diff --git a/examples/ADCP_Delft3D_TRTS_2021_2023_all_transects.ipynb b/examples/ADCP_Delft3D_TRTS_2021_2023_all_transects.ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "70a6ebdd-7295-44ee-9654-65645b559196",
+   "metadata": {},
+   "source": [
+    "# Comparing Transect Data at the Tanana River Test Site \n",
+    "\n",
+    "The TRTS is located near Nenana, Alaska, and was first investigated as a test site in 2008 after a CEC deployment in Eagle Alaska ended early due to debris-related issues.  Since then, it has become an established test site with modular deployment infrastructure that can be adapted for different CEC deployments. The TRTS allows CECs to be tested in a harsh environment with cold temperatures, sediment, and heavy debris, representing the conditions of many remote Alaska river communities that could greatly benefit from ME. The following example will familiarize the user with the MHKiT DOLfYN and Delft3D modules by performing a Deflt3d numerical model validation of the Tanana River Test Site (TRTS) against field data.\n",
+    "\n",
+    "Start by importing the necessary python packages and MHKiT module."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "447c788c-a042-4649-81c6-532fcd464c0e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from os.path import abspath, dirname, join, normpath, relpath\n",
+    "from matplotlib.pyplot import figure\n",
+    "import scipy.interpolate as interp\n",
+    "import matplotlib.pyplot as plt\n",
+    "from datetime import datetime\n",
+    "import xarray as xr\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib\n",
+    "import scipy.io\n",
+    "import netCDF4\n",
+    "import math\n",
+    "import utm\n",
+    "\n",
+    "# MHKiT Imports\n",
+    "from mhkit.dolfyn.rotate import api as ap\n",
+    "from mhkit.dolfyn.adp import api\n",
+    "from mhkit import dolfyn as dlfn\n",
+    "from mhkit.river.io import d3d\n",
+    "from mhkit import river"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "552c226f-c933-4411-bcb0-bc012e74cfa4",
+   "metadata": {},
+   "source": [
+    "## 1. Preparing the ADCP Field Data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e7cbf99b-302c-42a9-bc9a-f2029fbd5ed9",
+   "metadata": {},
+   "source": [
+    "### ADCP: Importing Data\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "079e0248-bb41-4c99-a954-19735dbe5e90",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Reading file data/TRTS/ADCP_transect/2023-07-07_Transects/StationTRTS_0_000_23-07-07_235212.PD0 ...\n",
+      "\n",
+      "Reading file data/TRTS/ADCP_transect/2023-07-07_Transects/StationTRTS_0_001_23-07-07_235531.PD0 ...\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Read in the two transect passes\n",
+    "transect_0_raw = api.read(\n",
+    "    \"data/TRTS/ADCP_transect/2023-07-07_Transects/StationTRTS_0_000_23-07-07_235212.PD0\"\n",
+    ")\n",
+    "transect_1_raw = api.read(\n",
+    "    \"data/TRTS/ADCP_transect/2023-07-07_Transects/StationTRTS_0_001_23-07-07_235531.PD0\"\n",
+    ")\n",
+    "# Create one dataset from the two passes\n",
+    "transect_0_1 = xr.merge([transect_0_raw, transect_1_raw])\n",
+    "transect_0_1 = transect_0_1.assign_attrs(name=\"transect_0_1\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "236956f5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Reading file data/TRTS/ADCP_transect/2023-07-07_Transects/StationTRTS_0_002_23-07-07_235744.PD0 ...\n",
+      "\n",
+      "Reading file data/TRTS/ADCP_transect/2023-07-07_Transects/StationTRTS_0_003_23-07-08_000111.PD0 ...\n"
+     ]
+    }
+   ],
+   "source": [
+    "transect_2_raw = api.read(\n",
+    "    \"data/TRTS/ADCP_transect/2023-07-07_Transects/StationTRTS_0_002_23-07-07_235744.PD0\"\n",
+    ")\n",
+    "transect_3_raw = api.read(\n",
+    "    \"data/TRTS/ADCP_transect/2023-07-07_Transects/StationTRTS_0_003_23-07-08_000111.PD0\"\n",
+    ")\n",
+    "# Create one dataset from the two passes\n",
+    "transect_2_3 = xr.merge([transect_2_raw, transect_3_raw])\n",
+    "transect_2_3 = transect_2_3.assign_attrs(name=\"transect_2_3\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "dee15628",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Reading file data/TRTS/ADCP_transect/2023-07-07_Transects/StationTRTS_0_004_23-07-08_000320.PD0 ...\n",
+      "\n",
+      "Reading file data/TRTS/ADCP_transect/2023-07-07_Transects/StationTRTS_0_005_23-07-08_000631.PD0 ...\n"
+     ]
+    }
+   ],
+   "source": [
+    "transect_4_raw = api.read(\n",
+    "    \"data/TRTS/ADCP_transect/2023-07-07_Transects/StationTRTS_0_004_23-07-08_000320.PD0\"\n",
+    ")\n",
+    "transect_5_raw = api.read(\n",
+    "    \"data/TRTS/ADCP_transect/2023-07-07_Transects/StationTRTS_0_005_23-07-08_000631.PD0\"\n",
+    ")\n",
+    "# Create one dataset from the two passes\n",
+    "transect_4_5 = xr.merge([transect_4_raw, transect_5_raw])\n",
+    "transect_4_5 = transect_4_5.assign_attrs(name=\"transect_4_5\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "5e69dc94",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Reading file data/TRTS/ADCP_transect/2023-07-07_Transects/StationTRTS_0_006_23-07-08_000841.PD0 ...\n",
+      "\n",
+      "Reading file data/TRTS/ADCP_transect/2023-07-07_Transects/StationTRTS_0_007_23-07-08_003209.PD0 ...\n"
+     ]
+    }
+   ],
+   "source": [
+    "transect_6_raw = api.read(\n",
+    "    \"data/TRTS/ADCP_transect/2023-07-07_Transects/StationTRTS_0_006_23-07-08_000841.PD0\"\n",
+    ")\n",
+    "transect_7_raw = api.read(\n",
+    "    \"data/TRTS/ADCP_transect/2023-07-07_Transects/StationTRTS_0_007_23-07-08_003209.PD0\"\n",
+    ")\n",
+    "# Create one dataset from the two passes\n",
+    "transect_6_7 = xr.merge([transect_6_raw, transect_7_raw])\n",
+    "transect_6_7= transect_6_7.assign_attrs(name=\"transect_6_7\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "4465697e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Reading file data/TRTS/ADCP_transect/2023-07-07_Transects/StationTRTS_0_008_23-07-08_003435.PD0 ...\n",
+      "\n",
+      "Reading file data/TRTS/ADCP_transect/2023-07-07_Transects/StationTRTS_0_009_23-07-08_003909.PD0 ...\n"
+     ]
+    }
+   ],
+   "source": [
+    "transect_8_raw = api.read(\n",
+    "    \"data/TRTS/ADCP_transect/2023-07-07_Transects/StationTRTS_0_008_23-07-08_003435.PD0\"\n",
+    ")\n",
+    "transect_9_raw = api.read(\n",
+    "    \"data/TRTS/ADCP_transect/2023-07-07_Transects/StationTRTS_0_009_23-07-08_003909.PD0\"\n",
+    ")\n",
+    "# Create one dataset from the two passes\n",
+    "transect_8_9 = xr.merge([transect_8_raw, transect_9_raw])\n",
+    "transect_8_9  = transect_8_9 .assign_attrs(name=\"transect_8_9 \")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "1b9684ff",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Reading file data/TRTS/ADCP_transect/2023-07-07_Transects/StationTRTS_0_010_23-07-08_004209.PD0 ...\n",
+      "\n",
+      "Reading file data/TRTS/ADCP_transect/2023-07-07_Transects/StationTRTS_0_011_23-07-08_004623.PD0 ...\n"
+     ]
+    }
+   ],
+   "source": [
+    "transect_10_raw = api.read(\n",
+    "    \"data/TRTS/ADCP_transect/2023-07-07_Transects/StationTRTS_0_010_23-07-08_004209.PD0\"\n",
+    ")\n",
+    "transect_11_raw = api.read(\n",
+    "    \"data/TRTS/ADCP_transect/2023-07-07_Transects/StationTRTS_0_011_23-07-08_004623.PD0\"\n",
+    ")\n",
+    "# Create one dataset from the two passes\n",
+    "transect_10_11 = xr.merge([transect_10_raw, transect_11_raw])\n",
+    "transect_10_11 = transect_10_11.assign_attrs(name=\"transect_10_11\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "c04574ce",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Reading file data/TRTS/ADCP_transect/2023-07-07_Transects/StationTRTS_0_012_23-07-08_004858.PD0 ...\n",
+      "\n",
+      "Reading file data/TRTS/ADCP_transect/2023-07-07_Transects/StationTRTS_0_013_23-07-08_005327.PD0 ...\n"
+     ]
+    }
+   ],
+   "source": [
+    "transect_12_raw = api.read(\n",
+    "    \"data/TRTS/ADCP_transect/2023-07-07_Transects/StationTRTS_0_012_23-07-08_004858.PD0\"\n",
+    ")\n",
+    "transect_13_raw = api.read(\n",
+    "    \"data/TRTS/ADCP_transect/2023-07-07_Transects/StationTRTS_0_013_23-07-08_005327.PD0\"\n",
+    ")\n",
+    "# Create one dataset from the two passes\n",
+    "transect_12_13 = xr.merge([transect_12_raw, transect_13_raw])\n",
+    "transect_12_13= transect_12_13.assign_attrs(name=\"transect_12_13\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "96b76ca6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Reading file data/TRTS/ADCP_transect/2023-07-07_Transects/StationTRTS_0_014_23-07-08_005522.PD0 ...\n",
+      "\n",
+      "Reading file data/TRTS/ADCP_transect/2023-07-07_Transects/StationTRTS_0_015_23-07-08_005916.PD0 ...\n"
+     ]
+    }
+   ],
+   "source": [
+    "transect_14_raw = api.read(\n",
+    "    \"data/TRTS/ADCP_transect/2023-07-07_Transects/StationTRTS_0_014_23-07-08_005522.PD0\"\n",
+    ")\n",
+    "transect_15_raw = api.read(\n",
+    "    \"data/TRTS/ADCP_transect/2023-07-07_Transects/StationTRTS_0_015_23-07-08_005916.PD0\"\n",
+    ")\n",
+    "# Create one dataset from the two passes\n",
+    "transect_14_15 = xr.merge([transect_14_raw, transect_15_raw])\n",
+    "transect_14_15= transect_14_15.assign_attrs(name=\"transect_14_15\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "4cf36e64",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Import the simulated data\n",
+    "d3d_data = netCDF4.Dataset(\"data/TRTS/D3D/TRTS_2021_2023_hydroball_vertical_viscocity.nc\")\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b6d6ddf5-15ef-4177-afbe-a29173ebf0a2",
+   "metadata": {},
+   "source": [
+    "### ADCP: Convert the Coordinate System to UTM\n",
+    "\n",
+    "By default, the ADCP coordinates are stored as latitude and longitude coordinates. However, latitude and longitude cannot be directly converted to a distance therefor here we convert from latitude and longitude to $UTM_x$ and $UTM_y$. This gives us the ability to calculate the linear line of best fit for the idealized transect."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "16d5e6bf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "xarray_names=[transect_0_1,\n",
+    "                transect_2_3,\n",
+    "                transect_4_5,\n",
+    "                transect_6_7,\n",
+    "                transect_8_9, \n",
+    "                transect_10_11,\n",
+    "                transect_12_13,\n",
+    "                transect_14_15]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "158b216f-f71a-44aa-93ec-a81e5c1f45c9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x440 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x440 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "ename": "Exception",
+     "evalue": "Coordinates not recognized.",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "File \u001b[1;32mc:\\Users\\ashly\\anaconda3\\envs\\Python_310\\lib\\site-packages\\mhkit\\river\\io\\d3d.py:636\u001b[0m, in \u001b[0;36mget_all_data_points\u001b[1;34m(data, variable, time_index, to_pandas)\u001b[0m\n\u001b[0;32m    635\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 636\u001b[0m     cord_sys \u001b[38;5;241m=\u001b[39m \u001b[43mcords_to_layers\u001b[49m\u001b[43m[\u001b[49m\u001b[43mlayer_dim\u001b[49m\u001b[43m]\u001b[49m[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcoords\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[0;32m    637\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m:\n",
+      "\u001b[1;31mKeyError\u001b[0m: 'mesh2d_face_x mesh2d_face_y mesh2d_layer_sigma'",
+      "\nDuring handling of the above exception, another exception occurred:\n",
+      "\u001b[1;31mException\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[12], line 243\u001b[0m\n\u001b[0;32m    238\u001b[0m ADCP_ideal_points_downsamples_xy \u001b[38;5;241m=\u001b[39m ADCP_ideal_points_downsamples\u001b[38;5;241m.\u001b[39mrename(\n\u001b[0;32m    239\u001b[0m columns\u001b[38;5;241m=\u001b[39m{\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mutm_x\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mx\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mutm_y\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124my\u001b[39m\u001b[38;5;124m\"\u001b[39m}\n\u001b[0;32m    240\u001b[0m )\n\u001b[0;32m    242\u001b[0m variables \u001b[38;5;241m=\u001b[39m [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmesh2d_ucx\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmesh2d_ucy\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmesh2d_ucz\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m--> 243\u001b[0m D3D \u001b[38;5;241m=\u001b[39m \u001b[43md3d\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvariable_interpolation\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    244\u001b[0m \u001b[43m    \u001b[49m\u001b[43md3d_data\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvariables\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpoints\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mADCP_ideal_points_downsamples_xy\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43medges\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mnearest\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\n\u001b[0;32m    245\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    246\u001b[0m \u001b[38;5;66;03m# Calculate the magnitude of the velocity\u001b[39;00m\n\u001b[0;32m    247\u001b[0m D3D[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmagnitude\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39msqrt(D3D\u001b[38;5;241m.\u001b[39mmesh2d_ucy\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m2\u001b[39m \u001b[38;5;241m+\u001b[39m D3D\u001b[38;5;241m.\u001b[39mmesh2d_ucx\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m2\u001b[39m \u001b[38;5;241m+\u001b[39m D3D\u001b[38;5;241m.\u001b[39mmesh2d_ucz\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m2\u001b[39m)\n",
+      "File \u001b[1;32mc:\\Users\\ashly\\anaconda3\\envs\\Python_310\\lib\\site-packages\\mhkit\\river\\io\\d3d.py:511\u001b[0m, in \u001b[0;36mvariable_interpolation\u001b[1;34m(data, variables, points, edges, x_max_lim, x_min_lim, y_max_lim, y_min_lim, to_pandas)\u001b[0m\n\u001b[0;32m    509\u001b[0m data_raw \u001b[38;5;241m=\u001b[39m {}\n\u001b[0;32m    510\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m var \u001b[38;5;129;01min\u001b[39;00m variables:\n\u001b[1;32m--> 511\u001b[0m     var_data_df \u001b[38;5;241m=\u001b[39m \u001b[43mget_all_data_points\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvar\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtime_index\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mto_pandas\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[0;32m    512\u001b[0m     var_data_df[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdepth\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m var_data_df\u001b[38;5;241m.\u001b[39mwaterdepth \u001b[38;5;241m-\u001b[39m var_data_df\u001b[38;5;241m.\u001b[39mwaterlevel  \u001b[38;5;66;03m# added\u001b[39;00m\n\u001b[0;32m    513\u001b[0m     var_data_df \u001b[38;5;241m=\u001b[39m var_data_df\u001b[38;5;241m.\u001b[39mloc[:, \u001b[38;5;241m~\u001b[39mvar_data_df\u001b[38;5;241m.\u001b[39mT\u001b[38;5;241m.\u001b[39mduplicated(keep\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfirst\u001b[39m\u001b[38;5;124m\"\u001b[39m)]\n",
+      "File \u001b[1;32mc:\\Users\\ashly\\anaconda3\\envs\\Python_310\\lib\\site-packages\\mhkit\\river\\io\\d3d.py:638\u001b[0m, in \u001b[0;36mget_all_data_points\u001b[1;34m(data, variable, time_index, to_pandas)\u001b[0m\n\u001b[0;32m    636\u001b[0m     cord_sys \u001b[38;5;241m=\u001b[39m cords_to_layers[layer_dim][\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcoords\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[0;32m    637\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m:\n\u001b[1;32m--> 638\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCoordinates not recognized.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m    639\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    640\u001b[0m     layer_percentages \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mma\u001b[38;5;241m.\u001b[39mgetdata(cord_sys, \u001b[38;5;28;01mFalse\u001b[39;00m)\n",
+      "\u001b[1;31mException\u001b[0m: Coordinates not recognized."
+     ]
+    }
+   ],
+   "source": [
+    "# Convert Coordiantes to UTM using utm module\n",
+    "angle = 15.4\n",
+    "offset = 0.35 \n",
+    "min_correlation = 40\n",
+    "N = 10\n",
+    "N_layers = 12\n",
+    "\n",
+    "for name in xarray_names:\n",
+    "    ap.set_declination(name, angle, inplace=True)\n",
+    "    api.rotate2(name, \"earth\", inplace=True)\n",
+    "    api.clean.set_range_offset(name, offset)\n",
+    "    name= api.clean.correlation_filter(name, thresh=min_correlation)\n",
+    "    \n",
+    "    #Ideal transect \n",
+    "    utm_x_y = utm.from_latlon(name.latitude_gps, name.longitude_gps, 6, \"W\")\n",
+    "    gps = [[x, y] for x, y in zip(utm_x_y[0], utm_x_y[1])]\n",
+    "    gps_points = pd.DataFrame(np.array(gps), columns=[\"utm_x\", \"utm_y\"])\n",
+    "    # Linear regression using first order polyfit\n",
+    "    a, b = np.polyfit(gps_points.utm_x, gps_points.utm_y, 1)\n",
+    "    # Generate a DataFrame of points from the linear regression\n",
+    "    ideal = [[x, y] for x, y in zip(gps_points.utm_x, a * gps_points.utm_x + b)]\n",
+    "    ideal_points = pd.DataFrame(np.array(ideal), columns=[\"utm_x\", \"utm_y\"])\n",
+    "    ADCP_ideal_points = {\n",
+    "        \"utm_x\": np.tile(gps_points.utm_x, np.size(name.range)),\n",
+    "        \"utm_y\": np.tile(a * gps_points.utm_x + b, np.size(name.range)),\n",
+    "        \"waterdepth\": np.repeat(name.range, np.size(gps_points.utm_x)),\n",
+    "    }\n",
+    "    ADCP_ideal_points = pd.DataFrame(ADCP_ideal_points)\n",
+    "\n",
+    "    # Bottom Filter\n",
+    "    # Filtering out depth sounder values above the river surface\n",
+    "    depth_sounder = name.where(name.dist_bt > 0)\n",
+    "    # Of the 4 values beams get the shallowest depth value at each location\n",
+    "    bottom = np.min(depth_sounder.dist_bt, axis=0)\n",
+    "    # River bottom for ideal transect\n",
+    "    bottom_avg = interp.griddata(gps_points, bottom, ideal_points, method=\"linear\")\n",
+    "    # Create a matrix of depths\n",
+    "    bottom_filter = d3d.create_points(\n",
+    "        x=bottom_avg, y=name.range.to_numpy(), waterdepth=1\n",
+    "    )\n",
+    "    # Creating a mask matrix with ones in the area of the river cross section and nan's outside\n",
+    "    river_bottom_filter = []\n",
+    "    for index, row in bottom_filter.iterrows():\n",
+    "        if row[\"x\"] > row[\"y\"]:\n",
+    "            filter = 1\n",
+    "        else:\n",
+    "            filter = float(\"nan\")\n",
+    "        river_bottom_filter = np.append(river_bottom_filter, filter)\n",
+    "\n",
+    "        # Tiling the GPS data for each depth bin\n",
+    "    gps_utm_x = np.tile(gps_points.utm_x, np.size(name.range))\n",
+    "    gps_utm_y = np.tile(gps_points.utm_y, np.size(name.range))\n",
+    "\n",
+    "    # Repeating the depth bins for each GPS point\n",
+    "    depth = np.repeat(name.range, np.size(gps_points.utm_x))\n",
+    "\n",
+    "    # Create Dataframe from the calculated points\n",
+    "    ADCP_points = pd.DataFrame(\n",
+    "        {\"utm_x\": gps_utm_x, \"utm_y\": gps_utm_y, \"waterdepth\": depth}\n",
+    "    )\n",
+    "\n",
+    "    # Raveling the veocity data to correspond with 'ADCP_points' and filtering out velocity data bellow the river bottom\n",
+    "    ADCP_points[\"east_velocity\"] = np.ravel(name.vel[0, :, :]) * river_bottom_filter\n",
+    "    ADCP_points[\"north_velocity\"] = (\n",
+    "        np.ravel(name.vel[1, :, :]) * river_bottom_filter\n",
+    "    )\n",
+    "    ADCP_points[\"vertical_velocity\"] = (\n",
+    "        np.ravel(name.vel[2, :, :]) * river_bottom_filter\n",
+    "    )\n",
+    "    ADCP_points = ADCP_points.dropna()\n",
+    "\n",
+    "    # Project velocity onto ideal tansect\n",
+    "    ADCP_ideal = pd.DataFrame()\n",
+    "    ADCP_ideal[\"east_velocity\"] = interp.griddata(\n",
+    "        ADCP_points[[\"utm_x\", \"utm_y\", \"waterdepth\"]],\n",
+    "        ADCP_points[\"east_velocity\"],\n",
+    "        ADCP_ideal_points[[\"utm_x\", \"utm_y\", \"waterdepth\"]],\n",
+    "        method=\"linear\",\n",
+    "    )\n",
+    "    ADCP_ideal[\"north_velocity\"] = interp.griddata(\n",
+    "        ADCP_points[[\"utm_x\", \"utm_y\", \"waterdepth\"]],\n",
+    "        ADCP_points[\"north_velocity\"],\n",
+    "        ADCP_ideal_points[[\"utm_x\", \"utm_y\", \"waterdepth\"]],\n",
+    "        method=\"linear\",\n",
+    "    )\n",
+    "    ADCP_ideal[\"vertical_velocity\"] = interp.griddata(\n",
+    "        ADCP_points[[\"utm_x\", \"utm_y\", \"waterdepth\"]],\n",
+    "        ADCP_points[\"vertical_velocity\"],\n",
+    "        ADCP_ideal_points[[\"utm_x\", \"utm_y\", \"waterdepth\"]],\n",
+    "        method=\"linear\",\n",
+    "    )\n",
+    "\n",
+    "    # Calculate the magnitude of the velocity components\n",
+    "    ADCP_ideal[\"magnitude\"] = np.sqrt(\n",
+    "        ADCP_ideal.east_velocity**2\n",
+    "        + ADCP_ideal.north_velocity**2\n",
+    "        + ADCP_ideal.vertical_velocity**2\n",
+    "    )\n",
+    "    # Set the contour color bar bounds\n",
+    "    min_plot = 0\n",
+    "    max_plot = 3\n",
+    "\n",
+    "    # The Contour of velocity magnitude from the ADCP transect data\n",
+    "    fig, ax = plt.subplots(figsize=(10, 4.4))\n",
+    "\n",
+    "    contour_plot = plt.tripcolor(\n",
+    "        ADCP_ideal_points.utm_x,\n",
+    "        -ADCP_ideal_points.waterdepth,\n",
+    "        ADCP_ideal.magnitude * river_bottom_filter,\n",
+    "        vmin=min_plot,\n",
+    "        vmax=max_plot,\n",
+    "    )\n",
+    "\n",
+    "    plt.xlabel(\"$UTM_x (m)$\")\n",
+    "    plt.ylabel(\"Water Depth (m)\")\n",
+    "    cbar = plt.colorbar(contour_plot)\n",
+    "    cbar.set_label(\"velocity [m/s]\")\n",
+    "    plt.ylim([-10, -1])\n",
+    "    plt.xlim([400940, 401090])\n",
+    "    plt.plot(ideal_points.utm_x, -bottom_avg, \"k\", label=\"river bottom\")\n",
+    "    plt.legend(loc=7)\n",
+    "    ax.get_xaxis().set_major_formatter(\n",
+    "        matplotlib.ticker.FuncFormatter(lambda x, p: format(int(x), \",\"))\n",
+    "    )\n",
+    "    plt.xticks(rotation=45)\n",
+    "    plt.title(name.name)\n",
+    "    plt.show()\n",
+    "\n",
+    "\n",
+    "        # Interpolate points by getting min & max first\n",
+    "    start_utmx = min(ADCP_ideal_points.utm_x)\n",
+    "    start_utmy = min(ADCP_ideal_points.utm_y)\n",
+    "\n",
+    "    end_utmx = max(ADCP_ideal_points.utm_x)\n",
+    "    end_utmy = min(ADCP_ideal_points.utm_y)\n",
+    "\n",
+    "    # Using N points for x calculate the y values on an ideal transect from the linear regression used earlier\n",
+    "    \n",
+    "    utm_x_ideal_downsampeled = np.linspace(start_utmx, end_utmx, N)\n",
+    "    utm_y_ideal_downsampeled = (a * utm_x_ideal_downsampeled) + b\n",
+    "    # Create an idealized depth N layers deep\n",
+    "    \n",
+    "    downsampled_depth = np.linspace(\n",
+    "        name.range.min(), np.nanmax(bottom_avg), N_layers\n",
+    "    )\n",
+    "\n",
+    "    # Create a Dataframe of our idealized x,y,depth points\n",
+    "    ADCP_ideal_points_downsamples = pd.DataFrame(\n",
+    "        {\n",
+    "            \"utm_x\": np.tile(utm_x_ideal_downsampeled, N_layers),\n",
+    "            \"utm_y\": np.tile(utm_y_ideal_downsampeled, N_layers),\n",
+    "            \"waterdepth\": np.repeat(downsampled_depth, N),\n",
+    "        }\n",
+    "    )\n",
+    "\n",
+    "\n",
+    "    # Create a DataFrame of downsampled points\n",
+    "    ideal_downsampeled = [\n",
+    "        [x, y] for x, y in zip(utm_x_ideal_downsampeled, utm_y_ideal_downsampeled)\n",
+    "    ]\n",
+    "    ideal_points_downsampled = pd.DataFrame(\n",
+    "        np.array(ideal_downsampeled), columns=[\"utm_x\", \"utm_y\"]\n",
+    "    )\n",
+    "\n",
+    "    # River bottom for downsampled ideal transect\n",
+    "    bottom_avg_downsampled = interp.griddata(\n",
+    "        gps_points, bottom, ideal_points_downsampled, method=\"linear\"\n",
+    "    )\n",
+    "\n",
+    "    # Create a matrix of depths\n",
+    "    bottom_filter_downsampled = d3d.create_points(\n",
+    "        x=bottom_avg_downsampled, y=downsampled_depth, waterdepth=1\n",
+    "    )\n",
+    "\n",
+    "    # Creating a mask matrix with ones in the area of the river cross section and nan's outside\n",
+    "    river_bottom_filter_downsampled = []\n",
+    "    for index, row in bottom_filter_downsampled.iterrows():\n",
+    "        if row[\"x\"] > row[\"y\"]:\n",
+    "            filter = 1\n",
+    "        else:\n",
+    "            filter = float(\"nan\")\n",
+    "        river_bottom_filter_downsampled = np.append(river_bottom_filter_downsampled, filter)\n",
+    "\n",
+    "    # Project velocity onto ideal tansect\n",
+    "    ADCP_ideal_downsamples = pd.DataFrame()\n",
+    "    ADCP_ideal_downsamples[\"east_velocity\"] = interp.griddata(\n",
+    "        ADCP_points[[\"utm_x\", \"utm_y\", \"waterdepth\"]],\n",
+    "        ADCP_points[\"east_velocity\"],\n",
+    "        ADCP_ideal_points_downsamples[[\"utm_x\", \"utm_y\", \"waterdepth\"]],\n",
+    "        method=\"linear\",\n",
+    "    )\n",
+    "    ADCP_ideal_downsamples[\"north_velocity\"] = interp.griddata(\n",
+    "        ADCP_points[[\"utm_x\", \"utm_y\", \"waterdepth\"]],\n",
+    "        ADCP_points[\"north_velocity\"],\n",
+    "        ADCP_ideal_points_downsamples[[\"utm_x\", \"utm_y\", \"waterdepth\"]],\n",
+    "        method=\"linear\",\n",
+    "    )\n",
+    "    ADCP_ideal_downsamples[\"vertical_velocity\"] = interp.griddata(\n",
+    "        ADCP_points[[\"utm_x\", \"utm_y\", \"waterdepth\"]],\n",
+    "        ADCP_points[\"vertical_velocity\"],\n",
+    "        ADCP_ideal_points_downsamples[[\"utm_x\", \"utm_y\", \"waterdepth\"]],\n",
+    "        method=\"linear\",\n",
+    "    )\n",
+    "    ADCP_ideal_downsamples[\"magnitude\"] = np.sqrt(\n",
+    "        ADCP_ideal_downsamples.east_velocity**2\n",
+    "        + ADCP_ideal_downsamples.north_velocity**2\n",
+    "        + ADCP_ideal_downsamples.vertical_velocity**2\n",
+    "    )\n",
+    "    # Plotting\n",
+    "    fig, ax = plt.subplots(figsize=(10, 4.4))\n",
+    "    contour_plot = plt.tripcolor(\n",
+    "        ADCP_ideal_points_downsamples.utm_x,\n",
+    "        -ADCP_ideal_points_downsamples.waterdepth,\n",
+    "        ADCP_ideal_downsamples.magnitude,# river_bottom_filter_downsampled,\n",
+    "        vmin=min_plot,\n",
+    "        vmax=4,\n",
+    "    )\n",
+    "\n",
+    "    # Plot river bottom for comparison\n",
+    "    plt.plot(ideal_points.utm_x, -bottom_avg, \"k\", label=\"river bottom\")\n",
+    "\n",
+    "    # Plot Settings\n",
+    "    plt.xlabel(\"$UTM_x$ (m)\")\n",
+    "    plt.ylabel(\"Water Depth (m)\")\n",
+    "    cbar = plt.colorbar(contour_plot)\n",
+    "    cbar.set_label(\"Velocity [m/s]\")\n",
+    "    plt.ylim([-10, -1])\n",
+    "    plt.xlim([400950, 401090])\n",
+    "    plt.legend(loc=7)\n",
+    "    ax.get_xaxis().set_major_formatter(\n",
+    "        matplotlib.ticker.FuncFormatter(lambda x, p: format(int(x), \",\"))\n",
+    "    )\n",
+    "    plt.xticks(rotation=45)\n",
+    "    plt.title(name.name)\n",
+    "    plt.show()\n",
+    "\n",
+    "    # Get the ADCP sample points\n",
+    "    ADCP_ideal_points_downsamples_xy = ADCP_ideal_points_downsamples.rename(\n",
+    "    columns={\"utm_x\": \"x\", \"utm_y\": \"y\"}\n",
+    "    )\n",
+    "\n",
+    "    variables = [\"mesh2d_ucx\", \"mesh2d_ucy\", \"mesh2d_ucz\"]\n",
+    "    D3D = d3d.variable_interpolation(\n",
+    "        d3d_data, variables, points=ADCP_ideal_points_downsamples_xy, edges= 'nearest'\n",
+    "    )\n",
+    "    # Calculate the magnitude of the velocity\n",
+    "    D3D[\"magnitude\"] = np.sqrt(D3D.mesh2d_ucy**2 + D3D.mesh2d_ucx**2 + D3D.mesh2d_ucz**2)\n",
+    "    \n",
+    "    # Plot Delft3D interpolated Data\n",
+    "    fig, ax = plt.subplots(figsize=(10, 4.4))\n",
+    "    contour_plot = plt.tripcolor(\n",
+    "        D3D.x,\n",
+    "        -D3D.waterdepth,\n",
+    "        D3D.magnitude* river_bottom_filter_downsampled,\n",
+    "        vmin=min_plot,\n",
+    "        vmax=3,\n",
+    "        # shading='gouraud'\n",
+    "        alpha=1,\n",
+    "    )\n",
+    "\n",
+    "    # Plot the river bottom calculated frol ADCP for comparison\n",
+    "    plt.plot(ideal_points.utm_x, -bottom_avg, \"k\", label=\"river bottom\")\n",
+    "\n",
+    "    # Figure settings\n",
+    "    plt.xlabel(\"$UTM_x (m)$\")\n",
+    "    plt.ylabel(\"Water Depth (m)\")\n",
+    "    cbar = plt.colorbar(contour_plot)\n",
+    "    cbar.set_label(\"velocity [m/s]\")\n",
+    "    #plt.ylim([-8.5, -1])\n",
+    "    plt.xlim([400960, 401090])\n",
+    "    plt.legend(loc=7)\n",
+    "    ax.get_xaxis().set_major_formatter(\n",
+    "        matplotlib.ticker.FuncFormatter(lambda x, p: format(int(x), \",\"))\n",
+    "    )\n",
+    "    plt.xticks(rotation=45)\n",
+    "    plt.title(name.name)\n",
+    "    plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "afa77384-5c3f-424b-a775-16e03c2a6699",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "notes"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "## Initialize the figure\n",
+    "#figure(figsize=(8, 6))\n",
+    "#fig, ax = plt.subplots()\n",
+    "\n",
+    "# Get data from the original transect in UTM for comparison\n",
+    "#transect_1 = utm.from_latlon(\n",
+    "#    transect_1_raw.latitude_gps, transect_1_raw.longitude_gps, 6, \"W\"\n",
+    "#)\n",
+    "#transect_2 = utm.from_latlon(\n",
+    "#    transect_2_raw.latitude_gps, transect_2_raw.longitude_gps, 6, \"W\"\n",
+    "#)\n",
+    "\n",
+    "## Plot the original transect data for comparison\n",
+    "#plt.plot(transect_1[0], transect_1[1], \"b\", label=\"GPS Transect 1\")\n",
+    "#plt.plot(transect_2[0], transect_2[1], \"r--\", label=\"GPS Transect 2\")\n",
+    "\n",
+    "## Plot the Idealized Transect\n",
+    "#plt.plot(\n",
+    "#    ADCP_ideal_points.utm_x, ADCP_ideal_points.utm_y, \"k-.\", label=\"Ideal Transect\"\n",
+    "#)\n",
+    "#plt.ticklabel_format(style=\"scientific\", useOffset=False)\n",
+    "#ax.get_xaxis().set_major_formatter(\n",
+    "#    matplotlib.ticker.FuncFormatter(lambda x, p: format(int(x), \",\"))\n",
+    "#)\n",
+    "#plt.xticks(rotation=45)\n",
+    "\n",
+    "## Plot Settings\n",
+    "#plt.legend()\n",
+    "#plt.xlabel(\"$UTM_x (m)$\")\n",
+    "#plt.ylabel(\"$UTM_y (m)$\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ae0fef1b-ffb2-4d25-adb9-378e54972ba0",
+   "metadata": {},
+   "source": [
+    "### ADCP: Get Original Transect Data \n",
+    "\n",
+    "Here we create a DataFrame of the original velocity components from the transect data. This DataFrame will be used to interpolate the velocity data onto the ideal transect."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1880fb38-d487-415f-b011-4de37fc44878",
+   "metadata": {},
+   "source": [
+    "### ADCP: Interpolating Velocities for Ideal Transect \n",
+    "\n",
+    "Using the original transect data saved in `ADCP_points` the velocity values are interpolated onto the ideal transect points using the scipy interpolate `griddata` function. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d152f9f1-aa68-4504-bfbf-cad381d56d4c",
+   "metadata": {},
+   "source": [
+    " ## 2. Delft3D Data\n",
+    " \n",
+    "Typically researchers use a modeling tool such as Deflt3D which, relative to field deployments, can perform low-cost simulations where changes to the environment can be implemented and interpreted quickly. This allows researchers to narrow in on a limited design set for field investigation thereby increasing the efficiency of field deployments. To initiate the numerical model, a 2010 bathymetry survey taken by Terrasond under contract to the University of Alaska Fairbanks (UAF) was used to represent the domain. This domain was discretized using the Delft3D built-in mesh generation tool, RGF grid, to roughly 5m x 5m unstructured squares from a bird's eye view. Delft3D represents depth using shallow water equations with a sigma layer approach which distributes the layers between the specified bottom of the river and the water level. An initial 12 sigma layers were specified to compare to filed data for this study. The domain was simulated in Delft3D at a constant discharge of $1789 \n",
+    " m^{3}/s$ and a $2.7 m$ water level, as recorded by the USGS survey station for August $10^{th}$ 2010.  The simulation shown in the example is designed to match the experimental conditions of when the ADCP plotted above was collected. The Delft3D model takes inputs of water level, discharge, and the river bathymetry. The United States Geological Survey (USGS) has a data collection downstream of the TRTS (station:\"15515500\"). The water level and discharge are taken from the USGS data, and the bathymetry data was collected in is the summer of 2010. In this example we will show how to get data from the USGS station and then jump to analyzing the output from the Delft3D model.\n",
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0382e60e-a57a-4899-baf9-1281f39f82ae",
+   "metadata": {},
+   "source": [
+    "### D3D: USGS Water Level\n",
+    "The United States Geological Survey has a nearby survey station (#15515500) downstream of the TRTS which records discharge and water level. The MHKiT River module has the capability to request data from USGS survey stations as demonstrated in the river example. Using MHKiT's river module the water level for the USGS station was collected for the same day as the ADCP data was collected. The USGS assigns a parameter number of '00065' for water level or Gage height. The water level was averaged to be roughly 2.7m. This number is directly input into the Delft3D model and isn't used in this example notebook. Further examples of using the river module are shown in the `river_example`. Similar to the water level the discharge was found using the same MHKit river module except swapping out the parameter number for '00060' to get the discharge data. The discharge was averaged to be 1789 $m^3/s$. This value was also directly input to the Delft3D model and isn't used in this example notebook."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "2727bb7b-9994-450f-8cf7-43bdf0934b2e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Dischage ($f^3/s$)')"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Use the requests method to obtain 1 day of instantneous gage height data\n",
+    "water_level_USGS_data = river.io.usgs.request_usgs_data(\n",
+    "    station=\"15515500\",\n",
+    "    parameter=\"00065\",\n",
+    "    start_date=\"2023-07-08\",\n",
+    "    end_date=\"2023-07-08\",\n",
+    "    data_type=\"Instantaneous\",\n",
+    ")\n",
+    "\n",
+    "# Plot data\n",
+    "water_level_USGS_data.plot()\n",
+    "\n",
+    "# Plot Settings\n",
+    "plt.xlabel(\"Time\")\n",
+    "plt.ylabel(\"Gage Height (feet)\")\n",
+    "\n",
+    "# Use the requests method to obtain 1 day of instantneous discharge data\n",
+    "discharge_USGS_data = river.io.usgs.request_usgs_data(\n",
+    "    station=\"15515500\",\n",
+    "    parameter=\"00060\",\n",
+    "    start_date=\"2023-07-08\",\n",
+    "    end_date=\"2023-07-08\",\n",
+    "    data_type=\"Instantaneous\",\n",
+    ")\n",
+    "\n",
+    "# Print data\n",
+    "discharge_USGS_data.plot()\n",
+    "# Plot Settings\n",
+    "plt.xlabel(\"Time\")\n",
+    "plt.ylabel(\"Dischage ($f^3/s$)\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "1b3e78c9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Discharge, cubic feet per second    1985.00768\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "discharge_USGS_data.max()*0.0283168"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "a30f2de2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Gage height, feet    117.779712\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "water_level_USGS_data.max()*0.3048+114.75"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "8643dfb1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "117.036"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "2.286+114.75\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "204a5a08-50fd-4b70-be89-d82b338177b4",
+   "metadata": {},
+   "source": [
+    "### D3D: Importing Delft3D Data \n",
+    "Here we are importing the NetCDF data exported by the Delft3D simulation described in the Delft3D introduction."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "745835ce-0224-4121-9d1b-a135c8ee3e0c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Import the simulated data\n",
+    "d3d_data = netCDF4.Dataset(\"data/TRTS/D3D/TRTS_2021_2023_hydroball_vertical_viscocity.nc\")\n",
+    "\n",
+    "# Get the ADCP sample points\n",
+    "ADCP_ideal_points_downsamples_xy = ADCP_ideal_points_downsamples.rename(\n",
+    "    columns={\"utm_x\": \"x\", \"utm_y\": \"y\"}\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "22ffc77a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\"projected_coordinate_system\"\n",
+      "\"mesh2d\": Topology data of 2D mesh\n",
+      "\"mesh2d_node_x\": x-coordinate of mesh nodes\n",
+      "\"mesh2d_node_y\": y-coordinate of mesh nodes\n",
+      "\"mesh2d_node_z\": z-coordinate of mesh nodes\n",
+      "\"mesh2d_edge_x\": characteristic x-coordinate of the mesh edge (e.g. midpoint)\n",
+      "\"mesh2d_edge_y\": characteristic y-coordinate of the mesh edge (e.g. midpoint)\n",
+      "\"mesh2d_edge_nodes\": Start and end nodes of mesh edges\n",
+      "\"mesh2d_face_nodes\": Vertex nodes of mesh faces (counterclockwise)\n",
+      "\"mesh2d_edge_faces\": Neighboring faces of mesh edges\n",
+      "\"mesh2d_face_x\": Characteristic x-coordinate of mesh face\n",
+      "\"mesh2d_face_y\": Characteristic y-coordinate of mesh face\n",
+      "\"mesh2d_face_x_bnd\": x-coordinate bounds of mesh faces (i.e. corner coordinates)\n",
+      "\"mesh2d_face_y_bnd\": y-coordinate bounds of mesh faces (i.e. corner coordinates)\n",
+      "\"mesh2d_layer_sigma\": Sigma coordinate of layer centres\n",
+      "\"mesh2d_interface_sigma\": Sigma coordinate of layer interfaces\n",
+      "\"mesh2d_edge_type\": edge type (relation between edge and flow geometry)\n",
+      "\"mesh2d_bldepth\"\n",
+      "\"mesh2d_flowelem_ba\"\n",
+      "\"mesh2d_flowelem_bl\": flow element center bedlevel (bl)\n",
+      "\"time\"\n",
+      "\"timestep\": Latest computational timestep size in each output interval\n",
+      "\"mesh2d_Numlimdt\": Number of times flow element was Courant limiting\n",
+      "\"mesh2d_s1\": Water level\n",
+      "\"mesh2d_s0\": Water level on previous timestep\n",
+      "\"mesh2d_waterdepth\": Water depth at pressure points\n",
+      "\"mesh2d_u1\": Velocity at velocity point, n-component\n",
+      "\"mesh2d_u0\": Velocity at velocity point at previous time step, n-component\n",
+      "\"mesh2d_ucx\": Flow element center velocity vector, x-component\n",
+      "\"mesh2d_ucy\": Flow element center velocity vector, y-component\n",
+      "\"mesh2d_ucz\": Flow element center velocity vector, z-component\n",
+      "\"mesh2d_ucxa\": Flow element center depth-averaged velocity, x-component\n",
+      "\"mesh2d_ucya\": Flow element center depth-averaged velocity, y-component\n",
+      "\"mesh2d_ucmag\": Flow element center velocity magnitude\n",
+      "\"mesh2d_ucmaga\": Flow element center depth-averaged velocity magnitude\n",
+      "\"mesh2d_rho\": Flow element center potential density\n",
+      "\"mesh2d_q1\": Discharge through flow link at current time\n",
+      "\"mesh2d_viu\": Horizontal eddy viscosity\n",
+      "\"mesh2d_diu\": Horizontal eddy diffusivity\n",
+      "\"mesh2d_tausx\": Total bed shear stress vector, x-component\n",
+      "\"mesh2d_tausy\": Total bed shear stress vector, y-component\n",
+      "\"mesh2d_taus\": Total bed shear stress magnitude\n",
+      "\"mesh2d_czs\": Chezy roughness in flow element center\n",
+      "\"mesh2d_turkin1\": turbulent kinetic energy\n",
+      "\"mesh2d_vicwwu\": turbulent vertical eddy viscosity\n",
+      "\"mesh2d_tureps1\": turbulent energy dissipation\n"
+     ]
+    }
+   ],
+   "source": [
+    "for var in d3d_data.variables.keys():\n",
+    "    try:\n",
+    "        d3d_data[var].long_name\n",
+    "    except:\n",
+    "        print(f'\"{var}\"')\n",
+    "    else:\n",
+    "        print(f'\"{var}\": {d3d_data[var].long_name}')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "a8809863",
+   "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>x</th>\n",
+       "      <th>y</th>\n",
+       "      <th>waterdepth</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>400948.728297</td>\n",
+       "      <td>7.161181e+06</td>\n",
+       "      <td>0.410000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>400962.931236</td>\n",
+       "      <td>7.161185e+06</td>\n",
+       "      <td>0.410000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>400977.134176</td>\n",
+       "      <td>7.161190e+06</td>\n",
+       "      <td>0.410000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>400991.337115</td>\n",
+       "      <td>7.161194e+06</td>\n",
+       "      <td>0.410000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>401005.540054</td>\n",
+       "      <td>7.161199e+06</td>\n",
+       "      <td>0.410000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>115</th>\n",
+       "      <td>401019.742994</td>\n",
+       "      <td>7.161203e+06</td>\n",
+       "      <td>8.461812</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>116</th>\n",
+       "      <td>401033.945933</td>\n",
+       "      <td>7.161208e+06</td>\n",
+       "      <td>8.461812</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>117</th>\n",
+       "      <td>401048.148872</td>\n",
+       "      <td>7.161212e+06</td>\n",
+       "      <td>8.461812</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>118</th>\n",
+       "      <td>401062.351812</td>\n",
+       "      <td>7.161217e+06</td>\n",
+       "      <td>8.461812</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>119</th>\n",
+       "      <td>401076.554751</td>\n",
+       "      <td>7.161221e+06</td>\n",
+       "      <td>8.461812</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>120 rows × 3 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                 x             y  waterdepth\n",
+       "0    400948.728297  7.161181e+06    0.410000\n",
+       "1    400962.931236  7.161185e+06    0.410000\n",
+       "2    400977.134176  7.161190e+06    0.410000\n",
+       "3    400991.337115  7.161194e+06    0.410000\n",
+       "4    401005.540054  7.161199e+06    0.410000\n",
+       "..             ...           ...         ...\n",
+       "115  401019.742994  7.161203e+06    8.461812\n",
+       "116  401033.945933  7.161208e+06    8.461812\n",
+       "117  401048.148872  7.161212e+06    8.461812\n",
+       "118  401062.351812  7.161217e+06    8.461812\n",
+       "119  401076.554751  7.161221e+06    8.461812\n",
+       "\n",
+       "[120 rows x 3 columns]"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#ADCP_ideal_points_downsamples_xy.waterdepth=ADCP_ideal_points_downsamples_xy.waterdepth-114.75\n",
+    "ADCP_ideal_points_downsamples_xy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c664e8af-d29c-472b-8e2d-d3c4057ea5a9",
+   "metadata": {},
+   "source": [
+    "### D3D: Ideal Transect Data\n",
+    "Next, we get the velocity data at the same points as the down-sampled ideal ADCP data. In Delft3D velocity in the y direction is `ucy`, velocity in the x direction is `ucx` and velocity in the z direction is `ucz`. From these velocity components the velocity magnitude is calculated using the root mean squared formula."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "64e7f82b-3632-41f4-a5be-9421a817b3d4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Interpolate the Delft3D simulated data onto the the sample points\n",
+    "#variables = [\"mesh2d_s1\"]\n",
+    "#D3D_water_level = d3d.variable_interpolation(\n",
+    "#    d3d_data, variables, points=ADCP_ideal_points_downsamples_xy, edges= 'nearest'\n",
+    "#)\n",
+    "#variables = [\"mesh2d_waterdepth\"]\n",
+    "#D3D_waterdepth = d3d.variable_interpolation(\n",
+    "#    d3d_data, variables, points=ADCP_ideal_points_downsamples_xy, edges= 'nearest'\n",
+    "#)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "16c30ac4",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "Exception",
+     "evalue": "Coordinates not recognized.",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "File \u001b[1;32mc:\\Users\\ashly\\anaconda3\\envs\\Python_310\\lib\\site-packages\\mhkit\\river\\io\\d3d.py:636\u001b[0m, in \u001b[0;36mget_all_data_points\u001b[1;34m(data, variable, time_index, to_pandas)\u001b[0m\n\u001b[0;32m    635\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 636\u001b[0m     cord_sys \u001b[38;5;241m=\u001b[39m \u001b[43mcords_to_layers\u001b[49m\u001b[43m[\u001b[49m\u001b[43mlayer_dim\u001b[49m\u001b[43m]\u001b[49m[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcoords\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[0;32m    637\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m:\n",
+      "\u001b[1;31mKeyError\u001b[0m: 'mesh2d_face_x mesh2d_face_y mesh2d_layer_sigma'",
+      "\nDuring handling of the above exception, another exception occurred:\n",
+      "\u001b[1;31mException\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[13], line 2\u001b[0m\n\u001b[0;32m      1\u001b[0m variables \u001b[38;5;241m=\u001b[39m [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmesh2d_ucx\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmesh2d_ucy\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmesh2d_ucz\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m----> 2\u001b[0m D3D \u001b[38;5;241m=\u001b[39m \u001b[43md3d\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvariable_interpolation\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m      3\u001b[0m \u001b[43m    \u001b[49m\u001b[43md3d_data\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvariables\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpoints\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mADCP_ideal_points_downsamples_xy\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43medges\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mnearest\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\n\u001b[0;32m      4\u001b[0m \u001b[43m)\u001b[49m\n\u001b[0;32m      5\u001b[0m \u001b[38;5;66;03m# Calculate the magnitude of the velocity\u001b[39;00m\n\u001b[0;32m      6\u001b[0m D3D[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmagnitude\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39msqrt(D3D\u001b[38;5;241m.\u001b[39mmesh2d_ucy\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m2\u001b[39m \u001b[38;5;241m+\u001b[39m D3D\u001b[38;5;241m.\u001b[39mmesh2d_ucx\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m2\u001b[39m \u001b[38;5;241m+\u001b[39m D3D\u001b[38;5;241m.\u001b[39mmesh2d_ucz\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m2\u001b[39m)\n",
+      "File \u001b[1;32mc:\\Users\\ashly\\anaconda3\\envs\\Python_310\\lib\\site-packages\\mhkit\\river\\io\\d3d.py:511\u001b[0m, in \u001b[0;36mvariable_interpolation\u001b[1;34m(data, variables, points, edges, x_max_lim, x_min_lim, y_max_lim, y_min_lim, to_pandas)\u001b[0m\n\u001b[0;32m    509\u001b[0m data_raw \u001b[38;5;241m=\u001b[39m {}\n\u001b[0;32m    510\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m var \u001b[38;5;129;01min\u001b[39;00m variables:\n\u001b[1;32m--> 511\u001b[0m     var_data_df \u001b[38;5;241m=\u001b[39m \u001b[43mget_all_data_points\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvar\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtime_index\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mto_pandas\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[0;32m    512\u001b[0m     var_data_df[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdepth\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m var_data_df\u001b[38;5;241m.\u001b[39mwaterdepth \u001b[38;5;241m-\u001b[39m var_data_df\u001b[38;5;241m.\u001b[39mwaterlevel  \u001b[38;5;66;03m# added\u001b[39;00m\n\u001b[0;32m    513\u001b[0m     var_data_df \u001b[38;5;241m=\u001b[39m var_data_df\u001b[38;5;241m.\u001b[39mloc[:, \u001b[38;5;241m~\u001b[39mvar_data_df\u001b[38;5;241m.\u001b[39mT\u001b[38;5;241m.\u001b[39mduplicated(keep\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfirst\u001b[39m\u001b[38;5;124m\"\u001b[39m)]\n",
+      "File \u001b[1;32mc:\\Users\\ashly\\anaconda3\\envs\\Python_310\\lib\\site-packages\\mhkit\\river\\io\\d3d.py:638\u001b[0m, in \u001b[0;36mget_all_data_points\u001b[1;34m(data, variable, time_index, to_pandas)\u001b[0m\n\u001b[0;32m    636\u001b[0m     cord_sys \u001b[38;5;241m=\u001b[39m cords_to_layers[layer_dim][\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcoords\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[0;32m    637\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m:\n\u001b[1;32m--> 638\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCoordinates not recognized.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m    639\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    640\u001b[0m     layer_percentages \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mma\u001b[38;5;241m.\u001b[39mgetdata(cord_sys, \u001b[38;5;28;01mFalse\u001b[39;00m)\n",
+      "\u001b[1;31mException\u001b[0m: Coordinates not recognized."
+     ]
+    }
+   ],
+   "source": [
+    "variables = [\"mesh2d_ucx\", \"mesh2d_ucy\", \"mesh2d_ucz\"]\n",
+    "D3D = d3d.variable_interpolation(\n",
+    "    d3d_data, variables, points=ADCP_ideal_points_downsamples_xy, edges= 'nearest'\n",
+    ")\n",
+    "# Calculate the magnitude of the velocity\n",
+    "D3D[\"magnitude\"] = np.sqrt(D3D.mesh2d_ucy**2 + D3D.mesh2d_ucx**2 + D3D.mesh2d_ucz**2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "2defcf48",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "variable = \"mesh2d_waterdepth\"\n",
+    "var_data_df = d3d.get_all_data_points(d3d_data, variable, time_index=4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "338a279b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "12.699487975086129"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "var_data_df.waterdepth.max()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "b6e3ac34",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([400600., 400700., 400800., 400900., 401000., 401100., 401200.,\n",
+       "        401300., 401400., 401500.]),\n",
+       " [Text(400600.0, 0, '400600'),\n",
+       "  Text(400700.0, 0, '400700'),\n",
+       "  Text(400800.0, 0, '400800'),\n",
+       "  Text(400900.0, 0, '400900'),\n",
+       "  Text(401000.0, 0, '401000'),\n",
+       "  Text(401100.0, 0, '401100'),\n",
+       "  Text(401200.0, 0, '401200'),\n",
+       "  Text(401300.0, 0, '401300'),\n",
+       "  Text(401400.0, 0, '401400'),\n",
+       "  Text(401500.0, 0, '401500')])"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "depth_contour_plot = plt.tripcolor(\n",
+    "    var_data_df.x,\n",
+    "    var_data_df.y,\n",
+    "    var_data_df.waterdepth,\n",
+    "    #vmin=min_plot,\n",
+    "    #vmax=4,\n",
+    "    # shading='gouraud'\n",
+    "    alpha=1,\n",
+    ")\n",
+    "\n",
+    "# Figure settings\n",
+    "plt.xlabel(\"$UTM_x (m)$\")\n",
+    "plt.ylabel(\"$UTM_y (m)$\")\n",
+    "cbar = plt.colorbar(depth_contour_plot)\n",
+    "cbar.set_label(\"Water Depth [m]\")\n",
+    "#plt.ylim([-8.5, -1])\n",
+    "#plt.xlim([400960, 401090])\n",
+    "plt.legend(loc=7)\n",
+    "ax.get_xaxis().set_major_formatter(\n",
+    "    matplotlib.ticker.FuncFormatter(lambda x, p: format(int(x), \",\"))\n",
+    ")\n",
+    "plt.xticks(rotation=45)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "104a28b1-b24d-4329-a8f6-0c133714cb25",
+   "metadata": {},
+   "source": [
+    "Using our Delft3D velocity magnitude a contour plot is created. The results are plotted with the original ideal ADCP river bottom for reference."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "10057916",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "count    120.000000\n",
+       "mean       1.895274\n",
+       "std        0.434225\n",
+       "min        1.047165\n",
+       "25%        1.560212\n",
+       "50%        2.067733\n",
+       "75%        2.271975\n",
+       "max        2.396330\n",
+       "Name: magnitude, dtype: float64"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "D3D.magnitude.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "dac2abf7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "count    69.000000\n",
+       "mean      2.135928\n",
+       "std       0.247362\n",
+       "min       1.560212\n",
+       "25%       2.007290\n",
+       "50%       2.129126\n",
+       "75%       2.339263\n",
+       "max       2.396330\n",
+       "Name: magnitude, dtype: float64"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(D3D.magnitude*river_bottom_filter_downsampled).describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "69a3a77d-2a2a-4944-8923-05159d52ad8d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([400960., 400980., 401000., 401020., 401040., 401060., 401080.,\n",
+       "        401100.]),\n",
+       " [Text(400960.0, 0, '400,960'),\n",
+       "  Text(400980.0, 0, '400,980'),\n",
+       "  Text(401000.0, 0, '401,000'),\n",
+       "  Text(401020.0, 0, '401,020'),\n",
+       "  Text(401040.0, 0, '401,040'),\n",
+       "  Text(401060.0, 0, '401,060'),\n",
+       "  Text(401080.0, 0, '401,080'),\n",
+       "  Text(401100.0, 0, '401,100')])"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x440 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot Delft3D interpolated Data\n",
+    "fig, ax = plt.subplots(figsize=(10, 4.4))\n",
+    "contour_plot = plt.tripcolor(\n",
+    "    D3D.x,\n",
+    "    -D3D.waterdepth,\n",
+    "    D3D.magnitude* river_bottom_filter_downsampled,\n",
+    "    vmin=min_plot,\n",
+    "    vmax=3,\n",
+    "    # shading='gouraud'\n",
+    "    alpha=1,\n",
+    ")\n",
+    "\n",
+    "# Plot the river bottom calculated frol ADCP for comparison\n",
+    "plt.plot(ideal_points.utm_x, -bottom_avg, \"k\", label=\"river bottom\")\n",
+    "\n",
+    "# Figure settings\n",
+    "plt.xlabel(\"$UTM_x (m)$\")\n",
+    "plt.ylabel(\"Water Depth (m)\")\n",
+    "cbar = plt.colorbar(contour_plot)\n",
+    "cbar.set_label(\"velocity [m/s]\")\n",
+    "#plt.ylim([-8.5, -1])\n",
+    "plt.xlim([400960, 401090])\n",
+    "plt.legend(loc=7)\n",
+    "ax.get_xaxis().set_major_formatter(\n",
+    "    matplotlib.ticker.FuncFormatter(lambda x, p: format(int(x), \",\"))\n",
+    ")\n",
+    "plt.xticks(rotation=45)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d419e6b1-09fa-44c2-955f-178aaf378976",
+   "metadata": {},
+   "source": [
+    "## 3. Error Calulations\n",
+    "Data from Delft3D and the ADCP were now on an equivalent set of points in our data processing environment. For comparison the $L_1$, $L_2$, and $L_{\\infty}$ norms were calculated for the Delft3D transect, and the down-sampled ADCP data. The L1 norm is equal to the absolute value of the difference between modeled and measured velocities, Delft3D and ADCP, respectively, over the measured velocity. This gives a fraction that can be converted into the percent difference between measured and modeled velocities. The mean absolute error (MAE) in is an average value of L1. The error calculation process was repeated to calculate the L2 norm which is commonly referred to as the mean squared error (MSE). $L_1$, $L_2$, and $L_{\\infty}$ are dimensionless.\n",
+    "\n",
+    "### Error:  L1 \n",
+    "\n",
+    "$L_1= \\frac{|D3D-ADCP|}{ADCP}$\n",
+    "\n",
+    "$L_1$ is a dimensionless value equal to the difference between the modeled and measured value divided by the actual value. In this case the measured ADCP is considered to be the actual value."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "d01e3d76-daab-4597-8b5d-5b588f6c81c2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# L1\n",
+    "L1_Magnitude = (\n",
+    "    abs(ADCP_ideal_downsamples.magnitude - D3D.magnitude)\n",
+    "    / ADCP_ideal_downsamples.magnitude\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a1b9f829-9046-4f22-98c0-f98c8235f8e1",
+   "metadata": {},
+   "source": [
+    "Similar to the `river_bottom_filter` this filter replaces extreme values (100%) values with 'nan' and is then combined with the `river_bottom_filter` to create `error_filter`. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "147f4bf3-7ad3-4cb0-8c08-fd946557e2f8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "river_bottom_edge_filter_downsampled = []\n",
+    "for i in L1_Magnitude:\n",
+    "    if 1 > i:\n",
+    "        filter = 1\n",
+    "    else:\n",
+    "        filter = float(\"nan\")\n",
+    "    river_bottom_edge_filter_downsampled = np.append(\n",
+    "        river_bottom_edge_filter_downsampled, filter\n",
+    "    )\n",
+    "\n",
+    "error_filter = river_bottom_edge_filter_downsampled * river_bottom_filter_downsampled"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f2532dd2-e4a8-4c4f-8070-8799d69effd1",
+   "metadata": {},
+   "source": [
+    "The mean absolute error (MAE) is the average $L_1$ error over the transect area. The MAE is displayed with the fil contour plot of $L_1$. $N$ is equation is equle to the number of points in the river cross-sectional area. \n",
+    "\n",
+    "$MAE= \\frac{\\sum{L_1}}{N}$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "bf78c6e0-c4cd-421e-b9c0-3465d05c6dd3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.1492904779996135"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Calculate and priont the Mean Absolute Error\n",
+    "MAE = np.sum(L1_Magnitude * error_filter) / len(L1_Magnitude[L1_Magnitude < 1000])\n",
+    "MAE"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "0acdaf51-d08e-4d9e-8070-4d2e5477b456",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([400960., 400980., 401000., 401020., 401040., 401060., 401080.,\n",
+       "        401100.]),\n",
+       " [Text(400960.0, 0, '400,960'),\n",
+       "  Text(400980.0, 0, '400,980'),\n",
+       "  Text(401000.0, 0, '401,000'),\n",
+       "  Text(401020.0, 0, '401,020'),\n",
+       "  Text(401040.0, 0, '401,040'),\n",
+       "  Text(401060.0, 0, '401,060'),\n",
+       "  Text(401080.0, 0, '401,080'),\n",
+       "  Text(401100.0, 0, '401,100')])"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x440 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Set the min and max error values\n",
+    "max_plot_error = 1\n",
+    "min_plot_error = 0\n",
+    "\n",
+    "# Plotting the L1 error\n",
+    "fig, ax = plt.subplots(figsize=(10, 4.4))\n",
+    "contour_plot_L1 = plt.tripcolor(\n",
+    "    D3D.x,\n",
+    "    -D3D.waterdepth,\n",
+    "    L1_Magnitude * error_filter,\n",
+    "    vmin=min_plot_error,\n",
+    "    vmax=max_plot_error,\n",
+    ")\n",
+    "\n",
+    "# Plot the river bottom for comparison\n",
+    "plt.plot(ideal_points.utm_x, -bottom_avg, \"k\", label=\"river bottom\")\n",
+    "\n",
+    "# Plot settings\n",
+    "plt.xlim([400960, 401090])\n",
+    "plt.ylim([-8.5, -1])\n",
+    "plt.xlabel(\"UTM x (m)\")\n",
+    "plt.ylabel(\"Water Depth (m)\")\n",
+    "cbar = plt.colorbar(contour_plot_L1)\n",
+    "cbar.set_label(\"$L_1$ Velocity Error\")\n",
+    "plt.legend(loc=7)\n",
+    "\n",
+    "ax.get_xaxis().set_major_formatter(\n",
+    "    matplotlib.ticker.FuncFormatter(lambda x, p: format(int(x), \",\"))\n",
+    ")\n",
+    "plt.xticks(rotation=45)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "45185463-73d7-4851-85c8-083ade7a50f2",
+   "metadata": {},
+   "source": [
+    "### Error: L2\n",
+    "\n",
+    "$L_2=\\left(\\frac{D3D-ADCP}{ADCP}\\right)^2$\n",
+    "\n",
+    "$L_2$ is similar to $L_1$ except $L_2$ squares the difference between the modeled and measured values."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "1d10a637-2105-40de-8f22-e8b46b5c2d16",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# L2\n",
+    "L2_Magnitude = (\n",
+    "    (ADCP_ideal_downsamples.magnitude - D3D.magnitude)\n",
+    "    / ADCP_ideal_downsamples.magnitude\n",
+    ") ** 2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "39753c37-c501-462f-b45f-2030f16632c4",
+   "metadata": {},
+   "source": [
+    "The mean squared error (MSE) is the average of $L_2$ error over the transect area. $N$ is equation is equle to the number of points in the river cross-sectional area. \n",
+    "\n",
+    "\n",
+    "$MSE= \\frac{\\sum{L_2}}{N}$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "e71c17c4-93f7-4ad2-8b66-bff1b7da0519",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.04877868538163196"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "MSE = np.sum(L2_Magnitude * error_filter) / np.size(L2_Magnitude[L2_Magnitude < 1000])\n",
+    "MSE"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "3921f31e-1f0e-45a6-9394-c7e42e1ebb2d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([400960., 400980., 401000., 401020., 401040., 401060., 401080.,\n",
+       "        401100.]),\n",
+       " [Text(400960.0, 0, '400,960'),\n",
+       "  Text(400980.0, 0, '400,980'),\n",
+       "  Text(401000.0, 0, '401,000'),\n",
+       "  Text(401020.0, 0, '401,020'),\n",
+       "  Text(401040.0, 0, '401,040'),\n",
+       "  Text(401060.0, 0, '401,060'),\n",
+       "  Text(401080.0, 0, '401,080'),\n",
+       "  Text(401100.0, 0, '401,100')])"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x440 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Create a contour plot of the error\n",
+    "# Plotting\n",
+    "fig, ax = plt.subplots(figsize=(10, 4.4))\n",
+    "contour_plot_L2 = plt.tripcolor(\n",
+    "    D3D.x,\n",
+    "    -D3D.waterdepth,\n",
+    "    L2_Magnitude * error_filter,\n",
+    "    vmin=min_plot_error,\n",
+    "    vmax=max_plot_error,\n",
+    ")\n",
+    "\n",
+    "# Plot the river bottom for comparison\n",
+    "plt.plot(ideal_points.utm_x, -bottom_avg, \"k\", label=\"river bottom\")\n",
+    "\n",
+    "# Plot settings\n",
+    "plt.xlim([400960, 401090])\n",
+    "plt.ylim([-8.5, -1])\n",
+    "plt.xlabel(\"UTM x (m)\")\n",
+    "plt.ylabel(\"Water Depth (m)\")\n",
+    "cbar = plt.colorbar(contour_plot_L2)\n",
+    "cbar.set_label(\"$L_2$ Velocity Error\")\n",
+    "plt.legend(loc=7)\n",
+    "\n",
+    "ax.get_xaxis().set_major_formatter(\n",
+    "    matplotlib.ticker.FuncFormatter(lambda x, p: format(int(x), \",\"))\n",
+    ")\n",
+    "plt.xticks(rotation=45)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "773b015e-9658-4008-ad69-e93a512c748e",
+   "metadata": {},
+   "source": [
+    "### Error: $L_\\infty$\n",
+    "\n",
+    "$L_\\infty = max(L_1)$\n",
+    "\n",
+    "$L_\\infty$ is the maximum L1 error "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "de8f93fc-8254-4181-817c-dae48b39456b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.8053485889191354"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# L inf\n",
+    "L_inf = np.nanmax(L1_Magnitude * error_filter)\n",
+    "L_inf"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5d52f895-bbc6-4851-b19d-b19affb5e3c2",
+   "metadata": {},
+   "source": [
+    "## 4. Discussion\n",
+    "\n",
+    "A model verification using simulated data of TRTS was compared to field data collected in 2010 using MHKiT. This example showcased the functionality of the MHKiT river and DOLfYN modules using a real-world study. The findings demonstrated how MHKiT modules may be combined to analyze simulated data and collected field data in a simple and quick manner. Other transect plotting software run separately from data analysis software. MHKiT's ability to process data from multiple common marine energy sources will help ME practitioners reach unique and novel results quickly by eliminating much of the start-up processing scripts most analysts must perform and maintain today. Although this is a river example, this framework of analysing modeled data and comparing it to wave and tidal experimental data is possible as well.\n",
+    "\n",
+    "The high error values found between the model and field data were expected for a first attempt on a coarse numerical model domain. The coarse model domain allows MHKiT to host the data in this repository for public use and replication of this study's results. This example further allows users to swap in their own data and build on this working example. Using this template the TRTS Delft3D model may now be tuned and easily compared to the error metrics calculated to improve our Delft3D Tanana River model. Looking forward, a primary focus of model improvement would be the resolution of the Delft3D model, especially on the TRTS section of the Tanana river. Improving the Delft3D model is of particular interest to the TRTS staff as it can be used to predict the impact of the wake of a CEC on the surrounding environment such as the river bottom before it's deployed long-term. This can save time and funding when implementing CECs onto an electrical grid while also mitigating environmental impact."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d68c5617-4648-4bae-aa1f-06d811d845c0",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4ab37171",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python_310",
+   "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": "3.10.15"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/mhkit/river/io/d3d.py b/mhkit/river/io/d3d.py
index 19a61df6..f19bf8ea 100644
--- a/mhkit/river/io/d3d.py
+++ b/mhkit/river/io/d3d.py
@@ -221,6 +221,10 @@ def get_layer_data(data, variable, layer_index=-1, time_index=-1, to_pandas=True
                 "name": "mesh2d_nLayers",
                 "coords": data.variables["mesh2d_layer_sigma"][:],
             },
+            "mesh2d_face_x mesh2d_face_y mesh2d_layer_sigma": {
+                "name": "mesh2d_nLayers",
+                "coords": data.variables["mesh2d_layer_sigma"][:],
+            },
             "mesh2d_edge_x mesh2d_edge_y": {
                 "name": "mesh2d_nInterfaces",
                 "coords": data.variables["mesh2d_interface_sigma"][:],
@@ -230,7 +234,7 @@ def get_layer_data(data, variable, layer_index=-1, time_index=-1, to_pandas=True
             data.variables["mesh2d_waterdepth"][time_index, :], False
         )
         waterlevel = np.ma.getdata(data.variables["mesh2d_s1"][time_index, :], False)
-        coords = str(data.variables["waterdepth"].coordinates).split()
+        coords = str(data.variables["mesh2d_waterdepth"].coordinates).split()
 
     elif str(data.variables[variable].coordinates) == "FlowElem_xcc FlowElem_ycc":
         cords_to_layers = {
@@ -601,6 +605,10 @@ def get_all_data_points(data, variable, time_index=-1, to_pandas=True):
                 "name": "mesh2d_nLayers",
                 "coords": data.variables["mesh2d_layer_sigma"][:],
             },
+            "mesh2d_face_x mesh2d_face_y mesh2d_layer_sigma": {
+                "name": "mesh2d_nLayers",
+                "coords": data.variables["mesh2d_layer_sigma"][:],
+            },
             "mesh2d_edge_x mesh2d_edge_y": {
                 "name": "mesh2d_nInterfaces",
                 "coords": data.variables["mesh2d_interface_sigma"][:],