tbplas.Visualizer

class tbplas.Visualizer(enable_mpi: bool = False, echo_details: bool = False)

Class for quick data visualization.

Notes

Order of arguments

The arguments should follow:: data (x, y, z, u, v, …), figure settings (fig_name, fig_size, fig_dpi) model [optional], model settings [optional], axis settings (x_label, y_label, x_lim, y_lim) beautifier, arguments for low level matplotlib functions

__init__(enable_mpi: bool = False, echo_details: bool = False) → None

Parameters:

enable_mpi – whether to enable mpi parallelism
echo_details – whether to output parallelization details

Methods

`__init__`([enable_mpi, echo_details])	param enable_mpi: whether to enable mpi parallelism
`all_average`(data_local)	Average results over random samples broadcast to all process.
`all_reduce`(data_local)	Reduce local data and broadcast to all processes.
`average`(data_local)	Average results over random samples and store results to master process.
`barrier`()	Wrapper for self.comm.Barrier.
`bcast`(data_local)	Broadcast data from master to other processes.
`dist_bound`(n_max)	Same as dist_range, but returns the lower and upper bounds.
`dist_list`(raw_list[, algorithm])	Distribute a list over processes.
`dist_range`(n_max)	Distribute range(n_max) over processes.
`log`([event, fmt])	Log the date and time of event.
`plot_bands`(k_len, bands, k_idx, k_label[, ...])	Plot band structure.
`plot_dos`(energies, dos[, x_label, y_label])	Plot density of states.
`plot_phases`(kb_array, phases[, fig_name, ...])	param kb_array: (num_kb,) float64 array
`plot_scalar`(x, y, z[, fig_name, fig_size, ...])	Plot 2d scalar field z = f(x, y).
`plot_vector`(x, y, u, v[, fig_name, ...])	Plot 2d vector field [u(x, y), v(x, y)].
`plot_wfc`(model, wfc[, with_model])	Plot wave function in real space.
`plot_wfc3d`(model, wfc, quantum_numbers[, ...])	Plot wave function to cube file.
`plot_xy`(x, y[, fig_name, fig_size, fig_dpi, ...])	Plot y as function of x.
`print`([text])	Print text on master process.
`reduce`(data_local)	Reduce local data to master process.

Attributes

`is_master`	Determine whether this is the master process.
`mpi_enabled`	Determine whether MPI is enabled.
`rank`	Interface for the '__rank' attribute.
`size`	Interface for the '__size' attribute.

__init__(enable_mpi: bool = False, echo_details: bool = False) → None

Parameters:

enable_mpi – whether to enable mpi parallelism
echo_details – whether to output parallelization details

all_average(data_local: ndarray) → ndarray

Average results over random samples broadcast to all process.

Parameters:: data_local – local results on each process
Returns:: averaged data from data_local

all_reduce(data_local: ndarray) → ndarray

Reduce local data and broadcast to all processes.

Parameters:: data_local – local results on each process
Returns:: summed data from data_local

average(data_local: ndarray) → ndarray

Average results over random samples and store results to master process.

Parameters:: data_local – local results on each process
Returns:: averaged data from data_local

barrier() → None: Wrapper for self.comm.Barrier.

bcast(data_local: ndarray) → None

Broadcast data from master to other processes.

Parameters:: data_local – local results on each process
Returns:: None

dist_bound(n_max: int) → Tuple[int, int]

Same as dist_range, but returns the lower and upper bounds. Both of the bounds are close, i.e. [i_min, i_max].

Parameters:: n_max – upper bound of range
Returns:: lower and upper bounds of subrange assigned to this process

dist_list(raw_list: List[Any], algorithm: str = 'range') → List[Any]

Distribute a list over processes.

Parameters:

raw_list – raw list to distribute
algorithm – distribution algorithm, should be either “remainder” or “range”

Returns:

sublist assigned to this process

dist_range(n_max: int) → range

Distribute range(n_max) over processes.

Parameters:: n_max – upper bound of the range
Returns:: subrange assigned to this process

log(event: str = '', fmt: str = '%x %X') → None

Log the date and time of event.

Parameters:

event – notice of the event
fmt – date and time format

Returns:

None.

plot_bands(k_len: ndarray, bands: ndarray, k_idx: ndarray, k_label: List[str], fig_name: str = None, fig_size: Tuple[float, float] = None, fig_dpi: int = 300, x_label: str = 'k (1/nm)', y_label: str = 'Energy (eV)', beautifier: Callable = None, color: str = 'r', linewidth: float = 1.2, **kwargs) → None

Plot band structure.

Parameters:

k_len – (num_kpt,) float64 array distance of k-path in reciprocal space
bands – (num_kpt, num_band) float64 array energies corresponding to k_len
k_idx – (num_hsk,) int32 array indices of highly-symmetric k-points in k_len
k_label – (num_hsk,) labels of highly-symmetric k-points
fig_name – file name of figure to save
fig_size – width and height of the figure
fig_dpi – resolution of figure
x_label – label of x-axis
y_label – label of y-axis
beautifier – function for improving the plot
color – line color
linewidth – line width
kwargs – parameters for plt.plot()

Returns:

None

plot_dos(energies: ndarray, dos: ndarray, x_label: str = 'Energy (eV)', y_label: str = 'DOS (1/eV)', **kwargs) → None

Plot density of states.

Parameters:

energies – (num_eng,) float64 array energy grids
dos – (num_eng,) float64 array density of states
x_label – label for x-axis
y_label – label for y-axis
kwargs – arguments for ‘plot_xy’

Returns:

None

plot_phases(kb_array: ndarray, phases: ndarray, fig_name: str = None, fig_size: Tuple[float, float] = None, fig_dpi: int = 300, scatter: bool = True, polar: bool = False, x_label: str = '$k_b (G_b)$', y_label: str = '$\\theta$', beautifier: Callable = None, color: str = 'r', linewidth: float = 1.2, **kwargs) → None

Parameters:

kb_array – (num_kb,) float64 array FRACTIONAL coordinates of the loop along b-axis
phases – (num_kpt, num_occ) float64 array phases of WF centers
fig_name – file name of figure
fig_size – width and height of the figure
fig_dpi – dpi of output figure
scatter – whether to do scatter plot instead of line plot
polar – whether to plot in polar coordinate system
x_label – label for x-axis
y_label – label for y-axis
beautifier – function for improving the plot
color – line color
linewidth – line width
kwargs – parameters for plt.plot() and plt.scatter()

Returns:

None

plot_scalar(x: ndarray, y: ndarray, z: ndarray, fig_name: str = None, fig_size: Tuple[float, float] = None, fig_dpi: int = 300, model: Union[PrimitiveCell, Sample] = None, model_style: Dict[str, Any] = None, scatter: bool = False, site_size: Union[int, ndarray] = 5, site_color: Union[str, List[str]] = 'cmap', num_grid: Tuple[int, int] = (200, 200), cmap: str = 'viridis', with_colorbar: bool = False, beautifier: Callable = None, **kwargs) → None

Plot 2d scalar field z = f(x, y).

Parameters:

x – (num_data,) float64 array x-component of Cartesian coordinates of data points
y – (num_data,) float64 array y-component of Cartesian coordinates of data points
z – (num_data,) float64 array z-value of data points
fig_name – image file name
fig_size – width and height of the figure
fig_dpi – dpi of output figure
model – model with which the data will be plotted
model_style – style of the model
scatter – whether to plot the wave function as scatter
site_size – size of the sites for scatter plot
site_color – color of the sites, use colormap from z-value if set to camp, otherwise monochromatic
num_grid – (nx, ny) number of grid-points for interpolation along x and y directions when plotting the wave function
cmap – color map for plotting the wave function
with_colorbar – whether to add colorbar to figure
beautifier – function for improving the plot
kwargs – parameters for plt.scatter() and plt.imshow()

Returns:

None

plot_vector(x: ndarray, y: ndarray, u: ndarray, v: ndarray, fig_name: str = None, fig_size: Tuple[float, float] = None, fig_dpi: int = 300, model: Union[PrimitiveCell, Sample] = None, model_style: Dict[str, Any] = None, arrow_color: Union[str, List[str]] = 'cmap', cmap: str = 'viridis', with_colorbar: bool = False, beautifier: Callable = None, **kwargs) → None

Plot 2d vector field [u(x, y), v(x, y)].

Parameters:

x – (num_data,) float64 array x-component of Cartesian coordinates of data points
y – (num_data,) float64 array y-component of Cartesian coordinates of data points
u – (num_data,) float64 array x component of arrow directions
v – (num_data,) float64 array y component of arrow directions
fig_name – image file name
fig_size – width and height of the figure
fig_dpi – dpi of output figure
model – model with which the data will be plotted
model_style – style of the model
arrow_color – color for the arrows
cmap – color map for plotting the arrows
with_colorbar – whether to add colorbar to figure
beautifier – function for improving the plot
kwargs – keyword arguments for quiver function

Returns:

None

plot_wfc(model: Union[PrimitiveCell, Sample], wfc: ndarray, with_model: bool = True, **kwargs) → None

Plot wave function in real space.

Parameters:

model – model to which the wave function belongs
with_model – whether to plot model along with the wave function
wfc – (num_orb,) float64 array projection of wave function on all the sites
kwargs – arguments for ‘plot_scalar’

Returns:

None

plot_wfc3d(model: Union[PrimitiveCell, Sample], wfc: ndarray, quantum_numbers: ndarray, convention: int = 1, k_point: ndarray = None, rn_max: ndarray = None, cube_name: str = 'wfc.cube', cube_origin: ndarray = None, cube_size: ndarray = None, resolution: float = 0.005, kind: str = 'real') → None

Plot wave function to cube file.

Parameters:

model – model to which the wave function belongs
wfc – (num_orb,) float64 array projection of wave function on all the sites
quantum_numbers – (num_orb, 4) int32 array z, n, l, m of each orbital
convention – convention of Hamiltonian from which the wave function is obtained
k_point – (3,) float64 array fractional coordinate of the k-point
rn_max – (3,) int64 array range of rn to evaluate the Bloch function, the actual range is [-rn_max[i], rn_max[i]] along 3 directions
cube_name – name of output cube file
cube_origin – (3,) float64 array Cartesian coordinate of origin of plotting range in nm
cube_size – (3,) float64 array size of plotting range in nm
resolution – resolution of plotting range in nm
kind – data to plot, should be real, imag or abs2

Returns:

None

plot_xy(x: ndarray, y: ndarray, fig_name: str = None, fig_size: Tuple[float, float] = None, fig_dpi: int = 300, x_label: str = None, y_label: str = None, x_lim: Tuple[float, float] = None, y_lim: Tuple[float, float] = None, beautifier: Callable = None, color: str = 'r', linewidth: float = 1.2, **kwargs) → None

Plot y as function of x.

Parameters:

x – (num_data,) float64 array data x for plot
y – (num_data,) float64 array data y for plot
fig_name – file name of figure to save
fig_size – width and height of the figure
fig_dpi – resolution of figure
x_label – label for x-axis
y_label – label for y-axis
x_lim – (x_min, x_max) range of x
y_lim – (y_min, y_max) range of y
beautifier – function for improving the plot
color – line color
linewidth – line width
kwargs – parameters for plt.plot()

Returns:

None

print(text: str = '') → None

Print text on master process.

NOTE: flush=True is essential for some MPI implementations, e.g. MPICH3.

Parameters:: text – text to print
Returns:: None

reduce(data_local: ndarray) → ndarray

Reduce local data to master process.

Parameters:: data_local – local results on each process
Returns:: summed data from data_local

__weakref__: list of weak references to the object (if defined)

property is_master: bool: Determine whether this is the master process.

property mpi_enabled: bool: Determine whether MPI is enabled.

property rank: int

Interface for the ‘__rank’ attribute.

Returns:: rank of this MPI process

property size: int

Interface for the ‘__size’ attribute.

Returns:: number of MPI processes