convolution_detection

ConvolutionDetection

Bases: RotorDetection

KERNELS = {'nabla_2x2': np.array([[-1, 1], [0, 0]]), 'nabla_3x3': np.array([[-1, 0, 1], [-1, 0, 1], [0, 0, 0]]), 'sobel_3x3': np.array([[-1 / 2, 0, 1 / 2], [-1, 0, 1], [-1 / 2, 0, 1 / 2]]), 'sobel_5x5': np.array([[-1 / 4, -1 / 5, 0, 1 / 5, 1 / 4], [-2 / 5, -1 / 2, 0, 1 / 2, 2 / 5], [-1 / 2, -1, 0, 1, 1 / 2], [-2 / 5, -1 / 2, 0, 1 / 2, 2 / 5], [-1 / 4, -1 / 5, 0, 1 / 5, 1 / 4]])} class-attribute instance-attribute

kernel = self.KERNELS['nabla_3x3'] instance-attribute

tc_threshold = 0.95 instance-attribute

__init__(phase_maps)

Initialize the ConvolutionDetection class.

Parameters:

Name	Type	Description	Default
phase_maps	ndarray[T, N, M]	T phases maps with shape (N, M).	required

calc_rotors(topocharge, direction)

calc_topocharge()

Calculate topological charge via 2D matrix convolution with kernels.

Returns:

Type	Description
ndarray	np.ndarray[T, N, M]: Topological charge array.

convolve(x, kernel)

Compute convolution of data using a certain kernel.

Parameters:

Name	Type	Description	Default
x	ndarray	Input array to be convoluted with arbitrary ndim.	required
kernel	ndarray	Convolution kernel with dimension ndim-1.	required

Returns:

Type	Description
ndarray	np.ndarray: Convoluted array with same shape as input array.

detect()

Calculate (t_ind, node_ind, direction) of rotor centers.

Returns:

Type	Description
DataFrame	pd.DataFrame: Dataframe containing ['t_ind', 'x_ind', 'y_ind', 'direction'].

set_kernel(kernel_name)

Set convolution kernel. Kernel: 'nabla_3x3'

set_tc_threshold(tc_threshold)

Threshold value for detecting rotor. Default: 0.95

unwrap_period(phases)

Keep every phase difference in [-pi, pi] range.

Parameters:

Name	Type	Description	Default
phases	ndarray	Array of phases.	required

Returns:

Type	Description
ndarray	np.ndarray: Array of phases in range [-pi, pi] where every phase is restricted to its 2pi counterpart where needed.