sisl.io.siesta.ncSileSiesta

class sisl.io.siesta.ncSileSiesta(filename, mode='r', lvl=0, access=1, *args, **kwargs)

Bases: SileCDFSiesta

Generic NetCDF output file containing a large variety of information

Plotting

`plot`	Plotting functions for the `ncSileSiesta` class.
`plot.geometry`(*[, axes, atoms, ...])	Calls `read_geometry` and creates a `GeometryPlot` from its output.
`plot.grid`(name[, index, ...])	Calls `read_grid` and creates a `GridPlot` from its output.

Methods

`base_directory`([relative_to])	Retrieve the base directory of the file, relative to the path relative_to
`close`()
`dir_file`([filename, filename_base])	File of the current Sile
`iter`([group, dimension, variable, levels, root])	Iterator on all groups, variables and dimensions.
`read`(args, *kwargs)	Generic read method which should be overloaded in child-classes
`read_basis`()	Returns a set of atoms corresponding to the basis-sets in the nc file
`read_density_matrix`(**kwargs)	Returns a density matrix from the underlying NetCDF file
`read_dynamical_matrix`(**kwargs)	Returns a dynamical matrix from the underlying NetCDF file
`read_energy_density_matrix`(**kwargs)	Returns energy density matrix from the underlying NetCDF file
`read_fermi_level`()	Returns the fermi-level
`read_force`()	Returns a vector with final forces contained.
`read_force_constant`()	Reads the force-constant stored in the nc file
`read_geometry`()	Returns Geometry object from a Siesta.nc file
`read_grid`(name[, index])	Reads a grid in the current Siesta.nc file
`read_hamiltonian`(**kwargs)	Returns a Hamiltonian from the underlying NetCDF file
`read_hessian`()	Reads the force-constant stored in the nc file
`read_lattice`()	Returns a Lattice object from a Siesta.nc file
`read_lattice_nsc`()	Returns number of supercell connections
`read_overlap`(**kwargs)	Returns a overlap matrix from the underlying NetCDF file
`write`(args, *kwargs)	Generic write method which should be overloaded in child-classes
`write_basis`(atoms)	Write the current atoms orbitals as the basis
`write_density_matrix`(DM, **kwargs)	Writes density matrix model to file
`write_dynamical_matrix`(D, **kwargs)	Writes dynamical matrix model to file
`write_energy_density_matrix`(EDM, **kwargs)	Writes energy density matrix model to file
`write_geometry`(geometry)	Creates the NetCDF file and writes the geometry information
`write_hamiltonian`(H, **kwargs)	Writes Hamiltonian model to file
`write_overlap`(S, **kwargs)	Write the overlap matrix to the NetCDF file

Attributes

`base_file`	File of the current Sile
`file`	File of the current Sile
`grids`	Return a list of available grids in this file.

base_directory(relative_to='.'): Retrieve the base directory of the file, relative to the path relative_to

close()

dir_file(filename=None, filename_base=''): File of the current Sile

iter(group=True, dimension=True, variable=True, levels=-1, root=None)

Iterator on all groups, variables and dimensions.

This iterator iterates through all groups, variables and dimensions in the Dataset

The generator sequence will _always_ be:

Group

Dimensions in group

Variables in group

As the dimensions are generated before the variables it is possible to copy groups, dimensions, and then variables such that one always ensures correct dependencies in the generation of a new SileCDF.

Parameters:

group (bool (True)) – whether the iterator yields Group instances
dimension (bool (True)) – whether the iterator yields Dimension instances
variable (bool (True)) – whether the iterator yields Variable instances
levels (int (-1)) – number of levels to traverse, with respect to root variable, i.e. number of sub-groups this iterator will return.
root (str (None)) – the base root to start iterating from.

Examples

Script for looping and checking each instance.

>>> for gv in self.iter():
...     if self.isGroup(gv):
...         # is group
...     elif self.isDimension(gv):
...         # is dimension
...     elif self.isVariable(gv):
...         # is variable

plot.geometry(*, axes: Axes = ['x', 'y', 'z'], atoms: AtomsIndex = None, atoms_style: Sequence[AtomsStyleSpec] = [], atoms_scale: float = 1.0, atoms_colorscale: Colorscale | None = None, drawing_mode: Literal['scatter', 'balls', None] = None, bind_bonds_to_ats: bool = True, points_per_bond: int = 20, bonds_style: StyleSpec = {}, bonds_scale: float = 1.0, bonds_colorscale: Colorscale | None = None, show_atoms: bool = True, show_bonds: bool = True, show_cell: Literal['box', 'axes', False] = 'box', cell_style: StyleSpec = {}, nsc: tuple[int, int, int] = (1, 1, 1), atoms_ndim_scale: tuple[float, float, float] = (16, 16, 1), bonds_ndim_scale: tuple[float, float, float] = (1, 1, 10), dataaxis_1d: np.ndarray | Callable | None = None, arrows: Sequence[AtomArrowSpec] = (), backend='plotly') → GeometryPlot

Calls read_geometry and creates a GeometryPlot from its output.

Parameters:

axes – The axes to project the geometry to.
atoms – The atoms to plot. If None, all atoms are plotted.
atoms_style – List of style specifications for the atoms. See the showcase notebooks for examples.
atoms_scale – Scaling factor for the size of all atoms.
atoms_colorscale – Colorscale to use for the atoms in case the color attribute is an array of values. If None, the default colorscale is used for each backend.
drawing_mode – The method used to draw the atoms.
bind_bonds_to_ats – Whether to display only bonds between atoms that are being displayed.
points_per_bond – When the points are drawn using points instead of lines (e.g. in some frameworks to draw multicolor bonds), the number of points used per bond.
bonds_style – Style specification for the bonds. See the showcase notebooks for examples.
bonds_scale – Scaling factor for the width of all bonds.
bonds_colorscale – Colorscale to use for the bonds in case the color attribute is an array of values. If None, the default colorscale is used for each backend.
show_atoms – Whether to display the atoms.
show_bonds – Whether to display the bonds.
show_cell – Mode to display the cell. If False, the cell is not displayed.
cell_style – Style specification for the cell. See the showcase notebooks for examples.
nsc – Number of unit cells to display in each direction.
atoms_ndim_scale – Scaling factor for the size of the atoms for different dimensionalities (1D, 2D, 3D).
bonds_ndim_scale – Scaling factor for the width of the bonds for different dimensionalities (1D, 2D, 3D).
dataaxis_1d – Only meaningful for 1D plots. The data to plot on the Y axis.
arrows – List of arrow specifications to display. See the showcase notebooks for examples.
backend – The backend to use to generate the figure.

See also

GeometryPlot: The plot class used to generate the plot.
read_geometry: The method called to get the data.

plot.grid(name, index=0, *, data_kwargs={}, axes: Axes = ['z'], represent: Literal['real', 'imag', 'mod', 'phase', 'deg_phase', 'rad_phase'] = 'real', transforms: Sequence[str | Callable] = (), reduce_method: Literal['average', 'sum'] = 'average', boundary_mode: str = 'grid-wrap', nsc: tuple[int, int, int] = (1, 1, 1), interp: tuple[int, int, int] = (1, 1, 1), isos: Sequence[dict] = [], smooth: bool = False, colorscale: Colorscale | None = None, crange: tuple[float, float] | None = None, cmid: float | None = None, show_cell: Literal['box', 'axes', False] = 'box', cell_style: dict = {}, x_range: Sequence[float] | None = None, y_range: Sequence[float] | None = None, z_range: Sequence[float] | None = None, plot_geom: bool = False, geom_kwargs: dict = {}, backend: str = 'plotly') → GridPlot

Calls read_grid and creates a GridPlot from its output.

Parameters:

name (str) – name of the grid variable to read
index (int or array_like, optional) – the spin-index for retrieving one of the components. If a vector is passed it refers to the fraction per indexed component. I.e. [0.5, 0.5] will return sum of half the first two components. Default to the first component.
spin (optional) – same as index argument. spin argument has precedence.
axes – The axes to project the grid to.
represent – The representation of the grid to plot.
transforms – List of transforms to apply to the grid before plotting.
reduce_method – The method used to reduce the grid axes that are not displayed.
boundary_mode – The method used to deal with the boundary conditions. Only used if the grid is to be orthogonalized. See scipy docs for more info on the possible values.
nsc – The number of unit cells to display in each direction.
interp – The interpolation factor to use for each axis to make the grid smoother.
isos – List of isosurfaces or isocontours to plot. See the showcase notebooks for examples.
smooth – Whether to ask the plotting backend to make an attempt at smoothing the grid display.
colorscale – Colorscale to use for the grid display in the 2D representation. If None, the default colorscale is used for each backend.
crange – Min and max values for the colorscale.
cmid – The value at which the colorscale is centered.
show_cell – Method used to display the unit cell. If False, the cell is not displayed.
cell_style – Style specification for the cell. See the showcase notebooks for examples.
x_range – The range of the x axis to take into account. Even if the X axis is not displayed! This is important because the reducing operation will only be applied on this range.
y_range – The range of the y axis to take into account. Even if the Y axis is not displayed! This is important because the reducing operation will only be applied on this range.
z_range – The range of the z axis to take into account. Even if the Z axis is not displayed! This is important because the reducing operation will only be applied on this range.
plot_geom – Whether to plot the associated geometry (if any).
geom_kwargs – Keyword arguments to pass to the geometry plot of the associated geometry.
backend – The backend to use to generate the figure.