ipsuite.analysis package¶

Subpackages¶

ipsuite.analysis.model package

Submodules¶

ipsuite.analysis.bin_property module¶

class ipsuite.analysis.bin_property.DipoleHistogram(*args, **kwargs)[source]¶

Bases: LabelHistogram

Creates a histogram of all dipole labels contained in a dataset.

data: list[ase.Atoms]¶

datalabel: str = 'dipole'¶

get_labels()[source]¶

xlabel: str = '$\\mu$ / eV Ang'¶

class ipsuite.analysis.bin_property.EnergyHistogram(*args, **kwargs)[source]¶

Bases: LabelHistogram

Creates a histogram of all energy labels contained in a dataset.

data: list[ase.Atoms]¶

datalabel: str = 'energy'¶

get_labels()[source]¶

xlabel: str = '$E$ / eV'¶

class ipsuite.analysis.bin_property.EnergyUncertaintyHistogram(*args, **kwargs)[source]¶

Bases: LabelHistogram

Creates a histogram of all energy uncertainties in a prediction.

data: list[ase.Atoms]¶

datalabel: str = 'energy-uncertainty'¶

get_labels()[source]¶

xlabel: str = '$\\sigma(E)$ / eV'¶

class ipsuite.analysis.bin_property.ForcesHistogram(*args, **kwargs)[source]¶

Bases: LabelHistogram

Creates a histogram of all force labels contained in a dataset.

data: list[ase.Atoms]¶

datalabel: str = 'forces'¶

get_labels()[source]¶

xlabel: str = '$F$ / eV/Ang'¶

class ipsuite.analysis.bin_property.ForcesUncertaintyHistogram(*args, **kwargs)[source]¶

Bases: LabelHistogram

Creates a histogram of all force uncertainties in a prediction.

data: list[ase.Atoms]¶

datalabel: str = 'forces-uncertainty'¶

get_labels()[source]¶

xlabel: str = '$\\sigma(F)$ / eV/Ang'¶

class ipsuite.analysis.bin_property.LabelHistogram(*args, **kwargs)[source]¶

Bases: AnalyseAtoms

Base class for creating histogram of a dataset.

Parameters¶

data: list: List of Atoms objects.
bins: int | str: Number of bins in the histogram, or string indicating how to find the number of bins. See https://numpy.org/devdocs/reference/generated/numpy.histogram_bin_edges.html#numpy.histogram_bin_edges

bins: int | str = 'auto'¶

get_hist()[source]¶: Create a pandas dataframe from the given data.

get_labels()[source]¶

get_plots(counts, bin_edges)[source]¶: Create figures for all available data.

labels_df: DataFrame = NOT_AVAILABLE¶

logy_scale: bool = True¶

metrics: float = NOT_AVAILABLE¶

plots_dir: Path = PosixPath('$nwd$/plots')¶

run()[source]¶

x_lim: tuple = None¶

y_lim: tuple = None¶

class ipsuite.analysis.bin_property.StressHistogram(*args, **kwargs)[source]¶

Bases: AnalyseAtoms

Creates histograms for the hydrostatic and deviatoric components of the stress tensor.

Parameters¶

data: list: List of Atoms objects.
bins: int: Number of bins in the histogram.

bins: int = None¶

data: list[ase.Atoms]¶

get_hist()[source]¶: Create a pandas dataframe from the given data.

get_labels()[source]¶

get_plots(counts, bin_edges, hydrostatic=True)[source]¶: Create figures for all available data.

labels_df: DataFrame = NOT_AVAILABLE¶

logy_scale: bool = True¶

plots_dir: Path = PosixPath('$nwd$/plots')¶

run()[source]¶

ipsuite.analysis.bond_stretch module¶

class ipsuite.analysis.bond_stretch.BondStretchAnalyses(*args, **kwargs)[source]¶

Bases: ProcessAtoms

Analyses a Model by evaluating the elongation of a bond: in terms of energy, forces and optionally the uncertainty.

Attributes¶

ase_calculator: ase.calculator: ase calculator to use for simulation
idxs: [int, int]: indices of the two atoms that should be analyzed
r_min: float: minimal bond length
r_max: float: maximal bond length
n_steps: int: number of steps that should be used for the bond elongation
data_id: int: index of the structure in the list of structures used for the bond stretch analyses
fig_size: (float, float): size of the plot

ase_calculator: Any¶

data_id: int | None = 0¶

fig_size: Tuple[int, int] = (10, 7)¶

get_plots(train_bond_length, bond_lengths, results, chem_symbols, figsize)[source]¶

idxs: Tuple[int, int]¶

n_steps: int¶

plots_dir: Path = PosixPath('$nwd$/plots')¶

r_max: float¶

r_min: float¶

run()[source]¶

ipsuite.analysis.ensemble module¶

ipsuite.analysis.ensemble.plot_with_uncertainty(value, ylabel: str, xlabel: str, x=None, **kwargs) → dict[source]¶: Parameters¶

value: data of shape (n, m) where n is the number of ensembles. x: optional x values of shape (m,)

Returns¶

ipsuite.analysis.md module¶

class ipsuite.analysis.md.AnalyseDensity(*, name: str | None = None, always_changed: bool = False, data: list[ase.atoms.Atoms], window: int = 1000, start: int = 0, end: int = None, figure: pathlib.Path = PosixPath('$nwd$/density.png'))[source]¶

Bases: AnalyseAtoms

data: list[ase.Atoms]¶

density: dict = NOT_AVAILABLE¶

end: int = None¶

figure: Path = PosixPath('$nwd$/density.png')¶

results: DataFrame = NOT_AVAILABLE¶

run()[source]¶

start: int = 0¶

window: int = 1000¶

class ipsuite.analysis.md.CollectMDSteps(*, name: str | None = None, always_changed: bool = False, mds: list[ipsuite.dynamics.md_nodes.ASEMD])[source]¶

Bases: IPSNode

mds: list[ASEMD]¶

metrics: dict = NOT_AVAILABLE¶

run()[source]¶

ipsuite.analysis.sensitivity module¶

class ipsuite.analysis.sensitivity.AnalyseGlobalForceSensitivity(*, name: str | None = None, always_changed: bool = False, atoms_list: list[ase.atoms.Atoms], plots: pathlib.Path = PosixPath('$nwd$/plots'))[source]¶

Bases: IPSNode

atoms_list: list[Atoms]¶

plots: Path = PosixPath('$nwd$/plots')¶

run()[source]¶

class ipsuite.analysis.sensitivity.AnalyseSingleForceSensitivity(*, name: str | None = None, always_changed: bool = False, data: list[list[ase.atoms.Atoms]], sim_list: list, alpha: float = 0.05, sensitivity_plot: str = PosixPath('$nwd$/sensitivity.png'))[source]¶

Bases: IPSNode

alpha: float = 0.05¶

data: list[list[Atoms]]¶

get_values(data, item)[source]¶

run()[source]¶

sensitivity: DataFrame = NOT_AVAILABLE¶

sensitivity_plot: str = PosixPath('$nwd$/sensitivity.png')¶

sim_list: list¶

t_confidence_interval(data)[source]¶: Returns the confidence interval for the given data and significance level.

class ipsuite.analysis.sensitivity.IsConstraintMD(*args, **kwargs)[source]¶

Bases: Protocol

Protocol for objects that have a results attribute.

radius: float¶

selected_atom_id: int¶

class ipsuite.analysis.sensitivity.MoveSingleParticle(*args, **kwargs)[source]¶

Bases: IPSNode

Move a single particle in a given direction.

atom_id: int = 0¶

atoms: list = NOT_AVAILABLE¶

atoms_list: Any¶

atoms_list_id: int = 0¶

atoms_path: Path = PosixPath('$nwd$/atoms')¶

get_atom_filenames()[source]¶

run()[source]¶: ZnTrack run method.

samples: int = 10¶

scale: float = 0.5¶

seed: int = 1234¶

ipsuite.analysis.sensitivity.nonuniform_imshow(ax, x, y, z, aspect=1, cmap=<matplotlib.colors.LinearSegmentedColormap object>)[source]¶: Plot a non-uniformly sampled 2D array.

References¶

adapted from https://stackoverflow.com/a/53780594/10504481

Module contents¶

class ipsuite.analysis.AllowedStructuresFilter(*args, **kwargs)[source]¶

Bases: IPSNode

Search a given dataset for outliers.

Iterates all structures in the dataset, uses covalent radii to determine the atoms in each molecule, and checks if the molecule is allowed.

Attributes¶

datalist[ase.Atoms]: The dataset to search.
moleculeslist[ase.Atoms], optional: The molecules that are allowed.
smileslist[str], optional: The SMILES strings of the allowed molecules.
cutoffsdict[str, float] | None, optional: The cutoffs for each element. If None, use the ase.data.covalent_radii. Default: None

cutoffs: dict[str, float] | None = None¶

data: list[Atoms]¶

property excluded_frames: list[Atoms]¶

fail: bool = False¶

property frames: list[Atoms]¶

molecules: list[Atoms]¶

outliers: list[int] = NOT_AVAILABLE¶

run()[source]¶

smiles: list[str]¶

class ipsuite.analysis.AnalyseDensity(*, name: str | None = None, always_changed: bool = False, data: list[ase.atoms.Atoms], window: int = 1000, start: int = 0, end: int = None, figure: pathlib.Path = PosixPath('$nwd$/density.png'))[source]¶

Bases: AnalyseAtoms

data: list[ase.Atoms]¶

density: dict = NOT_AVAILABLE¶

end: int = None¶

figure: Path = PosixPath('$nwd$/density.png')¶

results: DataFrame = NOT_AVAILABLE¶

run()[source]¶

start: int = 0¶

window: int = 1000¶

class ipsuite.analysis.AnalyseGlobalForceSensitivity(*, name: str | None = None, always_changed: bool = False, atoms_list: list[ase.atoms.Atoms], plots: pathlib.Path = PosixPath('$nwd$/plots'))[source]¶

Bases: IPSNode

atoms_list: list[Atoms]¶

plots: Path = PosixPath('$nwd$/plots')¶

run()[source]¶

class ipsuite.analysis.AnalyseSingleForceSensitivity(*, name: str | None = None, always_changed: bool = False, data: list[list[ase.atoms.Atoms]], sim_list: list, alpha: float = 0.05, sensitivity_plot: str = PosixPath('$nwd$/sensitivity.png'))[source]¶

Bases: IPSNode

alpha: float = 0.05¶

data: list[list[Atoms]]¶

get_values(data, item)[source]¶

run()[source]¶

sensitivity: DataFrame = NOT_AVAILABLE¶

sensitivity_plot: str = PosixPath('$nwd$/sensitivity.png')¶

sim_list: list¶

t_confidence_interval(data)[source]¶: Returns the confidence interval for the given data and significance level.

class ipsuite.analysis.AnalyseStructureMeanForce(*args, **kwargs)[source]¶

Bases: IPSNode

Analyze mean force magnitude across atomic configurations.

Computes the magnitude of the total force vector (sum of all atomic forces) for each configuration. For well-converged periodic structures, this should approach zero. Useful for checking DFT convergence, force consistency, and identifying problematic structures in datasets.

Parameters¶

datalist[ase.Atoms]: Atomic configurations with calculated forces to analyze.

Attributes¶

forcesdict: Statistical summary containing mean, std, min, max force magnitudes.
figure_pathPath: Path to the generated force analysis plot.

Examples¶

>>> model = ips.MACEMPModel()
>>> with project:
...     data = ips.AddData(file="ethanol.xyz")
...     calc_data = ips.ApplyCalculator(data=data.frames, model=model)
...     force_analysis = ips.AnalyseStructureMeanForce(data=calc_data.frames)
>>> project.repro()
>>> print(f"Mean force magnitude: {force_analysis.forces['mean']:.4f} eV/Å")
Mean force magnitude: 0.0000 eV/Å

data: list[Atoms]¶

figure_path: Path = PosixPath('$nwd$/forces.png')¶

forces: dict = NOT_AVAILABLE¶

run()[source]¶

class ipsuite.analysis.BondStretchAnalyses(*args, **kwargs)[source]¶

Bases: ProcessAtoms

Analyses a Model by evaluating the elongation of a bond: in terms of energy, forces and optionally the uncertainty.

Attributes¶

ase_calculator: ase.calculator: ase calculator to use for simulation
idxs: [int, int]: indices of the two atoms that should be analyzed
r_min: float: minimal bond length
r_max: float: maximal bond length
n_steps: int: number of steps that should be used for the bond elongation
data_id: int: index of the structure in the list of structures used for the bond stretch analyses
fig_size: (float, float): size of the plot

ase_calculator: Any¶

data: list[ase.Atoms]¶

data_id: int | None = 0¶

fig_size: Tuple[int, int] = (10, 7)¶

get_plots(train_bond_length, bond_lengths, results, chem_symbols, figsize)[source]¶

idxs: Tuple[int, int]¶

n_steps: int¶

plots_dir: Path = PosixPath('$nwd$/plots')¶

r_max: float¶

r_min: float¶

run()[source]¶

class ipsuite.analysis.BoxHeatUp(*args, **kwargs)[source]¶

Bases: ProcessSingleAtom

Perform a heat-up analysis by gradually increasing the temperature of the system and monitoring the measured temperature and energy.

Parameters¶

start_temperaturefloat: The initial temperature (in Kelvin) to start the analysis.
stop_temperaturefloat: The upper bound of the temperature range (in Kelvin).
stepsint: Number of temperature increments between start and stop temperature.
time_stepfloat, optional: Time step of the simulation in femtoseconds. Default is 0.5 fs.
frictionfloat: Langevin friction coefficient.
repeattuple of int, optional: Number of repetitions of the unit cell in each direction. Default is (1, 1, 1).
max_temperaturefloat or None, optional: Maximum allowed temperature before stopping the simulation. If None, set to 1.5 * stop_temperature.
modeltyping.Any: The MLModel node that implements the ‘get_calculator’ method.
model_outspathlib.Path: Output directory for model results.
plotspathlib.Path: Path to save the temperature plot.

Attributes¶

flux_datapd.DataFrame: DataFrame containing measured temperature, total energy, and set temperature at each step.
steps_before_explosionint: Number of steps completed before exceeding max_temperature, or -1 if not exceeded.
frameslist of ase.Atoms: List of atomic configurations at each step.

data: List[ase.Atoms]¶

flux_data: DataFrame = NOT_AVAILABLE¶

friction: float¶

get_atoms() → Atoms[source]¶

max_temperature: float | None = None¶

model: Any¶

model_outs: Path = PosixPath('$nwd$/model')¶

plot_temperature()[source]¶

plots: Path = PosixPath('$nwd$/temperature.png')¶

repeat: tuple[int, int, int] = (1, 1, 1)¶

run()[source]¶

start_temperature: float¶

steps: int¶

stop_temperature: float¶

time_step: float = 0.5¶

class ipsuite.analysis.BoxScale(*args, **kwargs)[source]¶

Bases: ProcessSingleAtom

Scale all particles and predict energies.

Attributes¶

model: The MLModel node that implements the ‘predict’ method atoms: list[Atoms] to predict properties for start: int, default = None

The initial box scale, default value is the original box size.

stop: float, default = 1.0: The stop value for the generated space of stdev points
num: int, default = 100: The size of the generated space of stdev points

data: List[ase.Atoms]¶

energies: DataFrame = NOT_AVAILABLE¶

mapping: Any | None = None¶

model: MLModel¶

model_outs: Path = PosixPath('$nwd$/model')¶

num: int = 100¶

plot: Path = PosixPath('$nwd$/energy.png')¶

run()[source]¶

start: float = 1¶

stop: float = 2.0¶

class ipsuite.analysis.CalibrationMetrics(*args, **kwargs)[source]¶

Bases: ComparePredictions

Analyse the calibration of a models uncertainty estimate. Plots the empirical vs predicted error distribution, a log-log calibration plot and the miscalibration area. Further, various UQ metrics are computed: - Mean absolute calibration error - Root mean square miscalibration error - Miscalibration area - NLL - RLL

For more information checkout the uncertainty toolbox or the following paper: 10.1088/2632-2153/ad594a

Parameters¶

force_dist_slices: List[tuple]: Interval in which to analyse the gassianity of error distributions.

data_file: Path = PosixPath('$nwd$/data.npz')¶

energy: dict = NOT_AVAILABLE¶

force_dist_slices: List[tuple] | None = None¶

forces: dict = NOT_AVAILABLE¶

get_data()[source]¶: Create dict of all data.

get_metrics()[source]¶: Update the metrics.

get_plots(save=False)[source]¶: Create figures for all available data.

plots_dir: Path = PosixPath('$nwd$/plots')¶

run()[source]¶

x: list[ase.Atoms]¶

y: list[ase.Atoms]¶

class ipsuite.analysis.CollectMDSteps(*, name: str | None = None, always_changed: bool = False, mds: list[ipsuite.dynamics.md_nodes.ASEMD])[source]¶

Bases: IPSNode

mds: list[ASEMD]¶

metrics: dict = NOT_AVAILABLE¶

run()[source]¶

class ipsuite.analysis.DipoleHistogram(*args, **kwargs)[source]¶

Bases: LabelHistogram

Creates a histogram of all dipole labels contained in a dataset.

data: list[ase.Atoms]¶

datalabel: str = 'dipole'¶

get_labels()[source]¶

xlabel: str = '$\\mu$ / eV Ang'¶

class ipsuite.analysis.EnergyHistogram(*args, **kwargs)[source]¶

Bases: LabelHistogram

Creates a histogram of all energy labels contained in a dataset.

data: list[ase.Atoms]¶

datalabel: str = 'energy'¶

get_labels()[source]¶

xlabel: str = '$E$ / eV'¶

class ipsuite.analysis.EnergyUncertaintyHistogram(*args, **kwargs)[source]¶

Bases: LabelHistogram

Creates a histogram of all energy uncertainties in a prediction.

data: list[ase.Atoms]¶

datalabel: str = 'energy-uncertainty'¶

get_labels()[source]¶

xlabel: str = '$\\sigma(E)$ / eV'¶

class ipsuite.analysis.ForceAngles(*, name: str | None = None, always_changed: bool = False, x: list[ase.atoms.Atoms], y: list[ase.atoms.Atoms], plot: pathlib.Path = PosixPath('$nwd$/angle.png'), log_plot: pathlib.Path = PosixPath('$nwd$/angle_ylog.png'))[source]¶

Bases: ComparePredictions

angles: dict = NOT_AVAILABLE¶

log_plot: Path = PosixPath('$nwd$/angle_ylog.png')¶

plot: Path = PosixPath('$nwd$/angle.png')¶

run()[source]¶

x: list[ase.Atoms]¶

y: list[ase.Atoms]¶

class ipsuite.analysis.ForceDecomposition(*args, **kwargs)[source]¶

Bases: ComparePredictions

Node for decomposing forces in a system of molecular units into translational, rotational and vibrational components.

The implementation follows the method described in https://doi.org/10.26434/chemrxiv-2022-l4tb9

Currently, single atoms and diatomic molecules are simply filtered out. Please raise an issue if you need those cases to be treated correctly in your work.

Attributes¶

wasserstein_distance: float: Compute the wasserstein distance between the distributions of the predicted and true forces for each trans, rot, vib component.

get_histogram()[source]¶

get_metrics()[source]¶: Update the metrics.

get_plots()[source]¶

histogram_plt: Path = PosixPath('$nwd$/histogram.png')¶

rot_force_plt: Path = PosixPath('$nwd$/rot_force.png')¶

rot_forces: dict = NOT_AVAILABLE¶

run()[source]¶

trans_force_plt: Path = PosixPath('$nwd$/trans_force.png')¶

trans_forces: dict = NOT_AVAILABLE¶

vib_force_plt: Path = PosixPath('$nwd$/vib_force.png')¶

vib_forces: dict = NOT_AVAILABLE¶

wasserstein_distance: dict = NOT_AVAILABLE¶

x: list[ase.Atoms]¶

y: list[ase.Atoms]¶

class ipsuite.analysis.ForceUncertaintyDecomposition(*args, **kwargs)[source]¶

Bases: ComparePredictions

Node for decomposing force uncertainties in a system of molecular units into translational, rotational and vibrational components.

The implementation follows the method described in https://doi.org/10.26434/chemrxiv-2022-l4tb9

Currently, single atoms and diatomic molecules are simply filtered out. Please raise an issue if you need those cases to be treated correctly in your work.

get_metrics()[source]¶: Update the metrics.

get_plots()[source]¶

plots_dir: Path = PosixPath('$nwd$/plots')¶

rot_forces: dict = NOT_AVAILABLE¶

run()[source]¶

trans_forces: dict = NOT_AVAILABLE¶

vib_forces: dict = NOT_AVAILABLE¶

x: list[ase.Atoms]¶

y: list[ase.Atoms]¶

class ipsuite.analysis.ForcesHistogram(*args, **kwargs)[source]¶

Bases: LabelHistogram

Creates a histogram of all force labels contained in a dataset.

data: list[ase.Atoms]¶

datalabel: str = 'forces'¶

get_labels()[source]¶

xlabel: str = '$F$ / eV/Ang'¶

class ipsuite.analysis.ForcesUncertaintyHistogram(*args, **kwargs)[source]¶

Bases: LabelHistogram

Creates a histogram of all force uncertainties in a prediction.

data: list[ase.Atoms]¶

datalabel: str = 'forces-uncertainty'¶

get_labels()[source]¶

xlabel: str = '$\\sigma(F)$ / eV/Ang'¶

class ipsuite.analysis.MDStability(*args, **kwargs)[source]¶

Bases: IPSNode

Perform NVE molecular dynamics for all supplied atoms using a trained model. Several stability checks can be supplied to judge whether a particular trajectory is stable. If the check fails, the trajectory is terminated. After all trajectories are done, a histogram of the duration of stability is created.

Attributes¶

model: A node which implements the calc property. Typically an MLModel instance. data: list[Atoms] to run MD for for max_steps: Maximum number of steps for each trajectory time_step: MD integration time step initial_temperature: Initial velocities are drawn from a maxwell boltzman distribution. save_last_n: how many configurations before the instability should be saved bins: number of bins in the histogram seed: seed for the MaxwellBoltzmann distribution

bins: int = None¶

checks: list[Node] = None¶

data: list[Atoms]¶

property frames: List[Atoms]¶

get_plots(stable_steps: int) → None[source]¶: Create figures for all available data.

initial_temperature: float = 300¶

max_steps: int¶

model: Any¶

model_outs: Path = PosixPath('$nwd$/model_outs')¶

plots_dir: Path = PosixPath('$nwd$/plots')¶

run() → None[source]¶

save_last_n: int = 1¶

seed: int = 0¶

stable_steps_df: DataFrame = NOT_AVAILABLE¶

time_step: float = 0.5¶

traj_file: Path = PosixPath('$nwd$/structures.h5')¶

class ipsuite.analysis.MoveSingleParticle(*args, **kwargs)[source]¶

Bases: IPSNode

Move a single particle in a given direction.

atom_id: int = 0¶

atoms: list = NOT_AVAILABLE¶

atoms_list: Any¶

atoms_list_id: int = 0¶

atoms_path: Path = PosixPath('$nwd$/atoms')¶

get_atom_filenames()[source]¶

run()[source]¶: ZnTrack run method.

samples: int = 10¶

scale: float = 0.5¶

seed: int = 1234¶

class ipsuite.analysis.Prediction(*args, **kwargs)[source]¶

Bases: ProcessAtoms

Create and Save the predictions from model on atoms.

Attributes¶

model: The MLModel node that implements the ‘predict’ method atoms: list[Atoms] to predict properties for

predictions: list[Atoms] the atoms that have the predicted properties from model

data: list[ase.Atoms]¶

model: MLModel¶

run()[source]¶

class ipsuite.analysis.PredictionMetrics(*args, **kwargs)[source]¶

Bases: ComparePredictions

Compare model predictions against reference data with comprehensive metrics.

Computes and visualizes prediction accuracy for energy, forces, and stress with statistical measures including MAE, RMSE, and correlation coefficients. Useful for benchmarking different models and analyzing prediction quality.

Parameters¶

xlist[ase.Atoms]: Reference (ground truth) atomic configurations with calculated properties.
ylist[ase.Atoms]: Model predictions on the same configurations.
figure_ymaxdict[str, float], optional: Y-axis limits for plots by property type (energy, forces, stress).

Attributes¶

energydict: Energy prediction metrics (MAE, RMSE, R², etc.) in meV/atom.
forcesdict: Force prediction metrics (MAE, RMSE, R², etc.) in meV/Å.
stressdict: Stress prediction metrics in eV/Å³.
plots_dirPath: Directory containing generated comparison plots.

Examples¶

>>> medium_model = ips.MACEMPModel(model="medium")
>>> small_model = ips.MACEMPModel(model="small")
>>> with project:
...     data = ips.AddData(file="ethanol.xyz")
...     medium_data = ips.ApplyCalculator(data=data.frames, model=medium_model)
...     small_data = ips.ApplyCalculator(data=data.frames, model=small_model)
...     metrics = ips.PredictionMetrics(x=medium_data.frames, y=small_data.frames)
>>> project.repro()
>>> print(f"Energy MAE: {metrics.energy['mae']:.2f} meV/atom")
Energy MAE: 52.23 meV/atom
>>> print(f"Force MAE: {metrics.forces['mae']:.2f} meV/Å")
Force MAE: 213.22 meV/Å

data_file: Path = PosixPath('$nwd$/data.npz')¶

energy: dict = NOT_AVAILABLE¶

figure_ymax: dict[str, float]¶

forces: dict = NOT_AVAILABLE¶

get_content()[source]¶

get_data()[source]¶: Create dict of all data.

get_metrics()[source]¶: Update the metrics.

get_plots(save=False)[source]¶: Create figures for all available data.

plots_dir: Path = PosixPath('$nwd$/plots')¶

run()[source]¶

stress: dict = NOT_AVAILABLE¶

stress_deviat: dict = NOT_AVAILABLE¶

stress_hydro: dict = NOT_AVAILABLE¶

x: list[ase.Atoms]¶

y: list[ase.Atoms]¶

class ipsuite.analysis.RattleAnalysis(*args, **kwargs)[source]¶

Bases: ProcessSingleAtom

Move particles with a given stdev from a starting configuration and predict.

Attributes¶

model: The MLModel node that implements the ‘predict’ method atoms: list[Atoms] to predict properties for logspace: bool, default=True

Increase the stdev of rattle with ‘np.logspace’ instead of ‘np.linspace’

stop: float, default = 1.0: The stop value for the generated space of stdev points
num: int, default = 100: The size of the generated space of stdev points
factor: float, default = 0.001: The ‘np.linspace(0.0, stop, num) * factor’
atom_id: int, default = 0: The atom to pick from self.atoms as a starting point

data: List[ase.Atoms]¶

energies: DataFrame = NOT_AVAILABLE¶

factor: float = 0.001¶

logspace: bool = True¶

model: MLModel¶

model_outs: Path = PosixPath('$nwd$/model')¶

num: int = 100¶

run()[source]¶

seed: int = 1234¶

stop: float = 3.0¶

class ipsuite.analysis.StressHistogram(*args, **kwargs)[source]¶

Bases: AnalyseAtoms

Creates histograms for the hydrostatic and deviatoric components of the stress tensor.

Parameters¶

data: list: List of Atoms objects.
bins: int: Number of bins in the histogram.

bins: int = None¶

data: list[ase.Atoms]¶

get_hist()[source]¶: Create a pandas dataframe from the given data.

get_labels()[source]¶

get_plots(counts, bin_edges, hydrostatic=True)[source]¶: Create figures for all available data.

labels_df: DataFrame = NOT_AVAILABLE¶

logy_scale: bool = True¶

plots_dir: Path = PosixPath('$nwd$/plots')¶

run()[source]¶

ipsuite.analysis package¶

Subpackages¶

Submodules¶

ipsuite.analysis.bin_property module¶

Parameters¶

Parameters¶

ipsuite.analysis.bond_stretch module¶

Attributes¶

ipsuite.analysis.ensemble module¶

Parameters¶

Returns¶

ipsuite.analysis.md module¶

ipsuite.analysis.sensitivity module¶

References¶

Module contents¶

Attributes¶

Parameters¶

Attributes¶

Examples¶

Attributes¶

Parameters¶

Attributes¶

Attributes¶

Parameters¶

Attributes¶

Attributes¶

Attributes¶

Parameters¶

Attributes¶

Examples¶

Attributes¶

Parameters¶