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.S13 { border-left: 1px solid rgb(217, 217, 217); border-right: 1px solid rgb(217, 217, 217); border-top: 1px solid rgb(217, 217, 217); border-bottom: 0px none rgb(33, 33, 33); border-radius: 0px; padding: 6px 45px 0px 13px; line-height: 18.004px; min-height: 0px; white-space: nowrap; color: rgb(33, 33, 33); font-family: Menlo, Monaco, Consolas, "Courier New", monospace; font-size: 14px; }</style></head><body><div class = rtcContent><h1 class = 'S0'><span>MHKiT SWAN Example</span></h1><div class = 'S1'><span>This example notebook demonstrates the input and plotting of output data from the software</span><span> </span><a href = "http://swanmodel.sourceforge.net/"><span>Simulating WAves Nearshore (SWAN)</span></a><span> using MHKiT. In this example the</span><span> </span><a href = "https://github.com/SNL-WaterPower/SNL-SWAN"><span>SNL-SWAN</span></a><span> tutorial was run for a wave energy converter. The output was written in ASCII and binary </span><span>(*</span><span>.mat) files. This MHKiT example notebook demonstrates how to import these different files into MHKiT and plot the output data.</span></div><h2 class = 'S2'><span>Supported SWAN Output Files</span></h2><div class = 'S1'><span>MHKiT currently supports block and table SWAN output files in ASCII or binary </span><span>(*</span><span>.mat) files. Detailed descriptions of these file types may be found in the</span><span> </span><a href = "http://swanmodel.sourceforge.net/download/zip/swanuse.pdf"><span>SWAN User Manual</span></a><span>. In the following cells, SWAN table and block data will be imported, discussed, and plotted. Three SWAN output files will be imported:</span></div><ol class = 'S3'><li class = 'S4'><span>An ASCII table file ('SWANOUT.DAT'),</span></li><li class = 'S4'><span>An ASCII block file ('SWANOUTBlock.DAT')</span></li><li class = 'S4'><span>A binary block file ('SWANOUT.mat')</span></li></ol><div class="CodeBlock"><div class="inlineWrapper"><div class = 'S5'><span style="white-space: pre"><span >swan_path = </span><span style="color: #a709f5;">"./data/wave/SWAN/"</span><span >;</span></span></div></div><div class="inlineWrapper"><div class = 'S6'><span style="white-space: pre"><span >swan_table_file = append(swan_path,</span><span style="color: #a709f5;">"SWANOUT.DAT"</span><span >);</span></span></div></div><div class="inlineWrapper"><div class = 'S6'><span style="white-space: pre"><span >swan_block_file = append(swan_path,</span><span style="color: #a709f5;">"SWANOUTBlock.DAT"</span><span >);</span></span></div></div><div class="inlineWrapper"><div class = 'S7'><span style="white-space: pre"><span >swan_block_mat_file = append(swan_path,</span><span style="color: #a709f5;">"SWANOUT.mat"</span><span >) ;</span></span></div></div></div><h2 class = 'S8'><span>Load SWAN Files with MHKiT</span></h2><div class = 'S1'><span>To load a supported non .mat SWAN file simply call the</span><span> </span><span style=' font-family: monospace;'>swan_read_table</span><span> or</span><span> </span><span style=' font-family: monospace;'>swan_read_block</span><span> as appropriate for the swan output. The MHKiT function will read in the SWAN output and return the data as a structure. The structure will also contain any metadata that the file may contain which will vary based on the file type and options specified in the SWAN run. MHKiT requires that for block data written in ASCII format that the file was written with headers. The</span><span> </span><span style=' font-family: monospace;'>swan_read_block</span><span> function accepts both binary and ASCII format by assuming that any non-'.mat' extension is ASCII format.</span></div><h2 class = 'S2'><span>SWAN Table Data and Metadata</span></h2><div class = 'S1'><span>The SWAN output table is parsed from the MHKiT function</span><span> </span><span style=' font-family: monospace;'>swan_read_table</span><span> into a structure that is displayed below. The structure fields contain a series of x-points ('Xp'), y-points ('Yp'), and keyword values at a given (x,y) point. The keywords are specified in the SWAN user manual and here can be seen as: 'Hsig' (significant wave height), 'Dir' (average wave direction), 'RTpeak' (Relative peak period), 'TDir' (direction of the energy transport).</span></div><div class="CodeBlock"><div class="inlineWrapper outputs"><div class = 'S9'><span style="white-space: pre"><span >swan_table = swan_read_table(swan_table_file)</span></span></div><div class = 'S10'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsVariableStringElement" uid="CDD49EF7" prevent-scroll="true" data-testid="output_0" tabindex="-1" style="width: 1039px; opacity: 1; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="textElement eoOutputContent" data-previous-available-width="1002" data-previous-scroll-height="138" data-hashorizontaloverflow="false" role="article" aria-roledescription="Use Browse Mode to explore " aria-description="variable output " tabindex="-1" style="max-height: 261px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><span class="variableNameElement" style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">swan_table = <span class="headerElement" style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">struct with fields:</span></span></div><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"> Xp: [10201×1 double]
Yp: [10201×1 double]
Hsig: [10201×1 double]
Dir: [10201×1 double]
RTpeak: [10201×1 double]
TDir: [10201×1 double]
units: [1×1 struct]
metadata: [1×1 struct]
</div></div></div></div></div></div><div class = 'S1'><span>In the cell below, metadata is written to screen and can be seen to be a structure of keywords which contains the SWAN run name, the type of table written, and the version of SWAN run. The units show the column headers, and the associated units.</span></div><div class="CodeBlock"><div class="inlineWrapper outputs"><div class = 'S9'><span style="white-space: pre"><span >swan_table.metadata</span></span></div><div class = 'S10'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsVariableStringElement" uid="5F8D7836" prevent-scroll="true" data-testid="output_1" tabindex="-1" style="width: 1039px; opacity: 1; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="textElement eoOutputContent" data-previous-available-width="1002" data-previous-scroll-height="64" data-hashorizontaloverflow="false" role="article" aria-roledescription="Use Browse Mode to explore " aria-description="variable output " tabindex="-1" style="max-height: 261px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><span class="variableNameElement" style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">ans = <span class="headerElement" style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">struct with fields:</span></span></div><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"> Run: "TEST"
Table: "COMPGRID"
version: "41.20"
</div></div></div></div></div><div class="inlineWrapper outputs"><div class = 'S11'><span style="white-space: pre"><span >swan_table.units</span></span></div><div class = 'S10'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsVariableStringElement" uid="2595ABAF" prevent-scroll="true" data-testid="output_2" tabindex="-1" style="width: 1039px; opacity: 1; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="textElement eoOutputContent" data-previous-available-width="1002" data-previous-scroll-height="108" data-hashorizontaloverflow="false" role="article" aria-roledescription="Use Browse Mode to explore " aria-description="variable output " tabindex="-1" style="max-height: 261px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><span class="variableNameElement" style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">ans = <span class="headerElement" style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">struct with fields:</span></span></div><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"> Xp: "[m]"
Yp: "[m]"
Hsig: "[m]"
Dir: "[degr]"
RTpeak: "[sec]"
TDir: "[degr]"
</div></div></div></div></div></div><h2 class = 'S8'><span>SWAN Block (ASCII) Data and Metadata</span></h2><div class = 'S1'><span>MHKiT will read in block data as a structure. The structure swan_block (shown below) is read using</span><span> </span><span style=' font-family: monospace;'>swan_read_block</span><span> on the ASCII block data, and has the same four keys from the table data shown previously. In the cell below the structure for the 'Significant_wave_height' is shown by using the specified key. This structure has the metadata for Significant_wave_height. The last line of code looks at the values from Significant_wave_height. This matrix has a value of significant wave height at each point on the modeled output grid.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div class = 'S5'><span style="white-space: pre"><span >swan_block = swan_read_block(swan_block_file);</span></span></div></div><div class="inlineWrapper outputs"><div class = 'S12'><span style="white-space: pre"><span >swan_block.Significant_wave_height</span></span></div><div class = 'S10'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsVariableStringElement" uid="B8257746" prevent-scroll="true" data-testid="output_3" tabindex="-1" style="width: 1039px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="textElement eoOutputContent" tabindex="-1" data-previous-available-width="1002" data-previous-scroll-height="94" data-hashorizontaloverflow="false" role="article" aria-roledescription="Use Browse Mode to explore " aria-description="variable output " style="max-height: 261px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><span class="variableNameElement" style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">ans = <span class="headerElement" style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">struct with fields:</span></span></div><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"> values: [101×101 double]
Run: "TEST"
Frame: "COMPGRID"
Unit: "0.1000E-01 m"
unitMultiplier: 0.0100
</div></div></div></div></div><div class="inlineWrapper outputs"><div class = 'S11'><span style="white-space: pre"><span >swan_block.Significant_wave_height.values</span></span></div><div class = 'S10'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsVariableMatrixElement" uid="9B25126E" prevent-scroll="true" data-testid="output_4" data-width="1009" tabindex="-1" style="width: 1039px; white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="matrixElement veSpecifier eoOutputContent" role="article" aria-roledescription="Use Browse Mode to explore " aria-description="variable output " style="white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="veVariableName variableNameElement double" style="width: 1009px; white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="headerElementClickToInteract" style="white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><span style="white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">ans = </span><span class="veVariableValueSummary veMetaSummary" style="white-space: normal; font-style: italic; color: rgb(97, 97, 97); font-size: 12px;">101×101</span></div></div><div class="valueContainer" data-layout="{"columnWidth":80,"totalColumns":101,"totalRows":101,"charsPerColumn":6}" style="white-space: nowrap; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="variableValue" style="width: 962px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"> 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
</div><div class="horizontalEllipsis" style="white-space: nowrap; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><mw-icon icon-id="meatballMenuUI" icon-width="16" icon-height="16" style="white-space: nowrap; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"></mw-icon></div><div class="verticalEllipsis" style="white-space: nowrap; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><mw-icon icon-id="kebabMenuUI" icon-width="16" icon-height="16" style="white-space: nowrap; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"></mw-icon></div></div></div><div class="outputLayer selectedOutputDecorationLayer doNotExport" aria-hidden="true" style="white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"></div><div class="outputLayer activeOutputDecorationLayer doNotExport" aria-hidden="true" style="white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"></div><div class="outputLayer scrollableOutputDecorationLayer doNotExport" aria-hidden="true" style="white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"></div><div class="outputLayer navigationFocusLayer doNotExport" aria-hidden="false" tabindex="-1" role="application" aria-label="variable output starting with ans = 101×101
1 1 1 1 1 1 1 1 1 1 1 1 1 " style="white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"></div></div></div></div></div><h2 class = 'S8'><span>SWAN Block (.mat) Data and Metadata</span></h2><div class = 'S1'><span>Reading in SWAN .mat data files is as simple as loading the file into the Matlab workspace.</span></div><div class="CodeBlock"><div class="inlineWrapper outputs"><div class = 'S9'><span style="white-space: pre"><span >swan_block_mat = load(swan_block_mat_file)</span></span></div><div class = 'S10'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsVariableStringElement" uid="87E77D87" prevent-scroll="true" data-testid="output_5" tabindex="-1" style="width: 1039px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="textElement eoOutputContent" tabindex="-1" data-previous-available-width="1002" data-previous-scroll-height="79" data-hashorizontaloverflow="false" role="article" aria-roledescription="Use Browse Mode to explore " aria-description="variable output " style="max-height: 261px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><span class="variableNameElement" style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">swan_block_mat = <span class="headerElement" style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">struct with fields:</span></span></div><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"> Hsig: [101×101 single]
Dir: [101×101 single]
RTpeak: [101×101 single]
TDir: [101×101 single]
</div></div></div></div></div></div><h2 class = 'S8'><span>Example Plots from SWAN Data</span></h2><div class = 'S1'><span>This last section shows a couple of plots for the significant wave height using each of the imported results. Plot Table Data To plot 1D vector data, we must grid the swan data to make a 2D matrix. We do this by first creating xx and yy vectors spanning the x and y dimensions. We use meshgrid to create a 2D mesh. Finally we grid the Hsig data to the mesh.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div class = 'S5'><span style="white-space: pre"><span >xx = linspace(min(swan_table.Xp),max(swan_table.Xp),20);</span></span></div></div><div class="inlineWrapper"><div class = 'S6'><span style="white-space: pre"><span >yy = linspace(min(swan_table.Yp),max(swan_table.Yp),20);</span></span></div></div><div class="inlineWrapper"><div class = 'S6'><span style="white-space: pre"><span >[X,Y] = meshgrid(xx,yy);</span></span></div></div><div class="inlineWrapper"><div class = 'S6'><span style="white-space: pre"><span >Z = griddata(swan_table.Xp,swan_table.Yp,swan_table.Hsig,X,Y);</span></span></div></div><div class="inlineWrapper"><div class = 'S6'><span style="white-space: pre"><span >figure(</span><span style="color: #a709f5;">'Position'</span><span >, [100, 100, 1600, 600]);</span></span></div></div><div class="inlineWrapper"><div class = 'S6'><span style="white-space: pre"><span >plot_table = pcolor(X,Y,Z); </span><span style="color: #008013;">% Note: you should include X,Y coordinates</span></span></div></div><div class="inlineWrapper"><div class = 'S6'><span style="white-space: pre"><span >shading </span><span style="color: #a709f5;">interp</span><span >;</span></span></div></div><div class="inlineWrapper"><div class = 'S6'><span style="white-space: pre"><span >colormap(viridis_colormap(256)); </span><span style="color: #008013;">% Default MATLAB colormap</span></span></div></div><div class="inlineWrapper outputs"><div class = 'S12'><span style="white-space: pre"><span >colorbar; </span><span style="color: #008013;">% Add a colorbar to show the scale</span></span></div><div class = 'S10'><img 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"></div></div><div class="inlineWrapper"><div class = 'S13'> </div></div><div class="inlineWrapper"><div class = 'S6'><span style="white-space: pre"><span style="color: #008013;">% Plotting SWAN block data</span></span></div></div><div class="inlineWrapper"><div class = 'S6'> </div></div><div class="inlineWrapper"><div class = 'S6'><span style="white-space: pre"><span >figure(</span><span style="color: #a709f5;">'Position'</span><span >, [100, 100, 1600, 600]);</span></span></div></div><div class="inlineWrapper"><div class = 'S6'><span style="white-space: pre"><span >colormap(viridis_colormap(256));</span></span></div></div><div class="inlineWrapper outputs"><div class = 'S12'><span style="white-space: pre"><span >plot_block = pcolor(swan_block.Significant_wave_height.values)</span></span></div><div class = 'S10'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsVariableStringElement scrollableOutput" uid="BCCE92C2" prevent-scroll="true" data-testid="output_7" tabindex="-1" style="width: 1039px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="textElement eoOutputContent scrollArea" tabindex="-1" data-previous-available-width="1002" data-previous-scroll-height="212" data-hashorizontaloverflow="true" role="article" aria-roledescription="Use Browse Mode to explore " aria-description="variable output " style="max-height: 261px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><span class="variableNameElement" style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">plot_block = </span></div><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"> Surface with properties:
EdgeColor: [0 0 0]
LineStyle: '-'
FaceColor: 'flat'
FaceLighting: 'flat'
FaceAlpha: 1
XData: [1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 … ] (1×101 double)
YData: [101×1 double]
ZData: [101×101 double]
CData: [101×101 double]
Show all properties
</div></div></div></div></div><div class="inlineWrapper"><div class = 'S13'><span style="white-space: pre"><span >shading </span><span style="color: #a709f5;">interp</span><span >;</span></span></div></div><div class="inlineWrapper outputs"><div class = 'S12'><span style="white-space: pre"><span >colorbar;</span></span></div><div class = 'S10'><img 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"></div></div></div>
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<!--
##### SOURCE BEGIN #####
%% MHKiT SWAN Example
% This example notebook demonstrates the input and plotting of output data from
% the software <http://swanmodel.sourceforge.net/ Simulating WAves Nearshore (SWAN)>
% using MHKiT. In this example the <https://github.com/SNL-WaterPower/SNL-SWAN
% SNL-SWAN> tutorial was run for a wave energy converter. The output was written
% in ASCII and binary (*.mat) files. This MHKiT example notebook demonstrates
% how to import these different files into MHKiT and plot the output data.
%% Supported SWAN Output Files
% MHKiT currently supports block and table SWAN output files in ASCII or binary
% (*.mat) files. Detailed descriptions of these file types may be found in the
% <http://swanmodel.sourceforge.net/download/zip/swanuse.pdf SWAN User Manual>.
% In the following cells, SWAN table and block data will be imported, discussed,
% and plotted. Three SWAN output files will be imported:
%%
% # An ASCII table file ('SWANOUT.DAT'),
% # An ASCII block file ('SWANOUTBlock.DAT')
% # A binary block file ('SWANOUT.mat')
swan_path = "./data/wave/SWAN/";
swan_table_file = append(swan_path,"SWANOUT.DAT");
swan_block_file = append(swan_path,"SWANOUTBlock.DAT");
swan_block_mat_file = append(swan_path,"SWANOUT.mat") ;
%% Load SWAN Files with MHKiT
% To load a supported non .mat SWAN file simply call the |swan_read_table| or
% |swan_read_block| as appropriate for the swan output. The MHKiT function will
% read in the SWAN output and return the data as a structure. The structure will
% also contain any metadata that the file may contain which will vary based on
% the file type and options specified in the SWAN run. MHKiT requires that for
% block data written in ASCII format that the file was written with headers. The
% |swan_read_block| function accepts both binary and ASCII format by assuming
% that any non-'.mat' extension is ASCII format.
%% SWAN Table Data and Metadata
% The SWAN output table is parsed from the MHKiT function |swan_read_table|
% into a structure that is displayed below. The structure fields contain a series
% of x-points ('Xp'), y-points ('Yp'), and keyword values at a given (x,y) point.
% The keywords are specified in the SWAN user manual and here can be seen as:
% 'Hsig' (significant wave height), 'Dir' (average wave direction), 'RTpeak' (Relative
% peak period), 'TDir' (direction of the energy transport).
swan_table = swan_read_table(swan_table_file)
%%
% In the cell below, metadata is written to screen and can be seen to be a structure
% of keywords which contains the SWAN run name, the type of table written, and
% the version of SWAN run. The units show the column headers, and the associated
% units.
swan_table.metadata
swan_table.units
%% SWAN Block (ASCII) Data and Metadata
% MHKiT will read in block data as a structure. The structure swan_block (shown
% below) is read using |swan_read_block| on the ASCII block data, and has the
% same four keys from the table data shown previously. In the cell below the structure
% for the 'Significant_wave_height' is shown by using the specified key. This
% structure has the metadata for Significant_wave_height. The last line of code
% looks at the values from Significant_wave_height. This matrix has a value of
% significant wave height at each point on the modeled output grid.
swan_block = swan_read_block(swan_block_file);
swan_block.Significant_wave_height
swan_block.Significant_wave_height.values
%% SWAN Block (.mat) Data and Metadata
% Reading in SWAN .mat data files is as simple as loading the file into the
% Matlab workspace.
swan_block_mat = load(swan_block_mat_file)
%% Example Plots from SWAN Data
% This last section shows a couple of plots for the significant wave height
% using each of the imported results. Plot Table Data To plot 1D vector data,
% we must grid the swan data to make a 2D matrix. We do this by first creating
% xx and yy vectors spanning the x and y dimensions. We use meshgrid to create
% a 2D mesh. Finally we grid the Hsig data to the mesh.
xx = linspace(min(swan_table.Xp),max(swan_table.Xp),20);
yy = linspace(min(swan_table.Yp),max(swan_table.Yp),20);
[X,Y] = meshgrid(xx,yy);
Z = griddata(swan_table.Xp,swan_table.Yp,swan_table.Hsig,X,Y);
figure('Position', [100, 100, 1600, 600]);
plot_table = pcolor(X,Y,Z); % Note: you should include X,Y coordinates
shading interp;
colormap(viridis_colormap(256)); % Default MATLAB colormap
colorbar; % Add a colorbar to show the scale
% Plotting SWAN block data
figure('Position', [100, 100, 1600, 600]);
colormap(viridis_colormap(256));
plot_block = pcolor(swan_block.Significant_wave_height.values)
shading interp;
colorbar;
##### SOURCE END #####
-->
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