Hi Graeme,<div><br><div>Are you aware of the</div><div><br></div><div>mtz.object.as_miller_arrays(..., merge_equivalents=False) option?<br><br>If this is too high-level for your purposes, you can loop over the columns and call:</div>
<div><br></div><div>column.extract_values(not_a_number_substitute=0.0)<br><div class="gmail_quote"><br></div><div class="gmail_quote">You may also want to look at column.extract_valid_values() and column.column.selection_valid() as an alternative. There are other medium-level methods such as mtz_object.extract_integers() or .extract_reals(). All the extract methods run in C++ and produce C++ arrays.</div>
<div class="gmail_quote"><br></div><div class="gmail_quote">Ralf</div><div class="gmail_quote"><br></div><div class="gmail_quote">On Tue, Apr 24, 2012 at 8:03 AM, <span dir="ltr"><<a href="mailto:Graeme.Winter@diamond.ac.uk">Graeme.Winter@diamond.ac.uk</a>></span> wrote:<br>
<blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex">Hi Folks,<br>
<br>
I have been writing very bad Python code to handle unmerged MTZ files using CCTBX - in essence copying all of the measurements to lists for each Miller index. Inefficient. Has anyone looked a neater way of handling these files perhaps with doing the "hard work" from C++? Reading the file is very easy, but managing the data is harder.<br>
<br>
Essentially I would like to be able to read in a scaled-summed-but-unmerged MTZ file (equivalent to unmerged scale pack no-merge-original-index) and get back all observations of a given HKL, which I can then subsequently analyse…<br>
<br>
I could crack on and write something but it strikes me as a good idea to ask first.<br>
<br>
Thanks,<br>
<br>
Graeme<br>
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