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efficiently searching a large array

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I'm working on a script that (among other things) takes a pair of x,y coordinates and then searches a large (20000 x 6) array for the nearest xy location (the other 4 columns in the array are irrelevant here). It's not quite a straight dist = sqrt((x1-x2)^2 + (y1-y2)^2) but Sinnott's formula for spherical geometry (the xy coords are actually angles, latitude and longitude:

Func _LLDist($lat1, $lon1, $lat2, $lon2)
    ;using Sinnott's formula for great circle distance between two points
    ;first convert degrees to radians for trig functions
    $lat1 = $lat1 * (ACos(-1) / 180)
    $lon1 = $lon1 * (ACos(-1) / 180)
    $lat2 = $lat2 * (ACos(-1) / 180)
    $lon2 = $lon2 * (ACos(-1) / 180)
    Local $R = 6371000 ; Earth radius in meters
    Local $dlon = $lon2 - $lon1
    Local $dlat = $lat2 - $lat1
    Local $a = (Sin($dlat / 2)) ^ 2 + Cos($lat1) * Cos($lat2) * (Sin($dlon / 2)) ^ 2
    Local $c = 2 * ASin(Sqrt($a))
    Local $d = $R * $c
    Return $d
EndFunc   ;==>_LLDist

If no point is closer than the minimum distance ($minsize) then it returns 0 (no match). The actual comparison I'm using now is a simple brute force comparison of each of 20000 lines in the array:

Global $apdb[20000][6]
; the actual $apdb array is read in from a text file
Global $minsize = 1609.344 ; 1 mile
Func _APnearest($alat, $along)
    ;ConsoleWrite("checking stop at " & $alat & "," & $along & ":" & @CRLF)
    Local $mindist = 20015087 ; Earth circumference
    For $a = 1 To $apnum
        Local $apdist = _LLDist($alat, $along, $apdb[$a][3], $apdb[$a][4])
        If $apdist < $mindist Then
            $mindist = $apdist
            $closest = $a
        ;ConsoleWrite($a & " " & $apdb[$a][1] & " " & $alat & "," & $along & "  " & $apdb[$a][3] & "," & $apdb[$a][4] & ", apdist:  " & $apdist & " mindist: " & $mindist & @CRLF)
    If $mindist < $minsize Then Return $closest ; else returns 0
EndFunc   ;==>_APnearest

The problem is that it takes quite some time to process 20000 lines, so I'm trying to figure out a way to make it faster. I know I could simply stop when it finds the first value less than $minsize, but that's not necessarily unique. I could also pre-check the latitude and longitude for approximate proximity (say, 2 minutes of arc which (for latitude at least) equals 2 nautical miles) and then run the _LLDist function only on the close pairs, but I'm unclear on how much time that function takes compared with stepping through the array.

I know there are ways with single variable and a sorted list to zero in on the target by jumping around without fully checking each value. I suppose I could do that, sorting the array first by latitude, zero in on the target latitude and then do the actual calculation only on close latitudes... so I guess one question is has anybody created or seen a UDF already that does this? Or any other suggestions?

Edited by Dana
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In my signature is a link to a modified array UDF. In that UDF is the _ArrayBinarySearch function modifed to work with 2D arrays.If you sort the array first, and search on the sorted "column" of the array you'll find that this search is much faster than normal searching in an array.

This UDF by the way should be compatible with the normal Array.au3 UDF file except I modified most of the 1D only functions to work with 2D arrays, plus there are some new functions added.

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How about a simple flat geometry (root of sum of squared x/y differences) comprison on the first pass and then a full spherical geometry comparison with the X closest matches that you find? That way you get a fast run through the whole array and only do the really expensive calculations on the likely positions. Adjust X as needed depending on the spread of the points. :D


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ArrayMultiColSort ---- Sort arrays on multiple columns
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Interesting question. This depends entirely on how you have the coordinates organized in your array. You currently have two columns, and if they are layed out in order of coordinate magnitude, the job will be made easier using binary search methods as suggested by BrewManNH. If the array is disorganized, then it will be more difficut to improve on search speed.


The nearest value for X may not be one of the closest set of coordinates, since the y coordinate may be a long distance away. I think you need to search outwards from an artificial set of coordinates which include the closest value of x and the closest value of y. You will probably need to compare a number of candidate close matches using trig, also taking into consideration the fact that longditudinal lines get closer towards the poles.

Edited by czardas
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It's not going to make the difference between night and day, but I don't see why you'd want to execute this portion 80,000 times:

ACos(-1) / 180

when it could be defined once as a constant...

Edit: fixed whacked-out code tags

Edited by Spiff59
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If you have to perform such massive geodata search, I'd look into the Spatialite extension to SQLite.

This wonderful site allows debugging and testing regular expressions (many flavors available). An absolute must have in your bookmarks.
Another excellent RegExp tutorial. Don't forget downloading your copy of up-to-date pcretest.exe and pcregrep.exe here
RegExp tutorial: enough to get started
PCRE v8.33 regexp documentation latest available release and currently implemented in AutoIt beta.

SQLitespeed is another feature-rich premier SQLite manager (includes import/export). Well worth a try.
SQLite Expert (freeware Personal Edition or payware Pro version) is a very useful SQLite database manager.
An excellent eBook covering almost every aspect of SQLite3: a must-read for anyone doing serious work.
SQL tutorial (covers "generic" SQL, but most of it applies to SQLite as well)
A work-in-progress SQLite3 tutorial. Don't miss other LxyzTHW pages!
SQLite official website with full documentation (may be newer than the SQLite library that comes standard with AutoIt)

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Thanks all,

The data in the large array initially isn't sorted. I altered the code to first ReDim the array to the actual size to eliminate blank lines (I initially set it to 25,000 prior to reading the data in whereas the actual number at this moment is 19,782), then I use _ArraySort to sort it on latitude. I then modified my function to use the same kind of search as _ArrayBinarySearch:

Func _APnearest($alat, $along)
    Local $mindist = 20015087 ; Earth circumference

    Local $amid = $apnum / 2
    Local $astart = 1
    Local $aend = $apnum

    While 1

        If ($alat < ($apdb[$amid][3] - $minsize/96560.64)) Then ; meters to minutes of latitude
            $aend = $amid - 1
        Elseif ($alat &gt; ($apdb[$amid][3] + $minsize/96560.64)) then
            $astart = $amid + 1
        $amid = Int(($aend + $astart) / 2)

    For $a = $astart To $apnum ; was 1 To $apnum
        Local $apdist = _LLDist($alat, $along, $apdb[$a][3], $apdb[$a][4])
     If $apdist &lt; $mindist Then
            $mindist = $apdist
            $closest = $a
        If ($alat &lt; ($apdb[$a][3] - ($minsize/96560.64))) Then
$apdist &amp; " mindist: " &amp; $mindist &amp; @CRLF)
    If $mindist &lt; $minsize Then Return $closest ; else returns 0
EndFunc   ;==&gt;_APnearest

This starts the search at the first location within $minsize to the south, and stops the same distance to the north, which restricts the actual distance calculation to perhaps a couple of hundred locations. The search naturally goes much faster, but the time saved over (in this case) six locations is offset by the time it takes to do the _ArraySort initially. If I was doing a lot more locations sorting first would make a lot of sense; in this case it doesn't seem to make much difference.

Melba23, the time intensive part seems to be stepping through the array... I tried doing a simple latitude comparison first and then the spherical geometry only on the locations close in latitude, and also the root sum of squares as a first test, but it made little or no difference.

Spiff59, I tried your suggestion and made (ACos(-1) / 180) a constant. It made 0.005 seconds difference. I spelled it out each time to make it self documenting code.

At this point it takes under 10 seconds to run six locations. Slower than I'd like, but acceptable.

Edited by Dana
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Spiff59, I tried your suggestion and made (ACos(-1) / 180) a constant. It made 0.005 seconds difference. I spelled it out each time to make it self documenting code.

I was sceptical that 80,000 calls to ACos() and 80,000 division operations could be executed in 5 milliseconds (you have a Cray?), so I threw together a test. The interesting thing is; the 32-bit ACos() function doesn't work! I started a related thread, and just now opened a bug tracker on the issue. I assume you must be running a 64-bit OS, where ACos() does not exhibit the bug?

Self-documentation for that formula, as well as the repeatedly executed "$minsize/96560.64", could also be accomplished by choosing meaningful variable names.

Edited by Spiff59
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Interesting... yes, I'm running 64bit Win7. It's a fast engineering workstation, but no Cray... I'll try it on my 32bit XP laptop.

Of course the timing information I was simply getting from the SciTE console, which I'm sure is not accurate to .005 seconds... but the point is that the difference is not noticeable.

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