{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DeudRk4Y1sJJ"
   },
   "source": [
    "# Database Example  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-17T19:52:35.434872Z",
     "start_time": "2023-07-17T19:52:33.950791Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 350
    },
    "execution": {
     "iopub.execute_input": "2024-07-10T21:22:25.849177Z",
     "iopub.status.busy": "2024-07-10T21:22:25.848235Z",
     "iopub.status.idle": "2024-07-10T21:22:29.729455Z",
     "shell.execute_reply": "2024-07-10T21:22:29.728253Z",
     "shell.execute_reply.started": "2024-07-10T21:22:25.849115Z"
    },
    "executionInfo": {
     "elapsed": 420,
     "status": "error",
     "timestamp": 1615494076728,
     "user": {
      "displayName": "Leo Scholl",
      "photoUrl": "",
      "userId": "08377480612982390884"
     },
     "user_tz": 480
    },
    "id": "8CzeKK8Hg-bI",
    "outputId": "c5c454f6-5cf0-4f77-c822-1602cd207ed8",
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Import libraries\n",
    "import aopy\n",
    "from aopy.data import db\n",
    "from ipywidgets import interactive, widgets\n",
    "from datetime import date, datetime, timedelta\n",
    "import re\n",
    "from tqdm.notebook import tqdm\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-17T19:52:35.505436Z",
     "start_time": "2023-07-17T19:52:35.500179Z"
    },
    "execution": {
     "iopub.execute_input": "2024-07-10T21:22:30.893900Z",
     "iopub.status.busy": "2024-07-10T21:22:30.892630Z",
     "iopub.status.idle": "2024-07-10T21:22:30.902121Z",
     "shell.execute_reply": "2024-07-10T21:22:30.900195Z",
     "shell.execute_reply.started": "2024-07-10T21:22:30.893834Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# set up constants\n",
    "preproc_dir = '/data/preprocessed'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Finding a single recording"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-17T19:52:37.746216Z",
     "start_time": "2023-07-17T19:52:37.683471Z"
    },
    "execution": {
     "iopub.execute_input": "2024-07-10T21:17:51.454392Z",
     "iopub.status.busy": "2024-07-10T21:17:51.452733Z",
     "iopub.status.idle": "2024-07-10T21:17:51.551193Z",
     "shell.execute_reply": "2024-07-10T21:17:51.549087Z",
     "shell.execute_reply.started": "2024-07-10T21:17:51.454320Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-06-11 09:55:10.450000: beignet on manual control task, id=5624\n",
      "baseline flash\n"
     ]
    }
   ],
   "source": [
    "subject = 'beignet'\n",
    "task = 'manual control'\n",
    "project = 'baseline flash'\n",
    "day = date(2022, 6, 11) # June 11, 2022\n",
    "\n",
    "sessions = db.lookup_sessions(subject=subject, task_name=task, date=day, project=project)\n",
    "e = sessions[-1]\n",
    "print(e)\n",
    "print(e.project)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-17T19:52:39.155712Z",
     "start_time": "2023-07-17T19:52:38.916720Z"
    },
    "execution": {
     "iopub.execute_input": "2024-07-10T21:17:52.268969Z",
     "iopub.status.busy": "2024-07-10T21:17:52.267162Z",
     "iopub.status.idle": "2024-07-10T21:17:52.942184Z",
     "shell.execute_reply": "2024-07-10T21:17:52.940941Z",
     "shell.execute_reply.started": "2024-07-10T21:17:52.268886Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "412 sessions found with ecog data\n"
     ]
    }
   ],
   "source": [
    "# Filter on recordings with neural data\n",
    "filter_fn = db.filter_has_neural_data('ecog')\n",
    "sessions = db.lookup_sessions(subject='affi', task_desc='resting data', experimenter='leo', filter_fn=filter_fn)\n",
    "print(f\"{len(sessions)} sessions found with ecog data\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-17T19:52:42.363253Z",
     "start_time": "2023-07-17T19:52:39.855627Z"
    },
    "execution": {
     "iopub.execute_input": "2024-07-10T21:17:53.339014Z",
     "iopub.status.busy": "2024-07-10T21:17:53.337488Z",
     "iopub.status.idle": "2024-07-10T21:18:00.979077Z",
     "shell.execute_reply": "2024-07-10T21:18:00.977717Z",
     "shell.execute_reply.started": "2024-07-10T21:17:53.338940Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "197 sessions found with 250ms rewards\n"
     ]
    }
   ],
   "source": [
    "# Filter specific parameters\n",
    "filter_fn = lambda x: x.get_task_param('reward_time') == 0.25\n",
    "sessions = db.lookup_sessions(subject='affi', filter_fn=filter_fn)\n",
    "print(f\"{len(sessions)} sessions found with 250ms rewards\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-17T19:52:42.831884Z",
     "start_time": "2023-07-17T19:52:42.781519Z"
    },
    "execution": {
     "iopub.execute_input": "2024-07-10T21:18:00.980964Z",
     "iopub.status.busy": "2024-07-10T21:18:00.980734Z",
     "iopub.status.idle": "2024-07-10T21:18:01.020791Z",
     "shell.execute_reply": "2024-07-10T21:18:01.019744Z",
     "shell.execute_reply.started": "2024-07-10T21:18:00.980941Z"
    },
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('beignet', 'beignet', 'beignet', 'beignet') (9609, 9631, 9638, 9667) (datetime.date(2023, 6, 5), datetime.date(2023, 6, 6), datetime.date(2023, 6, 8), datetime.date(2023, 6, 9))\n"
     ]
    }
   ],
   "source": [
    "# Or use a shortcut\n",
    "sessions = db.lookup_mc_sessions(subject=subject, date=('2023-06-05', '2023-06-10'))\n",
    "subjects, ids, dates = db.list_entry_details(sessions)\n",
    "print(subjects, ids, dates)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Finding a range of dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-17T19:52:44.496258Z",
     "start_time": "2023-07-17T19:52:44.470187Z"
    },
    "execution": {
     "iopub.execute_input": "2024-07-10T21:18:02.314040Z",
     "iopub.status.busy": "2024-07-10T21:18:02.312248Z",
     "iopub.status.idle": "2024-07-10T21:18:02.342179Z",
     "shell.execute_reply": "2024-07-10T21:18:02.341070Z",
     "shell.execute_reply.started": "2024-07-10T21:18:02.313961Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8692f73cc35645f5ab31cc31bdecb175",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "DatePicker(value=None, description='Start date', step=1)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e5c60185a8ca47a0b3ebc91b60004671",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "DatePicker(value=None, description='End date', step=1)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "start = widgets.DatePicker(\n",
    "    description='Start date',\n",
    "    disabled=False\n",
    ")\n",
    "end = widgets.DatePicker(\n",
    "    description='End date',\n",
    "    disabled=False\n",
    ")\n",
    "display(start)\n",
    "display(end)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-17T19:53:01.858299Z",
     "start_time": "2023-07-17T19:53:01.745774Z"
    },
    "execution": {
     "iopub.execute_input": "2024-07-10T21:18:34.589073Z",
     "iopub.status.busy": "2024-07-10T21:18:34.587359Z",
     "iopub.status.idle": "2024-07-10T21:18:34.751955Z",
     "shell.execute_reply": "2024-07-10T21:18:34.750813Z",
     "shell.execute_reply.started": "2024-07-10T21:18:34.588996Z"
    },
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "101 matching recordings\n"
     ]
    }
   ],
   "source": [
    "# Might want to do some additional filtering by date\n",
    "subject = 'beignet'\n",
    "task = 'bmi control'\n",
    "project = 'linear map'\n",
    "entries = db.lookup_sessions(subject=subject, task_name=task, date=(start.value, end.value), project=project)\n",
    "print(f\"{len(entries)} matching recordings\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Select from a dropdown menu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-17T19:53:04.033335Z",
     "start_time": "2023-07-17T19:53:03.921534Z"
    },
    "execution": {
     "iopub.execute_input": "2024-07-10T21:18:37.645625Z",
     "iopub.status.busy": "2024-07-10T21:18:37.643616Z",
     "iopub.status.idle": "2024-07-10T21:18:37.813796Z",
     "shell.execute_reply": "2024-07-10T21:18:37.812629Z",
     "shell.execute_reply.started": "2024-07-10T21:18:37.645549Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "80ad680daa424049a31054552bf5c755",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Dropdown(options=(2022-06-06 13:44:42.434000: beignet on bmi control task, id=5512, 2022-06-06 13:45:31.687000…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "entry_drop = widgets.Dropdown(options=entries)\n",
    "display(entry_drop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-17T19:53:06.309237Z",
     "start_time": "2023-07-17T19:53:06.302092Z"
    },
    "execution": {
     "iopub.execute_input": "2024-07-10T21:18:38.755685Z",
     "iopub.status.busy": "2024-07-10T21:18:38.754400Z",
     "iopub.status.idle": "2024-07-10T21:18:38.763185Z",
     "shell.execute_reply": "2024-07-10T21:18:38.761629Z",
     "shell.execute_reply.started": "2024-07-10T21:18:38.755613Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-06-06\n",
      "2022-06-06 13:44:42.434000\n"
     ]
    }
   ],
   "source": [
    "e = entry_drop.value\n",
    "print(e.date)\n",
    "print(e.datetime)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Select anything"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-17T19:53:11.922516Z",
     "start_time": "2023-07-17T19:53:11.316184Z"
    },
    "execution": {
     "iopub.execute_input": "2024-07-10T21:18:42.734010Z",
     "iopub.status.busy": "2024-07-10T21:18:42.732294Z",
     "iopub.status.idle": "2024-07-10T21:18:43.225659Z",
     "shell.execute_reply": "2024-07-10T21:18:43.224876Z",
     "shell.execute_reply.started": "2024-07-10T21:18:42.733936Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "812a1689d5af4f20b922e4e0a344bb8d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(Dropdown(description='subject', options=('affi', 'churro', 'test'), value='affi'), Dropd…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "start_date = widgets.DatePicker(value = datetime.today() - timedelta(days=30))\n",
    "end_date = widgets.DatePicker(value = datetime.today())\n",
    "\n",
    "# Filter by task and subject\n",
    "entries = db.lookup_sessions(date=(start_date.value, end_date.value))\n",
    "subjects = np.unique([e.subject for e in entries])\n",
    "tasks = np.unique([e.task_name for e in entries])\n",
    "\n",
    "subj_drop = widgets.Dropdown(options=subjects)\n",
    "task_drop = widgets.Dropdown(options=tasks) \n",
    "entry_select = widgets.SelectMultiple(\n",
    "    options=[],\n",
    "    description='entries',\n",
    "    disabled=False\n",
    ")\n",
    "entry_select.layout.width = '600px'\n",
    "\n",
    "def update_entries(*args):\n",
    "    entries = db.lookup_sessions(\n",
    "        subject=subj_drop.value, \n",
    "        task_name=task_drop.value,\n",
    "        date=(start_date.value,end_date.value)\n",
    "    )\n",
    "    entry_select.options = [\"{} - {}: {}\".format(e.id, e.task_desc, e.date) for i, e in enumerate(entries)]\n",
    "    \n",
    "start_date.observe(update_entries)\n",
    "end_date.observe(update_entries)\n",
    "subj_drop.observe(update_entries)\n",
    "task_drop.observe(update_entries)\n",
    "\n",
    "def select_entry(subject, task, start_date, end_date, entries):\n",
    "    ids = [int(re.search(r'\\d+', e).group()) for e in entries]\n",
    "    return db.lookup_sessions(id=ids)\n",
    "\n",
    "\n",
    "selection = interactive(select_entry,\n",
    "                     subject=subj_drop,\n",
    "                     task=task_drop,\n",
    "                     start_date=start_date,\n",
    "                     end_date=end_date,\n",
    "                     entries=entry_select)\n",
    "\n",
    "display(selection)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-17T19:53:15.618678Z",
     "start_time": "2023-07-17T19:53:15.610971Z"
    },
    "execution": {
     "iopub.execute_input": "2024-07-10T21:18:51.644850Z",
     "iopub.status.busy": "2024-07-10T21:18:51.643187Z",
     "iopub.status.idle": "2024-07-10T21:18:51.653220Z",
     "shell.execute_reply": "2024-07-10T21:18:51.651720Z",
     "shell.execute_reply.started": "2024-07-10T21:18:51.644777Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 selected\n"
     ]
    }
   ],
   "source": [
    "entries = selection.result\n",
    "print(f\"{len(entries)} selected\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-17T19:53:21.093240Z",
     "start_time": "2023-07-17T19:53:20.942158Z"
    },
    "execution": {
     "iopub.execute_input": "2024-07-10T21:18:53.725353Z",
     "iopub.status.busy": "2024-07-10T21:18:53.723661Z",
     "iopub.status.idle": "2024-07-10T21:18:53.850470Z",
     "shell.execute_reply": "2024-07-10T21:18:53.849215Z",
     "shell.execute_reply.started": "2024-07-10T21:18:53.725279Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "89f3c1330bd544afa79337e8a180ec05",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "entry 17676 has 4256 clock cycles\n"
     ]
    }
   ],
   "source": [
    "for e in tqdm(entries):\n",
    "    \n",
    "    # Load the data\n",
    "    exp_data, exp_metadata = aopy.data.load_preproc_exp_data(preproc_dir, e.subject, e.id, e.date)\n",
    "\n",
    "    print(f\"entry {e.id} has {len(exp_data['clock'])} clock cycles\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-10T21:19:53.480935Z",
     "iopub.status.busy": "2024-07-10T21:19:53.479471Z",
     "iopub.status.idle": "2024-07-10T21:19:53.487561Z",
     "shell.execute_reply": "2024-07-10T21:19:53.485726Z",
     "shell.execute_reply.started": "2024-07-10T21:19:53.480860Z"
    },
    "tags": []
   },
   "source": [
    "## Setting filter function to a defined function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-10T21:22:37.593569Z",
     "iopub.status.busy": "2024-07-10T21:22:37.592179Z",
     "iopub.status.idle": "2024-07-10T21:22:37.601645Z",
     "shell.execute_reply": "2024-07-10T21:22:37.599664Z",
     "shell.execute_reply.started": "2024-07-10T21:22:37.593469Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "db.BMI3D_DBNAME = 'booth' "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-10T21:22:38.534392Z",
     "iopub.status.busy": "2024-07-10T21:22:38.532942Z",
     "iopub.status.idle": "2024-07-10T21:22:38.548916Z",
     "shell.execute_reply": "2024-07-10T21:22:38.547469Z",
     "shell.execute_reply.started": "2024-07-10T21:22:38.534295Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def filter_for_desired_sessions(e):\n",
    "    \n",
    "    value = (\n",
    "        e.has_feature('optitrack') and \n",
    "        'flash' not in e.task_desc and \n",
    "        'centerout_2D' in e.sequence_name and \n",
    "        e.subject != 'test'\n",
    "        )\n",
    "    \n",
    "    try:\n",
    "        exp_data, exp_metadata = aopy.data.load_preproc_exp_data(preproc_dir, e.subject, e.id, e.date)\n",
    "        t = exp_data['events']['timestamp']\n",
    "        \n",
    "        #hand_offset exp_meta\n",
    "        \n",
    "    except:\n",
    "        return False\n",
    "    \n",
    "    return value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-10T21:22:40.149980Z",
     "iopub.status.busy": "2024-07-10T21:22:40.148614Z",
     "iopub.status.idle": "2024-07-10T21:22:41.735594Z",
     "shell.execute_reply": "2024-07-10T21:22:41.734422Z",
     "shell.execute_reply.started": "2024-07-10T21:22:40.149921Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#constants\n",
    "preproc_dir = '/data/preprocessed'\n",
    "\n",
    "affi_dates = ('2021-12-02','2021-12-27')\n",
    "\n",
    "affi_sessions = db.lookup_sessions(task_name='manual control',\n",
    "                              subject = 'affi',\n",
    "                              date = affi_dates,\n",
    "                              filter_fn = filter_for_desired_sessions) #func that finds all manual control sessions and returns as an array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-10T21:22:41.737462Z",
     "iopub.status.busy": "2024-07-10T21:22:41.737222Z",
     "iopub.status.idle": "2024-07-10T21:22:41.761170Z",
     "shell.execute_reply": "2024-07-10T21:22:41.760350Z",
     "shell.execute_reply.started": "2024-07-10T21:22:41.737439Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2021-12-02 14:23:35.041000: affi on manual control task, id=3407,\n",
       " 2021-12-02 14:24:38.642000: affi on manual control task, id=3408,\n",
       " 2021-12-16 11:59:21.893000: affi on manual control task, id=3545,\n",
       " 2021-12-16 12:01:12.051000: affi on manual control task, id=3546,\n",
       " 2021-12-17 12:40:20.725000: affi on manual control task, id=3567,\n",
       " 2021-12-17 12:57:34.536000: affi on manual control task, id=3568,\n",
       " 2021-12-17 13:02:13.886000: affi on manual control task, id=3569,\n",
       " 2021-12-20 13:37:26.169000: affi on manual control task, id=3593,\n",
       " 2021-12-20 13:40:50.615000: affi on manual control task, id=3594,\n",
       " 2021-12-20 13:43:03.629000: affi on manual control task, id=3595,\n",
       " 2021-12-21 13:04:31.829000: affi on manual control task, id=3611,\n",
       " 2021-12-21 13:06:16.866000: affi on manual control task, id=3612,\n",
       " 2021-12-23 13:19:47.602000: affi on manual control task, id=3613]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(affi_sessions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Querying a different rig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-10T21:18:56.954919Z",
     "iopub.status.busy": "2024-07-10T21:18:56.953381Z",
     "iopub.status.idle": "2024-07-10T21:18:57.309333Z",
     "shell.execute_reply": "2024-07-10T21:18:57.307704Z",
     "shell.execute_reply.started": "2024-07-10T21:18:56.954843Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2024-07-08 14:55:05.579804: churro on tracking task task, id=703\n",
      "bumpers\n"
     ]
    }
   ],
   "source": [
    "db.BMI3D_DBNAME = 'tablet' # booth, tablet, human, test\n",
    "subject = 'churro'\n",
    "\n",
    "sessions = db.lookup_sessions(subject=subject)\n",
    "e = sessions[-1]\n",
    "print(e)\n",
    "print(e.project)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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