AJMR-Python-Baird/Mustique/WestCoastDataTemplate_V2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "pycharm": {
     "name": "#%% Project Setup\n"
    }
   },
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "import jupyter\n",
    "import sys\n",
    "import pandas as pd\n",
    "import geopandas as gp\n",
    "from scipy.signal import argrelextrema\n",
    "import numpy as np\n",
    "import math\n",
    "from mpl_toolkits.axes_grid1.anchored_artists import AnchoredSizeBar, AnchoredDirectionArrows\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.font_manager as fm\n",
    "import matplotlib as mpl\n",
    "\n",
    "import cartopy.crs as ccrs\n",
    "import contextily as ctx\n",
    "import cmocean.cm as cmo\n",
    "from shapely.geometry import Point, LineString\n",
    "\n",
    "from xarray.backends import NetCDF4DataStore\n",
    "import xarray as xr\n",
    "\n",
    "from datetime import datetime, timedelta\n",
    "from netCDF4 import num2date\n",
    "from metpy.units import units\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import cartopy.crs as ccrs\n",
    "import cartopy.feature as cfeature\n",
    "from metpy.plots import ctables\n",
    "\n",
    "from siphon.catalog import TDSCatalog"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [],
   "source": [
    "def find_time_var(var, time_basename='time'):\n",
    "    for coord_name in var.coords:\n",
    "        if coord_name.startswith(time_basename):\n",
    "            return var.coords[coord_name]\n",
    "    raise ValueError('No time variable found for ' + var.name)\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%  Helper function for finding proper time variable\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\arey\\Anaconda3\\envs\\BairdBase\\lib\\site-packages\\openpyxl\\styles\\stylesheet.py:221: UserWarning: Workbook contains no default style, apply openpyxl's default\n",
      "  warn(\"Workbook contains no default style, apply openpyxl's default\")\n"
     ]
    }
   ],
   "source": [
    "importPaths = ['C:/Users/arey/files/Projects/West Coast/Observations/Great House/',\n",
    "               'C:/Users/arey/files/Projects/West Coast/Observations/Greensleeves/',\n",
    "               'C:/Users/arey/files/Projects/West Coast/Observations/Old Queens Fort/',\n",
    "               'C:/Users/arey/files/Projects/West Coast/Updated Observations/Great House/',\n",
    "               'C:/Users/arey/files/Projects/West Coast/Updated Observations/Greensleeves/',\n",
    "               'C:/Users/arey/files/Projects/West Coast/Updated Observations/Old Queens Fort/',\n",
    "               'C:/Users/arey/files/Projects/West Coast/Observations3/Great House/',\n",
    "               'C:/Users/arey/files/Projects/West Coast/Observations3/Greensleeves/',\n",
    "               'C:/Users/arey/files/Projects/West Coast/Observations3/Old Queens Fort/',\n",
    "               'C:/Users/arey/files/Projects/West Coast/Monitoring_Nov/Great House/',\n",
    "               'C:/Users/arey/files/Projects/West Coast/Monitoring_Nov/Greensleeves/',\n",
    "               'C:/Users/arey/files/Projects/West Coast/Monitoring_Nov/Old Queens Fort/']\n",
    "\n",
    "siteNames   = ['Great House',\n",
    "               'Greensleeves',\n",
    "               'Old Queens Fort',\n",
    "               'Great House',\n",
    "               'Greensleeves',\n",
    "               'Old Queens Fort',\n",
    "               'Great House',\n",
    "               'Greensleeves',\n",
    "               'Old Queens Fort',\n",
    "               'Great House',\n",
    "               'Greensleeves',\n",
    "               'Old Queens Fort']\n",
    "\n",
    "timeLabels=   ['Before Construction',\n",
    "               'Before Construction',\n",
    "               'Before Construction',\n",
    "               'During Construction',\n",
    "               'During Construction',\n",
    "               'During Construction',\n",
    "               'After Construction',\n",
    "               'After Construction',\n",
    "               'After Construction',\n",
    "               'November Monitoring',\n",
    "               'November Monitoring',\n",
    "               'November Monitoring']\n",
    "\n",
    "wave_bts_file = None\n",
    "\n",
    "for s in range(11, 12):\n",
    "    ## Define master import path\n",
    "    importPath  = importPaths[s]\n",
    "    siteName    = siteNames[s]\n",
    "    timeLabel   = timeLabels[s]\n",
    "    importFiles = os.listdir(importPath)\n",
    "\n",
    "    # Initialize import variables\n",
    "    RBR_File = None\n",
    "    JFE_File = None\n",
    "    GPS_File = None\n",
    "\n",
    "    for i in range(0, len(importFiles)):\n",
    "        if '.xlsx' in importFiles[i] and 'Summary' not in importFiles[i]:\n",
    "            RBR_File = importFiles[i]\n",
    "        elif '_A.csv' in importFiles[i] and 'Summary' not in importFiles[i]:\n",
    "            JFE_File = importFiles[i]\n",
    "        elif '.csv' in importFiles[i] and 'Summary' not in importFiles[i]:\n",
    "            GPS_File = importFiles[i]\n",
    "\n",
    "    #%% RBR Import Data\n",
    "    if RBR_File is not None:\n",
    "        RBR_Obs = pd.read_excel(importPath + RBR_File,\n",
    "                             sheet_name='Data', skiprows=0, header=1)\n",
    "\n",
    "    #%% JFE Import Data\n",
    "    if JFE_File is not None:\n",
    "        JFE_Obs = pd.read_csv(importPath + JFE_File, skiprows=30)\n",
    "\n",
    "    #%% GPS Import Data\n",
    "    GPS = pd.read_csv(importPath + GPS_File,\n",
    "                        header=None, names=['Index', 'Date1', 'Time1', 'Date2', 'Time2', 'Northing', 'North', 'Easting', 'East', 'Var1', 'Var2'])\n",
    "    #convert GPS data to geodataframe\n",
    "    GPS_gdf = gp.GeoDataFrame(GPS, geometry=gp.points_from_xy(-GPS.Easting, GPS.Northing, crs=\"EPSG:4326\"))\n",
    "\n",
    "    GPS_gdf['DateTime'] = pd.to_datetime(GPS_gdf['Date2'].astype(str) + ' ' + GPS_gdf['Time2'].astype(str))\n",
    "\n",
    "    GPS_gdf.set_index('DateTime', inplace=True)\n",
    "\n",
    "    # Convert to UTM\n",
    "    GPS_gdf.geometry = GPS_gdf.geometry.to_crs(\"EPSG:32621\")\n",
    "\n",
    "    # Read in site shapefile\n",
    "    siteShp = gp.read_file('C:/Users/arey/files/Projects/West Coast/SitePolygons.shp')\n",
    "    siteShp.geometry = siteShp.geometry.to_crs(\"EPSG:32621\")\n",
    "\n",
    "\n",
    "    #%% Merge GPS to RBR\n",
    "    # Process RBR into datetime\n",
    "    if RBR_File is not None:\n",
    "        RBR_Obs['DateTime'] = pd.to_datetime(RBR_Obs['Time'])\n",
    "        RBR_Obs.set_index('DateTime', inplace=True)\n",
    "\n",
    "        # Merge with GPS as dataframe\n",
    "        RBR_Obs_geo = pd.merge_asof(RBR_Obs, GPS_gdf,\n",
    "                                     left_index=True, right_index=True,  direction='nearest', tolerance=pd.Timedelta('300s'))\n",
    "        RBR_Obs_geo = gp.GeoDataFrame(RBR_Obs_geo, geometry=RBR_Obs_geo.geometry, crs=\"EPSG:32621\")\n",
    "\n",
    "\n",
    "\n",
    "    #%% Merge GPS to JFE\n",
    "    if JFE_File is not None:\n",
    "        # Process JFE into datetime\n",
    "        JFE_Obs['DateTime'] = pd.to_datetime(JFE_Obs['Date'])\n",
    "        JFE_Obs.set_index('DateTime', inplace=True)\n",
    "\n",
    "        # Merge with GPS as dataframe\n",
    "        JFE_Obs_geo = pd.merge_asof(JFE_Obs, GPS_gdf, left_index=True, right_index=True,  direction='nearest', tolerance=pd.Timedelta('60s'))\n",
    "        JFE_Obs_geo = gp.GeoDataFrame(JFE_Obs_geo, geometry=JFE_Obs_geo.geometry, crs=\"EPSG:32621\")\n",
    "\n",
    "    #%% Find and setup key plotting details\n",
    "\n",
    "    # Shaded Water\n",
    "    mapbox = 'https://api.mapbox.com/styles/v1/alexander0042/ckemxgtk51fgp19nybfmdcb1e/tiles/256/{z}/{x}/{y}@2x?access_token=pk.eyJ1IjoiYWxleGFuZGVyMDA0MiIsImEiOiJjazVmdG4zbncwMHY4M2VrcThwZGUzZDFhIn0.w6oDHoo1eCeRlSBpwzwVtw'\n",
    "    # Sat water\n",
    "    # mapbox = 'https://api.mapbox.com/styles/v1/alexander0042/ckekcw3pn08am19qmqbhtq8sb/tiles/256/{z}/{x}/{y}@2x?access_token=pk.eyJ1IjoiYWxleGFuZGVyMDA0MiIsImEiOiJjazVmdG4zbncwMHY4M2VrcThwZGUzZDFhIn0.w6oDHoo1eCeRlSBpwzwVtw'\n",
    "\n",
    "    if siteName == 'Great House':\n",
    "        axXlim  = (213210.7529575412, 213562.64172686986)\n",
    "        axYlim  = (1464769.2243017585, 1465135.2219089477)\n",
    "        GFS_Lon = -59.6441\n",
    "        GFS_Lat = 13.2372\n",
    "        RBR_Obs_geo['inArea'] = RBR_Obs_geo.within(siteShp.iloc[2,1])\n",
    "    elif siteName == 'Greensleeves':\n",
    "        axXlim  = (213269.99233348924, 213648.1643157148)\n",
    "        # axYlim  = (1463378.1020314451, 1463843.5442048472)\n",
    "        axYlim  = (1463378.1020314451, 1463950.5442048472)\n",
    "        GFS_Lon = -59.6428\n",
    "        GFS_Lat = 13.2289\n",
    "        RBR_Obs_geo['inArea'] = RBR_Obs_geo.within(siteShp.iloc[1,1])\n",
    "    elif siteName == 'Old Queens Fort':\n",
    "        axXlim  = (213368.59866770002, 213745.6997016811)\n",
    "        axYlim  = (1460192.707288096, 1460672.371780407)\n",
    "        GFS_Lon = -59.6419\n",
    "        GFS_Lat = 13.1960\n",
    "        RBR_Obs_geo['inArea'] = RBR_Obs_geo.within(siteShp.iloc[0,1])\n",
    "\n",
    "\n",
    "    # Set min and max times using conductivity\n",
    "    if JFE_File is None:\n",
    "        if RBR_Obs_geo['inArea'].any():\n",
    "            # First and last times from area in shapefile\n",
    "            minTime   = RBR_Obs_geo[RBR_Obs_geo['inArea']==True].iloc[20, 0]\n",
    "            maxTime   = RBR_Obs_geo[RBR_Obs_geo['inArea']==True].iloc[-20, 0]\n",
    "        else:\n",
    "            # First and last times if no GPS data\n",
    "            minTime   = RBR_Obs_geo.iloc[20, 0]\n",
    "            maxTime   = RBR_Obs_geo.iloc[-20, 0]\n",
    "    else:\n",
    "        minTime   = (RBR_Obs['Conductivity ']>5).idxmax()\n",
    "        minTime   = minTime + timedelta(seconds=30)\n",
    "        maxTime   = ((RBR_Obs['Conductivity ']<5) & (RBR_Obs['Time']>minTime)).idxmax()\n",
    "        maxTime   = maxTime - timedelta(seconds=30)\n",
    "        obsPeriod = maxTime-minTime\n",
    "\n",
    "        if (obsPeriod.seconds<180) | (maxTime<minTime):\n",
    "            minTime   = ((RBR_Obs['Conductivity ']>5) & (RBR_Obs['Time']>(minTime+timedelta(seconds=180)))).idxmax()\n",
    "            minTime   = minTime + timedelta(seconds=30)\n",
    "            maxTime   = ((RBR_Obs['Conductivity ']<5) & (RBR_Obs['Time']>minTime)).idxmax()\n",
    "            maxTime   = maxTime - timedelta(seconds=30)\n",
    "\n",
    "    metDate  = minTime - timedelta(\n",
    "                                hours = minTime.hour % 6,\n",
    "                                minutes=minTime.minute,\n",
    "                                seconds=minTime.second,\n",
    "                                microseconds=minTime.microsecond)\n",
    "\n",
    "\n",
    "    #%% GFS Met Import\n",
    "    var = ['Temperature_surface', 'Wind_speed_gust_surface',\n",
    "           'u-component_of_wind_height_above_ground', 'v-component_of_wind_height_above_ground']\n",
    "    var_precp = ['Total_precipitation_surface_6_Hour_Accumulation']\n",
    "\n",
    "    temp_1d = []\n",
    "    gust_1d = []\n",
    "    wndu_1d = []\n",
    "    wndv_1d = []\n",
    "    prep_1d = []\n",
    "    time_1d = []\n",
    "\n",
    "    # Set times to download\n",
    "    startdate  = metDate - timedelta(hours=18)\n",
    "    enddate    = metDate + timedelta(hours=6)\n",
    "    date_list = pd.date_range(startdate, enddate, freq='6H')\n",
    "\n",
    "    # Loop through dates\n",
    "    for date in date_list:\n",
    "        # Base URL for 0.5 degree GFS data\n",
    "        best_gfs = TDSCatalog('https://www.ncei.noaa.gov/thredds/catalog/model-gfs-g4-anl-files/' +\n",
    "                            date.strftime('%Y%m') + '/' + date.strftime('%Y%m%d') + '/' + 'catalog.xml')\n",
    "\n",
    "        # Generate URLs for specific grib file\n",
    "        best_ds         = best_gfs.datasets['gfs_4_'+date.strftime('%Y%m%d_%H%M')+'_000.grb2']\n",
    "        best_ds_precp   = best_gfs.datasets['gfs_4_'+date.strftime('%Y%m%d_%H%M')+'_006.grb2']\n",
    "\n",
    "        # Format the query parameters\n",
    "        ncss  = best_ds.subset()\n",
    "        query = ncss.query()\n",
    "\n",
    "        ncss_precp  = best_ds_precp.subset()\n",
    "        query_precp = ncss_precp.query()\n",
    "\n",
    "        # Extract data from specific point\n",
    "        query.lonlat_point(GFS_Lon, GFS_Lat).time(date)\n",
    "        query.accept('netcdf')\n",
    "        query.variables(var[0], var[1], var[2], var[3])\n",
    "        query.vertical_level(10)\n",
    "\n",
    "        data = ncss.get_data(query)\n",
    "        data = xr.open_dataset(NetCDF4DataStore(data), drop_variables='height_above_ground4')\n",
    "\n",
    "        query_precp.lonlat_point(GFS_Lon, GFS_Lat).time(date + timedelta(hours=6))\n",
    "        query_precp.accept('netcdf')\n",
    "        query_precp.variables(var_precp[0])\n",
    "\n",
    "\n",
    "        data_precp = ncss_precp.get_data(query_precp)\n",
    "        data_precp = xr.open_dataset(NetCDF4DataStore(data_precp))\n",
    "\n",
    "        temp_3d = data[var[0]]\n",
    "        gust_3d = data[var[1]]\n",
    "        wndu_3d = data[var[2]]\n",
    "        wndv_3d = data[var[3]]\n",
    "        prep_3d = data_precp[var_precp[0]]\n",
    "\n",
    "        # Read the individual point (with units) and append to the list\n",
    "        temp_1d.append(temp_3d.metpy.unit_array.squeeze())\n",
    "        gust_1d.append(gust_3d.metpy.unit_array.squeeze())\n",
    "        wndu_1d.append(wndu_3d.metpy.unit_array.squeeze())\n",
    "        wndv_1d.append(wndv_3d.metpy.unit_array.squeeze())\n",
    "        prep_1d.append(prep_3d.metpy.unit_array.squeeze())\n",
    "        time_1d.append(find_time_var(temp_3d))\n",
    "\n",
    "    #%% Process Met Data\n",
    "    # 24h Precipitation Total\n",
    "    # Time weighted average of last two points for everything else\n",
    "\n",
    "    met_prep = prep_1d[0] + prep_1d[1] + prep_1d[2] + prep_1d[3]\n",
    "\n",
    "    timeWeight1 = minTime-metDate\n",
    "    timeWeight2 = (metDate+timedelta(hours=6))-minTime\n",
    "\n",
    "    timeWeight1 = timeWeight1.seconds/21600\n",
    "    timeWeight2 = timeWeight2.seconds/21600\n",
    "\n",
    "    met_gust = gust_1d[3] * timeWeight1 + gust_1d[4] * timeWeight2\n",
    "    met_temp = temp_1d[3] * timeWeight1 + temp_1d[4] * timeWeight2\n",
    "    met_wind =  math.sqrt((wndv_1d[3].m.item(0) * timeWeight1 + wndv_1d[4].m.item(0)* timeWeight2) ** 2 +\n",
    "                          (wndu_1d[3].m.item(0) * timeWeight1 + wndu_1d[4].m.item(0)* timeWeight2) **2 )\n",
    "    met_wdir = math.degrees(math.atan2(\n",
    "                wndv_1d[3].m.item(0) * timeWeight1 + wndv_1d[4].m.item(0)* timeWeight2,\n",
    "                wndu_1d[3].m.item(0) * timeWeight1 + wndu_1d[4].m.item(0)* timeWeight2)) % 360\n",
    "\n",
    "    #%% Read in wave conditions from BTS file\n",
    "    if wave_bts_file is not None:\n",
    "        if siteName == 'Great House':\n",
    "            wave_bts = pd.read_csv(wave_bts_file,\n",
    "                        names=['date', 'time', 'HM0', 'TP', 'TM', 'MWD', 'DPK', 'HSWL', 'TSWL', 'DPSWL', 'HSEA', 'TSEA', 'DPSEA'],\n",
    "                        header=0, skiprows=22, delim_whitespace=True)\n",
    "            wave_bts['datetime'] = pd.to_datetime(wave_bts['date'] + ' ' + wave_bts['time'])\n",
    "            wave_bts.set_index('datetime', inplace=True)\n",
    "\n",
    "\n",
    "        met_hmo = wave_bts.iloc[wave_bts.index.get_loc(minTime, method='nearest'), :].HM0\n",
    "        met_tp  = wave_bts.iloc[wave_bts.index.get_loc(minTime, method='nearest'), :].TP\n",
    "        met_mwd = wave_bts.iloc[wave_bts.index.get_loc(minTime, method='nearest'), :].MWD\n",
    "    else:\n",
    "        met_hmo = -999\n",
    "        met_tp  = -999\n",
    "        met_mwd = -999\n",
    "\n",
    "\n",
    "    #%% Plot time series for Geo data\n",
    "    # RBRparam = ['Depth ', 'Salinity ', 'Dissolved O₂ saturation ', 'Temperature ']\n",
    "    # RBRparamName = ['Depth [m]', 'Salinity [PSU]', 'Dissolved O₂ saturation [%]', 'Temperature [degC]']\n",
    "    # RBRparamMin = [0.0, 34.5, 32.0, 29.0]\n",
    "    # RBRparamMax = [1.0, 36.0, 34.5, 31.0]\n",
    "    #\n",
    "    # JFEparam     = ['Turb. -M[FTU]', 'Chl-Flu.[ppb]', 'Chl-a[ug/l]']\n",
    "    # JFEparamName = ['Turbidity [FTU]', 'Chl-Flu. [ppb]', 'Chl-a [ug/l]']\n",
    "    # JFEparamMin  = [0.0, 0.0, 0.0]\n",
    "    # JFEparamMax  = [20.0, 1.0, 1.0]\n",
    "\n",
    "    if JFE_File is None:\n",
    "        fig, axesTMP = plt.subplots(nrows=1, ncols=1, figsize=(19, 5), constrained_layout=True)\n",
    "\n",
    "        RBRparam        = ['Turbidity ']\n",
    "        RBRparamName    = ['Turbidity [NTU]']\n",
    "        RBRparmCmap     = [cmo.turbid]\n",
    "        RBRparamMin     = [0.0]\n",
    "        RBRparamMax     = [60.0]\n",
    "\n",
    "        dataTable = np.zeros([9, 4])\n",
    "        axes = []\n",
    "        axes.append(axesTMP)\n",
    "    else:\n",
    "        fig, axes = plt.subplots(nrows=7, ncols=1, figsize=(19, 25), constrained_layout=True)\n",
    "        dataTable = np.zeros([15, 4])\n",
    "\n",
    "        RBRparam        = ['Depth ', 'Salinity ', 'Dissolved O₂ saturation ', 'Temperature ']\n",
    "        RBRparamName    = ['Depth [m]', 'Salinity [PSU]', 'Dissolved O₂ saturation [%]', 'Temperature [degC]']\n",
    "        RBRparmCmap     = [cmo.deep, 'cividis', cmo.dense, cmo.thermal]\n",
    "        RBRparamMin     = [0.0, 35.0, 32.5, 29.0]\n",
    "        RBRparamMax     = [1.0, 36.0, 33.5, 31.0]\n",
    "\n",
    "    fig.patch.set_facecolor('white')\n",
    "    # fig.tight_layout(pad=1.05)\n",
    "\n",
    "    fontprops = fm.FontProperties(size=25)\n",
    "\n",
    "    dataTable[0, 0] = met_temp.m.item(0)-272.15\n",
    "    dataTable[1, 0] = met_wind\n",
    "    dataTable[2, 0] = met_wdir\n",
    "    dataTable[3, 0] = met_prep.m.item(0)\n",
    "    dataTable[4, 0] = met_hmo\n",
    "    dataTable[5, 0] = met_tp\n",
    "    dataTable[6, 0] = met_mwd\n",
    "\n",
    "    for paramIDX, param in enumerate(RBRparam):\n",
    "        RBR_Obs_geo.loc[minTime:maxTime, param].plot(\n",
    "                        ax=axes[paramIDX], label='1 Second Observations', color='lightgrey') # Note the space in the col name\n",
    "\n",
    "        # Create mask for RBR data based on time and parameter minimum\n",
    "        RBR_mask = ((RBR_Obs_geo.index>minTime) &\n",
    "                         (RBR_Obs_geo.index<maxTime) &\n",
    "                         (RBR_Obs_geo[param]>RBRparamMin[paramIDX]))\n",
    "        RBR_smoothed = RBR_Obs_geo.loc[RBR_mask, param].rolling(\n",
    "                60, win_type='nuttall',center=True).mean()\n",
    "\n",
    "        RBR_smoothed.plot(\n",
    "                ax=axes[paramIDX], label='1 Minute Average', color='black', linewidth=3) # Note the space in the col name\n",
    "\n",
    "        # Find the local maximums\n",
    "        ilocs_max = argrelextrema(\n",
    "                            RBR_smoothed.values,\n",
    "                            np.greater_equal, order=60)[0]\n",
    "\n",
    "        # Add points at beginning and end\n",
    "        ilocs_max       = np.insert(ilocs_max, 0, 10)\n",
    "        ilocs_max_pts   = RBR_smoothed.iloc[ilocs_max].index.values\n",
    "        # ilocs_max[-1]   = len(RBR_smoothed.values)-10\n",
    "\n",
    "        plt.scatter(RBR_smoothed.iloc[\n",
    "                         ilocs_max].index, np.ones(len(ilocs_max)) * 30, 75,\n",
    "                         color='blue')\n",
    "\n",
    "        # Add labels\n",
    "        for a in range(0, len(ilocs_max)):\n",
    "            plt.annotate(str(a+1), (ilocs_max_pts[a], 30), fontsize=30)\n",
    "\n",
    "\n",
    "        dataTable[paramIDX+7, 0] = RBR_Obs_geo.loc[minTime:maxTime, param].mean(skipna=True)\n",
    "        dataTable[paramIDX+7, 1] = RBR_Obs_geo.loc[minTime:maxTime, param].std(skipna=True)\n",
    "        dataTable[paramIDX+7, 2] = max(RBR_Obs_geo.loc[minTime:maxTime, param].min(skipna=True), 0)\n",
    "        dataTable[paramIDX+7, 3] = RBR_Obs_geo.loc[minTime:maxTime, param].max(skipna=True)\n",
    "\n",
    "        axes[paramIDX].set_ylabel(RBRparamName[paramIDX])\n",
    "        axes[paramIDX].set_title('RBR: ' + RBRparamName[paramIDX])\n",
    "        axes[paramIDX].set_xlabel('')\n",
    "        axes[paramIDX].set_ylim(RBRparamMin[paramIDX], RBRparamMax[paramIDX])\n",
    "        axes[paramIDX].legend(loc='upper right')\n",
    "\n",
    "    if JFE_File is not None:\n",
    "        for paramIDX, param in enumerate(JFEparam):\n",
    "            JFE_Obs_geo.loc[minTime:maxTime, param].plot(\n",
    "                            ax=axes[paramIDX+4], label='15 Second Observations') # Note the space in the col name\n",
    "            # JFEDats[siteIDX].loc[minTime:maxTime, param].rolling(\n",
    "            #             300, win_type='gaussian',center=True).mean(std=5).plot(\n",
    "            #             ax=axes[paramIDX+4], label='15 Minute Average') # Note the space in the col name\n",
    "\n",
    "            dataTable[paramIDX+4+7, 0] = JFE_Obs_geo.loc[minTime:maxTime, param].mean(skipna=True)\n",
    "            dataTable[paramIDX+4+7, 1] = JFE_Obs_geo.loc[minTime:maxTime, param].std(skipna=True)\n",
    "            dataTable[paramIDX+4+7, 2] = max(JFE_Obs_geo.loc[minTime:maxTime, param].min(skipna=True), 0)\n",
    "            dataTable[paramIDX+4+7, 3] = JFE_Obs_geo.loc[minTime:maxTime, param].max(skipna=True)\n",
    "\n",
    "            axes[paramIDX+4].set_ylabel(JFEparamName[paramIDX])\n",
    "            axes[paramIDX+4].set_title('JFE: ' + JFEparamName[paramIDX])\n",
    "            axes[paramIDX+4].set_xlabel('')\n",
    "            # Set turbidity to log\n",
    "            # if paramIDX==2:\n",
    "            #     axes[6].set_yscale('log')\n",
    "            axes[paramIDX+4].set_ylim(JFEparamMin[paramIDX], JFEparamMax[paramIDX])\n",
    "            axes[paramIDX+4].legend(loc='upper right')\n",
    "\n",
    "        axes[paramIDX+4].set_xlabel(minTime.strftime('%B %#d, %Y'))\n",
    "    else:\n",
    "        axes[paramIDX].set_xlabel(minTime.strftime('%B %#d, %Y'))\n",
    "\n",
    "    dfOut = pd.DataFrame (dataTable[:, :])\n",
    "    # Change the column names\n",
    "    dfOut.columns =['Mean', 'Standard Deviation', 'Min', 'Max']\n",
    "    # Change the row indexes\n",
    "    if JFE_File is not None:\n",
    "        dfOut.iloc[14, 0] = minTime.strftime('%B %#d, %Y')\n",
    "        dfOut.index = ['Air Temperature [degC]', 'Wind Speed [m/s]', 'Wind Direction [deg]', '24h Precipitation [mm]',\n",
    "                   'Significant Wave Height Offshore [m]' ,'Peak Wave Period Offshore [s]',\n",
    "                   'Mean Wave Direction Offshore [deg]', 'Depth [m]', 'Salinity [PSU]',\n",
    "                   'Dissolved O₂ saturation [%]', 'Temperature [degC]',\n",
    "                   'Turbidity [FTU]', 'Chl-Flu. [ppb]', 'Chl-a [ug/l]', 'Observation Date']\n",
    "    else:\n",
    "        dfOut.iloc[8, 0] = minTime.strftime('%B %#d, %Y')\n",
    "        dfOut.index = ['Air Temperature [degC]', 'Wind Speed [m/s]', 'Wind Direction [deg]', '24h Precipitation [mm]',\n",
    "                   'Significant Wave Height Offshore [m]' ,'Peak Wave Period Offshore [s]',\n",
    "                   'Mean Wave Direction Offshore [deg]',\n",
    "                   'Turbidity [NTU]', 'Observation Date']\n",
    "\n",
    "    fig.suptitle(siteName + ', ' +minTime.strftime('%B %#d, %Y') + ' (' + timeLabel + ')', fontsize=30)\n",
    "\n",
    "    plt.show()\n",
    "    dfOut.to_excel(importPath + '/Summary_Stats_' + siteName + '.xlsx')\n",
    "    dfOut.to_csv(importPath + '/Summary_Stats_' + siteName + '.csv')\n",
    "\n",
    "    fig.savefig(importPath + '/Figures/SummaryTimeSeries_' + siteName + '.pdf',\n",
    "                 bbox_inches='tight')\n",
    "    fig.savefig(importPath + '/Figures/SummaryTimeSeries_' + siteName + '.png',\n",
    "                 bbox_inches='tight', dpi=500)\n",
    "\n",
    "\n",
    "    #%% Plot Points\n",
    "    if JFE_File is None:\n",
    "        # Only Turbidity Data\n",
    "        fig, axes = plt.subplots(nrows=1, ncols=1, figsize=(9, 9), constrained_layout=True)\n",
    "        RBRparam        = ['Turbidity ']\n",
    "        RBRparamName    = ['Turbidity [NTU]']\n",
    "        RBRparmCmap     = [cmo.turbid]\n",
    "        RBRparamMin     = [0.0]\n",
    "        RBRparamMax     = [60.0]\n",
    "\n",
    "        ax = []\n",
    "        ax.append(axes)\n",
    "    else:\n",
    "        fig, axes = plt.subplots(nrows=3, ncols=2, figsize=(19, 25), constrained_layout=True)\n",
    "        # List plotting parameters\n",
    "        RBRparam        = ['Depth ', 'Salinity ', 'Dissolved O₂ saturation ', 'Temperature ']\n",
    "        RBRparamName    = ['Depth [m]', 'Salinity [PSU]', 'Dissolved O₂ saturation [%]', 'Temperature [degC]']\n",
    "        RBRparmCmap     = [cmo.deep, 'cividis', cmo.dense, cmo.thermal]\n",
    "        RBRparamMin     = [0.0, 35.0, 32.5, 29.0]\n",
    "        RBRparamMax     = [1.0, 36.0, 33.5, 31.0]\n",
    "\n",
    "        ax = axes.flat\n",
    "\n",
    "    fig.patch.set_facecolor('white')\n",
    "    # fig.tight_layout(pad=1.05)\n",
    "\n",
    "    fontprops = fm.FontProperties(size=25)\n",
    "    x, y, arrow_length = 0.95, 0.93, 0.20\n",
    "    plt.rcParams.update({'font.size': 22})\n",
    "\n",
    "    axXlimTT = (RBR_Obs_geo.loc[minTime:maxTime].geometry.x.min()-100,\n",
    "                     RBR_Obs_geo.loc[minTime:maxTime].geometry.x.max()+100)\n",
    "    axYlimTT = (RBR_Obs_geo.loc[minTime:maxTime].geometry.y.min()-100,\n",
    "                     RBR_Obs_geo.loc[minTime:maxTime].geometry.y.max()+100)\n",
    "\n",
    "    plt.setp(axes, xlim=axXlim, ylim=axYlim)\n",
    "    # plt.setp(axes, xlim=axXlimTT, ylim=axYlimTT)\n",
    "\n",
    "    # Plot the RBR observations\n",
    "    # Salinity\n",
    "    for paramIDX, param in enumerate(RBRparam):\n",
    "        if RBR_File is not None:\n",
    "            RBR_Obs_geo.loc[minTime:maxTime].plot(\n",
    "                                column=param, ax=ax[paramIDX], vmin=RBRparamMin[paramIDX], vmax=RBRparamMax[paramIDX],\n",
    "                                legend=True, legend_kwds={'label': RBRparamName[paramIDX]},\n",
    "                                cmap=RBRparmCmap[paramIDX], markersize=20) # Note the space in the col name\n",
    "            ctx.add_basemap(ax[paramIDX], source=mapbox, crs='EPSG:32621')\n",
    "\n",
    "            ax[paramIDX].set_title(RBRparamName[paramIDX])\n",
    "            # ax[paramIDX].set_ylabel('UTM 21N [m]')\n",
    "            # ax[paramIDX].set_xlabel('UTM 21N [m]')\n",
    "            ax[paramIDX].locator_params(axis='y', nbins=3)\n",
    "            ax[paramIDX].ticklabel_format(useOffset=False, style='plain', axis='both')\n",
    "\n",
    "            ax[paramIDX].get_xaxis().set_ticks([])\n",
    "            ax[paramIDX].get_yaxis().set_ticks([])\n",
    "\n",
    "            #Add scale-bar\n",
    "            scalebar = AnchoredSizeBar(ax[paramIDX].transData,\n",
    "                                   100, '100 m', 'lower right', pad=0.5, size_vertical=10, fontproperties=fontprops)\n",
    "            ax[paramIDX].add_artist(scalebar)\n",
    "            ax[paramIDX].annotate('N', xy=(x, y), xytext=(x, y-arrow_length),\n",
    "                    arrowprops=dict(facecolor='black', width=6, headwidth=30),\n",
    "                    ha='center', va='center', fontsize=35,\n",
    "                    xycoords=ax[paramIDX].transAxes)\n",
    "\n",
    "    # Plot Plot JFE Points\n",
    "\n",
    "    # JFEparam     = ['Temp.[deg C]', 'Turb. -M[FTU]', 'Chl-a[ug/l]', 'Chl-Flu.[ppb]']\n",
    "    # JFEparamName = ['Temperature [degC]', 'Turbidity [FTU]', 'Chl-a [ug/l]', 'Chl-Flu. [ppb]']\n",
    "    # JFEparamCmp  = [cmo.thermal, cmo.turbid, cmo.algae, cmo.algae]\n",
    "    JFEparam     = ['Turb. -M[FTU]', 'Chl-Flu.[ppb]']\n",
    "    JFEparamName = ['Turbidity [FTU]', 'Chl-Flu. [ppb]']\n",
    "    JFEparamCmp  = [cmo.turbid,  cmo.algae]\n",
    "    JFEparamMin = [0.0, 0.0]\n",
    "    JFEparamMax = [1.0, 1.0]\n",
    "    if JFE_File is not None:\n",
    "        for paramIDX, param in enumerate(JFEparam):\n",
    "            if JFE_File is not None:\n",
    "                JFE_Obs_geo.loc[minTime:maxTime].plot(\n",
    "                                    column=param, ax=ax[paramIDX+4], vmin=JFEparamMin[paramIDX], vmax=JFEparamMax[paramIDX],\n",
    "                                    legend=True, legend_kwds={'label': JFEparamName[paramIDX]},\n",
    "                                    cmap=JFEparamCmp[paramIDX], markersize=20) # Note the space in the col name\n",
    "                ctx.add_basemap(ax[paramIDX+4], source=mapbox, crs='EPSG:32621')\n",
    "\n",
    "                ax[paramIDX+4].set_title(JFEparamName[paramIDX])\n",
    "                # ax[paramIDX+4].set_ylabel('UTM 21N [m]')\n",
    "                # ax[paramIDX+4].set_xlabel('UTM 21N [m]')\n",
    "                ax[paramIDX+4].locator_params(axis='y', nbins=3)\n",
    "                ax[paramIDX+4].ticklabel_format(useOffset=False, style='plain', axis='both')\n",
    "                ax[paramIDX+4].get_xaxis().set_ticks([])\n",
    "                ax[paramIDX+4].get_yaxis().set_ticks([])\n",
    "\n",
    "                #Add scale-bar\n",
    "                scalebar = AnchoredSizeBar(ax[paramIDX+4].transData,\n",
    "                                       100, '100 m', 'lower right', pad=0.5, size_vertical=10, fontproperties=fontprops)\n",
    "                ax[paramIDX+4].add_artist(scalebar)\n",
    "                ax[paramIDX+4].annotate('N', xy=(x, y), xytext=(x, y-arrow_length),\n",
    "                        arrowprops=dict(facecolor='black', width=6, headwidth=30),\n",
    "                        ha='center', va='center', fontsize=25,\n",
    "                        xycoords=ax[paramIDX+4].transAxes)\n",
    "\n",
    "    fig.suptitle(siteName + ', ' + minTime.strftime('%B %#d, %Y') + ' (' + timeLabel + ')', fontsize=30)\n",
    "    plt.show()\n",
    "\n",
    "    if not os.path.exists(importPath + '/Figures'):\n",
    "        os.mkdir(importPath + '/Figures')\n",
    "\n",
    "    fig.savefig(importPath + '/Figures/SummaryMap_' + siteName + '.pdf',\n",
    "                 bbox_inches='tight')\n",
    "\n",
    "    fig.savefig(importPath + '/Figures/SummaryMap_' + siteName + '.png',\n",
    "                 bbox_inches='tight', dpi=500)\n",
    "\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
     "is_executing": true
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 2736x3600 with 10 Axes>",
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NAAAAAFqyJ598Mvvtt1+qqqpKY127ds3999+fAQMGNGjudu3a5fe//33GjBmTCy+8MHvvvXfWXHPNdOzYMe3atcuAAQPyla98JTfccENee+210ta5o0aNqjHP57fjbQkGDhxY43j69OmZOXNmmboBAAAAaHz1DvB9/huhY8aMaXAztfH5Or169Wr0Gg0JIw4fPjwXXnhhjbFddtklHTt2LHutBYYNG5ZCoVDjMWzYsKXWO/DAA9O/f//S8YQJE3LmmWfWqtc//vGPee+992qMHXHEEbW6FgAAAACWZS+88EL23HPPTJ8+vTTWoUOH3HPPPdlggw0arU7Pnj1z/PHH54477siYMWMyZcqUzJgxI6+//npuv/32HHrooWnXrl2S+UG4l19+ucb1gwcPbrReGstKK6200Ninn35ahk4AAAAAmka9A3zrrLNOkqRQKCRJbr311sbpaCluueWWJEmxWEyy8DcwG8ORRx6Zk08+OR9//HGdrrv99tuz22671fgWbUVFRX73u9+1iFoN1aZNm/z617+uMXbmmWfmr3/96xKvu+WWW/LLX/6yxtjmm2++0Ja8AAAAAFAbo0ePzkMPPZShQ4fmr3/960K7VLQkr7/+er785S/X2FWkbdu2ue2227LFFluUra/HH3+8xla+Xbp0yXrrrVe2fhZn8uTJC4117ty5DJ0AAAAANI16B/g22GCDdOrUKcn8MN11111X44ZPU6iurs51111XCg127NgxG264YaPXmTlzZv74xz9m1VVXza677pqLLrooTzzxRKZMmVLjvLlz5+aVV17JlVdeme222y777rvvQt/+/NnPfpaNN964RdRqDIccckh23XXXGn0dfvjhOeSQQ/LEE0+UfgfmzZuXp59+OkcddVS+9rWvZc6cOaVrOnTokGuuuaZJ+wQAAABg+XLHHXdkn332yUorrZT1118/u+yyS77+9a/nyCOPzFFHHbXY6y6++OKcd955Oe+88/Kvf/2r+RpOMnbs2Oyyyy41vrzbunXr3HTTTdlpp52atZfP+3zo8bDDDkurVq3K1M3ivf766zWO27Vrl/bt25epGwAAAIDG17q+F1ZUVGSvvfbKjTfemCR54403cuaZZy600lpjOvPMM/PGG2+Utn3dc889S2G+pjB37tw88MADeeCBB0pjbdq0SadOnVJVVZVp06aVVgJclBNOOCG//e1vW1ythigUCrnxxhuzww475KWXXiqN//3vf8/f//73tGrVKp07d87kyZMXGehs27Ztbrjhhhb5bV4AAAAAWp6RI0fm8MMPz6hRo5JkkffIlnSPcPjw4bn++uuTJJ06dcoee+yRtm3bNk2znzFu3Ljssssueffdd0tjFRUVufbaa7PPPvs0ef0lGTdu3EI7qhx99NFl6mbJ7rnnnhrHTfGFbgAAAIByqvcKfEly7LHHJpl/g6xYLOZ3v/td7r777kZp7PPuuuuunHnmmaVaSXLMMcc0Sa0lmTNnTiZMmJCpU6cuNlDXu3fv3HLLLbnggguWmVp10a1btzz88MOL3AJ37ty5mThx4iLDe6uuumoefvjhst+gBAAAAGDZcM0112TIkCEZNWpU6f7YZ8N6tfly7wknnJBisZhisZgpU6YsFFxrCpMnT85uu+2W1157rcb4JZdckkMPPbTJ6y/NySefnBkzZpSOt91222yyySZl7GjRHn/88Tz66KM1xhZ1TxIAAABgWdagAN/222+fbbfdNsn8m2VVVVXZf//9c8MNNzRKcwtcf/31OeCAAzJnzpwUi8UUCoUMGTIkO+64Y6PWWeDKK6/Mddddl0MOOSSrr756ra5p27Ztdtxxx1x77bV5/fXXs//++7e4Wo1ppZVWyr///e/84x//yODBg5d4bv/+/fOHP/whr7/+erbYYotm6hAAAACAZdltt92WY445JlVVVaWxYrGYVVZZJXvssUc222yzJe5YscCmm26atddeu3R85513Nkm/C8yYMSN77713nnvuuRrj55xzTr7zne80ae3/j737jrKrrNuHf51Mek8ISegdQpPeAqZQH5EmCgoKoanwAAZEbCgEFRTlpxQFkRI6ikgxIiAtoYQSICI8JECooYb0nslMzvtH3jkykD5z5kzC57PWWe69Z+/v/T0yyWLdXPu+l8V1111Xb/vcVq1a5bLLLlumZ4cPH17aHaXuM3z48CU+syz/jBZl4sSJGTRoUL1rVVVVzSIACQAAANCYVngL3TqXX355tt9++8yfPz+FQiHz58/P0Ucfnb/85S+59NJLlzmUtihvvfVWTj311Nx9992l4F6ycFLp8ssvb2jri9WjR48cddRROeqoo5Is3FLi5ZdfzptvvplJkyZl1qxZadmyZTp37pyuXbtm8803z5ZbbplWrVo167HqDBgwYIUnzj7p8MMPz+GHH57x48fn6aefzltvvZXZs2enS5cu6dmzZ3bcccdstNFGjTIWAAAAAJ8NkydPzjHHHJMFCxaUduTYaaed8pvf/Cb9+vVLklxxxRUZNWrUMtX70pe+lAsuuCDFYjEPPvhg2fqeP39+vvKVr+Sxxx6rd/0nP/lJzjzzzEYf74MPPsjYsWMzYMCApd5bW1ubiy+++FN9nHnmmdlqq60avbc6gwcPTocOHXLaaaelV69ey/TMSy+9lMMOOyyvvfZavevHHXdcNttss3K0CQAAAFAxDQ7wbbHFFvnDH/6Qb37zm6W3LovFYu6+++7ce++92W+//TJo0KDsvffe6dq161LrTZ06NQ8++GCuvfba3HfffamtrS2F9+r+9+KLLy7rpNIn9ezZMz179iytNriqjNWY1llnnayzzjqVbgMAAACAVcD555+fadOmlV7oPeyww3LjjTeu8Eutffv2LR1PmDAhb7/9dtZdd91G6fXjBg8enHvuuafetY022ihrrbVW/vjHPy53vc0222yJu5B88MEHGThwYPr06ZNDDz00e++9d7bddtt069YtycLQ3vjx43P33XfnyiuvzPPPP1/v+b333jtDhgxZ7r6Wx/Tp03PppZfmwgsvzMCBA3PwwQdn++23z1ZbbZVOnTrVu2/kyJH585//nJtvvjnz58+vV2fzzTfPBRdcUNZeAQAAACqhwQG+JDn++OPz0Ucf5cc//nG9EF9NTU3++c9/5p///GeSZP3118/nPve59OjRI126dEmHDh0ya9asTJ8+PRMnTsx//vOfvPHGG6W6davE1U3UJck555zTLLaaAAAAAADK48YbbyzNMW666aa54YYbGrQjxTbbbFPvfOzYsWUJ8L300kufuvbaa6/lpJNOWqF6gwYNWmKAr87YsWNz/vnn5/zzz0+StGvXLu3atcvUqVOzYMGCRT6zzz775I477mjQ/6/Lo6amJvfff3/uv//+0rXWrVunU6dOmT17dubMmbPYZzfaaKPce++9pWAiAAAAwKqkUQJ8SfLDH/4wG2ywQU466aRMnTq1FLr7+Fatb7zxRt58883F1vjktq4fr9GpU6f84Q9/yDe+8Y3GahkAAAAAaGaeffbZTJgwofSi8I9+9KO0bt26QTXXWWedei8Jv/322w1ts1mbM2fOYgNxrVu3zg9/+MOcffbZqaqqauLO6quurs6kSZMW+/NCoZBjjjkml1xySTp27NiEnQEAAAA0nRaNWeyrX/1qnn/++QwcODDFYrG05e3HP3XXF/VZ3L177LFHnn/+eeE9AAAAAFjFjR07Nsl/X/b94he/2OCahUKhXgBs+vTpDa7ZHGywwQY5++yzs+OOOy41jNe9e/ecfPLJGTNmTM4999wmC++dfvrp+elPf5p+/fotcwhvzTXXzMknn5yXXnop11xzjfAeAAAAsEprtBX46qyzzjp58MEHM3LkyPzud7/LnXfemdra2tLPP/6m6yd9fAW+qqqqHHTQQRk8eHD69evX2G0CAAAAAM3QRx99VDpu3759evTo0Sh1P75V7Ny5cxul5icNHz68LHUXp0uXLjn33HNz7rnnZtasWXn++efz2muvZcKECZk9e3batGmT3r17Z8stt8x2222XFi0a9j73gAEDPrWLytJss802pS2Mi8Vixo0bl9dffz3vvPNOpkyZkrlz56ZNmzbp1q1bevToke23374s2xsDAAAANFeNHuCr07dv3/Tt2zcffvhhRowYkccffzwjR47Mm2++malTp9YL9VVVVaVr165Zd91107dv3+y+++4ZMGBAevfuXa72AAAAAIBmqLq6unTc0K1zP27atGml465duzZa3eaiQ4cOpTnZ5qpQKGSTTTbJJptsUulWAAAAAJqNsgX46vTq1SuHH354Dj/88HrXp0+fnhkzZqRTp07p3LlzudsAAAAAAFYCq6++eul42rRpqa2tbfB2r2+88UZqa2tLu4M01qp+AAAAANBQDdszoQE6d+6ctdZaS3gPAAAAACjp1atX6bhYLGb06NENrvnII4+U6iXJRhtt1OCaAAAAANAYKhbgAwAAAAD4pN122y0tWrQorZZ35513NrjmlVdeWTru3Llztt9++wbXBAAAAIDGIMAHAAAAADQb3bp1y84775xk4Yp5f/zjHzNhwoQVrve3v/0tI0eOTKFQSKFQyF577VUKBwIAAABApQnwAQAAAADNyre//e0Ui8UUCoVMmTIlRx55ZObOnbvcdZ5++umccMIJKRQKpe1zTz/99MZuFwAAAABWmAAfAAAAANCsHH300dlyyy1L5w8//HD69++f559/fpmenzdvXi688MIMHDgw06ZNK4UBBw4cmN13371cbQMAAADAcmtZ6QYAAAAAAD6uUCjk+uuvT//+/TNr1qwkyahRo7LDDjtkt912y7777pu33nqr3jOPPvpoXnvttTzyyCP5xz/+kUmTJpWCe8ViMT169Mi1115bgW8DAAAAAIsnwAcAAAAANDvbbbdd/vznP+fLX/5yqqurUygUsmDBgowcOTIjR46sd2+xWMyAAQPqnScphfc6dOiQW2+9NWu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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotvars = ['Air Temperature [degC]', 'Wind Speed [m/s]', 'Wind Direction [deg]', '24h Precipitation [mm]',\n",
    "           'Significant Wave Height [m]', 'Salinity [PSU]',\n",
    "           'Dissolved O₂ [%]', 'Temperature [degC]',\n",
    "           'Turbidity [FTU]', 'Chl-Flu. [ppb]']\n",
    "\n",
    "\n",
    "plotIDXs = [0, 3, 6]\n",
    "# plotIDXs = [1, 4, 7]\n",
    "# plotIDXs = [2, 5, 8]\n",
    "plotDates = []\n",
    "plotTable = np.zeros([10, len(plotIDXs)])\n",
    "\n",
    "for s, plotIDX in enumerate(plotIDXs):\n",
    "    ## Define master import path\n",
    "    importPath  = importPaths[plotIDX]\n",
    "    siteName    = siteNames[plotIDX]\n",
    "\n",
    "    obsStatsIN  = pd.read_excel(importPath + '/Summary_Stats.xlsx')\n",
    "\n",
    "    plotTable[:, s] = obsStatsIN.iloc[[0, 1, 2, 3, 4, 8, 9, 10, 11, 12], 1]\n",
    "    plotDates.append(datetime.strptime(obsStatsIN.iloc[14, 1], '%B %d, %Y'))\n",
    "\n",
    "\n",
    "fig, axes = plt.subplots(nrows=5, ncols=2, figsize=(19, 25))\n",
    "fig.patch.set_facecolor('white')\n",
    "fig.tight_layout(pad=3)\n",
    "ax = axes.flat\n",
    "\n",
    "for v in range(0, 10):\n",
    "    ax[v].scatter(plotDates, plotTable[v, :], 250)\n",
    "    ax[v].set_ylabel(plotvars[v])\n",
    "\n",
    "fig.suptitle(siteName, fontsize=35)\n",
    "plt.gcf().autofmt_xdate()\n",
    "plt.gcf().align_ylabels()\n",
    "plt.show()\n",
    "\n",
    "fig.savefig('C:/Users/arey/files/Projects/West Coast/Scatterplots/' + siteName + '.pdf',\n",
    "             bbox_inches='tight')\n",
    "fig.savefig('C:/Users/arey/files/Projects/West Coast/Scatterplots/' + siteName + '.png',\n",
    "             bbox_inches='tight', dpi=500)"
   ],
   "metadata": {
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