# [phenixbb] Filtering CCP4 maps to lower resolution

Pavel Afonine pafonine at lbl.gov
Sun Nov 16 10:43:29 PST 2014

```Hi Ben,

>     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.
>
>     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.
>
>
> My understanding is that the loss of information would happen at high
> resolution (which I want to be filtering out anyway),

Discrete 3D function (map calculated on N1,N2,N3 regular grid) in
reciprocal space corresponds to Fourier map coefficients with indices
|h|<N1/2, |k|<N2/2, |l|<N3/2, which corresponds to a box. Any missing
coefficients from this box (no matter which one, low or high resolution)
will result in a loss of information and artifacts.

I guess one way would be to calculate a box of Fourier coefficients from
your map, then remove those coefficients that have resolution higher
than certain predefined value.

Thinking of available tools... You can convert your CCP4 map into box of
reflections:

phenix.map_to_structure_factors map_real_space.ccp4 box=true

then convert compute map again but leave out high-res terms up to
certain d_min:

phenix.mtz2map map_reciprocal_space_box.mtz d_min=3.21

By default this will result in a map with grid defined as
d_min/grid_resolution_factor, where grid_resolution_factor=0.25 (you can
change it). Perhaps you want to use the same gridding for the new map as
the original map has.

Alternatively, you can just do local map averaging as I explained in
previous email: for each map grid node value replace it with a local
average (calculated in a sphere of radius R around this node or simply
over all nearest neighbors. This is not the same as truncating high
resolution, but may give you similar result.

Pavel

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