dynamics Package

dynamics Package

analyzer Module

Tools to analyze MD trajectories and compute diffusion coefficients.

abipy.dynamics.analyzer.common_oxidation_states() → dict

abipy.dynamics.analyzer.read_structure_postac_ucmats(traj_filepath: str | PathLike, step_skip: int) → tuple[Structure, ndarray, ndarray, int]

Read all configurations from an ASE trajectory file.

Parameters:

• traj_filepath – File path.

• step_skip – Sampling frequency. time_step should be multiplied by this number to get the real time between measurements.

Returns:

initial Structure, (nsteps, natom, 3) array with the Cartesian coords, (nsteps,3,3) array with cell vectors.

Return type:

tuple with

abipy.dynamics.analyzer.parse_lammps_input(filepath: str | PathLike, verbose: int = 0)

Extract parameters, atoms and structure from a LAMMPS input file. Returns namedtuple with results.

class abipy.dynamics.analyzer.MdAnalyzer(structure: Structure, temperature: float, times: ndarray, cart_positions: ndarray, ucmats: ndarray, engine: str, pos_order: str = 'tac', evp_df=None | pandas.core.frame.DataFrame)

Bases: HasPickleIO

High-level interface to read MD trajectories and metadata from external files, compute the MSQD and plot the results.

classmethod from_abiml_dir(directory: str | PathLike, step_skip: int = 1) → MdAnalyzer

Build an instance from a directory containing an ASE trajectory file and a JSON file with the MD parameters as produced by the abiml.py md script.

classmethod from_hist_file(hist_filepath: str | PathLike, step_skip: int = 1) → MdAnalyzer

Build an instance from an ABINIT HIST.nc file.

classmethod from_vaspruns(filepaths: list) → MdAnalyzer

Build an instance from a list of Vasprun files (must be ordered in sequence of MD simulation).

classmethod from_qe_input(filepath: str | PathLike, step_skip: int = 1)

Build an instance from a CP/PW input file.

Conventions for Quantum ESPRESSO input files:

“.pwi” -> pw.x “.cpi” -> cp.x

classmethod from_lammps_dir(directory: str | PathLike, step_skip: int = 1, basename='in.lammps') → MdAnalyzer

Build an instance from a directory containing a LAMMPS input file.

classmethod from_lammps_input(input_filepath: str | PathLike, step_skip: int = 1) → MdAnalyzer

Build an instance from a LAMMPS input file.

Parameters:

input_filepath – LAMMPS input file.

classmethod from_lammpstrj(traj_filepath: str | PathLike, input_filepath: str | PathLike, step_skip: int = 1) → MdAnalyzer

Build an instance from a LAMMPS trajectory file and a log file.

Parameters:

• traj_filepath

• input_filepath

consistency_check() → None

Perform internal consistency check.

get_params_dict() → dict

Dictionary with the most important parameters.

deepcopy() → MdAnalyzer

Deep copy of the object.

iter_structures()

Generate pymatgen structures.

resample_step(start_at_step: int, take_every: int) → MdAnalyzer

Resample the trajectory. Start at iteration start_at_step and increase the timestep by taking every take_every iteration.

resample_time(start_time: float, new_timestep: float) → MdAnalyzer

Resample the trajectory. Start at time start_time and use new timestep new_timestep.

property timestep: float

Timestep in ps.

property max_time: float

Maximum simulation time in ps.

property nt: int

Number of points in the MD trajectory.

natom()

Number of atoms.

property temperature: float

Temperature in Kelvin.

property verbose: int

Verbosity level.

property engine: str

property formula: str

Returns the formula as a string.

property latex_formula: str

property latex_formula_n_temp: str

property latex_avg_volume: str

property avg_volume: float

Average unit cell volume in Ang^3.

set_color_symbol(dict_or_string: dict | str) → None

Set the dictionary mapping chemical_symbol –> color used in the matplotlib plots.

Parameters:

dict_or_string – “VESTA”, “Jmol”

get_it_ts(t0: float) → tuple[int, ndarray]

Return the index of time t0 in self.times and the array with the time values.

to_string(verbose: int = 0) → str

String representation with verbosity level verbose.

iatoms_with_symbol(symbol: str, atom_inds=None) → ndarray

Array with the index of the atoms with the given chemical symbol. If atom_inds is not None, filter sites accordingly.

get_sqdt_iatom(iatom: int, it0: int = 0) → array

Compute the square displacement vs time for a given atomic index starting from time index it0.

get_sqdt_symbol(symbol: str, it0: int = 0, atom_inds=None) → array

Compute the square displacement vs time averaged over atoms with the same chemical symbol starting from time index it0. atoms_inds adds an additional filter on the site index.

get_dw_symbol(symbol, t0: float = 0.0, tmax=None, atom_inds=None)

Compute diffusion coefficient by performing a naive linear regression of the raw MQST.

get_msdtt0_symbol_tmax(symbol: str, tmax: float, atom_inds=None, nprocs=None) → Msdtt0

Calculates the MSD for every possible pair of time points using the formula:

$$MSD(t,t_0) = frac{1}{N} sum_{i=1}^{N} (vec{r}_i(t+t_0) - vec{r}_i(t_0))^2$$

where $N$ is the number of particles with the given symbol, and $vec{r}_i(t)$ is the position vector.

Parameters:

• symbols

• tmax

• atoms_ins

plot_sqdt_atoms(symbols='all', t0: float = 0.0, atom_inds=None, ax=None, xy_log=None, fontsize=8, xlims=None, **kwargs) → Any

Plot the square displacement of atoms vs time.

Parameters:

• symbols – List of chemical symbols to consider.

• t0 – Initial time in ps.

• atom_inds – List of atom indices to include. None to disable filtering.

• ax – matplotlib.axes.Axes or None if a new figure should be created.

• xy_log – None or empty string for linear scale. “x” for log scale on x-axis. “xy” for log scale on x- and y-axis. “x:semilog” for semilog scale on x-axis.

• fontsize – fontsize for legends and titles.

• xlims – Set the data limits for the x-axis. Accept tuple e.g. (left, right) or scalar e.g. left. If left (right) is None, default values are used.

Keyword arguments controlling the display of the figure:

kwargs	Meaning
title	Title of the plot (Default: None).
show	True to show the figure (default: True).
savefig	“abc.png” or “abc.eps” to save the figure to a file.
size_kwargs	Dictionary with options passed to fig.set_size_inches e.g. size_kwargs=dict(w=3, h=4)
tight_layout	True to call fig.tight_layout (default: False)
ax_grid	True (False) to add (remove) grid from all axes in fig. Default: None i.e. fig is left unchanged.
ax_annotate	Add labels to subplots e.g. (a), (b). Default: False
fig_close	Close figure. Default: False.
plotly	Try to convert mpl figure to plotly.

plot_sqdt_symbols(symbols, t0: float = 0.0, atom_inds=None, with_dw=0, ax=None, xy_log=None, fontsize=8, xlims=None, **kwargs) → Any

Plot the square displacement averaged over all atoms of the same specie vs time.

Parameters:

• symbols – List of chemical symbols to consider. “all” for all symbols in structure.

• t0 – Initial time in ps.

• atom_inds – List of atom indices to include. None to disable filtering.

• with_dw – If != 0 compute diffusion coefficient via least-squares fit in the time-interval [t0, with_dw]. If with_dw < 0 e.g. -1 the time-interval is set to [t0, tmax].

• ax – matplotlib.axes.Axes or None if a new figure should be created.

• xy_log – None or empty string for linear scale. “x” for log scale on x-axis. “xy” for log scale on x- and y-axis. “x:semilog” for semilog scale on x-axis.

• fontsize – fontsize for legends and titles.

• xlims – Set the data limits for the x-axis. Accept tuple e.g. (left, right) or scalar e.g. left. If left (right) is None, default values are used.

Keyword arguments controlling the display of the figure:

kwargs	Meaning
title	Title of the plot (Default: None).
show	True to show the figure (default: True).
savefig	“abc.png” or “abc.eps” to save the figure to a file.
size_kwargs	Dictionary with options passed to fig.set_size_inches e.g. size_kwargs=dict(w=3, h=4)
tight_layout	True to call fig.tight_layout (default: False)
ax_grid	True (False) to add (remove) grid from all axes in fig. Default: None i.e. fig is left unchanged.
ax_annotate	Add labels to subplots e.g. (a), (b). Default: False
fig_close	Close figure. Default: False.
plotly	Try to convert mpl figure to plotly.

plot_sqdt_symbols_tmax(symbols, tmax: float, atom_inds=None, nprocs=None, ax=None, xy_log=None, fontsize=8, xlims=None, **kwargs) → Any

Plot the square displacement averaged over all atoms of the same specie vs time.

Parameters:

• symbols – List of chemical symbols to consider. “all” for all symbols in structure.

• tmax – Max time in ps.

• atom_inds – List of atom indices to include. None to disable filtering.

• nprocs – Number of procs to use.

• ax – matplotlib.axes.Axes or None if a new figure should be created.

• xy_log – None or empty string for linear scale. “x” for log scale on x-axis. “xy” for log scale on x- and y-axis. “x:semilog” for semilog scale on x-axis.

• fontsize – fontsize for legends and titles

• xlims – Set the data limits for the x-axis. Accept tuple e.g. (left, right) or scalar e.g. left. If left (right) is None, default values are used.

Keyword arguments controlling the display of the figure:

kwargs	Meaning
title	Title of the plot (Default: None).
show	True to show the figure (default: True).
savefig	“abc.png” or “abc.eps” to save the figure to a file.
size_kwargs	Dictionary with options passed to fig.set_size_inches e.g. size_kwargs=dict(w=3, h=4)
tight_layout	True to call fig.tight_layout (default: False)
ax_grid	True (False) to add (remove) grid from all axes in fig. Default: None i.e. fig is left unchanged.
ax_annotate	Add labels to subplots e.g. (a), (b). Default: False
fig_close	Close figure. Default: False.
plotly	Try to convert mpl figure to plotly.

plot_lattices(what_list=('abc', 'angles', 'volume'), ax_list=None, xy_log=None, fontsize=8, xlims=None, **kwargs) → Any

Plot lattice lengths/angles/volume as a function of time.

Parameters:

• what_list – List of strings specifying the quantities to plot. Default all

• ax_list – List of axis or None if a new figure should be created.

• xy_log – None or empty string for linear scale. “x” for log scale on x-axis. “xy” for log scale on x- and y-axis. “x:semilog” for semilog scale on x-axis.

• fontsize – fontsize for legends and titles

• xlims – Set the data limits for the x-axis. Accept tuple e.g. (left, right) or scalar e.g. left. If left (right) is None, default values are used.

Keyword arguments controlling the display of the figure:

kwargs	Meaning
title	Title of the plot (Default: None).
show	True to show the figure (default: True).
savefig	“abc.png” or “abc.eps” to save the figure to a file.
size_kwargs	Dictionary with options passed to fig.set_size_inches e.g. size_kwargs=dict(w=3, h=4)
tight_layout	True to call fig.tight_layout (default: False)
ax_grid	True (False) to add (remove) grid from all axes in fig. Default: None i.e. fig is left unchanged.
ax_annotate	Add labels to subplots e.g. (a), (b). Default: False
fig_close	Close figure. Default: False.
plotly	Try to convert mpl figure to plotly.

class abipy.dynamics.analyzer.Msdtt0(*, index_tmax: int, symbol: str, arr_tt0: ndarray, mda: MdAnalyzer)

Bases: object

This object stores:

$$MSD(t,t_0) = frac{1}{N} sum_{i=1}^{N} (vec{r}_i(t+t_0) - vec{r}_i(t_0))^2$$

where $N$ is the number of particles of a particular chemical symbol and $vec{r}_i(t)$ is the position vector.

index_tmax: int

symbol: str

arr_tt0: ndarray

mda: MdAnalyzer

property times: ndarray

property temperature: float

msd_t()

Average of MSD(t,t_0) over t0.

to_string(verbose=0) → str

String representation with verbosity level verbose.

get_linfit_results()

Perform linear fit.

plot(ax=None, xy_log=None, fontsize=8, xlims=None, **kwargs) → Any

Plot <msd($t, t_0$)>$ averaged over the initial time t0.

Parameters:

• ax – matplotlib.axes.Axes or None if a new figure should be created.

• xy_log – None or empty string for linear scale. “x” for log scale on x-axis. “xy” for log scale on x- and y-axis. “x:semilog” for semilog scale on x-axis.

• fontsize – fontsize for legends and titles

• xlims – Set the data limits for the x-axis. Accept tuple e.g. (left, right) or scalar e.g. left. If left (right) is None, default values are used.

Keyword arguments controlling the display of the figure:

kwargs	Meaning
title	Title of the plot (Default: None).
show	True to show the figure (default: True).
savefig	“abc.png” or “abc.eps” to save the figure to a file.
size_kwargs	Dictionary with options passed to fig.set_size_inches e.g. size_kwargs=dict(w=3, h=4)
tight_layout	True to call fig.tight_layout (default: False)
ax_grid	True (False) to add (remove) grid from all axes in fig. Default: None i.e. fig is left unchanged.
ax_annotate	Add labels to subplots e.g. (a), (b). Default: False
fig_close	Close figure. Default: False.
plotly	Try to convert mpl figure to plotly.

get_sigma_berend(t1: float, t2: float, nblock_step: int = 1, tot_block: int = 1000) → SigmaBerend

Parameters:

• t1

• t2

• nblock_step

• tot_block

get_diffusion_with_sigma(block_size1: int, block_size2: int, fit_time_start: float, fit_time_stop: float, sigma_berend: SigmaBerend) → DiffusionData

Compute diffusion coefficient with uncertainty.

plot_mat(cmap='jet', fontsize=8, ax=None, **kwargs) → Any

Keyword arguments controlling the display of the figure:

kwargs	Meaning
title	Title of the plot (Default: None).
show	True to show the figure (default: True).
savefig	“abc.png” or “abc.eps” to save the figure to a file.
size_kwargs	Dictionary with options passed to fig.set_size_inches e.g. size_kwargs=dict(w=3, h=4)
tight_layout	True to call fig.tight_layout (default: False)
ax_grid	True (False) to add (remove) grid from all axes in fig. Default: None i.e. fig is left unchanged.
ax_annotate	Add labels to subplots e.g. (a), (b). Default: False
fig_close	Close figure. Default: False.
plotly	Try to convert mpl figure to plotly.

class abipy.dynamics.analyzer.Msdtt0List(iterable=(), /)

Bases: list

A list of Msdtt0 objects.

to_string(verbose=0) → str

String representation with verbosity level verbose.

plot(sharex=True, sharey=True, fontsize=8, **kwargs) → Any

Plot all Msdtt0 objects on a grid.

Keyword arguments controlling the display of the figure:

kwargs	Meaning
title	Title of the plot (Default: None).
show	True to show the figure (default: True).
savefig	“abc.png” or “abc.eps” to save the figure to a file.
size_kwargs	Dictionary with options passed to fig.set_size_inches e.g. size_kwargs=dict(w=3, h=4)
tight_layout	True to call fig.tight_layout (default: False)
ax_grid	True (False) to add (remove) grid from all axes in fig. Default: None i.e. fig is left unchanged.
ax_annotate	Add labels to subplots e.g. (a), (b). Default: False
fig_close	Close figure. Default: False.
plotly	Try to convert mpl figure to plotly.

class abipy.dynamics.analyzer.SigmaBerend(*, temperature: float, latex_formula: str, it1: int, time1: float, block_sizes1: ndarray, sigmas1: ndarray, delta_sigmas1: ndarray, it2: int, time2: float, block_sizes2: ndarray, sigmas2: ndarray, delta_sigmas2: ndarray)

Bases: object

Stores the variance of correlated data as function of block number.

temperature: float

latex_formula: str

it1: int

time1: float

block_sizes1: ndarray

sigmas1: ndarray

delta_sigmas1: ndarray

it2: int

time2: float

block_sizes2: ndarray

sigmas2: ndarray

delta_sigmas2: ndarray

plot(fontsize=8, ax_list=None, **kwargs) → Any

Plot variance of correlated data as function of block number.

Keyword arguments controlling the display of the figure:

kwargs	Meaning
title	Title of the plot (Default: None).
show	True to show the figure (default: True).
savefig	“abc.png” or “abc.eps” to save the figure to a file.
size_kwargs	Dictionary with options passed to fig.set_size_inches e.g. size_kwargs=dict(w=3, h=4)
tight_layout	True to call fig.tight_layout (default: False)
ax_grid	True (False) to add (remove) grid from all axes in fig. Default: None i.e. fig is left unchanged.
ax_annotate	Add labels to subplots e.g. (a), (b). Default: False
fig_close	Close figure. Default: False.
plotly	Try to convert mpl figure to plotly.

class abipy.dynamics.analyzer.DiffusionData(*, diffusion_coeff: float, err_diffusion_coeff: float, conductivity: float, err_conductivity: float, temperature: float, symbol: str, latex_formula: str, avg_volume: float, ncarriers: int, block_size1: int, block_size2: int, fit_time_start: float, fit_time_stop: float, min_angcoeff: float, max_angcoeff: float, best_angcoeff: float, engine: str, msd_t: ndarray, err_msd: ndarray, sigma_berend: SigmaBerend, times: ndarray, quote: float, timeArrScorrelated: ndarray, msdSScorrelated: ndarray, errMSDScorrelated: ndarray)

Bases: HasPickleIO

Diffusion results for a given temperature.

diffusion_coeff: float

err_diffusion_coeff: float

conductivity: float

err_conductivity: float

temperature: float

symbol: str

latex_formula: str

avg_volume: float

ncarriers: int

block_size1: int

block_size2: int

fit_time_start: float

fit_time_stop: float

min_angcoeff: float

max_angcoeff: float

best_angcoeff: float

engine: str

msd_t: ndarray

err_msd: ndarray

sigma_berend: SigmaBerend

times: ndarray

quote: float

timeArrScorrelated: ndarray

msdSScorrelated: ndarray

errMSDScorrelated: ndarray

plot(ax=None, fontsize=8, **kwargs) → Any

Plot MDS(t) with errors.

Keyword arguments controlling the display of the figure:

kwargs	Meaning
title	Title of the plot (Default: None).
show	True to show the figure (default: True).
savefig	“abc.png” or “abc.eps” to save the figure to a file.
size_kwargs	Dictionary with options passed to fig.set_size_inches e.g. size_kwargs=dict(w=3, h=4)
tight_layout	True to call fig.tight_layout (default: False)
ax_grid	True (False) to add (remove) grid from all axes in fig. Default: None i.e. fig is left unchanged.
ax_annotate	Add labels to subplots e.g. (a), (b). Default: False
fig_close	Close figure. Default: False.
plotly	Try to convert mpl figure to plotly.

class abipy.dynamics.analyzer.DiffusionDataList(iterable=(), /)

Bases: list

A list of DiffusionData objects.

get_dataframe(add_keys=None) → DataFrame

Dataframe with diffusion results.

Parameters:

add_keys – optional list of attributes to add.

plot_sigma_berend(**kwargs) → Any

Plot variance of correlated data as function of block number for all objects stored in DiffusionDataList.

Keyword arguments controlling the display of the figure:

kwargs	Meaning
title	Title of the plot (Default: None).
show	True to show the figure (default: True).
savefig	“abc.png” or “abc.eps” to save the figure to a file.
size_kwargs	Dictionary with options passed to fig.set_size_inches e.g. size_kwargs=dict(w=3, h=4)
tight_layout	True to call fig.tight_layout (default: False)
ax_grid	True (False) to add (remove) grid from all axes in fig. Default: None i.e. fig is left unchanged.
ax_annotate	Add labels to subplots e.g. (a), (b). Default: False
fig_close	Close figure. Default: False.
plotly	Try to convert mpl figure to plotly.

plot(**kwargs) → Any

Plot … for all objects stored DiffusionDataList.

Keyword arguments controlling the display of the figure:

kwargs	Meaning
title	Title of the plot (Default: None).
show	True to show the figure (default: True).
savefig	“abc.png” or “abc.eps” to save the figure to a file.
size_kwargs	Dictionary with options passed to fig.set_size_inches e.g. size_kwargs=dict(w=3, h=4)
tight_layout	True to call fig.tight_layout (default: False)
ax_grid	True (False) to add (remove) grid from all axes in fig. Default: None i.e. fig is left unchanged.
ax_annotate	Add labels to subplots e.g. (a), (b). Default: False
fig_close	Close figure. Default: False.
plotly	Try to convert mpl figure to plotly.

get_arrhenius_nmtuples(df: pd.Dataframe, hue: str | None) → list

Return list of namedtuple objects.

Parameters:

• df – pandas.DataFrame.

• hue – Variable defining how to group data. If None, no grouping is performed.

yield_figs(**kwargs)

This function generates a predefined list of matplotlib figures with minimal input from the user.

expose(exposer='mpl', **kwargs)

class abipy.dynamics.analyzer.MultiMdAnalyzer(mdas: list[MdAnalyzer], temp_colormap='jet')

Bases: HasPickleIO

High-level interface to analyze multiple MD trajectories

classmethod from_abiml_dirs(directories: list, step_skip=1, pmode='processes') → MultiMdAnalyzer

Build an instance from a list of directories produced by abiml.py md

classmethod from_lammps_dirs(directories: list, step_skip=1, basename='in.lammps', pmode='processes') → MdAnalyzer

Build an instance from a list of directories containing LAMMPS results.

classmethod from_vaspruns(vasprun_filepaths: list, step_skip=1, pmode='processes') → MultiMdAnalyzer

Build an instance from a list of vasprun files.

classmethod from_hist_files(hist_filepaths: list, step_skip=1, pmode='processes') → MultiMdAnalyzer

Build an instance from a list of ABINIT HIST.nc files.

has_same_system() → bool

set_temp_colormap(colormap) → None

Set the colormap for the list of temperatures.

get_params_dataframe() → DataFrame

Dataframe with the parameters of the different MdAnalyzers.

to_string(verbose: int = 0) → str

String representation with verbosity level verbose.

iter_mdat()

Iterate over (MdAnalyzer, temperature).

iter_mdatc()

Iterate over (MdAnalyzer, temperature, color).

get_msdtt0_symbol_tmax(symbol: str, tmax: float, atom_inds=None, nprocs=None) → Msdtt0List

plot_sqdt_symbols(symbols, t0: float = 0.0, xy_log=None, fontsize=8, xlims=None, **kwargs) → Any

Plot the square displacement averaged over all atoms of the same specie vs time for the different temperatures.

Parameters:

• symbols – List of chemical symbols to consider. “all” for all symbols in structure.

• t0 – Initial time in ps.

• ax – matplotlib.axes.Axes or None if a new figure should be created.

• xy_log – None or empty string for linear scale. “x” for log scale on x-axis. “xy” for log scale on x- and y-axis. “x:semilog” for semilog scale on x-axis.

• fontsize – fontsize for legends and titles.

• xlims – Set the data limits for the x-axis. Accept tuple e.g. (left, right) or scalar e.g. left. If left (right) is None, default values are used.

Keyword arguments controlling the display of the figure:

kwargs	Meaning
title	Title of the plot (Default: None).
show	True to show the figure (default: True).
savefig	“abc.png” or “abc.eps” to save the figure to a file.
size_kwargs	Dictionary with options passed to fig.set_size_inches e.g. size_kwargs=dict(w=3, h=4)
tight_layout	True to call fig.tight_layout (default: False)
ax_grid	True (False) to add (remove) grid from all axes in fig. Default: None i.e. fig is left unchanged.
ax_annotate	Add labels to subplots e.g. (a), (b). Default: False
fig_close	Close figure. Default: False.
plotly	Try to convert mpl figure to plotly.

abipy.dynamics.analyzer.sfind_ind_val(array, value, arr_is_sorted=False) → tuple

abipy.dynamics.analyzer.linear_lsq_linefit(x, z, weights)

abipy.dynamics.analyzer.msd_tt0_from_tac(pos_tac: ndarray, size_t: int, nprocs=None) → ndarray

Calculates the MSD for every possible pair of time points in the input array, using the formula:

$$MSD(t,t_0) = frac{1}{N} sum_{i=1}^{N} (vec{r}_i(t+t_0) - vec{r}_i(t_0))^2$$

where $N$ is the number of particles, and $vec{r}_i(t)$ is the position vector.

Parameters:

• pos_tac

• size_t

• nprocs

abipy.dynamics.analyzer.block_mean_var(data, data_mean, n_block) → tuple[float, float]

Perform the block mean and the block variance of data.

abipy.dynamics.analyzer.sigma_berend(nblock_step: int, tot_block: int, data: ndarray) → tuple[float, float, float]

Parameters:

• nblock_step

• tot_block

• data

Return: (data_in_block, sigma, delta_sigma)

class abipy.dynamics.analyzer.ArrheniusEntry(*, key: str, symbol: str, composition: str, temps: ndarray, diffusions: ndarray, err_diffusions: ndarray, volumes: ndarray, mpl_style: dict)

Bases: object

key: str

symbol: str

composition: str

temps: ndarray

diffusions: ndarray

err_diffusions: ndarray

volumes: ndarray

mpl_style: dict

classmethod from_file(filepath: str | PathLike, key, mpl_style) → ArrheniusEntry

get_diffusion_data(fit_thinvt=None) → AttrDict

Fit diffusion(T) taking into account uncertainties. Return dict with activation energy in eV and fit parameters.

get_conductivity_data(ncar: float, fit_thinvt=None) → tuple[AttrDict, AttrDict]

Compute conductivity sigma and T x sigma(T) assuming ncar carriers. Fit values taking into account uncertainties.

sigma(T) = Nq^2 D(T) / (VkT)

class abipy.dynamics.analyzer.ArrheniusPlotter(entries=None)

Bases: object

This object stores conductivities D(T) computed for different structures and/or different ML-potentials and allows one to produce Arrnehnius plots on the same figure. Internally, the results are indexed by a unique key that is be used as label in the matplotlib plot. The style for each key can be customized by setting the mpl_style dict with the options that will be passed to ax.plot.

In the simplest case, one reads the data from external files in CSV format.

Example

from abipy.dynamics.analyzer import ArrheniusPlotter

key_path = {

“matgl-MD”: “diffusion_cLLZO-matgl.csv”, “m3gnet-MD”: “diffusion_cLLZO-m3gnet.csv”,

}

mpl_style_key = {

“matgl-MD” : dict(c=’blue’), “m3gnet-MD”: dict(c=’purple’),

}

plotter = ArrheniusPlotter() for key, path in key_path.items():

plotter.add_entry_from_file(path, key, mpl_style=mpl_style_key[key])

# temperature grid refined thinvt_arange = (0.6, 2.5, 0.01) xlims, ylims = (0.5, 2.0), (-7, -4)

plotter.plot(thinvt_arange=thinvt_arange,

xlims=xlims, ylims=ylims, text=’LLZO cubic’, savefig=None)

copy()

Deep copy of the object.

keys() → list[str]

List of keys. Must be unique

symbols() → set[str]

Return set with chemical symbols.

index_key(key: str) → int

Find the index of key. Raise KeyError if not found.

pop_key(key: str) → ArrheniusEntry

Pop entry by key

append(entry: ArrheniusEntry) → None

Append new entry.

set_style(key: str, mpl_style: dict) → None

Set matplotlib style for key.

get_min_max_temp() → tuple[float, float]

Compute the min and max temperature for all entries.

add_entry_from_file(filepath: str | PathLike, key: str, mpl_style=None) → None

plot(thinvt_arange=None, what='diffusion', ncar=None, colormap='jet', with_t=True, text=None, ax=None, fontsize=8, xlims=None, ylims=None, **kwargs) → Any

Arrhenius plot.

Parameters:

• thinvt_arange – start, stop, step for 1000/T mesh. If None, the mesh is automatically computed.

• what – Selects the quantity to plot. Possibile values: “diffusion”, “sigma”, “tsigma”.

• ncar – Number of carriers. Required if what is “sigma” or “tsigma”.

• colormap – Colormap used to select the color if entry.mpl_style does not provide it.

• with_t – True to dd a twin axes with the value of T

• text

• ax – matplotlib.axes.Axes or None if a new figure should be created.

• fontsize – fontsize for legends and titles.

• xlims – Set the data limits for the x-axis. Accept tuple e.g. (left, right) or scalar e.g. left. If left (right) is None, default values are used.

• ylims – Similar to xlims but for the y-axis.

Keyword arguments controlling the display of the figure:

kwargs	Meaning
title	Title of the plot (Default: None).
show	True to show the figure (default: True).
savefig	“abc.png” or “abc.eps” to save the figure to a file.
size_kwargs	Dictionary with options passed to fig.set_size_inches e.g. size_kwargs=dict(w=3, h=4)
tight_layout	True to call fig.tight_layout (default: False)
ax_grid	True (False) to add (remove) grid from all axes in fig. Default: None i.e. fig is left unchanged.
ax_annotate	Add labels to subplots e.g. (a), (b). Default: False
fig_close	Close figure. Default: False.
plotly	Try to convert mpl figure to plotly.

hist Module

History file with structural relaxation results.

class abipy.dynamics.hist.HistFile(filepath: str)

Bases: AbinitNcFile, NotebookWriter

File with the history of a structural relaxation or molecular dynamics calculation.

Usage example:

with HistFile("foo_HIST") as hist:
    hist.plot()

Inheritance Diagram

classmethod from_file(filepath: str) → HistFile

Initialize the object from a netcdf file

close() → None

Close the file.

params()

dict with parameters that might be subject to convergence studies.

final_energy()

Total energy in eV of the last iteration.

final_pressure()

Final pressure in Gpa.

get_fstats_dict(step) → AttrDict

Return monty.collections.AttrDict with stats on the forces at the given step.

to_string(verbose=0, title=None) → str

String representation.

property num_steps: int

Number of iterations performed.

steps()

Step indices.

property initial_structure: Structure

The initial abipy.core.structure.Structure.

property final_structure: Structure

The abipy.core.structure.Structure of the last iteration.

structures()

List of abipy.core.structure.Structure objects at the different steps.

etotals()

numpy.ndarray with total energies in eV at the different steps.

get_relaxation_analyzer() → RelaxationAnalyzer

Return a pymatgen RelaxationAnalyzer object to analyze the relaxation in a calculation.

to_xdatcar(filepath=None, groupby_type=True, to_unit_cell=False, **kwargs)

Return Xdatcar pymatgen object. See write_xdatcar for the meaning of arguments.

Parameters:

• to_unit_cell (bool) – Whether to translate sites into the unit cell.

• kwargs – keywords arguments passed to Xdatcar constructor.

write_xdatcar(filepath='XDATCAR', groupby_type=True, overwrite=False, to_unit_cell=False) → str

Write Xdatcar file with unit cell and atomic positions to file filepath.

Parameters:

• filepath – Xdatcar filename. If None, a temporary file is created.

• groupby_type – If True, atoms are grouped by type. Note that this option may change the order of the atoms. This option is needed because there are post-processing tools (e.g. ovito) that do not work as expected if the atoms in the structure are not grouped by type.

• overwrite – raise RuntimeError, if False and filepath exists.

• to_unit_cell (bool) – Whether to translate sites into the unit cell.

Returns:

path to Xdatcar file.

visualize(appname='ovito', to_unit_cell=False)

Visualize the crystalline structure with visualizer. See Visualizer for the list of applications and formats supported.

Parameters:

to_unit_cell (bool) – Whether to translate sites into the unit cell.

plot_ax(ax, what, fontsize=8, **kwargs) → None

Helper function to plot quantity what on axis ax with matplotlib.

Parameters:

• fontsize – fontsize for legend.

• method. (kwargs are passed to matplotlib plot)

plotly_traces(fig, what, rcd=None, fontsize=8, showlegend=False, **kwargs)

Helper function to plot quantity what on figure fig with plotly.

Parameters:

• rcd – If fig has subplots, rcd is used to add traces on these subplots.

• fontsize – fontsize for legend.

• method. (kwargs are passed to fig.add_scatter)

plot(what_list=None, ax_list=None, fontsize=8, **kwargs) → Any

Plot the evolution of structural parameters (lattice lengths, angles and volume) as well as pressure, info on forces and total energy with matplotlib.

Parameters:

• what_list

• ax_list – List of matplotlib.axes.Axes. If None, a new figure is created.

• fontsize – fontsize for legend

Returns: matplotlib.figure.Figure

Keyword arguments controlling the display of the figure:

kwargs	Meaning
title	Title of the plot (Default: None).
show	True to show the figure (default: True).
savefig	“abc.png” or “abc.eps” to save the figure to a file.
size_kwargs	Dictionary with options passed to fig.set_size_inches e.g. size_kwargs=dict(w=3, h=4)
tight_layout	True to call fig.tight_layout (default: False)
ax_grid	True (False) to add (remove) grid from all axes in fig. Default: None i.e. fig is left unchanged.
ax_annotate	Add labels to subplots e.g. (a), (b). Default: False
fig_close	Close figure. Default: False.
plotly	Try to convert mpl figure to plotly.

plotly(what_list=None, fig=None, fontsize=12, **kwargs)

Plot the evolution of structural parameters (lattice lengths, angles and volume) as well as pressure, info on forces and total energy with plotly.

Parameters:

• what_list

• fig – The fig for plot and the DOS plot. If None, a new figure is created.

• fontsize – fontsize for legend

Returns: plotly.graph_objects.Figure

Keyword arguments controlling the display of the figure: ================ ==================================================================== kwargs Meaning ================ ==================================================================== title Title of the plot (Default: None). show True to show the figure (default: True). hovermode True to show the hover info (default: False) savefig “abc.png” , “abc.jpeg” or “abc.webp” to save the figure to a file. write_json Write plotly figure to write_json JSON file.

Inside jupyter-lab, one can right-click the write_json file from the file menu and open with “Plotly Editor”. Make some changes to the figure, then use the file menu to save the customized plotly plot. Requires jupyter labextension install jupyterlab-chart-editor. See https://github.com/plotly/jupyterlab-chart-editor

renderer (str or None (default None)) –

A string containing the names of one or more registered renderers (separated by ‘+’ characters) or None. If None, then the default renderers specified in plotly.io.renderers.default are used. See https://plotly.com/python-api-reference/generated/plotly.graph_objects.Figure.html

config (dict) A dict of parameters to configure the figure. The defaults are set in plotly.js. chart_studio True to push figure to chart_studio server. Requires authenticatios.

Default: False.

template Plotly template. See https://plotly.com/python/templates/

[“plotly”, “plotly_white”, “plotly_dark”, “ggplot2”,

“seaborn”, “simple_white”, “none”]

Default is None that is the default template is used.

plotly_energies(fig=None, fontsize=12, **kwargs)

Plot the total energies as function of the iteration step with plotly.

Keyword arguments controlling the display of the figure: ================ ==================================================================== kwargs Meaning ================ ==================================================================== title Title of the plot (Default: None). show True to show the figure (default: True). hovermode True to show the hover info (default: False) savefig “abc.png” , “abc.jpeg” or “abc.webp” to save the figure to a file. write_json Write plotly figure to write_json JSON file.

Inside jupyter-lab, one can right-click the write_json file from the file menu and open with “Plotly Editor”. Make some changes to the figure, then use the file menu to save the customized plotly plot. Requires jupyter labextension install jupyterlab-chart-editor. See https://github.com/plotly/jupyterlab-chart-editor

renderer (str or None (default None)) –

A string containing the names of one or more registered renderers (separated by ‘+’ characters) or None. If None, then the default renderers specified in plotly.io.renderers.default are used. See https://plotly.com/python-api-reference/generated/plotly.graph_objects.Figure.html

config (dict) A dict of parameters to configure the figure. The defaults are set in plotly.js. chart_studio True to push figure to chart_studio server. Requires authenticatios.

Default: False.

template Plotly template. See https://plotly.com/python/templates/

[“plotly”, “plotly_white”, “plotly_dark”, “ggplot2”,

“seaborn”, “simple_white”, “none”]

Default is None that is the default template is used.

plot_energies(ax=None, fontsize=8, **kwargs) → Any

Plot the total energies as function of the iteration step with matplotlib.

Parameters:

• ax – matplotlib.axes.Axes or None if a new figure should be created.

• fontsize – Legend and title fontsize.

Returns: matplotlib.figure.Figure

Keyword arguments controlling the display of the figure:

kwargs	Meaning
title	Title of the plot (Default: None).
show	True to show the figure (default: True).
savefig	“abc.png” or “abc.eps” to save the figure to a file.
size_kwargs	Dictionary with options passed to fig.set_size_inches e.g. size_kwargs=dict(w=3, h=4)
tight_layout	True to call fig.tight_layout (default: False)
ax_grid	True (False) to add (remove) grid from all axes in fig. Default: None i.e. fig is left unchanged.
ax_annotate	Add labels to subplots e.g. (a), (b). Default: False
fig_close	Close figure. Default: False.
plotly	Try to convert mpl figure to plotly.

yield_figs(**kwargs)

This function generates a predefined list of matplotlib figures with minimal input from the user.

yield_plotly_figs(**kwargs)

This function generates a predefined list of matplotlib figures with minimal input from the user.

mvplot_trajectories(colormap='hot', sampling=1, figure=None, show=True, with_forces=True, **kwargs)

Call mayavi to plot atomic trajectories and the variation of the unit cell.

mvanimate(delay=500)

get_panel(**kwargs)

Build panel with widgets to interact with the abipy.dynamics.hist.HistFile either in a notebook or in panel app.

write_notebook(nbpath=None) → str

Write a jupyter notebook to nbpath. If nbpath is None, a temporay file in the current working directory is created. Return path to the notebook.

class abipy.dynamics.hist.HistRobot(*args)

Bases: Robot

This robot analyzes the results contained in multiple HIST.nc files.

Inheritance Diagram

EXT = 'HIST'

to_string(verbose: int = 0) → str

String representation with verbosity level verbose.

get_dataframe(with_geo=True, index=None, abspath=False, with_spglib=True, funcs=None, **kwargs) → DataFrame

Return a pandas.DataFrame with the most important final results and the filenames as index.

Parameters:

• with_geo – True if structure info should be added to the dataframe

• abspath – True if paths in index should be absolute. Default: Relative to getcwd().

• index – Index of the dataframe, if None, robot labels are used

• with_spglib – If True, spglib is invoked to get the space group symbol and number

kwargs:

attrs:

List of additional attributes of the abipy.electrons.gsr.GsrFile to add to the pandas.DataFrame.

funcs: Function or list of functions to execute to add more data to the DataFrame.

Each function receives a abipy.electrons.gsr.GsrFile object and returns a tuple (key, value) where key is a string with the name of column and value is the value to be inserted.

property what_list: list[str]

List with all quantities that can be plotted (what_list).

gridplot(what_list=None, sharex='row', sharey='row', fontsize=8, **kwargs) → Any

Plot the what value extracted from multiple HIST.nc files on a grid.

Parameters:

• what_list – List of quantities to plot. Must be in [“energy”, “abc”, “angles”, “volume”, “pressure”, “forces”]

• sharex – True if xaxis should be shared.

• sharey – True if yaxis should be shared.

• fontsize – fontsize for legend.

Returns: matplotlib.figure.Figure

Keyword arguments controlling the display of the figure:

kwargs	Meaning
title	Title of the plot (Default: None).
show	True to show the figure (default: True).
savefig	“abc.png” or “abc.eps” to save the figure to a file.
size_kwargs	Dictionary with options passed to fig.set_size_inches e.g. size_kwargs=dict(w=3, h=4)
tight_layout	True to call fig.tight_layout (default: False)
ax_grid	True (False) to add (remove) grid from all axes in fig. Default: None i.e. fig is left unchanged.
ax_annotate	Add labels to subplots e.g. (a), (b). Default: False
fig_close	Close figure. Default: False.
plotly	Try to convert mpl figure to plotly.

combiplot(what_list=None, colormap='jet', fontsize=6, **kwargs) → Any

Plot multiple HIST.nc files on a grid. One plot for each what value.

Parameters:

• what_list – List of strings with the quantities to plot. If None, all quanties are plotted.

• colormap – matplotlib color map.

• fontsize – fontisize for legend.

Returns: matplotlib.figure.Figure.

Keyword arguments controlling the display of the figure:

kwargs	Meaning
title	Title of the plot (Default: None).
show	True to show the figure (default: True).
savefig	“abc.png” or “abc.eps” to save the figure to a file.
size_kwargs	Dictionary with options passed to fig.set_size_inches e.g. size_kwargs=dict(w=3, h=4)
tight_layout	True to call fig.tight_layout (default: False)
ax_grid	True (False) to add (remove) grid from all axes in fig. Default: None i.e. fig is left unchanged.
ax_annotate	Add labels to subplots e.g. (a), (b). Default: False
fig_close	Close figure. Default: False.
plotly	Try to convert mpl figure to plotly.

yield_figs(**kwargs)

This function generates a predefined list of matplotlib figures with minimal input from the user.

write_notebook(nbpath=None) → str

Write a jupyter notebook to nbpath. If nbpath is None, a temporay file in the current working directory is created. Return path to the notebook.

class abipy.dynamics.hist.HistReader(path)

Bases: ETSF_Reader

This object reads data from the HIST file.

Inheritance Diagram

num_steps()

Number of iterations present in the HIST.nc file.

natom()

Number of atoms un the unit cell.

read_all_structures() → list[Structure]

Return the list of structures at the different iteration steps.

read_eterms(unit: str = 'eV') → AttrDict

monty.collections.AttrDict with the decomposition of the total energy in units unit

read_cart_forces(unit: str = 'eV ang^-1') → ndarray

Read and return a numpy.ndarray with the cartesian forces in unit unit. Shape (num_steps, natom, 3)

read_reduced_forces() → ndarray

Read and return a numpy.ndarray with the forces in reduced coordinates Shape (num_steps, natom, 3)

read_cart_stress_tensors() → tuple[ndarray, ndarray]

Return the stress tensors (nstep x 3 x 3) in cartesian coordinates (GPa) and the list of pressures in GPa unit.