Energy¶

class py4vasp.data.Energy(*args, **kwargs)¶

Bases: Mixin, Refinery, Mixin

The energy data for one or several steps of a relaxation or MD simulation.

You can use this class to inspect how the ionic relaxation converges or during an MD simulation whether the total energy is conserved.

Examples

If you access a method of this class, the result will depend on the steps that you selected with the [] operator. Without any selection the results from the final step will be used.

>>> calc.energy.read()

To select the results for all steps, you don’t specify the array boundaries.

>>> calc.energy[:].read()

You can also select specific steps or a subset of steps as follows

>>> calc.energy[5].read()
>>> calc.energy[1:6].read()

Attributes Summary

path	Returns the path from which the output is obtained.

Methods Summary

from_data(raw_data)	Create the instance directly from the raw data.
from_file(file)	Read the quantities from the given file.
from_path([path])	Read the quantities from the given path.
labels([selection])	Return the labels corresponding to a particular selection defaulting to all labels.
plot(*args, **kwargs)	Wrapper around the to_graph() function.
print()	Print a string representation of this instance.
read(*args, **kwargs)	Convenient wrapper around to_dict.
to_dict([selection])	Read the energy data and store it in a dictionary.
to_graph([selection])	Read the energy data and generate a figure of the selected components.
to_image(*args[, filename])	Read the data and generate an image writing to the given filename.
to_numpy([selection])	Read the energy of the selected steps.
to_plotly(*args, **kwargs)	Convert the graph of this quantity to a plotly figure.

Attributes Documentation

path¶

Returns the path from which the output is obtained.

Methods Documentation

classmethod from_data(raw_data)¶

Create the instance directly from the raw data.

Use this approach when the data is put into the correct format by other means than reading from the VASP output files. A typical use case is to read the data with from_path and then act on it with some postprocessing and pass the results to this method.

Parameters:

raw_data – The raw data required to produce this Refinery.

Return type:

A Refinery instance to handle the passed data.

classmethod from_file(file)¶

Read the quantities from the given file.

You want to use this method if you want to avoid using the Calculation wrapper, for example because you renamed the output of the VASP calculation.

Parameters:

file (str or io.BufferedReader) – Filename from which the data is extracted. Alternatively, you can open the file yourself and pass the Reader object. In that case, you need to take care the file is properly closed again and be aware the generated instance of this class becomes unusable after the file is closed.

Returns:

The returned instance handles opening and closing the file for every function called on it, unless a Reader object is passed in which case this is left to the user.

Return type:

Refinery

Notes

VASP produces multiple output files whereas this routine will only link to the single specified file. Prefer from_path routine over this method unless you renamed the VASP output files, because from_path can collate results from multiple files.

classmethod from_path(path=None)¶

Read the quantities from the given path.

The VASP schema determines the particular files accessed. The files will only be accessed when the data is required for a particular postprocessing call.

Parameters:

path (str or pathlib.Path) – Path to the directory with the outputs of the VASP calculation. If not set explicitly the current directory will be used.

Returns:

The returned instance handles opening and closing the files for every function called on it.

Return type:

Refinery

labels(selection='__all__')¶

Return the labels corresponding to a particular selection defaulting to all labels.

plot(*args, **kwargs)¶

Wrapper around the to_graph() function.

This will merge multiple graphs if you specify different sources with the selection arguments. All arguments are passed to the wrapped function.

print()¶

Print a string representation of this instance.

read(*args, **kwargs)¶

Convenient wrapper around to_dict. Check that function for examples and optional arguments.

to_dict(selection='__all__')¶

Read the energy data and store it in a dictionary.

Parameters:

selection (str or None) – String specifying the labels of the energy to be read. A substring of the label is sufficient. If no energy is select this will default to selecting all energies. Separate distinct labels by commas. For a complete list of all possible selections, please use >>> calc.energy.labels()

Returns:

Contains the exact labels corresponding to the selection and the associated energies for every selected ionic step.

Return type:

dict

Examples

If you access this method, the result will depend on the steps of the class that you selected with the [] operator. Without any selection the results from the final step will be used.

>>> calc.energy.to_dict()

To select the results for all steps, you don’t specify the array boundaries.

>>> calc.energy[:].to_dict()

You can also select specific steps or a subset of steps as follows

>>> calc.energy[5].to_dict()
>>> calc.energy[1:6].to_dict()

to_graph(selection='TOTEN')¶

Read the energy data and generate a figure of the selected components.

Parameters:

selection (str or None) – String specifying the labels of the energy to be read. A substring of the label is sufficient. If no energy is select this will default to selecting the total energy. Separate distinct labels by commas. For a complete list of all possible selections, please use >>> calc.energy.labels()

Returns:

figure containing the selected energies for every selected ionic step.

Return type:

Graph

Examples

If you access this method, the result will depend on the steps of the class that you selected with the [] operator. Without any selection the results from the final step will be used.

>>> calc.energy.to_graph()

To select the results for all steps, you don’t specify the array boundaries.

>>> calc.energy[:].to_graph()

You can also select specific steps or a subset of steps as follows

>>> calc.energy[5].to_graph()
>>> calc.energy[1:6].to_graph()

to_image(*args, filename=None, **kwargs)¶

Read the data and generate an image writing to the given filename.

The filetype is automatically deduced from the filename; possible are common raster (png, jpg) and vector (svg, pdf) formats. If no filename is provided a default filename is deduced from the name of the class and the picture has png format.

Note that the filename must be a keyword argument, i.e., you explicitly need to write filename=”name_of_file” because the arguments are passed on to the to_graph() function. Please check the documentation of that function to learn which arguments are allowed.

to_numpy(selection='TOTEN')¶

Read the energy of the selected steps.

Parameters:

selection (str or None) – String specifying the labels of the energy to be read. A substring of the label is sufficient. If no energy is select this will default to selecting the total energy. Separate distinct labels by commas. For a complete list of all possible selections, please use >>> calc.energy.labels()

Returns:

Contains energies associated with the selection for the selected ionic step(s). When only a single step is inquired, result is a float otherwise an array. If you select multiple quantities a tuple of them is returned.

Return type:

float or np.ndarray or tuple

Examples

If you access this method, the result will depend on the steps of the class that you selected with the [] operator. Without any selection the results from the final step will be used.

>>> calc.energy.to_numpy()

To select the results for all steps, you don’t specify the array boundaries.

>>> calc.energy[:].to_numpy()

You can also select specific steps or a subset of steps as follows

>>> calc.energy[5].to_numpy()
>>> calc.energy[1:6].to_numpy()

to_plotly(*args, **kwargs)¶

Convert the graph of this quantity to a plotly figure.

The arguments to this function are automatically passed on to the to_graph() function. Please check the documentation of that function to learn which arguments are allowed.