diff --git a/CHANGELOG.md b/CHANGELOG.md
index 78b882663..dc991e655 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -28,7 +28,6 @@
 - Spike sorting:
     - Add SpikeSorting V1 pipeline. #651
     - Move modules into spikesorting.v0 #807
-    - Add MUA analysis to spike sorting pipeline
 - LFP:
     - Minor fixes to LFPBandV1 populator and `make`. #706, #795
     - LFPV1: Fix error for multiple lfp settings on same data #775
@@ -41,7 +40,7 @@
     - DLC path handling from config, and normalize naming convention. #722
     - Fix in place column bug #752
 - Decoding:
-    - Add `decoding` pipeline V1. #731, #769
+    - Add `decoding` pipeline V1. #731, #769, #819
     - Add a table to store the decoding results #731
     - Use the new `non_local_detector` package for decoding #731
     - Allow multiple spike waveform features for clusterless decoding #731
@@ -51,6 +50,10 @@
     - Rename SortedSpikesGroup.SortGroup to SortedSpikesGroup.Units #807
     - Change methods with load\_... to fetch\_... for consistency #807
     - Use merge table methods to access part methods #807
+- MUA
+    - Add MUA pipeline V1. #731, #819
+- Ripple
+    - Add figurl to Ripple pipeline #819
 
 ## [0.4.3] (November 7, 2023)
 
diff --git a/README.md b/README.md
index c627581ff..2241868e1 100644
--- a/README.md
+++ b/README.md
@@ -10,6 +10,8 @@ visualization, and sharing of neuroscience data to support reproducible
 research. It is designed to be interoperable with the NWB format and integrates
 open-source tools into a coherent framework.
 
+Try out a demo [here](https://spyglass.hhmi.2i2c.cloud/hub/user-redirect/git-pull?repo=https%3A%2F%2Fgithub.com%2FLorenFrankLab%2Fspyglass-demo&urlpath=lab%2Ftree%2Fspyglass-demo%2Fnotebooks%2F01_Insert_Data.ipynb&branch=main)!
+
 Features of Spyglass include:
 
 - **Standardized data storage** - Spyglass uses the open-source
@@ -86,3 +88,5 @@ a data analysis framework for reproducible and shareable neuroscience research.
    [10.1101/2024.01.25.577295](https://doi.org/10.1101/2024.01.25.577295 ).
 
 *\* Equal contribution*
+
+See paper related code [here](https://github.com/LorenFrankLab/spyglass-paper).
diff --git a/notebooks/43_Decoding_SortedSpikes.ipynb b/notebooks/43_Decoding_SortedSpikes.ipynb
index dc0cc9b61..23e1888fe 100644
--- a/notebooks/43_Decoding_SortedSpikes.ipynb
+++ b/notebooks/43_Decoding_SortedSpikes.ipynb
@@ -655,7 +655,7 @@
    ],
    "source": [
     "# look at the sorting within the group we just made\n",
-    "SortedSpikesGroup.SortGroup & {\n",
+    "SortedSpikesGroup.Units & {\n",
     "    \"nwb_file_name\": nwb_copy_file_name,\n",
     "    \"sorted_spikes_group_name\": \"test_group\",\n",
     "    \"unit_filter_params_name\": unit_filter_params_name,\n",
diff --git a/notebooks/51_MUA_Detection.ipynb b/notebooks/51_MUA_Detection.ipynb
index e8e51f66d..5d7f9a061 100644
--- a/notebooks/51_MUA_Detection.ipynb
+++ b/notebooks/51_MUA_Detection.ipynb
@@ -4,23 +4,25 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[2024-02-07 14:44:16,324][INFO]: Connecting edeno@lmf-db.cin.ucsf.edu:3306\n",
+      "[2024-02-07 14:44:16,357][INFO]: Connected edeno@lmf-db.cin.ucsf.edu:3306\n"
+     ]
+    }
+   ],
    "source": [
-    "from pathlib import Path\n",
     "import datajoint as dj\n",
+    "from pathlib import Path\n",
     "\n",
     "dj.config.load(\n",
     "    Path(\"../dj_local_conf.json\").absolute()\n",
-    ")  # load config for database connection info"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# MUA Analysis and Detection\n",
+    ")  # load config for database connection info\n",
     "\n",
-    "NOTE: This notebook is a work in progress. It is not yet complete and may contain errors."
+    "from spyglass.mua.v1.mua import MuaEventsV1, MuaEventsParameters"
    ]
   },
   {
@@ -28,38 +30,27 @@
    "execution_count": 2,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[2024-01-29 13:51:25,319][INFO]: Connecting root@localhost:3306\n",
-      "[2024-01-29 13:51:25,405][INFO]: Connected root@localhost:3306\n",
-      "[13:51:26][WARNING] Spyglass: Please update position_tools to >= 0.1.0\n"
-     ]
-    },
     {
      "data": {
       "text/html": [
        "\n",
        "    \n",
        "    <style type=\"text/css\">\n",
-       "        .Table{\n",
+       "        .Relation{\n",
        "            border-collapse:collapse;\n",
        "        }\n",
-       "        .Table th{\n",
+       "        .Relation th{\n",
        "            background: #A0A0A0; color: #ffffff; padding:4px; border:#f0e0e0 1px solid;\n",
        "            font-weight: normal; font-family: monospace; font-size: 100%;\n",
        "        }\n",
-       "        .Table td{\n",
+       "        .Relation td{\n",
        "            padding:4px; border:#f0e0e0 1px solid; font-size:100%;\n",
        "        }\n",
-       "        .Table tr:nth-child(odd){\n",
+       "        .Relation tr:nth-child(odd){\n",
        "            background: #ffffff;\n",
-       "            color: #000000;\n",
        "        }\n",
-       "        .Table tr:nth-child(even){\n",
+       "        .Relation tr:nth-child(even){\n",
        "            background: #f3f1ff;\n",
-       "            color: #000000;\n",
        "        }\n",
        "        /* Tooltip container */\n",
        "        .djtooltip {\n",
@@ -92,141 +83,28 @@
        "        }\n",
        "    </style>\n",
        "    \n",
-       "    \n",
+       "    <b></b>\n",
        "        <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
-       "        <table border=\"1\" class=\"Table\">\n",
+       "        <table border=\"1\" class=\"Relation\">\n",
        "            <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">sorting_id</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">merge_id</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">recording_id</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">sorter</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">sorter_param_name</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">nwb_file_name</p>\n",
-       "                            <span class=\"djtooltiptext\">name of the NWB file</span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">interval_list_name</p>\n",
-       "                            <span class=\"djtooltiptext\">descriptive name of this interval list</span>\n",
+       "                            <p id=\"primary\">mua_param_name</p>\n",
+       "                            <span class=\"djtooltiptext\">a name for this set of parameters</span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">curation_id</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                            <p id=\"nonprimary\">mua_param_dict</p>\n",
+       "                            <span class=\"djtooltiptext\">dictionary of parameters</span>\n",
        "                        </div> </th> </tr> </thead>\n",
-       "            <tbody> <tr> <td>08a302b6-5505-40fa-b4d5-62162f8eef58</td>\n",
-       "<td>485a4ddf-332d-35b5-3ad4-0561736c1844</td>\n",
-       "<td>449b64e3-db0b-437e-a1b9-0d29928aa2dd</td>\n",
-       "<td>clusterless_thresholder</td>\n",
-       "<td>default_clusterless</td>\n",
-       "<td>mediumnwb20230802_.nwb</td>\n",
-       "<td>45f6b9a1-eef3-46eb-866d-d0999afebda6</td>\n",
-       "<td>0</td></tr><tr><td>0ca508ee-af4c-4a89-8181-d48bd209bfd4</td>\n",
-       "<td>6acb99b8-6a0c-eb83-1141-5f603c5895e0</td>\n",
-       "<td>328da21c-1d9c-41e2-9800-76b3484b707b</td>\n",
-       "<td>clusterless_thresholder</td>\n",
-       "<td>default_clusterless</td>\n",
-       "<td>mediumnwb20230802_.nwb</td>\n",
-       "<td>686d9951-1c0f-4d5e-9f5c-09e6fd8bdd4c</td>\n",
-       "<td>0</td></tr><tr><td>209dc048-6fae-4315-b293-c06fff29f947</td>\n",
-       "<td>f7237e18-4e73-4aee-805b-90735e9147de</td>\n",
-       "<td>aff78f2f-2ba0-412a-95cc-447c3a2f4683</td>\n",
-       "<td>clusterless_thresholder</td>\n",
-       "<td>default_clusterless</td>\n",
-       "<td>mediumnwb20230802_.nwb</td>\n",
-       "<td>719e8a86-fcf1-4ffc-8c1f-ea912f67ad5d</td>\n",
-       "<td>0</td></tr><tr><td>21a9a593-f6f3-4b82-99d7-8fc46556eff3</td>\n",
-       "<td>7e3fa66e-727e-1541-819a-b01309bb30ae</td>\n",
-       "<td>2402805a-04f9-4a88-9ccf-071376c8de19</td>\n",
-       "<td>clusterless_thresholder</td>\n",
-       "<td>default_clusterless</td>\n",
-       "<td>mediumnwb20230802_.nwb</td>\n",
-       "<td>d581b117-160e-4311-b096-7781a4de4394</td>\n",
-       "<td>0</td></tr><tr><td>406a20e3-5a9f-4fec-b046-a6561f72461e</td>\n",
-       "<td>6d039a63-17ad-0b78-4b1e-f02d5f3dbbc5</td>\n",
-       "<td>f1427e00-2974-4301-b2ac-b4dc29277c51</td>\n",
-       "<td>clusterless_thresholder</td>\n",
-       "<td>default_clusterless</td>\n",
-       "<td>mediumnwb20230802_.nwb</td>\n",
-       "<td>0e848c38-9105-4ea4-b6ba-dbdd5b46a088</td>\n",
-       "<td>0</td></tr><tr><td>4131c51b-c56d-41fa-b046-46635fc17fd9</td>\n",
-       "<td>e0e9133a-7a4e-1321-a43a-e8afcb2f25da</td>\n",
-       "<td>9e332d82-1daf-4e92-bb50-12e4f9430875</td>\n",
-       "<td>clusterless_thresholder</td>\n",
-       "<td>default_clusterless</td>\n",
-       "<td>mediumnwb20230802_.nwb</td>\n",
-       "<td>9ed11db5-c42e-491a-8caf-7d9a37a65f13</td>\n",
-       "<td>0</td></tr><tr><td>4c5a629a-71d9-481d-ab11-a4cb0fc16087</td>\n",
-       "<td>9959b614-2318-f597-6651-a3a82124d28a</td>\n",
-       "<td>3a2c3eed-413a-452a-83c8-0e4648141bde</td>\n",
-       "<td>clusterless_thresholder</td>\n",
-       "<td>default_clusterless</td>\n",
-       "<td>mediumnwb20230802_.nwb</td>\n",
-       "<td>2b9fbf14-74a0-4294-a805-26702340aac9</td>\n",
-       "<td>0</td></tr><tr><td>4d629c07-1931-4e1f-a3a8-cbf1b72161e3</td>\n",
-       "<td>c0eb6455-fc41-c200-b62e-e3ca81b9a3f7</td>\n",
-       "<td>f07bc0b0-de6b-4424-8ef9-766213aaca26</td>\n",
-       "<td>clusterless_thresholder</td>\n",
-       "<td>default_clusterless</td>\n",
-       "<td>mediumnwb20230802_.nwb</td>\n",
-       "<td>5c68f0f0-f577-4905-8a09-e4d171d0a22d</td>\n",
-       "<td>0</td></tr><tr><td>554a9a3c-0461-48be-8435-123eed59c228</td>\n",
-       "<td>912e250e-56d8-ee33-4525-c844d810971b</td>\n",
-       "<td>7f128981-6868-4976-ba20-248655dcac21</td>\n",
-       "<td>clusterless_thresholder</td>\n",
-       "<td>default_clusterless</td>\n",
-       "<td>mediumnwb20230802_.nwb</td>\n",
-       "<td>f4b9301f-bc91-455b-9474-c801093f3856</td>\n",
-       "<td>0</td></tr><tr><td>7bb007f2-26d3-463f-b7dc-7bd4d271725e</td>\n",
-       "<td>d7d2c97a-0e6e-d1b8-735c-d55dc66a30e1</td>\n",
-       "<td>a9b7cec0-1256-49cf-abf0-8c45fd155379</td>\n",
-       "<td>clusterless_thresholder</td>\n",
-       "<td>default_clusterless</td>\n",
-       "<td>mediumnwb20230802_.nwb</td>\n",
-       "<td>74270cba-36ee-4afb-ab50-2a6cc948e68c</td>\n",
-       "<td>0</td></tr><tr><td>80e1f37f-48a7-4087-bd37-7a37b6a2c160</td>\n",
-       "<td>abb92dce-4410-8f17-a501-a4104bda0dcf</td>\n",
-       "<td>3c40ebdc-0b61-4105-9971-e1348bd49bc7</td>\n",
-       "<td>clusterless_thresholder</td>\n",
-       "<td>default_clusterless</td>\n",
-       "<td>mediumnwb20230802_.nwb</td>\n",
-       "<td>0f91197e-bebb-4dc6-ad41-5bf89c3eed28</td>\n",
-       "<td>0</td></tr><tr><td>8848c4a8-a2f2-4f3d-82cd-51b13b8bae3c</td>\n",
-       "<td>74e10781-1228-4075-0870-af224024ffdc</td>\n",
-       "<td>257c077b-8f3b-4abb-a631-6b8084d6a1ea</td>\n",
-       "<td>clusterless_thresholder</td>\n",
-       "<td>default_clusterless</td>\n",
-       "<td>mediumnwb20230802_.nwb</td>\n",
-       "<td>e289e03d-32ad-461a-a1cc-c88537343149</td>\n",
-       "<td>0</td> </tr> </tbody>\n",
+       "            <tbody> <tr> <td>default</td>\n",
+       "<td>=BLOB=</td> </tr> </tbody>\n",
        "        </table>\n",
-       "        <p>...</p>\n",
-       "        <p>Total: 23</p></div>\n",
+       "        \n",
+       "        <p>Total: 1</p></div>\n",
        "        "
       ],
       "text/plain": [
-       "*sorting_id    *merge_id      recording_id   sorter         sorter_param_n nwb_file_name  interval_list_ curation_id   \n",
-       "+------------+ +------------+ +------------+ +------------+ +------------+ +------------+ +------------+ +------------+\n",
-       "08a302b6-5505- 485a4ddf-332d- 449b64e3-db0b- clusterless_th default_cluste mediumnwb20230 45f6b9a1-eef3- 0             \n",
-       "0ca508ee-af4c- 6acb99b8-6a0c- 328da21c-1d9c- clusterless_th default_cluste mediumnwb20230 686d9951-1c0f- 0             \n",
-       "209dc048-6fae- f7237e18-4e73- aff78f2f-2ba0- clusterless_th default_cluste mediumnwb20230 719e8a86-fcf1- 0             \n",
-       "21a9a593-f6f3- 7e3fa66e-727e- 2402805a-04f9- clusterless_th default_cluste mediumnwb20230 d581b117-160e- 0             \n",
-       "406a20e3-5a9f- 6d039a63-17ad- f1427e00-2974- clusterless_th default_cluste mediumnwb20230 0e848c38-9105- 0             \n",
-       "4131c51b-c56d- e0e9133a-7a4e- 9e332d82-1daf- clusterless_th default_cluste mediumnwb20230 9ed11db5-c42e- 0             \n",
-       "4c5a629a-71d9- 9959b614-2318- 3a2c3eed-413a- clusterless_th default_cluste mediumnwb20230 2b9fbf14-74a0- 0             \n",
-       "4d629c07-1931- c0eb6455-fc41- f07bc0b0-de6b- clusterless_th default_cluste mediumnwb20230 5c68f0f0-f577- 0             \n",
-       "554a9a3c-0461- 912e250e-56d8- 7f128981-6868- clusterless_th default_cluste mediumnwb20230 f4b9301f-bc91- 0             \n",
-       "7bb007f2-26d3- d7d2c97a-0e6e- a9b7cec0-1256- clusterless_th default_cluste mediumnwb20230 74270cba-36ee- 0             \n",
-       "80e1f37f-48a7- abb92dce-4410- 3c40ebdc-0b61- clusterless_th default_cluste mediumnwb20230 0f91197e-bebb- 0             \n",
-       "8848c4a8-a2f2- 74e10781-1228- 257c077b-8f3b- clusterless_th default_cluste mediumnwb20230 e289e03d-32ad- 0             \n",
-       "   ...\n",
-       " (Total: 23)"
+       "*mua_param_nam mua_param_\n",
+       "+------------+ +--------+\n",
+       "default        =BLOB=    \n",
+       " (Total: 1)"
       ]
      },
      "execution_count": 2,
@@ -235,71 +113,13 @@
     }
    ],
    "source": [
-    "from spyglass.spikesorting.spikesorting_merge import SpikeSortingOutput\n",
-    "import spyglass.spikesorting.v1 as sgs\n",
-    "\n",
-    "\n",
-    "nwb_copy_file_name = \"mediumnwb20230802_.nwb\"\n",
-    "\n",
-    "sorter_keys = {\n",
-    "    \"nwb_file_name\": nwb_copy_file_name,\n",
-    "    \"sorter\": \"clusterless_thresholder\",\n",
-    "    \"sorter_param_name\": \"default_clusterless\",\n",
-    "}\n",
-    "\n",
-    "(sgs.SpikeSortingSelection & sorter_keys) * SpikeSortingOutput.CurationV1"
+    "MuaEventsParameters()"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 3,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([UUID('485a4ddf-332d-35b5-3ad4-0561736c1844'),\n",
-       "       UUID('6acb99b8-6a0c-eb83-1141-5f603c5895e0'),\n",
-       "       UUID('f7237e18-4e73-4aee-805b-90735e9147de'),\n",
-       "       UUID('7e3fa66e-727e-1541-819a-b01309bb30ae'),\n",
-       "       UUID('6d039a63-17ad-0b78-4b1e-f02d5f3dbbc5'),\n",
-       "       UUID('e0e9133a-7a4e-1321-a43a-e8afcb2f25da'),\n",
-       "       UUID('9959b614-2318-f597-6651-a3a82124d28a'),\n",
-       "       UUID('c0eb6455-fc41-c200-b62e-e3ca81b9a3f7'),\n",
-       "       UUID('912e250e-56d8-ee33-4525-c844d810971b'),\n",
-       "       UUID('d7d2c97a-0e6e-d1b8-735c-d55dc66a30e1'),\n",
-       "       UUID('abb92dce-4410-8f17-a501-a4104bda0dcf'),\n",
-       "       UUID('74e10781-1228-4075-0870-af224024ffdc'),\n",
-       "       UUID('8bbddc0f-d6ae-6260-9400-f884a6e25ae8'),\n",
-       "       UUID('614d796c-0b95-6364-aaa0-b6cb1e7bbb83'),\n",
-       "       UUID('b332482b-e430-169d-8ac0-0a73ce968ed7'),\n",
-       "       UUID('86897349-ff68-ac72-02eb-739dd88936e6'),\n",
-       "       UUID('4a712103-c223-864f-82e0-6c23de79cc14'),\n",
-       "       UUID('cf858380-e8a3-49de-c2a9-1a277e307a68'),\n",
-       "       UUID('cc4ee561-f974-f8e5-0ea4-83185263ac67'),\n",
-       "       UUID('4a72c253-b3ca-8c13-e615-736a7ebff35c'),\n",
-       "       UUID('b92a94d8-ee1e-2097-a81f-5c1e1556ed24'),\n",
-       "       UUID('5c53bd33-d57c-fbba-e0fb-55e0bcb85d03'),\n",
-       "       UUID('0751a1e1-a406-7f87-ae6f-ce4ffc60621c')], dtype=object)"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "spikesorting_merge_ids = (\n",
-    "    (sgs.SpikeSortingSelection & sorter_keys) * SpikeSortingOutput.CurationV1\n",
-    ").fetch(\"merge_id\")\n",
-    "\n",
-    "spikesorting_merge_ids"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -307,23 +127,21 @@
        "\n",
        "    \n",
        "    <style type=\"text/css\">\n",
-       "        .Table{\n",
+       "        .Relation{\n",
        "            border-collapse:collapse;\n",
        "        }\n",
-       "        .Table th{\n",
+       "        .Relation th{\n",
        "            background: #A0A0A0; color: #ffffff; padding:4px; border:#f0e0e0 1px solid;\n",
        "            font-weight: normal; font-family: monospace; font-size: 100%;\n",
        "        }\n",
-       "        .Table td{\n",
+       "        .Relation td{\n",
        "            padding:4px; border:#f0e0e0 1px solid; font-size:100%;\n",
        "        }\n",
-       "        .Table tr:nth-child(odd){\n",
+       "        .Relation tr:nth-child(odd){\n",
        "            background: #ffffff;\n",
-       "            color: #000000;\n",
        "        }\n",
-       "        .Table tr:nth-child(even){\n",
+       "        .Relation tr:nth-child(even){\n",
        "            background: #f3f1ff;\n",
-       "            color: #000000;\n",
        "        }\n",
        "        /* Tooltip container */\n",
        "        .djtooltip {\n",
@@ -358,227 +176,95 @@
        "    \n",
        "    <b></b>\n",
        "        <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
-       "        <table border=\"1\" class=\"Table\">\n",
+       "        <table border=\"1\" class=\"Relation\">\n",
        "            <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">merge_id</p>\n",
+       "                            <p id=\"primary\">mua_param_name</p>\n",
+       "                            <span class=\"djtooltiptext\">a name for this set of parameters</span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">nwb_file_name</p>\n",
+       "                            <span class=\"djtooltiptext\">name of the NWB file</span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">unit_filter_params_name</p>\n",
        "                            <span class=\"djtooltiptext\"></span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">spikesorting_merge_id</p>\n",
+       "                            <p id=\"primary\">sorted_spikes_group_name</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">pos_merge_id</p>\n",
        "                            <span class=\"djtooltiptext\"></span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">unit_group_name</p>\n",
-       "                            <span class=\"djtooltiptext\">Name of unit group</span>\n",
+       "                            <p id=\"primary\">detection_interval</p>\n",
+       "                            <span class=\"djtooltiptext\">descriptive name of this interval list</span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">analysis_file_name</p>\n",
+       "                            <span class=\"djtooltiptext\">name of the file</span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">mua_times_object_id</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
        "                        </div> </th> </tr> </thead>\n",
-       "            <tbody> <tr> <td>39b22078-84fa-d3aa-8ca9-2368603d6a47</td>\n",
-       "<td>0751a1e1-a406-7f87-ae6f-ce4ffc60621c</td>\n",
-       "<td>all units</td></tr><tr><td>7bc69ef8-ae89-f30f-c236-56a87a49ed63</td>\n",
-       "<td>485a4ddf-332d-35b5-3ad4-0561736c1844</td>\n",
-       "<td>all units</td></tr><tr><td>2f6295c6-ad14-dde0-6b14-ee29bc2ed849</td>\n",
-       "<td>4a712103-c223-864f-82e0-6c23de79cc14</td>\n",
-       "<td>all units</td></tr><tr><td>7fa3c0ad-36a8-c14c-d84a-890c6002a457</td>\n",
-       "<td>4a72c253-b3ca-8c13-e615-736a7ebff35c</td>\n",
-       "<td>all units</td></tr><tr><td>4b025478-ab06-8e65-ef09-7e32be5a624c</td>\n",
-       "<td>5c53bd33-d57c-fbba-e0fb-55e0bcb85d03</td>\n",
-       "<td>all units</td></tr><tr><td>9658d944-1f1f-d467-1f56-bcc32d63e6e8</td>\n",
-       "<td>614d796c-0b95-6364-aaa0-b6cb1e7bbb83</td>\n",
-       "<td>all units</td></tr><tr><td>76cb7574-a687-37c9-1045-8e4e5d13a0b0</td>\n",
-       "<td>6acb99b8-6a0c-eb83-1141-5f603c5895e0</td>\n",
-       "<td>all units</td></tr><tr><td>87af74fa-a77e-f29b-99f7-f5720aa4816f</td>\n",
-       "<td>6d039a63-17ad-0b78-4b1e-f02d5f3dbbc5</td>\n",
-       "<td>all units</td></tr><tr><td>3cccfce5-dad9-61c5-7eee-1c7081837100</td>\n",
-       "<td>74e10781-1228-4075-0870-af224024ffdc</td>\n",
-       "<td>all units</td></tr><tr><td>bbddf25f-d810-f586-e3b8-cb96350fbbde</td>\n",
-       "<td>7e3fa66e-727e-1541-819a-b01309bb30ae</td>\n",
-       "<td>all units</td></tr><tr><td>7887e5db-da1d-d261-d58a-a51757593965</td>\n",
-       "<td>86897349-ff68-ac72-02eb-739dd88936e6</td>\n",
-       "<td>all units</td></tr><tr><td>1b7ba376-89f9-e0ac-9104-fbd1bd984e41</td>\n",
-       "<td>8bbddc0f-d6ae-6260-9400-f884a6e25ae8</td>\n",
-       "<td>all units</td> </tr> </tbody>\n",
+       "            <tbody> <tr> <td>default</td>\n",
+       "<td>mediumnwb20230802_.nwb</td>\n",
+       "<td>default_exclusion</td>\n",
+       "<td>test_group</td>\n",
+       "<td>4eb59a18-045a-5768-d12e-b6473415ae1c</td>\n",
+       "<td>pos 0 valid times</td>\n",
+       "<td>mediumnwb20230802_235KSEV39O.nwb</td>\n",
+       "<td>a93532eb-2947-4552-8877-19ea0d2dcc4f</td> </tr> </tbody>\n",
        "        </table>\n",
-       "        <p>...</p>\n",
-       "        <p>Total: 23</p></div>\n",
+       "        \n",
+       "        <p>Total: 1</p></div>\n",
        "        "
       ],
       "text/plain": [
-       "*merge_id      spikesorting_m unit_group_nam\n",
-       "+------------+ +------------+ +------------+\n",
-       "39b22078-84fa- 0751a1e1-a406- all units     \n",
-       "7bc69ef8-ae89- 485a4ddf-332d- all units     \n",
-       "2f6295c6-ad14- 4a712103-c223- all units     \n",
-       "7fa3c0ad-36a8- 4a72c253-b3ca- all units     \n",
-       "4b025478-ab06- 5c53bd33-d57c- all units     \n",
-       "9658d944-1f1f- 614d796c-0b95- all units     \n",
-       "76cb7574-a687- 6acb99b8-6a0c- all units     \n",
-       "87af74fa-a77e- 6d039a63-17ad- all units     \n",
-       "3cccfce5-dad9- 74e10781-1228- all units     \n",
-       "bbddf25f-d810- 7e3fa66e-727e- all units     \n",
-       "7887e5db-da1d- 86897349-ff68- all units     \n",
-       "1b7ba376-89f9- 8bbddc0f-d6ae- all units     \n",
-       "   ...\n",
-       " (Total: 23)"
+       "*mua_param_nam *nwb_file_name *unit_filter_p *sorted_spikes *pos_merge_id  *detection_int analysis_file_ mua_times_obje\n",
+       "+------------+ +------------+ +------------+ +------------+ +------------+ +------------+ +------------+ +------------+\n",
+       "default        mediumnwb20230 default_exclus test_group     4eb59a18-045a- pos 0 valid ti mediumnwb20230 a93532eb-2947-\n",
+       " (Total: 1)"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "from spyglass.spikesorting.unit_inclusion_merge import (\n",
-    "    ImportedUnitInclusionV1,\n",
-    "    UnitInclusionOutput,\n",
-    ")\n",
-    "\n",
-    "ImportedUnitInclusionV1().insert_all_units(spikesorting_merge_ids)\n",
-    "\n",
-    "UnitInclusionOutput.ImportedUnitInclusionV1() & [\n",
-    "    {\"spikesorting_merge_id\": id} for id in spikesorting_merge_ids\n",
-    "]"
+    "MuaEventsV1()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "text/html": [
-       "\n",
-       "    \n",
-       "    <style type=\"text/css\">\n",
-       "        .Table{\n",
-       "            border-collapse:collapse;\n",
-       "        }\n",
-       "        .Table th{\n",
-       "            background: #A0A0A0; color: #ffffff; padding:4px; border:#f0e0e0 1px solid;\n",
-       "            font-weight: normal; font-family: monospace; font-size: 100%;\n",
-       "        }\n",
-       "        .Table td{\n",
-       "            padding:4px; border:#f0e0e0 1px solid; font-size:100%;\n",
-       "        }\n",
-       "        .Table tr:nth-child(odd){\n",
-       "            background: #ffffff;\n",
-       "            color: #000000;\n",
-       "        }\n",
-       "        .Table tr:nth-child(even){\n",
-       "            background: #f3f1ff;\n",
-       "            color: #000000;\n",
-       "        }\n",
-       "        /* Tooltip container */\n",
-       "        .djtooltip {\n",
-       "        }\n",
-       "        /* Tooltip text */\n",
-       "        .djtooltip .djtooltiptext {\n",
-       "            visibility: hidden;\n",
-       "            width: 120px;\n",
-       "            background-color: black;\n",
-       "            color: #fff;\n",
-       "            text-align: center;\n",
-       "            padding: 5px 0;\n",
-       "            border-radius: 6px;\n",
-       "            /* Position the tooltip text - see examples below! */\n",
-       "            position: absolute;\n",
-       "            z-index: 1;\n",
-       "        }\n",
-       "        #primary {\n",
-       "            font-weight: bold;\n",
-       "            color: black;\n",
-       "        }\n",
-       "        #nonprimary {\n",
-       "            font-weight: normal;\n",
-       "            color: white;\n",
-       "        }\n",
-       "\n",
-       "        /* Show the tooltip text when you mouse over the tooltip container */\n",
-       "        .djtooltip:hover .djtooltiptext {\n",
-       "            visibility: visible;\n",
-       "        }\n",
-       "    </style>\n",
-       "    \n",
-       "    <b></b>\n",
-       "        <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
-       "        <table border=\"1\" class=\"Table\">\n",
-       "            <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">merge_id</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">spikesorting_merge_id</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">unit_group_name</p>\n",
-       "                            <span class=\"djtooltiptext\">Name of unit group</span>\n",
-       "                        </div> </th> </tr> </thead>\n",
-       "            <tbody> <tr> <td>39b22078-84fa-d3aa-8ca9-2368603d6a47</td>\n",
-       "<td>0751a1e1-a406-7f87-ae6f-ce4ffc60621c</td>\n",
-       "<td>all units</td></tr><tr><td>7bc69ef8-ae89-f30f-c236-56a87a49ed63</td>\n",
-       "<td>485a4ddf-332d-35b5-3ad4-0561736c1844</td>\n",
-       "<td>all units</td></tr><tr><td>2f6295c6-ad14-dde0-6b14-ee29bc2ed849</td>\n",
-       "<td>4a712103-c223-864f-82e0-6c23de79cc14</td>\n",
-       "<td>all units</td></tr><tr><td>7fa3c0ad-36a8-c14c-d84a-890c6002a457</td>\n",
-       "<td>4a72c253-b3ca-8c13-e615-736a7ebff35c</td>\n",
-       "<td>all units</td></tr><tr><td>4b025478-ab06-8e65-ef09-7e32be5a624c</td>\n",
-       "<td>5c53bd33-d57c-fbba-e0fb-55e0bcb85d03</td>\n",
-       "<td>all units</td></tr><tr><td>9658d944-1f1f-d467-1f56-bcc32d63e6e8</td>\n",
-       "<td>614d796c-0b95-6364-aaa0-b6cb1e7bbb83</td>\n",
-       "<td>all units</td></tr><tr><td>76cb7574-a687-37c9-1045-8e4e5d13a0b0</td>\n",
-       "<td>6acb99b8-6a0c-eb83-1141-5f603c5895e0</td>\n",
-       "<td>all units</td></tr><tr><td>87af74fa-a77e-f29b-99f7-f5720aa4816f</td>\n",
-       "<td>6d039a63-17ad-0b78-4b1e-f02d5f3dbbc5</td>\n",
-       "<td>all units</td></tr><tr><td>3cccfce5-dad9-61c5-7eee-1c7081837100</td>\n",
-       "<td>74e10781-1228-4075-0870-af224024ffdc</td>\n",
-       "<td>all units</td></tr><tr><td>bbddf25f-d810-f586-e3b8-cb96350fbbde</td>\n",
-       "<td>7e3fa66e-727e-1541-819a-b01309bb30ae</td>\n",
-       "<td>all units</td></tr><tr><td>7887e5db-da1d-d261-d58a-a51757593965</td>\n",
-       "<td>86897349-ff68-ac72-02eb-739dd88936e6</td>\n",
-       "<td>all units</td></tr><tr><td>1b7ba376-89f9-e0ac-9104-fbd1bd984e41</td>\n",
-       "<td>8bbddc0f-d6ae-6260-9400-f884a6e25ae8</td>\n",
-       "<td>all units</td> </tr> </tbody>\n",
-       "        </table>\n",
-       "        <p>...</p>\n",
-       "        <p>Total: 23</p></div>\n",
-       "        "
-      ],
       "text/plain": [
-       "*merge_id      spikesorting_m unit_group_nam\n",
-       "+------------+ +------------+ +------------+\n",
-       "39b22078-84fa- 0751a1e1-a406- all units     \n",
-       "7bc69ef8-ae89- 485a4ddf-332d- all units     \n",
-       "2f6295c6-ad14- 4a712103-c223- all units     \n",
-       "7fa3c0ad-36a8- 4a72c253-b3ca- all units     \n",
-       "4b025478-ab06- 5c53bd33-d57c- all units     \n",
-       "9658d944-1f1f- 614d796c-0b95- all units     \n",
-       "76cb7574-a687- 6acb99b8-6a0c- all units     \n",
-       "87af74fa-a77e- 6d039a63-17ad- all units     \n",
-       "3cccfce5-dad9- 74e10781-1228- all units     \n",
-       "bbddf25f-d810- 7e3fa66e-727e- all units     \n",
-       "7887e5db-da1d- 86897349-ff68- all units     \n",
-       "1b7ba376-89f9- 8bbddc0f-d6ae- all units     \n",
-       "   ...\n",
-       " (Total: 23)"
+       "UUID('4eb59a18-045a-5768-d12e-b6473415ae1c')"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "from spyglass.spikesorting.unit_inclusion_merge import (\n",
-    "    ImportedUnitInclusionV1,\n",
-    "    UnitInclusionOutput,\n",
-    ")\n",
+    "from spyglass.position import PositionOutput\n",
     "\n",
-    "ImportedUnitInclusionV1().insert_all_units(spikesorting_merge_ids)\n",
+    "nwb_copy_file_name = \"mediumnwb20230802_.nwb\"\n",
+    "\n",
+    "trodes_s_key = {\n",
+    "    \"nwb_file_name\": nwb_copy_file_name,\n",
+    "    \"interval_list_name\": \"pos 0 valid times\",\n",
+    "    \"trodes_pos_params_name\": \"single_led_upsampled\",\n",
+    "}\n",
     "\n",
-    "UnitInclusionOutput.ImportedUnitInclusionV1() & [\n",
-    "    {\"spikesorting_merge_id\": id} for id in spikesorting_merge_ids\n",
-    "]"
+    "pos_merge_id = (PositionOutput.TrodesPosV1 & trodes_s_key).fetch1(\"merge_id\")\n",
+    "pos_merge_id"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -587,23 +273,21 @@
        "\n",
        "    \n",
        "    <style type=\"text/css\">\n",
-       "        .Table{\n",
+       "        .Relation{\n",
        "            border-collapse:collapse;\n",
        "        }\n",
-       "        .Table th{\n",
+       "        .Relation th{\n",
        "            background: #A0A0A0; color: #ffffff; padding:4px; border:#f0e0e0 1px solid;\n",
        "            font-weight: normal; font-family: monospace; font-size: 100%;\n",
        "        }\n",
-       "        .Table td{\n",
+       "        .Relation td{\n",
        "            padding:4px; border:#f0e0e0 1px solid; font-size:100%;\n",
        "        }\n",
-       "        .Table tr:nth-child(odd){\n",
+       "        .Relation tr:nth-child(odd){\n",
        "            background: #ffffff;\n",
-       "            color: #000000;\n",
        "        }\n",
-       "        .Table tr:nth-child(even){\n",
+       "        .Relation tr:nth-child(even){\n",
        "            background: #f3f1ff;\n",
-       "            color: #000000;\n",
        "        }\n",
        "        /* Tooltip container */\n",
        "        .djtooltip {\n",
@@ -638,15 +322,19 @@
        "    \n",
        "    <b></b>\n",
        "        <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
-       "        <table border=\"1\" class=\"Table\">\n",
+       "        <table border=\"1\" class=\"Relation\">\n",
        "            <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
        "                            <p id=\"primary\">nwb_file_name</p>\n",
        "                            <span class=\"djtooltiptext\">name of the NWB file</span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">unit_filter_params_name</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
        "                            <p id=\"primary\">sorted_spikes_group_name</p>\n",
        "                            <span class=\"djtooltiptext\"></span>\n",
        "                        </div> </th> </tr> </thead>\n",
        "            <tbody> <tr> <td>mediumnwb20230802_.nwb</td>\n",
+       "<td>default_exclusion</td>\n",
        "<td>test_group</td> </tr> </tbody>\n",
        "        </table>\n",
        "        \n",
@@ -654,42 +342,34 @@
        "        "
       ],
       "text/plain": [
-       "*nwb_file_name *sorted_spikes\n",
-       "+------------+ +------------+\n",
-       "mediumnwb20230 test_group    \n",
+       "*nwb_file_name *unit_filter_p *sorted_spikes\n",
+       "+------------+ +------------+ +------------+\n",
+       "mediumnwb20230 default_exclus test_group    \n",
        " (Total: 1)"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "from spyglass.spikesorting.unit_inclusion_merge import SortedSpikesGroup\n",
-    "\n",
-    "unit_inclusion_merge_ids = (\n",
-    "    UnitInclusionOutput.ImportedUnitInclusionV1\n",
-    "    & [{\"spikesorting_merge_id\": id} for id in spikesorting_merge_ids]\n",
-    ").fetch(\"merge_id\")\n",
-    "\n",
-    "SortedSpikesGroup().create_group(\n",
-    "    group_name=\"test_group\",\n",
-    "    nwb_file_name=nwb_copy_file_name,\n",
-    "    unit_inclusion_merge_ids=unit_inclusion_merge_ids,\n",
+    "from spyglass.spikesorting.analysis.v1.group import (\n",
+    "    SortedSpikesGroup,\n",
     ")\n",
     "\n",
-    "group_key = {\n",
+    "sorted_spikes_group_key = {\n",
     "    \"nwb_file_name\": nwb_copy_file_name,\n",
     "    \"sorted_spikes_group_name\": \"test_group\",\n",
+    "    \"unit_filter_params_name\": \"default_exclusion\",\n",
     "}\n",
     "\n",
-    "SortedSpikesGroup & group_key"
+    "SortedSpikesGroup & sorted_spikes_group_key"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -698,23 +378,21 @@
        "\n",
        "    \n",
        "    <style type=\"text/css\">\n",
-       "        .Table{\n",
+       "        .Relation{\n",
        "            border-collapse:collapse;\n",
        "        }\n",
-       "        .Table th{\n",
+       "        .Relation th{\n",
        "            background: #A0A0A0; color: #ffffff; padding:4px; border:#f0e0e0 1px solid;\n",
        "            font-weight: normal; font-family: monospace; font-size: 100%;\n",
        "        }\n",
-       "        .Table td{\n",
+       "        .Relation td{\n",
        "            padding:4px; border:#f0e0e0 1px solid; font-size:100%;\n",
        "        }\n",
-       "        .Table tr:nth-child(odd){\n",
+       "        .Relation tr:nth-child(odd){\n",
        "            background: #ffffff;\n",
-       "            color: #000000;\n",
        "        }\n",
-       "        .Table tr:nth-child(even){\n",
+       "        .Relation tr:nth-child(even){\n",
        "            background: #f3f1ff;\n",
-       "            color: #000000;\n",
        "        }\n",
        "        /* Tooltip container */\n",
        "        .djtooltip {\n",
@@ -749,146 +427,74 @@
        "    \n",
        "    <b></b>\n",
        "        <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
-       "        <table border=\"1\" class=\"Table\">\n",
+       "        <table border=\"1\" class=\"Relation\">\n",
        "            <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">mua_param_name</p>\n",
+       "                            <span class=\"djtooltiptext\">a name for this set of parameters</span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
        "                            <p id=\"primary\">nwb_file_name</p>\n",
        "                            <span class=\"djtooltiptext\">name of the NWB file</span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">unit_filter_params_name</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
        "                            <p id=\"primary\">sorted_spikes_group_name</p>\n",
        "                            <span class=\"djtooltiptext\"></span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">unit_inclusion_merge_id</p>\n",
+       "                            <p id=\"primary\">pos_merge_id</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">detection_interval</p>\n",
+       "                            <span class=\"djtooltiptext\">descriptive name of this interval list</span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">analysis_file_name</p>\n",
+       "                            <span class=\"djtooltiptext\">name of the file</span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">mua_times_object_id</p>\n",
        "                            <span class=\"djtooltiptext\"></span>\n",
        "                        </div> </th> </tr> </thead>\n",
-       "            <tbody> <tr> <td>mediumnwb20230802_.nwb</td>\n",
-       "<td>test_group</td>\n",
-       "<td>0363fb94-c5de-d653-ed33-1807fa523790</td></tr><tr><td>mediumnwb20230802_.nwb</td>\n",
-       "<td>test_group</td>\n",
-       "<td>12da7476-bb70-a4ee-0558-99ca5bcc0958</td></tr><tr><td>mediumnwb20230802_.nwb</td>\n",
-       "<td>test_group</td>\n",
-       "<td>151976b9-9dc6-74f6-65d3-23ce96d006d1</td></tr><tr><td>mediumnwb20230802_.nwb</td>\n",
-       "<td>test_group</td>\n",
-       "<td>1b7ba376-89f9-e0ac-9104-fbd1bd984e41</td></tr><tr><td>mediumnwb20230802_.nwb</td>\n",
-       "<td>test_group</td>\n",
-       "<td>2f6295c6-ad14-dde0-6b14-ee29bc2ed849</td></tr><tr><td>mediumnwb20230802_.nwb</td>\n",
-       "<td>test_group</td>\n",
-       "<td>357a0267-874e-04f0-410e-9711f29e1236</td></tr><tr><td>mediumnwb20230802_.nwb</td>\n",
-       "<td>test_group</td>\n",
-       "<td>39b22078-84fa-d3aa-8ca9-2368603d6a47</td></tr><tr><td>mediumnwb20230802_.nwb</td>\n",
-       "<td>test_group</td>\n",
-       "<td>3cccfce5-dad9-61c5-7eee-1c7081837100</td></tr><tr><td>mediumnwb20230802_.nwb</td>\n",
-       "<td>test_group</td>\n",
-       "<td>4b025478-ab06-8e65-ef09-7e32be5a624c</td></tr><tr><td>mediumnwb20230802_.nwb</td>\n",
-       "<td>test_group</td>\n",
-       "<td>7591b69d-a902-9058-7597-04632de9e08c</td></tr><tr><td>mediumnwb20230802_.nwb</td>\n",
-       "<td>test_group</td>\n",
-       "<td>76cb7574-a687-37c9-1045-8e4e5d13a0b0</td></tr><tr><td>mediumnwb20230802_.nwb</td>\n",
+       "            <tbody> <tr> <td>default</td>\n",
+       "<td>mediumnwb20230802_.nwb</td>\n",
+       "<td>default_exclusion</td>\n",
        "<td>test_group</td>\n",
-       "<td>7887e5db-da1d-d261-d58a-a51757593965</td> </tr> </tbody>\n",
+       "<td>4eb59a18-045a-5768-d12e-b6473415ae1c</td>\n",
+       "<td>pos 0 valid times</td>\n",
+       "<td>mediumnwb20230802_235KSEV39O.nwb</td>\n",
+       "<td>a93532eb-2947-4552-8877-19ea0d2dcc4f</td> </tr> </tbody>\n",
        "        </table>\n",
-       "        <p>...</p>\n",
-       "        <p>Total: 23</p></div>\n",
+       "        \n",
+       "        <p>Total: 1</p></div>\n",
        "        "
       ],
       "text/plain": [
-       "*nwb_file_name *sorted_spikes *unit_inclusio\n",
-       "+------------+ +------------+ +------------+\n",
-       "mediumnwb20230 test_group     0363fb94-c5de-\n",
-       "mediumnwb20230 test_group     12da7476-bb70-\n",
-       "mediumnwb20230 test_group     151976b9-9dc6-\n",
-       "mediumnwb20230 test_group     1b7ba376-89f9-\n",
-       "mediumnwb20230 test_group     2f6295c6-ad14-\n",
-       "mediumnwb20230 test_group     357a0267-874e-\n",
-       "mediumnwb20230 test_group     39b22078-84fa-\n",
-       "mediumnwb20230 test_group     3cccfce5-dad9-\n",
-       "mediumnwb20230 test_group     4b025478-ab06-\n",
-       "mediumnwb20230 test_group     7591b69d-a902-\n",
-       "mediumnwb20230 test_group     76cb7574-a687-\n",
-       "mediumnwb20230 test_group     7887e5db-da1d-\n",
-       "   ...\n",
-       " (Total: 23)"
+       "*mua_param_nam *nwb_file_name *unit_filter_p *sorted_spikes *pos_merge_id  *detection_int analysis_file_ mua_times_obje\n",
+       "+------------+ +------------+ +------------+ +------------+ +------------+ +------------+ +------------+ +------------+\n",
+       "default        mediumnwb20230 default_exclus test_group     4eb59a18-045a- pos 0 valid ti mediumnwb20230 a93532eb-2947-\n",
+       " (Total: 1)"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "SortedSpikesGroup.Units() & group_key"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "An example of how to get spike times"
+    "mua_key = {\n",
+    "    \"mua_param_name\": \"default\",\n",
+    "    **sorted_spikes_group_key,\n",
+    "    \"pos_merge_id\": pos_merge_id,\n",
+    "    \"detection_interval\": \"pos 0 valid times\",\n",
+    "}\n",
+    "\n",
+    "MuaEventsV1().populate(mua_key)\n",
+    "MuaEventsV1 & mua_key"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[2024-01-29 13:51:31,631][WARNING]: Skipped checksum for file with hash: 148d9058-e6dc-e959-4c4d-75db9aa0b6e4, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_EF6N6XI3AH.nwb\n",
-      "[2024-01-29 13:51:32,089][WARNING]: Skipped checksum for file with hash: b4b6404f-aaf8-c4cc-9abe-ceea56e103f3, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_O7ZZ0F1XN7.nwb\n",
-      "[2024-01-29 13:51:32,499][WARNING]: Skipped checksum for file with hash: 4357905c-c6b9-3990-4d62-740a54cfc667, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_X84BYVM2B0.nwb\n",
-      "[2024-01-29 13:51:32,924][WARNING]: Skipped checksum for file with hash: ff81d274-17f7-702d-a2b4-92ac43c29316, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_Y2YF504C5D.nwb\n",
-      "[2024-01-29 13:51:33,306][WARNING]: Skipped checksum for file with hash: 8993754e-7dbe-94a1-403d-8c55aa9c6c42, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_JN4A4GSLZB.nwb\n",
-      "[2024-01-29 13:51:33,741][WARNING]: Skipped checksum for file with hash: 7d05460d-7366-27c9-2ba7-de2ad5d402f2, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_4JXWFJ3JRI.nwb\n",
-      "[2024-01-29 13:51:34,202][WARNING]: Skipped checksum for file with hash: 6629fd95-636a-4ad4-c9af-cee507de2130, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_AMBBKQ9RIY.nwb\n",
-      "[2024-01-29 13:51:34,735][WARNING]: Skipped checksum for file with hash: fa76d419-77a4-697a-325d-5c2ddbe517f9, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_0R6AWXMC6G.nwb\n",
-      "[2024-01-29 13:51:35,159][WARNING]: Skipped checksum for file with hash: f64f34ee-e72d-e566-a048-65f2ea31708a, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_USMRXAAV8I.nwb\n",
-      "[2024-01-29 13:51:35,536][WARNING]: Skipped checksum for file with hash: e282a8e5-844b-20f6-345c-cded12e761a9, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_DUNM1TZUGR.nwb\n",
-      "[2024-01-29 13:51:35,889][WARNING]: Skipped checksum for file with hash: d740eb7d-ce29-e140-06a2-c56655e0842a, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_L92EE1VRPB.nwb\n",
-      "[2024-01-29 13:51:36,277][WARNING]: Skipped checksum for file with hash: e43f95ff-9779-b980-00a3-99e104864462, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_AKOI7OTASI.nwb\n",
-      "[2024-01-29 13:51:36,679][WARNING]: Skipped checksum for file with hash: 6d04cbdb-e1e4-f44f-7274-0e1ab0356d75, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_W1MLF0Q86S.nwb\n",
-      "[2024-01-29 13:51:37,056][WARNING]: Skipped checksum for file with hash: 9e24661c-b021-6ad4-f224-89e331334f18, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_T2DBO3EMZ8.nwb\n",
-      "[2024-01-29 13:51:37,738][WARNING]: Skipped checksum for file with hash: 1f386cd3-89da-0233-03ff-76ba94e91a3a, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_TX2ZX3DAP4.nwb\n",
-      "[2024-01-29 13:51:38,180][WARNING]: Skipped checksum for file with hash: fde8b240-6adc-86f0-6391-f3f6fad72ee9, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_HWU3E4EKP4.nwb\n",
-      "[2024-01-29 13:51:38,798][WARNING]: Skipped checksum for file with hash: 6d13e338-41bd-b011-beb5-4de53d9d467b, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_JA2OA12RPN.nwb\n",
-      "[2024-01-29 13:51:39,249][WARNING]: Skipped checksum for file with hash: c202eb9e-ca43-0a72-4086-57a5bb6eb937, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_5TY04H3B5T.nwb\n",
-      "[2024-01-29 13:51:39,931][WARNING]: Skipped checksum for file with hash: 26f7bdc7-da8d-6ad5-3f4a-554ceb48755e, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_0TKF5589B7.nwb\n",
-      "[2024-01-29 13:51:40,547][WARNING]: Skipped checksum for file with hash: 023c874f-8114-3ef6-7fcf-813844787d5f, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_L7HDY9IDHO.nwb\n",
-      "[2024-01-29 13:51:40,963][WARNING]: Skipped checksum for file with hash: ce4cb0c3-3dd0-70fd-8ea0-98a8b84592d9, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_7UIA2ILMG6.nwb\n",
-      "[2024-01-29 13:51:41,407][WARNING]: Skipped checksum for file with hash: c592e63b-4db1-40be-632e-0180e6fa02d7, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_SGAU9PX7US.nwb\n",
-      "[2024-01-29 13:51:41,819][WARNING]: Skipped checksum for file with hash: 4c1103ac-eaca-b282-e5ff-aa2194e65a43, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_2R6VQ8EDL4.nwb\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "array([1.62593570e+09, 1.62593570e+09, 1.62593570e+09, ...,\n",
-       "       1.62593718e+09, 1.62593718e+09, 1.62593718e+09])"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "spike_times = SortedSpikesGroup.fetch_spike_data(group_key)\n",
-    "spike_times[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[13:51:42][WARNING] Spyglass: Upsampled position data, frame indices are invalid. Setting add_frame_ind=False\n",
-      "[2024-01-29 13:51:42,217][WARNING]: Skipped checksum for file with hash: 0cd40383-03e0-44ec-5dac-36c66063796a, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_FUSH604NQA.nwb\n"
-     ]
-    },
     {
      "data": {
       "text/html": [
@@ -910,15 +516,29 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>position_x</th>\n",
-       "      <th>position_y</th>\n",
-       "      <th>orientation</th>\n",
-       "      <th>velocity_x</th>\n",
-       "      <th>velocity_y</th>\n",
-       "      <th>speed</th>\n",
+       "      <th>start_time</th>\n",
+       "      <th>end_time</th>\n",
+       "      <th>duration</th>\n",
+       "      <th>mean_zscore</th>\n",
+       "      <th>median_zscore</th>\n",
+       "      <th>max_zscore</th>\n",
+       "      <th>min_zscore</th>\n",
+       "      <th>speed_at_start</th>\n",
+       "      <th>speed_at_end</th>\n",
+       "      <th>max_speed</th>\n",
+       "      <th>min_speed</th>\n",
+       "      <th>median_speed</th>\n",
+       "      <th>mean_speed</th>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>time</th>\n",
+       "      <th>id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
@@ -929,49 +549,84 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>1.625936e+09</th>\n",
-       "      <td>183.396148</td>\n",
-       "      <td>194.044480</td>\n",
-       "      <td>-1.421292</td>\n",
-       "      <td>4.893770</td>\n",
-       "      <td>-8.354365</td>\n",
-       "      <td>9.682170</td>\n",
+       "      <th>0</th>\n",
+       "      <td>1.625936e+09</td>\n",
+       "      <td>1.625936e+09</td>\n",
+       "      <td>0.238</td>\n",
+       "      <td>1.204139</td>\n",
+       "      <td>1.220925</td>\n",
+       "      <td>2.258331</td>\n",
+       "      <td>0.028349</td>\n",
+       "      <td>2.760825</td>\n",
+       "      <td>1.517296</td>\n",
+       "      <td>3.576624</td>\n",
+       "      <td>1.427160</td>\n",
+       "      <td>2.851154</td>\n",
+       "      <td>2.616020</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1.625936e+09</th>\n",
-       "      <td>183.414273</td>\n",
-       "      <td>194.013538</td>\n",
-       "      <td>-1.420984</td>\n",
-       "      <td>4.893770</td>\n",
-       "      <td>-8.354365</td>\n",
-       "      <td>9.682170</td>\n",
+       "      <th>1</th>\n",
+       "      <td>1.625936e+09</td>\n",
+       "      <td>1.625936e+09</td>\n",
+       "      <td>0.120</td>\n",
+       "      <td>1.364368</td>\n",
+       "      <td>1.349665</td>\n",
+       "      <td>2.412096</td>\n",
+       "      <td>0.015503</td>\n",
+       "      <td>1.803533</td>\n",
+       "      <td>0.497099</td>\n",
+       "      <td>1.803533</td>\n",
+       "      <td>0.464203</td>\n",
+       "      <td>0.553494</td>\n",
+       "      <td>0.722099</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1.625936e+09</th>\n",
-       "      <td>183.432398</td>\n",
-       "      <td>193.982596</td>\n",
-       "      <td>-1.420676</td>\n",
-       "      <td>4.893770</td>\n",
-       "      <td>-8.354365</td>\n",
-       "      <td>9.682170</td>\n",
+       "      <th>2</th>\n",
+       "      <td>1.625936e+09</td>\n",
+       "      <td>1.625936e+09</td>\n",
+       "      <td>0.082</td>\n",
+       "      <td>1.350428</td>\n",
+       "      <td>1.490812</td>\n",
+       "      <td>2.229001</td>\n",
+       "      <td>0.016666</td>\n",
+       "      <td>0.639882</td>\n",
+       "      <td>0.830598</td>\n",
+       "      <td>0.838116</td>\n",
+       "      <td>0.639882</td>\n",
+       "      <td>0.791716</td>\n",
+       "      <td>0.772154</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1.625936e+09</th>\n",
-       "      <td>183.450524</td>\n",
-       "      <td>193.951654</td>\n",
-       "      <td>-1.420368</td>\n",
-       "      <td>4.893770</td>\n",
-       "      <td>-8.354365</td>\n",
-       "      <td>9.682170</td>\n",
+       "      <th>3</th>\n",
+       "      <td>1.625936e+09</td>\n",
+       "      <td>1.625936e+09</td>\n",
+       "      <td>0.074</td>\n",
+       "      <td>1.609420</td>\n",
+       "      <td>1.803123</td>\n",
+       "      <td>2.578417</td>\n",
+       "      <td>0.013637</td>\n",
+       "      <td>2.690052</td>\n",
+       "      <td>0.684351</td>\n",
+       "      <td>2.690052</td>\n",
+       "      <td>0.684351</td>\n",
+       "      <td>1.327693</td>\n",
+       "      <td>1.451886</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1.625936e+09</th>\n",
-       "      <td>183.468649</td>\n",
-       "      <td>193.920712</td>\n",
-       "      <td>-1.420059</td>\n",
-       "      <td>4.893770</td>\n",
-       "      <td>-8.354365</td>\n",
-       "      <td>9.682170</td>\n",
+       "      <th>4</th>\n",
+       "      <td>1.625936e+09</td>\n",
+       "      <td>1.625936e+09</td>\n",
+       "      <td>0.208</td>\n",
+       "      <td>1.459232</td>\n",
+       "      <td>1.441996</td>\n",
+       "      <td>2.610289</td>\n",
+       "      <td>0.040791</td>\n",
+       "      <td>0.208621</td>\n",
+       "      <td>1.136773</td>\n",
+       "      <td>1.298535</td>\n",
+       "      <td>0.162133</td>\n",
+       "      <td>1.088990</td>\n",
+       "      <td>0.935486</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
@@ -981,219 +636,213 @@
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1.625937e+09</th>\n",
-       "      <td>38.376407</td>\n",
-       "      <td>113.604645</td>\n",
-       "      <td>-1.433903</td>\n",
-       "      <td>-0.067077</td>\n",
-       "      <td>0.026783</td>\n",
-       "      <td>0.072226</td>\n",
+       "      <th>160</th>\n",
+       "      <td>1.625937e+09</td>\n",
+       "      <td>1.625937e+09</td>\n",
+       "      <td>0.212</td>\n",
+       "      <td>3.099559</td>\n",
+       "      <td>3.296528</td>\n",
+       "      <td>5.312823</td>\n",
+       "      <td>0.013042</td>\n",
+       "      <td>1.183369</td>\n",
+       "      <td>0.500407</td>\n",
+       "      <td>1.183369</td>\n",
+       "      <td>0.386452</td>\n",
+       "      <td>0.827476</td>\n",
+       "      <td>0.791367</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1.625937e+09</th>\n",
-       "      <td>38.376407</td>\n",
-       "      <td>113.610601</td>\n",
-       "      <td>-1.434247</td>\n",
-       "      <td>-0.080849</td>\n",
-       "      <td>0.043172</td>\n",
-       "      <td>0.091653</td>\n",
+       "      <th>161</th>\n",
+       "      <td>1.625937e+09</td>\n",
+       "      <td>1.625937e+09</td>\n",
+       "      <td>0.406</td>\n",
+       "      <td>2.234536</td>\n",
+       "      <td>2.555549</td>\n",
+       "      <td>4.133360</td>\n",
+       "      <td>0.013230</td>\n",
+       "      <td>0.483370</td>\n",
+       "      <td>0.415434</td>\n",
+       "      <td>0.967244</td>\n",
+       "      <td>0.170981</td>\n",
+       "      <td>0.615845</td>\n",
+       "      <td>0.603776</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1.625937e+09</th>\n",
-       "      <td>38.376407</td>\n",
-       "      <td>113.616556</td>\n",
-       "      <td>-1.434589</td>\n",
-       "      <td>-0.093116</td>\n",
-       "      <td>0.058259</td>\n",
-       "      <td>0.109840</td>\n",
+       "      <th>162</th>\n",
+       "      <td>1.625937e+09</td>\n",
+       "      <td>1.625937e+09</td>\n",
+       "      <td>0.106</td>\n",
+       "      <td>1.272785</td>\n",
+       "      <td>1.090846</td>\n",
+       "      <td>2.567647</td>\n",
+       "      <td>0.029594</td>\n",
+       "      <td>1.173970</td>\n",
+       "      <td>1.531232</td>\n",
+       "      <td>1.586072</td>\n",
+       "      <td>1.173970</td>\n",
+       "      <td>1.533011</td>\n",
+       "      <td>1.472277</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1.625937e+09</th>\n",
-       "      <td>38.376407</td>\n",
-       "      <td>113.622511</td>\n",
-       "      <td>-1.434930</td>\n",
-       "      <td>-0.103935</td>\n",
-       "      <td>0.072067</td>\n",
-       "      <td>0.126476</td>\n",
+       "      <th>163</th>\n",
+       "      <td>1.625937e+09</td>\n",
+       "      <td>1.625937e+09</td>\n",
+       "      <td>0.130</td>\n",
+       "      <td>2.206783</td>\n",
+       "      <td>2.288842</td>\n",
+       "      <td>3.916084</td>\n",
+       "      <td>0.000061</td>\n",
+       "      <td>1.843658</td>\n",
+       "      <td>2.197606</td>\n",
+       "      <td>2.263417</td>\n",
+       "      <td>1.843658</td>\n",
+       "      <td>2.212691</td>\n",
+       "      <td>2.156476</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1.625937e+09</th>\n",
-       "      <td>38.376407</td>\n",
-       "      <td>113.628467</td>\n",
-       "      <td>-1.371597</td>\n",
-       "      <td>-0.113366</td>\n",
-       "      <td>0.084619</td>\n",
-       "      <td>0.141464</td>\n",
+       "      <th>164</th>\n",
+       "      <td>1.625937e+09</td>\n",
+       "      <td>1.625937e+09</td>\n",
+       "      <td>0.088</td>\n",
+       "      <td>1.682973</td>\n",
+       "      <td>1.689252</td>\n",
+       "      <td>3.048358</td>\n",
+       "      <td>0.021588</td>\n",
+       "      <td>1.288769</td>\n",
+       "      <td>1.058330</td>\n",
+       "      <td>1.334759</td>\n",
+       "      <td>1.058330</td>\n",
+       "      <td>1.296841</td>\n",
+       "      <td>1.260912</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>365438 rows × 6 columns</p>\n",
+       "<p>165 rows × 13 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "              position_x  position_y  orientation  velocity_x  velocity_y  \\\n",
-       "time                                                                        \n",
-       "1.625936e+09  183.396148  194.044480    -1.421292    4.893770   -8.354365   \n",
-       "1.625936e+09  183.414273  194.013538    -1.420984    4.893770   -8.354365   \n",
-       "1.625936e+09  183.432398  193.982596    -1.420676    4.893770   -8.354365   \n",
-       "1.625936e+09  183.450524  193.951654    -1.420368    4.893770   -8.354365   \n",
-       "1.625936e+09  183.468649  193.920712    -1.420059    4.893770   -8.354365   \n",
-       "...                  ...         ...          ...         ...         ...   \n",
-       "1.625937e+09   38.376407  113.604645    -1.433903   -0.067077    0.026783   \n",
-       "1.625937e+09   38.376407  113.610601    -1.434247   -0.080849    0.043172   \n",
-       "1.625937e+09   38.376407  113.616556    -1.434589   -0.093116    0.058259   \n",
-       "1.625937e+09   38.376407  113.622511    -1.434930   -0.103935    0.072067   \n",
-       "1.625937e+09   38.376407  113.628467    -1.371597   -0.113366    0.084619   \n",
+       "       start_time      end_time  duration  mean_zscore  median_zscore  \\\n",
+       "id                                                                      \n",
+       "0    1.625936e+09  1.625936e+09     0.238     1.204139       1.220925   \n",
+       "1    1.625936e+09  1.625936e+09     0.120     1.364368       1.349665   \n",
+       "2    1.625936e+09  1.625936e+09     0.082     1.350428       1.490812   \n",
+       "3    1.625936e+09  1.625936e+09     0.074     1.609420       1.803123   \n",
+       "4    1.625936e+09  1.625936e+09     0.208     1.459232       1.441996   \n",
+       "..            ...           ...       ...          ...            ...   \n",
+       "160  1.625937e+09  1.625937e+09     0.212     3.099559       3.296528   \n",
+       "161  1.625937e+09  1.625937e+09     0.406     2.234536       2.555549   \n",
+       "162  1.625937e+09  1.625937e+09     0.106     1.272785       1.090846   \n",
+       "163  1.625937e+09  1.625937e+09     0.130     2.206783       2.288842   \n",
+       "164  1.625937e+09  1.625937e+09     0.088     1.682973       1.689252   \n",
        "\n",
-       "                 speed  \n",
-       "time                    \n",
-       "1.625936e+09  9.682170  \n",
-       "1.625936e+09  9.682170  \n",
-       "1.625936e+09  9.682170  \n",
-       "1.625936e+09  9.682170  \n",
-       "1.625936e+09  9.682170  \n",
-       "...                ...  \n",
-       "1.625937e+09  0.072226  \n",
-       "1.625937e+09  0.091653  \n",
-       "1.625937e+09  0.109840  \n",
-       "1.625937e+09  0.126476  \n",
-       "1.625937e+09  0.141464  \n",
+       "     max_zscore  min_zscore  speed_at_start  speed_at_end  max_speed  \\\n",
+       "id                                                                     \n",
+       "0      2.258331    0.028349        2.760825      1.517296   3.576624   \n",
+       "1      2.412096    0.015503        1.803533      0.497099   1.803533   \n",
+       "2      2.229001    0.016666        0.639882      0.830598   0.838116   \n",
+       "3      2.578417    0.013637        2.690052      0.684351   2.690052   \n",
+       "4      2.610289    0.040791        0.208621      1.136773   1.298535   \n",
+       "..          ...         ...             ...           ...        ...   \n",
+       "160    5.312823    0.013042        1.183369      0.500407   1.183369   \n",
+       "161    4.133360    0.013230        0.483370      0.415434   0.967244   \n",
+       "162    2.567647    0.029594        1.173970      1.531232   1.586072   \n",
+       "163    3.916084    0.000061        1.843658      2.197606   2.263417   \n",
+       "164    3.048358    0.021588        1.288769      1.058330   1.334759   \n",
        "\n",
-       "[365438 rows x 6 columns]"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from spyglass.position import PositionOutput\n",
-    "\n",
-    "position_merge_id = (\n",
-    "    PositionOutput.TrodesPosV1\n",
-    "    & {\n",
-    "        \"nwb_file_name\": nwb_copy_file_name,\n",
-    "        \"interval_list_name\": \"pos 0 valid times\",\n",
-    "        \"trodes_pos_params_name\": \"default_decoding\",\n",
-    "    }\n",
-    ").fetch1(\"merge_id\")\n",
-    "\n",
-    "position_info = (\n",
-    "    (PositionOutput & {\"merge_id\": position_merge_id})\n",
-    "    .fetch1_dataframe()\n",
-    "    .dropna()\n",
-    ")\n",
-    "position_info"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[2024-01-29 13:51:42,682][WARNING]: Skipped checksum for file with hash: 148d9058-e6dc-e959-4c4d-75db9aa0b6e4, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_EF6N6XI3AH.nwb\n",
-      "[2024-01-29 13:51:43,058][WARNING]: Skipped checksum for file with hash: b4b6404f-aaf8-c4cc-9abe-ceea56e103f3, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_O7ZZ0F1XN7.nwb\n",
-      "[2024-01-29 13:51:43,392][WARNING]: Skipped checksum for file with hash: 4357905c-c6b9-3990-4d62-740a54cfc667, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_X84BYVM2B0.nwb\n",
-      "[2024-01-29 13:51:43,770][WARNING]: Skipped checksum for file with hash: ff81d274-17f7-702d-a2b4-92ac43c29316, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_Y2YF504C5D.nwb\n",
-      "[2024-01-29 13:51:44,190][WARNING]: Skipped checksum for file with hash: 8993754e-7dbe-94a1-403d-8c55aa9c6c42, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_JN4A4GSLZB.nwb\n",
-      "[2024-01-29 13:51:44,536][WARNING]: Skipped checksum for file with hash: 7d05460d-7366-27c9-2ba7-de2ad5d402f2, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_4JXWFJ3JRI.nwb\n",
-      "[2024-01-29 13:51:44,845][WARNING]: Skipped checksum for file with hash: 6629fd95-636a-4ad4-c9af-cee507de2130, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_AMBBKQ9RIY.nwb\n",
-      "[2024-01-29 13:51:45,223][WARNING]: Skipped checksum for file with hash: fa76d419-77a4-697a-325d-5c2ddbe517f9, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_0R6AWXMC6G.nwb\n",
-      "[2024-01-29 13:51:45,600][WARNING]: Skipped checksum for file with hash: f64f34ee-e72d-e566-a048-65f2ea31708a, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_USMRXAAV8I.nwb\n",
-      "[2024-01-29 13:51:45,991][WARNING]: Skipped checksum for file with hash: e282a8e5-844b-20f6-345c-cded12e761a9, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_DUNM1TZUGR.nwb\n",
-      "[2024-01-29 13:51:46,369][WARNING]: Skipped checksum for file with hash: d740eb7d-ce29-e140-06a2-c56655e0842a, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_L92EE1VRPB.nwb\n",
-      "[2024-01-29 13:51:46,765][WARNING]: Skipped checksum for file with hash: e43f95ff-9779-b980-00a3-99e104864462, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_AKOI7OTASI.nwb\n",
-      "[2024-01-29 13:51:47,172][WARNING]: Skipped checksum for file with hash: 6d04cbdb-e1e4-f44f-7274-0e1ab0356d75, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_W1MLF0Q86S.nwb\n",
-      "[2024-01-29 13:51:47,527][WARNING]: Skipped checksum for file with hash: 9e24661c-b021-6ad4-f224-89e331334f18, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_T2DBO3EMZ8.nwb\n",
-      "[2024-01-29 13:51:47,895][WARNING]: Skipped checksum for file with hash: 1f386cd3-89da-0233-03ff-76ba94e91a3a, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_TX2ZX3DAP4.nwb\n",
-      "[2024-01-29 13:51:48,301][WARNING]: Skipped checksum for file with hash: fde8b240-6adc-86f0-6391-f3f6fad72ee9, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_HWU3E4EKP4.nwb\n",
-      "[2024-01-29 13:51:48,671][WARNING]: Skipped checksum for file with hash: 6d13e338-41bd-b011-beb5-4de53d9d467b, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_JA2OA12RPN.nwb\n",
-      "[2024-01-29 13:51:49,020][WARNING]: Skipped checksum for file with hash: c202eb9e-ca43-0a72-4086-57a5bb6eb937, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_5TY04H3B5T.nwb\n",
-      "[2024-01-29 13:51:49,400][WARNING]: Skipped checksum for file with hash: 26f7bdc7-da8d-6ad5-3f4a-554ceb48755e, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_0TKF5589B7.nwb\n",
-      "[2024-01-29 13:51:49,798][WARNING]: Skipped checksum for file with hash: 023c874f-8114-3ef6-7fcf-813844787d5f, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_L7HDY9IDHO.nwb\n",
-      "[2024-01-29 13:51:50,175][WARNING]: Skipped checksum for file with hash: ce4cb0c3-3dd0-70fd-8ea0-98a8b84592d9, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_7UIA2ILMG6.nwb\n",
-      "[2024-01-29 13:51:50,560][WARNING]: Skipped checksum for file with hash: c592e63b-4db1-40be-632e-0180e6fa02d7, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_SGAU9PX7US.nwb\n",
-      "[2024-01-29 13:51:50,963][WARNING]: Skipped checksum for file with hash: 4c1103ac-eaca-b282-e5ff-aa2194e65a43, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_2R6VQ8EDL4.nwb\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "array([[0., 0., 0., ..., 0., 1., 0.],\n",
-       "       [0., 0., 0., ..., 0., 0., 0.],\n",
-       "       [0., 0., 0., ..., 0., 1., 1.],\n",
-       "       ...,\n",
-       "       [0., 1., 0., ..., 0., 0., 0.],\n",
-       "       [0., 0., 0., ..., 0., 0., 0.],\n",
-       "       [0., 0., 0., ..., 0., 0., 0.]])"
+       "     min_speed  median_speed  mean_speed  \n",
+       "id                                        \n",
+       "0     1.427160      2.851154    2.616020  \n",
+       "1     0.464203      0.553494    0.722099  \n",
+       "2     0.639882      0.791716    0.772154  \n",
+       "3     0.684351      1.327693    1.451886  \n",
+       "4     0.162133      1.088990    0.935486  \n",
+       "..         ...           ...         ...  \n",
+       "160   0.386452      0.827476    0.791367  \n",
+       "161   0.170981      0.615845    0.603776  \n",
+       "162   1.173970      1.533011    1.472277  \n",
+       "163   1.843658      2.212691    2.156476  \n",
+       "164   1.058330      1.296841    1.260912  \n",
+       "\n",
+       "[165 rows x 13 columns]"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "time_ind_slice = slice(63_000, 70_000)\n",
-    "time = position_info.index[time_ind_slice]\n",
-    "\n",
-    "SortedSpikesGroup.get_spike_indicator(group_key, time)"
+    "mua_times = (MuaEventsV1 & mua_key).fetch1_dataframe()\n",
+    "mua_times"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[2024-01-29 13:51:51,323][WARNING]: Skipped checksum for file with hash: 148d9058-e6dc-e959-4c4d-75db9aa0b6e4, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_EF6N6XI3AH.nwb\n",
-      "[2024-01-29 13:51:51,656][WARNING]: Skipped checksum for file with hash: b4b6404f-aaf8-c4cc-9abe-ceea56e103f3, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_O7ZZ0F1XN7.nwb\n",
-      "[2024-01-29 13:51:52,034][WARNING]: Skipped checksum for file with hash: 4357905c-c6b9-3990-4d62-740a54cfc667, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_X84BYVM2B0.nwb\n",
-      "[2024-01-29 13:51:52,374][WARNING]: Skipped checksum for file with hash: ff81d274-17f7-702d-a2b4-92ac43c29316, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_Y2YF504C5D.nwb\n",
-      "[2024-01-29 13:51:52,694][WARNING]: Skipped checksum for file with hash: 8993754e-7dbe-94a1-403d-8c55aa9c6c42, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_JN4A4GSLZB.nwb\n",
-      "[2024-01-29 13:51:53,025][WARNING]: Skipped checksum for file with hash: 7d05460d-7366-27c9-2ba7-de2ad5d402f2, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_4JXWFJ3JRI.nwb\n",
-      "[2024-01-29 13:51:53,389][WARNING]: Skipped checksum for file with hash: 6629fd95-636a-4ad4-c9af-cee507de2130, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_AMBBKQ9RIY.nwb\n",
-      "[2024-01-29 13:51:53,744][WARNING]: Skipped checksum for file with hash: fa76d419-77a4-697a-325d-5c2ddbe517f9, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_0R6AWXMC6G.nwb\n",
-      "[2024-01-29 13:51:54,114][WARNING]: Skipped checksum for file with hash: f64f34ee-e72d-e566-a048-65f2ea31708a, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_USMRXAAV8I.nwb\n",
-      "[2024-01-29 13:51:54,482][WARNING]: Skipped checksum for file with hash: e282a8e5-844b-20f6-345c-cded12e761a9, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_DUNM1TZUGR.nwb\n",
-      "[2024-01-29 13:51:54,884][WARNING]: Skipped checksum for file with hash: d740eb7d-ce29-e140-06a2-c56655e0842a, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_L92EE1VRPB.nwb\n",
-      "[2024-01-29 13:51:55,251][WARNING]: Skipped checksum for file with hash: e43f95ff-9779-b980-00a3-99e104864462, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_AKOI7OTASI.nwb\n",
-      "[2024-01-29 13:51:55,600][WARNING]: Skipped checksum for file with hash: 6d04cbdb-e1e4-f44f-7274-0e1ab0356d75, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_W1MLF0Q86S.nwb\n",
-      "[2024-01-29 13:51:55,989][WARNING]: Skipped checksum for file with hash: 9e24661c-b021-6ad4-f224-89e331334f18, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_T2DBO3EMZ8.nwb\n",
-      "[2024-01-29 13:51:56,379][WARNING]: Skipped checksum for file with hash: 1f386cd3-89da-0233-03ff-76ba94e91a3a, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_TX2ZX3DAP4.nwb\n",
-      "[2024-01-29 13:51:56,740][WARNING]: Skipped checksum for file with hash: fde8b240-6adc-86f0-6391-f3f6fad72ee9, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_HWU3E4EKP4.nwb\n",
-      "[2024-01-29 13:51:57,159][WARNING]: Skipped checksum for file with hash: 6d13e338-41bd-b011-beb5-4de53d9d467b, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_JA2OA12RPN.nwb\n",
-      "[2024-01-29 13:51:57,506][WARNING]: Skipped checksum for file with hash: c202eb9e-ca43-0a72-4086-57a5bb6eb937, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_5TY04H3B5T.nwb\n",
-      "[2024-01-29 13:51:57,880][WARNING]: Skipped checksum for file with hash: 26f7bdc7-da8d-6ad5-3f4a-554ceb48755e, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_0TKF5589B7.nwb\n",
-      "[2024-01-29 13:51:58,241][WARNING]: Skipped checksum for file with hash: 023c874f-8114-3ef6-7fcf-813844787d5f, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_L7HDY9IDHO.nwb\n",
-      "[2024-01-29 13:51:58,599][WARNING]: Skipped checksum for file with hash: ce4cb0c3-3dd0-70fd-8ea0-98a8b84592d9, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_7UIA2ILMG6.nwb\n",
-      "[2024-01-29 13:51:58,991][WARNING]: Skipped checksum for file with hash: c592e63b-4db1-40be-632e-0180e6fa02d7, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_SGAU9PX7US.nwb\n",
-      "[2024-01-29 13:51:59,349][WARNING]: Skipped checksum for file with hash: 4c1103ac-eaca-b282-e5ff-aa2194e65a43, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_2R6VQ8EDL4.nwb\n"
+      "[14:44:19][WARNING] Spyglass: Upsampled position data, frame indices are invalid. Setting add_frame_ind=False\n",
+      "/home/edeno/miniconda3/envs/spyglass/lib/python3.9/site-packages/hdmf/spec/namespace.py:531: UserWarning: Ignoring cached namespace 'hdmf-common' version 1.6.0 because version 1.5.1 is already loaded.\n",
+      "  warn(\"Ignoring cached namespace '%s' version %s because version %s is already loaded.\"\n",
+      "/home/edeno/miniconda3/envs/spyglass/lib/python3.9/site-packages/hdmf/spec/namespace.py:531: UserWarning: Ignoring cached namespace 'core' version 2.6.0-alpha because version 2.5.0 is already loaded.\n",
+      "  warn(\"Ignoring cached namespace '%s' version %s because version %s is already loaded.\"\n",
+      "/home/edeno/miniconda3/envs/spyglass/lib/python3.9/site-packages/hdmf/spec/namespace.py:531: UserWarning: Ignoring cached namespace 'hdmf-experimental' version 0.3.0 because version 0.2.0 is already loaded.\n",
+      "  warn(\"Ignoring cached namespace '%s' version %s because version %s is already loaded.\"\n",
+      "/home/edeno/miniconda3/envs/spyglass/lib/python3.9/site-packages/hdmf/spec/namespace.py:531: UserWarning: Ignoring cached namespace 'hdmf-common' version 1.6.0 because version 1.5.1 is already loaded.\n",
+      "  warn(\"Ignoring cached namespace '%s' version %s because version %s is already loaded.\"\n",
+      "/home/edeno/miniconda3/envs/spyglass/lib/python3.9/site-packages/hdmf/spec/namespace.py:531: UserWarning: Ignoring cached namespace 'core' version 2.6.0-alpha because version 2.5.0 is already loaded.\n",
+      "  warn(\"Ignoring cached namespace '%s' version %s because version %s is already loaded.\"\n",
+      "/home/edeno/miniconda3/envs/spyglass/lib/python3.9/site-packages/hdmf/spec/namespace.py:531: UserWarning: Ignoring cached namespace 'hdmf-experimental' version 0.3.0 because version 0.2.0 is already loaded.\n",
+      "  warn(\"Ignoring cached namespace '%s' version %s because version %s is already loaded.\"\n",
+      "/home/edeno/miniconda3/envs/spyglass/lib/python3.9/site-packages/hdmf/spec/namespace.py:531: UserWarning: Ignoring cached namespace 'hdmf-common' version 1.6.0 because version 1.5.1 is already loaded.\n",
+      "  warn(\"Ignoring cached namespace '%s' version %s because version %s is already loaded.\"\n",
+      "/home/edeno/miniconda3/envs/spyglass/lib/python3.9/site-packages/hdmf/spec/namespace.py:531: UserWarning: Ignoring cached namespace 'core' version 2.6.0-alpha because version 2.5.0 is already loaded.\n",
+      "  warn(\"Ignoring cached namespace '%s' version %s because version %s is already loaded.\"\n",
+      "/home/edeno/miniconda3/envs/spyglass/lib/python3.9/site-packages/hdmf/spec/namespace.py:531: UserWarning: Ignoring cached namespace 'hdmf-experimental' version 0.3.0 because version 0.2.0 is already loaded.\n",
+      "  warn(\"Ignoring cached namespace '%s' version %s because version %s is already loaded.\"\n",
+      "/home/edeno/miniconda3/envs/spyglass/lib/python3.9/site-packages/hdmf/spec/namespace.py:531: UserWarning: Ignoring cached namespace 'hdmf-common' version 1.6.0 because version 1.5.1 is already loaded.\n",
+      "  warn(\"Ignoring cached namespace '%s' version %s because version %s is already loaded.\"\n",
+      "/home/edeno/miniconda3/envs/spyglass/lib/python3.9/site-packages/hdmf/spec/namespace.py:531: UserWarning: Ignoring cached namespace 'core' version 2.6.0-alpha because version 2.5.0 is already loaded.\n",
+      "  warn(\"Ignoring cached namespace '%s' version %s because version %s is already loaded.\"\n",
+      "/home/edeno/miniconda3/envs/spyglass/lib/python3.9/site-packages/hdmf/spec/namespace.py:531: UserWarning: Ignoring cached namespace 'hdmf-experimental' version 0.3.0 because version 0.2.0 is already loaded.\n",
+      "  warn(\"Ignoring cached namespace '%s' version %s because version %s is already loaded.\"\n",
+      "/home/edeno/miniconda3/envs/spyglass/lib/python3.9/site-packages/hdmf/spec/namespace.py:531: UserWarning: Ignoring cached namespace 'hdmf-common' version 1.6.0 because version 1.5.1 is already loaded.\n",
+      "  warn(\"Ignoring cached namespace '%s' version %s because version %s is already loaded.\"\n",
+      "/home/edeno/miniconda3/envs/spyglass/lib/python3.9/site-packages/hdmf/spec/namespace.py:531: UserWarning: Ignoring cached namespace 'core' version 2.6.0-alpha because version 2.5.0 is already loaded.\n",
+      "  warn(\"Ignoring cached namespace '%s' version %s because version %s is already loaded.\"\n",
+      "/home/edeno/miniconda3/envs/spyglass/lib/python3.9/site-packages/hdmf/spec/namespace.py:531: UserWarning: Ignoring cached namespace 'hdmf-experimental' version 0.3.0 because version 0.2.0 is already loaded.\n",
+      "  warn(\"Ignoring cached namespace '%s' version %s because version %s is already loaded.\"\n",
+      "/home/edeno/miniconda3/envs/spyglass/lib/python3.9/site-packages/hdmf/spec/namespace.py:531: UserWarning: Ignoring cached namespace 'hdmf-common' version 1.6.0 because version 1.5.1 is already loaded.\n",
+      "  warn(\"Ignoring cached namespace '%s' version %s because version %s is already loaded.\"\n",
+      "/home/edeno/miniconda3/envs/spyglass/lib/python3.9/site-packages/hdmf/spec/namespace.py:531: UserWarning: Ignoring cached namespace 'core' version 2.6.0-alpha because version 2.5.0 is already loaded.\n",
+      "  warn(\"Ignoring cached namespace '%s' version %s because version %s is already loaded.\"\n",
+      "/home/edeno/miniconda3/envs/spyglass/lib/python3.9/site-packages/hdmf/spec/namespace.py:531: UserWarning: Ignoring cached namespace 'hdmf-experimental' version 0.3.0 because version 0.2.0 is already loaded.\n",
+      "  warn(\"Ignoring cached namespace '%s' version %s because version %s is already loaded.\"\n",
+      "/home/edeno/miniconda3/envs/spyglass/lib/python3.9/site-packages/hdmf/spec/namespace.py:531: UserWarning: Ignoring cached namespace 'hdmf-common' version 1.6.0 because version 1.5.1 is already loaded.\n",
+      "  warn(\"Ignoring cached namespace '%s' version %s because version %s is already loaded.\"\n",
+      "/home/edeno/miniconda3/envs/spyglass/lib/python3.9/site-packages/hdmf/spec/namespace.py:531: UserWarning: Ignoring cached namespace 'core' version 2.6.0-alpha because version 2.5.0 is already loaded.\n",
+      "  warn(\"Ignoring cached namespace '%s' version %s because version %s is already loaded.\"\n",
+      "/home/edeno/miniconda3/envs/spyglass/lib/python3.9/site-packages/hdmf/spec/namespace.py:531: UserWarning: Ignoring cached namespace 'hdmf-experimental' version 0.3.0 because version 0.2.0 is already loaded.\n",
+      "  warn(\"Ignoring cached namespace '%s' version %s because version %s is already loaded.\"\n",
+      "[14:44:24][WARNING] Spyglass: Upsampled position data, frame indices are invalid. Setting add_frame_ind=False\n"
      ]
     },
     {
      "data": {
-      "text/plain": [
-       "Text(0.5, 0, 'time (s)')"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 1500x400 with 2 Axes>"
       ]
@@ -1204,1578 +853,77 @@
    ],
    "source": [
     "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
     "\n",
     "fig, axes = plt.subplots(2, 1, sharex=True, figsize=(15, 4))\n",
-    "multiunit_firing_rate = SortedSpikesGroup.get_firing_rate(\n",
-    "    group_key, time, multiunit=True\n",
+    "speed = MuaEventsV1.get_speed(mua_key).to_numpy()\n",
+    "time = speed.index.to_numpy()\n",
+    "multiunit_firing_rate = MuaEventsV1.get_firing_rate(mua_key, time)\n",
+    "\n",
+    "time_slice = slice(\n",
+    "    np.searchsorted(time, mua_times.loc[10].start_time) - 1_000,\n",
+    "    np.searchsorted(time, mua_times.loc[10].start_time) + 5_000,\n",
     ")\n",
+    "\n",
     "axes[0].plot(\n",
-    "    time,\n",
-    "    multiunit_firing_rate,\n",
+    "    time[time_slice],\n",
+    "    multiunit_firing_rate[time_slice],\n",
+    "    color=\"black\",\n",
     ")\n",
     "axes[0].set_ylabel(\"firing rate (Hz)\")\n",
     "axes[0].set_title(\"multiunit\")\n",
-    "axes[1].fill_between(\n",
-    "    time, position_info[\"speed\"].iloc[time_ind_slice], color=\"lightgrey\"\n",
-    ")\n",
+    "axes[1].fill_between(time[time_slice], speed[time_slice], color=\"lightgrey\")\n",
     "axes[1].set_ylabel(\"speed (cm/s)\")\n",
-    "axes[1].set_xlabel(\"time (s)\")"
+    "axes[1].set_xlabel(\"time (s)\")\n",
+    "\n",
+    "for id, mua_time in mua_times.loc[\n",
+    "    np.logical_and(\n",
+    "        mua_times[\"start_time\"] > time[time_slice].min(),\n",
+    "        mua_times[\"end_time\"] < time[time_slice].max(),\n",
+    "    )\n",
+    "].iterrows():\n",
+    "    axes[0].axvspan(\n",
+    "        mua_time[\"start_time\"], mua_time[\"end_time\"], color=\"red\", alpha=0.5\n",
+    "    )"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[14:44:25][WARNING] Spyglass: Upsampled position data, frame indices are invalid. Setting add_frame_ind=False\n",
+      "[14:44:29][WARNING] Spyglass: Upsampled position data, frame indices are invalid. Setting add_frame_ind=False\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING: TimeseriesGraph::_add_series y argument is not 1D array. Using squeeze.\n"
+     ]
+    },
     {
      "data": {
-      "text/html": [
-       "\n",
-       "    \n",
-       "    <style type=\"text/css\">\n",
-       "        .Table{\n",
-       "            border-collapse:collapse;\n",
-       "        }\n",
-       "        .Table th{\n",
-       "            background: #A0A0A0; color: #ffffff; padding:4px; border:#f0e0e0 1px solid;\n",
-       "            font-weight: normal; font-family: monospace; font-size: 100%;\n",
-       "        }\n",
-       "        .Table td{\n",
-       "            padding:4px; border:#f0e0e0 1px solid; font-size:100%;\n",
-       "        }\n",
-       "        .Table tr:nth-child(odd){\n",
-       "            background: #ffffff;\n",
-       "            color: #000000;\n",
-       "        }\n",
-       "        .Table tr:nth-child(even){\n",
-       "            background: #f3f1ff;\n",
-       "            color: #000000;\n",
-       "        }\n",
-       "        /* Tooltip container */\n",
-       "        .djtooltip {\n",
-       "        }\n",
-       "        /* Tooltip text */\n",
-       "        .djtooltip .djtooltiptext {\n",
-       "            visibility: hidden;\n",
-       "            width: 120px;\n",
-       "            background-color: black;\n",
-       "            color: #fff;\n",
-       "            text-align: center;\n",
-       "            padding: 5px 0;\n",
-       "            border-radius: 6px;\n",
-       "            /* Position the tooltip text - see examples below! */\n",
-       "            position: absolute;\n",
-       "            z-index: 1;\n",
-       "        }\n",
-       "        #primary {\n",
-       "            font-weight: bold;\n",
-       "            color: black;\n",
-       "        }\n",
-       "        #nonprimary {\n",
-       "            font-weight: normal;\n",
-       "            color: white;\n",
-       "        }\n",
-       "\n",
-       "        /* Show the tooltip text when you mouse over the tooltip container */\n",
-       "        .djtooltip:hover .djtooltiptext {\n",
-       "            visibility: visible;\n",
-       "        }\n",
-       "    </style>\n",
-       "    \n",
-       "    <b></b>\n",
-       "        <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
-       "        <table border=\"1\" class=\"Table\">\n",
-       "            <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">mua_param_name</p>\n",
-       "                            <span class=\"djtooltiptext\">a name for this set of parameters</span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">mua_param_dict</p>\n",
-       "                            <span class=\"djtooltiptext\">dictionary of parameters</span>\n",
-       "                        </div> </th> </tr> </thead>\n",
-       "            <tbody> <tr> <td>default</td>\n",
-       "<td>=BLOB=</td> </tr> </tbody>\n",
-       "        </table>\n",
-       "        \n",
-       "        <p>Total: 1</p></div>\n",
-       "        "
-      ],
       "text/plain": [
-       "*mua_param_nam mua_param_\n",
-       "+------------+ +--------+\n",
-       "default        =BLOB=    \n",
-       " (Total: 1)"
+       "'https://figurl.org/f?v=gs://figurl/spikesortingview-10&d=sha1://08d7c35434e8469686fca8aff3ec9319bccb44f9&label=Multiunit%20Detection&zone=franklab.default'"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "from spyglass.mua.v1.mua import MuaEventsParameters, MuaEventsV1\n",
-    "\n",
-    "MuaEventsParameters().insert_default()\n",
-    "MuaEventsParameters()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[13:51:59][WARNING] Spyglass: Upsampled position data, frame indices are invalid. Setting add_frame_ind=False\n",
-      "[2024-01-29 13:52:00,002][WARNING]: Skipped checksum for file with hash: 0cd40383-03e0-44ec-5dac-36c66063796a, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_FUSH604NQA.nwb\n",
-      "[2024-01-29 13:52:00,421][WARNING]: Skipped checksum for file with hash: 148d9058-e6dc-e959-4c4d-75db9aa0b6e4, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_EF6N6XI3AH.nwb\n",
-      "[2024-01-29 13:52:00,785][WARNING]: Skipped checksum for file with hash: b4b6404f-aaf8-c4cc-9abe-ceea56e103f3, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_O7ZZ0F1XN7.nwb\n",
-      "[2024-01-29 13:52:01,131][WARNING]: Skipped checksum for file with hash: 4357905c-c6b9-3990-4d62-740a54cfc667, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_X84BYVM2B0.nwb\n",
-      "[2024-01-29 13:52:01,498][WARNING]: Skipped checksum for file with hash: ff81d274-17f7-702d-a2b4-92ac43c29316, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_Y2YF504C5D.nwb\n",
-      "[2024-01-29 13:52:01,888][WARNING]: Skipped checksum for file with hash: 8993754e-7dbe-94a1-403d-8c55aa9c6c42, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_JN4A4GSLZB.nwb\n",
-      "[2024-01-29 13:52:02,230][WARNING]: Skipped checksum for file with hash: 7d05460d-7366-27c9-2ba7-de2ad5d402f2, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_4JXWFJ3JRI.nwb\n",
-      "[2024-01-29 13:52:02,608][WARNING]: Skipped checksum for file with hash: 6629fd95-636a-4ad4-c9af-cee507de2130, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_AMBBKQ9RIY.nwb\n",
-      "[2024-01-29 13:52:02,984][WARNING]: Skipped checksum for file with hash: fa76d419-77a4-697a-325d-5c2ddbe517f9, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_0R6AWXMC6G.nwb\n",
-      "[2024-01-29 13:52:03,365][WARNING]: Skipped checksum for file with hash: f64f34ee-e72d-e566-a048-65f2ea31708a, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_USMRXAAV8I.nwb\n",
-      "[2024-01-29 13:52:03,762][WARNING]: Skipped checksum for file with hash: e282a8e5-844b-20f6-345c-cded12e761a9, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_DUNM1TZUGR.nwb\n",
-      "[2024-01-29 13:52:04,194][WARNING]: Skipped checksum for file with hash: d740eb7d-ce29-e140-06a2-c56655e0842a, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_L92EE1VRPB.nwb\n",
-      "[2024-01-29 13:52:04,662][WARNING]: Skipped checksum for file with hash: e43f95ff-9779-b980-00a3-99e104864462, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_AKOI7OTASI.nwb\n",
-      "[2024-01-29 13:52:05,074][WARNING]: Skipped checksum for file with hash: 6d04cbdb-e1e4-f44f-7274-0e1ab0356d75, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_W1MLF0Q86S.nwb\n",
-      "[2024-01-29 13:52:05,558][WARNING]: Skipped checksum for file with hash: 9e24661c-b021-6ad4-f224-89e331334f18, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_T2DBO3EMZ8.nwb\n",
-      "[2024-01-29 13:52:05,938][WARNING]: Skipped checksum for file with hash: 1f386cd3-89da-0233-03ff-76ba94e91a3a, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_TX2ZX3DAP4.nwb\n",
-      "[2024-01-29 13:52:06,285][WARNING]: Skipped checksum for file with hash: fde8b240-6adc-86f0-6391-f3f6fad72ee9, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_HWU3E4EKP4.nwb\n",
-      "[2024-01-29 13:52:06,641][WARNING]: Skipped checksum for file with hash: 6d13e338-41bd-b011-beb5-4de53d9d467b, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_JA2OA12RPN.nwb\n",
-      "[2024-01-29 13:52:07,019][WARNING]: Skipped checksum for file with hash: c202eb9e-ca43-0a72-4086-57a5bb6eb937, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_5TY04H3B5T.nwb\n",
-      "[2024-01-29 13:52:07,412][WARNING]: Skipped checksum for file with hash: 26f7bdc7-da8d-6ad5-3f4a-554ceb48755e, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_0TKF5589B7.nwb\n",
-      "[2024-01-29 13:52:07,806][WARNING]: Skipped checksum for file with hash: 023c874f-8114-3ef6-7fcf-813844787d5f, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_L7HDY9IDHO.nwb\n",
-      "[2024-01-29 13:52:08,189][WARNING]: Skipped checksum for file with hash: ce4cb0c3-3dd0-70fd-8ea0-98a8b84592d9, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_7UIA2ILMG6.nwb\n",
-      "[2024-01-29 13:52:08,553][WARNING]: Skipped checksum for file with hash: c592e63b-4db1-40be-632e-0180e6fa02d7, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_SGAU9PX7US.nwb\n",
-      "[2024-01-29 13:52:08,875][WARNING]: Skipped checksum for file with hash: 4c1103ac-eaca-b282-e5ff-aa2194e65a43, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_2R6VQ8EDL4.nwb\n",
-      "/Users/edeno/miniconda3/envs/spyglass/lib/python3.9/site-packages/pynwb/ecephys.py:90: UserWarning: ElectricalSeries 'e-series': The second dimension of data does not match the length of electrodes. Your data may be transposed.\n",
-      "  warnings.warn(\"%s '%s': The second dimension of data does not match the length of electrodes. \"\n",
-      "/Users/edeno/miniconda3/envs/spyglass/lib/python3.9/site-packages/pynwb/base.py:193: UserWarning: TimeSeries 'analog': Length of data does not match length of timestamps. Your data may be transposed. Time should be on the 0th dimension\n",
-      "  warn(\"%s '%s': Length of data does not match length of timestamps. Your data may be transposed. \"\n",
-      "[13:52:10][INFO] Spyglass: Writing new NWB file mediumnwb20230802_0ADLJ3W6MJ.nwb\n"
-     ]
-    }
-   ],
-   "source": [
-    "selection_key = {\n",
-    "    \"mua_param_name\": \"default\",\n",
-    "    \"nwb_file_name\": nwb_copy_file_name,\n",
-    "    \"sorted_spikes_group_name\": \"test_group\",\n",
-    "    \"pos_merge_id\": position_merge_id,\n",
-    "    \"artifact_interval_list_name\": \"test_artifact_times\",\n",
-    "}\n",
-    "\n",
-    "MuaEventsV1.populate(selection_key)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "    \n",
-       "    <style type=\"text/css\">\n",
-       "        .Table{\n",
-       "            border-collapse:collapse;\n",
-       "        }\n",
-       "        .Table th{\n",
-       "            background: #A0A0A0; color: #ffffff; padding:4px; border:#f0e0e0 1px solid;\n",
-       "            font-weight: normal; font-family: monospace; font-size: 100%;\n",
-       "        }\n",
-       "        .Table td{\n",
-       "            padding:4px; border:#f0e0e0 1px solid; font-size:100%;\n",
-       "        }\n",
-       "        .Table tr:nth-child(odd){\n",
-       "            background: #ffffff;\n",
-       "            color: #000000;\n",
-       "        }\n",
-       "        .Table tr:nth-child(even){\n",
-       "            background: #f3f1ff;\n",
-       "            color: #000000;\n",
-       "        }\n",
-       "        /* Tooltip container */\n",
-       "        .djtooltip {\n",
-       "        }\n",
-       "        /* Tooltip text */\n",
-       "        .djtooltip .djtooltiptext {\n",
-       "            visibility: hidden;\n",
-       "            width: 120px;\n",
-       "            background-color: black;\n",
-       "            color: #fff;\n",
-       "            text-align: center;\n",
-       "            padding: 5px 0;\n",
-       "            border-radius: 6px;\n",
-       "            /* Position the tooltip text - see examples below! */\n",
-       "            position: absolute;\n",
-       "            z-index: 1;\n",
-       "        }\n",
-       "        #primary {\n",
-       "            font-weight: bold;\n",
-       "            color: black;\n",
-       "        }\n",
-       "        #nonprimary {\n",
-       "            font-weight: normal;\n",
-       "            color: white;\n",
-       "        }\n",
-       "\n",
-       "        /* Show the tooltip text when you mouse over the tooltip container */\n",
-       "        .djtooltip:hover .djtooltiptext {\n",
-       "            visibility: visible;\n",
-       "        }\n",
-       "    </style>\n",
-       "    \n",
-       "    <b></b>\n",
-       "        <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
-       "        <table border=\"1\" class=\"Table\">\n",
-       "            <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">mua_param_name</p>\n",
-       "                            <span class=\"djtooltiptext\">a name for this set of parameters</span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">nwb_file_name</p>\n",
-       "                            <span class=\"djtooltiptext\">name of the NWB file</span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">sorted_spikes_group_name</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">pos_merge_id</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">artifact_interval_list_name</p>\n",
-       "                            <span class=\"djtooltiptext\">descriptive name of this interval list</span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">analysis_file_name</p>\n",
-       "                            <span class=\"djtooltiptext\">name of the file</span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">mua_times_object_id</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div> </th> </tr> </thead>\n",
-       "            <tbody> <tr> <td>default</td>\n",
-       "<td>mediumnwb20230802_.nwb</td>\n",
-       "<td>test_group</td>\n",
-       "<td>6dfae23d-6034-e483-06e7-28ab4c29282f</td>\n",
-       "<td>test_artifact_times</td>\n",
-       "<td>mediumnwb20230802_0ADLJ3W6MJ.nwb</td>\n",
-       "<td>56d076f9-8751-4cec-b7b4-21a14058fcff</td> </tr> </tbody>\n",
-       "        </table>\n",
-       "        \n",
-       "        <p>Total: 1</p></div>\n",
-       "        "
-      ],
-      "text/plain": [
-       "*mua_param_nam *nwb_file_name *sorted_spikes *pos_merge_id  *artifact_inte analysis_file_ mua_times_obje\n",
-       "+------------+ +------------+ +------------+ +------------+ +------------+ +------------+ +------------+\n",
-       "default        mediumnwb20230 test_group     6dfae23d-6034- test_artifact_ mediumnwb20230 56d076f9-8751-\n",
-       " (Total: 1)"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "MuaEventsV1 & selection_key"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[2024-01-29 13:52:11,975][WARNING]: Skipped checksum for file with hash: 6b7e200d-7337-6c3e-ae61-b52ac7b9d6ca, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_0ADLJ3W6MJ.nwb\n"
-     ]
-    },
-    {
-     "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>start_time</th>\n",
-       "      <th>end_time</th>\n",
-       "      <th>duration</th>\n",
-       "      <th>mean_zscore</th>\n",
-       "      <th>median_zscore</th>\n",
-       "      <th>max_zscore</th>\n",
-       "      <th>min_zscore</th>\n",
-       "      <th>speed_at_start</th>\n",
-       "      <th>speed_at_end</th>\n",
-       "      <th>max_speed</th>\n",
-       "      <th>min_speed</th>\n",
-       "      <th>median_speed</th>\n",
-       "      <th>mean_speed</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>id</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1.625936e+09</td>\n",
-       "      <td>1.625936e+09</td>\n",
-       "      <td>0.084000</td>\n",
-       "      <td>1.250994</td>\n",
-       "      <td>1.225517</td>\n",
-       "      <td>2.346275</td>\n",
-       "      <td>0.026900</td>\n",
-       "      <td>2.845775</td>\n",
-       "      <td>1.442441</td>\n",
-       "      <td>2.845775</td>\n",
-       "      <td>1.352343</td>\n",
-       "      <td>1.978832</td>\n",
-       "      <td>2.010321</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1.625936e+09</td>\n",
-       "      <td>1.625936e+09</td>\n",
-       "      <td>0.068000</td>\n",
-       "      <td>1.867170</td>\n",
-       "      <td>1.994518</td>\n",
-       "      <td>3.243124</td>\n",
-       "      <td>0.061468</td>\n",
-       "      <td>0.287511</td>\n",
-       "      <td>0.116674</td>\n",
-       "      <td>0.287511</td>\n",
-       "      <td>0.116674</td>\n",
-       "      <td>0.184148</td>\n",
-       "      <td>0.190783</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1.625936e+09</td>\n",
-       "      <td>1.625936e+09</td>\n",
-       "      <td>0.128000</td>\n",
-       "      <td>1.308437</td>\n",
-       "      <td>1.048030</td>\n",
-       "      <td>3.013205</td>\n",
-       "      <td>0.022677</td>\n",
-       "      <td>2.097526</td>\n",
-       "      <td>1.261806</td>\n",
-       "      <td>2.131399</td>\n",
-       "      <td>1.261806</td>\n",
-       "      <td>1.945054</td>\n",
-       "      <td>1.848956</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1.625936e+09</td>\n",
-       "      <td>1.625936e+09</td>\n",
-       "      <td>0.260000</td>\n",
-       "      <td>1.847013</td>\n",
-       "      <td>2.225766</td>\n",
-       "      <td>2.979140</td>\n",
-       "      <td>0.028124</td>\n",
-       "      <td>0.793993</td>\n",
-       "      <td>0.523751</td>\n",
-       "      <td>2.196469</td>\n",
-       "      <td>0.523751</td>\n",
-       "      <td>1.697052</td>\n",
-       "      <td>1.616889</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1.625936e+09</td>\n",
-       "      <td>1.625936e+09</td>\n",
-       "      <td>0.092000</td>\n",
-       "      <td>1.320218</td>\n",
-       "      <td>1.367887</td>\n",
-       "      <td>2.377142</td>\n",
-       "      <td>0.021857</td>\n",
-       "      <td>2.139993</td>\n",
-       "      <td>1.304687</td>\n",
-       "      <td>2.139993</td>\n",
-       "      <td>1.142313</td>\n",
-       "      <td>1.321552</td>\n",
-       "      <td>1.465801</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>212</th>\n",
-       "      <td>1.625937e+09</td>\n",
-       "      <td>1.625937e+09</td>\n",
-       "      <td>0.228000</td>\n",
-       "      <td>5.020090</td>\n",
-       "      <td>4.976216</td>\n",
-       "      <td>10.539919</td>\n",
-       "      <td>0.239889</td>\n",
-       "      <td>1.336351</td>\n",
-       "      <td>0.441290</td>\n",
-       "      <td>1.336351</td>\n",
-       "      <td>0.389653</td>\n",
-       "      <td>0.828371</td>\n",
-       "      <td>0.804487</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>213</th>\n",
-       "      <td>1.625937e+09</td>\n",
-       "      <td>1.625937e+09</td>\n",
-       "      <td>0.303999</td>\n",
-       "      <td>4.870160</td>\n",
-       "      <td>4.968471</td>\n",
-       "      <td>8.561497</td>\n",
-       "      <td>0.129386</td>\n",
-       "      <td>0.956787</td>\n",
-       "      <td>0.408402</td>\n",
-       "      <td>0.966132</td>\n",
-       "      <td>0.170895</td>\n",
-       "      <td>0.416961</td>\n",
-       "      <td>0.540956</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>214</th>\n",
-       "      <td>1.625937e+09</td>\n",
-       "      <td>1.625937e+09</td>\n",
-       "      <td>0.140000</td>\n",
-       "      <td>3.250498</td>\n",
-       "      <td>3.146104</td>\n",
-       "      <td>6.157476</td>\n",
-       "      <td>0.122835</td>\n",
-       "      <td>1.745086</td>\n",
-       "      <td>2.199006</td>\n",
-       "      <td>2.262194</td>\n",
-       "      <td>1.745086</td>\n",
-       "      <td>2.202081</td>\n",
-       "      <td>2.125525</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>215</th>\n",
-       "      <td>1.625937e+09</td>\n",
-       "      <td>1.625937e+09</td>\n",
-       "      <td>0.060000</td>\n",
-       "      <td>2.129116</td>\n",
-       "      <td>2.216616</td>\n",
-       "      <td>3.790466</td>\n",
-       "      <td>0.127067</td>\n",
-       "      <td>1.332429</td>\n",
-       "      <td>1.077809</td>\n",
-       "      <td>1.332429</td>\n",
-       "      <td>1.077809</td>\n",
-       "      <td>1.265536</td>\n",
-       "      <td>1.242683</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>216</th>\n",
-       "      <td>1.625937e+09</td>\n",
-       "      <td>1.625937e+09</td>\n",
-       "      <td>0.104000</td>\n",
-       "      <td>1.546942</td>\n",
-       "      <td>1.681691</td>\n",
-       "      <td>2.594644</td>\n",
-       "      <td>0.007079</td>\n",
-       "      <td>0.369586</td>\n",
-       "      <td>0.185078</td>\n",
-       "      <td>0.375829</td>\n",
-       "      <td>0.183949</td>\n",
-       "      <td>0.303257</td>\n",
-       "      <td>0.291096</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>217 rows × 13 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       start_time      end_time  duration  mean_zscore  median_zscore  \\\n",
-       "id                                                                      \n",
-       "0    1.625936e+09  1.625936e+09  0.084000     1.250994       1.225517   \n",
-       "1    1.625936e+09  1.625936e+09  0.068000     1.867170       1.994518   \n",
-       "2    1.625936e+09  1.625936e+09  0.128000     1.308437       1.048030   \n",
-       "3    1.625936e+09  1.625936e+09  0.260000     1.847013       2.225766   \n",
-       "4    1.625936e+09  1.625936e+09  0.092000     1.320218       1.367887   \n",
-       "..            ...           ...       ...          ...            ...   \n",
-       "212  1.625937e+09  1.625937e+09  0.228000     5.020090       4.976216   \n",
-       "213  1.625937e+09  1.625937e+09  0.303999     4.870160       4.968471   \n",
-       "214  1.625937e+09  1.625937e+09  0.140000     3.250498       3.146104   \n",
-       "215  1.625937e+09  1.625937e+09  0.060000     2.129116       2.216616   \n",
-       "216  1.625937e+09  1.625937e+09  0.104000     1.546942       1.681691   \n",
-       "\n",
-       "     max_zscore  min_zscore  speed_at_start  speed_at_end  max_speed  \\\n",
-       "id                                                                     \n",
-       "0      2.346275    0.026900        2.845775      1.442441   2.845775   \n",
-       "1      3.243124    0.061468        0.287511      0.116674   0.287511   \n",
-       "2      3.013205    0.022677        2.097526      1.261806   2.131399   \n",
-       "3      2.979140    0.028124        0.793993      0.523751   2.196469   \n",
-       "4      2.377142    0.021857        2.139993      1.304687   2.139993   \n",
-       "..          ...         ...             ...           ...        ...   \n",
-       "212   10.539919    0.239889        1.336351      0.441290   1.336351   \n",
-       "213    8.561497    0.129386        0.956787      0.408402   0.966132   \n",
-       "214    6.157476    0.122835        1.745086      2.199006   2.262194   \n",
-       "215    3.790466    0.127067        1.332429      1.077809   1.332429   \n",
-       "216    2.594644    0.007079        0.369586      0.185078   0.375829   \n",
-       "\n",
-       "     min_speed  median_speed  mean_speed  \n",
-       "id                                        \n",
-       "0     1.352343      1.978832    2.010321  \n",
-       "1     0.116674      0.184148    0.190783  \n",
-       "2     1.261806      1.945054    1.848956  \n",
-       "3     0.523751      1.697052    1.616889  \n",
-       "4     1.142313      1.321552    1.465801  \n",
-       "..         ...           ...         ...  \n",
-       "212   0.389653      0.828371    0.804487  \n",
-       "213   0.170895      0.416961    0.540956  \n",
-       "214   1.745086      2.202081    2.125525  \n",
-       "215   1.077809      1.265536    1.242683  \n",
-       "216   0.183949      0.303257    0.291096  \n",
-       "\n",
-       "[217 rows x 13 columns]"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "mua_times = (MuaEventsV1 & selection_key).fetch1_dataframe()\n",
-    "mua_times"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(1625935966.608866, 1625935994.6048138)"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 1500x400 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
-    "\n",
-    "fig, axes = plt.subplots(2, 1, sharex=True, figsize=(15, 4))\n",
-    "axes[0].plot(\n",
-    "    time,\n",
-    "    multiunit_firing_rate,\n",
-    ")\n",
-    "axes[0].set_ylabel(\"firing rate (Hz)\")\n",
-    "axes[0].set_title(\"multiunit\")\n",
-    "axes[1].fill_between(\n",
-    "    time, position_info[\"speed\"].iloc[time_ind_slice], color=\"lightgrey\"\n",
-    ")\n",
-    "axes[1].set_ylabel(\"speed (cm/s)\")\n",
-    "axes[1].set_xlabel(\"time (s)\")\n",
-    "\n",
-    "in_bounds = np.logical_and(\n",
-    "    mua_times.start_time >= time[0], mua_times.end_time <= time[-1]\n",
-    ")\n",
-    "\n",
-    "for mua_time in mua_times.loc[in_bounds].itertuples():\n",
-    "    axes[0].axvspan(\n",
-    "        mua_time.start_time, mua_time.end_time, color=\"red\", alpha=0.3\n",
-    "    )\n",
-    "    axes[1].axvspan(\n",
-    "        mua_time.start_time, mua_time.end_time, color=\"red\", alpha=0.3\n",
-    "    )\n",
-    "axes[1].set_ylim((0, 80))\n",
-    "axes[1].axhline(4, color=\"black\", linestyle=\"--\")\n",
-    "axes[1].set_xlim((time[0], time[-1]))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "    \n",
-       "    <style type=\"text/css\">\n",
-       "        .Table{\n",
-       "            border-collapse:collapse;\n",
-       "        }\n",
-       "        .Table th{\n",
-       "            background: #A0A0A0; color: #ffffff; padding:4px; border:#f0e0e0 1px solid;\n",
-       "            font-weight: normal; font-family: monospace; font-size: 100%;\n",
-       "        }\n",
-       "        .Table td{\n",
-       "            padding:4px; border:#f0e0e0 1px solid; font-size:100%;\n",
-       "        }\n",
-       "        .Table tr:nth-child(odd){\n",
-       "            background: #ffffff;\n",
-       "            color: #000000;\n",
-       "        }\n",
-       "        .Table tr:nth-child(even){\n",
-       "            background: #f3f1ff;\n",
-       "            color: #000000;\n",
-       "        }\n",
-       "        /* Tooltip container */\n",
-       "        .djtooltip {\n",
-       "        }\n",
-       "        /* Tooltip text */\n",
-       "        .djtooltip .djtooltiptext {\n",
-       "            visibility: hidden;\n",
-       "            width: 120px;\n",
-       "            background-color: black;\n",
-       "            color: #fff;\n",
-       "            text-align: center;\n",
-       "            padding: 5px 0;\n",
-       "            border-radius: 6px;\n",
-       "            /* Position the tooltip text - see examples below! */\n",
-       "            position: absolute;\n",
-       "            z-index: 1;\n",
-       "        }\n",
-       "        #primary {\n",
-       "            font-weight: bold;\n",
-       "            color: black;\n",
-       "        }\n",
-       "        #nonprimary {\n",
-       "            font-weight: normal;\n",
-       "            color: white;\n",
-       "        }\n",
-       "\n",
-       "        /* Show the tooltip text when you mouse over the tooltip container */\n",
-       "        .djtooltip:hover .djtooltiptext {\n",
-       "            visibility: visible;\n",
-       "        }\n",
-       "    </style>\n",
-       "    \n",
-       "    <b>Time intervals used for analysis</b>\n",
-       "        <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
-       "        <table border=\"1\" class=\"Table\">\n",
-       "            <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">nwb_file_name</p>\n",
-       "                            <span class=\"djtooltiptext\">name of the NWB file</span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">interval_list_name</p>\n",
-       "                            <span class=\"djtooltiptext\">descriptive name of this interval list</span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">valid_times</p>\n",
-       "                            <span class=\"djtooltiptext\">numpy array with start/end times for each interval</span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">pipeline</p>\n",
-       "                            <span class=\"djtooltiptext\">type of interval list (e.g. 'position', 'spikesorting_recording_v1')</span>\n",
-       "                        </div> </th> </tr> </thead>\n",
-       "            <tbody> <tr> <td>mediumnwb20230802_.nwb</td>\n",
-       "<td>0e848c38-9105-4ea4-b6ba-dbdd5b46a088</td>\n",
-       "<td>=BLOB=</td>\n",
-       "<td>spikesorting_artifact_v1</td></tr><tr><td>mediumnwb20230802_.nwb</td>\n",
-       "<td>0f91197e-bebb-4dc6-ad41-5bf89c3eed28</td>\n",
-       "<td>=BLOB=</td>\n",
-       "<td>spikesorting_artifact_v1</td></tr><tr><td>mediumnwb20230802_.nwb</td>\n",
-       "<td>15c8a3e8-5ce9-4654-891e-6ee4109d6f1a</td>\n",
-       "<td>=BLOB=</td>\n",
-       "<td>spikesorting_artifact_v1</td></tr><tr><td>mediumnwb20230802_.nwb</td>\n",
-       "<td>2b93bcd0-7b05-457c-8aab-c41ef543ecf2</td>\n",
-       "<td>=BLOB=</td>\n",
-       "<td>spikesorting_artifact_v1</td></tr><tr><td>mediumnwb20230802_.nwb</td>\n",
-       "<td>2b9fbf14-74a0-4294-a805-26702340aac9</td>\n",
-       "<td>=BLOB=</td>\n",
-       "<td>spikesorting_artifact_v1</td></tr><tr><td>mediumnwb20230802_.nwb</td>\n",
-       "<td>3a34ab35-eca6-406b-abab-c866a9f41fb9</td>\n",
-       "<td>=BLOB=</td>\n",
-       "<td>spikesorting_artifact_v1</td></tr><tr><td>mediumnwb20230802_.nwb</td>\n",
-       "<td>3fecf0af-6ce7-474c-b933-9feb746993a0</td>\n",
-       "<td>=BLOB=</td>\n",
-       "<td>spikesorting_artifact_v1</td></tr><tr><td>mediumnwb20230802_.nwb</td>\n",
-       "<td>45f6b9a1-eef3-46eb-866d-d0999afebda6</td>\n",
-       "<td>=BLOB=</td>\n",
-       "<td>spikesorting_artifact_v1</td></tr><tr><td>mediumnwb20230802_.nwb</td>\n",
-       "<td>5c68f0f0-f577-4905-8a09-e4d171d0a22d</td>\n",
-       "<td>=BLOB=</td>\n",
-       "<td>spikesorting_artifact_v1</td></tr><tr><td>mediumnwb20230802_.nwb</td>\n",
-       "<td>686d9951-1c0f-4d5e-9f5c-09e6fd8bdd4c</td>\n",
-       "<td>=BLOB=</td>\n",
-       "<td>spikesorting_artifact_v1</td></tr><tr><td>mediumnwb20230802_.nwb</td>\n",
-       "<td>69f7d164-efc8-4621-b83c-77bc7459dbc2</td>\n",
-       "<td>=BLOB=</td>\n",
-       "<td>spikesorting_artifact_v1</td></tr><tr><td>mediumnwb20230802_.nwb</td>\n",
-       "<td>719e8a86-fcf1-4ffc-8c1f-ea912f67ad5d</td>\n",
-       "<td>=BLOB=</td>\n",
-       "<td>spikesorting_artifact_v1</td> </tr> </tbody>\n",
-       "        </table>\n",
-       "        <p>...</p>\n",
-       "        <p>Total: 24</p></div>\n",
-       "        "
-      ],
-      "text/plain": [
-       "*nwb_file_name *interval_list valid_time pipeline      \n",
-       "+------------+ +------------+ +--------+ +------------+\n",
-       "mediumnwb20230 0e848c38-9105- =BLOB=     spikesorting_a\n",
-       "mediumnwb20230 0f91197e-bebb- =BLOB=     spikesorting_a\n",
-       "mediumnwb20230 15c8a3e8-5ce9- =BLOB=     spikesorting_a\n",
-       "mediumnwb20230 2b93bcd0-7b05- =BLOB=     spikesorting_a\n",
-       "mediumnwb20230 2b9fbf14-74a0- =BLOB=     spikesorting_a\n",
-       "mediumnwb20230 3a34ab35-eca6- =BLOB=     spikesorting_a\n",
-       "mediumnwb20230 3fecf0af-6ce7- =BLOB=     spikesorting_a\n",
-       "mediumnwb20230 45f6b9a1-eef3- =BLOB=     spikesorting_a\n",
-       "mediumnwb20230 5c68f0f0-f577- =BLOB=     spikesorting_a\n",
-       "mediumnwb20230 686d9951-1c0f- =BLOB=     spikesorting_a\n",
-       "mediumnwb20230 69f7d164-efc8- =BLOB=     spikesorting_a\n",
-       "mediumnwb20230 719e8a86-fcf1- =BLOB=     spikesorting_a\n",
-       "   ...\n",
-       " (Total: 24)"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from spyglass.common import IntervalList\n",
-    "\n",
-    "IntervalList() & {\"nwb_file_name\": nwb_copy_file_name, \"pipeline\": \"spikesorting_artifact_v1\"}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "    \n",
-       "    <style type=\"text/css\">\n",
-       "        .Table{\n",
-       "            border-collapse:collapse;\n",
-       "        }\n",
-       "        .Table th{\n",
-       "            background: #A0A0A0; color: #ffffff; padding:4px; border:#f0e0e0 1px solid;\n",
-       "            font-weight: normal; font-family: monospace; font-size: 100%;\n",
-       "        }\n",
-       "        .Table td{\n",
-       "            padding:4px; border:#f0e0e0 1px solid; font-size:100%;\n",
-       "        }\n",
-       "        .Table tr:nth-child(odd){\n",
-       "            background: #ffffff;\n",
-       "            color: #000000;\n",
-       "        }\n",
-       "        .Table tr:nth-child(even){\n",
-       "            background: #f3f1ff;\n",
-       "            color: #000000;\n",
-       "        }\n",
-       "        /* Tooltip container */\n",
-       "        .djtooltip {\n",
-       "        }\n",
-       "        /* Tooltip text */\n",
-       "        .djtooltip .djtooltiptext {\n",
-       "            visibility: hidden;\n",
-       "            width: 120px;\n",
-       "            background-color: black;\n",
-       "            color: #fff;\n",
-       "            text-align: center;\n",
-       "            padding: 5px 0;\n",
-       "            border-radius: 6px;\n",
-       "            /* Position the tooltip text - see examples below! */\n",
-       "            position: absolute;\n",
-       "            z-index: 1;\n",
-       "        }\n",
-       "        #primary {\n",
-       "            font-weight: bold;\n",
-       "            color: black;\n",
-       "        }\n",
-       "        #nonprimary {\n",
-       "            font-weight: normal;\n",
-       "            color: white;\n",
-       "        }\n",
-       "\n",
-       "        /* Show the tooltip text when you mouse over the tooltip container */\n",
-       "        .djtooltip:hover .djtooltiptext {\n",
-       "            visibility: visible;\n",
-       "        }\n",
-       "    </style>\n",
-       "    \n",
-       "    \n",
-       "        <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
-       "        <table border=\"1\" class=\"Table\">\n",
-       "            <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">artifact_param_name</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">recording_id</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">artifact_id</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">artifact_params</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">analysis_file_name</p>\n",
-       "                            <span class=\"djtooltiptext\">name of the file</span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">object_id</p>\n",
-       "                            <span class=\"djtooltiptext\">Object ID for the processed recording in NWB file</span>\n",
-       "                        </div> </th> </tr> </thead>\n",
-       "            <tbody> <tr> <td>none</td>\n",
-       "<td>f1427e00-2974-4301-b2ac-b4dc29277c51</td>\n",
-       "<td>0e848c38-9105-4ea4-b6ba-dbdd5b46a088</td>\n",
-       "<td>=BLOB=</td>\n",
-       "<td>mediumnwb20230802_3BWJOEGAO1.nwb</td>\n",
-       "<td>8366dcfd-bd90-470f-b08c-183bd44dcd1f</td></tr><tr><td>none</td>\n",
-       "<td>3c40ebdc-0b61-4105-9971-e1348bd49bc7</td>\n",
-       "<td>0f91197e-bebb-4dc6-ad41-5bf89c3eed28</td>\n",
-       "<td>=BLOB=</td>\n",
-       "<td>mediumnwb20230802_85VG1GLTSR.nwb</td>\n",
-       "<td>2d6e841c-4f98-42b7-9cbf-097fa8ba0766</td></tr><tr><td>none</td>\n",
-       "<td>1e3f3707-613e-4a44-93f1-c7e5484112cd</td>\n",
-       "<td>15c8a3e8-5ce9-4654-891e-6ee4109d6f1a</td>\n",
-       "<td>=BLOB=</td>\n",
-       "<td>mediumnwb20230802_H70AAE9UEI.nwb</td>\n",
-       "<td>34550b0b-36c7-4dfb-b1ba-900fc9247da8</td></tr><tr><td>none</td>\n",
-       "<td>72f70c5a-bc0f-46cb-b22f-33bafe2315df</td>\n",
-       "<td>2b93bcd0-7b05-457c-8aab-c41ef543ecf2</td>\n",
-       "<td>=BLOB=</td>\n",
-       "<td>mediumnwb20230802_OFQSUAGB74.nwb</td>\n",
-       "<td>3a1b3df6-fa85-4006-ab9b-4bff45680516</td></tr><tr><td>none</td>\n",
-       "<td>3a2c3eed-413a-452a-83c8-0e4648141bde</td>\n",
-       "<td>2b9fbf14-74a0-4294-a805-26702340aac9</td>\n",
-       "<td>=BLOB=</td>\n",
-       "<td>mediumnwb20230802_G0STLTBOSC.nwb</td>\n",
-       "<td>ec5170e0-277a-4626-9120-b24574eae650</td></tr><tr><td>none</td>\n",
-       "<td>d14ae25a-2796-4203-ba0b-f28768536cbe</td>\n",
-       "<td>3a34ab35-eca6-406b-abab-c866a9f41fb9</td>\n",
-       "<td>=BLOB=</td>\n",
-       "<td>mediumnwb20230802_IG73IGEYPA.nwb</td>\n",
-       "<td>a3406f7e-1137-4c34-8715-e0323d074153</td></tr><tr><td>none</td>\n",
-       "<td>76ee4ab3-da3e-4a68-9159-635fbf5f8a04</td>\n",
-       "<td>3fecf0af-6ce7-474c-b933-9feb746993a0</td>\n",
-       "<td>=BLOB=</td>\n",
-       "<td>mediumnwb20230802_LFO7YW7IIA.nwb</td>\n",
-       "<td>cfe309be-3748-4dc8-9c71-81bd5c1067b7</td></tr><tr><td>none</td>\n",
-       "<td>449b64e3-db0b-437e-a1b9-0d29928aa2dd</td>\n",
-       "<td>45f6b9a1-eef3-46eb-866d-d0999afebda6</td>\n",
-       "<td>=BLOB=</td>\n",
-       "<td>mediumnwb20230802_R19M5A499A.nwb</td>\n",
-       "<td>68db8ac5-d008-4720-a3f8-162b0671313d</td></tr><tr><td>none</td>\n",
-       "<td>f07bc0b0-de6b-4424-8ef9-766213aaca26</td>\n",
-       "<td>5c68f0f0-f577-4905-8a09-e4d171d0a22d</td>\n",
-       "<td>=BLOB=</td>\n",
-       "<td>mediumnwb20230802_GF6Z0P5KYV.nwb</td>\n",
-       "<td>80c14292-a179-4c0f-9ff9-79016038ce95</td></tr><tr><td>none</td>\n",
-       "<td>328da21c-1d9c-41e2-9800-76b3484b707b</td>\n",
-       "<td>686d9951-1c0f-4d5e-9f5c-09e6fd8bdd4c</td>\n",
-       "<td>=BLOB=</td>\n",
-       "<td>mediumnwb20230802_YSZXPEGP4X.nwb</td>\n",
-       "<td>623a46f8-be44-434b-8aee-49155070f3f7</td></tr><tr><td>none</td>\n",
-       "<td>b81f1ed9-fb2e-4f5c-93d8-d9cc0f118875</td>\n",
-       "<td>69f7d164-efc8-4621-b83c-77bc7459dbc2</td>\n",
-       "<td>=BLOB=</td>\n",
-       "<td>mediumnwb20230802_QBQBH0I2OO.nwb</td>\n",
-       "<td>101d753a-72fb-47a4-8734-962f8ef4f477</td></tr><tr><td>none</td>\n",
-       "<td>aff78f2f-2ba0-412a-95cc-447c3a2f4683</td>\n",
-       "<td>719e8a86-fcf1-4ffc-8c1f-ea912f67ad5d</td>\n",
-       "<td>=BLOB=</td>\n",
-       "<td>mediumnwb20230802_42H3PR863Y.nwb</td>\n",
-       "<td>6c5f3cd0-903a-42b5-ad68-261789bfd1a8</td> </tr> </tbody>\n",
-       "        </table>\n",
-       "        <p>...</p>\n",
-       "        <p>Total: 24</p></div>\n",
-       "        "
-      ],
-      "text/plain": [
-       "*artifact_para *recording_id  *artifact_id   artifact_p analysis_file_ object_id     \n",
-       "+------------+ +------------+ +------------+ +--------+ +------------+ +------------+\n",
-       "none           f1427e00-2974- 0e848c38-9105- =BLOB=     mediumnwb20230 8366dcfd-bd90-\n",
-       "none           3c40ebdc-0b61- 0f91197e-bebb- =BLOB=     mediumnwb20230 2d6e841c-4f98-\n",
-       "none           1e3f3707-613e- 15c8a3e8-5ce9- =BLOB=     mediumnwb20230 34550b0b-36c7-\n",
-       "none           72f70c5a-bc0f- 2b93bcd0-7b05- =BLOB=     mediumnwb20230 3a1b3df6-fa85-\n",
-       "none           3a2c3eed-413a- 2b9fbf14-74a0- =BLOB=     mediumnwb20230 ec5170e0-277a-\n",
-       "none           d14ae25a-2796- 3a34ab35-eca6- =BLOB=     mediumnwb20230 a3406f7e-1137-\n",
-       "none           76ee4ab3-da3e- 3fecf0af-6ce7- =BLOB=     mediumnwb20230 cfe309be-3748-\n",
-       "none           449b64e3-db0b- 45f6b9a1-eef3- =BLOB=     mediumnwb20230 68db8ac5-d008-\n",
-       "none           f07bc0b0-de6b- 5c68f0f0-f577- =BLOB=     mediumnwb20230 80c14292-a179-\n",
-       "none           328da21c-1d9c- 686d9951-1c0f- =BLOB=     mediumnwb20230 623a46f8-be44-\n",
-       "none           b81f1ed9-fb2e- 69f7d164-efc8- =BLOB=     mediumnwb20230 101d753a-72fb-\n",
-       "none           aff78f2f-2ba0- 719e8a86-fcf1- =BLOB=     mediumnwb20230 6c5f3cd0-903a-\n",
-       "   ...\n",
-       " (Total: 24)"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "(sgs.ArtifactDetectionParameters\n",
-    "* sgs.SpikeSortingRecording\n",
-    "* sgs.ArtifactDetectionSelection)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "    \n",
-       "    <style type=\"text/css\">\n",
-       "        .Table{\n",
-       "            border-collapse:collapse;\n",
-       "        }\n",
-       "        .Table th{\n",
-       "            background: #A0A0A0; color: #ffffff; padding:4px; border:#f0e0e0 1px solid;\n",
-       "            font-weight: normal; font-family: monospace; font-size: 100%;\n",
-       "        }\n",
-       "        .Table td{\n",
-       "            padding:4px; border:#f0e0e0 1px solid; font-size:100%;\n",
-       "        }\n",
-       "        .Table tr:nth-child(odd){\n",
-       "            background: #ffffff;\n",
-       "            color: #000000;\n",
-       "        }\n",
-       "        .Table tr:nth-child(even){\n",
-       "            background: #f3f1ff;\n",
-       "            color: #000000;\n",
-       "        }\n",
-       "        /* Tooltip container */\n",
-       "        .djtooltip {\n",
-       "        }\n",
-       "        /* Tooltip text */\n",
-       "        .djtooltip .djtooltiptext {\n",
-       "            visibility: hidden;\n",
-       "            width: 120px;\n",
-       "            background-color: black;\n",
-       "            color: #fff;\n",
-       "            text-align: center;\n",
-       "            padding: 5px 0;\n",
-       "            border-radius: 6px;\n",
-       "            /* Position the tooltip text - see examples below! */\n",
-       "            position: absolute;\n",
-       "            z-index: 1;\n",
-       "        }\n",
-       "        #primary {\n",
-       "            font-weight: bold;\n",
-       "            color: black;\n",
-       "        }\n",
-       "        #nonprimary {\n",
-       "            font-weight: normal;\n",
-       "            color: white;\n",
-       "        }\n",
-       "\n",
-       "        /* Show the tooltip text when you mouse over the tooltip container */\n",
-       "        .djtooltip:hover .djtooltiptext {\n",
-       "            visibility: visible;\n",
-       "        }\n",
-       "    </style>\n",
-       "    \n",
-       "    \n",
-       "        <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
-       "        <table border=\"1\" class=\"Table\">\n",
-       "            <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">merge_id</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">artifact_param_name</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">recording_id</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">artifact_id</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">sorting_id</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">curation_id</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">artifact_params</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">analysis_file_name</p>\n",
-       "                            <span class=\"djtooltiptext\">name of the file</span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">object_id</p>\n",
-       "                            <span class=\"djtooltiptext\">Object ID for the processed recording in NWB file</span>\n",
-       "                        </div> </th> </tr> </thead>\n",
-       "            <tbody> <tr> <td>485a4ddf-332d-35b5-3ad4-0561736c1844</td>\n",
-       "<td>none</td>\n",
-       "<td>f15351b3-d8e9-49a1-a306-4f366f1ee535</td>\n",
-       "<td>a5470cef-fc31-4723-9e1b-a024e73ef3b1</td>\n",
-       "<td>08a302b6-5505-40fa-b4d5-62162f8eef58</td>\n",
-       "<td>0</td>\n",
-       "<td>=BLOB=</td>\n",
-       "<td>mediumnwb20230802_JVBY43AQFF.nwb</td>\n",
-       "<td>f639dd9a-fc4f-483e-a9c5-f487ecd11292</td></tr><tr><td>485a4ddf-332d-35b5-3ad4-0561736c1844</td>\n",
-       "<td>none</td>\n",
-       "<td>f1427e00-2974-4301-b2ac-b4dc29277c51</td>\n",
-       "<td>0e848c38-9105-4ea4-b6ba-dbdd5b46a088</td>\n",
-       "<td>08a302b6-5505-40fa-b4d5-62162f8eef58</td>\n",
-       "<td>0</td>\n",
-       "<td>=BLOB=</td>\n",
-       "<td>mediumnwb20230802_3BWJOEGAO1.nwb</td>\n",
-       "<td>8366dcfd-bd90-470f-b08c-183bd44dcd1f</td></tr><tr><td>485a4ddf-332d-35b5-3ad4-0561736c1844</td>\n",
-       "<td>none</td>\n",
-       "<td>f07bc0b0-de6b-4424-8ef9-766213aaca26</td>\n",
-       "<td>5c68f0f0-f577-4905-8a09-e4d171d0a22d</td>\n",
-       "<td>08a302b6-5505-40fa-b4d5-62162f8eef58</td>\n",
-       "<td>0</td>\n",
-       "<td>=BLOB=</td>\n",
-       "<td>mediumnwb20230802_GF6Z0P5KYV.nwb</td>\n",
-       "<td>80c14292-a179-4c0f-9ff9-79016038ce95</td></tr><tr><td>485a4ddf-332d-35b5-3ad4-0561736c1844</td>\n",
-       "<td>none</td>\n",
-       "<td>e59e77e9-dd41-4f58-a75b-17271b78c0d8</td>\n",
-       "<td>8cc7a814-8e4a-4b87-ae71-80ca1f5771b1</td>\n",
-       "<td>08a302b6-5505-40fa-b4d5-62162f8eef58</td>\n",
-       "<td>0</td>\n",
-       "<td>=BLOB=</td>\n",
-       "<td>mediumnwb20230802_8SB3X7Y9YA.nwb</td>\n",
-       "<td>c880110b-1dbf-4cc1-99ff-3766ade81fed</td></tr><tr><td>485a4ddf-332d-35b5-3ad4-0561736c1844</td>\n",
-       "<td>none</td>\n",
-       "<td>d14ae25a-2796-4203-ba0b-f28768536cbe</td>\n",
-       "<td>3a34ab35-eca6-406b-abab-c866a9f41fb9</td>\n",
-       "<td>08a302b6-5505-40fa-b4d5-62162f8eef58</td>\n",
-       "<td>0</td>\n",
-       "<td>=BLOB=</td>\n",
-       "<td>mediumnwb20230802_IG73IGEYPA.nwb</td>\n",
-       "<td>a3406f7e-1137-4c34-8715-e0323d074153</td></tr><tr><td>485a4ddf-332d-35b5-3ad4-0561736c1844</td>\n",
-       "<td>none</td>\n",
-       "<td>b81f1ed9-fb2e-4f5c-93d8-d9cc0f118875</td>\n",
-       "<td>69f7d164-efc8-4621-b83c-77bc7459dbc2</td>\n",
-       "<td>08a302b6-5505-40fa-b4d5-62162f8eef58</td>\n",
-       "<td>0</td>\n",
-       "<td>=BLOB=</td>\n",
-       "<td>mediumnwb20230802_QBQBH0I2OO.nwb</td>\n",
-       "<td>101d753a-72fb-47a4-8734-962f8ef4f477</td></tr><tr><td>485a4ddf-332d-35b5-3ad4-0561736c1844</td>\n",
-       "<td>none</td>\n",
-       "<td>aff78f2f-2ba0-412a-95cc-447c3a2f4683</td>\n",
-       "<td>719e8a86-fcf1-4ffc-8c1f-ea912f67ad5d</td>\n",
-       "<td>08a302b6-5505-40fa-b4d5-62162f8eef58</td>\n",
-       "<td>0</td>\n",
-       "<td>=BLOB=</td>\n",
-       "<td>mediumnwb20230802_42H3PR863Y.nwb</td>\n",
-       "<td>6c5f3cd0-903a-42b5-ad68-261789bfd1a8</td></tr><tr><td>485a4ddf-332d-35b5-3ad4-0561736c1844</td>\n",
-       "<td>none</td>\n",
-       "<td>a9b7cec0-1256-49cf-abf0-8c45fd155379</td>\n",
-       "<td>74270cba-36ee-4afb-ab50-2a6cc948e68c</td>\n",
-       "<td>08a302b6-5505-40fa-b4d5-62162f8eef58</td>\n",
-       "<td>0</td>\n",
-       "<td>=BLOB=</td>\n",
-       "<td>mediumnwb20230802_5GUFKQ59T6.nwb</td>\n",
-       "<td>498d52a8-47d1-41c2-bd45-2ac796fce4cd</td></tr><tr><td>485a4ddf-332d-35b5-3ad4-0561736c1844</td>\n",
-       "<td>none</td>\n",
-       "<td>a3f5b9e7-7c79-4eb4-b5ed-910191c615c2</td>\n",
-       "<td>f9cc9158-bea2-4de2-811d-1fb895c048a9</td>\n",
-       "<td>08a302b6-5505-40fa-b4d5-62162f8eef58</td>\n",
-       "<td>0</td>\n",
-       "<td>=BLOB=</td>\n",
-       "<td>mediumnwb20230802_OQZ8C41NHS.nwb</td>\n",
-       "<td>d49e9245-149c-4725-bd0f-d69b78e0a33a</td></tr><tr><td>485a4ddf-332d-35b5-3ad4-0561736c1844</td>\n",
-       "<td>none</td>\n",
-       "<td>9e332d82-1daf-4e92-bb50-12e4f9430875</td>\n",
-       "<td>9ed11db5-c42e-491a-8caf-7d9a37a65f13</td>\n",
-       "<td>08a302b6-5505-40fa-b4d5-62162f8eef58</td>\n",
-       "<td>0</td>\n",
-       "<td>=BLOB=</td>\n",
-       "<td>mediumnwb20230802_30PFKCH9HH.nwb</td>\n",
-       "<td>a45a4c86-d748-48f8-b406-4da381a74737</td></tr><tr><td>485a4ddf-332d-35b5-3ad4-0561736c1844</td>\n",
-       "<td>none</td>\n",
-       "<td>7f128981-6868-4976-ba20-248655dcac21</td>\n",
-       "<td>f4b9301f-bc91-455b-9474-c801093f3856</td>\n",
-       "<td>08a302b6-5505-40fa-b4d5-62162f8eef58</td>\n",
-       "<td>0</td>\n",
-       "<td>=BLOB=</td>\n",
-       "<td>mediumnwb20230802_F9B938GB2N.nwb</td>\n",
-       "<td>b06a3ec5-17a2-49a3-bd7f-475cb07081a0</td></tr><tr><td>485a4ddf-332d-35b5-3ad4-0561736c1844</td>\n",
-       "<td>none</td>\n",
-       "<td>7cc37209-7c28-443d-8f65-d8b01ca49a9a</td>\n",
-       "<td>f8650c19-8964-44ea-bd87-0245f1d2f934</td>\n",
-       "<td>08a302b6-5505-40fa-b4d5-62162f8eef58</td>\n",
-       "<td>0</td>\n",
-       "<td>=BLOB=</td>\n",
-       "<td>mediumnwb20230802_XAQMO7WRC5.nwb</td>\n",
-       "<td>38522acb-ba02-4fe1-b715-3c7485cca749</td> </tr> </tbody>\n",
-       "        </table>\n",
-       "        <p>...</p>\n",
-       "        <p>Total: 552</p></div>\n",
-       "        "
-      ],
-      "text/plain": [
-       "*merge_id      *artifact_para *recording_id  *artifact_id   sorting_id     curation_id    artifact_p analysis_file_ object_id     \n",
-       "+------------+ +------------+ +------------+ +------------+ +------------+ +------------+ +--------+ +------------+ +------------+\n",
-       "485a4ddf-332d- none           f15351b3-d8e9- a5470cef-fc31- 08a302b6-5505- 0              =BLOB=     mediumnwb20230 f639dd9a-fc4f-\n",
-       "485a4ddf-332d- none           f1427e00-2974- 0e848c38-9105- 08a302b6-5505- 0              =BLOB=     mediumnwb20230 8366dcfd-bd90-\n",
-       "485a4ddf-332d- none           f07bc0b0-de6b- 5c68f0f0-f577- 08a302b6-5505- 0              =BLOB=     mediumnwb20230 80c14292-a179-\n",
-       "485a4ddf-332d- none           e59e77e9-dd41- 8cc7a814-8e4a- 08a302b6-5505- 0              =BLOB=     mediumnwb20230 c880110b-1dbf-\n",
-       "485a4ddf-332d- none           d14ae25a-2796- 3a34ab35-eca6- 08a302b6-5505- 0              =BLOB=     mediumnwb20230 a3406f7e-1137-\n",
-       "485a4ddf-332d- none           b81f1ed9-fb2e- 69f7d164-efc8- 08a302b6-5505- 0              =BLOB=     mediumnwb20230 101d753a-72fb-\n",
-       "485a4ddf-332d- none           aff78f2f-2ba0- 719e8a86-fcf1- 08a302b6-5505- 0              =BLOB=     mediumnwb20230 6c5f3cd0-903a-\n",
-       "485a4ddf-332d- none           a9b7cec0-1256- 74270cba-36ee- 08a302b6-5505- 0              =BLOB=     mediumnwb20230 498d52a8-47d1-\n",
-       "485a4ddf-332d- none           a3f5b9e7-7c79- f9cc9158-bea2- 08a302b6-5505- 0              =BLOB=     mediumnwb20230 d49e9245-149c-\n",
-       "485a4ddf-332d- none           9e332d82-1daf- 9ed11db5-c42e- 08a302b6-5505- 0              =BLOB=     mediumnwb20230 a45a4c86-d748-\n",
-       "485a4ddf-332d- none           7f128981-6868- f4b9301f-bc91- 08a302b6-5505- 0              =BLOB=     mediumnwb20230 b06a3ec5-17a2-\n",
-       "485a4ddf-332d- none           7cc37209-7c28- f8650c19-8964- 08a302b6-5505- 0              =BLOB=     mediumnwb20230 38522acb-ba02-\n",
-       "   ...\n",
-       " (Total: 552)"
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "SpikeSortingOutput.CurationV1() * (\n",
-    "    sgs.ArtifactDetectionParameters\n",
-    "    * sgs.SpikeSortingRecording\n",
-    "    * sgs.ArtifactDetectionSelection\n",
+    "(MuaEventsV1 & mua_key).create_figurl(\n",
+    "    zscore_mua=True,\n",
     ")"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "(\n",
-    "    IntervalList()\n",
-    "    & {\n",
-    "        \"nwb_file_name\": nwb_copy_file_name,\n",
-    "        \"pipeline\": \"spikesorting_artifact_v1\",\n",
-    "    }\n",
-    ").proj(artifact_id=\"interval_list_name\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "    \n",
-       "    <style type=\"text/css\">\n",
-       "        .Table{\n",
-       "            border-collapse:collapse;\n",
-       "        }\n",
-       "        .Table th{\n",
-       "            background: #A0A0A0; color: #ffffff; padding:4px; border:#f0e0e0 1px solid;\n",
-       "            font-weight: normal; font-family: monospace; font-size: 100%;\n",
-       "        }\n",
-       "        .Table td{\n",
-       "            padding:4px; border:#f0e0e0 1px solid; font-size:100%;\n",
-       "        }\n",
-       "        .Table tr:nth-child(odd){\n",
-       "            background: #ffffff;\n",
-       "            color: #000000;\n",
-       "        }\n",
-       "        .Table tr:nth-child(even){\n",
-       "            background: #f3f1ff;\n",
-       "            color: #000000;\n",
-       "        }\n",
-       "        /* Tooltip container */\n",
-       "        .djtooltip {\n",
-       "        }\n",
-       "        /* Tooltip text */\n",
-       "        .djtooltip .djtooltiptext {\n",
-       "            visibility: hidden;\n",
-       "            width: 120px;\n",
-       "            background-color: black;\n",
-       "            color: #fff;\n",
-       "            text-align: center;\n",
-       "            padding: 5px 0;\n",
-       "            border-radius: 6px;\n",
-       "            /* Position the tooltip text - see examples below! */\n",
-       "            position: absolute;\n",
-       "            z-index: 1;\n",
-       "        }\n",
-       "        #primary {\n",
-       "            font-weight: bold;\n",
-       "            color: black;\n",
-       "        }\n",
-       "        #nonprimary {\n",
-       "            font-weight: normal;\n",
-       "            color: white;\n",
-       "        }\n",
-       "\n",
-       "        /* Show the tooltip text when you mouse over the tooltip container */\n",
-       "        .djtooltip:hover .djtooltiptext {\n",
-       "            visibility: visible;\n",
-       "        }\n",
-       "    </style>\n",
-       "    \n",
-       "    \n",
-       "        <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
-       "        <table border=\"1\" class=\"Table\">\n",
-       "            <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">recording_id</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">artifact_id</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">analysis_file_name</p>\n",
-       "                            <span class=\"djtooltiptext\">name of the file</span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">object_id</p>\n",
-       "                            <span class=\"djtooltiptext\">Object ID for the processed recording in NWB file</span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">artifact_param_name</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div> </th> </tr> </thead>\n",
-       "            <tbody> <tr> <td>04f3ecb4-a18c-4ffb-85d8-2f5f62d4d6d4</td>\n",
-       "<td>7a93bd27-5108-4290-89a2-2f8770664b35</td>\n",
-       "<td>mediumnwb20230802_F7HG9E9J3I.nwb</td>\n",
-       "<td>16bded49-3fc2-4e37-9ac6-385eec091740</td>\n",
-       "<td>none</td></tr><tr><td>1d2b5966-415a-4c65-955a-0e422d8b5b00</td>\n",
-       "<td>e4921df8-99b5-4f52-b3c2-988b9433e0cf</td>\n",
-       "<td>mediumnwb20230802_C0I26JVUNV.nwb</td>\n",
-       "<td>a07a4fcc-ac84-4d07-a036-b32c7e2ceb28</td>\n",
-       "<td>none</td></tr><tr><td>1e3f3707-613e-4a44-93f1-c7e5484112cd</td>\n",
-       "<td>15c8a3e8-5ce9-4654-891e-6ee4109d6f1a</td>\n",
-       "<td>mediumnwb20230802_H70AAE9UEI.nwb</td>\n",
-       "<td>34550b0b-36c7-4dfb-b1ba-900fc9247da8</td>\n",
-       "<td>none</td></tr><tr><td>2402805a-04f9-4a88-9ccf-071376c8de19</td>\n",
-       "<td>d581b117-160e-4311-b096-7781a4de4394</td>\n",
-       "<td>mediumnwb20230802_5MEOIHZ5E5.nwb</td>\n",
-       "<td>bf877f6e-5daf-4d70-ab23-fc2b7c132370</td>\n",
-       "<td>none</td></tr><tr><td>24107d8c-ce26-4c77-8f6a-bf6955d8a3c7</td>\n",
-       "<td>d1925dc7-e3b0-47ef-adbc-1af129b67048</td>\n",
-       "<td>mediumnwb20230802_MKQ2TG5YZC.nwb</td>\n",
-       "<td>7f55e942-7563-466a-ba56-b82ea5089be4</td>\n",
-       "<td>none</td></tr><tr><td>257c077b-8f3b-4abb-a631-6b8084d6a1ea</td>\n",
-       "<td>e289e03d-32ad-461a-a1cc-c88537343149</td>\n",
-       "<td>mediumnwb20230802_C2DEV7V2C6.nwb</td>\n",
-       "<td>c19ca39e-50a4-4e87-a8e7-22ccf5979f27</td>\n",
-       "<td>none</td></tr><tr><td>328da21c-1d9c-41e2-9800-76b3484b707b</td>\n",
-       "<td>686d9951-1c0f-4d5e-9f5c-09e6fd8bdd4c</td>\n",
-       "<td>mediumnwb20230802_YSZXPEGP4X.nwb</td>\n",
-       "<td>623a46f8-be44-434b-8aee-49155070f3f7</td>\n",
-       "<td>none</td></tr><tr><td>3a2c3eed-413a-452a-83c8-0e4648141bde</td>\n",
-       "<td>2b9fbf14-74a0-4294-a805-26702340aac9</td>\n",
-       "<td>mediumnwb20230802_G0STLTBOSC.nwb</td>\n",
-       "<td>ec5170e0-277a-4626-9120-b24574eae650</td>\n",
-       "<td>none</td></tr><tr><td>3c40ebdc-0b61-4105-9971-e1348bd49bc7</td>\n",
-       "<td>0f91197e-bebb-4dc6-ad41-5bf89c3eed28</td>\n",
-       "<td>mediumnwb20230802_85VG1GLTSR.nwb</td>\n",
-       "<td>2d6e841c-4f98-42b7-9cbf-097fa8ba0766</td>\n",
-       "<td>none</td></tr><tr><td>449b64e3-db0b-437e-a1b9-0d29928aa2dd</td>\n",
-       "<td>45f6b9a1-eef3-46eb-866d-d0999afebda6</td>\n",
-       "<td>mediumnwb20230802_R19M5A499A.nwb</td>\n",
-       "<td>68db8ac5-d008-4720-a3f8-162b0671313d</td>\n",
-       "<td>none</td></tr><tr><td>72f70c5a-bc0f-46cb-b22f-33bafe2315df</td>\n",
-       "<td>2b93bcd0-7b05-457c-8aab-c41ef543ecf2</td>\n",
-       "<td>mediumnwb20230802_OFQSUAGB74.nwb</td>\n",
-       "<td>3a1b3df6-fa85-4006-ab9b-4bff45680516</td>\n",
-       "<td>none</td></tr><tr><td>76ee4ab3-da3e-4a68-9159-635fbf5f8a04</td>\n",
-       "<td>3fecf0af-6ce7-474c-b933-9feb746993a0</td>\n",
-       "<td>mediumnwb20230802_LFO7YW7IIA.nwb</td>\n",
-       "<td>cfe309be-3748-4dc8-9c71-81bd5c1067b7</td>\n",
-       "<td>none</td> </tr> </tbody>\n",
-       "        </table>\n",
-       "        <p>...</p>\n",
-       "        <p>Total: 24</p></div>\n",
-       "        "
-      ],
-      "text/plain": [
-       "*recording_id  *artifact_id   analysis_file_ object_id      artifact_param\n",
-       "+------------+ +------------+ +------------+ +------------+ +------------+\n",
-       "04f3ecb4-a18c- 7a93bd27-5108- mediumnwb20230 16bded49-3fc2- none          \n",
-       "1d2b5966-415a- e4921df8-99b5- mediumnwb20230 a07a4fcc-ac84- none          \n",
-       "1e3f3707-613e- 15c8a3e8-5ce9- mediumnwb20230 34550b0b-36c7- none          \n",
-       "2402805a-04f9- d581b117-160e- mediumnwb20230 bf877f6e-5daf- none          \n",
-       "24107d8c-ce26- d1925dc7-e3b0- mediumnwb20230 7f55e942-7563- none          \n",
-       "257c077b-8f3b- e289e03d-32ad- mediumnwb20230 c19ca39e-50a4- none          \n",
-       "328da21c-1d9c- 686d9951-1c0f- mediumnwb20230 623a46f8-be44- none          \n",
-       "3a2c3eed-413a- 2b9fbf14-74a0- mediumnwb20230 ec5170e0-277a- none          \n",
-       "3c40ebdc-0b61- 0f91197e-bebb- mediumnwb20230 2d6e841c-4f98- none          \n",
-       "449b64e3-db0b- 45f6b9a1-eef3- mediumnwb20230 68db8ac5-d008- none          \n",
-       "72f70c5a-bc0f- 2b93bcd0-7b05- mediumnwb20230 3a1b3df6-fa85- none          \n",
-       "76ee4ab3-da3e- 3fecf0af-6ce7- mediumnwb20230 cfe309be-3748- none          \n",
-       "   ...\n",
-       " (Total: 24)"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sgs.SpikeSortingRecording() * sgs.ArtifactDetectionSelection()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "    \n",
-       "    <style type=\"text/css\">\n",
-       "        .Table{\n",
-       "            border-collapse:collapse;\n",
-       "        }\n",
-       "        .Table th{\n",
-       "            background: #A0A0A0; color: #ffffff; padding:4px; border:#f0e0e0 1px solid;\n",
-       "            font-weight: normal; font-family: monospace; font-size: 100%;\n",
-       "        }\n",
-       "        .Table td{\n",
-       "            padding:4px; border:#f0e0e0 1px solid; font-size:100%;\n",
-       "        }\n",
-       "        .Table tr:nth-child(odd){\n",
-       "            background: #ffffff;\n",
-       "            color: #000000;\n",
-       "        }\n",
-       "        .Table tr:nth-child(even){\n",
-       "            background: #f3f1ff;\n",
-       "            color: #000000;\n",
-       "        }\n",
-       "        /* Tooltip container */\n",
-       "        .djtooltip {\n",
-       "        }\n",
-       "        /* Tooltip text */\n",
-       "        .djtooltip .djtooltiptext {\n",
-       "            visibility: hidden;\n",
-       "            width: 120px;\n",
-       "            background-color: black;\n",
-       "            color: #fff;\n",
-       "            text-align: center;\n",
-       "            padding: 5px 0;\n",
-       "            border-radius: 6px;\n",
-       "            /* Position the tooltip text - see examples below! */\n",
-       "            position: absolute;\n",
-       "            z-index: 1;\n",
-       "        }\n",
-       "        #primary {\n",
-       "            font-weight: bold;\n",
-       "            color: black;\n",
-       "        }\n",
-       "        #nonprimary {\n",
-       "            font-weight: normal;\n",
-       "            color: white;\n",
-       "        }\n",
-       "\n",
-       "        /* Show the tooltip text when you mouse over the tooltip container */\n",
-       "        .djtooltip:hover .djtooltiptext {\n",
-       "            visibility: visible;\n",
-       "        }\n",
-       "    </style>\n",
-       "    \n",
-       "    \n",
-       "        <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
-       "        <table border=\"1\" class=\"Table\">\n",
-       "            <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">merge_id</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">recording_id</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">sorting_id</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">curation_id</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">analysis_file_name</p>\n",
-       "                            <span class=\"djtooltiptext\">name of the file</span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">object_id</p>\n",
-       "                            <span class=\"djtooltiptext\">Object ID for the processed recording in NWB file</span>\n",
-       "                        </div> </th> </tr> </thead>\n",
-       "            <tbody> <tr> <td>485a4ddf-332d-35b5-3ad4-0561736c1844</td>\n",
-       "<td>f15351b3-d8e9-49a1-a306-4f366f1ee535</td>\n",
-       "<td>08a302b6-5505-40fa-b4d5-62162f8eef58</td>\n",
-       "<td>0</td>\n",
-       "<td>mediumnwb20230802_JVBY43AQFF.nwb</td>\n",
-       "<td>f639dd9a-fc4f-483e-a9c5-f487ecd11292</td></tr><tr><td>485a4ddf-332d-35b5-3ad4-0561736c1844</td>\n",
-       "<td>f1427e00-2974-4301-b2ac-b4dc29277c51</td>\n",
-       "<td>08a302b6-5505-40fa-b4d5-62162f8eef58</td>\n",
-       "<td>0</td>\n",
-       "<td>mediumnwb20230802_3BWJOEGAO1.nwb</td>\n",
-       "<td>8366dcfd-bd90-470f-b08c-183bd44dcd1f</td></tr><tr><td>485a4ddf-332d-35b5-3ad4-0561736c1844</td>\n",
-       "<td>f07bc0b0-de6b-4424-8ef9-766213aaca26</td>\n",
-       "<td>08a302b6-5505-40fa-b4d5-62162f8eef58</td>\n",
-       "<td>0</td>\n",
-       "<td>mediumnwb20230802_GF6Z0P5KYV.nwb</td>\n",
-       "<td>80c14292-a179-4c0f-9ff9-79016038ce95</td></tr><tr><td>485a4ddf-332d-35b5-3ad4-0561736c1844</td>\n",
-       "<td>e59e77e9-dd41-4f58-a75b-17271b78c0d8</td>\n",
-       "<td>08a302b6-5505-40fa-b4d5-62162f8eef58</td>\n",
-       "<td>0</td>\n",
-       "<td>mediumnwb20230802_8SB3X7Y9YA.nwb</td>\n",
-       "<td>c880110b-1dbf-4cc1-99ff-3766ade81fed</td></tr><tr><td>485a4ddf-332d-35b5-3ad4-0561736c1844</td>\n",
-       "<td>d14ae25a-2796-4203-ba0b-f28768536cbe</td>\n",
-       "<td>08a302b6-5505-40fa-b4d5-62162f8eef58</td>\n",
-       "<td>0</td>\n",
-       "<td>mediumnwb20230802_IG73IGEYPA.nwb</td>\n",
-       "<td>a3406f7e-1137-4c34-8715-e0323d074153</td></tr><tr><td>485a4ddf-332d-35b5-3ad4-0561736c1844</td>\n",
-       "<td>b81f1ed9-fb2e-4f5c-93d8-d9cc0f118875</td>\n",
-       "<td>08a302b6-5505-40fa-b4d5-62162f8eef58</td>\n",
-       "<td>0</td>\n",
-       "<td>mediumnwb20230802_QBQBH0I2OO.nwb</td>\n",
-       "<td>101d753a-72fb-47a4-8734-962f8ef4f477</td></tr><tr><td>485a4ddf-332d-35b5-3ad4-0561736c1844</td>\n",
-       "<td>aff78f2f-2ba0-412a-95cc-447c3a2f4683</td>\n",
-       "<td>08a302b6-5505-40fa-b4d5-62162f8eef58</td>\n",
-       "<td>0</td>\n",
-       "<td>mediumnwb20230802_42H3PR863Y.nwb</td>\n",
-       "<td>6c5f3cd0-903a-42b5-ad68-261789bfd1a8</td></tr><tr><td>485a4ddf-332d-35b5-3ad4-0561736c1844</td>\n",
-       "<td>a9b7cec0-1256-49cf-abf0-8c45fd155379</td>\n",
-       "<td>08a302b6-5505-40fa-b4d5-62162f8eef58</td>\n",
-       "<td>0</td>\n",
-       "<td>mediumnwb20230802_5GUFKQ59T6.nwb</td>\n",
-       "<td>498d52a8-47d1-41c2-bd45-2ac796fce4cd</td></tr><tr><td>485a4ddf-332d-35b5-3ad4-0561736c1844</td>\n",
-       "<td>a3f5b9e7-7c79-4eb4-b5ed-910191c615c2</td>\n",
-       "<td>08a302b6-5505-40fa-b4d5-62162f8eef58</td>\n",
-       "<td>0</td>\n",
-       "<td>mediumnwb20230802_OQZ8C41NHS.nwb</td>\n",
-       "<td>d49e9245-149c-4725-bd0f-d69b78e0a33a</td></tr><tr><td>485a4ddf-332d-35b5-3ad4-0561736c1844</td>\n",
-       "<td>9e332d82-1daf-4e92-bb50-12e4f9430875</td>\n",
-       "<td>08a302b6-5505-40fa-b4d5-62162f8eef58</td>\n",
-       "<td>0</td>\n",
-       "<td>mediumnwb20230802_30PFKCH9HH.nwb</td>\n",
-       "<td>a45a4c86-d748-48f8-b406-4da381a74737</td></tr><tr><td>485a4ddf-332d-35b5-3ad4-0561736c1844</td>\n",
-       "<td>7f128981-6868-4976-ba20-248655dcac21</td>\n",
-       "<td>08a302b6-5505-40fa-b4d5-62162f8eef58</td>\n",
-       "<td>0</td>\n",
-       "<td>mediumnwb20230802_F9B938GB2N.nwb</td>\n",
-       "<td>b06a3ec5-17a2-49a3-bd7f-475cb07081a0</td></tr><tr><td>485a4ddf-332d-35b5-3ad4-0561736c1844</td>\n",
-       "<td>7cc37209-7c28-443d-8f65-d8b01ca49a9a</td>\n",
-       "<td>08a302b6-5505-40fa-b4d5-62162f8eef58</td>\n",
-       "<td>0</td>\n",
-       "<td>mediumnwb20230802_XAQMO7WRC5.nwb</td>\n",
-       "<td>38522acb-ba02-4fe1-b715-3c7485cca749</td> </tr> </tbody>\n",
-       "        </table>\n",
-       "        <p>...</p>\n",
-       "        <p>Total: 552</p></div>\n",
-       "        "
-      ],
-      "text/plain": [
-       "*merge_id      *recording_id  sorting_id     curation_id    analysis_file_ object_id     \n",
-       "+------------+ +------------+ +------------+ +------------+ +------------+ +------------+\n",
-       "485a4ddf-332d- f15351b3-d8e9- 08a302b6-5505- 0              mediumnwb20230 f639dd9a-fc4f-\n",
-       "485a4ddf-332d- f1427e00-2974- 08a302b6-5505- 0              mediumnwb20230 8366dcfd-bd90-\n",
-       "485a4ddf-332d- f07bc0b0-de6b- 08a302b6-5505- 0              mediumnwb20230 80c14292-a179-\n",
-       "485a4ddf-332d- e59e77e9-dd41- 08a302b6-5505- 0              mediumnwb20230 c880110b-1dbf-\n",
-       "485a4ddf-332d- d14ae25a-2796- 08a302b6-5505- 0              mediumnwb20230 a3406f7e-1137-\n",
-       "485a4ddf-332d- b81f1ed9-fb2e- 08a302b6-5505- 0              mediumnwb20230 101d753a-72fb-\n",
-       "485a4ddf-332d- aff78f2f-2ba0- 08a302b6-5505- 0              mediumnwb20230 6c5f3cd0-903a-\n",
-       "485a4ddf-332d- a9b7cec0-1256- 08a302b6-5505- 0              mediumnwb20230 498d52a8-47d1-\n",
-       "485a4ddf-332d- a3f5b9e7-7c79- 08a302b6-5505- 0              mediumnwb20230 d49e9245-149c-\n",
-       "485a4ddf-332d- 9e332d82-1daf- 08a302b6-5505- 0              mediumnwb20230 a45a4c86-d748-\n",
-       "485a4ddf-332d- 7f128981-6868- 08a302b6-5505- 0              mediumnwb20230 b06a3ec5-17a2-\n",
-       "485a4ddf-332d- 7cc37209-7c28- 08a302b6-5505- 0              mediumnwb20230 38522acb-ba02-\n",
-       "   ...\n",
-       " (Total: 552)"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "SpikeSortingOutput.CurationV1() * sgs.SpikeSortingRecording()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "DuplicateError",
-     "evalue": "(\"Duplicate entry 'mediumnwb20230802_.nwb-test_artifact_times' for key 'interval_list.PRIMARY'\", 'To ignore duplicate entries in insert, set skip_duplicates=True')",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mDuplicateError\u001b[0m                            Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[22], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mIntervalList\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minsert1\u001b[49m\u001b[43m(\u001b[49m\u001b[43m{\u001b[49m\n\u001b[1;32m      2\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mnwb_file_name\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mnwb_copy_file_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m      3\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43minterval_list_name\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mtest_artifact_times\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m      4\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mvalid_times\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m      5\u001b[0m \u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/miniconda3/envs/spyglass/lib/python3.9/site-packages/datajoint/table.py:337\u001b[0m, in \u001b[0;36mTable.insert1\u001b[0;34m(self, row, **kwargs)\u001b[0m\n\u001b[1;32m    330\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21minsert1\u001b[39m(\u001b[38;5;28mself\u001b[39m, row, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m    331\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    332\u001b[0m \u001b[38;5;124;03m    Insert one data record into the table. For ``kwargs``, see ``insert()``.\u001b[39;00m\n\u001b[1;32m    333\u001b[0m \n\u001b[1;32m    334\u001b[0m \u001b[38;5;124;03m    :param row: a numpy record, a dict-like object, or an ordered sequence to be inserted\u001b[39;00m\n\u001b[1;32m    335\u001b[0m \u001b[38;5;124;03m        as one row.\u001b[39;00m\n\u001b[1;32m    336\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 337\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minsert\u001b[49m\u001b[43m(\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrow\u001b[49m\u001b[43m,\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/miniconda3/envs/spyglass/lib/python3.9/site-packages/datajoint/table.py:453\u001b[0m, in \u001b[0;36mTable.insert\u001b[0;34m(self, rows, replace, skip_duplicates, ignore_extra_fields, allow_direct_insert)\u001b[0m\n\u001b[1;32m    449\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m err\u001b[38;5;241m.\u001b[39msuggest(\n\u001b[1;32m    450\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTo ignore extra fields in insert, set ignore_extra_fields=True\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    451\u001b[0m     )\n\u001b[1;32m    452\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m DuplicateError \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[0;32m--> 453\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m err\u001b[38;5;241m.\u001b[39msuggest(\n\u001b[1;32m    454\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTo ignore duplicate entries in insert, set skip_duplicates=True\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    455\u001b[0m     )\n",
-      "\u001b[0;31mDuplicateError\u001b[0m: (\"Duplicate entry 'mediumnwb20230802_.nwb-test_artifact_times' for key 'interval_list.PRIMARY'\", 'To ignore duplicate entries in insert, set skip_duplicates=True')"
-     ]
-    }
-   ],
-   "source": [
-    "IntervalList.insert1({\n",
-    "    \"nwb_file_name\": nwb_copy_file_name,\n",
-    "    \"interval_list_name\": \"test_artifact_times\",\n",
-    "    \"valid_times\": [],\n",
-    "})"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
diff --git a/notebooks/py_scripts/43_Decoding_SortedSpikes.py b/notebooks/py_scripts/43_Decoding_SortedSpikes.py
index 8f51cd4a7..d1449352c 100644
--- a/notebooks/py_scripts/43_Decoding_SortedSpikes.py
+++ b/notebooks/py_scripts/43_Decoding_SortedSpikes.py
@@ -108,7 +108,7 @@
 # -
 
 # look at the sorting within the group we just made
-SortedSpikesGroup.SortGroup & {
+SortedSpikesGroup.Units & {
     "nwb_file_name": nwb_copy_file_name,
     "sorted_spikes_group_name": "test_group",
     "unit_filter_params_name": unit_filter_params_name,
diff --git a/notebooks/py_scripts/51_MUA_Detection.py b/notebooks/py_scripts/51_MUA_Detection.py
index 506217553..bc319ff82 100644
--- a/notebooks/py_scripts/51_MUA_Detection.py
+++ b/notebooks/py_scripts/51_MUA_Detection.py
@@ -13,158 +13,61 @@
 # ---
 
 # +
-from pathlib import Path
 import datajoint as dj
+from pathlib import Path
 
 dj.config.load(
     Path("../dj_local_conf.json").absolute()
 )  # load config for database connection info
+
+from spyglass.mua.v1.mua import MuaEventsV1, MuaEventsParameters
+
 # -
 
-# # MUA Analysis and Detection
-#
-# NOTE: This notebook is a work in progress. It is not yet complete and may contain errors.
+MuaEventsParameters()
+
+MuaEventsV1()
 
 # +
-from spyglass.spikesorting.spikesorting_merge import SpikeSortingOutput
-import spyglass.spikesorting.v1 as sgs
-
+from spyglass.position import PositionOutput
 
 nwb_copy_file_name = "mediumnwb20230802_.nwb"
 
-sorter_keys = {
+trodes_s_key = {
     "nwb_file_name": nwb_copy_file_name,
-    "sorter": "clusterless_thresholder",
-    "sorter_param_name": "default_clusterless",
+    "interval_list_name": "pos 0 valid times",
+    "trodes_pos_params_name": "single_led_upsampled",
 }
 
-(sgs.SpikeSortingSelection & sorter_keys) * SpikeSortingOutput.CurationV1
-
-# +
-spikesorting_merge_ids = (
-    (sgs.SpikeSortingSelection & sorter_keys) * SpikeSortingOutput.CurationV1
-).fetch("merge_id")
-
-spikesorting_merge_ids
-
-# +
-from spyglass.spikesorting.unit_inclusion_merge import (
-    ImportedUnitInclusionV1,
-    UnitInclusionOutput,
-)
-
-ImportedUnitInclusionV1().insert_all_units(spikesorting_merge_ids)
-
-UnitInclusionOutput.ImportedUnitInclusionV1() & [
-    {"spikesorting_merge_id": id} for id in spikesorting_merge_ids
-]
-
-# +
-from spyglass.spikesorting.unit_inclusion_merge import (
-    ImportedUnitInclusionV1,
-    UnitInclusionOutput,
-)
-
-ImportedUnitInclusionV1().insert_all_units(spikesorting_merge_ids)
-
-UnitInclusionOutput.ImportedUnitInclusionV1() & [
-    {"spikesorting_merge_id": id} for id in spikesorting_merge_ids
-]
+pos_merge_id = (PositionOutput.TrodesPosV1 & trodes_s_key).fetch1("merge_id")
+pos_merge_id
 
 # +
-from spyglass.spikesorting.unit_inclusion_merge import SortedSpikesGroup
-
-unit_inclusion_merge_ids = (
-    UnitInclusionOutput.ImportedUnitInclusionV1
-    & [{"spikesorting_merge_id": id} for id in spikesorting_merge_ids]
-).fetch("merge_id")
-
-SortedSpikesGroup().create_group(
-    group_name="test_group",
-    nwb_file_name=nwb_copy_file_name,
-    unit_inclusion_merge_ids=unit_inclusion_merge_ids,
+from spyglass.spikesorting.analysis.v1.group import (
+    SortedSpikesGroup,
 )
 
-group_key = {
+sorted_spikes_group_key = {
     "nwb_file_name": nwb_copy_file_name,
     "sorted_spikes_group_name": "test_group",
+    "unit_filter_params_name": "default_exclusion",
 }
 
-SortedSpikesGroup & group_key
-# -
-
-SortedSpikesGroup.Units() & group_key
-
-# An example of how to get spike times
-
-spike_times = SortedSpikesGroup.fetch_spike_data(group_key)
-spike_times[0]
+SortedSpikesGroup & sorted_spikes_group_key
 
 # +
-from spyglass.position import PositionOutput
-
-position_merge_id = (
-    PositionOutput.TrodesPosV1
-    & {
-        "nwb_file_name": nwb_copy_file_name,
-        "interval_list_name": "pos 0 valid times",
-        "trodes_pos_params_name": "default_decoding",
-    }
-).fetch1("merge_id")
-
-position_info = (
-    (PositionOutput & {"merge_id": position_merge_id})
-    .fetch1_dataframe()
-    .dropna()
-)
-position_info
-
-# +
-time_ind_slice = slice(63_000, 70_000)
-time = position_info.index[time_ind_slice]
-
-SortedSpikesGroup.get_spike_indicator(group_key, time)
-
-# +
-import matplotlib.pyplot as plt
-
-fig, axes = plt.subplots(2, 1, sharex=True, figsize=(15, 4))
-multiunit_firing_rate = SortedSpikesGroup.get_firing_rate(
-    group_key, time, multiunit=True
-)
-axes[0].plot(
-    time,
-    multiunit_firing_rate,
-)
-axes[0].set_ylabel("firing rate (Hz)")
-axes[0].set_title("multiunit")
-axes[1].fill_between(
-    time, position_info["speed"].iloc[time_ind_slice], color="lightgrey"
-)
-axes[1].set_ylabel("speed (cm/s)")
-axes[1].set_xlabel("time (s)")
-
-# +
-from spyglass.mua.v1.mua import MuaEventsParameters, MuaEventsV1
-
-MuaEventsParameters().insert_default()
-MuaEventsParameters()
-
-# +
-selection_key = {
+mua_key = {
     "mua_param_name": "default",
-    "nwb_file_name": nwb_copy_file_name,
-    "sorted_spikes_group_name": "test_group",
-    "pos_merge_id": position_merge_id,
-    "artifact_interval_list_name": "test_artifact_times",
+    **sorted_spikes_group_key,
+    "pos_merge_id": pos_merge_id,
+    "detection_interval": "pos 0 valid times",
 }
 
-MuaEventsV1.populate(selection_key)
+MuaEventsV1().populate(mua_key)
+MuaEventsV1 & mua_key
 # -
 
-MuaEventsV1 & selection_key
-
-mua_times = (MuaEventsV1 & selection_key).fetch1_dataframe()
+mua_times = (MuaEventsV1 & mua_key).fetch1_dataframe()
 mua_times
 
 # +
@@ -172,70 +75,37 @@
 import numpy as np
 
 fig, axes = plt.subplots(2, 1, sharex=True, figsize=(15, 4))
+speed = MuaEventsV1.get_speed(mua_key).to_numpy()
+time = speed.index.to_numpy()
+multiunit_firing_rate = MuaEventsV1.get_firing_rate(mua_key, time)
+
+time_slice = slice(
+    np.searchsorted(time, mua_times.loc[10].start_time) - 1_000,
+    np.searchsorted(time, mua_times.loc[10].start_time) + 5_000,
+)
+
 axes[0].plot(
-    time,
-    multiunit_firing_rate,
+    time[time_slice],
+    multiunit_firing_rate[time_slice],
+    color="black",
 )
 axes[0].set_ylabel("firing rate (Hz)")
 axes[0].set_title("multiunit")
-axes[1].fill_between(
-    time, position_info["speed"].iloc[time_ind_slice], color="lightgrey"
-)
+axes[1].fill_between(time[time_slice], speed[time_slice], color="lightgrey")
 axes[1].set_ylabel("speed (cm/s)")
 axes[1].set_xlabel("time (s)")
 
-in_bounds = np.logical_and(
-    mua_times.start_time >= time[0], mua_times.end_time <= time[-1]
-)
-
-for mua_time in mua_times.loc[in_bounds].itertuples():
-    axes[0].axvspan(
-        mua_time.start_time, mua_time.end_time, color="red", alpha=0.3
+for id, mua_time in mua_times.loc[
+    np.logical_and(
+        mua_times["start_time"] > time[time_slice].min(),
+        mua_times["end_time"] < time[time_slice].max(),
     )
-    axes[1].axvspan(
-        mua_time.start_time, mua_time.end_time, color="red", alpha=0.3
+].iterrows():
+    axes[0].axvspan(
+        mua_time["start_time"], mua_time["end_time"], color="red", alpha=0.5
     )
-axes[1].set_ylim((0, 80))
-axes[1].axhline(4, color="black", linestyle="--")
-axes[1].set_xlim((time[0], time[-1]))
-
-# +
-from spyglass.common import IntervalList
-
-IntervalList() & {
-    "nwb_file_name": nwb_copy_file_name,
-    "pipeline": "spikesorting_artifact_v1",
-}
 # -
 
-(
-    sgs.ArtifactDetectionParameters
-    * sgs.SpikeSortingRecording
-    * sgs.ArtifactDetectionSelection
-)
-
-SpikeSortingOutput.CurationV1() * (
-    sgs.ArtifactDetectionParameters
-    * sgs.SpikeSortingRecording
-    * sgs.ArtifactDetectionSelection
-)
-
-(
-    IntervalList()
-    & {
-        "nwb_file_name": nwb_copy_file_name,
-        "pipeline": "spikesorting_artifact_v1",
-    }
-).proj(artifact_id="interval_list_name")
-
-sgs.SpikeSortingRecording() * sgs.ArtifactDetectionSelection()
-
-SpikeSortingOutput.CurationV1() * sgs.SpikeSortingRecording()
-
-IntervalList.insert1(
-    {
-        "nwb_file_name": nwb_copy_file_name,
-        "interval_list_name": "test_artifact_times",
-        "valid_times": [],
-    }
+(MuaEventsV1 & mua_key).create_figurl(
+    zscore_mua=True,
 )
diff --git a/src/spyglass/mua/__init__.py b/src/spyglass/mua/__init__.py
index e69de29bb..948eabee4 100644
--- a/src/spyglass/mua/__init__.py
+++ b/src/spyglass/mua/__init__.py
@@ -0,0 +1 @@
+from spyglass.mua.v1.mua import MuaEventsParameters, MuaEventsV1  # noqa: F401
diff --git a/src/spyglass/mua/v1/__init__.py b/src/spyglass/mua/v1/__init__.py
index e69de29bb..948eabee4 100644
--- a/src/spyglass/mua/v1/__init__.py
+++ b/src/spyglass/mua/v1/__init__.py
@@ -0,0 +1 @@
+from spyglass.mua.v1.mua import MuaEventsParameters, MuaEventsV1  # noqa: F401
diff --git a/src/spyglass/mua/v1/mua.py b/src/spyglass/mua/v1/mua.py
index 86c93de2e..c31320015 100644
--- a/src/spyglass/mua/v1/mua.py
+++ b/src/spyglass/mua/v1/mua.py
@@ -1,6 +1,8 @@
 import datajoint as dj
 import numpy as np
+import sortingview.views as vv
 from ripple_detection import multiunit_HSE_detector
+from scipy.stats import zscore
 
 from spyglass.common.common_interval import IntervalList
 from spyglass.common.common_nwbfile import AnalysisNwbfile
@@ -46,22 +48,16 @@ class MuaEventsV1(SpyglassMixin, dj.Computed):
     -> MuaEventsParameters
     -> SortedSpikesGroup
     -> PositionOutput.proj(pos_merge_id='merge_id')
-    -> IntervalList.proj(artifact_interval_list_name='interval_list_name') # exclude artifact times
+    -> IntervalList.proj(detection_interval='interval_list_name')
     ---
     -> AnalysisNwbfile
     mua_times_object_id : varchar(40)
     """
 
     def make(self, key):
-        # TODO: exclude artifact times
-        position_info = (
-            PositionOutput & {"merge_id": key["pos_merge_id"]}
-        ).fetch1_dataframe()
-        speed_name = (
-            "speed" if "speed" in position_info.columns else "head_speed"
-        )
-        speed = position_info[speed_name].to_numpy()
-        time = position_info.index.to_numpy()
+        speed = self.get_speed(key)
+        time = speed.index.to_numpy()
+        speed = speed.to_numpy()
 
         spike_indicator = SortedSpikesGroup.get_spike_indicator(key, time)
         spike_indicator = spike_indicator.sum(axis=1, keepdims=True)
@@ -70,21 +66,20 @@ def make(self, key):
 
         mua_params = (MuaEventsParameters & key).fetch1("mua_param_dict")
 
-        # Exclude artifact times
-        # Alternatively could set to NaN and leave them out of the firing rate calculation
-        # in the multiunit_HSE_detector function
-        artifact_key = {
-            "nwb_file_name": key["nwb_file_name"],
-            "interval_list_name": key["artifact_interval_list_name"],
-        }
-        artifact_times = (IntervalList & artifact_key).fetch1("valid_times")
-        mean_n_spikes = np.mean(spike_indicator)
-        for artifact_time in artifact_times:
-            spike_indicator[
-                np.logical_and(
-                    time >= artifact_time.start, time <= artifact_time.stop
-                )
-            ] = mean_n_spikes
+        valid_times = (
+            IntervalList
+            & {
+                "nwb_file_name": key["nwb_file_name"],
+                "interval_list_name": key["detection_interval"],
+            }
+        ).fetch1("valid_times")
+        mask = np.zeros_like(time, dtype=bool)
+        for start, end in valid_times:
+            mask = mask | ((time >= start) & (time <= end))
+
+        time = time[mask]
+        speed = speed[mask]
+        spike_indicator = spike_indicator[mask]
 
         mua_times = multiunit_HSE_detector(
             time, spike_indicator, speed, sampling_frequency, **mua_params
@@ -110,3 +105,99 @@ def fetch1_dataframe(self):
 
     def fetch_dataframe(self):
         return [data["mua_times"] for data in self.fetch_nwb()]
+
+    @classmethod
+    def get_firing_rate(cls, key, time):
+        return SortedSpikesGroup.get_firing_rate(key, time, multiunit=True)
+
+    @staticmethod
+    def get_speed(key):
+        position_info = (
+            PositionOutput & {"merge_id": key["pos_merge_id"]}
+        ).fetch1_dataframe()
+        speed_name = (
+            "speed" if "speed" in position_info.columns else "head_speed"
+        )
+        return position_info[speed_name]
+
+    def create_figurl(
+        self,
+        zscore_mua=True,
+        mua_times_color="red",
+        speed_color="black",
+        mua_color="black",
+        view_height=800,
+    ):
+        key = self.fetch1("KEY")
+        speed = self.get_speed(key)
+        time = speed.index.to_numpy()
+        multiunit_firing_rate = self.get_firing_rate(key, time)
+        if zscore_mua:
+            multiunit_firing_rate = zscore(multiunit_firing_rate)
+
+        mua_times = self.fetch1_dataframe()
+
+        multiunit_firing_rate_view = vv.TimeseriesGraph()
+        multiunit_firing_rate_view.add_interval_series(
+            name="MUA Events",
+            t_start=mua_times.start_time.to_numpy(),
+            t_end=mua_times.end_time.to_numpy(),
+            color=mua_times_color,
+        )
+        name = "Z-Scored Multiunit Rate" if zscore_mua else "Multiunit Rate"
+        multiunit_firing_rate_view.add_line_series(
+            name=name,
+            t=np.asarray(time),
+            y=np.asarray(multiunit_firing_rate, dtype=np.float32),
+            color=mua_color,
+            width=1,
+        )
+        if zscore_mua:
+            mua_params = (MuaEventsParameters & key).fetch1("mua_param_dict")
+            zscore_threshold = mua_params.get("zscore_threshold")
+            multiunit_firing_rate_view.add_line_series(
+                name="Z-Score Threshold",
+                t=np.asarray(time).squeeze(),
+                y=np.ones_like(
+                    multiunit_firing_rate, dtype=np.float32
+                ).squeeze()
+                * zscore_threshold,
+                color=mua_times_color,
+                width=1,
+            )
+        speed_view = vv.TimeseriesGraph().add_line_series(
+            name="Speed [cm/s]",
+            t=np.asarray(time),
+            y=np.asarray(speed, dtype=np.float32),
+            color=speed_color,
+            width=1,
+        )
+        speed_view.add_interval_series(
+            name="MUA Events",
+            t_start=mua_times.start_time.to_numpy(),
+            t_end=mua_times.end_time.to_numpy(),
+            color=mua_times_color,
+        )
+        vertical_panel_content = [
+            vv.LayoutItem(
+                multiunit_firing_rate_view, stretch=2, title="Multiunit"
+            ),
+            vv.LayoutItem(speed_view, stretch=2, title="Speed"),
+        ]
+
+        view = vv.Box(
+            direction="horizontal",
+            show_titles=True,
+            height=view_height,
+            items=[
+                vv.LayoutItem(
+                    vv.Box(
+                        direction="vertical",
+                        show_titles=True,
+                        items=vertical_panel_content,
+                    )
+                ),
+            ],
+        )
+
+        return view.url(label="Multiunit Detection")
diff --git a/src/spyglass/ripple/v1/ripple.py b/src/spyglass/ripple/v1/ripple.py
index 38618f301..336e07799 100644
--- a/src/spyglass/ripple/v1/ripple.py
+++ b/src/spyglass/ripple/v1/ripple.py
@@ -2,8 +2,10 @@
 import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
+import sortingview.views as vv
 from ripple_detection import Karlsson_ripple_detector, Kay_ripple_detector
 from ripple_detection.core import gaussian_smooth, get_envelope
+from scipy.stats import zscore
 
 from spyglass.common.common_interval import (
     IntervalList,
@@ -11,6 +13,7 @@
 )
 from spyglass.common.common_nwbfile import AnalysisNwbfile
 from spyglass.lfp.analysis.v1.lfp_band import LFPBandSelection, LFPBandV1
+from spyglass.lfp.lfp_merge import LFPOutput
 from spyglass.position import PositionOutput
 from spyglass.utils import SpyglassMixin, logger
 from spyglass.utils.nwb_helper_fn import get_electrode_indices
@@ -155,9 +158,7 @@ class RippleTimesV1(SpyglassMixin, dj.Computed):
      """
 
     def make(self, key):
-        nwb_file_name, interval_list_name = (LFPBandV1 & key).fetch1(
-            "nwb_file_name", "target_interval_list_name"
-        )
+        nwb_file_name = (LFPBandV1 & key).fetch1("nwb_file_name")
 
         logger.info(f"Computing ripple times for: {key}")
         ripple_params = (
@@ -171,9 +172,7 @@ def make(self, key):
             speed,
             interval_ripple_lfps,
             sampling_frequency,
-        ) = self.get_ripple_lfps_and_position_info(
-            key, nwb_file_name, interval_list_name
-        )
+        ) = self.get_ripple_lfps_and_position_info(key)
         ripple_times = RIPPLE_DETECTION_ALGORITHMS[ripple_detection_algorithm](
             time=np.asarray(interval_ripple_lfps.index),
             filtered_lfps=np.asarray(interval_ripple_lfps),
@@ -203,9 +202,7 @@ def fetch_dataframe(self):
         return [data["ripple_times"] for data in self.fetch_nwb()]
 
     @staticmethod
-    def get_ripple_lfps_and_position_info(
-        key, nwb_file_name, interval_list_name
-    ):
+    def get_ripple_lfps_and_position_info(key):
         ripple_params = (
             RippleParameters & {"ripple_param_name": key["ripple_param_name"]}
         ).fetch1("ripple_param_dict")
@@ -219,7 +216,7 @@ def get_ripple_lfps_and_position_info(
         # warn/validate that there is only one wire per electrode
         ripple_lfp_nwb = (LFPBandV1 & key).fetch_nwb()[0]
         ripple_lfp_electrodes = ripple_lfp_nwb["lfp_band"].electrodes.data[:]
-        elec_mask = np.full_like(ripple_lfp_electrodes, 0, dtype=bool)
+        elec_mask = np.zeros_like(ripple_lfp_electrodes, dtype=bool)
         valid_elecs = [
             elec for elec in electrode_keys if elec in ripple_lfp_electrodes
         ]
@@ -230,16 +227,14 @@ def get_ripple_lfps_and_position_info(
         ripple_lfp = pd.DataFrame(
             ripple_lfp_nwb["lfp_band"].data,
             index=pd.Index(ripple_lfp_nwb["lfp_band"].timestamps, name="time"),
-        )
+        ).loc[:, elec_mask]
         sampling_frequency = ripple_lfp_nwb["lfp_band_sampling_rate"]
 
-        ripple_lfp = ripple_lfp.loc[:, elec_mask]
-
         position_valid_times = (
             IntervalList
             & {
-                "nwb_file_name": nwb_file_name,
-                "interval_list_name": interval_list_name,
+                "nwb_file_name": key["nwb_file_name"],
+                "interval_list_name": key["target_interval_list_name"],
             }
         ).fetch1("valid_times")
         position_info = (
@@ -366,3 +361,130 @@ def plot_ripple(
         )
         ax.set_ylabel("LFPs")
         ax.set_xlabel("Time [s]")
+
+    def create_figurl(
+        self,
+        zscore_ripple=False,
+        ripple_times_color="red",
+        consensus_color="black",
+        speed_color="black",
+        view_height=800,
+        use_ripple_filtered_lfps=False,
+        lfp_offset=1,
+        lfp_channel_ind=None,
+    ):
+
+        ripple_times = self.fetch1_dataframe()
+
+        def _add_ripple_times(
+            view,
+            ripple_times=ripple_times,
+            ripple_times_color=ripple_times_color,
+        ):
+            return view.add_interval_series(
+                name="Ripple Events",
+                t_start=ripple_times.start_time.to_numpy(),
+                t_end=ripple_times.end_time.to_numpy(),
+                color=ripple_times_color,
+            )
+
+        key = self.fetch1("KEY")
+        (
+            speed,
+            ripple_filtered_lfps,
+            sampling_frequency,
+        ) = self.get_ripple_lfps_and_position_info(key)
+        ripple_consensus_trace = self.get_Kay_ripple_consensus_trace(
+            ripple_filtered_lfps, sampling_frequency
+        )
+
+        if zscore_ripple:
+            ripple_consensus_trace = zscore(ripple_consensus_trace)
+
+        consensus_view = _add_ripple_times(vv.TimeseriesGraph())
+        consensus_name = (
+            "Z-Scored Consensus Trace" if zscore_ripple else "Consensus Trace"
+        )
+        consensus_view.add_line_series(
+            name=consensus_name,
+            t=np.asarray(ripple_consensus_trace.index).squeeze(),
+            y=np.asarray(ripple_consensus_trace, dtype=np.float32).squeeze(),
+            color=consensus_color,
+            width=1,
+        )
+        if zscore_ripple:
+            ripple_params = (
+                RippleParameters
+                & {"ripple_param_name": key["ripple_param_name"]}
+            ).fetch1("ripple_param_dict")
+
+            zscore_threshold = ripple_params["ripple_detection_params"].get(
+                "zscore_threshold"
+            )
+            if zscore_threshold is not None:
+                consensus_view.add_line_series(
+                    name="Z-Score Threshold",
+                    t=np.asarray(ripple_consensus_trace.index).squeeze(),
+                    y=np.ones_like(
+                        ripple_consensus_trace, dtype=np.float32
+                    ).squeeze()
+                    * zscore_threshold,
+                    color=ripple_times_color,
+                    width=1,
+                )
+
+        if use_ripple_filtered_lfps:
+            interval_ripple_lfps = ripple_filtered_lfps
+        else:
+            lfp_merge_id = (LFPBandSelection & key).fetch1("lfp_merge_id")
+            lfp_df = (LFPOutput & {"merge_id": lfp_merge_id}).fetch1_dataframe()
+            interval_ripple_lfps = lfp_df.loc[speed.index[0] : speed.index[-1]]
+        if lfp_channel_ind is not None:
+            if lfp_channel_ind.max() >= interval_ripple_lfps.shape[1]:
+                raise ValueError(
+                    "lfp_channel_ind is out of range for the number of LFPs"
+                )
+            interval_ripple_lfps = interval_ripple_lfps.iloc[:, lfp_channel_ind]
+
+        lfp_view = _add_ripple_times(vv.TimeseriesGraph())
+        max_lfp_value = interval_ripple_lfps.to_numpy().max()
+        lfp_offset *= max_lfp_value
+
+        for i, lfp in enumerate(interval_ripple_lfps.to_numpy().T):
+            lfp_view.add_line_series(
+                name=f"LFP {i}",
+                t=np.asarray(interval_ripple_lfps.index).squeeze(),
+                y=np.asarray(lfp + lfp_offset * i, dtype=np.int16).squeeze(),
+                color="black",
+                width=1,
+            )
+        speed_view = _add_ripple_times(vv.TimeseriesGraph())
+        speed_view.add_line_series(
+            name="Speed [cm/s]",
+            t=np.asarray(speed.index).squeeze(),
+            y=np.asarray(speed, dtype=np.float32).squeeze(),
+            color=speed_color,
+            width=1,
+        )
+        vertical_panel_content = [
+            vv.LayoutItem(consensus_view, stretch=2, title="Consensus"),
+            vv.LayoutItem(lfp_view, stretch=8, title="LFPs"),
+            vv.LayoutItem(speed_view, stretch=2, title="Speed"),
+        ]
+
+        view = vv.Box(
+            direction="horizontal",
+            show_titles=True,
+            height=view_height,
+            items=[
+                vv.LayoutItem(
+                    vv.Box(
+                        direction="vertical",
+                        show_titles=True,
+                        items=vertical_panel_content,
+                    )
+                ),
+            ],
+        )
+
+        return view.url(label="Ripple Detection")
diff --git a/src/spyglass/spikesorting/analysis/v1/group.py b/src/spyglass/spikesorting/analysis/v1/group.py
index 0f52fdd0e..573697f1a 100644
--- a/src/spyglass/spikesorting/analysis/v1/group.py
+++ b/src/spyglass/spikesorting/analysis/v1/group.py
@@ -50,7 +50,7 @@ class SortedSpikesGroup(SpyglassMixin, dj.Manual):
     sorted_spikes_group_name: varchar(80)
     """
 
-    class SortGroup(SpyglassMixin, dj.Part):
+    class Units(SpyglassMixin, dj.Part):
         definition = """
         -> master
         -> SpikeSortingOutput.proj(spikesorting_merge_id='merge_id')
@@ -74,7 +74,7 @@ def create_group(
             group_key,
             skip_duplicates=True,
         )
-        self.SortGroup.insert(parts_insert)
+        self.Units.insert(parts_insert, skip_duplicates=True)
 
     @staticmethod
     def filter_units(
@@ -88,16 +88,19 @@ def filter_units(
         include_labels = np.unique(include_labels)
         exclude_labels = np.unique(exclude_labels)
 
+        if include_labels.size == 0 and exclude_labels.size == 0:
+            # if no labels are provided, include all units
+            return np.ones(len(labels), dtype=bool)
+
         include_mask = np.zeros(len(labels), dtype=bool)
         for ind, unit_labels in enumerate(labels):
             if isinstance(unit_labels, str):
                 unit_labels = [unit_labels]
-            if (
-                include_labels
-                and not np.isin(include_labels, unit_labels).any()
+            if np.all(~np.isin(unit_labels, include_labels)) or np.any(
+                np.isin(unit_labels, exclude_labels)
             ):
-                continue
-            if np.isin(exclude_labels, unit_labels).any():
+                # if the unit does not have any of the include labels
+                # or has any of the exclude labels, skip
                 continue
             include_mask[ind] = True
         return include_mask
@@ -107,7 +110,7 @@ def fetch_spike_data(key, time_slice=None):
         # get merge_ids for SpikeSortingOutput
         merge_ids = (
             (
-                SortedSpikesGroup.SortGroup
+                SortedSpikesGroup.Units
                 & {
                     "nwb_file_name": key["nwb_file_name"],
                     "sorted_spikes_group_name": key["sorted_spikes_group_name"],