Hi Pavel

In general, given highly anisotropic data set:

1) maps calculated using all (unmodified) data by phenix.refine,
phenix.maps and similar tools are better than maps calculated using
anisotropy truncated data. So, yes, for the purpose of map calculation
there is no need to do anything: Phenix map calculation tools deal with
anisotropy very well.

If there are a lot of reflections without signal, that makes them essentially missing, so by including them, you're effectively filling in for those reflections with only DFc. If anisotropy is very strong (i.e. many missing reflections), does that not introduce very significant model bias? The maps would look cleaner, though.

That's a different story. If you do anisotropy truncation then in case of severe anisotropy there will be lots of removed weak Fobs, which will be subsequently filled in with DFc, and such maps will have a better chance to be more model biased. However, phenix.refine always creates two 2mFo-DFc maps: with and without filling missing Fobs, so you can quickly compare them and get an idea.

No, the comparison I mean is

no anisotropy cut-off --vs-- anisotropy cut-off WITHOUT filling in missing reflections.

I'm wondering about what happens when you do NOT do anisotropy truncation: that generates large volumes of reciprocal space where Fobs is approximately zero, and therefore the map coefficients (2mFo-DFc) become DFc -- i.e. the equivalent to filling in missing Fobs for very incomplete data.

The maps to compare would be:

(Of course, it presumably matters how effectively D down-weights those reflections; but how is calculation of D affected by a resolution bin being dominated by near-zero Fobs?)

phx.