SpatialMap

class SpatialMap(data: ndarray, positions: _Buffer | _SupportsArray[dtype[Any]] | _NestedSequence[_SupportsArray[dtype[Any]]] | bool | int | float | complex | str | bytes | _NestedSequence[bool | int | float | complex | str | bytes] | AbstractSites, sublattices: _Buffer | _SupportsArray[dtype[Any]] | _NestedSequence[_SupportsArray[dtype[Any]]] | bool | int | float | complex | str | bytes | _NestedSequence[bool | int | float | complex | str | bytes] | None = None)

Represents some spatially dependent property: data mapped to site positions

Attributes

callsig
data	1D array of values for each site, i.e. maps directly to x, y, z site coordinates.
num_sites	Total number of lattice sites
positions	Lattice site positions.
sub	1D array of sublattices IDs, short for .sublattices
sublattices	1D array of sublattices IDs
x	1D array of coordinates, short for .positions.x
xyz	Return a new array with shape=(N, 3).
y	1D array of coordinates, short for .positions.y
z	1D array of coordinates, short for .positions.z

Methods

__getitem__(idx)	Same rules as numpy indexing
_decorate_plot([ax])
_make_crop_indices(limits)	Return the indices into obj which retain only the data within the given limits
clipped(v_min, v_max)	Clip (limit) the values in the data array, see clip()
convolve([sigma])	Convolve the data with a Gaussian kernel of the given standard deviation
cropped(**limits)	Return a copy which retains only the sites within the given limits
plot_contour([ax])	Contour plot of the xy plane
plot_contourf([num_levels, ax])	Filled contour plot of the xy plane
plot_pcolor([ax])	Color plot of the xy plane
save_txt(filename)
with_data(data)	Return a copy of this object with different data mapped to the sites

__getitem__(idx: int | _Buffer | _SupportsArray[dtype[Any]] | _NestedSequence[_SupportsArray[dtype[Any]]] | bool | float | complex | str | bytes | _NestedSequence[bool | int | float | complex | str | bytes]) → SpatialMap

Same rules as numpy indexing

clipped(v_min: int, v_max: int) → SpatialMap

Clip (limit) the values in the data array, see clip()

convolve(sigma: float = 0.25)

Convolve the data with a Gaussian kernel of the given standard deviation

Parameters:

sigmafloat

Standard deviation of the Gaussian kernel

cropped(**limits) → SpatialMap

Return a copy which retains only the sites within the given limits

Parameters:

**limits

Attribute names and corresponding limits. See example.

Examples

Leave only the data where -10 <= x < 10 and 2 <= y < 4:

new = original.cropped(x=[-10, 10], y=[2, 4])

plot_contour(ax: Axes | None = None, **kwargs) → TriContourSet

Contour plot of the xy plane

Parameters:

axOptional[plt.Axes]

The axis to plot on.

**kwargs

Forwarded to tricontour().

plot_contourf(num_levels: int = 50, ax: Axes | None = None, **kwargs) → TriContourSet

Filled contour plot of the xy plane

Parameters:

num_levelsint

Number of contour levels.

axOptional[plt.Axes]

The axis to plot on.

**kwargs

Forwarded to tricontourf().

plot_pcolor(ax: Axes | None = None, **kwargs) → TriMesh | PolyCollection

Color plot of the xy plane

Parameters:

axOptional[plt.Axes]

The axis to plot on.

**kwargs

Forwarded to tripcolor().

with_data(data) → SpatialMap

Return a copy of this object with different data mapped to the sites

property data: ndarray

1D array of values for each site, i.e. maps directly to x, y, z site coordinates

property num_sites: int

Total number of lattice sites

property positions: Positions

Lattice site positions. Named tuple with x, y, z fields, each a 1D array.

property sub: ndarray

1D array of sublattices IDs, short for .sublattices

property sublattices: ndarray

1D array of sublattices IDs

property x: ndarray

1D array of coordinates, short for .positions.x

property xyz: ndarray

Return a new array with shape=(N, 3). Convenient, but slow for big systems.

property y: ndarray

1D array of coordinates, short for .positions.y

property z: ndarray

1D array of coordinates, short for .positions.z