sisl.physics.BandStructure

class sisl.physics.BandStructure(parent, *args, **kwargs)

Bases: BrillouinZone

Create a path in the Brillouin zone for plotting band-structures etc.

Parameters:

parent (object or array_like) – An object with associated parent.cell and parent.rcell or an array of floats which may be turned into a Lattice
points (array_like of float) – a list of points that are the corners of the path. Define a discontinuity in the points by adding a None in the list.
divisions (int or array_like of int) – number of divisions in each segment. If a single integer is passed it is the total number of points on the path (equally separated). If it is an array_like input it must have length one less than point, in this case the total number of points will be sum(divisions) + 1 due to the end-point constraint.
names (array_like of str) – the associated names of the points on the Brillouin Zone path
jump_dk (float or array_like, optional) – Percentage of self.lineark()[-1] that is used as separation between discontinued jumps in the band-structure. For band-structures with disconnected jumps the lineark and lineartick methods returns a separation between the disconnected points according to this percentage. Default value is 5% of the total distance. Alternatively an array equal to the number of discontinuity jumps may be passed for individual percentages. Keyword only, argument.

Examples

>>> lattice = Lattice(10)
>>> bs = BandStructure(lattice, [[0] * 3, [0.5] * 3], 200)
>>> bs = BandStructure(lattice, [[0] * 3, [0.5] * 3, [1.] * 3], 200)
>>> bs = BandStructure(lattice, [[0] * 3, [0.5] * 3, [1.] * 3], 200, ['Gamma', 'M', 'Gamma'])

A disconnected band structure may be created by having None as the element. Note that the number of names does not contain the empty points (they are simply removed). Such a band-structure may be useful when one is interested in a discontinuous band structure.

>>> bs = BandStructure(lattice, [[0, 0, 0], [0, 0.5, 0], None, [0.5, 0, 0], [0.5, 0.5, 0]], 200)

Plotting

`plot`	Plotting functions for the `BrillouinZone` class.
`plot.bands`([H, extra_vars, ...])	Creates a `BandsData` object and then plots a `BandsPlot` from it.
`plot.fatbands`([H, extra_vars, ...])	Creates a `BandsData` object and then plots a `FatbandsPlot` from it.
`plot.sites`([axes, sites, ...])	Builds a `SitesPlot` by setting the value of "sites_obj" to the current object.

\(k\)-point calculations

`apply`	Loop over all k-points by applying parent methods for all k.
`apply.array`(method[, eta_key])	Dispatch the method by one array
`apply.average`(method)	Dispatch the method by averaging
`apply.dataarray`(method)	Dispatch the method by returning a DataArray or data-set
`apply.grid`(method[, eta_key])	Dispatch the method by putting values on the grid
`apply.iter`(method[, eta_key])	Dispatch the method by iterating values
`apply.list`(method)	Dispatch the method by returning list of values
`apply.ndarray`(method[, eta_key])	Dispatch the method by one array
`apply.none`(method)	Dispatch the method by doing nothing (mostly useful if wrapped)
`apply.oplist`(method)	Dispatch the method by returning oplist of values
`apply.sum`(method)	Dispatch the method by summing
`apply.xarray`(method)	Dispatch the method by returning a DataArray or data-set

Methods

`copy`([parent])	Create a copy of this object, optionally changing the parent
`in_primitive`(k)	Move the k-point into the primitive point(s) ]-0.5 ; 0.5]
`insert_jump`(*arrays[, value])	Return a copy of arrays filled with value at indices of discontinuity jumps
`iter`([ret_weight])	An iterator for the k-points and (possibly) the weights
`lineark`([ticks])	A 1D array which corresponds to the delta-k values of the path
`lineartick`()	The tick-marks corresponding to the linear-k values
`merge`(bzs[, weight_scale, parent])	Merge several BrillouinZone objects into one
`param_circle`(parent, N_or_dk, kR, normal, origin)	Create a parameterized k-point list where the k-points are generated on a circle around an origin
`parametrize`(parent, func, N, args, *kwargs)	Generate a new `BrillouinZone` object with k-points parameterized via the function func in N separations
`set_parent`(parent)	Update the parent associated to this object
`tocartesian`([k])	Transfer a k-point in reduced coordinates to the Cartesian coordinates
`tolinear`(k[, ret_index, atol])	Convert a k-point into the equivalent linear k-point via the distance
`toreduced`(k)	Transfer a k-point in Cartesian coordinates to the reduced coordinates
`volume`([ret_dim, axes])	Calculate the volume of the BrillouinZone, optionally only on some axes axes
`write`(sile, args, *kwargs)	Writes k-points to a `tableSile`.

Attributes

`cell`
`k`	A list of all k-points (if available)
`rcell`
`weight`	Weight of the k-points in the `BrillouinZone` object

apply.array(method, eta_key='ndarray'): Dispatch the method by one array

apply.average(method): Dispatch the method by averaging

apply.dataarray(method): Dispatch the method by returning a DataArray or data-set

apply.grid(method, eta_key='grid'): Dispatch the method by putting values on the grid

apply.iter(method, eta_key='iter'): Dispatch the method by iterating values

apply.list(method): Dispatch the method by returning list of values

apply.ndarray(method, eta_key='ndarray'): Dispatch the method by one array

apply.none(method): Dispatch the method by doing nothing (mostly useful if wrapped)

apply.oplist(method): Dispatch the method by returning oplist of values

apply.sum(method): Dispatch the method by summing

apply.xarray(method): Dispatch the method by returning a DataArray or data-set

copy(parent=None) → BandStructure

Create a copy of this object, optionally changing the parent

Parameters:: parent (optional) – change the parent

static in_primitive(k: numpy.typing.ArrayLike) → ndarray

Move the k-point into the primitive point(s) ]-0.5 ; 0.5]

Parameters:: k (array_like) – k-point(s) to move into the primitive cell
Returns:: numpy.ndarray – all k-points moved into the primitive cell

insert_jump(*arrays, value=np.nan)[source]

Return a copy of arrays filled with value at indices of discontinuity jumps

Arrays with value in jumps is easier to plot since those lines will be naturally discontinued. For band structures without discontinuity jumps in the Brillouin zone the arrays will be return as is.

It will insert value along the first dimension matching the length of self. For each discontinuity jump an element will be inserted.

This may be useful for plotting since np.nan gets interpreted as a discontinuity in the graph thus removing connections between the segments.

Parameters:

*arrays (array_like) – arrays will get value inserted where there are jumps in the band structure
value (optional) – the value to be inserted at the jump points in the data array

Examples

Create a bandrstructure with a discontinuity.

>>> gr = geom.graphene()
>>> bs = BandStructure(gr, [[0, 0, 0], [0.5, 0, 0], None, [0, 0, 0], [0, 0.5, 0]], 4)
>>> data = np.zeros([len(bs), 10])
>>> data_with_jump = bs.insert_jump(data)
>>> assert data_with_jump.shape == (len(bs)+1, 10)
>>> np.all(data_with_jump[2] == np.nan)
True

iter(ret_weight: bool = False)

An iterator for the k-points and (possibly) the weights

Parameters:

ret_weight (bool, optional) – if true, also yield the weight for the respective k-point

Yields:

kpt (k-point)
weight (weight of k-point, only if `ret_weight is true.`)

lineark(ticks: bool = False)[source]

A 1D array which corresponds to the delta-k values of the path

This is mainly meant for plotting but may be useful for finding out distances in the reciprocal lattice.

Examples

>>> p = BandStructure(...)
>>> eigs = Hamiltonian.eigh(p)
>>> for i in range(len(Hamiltonian)):
...     plt.plot(p.lineark(), eigs[:, i])

>>> p = BandStructure(...)
>>> eigs = Hamiltonian.eigh(p)
>>> lk, kt, kl = p.lineark(True)
>>> plt.xticks(kt, kl)
>>> for i in range(len(Hamiltonian)):
...     plt.plot(lk, eigs[:, i])

Parameters:: ticks – if True the ticks for the points are also returned

See also

linspace_bz: converts k-points into a linear distance parameterization

Returns:

linear_k (numpy.ndarray) – the positions in reciprocal space determined by the distance between points
ticks (numpy.ndarray) – linear k-positions of the points, only returned if ticks is True
ticklabels (list of str) – labels at ticks, only returned if ticks is True

lineartick()[source]

The tick-marks corresponding to the linear-k values

Returns:: numpy.ndarray – the positions in reciprocal space determined by the distance between points

See also

lineark: Routine used to calculate the tick-marks.

static merge(bzs, weight_scale: Sequence[float] | float = 1.0, parent=None)

Merge several BrillouinZone objects into one

The merging strategy only stores the new list of k-points and weights. Information retained in the merged objects will not be stored.

Parameters:

bzs (list-like of BrillouinZone objects) – each element is a BrillouinZone object with bzs[i].k and bzs[i].weight fields.
weight_scale (list-like or float) – these are matched item-wise with bzs and applied to. Internally itertools.zip_longest(fillvalue=weight_scale[-1]) will be used to extend for all bzs.
parent (object, optional) – Associated parent in the returned object, will default to bzs[0].parent

Returns:

BrillouinZone – even if all objects are not BrillouinZone objects the returned object will be.

classmethod param_circle(parent, N_or_dk: int | float, kR: float, normal, origin, loop: bool = False)

Create a parameterized k-point list where the k-points are generated on a circle around an origin

The generated circle is a perfect circle in the reciprocal space (Cartesian coordinates). To generate a perfect circle in units of the reciprocal lattice vectors one can generate the circle for a diagonal supercell with side-length \(2\pi\), see example below.

Parameters:

parent (Lattice, or LatticeChild) – the parent object
N_or_dk (int) – number of k-points generated using the parameterization (if an integer), otherwise it specifies the discretization length on the circle (in 1/Ang), If the latter case will use less than 2 points a warning will be raised and the number of points increased to 2.
kR (float) – radius of the k-point. In 1/Ang
normal (array_like of float) – normal vector to determine the circle plane
origin (array_like of float) – origin of the circle used to generate the circular parameterization
loop – whether the first and last point are equal

Examples

>>> lattice = Lattice([1, 1, 10, 90, 90, 60])
>>> bz = BrillouinZone.param_circle(lattice, 10, 0.05, [0, 0, 1], [1./3, 2./3, 0])

To generate a circular set of k-points in reduced coordinates (reciprocal

>>> lattice = Lattice([1, 1, 10, 90, 90, 60])
>>> bz = BrillouinZone.param_circle(lattice, 10, 0.05, [0, 0, 1], [1./3, 2./3, 0])
>>> bz_rec = BrillouinZone.param_circle(2*np.pi, 10, 0.05, [0, 0, 1], [1./3, 2./3, 0])
>>> bz.k[:, :] = bz_rec.k[:, :]

Returns:: BrillouinZone – with the parameterized k-points.

static parametrize(parent, func, N: Sequence[int] | int, *args, **kwargs) → BrillouinZone

Generate a new BrillouinZone object with k-points parameterized via the function func in N separations

Generator of a parameterized Brillouin zone object that contains a parameterized k-point list.

Parameters:

parent (Lattice, or LatticeChild) – the object that the returned object will contain as parent
func (callable) – method that parameterizes the k-points, must at least accept three arguments, 1. parent: object 2. N: total number of k-points 3. i: current index of the k-point (starting from 0)

the function must return a k-point in 3 dimensions.
N (int or list of int) – number of k-points generated using the parameterization, or a list of integers that will be looped over. In this case arguments N and i in func will be lists accordingly.
*args – additional arguments passed directly to func
**kwargs – additional keyword arguments passed directly to func

Examples

Simple linear k-points

>>> def func(sc, N, i):
...    return [i/N, 0, 0]
>>> bz = BrillouinZone.parametrize(1, func, 10)
>>> assert len(bz) == 10
>>> assert np.allclose(bz.k[-1, :], [9./10, 0, 0])

For double looping, say to create your own grid

>>> def func(sc, N, i):
...    return [i[0]/N[0], i[1]/N[1], 0]
>>> bz = BrillouinZone.parametrize(1, func, [10, 5])
>>> assert len(bz) == 50

plot.bands(H: Hamiltonian | None = None, extra_vars: Sequence[dict | str] = (), *, Erange: tuple[float, float] | None = None, E0: float = 0.0, E_axis: Literal['x', 'y'] = 'y', bands_range: tuple[int, int] | None = None, spin: Literal[0, 1] | None = None, bands_style: StyleSpec = {'color': 'black', 'dash': 'solid', 'opacity': 1, 'width': 1}, spindown_style: StyleSpec = {'color': 'blue', 'width': 1}, colorscale: Colorscale | None = None, gap: bool = False, gap_tol: float = 0.01, gap_color: str = 'red', gap_marker: dict = {'size': 7}, direct_gaps_only: bool = False, custom_gaps: Sequence[dict] = [], line_mode: Literal['line', 'scatter', 'area_line'] = 'line', group_legend: bool = True, backend: str = 'plotly') → BandsPlot

Creates a BandsData object and then plots a BandsPlot from it.

Parameters:

H – The Hamiltonian to use for the calculations. If None, the parent of the Brillouin zone will be used, which is typically what you want!
extra_vars – Additional variables to calculate for each eigenstate, apart from their energy. Each item of the list should be a dictionary with the following keys:
- ‘name’, str: The name of the variable.
- ‘getter’, callable: A function that gets 3 arguments: eigenstate, plot and
spin index, and returns the values of the variable in a numpy array. This function will be called for each eigenstate object separately. That is, once for each (k-point, spin) combination. - ‘coords’, tuple of str: The names of the dimensions of the returned array. The number of coordinates should match the number of dimensions. - ‘coords_values’, dict: If this variable introduces a new coordinate, you should pass the values for that coordinate here. If the coordinates were already defined by another variable, they will already have values. If you are unsure that the coordinates are new, just pass the values for them, they will get overwritten.
Each item can also be a string indicating the name of a known variable: ‘norm2’, ‘spin_moment’, ‘ipr’.
Erange – The energy range to plot. If None, the range is determined by bands_range.
E0 – The energy reference.
E_axis – Axis to plot the energies.
bands_range – The bands to plot. Only used if Erange is None. If None, the 15 bands above and below the Fermi level are plotted.
spin – Which spin channel to display. Only meaningful for spin-polarized calculations. If None and the calculation is spin polarized, both are plotted.
bands_style – Styling attributes for bands.
spindown_style – Styling attributes for the spin down bands (if present). Any missing attribute will be taken from bands_style.
colorscale – Colorscale to use for the bands in case the color attribute is an array of values. If None, the default colorscale is used for each backend.
gap – Whether to display the gap.
gap_tol – Tolerance in k for determining whether two gaps are the same.
gap_color – Color of the gap.
gap_marker – Marker styles for the gap (as plotly marker’s styles).
direct_gaps_only – Whether to only display direct gaps.
custom_gaps – List of custom gaps to display. See the showcase notebooks for examples.
line_mode – The method used to draw the band lines.
group_legend – Whether to group all bands in the legend to show a single legend item.

If the bands are spin polarized, bands are grouped by spin channel.
backend – The backend to use to generate the figure.

See also

BandsPlot: The plot class used to generate the plot.
BandsData: The class to which data is converted.

plot.fatbands(H: Hamiltonian | None = None, extra_vars: Sequence[dict | str] = (), *, Erange: tuple[float, float] | None = None, E0: float = 0.0, E_axis: Literal['x', 'y'] = 'y', bands_range: tuple[int, int] | None = None, spin: Literal[0, 1] | None = None, bands_style: StyleSpec = {'color': 'black', 'opacity': 1, 'width': 1}, spindown_style: StyleSpec = {'color': 'blue', 'width': 1}, gap: bool = False, gap_tol: float = 0.01, gap_color: str = 'red', gap_marker: dict = {'size': 7}, direct_gaps_only: bool = False, custom_gaps: Sequence[dict] = [], bands_mode: Literal['line', 'scatter', 'area_line'] = 'line', bands_group_legend: bool = True, groups: OrbitalQueries = [], fatbands_var: str = 'norm2', fatbands_mode: Literal['line', 'scatter', 'area_line'] = 'area_line', fatbands_scale: float = 1.0, backend: str = 'plotly') → FatbandsPlot

Creates a BandsData object and then plots a FatbandsPlot from it.

Parameters:

H – The Hamiltonian to use for the calculations. If None, the parent of the Brillouin zone will be used, which is typically what you want!
extra_vars – Additional variables to calculate for each eigenstate, apart from their energy. Each item of the list should be a dictionary with the following keys:
- ‘name’, str: The name of the variable.
- ‘getter’, callable: A function that gets 3 arguments: eigenstate, plot and
spin index, and returns the values of the variable in a numpy array. This function will be called for each eigenstate object separately. That is, once for each (k-point, spin) combination. - ‘coords’, tuple of str: The names of the dimensions of the returned array. The number of coordinates should match the number of dimensions. - ‘coords_values’, dict: If this variable introduces a new coordinate, you should pass the values for that coordinate here. If the coordinates were already defined by another variable, they will already have values. If you are unsure that the coordinates are new, just pass the values for them, they will get overwritten.
Each item can also be a string indicating the name of a known variable: ‘norm2’, ‘spin_moment’, ‘ipr’.
Erange – The energy range to plot. If None, the range is determined by bands_range.
E0 – The energy reference.
E_axis – Axis to plot the energies.
bands_range – The bands to plot. Only used if Erange is None. If None, the 15 bands above and below the Fermi level are plotted.
spin – Which spin channel to display. Only meaningful for spin-polarized calculations. If None and the calculation is spin polarized, both are plotted.
bands_style – Styling attributes for bands.
spindown_style – Styling attributes for the spin down bands (if present). Any missing attribute will be taken from bands_style.
gap – Whether to display the gap.
gap_tol – Tolerance in k for determining whether two gaps are the same.
gap_color – Color of the gap.
gap_marker – Marker styles for the gap (as plotly marker’s styles).
direct_gaps_only – Whether to only display direct gaps.
custom_gaps – List of custom gaps to display. See the showcase notebooks for examples.
bands_mode – The method used to draw the band lines.
bands_group_legend – Whether to group all bands in the legend to show a single legend item.

If the bands are spin polarized, bands are grouped by spin channel.
groups – Orbital groups to plots. See showcase notebook for examples.
fatbands_var – The variable to use from bands_data to determine the width of the fatbands. This variable must have as coordinates (k, band, orb, [spin]).
fatbands_mode – The method used to draw the fatbands.
fatbands_scale – Factor that scales the size of all fatbands.
backend – The backend to use to generate the figure.

See also

FatbandsPlot: The plot class used to generate the plot.
BandsData: The class to which data is converted.

plot.sites(axes: Axes = ['x', 'y', 'z'], sites: AtomsIndex = None, sites_style: Sequence[AtomsStyleSpec] = [], sites_scale: float = 1.0, sites_name: str = 'Sites', sites_colorscale: Colorscale | None = None, drawing_mode: Literal['scatter', 'balls', 'line', None] = None, show_cell: Literal['box', 'axes', False] = False, cell_style: StyleSpec = {}, nsc: tuple[int, int, int] = (1, 1, 1), sites_ndim_scale: tuple[float, float, float] = (1, 1, 1), dataaxis_1d: np.ndarray | Callable | None = None, arrows: Sequence[AtomArrowSpec] = (), backend='plotly') → SitesPlot

Builds a SitesPlot by setting the value of “sites_obj” to the current object.

Plots sites from an object that can be parsed into a geometry.

The only differences between this plot and a geometry plot is the naming of the inputs and the fact that there are no options to plot bonds.

Parameters:

axes – The axes to project the sites to.
sites – The sites to plot. If None, all sites are plotted.
sites_style – List of style specifications for the sites. See the showcase notebooks for examples.
sites_scale – Scaling factor for the size of all sites.
sites_name – Name to give to the trace that draws the sites.
sites_colorscale – Colorscale to use for the sites 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 sites.
show_cell – Mode to display the reciprocal cell. If False, the cell is not displayed.
cell_style – Style specification for the reciprocal cell. See the showcase notebooks for examples.
nsc – Number of unit cells to display in each direction.
sites_ndim_scale – Scaling factor for the size of the sites 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.

set_parent(parent) → None

Update the parent associated to this object

Parameters:: parent (object or array_like) – an object containing cell vectors

tocartesian(k: npt.ArrayLike | None = None) → np.ndarray

Transfer a k-point in reduced coordinates to the Cartesian coordinates

Parameters:: k – k-point in reduced coordinates, defaults to this objects k-points.
Returns:: numpy.ndarray – in units of 1/Ang

tolinear(k, ret_index: bool = False, atol: float = 1e-4)[source]

Convert a k-point into the equivalent linear k-point via the distance

Finds the index of the k-point in self.k that is closests to k. The returned value is then the equivalent index in lineark.

This is very useful for extracting certain points along the band structure.

Parameters:

k (array_like) – the k-point(s) to locate in the linear values
ret_index – whether the indices are also returned
atol – when the found k-point has a distance (in Cartesian coordinates) is differing by more than tol a warning will be issued. The tolerance is in units 1/Ang.

toreduced(k: numpy.typing.ArrayLike) → ndarray

Transfer a k-point in Cartesian coordinates to the reduced coordinates

Parameters:: k (list of float) – k-point in Cartesian coordinates
Returns:: numpy.ndarray – in units of reciprocal lattice vectors ]-0.5 ; 0.5] (if k is in the primitive cell)

volume(ret_dim: bool = False, axes: CellAxes | None = None) → float | tuple[float, int]

Calculate the volume of the BrillouinZone, optionally only on some axes axes

This will return the volume of the Brillouin zone, depending on the dimensions of the system. Here the dimensions of the system is determined by how many dimensions have auxilliary supercells that can contribute to Brillouin zone integrals. Therefore the returned value will have differing units depending on dimensionality.

Parameters:

ret_dim – also return the dimensionality of the system
axes – estimate the volume using only the directions indexed by this array. The default axes are only the periodic ones (self.parent.pbc.nonzero()[0]). Hence the units might not necessarily be 1/Ang^3.

Returns:

vol – the volume of the Brillouin zone. Units are 1/Ang^D with D being the dimensionality. For 0D it will return 0.
dimensionality (int) – the dimensionality of the volume

write(sile: sisl.typing.SileLike, *args, **kwargs) → None

Writes k-points to a tableSile.

This allows one to pass a tableSile or a file-name.

apply

Loop over all k-points by applying parent methods for all k.

This allows potential for running and collecting various computationally heavy methods from a single point on all k-points.

The apply method will dispatch the parent methods through all k-points and passing k as arguments to the parent methods in a straight-forward manner.

For instance to iterate over all eigenvalues of a Hamiltonian

>>> H = Hamiltonian(...)
>>> bz = BrillouinZone(H)
>>> for ik, eigh in enumerate(bz.apply.eigh()):
...    # do something with eigh which corresponds to bz.k[ik]

By default the apply method exposes a set of dispatch methods:

apply.iter, the default iterator module
apply.average reduced result by averaging (using BrillouinZone.weight as the weight per k-point.
apply.sum reduced result without weighing
apply.array return a single array with all values; has len equal to number of k-points
apply.none, specialized method that is mainly useful when wrapping methods
apply.list same as apply.array but using Python list as return value
apply.oplist using sisl.oplist allows greater flexibility for mathematical operations element wise
apply.datarray if xarray is available one can retrieve an xarray.DataArray instance

Please see Brillouin zone for further examples.

property cell: ndarray

property k: ndarray: A list of all k-points (if available)

plot: Plotting functions for the BrillouinZone class.

property rcell: ndarray

property weight: ndarray: Weight of the k-points in the BrillouinZone object