ess.nmx.mtz_io.process_single_mtz_to_dataframe#

ess.nmx.mtz_io.process_single_mtz_to_dataframe(mtz, wavelength_column_name='LAMBDA', intensity_column_name='I', intensity_sig_col_name='SIGI')[source]#

Select and derive columns from the original MtzDataFrame.

Parameters:

  • mtz (Mtz) – The raw mtz dataset.

  • wavelength_column_name (mtz_io.WavelengthColumnName (str), default: 'LAMBDA') – The name of the wavelength column in the mtz file.

  • intensity_column_name (mtz_io.IntensityColumnName (str), default: 'I') – The name of the intensity column in the mtz file.

  • intensity_sig_col_name (mtz_io.StdDevColumnName (str), default: 'SIGI') – The name of the standard uncertainty of intensity column in the mtz file.

Returns:

mtz_io.MtzDataFrame (pandas.core.frame.DataFrame) – The new mtz dataframe with derived and renamed columns.

The derived columns are:

  • hkl: The miller indices as a list of integers.

  • d: The d-spacing calculated from the miller indices.

    :math:\dfrac{2}{d^{2}} = \dfrac{\sin^2(\theta)}{\lambda^2}

  • resolution: The resolution calculated from the d-spacing.

For consistent names of columns/coordinates, the following columns are renamed:

  • wavelength_column_name -> 'wavelength'

  • intensity_column_name -> 'I'

  • intensity_sig_col_name -> 'SIGI'

Other columns are kept as they are.

Notes

pandas.DataFrame is used from loading to merging, but gemmi.Mtz has gemmi.Mtz:calculate_d() that can derive the d using HKL. This part of the method must be called on each mtz file separately.