diff --git a/.ipynb_checkpoints/create_new_monitor-checkpoint.ipynb b/.ipynb_checkpoints/create_new_monitor-checkpoint.ipynb
new file mode 100644
index 0000000..7fec515
--- /dev/null
+++ b/.ipynb_checkpoints/create_new_monitor-checkpoint.ipynb
@@ -0,0 +1,6 @@
+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/.ipynb_checkpoints/dark_monitor_plots-checkpoint.ipynb b/.ipynb_checkpoints/dark_monitor_plots-checkpoint.ipynb
new file mode 100644
index 0000000..cef3ff0
--- /dev/null
+++ b/.ipynb_checkpoints/dark_monitor_plots-checkpoint.ipynb
@@ -0,0 +1,689 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import yaml\n",
+    "import cosmo\n",
+    "import numpy\n",
+    "from itertools import repeat\n",
+    "from glob import glob\n",
+    "from astropy.io import fits\n",
+    "import matplotlib.pyplot as plt\n",
+    "from matplotlib.ticker import FormatStrFormatter\n",
+    "from cosmo.filesystem import find_files, data_from_exposures, data_from_jitters\n",
+    "from cosmo.monitor_helpers import absolute_time, explode_df\n",
+    "from monitorframe.datamodel import BaseDataModel\n",
+    "from monitorframe.monitor import BaseMonitor"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# load settings\n",
+    "with open(os.environ['COSMO_CONFIG']) as yamlfile:\n",
+    "    SETTINGS = yaml.safe_load(yamlfile)\n",
+    "\n",
+    "FILES_SOURCE = SETTINGS['filesystem']['source']\n",
+    "COS_MONITORING = SETTINGS['output']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# working DataModel\n",
+    "class DarkDataModel(BaseDataModel):\n",
+    "    cosmo_layout = False\n",
+    "#     fuv_program_ids = ['15771/', '15533/', '14940/', '14520/', '14436/', '13968/', '13521/', '13121/', '12716/', '12423/',\n",
+    "#                   '11895/']\n",
+    "#     nuv_program_ids = ['15776/', '15538/', '14942/', '14521/', '14442/', '13974/', \n",
+    "#                        '13528/', '13126/', '12720/', '12420/', '11894/']\n",
+    "    fuv_program_ids = [\"15771/\"]\n",
+    "    nuv_program_ids = [\"15776/\"]\n",
+    "    program_id = fuv_program_ids + nuv_program_ids\n",
+    "\n",
+    "    def get_new_data(self):  # this way when you get new data it will get all the data\n",
+    "        header_request = {\n",
+    "            0: ['ROOTNAME', 'SEGMENT'],\n",
+    "            1: ['EXPTIME', 'EXPSTART']\n",
+    "        }\n",
+    "        table_request = {\n",
+    "            1: ['PHA', 'XCORR', 'YCORR', 'TIME'],\n",
+    "            3: ['TIME', 'LATITUDE', 'LONGITUDE']\n",
+    "        }\n",
+    "\n",
+    "        files = []\n",
+    "\n",
+    "        for prog_id in self.program_id:\n",
+    "            new_files_source = os.path.join(FILES_SOURCE, prog_id)\n",
+    "            files += find_files('*corrtag*', data_dir=new_files_source)\n",
+    "\n",
+    "        if self.model is not None:\n",
+    "            currently_ingested = [item.FILENAME for item in self.model.select(self.model.FILENAME)]\n",
+    "\n",
+    "            for file in currently_ingested:\n",
+    "                files.remove(file)\n",
+    "\n",
+    "        if not files:   # No new files\n",
+    "            return pd.DataFrame()\n",
+    "\n",
+    "        data_results = data_from_exposures(\n",
+    "            files,\n",
+    "            header_request=header_request,\n",
+    "            table_request=table_request\n",
+    "        )\n",
+    "\n",
+    "        return data_results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# base DarkMonitor\n",
+    "def dark_filter(df_row, filter_pha, location):\n",
+    "    good_pha = (2, 23)\n",
+    "    # time step stuff\n",
+    "    time_step = 25\n",
+    "    time_bins = df_row['TIME_3'][::time_step]\n",
+    "    lat = df_row['LATITUDE'][::time_step][:-1]\n",
+    "    lon = df_row['LONGITUDE'][::time_step][:-1]\n",
+    "    \n",
+    "    # try commenting these out, since lat and lon don't seem to be used\n",
+    "#     lat = df_row['LATITUDE'][::time_step][:-1]\n",
+    "#     lon = df_row['LONGITUDE'][::time_step][:-1]\n",
+    "    \n",
+    "    # filtering pha\n",
+    "    if filter_pha:\n",
+    "        event_df = df_row[['SEGMENT', 'XCORR', 'YCORR', 'PHA', 'TIME']].to_frame().T\n",
+    "        event_df = explode_df(event_df, ['XCORR', 'YCORR', 'PHA', 'TIME'])\n",
+    "    else:\n",
+    "        event_df = df_row[['SEGMENT', 'XCORR', 'YCORR', 'TIME']].to_frame().T\n",
+    "        event_df = explode_df(event_df, ['XCORR', 'YCORR', 'TIME'])\n",
+    "    \n",
+    "    # creating event dataframe and filtering it by location on the detector\n",
+    "    npix = (location[1] - location[0]) * (location[3] - location[2])\n",
+    "    index = np.where((event_df['XCORR'] > location[0]) &\n",
+    "                     (event_df['XCORR'] < location[1]) &\n",
+    "                     (event_df['YCORR'] > location[2]) &\n",
+    "                     (event_df['YCORR'] < location[3]))\n",
+    "    filtered_row = event_df.iloc[index].reset_index(drop=True)\n",
+    "\n",
+    "    #filtered events only need to be further filtered by PHA if not NUV\n",
+    "    if filter_pha:\n",
+    "        filtered_row = filtered_row[(filtered_row['PHA'] > good_pha[0]) & (filtered_row['PHA'] < good_pha[1])]\n",
+    "\n",
+    "    counts = np.histogram(filtered_row.TIME, bins=time_bins)[0]\n",
+    "\n",
+    "    date = absolute_time(\n",
+    "        expstart=list(repeat(df_row['EXPSTART'], len(time_bins))), time=time_bins.tolist()\n",
+    "    ).to_datetime()[:-1]\n",
+    "\n",
+    "    dark_rate = counts / npix / time_step\n",
+    "\n",
+    "    return pd.DataFrame({'segment': df_row['SEGMENT'], 'darks': [dark_rate], 'date': [date],\n",
+    "                        'ROOTNAME': df_row['ROOTNAME']})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# overwriting the plot function and making subplot of dark vs. time in base monitor\n",
+    "from plotly.subplots import make_subplots\n",
+    "import plotly.graph_objects as go\n",
+    "import plotly.express as px\n",
+    "\n",
+    "class DarkMonitor(BaseMonitor):\n",
+    "    \"\"\"Abstracted FUV Dark Monitor. Not meant to be used directly but rather inherited by specific segment and region\n",
+    "    dark monitors\"\"\"\n",
+    "    labels = ['ROOTNAME']\n",
+    "    output = COS_MONITORING\n",
+    "    docs = \"https://spacetelescope.github.io/cosmo/monitors.html#fuv-dark-rate-monitors\"\n",
+    "    segment = None\n",
+    "    location = None\n",
+    "    data_model = DarkDataModel\n",
+    "    plottype = 'scatter'\n",
+    "    x = 'date'\n",
+    "    y = 'darks'\n",
+    "\n",
+    "    def get_data(self): # -> Any: fix this later, should be fine in the monitor, just not in jupyter notebook\n",
+    "        filtered_rows = []\n",
+    "        for _, row in self.model.new_data.iterrows():\n",
+    "            if row.EXPSTART == 0:\n",
+    "                continue\n",
+    "            if row.SEGMENT == self.segment: \n",
+    "                if row.SEGMENT == \"N/A\":  #NUV\n",
+    "                    filtered_rows.append(dark_filter(row, False, self.location))\n",
+    "                else:   # Any of the FUV situations\n",
+    "                    filtered_rows.append(dark_filter(row, True, self.location))\n",
+    "        filtered_df = pd.concat(filtered_rows).reset_index(drop=True)\n",
+    "\n",
+    "        return explode_df(filtered_df, ['darks', 'date'])\n",
+    "\n",
+    "    def plot(self):\n",
+    "        # make the interactive plots with sub-solar plots\n",
+    "        self.figure = px.scatter(\n",
+    "            self.data,\n",
+    "            x=self.x,\n",
+    "            y=self.y,\n",
+    "            color=self.z,\n",
+    "            color_continuous_scale=px.colors.sequential.Viridis,\n",
+    "            hover_data=self.labels,\n",
+    "        )\n",
+    "        \n",
+    "        self.figure.update_layout(\n",
+    "            coloraxis_colorbar_len=0.8,\n",
+    "            coloraxis_colorbar_yanchor='bottom',\n",
+    "            coloraxis_colorbar_y=0\n",
+    "        )\n",
+    "    \n",
+    "    def store_results(self):\n",
+    "        # TODO: Define results to store\n",
+    "        pass\n",
+    "\n",
+    "    def track(self):\n",
+    "        # TODO: Define something to track\n",
+    "        pass\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class FUVAInnerDarkMonitor(DarkMonitor):\n",
+    "    \"\"\"FUVA dark monitor for inner region\"\"\"\n",
+    "    name = 'FUVA Dark Monitor - Inner'\n",
+    "    segment = 'FUVA'\n",
+    "    location = (1260, 15119, 375, 660)\n",
+    "    \n",
+    "class NUVDarkMonitor(DarkMonitor):\n",
+    "    name = \"NUV Dark Monitor\"\n",
+    "    segment = \"N/A\"\n",
+    "    location = (0, 1024, 0, 1024)\n",
+    "    \n",
+    "fuv_inner_monitor = FUVAInnerDarkMonitor()\n",
+    "fuv_inner_monitor.monitor()\n",
+    "\n",
+    "nuv_monitor = NUVDarkMonitor()\n",
+    "nuv_monitor.monitor()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# turn plot into a subplot and add another empty plot\n",
+    "\n",
+    "class DarkMonitor(BaseMonitor):\n",
+    "    \"\"\"Abstracted FUV Dark Monitor. Not meant to be used directly but rather inherited by specific segment and region\n",
+    "    dark monitors\"\"\"\n",
+    "    labels = ['ROOTNAME']\n",
+    "    output = COS_MONITORING\n",
+    "    docs = \"https://spacetelescope.github.io/cosmo/monitors.html#fuv-dark-rate-monitors\"\n",
+    "    segment = None\n",
+    "    location = None\n",
+    "    data_model = DarkDataModel\n",
+    "    plottype = 'scatter'\n",
+    "    x = 'date'\n",
+    "    y = 'darks'\n",
+    "\n",
+    "    def get_data(self): # -> Any: fix this later, should be fine in the monitor, just not in jupyter notebook\n",
+    "        filtered_rows = []\n",
+    "        for _, row in self.model.new_data.iterrows():\n",
+    "            if row.EXPSTART == 0:\n",
+    "                continue\n",
+    "            if row.SEGMENT == self.segment: \n",
+    "                if row.SEGMENT == \"N/A\":  #NUV\n",
+    "                    filtered_rows.append(dark_filter(row, False, self.location))\n",
+    "                else:   # Any of the FUV situations\n",
+    "                    filtered_rows.append(dark_filter(row, True, self.location))\n",
+    "        filtered_df = pd.concat(filtered_rows).reset_index(drop=True)\n",
+    "\n",
+    "        return explode_df(filtered_df, ['darks', 'date'])\n",
+    "\n",
+    "    \n",
+    "    def plot(self):\n",
+    "        # make the interactive plots with sub-solar plots\n",
+    "        self.figure = make_subplots(rows=2, cols=1, subplot_titles=(self.name, \"Solar Flux\"))\n",
+    "        \n",
+    "        self.figure.add_trace(\n",
+    "            go.Scatter(x=self.data[self.x], \n",
+    "                       y=self.data[self.y],\n",
+    "                       mode=\"markers\",\n",
+    "                       hovertext=self.labels,\n",
+    "                       hoverinfo=\"x+y+text\"), \n",
+    "            row=1, col=1)\n",
+    "    \n",
+    "    def store_results(self):\n",
+    "        # TODO: Define results to store\n",
+    "        pass\n",
+    "\n",
+    "    def track(self):\n",
+    "        # TODO: Define something to track\n",
+    "        pass\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class FUVBInnerDarkMonitor(DarkMonitor):\n",
+    "    \"\"\"FUVB dark monitor for inner region\"\"\"\n",
+    "    name = 'FUVB Dark Monitor - Inner'\n",
+    "    segment = 'FUVB'\n",
+    "    location = (1000, 14990, 405, 740)\n",
+    "    \n",
+    "fuv_inner_monitor = FUVBInnerDarkMonitor()\n",
+    "fuv_inner_monitor.monitor()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from astropy.time import Time\n",
+    "import scipy\n",
+    "from ftplib import FTP\n",
+    "\n",
+    "def grab_solar_files(file_dir):\n",
+    "    \"\"\"Pull solar data files from NOAA website\n",
+    "    Solar data is FTPd from NOAA and written to text files for use in plotting\n",
+    "    and monitoring of COS dark-rates and TDS.\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    file_dir : str\n",
+    "        Directory to write the files to\n",
+    "    \"\"\"\n",
+    "    ftp = FTP('ftp.swpc.noaa.gov')\n",
+    "    ftp.login()\n",
+    "\n",
+    "    ftp.cwd('/pub/indices/old_indices/')\n",
+    "\n",
+    "    for item in sorted(ftp.nlst()):\n",
+    "        if item.endswith('_DSD.txt'):\n",
+    "            year = int(item[:4])\n",
+    "            if year >= 2000:\n",
+    "                destination = os.path.join(file_dir, item)\n",
+    "                if not os.path.exists(destination):\n",
+    "                    ftp.retrbinary('RETR {}'.format(item),\n",
+    "                                   open(destination, 'wb').write)\n",
+    "\n",
+    "                    os.chmod(destination, 0o777)\n",
+    "\n",
+    "\n",
+    "def compile_solar_data(file_dir):\n",
+    "    \"\"\"Pull desired columns from solar data text files\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    file_dir : str\n",
+    "    Returns\n",
+    "    -------\n",
+    "    date : np.ndarray\n",
+    "        mjd of each measurements\n",
+    "    flux : np.ndarray\n",
+    "        solar flux measurements\n",
+    "    \"\"\"\n",
+    "    date = []\n",
+    "    flux = []\n",
+    "    input_list = glob(os.path.join(file_dir, '*DSD.txt'))\n",
+    "    input_list.sort()\n",
+    "\n",
+    "    for item in input_list:\n",
+    "        # clean up Q4 files when year-long file exists\n",
+    "        if ('Q4_' in item) and os.path.exists(item.replace('Q4_', '_')):\n",
+    "            try:\n",
+    "                os.remove(item)\n",
+    "            except PermissionError:\n",
+    "                continue\n",
+    "            continue  # i know this is stupid\n",
+    "\n",
+    "        # astropy.ascii no longer returns an empty table for empty files\n",
+    "        # Throws IndexError, we will go around it if empty.\n",
+    "        try:\n",
+    "            data = ascii.read(item, data_start=1, comment='[#,:]')\n",
+    "        except IndexError:\n",
+    "            continue\n",
+    "\n",
+    "        for line in data:\n",
+    "            line_date = Time('{}-{}-{} 00:00:00'.format(line['col1'],\n",
+    "                                                        line['col2'],\n",
+    "                                                        line['col3']),\n",
+    "                             scale='utc', format='iso').mjd\n",
+    "\n",
+    "            line_flux = line[3]\n",
+    "\n",
+    "            if line_flux > 0:\n",
+    "                date.append(line_date)\n",
+    "                flux.append(line_flux)\n",
+    "    \n",
+    "    solar_date = np.array(date)\n",
+    "    solar_flux = np.array(flux)\n",
+    "    solar_dec = Time(solar_date, format='mjd').decimalyear\n",
+    "    solar_smooth = scipy.convolve(solar_flux, np.ones(81)/81.0, mode=\"same\")\n",
+    "    \n",
+    "\n",
+    "    return solar_flux, solar_dec, solar_smooth\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# add solar flux data to second subplot\n",
+    "\n",
+    "class DarkMonitor(BaseMonitor):\n",
+    "    \"\"\"Abstracted FUV Dark Monitor. Not meant to be used directly but rather inherited by specific segment and region\n",
+    "    dark monitors\"\"\"\n",
+    "    labels = ['ROOTNAME']\n",
+    "    output = COS_MONITORING\n",
+    "    docs = \"https://spacetelescope.github.io/cosmo/monitors.html#fuv-dark-rate-monitors\"\n",
+    "    segment = None\n",
+    "    location = None\n",
+    "    data_model = DarkDataModel\n",
+    "    plottype = 'scatter'\n",
+    "    x = 'date'\n",
+    "    y = 'darks'\n",
+    "\n",
+    "    def get_data(self): # -> Any: fix this later, should be fine in the monitor, just not in jupyter notebook\n",
+    "        filtered_rows = []\n",
+    "        for _, row in self.model.new_data.iterrows():\n",
+    "            if row.EXPSTART == 0:\n",
+    "                continue\n",
+    "            if row.SEGMENT == self.segment: \n",
+    "                if row.SEGMENT == \"N/A\":  #NUV\n",
+    "                    filtered_rows.append(dark_filter(row, False, self.location))\n",
+    "                else:   # Any of the FUV situations\n",
+    "                    filtered_rows.append(dark_filter(row, True, self.location))\n",
+    "        filtered_df = pd.concat(filtered_rows).reset_index(drop=True)\n",
+    "\n",
+    "        return explode_df(filtered_df, ['darks', 'date'])\n",
+    "\n",
+    "    \n",
+    "    def plot(self):\n",
+    "        # make the interactive plots with sub-solar plots\n",
+    "        self.figure = make_subplots(rows=2, cols=1, subplot_titles=(self.name, \"Solar Radio Flux\"))\n",
+    "        \n",
+    "        self.figure.add_trace(\n",
+    "            go.Scatter(x=self.data[self.x], \n",
+    "                       y=self.data[self.y],\n",
+    "                       mode=\"markers\",\n",
+    "                       hovertext=self.labels,\n",
+    "                       hoverinfo=\"x+y+text\", \n",
+    "                       name=\"Dark Rates\"), \n",
+    "            row=1, col=1)\n",
+    "        \n",
+    "        grab_solar_files('/grp/hst/cos2/solar_data')\n",
+    "        solar_flux, solar_dec, solar_smooth = compile_solar_data('/grp/hst/cos2/solar_data')\n",
+    "        \n",
+    "        self.figure.add_trace(\n",
+    "            go.Scatter(x=solar_dec, \n",
+    "                       y=solar_flux,\n",
+    "                       mode=\"lines\",\n",
+    "                       name=\"10.7 cm\"),\n",
+    "            row=2, col=1\n",
+    "        )\n",
+    "        \n",
+    "        self.figure.add_trace(\n",
+    "            go.Scatter(x=solar_dec[:-41], \n",
+    "                       y=solar_smooth[:-41],\n",
+    "                       mode=\"lines\",\n",
+    "                       name=\"10.7 cm Smoothed\"),\n",
+    "            row=2, col=1\n",
+    "        )\n",
+    "                  \n",
+    "        date_min = self.data[self.x].min()\n",
+    "        date_max = self.data[self.x].max()\n",
+    "        print(date_min, date_max)\n",
+    "#         self.figure.update_layout(\n",
+    "#             xaxis2=dict(\n",
+    "#             )\n",
+    "#         )\n",
+    "        \n",
+    "        self.figure['layout']['xaxis1'].update(title=\"Year\")\n",
+    "        self.figure['layout']['yaxis1'].update(title=\"Dark Rate\")\n",
+    "        self.figure['layout']['xaxis2'].update(title=\"Year\", range=[date_min, date_max])\n",
+    "        self.figure['layout']['yaxis2'].update(title=\"Solar Radio Flux\")\n",
+    "\n",
+    "    \n",
+    "    def store_results(self):\n",
+    "        # TODO: Define results to store\n",
+    "        pass\n",
+    "\n",
+    "    def track(self):\n",
+    "        # TODO: Define something to track\n",
+    "        pass\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/cmagness/miniconda3/envs/cosmos/lib/python3.7/site-packages/ipykernel_launcher.py:79: DeprecationWarning:\n",
+      "\n",
+      "scipy.convolve is deprecated and will be removed in SciPy 2.0.0, use numpy.convolve instead\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2019-11-04 04:15:03.769408 2020-04-20 07:43:56.284317\n"
+     ]
+    }
+   ],
+   "source": [
+    "class FUVBInnerDarkMonitor(DarkMonitor):\n",
+    "    \"\"\"FUVB dark monitor for inner region\"\"\"\n",
+    "    name = 'FUVB Dark Monitor - Inner'\n",
+    "    segment = 'FUVB'\n",
+    "    location = (1000, 14990, 405, 740)\n",
+    "    \n",
+    "fuv_inner_monitor = FUVBInnerDarkMonitor()\n",
+    "fuv_inner_monitor.monitor()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# add histogram plotting function\n",
+    "\n",
+    "class DarkMonitor(BaseMonitor):\n",
+    "    \"\"\"Abstracted FUV Dark Monitor. Not meant to be used directly but rather inherited by specific segment and region\n",
+    "    dark monitors\"\"\"\n",
+    "    labels = ['ROOTNAME']\n",
+    "    output = COS_MONITORING\n",
+    "    docs = \"https://spacetelescope.github.io/cosmo/monitors.html#fuv-dark-rate-monitors\"\n",
+    "    segment = None\n",
+    "    location = None\n",
+    "    data_model = DarkDataModel\n",
+    "    plottype = 'scatter'\n",
+    "    x = 'date'\n",
+    "    y = 'darks'\n",
+    "\n",
+    "    def get_data(self): # -> Any: fix this later, should be fine in the monitor, just not in jupyter notebook\n",
+    "        filtered_rows = []\n",
+    "        for _, row in self.model.new_data.iterrows():\n",
+    "            if row.EXPSTART == 0:\n",
+    "                continue\n",
+    "            if row.SEGMENT == self.segment: \n",
+    "                if row.SEGMENT == \"N/A\":  #NUV\n",
+    "                    filtered_rows.append(dark_filter(row, False, self.location))\n",
+    "                else:   # Any of the FUV situations\n",
+    "                    filtered_rows.append(dark_filter(row, True, self.location))\n",
+    "        filtered_df = pd.concat(filtered_rows).reset_index(drop=True)\n",
+    "\n",
+    "        return explode_df(filtered_df, ['darks', 'date'])\n",
+    "\n",
+    "    \n",
+    "    def plot(self):\n",
+    "        # make the interactive plots with sub-solar plots\n",
+    "        self.figure = make_subplots(rows=2, cols=1, subplot_titles=(self.name, \"Solar Radio Flux\"))\n",
+    "        \n",
+    "        self.figure.add_trace(\n",
+    "            go.Scatter(x=self.data[self.x], \n",
+    "                       y=self.data[self.y],\n",
+    "                       mode=\"markers\",\n",
+    "                       hovertext=self.labels,\n",
+    "                       hoverinfo=\"x+y+text\", \n",
+    "                       name=\"Dark Rates\"), \n",
+    "            row=1, col=1)\n",
+    "        \n",
+    "        grab_solar_files('/grp/hst/cos2/solar_data')\n",
+    "        solar_flux, solar_dec, solar_smooth = compile_solar_data('/grp/hst/cos2/solar_data')\n",
+    "        \n",
+    "        self.figure.add_trace(\n",
+    "            go.Scatter(x=solar_dec, \n",
+    "                       y=solar_flux,\n",
+    "                       mode=\"lines\",\n",
+    "                       name=\"10.7 cm\"),\n",
+    "            row=2, col=1\n",
+    "        )\n",
+    "        \n",
+    "        self.figure.add_trace(\n",
+    "            go.Scatter(x=solar_dec[:-41], \n",
+    "                       y=solar_smooth[:-41],\n",
+    "                       mode=\"lines\",\n",
+    "                       name=\"10.7 cm Smoothed\"),\n",
+    "            row=2, col=1\n",
+    "        )\n",
+    "                  \n",
+    "        date_min = self.data[self.x].min()\n",
+    "        date_max = self.data[self.x].max()\n",
+    "        print(date_min, date_max)\n",
+    "        \n",
+    "        self.figure['layout']['xaxis1'].update(title=\"Year\")\n",
+    "        self.figure['layout']['yaxis1'].update(title=\"Dark Rate\")\n",
+    "        self.figure['layout']['xaxis2'].update(title=\"Year\", range=[date_min, date_max])\n",
+    "        self.figure['layout']['yaxis2'].update(title=\"Solar Radio Flux\")\n",
+    "\n",
+    "        \n",
+    "    def plot_histogram(self, nbins=30):\n",
+    "        if self.data is None:\n",
+    "            self.data = self.get_data()\n",
+    "        \n",
+    "        # self.data[self.y] should be all dark rates\n",
+    "        counts, bins = np.histogram(self.data[self.y], bins=nbins)\n",
+    "        cuml_dist = np.cumsum(counts)\n",
+    "        count_99 = abs(cuml_dist / float(cuml_dist.max()) - .99).argmin()\n",
+    "        count_95 = abs(cuml_dist / float(cuml_dist.max()) - .95).argmin()\n",
+    "        \n",
+    "        mean = self.data[self.y].mean()\n",
+    "        med = np.median(self.data[self.y])\n",
+    "        std = self.data[self.y].std()  \n",
+    "        onesig = med + std\n",
+    "        twosig = med + (2 * std)\n",
+    "        threesig = med + (3 * std)\n",
+    "        dist95 = bins[count_95]\n",
+    "        dist99 = bins[count_99]\n",
+    "        lines = [mean, med, onesig, twosig, threesig, dist95, dist99]\n",
+    "                   \n",
+    "        fig = go.Figure(data=[go.Histogram(x=self.data[self.y], nbinsx=nbins)])\n",
+    "        for value in lines:\n",
+    "            fig.add_shape(\n",
+    "                dict(type=\"line\",\n",
+    "                    xref=\"x\",\n",
+    "                    yref=\"paper\",\n",
+    "                    x0=value,\n",
+    "                    y0=0,\n",
+    "                    x1=value,\n",
+    "                    y1=1)    \n",
+    "            )\n",
+    "            \n",
+    "        # fix this naming convention later\n",
+    "        output = os.path.join(COS_MONITORING, \"FUVBDarkMonitor-Inner_hist_2020-05-15.html\")   \n",
+    "        fig.write_html(output)\n",
+    "        \n",
+    "        \n",
+    "    def store_results(self):\n",
+    "        # TODO: Define results to store\n",
+    "        pass\n",
+    "\n",
+    "    def track(self):\n",
+    "        # TODO: Define something to track\n",
+    "        pass\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class FUVBInnerDarkMonitor(DarkMonitor):\n",
+    "    \"\"\"FUVB dark monitor for inner region\"\"\"\n",
+    "    name = 'FUVB Dark Monitor - Inner'\n",
+    "    segment = 'FUVB'\n",
+    "    location = (1000, 14990, 405, 740)\n",
+    "\n",
+    "fuv_inner_monitor = FUVBInnerDarkMonitor()\n",
+    "fuv_inner_monitor.plot_histogram()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/.ipynb_checkpoints/nuv_dark_monitor-checkpoint.ipynb b/.ipynb_checkpoints/nuv_dark_monitor-checkpoint.ipynb
new file mode 100644
index 0000000..30195bb
--- /dev/null
+++ b/.ipynb_checkpoints/nuv_dark_monitor-checkpoint.ipynb
@@ -0,0 +1,905 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import yaml\n",
+    "import cosmo\n",
+    "import numpy\n",
+    "import itertools\n",
+    "from glob import glob\n",
+    "from astropy.io import fits\n",
+    "import matplotlib.pyplot as plt\n",
+    "from matplotlib.ticker import FormatStrFormatter\n",
+    "from cosmo.monitors.data_models import NUVDarkDataModel\n",
+    "from cosmo.filesystem import find_files, data_from_exposures, data_from_jitters\n",
+    "from cosmo.monitor_helpers import absolute_time, explode_df\n",
+    "from monitorframe.datamodel import BaseDataModel\n",
+    "from monitorframe.monitor import BaseMonitor"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(os.environ['COSMO_CONFIG']) as yamlfile:\n",
+    "    SETTINGS = yaml.safe_load(yamlfile)\n",
+    "\n",
+    "FILES_SOURCE = SETTINGS['filesystem']['source']\n",
+    "COS_MONITORING = SETTINGS['output']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Filename: /grp/hst/cos2/cosmo/15776/le090edhq_corrtag.fits.gz\n",
+      "No.    Name      Ver    Type      Cards   Dimensions   Format\n",
+      "  0  PRIMARY       1 PrimaryHDU     166   ()      \n",
+      "  1  EVENTS        1 BinTableHDU    250   1440318R x 12C   [1E, 1I, 1I, 1E, 1E, 1E, 1E, 1E, 1E, 1E, 1I, 1B]   \n",
+      "  2  GTI           1 BinTableHDU     22   1R x 2C   [1D, 1D]   \n",
+      "  3  TIMELINE      1 BinTableHDU    264   1331R x 12C   [1E, 1E, 1E, 1E, 1E, 1E, 1E, 1E, 1E, 1E, 1E, 1E]   \n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "ColDefs(\n",
+       "    name = 'TIME'; format = '1E'; unit = 's'; disp = 'F8.3'\n",
+       "    name = 'LONGITUDE'; format = '1E'; unit = 'degree'; disp = 'F10.6'; coord_type = 'RA---TAN'; coord_unit = 'deg'; coord_ref_point = 512.0; coord_ref_value = 189.955952071\n",
+       "    name = 'LATITUDE'; format = '1E'; unit = 'degree'; disp = 'F10.6'; coord_type = 'DEC--TAN'; coord_unit = 'deg'; coord_ref_point = 512.0; coord_ref_value = -52.86080947004\n",
+       "    name = 'SUN_ALT'; format = '1E'; unit = 'degree'; disp = 'F6.2'\n",
+       "    name = 'SUN_ZD'; format = '1E'; unit = 'degree'; disp = 'F6.2'\n",
+       "    name = 'TARGET_ALT'; format = '1E'; unit = 'degree'; disp = 'F6.2'\n",
+       "    name = 'RADIAL_VEL'; format = '1E'; unit = 'km /s'; disp = 'F7.5'\n",
+       "    name = 'SHIFT1'; format = '1E'; unit = 'pixel'; disp = 'F7.3'\n",
+       "    name = 'LY_ALPHA'; format = '1E'; unit = 'count /s'; disp = 'G15.6'\n",
+       "    name = 'OI_1304'; format = '1E'; unit = 'count /s'; disp = 'G15.6'\n",
+       "    name = 'OI_1356'; format = '1E'; unit = 'count /s'; disp = 'G15.6'\n",
+       "    name = 'DARKRATE'; format = '1E'; unit = 'count /s /pixel'; disp = 'G15.6'\n",
+       ")"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "testfile = FILES_SOURCE + \"/15776/le090edhq_corrtag.fits.gz\"\n",
+    "with fits.open(testfile) as f:\n",
+    "    f.info()\n",
+    "    data = f[\"EVENTS\"].data\n",
+    "    timeline = f[\"TIMELINE\"].data\n",
+    "    \n",
+    "data.columns  \n",
+    "timeline.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class NUVDarkDataModel(BaseDataModel):\n",
+    "    \"\"\"Datamodel for NUV Dark files.\"\"\"\n",
+    "    files_source = FILES_SOURCE\n",
+    "    subdir_pattern = '?????'\n",
+    "\n",
+    "    def get_new_data(self):\n",
+    "        header_request = {\n",
+    "            0: ['ROOTNAME'],\n",
+    "            1: ['EXPSTART','EXPTIME']\n",
+    "            }\n",
+    "\n",
+    "        table_request = {\n",
+    "            1: ['TIME','XCORR','YCORR'],\n",
+    "            3: ['TIME','LATITUDE','LONGITUDE','DARKRATE']\n",
+    "            }\n",
+    "\n",
+    "        # any special data requests\n",
+    "        # TODO: add spt support for temp, sun_lat, sun_long\n",
+    "        # TODO: is this a good place to add solar data scraping in the future?\n",
+    "\n",
+    "        # this is temporary to find the files from the dark programs until\n",
+    "        # we can add the dark files to the monitor_data database\n",
+    "        files = []\n",
+    "        # 27, 26, 25, 24, 23, 22, 21, 20, 19, 18, 17\n",
+    "        program_ids = ['15776/', '15538/', '14942/', '14521/', '14442/', '13974/', \n",
+    "                       '13528/', '13126/', '12720/', '12420/', '11894/']\n",
+    "#         program_ids = ['15538/']\n",
+    "        for program in program_ids:\n",
+    "            new_files_source = os.path.join(FILES_SOURCE, program)\n",
+    "            subfiles = glob(os.path.join(new_files_source, \"*corrtag*\"))\n",
+    "            files += subfiles\n",
+    "\n",
+    "        if not files:  # No new files\n",
+    "            return pd.DataFrame()\n",
+    "\n",
+    "        # need to add any other keywords that need to be set\n",
+    "        data_results = data_from_exposures(files,\n",
+    "                                           header_request=header_request,\n",
+    "                                           table_request=table_request)\n",
+    "\n",
+    "        return data_results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>ROOTNAME</th>\n",
+       "      <th>EXPSTART</th>\n",
+       "      <th>EXPTIME</th>\n",
+       "      <th>TIME</th>\n",
+       "      <th>XCORR</th>\n",
+       "      <th>YCORR</th>\n",
+       "      <th>TIME_3</th>\n",
+       "      <th>LATITUDE</th>\n",
+       "      <th>LONGITUDE</th>\n",
+       "      <th>DARKRATE</th>\n",
+       "      <th>FILENAME</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>le0905b0q</td>\n",
+       "      <td>58819.166718</td>\n",
+       "      <td>1330.208</td>\n",
+       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+       "      <td>[885.0, 997.0, 347.0, 553.0, 114.0, 535.0, 784...</td>\n",
+       "      <td>[906.0, 881.0, 139.0, 68.0, 423.0, 762.0, 370....</td>\n",
+       "      <td>[0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, ...</td>\n",
+       "      <td>[-18.725433, -18.702103, -18.678751, -18.65538...</td>\n",
+       "      <td>[21.055735, 21.113192, 21.170631, 21.228054, 2...</td>\n",
+       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+       "      <td>/grp/hst/cos2/cosmo/15776/le0905b0q_corrtag.fi...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>le090ahtq</td>\n",
+       "      <td>58853.130381</td>\n",
+       "      <td>1330.208</td>\n",
+       "      <td>[0.0, 0.0, 0.0, 0.0, 0.032, 0.032, 0.032, 0.03...</td>\n",
+       "      <td>[91.0, 709.0, 745.0, 625.0, 1003.0, 254.0, 434...</td>\n",
+       "      <td>[513.0, 907.0, 243.0, 348.0, 476.0, 1001.0, 70...</td>\n",
+       "      <td>[0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, ...</td>\n",
+       "      <td>[-2.6593285, -2.6892054, -2.7190795, -2.748951...</td>\n",
+       "      <td>[0.16533001, 0.2166269, 0.26792637, 0.31922853...</td>\n",
+       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+       "      <td>/grp/hst/cos2/cosmo/15776/le090ahtq_corrtag.fi...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>le090ldoq</td>\n",
+       "      <td>58936.527326</td>\n",
+       "      <td>1330.208</td>\n",
+       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+       "      <td>[140.0, 643.0, 812.0, 995.0, 461.0, 538.0, 830...</td>\n",
+       "      <td>[915.0, 752.0, 1011.0, 902.0, 539.0, 1017.0, 5...</td>\n",
+       "      <td>[0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, ...</td>\n",
+       "      <td>[-28.539606, -28.53875, -28.537857, -28.536926...</td>\n",
+       "      <td>[29.865618, 29.933025, 30.000433, 30.067837, 3...</td>\n",
+       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+       "      <td>/grp/hst/cos2/cosmo/15776/le090ldoq_corrtag.fi...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>le0903bgq</td>\n",
+       "      <td>58805.145954</td>\n",
+       "      <td>1330.176</td>\n",
+       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+       "      <td>[73.0, 726.0, 521.0, 305.0, 35.0, 1019.0, 668....</td>\n",
+       "      <td>[931.0, 736.0, 737.0, 863.0, 917.0, 399.0, 308...</td>\n",
+       "      <td>[0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, ...</td>\n",
+       "      <td>[-23.0323, -23.01384, -22.995354, -22.97684, -...</td>\n",
+       "      <td>[122.1993, 122.26049, 122.321655, 122.38281, 1...</td>\n",
+       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+       "      <td>/grp/hst/cos2/cosmo/15776/le0903bgq_corrtag.fi...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>le090hbhq</td>\n",
+       "      <td>58903.145983</td>\n",
+       "      <td>1330.208</td>\n",
+       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.032, 0.032, 0...</td>\n",
+       "      <td>[844.0, 984.0, 367.0, 996.0, 44.0, 674.0, 383....</td>\n",
+       "      <td>[751.0, 901.0, 255.0, 239.0, 570.0, 420.0, 292...</td>\n",
+       "      <td>[0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, ...</td>\n",
+       "      <td>[-14.438115, -14.411661, -14.385194, -14.35871...</td>\n",
+       "      <td>[121.25305, 121.307846, 121.362625, 121.41739,...</td>\n",
+       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+       "      <td>/grp/hst/cos2/cosmo/15776/le090hbhq_corrtag.fi...</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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>595</th>\n",
+       "      <td>lb8r3khgq</td>\n",
+       "      <td>55487.549559</td>\n",
+       "      <td>1330.176</td>\n",
+       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.032, 0.032, 0.032,...</td>\n",
+       "      <td>[742.0, 905.0, 340.0, 53.0, 980.0, 98.0, 901.0...</td>\n",
+       "      <td>[274.0, 726.0, 754.0, 382.0, 63.0, 444.0, 240....</td>\n",
+       "      <td>[0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, ...</td>\n",
+       "      <td>[10.690754, 10.662744, 10.634723, 10.606691, 1...</td>\n",
+       "      <td>[89.07274, 89.125565, 89.178375, 89.23118, 89....</td>\n",
+       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+       "      <td>/grp/hst/cos2/cosmo/11894/lb8r3khgq_corrtag.fi...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>596</th>\n",
+       "      <td>lb8r1ce4q</td>\n",
+       "      <td>55207.905883</td>\n",
+       "      <td>1330.176</td>\n",
+       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+       "      <td>[812.0, 472.0, 585.0, 712.0, 396.0, 478.0, 498...</td>\n",
+       "      <td>[496.0, 695.0, 635.0, 550.0, 353.0, 259.0, 594...</td>\n",
+       "      <td>[0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, ...</td>\n",
+       "      <td>[-26.498741, -26.487095, -26.475414, -26.46370...</td>\n",
+       "      <td>[35.329605, 35.394123, 35.45863, 35.523125, 35...</td>\n",
+       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+       "      <td>/grp/hst/cos2/cosmo/11894/lb8r1ce4q_corrtag.fi...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>597</th>\n",
+       "      <td>lb8r3ic6q</td>\n",
+       "      <td>55480.557615</td>\n",
+       "      <td>1330.208</td>\n",
+       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.032, 0.032, 0...</td>\n",
+       "      <td>[888.0, 835.0, 909.0, 848.0, 431.0, 239.0, 80....</td>\n",
+       "      <td>[384.0, 529.0, 539.0, 580.0, 627.0, 295.0, 803...</td>\n",
+       "      <td>[0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, ...</td>\n",
+       "      <td>[27.989649, 27.984045, 27.978405, 27.972727, 2...</td>\n",
+       "      <td>[79.8855, 79.95197, 80.01843, 80.08489, 80.151...</td>\n",
+       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+       "      <td>/grp/hst/cos2/cosmo/11894/lb8r3ic6q_corrtag.fi...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>598</th>\n",
+       "      <td>lb8r2cixq</td>\n",
+       "      <td>55334.454277</td>\n",
+       "      <td>1330.176</td>\n",
+       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+       "      <td>[831.0, 692.0, 60.0, 931.0, 808.0, 712.0, 30.0...</td>\n",
+       "      <td>[210.0, 214.0, 427.0, 440.0, 825.0, 227.0, 242...</td>\n",
+       "      <td>[0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, ...</td>\n",
+       "      <td>[-10.666136, -10.638098, -10.61005, -10.581992...</td>\n",
+       "      <td>[13.996996, 14.049734, 14.102462, 14.155179, 1...</td>\n",
+       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+       "      <td>/grp/hst/cos2/cosmo/11894/lb8r2cixq_corrtag.fi...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>599</th>\n",
+       "      <td>lb8r3mefq</td>\n",
+       "      <td>55494.622290</td>\n",
+       "      <td>1330.176</td>\n",
+       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+       "      <td>[117.0, 269.0, 524.0, 985.0, 279.0, 289.0, 727...</td>\n",
+       "      <td>[943.0, 905.0, 765.0, 493.0, 321.0, 973.0, 947...</td>\n",
+       "      <td>[0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, ...</td>\n",
+       "      <td>[-26.237272, -26.224733, -26.21216, -26.199556...</td>\n",
+       "      <td>[144.99263, 145.0567, 145.12076, 145.1848, 145...</td>\n",
+       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+       "      <td>/grp/hst/cos2/cosmo/11894/lb8r3mefq_corrtag.fi...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>600 rows × 11 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      ROOTNAME      EXPSTART   EXPTIME  \\\n",
+       "0    le0905b0q  58819.166718  1330.208   \n",
+       "1    le090ahtq  58853.130381  1330.208   \n",
+       "2    le090ldoq  58936.527326  1330.208   \n",
+       "3    le0903bgq  58805.145954  1330.176   \n",
+       "4    le090hbhq  58903.145983  1330.208   \n",
+       "..         ...           ...       ...   \n",
+       "595  lb8r3khgq  55487.549559  1330.176   \n",
+       "596  lb8r1ce4q  55207.905883  1330.176   \n",
+       "597  lb8r3ic6q  55480.557615  1330.208   \n",
+       "598  lb8r2cixq  55334.454277  1330.176   \n",
+       "599  lb8r3mefq  55494.622290  1330.176   \n",
+       "\n",
+       "                                                  TIME  \\\n",
+       "0    [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
+       "1    [0.0, 0.0, 0.0, 0.0, 0.032, 0.032, 0.032, 0.03...   \n",
+       "2    [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
+       "3    [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
+       "4    [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.032, 0.032, 0...   \n",
+       "..                                                 ...   \n",
+       "595  [0.0, 0.0, 0.0, 0.0, 0.0, 0.032, 0.032, 0.032,...   \n",
+       "596  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
+       "597  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.032, 0.032, 0...   \n",
+       "598  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
+       "599  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
+       "\n",
+       "                                                 XCORR  \\\n",
+       "0    [885.0, 997.0, 347.0, 553.0, 114.0, 535.0, 784...   \n",
+       "1    [91.0, 709.0, 745.0, 625.0, 1003.0, 254.0, 434...   \n",
+       "2    [140.0, 643.0, 812.0, 995.0, 461.0, 538.0, 830...   \n",
+       "3    [73.0, 726.0, 521.0, 305.0, 35.0, 1019.0, 668....   \n",
+       "4    [844.0, 984.0, 367.0, 996.0, 44.0, 674.0, 383....   \n",
+       "..                                                 ...   \n",
+       "595  [742.0, 905.0, 340.0, 53.0, 980.0, 98.0, 901.0...   \n",
+       "596  [812.0, 472.0, 585.0, 712.0, 396.0, 478.0, 498...   \n",
+       "597  [888.0, 835.0, 909.0, 848.0, 431.0, 239.0, 80....   \n",
+       "598  [831.0, 692.0, 60.0, 931.0, 808.0, 712.0, 30.0...   \n",
+       "599  [117.0, 269.0, 524.0, 985.0, 279.0, 289.0, 727...   \n",
+       "\n",
+       "                                                 YCORR  \\\n",
+       "0    [906.0, 881.0, 139.0, 68.0, 423.0, 762.0, 370....   \n",
+       "1    [513.0, 907.0, 243.0, 348.0, 476.0, 1001.0, 70...   \n",
+       "2    [915.0, 752.0, 1011.0, 902.0, 539.0, 1017.0, 5...   \n",
+       "3    [931.0, 736.0, 737.0, 863.0, 917.0, 399.0, 308...   \n",
+       "4    [751.0, 901.0, 255.0, 239.0, 570.0, 420.0, 292...   \n",
+       "..                                                 ...   \n",
+       "595  [274.0, 726.0, 754.0, 382.0, 63.0, 444.0, 240....   \n",
+       "596  [496.0, 695.0, 635.0, 550.0, 353.0, 259.0, 594...   \n",
+       "597  [384.0, 529.0, 539.0, 580.0, 627.0, 295.0, 803...   \n",
+       "598  [210.0, 214.0, 427.0, 440.0, 825.0, 227.0, 242...   \n",
+       "599  [943.0, 905.0, 765.0, 493.0, 321.0, 973.0, 947...   \n",
+       "\n",
+       "                                                TIME_3  \\\n",
+       "0    [0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, ...   \n",
+       "1    [0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, ...   \n",
+       "2    [0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, ...   \n",
+       "3    [0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, ...   \n",
+       "4    [0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, ...   \n",
+       "..                                                 ...   \n",
+       "595  [0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, ...   \n",
+       "596  [0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, ...   \n",
+       "597  [0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, ...   \n",
+       "598  [0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, ...   \n",
+       "599  [0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, ...   \n",
+       "\n",
+       "                                              LATITUDE  \\\n",
+       "0    [-18.725433, -18.702103, -18.678751, -18.65538...   \n",
+       "1    [-2.6593285, -2.6892054, -2.7190795, -2.748951...   \n",
+       "2    [-28.539606, -28.53875, -28.537857, -28.536926...   \n",
+       "3    [-23.0323, -23.01384, -22.995354, -22.97684, -...   \n",
+       "4    [-14.438115, -14.411661, -14.385194, -14.35871...   \n",
+       "..                                                 ...   \n",
+       "595  [10.690754, 10.662744, 10.634723, 10.606691, 1...   \n",
+       "596  [-26.498741, -26.487095, -26.475414, -26.46370...   \n",
+       "597  [27.989649, 27.984045, 27.978405, 27.972727, 2...   \n",
+       "598  [-10.666136, -10.638098, -10.61005, -10.581992...   \n",
+       "599  [-26.237272, -26.224733, -26.21216, -26.199556...   \n",
+       "\n",
+       "                                             LONGITUDE  \\\n",
+       "0    [21.055735, 21.113192, 21.170631, 21.228054, 2...   \n",
+       "1    [0.16533001, 0.2166269, 0.26792637, 0.31922853...   \n",
+       "2    [29.865618, 29.933025, 30.000433, 30.067837, 3...   \n",
+       "3    [122.1993, 122.26049, 122.321655, 122.38281, 1...   \n",
+       "4    [121.25305, 121.307846, 121.362625, 121.41739,...   \n",
+       "..                                                 ...   \n",
+       "595  [89.07274, 89.125565, 89.178375, 89.23118, 89....   \n",
+       "596  [35.329605, 35.394123, 35.45863, 35.523125, 35...   \n",
+       "597  [79.8855, 79.95197, 80.01843, 80.08489, 80.151...   \n",
+       "598  [13.996996, 14.049734, 14.102462, 14.155179, 1...   \n",
+       "599  [144.99263, 145.0567, 145.12076, 145.1848, 145...   \n",
+       "\n",
+       "                                              DARKRATE  \\\n",
+       "0    [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
+       "1    [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
+       "2    [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
+       "3    [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
+       "4    [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
+       "..                                                 ...   \n",
+       "595  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
+       "596  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
+       "597  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
+       "598  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
+       "599  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
+       "\n",
+       "                                              FILENAME  \n",
+       "0    /grp/hst/cos2/cosmo/15776/le0905b0q_corrtag.fi...  \n",
+       "1    /grp/hst/cos2/cosmo/15776/le090ahtq_corrtag.fi...  \n",
+       "2    /grp/hst/cos2/cosmo/15776/le090ldoq_corrtag.fi...  \n",
+       "3    /grp/hst/cos2/cosmo/15776/le0903bgq_corrtag.fi...  \n",
+       "4    /grp/hst/cos2/cosmo/15776/le090hbhq_corrtag.fi...  \n",
+       "..                                                 ...  \n",
+       "595  /grp/hst/cos2/cosmo/11894/lb8r3khgq_corrtag.fi...  \n",
+       "596  /grp/hst/cos2/cosmo/11894/lb8r1ce4q_corrtag.fi...  \n",
+       "597  /grp/hst/cos2/cosmo/11894/lb8r3ic6q_corrtag.fi...  \n",
+       "598  /grp/hst/cos2/cosmo/11894/lb8r2cixq_corrtag.fi...  \n",
+       "599  /grp/hst/cos2/cosmo/11894/lb8r3mefq_corrtag.fi...  \n",
+       "\n",
+       "[600 rows x 11 columns]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nuv_model = NUVDarkDataModel()\n",
+    "data_results = nuv_model.get_new_data()\n",
+    "data_results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class NUVDarkMonitor(BaseMonitor):\n",
+    "    data_model = NUVDarkDataModel\n",
+    "    # TODO: update docs\n",
+    "    # docs = \"https://spacetelescope.github.io/cosmo/monitors.html#<darks>\"\n",
+    "    output = COS_MONITORING\n",
+    "\n",
+    "    run = 'monthly'\n",
+    "        \n",
+    "    def get_data(self):\n",
+    "        # access data, perform any filtering required for analysis\n",
+    "        data = self.model.new_data\n",
+    "        dark_rate_column = []\n",
+    "        dec_year_column = []\n",
+    "        \n",
+    "        xlim = [0, 1024]\n",
+    "        ylim = [0, 1024]\n",
+    "                \n",
+    "        # parallelize, this is going to get bad when looking at a lot of data\n",
+    "        for index, row in data.iterrows():\n",
+    "            subdf = pd.DataFrame({\n",
+    "                \"EXPSTART\": row[\"EXPSTART\"],\n",
+    "                \"TIME\": [row[\"TIME\"]],\n",
+    "                \"XCORR\": [row[\"XCORR\"]],\n",
+    "                \"YCORR\": [row[\"YCORR\"]],\n",
+    "                \"TIME_3\": [row[\"TIME_3\"]]\n",
+    "            })\n",
+    "            \n",
+    "            # this is temporary until i understand james' suggestion to use df.apply and lambda functions\n",
+    "            xcorr = subdf[\"XCORR\"][0]\n",
+    "            ycorr = subdf[\"YCORR\"][0]\n",
+    "            filtered_xcorr = xcorr[np.where((xcorr > xlim[0]) & (xcorr < xlim[1]))]\n",
+    "            filtered_ycorr = ycorr[np.where((ycorr > ylim[0]) & (ycorr < ylim[1]))]\n",
+    "            subdf[\"XCORR\"] = [filtered_xcorr]\n",
+    "            subdf[\"YCORR\"] = [filtered_ycorr]\n",
+    "            \n",
+    "            dark_rate_array, dec_year_array = self.calculate_dark_rate(subdf, xlim, ylim)\n",
+    "            dark_rate_column.append(dark_rate_array)\n",
+    "            dec_year_column.append(dec_year_array)\n",
+    "         \n",
+    "        data[\"DARK_RATE\"] = dark_rate_column\n",
+    "        data[\"DECIMAL_YEAR\"] = dec_year_column\n",
+    "        \n",
+    "        # when the monitor method of the monitor is called, it will initialize the self.data attribute\n",
+    "        # with this method and then can be used by the other methods\n",
+    "        return data\n",
+    "    \n",
+    "    def calculate_dark_rate(self, dataframe, xlim, ylim):\n",
+    "        # calculate dark rate for one exposure, with a dataframe with TIME, XCORR, and YCORR values\n",
+    "        \n",
+    "        # need to set this somewhere\n",
+    "        timestep = 25\n",
+    "        time_bins = dataframe[\"TIME_3\"][0][::timestep]\n",
+    "        \n",
+    "        counts = np.histogram(dataframe[\"TIME\"][0], bins=time_bins)[0]\n",
+    "        npix = float((xlim[1] - xlim[0]) * (ylim[1] - ylim[0]))\n",
+    "        dark_rate_array = counts / npix / timestep\n",
+    "        # save the whole histogram in time bins, and then plot each of them\n",
+    "        \n",
+    "        # make a decimal year array corresponding to the time bins of the dark rates\n",
+    "        # do this with the expstart (mjd) and time array from the timeline extension\n",
+    "        # taking the expstart, binning the time array by the timestep, \n",
+    "        # removing the last element in the array (bin by front edge),\n",
+    "        # and then multiplying by the conversion factor\n",
+    "        # this is done by the absolute_time helper function\n",
+    "        mjd_array = absolute_time(expstart=dataframe['EXPSTART'][0], time=dataframe['TIME_3'][0][::timestep][:-1])\n",
+    "        dec_year_array = mjd_array.decimalyear\n",
+    "\n",
+    "        return dark_rate_array, dec_year_array\n",
+    "\n",
+    "    def track(self):\n",
+    "        # track something. perhaps current dark rate?\n",
+    "        if self.data is None:\n",
+    "            self.data = self.get_data()\n",
+    "            \n",
+    "        plotdf = self.data[['DECIMAL_YEAR', 'DARK_RATE']]\n",
+    "        \n",
+    "        # i can't get explode_df to work so this is for now\n",
+    "        # i think i know why it doesn't work and i fixed it i just haven't switched to using it\n",
+    "        all_dec_year = []\n",
+    "        all_dark_rates = []\n",
+    "        for index, row in plotdf.iterrows():\n",
+    "            all_dec_year = list(itertools.chain(all_dec_year, row[\"DECIMAL_YEAR\"]))\n",
+    "            all_dark_rates = list(itertools.chain(all_dark_rates, row[\"DARK_RATE\"]))\n",
+    "            \n",
+    "        dark_counts = np.asarray(all_dark_rates)\n",
+    "        fig = plt.figure(figsize=(12, 9))\n",
+    "        # bin_size = 1e-5\n",
+    "        # n_bins = int((dark_counts.max() - dark_counts.min()) / bin_size)\n",
+    "        n_bins = 30\n",
+    "        ax = fig.add_subplot(2, 1, 1)\n",
+    "        ax.hist(dark_counts, bins=n_bins, align='mid', histtype='stepfilled')\n",
+    "        counts, bins = np.histogram(dark_counts, bins=n_bins)\n",
+    "        cuml_dist = np.cumsum(counts)\n",
+    "        count_99 = abs(cuml_dist / float(cuml_dist.max()) - .99).argmin()\n",
+    "        count_95 = abs(cuml_dist / float(cuml_dist.max()) - .95).argmin()\n",
+    "\n",
+    "        mean = dark_counts.mean()\n",
+    "        med = np.median(dark_counts)\n",
+    "        std = dark_counts.std()\n",
+    "        mean_obj = ax.axvline(x=mean, lw=2, ls='--', color='r', label='Mean ')\n",
+    "        med_obj = ax.axvline(x=med, lw=2, ls='-', color='r', label='Median')\n",
+    "        two_sig = ax.axvline(x=med + (2 * std), lw=2, ls='-', color='gold')\n",
+    "        three_sig = ax.axvline(x=med + (3 * std), lw=2, ls='-', color='DarkOrange')\n",
+    "        dist_95 = ax.axvline(x=bins[count_95], lw=2, ls='-', color='LightGreen')\n",
+    "        dist_99 = ax.axvline(x=bins[count_99], lw=2, ls='-', color='DarkGreen')\n",
+    "\n",
+    "        ax.grid(True, which='both')\n",
+    "        ax.set_title('Histogram of Dark Rates', fontsize=15, fontweight='bold')\n",
+    "        ax.set_ylabel('Frequency', fontsize=15, fontweight='bold')\n",
+    "        ax.set_xlabel('Counts/pix/sec', fontsize=15, fontweight='bold')\n",
+    "        ax.set_xlim(dark_counts.min(), dark_counts.max())\n",
+    "        ax.xaxis.set_major_formatter(FormatStrFormatter('%3.2e'))\n",
+    "\n",
+    "        ax = fig.add_subplot(2, 1, 2)\n",
+    "        # log_bins = np.logspace(np.log10(dark.min()), np.log10(dark.max()), 100)\n",
+    "        ax.hist(dark_counts, bins=n_bins, align='mid', log=True, histtype='stepfilled')\n",
+    "\n",
+    "        ax.axvline(x=mean, lw=2, ls='--', color='r', label='Mean')\n",
+    "        ax.axvline(x=med, lw=2, ls='-', color='r', label='Median')\n",
+    "        ax.axvline(x=med + (2 * std), lw=2, ls='-', color='gold')\n",
+    "        ax.axvline(x=med + (3 * std), lw=2, ls='-', color='DarkOrange')\n",
+    "        ax.axvline(x=bins[count_95], lw=2, ls='-', color='LightGreen')\n",
+    "        ax.axvline(x=bins[count_99], lw=2, ls='-', color='DarkGreen')\n",
+    "\n",
+    "        # ax.set_xscale('log')\n",
+    "        ax.grid(True, which='both')\n",
+    "        ax.set_ylabel('Log Frequency', fontsize=15, fontweight='bold')\n",
+    "        ax.set_xlabel('Counts/pix/sec', fontsize=15, fontweight='bold')\n",
+    "        ax.set_xlim(dark_counts.min(), dark_counts.max())\n",
+    "        ax.xaxis.set_major_formatter(FormatStrFormatter('%3.2e'))\n",
+    "\n",
+    "        fig.legend([med_obj, mean_obj, two_sig, three_sig, dist_95, dist_99],\n",
+    "                   ['Median: {0:.2e}'.format(med),\n",
+    "                    'Mean: {0:.2e}'.format(mean),\n",
+    "                    r'2$\\sigma$: {0:.2e}'.format(med + (2 * std)),\n",
+    "                    r'3$\\sigma$: {0:.2e}'.format(med + (3 * std)),\n",
+    "                    r'95$\\%$: {0:.2e}'.format(bins[count_95]),\n",
+    "                    r'99$\\%$: {0:.2e}'.format(bins[count_99])],\n",
+    "                   shadow=True,\n",
+    "                   numpoints=1,\n",
+    "                   bbox_to_anchor=[0.8, 0.8])\n",
+    "\n",
+    "\n",
+    "    def plot(self):\n",
+    "        # select the important columns from the dataframe\n",
+    "        if self.data is None:\n",
+    "            self.data = self.get_data()\n",
+    "            \n",
+    "        plotdf = pd.DataFrame({\n",
+    "            \"DECIMAL_YEAR\": self.data[\"DECIMAL_YEAR\"],\n",
+    "            \"DARK_RATE\": self.data[\"DARK_RATE\"]\n",
+    "        })\n",
+    "        \n",
+    "        # i can't get explode_df to work so this is for now\n",
+    "        all_dec_year = []\n",
+    "        all_dark_rates = []\n",
+    "        for index, row in plotdf.iterrows():\n",
+    "            all_dec_year = list(itertools.chain(all_dec_year, row[\"DECIMAL_YEAR\"]))\n",
+    "            all_dark_rates = list(itertools.chain(all_dark_rates, row[\"DARK_RATE\"]))\n",
+    "        \n",
+    "        # not sure what is happening here tbh, still figuring it out\n",
+    "#         self.x = all_dec_year\n",
+    "#         self.y = all_dark_rates\n",
+    "        \n",
+    "#         self.basic_scatter()\n",
+    "        \n",
+    "        fig = plt.figure(figsize=(12, 9))\n",
+    "        plt.scatter(all_dec_year, all_dark_rates)\n",
+    "        # plt.xlim(min(all_dec_year), max(all_dec_year))\n",
+    "        plt.ylim(0, max(all_dark_rates) + 0.5e-5)\n",
+    "        plt.ticklabel_format(axis='y', style='sci', scilimits=(-2, 2))\n",
+    "        plt.ticklabel_format(axis='x', style='plain')\n",
+    "        plt.xlabel(\"Decimal Year\")\n",
+    "        plt.ylabel(\"Dark Rate (c/p/s)\")\n",
+    "        plt.grid(True)\n",
+    "\n",
+    "    def store_results(self):\n",
+    "        # need to store results if not going in the database\n",
+    "        pass\n",
+    "\n",
+    "nuv_monitor = NUVDarkMonitor()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x648 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x648 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# nuv_monitor.model.new_data\n",
+    "# nuv_monitor.get_data()\n",
+    "nuv_monitor.plot()\n",
+    "nuv_monitor.track()\n",
+    "# nuv_monitor.monitor()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class NUVDarkMonitor(BaseMonitor):\n",
+    "    data_model = NUVDarkDataModel\n",
+    "    # TODO: update docs\n",
+    "    # docs = \"https://spacetelescope.github.io/cosmo/monitors.html#<darks>\"\n",
+    "    output = COS_MONITORING\n",
+    "\n",
+    "    run = 'monthly'\n",
+    "        \n",
+    "    def get_data(self):\n",
+    "        # access data, perform any filtering required for analysis\n",
+    "        data = self.model.new_data\n",
+    "        \n",
+    "        xlim = (0, 1024)\n",
+    "        ylim = (0, 1024)\n",
+    "                \n",
+    "        data = self.get_dark_rate(data)\n",
+    "        \n",
+    "        # when the monitor method of the monitor is called, it will initialize the self.data attribute\n",
+    "        # with this method and then can be used by the other methods\n",
+    "        return data\n",
+    "    \n",
+    "    def get_dark_rate(df, x_range=(0, 1024), y_range=(0, 1024), time_step=25):\n",
+    "        \"\"\"Calculate the dark rate per input DataFrame row which should correspond to the \n",
+    "        data from a single dark.\"\"\"\n",
+    "        \n",
+    "        def _calculate_darkrate_per_row(row):\n",
+    "            time_bins = row['TIME_3'][::time_step]\n",
+    "            npix = (x_range[1] - x_range[0]) * (y_range[1] - y_range[0])\n",
+    "            \n",
+    "            filtered_time = row.TIME[\n",
+    "                (row.XCORR > x_range[0])\n",
+    "                & (row.XCORR < x_range[1])\n",
+    "                & (row.YCORR > y_range[0])\n",
+    "                & (row.YCORR < y_range[1])\n",
+    "            ]\n",
+    "            counts, _ = np.histogram(filtered_time, bins=time_bins)\n",
+    "            \n",
+    "            return counts / npix / time_step\n",
+    "        \n",
+    "        def _calculate_datetime_bins_per_row(row):\n",
+    "            time_bins = row['TIME_3'][::time_step]\n",
+    "            \n",
+    "            return absolute_time(expstart=row['EXPSTART'], time=time_bins).to_datetime()[:-1]\n",
+    "        \n",
+    "        df['DARK_RATE'] = df.apply(_calculate_darkrate_per_row, axis=1)\n",
+    "        df['DR_TIME'] = df.apply(_calculate_datetime_bins_per_row, axis=1)\n",
+    "        \n",
+    "        return df\n",
+    "\n",
+    "    def track(self):\n",
+    "        # track something. perhaps current dark rate?\n",
+    "        if self.data is None:\n",
+    "            self.data = self.get_data()\n",
+    "            \n",
+    "        plotdf = self.data[['DECIMAL_YEAR', 'DARK_RATE']]\n",
+    "        \n",
+    "        # i can't get explode_df to work so this is for now\n",
+    "        # i think i know why it doesn't work and i fixed it i just haven't switched to using it\n",
+    "        all_dec_year = []\n",
+    "        all_dark_rates = []\n",
+    "        for index, row in plotdf.iterrows():\n",
+    "            all_dec_year = list(itertools.chain(all_dec_year, row[\"DECIMAL_YEAR\"]))\n",
+    "            all_dark_rates = list(itertools.chain(all_dark_rates, row[\"DARK_RATE\"]))\n",
+    "            \n",
+    "        dark_counts = np.asarray(all_dark_rates)\n",
+    "        fig = plt.figure(figsize=(12, 9))\n",
+    "#         bin_size = 1e-8\n",
+    "#         n_bins = int((dark_counts.max() - dark_counts.min()) / bin_size)\n",
+    "        n_bins = 30\n",
+    "        ax = fig.add_subplot(2, 1, 1)\n",
+    "        ax.hist(dark_counts, bins=n_bins, align='mid', histtype='stepfilled')\n",
+    "        counts, bins = np.histogram(dark_counts, bins=n_bins)\n",
+    "        cuml_dist = np.cumsum(counts)\n",
+    "        count_99 = abs(cuml_dist / float(cuml_dist.max()) - .99).argmin()\n",
+    "        count_95 = abs(cuml_dist / float(cuml_dist.max()) - .95).argmin()\n",
+    "\n",
+    "        mean = dark_counts.mean()\n",
+    "        med = np.median(dark_counts)\n",
+    "        std = dark_counts.std()\n",
+    "        mean_obj = ax.axvline(x=mean, lw=2, ls='--', color='r', label='Mean ')\n",
+    "        med_obj = ax.axvline(x=med, lw=2, ls='-', color='r', label='Median')\n",
+    "        two_sig = ax.axvline(x=med + (2 * std), lw=2, ls='-', color='gold')\n",
+    "        three_sig = ax.axvline(x=med + (3 * std), lw=2, ls='-', color='DarkOrange')\n",
+    "        dist_95 = ax.axvline(x=bins[count_95], lw=2, ls='-', color='LightGreen')\n",
+    "        dist_99 = ax.axvline(x=bins[count_99], lw=2, ls='-', color='DarkGreen')\n",
+    "\n",
+    "        ax.grid(True, which='both')\n",
+    "        ax.set_title('Histogram of Dark Rates', fontsize=15, fontweight='bold')\n",
+    "        ax.set_ylabel('Frequency', fontsize=15, fontweight='bold')\n",
+    "        ax.set_xlabel('Counts/pix/sec', fontsize=15, fontweight='bold')\n",
+    "        ax.set_xlim(dark_counts.min(), dark_counts.max())\n",
+    "        ax.xaxis.set_major_formatter(FormatStrFormatter('%3.2e'))\n",
+    "\n",
+    "        ax = fig.add_subplot(2, 1, 2)\n",
+    "        # log_bins = np.logspace(np.log10(dark.min()), np.log10(dark.max()), 100)\n",
+    "        ax.hist(dark_counts, bins=n_bins, align='mid', log=True, histtype='stepfilled')\n",
+    "\n",
+    "        ax.axvline(x=mean, lw=2, ls='--', color='r', label='Mean')\n",
+    "        ax.axvline(x=med, lw=2, ls='-', color='r', label='Median')\n",
+    "        ax.axvline(x=med + (2 * std), lw=2, ls='-', color='gold')\n",
+    "        ax.axvline(x=med + (3 * std), lw=2, ls='-', color='DarkOrange')\n",
+    "        ax.axvline(x=bins[count_95], lw=2, ls='-', color='LightGreen')\n",
+    "        ax.axvline(x=bins[count_99], lw=2, ls='-', color='DarkGreen')\n",
+    "\n",
+    "        # ax.set_xscale('log')\n",
+    "        ax.grid(True, which='both')\n",
+    "        ax.set_ylabel('Log Frequency', fontsize=15, fontweight='bold')\n",
+    "        ax.set_xlabel('Counts/pix/sec', fontsize=15, fontweight='bold')\n",
+    "        ax.set_xlim(dark_counts.min(), dark_counts.max())\n",
+    "        ax.xaxis.set_major_formatter(FormatStrFormatter('%3.2e'))\n",
+    "\n",
+    "        fig.legend([med_obj, mean_obj, two_sig, three_sig, dist_95, dist_99],\n",
+    "                   ['Median: {0:.2e}'.format(med),\n",
+    "                    'Mean: {0:.2e}'.format(mean),\n",
+    "                    r'2$\\sigma$: {0:.2e}'.format(med + (2 * std)),\n",
+    "                    r'3$\\sigma$: {0:.2e}'.format(med + (3 * std)),\n",
+    "                    r'95$\\%$: {0:.2e}'.format(bins[count_95]),\n",
+    "                    r'99$\\%$: {0:.2e}'.format(bins[count_99])],\n",
+    "                   shadow=True,\n",
+    "                   numpoints=1,\n",
+    "                   bbox_to_anchor=[0.8, 0.8])\n",
+    "\n",
+    "\n",
+    "    def plot(self):\n",
+    "        # select the important columns from the dataframe\n",
+    "        if self.data is None:\n",
+    "            self.data = self.get_data()\n",
+    "            \n",
+    "        plotdf = pd.DataFrame({\n",
+    "            \"DECIMAL_YEAR\": self.data[\"DECIMAL_YEAR\"],\n",
+    "            \"DARK_RATE\": self.data[\"DARK_RATE\"]\n",
+    "        })\n",
+    "        \n",
+    "        # i can't get explode_df to work so this is for now\n",
+    "        all_dec_year = []\n",
+    "        all_dark_rates = []\n",
+    "        for index, row in plotdf.iterrows():\n",
+    "            all_dec_year = list(itertools.chain(all_dec_year, row[\"DECIMAL_YEAR\"]))\n",
+    "            all_dark_rates = list(itertools.chain(all_dark_rates, row[\"DARK_RATE\"]))\n",
+    "        \n",
+    "        # not sure what is happening here tbh, still figuring it out\n",
+    "#         self.x = all_dec_year\n",
+    "#         self.y = all_dark_rates\n",
+    "        \n",
+    "#         self.basic_scatter()\n",
+    "        \n",
+    "        fig = plt.figure(figsize=(12, 9))\n",
+    "        plt.scatter(all_dec_year, all_dark_rates)\n",
+    "        # plt.xlim(min(all_dec_year), max(all_dec_year))\n",
+    "        plt.ylim(0, max(all_dark_rates))\n",
+    "        plt.ticklabel_format(axis='y', style='sci', scilimits=(-2, 2))\n",
+    "        plt.ticklabel_format(axis='x', style='plain')\n",
+    "        plt.xlabel(\"Decimal Year\")\n",
+    "        plt.ylabel(\"Dark Rate (c/p/s)\")\n",
+    "        plt.grid(True)\n",
+    "\n",
+    "    def store_results(self):\n",
+    "        # need to store results if not going in the database\n",
+    "        pass\n",
+    "\n",
+    "nuv_monitor = NUVDarkMonitor()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nuv_monitor.plot()\n",
+    "nuv_monitor.track()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/.ipynb_checkpoints/nuv_dark_monitor_refactor-checkpoint.ipynb b/.ipynb_checkpoints/nuv_dark_monitor_refactor-checkpoint.ipynb
new file mode 100644
index 0000000..43efff1
--- /dev/null
+++ b/.ipynb_checkpoints/nuv_dark_monitor_refactor-checkpoint.ipynb
@@ -0,0 +1,116 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import yaml\n",
+    "import cosmo\n",
+    "import numpy\n",
+    "import itertools\n",
+    "from glob import glob\n",
+    "from astropy.io import fits\n",
+    "import matplotlib.pyplot as plt\n",
+    "from matplotlib.ticker import FormatStrFormatter\n",
+    "from cosmo.monitors.data_models import NUVDarkDataModel\n",
+    "from cosmo.filesystem import find_files, data_from_exposures, data_from_jitters\n",
+    "from cosmo.monitor_helpers import absolute_time, explode_df\n",
+    "from monitorframe.datamodel import BaseDataModel\n",
+    "from monitorframe.monitor import BaseMonitor"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(os.environ['COSMO_CONFIG']) as yamlfile:\n",
+    "    SETTINGS = yaml.safe_load(yamlfile)\n",
+    "\n",
+    "FILES_SOURCE = SETTINGS['filesystem']['source']\n",
+    "COS_MONITORING = SETTINGS['output']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class NUVDarkDataModel(BaseDataModel):\n",
+    "    cosmo_layout = False\n",
+    "#     program_ids = ['15776/', '15538/', '14942/', '14521/', '14442/', '13974/', \n",
+    "#                        '13528/', '13126/', '12720/', '12420/', '11894/']\n",
+    "    program_ids = ['15538/']\n",
+    "    # subdir_pattern = '?????'\n",
+    "    # ^ this will need to be added back once we have an all corrtag model\n",
+    "\n",
+    "    def get_new_data(self):\n",
+    "        header_request = {\n",
+    "            0: ['ROOTNAME'],\n",
+    "            1: ['EXPTIME', 'EXPSTART']\n",
+    "        }\n",
+    "\n",
+    "        table_request = {\n",
+    "            1: ['XCORR', 'YCORR', 'TIME'],\n",
+    "            3: ['TIME', 'LATITUDE', 'LONGITUDE']\n",
+    "        }\n",
+    "\n",
+    "        files = []\n",
+    "\n",
+    "        for prog_id in self.program_ids:\n",
+    "            new_files_source = os.path.join(FILES_SOURCE, prog_id)\n",
+    "            files += find_files('*corrtag*', data_dir=new_files_source)\n",
+    "\n",
+    "        if self.model is not None:\n",
+    "            currently_ingested = [item.FILENAME for item in self.model.select(self.model.FILENAME)]\n",
+    "\n",
+    "            for file in currently_ingested:\n",
+    "                files.remove(file)\n",
+    "\n",
+    "        if not files:   # No new files\n",
+    "            return pd.DataFrame()\n",
+    "\n",
+    "        data_results = data_from_exposures(\n",
+    "            files,\n",
+    "            header_request=header_request,\n",
+    "            table_request=table_request\n",
+    "        )\n",
+    "\n",
+    "        return data_results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/.ipynb_checkpoints/refactor_FUV_and_NUV-checkpoint.ipynb b/.ipynb_checkpoints/refactor_FUV_and_NUV-checkpoint.ipynb
new file mode 100644
index 0000000..2032863
--- /dev/null
+++ b/.ipynb_checkpoints/refactor_FUV_and_NUV-checkpoint.ipynb
@@ -0,0 +1,888 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import yaml\n",
+    "import cosmo\n",
+    "import numpy\n",
+    "from itertools import repeat\n",
+    "from glob import glob\n",
+    "from astropy.io import fits\n",
+    "import matplotlib.pyplot as plt\n",
+    "from matplotlib.ticker import FormatStrFormatter\n",
+    "from cosmo.filesystem import find_files, data_from_exposures, data_from_jitters\n",
+    "from cosmo.monitor_helpers import absolute_time, explode_df\n",
+    "from monitorframe.datamodel import BaseDataModel\n",
+    "from monitorframe.monitor import BaseMonitor"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/grp/hst/cos2/cosmo/\n"
+     ]
+    }
+   ],
+   "source": [
+    "# load settings\n",
+    "with open(os.environ['COSMO_CONFIG']) as yamlfile:\n",
+    "    SETTINGS = yaml.safe_load(yamlfile)\n",
+    "\n",
+    "FILES_SOURCE = SETTINGS['filesystem']['source']\n",
+    "COS_MONITORING = SETTINGS['output']\n",
+    "print(FILES_SOURCE)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Filename: /grp/hst/cos2/cosmo/15776/le090edhq_corrtag.fits.gz\n",
+      "No.    Name      Ver    Type      Cards   Dimensions   Format\n",
+      "  0  PRIMARY       1 PrimaryHDU     166   ()      \n",
+      "  1  EVENTS        1 BinTableHDU    250   1440318R x 12C   [1E, 1I, 1I, 1E, 1E, 1E, 1E, 1E, 1E, 1E, 1I, 1B]   \n",
+      "  2  GTI           1 BinTableHDU     22   1R x 2C   [1D, 1D]   \n",
+      "  3  TIMELINE      1 BinTableHDU    264   1331R x 12C   [1E, 1E, 1E, 1E, 1E, 1E, 1E, 1E, 1E, 1E, 1E, 1E]   \n"
+     ]
+    }
+   ],
+   "source": [
+    "# testfile\n",
+    "testfile = FILES_SOURCE + \"15776/le090edhq_corrtag.fits.gz\"\n",
+    "with fits.open(testfile) as f:\n",
+    "    f.info()\n",
+    "    data = f[\"EVENTS\"].data\n",
+    "    timeline = f[\"TIMELINE\"].data\n",
+    "    header = f[\"PRIMARY\"].header\n",
+    "    \n",
+    "# data.columns  \n",
+    "# timeline.columns\n",
+    "# header"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# # combined DataModel\n",
+    "# class DarkDataModel(BaseDataModel):\n",
+    "#     cosmo_layout = False\n",
+    "#     fuv_program_ids = ['15771', '15533/', '14940/', '14520/', '14436/', '13968/', '13521/', '13121/', '12716/', '12423/',\n",
+    "#                   '11895/']\n",
+    "#     nuv_program_ids = ['15776/', '15538/', '14942/', '14521/', '14442/', '13974/', \n",
+    "#                        '13528/', '13126/', '12720/', '12420/', '11894/']\n",
+    "#     # subdir_pattern = '?????'\n",
+    "\n",
+    "#     def get_new_data(self, segment=\"FUV\"):  # this way when you get new data it will be based on whether it is fuv or nuv\n",
+    "#         if segment == \"NUV\":\n",
+    "#             header_request = {\n",
+    "#                 0: ['ROOTNAME'],\n",
+    "#                 1: ['EXPTIME', 'EXPSTART']\n",
+    "#             }\n",
+    "#             table_request = {\n",
+    "#                 1: ['XCORR', 'YCORR', 'TIME'],\n",
+    "#                 3: ['TIME', 'LATITUDE', 'LONGITUDE']\n",
+    "#             }\n",
+    "#             program_id = nuv_program_ids\n",
+    "\n",
+    "#         else: \n",
+    "#             header_request = {\n",
+    "#                 0: ['ROOTNAME', 'SEGMENT'],\n",
+    "#                 1: ['EXPTIME', 'EXPSTART']\n",
+    "#             }\n",
+    "#             table_request = {\n",
+    "#                 1: ['PHA', 'XCORR', 'YCORR', 'TIME'],\n",
+    "#                 3: ['TIME', 'LATITUDE', 'LONGITUDE']\n",
+    "#             }\n",
+    "#             program_id = fuv_program_ids\n",
+    "\n",
+    "#         files = []\n",
+    "\n",
+    "#         for prog_id in self.program_id:\n",
+    "\n",
+    "#             new_files_source = os.path.join(FILES_SOURCE, prog_id)\n",
+    "#             files += find_files('*corrtag*', data_dir=new_files_source)\n",
+    "\n",
+    "#         if self.model is not None:\n",
+    "#             currently_ingested = [item.FILENAME for item in self.model.select(self.model.FILENAME)]\n",
+    "\n",
+    "#             for file in currently_ingested:\n",
+    "#                 files.remove(file)\n",
+    "\n",
+    "#         if not files:   # No new files\n",
+    "#             return pd.DataFrame()\n",
+    "\n",
+    "#         data_results = data_from_exposures(\n",
+    "#             files,\n",
+    "#             header_request=header_request,\n",
+    "#             table_request=table_request\n",
+    "#         )\n",
+    "\n",
+    "#         return data_results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Changes to DataModel (e.g. program ids):\n",
+    "- Re-run DataModel cell\n",
+    "- Run Monitor cell for FUV and/or NUV\n",
+    "- Run monitor execution cell for FUV and/or NUV\n",
+    "\n",
+    "#### Changes to Monitor:\n",
+    "- Run Monitor cell for FUV and/or NUV\n",
+    "- Run monitor execution cell for FUV and/or NUV\n",
+    "- If successful for FUV make changes as necessary for NUV"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Working DataModel for both FUV and NUV"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# combined DataModel\n",
+    "class DarkDataModel(BaseDataModel):\n",
+    "    cosmo_layout = False\n",
+    "#     fuv_program_ids = ['15771/', '15533/', '14940/', '14520/', '14436/', '13968/', '13521/', '13121/', '12716/', '12423/',\n",
+    "#                   '11895/']\n",
+    "#     nuv_program_ids = ['15776/', '15538/', '14942/', '14521/', '14442/', '13974/', \n",
+    "#                        '13528/', '13126/', '12720/', '12420/', '11894/']\n",
+    "    fuv_program_ids = [\"15771/\"]\n",
+    "    nuv_program_ids = [\"15776/\"]\n",
+    "    program_id = fuv_program_ids + nuv_program_ids\n",
+    "    # subdir_pattern = '?????'\n",
+    "\n",
+    "    def get_new_data(self):  # this way when you get new data it will get all the data\n",
+    "        header_request = {\n",
+    "            0: ['ROOTNAME', 'SEGMENT'],\n",
+    "            1: ['EXPTIME', 'EXPSTART']\n",
+    "        }\n",
+    "        table_request = {\n",
+    "            1: ['PHA', 'XCORR', 'YCORR', 'TIME'],\n",
+    "            3: ['TIME', 'LATITUDE', 'LONGITUDE']\n",
+    "        }\n",
+    "\n",
+    "        files = []\n",
+    "\n",
+    "        # this is not finding files for some reason. i need to explore that\n",
+    "#         for prog_id in self.program_id:\n",
+    "#             new_files_source = os.path.join(FILES_SOURCE, prog_id)\n",
+    "#             print(new_files_source)\n",
+    "#             files += find_files('*corrtag*', data_dir=new_files_source)\n",
+    "#             print(files)\n",
+    "\n",
+    "        for program in self.program_id:\n",
+    "            new_files_source = os.path.join(FILES_SOURCE, program)\n",
+    "            subfiles = glob(os.path.join(new_files_source, \"*corrtag*\"))\n",
+    "            files += subfiles\n",
+    "\n",
+    "        if self.model is not None:\n",
+    "            currently_ingested = [item.FILENAME for item in self.model.select(self.model.FILENAME)]\n",
+    "\n",
+    "            for file in currently_ingested:\n",
+    "                files.remove(file)\n",
+    "\n",
+    "        if not files:   # No new files\n",
+    "            return pd.DataFrame()\n",
+    "\n",
+    "        data_results = data_from_exposures(\n",
+    "            files,\n",
+    "            header_request=header_request,\n",
+    "            table_request=table_request\n",
+    "        )\n",
+    "\n",
+    "        return data_results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# model = DarkDataModel()\n",
+    "# data_results = model.get_new_data()\n",
+    "# data_results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# FUV monitors\n",
+    "\n",
+    "def dark_filter(df_row, filter_pha, location):\n",
+    "    good_pha = (2, 23)\n",
+    "    time_step = 25\n",
+    "    time_bins = df_row['TIME_3'][::time_step]\n",
+    "    # try commenting these out, since lat and lon don't seem to be used\n",
+    "    lat = df_row['LATITUDE'][::time_step][:-1]\n",
+    "    lon = df_row['LONGITUDE'][::time_step][:-1]\n",
+    "    event_df = df_row[['SEGMENT', 'XCORR', 'YCORR', 'PHA', 'TIME']].to_frame().T\n",
+    "    event_df = explode_df(event_df, ['XCORR', 'YCORR', 'PHA', 'TIME'])\n",
+    "    npix = (location[1] - location[0]) * (location[3] - location[2])\n",
+    "    index = np.where((event_df['XCORR'] > location[0]) &\n",
+    "                     (event_df['XCORR'] < location[1]) &\n",
+    "                     (event_df['YCORR'] > location[2]) &\n",
+    "                     (event_df['YCORR'] < location[3]))\n",
+    "    filtered_row = event_df.iloc[index].reset_index(drop=True)\n",
+    "\n",
+    "    if filter_pha:\n",
+    "        filtered_row = filtered_row[(filtered_row['PHA'] > good_pha[0]) & (filtered_row['PHA'] < good_pha[1])]\n",
+    "\n",
+    "    counts = np.histogram(filtered_row.TIME, bins=time_bins)[0]\n",
+    "\n",
+    "    date = absolute_time(\n",
+    "        expstart=list(repeat(df_row['EXPSTART'], len(time_bins))), time=time_bins.tolist()\n",
+    "    ).to_datetime()[:-1]\n",
+    "\n",
+    "    dark_rate = counts / npix / time_step\n",
+    "\n",
+    "    return pd.DataFrame({'segment': df_row['SEGMENT'], 'darks': [dark_rate], 'date': [date],\n",
+    "                        'ROOTNAME': df_row['ROOTNAME']})\n",
+    "\n",
+    "\n",
+    "class FUVDarkMonitor(BaseMonitor):\n",
+    "    \"\"\"Abstracted FUV Dark Monitor. Not meant to be used directly but rather inherited by specific segment and region\n",
+    "    dark monitors\"\"\"\n",
+    "    labels = ['ROOTNAME']\n",
+    "    output = COS_MONITORING\n",
+    "    docs = \"https://spacetelescope.github.io/cosmo/monitors.html#fuv-dark-rate-monitors\"\n",
+    "    segment = None\n",
+    "    location = None\n",
+    "    data_model = DarkDataModel\n",
+    "    plottype = 'scatter'\n",
+    "    x = 'date'\n",
+    "    y = 'darks'\n",
+    "\n",
+    "    def get_data(self): # -> Any: fix this later, should be fine in the monitor, just not in jupyter notebook\n",
+    "        filtered_rows = []\n",
+    "#         print(self.model.new_data)\n",
+    "        for _, row in self.model.new_data.iterrows():\n",
+    "#             print(row.SEGMENT, self.segment)\n",
+    "            if row.EXPSTART == 0:\n",
+    "                continue\n",
+    "            if row.SEGMENT == self.segment:\n",
+    "                filtered_rows.append(dark_filter(row, True, self.location))\n",
+    "        filtered_df = pd.concat(filtered_rows).reset_index(drop=True)\n",
+    "\n",
+    "        return explode_df(filtered_df, ['darks', 'date'])\n",
+    "\n",
+    "    def store_results(self):\n",
+    "        # TODO: Define results to store\n",
+    "        pass\n",
+    "\n",
+    "    def track(self):\n",
+    "        # TODO: Define something to track\n",
+    "        pass\n",
+    "\n",
+    "\n",
+    "class FUVABottomDarkMonitor(FUVDarkMonitor):\n",
+    "    \"\"\"FUVA dark monitor for bottom edge\"\"\"\n",
+    "#     data_model = FUVDarkDataModel\n",
+    "    segment = 'FUVA'\n",
+    "    location = (1060, 15250, 296, 375)\n",
+    "    name = f'FUVA Dark Monitor - Bottom'\n",
+    "\n",
+    "\n",
+    "class FUVALeftDarkMonitor(FUVDarkMonitor):\n",
+    "    \"\"\"FUVA dark monitor for left edge\"\"\"\n",
+    "    name = 'FUVA Dark Monitor - Left'\n",
+    "#     data_model = FUVDarkDataModel\n",
+    "    segment = 'FUVA'\n",
+    "    location = (1060, 1260, 296, 734)\n",
+    "\n",
+    "\n",
+    "class FUVATopDarkMonitor(FUVDarkMonitor):\n",
+    "    \"\"\"FUVA dark monitor for top edge\"\"\"\n",
+    "    name = 'FUVA Dark Monitor - Top'\n",
+    "#     data_model = FUVDarkDataModel\n",
+    "    segment = 'FUVA'\n",
+    "    location = (1060, 15250, 660, 734)\n",
+    "\n",
+    "\n",
+    "class FUVARightDarkMonitor(FUVDarkMonitor):\n",
+    "    \"\"\"FUVA dark monitor for right edge\"\"\"\n",
+    "    name = 'FUVA Dark Monitor - Right'\n",
+    "#     data_model = FUVDarkDataModel\n",
+    "    segment = 'FUVA'\n",
+    "    location = (15119, 15250, 296, 734)\n",
+    "\n",
+    "\n",
+    "class FUVAInnerDarkMonitor(FUVDarkMonitor):\n",
+    "    \"\"\"FUVA dark monitor for inner region\"\"\"\n",
+    "    name = 'FUVA Dark Monitor - Inner'\n",
+    "#     data_model = FUVDarkDataModel\n",
+    "    segment = 'FUVA'\n",
+    "    location = (1260, 15119, 375, 660)\n",
+    "\n",
+    "\n",
+    "class FUVBBottomDarkMonitor(FUVDarkMonitor):\n",
+    "    \"\"\"FUVB dark monitor for bottom edge\"\"\"\n",
+    "    name = 'FUVB Dark Monitor - Bottom'\n",
+    "#     data_model = FUVDarkDataModel\n",
+    "    segment = 'FUVB'\n",
+    "    location = (809, 15182, 360, 405)\n",
+    "\n",
+    "\n",
+    "class FUVBLeftDarkMonitor(FUVDarkMonitor):\n",
+    "    \"\"\"FUVB dark monitor for left edge\"\"\"\n",
+    "    name = 'FUVB Dark Monitor - Left'\n",
+    "#     data_model = FUVDarkDataModel\n",
+    "    segment = 'FUVB'\n",
+    "    location = (809, 1000, 360, 785)\n",
+    "\n",
+    "\n",
+    "class FUVBTopDarkMonitor(FUVDarkMonitor):\n",
+    "    \"\"\"FUVB dark monitor for top edge\"\"\"\n",
+    "    name = 'FUVB Dark Monitor - Top'\n",
+    "#     data_model = FUVDarkDataModel\n",
+    "    segment = 'FUVB'\n",
+    "    location = (809, 15182, 740, 785)\n",
+    "\n",
+    "\n",
+    "class FUVBRightDarkMonitor(FUVDarkMonitor):\n",
+    "    \"\"\"FUVB dark monitor for right edge\"\"\"\n",
+    "    name = 'FUVB Dark Monitor - Right'\n",
+    "#     data_model = FUVDarkDataModel\n",
+    "    segment = 'FUVB'\n",
+    "    location = (14990, 15182, 360, 785)\n",
+    "\n",
+    "\n",
+    "class FUVBInnerDarkMonitor(FUVDarkMonitor):\n",
+    "    \"\"\"FUVB dark monitor for inner region\"\"\"\n",
+    "    name = 'FUVB Dark Monitor - Inner'\n",
+    "#     data_model = FUVDarkDataModel\n",
+    "    segment = 'FUVB'\n",
+    "    location = (1000, 14990, 405, 740)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "fuv_monitor = FUVAInnerDarkMonitor()\n",
+    "fuv_monitor.monitor()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Working Dark Monitor for both FUV and NUV"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# include NUV\n",
+    "def dark_filter(df_row, filter_pha, location):\n",
+    "    good_pha = (2, 23)\n",
+    "    # time step stuff\n",
+    "    time_step = 25\n",
+    "    time_bins = df_row['TIME_3'][::time_step]\n",
+    "    lat = df_row['LATITUDE'][::time_step][:-1]\n",
+    "    lon = df_row['LONGITUDE'][::time_step][:-1]\n",
+    "    \n",
+    "    # try commenting these out, since lat and lon don't seem to be used\n",
+    "#     lat = df_row['LATITUDE'][::time_step][:-1]\n",
+    "#     lon = df_row['LONGITUDE'][::time_step][:-1]\n",
+    "    \n",
+    "    # filtering pha\n",
+    "    if filter_pha:\n",
+    "        event_df = df_row[['SEGMENT', 'XCORR', 'YCORR', 'PHA', 'TIME']].to_frame().T\n",
+    "        event_df = explode_df(event_df, ['XCORR', 'YCORR', 'PHA', 'TIME'])\n",
+    "    else:\n",
+    "        event_df = df_row[['SEGMENT', 'XCORR', 'YCORR', 'TIME']].to_frame().T\n",
+    "        event_df = explode_df(event_df, ['XCORR', 'YCORR', 'TIME'])\n",
+    "    \n",
+    "    # creating event dataframe and filtering it by location on the detector\n",
+    "    npix = (location[1] - location[0]) * (location[3] - location[2])\n",
+    "    index = np.where((event_df['XCORR'] > location[0]) &\n",
+    "                     (event_df['XCORR'] < location[1]) &\n",
+    "                     (event_df['YCORR'] > location[2]) &\n",
+    "                     (event_df['YCORR'] < location[3]))\n",
+    "    filtered_row = event_df.iloc[index].reset_index(drop=True)\n",
+    "\n",
+    "    #filtered events only need to be further filtered by PHA if not NUV\n",
+    "    if filter_pha:\n",
+    "        filtered_row = filtered_row[(filtered_row['PHA'] > good_pha[0]) & (filtered_row['PHA'] < good_pha[1])]\n",
+    "\n",
+    "    counts = np.histogram(filtered_row.TIME, bins=time_bins)[0]\n",
+    "\n",
+    "    date = absolute_time(\n",
+    "        expstart=list(repeat(df_row['EXPSTART'], len(time_bins))), time=time_bins.tolist()\n",
+    "    ).to_datetime()[:-1]\n",
+    "\n",
+    "    dark_rate = counts / npix / time_step\n",
+    "\n",
+    "    return pd.DataFrame({'segment': df_row['SEGMENT'], 'darks': [dark_rate], 'date': [date],\n",
+    "                        'ROOTNAME': df_row['ROOTNAME']})\n",
+    "\n",
+    "\n",
+    "class DarkMonitor(BaseMonitor):\n",
+    "    \"\"\"Abstracted FUV Dark Monitor. Not meant to be used directly but rather inherited by specific segment and region\n",
+    "    dark monitors\"\"\"\n",
+    "    labels = ['ROOTNAME']\n",
+    "    output = COS_MONITORING\n",
+    "    docs = \"https://spacetelescope.github.io/cosmo/monitors.html#fuv-dark-rate-monitors\"\n",
+    "    segment = None\n",
+    "    location = None\n",
+    "    data_model = DarkDataModel\n",
+    "    plottype = 'scatter'\n",
+    "    x = 'date'\n",
+    "    y = 'darks'\n",
+    "\n",
+    "    def get_data(self): # -> Any: fix this later, should be fine in the monitor, just not in jupyter notebook\n",
+    "        filtered_rows = []\n",
+    "        for _, row in self.model.new_data.iterrows():\n",
+    "            if row.EXPSTART == 0:\n",
+    "                continue\n",
+    "            if row.SEGMENT == self.segment: \n",
+    "                if row.SEGMENT == \"N/A\":  #NUV\n",
+    "                    filtered_rows.append(dark_filter(row, False, self.location))\n",
+    "                else:   # Any of the FUV situations\n",
+    "                    filtered_rows.append(dark_filter(row, True, self.location))\n",
+    "        filtered_df = pd.concat(filtered_rows).reset_index(drop=True)\n",
+    "\n",
+    "        return explode_df(filtered_df, ['darks', 'date'])\n",
+    "\n",
+    "    def store_results(self):\n",
+    "        # TODO: Define results to store\n",
+    "        pass\n",
+    "\n",
+    "    def track(self):\n",
+    "        # TODO: Define something to track\n",
+    "        pass\n",
+    "\n",
+    "\n",
+    "class FUVABottomDarkMonitor(DarkMonitor):\n",
+    "    \"\"\"FUVA dark monitor for bottom edge\"\"\"\n",
+    "#     data_model = FUVDarkDataModel\n",
+    "    segment = 'FUVA'\n",
+    "    location = (1060, 15250, 296, 375)\n",
+    "    name = f'FUVA Dark Monitor - Bottom'\n",
+    "\n",
+    "\n",
+    "class FUVALeftDarkMonitor(DarkMonitor):\n",
+    "    \"\"\"FUVA dark monitor for left edge\"\"\"\n",
+    "    name = 'FUVA Dark Monitor - Left'\n",
+    "#     data_model = FUVDarkDataModel\n",
+    "    segment = 'FUVA'\n",
+    "    location = (1060, 1260, 296, 734)\n",
+    "\n",
+    "\n",
+    "class FUVATopDarkMonitor(DarkMonitor):\n",
+    "    \"\"\"FUVA dark monitor for top edge\"\"\"\n",
+    "    name = 'FUVA Dark Monitor - Top'\n",
+    "#     data_model = FUVDarkDataModel\n",
+    "    segment = 'FUVA'\n",
+    "    location = (1060, 15250, 660, 734)\n",
+    "\n",
+    "\n",
+    "class FUVARightDarkMonitor(DarkMonitor):\n",
+    "    \"\"\"FUVA dark monitor for right edge\"\"\"\n",
+    "    name = 'FUVA Dark Monitor - Right'\n",
+    "#     data_model = FUVDarkDataModel\n",
+    "    segment = 'FUVA'\n",
+    "    location = (15119, 15250, 296, 734)\n",
+    "\n",
+    "\n",
+    "class FUVAInnerDarkMonitor(DarkMonitor):\n",
+    "    \"\"\"FUVA dark monitor for inner region\"\"\"\n",
+    "    name = 'FUVA Dark Monitor - Inner'\n",
+    "#     data_model = FUVDarkDataModel\n",
+    "    segment = 'FUVA'\n",
+    "    location = (1260, 15119, 375, 660)\n",
+    "\n",
+    "\n",
+    "class FUVBBottomDarkMonitor(DarkMonitor):\n",
+    "    \"\"\"FUVB dark monitor for bottom edge\"\"\"\n",
+    "    name = 'FUVB Dark Monitor - Bottom'\n",
+    "#     data_model = FUVDarkDataModel\n",
+    "    segment = 'FUVB'\n",
+    "    location = (809, 15182, 360, 405)\n",
+    "\n",
+    "\n",
+    "class FUVBLeftDarkMonitor(DarkMonitor):\n",
+    "    \"\"\"FUVB dark monitor for left edge\"\"\"\n",
+    "    name = 'FUVB Dark Monitor - Left'\n",
+    "#     data_model = FUVDarkDataModel\n",
+    "    segment = 'FUVB'\n",
+    "    location = (809, 1000, 360, 785)\n",
+    "\n",
+    "\n",
+    "class FUVBTopDarkMonitor(DarkMonitor):\n",
+    "    \"\"\"FUVB dark monitor for top edge\"\"\"\n",
+    "    name = 'FUVB Dark Monitor - Top'\n",
+    "#     data_model = FUVDarkDataModel\n",
+    "    segment = 'FUVB'\n",
+    "    location = (809, 15182, 740, 785)\n",
+    "\n",
+    "\n",
+    "class FUVBRightDarkMonitor(DarkMonitor):\n",
+    "    \"\"\"FUVB dark monitor for right edge\"\"\"\n",
+    "    name = 'FUVB Dark Monitor - Right'\n",
+    "#     data_model = FUVDarkDataModel\n",
+    "    segment = 'FUVB'\n",
+    "    location = (14990, 15182, 360, 785)\n",
+    "\n",
+    "\n",
+    "class FUVBInnerDarkMonitor(DarkMonitor):\n",
+    "    \"\"\"FUVB dark monitor for inner region\"\"\"\n",
+    "    name = 'FUVB Dark Monitor - Inner'\n",
+    "#     data_model = FUVDarkDataModel\n",
+    "    segment = 'FUVB'\n",
+    "    location = (1000, 14990, 405, 740)\n",
+    "       \n",
+    "    \n",
+    "class NUVDarkMonitor(DarkMonitor):\n",
+    "    name = \"NUV Dark Monitor\"\n",
+    "    segment = \"N/A\"\n",
+    "    location = (0, 1024, 0, 1024)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nuv_monitor = NUVDarkMonitor()\n",
+    "nuv_monitor.monitor()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# # run everything\n",
+    "# from tqdm import tqdm\n",
+    "# def run_all_dark_monitors():\n",
+    "#     fuva_bottom_monitor = FUVABottomDarkMonitor()\n",
+    "#     fuva_left_monitor = FUVALeftDarkMonitor()\n",
+    "#     fuva_top_monitor = FUVATopDarkMonitor()\n",
+    "#     fuva_right_monitor = FUVARightDarkMonitor()\n",
+    "#     fuva_inner_monitor = FUVAInnerDarkMonitor()\n",
+    "#     fuvb_bottom_monitor = FUVbBottomDarkMonitor()\n",
+    "#     fuvb_left_monitor = FUVbLeftDarkMonitor()\n",
+    "#     fuvb_top_monitor = FUVbTopDarkMonitor()\n",
+    "#     fuvb_right_monitor = FUVbRightDarkMonitor()\n",
+    "#     fuvb_inner_monitor = FUVbInnerDarkMonitor()\n",
+    "#     nuv_monitor = NUVDarkMonitor()\n",
+    "#     for monitor in tqdm([fuva_bottom_monitor, fuva_left_monitor, fuva_top_monitor, \n",
+    "#                     fuva_right_monitor, fuva_inner_monitor, fuvb_bottom_monitor,\n",
+    "#                    fuvb_left_monitor, fuvb_top_monitor, fuvb_right_monitor, \n",
+    "#                    fuvb_inner_monitor, nuv_monitor]):\n",
+    "#         monitor.monitor()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# combined DataModel\n",
+    "from tqdm import tqdm\n",
+    "\n",
+    "class DarkDataModel(BaseDataModel):\n",
+    "    cosmo_layout = False\n",
+    "#     fuv_program_ids = ['15771/', '15533/', '14940/', '14520/', '14436/', '13968/', '13521/', '13121/', '12716/', '12423/',\n",
+    "#                   '11895/']\n",
+    "#     nuv_program_ids = ['15776/', '15538/', '14942/', '14521/', '14442/', '13974/', \n",
+    "#                        '13528/', '13126/', '12720/', '12420/', '11894/']\n",
+    "    fuv_program_ids = [\"15771/\"]\n",
+    "    nuv_program_ids = [\"15776/\"]\n",
+    "    program_id = fuv_program_ids + nuv_program_ids\n",
+    "\n",
+    "    def get_new_data(self):  # this way when you get new data it will get all the data\n",
+    "        header_request = {\n",
+    "            0: ['ROOTNAME', 'SEGMENT'],\n",
+    "            1: ['EXPTIME', 'EXPSTART']\n",
+    "        }\n",
+    "        table_request = {\n",
+    "            1: ['PHA', 'XCORR', 'YCORR', 'TIME'],\n",
+    "            3: ['TIME', 'LATITUDE', 'LONGITUDE']\n",
+    "        }\n",
+    "\n",
+    "        files = []\n",
+    "\n",
+    "        for prog_id in tqdm(self.program_id):\n",
+    "            new_files_source = os.path.join(FILES_SOURCE, prog_id)\n",
+    "            files += find_files('*corrtag*', data_dir=new_files_source)\n",
+    "\n",
+    "#         for program in self.program_id:\n",
+    "#             new_files_source = os.path.join(FILES_SOURCE, program)\n",
+    "#             subfiles = glob(os.path.join(new_files_source, \"*corrtag*\"))\n",
+    "#             files += subfiles\n",
+    "\n",
+    "        if self.model is not None:\n",
+    "            currently_ingested = [item.FILENAME for item in self.model.select(self.model.FILENAME)]\n",
+    "\n",
+    "            for file in currently_ingested:\n",
+    "                files.remove(file)\n",
+    "\n",
+    "        if not files:   # No new files\n",
+    "            return pd.DataFrame()\n",
+    "\n",
+    "        data_results = data_from_exposures(\n",
+    "            files,\n",
+    "            header_request=header_request,\n",
+    "            table_request=table_request\n",
+    "        )\n",
+    "\n",
+    "        return data_results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# include NUV\n",
+    "def dark_filter(df_row, filter_pha, location):\n",
+    "    good_pha = (2, 23)\n",
+    "    # time step stuff\n",
+    "    time_step = 25\n",
+    "    time_bins = df_row['TIME_3'][::time_step]\n",
+    "    lat = df_row['LATITUDE'][::time_step][:-1]\n",
+    "    lon = df_row['LONGITUDE'][::time_step][:-1]\n",
+    "    \n",
+    "    # try commenting these out, since lat and lon don't seem to be used\n",
+    "#     lat = df_row['LATITUDE'][::time_step][:-1]\n",
+    "#     lon = df_row['LONGITUDE'][::time_step][:-1]\n",
+    "    \n",
+    "    # filtering pha\n",
+    "    if filter_pha:\n",
+    "        event_df = df_row[['SEGMENT', 'XCORR', 'YCORR', 'PHA', 'TIME']].to_frame().T\n",
+    "        event_df = explode_df(event_df, ['XCORR', 'YCORR', 'PHA', 'TIME'])\n",
+    "    else:\n",
+    "        event_df = df_row[['SEGMENT', 'XCORR', 'YCORR', 'TIME']].to_frame().T\n",
+    "        event_df = explode_df(event_df, ['XCORR', 'YCORR', 'TIME'])\n",
+    "    \n",
+    "    # creating event dataframe and filtering it by location on the detector\n",
+    "    npix = (location[1] - location[0]) * (location[3] - location[2])\n",
+    "    index = np.where((event_df['XCORR'] > location[0]) &\n",
+    "                     (event_df['XCORR'] < location[1]) &\n",
+    "                     (event_df['YCORR'] > location[2]) &\n",
+    "                     (event_df['YCORR'] < location[3]))\n",
+    "    filtered_row = event_df.iloc[index].reset_index(drop=True)\n",
+    "\n",
+    "    #filtered events only need to be further filtered by PHA if not NUV\n",
+    "    if filter_pha:\n",
+    "        filtered_row = filtered_row[(filtered_row['PHA'] > good_pha[0]) & (filtered_row['PHA'] < good_pha[1])]\n",
+    "\n",
+    "    counts = np.histogram(filtered_row.TIME, bins=time_bins)[0]\n",
+    "\n",
+    "    date = absolute_time(\n",
+    "        expstart=list(repeat(df_row['EXPSTART'], len(time_bins))), time=time_bins.tolist()\n",
+    "    ).to_datetime()[:-1]\n",
+    "\n",
+    "    dark_rate = counts / npix / time_step\n",
+    "\n",
+    "    return pd.DataFrame({'segment': df_row['SEGMENT'], 'darks': [dark_rate], 'date': [date],\n",
+    "                        'ROOTNAME': df_row['ROOTNAME']})\n",
+    "\n",
+    "\n",
+    "class DarkMonitor(BaseMonitor):\n",
+    "    \"\"\"Abstracted FUV Dark Monitor. Not meant to be used directly but rather inherited by specific segment and region\n",
+    "    dark monitors\"\"\"\n",
+    "    labels = ['ROOTNAME']\n",
+    "    output = COS_MONITORING\n",
+    "    docs = \"https://spacetelescope.github.io/cosmo/monitors.html#fuv-dark-rate-monitors\"\n",
+    "    segment = None\n",
+    "    location = None\n",
+    "    data_model = DarkDataModel\n",
+    "    plottype = 'scatter'\n",
+    "    x = 'date'\n",
+    "    y = 'darks'\n",
+    "\n",
+    "    def get_data(self): # -> Any: fix this later, should be fine in the monitor, just not in jupyter notebook\n",
+    "        filtered_rows = []\n",
+    "        for _, row in self.model.new_data.iterrows():\n",
+    "            if row.EXPSTART == 0:\n",
+    "                continue\n",
+    "            if row.SEGMENT == self.segment: \n",
+    "                if row.SEGMENT == \"N/A\":  #NUV\n",
+    "                    filtered_rows.append(dark_filter(row, False, self.location))\n",
+    "                else:   # Any of the FUV situations\n",
+    "                    filtered_rows.append(dark_filter(row, True, self.location))\n",
+    "        filtered_df = pd.concat(filtered_rows).reset_index(drop=True)\n",
+    "\n",
+    "        return explode_df(filtered_df, ['darks', 'date'])\n",
+    "\n",
+    "    def store_results(self):\n",
+    "        # TODO: Define results to store\n",
+    "        pass\n",
+    "\n",
+    "    def track(self):\n",
+    "        # TODO: Define something to track\n",
+    "        pass\n",
+    "\n",
+    "\n",
+    "class FUVABottomDarkMonitor(DarkMonitor):\n",
+    "    \"\"\"FUVA dark monitor for bottom edge\"\"\"\n",
+    "#     data_model = FUVDarkDataModel\n",
+    "    segment = 'FUVA'\n",
+    "    location = (1060, 15250, 296, 375)\n",
+    "    name = f'FUVA Dark Monitor - Bottom'\n",
+    "\n",
+    "\n",
+    "class FUVALeftDarkMonitor(DarkMonitor):\n",
+    "    \"\"\"FUVA dark monitor for left edge\"\"\"\n",
+    "    name = 'FUVA Dark Monitor - Left'\n",
+    "#     data_model = FUVDarkDataModel\n",
+    "    segment = 'FUVA'\n",
+    "    location = (1060, 1260, 296, 734)\n",
+    "\n",
+    "\n",
+    "class FUVATopDarkMonitor(DarkMonitor):\n",
+    "    \"\"\"FUVA dark monitor for top edge\"\"\"\n",
+    "    name = 'FUVA Dark Monitor - Top'\n",
+    "#     data_model = FUVDarkDataModel\n",
+    "    segment = 'FUVA'\n",
+    "    location = (1060, 15250, 660, 734)\n",
+    "\n",
+    "\n",
+    "class FUVARightDarkMonitor(DarkMonitor):\n",
+    "    \"\"\"FUVA dark monitor for right edge\"\"\"\n",
+    "    name = 'FUVA Dark Monitor - Right'\n",
+    "#     data_model = FUVDarkDataModel\n",
+    "    segment = 'FUVA'\n",
+    "    location = (15119, 15250, 296, 734)\n",
+    "\n",
+    "\n",
+    "class FUVAInnerDarkMonitor(DarkMonitor):\n",
+    "    \"\"\"FUVA dark monitor for inner region\"\"\"\n",
+    "    name = 'FUVA Dark Monitor - Inner'\n",
+    "#     data_model = FUVDarkDataModel\n",
+    "    segment = 'FUVA'\n",
+    "    location = (1260, 15119, 375, 660)\n",
+    "\n",
+    "\n",
+    "class FUVBBottomDarkMonitor(DarkMonitor):\n",
+    "    \"\"\"FUVB dark monitor for bottom edge\"\"\"\n",
+    "    name = 'FUVB Dark Monitor - Bottom'\n",
+    "#     data_model = FUVDarkDataModel\n",
+    "    segment = 'FUVB'\n",
+    "    location = (809, 15182, 360, 405)\n",
+    "\n",
+    "\n",
+    "class FUVBLeftDarkMonitor(DarkMonitor):\n",
+    "    \"\"\"FUVB dark monitor for left edge\"\"\"\n",
+    "    name = 'FUVB Dark Monitor - Left'\n",
+    "#     data_model = FUVDarkDataModel\n",
+    "    segment = 'FUVB'\n",
+    "    location = (809, 1000, 360, 785)\n",
+    "\n",
+    "\n",
+    "class FUVBTopDarkMonitor(DarkMonitor):\n",
+    "    \"\"\"FUVB dark monitor for top edge\"\"\"\n",
+    "    name = 'FUVB Dark Monitor - Top'\n",
+    "#     data_model = FUVDarkDataModel\n",
+    "    segment = 'FUVB'\n",
+    "    location = (809, 15182, 740, 785)\n",
+    "\n",
+    "\n",
+    "class FUVBRightDarkMonitor(DarkMonitor):\n",
+    "    \"\"\"FUVB dark monitor for right edge\"\"\"\n",
+    "    name = 'FUVB Dark Monitor - Right'\n",
+    "#     data_model = FUVDarkDataModel\n",
+    "    segment = 'FUVB'\n",
+    "    location = (14990, 15182, 360, 785)\n",
+    "\n",
+    "\n",
+    "class FUVBInnerDarkMonitor(DarkMonitor):\n",
+    "    \"\"\"FUVB dark monitor for inner region\"\"\"\n",
+    "    name = 'FUVB Dark Monitor - Inner'\n",
+    "#     data_model = FUVDarkDataModel\n",
+    "    segment = 'FUVB'\n",
+    "    location = (1000, 14990, 405, 740)\n",
+    "       \n",
+    "    \n",
+    "class NUVDarkMonitor(DarkMonitor):\n",
+    "    name = \"NUV Dark Monitor\"\n",
+    "    segment = \"N/A\"\n",
+    "    location = (0, 1024, 0, 1024)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nuv_monitor = NUVDarkMonitor()\n",
+    "nuv_monitor.monitor()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/.ipynb_checkpoints/solar_plots-checkpoint.ipynb b/.ipynb_checkpoints/solar_plots-checkpoint.ipynb
new file mode 100644
index 0000000..8b4f6fc
--- /dev/null
+++ b/.ipynb_checkpoints/solar_plots-checkpoint.ipynb
@@ -0,0 +1,996 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import yaml\n",
+    "import cosmo\n",
+    "import numpy\n",
+    "import scipy\n",
+    "import sunpy\n",
+    "import datetime\n",
+    "\n",
+    "import plotly.express as px\n",
+    "import matplotlib.pyplot as plt\n",
+    "import plotly.graph_objects as go\n",
+    "\n",
+    "from glob import glob\n",
+    "from ftplib import FTP\n",
+    "from astropy.io import fits\n",
+    "from itertools import repeat\n",
+    "from astropy.time import Time\n",
+    "from sunpy.net import Fido, attrs as a\n",
+    "from sunpy.timeseries import TimeSeries\n",
+    "from plotly.subplots import make_subplots\n",
+    "from monitorframe.monitor import BaseMonitor\n",
+    "from monitorframe.datamodel import BaseDataModel\n",
+    "from matplotlib.ticker import FormatStrFormatter\n",
+    "from cosmo.monitor_helpers import absolute_time, explode_df\n",
+    "from cosmo.filesystem import find_files, data_from_exposures, data_from_jitters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# load settings\n",
+    "with open(os.environ['COSMO_CONFIG']) as yamlfile:\n",
+    "    SETTINGS = yaml.safe_load(yamlfile)\n",
+    "\n",
+    "FILES_SOURCE = SETTINGS['filesystem']['source']\n",
+    "COS_MONITORING = SETTINGS['output']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# working DataModel\n",
+    "class DarkDataModel(BaseDataModel):\n",
+    "    cosmo_layout = False\n",
+    "    fuv_program_ids = ['15771/', '15533/', '14940/', '14520/', '14436/', '13968/', '13521/', '13121/', '12716/', '12423/',\n",
+    "                  '11895/']\n",
+    "    program_id = fuv_program_ids\n",
+    "#     nuv_program_ids = ['15776/', '15538/', '14942/', '14521/', '14442/', '13974/', \n",
+    "#                        '13528/', '13126/', '12720/', '12420/', '11894/']\n",
+    "#     fuv_program_ids = [\"15771/\"]\n",
+    "#     nuv_program_ids = [\"15776/\"]\n",
+    "#     program_id = fuv_program_ids + nuv_program_ids\n",
+    "\n",
+    "    def get_new_data(self):  # this way when you get new data it will get all the data\n",
+    "        header_request = {\n",
+    "            0: ['ROOTNAME', 'SEGMENT'],\n",
+    "            1: ['EXPTIME', 'EXPSTART']\n",
+    "        }\n",
+    "        table_request = {\n",
+    "            1: ['PHA', 'XCORR', 'YCORR', 'TIME'],\n",
+    "            3: ['TIME', 'LATITUDE', 'LONGITUDE']\n",
+    "        }\n",
+    "\n",
+    "        files = []\n",
+    "\n",
+    "        for prog_id in self.program_id:\n",
+    "            new_files_source = os.path.join(FILES_SOURCE, prog_id)\n",
+    "            files += find_files('*corrtag*', data_dir=new_files_source)\n",
+    "\n",
+    "        if self.model is not None:\n",
+    "            currently_ingested = [item.FILENAME for item in self.model.select(self.model.FILENAME)]\n",
+    "\n",
+    "            for file in currently_ingested:\n",
+    "                files.remove(file)\n",
+    "\n",
+    "        if not files:   # No new files\n",
+    "            return pd.DataFrame()\n",
+    "\n",
+    "        data_results = data_from_exposures(\n",
+    "            files,\n",
+    "            header_request=header_request,\n",
+    "            table_request=table_request\n",
+    "        )\n",
+    "\n",
+    "        return data_results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# monitor functions\n",
+    "def dark_filter(df_row, filter_pha, location):\n",
+    "    good_pha = (2, 23)\n",
+    "    # time step stuff\n",
+    "    time_step = 25\n",
+    "    time_bins = df_row['TIME_3'][::time_step]\n",
+    "    lat = df_row['LATITUDE'][::time_step][:-1]\n",
+    "    lon = df_row['LONGITUDE'][::time_step][:-1]\n",
+    "    \n",
+    "    # try commenting these out, since lat and lon don't seem to be used\n",
+    "#     lat = df_row['LATITUDE'][::time_step][:-1]\n",
+    "#     lon = df_row['LONGITUDE'][::time_step][:-1]\n",
+    "    \n",
+    "    # filtering pha\n",
+    "    if filter_pha:\n",
+    "        event_df = df_row[['SEGMENT', 'XCORR', 'YCORR', 'PHA', 'TIME']].to_frame().T\n",
+    "        event_df = explode_df(event_df, ['XCORR', 'YCORR', 'PHA', 'TIME'])\n",
+    "    else:\n",
+    "        event_df = df_row[['SEGMENT', 'XCORR', 'YCORR', 'TIME']].to_frame().T\n",
+    "        event_df = explode_df(event_df, ['XCORR', 'YCORR', 'TIME'])\n",
+    "    \n",
+    "    # creating event dataframe and filtering it by location on the detector\n",
+    "    npix = (location[1] - location[0]) * (location[3] - location[2])\n",
+    "    index = np.where((event_df['XCORR'] > location[0]) &\n",
+    "                     (event_df['XCORR'] < location[1]) &\n",
+    "                     (event_df['YCORR'] > location[2]) &\n",
+    "                     (event_df['YCORR'] < location[3]))\n",
+    "    filtered_row = event_df.iloc[index].reset_index(drop=True)\n",
+    "\n",
+    "    #filtered events only need to be further filtered by PHA if not NUV\n",
+    "    if filter_pha:\n",
+    "        filtered_row = filtered_row[(filtered_row['PHA'] > good_pha[0]) & (filtered_row['PHA'] < good_pha[1])]\n",
+    "\n",
+    "    counts = np.histogram(filtered_row.TIME, bins=time_bins)[0]\n",
+    "\n",
+    "    date = absolute_time(\n",
+    "        expstart=list(repeat(df_row['EXPSTART'], len(time_bins))), time=time_bins.tolist()\n",
+    "    ).to_datetime()[:-1]\n",
+    "\n",
+    "    dark_rate = counts / npix / time_step\n",
+    "\n",
+    "    return pd.DataFrame({'segment': df_row['SEGMENT'], 'darks': [dark_rate], 'date': [date],\n",
+    "                        'ROOTNAME': df_row['ROOTNAME']})\n",
+    "\n",
+    "# solar functions\n",
+    "def grab_solar_files(file_dir):\n",
+    "    \"\"\"Pull solar data files from NOAA website\n",
+    "    Solar data is FTPd from NOAA and written to text files for use in plotting\n",
+    "    and monitoring of COS dark-rates and TDS.\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    file_dir : str\n",
+    "        Directory to write the files to\n",
+    "    \"\"\"\n",
+    "    ftp = FTP('ftp.swpc.noaa.gov')\n",
+    "    ftp.login()\n",
+    "\n",
+    "    ftp.cwd('/pub/indices/old_indices/')\n",
+    "\n",
+    "    for item in sorted(ftp.nlst()):\n",
+    "        if item.endswith('_DSD.txt'):\n",
+    "            year = int(item[:4])\n",
+    "            if year >= 2000:\n",
+    "                destination = os.path.join(file_dir, item)\n",
+    "                if not os.path.exists(destination):\n",
+    "                    ftp.retrbinary('RETR {}'.format(item),\n",
+    "                                   open(destination, 'wb').write)\n",
+    "\n",
+    "                    os.chmod(destination, 0o777)\n",
+    "\n",
+    "\n",
+    "def compile_solar_data(file_dir):\n",
+    "    \"\"\"Pull desired columns from solar data text files\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    file_dir : str\n",
+    "    Returns\n",
+    "    -------\n",
+    "    date : np.ndarray\n",
+    "        mjd of each measurements\n",
+    "    flux : np.ndarray\n",
+    "        solar flux measurements\n",
+    "    \"\"\"\n",
+    "    date = []\n",
+    "    flux = []\n",
+    "    input_list = glob(os.path.join(file_dir, '*DSD.txt'))\n",
+    "    input_list.sort()\n",
+    "\n",
+    "    for item in input_list:\n",
+    "        # clean up Q4 files when year-long file exists\n",
+    "        if ('Q4_' in item) and os.path.exists(item.replace('Q4_', '_')):\n",
+    "            try:\n",
+    "                os.remove(item)\n",
+    "            except PermissionError:\n",
+    "                continue\n",
+    "            continue  # i know this is stupid\n",
+    "\n",
+    "        # astropy.ascii no longer returns an empty table for empty files\n",
+    "        # Throws IndexError, we will go around it if empty.\n",
+    "        try:\n",
+    "            data = ascii.read(item, data_start=1, comment='[#,:]')\n",
+    "        except IndexError:\n",
+    "            continue\n",
+    "\n",
+    "        for line in data:\n",
+    "            line_date = Time('{}-{}-{} 00:00:00'.format(line['col1'],\n",
+    "                                                        line['col2'],\n",
+    "                                                        line['col3']),\n",
+    "                             scale='utc', format='iso').mjd\n",
+    "\n",
+    "            line_flux = line[3]\n",
+    "\n",
+    "            if line_flux > 0:\n",
+    "                date.append(line_date)\n",
+    "                flux.append(line_flux)\n",
+    "    \n",
+    "    solar_date = np.array(date)\n",
+    "    solar_flux = np.array(flux)\n",
+    "    solar_dec = Time(solar_date, format='mjd').decimalyear\n",
+    "    solar_smooth = scipy.convolve(solar_flux, np.ones(81)/81.0, mode=\"same\")\n",
+    "    \n",
+    "\n",
+    "    return solar_flux, solar_dec, solar_smooth\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# add histogram plotting function -- simple one\n",
+    "\n",
+    "# change solar plot\n",
+    "\n",
+    "class DarkMonitor(BaseMonitor):\n",
+    "    \"\"\"Abstracted FUV Dark Monitor. Not meant to be used directly but rather inherited by specific segment and region\n",
+    "    dark monitors\"\"\"\n",
+    "    labels = ['ROOTNAME']\n",
+    "    output = COS_MONITORING\n",
+    "    docs = \"https://spacetelescope.github.io/cosmo/monitors.html#fuv-dark-rate-monitors\"\n",
+    "    segment = None\n",
+    "    location = None\n",
+    "    data_model = DarkDataModel\n",
+    "    plottype = 'scatter'\n",
+    "    x = 'date'\n",
+    "    y = 'darks'\n",
+    "\n",
+    "    def get_data(self): # -> Any: fix this later, should be fine in the monitor, just not in jupyter notebook\n",
+    "        filtered_rows = []\n",
+    "        for _, row in self.model.new_data.iterrows():\n",
+    "            if row.EXPSTART == 0:\n",
+    "                continue\n",
+    "            if row.SEGMENT == self.segment: \n",
+    "                if row.SEGMENT == \"N/A\":  #NUV\n",
+    "                    filtered_rows.append(dark_filter(row, False, self.location))\n",
+    "                else:   # Any of the FUV situations\n",
+    "                    filtered_rows.append(dark_filter(row, True, self.location))\n",
+    "        filtered_df = pd.concat(filtered_rows).reset_index(drop=True)\n",
+    "\n",
+    "        return explode_df(filtered_df, ['darks', 'date'])\n",
+    "\n",
+    "    \n",
+    "    def plot(self):\n",
+    "        # make the interactive plots with sub-solar plots\n",
+    "        self.figure = make_subplots(rows=2, cols=1, subplot_titles=(self.name, \"Solar Radio Flux\"))\n",
+    "        \n",
+    "        self.figure.add_trace(\n",
+    "            go.Scatter(x=self.data[self.x], \n",
+    "                       y=self.data[self.y],\n",
+    "                       mode=\"markers\",\n",
+    "                       hovertext=self.labels,\n",
+    "                       hoverinfo=\"x+y+text\", \n",
+    "                       name=\"Dark Rates\"), \n",
+    "            row=1, col=1)\n",
+    "        \n",
+    "        grab_solar_files('/grp/hst/cos2/solar_data')\n",
+    "        solar_flux, solar_dec, solar_smooth = compile_solar_data('/grp/hst/cos2/solar_data')\n",
+    "        \n",
+    "        self.figure.add_trace(\n",
+    "            go.Scatter(x=solar_dec, \n",
+    "                       y=solar_flux,\n",
+    "                       mode=\"lines\",\n",
+    "                       name=\"10.7 cm\"),\n",
+    "            row=2, col=1\n",
+    "        )\n",
+    "        \n",
+    "        self.figure.add_trace(\n",
+    "            go.Scatter(x=solar_dec[:-41], \n",
+    "                       y=solar_smooth[:-41],\n",
+    "                       mode=\"lines\",\n",
+    "                       name=\"10.7 cm Smoothed\"),\n",
+    "            row=2, col=1\n",
+    "        )\n",
+    "                  \n",
+    "        date_min = self.data[self.x].min()\n",
+    "        date_max = self.data[self.x].max()\n",
+    "        print(date_min, date_max)\n",
+    "        \n",
+    "        self.figure['layout']['xaxis1'].update(title=\"Year\")\n",
+    "        self.figure['layout']['yaxis1'].update(title=\"Dark Rate\")\n",
+    "        self.figure['layout']['xaxis2'].update(title=\"Year\", range=[date_min, date_max])\n",
+    "        self.figure['layout']['yaxis2'].update(title=\"Solar Radio Flux\")\n",
+    "\n",
+    "        \n",
+    "    def plot_histogram(self, nbins=30):\n",
+    "        if self.data is None:\n",
+    "            self.data = self.get_data()\n",
+    "        \n",
+    "        # self.data[self.y] should be all dark rates\n",
+    "        counts, bins = np.histogram(self.data[self.y], bins=nbins)\n",
+    "        cuml_dist = np.cumsum(counts)\n",
+    "        count_99 = abs(cuml_dist / float(cuml_dist.max()) - .99).argmin()\n",
+    "        count_95 = abs(cuml_dist / float(cuml_dist.max()) - .95).argmin()\n",
+    "        \n",
+    "        mean = self.data[self.y].mean()\n",
+    "        med = np.median(self.data[self.y])\n",
+    "        std = self.data[self.y].std()  \n",
+    "        onesig = med + std\n",
+    "        twosig = med + (2 * std)\n",
+    "        threesig = med + (3 * std)\n",
+    "        dist95 = bins[count_95]\n",
+    "        dist99 = bins[count_99]\n",
+    "        lines = [mean, med, onesig, twosig, threesig, dist95, dist99]\n",
+    "                   \n",
+    "        fig = go.Figure(data=[go.Histogram(x=self.data[self.y], nbinsx=nbins)])\n",
+    "        for value in lines:\n",
+    "            fig.add_shape(\n",
+    "                dict(type=\"line\",\n",
+    "                    xref=\"x\",\n",
+    "                    yref=\"paper\",\n",
+    "                    x0=value,\n",
+    "                    y0=0,\n",
+    "                    x1=value,\n",
+    "                    y1=1)    \n",
+    "            )\n",
+    "            \n",
+    "        # fix this naming convention later\n",
+    "        output = os.path.join(COS_MONITORING, \"FUVBDarkMonitor-Inner_hist_2020-05-15.html\")   \n",
+    "        fig.write_html(output)\n",
+    "        \n",
+    "        \n",
+    "    def store_results(self):\n",
+    "        # TODO: Define results to store\n",
+    "        pass\n",
+    "\n",
+    "    def track(self):\n",
+    "        # TODO: Define something to track\n",
+    "        pass\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class FUVBInnerDarkMonitor(DarkMonitor):\n",
+    "    \"\"\"FUVB dark monitor for inner region\"\"\"\n",
+    "    name = 'FUVB Dark Monitor - Inner'\n",
+    "    segment = 'FUVB'\n",
+    "    location = (1000, 14990, 405, 740)\n",
+    "    \n",
+    "fuv_inner_monitor = FUVBInnerDarkMonitor()\n",
+    "fuv_inner_monitor.monitor()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# new functions for retrieving and organizing data with sunpy\n",
+    "\n",
+    "# a.Time takes datetime.datetime objects, strings, etc\n",
+    "def sunpy_retriever(start_date, end_date):\n",
+    "    dummy_dates = [\"2009/1/1\", \"2009/2/1\"]  \n",
+    "    # ^ it literally does not matter what these dates are because of how Fido works\n",
+    "    solar_flux_search = Fido.search(a.Time(dummy_dates[0], dummy_dates[1]),\n",
+    "                                  a.Instrument('noaa-indices')) \n",
+    "    solar_flux_results = Fido.fetch(solar_flux_search, path=SETTINGS[\"output\"])\n",
+    "    solar_flux_file = solar_flux_results.data[0]\n",
+    "    df_solar_flux = TimeSeries(solar_flux_file, source=\"NOAAIndices\").to_dataframe()\n",
+    "    df_solar_flux_filtered = df_solar_flux[start_date: end_date]\n",
+    "    \n",
+    "    return df_solar_flux_filtered"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/cmagness/miniconda3/envs/cosmos/lib/python3.7/site-packages/parfive/downloader.py:279: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
+      "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n",
+      "  position=0)\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e29f5560404243a7a8f1923bcdfb3653",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HBox(children=(FloatProgress(value=0.0, description='Files Downloaded', max=1.0, style=ProgressStyle(descripti…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\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>sunspot SWO</th>\n",
+       "      <th>sunspot RI</th>\n",
+       "      <th>sunspot ratio</th>\n",
+       "      <th>sunspot SWO smooth</th>\n",
+       "      <th>sunspot RI smooth</th>\n",
+       "      <th>radio flux</th>\n",
+       "      <th>radio flux smooth</th>\n",
+       "      <th>geomagnetic ap</th>\n",
+       "      <th>geomagnetic smooth</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>time</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>2019-01-01</th>\n",
+       "      <td>16.0</td>\n",
+       "      <td>4.6</td>\n",
+       "      <td>0.29</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>3.2</td>\n",
+       "      <td>71.6</td>\n",
+       "      <td>70.0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>6.8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2019-02-01</th>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>-1.00</td>\n",
+       "      <td>8.7</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>70.6</td>\n",
+       "      <td>69.8</td>\n",
+       "      <td>7</td>\n",
+       "      <td>6.7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2019-03-01</th>\n",
+       "      <td>14.8</td>\n",
+       "      <td>5.6</td>\n",
+       "      <td>0.39</td>\n",
+       "      <td>8.3</td>\n",
+       "      <td>2.8</td>\n",
+       "      <td>71.5</td>\n",
+       "      <td>69.7</td>\n",
+       "      <td>6</td>\n",
+       "      <td>6.6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2019-04-01</th>\n",
+       "      <td>11.5</td>\n",
+       "      <td>5.5</td>\n",
+       "      <td>0.48</td>\n",
+       "      <td>7.9</td>\n",
+       "      <td>2.6</td>\n",
+       "      <td>72.4</td>\n",
+       "      <td>69.6</td>\n",
+       "      <td>6</td>\n",
+       "      <td>6.7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2019-05-01</th>\n",
+       "      <td>18.1</td>\n",
+       "      <td>5.9</td>\n",
+       "      <td>0.34</td>\n",
+       "      <td>7.4</td>\n",
+       "      <td>2.3</td>\n",
+       "      <td>71.3</td>\n",
+       "      <td>69.6</td>\n",
+       "      <td>7</td>\n",
+       "      <td>6.7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2019-06-01</th>\n",
+       "      <td>11.6</td>\n",
+       "      <td>0.7</td>\n",
+       "      <td>0.06</td>\n",
+       "      <td>7.3</td>\n",
+       "      <td>2.2</td>\n",
+       "      <td>68.1</td>\n",
+       "      <td>69.6</td>\n",
+       "      <td>5</td>\n",
+       "      <td>6.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2019-07-01</th>\n",
+       "      <td>1.6</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>0.31</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>2.1</td>\n",
+       "      <td>67.1</td>\n",
+       "      <td>69.7</td>\n",
+       "      <td>6</td>\n",
+       "      <td>6.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2019-08-01</th>\n",
+       "      <td>2.5</td>\n",
+       "      <td>0.3</td>\n",
+       "      <td>0.16</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>2.1</td>\n",
+       "      <td>67.0</td>\n",
+       "      <td>69.8</td>\n",
+       "      <td>7</td>\n",
+       "      <td>6.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2019-09-01</th>\n",
+       "      <td>2.6</td>\n",
+       "      <td>0.7</td>\n",
+       "      <td>0.27</td>\n",
+       "      <td>6.8</td>\n",
+       "      <td>1.9</td>\n",
+       "      <td>68.1</td>\n",
+       "      <td>69.7</td>\n",
+       "      <td>10</td>\n",
+       "      <td>6.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2019-10-01</th>\n",
+       "      <td>1.8</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0.11</td>\n",
+       "      <td>6.2</td>\n",
+       "      <td>1.6</td>\n",
+       "      <td>67.4</td>\n",
+       "      <td>69.5</td>\n",
+       "      <td>8</td>\n",
+       "      <td>6.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2019-11-01</th>\n",
+       "      <td>1.1</td>\n",
+       "      <td>0.3</td>\n",
+       "      <td>0.27</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>-0.6</td>\n",
+       "      <td>70.2</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>-1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2019-12-01</th>\n",
+       "      <td>7.2</td>\n",
+       "      <td>0.9</td>\n",
+       "      <td>0.14</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>-0.6</td>\n",
+       "      <td>70.9</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>-1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2020-01-01</th>\n",
+       "      <td>9.2</td>\n",
+       "      <td>3.8</td>\n",
+       "      <td>0.41</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>-0.6</td>\n",
+       "      <td>72.3</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>-1.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            sunspot SWO  sunspot RI  sunspot ratio  sunspot SWO smooth  \\\n",
+       "time                                                                     \n",
+       "2019-01-01         16.0         4.6           0.29                 9.0   \n",
+       "2019-02-01         -1.0         0.5          -1.00                 8.7   \n",
+       "2019-03-01         14.8         5.6           0.39                 8.3   \n",
+       "2019-04-01         11.5         5.5           0.48                 7.9   \n",
+       "2019-05-01         18.1         5.9           0.34                 7.4   \n",
+       "2019-06-01         11.6         0.7           0.06                 7.3   \n",
+       "2019-07-01          1.6         0.5           0.31                 7.0   \n",
+       "2019-08-01          2.5         0.3           0.16                 7.0   \n",
+       "2019-09-01          2.6         0.7           0.27                 6.8   \n",
+       "2019-10-01          1.8         0.2           0.11                 6.2   \n",
+       "2019-11-01          1.1         0.3           0.27                -1.0   \n",
+       "2019-12-01          7.2         0.9           0.14                -1.0   \n",
+       "2020-01-01          9.2         3.8           0.41                -1.0   \n",
+       "\n",
+       "            sunspot RI smooth  radio flux  radio flux smooth  geomagnetic ap  \\\n",
+       "time                                                                           \n",
+       "2019-01-01                3.2        71.6               70.0               6   \n",
+       "2019-02-01                3.0        70.6               69.8               7   \n",
+       "2019-03-01                2.8        71.5               69.7               6   \n",
+       "2019-04-01                2.6        72.4               69.6               6   \n",
+       "2019-05-01                2.3        71.3               69.6               7   \n",
+       "2019-06-01                2.2        68.1               69.6               5   \n",
+       "2019-07-01                2.1        67.1               69.7               6   \n",
+       "2019-08-01                2.1        67.0               69.8               7   \n",
+       "2019-09-01                1.9        68.1               69.7              10   \n",
+       "2019-10-01                1.6        67.4               69.5               8   \n",
+       "2019-11-01               -0.6        70.2               -1.0               4   \n",
+       "2019-12-01               -0.6        70.9               -1.0               4   \n",
+       "2020-01-01               -0.6        72.3               -1.0               5   \n",
+       "\n",
+       "            geomagnetic smooth  \n",
+       "time                            \n",
+       "2019-01-01                 6.8  \n",
+       "2019-02-01                 6.7  \n",
+       "2019-03-01                 6.6  \n",
+       "2019-04-01                 6.7  \n",
+       "2019-05-01                 6.7  \n",
+       "2019-06-01                 6.5  \n",
+       "2019-07-01                 6.3  \n",
+       "2019-08-01                 6.2  \n",
+       "2019-09-01                 6.2  \n",
+       "2019-10-01                 6.2  \n",
+       "2019-11-01                -1.0  \n",
+       "2019-12-01                -1.0  \n",
+       "2020-01-01                -1.0  "
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "start_date = datetime.datetime(2019, 1, 1)\n",
+    "end_date = datetime.datetime(2020, 1, 1)\n",
+    "data_df = sunpy_retriever(start_date, end_date)\n",
+    "data_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# new monitor functions\n",
+    "def dark_filter(df_row, filter_pha, location):\n",
+    "    good_pha = (2, 23)\n",
+    "    # time step stuff\n",
+    "    time_step = 25\n",
+    "    time_bins = df_row['TIME_3'][::time_step]\n",
+    "    lat = df_row['LATITUDE'][::time_step][:-1]\n",
+    "    lon = df_row['LONGITUDE'][::time_step][:-1]\n",
+    "    \n",
+    "    # try commenting these out, since lat and lon don't seem to be used\n",
+    "#     lat = df_row['LATITUDE'][::time_step][:-1]\n",
+    "#     lon = df_row['LONGITUDE'][::time_step][:-1]\n",
+    "    \n",
+    "    # filtering pha\n",
+    "    if filter_pha:\n",
+    "        event_df = df_row[['SEGMENT', 'XCORR', 'YCORR', 'PHA', 'TIME']].to_frame().T\n",
+    "        event_df = explode_df(event_df, ['XCORR', 'YCORR', 'PHA', 'TIME'])\n",
+    "    else:\n",
+    "        event_df = df_row[['SEGMENT', 'XCORR', 'YCORR', 'TIME']].to_frame().T\n",
+    "        event_df = explode_df(event_df, ['XCORR', 'YCORR', 'TIME'])\n",
+    "    \n",
+    "    # creating event dataframe and filtering it by location on the detector\n",
+    "    npix = (location[1] - location[0]) * (location[3] - location[2])\n",
+    "    index = np.where((event_df['XCORR'] > location[0]) &\n",
+    "                     (event_df['XCORR'] < location[1]) &\n",
+    "                     (event_df['YCORR'] > location[2]) &\n",
+    "                     (event_df['YCORR'] < location[3]))\n",
+    "    filtered_row = event_df.iloc[index].reset_index(drop=True)\n",
+    "\n",
+    "    #filtered events only need to be further filtered by PHA if not NUV\n",
+    "    if filter_pha:\n",
+    "        filtered_row = filtered_row[(filtered_row['PHA'] > good_pha[0]) & (filtered_row['PHA'] < good_pha[1])]\n",
+    "\n",
+    "    counts = np.histogram(filtered_row.TIME, bins=time_bins)[0]\n",
+    "\n",
+    "    date = absolute_time(\n",
+    "        expstart=list(repeat(df_row['EXPSTART'], len(time_bins))), time=time_bins.tolist()\n",
+    "    ).to_datetime()[:-1]\n",
+    "\n",
+    "    dark_rate = counts / npix / time_step\n",
+    "\n",
+    "    return pd.DataFrame({'segment': df_row['SEGMENT'], 'darks': [dark_rate], 'date': [date],\n",
+    "                        'ROOTNAME': df_row['ROOTNAME']})\n",
+    "\n",
+    "\n",
+    "# a.Time takes datetime.datetime objects, strings, etc\n",
+    "# None default so there is no filtering if not necessary\n",
+    "def sunpy_retriever(start_date=None, end_date=None):\n",
+    "    dummy_dates = [\"2009/1/1\", \"2009/2/1\"]  \n",
+    "    # ^ it literally does not matter what these dates are because of how Fido works\n",
+    "    # still will get all available data and needs to be filtered later\n",
+    "    # at least, as of the June 2020 version of sunpy\n",
+    "    # can change that to start_date and end_date if sunpy changes\n",
+    "    solar_flux_search = Fido.search(a.Time(dummy_dates[0], dummy_dates[1]),\n",
+    "                                  a.Instrument('noaa-indices')) \n",
+    "    solar_flux_results = Fido.fetch(solar_flux_search, path=SETTINGS[\"output\"])\n",
+    "    solar_flux_file = solar_flux_results.data[0]\n",
+    "    df_solar_flux = TimeSeries(solar_flux_file, source=\"NOAAIndices\").to_dataframe()\n",
+    "    \n",
+    "    if start_date and end_date:\n",
+    "        df_solar_flux_filtered = df_solar_flux[start_date: end_date]\n",
+    "    elif not end_date:  # if there is a start date but no end date\n",
+    "        df_solar_flux_filtered = df_solar_flux[start_date:]\n",
+    "    elif not start_date:  # if there is an end date but not start date\n",
+    "        df_solar_flux_filtered = df_solar_flux[0:end_date]\n",
+    "        \n",
+    "    return df_solar_flux_filtered"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class DarkMonitor(BaseMonitor):\n",
+    "    \"\"\"Abstracted Dark Monitor. Not meant to be used directly but rather inherited by specific segment and region\n",
+    "    dark monitors\"\"\"\n",
+    "    labels = ['ROOTNAME']\n",
+    "    output = COS_MONITORING\n",
+    "    docs = \"https://spacetelescope.github.io/cosmo/monitors.html#dark-rate-monitors\"\n",
+    "    segment = None\n",
+    "    location = None\n",
+    "    data_model = DarkDataModel\n",
+    "    plottype = 'scatter'\n",
+    "    x = 'date'\n",
+    "    y = 'darks'\n",
+    "\n",
+    "    def get_data(self): # -> Any: fix this later, should be fine in the monitor, just not in jupyter notebook\n",
+    "        filtered_rows = []\n",
+    "        for _, row in self.model.new_data.iterrows():\n",
+    "            if row.EXPSTART == 0:\n",
+    "                continue\n",
+    "            if row.SEGMENT == self.segment: \n",
+    "                if row.SEGMENT == \"N/A\":  #NUV\n",
+    "                    filtered_rows.append(dark_filter(row, False, self.location))\n",
+    "                else:   # Any of the FUV situations\n",
+    "                    filtered_rows.append(dark_filter(row, True, self.location))\n",
+    "        filtered_df = pd.concat(filtered_rows).reset_index(drop=True)\n",
+    "\n",
+    "        return explode_df(filtered_df, ['darks', 'date'])\n",
+    "\n",
+    "    \n",
+    "    def plot(self):\n",
+    "        # make the interactive plots with sub-solar plots\n",
+    "        self.figure = make_subplots(rows=2, cols=1, subplot_titles=(self.name, \"Solar Radio Flux\"))\n",
+    "        \n",
+    "        self.figure.add_trace(\n",
+    "            go.Scatter(x=self.data[self.x], \n",
+    "                       y=self.data[self.y],\n",
+    "                       mode=\"markers\",\n",
+    "                       hovertext=self.labels,\n",
+    "                       hoverinfo=\"x+y+text\", \n",
+    "                       name=\"Dark Rates\"), \n",
+    "            row=1, col=1)\n",
+    "        \n",
+    "        date_min = self.data[self.x].min()\n",
+    "        date_max = self.data[self.x].max()\n",
+    "        print(date_min, date_max)\n",
+    "        \n",
+    "        sunpy_data = sunpy_retriever(date_min, date_max)\n",
+    "        solar_time = sunpy_data.index\n",
+    "        solar_flux = sunpy_data[\"radio flux\"]\n",
+    "        solar_flux_smooth = sunpy_data[\"radio flux smooth\"]\n",
+    "        \n",
+    "        self.figure.add_trace(\n",
+    "            go.Scatter(x=solar_time, \n",
+    "                       y=solar_flux,\n",
+    "                       mode=\"lines\",\n",
+    "                       name=\"10.7 cm\"),\n",
+    "            row=2, col=1\n",
+    "        )\n",
+    "        \n",
+    "        self.figure.add_trace(\n",
+    "            go.Scatter(x=solar_time, \n",
+    "                       y=solar_flux_smoothed,\n",
+    "                       mode=\"lines\",\n",
+    "                       name=\"10.7 cm Smoothed\"),\n",
+    "            row=2, col=1\n",
+    "        )\n",
+    "        \n",
+    "        self.figure['layout']['xaxis1'].update(title=\"Year\")\n",
+    "        self.figure['layout']['yaxis1'].update(title=\"Dark Rate\")\n",
+    "        self.figure['layout']['xaxis2'].update(title=\"Year\", range=[date_min, date_max])\n",
+    "        self.figure['layout']['yaxis2'].update(title=\"Solar Radio Flux\")\n",
+    "\n",
+    "        \n",
+    "    def plot_histogram(self, nbins=30):\n",
+    "        if self.data is None:\n",
+    "            self.data = self.get_data()\n",
+    "        \n",
+    "        # self.data[self.y] should be all dark rates\n",
+    "        counts, bins = np.histogram(self.data[self.y], bins=nbins)\n",
+    "        cuml_dist = np.cumsum(counts)\n",
+    "        count_99 = abs(cuml_dist / float(cuml_dist.max()) - .99).argmin()\n",
+    "        count_95 = abs(cuml_dist / float(cuml_dist.max()) - .95).argmin()\n",
+    "        \n",
+    "        mean = self.data[self.y].mean()\n",
+    "        med = np.median(self.data[self.y])\n",
+    "        std = self.data[self.y].std()  \n",
+    "        onesig = med + std\n",
+    "        twosig = med + (2 * std)\n",
+    "        threesig = med + (3 * std)\n",
+    "        dist95 = bins[count_95]\n",
+    "        dist99 = bins[count_99]\n",
+    "        lines = [mean, med, onesig, twosig, threesig, dist95, dist99]\n",
+    "                   \n",
+    "        fig = go.Figure(data=[go.Histogram(x=self.data[self.y], nbinsx=nbins)])\n",
+    "        for value in lines:\n",
+    "            fig.add_shape(\n",
+    "                dict(type=\"line\",\n",
+    "                    xref=\"x\",\n",
+    "                    yref=\"paper\",\n",
+    "                    x0=value,\n",
+    "                    y0=0,\n",
+    "                    x1=value,\n",
+    "                    y1=1)    \n",
+    "            )\n",
+    "            \n",
+    "        # fix this naming convention later\n",
+    "        output = os.path.join(COS_MONITORING, \"FUVBDarkMonitor-Inner_hist_2020-05-15.html\")   \n",
+    "        fig.write_html(output)\n",
+    "        \n",
+    "        \n",
+    "    def store_results(self):\n",
+    "        # TODO: Define results to store\n",
+    "        pass\n",
+    "\n",
+    "    def track(self):\n",
+    "        # TODO: Define something to track\n",
+    "        pass\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "OperationalError",
+     "evalue": "unable to open database file",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mOperationalError\u001b[0m                          Traceback (most recent call last)",
+      "\u001b[0;32m~/miniconda3/envs/cosmos/lib/python3.7/site-packages/peewee.py\u001b[0m in \u001b[0;36mconnect\u001b[0;34m(self, reuse_if_open)\u001b[0m\n\u001b[1;32m   3034\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0m__exception_wrapper__\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3035\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_state\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_connection\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_connect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3036\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mserver_version\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/miniconda3/envs/cosmos/lib/python3.7/site-packages/peewee.py\u001b[0m in \u001b[0;36m_connect\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   3372\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3373\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_add_conn_hooks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3374\u001b[0m         \u001b[0;32mexcept\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/miniconda3/envs/cosmos/lib/python3.7/site-packages/playhouse/sqlite_ext.py\u001b[0m in \u001b[0;36m_add_conn_hooks\u001b[0;34m(self, conn)\u001b[0m\n\u001b[1;32m    964\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_add_conn_hooks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 965\u001b[0;31m         \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mSqliteExtDatabase\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_add_conn_hooks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    966\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_row_factory\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/miniconda3/envs/cosmos/lib/python3.7/site-packages/peewee.py\u001b[0m in \u001b[0;36m_add_conn_hooks\u001b[0;34m(self, conn)\u001b[0m\n\u001b[1;32m   3382\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_pragmas\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3383\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_set_pragmas\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3384\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_load_aggregates\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/miniconda3/envs/cosmos/lib/python3.7/site-packages/peewee.py\u001b[0m in \u001b[0;36m_set_pragmas\u001b[0;34m(self, conn)\u001b[0m\n\u001b[1;32m   3397\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mpragma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_pragmas\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3398\u001b[0;31m             \u001b[0mcursor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexecute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'PRAGMA %s = %s;'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mpragma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3399\u001b[0m         \u001b[0mcursor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mOperationalError\u001b[0m: unable to open database file",
+      "\nDuring handling of the above exception, another exception occurred:\n",
+      "\u001b[0;31mOperationalError\u001b[0m                          Traceback (most recent call last)",
+      "\u001b[0;32m~/miniconda3/envs/cosmos/lib/python3.7/site-packages/peewee.py\u001b[0m in \u001b[0;36mexecute_sql\u001b[0;34m(self, sql, params, commit)\u001b[0m\n\u001b[1;32m   3096\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0m__exception_wrapper__\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3097\u001b[0;31m             \u001b[0mcursor\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcursor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcommit\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3098\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/miniconda3/envs/cosmos/lib/python3.7/site-packages/peewee.py\u001b[0m in \u001b[0;36mcursor\u001b[0;34m(self, commit)\u001b[0m\n\u001b[1;32m   3080\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mautoconnect\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3081\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconnect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3082\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/miniconda3/envs/cosmos/lib/python3.7/site-packages/peewee.py\u001b[0m in \u001b[0;36mconnect\u001b[0;34m(self, reuse_if_open)\u001b[0m\n\u001b[1;32m   3037\u001b[0m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_set_server_version\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_state\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3038\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_initialize_connection\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_state\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3039\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/miniconda3/envs/cosmos/lib/python3.7/site-packages/peewee.py\u001b[0m in \u001b[0;36m__exit__\u001b[0;34m(self, exc_type, exc_value, traceback)\u001b[0m\n\u001b[1;32m   2872\u001b[0m             \u001b[0mexc_args\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mexc_value\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2873\u001b[0;31m             \u001b[0mreraise\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnew_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexc_value\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mexc_args\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtraceback\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2874\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/miniconda3/envs/cosmos/lib/python3.7/site-packages/peewee.py\u001b[0m in \u001b[0;36mreraise\u001b[0;34m(tp, value, tb)\u001b[0m\n\u001b[1;32m    182\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__traceback__\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mtb\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 183\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    184\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/miniconda3/envs/cosmos/lib/python3.7/site-packages/peewee.py\u001b[0m in \u001b[0;36mconnect\u001b[0;34m(self, reuse_if_open)\u001b[0m\n\u001b[1;32m   3034\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0m__exception_wrapper__\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3035\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_state\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_connection\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_connect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3036\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mserver_version\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/miniconda3/envs/cosmos/lib/python3.7/site-packages/peewee.py\u001b[0m in \u001b[0;36m_connect\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   3372\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3373\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_add_conn_hooks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3374\u001b[0m         \u001b[0;32mexcept\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/miniconda3/envs/cosmos/lib/python3.7/site-packages/playhouse/sqlite_ext.py\u001b[0m in \u001b[0;36m_add_conn_hooks\u001b[0;34m(self, conn)\u001b[0m\n\u001b[1;32m    964\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_add_conn_hooks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 965\u001b[0;31m         \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mSqliteExtDatabase\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_add_conn_hooks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    966\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_row_factory\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/miniconda3/envs/cosmos/lib/python3.7/site-packages/peewee.py\u001b[0m in \u001b[0;36m_add_conn_hooks\u001b[0;34m(self, conn)\u001b[0m\n\u001b[1;32m   3382\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_pragmas\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3383\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_set_pragmas\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3384\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_load_aggregates\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/miniconda3/envs/cosmos/lib/python3.7/site-packages/peewee.py\u001b[0m in \u001b[0;36m_set_pragmas\u001b[0;34m(self, conn)\u001b[0m\n\u001b[1;32m   3397\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mpragma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_pragmas\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3398\u001b[0;31m             \u001b[0mcursor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexecute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'PRAGMA %s = %s;'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mpragma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3399\u001b[0m         \u001b[0mcursor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mOperationalError\u001b[0m: unable to open database file",
+      "\nDuring handling of the above exception, another exception occurred:\n",
+      "\u001b[0;31mOperationalError\u001b[0m                          Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-21-cb132689594d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0mlocation\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m1000\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m14990\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m405\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m740\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0mfuv_inner_monitor\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mFUVBInnerDarkMonitor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      8\u001b[0m \u001b[0mfuv_inner_monitor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmonitor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/miniconda3/envs/cosmos/lib/python3.7/site-packages/monitorframe/monitor.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, find_new_data)\u001b[0m\n\u001b[1;32m    115\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresultcol\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    116\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 117\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfind_new\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfind_new_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    118\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdate\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdatetime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtoday\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    119\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_define_results_table\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/miniconda3/envs/cosmos/lib/python3.7/site-packages/monitorframe/datamodel.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, find_new)\u001b[0m\n\u001b[1;32m     59\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     60\u001b[0m         \u001b[0;31m# Attempt to create a database table model\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 61\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_generate_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     62\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     63\u001b[0m         \u001b[0;31m# Read in and ingest new data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/miniconda3/envs/cosmos/lib/python3.7/site-packages/monitorframe/datamodel.py\u001b[0m in \u001b[0;36m_generate_model\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     67\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_generate_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     68\u001b[0m         \u001b[0;34m\"\"\"Return the database table model object if the table exists in the database.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 69\u001b[0;31m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_database\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtable_exists\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtable_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     70\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_database\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mdb\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     71\u001b[0m                 self.model = generate_models(\n",
+      "\u001b[0;32m~/miniconda3/envs/cosmos/lib/python3.7/site-packages/peewee.py\u001b[0m in \u001b[0;36mtable_exists\u001b[0;34m(self, table_name, schema)\u001b[0m\n\u001b[1;32m   3262\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3263\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mtable_exists\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtable_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mschema\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3264\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mtable_name\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_tables\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mschema\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mschema\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3265\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3266\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget_tables\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mschema\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/miniconda3/envs/cosmos/lib/python3.7/site-packages/peewee.py\u001b[0m in \u001b[0;36mget_tables\u001b[0;34m(self, schema)\u001b[0m\n\u001b[1;32m   3588\u001b[0m         \u001b[0mschema\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mschema\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m'main'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3589\u001b[0m         cursor = self.execute_sql('SELECT name FROM \"%s\".sqlite_master WHERE '\n\u001b[0;32m-> 3590\u001b[0;31m                                   'type=? ORDER BY name' % schema, ('table',))\n\u001b[0m\u001b[1;32m   3591\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mrow\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mrow\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcursor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfetchall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3592\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/miniconda3/envs/cosmos/lib/python3.7/site-packages/peewee.py\u001b[0m in \u001b[0;36mexecute_sql\u001b[0;34m(self, sql, params, commit)\u001b[0m\n\u001b[1;32m   3104\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3105\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mcommit\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0min_transaction\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3106\u001b[0;31m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcommit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3107\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mcursor\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3108\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/miniconda3/envs/cosmos/lib/python3.7/site-packages/peewee.py\u001b[0m in \u001b[0;36m__exit__\u001b[0;34m(self, exc_type, exc_value, traceback)\u001b[0m\n\u001b[1;32m   2871\u001b[0m             \u001b[0mnew_type\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexceptions\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mexc_type\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2872\u001b[0m             \u001b[0mexc_args\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mexc_value\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2873\u001b[0;31m             \u001b[0mreraise\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnew_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexc_value\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mexc_args\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtraceback\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2874\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2875\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/miniconda3/envs/cosmos/lib/python3.7/site-packages/peewee.py\u001b[0m in \u001b[0;36mreraise\u001b[0;34m(tp, value, tb)\u001b[0m\n\u001b[1;32m    181\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mreraise\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtb\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    182\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__traceback__\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mtb\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 183\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    184\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    185\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/miniconda3/envs/cosmos/lib/python3.7/site-packages/peewee.py\u001b[0m in \u001b[0;36mexecute_sql\u001b[0;34m(self, sql, params, commit)\u001b[0m\n\u001b[1;32m   3095\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3096\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0m__exception_wrapper__\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3097\u001b[0;31m             \u001b[0mcursor\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcursor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcommit\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3098\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3099\u001b[0m                 \u001b[0mcursor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexecute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msql\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/miniconda3/envs/cosmos/lib/python3.7/site-packages/peewee.py\u001b[0m in \u001b[0;36mcursor\u001b[0;34m(self, commit)\u001b[0m\n\u001b[1;32m   3079\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_closed\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3080\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mautoconnect\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3081\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconnect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3082\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3083\u001b[0m                 \u001b[0;32mraise\u001b[0m \u001b[0mInterfaceError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Error, database connection not opened.'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/miniconda3/envs/cosmos/lib/python3.7/site-packages/peewee.py\u001b[0m in \u001b[0;36mconnect\u001b[0;34m(self, reuse_if_open)\u001b[0m\n\u001b[1;32m   3036\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mserver_version\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3037\u001b[0m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_set_server_version\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_state\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3038\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_initialize_connection\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_state\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3039\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3040\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/miniconda3/envs/cosmos/lib/python3.7/site-packages/peewee.py\u001b[0m in \u001b[0;36m__exit__\u001b[0;34m(self, exc_type, exc_value, traceback)\u001b[0m\n\u001b[1;32m   2871\u001b[0m             \u001b[0mnew_type\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexceptions\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mexc_type\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2872\u001b[0m             \u001b[0mexc_args\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mexc_value\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2873\u001b[0;31m             \u001b[0mreraise\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnew_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexc_value\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mexc_args\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtraceback\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2874\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2875\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/miniconda3/envs/cosmos/lib/python3.7/site-packages/peewee.py\u001b[0m in \u001b[0;36mreraise\u001b[0;34m(tp, value, tb)\u001b[0m\n\u001b[1;32m    181\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mreraise\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtb\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    182\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__traceback__\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mtb\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 183\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    184\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    185\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/miniconda3/envs/cosmos/lib/python3.7/site-packages/peewee.py\u001b[0m in \u001b[0;36mconnect\u001b[0;34m(self, reuse_if_open)\u001b[0m\n\u001b[1;32m   3033\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_state\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3034\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0m__exception_wrapper__\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3035\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_state\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_connection\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_connect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3036\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mserver_version\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3037\u001b[0m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_set_server_version\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_state\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/miniconda3/envs/cosmos/lib/python3.7/site-packages/peewee.py\u001b[0m in \u001b[0;36m_connect\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   3371\u001b[0m                                isolation_level=None, **self.connect_params)\n\u001b[1;32m   3372\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3373\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_add_conn_hooks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3374\u001b[0m         \u001b[0;32mexcept\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3375\u001b[0m             \u001b[0mconn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/miniconda3/envs/cosmos/lib/python3.7/site-packages/playhouse/sqlite_ext.py\u001b[0m in \u001b[0;36m_add_conn_hooks\u001b[0;34m(self, conn)\u001b[0m\n\u001b[1;32m    963\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    964\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_add_conn_hooks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 965\u001b[0;31m         \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mSqliteExtDatabase\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_add_conn_hooks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    966\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_row_factory\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    967\u001b[0m             \u001b[0mconn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrow_factory\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_row_factory\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/miniconda3/envs/cosmos/lib/python3.7/site-packages/peewee.py\u001b[0m in \u001b[0;36m_add_conn_hooks\u001b[0;34m(self, conn)\u001b[0m\n\u001b[1;32m   3381\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_attach_databases\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3382\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_pragmas\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3383\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_set_pragmas\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3384\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_load_aggregates\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3385\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_load_collations\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/miniconda3/envs/cosmos/lib/python3.7/site-packages/peewee.py\u001b[0m in \u001b[0;36m_set_pragmas\u001b[0;34m(self, conn)\u001b[0m\n\u001b[1;32m   3396\u001b[0m         \u001b[0mcursor\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcursor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3397\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mpragma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_pragmas\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3398\u001b[0;31m             \u001b[0mcursor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexecute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'PRAGMA %s = %s;'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mpragma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3399\u001b[0m         \u001b[0mcursor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3400\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mOperationalError\u001b[0m: unable to open database file"
+     ]
+    }
+   ],
+   "source": [
+    "class FUVBInnerDarkMonitor(DarkMonitor):\n",
+    "    \"\"\"FUVB dark monitor for inner region\"\"\"\n",
+    "    name = 'FUVB Dark Monitor - Inner'\n",
+    "    segment = 'FUVB'\n",
+    "    location = (1000, 14990, 405, 740)\n",
+    "    \n",
+    "fuv_inner_monitor = FUVBInnerDarkMonitor()\n",
+    "fuv_inner_monitor.monitor()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/README.md b/README.md
index cc4cfc8..f01e8e6 100644
--- a/README.md
+++ b/README.md
@@ -9,7 +9,8 @@ COSMO is made up of a series of monitors and their data built on the light-weigh
 [monitorframe framework](https://github.com/spacetelescope/monitor-framework).
 These monitors are important for ensuring that COS is operating nominally as well as extending its mission lifetime.
 
-Check out the COSMO documenation [here](https://spacetelescope.github.io/cosmo/)
+Check out the COSMO documentation [here](https://spacetelescope.github.io
+/cosmo/)
 
 ## Contributing
 Please open a new [issue](https://github.com/spacetelescope/cosmo/issues) or new
diff --git a/cosmo/filesystem.py b/cosmo/filesystem.py
index dae743c..7c0591d 100644
--- a/cosmo/filesystem.py
+++ b/cosmo/filesystem.py
@@ -278,7 +278,8 @@ def reduce_to_stat(self, description: dict):
                 del filedata[key]
 
 
-def find_files(file_pattern: str, data_dir: str = FILES_SOURCE, subdir_pattern: Union[str, None] = '?????') -> list:
+def find_files(file_pattern: str, data_dir: str = FILES_SOURCE,
+               subdir_pattern: Union[str, None] = None) -> list:
     """Find COS data files from a source directory. The default is the cosmo data directory subdirectories layout
     pattern. A different subdirectory pattern can be used or
     """
diff --git a/cosmo/monitors/dark_monitors.py b/cosmo/monitors/dark_monitors.py
new file mode 100644
index 0000000..63ceb6b
--- /dev/null
+++ b/cosmo/monitors/dark_monitors.py
@@ -0,0 +1,601 @@
+
+import os
+import json
+import datetime
+
+import numpy as np
+import pandas as pd
+import plotly.io as pio
+import plotly.express as px
+import plotly.graph_objs as go
+
+# from tqdm import tqdm
+from typing import Any
+from urllib import request
+from itertools import repeat
+from plotly.subplots import make_subplots
+from monitorframe.monitor import BaseMonitor
+from astropy.convolution import Box1DKernel, convolve
+
+from .. import SETTINGS
+from .data_models import DarkDataModel
+from ..monitor_helpers import explode_df, absolute_time
+
+COS_MONITORING = SETTINGS['output']
+NOAA_URL = 'https://services.swpc.noaa.gov/json/solar-cycle/observed-solar-cycle-indices.json'
+
+# ----------------------------------------------------------------------------#
+
+
+# def run_all_dark_monitors():
+#     fuva_bottom_monitor = FUVABottomDarkMonitor()
+#     fuva_left_monitor = FUVALeftDarkMonitor()
+#     fuva_top_monitor = FUVATopDarkMonitor()
+#     fuva_right_monitor = FUVARightDarkMonitor()
+#     fuva_inner_monitor = FUVAInnerDarkMonitor()
+#     fuvb_bottom_monitor = FUVBBottomDarkMonitor()
+#     fuvb_left_monitor = FUVBLeftDarkMonitor()
+#     fuvb_top_monitor = FUVBTopDarkMonitor()
+#     fuvb_right_monitor = FUVBRightDarkMonitor()
+#     fuvb_inner_monitor = FUVBInnerDarkMonitor()
+#     nuv_monitor = NUVDarkMonitor()
+#     for monitor in tqdm([fuva_bottom_monitor, fuva_left_monitor,
+#                          fuva_top_monitor, fuva_right_monitor,
+#                          fuva_inner_monitor, fuvb_bottom_monitor,
+#                          fuvb_left_monitor, fuvb_top_monitor,
+#                          fuvb_right_monitor, fuvb_inner_monitor,
+#                          nuv_monitor]):
+#         monitor.monitor()
+
+
+def dark_filter(df_row, filter_pha, location):
+    """Given a row corresponding to a dark corrtag file, filter it based on
+    the location and PHA (if FUV), and calculate dark rate information. Will
+    return the exploded dataframe with the correct dark information for that
+    one file."""
+    good_pha = (2, 23)
+    # time step stuff
+    time_step = 25
+    time_bins = df_row['TIME_3'][::time_step]
+    lat = df_row['LATITUDE'][::time_step][:-1]
+    lon = df_row['LONGITUDE'][::time_step][:-1]
+
+    # filtering pha
+    if filter_pha:
+        event_df = df_row[
+            ['SEGMENT', 'XCORR', 'YCORR', 'PHA', 'TIME']].to_frame().T
+        event_df = explode_df(event_df, ['XCORR', 'YCORR', 'PHA', 'TIME'])
+    else:
+        event_df = df_row[['SEGMENT', 'XCORR', 'YCORR', 'TIME']].to_frame().T
+        event_df = explode_df(event_df, ['XCORR', 'YCORR', 'TIME'])
+
+    # creating event dataframe and filtering it by location on the detector
+    npix = (location[1] - location[0]) * (location[3] - location[2])
+    index = np.where((event_df['XCORR'] > location[0]) & (
+                event_df['XCORR'] < location[1]) & (
+                                 event_df['YCORR'] > location[2]) & (
+                                 event_df['YCORR'] < location[3]))
+    filtered_row = event_df.iloc[index].reset_index(drop=True)
+
+    # filtered events only need to be further filtered by PHA if not NUV
+    if filter_pha:
+        filtered_row = filtered_row[(filtered_row['PHA'] > good_pha[0]) & (
+                filtered_row['PHA'] < good_pha[1])]
+
+    counts = np.histogram(filtered_row.TIME, bins=time_bins)[0]
+
+    date = absolute_time(
+        expstart=list(repeat(df_row['EXPSTART'], len(time_bins))),
+        time=time_bins.tolist()).to_datetime()[:-1]
+
+    dark_rate = counts / npix / time_step
+
+    return pd.DataFrame({
+        'segment': df_row['SEGMENT'], 'darks': [dark_rate], 'date': [date],
+        'rootname': df_row['ROOTNAME'], 'latitude': [lat], "longitude": [lon]
+        })
+
+
+def get_solar_data(url, datemin, datemax, box=4):
+    """Download the most recent solar data, save as file and dataframe,
+    filter dataframe to date range. Also replace -1 values in the smoothed
+    flux."""
+    response = request.urlopen(url)
+    if response.status == 200:
+        data = json.loads(response.read())
+    else:
+        print("Invalid response! HTTP Status Code: {}".format(response.status))
+    df = pd.DataFrame(data)
+    dates = [datetime.datetime.strptime(val, '%Y-%m') for val in
+             df['time-tag']]
+    df.index = pd.DatetimeIndex(dates)
+
+    todays_date = datetime.datetime.today().strftime('%b%d_%Y')
+    outfile = os.path.join(COS_MONITORING,
+                           "noaa_solar_indices_{}.txt".format(todays_date))
+    # print("Saving outfile: {}".format(outfile))
+    df.to_csv(outfile, header=True, index=True)
+
+    # print("Filtering the dataframe to the date range: {}, {}".format(datemin,
+    #                                                                  datemax))
+    df = df.loc[datemin:datemax]
+    # smoothing the f10.7 data
+    kernel = Box1DKernel(box)
+    smoothed_107 = convolve(df["f10.7"], kernel)
+    df["box_convolved_f10.7"] = smoothed_107
+
+    return df
+
+
+class DarkMonitor(BaseMonitor):
+    """Abstracted Dark Monitor. Not meant to be used directly but rather
+    inherited by specific segment and region dark monitors"""
+    labels = ['rootname']
+    output = COS_MONITORING
+    docs = "https://spacetelescope.github.io/cosmo/monitors.html#dark-rate" \
+           "-monitors"
+    segment = None
+    location = None
+    data_model = DarkDataModel
+    plottype = 'scatter'
+    x = 'date'
+    y = 'darks'
+    # # # defaults, can change these but optional definitions in the Monitor
+    # objects # # #
+    multi = False
+    sub_names = None
+    filter_saa = True
+    inner_region = 4  # number region that corresponds to the inner_region,
+
+    # for FUV use only.
+
+    def __init__(self, datemin=None, datemax=None, *args):
+        """Class Initialization to subclass superclass __init__ and add
+        optional date parameters."""
+        super().__init__(*args)
+        if datemin:
+            self.datemin = datemin
+        else:
+            self.datemin = "2009-01-01"  # earliest start date
+        if datemax:
+            self.datemax = datemax
+        else:
+            self.datemax = self.date  # this is defined in the superclass
+
+    def get_data(self):  # -> Any: fix this later,
+        """Required method to get the data necessary for plotting in the
+        correct format. Takes care of all data organization and "explosion"
+        of dataframes. Returns full dataframe with correct calculations and
+        columns."""
+        # should be fine in the monitor, just not in jupyter notebook
+        if self.multi:
+            # prime the pump
+            exploded_df = self.filter_data(self.location[0])
+            exploded_df["region"] = 0
+            for index, location in enumerate(self.location[1:]):
+                sub_exploded_df = self.filter_data(location)
+                sub_exploded_df["region"] = index + 1
+                exploded_df = exploded_df.append(sub_exploded_df)
+
+        else:
+            exploded_df = self.filter_data(self.location)
+
+        return exploded_df
+
+    def filter_data(self, location):
+        """Given a location (region) on the detector, filter down the new
+        data from the DataModel as appropriate and perform dark filtering
+        and "explosion" of dataframe as necessary. Return the fully
+        "exploded" data for that location."""
+        filtered_rows = []
+        for _, row in self.model.new_data.iterrows():
+            if row.EXPSTART == 0:
+                continue
+            if row.SEGMENT == self.segment:
+                if row.SEGMENT == "N/A":  # NUV
+                    filtered_rows.append(dark_filter(row, False, location))
+                else:  # Any of the FUV situations
+                    filtered_rows.append(dark_filter(row, True, location))
+        filtered_df = pd.concat(filtered_rows).reset_index(drop=True)
+
+        exploded_df = explode_df(filtered_df,
+                                 ['darks', 'date', 'latitude', 'longitude'])
+
+        # filtering on date
+        index = np.where((exploded_df["date"] >= self.datemin) & (
+                    exploded_df["date"] <= self.datemax))
+        exploded_df = exploded_df.iloc[index].reset_index(drop=True)
+
+        # after exploding, add SAA filtering if required
+        if self.filter_saa:
+            exploded_df["no_saa"] = np.where(
+                exploded_df.eval("latitude > 10 or longitude < 260"), 1, 0)
+
+        return exploded_df
+
+    def plot(self):
+        """Make the interactive subplots, including the solar plot, based on
+        how many locations (regions) are given. Write out the file to the
+        correct outpath."""
+        # make the interactive plots with sub-solar plots
+        if self.multi:
+            rows = len(self.location) + 1
+            self.sub_names += ["Solar Radio Flux"]
+            titles = tuple(self.sub_names)
+        else:
+            # only one region means two subplots
+            rows = 2
+            titles = (self.name, "Solar Radio Flux")
+
+        fig_height = 750
+        delta = 250
+        if rows > 3:
+            fig_height = delta * rows
+
+        pio.templates.default = "simple_white"
+
+        datemin = self.data[self.x].min()
+        datemax = self.data[self.x].max()
+
+        self.figure = make_subplots(rows=rows, cols=1, shared_xaxes=True,
+                                    subplot_titles=titles, x_title="Year",
+                                    vertical_spacing=0.05)
+        self.figure.update_layout(height=fig_height, width=1200,
+                                  title_text=self.name + f": "
+                                                         f"{
+        datemin:%Y-%m-%d} - "
+                                                         f"{datemax:%Y-%m-%d}")
+
+        if self.multi:
+            # prime the pump again
+            region_x_data = self.data[self.x].where(self.data["region"] == 0)
+            region_y_data = self.data[self.y].where(self.data["region"] == 0)
+            self.figure.add_trace(
+                go.Scatter(x=region_x_data, y=region_y_data, mode="markers",
+                           marker=dict(color=self.data["no_saa"],
+                                       colorscale=["red", "black"], size=5),
+                           hovertext=self.labels, hoverinfo="x+y+text",
+                           name="Mean Dark Rate"), row=1, col=1)
+            self.figure.update_yaxes(
+                title_text="Mean Dark Rate<br>(counts/pix/sec)", row=1, col=1)
+            for index, location in enumerate(self.location[1:]):
+                index = index + 1
+                region_x_data = self.data[self.x].where(
+                    self.data["region"] == index)
+                region_y_data = self.data[self.y].where(
+                    self.data["region"] == index)
+                self.figure.add_trace(
+                    go.Scatter(x=region_x_data, y=region_y_data,
+                               showlegend=False, mode="markers",
+                               marker=dict(color=self.data["no_saa"],
+                                           colorscale=["red", "black"],
+                                           size=5), hovertext=self.labels,
+                               hoverinfo="x+y+text", name="Mean Dark Rate"),
+                    row=index + 1, col=1)
+                self.figure.update_yaxes(
+                    title_text="Mean Dark Rate<br>(counts/pix/sec)",
+                    row=index + 1, col=1)
+
+        else:
+            # single plot
+            self.figure.add_trace(
+                go.Scatter(x=self.data[self.x], y=self.data[self.y],
+                           mode="markers",
+                           marker=dict(color=self.data["no_saa"],
+                                       colorscale=["red", "black"], size=5),
+                           hovertext=self.labels, hoverinfo="x+y+text",
+                           name="Mean Dark Rate"), row=1, col=1)
+            self.figure.update_yaxes(
+                title_text="Mean Dark Rate<br>(counts/pix/sec)", row=1, col=1)
+
+        ## this is solar stuff only until the next ##
+
+        # sunpy_data = sunpy_retriever(date_min, date_max)
+        solar_data = get_solar_data(NOAA_URL, datemin, datemax)
+        solar_time = solar_data.index
+        solar_flux = solar_data["f10.7"]
+        solar_flux_smooth = solar_data["box_convolved_f10.7"]
+
+        self.figure.add_trace(
+            go.Scatter(x=solar_time, y=solar_flux, mode="lines",
+                       line=dict(dash="dot", color="#0F2080"), name="10.7 cm"),
+            row=rows, col=1)
+
+        self.figure.add_trace(
+            go.Scatter(x=solar_time, y=solar_flux_smooth, mode="lines",
+                       line=dict(color="#85C0F9"), name="10.7 cm Smoothed"),
+            row=rows, col=1)
+
+        self.figure.update_yaxes(title_text="Solar Radio Flux", row=rows,
+                                 col=1)
+
+        ##
+
+        self.figure.update_xaxes(showgrid=True, showline=True, mirror=True)
+        self.figure.update_yaxes(showgrid=True, showline=True, mirror=True)
+
+    def plot_histogram(self, nbins=100):
+        """Make the interactive histogram which displays the distribution of
+        the data and the ETC dark rates. Write out the file to the
+        correct outpath."""
+        if self.data is None:
+            self.data = self.get_data()
+
+        dist995, dark_column, lines = self.calculate_histogram(nbins)
+        full_names = [f"Mean: {lines[0]:.2e}", f"Median: {lines[1]:.2e}",
+                      f"2 sigma: {lines[3]:.2e}", f"3 sigma: {lines[4]:.2e}",
+                      f"95%: {lines[5]:.2e}", f"99%: {lines[6]:.2e}"]
+
+        # histogram
+        fig = go.Figure(
+            data=[go.Histogram(x=dark_column, nbinsx=nbins, showlegend=False)])
+
+        # value lines--have to do a shape and trace for both of them until
+        # plotly adds vertical line plotting features (because shapes can't
+        # be in the legend, only traces)
+        # also the indexing is all weird but it is correct (has to do with
+        # the onesig)
+        # should fix this later
+        fig.add_trace(
+            go.Scatter(x=[lines[0], lines[0]], y=[0, 1], mode="lines",
+                       line=dict(color="#DC267F"), name=full_names[0]))
+        fig.add_shape(
+            dict(type="line", xref="x", yref="paper", x0=lines[0], y0=0,
+                 x1=lines[0], y1=1, line=dict(color="#DC267F")))
+
+        fig.add_trace(
+            go.Scatter(x=[lines[1], lines[1]], y=[0, 1], mode="lines",
+                       line=dict(color="#DC267F", dash="dash"),
+                       name=full_names[1]))
+        fig.add_shape(
+            dict(type="line", xref="x", yref="paper", x0=lines[1], y0=0,
+                 x1=lines[1], y1=1, line=dict(color="#DC267F", dash="dash")))
+
+        fig.add_trace(
+            go.Scatter(x=[lines[3], lines[3]], y=[0, 1], mode="lines",
+                       line=dict(color="#FE6100"), name=full_names[2]))
+        fig.add_shape(
+            dict(type="line", xref="x", yref="paper", x0=lines[3], y0=0,
+                 x1=lines[3], y1=1, line=dict(color="#FE6100")))
+
+        fig.add_trace(
+            go.Scatter(x=[lines[4], lines[4]], y=[0, 1], mode="lines",
+                       line=dict(color="#FE6100", dash="dash"),
+                       name=full_names[3]))
+        fig.add_shape(
+            dict(type="line", xref="x", yref="paper", x0=lines[4], y0=0,
+                 x1=lines[4], y1=1, line=dict(color="#FE6100", dash="dash")))
+
+        fig.add_trace(
+            go.Scatter(x=[lines[5], lines[5]], y=[0, 1], mode="lines",
+                       line=dict(color="#FFB000"), name=full_names[4]))
+        fig.add_shape(
+            dict(type="line", xref="x", yref="paper", x0=lines[5], y0=0,
+                 x1=lines[5], y1=1, line=dict(color="#FFB000")))
+
+        fig.add_trace(
+            go.Scatter(x=[lines[6], lines[6]], y=[0, 1], mode="lines",
+                       line=dict(color="#FFB000", dash="dash"),
+                       name=full_names[5]))
+        fig.add_shape(
+            dict(type="line", xref="x", yref="paper", x0=lines[6], y0=0,
+                 x1=lines[6], y1=1, line=dict(color="#FFB000", dash="dash")))
+
+        datemin = self.data[self.x].min()
+        datemax = self.data[self.x].max()
+        fig.update_xaxes(range=[0, dist995], title_text="Counts/Pix/Sec",
+                         showline=True)
+        fig.update_yaxes(rangemode="tozero", title_text="Frequency",
+                         showline=True)
+        fig.update_layout(
+            title_text=self.name + f" Histogram: {datemin:%Y-%m-%d} - "
+                                   f"{datemax:%Y-%m-%d}")
+        fig.update_layout(xaxis=dict(showexponent='all', exponentformat='e'))
+        fig.update_layout(yaxis_showgrid=True)
+
+        # fix this naming convention later
+        if not self.output:
+            output = f'{os.path.join(os.getcwd(), f"{self._filename}_hist.html")}'
+        else:
+            # you would think you could use self.output but that gets
+            # updated somewhere
+            # in the superclass to include the full plot name so we can't
+            # use that.
+            # kind of a bug
+            output = os.path.join(COS_MONITORING,
+                                  f"{self._filename}_hist.html")
+
+        fig.write_html(output)
+
+    def calculate_histogram(self, nbins=100):
+        """Calculate the histogram distribution for the important plot and
+        ETC values."""
+        if self.data is None:
+            self.data = self.get_data()
+
+        # filter out the flag == 0 / grab the flag == 1 from self.data[self.y]
+        if self.filter_saa:
+            dark_column = self.data[self.y].loc[self.data["no_saa"] == 1]
+            if "FUV" in self.segment:
+                dark_column = self.data[self.y].loc[
+                    (self.data["no_saa"] == 1) & (
+                            self.data["region"] == self.inner_region)]
+        else:
+            dark_column = self.data[self.y]
+
+        counts, bins = np.histogram(dark_column, bins=nbins)
+        cuml_dist = np.cumsum(counts)
+        count_99 = abs(cuml_dist / float(cuml_dist.max()) - .99).argmin()
+        count_95 = abs(cuml_dist / float(cuml_dist.max()) - .95).argmin()
+        # only used for plotting
+        count995 = abs(cuml_dist / float(cuml_dist.max()) - .995).argmin()
+
+        mean = dark_column.mean()
+        med = np.median(dark_column)
+        std = dark_column.std()
+        onesig = med + std
+        twosig = med + (2 * std)
+        threesig = med + (3 * std)
+        dist95 = bins[count_95]
+        dist99 = bins[count_99]
+        dist995 = bins[count995]
+        values = [mean, med, onesig, twosig, threesig, dist95, dist99]
+
+        return dist995, dark_column, values
+
+    def track(self, nbins=100):
+        "Tracking method to track ETC Dark Rate measurements."
+        _, _, track_list = self.calculate_histogram(nbins)
+        return track_list
+
+    def plot_orbital_variation(self):
+        """Make the orbital variation plot and write out the file to the
+        correct outpath."""
+        if self.data is None:
+            self.data = self.get_data()
+
+        colormin = self.data["darks"].min()
+        colormax = self.data["darks"].max()
+        fig = go.Figure(data=[
+            go.Scatter(x=self.data["longitude"], y=self.data["latitude"],
+                       mode="markers",
+                       marker=dict(color=self.data["darks"], size=2,
+                                   colorscale='Viridis', opacity=0.5,
+                                   colorbar=dict(thickness=20,
+                                                 exponentformat="e",
+                                                 title=dict(text="Dark Rate")),
+                                   cmin=colormin, cmax=colormax))])
+
+        datemin = self.data[self.x].min()
+        datemax = self.data[self.x].max()
+        fig.update_layout(
+            title_text=self.name + f" Orbital Variation: {datemin:%Y-%m-%d} - "
+                                   f"{datemax:%Y-%m-%d}")
+        fig.update_xaxes(title_text="Longitude", showline=True)
+        fig.update_yaxes(title_text="Latitude", showline=True)
+
+        if not self.output:
+            output = os.path.join(os.getcwd(),
+                                  f"{self._filename}_orbital.html")
+        else:
+            # you would think you could use self.output but that gets
+            # updated somewhere
+            # in the superclass to include the full plot name so we can't
+            # use that.
+            # kind of a bug
+            output = os.path.join(COS_MONITORING,
+                                  f"{self._filename}_orbital.html")
+
+        fig.write_html(output)
+
+    def store_results(self):
+        # TODO: Define results to store
+        pass
+
+
+# ----------------------------------------------------------------------------#
+
+
+class FUVADarkMonitor(DarkMonitor):
+    """FUVA Dark Monitor for all edges and inner region."""
+    name = 'FUVA Dark Monitor'
+    segment = 'FUVA'
+    multi = True
+    location = [(1060, 15250, 296, 375), (1060, 1260, 296, 734),
+                (1060, 15250, 660, 734), (15119, 15250, 296, 734),
+                (1260, 15119, 375, 660)]
+    sub_names = ["FUVA Dark Monitor - Bottom", "FUVA Dark Monitor - Left",
+                 "FUVA Dark Monitor - Top", "FUVA Dark Monitor - Right",
+                 "FUVA Dark Monitor - Inner"]
+
+
+class FUVBDarkMonitor(DarkMonitor):
+    """FUVB Dark Monitor for all edges and inner region."""
+    name = 'FUVB Dark Monitor'
+    segment = 'FUVB'
+    multi = True
+    location = [(809, 15182, 360, 405), (809, 1000, 360, 785),
+                (809, 15182, 740, 785), (14990, 15182, 360, 785),
+                (1000, 14990, 405, 740)]
+    sub_names = ["FUVB Dark Monitor - Bottom", "FUVB Dark Monitor - Left",
+                 "FUVB Dark Monitor - Top", "FUVB Dark Monitor - Right",
+                 "FUVB Dark Monitor - Inner"]
+
+
+class FUVABottomDarkMonitor(DarkMonitor):
+    """FUVA Dark Monitor for bottom edge."""
+    segment = 'FUVA'
+    location = (1060, 15250, 296, 375)
+    name = 'FUVA Dark Monitor - Bottom'
+
+
+class FUVALeftDarkMonitor(DarkMonitor):
+    """FUVA Dark Monitor for left edge."""
+    name = 'FUVA Dark Monitor - Left'
+    segment = 'FUVA'
+    location = (1060, 1260, 296, 734)
+
+
+class FUVATopDarkMonitor(DarkMonitor):
+    """FUVA Dark Monitor for top edge."""
+    name = 'FUVA Dark Monitor - Top'
+    segment = 'FUVA'
+    location = (1060, 15250, 660, 734)
+
+
+class FUVARightDarkMonitor(DarkMonitor):
+    """FUVA Dark Monitor for right edge."""
+    name = 'FUVA Dark Monitor - Right'
+    segment = 'FUVA'
+    location = (15119, 15250, 296, 734)
+
+
+class FUVAInnerDarkMonitor(DarkMonitor):
+    """FUVA Dark Monitor for inner region."""
+    name = 'FUVA Dark Monitor - Inner'
+    segment = 'FUVA'
+    location = (1260, 15119, 375, 660)
+
+
+class FUVBBottomDarkMonitor(DarkMonitor):
+    """FUVB Dark Monitor for bottom edge."""
+    name = 'FUVB Dark Monitor - Bottom'
+    segment = 'FUVB'
+    location = (809, 15182, 360, 405)
+
+
+class FUVBLeftDarkMonitor(DarkMonitor):
+    """FUVB Dark Monitor for left edge."""
+    name = 'FUVB Dark Monitor - Left'
+    segment = 'FUVB'
+    location = (809, 1000, 360, 785)
+
+
+class FUVBTopDarkMonitor(DarkMonitor):
+    """FUVB Dark Monitor for top edge."""
+    name = 'FUVB Dark Monitor - Top'
+    segment = 'FUVB'
+    location = (809, 15182, 740, 785)
+
+
+class FUVBRightDarkMonitor(DarkMonitor):
+    """FUVB Dark Monitor for right edge."""
+    name = 'FUVB Dark Monitor - Right'
+    segment = 'FUVB'
+    location = (14990, 15182, 360, 785)
+
+
+class FUVBInnerDarkMonitor(DarkMonitor):
+    """FUVB Dark Monitor for inner region."""
+    name = 'FUVB Dark Monitor - Inner'
+    segment = 'FUVB'
+    location = (1000, 14990, 405, 740)
+
+
+class NUVDarkMonitor(DarkMonitor):
+    """NUV Dark Monitor for full detector."""
+    name = "NUV Dark Monitor"
+    segment = "N/A"
+    location = (0, 1024, 0, 1024)
+
+
diff --git a/cosmo/monitors/data_models.py b/cosmo/monitors/data_models.py
index 503251e..b0a34f2 100644
--- a/cosmo/monitors/data_models.py
+++ b/cosmo/monitors/data_models.py
@@ -1,5 +1,7 @@
 import pandas as pd
 import numpy as np
+import os
+from glob import glob
 
 from typing import List
 from monitorframe.datamodel import BaseDataModel
@@ -10,12 +12,16 @@
 from .. import SETTINGS
 
 FILES_SOURCE = SETTINGS['filesystem']['source']
+PROGRAMS = SETTINGS['dark_programs']
 
 
 def dgestar_to_fgs(results: List[dict]) -> None:
     """Add a FGS key to each row dictionary."""
     for item in results:
-        item.update({'FGS': item['DGESTAR'][-2:]})  # The dominant guide star key is the last 2 values in the string
+        item.update({
+            'FGS': item['DGESTAR'][-2:]
+            })  # The dominant guide star key is the last 2  # values in the
+        # string
 
 
 class AcqDataModel(BaseDataModel):
@@ -26,37 +32,26 @@ class AcqDataModel(BaseDataModel):
 
     def get_new_data(self):
         header_request = {
-            0: [
-                'ACQSLEWX',
-                'ACQSLEWY',
-                'ROOTNAME',
-                'PROPOSID',
-                'OBSTYPE',
-                'SHUTTER',
-                'LAMPEVNT',
-                'ACQSTAT',
-                'EXTENDED',
-                'LINENUM',
-                'APERTURE',
-                'OPT_ELEM',
-                'LIFE_ADJ',
-                'CENWAVE',
-                'DETECTOR',
-                'EXPTYPE'
-            ],
-            1: ['EXPSTART', 'NEVENTS']
-        }
+            0: ['ACQSLEWX', 'ACQSLEWY', 'ROOTNAME', 'PROPOSID', 'OBSTYPE',
+                'SHUTTER', 'LAMPEVNT', 'ACQSTAT', 'EXTENDED', 'LINENUM',
+                'APERTURE', 'OPT_ELEM', 'LIFE_ADJ', 'CENWAVE', 'DETECTOR',
+                'EXPTYPE'], 1: ['EXPSTART', 'NEVENTS']
+            }
 
         # Different ACQ types may not have the full set
-        header_defaults = {'ACQSLEWX': 0.0, 'ACQSLEWY': 0.0, 'NEVENTS': 0.0, 'LAMPEVNT': 0.0}
+        header_defaults = {
+            'ACQSLEWX': 0.0, 'ACQSLEWY': 0.0, 'NEVENTS': 0.0, 'LAMPEVNT': 0.0
+            }
 
         # SPT file header keys, extensions
         spt_header_request = {0: ['DGESTAR']}
 
-        files = find_files('*rawacq*', data_dir=self.files_source, subdir_pattern=self.subdir_pattern)
+        files = find_files('*rawacq*', data_dir=self.files_source,
+                           subdir_pattern=self.subdir_pattern)
 
         if self.model is not None:
-            currently_ingested = [item.FILENAME for item in self.model.select(self.model.FILENAME)]
+            currently_ingested = [item.FILENAME for item in
+                                  self.model.select(self.model.FILENAME)]
 
             for file in currently_ingested:
                 files.remove(file)
@@ -64,12 +59,10 @@ def get_new_data(self):
         if not files:  # No new files
             return pd.DataFrame()
 
-        data_results = data_from_exposures(
-            files,
-            header_request=header_request,
-            header_defaults=header_defaults,
-            spt_header_request=spt_header_request,
-        )
+        data_results = data_from_exposures(files,
+                                           header_request=header_request,
+                                           header_defaults=header_defaults,
+                                           spt_header_request=spt_header_request, )
 
         dgestar_to_fgs(data_results)
 
@@ -88,9 +81,9 @@ class OSMDataModel(BaseDataModel):
     def get_new_data(self):
         """Retrieve data."""
         header_request = {
-            0: ['ROOTNAME', 'DETECTOR', 'LIFE_ADJ', 'OPT_ELEM', 'CENWAVE', 'FPPOS', 'PROPOSID', 'OBSET_ID'],
-            1: ['EXPSTART']
-        }
+            0: ['ROOTNAME', 'DETECTOR', 'LIFE_ADJ', 'OPT_ELEM', 'CENWAVE',
+                'FPPOS', 'PROPOSID', 'OBSET_ID'], 1: ['EXPSTART']
+            }
 
         table_request = {1: ['TIME', 'SHIFT_DISP', 'SHIFT_XDISP', 'SEGMENT']}
 
@@ -98,58 +91,64 @@ def get_new_data(self):
             'LAMPTAB': {
                 'match_keys': ['OPT_ELEM', 'CENWAVE', 'FPOFFSET'],
                 'table_request': {1: ['SEGMENT', 'FP_PIXEL_SHIFT']},
-            },
-            'WCPTAB': {'match_keys': ['OPT_ELEM'], 'table_request': {1: ['XC_RANGE', 'SEARCH_OFFSET']}}
-        }
+                }, 'WCPTAB': {
+                'match_keys': ['OPT_ELEM'],
+                'table_request': {1: ['XC_RANGE', 'SEARCH_OFFSET']}
+                }
+            }
 
-        files = find_files('*lampflash*', data_dir=self.files_source, subdir_pattern=self.subdir_pattern)
+        files = find_files('*lampflash*', data_dir=self.files_source,
+                           subdir_pattern=self.subdir_pattern)
 
         if self.model is not None:
-            currently_ingested = [item.FILENAME for item in self.model.select(self.model.FILENAME)]
+            currently_ingested = [item.FILENAME for item in
+                                  self.model.select(self.model.FILENAME)]
 
             for file in currently_ingested:
                 files.remove(file)
 
-        if not files:   # No new files
+        if not files:  # No new files
             return pd.DataFrame()
 
         data_results = pd.DataFrame(
-            data_from_exposures(
-                files,
-                header_request=header_request,
-                table_request=table_request,
-                reference_request=reference_request
-            )
-        )
+            data_from_exposures(files, header_request=header_request,
+                                table_request=table_request,
+                                reference_request=reference_request))
 
         # Remove any rows that have empty data columns
-        data_results = data_results.drop(
-            data_results[data_results.apply(lambda x: not bool(len(x.SHIFT_DISP)), axis=1)].index.values
-        ).reset_index(drop=True)
+        data_results = data_results.drop(data_results[data_results.apply(
+            lambda x: not bool(len(x.SHIFT_DISP)),
+            axis=1)].index.values).reset_index(drop=True)
 
         # Add tsince data from SMSTable.
         try:
             sms_data = pd.DataFrame(
-                    SMSTable.select(SMSTable.ROOTNAME, SMSTable.TSINCEOSM1, SMSTable.TSINCEOSM2).where(
-                        # x << y -> x IN y (y must be a list)
-                        SMSTable.ROOTNAME + 'q' << data_results.ROOTNAME.to_list()).dicts()
-            )
+                SMSTable.select(SMSTable.ROOTNAME, SMSTable.TSINCEOSM1,
+                                SMSTable.TSINCEOSM2).where(
+                    # x << y -> x IN y (y must be a list)
+                    SMSTable.ROOTNAME + 'q' <<
+                    data_results.ROOTNAME.to_list()).dicts())
 
         except OperationalError as e:
             raise type(e)(str(e) + '\nSMS database is required.')
 
-        # It's possible that there could be a lag in between when the SMS data is updated and when new lampflashes
+        # It's possible that there could be a lag in between when the SMS
+        # data is updated and when new lampflashes
         # are added.
-        # Returning the empty data frame ensures that only files with a match in the SMS data are added...
+        # Returning the empty data frame ensures that only files with a
+        # match in the SMS data are added...
         # This may not be the best idea
         if sms_data.empty:
             return sms_data
 
-        # Need to add the 'q' at the end of the rootname.. For some reason those are missing from the SMS rootnames
+        # Need to add the 'q' at the end of the rootname.. For some reason
+        # those are missing from the SMS rootnames
         sms_data.ROOTNAME += 'q'
 
-        # Combine the data from the files with the data from the SMS table with an inner merge between the two.
-        # NOTE: this means that if a file does not have a corresponding entry in the SMSTable, it will not be in the
+        # Combine the data from the files with the data from the SMS table
+        # with an inner merge between the two.
+        # NOTE: this means that if a file does not have a corresponding
+        # entry in the SMSTable, it will not be in the
         # dataset used for monitoring.
         merged = pd.merge(data_results, sms_data, on='ROOTNAME')
 
@@ -165,30 +164,85 @@ def get_new_data(self):
         extension_header_keys = ('EXPNAME',)
 
         data_keys = ('SI_V2_AVG', 'SI_V3_AVG')
-        reduce = {'SI_V2_AVG': ('mean', 'std', 'max'), 'SI_V3_AVG': ('mean', 'std', 'max')}
+        reduce = {
+            'SI_V2_AVG': ('mean', 'std', 'max'),
+            'SI_V3_AVG': ('mean', 'std', 'max')
+            }
 
-        files = find_files('*jit*', data_dir=self.files_source, subdir_pattern=self.subdir_pattern)
+        files = find_files('*jit*', data_dir=self.files_source,
+                           subdir_pattern=self.subdir_pattern)
 
         if self.model is not None:
-            currently_ingested = [item.FILENAME for item in self.model.select(self.model.FILENAME)]
+            currently_ingested = [item.FILENAME for item in
+                                  self.model.select(self.model.FILENAME)]
 
             for file in currently_ingested:
                 files.remove(file)
 
-        if not files:   # No new files
+        if not files:  # No new files
             return pd.DataFrame()
 
         data_results = pd.DataFrame(
-            data_from_jitters(
-                files,
-                primary_header_keys,
-                extension_header_keys,
-                data_keys,
-                reduce_to_stats=reduce
-            )
-        )
-
-        # Remove any NaNs or inf that may occur from the statistics calculations.
-        data_results = data_results.replace([np.inf, -np.inf], np.nan).dropna().reset_index(drop=True)
-
-        return data_results[~data_results.EXPTYPE.str.contains('ACQ|DARK|FLAT')]
+            data_from_jitters(files, primary_header_keys,
+                              extension_header_keys, data_keys,
+                              reduce_to_stats=reduce))
+
+        # Remove any NaNs or inf that may occur from the statistics
+        # calculations.
+        data_results = data_results.replace([np.inf, -np.inf],
+                                            np.nan).dropna().reset_index(
+            drop=True)
+
+        return data_results[
+            ~data_results.EXPTYPE.str.contains('ACQ|DARK|FLAT')]
+
+
+def get_program_ids(pid_file):
+    """Retrieve the program IDs from the given text file."""
+    programs_df = pd.read_csv(pid_file, delim_whitespace=True)
+    all_programs = []
+    for col, col_data in programs_df.iteritems():
+        all_programs += col_data.to_numpy(dtype=str).tolist()
+
+    return all_programs
+
+
+class DarkDataModel(BaseDataModel):
+    """DataModel for dark corrtag files."""
+    cosmo_layout = False
+
+    def get_new_data(self):
+        """Set the model for what data is to be retrieved from each dark
+        file."""
+        # this way when you get new data it will get all the data
+        header_request = {
+            0: ['ROOTNAME', 'SEGMENT'], 1: ['EXPTIME', 'EXPSTART']
+            }
+        table_request = {
+            1: ['PHA', 'XCORR', 'YCORR', 'TIME'],
+            3: ['TIME', 'LATITUDE', 'LONGITUDE']
+            }
+
+        files = []
+
+        program_ids = get_program_ids(PROGRAMS)
+
+        for prog_id in program_ids:
+            new_files_source = os.path.join(FILES_SOURCE, prog_id)
+            files += find_files('*corrtag*', data_dir=new_files_source)
+
+        if self.model is not None:
+            currently_ingested = [item.FILENAME for item in
+                                  self.model.select(self.model.FILENAME)]
+
+            for file in currently_ingested:
+                files.remove(file)
+
+        if not files:  # No new files
+            return pd.DataFrame()
+
+        data_results = data_from_exposures(files,
+                                           header_request=header_request,
+                                           table_request=table_request)
+
+        return data_results
diff --git a/setup.py b/setup.py
index 4ee0534..31a344c 100644
--- a/setup.py
+++ b/setup.py
@@ -16,15 +16,18 @@
         'setuptools',
         'numpy>=1.11.1',
         'astropy>=1.0.1',
-        'plotly>=4.0.0',
+        'plotly>=4.9.0',
         'dask',
         'pandas>=0.25.0',
         'pytest',
         'pyyaml',
-        'peewee',
+        'peewee>=3.13.3',  # needs this for database model reasons
         'crds',
+        'sunpy[all]',
         'monitorframe @ git+https://github.com/spacetelescope/monitor-framework@v1.2.0#egg=monitorframe'
     ],
+    dependency_links=['http://github.com/spacetelescope/monitor-framework'
+                      '/tarball/master#egg=monitorframe'],
     package_data={'cosmo': ['pytest.ini']},
     entry_points={
         'console_scripts':
diff --git a/tests/cosmoconfig_test.yaml b/tests/cosmoconfig_test.yaml
index 3ccc9dc..d9f3e19 100644
--- a/tests/cosmoconfig_test.yaml
+++ b/tests/cosmoconfig_test.yaml
@@ -12,6 +12,7 @@ sms:
       synchronous: 0
 
 output: ''
+dark_programs: './data/programs_test.txt'
 
 # Monitor data database
 data:
diff --git a/tests/data/programs_test.txt b/tests/data/programs_test.txt
new file mode 100644
index 0000000..3d488b5
--- /dev/null
+++ b/tests/data/programs_test.txt
@@ -0,0 +1,2 @@
+FUV NUV
+15771 15776
\ No newline at end of file
diff --git a/tests/test_dark_monitors.py b/tests/test_dark_monitors.py
new file mode 100644
index 0000000..1fffda2
--- /dev/null
+++ b/tests/test_dark_monitors.py
@@ -0,0 +1,52 @@
+import os
+import pytest
+
+from cosmo.monitors.dark_monitors import DarkMonitor, FUVADarkMonitor, \
+    FUVBDarkMonitor, NUVDarkMonitor
+from cosmo.monitors.data_models import DarkDataModel
+
+TEST_DATA = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'data/')
+
+@pytest.fixture(params=[False, True])
+def set_darkmonitor(request, data_dir, here):
+    if request.param:
+        model = DarkDataModel()
+        model.ingest()
+
+    def _set_monitor(monitor):
+        DarkDataModel.files_source = data_dir
+        DarkDataModel.subdir_pattern = None
+
+        monitor.data_model = DarkDataModel
+        monitor.output = here
+
+        active = monitor()
+
+        return active
+
+    return _set_monitor
+
+
+class TestDarkMonitors:
+
+    @pytest.fixture(autouse=True, params=[FUVADarkMonitor,
+                                          FUVBDarkMonitor,
+                                          NUVDarkMonitor])
+    def darkmonitor(self, request, set_darkmonitor):
+        darkmonitor = set_darkmonitor(request.param)
+
+        request.cls.darkmonitor = darkmonitor
+
+        yield
+
+        if request.cls.darkmonitor.model.model is not None:
+            request.cls.darkmonitor.model.model.drop_table(safe=True)
+
+    def test_monitor_steps(self):
+        self.darkmonitor.initialize_data()
+        self.darkmonitor.run_analysis()
+        self.darkmonitor.plot()
+        self.darkmonitor.write_figure()
+        self.darkmonitor.store_results()
+
+        assert os.path.exists(self.darkmonitor.output)
diff --git a/tests/test_data_models.py b/tests/test_data_models.py
index 6ed9a88..6d5c53c 100644
--- a/tests/test_data_models.py
+++ b/tests/test_data_models.py
@@ -2,7 +2,8 @@
 import numpy as np
 import pytest
 
-from cosmo.monitors.data_models import AcqDataModel, OSMDataModel
+from cosmo.monitors.data_models import AcqDataModel, OSMDataModel, \
+    DarkDataModel
 from cosmo.sms import SMSFinder
 
 
@@ -119,3 +120,35 @@ def test_data_ingest(self):
 
         assert self.acqmodel.model is not None
         assert len(list(self.acqmodel.model.select())) == 9
+
+
+class TestDarkDataModel:
+
+    @pytest.fixture(autouse=True)
+    def darkmodel(self, request, make_datamodel):
+        darkmodel = make_datamodel(DarkDataModel)
+
+        request.cls.darkmodel = darkmodel  # Add the data model to the test
+        # class
+
+        yield
+
+        if request.cls.darkmodel.model is not None:
+            request.cls.darkmodel.model.drop_table(safe=True)
+
+    def test_data_collection(self):
+        assert isinstance(self.darkmodel.new_data, pd.DataFrame)
+        assert len(self.darkmodel.new_data) == 9  # There are 9 test data sets
+
+    def test_content_collected(self):
+        keys_that_should_be_there = (
+            # Header keywords
+            "ROOTNAME", "SEGMENT", "EXPTIME", "EXPSTART",
+            # Table keywords data extension
+            "PHA", "XCORR", "YCORR", "TIME",
+            # Table keywords third extension
+            "TIME_3", "LATITUDE", "LONGITUDE")
+        # seems like TIME_3 might not work...should check that
+
+        for key in keys_that_should_be_there:
+            assert key in self.darkmodel.new_data