Latest Contributions by JohannesLindner

You're telling your DBMS to use the literal SQL query "ID = $feature.ID". This isn't a valid where clause and so it fails. What you want to do is store the $feature.ID in a variable and use the @ notation of Filter() to take care of the type formatting for you. Also, you aren't looping over the filtered feature set, but over the whole table... var Land = FeatureSetByName($datastore, "Land", [ "Location", "Name", "County", "School_District", "ID"]); var id = $feature.ID var related = Filter(Land,"ID=@id") var location = First(related) if(location == null) { // no related features found -> return a default value return null } // else return the related feature's ID return location.ID   ... View more

Just had another look at the docs: Number() also supports a format string. So if the part after the underscore is the same for all features, here is another possibility: Number(Split($feature.RouteName, "_")[1], "####KM") + " km" ... View more

My apologies, I really fudged that up... I know that limiting what you load can speed scripts up, so that was the first thing I tried. I got a huge speed boost and left it at that without checking the result or diving deeper into your script. And I must have some confusion about loading geometries and Intersects(). Could have sworn you didn't need the geometries, but obviously that's wrong. At least loading only the needed fields really gives a significant (although not 33 seconds...) speed boost: loading time in seconds boundaries, all fields: 0.641 boundaries, spec fields: 0.002 nno, all fields: 0.312 nno, spec fields: 0.003   Anyway, I did a quick profiling of your script. Turns out the loading (esp. with only needed fields) and Intersecting is quite fast. What's dragging the speed down is the Count() and Sum() functions. They seem to be implemented as simple for loops, and they take a long time, especially for large nnoint. In this profile, I measured the time to do both Count and Sum, they took roughly the equal amount of time each. Load 1: 0.009 Load 2: 0.001 boundary 0 Intersects: 0 Count & Sum: 4.3629999999999995 total: 4.367 boundary 1 Intersects: 0 Count & Sum: 2.787 total: 2.791 boundary 2 Intersects: 0 Count & Sum: 0.864 total: 0.866 boundary 3 Intersects: 0 Count & Sum: 1.081 total: 1.085 boundary 4 Intersects: 0.001 Count & Sum: 1.321 total: 1.325 boundary 5 Intersects: 0 Count & Sum: 0.947 total: 0.949 boundary 6 Intersects: 0 Count & Sum: 0.484 total: 0.486 boundary 7 Intersects: 0 Count & Sum: 0.894 total: 0.895 boundary 8 Intersects: 0 Count & Sum: 1.155 total: 1.157 boundary 9 Intersects: 0 Count & Sum: 0.668 total: 0.67 boundary 10 Intersects: 0 Count & Sum: 0.544 total: 0.547 boundary 11 Intersects: 0.001 Count & Sum: 0.509 total: 0.512 boundary 12 Intersects: 0 Count & Sum: 0.594 total: 0.595 boundary 13 Intersects: 0 Count & Sum: 0.469 total: 0.471 boundary 14 Intersects: 0 Count & Sum: 0.512 total: 0.513 boundary 15 Intersects: 0 Count & Sum: 0.538 total: 0.54 boundary 16 Intersects: 0 Count & Sum: 0.788 total: 0.791 boundary 17 Intersects: 0.001 Count & Sum: 0.595 total: 0.598 boundary 18 Intersects: 0 Count & Sum: 0.641 total: 0.644 boundary 19 Intersects: 0 Count & Sum: 0.845 total: 0.847 boundary 20 Intersects: 0 Count & Sum: 0.533 total: 0.535 boundary 21 Intersects: 0 Count & Sum: 0.532 total: 0.533 boundary 22 Intersects: 0 Count & Sum: 0.505 total: 0.507 boundary 23 Intersects: 0 Count & Sum: 0.711 total: 0.713 boundary 24 Intersects: 0 Count & Sum: 0.657 total: 0.659 boundary 25 Intersects: 0 Count & Sum: 0.542 total: 0.544 boundary 26 Intersects: 0 Count & Sum: 0.53 total: 0.531 boundary 27 Intersects: 0 Count & Sum: 0.466 total: 0.467 boundary 28 Intersects: 0 Count & Sum: 0.924 total: 0.925 boundary 29 Intersects: 0 Count & Sum: 0.523 total: 0.523 boundary 30 Intersects: 0 Count & Sum: 0.648 total: 0.649 boundary 31 Intersects: 0 Count & Sum: 0.512 total: 0.513 boundary 32 Intersects: 0 Count & Sum: 0.501 total: 0.502 boundary 33 Intersects: 0 Count & Sum: 0.658 total: 0.659 total: 29.769   As Count() and Sum() are implemented as for loops under the hood, you can speed things up by writing the code yourself, using only one for loop to calculate both sum and count:   for (var boundary in boundaries) { var nnoint = Intersects(boundary,nno) var nnocount = 0 var nnosum = 0 for(var nnoi in nnoint) { nnocount ++ nnosum += nnoi.Expected_Attendance } // append feature }     This speeds up the script by about 50%, which is obvious, because we cut 50% of the loops. Load 1: 0.007 Load 2: 0.002 boundary 0 Intersect: 0 Count & Sum: 2.073 Append feature: 0 total: 2.075 boundary 1 Intersect: 0 Count & Sum: 1.004 Append feature: 0.001 total: 1.007 boundary 2 Intersect: 0 Count & Sum: 0.745 Append feature: 0 total: 0.748 boundary 3 Intersect: 0 Count & Sum: 0.517 Append feature: 0 total: 0.52 boundary 4 Intersect: 0 Count & Sum: 0.544 Append feature: 0 total: 0.547 boundary 5 Intersect: 0 Count & Sum: 0.71 Append feature: 0.001 total: 0.713 boundary 6 Intersect: 0 Count & Sum: 0.497 Append feature: 0 total: 0.499 boundary 7 Intersect: 0 Count & Sum: 0.537 Append feature: 0 total: 0.539 boundary 8 Intersect: 0 Count & Sum: 0.759 Append feature: 0 total: 0.761 boundary 9 Intersect: 0 Count & Sum: 0.326 Append feature: 0 total: 0.329 boundary 10 Intersect: 0 Count & Sum: 0.573 Append feature: 0 total: 0.577 boundary 11 Intersect: 0 Count & Sum: 0.317 Append feature: 0.001 total: 0.319 boundary 12 Intersect: 0 Count & Sum: 0.289 Append feature: 0 total: 0.292 boundary 13 Intersect: 0 Count & Sum: 0.241 Append feature: 0 total: 0.243 boundary 14 Intersect: 0 Count & Sum: 0.229 Append feature: 0 total: 0.231 boundary 15 Intersect: 0 Count & Sum: 0.261 Append feature: 0 total: 0.262 boundary 16 Intersect: 0 Count & Sum: 0.469 Append feature: 0 total: 0.472 boundary 17 Intersect: 0.001 Count & Sum: 0.249 Append feature: 0 total: 0.253 boundary 18 Intersect: 0 Count & Sum: 0.288 Append feature: 0 total: 0.291 boundary 19 Intersect: 0 Count & Sum: 0.395 Append feature: 0 total: 0.398 boundary 20 Intersect: 0 Count & Sum: 0.231 Append feature: 0 total: 0.234 boundary 21 Intersect: 0 Count & Sum: 0.245 Append feature: 0 total: 0.247 boundary 22 Intersect: 0 Count & Sum: 0.205 Append feature: 0 total: 0.206 boundary 23 Intersect: 0 Count & Sum: 0.284 Append feature: 0 total: 0.285 boundary 24 Intersect: 0 Count & Sum: 0.225 Append feature: 0 total: 0.228 boundary 25 Intersect: 0 Count & Sum: 0.254 Append feature: 0.001 total: 0.259 boundary 26 Intersect: 0 Count & Sum: 0.256 Append feature: 0 total: 0.261 boundary 27 Intersect: 0 Count & Sum: 0.244 Append feature: 0 total: 0.25 boundary 28 Intersect: 0 Count & Sum: 0.386 Append feature: 0.001 total: 0.389 boundary 29 Intersect: 0 Count & Sum: 0.27 Append feature: 0 total: 0.274 boundary 30 Intersect: 0.001 Count & Sum: 0.273 Append feature: 0 total: 0.275 boundary 31 Intersect: 0 Count & Sum: 0.248 Append feature: 0.001 total: 0.25 boundary 32 Intersect: 0 Count & Sum: 0.235 Append feature: 0 total: 0.238 boundary 33 Intersect: 0 Count & Sum: 0.321 Append feature: 0 total: 0.322 total: 15.649000000000001   Sadly, I don't see a way to speed it up further. ... 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You could use marker layers. create a marker layer, delete the stroke layers Choose this form   rotation: 90°, Placement = Along line, check Angle to line play with the other parameters eg: Line width = 1pt, Size = 15pt, Placement template = 30 Copy the layer. in the copy: rotation = 0°, change line width, size, and Perpendicular offset to match eg: Line width = 3pt, Size = 7pt, Perpendicular offset = 3pt     ... View more

Indexes speed up everything that uses WHERE statements. I don't know if the search feature does that, it's possible that you won't get a speed boost here.   But there is a noticeable difference in selecting a record (table from a fgdb, can't test in an egdb right now):   from timeit import timeit timeit(lambda: arcpy.management.SelectLayerByAttribute("TestTable", "NEW_SELECTION", "IntegerField = 172649"), number=100) #301.5261236000006 timeit(lambda: arcpy.management.SelectLayerByAttribute("TestTable", "NEW_SELECTION", "OBJECTID = 172649"), number=100) #244.7347675000001   A query on the non-indexed field took 3.01 seconds on average. On the indexed field it took 2.44 seconds, that's a 20% speedup. ... View more