Auxiliary files

Auxiliary readers allow you to read in timeseries data accompanying a trajectory that is not stored in the regular trajectory file.

Supported formats



Extension (if file)






Produced by Gromacs during simulation or analysis. Reads full file on initialisation.




Alternate xvg file reader, reading each step from the file in turn for a lower memory footprint. XVGReader is the default reader for .xvg files.




Produced by Gromacs during simulation or analysis. Reads full file on initialisation.

XVG Files

Reading data directly

In [1]: import MDAnalysis as mda

In [2]: from MDAnalysis.tests.datafiles import XVG_BZ2  # cobrotoxin protein forces

In [3]: aux = mda.auxiliary.core.auxreader(XVG_BZ2)

In [4]: aux
Out[4]: <MDAnalysis.auxiliary.XVG.XVGReader at 0x7f0af0990b20>

In stand-alone use, an auxiliary reader allows you to iterate over each step in a set of auxiliary data.

In [5]: for step in aux:
   ...:     print(
[    0.        200.71288 -1552.2849  ...   128.4072   1386.0378
 -2699.3118 ]
[   50.      -1082.6454   -658.32166 ...  -493.02954   589.8844
  -739.2124 ]
[  100.       -246.27269   146.52911 ...   484.32501  2332.3767
 -1801.6234 ]

Use slicing to skip steps.

In [6]: for step in aux[1:2]:
   ...:     print(step.time)

The auxreader() function uses the get_auxreader_for() to return an appropriate class. This can guess the format either from a filename, ‘

In [7]: mda.auxiliary.core.get_auxreader_for(XVG_BZ2)
Out[7]: MDAnalysis.auxiliary.XVG.XVGReader

or return the reader for a specified format.

In [8]: mda.auxiliary.core.get_auxreader_for(format='XVG-F')
Out[8]: MDAnalysis.auxiliary.XVG.XVGFileReader

Loading data into a Universe

Auxiliary data may be added to a trajectory Reader through the add_auxiliary() method. Auxiliary data may be passed in as a AuxReader instance, or directly as e.g. a filename, in which case get_auxreader_for() is used to guess an appropriate reader.

In [9]: from MDAnalysis.tests.datafiles import PDB_xvf, TRR_xvf

In [10]: u = mda.Universe(PDB_xvf, TRR_xvf)

In [11]: u.trajectory.add_auxiliary('protein_force', XVG_BZ2)

In [12]: for ts in u.trajectory:
   ....:     print(ts.aux.protein_force)
[    0.        200.71288 -1552.2849  ...   128.4072   1386.0378
 -2699.3118 ]
[   50.      -1082.6454   -658.32166 ...  -493.02954   589.8844
  -739.2124 ]
[  100.       -246.27269   146.52911 ...   484.32501  2332.3767
 -1801.6234 ]

Passing arguments to auxiliary data

For alignment with trajectory data, auxiliary readers provide methods to assign each auxiliary step to the nearest trajectory timestep, read all steps assigned to a trajectory timestep and calculate ‘representative’ value(s) of the auxiliary data for that timestep.

To set a timestep or ??

‘Assignment’ of auxiliary steps to trajectory timesteps is determined from the time of the auxiliary step, dt of the trajectory and time at the first frame of the trajectory. If there are no auxiliary steps assigned to a given timestep (or none within cutoff, if set), the representative value(s) are set to np.nan.

Iterating over auxiliary data

Auxiliary data may not perfectly line up with the trajectory, or have missing data.

In [13]: from MDAnalysis.tests.datafiles import PDB, TRR

In [14]: u_long = mda.Universe(PDB, TRR)

In [15]: u_long.trajectory.add_auxiliary('protein_force', XVG_BZ2, dt=200)

In [16]: for ts in u_long.trajectory:
   ....:     print(ts.time, ts.aux.protein_force[:4])
0.0 [    0.        200.71288 -1552.2849   -967.21124]
100.00000762939453 [  100.       -246.27269   146.52911 -1084.2484 ]
200.00001525878906 [nan nan nan nan]
300.0 [nan nan nan nan]
400.0000305175781 [nan nan nan nan]
500.0000305175781 [nan nan nan nan]
600.0 [nan nan nan nan]
700.0000610351562 [nan nan nan nan]
800.0000610351562 [nan nan nan nan]
900.0000610351562 [nan nan nan nan]

The trajectory ProtoReader methods next_as_aux() and iter_as_aux() allow for movement through only trajectory timesteps for which auxiliary data is available.

In [17]: for ts in u_long.trajectory.iter_as_aux('protein_force'):
   ....:     print(ts.time, ts.aux.protein_force[:4])
0.0 [    0.        200.71288 -1552.2849   -967.21124]
100.00000762939453 [  100.       -246.27269   146.52911 -1084.2484 ]

This may be used to avoid representative values set to np.nan, particularly when auxiliary data is less frequent.

Sometimes the auxiliary data is longer than the trajectory.

In [18]: u_short = mda.Universe(PDB)

In [19]: u_short.trajectory.add_auxiliary('protein_force', XVG_BZ2)

In [20]: for ts in u_short.trajectory:
   ....:     print(ts.time, ts.aux.protein_force)
0.0 [    0.        200.71288 -1552.2849  ...   128.4072   1386.0378
 -2699.3118 ]

In order to acess auxiliary values at every individual step, including those outside the time range of the trajectory, iter_auxiliary() allows iteration over the auxiliary independent of the trajectory.

In [21]: for step in u_short.trajectory.iter_auxiliary('protein_force'):
   ....:     print(
[    0.        200.71288 -1552.2849  ...   128.4072   1386.0378
 -2699.3118 ]
[   50.      -1082.6454   -658.32166 ...  -493.02954   589.8844
  -739.2124 ]
[  100.       -246.27269   146.52911 ...   484.32501  2332.3767
 -1801.6234 ]

To iterate over only a certain section of the auxiliary:

In [22]: for step in u_short.trajectory.iter_auxiliary('protein_force', start=1, step=2):
   ....:     print(step.time)

The trajectory remains unchanged, and the auxiliary will be returned to the current timestep after iteration is complete.

Accessing auxiliary attributes

To check the values of attributes of an added auxiliary, use get_aux_attribute().

In [23]: u.trajectory.get_aux_attribute('protein_force', 'dt')
Out[23]: 50.0

If attributes are settable, they can be changed using set_aux_attribute().

In [24]: u.trajectory.set_aux_attribute('protein_force', 'data_selector', [1])

The auxiliary may be renamed using set_aux_attribute with ‘auxname’, or more directly by using rename_aux().

In [25]: u.trajectory.ts.aux.protein_force
array([    0.     ,   200.71288, -1552.2849 , ...,   128.4072 ,
        1386.0378 , -2699.3118 ])

In [26]: u.trajectory.rename_aux('protein_force', 'f')

In [27]: u.trajectory.ts.aux.f
array([    0.     ,   200.71288, -1552.2849 , ...,   128.4072 ,
        1386.0378 , -2699.3118 ])

Recreating auxiliaries

To recreate an auxiliary, the set of attributes necessary to replicate it can first be obtained with get_description(). The returned dictionary can then be passed to auxreader() to load a new copy of the original auxiliary reader.

In [28]: description = aux.get_description()

In [29]: list(description.keys())

In [30]: del aux

In [31]: mda.auxiliary.core.auxreader(**description)
Out[31]: <MDAnalysis.auxiliary.XVG.XVGReader at 0x7f0af0cad5e0>

The ‘description’ of any or all the auxiliaries added to a trajectory can be obtained using get_aux_descriptions().

In [32]: descriptions = u.trajectory.get_aux_descriptions(['f'])

To reload, pass the dictionary into add_auxiliary().

In [33]: u2 = mda.Universe(PDB, TRR)

In [34]: for desc in descriptions:
   ....:      u2.trajectory.add_auxiliary(**desc)

EDR Files

EDR files are created by GROMACS during simulations and contain additional non-trajectory time-series data of the system, such as energies, temperature, or pressure. The EDRReader allows direct reading of these binary files and associating of the data with trajectory time steps just like the XVGReader does. As all functionality of the base AuxReaders (see XVGReader above) also works with the EDRReader, this section will highlight functionality unique to the EDRReader.

Standalone Usage

The EDRReader is initialised by passing the path to an EDR file as an argument.

In [35]: import MDAnalysis as mda

In [36]: from MDAnalysisTests.datafiles import AUX_EDR

In [37]: aux = mda.auxiliary.EDR.EDRReader(AUX_EDR)

 # Or, for example
 # aux = mda.auxiliary.EDR.EDRReader("path/to/file/ener.edr")

Dozens of terms can be defined in EDR files. A list of available data is conveniently available unter the terms attribute.

In [38]: aux.terms
 'Proper Dih.',
 'LJ (SR)',
 'Disper. corr.',
 'Coulomb (SR)',
 'Coul. recip.',
 'Kinetic En.',
 'Total Energy',
 'Conserved En.',
 'Pres. DC',
 'Constr. rmsd',

To extract data for plotting, the get_data method can be used. It can be used to extract a single, multiple, or all terms as follows:

In [39]: temp = aux.get_data("Temperature")

In [40]: print(temp.keys())
dict_keys(['Time', 'Temperature'])

In [41]: energies = aux.get_data(["Potential", "Kinetic En."])

In [42]: print(energies.keys())
dict_keys(['Time', 'Potential', 'Kinetic En.'])

In [43]: all_data = aux.get_data()

In [44]: print(all_data.keys())
dict_keys(['Time', 'Bond', 'Angle', 'Proper Dih.', 'Ryckaert-Bell.', 'LJ-14', 'Coulomb-14', 'LJ (SR)', 'Disper. corr.', 'Coulomb (SR)', 'Coul. recip.', 'Potential', 'Kinetic En.', 'Total Energy', 'Conserved En.', 'Temperature', 'Pres. DC', 'Pressure', 'Constr. rmsd', 'Box-X', 'Box-Y', 'Box-Z', 'Volume', 'Density', 'pV', 'Enthalpy', 'Vir-XX', 'Vir-XY', 'Vir-XZ', 'Vir-YX', 'Vir-YY', 'Vir-YZ', 'Vir-ZX', 'Vir-ZY', 'Vir-ZZ', 'Pres-XX', 'Pres-XY', 'Pres-XZ', 'Pres-YX', 'Pres-YY', 'Pres-YZ', 'Pres-ZX', 'Pres-ZY', 'Pres-ZZ', '#Surf*SurfTen', 'Box-Vel-XX', 'Box-Vel-YY', 'Box-Vel-ZZ', 'T-Protein', 'T-non-Protein', 'Lamb-Protein', 'Lamb-non-Protein'])

The times of each data point are always part of the returned dictionary to facilicate plotting.

In [45]: import matplotlib.pyplot as plt

In [46]: plt.plot(temp["Time"], temp["Temperature"])
Out[46]: [<matplotlib.lines.Line2D at 0x7f0b01cef820>]

In [47]: plt.ylabel("Temperature [K]")
Out[47]: Text(0, 0.5, 'Temperature [K]')

In [48]: plt.xlabel("Time [ps]")
Out[48]: Text(0.5, 0, 'Time [ps]')

In [49]:

(Source code, png, hires.png, pdf)


Unit Handling

On object creation, the EDRReader creates a unit_dict attribute which contains information on the units of the data stored within. These units are read from the EDR file automatically and by default converted to MDAnalysis base units where such units are defined. The automatic unit conversion can be disabled by setting the convert_units kwarg to False.

In [50]: aux.unit_dict["Box-X"]
Out[50]: 'A'

In [51]: aux_native = mda.auxiliary.EDR.EDRReader(AUX_EDR, convert_units=False)

In [52]: aux_native.unit_dict["Box-X"]
Out[52]: 'nm'

Use with Trajectories

An arbitrary number of terms to be associated with a trajectory can be specified as a dictionary. The dictionary is chosen so that the name to be used in MDAnalysis to access the data is mapped to the name in aux.terms.

In [53]: from MDAnalysisTests.datafiles import AUX_EDR_TPR, AUX_EDR_XTC

In [54]: u = mda.Universe(AUX_EDR_TPR, AUX_EDR_XTC)

In [55]: term_dict = {"temp": "Temperature", "epot": "Potential"}

In [56]: u.trajectory.add_auxiliary(term_dict, aux)

In [57]: u.trajectory.ts.aux.epot
Out[57]: -525164.0625

Adding all data is possible by omitting the dictionary as follows. It is then necessary to specify that the EDRReader should be passed as the auxdata argument as such:

In [58]: u = mda.Universe(AUX_EDR_TPR, AUX_EDR_XTC)

In [59]: u.trajectory.add_auxiliary(auxdata=aux)

In [60]: u.trajectory.aux_list
Out[60]: dict_keys(['Time', 'Bond', 'Angle', 'Proper Dih.', 'Ryckaert-Bell.', 'LJ-14', 'Coulomb-14', 'LJ (SR)', 'Disper. corr.', 'Coulomb (SR)', 'Coul. recip.', 'Potential', 'Kinetic En.', 'Total Energy', 'Conserved En.', 'Temperature', 'Pres. DC', 'Pressure', 'Constr. rmsd', 'Box-X', 'Box-Y', 'Box-Z', 'Volume', 'Density', 'pV', 'Enthalpy', 'Vir-XX', 'Vir-XY', 'Vir-XZ', 'Vir-YX', 'Vir-YY', 'Vir-YZ', 'Vir-ZX', 'Vir-ZY', 'Vir-ZZ', 'Pres-XX', 'Pres-XY', 'Pres-XZ', 'Pres-YX', 'Pres-YY', 'Pres-YZ', 'Pres-ZX', 'Pres-ZY', 'Pres-ZZ', '#Surf*SurfTen', 'Box-Vel-XX', 'Box-Vel-YY', 'Box-Vel-ZZ', 'T-Protein', 'T-non-Protein', 'Lamb-Protein', 'Lamb-non-Protein'])

Selecting Trajectory Frames Based on Auxiliary Data

One use case for the new auxiliary readers is the selection of frames based on auxiliary data. To select only those frames with a potential energy below a certain threshold, the following can be used:

In [61]: u = mda.Universe(AUX_EDR_TPR, AUX_EDR_XTC)

In [62]: term_dict = {"epot": "Potential"}

In [63]: u.trajectory.add_auxiliary(term_dict, aux)

In [64]: selected_frames = np.array([ts.frame for ts in u.trajectory if ts.aux.epot < -524600])

A slice of the trajectory can then be obtained from the list of frames as such:

In [65]: trajectory_slice = u.trajectory[selected_frames]

In [66]: print(len(u.trajectory))

In [67]: print(len(trajectory_slice))

Memory Usage

It is assumed that the EDR file is small enough to be read in full. However, since one EDRReader instance is created for each term added to a trajectory, memory usage monitoring was implemented. A warning will be issued if 1 GB of memory is used by auxiliary data. This warning limit can optionally be changed by defining the memory_limit (in bytes) when adding data to a trajectory. Below, the memory limit is set to 200 MB.

In [68]: u = mda.Universe(AUX_EDR_TPR, AUX_EDR_XTC)

In [69]: term_dict = {"temp": "Temperature", "epot": "Potential"}

In [70]: u.trajectory.add_auxiliary(term_dict, aux, memory_limit=2e+08)