Source code

Analytical

Monte Carlo

class Result(binding)

Bases: object

Output container for an RSA simulation run.

Holds arrays plus domain metadata, computed statistics, and plotting helpers.

property positions: ndarray

Get the sphere center positions as a NumPy array of shape (N, 3).

Returns:

Array of sphere center positions.

Return type:

np.ndarray

property radii: ndarray

Get the sphere radii as a NumPy array of shape (N,).

Returns:

Array of sphere radii.

Return type:

np.ndarray

property statistics

Get the simulation statistics.

Returns:

The simulation statistics.

Return type:

Statistics

property sphere_configuration

Get the sphere configuration object.

Returns:

The configuration of spheres in the simulation.

Return type:

SphereConfiguration

property domain

Get the simulation domain.

Returns:

The domain (box) dimensions and boundary conditions.

Return type:

Domain

compute_partial_pair_correlation_function(**kwargs) → tuple

Compute the partial pair correlation function g_ij(r).

Parameters:

**kwargs (dict) – Keyword arguments passed to the binding method.

Returns:

A tuple containing: - centers (Quantity): The radial distances with units. - g_ij (np.ndarray): The partial pair correlation values.

Return type:

tuple

property partial_volume_fractions: ndarray

Get the partial volume fractions of each component.

Returns:

Array of partial volume fractions.

Return type:

np.ndarray

property partial_volumes: ndarray

Get the partial volumes of each component.

Returns:

Array of partial volumes.

Return type:

np.ndarray

compute_pair_correlation_function(**kwargs) → None

Compute the total pair correlation function g(r).

Parameters:

**kwargs (dict) – Keyword arguments passed to the binding method.

property pair_correlation_centers: ndarray

Get the radial centers for the pair correlation function.

Returns:

Array of radial centers.

Return type:

np.ndarray

property pair_correlation_values: ndarray

Get the values of the pair correlation function g(r).

Returns:

Array of pair correlation values.

Return type:

np.ndarray

plot_centers_3d(maximum_points_3d: int = 10000) → Figure

Plot the sphere centers in a 3D scatter plot.

maximum_points_3dint

Maximum number of points to plot (subsampling if necessary).

plt.Figure

The matplotlib Figure object containing the 3D scatter plot.

Extra Parameters

showbool

Whether to display the plot after creation.

save_asstr, optional

If provided, save the figure to this path.

figure_sizetuple, optional

Size of the figure in inches.

tight_layoutbool, default=True

Whether to use tight layout for the figure.

axesmatplotlib.axes.Axes, optional

Axes to draw on. If None, a new figure+axes are created.

xscalestr, optional

X-axis scale type (e.g., ‘linear’, ‘log’).

yscalestr, optional

Y-axis scale type (e.g., ‘linear’, ‘log’).

xlimtuple, optional

X-axis limits as (min, max).

ylimtuple, optional

Y-axis limits as (min, max).

stylestr, default=plot_style

Matplotlib style to use for the plot.

**kwargs

Additional keyword arguments to pass to the plotting function.

returns:

Figure – The figure with the market prices plot.

rtype:

matplotlib.figure.Figure

plot_radius_distribution(bins: int = 40, density: bool = True, alpha: float = 0.85) → Figure

Plot the distribution of sphere radii.

binsint

Number of histogram bins.

densitybool

Whether to normalize the histogram to form a probability density.

alphafloat

Transparency level for the histogram bars.

Extra Parameters

showbool

Whether to display the plot after creation.

save_asstr, optional

If provided, save the figure to this path.

figure_sizetuple, optional

Size of the figure in inches.

tight_layoutbool, default=True

Whether to use tight layout for the figure.

axesmatplotlib.axes.Axes, optional

Axes to draw on. If None, a new figure+axes are created.

xscalestr, optional

X-axis scale type (e.g., ‘linear’, ‘log’).

yscalestr, optional

Y-axis scale type (e.g., ‘linear’, ‘log’).

xlimtuple, optional

X-axis limits as (min, max).

ylimtuple, optional

Y-axis limits as (min, max).

stylestr, default=plot_style

Matplotlib style to use for the plot.

**kwargs

Additional keyword arguments to pass to the plotting function.

returns:

Figure – The figure with the market prices plot.

rtype:

matplotlib.figure.Figure

plot_slice_2d(slice_axis: Literal['x', 'y', 'z'] = 'z', slice_center_fraction: float = 0.5, slice_thickness_fraction: float = 0.08, maximum_circles_in_slice: int = 2500) → Figure

Plot a 2D slice of the sphere configuration.

slice_axisLiteral[“x”, “y”, “z”]

Axis along which to take the slice.

slice_center_fractionfloat

Fractional position along the slice axis where the slice is centered (0.0 to 1.0).

slice_thickness_fractionfloat

Fractional thickness of the slice relative to the box length along the slice axis (0.0 to 1.0).

maximum_circles_in_sliceint

Maximum number of circles to plot in the slice (subsampling if necessary).

Extra Parameters

showbool

Whether to display the plot after creation.

save_asstr, optional

If provided, save the figure to this path.

figure_sizetuple, optional

Size of the figure in inches.

tight_layoutbool, default=True

Whether to use tight layout for the figure.

axesmatplotlib.axes.Axes, optional

Axes to draw on. If None, a new figure+axes are created.

xscalestr, optional

X-axis scale type (e.g., ‘linear’, ‘log’).

yscalestr, optional

Y-axis scale type (e.g., ‘linear’, ‘log’).

xlimtuple, optional

X-axis limits as (min, max).

ylimtuple, optional

Y-axis limits as (min, max).

stylestr, default=plot_style

Matplotlib style to use for the plot.

**kwargs

Additional keyword arguments to pass to the plotting function.

returns:

Figure – The figure with the market prices plot.

rtype:

matplotlib.figure.Figure

plot_pair_correlation(n_bins: int = 80, maximum_pairs: int = 300000) → Figure

Plot the partial pair correlation functions g_ij(r) obtained from the RSA configuration, overlaying all (i, j) curves on a single axis.

n_binsint

Number of radial distance bins.

maximum_pairsint

Number of Monte Carlo sampled pairs used for estimation.

matplotlib.figure.Figure

Figure containing the overlaid plot of all g_ij(r) curves.

Extra Parameters

showbool

Whether to display the plot after creation.

save_asstr, optional

If provided, save the figure to this path.

figure_sizetuple, optional

Size of the figure in inches.

tight_layoutbool, default=True

Whether to use tight layout for the figure.

axesmatplotlib.axes.Axes, optional

Axes to draw on. If None, a new figure+axes are created.

xscalestr, optional

X-axis scale type (e.g., ‘linear’, ‘log’).

yscalestr, optional

Y-axis scale type (e.g., ‘linear’, ‘log’).

xlimtuple, optional

X-axis limits as (min, max).

ylimtuple, optional

Y-axis limits as (min, max).

stylestr, default=plot_style

Matplotlib style to use for the plot.

**kwargs

Additional keyword arguments to pass to the plotting function.

returns:

Figure – The figure with the market prices plot.

rtype:

matplotlib.figure.Figure