{ "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 scipy.io\n", "import netCDF4\n", "import math \n", "import utm\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\n", "\n", "Acoustic Dopple Curent Profiler (ADCP) field data was collected in 2010 on the Tanana river near Nenana. From the picture shown below the flow can be seen enetering the picture from the bottom heading north. The figure further shows the two passes (Transects) of data collected. The data is collected using a barge used with a debris shield infront to protect instruments and test devices. The transect we are looking at are up-steam of the testing barge making them a good repesentiaion of the flow field the Curent Energy Convertes (CEC) are operating in. This transect data was collected using a Teledyne Rio Grand ADCP monted at the Bow of the boat facing downward. The boat faced and moved perpendicular to the river flow across the river as the ADCP was recording. \n", "\n", "In the next section we will import the two transects shown here into MHKiT and then interpolate these two passes into a single idealized transect for comparison with the simulated Deflt3D data.\n", "\n", "" ] }, { "cell_type": "markdown", "id": "e7cbf99b-302c-42a9-bc9a-f2029fbd5ed9", "metadata": {}, "source": [ "### ADCP: Importing Data\n", "\n", "The MHKiT DOLfYN api module can import `.PDO` and other binary formats from ADCP and ADV to an xarray. For this analysis we import two transect passes stored in the 'data' folder in `.PDO` format. For comparison to the numerical model we want to average the two transects into one xarray ('transect_1_2')." ] }, { "cell_type": "code", "execution_count": 2, "id": "079e0248-bb41-4c99-a954-19735dbe5e90", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Reading file data/river/ADCP_transect/tanana_transects_08_10_10_0_002_10-08-10_142214.PD0 ...\n", "\n", "Reading file data/river/ADCP_transect/tanana_transects_08_10_10_0_003_10-08-10_143335.PD0 ...\n" ] }, { "data": { "text/html": [ "
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<xarray.Dataset>\n",
       "Dimensions:              (time_gps: 1171, time: 1171, beam: 4, dir: 4, range: 47, x: 4, x*: 4, earth: 3, inst: 3)\n",
       "Coordinates:\n",
       "  * time_gps             (time_gps) datetime64[ns] 2010-08-10T22:28:17.200000...\n",
       "  * time                 (time) datetime64[ns] 2010-08-10T14:28:15.559999942 ...\n",
       "  * beam                 (beam) int32 1 2 3 4\n",
       "  * dir                  (dir) <U3 'X' 'Y' 'Z' 'err'\n",
       "  * range                (range) float64 0.57 0.82 1.07 ... 11.57 11.82 12.07\n",
       "  * x                    (x) int32 1 2 3 4\n",
       "  * x*                   (x*) int32 1 2 3 4\n",
       "  * earth                (earth) <U1 'E' 'N' 'U'\n",
       "  * inst                 (inst) <U1 'X' 'Y' 'Z'\n",
       "Data variables: (12/36)\n",
       "    number               (time) float64 3.652e+03 3.653e+03 ... 4.882e+03\n",
       "    builtin_test_fail    (time) float64 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0\n",
       "    c_sound              (time) float32 1.466e+03 1.466e+03 ... 1.466e+03\n",
       "    depth                (time) float32 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0\n",
       "    pitch                (time) float32 -0.1 -0.09 -0.21 ... -0.37 -0.29 -0.11\n",
       "    roll                 (time) float32 3.33 3.32 3.33 3.49 ... 2.34 2.37 2.38\n",
       "    ...                   ...\n",
       "    rtk_age_gps          (time_gps) float32 5.0 6.0 2.0 3.0 ... 2.0 2.0 3.0 3.0\n",
       "    speed_over_grnd_gps  (time_gps) float32 0.03549 0.03395 ... 0.2541 0.285\n",
       "    dir_over_grnd_gps    (time_gps) float32 115.5 94.08 6.931 ... 262.1 261.0\n",
       "    hdwtime_gps          (time_gps) datetime64[ns] 2010-08-10T14:28:15.559999...\n",
       "    beam2inst_orientmat  (x, x*) float64 1.462 -1.462 0.0 ... -1.034 -1.034\n",
       "    orientmat            (earth, inst, time) float64 -0.9037 -0.9025 ... 1.0 1.0\n",
       "Attributes: (12/38)\n",
       "    inst_make:                TRDI\n",
       "    inst_type:                ADCP\n",
       "    rotate_vars:              ['vel', 'vel_bt']\n",
       "    has_imu:                  0\n",
       "    prog_ver:                 10.16\n",
       "    inst_model:               Rio Grande\n",
       "    ...                       ...\n",
       "    false_target_threshold:   50\n",
       "    transmit_lag_m:           0.08\n",
       "    bandwidth:                0\n",
       "    sourceprog:               WINRIVER\n",
       "    fs:                       11.11111111111111\n",
       "    vel_gps_corrected:        0
" ], "text/plain": [ "\n", "Dimensions: (time_gps: 1171, time: 1171, beam: 4, dir: 4, range: 47, x: 4, x*: 4, earth: 3, inst: 3)\n", "Coordinates:\n", " * time_gps (time_gps) datetime64[ns] 2010-08-10T22:28:17.200000...\n", " * time (time) datetime64[ns] 2010-08-10T14:28:15.559999942 ...\n", " * beam (beam) int32 1 2 3 4\n", " * dir (dir) " ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Linear regression using first order polyfit\n", "a,b = np.polyfit(gps_points.utm_x, gps_points.utm_y,1)\n", "\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", "\n", "# Repeat UTM corrdinates to match the ADCP points matrix (dir, range, time)\n", "utm_x_points = np.tile(gps_points.utm_x, np.size(transect_1_2.range))\n", "utm_y_points = np.tile(a*gps_points.utm_x+b, np.size(transect_1_2.range))\n", "depth_points = np.repeat( transect_1_2.range, np.size(gps_points.utm_x))\n", "\n", "ADCP_ideal_points={\n", " 'utm_x': utm_x_points, \n", " 'utm_y': utm_y_points, \n", " 'waterdepth': depth_points\n", " }\n", "ADCP_ideal_points=pd.DataFrame(ADCP_ideal_points)\n", "\n", "# Initialize the figure\n", "figure(figsize=(8,6))\n", "\n", "# Get data from the original transect in UTM for comparison\n", "transect_1 = utm.from_latlon(transect_1_raw.latitude_gps, transect_1_raw.longitude_gps, 6, 'W') \n", "transect_2 = utm.from_latlon(transect_2_raw.latitude_gps, transect_2_raw.longitude_gps, 6, 'W') \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(ADCP_ideal_points.utm_x, ADCP_ideal_points.utm_y, 'k-.', label='Ideal Transect')\n", "\n", "# Plot Settings\n", "plt.legend()\n", "plt.xlabel('$UTM_x (m)$')\n", "plt.ylabel('$UTM_y (m)$')" ] }, { "cell_type": "markdown", "id": "1f4a7b28-63af-498f-bf82-574d0a428dd9", "metadata": {}, "source": [ "### ADCP: Range Offset\n", "\n", "Depending on where the ADCP is deployed the `range` values might need to be offset by the deployment depth. For the TRTS dataset used here the range was corrected during the deployment setup, therefore, a zero-input used. This step is provided as a reference for other users." ] }, { "cell_type": "code", "execution_count": 7, "id": "67f37e4f-a889-4676-9797-82d7c13fbe94", "metadata": {}, "outputs": [], "source": [ "# Adjust the range offset, included here for reference\n", "offset=0\n", "api.clean.set_range_offset(transect_1_2, offset)" ] }, { "cell_type": "markdown", "id": "2f20082e-0d10-4b50-85c2-744179a6f91d", "metadata": {}, "source": [ "### ADCP: Correlation Filter \n", "Correlation is a measure of data quality, and its output is scaled in units such that the expected correlation (given high signal/noise ratio, S/N) is 128. Here data was fileted for a minimum correlation of 40 counts. [https://www.comm-tec.com/Docs/Manuali/RDI/BBPRIME.pdf]\n", "\n", "Looking at the plot in the figure below the ADCP is facing downward from the surface of the river and is defined as a range of 0. This means the top of the river is at the bottom of the plot. This ADCP has 4 beams the plot shown is one the 4 beams. From the plot of one of the beams shown below we can see that some of the values below the river bottom are filtered out." ] }, { "cell_type": "code", "execution_count": 8, "id": "87eb43c7-486f-497f-b1b6-dd93330a2d18", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Apply the correlation filter\n", "min_correlation=40\n", "transect_1_2 = api.clean.correlation_filter(transect_1_2, thresh=min_correlation)\n", "\n", "# Plot the results the (data is displayed upside-down)\n", "transect_1_2.corr.sel(beam=1).plot() " ] }, { "cell_type": "markdown", "id": "85175b94-e0fa-488c-929c-a33a20a8bfe0", "metadata": {}, "source": [ "### ADCP: River Bottom Filter \n", "MHKiT DOLfYN has the functionality to find the river bottom based on the along-beam acoustic amplitude data recorded from the ADCP. However, if depth sounder data is available it can be more reliable at representing the river bottom. Here we use the depth sounder (`depth_sounder`) to create an array that can be used as a filter when multiplied with the velocity data." ] }, { "cell_type": "code", "execution_count": 9, "id": "88c8358f-d05f-47c9-9ce3-66f5c9a491e7", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[0, 1, 2]\n" ] } ], "source": [ "# Filtering out depth sounder values above the river surface\n", "depth_sounder = transect_1_2.where(transect_1_2.dist_bt > 0 )\n", "\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", "\n", "# River bottom for ideal transect \n", "bottom_avg = interp.griddata(gps_points, bottom, ideal_points, method='linear')\n", "\n", "# Create a matrix of depths\n", "bottom_filter = d3d.create_points(x=bottom_avg, y=transect_1_2.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)" ] }, { "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": "code", "execution_count": 10, "id": "609dc780-401c-4814-a3ca-46d0bcdcb3be", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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21850 rows × 6 columns

\n", "
" ], "text/plain": [ " utm_x utm_y waterdepth east_velocity north_velocity \\\n", "0 400953.813014 7.161133e+06 0.57 -0.094245 0.214233 \n", "1 400953.813014 7.161133e+06 0.57 0.181185 0.079026 \n", "2 400953.813014 7.161133e+06 0.57 -0.143789 0.129486 \n", "3 400953.813014 7.161133e+06 0.57 -0.432847 0.519561 \n", "4 400953.813014 7.161133e+06 0.57 -0.258352 0.393507 \n", "... ... ... ... ... ... \n", "36171 400989.066656 7.161150e+06 8.07 0.074346 1.819609 \n", "37323 400993.746501 7.161153e+06 8.32 0.409135 0.774590 \n", "37324 400993.746501 7.161153e+06 8.32 0.230414 0.237873 \n", "37328 400992.412805 7.161152e+06 8.32 0.381595 0.560519 \n", "37331 400991.734048 7.161151e+06 8.32 0.058718 0.515059 \n", "\n", " vertical_velocity \n", "0 -0.010 \n", "1 0.022 \n", "2 -0.119 \n", "3 -0.095 \n", "4 -0.106 \n", "... ... \n", "36171 0.084 \n", "37323 0.055 \n", "37324 0.038 \n", "37328 0.104 \n", "37331 0.152 \n", "\n", "[21850 rows x 6 columns]" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Tiling the GPS data for each depth bin\n", "gps_utm_x = np.tile(\n", " gps_points.utm_x, \n", " np.size(transect_1_2.range)\n", " )\n", "gps_utm_y = np.tile(\n", " gps_points.utm_y, \n", " np.size(transect_1_2.range)\n", " )\n", "\n", "# Repeating the depth bins for each GPS point\n", "depth = np.repeat( \n", " transect_1_2.range, \n", " np.size(gps_points.utm_x)\n", " )\n", "\n", "# Create Dataframe from the calculated points\n", "ADCP_points = pd.DataFrame({\n", " 'utm_x': gps_utm_x, \n", " 'utm_y': gps_utm_y, \n", " '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(transect_1_2.vel[0, :,:]) * river_bottom_filter\n", "ADCP_points['north_velocity']= np.ravel(transect_1_2.vel[1, :,:]) * river_bottom_filter\n", "ADCP_points['vertical_velocity']= np.ravel(transect_1_2.vel[2, :,:])* river_bottom_filter\n", "ADCP_points= ADCP_points.dropna()\n", "\n", "# Show points\n", "ADCP_points" ] }, { "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": "code", "execution_count": 11, "id": "767587a8-2248-4ad5-850e-68e7dda56441", "metadata": {}, "outputs": [], "source": [ "# 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(ADCP_ideal.east_velocity**2+ADCP_ideal.north_velocity**2+ADCP_ideal.vertical_velocity**2)" ] }, { "cell_type": "markdown", "id": "9af85dfb-c45e-4ec9-8fa1-9b14970963cc", "metadata": {}, "source": [ "Using the calculated magnitude of velocity from the ADCP we can create a contour plot of the velocity magnitude. " ] }, { "cell_type": "code", "execution_count": 12, "id": "c70be4c3-3082-4ec0-bacf-e40592838abd", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# 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", "plt.figure(figsize=(10,4.4))\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([-8.5,-1])\n", "plt.xlim([400950,401090])\n", "plt.plot(ideal_points.utm_x,-bottom_avg,'k', label= 'river bottom')\n", "plt.legend(loc= 7)" ] }, { "cell_type": "markdown", "id": "3dc22491-18ac-459d-a223-e251a89a309c", "metadata": {}, "source": [ "### ADCP: Ideal Transect Down-sampled\n", "\n", "Numerical models such as Delft3D use discretized points to represent the domain. Environmental models tend to have a low number of points to represent large continuous areas. For instance, the bird's eye view of Delft3D only calculated numerical effects at a roughly 5m by 5m square. Delft3D then discretizes the depth with an even percentage from the water level to the bed by 12 layers as specified for this initial case. As the collected ADCP data had far more resolution than the numerical model a down-sampling was applied using an average to match the locations at which the Delft3D model returned data. This roughly equated to ten velocity measurements spanning the width of the river and 12 measurments spanning the depth." ] }, { "cell_type": "code", "execution_count": 13, "id": "3870861e-3476-4e47-95e8-ed9f4534dc0b", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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yTWGdfBpOMZ1927M+d+6ziQ7roNAU1g3EWyquFZeQq+Hhf5WvvPIKN9xwAwCnnHIK2dnZfPDBB4wYMYJZs2bRr18/xo8fz/PPP8+OHTtYs2YN/fv3Z+3atZx22mn07dsXgIKCAtasWcM3vvGNeue54447mDRpEosWLeLXv/41jz76KEuWLGHLli1MmTKF1atXY2bs3r279pjRo0fTuXNnOnfuTNeuXbngggsAyMvLq62tAFx++eVAMAHf1q1b+eKLL+qd+4UXXqi3utnWrVvZtm0bS5curZ0ee/z48XTv3v2g79XLL7/Mt7/9bTIzMwG48MILgWDa7FdffZVLL720dt9du3YBUF1dzWWXXcbatWv56quv6Nev30HPEUtGRgYTJ+6/BnTx4sXMmDGDHTt2sHnzZnJzc2vfm7pTf69ZswaA008/nenTp1NdXc2ECRM46aSTePHFF3njjTcYOnQoADt37qR379689tprjBw5sjZOTf2dAmI0Kd/ydreYI4VSoW8gnvaZDLKygqahWOWt6OOPPyYjI4PevXvHbSIZOnQoy5cv54QTTuCcc85h48aNPPjggwwZMqR2n5rppqH+lNMNnXjiiVx33XVcffXV9OrVi02bNvGDH/yA0aNH8+STT7JmzRpGjRoV83U7dOhQ+7xDhw71ztFwVteGz/ft28eyZcs48sgjD4ipqTPCxtp/3759dOvWrbY/pq4bbriBm2++mQsvvJAlS5Y0q+O8U6dOtf0EX375Jddffz3Lly/n+OOP56677jrk1N/f+973GDZsGAsXLuTcc8/loYcewt2ZMmUKP/7xj+uda8GCBZr6O5XUNCnXrDxYWcmOqVex+7xpkDsaSI2RQo3RPvsMpk8Ployrq5WXkNuwYQPXXnst06ZNw8wYOXJk7dTUH3zwAVVVVfTv35/DDz+c448/nnnz5jF8+HBGjBjBzJkzGTFiRJPOt3DhwtqEs3r1ajIyMujWrRtbtmzha1/7GhD0EzTH448/DgS1m65du9K1a/2FgMaNG8fs2bNrn9d8adf9mX//+9/z17/+9aDnGTlyJE8++SQ7d+5k27ZtPPPMMwB06dKFfv368cQTTwBBbWvFihUA9X6+uXPnNuvnq6vmi79nz55s376d+fPnH/KYjz/+mBNOOIEbb7yRCy+8kLfeeosxY8Ywf/782iVPN2/eTGVlJaeffjovvfQSn3zySW05aOrvpFVauj8RhDL37OLWpeVA6tcG6mqfyWDSJCgrg+xsMAvuy8paPJpo586dtUNLx44dy7hx42rXDL7++uvZu3cveXl5XHbZZcyZM6f2v8wRI0Zw7LHHkpmZyYgRI6iurm5yMvjVr35F//79KSgo4Pvf/z4VFRVkZGRw6623cvvtt3PmmWfWdmA2Vffu3TnjjDO49tpr+cUvfnHA9lmzZrF8+XIGDRrEgAEDeOCBBwC48847Wbp0KYMHD2bRokVkHaLmNXjwYC677DIKCgqYOHFivfegoqKCX/ziF+Tn55Obm8vTTz8NBMNPL730UkaMGHHAyKDm6NatG1dffTV5eXlcfPHFtc08B/P4448zcOBACgoKWLVqFZMnT2bAgAH86Ec/Yty4cQwaNIhzzjmHtWvX0qtXL8rKypgwYQL5+flcdtllAFxwwQU8+eST6kBONnGajo/bupF7BiTvBWTNoSms5aBGjRrFzJkzKSqKOeutRER/39HLKVnIKz+/kr5bNxy4MTsbwj6jVKIprEVEmqDmorEZIyezo+MR9Te2cpNysmifHcjSaM1Zi1kkVTWcT2hB2Ek86+35rXqBajJSMhARIf4007OemQk0b1qUVJJWycDdNWxP0k4q9uulkhUrVnDRo9UHlD99eV/y8/MTEFFipE0y6NSpE5s2baJHjx5KCJI23J1NmzbRqVOnRIeSlsaWLOTDGOX3DKBdJQJIo2TQt29fqqur2bAhRs+/SArr1KlT7RXo0jriLTrzdeCFNBou2hRpkwwOO+ywZk1FICLth5JAfBpaKiLtRrzZRdt7IoA0qhmIiMSSSusQJ5KSgYikrXiJIJUWnYmKkoGIpJ14w0UhGDIqB1IyEJG0Eu/isfZ23UBTRZYMzKw/8HidohOAO4DysDwHWAN8x90PPtexiEgD8WoDGinUOJElA3d/HygAMLMM4DPgSaAEeNHd7zWzkvD5bVHFJSKpLV4SmEh6TTHd1hI1tHQM8JG7VwIXATWrkswFLk5QTCKSgmIlgnRbayAKieoz+C7waPj4WHdfC+Dua82sd6wDzKwYKAYOuUiKiKS3eKOEQH0DzRV5MjCzw4ELgdubcpy7lwFlECxu0wahiUgKWLFiRcxEoCTQMomoGXwT+B93Xxc+X2dmfcJaQR9gfQJiEpFkU1ERrEFcZx2BW97udsBIIV081joS0WdwOfubiAAWAFPCx1OApyOPSESSS0UFFBdDZSW4Q2UlO6Zexe53F9fbTYmg9US6BrKZZQKfAie4+5awrAcwD8gCqoBL3X3zwV4n1hrIIpJGcnKCRNBAdZdefOO6hzVSqJkOtgZypM1E7r4D6NGgbBPB6CIRkUBVVczi47ZuVG2gjWjWUhFJKuXlC6nu3DPmtg7ZWUoEbUTJQESSRs2Q0RkjJ7Oj4xH1N2ZmBovRS5vQ3EQiknANrxtYkDsagLuWlnPMto21o4mYNClBEaY/JQMRSah4E8td9aPvc0z+zMjjaa+UDEQkIWJdRazO4cRRMhCRyMWqDUxEiSCRlAxEJDJagjJ5KRmISCRyShbGLFciSA5KBiLSpuLVBrToTHJRMhCRNhNvhlEtSJ98lAxEpNWNLVnIh3G2KREkJyUDEWlV8foGQIkgmSkZiEiriJUE1C+QOpQMRKRF4i1ID0oEqUTJQESaLV4i0BKUqUfJQESaLN5wUS06k7qUDESkSXQVcXpSMhCRRok3XFSdxOlBi9uISH0VFcEaxB06BPcVFQAxE8E9A5QI0oVqBiKyX0UFFBfDjh3B88pKdky9ipLH/h+EC86AmoTSkZKBiOxXWro/EYQy9+zi1qXlLMgdrYvG0piaiURkv6qqmMXHbd3IPQMijkUipWQgIrXWde4Zs7xDdpaahdKckoGIkFOykJyShUwfOZkdHY+ovzEzM1iMXtKa+gxE2rGG1wwsCDuJS5eWc+y2jZCVFSSCSZMSFKFERclApJ2KtQ4xQNGlozn2mZmRxyOJpWQg0s7ESwKaSqJ9UzIQaUfiXUWs6wZEyUCkHYg1n5BqAlKXkoFImou36IwSgdSlZCCSpjS7qDSFrjMQSUNKBNJUkdYMzKwb8BAwEHDg74H3gceBHGAN8B13/2uUcYmki3gjhZQE5FCirhn8J/Ccu58C5APvASXAi+5+EvBi+FxEmuhgQ0aVCORQIqsZmFkXYCQwFcDdvwK+MrOLgFHhbnOBJcBtUcUlkg7Kyw9MBFp0RpoiymaiE4ANwMNmlg+8AfwTcKy7rwVw97Vm1jvWwWZWDBQDZGVlRROxSBLTOsTSmqJsJuoIDAZ+7u6FwN9oQpOQu5e5e5G7F/Xq1autYhRJCStWrIjbQaxEIM0RZc2gGqh29z+Fz+cTJIN1ZtYnrBX0AdZHGJNISol3BbFqA9JSkdUM3P0vwKdm1j8sGgOsBBYAU8KyKcDTUcUkkkpylAikDUV90dkNQIWZHQ58DFxJkJDmmdlVQBVwacQxiSS9WFcRA1qGUlpNpMnA3d8EimJsGhNlHCJJq6IiWIe4qoqv/u7v+Och36tdY6AuJQFpbZqOQiRZVFRAcXHtgvSHr13Lvc/NBqiXEJQIpC00q8/AzI449F4i0iSlpbWJoEbmnl3curQcCPoGlAikrTS3ZvB/zOwxd3++VaMRacf2VVbF/O/suK0blQSkzTWrZuDuVwFZZjbLzHq2ckwi7U5OyUI+7xL7o9QhWxdZSttrbjPRuUA/4ETgITP7dqtGJdJOjC1ZWDtSaMbIyezo2KAFNjMzWJBepI01t5moD/BLd/8YwMz+C3iy1aISSXOxJpWr6SSe9fZ8qKqCrKwgEUyaFH2A0u4cMhmY2dnA2+6+oabM3ec02K20leMSSVsHXZD+mZnAzIgjEmlczeAFYL2Z7QPeAd4C3g7vV7r7Lnff2oYxiqSNeBePab0BSbTGJIMbCRahmQe8CvQHhhBMRX0q8HdtFZxIuoiXBDTNtCSLQyYDd59tZr8gSAo/BWYB/+Tu3saxiaSFWzSVhKSARo0mcved7v7vBIvQfB143cyGtWVgIqmuZqRQw/4BXTwmyagxHcgjCJqDTgnvewPbgB5tG5pI6orVLKTZRSWZNabP4CVgBfAoMMvd17RpRCIpLNZ6A+oXkFTQmGRwLTAIGA/cYmabCEYTvQ284+5PtV14IqkhXgcxKBFIamhMMjjZ3afVPDGzvgTJIY+g5vtU24QmkhoOlgjUNyCpojHJ4Oy6T9y9mmAJy9+1SUQiKSLeEpRqFpJUpPUMRJpBK49JumlMMsg3s08I+wjq3K9y991tGZxIMop13YBGCkmqa0wyeIug83ggQT/BOOAW4CQz+9TdB7ZhfCJJId58QqDagKSHRjUTufvnwOfAopoyMzOC5lGRtBavSUi1AUknjUkGv4xVGE5Hsbp1wxFJHuoglvakMdNR3GNm75vZE2b2AzO7yMyOafPIRKJUUQE5OdChA+Tk8MgF/6xEIO1KYyaq625m/QiuLRgEXAH83MxeAq519y1tHKNI26qogOLi/YvRV1Yy4bPZvM7+BWc0xbSku8ZOVPeJuz/t7j9090uBrwHvEsxiKpLaSkv3J4JQ5p5d3Lq0HAj6BpQIJN016zqDsL/gR2b2XivHIxI5r6zCYpQft3WjRgpJu3HImoGZ3WxmY82sd4PyI4BObRaZSARuKVnIZ116xtzWITsr4mhEEqcxNYO/A8YCeWZ2GMF1Bx8BQyHu0GuRpFb3uoHdIydz73Ozydyza/8OmZnBYvQi7URjOpBvrXlsZt0ILjzrD/zW3Z9vu9BE2kbD6wZqOon/bVk5R2/eCFlZQSKYNCkR4YkkRJP6DNz9C+Dl8CaSUsrLF3LHyvpl+y8cGw/MTEBUIslBE9VJ2jvYVBK6glgk0KihpSKpKl4i0DrEIvVFWjMwszUE6yfvBfa4e1F4NfPjQA6wBviOu/81yrgk/cRqEqqhOYVEDpSIZqLR7r6xzvMS4EV3v9fMSsLntyUgLkkTB6sNKAmIxJYMfQYXAaPCx3OBJSgZSDPFm2FU00mIHFzUycCBRWbmwP919zLgWHdfC+Duaxte3CbSWFp0RqT5ok4GZ7r75+EX/vNmtqqxB5pZMVAMkJWlK0MloEVnRFpHpKOJwkVycPf1wJPAacA6M+sDEN6vj3NsmbsXuXtRr169ogpZkphGCom0nshqBmZ2FNDB3beFj8cB9wALgCnAveH901HFJKkp3kgh9QuINF+UzUTHAk8Gq2XSEfi1uz9nZn8G5pnZVUAVcGmEMUmKUQexSNuILBm4+8dAfozyTcCYqOKQ1HSw6wbUJCTScskwtFQkLnUQi0RD01FI0iovVwexSFSUDCRpxWoW0nUDIm1DzUSSWBUVwRrEVVWQlcWNeZfUri/wdeDDcDd1EIu0LSUDSZyKCigu3r8YfWUl9342GwgWnPkQNQeJREXNRJI4paX7E0Eoc88ubl1aDgS1ARGJhmoGkjD7Kqti/jdy3NaNqhGIREw1A0mIsSUL+bxLz5jbOmRr7imRqCkZSKRWrFhBTslCPgRmjJzMjo5H1N8hMzNYjF5EIqVmIolMw6kkakYN/duyco7evBGysoJEMGlSIsITadeUDCQSsdYaAJj1zExgZrTBiMgBlAykTcWbU0gdxCLJRclA2oTWIRZJLUoG0upizSmkK4hFkpuSgbSaeGsNfB0lApFkp6Gl0irGHmTRmRfULCSS9FQzkBaJ10GsvgGR1KJkIM02Nrx4rK6vo5qASCpSM5E0y4oVKw5IBGoSEkldqhlIo61YsYKLHq0+oFy1AZHUp2QgjRKrSQjg6cv7kp+fH3k8ItK6lAzkoOLVBtRBLJJelAwkrnhXEesCMpH0o2QgB4hXG1ASEElfSgZST7yriNU3IJLelAzaq4qKYA3iqirIymLpeZcwudvomLtqhlGR9Kdk0B5VVEBx8f7F6CsrKfrFbC48b/+CM6AkINKe6KKz9qi0dH8iCGXu2cWtS8uBYKSQEoFI+6Jk0B5VVcUsPm7rRr6OhoyKtEdKBu3M2JKFVHfuGXNbh+wsXUks0k4pGbQTt5QsJCe8injGyMns6HhE/R0yM4PF6EWkXVIyaAcaXjy2IHc0JedNY3OXXmAG2dlQVgaTJiUsRhFJrMhHE5lZBrAc+MzdzzezY4DHgRxgDfAdd/9r1HGlo3hrDdwzACbfOxOYGXlMIpKcElEz+CfgvTrPS4AX3f0k4MXwubTQ2JKDJAJdRSwiDURaMzCzvsB4YDpwc1h8ETAqfDwXWALcFmVc6eaWODOMarioiMQTdTPRT4Fbgc51yo5197UA7r7WzHrHOtDMioFigKysrDYOMzXFahbSWgMi0hiRNROZ2fnAend/oznHu3uZuxe5e1GvXr1aObrUlxOjWWgiSgQi0jhR1gzOBC40s28BnYAuZvYIsM7M+oS1gj7A+ghjSmmaYlpEWktkNQN3v93d+7p7DvBd4L/d/QpgATAl3G0K8HRUMaWynDiJAJQIRKTpkuE6g3uBc8xsNXBO+FwOIt4006BOYhFpnoTMWuruSwhGDeHum4AxiYgj1cRLAmoWEpGW0hTWKSJeIlBNQERag5JBCoiVCJQERKQ1JUOfgRzELUoEIhIB1QySkDqIRSRqqhkkkfLyhXETgVYfE5G2pJpBkoiXBDSdhIhEQckgahUVwRrEVVWQlQXTp5PzdrcDdpuIlp8UkegoGUSpogKKi/cvRl9ZyY6pV3HhedNYkDu6djc1B4lI1NRnEKXS0v2JIJS5Zxe3Li2vfa5EICKJoGQQpaqqmMXHbd0IKBGISOIoGUTklpKFVHfuGXNbh+wsJQIRSSglgwjUzDA6Y+RkdnQ8ov7GzEyYPj0hcYmI1FAyaGN1h4wuyB3Nb8+bBtnZYBbcl5XBpEkJjFBERKOJ2ky86waueGYmMDPaYEREDkHJoJVpKgkRSUVKBq1ESUBEUpn6DFqBEoGIpDrVDFpIaw2ISDpQzaAFtNaAiKQL1QyaaGzJQj6Ms02JQERSlZJBI90SXjgWi2YYFZFUp2TQCPFqA0oCIpIulAwO4WArjykRiEi6UDKIo7x8IXesrF+mVcdEJF0pGTQQKwnUUCIQkXSloaV1xEsEWoxeRNKdagZopJCISLtPBhopJCLS3pJBRUWwDnFVFdWdezJj5GQ+rLMQPag5SETap/aTDCoqoLi4dkH6vls3cO9zs4Fg0RmAewYkLDoRkYRqP8mgtLQ2EdTI3LOLW5eWszJ3tEYKiUi71n5GE1VVxSz+2taNSgQi0u5FlgzMrJOZvW5mK8zsXTO7Oyw/xsyeN7PV4X33NgkgKyt2XNmxy0VE2pMoawa7gLPdPR8oAM4zs+FACfCiu58EvBg+b33Tp0NmZv2yzMygXESknYssGXhge/j0sPDmwEXA3LB8LnBxmwQwaRKUlUF2NpgF92VlQbmISDtn7h7dycwygDcIpvn5mbvfZmZfuHu3Ovv81d0PaCoys2KgGCArK2tIZWVlRFGLiKQHM3vD3YtibYu0A9nd97p7AdAXOM3MBjbh2DJ3L3L3ol69erVZjCIi7VFCRhO5+xfAEuA8YJ2Z9QEI79cnIiYRkfYsytFEvcysW/j4SGAssApYAEwJd5sCPB1VTCIiEojyorM+wNyw36ADMM/dnzWzZcA8M7sKqAIujTAmEREhwmTg7m8BhTHKNwFjoopDREQOFOlootZiZhuAqIcT9QQ2RnzOplB8zZfMsYHia4lkjg2ijy/b3WOOwEnJZJAIZrY83pCsZKD4mi+ZYwPF1xLJHBskV3ztZ24iERGJS8lARESUDJqgLNEBHILia75kjg0UX0skc2yQRPGpz0BERFQzEBERJQMREQFw93ZxAzKA/wc8Gz4/BngeWB3ed6+z7+3Ah8D7wLl1yi8D3gLeBWY0eP3vACvDbb+uUz4lPMdqYErUsQFZwOLw9d8CvtWU2JoSH9AjPNd2YHaD1xgCvB3GPov9TZRHAI+H5X8CcpIsvpvD3+tbBOttZLfF77Y5sdXZfgnBdPBFyfTetfRzEcHvtkWfjVaKbTrwKbC9QXmLPxdNvSX8SzqqG8GH+td1fnEzgJLwcQnw7+HjAcCK8JfRD/go/KX3IJguo1e431xgTPj4pPCPouaX37vOH8fH4X338HH3iGMrA66rc/yapsTWxPiOAr4BXBvjj/514HTAgN8D3wzLrwceCB9/F3g8yeIbDWSGj69ranxtGVu4rTOwFHiNMBkk0XvXos9FBPG16LPRSrENJ5iqp2EyaPHnoqm3dtFMZGZ9gfHAQ3WKLyL2ojoXAY+5+y53/4QgM58GnAB84O4bwv1eACaGj68mWJ/hrwDuXjPz6rnA8+6+Odz2PMFMrVHG5kCX8HFX4PPGxtbU+Nz9b+7+CvBlg9foA3Rx92Ue/EWXN/iZal5rPjDGzCxZ4nP3xe6+I9z1NYLp12lMfBG8dwA/JPgSqntcUrx3tOBzEVF8zf5stEZs4bbX3H1tw3Ja+LlojnaRDICfArcC++qUHVvzSwjve4flXyOottWoDss+BE4xsxwz60jwiz4+3Odk4GQz+6OZvWZm5x3itaKM7S7gCjOrBn4H3NCE2JoaXzxfC18/1rlq43D3PcAWgppOssRX11UE/1nWi/sgx7RpbGZWCBzv7s/GOCYZ3ruWfC6iiO8umv/ZaI3YDhV3Sz4XTZb2ycDMzgfWu/sbjT0kRpmHWfg6gna8l4E1wJ5we0eCKvEo4HLgoXC67pivFXFslwNz3L0v8C3gV2bW4VCxNTO+JsV9iG3JEl/Nua4AioD7GnNMW8cW/g5/AtzS2GPq7RDNe9esz0WE8TXrs9GKsR1Msz8XzZX2yQA4E7jQzNYAjwFnm9kjxF9Up5r9/1VD0CzwOYC7P+Puw9z9dIIO3NV1jnna3XeHzTfvE3wI4r5WhLFdBcwL91kGdCKYHOtQsTUnvniq2d+80vBctXGEtZquwOYkig8zGwuUAhe6+66GcceJr61j6wwMBJaE5xgOLDCzokbEFkV8Ndua87mIKr7mfjZaK7ZDxd3cz0XztEbHQ6rcCP5DqensuY/6nT0zwse51O+k/RjI8PodYN2BN4GTw+fnAXPDxz0JqnE9CDp5Pgn37x4+Pibi2H4PTA0fn0rwh2NNia2x8dXZdyoHdpT9meALq6YT71th+T9Sv6Nsnu/vKEuG+AoJOupParB/q/5umxNbg32WUL8DORneuxZ/Lto4vhZ/NloaW51tDTuQW+Vz0ZRbwr+go7w1+MX1IBgquDq8P6bOfqUEXwDvU3/kxqMEw+RWAt+tU27A/WH52w22/T1Bm/6HwJUJiG0A8EeCJPImMK6psTUxvjUE/8FsJ/gvZkBYXgS8E8Y+m/3D+zoBT4QxvA6ckGTxvQCsC9+7N4EFbfi7bVJsDc6xhPpDS5PhvWvx56KN42vxZ6MVYpsRPt8X3t/Vmp+Lptw0HYWIiLSLPgMRETkEJQMREVEyEBERJQMREUHJQEREUDIQERGUDEQOyczuM7N/rvPczOwLM/snM3szvO2r8/h+M7vGzNzMzqpz3LSwbGxifhKR+JQMRA5tIMF89zX6ARvc/T/dvYBg9spP3b0gvN0MDAqPORXAzDIJpj/YQHABlkhSUTIQObQ86ieDQQ2eD+TAL/g8gqvCTwmf30hwRek+d1/XRnGKNJuSgchBmFl34HB3/0ud4obJII9guoO6TiWYBO0UM+tKsBLdqzH2E0kKSgYiB9ewVgCHqBmY2fHAJnf/mGBO+1uB/yKY37/ha4kkBSUDkYPrTzDBGQDhfPdnEqwbUaNhzWAQ+5PDNsLZO8P93g5fp5eZPWxmfc3sl2Z2WNv9CCKHpmQgcnCVwFAz6xQ+/9/AUnffCLXJ4SRgVZ1jar/0CaY1nubue6lTy/BgidIq4D+AG919d1v/ICIH0zHRAYgkM3dfZGbnAqvMbBfBOsjX1tnl60C171/0BoIv/d+Ex9ddknIAwXTOmNnRBGtX73H37W34I4g0iqawFolYuHJVGXA38B3gz+6+JKFBSbunZCAiIuozEBERJQMREUHJQEREUDIQERGUDEREBCUDERFByUBERFAyEBERlAxERAT4/6UkLK4ah0GNAAAAAElFTkSuQmCC\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# 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=10\n", "utm_x_ideal_downsampeled = np.linspace(start_utmx, end_utmx, N)\n", "utm_y_ideal_downsampeled = (a*utm_x_ideal_downsampeled) + b\n", "\n", "\n", "\n", "# Plot the Idealized Transect for comparison\n", "plt.plot(\n", " ADCP_ideal_points.utm_x, \n", " ADCP_ideal_points.utm_y, \n", " '.', ms=1, label='Ideal Transect'\n", " )\n", "\n", "# Plot the downsampled transect\n", "plt.plot(\n", " utm_x_ideal_downsampeled, \n", " utm_y_ideal_downsampeled, \n", " 'ro', label='Down Sampled Ideal Transect')\n", "\n", "\n", "# Plot settings\n", "plt.xlabel('$UTM_x$')\n", "plt.ylabel('$UTM_y$')\n", "plt.legend()" ] }, { "cell_type": "markdown", "id": "466a1c01-d12c-4ccd-95e5-5c68d8fe448c", "metadata": {}, "source": [ "With our down-sampled x,y points we can now create a down-sampled z at our x,y locations. As shown in the figure red dots below the river bottom will be excluded from the interpolation. " ] }, { "cell_type": "code", "execution_count": 14, "id": "fd9d270d-0b41-4fff-9b3e-e5626197b24a", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, '$ Depth [m]$')" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Create an idealized depth N layers deep\n", "N_layers=12\n", "downsampled_depth = np.linspace(\n", " transect_1_2.range.min(), \n", " np.nanmax(bottom_avg), \n", " N_layers\n", " )\n", "\n", "# Repeat this over the N points of the DownSampled Ideal Transect above \n", "depth_ideal_points_downsampled = np.repeat(\n", " downsampled_depth,\n", " N\n", " )\n", "\n", "# Tile the x, y over the N of layers to add to a DataFrame\n", "utm_x_ideal_points_downsampled= np.tile(\n", " utm_x_ideal_downsampeled, \n", " N_layers\n", " )\n", "utm_y_ideal_points_downsampled= np.tile(\n", " utm_y_ideal_downsampeled, \n", " N_layers\n", " )\n", "\n", "# Create a Dataframe of our idealized x,y,depth points\n", "ADCP_ideal_points_downsamples=pd.DataFrame({\n", " 'utm_x': utm_x_ideal_points_downsampled, \n", " 'utm_y': utm_y_ideal_points_downsampled,\n", " 'waterdepth': depth_ideal_points_downsampled\n", " })\n", "\n", "# Plot the Down sampled data points at the x locations\n", "plt.plot(ADCP_ideal_points_downsamples.utm_x, \n", " ADCP_ideal_points_downsamples.waterdepth * -1, \n", " 'ro', \n", " )\n", "\n", "# Plot the ADCP river bed\n", "plt.plot(ideal_points.utm_x,-bottom_avg,'k', label= 'river bottom')\n", "\n", "# Plot settings\n", "plt.title('DownSampled Ideal Transect Depth')\n", "plt.xlabel('$UTM_x [m]$')\n", "plt.ylabel('$ Depth [m]$')" ] }, { "cell_type": "markdown", "id": "58d92d94-478f-4b48-a6b1-c691badbbac1", "metadata": {}, "source": [ "### ADCP: Interpolating the Downsampled Velocites for the Ideal Transect \n", "\n", "The above process of creating a `river_bottom_filter` and interpolating onto the ideal transect is repeated for the down-sampled grid shown in the figure above. The depth sounder data is saved above in the variable `'bottom'` and used again here. " ] }, { "cell_type": "code", "execution_count": 15, "id": "c854c7d9-9efe-4540-a533-f7d7ab39486c", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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east_velocitynorth_velocityvertical_velocitymagnitude
0NaNNaNNaNNaN
1-0.3006361.4215350.0427121.453605
2-0.7584311.832806-0.0167201.983601
3-0.9721102.340159-0.0435992.534412
4-1.0113642.046775-0.0344622.283273
...............
115NaNNaNNaNNaN
116NaNNaNNaNNaN
117NaNNaNNaNNaN
118NaNNaNNaNNaN
119NaNNaNNaNNaN
\n", "

120 rows × 4 columns

\n", "
" ], "text/plain": [ " east_velocity north_velocity vertical_velocity magnitude\n", "0 NaN NaN NaN NaN\n", "1 -0.300636 1.421535 0.042712 1.453605\n", "2 -0.758431 1.832806 -0.016720 1.983601\n", "3 -0.972110 2.340159 -0.043599 2.534412\n", "4 -1.011364 2.046775 -0.034462 2.283273\n", ".. ... ... ... ...\n", "115 NaN NaN NaN NaN\n", "116 NaN NaN NaN NaN\n", "117 NaN NaN NaN NaN\n", "118 NaN NaN NaN NaN\n", "119 NaN NaN NaN NaN\n", "\n", "[120 rows x 4 columns]" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 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(ADCP_ideal_downsamples.east_velocity**2+ADCP_ideal_downsamples.north_velocity**2+ADCP_ideal_downsamples.vertical_velocity**2)\n", "ADCP_ideal_downsamples" ] }, { "cell_type": "markdown", "id": "5f47039f-7457-48f3-8688-517c645cd64b", "metadata": {}, "source": [ "### ADCP: Plot Down-Sampled Ideal Transect \n", "\n", "To plot the contour of the ideal sample points we need to first create a mask to remove data outside of the river bottom because the ideal transect grid (shown as red dots in the figure above) includes points below the river bottom. " ] }, { "cell_type": "code", "execution_count": 16, "id": "69b668e7-8a41-40bb-841e-de8fc62e7b01", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[0, 1, 2]\n" ] } ], "source": [ "# Create a DataFrame of downsampled points\n", "ideal_downsampeled= [ [x, y] for x, y in zip(utm_x_ideal_downsampeled, utm_y_ideal_downsampeled)] \n", "ideal_points_downsampled = pd.DataFrame(np.array(ideal_downsampeled), columns= ['utm_x','utm_y'])\n", "\n", "# River bottom for downsampled ideal transect \n", "bottom_avg_downsampled= interp.griddata(gps_points, bottom, ideal_points_downsampled, method='linear')\n", "\n", "# Create a matrix of depths\n", "bottom_filter_downsampled = d3d.create_points(x=bottom_avg_downsampled, y=downsampled_depth, 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_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)" ] }, { "cell_type": "markdown", "id": "a54bddc5-ccc8-4057-8462-f71276121e7e", "metadata": {}, "source": [ "Using our down-sampled velocity magnitude a contour plot is created. For comparison the river bottom from the original ideal transect is shown. " ] }, { "cell_type": "code", "execution_count": 17, "id": "e90c6c52-3af2-4a06-ba5f-ff364256c2c5", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Plotting \n", "plt.figure(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=max_plot\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([-8.5,-1])\n", "plt.xlim([400950,401090])\n", "plt.legend(loc= 7)" ] }, { "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": 18, "id": "2727bb7b-9994-450f-8cf7-43bdf0934b2e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Data request URL: https://waterservices.usgs.gov/nwis/iv/?format=json&sites=15515500&startDT=2010-08-10&endDT=2010-08-10¶meterCd=00065&siteStatus=all\n", "Data request URL: https://waterservices.usgs.gov/nwis/iv/?format=json&sites=15515500&startDT=2010-08-10&endDT=2010-08-10¶meterCd=00060&siteStatus=all\n" ] }, { "data": { "text/plain": [ "Text(0, 0.5, 'Dischage ($f^3/s$)')" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "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='2010-08-10',\n", " end_date='2010-08-10',\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='2010-08-10',\n", " end_date='2010-08-10',\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": "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": 19, "id": "745835ce-0224-4121-9d1b-a135c8ee3e0c", "metadata": {}, "outputs": [], "source": [ "# Import the simulated data\n", "d3d_data = netCDF4.Dataset('data/river/ADCP_transect/tanana81010_final_map.nc')\n", "\n", "# Get the ADCP sample points\n", "ADCP_ideal_points_downsamples_xy = ADCP_ideal_points_downsamples.rename(columns={\"utm_x\": \"x\", \"utm_y\": \"y\"})" ] }, { "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": 20, "id": "64e7f82b-3632-41f4-a5be-9421a817b3d4", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "points provided\n" ] } ], "source": [ "# Interpolate the Delft3D simulated data onto the the sample points\n", "variables= ['ucy', 'ucx', 'ucz']\n", "D3D= d3d.variable_interpolation(d3d_data, variables, points= ADCP_ideal_points_downsamples_xy)\n", "\n", "# Calculate the magnitude of the velocity\n", "D3D['magnitude'] = np.sqrt(D3D.ucy**2 + D3D.ucx**2 + D3D.ucz**2)" ] }, { "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": 21, "id": "69a3a77d-2a2a-4944-8923-05159d52ad8d", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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LlFjIcZ9Vf4xbLOR4r7S0lMaNGwPQqFEj1q9fT15e8L7Y68JGZM6OEaraTVV7AK8AN3nUjtCzTsm5YdUfUxtW1fGP0tJS6tevDziBZ8OGDZELPAkxJO0laDz5H1XVX1Pu7kBoR9TwjgUd70QmAFnn51qxoOM/S5YsSQYegA0bNiARGrcrVdiv3vKsI7OI3AGcDfwCHOFVO8LEQo7/RO70V9jfZx1YyPGf4uJiDjzwQD799FMAdt11V7777rvIBp6EIPbVSVfW3pmIvCkiCypZBgCo6g2q2hZ4Gri0mu0MEZFZIjJr9erV2WpuoFlVx/+s+hNNVtXxr6uuuorCwsJk4HnyySdZuXIl9epF/KLmDKo8VulJoap90lz1GeBV4OYqtjMOGAdQVFRkn6RxFnKCK3LVH4hcBchCjn9NmDCBU089NXn/jDPO4Kmnnop8dSdBIZB9ddLl1dVbHVT1q/jdE4BFXrQjiCzshEdq1ScyASjE79OCjj9t3bqVmTNnct999zFhwoTk461atWLx4sWhmijULUGs4KTLqzre30SkE84l68uAoR61IxAs6ISfVX+Cy8KO/6xbt46TTz6Zd955p9LnFy5cSOfOnXPcqmBIdGQOK09Cj6qe7MV+g8SCTjRZ9ScYLOj415///Gfuvvvuco/95je/4ZJLLuH888+3yk4aLPSYnLGwYxKs+uM/Fnb8a926dTRr1ix5/9JLL2XUqFHbTSdhqhf2wQkt9PiABR1THav+eMuCjv8tWrSILl26JO//9NNP7Lzzzh62KNjC3JE5vBfjB4Bdam4yFYnL3sEXl77bpebBsGDBgmTgOfvss1FVCzx1oTY4oXGRhRzjhkhWfyDrFSALOcEyd+5cevbsCcCNN97Ibbfd5nGLgk+B0lh46yHhfWc+Y1Udky2Rqf5A1qo/VtUJnvvuuy8ZeIYPH26BxyWJPj1W6TEZs5BjcsmqP5mxkBNMv/zyCzvttFPy/oMPPsjQoTbqiZs0gGEmXVbpcZnNam78wKo/VbOqTnCNHj26XOBZtmyZBZ4ssFnWTY0s6Bg/isycX1Bt52ebAyvY1q5di4gwbNgwAIYNG4aq0q5dO28bFkLqckdmEWkrIu+IyEIR+VxErqhkHRGR+0RkiYjMF5FeWXlz2OmtOrGQY4IkMuP+gBN8iu0UVhiMGDGCa665Jnl/+fLltG7d2sMWhZ/Lp7dKgatUdY6INAFmi8gbqvpFyjr9gA7xZX/gwfi/rrPQUwtLz76WPZ+50+tmZEV+/ZjXTcgpTSkKpEZYLQt3EVTzw1/50XqK2t8lgTV9+nQOO+yw5P0bbriB22+/3cMWRYW7HZRVdSWwMn57vYgsBFoDqaFnAPCEqirwkYjsJCKt4q91lYUeE+mgUxXJ33ZMwh6AwizWwPnPziu2ik8QbNmyhT//+c+MHj263OMrVqygVatWHrUqerLVkVlE2gM9gY8rPNUa+C7l/vL4Y7kNPSJSCPQHDgV2AzYDC4BXVfVztxtjcsvCTnoSAcjCT3Alwg9YAPKjzZs3c/LJJzN58uRyj7///vscdNBBHrUqmmox4WgLEZmVcn+cqo6ruJKINAaeB4ap6q8Vn66iKa6rMvSIyC3A8cC7OKlsFVAIdMSZJb0Q5zzd/Gw0zGSHBZ3as+pPOFgA8o+ysjLOOOMMxo8fn3zs97//PU888YRNDOoVzfhzc42qFlW3gojUxwk8T6vqC5Wsshxom3K/DbAio1akqbpKzyeqeksVz40UkV0A6zofEFEKO24GnapYAAoHO/3lnfnz59O9e/fk/TPOOIPHH3+cevWs14XX3LwUXUQE+DewUFVHVrHaS8ClIvIsTgfmX7LRnweqCT2q+mp1L1TVVTjVH+NTUQo6kJuwUxk7/RV8Vv3JHVXlhBNO4JVXXgGgZcuWfPXVVzRt2tTjlhlwzim53KfnYOAs4DMRmRt/7HriRRNVHQu8BhwHLAE2Aee62YBUNUZqESkCbgB2j68vTju1W7YaZeomSmHHq6BTGav+hINVf7Ln/fff55BDDkneHz9+PAMHDvSwRWZ7rl+9NYPK++ykrqPAJa7ttBrp1BGfBq4GPgOi820aMBZ0/MWqP8Fn1R/3lJSUsM8++7B48WIA2rdvz+LFiykoKPC4ZaYyQfiMra10Qs9qVX0p6y0xGYtS0IFg/iJa9SccLADV3sSJE8tVc9555x0OP/xw7xpkqqUKsRDPsp5O6LlZRB4G3gKKEw9W0QPb5ECUwk4Qg05VLACFg53+Ss/69evZeeedKStzRog8+uijef3113H6tRo/8+Ps6SJyZRqrbVTVf1W3QjqfvOcCPYBjcS5hPx5n7B6TQ/n1Y8kl7FS3LWEl+bFyIcgET6yBJhdT3oMPPsiOO+6YDDwLFixg6tSpFngCIvUzuKYlh64GGgNNqlmuqmkj6VR6uqvqPrVvp6mLKIQcCHfAqY5Vf8LBTn85fvrpJ1q2bEks5vxcX3TRRTzwwAMWdgImWyMy19GTqvrX6lYQkR1q2kg6n7IfiUjXtJtl6iyKVR2zrfpjFaBgi1r1Z8OGDTzxxBO0aNGC5s2bJwPPkiVLGDNmjAWegFEE1fSXnLVL9Ro31kkn9BwCzBWRxfEp3z8TERuFOQuiFnQs7FTNwk/wReH016hRo2jSpAnnnHMOa9euBeCCCy6gtLSUvfbay+PWmdrSDJZcE5ErRGRHcfxbROaIyDHpvj6d01vH1qF91RKRPwEjgJaquiZb+/GzKIQcsIBTW3b6KxzCePrrrrvu4vrrrwega9euPPTQQxx44IFW2Qk69e3prYTzVHW0iPQFWuL0O34UmJrOi6ube6uxqm5Q1WU1rZNpi+OvbQscDXxbm9cHnYUdkykLQOEQhqu//vvf/yYDz7x58+jWzcaqDRV/f24nfnGOAx5V1XmSQdKu7pPzRRH5h4j8X2rnIBHZU0TOF5HXqVsV6F7gGvx+eF0Ulb46dvoq++z0V/AF9fTXp59+ykknnQTA1KlTLfCEkB/79KSYLSJTcULP6yLShAwGTq5u7q2jROQ44ELgYBHZGSgFFgOvAueo6g+1abGInAB8H09otdlEYIQ94CRYwPGGVX/CISjVn5UrV9KrVy8AxowZw9FHH+1xi0w2+PHzXETqqWopcD7OMDrfqOomEWlOBnN1VdunR1Vfw5kIrDYNfBPYtZKnbsCZbCytjkciMgQYAtCuXXAmdbewY3LNpr4IPj/3/Zk9ezZFRUWAcyn6RRdd5HGLTDZkYcJRt3wkIsuBKcAUVf0ZQFXXAmvT3Ug6HZlrRVX7VPa4iOwD7AEkqjxtgDkisl9llSNVHQeMAygqKvL1V6wFHeMHVv0JB78EoOeff55TTjklef93v/sdY8aM8aw9JssU8GHoUdUiEdkd6AeMEpHWwAxgMjBNVYur3UBc1kJPVVT1M2CXxH0RWQoUBfnqrSiEHQs6wWQBKBy8OP01bdq07ebIGjNmjFV4IsCvn/fxC6vGAmNFpD5wKE7f4ttFZLWq/q6mbeQ89IRFFIIO+PeH32QuefqrJN/jlpjaylX159FHH+W8884DQESYP38+e++9d9b2Z3wmAJ/7qloCvB1fiFd+apRW6BGRfOD/pa6vqq5caq6q7d3YTi51euGv5caikLwA/ITUkv+KnO5KTKxXsiU6+V9SA3uIqz/auAw2h/f9lTXa9rnTfsw9LL34T65s99///jd//OMfAZg0aRIDBgxwZbsmKASN+feTX0T6A7cB7YF8nK8pVdUd03l9jZ/0InIZcDPwI9suC1MgstcpLj7pJjpOvC15P/EDEubwEzYVZxGuX1hKWWkesdLwfklWKnH6K8ThJ+zcCjuqyoknnsiLL74IwBtvvEGfPpV2zTRh5v/BCUcBJwGfqWZ+LiKdP2+vADrFe0ibaqSmYwtA/lMx6FQmr962KkikAlB+NKo/YeFW0AH4/vvvueGGG3j88ceTj3388cfst99+ru3DBIy/v76+AxbUJvBAeqHnO+CX2mw8yqz64w/pBJ2qJAJQpMIPWADyMTerOo8//jjnnlt+eJPWrVvz9ddf06BBA1f2Y4LK15Wea4DXRGQakLxiS1VHpvPi6qahuDJ+8xvgXRF5tTY7iDqr/nijLmGnoshWf8BOf/mAm1WdhH79+vH6668n7//lL3/h2muvZYcddqjmVSYy/P1VdQewASgECjJ9cXWVnibxf7+NLwUpO/D3IfEpq/5kl5tBpypW/cECUI5kI+wAPPLII8nAY/NmmUr5+yuqmaqmPat6RdVNQ3ErgIgMVNUJqc+JyMDa7tBY9cdtuQg7FVn1Bws/WZCtoJOwZcsWzj//fAC+/PJLOnTokNX9mQDy6eCEKd4UkWNUNa1Z1StKp0/PdcCENB4ztWABqHa8CDpViWwAsuqPa7IddhJatWoFwMCBAy3wmCr5fHy2S4BrRKQYKMGtS9ZFpB/OLKatReS+lKd2xJl41LjMTn/VzE9hpzKRP/1l4SdtuQo6CX/5y1/4+eefAXjuuedyum8TMD7+ClLVJjWvVbXqKj0rgFnACcDslMfXA8PrstPa+uWXaFxEZtWf8vwedCpj1R8sAFUiV0Hnl19+4ddff+Wnn35i+vTpXH755cnn5s+fX25wVWO248PPXBHZtbL5OTNdp7o+PfNwJgV9Bqd81Bkn/y1W1a21aHOdrVixwovdeiqq1Z8gBp2qRL76A5EPQLkKO7FYjO7du7NgwYJKn//ss89sOglTI/Hn181rQK+6rpNOn56jgX8BX+OEnz1E5EJVnZxOK920adOmXO/SN6JS/QlT2KkostUfiOTpr1yfvlq7di0tWrQo91jr1q055phj6N+/P8ceeyyNGjWq9LUlJSUsX76cLVu25KKpphKFhYW0adOG+vXre9sQxa+nt7qLyK/VPC9Adc8D6YWekcARqroEQET2Al7Fmc4958rKysjPj/aEiWGr/oQ56FTFqj+ENgDlOuwAPPbYY+UGGty4cWOVAacyy5cvp0mTJrRv395OfXlAVVm7di3Lly9njz328Lg14svTW6rqyhd/OqFnVSLwxH0DrHJj57Uxc+ZMDjzwQK927ytBr/5EMexUZNUfQhF+vAg6AC+99FK5CUEbN27MqlWraNiwYUbb2bJliwUeD4kIzZs3Z/Xq1V43xRG8r5O0pRN6PheR14DxOIdiIPCJiJwEoKovZLF927n//vst9FQiKNUfCzpVi2wACnD1x6uwE4vF6NGjB5999hkAzZs35/XXX6dXr161Di4WeLzlq+Pv76+ROknnE6YQZ4b1w4DDgdVAM+B4oH/WWlaFZ555Jte7DBSNSXLxk5iKBZ4M5NWLlQtBkZEfKx+CfGjpxX9KLl74/PPPyc/PTwae2bNns2bNGnr37u2vL846Ou6445KX2Lupffv2rFmzJu31J02axBdffJG8/9hjj4X7ohoFYpL+EjA1VnpU9dya1sk1VQ3VL3e2eF39sZBTd1b9wRfVH68CTkWXXXYZ999/PwB77bUXixcvDl0fR1VFVXnttddc21ZeXu1/hiZNmkT//v3p2rUr4ISevffem912263O7fMrN6/eEpFHcAokq1R1u0sHReRw4EXgf/GHXlDVv1azvXuAR1X189q0p8afBBHpKCJviciC+P1uInJjbXbmli+//NLL3QdOLqs/iYqOBR73Rb7640EFyMuKTrl2LF2KiCQDzzPPPMOSJUtCE3iWLl1Kly5duPjii+nVqxffffddsiJz7bXXMmbMmOS6t9xyC//4xz8AGDFiBPvuuy/dunXj5ptvrnJbFY0YMYL99tuP/fbbjyVLnC6ry5Yt46ijjqJbt24cddRRfPvtt3zwwQe89NJLXH311fTo0YO7776bWbNmceaZZ9KjRw82b97MW2+9Rc+ePdlnn30477zzKC525uVu3749119/PQceeCBFRUXMmTOHvn37stdeezF27NhsH9K60QyWmj0GHFvDOu+pao/4UmXgiVsEjBORj0VkqIg0TasVcen06XkIuBrnsnVUdX587J7bM9mRG3baaSd+/vlnpk6dSqdOnXK9+1DIVvXHQk7uRLb6Aznp/OyHkDNnzhxeffVVpkyZwgcffFDuuZ9//pmmTTP6nM/IsGHDmDt3rqvb7NGjB6NGjap2ncWLF/Poo4+WCzgAgwYNYtiwYVx88cUAjB8/nilTpjB16lS++uorZs6ciapywgknMH36dNq1a1flthJ23HFHZs6cyRNPPMGwYcN45ZVXuPTSSzn77LM555xzeOSRR7j88suZNGkSJ5xwAv379+eUU04BYPLkydxzzz0UFRWxZcsWBg8ezFtvvUXHjh05++yzefDBBxk2bBgAbdu25cMPP2T48OEMHjyY999/ny1btvDb3/6WoUOH1u2gBoSqTheR9i5u72HgYRHpBJwLzBeR94GHVPWdml6fzidHI1WdWeExT6ahaNasGQAPPPCAF7sPFTeqP1bV8Z5Vf9x7715XdYqLizn++OMREXr37s1NN91ULvA8/PDDqGpWA4+Xdt99dw444IDtHu/ZsyerVq1ixYoVzJs3j5133pl27doxdepUpk6dSs+ePenVqxeLFi3iq6++qnZbCaeffnry3w8//BCADz/8kDPOOAOAs846ixkzZtTY5sWLF7PHHnvQsWNHAM455xymT5+efP6EE04AYJ999mH//fenSZMmtGzZksLCwqz0V3KLaPqLSw4UkXkiMllEfltj+0TycQZM7gysAeYBV4rIszW9Np1Kz5r42Dwa39kpwMo0Xue6HXbYAXB+0Ix7Mq3+WMjxH6v+UKvqjx+qOlu3bmXAgAFMmTIl+VjLli0555xz6NOnD0ceeWROB6yrqSKTLYnP98qccsopTJw4kR9++IFBgwYBTn+d6667jgsvvLDcukuXLq12W1D+Sqmq+oem029Ua5iZs0GDBgDk5eUlbyful5b6eArLzD7jW4jIrJT741R1XAavnwPsrqobROQ4YBJQ5Wy4IjIS50Kqt4E7U4oyd4tIjeEgnU+JS3BObXUWke+BYYAndbmCgoLk7WXLlnnRhFCrrvpjVZ3gsOpPze/d66oOOF+Y559/Pg0aNEgGntNPP53i4mJWrVrFiBEj6Nu3r/cj9PrAoEGDePbZZ5k4cWLyNFPfvn155JFH2LBhAwDff/89q1alN4RcYsLV5557LjkEykEHHcSzzzqFgqeffppDDjkEgCZNmrB+/frka1Pvd+7cmaVLlyb7BT355JMcdthhdX273sqkP4+T+daoalHKkkngQVV/VdUN8duvAfVFpEU1L1kAdFfVCys5C7VfTftL5+qtb4A+IrIDkKeq62t6TTb16NGDuXPn8s477zB48GAvmxJqieBjGSe4rPoDqX/XeR1yUj300EMMGTIkef/000/nySefDE3HZLf99re/Zf369bRu3ZpWrVoBcMwxx7Bw4cJkaGncuDFPPfVUWsewuLiY/fffn1gsxn/+8x8A7rvvPs477zxGjBhBy5YtefTRRwEncF1wwQXcd999TJw4kcGDBzN06FAaNmzIhx9+yKOPPsrAgQMpLS1l3333DUdfnRxe8CsiuwI/qqqKyH44v7Rrq3nJmar6SIVtvKWqR6lqjbOSS3XluXhHoSE4580AFuKUrjy5fKqoqEh32203Xn75Zbp27crnn9fqirU66/xCTZ3Lg61+vbLk7ZLSfPJ9Pm5KXSR+/EtK0jnTGw6lxdH5Yv3fH67zugnlTJs2jcMPPzx5f++992bmzJkZj6DstoULF9KlSxdP22Aq/38QkdmqWpSrNjRo21bbDB+e9vrfXHVVte0Tkf/gjPHXAmfMv5uB+gCqOlZELgUuwukrvBm4UlU/qGQ7hUAj4J349hJ/ku8ITFbVtH6Aq/ykF5EDgRdwTm2Ni++gJ/CuiJykqh+ls4Mqtn0LcAHOQIcA18fLWjWKxZwv4NTBoow7UsNOQlgDTw2n4kMpSu/Zb2Fn0aJF232Zffvtt7Rt29ajFhlTDRc/K1T19Bqevx+4P41NXYjTvWY3nH5ACb8CaV/dVN2ftzcBp6vquymPTRKRt3GSWr90d1KFe1X1nkxf9PjjjydnEv7hhx/Ydddd69iMaKss6IRVlL70U0Xlffst6Pz444/cfffd3HvvveUe/+CDD2wqHeNvPvzMUNXRwGgRuUxV/1nb7VQXevaqEHgSO54mIhl1VHJT8+bNk7cnTpzIpZde6lVTAi0qYScqX/gVRel9exl2Zs2aRf/+/fnxxx9rXHfChAnJTrjG+JXLl6K7RkSOVNW3ge8Tc3+mSnce0OpCT3Udljems/EaXCoiZwOzgKtUdV26L9x9991ZtmwZl112mYWeDEQl6EC0vvRTReV9e13VWb58eVqnpi655BL++te/JscY8zOb3sdbNV3+nlP+vILlMJzL1I+v5DnF6Y5To+pCT1sRua+SxwVoXdOGReRNoLJzTzcADwK34TT0NuAfwHlVbGcITmdq2rVrBzgT7CVOcZmaRSXs+OkzI5ei9L69DjsA99xzD1dffXXy/oQJEzjppJPqNL+T1woLC1m7di3Nmze34OMBVWXt2rUUFhZ63RSHDz9TVPXm+L91mg+0utBzdTXPzarmOQBUtU86DRCRh4BXqtnOOJyO1BQVFSmUP8W1ZMkSfvOb36Szq0iJStCBaH3pp4rK+/ZD0AEoKyujZcuWrFvnFKXvuecerrrqKo9b5Y42bdqwfPlyVq9eXfPKJisKCwtp06aN180AQHx8/YqI3An8XVV/jt/fGedsUVpzglYZelT1cVdaWAkRaaWqiVGdT8QZbKhWunfvzsaNbpxtCz4LOuEXlfftl6CT8NVXXyWnGgBn1N/dd9/dwxa5q379+uyxxx5eN8P4gU/79KTop6rXJ+6o6rr4SM51Cz1Z9ncR6YFTRFuKcylaRrZu3UpBQQGbNm0iFosFurRcVxZ2wi1K79lvYQecUXsTUx90796dTz/91E4BmXDz92dOvog0UNViABFpCDSo4TVJnoQeVT2rrtuoX78+nTp1YvHixdxzzz1cc801bjQtMCzohF9U3rcfg07C8OHDk3NRjRo1iiuuuMLbBhmTC/7+7HkKeEtEHsVp6XlA2memqi2PiEi+iKQ/NGOOJearufbaa5ODFoZd/XplkQg8qtuWKInS+/7fH67zNPCsWbOGu+66iz333BMRSS6HH344t99+OyKSDDwffvihBR4TGR7Msp42Vf07cDvQBegK3BZ/LC3VVnpUtUxEBgD3VreeV9q3b5+8/eyzz3LGGWd415gsikLISYjCl31lovK+vQo5qsqbb77JPffcw9SpU6tdd9q0aUybNi15f+XKlTYIqjH+8inOVBYav522dDrCvC8i94vIoSLSK7HUppXZ8PrrrwNw5plnhq7aY1WdcIvS+851VWf9+vWMGDGCXXbZBREhLy+PY445ZrvAM2TIEObMmUMsFkNVKS4u5qmnnmLw4MGMGjWKsrIyCzwmejKbZT2nRORUYCZwCnAq8LGIpD3qZzp9eg6K/5s6y6YCR6a7k2w65phjqF+/PiUlJdx0003cfvvtXjepTqIQchKi8GVfmai8by+qOhMnTmTgwIGVPtejRw/+9Kc/ceqpp1K/fv1K1ykoKODMM8/kzDPPzGYzjfEv/1+9dQOwr6quAhCRlsCbwMR0Xlxj6FHVI+rUvBxYuXIlLVq04I477uDGG2/0zwBPGYhK2InKF35FUXnfXp6+OuKII8qdljr77LO5+uqr2XvvvT1pkzGB5e/Pq7xE4IlbS3pnrYA0Qo+I/D/gTmA3Ve0nIl2BA1X13xk3NUuaN2/OoEGDePbZZ9l333357LPPvG5SWqISdCA6X/qpovSeveyQvHHjRho3bpy8P2vWLHr37u1Ze4wJPH9/dk0RkdeB/8Tvnwa8lu6L00lHjwGv40znDvAlzvTuvvLkk08CsGDBAt5++22PW1M966sTblF5z4l+Ol4Gnrfffrtc4Nm8ebMFHmPqQPD91VtX48zS0A3oDoxT1WvTfX06oaeFqo4HYvEdlgK++8auV68eL7zgzDd21FFHsXnzZo9bVF4i6IQ97EQ96EThfXsddAC+/vprCgsLOeqoowA47bTTUNVAnto2xnd83JEZQFWfV9UrVXW4qv43k9em05F5o4g0J/72ROQA4JdatDPrTjzxRI4//nhefvllGjVq5ItZa8MechJ8cKg9EZX37XXISYjFYhxwwAF88sknycemT5/OoYce6mGrjAkRn3ZkFpH1VB6zBFBV3TGd7aQTeq4EXgL2EpH3gZZA5ZdH+MBLL72UHCK+T58+vPnmmzlvgwWdcIvS+/ZL2AGns3J+fn7y/kMPPcQf//hHD1tkTEj58DNOVZu4sZ10Qs/nwGFAJ5xEtZgMekp7YdOmTTRq1Ii33nqLkSNHcuWVV+ZkvxZ2wi0q79tPQSdBVcvNr/fTTz+x8847e9giY8LLz7OsA4jIIUAHVX1URFoATVT1f+m8Np3Q86Gq9sIJP4kdzgF8M0BhRQ0bNuTLL7+kY8eOXHXVVRxwwAEcdNBBNb+wFizohFtU3rcfg05CWVkZ9erVK3c/yhMMG5N1Pv7cE5GbgSKcQsyjQAHOfFwHp/P6KkOPiOwKtAYaikhPnCoPwI5Aozq0OSc6dOjAxIkTOeWUUzj44INZtWoVLVu2dG37FnbCK0rv2c9hB+Dnn38uV9GxwGNMlnnYQTlNJwI9gTkAqrpCRNI+9VVdpacvMBhoA4xMeXw9cH3GzfTAySefzKWXXsr999/PLrvsQmlpabk+AbWVnx8jppK8Xy8/vAEo9XCVlYX3yya/QQlbS+v+sxEES0690esmpGXChAmceuqpADRt2pR169Yl++sZY7LHjx2ZU2xVVRWRxMVVO2Ty4iq/xVT18fhozINV9YiU5QRVfaGOjc6Zf/7zn8m5czp37pyVfZSW5VNaFv4vzPz8GPn5Pj/Za6qkMUFj/g8NM2bMQESSgeeiiy7i559/tsBjTK74+5L18SLyL2AnEbkAZwqKh9J9cTrTUDwvIr8DfgsUpjz+16pf5S8rVqwgLy+PJUuWMGfOHHr1yk53pNTgE+7qz7bgE+bqTxgEIeQArFq1ismTJzN48OByj3/22Wc2jYQxOebnSo+q3iMiRwO/4vTruUlV30j39elMQzEWpw/PEcDDODObzqxdc70hIkyePJl+/frRu3fvnIzfkwhAYQ4/YAHIr4IQdlavXs0DDzzAv/71L3744Ydyz82YMYODD06rX6Ixxm0+Dj0iMhyYkEnQSZXOt9RBqno2sE5VbwUOBNrWZmdeOvbYY5O3b7nllpztN3Hqy05/mWxLnL7ye+ApKSnhoIMOYpddduHWW28tF3jGjh1LLBazwGOMVzI5teVNONoReF1E3hORS+Lzg6YtndCTmM9hk4jsBpQAe2TYSF9Yu3YtALfeeislJSU5339UAlAi/FgAyo0gBJ2EMWPGUFBQwIcffghAt27dGD16NDNnzqS4uJgLL7zQ+u4Y4yHJcMk1Vb1VVX8LXIIzJ+g0EUl7FOJ0xul5RUR2AkbgXCKmZNBpyE+aNWvGiSeeyH//+1+OPfZY3nrrLc/aErXTX3bqy11BCTkJS5cuZY89tv2tdPzxx/Piiy9awDHGj3x8eivFKuAHYC2wS7ovqm6cnmHA+8Bd8UlGnxeRV4BCVfXl3FvpmDBhAvXq1ePtt99m3bp1no/qap2fTSaCFnZUlQEDBvDyyy8nH/vuu+9o06aNh60yxlTHzx2ZReQi4DScKbEmAheo6hfpvr66b582wGhglYi8KyJ3An2AQJ+byc/P5+abbwbgN7/5jcetKS8Kp77A+v5kKih9dSqaP38+eXl5ycDz8MMPo6oWeIzxO3/36dkdGKaqv1XVmzMJPFD9OD1/UtWDgF1xBiP8CTgPWCAiGe3EbxIdmX/66Se2bNnibWMqYX1/DASrr05FY8aMoXv37gC0atWK4uJizj//fI9bZYxJi49Dj6r+WVXn1vb16ZxnaIjTW7ppfFkBfFzbHSaIyGUislhEPheRv9d1e5nq168fAE8//XSud52RKIQfsACUENSqTqpzzz2XSy65BICnnnqKFStWUFBQ4HGrjDFpUef0VrpLTUTkERFZJSILqnheROQ+EVkiIvNFJKvzelbXp2cczoCE63FCzgfASFVdV9edisgRwACgm6oWi0janZDccvPNNzN58mRuvvnmQPwFGpW+PxDNzs9BDjkJ//vf/9hzzz2T9+fPn88+++zjYYuMMbXh8izrjwH3A09U8Xw/oEN82R94MP5vVlT3rdIOaIDTO/p7YDnws0v7vQj4m6oWA6jqKpe2m7b99tsPgO+//z7Xu64zO/0VDmGo6gB88MEHiEi5wLNmzRoLPMYElYunt1R1Ok73mKoMAJ5Qx0c400u0qkvzq1Ndn55jgX2Be+IPXQV8IiJTReTWOu63I3CoiHwsItNEZN+qVhSRISIyS0RmrV69uo67Lbfd5O2tW7e6tt1ci0L4gXB1fg5D0CktLeWqq65CRMoNJPjII4+gqjRv3tzD1hlj6iLD01stEt/R8WVIhrtrDXyXcn95/LGsqPb8QTx5LQBeAybjXMK+F3BFTRsWkTdFZEElywCc02o7AwcAV+NMIFbpt4CqjlPVIlUtatmyZWbvrgb9+/cH4LHHHnN1u16w6o+/haWqE4vFuPzyy6lfvz4jR45MPv7222+jqpx77rkets4YU2eZj8i8JvEdHV/GZbjHyj4Us9ZFusrQIyKXi8izIvIdMB3oDywGTgKa1bRhVe2jqntXsryIk+ReiIeqmUAMaOHKO8pA4iquXE5LkQtRCD8QjOpPGIJOwtixY8nPz+ef//wnAEcccQS//vorqsoRRxzhceuMMa7J7dVbyyk/tVUbnAumsqK6EZnb4wz8M1xVV7q830nAkcC7ItIRKADWuLyPGvXu3RuAlSvdfnv+EJXOz34b+DAsISehpKSEli1b8ssvzpikXbt2ZebMmeywww4et8wY4zYh54MTvgRcKiLP4nRg/iULmSOpytCjqldma6fAI8Aj8UvYtgLnaC6mPq9GcXExDRo08LIJWRW1aS8gtwEobEEnYc2aNaSeVl64cCGdO3f2sEXGmKxz8dtYRP4DHI7T92c5cDNQH0BVx+J0nzkOWAJsArJ6jjydubdcp6pbgT94se+q/PGPf+TJJ5/0uhlZF5XqD+Tm0vewhh2AefPm0aNHDwDatWvH0qVLba4sYyJAXKxBqOrpNTyvOJOH5oT35wI89sQTztAB7777rrcN8YB1fq6dsHRKrs748eOTgef8889n2bJlFniMiYLMOzIHSuRDzx/+4BScli9f7nFLvBWF8AN16/wc9qCTcN1113HaaacB8NBDD/Hwww973CJjTC65OSKz33hyestP7K/X8qJy+ivdvj9RCDmpDj30UGbMmAHA+++/z0EHHeRxi4wxORfAMJOuyIeeVKtWrWKXXXI+I4ZvJQJQ/XrhDT+QEoBKtwW+qIWdTZs2lbsa69tvv6Vt27bVvMIYE1ZBrOCky0IPcOGFF/Kvf/2Lhx9+mOuvv77G9T8fcAu9XrsxBy0z2VaWEm4Wn3SThy3JnXXr1jFs2LBkf7aKNm3aRMOGDXPcKmOMb4Q49ES+Tw+QnBH6gQceSPs1ZTEp94VpgiWK/3/ffvstIkKzZs0qDTynnHIKsVjMAo8xUebyLOt+Y6EHkhMjrliR+SCQiS/PqH2BBlFU/69UlYEDB7L77rsnH7v66qtZt24dK1euZNmyZagqEyZMsD5uxkSc4Myynu4SNHZ6y0WJL9P8vADG35CKWsCpqLS0lPr16yfvjx07lgsvvNDDFhljfM/bsYKzykJPXPPmzVm7di2LFy+mU6dOddpW6hetBSBvRD3sJBx99NHJ25s3b6awsNDD1hhjgiCIp63SZae34hL9esaMGePqdqN4OsUrUT19VZUxY8YkB91cs2aNBR5jTM1scMJoGDJkCJBZZ+ZM2Bdy9thx3d5nn32WDPKzZ8+mefPmHrfIGBMU1qcnAlq3bg1AWVn2x6Sxvj91ZyGnaps2baJbt24A/POf/6RXr14et8gYEygh/mqy0FMJVc3JVSzW9ydzFnaqp6rJQQYPO+wwLr30Uo9bZIwJGuvTExFdu3YFSA7Dn0t2iqZqdmowfSeccELy9jvvvONhS4wxgaQ4V2+luwSMhZ4UtRmk0G32Be+w45C5xx9/nFdeeQWADRs22Jg7xphascEJI+Kss84C4LnnnvO4JY4ofulH8T274auvvmLw4MEAzJs3r9w8WsYYk5EQX71lfXpSNGnSxOsmVCrsfX8s5NTNhg0b6NixI+B0XE50YjbGmEwJwazgpMtCTxVKSkrKjWTrF2G68svCTt2VlJQkw3qfPn2s47Ixpm4C2lcnXXZ6q4IjjjgCgBdffNHjllQvqH1egtpuP4rFYhQUFABQWFjIG2+84XGLjDFhYH16IsQPnZkzFYQQEYQ2Bs1pp52WvL1+/XoPW2KMCRXr0xMdiUt+E8P3B4nf+v5YyMmeJ598kokTJwKwcuVK6tWzX2VjjDuCWMFJl31SVpDajycWi5GXF8ximFcByIJOdpSUlDBjxgwmTZrEfffdl3x8ypQp7Lrrrh62zBgTKgqUhTf1BPMbPcvat28PwNSpU71tiEtycWrJTl+5a9OmTZx44omICCJCQUEBRx55ZLnAM2nSJPr27ethK40xYWR9elwmIs+JyNz4slRE5nrRjqrcfffdAAwfPtzjlrjL7U7E1ik5O3744Qd22GEHJk2aVO7x/fffn7vuuouvv/6aWCzGgAEDvGmgMSbcQjwisyent1Q12QNTRP4B/OJFO6rSp08fABYtWuTbS9frqi6XvlvIyZ5YLEarVq0A2HHHHfn+++9p3Lixx60yxkRJECs46fL09JY44+SfCvzHy3ZU1KxZs+TtsF8GnG61xqo6udGlSxcAdt55Z3755RcLPMaY3Mrkyq0AhiOv+/QcCvyoql9VtYKIDBGRWSIya/Xq1TlrWGJKiqeffjpn+/RadaEmP09Dv3jt5Zdf5ssvvwQglz/rxhiT4IzIrGkvQZO101si8iZQ2WUlN6hqYuS/06mhyqOq44BxAEVFRTk7wocddhhPPvkkzzzzDA899BCNGjUq9/y8/rflqime2nfy9ZSp19k4/LZu3ZocLmH27Nnk5+d73CJjTGTFvG5A9mQt9Khqn+qeF5F6wElA72y1oS4OOuig5O2XX3653EBwUZMv234DLABlR+vWrQH43e9+R69evTxujTEmyoJYwUmXl99gfYBFqrrcwzZUqVOnTjRt2hSAZ555xuPW+Ee+xMqFIFN3kydPZs2aNYATsI0xxjPWpydrBuGzDsyp8vLyOPjggwHni+inn37yuEX+kgg/FoDqprS0lOOOOw6ATz75BKdvvzHGeCWDy9UDWBHyLPSo6mBVHevV/tNx9tlnA6CqPP/88x63xr8s/NRe4mesS5cuFBUVedwaY4xxf3BCETlWRBaLyBIR+XMlzx8uIr+kjN93k9vvKcE6aFTjtNNOY+jQoQAMGTLE49b4n1V/MrNu3Tr+8x+n2Dlr1iyPW2OMMXEuVnpEJB94AOgHdAVOF5Gulaz6nqr2iC9/dfcNbWOhpwajRo1K3q44Qq6pmoWfmu25554AXHvttdtdHWiMMZ5QkFj6Sxr2A5ao6jequhV4FvBsOHkLPTVo0KABH3/8MQAnnngiP/zwg8ctChar/lTu008/5eeffwbgrrvu8rYxxhiTKrNKT4vEWHrxpeJpkdbAdyn3l8cfq+hAEZknIpNF5LdZemcWetKx3377cf311wPQqlUrNICdt/zAAtA2icvSX331Veu8bIzxFYlp2guwRlWLUpZxFTdXyS4qfonOAXZX1e7AP4FJrr+pOAs9abrjjjuSl7An5kYytRfl8PPQQw8lbyeu3DLGGN9w9+qt5UDblPttgBXld6e/quqG+O3XgPoi0sKtt5PKQk8GEpet//jjj5x77rketyYcolb9UdVkp/hly5Z53BpjjKlAcUZkTnep2SdABxHZQ0QKcIareSl1BRHZNT4XJyKyH042WevCu9mOJ7OsB1VeXh5r1qyhRYsWPPbYY+y///7Jq7sAvvnmG5544gnee+89RIROnTpx1FFHcdxxx1FYWMjWrVtZvXp1cvRdU14i+IR51OdTTjkFgN69e9OuXTuPW2OMMeUJ7s6ppaqlInIp8DqQDzyiqp+LyND482OBU4CLRKQU2AwM0iz1I5Eg9U8pKipSP1zaO3fuXHr27Jm8//TTT3PmmWem/fqhQ4dy7733UlhYmI3muWrfydd7uv9cBqA5x92e1e1/9NFHHHjggQAUFxdTUFCQ1f0ZY4JPRGaras4G8Wq6w256QJf0h2iZOvvWnLavrsL7J3UW9ejRo9y4KqmB58orr+S1115jypQpjBo1iv/7v//b7vVjx46lYcOGvPbaazlpb5CF5dTXt99+mww8//nPfyzwGGP8y0ZkNhX17t0bVWXkyJG0atWKDz74AFXlH//4B/369aNv375cccUVTJs2DVVFVVmxYgUPPPBAchu/+93v6N+/v4fvIjiC3Pdn8eLF7L777gAcffTRDBo0yOMWGWNMFdzv0+MrFnrqaPjw4axYsSL5V3x1WrVqxcUXX4yq8tFHHwHbLll+7733st3U0AhSAPrss8/o3LkzAGeccQZTp071uEXGGFM9UU17CRoLPR7Zf//92bhxY/L+//3f/yEiFBQUMHr0aLZu3eph64LDz+Hn+++/p1u3bgDceuutPP300x63yBhj0mCnt0w2NGrUCFVlypQpyWkISkpKGDZsGA0aNEBEql1OPvlkSkpKPH4X/uC36s/mzZtp06YN4FQDb7opa/PnGWOMi2yWdZNlffv2ZePGjagqixYt4qSTTkrrdS+88AIFBQWcddZZxGL++LL3A6/DT1lZWTLEdu/enZEjR3rWFmOMyYhiocfkTqdOnXj++eeTnZ83bdpELBZL3k8sxcXFDBw4EICnnnqK/Px8xo8f73Hr/cWL6o+qlhuH6dNPP83Zvo0xxhXWkdl4pWHDhpXOzVRQUMD48ePZsmUL++yzDwCnnXYaPXr0YPPmzblupu/lIvy888475OXl8eOPPwLwyy+/2LxaxpjAsY7MxrcaNGjA/Pnzk1d/zZs3j0aNGrFo0SKPW+ZP2aj+fPDBB4gIRx55JOAE1eLiYnbccUfX9mGMMTkT4tNbNg1FSBxyyCGsW7eONm3asHHjRrp06UL//v2ZMGECGzduZNOmTbRt27bmDVXwSb87s9Ba/+n12o0Zv+bDDz/koIMOKvfYRx99xP777+9Ws4wxJrdUoSyA563SZJWeENlpp51Yv3598kqhV155hYYNG9KiRQvatWuHiNg4MVWoaQqKjRs3smjRIt544w1uvvlmRKRc4HnvvfdQVQs8xpjgC3Glx0JPyIgIt956K8XFxcnTLan69u2LiLBw4UIPWhcc69at47LLLksOD9C4cWO6dOnCMcccw1//+tfketOnT0dVOeSQQzxsrTHGuMhCjwmagoIC3nrrrXJXfM2ZMyf5fNeuXTnuuOM8bKH3ysrKuP322ysdA6lZs2bcf//9272mVatWnHbaaXz99deoKoceeqgHLTfGmCxRIKbpLwFjoSdCevbsiaomL22fPHkyIhKpq73mzJnDPvvsg4hQr149/vKXv1S57rBhw1i3bl254LhixQqeffZZ9txzzxy22hhjckVBY+kvAeNJ6BGRHiLykYjMFZFZIrKfF+2IqoEDB5YLOo0aNUpeZh1mw4YNo3fv3ixYsCD52DHHHMPnn3/O1q1bWb16NV9++WVyXKR7772XnXbaybsGG2OMF+z0luv+Dtyqqj2Am+L3TQ4VFhaiqhx88MEA7Lrrrvzwww8etyp7Lr74YkaPHg3AQw89RElJCarK66+/TteuXalfvz4tWrSgQ4cONraOMSa67PRWViiQGMSkKbDCo3ZE3owZM5IjO7dq1Yq1a9d63CL33XbbbTz44IMAfPnll/zxj3+kXj0brcEYYypllR7XDQNGiMh3wD3AdR61wwDjx4/n2GOPBaBFixb8+uuvHrfIPePHj09ewr948WI6dOjgcYuMMcbnLPRkTkTeFJEFlSwDgIuA4araFhgO/Lua7QyJ9/uZtXr16mw1N/ImT57MAQccAEDTpk3ZuHGjxy2qu48++ojTTjsNgHfffZeOHTt63CJjjPE7m2W9VlS1j6ruXcnyInAO8EJ81QlAlR2ZVXWcqhapalHLli2z1VyDM8Jw586dAWjcuDFr1qzxuEW1U1xczB133MGBBx4IwLhx4zjssMM8bpUxxgSAArFY+kvAeHV6awWQ+BY6EvjKo3aYChYuXJicwLRly5a88cYbHrcofV999RV9+/alsLCQG290ppW45ZZbuOCCCzxumTHGBIhVelx3AfAPEZkH3AkM8agdphLz589n6NChgHNJd//+/VGf/nA/9thjyQEFO3bsWG6ajRkzZnDzzTd72DpjjAmgEIceTy5hUdUZQG8v9m3S8+CDDzJo0CAOP/xwXn31VfLy8li+fDmtW7f2umkArF27lhYtWmz3+D333MOJJ55I+/btycuzsTeNMSYzwbwUPV32rWCqdNhhh5UbxLBNmzYMHz6cWI7O437zzTcUFRUlKzl33HEHqsqUKVPKBZ5FixYlR0y+6qqr2HPPPS3wGGNMbShoWVnaS9DYN4OpVmIQw7/97W8AjBo1ivz8fESE6667zvUAFIvFuOmmmxAR9tprL2bPnp187sYbbyQvL49+/foBMHz4cFSVTp06udoGY4yJtBCf3rLQY9Jy7bXXsm7dOnr27Jl87G9/+xv5+fmcd955FBcX13kfU6ZMIT8/n9tuuy352Lhx4/juu++YPn06DRo0SD7+zjvvMHLkyDrv0xhjTApVu3rLGICddtqJOXPmEIvFWLhwIfvuuy8Ajz76KIWFhYgII0aMoKwWJc958+YlKziFhYXJiT4vuOAC2rRpw6GHHsqWLVuYO3cumzdv5vDDD3fzrRljjEmwSo8x24gInTt3ZubMmaxfv56zzjor+dw111xDvXr1EBGmTp2a1lVfkydPpkePHgBcdtllbNq0qcqJPrt3705hYaEbb8MYY0wlNBZLewkaCz2mTho3bswTTzyBqrJ8+XL69u2bfK5v374MGDCgytf++uuvFBUVcdxxxwFOYLrvvvtswk9jjPGMjchsTFpat27NlClTUFWeeeYZAF5++WUee+yxcusVFxdzxhln0LRp02RH5bvuuou777471002xhiTKuSzrNtU0yYrTj/9dOrXr8/AgQM599xz+eGHH9hjjz0YNGjQduuNHTuWHXfc0aOWGmOMKUfdPW0lIscCo4F84GFV/VuF5yX+/HHAJmCwqs5xtRFxVukxWXPKKacwevRoAK677rpygecPf/gDmzZt4plnnrHAY4wxPqGAxjTtpSYikg88APQDugKni0jXCqv1AzrElyHAg66+qRRW6TFZdfnllwNwxRVX0KlTJ8aPH0+3bt08bpUxxphKqbpd6dkPWKKq3wCIyLPAAOCLlHUGAE+oc+XLRyKyk4i0UtWVbjYErNJjcuDyyy9HVVm0aJEFHmOM8Tk3Kz1Aa+C7lPvL449luo4rAlXpmT179gYRWex1O3ymBbDG60b4iB2P7dkx2Z4dk/LseGzPL8dk91zubD3rXn8zNn77iQ2rVigis1Luj1PVcSn3K7sct2JaSmcdVwQq9ACLVbXI60b4iYjMsmOyjR2P7dkx2Z4dk/LseGwvqsdEVY91eZPLgbYp99sAK2qxjivs9JYxxhhjsuUToIOI7CEiBcAg4KUK67wEnC2OA4BfstGfB4JX6THGGGNMQKhqqYhcCryOc8n6I6r6uYgMjT8/FngN53L1JTiXrJ+brfYELfSMq3mVyLFjUp4dj+3ZMdmeHZPy7Hhsz46JS1T1NZxgk/rY2JTbClySi7ZIOnMjGWOMMcYEnfXpMcYYY0wk5Cz0iEi+iHwqIq/E7zcTkTdE5Kv4vzunrHudiCwRkcUi0jfl8dNEZL6IfC4if6+w/VNF5Iv4c8+kPH5OfB9ficg5uXiv6crmMRGRdiLyTnz780XkuJTnAn9MRKR5/P1tEJH7K2yjt4h8Fj9e98WHOEdEGojIc/HHPxaR9imv8eUxyfLxuDL+OzNfRN4Skd1TXuPL4wHZPSYpz58iIioiRSmPRfaYSMA+X7P8exPIz1YTp6o5WYArgWeAV+L3/w78OX77z8Dd8dtdgXlAA2AP4Guczk/NgW+BlvH1HgeOit/uAHwK7By/v0v832bAN/F/d47f3jlX79njYzIOuCjl9UtDdkx2AA4BhgL3V9jGTOBAnLEfJgP94o9fDIyN3x4EPOf3Y5Ll43EE0Ch++6IgHI9sH5P4c02A6cBHQFHUjwkB/HzN8vEI5GerLc6Sk0qPiLQBfgc8nPLwAJwvaeL//j7l8WdVtVhV/4fTm3s/YE/gS1VdHV/vTeDk+O0LgAdUdR2Aqq6KP94XeENVf4o/9wbg9hgEtZKDY6JAYlKrpmwb8yAUx0RVN6rqDGBLhW20AnZU1Q/V+SR6gvLHMbGticBR8b/efHlMsn08VPUdVd0UX/UjnLExwKfHA3LyMwJwG86XZOrronxMAvX5moPjEbjPVrNNrk5vjQKuAVIn9Ph/Gr8OP/7vLvHHqxqOegnQWUTai0g9nB/AxGBGHYGOIvK+iHwkzoyu1W3LD0aR3WNyC/AHEVmO02v+shq25QejSP+YVKU1zntKSH1/yfeuqqXALzjVMr8ek1Fk93ikOh/nr9nEa/x4PCDLx0REegJtVfWVSl4TyWNC8D5fR5Hd43ELwftsNXFZDz0i0h9Ypaqz031JJY9pPDlfBDwHvAcsBUrjz9fDKcEeDpwOPCwiO1W1rXTbni05OianA4+pahuc8Q+eFJG8qraVQfOzohbHpMpNVfKY1vCc745Jjo5HYl9/AIqAEem+xgvZPibx3497gavSfU0d21FnOfo5Cczna46OR6A+W015uRin52DghHhnr0JgRxF5CvhR4rOoxkuJiZJplcNRq+rLwMsAIjIEKEt5zUeqWgL8T5z5uTrEHz+8wrbedf0dZi4Xx+R84qVVVf1QRApx5pIJyzGpynK2naaB8sOZJ47j8nhlrCnwE/48Jrk4HohIH+AG4DBVLU55zeEVXvNuHd6LW7J9TJoAewPvxvus7gq8JCInEN1jknguKJ+vuTgeQftsNaly2YEI5wci0bFsBOU7lv09fvu3lO+0+w2QH38u0YFuZ2Au0DF+/1jg8fjtFjglxuY4Hcr+F19/5/jtZrl8zx4ek8nA4PjtLji/sBKWY5Ky7mC274D4CXAA2zogHhd//BLKd2QeH7/t62OSxePRE6dTfIcK6/v6eGTzmFRY513Kd2SO5DEhoJ+vWTwegf1stUU9DT3NgbeAr+L/NktZ74b4h/Fiyl9V8R/gi/gyKOVxAUbGH/+swnPn4fR9WQKc6/UBz+Ex6Qq8jxOW5gLHhPCYLMWp1GzA+Sura/zxImBB/Hjdz7ZBOAuBCfH3PRPYMwjHJIvH403gx/jPx1zgpSAcj2wekwr7eJd46InyMSGgn69ZPB6B/Wy1RW1EZmOMMcZEg43IbIwxxphIsNBjjDHGmEiw0GOMMcaYSLDQY4wxxphIsNBjjDHGmEiw0GOMMcaYSLDQY4wxxphIsNBjTAiJyAgR+VPKfRGRn0XkChGZG19iKbdHisiFIqIicljK6y6NP9aniv00FJFpIpKfQdsKRGR6fCoQY4zJGQs9xoTT3sD8lPt7AKtVdbSq9gB+B3ynqj3iy5VAt/hrugCISCOceYZW44zEW5nzgBdUtayK57ejqltxRsU9LbO3ZIwxdWOhx5hw2ofyoadbhft7s32Q2QdnWpPO8fuX40zbEVPVH6vYz5nAi4k7IjJBRO4XkRkiskxEDhGRJ0TkSxH5d8rrJsVfa4wxOWOhx5iQEZGdgQJV/SHl4YqhZx+ceYVSdQHGA51FpClOJeaDStZL7KcAZ/6ypRW2+42qHgI8DvwbuBYnZJ0kIg3i6y0A9s383RljTO1Z6DEmfCpWeaCGSo+ItAXWquo3wC7ANcA/gY6VbCuhBfBzyjYKgZ2AUfGHNgP/VtWV8VNam4CtAPHTYVtFpEnG784YY2rJQo8x4dMJZ2ZoAEQkDzgYeC9lnYqVnm5sC0HrgWNxKjX7JB4XkZYi8qiItBGRR4ASnJnrE34LzFHVWPx+d+Dj+GvbACu0/AzHDYAtdXifxhiTEQs9xoTPMmDfeOUF4HpguqqugWQI6gAsSnlNMtwAI4BL49WYZNVIVVcD3wL/AC6P389P2c8+wLyUbaZWl7qn3EZEmuN0rC6p+9s1xpj0WOgxJmRUdSrwDrBIRBbjBJyhKav8BliuqsUpjyUrP6r6iqp+GH+8K/AFgIg0BvYESlV1Q/z5qcAhKduYG1+3EGioquviz1U8vXYE8Frd3qkxxmRGylebjTFme/ExdcYBtwKnAp+o6rsi0hO4UlXPynB7LwDXqepi91trjDGVs9BjjKkTETkPeDzdsXriV30NUtUnstsyY4wpz0KPMcYYYyLB+vQYY4wxJhIs9BhjjDEmEiz0GGOMMSYSLPQYY4wxJhIs9BhjjDEmEiz0GGOMMSYSLPQYY4wxJhIs9BhjjDEmEv4/8+b6oP8hFd8AAAAASUVORK5CYII=\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Plot Delft3D interpolated Data\n", "plt.figure(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=max_plot,\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)" ] }, { "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= abs(ADCP_ideal_downsamples.magnitude-D3D.magnitude)/ADCP_ideal_downsamples.magnitude" ] }, { "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(river_bottom_edge_filter_downsampled, filter)\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": 24, "id": "bf78c6e0-c4cd-421e-b9c0-3465d05c6dd3", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.2706051947034277" ] }, "execution_count": 24, "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": 25, "id": "0acdaf51-d08e-4d9e-8070-4d2e5477b456", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Set the min and max error values\n", "max_plot=1\n", "min_plot=0\n", "\n", "# Plotting the L1 error\n", "plt.figure(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,\n", " vmax=max_plot\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('L1 velocity error')\n", "plt.legend(loc= 7)\n", "\n" ] }, { "cell_type": "markdown", "id": "45185463-73d7-4851-85c8-083ade7a50f2", "metadata": {}, "source": [ "### Error: L2\n", "\n", "$L_2=\\sqrt{\\left(\\frac{D3D-ADCP}{ADCP}\\right)^2}$\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": 30, "id": "1d10a637-2105-40de-8f22-e8b46b5c2d16", "metadata": {}, "outputs": [], "source": [ "# L2 \n", "L2_Magnitude= ((ADCP_ideal_downsamples.magnitude-D3D.magnitude)/ADCP_ideal_downsamples.magnitude)**2" ] }, { "cell_type": "markdown", "id": "39753c37-c501-462f-b45f-2030f16632c4", "metadata": {}, "source": [ "The root mean squared error (RMSE) 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", "$RMSE= \\frac{\\sum{L_2}}{N}$" ] }, { "cell_type": "code", "execution_count": 31, "id": "e71c17c4-93f7-4ad2-8b66-bff1b7da0519", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.11117035854551333" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "RMSE=np.sum(L2_Magnitude*error_filter)/np.size(L2_Magnitude[L2_Magnitude< 1000])\n", "RMSE" ] }, { "cell_type": "code", "execution_count": 33, "id": "3921f31e-1f0e-45a6-9394-c7e42e1ebb2d", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Create a contour plot of the error\n", "max_plot=1\n", "min_plot=0\n", "# Plotting \n", "plt.figure(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,\n", " vmax=max_plot\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('L2 velocity error')\n", "plt.legend(loc= 7)" ] }, { "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": 35, "id": "de8f93fc-8254-4181-817c-dae48b39456b", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.8140245756783895" ] }, "execution_count": 35, "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": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.8" } }, "nbformat": 4, "nbformat_minor": 5 }