remove uff dependency

kwave/reconstruction/beamform.py

@@ -1,139 +1,11 @@
 import logging
-import os
 from typing import Tuple, Optional
-from warnings import warn
-
 import numpy as np
 import scipy
-from matplotlib import pyplot as plt
-from scipy.interpolate import interp1d
-from scipy.signal import hilbert
-from uff import ChannelData
-from uff.position import Position
 
 from kwave.utils.conversion import cart2pol
 from kwave.utils.data import scale_time
 from kwave.utils.tictoc import TicToc
-from .shifted_transform import ShiftedTransform
-from .tools import make_time_vector, get_t0, get_origin_array, apodize
-
-
-def beamform(channel_data: ChannelData) -> None:
-    """
-
-    Args:
-        channel_data: shape => (1, 96, 32, 1585)
-
-    Returns:
-
-    """
-    warn("This method is no longer supported and will be depricated in v0.4.0 of k-wave-python.", DeprecationWarning, stacklevel=2)
-    f_number = 1.2
-    num_px_z = 256
-    imaging_depth = 40e-3
-    # apodization_window = 'boxcar'
-    apodization_window = "none"
-    number_samples = np.size(channel_data.data, axis=-1)
-
-    # create depth vector
-    z = np.linspace(0, imaging_depth, num_px_z)
-
-    # allocate memory for beamformed image
-    beamformed_data = np.zeros((len(z), len(channel_data.sequence)), dtype=complex)
-
-    # hilbert transform rf data to get envelope
-    channel_data.data = hilbert(channel_data.data, axis=3)
-
-    # allocate memory for
-    wave_origin_x = np.empty(len(channel_data.sequence))
-
-    for e_id, event in enumerate(channel_data.sequence):
-        # todo event.event should be event.event_id or event.key
-        # todo make itteratable getter for events
-        event = channel_data.unique_events[e_id]
-        probe = event.receive_setup.probe
-        sampling_freq = event.receive_setup.sampling_frequency
-        # We assume one transmit wave per transmit event... hence 0 index
-        transmit_wave = event.transmit_setup.transmit_waves[0]
-
-        # make time vector
-        time_vector = make_time_vector(num_samples=number_samples, sampling_freq=sampling_freq,
-                                       time_offset=event.receive_setup.time_offset)
-
-        # todo: make indexing 0 min and not 1 min
-        wave_origin_x[e_id] = channel_data.unique_waves[transmit_wave.wave - 1].origin.position.x
-
-        # todo: make position objects
-        pixel_positions = np.stack([wave_origin_x[e_id] * np.ones(len(z)), np.zeros(len(z)), z]).T
-        expanding_aperture = pixel_positions[:, 2] / f_number
-
-        # time zero delays for spherical waves
-        origin = get_origin_array(channel_data, transmit_wave)
-        t0_point = get_t0(transmit_wave)
-
-        # logging.log(logging.INFO, origin, t0_point)
-
-        transmit_distance = np.sign(pixel_positions[:, 2] - origin[2]) * \
-                            np.sqrt(np.sum((pixel_positions - origin) ** 2, axis=1)) + \
-                            np.abs(1.2 * t0_point[0])
-        # np.sqrt(np.sum((origin - t0_point) ** 2))
-
-        probe = channel_data.probes[probe - 1]
-        # todo: why are element positions saved as transforms and not positions?
-        transform = ShiftedTransform.deserialize(probe.transform.serialize())
-        # todo: remove list from channel mapping. currently [[<element_number>,]...]
-
-        # dataset.channel_data.unique_waves[transmit_wave.wave - 1].origin.position.x
-
-        # event.transmit_setup.channel_mapping = np.arange(1, 33)  # Added by Farid
-        plt.plot(transmit_distance)
-
-        for element_number in event.transmit_setup.channel_mapping:
-            element_number = element_number[0]  # Changed by Farid
-
-            # todo: why are element positions saved as transformations?
-            element_position = Position.deserialize(
-                probe.element[element_number - 1].transform.translation.serialize())
-            element_location = Position.deserialize(transform(element_position).serialize())
-
-            pixel_element_lateral_distance = abs(pixel_positions[:, 0] - element_location[0])
-            # logging.log(logging.INFO, pixel_element_lateral_distance)
-            receive_apodization = apodize(pixel_element_lateral_distance, expanding_aperture, apodization_window)
-
-            # receive distance
-            receive_distance = np.sqrt(np.sum((pixel_positions - np.array(element_location)) ** 2, axis=1))
-
-            t0 = transmit_wave.time_offset
-
-            # round trip delay
-            delay = (transmit_distance + receive_distance) / channel_data.sound_speed + t0
-
-            # beamformed data
-            chan_id = element_number - 1 - event.transmit_setup.channel_mapping[0][0]  # tricky part
-            signal = np.squeeze(channel_data.data[:, e_id, chan_id, :])
-            interp = interp1d(x=time_vector, y=signal, kind='cubic', bounds_error=False, fill_value=0)
-            beamformed_data[:, e_id] += np.squeeze(receive_apodization * interp(delay).T)
-
-    # Envelope and plot
-    envelope_beamformed_data = np.absolute(beamformed_data)
-    compressed_beamformed_data = 20 * np.log10(envelope_beamformed_data / np.amax(envelope_beamformed_data) + 1e-12)
-
-    plt.figure
-    x_dis = 1e3 * wave_origin_x
-    z_dis = 1e3 * z
-    plt.imshow(compressed_beamformed_data, vmin=-60, vmax=0, cmap='Greys_r',
-               extent=[min(x_dis), max(x_dis), max(z_dis), min(z_dis)])
-    plt.xlabel('x[mm]', fontsize=12)
-    plt.ylabel('z[mm]', fontsize=12)
-    plt.title(channel_data.description)
-    plt.colorbar()
-
-    filename = "example_bmode.png"
-    plt.savefig(os.path.join(os.getcwd(), filename))
-    plt.plot()
-    logging.log(logging.INFO, f"Plot saved to {os.path.join(os.getcwd(), filename)}")
-
-    pass
 
 
 def focus(kgrid, input_signal, source_mask, focus_position, sound_speed):

kwave/reconstruction/tools.py

@@ -1,5 +1,4 @@
 import numpy as np
-from uff import Position
 
 
 def log_compression(signal, cf, normalize=False):
@@ -58,17 +57,6 @@ def apodize(distance, aperture, window):
     return apod
 
 
-def get_t0(transmit_wave):
-    serialized_tx_wave = transmit_wave.time_zero_reference_point.serialize()
-    return np.array(Position.deserialize(serialized_tx_wave))
-
-
-def get_origin_array(channel_data, transmit_wave):
-    serialized_origin = channel_data.unique_waves[transmit_wave.wave - 1].origin.position.serialize()
-    return np.array(
-        Position.deserialize(serialized_origin))
-
-
 def make_time_vector(num_samples, sampling_freq, time_offset):
     return np.linspace(start=0, num=num_samples,
                        stop=num_samples / sampling_freq) + time_offset

pyproject.toml

@@ -30,7 +30,6 @@ dependencies = [
     "matplotlib==3.7.2",
     "numpy>=1.22.2,<1.25.0",
     "scikit-image==0.21.0",
-    "uff-reader==0.0.2",
 ]
 
 [project.urls]