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Basics

reciprocalspaceship provides methods for reading and writing MTZ files, and can be easily used to join reflection data by Miller indices. We will demonstrate these uses by loading diffraction data of tetragonal hen egg-white lysozyme (HEWL).

import reciprocalspaceship as rs
print(rs.__version__)

0.9.9

This diffraction data was collected at the Sector 24-ID-C beamline at NE-CAT at APS. Diffraction images were collected at ambient room temperature (295K), and low energy (6550 eV) in order to collect native sulfur anomalous diffraction for experimental phasing. The diffraction images were processed in DIALS for indexing, geometry refinement, and spot integration, and scaling and merging was done in AIMLESS. This data reduction yielded an MTZ file that is included in the data/ subdirectory. Here, we will load the MTZ file and inspect its contents.

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Loading reflection data

Reflection tables can be loaded using the top-level function, rs.read_mtz(). This returns a DataSet object, that is analogous to a pandas.DataFrame.

refltable = rs.read_mtz("data/HEWL_SSAD_24IDC.mtz")
type(refltable).__name__

'DataSet'

This reflection table was produced directly from AIMLESS, and contains several different data columns:

refltable.head()

			FreeR_flag	IMEAN	SIGIMEAN	I(+)	SIGI(+)	I(-)	SIGI(-)	N(+)	N(-)
H	K	L
0	0	4	14	661.29987	21.953098	661.29987	21.953098	661.29987	21.953098	16	16
		8	4	3229.649	105.980934	3229.649	105.980934	3229.649	105.980934	16	16
		12	6	1361.8672	43.06085	1361.8672	43.06085	1361.8672	43.06085	16	16
		16	19	4124.393	196.89108	4124.393	196.89108	4124.393	196.89108	8	8
1	0	1	16	559.33685	8.6263	559.33685	8.6263	559.33685	8.6263	64	64

print(f"Number of reflections: {len(refltable)}")

Number of reflections: 12542

Internally, each of these data columns is stored using a custom dtype that was added to the conventional pandas and numpy datatypes. This enables DataSet reflection tables to be written back to MTZ files. There is a dtype for each of the possible datatypes listed in the MTZ file specification.

refltable.dtypes

FreeR_flag              MTZInt
IMEAN                Intensity
SIGIMEAN                Stddev
I(+)          FriedelIntensity
SIGI(+)         StddevFriedelI
I(-)          FriedelIntensity
SIGI(-)         StddevFriedelI
N(+)                    MTZInt
N(-)                    MTZInt
dtype: object

Additional crystallographic metadata is read from the MTZ file and can be stored as attributes of the DataSet. These include the crystallographic spacegroup and unit cell parameters, which are stored as gemmi.SpaceGroup and gemmi.UnitCell objects.

refltable.spacegroup

<gemmi.SpaceGroup("P 43 21 2")>

refltable.cell

<gemmi.UnitCell(79.3439, 79.3439, 37.8099, 90, 90, 90)>

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Plotting reflection data

For illustrative purposes, let’s plot the \(I(+)\) data against the \(I(-)\) data

%matplotlib inline
import matplotlib.pyplot as plt

plt.figure(figsize=(6, 6))
plt.plot(refltable['I(+)'].to_numpy(), refltable['I(-)'].to_numpy(), 'k.', alpha=0.1)
plt.xlabel("I(+)")
plt.ylabel("I(-)")
plt.show()

In the next example, we will investigate this anomalous signal in more detail.

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Writing Reflection Data

It is also possible to write out MTZ files using DataSet.write_mtz(). This functionality depends on the correct setting of each column’s dtype.

refltable.write_mtz("data/HEWL_SSAD_24IDC.mtz")