[cctbxbb] iotbx xray.reconstructed_amplitude and changed SIGF

[Huw Jenkins]

Hi,

This is my first post to this list and I hope I can explain my confusion properly. I have been using iotbx to manipulate data stored in mtz files and found that values in columns that should not have been altered by my code are different in the output mtz file. This seems only to occur when the input mtz file contains DANO, SIGDANO columns. 

When I run the code below F and DANO in input.mtz and output.mtz are the same but SIGF and SIGDANO are different (shown for 5 reflections listed). Is this expected behaviour? Given that in my understanding that code should just read a mtz file, convert the data to a miller array then write the array back out to a new mtz I would expect the values in the input and output to be identical (within rounding error).

Thanks,


Huw


from __future__ import division
import sys
from iotbx import mtz
from iotbx import reflection_file_reader 
from iotbx import reflection_file_utils

def run(args):
  
  mtz_file = reflection_file_reader.any_reflection_file("input.mtz")
  mtz_as_miller_array = mtz_file.as_miller_arrays() 
  f_obs = mtz_as_miller_array[0]
  mtz_out = f_obs.as_mtz_dataset(column_root_label="F_iotbx")
  mtz_out.mtz_object().write("output.mtz")
  
if (__name__ == "__main__"):
  run(sys.argv[1:])

some reflections from input.mtz are:

  H  K   L          F_ccp4      SIGF_ccp4  DANO_ccp4  SIGDANO_ccp4  ISYM_ccp4
  1   2   2         4.90461         2.31536               0         3.27441         0
  1   2   3         186.172         1.86381         119.745         3.84862         0
  1   2   4          289.31         3.06709        -5.42197         6.46531         0
  1   2   5         378.928          2.7118         87.3381         5.56933         0
  1   2   6         540.866         4.38513         46.4155          8.8053         0

The same reflections in output.mtz are:

  H  K   L          F_iotbx      SIGF_iotbx  DANOF_iotbx  SIGDANOF_iotbx  ISYMF_iotbx  
  1   2   2         4.90461         2.00516               0         4.01032         0
  1   2   3         186.172          1.8943         119.745          3.7886         0
  1   2   4          289.31         3.15096        -5.42197         6.30192         0
  1   2   5         378.928         2.74848         87.3381         5.49695         0
  1   2   6         540.866          4.3939         46.4155          8.7878         0