ultraspy core methods

The main methods in ultraspy are defined both in GPU and in CPU, and are stored within the dedicated repositories gpu/ and cpu/. The GPU version are highlighted, so those can be called directly from the ultraspy module. Their CPU alternatives, however, need to be called using the ultraspy.cpu module. For example:

import ultraspy as us

# Call the GPU method, input / output are on the GPU device
d_out = us.some_method(d_input, ...)

# Call the CPU alternative
out = us.cpu.some_method(input, ...)

Signal-related methods

The detailed description can be found in the API reference.

• down_mix: Down-mixing operation, performs a phase rotation on our data to move the spectrum around 0 Hz (involving the returned signal to be complex).

• filtfilt: Butterworth filtfilt, greatly inspired by Matlab’s filtfilt version, zero-phase forward and reverse digital filtering.

• rf2iq: Converts raw RFs data signals into I/Qs (In-Phase Quadrature). Consists in a down-mixing operation (centers the spectrum at 0Hz), followed by a low-pass filter to keep only the newly centered at 0 spectrum (remove the old negative component).

• matched_filter: Applies a matched filter to our data, given a reference signal which is supposed to match.

• normalize: Simply normalizes a signal to values between -1 and 1.

Doppler-related methods

The detailed description can be found in the API reference.

• apply_wall_filter: Applies a clutter filter along slow time. The clutter type can be either ‘mean’ (subtract the mean of the data), ‘poly’ (applies a polynomial filter) or ‘hp_filter’ (applies a high-pass butterworth filter).

• spatial_smoothing: Performs a spatial smoothing on data, using a given window function to compute the squared convolution kernel (either hamming or median).

• get_color_doppler_map: Computes the color doppler map, which is using the correlation of our data along slow time, which are then converted to doppler velocity using the Doppler formula. It also can perform a spatial smoothing of a given number of pixels.

• get_power_doppler_map: Computes the power doppler map, which is using the mean of squared values along slow time. The result is then returned in dB. It can also perform a spatial smoothing of a given number of pixels.

• gpu.dual_frequency_unalias: Uses the dual-frequency approach to compute two Doppler map that can be matched together to remove aliasing ambiguity.

Displaying methods

The detailed description can be found in the API reference.

• to_b_mode: Computes the B-Mode of our beamformed data. Simply returns 20 * log10(data).

• get_spectrum: Returns the spectrum of a data signal.

• get_doppler_colormap: Returns a color map proposition for Doppler, based on typical echographs, from blue (flow going away from the probe), to red (going toward to).

Post-processing methods

The detailed description can be found in the API reference.

• distort_dynamic: Post-processing method to distort the dynamic of a B-Mode image (already in dB). This is applying a mathematical function, either curved or sigmoid to distort the values of the image.

Metrics methods

The detailed description can be found in the API reference.

• metrics.signal_noise_ratio: Returns the Signal to Noise ratio of the sample, based on the location of both the pulse and the noise.

• metrics.get_full_width_at_half_maximum: Returns the Full-Width at Half Maximum of a signal, given a focus index. It is basically the width of the focused lobe at -6dB.

• metrics.get_peak_side_lobe: Returns the Peak Side Lobe of a signal, given a focus index. It is basically the difference in dB between the focused lobe and its closest neighbor.

• metrics.get_contrat_noise_ratio: Returns the Contrast to Noise ratio of the data, using two masks, one for the position of the signal, the other one for the noise.