Latest Contributions by DarrenWiens2

If the above link does not solve your problem, this (admittedly simplified) script creates lines for each set of points, after converting your csv to a point feature class (here named, "csv_points"). >>> lines = [] ... points = [] ... redd = 1 ... with arcpy.da.SearchCursor("csv_points",["SHAPE@","Redd"]) as cursor: ...    for row in cursor: ...        if row[1] != redd: ...            line = arcpy.Polyline(arcpy.Array(points)) ...            lines.append(line) ...            points = [] ...        points.append(row[0].centroid) ...        redd = row[1] ... arcpy.CopyFeatures_management(lines,r'in_memory\lines') ... View more

^ but chances are it's not that bad ... View more

You can certainly automate the process with python (if that's how you want to go, please indicate). However, if your data truly are in geodatabases (there is some confusion whether you're talking about geodatabases or folders of shapefiles), each feature class will already have the auto-populated field SHAPE_AREA. Then, right-click the field and Summarize to get the total, or batch process Summary Statistics on all feature classes to semi-automate the totals. ... View more

That's a good point. If so, multiply the percent by the area to a new column (Clay_Area). Then Summary Statistics, Clay_Area = SUM, Area = SUM, County = case field. Then divide Clay_Area_SUM by Area_SUM to get county clay percent. This should work, but it feels like I'm missing a time-saving step. ... View more

You can override the Snap Raster environment (align the output raster grid to a specified raster) at any tool in your model. 1.) Right-click the tool, choose Properties. 2.) Select Snap Raster inside Processing Extent 3.) Click Values 4.) Expand Processing Extent 5.) Enter Raster to snap output grid to. ... View more

You are looking for Summary Statistics. Clay content = MEAN, County = case field. ... View more

Just a small nit-pick, if you add NoData to Data in Raster Calculator, you will be left with NoData. You can get around it by using Con(IsNull("raster"), "otherraster", "raster") and setting the processing extent to the entire shared area. ... View more

My main question for you is, are you trying to remove pixel values (e.g. '0') or NoData? Depending on your data, it may be possible to mosaic the rasters together using either minimum or maximum as the mosaic operator, in order to identify the pixels where data exists for all rasters. For example, if you want to identify and remove all '0' pixels, then mosaicking with maximum would identify those where all rasters had '0', and vice versa if you want to remove '255' (or other max number). If you are trying to ignore NoData values, I believe they should automatically remove themselves from the mosaic. Once you have your mosaic, you should be able to convert to polygon for your clipping mask. ... View more

Is the problem that you have pairs of related points (same cross-section line) in one feature class? You can run Summary Statistics, using both MIN and MAX (elevations) as the statistics types and Line ID as the case field. Then join the result back to the point feature class based on Line ID. Finally, calculate elevation difference for each point using the field calculator. ... View more

Are you just selecting the street in order to zoom to it, and then selecting the portion to update? If so, you could skip the initial selection and use the streets as a data driven page index, instead. This will let you cycle through each street name, zooming to each street in order. ... View more