Changeset 957

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Ignore:
Timestamp:
04/05/12 21:34:20 (14 months ago)
Author:
jjr8
Message:

Rebuilt installation packages. This will be merged with the Trunk and released as MGET 0.8a38.

Location:
MGET/Branches/Jason/PythonPackage/dist
Files:
62 added
3 removed
72 modified

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  • MGET/Branches/Jason/PythonPackage/dist/MGETArcGISToolbox_jsTree.xml

    r945 r957  
    372372   <item> 
    373373    <item> 
     374      <item><content><name href="/projects/mget/export/HEAD/MGET/Trunk/PythonPackage/dist/TracOnlineDocumentation/Documentation/ArcGISReference/GHRSSTLevel4.CreateClimatologicalArcGISRasters.html" icon="/projects/mget/export/HEAD/WikiFiles/MgetTree/img/icon_script.png">Create Climatological Rasters for GHRSST L4 SST</name></content></item> 
     375      <item><content><name href="/projects/mget/export/HEAD/MGET/Trunk/PythonPackage/dist/TracOnlineDocumentation/Documentation/ArcGISReference/GHRSSTLevel4.CreateArcGISRasters.html" icon="/projects/mget/export/HEAD/WikiFiles/MgetTree/img/icon_script.png">Create Rasters for GHRSST L4 SST</name></content></item> 
     376      <item><content><name href="/projects/mget/export/HEAD/MGET/Trunk/PythonPackage/dist/TracOnlineDocumentation/Documentation/ArcGISReference/GHRSSTLevel4.CreateCayulaCornillonFrontsAsArcGISRasters.html" icon="/projects/mget/export/HEAD/WikiFiles/MgetTree/img/icon_script.png">Find Cayula-Cornillon Fronts in GHRSST L4 SST</name></content></item> 
     377      <item><content><name href="/projects/mget/export/HEAD/MGET/Trunk/PythonPackage/dist/TracOnlineDocumentation/Documentation/ArcGISReference/GHRSSTLevel4.InterpolateAtArcGISPoints.html" icon="/projects/mget/export/HEAD/WikiFiles/MgetTree/img/icon_script.png">Interpolate GHRSST L4 SST at Points</name></content></item> 
     378     <content><name icon="/projects/mget/export/HEAD/WikiFiles/MgetTree/img/icon_toolset.png">GHRSST L4 SST</name></content> 
     379    </item> 
     380    <item> 
    374381      <item><content><name href="/projects/mget/export/HEAD/MGET/Trunk/PythonPackage/dist/TracOnlineDocumentation/Documentation/ArcGISReference/MODISL3SSTTimeSeries.CreateClimatologicalArcGISRasters.html" icon="/projects/mget/export/HEAD/WikiFiles/MgetTree/img/icon_script.png">Create Climatological Rasters for PO.DAAC MODIS L3 SST</name></content></item> 
    375382      <item><content><name href="/projects/mget/export/HEAD/MGET/Trunk/PythonPackage/dist/TracOnlineDocumentation/Documentation/ArcGISReference/MODISL3SSTTimeSeries.CreateArcGISRasters.html" icon="/projects/mget/export/HEAD/WikiFiles/MgetTree/img/icon_script.png">Create Rasters for PO.DAAC MODIS L3 SST</name></content></item> 
    … …  
    680687   <content><name icon="/projects/mget/export/HEAD/WikiFiles/MgetTree/img/icon_toolset.png">Statistics</name></content> 
    681688  </item> 
    682   <content><name icon="/projects/mget/export/HEAD/WikiFiles/MgetTree/img/icon_toolbox.png">Marine Geospatial Ecology Tools 0.8a37</name></content> 
     689  <content><name icon="/projects/mget/export/HEAD/WikiFiles/MgetTree/img/icon_toolbox.png">Marine Geospatial Ecology Tools 0.8a38</name></content> 
    683690 </item> 
    684691</root> 

  • MGET/Branches/Jason/PythonPackage/dist/TracOnlineDocumentation/Documentation/ArcGISReference/AVHRRPathfinderSSTTimeSeries.CreateCayulaCornillonFrontsAsArcGISRasters.html

    r945 r957  
    24245.2, please contact the MGET development team.</p><p>Given a temporal resolution and observation time, this tool 
    2525efficiently downloads a time series of Pathfinder SST images, executes 
    26 the Cayula-Cornillon SIED algorithm to identify fronts, and creates 
     26the Cayula and Cornillon SIED algorithm to identify fronts, and creates 
    2727rasters showing the locations of the fronts.</p><p>This tool is complicated and has a lot of parameters. The complex 
    2828dynamics of the ocean, the presence of clouds, the difficulty of 
    … …  
    8585long as a sufficient temperature gradient continues in the direction 
    8686the front was pointing.</p></li></ul><p>In 2005, I obtained a Rational Fortran (Ratfor) version of the 
    87 Cayula-Cornillon algorithm from Dave Ullman. Although it had been 
     87Cayula and Cornillon algorithm from Dave Ullman. Although it had been 
    8888modified extensively from the 1992 version, mainly to incorporate the 
    8989multi-image edge detection (MIED) algorithm (Cayula and Cornillon 
    … …  
    126126coast. Journal of Geophysical Research 104: 23459-23478.</p><p>Ullman, D. S. and P. C. Cornillon. 2000. Evaluation of front detection 
    127127methods for satellite-derived SST data using in situ observations. 
    128 Journal of Atmospheric and Oceanic Technology 17: 1667-1675.</p><br /><p><h2><img width="11" height="11" border="0" src="sm_arrow_down.gif?format=raw" /> Command line syntax</h2></p><div Class="expand" id="id103142">AVHRRPathfinderSSTTimeSeriesCreateCayulaCornillonFrontsAsArcGISRasters_GeoEco &lt;Daily | 5day | 8day | Monthly | Yearly&gt; &lt;Daytime | Nighttime&gt; &lt;outputWorkspace&gt; {Add | Replace} {rasterNameExpressions;rasterNameExpressions...} {Optimized | Standard} {largeCloudSize} {largeCloudDilations} {smallCloudDilations} {cloudErosions} {minCloudSize} {maskLand} {4 | 0 | 1 | 2 | 3 | 5 | 6 | 7} {maskIfBrightnessTempTestFailed} {maskIfCloudTestFailed} {maskIfUniformityTest1Failed} {maskIfUniformityTest2Failed} {maskIfZenithAngleTest1Failed} {maskIfZenithAngleTest2Failed} {maskIfReferenceTestFailed} {maskIfStraySunlightTestFailed} {maskIfEdgeTestFailed} {maskIfGlintTestFailed} {maskIfSSTTestFailed} {medianFilterWindowSize} {histogramWindowSize} {histogramWindowStride} {minPropNonMaskedCells} {minPopProp} {minPopMeanDifference} {minTheta} {minSinglePopCohesion} {minGlobalPopCohesion} {threads} {fillHoles} {thin} {minSize} {rotationOffset} {spatialExtent} {startDate} {endDate} {timeout} {maxRetryTime} {cacheDirectory} {calculateStatistics} {buildRAT} {buildPyramids} {outputCandidateCounts} {outputFrontCounts} {outputWindowStatusCodes} {outputWindowStatusValues} <br /><br /><b>Parameters</b><br /><table width="100%" border="0" cellpadding="5"><tbody><tr><th width="40%"><b>Expression</b></th><th width="60%"><b>Explanation</b></th></tr><tr><td class="info">&lt;Daily | 5day | 8day | Monthly | Yearly&gt;</td><td class="info" align="left"><p>Temporal resolution to use, one of:</p><ul><li><p>Daily - daily images. There are 365 during normal years and 366 
     128Journal of Atmospheric and Oceanic Technology 17: 1667-1675.</p><br /><p><h2><img width="11" height="11" border="0" src="sm_arrow_down.gif?format=raw" /> Command line syntax</h2></p><div Class="expand" id="id103142">AVHRRPathfinderSSTTimeSeriesCreateCayulaCornillonFrontsAsArcGISRasters_GeoEco &lt;Daily | 5day | 8day | Monthly | Yearly&gt; &lt;Daytime | Nighttime&gt; &lt;minPopMeanDifference&gt; &lt;outputWorkspace&gt; {Add | Replace} {rasterNameExpressions;rasterNameExpressions...} {Optimized | Standard} {largeCloudSize} {largeCloudDilations} {smallCloudDilations} {cloudErosions} {minCloudSize} {maskLand} {4 | 0 | 1 | 2 | 3 | 5 | 6 | 7} {maskIfBrightnessTempTestFailed} {maskIfCloudTestFailed} {maskIfUniformityTest1Failed} {maskIfUniformityTest2Failed} {maskIfZenithAngleTest1Failed} {maskIfZenithAngleTest2Failed} {maskIfReferenceTestFailed} {maskIfStraySunlightTestFailed} {maskIfEdgeTestFailed} {maskIfGlintTestFailed} {maskIfSSTTestFailed} {medianFilterWindowSize} {histogramWindowSize} {histogramWindowStride} {minPropNonMaskedCells} {minPopProp} {minTheta} {minSinglePopCohesion} {minGlobalPopCohesion} {threads} {fillHoles} {thin} {minSize} {rotationOffset} {spatialExtent} {startDate} {endDate} {timeout} {maxRetryTime} {cacheDirectory} {calculateStatistics} {buildRAT} {buildPyramids} {outputCandidateCounts} {outputFrontCounts} {outputWindowStatusCodes} {outputWindowStatusValues} <br /><br /><b>Parameters</b><br /><table width="100%" border="0" cellpadding="5"><tbody><tr><th width="40%"><b>Expression</b></th><th width="60%"><b>Explanation</b></th></tr><tr><td class="info">&lt;Daily | 5day | 8day | Monthly | Yearly&gt;</td><td class="info" align="left"><p>Temporal resolution to use, one of:</p><ul><li><p>Daily - daily images. There are 365 during normal years and 366 
    129129during leap years.</p></li></ul><ul><li><p>5day - 5-day images. There are 73 per year. The first image of the 
    130130year starts on January 1. The last image ends on day 365. NODC does 
    … …  
    141141was opposite that used by NOAA-18, combining the data from both into 
    142142a single yearly average was considered inappropriate.</p></li></ul><p>The choice of an appropriate temporal resolution is critical to the 
    143 successful operation of the Cayula-Cornillon algorithm. The algorithm 
     143successful operation of the Cayula and Cornillon algorithm. The algorithm 
    144144is designed to operate on instantaneous images of SST, not averages. 
    145145Therefore, for best results, choose daily temporal resolution.</p><p>You may be tempted to try a lower temporal resolution, such as 5-day, 
    … …  
    169169at 00:00:00 on the first day of the averaging period, while nighttime 
    170170images start at 12:00:00 on the previous day.</p><p>For more information on which satellites were used and their pass 
    171 times and drift rates, please see the <a href="http://www.nodc.noaa.gov/SatelliteData/pathfinder4km/userguide.html">Pathfinder User Guide</a>.</p></td></tr><tr><td class="info">&lt;outputWorkspace&gt;</td><td class="info" align="left"><p>Directory or geodatabase to receive the rasters.</p><p>Unless you have a specific reason to store the rasters in a 
     171times and drift rates, please see the <a href="http://www.nodc.noaa.gov/SatelliteData/pathfinder4km/userguide.html">Pathfinder User Guide</a>.</p></td></tr><tr><td class="info">&lt;minPopMeanDifference&gt;</td><td class="info" align="left"><p>Minimum difference, in degrees C, between the mean temperatures of 
     172two adjacent populations of pixels for a front to be detected between 
     173those two populations.</p><p>The Cayula and Cornillon algorithm passes a moving window over the 
     174image, checking each window for a bimodal distribution in the 
     175temperatures of the pixels within it. When the algorithm detects a 
     176bimodal distribution, it computes the mean temperatures of the two 
     177populations and compares the difference between the means to this 
     178threshold. If the difference is less than this threshold, the 
     179algorithm concludes there is no front present and moves on to the next 
     180window.</p><p>You can use this parameter to eliminate weak fronts by selecting a 
     181value that corresponds to a desired minimum mean temperature 
     182difference. Larger values will detect fewer fronts; smaller values 
     183will detect more fronts. However, bear in mind that Kilpatrick et al. 
     184(2001) conclude that "the global accuracy of the current [circa 2001] 
     185Pathfinder algorithm is 0.02 +/- 0.5 deg C, while comparison with a 
     186radiometric reference of skin temperature (MAERI) yields 0.14 +/- 0.31 
     187deg C". Because of that, we do not advise thresholds below 0.3 to 0.5 
     188deg C. Cayula and Cornillon's (1992) study used data from early 
     189satellites that contributed to the Pathfinder program; they used a 
     190threshold of 0.45 deg C.</p></td></tr><tr><td class="info">&lt;outputWorkspace&gt;</td><td class="info" align="left"><p>Directory or geodatabase to receive the rasters.</p><p>Unless you have a specific reason to store the rasters in a 
    172191geodatabase, we recommend you store them in a directory because it 
    173192will be much faster and allows the rasters to be organized in a tree. 
    … …  
    179198any histogram windows that had sufficiently large numbers of 
    180199non-masked pixels to proceed with the histogramming step of the 
    181 Cayula-Cornillon algorithm.</p></li></ul><ul><li><p>0 - The pixel was a candidate for containing a front -- it was not 
     200Cayula and Cornillon algorithm.</p></li></ul><ul><li><p>0 - The pixel was a candidate for containing a front -- it was not 
    182201masked and it appeared in at least one histogram window with a 
    183202sufficient number of non-masked pixels to proceed with the 
    … …  
    474493to 0.</p></td></tr><tr><td class="info">{medianFilterWindowSize}</td><td class="info" align="left"><p>Window size, in pixels, of the median filter to apply to the input 
    475494image prior to running the histogram analysis step of the 
    476 Cayula-Cornillon algorithm. If not provided, median filtering will not 
     495Cayula and Cornillon algorithm. If not provided, median filtering will not 
    477496be performed.</p><p>If you provide a value, it must be an odd integer greater than or 
    478497equal to 3. The filter window is square and advances across the image 
    … …  
    480499with the median value of the non-masked pixels in the surrounding 
    481500window. All masks are applied before the median filter is executed.</p><p>Median filtering is a traditional first step for certain classes of 
    482 edge detection algorithms. The original Cayula-Cornillon paper used a 
    483 window size of 3.</p></td></tr><tr><td class="info">{histogramWindowSize}</td><td class="info" align="left"><p>Size of the histogram window to use for the Cayula-Cornillon 
     501edge detection algorithms. The original Cayula and Cornillon paper used a 
     502window size of 3.</p></td></tr><tr><td class="info">{histogramWindowSize}</td><td class="info" align="left"><p>Size of the histogram window to use for the Cayula and Cornillon 
    484503algorithm.</p><p>The window is square. The original paper used a window size of 32. 
    485504Although the algorithm is claimed to obtain similar results regardless 
    … …  
    488507the paper carefully before experimenting with different window 
    489508sizes.</p></td></tr><tr><td class="info">{histogramWindowStride}</td><td class="info" align="left"><p>Number of pixels to move the histogram window after each iteration 
    490 of the Cayula-Cornillon algorithm.</p><p>The original paper used a 32x32 window and a stride of 16, to minimize 
     509of the Cayula and Cornillon algorithm.</p><p>The original paper used a 32x32 window and a stride of 16, to minimize 
    491510the CPU time required to execute the algorithm. Cutting the stride in 
    492511half increases the CPU time by a factor of about four. For example, a 
    … …  
    512531accurate. In this case, and the algorithm discards the current window, 
    513532advances to next one, and starts over.</p><p>I do not recommend selecting a value other than 0.25 unless you 
    514 understand the statistical analysis presented in the 1992 paper.</p></td></tr><tr><td class="info">{minPopMeanDifference}</td><td class="info" align="left"><p>Minimum difference in population means.</p><p>After the histogram algorithm separates the non-masked pixels into two 
    515 populations, it computes the means of the two populations. If the 
    516 means differ by less than this parameter value, the algorithm discards 
    517 the current window, advances to next one, and starts over.</p><p>The original intent of this parameter is obscure. The Ratfor code I 
    518 obtained from Dave Ullman used the value 3 and contained the 
    519 explanation "a temperature difference of less than three digital 
    520 counts between the 2 populations is likely to be a result of the 
    521 discrete nature of the data."</p><p>You can use this parameter to eliminate weak fronts by selecting a 
    522 value that corresponds to a desired minimum mean temperature 
    523 difference. For example, for the NOAA NODC 4km AVHRR Pathfinder SST 
    524 data, the value of 1 corresponds to 0.075 degrees. To eliminate fronts 
    525 where the mean temperature difference is less than 0.5 degrees, set 
    526 this parameter to 0.5 / 0.075 = 6.666667.</p></td></tr><tr><td class="info">{minTheta}</td><td class="info" align="left"><p>Minimum criterion function, theta(Topt), as described on pages 
     533understand the statistical analysis presented in the 1992 paper.</p></td></tr><tr><td class="info">{minTheta}</td><td class="info" align="left"><p>Minimum criterion function, theta(Topt), as described on pages 
    52753471-72 of the 1992 paper.</p><p>The criterion function is used to determine whether the histogram 
    528535window contains a bimodal distribution, as would be expected if it 
    … …  
    686693in a histogram window that had a sufficiently large number of 
    687694non-masked pixels to proceed with the histogramming step of the 
    688 Cayula-Cornillon algorithm. If the histogram window stride is less 
     695Cayula and Cornillon algorithm. If the histogram window stride is less 
    689696than the window size, successive histogram windows will overlap, and 
    690697many pixels will have candidate counts greater than 1. Masked pixels 
    … …  
    753760algorithm's tests, increasing or decreasing the number of fronts 
    754761identified in the image. You should only adjust the parameters if you 
    755 feel comfortable deviating from their published values.</p></td></tr></tbody></table></div><p><h2><img width="11" height="11" border="0" src="sm_arrow_down.gif?format=raw" /> Scripting syntax</h2></p><div Class="expand" id="TEST">AVHRRPathfinderSSTTimeSeriesCreateCayulaCornillonFrontsAsArcGISRasters_GeoEco (temporalResolution, observationTime, outputWorkspace, mode, rasterNameExpressions, maskingMethod, largeCloudSize, largeCloudDilations, smallCloudDilations, cloudErosions, minCloudSize, maskLand, minOverallQualityFlag, maskIfBrightnessTempTestFailed, maskIfCloudTestFailed, maskIfUniformityTest1Failed, maskIfUniformityTest2Failed, maskIfZenithAngleTest1Failed, maskIfZenithAngleTest2Failed, maskIfReferenceTestFailed, maskIfStraySunlightTestFailed, maskIfEdgeTestFailed, maskIfGlintTestFailed, maskIfSSTTestFailed, medianFilterWindowSize, histogramWindowSize, histogramWindowStride, minPropNonMaskedCells, minPopProp, minPopMeanDifference, minTheta, minSinglePopCohesion, minGlobalPopCohesion, threads, fillHoles, thin, minSize, rotationOffset, spatialExtent, startDate, endDate, timeout, maxRetryTime, cacheDirectory, calculateStatistics, buildRAT, buildPyramids, outputCandidateCounts, outputFrontCounts, outputWindowStatusCodes, outputWindowStatusValues) <br /><br /><b>Parameters</b><br /><table width="100%" border="0" cellpadding="5"><tbody><tr><th width="40%"><b>Expression</b></th><th width="60%"><b>Explanation</b></th></tr><tr><td class="info">Temporal resolution (Required) </td><td class="info" align="left"><p>Temporal resolution to use, one of:</p><ul><li><p>Daily - daily images. There are 365 during normal years and 366 
     762feel comfortable deviating from their published values.</p></td></tr></tbody></table></div><p><h2><img width="11" height="11" border="0" src="sm_arrow_down.gif?format=raw" /> Scripting syntax</h2></p><div Class="expand" id="TEST">AVHRRPathfinderSSTTimeSeriesCreateCayulaCornillonFrontsAsArcGISRasters_GeoEco (temporalResolution, observationTime, minPopMeanDifference, outputWorkspace, mode, rasterNameExpressions, maskingMethod, largeCloudSize, largeCloudDilations, smallCloudDilations, cloudErosions, minCloudSize, maskLand, minOverallQualityFlag, maskIfBrightnessTempTestFailed, maskIfCloudTestFailed, maskIfUniformityTest1Failed, maskIfUniformityTest2Failed, maskIfZenithAngleTest1Failed, maskIfZenithAngleTest2Failed, maskIfReferenceTestFailed, maskIfStraySunlightTestFailed, maskIfEdgeTestFailed, maskIfGlintTestFailed, maskIfSSTTestFailed, medianFilterWindowSize, histogramWindowSize, histogramWindowStride, minPropNonMaskedCells, minPopProp, minTheta, minSinglePopCohesion, minGlobalPopCohesion, threads, fillHoles, thin, minSize, rotationOffset, spatialExtent, startDate, endDate, timeout, maxRetryTime, cacheDirectory, calculateStatistics, buildRAT, buildPyramids, outputCandidateCounts, outputFrontCounts, outputWindowStatusCodes, outputWindowStatusValues) <br /><br /><b>Parameters</b><br /><table width="100%" border="0" cellpadding="5"><tbody><tr><th width="40%"><b>Expression</b></th><th width="60%"><b>Explanation</b></th></tr><tr><td class="info">Temporal resolution (Required) </td><td class="info" align="left"><p>Temporal resolution to use, one of:</p><ul><li><p>Daily - daily images. There are 365 during normal years and 366 
    756763during leap years.</p></li></ul><ul><li><p>5day - 5-day images. There are 73 per year. The first image of the 
    757764year starts on January 1. The last image ends on day 365. NODC does 
    … …  
    768775was opposite that used by NOAA-18, combining the data from both into 
    769776a single yearly average was considered inappropriate.</p></li></ul><p>The choice of an appropriate temporal resolution is critical to the 
    770 successful operation of the Cayula-Cornillon algorithm. The algorithm 
     777successful operation of the Cayula and Cornillon algorithm. The algorithm 
    771778is designed to operate on instantaneous images of SST, not averages. 
    772779Therefore, for best results, choose daily temporal resolution.</p><p>You may be tempted to try a lower temporal resolution, such as 5-day, 
    … …  
    796803at 00:00:00 on the first day of the averaging period, while nighttime 
    797804images start at 12:00:00 on the previous day.</p><p>For more information on which satellites were used and their pass 
    798 times and drift rates, please see the <a href="http://www.nodc.noaa.gov/SatelliteData/pathfinder4km/userguide.html">Pathfinder User Guide</a>.</p></td></tr><tr><td class="info">Output workspace (Required) </td><td class="info" align="left"><p>Directory or geodatabase to receive the rasters.</p><p>Unless you have a specific reason to store the rasters in a 
     805times and drift rates, please see the <a href="http://www.nodc.noaa.gov/SatelliteData/pathfinder4km/userguide.html">Pathfinder User Guide</a>.</p></td></tr><tr><td class="info">Front detection threshold (Required) </td><td class="info" align="left"><p>Minimum difference, in degrees C, between the mean temperatures of 
     806two adjacent populations of pixels for a front to be detected between 
     807those two populations.</p><p>The Cayula and Cornillon algorithm passes a moving window over the 
     808image, checking each window for a bimodal distribution in the 
     809temperatures of the pixels within it. When the algorithm detects a 
     810bimodal distribution, it computes the mean temperatures of the two 
     811populations and compares the difference between the means to this 
     812threshold. If the difference is less than this threshold, the 
     813algorithm concludes there is no front present and moves on to the next 
     814window.</p><p>You can use this parameter to eliminate weak fronts by selecting a 
     815value that corresponds to a desired minimum mean temperature 
     816difference. Larger values will detect fewer fronts; smaller values 
     817will detect more fronts. However, bear in mind that Kilpatrick et al. 
     818(2001) conclude that "the global accuracy of the current [circa 2001] 
     819Pathfinder algorithm is 0.02 +/- 0.5 deg C, while comparison with a 
     820radiometric reference of skin temperature (MAERI) yields 0.14 +/- 0.31 
     821deg C". Because of that, we do not advise thresholds below 0.3 to 0.5 
     822deg C. Cayula and Cornillon's (1992) study used data from early 
     823satellites that contributed to the Pathfinder program; they used a 
     824threshold of 0.45 deg C.</p></td></tr><tr><td class="info">Output workspace (Required) </td><td class="info" align="left"><p>Directory or geodatabase to receive the rasters.</p><p>Unless you have a specific reason to store the rasters in a 
    799825geodatabase, we recommend you store them in a directory because it 
    800826will be much faster and allows the rasters to be organized in a tree. 
    … …  
    806832any histogram windows that had sufficiently large numbers of 
    807833non-masked pixels to proceed with the histogramming step of the 
    808 Cayula-Cornillon algorithm.</p></li></ul><ul><li><p>0 - The pixel was a candidate for containing a front -- it was not 
     834Cayula and Cornillon algorithm.</p></li></ul><ul><li><p>0 - The pixel was a candidate for containing a front -- it was not 
    809835masked and it appeared in at least one histogram window with a 
    810836sufficient number of non-masked pixels to proceed with the 
    … …  
    11011127to 0.</p></td></tr><tr><td class="info">Median filter window size (Optional) </td><td class="info" align="left"><p>Window size, in pixels, of the median filter to apply to the input 
    11021128image prior to running the histogram analysis step of the 
    1103 Cayula-Cornillon algorithm. If not provided, median filtering will not 
     1129Cayula and Cornillon algorithm. If not provided, median filtering will not 
    11041130be performed.</p><p>If you provide a value, it must be an odd integer greater than or 
    11051131equal to 3. The filter window is square and advances across the image 
    … …  
    11071133with the median value of the non-masked pixels in the surrounding 
    11081134window. All masks are applied before the median filter is executed.</p><p>Median filtering is a traditional first step for certain classes of 
    1109 edge detection algorithms. The original Cayula-Cornillon paper used a 
    1110 window size of 3.</p></td></tr><tr><td class="info">Histogram window size (Optional) </td><td class="info" align="left"><p>Size of the histogram window to use for the Cayula-Cornillon 
     1135edge detection algorithms. The original Cayula and Cornillon paper used a 
     1136window size of 3.</p></td></tr><tr><td class="info">Histogram window size (Optional) </td><td class="info" align="left"><p>Size of the histogram window to use for the Cayula and Cornillon 
    11111137algorithm.</p><p>The window is square. The original paper used a window size of 32. 
    11121138Although the algorithm is claimed to obtain similar results regardless 
    … …  
    11151141the paper carefully before experimenting with different window 
    11161142sizes.</p></td></tr><tr><td class="info">Histogram window stride (Optional) </td><td class="info" align="left"><p>Number of pixels to move the histogram window after each iteration 
    1117 of the Cayula-Cornillon algorithm.</p><p>The original paper used a 32x32 window and a stride of 16, to minimize 
     1143of the Cayula and Cornillon algorithm.</p><p>The original paper used a 32x32 window and a stride of 16, to minimize 
    11181144the CPU time required to execute the algorithm. Cutting the stride in 
    11191145half increases the CPU time by a factor of about four. For example, a 
    … …  
    11391165accurate. In this case, and the algorithm discards the current window, 
    11401166advances to next one, and starts over.</p><p>I do not recommend selecting a value other than 0.25 unless you 
    1141 understand the statistical analysis presented in the 1992 paper.</p></td></tr><tr><td class="info">Minimum population mean difference (Optional) </td><td class="info" align="left"><p>Minimum difference in population means.</p><p>After the histogram algorithm separates the non-masked pixels into two 
    1142 populations, it computes the means of the two populations. If the 
    1143 means differ by less than this parameter value, the algorithm discards 
    1144 the current window, advances to next one, and starts over.</p><p>The original intent of this parameter is obscure. The Ratfor code I 
    1145 obtained from Dave Ullman used the value 3 and contained the 
    1146 explanation "a temperature difference of less than three digital 
    1147 counts between the 2 populations is likely to be a result of the 
    1148 discrete nature of the data."</p><p>You can use this parameter to eliminate weak fronts by selecting a 
    1149 value that corresponds to a desired minimum mean temperature 
    1150 difference. For example, for the NOAA NODC 4km AVHRR Pathfinder SST 
    1151 data, the value of 1 corresponds to 0.075 degrees. To eliminate fronts 
    1152 where the mean temperature difference is less than 0.5 degrees, set 
    1153 this parameter to 0.5 / 0.075 = 6.666667.</p></td></tr><tr><td class="info">Minimum value for criterion function (Optional) </td><td class="info" align="left"><p>Minimum criterion function, theta(Topt), as described on pages 
     1167understand the statistical analysis presented in the 1992 paper.</p></td></tr><tr><td class="info">Minimum value for criterion function (Optional) </td><td class="info" align="left"><p>Minimum criterion function, theta(Topt), as described on pages 
    1154116871-72 of the 1992 paper.</p><p>The criterion function is used to determine whether the histogram 
    11551169window contains a bimodal distribution, as would be expected if it 
    … …  
    13131327in a histogram window that had a sufficiently large number of 
    13141328non-masked pixels to proceed with the histogramming step of the 
    1315 Cayula-Cornillon algorithm. If the histogram window stride is less 
     1329Cayula and Cornillon algorithm. If the histogram window stride is less 
    13161330than the window size, successive histogram windows will overlap, and 
    13171331many pixels will have candidate counts greater than 1. Masked pixels 

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    11<?xml version="1.0" encoding="utf-8"?> 
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    3 <html xmlns="http://www.w3.org/1999/xhtml"><head><title>Marine Geospatial Ecology Tools - ArcGIS Reference</title><link rel="stylesheet" type="text/css" href="../Documentation.css?format=raw" /><meta http-equiv="Content-Type" content="text/html; charset=UTF-8" /></head><body><p class="title1">Marine Geospatial Ecology Tools</p><p class="title2">ArcGIS Geoprocessing Reference</p><table class="docmetadata"><tr><td class="docmetadata1">MGET version:</td><td class="docmetadata2">0.8a37</td></tr><tr><td class="docmetadata1">Document version:</td><td class="docmetadata2">$Id: ArcGISReference.xsl 497 2010-04-01 18:41:19Z jjr8 $</td></tr><tr><td class="docmetadata1">Maintainer email:</td><td class="docmetadata2"><a href="mailto:jason.roberts@duke.edu">jason.roberts@duke.edu</a></td></tr><tr><td class="docmetadata1">MGET home page:</td><td class="docmetadata2"><a href="http://code.nicholas.duke.edu/projects/mget">http://code.nicholas.duke.edu/projects/mget</a></td></tr></table><h1><a id="Contents">Contents</a></h1><ul><li><a href="#IndexByLabel">Tool Index, By Label</a></li><li><a href="#IndexByName">Tool Index, By Name</a></li><li><a href="#Copyright">Copyright and License</a></li></ul><h1><a id="IndexByLabel">Tool Index, By Label</a></h1><p>In the table below, <i>Tool Label</i> is the name of the tool as it appears in the 
    4         ArcGIS toolbox.</p><p><table><tr><th>Tool Label</th><th>Description</th></tr><tr><td><a href="ArcGISPoints.AppendPointsToFeatureClass2.html?format=raw">Append Points</a></td><td>Appends points to an existing ArcGIS point feature class.</td></tr><tr><td><a href="ArcGISPolygons.AppendPolygonToFeatureClass2.html?format=raw">Append Polygon</a></td><td>Appends a polygon to an existing ArcGIS polygon feature class.</td></tr><tr><td><a href="ArcGISPolygons.AppendRectangleToFeatureClass2.html?format=raw">Append Rectangle</a></td><td>Appends a rectangle to an existing ArcGIS polygon feature class.</td></tr><tr><td><a href="Field.CalculateArcGISField.html?format=raw">Calculate Field Using a Python Expression</a></td><td>Calculates the value of a table field using a Python expression.</td></tr><tr><td><a href="Field.CalculateArcGISFields.html?format=raw">Calculate Fields Using Python Expressions</a></td><td>Calculates values for one or more fields of a table using Python expressions.</td></tr><tr><td><a href="SpeciesDiversity.CalculateDiversityIndexForArcGISPolygons.html?format=raw">Calculate Species Diversity Index for Polygons</a></td><td>Given polygons representing zones of interest and points representing species occurrence observations, calculates a species diversity index for each polygon.</td></tr><tr><td><a href="CayulaCornillonEdgeDetection.DetectEdgesInArcGISRaster.html?format=raw">Cayula-Cornillon Fronts in ArcGIS Raster</a></td><td>Finds fronts in an ArcGIS raster using the Cayula and Cornillon (1992) single image edge detection (SIED) algorithm.</td></tr><tr><td><a href="CayulaCornillonEdgeDetection.DetectEdgesInArcGISRastersArcGISTable.html?format=raw">Cayula-Cornillon Fronts in ArcGIS Rasters Listed in Table</a></td><td>Finds fronts in ArcGIS rasters listed in a table using the Cayula-Cornillon (1992) single-image edge detection algorithm.</td></tr><tr><td><a href="CayulaCornillonEdgeDetection.DetectEdgesInBinaryRaster.html?format=raw">Cayula-Cornillon Fronts in Binary Raster</a></td><td>Finds fronts in a binary raster using the Cayula and Cornillon (1992) single image edge detection (SIED) algorithm.</td></tr><tr><td><a href="CoastWatchAVHRR.FindFrontsAsArcGISRaster.html?format=raw">Cayula-Cornillon Fronts in CoastWatch Image as ArcGIS Raster</a></td><td>Finds fronts in a CoastWatch POES AVHRR image using the Cayula-Cornillon (1992) single-image edge detection algorithm and outputs them to an ArcGIS raster.</td></tr><tr><td><a href="CoastWatchAVHRR.FindFrontsAsBinaryRaster.html?format=raw">Cayula-Cornillon Fronts in CoastWatch Image as Binary Raster</a></td><td>Finds fronts in a CoastWatch POES AVHRR image using the Cayula-Cornillon (1992) single-image edge detection algorithm and outputs them to a binary raster.</td></tr><tr><td><a href="CoastWatchAVHRR.FindFrontsAsArcGISRastersArcGISTable.html?format=raw">Cayula-Cornillon Fronts in CoastWatch Images Listed in Table as ArcGIS Rasters</a></td><td>Finds fronts in CoastWatch POES AVHRR images listed in a table using the Cayula-Cornillon (1992) single-image edge detection algorithm and outputs them as ArcGIS rasters.</td></tr><tr><td><a href="ESRLClimateIndices.ClassifyONIEpisodesInTimeSeriesArcGISTable.html?format=raw">Classify Oceanic Nino Index (ONI) Episodes in Table</a></td><td>Given a time series table of monthly Oceanic Nino Index (ONI) numerical values, classifies each month as part of a normal, El Nino (warm), or La Nina (cold) episode.</td></tr><tr><td><a href="RExploratoryPlots.ClevelandPlotForArcGISTable.html?format=raw">Cleveland Plot for Table</a></td><td>Creates a multi-panel Cleveland dotplot for a table.</td></tr><tr><td><a href="CoastWatchAVHRR.ToArcGISRaster.html?format=raw">CoastWatch Image to ArcGIS Raster</a></td><td>Extracts and converts CoastWatch POES AVHRR image, specified by a file plus a variable in that file, to an ArcGIS raster.</td></tr><tr><td><a href="CoastWatchAVHRR.ToBinaryRaster.html?format=raw">CoastWatch Image to Binary Raster</a></td><td>Extracts and converts CoastWatch POES AVHRR image, specified by a file plus a variable in that file, to a binary raster.</td></tr><tr><td><a href="CoastWatchAVHRR.ToArcGISRasterArcGISTable.html?format=raw">CoastWatch Images Listed in Table To ArcGIS Rasters</a></td><td>Creates an ArcGIS raster from each CoastWatch POES AVHRR image listed in a table of images, where one field specifies the file path containing the image and another specifies the variable that represents the image.</td></tr><tr><td><a href="CoastWatchAVHRR.ToBinaryRasterArcGISTable.html?format=raw">CoastWatch Images Listed in Table To Binary Rasters</a></td><td>Creates a binary raster from each CoastWatch POES AVHRR image listed in a table of images, where one field specifies the file path containing the image and another specifies the variable that represents the image.</td></tr><tr><td><a href="CoastWatchAVHRR.ToCoastWatchHDF.html?format=raw">CoastWatch Images to HDF</a></td><td>Creates a CoastWatch HDF by importing images from a list of CoastWatch POES AVHRR CWFs or HDFs.</td></tr><tr><td><a href="NetCDF.Convert2DVariableToArcGISRaster.html?format=raw">Convert 2D Variable in NetCDF to ArcGIS Raster</a></td><td>Converts a two-dimensional variable in a netCDF file to an ArcGIS raster.</td></tr><tr><td><a href="NetCDF.Convert2DVariableToArcInfoASCIIGrid.html?format=raw">Convert 2D Variable in NetCDF to ArcInfo ASCII Grid</a></td><td>Converts a two-dimensional variable in a netCDF file to a text file in ArcInfo ASCII Grid format.</td></tr><tr><td><a href="NetCDF.Convert2DVariableToBinaryRaster.html?format=raw">Convert 2D Variable in NetCDF to Binary Raster</a></td><td>Converts a two-dimensional variable in a netCDF file to a binary raster.</td></tr><tr><td><a href="NetCDF.Convert2DVariableInNetCDFsInArcGISTableToArcGISRasters.html?format=raw">Convert 2D Variable in NetCDFs Listed in Table To ArcGIS Rasters</a></td><td>Converts a two-dimensional variable in each netCDF file in a table to an ArcGIS raster.</td></tr><tr><td><a href="NetCDF.Convert2DVariableInNetCDFsInArcGISTableToArcInfoASCIIGrids.html?format=raw">Convert 2D Variable in NetCDFs Listed in Table To ArcInfo ASCII Grids</a></td><td>Converts a two-dimensional variable in each netCDF file in a table to a text file in ArcInfo ASCII Grid format.</td></tr><tr><td><a href="NetCDF.Convert2DVariableInNetCDFsInArcGISTableToBinaryRasters.html?format=raw">Convert 2D Variable in NetCDFs Listed in Table To Binary Rasters</a></td><td>Converts a two-dimensional variable in each netCDF file in a table to a binary raster.</td></tr><tr><td><a href="SpatiaLiteDatabase.ImportFromArcGISWorkspace.html?format=raw">Convert ArcGIS Geodatasets to SpatiaLite Tables</a></td><td>Converts ArcGIS tables, shapefiles, and feature classes to tables in a SpatiaLite database.</td></tr><tr><td><a href="ArcGISRaster.ToLines.html?format=raw">Convert ArcGIS Raster to Lines</a></td><td>Converts an ArcGIS raster to a feature class of lines that connect adjacent foreground raster cells.</td></tr><tr><td><a href="ArcGISRaster.ToPoints.html?format=raw">Convert ArcGIS Raster to Points</a></td><td>Converts an ArcGIS raster to a feature class of points that occur at the centers of the raster cells.</td></tr><tr><td><a href="ArcGISRaster.ToPolygonOutlines.html?format=raw">Convert ArcGIS Raster to Polygon Outlines</a></td><td>Converts an ArcGIS raster to lines that outline groups of adjacent raster cells having the same value.</td></tr><tr><td><a href="ArcGISRaster.ToPolygons.html?format=raw">Convert ArcGIS Raster to Polygons</a></td><td>Converts an ArcGIS raster to polygons that encompass groups of adjacent raster cells having the same value.</td></tr><tr><td><a href="ArcGISRaster.ToLinesArcGISTable.html?format=raw">Convert ArcGIS Rasters Listed in Table to Lines</a></td><td>Converts the ArcGIS rasters listed in a table to lines that outline groups of adjacent raster cells having the same value.</td></tr><tr><td><a href="ArcGISRaster.ToPointsArcGISTable.html?format=raw">Convert ArcGIS Rasters Listed in Table to Points</a></td><td>Converts the ArcGIS rasters listed in a table to points that occur at the centers of the raster cells.</td></tr><tr><td><a href="ArcGISRaster.ToPolygonOutlinesArcGISTable.html?format=raw">Convert ArcGIS Rasters Listed in Table to Polygon Outlines</a></td><td>Converts the ArcGIS rasters listed in a table to lines that outline groups of adjacent raster cells having the same value.</td></tr><tr><td><a href="ArcGISRaster.ToPolygonsArcGISTable.html?format=raw">Convert ArcGIS Rasters Listed in Table to Polygons</a></td><td>Converts the ArcGIS rasters listed in a table to polygons that encompass groups of adjacent raster cells having the same value.</td></tr><tr><td><a href="ArcInfoASCIIGrid.ToArcGISRaster.html?format=raw">Convert ArcInfo ASCII Grid to ArcGIS Raster</a></td><td>Converts a text file in ArcInfo ASCII Grid format to an ArcGIS raster.</td></tr><tr><td><a href="ArcInfoASCIIGrid.ToArcGISRasterArcGISTable.html?format=raw">Convert ArcInfo ASCII Grids Listed in Table To ArcGIS Rasters</a></td><td>Converts each ArcInfo ASCII Grid text file in a table to an ArcGIS raster.</td></tr><tr><td><a href="BinaryRaster.ToArcGISRaster.html?format=raw">Convert Binary Raster to ArcGIS Raster</a></td><td>Converts a two-dimensional binary raster to an ArcGIS raster.</td></tr><tr><td><a href="BinaryRaster.ToArcInfoASCIIGrid.html?format=raw">Convert Binary Raster to ArcInfo ASCII Grid</a></td><td>Converts a two-dimensional binary raster to a text file in ArcGIS ASCII Grid format.</td></tr><tr><td><a href="BinaryRaster.ToArcGISRasterArcGISTable.html?format=raw">Convert Binary Rasters Listed in Table To ArcGIS Rasters</a></td><td>Converts each two-dimensional binary raster in a table to an ArcGIS raster.</td></tr><tr><td><a href="BinaryRaster.ToArcInfoASCIIGridArcGISTable.html?format=raw">Convert Binary Rasters Listed in Table To ArcInfo ASCII Grids</a></td><td>Converts each two-dimensional binary raster in a table to a text file in ArcInfo ASCII Grid format.</td></tr><tr><td><a href="CoastWatchAVHRR.ToCoastWatchHDFArcGISTable.html?format=raw">Convert CoastWatch Images Listed in Table to HDFs</a></td><td>Creates CoastWatch HDFs in a specified output directory by importing images from the CoastWatch POES AVHRR CWFs or HDFs listed in a table.</td></tr><tr><td><a href="CheltonMesocaleEddyPoints.ConvertToSpatiaLite.html?format=raw">Convert Mesoscale Eddies NetCDF to SpatiaLite Database</a></td><td>Converts the Chelton et al. (2011) mesoscale eddy database netCDF file to a SpatiaLite database.</td></tr><tr><td><a href="HDF.SDSToArcGISRaster.html?format=raw">Convert SDS in HDF to ArcGIS Raster</a></td><td>Converts a Scientific Data Set (SDS) in an HDF file to an ArcGIS raster.</td></tr><tr><td><a href="HDF.SDSToArcInfoASCIIGrid.html?format=raw">Convert SDS in HDF to ArcInfo ASCII Grid</a></td><td>Converts a Scientific Data Set (SDS) in an HDF file to a text file in ArcInfo ASCII Grid format.</td></tr><tr><td><a href="HDF.SDSToBinaryRaster.html?format=raw">Convert SDS in HDF to Binary Raster</a></td><td>Converts a Scientific Data Set (SDS) in an HDF file to a binary raster.</td></tr><tr><td><a href="HDF.ToArcGISRasterArcGISTable.html?format=raw">Convert SDS in HDFs Listed in Table To ArcGIS Rasters</a></td><td>Converts a Scientific Data Set (SDS) in each HDF file in a table to an ArcGIS raster.</td></tr><tr><td><a href="HDF.ToArcInfoASCIIGridArcGISTable.html?format=raw">Convert SDS in HDFs Listed in Table To ArcInfo ASCII Grids</a></td><td>Converts a Scientific Data Set (SDS) in each HDF file in a table to a text file in an ArcInfo ASCII Grid format.</td></tr><tr><td><a href="HDF.ToBinaryRasterArcGISTable.html?format=raw">Convert SDS in HDFs Listed in Table To Binary Rasters</a></td><td>Converts a Scientific Data Set (SDS) in each HDF file in a table to a binary raster.</td></tr><tr><td><a href="SIRFile.ToArcGISRaster.html?format=raw">Convert SIR File to ArcGIS Raster</a></td><td>Converts a SIR file to an ArcGIS raster.</td></tr><tr><td><a href="SIRFile.ToArcInfoASCIIGrid.html?format=raw">Convert SIR File to ArcInfo ASCII Grid</a></td><td>Converts a SIR file to a text file in ArcInfo ASCII Grid format.</td></tr><tr><td><a href="SIRFile.ToBinaryRaster.html?format=raw">Convert SIR File to Binary Raster</a></td><td>Converts a SIR file to a binary raster.</td></tr><tr><td><a href="SIRFile.ToArcGISRasterArcGISTable.html?format=raw">Convert SIR Files Listed in Table To ArcGIS Rasters</a></td><td>Converts each SIR file in a table to an ArcGIS raster.</td></tr><tr><td><a href="SIRFile.ToArcInfoASCIIGridArcGISTable.html?format=raw">Convert SIR Files Listed in Table To ArcInfo ASCII Grids</a></td><td>Converts each SIR file in a table to a text file in ArcInfo ASCII grid format.</td></tr><tr><td><a href="SIRFile.ToBinaryRasterArcGISTable.html?format=raw">Convert SIR Files Listed in Table To Binary Rasters</a></td><td>Converts each SIR file in a table to a binary raster.</td></tr><tr><td><a href="SpatiaLiteDatabase.ExportToArcGISWorkspace.html?format=raw">Convert SpatiaLite Tables to ArcGIS Geodatasets</a></td><td>Converts tables in a SpatiaLite database to ArcGIS tables, shapefiles, and feature classes.</td></tr><tr><td><a href="CoastWatchAVHRR.CopyNavigationOffsets.html?format=raw">Copy CoastWatch Navigation Offsets</a></td><td>Copies the navigation offsets from one variable in a CoastWatch POES AVHRR CWF or HDF file to one or more variables in another file.</td></tr><tr><td><a href="CoastWatchAVHRR.CopyNavigationOffsetsArcGISTable.html?format=raw">Copy CoastWatch Navigation Offsets for Files Listed in Table</a></td><td>Copies navigation offsets from a source variable to destination variables in CoastWatch POES AVHRR CWF or HDF files listed in a table.</td></tr><tr><td><a href="Directory.CopyArcGISTable.html?format=raw">Copy Directories Listed in Table</a></td><td>Copies the directories listed in a table.</td></tr><tr><td><a href="Directory.Copy.html?format=raw">Copy Directory</a></td><td>Copies a directory, including its subdirectories and files.</td></tr><tr><td><a href="File.Copy.html?format=raw">Copy File</a></td><td>Copies a file.</td></tr><tr><td><a href="File.CopyArcGISTable.html?format=raw">Copy Files Listed in Table</a></td><td>Copies the files listed in a table.</td></tr><tr><td><a href="ArcGISRaster.Copy.html?format=raw">Copy Raster</a></td><td>Copies an ArcGIS raster.</td></tr><tr><td><a href="ArcGISRaster.CopyArcGISTable.html?format=raw">Copy Rasters Listed in Table</a></td><td>Copies the ArcGIS rasters listed in a table.</td></tr><tr><td><a href="Shapefile.Copy.html?format=raw">Copy Shapefile</a></td><td>Copies a shapefile.</td></tr><tr><td><a href="Shapefile.CopyToDirectory.html?format=raw">Copy Shapefile To Directory</a></td><td>Copies a shapefile to a directory.</td></tr><tr><td><a href="ArcGISPolygons.CreateBoundingBoxForArcGISGeoDatasets.html?format=raw">Create Bounding Box for Geodatasets</a></td><td>Creates a new ArcGIS polygon feature class and a bounding box (a minimum bounding rectangle) within it that encompasses the extents of one or more ArcGIS geodatasets.</td></tr><tr><td><a href="AVHRRPathfinderSSTTimeSeries.CreateClimatologicalArcGISRasters.html?format=raw">Create Climatological Rasters for AVHRR Pathfinder V5 SST</a></td><td>Creates climatological rasters from AVHRR Pathfinder Version 5 SST images published by NOAA NODC.</td></tr><tr><td><a href="AvisoGriddedGeostrophicCurrents.CreateClimatologicalArcGISRasters.html?format=raw">Create Climatological Rasters for Aviso Geostrophic Currents Product</a></td><td>Creates climatological rasters for an Aviso gridded geostrophic currents product.</td></tr><tr><td><a href="AvisoGriddedSSH.CreateClimatologicalArcGISRasters.html?format=raw">Create Climatological Rasters for Aviso SSH Product</a></td><td>Creates climatological rasters for an Aviso gridded sea surface height product.</td></tr><tr><td><a href="AvisoGriddedSignificantWaveHeight.CreateClimatologicalArcGISRasters.html?format=raw">Create Climatological Rasters for Aviso Significant Wave Height Product</a></td><td>Creates climatological rasters for an Aviso gridded significant wave height product.</td></tr><tr><td><a href="AvisoGriddedWindSpeedModulus.CreateClimatologicalArcGISRasters.html?format=raw">Create Climatological Rasters for Aviso Wind Speed Modulus Product</a></td><td>Creates climatological rasters for an Aviso gridded wind speed modulus product.</td></tr><tr><td><a href="HYCOMGLBa008Equatorial3D.CreateClimatologicalArcGISRasters.html?format=raw">Create Climatological Rasters for HYCOM GLBa0.08 Equatorial 3D Variable</a></td><td>Creates climatological rasters for a 3D variable of the equatorial (Mercator) region of the HYCOM GLBa0.08 dataset</td></tr><tr><td><a href="HYCOMGLBa008Equatorial4D.CreateClimatologicalArcGISRasters.html?format=raw">Create Climatological Rasters for HYCOM GLBa0.08 Equatorial 4D Variable</a></td><td>Creates climatological rasters for a 4D variable of the equatorial (Mercator) region of the HYCOM GLBa0.08 dataset</td></tr><tr><td><a href="HYCOMGOMl0043D.CreateClimatologicalArcGISRasters.html?format=raw">Create Climatological Rasters for HYCOM GOMl0.04 3D Variable</a></td><td>Creates climatological rasters for a HYCOM GOMl0.04 3D variable</td></tr><tr><td><a href="HYCOMGOMl0044D.CreateClimatologicalArcGISRasters.html?format=raw">Create Climatological Rasters for HYCOM GOMl0.04 4D Variable</a></td><td>Creates climatological rasters for a HYCOM GOMl0.04 4D variable</td></tr><tr><td><a href="OceanColorLevel3SMITimeSeries.CreateClimatologicalArcGISRasters.html?format=raw">Create Climatological Rasters for NASA OceanColor L3 SMI Product</a></td><td>Creates climatological rasters for a Level 3 Standard Mapped Image (SMI) product published by the NASA GSFC OceanColor Group.</td></tr><tr><td><a href="OSCAR5DayThirdDegreeCurrents.CreateClimatologicalArcGISRasters.html?format=raw">Create Climatological Rasters for OSCAR Currents</a></td><td>Creates climatological rasters for NOAA OSCAR 5-day unfiltered 1/3 degree ocean surface currents.</td></tr><tr><td><a href="MODISL3SSTTimeSeries.CreateClimatologicalArcGISRasters.html?format=raw">Create Climatological Rasters for PO.DAAC MODIS L3 SST</a></td><td>Creates climatological rasters from MODIS Level 3 SST images published by NASA JPL PO.DAAC.</td></tr><tr><td><a href="ROMSCoSiNE3D.CreateClimatologicalArcGISRasters.html?format=raw">Create Climatological Rasters for Pacific ROMS-CoSiNE 3D Variable</a></td><td>Creates climatological rasters for a Pacific ROMS-CoSiNE 3D variable</td></tr><tr><td><a href="ROMSCoSiNE4D.CreateClimatologicalArcGISRasters.html?format=raw">Create Climatological Rasters for Pacific ROMS-CoSiNE 4D Variable</a></td><td>Creates climatological rasters for a Pacific ROMS-CoSiNE 4D variable</td></tr><tr><td><a href="CoastWatchAVHRR.CreateMaskAsArcGISRaster.html?format=raw">Create CoastWatch Mask as ArcGIS Raster</a></td><td>Creates a mask, in ArcGIS raster format, for a CoastWatch POES AVHRR image.</td></tr><tr><td><a href="CoastWatchAVHRR.CreateMaskAsBinaryRaster.html?format=raw">Create CoastWatch Mask as Binary Raster</a></td><td>Creates a mask, in binary raster format, for a CoastWatch POES AVHRR image.</td></tr><tr><td><a href="CoralReefConnectivity.CreateSimulationFromArcGISRasters.html?format=raw">Create Coral Reef Connectivity Simulation From ArcGIS Rasters</a></td><td>Creates a coral reef connectivity simulation and initializes it with reef data in ArcGIS rasters.</td></tr><tr><td><a href="HYCOMGLBa008Equatorial4D.CreateCurrentVectorsAsArcGISFeatureClasses.html?format=raw">Create Current Vectors for HYCOM GLBa0.08 Equatorial Region</a></td><td>Creates line feature classes representing the current vectors for the equatorial (Mercator) region of the HYCOM GLBa0.08 dataset.</td></tr><tr><td><a href="HYCOMGOMl0044D.CreateCurrentVectorsAsArcGISFeatureClasses.html?format=raw">Create Current Vectors for HYCOM GOMl0.04</a></td><td>Creates line feature classes representing the vectors of HYCOM GOMl0.04 currents.</td></tr><tr><td><a href="Directory.Create.html?format=raw">Create Directory</a></td><td>Creates a directory, including any parent directories that are missing.</td></tr><tr><td><a href="FEET.CreateEnvelopes.html?format=raw">Create Fishery Effort Envelopes</a></td><td>Models the spatial distribution of fishing effort for fisheries for which no spatially-explicit effort data is available.</td></tr><tr><td><a href="ArcGISFishnets.CreateFishnet.html?format=raw">Create Fishnet</a></td><td>Creates a grid of rectangular polygons.</td></tr><tr><td><a href="ArcGISFishnets.CreateFishnetForPoints.html?format=raw">Create Fishnet for Points</a></td><td>Creates a grid of rectangular cells that overlap the input points and optionally calculates summary statistics for the points that intersect each cell.</td></tr><tr><td><a href="ArcGISLines.FromVectorComponentRasters.html?format=raw">Create Lines From Vector Component Rasters</a></td><td>Given rasters representing the x and y components of a vector field, such as the u and v rasters for ocean currents, this tool creates a feature class of lines representing the vectors, similar to a "quiver plot".</td></tr><tr><td><a href="ArcGISLines.FromVectorComponentRastersInArcGISTable.html?format=raw">Create Lines From Vector Component Rasters Listed in Table</a></td><td>Given a table of rasters representing the x and y components of vector fields, such as the u and v rasters for ocean currents, this tool creates feature classes of lines representing the vectors, similar to a "quiver plot".</td></tr><tr><td><a href="CoastWatchAVHRR.CreateMasksAsArcGISRastersArcGISTable.html?format=raw">Create Masks as ArcGIS Rasters for CoastWatch Images Listed in Table</a></td><td>Creates masks, in ArcGIS raster format, for CoastWatch POES AVHRR images listed in a table.</td></tr><tr><td><a href="CoastWatchAVHRR.CreateMasksAsBinaryRastersArcGISTable.html?format=raw">Create Masks as Binary Rasters for CoastWatch Images Listed in Table</a></td><td>Creates masks, in binary raster format, for CoastWatch POES AVHRR images listed in a table.</td></tr><tr><td><a href="ArcGISPoints.CreateFeatureClassWithPoints2.html?format=raw">Create Points</a></td><td>Creates points in a new ArcGIS point feature class.</td></tr><tr><td><a href="ArcGISPoints.CreatePointsAlongLines.html?format=raw">Create Points Along Lines</a></td><td>Creates points along lines at a specified interval.</td></tr><tr><td><a href="ArcGISPolygons.CreateFeatureClassWithPolygon2.html?format=raw">Create Polygon</a></td><td>Creates a polygon in a new ArcGIS polygon feature class.</td></tr><tr><td><a href="CoRTADv32D.CreateArcGISRaster.html?format=raw">Create Raster for CoRTAD 2D Variable</a></td><td>Creates a raster for a CoRTAD 2D variable.</td></tr><tr><td><a href="ROMSCoSiNE2D.CreateArcGISRaster.html?format=raw">Create Raster for Pacific ROMS-CoSiNE 2D Variable</a></td><td>Creates a raster for a Pacific ROMS-CoSiNE 2D variable.</td></tr><tr><td><a href="AVHRRPathfinderSSTTimeSeries.CreateArcGISRasters.html?format=raw">Create Rasters for AVHRR Pathfinder V5 SST</a></td><td>Creates rasters for AVHRR Pathfinder Version 5 SST images published by NOAA NODC.</td></tr><tr><td><a href="AvisoGriddedGeostrophicCurrents.CreateArcGISRasters.html?format=raw">Create Rasters for Aviso Geostrophic Currents Product</a></td><td>Creates rasters for an Aviso gridded geostrophic currents product.</td></tr><tr><td><a href="AvisoGriddedSSH.CreateArcGISRasters.html?format=raw">Create Rasters for Aviso SSH Product</a></td><td>Creates rasters for an Aviso gridded sea surface height product.</td></tr><tr><td><a href="AvisoGriddedSignificantWaveHeight.CreateArcGISRasters.html?format=raw">Create Rasters for Aviso Significant Wave Height Product</a></td><td>Creates rasters for an Aviso gridded significant wave height product.</td></tr><tr><td><a href="AvisoGriddedWindSpeedModulus.CreateArcGISRasters.html?format=raw">Create Rasters for Aviso Wind Speed Modulus Product</a></td><td>Creates rasters for an Aviso gridded wind speed modulus product.</td></tr><tr><td><a href="CoRTADv33D.CreateArcGISRasters.html?format=raw">Create Rasters for CoRTAD 3D Variable</a></td><td>Creates rasters a CoRTAD 3D variable.</td></tr><tr><td><a href="HYCOMGLBa008Equatorial3D.CreateArcGISRasters.html?format=raw">Create Rasters for HYCOM GLBa0.08 Equatorial 3D Variable</a></td><td>Creates rasters for a 3D variable of the equatorial (Mercator) region of the HYCOM GLBa0.08 dataset.</td></tr><tr><td><a href="HYCOMGLBa008Equatorial4D.CreateArcGISRasters.html?format=raw">Create Rasters for HYCOM GLBa0.08 Equatorial 4D Variable</a></td><td>Creates rasters for a 4D variable of the equatorial (Mercator) region of the HYCOM GLBa0.08 dataset.</td></tr><tr><td><a href="HYCOMGOMl0043D.CreateArcGISRasters.html?format=raw">Create Rasters for HYCOM GOMl0.04 3D Variable</a></td><td>Creates rasters for a HYCOM GOMl0.04 3D variable.</td></tr><tr><td><a href="HYCOMGOMl0044D.CreateArcGISRasters.html?format=raw">Create Rasters for HYCOM GOMl0.04 4D Variable</a></td><td>Creates rasters for a HYCOM GOMl0.04 4D variable.</td></tr><tr><td><a href="OceanColorLevel3SMITimeSeries.CreateArcGISRasters.html?format=raw">Create Rasters for NASA OceanColor L3 SMI Product</a></td><td>Creates rasters for a Level 3 Standard Mapped Image (SMI) product published by the NASA GSFC OceanColor Group.</td></tr><tr><td><a href="OSCAR5DayThirdDegreeCurrents.CreateArcGISRasters.html?format=raw">Create Rasters for OSCAR Currents</a></td><td>Creates rasters for NOAA OSCAR 5-day unfiltered 1/3 degree ocean surface currents.</td></tr><tr><td><a href="MODISL3SSTTimeSeries.CreateArcGISRasters.html?format=raw">Create Rasters for PO.DAAC MODIS L3 SST</a></td><td>Creates rasters for MODIS Level 3 SST images published by NASA JPL PO.DAAC.</td></tr><tr><td><a href="ROMSCoSiNE3D.CreateArcGISRasters.html?format=raw">Create Rasters for Pacific ROMS-CoSiNE 3D Variable</a></td><td>Creates rasters for a Pacific ROMS-CoSiNE 3D variable.</td></tr><tr><td><a href="ROMSCoSiNE4D.CreateArcGISRasters.html?format=raw">Create Rasters for Pacific ROMS-CoSiNE 4D Variable</a></td><td>Creates rasters for a Pacific ROMS-CoSiNE 4D variable.</td></tr><tr><td><a href="ArcGISPolygons.CreateFeatureClassWithRectangle2.html?format=raw">Create Rectangle</a></td><td>Creates a rectangle in a new ArcGIS polygon feature class.</td></tr><tr><td><a href="Directory.CreateSubdirectory.html?format=raw">Create Subdirectory</a></td><td>Creates a subdirectory within a parent directory.</td></tr><tr><td><a href="ESRLClimateIndices.UrlToArcGISTable.html?format=raw">Create Table from ESRL Climate Index Time Series at URL</a></td><td>Creates and populates a table of climate index values parsed from NOAA ESRL climate index time series data downloaded from a URL.</td></tr><tr><td><a href="ESRLClimateIndices.UrlsToArcGISTable.html?format=raw">Create Table from ESRL Climate Index Time Series at URLs</a></td><td>Creates and populates a table of climate index values parsed from NOAA ESRL climate index time series data downloaded from a list of URLs.</td></tr><tr><td><a href="ESRLClimateIndices.FileToArcGISTable.html?format=raw">Create Table from ESRL Climate Index Time Series in Text File</a></td><td>Creates and populates a table of climate index values parsed from a text file in NOAA ESRL time series format.</td></tr><tr><td><a href="ESRLClimateIndices.FilesToArcGISTable.html?format=raw">Create Table from ESRL Climate Index Time Series in Text Files</a></td><td>Creates and populates a table of climate index values parsed from a list of text files, where each file contains the data for a single climate index in NOAA ESRL time series format.</td></tr><tr><td><a href="Directory.CreateTemporaryDirectory.html?format=raw">Create Temporary Directory</a></td><td>Creates a directory suitable for holding temporary files.</td></tr><tr><td><a href="AvisoGriddedGeostrophicCurrents.CreateVectorsAsArcGISFeatureClasses.html?format=raw">Create Vectors for Aviso Geostrophic Currents Product</a></td><td>Creates line feature classes representing the current vectors of an Aviso gridded geostrophic currents product.</td></tr><tr><td><a href="OSCAR5DayThirdDegreeCurrents.CreateVectorsAsArcGISFeatureClasses.html?format=raw">Create Vectors for OSCAR Currents</a></td><td>Creates line feature classes representing the vectors of NOAA OSCAR 5-day unfiltered 1/3 degree ocean surface currents.</td></tr><tr><td><a href="ArcGISRaster.CreateXRaster.html?format=raw">Create X Coordinate Raster</a></td><td>Creates an ArcGIS raster where the value of each cell is the X coordinate of the cell.</td></tr><tr><td><a href="ArcGISRaster.CreateYRaster.html?format=raw">Create Y Coordinate Raster</a></td><td>Creates an ArcGIS raster where the value of each cell is the Y coordinate of the cell.</td></tr><tr><td><a href="File.Decompress.html?format=raw">Decompress File</a></td><td>Decompresses a file into a directory.</td></tr><tr><td><a href="Directory.DeleteArcGISTable.html?format=raw">Delete Directories Listed in Table</a></td><td>Deletes the directories listed in a table.</td></tr><tr><td><a href="Directory.Delete.html?format=raw">Delete Directory</a></td><td>Deletes a directory.</td></tr><tr><td><a href="File.Delete.html?format=raw">Delete File</a></td><td>Deletes a file.</td></tr><tr><td><a href="File.DeleteArcGISTable.html?format=raw">Delete Files Listed in Table</a></td><td>Deletes the files listed in a table.</td></tr><tr><td><a href="ArcGISRaster.Delete.html?format=raw">Delete Raster</a></td><td>Deletes an ArcGIS raster.</td></tr><tr><td><a href="ArcGISRaster.DeleteArcGISTable.html?format=raw">Delete Rasters Listed in Table</a></td><td>Deletes the ArcGIS rasters listed in a table.</td></tr><tr><td><a href="RExploratoryPlots.DensityHistogramForArcGISField.html?format=raw">Density Histogram for Field</a></td><td>Creates a density histogram for a field of a table.</td></tr><tr><td><a href="RExploratoryPlots.DensityHistogramForArcGISPointsCoordinates.html?format=raw">Density Histogram for Point Coordinate</a></td><td>Creates a density histogram for one of the coordinates of a point feature class or layer.</td></tr><tr><td><a href="R.Evaluate.html?format=raw">Evaluate R Statements</a></td><td>Evalutes one or more R statements using the R interpreter and returns the result of the last statement.</td></tr><tr><td><a href="R.EvaluateFile.html?format=raw">Evaluate R Statements in Text File</a></td><td>Evalutes the R statements in a text file using the R interpreter and returns the result of the last statement.</td></tr><tr><td><a href="Table.ExecuteADOCommandForArcGISTable.html?format=raw">Execute Database Command for Table</a></td><td>Opens a Microsoft ActiveX Data Objects (ADO) connection to the database containing a table and executes a command.</td></tr><tr><td><a href="ADODatabaseConnection.ConnectAndExecuteCommand.html?format=raw">Execute Database Command on ADO Connection</a></td><td>Opens a Microsoft ActiveX Data Objects (ADO) connection to a database and executes a command.</td></tr><tr><td><a href="ChildProcess.ExecuteProgram.html?format=raw">Execute Program</a></td><td>Executes the specified program and captures its output.</td></tr><tr><td><a href="ArcGISRaster.ExtractByMaskArcGISTable.html?format=raw">Extract By Mask for ArcGIS Rasters Listed in Table</a></td><td>For each ArcGIS raster in a table, extracts the cells that correspond to the areas defined by a mask.</td></tr><tr><td><a href="HDF.ExtractHeader.html?format=raw">Extract HDF Header</a></td><td>Extracts the header of an HDF file and saves it to a text file.</td></tr><tr><td><a href="HDF.ExtractHeaderArcGISTable.html?format=raw">Extract Headers of HDFs Listed in Table</a></td><td>Extracts the headers of HDF files in a table and saves them to text files.</td></tr><tr><td><a href="NetCDF.ExtractHeaderArcGISTable.html?format=raw">Extract Headers of NetCDFs Listed in Table</a></td><td>Extracts the headers of netCDF files in a table and saves them to text files.</td></tr><tr><td><a href="SIRFile.ExtractHeaderArcGISTable.html?format=raw">Extract Headers of SIR Files Listed in Table</a></td><td>Extracts the headers of SIR files in a table and saves them to text files.</td></tr><tr><td><a href="CheltonMesocaleEddyPoints.ExtractArcGISPointsFromSpatiaLite.html?format=raw">Extract Mesoscale Eddy Centroids From SpatiaLite Database</a></td><td>Extracts eddy centroid points from the Chelton et al. (2011) mesoscale eddy database in SpatiaLite format.</td></tr><tr><td><a href="CheltonMesocaleEddyPoints.ExtractArcGISLinesFromSpatiaLite.html?format=raw">Extract Mesoscale Eddy Tracklines From SpatiaLite Database</a></td><td>Extracts eddy tracklines from the Chelton et al. (2011) mesoscale eddy database in SpatiaLite format.</td></tr><tr><td><a href="NetCDF.ExtractHeader.html?format=raw">Extract NetCDF Header</a></td><td>Extracts the header of a netCDF file and saves it to a text file.</td></tr><tr><td><a href="SIRFile.ExtractHeader.html?format=raw">Extract SIR File Header</a></td><td>Extracts the header of a SIR file.</td></tr><tr><td><a href="File.IsDecompressible.html?format=raw">File Is Decompressible</a></td><td>Returns True if the specified file is in a format that can be decompressed.</td></tr><tr><td><a href="ArcGISRaster.FindAndExtractByMask.html?format=raw">Find ArcGIS Rasters and Extract By Mask</a></td><td>Finds rasters in an ArcGIS workspace and extracts the cells that correspond to the areas defined by a mask.</td></tr><tr><td><a href="CayulaCornillonEdgeDetection.FindArcGISRastersAndDetectEdges.html?format=raw">Find ArcGIS Rasters and Find Cayula-Cornillon Fronts</a></td><td>Finds ArcGIS rasters in a workspace and finds fronts within them using the Cayula-Cornillon (1992) single-image edge detection algorithm.</td></tr><tr><td><a href="Interpolator.FindAndInpaintArcGISRasters.html?format=raw">Find ArcGIS Rasters and Interpolate No Data Cells</a></td><td>Finds rasters in an ArcGIS workspace and interpolates values for the No Data cells.</td></tr><tr><td><a href="ArcGISRaster.FindAndProjectRastersToTemplate.html?format=raw">Find ArcGIS Rasters and Project to Template</a></td><td>Finds rasters in an ArcGIS workspace and projects them to the coordinate system, cell size, and extent of a template raster.</td></tr><tr><td><a href="ArcGISRaster.FindAndProjectClipAndOrExecuteMapAlgebra.html?format=raw">Find ArcGIS Rasters and Project, Clip, and/or Execute Map Algebra</a></td><td>Finds rasters in an ArcGIS workspace and projects, clips, and/or perfoms map algebra on them. You must request at least one of these three operations. If you request multiple operations, the tool performs them in the order they are listed.</td></tr><tr><td><a href="ArcInfoASCIIGrid.FindAndConvertToArcGISRaster.html?format=raw">Find ArcInfo ASCII Grids and Convert To ArcGIS Rasters</a></td><td>Finds ArcInfo ASCII Grid text files in a directory and converts them to ArcGIS rasters.</td></tr><tr><td><a href="BinaryRaster.FindAndConvertToArcGISRaster.html?format=raw">Find Binary Rasters and Convert To ArcGIS Rasters</a></td><td>Finds two-dimensional binary rasters in a directory and converts them to ArcGIS rasters.</td></tr><tr><td><a href="BinaryRaster.FindAndConvertToArcInfoASCIIGrid.html?format=raw">Find Binary Rasters and Convert To ArcInfo ASCII Grids</a></td><td>Finds two-dimensional binary rasters in a directory and converts them to text files in ArcInfo ASCII Grid format.</td></tr><tr><td><a href="BinaryRaster.FindAndSwapBytes.html?format=raw">Find Binary Rasters and Swap Bytes</a></td><td>Finds and reverses the byte order of binary rasters in a directory (i.e. converts "little endian" to "big endian", or visa versa).</td></tr><tr><td><a href="AVHRRPathfinderSSTTimeSeries.CreateCayulaCornillonFrontsAsArcGISRasters.html?format=raw">Find Cayula-Cornillon Fronts in AVHRR Pathfinder V5 SST</a></td><td>Creates rasters indicating the positions of fronts in AVHRR Pathfinder Version 5 SST images published by NOAA NODC, using the Cayula and Cornillon (1992) single image edge detection (SIED) algorithm.</td></tr><tr><td><a href="HYCOMGLBa008Equatorial4D.CreateCayulaCornillonFrontsAsArcGISRasters.html?format=raw">Find Cayula-Cornillon Fronts in HYCOM GLBa0.08 Equatorial 4D Variable</a></td><td>Creates rasters indicating the positions of fronts in the 2D slices of a 4D variable of the equatorial (Mercator) region of the HYCOM GLBa0.08 dataset using the Cayula and Cornillon (1992) single image edge detection (SIED) algorithm.</td></tr><tr><td><a href="HYCOMGOMl0044D.CreateCayulaCornillonFrontsAsArcGISRasters.html?format=raw">Find Cayula-Cornillon Fronts in HYCOM GOMl0.04 4D Variable</a></td><td>Creates rasters indicating the positions of fronts in the 2D slices of a HYCOM GOMl0.04 4D variable using the Cayula and Cornillon (1992) single image edge detection (SIED) algorithm.</td></tr><tr><td><a href="MODISL3SSTTimeSeries.CreateCayulaCornillonFrontsAsArcGISRasters.html?format=raw">Find Cayula-Cornillon Fronts in PO.DAAC MODIS L3 SST</a></td><td>Creates rasters indicating the positions of fronts in MODIS Level 3 SST images published by NASA JPL PO.DAAC, using the Cayula and Cornillon (1992) single image edge detection (SIED) algorithm.</td></tr><tr><td><a href="CoastWatchAVHRR.FindFilesAndCreateArcGISTable.html?format=raw">Find CoastWatch Files</a></td><td>Finds CoastWatch POES AVHRR files within a directory and creates a table that lists them.</td></tr><tr><td><a href="CoastWatchAVHRR.FindImagesInFilesAndCreateArcGISTable.html?format=raw">Find CoastWatch Images In Files</a></td><td>Finds CoastWatch POES AVHRR files within a directory and creates a table that lists the images within them.</td></tr><tr><td><a href="CoastWatchAVHRR.FindAndConvertToArcGISRasters.html?format=raw">Find CoastWatch Images and Convert To ArcGIS Rasters</a></td><td>Finds images in CoastWatch POES AVHRR files in a directory and converts them to ArcGIS rasters.</td></tr><tr><td><a href="CoastWatchAVHRR.FindAndConvertToBinaryRasters.html?format=raw">Find CoastWatch Images and Convert To Binary Rasters</a></td><td>Finds images in CoastWatch POES AVHRR files in a directory and converts them to binary rasters.</td></tr><tr><td><a href="CoastWatchAVHRR.FindAndConvertToCoastWatchHDFs.html?format=raw">Find CoastWatch Images and Convert to HDFs</a></td><td>Finds images in CoastWatch POES AVHRR files in a directory and imports them into CoastWatch HDFs.</td></tr><tr><td><a href="CoastWatchAVHRR.FindAndCreateMasksAsArcGISRasters.html?format=raw">Find CoastWatch Images and Create Masks as ArcGIS Rasters</a></td><td>Finds images in CoastWatch POES AVHRR files in a directory creates masks for them, in ArcGIS raster format.</td></tr><tr><td><a href="CoastWatchAVHRR.FindAndCreateMasksAsBinaryRasters.html?format=raw">Find CoastWatch Images and Create Masks as Binary Rasters</a></td><td>Finds images in CoastWatch POES AVHRR files in a directory creates masks for them, in binary raster format.</td></tr><tr><td><a href="CoastWatchAVHRR.FindCoastWatchFilesAndFindFrontsAsArcGISRasters.html?format=raw">Find CoastWatch Images and Find Cayula-Cornillon Fronts as ArcGIS Rasters</a></td><td>Uses the Cayula-Cornillon (1992) single-image edge detection algorithm to find fronts in the CoastWatch POES AVHRR images found in a directory, and outputs the fronts as ArcGIS rasters.</td></tr><tr><td><a href="Directory.FindAndCreateArcGISTable.html?format=raw">Find Directories</a></td><td>Finds subdirectories within a directory and creates a table that lists them.</td></tr><tr><td><a href="File.FindAndCreateArcGISTable.html?format=raw">Find Files</a></td><td>Finds files within a directory and creates a table that lists them.</td></tr><tr><td><a href="HDF.FindAndConvertToArcGISRasters.html?format=raw">Find HDFs and Convert SDS To ArcGIS Rasters</a></td><td>Finds HDF files in a directory and converts a Scientific Data Sets (SDS) in each file to an ArcGIS raster.</td></tr><tr><td><a href="HDF.FindAndConvertToArcInfoASCIIGrids.html?format=raw">Find HDFs and Convert SDS To ArcInfo ASCII Grids</a></td><td>Finds HDF files in a directory and converts a Scientific Data Sets (SDS) in each file to a text file in ArcInfo ASCII Grid format.</td></tr><tr><td><a href="HDF.FindAndConvertToBinaryRasters.html?format=raw">Find HDFs and Convert SDS To Binary Rasters</a></td><td>Finds HDF files in a directory and converts a Scientific Data Sets (SDS) in each file to a binary raster.</td></tr><tr><td><a href="HDF.FindAndExtractHeaders.html?format=raw">Find HDFs and Extract Headers</a></td><td>Finds HDF files in a directory, extracts their headers, and saves the headers to text files.</td></tr><tr><td><a href="ArcGISPoints.FindNearestFeatures.html?format=raw">Find Nearest Features</a></td><td>For each point, finds the nearest feature and writes its ID, distance, angle, and/or values of specified fields to fields of the point.</td></tr><tr><td><a href="ArcGISPoints.FindNearestFeaturesListedInField.html?format=raw">Find Nearest Features Listed in Field</a></td><td>For each point, using the feature class or layer listed in a field, finds the nearest feature and writes its ID, distance, angle, and/or values of specified fields to fields of the point. Use this tool when you have a single point feature class but need to find distances to different near feature classes or layers for different points.</td></tr><tr><td><a href="NetCDF.FindNetCDFsAndConvert2DVariableToArcGISRasters.html?format=raw">Find NetCDFs and Convert 2D Variable to ArcGIS Rasters</a></td><td>Finds netCDF files in a directory and converts a two-dimensional variable in each file to an ArcGIS raster.</td></tr><tr><td><a href="NetCDF.FindNetCDFsAndConvert2DVariableToArcInfoASCIIGrids.html?format=raw">Find NetCDFs and Convert 2D Variable to ArcInfo ASCII Grids</a></td><td>Finds netCDF files in a directory and converts a two-dimensional variable in each file to a text file in ArcInfo ASCII Grid format.</td></tr><tr><td><a href="NetCDF.FindNetCDFsAndConvert2DVariableToBinaryRasters.html?format=raw">Find NetCDFs and Convert 2D Variable to Binary Rasters</a></td><td>Finds netCDF files in a directory and converts a two-dimensional variable in each file to a binary raster.</td></tr><tr><td><a href="NetCDF.FindAndExtractHeaders.html?format=raw">Find NetCDFs and Extract Headers</a></td><td>Finds netCDF files in a directory, extracts their headers, and saves the headers to text files.</td></tr><tr><td><a href="AvisoGriddedSSH.FindOkuboWeissEddies.html?format=raw">Find Okubo-Weiss Eddies in Aviso SSH Product</a></td><td>Creates rasters showing the cores of geostrophic eddies detected in an Aviso gridded sea surface height product using the Okubo-Weiss algorithm.</td></tr><tr><td><a href="ArcGISRaster.FindAndCreateArcGISTable.html?format=raw">Find Rasters</a></td><td>Finds rasters within an ArcGIS workspace and creates a table that lists them.</td></tr><tr><td><a href="ArcGISRasterMapAlgebra.FindRastersAndExecuteSingleOutputMapAlgebra.html?format=raw">Find Rasters and Execute Single Output Map Algebra</a></td><td>Finds rasters in a workspace and executes a map algebra expression on them.</td></tr><tr><td><a href="SIRFile.FindAndConvertToArcGISRasters.html?format=raw">Find SIR Files and Convert To ArcGIS Rasters</a></td><td>Finds SIR files in a directory and converts them to ArcGIS rasters.</td></tr><tr><td><a href="SIRFile.FindAndConvertToArcInfoASCIIGrids.html?format=raw">Find SIR Files and Convert To ArcInfo ASCII Grids</a></td><td>Finds SIR files in a directory and converts them to text files in ArcInfo ASCII grid format.</td></tr><tr><td><a href="SIRFile.FindAndConvertToBinaryRasters.html?format=raw">Find SIR Files and Convert To Binary Rasters</a></td><td>Finds SIR files in a directory and converts them to binary rasters.</td></tr><tr><td><a href="SIRFile.FindAndExtractHeaders.html?format=raw">Find SIR Files and Extract Headers</a></td><td>Finds SIR files in a directory, extracts their headers, and saves the headers to text files.</td></tr><tr><td><a href="ArcGISRaster.FindAndConvertToLines.html?format=raw">Find and Convert ArcGIS Rasters to Lines</a></td><td>Finds rasters in an ArcGIS workspace and converts them to lines that outline groups of adjacent raster cells having the same value.</td></tr><tr><td><a href="ArcGISRaster.FindAndConvertToPoints.html?format=raw">Find and Convert ArcGIS Rasters to Points</a></td><td>Finds rasters in an ArcGIS workspace and converts them to points that occur at the centers of the raster cells.</td></tr><tr><td><a href="ArcGISRaster.FindAndConvertToPolygonOutlines.html?format=raw">Find and Convert ArcGIS Rasters to Polygon Outlines</a></td><td>Finds rasters in an ArcGIS workspace and converts them to lines that outline groups of adjacent raster cells having the same value.</td></tr><tr><td><a href="ArcGISRaster.FindAndConvertToPolygons.html?format=raw">Find and Convert ArcGIS Rasters to Polygons</a></td><td>Finds rasters in an ArcGIS workspace and converts them to polygons that encompass groups of adjacent raster cells having the same value.</td></tr><tr><td><a href="Directory.FindAndCopy.html?format=raw">Find and Copy Directories</a></td><td>Finds and copies directories in a directory.</td></tr><tr><td><a href="File.FindAndCopy.html?format=raw">Find and Copy Files</a></td><td>Finds and copies files in a directory.</td></tr><tr><td><a href="ArcGISRaster.FindAndCopy.html?format=raw">Find and Copy Rasters</a></td><td>Finds and copies rasters in an ArcGIS workspace.</td></tr><tr><td><a href="Directory.FindAndDelete.html?format=raw">Find and Delete Directories</a></td><td>Finds and deletes directories in a directory.</td></tr><tr><td><a href="File.FindAndDelete.html?format=raw">Find and Delete Files</a></td><td>Finds and deletes files in a directory.</td></tr><tr><td><a href="ArcGISRaster.FindAndDelete.html?format=raw">Find and Delete Rasters</a></td><td>Finds and deletes rasters in an ArcGIS workspace.</td></tr><tr><td><a href="Directory.FindAndMove.html?format=raw">Find and Move Directories</a></td><td>Finds and moves directories in a directory.</td></tr><tr><td><a href="File.FindAndMove.html?format=raw">Find and Move Files</a></td><td>Finds and moves files in a directory.</td></tr><tr><td><a href="ArcGISRaster.FindAndMove.html?format=raw">Find and Move Rasters</a></td><td>Finds and moves rasters in an ArcGIS workspace.</td></tr><tr><td><a href="GAM.FitToArcGISTable.html?format=raw">Fit GAM</a></td><td>Fits a generalized additive model (GAM) to data in an ArcGIS table.</td></tr><tr><td><a href="GLM.FitToArcGISTable.html?format=raw">Fit GLM</a></td><td>Fits a generalized linear model (GLM) to data in an ArcGIS table using the R glm function.</td></tr><tr><td><a href="LinearMixedModel.FitToArcGISTable.html?format=raw">Fit Linear Mixed Model</a></td><td>Fits a linear mixed-effects model to data in an ArcGIS table.</td></tr><tr><td><a href="TreeModel.FitToArcGISTable.html?format=raw">Fit Tree Model</a></td><td>Fits a tree model to data in an ArcGIS table.</td></tr><tr><td><a href="DiGIR.GetResourcesAsArcGISTable.html?format=raw">Get DiGIR Resources as Table</a></td><td>Gets the list of Distributed Generic Information Retrieval (DiGIR) resources available from a DiGIR server and writes it to an ArcGIS table.</td></tr><tr><td><a href="DiGIR.GetOBISResourcesAsArcGISTable.html?format=raw">Get OBIS DiGIR Resources as Table</a></td><td>Gets the list of Distributed Generic Information Retrieval (DiGIR) resources available from OBIS and writes it to an ArcGIS table.</td></tr><tr><td><a href="DiGIR.GetOBISSEAMAPResourcesAsArcGISTable.html?format=raw">Get OBIS-SEAMAP DiGIR Resources as Table</a></td><td>Gets the list of Distributed Generic Information Retrieval (DiGIR) resources available from OBIS-SEAMAP and writes it to an ArcGIS table.</td></tr><tr><td><a href="AVHRRPathfinderSSTTimeSeries.InterpolateAtArcGISPoints.html?format=raw">Interpolate AVHRR Pathfinder V5 SST at Points</a></td><td>Interpolates AVHRR Pathfinder Version 5 SST values at points.</td></tr><tr><td><a href="AvisoGriddedGeostrophicCurrents.InterpolateAtArcGISPoints.html?format=raw">Interpolate Aviso Geostrophic Currents Product at Points</a></td><td>Interpolates the values of an Aviso gridded geostrophic currents product at points.</td></tr><tr><td><a href="AvisoGriddedSSH.InterpolateAtArcGISPoints.html?format=raw">Interpolate Aviso SSH Product at Points</a></td><td>Interpolates the values of an Aviso gridded sea surface height product at points.</td></tr><tr><td><a href="AvisoGriddedSignificantWaveHeight.InterpolateAtArcGISPoints.html?format=raw">Interpolate Aviso Significant Wave Height Product at Points</a></td><td>Interpolates the values of an Aviso gridded significant wave height product at points.</td></tr><tr><td><a href="AvisoGriddedWindSpeedModulus.InterpolateAtArcGISPoints.html?format=raw">Interpolate Aviso Wind Speed Modulus Product at Points</a></td><td>Interpolates the values of an Aviso gridded wind speed modulus product at points.</td></tr><tr><td><a href="CoRTADv32D.InterpolateAtArcGISPoints.html?format=raw">Interpolate CoRTAD 2D Variables at Points</a></td><td>Interpolates CoRTAD 2D variables at points.</td></tr><tr><td><a href="CoRTADv33D.InterpolateAtArcGISPoints.html?format=raw">Interpolate CoRTAD 3D Variables at Points</a></td><td>Interpolates CoRTAD 3D variables at points.</td></tr><tr><td><a href="HYCOMGLBa008Equatorial3D.InterpolateAtArcGISPoints.html?format=raw">Interpolate HYCOM GLBa0.08 Equatorial 3D Variables at Points</a></td><td>Interpolates 3D variables of the equatorial (Mercator) region of the HYCOM GLBa0.08 dataset at points.</td></tr><tr><td><a href="HYCOMGLBa008Equatorial4D.InterpolateAtArcGISPoints.html?format=raw">Interpolate HYCOM GLBa0.08 Equatorial 4D Variables at Points</a></td><td>Interpolates 4D variables of the equatorial (Mercator) region of the HYCOM GLBa0.08 dataset at points.</td></tr><tr><td><a href="HYCOMGOMl0043D.InterpolateAtArcGISPoints.html?format=raw">Interpolate HYCOM GOMl0.04 3D Variables at Points</a></td><td>Interpolates HYCOM GOMl0.04 3D variables at points.</td></tr><tr><td><a href="HYCOMGOMl0044D.InterpolateAtArcGISPoints.html?format=raw">Interpolate HYCOM GOMl0.04 4D Variables at Points</a></td><td>Interpolates HYCOM GOMl0.04 4D variables at points.</td></tr><tr><td><a href="OceanColorLevel3SMITimeSeries.InterpolateAtArcGISPoints.html?format=raw">Interpolate NASA OceanColor L3 SMI Product at Points</a></td><td>Interpolates the values of a Level 3 Standard Mapped Image (SMI) product published by the NASA GSFC OceanColor Group at points.</td></tr><tr><td><a href="Interpolator.InpaintArcGISRaster.html?format=raw">Interpolate No Data Cells</a></td><td>Interpolates values for the No Data cells of a raster.</td></tr><tr><td><a href="Interpolator.InpaintArcGISRasterArcGISTable.html?format=raw">Interpolate No Data Cells for ArcGIS Rasters Listed in Table</a></td><td>Interpolates values for the No Data cells for each ArcGIS raster in a table.</td></tr><tr><td><a href="OSCAR5DayThirdDegreeCurrents.InterpolateAtArcGISPoints.html?format=raw">Interpolate OSCAR Currents at Points</a></td><td>Interpolates the values of NOAA OSCAR 5-day unfiltered 1/3 degree ocean surface currents at points.</td></tr><tr><td><a href="MODISL3SSTTimeSeries.InterpolateAtArcGISPoints.html?format=raw">Interpolate PO.DAAC MODIS L3 SST at Points</a></td><td>Interpolates PO.DAAC MODIS Level 3 SST values at points.</td></tr><tr><td><a href="ROMSCoSiNE2D.InterpolateAtArcGISPoints.html?format=raw">Interpolate Pacific ROMS-CoSiNE 2D Variable at Points</a></td><td>Interpolates a Pacific ROMS-CoSiNE 2D variable at points.</td></tr><tr><td><a href="ROMSCoSiNE3D.InterpolateAtArcGISPoints.html?format=raw">Interpolate Pacific ROMS-CoSiNE 3D Variables at Points</a></td><td>Interpolates Pacific ROMS-CoSiNE 3D variables at points.</td></tr><tr><td><a href="ROMSCoSiNE4D.InterpolateAtArcGISPoints.html?format=raw">Interpolate Pacific ROMS-CoSiNE 4D Variables at Points</a></td><td>Interpolates Pacific ROMS-CoSiNE 4D variables at points.</td></tr><tr><td><a href="Interpolator.InterpolateArcGISRasterValuesAtPoints.html?format=raw">Interpolate Raster Values at Points</a></td><td>Interpolates the values of rasters at points.</td></tr><tr><td><a href="CoralReefConnectivity.LoadAvisoGeostrophicCurrentsIntoSimulation.html?format=raw">Load Aviso Geostrophic Currents Into Coral Reef Connectivity Simulation</a></td><td>Downloads Aviso geostrophic currents into a coral reef connectivity simulation.</td></tr><tr><td><a href="CoralReefConnectivity.LoadHYCOMGLBa0084DEquatorialCurrentsIntoSimulation.html?format=raw">Load HYCOM GLBa0.08 Currents Into Coral Reef Connectivity Simulation</a></td><td>Downloads HYCOM GLBa0.08 ocean currents into a coral reef connectivity simulation.</td></tr><tr><td><a href="CoralReefConnectivity.LoadHYCOMGOMl0044DCurrentsIntoSimulation.html?format=raw">Load HYCOM GOMl0.04 Currents Into Coral Reef Connectivity Simulation</a></td><td>Downloads HYCOM GOMl0.04 ocean currents into a coral reef connectivity simulation.</td></tr><tr><td><a href="CoralReefConnectivity.LoadOSCARCurrentsIntoSimulation.html?format=raw">Load OSCAR Currents Into Coral Reef Connectivity Simulation</a></td><td>Downloads NOAA OSCAR 5-day unfiltered 1/3 degree ocean surface currents into a coral reef connectivity simulation.</td></tr><tr><td><a href="CoralReefConnectivity.LoadROMSCoSiNE4DCurrentsIntoSimulation.html?format=raw">Load Pacific ROMS-CoSiNE Currents Into Coral Reef Connectivity Simulation</a></td><td>Downloads Pacific ROMS-CoSiNE ocean currents into a coral reef connectivity simulation.</td></tr><tr><td><a href="Directory.MoveArcGISTable.html?format=raw">Move Directories Listed in Table</a></td><td>Moves the directories listed in a table.</td></tr><tr><td><a href="Directory.Move.html?format=raw">Move Directory</a></td><td>Moves a directory, including its subdirectories and files.</td></tr><tr><td><a href="File.Move.html?format=raw">Move File</a></td><td>Moves a file.</td></tr><tr><td><a href="File.MoveArcGISTable.html?format=raw">Move Files Listed in Table</a></td><td>Moves the files listed in a table.</td></tr><tr><td><a href="ArcGISRaster.Move.html?format=raw">Move Raster</a></td><td>Moves an ArcGIS raster.</td></tr><tr><td><a href="ArcGISRaster.MoveArcGISTable.html?format=raw">Move Rasters Listed in Table</a></td><td>Moves the ArcGIS rasters listed in a table.</td></tr><tr><td><a href="ModelEvaluation.PlotPerformanceOfBinaryClassificationModel.html?format=raw">Plot Performance of Binary Classification Model</a></td><td>Plots the performance of a binary classification model (a model where the response variable has two possible values) using the R ROCR package.</td></tr><tr><td><a href="ModelEvaluation.PlotROCOfBinaryClassificationModel.html?format=raw">Plot ROC of Binary Classification Model</a></td><td>Plots the receiver operating characteristic (ROC) curve of a binary classification model (a model where the response variable has two possible values) using the R ROCR package.</td></tr><tr><td><a href="GAM.BayesPredictFromArcGISRasters.html?format=raw">Predict Bayesian Probabilities for GAM From Rasters</a></td><td>Using a binomial generalized additive model (GAM) fitted using the R mgcv package, this tool creates rasters representing the estimated probabilities that the response variable will equal or exceed specified thresholds, given ArcGIS rasters representing the predictor variables.</td></tr><tr><td><a href="GAM.PredictFromArcGISRasters.html?format=raw">Predict GAM From Rasters</a></td><td>Using a fitted generalized additive model (GAM), this tool creates a raster representing the response variable predicted from ArcGIS rasters representing the predictor variables.</td></tr><tr><td><a href="GLM.PredictFromArcGISRasters.html?format=raw">Predict GLM From Rasters</a></td><td>Using a fitted generalized linear model (GLM), this tool creates a raster representing the response variable predicted from ArcGIS rasters representing the predictor variables.</td></tr><tr><td><a href="LinearMixedModel.PredictFromArcGISRasters.html?format=raw">Predict Linear Mixed Model From Rasters</a></td><td>Using a fitted linear mixed model, this tool creates a raster representing the response variable predicted from rasters representing the predictor variables.</td></tr><tr><td><a href="TreeModel.PredictFromArcGISRasters.html?format=raw">Predict Tree Model From Rasters</a></td><td>Using a fitted tree model, this tool creates a raster representing the response variable predicted from rasters representing the predictor variables.</td></tr><tr><td><a href="ArcGISRaster.ProjectToTemplate.html?format=raw">Project Raster to Template</a></td><td>Projects a raster to the coordinate system, cell size, and extent of a template raster.</td></tr><tr><td><a href="ArcGISRaster.ProjectToTemplateArcGISTable.html?format=raw">Project Rasters Listed in Table to Template</a></td><td>Projects a table of rasters to the coordinate system, cell size, and extent of a template raster.</td></tr><tr><td><a href="ArcGISRaster.ProjectClipAndOrExecuteMapAlgebra.html?format=raw">Project, Clip, and/or Execute Map Algebra</a></td><td>Projects, clips, and/or perfoms map algebra on an ArcGIS raster. You must request at least one of these three operations. If you request multiple operations, the tool performs them in the order they are listed.</td></tr><tr><td><a href="ArcGISRaster.ProjectClipAndOrExecuteMapAlgebraArcGISTable.html?format=raw">Project, Clip, and/or Execute Map Algebra on ArcGIS Rasters Listed in Table</a></td><td>Projects, clips, and/or perfoms map algebra on the ArcGIS rasters listed in a table. You must request at least one of these three operations. If you request multiple operations, the tool performs them in the order they are listed.</td></tr><tr><td><a href="ModelEvaluation.RandomlySplitArcGISTableIntoTrainingAndEvaluationRecords.html?format=raw">Randomly Split Table Into Training and Evaluation Records</a></td><td>Randomly designates the records of a table as either training records (for fitting a statistical model) or evaluation records (for evaluating the model).</td></tr><tr><td><a href="CoralReefConnectivity.RunSimulation.html?format=raw">Run Coral Reef Connectivity Simulation</a></td><td>Executes a coral coral reef connectivity simulation.</td></tr><tr><td><a href="ArcGISFeatureSampler.SamplePolygons.html?format=raw">Sample Polygons</a></td><td>Samples the values of polygon fields at points that intersect the polygons.</td></tr><tr><td><a href="ArcGISRasterSampler.SampleRasters.html?format=raw">Sample Rasters</a></td><td>Samples rasters using a point feature class and stores the sampled values in fields of the feature class.</td></tr><tr><td><a href="ArcGISRasterSampler.SampleRastersInFields.html?format=raw">Sample Rasters Listed in Fields</a></td><td>Samples rasters listed in fields of a point feature class and stores the sampled values in other fields.</td></tr><tr><td><a href="ArcGISTableSampler.SampleTimeSeriesTable.html?format=raw">Sample Time Series Table</a></td><td>Matches records in a destination table to records in a time series table by date, and copies values of fields of the time series table to fields of the destination table.</td></tr><tr><td><a href="RExploratoryPlots.ScatterplotMatrixForArcGISTable.html?format=raw">Scatterplot Matrix for Table</a></td><td>Creates a matrix of scatterplots for a table using the R pairs function.</td></tr><tr><td><a href="DiGIR.SearchAndCreateArcGISPoints.html?format=raw">Search DiGIR Records and Create Points</a></td><td>Searches a Distributed Generic Information Retrieval (DiGIR) server for georeferenced records and creates an ArcGIS point feature class from them.</td></tr><tr><td><a href="DiGIR.SearchOBISAndCreateArcGISPoints.html?format=raw">Search OBIS DiGIR Records and Create Points</a></td><td>Searches OBIS for georeferenced records using the Distributed Generic Information Retrieval (DiGIR) protocol and creates an ArcGIS point feature class from them.</td></tr><tr><td><a href="DiGIR.SearchOBISSEAMAPAndCreateArcGISPoints.html?format=raw">Search OBIS-SEAMAP DiGIR Records and Create Points</a></td><td>Searches OBIS-SEAMAP for georeferenced records using the Distributed Generic Information Retrieval (DiGIR) protocol and creates an ArcGIS point feature class from them.</td></tr><tr><td><a href="CoastWatchAVHRR.SetNavigationOffsets.html?format=raw">Set CoastWatch Navigation Offsets</a></td><td>Sets the navigation offsets of a CoastWatch POES AVHRR CWF or HDF file.</td></tr><tr><td><a href="CoastWatchAVHRR.SetNavigationOffsetsArcGISTable.html?format=raw">Set Navigation Offsets of CoastWatch Files Listed in Table</a></td><td>Sets the navigation offsets of the CoastWatch POES AVHRR CWF or HDF files listed in a table to the values specified by two fields.</td></tr><tr><td><a href="ArcGISRasterMapAlgebra.SingleOutputMapAlgebraForRasterArcGISTable.html?format=raw">Single Output Map Algebra For Rasters Listed in Table</a></td><td>Executes a map algebra expression on the rasters listed in a table.</td></tr><tr><td><a href="ArcGISRasterMapAlgebra.SingleOutputMapAlgebraForArcGISTableRows.html?format=raw">Single Output Map Algebra for Table Rows</a></td><td>For each row of a table, calculates an output raster and map algebra expression from the row using Python expressions and executes the map algebra expression to produce the output raster.</td></tr><tr><td><a href="BinaryRaster.SwapBytes.html?format=raw">Swap Bytes of Binary Raster</a></td><td>Reverses the byte order of a binary raster (e.g. converts "little endian" to "big endian", or visa versa).</td></tr><tr><td><a href="BinaryRaster.SwapBytesArcGISTable.html?format=raw">Swap Bytes of Binary Rasters Listed in Table</a></td><td>Reverses the byte order of each binary raster listed in a table (i.e. converts "little endian" to "big endian", or visa versa).</td></tr></table></p><h1><a id="IndexByName">Tool Index, By Name</a></h1><p>In the table below, <i>Tool Name</i> is the programming name used to invoke the tool 
    5          from the ArcGIS command line or from the geoprocessor object in a geoprocessing script.</p><p><table><tr><th>Tool Name</th><th>Description</th></tr><tr><td><a href="ADODatabaseConnection.ConnectAndExecuteCommand.html?format=raw">ADODatabaseConnectionConnectAndExecuteCommand_GeoEco</a></td><td>Opens a Microsoft ActiveX Data Objects (ADO) connection to a database and executes a command.</td></tr><tr><td><a href="AVHRRPathfinderSSTTimeSeries.CreateArcGISRasters.html?format=raw">AVHRRPathfinderSSTTimeSeriesCreateArcGISRasters_GeoEco</a></td><td>Creates rasters for AVHRR Pathfinder Version 5 SST images published by NOAA NODC.</td></tr><tr><td><a href="AVHRRPathfinderSSTTimeSeries.CreateCayulaCornillonFrontsAsArcGISRasters.html?format=raw">AVHRRPathfinderSSTTimeSeriesCreateCayulaCornillonFrontsAsArcGISRasters_GeoEco</a></td><td>Creates rasters indicating the positions of fronts in AVHRR Pathfinder Version 5 SST images published by NOAA NODC, using the Cayula and Cornillon (1992) single image edge detection (SIED) algorithm.</td></tr><tr><td><a href="AVHRRPathfinderSSTTimeSeries.CreateClimatologicalArcGISRasters.html?format=raw">AVHRRPathfinderSSTTimeSeriesCreateClimatologicalArcGISRasters_GeoEco</a></td><td>Creates climatological rasters from AVHRR Pathfinder Version 5 SST images published by NOAA NODC.</td></tr><tr><td><a href="AVHRRPathfinderSSTTimeSeries.InterpolateAtArcGISPoints.html?format=raw">AVHRRPathfinderSSTTimeSeriesInterpolateAtArcGISPoints_GeoEco</a></td><td>Interpolates AVHRR Pathfinder Version 5 SST values at points.</td></tr><tr><td><a href="ArcGISFeatureSampler.SamplePolygons.html?format=raw">ArcGISFeatureSamplerSamplePolygons_GeoEco</a></td><td>Samples the values of polygon fields at points that intersect the polygons.</td></tr><tr><td><a href="ArcGISFishnets.CreateFishnetForPoints.html?format=raw">ArcGISFishnetsCreateFishnetForPoints_GeoEco</a></td><td>Creates a grid of rectangular cells that overlap the input points and optionally calculates summary statistics for the points that intersect each cell.</td></tr><tr><td><a href="ArcGISFishnets.CreateFishnet.html?format=raw">ArcGISFishnetsCreateFishnet_GeoEco</a></td><td>Creates a grid of rectangular polygons.</td></tr><tr><td><a href="ArcGISLines.FromVectorComponentRastersInArcGISTable.html?format=raw">ArcGISLinesFromVectorComponentRastersInArcGISTable_GeoEco</a></td><td>Given a table of rasters representing the x and y components of vector fields, such as the u and v rasters for ocean currents, this tool creates feature classes of lines representing the vectors, similar to a "quiver plot".</td></tr><tr><td><a href="ArcGISLines.FromVectorComponentRasters.html?format=raw">ArcGISLinesFromVectorComponentRasters_GeoEco</a></td><td>Given rasters representing the x and y components of a vector field, such as the u and v rasters for ocean currents, this tool creates a feature class of lines representing the vectors, similar to a "quiver plot".</td></tr><tr><td><a href="ArcGISPoints.AppendPointsToFeatureClass2.html?format=raw">ArcGISPointsAppendPointsToFeatureClass2_GeoEco</a></td><td>Appends points to an existing ArcGIS point feature class.</td></tr><tr><td><a href="ArcGISPoints.CreateFeatureClassWithPoints2.html?format=raw">ArcGISPointsCreateFeatureClassWithPoints2_GeoEco</a></td><td>Creates points in a new ArcGIS point feature class.</td></tr><tr><td><a href="ArcGISPoints.CreatePointsAlongLines.html?format=raw">ArcGISPointsCreatePointsAlongLines_GeoEco</a></td><td>Creates points along lines at a specified interval.</td></tr><tr><td><a href="ArcGISPoints.FindNearestFeaturesListedInField.html?format=raw">ArcGISPointsFindNearestFeaturesListedInField_GeoEco</a></td><td>For each point, using the feature class or layer listed in a field, finds the nearest feature and writes its ID, distance, angle, and/or values of specified fields to fields of the point. Use this tool when you have a single point feature class but need to find distances to different near feature classes or layers for different points.</td></tr><tr><td><a href="ArcGISPoints.FindNearestFeatures.html?format=raw">ArcGISPointsFindNearestFeatures_GeoEco</a></td><td>For each point, finds the nearest feature and writes its ID, distance, angle, and/or values of specified fields to fields of the point.</td></tr><tr><td><a href="ArcGISPolygons.AppendPolygonToFeatureClass2.html?format=raw">ArcGISPolygonsAppendPolygonToFeatureClass2_GeoEco</a></td><td>Appends a polygon to an existing ArcGIS polygon feature class.</td></tr><tr><td><a href="ArcGISPolygons.AppendRectangleToFeatureClass2.html?format=raw">ArcGISPolygonsAppendRectangleToFeatureClass2_GeoEco</a></td><td>Appends a rectangle to an existing ArcGIS polygon feature class.</td></tr><tr><td><a href="ArcGISPolygons.CreateBoundingBoxForArcGISGeoDatasets.html?format=raw">ArcGISPolygonsCreateBoundingBoxForArcGISGeoDatasets_GeoEco</a></td><td>Creates a new ArcGIS polygon feature class and a bounding box (a minimum bounding rectangle) within it that encompasses the extents of one or more ArcGIS geodatasets.</td></tr><tr><td><a href="ArcGISPolygons.CreateFeatureClassWithPolygon2.html?format=raw">ArcGISPolygonsCreateFeatureClassWithPolygon2_GeoEco</a></td><td>Creates a polygon in a new ArcGIS polygon feature class.</td></tr><tr><td><a href="ArcGISPolygons.CreateFeatureClassWithRectangle2.html?format=raw">ArcGISPolygonsCreateFeatureClassWithRectangle2_GeoEco</a></td><td>Creates a rectangle in a new ArcGIS polygon feature class.</td></tr><tr><td><a href="ArcGISRaster.CopyArcGISTable.html?format=raw">ArcGISRasterCopyArcGISTable_GeoEco</a></td><td>Copies the ArcGIS rasters listed in a table.</td></tr><tr><td><a href="ArcGISRaster.Copy.html?format=raw">ArcGISRasterCopy_GeoEco</a></td><td>Copies an ArcGIS raster.</td></tr><tr><td><a href="ArcGISRaster.CreateXRaster.html?format=raw">ArcGISRasterCreateXRaster_GeoEco</a></td><td>Creates an ArcGIS raster where the value of each cell is the X coordinate of the cell.</td></tr><tr><td><a href="ArcGISRaster.CreateYRaster.html?format=raw">ArcGISRasterCreateYRaster_GeoEco</a></td><td>Creates an ArcGIS raster where the value of each cell is the Y coordinate of the cell.</td></tr><tr><td><a href="ArcGISRaster.DeleteArcGISTable.html?format=raw">ArcGISRasterDeleteArcGISTable_GeoEco</a></td><td>Deletes the ArcGIS rasters listed in a table.</td></tr><tr><td><a href="ArcGISRaster.Delete.html?format=raw">ArcGISRasterDelete_GeoEco</a></td><td>Deletes an ArcGIS raster.</td></tr><tr><td><a href="ArcGISRaster.ExtractByMaskArcGISTable.html?format=raw">ArcGISRasterExtractByMaskArcGISTable_GeoEco</a></td><td>For each ArcGIS raster in a table, extracts the cells that correspond to the areas defined by a mask.</td></tr><tr><td><a href="ArcGISRaster.FindAndConvertToLines.html?format=raw">ArcGISRasterFindAndConvertToLines_GeoEco</a></td><td>Finds rasters in an ArcGIS workspace and converts them to lines that outline groups of adjacent raster cells having the same value.</td></tr><tr><td><a href="ArcGISRaster.FindAndConvertToPoints.html?format=raw">ArcGISRasterFindAndConvertToPoints_GeoEco</a></td><td>Finds rasters in an ArcGIS workspace and converts them to points that occur at the centers of the raster cells.</td></tr><tr><td><a href="ArcGISRaster.FindAndConvertToPolygonOutlines.html?format=raw">ArcGISRasterFindAndConvertToPolygonOutlines_GeoEco</a></td><td>Finds rasters in an ArcGIS workspace and converts them to lines that outline groups of adjacent raster cells having the same value.</td></tr><tr><td><a href="ArcGISRaster.FindAndConvertToPolygons.html?format=raw">ArcGISRasterFindAndConvertToPolygons_GeoEco</a></td><td>Finds rasters in an ArcGIS workspace and converts them to polygons that encompass groups of adjacent raster cells having the same value.</td></tr><tr><td><a href="ArcGISRaster.FindAndCopy.html?format=raw">ArcGISRasterFindAndCopy_GeoEco</a></td><td>Finds and copies rasters in an ArcGIS workspace.</td></tr><tr><td><a href="ArcGISRaster.FindAndCreateArcGISTable.html?format=raw">ArcGISRasterFindAndCreateArcGISTable_GeoEco</a></td><td>Finds rasters within an ArcGIS workspace and creates a table that lists them.</td></tr><tr><td><a href="ArcGISRaster.FindAndDelete.html?format=raw">ArcGISRasterFindAndDelete_GeoEco</a></td><td>Finds and deletes rasters in an ArcGIS workspace.</td></tr><tr><td><a href="ArcGISRaster.FindAndExtractByMask.html?format=raw">ArcGISRasterFindAndExtractByMask_GeoEco</a></td><td>Finds rasters in an ArcGIS workspace and extracts the cells that correspond to the areas defined by a mask.</td></tr><tr><td><a href="ArcGISRaster.FindAndMove.html?format=raw">ArcGISRasterFindAndMove_GeoEco</a></td><td>Finds and moves rasters in an ArcGIS workspace.</td></tr><tr><td><a href="ArcGISRaster.FindAndProjectClipAndOrExecuteMapAlgebra.html?format=raw">ArcGISRasterFindAndProjectClipAndOrExecuteMapAlgebra_GeoEco</a></td><td>Finds rasters in an ArcGIS workspace and projects, clips, and/or perfoms map algebra on them. You must request at least one of these three operations. If you request multiple operations, the tool performs them in the order they are listed.</td></tr><tr><td><a href="ArcGISRaster.FindAndProjectRastersToTemplate.html?format=raw">ArcGISRasterFindAndProjectRastersToTemplate_GeoEco</a></td><td>Finds rasters in an ArcGIS workspace and projects them to the coordinate system, cell size, and extent of a template raster.</td></tr><tr><td><a href="ArcGISRaster.MoveArcGISTable.html?format=raw">ArcGISRasterMoveArcGISTable_GeoEco</a></td><td>Moves the ArcGIS rasters listed in a table.</td></tr><tr><td><a href="ArcGISRaster.Move.html?format=raw">ArcGISRasterMove_GeoEco</a></td><td>Moves an ArcGIS raster.</td></tr><tr><td><a href="ArcGISRaster.ProjectClipAndOrExecuteMapAlgebraArcGISTable.html?format=raw">ArcGISRasterProjectClipAndOrExecuteMapAlgebraArcGISTable_GeoEco</a></td><td>Projects, clips, and/or perfoms map algebra on the ArcGIS rasters listed in a table. You must request at least one of these three operations. If you request multiple operations, the tool performs them in the order they are listed.</td></tr><tr><td><a href="ArcGISRaster.ProjectClipAndOrExecuteMapAlgebra.html?format=raw">ArcGISRasterProjectClipAndOrExecuteMapAlgebra_GeoEco</a></td><td>Projects, clips, and/or perfoms map algebra on an ArcGIS raster. You must request at least one of these three operations. If you request multiple operations, the tool performs them in the order they are listed.</td></tr><tr><td><a href="ArcGISRaster.ProjectToTemplateArcGISTable.html?format=raw">ArcGISRasterProjectToTemplateArcGISTable_GeoEco</a></td><td>Projects a table of rasters to the coordinate system, cell size, and extent of a template raster.</td></tr><tr><td><a href="ArcGISRaster.ProjectToTemplate.html?format=raw">ArcGISRasterProjectToTemplate_GeoEco</a></td><td>Projects a raster to the coordinate system, cell size, and extent of a template raster.</td></tr><tr><td><a href="ArcGISRaster.ToLinesArcGISTable.html?format=raw">ArcGISRasterToLinesArcGISTable_GeoEco</a></td><td>Converts the ArcGIS rasters listed in a table to lines that outline groups of adjacent raster cells having the same value.</td></tr><tr><td><a href="ArcGISRaster.ToLines.html?format=raw">ArcGISRasterToLines_GeoEco</a></td><td>Converts an ArcGIS raster to a feature class of lines that connect adjacent foreground raster cells.</td></tr><tr><td><a href="ArcGISRaster.ToPointsArcGISTable.html?format=raw">ArcGISRasterToPointsArcGISTable_GeoEco</a></td><td>Converts the ArcGIS rasters listed in a table to points that occur at the centers of the raster cells.</td></tr><tr><td><a href="ArcGISRaster.ToPoints.html?format=raw">ArcGISRasterToPoints_GeoEco</a></td><td>Converts an ArcGIS raster to a feature class of points that occur at the centers of the raster cells.</td></tr><tr><td><a href="ArcGISRaster.ToPolygonOutlinesArcGISTable.html?format=raw">ArcGISRasterToPolygonOutlinesArcGISTable_GeoEco</a></td><td>Converts the ArcGIS rasters listed in a table to lines that outline groups of adjacent raster cells having the same value.</td></tr><tr><td><a href="ArcGISRaster.ToPolygonOutlines.html?format=raw">ArcGISRasterToPolygonOutlines_GeoEco</a></td><td>Converts an ArcGIS raster to lines that outline groups of adjacent raster cells having the same value.</td></tr><tr><td><a href="ArcGISRaster.ToPolygonsArcGISTable.html?format=raw">ArcGISRasterToPolygonsArcGISTable_GeoEco</a></td><td>Converts the ArcGIS rasters listed in a table to polygons that encompass groups of adjacent raster cells having the same value.</td></tr><tr><td><a href="ArcGISRaster.ToPolygons.html?format=raw">ArcGISRasterToPolygons_GeoEco</a></td><td>Converts an ArcGIS raster to polygons that encompass groups of adjacent raster cells having the same value.</td></tr><tr><td><a href="ArcGISRasterMapAlgebra.FindRastersAndExecuteSingleOutputMapAlgebra.html?format=raw">ArcGISRasterMapAlgebraFindRastersAndExecuteSingleOutputMapAlgebra_GeoEco</a></td><td>Finds rasters in a workspace and executes a map algebra expression on them.</td></tr><tr><td><a href="ArcGISRasterMapAlgebra.SingleOutputMapAlgebraForArcGISTableRows.html?format=raw">ArcGISRasterMapAlgebraSingleOutputMapAlgebraForArcGISTableRows_GeoEco</a></td><td>For each row of a table, calculates an output raster and map algebra expression from the row using Python expressions and executes the map algebra expression to produce the output raster.</td></tr><tr><td><a href="ArcGISRasterMapAlgebra.SingleOutputMapAlgebraForRasterArcGISTable.html?format=raw">ArcGISRasterMapAlgebraSingleOutputMapAlgebraForRasterArcGISTable_GeoEco</a></td><td>Executes a map algebra expression on the rasters listed in a table.</td></tr><tr><td><a href="ArcGISRasterSampler.SampleRastersInFields.html?format=raw">ArcGISRasterSamplerSampleRastersInFields_GeoEco</a></td><td>Samples rasters listed in fields of a point feature class and stores the sampled values in other fields.</td></tr><tr><td><a href="ArcGISRasterSampler.SampleRasters.html?format=raw">ArcGISRasterSamplerSampleRasters_GeoEco</a></td><td>Samples rasters using a point feature class and stores the sampled values in fields of the feature class.</td></tr><tr><td><a href="ArcGISTableSampler.SampleTimeSeriesTable.html?format=raw">ArcGISTableSamplerSampleTimeSeriesTable_GeoEco</a></td><td>Matches records in a destination table to records in a time series table by date, and copies values of fields of the time series table to fields of the destination table.</td></tr><tr><td><a href="ArcInfoASCIIGrid.FindAndConvertToArcGISRaster.html?format=raw">ArcInfoASCIIGridFindAndConvertToArcGISRaster_GeoEco</a></td><td>Finds ArcInfo ASCII Grid text files in a directory and converts them to ArcGIS rasters.</td></tr><tr><td><a href="ArcInfoASCIIGrid.ToArcGISRasterArcGISTable.html?format=raw">ArcInfoASCIIGridToArcGISRasterArcGISTable_GeoEco</a></td><td>Converts each ArcInfo ASCII Grid text file in a table to an ArcGIS raster.</td></tr><tr><td><a href="ArcInfoASCIIGrid.ToArcGISRaster.html?format=raw">ArcInfoASCIIGridToArcGISRaster_GeoEco</a></td><td>Converts a text file in ArcInfo ASCII Grid format to an ArcGIS raster.</td></tr><tr><td><a href="AvisoGriddedGeostrophicCurrents.CreateArcGISRasters.html?format=raw">AvisoGriddedGeostrophicCurrentsCreateArcGISRasters_GeoEco</a></td><td>Creates rasters for an Aviso gridded geostrophic currents product.</td></tr><tr><td><a href="AvisoGriddedGeostrophicCurrents.CreateClimatologicalArcGISRasters.html?format=raw">AvisoGriddedGeostrophicCurrentsCreateClimatologicalArcGISRasters_GeoEco</a></td><td>Creates climatological rasters for an Aviso gridded geostrophic currents product.</td></tr><tr><td><a href="AvisoGriddedGeostrophicCurrents.CreateVectorsAsArcGISFeatureClasses.html?format=raw">AvisoGriddedGeostrophicCurrentsCreateVectorsAsArcGISFeatureClasses_GeoEco</a></td><td>Creates line feature classes representing the current vectors of an Aviso gridded geostrophic currents product.</td></tr><tr><td><a href="AvisoGriddedGeostrophicCurrents.InterpolateAtArcGISPoints.html?format=raw">AvisoGriddedGeostrophicCurrentsInterpolateAtArcGISPoints_GeoEco</a></td><td>Interpolates the values of an Aviso gridded geostrophic currents product at points.</td></tr><tr><td><a href="AvisoGriddedSSH.CreateArcGISRasters.html?format=raw">AvisoGriddedSSHCreateArcGISRasters_GeoEco</a></td><td>Creates rasters for an Aviso gridded sea surface height product.</td></tr><tr><td><a href="AvisoGriddedSSH.CreateClimatologicalArcGISRasters.html?format=raw">AvisoGriddedSSHCreateClimatologicalArcGISRasters_GeoEco</a></td><td>Creates climatological rasters for an Aviso gridded sea surface height product.</td></tr><tr><td><a href="AvisoGriddedSSH.FindOkuboWeissEddies.html?format=raw">AvisoGriddedSSHFindOkuboWeissEddies_GeoEco</a></td><td>Creates rasters showing the cores of geostrophic eddies detected in an Aviso gridded sea surface height product using the Okubo-Weiss algorithm.</td></tr><tr><td><a href="AvisoGriddedSSH.InterpolateAtArcGISPoints.html?format=raw">AvisoGriddedSSHInterpolateAtArcGISPoints_GeoEco</a></td><td>Interpolates the values of an Aviso gridded sea surface height product at points.</td></tr><tr><td><a href="AvisoGriddedSignificantWaveHeight.CreateArcGISRasters.html?format=raw">AvisoGriddedSignificantWaveHeightCreateArcGISRasters_GeoEco</a></td><td>Creates rasters for an Aviso gridded significant wave height product.</td></tr><tr><td><a href="AvisoGriddedSignificantWaveHeight.CreateClimatologicalArcGISRasters.html?format=raw">AvisoGriddedSignificantWaveHeightCreateClimatologicalArcGISRasters_GeoEco</a></td><td>Creates climatological rasters for an Aviso gridded significant wave height product.</td></tr><tr><td><a href="AvisoGriddedSignificantWaveHeight.InterpolateAtArcGISPoints.html?format=raw">AvisoGriddedSignificantWaveHeightInterpolateAtArcGISPoints_GeoEco</a></td><td>Interpolates the values of an Aviso gridded significant wave height product at points.</td></tr><tr><td><a href="AvisoGriddedWindSpeedModulus.CreateArcGISRasters.html?format=raw">AvisoGriddedWindSpeedModulusCreateArcGISRasters_GeoEco</a></td><td>Creates rasters for an Aviso gridded wind speed modulus product.</td></tr><tr><td><a href="AvisoGriddedWindSpeedModulus.CreateClimatologicalArcGISRasters.html?format=raw">AvisoGriddedWindSpeedModulusCreateClimatologicalArcGISRasters_GeoEco</a></td><td>Creates climatological rasters for an Aviso gridded wind speed modulus product.</td></tr><tr><td><a href="AvisoGriddedWindSpeedModulus.InterpolateAtArcGISPoints.html?format=raw">AvisoGriddedWindSpeedModulusInterpolateAtArcGISPoints_GeoEco</a></td><td>Interpolates the values of an Aviso gridded wind speed modulus product at points.</td></tr><tr><td><a href="BinaryRaster.FindAndConvertToArcGISRaster.html?format=raw">BinaryRasterFindAndConvertToArcGISRaster_GeoEco</a></td><td>Finds two-dimensional binary rasters in a directory and converts them to ArcGIS rasters.</td></tr><tr><td><a href="BinaryRaster.FindAndConvertToArcInfoASCIIGrid.html?format=raw">BinaryRasterFindAndConvertToArcInfoASCIIGrid_GeoEco</a></td><td>Finds two-dimensional binary rasters in a directory and converts them to text files in ArcInfo ASCII Grid format.</td></tr><tr><td><a href="BinaryRaster.FindAndSwapBytes.html?format=raw">BinaryRasterFindAndSwapBytes_GeoEco</a></td><td>Finds and reverses the byte order of binary rasters in a directory (i.e. converts "little endian" to "big endian", or visa versa).</td></tr><tr><td><a href="BinaryRaster.SwapBytesArcGISTable.html?format=raw">BinaryRasterSwapBytesArcGISTable_GeoEco</a></td><td>Reverses the byte order of each binary raster listed in a table (i.e. converts "little endian" to "big endian", or visa versa).</td></tr><tr><td><a href="BinaryRaster.SwapBytes.html?format=raw">BinaryRasterSwapBytes_GeoEco</a></td><td>Reverses the byte order of a binary raster (e.g. converts "little endian" to "big endian", or visa versa).</td></tr><tr><td><a href="BinaryRaster.ToArcGISRasterArcGISTable.html?format=raw">BinaryRasterToArcGISRasterArcGISTable_GeoEco</a></td><td>Converts each two-dimensional binary raster in a table to an ArcGIS raster.</td></tr><tr><td><a href="BinaryRaster.ToArcGISRaster.html?format=raw">BinaryRasterToArcGISRaster_GeoEco</a></td><td>Converts a two-dimensional binary raster to an ArcGIS raster.</td></tr><tr><td><a href="BinaryRaster.ToArcInfoASCIIGridArcGISTable.html?format=raw">BinaryRasterToArcInfoASCIIGridArcGISTable_GeoEco</a></td><td>Converts each two-dimensional binary raster in a table to a text file in ArcInfo ASCII Grid format.</td></tr><tr><td><a href="BinaryRaster.ToArcInfoASCIIGrid.html?format=raw">BinaryRasterToArcInfoASCIIGrid_GeoEco</a></td><td>Converts a two-dimensional binary raster to a text file in ArcGIS ASCII Grid format.</td></tr><tr><td><a href="CayulaCornillonEdgeDetection.DetectEdgesInArcGISRaster.html?format=raw">CayulaCornillonEdgeDetectionDetectEdgesInArcGISRaster_GeoEco</a></td><td>Finds fronts in an ArcGIS raster using the Cayula and Cornillon (1992) single image edge detection (SIED) algorithm.</td></tr><tr><td><a href="CayulaCornillonEdgeDetection.DetectEdgesInArcGISRastersArcGISTable.html?format=raw">CayulaCornillonEdgeDetectionDetectEdgesInArcGISRastersArcGISTable_GeoEco</a></td><td>Finds fronts in ArcGIS rasters listed in a table using the Cayula-Cornillon (1992) single-image edge detection algorithm.</td></tr><tr><td><a href="CayulaCornillonEdgeDetection.DetectEdgesInBinaryRaster.html?format=raw">CayulaCornillonEdgeDetectionDetectEdgesInBinaryRaster_GeoEco</a></td><td>Finds fronts in a binary raster using the Cayula and Cornillon (1992) single image edge detection (SIED) algorithm.</td></tr><tr><td><a href="CayulaCornillonEdgeDetection.FindArcGISRastersAndDetectEdges.html?format=raw">CayulaCornillonEdgeDetectionFindArcGISRastersAndDetectEdges_GeoEco</a></td><td>Finds ArcGIS rasters in a workspace and finds fronts within them using the Cayula-Cornillon (1992) single-image edge detection algorithm.</td></tr><tr><td><a href="CheltonMesocaleEddyPoints.ConvertToSpatiaLite.html?format=raw">CheltonMesocaleEddyPointsConvertToSpatiaLite_GeoEco</a></td><td>Converts the Chelton et al. (2011) mesoscale eddy database netCDF file to a SpatiaLite database.</td></tr><tr><td><a href="CheltonMesocaleEddyPoints.ExtractArcGISLinesFromSpatiaLite.html?format=raw">CheltonMesocaleEddyPointsExtractArcGISLinesFromSpatiaLite_GeoEco</a></td><td>Extracts eddy tracklines from the Chelton et al. (2011) mesoscale eddy database in SpatiaLite format.</td></tr><tr><td><a href="CheltonMesocaleEddyPoints.ExtractArcGISPointsFromSpatiaLite.html?format=raw">CheltonMesocaleEddyPointsExtractArcGISPointsFromSpatiaLite_GeoEco</a></td><td>Extracts eddy centroid points from the Chelton et al. (2011) mesoscale eddy database in SpatiaLite format.</td></tr><tr><td><a href="ChildProcess.ExecuteProgram.html?format=raw">ChildProcessExecuteProgram_GeoEco</a></td><td>Executes the specified program and captures its output.</td></tr><tr><td><a href="CoRTADv32D.CreateArcGISRaster.html?format=raw">CoRTADv32DCreateArcGISRaster_GeoEco</a></td><td>Creates a raster for a CoRTAD 2D variable.</td></tr><tr><td><a href="CoRTADv32D.InterpolateAtArcGISPoints.html?format=raw">CoRTADv32DInterpolateAtArcGISPoints_GeoEco</a></td><td>Interpolates CoRTAD 2D variables at points.</td></tr><tr><td><a href="CoRTADv33D.CreateArcGISRasters.html?format=raw">CoRTADv33DCreateArcGISRasters_GeoEco</a></td><td>Creates rasters a CoRTAD 3D variable.</td></tr><tr><td><a href="CoRTADv33D.InterpolateAtArcGISPoints.html?format=raw">CoRTADv33DInterpolateAtArcGISPoints_GeoEco</a></td><td>Interpolates CoRTAD 3D variables at points.</td></tr><tr><td><a href="CoastWatchAVHRR.CopyNavigationOffsetsArcGISTable.html?format=raw">CoastWatchAVHRRCopyNavigationOffsetsArcGISTable_GeoEco</a></td><td>Copies navigation offsets from a source variable to destination variables in CoastWatch POES AVHRR CWF or HDF files listed in a table.</td></tr><tr><td><a href="CoastWatchAVHRR.CopyNavigationOffsets.html?format=raw">CoastWatchAVHRRCopyNavigationOffsets_GeoEco</a></td><td>Copies the navigation offsets from one variable in a CoastWatch POES AVHRR CWF or HDF file to one or more variables in another file.</td></tr><tr><td><a href="CoastWatchAVHRR.CreateMaskAsArcGISRaster.html?format=raw">CoastWatchAVHRRCreateMaskAsArcGISRaster_GeoEco</a></td><td>Creates a mask, in ArcGIS raster format, for a CoastWatch POES AVHRR image.</td></tr><tr><td><a href="CoastWatchAVHRR.CreateMaskAsBinaryRaster.html?format=raw">CoastWatchAVHRRCreateMaskAsBinaryRaster_GeoEco</a></td><td>Creates a mask, in binary raster format, for a CoastWatch POES AVHRR image.</td></tr><tr><td><a href="CoastWatchAVHRR.CreateMasksAsArcGISRastersArcGISTable.html?format=raw">CoastWatchAVHRRCreateMasksAsArcGISRastersArcGISTable_GeoEco</a></td><td>Creates masks, in ArcGIS raster format, for CoastWatch POES AVHRR images listed in a table.</td></tr><tr><td><a href="CoastWatchAVHRR.CreateMasksAsBinaryRastersArcGISTable.html?format=raw">CoastWatchAVHRRCreateMasksAsBinaryRastersArcGISTable_GeoEco</a></td><td>Creates masks, in binary raster format, for CoastWatch POES AVHRR images listed in a table.</td></tr><tr><td><a href="CoastWatchAVHRR.FindAndConvertToArcGISRasters.html?format=raw">CoastWatchAVHRRFindAndConvertToArcGISRasters_GeoEco</a></td><td>Finds images in CoastWatch POES AVHRR files in a directory and converts them to ArcGIS rasters.</td></tr><tr><td><a href="CoastWatchAVHRR.FindAndConvertToBinaryRasters.html?format=raw">CoastWatchAVHRRFindAndConvertToBinaryRasters_GeoEco</a></td><td>Finds images in CoastWatch POES AVHRR files in a directory and converts them to binary rasters.</td></tr><tr><td><a href="CoastWatchAVHRR.FindAndConvertToCoastWatchHDFs.html?format=raw">CoastWatchAVHRRFindAndConvertToCoastWatchHDFs_GeoEco</a></td><td>Finds images in CoastWatch POES AVHRR files in a directory and imports them into CoastWatch HDFs.</td></tr><tr><td><a href="CoastWatchAVHRR.FindAndCreateMasksAsArcGISRasters.html?format=raw">CoastWatchAVHRRFindAndCreateMasksAsArcGISRasters_GeoEco</a></td><td>Finds images in CoastWatch POES AVHRR files in a directory creates masks for them, in ArcGIS raster format.</td></tr><tr><td><a href="CoastWatchAVHRR.FindAndCreateMasksAsBinaryRasters.html?format=raw">CoastWatchAVHRRFindAndCreateMasksAsBinaryRasters_GeoEco</a></td><td>Finds images in CoastWatch POES AVHRR files in a directory creates masks for them, in binary raster format.</td></tr><tr><td><a href="CoastWatchAVHRR.FindCoastWatchFilesAndFindFrontsAsArcGISRasters.html?format=raw">CoastWatchAVHRRFindCoastWatchFilesAndFindFrontsAsArcGISRasters_GeoEco</a></td><td>Uses the Cayula-Cornillon (1992) single-image edge detection algorithm to find fronts in the CoastWatch POES AVHRR images found in a directory, and outputs the fronts as ArcGIS rasters.</td></tr><tr><td><a href="CoastWatchAVHRR.FindFilesAndCreateArcGISTable.html?format=raw">CoastWatchAVHRRFindFilesAndCreateArcGISTable_GeoEco</a></td><td>Finds CoastWatch POES AVHRR files within a directory and creates a table that lists them.</td></tr><tr><td><a href="CoastWatchAVHRR.FindFrontsAsArcGISRaster.html?format=raw">CoastWatchAVHRRFindFrontsAsArcGISRaster_GeoEco</a></td><td>Finds fronts in a CoastWatch POES AVHRR image using the Cayula-Cornillon (1992) single-image edge detection algorithm and outputs them to an ArcGIS raster.</td></tr><tr><td><a href="CoastWatchAVHRR.FindFrontsAsArcGISRastersArcGISTable.html?format=raw">CoastWatchAVHRRFindFrontsAsArcGISRastersArcGISTable_GeoEco</a></td><td>Finds fronts in CoastWatch POES AVHRR images listed in a table using the Cayula-Cornillon (1992) single-image edge detection algorithm and outputs them as ArcGIS rasters.</td></tr><tr><td><a href="CoastWatchAVHRR.FindFrontsAsBinaryRaster.html?format=raw">CoastWatchAVHRRFindFrontsAsBinaryRaster_GeoEco</a></td><td>Finds fronts in a CoastWatch POES AVHRR image using the Cayula-Cornillon (1992) single-image edge detection algorithm and outputs them to a binary raster.</td></tr><tr><td><a href="CoastWatchAVHRR.FindImagesInFilesAndCreateArcGISTable.html?format=raw">CoastWatchAVHRRFindImagesInFilesAndCreateArcGISTable_GeoEco</a></td><td>Finds CoastWatch POES AVHRR files within a directory and creates a table that lists the images within them.</td></tr><tr><td><a href="CoastWatchAVHRR.SetNavigationOffsetsArcGISTable.html?format=raw">CoastWatchAVHRRSetNavigationOffsetsArcGISTable_GeoEco</a></td><td>Sets the navigation offsets of the CoastWatch POES AVHRR CWF or HDF files listed in a table to the values specified by two fields.</td></tr><tr><td><a href="CoastWatchAVHRR.SetNavigationOffsets.html?format=raw">CoastWatchAVHRRSetNavigationOffsets_GeoEco</a></td><td>Sets the navigation offsets of a CoastWatch POES AVHRR CWF or HDF file.</td></tr><tr><td><a href="CoastWatchAVHRR.ToArcGISRasterArcGISTable.html?format=raw">CoastWatchAVHRRToArcGISRasterArcGISTable_GeoEco</a></td><td>Creates an ArcGIS raster from each CoastWatch POES AVHRR image listed in a table of images, where one field specifies the file path containing the image and another specifies the variable that represents the image.</td></tr><tr><td><a href="CoastWatchAVHRR.ToArcGISRaster.html?format=raw">CoastWatchAVHRRToArcGISRaster_GeoEco</a></td><td>Extracts and converts CoastWatch POES AVHRR image, specified by a file plus a variable in that file, to an ArcGIS raster.</td></tr><tr><td><a href="CoastWatchAVHRR.ToBinaryRasterArcGISTable.html?format=raw">CoastWatchAVHRRToBinaryRasterArcGISTable_GeoEco</a></td><td>Creates a binary raster from each CoastWatch POES AVHRR image listed in a table of images, where one field specifies the file path containing the image and another specifies the variable that represents the image.</td></tr><tr><td><a href="CoastWatchAVHRR.ToBinaryRaster.html?format=raw">CoastWatchAVHRRToBinaryRaster_GeoEco</a></td><td>Extracts and converts CoastWatch POES AVHRR image, specified by a file plus a variable in that file, to a binary raster.</td></tr><tr><td><a href="CoastWatchAVHRR.ToCoastWatchHDFArcGISTable.html?format=raw">CoastWatchAVHRRToCoastWatchHDFArcGISTable_GeoEco</a></td><td>Creates CoastWatch HDFs in a specified output directory by importing images from the CoastWatch POES AVHRR CWFs or HDFs listed in a table.</td></tr><tr><td><a href="CoastWatchAVHRR.ToCoastWatchHDF.html?format=raw">CoastWatchAVHRRToCoastWatchHDF_GeoEco</a></td><td>Creates a CoastWatch HDF by importing images from a list of CoastWatch POES AVHRR CWFs or HDFs.</td></tr><tr><td><a href="CoralReefConnectivity.CreateSimulationFromArcGISRasters.html?format=raw">CoralReefConnectivityCreateSimulationFromArcGISRasters_GeoEco</a></td><td>Creates a coral reef connectivity simulation and initializes it with reef data in ArcGIS rasters.</td></tr><tr><td><a href="CoralReefConnectivity.LoadAvisoGeostrophicCurrentsIntoSimulation.html?format=raw">CoralReefConnectivityLoadAvisoGeostrophicCurrentsIntoSimulation_GeoEco</a></td><td>Downloads Aviso geostrophic currents into a coral reef connectivity simulation.</td></tr><tr><td><a href="CoralReefConnectivity.LoadHYCOMGLBa0084DEquatorialCurrentsIntoSimulation.html?format=raw">CoralReefConnectivityLoadHYCOMGLBa0084DEquatorialCurrentsIntoSimulation_GeoEco</a></td><td>Downloads HYCOM GLBa0.08 ocean currents into a coral reef connectivity simulation.</td></tr><tr><td><a href="CoralReefConnectivity.LoadHYCOMGOMl0044DCurrentsIntoSimulation.html?format=raw">CoralReefConnectivityLoadHYCOMGOMl0044DCurrentsIntoSimulation_GeoEco</a></td><td>Downloads HYCOM GOMl0.04 ocean currents into a coral reef connectivity simulation.</td></tr><tr><td><a href="CoralReefConnectivity.LoadOSCARCurrentsIntoSimulation.html?format=raw">CoralReefConnectivityLoadOSCARCurrentsIntoSimulation_GeoEco</a></td><td>Downloads NOAA OSCAR 5-day unfiltered 1/3 degree ocean surface currents into a coral reef connectivity simulation.</td></tr><tr><td><a href="CoralReefConnectivity.LoadROMSCoSiNE4DCurrentsIntoSimulation.html?format=raw">CoralReefConnectivityLoadROMSCoSiNE4DCurrentsIntoSimulation_GeoEco</a></td><td>Downloads Pacific ROMS-CoSiNE ocean currents into a coral reef connectivity simulation.</td></tr><tr><td><a href="CoralReefConnectivity.RunSimulation.html?format=raw">CoralReefConnectivityRunSimulation_GeoEco</a></td><td>Executes a coral coral reef connectivity simulation.</td></tr><tr><td><a href="DiGIR.GetOBISResourcesAsArcGISTable.html?format=raw">DiGIRGetOBISResourcesAsArcGISTable_GeoEco</a></td><td>Gets the list of Distributed Generic Information Retrieval (DiGIR) resources available from OBIS and writes it to an ArcGIS table.</td></tr><tr><td><a href="DiGIR.GetOBISSEAMAPResourcesAsArcGISTable.html?format=raw">DiGIRGetOBISSEAMAPResourcesAsArcGISTable_GeoEco</a></td><td>Gets the list of Distributed Generic Information Retrieval (DiGIR) resources available from OBIS-SEAMAP and writes it to an ArcGIS table.</td></tr><tr><td><a href="DiGIR.GetResourcesAsArcGISTable.html?format=raw">DiGIRGetResourcesAsArcGISTable_GeoEco</a></td><td>Gets the list of Distributed Generic Information Retrieval (DiGIR) resources available from a DiGIR server and writes it to an ArcGIS table.</td></tr><tr><td><a href="DiGIR.SearchAndCreateArcGISPoints.html?format=raw">DiGIRSearchAndCreateArcGISPoints_GeoEco</a></td><td>Searches a Distributed Generic Information Retrieval (DiGIR) server for georeferenced records and creates an ArcGIS point feature class from them.</td></tr><tr><td><a href="DiGIR.SearchOBISAndCreateArcGISPoints.html?format=raw">DiGIRSearchOBISAndCreateArcGISPoints_GeoEco</a></td><td>Searches OBIS for georeferenced records using the Distributed Generic Information Retrieval (DiGIR) protocol and creates an ArcGIS point feature class from them.</td></tr><tr><td><a href="DiGIR.SearchOBISSEAMAPAndCreateArcGISPoints.html?format=raw">DiGIRSearchOBISSEAMAPAndCreateArcGISPoints_GeoEco</a></td><td>Searches OBIS-SEAMAP for georeferenced records using the Distributed Generic Information Retrieval (DiGIR) protocol and creates an ArcGIS point feature class from them.</td></tr><tr><td><a href="Directory.CopyArcGISTable.html?format=raw">DirectoryCopyArcGISTable_GeoEco</a></td><td>Copies the directories listed in a table.</td></tr><tr><td><a href="Directory.Copy.html?format=raw">DirectoryCopy_GeoEco</a></td><td>Copies a directory, including its subdirectories and files.</td></tr><tr><td><a href="Directory.CreateSubdirectory.html?format=raw">DirectoryCreateSubdirectory_GeoEco</a></td><td>Creates a subdirectory within a parent directory.</td></tr><tr><td><a href="Directory.CreateTemporaryDirectory.html?format=raw">DirectoryCreateTemporaryDirectory_GeoEco</a></td><td>Creates a directory suitable for holding temporary files.</td></tr><tr><td><a href="Directory.Create.html?format=raw">DirectoryCreate_GeoEco</a></td><td>Creates a directory, including any parent directories that are missing.</td></tr><tr><td><a href="Directory.DeleteArcGISTable.html?format=raw">DirectoryDeleteArcGISTable_GeoEco</a></td><td>Deletes the directories listed in a table.</td></tr><tr><td><a href="Directory.Delete.html?format=raw">DirectoryDelete_GeoEco</a></td><td>Deletes a directory.</td></tr><tr><td><a href="Directory.FindAndCopy.html?format=raw">DirectoryFindAndCopy_GeoEco</a></td><td>Finds and copies directories in a directory.</td></tr><tr><td><a href="Directory.FindAndCreateArcGISTable.html?format=raw">DirectoryFindAndCreateArcGISTable_GeoEco</a></td><td>Finds subdirectories within a directory and creates a table that lists them.</td></tr><tr><td><a href="Directory.FindAndDelete.html?format=raw">DirectoryFindAndDelete_GeoEco</a></td><td>Finds and deletes directories in a directory.</td></tr><tr><td><a href="Directory.FindAndMove.html?format=raw">DirectoryFindAndMove_GeoEco</a></td><td>Finds and moves directories in a directory.</td></tr><tr><td><a href="Directory.MoveArcGISTable.html?format=raw">DirectoryMoveArcGISTable_GeoEco</a></td><td>Moves the directories listed in a table.</td></tr><tr><td><a href="Directory.Move.html?format=raw">DirectoryMove_GeoEco</a></td><td>Moves a directory, including its subdirectories and files.</td></tr><tr><td><a href="ESRLClimateIndices.ClassifyONIEpisodesInTimeSeriesArcGISTable.html?format=raw">ESRLClimateIndicesClassifyONIEpisodesInTimeSeriesArcGISTable_GeoEco</a></td><td>Given a time series table of monthly Oceanic Nino Index (ONI) numerical values, classifies each month as part of a normal, El Nino (warm), or La Nina (cold) episode.</td></tr><tr><td><a href="ESRLClimateIndices.FileToArcGISTable.html?format=raw">ESRLClimateIndicesFileToArcGISTable_GeoEco</a></td><td>Creates and populates a table of climate index values parsed from a text file in NOAA ESRL time series format.</td></tr><tr><td><a href="ESRLClimateIndices.FilesToArcGISTable.html?format=raw">ESRLClimateIndicesFilesToArcGISTable_GeoEco</a></td><td>Creates and populates a table of climate index values parsed from a list of text files, where each file contains the data for a single climate index in NOAA ESRL time series format.</td></tr><tr><td><a href="ESRLClimateIndices.UrlToArcGISTable.html?format=raw">ESRLClimateIndicesUrlToArcGISTable_GeoEco</a></td><td>Creates and populates a table of climate index values parsed from NOAA ESRL climate index time series data downloaded from a URL.</td></tr><tr><td><a href="ESRLClimateIndices.UrlsToArcGISTable.html?format=raw">ESRLClimateIndicesUrlsToArcGISTable_GeoEco</a></td><td>Creates and populates a table of climate index values parsed from NOAA ESRL climate index time series data downloaded from a list of URLs.</td></tr><tr><td><a href="FEET.CreateEnvelopes.html?format=raw">FEETCreateEnvelopes_GeoEco</a></td><td>Models the spatial distribution of fishing effort for fisheries for which no spatially-explicit effort data is available.</td></tr><tr><td><a href="Field.CalculateArcGISField.html?format=raw">FieldCalculateArcGISField_GeoEco</a></td><td>Calculates the value of a table field using a Python expression.</td></tr><tr><td><a href="Field.CalculateArcGISFields.html?format=raw">FieldCalculateArcGISFields_GeoEco</a></td><td>Calculates values for one or more fields of a table using Python expressions.</td></tr><tr><td><a href="File.CopyArcGISTable.html?format=raw">FileCopyArcGISTable_GeoEco</a></td><td>Copies the files listed in a table.</td></tr><tr><td><a href="File.Copy.html?format=raw">FileCopy_GeoEco</a></td><td>Copies a file.</td></tr><tr><td><a href="File.Decompress.html?format=raw">FileDecompress_GeoEco</a></td><td>Decompresses a file into a directory.</td></tr><tr><td><a href="File.DeleteArcGISTable.html?format=raw">FileDeleteArcGISTable_GeoEco</a></td><td>Deletes the files listed in a table.</td></tr><tr><td><a href="File.Delete.html?format=raw">FileDelete_GeoEco</a></td><td>Deletes a file.</td></tr><tr><td><a href="File.FindAndCopy.html?format=raw">FileFindAndCopy_GeoEco</a></td><td>Finds and copies files in a directory.</td></tr><tr><td><a href="File.FindAndCreateArcGISTable.html?format=raw">FileFindAndCreateArcGISTable_GeoEco</a></td><td>Finds files within a directory and creates a table that lists them.</td></tr><tr><td><a href="File.FindAndDelete.html?format=raw">FileFindAndDelete_GeoEco</a></td><td>Finds and deletes files in a directory.</td></tr><tr><td><a href="File.FindAndMove.html?format=raw">FileFindAndMove_GeoEco</a></td><td>Finds and moves files in a directory.</td></tr><tr><td><a href="File.IsDecompressible.html?format=raw">FileIsDecompressible_GeoEco</a></td><td>Returns True if the specified file is in a format that can be decompressed.</td></tr><tr><td><a href="File.MoveArcGISTable.html?format=raw">FileMoveArcGISTable_GeoEco</a></td><td>Moves the files listed in a table.</td></tr><tr><td><a href="File.Move.html?format=raw">FileMove_GeoEco</a></td><td>Moves a file.</td></tr><tr><td><a href="GAM.BayesPredictFromArcGISRasters.html?format=raw">GAMBayesPredictFromArcGISRasters_GeoEco</a></td><td>Using a binomial generalized additive model (GAM) fitted using the R mgcv package, this tool creates rasters representing the estimated probabilities that the response variable will equal or exceed specified thresholds, given ArcGIS rasters representing the predictor variables.</td></tr><tr><td><a href="GAM.FitToArcGISTable.html?format=raw">GAMFitToArcGISTable_GeoEco</a></td><td>Fits a generalized additive model (GAM) to data in an ArcGIS table.</td></tr><tr><td><a href="GAM.PredictFromArcGISRasters.html?format=raw">GAMPredictFromArcGISRasters_GeoEco</a></td><td>Using a fitted generalized additive model (GAM), this tool creates a raster representing the response variable predicted from ArcGIS rasters representing the predictor variables.</td></tr><tr><td><a href="GLM.FitToArcGISTable.html?format=raw">GLMFitToArcGISTable_GeoEco</a></td><td>Fits a generalized linear model (GLM) to data in an ArcGIS table using the R glm function.</td></tr><tr><td><a href="GLM.PredictFromArcGISRasters.html?format=raw">GLMPredictFromArcGISRasters_GeoEco</a></td><td>Using a fitted generalized linear model (GLM), this tool creates a raster representing the response variable predicted from ArcGIS rasters representing the predictor variables.</td></tr><tr><td><a href="HDF.ExtractHeaderArcGISTable.html?format=raw">HDFExtractHeaderArcGISTable_GeoEco</a></td><td>Extracts the headers of HDF files in a table and saves them to text files.</td></tr><tr><td><a href="HDF.ExtractHeader.html?format=raw">HDFExtractHeader_GeoEco</a></td><td>Extracts the header of an HDF file and saves it to a text file.</td></tr><tr><td><a href="HDF.FindAndConvertToArcGISRasters.html?format=raw">HDFFindAndConvertToArcGISRasters_GeoEco</a></td><td>Finds HDF files in a directory and converts a Scientific Data Sets (SDS) in each file to an ArcGIS raster.</td></tr><tr><td><a href="HDF.FindAndConvertToArcInfoASCIIGrids.html?format=raw">HDFFindAndConvertToArcInfoASCIIGrids_GeoEco</a></td><td>Finds HDF files in a directory and converts a Scientific Data Sets (SDS) in each file to a text file in ArcInfo ASCII Grid format.</td></tr><tr><td><a href="HDF.FindAndConvertToBinaryRasters.html?format=raw">HDFFindAndConvertToBinaryRasters_GeoEco</a></td><td>Finds HDF files in a directory and converts a Scientific Data Sets (SDS) in each file to a binary raster.</td></tr><tr><td><a href="HDF.FindAndExtractHeaders.html?format=raw">HDFFindAndExtractHeaders_GeoEco</a></td><td>Finds HDF files in a directory, extracts their headers, and saves the headers to text files.</td></tr><tr><td><a href="HDF.SDSToArcGISRaster.html?format=raw">HDFSDSToArcGISRaster_GeoEco</a></td><td>Converts a Scientific Data Set (SDS) in an HDF file to an ArcGIS raster.</td></tr><tr><td><a href="HDF.SDSToArcInfoASCIIGrid.html?format=raw">HDFSDSToArcInfoASCIIGrid_GeoEco</a></td><td>Converts a Scientific Data Set (SDS) in an HDF file to a text file in ArcInfo ASCII Grid format.</td></tr><tr><td><a href="HDF.SDSToBinaryRaster.html?format=raw">HDFSDSToBinaryRaster_GeoEco</a></td><td>Converts a Scientific Data Set (SDS) in an HDF file to a binary raster.</td></tr><tr><td><a href="HDF.ToArcGISRasterArcGISTable.html?format=raw">HDFToArcGISRasterArcGISTable_GeoEco</a></td><td>Converts a Scientific Data Set (SDS) in each HDF file in a table to an ArcGIS raster.</td></tr><tr><td><a href="HDF.ToArcInfoASCIIGridArcGISTable.html?format=raw">HDFToArcInfoASCIIGridArcGISTable_GeoEco</a></td><td>Converts a Scientific Data Set (SDS) in each HDF file in a table to a text file in an ArcInfo ASCII Grid format.</td></tr><tr><td><a href="HDF.ToBinaryRasterArcGISTable.html?format=raw">HDFToBinaryRasterArcGISTable_GeoEco</a></td><td>Converts a Scientific Data Set (SDS) in each HDF file in a table to a binary raster.</td></tr><tr><td><a href="HYCOMGLBa008Equatorial3D.CreateArcGISRasters.html?format=raw">HYCOMGLBa008Equatorial3DCreateArcGISRasters_GeoEco</a></td><td>Creates rasters for a 3D variable of the equatorial (Mercator) region of the HYCOM GLBa0.08 dataset.</td></tr><tr><td><a href="HYCOMGLBa008Equatorial3D.CreateClimatologicalArcGISRasters.html?format=raw">HYCOMGLBa008Equatorial3DCreateClimatologicalArcGISRasters_GeoEco</a></td><td>Creates climatological rasters for a 3D variable of the equatorial (Mercator) region of the HYCOM GLBa0.08 dataset</td></tr><tr><td><a href="HYCOMGLBa008Equatorial3D.InterpolateAtArcGISPoints.html?format=raw">HYCOMGLBa008Equatorial3DInterpolateAtArcGISPoints_GeoEco</a></td><td>Interpolates 3D variables of the equatorial (Mercator) region of the HYCOM GLBa0.08 dataset at points.</td></tr><tr><td><a href="HYCOMGLBa008Equatorial4D.CreateArcGISRasters.html?format=raw">HYCOMGLBa008Equatorial4DCreateArcGISRasters_GeoEco</a></td><td>Creates rasters for a 4D variable of the equatorial (Mercator) region of the HYCOM GLBa0.08 dataset.</td></tr><tr><td><a href="HYCOMGLBa008Equatorial4D.CreateCayulaCornillonFrontsAsArcGISRasters.html?format=raw">HYCOMGLBa008Equatorial4DCreateCayulaCornillonFrontsAsArcGISRasters_GeoEco</a></td><td>Creates rasters indicating the positions of fronts in the 2D slices of a 4D variable of the equatorial (Mercator) region of the HYCOM GLBa0.08 dataset using the Cayula and Cornillon (1992) single image edge detection (SIED) algorithm.</td></tr><tr><td><a href="HYCOMGLBa008Equatorial4D.CreateClimatologicalArcGISRasters.html?format=raw">HYCOMGLBa008Equatorial4DCreateClimatologicalArcGISRasters_GeoEco</a></td><td>Creates climatological rasters for a 4D variable of the equatorial (Mercator) region of the HYCOM GLBa0.08 dataset</td></tr><tr><td><a href="HYCOMGLBa008Equatorial4D.CreateCurrentVectorsAsArcGISFeatureClasses.html?format=raw">HYCOMGLBa008Equatorial4DCreateCurrentVectorsAsArcGISFeatureClasses_GeoEco</a></td><td>Creates line feature classes representing the current vectors for the equatorial (Mercator) region of the HYCOM GLBa0.08 dataset.</td></tr><tr><td><a href="HYCOMGLBa008Equatorial4D.InterpolateAtArcGISPoints.html?format=raw">HYCOMGLBa008Equatorial4DInterpolateAtArcGISPoints_GeoEco</a></td><td>Interpolates 4D variables of the equatorial (Mercator) region of the HYCOM GLBa0.08 dataset at points.</td></tr><tr><td><a href="HYCOMGOMl0043D.CreateArcGISRasters.html?format=raw">HYCOMGOMl0043DCreateArcGISRasters_GeoEco</a></td><td>Creates rasters for a HYCOM GOMl0.04 3D variable.</td></tr><tr><td><a href="HYCOMGOMl0043D.CreateClimatologicalArcGISRasters.html?format=raw">HYCOMGOMl0043DCreateClimatologicalArcGISRasters_GeoEco</a></td><td>Creates climatological rasters for a HYCOM GOMl0.04 3D variable</td></tr><tr><td><a href="HYCOMGOMl0043D.InterpolateAtArcGISPoints.html?format=raw">HYCOMGOMl0043DInterpolateAtArcGISPoints_GeoEco</a></td><td>Interpolates HYCOM GOMl0.04 3D variables at points.</td></tr><tr><td><a href="HYCOMGOMl0044D.CreateArcGISRasters.html?format=raw">HYCOMGOMl0044DCreateArcGISRasters_GeoEco</a></td><td>Creates rasters for a HYCOM GOMl0.04 4D variable.</td></tr><tr><td><a href="HYCOMGOMl0044D.CreateCayulaCornillonFrontsAsArcGISRasters.html?format=raw">HYCOMGOMl0044DCreateCayulaCornillonFrontsAsArcGISRasters_GeoEco</a></td><td>Creates rasters indicating the positions of fronts in the 2D slices of a HYCOM GOMl0.04 4D variable using the Cayula and Cornillon (1992) single image edge detection (SIED) algorithm.</td></tr><tr><td><a href="HYCOMGOMl0044D.CreateClimatologicalArcGISRasters.html?format=raw">HYCOMGOMl0044DCreateClimatologicalArcGISRasters_GeoEco</a></td><td>Creates climatological rasters for a HYCOM GOMl0.04 4D variable</td></tr><tr><td><a href="HYCOMGOMl0044D.CreateCurrentVectorsAsArcGISFeatureClasses.html?format=raw">HYCOMGOMl0044DCreateCurrentVectorsAsArcGISFeatureClasses_GeoEco</a></td><td>Creates line feature classes representing the vectors of HYCOM GOMl0.04 currents.</td></tr><tr><td><a href="HYCOMGOMl0044D.InterpolateAtArcGISPoints.html?format=raw">HYCOMGOMl0044DInterpolateAtArcGISPoints_GeoEco</a></td><td>Interpolates HYCOM GOMl0.04 4D variables at points.</td></tr><tr><td><a href="Interpolator.FindAndInpaintArcGISRasters.html?format=raw">InterpolatorFindAndInpaintArcGISRasters_GeoEco</a></td><td>Finds rasters in an ArcGIS workspace and interpolates values for the No Data cells.</td></tr><tr><td><a href="Interpolator.InpaintArcGISRasterArcGISTable.html?format=raw">InterpolatorInpaintArcGISRasterArcGISTable_GeoEco</a></td><td>Interpolates values for the No Data cells for each ArcGIS raster in a table.</td></tr><tr><td><a href="Interpolator.InpaintArcGISRaster.html?format=raw">InterpolatorInpaintArcGISRaster_GeoEco</a></td><td>Interpolates values for the No Data cells of a raster.</td></tr><tr><td><a href="Interpolator.InterpolateArcGISRasterValuesAtPoints.html?format=raw">InterpolatorInterpolateArcGISRasterValuesAtPoints_GeoEco</a></td><td>Interpolates the values of rasters at points.</td></tr><tr><td><a href="LinearMixedModel.FitToArcGISTable.html?format=raw">LinearMixedModelFitToArcGISTable_GeoEco</a></td><td>Fits a linear mixed-effects model to data in an ArcGIS table.</td></tr><tr><td><a href="LinearMixedModel.PredictFromArcGISRasters.html?format=raw">LinearMixedModelPredictFromArcGISRasters_GeoEco</a></td><td>Using a fitted linear mixed model, this tool creates a raster representing the response variable predicted from rasters representing the predictor variables.</td></tr><tr><td><a href="MODISL3SSTTimeSeries.CreateArcGISRasters.html?format=raw">MODISL3SSTTimeSeriesCreateArcGISRasters_GeoEco</a></td><td>Creates rasters for MODIS Level 3 SST images published by NASA JPL PO.DAAC.</td></tr><tr><td><a href="MODISL3SSTTimeSeries.CreateCayulaCornillonFrontsAsArcGISRasters.html?format=raw">MODISL3SSTTimeSeriesCreateCayulaCornillonFrontsAsArcGISRasters_GeoEco</a></td><td>Creates rasters indicating the positions of fronts in MODIS Level 3 SST images published by NASA JPL PO.DAAC, using the Cayula and Cornillon (1992) single image edge detection (SIED) algorithm.</td></tr><tr><td><a href="MODISL3SSTTimeSeries.CreateClimatologicalArcGISRasters.html?format=raw">MODISL3SSTTimeSeriesCreateClimatologicalArcGISRasters_GeoEco</a></td><td>Creates climatological rasters from MODIS Level 3 SST images published by NASA JPL PO.DAAC.</td></tr><tr><td><a href="MODISL3SSTTimeSeries.InterpolateAtArcGISPoints.html?format=raw">MODISL3SSTTimeSeriesInterpolateAtArcGISPoints_GeoEco</a></td><td>Interpolates PO.DAAC MODIS Level 3 SST values at points.</td></tr><tr><td><a href="ModelEvaluation.PlotPerformanceOfBinaryClassificationModel.html?format=raw">ModelEvaluationPlotPerformanceOfBinaryClassificationModel_GeoEco</a></td><td>Plots the performance of a binary classification model (a model where the response variable has two possible values) using the R ROCR package.</td></tr><tr><td><a href="ModelEvaluation.PlotROCOfBinaryClassificationModel.html?format=raw">ModelEvaluationPlotROCOfBinaryClassificationModel_GeoEco</a></td><td>Plots the receiver operating characteristic (ROC) curve of a binary classification model (a model where the response variable has two possible values) using the R ROCR package.</td></tr><tr><td><a href="ModelEvaluation.RandomlySplitArcGISTableIntoTrainingAndEvaluationRecords.html?format=raw">ModelEvaluationRandomlySplitArcGISTableIntoTrainingAndEvaluationRecords_GeoEco</a></td><td>Randomly designates the records of a table as either training records (for fitting a statistical model) or evaluation records (for evaluating the model).</td></tr><tr><td><a href="NetCDF.Convert2DVariableInNetCDFsInArcGISTableToArcGISRasters.html?format=raw">NetCDFConvert2DVariableInNetCDFsInArcGISTableToArcGISRasters_GeoEco</a></td><td>Converts a two-dimensional variable in each netCDF file in a table to an ArcGIS raster.</td></tr><tr><td><a href="NetCDF.Convert2DVariableInNetCDFsInArcGISTableToArcInfoASCIIGrids.html?format=raw">NetCDFConvert2DVariableInNetCDFsInArcGISTableToArcInfoASCIIGrids_GeoEco</a></td><td>Converts a two-dimensional variable in each netCDF file in a table to a text file in ArcInfo ASCII Grid format.</td></tr><tr><td><a href="NetCDF.Convert2DVariableInNetCDFsInArcGISTableToBinaryRasters.html?format=raw">NetCDFConvert2DVariableInNetCDFsInArcGISTableToBinaryRasters_GeoEco</a></td><td>Converts a two-dimensional variable in each netCDF file in a table to a binary raster.</td></tr><tr><td><a href="NetCDF.Convert2DVariableToArcGISRaster.html?format=raw">NetCDFConvert2DVariableToArcGISRaster_GeoEco</a></td><td>Converts a two-dimensional variable in a netCDF file to an ArcGIS raster.</td></tr><tr><td><a href="NetCDF.Convert2DVariableToArcInfoASCIIGrid.html?format=raw">NetCDFConvert2DVariableToArcInfoASCIIGrid_GeoEco</a></td><td>Converts a two-dimensional variable in a netCDF file to a text file in ArcInfo ASCII Grid format.</td></tr><tr><td><a href="NetCDF.Convert2DVariableToBinaryRaster.html?format=raw">NetCDFConvert2DVariableToBinaryRaster_GeoEco</a></td><td>Converts a two-dimensional variable in a netCDF file to a binary raster.</td></tr><tr><td><a href="NetCDF.ExtractHeaderArcGISTable.html?format=raw">NetCDFExtractHeaderArcGISTable_GeoEco</a></td><td>Extracts the headers of netCDF files in a table and saves them to text files.</td></tr><tr><td><a href="NetCDF.ExtractHeader.html?format=raw">NetCDFExtractHeader_GeoEco</a></td><td>Extracts the header of a netCDF file and saves it to a text file.</td></tr><tr><td><a href="NetCDF.FindAndExtractHeaders.html?format=raw">NetCDFFindAndExtractHeaders_GeoEco</a></td><td>Finds netCDF files in a directory, extracts their headers, and saves the headers to text files.</td></tr><tr><td><a href="NetCDF.FindNetCDFsAndConvert2DVariableToArcGISRasters.html?format=raw">NetCDFFindNetCDFsAndConvert2DVariableToArcGISRasters_GeoEco</a></td><td>Finds netCDF files in a directory and converts a two-dimensional variable in each file to an ArcGIS raster.</td></tr><tr><td><a href="NetCDF.FindNetCDFsAndConvert2DVariableToArcInfoASCIIGrids.html?format=raw">NetCDFFindNetCDFsAndConvert2DVariableToArcInfoASCIIGrids_GeoEco</a></td><td>Finds netCDF files in a directory and converts a two-dimensional variable in each file to a text file in ArcInfo ASCII Grid format.</td></tr><tr><td><a href="NetCDF.FindNetCDFsAndConvert2DVariableToBinaryRasters.html?format=raw">NetCDFFindNetCDFsAndConvert2DVariableToBinaryRasters_GeoEco</a></td><td>Finds netCDF files in a directory and converts a two-dimensional variable in each file to a binary raster.</td></tr><tr><td><a href="OSCAR5DayThirdDegreeCurrents.CreateArcGISRasters.html?format=raw">OSCAR5DayThirdDegreeCurrentsCreateArcGISRasters_GeoEco</a></td><td>Creates rasters for NOAA OSCAR 5-day unfiltered 1/3 degree ocean surface currents.</td></tr><tr><td><a href="OSCAR5DayThirdDegreeCurrents.CreateClimatologicalArcGISRasters.html?format=raw">OSCAR5DayThirdDegreeCurrentsCreateClimatologicalArcGISRasters_GeoEco</a></td><td>Creates climatological rasters for NOAA OSCAR 5-day unfiltered 1/3 degree ocean surface currents.</td></tr><tr><td><a href="OSCAR5DayThirdDegreeCurrents.CreateVectorsAsArcGISFeatureClasses.html?format=raw">OSCAR5DayThirdDegreeCurrentsCreateVectorsAsArcGISFeatureClasses_GeoEco</a></td><td>Creates line feature classes representing the vectors of NOAA OSCAR 5-day unfiltered 1/3 degree ocean surface currents.</td></tr><tr><td><a href="OSCAR5DayThirdDegreeCurrents.InterpolateAtArcGISPoints.html?format=raw">OSCAR5DayThirdDegreeCurrentsInterpolateAtArcGISPoints_GeoEco</a></td><td>Interpolates the values of NOAA OSCAR 5-day unfiltered 1/3 degree ocean surface currents at points.</td></tr><tr><td><a href="OceanColorLevel3SMITimeSeries.CreateArcGISRasters.html?format=raw">OceanColorLevel3SMITimeSeriesCreateArcGISRasters_GeoEco</a></td><td>Creates rasters for a Level 3 Standard Mapped Image (SMI) product published by the NASA GSFC OceanColor Group.</td></tr><tr><td><a href="OceanColorLevel3SMITimeSeries.CreateClimatologicalArcGISRasters.html?format=raw">OceanColorLevel3SMITimeSeriesCreateClimatologicalArcGISRasters_GeoEco</a></td><td>Creates climatological rasters for a Level 3 Standard Mapped Image (SMI) product published by the NASA GSFC OceanColor Group.</td></tr><tr><td><a href="OceanColorLevel3SMITimeSeries.InterpolateAtArcGISPoints.html?format=raw">OceanColorLevel3SMITimeSeriesInterpolateAtArcGISPoints_GeoEco</a></td><td>Interpolates the values of a Level 3 Standard Mapped Image (SMI) product published by the NASA GSFC OceanColor Group at points.</td></tr><tr><td><a href="R.EvaluateFile.html?format=raw">REvaluateFile_GeoEco</a></td><td>Evalutes the R statements in a text file using the R interpreter and returns the result of the last statement.</td></tr><tr><td><a href="R.Evaluate.html?format=raw">REvaluate_GeoEco</a></td><td>Evalutes one or more R statements using the R interpreter and returns the result of the last statement.</td></tr><tr><td><a href="RExploratoryPlots.ClevelandPlotForArcGISTable.html?format=raw">RExploratoryPlotsClevelandPlotForArcGISTable_GeoEco</a></td><td>Creates a multi-panel Cleveland dotplot for a table.</td></tr><tr><td><a href="RExploratoryPlots.DensityHistogramForArcGISField.html?format=raw">RExploratoryPlotsDensityHistogramForArcGISField_GeoEco</a></td><td>Creates a density histogram for a field of a table.</td></tr><tr><td><a href="RExploratoryPlots.DensityHistogramForArcGISPointsCoordinates.html?format=raw">RExploratoryPlotsDensityHistogramForArcGISPointsCoordinates_GeoEco</a></td><td>Creates a density histogram for one of the coordinates of a point feature class or layer.</td></tr><tr><td><a href="RExploratoryPlots.ScatterplotMatrixForArcGISTable.html?format=raw">RExploratoryPlotsScatterplotMatrixForArcGISTable_GeoEco</a></td><td>Creates a matrix of scatterplots for a table using the R pairs function.</td></tr><tr><td><a href="ROMSCoSiNE2D.CreateArcGISRaster.html?format=raw">ROMSCoSiNE2DCreateArcGISRaster_GeoEco</a></td><td>Creates a raster for a Pacific ROMS-CoSiNE 2D variable.</td></tr><tr><td><a href="ROMSCoSiNE2D.InterpolateAtArcGISPoints.html?format=raw">ROMSCoSiNE2DInterpolateAtArcGISPoints_GeoEco</a></td><td>Interpolates a Pacific ROMS-CoSiNE 2D variable at points.</td></tr><tr><td><a href="ROMSCoSiNE3D.CreateArcGISRasters.html?format=raw">ROMSCoSiNE3DCreateArcGISRasters_GeoEco</a></td><td>Creates rasters for a Pacific ROMS-CoSiNE 3D variable.</td></tr><tr><td><a href="ROMSCoSiNE3D.CreateClimatologicalArcGISRasters.html?format=raw">ROMSCoSiNE3DCreateClimatologicalArcGISRasters_GeoEco</a></td><td>Creates climatological rasters for a Pacific ROMS-CoSiNE 3D variable</td></tr><tr><td><a href="ROMSCoSiNE3D.InterpolateAtArcGISPoints.html?format=raw">ROMSCoSiNE3DInterpolateAtArcGISPoints_GeoEco</a></td><td>Interpolates Pacific ROMS-CoSiNE 3D variables at points.</td></tr><tr><td><a href="ROMSCoSiNE4D.CreateArcGISRasters.html?format=raw">ROMSCoSiNE4DCreateArcGISRasters_GeoEco</a></td><td>Creates rasters for a Pacific ROMS-CoSiNE 4D variable.</td></tr><tr><td><a href="ROMSCoSiNE4D.CreateClimatologicalArcGISRasters.html?format=raw">ROMSCoSiNE4DCreateClimatologicalArcGISRasters_GeoEco</a></td><td>Creates climatological rasters for a Pacific ROMS-CoSiNE 4D variable</td></tr><tr><td><a href="ROMSCoSiNE4D.InterpolateAtArcGISPoints.html?format=raw">ROMSCoSiNE4DInterpolateAtArcGISPoints_GeoEco</a></td><td>Interpolates Pacific ROMS-CoSiNE 4D variables at points.</td></tr><tr><td><a href="SIRFile.ExtractHeaderArcGISTable.html?format=raw">SIRFileExtractHeaderArcGISTable_GeoEco</a></td><td>Extracts the headers of SIR files in a table and saves them to text files.</td></tr><tr><td><a href="SIRFile.ExtractHeader.html?format=raw">SIRFileExtractHeader_GeoEco</a></td><td>Extracts the header of a SIR file.</td></tr><tr><td><a href="SIRFile.FindAndConvertToArcGISRasters.html?format=raw">SIRFileFindAndConvertToArcGISRasters_GeoEco</a></td><td>Finds SIR files in a directory and converts them to ArcGIS rasters.</td></tr><tr><td><a href="SIRFile.FindAndConvertToArcInfoASCIIGrids.html?format=raw">SIRFileFindAndConvertToArcInfoASCIIGrids_GeoEco</a></td><td>Finds SIR files in a directory and converts them to text files in ArcInfo ASCII grid format.</td></tr><tr><td><a href="SIRFile.FindAndConvertToBinaryRasters.html?format=raw">SIRFileFindAndConvertToBinaryRasters_GeoEco</a></td><td>Finds SIR files in a directory and converts them to binary rasters.</td></tr><tr><td><a href="SIRFile.FindAndExtractHeaders.html?format=raw">SIRFileFindAndExtractHeaders_GeoEco</a></td><td>Finds SIR files in a directory, extracts their headers, and saves the headers to text files.</td></tr><tr><td><a href="SIRFile.ToArcGISRasterArcGISTable.html?format=raw">SIRFileToArcGISRasterArcGISTable_GeoEco</a></td><td>Converts each SIR file in a table to an ArcGIS raster.</td></tr><tr><td><a href="SIRFile.ToArcGISRaster.html?format=raw">SIRFileToArcGISRaster_GeoEco</a></td><td>Converts a SIR file to an ArcGIS raster.</td></tr><tr><td><a href="SIRFile.ToArcInfoASCIIGridArcGISTable.html?format=raw">SIRFileToArcInfoASCIIGridArcGISTable_GeoEco</a></td><td>Converts each SIR file in a table to a text file in ArcInfo ASCII grid format.</td></tr><tr><td><a href="SIRFile.ToArcInfoASCIIGrid.html?format=raw">SIRFileToArcInfoASCIIGrid_GeoEco</a></td><td>Converts a SIR file to a text file in ArcInfo ASCII Grid format.</td></tr><tr><td><a href="SIRFile.ToBinaryRasterArcGISTable.html?format=raw">SIRFileToBinaryRasterArcGISTable_GeoEco</a></td><td>Converts each SIR file in a table to a binary raster.</td></tr><tr><td><a href="SIRFile.ToBinaryRaster.html?format=raw">SIRFileToBinaryRaster_GeoEco</a></td><td>Converts a SIR file to a binary raster.</td></tr><tr><td><a href="Shapefile.CopyToDirectory.html?format=raw">ShapefileCopyToDirectory_GeoEco</a></td><td>Copies a shapefile to a directory.</td></tr><tr><td><a href="Shapefile.Copy.html?format=raw">ShapefileCopy_GeoEco</a></td><td>Copies a shapefile.</td></tr><tr><td><a href="SpatiaLiteDatabase.ExportToArcGISWorkspace.html?format=raw">SpatiaLiteDatabaseExportToArcGISWorkspace_GeoEco</a></td><td>Converts tables in a SpatiaLite database to ArcGIS tables, shapefiles, and feature classes.</td></tr><tr><td><a href="SpatiaLiteDatabase.ImportFromArcGISWorkspace.html?format=raw">SpatiaLiteDatabaseImportFromArcGISWorkspace_GeoEco</a></td><td>Converts ArcGIS tables, shapefiles, and feature classes to tables in a SpatiaLite database.</td></tr><tr><td><a href="SpeciesDiversity.CalculateDiversityIndexForArcGISPolygons.html?format=raw">SpeciesDiversityCalculateDiversityIndexForArcGISPolygons_GeoEco</a></td><td>Given polygons representing zones of interest and points representing species occurrence observations, calculates a species diversity index for each polygon.</td></tr><tr><td><a href="Table.ExecuteADOCommandForArcGISTable.html?format=raw">TableExecuteADOCommandForArcGISTable_GeoEco</a></td><td>Opens a Microsoft ActiveX Data Objects (ADO) connection to the database containing a table and executes a command.</td></tr><tr><td><a href="TreeModel.FitToArcGISTable.html?format=raw">TreeModelFitToArcGISTable_GeoEco</a></td><td>Fits a tree model to data in an ArcGIS table.</td></tr><tr><td><a href="TreeModel.PredictFromArcGISRasters.html?format=raw">TreeModelPredictFromArcGISRasters_GeoEco</a></td><td>Using a fitted tree model, this tool creates a raster representing the response variable predicted from rasters representing the predictor variables.</td></tr></table></p><h1><a id="Copyright">Copyright and License</a></h1><p>Except where otherwise noted, this document and the Marine Geospatial Ecology 
     3<html xmlns="http://www.w3.org/1999/xhtml"><head><title>Marine Geospatial Ecology Tools - ArcGIS Reference</title><link rel="stylesheet" type="text/css" href="../Documentation.css?format=raw" /><meta http-equiv="Content-Type" content="text/html; charset=UTF-8" /></head><body><p class="title1">Marine Geospatial Ecology Tools</p><p class="title2">ArcGIS Geoprocessing Reference</p><table class="docmetadata"><tr><td class="docmetadata1">MGET version:</td><td class="docmetadata2">0.8a38</td></tr><tr><td class="docmetadata1">Document version:</td><td class="docmetadata2">$Id: ArcGISReference.xsl 497 2010-04-01 18:41:19Z jjr8 $</td></tr><tr><td class="docmetadata1">Maintainer email:</td><td class="docmetadata2"><a href="mailto:jason.roberts@duke.edu">jason.roberts@duke.edu</a></td></tr><tr><td class="docmetadata1">MGET home page:</td><td class="docmetadata2"><a href="http://code.nicholas.duke.edu/projects/mget">http://code.nicholas.duke.edu/projects/mget</a></td></tr></table><h1><a id="Contents">Contents</a></h1><ul><li><a href="#IndexByLabel">Tool Index, By Label</a></li><li><a href="#IndexByName">Tool Index, By Name</a></li><li><a href="#Copyright">Copyright and License</a></li></ul><h1><a id="IndexByLabel">Tool Index, By Label</a></h1><p>In the table below, <i>Tool Label</i> is the name of the tool as it appears in the 
     4        ArcGIS toolbox.</p><p><table><tr><th>Tool Label</th><th>Description</th></tr><tr><td><a href="ArcGISPoints.AppendPointsToFeatureClass2.html?format=raw">Append Points</a></td><td>Appends points to an existing ArcGIS point feature class.</td></tr><tr><td><a href="ArcGISPolygons.AppendPolygonToFeatureClass2.html?format=raw">Append Polygon</a></td><td>Appends a polygon to an existing ArcGIS polygon feature class.</td></tr><tr><td><a href="ArcGISPolygons.AppendRectangleToFeatureClass2.html?format=raw">Append Rectangle</a></td><td>Appends a rectangle to an existing ArcGIS polygon feature class.</td></tr><tr><td><a href="Field.CalculateArcGISField.html?format=raw">Calculate Field Using a Python Expression</a></td><td>Calculates the value of a table field using a Python expression.</td></tr><tr><td><a href="Field.CalculateArcGISFields.html?format=raw">Calculate Fields Using Python Expressions</a></td><td>Calculates values for one or more fields of a table using Python expressions.</td></tr><tr><td><a href="SpeciesDiversity.CalculateDiversityIndexForArcGISPolygons.html?format=raw">Calculate Species Diversity Index for Polygons</a></td><td>Given polygons representing zones of interest and points representing species occurrence observations, calculates a species diversity index for each polygon.</td></tr><tr><td><a href="CayulaCornillonEdgeDetection.DetectEdgesInArcGISRaster.html?format=raw">Cayula-Cornillon Fronts in ArcGIS Raster</a></td><td>Finds fronts in an ArcGIS raster using the Cayula and Cornillon (1992) single image edge detection (SIED) algorithm.</td></tr><tr><td><a href="CayulaCornillonEdgeDetection.DetectEdgesInArcGISRastersArcGISTable.html?format=raw">Cayula-Cornillon Fronts in ArcGIS Rasters Listed in Table</a></td><td>Finds fronts in ArcGIS rasters listed in a table using the Cayula and Cornillon (1992) single-image edge detection algorithm.</td></tr><tr><td><a href="CayulaCornillonEdgeDetection.DetectEdgesInBinaryRaster.html?format=raw">Cayula-Cornillon Fronts in Binary Raster</a></td><td>Finds fronts in a binary raster using the Cayula and Cornillon (1992) single image edge detection (SIED) algorithm.</td></tr><tr><td><a href="CoastWatchAVHRR.FindFrontsAsArcGISRaster.html?format=raw">Cayula-Cornillon Fronts in CoastWatch Image as ArcGIS Raster</a></td><td>Finds fronts in a CoastWatch POES AVHRR image using the Cayula and Cornillon (1992) single-image edge detection algorithm and outputs them to an ArcGIS raster.</td></tr><tr><td><a href="CoastWatchAVHRR.FindFrontsAsBinaryRaster.html?format=raw">Cayula-Cornillon Fronts in CoastWatch Image as Binary Raster</a></td><td>Finds fronts in a CoastWatch POES AVHRR image using the Cayula and Cornillon (1992) single-image edge detection algorithm and outputs them to a binary raster.</td></tr><tr><td><a href="CoastWatchAVHRR.FindFrontsAsArcGISRastersArcGISTable.html?format=raw">Cayula-Cornillon Fronts in CoastWatch Images Listed in Table as ArcGIS Rasters</a></td><td>Finds fronts in CoastWatch POES AVHRR images listed in a table using the Cayula and Cornillon (1992) single-image edge detection algorithm and outputs them as ArcGIS rasters.</td></tr><tr><td><a href="ESRLClimateIndices.ClassifyONIEpisodesInTimeSeriesArcGISTable.html?format=raw">Classify Oceanic Nino Index (ONI) Episodes in Table</a></td><td>Given a time series table of monthly Oceanic Nino Index (ONI) numerical values, classifies each month as part of a normal, El Nino (warm), or La Nina (cold) episode.</td></tr><tr><td><a href="RExploratoryPlots.ClevelandPlotForArcGISTable.html?format=raw">Cleveland Plot for Table</a></td><td>Creates a multi-panel Cleveland dotplot for a table.</td></tr><tr><td><a href="CoastWatchAVHRR.ToArcGISRaster.html?format=raw">CoastWatch Image to ArcGIS Raster</a></td><td>Extracts and converts CoastWatch POES AVHRR image, specified by a file plus a variable in that file, to an ArcGIS raster.</td></tr><tr><td><a href="CoastWatchAVHRR.ToBinaryRaster.html?format=raw">CoastWatch Image to Binary Raster</a></td><td>Extracts and converts CoastWatch POES AVHRR image, specified by a file plus a variable in that file, to a binary raster.</td></tr><tr><td><a href="CoastWatchAVHRR.ToArcGISRasterArcGISTable.html?format=raw">CoastWatch Images Listed in Table To ArcGIS Rasters</a></td><td>Creates an ArcGIS raster from each CoastWatch POES AVHRR image listed in a table of images, where one field specifies the file path containing the image and another specifies the variable that represents the image.</td></tr><tr><td><a href="CoastWatchAVHRR.ToBinaryRasterArcGISTable.html?format=raw">CoastWatch Images Listed in Table To Binary Rasters</a></td><td>Creates a binary raster from each CoastWatch POES AVHRR image listed in a table of images, where one field specifies the file path containing the image and another specifies the variable that represents the image.</td></tr><tr><td><a href="CoastWatchAVHRR.ToCoastWatchHDF.html?format=raw">CoastWatch Images to HDF</a></td><td>Creates a CoastWatch HDF by importing images from a list of CoastWatch POES AVHRR CWFs or HDFs.</td></tr><tr><td><a href="NetCDF.Convert2DVariableToArcGISRaster.html?format=raw">Convert 2D Variable in NetCDF to ArcGIS Raster</a></td><td>Converts a two-dimensional variable in a netCDF file to an ArcGIS raster.</td></tr><tr><td><a href="NetCDF.Convert2DVariableToArcInfoASCIIGrid.html?format=raw">Convert 2D Variable in NetCDF to ArcInfo ASCII Grid</a></td><td>Converts a two-dimensional variable in a netCDF file to a text file in ArcInfo ASCII Grid format.</td></tr><tr><td><a href="NetCDF.Convert2DVariableToBinaryRaster.html?format=raw">Convert 2D Variable in NetCDF to Binary Raster</a></td><td>Converts a two-dimensional variable in a netCDF file to a binary raster.</td></tr><tr><td><a href="NetCDF.Convert2DVariableInNetCDFsInArcGISTableToArcGISRasters.html?format=raw">Convert 2D Variable in NetCDFs Listed in Table To ArcGIS Rasters</a></td><td>Converts a two-dimensional variable in each netCDF file in a table to an ArcGIS raster.</td></tr><tr><td><a href="NetCDF.Convert2DVariableInNetCDFsInArcGISTableToArcInfoASCIIGrids.html?format=raw">Convert 2D Variable in NetCDFs Listed in Table To ArcInfo ASCII Grids</a></td><td>Converts a two-dimensional variable in each netCDF file in a table to a text file in ArcInfo ASCII Grid format.</td></tr><tr><td><a href="NetCDF.Convert2DVariableInNetCDFsInArcGISTableToBinaryRasters.html?format=raw">Convert 2D Variable in NetCDFs Listed in Table To Binary Rasters</a></td><td>Converts a two-dimensional variable in each netCDF file in a table to a binary raster.</td></tr><tr><td><a href="SpatiaLiteDatabase.ImportFromArcGISWorkspace.html?format=raw">Convert ArcGIS Geodatasets to SpatiaLite Tables</a></td><td>Converts ArcGIS tables, shapefiles, and feature classes to tables in a SpatiaLite database.</td></tr><tr><td><a href="ArcGISRaster.ToLines.html?format=raw">Convert ArcGIS Raster to Lines</a></td><td>Converts an ArcGIS raster to a feature class of lines that connect adjacent foreground raster cells.</td></tr><tr><td><a href="ArcGISRaster.ToPoints.html?format=raw">Convert ArcGIS Raster to Points</a></td><td>Converts an ArcGIS raster to a feature class of points that occur at the centers of the raster cells.</td></tr><tr><td><a href="ArcGISRaster.ToPolygonOutlines.html?format=raw">Convert ArcGIS Raster to Polygon Outlines</a></td><td>Converts an ArcGIS raster to lines that outline groups of adjacent raster cells having the same value.</td></tr><tr><td><a href="ArcGISRaster.ToPolygons.html?format=raw">Convert ArcGIS Raster to Polygons</a></td><td>Converts an ArcGIS raster to polygons that encompass groups of adjacent raster cells having the same value.</td></tr><tr><td><a href="ArcGISRaster.ToLinesArcGISTable.html?format=raw">Convert ArcGIS Rasters Listed in Table to Lines</a></td><td>Converts the ArcGIS rasters listed in a table to lines that outline groups of adjacent raster cells having the same value.</td></tr><tr><td><a href="ArcGISRaster.ToPointsArcGISTable.html?format=raw">Convert ArcGIS Rasters Listed in Table to Points</a></td><td>Converts the ArcGIS rasters listed in a table to points that occur at the centers of the raster cells.</td></tr><tr><td><a href="ArcGISRaster.ToPolygonOutlinesArcGISTable.html?format=raw">Convert ArcGIS Rasters Listed in Table to Polygon Outlines</a></td><td>Converts the ArcGIS rasters listed in a table to lines that outline groups of adjacent raster cells having the same value.</td></tr><tr><td><a href="ArcGISRaster.ToPolygonsArcGISTable.html?format=raw">Convert ArcGIS Rasters Listed in Table to Polygons</a></td><td>Converts the ArcGIS rasters listed in a table to polygons that encompass groups of adjacent raster cells having the same value.</td></tr><tr><td><a href="ArcInfoASCIIGrid.ToArcGISRaster.html?format=raw">Convert ArcInfo ASCII Grid to ArcGIS Raster</a></td><td>Converts a text file in ArcInfo ASCII Grid format to an ArcGIS raster.</td></tr><tr><td><a href="ArcInfoASCIIGrid.ToArcGISRasterArcGISTable.html?format=raw">Convert ArcInfo ASCII Grids Listed in Table To ArcGIS Rasters</a></td><td>Converts each ArcInfo ASCII Grid text file in a table to an ArcGIS raster.</td></tr><tr><td><a href="BinaryRaster.ToArcGISRaster.html?format=raw">Convert Binary Raster to ArcGIS Raster</a></td><td>Converts a two-dimensional binary raster to an ArcGIS raster.</td></tr><tr><td><a href="BinaryRaster.ToArcInfoASCIIGrid.html?format=raw">Convert Binary Raster to ArcInfo ASCII Grid</a></td><td>Converts a two-dimensional binary raster to a text file in ArcGIS ASCII Grid format.</td></tr><tr><td><a href="BinaryRaster.ToArcGISRasterArcGISTable.html?format=raw">Convert Binary Rasters Listed in Table To ArcGIS Rasters</a></td><td>Converts each two-dimensional binary raster in a table to an ArcGIS raster.</td></tr><tr><td><a href="BinaryRaster.ToArcInfoASCIIGridArcGISTable.html?format=raw">Convert Binary Rasters Listed in Table To ArcInfo ASCII Grids</a></td><td>Converts each two-dimensional binary raster in a table to a text file in ArcInfo ASCII Grid format.</td></tr><tr><td><a href="CoastWatchAVHRR.ToCoastWatchHDFArcGISTable.html?format=raw">Convert CoastWatch Images Listed in Table to HDFs</a></td><td>Creates CoastWatch HDFs in a specified output directory by importing images from the CoastWatch POES AVHRR CWFs or HDFs listed in a table.</td></tr><tr><td><a href="CheltonMesocaleEddyPoints.ConvertToSpatiaLite.html?format=raw">Convert Mesoscale Eddies NetCDF to SpatiaLite Database</a></td><td>Converts the Chelton et al. (2011) mesoscale eddy database netCDF file to a SpatiaLite database.</td></tr><tr><td><a href="HDF.SDSToArcGISRaster.html?format=raw">Convert SDS in HDF to ArcGIS Raster</a></td><td>Converts a Scientific Data Set (SDS) in an HDF file to an ArcGIS raster.</td></tr><tr><td><a href="HDF.SDSToArcInfoASCIIGrid.html?format=raw">Convert SDS in HDF to ArcInfo ASCII Grid</a></td><td>Converts a Scientific Data Set (SDS) in an HDF file to a text file in ArcInfo ASCII Grid format.</td></tr><tr><td><a href="HDF.SDSToBinaryRaster.html?format=raw">Convert SDS in HDF to Binary Raster</a></td><td>Converts a Scientific Data Set (SDS) in an HDF file to a binary raster.</td></tr><tr><td><a href="HDF.ToArcGISRasterArcGISTable.html?format=raw">Convert SDS in HDFs Listed in Table To ArcGIS Rasters</a></td><td>Converts a Scientific Data Set (SDS) in each HDF file in a table to an ArcGIS raster.</td></tr><tr><td><a href="HDF.ToArcInfoASCIIGridArcGISTable.html?format=raw">Convert SDS in HDFs Listed in Table To ArcInfo ASCII Grids</a></td><td>Converts a Scientific Data Set (SDS) in each HDF file in a table to a text file in an ArcInfo ASCII Grid format.</td></tr><tr><td><a href="HDF.ToBinaryRasterArcGISTable.html?format=raw">Convert SDS in HDFs Listed in Table To Binary Rasters</a></td><td>Converts a Scientific Data Set (SDS) in each HDF file in a table to a binary raster.</td></tr><tr><td><a href="SIRFile.ToArcGISRaster.html?format=raw">Convert SIR File to ArcGIS Raster</a></td><td>Converts a SIR file to an ArcGIS raster.</td></tr><tr><td><a href="SIRFile.ToArcInfoASCIIGrid.html?format=raw">Convert SIR File to ArcInfo ASCII Grid</a></td><td>Converts a SIR file to a text file in ArcInfo ASCII Grid format.</td></tr><tr><td><a href="SIRFile.ToBinaryRaster.html?format=raw">Convert SIR File to Binary Raster</a></td><td>Converts a SIR file to a binary raster.</td></tr><tr><td><a href="SIRFile.ToArcGISRasterArcGISTable.html?format=raw">Convert SIR Files Listed in Table To ArcGIS Rasters</a></td><td>Converts each SIR file in a table to an ArcGIS raster.</td></tr><tr><td><a href="SIRFile.ToArcInfoASCIIGridArcGISTable.html?format=raw">Convert SIR Files Listed in Table To ArcInfo ASCII Grids</a></td><td>Converts each SIR file in a table to a text file in ArcInfo ASCII grid format.</td></tr><tr><td><a href="SIRFile.ToBinaryRasterArcGISTable.html?format=raw">Convert SIR Files Listed in Table To Binary Rasters</a></td><td>Converts each SIR file in a table to a binary raster.</td></tr><tr><td><a href="SpatiaLiteDatabase.ExportToArcGISWorkspace.html?format=raw">Convert SpatiaLite Tables to ArcGIS Geodatasets</a></td><td>Converts tables in a SpatiaLite database to ArcGIS tables, shapefiles, and feature classes.</td></tr><tr><td><a href="CoastWatchAVHRR.CopyNavigationOffsets.html?format=raw">Copy CoastWatch Navigation Offsets</a></td><td>Copies the navigation offsets from one variable in a CoastWatch POES AVHRR CWF or HDF file to one or more variables in another file.</td></tr><tr><td><a href="CoastWatchAVHRR.CopyNavigationOffsetsArcGISTable.html?format=raw">Copy CoastWatch Navigation Offsets for Files Listed in Table</a></td><td>Copies navigation offsets from a source variable to destination variables in CoastWatch POES AVHRR CWF or HDF files listed in a table.</td></tr><tr><td><a href="Directory.CopyArcGISTable.html?format=raw">Copy Directories Listed in Table</a></td><td>Copies the directories listed in a table.</td></tr><tr><td><a href="Directory.Copy.html?format=raw">Copy Directory</a></td><td>Copies a directory, including its subdirectories and files.</td></tr><tr><td><a href="File.Copy.html?format=raw">Copy File</a></td><td>Copies a file.</td></tr><tr><td><a href="File.CopyArcGISTable.html?format=raw">Copy Files Listed in Table</a></td><td>Copies the files listed in a table.</td></tr><tr><td><a href="ArcGISRaster.Copy.html?format=raw">Copy Raster</a></td><td>Copies an ArcGIS raster.</td></tr><tr><td><a href="ArcGISRaster.CopyArcGISTable.html?format=raw">Copy Rasters Listed in Table</a></td><td>Copies the ArcGIS rasters listed in a table.</td></tr><tr><td><a href="Shapefile.Copy.html?format=raw">Copy Shapefile</a></td><td>Copies a shapefile.</td></tr><tr><td><a href="Shapefile.CopyToDirectory.html?format=raw">Copy Shapefile To Directory</a></td><td>Copies a shapefile to a directory.</td></tr><tr><td><a href="ArcGISPolygons.CreateBoundingBoxForArcGISGeoDatasets.html?format=raw">Create Bounding Box for Geodatasets</a></td><td>Creates a new ArcGIS polygon feature class and a bounding box (a minimum bounding rectangle) within it that encompasses the extents of one or more ArcGIS geodatasets.</td></tr><tr><td><a href="AVHRRPathfinderSSTTimeSeries.CreateClimatologicalArcGISRasters.html?format=raw">Create Climatological Rasters for AVHRR Pathfinder V5 SST</a></td><td>Creates climatological rasters from AVHRR Pathfinder Version 5 SST images published by NOAA NODC.</td></tr><tr><td><a href="AvisoGriddedGeostrophicCurrents.CreateClimatologicalArcGISRasters.html?format=raw">Create Climatological Rasters for Aviso Geostrophic Currents Product</a></td><td>Creates climatological rasters for an Aviso gridded geostrophic currents product.</td></tr><tr><td><a href="AvisoGriddedSSH.CreateClimatologicalArcGISRasters.html?format=raw">Create Climatological Rasters for Aviso SSH Product</a></td><td>Creates climatological rasters for an Aviso gridded sea surface height product.</td></tr><tr><td><a href="AvisoGriddedSignificantWaveHeight.CreateClimatologicalArcGISRasters.html?format=raw">Create Climatological Rasters for Aviso Significant Wave Height Product</a></td><td>Creates climatological rasters for an Aviso gridded significant wave height product.</td></tr><tr><td><a href="AvisoGriddedWindSpeedModulus.CreateClimatologicalArcGISRasters.html?format=raw">Create Climatological Rasters for Aviso Wind Speed Modulus Product</a></td><td>Creates climatological rasters for an Aviso gridded wind speed modulus product.</td></tr><tr><td><a href="GHRSSTLevel4.CreateClimatologicalArcGISRasters.html?format=raw">Create Climatological Rasters for GHRSST L4 SST</a></td><td>Creates climatological rasters for a GHRSST L4 product hosted by NASA JPL PO.DAAC.</td></tr><tr><td><a href="HYCOMGLBa008Equatorial3D.CreateClimatologicalArcGISRasters.html?format=raw">Create Climatological Rasters for HYCOM GLBa0.08 Equatorial 3D Variable</a></td><td>Creates climatological rasters for a 3D variable of the equatorial (Mercator) region of the HYCOM GLBa0.08 dataset</td></tr><tr><td><a href="HYCOMGLBa008Equatorial4D.CreateClimatologicalArcGISRasters.html?format=raw">Create Climatological Rasters for HYCOM GLBa0.08 Equatorial 4D Variable</a></td><td>Creates climatological rasters for a 4D variable of the equatorial (Mercator) region of the HYCOM GLBa0.08 dataset</td></tr><tr><td><a href="HYCOMGOMl0043D.CreateClimatologicalArcGISRasters.html?format=raw">Create Climatological Rasters for HYCOM GOMl0.04 3D Variable</a></td><td>Creates climatological rasters for a HYCOM GOMl0.04 3D variable</td></tr><tr><td><a href="HYCOMGOMl0044D.CreateClimatologicalArcGISRasters.html?format=raw">Create Climatological Rasters for HYCOM GOMl0.04 4D Variable</a></td><td>Creates climatological rasters for a HYCOM GOMl0.04 4D variable</td></tr><tr><td><a href="OceanColorLevel3SMITimeSeries.CreateClimatologicalArcGISRasters.html?format=raw">Create Climatological Rasters for NASA OceanColor L3 SMI Product</a></td><td>Creates climatological rasters for a Level 3 Standard Mapped Image (SMI) product published by the NASA GSFC OceanColor Group.</td></tr><tr><td><a href="OSCAR5DayThirdDegreeCurrents.CreateClimatologicalArcGISRasters.html?format=raw">Create Climatological Rasters for OSCAR Currents</a></td><td>Creates climatological rasters for NOAA OSCAR 5-day unfiltered 1/3 degree ocean surface currents.</td></tr><tr><td><a href="MODISL3SSTTimeSeries.CreateClimatologicalArcGISRasters.html?format=raw">Create Climatological Rasters for PO.DAAC MODIS L3 SST</a></td><td>Creates climatological rasters from MODIS Level 3 SST images published by NASA JPL PO.DAAC.</td></tr><tr><td><a href="ROMSCoSiNE3D.CreateClimatologicalArcGISRasters.html?format=raw">Create Climatological Rasters for Pacific ROMS-CoSiNE 3D Variable</a></td><td>Creates climatological rasters for a Pacific ROMS-CoSiNE 3D variable</td></tr><tr><td><a href="ROMSCoSiNE4D.CreateClimatologicalArcGISRasters.html?format=raw">Create Climatological Rasters for Pacific ROMS-CoSiNE 4D Variable</a></td><td>Creates climatological rasters for a Pacific ROMS-CoSiNE 4D variable</td></tr><tr><td><a href="CoastWatchAVHRR.CreateMaskAsArcGISRaster.html?format=raw">Create CoastWatch Mask as ArcGIS Raster</a></td><td>Creates a mask, in ArcGIS raster format, for a CoastWatch POES AVHRR image.</td></tr><tr><td><a href="CoastWatchAVHRR.CreateMaskAsBinaryRaster.html?format=raw">Create CoastWatch Mask as Binary Raster</a></td><td>Creates a mask, in binary raster format, for a CoastWatch POES AVHRR image.</td></tr><tr><td><a href="CoralReefConnectivity.CreateSimulationFromArcGISRasters.html?format=raw">Create Coral Reef Connectivity Simulation From ArcGIS Rasters</a></td><td>Creates a coral reef connectivity simulation and initializes it with reef data in ArcGIS rasters.</td></tr><tr><td><a href="HYCOMGLBa008Equatorial4D.CreateCurrentVectorsAsArcGISFeatureClasses.html?format=raw">Create Current Vectors for HYCOM GLBa0.08 Equatorial Region</a></td><td>Creates line feature classes representing the current vectors for the equatorial (Mercator) region of the HYCOM GLBa0.08 dataset.</td></tr><tr><td><a href="HYCOMGOMl0044D.CreateCurrentVectorsAsArcGISFeatureClasses.html?format=raw">Create Current Vectors for HYCOM GOMl0.04</a></td><td>Creates line feature classes representing the vectors of HYCOM GOMl0.04 currents.</td></tr><tr><td><a href="Directory.Create.html?format=raw">Create Directory</a></td><td>Creates a directory, including any parent directories that are missing.</td></tr><tr><td><a href="FEET.CreateEnvelopes.html?format=raw">Create Fishery Effort Envelopes</a></td><td>Models the spatial distribution of fishing effort for fisheries for which no spatially-explicit effort data is available.</td></tr><tr><td><a href="ArcGISFishnets.CreateFishnet.html?format=raw">Create Fishnet</a></td><td>Creates a grid of rectangular polygons.</td></tr><tr><td><a href="ArcGISFishnets.CreateFishnetForPoints.html?format=raw">Create Fishnet for Points</a></td><td>Creates a grid of rectangular cells that overlap the input points and optionally calculates summary statistics for the points that intersect each cell.</td></tr><tr><td><a href="ArcGISLines.FromVectorComponentRasters.html?format=raw">Create Lines From Vector Component Rasters</a></td><td>Given rasters representing the x and y components of a vector field, such as the u and v rasters for ocean currents, this tool creates a feature class of lines representing the vectors, similar to a "quiver plot".</td></tr><tr><td><a href="ArcGISLines.FromVectorComponentRastersInArcGISTable.html?format=raw">Create Lines From Vector Component Rasters Listed in Table</a></td><td>Given a table of rasters representing the x and y components of vector fields, such as the u and v rasters for ocean currents, this tool creates feature classes of lines representing the vectors, similar to a "quiver plot".</td></tr><tr><td><a href="CoastWatchAVHRR.CreateMasksAsArcGISRastersArcGISTable.html?format=raw">Create Masks as ArcGIS Rasters for CoastWatch Images Listed in Table</a></td><td>Creates masks, in ArcGIS raster format, for CoastWatch POES AVHRR images listed in a table.</td></tr><tr><td><a href="CoastWatchAVHRR.CreateMasksAsBinaryRastersArcGISTable.html?format=raw">Create Masks as Binary Rasters for CoastWatch Images Listed in Table</a></td><td>Creates masks, in binary raster format, for CoastWatch POES AVHRR images listed in a table.</td></tr><tr><td><a href="ArcGISPoints.CreateFeatureClassWithPoints2.html?format=raw">Create Points</a></td><td>Creates points in a new ArcGIS point feature class.</td></tr><tr><td><a href="ArcGISPoints.CreatePointsAlongLines.html?format=raw">Create Points Along Lines</a></td><td>Creates points along lines at a specified interval.</td></tr><tr><td><a href="ArcGISPolygons.CreateFeatureClassWithPolygon2.html?format=raw">Create Polygon</a></td><td>Creates a polygon in a new ArcGIS polygon feature class.</td></tr><tr><td><a href="CoRTADv32D.CreateArcGISRaster.html?format=raw">Create Raster for CoRTAD 2D Variable</a></td><td>Creates a raster for a CoRTAD 2D variable.</td></tr><tr><td><a href="ROMSCoSiNE2D.CreateArcGISRaster.html?format=raw">Create Raster for Pacific ROMS-CoSiNE 2D Variable</a></td><td>Creates a raster for a Pacific ROMS-CoSiNE 2D variable.</td></tr><tr><td><a href="AVHRRPathfinderSSTTimeSeries.CreateArcGISRasters.html?format=raw">Create Rasters for AVHRR Pathfinder V5 SST</a></td><td>Creates rasters for AVHRR Pathfinder Version 5 SST images published by NOAA NODC.</td></tr><tr><td><a href="AvisoGriddedGeostrophicCurrents.CreateArcGISRasters.html?format=raw">Create Rasters for Aviso Geostrophic Currents Product</a></td><td>Creates rasters for an Aviso gridded geostrophic currents product.</td></tr><tr><td><a href="AvisoGriddedSSH.CreateArcGISRasters.html?format=raw">Create Rasters for Aviso SSH Product</a></td><td>Creates rasters for an Aviso gridded sea surface height product.</td></tr><tr><td><a href="AvisoGriddedSignificantWaveHeight.CreateArcGISRasters.html?format=raw">Create Rasters for Aviso Significant Wave Height Product</a></td><td>Creates rasters for an Aviso gridded significant wave height product.</td></tr><tr><td><a href="AvisoGriddedWindSpeedModulus.CreateArcGISRasters.html?format=raw">Create Rasters for Aviso Wind Speed Modulus Product</a></td><td>Creates rasters for an Aviso gridded wind speed modulus product.</td></tr><tr><td><a href="CoRTADv33D.CreateArcGISRasters.html?format=raw">Create Rasters for CoRTAD 3D Variable</a></td><td>Creates rasters a CoRTAD 3D variable.</td></tr><tr><td><a href="GHRSSTLevel4.CreateArcGISRasters.html?format=raw">Create Rasters for GHRSST L4 SST</a></td><td>Creates rasters for a GHRSST L4 product hosted by NASA JPL PO.DAAC.</td></tr><tr><td><a href="HYCOMGLBa008Equatorial3D.CreateArcGISRasters.html?format=raw">Create Rasters for HYCOM GLBa0.08 Equatorial 3D Variable</a></td><td>Creates rasters for a 3D variable of the equatorial (Mercator) region of the HYCOM GLBa0.08 dataset.</td></tr><tr><td><a href="HYCOMGLBa008Equatorial4D.CreateArcGISRasters.html?format=raw">Create Rasters for HYCOM GLBa0.08 Equatorial 4D Variable</a></td><td>Creates rasters for a 4D variable of the equatorial (Mercator) region of the HYCOM GLBa0.08 dataset.</td></tr><tr><td><a href="HYCOMGOMl0043D.CreateArcGISRasters.html?format=raw">Create Rasters for HYCOM GOMl0.04 3D Variable</a></td><td>Creates rasters for a HYCOM GOMl0.04 3D variable.</td></tr><tr><td><a href="HYCOMGOMl0044D.CreateArcGISRasters.html?format=raw">Create Rasters for HYCOM GOMl0.04 4D Variable</a></td><td>Creates rasters for a HYCOM GOMl0.04 4D variable.</td></tr><tr><td><a href="OceanColorLevel3SMITimeSeries.CreateArcGISRasters.html?format=raw">Create Rasters for NASA OceanColor L3 SMI Product</a></td><td>Creates rasters for a Level 3 Standard Mapped Image (SMI) product published by the NASA GSFC OceanColor Group.</td></tr><tr><td><a href="OSCAR5DayThirdDegreeCurrents.CreateArcGISRasters.html?format=raw">Create Rasters for OSCAR Currents</a></td><td>Creates rasters for NOAA OSCAR 5-day unfiltered 1/3 degree ocean surface currents.</td></tr><tr><td><a href="MODISL3SSTTimeSeries.CreateArcGISRasters.html?format=raw">Create Rasters for PO.DAAC MODIS L3 SST</a></td><td>Creates rasters for MODIS Level 3 SST images published by NASA JPL PO.DAAC.</td></tr><tr><td><a href="ROMSCoSiNE3D.CreateArcGISRasters.html?format=raw">Create Rasters for Pacific ROMS-CoSiNE 3D Variable</a></td><td>Creates rasters for a Pacific ROMS-CoSiNE 3D variable.</td></tr><tr><td><a href="ROMSCoSiNE4D.CreateArcGISRasters.html?format=raw">Create Rasters for Pacific ROMS-CoSiNE 4D Variable</a></td><td>Creates rasters for a Pacific ROMS-CoSiNE 4D variable.</td></tr><tr><td><a href="ArcGISPolygons.CreateFeatureClassWithRectangle2.html?format=raw">Create Rectangle</a></td><td>Creates a rectangle in a new ArcGIS polygon feature class.</td></tr><tr><td><a href="Directory.CreateSubdirectory.html?format=raw">Create Subdirectory</a></td><td>Creates a subdirectory within a parent directory.</td></tr><tr><td><a href="ESRLClimateIndices.UrlToArcGISTable.html?format=raw">Create Table from ESRL Climate Index Time Series at URL</a></td><td>Creates and populates a table of climate index values parsed from NOAA ESRL climate index time series data downloaded from a URL.</td></tr><tr><td><a href="ESRLClimateIndices.UrlsToArcGISTable.html?format=raw">Create Table from ESRL Climate Index Time Series at URLs</a></td><td>Creates and populates a table of climate index values parsed from NOAA ESRL climate index time series data downloaded from a list of URLs.</td></tr><tr><td><a href="ESRLClimateIndices.FileToArcGISTable.html?format=raw">Create Table from ESRL Climate Index Time Series in Text File</a></td><td>Creates and populates a table of climate index values parsed from a text file in NOAA ESRL time series format.</td></tr><tr><td><a href="ESRLClimateIndices.FilesToArcGISTable.html?format=raw">Create Table from ESRL Climate Index Time Series in Text Files</a></td><td>Creates and populates a table of climate index values parsed from a list of text files, where each file contains the data for a single climate index in NOAA ESRL time series format.</td></tr><tr><td><a href="Directory.CreateTemporaryDirectory.html?format=raw">Create Temporary Directory</a></td><td>Creates a directory suitable for holding temporary files.</td></tr><tr><td><a href="AvisoGriddedGeostrophicCurrents.CreateVectorsAsArcGISFeatureClasses.html?format=raw">Create Vectors for Aviso Geostrophic Currents Product</a></td><td>Creates line feature classes representing the current vectors of an Aviso gridded geostrophic currents product.</td></tr><tr><td><a href="OSCAR5DayThirdDegreeCurrents.CreateVectorsAsArcGISFeatureClasses.html?format=raw">Create Vectors for OSCAR Currents</a></td><td>Creates line feature classes representing the vectors of NOAA OSCAR 5-day unfiltered 1/3 degree ocean surface currents.</td></tr><tr><td><a href="ArcGISRaster.CreateXRaster.html?format=raw">Create X Coordinate Raster</a></td><td>Creates an ArcGIS raster where the value of each cell is the X coordinate of the cell.</td></tr><tr><td><a href="ArcGISRaster.CreateYRaster.html?format=raw">Create Y Coordinate Raster</a></td><td>Creates an ArcGIS raster where the value of each cell is the Y coordinate of the cell.</td></tr><tr><td><a href="File.Decompress.html?format=raw">Decompress File</a></td><td>Decompresses a file into a directory.</td></tr><tr><td><a href="Directory.DeleteArcGISTable.html?format=raw">Delete Directories Listed in Table</a></td><td>Deletes the directories listed in a table.</td></tr><tr><td><a href="Directory.Delete.html?format=raw">Delete Directory</a></td><td>Deletes a directory.</td></tr><tr><td><a href="File.Delete.html?format=raw">Delete File</a></td><td>Deletes a file.</td></tr><tr><td><a href="File.DeleteArcGISTable.html?format=raw">Delete Files Listed in Table</a></td><td>Deletes the files listed in a table.</td></tr><tr><td><a href="ArcGISRaster.Delete.html?format=raw">Delete Raster</a></td><td>Deletes an ArcGIS raster.</td></tr><tr><td><a href="ArcGISRaster.DeleteArcGISTable.html?format=raw">Delete Rasters Listed in Table</a></td><td>Deletes the ArcGIS rasters listed in a table.</td></tr><tr><td><a href="RExploratoryPlots.DensityHistogramForArcGISField.html?format=raw">Density Histogram for Field</a></td><td>Creates a density histogram for a field of a table.</td></tr><tr><td><a href="RExploratoryPlots.DensityHistogramForArcGISPointsCoordinates.html?format=raw">Density Histogram for Point Coordinate</a></td><td>Creates a density histogram for one of the coordinates of a point feature class or layer.</td></tr><tr><td><a href="R.Evaluate.html?format=raw">Evaluate R Statements</a></td><td>Evalutes one or more R statements using the R interpreter and returns the result of the last statement.</td></tr><tr><td><a href="R.EvaluateFile.html?format=raw">Evaluate R Statements in Text File</a></td><td>Evalutes the R statements in a text file using the R interpreter and returns the result of the last statement.</td></tr><tr><td><a href="Table.ExecuteADOCommandForArcGISTable.html?format=raw">Execute Database Command for Table</a></td><td>Opens a Microsoft ActiveX Data Objects (ADO) connection to the database containing a table and executes a command.</td></tr><tr><td><a href="ADODatabaseConnection.ConnectAndExecuteCommand.html?format=raw">Execute Database Command on ADO Connection</a></td><td>Opens a Microsoft ActiveX Data Objects (ADO) connection to a database and executes a command.</td></tr><tr><td><a href="ChildProcess.ExecuteProgram.html?format=raw">Execute Program</a></td><td>Executes the specified program and captures its output.</td></tr><tr><td><a href="ArcGISRaster.ExtractByMaskArcGISTable.html?format=raw">Extract By Mask for ArcGIS Rasters Listed in Table</a></td><td>For each ArcGIS raster in a table, extracts the cells that correspond to the areas defined by a mask.</td></tr><tr><td><a href="HDF.ExtractHeader.html?format=raw">Extract HDF Header</a></td><td>Extracts the header of an HDF file and saves it to a text file.</td></tr><tr><td><a href="HDF.ExtractHeaderArcGISTable.html?format=raw">Extract Headers of HDFs Listed in Table</a></td><td>Extracts the headers of HDF files in a table and saves them to text files.</td></tr><tr><td><a href="NetCDF.ExtractHeaderArcGISTable.html?format=raw">Extract Headers of NetCDFs Listed in Table</a></td><td>Extracts the headers of netCDF files in a table and saves them to text files.</td></tr><tr><td><a href="SIRFile.ExtractHeaderArcGISTable.html?format=raw">Extract Headers of SIR Files Listed in Table</a></td><td>Extracts the headers of SIR files in a table and saves them to text files.</td></tr><tr><td><a href="CheltonMesocaleEddyPoints.ExtractArcGISPointsFromSpatiaLite.html?format=raw">Extract Mesoscale Eddy Centroids From SpatiaLite Database</a></td><td>Extracts eddy centroid points from the Chelton et al. (2011) mesoscale eddy database in SpatiaLite format.</td></tr><tr><td><a href="CheltonMesocaleEddyPoints.ExtractArcGISLinesFromSpatiaLite.html?format=raw">Extract Mesoscale Eddy Tracklines From SpatiaLite Database</a></td><td>Extracts eddy tracklines from the Chelton et al. (2011) mesoscale eddy database in SpatiaLite format.</td></tr><tr><td><a href="NetCDF.ExtractHeader.html?format=raw">Extract NetCDF Header</a></td><td>Extracts the header of a netCDF file and saves it to a text file.</td></tr><tr><td><a href="SIRFile.ExtractHeader.html?format=raw">Extract SIR File Header</a></td><td>Extracts the header of a SIR file.</td></tr><tr><td><a href="File.IsDecompressible.html?format=raw">File Is Decompressible</a></td><td>Returns True if the specified file is in a format that can be decompressed.</td></tr><tr><td><a href="ArcGISRaster.FindAndExtractByMask.html?format=raw">Find ArcGIS Rasters and Extract By Mask</a></td><td>Finds rasters in an ArcGIS workspace and extracts the cells that correspond to the areas defined by a mask.</td></tr><tr><td><a href="CayulaCornillonEdgeDetection.FindArcGISRastersAndDetectEdges.html?format=raw">Find ArcGIS Rasters and Find Cayula-Cornillon Fronts</a></td><td>Finds ArcGIS rasters in a workspace and finds fronts within them using the Cayula and Cornillon (1992) single-image edge detection algorithm.</td></tr><tr><td><a href="Interpolator.FindAndInpaintArcGISRasters.html?format=raw">Find ArcGIS Rasters and Interpolate No Data Cells</a></td><td>Finds rasters in an ArcGIS workspace and interpolates values for the No Data cells.</td></tr><tr><td><a href="ArcGISRaster.FindAndProjectRastersToTemplate.html?format=raw">Find ArcGIS Rasters and Project to Template</a></td><td>Finds rasters in an ArcGIS workspace and projects them to the coordinate system, cell size, and extent of a template raster.</td></tr><tr><td><a href="ArcGISRaster.FindAndProjectClipAndOrExecuteMapAlgebra.html?format=raw">Find ArcGIS Rasters and Project, Clip, and/or Execute Map Algebra</a></td><td>Finds rasters in an ArcGIS workspace and projects, clips, and/or perfoms map algebra on them. You must request at least one of these three operations. If you request multiple operations, the tool performs them in the order they are listed.</td></tr><tr><td><a href="ArcInfoASCIIGrid.FindAndConvertToArcGISRaster.html?format=raw">Find ArcInfo ASCII Grids and Convert To ArcGIS Rasters</a></td><td>Finds ArcInfo ASCII Grid text files in a directory and converts them to ArcGIS rasters.</td></tr><tr><td><a href="BinaryRaster.FindAndConvertToArcGISRaster.html?format=raw">Find Binary Rasters and Convert To ArcGIS Rasters</a></td><td>Finds two-dimensional binary rasters in a directory and converts them to ArcGIS rasters.</td></tr><tr><td><a href="BinaryRaster.FindAndConvertToArcInfoASCIIGrid.html?format=raw">Find Binary Rasters and Convert To ArcInfo ASCII Grids</a></td><td>Finds two-dimensional binary rasters in a directory and converts them to text files in ArcInfo ASCII Grid format.</td></tr><tr><td><a href="BinaryRaster.FindAndSwapBytes.html?format=raw">Find Binary Rasters and Swap Bytes</a></td><td>Finds and reverses the byte order of binary rasters in a directory (i.e. converts "little endian" to "big endian", or visa versa).</td></tr><tr><td><a href="AVHRRPathfinderSSTTimeSeries.CreateCayulaCornillonFrontsAsArcGISRasters.html?format=raw">Find Cayula-Cornillon Fronts in AVHRR Pathfinder V5 SST</a></td><td>Creates rasters indicating the positions of fronts in AVHRR Pathfinder Version 5 SST images published by NOAA NODC, using the Cayula and Cornillon (1992) single image edge detection (SIED) algorithm.</td></tr><tr><td><a href="GHRSSTLevel4.CreateCayulaCornillonFrontsAsArcGISRasters.html?format=raw">Find Cayula-Cornillon Fronts in GHRSST L4 SST</a></td><td>Creates rasters indicating the positions of fronts in GHRSST L4 SST images hosted by NASA JPL PO.DAAC, using the Cayula and Cornillon (1992) single image edge detection (SIED) algorithm.</td></tr><tr><td><a href="HYCOMGLBa008Equatorial4D.CreateCayulaCornillonFrontsAsArcGISRasters.html?format=raw">Find Cayula-Cornillon Fronts in HYCOM GLBa0.08 Equatorial 4D Variable</a></td><td>Creates rasters indicating the positions of fronts in the 2D slices of a 4D variable of the equatorial (Mercator) region of the HYCOM GLBa0.08 dataset using the Cayula and Cornillon (1992) single image edge detection (SIED) algorithm.</td></tr><tr><td><a href="HYCOMGOMl0044D.CreateCayulaCornillonFrontsAsArcGISRasters.html?format=raw">Find Cayula-Cornillon Fronts in HYCOM GOMl0.04 4D Variable</a></td><td>Creates rasters indicating the positions of fronts in the 2D slices of a HYCOM GOMl0.04 4D variable using the Cayula and Cornillon (1992) single image edge detection (SIED) algorithm.</td></tr><tr><td><a href="MODISL3SSTTimeSeries.CreateCayulaCornillonFrontsAsArcGISRasters.html?format=raw">Find Cayula-Cornillon Fronts in PO.DAAC MODIS L3 SST</a></td><td>Creates rasters indicating the positions of fronts in MODIS Level 3 SST images published by NASA JPL PO.DAAC, using the Cayula and Cornillon (1992) single image edge detection (SIED) algorithm.</td></tr><tr><td><a href="CoastWatchAVHRR.FindFilesAndCreateArcGISTable.html?format=raw">Find CoastWatch Files</a></td><td>Finds CoastWatch POES AVHRR files within a directory and creates a table that lists them.</td></tr><tr><td><a href="CoastWatchAVHRR.FindImagesInFilesAndCreateArcGISTable.html?format=raw">Find CoastWatch Images In Files</a></td><td>Finds CoastWatch POES AVHRR files within a directory and creates a table that lists the images within them.</td></tr><tr><td><a href="CoastWatchAVHRR.FindAndConvertToArcGISRasters.html?format=raw">Find CoastWatch Images and Convert To ArcGIS Rasters</a></td><td>Finds images in CoastWatch POES AVHRR files in a directory and converts them to ArcGIS rasters.</td></tr><tr><td><a href="CoastWatchAVHRR.FindAndConvertToBinaryRasters.html?format=raw">Find CoastWatch Images and Convert To Binary Rasters</a></td><td>Finds images in CoastWatch POES AVHRR files in a directory and converts them to binary rasters.</td></tr><tr><td><a href="CoastWatchAVHRR.FindAndConvertToCoastWatchHDFs.html?format=raw">Find CoastWatch Images and Convert to HDFs</a></td><td>Finds images in CoastWatch POES AVHRR files in a directory and imports them into CoastWatch HDFs.</td></tr><tr><td><a href="CoastWatchAVHRR.FindAndCreateMasksAsArcGISRasters.html?format=raw">Find CoastWatch Images and Create Masks as ArcGIS Rasters</a></td><td>Finds images in CoastWatch POES AVHRR files in a directory creates masks for them, in ArcGIS raster format.</td></tr><tr><td><a href="CoastWatchAVHRR.FindAndCreateMasksAsBinaryRasters.html?format=raw">Find CoastWatch Images and Create Masks as Binary Rasters</a></td><td>Finds images in CoastWatch POES AVHRR files in a directory creates masks for them, in binary raster format.</td></tr><tr><td><a href="CoastWatchAVHRR.FindCoastWatchFilesAndFindFrontsAsArcGISRasters.html?format=raw">Find CoastWatch Images and Find Cayula-Cornillon Fronts as ArcGIS Rasters</a></td><td>Uses the Cayula and Cornillon (1992) single-image edge detection algorithm to find fronts in the CoastWatch POES AVHRR images found in a directory, and outputs the fronts as ArcGIS rasters.</td></tr><tr><td><a href="Directory.FindAndCreateArcGISTable.html?format=raw">Find Directories</a></td><td>Finds subdirectories within a directory and creates a table that lists them.</td></tr><tr><td><a href="File.FindAndCreateArcGISTable.html?format=raw">Find Files</a></td><td>Finds files within a directory and creates a table that lists them.</td></tr><tr><td><a href="HDF.FindAndConvertToArcGISRasters.html?format=raw">Find HDFs and Convert SDS To ArcGIS Rasters</a></td><td>Finds HDF files in a directory and converts a Scientific Data Sets (SDS) in each file to an ArcGIS raster.</td></tr><tr><td><a href="HDF.FindAndConvertToArcInfoASCIIGrids.html?format=raw">Find HDFs and Convert SDS To ArcInfo ASCII Grids</a></td><td>Finds HDF files in a directory and converts a Scientific Data Sets (SDS) in each file to a text file in ArcInfo ASCII Grid format.</td></tr><tr><td><a href="HDF.FindAndConvertToBinaryRasters.html?format=raw">Find HDFs and Convert SDS To Binary Rasters</a></td><td>Finds HDF files in a directory and converts a Scientific Data Sets (SDS) in each file to a binary raster.</td></tr><tr><td><a href="HDF.FindAndExtractHeaders.html?format=raw">Find HDFs and Extract Headers</a></td><td>Finds HDF files in a directory, extracts their headers, and saves the headers to text files.</td></tr><tr><td><a href="ArcGISPoints.FindNearestFeatures.html?format=raw">Find Nearest Features</a></td><td>For each point, finds the nearest feature and writes its ID, distance, angle, and/or values of specified fields to fields of the point.</td></tr><tr><td><a href="ArcGISPoints.FindNearestFeaturesListedInField.html?format=raw">Find Nearest Features Listed in Field</a></td><td>For each point, using the feature class or layer listed in a field, finds the nearest feature and writes its ID, distance, angle, and/or values of specified fields to fields of the point. Use this tool when you have a single point feature class but need to find distances to different near feature classes or layers for different points.</td></tr><tr><td><a href="NetCDF.FindNetCDFsAndConvert2DVariableToArcGISRasters.html?format=raw">Find NetCDFs and Convert 2D Variable to ArcGIS Rasters</a></td><td>Finds netCDF files in a directory and converts a two-dimensional variable in each file to an ArcGIS raster.</td></tr><tr><td><a href="NetCDF.FindNetCDFsAndConvert2DVariableToArcInfoASCIIGrids.html?format=raw">Find NetCDFs and Convert 2D Variable to ArcInfo ASCII Grids</a></td><td>Finds netCDF files in a directory and converts a two-dimensional variable in each file to a text file in ArcInfo ASCII Grid format.</td></tr><tr><td><a href="NetCDF.FindNetCDFsAndConvert2DVariableToBinaryRasters.html?format=raw">Find NetCDFs and Convert 2D Variable to Binary Rasters</a></td><td>Finds netCDF files in a directory and converts a two-dimensional variable in each file to a binary raster.</td></tr><tr><td><a href="NetCDF.FindAndExtractHeaders.html?format=raw">Find NetCDFs and Extract Headers</a></td><td>Finds netCDF files in a directory, extracts their headers, and saves the headers to text files.</td></tr><tr><td><a href="AvisoGriddedSSH.FindOkuboWeissEddies.html?format=raw">Find Okubo-Weiss Eddies in Aviso SSH Product</a></td><td>Creates rasters showing the cores of geostrophic eddies detected in an Aviso gridded sea surface height product using the Okubo-Weiss algorithm.</td></tr><tr><td><a href="ArcGISRaster.FindAndCreateArcGISTable.html?format=raw">Find Rasters</a></td><td>Finds rasters within an ArcGIS workspace and creates a table that lists them.</td></tr><tr><td><a href="ArcGISRasterMapAlgebra.FindRastersAndExecuteSingleOutputMapAlgebra.html?format=raw">Find Rasters and Execute Single Output Map Algebra</a></td><td>Finds rasters in a workspace and executes a map algebra expression on them.</td></tr><tr><td><a href="SIRFile.FindAndConvertToArcGISRasters.html?format=raw">Find SIR Files and Convert To ArcGIS Rasters</a></td><td>Finds SIR files in a directory and converts them to ArcGIS rasters.</td></tr><tr><td><a href="SIRFile.FindAndConvertToArcInfoASCIIGrids.html?format=raw">Find SIR Files and Convert To ArcInfo ASCII Grids</a></td><td>Finds SIR files in a directory and converts them to text files in ArcInfo ASCII grid format.</td></tr><tr><td><a href="SIRFile.FindAndConvertToBinaryRasters.html?format=raw">Find SIR Files and Convert To Binary Rasters</a></td><td>Finds SIR files in a directory and converts them to binary rasters.</td></tr><tr><td><a href="SIRFile.FindAndExtractHeaders.html?format=raw">Find SIR Files and Extract Headers</a></td><td>Finds SIR files in a directory, extracts their headers, and saves the headers to text files.</td></tr><tr><td><a href="ArcGISRaster.FindAndConvertToLines.html?format=raw">Find and Convert ArcGIS Rasters to Lines</a></td><td>Finds rasters in an ArcGIS workspace and converts them to lines that outline groups of adjacent raster cells having the same value.</td></tr><tr><td><a href="ArcGISRaster.FindAndConvertToPoints.html?format=raw">Find and Convert ArcGIS Rasters to Points</a></td><td>Finds rasters in an ArcGIS workspace and converts them to points that occur at the centers of the raster cells.</td></tr><tr><td><a href="ArcGISRaster.FindAndConvertToPolygonOutlines.html?format=raw">Find and Convert ArcGIS Rasters to Polygon Outlines</a></td><td>Finds rasters in an ArcGIS workspace and converts them to lines that outline groups of adjacent raster cells having the same value.</td></tr><tr><td><a href="ArcGISRaster.FindAndConvertToPolygons.html?format=raw">Find and Convert ArcGIS Rasters to Polygons</a></td><td>Finds rasters in an ArcGIS workspace and converts them to polygons that encompass groups of adjacent raster cells having the same value.</td></tr><tr><td><a href="Directory.FindAndCopy.html?format=raw">Find and Copy Directories</a></td><td>Finds and copies directories in a directory.</td></tr><tr><td><a href="File.FindAndCopy.html?format=raw">Find and Copy Files</a></td><td>Finds and copies files in a directory.</td></tr><tr><td><a href="ArcGISRaster.FindAndCopy.html?format=raw">Find and Copy Rasters</a></td><td>Finds and copies rasters in an ArcGIS workspace.</td></tr><tr><td><a href="Directory.FindAndDelete.html?format=raw">Find and Delete Directories</a></td><td>Finds and deletes directories in a directory.</td></tr><tr><td><a href="File.FindAndDelete.html?format=raw">Find and Delete Files</a></td><td>Finds and deletes files in a directory.</td></tr><tr><td><a href="ArcGISRaster.FindAndDelete.html?format=raw">Find and Delete Rasters</a></td><td>Finds and deletes rasters in an ArcGIS workspace.</td></tr><tr><td><a href="Directory.FindAndMove.html?format=raw">Find and Move Directories</a></td><td>Finds and moves directories in a directory.</td></tr><tr><td><a href="File.FindAndMove.html?format=raw">Find and Move Files</a></td><td>Finds and moves files in a directory.</td></tr><tr><td><a href="ArcGISRaster.FindAndMove.html?format=raw">Find and Move Rasters</a></td><td>Finds and moves rasters in an ArcGIS workspace.</td></tr><tr><td><a href="GAM.FitToArcGISTable.html?format=raw">Fit GAM</a></td><td>Fits a generalized additive model (GAM) to data in an ArcGIS table.</td></tr><tr><td><a href="GLM.FitToArcGISTable.html?format=raw">Fit GLM</a></td><td>Fits a generalized linear model (GLM) to data in an ArcGIS table using the R glm function.</td></tr><tr><td><a href="LinearMixedModel.FitToArcGISTable.html?format=raw">Fit Linear Mixed Model</a></td><td>Fits a linear mixed-effects model to data in an ArcGIS table.</td></tr><tr><td><a href="TreeModel.FitToArcGISTable.html?format=raw">Fit Tree Model</a></td><td>Fits a tree model to data in an ArcGIS table.</td></tr><tr><td><a href="DiGIR.GetResourcesAsArcGISTable.html?format=raw">Get DiGIR Resources as Table</a></td><td>Gets the list of Distributed Generic Information Retrieval (DiGIR) resources available from a DiGIR server and writes it to an ArcGIS table.</td></tr><tr><td><a href="DiGIR.GetOBISResourcesAsArcGISTable.html?format=raw">Get OBIS DiGIR Resources as Table</a></td><td>Gets the list of Distributed Generic Information Retrieval (DiGIR) resources available from OBIS and writes it to an ArcGIS table.</td></tr><tr><td><a href="DiGIR.GetOBISSEAMAPResourcesAsArcGISTable.html?format=raw">Get OBIS-SEAMAP DiGIR Resources as Table</a></td><td>Gets the list of Distributed Generic Information Retrieval (DiGIR) resources available from OBIS-SEAMAP and writes it to an ArcGIS table.</td></tr><tr><td><a href="AVHRRPathfinderSSTTimeSeries.InterpolateAtArcGISPoints.html?format=raw">Interpolate AVHRR Pathfinder V5 SST at Points</a></td><td>Interpolates AVHRR Pathfinder Version 5 SST values at points.</td></tr><tr><td><a href="AvisoGriddedGeostrophicCurrents.InterpolateAtArcGISPoints.html?format=raw">Interpolate Aviso Geostrophic Currents Product at Points</a></td><td>Interpolates the values of an Aviso gridded geostrophic currents product at points.</td></tr><tr><td><a href="AvisoGriddedSSH.InterpolateAtArcGISPoints.html?format=raw">Interpolate Aviso SSH Product at Points</a></td><td>Interpolates the values of an Aviso gridded sea surface height product at points.</td></tr><tr><td><a href="AvisoGriddedSignificantWaveHeight.InterpolateAtArcGISPoints.html?format=raw">Interpolate Aviso Significant Wave Height Product at Points</a></td><td>Interpolates the values of an Aviso gridded significant wave height product at points.</td></tr><tr><td><a href="AvisoGriddedWindSpeedModulus.InterpolateAtArcGISPoints.html?format=raw">Interpolate Aviso Wind Speed Modulus Product at Points</a></td><td>Interpolates the values of an Aviso gridded wind speed modulus product at points.</td></tr><tr><td><a href="CoRTADv32D.InterpolateAtArcGISPoints.html?format=raw">Interpolate CoRTAD 2D Variables at Points</a></td><td>Interpolates CoRTAD 2D variables at points.</td></tr><tr><td><a href="CoRTADv33D.InterpolateAtArcGISPoints.html?format=raw">Interpolate CoRTAD 3D Variables at Points</a></td><td>Interpolates CoRTAD 3D variables at points.</td></tr><tr><td><a href="GHRSSTLevel4.InterpolateAtArcGISPoints.html?format=raw">Interpolate GHRSST L4 SST at Points</a></td><td>Interpolates values of a GHRSST L4 product hosted by NASA JPL PO.DAAC at points.</td></tr><tr><td><a href="HYCOMGLBa008Equatorial3D.InterpolateAtArcGISPoints.html?format=raw">Interpolate HYCOM GLBa0.08 Equatorial 3D Variables at Points</a></td><td>Interpolates 3D variables of the equatorial (Mercator) region of the HYCOM GLBa0.08 dataset at points.</td></tr><tr><td><a href="HYCOMGLBa008Equatorial4D.InterpolateAtArcGISPoints.html?format=raw">Interpolate HYCOM GLBa0.08 Equatorial 4D Variables at Points</a></td><td>Interpolates 4D variables of the equatorial (Mercator) region of the HYCOM GLBa0.08 dataset at points.</td></tr><tr><td><a href="HYCOMGOMl0043D.InterpolateAtArcGISPoints.html?format=raw">Interpolate HYCOM GOMl0.04 3D Variables at Points</a></td><td>Interpolates HYCOM GOMl0.04 3D variables at points.</td></tr><tr><td><a href="HYCOMGOMl0044D.InterpolateAtArcGISPoints.html?format=raw">Interpolate HYCOM GOMl0.04 4D Variables at Points</a></td><td>Interpolates HYCOM GOMl0.04 4D variables at points.</td></tr><tr><td><a href="OceanColorLevel3SMITimeSeries.InterpolateAtArcGISPoints.html?format=raw">Interpolate NASA OceanColor L3 SMI Product at Points</a></td><td>Interpolates the values of a Level 3 Standard Mapped Image (SMI) product published by the NASA GSFC OceanColor Group at points.</td></tr><tr><td><a href="Interpolator.InpaintArcGISRaster.html?format=raw">Interpolate No Data Cells</a></td><td>Interpolates values for the No Data cells of a raster.</td></tr><tr><td><a href="Interpolator.InpaintArcGISRasterArcGISTable.html?format=raw">Interpolate No Data Cells for ArcGIS Rasters Listed in Table</a></td><td>Interpolates values for the No Data cells for each ArcGIS raster in a table.</td></tr><tr><td><a href="OSCAR5DayThirdDegreeCurrents.InterpolateAtArcGISPoints.html?format=raw">Interpolate OSCAR Currents at Points</a></td><td>Interpolates the values of NOAA OSCAR 5-day unfiltered 1/3 degree ocean surface currents at points.</td></tr><tr><td><a href="MODISL3SSTTimeSeries.InterpolateAtArcGISPoints.html?format=raw">Interpolate PO.DAAC MODIS L3 SST at Points</a></td><td>Interpolates PO.DAAC MODIS Level 3 SST values at points.</td></tr><tr><td><a href="ROMSCoSiNE2D.InterpolateAtArcGISPoints.html?format=raw">Interpolate Pacific ROMS-CoSiNE 2D Variable at Points</a></td><td>Interpolates a Pacific ROMS-CoSiNE 2D variable at points.</td></tr><tr><td><a href="ROMSCoSiNE3D.InterpolateAtArcGISPoints.html?format=raw">Interpolate Pacific ROMS-CoSiNE 3D Variables at Points</a></td><td>Interpolates Pacific ROMS-CoSiNE 3D variables at points.</td></tr><tr><td><a href="ROMSCoSiNE4D.InterpolateAtArcGISPoints.html?format=raw">Interpolate Pacific ROMS-CoSiNE 4D Variables at Points</a></td><td>Interpolates Pacific ROMS-CoSiNE 4D variables at points.</td></tr><tr><td><a href="Interpolator.InterpolateArcGISRasterValuesAtPoints.html?format=raw">Interpolate Raster Values at Points</a></td><td>Interpolates the values of rasters at points.</td></tr><tr><td><a href="CoralReefConnectivity.LoadAvisoGeostrophicCurrentsIntoSimulation.html?format=raw">Load Aviso Geostrophic Currents Into Coral Reef Connectivity Simulation</a></td><td>Downloads Aviso geostrophic currents into a coral reef connectivity simulation.</td></tr><tr><td><a href="CoralReefConnectivity.LoadHYCOMGLBa0084DEquatorialCurrentsIntoSimulation.html?format=raw">Load HYCOM GLBa0.08 Currents Into Coral Reef Connectivity Simulation</a></td><td>Downloads HYCOM GLBa0.08 ocean currents into a coral reef connectivity simulation.</td></tr><tr><td><a href="CoralReefConnectivity.LoadHYCOMGOMl0044DCurrentsIntoSimulation.html?format=raw">Load HYCOM GOMl0.04 Currents Into Coral Reef Connectivity Simulation</a></td><td>Downloads HYCOM GOMl0.04 ocean currents into a coral reef connectivity simulation.</td></tr><tr><td><a href="CoralReefConnectivity.LoadOSCARCurrentsIntoSimulation.html?format=raw">Load OSCAR Currents Into Coral Reef Connectivity Simulation</a></td><td>Downloads NOAA OSCAR 5-day unfiltered 1/3 degree ocean surface currents into a coral reef connectivity simulation.</td></tr><tr><td><a href="CoralReefConnectivity.LoadROMSCoSiNE4DCurrentsIntoSimulation.html?format=raw">Load Pacific ROMS-CoSiNE Currents Into Coral Reef Connectivity Simulation</a></td><td>Downloads Pacific ROMS-CoSiNE ocean currents into a coral reef connectivity simulation.</td></tr><tr><td><a href="Directory.MoveArcGISTable.html?format=raw">Move Directories Listed in Table</a></td><td>Moves the directories listed in a table.</td></tr><tr><td><a href="Directory.Move.html?format=raw">Move Directory</a></td><td>Moves a directory, including its subdirectories and files.</td></tr><tr><td><a href="File.Move.html?format=raw">Move File</a></td><td>Moves a file.</td></tr><tr><td><a href="File.MoveArcGISTable.html?format=raw">Move Files Listed in Table</a></td><td>Moves the files listed in a table.</td></tr><tr><td><a href="ArcGISRaster.Move.html?format=raw">Move Raster</a></td><td>Moves an ArcGIS raster.</td></tr><tr><td><a href="ArcGISRaster.MoveArcGISTable.html?format=raw">Move Rasters Listed in Table</a></td><td>Moves the ArcGIS rasters listed in a table.</td></tr><tr><td><a href="ModelEvaluation.PlotPerformanceOfBinaryClassificationModel.html?format=raw">Plot Performance of Binary Classification Model</a></td><td>Plots the performance of a binary classification model (a model where the response variable has two possible values) using the R ROCR package.</td></tr><tr><td><a href="ModelEvaluation.PlotROCOfBinaryClassificationModel.html?format=raw">Plot ROC of Binary Classification Model</a></td><td>Plots the receiver operating characteristic (ROC) curve of a binary classification model (a model where the response variable has two possible values) using the R ROCR package.</td></tr><tr><td><a href="GAM.BayesPredictFromArcGISRasters.html?format=raw">Predict Bayesian Probabilities for GAM From Rasters</a></td><td>Using a binomial generalized additive model (GAM) fitted using the R mgcv package, this tool creates rasters representing the estimated probabilities that the response variable will equal or exceed specified thresholds, given ArcGIS rasters representing the predictor variables.</td></tr><tr><td><a href="GAM.PredictFromArcGISRasters.html?format=raw">Predict GAM From Rasters</a></td><td>Using a fitted generalized additive model (GAM), this tool creates a raster representing the response variable predicted from ArcGIS rasters representing the predictor variables.</td></tr><tr><td><a href="GLM.PredictFromArcGISRasters.html?format=raw">Predict GLM From Rasters</a></td><td>Using a fitted generalized linear model (GLM), this tool creates a raster representing the response variable predicted from ArcGIS rasters representing the predictor variables.</td></tr><tr><td><a href="LinearMixedModel.PredictFromArcGISRasters.html?format=raw">Predict Linear Mixed Model From Rasters</a></td><td>Using a fitted linear mixed model, this tool creates a raster representing the response variable predicted from rasters representing the predictor variables.</td></tr><tr><td><a href="TreeModel.PredictFromArcGISRasters.html?format=raw">Predict Tree Model From Rasters</a></td><td>Using a fitted tree model, this tool creates a raster representing the response variable predicted from rasters representing the predictor variables.</td></tr><tr><td><a href="ArcGISRaster.ProjectToTemplate.html?format=raw">Project Raster to Template</a></td><td>Projects a raster to the coordinate system, cell size, and extent of a template raster.</td></tr><tr><td><a href="ArcGISRaster.ProjectToTemplateArcGISTable.html?format=raw">Project Rasters Listed in Table to Template</a></td><td>Projects a table of rasters to the coordinate system, cell size, and extent of a template raster.</td></tr><tr><td><a href="ArcGISRaster.ProjectClipAndOrExecuteMapAlgebra.html?format=raw">Project, Clip, and/or Execute Map Algebra</a></td><td>Projects, clips, and/or perfoms map algebra on an ArcGIS raster. You must request at least one of these three operations. If you request multiple operations, the tool performs them in the order they are listed.</td></tr><tr><td><a href="ArcGISRaster.ProjectClipAndOrExecuteMapAlgebraArcGISTable.html?format=raw">Project, Clip, and/or Execute Map Algebra on ArcGIS Rasters Listed in Table</a></td><td>Projects, clips, and/or perfoms map algebra on the ArcGIS rasters listed in a table. You must request at least one of these three operations. If you request multiple operations, the tool performs them in the order they are listed.</td></tr><tr><td><a href="ModelEvaluation.RandomlySplitArcGISTableIntoTrainingAndEvaluationRecords.html?format=raw">Randomly Split Table Into Training and Evaluation Records</a></td><td>Randomly designates the records of a table as either training records (for fitting a statistical model) or evaluation records (for evaluating the model).</td></tr><tr><td><a href="CoralReefConnectivity.RunSimulation.html?format=raw">Run Coral Reef Connectivity Simulation</a></td><td>Executes a coral coral reef connectivity simulation.</td></tr><tr><td><a href="ArcGISFeatureSampler.SamplePolygons.html?format=raw">Sample Polygons</a></td><td>Samples the values of polygon fields at points that intersect the polygons.</td></tr><tr><td><a href="ArcGISRasterSampler.SampleRasters.html?format=raw">Sample Rasters</a></td><td>Samples rasters using a point feature class and stores the sampled values in fields of the feature class.</td></tr><tr><td><a href="ArcGISRasterSampler.SampleRastersInFields.html?format=raw">Sample Rasters Listed in Fields</a></td><td>Samples rasters listed in fields of a point feature class and stores the sampled values in other fields.</td></tr><tr><td><a href="ArcGISTableSampler.SampleTimeSeriesTable.html?format=raw">Sample Time Series Table</a></td><td>Matches records in a destination table to records in a time series table by date, and copies values of fields of the time series table to fields of the destination table.</td></tr><tr><td><a href="RExploratoryPlots.ScatterplotMatrixForArcGISTable.html?format=raw">Scatterplot Matrix for Table</a></td><td>Creates a matrix of scatterplots for a table using the R pairs function.</td></tr><tr><td><a href="DiGIR.SearchAndCreateArcGISPoints.html?format=raw">Search DiGIR Records and Create Points</a></td><td>Searches a Distributed Generic Information Retrieval (DiGIR) server for georeferenced records and creates an ArcGIS point feature class from them.</td></tr><tr><td><a href="DiGIR.SearchOBISAndCreateArcGISPoints.html?format=raw">Search OBIS DiGIR Records and Create Points</a></td><td>Searches OBIS for georeferenced records using the Distributed Generic Information Retrieval (DiGIR) protocol and creates an ArcGIS point feature class from them.</td></tr><tr><td><a href="DiGIR.SearchOBISSEAMAPAndCreateArcGISPoints.html?format=raw">Search OBIS-SEAMAP DiGIR Records and Create Points</a></td><td>Searches OBIS-SEAMAP for georeferenced records using the Distributed Generic Information Retrieval (DiGIR) protocol and creates an ArcGIS point feature class from them.</td></tr><tr><td><a href="CoastWatchAVHRR.SetNavigationOffsets.html?format=raw">Set CoastWatch Navigation Offsets</a></td><td>Sets the navigation offsets of a CoastWatch POES AVHRR CWF or HDF file.</td></tr><tr><td><a href="CoastWatchAVHRR.SetNavigationOffsetsArcGISTable.html?format=raw">Set Navigation Offsets of CoastWatch Files Listed in Table</a></td><td>Sets the navigation offsets of the CoastWatch POES AVHRR CWF or HDF files listed in a table to the values specified by two fields.</td></tr><tr><td><a href="ArcGISRasterMapAlgebra.SingleOutputMapAlgebraForRasterArcGISTable.html?format=raw">Single Output Map Algebra For Rasters Listed in Table</a></td><td>Executes a map algebra expression on the rasters listed in a table.</td></tr><tr><td><a href="ArcGISRasterMapAlgebra.SingleOutputMapAlgebraForArcGISTableRows.html?format=raw">Single Output Map Algebra for Table Rows</a></td><td>For each row of a table, calculates an output raster and map algebra expression from the row using Python expressions and executes the map algebra expression to produce the output raster.</td></tr><tr><td><a href="BinaryRaster.SwapBytes.html?format=raw">Swap Bytes of Binary Raster</a></td><td>Reverses the byte order of a binary raster (e.g. converts "little endian" to "big endian", or visa versa).</td></tr><tr><td><a href="BinaryRaster.SwapBytesArcGISTable.html?format=raw">Swap Bytes of Binary Rasters Listed in Table</a></td><td>Reverses the byte order of each binary raster listed in a table (i.e. converts "little endian" to "big endian", or visa versa).</td></tr></table></p><h1><a id="IndexByName">Tool Index, By Name</a></h1><p>In the table below, <i>Tool Name</i> is the programming name used to invoke the tool 
     5         from the ArcGIS command line or from the geoprocessor object in a geoprocessing script.</p><p><table><tr><th>Tool Name</th><th>Description</th></tr><tr><td><a href="ADODatabaseConnection.ConnectAndExecuteCommand.html?format=raw">ADODatabaseConnectionConnectAndExecuteCommand_GeoEco</a></td><td>Opens a Microsoft ActiveX Data Objects (ADO) connection to a database and executes a command.</td></tr><tr><td><a href="AVHRRPathfinderSSTTimeSeries.CreateArcGISRasters.html?format=raw">AVHRRPathfinderSSTTimeSeriesCreateArcGISRasters_GeoEco</a></td><td>Creates rasters for AVHRR Pathfinder Version 5 SST images published by NOAA NODC.</td></tr><tr><td><a href="AVHRRPathfinderSSTTimeSeries.CreateCayulaCornillonFrontsAsArcGISRasters.html?format=raw">AVHRRPathfinderSSTTimeSeriesCreateCayulaCornillonFrontsAsArcGISRasters_GeoEco</a></td><td>Creates rasters indicating the positions of fronts in AVHRR Pathfinder Version 5 SST images published by NOAA NODC, using the Cayula and Cornillon (1992) single image edge detection (SIED) algorithm.</td></tr><tr><td><a href="AVHRRPathfinderSSTTimeSeries.CreateClimatologicalArcGISRasters.html?format=raw">AVHRRPathfinderSSTTimeSeriesCreateClimatologicalArcGISRasters_GeoEco</a></td><td>Creates climatological rasters from AVHRR Pathfinder Version 5 SST images published by NOAA NODC.</td></tr><tr><td><a href="AVHRRPathfinderSSTTimeSeries.InterpolateAtArcGISPoints.html?format=raw">AVHRRPathfinderSSTTimeSeriesInterpolateAtArcGISPoints_GeoEco</a></td><td>Interpolates AVHRR Pathfinder Version 5 SST values at points.</td></tr><tr><td><a href="ArcGISFeatureSampler.SamplePolygons.html?format=raw">ArcGISFeatureSamplerSamplePolygons_GeoEco</a></td><td>Samples the values of polygon fields at points that intersect the polygons.</td></tr><tr><td><a href="ArcGISFishnets.CreateFishnetForPoints.html?format=raw">ArcGISFishnetsCreateFishnetForPoints_GeoEco</a></td><td>Creates a grid of rectangular cells that overlap the input points and optionally calculates summary statistics for the points that intersect each cell.</td></tr><tr><td><a href="ArcGISFishnets.CreateFishnet.html?format=raw">ArcGISFishnetsCreateFishnet_GeoEco</a></td><td>Creates a grid of rectangular polygons.</td></tr><tr><td><a href="ArcGISLines.FromVectorComponentRastersInArcGISTable.html?format=raw">ArcGISLinesFromVectorComponentRastersInArcGISTable_GeoEco</a></td><td>Given a table of rasters representing the x and y components of vector fields, such as the u and v rasters for ocean currents, this tool creates feature classes of lines representing the vectors, similar to a "quiver plot".</td></tr><tr><td><a href="ArcGISLines.FromVectorComponentRasters.html?format=raw">ArcGISLinesFromVectorComponentRasters_GeoEco</a></td><td>Given rasters representing the x and y components of a vector field, such as the u and v rasters for ocean currents, this tool creates a feature class of lines representing the vectors, similar to a "quiver plot".</td></tr><tr><td><a href="ArcGISPoints.AppendPointsToFeatureClass2.html?format=raw">ArcGISPointsAppendPointsToFeatureClass2_GeoEco</a></td><td>Appends points to an existing ArcGIS point feature class.</td></tr><tr><td><a href="ArcGISPoints.CreateFeatureClassWithPoints2.html?format=raw">ArcGISPointsCreateFeatureClassWithPoints2_GeoEco</a></td><td>Creates points in a new ArcGIS point feature class.</td></tr><tr><td><a href="ArcGISPoints.CreatePointsAlongLines.html?format=raw">ArcGISPointsCreatePointsAlongLines_GeoEco</a></td><td>Creates points along lines at a specified interval.</td></tr><tr><td><a href="ArcGISPoints.FindNearestFeaturesListedInField.html?format=raw">ArcGISPointsFindNearestFeaturesListedInField_GeoEco</a></td><td>For each point, using the feature class or layer listed in a field, finds the nearest feature and writes its ID, distance, angle, and/or values of specified fields to fields of the point. Use this tool when you have a single point feature class but need to find distances to different near feature classes or layers for different points.</td></tr><tr><td><a href="ArcGISPoints.FindNearestFeatures.html?format=raw">ArcGISPointsFindNearestFeatures_GeoEco</a></td><td>For each point, finds the nearest feature and writes its ID, distance, angle, and/or values of specified fields to fields of the point.</td></tr><tr><td><a href="ArcGISPolygons.AppendPolygonToFeatureClass2.html?format=raw">ArcGISPolygonsAppendPolygonToFeatureClass2_GeoEco</a></td><td>Appends a polygon to an existing ArcGIS polygon feature class.</td></tr><tr><td><a href="ArcGISPolygons.AppendRectangleToFeatureClass2.html?format=raw">ArcGISPolygonsAppendRectangleToFeatureClass2_GeoEco</a></td><td>Appends a rectangle to an existing ArcGIS polygon feature class.</td></tr><tr><td><a href="ArcGISPolygons.CreateBoundingBoxForArcGISGeoDatasets.html?format=raw">ArcGISPolygonsCreateBoundingBoxForArcGISGeoDatasets_GeoEco</a></td><td>Creates a new ArcGIS polygon feature class and a bounding box (a minimum bounding rectangle) within it that encompasses the extents of one or more ArcGIS geodatasets.</td></tr><tr><td><a href="ArcGISPolygons.CreateFeatureClassWithPolygon2.html?format=raw">ArcGISPolygonsCreateFeatureClassWithPolygon2_GeoEco</a></td><td>Creates a polygon in a new ArcGIS polygon feature class.</td></tr><tr><td><a href="ArcGISPolygons.CreateFeatureClassWithRectangle2.html?format=raw">ArcGISPolygonsCreateFeatureClassWithRectangle2_GeoEco</a></td><td>Creates a rectangle in a new ArcGIS polygon feature class.</td></tr><tr><td><a href="ArcGISRaster.CopyArcGISTable.html?format=raw">ArcGISRasterCopyArcGISTable_GeoEco</a></td><td>Copies the ArcGIS rasters listed in a table.</td></tr><tr><td><a href="ArcGISRaster.Copy.html?format=raw">ArcGISRasterCopy_GeoEco</a></td><td>Copies an ArcGIS raster.</td></tr><tr><td><a href="ArcGISRaster.CreateXRaster.html?format=raw">ArcGISRasterCreateXRaster_GeoEco</a></td><td>Creates an ArcGIS raster where the value of each cell is the X coordinate of the cell.</td></tr><tr><td><a href="ArcGISRaster.CreateYRaster.html?format=raw">ArcGISRasterCreateYRaster_GeoEco</a></td><td>Creates an ArcGIS raster where the value of each cell is the Y coordinate of the cell.</td></tr><tr><td><a href="ArcGISRaster.DeleteArcGISTable.html?format=raw">ArcGISRasterDeleteArcGISTable_GeoEco</a></td><td>Deletes the ArcGIS rasters listed in a table.</td></tr><tr><td><a href="ArcGISRaster.Delete.html?format=raw">ArcGISRasterDelete_GeoEco</a></td><td>Deletes an ArcGIS raster.</td></tr><tr><td><a href="ArcGISRaster.ExtractByMaskArcGISTable.html?format=raw">ArcGISRasterExtractByMaskArcGISTable_GeoEco</a></td><td>For each ArcGIS raster in a table, extracts the cells that correspond to the areas defined by a mask.</td></tr><tr><td><a href="ArcGISRaster.FindAndConvertToLines.html?format=raw">ArcGISRasterFindAndConvertToLines_GeoEco</a></td><td>Finds rasters in an ArcGIS workspace and converts them to lines that outline groups of adjacent raster cells having the same value.</td></tr><tr><td><a href="ArcGISRaster.FindAndConvertToPoints.html?format=raw">ArcGISRasterFindAndConvertToPoints_GeoEco</a></td><td>Finds rasters in an ArcGIS workspace and converts them to points that occur at the centers of the raster cells.</td></tr><tr><td><a href="ArcGISRaster.FindAndConvertToPolygonOutlines.html?format=raw">ArcGISRasterFindAndConvertToPolygonOutlines_GeoEco</a></td><td>Finds rasters in an ArcGIS workspace and converts them to lines that outline groups of adjacent raster cells having the same value.</td></tr><tr><td><a href="ArcGISRaster.FindAndConvertToPolygons.html?format=raw">ArcGISRasterFindAndConvertToPolygons_GeoEco</a></td><td>Finds rasters in an ArcGIS workspace and converts them to polygons that encompass groups of adjacent raster cells having the same value.</td></tr><tr><td><a href="ArcGISRaster.FindAndCopy.html?format=raw">ArcGISRasterFindAndCopy_GeoEco</a></td><td>Finds and copies rasters in an ArcGIS workspace.</td></tr><tr><td><a href="ArcGISRaster.FindAndCreateArcGISTable.html?format=raw">ArcGISRasterFindAndCreateArcGISTable_GeoEco</a></td><td>Finds rasters within an ArcGIS workspace and creates a table that lists them.</td></tr><tr><td><a href="ArcGISRaster.FindAndDelete.html?format=raw">ArcGISRasterFindAndDelete_GeoEco</a></td><td>Finds and deletes rasters in an ArcGIS workspace.</td></tr><tr><td><a href="ArcGISRaster.FindAndExtractByMask.html?format=raw">ArcGISRasterFindAndExtractByMask_GeoEco</a></td><td>Finds rasters in an ArcGIS workspace and extracts the cells that correspond to the areas defined by a mask.</td></tr><tr><td><a href="ArcGISRaster.FindAndMove.html?format=raw">ArcGISRasterFindAndMove_GeoEco</a></td><td>Finds and moves rasters in an ArcGIS workspace.</td></tr><tr><td><a href="ArcGISRaster.FindAndProjectClipAndOrExecuteMapAlgebra.html?format=raw">ArcGISRasterFindAndProjectClipAndOrExecuteMapAlgebra_GeoEco</a></td><td>Finds rasters in an ArcGIS workspace and projects, clips, and/or perfoms map algebra on them. You must request at least one of these three operations. If you request multiple operations, the tool performs them in the order they are listed.</td></tr><tr><td><a href="ArcGISRaster.FindAndProjectRastersToTemplate.html?format=raw">ArcGISRasterFindAndProjectRastersToTemplate_GeoEco</a></td><td>Finds rasters in an ArcGIS workspace and projects them to the coordinate system, cell size, and extent of a template raster.</td></tr><tr><td><a href="ArcGISRaster.MoveArcGISTable.html?format=raw">ArcGISRasterMoveArcGISTable_GeoEco</a></td><td>Moves the ArcGIS rasters listed in a table.</td></tr><tr><td><a href="ArcGISRaster.Move.html?format=raw">ArcGISRasterMove_GeoEco</a></td><td>Moves an ArcGIS raster.</td></tr><tr><td><a href="ArcGISRaster.ProjectClipAndOrExecuteMapAlgebraArcGISTable.html?format=raw">ArcGISRasterProjectClipAndOrExecuteMapAlgebraArcGISTable_GeoEco</a></td><td>Projects, clips, and/or perfoms map algebra on the ArcGIS rasters listed in a table. You must request at least one of these three operations. If you request multiple operations, the tool performs them in the order they are listed.</td></tr><tr><td><a href="ArcGISRaster.ProjectClipAndOrExecuteMapAlgebra.html?format=raw">ArcGISRasterProjectClipAndOrExecuteMapAlgebra_GeoEco</a></td><td>Projects, clips, and/or perfoms map algebra on an ArcGIS raster. You must request at least one of these three operations. If you request multiple operations, the tool performs them in the order they are listed.</td></tr><tr><td><a href="ArcGISRaster.ProjectToTemplateArcGISTable.html?format=raw">ArcGISRasterProjectToTemplateArcGISTable_GeoEco</a></td><td>Projects a table of rasters to the coordinate system, cell size, and extent of a template raster.</td></tr><tr><td><a href="ArcGISRaster.ProjectToTemplate.html?format=raw">ArcGISRasterProjectToTemplate_GeoEco</a></td><td>Projects a raster to the coordinate system, cell size, and extent of a template raster.</td></tr><tr><td><a href="ArcGISRaster.ToLinesArcGISTable.html?format=raw">ArcGISRasterToLinesArcGISTable_GeoEco</a></td><td>Converts the ArcGIS rasters listed in a table to lines that outline groups of adjacent raster cells having the same value.</td></tr><tr><td><a href="ArcGISRaster.ToLines.html?format=raw">ArcGISRasterToLines_GeoEco</a></td><td>Converts an ArcGIS raster to a feature class of lines that connect adjacent foreground raster cells.</td></tr><tr><td><a href="ArcGISRaster.ToPointsArcGISTable.html?format=raw">ArcGISRasterToPointsArcGISTable_GeoEco</a></td><td>Converts the ArcGIS rasters listed in a table to points that occur at the centers of the raster cells.</td></tr><tr><td><a href="ArcGISRaster.ToPoints.html?format=raw">ArcGISRasterToPoints_GeoEco</a></td><td>Converts an ArcGIS raster to a feature class of points that occur at the centers of the raster cells.</td></tr><tr><td><a href="ArcGISRaster.ToPolygonOutlinesArcGISTable.html?format=raw">ArcGISRasterToPolygonOutlinesArcGISTable_GeoEco</a></td><td>Converts the ArcGIS rasters listed in a table to lines that outline groups of adjacent raster cells having the same value.</td></tr><tr><td><a href="ArcGISRaster.ToPolygonOutlines.html?format=raw">ArcGISRasterToPolygonOutlines_GeoEco</a></td><td>Converts an ArcGIS raster to lines that outline groups of adjacent raster cells having the same value.</td></tr><tr><td><a href="ArcGISRaster.ToPolygonsArcGISTable.html?format=raw">ArcGISRasterToPolygonsArcGISTable_GeoEco</a></td><td>Converts the ArcGIS rasters listed in a table to polygons that encompass groups of adjacent raster cells having the same value.</td></tr><tr><td><a href="ArcGISRaster.ToPolygons.html?format=raw">ArcGISRasterToPolygons_GeoEco</a></td><td>Converts an ArcGIS raster to polygons that encompass groups of adjacent raster cells having the same value.</td></tr><tr><td><a href="ArcGISRasterMapAlgebra.FindRastersAndExecuteSingleOutputMapAlgebra.html?format=raw">ArcGISRasterMapAlgebraFindRastersAndExecuteSingleOutputMapAlgebra_GeoEco</a></td><td>Finds rasters in a workspace and executes a map algebra expression on them.</td></tr><tr><td><a href="ArcGISRasterMapAlgebra.SingleOutputMapAlgebraForArcGISTableRows.html?format=raw">ArcGISRasterMapAlgebraSingleOutputMapAlgebraForArcGISTableRows_GeoEco</a></td><td>For each row of a table, calculates an output raster and map algebra expression from the row using Python expressions and executes the map algebra expression to produce the output raster.</td></tr><tr><td><a href="ArcGISRasterMapAlgebra.SingleOutputMapAlgebraForRasterArcGISTable.html?format=raw">ArcGISRasterMapAlgebraSingleOutputMapAlgebraForRasterArcGISTable_GeoEco</a></td><td>Executes a map algebra expression on the rasters listed in a table.</td></tr><tr><td><a href="ArcGISRasterSampler.SampleRastersInFields.html?format=raw">ArcGISRasterSamplerSampleRastersInFields_GeoEco</a></td><td>Samples rasters listed in fields of a point feature class and stores the sampled values in other fields.</td></tr><tr><td><a href="ArcGISRasterSampler.SampleRasters.html?format=raw">ArcGISRasterSamplerSampleRasters_GeoEco</a></td><td>Samples rasters using a point feature class and stores the sampled values in fields of the feature class.</td></tr><tr><td><a href="ArcGISTableSampler.SampleTimeSeriesTable.html?format=raw">ArcGISTableSamplerSampleTimeSeriesTable_GeoEco</a></td><td>Matches records in a destination table to records in a time series table by date, and copies values of fields of the time series table to fields of the destination table.</td></tr><tr><td><a href="ArcInfoASCIIGrid.FindAndConvertToArcGISRaster.html?format=raw">ArcInfoASCIIGridFindAndConvertToArcGISRaster_GeoEco</a></td><td>Finds ArcInfo ASCII Grid text files in a directory and converts them to ArcGIS rasters.</td></tr><tr><td><a href="ArcInfoASCIIGrid.ToArcGISRasterArcGISTable.html?format=raw">ArcInfoASCIIGridToArcGISRasterArcGISTable_GeoEco</a></td><td>Converts each ArcInfo ASCII Grid text file in a table to an ArcGIS raster.</td></tr><tr><td><a href="ArcInfoASCIIGrid.ToArcGISRaster.html?format=raw">ArcInfoASCIIGridToArcGISRaster_GeoEco</a></td><td>Converts a text file in ArcInfo ASCII Grid format to an ArcGIS raster.</td></tr><tr><td><a href="AvisoGriddedGeostrophicCurrents.CreateArcGISRasters.html?format=raw">AvisoGriddedGeostrophicCurrentsCreateArcGISRasters_GeoEco</a></td><td>Creates rasters for an Aviso gridded geostrophic currents product.</td></tr><tr><td><a href="AvisoGriddedGeostrophicCurrents.CreateClimatologicalArcGISRasters.html?format=raw">AvisoGriddedGeostrophicCurrentsCreateClimatologicalArcGISRasters_GeoEco</a></td><td>Creates climatological rasters for an Aviso gridded geostrophic currents product.</td></tr><tr><td><a href="AvisoGriddedGeostrophicCurrents.CreateVectorsAsArcGISFeatureClasses.html?format=raw">AvisoGriddedGeostrophicCurrentsCreateVectorsAsArcGISFeatureClasses_GeoEco</a></td><td>Creates line feature classes representing the current vectors of an Aviso gridded geostrophic currents product.</td></tr><tr><td><a href="AvisoGriddedGeostrophicCurrents.InterpolateAtArcGISPoints.html?format=raw">AvisoGriddedGeostrophicCurrentsInterpolateAtArcGISPoints_GeoEco</a></td><td>Interpolates the values of an Aviso gridded geostrophic currents product at points.</td></tr><tr><td><a href="AvisoGriddedSSH.CreateArcGISRasters.html?format=raw">AvisoGriddedSSHCreateArcGISRasters_GeoEco</a></td><td>Creates rasters for an Aviso gridded sea surface height product.</td></tr><tr><td><a href="AvisoGriddedSSH.CreateClimatologicalArcGISRasters.html?format=raw">AvisoGriddedSSHCreateClimatologicalArcGISRasters_GeoEco</a></td><td>Creates climatological rasters for an Aviso gridded sea surface height product.</td></tr><tr><td><a href="AvisoGriddedSSH.FindOkuboWeissEddies.html?format=raw">AvisoGriddedSSHFindOkuboWeissEddies_GeoEco</a></td><td>Creates rasters showing the cores of geostrophic eddies detected in an Aviso gridded sea surface height product using the Okubo-Weiss algorithm.</td></tr><tr><td><a href="AvisoGriddedSSH.InterpolateAtArcGISPoints.html?format=raw">AvisoGriddedSSHInterpolateAtArcGISPoints_GeoEco</a></td><td>Interpolates the values of an Aviso gridded sea surface height product at points.</td></tr><tr><td><a href="AvisoGriddedSignificantWaveHeight.CreateArcGISRasters.html?format=raw">AvisoGriddedSignificantWaveHeightCreateArcGISRasters_GeoEco</a></td><td>Creates rasters for an Aviso gridded significant wave height product.</td></tr><tr><td><a href="AvisoGriddedSignificantWaveHeight.CreateClimatologicalArcGISRasters.html?format=raw">AvisoGriddedSignificantWaveHeightCreateClimatologicalArcGISRasters_GeoEco</a></td><td>Creates climatological rasters for an Aviso gridded significant wave height product.</td></tr><tr><td><a href="AvisoGriddedSignificantWaveHeight.InterpolateAtArcGISPoints.html?format=raw">AvisoGriddedSignificantWaveHeightInterpolateAtArcGISPoints_GeoEco</a></td><td>Interpolates the values of an Aviso gridded significant wave height product at points.</td></tr><tr><td><a href="AvisoGriddedWindSpeedModulus.CreateArcGISRasters.html?format=raw">AvisoGriddedWindSpeedModulusCreateArcGISRasters_GeoEco</a></td><td>Creates rasters for an Aviso gridded wind speed modulus product.</td></tr><tr><td><a href="AvisoGriddedWindSpeedModulus.CreateClimatologicalArcGISRasters.html?format=raw">AvisoGriddedWindSpeedModulusCreateClimatologicalArcGISRasters_GeoEco</a></td><td>Creates climatological rasters for an Aviso gridded wind speed modulus product.</td></tr><tr><td><a href="AvisoGriddedWindSpeedModulus.InterpolateAtArcGISPoints.html?format=raw">AvisoGriddedWindSpeedModulusInterpolateAtArcGISPoints_GeoEco</a></td><td>Interpolates the values of an Aviso gridded wind speed modulus product at points.</td></tr><tr><td><a href="BinaryRaster.FindAndConvertToArcGISRaster.html?format=raw">BinaryRasterFindAndConvertToArcGISRaster_GeoEco</a></td><td>Finds two-dimensional binary rasters in a directory and converts them to ArcGIS rasters.</td></tr><tr><td><a href="BinaryRaster.FindAndConvertToArcInfoASCIIGrid.html?format=raw">BinaryRasterFindAndConvertToArcInfoASCIIGrid_GeoEco</a></td><td>Finds two-dimensional binary rasters in a directory and converts them to text files in ArcInfo ASCII Grid format.</td></tr><tr><td><a href="BinaryRaster.FindAndSwapBytes.html?format=raw">BinaryRasterFindAndSwapBytes_GeoEco</a></td><td>Finds and reverses the byte order of binary rasters in a directory (i.e. converts "little endian" to "big endian", or visa versa).</td></tr><tr><td><a href="BinaryRaster.SwapBytesArcGISTable.html?format=raw">BinaryRasterSwapBytesArcGISTable_GeoEco</a></td><td>Reverses the byte order of each binary raster listed in a table (i.e. converts "little endian" to "big endian", or visa versa).</td></tr><tr><td><a href="BinaryRaster.SwapBytes.html?format=raw">BinaryRasterSwapBytes_GeoEco</a></td><td>Reverses the byte order of a binary raster (e.g. converts "little endian" to "big endian", or visa versa).</td></tr><tr><td><a href="BinaryRaster.ToArcGISRasterArcGISTable.html?format=raw">BinaryRasterToArcGISRasterArcGISTable_GeoEco</a></td><td>Converts each two-dimensional binary raster in a table to an ArcGIS raster.</td></tr><tr><td><a href="BinaryRaster.ToArcGISRaster.html?format=raw">BinaryRasterToArcGISRaster_GeoEco</a></td><td>Converts a two-dimensional binary raster to an ArcGIS raster.</td></tr><tr><td><a href="BinaryRaster.ToArcInfoASCIIGridArcGISTable.html?format=raw">BinaryRasterToArcInfoASCIIGridArcGISTable_GeoEco</a></td><td>Converts each two-dimensional binary raster in a table to a text file in ArcInfo ASCII Grid format.</td></tr><tr><td><a href="BinaryRaster.ToArcInfoASCIIGrid.html?format=raw">BinaryRasterToArcInfoASCIIGrid_GeoEco</a></td><td>Converts a two-dimensional binary raster to a text file in ArcGIS ASCII Grid format.</td></tr><tr><td><a href="CayulaCornillonEdgeDetection.DetectEdgesInArcGISRaster.html?format=raw">CayulaCornillonEdgeDetectionDetectEdgesInArcGISRaster_GeoEco</a></td><td>Finds fronts in an ArcGIS raster using the Cayula and Cornillon (1992) single image edge detection (SIED) algorithm.</td></tr><tr><td><a href="CayulaCornillonEdgeDetection.DetectEdgesInArcGISRastersArcGISTable.html?format=raw">CayulaCornillonEdgeDetectionDetectEdgesInArcGISRastersArcGISTable_GeoEco</a></td><td>Finds fronts in ArcGIS rasters listed in a table using the Cayula and Cornillon (1992) single-image edge detection algorithm.</td></tr><tr><td><a href="CayulaCornillonEdgeDetection.DetectEdgesInBinaryRaster.html?format=raw">CayulaCornillonEdgeDetectionDetectEdgesInBinaryRaster_GeoEco</a></td><td>Finds fronts in a binary raster using the Cayula and Cornillon (1992) single image edge detection (SIED) algorithm.</td></tr><tr><td><a href="CayulaCornillonEdgeDetection.FindArcGISRastersAndDetectEdges.html?format=raw">CayulaCornillonEdgeDetectionFindArcGISRastersAndDetectEdges_GeoEco</a></td><td>Finds ArcGIS rasters in a workspace and finds fronts within them using the Cayula and Cornillon (1992) single-image edge detection algorithm.</td></tr><tr><td><a href="CheltonMesocaleEddyPoints.ConvertToSpatiaLite.html?format=raw">CheltonMesocaleEddyPointsConvertToSpatiaLite_GeoEco</a></td><td>Converts the Chelton et al. (2011) mesoscale eddy database netCDF file to a SpatiaLite database.</td></tr><tr><td><a href="CheltonMesocaleEddyPoints.ExtractArcGISLinesFromSpatiaLite.html?format=raw">CheltonMesocaleEddyPointsExtractArcGISLinesFromSpatiaLite_GeoEco</a></td><td>Extracts eddy tracklines from the Chelton et al. (2011) mesoscale eddy database in SpatiaLite format.</td></tr><tr><td><a href="CheltonMesocaleEddyPoints.ExtractArcGISPointsFromSpatiaLite.html?format=raw">CheltonMesocaleEddyPointsExtractArcGISPointsFromSpatiaLite_GeoEco</a></td><td>Extracts eddy centroid points from the Chelton et al. (2011) mesoscale eddy database in SpatiaLite format.</td></tr><tr><td><a href="ChildProcess.ExecuteProgram.html?format=raw">ChildProcessExecuteProgram_GeoEco</a></td><td>Executes the specified program and captures its output.</td></tr><tr><td><a href="CoRTADv32D.CreateArcGISRaster.html?format=raw">CoRTADv32DCreateArcGISRaster_GeoEco</a></td><td>Creates a raster for a CoRTAD 2D variable.</td></tr><tr><td><a href="CoRTADv32D.InterpolateAtArcGISPoints.html?format=raw">CoRTADv32DInterpolateAtArcGISPoints_GeoEco</a></td><td>Interpolates CoRTAD 2D variables at points.</td></tr><tr><td><a href="CoRTADv33D.CreateArcGISRasters.html?format=raw">CoRTADv33DCreateArcGISRasters_GeoEco</a></td><td>Creates rasters a CoRTAD 3D variable.</td></tr><tr><td><a href="CoRTADv33D.InterpolateAtArcGISPoints.html?format=raw">CoRTADv33DInterpolateAtArcGISPoints_GeoEco</a></td><td>Interpolates CoRTAD 3D variables at points.</td></tr><tr><td><a href="CoastWatchAVHRR.CopyNavigationOffsetsArcGISTable.html?format=raw">CoastWatchAVHRRCopyNavigationOffsetsArcGISTable_GeoEco</a></td><td>Copies navigation offsets from a source variable to destination variables in CoastWatch POES AVHRR CWF or HDF files listed in a table.</td></tr><tr><td><a href="CoastWatchAVHRR.CopyNavigationOffsets.html?format=raw">CoastWatchAVHRRCopyNavigationOffsets_GeoEco</a></td><td>Copies the navigation offsets from one variable in a CoastWatch POES AVHRR CWF or HDF file to one or more variables in another file.</td></tr><tr><td><a href="CoastWatchAVHRR.CreateMaskAsArcGISRaster.html?format=raw">CoastWatchAVHRRCreateMaskAsArcGISRaster_GeoEco</a></td><td>Creates a mask, in ArcGIS raster format, for a CoastWatch POES AVHRR image.</td></tr><tr><td><a href="CoastWatchAVHRR.CreateMaskAsBinaryRaster.html?format=raw">CoastWatchAVHRRCreateMaskAsBinaryRaster_GeoEco</a></td><td>Creates a mask, in binary raster format, for a CoastWatch POES AVHRR image.</td></tr><tr><td><a href="CoastWatchAVHRR.CreateMasksAsArcGISRastersArcGISTable.html?format=raw">CoastWatchAVHRRCreateMasksAsArcGISRastersArcGISTable_GeoEco</a></td><td>Creates masks, in ArcGIS raster format, for CoastWatch POES AVHRR images listed in a table.</td></tr><tr><td><a href="CoastWatchAVHRR.CreateMasksAsBinaryRastersArcGISTable.html?format=raw">CoastWatchAVHRRCreateMasksAsBinaryRastersArcGISTable_GeoEco</a></td><td>Creates masks, in binary raster format, for CoastWatch POES AVHRR images listed in a table.</td></tr><tr><td><a href="CoastWatchAVHRR.FindAndConvertToArcGISRasters.html?format=raw">CoastWatchAVHRRFindAndConvertToArcGISRasters_GeoEco</a></td><td>Finds images in CoastWatch POES AVHRR files in a directory and converts them to ArcGIS rasters.</td></tr><tr><td><a href="CoastWatchAVHRR.FindAndConvertToBinaryRasters.html?format=raw">CoastWatchAVHRRFindAndConvertToBinaryRasters_GeoEco</a></td><td>Finds images in CoastWatch POES AVHRR files in a directory and converts them to binary rasters.</td></tr><tr><td><a href="CoastWatchAVHRR.FindAndConvertToCoastWatchHDFs.html?format=raw">CoastWatchAVHRRFindAndConvertToCoastWatchHDFs_GeoEco</a></td><td>Finds images in CoastWatch POES AVHRR files in a directory and imports them into CoastWatch HDFs.</td></tr><tr><td><a href="CoastWatchAVHRR.FindAndCreateMasksAsArcGISRasters.html?format=raw">CoastWatchAVHRRFindAndCreateMasksAsArcGISRasters_GeoEco</a></td><td>Finds images in CoastWatch POES AVHRR files in a directory creates masks for them, in ArcGIS raster format.</td></tr><tr><td><a href="CoastWatchAVHRR.FindAndCreateMasksAsBinaryRasters.html?format=raw">CoastWatchAVHRRFindAndCreateMasksAsBinaryRasters_GeoEco</a></td><td>Finds images in CoastWatch POES AVHRR files in a directory creates masks for them, in binary raster format.</td></tr><tr><td><a href="CoastWatchAVHRR.FindCoastWatchFilesAndFindFrontsAsArcGISRasters.html?format=raw">CoastWatchAVHRRFindCoastWatchFilesAndFindFrontsAsArcGISRasters_GeoEco</a></td><td>Uses the Cayula and Cornillon (1992) single-image edge detection algorithm to find fronts in the CoastWatch POES AVHRR images found in a directory, and outputs the fronts as ArcGIS rasters.</td></tr><tr><td><a href="CoastWatchAVHRR.FindFilesAndCreateArcGISTable.html?format=raw">CoastWatchAVHRRFindFilesAndCreateArcGISTable_GeoEco</a></td><td>Finds CoastWatch POES AVHRR files within a directory and creates a table that lists them.</td></tr><tr><td><a href="CoastWatchAVHRR.FindFrontsAsArcGISRaster.html?format=raw">CoastWatchAVHRRFindFrontsAsArcGISRaster_GeoEco</a></td><td>Finds fronts in a CoastWatch POES AVHRR image using the Cayula and Cornillon (1992) single-image edge detection algorithm and outputs them to an ArcGIS raster.</td></tr><tr><td><a href="CoastWatchAVHRR.FindFrontsAsArcGISRastersArcGISTable.html?format=raw">CoastWatchAVHRRFindFrontsAsArcGISRastersArcGISTable_GeoEco</a></td><td>Finds fronts in CoastWatch POES AVHRR images listed in a table using the Cayula and Cornillon (1992) single-image edge detection algorithm and outputs them as ArcGIS rasters.</td></tr><tr><td><a href="CoastWatchAVHRR.FindFrontsAsBinaryRaster.html?format=raw">CoastWatchAVHRRFindFrontsAsBinaryRaster_GeoEco</a></td><td>Finds fronts in a CoastWatch POES AVHRR image using the Cayula and Cornillon (1992) single-image edge detection algorithm and outputs them to a binary raster.</td></tr><tr><td><a href="CoastWatchAVHRR.FindImagesInFilesAndCreateArcGISTable.html?format=raw">CoastWatchAVHRRFindImagesInFilesAndCreateArcGISTable_GeoEco</a></td><td>Finds CoastWatch POES AVHRR files within a directory and creates a table that lists the images within them.</td></tr><tr><td><a href="CoastWatchAVHRR.SetNavigationOffsetsArcGISTable.html?format=raw">CoastWatchAVHRRSetNavigationOffsetsArcGISTable_GeoEco</a></td><td>Sets the navigation offsets of the CoastWatch POES AVHRR CWF or HDF files listed in a table to the values specified by two fields.</td></tr><tr><td><a href="CoastWatchAVHRR.SetNavigationOffsets.html?format=raw">CoastWatchAVHRRSetNavigationOffsets_GeoEco</a></td><td>Sets the navigation offsets of a CoastWatch POES AVHRR CWF or HDF file.</td></tr><tr><td><a href="CoastWatchAVHRR.ToArcGISRasterArcGISTable.html?format=raw">CoastWatchAVHRRToArcGISRasterArcGISTable_GeoEco</a></td><td>Creates an ArcGIS raster from each CoastWatch POES AVHRR image listed in a table of images, where one field specifies the file path containing the image and another specifies the variable that represents the image.</td></tr><tr><td><a href="CoastWatchAVHRR.ToArcGISRaster.html?format=raw">CoastWatchAVHRRToArcGISRaster_GeoEco</a></td><td>Extracts and converts CoastWatch POES AVHRR image, specified by a file plus a variable in that file, to an ArcGIS raster.</td></tr><tr><td><a href="CoastWatchAVHRR.ToBinaryRasterArcGISTable.html?format=raw">CoastWatchAVHRRToBinaryRasterArcGISTable_GeoEco</a></td><td>Creates a binary raster from each CoastWatch POES AVHRR image listed in a table of images, where one field specifies the file path containing the image and another specifies the variable that represents the image.</td></tr><tr><td><a href="CoastWatchAVHRR.ToBinaryRaster.html?format=raw">CoastWatchAVHRRToBinaryRaster_GeoEco</a></td><td>Extracts and converts CoastWatch POES AVHRR image, specified by a file plus a variable in that file, to a binary raster.</td></tr><tr><td><a href="CoastWatchAVHRR.ToCoastWatchHDFArcGISTable.html?format=raw">CoastWatchAVHRRToCoastWatchHDFArcGISTable_GeoEco</a></td><td>Creates CoastWatch HDFs in a specified output directory by importing images from the CoastWatch POES AVHRR CWFs or HDFs listed in a table.</td></tr><tr><td><a href="CoastWatchAVHRR.ToCoastWatchHDF.html?format=raw">CoastWatchAVHRRToCoastWatchHDF_GeoEco</a></td><td>Creates a CoastWatch HDF by importing images from a list of CoastWatch POES AVHRR CWFs or HDFs.</td></tr><tr><td><a href="CoralReefConnectivity.CreateSimulationFromArcGISRasters.html?format=raw">CoralReefConnectivityCreateSimulationFromArcGISRasters_GeoEco</a></td><td>Creates a coral reef connectivity simulation and initializes it with reef data in ArcGIS rasters.</td></tr><tr><td><a href="CoralReefConnectivity.LoadAvisoGeostrophicCurrentsIntoSimulation.html?format=raw">CoralReefConnectivityLoadAvisoGeostrophicCurrentsIntoSimulation_GeoEco</a></td><td>Downloads Aviso geostrophic currents into a coral reef connectivity simulation.</td></tr><tr><td><a href="CoralReefConnectivity.LoadHYCOMGLBa0084DEquatorialCurrentsIntoSimulation.html?format=raw">CoralReefConnectivityLoadHYCOMGLBa0084DEquatorialCurrentsIntoSimulation_GeoEco</a></td><td>Downloads HYCOM GLBa0.08 ocean currents into a coral reef connectivity simulation.</td></tr><tr><td><a href="CoralReefConnectivity.LoadHYCOMGOMl0044DCurrentsIntoSimulation.html?format=raw">CoralReefConnectivityLoadHYCOMGOMl0044DCurrentsIntoSimulation_GeoEco</a></td><td>Downloads HYCOM GOMl0.04 ocean currents into a coral reef connectivity simulation.</td></tr><tr><td><a href="CoralReefConnectivity.LoadOSCARCurrentsIntoSimulation.html?format=raw">CoralReefConnectivityLoadOSCARCurrentsIntoSimulation_GeoEco</a></td><td>Downloads NOAA OSCAR 5-day unfiltered 1/3 degree ocean surface currents into a coral reef connectivity simulation.</td></tr><tr><td><a href="CoralReefConnectivity.LoadROMSCoSiNE4DCurrentsIntoSimulation.html?format=raw">CoralReefConnectivityLoadROMSCoSiNE4DCurrentsIntoSimulation_GeoEco</a></td><td>Downloads Pacific ROMS-CoSiNE ocean currents into a coral reef connectivity simulation.</td></tr><tr><td><a href="CoralReefConnectivity.RunSimulation.html?format=raw">CoralReefConnectivityRunSimulation_GeoEco</a></td><td>Executes a coral coral reef connectivity simulation.</td></tr><tr><td><a href="DiGIR.GetOBISResourcesAsArcGISTable.html?format=raw">DiGIRGetOBISResourcesAsArcGISTable_GeoEco</a></td><td>Gets the list of Distributed Generic Information Retrieval (DiGIR) resources available from OBIS and writes it to an ArcGIS table.</td></tr><tr><td><a href="DiGIR.GetOBISSEAMAPResourcesAsArcGISTable.html?format=raw">DiGIRGetOBISSEAMAPResourcesAsArcGISTable_GeoEco</a></td><td>Gets the list of Distributed Generic Information Retrieval (DiGIR) resources available from OBIS-SEAMAP and writes it to an ArcGIS table.</td></tr><tr><td><a href="DiGIR.GetResourcesAsArcGISTable.html?format=raw">DiGIRGetResourcesAsArcGISTable_GeoEco</a></td><td>Gets the list of Distributed Generic Information Retrieval (DiGIR) resources available from a DiGIR server and writes it to an ArcGIS table.</td></tr><tr><td><a href="DiGIR.SearchAndCreateArcGISPoints.html?format=raw">DiGIRSearchAndCreateArcGISPoints_GeoEco</a></td><td>Searches a Distributed Generic Information Retrieval (DiGIR) server for georeferenced records and creates an ArcGIS point feature class from them.</td></tr><tr><td><a href="DiGIR.SearchOBISAndCreateArcGISPoints.html?format=raw">DiGIRSearchOBISAndCreateArcGISPoints_GeoEco</a></td><td>Searches OBIS for georeferenced records using the Distributed Generic Information Retrieval (DiGIR) protocol and creates an ArcGIS point feature class from them.</td></tr><tr><td><a href="DiGIR.SearchOBISSEAMAPAndCreateArcGISPoints.html?format=raw">DiGIRSearchOBISSEAMAPAndCreateArcGISPoints_GeoEco</a></td><td>Searches OBIS-SEAMAP for georeferenced records using the Distributed Generic Information Retrieval (DiGIR) protocol and creates an ArcGIS point feature class from them.</td></tr><tr><td><a href="Directory.CopyArcGISTable.html?format=raw">DirectoryCopyArcGISTable_GeoEco</a></td><td>Copies the directories listed in a table.</td></tr><tr><td><a href="Directory.Copy.html?format=raw">DirectoryCopy_GeoEco</a></td><td>Copies a directory, including its subdirectories and files.</td></tr><tr><td><a href="Directory.CreateSubdirectory.html?format=raw">DirectoryCreateSubdirectory_GeoEco</a></td><td>Creates a subdirectory within a parent directory.</td></tr><tr><td><a href="Directory.CreateTemporaryDirectory.html?format=raw">DirectoryCreateTemporaryDirectory_GeoEco</a></td><td>Creates a directory suitable for holding temporary files.</td></tr><tr><td><a href="Directory.Create.html?format=raw">DirectoryCreate_GeoEco</a></td><td>Creates a directory, including any parent directories that are missing.</td></tr><tr><td><a href="Directory.DeleteArcGISTable.html?format=raw">DirectoryDeleteArcGISTable_GeoEco</a></td><td>Deletes the directories listed in a table.</td></tr><tr><td><a href="Directory.Delete.html?format=raw">DirectoryDelete_GeoEco</a></td><td>Deletes a directory.</td></tr><tr><td><a href="Directory.FindAndCopy.html?format=raw">DirectoryFindAndCopy_GeoEco</a></td><td>Finds and copies directories in a directory.</td></tr><tr><td><a href="Directory.FindAndCreateArcGISTable.html?format=raw">DirectoryFindAndCreateArcGISTable_GeoEco</a></td><td>Finds subdirectories within a directory and creates a table that lists them.</td></tr><tr><td><a href="Directory.FindAndDelete.html?format=raw">DirectoryFindAndDelete_GeoEco</a></td><td>Finds and deletes directories in a directory.</td></tr><tr><td><a href="Directory.FindAndMove.html?format=raw">DirectoryFindAndMove_GeoEco</a></td><td>Finds and moves directories in a directory.</td></tr><tr><td><a href="Directory.MoveArcGISTable.html?format=raw">DirectoryMoveArcGISTable_GeoEco</a></td><td>Moves the directories listed in a table.</td></tr><tr><td><a href="Directory.Move.html?format=raw">DirectoryMove_GeoEco</a></td><td>Moves a directory, including its subdirectories and files.</td></tr><tr><td><a href="ESRLClimateIndices.ClassifyONIEpisodesInTimeSeriesArcGISTable.html?format=raw">ESRLClimateIndicesClassifyONIEpisodesInTimeSeriesArcGISTable_GeoEco</a></td><td>Given a time series table of monthly Oceanic Nino Index (ONI) numerical values, classifies each month as part of a normal, El Nino (warm), or La Nina (cold) episode.</td></tr><tr><td><a href="ESRLClimateIndices.FileToArcGISTable.html?format=raw">ESRLClimateIndicesFileToArcGISTable_GeoEco</a></td><td>Creates and populates a table of climate index values parsed from a text file in NOAA ESRL time series format.</td></tr><tr><td><a href="ESRLClimateIndices.FilesToArcGISTable.html?format=raw">ESRLClimateIndicesFilesToArcGISTable_GeoEco</a></td><td>Creates and populates a table of climate index values parsed from a list of text files, where each file contains the data for a single climate index in NOAA ESRL time series format.</td></tr><tr><td><a href="ESRLClimateIndices.UrlToArcGISTable.html?format=raw">ESRLClimateIndicesUrlToArcGISTable_GeoEco</a></td><td>Creates and populates a table of climate index values parsed from NOAA ESRL climate index time series data downloaded from a URL.</td></tr><tr><td><a href="ESRLClimateIndices.UrlsToArcGISTable.html?format=raw">ESRLClimateIndicesUrlsToArcGISTable_GeoEco</a></td><td>Creates and populates a table of climate index values parsed from NOAA ESRL climate index time series data downloaded from a list of URLs.</td></tr><tr><td><a href="FEET.CreateEnvelopes.html?format=raw">FEETCreateEnvelopes_GeoEco</a></td><td>Models the spatial distribution of fishing effort for fisheries for which no spatially-explicit effort data is available.</td></tr><tr><td><a href="Field.CalculateArcGISField.html?format=raw">FieldCalculateArcGISField_GeoEco</a></td><td>Calculates the value of a table field using a Python expression.</td></tr><tr><td><a href="Field.CalculateArcGISFields.html?format=raw">FieldCalculateArcGISFields_GeoEco</a></td><td>Calculates values for one or more fields of a table using Python expressions.</td></tr><tr><td><a href="File.CopyArcGISTable.html?format=raw">FileCopyArcGISTable_GeoEco</a></td><td>Copies the files listed in a table.</td></tr><tr><td><a href="File.Copy.html?format=raw">FileCopy_GeoEco</a></td><td>Copies a file.</td></tr><tr><td><a href="File.Decompress.html?format=raw">FileDecompress_GeoEco</a></td><td>Decompresses a file into a directory.</td></tr><tr><td><a href="File.DeleteArcGISTable.html?format=raw">FileDeleteArcGISTable_GeoEco</a></td><td>Deletes the files listed in a table.</td></tr><tr><td><a href="File.Delete.html?format=raw">FileDelete_GeoEco</a></td><td>Deletes a file.</td></tr><tr><td><a href="File.FindAndCopy.html?format=raw">FileFindAndCopy_GeoEco</a></td><td>Finds and copies files in a directory.</td></tr><tr><td><a href="File.FindAndCreateArcGISTable.html?format=raw">FileFindAndCreateArcGISTable_GeoEco</a></td><td>Finds files within a directory and creates a table that lists them.</td></tr><tr><td><a href="File.FindAndDelete.html?format=raw">FileFindAndDelete_GeoEco</a></td><td>Finds and deletes files in a directory.</td></tr><tr><td><a href="File.FindAndMove.html?format=raw">FileFindAndMove_GeoEco</a></td><td>Finds and moves files in a directory.</td></tr><tr><td><a href="File.IsDecompressible.html?format=raw">FileIsDecompressible_GeoEco</a></td><td>Returns True if the specified file is in a format that can be decompressed.</td></tr><tr><td><a href="File.MoveArcGISTable.html?format=raw">FileMoveArcGISTable_GeoEco</a></td><td>Moves the files listed in a table.</td></tr><tr><td><a href="File.Move.html?format=raw">FileMove_GeoEco</a></td><td>Moves a file.</td></tr><tr><td><a href="GAM.BayesPredictFromArcGISRasters.html?format=raw">GAMBayesPredictFromArcGISRasters_GeoEco</a></td><td>Using a binomial generalized additive model (GAM) fitted using the R mgcv package, this tool creates rasters representing the estimated probabilities that the response variable will equal or exceed specified thresholds, given ArcGIS rasters representing the predictor variables.</td></tr><tr><td><a href="GAM.FitToArcGISTable.html?format=raw">GAMFitToArcGISTable_GeoEco</a></td><td>Fits a generalized additive model (GAM) to data in an ArcGIS table.</td></tr><tr><td><a href="GAM.PredictFromArcGISRasters.html?format=raw">GAMPredictFromArcGISRasters_GeoEco</a></td><td>Using a fitted generalized additive model (GAM), this tool creates a raster representing the response variable predicted from ArcGIS rasters representing the predictor variables.</td></tr><tr><td><a href="GHRSSTLevel4.CreateArcGISRasters.html?format=raw">GHRSSTLevel4CreateArcGISRasters_GeoEco</a></td><td>Creates rasters for a GHRSST L4 product hosted by NASA JPL PO.DAAC.</td></tr><tr><td><a href="GHRSSTLevel4.CreateCayulaCornillonFrontsAsArcGISRasters.html?format=raw">GHRSSTLevel4CreateCayulaCornillonFrontsAsArcGISRasters_GeoEco</a></td><td>Creates rasters indicating the positions of fronts in GHRSST L4 SST images hosted by NASA JPL PO.DAAC, using the Cayula and Cornillon (1992) single image edge detection (SIED) algorithm.</td></tr><tr><td><a href="GHRSSTLevel4.CreateClimatologicalArcGISRasters.html?format=raw">GHRSSTLevel4CreateClimatologicalArcGISRasters_GeoEco</a></td><td>Creates climatological rasters for a GHRSST L4 product hosted by NASA JPL PO.DAAC.</td></tr><tr><td><a href="GHRSSTLevel4.InterpolateAtArcGISPoints.html?format=raw">GHRSSTLevel4InterpolateAtArcGISPoints_GeoEco</a></td><td>Interpolates values of a GHRSST L4 product hosted by NASA JPL PO.DAAC at points.</td></tr><tr><td><a href="GLM.FitToArcGISTable.html?format=raw">GLMFitToArcGISTable_GeoEco</a></td><td>Fits a generalized linear model (GLM) to data in an ArcGIS table using the R glm function.</td></tr><tr><td><a href="GLM.PredictFromArcGISRasters.html?format=raw">GLMPredictFromArcGISRasters_GeoEco</a></td><td>Using a fitted generalized linear model (GLM), this tool creates a raster representing the response variable predicted from ArcGIS rasters representing the predictor variables.</td></tr><tr><td><a href="HDF.ExtractHeaderArcGISTable.html?format=raw">HDFExtractHeaderArcGISTable_GeoEco</a></td><td>Extracts the headers of HDF files in a table and saves them to text files.</td></tr><tr><td><a href="HDF.ExtractHeader.html?format=raw">HDFExtractHeader_GeoEco</a></td><td>Extracts the header of an HDF file and saves it to a text file.</td></tr><tr><td><a href="HDF.FindAndConvertToArcGISRasters.html?format=raw">HDFFindAndConvertToArcGISRasters_GeoEco</a></td><td>Finds HDF files in a directory and converts a Scientific Data Sets (SDS) in each file to an ArcGIS raster.</td></tr><tr><td><a href="HDF.FindAndConvertToArcInfoASCIIGrids.html?format=raw">HDFFindAndConvertToArcInfoASCIIGrids_GeoEco</a></td><td>Finds HDF files in a directory and converts a Scientific Data Sets (SDS) in each file to a text file in ArcInfo ASCII Grid format.</td></tr><tr><td><a href="HDF.FindAndConvertToBinaryRasters.html?format=raw">HDFFindAndConvertToBinaryRasters_GeoEco</a></td><td>Finds HDF files in a directory and converts a Scientific Data Sets (SDS) in each file to a binary raster.</td></tr><tr><td><a href="HDF.FindAndExtractHeaders.html?format=raw">HDFFindAndExtractHeaders_GeoEco</a></td><td>Finds HDF files in a directory, extracts their headers, and saves the headers to text files.</td></tr><tr><td><a href="HDF.SDSToArcGISRaster.html?format=raw">HDFSDSToArcGISRaster_GeoEco</a></td><td>Converts a Scientific Data Set (SDS) in an HDF file to an ArcGIS raster.</td></tr><tr><td><a href="HDF.SDSToArcInfoASCIIGrid.html?format=raw">HDFSDSToArcInfoASCIIGrid_GeoEco</a></td><td>Converts a Scientific Data Set (SDS) in an HDF file to a text file in ArcInfo ASCII Grid format.</td></tr><tr><td><a href="HDF.SDSToBinaryRaster.html?format=raw">HDFSDSToBinaryRaster_GeoEco</a></td><td>Converts a Scientific Data Set (SDS) in an HDF file to a binary raster.</td></tr><tr><td><a href="HDF.ToArcGISRasterArcGISTable.html?format=raw">HDFToArcGISRasterArcGISTable_GeoEco</a></td><td>Converts a Scientific Data Set (SDS) in each HDF file in a table to an ArcGIS raster.</td></tr><tr><td><a href="HDF.ToArcInfoASCIIGridArcGISTable.html?format=raw">HDFToArcInfoASCIIGridArcGISTable_GeoEco</a></td><td>Converts a Scientific Data Set (SDS) in each HDF file in a table to a text file in an ArcInfo ASCII Grid format.</td></tr><tr><td><a href="HDF.ToBinaryRasterArcGISTable.html?format=raw">HDFToBinaryRasterArcGISTable_GeoEco</a></td><td>Converts a Scientific Data Set (SDS) in each HDF file in a table to a binary raster.</td></tr><tr><td><a href="HYCOMGLBa008Equatorial3D.CreateArcGISRasters.html?format=raw">HYCOMGLBa008Equatorial3DCreateArcGISRasters_GeoEco</a></td><td>Creates rasters for a 3D variable of the equatorial (Mercator) region of the HYCOM GLBa0.08 dataset.</td></tr><tr><td><a href="HYCOMGLBa008Equatorial3D.CreateClimatologicalArcGISRasters.html?format=raw">HYCOMGLBa008Equatorial3DCreateClimatologicalArcGISRasters_GeoEco</a></td><td>Creates climatological rasters for a 3D variable of the equatorial (Mercator) region of the HYCOM GLBa0.08 dataset</td></tr><tr><td><a href="HYCOMGLBa008Equatorial3D.InterpolateAtArcGISPoints.html?format=raw">HYCOMGLBa008Equatorial3DInterpolateAtArcGISPoints_GeoEco</a></td><td>Interpolates 3D variables of the equatorial (Mercator) region of the HYCOM GLBa0.08 dataset at points.</td></tr><tr><td><a href="HYCOMGLBa008Equatorial4D.CreateArcGISRasters.html?format=raw">HYCOMGLBa008Equatorial4DCreateArcGISRasters_GeoEco</a></td><td>Creates rasters for a 4D variable of the equatorial (Mercator) region of the HYCOM GLBa0.08 dataset.</td></tr><tr><td><a href="HYCOMGLBa008Equatorial4D.CreateCayulaCornillonFrontsAsArcGISRasters.html?format=raw">HYCOMGLBa008Equatorial4DCreateCayulaCornillonFrontsAsArcGISRasters_GeoEco</a></td><td>Creates rasters indicating the positions of fronts in the 2D slices of a 4D variable of the equatorial (Mercator) region of the HYCOM GLBa0.08 dataset using the Cayula and Cornillon (1992) single image edge detection (SIED) algorithm.</td></tr><tr><td><a href="HYCOMGLBa008Equatorial4D.CreateClimatologicalArcGISRasters.html?format=raw">HYCOMGLBa008Equatorial4DCreateClimatologicalArcGISRasters_GeoEco</a></td><td>Creates climatological rasters for a 4D variable of the equatorial (Mercator) region of the HYCOM GLBa0.08 dataset</td></tr><tr><td><a href="HYCOMGLBa008Equatorial4D.CreateCurrentVectorsAsArcGISFeatureClasses.html?format=raw">HYCOMGLBa008Equatorial4DCreateCurrentVectorsAsArcGISFeatureClasses_GeoEco</a></td><td>Creates line feature classes representing the current vectors for the equatorial (Mercator) region of the HYCOM GLBa0.08 dataset.</td></tr><tr><td><a href="HYCOMGLBa008Equatorial4D.InterpolateAtArcGISPoints.html?format=raw">HYCOMGLBa008Equatorial4DInterpolateAtArcGISPoints_GeoEco</a></td><td>Interpolates 4D variables of the equatorial (Mercator) region of the HYCOM GLBa0.08 dataset at points.</td></tr><tr><td><a href="HYCOMGOMl0043D.CreateArcGISRasters.html?format=raw">HYCOMGOMl0043DCreateArcGISRasters_GeoEco</a></td><td>Creates rasters for a HYCOM GOMl0.04 3D variable.</td></tr><tr><td><a href="HYCOMGOMl0043D.CreateClimatologicalArcGISRasters.html?format=raw">HYCOMGOMl0043DCreateClimatologicalArcGISRasters_GeoEco</a></td><td>Creates climatological rasters for a HYCOM GOMl0.04 3D variable</td></tr><tr><td><a href="HYCOMGOMl0043D.InterpolateAtArcGISPoints.html?format=raw">HYCOMGOMl0043DInterpolateAtArcGISPoints_GeoEco</a></td><td>Interpolates HYCOM GOMl0.04 3D variables at points.</td></tr><tr><td><a href="HYCOMGOMl0044D.CreateArcGISRasters.html?format=raw">HYCOMGOMl0044DCreateArcGISRasters_GeoEco</a></td><td>Creates rasters for a HYCOM GOMl0.04 4D variable.</td></tr><tr><td><a href="HYCOMGOMl0044D.CreateCayulaCornillonFrontsAsArcGISRasters.html?format=raw">HYCOMGOMl0044DCreateCayulaCornillonFrontsAsArcGISRasters_GeoEco</a></td><td>Creates rasters indicating the positions of fronts in the 2D slices of a HYCOM GOMl0.04 4D variable using the Cayula and Cornillon (1992) single image edge detection (SIED) algorithm.</td></tr><tr><td><a href="HYCOMGOMl0044D.CreateClimatologicalArcGISRasters.html?format=raw">HYCOMGOMl0044DCreateClimatologicalArcGISRasters_GeoEco</a></td><td>Creates climatological rasters for a HYCOM GOMl0.04 4D variable</td></tr><tr><td><a href="HYCOMGOMl0044D.CreateCurrentVectorsAsArcGISFeatureClasses.html?format=raw">HYCOMGOMl0044DCreateCurrentVectorsAsArcGISFeatureClasses_GeoEco</a></td><td>Creates line feature classes representing the vectors of HYCOM GOMl0.04 currents.</td></tr><tr><td><a href="HYCOMGOMl0044D.InterpolateAtArcGISPoints.html?format=raw">HYCOMGOMl0044DInterpolateAtArcGISPoints_GeoEco</a></td><td>Interpolates HYCOM GOMl0.04 4D variables at points.</td></tr><tr><td><a href="Interpolator.FindAndInpaintArcGISRasters.html?format=raw">InterpolatorFindAndInpaintArcGISRasters_GeoEco</a></td><td>Finds rasters in an ArcGIS workspace and interpolates values for the No Data cells.</td></tr><tr><td><a href="Interpolator.InpaintArcGISRasterArcGISTable.html?format=raw">InterpolatorInpaintArcGISRasterArcGISTable_GeoEco</a></td><td>Interpolates values for the No Data cells for each ArcGIS raster in a table.</td></tr><tr><td><a href="Interpolator.InpaintArcGISRaster.html?format=raw">InterpolatorInpaintArcGISRaster_GeoEco</a></td><td>Interpolates values for the No Data cells of a raster.</td></tr><tr><td><a href="Interpolator.InterpolateArcGISRasterValuesAtPoints.html?format=raw">InterpolatorInterpolateArcGISRasterValuesAtPoints_GeoEco</a></td><td>Interpolates the values of rasters at points.</td></tr><tr><td><a href="LinearMixedModel.FitToArcGISTable.html?format=raw">LinearMixedModelFitToArcGISTable_GeoEco</a></td><td>Fits a linear mixed-effects model to data in an ArcGIS table.</td></tr><tr><td><a href="LinearMixedModel.PredictFromArcGISRasters.html?format=raw">LinearMixedModelPredictFromArcGISRasters_GeoEco</a></td><td>Using a fitted linear mixed model, this tool creates a raster representing the response variable predicted from rasters representing the predictor variables.</td></tr><tr><td><a href="MODISL3SSTTimeSeries.CreateArcGISRasters.html?format=raw">MODISL3SSTTimeSeriesCreateArcGISRasters_GeoEco</a></td><td>Creates rasters for MODIS Level 3 SST images published by NASA JPL PO.DAAC.</td></tr><tr><td><a href="MODISL3SSTTimeSeries.CreateCayulaCornillonFrontsAsArcGISRasters.html?format=raw">MODISL3SSTTimeSeriesCreateCayulaCornillonFrontsAsArcGISRasters_GeoEco</a></td><td>Creates rasters indicating the positions of fronts in MODIS Level 3 SST images published by NASA JPL PO.DAAC, using the Cayula and Cornillon (1992) single image edge detection (SIED) algorithm.</td></tr><tr><td><a href="MODISL3SSTTimeSeries.CreateClimatologicalArcGISRasters.html?format=raw">MODISL3SSTTimeSeriesCreateClimatologicalArcGISRasters_GeoEco</a></td><td>Creates climatological rasters from MODIS Level 3 SST images published by NASA JPL PO.DAAC.</td></tr><tr><td><a href="MODISL3SSTTimeSeries.InterpolateAtArcGISPoints.html?format=raw">MODISL3SSTTimeSeriesInterpolateAtArcGISPoints_GeoEco</a></td><td>Interpolates PO.DAAC MODIS Level 3 SST values at points.</td></tr><tr><td><a href="ModelEvaluation.PlotPerformanceOfBinaryClassificationModel.html?format=raw">ModelEvaluationPlotPerformanceOfBinaryClassificationModel_GeoEco</a></td><td>Plots the performance of a binary classification model (a model where the response variable has two possible values) using the R ROCR package.</td></tr><tr><td><a href="ModelEvaluation.PlotROCOfBinaryClassificationModel.html?format=raw">ModelEvaluationPlotROCOfBinaryClassificationModel_GeoEco</a></td><td>Plots the receiver operating characteristic (ROC) curve of a binary classification model (a model where the response variable has two possible values) using the R ROCR package.</td></tr><tr><td><a href="ModelEvaluation.RandomlySplitArcGISTableIntoTrainingAndEvaluationRecords.html?format=raw">ModelEvaluationRandomlySplitArcGISTableIntoTrainingAndEvaluationRecords_GeoEco</a></td><td>Randomly designates the records of a table as either training records (for fitting a statistical model) or evaluation records (for evaluating the model).</td></tr><tr><td><a href="NetCDF.Convert2DVariableInNetCDFsInArcGISTableToArcGISRasters.html?format=raw">NetCDFConvert2DVariableInNetCDFsInArcGISTableToArcGISRasters_GeoEco</a></td><td>Converts a two-dimensional variable in each netCDF file in a table to an ArcGIS raster.</td></tr><tr><td><a href="NetCDF.Convert2DVariableInNetCDFsInArcGISTableToArcInfoASCIIGrids.html?format=raw">NetCDFConvert2DVariableInNetCDFsInArcGISTableToArcInfoASCIIGrids_GeoEco</a></td><td>Converts a two-dimensional variable in each netCDF file in a table to a text file in ArcInfo ASCII Grid format.</td></tr><tr><td><a href="NetCDF.Convert2DVariableInNetCDFsInArcGISTableToBinaryRasters.html?format=raw">NetCDFConvert2DVariableInNetCDFsInArcGISTableToBinaryRasters_GeoEco</a></td><td>Converts a two-dimensional variable in each netCDF file in a table to a binary raster.</td></tr><tr><td><a href="NetCDF.Convert2DVariableToArcGISRaster.html?format=raw">NetCDFConvert2DVariableToArcGISRaster_GeoEco</a></td><td>Converts a two-dimensional variable in a netCDF file to an ArcGIS raster.</td></tr><tr><td><a href="NetCDF.Convert2DVariableToArcInfoASCIIGrid.html?format=raw">NetCDFConvert2DVariableToArcInfoASCIIGrid_GeoEco</a></td><td>Converts a two-dimensional variable in a netCDF file to a text file in ArcInfo ASCII Grid format.</td></tr><tr><td><a href="NetCDF.Convert2DVariableToBinaryRaster.html?format=raw">NetCDFConvert2DVariableToBinaryRaster_GeoEco</a></td><td>Converts a two-dimensional variable in a netCDF file to a binary raster.</td></tr><tr><td><a href="NetCDF.ExtractHeaderArcGISTable.html?format=raw">NetCDFExtractHeaderArcGISTable_GeoEco</a></td><td>Extracts the headers of netCDF files in a table and saves them to text files.</td></tr><tr><td><a href="NetCDF.ExtractHeader.html?format=raw">NetCDFExtractHeader_GeoEco</a></td><td>Extracts the header of a netCDF file and saves it to a text file.</td></tr><tr><td><a href="NetCDF.FindAndExtractHeaders.html?format=raw">NetCDFFindAndExtractHeaders_GeoEco</a></td><td>Finds netCDF files in a directory, extracts their headers, and saves the headers to text files.</td></tr><tr><td><a href="NetCDF.FindNetCDFsAndConvert2DVariableToArcGISRasters.html?format=raw">NetCDFFindNetCDFsAndConvert2DVariableToArcGISRasters_GeoEco</a></td><td>Finds netCDF files in a directory and converts a two-dimensional variable in each file to an ArcGIS raster.</td></tr><tr><td><a href="NetCDF.FindNetCDFsAndConvert2DVariableToArcInfoASCIIGrids.html?format=raw">NetCDFFindNetCDFsAndConvert2DVariableToArcInfoASCIIGrids_GeoEco</a></td><td>Finds netCDF files in a directory and converts a two-dimensional variable in each file to a text file in ArcInfo ASCII Grid format.</td></tr><tr><td><a href="NetCDF.FindNetCDFsAndConvert2DVariableToBinaryRasters.html?format=raw">NetCDFFindNetCDFsAndConvert2DVariableToBinaryRasters_GeoEco</a></td><td>Finds netCDF files in a directory and converts a two-dimensional variable in each file to a binary raster.</td></tr><tr><td><a href="OSCAR5DayThirdDegreeCurrents.CreateArcGISRasters.html?format=raw">OSCAR5DayThirdDegreeCurrentsCreateArcGISRasters_GeoEco</a></td><td>Creates rasters for NOAA OSCAR 5-day unfiltered 1/3 degree ocean surface currents.</td></tr><tr><td><a href="OSCAR5DayThirdDegreeCurrents.CreateClimatologicalArcGISRasters.html?format=raw">OSCAR5DayThirdDegreeCurrentsCreateClimatologicalArcGISRasters_GeoEco</a></td><td>Creates climatological rasters for NOAA OSCAR 5-day unfiltered 1/3 degree ocean surface currents.</td></tr><tr><td><a href="OSCAR5DayThirdDegreeCurrents.CreateVectorsAsArcGISFeatureClasses.html?format=raw">OSCAR5DayThirdDegreeCurrentsCreateVectorsAsArcGISFeatureClasses_GeoEco</a></td><td>Creates line feature classes representing the vectors of NOAA OSCAR 5-day unfiltered 1/3 degree ocean surface currents.</td></tr><tr><td><a href="OSCAR5DayThirdDegreeCurrents.InterpolateAtArcGISPoints.html?format=raw">OSCAR5DayThirdDegreeCurrentsInterpolateAtArcGISPoints_GeoEco</a></td><td>Interpolates the values of NOAA OSCAR 5-day unfiltered 1/3 degree ocean surface currents at points.</td></tr><tr><td><a href="OceanColorLevel3SMITimeSeries.CreateArcGISRasters.html?format=raw">OceanColorLevel3SMITimeSeriesCreateArcGISRasters_GeoEco</a></td><td>Creates rasters for a Level 3 Standard Mapped Image (SMI) product published by the NASA GSFC OceanColor Group.</td></tr><tr><td><a href="OceanColorLevel3SMITimeSeries.CreateClimatologicalArcGISRasters.html?format=raw">OceanColorLevel3SMITimeSeriesCreateClimatologicalArcGISRasters_GeoEco</a></td><td>Creates climatological rasters for a Level 3 Standard Mapped Image (SMI) product published by the NASA GSFC OceanColor Group.</td></tr><tr><td><a href="OceanColorLevel3SMITimeSeries.InterpolateAtArcGISPoints.html?format=raw">OceanColorLevel3SMITimeSeriesInterpolateAtArcGISPoints_GeoEco</a></td><td>Interpolates the values of a Level 3 Standard Mapped Image (SMI) product published by the NASA GSFC OceanColor Group at points.</td></tr><tr><td><a href="R.EvaluateFile.html?format=raw">REvaluateFile_GeoEco</a></td><td>Evalutes the R statements in a text file using the R interpreter and returns the result of the last statement.</td></tr><tr><td><a href="R.Evaluate.html?format=raw">REvaluate_GeoEco</a></td><td>Evalutes one or more R statements using the R interpreter and returns the result of the last statement.</td></tr><tr><td><a href="RExploratoryPlots.ClevelandPlotForArcGISTable.html?format=raw">RExploratoryPlotsClevelandPlotForArcGISTable_GeoEco</a></td><td>Creates a multi-panel Cleveland dotplot for a table.</td></tr><tr><td><a href="RExploratoryPlots.DensityHistogramForArcGISField.html?format=raw">RExploratoryPlotsDensityHistogramForArcGISField_GeoEco</a></td><td>Creates a density histogram for a field of a table.</td></tr><tr><td><a href="RExploratoryPlots.DensityHistogramForArcGISPointsCoordinates.html?format=raw">RExploratoryPlotsDensityHistogramForArcGISPointsCoordinates_GeoEco</a></td><td>Creates a density histogram for one of the coordinates of a point feature class or layer.</td></tr><tr><td><a href="RExploratoryPlots.ScatterplotMatrixForArcGISTable.html?format=raw">RExploratoryPlotsScatterplotMatrixForArcGISTable_GeoEco</a></td><td>Creates a matrix of scatterplots for a table using the R pairs function.</td></tr><tr><td><a href="ROMSCoSiNE2D.CreateArcGISRaster.html?format=raw">ROMSCoSiNE2DCreateArcGISRaster_GeoEco</a></td><td>Creates a raster for a Pacific ROMS-CoSiNE 2D variable.</td></tr><tr><td><a href="ROMSCoSiNE2D.InterpolateAtArcGISPoints.html?format=raw">ROMSCoSiNE2DInterpolateAtArcGISPoints_GeoEco</a></td><td>Interpolates a Pacific ROMS-CoSiNE 2D variable at points.</td></tr><tr><td><a href="ROMSCoSiNE3D.CreateArcGISRasters.html?format=raw">ROMSCoSiNE3DCreateArcGISRasters_GeoEco</a></td><td>Creates rasters for a Pacific ROMS-CoSiNE 3D variable.</td></tr><tr><td><a href="ROMSCoSiNE3D.CreateClimatologicalArcGISRasters.html?format=raw">ROMSCoSiNE3DCreateClimatologicalArcGISRasters_GeoEco</a></td><td>Creates climatological rasters for a Pacific ROMS-CoSiNE 3D variable</td></tr><tr><td><a href="ROMSCoSiNE3D.InterpolateAtArcGISPoints.html?format=raw">ROMSCoSiNE3DInterpolateAtArcGISPoints_GeoEco</a></td><td>Interpolates Pacific ROMS-CoSiNE 3D variables at points.</td></tr><tr><td><a href="ROMSCoSiNE4D.CreateArcGISRasters.html?format=raw">ROMSCoSiNE4DCreateArcGISRasters_GeoEco</a></td><td>Creates rasters for a Pacific ROMS-CoSiNE 4D variable.</td></tr><tr><td><a href="ROMSCoSiNE4D.CreateClimatologicalArcGISRasters.html?format=raw">ROMSCoSiNE4DCreateClimatologicalArcGISRasters_GeoEco</a></td><td>Creates climatological rasters for a Pacific ROMS-CoSiNE 4D variable</td></tr><tr><td><a href="ROMSCoSiNE4D.InterpolateAtArcGISPoints.html?format=raw">ROMSCoSiNE4DInterpolateAtArcGISPoints_GeoEco</a></td><td>Interpolates Pacific ROMS-CoSiNE 4D variables at points.</td></tr><tr><td><a href="SIRFile.ExtractHeaderArcGISTable.html?format=raw">SIRFileExtractHeaderArcGISTable_GeoEco</a></td><td>Extracts the headers of SIR files in a table and saves them to text files.</td></tr><tr><td><a href="SIRFile.ExtractHeader.html?format=raw">SIRFileExtractHeader_GeoEco</a></td><td>Extracts the header of a SIR file.</td></tr><tr><td><a href="SIRFile.FindAndConvertToArcGISRasters.html?format=raw">SIRFileFindAndConvertToArcGISRasters_GeoEco</a></td><td>Finds SIR files in a directory and converts them to ArcGIS rasters.</td></tr><tr><td><a href="SIRFile.FindAndConvertToArcInfoASCIIGrids.html?format=raw">SIRFileFindAndConvertToArcInfoASCIIGrids_GeoEco</a></td><td>Finds SIR files in a directory and converts them to text files in ArcInfo ASCII grid format.</td></tr><tr><td><a href="SIRFile.FindAndConvertToBinaryRasters.html?format=raw">SIRFileFindAndConvertToBinaryRasters_GeoEco</a></td><td>Finds SIR files in a directory and converts them to binary rasters.</td></tr><tr><td><a href="SIRFile.FindAndExtractHeaders.html?format=raw">SIRFileFindAndExtractHeaders_GeoEco</a></td><td>Finds SIR files in a directory, extracts their headers, and saves the headers to text files.</td></tr><tr><td><a href="SIRFile.ToArcGISRasterArcGISTable.html?format=raw">SIRFileToArcGISRasterArcGISTable_GeoEco</a></td><td>Converts each SIR file in a table to an ArcGIS raster.</td></tr><tr><td><a href="SIRFile.ToArcGISRaster.html?format=raw">SIRFileToArcGISRaster_GeoEco</a></td><td>Converts a SIR file to an ArcGIS raster.</td></tr><tr><td><a href="SIRFile.ToArcInfoASCIIGridArcGISTable.html?format=raw">SIRFileToArcInfoASCIIGridArcGISTable_GeoEco</a></td><td>Converts each SIR file in a table to a text file in ArcInfo ASCII grid format.</td></tr><tr><td><a href="SIRFile.ToArcInfoASCIIGrid.html?format=raw">SIRFileToArcInfoASCIIGrid_GeoEco</a></td><td>Converts a SIR file to a text file in ArcInfo ASCII Grid format.</td></tr><tr><td><a href="SIRFile.ToBinaryRasterArcGISTable.html?format=raw">SIRFileToBinaryRasterArcGISTable_GeoEco</a></td><td>Converts each SIR file in a table to a binary raster.</td></tr><tr><td><a href="SIRFile.ToBinaryRaster.html?format=raw">SIRFileToBinaryRaster_GeoEco</a></td><td>Converts a SIR file to a binary raster.</td></tr><tr><td><a href="Shapefile.CopyToDirectory.html?format=raw">ShapefileCopyToDirectory_GeoEco</a></td><td>Copies a shapefile to a directory.</td></tr><tr><td><a href="Shapefile.Copy.html?format=raw">ShapefileCopy_GeoEco</a></td><td>Copies a shapefile.</td></tr><tr><td><a href="SpatiaLiteDatabase.ExportToArcGISWorkspace.html?format=raw">SpatiaLiteDatabaseExportToArcGISWorkspace_GeoEco</a></td><td>Converts tables in a SpatiaLite database to ArcGIS tables, shapefiles, and feature classes.</td></tr><tr><td><a href="SpatiaLiteDatabase.ImportFromArcGISWorkspace.html?format=raw">SpatiaLiteDatabaseImportFromArcGISWorkspace_GeoEco</a></td><td>Converts ArcGIS tables, shapefiles, and feature classes to tables in a SpatiaLite database.</td></tr><tr><td><a href="SpeciesDiversity.CalculateDiversityIndexForArcGISPolygons.html?format=raw">SpeciesDiversityCalculateDiversityIndexForArcGISPolygons_GeoEco</a></td><td>Given polygons representing zones of interest and points representing species occurrence observations, calculates a species diversity index for each polygon.</td></tr><tr><td><a href="Table.ExecuteADOCommandForArcGISTable.html?format=raw">TableExecuteADOCommandForArcGISTable_GeoEco</a></td><td>Opens a Microsoft ActiveX Data Objects (ADO) connection to the database containing a table and executes a command.</td></tr><tr><td><a href="TreeModel.FitToArcGISTable.html?format=raw">TreeModelFitToArcGISTable_GeoEco</a></td><td>Fits a tree model to data in an ArcGIS table.</td></tr><tr><td><a href="TreeModel.PredictFromArcGISRasters.html?format=raw">TreeModelPredictFromArcGISRasters_GeoEco</a></td><td>Using a fitted tree model, this tool creates a raster representing the response variable predicted from rasters representing the predictor variables.</td></tr></table></p><h1><a id="Copyright">Copyright and License</a></h1><p>Except where otherwise noted, this document and the Marine Geospatial Ecology 
    66        Tools software is Copyright © 2007 by Jason J. Roberts.</p><p>The terms "MGET" and "GeoEco" are synonymous with, and occasionally used 
    77        instead of, "Marine Geospatial Ecology Tools".</p><p>MGET is free software; you can redistribute it and/or modify it under the 

  • MGET/Branches/Jason/PythonPackage/dist/TracOnlineDocumentation/Documentation/ArcGISReference/CayulaCornillonEdgeDetection.DetectEdgesInArcGISRaster.html

    r945 r957  
    3333long as a sufficient temperature gradient continues in the direction 
    3434the front was pointing.</p></li></ul><p>In 2005, I obtained a Rational Fortran (Ratfor) version of the 
    35 Cayula-Cornillon algorithm from Dave Ullman. Although it had been 
     35Cayula and Cornillon algorithm from Dave Ullman. Although it had been 
    3636modified extensively from the 1992 version, mainly to incorporate the 
    3737multi-image edge detection (MIED) algorithm (Cayula and Cornillon 
    … …  
    6868coast. Journal of Geophysical Research 104: 23459-23478.</p><p>Ullman, D. S. and P. C. Cornillon. 2000. Evaluation of front detection 
    6969methods for satellite-derived SST data using in situ observations. 
    70 Journal of Atmospheric and Oceanic Technology 17: 1667-1675.</p><br /><p><h2><img width="11" height="11" border="0" src="sm_arrow_down.gif?format=raw" /> Command line syntax</h2></p><div Class="expand" id="id103141">CayulaCornillonEdgeDetectionDetectEdgesInArcGISRaster_GeoEco &lt;inputRaster&gt; &lt;outputFrontsRaster&gt; {wrapEdges} {mapAlgebraExpression} {medianFilterWindowSize} {histogramWindowSize} {histogramWindowStride} {minPropNonMaskedCells} {minPopProp} {minPopMeanDifference} {minTheta} {minSinglePopCohesion} {minGlobalPopCohesion} {threads} {fillHoles} {thin} {minSize} {outputFilteredImageRaster} {outputCandidateCountsRaster} {outputFrontCountsRaster} {outputWindowStatusCodesRaster} {outputWindowStatusValuesRaster} <br /><br /><b>Parameters</b><br /><table width="100%" border="0" cellpadding="5"><tbody><tr><th width="40%"><b>Expression</b></th><th width="60%"><b>Explanation</b></th></tr><tr><td class="info">&lt;inputRaster&gt;</td><td class="info" align="left"><p>Raster that is the input satellite image of sea surface 
     70Journal of Atmospheric and Oceanic Technology 17: 1667-1675.</p><br /><p><h2><img width="11" height="11" border="0" src="sm_arrow_down.gif?format=raw" /> Command line syntax</h2></p><div Class="expand" id="id103141">CayulaCornillonEdgeDetectionDetectEdgesInArcGISRaster_GeoEco &lt;inputRaster&gt; &lt;minPopMeanDifference&gt; &lt;outputFrontsRaster&gt; {wrapEdges} {mapAlgebraExpression} {medianFilterWindowSize} {histogramWindowSize} {histogramWindowStride} {minPropNonMaskedCells} {minPopProp} {minTheta} {minSinglePopCohesion} {minGlobalPopCohesion} {threads} {fillHoles} {thin} {minSize} {outputFilteredImageRaster} {outputCandidateCountsRaster} {outputFrontCountsRaster} {outputWindowStatusCodesRaster} {outputWindowStatusValuesRaster} <br /><br /><b>Parameters</b><br /><table width="100%" border="0" cellpadding="5"><tbody><tr><th width="40%"><b>Expression</b></th><th width="60%"><b>Explanation</b></th></tr><tr><td class="info">&lt;inputRaster&gt;</td><td class="info" align="left"><p>Raster that is the input satellite image of sea surface 
    7171temperature, chlorophyll density, or other data that exhibits fronts.</p><p>The raster must contain integers that fall within the range of 8-bit 
    7272or 16-bit signed or unsigned data type (i.e. the numbers fall within 
    … …  
    747465535). If the raster's pixel type is 32-bit signed or unsigned 
    7575integer, it may still be used so long as the actual values fall within 
    76 one of the acceptable ranges.</p><p>The Cayula-Cornillon algorithm cannot operate on floating-point data. 
     76one of the acceptable ranges.</p><p>The Cayula and Cornillon algorithm cannot operate on floating-point data. 
    7777If your input raster contains floating-point numbers, use the Map 
    7878Algebra Expression option to instruct this tool to convert the raster 
    … …  
    8080clouds, land or other invalid pixels to NoData before running this 
    8181tool. If you do not, the algorithm will find fronts around these 
    82 regions.</p></td></tr><tr><td class="info">&lt;outputFrontsRaster&gt;</td><td class="info" align="left"><p>Output raster that shows the fronts detected in the input 
     82regions.</p></td></tr><tr><td class="info">&lt;minPopMeanDifference&gt;</td><td class="info" align="left"><p>Minimum difference in the mean values of two adjacent populations 
     83of pixels for a front to be detected between those two populations.</p><p>The Cayula and Cornillon algorithm passes a moving window over the 
     84image, checking each window for a bimodal distribution in the values 
     85of the pixels within it. When the algorithm detects a bimodal 
     86distribution, it computes the mean values of the two populations and 
     87compares the difference between the means to this threshold. If the 
     88difference is less than this threshold, the algorithm concludes there 
     89is no front present and moves on to the next window.</p><p>The value of the threshold is expressed in integer values. If your 
     90input raster has an integer data type, then the units of the threshold 
     91are the units of the input raster. For example, if your raster's 
     92integers are scaled such an increase of 1 integer value corresponds to 
     93an increase in 0.1 degrees C, then the same thing applies to the 
     94threshold. Under this example, a threshold value of 5 corresponds to a 
     95temperature difference of 0.5 degrees C.</p><p>If your input raster has a floating-point data type, then the units of 
     96the threshold depend the Map Algebra Expression that you also must 
     97specify, to convert the floating-point values to integers. For 
     98example, if your Map Algebra Expression multiplies the floating-point 
     99values by 10 (e.g. the value 20 degrees C is converted to the integer 
     100value 200), then each integer value corresponds to a change of 0.1 
     101degrees C. Under this example, a threshold value of 5 would correspond 
     102to a temperature difference of 0.5 degrees C.</p><p>The minimum allowed value of the threshold is 3, following Cayula's 
     103original Fortran code which contained the explanation "a temperature 
     104difference of less than three digital counts between the two 
     105populations is likely to be a result of the discrete nature of the 
     106data." In Cayula and Cornillon's study they used threshold of 3 and 
     107their data used a scale factor of 0.15 (i.e. each integer value 
     108corresponded to a change of 0.15 degrees C). Therefore they detected 
     109fronts between water masses that differed in temperature by at least 
     1100.45 degrees C.</p><p>You can use this parameter to eliminate weak fronts by selecting a 
     111value that corresponds to a desired minimum mean temperature 
     112difference. Suppose, for example, you are working with NOAA NODC 4km 
     113AVHRR Pathfinder SST version 5.0 data, which uses a scale factor of 
     1140.075. To eliminate fronts where the mean temperature difference is 
     115less than 1 degree C, set this parameter to 1 / 0.075 = 
     11613.333333.</p></td></tr><tr><td class="info">&lt;outputFrontsRaster&gt;</td><td class="info" align="left"><p>Output raster that shows the fronts detected in the input 
    83117image.</p><p>The raster will have the same dimensions as the input raster and 
    84118contain 8-bit signed integers with three possible values:</p><ul><li><p>NoData - the pixel was never a candidate for containing a front, 
    … …  
    86120it did not appear in any histogram windows that had sufficiently 
    87121large numbers of pixels that were not NoData in the input image to 
    88 proceed with the histogramming step of the Cayula-Cornillon 
     122proceed with the histogramming step of the Cayula and Cornillon 
    89123algorithm.</p></li></ul><ul><li><p>0 - The pixel was a candidate for containing a front -- it was not 
    90124NoData in the input image and it appeared in at least one histogram 
    … …  
    95129marked as a front pixel in at least one of the histogram windows it 
    96130appeared in.</p></li></ul></td></tr><tr><td class="info">{wrapEdges}</td><td class="info" align="left"><p>If True, the input image is assumed to be a cylinder, with the 
    97 east and west edges connected. The Cayula-Cornillon algorithm will 
     131east and west edges connected. The Cayula and Cornillon algorithm will 
    98132"wrap around" to the other side of the image when needed. If False, 
    99133the east and west edges are assumed to not be connected, and the 
    … …  
    101135Pathfinder AVHRR SST) and False if your image is regional (e.g. NOAA 
    102136CoastWatch AVHRR SST).</p></td></tr><tr><td class="info">{mapAlgebraExpression}</td><td class="info" align="left"><p>Map algebra expression to execute on the input raster before 
    103 running the Cayula-Cornillon algorithm.</p><p><b>WARNING:</b> The ArcGIS Geoprocessing Model Builder may randomly and 
     137running the Cayula and Cornillon algorithm.</p><p><b>WARNING:</b> The ArcGIS Geoprocessing Model Builder may randomly and 
    104138silently delete the value of this parameter. This is a bug in ArcGIS. 
    105139Before running a model that you have saved, open this tool and 
    … …  
    134168down can be very frustrating.</p></li></ul></td></tr><tr><td class="info">{medianFilterWindowSize}</td><td class="info" align="left"><p>Window size, in pixels, of the median filter to apply to the input 
    135169image prior to running the histogram analysis step of the 
    136 Cayula-Cornillon algorithm. If not provided, median filtering will not 
     170Cayula and Cornillon algorithm. If not provided, median filtering will not 
    137171be performed.</p><p>If you provide a value, it must be an odd integer greater than or 
    138172equal to 3. The filter window is square and advances across the image 
    1391731 pixel at a time. The center pixel, if it is not NoData, is replaced 
    140174with the median value of the pixels in the surrounding window that are 
    141 not NoData. If the center pixel is NoData, it remains NoData.</p><p>The original paper used a window size of 3.</p></td></tr><tr><td class="info">{histogramWindowSize}</td><td class="info" align="left"><p>Size of the histogram window to use for the Cayula-Cornillon 
     175not NoData. If the center pixel is NoData, it remains NoData.</p><p>The original paper used a window size of 3.</p></td></tr><tr><td class="info">{histogramWindowSize}</td><td class="info" align="left"><p>Size of the histogram window to use for the Cayula and Cornillon 
    142176algorithm.</p><p>The window is square. The original paper used a window size of 32. 
    143177Although the algorithm is claimed to obtain similar results regardless 
    … …  
    146180the paper carefully before experimenting with different window 
    147181sizes.</p></td></tr><tr><td class="info">{histogramWindowStride}</td><td class="info" align="left"><p>Number of pixels to move the histogram window after each iteration 
    148 of the Cayula-Cornillon algorithm.</p><p>The original paper used a 32x32 window and a stride of 16, to minimize 
     182of the Cayula and Cornillon algorithm.</p><p>The original paper used a 32x32 window and a stride of 16, to minimize 
    149183the CPU time required to execute the algorithm. Cutting the stride in 
    150184half increases the CPU time by a factor of about four. For example, a 
    … …  
    168202tests will not be accurate. In this case, and the algorithm discards 
    169203the current window, advances to next one, and starts over.</p><p>I do not recommend selecting a value other than 0.25 unless you 
    170 understand the statistical analysis presented in the 1992 paper.</p></td></tr><tr><td class="info">{minPopMeanDifference}</td><td class="info" align="left"><p>Minimum difference in population means.</p><p>After the histogram algorithm separates the pixels into two 
    171 populations, it computes the means of the two populations. If the 
    172 means differ by less than this parameter value, the algorithm discards 
    173 the current window, advances to next one, and starts over.</p><p>The original intent of this parameter is obscure. The Fortran code I 
    174 obtained from Dave Ullman used the value 3 and contained the 
    175 explanation "a temperature difference of less than three digital 
    176 counts between the 2 populations is likely to be a result of the 
    177 discrete nature of the data."</p><p>You can use this parameter to eliminate weak fronts by selecting a 
    178 value that corresponds to a desired minimum mean temperature 
    179 difference. For example, the NOAA NODC 4km AVHRR Pathfinder Project 
    180 (<a href="http://www.nodc.noaa.gov/SatelliteData/pathfinder4km/">http://www.nodc.noaa.gov/SatelliteData/pathfinder4km/</a>) publishes SST 
    181 data as 16-bit unsigned integers where each integer represents 0.075 
    182 degrees. To eliminate fronts where the mean temperature difference is 
    183 less than 0.5 degrees, set this parameter to 0.5 / 0.075 = 
    184 6.666667.</p></td></tr><tr><td class="info">{minTheta}</td><td class="info" align="left"><p>Minimum criterion function, theta(Topt), as described on pages 
     204understand the statistical analysis presented in the 1992 paper.</p></td></tr><tr><td class="info">{minTheta}</td><td class="info" align="left"><p>Minimum criterion function, theta(Topt), as described on pages 
    18520571-72 of the 1992 paper.</p><p>The criterion function is used to determine whether the histogram 
    186206window contains a bimodal distribution, as would be expected if it 
    … …  
    250270the number of times it appeared in a histogram window that had a 
    251271sufficiently large number of pixels to proceed with the histogramming 
    252 step of the Cayula-Cornillon algorithm.</p><p>The raster will contain 16-bit signed integers and have the same 
     272step of the Cayula and Cornillon algorithm.</p><p>The raster will contain 16-bit signed integers and have the same 
    253273dimensions as the input raster. Pixels that are NoData in the input 
    254274raster will be NoData in the output raster. The remaining pixels will 
    … …  
    323343algorithm's tests, increasing or decreasing the number of fronts 
    324344identified in the image. You should only adjust the parameters if you 
    325 feel comfortable deviating from their published values.</p></td></tr></tbody></table></div><p><h2><img width="11" height="11" border="0" src="sm_arrow_down.gif?format=raw" /> Scripting syntax</h2></p><div Class="expand" id="TEST">CayulaCornillonEdgeDetectionDetectEdgesInArcGISRaster_GeoEco (inputRaster, outputFrontsRaster, wrapEdges, mapAlgebraExpression, medianFilterWindowSize, histogramWindowSize, histogramWindowStride, minPropNonMaskedCells, minPopProp, minPopMeanDifference, minTheta, minSinglePopCohesion, minGlobalPopCohesion, threads, fillHoles, thin, minSize, outputFilteredImageRaster, outputCandidateCountsRaster, outputFrontCountsRaster, outputWindowStatusCodesRaster, outputWindowStatusValuesRaster) <br /><br /><b>Parameters</b><br /><table width="100%" border="0" cellpadding="5"><tbody><tr><th width="40%"><b>Expression</b></th><th width="60%"><b>Explanation</b></th></tr><tr><td class="info">Input raster (Required) </td><td class="info" align="left"><p>Raster that is the input satellite image of sea surface 
     345feel comfortable deviating from their published values.</p></td></tr></tbody></table></div><p><h2><img width="11" height="11" border="0" src="sm_arrow_down.gif?format=raw" /> Scripting syntax</h2></p><div Class="expand" id="TEST">CayulaCornillonEdgeDetectionDetectEdgesInArcGISRaster_GeoEco (inputRaster, minPopMeanDifference, outputFrontsRaster, wrapEdges, mapAlgebraExpression, medianFilterWindowSize, histogramWindowSize, histogramWindowStride, minPropNonMaskedCells, minPopProp, minTheta, minSinglePopCohesion, minGlobalPopCohesion, threads, fillHoles, thin, minSize, outputFilteredImageRaster, outputCandidateCountsRaster, outputFrontCountsRaster, outputWindowStatusCodesRaster, outputWindowStatusValuesRaster) <br /><br /><b>Parameters</b><br /><table width="100%" border="0" cellpadding="5"><tbody><tr><th width="40%"><b>Expression</b></th><th width="60%"><b>Explanation</b></th></tr><tr><td class="info">Input raster (Required) </td><td class="info" align="left"><p>Raster that is the input satellite image of sea surface 
    326346temperature, chlorophyll density, or other data that exhibits fronts.</p><p>The raster must contain integers that fall within the range of 8-bit 
    327347or 16-bit signed or unsigned data type (i.e. the numbers fall within 
    … …  
    32934965535). If the raster's pixel type is 32-bit signed or unsigned 
    330350integer, it may still be used so long as the actual values fall within 
    331 one of the acceptable ranges.</p><p>The Cayula-Cornillon algorithm cannot operate on floating-point data. 
     351one of the acceptable ranges.</p><p>The Cayula and Cornillon algorithm cannot operate on floating-point data. 
    332352If your input raster contains floating-point numbers, use the Map 
    333353Algebra Expression option to instruct this tool to convert the raster 
    … …  
    335355clouds, land or other invalid pixels to NoData before running this 
    336356tool. If you do not, the algorithm will find fronts around these 
    337 regions.</p></td></tr><tr><td class="info">Output fronts image (Required) </td><td class="info" align="left"><p>Output raster that shows the fronts detected in the input 
     357regions.</p></td></tr><tr><td class="info">Front detection threshold (Required) </td><td class="info" align="left"><p>Minimum difference in the mean values of two adjacent populations 
     358of pixels for a front to be detected between those two populations.</p><p>The Cayula and Cornillon algorithm passes a moving window over the 
     359image, checking each window for a bimodal distribution in the values 
     360of the pixels within it. When the algorithm detects a bimodal 
     361distribution, it computes the mean values of the two populations and 
     362compares the difference between the means to this threshold. If the 
     363difference is less than this threshold, the algorithm concludes there 
     364is no front present and moves on to the next window.</p><p>The value of the threshold is expressed in integer values. If your 
     365input raster has an integer data type, then the units of the threshold 
     366are the units of the input raster. For example, if your raster's 
     367integers are scaled such an increase of 1 integer value corresponds to 
     368an increase in 0.1 degrees C, then the same thing applies to the 
     369threshold. Under this example, a threshold value of 5 corresponds to a 
     370temperature difference of 0.5 degrees C.</p><p>If your input raster has a floating-point data type, then the units of 
     371the threshold depend the Map Algebra Expression that you also must 
     372specify, to convert the floating-point values to integers. For 
     373example, if your Map Algebra Expression multiplies the floating-point 
     374values by 10 (e.g. the value 20 degrees C is converted to the integer 
     375value 200), then each integer value corresponds to a change of 0.1 
     376degrees C. Under this example, a threshold value of 5 would correspond 
     377to a temperature difference of 0.5 degrees C.</p><p>The minimum allowed value of the threshold is 3, following Cayula's 
     378original Fortran code which contained the explanation "a temperature 
     379difference of less than three digital counts between the two 
     380populations is likely to be a result of the discrete nature of the 
     381data." In Cayula and Cornillon's study they used threshold of 3 and 
     382their data used a scale factor of 0.15 (i.e. each integer value 
     383corresponded to a change of 0.15 degrees C). Therefore they detected 
     384fronts between water masses that differed in temperature by at least 
     3850.45 degrees C.</p><p>You can use this parameter to eliminate weak fronts by selecting a 
     386value that corresponds to a desired minimum mean temperature 
     387difference. Suppose, for example, you are working with NOAA NODC 4km 
     388AVHRR Pathfinder SST version 5.0 data, which uses a scale factor of 
     3890.075. To eliminate fronts where the mean temperature difference is 
     390less than 1 degree C, set this parameter to 1 / 0.075 = 
     39113.333333.</p></td></tr><tr><td class="info">Output fronts image (Required) </td><td class="info" align="left"><p>Output raster that shows the fronts detected in the input 
    338392image.</p><p>The raster will have the same dimensions as the input raster and 
    339393contain 8-bit signed integers with three possible values:</p><ul><li><p>NoData - the pixel was never a candidate for containing a front, 
    … …  
    341395it did not appear in any histogram windows that had sufficiently 
    342396large numbers of pixels that were not NoData in the input image to 
    343 proceed with the histogramming step of the Cayula-Cornillon 
     397proceed with the histogramming step of the Cayula and Cornillon 
    344398algorithm.</p></li></ul><ul><li><p>0 - The pixel was a candidate for containing a front -- it was not 
    345399NoData in the input image and it appeared in at least one histogram 
    … …  
    350404marked as a front pixel in at least one of the histogram windows it 
    351405appeared in.</p></li></ul></td></tr><tr><td class="info">Wrap edges (Optional) </td><td class="info" align="left"><p>If True, the input image is assumed to be a cylinder, with the 
    352 east and west edges connected. The Cayula-Cornillon algorithm will 
     406east and west edges connected. The Cayula and Cornillon algorithm will 
    353407"wrap around" to the other side of the image when needed. If False, 
    354408the east and west edges are assumed to not be connected, and the 
    … …  
    356410Pathfinder AVHRR SST) and False if your image is regional (e.g. NOAA 
    357411CoastWatch AVHRR SST).</p></td></tr><tr><td class="info">Map algebra expression (Optional) </td><td class="info" align="left"><p>Map algebra expression to execute on the input raster before 
    358 running the Cayula-Cornillon algorithm.</p><p><b>WARNING:</b> The ArcGIS Geoprocessing Model Builder may randomly and 
     412running the Cayula and Cornillon algorithm.</p><p><b>WARNING:</b> The ArcGIS Geoprocessing Model Builder may randomly and 
    359413silently delete the value of this parameter. This is a bug in ArcGIS. 
    360414Before running a model that you have saved, open this tool and 
    … …  
    389443down can be very frustrating.</p></li></ul></td></tr><tr><td class="info">Median filter window size (Optional) </td><td class="info" align="left"><p>Window size, in pixels, of the median filter to apply to the input 
    390444image prior to running the histogram analysis step of the 
    391 Cayula-Cornillon algorithm. If not provided, median filtering will not 
     445Cayula and Cornillon algorithm. If not provided, median filtering will not 
    392446be performed.</p><p>If you provide a value, it must be an odd integer greater than or 
    393447equal to 3. The filter window is square and advances across the image 
    3944481 pixel at a time. The center pixel, if it is not NoData, is replaced 
    395449with the median value of the pixels in the surrounding window that are 
    396 not NoData. If the center pixel is NoData, it remains NoData.</p><p>The original paper used a window size of 3.</p></td></tr><tr><td class="info">Histogram window size (Optional) </td><td class="info" align="left"><p>Size of the histogram window to use for the Cayula-Cornillon 
     450not NoData. If the center pixel is NoData, it remains NoData.</p><p>The original paper used a window size of 3.</p></td></tr><tr><td class="info">Histogram window size (Optional) </td><td class="info" align="left"><p>Size of the histogram window to use for the Cayula and Cornillon 
    397451algorithm.</p><p>The window is square. The original paper used a window size of 32. 
    398452Although the algorithm is claimed to obtain similar results regardless 
    … …  
    401455the paper carefully before experimenting with different window 
    402456sizes.</p></td></tr><tr><td class="info">Histogram window stride (Optional) </td><td class="info" align="left"><p>Number of pixels to move the histogram window after each iteration 
    403 of the Cayula-Cornillon algorithm.</p><p>The original paper used a 32x32 window and a stride of 16, to minimize 
     457of the Cayula and Cornillon algorithm.</p><p>The original paper used a 32x32 window and a stride of 16, to minimize 
    404458the CPU time required to execute the algorithm. Cutting the stride in 
    405459half increases the CPU time by a factor of about four. For example, a 
    … …  
    423477tests will not be accurate. In this case, and the algorithm discards 
    424478the current window, advances to next one, and starts over.</p><p>I do not recommend selecting a value other than 0.25 unless you 
    425 understand the statistical analysis presented in the 1992 paper.</p></td></tr><tr><td class="info">Minimum population mean difference (Optional) </td><td class="info" align="left"><p>Minimum difference in population means.</p><p>After the histogram algorithm separates the pixels into two 
    426 populations, it computes the means of the two populations. If the 
    427 means differ by less than this parameter value, the algorithm discards 
    428 the current window, advances to next one, and starts over.</p><p>The original intent of this parameter is obscure. The Fortran code I 
    429 obtained from Dave Ullman used the value 3 and contained the 
    430 explanation "a temperature difference of less than three digital 
    431 counts between the 2 populations is likely to be a result of the 
    432 discrete nature of the data."</p><p>You can use this parameter to eliminate weak fronts by selecting a 
    433 value that corresponds to a desired minimum mean temperature 
    434 difference. For example, the NOAA NODC 4km AVHRR Pathfinder Project 
    435 (<a href="http://www.nodc.noaa.gov/SatelliteData/pathfinder4km/">http://www.nodc.noaa.gov/SatelliteData/pathfinder4km/</a>) publishes SST 
    436 data as 16-bit unsigned integers where each integer represents 0.075 
    437 degrees. To eliminate fronts where the mean temperature difference is 
    438 less than 0.5 degrees, set this parameter to 0.5 / 0.075 = 
    439 6.666667.</p></td></tr><tr><td class="info">Minimum value for criterion function (Optional) </td><td class="info" align="left"><p>Minimum criterion function, theta(Topt), as described on pages 
     479understand the statistical analysis presented in the 1992 paper.</p></td></tr><tr><td class="info">Minimum value for criterion function (Optional) </td><td class="info" align="left"><p>Minimum criterion function, theta(Topt), as described on pages 
    44048071-72 of the 1992 paper.</p><p>The criterion function is used to determine whether the histogram 
    441481window contains a bimodal distribution, as would be expected if it 
    … …  
    505545the number of times it appeared in a histogram window that had a 
    506546sufficiently large number of pixels to proceed with the histogramming 
    507 step of the Cayula-Cornillon algorithm.</p><p>The raster will contain 16-bit signed integers and have the same 
     547step of the Cayula and Cornillon algorithm.</p><p>The raster will contain 16-bit signed integers and have the same 
    508548dimensions as the input raster. Pixels that are NoData in the input 
    509549raster will be NoData in the output raster. The remaining pixels will 

  • MGET/Branches/Jason/PythonPackage/dist/TracOnlineDocumentation/Documentation/ArcGISReference/CayulaCornillonEdgeDetection.DetectEdgesInArcGISRastersArcGISTable.html

    r945 r957  
    11<?xml version="1.0" encoding="utf-8"?> 
    22<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd"> 
    3 <html xmlns="http://www.w3.org/1999/xhtml"><head><link rel="stylesheet" type="text/css" href="81help.css?format=raw" /><meta http-equiv="Content-Type" content="text/html; charset=UTF-8" /><title>Cayula-Cornillon Fronts in ArcGIS Rasters Listed in Table</title></head><body><table style="margin-top:-1em; margin-bottom:0; padding:0; margin-left:-1em"><tr><td style="background:white"><img width="875" height="70" alt="ArcToolbox banner" src="AHBanner_ArcToolbox.gif?format=raw" /></td></tr></table><h1>Cayula-Cornillon Fronts in ArcGIS Rasters Listed in Table</h1><p></p><p>Finds fronts in ArcGIS rasters listed in a table using the Cayula-Cornillon (1992) single-image edge detection algorithm.</p><p>The Cayula and Cornillon (1992) single image edge detection (SIED) 
     3<html xmlns="http://www.w3.org/1999/xhtml"><head><link rel="stylesheet" type="text/css" href="81help.css?format=raw" /><meta http-equiv="Content-Type" content="text/html; charset=UTF-8" /><title>Cayula-Cornillon Fronts in ArcGIS Rasters Listed in Table</title></head><body><table style="margin-top:-1em; margin-bottom:0; padding:0; margin-left:-1em"><tr><td style="background:white"><img width="875" height="70" alt="ArcToolbox banner" src="AHBanner_ArcToolbox.gif?format=raw" /></td></tr></table><h1>Cayula-Cornillon Fronts in ArcGIS Rasters Listed in Table</h1><p></p><p>Finds fronts in ArcGIS rasters listed in a table using the Cayula and Cornillon (1992) single-image edge detection algorithm.</p><p>The Cayula and Cornillon (1992) single image edge detection (SIED) 
    44algorithm was designed to detect fronts in SST images and originally 
    55applied to data collected by the AVHRR sensor on the NOAA-7 satellite. 
    … …  
    3333long as a sufficient temperature gradient continues in the direction 
    3434the front was pointing.</p></li></ul><p>In 2005, I obtained a Rational Fortran (Ratfor) version of the 
    35 Cayula-Cornillon algorithm from Dave Ullman. Although it had been 
     35Cayula and Cornillon algorithm from Dave Ullman. Although it had been 
    3636modified extensively from the 1992 version, mainly to incorporate the 
    3737multi-image edge detection (MIED) algorithm (Cayula and Cornillon 
    … …  
    6868coast. Journal of Geophysical Research 104: 23459-23478.</p><p>Ullman, D. S. and P. C. Cornillon. 2000. Evaluation of front detection 
    6969methods for satellite-derived SST data using in situ observations. 
    70 Journal of Atmospheric and Oceanic Technology 17: 1667-1675.</p><br /><p><h2><img width="11" height="11" border="0" src="sm_arrow_down.gif?format=raw" /> Command line syntax</h2></p><div Class="expand" id="id103142">CayulaCornillonEdgeDetectionDetectEdgesInArcGISRastersArcGISTable_GeoEco &lt;table&gt; &lt;inputRasterField&gt; &lt;outputFrontsRasterField&gt; {wrapEdges} {mapAlgebraExpression} {medianFilterWindowSize} {histogramWindowSize} {histogramWindowStride} {minPropNonMaskedCells} {minPopProp} {minPopMeanDifference} {minTheta} {minSinglePopCohesion} {minGlobalPopCohesion} {threads} {fillHoles} {thin} {minSize} {outputFilteredImageRasterField} {outputCandidateCountsRasterField} {outputFrontCountsRasterField} {outputWindowStatusCodesRasterField} {outputWindowStatusValuesRasterField} {where} {orderBy;orderBy...} {directions;directions...} {skipExisting} {basePath} <br /><br /><b>Parameters</b><br /><table width="100%" border="0" cellpadding="5"><tbody><tr><th width="40%"><b>Expression</b></th><th width="60%"><b>Explanation</b></th></tr><tr><td class="info">&lt;table&gt;</td><td class="info" align="left"><p>Table to query.</p></td></tr><tr><td class="info">&lt;inputRasterField&gt;</td><td class="info" align="left"><p>Field containing the rasters that are the input satellite images of sea surface 
     70Journal of Atmospheric and Oceanic Technology 17: 1667-1675.</p><br /><p><h2><img width="11" height="11" border="0" src="sm_arrow_down.gif?format=raw" /> Command line syntax</h2></p><div Class="expand" id="id103142">CayulaCornillonEdgeDetectionDetectEdgesInArcGISRastersArcGISTable_GeoEco &lt;table&gt; &lt;inputRasterField&gt; &lt;minPopMeanDifference&gt; &lt;outputFrontsRasterField&gt; {wrapEdges} {mapAlgebraExpression} {medianFilterWindowSize} {histogramWindowSize} {histogramWindowStride} {minPropNonMaskedCells} {minPopProp} {minTheta} {minSinglePopCohesion} {minGlobalPopCohesion} {threads} {fillHoles} {thin} {minSize} {outputFilteredImageRasterField} {outputCandidateCountsRasterField} {outputFrontCountsRasterField} {outputWindowStatusCodesRasterField} {outputWindowStatusValuesRasterField} {where} {orderBy;orderBy...} {directions;directions...} {skipExisting} {basePath} <br /><br /><b>Parameters</b><br /><table width="100%" border="0" cellpadding="5"><tbody><tr><th width="40%"><b>Expression</b></th><th width="60%"><b>Explanation</b></th></tr><tr><td class="info">&lt;table&gt;</td><td class="info" align="left"><p>Table to query.</p></td></tr><tr><td class="info">&lt;inputRasterField&gt;</td><td class="info" align="left"><p>Field containing the rasters that are the input satellite images of sea surface 
    7171temperature, chlorophyll density, or other data that exhibits fronts.</p><p>Each raster must contain 8-bit or 16-bit integers (signed or 
    72 unsigned). The Cayula-Cornillon algorithm cannot operate on 
     72unsigned). The Cayula and Cornillon algorithm cannot operate on 
    7373floating-point data. If your input rasters contain floating-point 
    7474numbers, use the Map Algebra Expression option to instruct this tool 
    … …  
    7676clouds, land or other invalid pixels to NoData before running this 
    7777tool. If you do not, the algorithm will find fronts around these 
    78 regions.</p></td></tr><tr><td class="info">&lt;outputFrontsRasterField&gt;</td><td class="info" align="left"><p>Field containing the output rasters to create that show the fronts detected in the 
     78regions.</p></td></tr><tr><td class="info">&lt;minPopMeanDifference&gt;</td><td class="info" align="left"><p>Minimum difference in the mean values of two adjacent populations 
     79of pixels for a front to be detected between those two populations.</p><p>The Cayula and Cornillon algorithm passes a moving window over the 
     80image, checking each window for a bimodal distribution in the values 
     81of the pixels within it. When the algorithm detects a bimodal 
     82distribution, it computes the mean values of the two populations and 
     83compares the difference between the means to this threshold. If the 
     84difference is less than this threshold, the algorithm concludes there 
     85is no front present and moves on to the next window.</p><p>The value of the threshold is expressed in integer values. If your 
     86input raster has an integer data type, then the units of the threshold 
     87are the units of the input raster. For example, if your raster's 
     88integers are scaled such an increase of 1 integer value corresponds to 
     89an increase in 0.1 degrees C, then the same thing applies to the 
     90threshold. Under this example, a threshold value of 5 corresponds to a 
     91temperature difference of 0.5 degrees C.</p><p>If your input raster has a floating-point data type, then the units of 
     92the threshold depend the Map Algebra Expression that you also must 
     93specify, to convert the floating-point values to integers. For 
     94example, if your Map Algebra Expression multiplies the floating-point 
     95values by 10 (e.g. the value 20 degrees C is converted to the integer 
     96value 200), then each integer value corresponds to a change of 0.1 
     97degrees C. Under this example, a threshold value of 5 would correspond 
     98to a temperature difference of 0.5 degrees C.</p><p>The minimum allowed value of the threshold is 3, following Cayula's 
     99original Fortran code which contained the explanation "a temperature 
     100difference of less than three digital counts between the two 
     101populations is likely to be a result of the discrete nature of the 
     102data." In Cayula and Cornillon's study they used threshold of 3 and 
     103their data used a scale factor of 0.15 (i.e. each integer value 
     104corresponded to a change of 0.15 degrees C). Therefore they detected 
     105fronts between water masses that differed in temperature by at least 
     1060.45 degrees C.</p><p>You can use this parameter to eliminate weak fronts by selecting a 
     107value that corresponds to a desired minimum mean temperature 
     108difference. Suppose, for example, you are working with NOAA NODC 4km 
     109AVHRR Pathfinder SST version 5.0 data, which uses a scale factor of 
     1100.075. To eliminate fronts where the mean temperature difference is 
     111less than 1 degree C, set this parameter to 1 / 0.075 = 
     11213.333333.</p></td></tr><tr><td class="info">&lt;outputFrontsRasterField&gt;</td><td class="info" align="left"><p>Field containing the output rasters to create that show the fronts detected in the 
    79113input images.</p><p>The rasters will have the same dimensions as the input images and 
    80114contain 8-bit signed integers with three possible values:</p><ul><li><p>NoData - the pixel was never a candidate for containing a front, 
    … …  
    82116it did not appear in any histogram windows that had sufficiently 
    83117large numbers of pixels that were not NoData in the input image to 
    84 proceed with the histogramming step of the Cayula-Cornillon 
     118proceed with the histogramming step of the Cayula and Cornillon 
    85119algorithm.</p></li></ul><ul><li><p>0 - The pixel was a candidate for containing a front -- it was not 
    86120NoData in the input image and it appeared in at least one histogram 
    … …  
    91125marked as a front pixel in at least one of the histogram windows it 
    92126appeared in.</p></li></ul></td></tr><tr><td class="info">{wrapEdges}</td><td class="info" align="left"><p>If True, the input image is assumed to be a cylinder, with the 
    93 east and west edges connected. The Cayula-Cornillon algorithm will 
     127east and west edges connected. The Cayula and Cornillon algorithm will 
    94128"wrap around" to the other side of the image when needed. If False, 
    95129the east and west edges are assumed to not be connected, and the 
    … …  
    97131Pathfinder AVHRR SST) and False if your image is regional (e.g. NOAA 
    98132CoastWatch AVHRR SST).</p></td></tr><tr><td class="info">{mapAlgebraExpression}</td><td class="info" align="left"><p>Map algebra expression to execute on the input raster before 
    99 running the Cayula-Cornillon algorithm.</p><p><b>WARNING:</b> The ArcGIS Geoprocessing Model Builder may randomly and 
     133running the Cayula and Cornillon algorithm.</p><p><b>WARNING:</b> The ArcGIS Geoprocessing Model Builder may randomly and 
    100134silently delete the value of this parameter. This is a bug in ArcGIS. 
    101135Before running a model that you have saved, open this tool and 
    … …  
    130164down can be very frustrating.</p></li></ul></td></tr><tr><td class="info">{medianFilterWindowSize}</td><td class="info" align="left"><p>Window size, in pixels, of the median filter to apply to the input 
    131165image prior to running the histogram analysis step of the 
    132 Cayula-Cornillon algorithm. If not provided, median filtering will not 
     166Cayula and Cornillon algorithm. If not provided, median filtering will not 
    133167be performed.</p><p>If you provide a value, it must be an odd integer greater than or 
    134168equal to 3. The filter window is square and advances across the image 
    1351691 pixel at a time. The center pixel, if it is not NoData, is replaced 
    136170with the median value of the pixels in the surrounding window that are 
    137 not NoData. If the center pixel is NoData, it remains NoData.</p><p>The original paper used a window size of 3.</p></td></tr><tr><td class="info">{histogramWindowSize}</td><td class="info" align="left"><p>Size of the histogram window to use for the Cayula-Cornillon 
     171not NoData. If the center pixel is NoData, it remains NoData.</p><p>The original paper used a window size of 3.</p></td></tr><tr><td class="info">{histogramWindowSize}</td><td class="info" align="left"><p>Size of the histogram window to use for the Cayula and Cornillon 
    138172algorithm.</p><p>The window is square. The original paper used a window size of 32. 
    139173Although the algorithm is claimed to obtain similar results regardless 
    … …  
    142176the paper carefully before experimenting with different window 
    143177sizes.</p></td></tr><tr><td class="info">{histogramWindowStride}</td><td class="info" align="left"><p>Number of pixels to move the histogram window after each iteration 
    144 of the Cayula-Cornillon algorithm.</p><p>The original paper used a 32x32 window and a stride of 16, to minimize 
     178of the Cayula and Cornillon algorithm.</p><p>The original paper used a 32x32 window and a stride of 16, to minimize 
    145179the CPU time required to execute the algorithm. Cutting the stride in 
    146180half increases the CPU time by a factor of about four. For example, a 
    … …  
    164198tests will not be accurate. In this case, and the algorithm discards 
    165199the current window, advances to next one, and starts over.</p><p>I do not recommend selecting a value other than 0.25 unless you 
    166 understand the statistical analysis presented in the 1992 paper.</p></td></tr><tr><td class="info">{minPopMeanDifference}</td><td class="info" align="left"><p>Minimum difference in population means.</p><p>After the histogram algorithm separates the pixels into two 
    167 populations, it computes the means of the two populations. If the 
    168 means differ by less than this parameter value, the algorithm discards 
    169 the current window, advances to next one, and starts over.</p><p>The original intent of this parameter is obscure. The Fortran code I 
    170 obtained from Dave Ullman used the value 3 and contained the 
    171 explanation "a temperature difference of less than three digital 
    172 counts between the 2 populations is likely to be a result of the 
    173 discrete nature of the data."</p><p>You can use this parameter to eliminate weak fronts by selecting a 
    174 value that corresponds to a desired minimum mean temperature 
    175 difference. For example, the NOAA NODC 4km AVHRR Pathfinder Project 
    176 (<a href="http://www.nodc.noaa.gov/SatelliteData/pathfinder4km/">http://www.nodc.noaa.gov/SatelliteData/pathfinder4km/</a>) publishes SST 
    177 data as 16-bit unsigned integers where each integer represents 0.075 
    178 degrees. To eliminate fronts where the mean temperature difference is 
    179 less than 0.5 degrees, set this parameter to 0.5 / 0.075 = 
    180 6.666667.</p></td></tr><tr><td class="info">{minTheta}</td><td class="info" align="left"><p>Minimum criterion function, theta(Topt), as described on pages 
     200understand the statistical analysis presented in the 1992 paper.</p></td></tr><tr><td class="info">{minTheta}</td><td class="info" align="left"><p>Minimum criterion function, theta(Topt), as described on pages 
    18120171-72 of the 1992 paper.</p><p>The criterion function is used to determine whether the histogram 
    182202window contains a bimodal distribution, as would be expected if it 
    … …  
    247267containing fronts, i.e. the number of times the pixels appeared in 
    248268histogram windows that had a sufficiently large number of non-masked 
    249 pixels to proceed with the histogramming step of the Cayula-Cornillon 
     269pixels to proceed with the histogramming step of the Cayula and Cornillon 
    250270algorithm.</p><p>Each raster will contain 16-bit signed integers and have the same 
    251271dimensions as the input raster. Pixels that are NoData in the input 
    … …  
    343363that are obtained from the fields that list the inputs (and outputs, 
    344364if this tool has outputs). If a base path is not provided, the 
    345 workspace containing the table will be prepended instead.</p></td></tr></tbody></table></div><p><h2><img width="11" height="11" border="0" src="sm_arrow_down.gif?format=raw" /> Scripting syntax</h2></p><div Class="expand" id="TEST">CayulaCornillonEdgeDetectionDetectEdgesInArcGISRastersArcGISTable_GeoEco (table, inputRasterField, outputFrontsRasterField, wrapEdges, mapAlgebraExpression, medianFilterWindowSize, histogramWindowSize, histogramWindowStride, minPropNonMaskedCells, minPopProp, minPopMeanDifference, minTheta, minSinglePopCohesion, minGlobalPopCohesion, threads, fillHoles, thin, minSize, outputFilteredImageRasterField, outputCandidateCountsRasterField, outputFrontCountsRasterField, outputWindowStatusCodesRasterField, outputWindowStatusValuesRasterField, where, orderBy, directions, skipExisting, basePath) <br /><br /><b>Parameters</b><br /><table width="100%" border="0" cellpadding="5"><tbody><tr><th width="40%"><b>Expression</b></th><th width="60%"><b>Explanation</b></th></tr><tr><td class="info">Table (Required) </td><td class="info" align="left"><p>Table to query.</p></td></tr><tr><td class="info">Input raster field (Required) </td><td class="info" align="left"><p>Field containing the rasters that are the input satellite images of sea surface 
     365workspace containing the table will be prepended instead.</p></td></tr></tbody></table></div><p><h2><img width="11" height="11" border="0" src="sm_arrow_down.gif?format=raw" /> Scripting syntax</h2></p><div Class="expand" id="TEST">CayulaCornillonEdgeDetectionDetectEdgesInArcGISRastersArcGISTable_GeoEco (table, inputRasterField, minPopMeanDifference, outputFrontsRasterField, wrapEdges, mapAlgebraExpression, medianFilterWindowSize, histogramWindowSize, histogramWindowStride, minPropNonMaskedCells, minPopProp, minTheta, minSinglePopCohesion, minGlobalPopCohesion, threads, fillHoles, thin, minSize, outputFilteredImageRasterField, outputCandidateCountsRasterField, outputFrontCountsRasterField, outputWindowStatusCodesRasterField, outputWindowStatusValuesRasterField, where, orderBy, directions, skipExisting, basePath) <br /><br /><b>Parameters</b><br /><table width="100%" border="0" cellpadding="5"><tbody><tr><th width="40%"><b>Expression</b></th><th width="60%"><b>Explanation</b></th></tr><tr><td class="info">Table (Required) </td><td class="info" align="left"><p>Table to query.</p></td></tr><tr><td class="info">Input raster field (Required) </td><td class="info" align="left"><p>Field containing the rasters that are the input satellite images of sea surface 
    346366temperature, chlorophyll density, or other data that exhibits fronts.</p><p>Each raster must contain 8-bit or 16-bit integers (signed or 
    347 unsigned). The Cayula-Cornillon algorithm cannot operate on 
     367unsigned). The Cayula and Cornillon algorithm cannot operate on 
    348368floating-point data. If your input rasters contain floating-point 
    349369numbers, use the Map Algebra Expression option to instruct this tool 
    … …  
    351371clouds, land or other invalid pixels to NoData before running this 
    352372tool. If you do not, the algorithm will find fronts around these 
    353 regions.</p></td></tr><tr><td class="info">Output fronts raster field (Required) </td><td class="info" align="left"><p>Field containing the output rasters to create that show the fronts detected in the 
     373regions.</p></td></tr><tr><td class="info">Front detection threshold (Required) </td><td class="info" align="left"><p>Minimum difference in the mean values of two adjacent populations 
     374of pixels for a front to be detected between those two populations.</p><p>The Cayula and Cornillon algorithm passes a moving window over the 
     375image, checking each window for a bimodal distribution in the values 
     376of the pixels within it. When the algorithm detects a bimodal 
     377distribution, it computes the mean values of the two populations and 
     378compares the difference between the means to this threshold. If the 
     379difference is less than this threshold, the algorithm concludes there 
     380is no front present and moves on to the next window.</p><p>The value of the threshold is expressed in integer values. If your 
     381input raster has an integer data type, then the units of the threshold 
     382are the units of the input raster. For example, if your raster's 
     383integers are scaled such an increase of 1 integer value corresponds to 
     384an increase in 0.1 degrees C, then the same thing applies to the 
     385threshold. Under this example, a threshold value of 5 corresponds to a 
     386temperature difference of 0.5 degrees C.</p><p>If your input raster has a floating-point data type, then the units of 
     387the threshold depend the Map Algebra Expression that you also must 
     388specify, to convert the floating-point values to integers. For 
     389example, if your Map Algebra Expression multiplies the floating-point 
     390values by 10 (e.g. the value 20 degrees C is converted to the integer 
     391value 200), then each integer value corresponds to a change of 0.1 
     392degrees C. Under this example, a threshold value of 5 would correspond 
     393to a temperature difference of 0.5 degrees C.</p><p>The minimum allowed value of the threshold is 3, following Cayula's 
     394original Fortran code which contained the explanation "a temperature 
     395difference of less than three digital counts between the two 
     396populations is likely to be a result of the discrete nature of the 
     397data." In Cayula and Cornillon's study they used threshold of 3 and 
     398their data used a scale factor of 0.15 (i.e. each integer value 
     399corresponded to a change of 0.15 degrees C). Therefore they detected 
     400fronts between water masses that differed in temperature by at least 
     4010.45 degrees C.</p><p>You can use this parameter to eliminate weak fronts by selecting a 
     402value that corresponds to a desired minimum mean temperature 
     403difference. Suppose, for example, you are working with NOAA NODC 4km 
     404AVHRR Pathfinder SST version 5.0 data, which uses a scale factor of 
     4050.075. To eliminate fronts where the mean temperature difference is 
     406less than 1 degree C, set this parameter to 1 / 0.075 = 
     40713.333333.</p></td></tr><tr><td class="info">Output fronts raster field (Required) </td><td class="info" align="left"><p>Field containing the output rasters to create that show the fronts detected in the 
    354408input images.</p><p>The rasters will have the same dimensions as the input images and 
    355409contain 8-bit signed integers with three possible values:</p><ul><li><p>NoData - the pixel was never a candidate for containing a front, 
    … …  
    357411it did not appear in any histogram windows that had sufficiently 
    358412large numbers of pixels that were not NoData in the input image to 
    359 proceed with the histogramming step of the Cayula-Cornillon 
     413proceed with the histogramming step of the Cayula and Cornillon 
    360414algorithm.</p></li></ul><ul><li><p>0 - The pixel was a candidate for containing a front -- it was not 
    361415NoData in the input image and it appeared in at least one histogram 
    … …  
    366420marked as a front pixel in at least one of the histogram windows it 
    367421appeared in.</p></li></ul></td></tr><tr><td class="info">Wrap edges (Optional) </td><td class="info" align="left"><p>If True, the input image is assumed to be a cylinder, with the 
    368 east and west edges connected. The Cayula-Cornillon algorithm will 
     422east and west edges connected. The Cayula and Cornillon algorithm will 
    369423"wrap around" to the other side of the image when needed. If False, 
    370424the east and west edges are assumed to not be connected, and the 
    … …  
    372426Pathfinder AVHRR SST) and False if your image is regional (e.g. NOAA 
    373427CoastWatch AVHRR SST).</p></td></tr><tr><td class="info">Map algebra expression (Optional) </td><td class="info" align="left"><p>Map algebra expression to execute on the input raster before 
    374 running the Cayula-Cornillon algorithm.</p><p><b>WARNING:</b> The ArcGIS Geoprocessing Model Builder may randomly and 
     428running the Cayula and Cornillon algorithm.</p><p><b>WARNING:</b> The ArcGIS Geoprocessing Model Builder may randomly and 
    375429silently delete the value of this parameter. This is a bug in ArcGIS. 
    376430Before running a model that you have saved, open this tool and 
    … …  
    405459down can be very frustrating.</p></li></ul></td></tr><tr><td class="info">Median filter window size (Optional) </td><td class="info" align="left"><p>Window size, in pixels, of the median filter to apply to the input 
    406460image prior to running the histogram analysis step of the 
    407 Cayula-Cornillon algorithm. If not provided, median filtering will not 
     461Cayula and Cornillon algorithm. If not provided, median filtering will not 
    408462be performed.</p><p>If you provide a value, it must be an odd integer greater than or 
    409463equal to 3. The filter window is square and advances across the image 
    4104641 pixel at a time. The center pixel, if it is not NoData, is replaced 
    411465with the median value of the pixels in the surrounding window that are 
    412 not NoData. If the center pixel is NoData, it remains NoData.</p><p>The original paper used a window size of 3.</p></td></tr><tr><td class="info">Histogram window size (Optional) </td><td class="info" align="left"><p>Size of the histogram window to use for the Cayula-Cornillon 
     466not NoData. If the center pixel is NoData, it remains NoData.</p><p>The original paper used a window size of 3.</p></td></tr><tr><td class="info">Histogram window size (Optional) </td><td class="info" align="left"><p>Size of the histogram window to use for the Cayula and Cornillon 
    413467algorithm.</p><p>The window is square. The original paper used a window size of 32. 
    414468Although the algorithm is claimed to obtain similar results regardless 
    … …  
    417471the paper carefully before experimenting with different window 
    418472sizes.</p></td></tr><tr><td class="info">Histogram window stride (Optional) </td><td class="info" align="left"><p>Number of pixels to move the histogram window after each iteration 
    419 of the Cayula-Cornillon algorithm.</p><p>The original paper used a 32x32 window and a stride of 16, to minimize 
     473of the Cayula and Cornillon algorithm.</p><p>The original paper used a 32x32 window and a stride of 16, to minimize 
    420474the CPU time required to execute the algorithm. Cutting the stride in 
    421475half increases the CPU time by a factor of about four. For example, a 
    … …  
    439493tests will not be accurate. In this case, and the algorithm discards 
    440494the current window, advances to next one, and starts over.</p><p>I do not recommend selecting a value other than 0.25 unless you 
    441 understand the statistical analysis presented in the 1992 paper.</p></td></tr><tr><td class="info">Minimum population mean difference (Optional) </td><td class="info" align="left"><p>Minimum difference in population means.</p><p>After the histogram algorithm separates the pixels into two 
    442 populations, it computes the means of the two populations. If the 
    443 means differ by less than this parameter value, the algorithm discards 
    444 the current window, advances to next one, and starts over.</p><p>The original intent of this parameter is obscure. The Fortran code I 
    445 obtained from Dave Ullman used the value 3 and contained the 
    446 explanation "a temperature difference of less than three digital 
    447 counts between the 2 populations is likely to be a result of the 
    448 discrete nature of the data."</p><p>You can use this parameter to eliminate weak fronts by selecting a 
    449 value that corresponds to a desired minimum mean temperature 
    450 difference. For example, the NOAA NODC 4km AVHRR Pathfinder Project 
    451 (<a href="http://www.nodc.noaa.gov/SatelliteData/pathfinder4km/">http://www.nodc.noaa.gov/SatelliteData/pathfinder4km/</a>) publishes SST 
    452 data as 16-bit unsigned integers where each integer represents 0.075 
    453 degrees. To eliminate fronts where the mean temperature difference is 
    454 less than 0.5 degrees, set this parameter to 0.5 / 0.075 = 
    455 6.666667.</p></td></tr><tr><td class="info">Minimum value for criterion function (Optional) </td><td class="info" align="left"><p>Minimum criterion function, theta(Topt), as described on pages 
     495understand the statistical analysis presented in the 1992 paper.</p></td></tr><tr><td class="info">Minimum value for criterion function (Optional) </td><td class="info" align="left"><p>Minimum criterion function, theta(Topt), as described on pages 
    45649671-72 of the 1992 paper.</p><p>The criterion function is used to determine whether the histogram 
    457497window contains a bimodal distribution, as would be expected if it 
    … …  
    522562containing fronts, i.e. the number of times the pixels appeared in 
    523563histogram windows that had a sufficiently large number of non-masked 
    524 pixels to proceed with the histogramming step of the Cayula-Cornillon 
     564pixels to proceed with the histogramming step of the Cayula and Cornillon 
    525565algorithm.</p><p>Each raster will contain 16-bit signed integers and have the same 
    526566dimensions as the input raster. Pixels that are NoData in the input 

  • MGET/Branches/Jason/PythonPackage/dist/TracOnlineDocumentation/Documentation/ArcGISReference/CayulaCornillonEdgeDetection.DetectEdgesInBinaryRaster.html

    r945 r957  
    3333long as a sufficient temperature gradient continues in the direction 
    3434the front was pointing.</p></li></ul><p>In 2005, I obtained a Rational Fortran (Ratfor) version of the 
    35 Cayula-Cornillon algorithm from Dave Ullman. Although it had been 
     35Cayula and Cornillon algorithm from Dave Ullman. Although it had been 
    3636modified extensively from the 1992 version, mainly to incorporate the 
    3737multi-image edge detection (MIED) algorithm (Cayula and Cornillon 
    … …  
    6868coast. Journal of Geophysical Research 104: 23459-23478.</p><p>Ullman, D. S. and P. C. Cornillon. 2000. Evaluation of front detection 
    6969methods for satellite-derived SST data using in situ observations. 
    70 Journal of Atmospheric and Oceanic Technology 17: 1667-1675.</p><br /><p><h2><img width="11" height="11" border="0" src="sm_arrow_down.gif?format=raw" /> Command line syntax</h2></p><div Class="expand" id="id103141">CayulaCornillonEdgeDetectionDetectEdgesInBinaryRaster_GeoEco &lt;imageFile&gt; &lt;int8 | uint8 | int16 | uint16&gt; &lt;columnCount&gt; &lt;rowCount&gt; &lt;outputFrontsFile&gt; {swapBytes} {wrapEdges} {minImageValue} {maxImageValue} {maskFiles;maskFiles...} {maskDataTypes;maskDataTypes...} {maskTests;maskTests...} {maskValues;maskValues...} {medianFilterWindowSize} {histogramWindowSize} {histogramWindowStride} {minPropNonMaskedCells} {minPopProp} {minPopMeanDifference} {minTheta} {minSinglePopCohesion} {minGlobalPopCohesion} {threads} {fillHoles} {thin} {minSize} {outputMaskFile} {outputFilteredImageFile} {outputCandidateCountsFile} {outputFrontCountsFile} {outputWindowStatusCodesFile} {outputWindowStatusValuesFile} <br /><br /><b>Parameters</b><br /><table width="100%" border="0" cellpadding="5"><tbody><tr><th width="40%"><b>Expression</b></th><th width="60%"><b>Explanation</b></th></tr><tr><td class="info">&lt;imageFile&gt;</td><td class="info" align="left"><p>Binary raster that is the input satellite image of sea surface 
     70Journal of Atmospheric and Oceanic Technology 17: 1667-1675.</p><br /><p><h2><img width="11" height="11" border="0" src="sm_arrow_down.gif?format=raw" /> Command line syntax</h2></p><div Class="expand" id="id103141">CayulaCornillonEdgeDetectionDetectEdgesInBinaryRaster_GeoEco &lt;imageFile&gt; &lt;int8 | uint8 | int16 | uint16&gt; &lt;columnCount&gt; &lt;rowCount&gt; &lt;minPopMeanDifference&gt; &lt;outputFrontsFile&gt; {swapBytes} {wrapEdges} {minImageValue} {maxImageValue} {maskFiles;maskFiles...} {maskDataTypes;maskDataTypes...} {maskTests;maskTests...} {maskValues;maskValues...} {medianFilterWindowSize} {histogramWindowSize} {histogramWindowStride} {minPropNonMaskedCells} {minPopProp} {minTheta} {minSinglePopCohesion} {minGlobalPopCohesion} {threads} {fillHoles} {thin} {minSize} {outputMaskFile} {outputFilteredImageFile} {outputCandidateCountsFile} {outputFrontCountsFile} {outputWindowStatusCodesFile} {outputWindowStatusValuesFile} <br /><br /><b>Parameters</b><br /><table width="100%" border="0" cellpadding="5"><tbody><tr><th width="40%"><b>Expression</b></th><th width="60%"><b>Explanation</b></th></tr><tr><td class="info">&lt;imageFile&gt;</td><td class="info" align="left"><p>Binary raster that is the input satellite image of sea surface 
    7171temperature, chlorophyll density, or other data that exhibits fronts.</p><p>A binary raster is a file that contains a raw array of numbers stored 
    7272in binary format, with no header, metadata, formatting markers and so 
    … …  
    8383lower-right pixel is the last pixel in the file.</p><p>If you provide a compressed file in a supported compression format, it will 
    8484be automatically decompressed. If it is an archive (e.g. .zip or .tar), it must 
    85 contain exactly one file, which must not be in a subdirectory.</p></td></tr><tr><td class="info">&lt;int8 | uint8 | int16 | uint16&gt;</td><td class="info" align="left"><p>Data type of the input image.</p><p>This may be one of:</p><ul><li><p>int8 - 8-bit signed integer, range -128 to 127</p></li></ul><ul><li><p>uint8 - 8-bit unsigned integer, range 0 to 255</p></li></ul><ul><li><p>int16 - 16-bit signed integer, range -32768 to 32767</p></li></ul><ul><li><p>uint16 - 16-bit unsigned integer, range 0 to 65535</p></li></ul><p>The Cayula-Cornillon algorithm is designed to operate on the original 
     85contain exactly one file, which must not be in a subdirectory.</p></td></tr><tr><td class="info">&lt;int8 | uint8 | int16 | uint16&gt;</td><td class="info" align="left"><p>Data type of the input image.</p><p>This may be one of:</p><ul><li><p>int8 - 8-bit signed integer, range -128 to 127</p></li></ul><ul><li><p>uint8 - 8-bit unsigned integer, range 0 to 255</p></li></ul><ul><li><p>int16 - 16-bit signed integer, range -32768 to 32767</p></li></ul><ul><li><p>uint16 - 16-bit unsigned integer, range 0 to 65535</p></li></ul><p>The Cayula and Cornillon algorithm is designed to operate on the original 
    8686unscaled integer data provided by the original data provider. For 
    8787example, the NOAA NODC 4km AVHRR Pathfinder version 5.0 dataset 
    … …  
    8989values as floating-point numbers, do not apply it and then try to 
    9090provide the floating-point data to this algorithm. Instead, provide 
    91 the integer data directly to this algorithm.</p></td></tr><tr><td class="info">&lt;columnCount&gt;</td><td class="info" align="left"><p>Number of columns in the input image and masks.</p></td></tr><tr><td class="info">&lt;rowCount&gt;</td><td class="info" align="left"><p>Number of rows in the input image and masks.</p></td></tr><tr><td class="info">&lt;outputFrontsFile&gt;</td><td class="info" align="left"><p>Output binary raster that shows the fronts detected in the input 
     91the integer data directly to this algorithm.</p></td></tr><tr><td class="info">&lt;columnCount&gt;</td><td class="info" align="left"><p>Number of columns in the input image and masks.</p></td></tr><tr><td class="info">&lt;rowCount&gt;</td><td class="info" align="left"><p>Number of rows in the input image and masks.</p></td></tr><tr><td class="info">&lt;minPopMeanDifference&gt;</td><td class="info" align="left"><p>Minimum difference in the mean values of two adjacent populations 
     92of pixels for a front to be detected between those two populations.</p><p>The Cayula and Cornillon algorithm passes a moving window over the 
     93image, checking each window for a bimodal distribution in the values 
     94of the pixels within it. When the algorithm detects a bimodal 
     95distribution, it computes the mean values of the two populations and 
     96compares the difference between the means to this threshold. If the 
     97difference is less than this threshold, the algorithm concludes there 
     98is no front present and moves on to the next window.</p><p>The value of the threshold is expressed in unscaled integer values, 
     99not the corresponding real-world values such as degrees Celsius. The 
     100minimum allowed value is 3, following Cayula's original Fortran code 
     101which contained the explanation "a temperature difference of less than 
     102three digital counts between the two populations is likely to be a 
     103result of the discrete nature of the data." By converting this value 
     104to the real-world value, e.g. degrees C, you can determine the minimum 
     105temperature difference that must exist between the two populations for 
     106a front to be detected.</p><p>For example, in Cayula and Cornillon's study they used a value of 3 
     107for this parameter and their data used a scale factor of 0.15 (i.e. 
     108the integer value 1 corresponded to 0.15 degrees C). Therefore they 
     109detected fronts between water masses that differed in temperature by 
     110at least 0.45 degrees C.</p><p>You can use this parameter to eliminate weak fronts by selecting a 
     111value that corresponds to a desired minimum mean temperature 
     112difference. Suppose, for example, you are working with NOAA NODC 4km 
     113AVHRR Pathfinder SST version 5.0 data, which uses a scale factor of 
     1140.075. To eliminate fronts where the mean temperature difference is 
     115less than 1 degree C, set this parameter to 1 / 0.075 = 
     11613.333333.</p></td></tr><tr><td class="info">&lt;outputFrontsFile&gt;</td><td class="info" align="left"><p>Output binary raster that shows the fronts detected in the input 
    92117image.</p><p>The file will have the same dimensions as the input image and contain 
    931188-bit signed integers with three possible values:</p><ul><li><p>-128 - the pixel was never a candidate for containing a front, 
    … …  
    95120any histogram windows that had sufficiently large numbers of 
    96121non-masked pixels to proceed with the histogramming step of the 
    97 Cayula-Cornillon algorithm.</p></li></ul><ul><li><p>0 - The pixel was a candidate for containing a front -- it was not 
     122Cayula and Cornillon algorithm.</p></li></ul><ul><li><p>0 - The pixel was a candidate for containing a front -- it was not 
    98123masked and it appeared in at least one histogram window with a 
    99124sufficient number of non-masked pixels to proceed with the 
    … …  
    109134this option to process data produced by a Sun SPARC processor, which 
    110135uses "big endian" byte ordering.</p></td></tr><tr><td class="info">{wrapEdges}</td><td class="info" align="left"><p>If True, the input image is assumed to be a cylinder, with the 
    111 east and west edges connected. The Cayula-Cornillon algorithm will 
     136east and west edges connected. The Cayula and Cornillon algorithm will 
    112137"wrap around" to the other side of the image when needed. If False, 
    113138the east and west edges are assumed to not be connected, and the 
    … …  
    116141CoastWatch AVHRR SST).</p></td></tr><tr><td class="info">{minImageValue}</td><td class="info" align="left"><p>Minimum allowed value for pixels of the input image. If a value is 
    117142provided, pixels less than this value will be masked prior to running 
    118 the Cayula-Cornillon algorithm.</p></td></tr><tr><td class="info">{maxImageValue}</td><td class="info" align="left"><p>Maximum allowed value for pixels of the input image. If a value is 
     143the Cayula and Cornillon algorithm.</p></td></tr><tr><td class="info">{maxImageValue}</td><td class="info" align="left"><p>Maximum allowed value for pixels of the input image. If a value is 
    119144provided, pixels greater than this value will be masked prior to 
    120 running the Cayula-Cornillon algorithm.</p></td></tr><tr><td class="info">{maskFiles;maskFiles...}</td><td class="info" align="left"><p>Binary rasters to apply as masks to the input image before running 
    121 the Cayula-Cornillon algorithm on it. Each item in this list 
     145running the Cayula and Cornillon algorithm.</p></td></tr><tr><td class="info">{maskFiles;maskFiles...}</td><td class="info" align="left"><p>Binary rasters to apply as masks to the input image before running 
     146the Cayula and Cornillon algorithm on it. Each item in this list 
    122147corresponds to parallel entries in the lists of mask data types, 
    123148tests, and values.</p><p>Use this parameter to pass in land masks, cloud masks, and so on. Any 
    … …  
    153178data types, and mask tests.</p></td></tr><tr><td class="info">{medianFilterWindowSize}</td><td class="info" align="left"><p>Window size, in pixels, of the median filter to apply to the input 
    154179image prior to running the histogram analysis step of the 
    155 Cayula-Cornillon algorithm. If not provided, median filtering will not 
     180Cayula and Cornillon algorithm. If not provided, median filtering will not 
    156181be performed.</p><p>If you provide a value, it must be an odd integer greater than or 
    157182equal to 3. The filter window is square and advances across the image 
    … …  
    159184with the median value of the non-masked pixels in the surrounding 
    160185window. All masks are applied before the median filter is executed.</p><p>Median filtering is a traditional first step for certain classes of 
    161 edge detection algorithms. The original Cayula-Cornillon paper used a 
    162 window size of 3.</p></td></tr><tr><td class="info">{histogramWindowSize}</td><td class="info" align="left"><p>Size of the histogram window to use for the Cayula-Cornillon 
     186edge detection algorithms. The original Cayula and Cornillon paper used a 
     187window size of 3.</p></td></tr><tr><td class="info">{histogramWindowSize}</td><td class="info" align="left"><p>Size of the histogram window to use for the Cayula and Cornillon 
    163188algorithm.</p><p>The window is square. The original paper used a window size of 32. 
    164189Although the algorithm is claimed to obtain similar results regardless 
    … …  
    167192the paper carefully before experimenting with different window 
    168193sizes.</p></td></tr><tr><td class="info">{histogramWindowStride}</td><td class="info" align="left"><p>Number of pixels to move the histogram window after each iteration 
    169 of the Cayula-Cornillon algorithm.</p><p>The original paper used a 32x32 window and a stride of 16, to minimize 
     194of the Cayula and Cornillon algorithm.</p><p>The original paper used a 32x32 window and a stride of 16, to minimize 
    170195the CPU time required to execute the algorithm. Cutting the stride in 
    171196half increases the CPU time by a factor of about four. For example, a 
    … …  
    191216accurate. In this case, and the algorithm discards the current window, 
    192217advances to next one, and starts over.</p><p>I do not recommend selecting a value other than 0.25 unless you 
    193 understand the statistical analysis presented in the 1992 paper.</p></td></tr><tr><td class="info">{minPopMeanDifference}</td><td class="info" align="left"><p>Minimum difference in population means.</p><p>After the histogram algorithm separates the non-masked pixels into two 
    194 populations, it computes the means of the two populations. If the 
    195 means differ by less than this parameter value, the algorithm discards 
    196 the current window, advances to next one, and starts over.</p><p>The original intent of this parameter is obscure. The Ratfor code I 
    197 obtained from Dave Ullman used the value 3 and contained the 
    198 explanation "a temperature difference of less than three digital 
    199 counts between the 2 populations is likely to be a result of the 
    200 discrete nature of the data."</p><p>You can use this parameter to eliminate weak fronts by selecting a 
    201 value that corresponds to a desired minimum mean temperature 
    202 difference. For example, for the NOAA NODC 4km AVHRR Pathfinder SST 
    203 data, the value of 1 corresponds to 0.075 degrees. To eliminate fronts 
    204 where the mean temperature difference is less than 0.5 degrees, set 
    205 this parameter to 0.5 / 0.075 = 6.666667.</p></td></tr><tr><td class="info">{minTheta}</td><td class="info" align="left"><p>Minimum criterion function, theta(Topt), as described on pages 
     218understand the statistical analysis presented in the 1992 paper.</p></td></tr><tr><td class="info">{minTheta}</td><td class="info" align="left"><p>Minimum criterion function, theta(Topt), as described on pages 
    20621971-72 of the 1992 paper.</p><p>The criterion function is used to determine whether the histogram 
    207220window contains a bimodal distribution, as would be expected if it 
    … …  
    264277MATLAB Image Processing Toolbox. Please see the MATLAB documentation 
    265278for more information.</p></td></tr><tr><td class="info">{outputMaskFile}</td><td class="info" align="left"><p>Output binary raster that shows the pixels of the input image that 
    266 were masked prior to executing the Cayula-Cornillon algorithm.</p><p>The file will have the same dimensions as the input image and contain 
     279were masked prior to executing the Cayula and Cornillon algorithm.</p><p>The file will have the same dimensions as the input image and contain 
    2672808-bit unsigned integers. The value 1 indicates that the corresponding 
    268281pixel of the input image was masked; 0 indicates the pixel was not 
    … …  
    349362algorithm's tests, increasing or decreasing the number of fronts 
    350363identified in the image. You should only adjust the parameters if you 
    351 feel comfortable deviating from their published values.</p></td></tr></tbody></table></div><p><h2><img width="11" height="11" border="0" src="sm_arrow_down.gif?format=raw" /> Scripting syntax</h2></p><div Class="expand" id="TEST">CayulaCornillonEdgeDetectionDetectEdgesInBinaryRaster_GeoEco (imageFile, dataType, columnCount, rowCount, outputFrontsFile, swapBytes, wrapEdges, minImageValue, maxImageValue, maskFiles, maskDataTypes, maskTests, maskValues, medianFilterWindowSize, histogramWindowSize, histogramWindowStride, minPropNonMaskedCells, minPopProp, minPopMeanDifference, minTheta, minSinglePopCohesion, minGlobalPopCohesion, threads, fillHoles, thin, minSize, outputMaskFile, outputFilteredImageFile, outputCandidateCountsFile, outputFrontCountsFile, outputWindowStatusCodesFile, outputWindowStatusValuesFile) <br /><br /><b>Parameters</b><br /><table width="100%" border="0" cellpadding="5"><tbody><tr><th width="40%"><b>Expression</b></th><th width="60%"><b>Explanation</b></th></tr><tr><td class="info">Input image (Required) </td><td class="info" align="left"><p>Binary raster that is the input satellite image of sea surface 
     364feel comfortable deviating from their published values.</p></td></tr></tbody></table></div><p><h2><img width="11" height="11" border="0" src="sm_arrow_down.gif?format=raw" /> Scripting syntax</h2></p><div Class="expand" id="TEST">CayulaCornillonEdgeDetectionDetectEdgesInBinaryRaster_GeoEco (imageFile, dataType, columnCount, rowCount, minPopMeanDifference, outputFrontsFile, swapBytes, wrapEdges, minImageValue, maxImageValue, maskFiles, maskDataTypes, maskTests, maskValues, medianFilterWindowSize, histogramWindowSize, histogramWindowStride, minPropNonMaskedCells, minPopProp, minTheta, minSinglePopCohesion, minGlobalPopCohesion, threads, fillHoles, thin, minSize, outputMaskFile, outputFilteredImageFile, outputCandidateCountsFile, outputFrontCountsFile, outputWindowStatusCodesFile, outputWindowStatusValuesFile) <br /><br /><b>Parameters</b><br /><table width="100%" border="0" cellpadding="5"><tbody><tr><th width="40%"><b>Expression</b></th><th width="60%"><b>Explanation</b></th></tr><tr><td class="info">Input image (Required) </td><td class="info" align="left"><p>Binary raster that is the input satellite image of sea surface 
    352365temperature, chlorophyll density, or other data that exhibits fronts.</p><p>A binary raster is a file that contains a raw array of numbers stored 
    353366in binary format, with no header, metadata, formatting markers and so 
    … …  
    364377lower-right pixel is the last pixel in the file.</p><p>If you provide a compressed file in a supported compression format, it will 
    365378be automatically decompressed. If it is an archive (e.g. .zip or .tar), it must 
    366 contain exactly one file, which must not be in a subdirectory.</p></td></tr><tr><td class="info">Data type (Required) </td><td class="info" align="left"><p>Data type of the input image.</p><p>This may be one of:</p><ul><li><p>int8 - 8-bit signed integer, range -128 to 127</p></li></ul><ul><li><p>uint8 - 8-bit unsigned integer, range 0 to 255</p></li></ul><ul><li><p>int16 - 16-bit signed integer, range -32768 to 32767</p></li></ul><ul><li><p>uint16 - 16-bit unsigned integer, range 0 to 65535</p></li></ul><p>The Cayula-Cornillon algorithm is designed to operate on the original 
     379contain exactly one file, which must not be in a subdirectory.</p></td></tr><tr><td class="info">Data type (Required) </td><td class="info" align="left"><p>Data type of the input image.</p><p>This may be one of:</p><ul><li><p>int8 - 8-bit signed integer, range -128 to 127</p></li></ul><ul><li><p>uint8 - 8-bit unsigned integer, range 0 to 255</p></li></ul><ul><li><p>int16 - 16-bit signed integer, range -32768 to 32767</p></li></ul><ul><li><p>uint16 - 16-bit unsigned integer, range 0 to 65535</p></li></ul><p>The Cayula and Cornillon algorithm is designed to operate on the original 
    367380unscaled integer data provided by the original data provider. For 
    368381example, the NOAA NODC 4km AVHRR Pathfinder version 5.0 dataset 
    … …  
    370383values as floating-point numbers, do not apply it and then try to 
    371384provide the floating-point data to this algorithm. Instead, provide 
    372 the integer data directly to this algorithm.</p></td></tr><tr><td class="info">Columns (Required) </td><td class="info" align="left"><p>Number of columns in the input image and masks.</p></td></tr><tr><td class="info">Rows (Required) </td><td class="info" align="left"><p>Number of rows in the input image and masks.</p></td></tr><tr><td class="info">Output fronts image (Required) </td><td class="info" align="left"><p>Output binary raster that shows the fronts detected in the input 
     385the integer data directly to this algorithm.</p></td></tr><tr><td class="info">Columns (Required) </td><td class="info" align="left"><p>Number of columns in the input image and masks.</p></td></tr><tr><td class="info">Rows (Required) </td><td class="info" align="left"><p>Number of rows in the input image and masks.</p></td></tr><tr><td class="info">Front detection threshold (Required) </td><td class="info" align="left"><p>Minimum difference in the mean values of two adjacent populations 
     386of pixels for a front to be detected between those two populations.</p><p>The Cayula and Cornillon algorithm passes a moving window over the 
     387image, checking each window for a bimodal distribution in the values 
     388of the pixels within it. When the algorithm detects a bimodal 
     389distribution, it computes the mean values of the two populations and 
     390compares the difference between the means to this threshold. If the 
     391difference is less than this threshold, the algorithm concludes there 
     392is no front present and moves on to the next window.</p><p>The value of the threshold is expressed in unscaled integer values, 
     393not the corresponding real-world values such as degrees Celsius. The 
     394minimum allowed value is 3, following Cayula's original Fortran code 
     395which contained the explanation "a temperature difference of less than 
     396three digital counts between the two populations is likely to be a 
     397result of the discrete nature of the data." By converting this value 
     398to the real-world value, e.g. degrees C, you can determine the minimum 
     399temperature difference that must exist between the two populations for 
     400a front to be detected.</p><p>For example, in Cayula and Cornillon's study they used a value of 3 
     401for this parameter and their data used a scale factor of 0.15 (i.e. 
     402the integer value 1 corresponded to 0.15 degrees C). Therefore they 
     403detected fronts between water masses that differed in temperature by 
     404at least 0.45 degrees C.</p><p>You can use this parameter to eliminate weak fronts by selecting a 
     405value that corresponds to a desired minimum mean temperature 
     406difference. Suppose, for example, you are working with NOAA NODC 4km 
     407AVHRR Pathfinder SST version 5.0 data, which uses a scale factor of 
     4080.075. To eliminate fronts where the mean temperature difference is 
     409less than 1 degree C, set this parameter to 1 / 0.075 = 
     41013.333333.</p></td></tr><tr><td class="info">Output fronts image (Required) </td><td class="info" align="left"><p>Output binary raster that shows the fronts detected in the input 
    373411image.</p><p>The file will have the same dimensions as the input image and contain 
    3744128-bit signed integers with three possible values:</p><ul><li><p>-128 - the pixel was never a candidate for containing a front, 
    … …  
    376414any histogram windows that had sufficiently large numbers of 
    377415non-masked pixels to proceed with the histogramming step of the 
    378 Cayula-Cornillon algorithm.</p></li></ul><ul><li><p>0 - The pixel was a candidate for containing a front -- it was not 
     416Cayula and Cornillon algorithm.</p></li></ul><ul><li><p>0 - The pixel was a candidate for containing a front -- it was not 
    379417masked and it appeared in at least one histogram window with a 
    380418sufficient number of non-masked pixels to proceed with the 
    … …  
    390428this option to process data produced by a Sun SPARC processor, which 
    391429uses "big endian" byte ordering.</p></td></tr><tr><td class="info">Wrap edges (Optional) </td><td class="info" align="left"><p>If True, the input image is assumed to be a cylinder, with the 
    392 east and west edges connected. The Cayula-Cornillon algorithm will 
     430east and west edges connected. The Cayula and Cornillon algorithm will 
    393431"wrap around" to the other side of the image when needed. If False, 
    394432the east and west edges are assumed to not be connected, and the 
    … …  
    397435CoastWatch AVHRR SST).</p></td></tr><tr><td class="info">Minimum allowed image value (Optional) </td><td class="info" align="left"><p>Minimum allowed value for pixels of the input image. If a value is 
    398436provided, pixels less than this value will be masked prior to running 
    399 the Cayula-Cornillon algorithm.</p></td></tr><tr><td class="info">Maximum allowed image value (Optional) </td><td class="info" align="left"><p>Maximum allowed value for pixels of the input image. If a value is 
     437the Cayula and Cornillon algorithm.</p></td></tr><tr><td class="info">Maximum allowed image value (Optional) </td><td class="info" align="left"><p>Maximum allowed value for pixels of the input image. If a value is 
    400438provided, pixels greater than this value will be masked prior to 
    401 running the Cayula-Cornillon algorithm.</p></td></tr><tr><td class="info">Masks to apply (Optional) </td><td class="info" align="left"><p>Binary rasters to apply as masks to the input image before running 
    402 the Cayula-Cornillon algorithm on it. Each item in this list 
     439running the Cayula and Cornillon algorithm.</p></td></tr><tr><td class="info">Masks to apply (Optional) </td><td class="info" align="left"><p>Binary rasters to apply as masks to the input image before running 
     440the Cayula and Cornillon algorithm on it. Each item in this list 
    403441corresponds to parallel entries in the lists of mask data types, 
    404442tests, and values.</p><p>Use this parameter to pass in land masks, cloud masks, and so on. Any 
    … …  
    434472data types, and mask tests.</p></td></tr><tr><td class="info">Median filter window size (Optional) </td><td class="info" align="left"><p>Window size, in pixels, of the median filter to apply to the input 
    435473image prior to running the histogram analysis step of the 
    436 Cayula-Cornillon algorithm. If not provided, median filtering will not 
     474Cayula and Cornillon algorithm. If not provided, median filtering will not 
    437475be performed.</p><p>If you provide a value, it must be an odd integer greater than or 
    438476equal to 3. The filter window is square and advances across the image 
    … …  
    440478with the median value of the non-masked pixels in the surrounding 
    441479window. All masks are applied before the median filter is executed.</p><p>Median filtering is a traditional first step for certain classes of 
    442 edge detection algorithms. The original Cayula-Cornillon paper used a 
    443 window size of 3.</p></td></tr><tr><td class="info">Histogram window size (Optional) </td><td class="info" align="left"><p>Size of the histogram window to use for the Cayula-Cornillon 
     480edge detection algorithms. The original Cayula and Cornillon paper used a 
     481window size of 3.</p></td></tr><tr><td class="info">Histogram window size (Optional) </td><td class="info" align="left"><p>Size of the histogram window to use for the Cayula and Cornillon 
    444482algorithm.</p><p>The window is square. The original paper used a window size of 32. 
    445483Although the algorithm is claimed to obtain similar results regardless 
    … …  
    448486the paper carefully before experimenting with different window 
    449487sizes.</p></td></tr><tr><td class="info">Histogram window stride (Optional) </td><td class="info" align="left"><p>Number of pixels to move the histogram window after each iteration 
    450 of the Cayula-Cornillon algorithm.</p><p>The original paper used a 32x32 window and a stride of 16, to minimize 
     488of the Cayula and Cornillon algorithm.</p><p>The original paper used a 32x32 window and a stride of 16, to minimize 
    451489the CPU time required to execute the algorithm. Cutting the stride in 
    452490half increases the CPU time by a factor of about four. For example, a 
    … …  
    472510accurate. In this case, and the algorithm discards the current window, 
    473511advances to next one, and starts over.</p><p>I do not recommend selecting a value other than 0.25 unless you 
    474 understand the statistical analysis presented in the 1992 paper.</p></td></tr><tr><td class="info">Minimum population mean difference (Optional) </td><td class="info" align="left"><p>Minimum difference in population means.</p><p>After the histogram algorithm separates the non-masked pixels into two 
    475 populations, it computes the means of the two populations. If the 
    476 means differ by less than this parameter value, the algorithm discards 
    477 the current window, advances to next one, and starts over.</p><p>The original intent of this parameter is obscure. The Ratfor code I 
    478 obtained from Dave Ullman used the value 3 and contained the 
    479 explanation "a temperature difference of less than three digital 
    480 counts between the 2 populations is likely to be a result of the 
    481 discrete nature of the data."</p><p>You can use this parameter to eliminate weak fronts by selecting a 
    482 value that corresponds to a desired minimum mean temperature 
    483 difference. For example, for the NOAA NODC 4km AVHRR Pathfinder SST 
    484 data, the value of 1 corresponds to 0.075 degrees. To eliminate fronts 
    485 where the mean temperature difference is less than 0.5 degrees, set 
    486 this parameter to 0.5 / 0.075 = 6.666667.</p></td></tr><tr><td class="info">Minimum value for criterion function (Optional) </td><td class="info" align="left"><p>Minimum criterion function, theta(Topt), as described on pages 
     512understand the statistical analysis presented in the 1992 paper.</p></td></tr><tr><td class="info">Minimum value for criterion function (Optional) </td><td class="info" align="left"><p>Minimum criterion function, theta(Topt), as described on pages 
    48751371-72 of the 1992 paper.</p><p>The criterion function is used to determine whether the histogram 
    488514window contains a bimodal distribution, as would be expected if it 
    … …  
    545571MATLAB Image Processing Toolbox. Please see the MATLAB documentation 
    546572for more information.</p></td></tr><tr><td class="info">Output mask image (Optional) </td><td class="info" align="left"><p>Output binary raster that shows the pixels of the input image that 
    547 were masked prior to executing the Cayula-Cornillon algorithm.</p><p>The file will have the same dimensions as the input image and contain 
     573were masked prior to executing the Cayula and Cornillon algorithm.</p><p>The file will have the same dimensions as the input image and contain 
    5485748-bit unsigned integers. The value 1 indicates that the corresponding 
    549575pixel of the input image was masked; 0 indicates the pixel was not 

  • MGET/Branches/Jason/PythonPackage/dist/TracOnlineDocumentation/Documentation/ArcGISReference/CayulaCornillonEdgeDetection.FindArcGISRastersAndDetectEdges.html

    r945 r957  
    11<?xml version="1.0" encoding="utf-8"?> 
    22<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd"> 
    3 <html xmlns="http://www.w3.org/1999/xhtml"><head><link rel="stylesheet" type="text/css" href="81help.css?format=raw" /><meta http-equiv="Content-Type" content="text/html; charset=UTF-8" /><title>Find ArcGIS Rasters and Find Cayula-Cornillon Fronts</title></head><body><table style="margin-top:-1em; margin-bottom:0; padding:0; margin-left:-1em"><tr><td style="background:white"><img width="875" height="70" alt="ArcToolbox banner" src="AHBanner_ArcToolbox.gif?format=raw" /></td></tr></table><h1>Find ArcGIS Rasters and Find Cayula-Cornillon Fronts</h1><p></p><p>Finds ArcGIS rasters in a workspace and finds fronts within them using the Cayula-Cornillon (1992) single-image edge detection algorithm.</p><p>The Cayula and Cornillon (1992) single image edge detection (SIED) 
     3<html xmlns="http://www.w3.org/1999/xhtml"><head><link rel="stylesheet" type="text/css" href="81help.css?format=raw" /><meta http-equiv="Content-Type" content="text/html; charset=UTF-8" /><title>Find ArcGIS Rasters and Find Cayula-Cornillon Fronts</title></head><body><table style="margin-top:-1em; margin-bottom:0; padding:0; margin-left:-1em"><tr><td style="background:white"><img width="875" height="70" alt="ArcToolbox banner" src="AHBanner_ArcToolbox.gif?format=raw" /></td></tr></table><h1>Find ArcGIS Rasters and Find Cayula-Cornillon Fronts</h1><p></p><p>Finds ArcGIS rasters in a workspace and finds fronts within them using the Cayula and Cornillon (1992) single-image edge detection algorithm.</p><p>The Cayula and Cornillon (1992) single image edge detection (SIED) 
    44algorithm was designed to detect fronts in SST images and originally 
    55applied to data collected by the AVHRR sensor on the NOAA-7 satellite. 
    … …  
    3333long as a sufficient temperature gradient continues in the direction 
    3434the front was pointing.</p></li></ul><p>In 2005, I obtained a Rational Fortran (Ratfor) version of the 
    35 Cayula-Cornillon algorithm from Dave Ullman. Although it had been 
     35Cayula and Cornillon algorithm from Dave Ullman. Although it had been 
    3636modified extensively from the 1992 version, mainly to incorporate the 
    3737multi-image edge detection (MIED) algorithm (Cayula and Cornillon 
    … …  
    6868coast. Journal of Geophysical Research 104: 23459-23478.</p><p>Ullman, D. S. and P. C. Cornillon. 2000. Evaluation of front detection 
    6969methods for satellite-derived SST data using in situ observations. 
    70 Journal of Atmospheric and Oceanic Technology 17: 1667-1675.</p><br /><p><h2><img width="11" height="11" border="0" src="sm_arrow_down.gif?format=raw" /> Command line syntax</h2></p><div Class="expand" id="id103141">CayulaCornillonEdgeDetectionFindArcGISRastersAndDetectEdges_GeoEco &lt;inputWorkspace&gt; &lt;outputWorkspace&gt; {wildcard} {searchTree} {rasterType} {wrapEdges} {mapAlgebraExpression} {medianFilterWindowSize} {histogramWindowSize} {histogramWindowStride} {minPropNonMaskedCells} {minPopProp} {minPopMeanDifference} {minTheta} {minSinglePopCohesion} {minGlobalPopCohesion} {threads} {fillHoles} {thin} {minSize} {outputFrontsRasterPythonExpression} {outputFilteredImageRasterPythonExpression} {outputCandidateCountsRasterPythonExpression} {outputFrontCountsRasterPythonExpression} {outputWindowStatusCodesRasterPythonExpression} {outputWindowStatusValuesRasterPythonExpression} {modulesToImport;modulesToImport...} {skipExisting} <br /><br /><b>Parameters</b><br /><table width="100%" border="0" cellpadding="5"><tbody><tr><th width="40%"><b>Expression</b></th><th width="60%"><b>Explanation</b></th></tr><tr><td class="info">&lt;inputWorkspace&gt;</td><td class="info" align="left"><p>Workspace to search.</p></td></tr><tr><td class="info">&lt;outputWorkspace&gt;</td><td class="info" align="left"><p>Workspace to receive the output rasters.</p></td></tr><tr><td class="info">{wildcard}</td><td class="info" align="left"><p>Wildcard expression specifying the rasters to find. Please see the 
     70Journal of Atmospheric and Oceanic Technology 17: 1667-1675.</p><br /><p><h2><img width="11" height="11" border="0" src="sm_arrow_down.gif?format=raw" /> Command line syntax</h2></p><div Class="expand" id="id103141">CayulaCornillonEdgeDetectionFindArcGISRastersAndDetectEdges_GeoEco &lt;inputWorkspace&gt; &lt;outputWorkspace&gt; &lt;minPopMeanDifference&gt; {wildcard} {searchTree} {rasterType} {wrapEdges} {mapAlgebraExpression} {medianFilterWindowSize} {histogramWindowSize} {histogramWindowStride} {minPropNonMaskedCells} {minPopProp} {minTheta} {minSinglePopCohesion} {minGlobalPopCohesion} {threads} {fillHoles} {thin} {minSize} {outputFrontsRasterPythonExpression} {outputFilteredImageRasterPythonExpression} {outputCandidateCountsRasterPythonExpression} {outputFrontCountsRasterPythonExpression} {outputWindowStatusCodesRasterPythonExpression} {outputWindowStatusValuesRasterPythonExpression} {modulesToImport;modulesToImport...} {skipExisting} <br /><br /><b>Parameters</b><br /><table width="100%" border="0" cellpadding="5"><tbody><tr><th width="40%"><b>Expression</b></th><th width="60%"><b>Explanation</b></th></tr><tr><td class="info">&lt;inputWorkspace&gt;</td><td class="info" align="left"><p>Workspace to search.</p></td></tr><tr><td class="info">&lt;outputWorkspace&gt;</td><td class="info" align="left"><p>Workspace to receive the output rasters.</p></td></tr><tr><td class="info">&lt;minPopMeanDifference&gt;</td><td class="info" align="left"><p>Minimum difference in the mean values of two adjacent populations 
     71of pixels for a front to be detected between those two populations.</p><p>The Cayula and Cornillon algorithm passes a moving window over the 
     72image, checking each window for a bimodal distribution in the values 
     73of the pixels within it. When the algorithm detects a bimodal 
     74distribution, it computes the mean values of the two populations and 
     75compares the difference between the means to this threshold. If the 
     76difference is less than this threshold, the algorithm concludes there 
     77is no front present and moves on to the next window.</p><p>The value of the threshold is expressed in integer values. If your 
     78input raster has an integer data type, then the units of the threshold 
     79are the units of the input raster. For example, if your raster's 
     80integers are scaled such an increase of 1 integer value corresponds to 
     81an increase in 0.1 degrees C, then the same thing applies to the 
     82threshold. Under this example, a threshold value of 5 corresponds to a 
     83temperature difference of 0.5 degrees C.</p><p>If your input raster has a floating-point data type, then the units of 
     84the threshold depend the Map Algebra Expression that you also must 
     85specify, to convert the floating-point values to integers. For 
     86example, if your Map Algebra Expression multiplies the floating-point 
     87values by 10 (e.g. the value 20 degrees C is converted to the integer 
     88value 200), then each integer value corresponds to a change of 0.1 
     89degrees C. Under this example, a threshold value of 5 would correspond 
     90to a temperature difference of 0.5 degrees C.</p><p>The minimum allowed value of the threshold is 3, following Cayula's 
     91original Fortran code which contained the explanation "a temperature 
     92difference of less than three digital counts between the two 
     93populations is likely to be a result of the discrete nature of the 
     94data." In Cayula and Cornillon's study they used threshold of 3 and 
     95their data used a scale factor of 0.15 (i.e. each integer value 
     96corresponded to a change of 0.15 degrees C). Therefore they detected 
     97fronts between water masses that differed in temperature by at least 
     980.45 degrees C.</p><p>You can use this parameter to eliminate weak fronts by selecting a 
     99value that corresponds to a desired minimum mean temperature 
     100difference. Suppose, for example, you are working with NOAA NODC 4km 
     101AVHRR Pathfinder SST version 5.0 data, which uses a scale factor of 
     1020.075. To eliminate fronts where the mean temperature difference is 
     103less than 1 degree C, set this parameter to 1 / 0.075 = 
     10413.333333.</p></td></tr><tr><td class="info">{wildcard}</td><td class="info" align="left"><p>Wildcard expression specifying the rasters to find. Please see the 
    71105documentation for the ArcGIS geoprocessor's ListRasters function for 
    72106more information about the syntax. At the time of this writing, only 
    … …  
    76110documentation specified that any of the following strings would be 
    77111accepted: ALL, BMP, GIF, GRID, IMG, JP2, JPG, PNG, TIFF.</p><p>This parameter requires ArcGIS 9.3 or later.</p></td></tr><tr><td class="info">{wrapEdges}</td><td class="info" align="left"><p>If True, the input image is assumed to be a cylinder, with the 
    78 east and west edges connected. The Cayula-Cornillon algorithm will 
     112east and west edges connected. The Cayula and Cornillon algorithm will 
    79113"wrap around" to the other side of the image when needed. If False, 
    80114the east and west edges are assumed to not be connected, and the 
    … …  
    82116Pathfinder AVHRR SST) and False if your image is regional (e.g. NOAA 
    83117CoastWatch AVHRR SST).</p></td></tr><tr><td class="info">{mapAlgebraExpression}</td><td class="info" align="left"><p>Map algebra expression to execute on the input raster before 
    84 running the Cayula-Cornillon algorithm.</p><p><b>WARNING:</b> The ArcGIS Geoprocessing Model Builder may randomly and 
     118running the Cayula and Cornillon algorithm.</p><p><b>WARNING:</b> The ArcGIS Geoprocessing Model Builder may randomly and 
    85119silently delete the value of this parameter. This is a bug in ArcGIS. 
    86120Before running a model that you have saved, open this tool and 
    … …  
    115149down can be very frustrating.</p></li></ul></td></tr><tr><td class="info">{medianFilterWindowSize}</td><td class="info" align="left"><p>Window size, in pixels, of the median filter to apply to the input 
    116150image prior to running the histogram analysis step of the 
    117 Cayula-Cornillon algorithm. If not provided, median filtering will not 
     151Cayula and Cornillon algorithm. If not provided, median filtering will not 
    118152be performed.</p><p>If you provide a value, it must be an odd integer greater than or 
    119153equal to 3. The filter window is square and advances across the image 
    1201541 pixel at a time. The center pixel, if it is not NoData, is replaced 
    121155with the median value of the pixels in the surrounding window that are 
    122 not NoData. If the center pixel is NoData, it remains NoData.</p><p>The original paper used a window size of 3.</p></td></tr><tr><td class="info">{histogramWindowSize}</td><td class="info" align="left"><p>Size of the histogram window to use for the Cayula-Cornillon 
     156not NoData. If the center pixel is NoData, it remains NoData.</p><p>The original paper used a window size of 3.</p></td></tr><tr><td class="info">{histogramWindowSize}</td><td class="info" align="left"><p>Size of the histogram window to use for the Cayula and Cornillon 
    123157algorithm.</p><p>The window is square. The original paper used a window size of 32. 
    124158Although the algorithm is claimed to obtain similar results regardless 
    … …  
    127161the paper carefully before experimenting with different window 
    128162sizes.</p></td></tr><tr><td class="info">{histogramWindowStride}</td><td class="info" align="left"><p>Number of pixels to move the histogram window after each iteration 
    129 of the Cayula-Cornillon algorithm.</p><p>The original paper used a 32x32 window and a stride of 16, to minimize 
     163of the Cayula and Cornillon algorithm.</p><p>The original paper used a 32x32 window and a stride of 16, to minimize 
    130164the CPU time required to execute the algorithm. Cutting the stride in 
    131165half increases the CPU time by a factor of about four. For example, a 
    … …  
    149183tests will not be accurate. In this case, and the algorithm discards 
    150184the current window, advances to next one, and starts over.</p><p>I do not recommend selecting a value other than 0.25 unless you 
    151 understand the statistical analysis presented in the 1992 paper.</p></td></tr><tr><td class="info">{minPopMeanDifference}</td><td class="info" align="left"><p>Minimum difference in population means.</p><p>After the histogram algorithm separates the pixels into two 
    152 populations, it computes the means of the two populations. If the 
    153 means differ by less than this parameter value, the algorithm discards 
    154 the current window, advances to next one, and starts over.</p><p>The original intent of this parameter is obscure. The Fortran code I 
    155 obtained from Dave Ullman used the value 3 and contained the 
    156 explanation "a temperature difference of less than three digital 
    157 counts between the 2 populations is likely to be a result of the 
    158 discrete nature of the data."</p><p>You can use this parameter to eliminate weak fronts by selecting a 
    159 value that corresponds to a desired minimum mean temperature 
    160 difference. For example, the NOAA NODC 4km AVHRR Pathfinder Project 
    161 (<a href="http://www.nodc.noaa.gov/SatelliteData/pathfinder4km/">http://www.nodc.noaa.gov/SatelliteData/pathfinder4km/</a>) publishes SST 
    162 data as 16-bit unsigned integers where each integer represents 0.075 
    163 degrees. To eliminate fronts where the mean temperature difference is 
    164 less than 0.5 degrees, set this parameter to 0.5 / 0.075 = 
    165 6.666667.</p></td></tr><tr><td class="info">{minTheta}</td><td class="info" align="left"><p>Minimum criterion function, theta(Topt), as described on pages 
     185understand the statistical analysis presented in the 1992 paper.</p></td></tr><tr><td class="info">{minTheta}</td><td class="info" align="left"><p>Minimum criterion function, theta(Topt), as described on pages 
    16618671-72 of the 1992 paper.</p><p>The criterion function is used to determine whether the histogram 
    167187window contains a bimodal distribution, as would be expected if it 
    … …  
    229249it did not appear in any histogram windows that had sufficiently 
    230250large numbers of pixels that were not NoData in the input image to 
    231 proceed with the histogramming step of the Cayula-Cornillon 
     251proceed with the histogramming step of the Cayula and Cornillon 
    232252algorithm.</p></li></ul><ul><li><p>0 - The pixel was a candidate for containing a front -- it was not 
    233253NoData in the input image and it appeared in at least one histogram 
    … …  
    259279number of times the pixels appeared in histogram windows that had a 
    260280sufficiently large number of pixels to proceed with the histogramming 
    261 step of the Cayula-Cornillon algorithm. If an expression is not 
     281step of the Cayula and Cornillon algorithm. If an expression is not 
    262282provided, this raster will not be created.</p><p>The output raster will contain 16-bit signed integers and have the 
    263283same dimensions as the input raster. Pixels that are NoData in the 
    … …  
    350370expression, list the datetime module here. In your expression, you 
    351371must refer to the class using its fully-qualified name, 
    352 datetime.datetime.</p></td></tr><tr><td class="info">{skipExisting}</td><td class="info" align="left"><p>If True, existing output rasters will be skipped.</p></td></tr></tbody></table></div><p><h2><img width="11" height="11" border="0" src="sm_arrow_down.gif?format=raw" /> Scripting syntax</h2></p><div Class="expand" id="TEST">CayulaCornillonEdgeDetectionFindArcGISRastersAndDetectEdges_GeoEco (inputWorkspace, outputWorkspace, wildcard, searchTree, rasterType, wrapEdges, mapAlgebraExpression, medianFilterWindowSize, histogramWindowSize, histogramWindowStride, minPropNonMaskedCells, minPopProp, minPopMeanDifference, minTheta, minSinglePopCohesion, minGlobalPopCohesion, threads, fillHoles, thin, minSize, outputFrontsRasterPythonExpression, outputFilteredImageRasterPythonExpression, outputCandidateCountsRasterPythonExpression, outputFrontCountsRasterPythonExpression, outputWindowStatusCodesRasterPythonExpression, outputWindowStatusValuesRasterPythonExpression, modulesToImport, skipExisting) <br /><br /><b>Parameters</b><br /><table width="100%" border="0" cellpadding="5"><tbody><tr><th width="40%"><b>Expression</b></th><th width="60%"><b>Explanation</b></th></tr><tr><td class="info">Workspace to search (Required) </td><td class="info" align="left"><p>Workspace to search.</p></td></tr><tr><td class="info">Output workspace (Required) </td><td class="info" align="left"><p>Workspace to receive the output rasters.</p></td></tr><tr><td class="info">Wildcard expression (Optional) </td><td class="info" align="left"><p>Wildcard expression specifying the rasters to find. Please see the 
     372datetime.datetime.</p></td></tr><tr><td class="info">{skipExisting}</td><td class="info" align="left"><p>If True, existing output rasters will be skipped.</p></td></tr></tbody></table></div><p><h2><img width="11" height="11" border="0" src="sm_arrow_down.gif?format=raw" /> Scripting syntax</h2></p><div Class="expand" id="TEST">CayulaCornillonEdgeDetectionFindArcGISRastersAndDetectEdges_GeoEco (inputWorkspace, outputWorkspace, minPopMeanDifference, wildcard, searchTree, rasterType, wrapEdges, mapAlgebraExpression, medianFilterWindowSize, histogramWindowSize, histogramWindowStride, minPropNonMaskedCells, minPopProp, minTheta, minSinglePopCohesion, minGlobalPopCohesion, threads, fillHoles, thin, minSize, outputFrontsRasterPythonExpression, outputFilteredImageRasterPythonExpression, outputCandidateCountsRasterPythonExpression, outputFrontCountsRasterPythonExpression, outputWindowStatusCodesRasterPythonExpression, outputWindowStatusValuesRasterPythonExpression, modulesToImport, skipExisting) <br /><br /><b>Parameters</b><br /><table width="100%" border="0" cellpadding="5"><tbody><tr><th width="40%"><b>Expression</b></th><th width="60%"><b>Explanation</b></th></tr><tr><td class="info">Workspace to search (Required) </td><td class="info" align="left"><p>Workspace to search.</p></td></tr><tr><td class="info">Output workspace (Required) </td><td class="info" align="left"><p>Workspace to receive the output rasters.</p></td></tr><tr><td class="info">Front detection threshold (Required) </td><td class="info" align="left"><p>Minimum difference in the mean values of two adjacent populations 
     373of pixels for a front to be detected between those two populations.</p><p>The Cayula and Cornillon algorithm passes a moving window over the 
     374image, checking each window for a bimodal distribution in the values 
     375of the pixels within it. When the algorithm detects a bimodal 
     376distribution, it computes the mean values of the two populations and 
     377compares the difference between the means to this threshold. If the 
     378difference is less than this threshold, the algorithm concludes there 
     379is no front present and moves on to the next window.</p><p>The value of the threshold is expressed in integer values. If your 
     380input raster has an integer data type, then the units of the threshold 
     381are the units of the input raster. For example, if your raster's 
     382integers are scaled such an increase of 1 integer value corresponds to 
     383an increase in 0.1 degrees C, then the same thing applies to the 
     384threshold. Under this example, a threshold value of 5 corresponds to a 
     385temperature difference of 0.5 degrees C.</p><p>If your input raster has a floating-point data type, then the units of 
     386the threshold depend the Map Algebra Expression that you also must 
     387specify, to convert the floating-point values to integers. For 
     388example, if your Map Algebra Expression multiplies the floating-point 
     389values by 10 (e.g. the value 20 degrees C is converted to the integer 
     390value 200), then each integer value corresponds to a change of 0.1 
     391degrees C. Under this example, a threshold value of 5 would correspond 
     392to a temperature difference of 0.5 degrees C.</p><p>The minimum allowed value of the threshold is 3, following Cayula's 
     393original Fortran code which contained the explanation "a temperature 
     394difference of less than three digital counts between the two 
     395populations is likely to be a result of the discrete nature of the 
     396data." In Cayula and Cornillon's study they used threshold of 3 and 
     397their data used a scale factor of 0.15 (i.e. each integer value 
     398corresponded to a change of 0.15 degrees C). Therefore they detected 
     399fronts between water masses that differed in temperature by at least 
     4000.45 degrees C.</p><p>You can use this parameter to eliminate weak fronts by selecting a 
     401value that corresponds to a desired minimum mean temperature 
     402difference. Suppose, for example, you are working with NOAA NODC 4km 
     403AVHRR Pathfinder SST version 5.0 data, which uses a scale factor of 
     4040.075. To eliminate fronts where the mean temperature difference is 
     405less than 1 degree C, set this parameter to 1 / 0.075 = 
     40613.333333.</p></td></tr><tr><td class="info">Wildcard expression (Optional) </td><td class="info" align="left"><p>Wildcard expression specifying the rasters to find. Please see the 
    353407documentation for the ArcGIS geoprocessor's ListRasters function for 
    354408more information about the syntax. At the time of this writing, only 
    … …  
    358412documentation specified that any of the following strings would be 
    359413accepted: ALL, BMP, GIF, GRID, IMG, JP2, JPG, PNG, TIFF.</p><p>This parameter requires ArcGIS 9.3 or later.</p></td></tr><tr><td class="info">Wrap edges (Optional) </td><td class="info" align="left"><p>If True, the input image is assumed to be a cylinder, with the 
    360 east and west edges connected. The Cayula-Cornillon algorithm will 
     414east and west edges connected. The Cayula and Cornillon algorithm will 
    361415"wrap around" to the other side of the image when needed. If False, 
    362416the east and west edges are assumed to not be connected, and the 
    … …  
    364418Pathfinder AVHRR SST) and False if your image is regional (e.g. NOAA 
    365419CoastWatch AVHRR SST).</p></td></tr><tr><td class="info">Map algebra expression (Optional) </td><td class="info" align="left"><p>Map algebra expression to execute on the input raster before 
    366 running the Cayula-Cornillon algorithm.</p><p><b>WARNING:</b> The ArcGIS Geoprocessing Model Builder may randomly and 
     420running the Cayula and Cornillon algorithm.</p><p><b>WARNING:</b> The ArcGIS Geoprocessing Model Builder may randomly and 
    367421silently delete the value of this parameter. This is a bug in ArcGIS. 
    368422Before running a model that you have saved, open this tool and 
    … …  
    397451down can be very frustrating.</p></li></ul></td></tr><tr><td class="info">Median filter window size (Optional) </td><td class="info" align="left"><p>Window size, in pixels, of the median filter to apply to the input 
    398452image prior to running the histogram analysis step of the 
    399 Cayula-Cornillon algorithm. If not provided, median filtering will not 
     453Cayula and Cornillon algorithm. If not provided, median filtering will not 
    400454be performed.</p><p>If you provide a value, it must be an odd integer greater than or 
    401455equal to 3. The filter window is square and advances across the image 
    4024561 pixel at a time. The center pixel, if it is not NoData, is replaced 
    403457with the median value of the pixels in the surrounding window that are 
    404 not NoData. If the center pixel is NoData, it remains NoData.</p><p>The original paper used a window size of 3.</p></td></tr><tr><td class="info">Histogram window size (Optional) </td><td class="info" align="left"><p>Size of the histogram window to use for the Cayula-Cornillon 
     458not NoData. If the center pixel is NoData, it remains NoData.</p><p>The original paper used a window size of 3.</p></td></tr><tr><td class="info">Histogram window size (Optional) </td><td class="info" align="left"><p>Size of the histogram window to use for the Cayula and Cornillon 
    405459algorithm.</p><p>The window is square. The original paper used a window size of 32. 
    406460Although the algorithm is claimed to obtain similar results regardless 
    … …  
    409463the paper carefully before experimenting with different window 
    410464sizes.</p></td></tr><tr><td class="info">Histogram window stride (Optional) </td><td class="info" align="left"><p>Number of pixels to move the histogram window after each iteration 
    411 of the Cayula-Cornillon algorithm.</p><p>The original paper used a 32x32 window and a stride of 16, to minimize 
     465of the Cayula and Cornillon algorithm.</p><p>The original paper used a 32x32 window and a stride of 16, to minimize 
    412466the CPU time required to execute the algorithm. Cutting the stride in 
    413467half increases the CPU time by a factor of about four. For example, a 
    … …  
    431485tests will not be accurate. In this case, and the algorithm discards 
    432486the current window, advances to next one, and starts over.</p><p>I do not recommend selecting a value other than 0.25 unless you 
    433 understand the statistical analysis presented in the 1992 paper.</p></td></tr><tr><td class="info">Minimum population mean difference (Optional) </td><td class="info" align="left"><p>Minimum difference in population means.</p><p>After the histogram algorithm separates the pixels into two 
    434 populations, it computes the means of the two populations. If the 
    435 means differ by less than this parameter value, the algorithm discards 
    436 the current window, advances to next one, and starts over.</p><p>The original intent of this parameter is obscure. The Fortran code I 
    437 obtained from Dave Ullman used the value 3 and contained the 
    438 explanation "a temperature difference of less than three digital 
    439 counts between the 2 populations is likely to be a result of the 
    440 discrete nature of the data."</p><p>You can use this parameter to eliminate weak fronts by selecting a 
    441 value that corresponds to a desired minimum mean temperature 
    442 difference. For example, the NOAA NODC 4km AVHRR Pathfinder Project 
    443 (<a href="http://www.nodc.noaa.gov/SatelliteData/pathfinder4km/">http://www.nodc.noaa.gov/SatelliteData/pathfinder4km/</a>) publishes SST 
    444 data as 16-bit unsigned integers where each integer represents 0.075 
    445 degrees. To eliminate fronts where the mean temperature difference is 
    446 less than 0.5 degrees, set this parameter to 0.5 / 0.075 = 
    447 6.666667.</p></td></tr><tr><td class="info">Minimum value for criterion function (Optional) </td><td class="info" align="left"><p>Minimum criterion function, theta(Topt), as described on pages 
     487understand the statistical analysis presented in the 1992 paper.</p></td></tr><tr><td class="info">Minimum value for criterion function (Optional) </td><td class="info" align="left"><p>Minimum criterion function, theta(Topt), as described on pages 
    44848871-72 of the 1992 paper.</p><p>The criterion function is used to determine whether the histogram 
    449489window contains a bimodal distribution, as would be expected if it 
    … …  
    511551it did not appear in any histogram windows that had sufficiently 
    512552large numbers of pixels that were not NoData in the input image to 
    513 proceed with the histogramming step of the Cayula-Cornillon 
     553proceed with the histogramming step of the Cayula and Cornillon 
    514554algorithm.</p></li></ul><ul><li><p>0 - The pixel was a candidate for containing a front -- it was not 
    515555NoData in the input image and it appeared in at least one histogram 
    … …  
    541581number of times the pixels appeared in histogram windows that had a 
    542582sufficiently large number of pixels to proceed with the histogramming 
    543 step of the Cayula-Cornillon algorithm. If an expression is not 
     583step of the Cayula and Cornillon algorithm. If an expression is not 
    544584provided, this raster will not be created.</p><p>The output raster will contain 16-bit signed integers and have the 
    545585same dimensions as the input raster. Pixels that are NoData in the 

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    11<?xml version="1.0" encoding="utf-8"?> 
    22<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd"> 
    3 <html xmlns="http://www.w3.org/1999/xhtml"><head><link rel="stylesheet" type="text/css" href="81help.css?format=raw" /><meta http-equiv="Content-Type" content="text/html; charset=UTF-8" /><title>Find CoastWatch Images and Find Cayula-Cornillon Fronts as ArcGIS Rasters</title></head><body><table style="margin-top:-1em; margin-bottom:0; padding:0; margin-left:-1em"><tr><td style="background:white"><img width="875" height="70" alt="ArcToolbox banner" src="AHBanner_ArcToolbox.gif?format=raw" /></td></tr></table><h1>Find CoastWatch Images and Find Cayula-Cornillon Fronts as ArcGIS Rasters</h1><p></p><p>Uses the Cayula-Cornillon (1992) single-image edge detection algorithm to find fronts in the CoastWatch POES AVHRR images found in a directory, and outputs the fronts as ArcGIS rasters.</p><br /><p><h2><img width="11" height="11" border="0" src="sm_arrow_down.gif?format=raw" /> Command line syntax</h2></p><div Class="expand" id="id103141">CoastWatchAVHRRFindCoastWatchFilesAndFindFrontsAsArcGISRasters_GeoEco &lt;inputDirectory&gt; &lt;outputWorkspace&gt; {wildcard} {searchTree} {minSize} {maxSize} {minDateCreated} {maxDateCreated} {minDateModified} {maxDateModified} {variables;variables...} {regionCodes;regionCodes...} {satellites;satellites...} {minImageDate} {maxImageDate} {minDayOfYear} {maxDayOfYear} {sceneTimes;sceneTimes...} {cloudVariable} {sunZenithVariable} {useDayCloudTest1} {useDayCloudTest2} {useDayCloudTest3} {useDayCloudTest4} {useDayCloudTest5} {useDayCloudTest6} {useDayCloudTest7} {maskWhenDayCloudMaskExceeds} {useNightCloudTest1} {useNightCloudTest2} {useNightCloudTest3} {useNightCloudTest4} {useNightCloudTest5} {useNightCloudTest6} {useNightCloudTest7} {maskWhenNightCloudMaskExceeds} {minCloudyNeighbors} {medianFilterWindowSize} {histogramWindowSize} {histogramWindowStride} {minPropNonMaskedCells} {minPopProp} {minPopMeanDifference} {minTheta} {minSinglePopCohesion} {minGlobalPopCohesion} {threads} {projectedCoordinateSystem} {geographicTransformation} {NEAREST | BILINEAR | CUBIC} {projectedCellSize} {registrationPoint} {clippingRectangle} {buildPyramids} {outputFrontsRasterPythonExpression} {outputMaskRasterPythonExpression} {outputFilteredImageRasterPythonExpression} {outputCandidateCountsRasterPythonExpression} {outputFrontCountsRasterPythonExpression} {outputWindowStatusCodesRasterPythonExpression} {outputWindowStatusValuesRasterPythonExpression} {modulesToImport;modulesToImport...} {skipExisting} <br /><br /><b>Parameters</b><br /><table width="100%" border="0" cellpadding="5"><tbody><tr><th width="40%"><b>Expression</b></th><th width="60%"><b>Explanation</b></th></tr><tr><td class="info">&lt;inputDirectory&gt;</td><td class="info" align="left"><p>Directory to search.</p></td></tr><tr><td class="info">&lt;outputWorkspace&gt;</td><td class="info" align="left"><p>Workspace to receive the ArcGIS rasters.</p></td></tr><tr><td class="info">{wildcard}</td><td class="info" align="left"><p>UNIX-style "glob" wildcard expression specifying the CoastWatch 
     3<html xmlns="http://www.w3.org/1999/xhtml"><head><link rel="stylesheet" type="text/css" href="81help.css?format=raw" /><meta http-equiv="Content-Type" content="text/html; charset=UTF-8" /><title>Find CoastWatch Images and Find Cayula-Cornillon Fronts as ArcGIS Rasters</title></head><body><table style="margin-top:-1em; margin-bottom:0; padding:0; margin-left:-1em"><tr><td style="background:white"><img width="875" height="70" alt="ArcToolbox banner" src="AHBanner_ArcToolbox.gif?format=raw" /></td></tr></table><h1>Find CoastWatch Images and Find Cayula-Cornillon Fronts as ArcGIS Rasters</h1><p></p><p>Uses the Cayula and Cornillon (1992) single-image edge detection algorithm to find fronts in the CoastWatch POES AVHRR images found in a directory, and outputs the fronts as ArcGIS rasters.</p><br /><p><h2><img width="11" height="11" border="0" src="sm_arrow_down.gif?format=raw" /> Command line syntax</h2></p><div Class="expand" id="id103141">CoastWatchAVHRRFindCoastWatchFilesAndFindFrontsAsArcGISRasters_GeoEco &lt;inputDirectory&gt; &lt;outputWorkspace&gt; &lt;minPopMeanDifference&gt; {wildcard} {searchTree} {minSize} {maxSize} {minDateCreated} {maxDateCreated} {minDateModified} {maxDateModified} {variables;variables...} {regionCodes;regionCodes...} {satellites;satellites...} {minImageDate} {maxImageDate} {minDayOfYear} {maxDayOfYear} {sceneTimes;sceneTimes...} {cloudVariable} {sunZenithVariable} {useDayCloudTest1} {useDayCloudTest2} {useDayCloudTest3} {useDayCloudTest4} {useDayCloudTest5} {useDayCloudTest6} {useDayCloudTest7} {maskWhenDayCloudMaskExceeds} {useNightCloudTest1} {useNightCloudTest2} {useNightCloudTest3} {useNightCloudTest4} {useNightCloudTest5} {useNightCloudTest6} {useNightCloudTest7} {maskWhenNightCloudMaskExceeds} {minCloudyNeighbors} {medianFilterWindowSize} {histogramWindowSize} {histogramWindowStride} {minPropNonMaskedCells} {minPopProp} {minTheta} {minSinglePopCohesion} {minGlobalPopCohesion} {threads} {projectedCoordinateSystem} {geographicTransformation} {NEAREST | BILINEAR | CUBIC} {projectedCellSize} {registrationPoint} {clippingRectangle} {buildPyramids} {outputFrontsRasterPythonExpression} {outputMaskRasterPythonExpression} {outputFilteredImageRasterPythonExpression} {outputCandidateCountsRasterPythonExpression} {outputFrontCountsRasterPythonExpression} {outputWindowStatusCodesRasterPythonExpression} {outputWindowStatusValuesRasterPythonExpression} {modulesToImport;modulesToImport...} {skipExisting} <br /><br /><b>Parameters</b><br /><table width="100%" border="0" cellpadding="5"><tbody><tr><th width="40%"><b>Expression</b></th><th width="60%"><b>Explanation</b></th></tr><tr><td class="info">&lt;inputDirectory&gt;</td><td class="info" align="left"><p>Directory to search.</p></td></tr><tr><td class="info">&lt;outputWorkspace&gt;</td><td class="info" align="left"><p>Workspace to receive the ArcGIS rasters.</p></td></tr><tr><td class="info">&lt;minPopMeanDifference&gt;</td><td class="info" align="left"><p>Minimum difference, in degrees C, between the mean temperatures of 
     4two adjacent populations of pixels for a front to be detected between 
     5those two populations.</p><p>The Cayula and Cornillon algorithm passes a moving window over the 
     6image, checking each window for a bimodal distribution in the 
     7temperatures of the pixels within it. When the algorithm detects a 
     8bimodal distribution, it computes the mean temperatures of the two 
     9populations and compares the difference between the means to this 
     10threshold. If the difference is less than this threshold, the 
     11algorithm concludes there is no front present and moves on to the next 
     12window.</p><p>You can use this parameter to eliminate weak fronts by selecting a 
     13value that corresponds to a desired minimum mean temperature 
     14difference. Bear in mind that Cayula and Cornillon's (1992) study used 
     15data from early satellites that contributed to the CoastWatch program. 
     16When I examined their Fortran code, I found a commenet suggesting that 
     17they believed the minimum allowable threshold given the measurement 
     18error of the sensors was 0.45 deg C.</p></td></tr><tr><td class="info">{wildcard}</td><td class="info" align="left"><p>UNIX-style "glob" wildcard expression specifying the CoastWatch 
    419files to find.</p><p>The glob syntax supports the following patterns:</p><ul><li><p>? - matches any single character</p></li></ul><ul><li><p>* - matches zero or more characters</p></li></ul><ul><li><p>[seq] - matches any single character in <i>seq</i></p></li></ul><ul><li><p>[!seq] - matches any single character not in <i>seq</i></p></li></ul><p><i>seq</i> is one or more characters, such as abc. You may specify 
    520character ranges using a dash. For example, a-z0-9 specifies all of 
    … …  
    340355(e.g. it is land), it does not count as being cloudy.</p><p>This option is ignored when cloud masking is not performed.</p></td></tr><tr><td class="info">{medianFilterWindowSize}</td><td class="info" align="left"><p>Window size, in pixels, of the median filter to apply to the input 
    341356image prior to running the histogram analysis step of the 
    342 Cayula-Cornillon algorithm. If not provided, median filtering will not 
     357Cayula and Cornillon algorithm. If not provided, median filtering will not 
    343358be performed.</p><p>If you provide a value, it must be an odd integer greater than or 
    344359equal to 3. The filter window is square and advances across the image 
    … …  
    346361with the median value of the non-masked pixels in the surrounding 
    347362window. All masks are applied before the median filter is executed.</p><p>Median filtering is a traditional first step for certain classes of 
    348 edge detection algorithms. The original Cayula-Cornillon paper used a 
    349 window size of 3.</p></td></tr><tr><td class="info">{histogramWindowSize}</td><td class="info" align="left"><p>Size of the histogram window to use for the Cayula-Cornillon 
     363edge detection algorithms. The original Cayula and Cornillon paper used a 
     364window size of 3.</p></td></tr><tr><td class="info">{histogramWindowSize}</td><td class="info" align="left"><p>Size of the histogram window to use for the Cayula and Cornillon 
    350365algorithm.</p><p>The window is square. The original paper used a window size of 32. 
    351366Although the algorithm is claimed to obtain similar results regardless 
    … …  
    354369the paper carefully before experimenting with different window 
    355370sizes.</p></td></tr><tr><td class="info">{histogramWindowStride}</td><td class="info" align="left"><p>Number of pixels to move the histogram window after each iteration 
    356 of the Cayula-Cornillon algorithm.</p><p>The original paper used a 32x32 window and a stride of 16, to minimize 
     371of the Cayula and Cornillon algorithm.</p><p>The original paper used a 32x32 window and a stride of 16, to minimize 
    357372the CPU time required to execute the algorithm. Cutting the stride in 
    358373half increases the CPU time by a factor of about four. For example, a 
    … …  
    378393accurate. In this case, and the algorithm discards the current window, 
    379394advances to next one, and starts over.</p><p>I do not recommend selecting a value other than 0.25 unless you 
    380 understand the statistical analysis presented in the 1992 paper.</p></td></tr><tr><td class="info">{minPopMeanDifference}</td><td class="info" align="left"><p>Minimum difference in population means.</p><p>After the histogram algorithm separates the non-masked pixels into two 
    381 populations, it computes the means of the two populations. If the 
    382 means differ by less than this parameter value, the algorithm discards 
    383 the current window, advances to next one, and starts over.</p><p>The original intent of this parameter is obscure. The Ratfor code I 
    384 obtained from Dave Ullman used the value 3 and contained the 
    385 explanation "a temperature difference of less than three digital 
    386 counts between the 2 populations is likely to be a result of the 
    387 discrete nature of the data."</p><p>You can use this parameter to eliminate weak fronts by selecting a 
    388 value that corresponds to a desired minimum mean temperature 
    389 difference. For example, for the NOAA NODC 4km AVHRR Pathfinder SST 
    390 data, the value of 1 corresponds to 0.075 degrees. To eliminate fronts 
    391 where the mean temperature difference is less than 0.5 degrees, set 
    392 this parameter to 0.5 / 0.075 = 6.666667.</p></td></tr><tr><td class="info">{minTheta}</td><td class="info" align="left"><p>Minimum criterion function, theta(Topt), as described on pages 
     395understand the statistical analysis presented in the 1992 paper.</p></td></tr><tr><td class="info">{minTheta}</td><td class="info" align="left"><p>Minimum criterion function, theta(Topt), as described on pages 
    39339671-72 of the 1992 paper.</p><p>The criterion function is used to determine whether the histogram 
    394397window contains a bimodal distribution, as would be expected if it 
    … …  
    503506any histogram windows that had sufficiently large numbers of 
    504507non-masked pixels to proceed with the histogramming step of the 
    505 Cayula-Cornillon algorithm.</p></li></ul><ul><li><p>0 - The pixel was a candidate for containing a front -- it was not 
     508Cayula and Cornillon algorithm.</p></li></ul><ul><li><p>0 - The pixel was a candidate for containing a front -- it was not 
    506509masked and it appeared in at least one histogram window with a 
    507510sufficient number of non-masked pixels to proceed with the 
    … …  
    630633documentation</a>.</p></td></tr><tr><td class="info">{outputMaskRasterPythonExpression}</td><td class="info" align="left"><p>Python expression used to calculate the absolute path of the 
    631634output raster that shows the pixels of the input image that were 
    632 masked prior to executing the Cayula-Cornillon algorithm. If an 
     635masked prior to executing the Cayula and Cornillon algorithm. If an 
    633636expression is not provided, this raster will not be created.</p><p>The raster will contain 8-bit integers and have the same dimensions as 
    634637the input image. The value 1 indicates that the corresponding pixel of 
    … …  
    652655number of times the pixels appeared in histogram windows that had a 
    653656sufficiently large number of non-masked pixels to proceed with the 
    654 histogramming step of the Cayula-Cornillon algorithm. If an expression 
     657histogramming step of the Cayula and Cornillon algorithm. If an expression 
    655658is not provided, this raster will not be created.</p><p>The raster will contain 16-bit signed integers and have the same 
    656659dimensions as the input image. Because the histogram window stride is 
    … …  
    743746expression, list the datetime module here. In your expression, you 
    744747must refer to the class using its fully-qualified name, 
    745 datetime.datetime.</p></td></tr><tr><td class="info">{skipExisting}</td><td class="info" align="left"><p>If True, conversion will be skipped for output rasters that already exist.</p></td></tr></tbody></table></div><p><h2><img width="11" height="11" border="0" src="sm_arrow_down.gif?format=raw" /> Scripting syntax</h2></p><div Class="expand" id="TEST">CoastWatchAVHRRFindCoastWatchFilesAndFindFrontsAsArcGISRasters_GeoEco (inputDirectory, outputWorkspace, wildcard, searchTree, minSize, maxSize, minDateCreated, maxDateCreated, minDateModified, maxDateModified, variables, regionCodes, satellites, minImageDate, maxImageDate, minDayOfYear, maxDayOfYear, sceneTimes, cloudVariable, sunZenithVariable, useDayCloudTest1, useDayCloudTest2, useDayCloudTest3, useDayCloudTest4, useDayCloudTest5, useDayCloudTest6, useDayCloudTest7, maskWhenDayCloudMaskExceeds, useNightCloudTest1, useNightCloudTest2, useNightCloudTest3, useNightCloudTest4, useNightCloudTest5, useNightCloudTest6, useNightCloudTest7, maskWhenNightCloudMaskExceeds, minCloudyNeighbors, medianFilterWindowSize, histogramWindowSize, histogramWindowStride, minPropNonMaskedCells, minPopProp, minPopMeanDifference, minTheta, minSinglePopCohesion, minGlobalPopCohesion, threads, projectedCoordinateSystem, geographicTransformation, resamplingTechnique, projectedCellSize, registrationPoint, clippingRectangle, buildPyramids, outputFrontsRasterPythonExpression, outputMaskRasterPythonExpression, outputFilteredImageRasterPythonExpression, outputCandidateCountsRasterPythonExpression, outputFrontCountsRasterPythonExpression, outputWindowStatusCodesRasterPythonExpression, outputWindowStatusValuesRasterPythonExpression, modulesToImport, skipExisting) <br /><br /><b>Parameters</b><br /><table width="100%" border="0" cellpadding="5"><tbody><tr><th width="40%"><b>Expression</b></th><th width="60%"><b>Explanation</b></th></tr><tr><td class="info">Directory to search (Required) </td><td class="info" align="left"><p>Directory to search.</p></td></tr><tr><td class="info">Output workspace (Required) </td><td class="info" align="left"><p>Workspace to receive the ArcGIS rasters.</p></td></tr><tr><td class="info">Wildcard expression (Optional) </td><td class="info" align="left"><p>UNIX-style "glob" wildcard expression specifying the CoastWatch 
     748datetime.datetime.</p></td></tr><tr><td class="info">{skipExisting}</td><td class="info" align="left"><p>If True, conversion will be skipped for output rasters that already exist.</p></td></tr></tbody></table></div><p><h2><img width="11" height="11" border="0" src="sm_arrow_down.gif?format=raw" /> Scripting syntax</h2></p><div Class="expand" id="TEST">CoastWatchAVHRRFindCoastWatchFilesAndFindFrontsAsArcGISRasters_GeoEco (inputDirectory, outputWorkspace, minPopMeanDifference, wildcard, searchTree, minSize, maxSize, minDateCreated, maxDateCreated, minDateModified, maxDateModified, variables, regionCodes, satellites, minImageDate, maxImageDate, minDayOfYear, maxDayOfYear, sceneTimes, cloudVariable, sunZenithVariable, useDayCloudTest1, useDayCloudTest2, useDayCloudTest3, useDayCloudTest4, useDayCloudTest5, useDayCloudTest6, useDayCloudTest7, maskWhenDayCloudMaskExceeds, useNightCloudTest1, useNightCloudTest2, useNightCloudTest3, useNightCloudTest4, useNightCloudTest5, useNightCloudTest6, useNightCloudTest7, maskWhenNightCloudMaskExceeds, minCloudyNeighbors, medianFilterWindowSize, histogramWindowSize, histogramWindowStride, minPropNonMaskedCells, minPopProp, minTheta, minSinglePopCohesion, minGlobalPopCohesion, threads, projectedCoordinateSystem, geographicTransformation, resamplingTechnique, projectedCellSize, registrationPoint, clippingRectangle, buildPyramids, outputFrontsRasterPythonExpression, outputMaskRasterPythonExpression, outputFilteredImageRasterPythonExpression, outputCandidateCountsRasterPythonExpression, outputFrontCountsRasterPythonExpression, outputWindowStatusCodesRasterPythonExpression, outputWindowStatusValuesRasterPythonExpression, modulesToImport, skipExisting) <br /><br /><b>Parameters</b><br /><table width="100%" border="0" cellpadding="5"><tbody><tr><th width="40%"><b>Expression</b></th><th width="60%"><b>Explanation</b></th></tr><tr><td class="info">Directory to search (Required) </td><td class="info" align="left"><p>Directory to search.</p></td></tr><tr><td class="info">Output workspace (Required) </td><td class="info" align="left"><p>Workspace to receive the ArcGIS rasters.</p></td></tr><tr><td class="info">Front detection threshold (Required) </td><td class="info" align="left"><p>Minimum difference, in degrees C, between the mean temperatures of 
     749two adjacent populations of pixels for a front to be detected between 
     750those two populations.</p><p>The Cayula and Cornillon algorithm passes a moving window over the 
     751image, checking each window for a bimodal distribution in the 
     752temperatures of the pixels within it. When the algorithm detects a 
     753bimodal distribution, it computes the mean temperatures of the two 
     754populations and compares the difference between the means to this 
     755threshold. If the difference is less than this threshold, the 
     756algorithm concludes there is no front present and moves on to the next 
     757window.</p><p>You can use this parameter to eliminate weak fronts by selecting a 
     758value that corresponds to a desired minimum mean temperature 
     759difference. Bear in mind that Cayula and Cornillon's (1992) study used 
     760data from early satellites that contributed to the CoastWatch program. 
     761When I examined their Fortran code, I found a commenet suggesting that 
     762they believed the minimum allowable threshold given the measurement 
     763error of the sensors was 0.45 deg C.</p></td></tr><tr><td class="info">Wildcard expression (Optional) </td><td class="info" align="left"><p>UNIX-style "glob" wildcard expression specifying the CoastWatch 
    746764files to find.</p><p>The glob syntax supports the following patterns:</p><ul><li><p>? - matches any single character</p></li></ul><ul><li><p>* - matches zero or more characters</p></li></ul><ul><li><p>[seq] - matches any single character in <i>seq</i></p></li></ul><ul><li><p>[!seq] - matches any single character not in <i>seq</i></p></li></ul><p><i>seq</i> is one or more characters, such as abc. You may specify 
    747765character ranges using a dash. For example, a-z0-9 specifies all of 
    … …  
    10821100(e.g. it is land), it does not count as being cloudy.</p><p>This option is ignored when cloud masking is not performed.</p></td></tr><tr><td class="info">Median filter window size (Optional) </td><td class="info" align="left"><p>Window size, in pixels, of the median filter to apply to the input 
    10831101image prior to running the histogram analysis step of the 
    1084 Cayula-Cornillon algorithm. If not provided, median filtering will not 
     1102Cayula and Cornillon algorithm. If not provided, median filtering will not 
    10851103be performed.</p><p>If you provide a value, it must be an odd integer greater than or 
    10861104equal to 3. The filter window is square and advances across the image 
    … …  
    10881106with the median value of the non-masked pixels in the surrounding 
    10891107window. All masks are applied before the median filter is executed.</p><p>Median filtering is a traditional first step for certain classes of 
    1090 edge detection algorithms. The original Cayula-Cornillon paper used a 
    1091 window size of 3.</p></td></tr><tr><td class="info">Histogram window size (Optional) </td><td class="info" align="left"><p>Size of the histogram window to use for the Cayula-Cornillon 
     1108edge detection algorithms. The original Cayula and Cornillon paper used a 
     1109window size of 3.</p></td></tr><tr><td class="info">Histogram window size (Optional) </td><td class="info" align="left"><p>Size of the histogram window to use for the Cayula and Cornillon 
    10921110algorithm.</p><p>The window is square. The original paper used a window size of 32. 
    10931111Although the algorithm is claimed to obtain similar results regardless 
    … …  
    10961114the paper carefully before experimenting with different window 
    10971115sizes.</p></td></tr><tr><td class="info">Histogram window stride (Optional) </td><td class="info" align="left"><p>Number of pixels to move the histogram window after each iteration 
    1098 of the Cayula-Cornillon algorithm.</p><p>The original paper used a 32x32 window and a stride of 16, to minimize 
     1116of the Cayula and Cornillon algorithm.</p><p>The original paper used a 32x32 window and a stride of 16, to minimize 
    10991117the CPU time required to execute the algorithm. Cutting the stride in 
    11001118half increases the CPU time by a factor of about four. For example, a 
    … …  
    11201138accurate. In this case, and the algorithm discards the current window, 
    11211139advances to next one, and starts over.</p><p>I do not recommend selecting a value other than 0.25 unless you 
    1122 understand the statistical analysis presented in the 1992 paper.</p></td></tr><tr><td class="info">Minimum population mean difference (Optional) </td><td class="info" align="left"><p>Minimum difference in population means.</p><p>After the histogram algorithm separates the non-masked pixels into two 
    1123 populations, it computes the means of the two populations. If the 
    1124 means differ by less than this parameter value, the algorithm discards 
    1125 the current window, advances to next one, and starts over.</p><p>The original intent of this parameter is obscure. The Ratfor code I 
    1126 obtained from Dave Ullman used the value 3 and contained the 
    1127 explanation "a temperature difference of less than three digital 
    1128 counts between the 2 populations is likely to be a result of the 
    1129 discrete nature of the data."</p><p>You can use this parameter to eliminate weak fronts by selecting a 
    1130 value that corresponds to a desired minimum mean temperature 
    1131 difference. For example, for the NOAA NODC 4km AVHRR Pathfinder SST 
    1132 data, the value of 1 corresponds to 0.075 degrees. To eliminate fronts 
    1133 where the mean temperature difference is less than 0.5 degrees, set 
    1134 this parameter to 0.5 / 0.075 = 6.666667.</p></td></tr><tr><td class="info">Minimum value for criterion function (Optional) </td><td class="info" align="left"><p>Minimum criterion function, theta(Topt), as described on pages 
     1140understand the statistical analysis presented in the 1992 paper.</p></td></tr><tr><td class="info">Minimum value for criterion function (Optional) </td><td class="info" align="left"><p>Minimum criterion function, theta(Topt), as described on pages 
    1135114171-72 of the 1992 paper.</p><p>The criterion function is used to determine whether the histogram 
    11361142window contains a bimodal distribution, as would be expected if it 
    … …  
    12451251any histogram windows that had sufficiently large numbers of 
    12461252non-masked pixels to proceed with the histogramming step of the 
    1247 Cayula-Cornillon algorithm.</p></li></ul><ul><li><p>0 - The pixel was a candidate for containing a front -- it was not 
     1253Cayula and Cornillon algorithm.</p></li></ul><ul><li><p>0 - The pixel was a candidate for containing a front -- it was not 
    12481254masked and it appeared in at least one histogram window with a 
    12491255sufficient number of non-masked pixels to proceed with the 
    … …  
    13721378documentation</a>.</p></td></tr><tr><td class="info">Output mask raster Python expression (Optional) </td><td class="info" align="left"><p>Python expression used to calculate the absolute path of the 
    13731379output raster that shows the pixels of the input image that were 
    1374 masked prior to executing the Cayula-Cornillon algorithm. If an 
     1380masked prior to executing the Cayula and Cornillon algorithm. If an 
    13751381expression is not provided, this raster will not be created.</p><p>The raster will contain 8-bit integers and have the same dimensions as 
    13761382the input image. The value 1 indicates that the corresponding pixel of 
    … …  
    13941400number of times the pixels appeared in histogram windows that had a 
    13951401sufficiently large number of non-masked pixels to proceed with the 
    1396 histogramming step of the Cayula-Cornillon algorithm. If an expression 
     1402histogramming step of the Cayula and Cornillon algorithm. If an expression 
    13971403is not provided, this raster will not be created.</p><p>The raster will contain 16-bit signed integers and have the same 
    13981404dimensions as the input image. Because the histogram window stride is 

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    r945 r957  
    11<?xml version="1.0" encoding="utf-8"?> 
    22<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd"> 
    3 <html xmlns="http://www.w3.org/1999/xhtml"><head><link rel="stylesheet" type="text/css" href="81help.css?format=raw" /><meta http-equiv="Content-Type" content="text/html; charset=UTF-8" /><title>Cayula-Cornillon Fronts in CoastWatch Image as ArcGIS Raster</title></head><body><table style="margin-top:-1em; margin-bottom:0; padding:0; margin-left:-1em"><tr><td style="background:white"><img width="875" height="70" alt="ArcToolbox banner" src="AHBanner_ArcToolbox.gif?format=raw" /></td></tr></table><h1>Cayula-Cornillon Fronts in CoastWatch Image as ArcGIS Raster</h1><p></p><p>Finds fronts in a CoastWatch POES AVHRR image using the Cayula-Cornillon (1992) single-image edge detection algorithm and outputs them to an ArcGIS raster.</p><br /><p><h2><img width="11" height="11" border="0" src="sm_arrow_down.gif?format=raw" /> Command line syntax</h2></p><div Class="expand" id="id103135">CoastWatchAVHRRFindFrontsAsArcGISRaster_GeoEco &lt;imageFile&gt; &lt;variable&gt; &lt;outputFrontsRaster&gt; {cloudMaskFile} {cloudVariable} {sunZenithFile} {sunZenithVariable} {useDayCloudTest1} {useDayCloudTest2} {useDayCloudTest3} {useDayCloudTest4} {useDayCloudTest5} {useDayCloudTest6} {useDayCloudTest7} {maskWhenDayCloudMaskExceeds} {useNightCloudTest1} {useNightCloudTest2} {useNightCloudTest3} {useNightCloudTest4} {useNightCloudTest5} {useNightCloudTest6} {useNightCloudTest7} {maskWhenNightCloudMaskExceeds} {minCloudyNeighbors} {medianFilterWindowSize} {histogramWindowSize} {histogramWindowStride} {minPropNonMaskedCells} {minPopProp} {minPopMeanDifference} {minTheta} {minSinglePopCohesion} {minGlobalPopCohesion} {threads} {outputMaskRaster} {outputFilteredImageRaster} {outputCandidateCountsRaster} {outputFrontCountsRaster} {outputWindowStatusCodesRaster} {outputWindowStatusValuesRaster} {projectedCoordinateSystem} {geographicTransformation} {NEAREST | BILINEAR | CUBIC} {projectedCellSize} {registrationPoint} {clippingRectangle} {buildPyramids} <br /><br /><b>Parameters</b><br /><table width="100%" border="0" cellpadding="5"><tbody><tr><th width="40%"><b>Expression</b></th><th width="60%"><b>Explanation</b></th></tr><tr><td class="info">&lt;imageFile&gt;</td><td class="info" align="left"><p>CoastWatch POES AVHRR CWF or HDF file in which fronts should be 
     3<html xmlns="http://www.w3.org/1999/xhtml"><head><link rel="stylesheet" type="text/css" href="81help.css?format=raw" /><meta http-equiv="Content-Type" content="text/html; charset=UTF-8" /><title>Cayula-Cornillon Fronts in CoastWatch Image as ArcGIS Raster</title></head><body><table style="margin-top:-1em; margin-bottom:0; padding:0; margin-left:-1em"><tr><td style="background:white"><img width="875" height="70" alt="ArcToolbox banner" src="AHBanner_ArcToolbox.gif?format=raw" /></td></tr></table><h1>Cayula-Cornillon Fronts in CoastWatch Image as ArcGIS Raster</h1><p></p><p>Finds fronts in a CoastWatch POES AVHRR image using the Cayula and Cornillon (1992) single-image edge detection algorithm and outputs them to an ArcGIS raster.</p><br /><p><h2><img width="11" height="11" border="0" src="sm_arrow_down.gif?format=raw" /> Command line syntax</h2></p><div Class="expand" id="id103135">CoastWatchAVHRRFindFrontsAsArcGISRaster_GeoEco &lt;imageFile&gt; &lt;variable&gt; &lt;minPopMeanDifference&gt; &lt;outputFrontsRaster&gt; {cloudMaskFile} {cloudVariable} {sunZenithFile} {sunZenithVariable} {useDayCloudTest1} {useDayCloudTest2} {useDayCloudTest3} {useDayCloudTest4} {useDayCloudTest5} {useDayCloudTest6} {useDayCloudTest7} {maskWhenDayCloudMaskExceeds} {useNightCloudTest1} {useNightCloudTest2} {useNightCloudTest3} {useNightCloudTest4} {useNightCloudTest5} {useNightCloudTest6} {useNightCloudTest7} {maskWhenNightCloudMaskExceeds} {minCloudyNeighbors} {medianFilterWindowSize} {histogramWindowSize} {histogramWindowStride} {minPropNonMaskedCells} {minPopProp} {minTheta} {minSinglePopCohesion} {minGlobalPopCohesion} {threads} {outputMaskRaster} {outputFilteredImageRaster} {outputCandidateCountsRaster} {outputFrontCountsRaster} {outputWindowStatusCodesRaster} {outputWindowStatusValuesRaster} {projectedCoordinateSystem} {geographicTransformation} {NEAREST | BILINEAR | CUBIC} {projectedCellSize} {registrationPoint} {clippingRectangle} {buildPyramids} <br /><br /><b>Parameters</b><br /><table width="100%" border="0" cellpadding="5"><tbody><tr><th width="40%"><b>Expression</b></th><th width="60%"><b>Explanation</b></th></tr><tr><td class="info">&lt;imageFile&gt;</td><td class="info" align="left"><p>CoastWatch POES AVHRR CWF or HDF file in which fronts should be 
    44detected.</p><p>Only CoastWatch POES AVHRR files are supported. An error will be raise 
    55for other CoastWatch files, such as those for the GOES satellite 
    … …  
    2424you may specify one of the others, if appropriate for your project. 
    2525Please see the CoastWatch documentation for more information about the 
    26 variables.</p></td></tr><tr><td class="info">&lt;outputFrontsRaster&gt;</td><td class="info" align="left"><p>Output raster that shows the fronts detected in the input image.</p><p>The raster will have the same dimensions as the input image and 
     26variables.</p></td></tr><tr><td class="info">&lt;minPopMeanDifference&gt;</td><td class="info" align="left"><p>Minimum difference, in degrees C, between the mean temperatures of 
     27two adjacent populations of pixels for a front to be detected between 
     28those two populations.</p><p>The Cayula and Cornillon algorithm passes a moving window over the 
     29image, checking each window for a bimodal distribution in the 
     30temperatures of the pixels within it. When the algorithm detects a 
     31bimodal distribution, it computes the mean temperatures of the two 
     32populations and compares the difference between the means to this 
     33threshold. If the difference is less than this threshold, the 
     34algorithm concludes there is no front present and moves on to the next 
     35window.</p><p>You can use this parameter to eliminate weak fronts by selecting a 
     36value that corresponds to a desired minimum mean temperature 
     37difference. Bear in mind that Cayula and Cornillon's (1992) study used 
     38data from early satellites that contributed to the CoastWatch program. 
     39When I examined their Fortran code, I found a commenet suggesting that 
     40they believed the minimum allowable threshold given the measurement 
     41error of the sensors was 0.45 deg C.</p></td></tr><tr><td class="info">&lt;outputFrontsRaster&gt;</td><td class="info" align="left"><p>Output raster that shows the fronts detected in the input image.</p><p>The raster will have the same dimensions as the input image and 
    2742contain 8-bit signed integers with three possible values:</p><ul><li><p>NoData - the pixel was never a candidate for containing a front, 
    2843either because it was masked or because because it did not appear in 
    2944any histogram windows that had sufficiently large numbers of 
    3045non-masked pixels to proceed with the histogramming step of the 
    31 Cayula-Cornillon algorithm.</p></li></ul><ul><li><p>0 - The pixel was a candidate for containing a front -- it was not 
     46Cayula and Cornillon algorithm.</p></li></ul><ul><li><p>0 - The pixel was a candidate for containing a front -- it was not 
    3247masked and it appeared in at least one histogram window with a 
    3348sufficient number of non-masked pixels to proceed with the 
    … …  
    260275(e.g. it is land), it does not count as being cloudy.</p><p>This option is ignored when cloud masking is not performed.</p></td></tr><tr><td class="info">{medianFilterWindowSize}</td><td class="info" align="left"><p>Window size, in pixels, of the median filter to apply to the input 
    261276image prior to running the histogram analysis step of the 
    262 Cayula-Cornillon algorithm. If not provided, median filtering will not 
     277Cayula and Cornillon algorithm. If not provided, median filtering will not 
    263278be performed.</p><p>If you provide a value, it must be an odd integer greater than or 
    264279equal to 3. The filter window is square and advances across the image 
    … …  
    266281with the median value of the non-masked pixels in the surrounding 
    267282window. All masks are applied before the median filter is executed.</p><p>Median filtering is a traditional first step for certain classes of 
    268 edge detection algorithms. The original Cayula-Cornillon paper used a 
    269 window size of 3.</p></td></tr><tr><td class="info">{histogramWindowSize}</td><td class="info" align="left"><p>Size of the histogram window to use for the Cayula-Cornillon 
     283edge detection algorithms. The original Cayula and Cornillon paper used a 
     284window size of 3.</p></td></tr><tr><td class="info">{histogramWindowSize}</td><td class="info" align="left"><p>Size of the histogram window to use for the Cayula and Cornillon 
    270285algorithm.</p><p>The window is square. The original paper used a window size of 32. 
    271286Although the algorithm is claimed to obtain similar results regardless 
    … …  
    274289the paper carefully before experimenting with different window 
    275290sizes.</p></td></tr><tr><td class="info">{histogramWindowStride}</td><td class="info" align="left"><p>Number of pixels to move the histogram window after each iteration 
    276 of the Cayula-Cornillon algorithm.</p><p>The original paper used a 32x32 window and a stride of 16, to minimize 
     291of the Cayula and Cornillon algorithm.</p><p>The original paper used a 32x32 window and a stride of 16, to minimize 
    277292the CPU time required to execute the algorithm. Cutting the stride in 
    278293half increases the CPU time by a factor of about four. For example, a 
    … …  
    298313accurate. In this case, and the algorithm discards the current window, 
    299314advances to next one, and starts over.</p><p>I do not recommend selecting a value other than 0.25 unless you 
    300 understand the statistical analysis presented in the 1992 paper.</p></td></tr><tr><td class="info">{minPopMeanDifference}</td><td class="info" align="left"><p>Minimum difference in population means.</p><p>After the histogram algorithm separates the non-masked pixels into two 
    301 populations, it computes the means of the two populations. If the 
    302 means differ by less than this parameter value, the algorithm discards 
    303 the current window, advances to next one, and starts over.</p><p>The original intent of this parameter is obscure. The Ratfor code I 
    304 obtained from Dave Ullman used the value 3 and contained the 
    305 explanation "a temperature difference of less than three digital 
    306 counts between the 2 populations is likely to be a result of the 
    307 discrete nature of the data."</p><p>You can use this parameter to eliminate weak fronts by selecting a 
    308 value that corresponds to a desired minimum mean temperature 
    309 difference. For example, for the NOAA NODC 4km AVHRR Pathfinder SST 
    310 data, the value of 1 corresponds to 0.075 degrees. To eliminate fronts 
    311 where the mean temperature difference is less than 0.5 degrees, set 
    312 this parameter to 0.5 / 0.075 = 6.666667.</p></td></tr><tr><td class="info">{minTheta}</td><td class="info" align="left"><p>Minimum criterion function, theta(Topt), as described on pages 
     315understand the statistical analysis presented in the 1992 paper.</p></td></tr><tr><td class="info">{minTheta}</td><td class="info" align="left"><p>Minimum criterion function, theta(Topt), as described on pages 
    31331671-72 of the 1992 paper.</p><p>The criterion function is used to determine whether the histogram 
    314317window contains a bimodal distribution, as would be expected if it 
    … …  
    345348threads to more than the number of processors you have; this will 
    346349reduce performance.</p></td></tr><tr><td class="info">{outputMaskRaster}</td><td class="info" align="left"><p>Output raster that shows the pixels of the input image that were 
    347 masked prior to executing the Cayula-Cornillon algorithm.</p><p>The raster will contain 8-bit integers and have the same dimensions as 
     350masked prior to executing the Cayula and Cornillon algorithm.</p><p>The raster will contain 8-bit integers and have the same dimensions as 
    348351the input image. The value 1 indicates that the corresponding pixel of 
    349352the input image was masked; 0 indicates the pixel was not masked.</p></td></tr><tr><td class="info">{outputFilteredImageRaster}</td><td class="info" align="left"><p>Output raster that shows the median-filtered input image.</p><p>The raster will have the same data type and dimensions as the input 
    … …  
    355358the number of times it appeared in a histogram window that had a 
    356359sufficiently large number of non-masked pixels to proceed with the 
    357 histogramming step of the Cayula-Cornillon algorithm.</p><p>The raster will contain 16-bit signed integers and have the same 
     360histogramming step of the Cayula and Cornillon algorithm.</p><p>The raster will contain 16-bit signed integers and have the same 
    358361dimensions as the input image. Because the histogram window stride is 
    359362typically less than the window size, successive histogram windows 
    … …  
    498501raster is in a geographic coordinate system, it may be clipped to 10 
    499502W, 15 S, 20 E, and 25 N with the string:</p><dl><dt></dt><dd><p>10 15 20 25</p></dd></dl><p>Integers or decimal numbers may be provided.</p></td></tr><tr><td class="info">{buildPyramids}</td><td class="info" align="left"><p>If True, pyramids will be built for the raster, which will improve 
    500 its display speed in the ArcGIS user interface.</p></td></tr></tbody></table></div><p><h2><img width="11" height="11" border="0" src="sm_arrow_down.gif?format=raw" /> Scripting syntax</h2></p><div Class="expand" id="TEST">CoastWatchAVHRRFindFrontsAsArcGISRaster_GeoEco (imageFile, variable, outputFrontsRaster, cloudMaskFile, cloudVariable, sunZenithFile, sunZenithVariable, useDayCloudTest1, useDayCloudTest2, useDayCloudTest3, useDayCloudTest4, useDayCloudTest5, useDayCloudTest6, useDayCloudTest7, maskWhenDayCloudMaskExceeds, useNightCloudTest1, useNightCloudTest2, useNightCloudTest3, useNightCloudTest4, useNightCloudTest5, useNightCloudTest6, useNightCloudTest7, maskWhenNightCloudMaskExceeds, minCloudyNeighbors, medianFilterWindowSize, histogramWindowSize, histogramWindowStride, minPropNonMaskedCells, minPopProp, minPopMeanDifference, minTheta, minSinglePopCohesion, minGlobalPopCohesion, threads, outputMaskRaster, outputFilteredImageRaster, outputCandidateCountsRaster, outputFrontCountsRaster, outputWindowStatusCodesRaster, outputWindowStatusValuesRaster, projectedCoordinateSystem, geographicTransformation, resamplingTechnique, projectedCellSize, registrationPoint, clippingRectangle, buildPyramids) <br /><br /><b>Parameters</b><br /><table width="100%" border="0" cellpadding="5"><tbody><tr><th width="40%"><b>Expression</b></th><th width="60%"><b>Explanation</b></th></tr><tr><td class="info">CoastWatch file (Required) </td><td class="info" align="left"><p>CoastWatch POES AVHRR CWF or HDF file in which fronts should be 
     503its display speed in the ArcGIS user interface.</p></td></tr></tbody></table></div><p><h2><img width="11" height="11" border="0" src="sm_arrow_down.gif?format=raw" /> Scripting syntax</h2></p><div Class="expand" id="TEST">CoastWatchAVHRRFindFrontsAsArcGISRaster_GeoEco (imageFile, variable, minPopMeanDifference, outputFrontsRaster, cloudMaskFile, cloudVariable, sunZenithFile, sunZenithVariable, useDayCloudTest1, useDayCloudTest2, useDayCloudTest3, useDayCloudTest4, useDayCloudTest5, useDayCloudTest6, useDayCloudTest7, maskWhenDayCloudMaskExceeds, useNightCloudTest1, useNightCloudTest2, useNightCloudTest3, useNightCloudTest4, useNightCloudTest5, useNightCloudTest6, useNightCloudTest7, maskWhenNightCloudMaskExceeds, minCloudyNeighbors, medianFilterWindowSize, histogramWindowSize, histogramWindowStride, minPropNonMaskedCells, minPopProp, minTheta, minSinglePopCohesion, minGlobalPopCohesion, threads, outputMaskRaster, outputFilteredImageRaster, outputCandidateCountsRaster, outputFrontCountsRaster, outputWindowStatusCodesRaster, outputWindowStatusValuesRaster, projectedCoordinateSystem, geographicTransformation, resamplingTechnique, projectedCellSize, registrationPoint, clippingRectangle, buildPyramids) <br /><br /><b>Parameters</b><br /><table width="100%" border="0" cellpadding="5"><tbody><tr><th width="40%"><b>Expression</b></th><th width="60%"><b>Explanation</b></th></tr><tr><td class="info">CoastWatch file (Required) </td><td class="info" align="left"><p>CoastWatch POES AVHRR CWF or HDF file in which fronts should be 
    501504detected.</p><p>Only CoastWatch POES AVHRR files are supported. An error will be raise 
    502505for other CoastWatch files, such as those for the GOES satellite 
    … …  
    521524you may specify one of the others, if appropriate for your project. 
    522525Please see the CoastWatch documentation for more information about the 
    523 variables.</p></td></tr><tr><td class="info">Output ArcGIS raster (Required) </td><td class="info" align="left"><p>Output raster that shows the fronts detected in the input image.</p><p>The raster will have the same dimensions as the input image and 
     526variables.</p></td></tr><tr><td class="info">Front detection threshold (Required) </td><td class="info" align="left"><p>Minimum difference, in degrees C, between the mean temperatures of 
     527two adjacent populations of pixels for a front to be detected between 
     528those two populations.</p><p>The Cayula and Cornillon algorithm passes a moving window over the 
     529image, checking each window for a bimodal distribution in the 
     530temperatures of the pixels within it. When the algorithm detects a 
     531bimodal distribution, it computes the mean temperatures of the two 
     532populations and compares the difference between the means to this 
     533threshold. If the difference is less than this threshold, the 
     534algorithm concludes there is no front present and moves on to the next 
     535window.</p><p>You can use this parameter to eliminate weak fronts by selecting a 
     536value that corresponds to a desired minimum mean temperature 
     537difference. Bear in mind that Cayula and Cornillon's (1992) study used 
     538data from early satellites that contributed to the CoastWatch program. 
     539When I examined their Fortran code, I found a commenet suggesting that 
     540they believed the minimum allowable threshold given the measurement 
     541error of the sensors was 0.45 deg C.</p></td></tr><tr><td class="info">Output ArcGIS raster (Required) </td><td class="info" align="left"><p>Output raster that shows the fronts detected in the input image.</p><p>The raster will have the same dimensions as the input image and 
    524542contain 8-bit signed integers with three possible values:</p><ul><li><p>NoData - the pixel was never a candidate for containing a front, 
    525543either because it was masked or because because it did not appear in 
    526544any histogram windows that had sufficiently large numbers of 
    527545non-masked pixels to proceed with the histogramming step of the 
    528 Cayula-Cornillon algorithm.</p></li></ul><ul><li><p>0 - The pixel was a candidate for containing a front -- it was not 
     546Cayula and Cornillon algorithm.</p></li></ul><ul><li><p>0 - The pixel was a candidate for containing a front -- it was not 
    529547masked and it appeared in at least one histogram window with a 
    530548sufficient number of non-masked pixels to proceed with the 
    … …  
    757775(e.g. it is land), it does not count as being cloudy.</p><p>This option is ignored when cloud masking is not performed.</p></td></tr><tr><td class="info">Median filter window size (Optional) </td><td class="info" align="left"><p>Window size, in pixels, of the median filter to apply to the input 
    758776image prior to running the histogram analysis step of the 
    759 Cayula-Cornillon algorithm. If not provided, median filtering will not 
     777Cayula and Cornillon algorithm. If not provided, median filtering will not 
    760778be performed.</p><p>If you provide a value, it must be an odd integer greater than or 
    761779equal to 3. The filter window is square and advances across the image 
    … …  
    763781with the median value of the non-masked pixels in the surrounding 
    764782window. All masks are applied before the median filter is executed.</p><p>Median filtering is a traditional first step for certain classes of 
    765 edge detection algorithms. The original Cayula-Cornillon paper used a 
    766 window size of 3.</p></td></tr><tr><td class="info">Histogram window size (Optional) </td><td class="info" align="left"><p>Size of the histogram window to use for the Cayula-Cornillon 
     783edge detection algorithms. The original Cayula and Cornillon paper used a 
     784window size of 3.</p></td></tr><tr><td class="info">Histogram window size (Optional) </td><td class="info" align="left"><p>Size of the histogram window to use for the Cayula and Cornillon 
    767785algorithm.</p><p>The window is square. The original paper used a window size of 32. 
    768786Although the algorithm is claimed to obtain similar results regardless 
    … …  
    771789the paper carefully before experimenting with different window 
    772790sizes.</p></td></tr><tr><td class="info">Histogram window stride (Optional) </td><td class="info" align="left"><p>Number of pixels to move the histogram window after each iteration 
    773 of the Cayula-Cornillon algorithm.</p><p>The original paper used a 32x32 window and a stride of 16, to minimize 
     791of the Cayula and Cornillon algorithm.</p><p>The original paper used a 32x32 window and a stride of 16, to minimize 
    774792the CPU time required to execute the algorithm. Cutting the stride in 
    775793half increases the CPU time by a factor of about four. For example, a 
    … …  
    795813accurate. In this case, and the algorithm discards the current window, 
    796814advances to next one, and starts over.</p><p>I do not recommend selecting a value other than 0.25 unless you 
    797 understand the statistical analysis presented in the 1992 paper.</p></td></tr><tr><td class="info">Minimum population mean difference (Optional) </td><td class="info" align="left"><p>Minimum difference in population means.</p><p>After the histogram algorithm separates the non-masked pixels into two 
    798 populations, it computes the means of the two populations. If the 
    799 means differ by less than this parameter value, the algorithm discards 
    800 the current window, advances to next one, and starts over.</p><p>The original intent of this parameter is obscure. The Ratfor code I 
    801 obtained from Dave Ullman used the value 3 and contained the 
    802 explanation "a temperature difference of less than three digital 
    803 counts between the 2 populations is likely to be a result of the 
    804 discrete nature of the data."</p><p>You can use this parameter to eliminate weak fronts by selecting a 
    805 value that corresponds to a desired minimum mean temperature 
    806 difference. For example, for the NOAA NODC 4km AVHRR Pathfinder SST 
    807 data, the value of 1 corresponds to 0.075 degrees. To eliminate fronts 
    808 where the mean temperature difference is less than 0.5 degrees, set 
    809 this parameter to 0.5 / 0.075 = 6.666667.</p></td></tr><tr><td class="info">Minimum value for criterion function (Optional) </td><td class="info" align="left"><p>Minimum criterion function, theta(Topt), as described on pages 
     815understand the statistical analysis presented in the 1992 paper.</p></td></tr><tr><td class="info">Minimum value for criterion function (Optional) </td><td class="info" align="left"><p>Minimum criterion function, theta(Topt), as described on pages 
    81081671-72 of the 1992 paper.</p><p>The criterion function is used to determine whether the histogram 
    811817window contains a bimodal distribution, as would be expected if it 
    … …  
    842848threads to more than the number of processors you have; this will 
    843849reduce performance.</p></td></tr><tr><td class="info">Output mask raster (Optional) </td><td class="info" align="left"><p>Output raster that shows the pixels of the input image that were 
    844 masked prior to executing the Cayula-Cornillon algorithm.</p><p>The raster will contain 8-bit integers and have the same dimensions as 
     850masked prior to executing the Cayula and Cornillon algorithm.</p><p>The raster will contain 8-bit integers and have the same dimensions as 
    845851the input image. The value 1 indicates that the corresponding pixel of 
    846852the input image was masked; 0 indicates the pixel was not masked.</p></td></tr><tr><td class="info">Output median filtered image (Optional) </td><td class="info" align="left"><p>Output raster that shows the median-filtered input image.</p><p>The raster will have the same data type and dimensions as the input 
    … …  
    852858the number of times it appeared in a histogram window that had a 
    853859sufficiently large number of non-masked pixels to proceed with the 
    854 histogramming step of the Cayula-Cornillon algorithm.</p><p>The raster will contain 16-bit signed integers and have the same 
     860histogramming step of the Cayula and Cornillon algorithm.</p><p>The raster will contain 16-bit signed integers and have the same 
    855861dimensions as the input image. Because the histogram window stride is 
    856862typically less than the window size, successive histogram windows 

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    11<?xml version="1.0" encoding="utf-8"?> 
    22<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd"> 
    3 <html xmlns="http://www.w3.org/1999/xhtml"><head><link rel="stylesheet" type="text/css" href="81help.css?format=raw" /><meta http-equiv="Content-Type" content="text/html; charset=UTF-8" /><title>Cayula-Cornillon Fronts in CoastWatch Images Listed in Table as ArcGIS Rasters</title></head><body><table style="margin-top:-1em; margin-bottom:0; padding:0; margin-left:-1em"><tr><td style="background:white"><img width="875" height="70" alt="ArcToolbox banner" src="AHBanner_ArcToolbox.gif?format=raw" /></td></tr></table><h1>Cayula-Cornillon Fronts in CoastWatch Images Listed in Table as ArcGIS Rasters</h1><p></p><p>Finds fronts in CoastWatch POES AVHRR images listed in a table using the Cayula-Cornillon (1992) single-image edge detection algorithm and outputs them as ArcGIS rasters.</p><br /><p><h2><img width="11" height="11" border="0" src="sm_arrow_down.gif?format=raw" /> Command line syntax</h2></p><div Class="expand" id="id103138">CoastWatchAVHRRFindFrontsAsArcGISRastersArcGISTable_GeoEco &lt;table&gt; &lt;imageFileField&gt; &lt;variableField&gt; &lt;outputFrontsRasterField&gt; {cloudMaskFileField} {cloudVariable} {sunZenithFileField} {sunZenithVariable} {useDayCloudTest1} {useDayCloudTest2} {useDayCloudTest3} {useDayCloudTest4} {useDayCloudTest5} {useDayCloudTest6} {useDayCloudTest7} {maskWhenDayCloudMaskExceeds} {useNightCloudTest1} {useNightCloudTest2} {useNightCloudTest3} {useNightCloudTest4} {useNightCloudTest5} {useNightCloudTest6} {useNightCloudTest7} {maskWhenNightCloudMaskExceeds} {minCloudyNeighbors} {medianFilterWindowSize} {histogramWindowSize} {histogramWindowStride} {minPropNonMaskedCells} {minPopProp} {minPopMeanDifference} {minTheta} {minSinglePopCohesion} {minGlobalPopCohesion} {threads} {outputMaskRasterField} {outputFilteredImageRasterField} {outputCandidateCountsRasterField} {outputFrontCountsRasterField} {outputWindowStatusCodesRasterField} {outputWindowStatusValuesRasterField} {projectedCoordinateSystem} {geographicTransformation} {NEAREST | BILINEAR | CUBIC} {projectedCellSize} {registrationPoint} {clippingRectangle} {buildPyramids} {where} {orderBy;orderBy...} {directions;directions...} {skipExisting} {basePath} <br /><br /><b>Parameters</b><br /><table width="100%" border="0" cellpadding="5"><tbody><tr><th width="40%"><b>Expression</b></th><th width="60%"><b>Explanation</b></th></tr><tr><td class="info">&lt;table&gt;</td><td class="info" align="left"><p>Table to query.</p></td></tr><tr><td class="info">&lt;imageFileField&gt;</td><td class="info" align="left"><p>Field containing the paths of the CoastWatch POES AVHRR CWF or HDF files.</p><p>Only CoastWatch POES AVHRR files are supported. Other CoastWatch 
     3<html xmlns="http://www.w3.org/1999/xhtml"><head><link rel="stylesheet" type="text/css" href="81help.css?format=raw" /><meta http-equiv="Content-Type" content="text/html; charset=UTF-8" /><title>Cayula-Cornillon Fronts in CoastWatch Images Listed in Table as ArcGIS Rasters</title></head><body><table style="margin-top:-1em; margin-bottom:0; padding:0; margin-left:-1em"><tr><td style="background:white"><img width="875" height="70" alt="ArcToolbox banner" src="AHBanner_ArcToolbox.gif?format=raw" /></td></tr></table><h1>Cayula-Cornillon Fronts in CoastWatch Images Listed in Table as ArcGIS Rasters</h1><p></p><p>Finds fronts in CoastWatch POES AVHRR images listed in a table using the Cayula and Cornillon (1992) single-image edge detection algorithm and outputs them as ArcGIS rasters.</p><br /><p><h2><img width="11" height="11" border="0" src="sm_arrow_down.gif?format=raw" /> Command line syntax</h2></p><div Class="expand" id="id103138">CoastWatchAVHRRFindFrontsAsArcGISRastersArcGISTable_GeoEco &lt;table&gt; &lt;imageFileField&gt; &lt;variableField&gt; &lt;minPopMeanDifference&gt; &lt;outputFrontsRasterField&gt; {cloudMaskFileField} {cloudVariable} {sunZenithFileField} {sunZenithVariable} {useDayCloudTest1} {useDayCloudTest2} {useDayCloudTest3} {useDayCloudTest4} {useDayCloudTest5} {useDayCloudTest6} {useDayCloudTest7} {maskWhenDayCloudMaskExceeds} {useNightCloudTest1} {useNightCloudTest2} {useNightCloudTest3} {useNightCloudTest4} {useNightCloudTest5} {useNightCloudTest6} {useNightCloudTest7} {maskWhenNightCloudMaskExceeds} {minCloudyNeighbors} {medianFilterWindowSize} {histogramWindowSize} {histogramWindowStride} {minPropNonMaskedCells} {minPopProp} {minTheta} {minSinglePopCohesion} {minGlobalPopCohesion} {threads} {outputMaskRasterField} {outputFilteredImageRasterField} {outputCandidateCountsRasterField} {outputFrontCountsRasterField} {outputWindowStatusCodesRasterField} {outputWindowStatusValuesRasterField} {projectedCoordinateSystem} {geographicTransformation} {NEAREST | BILINEAR | CUBIC} {projectedCellSize} {registrationPoint} {clippingRectangle} {buildPyramids} {where} {orderBy;orderBy...} {directions;directions...} {skipExisting} {basePath} <br /><br /><b>Parameters</b><br /><table width="100%" border="0" cellpadding="5"><tbody><tr><th width="40%"><b>Expression</b></th><th width="60%"><b>Explanation</b></th></tr><tr><td class="info">&lt;table&gt;</td><td class="info" align="left"><p>Table to query.</p></td></tr><tr><td class="info">&lt;imageFileField&gt;</td><td class="info" align="left"><p>Field containing the paths of the CoastWatch POES AVHRR CWF or HDF files.</p><p>Only CoastWatch POES AVHRR files are supported. Other CoastWatch 
    44files, such as those for the GOES satellite series, will be skipped 
    55and a warning will be reported.</p><p>Compressed files in a supported compression format will be 
    … …  
    2222you may specify one of the others, if appropriate for your project. 
    2323Please see the CoastWatch documentation for more information about the 
    24 variables.</p></td></tr><tr><td class="info">&lt;outputFrontsRasterField&gt;</td><td class="info" align="left"><p>Field containing the output rasters to create that show the fronts detected in the 
     24variables.</p></td></tr><tr><td class="info">&lt;minPopMeanDifference&gt;</td><td class="info" align="left"><p>Minimum difference, in degrees C, between the mean temperatures of 
     25two adjacent populations of pixels for a front to be detected between 
     26those two populations.</p><p>The Cayula and Cornillon algorithm passes a moving window over the 
     27image, checking each window for a bimodal distribution in the 
     28temperatures of the pixels within it. When the algorithm detects a 
     29bimodal distribution, it computes the mean temperatures of the two 
     30populations and compares the difference between the means to this 
     31threshold. If the difference is less than this threshold, the 
     32algorithm concludes there is no front present and moves on to the next 
     33window.</p><p>You can use this parameter to eliminate weak fronts by selecting a 
     34value that corresponds to a desired minimum mean temperature 
     35difference. Bear in mind that Cayula and Cornillon's (1992) study used 
     36data from early satellites that contributed to the CoastWatch program. 
     37When I examined their Fortran code, I found a commenet suggesting that 
     38they believed the minimum allowable threshold given the measurement 
     39error of the sensors was 0.45 deg C.</p></td></tr><tr><td class="info">&lt;outputFrontsRasterField&gt;</td><td class="info" align="left"><p>Field containing the output rasters to create that show the fronts detected in the 
    2540input images.</p><p>The output rasters will have the same dimensions as the input images 
    2641and contain 8-bit signed integers with three possible values:</p><ul><li><p>NoData - the pixel was never a candidate for containing a front, 
    … …  
    2843any histogram windows that had sufficiently large numbers of 
    2944non-masked pixels to proceed with the histogramming step of the 
    30 Cayula-Cornillon algorithm.</p></li></ul><ul><li><p>0 - The pixel was a candidate for containing a front -- it was not 
     45Cayula and Cornillon algorithm.</p></li></ul><ul><li><p>0 - The pixel was a candidate for containing a front -- it was not 
    3146masked and it appeared in at least one histogram window with a 
    3247sufficient number of non-masked pixels to proceed with the 
    … …  
    270285(e.g. it is land), it does not count as being cloudy.</p><p>This option is ignored when cloud masking is not performed.</p></td></tr><tr><td class="info">{medianFilterWindowSize}</td><td class="info" align="left"><p>Window size, in pixels, of the median filter to apply to the input 
    271286image prior to running the histogram analysis step of the 
    272 Cayula-Cornillon algorithm. If not provided, median filtering will not 
     287Cayula and Cornillon algorithm. If not provided, median filtering will not 
    273288be performed.</p><p>If you provide a value, it must be an odd integer greater than or 
    274289equal to 3. The filter window is square and advances across the image 
    … …  
    276291with the median value of the non-masked pixels in the surrounding 
    277292window. All masks are applied before the median filter is executed.</p><p>Median filtering is a traditional first step for certain classes of 
    278 edge detection algorithms. The original Cayula-Cornillon paper used a 
    279 window size of 3.</p></td></tr><tr><td class="info">{histogramWindowSize}</td><td class="info" align="left"><p>Size of the histogram window to use for the Cayula-Cornillon 
     293edge detection algorithms. The original Cayula and Cornillon paper used a 
     294window size of 3.</p></td></tr><tr><td class="info">{histogramWindowSize}</td><td class="info" align="left"><p>Size of the histogram window to use for the Cayula and Cornillon 
    280295algorithm.</p><p>The window is square. The original paper used a window size of 32. 
    281296Although the algorithm is claimed to obtain similar results regardless 
    … …  
    284299the paper carefully before experimenting with different window 
    285300sizes.</p></td></tr><tr><td class="info">{histogramWindowStride}</td><td class="info" align="left"><p>Number of pixels to move the histogram window after each iteration 
    286 of the Cayula-Cornillon algorithm.</p><p>The original paper used a 32x32 window and a stride of 16, to minimize 
     301of the Cayula and Cornillon algorithm.</p><p>The original paper used a 32x32 window and a stride of 16, to minimize 
    287302the CPU time required to execute the algorithm. Cutting the stride in 
    288303half increases the CPU time by a factor of about four. For example, a 
    … …  
    308323accurate. In this case, and the algorithm discards the current window, 
    309324advances to next one, and starts over.</p><p>I do not recommend selecting a value other than 0.25 unless you 
    310 understand the statistical analysis presented in the 1992 paper.</p></td></tr><tr><td class="info">{minPopMeanDifference}</td><td class="info" align="left"><p>Minimum difference in population means.</p><p>After the histogram algorithm separates the non-masked pixels into two 
    311 populations, it computes the means of the two populations. If the 
    312 means differ by less than this parameter value, the algorithm discards 
    313 the current window, advances to next one, and starts over.</p><p>The original intent of this parameter is obscure. The Ratfor code I 
    314 obtained from Dave Ullman used the value 3 and contained the 
    315 explanation "a temperature difference of less than three digital 
    316 counts between the 2 populations is likely to be a result of the 
    317 discrete nature of the data."</p><p>You can use this parameter to eliminate weak fronts by selecting a 
    318 value that corresponds to a desired minimum mean temperature 
    319 difference. For example, for the NOAA NODC 4km AVHRR Pathfinder SST 
    320 data, the value of 1 corresponds to 0.075 degrees. To eliminate fronts 
    321 where the mean temperature difference is less than 0.5 degrees, set 
    322 this parameter to 0.5 / 0.075 = 6.666667.</p></td></tr><tr><td class="info">{minTheta}</td><td class="info" align="left"><p>Minimum criterion function, theta(Topt), as described on pages 
     325understand the statistical analysis presented in the 1992 paper.</p></td></tr><tr><td class="info">{minTheta}</td><td class="info" align="left"><p>Minimum criterion function, theta(Topt), as described on pages 
    32332671-72 of the 1992 paper.</p><p>The criterion function is used to determine whether the histogram 
    324327window contains a bimodal distribution, as would be expected if it 
    … …  
    355358threads to more than the number of processors you have; this will 
    356359reduce performance.</p></td></tr><tr><td class="info">{outputMaskRasterField}</td><td class="info" align="left"><p>Field containing the output rasters to create that show the pixels of the input 
    357 images that were masked prior to executing the Cayula-Cornillon 
     360images that were masked prior to executing the Cayula and Cornillon 
    358361algorithm.</p><p>Each raster will contain 8-bit integers and have the same dimensions 
    359362as the input image. The value 1 indicates that the corresponding pixel 
    … …  
    368371containing fronts, i.e. the number of times the pixels appeared in 
    369372histogram windows that had a sufficiently large number of non-masked 
    370 pixels to proceed with the histogramming step of the Cayula-Cornillon 
     373pixels to proceed with the histogramming step of the Cayula and Cornillon 
    371374algorithm.</p><p>Each raster will contain 16-bit signed integers and have the same 
    372375dimensions as the input image. Because the histogram window stride is 
    … …  
    536539that are obtained from the fields that list the inputs (and outputs, 
    537540if this tool has outputs). If a base path is not provided, the 
    538 workspace containing the table will be prepended instead.</p></td></tr></tbody></table></div><p><h2><img width="11" height="11" border="0" src="sm_arrow_down.gif?format=raw" /> Scripting syntax</h2></p><div Class="expand" id="TEST">CoastWatchAVHRRFindFrontsAsArcGISRastersArcGISTable_GeoEco (table, imageFileField, variableField, outputFrontsRasterField, cloudMaskFileField, cloudVariable, sunZenithFileField, sunZenithVariable, useDayCloudTest1, useDayCloudTest2, useDayCloudTest3, useDayCloudTest4, useDayCloudTest5, useDayCloudTest6, useDayCloudTest7, maskWhenDayCloudMaskExceeds, useNightCloudTest1, useNightCloudTest2, useNightCloudTest3, useNightCloudTest4, useNightCloudTest5, useNightCloudTest6, useNightCloudTest7, maskWhenNightCloudMaskExceeds, minCloudyNeighbors, medianFilterWindowSize, histogramWindowSize, histogramWindowStride, minPropNonMaskedCells, minPopProp, minPopMeanDifference, minTheta, minSinglePopCohesion, minGlobalPopCohesion, threads, outputMaskRasterField, outputFilteredImageRasterField, outputCandidateCountsRasterField, outputFrontCountsRasterField, outputWindowStatusCodesRasterField, outputWindowStatusValuesRasterField, projectedCoordinateSystem, geographicTransformation, resamplingTechnique, projectedCellSize, registrationPoint, clippingRectangle, buildPyramids, where, orderBy, directions, skipExisting, basePath) <br /><br /><b>Parameters</b><br /><table width="100%" border="0" cellpadding="5"><tbody><tr><th width="40%"><b>Expression</b></th><th width="60%"><b>Explanation</b></th></tr><tr><td class="info">Table (Required) </td><td class="info" align="left"><p>Table to query.</p></td></tr><tr><td class="info">Input CoastWatch file field (Required) </td><td class="info" align="left"><p>Field containing the paths of the CoastWatch POES AVHRR CWF or HDF files.</p><p>Only CoastWatch POES AVHRR files are supported. Other CoastWatch 
     541workspace containing the table will be prepended instead.</p></td></tr></tbody></table></div><p><h2><img width="11" height="11" border="0" src="sm_arrow_down.gif?format=raw" /> Scripting syntax</h2></p><div Class="expand" id="TEST">CoastWatchAVHRRFindFrontsAsArcGISRastersArcGISTable_GeoEco (table, imageFileField, variableField, minPopMeanDifference, outputFrontsRasterField, cloudMaskFileField, cloudVariable, sunZenithFileField, sunZenithVariable, useDayCloudTest1, useDayCloudTest2, useDayCloudTest3, useDayCloudTest4, useDayCloudTest5, useDayCloudTest6, useDayCloudTest7, maskWhenDayCloudMaskExceeds, useNightCloudTest1, useNightCloudTest2, useNightCloudTest3, useNightCloudTest4, useNightCloudTest5, useNightCloudTest6, useNightCloudTest7, maskWhenNightCloudMaskExceeds, minCloudyNeighbors, medianFilterWindowSize, histogramWindowSize, histogramWindowStride, minPropNonMaskedCells, minPopProp, minTheta, minSinglePopCohesion, minGlobalPopCohesion, threads, outputMaskRasterField, outputFilteredImageRasterField, outputCandidateCountsRasterField, outputFrontCountsRasterField, outputWindowStatusCodesRasterField, outputWindowStatusValuesRasterField, projectedCoordinateSystem, geographicTransformation, resamplingTechnique, projectedCellSize, registrationPoint, clippingRectangle, buildPyramids, where, orderBy, directions, skipExisting, basePath) <br /><br /><b>Parameters</b><br /><table width="100%" border="0" cellpadding="5"><tbody><tr><th width="40%"><b>Expression</b></th><th width="60%"><b>Explanation</b></th></tr><tr><td class="info">Table (Required) </td><td class="info" align="left"><p>Table to query.</p></td></tr><tr><td class="info">Input CoastWatch file field (Required) </td><td class="info" align="left"><p>Field containing the paths of the CoastWatch POES AVHRR CWF or HDF files.</p><p>Only CoastWatch POES AVHRR files are supported. Other CoastWatch 
    539542files, such as those for the GOES satellite series, will be skipped 
    540543and a warning will be reported.</p><p>Compressed files in a supported compression format will be 
    … …  
    557560you may specify one of the others, if appropriate for your project. 
    558561Please see the CoastWatch documentation for more information about the 
    559 variables.</p></td></tr><tr><td class="info">Output fronts raster field (Required) </td><td class="info" align="left"><p>Field containing the output rasters to create that show the fronts detected in the 
     562variables.</p></td></tr><tr><td class="info">Front detection threshold (Required) </td><td class="info" align="left"><p>Minimum difference, in degrees C, between the mean temperatures of 
     563two adjacent populations of pixels for a front to be detected between 
     564those two populations.</p><p>The Cayula and Cornillon algorithm passes a moving window over the 
     565image, checking each window for a bimodal distribution in the 
     566temperatures of the pixels within it. When the algorithm detects a 
     567bimodal distribution, it computes the mean temperatures of the two 
     568populations and compares the difference between the means to this 
     569threshold. If the difference is less than this threshold, the 
     570algorithm concludes there is no front present and moves on to the next 
     571window.</p><p>You can use this parameter to eliminate weak fronts by selecting a 
     572value that corresponds to a desired minimum mean temperature 
     573difference. Bear in mind that Cayula and Cornillon's (1992) study used 
     574data from early satellites that contributed to the CoastWatch program. 
     575When I examined their Fortran code, I found a commenet suggesting that 
     576they believed the minimum allowable threshold given the measurement 
     577error of the sensors was 0.45 deg C.</p></td></tr><tr><td class="info">Output fronts raster field (Required) </td><td class="info" align="left"><p>Field containing the output rasters to create that show the fronts detected in the 
    560578input images.</p><p>The output rasters will have the same dimensions as the input images 
    561579and contain 8-bit signed integers with three possible values:</p><ul><li><p>NoData - the pixel was never a candidate for containing a front, 
    … …  
    563581any histogram windows that had sufficiently large numbers of 
    564582non-masked pixels to proceed with the histogramming step of the 
    565 Cayula-Cornillon algorithm.</p></li></ul><ul><li><p>0 - The pixel was a candidate for containing a front -- it was not 
     583Cayula and Cornillon algorithm.</p></li></ul><ul><li><p>0 - The pixel was a candidate for containing a front -- it was not 
    566584masked and it appeared in at least one histogram window with a 
    567585sufficient number of non-masked pixels to proceed with the 
    … …  
    805823(e.g. it is land), it does not count as being cloudy.</p><p>This option is ignored when cloud masking is not performed.</p></td></tr><tr><td class="info">Median filter window size (Optional) </td><td class="info" align="left"><p>Window size, in pixels, of the median filter to apply to the input 
    806824image prior to running the histogram analysis step of the 
    807 Cayula-Cornillon algorithm. If not provided, median filtering will not 
     825Cayula and Cornillon algorithm. If not provided, median filtering will not 
    808826be performed.</p><p>If you provide a value, it must be an odd integer greater than or 
    809827equal to 3. The filter window is square and advances across the image 
    … …  
    811829with the median value of the non-masked pixels in the surrounding 
    812830window. All masks are applied before the median filter is executed.</p><p>Median filtering is a traditional first step for certain classes of 
    813 edge detection algorithms. The original Cayula-Cornillon paper used a 
    814 window size of 3.</p></td></tr><tr><td class="info">Histogram window size (Optional) </td><td class="info" align="left"><p>Size of the histogram window to use for the Cayula-Cornillon 
     831edge detection algorithms. The original Cayula and Cornillon paper used a 
     832window size of 3.</p></td></tr><tr><td class="info">Histogram window size (Optional) </td><td class="info" align="left"><p>Size of the histogram window to use for the Cayula and Cornillon 
    815833algorithm.</p><p>The window is square. The original paper used a window size of 32. 
    816834Although the algorithm is claimed to obtain similar results regardless 
    … …  
    819837the paper carefully before experimenting with different window 
    820838sizes.</p></td></tr><tr><td class="info">Histogram window stride (Optional) </td><td class="info" align="left"><p>Number of pixels to move the histogram window after each iteration 
    821 of the Cayula-Cornillon algorithm.</p><p>The original paper used a 32x32 window and a stride of 16, to minimize 
     839of the Cayula and Cornillon algorithm.</p><p>The original paper used a 32x32 window and a stride of 16, to minimize 
    822840the CPU time required to execute the algorithm. Cutting the stride in 
    823841half increases the CPU time by a factor of about four. For example, a 
    … …  
    843861accurate. In this case, and the algorithm discards the current window, 
    844862advances to next one, and starts over.</p><p>I do not recommend selecting a value other than 0.25 unless you 
    845 understand the statistical analysis presented in the 1992 paper.</p></td></tr><tr><td class="info">Minimum population mean difference (Optional) </td><td class="info" align="left"><p>Minimum difference in population means.</p><p>After the histogram algorithm separates the non-masked pixels into two 
    846 populations, it computes the means of the two populations. If the 
    847 means differ by less than this parameter value, the algorithm discards 
    848 the current window, advances to next one, and starts over.</p><p>The original intent of this parameter is obscure. The Ratfor code I 
    849 obtained from Dave Ullman used the value 3 and contained the 
    850 explanation "a temperature difference of less than three digital 
    851 counts between the 2 populations is likely to be a result of the 
    852 discrete nature of the data."</p><p>You can use this parameter to eliminate weak fronts by selecting a 
    853 value that corresponds to a desired minimum mean temperature 
    854 difference. For example, for the NOAA NODC 4km AVHRR Pathfinder SST 
    855 data, the value of 1 corresponds to 0.075 degrees. To eliminate fronts 
    856 where the mean temperature difference is less than 0.5 degrees, set 
    857 this parameter to 0.5 / 0.075 = 6.666667.</p></td></tr><tr><td class="info">Minimum value for criterion function (Optional) </td><td class="info" align="left"><p>Minimum criterion function, theta(Topt), as described on pages 
     863understand the statistical analysis presented in the 1992 paper.</p></td></tr><tr><td class="info">Minimum value for criterion function (Optional) </td><td class="info" align="left"><p>Minimum criterion function, theta(Topt), as described on pages 
    85886471-72 of the 1992 paper.</p><p>The criterion function is used to determine whether the histogram 
    859865window contains a bimodal distribution, as would be expected if it 
    … …  
    890896threads to more than the number of processors you have; this will 
    891897reduce performance.</p></td></tr><tr><td class="info">Output mask raster field (Optional) </td><td class="info" align="left"><p>Field containing the output rasters to create that show the pixels of the input 
    892 images that were masked prior to executing the Cayula-Cornillon 
     898images that were masked prior to executing the Cayula and Cornillon 
    893899algorithm.</p><p>Each raster will contain 8-bit integers and have the same dimensions 
    894900as the input image. The value 1 indicates that the corresponding pixel 
    … …  
    903909containing fronts, i.e. the number of times the pixels appeared in 
    904910histogram windows that had a sufficiently large number of non-masked 
    905 pixels to proceed with the histogramming step of the Cayula-Cornillon 
     911pixels to proceed with the histogramming step of the Cayula and Cornillon 
    906912algorithm.</p><p>Each raster will contain 16-bit signed integers and have the same 
    907913dimensions as the input image. Because the histogram window stride is 

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    11<?xml version="1.0" encoding="utf-8"?> 
    22<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd"> 
    3 <html xmlns="http://www.w3.org/1999/xhtml"><head><link rel="stylesheet" type="text/css" href="81help.css?format=raw" /><meta http-equiv="Content-Type" content="text/html; charset=UTF-8" /><title>Cayula-Cornillon Fronts in CoastWatch Image as Binary Raster</title></head><body><table style="margin-top:-1em; margin-bottom:0; padding:0; margin-left:-1em"><tr><td style="background:white"><img width="875" height="70" alt="ArcToolbox banner" src="AHBanner_ArcToolbox.gif?format=raw" /></td></tr></table><h1>Cayula-Cornillon Fronts in CoastWatch Image as Binary Raster</h1><p></p><p>Finds fronts in a CoastWatch POES AVHRR image using the Cayula-Cornillon (1992) single-image edge detection algorithm and outputs them to a binary raster.</p><br /><p><h2><img width="11" height="11" border="0" src="sm_arrow_down.gif?format=raw" /> Command line syntax</h2></p><div Class="expand" id="id103135">CoastWatchAVHRRFindFrontsAsBinaryRaster_GeoEco &lt;imageFile&gt; &lt;variable&gt; &lt;outputFrontsFile&gt; {cloudMaskFile} {cloudVariable} {sunZenithFile} {sunZenithVariable} {useDayCloudTest1} {useDayCloudTest2} {useDayCloudTest3} {useDayCloudTest4} {useDayCloudTest5} {useDayCloudTest6} {useDayCloudTest7} {maskWhenDayCloudMaskExceeds} {useNightCloudTest1} {useNightCloudTest2} {useNightCloudTest3} {useNightCloudTest4} {useNightCloudTest5} {useNightCloudTest6} {useNightCloudTest7} {maskWhenNightCloudMaskExceeds} {minCloudyNeighbors} {medianFilterWindowSize} {histogramWindowSize} {histogramWindowStride} {minPropNonMaskedCells} {minPopProp} {minPopMeanDifference} {minTheta} {minSinglePopCohesion} {minGlobalPopCohesion} {threads} {outputMaskFile} {outputFilteredImageFile} {outputCandidateCountsFile} {outputFrontCountsFile} {outputWindowStatusCodesFile} {outputWindowStatusValuesFile} <br /><br /><b>Parameters</b><br /><table width="100%" border="0" cellpadding="5"><tbody><tr><th width="40%"><b>Expression</b></th><th width="60%"><b>Explanation</b></th></tr><tr><td class="info">&lt;imageFile&gt;</td><td class="info" align="left"><p>CoastWatch POES AVHRR CWF or HDF file in which fronts should be 
     3<html xmlns="http://www.w3.org/1999/xhtml"><head><link rel="stylesheet" type="text/css" href="81help.css?format=raw" /><meta http-equiv="Content-Type" content="text/html; charset=UTF-8" /><title>Cayula-Cornillon Fronts in CoastWatch Image as Binary Raster</title></head><body><table style="margin-top:-1em; margin-bottom:0; padding:0; margin-left:-1em"><tr><td style="background:white"><img width="875" height="70" alt="ArcToolbox banner" src="AHBanner_ArcToolbox.gif?format=raw" /></td></tr></table><h1>Cayula-Cornillon Fronts in CoastWatch Image as Binary Raster</h1><p></p><p>Finds fronts in a CoastWatch POES AVHRR image using the Cayula and Cornillon (1992) single-image edge detection algorithm and outputs them to a binary raster.</p><br /><p><h2><img width="11" height="11" border="0" src="sm_arrow_down.gif?format=raw" /> Command line syntax</h2></p><div Class="expand" id="id103135">CoastWatchAVHRRFindFrontsAsBinaryRaster_GeoEco &lt;imageFile&gt; &lt;variable&gt; &lt;minPopMeanDifference&gt; &lt;outputFrontsFile&gt; {cloudMaskFile} {cloudVariable} {sunZenithFile} {sunZenithVariable} {useDayCloudTest1} {useDayCloudTest2} {useDayCloudTest3} {useDayCloudTest4} {useDayCloudTest5} {useDayCloudTest6} {useDayCloudTest7} {maskWhenDayCloudMaskExceeds} {useNightCloudTest1} {useNightCloudTest2} {useNightCloudTest3} {useNightCloudTest4} {useNightCloudTest5} {useNightCloudTest6} {useNightCloudTest7} {maskWhenNightCloudMaskExceeds} {minCloudyNeighbors} {medianFilterWindowSize} {histogramWindowSize} {histogramWindowStride} {minPropNonMaskedCells} {minPopProp} {minTheta} {minSinglePopCohesion} {minGlobalPopCohesion} {threads} {outputMaskFile} {outputFilteredImageFile} {outputCandidateCountsFile} {outputFrontCountsFile} {outputWindowStatusCodesFile} {outputWindowStatusValuesFile} <br /><br /><b>Parameters</b><br /><table width="100%" border="0" cellpadding="5"><tbody><tr><th width="40%"><b>Expression</b></th><th width="60%"><b>Explanation</b></th></tr><tr><td class="info">&lt;imageFile&gt;</td><td class="info" align="left"><p>CoastWatch POES AVHRR CWF or HDF file in which fronts should be 
    44detected.</p><p>Only CoastWatch POES AVHRR files are supported. An error will be raise 
    55for other CoastWatch files, such as those for the GOES satellite 
    … …  
    2424you may specify one of the others, if appropriate for your project. 
    2525Please see the CoastWatch documentation for more information about the 
    26 variables.</p></td></tr><tr><td class="info">&lt;outputFrontsFile&gt;</td><td class="info" align="left"><p>Output binary raster that shows the fronts detected in the input 
     26variables.</p></td></tr><tr><td class="info">&lt;minPopMeanDifference&gt;</td><td class="info" align="left"><p>Minimum difference, in degrees C, between the mean temperatures of 
     27two adjacent populations of pixels for a front to be detected between 
     28those two populations.</p><p>The Cayula and Cornillon algorithm passes a moving window over the 
     29image, checking each window for a bimodal distribution in the 
     30temperatures of the pixels within it. When the algorithm detects a 
     31bimodal distribution, it computes the mean temperatures of the two 
     32populations and compares the difference between the means to this 
     33threshold. If the difference is less than this threshold, the 
     34algorithm concludes there is no front present and moves on to the next 
     35window.</p><p>You can use this parameter to eliminate weak fronts by selecting a 
     36value that corresponds to a desired minimum mean temperature 
     37difference. Bear in mind that Cayula and Cornillon's (1992) study used 
     38data from early satellites that contributed to the CoastWatch program. 
     39When I examined their Fortran code, I found a commenet suggesting that 
     40they believed the minimum allowable threshold given the measurement 
     41error of the sensors was 0.45 deg C.</p></td></tr><tr><td class="info">&lt;outputFrontsFile&gt;</td><td class="info" align="left"><p>Output binary raster that shows the fronts detected in the input 
    2742image.</p><p>The file will have the same dimensions as the input image and contain 
    28438-bit signed integers with three possible values:</p><ul><li><p>-128 - the pixel was never a candidate for containing a front, 
    … …  
    3045any histogram windows that had sufficiently large numbers of 
    3146non-masked pixels to proceed with the histogramming step of the 
    32 Cayula-Cornillon algorithm.</p></li></ul><ul><li><p>0 - The pixel was a candidate for containing a front -- it was not 
     47Cayula and Cornillon algorithm.</p></li></ul><ul><li><p>0 - The pixel was a candidate for containing a front -- it was not 
    3348masked and it appeared in at least one histogram window with a 
    3449sufficient number of non-masked pixels to proceed with the 
    … …  
    261276(e.g. it is land), it does not count as being cloudy.</p><p>This option is ignored when cloud masking is not performed.</p></td></tr><tr><td class="info">{medianFilterWindowSize}</td><td class="info" align="left"><p>Window size, in pixels, of the median filter to apply to the input 
    262277image prior to running the histogram analysis step of the 
    263 Cayula-Cornillon algorithm. If not provided, median filtering will not 
     278Cayula and Cornillon algorithm. If not provided, median filtering will not 
    264279be performed.</p><p>If you provide a value, it must be an odd integer greater than or 
    265280equal to 3. The filter window is square and advances across the image 
    … …  
    267282with the median value of the non-masked pixels in the surrounding 
    268283window. All masks are applied before the median filter is executed.</p><p>Median filtering is a traditional first step for certain classes of 
    269 edge detection algorithms. The original Cayula-Cornillon paper used a 
    270 window size of 3.</p></td></tr><tr><td class="info">{histogramWindowSize}</td><td class="info" align="left"><p>Size of the histogram window to use for the Cayula-Cornillon 
     284edge detection algorithms. The original Cayula and Cornillon paper used a 
     285window size of 3.</p></td></tr><tr><td class="info">{histogramWindowSize}</td><td class="info" align="left"><p>Size of the histogram window to use for the Cayula and Cornillon 
    271286algorithm.</p><p>The window is square. The original paper used a window size of 32. 
    272287Although the algorithm is claimed to obtain similar results regardless 
    … …  
    275290the paper carefully before experimenting with different window 
    276291sizes.</p></td></tr><tr><td class="info">{histogramWindowStride}</td><td class="info" align="left"><p>Number of pixels to move the histogram window after each iteration 
    277 of the Cayula-Cornillon algorithm.</p><p>The original paper used a 32x32 window and a stride of 16, to minimize 
     292of the Cayula and Cornillon algorithm.</p><p>The original paper used a 32x32 window and a stride of 16, to minimize 
    278293the CPU time required to execute the algorithm. Cutting the stride in 
    279294half increases the CPU time by a factor of about four. For example, a 
    … …  
    299314accurate. In this case, and the algorithm discards the current window, 
    300315advances to next one, and starts over.</p><p>I do not recommend selecting a value other than 0.25 unless you 
    301 understand the statistical analysis presented in the 1992 paper.</p></td></tr><tr><td class="info">{minPopMeanDifference}</td><td class="info" align="left"><p>Minimum difference in population means.</p><p>After the histogram algorithm separates the non-masked pixels into two 
    302 populations, it computes the means of the two populations. If the 
    303 means differ by less than this parameter value, the algorithm discards 
    304 the current window, advances to next one, and starts over.</p><p>The original intent of this parameter is obscure. The Ratfor code I 
    305 obtained from Dave Ullman used the value 3 and contained the 
    306 explanation "a temperature difference of less than three digital 
    307 counts between the 2 populations is likely to be a result of the 
    308 discrete nature of the data."</p><p>You can use this parameter to eliminate weak fronts by selecting a 
    309 value that corresponds to a desired minimum mean temperature 
    310 difference. For example, for the NOAA NODC 4km AVHRR Pathfinder SST 
    311 data, the value of 1 corresponds to 0.075 degrees. To eliminate fronts 
    312 where the mean temperature difference is less than 0.5 degrees, set 
    313 this parameter to 0.5 / 0.075 = 6.666667.</p></td></tr><tr><td class="info">{minTheta}</td><td class="info" align="left"><p>Minimum criterion function, theta(Topt), as described on pages 
     316understand the statistical analysis presented in the 1992 paper.</p></td></tr><tr><td class="info">{minTheta}</td><td class="info" align="left"><p>Minimum criterion function, theta(Topt), as described on pages 
    31431771-72 of the 1992 paper.</p><p>The criterion function is used to determine whether the histogram 
    315318window contains a bimodal distribution, as would be expected if it 
    … …  
    346349threads to more than the number of processors you have; this will 
    347350reduce performance.</p></td></tr><tr><td class="info">{outputMaskFile}</td><td class="info" align="left"><p>Output binary raster that shows the pixels of the input image that 
    348 were masked prior to executing the Cayula-Cornillon algorithm.</p><p>The file will have the same dimensions as the input image and contain 
     351were masked prior to executing the Cayula and Cornillon algorithm.</p><p>The file will have the same dimensions as the input image and contain 
    3493528-bit unsigned integers. The value 1 indicates that the corresponding 
    350353pixel of the input image was masked; 0 indicates the pixel was not 
    … …  
    431434algorithm's tests, increasing or decreasing the number of fronts 
    432435identified in the image. You should only adjust the parameters if you 
    433 feel comfortable deviating from their published values.</p></td></tr></tbody></table></div><p><h2><img width="11" height="11" border="0" src="sm_arrow_down.gif?format=raw" /> Scripting syntax</h2></p><div Class="expand" id="TEST">CoastWatchAVHRRFindFrontsAsBinaryRaster_GeoEco (imageFile, variable, outputFrontsFile, cloudMaskFile, cloudVariable, sunZenithFile, sunZenithVariable, useDayCloudTest1, useDayCloudTest2, useDayCloudTest3, useDayCloudTest4, useDayCloudTest5, useDayCloudTest6, useDayCloudTest7, maskWhenDayCloudMaskExceeds, useNightCloudTest1, useNightCloudTest2, useNightCloudTest3, useNightCloudTest4, useNightCloudTest5, useNightCloudTest6, useNightCloudTest7, maskWhenNightCloudMaskExceeds, minCloudyNeighbors, medianFilterWindowSize, histogramWindowSize, histogramWindowStride, minPropNonMaskedCells, minPopProp, minPopMeanDifference, minTheta, minSinglePopCohesion, minGlobalPopCohesion, threads, outputMaskFile, outputFilteredImageFile, outputCandidateCountsFile, outputFrontCountsFile, outputWindowStatusCodesFile, outputWindowStatusValuesFile) <br /><br /><b>Parameters</b><br /><table width="100%" border="0" cellpadding="5"><tbody><tr><th width="40%"><b>Expression</b></th><th width="60%"><b>Explanation</b></th></tr><tr><td class="info">CoastWatch file (Required) </td><td class="info" align="left"><p>CoastWatch POES AVHRR CWF or HDF file in which fronts should be 
     436feel comfortable deviating from their published values.</p></td></tr></tbody></table></div><p><h2><img width="11" height="11" border="0" src="sm_arrow_down.gif?format=raw" /> Scripting syntax</h2></p><div Class="expand" id="TEST">CoastWatchAVHRRFindFrontsAsBinaryRaster_GeoEco (imageFile, variable, minPopMeanDifference, outputFrontsFile, cloudMaskFile, cloudVariable, sunZenithFile, sunZenithVariable, useDayCloudTest1, useDayCloudTest2, useDayCloudTest3, useDayCloudTest4, useDayCloudTest5, useDayCloudTest6, useDayCloudTest7, maskWhenDayCloudMaskExceeds, useNightCloudTest1, useNightCloudTest2, useNightCloudTest3, useNightCloudTest4, useNightCloudTest5, useNightCloudTest6, useNightCloudTest7, maskWhenNightCloudMaskExceeds, minCloudyNeighbors, medianFilterWindowSize, histogramWindowSize, histogramWindowStride, minPropNonMaskedCells, minPopProp, minTheta, minSinglePopCohesion, minGlobalPopCohesion, threads, outputMaskFile, outputFilteredImageFile, outputCandidateCountsFile, outputFrontCountsFile, outputWindowStatusCodesFile, outputWindowStatusValuesFile) <br /><br /><b>Parameters</b><br /><table width="100%" border="0" cellpadding="5"><tbody><tr><th width="40%"><b>Expression</b></th><th width="60%"><b>Explanation</b></th></tr><tr><td class="info">CoastWatch file (Required) </td><td class="info" align="left"><p>CoastWatch POES AVHRR CWF or HDF file in which fronts should be 
    434437detected.</p><p>Only CoastWatch POES AVHRR files are supported. An error will be raise 
    435438for other CoastWatch files, such as those for the GOES satellite 
    … …  
    454457you may specify one of the others, if appropriate for your project. 
    455458Please see the CoastWatch documentation for more information about the 
    456 variables.</p></td></tr><tr><td class="info">Output fronts image (Required) </td><td class="info" align="left"><p>Output binary raster that shows the fronts detected in the input 
     459variables.</p></td></tr><tr><td class="info">Front detection threshold (Required) </td><td class="info" align="left"><p>Minimum difference, in degrees C, between the mean temperatures of 
     460two adjacent populations of pixels for a front to be detected between 
     461those two populations.</p><p>The Cayula and Cornillon algorithm passes a moving window over the 
     462image, checking each window for a bimodal distribution in the 
     463temperatures of the pixels within it. When the algorithm detects a 
     464bimodal distribution, it computes the mean temperatures of the two 
     465populations and compares the difference between the means to this 
     466threshold. If the difference is less than this threshold, the 
     467algorithm concludes there is no front present and moves on to the next 
     468window.</p><p>You can use this parameter to eliminate weak fronts by selecting a 
     469value that corresponds to a desired minimum mean temperature 
     470difference. Bear in mind that Cayula and Cornillon's (1992) study used 
     471data from early satellites that contributed to the CoastWatch program. 
     472When I examined their Fortran code, I found a commenet suggesting that 
     473they believed the minimum allowable threshold given the measurement 
     474error of the sensors was 0.45 deg C.</p></td></tr><tr><td class="info">Output fronts image (Required) </td><td class="info" align="left"><p>Output binary raster that shows the fronts detected in the input 
    457475image.</p><p>The file will have the same dimensions as the input image and contain 
    4584768-bit signed integers with three possible values:</p><ul><li><p>-128 - the pixel was never a candidate for containing a front, 
    … …  
    460478any histogram windows that had sufficiently large numbers of 
    461479non-masked pixels to proceed with the histogramming step of the 
    462 Cayula-Cornillon algorithm.</p></li></ul><ul><li><p>0 - The pixel was a candidate for containing a front -- it was not 
     480Cayula and Cornillon algorithm.</p></li></ul><ul><li><p>0 - The pixel was a candidate for containing a front -- it was not 
    463481masked and it appeared in at least one histogram window with a 
    464482sufficient number of non-masked pixels to proceed with the 
    … …  
    691709(e.g. it is land), it does not count as being cloudy.</p><p>This option is ignored when cloud masking is not performed.</p></td></tr><tr><td class="info">Median filter window size (Optional) </td><td class="info" align="left"><p>Window size, in pixels, of the median filter to apply to the input 
    692710image prior to running the histogram analysis step of the 
    693 Cayula-Cornillon algorithm. If not provided, median filtering will not 
     711Cayula and Cornillon algorithm. If not provided, median filtering will not 
    694712be performed.</p><p>If you provide a value, it must be an odd integer greater than or 
    695713equal to 3. The filter window is square and advances across the image 
    … …  
    697715with the median value of the non-masked pixels in the surrounding 
    698716window. All masks are applied before the median filter is executed.</p><p>Median filtering is a traditional first step for certain classes of 
    699 edge detection algorithms. The original Cayula-Cornillon paper used a 
    700 window size of 3.</p></td></tr><tr><td class="info">Histogram window size (Optional) </td><td class="info" align="left"><p>Size of the histogram window to use for the Cayula-Cornillon 
     717edge detection algorithms. The original Cayula and Cornillon paper used a 
     718window size of 3.</p></td></tr><tr><td class="info">Histogram window size (Optional) </td><td class="info" align="left"><p>Size of the histogram window to use for the Cayula and Cornillon 
    701719algorithm.</p><p>The window is square. The original paper used a window size of 32. 
    702720Although the algorithm is claimed to obtain similar results regardless 
    … …  
    705723the paper carefully before experimenting with different window 
    706724sizes.</p></td></tr><tr><td class="info">Histogram window stride (Optional) </td><td class="info" align="left"><p>Number of pixels to move the histogram window after each iteration 
    707 of the Cayula-Cornillon algorithm.</p><p>The original paper used a 32x32 window and a stride of 16, to minimize 
     725of the Cayula and Cornillon algorithm.</p><p>The original paper used a 32x32 window and a stride of 16, to minimize 
    708726the CPU time required to execute the algorithm. Cutting the stride in 
    709727half increases the CPU time by a factor of about four. For example, a 
    … …  
    729747accurate. In this case, and the algorithm discards the current window, 
    730748advances to next one, and starts over.</p><p>I do not recommend selecting a value other than 0.25 unless you 
    731 understand the statistical analysis presented in the 1992 paper.</p></td></tr><tr><td class="info">Minimum population mean difference (Optional) </td><td class="info" align="left"><p>Minimum difference in population means.</p><p>After the histogram algorithm separates the non-masked pixels into two 
    732 populations, it computes the means of the two populations. If the 
    733 means differ by less than this parameter value, the algorithm discards 
    734 the current window, advances to next one, and starts over.</p><p>The original intent of this parameter is obscure. The Ratfor code I 
    735 obtained from Dave Ullman used the value 3 and contained the 
    736 explanation "a temperature difference of less than three digital 
    737 counts between the 2 populations is likely to be a result of the 
    738 discrete nature of the data."</p><p>You can use this parameter to eliminate weak fronts by selecting a 
    739 value that corresponds to a desired minimum mean temperature 
    740 difference. For example, for the NOAA NODC 4km AVHRR Pathfinder SST 
    741 data, the value of 1 corresponds to 0.075 degrees. To eliminate fronts 
    742 where the mean temperature difference is less than 0.5 degrees, set 
    743 this parameter to 0.5 / 0.075 = 6.666667.</p></td></tr><tr><td class="info">Minimum value for criterion function (Optional) </td><td class="info" align="left"><p>Minimum criterion function, theta(Topt), as described on pages 
     749understand the statistical analysis presented in the 1992 paper.</p></td></tr><tr><td class="info">Minimum value for criterion function (Optional) </td><td class="info" align="left"><p>Minimum criterion function, theta(Topt), as described on pages 
    74475071-72 of the 1992 paper.</p><p>The criterion function is used to determine whether the histogram 
    745751window contains a bimodal distribution, as would be expected if it 
    … …  
    776782threads to more than the number of processors you have; this will 
    777783reduce performance.</p></td></tr><tr><td class="info">Output mask image (Optional) </td><td class="info" align="left"><p>Output binary raster that shows the pixels of the input image that 
    778 were masked prior to executing the Cayula-Cornillon algorithm.</p><p>The file will have the same dimensions as the input image and contain 
     784were masked prior to executing the Cayula and Cornillon algorithm.</p><p>The file will have the same dimensions as the input image and contain 
    7797858-bit unsigned integers. The value 1 indicates that the corresponding 
    780786pixel of the input image was masked; 0 indicates the pixel was not 

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    209209typically required by scientific journals that accept figures in PNG 
    210210format.</p><p>This parameter is ignored for EMF format because it is a vector 
    211 format.</p></td></tr><tr><td class="info">{width}</td><td class="info" align="left"><p>Plot file width in inches (for EMF format) or pixels (for PNG 
    212 format).</p></td></tr><tr><td class="info">{height}</td><td class="info" align="left"><p>Plot file width in inches (for EMF format) or pixels (for PNG 
    213 format).</p></td></tr><tr><td class="info">{pointSize}</td><td class="info" align="left"><p>The default pointsize of plotted text.</p></td></tr><tr><td class="info">{bg}</td><td class="info" align="left"><p>PNG plot file background color. The color must be a valid name in 
     211format.</p></td></tr><tr><td class="info">{width}</td><td class="info" align="left"><p>Plot file width in thousandths of inches (for EMF format; e.g. the 
     212value 3000 is 3 inches) or pixels (for PNG format).</p></td></tr><tr><td class="info">{height}</td><td class="info" align="left"><p>Plot file height in thousandths of inches (for EMF format; e.g. the 
     213value 3000 is 3 inches) or pixels (for PNG format).</p></td></tr><tr><td class="info">{pointSize}</td><td class="info" align="left"><p>The default pointsize of plotted text.</p></td></tr><tr><td class="info">{bg}</td><td class="info" align="left"><p>PNG plot file background color. The color must be a valid name in 
    214214R's color palette, or "transparent" if there is no background color. 
    215215This parameter is ignored if the plot format file is EMF.</p></td></tr></tbody></table></div><p><h2><img width="11" height="11" border="0" src="sm_arrow_down.gif?format=raw" /> Scripting syntax</h2></p><div Class="expand" id="TEST">GAMFitToArcGISTable_GeoEco (inputTable, outputModelFile, formula, family, rPackage, where, link, variance, theta, method, optimizer, alternativeOptimizer, xColumnName, yColumnName, zColumnName, mColumnName, selectionMethod, logSelectionDetails, writeSummaryFile, writeDiagnosticPlots, writeTermPlots, residuals, xAxis, commonScale, plotFileFormat, res, width, height, pointSize, bg) <br /><br /><b>Parameters</b><br /><table width="100%" border="0" cellpadding="5"><tbody><tr><th width="40%"><b>Expression</b></th><th width="60%"><b>Explanation</b></th></tr><tr><td class="info">Input table (Required) </td><td class="info" align="left"><p>ArcGIS table, table view, feature class, or feature layer 
    … …  
    421421typically required by scientific journals that accept figures in PNG 
    422422format.</p><p>This parameter is ignored for EMF format because it is a vector 
    423 format.</p></td></tr><tr><td class="info">Plot width (Optional) </td><td class="info" align="left"><p>Plot file width in inches (for EMF format) or pixels (for PNG 
    424 format).</p></td></tr><tr><td class="info">Plot height (Optional) </td><td class="info" align="left"><p>Plot file width in inches (for EMF format) or pixels (for PNG 
    425 format).</p></td></tr><tr><td class="info">Default pointsize of plotted text (Optional) </td><td class="info" align="left"><p>The default pointsize of plotted text.</p></td></tr><tr><td class="info">Plot background color (Optional) </td><td class="info" align="left"><p>PNG plot file background color. The color must be a valid name in 
     423format.</p></td></tr><tr><td class="info">Plot width (Optional) </td><td class="info" align="left"><p>Plot file width in thousandths of inches (for EMF format; e.g. the 
     424value 3000 is 3 inches) or pixels (for PNG format).</p></td></tr><tr><td class="info">Plot height (Optional) </td><td class="info" align="left"><p>Plot file height in thousandths of inches (for EMF format; e.g. the 
     425value 3000 is 3 inches) or pixels (for PNG format).</p></td></tr><tr><td class="info">Default pointsize of plotted text (Optional) </td><td class="info" align="left"><p>The default pointsize of plotted text.</p></td></tr><tr><td class="info">Plot background color (Optional) </td><td class="info" align="left"><p>PNG plot file background color. The color must be a valid name in 
    426426R's color palette, or "transparent" if there is no background color. 
    427427This parameter is ignored if the plot format file is EMF.</p></td></tr></tbody></table></div></body></html> 

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    r945 r957  
    135135typically required by scientific journals that accept figures in PNG 
    136136format.</p><p>This parameter is ignored for EMF format because it is a vector 
    137 format.</p></td></tr><tr><td class="info">{width}</td><td class="info" align="left"><p>Plot file width in inches (for EMF format) or pixels (for PNG 
    138 format).</p></td></tr><tr><td class="info">{height}</td><td class="info" align="left"><p>Plot file width in inches (for EMF format) or pixels (for PNG 
    139 format).</p></td></tr><tr><td class="info">{pointSize}</td><td class="info" align="left"><p>The default pointsize of plotted text.</p></td></tr><tr><td class="info">{bg}</td><td class="info" align="left"><p>PNG plot file background color. The color must be a valid name in 
     137format.</p></td></tr><tr><td class="info">{width}</td><td class="info" align="left"><p>Plot file width in thousandths of inches (for EMF format; e.g. the 
     138value 3000 is 3 inches) or pixels (for PNG format).</p></td></tr><tr><td class="info">{height}</td><td class="info" align="left"><p>Plot file height in thousandths of inches (for EMF format; e.g. the 
     139value 3000 is 3 inches) or pixels (for PNG format).</p></td></tr><tr><td class="info">{pointSize}</td><td class="info" align="left"><p>The default pointsize of plotted text.</p></td></tr><tr><td class="info">{bg}</td><td class="info" align="left"><p>PNG plot file background color. The color must be a valid name in 
    140140R's color palette, or "transparent" if there is no background color. 
    141141This parameter is ignored if the plot format file is EMF.</p></td></tr></tbody></table></div><p><h2><img width="11" height="11" border="0" src="sm_arrow_down.gif?format=raw" /> Scripting syntax</h2></p><div Class="expand" id="TEST">GLMFitToArcGISTable_GeoEco (inputTable, outputModelFile, formula, family, where, link, variance, xColumnName, yColumnName, zColumnName, mColumnName, selectionMethod, logSelectionDetails, writeSummaryFile, writeDiagnosticPlots, numDiagLabels, diagLabelField, writeTermPlots, residuals, xAxis, commonScale, plotFileFormat, res, width, height, pointSize, bg) <br /><br /><b>Parameters</b><br /><table width="100%" border="0" cellpadding="5"><tbody><tr><th width="40%"><b>Expression</b></th><th width="60%"><b>Explanation</b></th></tr><tr><td class="info">Input table (Required) </td><td class="info" align="left"><p>ArcGIS table, table view, feature class, or feature layer 
    … …  
    273273typically required by scientific journals that accept figures in PNG 
    274274format.</p><p>This parameter is ignored for EMF format because it is a vector 
    275 format.</p></td></tr><tr><td class="info">Plot width (Optional) </td><td class="info" align="left"><p>Plot file width in inches (for EMF format) or pixels (for PNG 
    276 format).</p></td></tr><tr><td class="info">Plot height (Optional) </td><td class="info" align="left"><p>Plot file width in inches (for EMF format) or pixels (for PNG 
    277 format).</p></td></tr><tr><td class="info">Default pointsize of plotted text (Optional) </td><td class="info" align="left"><p>The default pointsize of plotted text.</p></td></tr><tr><td class="info">Plot background color (Optional) </td><td class="info" align="left"><p>PNG plot file background color. The color must be a valid name in 
     275format.</p></td></tr><tr><td class="info">Plot width (Optional) </td><td class="info" align="left"><p>Plot file width in thousandths of inches (for EMF format; e.g. the 
     276value 3000 is 3 inches) or pixels (for PNG format).</p></td></tr><tr><td class="info">Plot height (Optional) </td><td class="info" align="left"><p>Plot file height in thousandths of inches (for EMF format; e.g. the 
     277value 3000 is 3 inches) or pixels (for PNG format).</p></td></tr><tr><td class="info">Default pointsize of plotted text (Optional) </td><td class="info" align="left"><p>The default pointsize of plotted text.</p></td></tr><tr><td class="info">Plot background color (Optional) </td><td class="info" align="left"><p>PNG plot file background color. The color must be a valid name in 
    278278R's color palette, or "transparent" if there is no background color. 
    279279This parameter is ignored if the plot format file is EMF.</p></td></tr></tbody></table></div></body></html> 

  • MGET/Branches/Jason/PythonPackage/dist/TracOnlineDocumentation/Documentation/ArcGISReference/HYCOMGLBa008Equatorial4D.CreateCayulaCornillonFrontsAsArcGISRasters.html

    r945 r957  
    22<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd"> 
    33<html xmlns="http://www.w3.org/1999/xhtml"><head><link rel="stylesheet" type="text/css" href="81help.css?format=raw" /><meta http-equiv="Content-Type" content="text/html; charset=UTF-8" /><title>Find Cayula-Cornillon Fronts in HYCOM GLBa0.08 Equatorial 4D Variable</title></head><body><table style="margin-top:-1em; margin-bottom:0; padding:0; margin-left:-1em"><tr><td style="background:white"><img width="875" height="70" alt="ArcToolbox banner" src="AHBanner_ArcToolbox.gif?format=raw" /></td></tr></table><h1>Find Cayula-Cornillon Fronts in HYCOM GLBa0.08 Equatorial 4D Variable</h1><p></p><p>Creates rasters indicating the positions of fronts in the 2D slices of a 4D variable of the equatorial (Mercator) region of the HYCOM GLBa0.08 dataset using the Cayula and Cornillon (1992) single image edge detection (SIED) algorithm.</p><p><b>Overview</b></p><p>This tool efficiently downloads 2D time/depth slices of a specified 4D 
    4 HYCOM variable, executes the Cayula-Cornillon SIED algorithm to 
     4HYCOM variable, executes the Cayula and Cornillon SIED algorithm to 
    55identify fronts in each 2D slice, and creates rasters showing the 
    66locations of the fronts.</p><p>This tool is complicated and has a lot of parameters. For the best 
    … …  
    104104long as a sufficient temperature gradient continues in the direction 
    105105the front was pointing.</p></li></ul><p>In 2005, I obtained a Rational Fortran (Ratfor) version of the 
    106 Cayula-Cornillon algorithm from Dave Ullman. Although it had been 
     106Cayula and Cornillon algorithm from Dave Ullman. Although it had been 
    107107modified extensively from the 1992 version, mainly to incorporate the 
    108108multi-image edge detection (MIED) algorithm (Cayula and Cornillon 
    …