FieldAnalyzer

class zeroheliumkit.fem.fieldreader.FieldAnalyzer(coupling_constants: CouplingConstants)

Bases: object

A class for analyzing and plotting field data extracted from FEM simulations. This class provides methods to: - Load and store field data as attributes. - Calculate potential distributions from coupling constants and voltages. - Plot 2D and 1D potential and electric field distributions.

Parameters:

coupling_constants (CouplingConstants) – The coupling constants used for field analysis.

Methods

accumulate_arrays()	Calculates the potential distribution based on the coupling constants and voltages.
get_data([slice])	Returns the potential distribution.
get_gradient(grad_axis[, slice])	Returns the gradient of the potential data.
plot1D_data(slice[, ax, scale, add_offset])	Plots the 1D potential distribution along a specified slice in the XY plane.
plot1D_gradient(grad_axis, slice[, ax, ...])	Plots the 1D gradient of the potential distribution along a specified slice in the XY plane.
plot2D_data([ax, num_levels, zero_line])	Plots the 2D potential distribution based on the coupling constants and voltages.
plot2D_gradient(grad_axis[, ax, num_levels])	Plots the 2D gradient distribution based on the coupling constants and voltages.
plot2D_vectorfield([ax, step])	Plots the 2D electric field distribution as a vector field.
print_voltages()
set_voltages(voltages)	Sets the voltages for the electrodes.
update()	Updates the potential distribution based on the current voltages.

Attributes

couplings
names
voltages
potential

set_voltages(voltages: dict) → None

Sets the voltages for the electrodes.

Parameters:

voltages (dict) – A dictionary containing the voltages.

update() → None

Updates the potential distribution based on the current voltages.

accumulate_arrays() → None

Calculates the potential distribution based on the coupling constants and voltages.

get_data(slice: tuple = None) → ndarray

Returns the potential distribution. If a slice is provided, returns the potential along that slice.

Parameters:

slice (tuple, optional) – A tuple specifying the slice of data to use. Defaults to None -> In this case, the full 2D array is returned.

get_gradient(grad_axis: str, slice: tuple = None) → ndarray

Returns the gradient of the potential data.

Parameters:

• grad_axis (str) – The axis along which to compute the gradient. Must be either ‘x’ or ‘y’.

• slice (tuple, optional) – A tuple specifying the slice of data to use. Defaults to None -> In this case, the full 2D array is used.

Raises:

ValueError – If grad_axis is not ‘x’ or ‘y’.

Returns:

The gradient of the potential data.

Return type:

np.ndarray

plot2D_data(ax=None, num_levels: int = 17, zero_line: float | int = None, **kwargs)

Plots the 2D potential distribution based on the coupling constants and voltages.

Parameters:

• ax (optional) – The matplotlib axes object to plot on. If not provided, a new figure and axes will be created.

• num_levels (int, optional) – The number of contour levels to use. Defaults to 17.

• zero_line (optional) – The value at which to draw a dashed line. If True, the zero line will be drawn at 0. If None, no zero line will be drawn.

• **kwargs – Additional keyword arguments to pass to the contourf function. See documentation <https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.contourf.html>`_.

plot2D_gradient(grad_axis: str, ax=None, num_levels: int = 17, **kwargs)

Plots the 2D gradient distribution based on the coupling constants and voltages.

Parameters:

• grad_axis (str) – The axis along which to compute the gradient. Must be either ‘x’ or ‘y’.

• ax (optional) – The matplotlib axes object to plot on. Defaults to None.

• num_levels (int, optional) – The number of contour levels to use. Defaults to 17.

• **kwargs – Additional keyword arguments to pass to the contourf function. See documentation <https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.contourf.html>`_.

Raises:

ValueError – If grad_axis is not ‘x’ or ‘y’.

plot2D_vectorfield(ax=None, step: int = 5, **kwargs)

Plots the 2D electric field distribution as a vector field.

Parameters:

• ax (optional) – The matplotlib axes object to plot on. Defaults to None. If not provided, a new figure and axes will be created.

• **kwargs – Additional keyword arguments to pass to the matplotlib quiver function. See documentation <https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.quiver.html>`_.

plot1D_data(slice: tuple, ax=None, scale=1, add_offset=0, **kwargs)

Plots the 1D potential distribution along a specified slice in the XY plane.

Parameters:

• slice (tuple) – A tuple specifying the slice of data to use. Must be of the form (‘x’ or ‘y’, value).

• scale (float, optional) – A scaling factor to apply to the potential data. Defaults to 1e3.

• add_offset (float, optional) – An offset to add to the potential data after scaling. Defaults to 0.

• ax (optional) – The matplotlib axes object to plot on. If not provided, a new figure and axes will be created.

• **kwargs – Additional keyword arguments to pass to the plot function.

plot1D_gradient(grad_axis: str, slice: tuple, ax=None, scale=1, add_offset=0, **kwargs)

Plots the 1D gradient of the potential distribution along a specified slice in the XY plane.

Parameters:

• grad_axis (str) – The axis along which to compute the gradient. Must be either ‘x’ or ‘y’.

• slice (tuple) – A tuple specifying the slice of data to use. Must be of the form (‘x’ or ‘y’, value).

• scale (float, optional) – A scaling factor to apply to the gradient data. Defaults to 1e3.

• add_offset (float, optional) – An offset to add to the gradient data after scaling. Defaults to 0.

• ax (optional) – The matplotlib axes object to plot on. If not provided, a new figure and axes will be created.

• **kwargs – Additional keyword arguments to pass to the plot function.