<div dir="ltr"><br><div class="gmail_extra"><div class="gmail_quote"><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex"><div bgcolor="#FFFFFF" text="#000000">
complete representation of your map (the one in CCP4 formatted file)
is a reciprocal space box of reflections with miller indices being
|h|<N1/2, |k|<N2/2, |l|<N3/2 , where N1,N2,N3 is map
gridding.<br>
<br>
Converting map (from CCP4 file) into a set of structure factors in a
sphere of given resolution is an operation associated with loss of
information. Further truncation of resolution obviously results in
further loss of information. All in all this means the artifacts you
observe after these manipulations are expected.<br></div></blockquote><div><br></div><div>My understanding is that the loss of information would happen at high resolution (which I want to be filtering out anyway), so it would result in smoothing rather than generation of lots of noise that I would associate with loss of low-resolution information. Am I incorrect in that interpretation?<br><br>If so, is there a better way to do a low-pass filter on real-space data in phenix? One that would only remove high-resolution information?<br></div><div> </div><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex"><div bgcolor="#FFFFFF" text="#000000">
<br>
Alternatively, I guess, you can do local averaging: replace each map
grid node with the average takes over all 27 nearest neighbors of
that node or all neighbors in a sphere of R around it (Wang's
averaging). As A. Leslie demonstrated original Wang's averaging can
be done efficiently in reciprocal space by convolution of structure
factors with some function (see that paper for details).<br></div></blockquote><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex"><div bgcolor="#FFFFFF" text="#000000">
<br>
I can write a simple Python script for you to do this task.<span><font color="#888888"><br>
<br>
Pavel</font></span><div><div><br></div></div></div></blockquote><div><br><div><br></div>This is an interesting option, and I would like to
try it... Working in reciprocal space would require an FFT,
however, which you suggest above would result in the same loss of data,
right?<br><br></div><div>Thanks very much,<br>Ben<br></div><div> </div><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex"><div bgcolor="#FFFFFF" text="#000000"><div><div>
<br>
<div>On 11/13/14 4:00 PM, Barad, Ben wrote:<br>
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<div style="direction:ltr;font-family:Tahoma;color:rgb(0,0,0);font-size:10pt">Hi All,<br>
<br>
I am currently trying to filter a CCP4-formatted real space map
from the EMDB, which is at 3.2 Å resolution, to different
resolutions from 3.2 to 5 Å, and I've been having a bit of a
difficult time getting it to work.<br>
<br>
My first thought was to do an fft to get structure factors, then
regenerate the map from those structure factors with a
resolution cutoff. To do this, I used the tools
"phenix.map_to_structure_factors" and "phenix.mtz2map" to
generate a structure factor file, then convert it back into a
map. When I do this, I end up with a very noisy map that is not
usable for what I would like to do.<br>
<br>
Is there a better way to accomplish this in phenix (or another
tool)? I've also tried some low-pass filtering with proc3d,
without much success...<br>
<br>
Thanks,<br>
Ben Barad<br>
Fraser Lab @ UCSF<br>
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</blockquote>
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