Hi,<br><br><div class="gmail_quote">2009/2/26 Ari Jolma <span dir="ltr"><<a href="mailto:ari.jolma@tkk.fi">ari.jolma@tkk.fi</a>></span><br><blockquote class="gmail_quote" style="border-left: 1px solid rgb(204, 204, 204); margin: 0pt 0pt 0pt 0.8ex; padding-left: 1ex;">
<div class="Ih2E3d"><a href="mailto:nicholas.g.lawrence@mainroads.qld.gov.au" target="_blank">nicholas.g.lawrence@mainroads.qld.gov.au</a> wrote:<br>
</div><blockquote class="gmail_quote" style="border-left: 1px solid rgb(204, 204, 204); margin: 0pt 0pt 0pt 0.8ex; padding-left: 1ex;"><div class="Ih2E3d"><blockquote class="gmail_quote" style="border-left: 1px solid rgb(204, 204, 204); margin: 0pt 0pt 0pt 0.8ex; padding-left: 1ex;">
<a href="mailto:nicholas.g.lawrence@mainroads.qld.gov.au" target="_blank"><br></a><blockquote class="gmail_quote" style="border-left: 1px solid rgb(204, 204, 204); margin: 0pt 0pt 0pt 0.8ex; padding-left: 1ex;">Can GDAL or OGR weed out 3D points based on proximity to each other?<br>
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I would use plain Perl (or Python or high level language X) to read in the points sequentially and discard new ones that are "too close" to existing ones. It may take a while & require a bit of memory it is probably doable that way (millions of points but not 100s of millions in the end set). The program shouldn't need to be more than 20 lines I guess.</blockquote>
<div><br>scipy (python's numeric/scientific workhorse) now has a Kdtrees capabilities, which are an efficient way of working out distances between points:<br><<a href="http://docs.scipy.org/doc/scipy/reference/spatial.html">http://docs.scipy.org/doc/scipy/reference/spatial.html</a>><br>
<br>Best regards,<br>Jose<br></div></div><br>