I need to determine the shortest distance between each point within a single feature class. Near Analysis would have worked perfectly, but unfortunately you need to have an Advanced ArcGIS Licence and I only have a Standard Licence. I found the following code on Stack Exchange written by PolyGeoâ: import arcpy , math # Set variables for input point feature classes and output table ptFC1 = "C:/temp/test.gdb/PointFC1" ptFC2 = "C:/temp/test.gdb/PointFC2" outGDB = "C:/temp/test.gdb" outTableName = "outTable" outTable = outGDB + "/" + outTableName arcpy . env . overwriteOutput = True # Create empty output table arcpy . CreateTable_management ( outGDB , outTableName ) arcpy . AddField_management ( outTable , "INPUT_FID" , "LONG" ) arcpy . AddField_management ( outTable , "NEAR_FID" , "LONG" ) arcpy . AddField_management ( outTable , "DISTANCE" , "DOUBLE" ) # Create and populate two dictionaries with X and Y coordinates for each # OBJECTID in second feature class using a SearchCursor ptFC2XCoordDict = {} ptFC2YCoordDict = {} with arcpy . da . SearchCursor ( ptFC2 ,[ "OBJECTID" , "SHAPE@XY" ]) as cursor : for row in cursor : ptFC2XCoordDict [ row [ 0 ]] = row [ 1 ][ 0 ] ptFC2YCoordDict [ row [ 0 ]] = row [ 1 ][ 1 ] # Open an InsertCursor ready to have rows written for each pair of OBJECTIDs iCursor = arcpy . da . InsertCursor ( outTable ,[ "INPUT_FID" , "NEAR_FID" , "DISTANCE" ]) # Use a SearchCursor to read the rows (and X,Y coordinates) of the first # feature class with arcpy . da . SearchCursor ( ptFC1 ,[ "OBJECTID" , "SHAPE@XY" ]) as cursor : for row in cursor : x1 = row [ 1 ][ 0 ] y1 = row [ 1 ][ 1 ] for i in range ( len ( ptFC2XCoordDict )): x2 = ptFC2XCoordDict [ i + 1 ] y2 = ptFC2YCoordDict [ i + 1 ] # Prepare and insert the InsertCursor row iRow = [ row [ 0 ], i + 1 , math . sqrt (( x2 - x1 )*( x2 - x1 ) + ( y2 - y1 )*( y2 - y1 ))] iCursor . insertRow ( iRow ) del iCursor print "Done!" The following reports the distance of the first point to the rest of the points: I would like to amend the following to iterate through each point within the feature class to report the shortest distance between the points, greater than zero of course. Any advice in amending the following will be appreciated. I'm trying to use the results from this as a way of validating that none of the points are within 3m or less from each other and if there are the filtered list is processed further to move the points away from each other within bounding box for each point.

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Python Near Analysis (No Advanced Licence)

PeterWilson

I need to determine the shortest distance between each point within a single feature class. Near Analysis would have worked perfectly, but unfortunately you need to have an Advanced ArcGIS Licence and I only have a Standard Licence.

I found the following code on Stack Exchange written by PolyGeoâ:

<SPAN class="kwd" style="color: #00008b;">import</SPAN><SPAN class="pln" style="color: #2e3133;"> arcpy</SPAN><SPAN class="pun" style="color: #2e3133;">,</SPAN><SPAN class="pln" style="color: #2e3133;">math

</SPAN><SPAN class="com" style="color: #848a91;"># Set variables for input point feature classes and output table</SPAN><SPAN class="pln" style="color: #2e3133;">
ptFC1 </SPAN><SPAN class="pun" style="color: #2e3133;">=</SPAN><SPAN class="pln" style="color: #2e3133;"> </SPAN><SPAN class="str" style="color: #6b291b;">"C:/temp/test.gdb/PointFC1"</SPAN><SPAN class="pln" style="color: #2e3133;">
ptFC2 </SPAN><SPAN class="pun" style="color: #2e3133;">=</SPAN><SPAN class="pln" style="color: #2e3133;"> </SPAN><SPAN class="str" style="color: #6b291b;">"C:/temp/test.gdb/PointFC2"</SPAN><SPAN class="pln" style="color: #2e3133;">
outGDB </SPAN><SPAN class="pun" style="color: #2e3133;">=</SPAN><SPAN class="pln" style="color: #2e3133;"> </SPAN><SPAN class="str" style="color: #6b291b;">"C:/temp/test.gdb"</SPAN><SPAN class="pln" style="color: #2e3133;">
outTableName </SPAN><SPAN class="pun" style="color: #2e3133;">=</SPAN><SPAN class="pln" style="color: #2e3133;"> </SPAN><SPAN class="str" style="color: #6b291b;">"outTable"</SPAN><SPAN class="pln" style="color: #2e3133;">
outTable </SPAN><SPAN class="pun" style="color: #2e3133;">=</SPAN><SPAN class="pln" style="color: #2e3133;"> outGDB </SPAN><SPAN class="pun" style="color: #2e3133;">+</SPAN><SPAN class="pln" style="color: #2e3133;"> </SPAN><SPAN class="str" style="color: #6b291b;">"/"</SPAN><SPAN class="pln" style="color: #2e3133;"> </SPAN><SPAN class="pun" style="color: #2e3133;">+</SPAN><SPAN class="pln" style="color: #2e3133;"> outTableName

arcpy</SPAN><SPAN class="pun" style="color: #2e3133;">.</SPAN><SPAN class="pln" style="color: #2e3133;">env</SPAN><SPAN class="pun" style="color: #2e3133;">.</SPAN><SPAN class="pln" style="color: #2e3133;">overwriteOutput </SPAN><SPAN class="pun" style="color: #2e3133;">=</SPAN><SPAN class="pln" style="color: #2e3133;"> </SPAN><SPAN class="kwd" style="color: #00008b;">True</SPAN><SPAN class="pln" style="color: #2e3133;">

</SPAN><SPAN class="com" style="color: #848a91;"># Create empty output table</SPAN><SPAN class="pln" style="color: #2e3133;">
arcpy</SPAN><SPAN class="pun" style="color: #2e3133;">.</SPAN><SPAN class="typ" style="color: #2b91af;">CreateTable_management</SPAN><SPAN class="pun" style="color: #2e3133;">(</SPAN><SPAN class="pln" style="color: #2e3133;">outGDB</SPAN><SPAN class="pun" style="color: #2e3133;">,</SPAN><SPAN class="pln" style="color: #2e3133;">outTableName</SPAN><SPAN class="pun" style="color: #2e3133;">)</SPAN><SPAN class="pln" style="color: #2e3133;">
arcpy</SPAN><SPAN class="pun" style="color: #2e3133;">.</SPAN><SPAN class="typ" style="color: #2b91af;">AddField_management</SPAN><SPAN class="pun" style="color: #2e3133;">(</SPAN><SPAN class="pln" style="color: #2e3133;">outTable</SPAN><SPAN class="pun" style="color: #2e3133;">,</SPAN><SPAN class="str" style="color: #6b291b;">"INPUT_FID"</SPAN><SPAN class="pun" style="color: #2e3133;">,</SPAN><SPAN class="str" style="color: #6b291b;">"LONG"</SPAN><SPAN class="pun" style="color: #2e3133;">)</SPAN><SPAN class="pln" style="color: #2e3133;">
arcpy</SPAN><SPAN class="pun" style="color: #2e3133;">.</SPAN><SPAN class="typ" style="color: #2b91af;">AddField_management</SPAN><SPAN class="pun" style="color: #2e3133;">(</SPAN><SPAN class="pln" style="color: #2e3133;">outTable</SPAN><SPAN class="pun" style="color: #2e3133;">,</SPAN><SPAN class="str" style="color: #6b291b;">"NEAR_FID"</SPAN><SPAN class="pun" style="color: #2e3133;">,</SPAN><SPAN class="str" style="color: #6b291b;">"LONG"</SPAN><SPAN class="pun" style="color: #2e3133;">)</SPAN><SPAN class="pln" style="color: #2e3133;">
arcpy</SPAN><SPAN class="pun" style="color: #2e3133;">.</SPAN><SPAN class="typ" style="color: #2b91af;">AddField_management</SPAN><SPAN class="pun" style="color: #2e3133;">(</SPAN><SPAN class="pln" style="color: #2e3133;">outTable</SPAN><SPAN class="pun" style="color: #2e3133;">,</SPAN><SPAN class="str" style="color: #6b291b;">"DISTANCE"</SPAN><SPAN class="pun" style="color: #2e3133;">,</SPAN><SPAN class="str" style="color: #6b291b;">"DOUBLE"</SPAN><SPAN class="pun" style="color: #2e3133;">)</SPAN><SPAN class="pln" style="color: #2e3133;">

</SPAN><SPAN class="com" style="color: #848a91;"># Create and populate two dictionaries with X and Y coordinates for each</SPAN><SPAN class="pln" style="color: #2e3133;">
</SPAN><SPAN class="com" style="color: #848a91;"># OBJECTID in second feature class using a SearchCursor</SPAN><SPAN class="pln" style="color: #2e3133;">
ptFC2XCoordDict </SPAN><SPAN class="pun" style="color: #2e3133;">=</SPAN><SPAN class="pln" style="color: #2e3133;"> </SPAN><SPAN class="pun" style="color: #2e3133;">{}</SPAN><SPAN class="pln" style="color: #2e3133;">
ptFC2YCoordDict </SPAN><SPAN class="pun" style="color: #2e3133;">=</SPAN><SPAN class="pln" style="color: #2e3133;"> </SPAN><SPAN class="pun" style="color: #2e3133;">{}</SPAN><SPAN class="pln" style="color: #2e3133;">
</SPAN><SPAN class="kwd" style="color: #00008b;">with</SPAN><SPAN class="pln" style="color: #2e3133;"> arcpy</SPAN><SPAN class="pun" style="color: #2e3133;">.</SPAN><SPAN class="pln" style="color: #2e3133;">da</SPAN><SPAN class="pun" style="color: #2e3133;">.</SPAN><SPAN class="typ" style="color: #2b91af;">SearchCursor</SPAN><SPAN class="pun" style="color: #2e3133;">(</SPAN><SPAN class="pln" style="color: #2e3133;">ptFC2</SPAN><SPAN class="pun" style="color: #2e3133;">,[</SPAN><SPAN class="str" style="color: #6b291b;">"OBJECTID"</SPAN><SPAN class="pun" style="color: #2e3133;">,</SPAN><SPAN class="str" style="color: #6b291b;">"SHAPE@XY"</SPAN><SPAN class="pun" style="color: #2e3133;">])</SPAN><SPAN class="pln" style="color: #2e3133;"> </SPAN><SPAN class="kwd" style="color: #00008b;">as</SPAN><SPAN class="pln" style="color: #2e3133;"> cursor</SPAN><SPAN class="pun" style="color: #2e3133;">:</SPAN><SPAN class="pln" style="color: #2e3133;">
&nbsp;&nbsp; </SPAN><SPAN class="kwd" style="color: #00008b;">for</SPAN><SPAN class="pln" style="color: #2e3133;"> row </SPAN><SPAN class="kwd" style="color: #00008b;">in</SPAN><SPAN class="pln" style="color: #2e3133;"> cursor</SPAN><SPAN class="pun" style="color: #2e3133;">:</SPAN><SPAN class="pln" style="color: #2e3133;">
&nbsp; ptFC2XCoordDict</SPAN><SPAN class="pun" style="color: #2e3133;">[</SPAN><SPAN class="pln" style="color: #2e3133;">row</SPAN><SPAN class="pun" style="color: #2e3133;">[</SPAN><SPAN class="lit" style="color: #6b291b;">0</SPAN><SPAN class="pun" style="color: #2e3133;">]]</SPAN><SPAN class="pln" style="color: #2e3133;"> </SPAN><SPAN class="pun" style="color: #2e3133;">=</SPAN><SPAN class="pln" style="color: #2e3133;"> row</SPAN><SPAN class="pun" style="color: #2e3133;">[</SPAN><SPAN class="lit" style="color: #6b291b;">1</SPAN><SPAN class="pun" style="color: #2e3133;">][</SPAN><SPAN class="lit" style="color: #6b291b;">0</SPAN><SPAN class="pun" style="color: #2e3133;">]</SPAN><SPAN class="pln" style="color: #2e3133;">
&nbsp; ptFC2YCoordDict</SPAN><SPAN class="pun" style="color: #2e3133;">[</SPAN><SPAN class="pln" style="color: #2e3133;">row</SPAN><SPAN class="pun" style="color: #2e3133;">[</SPAN><SPAN class="lit" style="color: #6b291b;">0</SPAN><SPAN class="pun" style="color: #2e3133;">]]</SPAN><SPAN class="pln" style="color: #2e3133;"> </SPAN><SPAN class="pun" style="color: #2e3133;">=</SPAN><SPAN class="pln" style="color: #2e3133;"> row</SPAN><SPAN class="pun" style="color: #2e3133;">[</SPAN><SPAN class="lit" style="color: #6b291b;">1</SPAN><SPAN class="pun" style="color: #2e3133;">][</SPAN><SPAN class="lit" style="color: #6b291b;">1</SPAN><SPAN class="pun" style="color: #2e3133;">]</SPAN><SPAN class="pln" style="color: #2e3133;">

</SPAN><SPAN class="com" style="color: #848a91;"># Open an InsertCursor ready to have rows written for each pair of OBJECTIDs</SPAN><SPAN class="pln" style="color: #2e3133;">
iCursor </SPAN><SPAN class="pun" style="color: #2e3133;">=</SPAN><SPAN class="pln" style="color: #2e3133;"> arcpy</SPAN><SPAN class="pun" style="color: #2e3133;">.</SPAN><SPAN class="pln" style="color: #2e3133;">da</SPAN><SPAN class="pun" style="color: #2e3133;">.</SPAN><SPAN class="typ" style="color: #2b91af;">InsertCursor</SPAN><SPAN class="pun" style="color: #2e3133;">(</SPAN><SPAN class="pln" style="color: #2e3133;">outTable</SPAN><SPAN class="pun" style="color: #2e3133;">,[</SPAN><SPAN class="str" style="color: #6b291b;">"INPUT_FID"</SPAN><SPAN class="pun" style="color: #2e3133;">,</SPAN><SPAN class="str" style="color: #6b291b;">"NEAR_FID"</SPAN><SPAN class="pun" style="color: #2e3133;">,</SPAN><SPAN class="str" style="color: #6b291b;">"DISTANCE"</SPAN><SPAN class="pun" style="color: #2e3133;">])</SPAN><SPAN class="pln" style="color: #2e3133;">
</SPAN><SPAN class="com" style="color: #848a91;"># Use a SearchCursor to read the rows (and X,Y coordinates) of the first</SPAN><SPAN class="pln" style="color: #2e3133;">
</SPAN><SPAN class="com" style="color: #848a91;"># feature class</SPAN><SPAN class="pln" style="color: #2e3133;">
</SPAN><SPAN class="kwd" style="color: #00008b;">with</SPAN><SPAN class="pln" style="color: #2e3133;"> arcpy</SPAN><SPAN class="pun" style="color: #2e3133;">.</SPAN><SPAN class="pln" style="color: #2e3133;">da</SPAN><SPAN class="pun" style="color: #2e3133;">.</SPAN><SPAN class="typ" style="color: #2b91af;">SearchCursor</SPAN><SPAN class="pun" style="color: #2e3133;">(</SPAN><SPAN class="pln" style="color: #2e3133;">ptFC1</SPAN><SPAN class="pun" style="color: #2e3133;">,[</SPAN><SPAN class="str" style="color: #6b291b;">"OBJECTID"</SPAN><SPAN class="pun" style="color: #2e3133;">,</SPAN><SPAN class="str" style="color: #6b291b;">"SHAPE@XY"</SPAN><SPAN class="pun" style="color: #2e3133;">])</SPAN><SPAN class="pln" style="color: #2e3133;"> </SPAN><SPAN class="kwd" style="color: #00008b;">as</SPAN><SPAN class="pln" style="color: #2e3133;"> cursor</SPAN><SPAN class="pun" style="color: #2e3133;">:</SPAN><SPAN class="pln" style="color: #2e3133;">
&nbsp;&nbsp; </SPAN><SPAN class="kwd" style="color: #00008b;">for</SPAN><SPAN class="pln" style="color: #2e3133;"> row </SPAN><SPAN class="kwd" style="color: #00008b;">in</SPAN><SPAN class="pln" style="color: #2e3133;"> cursor</SPAN><SPAN class="pun" style="color: #2e3133;">:</SPAN><SPAN class="pln" style="color: #2e3133;">
&nbsp; x1 </SPAN><SPAN class="pun" style="color: #2e3133;">=</SPAN><SPAN class="pln" style="color: #2e3133;"> row</SPAN><SPAN class="pun" style="color: #2e3133;">[</SPAN><SPAN class="lit" style="color: #6b291b;">1</SPAN><SPAN class="pun" style="color: #2e3133;">][</SPAN><SPAN class="lit" style="color: #6b291b;">0</SPAN><SPAN class="pun" style="color: #2e3133;">]</SPAN><SPAN class="pln" style="color: #2e3133;">
&nbsp; y1 </SPAN><SPAN class="pun" style="color: #2e3133;">=</SPAN><SPAN class="pln" style="color: #2e3133;"> row</SPAN><SPAN class="pun" style="color: #2e3133;">[</SPAN><SPAN class="lit" style="color: #6b291b;">1</SPAN><SPAN class="pun" style="color: #2e3133;">][</SPAN><SPAN class="lit" style="color: #6b291b;">1</SPAN><SPAN class="pun" style="color: #2e3133;">]</SPAN><SPAN class="pln" style="color: #2e3133;">
&nbsp;&nbsp; </SPAN><SPAN class="kwd" style="color: #00008b;">for</SPAN><SPAN class="pln" style="color: #2e3133;"> i </SPAN><SPAN class="kwd" style="color: #00008b;">in</SPAN><SPAN class="pln" style="color: #2e3133;"> range</SPAN><SPAN class="pun" style="color: #2e3133;">(</SPAN><SPAN class="pln" style="color: #2e3133;">len</SPAN><SPAN class="pun" style="color: #2e3133;">(</SPAN><SPAN class="pln" style="color: #2e3133;">ptFC2XCoordDict</SPAN><SPAN class="pun" style="color: #2e3133;">)):</SPAN><SPAN class="pln" style="color: #2e3133;">
&nbsp; x2 </SPAN><SPAN class="pun" style="color: #2e3133;">=</SPAN><SPAN class="pln" style="color: #2e3133;"> ptFC2XCoordDict</SPAN><SPAN class="pun" style="color: #2e3133;">[</SPAN><SPAN class="pln" style="color: #2e3133;">i</SPAN><SPAN class="pun" style="color: #2e3133;">+</SPAN><SPAN class="lit" style="color: #6b291b;">1</SPAN><SPAN class="pun" style="color: #2e3133;">]</SPAN><SPAN class="pln" style="color: #2e3133;">
&nbsp; y2 </SPAN><SPAN class="pun" style="color: #2e3133;">=</SPAN><SPAN class="pln" style="color: #2e3133;"> ptFC2YCoordDict</SPAN><SPAN class="pun" style="color: #2e3133;">[</SPAN><SPAN class="pln" style="color: #2e3133;">i</SPAN><SPAN class="pun" style="color: #2e3133;">+</SPAN><SPAN class="lit" style="color: #6b291b;">1</SPAN><SPAN class="pun" style="color: #2e3133;">]</SPAN><SPAN class="pln" style="color: #2e3133;">
&nbsp;&nbsp; </SPAN><SPAN class="com" style="color: #848a91;"># Prepare and insert the InsertCursor row</SPAN><SPAN class="pln" style="color: #2e3133;">
&nbsp; iRow </SPAN><SPAN class="pun" style="color: #2e3133;">=</SPAN><SPAN class="pln" style="color: #2e3133;"> </SPAN><SPAN class="pun" style="color: #2e3133;">[</SPAN><SPAN class="pln" style="color: #2e3133;">row</SPAN><SPAN class="pun" style="color: #2e3133;">[</SPAN><SPAN class="lit" style="color: #6b291b;">0</SPAN><SPAN class="pun" style="color: #2e3133;">],</SPAN><SPAN class="pln" style="color: #2e3133;">i</SPAN><SPAN class="pun" style="color: #2e3133;">+</SPAN><SPAN class="lit" style="color: #6b291b;">1</SPAN><SPAN class="pun" style="color: #2e3133;">,</SPAN><SPAN class="pln" style="color: #2e3133;">math</SPAN><SPAN class="pun" style="color: #2e3133;">.</SPAN><SPAN class="pln" style="color: #2e3133;">sqrt</SPAN><SPAN class="pun" style="color: #2e3133;">((</SPAN><SPAN class="pln" style="color: #2e3133;">x2</SPAN><SPAN class="pun" style="color: #2e3133;">-</SPAN><SPAN class="pln" style="color: #2e3133;">x1</SPAN><SPAN class="pun" style="color: #2e3133;">)*(</SPAN><SPAN class="pln" style="color: #2e3133;">x2</SPAN><SPAN class="pun" style="color: #2e3133;">-</SPAN><SPAN class="pln" style="color: #2e3133;">x1</SPAN><SPAN class="pun" style="color: #2e3133;">)</SPAN><SPAN class="pln" style="color: #2e3133;"> </SPAN><SPAN class="pun" style="color: #2e3133;">+</SPAN><SPAN class="pln" style="color: #2e3133;"> </SPAN><SPAN class="pun" style="color: #2e3133;">(</SPAN><SPAN class="pln" style="color: #2e3133;">y2</SPAN><SPAN class="pun" style="color: #2e3133;">-</SPAN><SPAN class="pln" style="color: #2e3133;">y1</SPAN><SPAN class="pun" style="color: #2e3133;">)*(</SPAN><SPAN class="pln" style="color: #2e3133;">y2</SPAN><SPAN class="pun" style="color: #2e3133;">-</SPAN><SPAN class="pln" style="color: #2e3133;">y1</SPAN><SPAN class="pun" style="color: #2e3133;">))]</SPAN><SPAN class="pln" style="color: #2e3133;">
&nbsp; iCursor</SPAN><SPAN class="pun" style="color: #2e3133;">.</SPAN><SPAN class="pln" style="color: #2e3133;">insertRow</SPAN><SPAN class="pun" style="color: #2e3133;">(</SPAN><SPAN class="pln" style="color: #2e3133;">iRow</SPAN><SPAN class="pun" style="color: #2e3133;">)</SPAN><SPAN class="pln" style="color: #2e3133;">
</SPAN><SPAN class="kwd" style="color: #00008b;">del</SPAN><SPAN class="pln" style="color: #2e3133;"> iCursor

</SPAN><SPAN class="kwd" style="color: #00008b;">print</SPAN><SPAN class="pln" style="color: #2e3133;"> </SPAN><SPAN class="str" style="color: #6b291b;">"Done!"</SPAN>

The following reports the distance of the first point to the rest of the points:

I would like to amend the following to iterate through each point within the feature class to report the shortest distance between the points, greater than zero of course. Any advice in amending the following will be appreciated. I'm trying to use the results from this as a way of validating that none of the points are within 3m or less from each other and if there are the filtered list is processed further to move the points away from each other within bounding box for each point.

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JeffreyWilkerson

Dan, I’ve reposted it at https://community.esri.com/message/939475-determine-minimum-distance-between-all-points-in-two-tables.

Thank you.

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danretired

Jeff

You should post a new question with formatted code. I came across this by chance since my current persona doesn't get mail links on old threads.

Also, use code formatting

/blogs/dan_patterson/2016/08/14/script-formatting

And you need to use projected data in order to get true distances. Decimal Degrees won't cut it.

There is no need to use pandas either.

JeffreyWilkerson

Dan,

I know this is an old post but your answer was almost exactly what I was looking for and I'm wondering if you can help me. I have bus location information that is coming in daily (approx 50K of points a day) that I need to verify that the location cited is using the correct bus stop. The initial data is individual records about each fare that boards the bus, but we have been aggregating that down to the individual stops to reduce the data size. It's still to large to push all of it into a featureclass, so it lives as SQL Server data only.

In order to get the nearness calculated, I've been trying to use SQL Server Spatial, but that's not as fast as I would like it to be. When I came across this post, the concept of using Numpy became an option and I've built the code below to compare 2 different tables and return the id from the first one, and the corresponding id/minimum distance from the second one. Using 2 small feature classes I have been able to verify that the minimum distance for each input point is correct, but when I expand that to a larger table (only ~750 records) I don't seem to be getting the minimum distance any more. Am I missing something about getting the minimum distance, or is there a better way to get the minimum distance from the results, or is the numpy.einsum request even working right.

Still trying to understand it all but I at least thought I had it working. At the end of my rope though so I'm hoping you might be able to shed some light.

<SPAN class="" style="color: #999999;"></SPAN>

##Script: distance_einsum.py
##Author: Dan.Patterson@carleton.ca, and others
##Purpose: Numpy Repository stuff
##References:
## https://community.esri.com/thread/175646
## https://numpy.org/doc/stable/reference/generated/numpy.einsum.html
## https://towardsdatascience.com/how-fast-numpy-really-is-e9111df44347
## https://en.wikipedia.org/wiki/Einstein_notation
## https://pro.arcgis.com/en/pro-app/arcpy/data-access/tabletonumpyarray.htm
##
import os, sys
import numpy as np
import arcpy
import numpy.lib.recfunctions, scipy.spatial, random, cProfile
from scipy.spatial import distance
import pandas as pd
np.set_printoptions(edgeitems=3, linewidth=80, precision=6, suppress=True, threshold=10)
def npNear(f1,f2):
f1shp_fields = ["DilaxID","LAT", "LON"] # DilaxBusTest
#f1shp_fields = ["OID@", "SHAPE@XY", "POINT_Y", "POINT_X"] # RandomPoints
f2shp_fields = ["OID@", "SHAPE@XY"]
#a = arcpy.da.FeatureClassToNumPyArray(f1, f1shp_fields)
#b = arcpy.da.FeatureClassToNumPyArray(f2, f2shp_fields)
a = arcpy.da.TableToNumPyArray(f1, f1shp_fields)
b = arcpy.da.TableToNumPyArray(f2, f2shp_fields)
origs = np.array(pd.DataFrame(a[:][['LON', 'LAT']])) ## Change if using different f1 fields
dests = b['SHAPE@XY'][:]
cols = range(2,3)
subts = origs[:,None,:] - dests
dist = np.sqrt(np.einsum('ijk,ijk->ij',subts,subts))
#print(dist)
out = np.zeros((len(dist),3))
out[:,0] = a['DilaxID'][:] # the initial table's ID field (f1, a, origs)
out[:,1] = np.argmin(dist, axis=1) # minimum value from the distance calc
out[:,2] = np.amin(dist, axis=1) # minimum value's id, to be associated to later
return out
if __name__=="__main__":
#f1 = r"d:\Projects\GTFS_Update_Checker\RandomPoints.shp"
#f2 = r"d:\Projects\GTFS_Update_Checker\Random2.shp"
f1 = os.path.join(sys.path[0], r"Ridership on W-SQL01.sde\Ridership.dbo.DilaxBusTest")
f2 = os.path.join(sys.path[0],r"GTFS on W-SQL01.sde\GTFS.dbo.BusStopsWM")
outPath = os.path.join(sys.path[0], r"Ridership on W-SQL01.sde\Ridership.dbo.DilaxBusDistTest")
start = time.time()
## Generate minimum distance array using Numpy Einsum
out = npNear(f1, f2)
## Remove output table if it exists
if arcpy.Exists(outPath):
arcpy.Delete_management(outPath)
## Create structured array for output
struct_out = numpy.core.records.fromarrays(
out.transpose(),
numpy.dtype([('dOID', numpy.int32), ('bOID', numpy.int32), ('distance', '<f8')]))
## Push structured array to output using ArcGIS NumPyArrayToTable function
arcpy.da.NumPyArrayToTable(struct_out, outPath)
end = time.time()
print(out)
print(end - start)

PeterWilson

HI Dan

Thanks, will give me a chance to figure it out first before you post the answer . Thanks for being such a great teacher. I have learnt a great deal from you already. My Python skill set has grown tremendously over the last 3 months.

DanPatterson_Retired

I can put an 'all points' to 'all points' incarnation online, but I will let you digest that version first.

PeterWilson

Thanks Dan, much appreciated. Will get back to you once I've done my homework

DanPatterson_Retired

Peter

distance from the first point in a point file to all other points in the file.

the rest of the problem is homework :

"""

Script<SPAN class="punctuation token">:</SPAN>&nbsp;&nbsp; distance_einsum<SPAN class="punctuation token">.</SPAN>py
Author<SPAN class="punctuation token">:</SPAN>&nbsp;&nbsp; 
Dan<SPAN class="punctuation token">.</SPAN>Patterson@carleton<SPAN class="punctuation token">.</SPAN>ca

Purpose<SPAN class="punctuation token">:</SPAN>&nbsp; Yes
References<SPAN class="punctuation token">:</SPAN>
&nbsp;&nbsp; Numpy Repository stuff
<SPAN class="string token">"""
import numpy as np
import arcpy
np.set_printoptions(edgeitems=3,linewidth=80,precision=2,suppress=True,threshold=10)
def einsum_dist(origin,destin):
&nbsp;&nbsp;&nbsp; """</SPAN>using einsum<SPAN class="string token">"""
&nbsp;&nbsp;&nbsp; deltas = destin - origin
&nbsp;&nbsp;&nbsp; return np.sqrt(np.einsum('ij,ij->i', deltas, deltas))
def demo(a):
&nbsp;&nbsp;&nbsp; """</SPAN> demo of one origin to all other points<SPAN class="string token">""</SPAN>"
&nbsp;&nbsp;&nbsp; orig <SPAN class="operator token">=</SPAN> a<SPAN class="punctuation token">[</SPAN><SPAN class="string token">'Shape'</SPAN><SPAN class="punctuation token">]</SPAN><SPAN class="punctuation token">[</SPAN><SPAN class="number token">0</SPAN><SPAN class="punctuation token">]</SPAN>
&nbsp;&nbsp;&nbsp; dest <SPAN class="operator token">=</SPAN> a<SPAN class="punctuation token">[</SPAN><SPAN class="string token">'Shape'</SPAN><SPAN class="punctuation token">]</SPAN><SPAN class="punctuation token">[</SPAN><SPAN class="number token">1</SPAN><SPAN class="punctuation token">:</SPAN><SPAN class="punctuation token">]</SPAN>
&nbsp;&nbsp;&nbsp; dist <SPAN class="operator token">=</SPAN> einsum_dist<SPAN class="punctuation token">(</SPAN>orig<SPAN class="punctuation token">,</SPAN>dest<SPAN class="punctuation token">)</SPAN>
&nbsp;&nbsp;&nbsp; out <SPAN class="operator token">=</SPAN> np<SPAN class="punctuation token">.</SPAN>zeros<SPAN class="punctuation token">(</SPAN><SPAN class="punctuation token">(</SPAN>len<SPAN class="punctuation token">(</SPAN>dist<SPAN class="punctuation token">)</SPAN><SPAN class="operator token">+</SPAN><SPAN class="number token">1</SPAN><SPAN class="punctuation token">,</SPAN><SPAN class="number token">3</SPAN><SPAN class="punctuation token">)</SPAN><SPAN class="punctuation token">)</SPAN>
&nbsp;&nbsp;&nbsp; out<SPAN class="punctuation token">[</SPAN><SPAN class="punctuation token">:</SPAN><SPAN class="punctuation token">,</SPAN><SPAN class="number token">0</SPAN><SPAN class="punctuation token">]</SPAN> <SPAN class="operator token">=</SPAN> a<SPAN class="punctuation token">[</SPAN><SPAN class="string token">'Shape'</SPAN><SPAN class="punctuation token">]</SPAN><SPAN class="punctuation token">[</SPAN><SPAN class="punctuation token">:</SPAN><SPAN class="punctuation token">,</SPAN><SPAN class="number token">0</SPAN><SPAN class="punctuation token">]</SPAN> <SPAN class="comment token"># the Xs</SPAN>
&nbsp;&nbsp;&nbsp; out<SPAN class="punctuation token">[</SPAN><SPAN class="punctuation token">:</SPAN><SPAN class="punctuation token">,</SPAN><SPAN class="number token">1</SPAN><SPAN class="punctuation token">]</SPAN> <SPAN class="operator token">=</SPAN> a<SPAN class="punctuation token">[</SPAN><SPAN class="string token">'Shape'</SPAN><SPAN class="punctuation token">]</SPAN><SPAN class="punctuation token">[</SPAN><SPAN class="punctuation token">:</SPAN><SPAN class="punctuation token">,</SPAN><SPAN class="number token">1</SPAN><SPAN class="punctuation token">]</SPAN> <SPAN class="comment token"># the Ys</SPAN>
&nbsp;&nbsp;&nbsp; out<SPAN class="punctuation token">[</SPAN><SPAN class="number token">1</SPAN><SPAN class="punctuation token">:</SPAN><SPAN class="punctuation token">,</SPAN><SPAN class="number token">2</SPAN><SPAN class="punctuation token">]</SPAN> <SPAN class="operator token">=</SPAN> dist
&nbsp;&nbsp;&nbsp; <SPAN class="keyword token">return</SPAN> out

<SPAN class="keyword token">if</SPAN> __name__<SPAN class="operator token">==</SPAN><SPAN class="string token">"__main__"</SPAN><SPAN class="punctuation token">:</SPAN>
&nbsp;&nbsp;&nbsp; <SPAN class="string token">"""sample run for distance demo"""</SPAN>
&nbsp;&nbsp;&nbsp; f <SPAN class="operator token">=</SPAN> r<SPAN class="string token">"F:\A2_in_progress\NearDemo\Shapefiles\RandomPnts.shp"</SPAN>
&nbsp;&nbsp;&nbsp; shp_field <SPAN class="operator token">=</SPAN> <SPAN class="string token">"Shape"</SPAN>
&nbsp;&nbsp;&nbsp; a <SPAN class="operator token">=</SPAN> arcpy<SPAN class="punctuation token">.</SPAN>da<SPAN class="punctuation token">.</SPAN>FeatureClassToNumPyArray<SPAN class="punctuation token">(</SPAN>f<SPAN class="punctuation token">,</SPAN> shp_field<SPAN class="punctuation token">)</SPAN>
&nbsp;&nbsp;&nbsp; b <SPAN class="operator token">=</SPAN> demo<SPAN class="punctuation token">(</SPAN>a<SPAN class="punctuation token">)</SPAN>
&nbsp;&nbsp;&nbsp; <SPAN class="comment token"># send it to a featureclass if desired</SPAN>
&nbsp;&nbsp;&nbsp; <SPAN class="keyword token">print</SPAN><SPAN class="punctuation token">(</SPAN><SPAN class="string token">"X, Y, distance from the first point \n{}"</SPAN><SPAN class="punctuation token">.</SPAN>format<SPAN class="punctuation token">(</SPAN>b<SPAN class="punctuation token">)</SPAN><SPAN class="punctuation token">)</SPAN>
<SPAN class="line-numbers-rows"><SPAN>‍</SPAN><SPAN>‍</SPAN><SPAN>‍</SPAN><SPAN>‍</SPAN><SPAN>‍</SPAN><SPAN>‍</SPAN><SPAN>‍</SPAN><SPAN>‍</SPAN><SPAN>‍</SPAN><SPAN>‍</SPAN><SPAN>‍</SPAN><SPAN>‍</SPAN><SPAN>‍</SPAN><SPAN>‍</SPAN><SPAN>‍</SPAN><SPAN>‍</SPAN><SPAN>‍</SPAN><SPAN>‍</SPAN><SPAN>‍</SPAN><SPAN>‍</SPAN><SPAN>‍</SPAN><SPAN>‍</SPAN><SPAN>‍</SPAN><SPAN>‍</SPAN><SPAN>‍</SPAN><SPAN>‍</SPAN><SPAN>‍</SPAN><SPAN>‍</SPAN><SPAN>‍</SPAN><SPAN>‍</SPAN><SPAN>‍</SPAN><SPAN>‍</SPAN><SPAN>‍</SPAN><SPAN>‍</SPAN></SPAN>

result

<SPAN class="operator token">>></SPAN><SPAN class="operator token">></SPAN> X<SPAN class="punctuation token">,</SPAN> Y<SPAN class="punctuation token">,</SPAN> distance <SPAN class="keyword token">from</SPAN> the first point 
<SPAN class="punctuation token">[</SPAN><SPAN class="punctuation token">[</SPAN>&nbsp; <SPAN class="number token">342059</SPAN><SPAN class="punctuation token">.</SPAN>&nbsp;&nbsp;&nbsp; <SPAN class="number token">5022985</SPAN><SPAN class="punctuation token">.</SPAN>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; <SPAN class="number token">0</SPAN><SPAN class="punctuation token">.</SPAN>&nbsp; <SPAN class="punctuation token">]</SPAN>
 <SPAN class="punctuation token">[</SPAN>&nbsp; <SPAN class="number token">342431</SPAN><SPAN class="punctuation token">.</SPAN>&nbsp;&nbsp;&nbsp; <SPAN class="number token">5025573</SPAN><SPAN class="punctuation token">.</SPAN>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; <SPAN class="number token">2614.6</SPAN> <SPAN class="punctuation token">]</SPAN>
 <SPAN class="punctuation token">[</SPAN>&nbsp; <SPAN class="number token">341341</SPAN><SPAN class="punctuation token">.</SPAN>&nbsp;&nbsp;&nbsp; <SPAN class="number token">5024714</SPAN><SPAN class="punctuation token">.</SPAN>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; <SPAN class="number token">1872.16</SPAN><SPAN class="punctuation token">]</SPAN>
 <SPAN class="punctuation token">.</SPAN><SPAN class="punctuation token">.</SPAN><SPAN class="punctuation token">.</SPAN><SPAN class="punctuation token">,</SPAN> 
 <SPAN class="punctuation token">[</SPAN>&nbsp; <SPAN class="number token">343020</SPAN><SPAN class="punctuation token">.</SPAN>&nbsp;&nbsp;&nbsp; <SPAN class="number token">5022952</SPAN><SPAN class="punctuation token">.</SPAN>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; <SPAN class="number token">961.57</SPAN><SPAN class="punctuation token">]</SPAN>
 <SPAN class="punctuation token">[</SPAN>&nbsp; <SPAN class="number token">343198</SPAN><SPAN class="punctuation token">.</SPAN>&nbsp;&nbsp;&nbsp; <SPAN class="number token">5025388</SPAN><SPAN class="punctuation token">.</SPAN>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; <SPAN class="number token">2659.27</SPAN><SPAN class="punctuation token">]</SPAN>
 <SPAN class="punctuation token">[</SPAN>&nbsp; <SPAN class="number token">342635</SPAN><SPAN class="punctuation token">.</SPAN>&nbsp;&nbsp;&nbsp; <SPAN class="number token">5023786</SPAN><SPAN class="punctuation token">.</SPAN>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; <SPAN class="number token">986.6</SPAN> <SPAN class="punctuation token">]</SPAN><SPAN class="punctuation token">]</SPAN>
<SPAN class="line-numbers-rows"><SPAN>‍</SPAN><SPAN>‍</SPAN><SPAN>‍</SPAN><SPAN>‍</SPAN><SPAN>‍</SPAN><SPAN>‍</SPAN><SPAN>‍</SPAN><SPAN>‍</SPAN></SPAN>

Now where are those close points and how many of them are there? slice and dice. b[:,2] is the distance column ... b[:,2] < 1000 are all the distances < 1000 m ... Therefore:

b[b[:,2]<1000] must be all the records where the distance is less than 1000m .. now what? save it to a featureclass. And yes you can get all fancy with inputs, retaining variable, etc. but that should suffice. It is pretty quick up about 50,000,000 origin destinations.

<SPAN class="operator token">>></SPAN><SPAN class="operator token">></SPAN> b<SPAN class="punctuation token">[</SPAN>b<SPAN class="punctuation token">[</SPAN><SPAN class="punctuation token">:</SPAN><SPAN class="punctuation token">,</SPAN><SPAN class="number token">2</SPAN><SPAN class="punctuation token">]</SPAN><SPAN class="operator token"><</SPAN><SPAN class="number token">1000</SPAN><SPAN class="punctuation token">]</SPAN>
array<SPAN class="punctuation token">(</SPAN><SPAN class="punctuation token">[</SPAN><SPAN class="punctuation token">[</SPAN>&nbsp; <SPAN class="number token">342059</SPAN><SPAN class="punctuation token">.</SPAN>&nbsp; <SPAN class="punctuation token">,</SPAN>&nbsp; <SPAN class="number token">5022985</SPAN><SPAN class="punctuation token">.</SPAN>&nbsp; <SPAN class="punctuation token">,</SPAN>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; <SPAN class="number token">0</SPAN><SPAN class="punctuation token">.</SPAN>&nbsp; <SPAN class="punctuation token">]</SPAN><SPAN class="punctuation token">,</SPAN>
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; <SPAN class="punctuation token">[</SPAN>&nbsp; <SPAN class="number token">341427</SPAN><SPAN class="punctuation token">.</SPAN>&nbsp; <SPAN class="punctuation token">,</SPAN>&nbsp; <SPAN class="number token">5023380</SPAN><SPAN class="punctuation token">.</SPAN>&nbsp; <SPAN class="punctuation token">,</SPAN>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; <SPAN class="number token">745.28</SPAN><SPAN class="punctuation token">]</SPAN><SPAN class="punctuation token">,</SPAN>
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; <SPAN class="punctuation token">[</SPAN>&nbsp; <SPAN class="number token">342494</SPAN><SPAN class="punctuation token">.</SPAN>&nbsp; <SPAN class="punctuation token">,</SPAN>&nbsp; <SPAN class="number token">5022158</SPAN><SPAN class="punctuation token">.</SPAN>&nbsp; <SPAN class="punctuation token">,</SPAN>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; <SPAN class="number token">934.43</SPAN><SPAN class="punctuation token">]</SPAN><SPAN class="punctuation token">,</SPAN>
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; <SPAN class="punctuation token">.</SPAN><SPAN class="punctuation token">.</SPAN><SPAN class="punctuation token">.</SPAN><SPAN class="punctuation token">,</SPAN> 
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; <SPAN class="punctuation token">[</SPAN>&nbsp; <SPAN class="number token">342195</SPAN><SPAN class="punctuation token">.</SPAN>&nbsp; <SPAN class="punctuation token">,</SPAN>&nbsp; <SPAN class="number token">5023291</SPAN><SPAN class="punctuation token">.</SPAN>&nbsp; <SPAN class="punctuation token">,</SPAN>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; <SPAN class="number token">334.86</SPAN><SPAN class="punctuation token">]</SPAN><SPAN class="punctuation token">,</SPAN>
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; <SPAN class="punctuation token">[</SPAN>&nbsp; <SPAN class="number token">343020</SPAN><SPAN class="punctuation token">.</SPAN>&nbsp; <SPAN class="punctuation token">,</SPAN>&nbsp; <SPAN class="number token">5022952</SPAN><SPAN class="punctuation token">.</SPAN>&nbsp; <SPAN class="punctuation token">,</SPAN>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; <SPAN class="number token">961.57</SPAN><SPAN class="punctuation token">]</SPAN><SPAN class="punctuation token">,</SPAN>
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; <SPAN class="punctuation token">[</SPAN>&nbsp; <SPAN class="number token">342635</SPAN><SPAN class="punctuation token">.</SPAN>&nbsp; <SPAN class="punctuation token">,</SPAN>&nbsp; <SPAN class="number token">5023786</SPAN><SPAN class="punctuation token">.</SPAN>&nbsp; <SPAN class="punctuation token">,</SPAN>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; <SPAN class="number token">986.6</SPAN> <SPAN class="punctuation token">]</SPAN><SPAN class="punctuation token">]</SPAN><SPAN class="punctuation token">)</SPAN>
<SPAN class="operator token">>></SPAN><SPAN class="operator token">></SPAN> len<SPAN class="punctuation token">(</SPAN>b<SPAN class="punctuation token">[</SPAN>b<SPAN class="punctuation token">[</SPAN><SPAN class="punctuation token">:</SPAN><SPAN class="punctuation token">,</SPAN><SPAN class="number token">2</SPAN><SPAN class="punctuation token">]</SPAN><SPAN class="operator token"><</SPAN><SPAN class="number token">1000</SPAN><SPAN class="punctuation token">]</SPAN><SPAN class="punctuation token">)</SPAN>
<SPAN class="number token">24</SPAN>
<SPAN class="operator token">>></SPAN><SPAN class="operator token">></SPAN>
<SPAN class="line-numbers-rows"><SPAN>‍</SPAN><SPAN>‍</SPAN><SPAN>‍</SPAN><SPAN>‍</SPAN><SPAN>‍</SPAN><SPAN>‍</SPAN><SPAN>‍</SPAN><SPAN>‍</SPAN><SPAN>‍</SPAN><SPAN>‍</SPAN><SPAN>‍</SPAN></SPAN>

Check Numpy Repository or contact me for further information.

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