsddyskbrmB9HTz69aP5wozh6SfWwCABGCewgSfsDk+do9cy8ziV0dUJmcHoneA72ie8D576GlQt5NPJP+BzZcfRr0ceD//fTAoHmEyeBh0P4wQ+hJFu56id+Vl2fIgy/3nz5rF8+XJXx67lCFbh5BR2Qns7gX35HvbtgkfOgbWv817xTZyxchaj++fz7HeON8zfIONhBEAnRzojZJzqrK+vj7WZn5/PJZdcwqxZs9rY/9V6SkpKKC0tjTtnmVec+tCekUBO9dpnNXHYuwUemg07Knltyl1c8PZYZozqw7wrj6V/j7y00GlgkEoYAdDJka4IGTemXFlZGWcbz8/Pj2P+9utCoRAzZ86MbRVpCYSKioqYqUi3YUx7RQK5hag6laF2Izz8OWTdVp4Y9we+8fYAzpw4kEcuPzqWM8jAINNhfAAGjkh04ZafFBV+fABevodUwk/20Fj7NZ/CP85FHqjl/uG3c9sHPblk5nB+e8Hk1OcnMjBIAYwPwCAw3MI0g1xnafz2WYGTyUiHdJuAdGsXtGV2VcEjZyPDddw9+A5u+6AnV508hlsuNMzfoPPBCACDdoFTDLzqTwDivsvKyqipqQFSbwJKKAR1x2p45HPI5kZuH3gH967O5ydnHMlPZ483Mf4GnRJGABj4ghrFE1QLD4VCMfu/vc6KigqtMzgUClFYWMiCBQuor69PWT+sdgPPJmo+hUfPRQK3DLyD+1Z35dozx/H908amlDYDg/aEEQAGWtgjcEpLSykpKaGmpiYhU4yTVi2ldDS/5OfnM2fOHPLz85M2AdnNT16zibh26rbDY+cjW8Lc0v82HvgwxE9nj+O7pxQmRIuBQabACACDNtBF4MyYMYPi4mKqqqriNPag9aqwooPUuuzRRH5s80H7o7ajoyuufEMtPH4hsn4n9w+9Ncb8rzrZMH+Dzg8jAAzawCkXT35+PkVFRTGN3B7X7wYnDd5pHUAq8wG5CQ/XtmmGJy6BnWt4Zuyt3PZBD7590hjD/A0OGRgBYKCFE7NU0zu7hRDrGL1bdI3uXDIOX4tG3czCre1Y+WwB8y+HDStYNPE3XPNuX744YzjXzR6XME0GBpkGIwAMfMNilgCVlZUUFxf71qq9Yvi99gQIAmt2snz58rjwU7dZSNz5xkZ48Rr46GUqim/g8pLhzJ44iN9eMCnhaB+vvrR3mgs3BJnVGXRupF0ACCEeEkLsEEJ8YDv+fSHEGiHEKiHE7emmwyA1UHP7Wzt96crYteqgDtxknL6Wz2LWrFkxH4OOLke6S/8KKx9hy+SrmFNaxNFHFHDPJVPJSWDjdj99SfcahyDwS0sm0WyQONK+ElgIcSJQDzwqpZzUeuwU4JfA2VLKRiHEACnlDq+6UrESOJ2rSQ0Ows8q3kRWFKeiXVdULoCnvkp94TmcUHUZBT268Ox3jqN3t+RWJKejr+mCX1oyiWYDd3TYSmAp5RKgxnb4O8CtUsrG1jKezD8VMFqLM5KN6LGfc4u60ZXRxfp7MRdd9I7qmA58vzevhGevpGnwNM7ffBk5Odn84/Kj2zD/kpKSGL06GnQIunq6I+GXlkyi2SAxdJQP4EjgM0KIt4UQbwohZjoVFEJcKYQoFUKU7ty5M6lG05lQrDMjKKP0Kq/6Cpyg3ov6+nrmz5/vueDL7lPQ0aDOaAPF+9dugH9dQiR/AN8I/4RN9ZK/fW0mwwu6tWmjubmZ8vJy7Spmo2AYdCa0SzI4IcQo4AXFBPQB8AbwQ2Am8CQwWnoQY5LBpQduidnUab7Tb13Z+vp6KisrfQvc+vr6OJ+CzpRjT9KWiFnJXrasrIypE48k9Ng5yN3ruXnQvTz8UYj7L5vOmRMHObYLB0NYdd8GBpmETEsGtwl4VkbxDhAB+nUQLYc1LAbnds4Kp7QvDrOXtSJurHDRwsJCR2Zo15LtzF9nQrJvz+iYsM0nQqEQU4uLCb18LWz7gBfG/oaH1oT42ezxnDlx0MEymvUIqmNZpdcwf4POhI4SAM8BpwIIIY4EQsCuDqLlsIbf+Hw/5hQrRNJi1lVVVVpziF8Tkqpxq2sQ/MJP2dC7f4f3n2Lt5B/xg5X9OLd4CFeeOLoNPW51qvQa849BJsDvc9geYaBPAMuBcUKITUKIK4CHgNGtpqB5wNe8zD8G6YPf+HyvcmrCt/z8/EALv7xi8wH/G7TTNsuoFp8ugVevp370WVxQcQxFg3ty20VTfGcutdNrfAAGmYAgz6HZEMYgrXAzi6h2c7fN160H2dcG7Rx8Aay0FVrUboQHTqKla1/Oa7yJLQ25LPje8Qzr0y3xOj36a2DQXrA/h5nmAzA4DOCmidjPeTH2IBFclgnKkVG3NMHT/4dsDvOrrr/gwxr486XTHJm/2r4b87fKqTCzAYOOgF8lxAgAg7TBj38BaOOEdgr39ONQ9uUveP3XsLmUReOu57GqED8/azzTh/fw1Z8gMCYhg0yHEQAG7Q41ksgrbYSXg1UXqQQ4r0P4eCEsu5c1/c/iypUjOH3CQL5y9FDPVA2JwCsLqYFBR8MIAIO0QacBe60Sdgq7dNKm1ZmE/do25fdugX9/i+Z+RXx95yX0yoPfnTeBvLw8z3TRie5K5jdZnoFBR8AIAIO0wYmZ+7H3+6lLhd1M1KZ8pAWe+SayqYFfd72W7eEQ9102gwG9u8fKO0X3FBUVBQ5BdUN7rkg3QsbADUYAdGJ0hpc76GIt+2zBz3VO6wbiyi+9B9a/xdJxP+fRj/P42VnjOXrMgLi2SktLtZq+W0irjk4vtFekkJlpGHjBCIBOikPx5Vb7FHTvYZX5W/sXx67dWgGLbmHv6LO5oqyQk8f154oTjmhTR1NTE+Xl5Y4zAZVOHc1+EsO1533zmmn4oeFQer4M2sIIgE6KRM0ITkwpVUjUVg4H+xQOh1mwYIFrKgm3OmbMmHFwH4CmA/DvbxHpWsBXN19I11AOt8+ZQlNTU5vr7PsH6ODkpIZ4n4ObzyLIgrZk4cb8vQTRoahkGMTDCIBOjFSEJabyJVezeiYTOZOfn8+cOXM8Y+7d6oiNzaLfwo5K/j3i55Tt6cqtF06md15WG2bd5jqXup1yA6nHnRh9Iikt0gE/s4P29FUYdAyMADiMkKhT1g4n5mUx7lAo5HtXKTdakxZM65fBsj+yc9ylXFs2gJNH5HHqkX3bhJYGbcfNF2HBidFnElP1MzvIBDoN0geTCsIgEFKVEkFNFw3uqagTYkRNDfCXWUSk5HPhW6iLdOH5q46loGfb1b5+6k+Eho5ioF7tpqu/BpkLkwrCICXwGxbpxYAqKyspKioCNPH6Sh2WUzfwTODN22H3pzwx6FpW10ju+EJxG+avmn7c4GQ680JHMX+vRW1+ZjyG+R8eOKwFQEfbYS1kCh12uJl6kjFjqHl1/JhEAs9St6+CZfey+8gvcGNFAZfMHM6sMX3jivh1gqr0Oq1WziQ4jadTXwwObxy2AiBTXuJMocMOp9THqUqXYPdDONUTCoU8I3Pi6Gg8AM//ENmlF9/deSF9uoX4+VkTtO27RePoon3Ua73SWXckdMzfbTMfg8MXh60A8NKUOpqOdMNL87VMNHYmZ6VF8BPy6Ld9P6GTfuvcsuA3sKmEJUf8mGVbJXM/X0Svbrnask4x/1abbvdFNy5+ZhOJINln0mj9Bk44bAUAeGtKHUVHqpCoHVg10ajXqMftIY9BGIx98ZQacZKUaelANSOrHuHA8BP5zvuFnDKuP+dMGexY3mnjl1h9PunwE1KZaFRUqp7Jjmb+mTRDMjiIw1oA2HEoaUp2xqG+gMkwbKeoHKcoHh3U9nXx835psiPy32ugpYm5kW8gpeDm8yc5MnlrwViq7rVbPX7G223209mfyUw1cxoYAdAGnf1Fs6CuUNW9gImsdg0SN+8VOaM6VIMID7Vua9VxOBymadXzZK15gSW9L+LJtTn86LQxrhu82NtN1HzlF4ksMPN7bTJoD6Z8KAixQxXtsSfwQ0KIHa37/1rHnhRClLV+1gkhytJNx6EOu1nFgpof30+4owXdgjHrW82172S20JWzp1V2EhJuQsaq2wpFra+vp6J0OTmvXEdTwTiu3jmbosH5TMjerp396Np1my3Z++lrr+EE0d5O5YRDbBOAYf6ZifaYATwCzFYPSCm/KKWcKqWcCjwDPNsOdBzSUBmuzqbuN9bdYnBe0DmD1XqsYyotuvUDuvBK+3Fd3fn5+TEn9VG7/4vYu4W/9fkRtU05/O78ieRkZ2npUcdK58Nw65O1eC3duXxSbTJxq6czLQQ1SD3aZSWwEGIU8IKUcpLtuAA2AKdKKT/2qsesBPYHJ7OK3+v8XO9WVj1mDz1UryvTbPLu1bZ6fUlJCfl7P2bKOz9mx5GXckz55/j6cSO58dxJbWhwo1E95rTKOZGx8QM3E1gy91D9X1pa6ujvSLQdg86FTF0J/BlguxvzF0JcKYQoFUKU7ty5sx1J67xIlnH4ud6trP2YLgbdyS7sx1Zufc+cNpXJnz4A+QP55ubPMax3F07ut68NU/Mb7aWLftK168c/4Ud793LUB4VT225KnmH+hzc6WgB8CXjCrYCU8gEp5Qwp5Yz+/fu3E1mZiURNAok4bFNFQxDnpp0RurVVX19PaOUDZO1YxQvDrqZiF/zq8xM4dsZRnkzaL02JhGXafR9u8GN60n37qU89FmQhXSbDRBGlHh0mAIQQOcCFwJMdRUNnQqJ2YZ3zVT0H/qM0gtJg1/rVY/Zydh9EfX29o4Oyvr6eV564D7n4VupHf46rK4Yxa2guXWs/9U2nn77GbSqjXOdnrPyOkyWs7L4FdRzKysp8b5CTTBRRJjPYVPtFDKLoyBnA6cBqKeWmDqSh08DOeLy0Qovhh0IhCgsL2zhfdaGUQWlwg1OEj9MxoM0iM8t0YS+f370752UvguwQP6n/Ml1zs7nzywc3c3EaKx2NblDXEAQ1zwQJe7SbnlSBYDm7q6qq0up8TgeDTWVdJpQ0PWiPMNAngOXAOCHEJiHEFa2nLsHD/GMQD3u0jFNIon1jFifmYUXl1NTUMH/+fGpqanzT4Kec7oV12o/A7oOwTBdwUJu2+t1c8hA5G5aycuwPeWWD4KdnjmVIQQ+t3d9J6KjHdYwqFDq4UCwRu35Q6MbEEgiWUPRKv50MEmWwbsLVaeaZKAzzTz3MfgCdFF5RKfX19XEapd0MY0XgQPTFqqmpoaqqKiValluEja5+S2DpdgGza97hbasJ/f1UGgdOZebG7zNuUE9+Mi2HaUcd5Ui3PSLIioqx4ESXVySRU3/bO7LGbWzt5VJJl7qng9Mz6HbeoP2QqVFABgnCLQonHA7HMVJdpI19kVZBQUHKmL9bhI3OLOO0BaRaV1lZGeED+wk9/11kdg5XN32bFim464tHuTJ/q20VltKjMxdZsM+u3Ji/bp1Be8KP9p5qE4/Ob2FHsmnDDdIPMwPoBPCruVkvt19t0HKyquaO9qDXr8aq1hUOhwktvRPevJXXJ97GFSuHc/ucKVw8Y3ja6LNs8H7KB3G0dhRDTHXbqRwXg/TCzAA6CewampMNW/2t2rTBnwMyFAoxZcoUjjvuuJQyf6tur/NBmD9AaNNyWPJ7asdexHfKRvLZooF8YfqwlNNntWnZ3FOhOasO+1TbxYPAq9+pri8TonY6uv1MxyEhAA6Vm+xkTtClS7CYfklJCdasKGiGz8rKylgb7Y1AIae718PT/0dLQSFf3DSH3t1yueXCyZ7pnINCbVM1sbmNqxOjszN9q04/22k6tZMupINZZ0LUTqYIoUxGpzcBBTEntBeSmfr6uVYtU19fH7NlW+f8ODWTpTPZOtyc2HHnZCM8fBZy93p+2ucentvYlccvn8ExhQOSojsRuvyU1Znh/I672/F0P+OdwVyTCI2doV/tgUPWBNTemoaXNpGs1uGnHyqzsbR467/TePhxViailSa7OM1trUBIROCJLyG3V/LPYTfy9LouXPfZQkTNujgNO5XwG/apmy04meF0Tni3+nQ0pcpB74RMZ5KJPmuZ3q+ORqefAbQnOirczosmlWmp0T12rdN+zF6PV9+cNN4gfbXX4TgDyBbw1NdgzX95bfzNfKNsDF89djg3nTeZpqamNn1O9Xj7cXCCXoj6DRcNej4ZJDpWyfQnkTq9rgPD1BOBEOJdKeV0+/FOPwNoT/jVxNrzAVXbsph/aWkpJSUlsYVgfrQnt5mD9a1LSRCUmehs7PYyIdkIT1wCa/7L2+N/xjfKxnDC8Dx+MXscQgjf9vl0wfK96BDUV+DnWuv6ROE2M/S6zonmZGd/ifYnk1crZypa+9hVd84IgIDoCGbjdU41PYTDYWbMmEFxcTEVFRUxRqWmg3Cq081sZDkwk0lJYDEht/6sWvEakYdmI9cu4pXRv+CLZVM4fcJA7r/8BLp0yfOkORXws+mLzgGdiID1AzvTDMK0VHNbkFBiL5r9OMd1SGYcUi3wvRz4Xsc6C1rHq0F3zgiADIaXBmYlK7NejHA4zPz58wmHowvBZsyYEUunUFVVRWFhIXBwMxcv2Bm2n5QEgGfdTu2HNr/N1JKrEbvX8eCw3/Gtykl8ccZw7r9sGvndunjSGwROL73lV3ETcqFQ23UTyWr4blAZX1ANOkjkkR8/kb1uv/X4uc4PUinwdQJFR3uys5YMgdbWb3wAGQ63yBDdRh9qCggV6rJ8i8n5jcX3G1UEUFNTw4IFC7Qre3W0hEIhaKyHRb+DFX+hqdcRXC1/xPM7+nHhkV245csnkJcXr/knayu390n3P9WRTUGud/rvddxv3X7s+qkY485qq3f0S3XS/oBzFJARAJ0YQZ2Kqt0+yAPt9ELYBYN1rLCwkIKCAkcaYu1nZ0HFvCjz37uZVUPm8JWN59Cc3Y1bLpjIGRMG+Go3EfhltkGvScbZqhNKut3J/NbnFm3kVm8qneupYpydnQF3NA7ZMNDDGV7MX10wZkG16dunueq3VztO9tipU6fGMX+77bqsrIxw/W5C5Y/CX46B/3yX2qw+fLfLrZz9yYUcNWYor/z4RM4uHpaQ7dkvVKGl+69C50h3MgskSp9lqlEd3MksGnMLKfWq18tX46d9LzqC1pcOE0wnN+mkBEYAHCJwYkR2J6D1YqvH1d9uTjFdG2oZyxFtpyEUCkEkQmjLOxy16RFCfyqG//6EXU153JB3HVO3/ZyqvIn89ctTuXKCpG/XbNe+pssJ6GX/tcf2OzHJRM0/dqacaDI1LyHkVq9OWQgCe+BAqoR1qqO9vPxrhwuMCShDkMwUV52yQ7w2a5lkrFTPcHCVquoLUGnQMfaioiLKy8tdtxds04c9m2H9Mqh6LfrZv4twVhfe63YCd9d+hhXNhUwd3odvfmY0sycNIjtLJG2aCgo/Zh2ntMapNJU4te10PlW2env9ifpC7LRB4oKwvUw9yZjwOptJyvgAMhipYCSWmcLOqHSMwm4CCoVCsZz85557biwJmrqfAKB1OofDYUI5OVC3FXZUwrb3o59NJbBnIwANOb1YmTudZ+qKeLl5Gl269eC8qUO5cNpQpgzr3aYffsYgSK75VDBLJ6bWXowgHI7fx8ASykHz7Xs5OBN1MLs51ROtJ5VIpWKRTjqD0uK3vBEAGY5UOWWdnHt2JqgyFOt/TU0Nn3zyCfv27WP9+vVccskl0RmBiMCeTTRVf0ruvq1QuxH2bCKyez3hnWvJa6xGRJpibVXnDOR9CllyYDQlLeOoZBRjB/bipHH9OXFMAVk165g+rW0Of6eZjK4vfh2kVj/Hjx+f1IY3Xi99ugVBOBwN+y0uLo5LUR30uXFy3HeEU91PPalAOhh2ewj+oHS7lTcC4BCApYUGCctUtfcpU6bExbiXlJRQPGUKoUgDlctfIa9hO6P75iL2biZr7yay6rbQXL2OUHh3XJ0RsqjN7st20Y8NLX1Z21TAFtmXjyLDWC1HkNetB1NG9GX8oB50b9jBxafOoF+v7q50+u2jnzrs5ZYtW0YoFE1/nUhEjVeb7aUR2mc9iWrr6ZrFZLJZxO3eZQLNqaIvbTMAIcSlwLlACyCA56WUnnv6CiEeAs4BdkgpJ7UemwrcD3QBmoGrpJTveNV1OAsAv9qx7prCUcNZ995iJg/rQWRXFTVrVzIw1EBk93qo3UhOS/wiwTAhdmT1Z5Psx7qmAjbLvmyW/dgs+7OZvtTl9qd/7x4M7tWl9dOV0f27M7pfPqP6dSMvS8YYVElJiavfwI32VDz41jlIzHwTRNCk0h/hxRCchE66NGgv5tReWnaqfR6JPp+pRHsoEKkQAH+VUn5L+f9nKeV3fVx3IlAPPKoIgFeBu6WULwkhPgf8VEp5slddh7MAAB8Pf3MYdn0UtcVv/wC5vZKWbavIqd8SV2w/XdjIQNa39GOz7MemVua+RfZlT94gQj36M6hXVwb2zEM01DL1yFEM75vP4F5dGNSrCz265KaO5iThNmPwEgpeM6n2tEmr2r1Tf7zqSMaJ69aG09jqzEl+Z6Ve9KXDXKUbL51fy4nudCLd7aVCADwCPA1sBIYBc6SUX/d57SjgBUUAvAI8JKV8UgjxJeDzUspLveo5VAWA0+pdT+zZBBtWwIYVNK9bRnb1GkSkGYAmcljLUCpbhrMuMoj1cgCbxSBaeo8iv1d/8iINjB85iJa6ao6eNJbh/XowqFcX8vNyEpptpBpum9pb0NHpxLyDMCc/TDRZh7LOaar6NILa9sG9/4nCafak0hjENKb21c2BnWqne5AZU3s6eRNF0HFIhQDoBlxIlPlvAp6VUu73ee0o4gXABOAVoqakLOA4KeV6h2uvBK4EGDFixPT167XFOi2s6Bu31AkxNOyGqtdpWf0SYsNysuo2Rw/ThXcjYyiPjGF1ZATrckbRddA4xgzsxZiBPRneO0T9lk845Zhi+vTqCXi/vDU1NXELuqD9ImDUMQmFQr6dr07MOxFnWrqjQLycpkFMTyUlJQgh9BFaSZi4/CgCiTjHgwgOp3YTgVdf7c9Mezh5g8zw1ONBn7+EBYAQ4mPgfaAcKAPKpZTrfLV6sI5RxAuAe4E3pZTPCCEuBq6UUp7uVc9hOQPYsxlW/Rs+ehm5fhlCtlBLT5a2FPFOZBxljKPLsCmMH9yTaaP6MXFIL47o152W5iaWL1/OrFmzCIWiYZ5ecfwWampqePbZZ2NRQBaDUZ2o6dKSrIfezwzA7Xr7MXCPKkq3jyJo3UEFFrhr6U7X+HG2e5XzM4NNxKdjtZtoOgy/7fqdlaQSfgRnKqPOkhEA1wCjgUpgEnAZ8Cnwb+BmKWWTy+VWHaOIFwB7gN5SSimieXX3SCl7etXTWQVAYGbR0gxVC2HlI8iPX0XICJ9mjeTFcDGvt0yjedBUZh7Rl6NH9qbrvq0cO6NtSGV9fT3z5s3jkksuiYUNqnHkbi98SUkJDQ0NnHjiiTFtKBwOU1FREadl+tGo/MIvQwqKZF+0VLSfCnt1om172e/Bf0BBouOXzBgHWe+RTLup1vyDzDacyqdSyUhGAJRJKacq/6cClxP1BYyUUn7fR+OjiBcAHwLfkVIuFkKcBtyu263Gjs4oAAI9/OH9UPp35PK/IOq2UJtVwGPhE3m65ST6Dx/HuVOHMmtYF4YU5Mc5DJ0eIkszUx84wDPywWL4qtZlCQbrukQdrPZydhONW3+Cwu9LnS5tviNtyX7MLImYnNzacqsrVfUH9Ze1tzPXatMtwCBdazHckEwyuD1CiCnWHyllGXCslPIO4HgfDT8BLAfGCSE2CSGuAL4J3CmEKAd+R6uN/1BEKOQjj0mkBd5+APmHYnj1esoa+nNl+MeclXUfWafdwGNXz+G6Gbl8fnxPlrz6AqWlpbFYflVDL7PlNrGYv3XcokG3mYkd9rw0oVAojvnb23Lqs1rGotP6rdJllXfrjx1e58rK/G2Ekk7N38vJ6XXMTzu6Y/Z7boduRuB3rO3l/NTldwzcaLV8Q7q9JNyexVQhCL1O9113zhePSBeklK4fYDywEvg78H3gT8Dy1nMfeF2fys/06dPlIYet70t5/2eknNtTlv/mBHnhz+6UZ9y1WD5VskEeaGqWUkrZ2NgoGxsbpZRS1tXVxX5b595+++24MnbYj9uv16Gurs61jK493X+VtrfeeksuXbo0Vs6pXq/+2Ms50VBXV6ctkwy8aHKjzTqnO28ds497ImPgRacX7W7tuLUZpG0/9eigjk+ydQWB0z3rKARtGyiVOv6uO9imEGQDXwBuBn4E9AW6A9f7uT5Vn0NOAHz4goz8ZpDc99vR8nu/vF7OvPlVOb90o2xpibgyEy/G6wVVmDgxUacX3jpmZ1iNjY1xzN3elnqdEz1B++NGt1uZRF9cL4ZrHw+363U0WvfDEvJ2genUn1TBr0BJVZvtJZhTVYeT4pWu9tyu89O2ej4pAZApn0NKAFQ+LyNze8kNtx0jZ1z3uPz2Y6VyT0NYStn2Bgd98LzOLV26NI7ZeNWh0vHWW2/JxYsXy8bGxjgBYp3zYnyJ9CcIvPruZxbip37ddfYx9Svs1P7X1dXFxtFttpcudKRWq0N70ZPI2CbL/JN9j4PUbwRAB6LNzdr6vpQ3D5Cbbp8lx133jLzlxQ9lJBKJKxvk5tvNBm4PlsUELeYS1HxQV1fnyED90NyeU2mnvrnR4rdep+tUIeC3bvtYqAI6nRp/OutMRfvtIfCc6Eh0Fppoe7pzTkqTX6iKiBEAHYQ20/6WFikfOFU23nKEPOq6f8mfPVMRx/zffvttWV1d3aYeJ029urpaPvzww542e7We6upqRyYVRDNxM6cko72k4ppEpsmpoieZl9bt+kTG2qtcKhhNkPac2vczE02mnSB1BHl2EhHyQWmyj5HX+22/ZunSpRJYKTU81ewIlmZYHn6Ixrg3ffhf2FzKH8Rl9Oo3mBvPKYpF5YRCIQoLC1mwYAE1NTWxOmpqarTRD+FwmKqqqlgOfy9YawPefffd2OKaRCISrEid0tJSSkpKtJE+ukgQpwVLXtEVfqJU7OW9+uG1OMwLXuPjt25ddI3uercx9TM2btE59pQUiUZe6dqzdpxzQiiU3NaXQZ8NN1rV8fcTleO3XKI0qs+BNUZOu/ZBfJSUSpuULqH+OqmQqZ/OOANQ0djYKOU/L5YHbh0rx1z3nHzuvU3acpaGbmnYTrOCWJ22/25RNNXV1Y62ej/1q8fsbdjNS050qcdSpak7aUtOZf2YoVI9e/C6V4k6+1NprvC630FMMpb2qXve/PTNrT2v5zwoUjFjTfWs16kOPzMAe3mMCSgD0NIi5e+GyRX3flUW3fCSbGxqid0kO8Pya4/UvQCNjY2uNuggD7T6kukYldp+Y2OjXLx4saszWL0mVS+wnSY/9nevKb8XswvKDJ3K6wRiIvWnC8maiJyez1SYTvwKkWSQDnNQe8BOj5MAMBvCtCdqN8I9k/hjt+9S0u98/nbZVMrKyhgxYgQvv/yya0I4p+Xjy5cvJycnJ25lr5rbJNlVk9ZUs7S0FCklM2fOjDtvb9/LtOKVhiDRFar2FaKJpKIIuno1FW3Yz6tjAx23BWUq2vSTDiFVSOcKW7+06lI7dMT90tGTzEpgg1QhvA+ALQfyGNKrS8zmv2HDBs4991zH1ZKW3U9n/8vNzaW4uFhrxw/K/J1sxKFQiBkzZsQJGatsTk5OXPtWeatOexuWPVMtZ6fB8nXU1NRofQx2WPli1JXGQV86p1WbTm3qrvGy8fq1KVvjYId9fNoLiTJ/r9XiqUQqV9gmuorZLnjc7PXphl/BYwRAeyK3CwBdOYAQIubEtTR1+8MSDkfz71RUVGidthZj1jH6RDRTVfPU1afWaQmYmTNnOu5BXFpaGmNW6kthd/hZQk51dlkZSRsaGuLKqWNkMXxrm0sgqRfOK62C17Vuju8g7arbdqoI4jBNVaqJRGC1097pDdyEuF/4dYT7SYWiC7JQ60gXAjmddXahTP349QFkih2uDZoapfxVb/n4b78hr3jkbSmlt61f51BVkW5HpO56y8bvZr9vbGyUr776qnbNgL3Pixcvlg8++KDcunVr3HknZ7LOh5CMjdo+Dk5l/Nal/vcznkHXRfi1R6vjl4iNOhEbfWNjo6PjN5VIxDcQ5D4mew+8rm3vBX04+AAOuRlAKsLC0oacEAyazIzsNZSvr/ZlE66srNQet76Dhkfay9sTselSB9thzV6WLVvG8uXL40wv6jXWHgL2sEb7NH369Omcc845vPDCC3HJ2/Lz87XjY5lH1BmLW+I3dbycxsMrgZcf6DR2PyG1OtNFkHZ054uKiqioqIiZ0IKaQxLVhHXmQqdrE4WbaUU3S0zEHONVNpmZTbqTvznwFW0GyENOAHRoZj0/OOIkCg+sIrx/L1vrDm6l4MWcdeWC9tWpvBPzcXvBQ6EQQgiklHEvmWW2qq+vZ926dXHt2AWFdayiooL8/HzGjh0b50j20wdLGDiZRpwYgG780vHM+KlTV0a18yfCLPPz89swYi+fhp0mu0PVfq3b8+TH2Z1Iv1STn+5Z0d1TL3OMHYkIy6BIV90uY6uN9jnkBACk50VOGSZ/gWzZzHnZS3mhYmvssOoctb9kFlLBtIKU93rBZ82axXHHHUd+fj6FhYUxBmwtbLN2FLNoVxeOqQxFShmrD6IRR/Yy9jGwj4XTC66W0S148hI4HQF1QU8yzNLN0e5XCKjXpMqpmYySpjJ0p744KThBn30/SKfFQTeb8ULQsT0kBUBGY/AUGDyVb3d5jXkrPqG5JQJEb255ebnjS2ZnWjq0ByOzCyeLgarO7PHjx7NgwYI2D/CUKVNiYaSqQ9iKIrL6Zc0qLCeyOruwfltCQmXgbg+9NVOwOygzxWSotp+fnx8LCU6EWQaZQfmBkxadrHAKQrd63q7dp0uIp4PhBoGT0uLnOg0ODxNQp8BnfsKQls1Mq1vEgvc2xg4LIVwfKDUyxE1ApAp25uumcalhpwUFBZx77rkUFBTE7PRWNJNa3n6dBctsYc0MLPMOHGTcsnX9ipOWZA+VtOiwp6/w+wKnM/RSN67qmARl/umwX+u06CDMzw9zt+j2Y9tXaUjVs2+faSbJcFMCnbALitY+dNWe1HmGM/XT6VcCW2hpkZG/zJLbfjVGnnbri7IhHL/xi25TkKVLl8pFixbFIjvsq2hTsaJWhbWatrq6Wi5atCgWzeM3MsQe5eC1kY3TKmO1vL0N+2/r+q1bt8YS5OnqDLpRTF1dXZuEe6lGslElqaor1VDvq9f4eUWipTIaTnd9Jm34EgR+aMYkg8sgZGUhzvo9A+VOzqubx9+WVMVOLV++nHnz5sW0bsvUMWXKFIQQMS3a0mjttlndzEAHN43WMs1YJp1QKBSL5vEz3dY5EO3RTFYZiM4OrBXeTv4NnV1X1eItc1BRUVFsYZ2lRdvrDBqfbZlk2kPTc4LTmDs56t2uaS9YAQHg7KRXYT1fiWi6yd4bXbsZ7UtshX3m5DJr0ad80EmFVH6Ah4AdKNtHAsVE9wl+H3ge6OmnrkNmBmDhmStleG5fedYvH5CfbK+VUh7UUK3fdu3broWrMfPWZifW9U6arl2j1WnmqrYWdL8Br+NOsf1e5d1mELqZQ1Ctzkv7TCSOPtH1CUHaThW9fuoNer36PGZ67HxHI9G++ZwBdEwyOOBEYJpNAJQAJ7X+/jpws5+6DhUBELtJddtl8y0j5Qc3FsuvPLBEtrRE2pStrq5uY0pxSipWV1fXZkMSpyRz9rzhTg9RECaa6MIntzKqQHKiQ93cxkuQ6LatVBFkNy+3c1ZbVnbUZDOeJsIcMoHhevU9iMmlI4VZupFuAddhAiDaNqNsAmAvxBLRDQcq/dRzKAgA+40OVzwr5dye8o+//Ip8dNmnceXq6urkgw8+qN3kQYWq/btpnU4PmZ8Xzy9DDLLFpPrbfkzXD11Za4yqq6s9V6A2NjpvW2mdD7qbl3qd28zGSzBlooabClqcnh3rnNXnoEIy6Ewsmcyz6bgnXu9Tqtt0EgAd5QP4ADi39fcXWoWAFkKIK4UQpUKI0p07d7YLcemEPbTrvYahtEy5lKtynuf1F59i9ba9MVsewMiRI7WLWixY9vXi4uLYAi23uGevhWBONlq7bVEXrmrR4hWhZA8jrKmpiYV8qmWtVcaqjV9X19ixY7ULn+x9CoVCritU/S4askcc6ezHFr26e6Gz20L7587xQrK0qH1zqn9qa/I7p/xHuvr8rkew/FyqvyloXH06ouu81i+0a2iyTiqk+kPbGcB44FVgJTAXqPZTz6EwA7CjsbFRygN1sunemXL33KHyi7c9Kfc2hGPaipfJIlaHx7FkzTOq5uU0E3GixX69emzr1q1y6dKl8tVXX5ULFy6M1a9q9nbN3R5R5LX5y6JFi3xpf360Lqvv9k17vMxmbm2l2vTjF+0x2/DzPLhteOR2nRucIrfs7fq954kgmfsa9Fqv+sikGYCUcrWU8rNSyunAE8DajqAjnfArvUOhEOTlk3PpE+TnwvX1v+P6p0vIzc0lFDqYdkGt12vlo1NkSCJ5adRzdm3VKwupvX275rts2TKee+45xo8fz/Tp01m3bl1Mc87Pz+fCCy+kqqqKcPjg6mJrwZy6Wthp1mF9V1VVeWqNXudVbb+oqIiqqioKCwuBg1FMftYW6Batuc001Hqd4PdZSzTOPRk4jYG6qNEazyAx916zE3Uxnf16q11dqgu/ffCC1/h61en2TOhm3k6LRz2hkwqp/tB2BjCg9TsLeBT4up96OssMIGF77uqXpJzbU86//mx51yurtfW+9dZbcvHixb40ILf/icJvPeoswUnbraurk4sXL46LWnKyldtnIE6zAbVttYyuLh2tun7o7qdqs/ZrW3ZrRwfLt5PorM2rXLIzgGSuD+qHCgo3X1TQ3d8SRbpmWInM+OnAKKAngK1AE7AJuAL4IfBR6+dWWh3CXp/OIgCkTPzmRxbdKuXcnvKuX1wu//3uwT2DVdOIblGWyuR0DDAVC5kSYWC6LSLV35YpRbdoy6l9dSzs/fIyETi14fRSuTkPE7nHQZydfsfaLx2pZEjpYpqpgNfz7nQvkzURZtpYqPR0mABI5aczCYCEEYnI5me/I+XcnvLnv7xGvvNpdUwTtDR/O5O3Hni7XVpFKmYAjY2NcW14waJbt45ADVm1Nqr3s/eBru/qOT+290R8F24I+uJngv0/Fchk+oI+70GUD7fz6VwtngycBIBZCZxpEILsc/9A0xGncHPO3/jHIw/w8vIKJkyYQG5uLtB2xaRl74SDkSSWDdGKhPDaHtKPnbmkpITVq1d7RmtY5SsrK5kwYUKbvDZFRUWUl5fH7XTW0NBAeXm5a32W/8Hed8tWrvZfZw+1ypWXl/u2h/vpp33bSi/4ifaw7NSZikynL8h2qOCdWM7Lh6bmrHJ7Fjp6dbYdRgBkIrJzyb3kMVr6T+T33MXLK0rZG8mLOYQtZmZ3uN55552xUEqIMn81rYQT/DgErRBKpy0odeWdnHvWVpJqXV27dm2zt7GuPtWJZ++DVc7eH/W/LlQ0mURbAM3NzQld5xQ+2q5hgAmgI+lLZ5teieX8OJ69wof9jlt7ja0RAJmKvB6EvvYs2b2HcU/L7/jtA49S3dAS02LtWmd+fj4nn3wyoVAo7tzYsWMB9x2OnBigLt49CJO0XggnqBEdTnsLq7RYewVbseC6unQRHqp2Zv33iqSy6vIDa2YW9Dp7u6laE5BuBpOswEwU6mwrUfq9rku2b07vUJC621PAGgGQycgfQOjrL5DToz93Nv6aX93/OHsaZZwWqz4kJ510EqFQiObmZsLh6E5b06dP92TE4BxKmopNQPyErTmZbdTzavhlZWVl3EKfGTNmxLVl7486BvZwVB28XkJVOKqhun6v00FlEMkwf52AVM+ngsEkIzCTEVDWdqSJ0O/3umT6pmtPvRdOJiZ7++0lYI0AyHT0HEKXK14klF/Abft/xS/u+yc1DRFCoVBcvn7dikspZawc6BmxE/yuinWDU7y1k+nDyWxjwaKloKCgjb3V7hex99H6VjVstzHxiuUvKSlh27ZtzJ8/37eW5ydeO9FxVq/32h4TUr/q2C9z9VPOSYhZgj6RZ9LpuXMqCwf9Z4kKHN290NUVxNzk9/31DZ1nOFM/h0UUkA2xqIOaT+WB28fL2hsHy2/fcp/8dHttXNSBGiFjRdRUV1fHRQipqy39RPMkG+dtz9AZNKxSV0a3V4KuHrU9e8y+UxvW2PkJ91u8eLFcunRpbEyDrI+w/08mnDJIWKvb8VQgleGofpPIBVl/4feZs+6r9d74bcetfaf/Qe6//f1x2jtDB0wYaOeBelPjQstq1smGOybJfXP7y+/99h5Zta027ho1NHLhwoXyj3/8Y4zxL168WD744INy/fr1cULB3qauvmTot9dl0ZdI3dZL6ZXJ1No8R2X4fl4U61q/L6NVzs86C7fQz2TCSINc395hm8k8O27X6wR7IrQ4PT+WEhAk5DkZBKnfrlBZv73oNAKgk8B+M9W0zo2NjfLdN1+U+++eLhvm9pU/vvlWuWbbXi2TW7hwobz77rvliy++GLv2448/ljfccENsZuDUpno8EebkppEGiZfWaUr2nDE6xq4ulrP3JdWzHvU+2Y/rXtBEhZ9abyLXJzvTsNeVbHt+GbLTdbr7nqzA0R1rb6Hpp02d9m9mAIcQ7DezjQmjfpfc/8fjZdPcPvK6udfLR/77vzYPQV1dnVy/fr2877775OLFi2PazMcff+yrTeuYFzPXwc/Wf17nnbQzJzp0ZXVptBNZIOR2zr7QTT2uCiG1T6lYke2HPvv5VKwqDiJI/KZj0AnqoG0nInAyEX7HN4j2L6WzADBO4AyEUwRLLDqke1+6fuO/NA89hlu5l53LH+eVVdviQuTKy8vZtGkTF198MZMnT6aqqooRI0bw1ltvaSNEdFFAbs5ap+ggK1zTy8HsFX0DBx21OhpVOpzonD9/fux8UVERFRUV2kVgTvATLdTc3NymzlAoFJee2zqmOtaTQZCII5V++31xchLr6vPjHLeXd3oO7Iuu7G26xeG7tR3U+Z5pCOKotoc4q89EIOikQqZ+DpcZgG80HZCN8/5Pyrk95aPXXyhveuzVmAalflSt0y3trl+twmsG4Oc6v/XbtWgVbrujWeftx+2mGSebv11zt7Y21LWla6e98uQEmQHoTGp+ZnfJmJ2ClvNjztBdn86AhvaA26zI7Rr1/XYbB8wM4BBETh6hL/ydplk/5CvZrzFrze+49+UK3n3vPeCg1mmlWwiHD6ZGBuI0W1VL8gplVLUPHby0MD/L6i1En119umZrZuTUD10qYIjXMK36VTrttAI0NTWxcuVKbdqH8vLyNrHeunUXbuGfuv9+oM6mnMJZ1bJ2Gu2zMZ1Wnmhcut/y9hlNUPihL9Ux9amaTejGWBcSbS+vjpW6yDHwfdJJhUz9mBlAW1jSvnnFX2XL3N7yvRumyp/8/RW5r7Epdl7VCixfgLWFohUtI6U/230yKYUTddLZZzFqPdXV1fLBBx+MS5HtlezNyS6u9s8e/mdpWDptX5fx1D47sY+d13k3+u3H7fsOB9HYE/XzpAuZrKnbZ9VuM0e/9fnZgMlP6GeiPoAOZ+pBPkYAxMP+QEQ+fEE23TRAbrrxCPm9Ox+WW2r3x8qpO2tZzN/61jFZtzad2k8n7C+EOuXdunWrKyN1MvXoylqO4ocffji2W5kaiaUbI7tZyapXJ0TUdp0imnR9d2MU6j1MhHG7tbto0aKMyHAZxNSVrvbVPaed7kXQ98GvkLaQ6L0AVkpjAjq0YJ82ivFnk/PNV+nbPcTte67lD/f+nrKNtdTX13PnnXdSU1ODEIJQKISUMraPrmoqUh13qlnDyaSQyIrMRPtqfatT3qKiIjZs2NBmKm1vs6mpSVunSn99fT0VFRWEQiFmz57NJ598Emeyys/Pj7VtN/moK7MtutSdyOrr62Njq6a18LsK2G2nNtXZbO0g5uee6O6p/fzq1atZuXJluzhQ/TizEzmfSJt2WONspWBxuhdB3wc/K35VE5lXgIULpP5oBmj2fj9mBqBHG82jbrus/8upUs7tKf90w//JZ975RL766qsxE4bOxGHXINXFTX5mB340tGRNSE7t6cwp6v4CdlOXClUrt/ZcsPYxsGZN9nZ1zmfdGPnZicwLujUNTrA0dl07fmY/Tu231wwvmTDORM2LmbgTmNeML5HnCOMEPnTRJt9OqDcVk29g/8Qv8d2sZ+mx4ApW7u1GuDnCypUrWbJkCU899RTbtm2LaaclJSUsX74cIBY6ae2p6pTTxC2/iQqnHDROOV/89tnNSdnc3MzKlSsBmDFjBscdd1wbmurr62OhogAzZ85k1qxZhMNhmpubWb16tbYfuvbUWUJ9fX0bjd8qY9Wj/neCFc6rm724jYt6fTgc3XvZvrewX23Vuv/pRpDAAGib4ylRGjsqJ5IbvMbCKWdWIm0aAXCIwP6wREQuOef+gcbTfssp2eVcVvFVbvrr4+wOZzFt2jRGjhzJmjVrKCoqAqC4uJjc3FxqamqYN28ey5cvj6tTXYsQDsenpHaKKFFps2e5VE0hiUxr7esR7Jg8eXLUyaWMjfqiWIx8zpw5hEKh2IYy4XCYBQsWMHny5DjTilWPFXPtZAKw9mAoLS2Nja2ObrdYe7W+mTNnMn36dF9MSjUHhcPhuPszfvx4bcSPU9sdAb+M2H7vEzX9JBJxpKtHRaIRU3a4CUJ7qnOnxIY2CO1R3bQglR9gOLAI+BBYBfyw9XgBsBD4uPW7j1ddxgTkH+q0MPzpcrnvtgkyfGMfecfcq+TzKz+RixcvlosWLYrLr6Nu0ejXpGM/5sfE4+QkTbSPdnOQZcpRI4Osczq6LPOPav5y658uGkmFer1XojavyBK3MXVyRlrnLNORHye20/FUwW9dQcslQ6PXffRDQ6as93Ar09jYKIFKqePPuoOp/ACDgWmtv3sQ3Qi+CLgd+Fnr8Z8Bt3nVZQRAEti/W9b944tSzu0pX7/+RHnzvEVyT300Smjr1q2xYnabrxPzcjpuTzDn9ILYE7vZkUgkhcrI7MwxiI3ZEgi6fEJ2ZpqIbVqld9GiRXFRPLprdP4OS9C5+Qd091HnL3E6nsrcQX7Hqj0Zqv0+egkEt3uZKA1BaHUr71UX8K7sCAHQpkH4D3AGsAYYLA8KiTVe1xoBEAxtGOCKFbLhf3+WTb/qK7feOFL+9La75bLKDXHOXoupWDMCNezNOm93TKoavVqXnQY7XU4OxiCMwF42GUe1vaz6UcfCr+bo1DdVM7eczk79sdNjn4Ekogk7MflUzQCSYZTtWc5+f60MuX5nYkHo0LUdJC+UVz+8lBunGUC7+gCEEKOAo4C3gYFSyq0Ard8DHK65UghRKoQo3blzZ7vR2tmhXc151FF0OeEqcq58g569Crht/1w++udPqOs3ka7dugNRR+j48eOpqKhgxYoVNDc3x7ZhtJyjEL+CVnWAWjZ1+0paHV1quUQclGpZq243f4Ifp6u9rOUcX7lyZWynNSsM1GnFs72f6rlwOIyUknA4zMqVK5FSuobWWteUlJQAxOUSsnwRiTgedb4bv74Y3QpV9ZyOlqCrgr3aT9XmM5YfwHp2CwoKtM+e7plMxgEbxP/lFdbr04HeoD2pkwrp+AD5wErgwtb/tbbzu73qMDOAYHDVGupr5YYHvyrl3J7y4xvGy+vufVj+d9GyuJXCixYtklu3bpUPPvhgzC9gN1eobVkzB+u/X9NEoimSdZpsqu25qjlJzaekhpmqZZ1WaqomG2t2pY6XW7/8hII6me2c7pNTuKj17abxepnAkjXh+J35paKuVNAadMbqdX8SrdsNdGQYqBAiF3gG+KeU8tnWw9uFEINbzw8GdrQHLYcTXLWG7r0Y+NUHkV95jiHdWrh519WsfeMh/vLSu0yYMIH8/PzYorGxY8fGwgHVhWN2hMPRfYgtjcYtq6OKysrKuIyGVl1usM8kktlL116HetxacGbRWVNTw/vvv09DQwOlpaWUlpZSX18fK6v2w76AR80QWllZyZQpU+LyFVmapBqyaWn8anSPDrrQWp0GHAqFmDJlirYu+2zCTRN225Yx6D2wz5L8aPepmlGkIlrH74zVmsktX75cG75qn1HoQqdTDp1USOWHaPjRo8A9tuO/J94JfLtXXWYGkDrEaRT7a+Texy6Tcm5P+d4NU+VP//S4XLutVr744ott8t3rnMSq5uhl/9ch0Wyaic4YnNrS0W3X4pcuXSpfffXVuNxAfmYfTjML65yqWVt+AbuN2O+4WLM43b1Q7f1OdSWzaU3QWZiuT8lqu051pKJeP3Cz66u+JB1SPQ4W6MAZwPHAV4BThRBlrZ/PAbcCZwghPibqFL61HWgx4KAmEtOyuvahx2WPET7vAcbl7eY3O77Pwj9/nxc/2M648eNjWuq2bdt45JFHWL58OTU1NbF6VI21oqKCZcuWxdkt7TZS+2/LDuq2sEuHIFqRm21ajaN2iy3Pz89nypQpdO/ePdZ2m70aHNq2/y8vL4/NHtT0EaFQiJycHK2N2GmWYq+7qqqKwsLC2OxFFzfvpsEnsuk6EFtQaPXLD5wW1eng187u5H9J554AVr32xYV25Ofnx9JJ6OBnHFIKnVTI1I+ZAaQGqi2/DfZVy71PXCHl3J5y7Q1Hyuvv/ous3LJH1tXVyT/+8Y/y7rvvluvXr4+L9tFpyWqEhZVEy/Ij6DRbN+02VdqS3xmFl2/B+u93A3lrXYLbzMlpJuIV1RQkp7+fMdDV47esuuuaW277IDMle/1+fR1eGWGThb1tv5FoQepNZXlMNlADFZ6M68NX5N5bi6Sc21POu/48eeszb8mXXlsUi/N3y2RpN2k0NjbGhYiqWUi9aLK/XHbmbN8II6gJQtee/b+OobkKURvsQtFP26rgTJRhBjmuK5dMZkt1rNQ+eAn6IGs13NIkp8JxmqgwSqa9IHQ7KSg6GAFgEByN+2T9gutk89w+cseNw+UNv75evlC2We7duzduhyzV5iyl3s5sMVJ1pW4yGrmufr9MU9eGrn6dIAvy0tnbcXrBneLvExVmbjMDu2/AiU6VjmSgE5SJCDSnsonMMvzWnUxyukRpCPrcOj2T9nqcBICInuscmDFjhrRythi0H5o2lLDnyavot+8j3o6M57H8bzBr/EiG9gzR3NxMVVUV559/Phs2bIjZre0pmUtKSiguLqayspLCwkLXJGNqFJHO5q1LqlZfXx/b9cw65gXLLmyPu7dgndP9tyJmdLToUFNTQ0FBQZuIHMtmbK2fsHwiajt+YafHaZwqKipoampi1qxZcf2wIpCklBQXF8d8P+p5NXooSO6edNizdffPre2gdCRDt0WbtRtfKtux7pOaq0p9Z9R30DovhFgppZxhr8skgzPwRO6ImXT59uusnfhjivO2cs++nxIueYjy+u5MnT6TCRMmxBbQWA5EiE++ZoWUWnnwneAWhmh/mS3nqd3x5pf5W/TYy+vC7+yhpiqd9fX1ccnx7KipqeGee+5h27ZtAHFl1cVzpaWllJeXx5LIBXFa2p2cFt01NTWx0ENLSE6ZMoWcnBxtqOiMGTNigloNR7QcyDU1Na59TRSJ1KU68HV16cIqvdqx7/OQKPwu9AqyqE2Fpbir99seousLumlBpn6MCahj0djYKOW+arnvme/Llrm95I4bh8ubfnODXLByvTxw4ECb8vawybh6XKAmpVOvcZvm6pyPbv1wWsxkN7t42az9mFWkjOZbUtNq6ByFVh8STVKmMy1ZO5stXLgwzjRm+WSqq6vb+FHsddlNbk7J8pxo8jKlJGNu8bL/B7HTq+kgUgU/5i6/z6vuOfVKG2Kdw/gADFKKTSvl3ns/I+XcnnLFDUfL79z6gCxbv6vNw7h161Z53333Bd5YRJcszumhtzNiP3ASIonke/HTZmNjYyy+X0erdX7hwoXywQcf9JXTyOnFV5mzmuPJztgsZ7x9m0O/TCvI6len47r1CvZ76xU3n0r7vy4XUCp8IXYEGT87DW606RQlKZ0FgDEBHeZIeBo/dBo9vruYlnP+QHHeNu5t+CkVD36Lax9YwLbd9bG616xZw5gxYwB/Jg3rfEFBQWxDGhX2uHE1tl3Nk+MFp9W6OpOQmylAXcfgp1/l5eUAWvNATk4Oxx57LOecc06c/d1ej7p+QDV3WKaeJUuWUFJSEstXFAqFKC4uZvXq1XH1FRQUtNnm0MkkYTct+V2roV6jO65u72n1TTUvWeZDHaw++23PD+y5gNK1fiDI+FnldfS45SzyVbdOKmTqx8wAUougWogj9lXLA8/9WDbP7SP33jhQ3jn3O/KhRZWybl+DY34g3XaJXuYWnWZmbW6fzMpV3e9E63A6rzMT6bQ3ywyhau72MXLS2NUcQzrt3m90lJtWmcw42Ms6mWnsswy3lOF+Q3GTQaJx/cmMV6LnnIAxARnokNKXZ8dquffvF0g5t6fceONoedPvbpIL3l0vI5FIrK23335bbt26NS71rp90Ck5TfTsz9NsnP7bnVCJIG6pwVNcP2PPWW2Usf4kur72fcfHDqBK1UzuVdQrXDWrOSbRPfpHoM+J1ndv5ZFJxOLVlBIBByuH4gK5dLPfcfbSUc3vKd2+YJn92zwPynU+j9mfr4V6/fn1cHV7M383ZZf/vZ/OMZLOH6pDMClALdnrcfCCq5u+0CteLJi/GHYQBBrnOSaAnahdX/yfDtIMc9yqTiPDymvUkWh+wUmp4qvEBGCQEV9vo6JPo8u3FfFT0I8Z13cMtu69h+98v4Rd/+w9b90kKCwt544032uTUt7IkOuXrccqcqP4H9+yJVvinzk6ajJ3XKweMjg7dMXuf7LmA7LmL7Hl9KisrGTFiRNwaAy+a3Gze6ljp8jnZ1zWov71s3G72ay843X/7/U0kjFb3/AWhxe06p/FSj+l8WX58TB65rvQLvnRSIVM/ZgaQWfDSshsbG2VjXY0Mv/ZbGb5pgAzf2Ec+fv0F8jdPvC6rNu+KK19XVycffPBBuXXrVt95ZLxyzuhoSjQNgR8EiUDym/fIjR5de2rUlVomSJ91swH7zMNtTwAvTVo13SUDP+ajoJFhqknNrW4vWpzKJDoz8ePrsJsHVWCigAxSDTct29JGyio/pu6obyO/V8qWobO5JHsxV3/4RV67/8fc9Z8V7D3QFJsJXHjhhbHVxPZ67P/tmp5fet1y2Cebd10XgaTT3LzocPuvjoW694J1bPXq1Rw4cCBOg3fTDnVRT2p91iIwIG7W5LQngFsUkXrcKbLHD9wiYHT900VpOd2XUChEYWEhCxYsoKamxrVPura9yiT6jEV5uDvC4XCgWShgUkEYpAfWqtOamhoWLFgQW+3atOMjahdcz9Adi6iV3XlEnM/qLjOYc/RoTjzhuNj11lJ3iH+xLWFhpSywQhj9pAVINbyW8NtpcmJcTsd1ZdR0FCUlJXGphS3BuGLFCrp27Rpb0euUVsKiT01XoNJs/S8vL0cIEZd6IJFxsa/kdlrl7XUs6H226lG/rdQkTmHDNTU1VFVVxd07v6agdDyDfp4ROPje2eGUCqLDzTpBPsYE1Dlgn+rqkp2FN5TK2gfPlXJuT7n1xpHylpuvk/9ZuU42NBxwNBGo03P7alTd9DeIOSXVTk77NY2NjXLRokVxU3mLfisixi3c0SkCSDXx2BPv+emXLoQ2kQyrfs1wbv2z983J7OHWlp9AgsbG6KK7RDOtuiEVQQWpMFHay2FMQAbtAZ1pRqeRvLe1ha5ffZqmy/4D3Qfys+b7mPjcmdxyxy0sWr0jpv1bsDT/wsJCnnrqqbjFUNZGJBUVFbHpr6WN+ZnG+5niuzlH3WB3jKrbMVpOa3v+HV0dlkZeXl4eZwaaP38+NTU1beixNq7XOWut/+FwdPMY1QxkOZ4h3uTjZ2zcNtNxu9Yy1ah9g7b5btTx0N0zr/uo9mfWrFkUFxcHclL76ZcfU5AbvJy5fmiyZji+aNBJhUz9mBlAZiNoyGBshrB3r2ypfEHuuWOalHN7yvIbiuUNd94r//7C/2KhjVZ6BEt706Wgtjsl7ekkktX2dCGZQeDHaelFoz2NtqrB27X4RYsWtXGsq7MOp/bsx9TFaU7lnGZ7TnWrDlc7Tfbx8MrFZK9XV0YX6ppo7n2nPiVat1d9QWnSjSVmHYBBeyAI87cvYGps2C+bVv5T1t0yTsq5PeVb18+S19x2r1y6enMsj47ahi5pl50RBM1B5MSkVTOFZaIKwkT9tOfFOKx2rZ3V7B/dXgb2/YGt33bzh/06O6w2LTjV4ba+Qm1fl9jPrd9Ox72ikazjbs+KHzg9R25M2E+dXvBjevN6/hobnReCpd0EJIQYLoRYJIT4UAixSgjxw9bjX2j9HxFCtHVOGHRK+HWUAbHU0IWFhQCUVbxPZNIc8q8p58CpNzOt61Z+v/96Gh+/hDc++JSXllfETAb19fUsWLCgTcSD3TwQCoXa5Jbxos0yj4TD4dh+x/a8NTNnzmTKlClRupVIGXs9bm0FNSuFQiEmTJjAs88+y5IlS1i+fHlcLhzVcWuNsZqmW43esTuPrT7oUj1b0UXWcSsCqbm5uQ199jUKuvP2fZRVep1MYE7jYTmwdeNmtWc9K5Y50KteO7zWUzi1nUyd4D8PkdNYq88YoA+70kmFVH6AwcC01t89gI+AImACMA5YDMzwU5eZAXR+6EwVjlpjY72se+V3sv6mIVLO7Slfuf4U+buHnpZrd9TFTD5OUGcLCxcudE15rH6rq2ntWq5dY7XOOaWVVr/tY5DMSmTVAa5q/35Sathp0o2BTpvV5THy24bTOSdzj5szXIVTf3WzEvv6Ei8a7XDKEhpkHYq9L+maAdhnhB06A5BSbpVSvtv6uw74EBgqpfxQSrkm3e0bZA5UTc+CXRuMK08ulb1PQ363lH2zruXE0Bp+vv4KPrz3Qn711yfYub8lVq+qAdXX18dtaFJVVYUVPmzXku2O5JUrV8Y2ZVE1ZZ02ZsWyW5qoCjXc0M2hl8hKZMupbl3rpFnr6rTPctSZklWXPezW2qjG3ocgDlR7+3aHsVWfW9y+bpW0unmOboMba2wGDRqkXfHspYFbsGcJVet2W2vhNgZ+ZiBuTmXd+2TNWgHH90pFu64DEEKMApYAk6SUe1uPLQaukVJ6BvibdQCdH37iqe3lofVFaNjNvsV/IKfkr+S0NPCCPJ61Rd9l/ICe9MoTzJw5Ezi4LZ7FKGtqauIikexx7rpIFx2NuthwiK5ZaGpqIicnp41pxb51n1s/y8rKKCwspKCgwFdZ3dZ/unK6xVpO8fj260tLS2ORS17x8H7vrS4uH/CkyR7jrpazhL7Tmgdd++o19vJu55z6G+TZDlrW6TlyWuOgPoeQAesAgHxgJXCh7fhiXExAwJVAKVA6YsQIzymTQeeFk2mijZOtfpesW/Az2fir/rLpxt7y6RvOlX+cv1BW1+tNC07mEafoFifadLHq1ncQZ7DTtF7dpcsL9lQPQdvSQWdKsJvAkkml4XTdW2+91Sa6yat+J7OHF/ykD/EyRenoSScaG9tGf9nP2++THcC7UsdfdQdT/QFygVeAqzXnXAWA+jE+gEMXdpulH9tq/ba1suqBr8rwr/rKxhv7yCduvEj++bnFMUGg1u1UT5Csi3aGn4hN2d5P+zV+9jZwEmpu9nA35iGltz3d6dtPG/Z+26GLKNLV5ySInOrW9cUeOuyEIPZ5t7H16pMf2Bm87rxXriA6KgyUqPf5UeAeh/NGABhIKfVMxa1s7GWu3SRrn/qebJpbIA/c2Ff+48Yvyj/+5602giBVNDoxSr8rg62PW058P/VI6Z0u2k+YpH2vXz/OTmujH7fc9U7CxK0/Tm1afXWbAdj/68Y3VbMZt7JO99eLkXu1k8x5JwGQdh+AEOIE4H/A+0Ck9fAvgDzgj0B/oBYok1Ke6VaX8QEYWNDlRgnvqGLLMz9n+PbXaJLZPM3p7J3+PS497Wj6dHe34+pssk62cif7rZdd126T1/XBKqfacZ3yu6i+ACfbuJOd3YJVt1f/7PUsW7YMIQQ5OTlMmDAhFmZq74PfHEJONKs2/nnz5jF27FhmzZrVpi9ONOv6bO+7jg6nMfeqR73HKqz+uOW4UusI4ifzgw73AaTiY2YABlK6h1E2NjZKuatK7vnXN2Tz3N7ywI195WM3Xiz/8p835e59ziYBN603lfZev1qlNaV381NY5/2sUPWj2fuhy26+qqurc8yr42cmY6/Xbbaihr/6MQHZx8Bel9N4uIX1utGuK6uj1X7Mvg1oOp41OmoGkEqYGYCBBUu7c82+WPMJe169lfzV82mWWTzDqawechHf++I5DOjdPVZP0BlAOvri1nZNTU2byCC7pqm73j42Xu34gS4Sx4oUctOY/cyOoG0El5++uM2kdDMua2Zip1mtx02r9xpDtR432tTnLpEZgFdEltp3MwMwOCThS1Oq+VTufuJbsnluH3ngxgL5xI0Xyb8uiM4IOowmpaxX+ocg2q3bebc2/NThRIuq6ets9EFt6jqanPIQ+Rk/u9atS+vgNePzOxOsq6uLy+7qJzVGoj4BP9FMFjC5gAwOe9Ssk9X/+pZsmlsgG2/sI+fdeJF8YMFiWbsvnLImEtnQ249DM1m4MW+36Bw/phC76STIdX5o9fNf920vqzJxJ5OaBadQXKd7Y69bNe/YafAzBm7Q9ddLCOKwJ3CHM/UgHyMADFKC3RtkzZNXxQTBUzdeKB9csChpQeDGXILWkQqmr6tb/bYf19ERZDtFtzac6ndjqEHqDyJ8vLay9LM+xM7w7Yzf3ke/Mwi39ux16s6rtFm/ly5dKoEPpYanGh+AweGLPZuoeeU2elQ+ATLCf8RJ7Jn+A+acfgK9uuYmVGUqfAXp9jf43bUqVXQ4+VmsdBQ6W3vQlc2q78CNbl3Uk65utyggtaxFv24lsjZSLYHVwzpfhtfqbSnjd8vLy8t7V0o53V7eCAADgz2bqXn1dnpU/hMiEZ4XJ7J72g+46PQT6N2tfbaXbE+kU8Do2nJKW2FlxJwzZ04bB6hXqK0fJ7AduvbsdfupRy2rczL7TdNhv96pr0799qLNgpsT2AgAAwMLe7dQ/ert9Fz1OCLSwvN8hh1HfZ85p59I3/y8jqYuLUhFxIlXeTXSxZ6czCkW32+EE+gjh5zgNgOw6kpkv2HdMTchZl8roApFiz5d5FOQ2Zu6DsNpBmC2hDQwsNBzCH3n3EPu1R+wd8rlnJ21nCveu5j/3T6H+555mR11BzqawpTCYih2jTHRck7lLWZYXl4e21/Bgo5ph0L6fQXU+tRMl/YMovZvFSpztc5bmUlBn9vfDW5lnc6p9Kt9VPcIsPrldp0XXcXFxerWqt105cwMwMDACXXb2f3aHXSv+AfZkTAvyllsmHgVF84+ncG9unY0dSlBe8wA1GPgfyMWtR6nVc9u5ewmGCeTir3eRNZL2OtLdBZhMX8g0EpqO+zZTIUQxgdgYJAQ6ney54276FL2MHmRBl6OHE3V+O9w/lmzGdZHq1gd1kilj0FlrOFwWGtXd3Ks+rnWfr2X6QW8F5yppi6/gkNXl/18ED+BfXGekw/AmIAMDLyQ359e595C3jWV7Jn5I07OWcX3PrqcNXefzb2PzmN99b6OpjBjENRc5Kcuy0SjM4Ho2lMZpsUMrU1+VGetvbz122njF3Df6tJi/papy77pjUqzG+1q++Xl5bGtP3X1WCY1ta5wOIyU0i4wtFtCmhmAgUFQNNSyd8mfyXnnfrq17GVJZAplR1zJ2edcwJj+/hKIHcpI1wzALfTRS8uG+P2i/TqNg9Jh1e00AwgS0qnSbtFvP6cmDbRHIVn+DoC8vLwPpZTxTgWMADAwSByNddT/737Eij/TvXk3yyJFlIz8JmedPYcjB/XsaOoMHOBnty8VXiYge1n7LMNPKGfQ9QE6M5eTf2Pq1KkmCsjAIOXI60H+6dfS/aerqD/510zO28EPN/6Y2r+czr0P3M+qzbUdTaGBBureyX5gNwG57SMMBzVz1XSjmoOcfAhupiH1t1qX3fxj0avS3fpfq+mbGYCBQarQdID9Kx6m+X930zO8nbLIGJYMvpyTzr6M4hF9Opo6gxTAafZgX0dgh5fZyW0G4LTuAYgtbHNzFIOzE9gIAAODVKM5zP6Sx2hafCe9GjfzQWQUr/X/KkfP/gqzCvsjhNYfZ9BJYGfWfjakT9YvojPxWG3bF47ZaQiHw+Tl5RkBYGDQrmhpouHdeTS+cTu9GzawOjKcF3pdyuQzv8YZRUPIyjKCoLMjUadyumiBtukpysrKOOaYY8w6AAODDkGkhXDFM+x/7VZ616/lk8ggnut2EUec+g3OmT6K3GzjiuvMSGXUUyqgMyeZGYCBQUcjEqGlcgF1r91O79pVbJe9mZ97LgUnXskFs4rokpvd0RQaHKLoMB+AEGI48CgwiOim8A9IKf8ghPg98HkgDKwFLpdS1rrVZQSAwSEBKYmsfZPdC2+j7/Zl7JVdeTbrTOQx3+bCk2YknIrawMAJHSkABgODpZTvCiF6ACuB84FhwBtSymYhxG0AUsrr3OoyAsDgUIPc/B41r/6ePutfoklmsYCT2DbpSs4/7USGF5g0EwapgZMAyEl3w1LKrcDW1t91QogPgaFSyleVYiuAOemmxcAg0yCGHkXfy/8F1WvZt/BOzl/zNNkfvMHLFTP516ivc8YZs5lmQkgN0oR29QEIIUYBS4BJUsq9yvHngSellI9rrrkSuLL17yTgg3YgNZXoB+zqaCICoLPRC4bm9kBnoxc6H83ppHeklLK//WC7CQAhRD7wJvBbKeWzyvFfAjOAC6UHMUKIUt00JpPR2WjubPSCobk90Nnohc5Hc0fQm3YTEIAQIhd4Bvinjfl/DTgHOM2L+RsYGBgYpBZpFwAiuuzx70R3pb9LOT4buA44SUq5P910GBgYGBjEoz1mAMcDXwHeF0KUtR77BXAvkAcsbF0av0JK+W2Puh5IF5FpRGejubPRC4bm9kBnoxc6H83tTm+nWghmYGBgYJA6mDXoBgYGBocpjAAwMDAwOEyRkQJACDFbCLFGCFElhPiZ5rwQQtzber5CCDGtI+hU6PGi98utdFYIIZYJIYo7gk4bTa40K+VmCiFahBAdulDPD71CiJOFEGVCiFVCiDfbm0YNPV7PRS8hxPNCiPJWmi/vCDoVeh4SQuwQQmjX2mTae9dKkxfNmfjuudKslEv/uyelzKgPkE00N9BoIASUA0W2Mp8DXiK60fGxwNsZTu9xQJ/W32d1JL1+aVbKvQG8CMzJZHqB3kAlMKL1/4BMH2OiwRC3tf7uD9QAoQ6k+URgGvCBw/mMee8C0JxR754fmpXnJ+3vXibOAI4GqqSUn0gpw8A84DxbmfOAR2UUK4DerTmHOgKe9Eopl0kpd7f+XUE0D1JHws8YA3yf6PqNHe1JnAZ+6L0UeFZKuQFAStkZaJZAj9ZQ6XyiAqC5fclUiJFySSsNTsik9w7wpjkD3z0/4wzt9O5logAYCmxU/m9qPRa0THshKC1XENWiOhKeNAshhgIXAPe3I11O8DPGRwJ9hBCLhRArhRBfbTfq9PBD85+ACcAW4H3gh1LKSPuQlxAy6b1LBJnw7nmiPd+9dlkJHBC6bZLssap+yrQXfNMihDiF6EN4Qlop8oYfmu8BrpNStmTAFoZ+6M0BpgOnAV2B5UKIFVLKj9JNnAP80HwmUAacCowhuibmf1LJk5VhyKT3LhAy6N3zg3top3cvEwXAJmC48n8YUQ0paJn2gi9ahBBTgL8BZ0kpq9uJNif4oXkGMK/1AewHfE4I0SylfK5dKIyH32dil5RyH7BPCLEEKAY6SgD4ofly4FYZNfpWCSE+BcYD77QPiYGRSe+db2TYu+cH7ffudbRDROP8yAE+AY7goPNsoq3M2cQ7o97JcHpHAFXAcR09vn5ptpV/hI51AvsZ4wnA661luxHNGjspw2m+D/hV6++BwGagXwc/G6NwdqhmzHsXgOaMevf80Gwrl9Z3L+NmADK6Qcz3gFeIesIfklKuEkJ8u/X8/UQ9458jemP3E9WkMpneG4G+wF9apXqz7MAshT5pzhj4oVdK+aEQ4mWggujOc3+TUnZY6nCfY3wz8IgQ4n2iTPU6KWWHpS8WQjwBnAz0E0JsAuYCuZB5750FHzRn1LsHvmhuP1papYyBgYGBwWGGTIwCMjAwMDBoBxgBYGBgYHCYwggAAwMDg8MURgAYGBgYHKYwAsDAwMDgMIURAAYGBgaHKYwAMDAwMDhMYQSAgUErhBBdhRBvCiGyU1zvcUKIm4QQISHEEiFExi3ANDg8YRaCGRi0QgjxXSBHSvmHNLYxl2ia6H+mqw0DA78wMwADg4P4MvAfACHEhFZtvUIIca0QoirRSoUQTwshrCyUz7W2Y2DQ4TACwMAAEEKEgNFSynWtJpp/Es3PP4Xorl7J5BWaRDTfP631zEyKWAODFMHYIg0MougH1Lb+vhAol1K+1/q/Es3OTEKI14BBmrp+KaW0ZhJdgFwp5R4AGc3xHhZC9CCatO4vQBhYbMxCBu0NIwAMDKJoALq0/p5CdKMWC5OAl+0XSClP91HvRKICREUecAC4BJgvpXxeCPEk0VmHgUG7wZiADAwAGd03NrtVY68musUkQoipwGVE8/kngslEU1TTWl9fYKeUsonohirWFostCdZvYJAwjAAwMDiIV4luGfgYMEMIUQJ8HVgnpfwkwTrjBABwCtG8+hDdYcvapNy8iwbtDhMGamDQCiHEUcDVwHeklPWtx64Fekkpr09RG88CP5dSrhFCdCe6MfwB4C3jAzBobxgBYGCgQAjxdaL73l4MNAFLgaullI0pqDsEXCKlfDTZugwMUgEjAAwMDAwOUxi7o4GBgcFhCiMADAwMDA5TGAFgYGBgcJjCCAADAwODwxRGABgYGBgcpjACwMDAwOAwhREABgYGBocp/h/B0pHykikTOgAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "plot_cmd(photo_table)\n",
-    "plt.plot(gi1, g1)\n",
-    "plt.plot(gi2, g2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 174,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# TODO\n",
-    "# ind = (poly[:,1]<21.) & (poly[:,1]>17.8)"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Drawing a polygon\n",
-    "\n",
-    "Matplotlib provides a function called `ginput` that lets us click on the figure and make a list of coordinates.\n",
-    "\n",
-    "It's a little tricky to use `ginput` in a Jupyter notebook.  \n",
-    "Before calling `plt.ginput` we have to tell Matplotlib to use `TkAgg` to draw the figure in a new window.\n",
-    "\n",
-    "When you run the following cell, a figure should appear in a new window.  Click on it 10 times to draw a polygon around the overdense area.  A red cross should appear where you click."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import matplotlib as mpl\n",
-    "\n",
-    "coords = None\n",
-    "\n",
-    "if not IN_COLAB:\n",
-    "    mpl.use('TkAgg')\n",
-    "    plot_cmd(photo_table)\n",
-    "    coords = plt.ginput(10)\n",
-    "    mpl.use('agg')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The argument to `ginput` is the number of times the user has to click on the figure.\n",
-    "\n",
-    "The result from `ginput` is a list of coordinate pairs."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[(0.2643369175627239, 17.84253127299485),\n",
-       " (0.3539426523297491, 18.799116997792495),\n",
-       " (0.47491039426523296, 19.682119205298015),\n",
-       " (0.6317204301075269, 20.454746136865342),\n",
-       " (0.7661290322580645, 20.785871964679913),\n",
-       " (0.8064516129032258, 21.41133186166299),\n",
-       " (0.5869175627240143, 21.300956585724798),\n",
-       " (0.39426523297491034, 20.565121412803535),\n",
-       " (0.22401433691756267, 19.240618101545255),\n",
-       " (0.19713261648745517, 18.02649006622517)]"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "coords"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If `ginput` doesn't work for you, you could use the following coordinates."
+    "Now we can read the file:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 8,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Reading in: MIST_iso_5fd2532653c27.iso.cmd\n"
+     ]
+    }
+   ],
    "source": [
-    "if coords is None:\n",
-    "    coords = [(0.2, 17.5), \n",
-    "              (0.2, 19.5), \n",
-    "              (0.65, 22),\n",
-    "              (0.75, 21),\n",
-    "              (0.4, 19),\n",
-    "              (0.4, 17.5)]"
+    "import read_mist_models\n",
+    "\n",
+    "filename = 'MIST_iso_5fd2532653c27.iso.cmd'\n",
+    "iso = read_mist_models.ISOCMD(filename)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The next step is to convert the coordinates to a format we can use to plot them, which is a sequence of `x` coordinates and a sequence of `y` coordinates.  The NumPy function `transpose` does what we want. "
+    "The result is an `ISOCMD` object."
    ]
   },
   {
@@ -838,10 +377,7 @@
     {
      "data": {
       "text/plain": [
-       "(array([0.26433692, 0.35394265, 0.47491039, 0.63172043, 0.76612903,\n",
-       "        0.80645161, 0.58691756, 0.39426523, 0.22401434, 0.19713262]),\n",
-       " array([17.84253127, 18.799117  , 19.68211921, 20.45474614, 20.78587196,\n",
-       "        21.41133186, 21.30095659, 20.56512141, 19.2406181 , 18.02649007]))"
+       "read_mist_models.ISOCMD"
       ]
      },
      "execution_count": 9,
@@ -850,17 +386,14 @@
     }
    ],
    "source": [
-    "import numpy as np\n",
-    "\n",
-    "xs, ys = np.transpose(coords)\n",
-    "xs, ys"
+    "type(iso)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "To display the polygon, we'll draw the figure again and use `plt.plot` to draw the polygon."
+    "It contains a list of arrays, one for each isochrone."
    ]
   },
   {
@@ -870,7 +403,389 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "text/plain": [
+       "list"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(iso.isocmds)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We only got one isochrone."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(iso.isocmds)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "So we can select it like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "iso_array = iso.isocmds[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It's a NumPy array:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "numpy.ndarray"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(iso_array)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "But it's an unusual NumPy array, because it contains names for the columns."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dtype([('EEP', '<i4'), ('isochrone_age_yr', '<f8'), ('initial_mass', '<f8'), ('star_mass', '<f8'), ('log_Teff', '<f8'), ('log_g', '<f8'), ('log_L', '<f8'), ('[Fe/H]_init', '<f8'), ('[Fe/H]', '<f8'), ('PS_g', '<f8'), ('PS_r', '<f8'), ('PS_i', '<f8'), ('PS_z', '<f8'), ('PS_y', '<f8'), ('PS_w', '<f8'), ('PS_open', '<f8'), ('phase', '<f8')])"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "iso_array.dtype"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Which means we can select columns using the bracket operator:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0., 0., 0., ..., 6., 6., 6.])"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "iso_array['phase']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can use `phase` to select the part of the isochrone for stars in the main sequence and red giant phases."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "354"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "phase_mask = (iso_array['phase'] >= 0) & (iso_array['phase'] < 3)\n",
+    "phase_mask.sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "354"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "main_sequence = iso_array[phase_mask]\n",
+    "len(main_sequence)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The other two columns we'll use are `PS_g` and `PS_i`, which contain simulated photometry data for stars with the given age and metallicity, based on a model of the Pan-STARRS sensors.\n",
+    "\n",
+    "We'll use these columns to superimpose the isochrone on the color-magnitude diagram, but first we have to use a [distance modulus](https://en.wikipedia.org/wiki/Distance_modulus) to scale the isochrone based on the estimated distance of GD-1.\n",
+    "\n",
+    "We can use the `Distance` object from Astropy to compute the distance modulus."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "14.4604730134524"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import astropy.coordinates as coord\n",
+    "import astropy.units as u\n",
+    "\n",
+    "distance = 7.8 * u.kpc\n",
+    "distmod = coord.Distance(distance).distmod.value\n",
+    "distmod"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can compute the scaled magnitude and color of the isochrone."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "g = main_sequence['PS_g'] + distmod\n",
+    "gi = main_sequence['PS_g'] - main_sequence['PS_i']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's what the it looks like on the color-magnitude diagram."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "plot_cmd(photo_table)\n",
+    "plt.plot(gi, g);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The theoretical isochrone passes through the overdense region where we expect to find stars in GD-1."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Making a polygon\n",
+    "\n",
+    "To select the stars in the overdense region of the color-magnitude diagram, we want to stretch the isochrone into a polygon.\n",
+    "\n",
+    "We'll use the following formulas to compute the left and right sides of the polygons.\n",
+    "\n",
+    "To explain the terms:\n",
+    "\n",
+    "* We divide magnitudes by 28 to normalize them onto the range from 0 to 1.\n",
+    "\n",
+    "* Raising the normalized magnitudes to the 5th power [DOES WHAT?]\n",
+    "\n",
+    "* Then we subtract the result from `gi` to account for [WHAT?].  The factors 0.4 and 0.7 were chosen by eye to enclose the overdense region."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "left_gi = gi - 0.4 * (g/28)**5\n",
+    "right_gi = gi + 0.7 * (g/28)**5"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To make these isochrones easier to work with, we'll put them in a Pandas `Series` with that contains both `g` and the scaled values of `gi`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1.773520    28.294743\n",
+       "1.752340    28.189718\n",
+       "1.725601    28.051761\n",
+       "1.699671    27.916194\n",
+       "1.674053    27.780024\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "left_series = pd.Series(g, index=left_gi)\n",
+    "left_series.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2.932648    28.294743\n",
+       "2.890114    28.189718\n",
+       "2.835806    28.051761\n",
+       "2.783308    27.916194\n",
+       "2.731517    27.780024\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "right_series = pd.Series(g, index=right_gi)\n",
+    "right_series.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can plot them on the color-magnitude diagram like this."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -883,18 +798,145 @@
    ],
    "source": [
     "plot_cmd(photo_table)\n",
-    "plt.plot(xs, ys);"
+    "left_series.plot()\n",
+    "right_series.plot();"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "If it looks like your polygon does a good job surrounding the overdense area, go on to the next section.  Otherwise you can try again.\n",
+    "It looks like the scaled isochrones bound the overdense area well, but they also include stars with magnitudes higher than we expect for stars in GD-1, so we'll use another mask to limit the range of `g`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "117"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "g_mask = (g > 18.0) & (g < 21.5)\n",
+    "g_mask.sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(117, 117)"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "left = left_series[g_mask]\n",
+    "right = right_series[g_mask]\n",
     "\n",
-    "If you want a polygon with more points (or fewer), you can change the argument to `ginput`.\n",
+    "len(left), len(right)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's what they look like:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_cmd(photo_table)\n",
+    "left.plot()\n",
+    "right.plot();"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we want to assemble the two halves into a polygon.  We can use `append` to make a new `Series` that contains both halves.\n",
     "\n",
-    "The polygon does not have to be \"closed\".  When we use this polygon in the next section, the last and first points will be connected by a straight line.\n"
+    "And we'll use the slice `[::-1]` to reverse the elements of `right` so the result forms a loop.  [See here for an explanation of this idiom](https://stackoverflow.com/questions/5876998/reversing-a-list-using-slice-notation)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loop = left.append(right[::-1])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And here's what it looks like"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "loop.plot()\n",
+    "plot_cmd(photo_table)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next we'll use this polygon to identify stars in the overdense region."
    ]
   },
   {
@@ -903,32 +945,376 @@
    "source": [
     "## Which points are in the polygon?\n",
     "\n",
-    "Matplotlib provides a `Path` object that we can use to check which points fall in the polygon we selected.\n",
+    "Matplotlib provides a `Path` object that we can use to check which points fall in the polygon we just constructed.\n",
     "\n",
-    "Here's how we make a `Path` using a list of coordinates."
+    "To make a `Path`, we need a list of coordinates in the form of an array with two columns.\n",
+    "\n",
+    "Currently `loop` is a `Series` with the values of `gi` in the index:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Path(array([[ 0.26433692, 17.84253127],\n",
-       "       [ 0.35394265, 18.799117  ],\n",
-       "       [ 0.47491039, 19.68211921],\n",
-       "       [ 0.63172043, 20.45474614],\n",
-       "       [ 0.76612903, 20.78587196],\n",
-       "       [ 0.80645161, 21.41133186],\n",
-       "       [ 0.58691756, 21.30095659],\n",
-       "       [ 0.39426523, 20.56512141],\n",
-       "       [ 0.22401434, 19.2406181 ],\n",
-       "       [ 0.19713262, 18.02649007]]), None)"
+       "0.587571    21.411746\n",
+       "0.567801    21.322466\n",
+       "0.548134    21.233380\n",
+       "0.528693    21.144427\n",
+       "0.509300    21.054549\n",
+       "dtype: float64"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "loop.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can move them out of the index into a column using `reset_index`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>index</th>\n",
+       "      <th>0</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.587571</td>\n",
+       "      <td>21.411746</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.567801</td>\n",
+       "      <td>21.322466</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.548134</td>\n",
+       "      <td>21.233380</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.528693</td>\n",
+       "      <td>21.144427</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.509300</td>\n",
+       "      <td>21.054549</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      index          0\n",
+       "0  0.587571  21.411746\n",
+       "1  0.567801  21.322466\n",
+       "2  0.548134  21.233380\n",
+       "3  0.528693  21.144427\n",
+       "4  0.509300  21.054549"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "loop_df = loop.reset_index()\n",
+    "loop_df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The result is a `DataFrame` with one column for the x-axis and one column for the y-axis, so we can pass it to `Path` like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Path(array([[ 0.58757135, 21.41174601],\n",
+       "       [ 0.56780097, 21.32246601],\n",
+       "       [ 0.54813409, 21.23338001],\n",
+       "       [ 0.5286928 , 21.14442701],\n",
+       "       [ 0.50929987, 21.05454901],\n",
+       "       [ 0.48991266, 20.96383501],\n",
+       "       [ 0.47084777, 20.87386601],\n",
+       "       [ 0.45222635, 20.78511001],\n",
+       "       [ 0.43438902, 20.69865301],\n",
+       "       [ 0.42745198, 20.66469601],\n",
+       "       [ 0.42067029, 20.63135301],\n",
+       "       [ 0.41402867, 20.59850601],\n",
+       "       [ 0.40738016, 20.56529901],\n",
+       "       [ 0.40088387, 20.53264001],\n",
+       "       [ 0.39449608, 20.50023501],\n",
+       "       [ 0.38843797, 20.46871801],\n",
+       "       [ 0.38251577, 20.43765101],\n",
+       "       [ 0.3766547 , 20.40653701],\n",
+       "       [ 0.37088531, 20.37564701],\n",
+       "       [ 0.36522325, 20.34505401],\n",
+       "       [ 0.35962415, 20.31443001],\n",
+       "       [ 0.35413292, 20.28413501],\n",
+       "       [ 0.34871894, 20.25390101],\n",
+       "       [ 0.34339273, 20.22385701],\n",
+       "       [ 0.33815825, 20.19395801],\n",
+       "       [ 0.33305724, 20.16427301],\n",
+       "       [ 0.32820637, 20.13508501],\n",
+       "       [ 0.32348139, 20.10604901],\n",
+       "       [ 0.31883343, 20.07716101],\n",
+       "       [ 0.31425423, 20.04833101],\n",
+       "       [ 0.30974976, 20.01961701],\n",
+       "       [ 0.30531997, 19.99097001],\n",
+       "       [ 0.30097354, 19.96246401],\n",
+       "       [ 0.29669999, 19.93401801],\n",
+       "       [ 0.29250157, 19.90573101],\n",
+       "       [ 0.28837983, 19.87746501],\n",
+       "       [ 0.28441584, 19.84955001],\n",
+       "       [ 0.28065057, 19.82188301],\n",
+       "       [ 0.27700644, 19.79450101],\n",
+       "       [ 0.27342328, 19.76713801],\n",
+       "       [ 0.26989305, 19.73985301],\n",
+       "       [ 0.26641258, 19.71265801],\n",
+       "       [ 0.26298257, 19.68540001],\n",
+       "       [ 0.25960216, 19.65824401],\n",
+       "       [ 0.2562733 , 19.63113701],\n",
+       "       [ 0.25299978, 19.60409301],\n",
+       "       [ 0.24977307, 19.57714401],\n",
+       "       [ 0.24660506, 19.55024001],\n",
+       "       [ 0.24348829, 19.52341001],\n",
+       "       [ 0.24042159, 19.49666601],\n",
+       "       [ 0.23741737, 19.46998501],\n",
+       "       [ 0.23447423, 19.44339301],\n",
+       "       [ 0.23158726, 19.41688701],\n",
+       "       [ 0.22876474, 19.39045101],\n",
+       "       [ 0.22600432, 19.36410901],\n",
+       "       [ 0.22330395, 19.33786601],\n",
+       "       [ 0.220663  , 19.31170101],\n",
+       "       [ 0.21808571, 19.28560101],\n",
+       "       [ 0.21557456, 19.25960101],\n",
+       "       [ 0.21312279, 19.23368701],\n",
+       "       [ 0.21073349, 19.20785601],\n",
+       "       [ 0.20840975, 19.18210401],\n",
+       "       [ 0.20614799, 19.15640601],\n",
+       "       [ 0.20395119, 19.13076401],\n",
+       "       [ 0.20182156, 19.10523201],\n",
+       "       [ 0.19975572, 19.07977101],\n",
+       "       [ 0.19775195, 19.05436401],\n",
+       "       [ 0.19581903, 19.02902801],\n",
+       "       [ 0.19395701, 19.00376101],\n",
+       "       [ 0.19216276, 18.97857301],\n",
+       "       [ 0.19044513, 18.95347601],\n",
+       "       [ 0.1888007 , 18.92850001],\n",
+       "       [ 0.18723796, 18.90368201],\n",
+       "       [ 0.18576648, 18.87905401],\n",
+       "       [ 0.18438763, 18.85466301],\n",
+       "       [ 0.18310871, 18.83056001],\n",
+       "       [ 0.18193706, 18.80672701],\n",
+       "       [ 0.18087817, 18.78327401],\n",
+       "       [ 0.17993184, 18.76015001],\n",
+       "       [ 0.17910244, 18.73740501],\n",
+       "       [ 0.17838817, 18.71496101],\n",
+       "       [ 0.17779005, 18.69282101],\n",
+       "       [ 0.177312  , 18.67099501],\n",
+       "       [ 0.17694971, 18.64944001],\n",
+       "       [ 0.1767112 , 18.62815801],\n",
+       "       [ 0.17659065, 18.60714001],\n",
+       "       [ 0.17658939, 18.58636601],\n",
+       "       [ 0.17671618, 18.56585701],\n",
+       "       [ 0.17696696, 18.54562201],\n",
+       "       [ 0.17733781, 18.52565801],\n",
+       "       [ 0.1778346 , 18.50597901],\n",
+       "       [ 0.17846661, 18.48656801],\n",
+       "       [ 0.17922891, 18.46742401],\n",
+       "       [ 0.18012796, 18.44859001],\n",
+       "       [ 0.18116197, 18.43005501],\n",
+       "       [ 0.18233604, 18.41181501],\n",
+       "       [ 0.18363223, 18.39379401],\n",
+       "       [ 0.18506009, 18.37602901],\n",
+       "       [ 0.18660932, 18.35862101],\n",
+       "       [ 0.18829849, 18.34153201],\n",
+       "       [ 0.19012805, 18.32480701],\n",
+       "       [ 0.19210919, 18.30851301],\n",
+       "       [ 0.19422686, 18.29250401],\n",
+       "       [ 0.1964951 , 18.27685701],\n",
+       "       [ 0.19890209, 18.26156301],\n",
+       "       [ 0.20145338, 18.24666001],\n",
+       "       [ 0.20417715, 18.23260501],\n",
+       "       [ 0.20705285, 18.21898101],\n",
+       "       [ 0.21005661, 18.20562501],\n",
+       "       [ 0.21319339, 18.19254201],\n",
+       "       [ 0.22126873, 18.16185301],\n",
+       "       [ 0.2300065 , 18.13259301],\n",
+       "       [ 0.23950909, 18.10508001],\n",
+       "       [ 0.24974677, 18.07932501],\n",
+       "       [ 0.26066153, 18.05527801],\n",
+       "       [ 0.27224553, 18.03295501],\n",
+       "       [ 0.28447607, 18.01227601],\n",
+       "       [ 0.40566013, 18.01227601],\n",
+       "       [ 0.39412682, 18.03295501],\n",
+       "       [ 0.38329907, 18.05527801],\n",
+       "       [ 0.37320316, 18.07932501],\n",
+       "       [ 0.36384734, 18.10508001],\n",
+       "       [ 0.35529237, 18.13259301],\n",
+       "       [ 0.34756872, 18.16185301],\n",
+       "       [ 0.34056407, 18.19254201],\n",
+       "       [ 0.33788593, 18.20562501],\n",
+       "       [ 0.33535176, 18.21898101],\n",
+       "       [ 0.33295648, 18.23260501],\n",
+       "       [ 0.33072983, 18.24666001],\n",
+       "       [ 0.32870734, 18.26156301],\n",
+       "       [ 0.32684482, 18.27685701],\n",
+       "       [ 0.3251355 , 18.29250401],\n",
+       "       [ 0.32359167, 18.30851301],\n",
+       "       [ 0.32219665, 18.32480701],\n",
+       "       [ 0.32097089, 18.34153201],\n",
+       "       [ 0.31990093, 18.35862101],\n",
+       "       [ 0.31898485, 18.37602901],\n",
+       "       [ 0.3182056 , 18.39379401],\n",
+       "       [ 0.31756993, 18.41181501],\n",
+       "       [ 0.31706705, 18.43005501],\n",
+       "       [ 0.31671781, 18.44859001],\n",
+       "       [ 0.3165174 , 18.46742401],\n",
+       "       [ 0.31646817, 18.48656801],\n",
+       "       [ 0.3165622 , 18.50597901],\n",
+       "       [ 0.31680458, 18.52565801],\n",
+       "       [ 0.31718682, 18.54562201],\n",
+       "       [ 0.31770268, 18.56585701],\n",
+       "       [ 0.31835632, 18.58636601],\n",
+       "       [ 0.31915162, 18.60714001],\n",
+       "       [ 0.32007915, 18.62815801],\n",
+       "       [ 0.3211385 , 18.64944001],\n",
+       "       [ 0.32233599, 18.67099501],\n",
+       "       [ 0.32366367, 18.69282101],\n",
+       "       [ 0.32512771, 18.71496101],\n",
+       "       [ 0.32672398, 18.73740501],\n",
+       "       [ 0.32845154, 18.76015001],\n",
+       "       [ 0.33031546, 18.78327401],\n",
+       "       [ 0.33230964, 18.80672701],\n",
+       "       [ 0.33443651, 18.83056001],\n",
+       "       [ 0.3366864 , 18.85466301],\n",
+       "       [ 0.3390529 , 18.87905401],\n",
+       "       [ 0.34152681, 18.90368201],\n",
+       "       [ 0.34410502, 18.92850001],\n",
+       "       [ 0.34677677, 18.95347601],\n",
+       "       [ 0.34953217, 18.97857301],\n",
+       "       [ 0.35237348, 19.00376101],\n",
+       "       [ 0.35529144, 19.02902801],\n",
+       "       [ 0.35828883, 19.05436401],\n",
+       "       [ 0.36136575, 19.07977101],\n",
+       "       [ 0.36451277, 19.10523201],\n",
+       "       [ 0.36773241, 19.13076401],\n",
+       "       [ 0.37102978, 19.15640601],\n",
+       "       [ 0.37440044, 19.18210401],\n",
+       "       [ 0.37784139, 19.20785601],\n",
+       "       [ 0.38135736, 19.23368701],\n",
+       "       [ 0.38494552, 19.25960101],\n",
+       "       [ 0.388603  , 19.28560101],\n",
+       "       [ 0.39233725, 19.31170101],\n",
+       "       [ 0.39614435, 19.33786601],\n",
+       "       [ 0.40002069, 19.36410901],\n",
+       "       [ 0.40396796, 19.39045101],\n",
+       "       [ 0.40798805, 19.41688701],\n",
+       "       [ 0.41208235, 19.44339301],\n",
+       "       [ 0.41624335, 19.46998501],\n",
+       "       [ 0.42047622, 19.49666601],\n",
+       "       [ 0.42478124, 19.52341001],\n",
+       "       [ 0.42914714, 19.55024001],\n",
+       "       [ 0.43357463, 19.57714401],\n",
+       "       [ 0.43806989, 19.60409301],\n",
+       "       [ 0.44262347, 19.63113701],\n",
+       "       [ 0.44724247, 19.65824401],\n",
+       "       [ 0.4519225 , 19.68540001],\n",
+       "       [ 0.45666424, 19.71265801],\n",
+       "       [ 0.46146067, 19.73985301],\n",
+       "       [ 0.46631851, 19.76713801],\n",
+       "       [ 0.47124047, 19.79450101],\n",
+       "       [ 0.47623175, 19.82188301],\n",
+       "       [ 0.48136578, 19.84955001],\n",
+       "       [ 0.48671855, 19.87746501],\n",
+       "       [ 0.49225451, 19.90573101],\n",
+       "       [ 0.49787627, 19.93401801],\n",
+       "       [ 0.50358931, 19.96246401],\n",
+       "       [ 0.50938655, 19.99097001],\n",
+       "       [ 0.51528266, 20.01961701],\n",
+       "       [ 0.52126534, 20.04833101],\n",
+       "       [ 0.52733726, 20.07716101],\n",
+       "       [ 0.53348957, 20.10604901],\n",
+       "       [ 0.53973535, 20.13508501],\n",
+       "       [ 0.54612384, 20.16427301],\n",
+       "       [ 0.55279781, 20.19395801],\n",
+       "       [ 0.55962597, 20.22385701],\n",
+       "       [ 0.56656311, 20.25390101],\n",
+       "       [ 0.57360789, 20.28413501],\n",
+       "       [ 0.58074299, 20.31443001],\n",
+       "       [ 0.5880138 , 20.34505401],\n",
+       "       [ 0.59535596, 20.37564701],\n",
+       "       [ 0.60283203, 20.40653701],\n",
+       "       [ 0.61042265, 20.43765101],\n",
+       "       [ 0.61808231, 20.46871801],\n",
+       "       [ 0.62591386, 20.50023501],\n",
+       "       [ 0.63413647, 20.53264001],\n",
+       "       [ 0.64249372, 20.56529901],\n",
+       "       [ 0.65104657, 20.59850601],\n",
+       "       [ 0.659584  , 20.63135301],\n",
+       "       [ 0.66830253, 20.66469601],\n",
+       "       [ 0.67722496, 20.69865301],\n",
+       "       [ 0.70017638, 20.78511001],\n",
+       "       [ 0.72413715, 20.87386601],\n",
+       "       [ 0.74870785, 20.96383501],\n",
+       "       [ 0.77374297, 21.05454901],\n",
+       "       [ 0.7988286 , 21.14442701],\n",
+       "       [ 0.8240001 , 21.23338001],\n",
+       "       [ 0.84950281, 21.32246601],\n",
+       "       [ 0.8752204 , 21.41174601]]), None)"
+      ]
+     },
+     "execution_count": 32,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -936,7 +1322,7 @@
    "source": [
     "from matplotlib.path import Path\n",
     "\n",
-    "path = Path(coords)\n",
+    "path = Path(loop_df)\n",
     "path"
    ]
   },
@@ -951,12 +1337,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [],
    "source": [
     "points = [(0.4, 20), \n",
-    "          (0.4, 30)]"
+    "          (0.4, 16)]"
    ]
   },
   {
@@ -968,7 +1354,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [
     {
@@ -977,7 +1363,7 @@
        "array([ True, False])"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 34,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1008,7 +1394,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1024,7 +1410,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1037,7 +1423,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "`candidate_df` is the Pandas DataFrame that contains the results from Notebook XX, which selects stars likely to be in GD-1 based on proper motion.  It also includes position and proper motion transformed to the ICRS frame."
+    "`candidate_df` is the Pandas DataFrame that contains the results from Lesson 4, which selects stars likely to be in GD-1 based on proper motion.  It also includes position and proper motion transformed to the ICRS frame."
    ]
   },
   {
@@ -1063,7 +1449,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [
     {
@@ -1096,7 +1482,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 38,
    "metadata": {
     "scrolled": true
    },
@@ -1128,7 +1514,6 @@
        "      <th>pmra</th>\n",
        "      <th>pmdec</th>\n",
        "      <th>parallax</th>\n",
-       "      <th>parallax_error</th>\n",
        "      <th>radial_velocity</th>\n",
        "      <th>phi1</th>\n",
        "      <th>phi2</th>\n",
@@ -1147,7 +1532,6 @@
        "      <td>-3.770522</td>\n",
        "      <td>-12.490482</td>\n",
        "      <td>0.791393</td>\n",
-       "      <td>0.271754</td>\n",
        "      <td>NaN</td>\n",
        "      <td>-59.630489</td>\n",
        "      <td>-1.216485</td>\n",
@@ -1164,7 +1548,6 @@
        "      <td>-5.941679</td>\n",
        "      <td>-11.346409</td>\n",
        "      <td>0.307456</td>\n",
-       "      <td>0.199466</td>\n",
        "      <td>NaN</td>\n",
        "      <td>-59.247330</td>\n",
        "      <td>-2.016078</td>\n",
@@ -1181,7 +1564,6 @@
        "      <td>-3.897001</td>\n",
        "      <td>-12.702780</td>\n",
        "      <td>0.779463</td>\n",
-       "      <td>0.223692</td>\n",
        "      <td>NaN</td>\n",
        "      <td>-59.133391</td>\n",
        "      <td>-2.306901</td>\n",
@@ -1198,7 +1580,6 @@
        "      <td>-4.335041</td>\n",
        "      <td>-14.492309</td>\n",
        "      <td>0.314514</td>\n",
-       "      <td>0.102775</td>\n",
        "      <td>NaN</td>\n",
        "      <td>-59.785300</td>\n",
        "      <td>-1.594569</td>\n",
@@ -1215,7 +1596,6 @@
        "      <td>-7.172931</td>\n",
        "      <td>-12.291499</td>\n",
        "      <td>0.425404</td>\n",
-       "      <td>0.337689</td>\n",
        "      <td>NaN</td>\n",
        "      <td>-59.557744</td>\n",
        "      <td>-1.682147</td>\n",
@@ -1236,22 +1616,22 @@
        "3  635535454774983040  137.837752  18.864007 -4.335041 -14.492309  0.314514   \n",
        "4  635497276810313600  138.044516  19.009471 -7.172931 -12.291499  0.425404   \n",
        "\n",
-       "   parallax_error  radial_velocity       phi1      phi2   pm_phi1   pm_phi2  \\\n",
-       "0        0.271754              NaN -59.630489 -1.216485 -7.361363 -0.592633   \n",
-       "1        0.199466              NaN -59.247330 -2.016078 -7.527126  1.748779   \n",
-       "2        0.223692              NaN -59.133391 -2.306901 -7.560608 -0.741800   \n",
-       "3        0.102775              NaN -59.785300 -1.594569 -9.357536 -1.218492   \n",
-       "4        0.337689              NaN -59.557744 -1.682147 -9.000831  2.334407   \n",
+       "   radial_velocity       phi1      phi2   pm_phi1   pm_phi2  g_mean_psf_mag  \\\n",
+       "0              NaN -59.630489 -1.216485 -7.361363 -0.592633             NaN   \n",
+       "1              NaN -59.247330 -2.016078 -7.527126  1.748779         17.8978   \n",
+       "2              NaN -59.133391 -2.306901 -7.560608 -0.741800         19.2873   \n",
+       "3              NaN -59.785300 -1.594569 -9.357536 -1.218492         16.9238   \n",
+       "4              NaN -59.557744 -1.682147 -9.000831  2.334407         19.9242   \n",
        "\n",
-       "   g_mean_psf_mag  i_mean_psf_mag  \n",
-       "0             NaN             NaN  \n",
-       "1         17.8978       17.517401  \n",
-       "2         19.2873       17.678101  \n",
-       "3         16.9238       16.478100  \n",
-       "4         19.9242       18.334000  "
+       "   i_mean_psf_mag  \n",
+       "0             NaN  \n",
+       "1       17.517401  \n",
+       "2       17.678101  \n",
+       "3       16.478100  \n",
+       "4       18.334000  "
       ]
      },
-     "execution_count": 17,
+     "execution_count": 38,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1281,7 +1661,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [
     {
@@ -1290,7 +1670,7 @@
        "(7346, 3724, 7346)"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 39,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1308,7 +1688,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [
     {
@@ -1321,7 +1701,6 @@
       "pmra\n",
       "pmdec\n",
       "parallax\n",
-      "parallax_error\n",
       "radial_velocity\n",
       "phi1\n",
       "phi2\n",
@@ -1357,7 +1736,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 41,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1376,7 +1755,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [
     {
@@ -1396,7 +1775,7 @@
        "Name: color, Length: 7346, dtype: bool"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 42,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1414,7 +1793,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 43,
    "metadata": {},
    "outputs": [
     {
@@ -1423,7 +1802,7 @@
        "3724"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 43,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1456,7 +1835,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 44,
    "metadata": {},
    "outputs": [
     {
@@ -1523,7 +1902,7 @@
        "4  1.5902  19.9242"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 44,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1542,7 +1921,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 45,
    "metadata": {},
    "outputs": [
     {
@@ -1551,7 +1930,7 @@
        "array([False, False, False, ..., False, False, False])"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 45,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1570,16 +1949,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 46,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "481"
+       "464"
       ]
      },
-     "execution_count": 25,
+     "execution_count": 46,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1597,7 +1976,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 47,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1613,12 +1992,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 48,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1631,9 +2010,10 @@
    ],
    "source": [
     "plot_cmd(photo_table)\n",
-    "plt.plot(xs, ys)\n",
+    "plt.plot(gi, g)\n",
+    "loop.plot()\n",
     "\n",
-    "plt.plot(selected2['color'], selected2['mag'], 'gx');"
+    "plt.plot(selected2['color'], selected2['mag'], 'g.');"
    ]
   },
   {
@@ -1647,12 +2027,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 49,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 720x180 with 1 Axes>"
       ]
@@ -1701,7 +2081,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 50,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1713,14 +2093,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 51,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "-rw-rw-r-- 1 downey downey 2.0M Nov 18 19:28 gd1_merged.hdf5\r\n"
+      "-rw-rw-r-- 1 downey downey 1.1M Dec 10 20:42 gd1_merged.hdf5\r\n"
      ]
     }
    ],
@@ -1749,30 +2129,17 @@
     "\n",
     "This Jupyter notebook is an example of reproducible research because it contains all of the code needed to reproduce the results, including the database queries that download the data and and analysis.\n",
     "\n",
-    "However, when we used `ginput` to define a polygon by hand, we introduced a non-reproducible element to the analysis.  If someone running this notebook chooses a different polygon, they will get different results.  So it is important to record the polygon we chose as part of the data analysis pipeline.\n",
-    "\n",
-    "Since `coords` is a NumPy array, we can't use `to_hdf` to save it in a file.  But we can convert it to a Pandas `DataFrame` and save that.\n",
-    "\n",
-    "As an alternative, we could use [PyTables](http://www.pytables.org/index.html), which is the library Pandas uses to read and write files.  It is a powerful library, but not easy to use directly.  So let's take advantage of Pandas."
+    "However, when we used `ginput` to define a polygon by hand, we introduced a non-reproducible element to the analysis.  If someone running this notebook chooses a different polygon, they will get different results.  So it is important to record the polygon we chose as part of the data analysis pipeline."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "coords_df = pd.DataFrame(coords)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 52,
    "metadata": {},
    "outputs": [],
    "source": [
     "filename = 'gd1_polygon.hdf5'\n",
-    "coords_df.to_hdf(filename, 'coords_df')"
+    "loop.to_hdf(filename, 'loop')"
    ]
   },
   {
@@ -1784,12 +2151,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 53,
    "metadata": {},
    "outputs": [],
    "source": [
-    "coords2_df = pd.read_hdf(filename, 'coords_df')\n",
-    "coords2 = coords2_df.to_numpy()"
+    "loop2 = pd.read_hdf(filename, 'loop')"
    ]
   },
   {
@@ -1801,22 +2167,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 54,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 34,
-     "metadata": {},
-     "output_type": "execute_result"
+     "ename": "NameError",
+     "evalue": "name 'np' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-54-27c208ed8a9e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloop\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mloop2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m: name 'np' is not defined"
+     ]
     }
    ],
    "source": [
-    "np.all(coords2 == coords)"
+    "np.all(loop == loop2)"
    ]
   },
   {
diff --git a/07_plot.ipynb b/07_plot.ipynb
index 15e961c..5765556 100644
--- a/07_plot.ipynb
+++ b/07_plot.ipynb
@@ -1,5 +1,31 @@
 {
  "cells": [
+  {
+   "cell_type": "raw",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "title: \"Title\"\n",
+    "teaching: 3000\n",
+    "exercises: 0\n",
+    "questions:\n",
+    "\n",
+    "- \"Question?\"\n",
+    "\n",
+    "objectives:\n",
+    "\n",
+    "- \"Objective.\"\n",
+    "\n",
+    "keypoints:\n",
+    "\n",
+    "- \"Keypoint.\"\n",
+    "\n",
+    "---\n",
+    "FIXME\n",
+    "\n",
+    "{% include links.md %}\n"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},