magni.imaging.measurements._lissajous module

Module providing public functions for the magni.imaging.measurements subpackage.

Routine listings

lissajous_sample_image(h, w, scan_length, num_points, f_y=1., f_x=1.,

theta_y=0., theta_x=np.pi / 2) Function for lissajous sampling an image.

lissajous_sample_surface(l, w, speed, sample_rate, time, f_y=1., f_x=1.,

theta_y=0., theta_x=np.pi / 2, speed_mode=0) Function for lissajous sampling a surface.

magni.imaging.measurements._lissajous.lissajous_sample_image(h, w, scan_length, num_points, f_y=1.0, f_x=1.0, theta_y=0.0, theta_x=1.5707963267948966)

Sample an image using a lissajous pattern.

The coordinates (in units of pixels) resulting from sampling an image of size h times w using a lissajous pattern are determined. The scan_length determines the length of the path scanned whereas num_points indicates the number of samples taken on that path.

Parameters:

• h (int) – The height of the area to scan in units of pixels.
• w (int) – The width of the area to scan in units of pixels.
• scan_length (float) – The length of the path to scan in units of pixels.
• num_points (int) – The number of samples to take on the scanned path.
• f_y (float) – The frequency of the y-sinusoid (the default value is 1.0).
• f_x (float) – The frequency of the x-sinusoid (the default value is 1.0).
• theta_y (float) – The starting phase of the y-sinusoid (the default is 0.0).
• theta_x (float) – The starting phase of the x-sinusoid (the default is pi / 2).

Returns:

coords (ndarray) – The coordinates of the samples arranged into a 2D array, such that each row is a coordinate pair (x, y).

Notes

The orientation of the coordinate system is such that the width w is measured along the x-axis whereas the height h is measured along the y-axis.

Examples

For example,

>>> import numpy as np
>>> from magni.imaging.measurements import lissajous_sample_image
>>> h = 10
>>> w = 10
>>> scan_length = 50.0
>>> num_points = 12
>>> np.set_printoptions(suppress=True)
>>> lissajous_sample_image(h, w, scan_length, num_points)
array([[ 5.        ,  9.5       ],
       [ 1.40370042,  7.70492686],
       [ 0.67656563,  3.75183526],
       [ 3.39871123,  0.79454232],
       [ 7.39838148,  1.19240676],
       [ 9.48459832,  4.62800824],
       [ 7.99295651,  8.36038857],
       [ 4.11350322,  9.41181634],
       [ 0.94130617,  6.94345168],
       [ 1.0071768 ,  2.92458128],
       [ 4.25856283,  0.56150128],
       [ 8.10147506,  1.7395012 ],
       [ 9.4699986 ,  5.51876059]])

magni.imaging.measurements._lissajous.lissajous_sample_surface(l, w, speed, sample_rate, time, f_y=1.0, f_x=1.0, theta_y=0.0, theta_x=1.5707963267948966, speed_mode=0)

Sample a surface area using a lissajous pattern.

The coordinates (in units of meters) resulting from sampling an area of size l times w using a lissajous pattern are determined. The scanned path is determined from the probe speed and the scan time.

Parameters:

• l (float) – The length of the area to scan in units of meters.
• w (float) – The width of the area to scan in units of meters.
• speed (float) – The probe speed in units of meters/second.
• sample_rate (float) – The sample rate in units of Hertz.
• time (float) – The scan time in units of seconds.
• f_y (float) – The frequency of the y-sinusoid (the default value is 1.0).
• f_x (float) – The frequency of the x-sinusoid (the default value is 1.0).
• theta_y (float) – The starting phase of the y-sinusoid (the default is 0.0).
• theta_x (float) – The starting phase of the x-sinusoid (the default is pi / 2).
• speed_mode (int) – The speed mode used to select sampling points (the default is 0 which implies that the speed argument determines the speed, and f_y and f_x determine the ratio between the relative frequencies used).

Returns:

coords (ndarray) – The coordinates of the samples arranged into a 2D array, such that each row is a coordinate pair (x, y).

Notes

The orientation of the coordinate system is such that the width w is measured along the x-axis whereas the length l is measured along the y-axis.

Generally, the lissajous sampling pattern does not provide constant speed, and this cannot be compensated for without violating f_y, f_x, or both. Therefore, speed_mode allows the user to determine how this issue is handled: In speed_mode 0, constant speed equal to speed is ensured by non-uniform sampling of a lissajous curve, whereby f_y and f_x are not constant frequencies. In speed_mode 1, average speed equal to speed is ensured by scaling f_y and f_x by the same constant. In speed_mode 2, f_y and f_x are kept constant and the speed is only used to determine the path length in combination with time.

Examples

For example,

>>> import numpy as np
>>> from magni.imaging.measurements import lissajous_sample_surface
>>> l = 1e-6
>>> w = 1e-6
>>> speed = 7e-7
>>> sample_rate = 1.0
>>> time = 12.0
>>> np.set_printoptions(suppress=True)
>>> lissajous_sample_surface(l, w, speed, sample_rate, time)
array([[ 0.0000005 ,  0.000001  ],
       [ 0.00000001,  0.00000058],
       [ 0.00000033,  0.00000003],
       [ 0.00000094,  0.00000025],
       [ 0.00000082,  0.00000089],
       [ 0.00000017,  0.00000088],
       [ 0.00000007,  0.00000024],
       [ 0.00000068,  0.00000003],
       [ 0.00000099,  0.0000006 ],
       [ 0.00000048,  0.000001  ],
       [ 0.        ,  0.00000057],
       [ 0.00000035,  0.00000002],
       [ 0.00000094,  0.00000027]])